diff --git a/.gitignore b/.gitignore index 828bbe9bd3363853ae3f58f54a8d5f60cefad837..b5306b8b79c37166e5496cf17a3e39b86b9a6314 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,7 @@ __pycache__ cmake_build/ .idea/** /build/ +[Bb]uild/ /tensorflow/core/util/version_info.cc /tensorflow/python/framework/fast_tensor_util.cpp Pods diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index db4b1581ae671b1e676e215c9a80dfaab832fa21..f598999f351c10f8bd01dfbd3ad8897f19d570e8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -107,7 +107,7 @@ diff /tmp/my_cc_file.cc #### Python coding style Changes to TensorFlow Python code should conform to -[Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) +[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md) Use `pylint` to check your Python changes. To install `pylint` and retrieve TensorFlow's custom style definition: diff --git a/README.md b/README.md index 63853137cfd30b396f8c7d204811f3e4a1794c07..05fcb23f7edd657f2ea495d848fadc226e56b524 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting -code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), a data visualization toolkit. +code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), a data visualization toolkit. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research @@ -96,6 +96,8 @@ The TensorFlow project strives to abide by generally accepted best practices in | --- | --- | --- | | **IBM s390x** | [![Build Status](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/badge/icon)](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/) | TBA | | **IBM ppc64le CPU** | [![Build Status](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_CPU/badge/icon)](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_CPU/) | TBA | +| **IBM ppc64le GPU** | [![Build Status](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_PPC64LE_GPU/badge/icon)](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_PPC64LE_GPU/) | TBA | +| **Linux CPU with IntelĀ® MKL-DNNĀ®** | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | TBA | ## For more information diff --git a/RELEASE.md b/RELEASE.md index e09e9c6190f57adec67c2ae1d85848dabfd9c2a7..4b0339442768afbd97ac21323bb0351eea13a6ca 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,18 +1,38 @@ # Release 1.9.0 ## Major Features And Improvements -* Update tf.keras to the Keras 2.1.6 API. -* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. -* Adding support of core feature columns and losses to gradient boosted trees estimators. -* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. -* Layered variable names have changed in the following conditions: - * Using `tf.keras.layers` with custom variable scopes. - * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details - +* Updated docs for `tf.keras`: New Keras-based [get started](http://tensorflow.org/versions/r1.9/get_started), + and [programmers guide page](http://tensorflow.org/versions/r1.9/programmers_guide/keras). +* Update `tf.keras` to the Keras 2.1.6 API. +* Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082). +* Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees). +* The [python interface](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/lite) + for the [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/README.md) + has been expanded, and the command line interface (AKA: `toco`, `tflite_convert`) is once again + included in the standard `pip` installation. +* Improved data-loading and text processing with: + * [`tf.decode_compressed`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/decode_compressed) + * [`tf.string_strip`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/string_strip) + * [`tf.strings.regex_full_match`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/strings/regex_full_match) +* Added experimental support for new pre-made Estimators: + * [`tf.contrib.estimator.BaselineEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/BaselineEstimator) + * [`tf.contrib.estimator.RNNClassifier`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNEstimator) + * [`tf.contrib.estimator.RNNEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNClassifier) +* The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector) + API supports broadcasting for Bijectors with new API changes. + ## Breaking Chances - * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). + * If you're opening empty variable scopes; replace `variable_scope('', ...)` by + `variable_scope(tf.get_variable_scope(), ...)`. + * Headers used for building custom ops have been moved from site-packages/external into site-packages/tensorflow/include/external. ## Bug Fixes and Other Changes + +* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. +* Layered variable names have changed in the following conditions: + * Using `tf.keras.layers` with custom variable scopes. + * Using `tf.layers` in a subclassed `tf.keras.Model` class. See + [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details * `tf.data`: * The `DatasetBase::DebugString()` method is now `const`. * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. @@ -465,7 +485,7 @@ answered questions, and were part of inspiring discussions. ## Major Features And Improvements * `tf.keras` is now part of the core TensorFlow API. -* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of +* [`tf.data`](http://tensorflow.org/guide/datasets) is now part of the core TensorFlow API. * The API is now subject to backwards compatibility guarantees. * For a guide to migrating from the `tf.contrib.data` API, see the @@ -485,7 +505,7 @@ answered questions, and were part of inspiring discussions. * TensorFlow Debugger (tfdbg): * Add `eval` command to allow evaluation of arbitrary Python/numpy expressions in tfdbg command-line interface. See - [Debugging TensorFlow Programs](https://www.tensorflow.org/programmers_guide/debugger) + [Debugging TensorFlow Programs](https://www.tensorflow.org/guide/debugger) for more details. * Usability improvement: The frequently used tensor filter `has_inf_or_nan` is now added to `Session` wrappers and hooks by default. So there is no need @@ -772,7 +792,7 @@ answered questions, and were part of inspiring discussions. * Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters. * TensorFlow C library now available for Windows. * We released a new open-source version of TensorBoard. -* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel +* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel * Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md diff --git a/SECURITY.md b/SECURITY.md index e2f6ff353a3c04a6ec6b8ccbaeb75db59fa22d54..0b52fdc7ab84b7bd5bce5d247ede81b40699005c 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -245,4 +245,4 @@ v//Fw6ZeY+HmRDFdirjD7wXtIuER4vqCryIqR6Xe9X8oJXz9L/Jhslc= ### Known Vulnerabilities For a list of known vulnerabilities and security advisories for TensorFlow, -(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/index.md)[click here]. +[click here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/index.md). diff --git a/configure.py b/configure.py index ada342a50ab5104509156d3e44e6435a308255a3..31a83b4a1589b7f038bcdde5cec9007cd16b261c 100644 --- a/configure.py +++ b/configure.py @@ -943,6 +943,35 @@ def set_tf_cudnn_version(environ_cp): write_action_env_to_bazelrc('TF_CUDNN_VERSION', tf_cudnn_version) +def is_cuda_compatible(lib, cuda_ver, cudnn_ver): + """Check compatibility between given library and cudnn/cudart libraries.""" + ldd_bin = which('ldd') or '/usr/bin/ldd' + ldd_out = run_shell([ldd_bin, lib], True) + ldd_out = ldd_out.split(os.linesep) + cudnn_pattern = re.compile('.*libcudnn.so\\.?(.*) =>.*$') + cuda_pattern = re.compile('.*libcudart.so\\.?(.*) =>.*$') + cudnn = None + cudart = None + cudnn_ok = True # assume no cudnn dependency by default + cuda_ok = True # assume no cuda dependency by default + for line in ldd_out: + if 'libcudnn.so' in line: + cudnn = cudnn_pattern.search(line) + cudnn_ok = False + elif 'libcudart.so' in line: + cudart = cuda_pattern.search(line) + cuda_ok = False + if cudnn and len(cudnn.group(1)): + cudnn = convert_version_to_int(cudnn.group(1)) + if cudart and len(cudart.group(1)): + cudart = convert_version_to_int(cudart.group(1)) + if cudnn is not None: + cudnn_ok = (cudnn == cudnn_ver) + if cudart is not None: + cuda_ok = (cudart == cuda_ver) + return cudnn_ok and cuda_ok + + def set_tf_tensorrt_install_path(environ_cp): """Set TENSORRT_INSTALL_PATH and TF_TENSORRT_VERSION. @@ -959,8 +988,8 @@ def set_tf_tensorrt_install_path(environ_cp): raise ValueError('Currently TensorRT is only supported on Linux platform.') # Ask user whether to add TensorRT support. - if str(int(get_var( - environ_cp, 'TF_NEED_TENSORRT', 'TensorRT', False))) != '1': + if str(int(get_var(environ_cp, 'TF_NEED_TENSORRT', 'TensorRT', + False))) != '1': return for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS): @@ -973,47 +1002,29 @@ def set_tf_tensorrt_install_path(environ_cp): # Result returned from "read" will be used unexpanded. That make "~" # unusable. Going through one more level of expansion to handle that. - trt_install_path = os.path.realpath( - os.path.expanduser(trt_install_path)) + trt_install_path = os.path.realpath(os.path.expanduser(trt_install_path)) def find_libs(search_path): """Search for libnvinfer.so in "search_path".""" fl = set() if os.path.exists(search_path) and os.path.isdir(search_path): - fl.update([os.path.realpath(os.path.join(search_path, x)) - for x in os.listdir(search_path) if 'libnvinfer.so' in x]) + fl.update([ + os.path.realpath(os.path.join(search_path, x)) + for x in os.listdir(search_path) + if 'libnvinfer.so' in x + ]) return fl possible_files = find_libs(trt_install_path) possible_files.update(find_libs(os.path.join(trt_install_path, 'lib'))) possible_files.update(find_libs(os.path.join(trt_install_path, 'lib64'))) - - def is_compatible(tensorrt_lib, cuda_ver, cudnn_ver): - """Check the compatibility between tensorrt and cudnn/cudart libraries.""" - ldd_bin = which('ldd') or '/usr/bin/ldd' - ldd_out = run_shell([ldd_bin, tensorrt_lib]).split(os.linesep) - cudnn_pattern = re.compile('.*libcudnn.so\\.?(.*) =>.*$') - cuda_pattern = re.compile('.*libcudart.so\\.?(.*) =>.*$') - cudnn = None - cudart = None - for line in ldd_out: - if 'libcudnn.so' in line: - cudnn = cudnn_pattern.search(line) - elif 'libcudart.so' in line: - cudart = cuda_pattern.search(line) - if cudnn and len(cudnn.group(1)): - cudnn = convert_version_to_int(cudnn.group(1)) - if cudart and len(cudart.group(1)): - cudart = convert_version_to_int(cudart.group(1)) - return (cudnn == cudnn_ver) and (cudart == cuda_ver) - cuda_ver = convert_version_to_int(environ_cp['TF_CUDA_VERSION']) cudnn_ver = convert_version_to_int(environ_cp['TF_CUDNN_VERSION']) nvinfer_pattern = re.compile('.*libnvinfer.so.?(.*)$') highest_ver = [0, None, None] for lib_file in possible_files: - if is_compatible(lib_file, cuda_ver, cudnn_ver): + if is_cuda_compatible(lib_file, cuda_ver, cudnn_ver): matches = nvinfer_pattern.search(lib_file) if len(matches.groups()) == 0: continue @@ -1029,12 +1040,13 @@ def set_tf_tensorrt_install_path(environ_cp): # Try another alternative from ldconfig. ldconfig_bin = which('ldconfig') or '/sbin/ldconfig' ldconfig_output = run_shell([ldconfig_bin, '-p']) - search_result = re.search( - '.*libnvinfer.so\\.?([0-9.]*).* => (.*)', ldconfig_output) + search_result = re.search('.*libnvinfer.so\\.?([0-9.]*).* => (.*)', + ldconfig_output) if search_result: libnvinfer_path_from_ldconfig = search_result.group(2) if os.path.exists(libnvinfer_path_from_ldconfig): - if is_compatible(libnvinfer_path_from_ldconfig, cuda_ver, cudnn_ver): + if is_cuda_compatible(libnvinfer_path_from_ldconfig, cuda_ver, + cudnn_ver): trt_install_path = os.path.dirname(libnvinfer_path_from_ldconfig) tf_tensorrt_version = search_result.group(1) break @@ -1122,7 +1134,9 @@ def set_tf_nccl_install_path(environ_cp): nccl_lib_path = os.path.join(nccl_install_path, nccl_lib_path) nccl_hdr_path = os.path.join(nccl_install_path, 'include/nccl.h') - if os.path.exists(nccl_lib_path) and os.path.exists(nccl_hdr_path): + nccl_license_path = os.path.join(nccl_install_path, 'NCCL-SLA.txt') + if os.path.exists(nccl_lib_path) and os.path.exists( + nccl_hdr_path) and os.path.exists(nccl_license_path): # Set NCCL_INSTALL_PATH environ_cp['NCCL_INSTALL_PATH'] = nccl_install_path write_action_env_to_bazelrc('NCCL_INSTALL_PATH', nccl_install_path) @@ -1435,7 +1449,7 @@ def main(): setup_python(environ_cp) if is_windows(): - environ_cp['TF_NEED_S3'] = '0' + environ_cp['TF_NEED_AWS'] = '0' environ_cp['TF_NEED_GCP'] = '0' environ_cp['TF_NEED_HDFS'] = '0' environ_cp['TF_NEED_JEMALLOC'] = '0' @@ -1459,8 +1473,8 @@ def main(): 'with_gcp_support', True, 'gcp') set_build_var(environ_cp, 'TF_NEED_HDFS', 'Hadoop File System', 'with_hdfs_support', True, 'hdfs') - set_build_var(environ_cp, 'TF_NEED_S3', 'Amazon S3 File System', - 'with_s3_support', True, 's3') + set_build_var(environ_cp, 'TF_NEED_AWS', 'Amazon AWS Platform', + 'with_aws_support', True, 'aws') set_build_var(environ_cp, 'TF_NEED_KAFKA', 'Apache Kafka Platform', 'with_kafka_support', True, 'kafka') set_build_var(environ_cp, 'TF_ENABLE_XLA', 'XLA JIT', 'with_xla_support', diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 9b07669a5d8e4da6ce202fc9196185b91d8e7e2e..51eea94847e47ac3ffee89ed6bbae269b7b92c77 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -154,6 +154,12 @@ config_setting( visibility = ["//visibility:public"], ) +config_setting( + name = "linux_s390x", + values = {"cpu": "s390x"}, + visibility = ["//visibility:public"], +) + config_setting( name = "debug", values = { @@ -210,8 +216,8 @@ config_setting( ) config_setting( - name = "with_s3_support", - define_values = {"with_s3_support": "true"}, + name = "with_aws_support", + define_values = {"with_aws_support": "true"}, visibility = ["//visibility:public"], ) @@ -238,8 +244,8 @@ config_setting( ) config_setting( - name = "with_s3_support_windows_override", - define_values = {"with_s3_support": "true"}, + name = "with_aws_support_windows_override", + define_values = {"with_aws_support": "true"}, values = {"cpu": "x64_windows"}, visibility = ["//visibility:public"], ) @@ -251,6 +257,13 @@ config_setting( visibility = ["//visibility:public"], ) +config_setting( + name = "with_cuda_support_windows_override", + define_values = {"using_cuda_nvcc": "true"}, + values = {"cpu": "x64_windows"}, + visibility = ["//visibility:public"], +) + config_setting( name = "with_gcp_support_android_override", define_values = {"with_gcp_support": "true"}, @@ -266,8 +279,8 @@ config_setting( ) config_setting( - name = "with_s3_support_android_override", - define_values = {"with_s3_support": "true"}, + name = "with_aws_support_android_override", + define_values = {"with_aws_support": "true"}, values = {"crosstool_top": "//external:android/crosstool"}, visibility = ["//visibility:public"], ) @@ -287,8 +300,8 @@ config_setting( ) config_setting( - name = "with_s3_support_ios_override", - define_values = {"with_s3_support": "true"}, + name = "with_aws_support_ios_override", + define_values = {"with_aws_support": "true"}, values = {"crosstool_top": "//tools/osx/crosstool:crosstool"}, visibility = ["//visibility:public"], ) @@ -398,6 +411,7 @@ config_setting( package_group( name = "internal", packages = [ + "-//third_party/tensorflow/python/estimator", "//learning/meta_rank/...", "//tensorflow/...", "//tensorflow_fold/llgtm/...", @@ -424,6 +438,22 @@ filegroup( data = glob(["docs_src/**/*.md"]), ) +cc_library( + name = "grpc", + deps = select({ + ":linux_s390x": ["@grpc//:grpc_unsecure"], + "//conditions:default": ["@grpc"], + }), +) + +cc_library( + name = "grpc++", + deps = select({ + ":linux_s390x": ["@grpc//:grpc++_unsecure"], + "//conditions:default": ["@grpc//:grpc++"], + }), +) + # A shared object which includes registration mechanisms for ops and # kernels. Does not include the implementations of any ops or kernels. Instead, # the library which loads libtensorflow_framework.so @@ -451,6 +481,15 @@ filegroup( tf_cc_shared_object( name = "libtensorflow_framework.so", framework_so = [], + linkopts = select({ + "//tensorflow:darwin": [], + "//tensorflow:windows": [], + "//tensorflow:windows_msvc": [], + "//conditions:default": [ + "-Wl,--version-script", # This line must be directly followed by the version_script.lds file + "$(location //tensorflow:tf_framework_version_script.lds)", + ], + }), linkstatic = 1, visibility = ["//visibility:public"], deps = [ @@ -460,6 +499,7 @@ tf_cc_shared_object( "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry_impl", "//tensorflow/core:lib_internal_impl", "//tensorflow/stream_executor:stream_executor_impl", + "//tensorflow:tf_framework_version_script.lds", ] + tf_additional_binary_deps(), ) @@ -539,15 +579,27 @@ exports_files( ) gen_api_init_files( - name = "python_api_gen", + name = "tensorflow_python_api_gen", srcs = ["api_template.__init__.py"], root_init_template = "api_template.__init__.py", ) py_library( name = "tensorflow_py", - srcs = [":python_api_gen"], + srcs = ["//tensorflow/python/estimator/api:estimator_python_api_gen"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":tensorflow_py_no_contrib", + "//tensorflow/contrib:contrib_py", + "//tensorflow/python/estimator:estimator_py", + ], +) + +py_library( + name = "tensorflow_py_no_contrib", + srcs = [":tensorflow_python_api_gen"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = ["//tensorflow/python"], + deps = ["//tensorflow/python:no_contrib"], ) diff --git a/tensorflow/api_template.__init__.py b/tensorflow/api_template.__init__.py index 9b0d7d48afd058607badc90b95c9dca0c4ceaa31..779f65d5b17c350833f67f07985b00e8eb561e72 100644 --- a/tensorflow/api_template.__init__.py +++ b/tensorflow/api_template.__init__.py @@ -20,9 +20,25 @@ from __future__ import print_function # pylint: disable=g-bad-import-order from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import + +try: + import os # pylint: disable=g-import-not-at-top + # Add `estimator` attribute to allow access to estimator APIs via + # "tf.estimator..." + from tensorflow.python.estimator.api import estimator # pylint: disable=g-import-not-at-top + + # Add `estimator` to the __path__ to allow "from tensorflow.estimator..." + # style imports. + from tensorflow.python.estimator import api as estimator_api # pylint: disable=g-import-not-at-top + __path__ += [os.path.dirname(estimator_api.__file__)] + del estimator_api + del os +except (ImportError, AttributeError): + print('tf.estimator package not installed.') + # API IMPORTS PLACEHOLDER -from tensorflow.python.util.lazy_loader import LazyLoader +from tensorflow.python.util.lazy_loader import LazyLoader # pylint: disable=g-import-not-at-top contrib = LazyLoader('contrib', globals(), 'tensorflow.contrib') del LazyLoader diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index b86b277ac3200b88ae03490a6c1b64d464e81950..5c218d3f25e01f0e78916d4a5a8b1d2751f9dc25 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -45,6 +45,7 @@ limitations under the License. #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/core/graph/validate.h" #include "tensorflow/core/lib/core/coding.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" @@ -390,64 +391,6 @@ void TF_Reset_Helper(const TF_SessionOptions* opt, const char** containers, status->status = Reset(opt->options, container_names); } -// This traverses the specified nodes in topological order to verify there are -// no cycles. Starting with inputless nodes, it visits nodes whose inputs have -// all been visited, and counts the total number of visited nodes. If there is a -// cycle, nodes in the cycle will never be visited, and the visited count will -// be less than the total node count. -Status ValidateNoCycles(const Graph& g) { - // TODO(nolivia): check this on a subset of the graph instead of all of it. - // A node is ready when all of its inputs have been visited. - std::vector ready; - std::vector pending_count(g.num_node_ids(), 0); - - for (int i = 0; i < g.num_node_ids(); ++i) { - const Node* n = g.FindNodeId(i); - if (n == nullptr) continue; - pending_count[i] = n->in_edges().size(); - if (n->IsMerge()) { - // While-loop cycles are legal cycles so we manually adjust the - // pending_count to make sure that the loop is visited. - for (const Edge* e : n->in_edges()) { - if (!e->IsControlEdge() && e->src()->IsNextIteration()) { - pending_count[i]--; - } - } - } - if (pending_count[i] == 0) { - ready.push_back(n); - } - } - - int processed = 0; - while (!ready.empty()) { - const Node* node = ready.back(); - ready.pop_back(); - ++processed; - - for (const Edge* out : node->out_edges()) { - const int output_id = out->dst()->id(); - pending_count[output_id]--; - if (pending_count[output_id] == 0) { - ready.push_back(out->dst()); - } - } - } - - if (processed < g.num_nodes()) { - std::vector nodes_in_cycle; - for (int i = 0; i < pending_count.size() && nodes_in_cycle.size() < 3; - ++i) { - if (pending_count[i] != 0) { - nodes_in_cycle.push_back(g.FindNodeId(i)->name()); - } - } - return errors::InvalidArgument( - "Graph is invalid, contains a cycle with ", g.num_nodes() - processed, - " nodes, including: ", str_util::Join(nodes_in_cycle, ", ")); - } - return Status::OK(); -} } // namespace } // namespace tensorflow @@ -631,7 +574,22 @@ Status MessageToBuffer(const tensorflow::protobuf::Message& in, "Failed to allocate memory to serialize message of type '", in.GetTypeName(), "' and size ", proto_size); } - in.SerializeToArray(buf, proto_size); + // SerializeToArray takes size as an int. + // This next 'if' is a workaround till we update to depend on a version + // of protocol buffers that includes + // https://github.com/google/protobuf/pull/4739 + if (proto_size > std::numeric_limits::max()) { + return InvalidArgument("Cannot serialize protocol buffer of type ", + in.GetTypeName(), " as the serialized size (", + proto_size, + "bytes) would be larger than the limit (", + std::numeric_limits::max(), " bytes)"); + } + if (!in.SerializeToArray(buf, proto_size)) { + return InvalidArgument("Unable to serialize ", in.GetTypeName(), + " protocol buffer, perhaps the serialized size (", + proto_size, " bytes) is too large?"); + } out->data = buf; out->length = proto_size; out->data_deallocator = [](void* data, size_t length) { @@ -731,7 +689,9 @@ bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { const auto num_nodes = graph.num_node_ids(); if (session->last_num_graph_nodes < num_nodes) { - status->status = tensorflow::ValidateNoCycles(session->graph->graph); + // TODO(nolivia): check this on a subset of the graph instead of all of + // it. + status->status = graph::ValidateGraphHasNoCycle(session->graph->graph); if (!status->status.ok()) { session->graph->mu.unlock(); return false; @@ -2108,7 +2068,8 @@ TF_ImportGraphDefResults* TF_GraphImportGraphDefWithResults( TF_Graph* graph, const TF_Buffer* graph_def, const TF_ImportGraphDefOptions* options, TF_Status* status) { GraphDef def; - if (!def.ParseFromArray(graph_def->data, graph_def->length)) { + if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, + graph_def->length)) { status->status = InvalidArgument("Invalid GraphDef"); return nullptr; } @@ -2138,7 +2099,8 @@ void TF_GraphImportGraphDefWithReturnOutputs( return; } GraphDef def; - if (!def.ParseFromArray(graph_def->data, graph_def->length)) { + if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, + graph_def->length)) { status->status = InvalidArgument("Invalid GraphDef"); return; } @@ -2454,7 +2416,18 @@ void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx, for (int i = first_new_node_id; i < g->graph.num_node_ids(); ++i) { Node* n = g->graph.FindNodeId(i); if (n == nullptr) continue; - g->name_map[n->name()] = n; + // We have a convoluted scheme here: Using the C++ graph construction API + // to add potentially many nodes to the graph without running the checks + // (such as uniqueness of the names of nodes) we run with other functions + // that add a node to the graph (like TF_FinishOperation). + if (!g->name_map.insert(std::make_pair(n->name(), n)).second) { + status->status = tensorflow::errors::Internal( + "BUG: The API allowed construction of a graph with duplicate node " + "names (", + n->name(), + "). This is a bug. Please file an issue at " + "https://github.com/tensorflow/tensorflow/issues."); + } } } diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index c8594347451dffd465d7fa926cc53818dc9e38d4..1eb75ef11ff337dfcb2e016e09804fc04662fcda 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -894,7 +894,8 @@ TF_CAPI_EXPORT extern void TF_DeleteImportGraphDefOptions( TF_ImportGraphDefOptions* opts); // Set the prefix to be prepended to the names of nodes in `graph_def` that will -// be imported into `graph`. +// be imported into `graph`. `prefix` is copied and has no lifetime +// requirements. TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsSetPrefix( TF_ImportGraphDefOptions* opts, const char* prefix); @@ -915,6 +916,7 @@ TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsSetUniquifyPrefix( // Set any imported nodes with input `src_name:src_index` to have that input // replaced with `dst`. `src_name` refers to a node in the graph to be imported, // `dst` references a node already existing in the graph being imported into. +// `src_name` is copied and has no lifetime requirements. TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddInputMapping( TF_ImportGraphDefOptions* opts, const char* src_name, int src_index, TF_Output dst); @@ -922,7 +924,7 @@ TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddInputMapping( // Set any imported nodes with control input `src_name` to have that input // replaced with `dst`. `src_name` refers to a node in the graph to be imported, // `dst` references an operation already existing in the graph being imported -// into. +// into. `src_name` is copied and has no lifetime requirements. TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsRemapControlDependency( TF_ImportGraphDefOptions* opts, const char* src_name, TF_Operation* dst); @@ -934,6 +936,7 @@ TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddControlDependency( // Add an output in `graph_def` to be returned via the `return_outputs` output // parameter of TF_GraphImportGraphDef(). If the output is remapped via an input // mapping, the corresponding existing tensor in `graph` will be returned. +// `oper_name` is copied and has no lifetime requirements. TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddReturnOutput( TF_ImportGraphDefOptions* opts, const char* oper_name, int index); @@ -943,7 +946,8 @@ TF_CAPI_EXPORT extern int TF_ImportGraphDefOptionsNumReturnOutputs( const TF_ImportGraphDefOptions* opts); // Add an operation in `graph_def` to be returned via the `return_opers` output -// parameter of TF_GraphImportGraphDef(). +// parameter of TF_GraphImportGraphDef(). `oper_name` is copied and has no +// lifetime requirements. TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddReturnOperation( TF_ImportGraphDefOptions* opts, const char* oper_name); diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index 577f10c5e69ea9ecbe8ce821c6bd5167e98bef25..bc04b53fbb7fa9ba46228ae5a4ec8ee96df5f3dc 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -1160,7 +1160,7 @@ TEST(CAPI, GetOpDef) { } void StringVectorToArrays(const std::vector& v, - std::unique_ptr* ptrs, + std::unique_ptr* ptrs, std::unique_ptr* lens) { ptrs->reset(new const void*[v.size()]); lens->reset(new size_t[v.size()]); @@ -1196,7 +1196,7 @@ class CApiColocationTest : public ::testing::Test { void SetViaStringList(TF_OperationDescription* desc, const std::vector& list) { - std::unique_ptr list_ptrs; + std::unique_ptr list_ptrs; std::unique_ptr list_lens; StringVectorToArrays(list, &list_ptrs, &list_lens); TF_SetAttrStringList(desc, tensorflow::kColocationAttrName, list_ptrs.get(), @@ -1700,6 +1700,61 @@ TEST_F(CApiGradientsTest, OpWithNoGradientRegistered_NoGradInputs) { TestGradientsError(false); } +void ScalarFloatFromTensor(const TF_Tensor* t, float* f) { + ASSERT_TRUE(t != nullptr); + ASSERT_EQ(TF_FLOAT, TF_TensorType(t)); + ASSERT_EQ(0, TF_NumDims(t)); + ASSERT_EQ(4, TF_TensorByteSize(t)); + float* p = static_cast(TF_TensorData(t)); + *f = *p; +} + +TEST_F(CApiGradientsTest, MultipleCallsToAddGradients) { + const float X = 3.0f, Y = 7.0f; + TF_Operation* x = Placeholder(graph_, s_, "x", TF_FLOAT); + TF_Operation* y = Placeholder(graph_, s_, "y", TF_FLOAT); + TF_Operation* xy = Mul(x, y, graph_, s_, "xy"); + TF_Output dxy_dx, dxy_dy; + + TF_Output outputs[1] = {{xy, 0}}; + TF_Output inputs[1] = {{x, 0}}; + TF_AddGradients(graph_, outputs, 1, inputs, 1, nullptr, s_, &dxy_dx); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + inputs[0] = {y, 0}; + TF_AddGradients(graph_, outputs, 1, inputs, 1, nullptr, s_, &dxy_dy); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_SessionOptions* opts = TF_NewSessionOptions(); + TF_Session* sess = TF_NewSession(graph_, opts, s_); + TF_DeleteSessionOptions(opts); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_Output feeds[] = {{x, 0}, {y, 0}}; + TF_Tensor* feedValues[] = {FloatTensor(X), FloatTensor(Y)}; + TF_Output fetches[] = {dxy_dx, dxy_dy}; + TF_Tensor* fetchValues[] = {nullptr, nullptr}; + + TF_SessionRun(sess, nullptr /* run_options */, feeds, feedValues, 2, fetches, + fetchValues, 2, nullptr /* target_opers */, 0, + nullptr /* run_metadata */, s_); + TF_DeleteTensor(feedValues[0]); + TF_DeleteTensor(feedValues[1]); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + TF_DeleteSession(sess, s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + float dxy_dxValue = 0.0f, dxy_dyValue = 0.0f; + ScalarFloatFromTensor(fetchValues[0], &dxy_dxValue); + EXPECT_EQ(Y, dxy_dxValue); + + ScalarFloatFromTensor(fetchValues[1], &dxy_dyValue); + EXPECT_EQ(X, dxy_dyValue); + + TF_DeleteTensor(fetchValues[0]); + TF_DeleteTensor(fetchValues[1]); +} + // REGISTER_OP for CApiAttributesTest test cases. // Registers two ops, each with a single attribute called 'v'. // The attribute in one op will have a type 'type', the other @@ -1784,7 +1839,7 @@ TEST_F(CApiAttributesTest, String) { TEST_F(CApiAttributesTest, StringList) { std::vector list = {"bugs", "bunny", "duck"}; - std::unique_ptr list_ptrs; + std::unique_ptr list_ptrs; std::unique_ptr list_lens; StringVectorToArrays(list, &list_ptrs, &list_lens); int list_total_size = 0; @@ -1800,7 +1855,7 @@ TEST_F(CApiAttributesTest, StringList) { ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); EXPECT_TF_META("v", list.size(), TF_ATTR_STRING, list_total_size); - std::unique_ptr values(new void*[list.size()]); + std::unique_ptr values(new void*[list.size()]); std::unique_ptr lens(new size_t[list.size()]); std::unique_ptr storage(new char[list_total_size]); TF_OperationGetAttrStringList(oper, "v", values.get(), lens.get(), @@ -2025,7 +2080,7 @@ TEST_F(CApiAttributesTest, TensorShapeProtoList) { tensorflow::PartialTensorShape(pts2).AsProto(&proto); proto.SerializeToString(&bytes2); - std::unique_ptr list_ptrs; + std::unique_ptr list_ptrs; std::unique_ptr list_lens; const std::vector list = {bytes1, bytes2}; StringVectorToArrays(list, &list_ptrs, &list_lens); diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc index f3b28c1708129d39e451d927a89c0d10e2193b63..24eb6c069b21349fce288db3e79fbf14e824ad11 100644 --- a/tensorflow/c/c_test_util.cc +++ b/tensorflow/c/c_test_util.cc @@ -216,6 +216,13 @@ TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph, return MinWithDevice(l, r, graph, /*op_device=*/"", s, name); } +TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name) { + TF_Operation* op; + BinaryOpHelper("Mul", l, r, graph, s, name, &op, "", true); + return op; +} + TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s, const char* name) { TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name); diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h index c16aba666ee6974fed5351c2d9ac291dcbcdecab..38313d647ca93d4779bb1325f8ed7bde4b743879 100644 --- a/tensorflow/c/c_test_util.h +++ b/tensorflow/c/c_test_util.h @@ -80,6 +80,9 @@ TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s, TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name = "min"); +TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph, + TF_Status* s, const char* name = "mul"); + // If `op_device` is non-empty, set the created op on that device. TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph, const string& op_device, TF_Status* s, diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD index f265da2c2c89c0e9caf14f2213c606fcb69997e0..37be52f57d865c1e59611540d5dab04b59e89444 100644 --- a/tensorflow/c/eager/BUILD +++ b/tensorflow/c/eager/BUILD @@ -54,7 +54,6 @@ tf_cuda_library( "//tensorflow/core/distributed_runtime/eager:eager_client", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", @@ -93,10 +92,10 @@ tf_cuda_library( "//tensorflow/core/distributed_runtime/eager:eager_client", "//tensorflow/core/distributed_runtime/eager:remote_tensor_handle", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", + "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client", ], ) @@ -122,6 +121,7 @@ tf_cuda_library( tf_cuda_cc_test( name = "c_api_test", + size = "small", srcs = [ "c_api_debug_test.cc", "c_api_test.cc", @@ -139,7 +139,7 @@ tf_cuda_cc_test( "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", + "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", ], ) diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 81221c4078bec9820ee187efdf0314da378be62b..82ca2be2cff885967dd798a1cb84b164a9df399e 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -36,9 +36,9 @@ limitations under the License. #include "tensorflow/core/common_runtime/eager/execute.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/server_lib.h" #include "tensorflow/core/distributed_runtime/worker_env.h" #include "tensorflow/core/framework/node_def_util.h" @@ -46,10 +46,12 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/refcount.h" +#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" +#include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/thread_annotations.h" @@ -107,7 +109,8 @@ tensorflow::Status GetAllRemoteDevices( } tensorflow::Status CreateRemoteContexts( - const std::vector& remote_workers, + const std::vector& remote_workers, int64 rendezvous_id, + const tensorflow::ServerDef& server_def, tensorflow::eager::EagerClientCache* remote_eager_workers, bool async, tensorflow::gtl::FlatMap* remote_contexts) { for (int i = 0; i < remote_workers.size(); i++) { @@ -115,12 +118,14 @@ tensorflow::Status CreateRemoteContexts( tensorflow::eager::CreateContextRequest request; tensorflow::eager::CreateContextResponse response; + request.set_rendezvous_id(rendezvous_id); tensorflow::DeviceNameUtils::ParsedName parsed_name; if (!tensorflow::DeviceNameUtils::ParseFullName(remote_worker, &parsed_name)) { return tensorflow::errors::InvalidArgument( "Unable to parse ", remote_worker, " as a device name"); } + *request.mutable_server_def() = server_def; request.mutable_server_def()->set_job_name(parsed_name.job); request.mutable_server_def()->set_task_index(parsed_name.task); request.set_async(async); @@ -147,46 +152,82 @@ tensorflow::Status CreateRemoteContexts( tensorflow::Status NewRemoteAwareTFE_Context(const TFE_ContextOptions* opts, TFE_Context** ctx) { + // We don't use the TF_RETURN_IF_ERROR macro directly since that destroys the + // server object (which currently CHECK-fails) and we miss the error, instead, + // we log the error, and then return to allow the user to see the error + // message. +#define LOG_AND_RETURN_IF_ERROR(...) \ + do { \ + const ::tensorflow::Status _status = (__VA_ARGS__); \ + if (TF_PREDICT_FALSE(!_status.ok())) { \ + LOG(ERROR) << _status.error_message(); \ + return _status; \ + } \ + } while (0); + string worker_name = tensorflow::strings::StrCat( "/job:", opts->server_def.job_name(), "/replica:0/task:", opts->server_def.task_index()); - std::unique_ptr server; - TF_RETURN_IF_ERROR( - tensorflow::eager::EagerGrpcServer::Create(opts->server_def, &server)); - TF_RETURN_IF_ERROR(server->Start()); + std::unique_ptr server; + LOG_AND_RETURN_IF_ERROR(tensorflow::NewServer(opts->server_def, &server)); + + tensorflow::GrpcServer* grpc_server = + dynamic_cast(server.get()); + if (grpc_server == nullptr) { + LOG_AND_RETURN_IF_ERROR(tensorflow::errors::Internal( + "Currently, TFE_NewContext only supports tensorflow::GrpcServer.")); + } + + LOG_AND_RETURN_IF_ERROR(grpc_server->Start()); + + int64 rendezvous_id = tensorflow::random::New64(); std::vector remote_workers; - server->master_env()->worker_cache->ListWorkers(&remote_workers); + grpc_server->master_env()->worker_cache->ListWorkers(&remote_workers); remote_workers.erase( std::remove(remote_workers.begin(), remote_workers.end(), worker_name), remote_workers.end()); std::unique_ptr remote_device_mgr; - TF_RETURN_IF_ERROR(GetAllRemoteDevices( - remote_workers, server->master_env()->worker_cache, &remote_device_mgr)); + LOG_AND_RETURN_IF_ERROR(GetAllRemoteDevices( + remote_workers, grpc_server->master_env()->worker_cache, + &remote_device_mgr)); std::shared_ptr channel_cache = - server->channel_cache(); + grpc_server->channel_cache(); std::unique_ptr remote_eager_workers( tensorflow::eager::NewGrpcEagerClientCache(channel_cache)); // Initialize remote eager workers. tensorflow::gtl::FlatMap remote_contexts; - TF_RETURN_IF_ERROR(CreateRemoteContexts(remote_workers, - remote_eager_workers.get(), - opts->async, &remote_contexts)); + LOG_AND_RETURN_IF_ERROR(CreateRemoteContexts( + remote_workers, rendezvous_id, opts->server_def, + remote_eager_workers.get(), opts->async, &remote_contexts)); tensorflow::RemoteRendezvous* r = - server->worker_env()->rendezvous_mgr->Find(0); + grpc_server->worker_env()->rendezvous_mgr->Find(rendezvous_id); + + auto session_name = tensorflow::strings::StrCat("eager_", rendezvous_id); + TF_RETURN_IF_ERROR(grpc_server->worker_env()->session_mgr->CreateSession( + session_name, opts->server_def, true)); + + std::shared_ptr worker_session; + TF_RETURN_IF_ERROR( + grpc_server->worker_env()->session_mgr->WorkerSessionForSession( + session_name, &worker_session)); + + // Initialize remote tensor communication based on worker session. + TF_RETURN_IF_ERROR(r->Initialize(worker_session.get())); - auto* device_mgr = server->worker_env()->device_mgr; + auto* device_mgr = grpc_server->worker_env()->device_mgr; *ctx = new TFE_Context(opts->session_options.options, opts->policy, opts->async, device_mgr, r, std::move(server), std::move(remote_eager_workers), std::move(remote_device_mgr), remote_contexts); return tensorflow::Status::OK(); +#undef LOG_AND_RETURN_IF_ERROR } } // namespace @@ -307,16 +348,16 @@ TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) { } int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) { - const tensorflow::Tensor* t = nullptr; - status->status = h->handle->Tensor(&t); - return t == nullptr ? 0 : t->dims(); + int result; + status->status = h->handle->NumDims(&result); + return result; } int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index, TF_Status* status) { - const tensorflow::Tensor* t = nullptr; - status->status = h->handle->Tensor(&t); - return t == nullptr ? 0 : t->dim_size(dim_index); + tensorflow::int64 result; + status->status = h->handle->Dim(dim_index, &result); + return result; } const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) { @@ -421,8 +462,11 @@ TF_AttrType TFE_OpNameGetAttrType(TFE_Context* ctx, return ret; } -void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const char* value) { - op->operation.MutableAttrs()->Set(attr_name, value); +void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const void* value, + size_t length) { + op->operation.MutableAttrs()->Set( + attr_name, + tensorflow::StringPiece(static_cast(value), length)); } void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) { @@ -473,16 +517,22 @@ void TFE_OpSetAttrFunction(TFE_Op* op, const char* attr_name, op->operation.MutableAttrs()->Set(attr_name, attr_value); } -#define TFE_OP_SET_ATTR_LIST(fn, type) \ - void fn(TFE_Op* op, const char* attr_name, const type* values, \ - int num_values) { \ - op->operation.MutableAttrs()->Set( \ - attr_name, \ - tensorflow::gtl::ArraySlice(values, num_values)); \ +void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name, + const void* const* values, const size_t* lengths, + int num_values) { + std::vector v(num_values); + for (int i = 0; i < num_values; ++i) { + v[i] = tensorflow::StringPiece(static_cast(values[i]), + lengths[i]); } -TFE_OP_SET_ATTR_LIST(TFE_OpSetAttrStringList, char*) -TFE_OP_SET_ATTR_LIST(TFE_OpSetAttrFloatList, float) -#undef TFE_OP_SET_ATTR_LIST + op->operation.MutableAttrs()->Set(attr_name, v); +} + +void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name, + const float* values, int num_values) { + op->operation.MutableAttrs()->Set( + attr_name, tensorflow::gtl::ArraySlice(values, num_values)); +} void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name, const int64_t* values, int num_values) { @@ -655,9 +705,11 @@ void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, const char* attr_name, TF_Status* status) { switch (default_value.value_case()) { - case tensorflow::AttrValue::kS: - TFE_OpSetAttrString(op, attr_name, default_value.s().data()); + case tensorflow::AttrValue::kS: { + const string& v = default_value.s(); + TFE_OpSetAttrString(op, attr_name, v.data(), v.size()); break; + } case tensorflow::AttrValue::kI: TFE_OpSetAttrInt(op, attr_name, static_cast(default_value.i())); break; diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 1862af3ce2f505a6e83b4805417eaf335ed07bc0..fdbd5374b2afe815c3a81b453930eb8f1fa351d3 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -278,7 +278,8 @@ TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType( TF_CAPI_EXPORT extern void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, - const char* value); + const void* value, + size_t length); TF_CAPI_EXPORT extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value); TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, @@ -305,7 +306,8 @@ TF_CAPI_EXPORT extern void TFE_OpSetAttrFunction(TFE_Op* op, TF_CAPI_EXPORT extern void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name, - const char** value, + const void* const* values, + const size_t* lengths, int num_values); TF_CAPI_EXPORT extern void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name, diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h index 04a6efc47c5177c82b7e88168b67cc584587de7c..4c5077023d5bb3b83808bf3908e7110dd026e3ad 100644 --- a/tensorflow/c/eager/c_api_internal.h +++ b/tensorflow/c/eager/c_api_internal.h @@ -39,7 +39,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/rendezvous_mgr.h" #include "tensorflow/core/distributed_runtime/eager/eager_client.h" #include "tensorflow/core/distributed_runtime/remote_device.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h" #include "tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h" @@ -78,7 +78,7 @@ struct TFE_Context { TFE_ContextDevicePlacementPolicy default_policy, bool async, tensorflow::DeviceMgr* local_device_mgr, tensorflow::Rendezvous* rendezvous, - std::unique_ptr server, + std::unique_ptr server, std::unique_ptr remote_eager_workers, std::unique_ptr remote_device_mgr, const tensorflow::gtl::FlatMap& diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 27ff5f7211b0592637a173d337f93c10d376443f..3504a8b5e78480732d3454097c1b2197ac2b2e17 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/c/eager/c_api_test_util.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/framework/function.pb.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" @@ -132,18 +132,20 @@ void TestRemoteExecute(bool async) { server_def.set_task_index(1); - std::unique_ptr worker_server; - ASSERT_TRUE( - tensorflow::eager::EagerGrpcServer::Create(server_def, &worker_server) - .ok()); + std::unique_ptr worker_server; + ASSERT_TRUE(tensorflow::GrpcServer::Create( + server_def, tensorflow::Env::Default(), &worker_server) + .ok()); ASSERT_TRUE(worker_server->Start().ok()); TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); TFE_ContextOptionsSetServerDef(opts, serialized.data(), serialized.size(), status); - TFE_ContextOptionsSetAsync(opts, static_cast(1)); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); + TFE_ContextOptionsSetDevicePlacementPolicy(opts, + TFE_DEVICE_PLACEMENT_EXPLICIT); TFE_Context* ctx = TFE_NewContext(opts, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -205,6 +207,95 @@ void TestRemoteExecute(bool async) { TEST(CAPI, RemoteExecute) { TestRemoteExecute(false); } TEST(CAPI, RemoteExecuteAsync) { TestRemoteExecute(true); } +void TestRemoteExecuteSilentCopies(bool async) { + tensorflow::ServerDef server_def = GetServerDef(3); + + // This server def has the task index set to 0. + string serialized = server_def.SerializeAsString(); + + server_def.set_task_index(1); + std::unique_ptr worker_server1; + ASSERT_TRUE(tensorflow::GrpcServer::Create( + server_def, tensorflow::Env::Default(), &worker_server1) + .ok()); + ASSERT_TRUE(worker_server1->Start().ok()); + + server_def.set_task_index(2); + std::unique_ptr worker_server2; + ASSERT_TRUE(tensorflow::GrpcServer::Create( + server_def, tensorflow::Env::Default(), &worker_server2) + .ok()); + ASSERT_TRUE(worker_server2->Start().ok()); + + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetServerDef(opts, serialized.data(), serialized.size(), + status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); + TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_SILENT); + TFE_Context* ctx = TFE_NewContext(opts, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(); + TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(); + const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0"; + const char task2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0"; + + auto* h1_task2 = + TFE_TensorHandleCopyToDevice(h1_task0, ctx, task2_name, status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + // Handles are on task0 (local), and task2, but op is on task1. + TFE_Op* matmul = MatMulOp(ctx, h0_task0, h1_task2); + TFE_OpSetDevice(matmul, task1_name, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + TFE_TensorHandle* retvals[1]; + int num_retvals = 1; + TFE_Execute(matmul, &retvals[0], &num_retvals, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + auto* retval_task0 = TFE_TensorHandleCopyToDevice( + retvals[0], ctx, "/job:localhost/replica:0/task:0/device:CPU:0", status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + TF_Tensor* t = TFE_TensorHandleResolve(retval_task0, status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteTensorHandle(retval_task0); + float product[4] = {0}; + EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); + memcpy(&product[0], TF_TensorData(t), TF_TensorByteSize(t)); + TF_DeleteTensor(t); + EXPECT_EQ(7, product[0]); + EXPECT_EQ(10, product[1]); + EXPECT_EQ(15, product[2]); + EXPECT_EQ(22, product[3]); + + TFE_DeleteTensorHandle(h0_task0); + TFE_DeleteTensorHandle(h1_task0); + TFE_DeleteTensorHandle(h1_task2); + TFE_DeleteTensorHandle(retvals[0]); + + TFE_DeleteOp(matmul); + + TFE_ContextAsyncWait(ctx, status); + TFE_DeleteContext(ctx, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + + TF_DeleteStatus(status); + + // TODO(nareshmodi): Figure out how to correctly shut the server down. + worker_server1.release(); + worker_server2.release(); +} + +TEST(CAPI, RemoteExecuteSilentCopies) { TestRemoteExecuteSilentCopies(false); } +TEST(CAPI, RemoteExecuteSilentCopiesAsync) { + TestRemoteExecuteSilentCopies(true); +} + TEST(CAPI, TensorHandle) { TFE_TensorHandle* h = TestMatrixTensorHandle(); EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(h)); @@ -1083,8 +1174,8 @@ TFE_TensorHandle* CreateVariable(TFE_Context* ctx, float value, if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op, "dtype", TF_FLOAT); TFE_OpSetAttrShape(op, "shape", {}, 0, status); - TFE_OpSetAttrString(op, "container", ""); - TFE_OpSetAttrString(op, "shared_name", ""); + TFE_OpSetAttrString(op, "container", "", 0); + TFE_OpSetAttrString(op, "shared_name", "", 0); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_TensorHandle* var_handle = nullptr; int num_retvals = 1; diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD index 079e063d3e3fbdaf833e9031f5f9438853c14099..a98f0b00b2c70055f697ed4f15cb14708384b62f 100644 --- a/tensorflow/cc/BUILD +++ b/tensorflow/cc/BUILD @@ -530,7 +530,7 @@ cc_library_with_android_deps( "//tensorflow/core/api_def:base_api_def", ], deps = [ - "//tensorflow/core:framework", + "//tensorflow/core:framework_headers_lib", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:op_gen_lib", diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc index d6a4f141b6bb8ccadb77f1fa83b5fb742d78f70f..dfdef88945deca376368edd6f7aa322b1e1cbf94 100644 --- a/tensorflow/cc/framework/cc_op_gen.cc +++ b/tensorflow/cc/framework/cc_op_gen.cc @@ -273,6 +273,12 @@ string PrintAttrValue(const string& op, const AttrValue& attr_value) { return ""; // Prevent missing return warning } +bool IsEmptyList(const AttrValue::ListValue& list) { + return list.s_size() == 0 && list.i_size() == 0 && list.f_size() == 0 && + list.b_size() == 0 && list.type_size() == 0 && + list.shape_size() == 0 && list.tensor_size() == 0; +} + string ToCamelCase(const string& str) { string result; const char joiner = '_'; @@ -297,9 +303,9 @@ string ToCamelCase(const string& str) { // indicate whether to treat the type as const when accepting the C++ type as an // argument to a function. std::pair AttrTypeName(StringPiece attr_type) { - static const std::unordered_map, - StringPieceHasher> - attr_type_map{ + static const auto* attr_type_map = + new std::unordered_map, + StringPieceHasher>{ {"string", {"StringPiece", false}}, {"list(string)", {"gtl::ArraySlice", true}}, {"int", {"int64", false}}, @@ -317,14 +323,34 @@ std::pair AttrTypeName(StringPiece attr_type) { {"func", {"NameAttrList", true}}, }; - auto entry = attr_type_map.find(attr_type); - if (entry == attr_type_map.end()) { + auto entry = attr_type_map->find(attr_type); + if (entry == attr_type_map->end()) { LOG(FATAL) << "Unsupported Attr type: " << attr_type; return {"", false}; } return entry->second; } +const char* ListElementTypeName(StringPiece attr_type) { + static const auto* attr_list_type_map = + new std::unordered_map{ + {"list(string)", "string"}, + {"list(int)", "int"}, + {"list(float)", "float"}, + {"list(bool)", "bool"}, + {"list(type)", "DataType"}, + {"list(shape)", "PartialTensorShape"}, + {"list(tensor)", "TensorProto"}, + }; + + auto entry = attr_list_type_map->find(attr_type); + if (entry == attr_list_type_map->end()) { + LOG(FATAL) << "Unsupported or non-list Attr type: " << attr_type; + return ""; + } + return entry->second; +} + bool IsCPPKeyword(StringPiece name) { static const std::unordered_set // Keywords obtained from http://en.cppreference.com/w/cpp/keyword @@ -668,6 +694,7 @@ OpInfo::OpInfo(const OpDef& graph_op_def, const ApiDef& api_def, string OpInfo::GetOpAttrStruct() const { string struct_fields; string setters; + string defaults_static_storage; for (int i = 0; i < graph_op_def.attr_size(); ++i) { const auto& attr(graph_op_def.attr(i)); @@ -705,11 +732,32 @@ string OpInfo::GetOpAttrStruct() const { "_ = x;\n"); strings::StrAppend(&setters, " return ret;\n }\n\n"); - strings::StrAppend( - &struct_fields, " ", attr_type_name, " ", api_def_attr.rename_to(), - "_ = ", - PrintAttrValue(graph_op_def.name(), api_def_attr.default_value()), - ";\n"); + string field_initiliazer; + auto& default_value = api_def_attr.default_value(); + if (default_value.value_case() == AttrValue::kList && + !IsEmptyList(default_value.list())) { + // Non-empty lists need static storage for their defaults. Define a + // function with static local variable that stores the array. + strings::StrAppend(&defaults_static_storage, " static ", + attr_type_name, " Default_", api_def_attr.rename_to(), + "() {\n"); + strings::StrAppend( + &defaults_static_storage, " static const ", + ListElementTypeName(attr.type()), " kStorage[] = ", + PrintAttrValue(graph_op_def.name(), api_def_attr.default_value()), + ";\n"); + strings::StrAppend(&defaults_static_storage, " return ", + attr_type_name, "(kStorage);\n }\n"); + // Set the field_initializer to call the defined function. + strings::StrAppend(&field_initiliazer, "Default_", + api_def_attr.rename_to(), "()"); + } else { + field_initiliazer = + PrintAttrValue(graph_op_def.name(), api_def_attr.default_value()); + } + strings::StrAppend(&struct_fields, " ", attr_type_name, " ", + api_def_attr.rename_to(), "_ = ", field_initiliazer, + ";\n"); } if (struct_fields.empty()) { @@ -721,6 +769,9 @@ string OpInfo::GetOpAttrStruct() const { string struct_decl = MakeComment(attrs_comment, " "); strings::StrAppend(&struct_decl, " struct Attrs {\n"); strings::StrAppend(&struct_decl, setters, struct_fields); + if (!defaults_static_storage.empty()) { + strings::StrAppend(&struct_decl, " private:\n", defaults_static_storage); + } strings::StrAppend(&struct_decl, " };\n"); return struct_decl; diff --git a/tensorflow/cc/framework/scope.cc b/tensorflow/cc/framework/scope.cc index 62a889181e787f2e181135ab0563c45e1bab8812..8c886f31711eb014fb9e9d600c9c78cf22073f71 100644 --- a/tensorflow/cc/framework/scope.cc +++ b/tensorflow/cc/framework/scope.cc @@ -37,6 +37,11 @@ Scope& Scope::operator=(const Scope& other) { return *this; } +namespace { +const char kScopeSeparator[] = "/"; +const char kSuffixSeparator[] = "_"; +} // namespace + Scope::Impl::Impl(Graph* graph, Status* status, NameMap* name_map, ShapeRefiner* refiner, bool disable_shape_inference) : graph_(graph), @@ -308,19 +313,23 @@ string Scope::Impl::GetUniqueName(const string& prefix, return prefix; } auto entry = name_map_->find(prefix); - string unique_name = prefix; if (entry == name_map_->end()) { name_map_->insert({prefix, 0}); - } else { - unique_name = strings::StrCat(unique_name, "_", ++entry->second); + return prefix; } + string unique_name; + do { + unique_name = strings::StrCat(prefix, kSuffixSeparator, ++entry->second); + } while (name_map_->find(unique_name) != name_map_->end()); + name_map_->insert({unique_name, 0}); return unique_name; } string Scope::Impl::GetNameForOp(const string& default_name) const { const string unique_name = GetUniqueName(default_name, true /* check_single_use */); - const string sep = name_.empty() || unique_name.empty() ? "" : "/"; + const string sep = + name_.empty() || unique_name.empty() ? "" : kScopeSeparator; return strings::StrCat(name_, sep, unique_name); } @@ -345,7 +354,8 @@ Scope Scope::NewSubScope(const string& child_scope_name) const { } const string unique_name = impl()->GetUniqueName(child_scope_name, false /* check_single_use */); - const string sep = impl()->name_.empty() || unique_name.empty() ? "" : "/"; + const string sep = + impl()->name_.empty() || unique_name.empty() ? "" : kScopeSeparator; return Scope(new Impl(*this, Impl::Tags::ScopeName(), strings::StrCat(impl()->name_, sep, unique_name), false /* copy_names */)); @@ -412,7 +422,7 @@ CompositeOpScopes Scope::GetCompositeOpScopes( if (!impl()->single_use_scope()) { Scope child = NewSubScope(impl()->op_name_.empty() ? composite_op_name : impl()->op_name_); - const string child_op_sep = impl()->name_.empty() ? "" : "_"; + const string child_op_sep = impl()->name_.empty() ? "" : kSuffixSeparator; const string child_name = strings::StrCat(impl()->name_, child_op_sep, child.impl()->name_); return {child, @@ -435,7 +445,13 @@ class InternalScope { static Scope NewScope(Graph* graph, Status* status, ShapeRefiner* refiner) { Scope::Impl::NameMap* name_map = new Scope::Impl::NameMap; for (const Node* node : graph->nodes()) { - (*name_map)[node->name()] = 0; + const string& name = node->name(); + (*name_map)[name] = 0; + // Add all name prefixes ('/' separated). + size_t idx = -1; + while ((idx = name.find(kScopeSeparator, idx + 1)) != string::npos) { + (*name_map)[name.substr(0, idx)] = 0; + } } // We provide null destructors for these shared ptrs (except for name_map) // since the caller owns them and doesn't want the scope to destroy them. diff --git a/tensorflow/cc/framework/scope_internal.h b/tensorflow/cc/framework/scope_internal.h index 8efcfed20d0b86d86d8c20a3d8630c7c6bc909c3..58adaef2e942a7fa6b0ce8d5534ac3e2fd380580 100644 --- a/tensorflow/cc/framework/scope_internal.h +++ b/tensorflow/cc/framework/scope_internal.h @@ -34,8 +34,7 @@ class Scope::Impl { // name that has not been used so far in a scope will get no suffix. Later // uses of the same name will get suffixes _1, _2, _3, etc. Multiple scopes // can share the same NameMap. For instance, a new scope created using - // WithControlDependencies() should would share the same NameMap with the - // parent. + // WithControlDependencies() would share the same NameMap with the parent. typedef std::unordered_map NameMap; Impl(const std::shared_ptr& graph, diff --git a/tensorflow/cc/framework/scope_test.cc b/tensorflow/cc/framework/scope_test.cc index 9eca9d3face34319413e1acbc2f5ac0b2ba85374..b40b345eb84237c34ea593021bea022ad28095f7 100644 --- a/tensorflow/cc/framework/scope_test.cc +++ b/tensorflow/cc/framework/scope_test.cc @@ -26,6 +26,16 @@ TEST(ScopeTest, BasicNames) { EXPECT_EQ(root.GetUniqueNameForOp("mul"), "mul"); } +TEST(ScopeTest, OpAndScopeNameCollision) { + Scope root = Scope::NewRootScope(); + EXPECT_EQ(root.GetUniqueNameForOp("foo"), "foo"); + EXPECT_EQ(root.GetUniqueNameForOp("foo"), "foo_1"); + EXPECT_EQ(root.GetUniqueNameForOp("foo_1"), "foo_1_1"); + EXPECT_EQ(root.GetUniqueNameForOp("foo_2"), "foo_2"); + EXPECT_EQ(root.GetUniqueNameForOp("foo"), "foo_3"); + EXPECT_EQ(root.GetUniqueNameForOp("foo_2"), "foo_2_1"); +} + TEST(ScopeTest, HierarchicalNames) { Scope root = Scope::NewRootScope(); Scope child = root.NewSubScope("child"); diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc index ff348fadb24e29a83bd6c8853aa67931f6df4182..b353accddcb6db9a07c112de03ead2f02c4ee6a6 100644 --- a/tensorflow/cc/gradients/array_grad.cc +++ b/tensorflow/cc/gradients/array_grad.cc @@ -421,6 +421,58 @@ Status StridedSliceGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("StridedSlice", StridedSliceGradHelper); +Status SliceGrad(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + // Propagate the incoming gradient along all the selected values, + // and zero everywhere else. Use the Pad operator for this. + // + // First create an Nx2 padding where N is the number of input + // dimensions. The first column is the number of prepended zeros + // for each dimension, and the second column is the number of + // appended zeros. + // + // The first column is just the begin vector. + // The second column is the shape of the input element-wise + // subtracted by begin+size + + // Running example: + // input.shape = [3, 5, 3] + // begin = [1, 2, 1], size = [1, 3, 2] + Input input = op.input(0); + Input begin = op.input(1); + // input_rank = 3 + auto input_rank = Rank(scope, input); + // slice_size = [1, 3, 2] + auto slice_size = Shape(scope, op.output(0)); + // padding_shape = [3, 1] + auto padding_shape = Stack(scope, {input_rank, 1}); + // before_padding = [[1] + // [2] + // [1]] + Input before_padding = Reshape(scope, begin, padding_shape); + // after_padding_sizes = shape(input) - slice_size - begin + // = [3, 5, 3] - [1, 3, 2] - [1, 2, 1] + // = [1, 0, 0] + auto after_padding_sizes = + Sub(scope, Sub(scope, Shape(scope, input), slice_size), begin); + // after_padding = [[1] + // [0] + // [0]] + Input after_padding = Reshape(scope, after_padding_sizes, padding_shape); + // paddings = [[1 1] + // [2 0] + // [1 0]] + auto paddings = + Concat(scope, {before_padding, after_padding}, Const(scope, 1)); + grad_outputs->push_back(Pad(scope, grad_inputs[0], paddings)); + // Nothing propagated for "begin" and "size" inputs + grad_outputs->push_back(NoGradient()); + grad_outputs->push_back(NoGradient()); + return scope.status(); +} +REGISTER_GRADIENT_OP("Slice", SliceGrad); + } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc index de3bd0fc9e2493f8ff76163f5be6bd4327c58c5a..d09275b6487b4212aa35a0476002f2bb587fa210 100644 --- a/tensorflow/cc/gradients/array_grad_test.cc +++ b/tensorflow/cc/gradients/array_grad_test.cc @@ -378,5 +378,12 @@ TEST_F(ArrayGradTest, StridedSliceGrad) { RunTest(x, x_shape, y, {1, 2, 2, 2}); } +TEST_F(ArrayGradTest, SliceGrad) { + TensorShape x_shape({3, 5, 3}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + auto y = Slice(scope_, x, {1, 2, 1}, {1, 3, 2}); + RunTest(x, x_shape, y, {1, 3, 2}); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen.cc b/tensorflow/compiler/aot/codegen.cc index 0025842aead53973befc794378a26fa8db2ae1cb..28070d60dbbe6dd8f930b8e6509cedcf09f94e11 100644 --- a/tensorflow/compiler/aot/codegen.cc +++ b/tensorflow/compiler/aot/codegen.cc @@ -287,7 +287,7 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, TF_RETURN_IF_ERROR(ValidateFeedFetchCppNames(config)); const int64 result_index = compile_result.aot->result_buffer_index(); const xla::BufferSizes& temp_sizes = compile_result.aot->buffer_sizes(); - if (result_index < 0 || result_index > temp_sizes.size()) { + if (result_index < 0 || result_index >= temp_sizes.size()) { return errors::InvalidArgument("result index: ", result_index, " is outside the range of temp sizes: [0,", temp_sizes.size(), ")"); diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index ab8cd8f4bcd3b5a102692b47cfedfce6a9d9cc47..c2245b8eae8fd27d96feaf58e26418b92e646910 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -176,11 +176,14 @@ cc_library( "//tensorflow/core/kernels:cast_op", "//tensorflow/core/kernels:constant_op", "//tensorflow/core/kernels:control_flow_ops", + "//tensorflow/core/kernels:fifo_queue", "//tensorflow/core/kernels:identity_n_op", "//tensorflow/core/kernels:identity_op", "//tensorflow/core/kernels:no_op", + "//tensorflow/core/kernels:queue_op", "//tensorflow/core/kernels:resource_variable_ops", "//tensorflow/core/kernels:sendrecv_ops", + "//tensorflow/core/kernels:shape_ops", "//tensorflow/core/kernels:variable_ops", ], ) @@ -316,7 +319,6 @@ cc_library( ":xla_cluster_util", "//tensorflow/compiler/jit/graphcycles", "//tensorflow/compiler/jit/kernels:parallel_check_op", - "//tensorflow/compiler/jit/legacy_flags:encapsulate_subgraphs_pass_flags", "//tensorflow/compiler/jit/legacy_flags:mark_for_compilation_pass_flags", "//tensorflow/compiler/jit/ops:parallel_check_op", "//tensorflow/compiler/jit/ops:xla_ops", @@ -342,6 +344,7 @@ cc_library( "//tensorflow/compiler/jit/graphcycles", "//tensorflow/core:framework", "//tensorflow/core:graph", + "//tensorflow/core:protos_all_cc", "//tensorflow/core/kernels:bounds_check", ], ) @@ -397,6 +400,32 @@ tf_cc_test( ], ) +tf_cc_test( + name = "xla_cluster_util_test", + size = "small", + srcs = [ + "xla_cluster_util_test.cc", + ], + deps = [ + ":common", + ":xla_cluster_util", + "//tensorflow/cc:cc_ops", + "//tensorflow/cc:cc_ops_internal", + "//tensorflow/cc:function_ops", + "//tensorflow/cc:ops", + "//tensorflow/compiler/jit/kernels:xla_launch_op", + "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/tf2xla/kernels:xla_ops", + "//tensorflow/core:core_cpu", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + tf_cc_test( name = "xla_launch_util_test", size = "small", diff --git a/tensorflow/compiler/jit/create_xla_launch_op.cc b/tensorflow/compiler/jit/create_xla_launch_op.cc index 731b8ebfdc6262500940274c94a03ae7c0376096..a2e6285339f9ed0bde8d72f5b4752b1ecc22f426 100644 --- a/tensorflow/compiler/jit/create_xla_launch_op.cc +++ b/tensorflow/compiler/jit/create_xla_launch_op.cc @@ -66,8 +66,28 @@ class SinglePassSearch { Status CompilationRequested(const FunctionLibraryRuntime& flr, const NodeDef& node_def) { + const FunctionDef* function_def = + flr.GetFunctionLibraryDefinition()->Find(node_def.name()); + if (function_def == nullptr) { + // The node def is not calling a function. Individual ops can be + // run directly using on-demand mode, no need to create XlaLaunch + // kernel for them. + // TODO(b/110359382): Make custom kernel creation return a bool instead of + // status. + // We don't set error messages here to avoid unnecessary string copy. + // Similarly below. + return Status(error::INVALID_ARGUMENT, ""); + } + + // If kXlaCompileAttr is set on the node_def, use its value. + const auto& it = node_def.attr().find(kXlaCompileAttr); + if (it != node_def.attr().end()) { + return it->second.b() ? Status::OK() : Status(error::INVALID_ARGUMENT, ""); + } + + // kXlaCompileAttr is not set on node_def, check if it is set on + // FunctionDef. bool xla_compile = false; - // Check if op is marked _XlaCompile=true. Status status = flr.GetFunctionLibraryDefinition()->GetAttr( node_def, kXlaCompileAttr, &xla_compile); if (!status.ok() || !xla_compile) { diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 6d1e3325ebd35b9608ea273fb7de39bad381e60d..e786d41887f1d539fe1ae122275d1c14c77309e8 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -23,7 +23,6 @@ limitations under the License. #include #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" -#include "tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h" #include "tensorflow/compiler/jit/mark_for_compilation_pass.h" #include "tensorflow/compiler/jit/shape_inference_helpers.h" #include "tensorflow/compiler/tf2xla/const_analysis.h" @@ -107,41 +106,11 @@ void MarkGuaranteedConstants( } } -// A node/slot pair. -// TODO(phawkins): is there a common definition of this? -struct NodeSlot { - NodeSlot() : node(nullptr), slot(-1), dtype(DT_INVALID) {} - NodeSlot(const Node* node, int slot) - : node(node), slot(slot), dtype(DT_INVALID) {} - NodeSlot(const Node* node, int slot, DataType dtype) - : node(node), slot(slot), dtype(dtype) {} - - const Node* node; - int slot; - - // Optional: used to record the destination type of a source NodeSlot in case - // the source output is a Ref type that is cast to a Tensor at the - // destination. - DataType dtype; - - bool operator==(const NodeSlot& other) const { - return node == other.node && slot == other.slot && dtype == other.dtype; - } - - // Leave dtype out of the hash since there are never two NodeSlots with the - // same node and slot and different dtypes. - struct Hasher { - uint64 operator()(NodeSlot const& s) const { - return Hash64Combine(std::hash()(s.node), - std::hash()(s.slot)); - } - }; - - struct PairHasher { - uint64 operator()(std::pair const& s) const { - return Hash64Combine(Hasher()(s.first), Hasher()(s.second)); - } - }; +struct OutputInputTensorPairHasher { + uint64 operator()(std::pair const& s) const { + return Hash64Combine(OutputTensor::Hash()(s.first), + InputTensor::Hash()(s.second)); + } }; // TODO(phawkins) add a canonical copy of these operator names and refactor @@ -182,8 +151,7 @@ class Encapsulator { // Write a copy of the input graph to 'graph_out', where the subgraphs are // replaced with calls to the new functions. - Status BuildOutputGraph(bool parallel_checking, Graph* graph_out, - FunctionLibraryDefinition* library); + Status BuildOutputGraph(Graph* graph_out, FunctionLibraryDefinition* library); private: // A subgraph of the input, all marked with a common 'group_attribute' @@ -271,7 +239,7 @@ class Encapsulator { // Adds the function call node to graph_out. Status AddFunctionCallNode( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Adds _RecvAtHost and _SendFromHost nodes, where needed, to graph_out. Status AddOutsideCompilationHostIONodes( @@ -284,11 +252,9 @@ class Encapsulator { // Subgraph. void GetOutsideCompilationSubgraphNames(std::vector* names) const; - // Returns the Node that inputs to the function should be wired up to. - Node* GetCallNodeForInputs() const; - - // Returns the Node that outputs to the function should be wired up to. - Node* GetCallNodeForOutputs() const; + // Returns the Node that the inputs and outputs of the function should be + // wired up to. + Node* GetCallNode() const; // Returns the index of the arg that the dst of edge should connect to. int GetArgIndexForEdge(const Edge* edge) const; @@ -380,7 +346,7 @@ class Encapsulator { // Map from source (producer node/slot) tensors in the original graph to // input index (slot number in the HostCompute/RecvAtHost nodes that will // be created) for the outside_compilation subgraph. - std::unordered_map inputs; + std::unordered_map inputs; // Set of nodes in the original graph that are the source of control edges // that cross from the containing compiled subgraph into the @@ -396,8 +362,15 @@ class Encapsulator { // node/slot) tensors in the original graph to output index (slot number // in the SendFromHost/HostCompute nodes that will be created) for the // outside_compilation subgraph. - std::unordered_map outputs_by_src; - std::unordered_map outputs_by_dst; + struct ArgNumAndType { + int index; + DataType dtype; + + ArgNumAndType(int i, DataType t) : index(i), dtype(t) {} + }; + std::unordered_map + outputs_by_src; + std::unordered_map outputs_by_dst; // Set of nodes in the original graph that are the destination of control // edges that cross from the outside_compilation subgraph into the @@ -425,12 +398,6 @@ class Encapsulator { OutsideCompilationSubgraph* LookupOrCreateOutsideCompilationSubgraph( const string& outside_compilation_id); - // Builds a ParallelCheck op that compares the output of the original - // subgraph with the encapsulated subgraph. - Status BuildParallelCheckOp( - const std::unordered_map& node_images, - Graph* graph_out); - // Builds a placeholder node used to provide the key input to a RecvAtHost // or SendFromHost node. This placeholder node will be removed by a later // pass. @@ -482,26 +449,21 @@ class Encapsulator { // Not owned. Node* host_compute_key_placeholder_ = nullptr; - // Function call node(s) in the output graph. Not owned. - // If parallel_checking is enabled, 'call_node_inputs' is the function call - // node to which inputs should be fed, and 'call_node_outputs' is the - // parallel check op from which outputs should be read. If parallel checking - // is disabled, both point to the function call node. - Node* call_node_inputs_; - Node* call_node_outputs_; + // Function call node in the output graph. Not owned. + Node* call_node_; // Maps from source (producer node/slot) and destination // (consumer node/slot) tensors in the input graph to _Arg numbers in // the subgraph. The source map is one-to-one, whereas the dest map may be // many-to-one. - std::unordered_map args_by_src_; - std::unordered_map args_by_dst_; + std::unordered_map args_by_src_; + std::unordered_map args_by_dst_; - // The _Arg nodes in the subgraph, in order by argument number. + // The arguments to the subgraph, in order. std::vector args_; // Map from source tensor in the input graph to result #. - std::unordered_map results_; + std::unordered_map results_; // The outside_compilation clusters in this subgraph. std::unordered_map @@ -541,13 +503,12 @@ class Encapsulator { // Copies all nodes that aren't in a compiled subgraph to the output graph. Status CopyNodesToOutputGraph( - bool parallel_checking, Graph* graph_out, - std::unordered_map* node_images); + Graph* graph_out, std::unordered_map* node_images); // Adds function call nodes for each compiled subgraph. Status AddFunctionCallNodes( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Adds _RecvAtHost and _SendFromHost nodes, where needed, for all // outside_compilation subgraphs. @@ -598,9 +559,9 @@ class Encapsulator { const string& src_outside_compilation_id, const string& dst_func_id, const string& dst_outside_compilation_id, const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out, - std::unordered_set, NodeSlot::PairHasher>* - edges_added); + Graph* graph_out, + std::unordered_set, + OutputInputTensorPairHasher>* edges_added); // Adds control dependencies between subgraph call nodes that have // dependencies via outside_compilation edges. @@ -609,7 +570,7 @@ class Encapsulator { // Adds all edges to the output graph. Status AddEdgesToOutputGraph( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Constructs a minimal shape inference graph that can be used to determine // the shape of send_node at the time that the subgraph is compiled. @@ -729,20 +690,14 @@ void TopologicalClusterSort( } // namespace -Node* Encapsulator::Subgraph::GetCallNodeForInputs() const { - return call_node_inputs_; -} - -Node* Encapsulator::Subgraph::GetCallNodeForOutputs() const { - return call_node_outputs_; -} +Node* Encapsulator::Subgraph::GetCallNode() const { return call_node_; } int Encapsulator::Subgraph::GetArgIndexForEdge(const Edge* edge) const { - return args_by_dst_.at(NodeSlot(edge->dst(), edge->dst_input())); + return args_by_dst_.at(InputTensor(edge->dst(), edge->dst_input())); } int Encapsulator::Subgraph::GetResultIndexForEdge(const Edge* edge) const { - return results_.at(NodeSlot(edge->src(), edge->src_output())); + return results_.at(OutputTensor(edge->src(), edge->src_output())); } Node* Encapsulator::Subgraph::GetRecvAtHostNode( @@ -754,7 +709,7 @@ Node* Encapsulator::Subgraph::GetRecvAtHostNode( int Encapsulator::Subgraph::GetRecvAtHostSlot( const string& outside_compilation_subgraph_name, const Edge* edge) const { return outside_compilation_subgraphs_.at(outside_compilation_subgraph_name) - .inputs.at(NodeSlot(edge->src(), edge->src_output())); + .inputs.at(OutputTensor(edge->src(), edge->src_output())); } Node* Encapsulator::Subgraph::GetSendFromHostNode( @@ -766,7 +721,7 @@ Node* Encapsulator::Subgraph::GetSendFromHostNode( int Encapsulator::Subgraph::GetSendFromHostSlot( const string& outside_compilation_subgraph_name, const Edge* edge) const { return outside_compilation_subgraphs_.at(outside_compilation_subgraph_name) - .outputs_by_dst.at(NodeSlot(edge->dst(), edge->dst_input())); + .outputs_by_dst.at(InputTensor(edge->dst(), edge->dst_input())); } Node* Encapsulator::Subgraph::MakeNodeImage(const Graph* graph_in, Node* node) { @@ -791,10 +746,10 @@ Status Encapsulator::Subgraph::RecordArg( std::vector>* src_arg_pairs) { Node* src_node = edge->src(); int src_slot = edge->src_output(); - std::unordered_map::iterator iter; + std::unordered_map::iterator iter; bool inserted; - std::tie(iter, inserted) = - args_by_src_.emplace(NodeSlot(src_node, src_slot), args_by_src_.size()); + std::tie(iter, inserted) = args_by_src_.emplace( + OutputTensor(src_node, src_slot), args_by_src_.size()); int arg_index = iter->second; if (inserted) { NodeDef arg_def; @@ -815,7 +770,7 @@ Status Encapsulator::Subgraph::RecordArg( Node* dst_node = edge->dst(); Node* dst_image = node_images.at(dst_node); int dst_slot = edge->dst_input(); - args_by_dst_[NodeSlot(dst_node, dst_slot)] = arg_index; + args_by_dst_[InputTensor(dst_node, dst_slot)] = arg_index; graph_->AddEdge(args_[arg_index], 0, dst_image, dst_slot); return Status::OK(); } @@ -826,10 +781,10 @@ Status Encapsulator::Subgraph::RecordResult( Node* src_node = edge->src(); Node* src_image = node_images.at(src_node); int src_slot = edge->src_output(); - std::unordered_map::iterator iter; + std::unordered_map::iterator iter; bool inserted; std::tie(iter, inserted) = - results_.emplace(NodeSlot(src_node, src_slot), results_.size()); + results_.emplace(OutputTensor(src_node, src_slot), results_.size()); int ret_index = iter->second; if (inserted) { NodeDef ret_def; @@ -867,8 +822,8 @@ void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( outside_subgraph->control_inputs.insert(edge->src()); } else { int input_index = outside_subgraph->inputs.size(); - outside_subgraph->inputs.emplace(NodeSlot(edge->src(), edge->src_output()), - input_index); + outside_subgraph->inputs.emplace( + OutputTensor(edge->src(), edge->src_output()), input_index); } } @@ -882,11 +837,13 @@ void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( DataType dtype = edge->dst()->input_type(edge->dst_input()); auto output_iter = outside_subgraph->outputs_by_src - .emplace(NodeSlot(edge->src(), edge->src_output(), dtype), - outside_subgraph->outputs_by_src.size()) + .emplace(OutputTensor(edge->src(), edge->src_output()), + OutsideCompilationSubgraph::ArgNumAndType( + outside_subgraph->outputs_by_src.size(), dtype)) .first; - int output_index = output_iter->second; - outside_subgraph->outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = + const int output_index = output_iter->second.index; + outside_subgraph + ->outputs_by_dst[InputTensor(edge->dst(), edge->dst_input())] = output_index; } } @@ -968,7 +925,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (const auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); - int src_slot = input_src.first.slot; + int src_slot = input_src.first.index; int input_index = input_src.second; DataType dtype = src_node->output_type(src_slot); @@ -976,8 +933,8 @@ Status Encapsulator::Subgraph::AddHostComputes( input_dtypes[input_index] = dtype; } for (const auto& output : oc_subgraph.outputs_by_src) { - DataType dtype = output.first.dtype; - int output_index = output.second; + DataType dtype = output.second.dtype; + int output_index = output.second.index; output_dtypes[output_index] = dtype; } @@ -1015,7 +972,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); - int src_slot = input_src.first.slot; + int src_slot = input_src.first.index; int input_index = input_src.second; graph_->AddEdge(src_image, src_slot, host_compute, input_index); } @@ -1037,7 +994,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (const auto& output : oc_subgraph.outputs_by_dst) { const Node* dst_node = output.first.node; Node* dst_image = node_images.at(dst_node); - int dst_slot = output.first.slot; + int dst_slot = output.first.index; int output_index = output.second; graph_->AddEdge(host_compute, output_index, dst_image, dst_slot); @@ -1075,7 +1032,7 @@ Status Encapsulator::Subgraph::MakeSequencingNode(const string& subgraph_name, void Encapsulator::Subgraph::ConnectSequencerToCallNode(Graph* graph_out) { if (sequencer_ != nullptr) { VLOG(2) << "ConnectSequencerToCallNode"; - graph_out->AddControlEdge(sequencer_, call_node_inputs_); + graph_out->AddControlEdge(sequencer_, call_node_); } } @@ -1090,14 +1047,19 @@ Status Encapsulator::Subgraph::BuildFunctionDef( call_node_def_.set_device(device_); if (rewrite_subgraph_fn) { + std::vector arg_source_tensors(args_by_src_.size()); + for (const auto& arg : args_by_src_) { + arg_source_tensors.at(arg.second) = arg.first; + } // Initialize the input and output permutations to the identity. std::vector input_permutation(args_by_src_.size()); std::iota(input_permutation.begin(), input_permutation.end(), 0); std::vector output_permutation(results_.size()); std::iota(output_permutation.begin(), output_permutation.end(), 0); - TF_RETURN_IF_ERROR(rewrite_subgraph_fn( - &graph_, &input_permutation, &output_permutation, &call_node_def_)); + TF_RETURN_IF_ERROR( + rewrite_subgraph_fn(arg_source_tensors, &graph_, &input_permutation, + &output_permutation, &call_node_def_)); // Apply the input/output permutations to the 'args_by_...' and 'results_' // mappings, so when we build edges in BuildOutputGraph() we @@ -1200,83 +1162,16 @@ Status Encapsulator::Subgraph::ReplaceFunctionDef( return Status::OK(); } -Status Encapsulator::Subgraph::BuildParallelCheckOp( - const std::unordered_map& node_images, - Graph* graph_out) { - // Build an index mapping output positions to node/slot pairs in the - // original graph. - std::vector results_by_num(results_.size()); - for (const auto& entry : results_) { - results_by_num[entry.second] = entry.first; - } - - // Build a parallel check NodeDef. - int num_results = results_by_num.size(); - std::vector result_dtypes(num_results); - std::vector expected_outputs(num_results); - std::vector actual_outputs(num_results); - for (int i = 0; i < num_results; ++i) { - const NodeSlot& node_slot = results_by_num[i]; - result_dtypes[i] = node_slot.node->output_type(node_slot.slot); - expected_outputs[i] = - NodeDefBuilder::NodeOut(node_images.at(node_slot.node)->name(), - node_slot.slot, result_dtypes[i]); - actual_outputs[i] = - NodeDefBuilder::NodeOut(call_node_def_.name(), i, result_dtypes[i]); - } - // Assign the parallel check op to a CPU on the same task as the cluster it is - // checking. - string device, dummy; - if (!DeviceNameUtils::SplitDeviceName( - call_node_inputs_->assigned_device_name(), &device, &dummy)) { - return errors::InvalidArgument("Could not parse device name"); - } - strings::StrAppend(&device, "/cpu:0"); - - NodeDef check_def; - TF_RETURN_IF_ERROR( - NodeDefBuilder(graph_out->NewName(strings::StrCat(call_node_def_.name(), - "_parallel_check")), - "ParallelCheck") - .Device(device) - .Attr("T", result_dtypes) - .Input(expected_outputs) - .Input(actual_outputs) - .Finalize(&check_def)); - - Status s; - Node* check_op = graph_out->AddNode(check_def, &s); - if (!s.ok()) return s; - check_op->set_assigned_device_name(device); - - // TODO(phawkins): it seems redundant to call AddEdge as well as - // pass Inputs to the NodeDefBuilder, but I have been unable to find a - // way to avoid it. - for (int i = 0; i < num_results; ++i) { - const NodeSlot& node_slot = results_by_num[i]; - graph_out->AddEdge(node_images.at(node_slot.node), node_slot.slot, check_op, - i); - graph_out->AddEdge(call_node_inputs_, i, check_op, num_results + i); - } - - call_node_outputs_ = check_op; - return Status::OK(); -} - Status Encapsulator::Subgraph::AddFunctionCallNode( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { Status s; - call_node_inputs_ = graph_out->AddNode(call_node_def_, &s); + call_node_ = graph_out->AddNode(call_node_def_, &s); if (!s.ok()) return s; // Copy the assigned device and the key_annotation over. - call_node_inputs_->set_assigned_device_name(device_); - call_node_outputs_ = call_node_inputs_; + call_node_->set_assigned_device_name(device_); - if (parallel_checking) { - TF_RETURN_IF_ERROR(BuildParallelCheckOp(node_images, graph_out)); - } return Status::OK(); } @@ -1315,7 +1210,7 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( for (const auto& input : oc_subgraph->inputs) { const Node* src_node = input.first.node; - int src_slot = input.first.slot; + int src_slot = input.first.index; int input_index = input.second; DataType dtype = src_node->output_type(src_slot); @@ -1369,8 +1264,8 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( for (const auto& output : oc_subgraph->outputs_by_src) { const Node* src_node = output.first.node; Node* src_image = node_images.at(src_node); - int src_slot = output.first.slot; - int output_index = output.second; + int src_slot = output.first.index; + int output_index = output.second.index; DataType dtype = src_node->output_type(src_slot); dtypes[output_index] = dtype; @@ -1609,6 +1504,9 @@ Status Encapsulator::SplitIntoSubgraphs() { for (auto& entry : subgraphs_) { Subgraph& subgraph = entry.second; FixupSourceAndSinkEdges(subgraph.GetGraph()); + // Verify that the graph has well-formed control flow structure. + std::vector dummy; + TF_RETURN_IF_ERROR(BuildControlFlowInfo(subgraph.GetGraph(), &dummy)); } return s; @@ -1627,27 +1525,17 @@ Status Encapsulator::BuildFunctionDefs( } Status Encapsulator::CopyNodesToOutputGraph( - bool parallel_checking, Graph* graph_out, - std::unordered_map* node_images) { + Graph* graph_out, std::unordered_map* node_images) { for (Node* node : graph_in_->op_nodes()) { string func_id; string outside_compilation_id; TF_RETURN_IF_ERROR( GetFunctionNameAttr(node, &func_id, &outside_compilation_id)); - // Don't copy nodes that going to be encapsulated, unless parallel checking - // is enabled. - if (IsInSubgraph(func_id, outside_compilation_id) && !parallel_checking) - continue; + // Don't copy nodes that are going to be encapsulated. + if (IsInSubgraph(func_id, outside_compilation_id)) continue; Node* image = graph_out->CopyNode(node); - if (!outside_compilation_id.empty()) { - if (parallel_checking) { - return errors::InvalidArgument( - "Parallel checking is not supported when outside_compilation " - "clusters are present."); - } - } (*node_images)[node] = image; } (*node_images)[graph_in_->source_node()] = graph_out->source_node(); @@ -1657,10 +1545,10 @@ Status Encapsulator::CopyNodesToOutputGraph( Status Encapsulator::AddFunctionCallNodes( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { for (auto& subgraph_entry : subgraphs_) { - TF_RETURN_IF_ERROR(subgraph_entry.second.AddFunctionCallNode( - node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR( + subgraph_entry.second.AddFunctionCallNode(node_images, graph_out)); } return Status::OK(); } @@ -1694,7 +1582,7 @@ Status Encapsulator::FindOutputImageOfEdgeSrc( } else { // The edge is from a subgraph to a regular node in the output graph so // use the subgraph's call node output. - *src_image = subgraphs_.at(src_func_id).GetCallNodeForOutputs(); + *src_image = subgraphs_.at(src_func_id).GetCallNode(); } } else { // The source of the edge is in the output graph so use the node image in @@ -1742,7 +1630,7 @@ Status Encapsulator::FindOutputImageOfEdgeDst( } else { // The edge is to a subgraph from a regular node in the output graph so // use the subgraph's call node input. - *dst_image = subgraphs_.at(dst_func_id).GetCallNodeForInputs(); + *dst_image = subgraphs_.at(dst_func_id).GetCallNode(); } } else { // The destination of the edge is in the output graph so use the node image @@ -1778,10 +1666,9 @@ Status Encapsulator::CopyEdgeToOutputGraph( const Edge* edge, const string& src_func_id, const string& src_outside_compilation_id, const string& dst_func_id, const string& dst_outside_compilation_id, - const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out, - std::unordered_set, NodeSlot::PairHasher>* - edges_added) { + const std::unordered_map& node_images, Graph* graph_out, + std::unordered_set, + OutputInputTensorPairHasher>* edges_added) { Node* src_image; TF_RETURN_IF_ERROR(FindOutputImageOfEdgeSrc( src_func_id, src_outside_compilation_id, dst_func_id, @@ -1796,16 +1683,12 @@ Status Encapsulator::CopyEdgeToOutputGraph( if (edge->IsControlEdge()) { // Add the control edge, if we have not already added it, using the images // determined above (potentially call operators or RecvAtHost/SendFromHost). - if (edges_added->emplace(NodeSlot(src_image, -1), NodeSlot(dst_image, -1)) + if (edges_added + ->emplace(OutputTensor(src_image, -1), InputTensor(dst_image, -1)) .second) { graph_out->AddControlEdge(src_image, dst_image); } - // If parallel checking is enabled, also add a control edge to the - // corresponding parallel check op. - if (parallel_checking) { - graph_out->AddControlEdge(src_image, node_images.at(edge->dst())); - } return Status::OK(); } @@ -1817,18 +1700,10 @@ Status Encapsulator::CopyEdgeToOutputGraph( FindOutputSlotOfEdgeDst(src_func_id, src_outside_compilation_id, dst_func_id, dst_outside_compilation_id, edge); - if (IsInSubgraph(dst_func_id, dst_outside_compilation_id) && - parallel_checking) { - // If we are parallel checking, also feed the tensor as an input to the - // corresponding parallel check subgraph. - graph_out->AddEdge(src_image, src_output, node_images.at(edge->dst()), - edge->dst_input()); - } - // Add the edge, if we have not already added it. if (edges_added - ->emplace(NodeSlot(src_image, src_output), - NodeSlot(dst_image, dst_input)) + ->emplace(OutputTensor(src_image, src_output), + InputTensor(dst_image, dst_input)) .second) { graph_out->AddEdge(src_image, src_output, dst_image, dst_input); } @@ -1839,8 +1714,8 @@ Status Encapsulator::AddCallNodeDependencies(Graph* graph_out) { for (const auto& ancestors : subgraph_ancestors_) { const string& subgraph = ancestors.first; for (const string& ancestor : ancestors.second) { - graph_out->AddControlEdge(subgraphs_[ancestor].GetCallNodeForOutputs(), - subgraphs_[subgraph].GetCallNodeForInputs()); + graph_out->AddControlEdge(subgraphs_[ancestor].GetCallNode(), + subgraphs_[subgraph].GetCallNode()); } } return Status::OK(); @@ -1848,11 +1723,12 @@ Status Encapsulator::AddCallNodeDependencies(Graph* graph_out) { Status Encapsulator::AddEdgesToOutputGraph( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { // Set of edges already added to the output graph, represented as (src, dst) // pairs. We use the set to deduplicate edges; multiple edges in the input // graph may map to one edge in the output graph. - std::unordered_set, NodeSlot::PairHasher> + std::unordered_set, + OutputInputTensorPairHasher> edges_added; for (const Edge* edge : graph_in_->edges()) { @@ -1870,16 +1746,6 @@ Status Encapsulator::AddEdgesToOutputGraph( if (IsInSubgraph(src_func_id, src_outside_compilation_id) && IsInSubgraph(dst_func_id, dst_outside_compilation_id) && src_func_id == dst_func_id) { - if (parallel_checking) { - Node* src_image = node_images.at(edge->src()); - Node* dst_image = node_images.at(edge->dst()); - if (edge->IsControlEdge()) { - graph_out->AddControlEdge(src_image, dst_image); - } else { - graph_out->AddEdge(src_image, edge->src_output(), dst_image, - edge->dst_input()); - } - } continue; } @@ -1887,8 +1753,7 @@ Status Encapsulator::AddEdgesToOutputGraph( // unclustered graph. TF_RETURN_IF_ERROR(CopyEdgeToOutputGraph( edge, src_func_id, src_outside_compilation_id, dst_func_id, - dst_outside_compilation_id, node_images, parallel_checking, graph_out, - &edges_added)); + dst_outside_compilation_id, node_images, graph_out, &edges_added)); } for (auto& subgraph_entry : subgraphs_) { @@ -2504,18 +2369,15 @@ Status Encapsulator::GetShapeInfoForOutsideCompilationSends( return Status::OK(); } -Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, +Status Encapsulator::BuildOutputGraph(Graph* graph_out, FunctionLibraryDefinition* library) { // Map from nodes in the input graph to nodes in the output graph. std::unordered_map node_images; - TF_RETURN_IF_ERROR( - CopyNodesToOutputGraph(parallel_checking, graph_out, &node_images)); - TF_RETURN_IF_ERROR( - AddFunctionCallNodes(node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR(CopyNodesToOutputGraph(graph_out, &node_images)); + TF_RETURN_IF_ERROR(AddFunctionCallNodes(node_images, graph_out)); TF_RETURN_IF_ERROR(AddOutsideCompilationHostIONodes(node_images, graph_out)); - TF_RETURN_IF_ERROR( - AddEdgesToOutputGraph(node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR(AddEdgesToOutputGraph(node_images, graph_out)); TF_RETURN_IF_ERROR( GetShapeInfoForOutsideCompilationSends(graph_out, library)); @@ -2528,8 +2390,8 @@ Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, Status EncapsulateSubgraphsInFunctions( string group_attribute, string outside_compilation_attribute, const Graph& graph_in, const RewriteSubgraphFn& rewrite_subgraph_fn, - bool parallel_checking, bool reuse_existing_functions, - std::unique_ptr* graph_out, FunctionLibraryDefinition* library) { + bool reuse_existing_functions, std::unique_ptr* graph_out, + FunctionLibraryDefinition* library) { Status s; Encapsulator encapsulator(std::move(group_attribute), @@ -2543,8 +2405,7 @@ Status EncapsulateSubgraphsInFunctions( std::unique_ptr out(new Graph(library)); out->set_versions(graph_in.versions()); - TF_RETURN_IF_ERROR( - encapsulator.BuildOutputGraph(parallel_checking, out.get(), library)); + TF_RETURN_IF_ERROR(encapsulator.BuildOutputGraph(out.get(), library)); *graph_out = std::move(out); return Status::OK(); @@ -2585,8 +2446,6 @@ static Status RenumberArguments(Graph* graph, Status EncapsulateSubgraphsPass::Run( const GraphOptimizationPassOptions& options) { VLOG(1) << "EncapsulateSubgraphsPass::Run"; - legacy_flags::EncapsulateSubgraphsPassFlags* flags = - legacy_flags::GetEncapsulateSubgraphsPassFlags(); if (VLOG_IS_ON(1)) { dump_graph::DumpGraphToFile("before_encapsulate_subgraphs", **options.graph, options.flib_def); @@ -2602,69 +2461,73 @@ Status EncapsulateSubgraphsPass::Run( FunctionLibraryRuntime* flr = pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice); - auto rewrite_subgraph = [flr](std::unique_ptr* subgraph, - std::vector* input_permutation, - std::vector* output_permutation, - NodeDef* node) { - // Optimize the subgraph. - OptimizeGraph(flr, subgraph); - - const int num_args = input_permutation->size(); - std::vector const_args(num_args); - TF_RETURN_IF_ERROR(BackwardsConstAnalysis(**subgraph, &const_args)); - - DataTypeVector arg_types(num_args); - TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types)); - - // Compute a permutation of the arguments such that the constant arguments - // are first. - const int num_consts = - std::count(const_args.begin(), const_args.end(), true); - - const int num_resources = - std::count(arg_types.begin(), arg_types.end(), DT_RESOURCE); - const int num_nonconsts = num_args - num_resources - num_consts; - if (num_nonconsts < 0) { - return errors::Internal("num_nonconsts should be >= 0, was ", - num_nonconsts); - } - - int const_pos = 0; - int arg_pos = num_consts; - int resource_pos = num_consts + num_nonconsts; - for (int i = 0; i < num_args; ++i) { - if (const_args[i]) { - if (arg_types[i] == DT_RESOURCE) { - return errors::Internal( - "Resource arguments cannot be constant (argument ", i, ")"); + auto rewrite_subgraph = + [flr](const std::vector& arg_source_tensors, + std::unique_ptr* subgraph, + std::vector* input_permutation, + std::vector* output_permutation, NodeDef* node) { + // Optimize the subgraph. + OptimizeGraph(flr, subgraph); + + const int num_args = input_permutation->size(); + std::vector const_args(num_args); + TF_RETURN_IF_ERROR(BackwardsConstAnalysis(**subgraph, &const_args)); + + DataTypeVector arg_types(num_args); + TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types)); + + // Compute a permutation of the arguments such that the constant + // arguments are first. + const int num_consts = + std::count(const_args.begin(), const_args.end(), true); + + const int num_resources = + std::count(arg_types.begin(), arg_types.end(), DT_RESOURCE); + const int num_nonconsts = num_args - num_resources - num_consts; + if (num_nonconsts < 0) { + return errors::Internal("num_nonconsts should be >= 0, was ", + num_nonconsts); } - (*input_permutation)[i] = const_pos; - ++const_pos; - } else if (arg_types[i] == DT_RESOURCE) { - (*input_permutation)[i] = resource_pos; - ++resource_pos; - } else { - (*input_permutation)[i] = arg_pos; - ++arg_pos; - } - } - // Renumber argument nodes in the graph. - TF_RETURN_IF_ERROR(RenumberArguments(subgraph->get(), *input_permutation)); - - // TODO(phawkins): add a forward is-constant analysis, similarly split - // outputs into host-memory constants and device-memory non-constants. - - AddNodeAttr(kXlaCompiledKernelAttr, true, node); - AddNodeAttr(kXlaNumConstantArgsAttr, num_consts, node); - AddNodeAttr(kXlaNumResourceArgsAttr, num_resources, node); - return Status::OK(); - }; + int const_pos = 0; + int arg_pos = num_consts; + int resource_pos = num_consts + num_nonconsts; + for (int i = 0; i < num_args; ++i) { + if (const_args[i]) { + if (arg_types[i] == DT_RESOURCE) { + return errors::Internal( + "Resource arguments cannot be constant (argument ", i, ")"); + } + (*input_permutation)[i] = const_pos; + ++const_pos; + } else if (arg_types[i] == DT_RESOURCE) { + (*input_permutation)[i] = resource_pos; + ++resource_pos; + } else { + (*input_permutation)[i] = arg_pos; + ++arg_pos; + } + } - TF_RETURN_IF_ERROR(EncapsulateSubgraphsInFunctions( - kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, - rewrite_subgraph, flags->tf_xla_parallel_checking, - /*reuse_existing_functions=*/false, &graph_out, library)); + // Renumber argument nodes in the graph. + TF_RETURN_IF_ERROR( + RenumberArguments(subgraph->get(), *input_permutation)); + + // TODO(phawkins): add a forward is-constant analysis, similarly split + // outputs into host-memory constants and device-memory non-constants. + + AddNodeAttr(kXlaCompiledKernelAttr, true, node); + AddNodeAttr(kXlaNumConstantArgsAttr, num_consts, node); + AddNodeAttr(kXlaNumResourceArgsAttr, num_resources, node); + return Status::OK(); + }; + + TF_RETURN_WITH_CONTEXT_IF_ERROR( + EncapsulateSubgraphsInFunctions( + kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, + rewrite_subgraph, /*reuse_existing_functions=*/false, &graph_out, + library), + "EncapsulateSubgraphsPass failed"); if (VLOG_IS_ON(1)) { dump_graph::DumpGraphToFile("after_encapsulate_subgraphs", *graph_out, diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h index 5fee36f022a7515504cb6faa5cca658481b784c5..926589546fec72048485d30966f31b24e44b1245 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h @@ -28,6 +28,9 @@ limitations under the License. namespace tensorflow { // A rewriting function to apply to each subgraph during encapsulation. +// 'arg_source_tensors' are the tensors corresponding to the arguments in the +// original source graph (*not* 'graph'). +// // 'graph' is the subgraph. The rewriting may renumber the inputs and outputs; // 'input_permutation' is a mapping from old argument numbers to new argument // numbers, whereas 'output_permutation' is the same for outputs. Both @@ -37,6 +40,7 @@ namespace tensorflow { // The rewrite may also change the NodeDef's operator name, and that // name will be used as the name of the generated function. typedef std::function& arg_source_tensors, std::unique_ptr* graph, std::vector* input_permutation, std::vector* output_permutation, NodeDef* node_def)> RewriteSubgraphFn; @@ -61,10 +65,6 @@ typedef std::function* graph_out, FunctionLibraryDefinition* library); + bool reuse_existing_functions, std::unique_ptr* graph_out, + FunctionLibraryDefinition* library); // The attribute that marks function calls produced by the encapsulate // subgraphs pass and that should in turn be compiled via XlaLaunch operators. diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index eef113a3547f0b2f648680d5f51650f70dbbd261..4eb389e0c653f2d32c17f448687f865a44a11b96 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -511,7 +511,6 @@ Status Encapsulate(GraphDef* graphdef, FunctionDefLibrary* library) { std::unique_ptr graph_out; s = EncapsulateSubgraphsInFunctions("_encapsulate", "_outside", *graph, /*rewrite_subgraph_fn=*/{}, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_out, lib_def.get()); if (!s.ok()) return s; @@ -560,8 +559,9 @@ TEST(EncapsulateSubgraphsTest, OneFunction) { Node* b = Input(b1.opts().WithName("B")); // Give nodes 'c' and 'd' names that collide after lowercasing. Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); - Node* d = Binary(b, c, b1.opts().WithName("c").WithControlInput(c).WithAttr( - "_encapsulate", "F1")); + Node* d = Binary(b, c, + b1.opts().WithName("c").WithControlInput(c).WithAttr( + "_encapsulate", "F1")); Binary(a, d, b1.opts().WithName("E")); TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); } @@ -614,8 +614,8 @@ TEST(EncapsulateSubgraphsTest, TwoFunctions) { Node* c = Unary(a, b1.opts().WithName("C").WithControlInput(control).WithAttr( "_encapsulate", "F1")); - Node* d = - Binary(b, c, b1.opts().WithName("D").WithControlInput(control).WithAttr( + Node* d = Binary(b, c, + b1.opts().WithName("D").WithControlInput(control).WithAttr( "_encapsulate", "F2")); Binary(a, d, b1.opts().WithName("E")); TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); @@ -707,7 +707,7 @@ TEST(EncapsulateSubgraphsTest, InputDeduplication) { std::unique_ptr graph; TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_cluster", "_outside", graph_before_encapsulation, - /*rewrite_subgraph_fn=*/{}, /*parallel_checking=*/false, + /*rewrite_subgraph_fn=*/{}, /*reuse_existing_functions=*/false, &graph, &library)); std::vector expected_nodes = {"cluster1", "cluster2", "mul", "x"}; @@ -721,47 +721,6 @@ TEST(EncapsulateSubgraphsTest, InputDeduplication) { EXPECT_EQ(expected_edges, GraphEdges(*graph)); } -TEST(EncapsulateSubgraphsTest, ParallelChecking) { - Scope root = Scope::NewRootScope().ExitOnError().WithDevice( - "/job:localhost/replica:0/task:0/cpu:0"); - auto x1 = ops::Placeholder(root.WithOpName("x1"), DT_FLOAT); - auto x2 = ops::Placeholder(root.WithOpName("x2"), DT_FLOAT); - auto add1 = ops::Add(root.WithOpName("add1"), x1, x2); - add1.node()->AddAttr("_cluster", "cluster1"); - auto add2 = ops::Add(root.WithOpName("add2"), add1, x2); - add2.node()->AddAttr("_cluster", "cluster1"); - auto out = ops::Mul(root.WithOpName("mul"), x1, add2); - - Graph graph_before_encapsulation(OpRegistry::Global()); - TF_ASSERT_OK(root.ToGraph(&graph_before_encapsulation)); - - FunctionLibraryDefinition library(OpRegistry::Global(), {}); - std::unique_ptr graph; - TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( - "_cluster", "_outside", graph_before_encapsulation, - /*rewrite_subgraph_fn=*/{}, /*parallel_checking=*/true, - /*reuse_existing_functions=*/false, &graph, &library)); - - std::vector expected_nodes = { - "add1", "add2", "cluster1", "cluster1_parallel_check/_0", - "mul", "x1", "x2"}; - EXPECT_EQ(expected_nodes, GraphNodes(*graph)); - - std::vector> expected_edges = { - {"add1:0", "add2:0"}, - {"add2:0", "cluster1_parallel_check/_0:0"}, - {"cluster1:0", "cluster1_parallel_check/_0:1"}, - {"cluster1_parallel_check/_0:0", "mul:1"}, - {"x1:0", "add1:0"}, - {"x1:0", "cluster1:0"}, - {"x1:0", "mul:0"}, - {"x2:0", "add1:1"}, - {"x2:0", "add2:1"}, - {"x2:0", "cluster1:1"}, - }; - EXPECT_EQ(expected_edges, GraphEdges(*graph)); -} - const Node* FindNodeByName(const Graph& graph, const string& name) { for (const Node* node : graph.nodes()) { if (node->name() == name) return node; @@ -798,7 +757,8 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Simple) { TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_encapsulate", "_outside", graph_before, /*rewrite_subgraph_fn=*/ - [&guaranteed_consts](std::unique_ptr* graph_ptr, + [&guaranteed_consts](const std::vector& arg_source_tensors, + std::unique_ptr* graph_ptr, std::vector* input_permutation, std::vector* output_permutation, NodeDef* call_def) { @@ -814,7 +774,6 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Simple) { } return Status::OK(); }, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_after, &library)); EXPECT_EQ(2, guaranteed_consts); } @@ -843,7 +802,8 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Add) { TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_encapsulate", "_outside", graph_before, /*rewrite_subgraph_fn=*/ - [&guaranteed_consts](std::unique_ptr* graph_ptr, + [&guaranteed_consts](const std::vector& arg_source_tensors, + std::unique_ptr* graph_ptr, std::vector* input_permutation, std::vector* output_permutation, NodeDef* call_def) { @@ -859,7 +819,6 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Add) { } return Status::OK(); }, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_after, &library)); // Only 1 runtime const, which is const_guarantee_add1. Add2 has one const // and another non-const, so overall non-const. diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index 902fe27acdec1cb323217e6409fbd02f62177612..251a07304eaeb21f1313d7a6ef6af668f99d8551 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -166,6 +166,14 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) { } XlaCompiler::CompileOptions compile_options; compile_options.is_entry_computation = true; + // Optimization: don't resolve constants. If we resolve constants we never + // emit them on the device, meaning that if they are needed by a following + // computation the host has to transfer them. + compile_options.resolve_compile_time_constants = false; + // Optimization: where possible, have the computation return a naked array + // rather than a one-element tuple. + compile_options.always_return_tuple = false; + OP_REQUIRES_OK( ctx, cache->Compile(options, function_, constant_args, variables, ctx, &kernel, &executable, &compile_options)); diff --git a/tensorflow/compiler/jit/legacy_flags/BUILD b/tensorflow/compiler/jit/legacy_flags/BUILD index 5d211f4d733d8d807426e62dd116092799184f35..5b6692f523658749f7ef48f9d7d89e97d4ce8b09 100644 --- a/tensorflow/compiler/jit/legacy_flags/BUILD +++ b/tensorflow/compiler/jit/legacy_flags/BUILD @@ -16,18 +16,6 @@ licenses(["notice"]) # Apache 2.0 package(default_visibility = ["//tensorflow:internal"]) -cc_library( - name = "encapsulate_subgraphs_pass_flags", - srcs = ["encapsulate_subgraphs_pass_flags.cc"], - hdrs = ["encapsulate_subgraphs_pass_flags.h"], - deps = - [ - "//tensorflow/compiler/xla/legacy_flags:parse_flags_from_env", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - ], -) - cc_library( name = "mark_for_compilation_pass_flags", srcs = ["mark_for_compilation_pass_flags.cc"], diff --git a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc b/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc deleted file mode 100644 index 856475f12c8a411cd80c1c1859323304ca4029e0..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Legacy flags for the XLA bridge's encapsulate_subgraphs_pass module. - -#include -#include - -#include "tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h" -#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h" -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Pointers to the parsed value of the flags and flag descriptors, initialized -// via flags_init. -static EncapsulateSubgraphsPassFlags* flags; -static std::vector* flag_list; -static std::once_flag flags_init; - -// Allocate *flags. Called via call_once(&flags_init,...). -static void AllocateFlags() { - flags = new EncapsulateSubgraphsPassFlags; - flags->tf_xla_parallel_checking = false; - flag_list = new std::vector({ - Flag("tf_xla_parallel_checking", &flags->tf_xla_parallel_checking, - "Debug tool. Runs both JIT-compiled and interpreted graphs in " - "parallel and verifies they produce the same outputs."), - }); - xla::legacy_flags::ParseFlagsFromEnv(*flag_list); -} - -// Append to *append_to flag definitions associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -void AppendEncapsulateSubgraphsPassFlags(std::vector* append_to) { - std::call_once(flags_init, &AllocateFlags); - append_to->insert(append_to->end(), flag_list->begin(), flag_list->end()); -} - -// Return a pointer to the EncapsulateSubgraphsPassFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -EncapsulateSubgraphsPassFlags* GetEncapsulateSubgraphsPassFlags() { - std::call_once(flags_init, &AllocateFlags); - return flags; -} - -} // namespace legacy_flags -} // namespace tensorflow diff --git a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h b/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h deleted file mode 100644 index d371bd269dbdfbf737d81490fb877fcf88661a8f..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ -#define TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ - -// Legacy flags for the XLA bridge's encapsulate_subgraphs_pass module. - -#include - -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Append to *flag_list flag definitions associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -void AppendEncapsulateSubgraphsPassFlags( - std::vector* flag_list); - -// The values of flags associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -typedef struct { - bool tf_xla_parallel_checking; // Debug tool. Runs both JIT-compiled and - // interpreted graphs in parallel and verifies - // they produce the same outputs. -} EncapsulateSubgraphsPassFlags; - -// Return a pointer to the EncapsulateSubgraphsPassFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -EncapsulateSubgraphsPassFlags* GetEncapsulateSubgraphsPassFlags(); - -} // namespace legacy_flags -} // namespace tensorflow - -#endif // TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index 74468266b9e983431732eafc801bc2d2ea682be9..8c3882116dd4f048ea3e32c037bf4139c67a3eb9 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -44,12 +44,6 @@ namespace tensorflow { namespace { -// Returns true if, when executed in TensorFlow, `node` is guaranteed to forward -// a ref tensor input to its output. -static bool AlwaysForwardsRefInput(const Node& node) { - return node.IsIdentity(); -} - bool HasXLAKernel(const Node& node, const DeviceType& jit_device_type) { // There is a SymbolicGradient kernel on the XLA_JIT device, but the gradient // is really a kind of function call and will be handled by @@ -68,20 +62,8 @@ bool HasXLAKernel(const Node& node, const DeviceType& jit_device_type) { // XLA does not offer guaranteed aliasing between the input and output of the // XLA cluster so it can't implement the forward-tensor-ref semantic. Leave // such nodes out of XLA clusters. - if (AlwaysForwardsRefInput(node)) { - for (const Edge* incoming_edge : node.in_edges()) { - if (incoming_edge->IsControlEdge()) { - continue; - } - - Node* incoming_node = incoming_edge->src(); - if (IsRefType(incoming_node->output_type(incoming_edge->src_output()))) { - VLOG(2) << "Not clustering " << node.def().ShortDebugString() - << " because of ref input " << incoming_node->name() << " " - << incoming_node->type_string(); - return false; - } - } + if (HasForwardedRefInput(node)) { + return false; } return FindKernelDef(jit_device_type, node.def(), nullptr, nullptr).ok(); diff --git a/tensorflow/compiler/jit/xla_cluster_util.cc b/tensorflow/compiler/jit/xla_cluster_util.cc index 70bd10336b824b4aaef6520f0b094f52e5a0d626..a5628b12a27c9ed052e22c784517a07f2c1c059a 100644 --- a/tensorflow/compiler/jit/xla_cluster_util.cc +++ b/tensorflow/compiler/jit/xla_cluster_util.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/graph/control_flow.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/util/device_name_utils.h" @@ -66,6 +67,9 @@ string DescribeCycle(const GraphCycles* cycles, const Graph& graph, int src, } return description; } + +bool AlwaysForwardsRefInput(const Node& node) { return node.IsIdentity(); } + } // namespace Status DeviceToDeviceType(const string& device, DeviceType* device_type) { @@ -77,6 +81,24 @@ Status DeviceToDeviceType(const string& device, DeviceType* device_type) { return Status::OK(); } +bool HasForwardedRefInput(const Node& node) { + if (AlwaysForwardsRefInput(node)) { + for (const Edge* incoming_edge : node.in_edges()) { + if (incoming_edge->IsControlEdge()) { + continue; + } + + Node* incoming_node = incoming_edge->src(); + if (IsRefType(incoming_node->output_type(incoming_edge->src_output()))) { + VLOG(2) << "Node " << node.def().ShortDebugString() << " has ref input " + << incoming_node->name() << " " << incoming_node->type_string(); + return true; + } + } + } + return false; +} + Status CreateCycleDetectionGraph(const Graph* graph, GraphCycles* cycles) { for (int i = 0; i < graph->num_node_ids(); ++i) { // We rely on the node IDs in the cycle detection graph being consecutive @@ -117,27 +139,32 @@ Status CreateCycleDetectionGraph(const Graph* graph, GraphCycles* cycles) { }; for (Edge const* edge : graph->edges()) { - if (edge->dst()->IsEnter()) { - // Lift edges to an "Enter" node to the corresponding frame node. - const string& frame_name = - control_flow_info[edge->dst()->id()].frame_name; - int dst = GetOrAddFrameNodeId(frame_name); - if (!cycles->InsertEdge(edge->src()->id(), dst)) { - return errors::Internal( - "Cycle detected when adding enter->frame edge: ", - DescribeCycle(cycles, *graph, edge->src()->id(), dst)); + if (edge->dst()->IsEnter() || edge->src()->IsExit()) { + const char* src_type = "pre-enter"; + const char* dst_type = "post-exit"; + int src = edge->src()->id(); + int dst = edge->dst()->id(); + + if (edge->dst()->IsEnter()) { + // Lift edges to an "Enter" node to the corresponding frame node. + const string& frame_name = + control_flow_info[edge->dst()->id()].frame_name; + dst = GetOrAddFrameNodeId(frame_name); + dst_type = "frame"; } - continue; - } - if (edge->src()->IsExit()) { - // Lift edges from an "Exit" node to the corresponding frame node. - const string& frame_name = - control_flow_info[edge->src()->id()].frame_name; - int src = GetOrAddFrameNodeId(frame_name); - if (!cycles->InsertEdge(src, edge->dst()->id())) { + + if (edge->src()->IsExit()) { + // Lift edges from an "Exit" node to the corresponding frame node. + const string& frame_name = + control_flow_info[edge->src()->id()].frame_name; + src = GetOrAddFrameNodeId(frame_name); + src_type = "frame"; + } + + if (!cycles->InsertEdge(src, dst)) { return errors::Internal( - "Cycle detected when adding frame->exit edge: ", - DescribeCycle(cycles, *graph, src, edge->dst()->id())); + "Cycle detected when adding ", src_type, "->", dst_type, + " edge: ", DescribeCycle(cycles, *graph, src, dst)); } // Drop the original edge. continue; diff --git a/tensorflow/compiler/jit/xla_cluster_util.h b/tensorflow/compiler/jit/xla_cluster_util.h index 5b673bdc27fccb4228b9e02cbf80d17aa35b5fe5..bcce082aaf6044ff0654efa4d78c0f493a350d00 100644 --- a/tensorflow/compiler/jit/xla_cluster_util.h +++ b/tensorflow/compiler/jit/xla_cluster_util.h @@ -36,6 +36,9 @@ using OrderedNodeSet = std::set; // Returns the DeviceType corresponding to 'device'. Status DeviceToDeviceType(const string& device, DeviceType* device_type); +// Returns true if `node` has a ref tensor input that it forwards to its output. +bool HasForwardedRefInput(const Node& node); + // Creates a graph representation to enable cycle detection when clustering. // This representation handles loops in graph by disconnecting each loop from // the enclosing graph. diff --git a/tensorflow/compiler/jit/xla_cluster_util_test.cc b/tensorflow/compiler/jit/xla_cluster_util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..2cb351e1ecdb4523a8652886af156540e4736b18 --- /dev/null +++ b/tensorflow/compiler/jit/xla_cluster_util_test.cc @@ -0,0 +1,69 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/jit/xla_cluster_util.h" + +#include "tensorflow/cc/framework/ops.h" +#include "tensorflow/cc/ops/control_flow_ops_internal.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/framework/function_testlib.h" +#include "tensorflow/core/framework/graph_to_functiondef.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/graph/graph_def_builder.h" +#include "tensorflow/core/graph/testlib.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +TEST(CreateCycleDetectionGraph, ConnectivityThroughEnterExitRegion) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output a = ops::Const(root.WithOpName("a"), Input::Initializer(0.0)); + Output enter = + ops::internal::Enter(root.WithOpName("enter"), a, "only_frame"); + Output exit = ops::internal::Exit(root.WithOpName("exit"), enter); + Output b = ops::Add(root.WithOpName("b"), a, exit); + + FixupSourceAndSinkEdges(root.graph()); + + GraphCycles cycles; + TF_ASSERT_OK(CreateCycleDetectionGraph(root.graph(), &cycles)); + EXPECT_FALSE(cycles.ContractEdge(a.node()->id(), b.node()->id())); +} + +TEST(CreateCycleDetectionGraph, ConnectivityThroughMultipleEnterExitRegions) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output a = ops::Const(root.WithOpName("a"), Input::Initializer(0.0)); + Output enter_0 = + ops::internal::Enter(root.WithOpName("enter_0"), a, "frame_0"); + Output exit_0 = ops::internal::Exit(root.WithOpName("exit_0"), enter_0); + Output enter_1 = + ops::internal::Enter(root.WithOpName("enter_1"), a, "frame_1"); + Output exit_1 = ops::internal::Exit(root.WithOpName("exit_1"), enter_1); + Output b = ops::Add(root.WithOpName("b"), a, exit_1); + + FixupSourceAndSinkEdges(root.graph()); + + GraphCycles cycles; + TF_ASSERT_OK(CreateCycleDetectionGraph(root.graph(), &cycles)); + EXPECT_FALSE(cycles.ContractEdge(a.node()->id(), b.node()->id())); +} +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index 7ed609c43748062656b631243c01d790519c54fd..54a41a4daa790401c797277e7eaab531dd34ac80 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -40,7 +40,23 @@ namespace tensorflow { XlaCompilationCache::XlaCompilationCache(xla::LocalClient* client, DeviceType device_type) : client_(client), device_type_(std::move(device_type)) {} -XlaCompilationCache::~XlaCompilationCache() = default; +XlaCompilationCache::~XlaCompilationCache() { + // Ensure any use of our programs have completed by waiting for all stream + // executors to complete. + for (auto* executor : client_->backend().stream_executors()) { + bool ok = executor->SynchronizeAllActivity(); + if (!ok) { + LOG(ERROR) << "Error synchronizing activity while waiting for all " + "programs to complete"; + } + } + // TODO(b/110813685): Think about the program ownership model. Programs are + // currently owned by the compilation cache which means we must wait for + // program completion in the destructor. There are multiple compilation caches + // around, which complicates things a little. Perhaps having programs be + // shared_ptrs (an invasive change) would make the model easier to reason + // about? +} string XlaCompilationCache::DebugString() { return "XLA JIT compilation cache"; diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc index b1943d3e1a7e321b5a3796a0c6e4f2b5d9ac7018..baccea2d6a793df8c5cf8c8941706d41d2c044ca 100644 --- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc +++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc @@ -61,14 +61,18 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx, ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr; TF_RET_CHECK(stream); - VLOG(2) << "Executing computation."; + VLOG(2) << "Executing computation: " << name(); + for (const xla::ShapedBuffer* arg : launch_context.arguments()) { + VLOG(2) << name() << ": " << *arg; + } xla::ExecutableRunOptions run_options; run_options.set_stream(stream); run_options.set_allocator(client->backend().memory_allocator()); run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device()); run_options.set_rng_seed(ctx->step_id()); - auto run_result = executable->Run(launch_context.arguments(), run_options); + xla::StatusOr run_result = + executable->Run(launch_context.arguments(), run_options); TF_RETURN_IF_ERROR(run_result.status()); launch_context.PopulateOutputs(ctx, result, run_result.ConsumeValueOrDie()); @@ -159,6 +163,13 @@ Status XlaCompileOnDemandOp::Compile( XlaCompiler::CompileOptions compile_options; compile_options.is_entry_computation = true; + // Optimization: don't resolve constants. If we resolve constants we never + // emit them on the device, meaning that if they are needed by a following + // computation the host has to transfer them. + compile_options.resolve_compile_time_constants = false; + // Optimization: where possible, have the computation return a naked array + // rather than a one-element tuple. + compile_options.always_return_tuple = false; std::map variable_args = GetVariables(ctx); return cache->CompileSingleOp(options, constant_arguments, variable_args, ctx, diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 71e63b110b3b132a57fc291e53a165954c72a03c..3bbf97afadd2c8a70add16b748a35832a2ef8538 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -56,9 +56,9 @@ XlaTransferManager::XlaTransferManager( transfer_as_literal_(transfer_as_literal), shape_representation_fn_(std::move(shape_representation_fn)) { if (!shape_representation_fn_) { - shape_representation_fn_ = [](const TensorShape& shape, DataType dtype) { - return shape; - }; + shape_representation_fn_ = + [](const TensorShape& shape, + DataType dtype) -> xla::StatusOr { return shape; }; } } @@ -67,36 +67,53 @@ Status XlaTransferManager::TransferLiteralToDevice( xla::Shape xla_shape; TF_RETURN_IF_ERROR(TensorShapeToXLAShape(host_tensor.dtype(), host_tensor.shape(), &xla_shape)); - xla::BorrowingLiteral literal( + // Create a reference to hold onto host_tensor until after the literal has + // been transferred. Also make sure the literal exists until the function + // asynchronously completes, as it will be wrapped in an xla::LiteralSlice. + TensorReference ref(host_tensor); + auto literal = std::make_shared( static_cast(DMAHelper::base(&host_tensor)), xla_shape); const xla::ShapedBuffer& shaped_buffer = XlaTensor::FromTensor(device_tensor)->shaped_buffer(); - VLOG(1) << "Transfer to device as literal: " << literal.ToString() << " " + VLOG(1) << "Transfer to device as literal: " << literal->ToString() << " " << shaped_buffer.ToString(); - return transfer_manager_->TransferLiteralToDevice(stream_->parent(), literal, - shaped_buffer); + TF_RETURN_IF_ERROR(transfer_manager_->TransferLiteralToDeviceAsync( + stream_, *literal, shaped_buffer)); + // Unref the host tensor, and capture the literal shared_ptr too so it goes + // out of scope when the lambda completes. + stream_->ThenDoHostCallback([ref, literal]() { ref.Unref(); }); + return Status::OK(); } -Status XlaTransferManager::TransferLiteralFromDevice( - Tensor* host_tensor, const Tensor& device_tensor) const { +void XlaTransferManager::TransferLiteralFromDevice( + Tensor* host_tensor, const Tensor& device_tensor, + const StatusCallback& done) const { const xla::ShapedBuffer& shaped_buffer = XlaTensor::FromTensor(&device_tensor)->shaped_buffer(); - TF_ASSIGN_OR_RETURN(std::unique_ptr literal, - transfer_manager_->TransferLiteralFromDevice( - stream_->parent(), shaped_buffer)); - VLOG(1) << "Transfer from device as literal: " << literal->ToString() << " " - << shaped_buffer.ToString(); - Tensor tensor; - TF_RETURN_IF_ERROR( - LiteralToHostTensor(*literal, host_tensor->dtype(), &tensor)); - // Reshape the tensor back to its declared shape. - if (!host_tensor->CopyFrom(tensor, device_tensor.shape())) { - return errors::Internal( - "Tensor::CopyFrom failed when copying from XLA device to CPU"); - } - return Status::OK(); + TensorReference ref(device_tensor); + transfer_manager_->TransferLiteralFromDevice( + stream_, shaped_buffer, + [=, &shaped_buffer]( + xla::StatusOr > literal_or) { + ref.Unref(); + done([&]() -> Status { + TF_ASSIGN_OR_RETURN(auto literal, std::move(literal_or)); + VLOG(1) << "Transfer from device as literal: " << literal->ToString() + << " " << shaped_buffer.ToString(); + Tensor tensor; + TF_RETURN_IF_ERROR( + LiteralToHostTensor(*literal, host_tensor->dtype(), &tensor)); + // Reshape the tensor back to its declared shape. + Status status; + if (!host_tensor->CopyFrom(tensor, device_tensor.shape())) { + status = errors::Internal( + "Tensor::CopyFrom failed when copying from XLA device to CPU"); + } + return status; + }()); + }); } void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, @@ -119,19 +136,23 @@ void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); CHECK(xla_tensor); - TensorShape shape = shape_representation_fn_(device_tensor->shape(), - device_tensor->dtype()); + Status status; + xla::StatusOr shape_or_status = shape_representation_fn_( + device_tensor->shape(), device_tensor->dtype()); + if (!shape_or_status.ok()) { + done(shape_or_status.status()); + return; + } + TensorShape shape = shape_or_status.ValueOrDie(); if (!xla_tensor->has_shaped_buffer()) { - Status s = xla_tensor->AllocateShapedBuffer( + status = xla_tensor->AllocateShapedBuffer( device_tensor->dtype(), shape, client_, stream_->parent()->device_ordinal()); - if (!s.ok()) { - done(s); - return; + if (!status.ok()) { + return done(status); } } - Status status; if (transfer_as_literal_) { Tensor reshaped_cpu_tensor; if (!reshaped_cpu_tensor.CopyFrom(*cpu_tensor, shape)) { @@ -184,7 +205,8 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, Status status; if (transfer_as_literal_) { - status = TransferLiteralFromDevice(cpu_tensor, *device_tensor); + TransferLiteralFromDevice(cpu_tensor, *device_tensor, done); + return; } else { stream_->ThenMemcpy(dst_ptr, dev_src_ptr, total_bytes); // TODO(hpucha): Make this asynchronous. @@ -194,9 +216,8 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, "Failed to complete data transfer on stream %p: %s", stream_, block_status.error_message().c_str()); } + done(status); } - - done(status); return; } @@ -207,8 +228,8 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, Tensor* dst_tensor, const StatusCallback& done) { - // TODO(phawkins): replace this code with an asynchronous implementation. - auto body = [&]() { + // Perform memory allocation now, and enqueue the device-to-device transfer. + Status status = [&]() -> Status { if (src_tensor.NumElements() == 0) { return Status::OK(); } @@ -217,27 +238,27 @@ void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, CHECK(xla_src && xla_dst) << "Missing destination tensor for device-to-device copy"; if (!xla_dst->has_shaped_buffer()) { - TensorShape shape = - shape_representation_fn_(src_tensor.shape(), src_tensor.dtype()); + TF_ASSIGN_OR_RETURN( + TensorShape shape, + shape_representation_fn_(src_tensor.shape(), src_tensor.dtype())); TF_RETURN_IF_ERROR( xla_dst->AllocateShapedBuffer(src_tensor.dtype(), shape, client_, stream_->parent()->device_ordinal())); } - TF_RETURN_IF_ERROR( - xla_dst->shaped_buffer().buffers().ForEachMutableElementWithStatus( - [&](const xla::ShapeIndex& index, se::DeviceMemoryBase* buffer) { - const se::DeviceMemoryBase& from_buffer = - xla_src->shaped_buffer().buffers().element(index); - CHECK_EQ(buffer->size(), from_buffer.size()); - if (!stream_->parent()->SynchronousMemcpy(buffer, from_buffer, - buffer->size())) { - return errors::Internal("Device to device memcpy failed"); - } - return Status::OK(); - })); + auto from_iter = xla_src->shaped_buffer().buffers().begin(); + auto to_iter = xla_dst->shaped_buffer().buffers().begin(); + for (auto end_iter = xla_src->shaped_buffer().buffers().end(); + from_iter != end_iter; ++from_iter, ++to_iter) { + stream_->ThenMemcpyD2D(&to_iter->second, from_iter->second, + to_iter->second.size()); + } return Status::OK(); - }; - done(body()); + }(); + if (!status.ok()) { + return done(status); + } else { + stream_->ThenDoHostCallback([=]() { done(Status::OK()); }); + } } XlaDeviceContext::XlaDeviceContext( diff --git a/tensorflow/compiler/jit/xla_device_context.h b/tensorflow/compiler/jit/xla_device_context.h index ee346e5653bbf9f393df202572c2150b4989506f..c5c81d65fe0f4a2774aab9f742454467e052071e 100644 --- a/tensorflow/compiler/jit/xla_device_context.h +++ b/tensorflow/compiler/jit/xla_device_context.h @@ -64,8 +64,9 @@ class XlaTransferManager { private: Status TransferLiteralToDevice(const Tensor& host_tensor, Tensor* device_tensor) const; - Status TransferLiteralFromDevice(Tensor* host_tensor, - const Tensor& device_tensor) const; + void TransferLiteralFromDevice(Tensor* host_tensor, + const Tensor& device_tensor, + const StatusCallback& done) const; // Stream obtained from a Device, used to transfer tensors between // CPU and device. diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h index 0c49286acd3abaf8ea1f12a90d86a1d1ff38b234..a605335a94f8687e0af4566f912b38dca9b5ac26 100644 --- a/tensorflow/compiler/jit/xla_device_ops.h +++ b/tensorflow/compiler/jit/xla_device_ops.h @@ -23,11 +23,14 @@ limitations under the License. #include "tensorflow/core/kernels/cast_op.h" #include "tensorflow/core/kernels/constant_op.h" #include "tensorflow/core/kernels/control_flow_ops.h" +#include "tensorflow/core/kernels/fifo_queue.h" #include "tensorflow/core/kernels/identity_n_op.h" #include "tensorflow/core/kernels/identity_op.h" #include "tensorflow/core/kernels/no_op.h" +#include "tensorflow/core/kernels/queue_op.h" #include "tensorflow/core/kernels/resource_variable_ops.h" #include "tensorflow/core/kernels/sendrecv_ops.h" +#include "tensorflow/core/kernels/shape_ops.h" #include "tensorflow/core/kernels/variable_ops.h" namespace tensorflow { @@ -87,6 +90,46 @@ class XlaAssignVariableOp : public AsyncOpKernel { REGISTER_KERNEL_BUILDER( \ Name("ReadVariableOp").Device(DEVICE).HostMemory("resource"), \ ReadVariableOp); \ + REGISTER_KERNEL_BUILDER(Name("Shape") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + ShapeOp); \ + REGISTER_KERNEL_BUILDER(Name("Shape") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + ShapeOp); \ + REGISTER_KERNEL_BUILDER(Name("ShapeN") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + ShapeNOp); \ + REGISTER_KERNEL_BUILDER(Name("ShapeN") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + ShapeNOp); \ + REGISTER_KERNEL_BUILDER(Name("Size") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + SizeOp); \ + REGISTER_KERNEL_BUILDER(Name("Size") \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("out_type") \ + .TypeConstraint("T", TYPES), \ + SizeOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("Rank").Device(DEVICE).HostMemory("output").TypeConstraint("T", \ + TYPES), \ + RankOp); \ REGISTER_KERNEL_BUILDER( \ Name("AssignVariableOp").Device(DEVICE).HostMemory("resource"), \ XlaAssignVariableOp); \ @@ -104,7 +147,32 @@ class XlaAssignVariableOp : public AsyncOpKernel { .Device(DEVICE) \ .HostMemory("input") \ .HostMemory("output"), \ - LoopCondOp); + LoopCondOp); \ + \ + REGISTER_KERNEL_BUILDER( \ + Name("QueueEnqueueV2").Device(DEVICE).HostMemory("handle"), EnqueueOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("QueueDequeueV2").Device(DEVICE).HostMemory("handle"), DequeueOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("QueueCloseV2").Device(DEVICE).HostMemory("handle"), QueueCloseOp); \ + REGISTER_KERNEL_BUILDER(Name("QueueSizeV2") \ + .Device(DEVICE) \ + .HostMemory("size") \ + .HostMemory("handle"), \ + QueueSizeOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("QueueIsClosedV2").Device(DEVICE).HostMemory("handle"), \ + QueueIsClosedOp); \ + \ + REGISTER_KERNEL_BUILDER( \ + Name("FIFOQueueV2").Device(DEVICE).HostMemory("handle"), FIFOQueueOp); + +// TODO(phawkins): currently we do not register the QueueEnqueueMany, +// QueueDequeueMany, or QueueDequeueUpTo kernels because they attempt to read +// and write the tensors they access in order to concatenate them into a batch. +// We would need either to call out to an XLA computation to perform the +// concatenation, or we would need to refactor those kernels so the splitting +// or merging is done in a separate operator that can be compiled. } // namespace tensorflow diff --git a/tensorflow/compiler/jit/xla_fusion_optimizer.cc b/tensorflow/compiler/jit/xla_fusion_optimizer.cc index 96016521ea902274e3ec1dcc35d3d070063eb1ae..74257b09a808a39454eace3b1a9bf57a2e071360 100644 --- a/tensorflow/compiler/jit/xla_fusion_optimizer.cc +++ b/tensorflow/compiler/jit/xla_fusion_optimizer.cc @@ -178,6 +178,13 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, continue; } + // XLA does not offer guaranteed aliasing between the input and output of + // the XLA cluster so it can't implement the forward-tensor-ref semantic. + // Leave such nodes out of XLA clusters. + if (HasForwardedRefInput(*node)) { + continue; + } + compilation_candidates.insert(node); } diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc index d0c7a9365125708b2af43f87c7617d8d84050a61..5ceccc769fa2e95d4cf4d2b4ebd8dbf312ebdfd0 100644 --- a/tensorflow/compiler/jit/xla_launch_util.cc +++ b/tensorflow/compiler/jit/xla_launch_util.cc @@ -176,6 +176,21 @@ void XlaComputationLaunchContext::PopulateOutputs( } CHECK_EQ(ctx->num_outputs(), kernel->outputs.size()); + // If the on-host-shape isn't a tuple, create a new single-element tuple + // buffer with a nullptr root index table. This allows the code below to treat + // output as a tuple unconditionally. + if (!xla::ShapeUtil::IsTuple(output.on_host_shape())) { + ShapedBuffer nontuple_buffer = output.release(); + ShapedBuffer buffer( + xla::ShapeUtil::MakeTupleShape({nontuple_buffer.on_host_shape()}), + xla::ShapeUtil::MakeTupleShape({nontuple_buffer.on_device_shape()}), + output.platform(), output.device_ordinal()); + buffer.buffers().CopySubtreeFrom(nontuple_buffer.buffers(), + /*source_base_index=*/{}, + /*target_base_index=*/{0}); + output = ScopedShapedBuffer(std::move(buffer), output.memory_allocator()); + } + // Copy XLA results to the OpOutputList. int output_num = 0; for (int i = 0; i < ctx->num_outputs(); ++i) { @@ -230,9 +245,14 @@ void XlaComputationLaunchContext::PopulateOutputs( Tensor* output_tensor; OP_REQUIRES_OK(ctx, ctx->allocate_output(i, shape, &output_tensor)); XlaTensor* xla_tensor = XlaTensor::FromTensor(output_tensor); - CHECK(xla_tensor); - xla_tensor->set_shaped_buffer(ScopedShapedBuffer( - ExtractSubShapedBuffer(&output, output_num, xla_allocator_))); + if (xla_tensor) { + xla_tensor->set_shaped_buffer(ScopedShapedBuffer( + ExtractSubShapedBuffer(&output, output_num, xla_allocator_))); + } else { + // xla_tensor wasn't valid, which must mean this is a zero-element + // tensor. + CHECK_EQ(output_tensor->TotalBytes(), 0); + } } else { Tensor output_tensor = XlaTensorBuffer::MakeTensor( ctx->expected_output_dtype(i), shape, buffer, allocator); diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index e6c92f9720e1285617280f60d1c5fea443c5ebef..080b1c9c3535b99800a256a595eb56df653bf53b 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -51,6 +51,38 @@ py_library( ], ) +py_library( + name = "test_utils", + testonly = 1, + srcs = ["test_utils.py"], + srcs_version = "PY2AND3", + deps = [ + "//third_party/py/numpy", + ], +) + +py_test( + name = "xla_test_test", + size = "small", + srcs = ["xla_test_test.py"], + deps = [ + ":xla_test", + ], +) + +tf_xla_py_test( + name = "adadelta_test", + size = "medium", + srcs = ["adadelta_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework", + "//tensorflow/python:platform_test", + "//tensorflow/python:training", + ], +) + tf_xla_py_test( name = "adagrad_test", size = "small", @@ -238,6 +270,7 @@ tf_xla_py_test( srcs = ["conv2d_test.py"], shard_count = 10, deps = [ + ":test_utils", ":xla_test", "//tensorflow/python:array_ops", "//tensorflow/python:framework", @@ -245,6 +278,7 @@ tf_xla_py_test( "//tensorflow/python:nn_ops", "//tensorflow/python:nn_ops_gen", "//tensorflow/python:platform_test", + "@absl_py//absl/testing:parameterized", ], ) @@ -350,6 +384,20 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "fifo_queue_test", + size = "medium", + srcs = ["fifo_queue_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:data_flow_ops", + "//tensorflow/python:extra_py_tests_deps", + "//tensorflow/python:framework", + "//tensorflow/python:platform_test", + ], +) + tf_xla_py_test( name = "fft_test", size = "medium", @@ -539,8 +587,11 @@ tf_xla_py_test( name = "random_ops_test", size = "small", srcs = ["random_ops_test.py"], - # TODO(b/31361304): enable RNG ops on GPU when parallelized. disabled_backends = [ + # TODO(b/110300529): RngNormal doesn't return values with the expected variance + "cpu", + "cpu_ondemand", + # TODO(b/31361304): enable RNG ops on GPU when parallelized. "gpu", ], deps = [ @@ -664,6 +715,19 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "sparse_to_dense_op_test", + size = "small", + srcs = ["sparse_to_dense_op_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework", + "//tensorflow/python:platform_test", + "//tensorflow/python:sparse_ops", + ], +) + tf_xla_py_test( name = "stack_ops_test", size = "small", @@ -743,9 +807,10 @@ tf_xla_py_test( tf_xla_py_test( name = "fused_batchnorm_test", - size = "small", + size = "medium", srcs = ["fused_batchnorm_test.py"], deps = [ + ":test_utils", ":xla_test", "//tensorflow/python:framework", "//tensorflow/python:math_ops", @@ -755,6 +820,7 @@ tf_xla_py_test( "//tensorflow/python:nn_ops_gen", "//tensorflow/python:platform_test", "//tensorflow/python:training", + "@absl_py//absl/testing:parameterized", ], ) @@ -830,6 +896,20 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "sort_ops_test", + size = "small", + srcs = ["sort_ops_test.py"], + # Times out in fastbuild mode. + tags = ["optonly"], + deps = [ + "//tensorflow/compiler/tests:xla_test", + "//tensorflow/compiler/tf2xla/python:xla", + "//tensorflow/python:array_ops", + "//tensorflow/python:dtypes", + ], +) + tf_xla_py_test( name = "xla_device_test", size = "small", diff --git a/tensorflow/compiler/tests/adadelta_test.py b/tensorflow/compiler/tests/adadelta_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3e3c09c66e72c4de141b64cea3c4693fabb7b2a2 --- /dev/null +++ b/tensorflow/compiler/tests/adadelta_test.py @@ -0,0 +1,134 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Adadelta Optimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import constant_op +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import adadelta + + +class AdadeltaOptimizerTest(xla_test.XLATestCase): + + def testBasic(self): + num_updates = 4 # number of ADADELTA steps to perform + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + for grad in [0.2, 0.1, 0.01]: + for lr in [1.0, 0.5, 0.1]: + var0_init = [1.0, 2.0] + var1_init = [3.0, 4.0] + var0 = resource_variable_ops.ResourceVariable( + var0_init, dtype=dtype) + var1 = resource_variable_ops.ResourceVariable( + var1_init, dtype=dtype) + + grads = constant_op.constant([grad, grad], dtype=dtype) + + accum = 0.0 + accum_update = 0.0 + + # ADADELTA gradient optimizer + rho = 0.95 + epsilon = 1e-8 + adadelta_opt = adadelta.AdadeltaOptimizer( + learning_rate=lr, rho=rho, epsilon=epsilon) + adadelta_update = adadelta_opt.apply_gradients( + zip([grads, grads], [var0, var1])) + self.evaluate(variables.global_variables_initializer()) + opt_vars = adadelta_opt.variables() + self.assertStartsWith(opt_vars[0].name, var0._shared_name) + self.assertStartsWith(opt_vars[1].name, var0._shared_name) + self.assertStartsWith(opt_vars[2].name, var1._shared_name) + self.assertStartsWith(opt_vars[3].name, var1._shared_name) + self.assertEqual(4, len(opt_vars)) + # Assign slots + slot = [None] * 2 + slot_update = [None] * 2 + self.assertEqual(["accum", "accum_update"], + adadelta_opt.get_slot_names()) + slot[0] = adadelta_opt.get_slot(var0, "accum") + self.assertEquals(slot[0].get_shape(), var0.get_shape()) + self.assertFalse(slot[0] in variables.trainable_variables()) + + slot_update[0] = adadelta_opt.get_slot(var0, "accum_update") + self.assertEquals(slot_update[0].get_shape(), var0.get_shape()) + self.assertFalse(slot_update[0] in variables.trainable_variables()) + + slot[1] = adadelta_opt.get_slot(var1, "accum") + self.assertEquals(slot[1].get_shape(), var1.get_shape()) + self.assertFalse(slot[1] in variables.trainable_variables()) + + slot_update[1] = adadelta_opt.get_slot(var1, "accum_update") + self.assertEquals(slot_update[1].get_shape(), var1.get_shape()) + self.assertFalse(slot_update[1] in variables.trainable_variables()) + + # Fetch params to validate initial values + self.assertAllClose(var0_init, self.evaluate(var0)) + self.assertAllClose(var1_init, self.evaluate(var1)) + + update = [None] * num_updates + tot_update = 0 + for step in range(num_updates): + # Run adadelta update for comparison + self.evaluate(adadelta_update) + + # Perform initial update without previous accum values + accum = accum * rho + (grad**2) * (1 - rho) + update[step] = ( + np.sqrt(accum_update + epsilon) * + (1. / np.sqrt(accum + epsilon)) * grad) + accum_update = ( + accum_update * rho + (update[step]**2) * (1.0 - rho)) + tot_update += update[step] * lr + + # Check that the accumulators have been updated + for slot_idx in range(2): + self.assertAllCloseAccordingToType( + np.array([accum, accum], dtype=dtype), + self.evaluate(slot[slot_idx]), + rtol=1e-5) + + self.assertAllCloseAccordingToType( + np.array([accum_update, accum_update], dtype=dtype), + self.evaluate(slot_update[slot_idx]), + rtol=1e-5) + + # Check that the parameters have been updated + self.assertAllCloseAccordingToType( + np.array( + [var0_init[0] - tot_update, var0_init[1] - tot_update], + dtype=dtype), + self.evaluate(var0), + rtol=1e-5) + + self.assertAllCloseAccordingToType( + np.array( + [var1_init[0] - tot_update, var1_init[1] - tot_update], + dtype=dtype), + self.evaluate(var1), + rtol=1e-5) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/adagrad_test.py b/tensorflow/compiler/tests/adagrad_test.py index 9a93b3216404d8ed21fd6c57757bec1730c119b4..d775850a80e9f83f7b2c9f1cf8997dd50e229635 100644 --- a/tensorflow/compiler/tests/adagrad_test.py +++ b/tensorflow/compiler/tests/adagrad_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables @@ -28,7 +28,7 @@ from tensorflow.python.platform import test from tensorflow.python.training import adagrad -class AdagradOptimizerTest(XLATestCase): +class AdagradOptimizerTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: diff --git a/tensorflow/compiler/tests/adam_test.py b/tensorflow/compiler/tests/adam_test.py index 3215dc36e5b2d517aa951db1b0d41188185ef93a..03554d6933aca39b428c6af4be0c78e2c7ccb0c9 100644 --- a/tensorflow/compiler/tests/adam_test.py +++ b/tensorflow/compiler/tests/adam_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops @@ -48,7 +48,7 @@ def adam_update_numpy(param, return param_t, m_t, v_t -class AdamOptimizerTest(XLATestCase): +class AdamOptimizerTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 1e4dd32916c3a40282735fb8f75670b0e9ef0dc9..9cb3d0454608c37e669d5b4360bc39bf1bf7e68c 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import array_ops @@ -32,7 +32,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.platform import googletest -class BinaryOpsTest(XLATestCase): +class BinaryOpsTest(xla_test.XLATestCase): """Test cases for binary operators.""" def _testBinary(self, op, a, b, expected, equality_test=None): @@ -226,6 +226,11 @@ class BinaryOpsTest(XLATestCase): np.array([0b1, 0b101, 0b1000], dtype=dtype), np.array([0b0, 0b101, 0b1001], dtype=dtype), expected=np.array([0b1, 0b101, 0b1001], dtype=dtype)) + self._testSymmetricBinary( + bitwise_ops.bitwise_xor, + np.array([0b1, 0b111, 0b1100], dtype=dtype), + np.array([0b0, 0b101, 0b1001], dtype=dtype), + expected=np.array([0b1, 0b010, 0b0101], dtype=dtype)) lhs = np.array([0, 5, 3, 14], dtype=dtype) rhs = np.array([5, 0, 7, 11], dtype=dtype) @@ -1216,6 +1221,24 @@ class BinaryOpsTest(XLATestCase): np.array([1, 0], dtype=np.int32), expected=np.array([[1, 3], [2, 4]], dtype=dtype)) + def testConjugateTranspose(self): + for dtype in self.complex_types: + self._testBinary( + array_ops.conjugate_transpose, + np.zeros(shape=[1, 0, 4], dtype=dtype), + np.array([1, 2, 0], dtype=np.int32), + expected=np.zeros(shape=[0, 4, 1], dtype=dtype)) + self._testBinary( + array_ops.conjugate_transpose, + np.array([[1 - 1j, 2 + 2j], [3 - 3j, 4 + 4j]], dtype=dtype), + np.array([0, 1], dtype=np.int32), + expected=np.array([[1 + 1j, 2 - 2j], [3 + 3j, 4 - 4j]], dtype=dtype)) + self._testBinary( + array_ops.conjugate_transpose, + np.array([[1 - 1j, 2 + 2j], [3 - 3j, 4 + 4j]], dtype=dtype), + np.array([1, 0], dtype=np.int32), + expected=np.array([[1 + 1j, 3 + 3j], [2 - 2j, 4 - 4j]], dtype=dtype)) + def testCross(self): for dtype in self.float_types: self._testBinary( diff --git a/tensorflow/compiler/tests/bucketize_op_test.py b/tensorflow/compiler/tests/bucketize_op_test.py index fde9759a1c209844caac99d5f303cd3e406e5370..ef4d5f6322b7ae79b051795b5af7e6f7f1e55550 100644 --- a/tensorflow/compiler/tests/bucketize_op_test.py +++ b/tensorflow/compiler/tests/bucketize_op_test.py @@ -18,7 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.ops import array_ops @@ -26,7 +26,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class BucketizationOpTest(XLATestCase): +class BucketizationOpTest(xla_test.XLATestCase): def testInt(self): with self.test_session() as sess: diff --git a/tensorflow/compiler/tests/categorical_op_test.py b/tensorflow/compiler/tests/categorical_op_test.py index 035cdea1786d39f3d21bb63be5c8ccffe1608bdf..a4e7f75081dfd07fd4b5c94c33908aab8e7d8aa9 100644 --- a/tensorflow/compiler/tests/categorical_op_test.py +++ b/tensorflow/compiler/tests/categorical_op_test.py @@ -22,7 +22,7 @@ import collections import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops @@ -32,7 +32,7 @@ from tensorflow.python.platform import googletest # TODO(srvasude): Merge this with # third_party/tensorflow/python/kernel_tests/random/multinomial_op_test.py. -class CategoricalTest(XLATestCase): +class CategoricalTest(xla_test.XLATestCase): """Test cases for random-number generating operators.""" def output_dtypes(self): diff --git a/tensorflow/compiler/tests/cholesky_op_test.py b/tensorflow/compiler/tests/cholesky_op_test.py index 1a8989d7c2f617525c301f30fd899a01362310bf..d2867278af93812eae804b66a7a6b706f98fa600 100644 --- a/tensorflow/compiler/tests/cholesky_op_test.py +++ b/tensorflow/compiler/tests/cholesky_op_test.py @@ -23,7 +23,7 @@ import unittest import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -32,7 +32,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class CholeskyOpTest(XLATestCase): +class CholeskyOpTest(xla_test.XLATestCase): # Cholesky defined for float64, float32, complex64, complex128 # (https://www.tensorflow.org/api_docs/python/tf/cholesky) diff --git a/tensorflow/compiler/tests/clustering_test.py b/tensorflow/compiler/tests/clustering_test.py index 574f82fc717818334ac5d72ebef2191f1c18e669..e42ebf8f9e01dab13cde15979ffc42b7c0fbc57b 100644 --- a/tensorflow/compiler/tests/clustering_test.py +++ b/tensorflow/compiler/tests/clustering_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -32,7 +32,7 @@ from tensorflow.python.platform import googletest CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0" -class ClusteringTest(XLATestCase): +class ClusteringTest(xla_test.XLATestCase): def testAdd(self): val1 = np.array([4, 3, 2, 1], dtype=np.float32) diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py index f10973e19f1945515b776cf86349445ed7334629..d9ad4281477e87f79f2ecb52989ae86a5030d0cc 100644 --- a/tensorflow/compiler/tests/concat_ops_test.py +++ b/tensorflow/compiler/tests/concat_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -30,7 +30,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest -class ConcatTest(XLATestCase): +class ConcatTest(xla_test.XLATestCase): def testHStack(self): with self.test_session(): @@ -292,7 +292,7 @@ class ConcatTest(XLATestCase): array_ops.concat([scalar, scalar, scalar], dim) -class ConcatOffsetTest(XLATestCase): +class ConcatOffsetTest(xla_test.XLATestCase): def testBasic(self): with self.test_session() as sess: @@ -306,7 +306,7 @@ class ConcatOffsetTest(XLATestCase): self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) -class PackTest(XLATestCase): +class PackTest(xla_test.XLATestCase): def testBasic(self): with self.test_session() as sess: diff --git a/tensorflow/compiler/tests/conv2d_test.py b/tensorflow/compiler/tests/conv2d_test.py index 62577b70ce96e220d79978f01614b2d9a3647680..98d41ba7edd52eedbf035097a48a1ce2ac7d5e9e 100644 --- a/tensorflow/compiler/tests/conv2d_test.py +++ b/tensorflow/compiler/tests/conv2d_test.py @@ -22,9 +22,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import test_utils +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_nn_ops @@ -32,7 +34,15 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.platform import googletest -class Conv2DTest(XLATestCase): +DATA_FORMATS = ( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), +) + + +class Conv2DTest(xla_test.XLATestCase, parameterized.TestCase): def _VerifyValues(self, input_sizes=None, @@ -40,6 +50,8 @@ class Conv2DTest(XLATestCase): strides=None, dilations=None, padding=None, + data_format_src="NHWC", + data_format_dst="NHWC", expected=None): """Tests that tf.nn.conv2d produces the expected value. @@ -51,8 +63,12 @@ class Conv2DTest(XLATestCase): strides: Strides. dilations: RHS dilations. padding: Padding type. + data_format_src: Data format input is in. + data_format_dst: Data format verification will run and input is converted + to. expected: Expected output. """ + total_size_1 = np.prod(input_sizes) total_size_2 = np.prod(filter_sizes) x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes) @@ -62,6 +78,18 @@ class Conv2DTest(XLATestCase): dilations = [1, 1] dilations = [1] + dilations + [1] + # Convert between data formats. + expected = test_utils.ConvertBetweenDataFormats(expected, data_format_src, + data_format_dst) + x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, + data_format_dst) + input_sizes = test_utils.PermuteDimsBetweenDataFormats( + input_sizes, data_format_src, data_format_dst) + strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, + data_format_dst) + dilations = test_utils.PermuteDimsBetweenDataFormats( + dilations, data_format_src, data_format_dst) + with self.test_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) @@ -71,12 +99,14 @@ class Conv2DTest(XLATestCase): t2, strides=strides, padding=padding, - data_format="NHWC", + data_format=data_format_dst, dilations=dilations) + value = sess.run(out, {t1: x1, t2: x2}) self.assertAllClose(expected, value, 1e-3) - def testConv2D1x1Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x1Filter(self, data_format): expected_output = np.reshape([ 30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0 @@ -86,9 +116,12 @@ class Conv2DTest(XLATestCase): filter_sizes=[1, 1, 3, 3], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Filter(self, data_format): expected_output = np.reshape( [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0], [1, 1, 2, 3]) self._VerifyValues( @@ -96,9 +129,12 @@ class Conv2DTest(XLATestCase): filter_sizes=[2, 2, 3, 3], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Filter2x1Dilation(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Filter2x1Dilation(self, data_format): expected_output = np.array([[[[72], [82], [92]], [[112], [122], [132]]]]) self._VerifyValues( input_sizes=[1, 4, 4, 1], @@ -106,9 +142,12 @@ class Conv2DTest(XLATestCase): strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2Filter(self, data_format): expected_output = np.reshape([ 231.0, 252.0, 273.0, 384.0, 423.0, 462.0, 690.0, 765.0, 840.0, 843.0, 936.0, 1029.0 @@ -118,18 +157,24 @@ class Conv2DTest(XLATestCase): filter_sizes=[1, 2, 3, 3], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2(self, data_format): expected_output = np.reshape([2271.0, 2367.0, 2463.0], [1, 1, 1, 3]) self._VerifyValues( input_sizes=[1, 2, 3, 3], filter_sizes=[2, 2, 3, 3], strides=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2Same(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2Same(self, data_format): expected_output = np.reshape( [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0], [1, 1, 2, 3]) self._VerifyValues( @@ -137,47 +182,61 @@ class Conv2DTest(XLATestCase): filter_sizes=[2, 2, 3, 3], strides=[2, 2], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2DEmptyDilation(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DEmptyDilation(self, data_format): self._VerifyValues( input_sizes=[0, 2, 3, 3], filter_sizes=[1, 1, 3, 3], strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.zeros([0, 2, 3, 3])) - def testConv2D2x2FilterDilation(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterDilation(self, data_format): self._VerifyValues( input_sizes=[1, 2, 3, 3], filter_sizes=[2, 2, 3, 3], strides=[1, 1], dilations=[1, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.reshape([2667, 2781, 2895], [1, 1, 1, 3])) - def testConv2D1x2FilterDilation(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterDilation(self, data_format): self._VerifyValues( input_sizes=[1, 2, 3, 3], filter_sizes=[1, 2, 3, 3], strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.array([[[[231, 252, 273], [384, 423, 462]], [[690, 765, 840], [843, 936, 1029]]]])) - def testConv2DKernelSizeMatchesInputSizeDilation(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DKernelSizeMatchesInputSizeDilation(self, data_format): self._VerifyValues( input_sizes=[1, 3, 3, 1], filter_sizes=[2, 2, 1, 2], strides=[1, 1], dilations=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.reshape([108, 128], [1, 1, 1, 2])) -class Conv2DBackpropInputTest(XLATestCase): +class Conv2DBackpropInputTest(xla_test.XLATestCase, parameterized.TestCase): def _VerifyValues(self, input_sizes=None, @@ -186,6 +245,8 @@ class Conv2DBackpropInputTest(XLATestCase): strides=None, dilations=None, padding=None, + data_format_src="NHWC", + data_format_dst="NHWC", expected=None): """Tests that gen_nn_ops.conv2d_backprop_input produces the expected output. @@ -198,8 +259,12 @@ class Conv2DBackpropInputTest(XLATestCase): strides: Strides. dilations: Dilations. padding: Padding type. + data_format_src: Data format input is in. + data_format_dst: Data format verification will run and input is converted + to. expected: Expected output. """ + total_size_1 = np.prod(filter_sizes) total_size_2 = np.prod(out_backprop_sizes) x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(filter_sizes) @@ -209,6 +274,23 @@ class Conv2DBackpropInputTest(XLATestCase): if dilations is not None: dilations = [1] + dilations + [1] + expected = np.reshape(expected, input_sizes) + + # Convert between data formats. + expected = test_utils.ConvertBetweenDataFormats(expected, data_format_src, + data_format_dst) + x2 = test_utils.ConvertBetweenDataFormats(x2, data_format_src, + data_format_dst) + input_sizes = test_utils.PermuteDimsBetweenDataFormats( + input_sizes, data_format_src, data_format_dst) + out_backprop_sizes = test_utils.PermuteDimsBetweenDataFormats( + out_backprop_sizes, data_format_src, data_format_dst) + strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, + data_format_dst) + if dilations is not None: + dilations = test_utils.PermuteDimsBetweenDataFormats( + dilations, data_format_src, data_format_dst) + with self.test_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) @@ -220,12 +302,14 @@ class Conv2DBackpropInputTest(XLATestCase): strides=strides, dilations=dilations, padding=padding, - data_format="NHWC") + data_format=data_format_dst) + value = sess.run(out, {t1: x1, t2: x2}) self.assertAllEqual(input_sizes, value.shape) - self.assertAllClose(expected, np.ravel(value), 1e-3) + self.assertAllClose(expected, value, 1e-3) - def testConv2D1x1Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x1Filter(self, data_format): expected_output = [ 5, 11, 17, 11, 25, 39, 17, 39, 61, 23, 53, 83, 29, 67, 105, 35, 81, 127, 41, 95, 149, 47, 109, 171, 53, 123, 193, 59, 137, 215, 65, 151, 237, 71, @@ -237,9 +321,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 4, 4, 2], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width5(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width5(self, data_format): expected_output = [1, 2, 0, 2, 4] self._VerifyValues( input_sizes=[1, 1, 5, 1], @@ -247,9 +334,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width6(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width6(self, data_format): expected_output = [1, 2, 0, 2, 4, 0] self._VerifyValues( input_sizes=[1, 1, 6, 1], @@ -257,9 +347,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width7(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width7(self, data_format): expected_output = [1, 2, 0, 2, 4, 0, 0] self._VerifyValues( input_sizes=[1, 1, 7, 1], @@ -267,9 +360,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterC1Same(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterC1Same(self, data_format): expected_output = [1, 4, 7, 7, 23, 33] self._VerifyValues( input_sizes=[1, 2, 3, 1], @@ -277,9 +373,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 2, 3, 1], strides=[1, 1], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Filter(self, data_format): expected_output = [ 14, 32, 50, 100, 163, 226, 167, 212, 257, 122, 140, 158, 478, 541, 604, 437, 482, 527 @@ -290,9 +389,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 3], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterSame(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterSame(self, data_format): expected_output = [ 14, 32, 50, 100, 163, 226, 217, 334, 451, 190, 307, 424, 929, 1217, 1505, 1487, 1883, 2279 @@ -303,9 +405,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 2, 3, 3], strides=[1, 1], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2Filter(self, data_format): expected_output = [1, 4, 4, 3, 10, 8, 5, 16, 12] self._VerifyValues( input_sizes=[1, 3, 3, 1], @@ -313,9 +418,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 3, 2, 1], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterSame(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterSame(self, data_format): expected_output = [1, 4, 7, 4, 13, 16, 7, 22, 25] self._VerifyValues( input_sizes=[1, 3, 3, 1], @@ -323,9 +431,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 3, 3, 1], strides=[1, 1], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2(self, data_format): expected_output = [1, 2, 5, 4, 6, 0, 0, 0, 0, 0, 3, 6, 13, 8, 12] self._VerifyValues( input_sizes=[1, 3, 5, 1], @@ -333,9 +444,12 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 2, 2, 1], strides=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2Same(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2Same(self, data_format): expected_output = [1, 2, 2, 3, 4, 6] self._VerifyValues( input_sizes=[1, 2, 3, 1], @@ -343,9 +457,13 @@ class Conv2DBackpropInputTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[2, 2], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Depth3ValidBackpropInputStride1x1Dilation2x1(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth3ValidBackpropInputStride1x1Dilation2x1( + self, data_format): self._VerifyValues( input_sizes=[1, 3, 6, 1], filter_sizes=[2, 2, 1, 1], @@ -353,9 +471,12 @@ class Conv2DBackpropInputTest(XLATestCase): strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[1, 4, 7, 10, 13, 10, 0, 0, 0, 0, 0, 0, 3, 10, 17, 24, 31, 20]) - def testConv2D2x2Depth1ValidBackpropInputDilation1x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth1ValidBackpropInputDilation1x2(self, data_format): self._VerifyValues( input_sizes=[1, 2, 3, 1], filter_sizes=[2, 2, 1, 1], @@ -363,9 +484,12 @@ class Conv2DBackpropInputTest(XLATestCase): strides=[1, 1], dilations=[1, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[1, 0, 2, 3, 0, 4]) - def testConv2DEmptyBackpropInputDilation1x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DEmptyBackpropInputDilation1x2(self, data_format): self._VerifyValues( input_sizes=[0, 2, 3, 1], filter_sizes=[2, 2, 1, 1], @@ -373,9 +497,12 @@ class Conv2DBackpropInputTest(XLATestCase): strides=[1, 1], dilations=[1, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.zeros([0])) - def testConv2D2x2Depth3ValidBackpropInputDilation2x1(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth3ValidBackpropInputDilation2x1(self, data_format): # The GPU version of this test is not very stable. So adjusting the # error threshold to 1e-4. self._VerifyValues( @@ -385,12 +512,16 @@ class Conv2DBackpropInputTest(XLATestCase): strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[ 14, 32, 50, 68, 86, 104, 0, 0, 0, 0, 0, 0, 122, 140, 158, 176, 194, 212 ]) - def testConv2DKernelSizeMatchesInputSizeBackpropInputDilation2x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DKernelSizeMatchesInputSizeBackpropInputDilation2x2( + self, data_format): self._VerifyValues( input_sizes=[1, 3, 3, 1], filter_sizes=[2, 2, 1, 2], @@ -398,10 +529,12 @@ class Conv2DBackpropInputTest(XLATestCase): strides=[1, 1], dilations=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[5, 0, 11, 0, 0, 0, 17, 0, 23]) -class Conv2DBackpropFilterTest(XLATestCase): +class Conv2DBackpropFilterTest(xla_test.XLATestCase, parameterized.TestCase): def _VerifyValues(self, input_sizes=None, @@ -410,6 +543,8 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=None, dilations=None, padding=None, + data_format_src="NHWC", + data_format_dst="NHWC", expected=None): """Tests that gen_nn_ops.conv2d_backprop_filter produces the right output. @@ -422,6 +557,9 @@ class Conv2DBackpropFilterTest(XLATestCase): strides: Stride. dilations: Dilations. padding: Padding type. + data_format_src: Data format input is in. + data_format_dst: Data format verification will run and input is converted + to. expected: Expected output. """ @@ -434,6 +572,23 @@ class Conv2DBackpropFilterTest(XLATestCase): if dilations is not None: dilations = [1] + dilations + [1] + expected = np.reshape(expected, filter_sizes) + + # Convert between data formats. + x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, + data_format_dst) + x2 = test_utils.ConvertBetweenDataFormats(x2, data_format_src, + data_format_dst) + input_sizes = test_utils.PermuteDimsBetweenDataFormats( + input_sizes, data_format_src, data_format_dst) + out_backprop_sizes = test_utils.PermuteDimsBetweenDataFormats( + out_backprop_sizes, data_format_src, data_format_dst) + strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, + data_format_dst) + if dilations is not None: + dilations = test_utils.PermuteDimsBetweenDataFormats( + dilations, data_format_src, data_format_dst) + with self.test_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) @@ -445,13 +600,14 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=strides, dilations=dilations, padding=padding, - data_format="NHWC") + data_format=data_format_dst) value = sess.run(tensor, {t1: x1, t2: x2}) self.assertAllEqual(filter_sizes, value.shape) - self.assertAllClose(expected, np.ravel(value), 1e-3) + self.assertAllClose(expected, value, 1e-3) - def testConv2D1x1Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x1Filter(self, data_format): expected_output = [8056, 8432, 8312, 8704, 8568, 8976] self._VerifyValues( input_sizes=[1, 4, 4, 3], @@ -459,9 +615,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 4, 4, 2], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2Filter(self, data_format): expected_output = [120, 141] self._VerifyValues( input_sizes=[1, 3, 3, 1], @@ -469,9 +628,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 3, 2, 1], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterDepth1(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterDepth1(self, data_format): expected_output = [5, 8, 14, 17] self._VerifyValues( input_sizes=[1, 2, 3, 1], @@ -479,9 +641,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Filter(self, data_format): expected_output = [ 17, 22, 27, 22, 29, 36, 27, 36, 45, 32, 43, 54, 37, 50, 63, 42, 57, 72, 62, 85, 108, 67, 92, 117, 72, 99, 126, 77, 106, 135, 82, 113, 144, 87, @@ -493,9 +658,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 3], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width5(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width5(self, data_format): expected_output = [9, 12] self._VerifyValues( input_sizes=[1, 1, 5, 1], @@ -503,9 +671,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width6(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width6(self, data_format): expected_output = [9, 12] self._VerifyValues( input_sizes=[1, 1, 6, 1], @@ -513,9 +684,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x2FilterStride3Width7(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x2FilterStride3Width7(self, data_format): expected_output = [9, 12] self._VerifyValues( input_sizes=[1, 1, 7, 1], @@ -523,9 +697,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[3, 3], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x3Filter(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x3Filter(self, data_format): expected_output = [5, 8, 11] self._VerifyValues( input_sizes=[1, 1, 4, 1], @@ -533,9 +710,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[1, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x3FilterSame(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x3FilterSame(self, data_format): expected_output = [20, 30, 20] self._VerifyValues( input_sizes=[1, 1, 4, 1], @@ -543,9 +723,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 4, 1], strides=[1, 1], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D1x3FilterSameOutbackprop2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D1x3FilterSameOutbackprop2(self, data_format): expected_output = [7, 10, 3] self._VerifyValues( input_sizes=[1, 1, 4, 1], @@ -553,9 +736,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[2, 2], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterC1Same(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterC1Same(self, data_format): expected_output = [91, 58, 32, 17] self._VerifyValues( input_sizes=[1, 2, 3, 1], @@ -563,9 +749,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 2, 3, 1], strides=[1, 1], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2(self, data_format): expected_output = [92, 102, 112] self._VerifyValues( input_sizes=[1, 3, 5, 1], @@ -573,9 +762,12 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 2, 2, 1], strides=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2FilterStride2Same(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2FilterStride2Same(self, data_format): expected_output = [7, 2, 16, 5] self._VerifyValues( input_sizes=[1, 2, 3, 1], @@ -583,9 +775,13 @@ class Conv2DBackpropFilterTest(XLATestCase): out_backprop_sizes=[1, 1, 2, 1], strides=[2, 2], padding="SAME", + data_format_src="NHWC", + data_format_dst=data_format, expected=expected_output) - def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1( + self, data_format): self._VerifyValues( input_sizes=[1, 3, 6, 1], filter_sizes=[2, 2, 1, 1], @@ -593,9 +789,12 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=[1, 1], dilations=[2, 1], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[55, 70, 235, 250]) - def testConv2D2x2Depth1ValidBackpropFilterDilation1x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth1ValidBackpropFilterDilation1x2(self, data_format): self._VerifyValues( input_sizes=[1, 2, 3, 1], filter_sizes=[2, 2, 1, 1], @@ -603,9 +802,12 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=[1, 1], dilations=[1, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[1, 3, 4, 6]) - def testConv2DEmptyBackpropFilterDilation1x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DEmptyBackpropFilterDilation1x2(self, data_format): self._VerifyValues( input_sizes=[1, 2, 3, 1], filter_sizes=[2, 2, 1, 0], @@ -613,9 +815,12 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=[1, 1], dilations=[1, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=np.zeros([0])) - def testConv2D2x2Depth3ValidBackpropFilterDilation2x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2D2x2Depth3ValidBackpropFilterDilation2x2(self, data_format): self._VerifyValues( input_sizes=[1, 3, 4, 3], filter_sizes=[2, 2, 3, 3], @@ -623,13 +828,17 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=[1, 1], dilations=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[ 17, 22, 27, 22, 29, 36, 27, 36, 45, 47, 64, 81, 52, 71, 90, 57, 78, 99, 137, 190, 243, 142, 197, 252, 147, 204, 261, 167, 232, 297, 172, 239, 306, 177, 246, 315 ]) - def testConv2DKernelSizeMatchesInputSizeBackpropFilterDilation2x2(self): + @parameterized.named_parameters(*DATA_FORMATS) + def testConv2DKernelSizeMatchesInputSizeBackpropFilterDilation2x2( + self, data_format): self._VerifyValues( input_sizes=[1, 3, 3, 1], filter_sizes=[2, 2, 1, 2], @@ -637,6 +846,8 @@ class Conv2DBackpropFilterTest(XLATestCase): strides=[1, 1], dilations=[2, 2], padding="VALID", + data_format_src="NHWC", + data_format_dst=data_format, expected=[1, 2, 3, 6, 7, 14, 9, 18]) diff --git a/tensorflow/compiler/tests/conv3d_test.py b/tensorflow/compiler/tests/conv3d_test.py index 3bebf46511cbc471d3fbbbe92d28511fcc717387..31ee41f04f27d387415e9fa2c4fa70b33cab7b04 100644 --- a/tensorflow/compiler/tests/conv3d_test.py +++ b/tensorflow/compiler/tests/conv3d_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -33,7 +33,7 @@ from tensorflow.python.platform import googletest # Test cloned from # tensorflow/python/kernel_tests/conv3d_backprop_filter_v2_grad_test.py -class Conv3DBackpropFilterV2GradTest(XLATestCase): +class Conv3DBackpropFilterV2GradTest(xla_test.XLATestCase): def testGradient(self): with self.test_session(), self.test_scope(): @@ -66,7 +66,7 @@ class Conv3DBackpropFilterV2GradTest(XLATestCase): # Test cloned from tensorflow/python/kernel_tests/conv3d_transpose_test.py -class Conv3DTransposeTest(XLATestCase): +class Conv3DTransposeTest(xla_test.XLATestCase): def testConv3DTransposeSingleStride(self): with self.test_session(), self.test_scope(): diff --git a/tensorflow/compiler/tests/depthwise_conv_op_test.py b/tensorflow/compiler/tests/depthwise_conv_op_test.py index 03d96a2cd8ab22a472a67f092e36224820405fa8..98dc73e189f99b7b811487756659d89dacb97d8a 100644 --- a/tensorflow/compiler/tests/depthwise_conv_op_test.py +++ b/tensorflow/compiler/tests/depthwise_conv_op_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -114,7 +114,7 @@ def CheckGradConfigsToTest(): yield i, f, o, s, p -class DepthwiseConv2DTest(XLATestCase): +class DepthwiseConv2DTest(xla_test.XLATestCase): # This is testing that depthwise_conv2d and depthwise_conv2d_native # produce the same results. It also tests that NCHW and NWHC diff --git a/tensorflow/compiler/tests/dynamic_slice_ops_test.py b/tensorflow/compiler/tests/dynamic_slice_ops_test.py index 6a46d2ec3e7aee3a4ecfbf1ab9f622d8eb659e3c..154e36b10e6da409606ae6022aaf53e34c8e37cc 100644 --- a/tensorflow/compiler/tests/dynamic_slice_ops_test.py +++ b/tensorflow/compiler/tests/dynamic_slice_ops_test.py @@ -20,14 +20,14 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.compiler.tf2xla.python import xla from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class DynamicUpdateSliceOpsTest(XLATestCase): +class DynamicUpdateSliceOpsTest(xla_test.XLATestCase): def _assertOpOutputMatchesExpected(self, op, args, expected): with self.test_session() as session: diff --git a/tensorflow/compiler/tests/dynamic_stitch_test.py b/tensorflow/compiler/tests/dynamic_stitch_test.py index c109c27abe2f145685f83251e1d21ec8ddad563a..edd78153b56bb5bf1c268936fb82a60581389733 100644 --- a/tensorflow/compiler/tests/dynamic_stitch_test.py +++ b/tensorflow/compiler/tests/dynamic_stitch_test.py @@ -20,14 +20,14 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.platform import googletest -class DynamicStitchTest(XLATestCase): +class DynamicStitchTest(xla_test.XLATestCase): def _AssertDynamicStitchResultIs(self, indices, data, expected): with self.test_session() as session: diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index 4dff5f0f405fb1d936ab2e6bcd82e05e926172c7..3524666499cbb2ef3eae2bb3b314dda0a9be64c8 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.core.protobuf import config_pb2 from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -31,14 +31,16 @@ from tensorflow.python.framework import ops from tensorflow.python.layers import convolutional from tensorflow.python.layers import pooling from tensorflow.python.ops import array_ops +from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import googletest +from tensorflow.python.training import adam -class EagerTest(XLATestCase): +class EagerTest(xla_test.XLATestCase): def testBasic(self): with self.test_scope(): @@ -47,6 +49,21 @@ class EagerTest(XLATestCase): product = three * five self.assertAllEqual(15, product) + def testGradientTape(self): + with self.test_scope(): + + x = constant_op.constant(1.0) + y = constant_op.constant(10.0) + with backprop.GradientTape(persistent=True) as tape: + tape.watch(x) + tape.watch(y) + a = x + y + x * y + da_dx = tape.gradient(a, x) + da_dy = tape.gradient(a, y) + + self.assertEqual(11.0, da_dx.numpy()) + self.assertEqual(2.0, da_dy.numpy()) + def testExecuteListOutputLen0(self): with self.test_scope(): empty = constant_op.constant([], dtype=dtypes.float32) @@ -160,12 +177,120 @@ class EagerTest(XLATestCase): for _ in range(100): values.append(var.value()) + # The shape, shape_n, size, and rank are tested here because their + # execution kernels (as opposed to compilation only tf2xla kernels) + # are distincts from tf2xla kernels. + + def testShape(self): + def const(value): + return array_ops.shape( + constant_op.constant(value)).numpy() + + def ones(value): + return array_ops.shape( + array_ops.ones(value)).numpy() + + with self.test_scope(): + # Shapes of directly constructed tensors + self.assertAllEqual([], const(3)) + self.assertAllEqual([3], const([1.0, 2.0, 3.0])) + self.assertAllEqual([2, 2], const([[1.0, 2.0], [3.0, 4.0]])) + self.assertAllEqual([2, 1, 2], const([[[1.0, 2.0]], [[3.0, 4.0]]])) + + # Shapes of tensors created by op running on device + # We make this distinction because directly constructed tensors + # are treated differently in a few places that can influence shape: + # - they always have on_host_tensor + # - they and their shapes can be cached + # - they end up on device via a copy, instead of as program output + self.assertAllEqual([], ones([])) + self.assertAllEqual([3], ones([3])) + self.assertAllEqual([2, 2], ones([2, 2])) + self.assertAllEqual([2, 1, 2], ones([2, 1, 2])) + + def testShapeN(self): + with self.test_scope(): + # Shapes of directly constructed tensors + shapes = array_ops.shape_n([ + constant_op.constant(1.0), + constant_op.constant([1.0, 2.0, 3.0]), + constant_op.constant([[1.0, 2.0], [3.0, 4.0]])]) + self.assertAllEqual( + [[], [3], [2, 2]], + [x.numpy().tolist() for x in shapes]) + + # Shapes of tensors created by op running on device + shapes = array_ops.shape_n([ + array_ops.ones([]), + array_ops.ones([3]), + array_ops.ones([2, 2])]) + self.assertAllEqual( + [[], [3], [2, 2]], + [x.numpy().tolist() for x in shapes]) + + def testSize(self): + with self.test_scope(): + self.assertEqual( + 1, array_ops.size(constant_op.constant(1.0)).numpy()) + self.assertEqual( + 3, array_ops.size(constant_op.constant([1.0, 2.0, 3.0])).numpy()) + self.assertEqual( + 4, array_ops.size( + constant_op.constant([[1.0, 2.0], [3.0, 4.0]])).numpy()) + + def testRank(self): + with self.test_scope(): + self.assertEqual( + 0, array_ops.rank(constant_op.constant(1.0)).numpy()) + self.assertEqual( + 1, array_ops.rank(constant_op.constant([1.0, 2.0, 3.0])).numpy()) + self.assertEqual( + 2, array_ops.rank( + constant_op.constant([[1.0, 2.0], [3.0, 4.0]])).numpy()) + + def testAdam(self): + with self.test_scope(): + optimizer = adam.AdamOptimizer(0.1) + x = resource_variable_ops.ResourceVariable(10.0) + with backprop.GradientTape() as tape: + y = x * x + dy_dx = tape.gradient(y, x) + optimizer.apply_gradients([(dy_dx, x)]) + self.assertAlmostEqual(9.9, x.numpy(), places=3) + + def testAdamSparse(self): + with ops.device('/cpu:0'): + # Create 2-D embedding for 3 objects on CPU because sparse/sliced updates + # are not implemented on TPU. + embedding_matrix = resource_variable_ops.ResourceVariable( + array_ops.ones([3, 2])) + + with self.test_scope(): + with backprop.GradientTape() as tape: + embedding = embedding_ops.embedding_lookup(embedding_matrix, [1]) + y = math_ops.reduce_sum(embedding) + dy_dx = tape.gradient(y, embedding_matrix) + self.assertIsInstance(dy_dx, ops.IndexedSlices) + optimizer = adam.AdamOptimizer(0.1) + # The gradient application operations will run on CPU because optimizer + # updates are always collocated with the variable. + optimizer.apply_gradients([(dy_dx, embedding_matrix)]) + + # This assign_add will run on CPU because when an input to an + # operation is a resource, this operation is placed on the resource's + # device by the eager runtime. + embedding_matrix.assign_add(array_ops.ones([3, 2])) + + self.assertAllClose([[2.0, 2.0], + [1.9, 1.9], + [2.0, 2.0]], embedding_matrix.numpy()) + -class EagerFunctionTest(XLATestCase): +class EagerFunctionTest(xla_test.XLATestCase): def testBasic(self): with self.test_scope(): - matmul = function.defun(math_ops.matmul, compiled=True) + matmul = function.defun(math_ops.matmul) t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) sq = matmul(t, t, transpose_a=True) self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20]) @@ -187,7 +312,7 @@ class EagerFunctionTest(XLATestCase): def model(x): x = conv(x) return pool(x) - model = function.defun(model, compiled=True) + model = function.defun(model) x = array_ops.ones([1, 4, 4, 1]) y = model(x) @@ -197,7 +322,7 @@ class EagerFunctionTest(XLATestCase): with self.test_scope(): v = resource_variable_ops.ResourceVariable(1.0) - @function.defun(compiled=True) + @function.defun def f(): return v.read_value() @@ -212,7 +337,7 @@ class EagerFunctionTest(XLATestCase): v.assign_add(1.0) return v - f = function.defun(f, compiled=True) + f = function.defun(f) var = f(v) self.assertEqual(2.0, var.numpy()) @@ -240,7 +365,7 @@ class EagerFunctionTest(XLATestCase): d = r2 * v2 return a, b, c, d - foo = function.defun(foo, compiled=True) + foo = function.defun(foo) c1 = [0, 0] c2 = array_ops.ones([2], dtype=dtypes.int32) @@ -262,7 +387,7 @@ class EagerFunctionTest(XLATestCase): with self.test_scope(): v0 = resource_variable_ops.ResourceVariable(5.0) - @function.defun(compiled=True) + @function.defun def f(x): x = v0 * v0 * x return x @@ -275,8 +400,26 @@ class EagerFunctionTest(XLATestCase): self.assertEqual(75, y.numpy()) self.assertEqual(30, dy.numpy()) + def testSliceInDefun(self): + with self.test_scope(): -class ExcessivePaddingTest(XLATestCase): + @function.defun(compiled=True) + def f(x, y): + return x[0::2, y:, ...] + + x = array_ops.ones([2, 3, 4]) + y = array_ops.ones([], dtype=dtypes.int32) + with backprop.GradientTape() as tape: + tape.watch(x) + tape.watch(y) + z = f(x, y) + dz = tape.gradient(z, x) + + self.assertAllEqual(np.ones([1, 2, 4]), z.numpy()) + self.assertAllEqual((2, 3, 4), dz.shape.as_list()) + + +class ExcessivePaddingTest(xla_test.XLATestCase): """Test that eager execution works with TPU flattened tensors. Tensors that would normally be excessively padded when written @@ -307,7 +450,7 @@ class ExcessivePaddingTest(XLATestCase): def testAsFunctionInput(self): with self.test_scope(): - @function.defun(compiled=True) + @function.defun def f(x): return math_ops.reduce_sum(x, axis=2) @@ -318,7 +461,7 @@ class ExcessivePaddingTest(XLATestCase): def testAsFunctionOutput(self): with self.test_scope(): - @function.defun(compiled=True) + @function.defun def f(x): return x * constant_op.constant(100 * [[[10.0, 2.0]]]) diff --git a/tensorflow/compiler/tests/extract_image_patches_op_test.py b/tensorflow/compiler/tests/extract_image_patches_op_test.py index 0361702e7af778176daed941d64e61198090daf2..5529fdbb090315e1d7f47589777d8a538c90db2b 100644 --- a/tensorflow/compiler/tests/extract_image_patches_op_test.py +++ b/tensorflow/compiler/tests/extract_image_patches_op_test.py @@ -20,13 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class ExtractImagePatches(XLATestCase): +class ExtractImagePatches(xla_test.XLATestCase): """Functional tests for ExtractImagePatches op.""" def _VerifyValues(self, image, ksizes, strides, rates, padding, patches): diff --git a/tensorflow/compiler/tests/fake_quant_ops_test.py b/tensorflow/compiler/tests/fake_quant_ops_test.py index dfe9400ef0f55ca011d4e23ba5d735899ca2e054..c48ab178bf53558084fb500b2811c6f0b77a7943 100644 --- a/tensorflow/compiler/tests/fake_quant_ops_test.py +++ b/tensorflow/compiler/tests/fake_quant_ops_test.py @@ -17,14 +17,14 @@ from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.platform import googletest -class FakeQuantWithMinMaxArgsTest(XLATestCase): +class FakeQuantWithMinMaxArgsTest(xla_test.XLATestCase): """Test cases for FakeQuantWithMinMaxArgs operation.""" # 8 bits, wide range. @@ -122,7 +122,7 @@ class FakeQuantWithMinMaxArgsTest(XLATestCase): result, expected, rtol=1e-3, atol=1e-5, bfloat16_rtol=0.03) -class FakeQuantWithMinMaxArgsGradientTest(XLATestCase): +class FakeQuantWithMinMaxArgsGradientTest(xla_test.XLATestCase): """Test cases for FakeQuantWithMinMaxArgsGradient operation.""" # 8 bits, wide range. @@ -223,7 +223,7 @@ class FakeQuantWithMinMaxArgsGradientTest(XLATestCase): bfloat16_rtol=0.03) -class FakeQuantWithMinMaxVarsTest(XLATestCase): +class FakeQuantWithMinMaxVarsTest(xla_test.XLATestCase): """Test cases for FakeQuantWithMinMaxVars operation.""" # 8 bits, wide range. @@ -328,7 +328,7 @@ class FakeQuantWithMinMaxVarsTest(XLATestCase): result, expected, rtol=1e-3, atol=1e-5, bfloat16_rtol=0.03) -class FakeQuantWithMinMaxVarsGradientTest(XLATestCase): +class FakeQuantWithMinMaxVarsGradientTest(xla_test.XLATestCase): """Test cases for FakeQuantWithMinMaxVarsGradient operation.""" # 8 bits, wide range. diff --git a/tensorflow/compiler/tests/fft_test.py b/tensorflow/compiler/tests/fft_test.py index afb5fa4bb4fefe5bc2ecded826143ffc83c2b559..c64ea249ecb97991952a960a6d16e1bb3be35b17 100644 --- a/tensorflow/compiler/tests/fft_test.py +++ b/tensorflow/compiler/tests/fft_test.py @@ -23,10 +23,11 @@ import itertools import numpy as np import scipy.signal as sps -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.contrib.signal.python.ops import spectral_ops as signal from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import spectral_ops from tensorflow.python.platform import googletest @@ -57,7 +58,7 @@ INNER_DIMS_2D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2)) INNER_DIMS_3D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2, POWS_OF_2)) -class FFTTest(XLATestCase): +class FFTTest(xla_test.XLATestCase): def _VerifyFftMethod(self, inner_dims, complex_to_input, input_to_expected, tf_method): @@ -97,8 +98,11 @@ class FFTTest(XLATestCase): ph = array_ops.placeholder( dtypes.as_dtype(data.dtype), shape=data.shape) out = signal.stft(ph, ws, hs) + grad = gradients_impl.gradients(out, ph, + grad_ys=array_ops.ones_like(out)) - value = sess.run(out, {ph: data}) + # For gradients, we simply verify that they compile & execute. + value, _ = sess.run([out, grad], {ph: data}) self.assertAllClose(expected, value, rtol=RTOL, atol=ATOL) def testFFT(self): diff --git a/tensorflow/compiler/tests/fifo_queue_test.py b/tensorflow/compiler/tests/fifo_queue_test.py new file mode 100644 index 0000000000000000000000000000000000000000..0f64cc87cde77fbbef6c4e570879e992bc34bafa --- /dev/null +++ b/tensorflow/compiler/tests/fifo_queue_test.py @@ -0,0 +1,201 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.ops.data_flow_ops.FIFOQueue.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time + +from six.moves import xrange # pylint: disable=redefined-builtin + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import dtypes as dtypes_lib +from tensorflow.python.ops import data_flow_ops +from tensorflow.python.platform import test + + +class FIFOQueueTest(xla_test.XLATestCase): + + def testEnqueue(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + enqueue_op = q.enqueue((10.0,)) + enqueue_op.run() + + def testEnqueueWithShape(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32, shapes=(3, 2)) + enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],)) + enqueue_correct_op.run() + with self.assertRaises(ValueError): + q.enqueue(([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],)) + self.assertEqual(1, q.size().eval()) + + def testMultipleDequeues(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q.enqueue([1])) + self.evaluate(q.enqueue([2])) + self.evaluate(q.enqueue([3])) + a, b, c = self.evaluate([q.dequeue(), q.dequeue(), q.dequeue()]) + self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) + + def testQueuesDontShare(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q.enqueue(1)) + q2 = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q2.enqueue(2)) + self.assertAllEqual(self.evaluate(q2.dequeue()), 2) + self.assertAllEqual(self.evaluate(q.dequeue()), 1) + + def testEnqueueDictWithoutNames(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + with self.assertRaisesRegexp(ValueError, "must have names"): + q.enqueue({"a": 12.0}) + + def testParallelEnqueue(self): + with self.test_session() as sess, self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] + enqueue_ops = [q.enqueue((x,)) for x in elems] + dequeued_t = q.dequeue() + + # Run one producer thread for each element in elems. + def enqueue(enqueue_op): + sess.run(enqueue_op) + + threads = [ + self.checkedThread(target=enqueue, args=(e,)) for e in enqueue_ops + ] + for thread in threads: + thread.start() + for thread in threads: + thread.join() + + # Dequeue every element using a single thread. + results = [] + for _ in xrange(len(elems)): + results.append(dequeued_t.eval()) + self.assertItemsEqual(elems, results) + + def testParallelDequeue(self): + with self.test_session() as sess, self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] + enqueue_ops = [q.enqueue((x,)) for x in elems] + dequeued_t = q.dequeue() + + # Enqueue every element using a single thread. + for enqueue_op in enqueue_ops: + enqueue_op.run() + + # Run one consumer thread for each element in elems. + results = [] + + def dequeue(): + results.append(sess.run(dequeued_t)) + + threads = [self.checkedThread(target=dequeue) for _ in enqueue_ops] + for thread in threads: + thread.start() + for thread in threads: + thread.join() + self.assertItemsEqual(elems, results) + + def testDequeue(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + elems = [10.0, 20.0, 30.0] + enqueue_ops = [q.enqueue((x,)) for x in elems] + dequeued_t = q.dequeue() + + for enqueue_op in enqueue_ops: + enqueue_op.run() + + for i in xrange(len(elems)): + vals = dequeued_t.eval() + self.assertEqual([elems[i]], vals) + + def testEnqueueAndBlockingDequeue(self): + with self.test_session() as sess, self.test_scope(): + q = data_flow_ops.FIFOQueue(3, dtypes_lib.float32) + elems = [10.0, 20.0, 30.0] + enqueue_ops = [q.enqueue((x,)) for x in elems] + dequeued_t = q.dequeue() + + def enqueue(): + # The enqueue_ops should run after the dequeue op has blocked. + # TODO(mrry): Figure out how to do this without sleeping. + time.sleep(0.1) + for enqueue_op in enqueue_ops: + sess.run(enqueue_op) + + results = [] + + def dequeue(): + for _ in xrange(len(elems)): + results.append(sess.run(dequeued_t)) + + enqueue_thread = self.checkedThread(target=enqueue) + dequeue_thread = self.checkedThread(target=dequeue) + enqueue_thread.start() + dequeue_thread.start() + enqueue_thread.join() + dequeue_thread.join() + + for elem, result in zip(elems, results): + self.assertEqual([elem], result) + + def testMultiEnqueueAndDequeue(self): + with self.test_session() as sess, self.test_scope(): + q = data_flow_ops.FIFOQueue(10, (dtypes_lib.int32, dtypes_lib.float32)) + elems = [(5, 10.0), (10, 20.0), (15, 30.0)] + enqueue_ops = [q.enqueue((x, y)) for x, y in elems] + dequeued_t = q.dequeue() + + for enqueue_op in enqueue_ops: + enqueue_op.run() + + for i in xrange(len(elems)): + x_val, y_val = sess.run(dequeued_t) + x, y = elems[i] + self.assertEqual([x], x_val) + self.assertEqual([y], y_val) + + def testQueueSizeEmpty(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + self.assertEqual([0], q.size().eval()) + + def testQueueSizeAfterEnqueueAndDequeue(self): + with self.test_session(), self.test_scope(): + q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) + enqueue_op = q.enqueue((10.0,)) + dequeued_t = q.dequeue() + size = q.size() + self.assertEqual([], size.get_shape()) + + enqueue_op.run() + self.assertEqual(1, size.eval()) + dequeued_t.op.run() + self.assertEqual(0, size.eval()) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py index 8e6407dffdac3adbcda8cbca2109ef9196defa8c..1da97fd51217a0f28d4b3ba2ccfae3f6b094e65b 100644 --- a/tensorflow/compiler/tests/ftrl_test.py +++ b/tensorflow/compiler/tests/ftrl_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables @@ -30,7 +30,7 @@ from tensorflow.python.training import ftrl from tensorflow.python.training import gradient_descent -class FtrlOptimizerTest(XLATestCase): +class FtrlOptimizerTest(xla_test.XLATestCase): def initVariableAndGradient(self, dtype): var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) diff --git a/tensorflow/compiler/tests/function_test.py b/tensorflow/compiler/tests/function_test.py index 8a3f4b0bdc7a61d6cfa2ba7474ce8579e293a5c7..04fba444460e714ce96205361ac02ed492206b04 100644 --- a/tensorflow/compiler/tests/function_test.py +++ b/tensorflow/compiler/tests/function_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function @@ -28,7 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import googletest -class FunctionTest(XLATestCase): +class FunctionTest(xla_test.XLATestCase): def testFunction(self): """Executes a simple TensorFlow function.""" diff --git a/tensorflow/compiler/tests/fused_batchnorm_test.py b/tensorflow/compiler/tests/fused_batchnorm_test.py index a80d69fa5f5099b8a8b67df0da9c92b957e9d194..132e42ac7a28d0769b0de12ea0cee6eae752b245 100644 --- a/tensorflow/compiler/tests/fused_batchnorm_test.py +++ b/tensorflow/compiler/tests/fused_batchnorm_test.py @@ -18,9 +18,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import test_utils +from tensorflow.compiler.tests import xla_test from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gradient_checker @@ -28,7 +30,7 @@ from tensorflow.python.ops import nn from tensorflow.python.platform import test -class FusedBatchNormTest(XLATestCase): +class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase): def _reference_training(self, x, scale, offset, epsilon, data_format): if data_format != "NHWC": @@ -63,24 +65,36 @@ class FusedBatchNormTest(XLATestCase): grad_offset = np.sum(grad_y, axis=(0, 1, 2)) return grad_x, grad_scale, grad_offset - def testInference(self): + @parameterized.named_parameters( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), + ) + def testInference(self, data_format): channel = 3 x_shape = [2, 2, 6, channel] scale_shape = [channel] x_val = np.random.random_sample(x_shape).astype(np.float32) scale_val = np.random.random_sample(scale_shape).astype(np.float32) - offset_val = np.random.random_sample(scale_shape).astype(np.float32) - data_format = "NHWC" + epsilon = 0.001 + data_format_src = "NHWC" + y_ref, mean_ref, var_ref = self._reference_training( + x_val, scale_val, offset_val, epsilon, data_format_src) + with self.test_session() as sess, self.test_scope(): # To avoid constant folding - t_val = array_ops.placeholder(np.float32, shape=x_shape, name="x") + x_val_converted = test_utils.ConvertBetweenDataFormats( + x_val, data_format_src, data_format) + y_ref_converted = test_utils.ConvertBetweenDataFormats( + y_ref, data_format_src, data_format) + + t_val = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="x") scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale") offset = array_ops.placeholder( np.float32, shape=scale_shape, name="offset") - epsilon = 0.001 - y_ref, mean_ref, var_ref = self._reference_training( - x_val, scale_val, offset_val, epsilon, data_format) y, mean, variance = nn.fused_batch_norm( t_val, scale, @@ -91,31 +105,39 @@ class FusedBatchNormTest(XLATestCase): data_format=data_format, is_training=False) - y_val, _, _ = sess.run( - [y, mean, - variance], {t_val: x_val, - scale: scale_val, - offset: offset_val}) - self.assertAllClose(y_val, y_ref, atol=1e-3) + y_val, _, _ = sess.run([y, mean, variance], { + t_val: x_val_converted, + scale: scale_val, + offset: offset_val + }) + self.assertAllClose(y_val, y_ref_converted, atol=1e-3) - def _testLearning(self, use_gradient_checker): + def _testLearning(self, use_gradient_checker, data_format): channel = 3 x_shape = [2, 2, 6, channel] scale_shape = [channel] x_val = np.random.random_sample(x_shape).astype(np.float32) scale_val = np.random.random_sample(scale_shape).astype(np.float32) - offset_val = np.random.random_sample(scale_shape).astype(np.float32) mean_val = np.random.random_sample(scale_shape).astype(np.float32) var_val = np.random.random_sample(scale_shape).astype(np.float32) - data_format = "NHWC" + epsilon = 0.001 + data_format_src = "NHWC" + y_ref, mean_ref, var_ref = self._reference_training( + x_val, scale_val, offset_val, epsilon, data_format_src) + with self.test_session() as sess, self.test_scope(): # To avoid constant folding - t_val = array_ops.placeholder(np.float32, shape=x_shape, name="x") + x_val_converted = test_utils.ConvertBetweenDataFormats( + x_val, data_format_src, data_format) + y_ref_converted = test_utils.ConvertBetweenDataFormats( + y_ref, data_format_src, data_format) + + t_val = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="x") scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale") offset = array_ops.placeholder( np.float32, shape=scale_shape, name="offset") - epsilon = 0.001 y, mean, var = nn.fused_batch_norm( t_val, scale, @@ -129,33 +151,50 @@ class FusedBatchNormTest(XLATestCase): if use_gradient_checker: err = gradient_checker.compute_gradient_error( t_val, - x_shape, + x_val_converted.shape, y, - x_shape, + x_val_converted.shape, extra_feed_dict={ - t_val: x_val, + t_val: x_val_converted, scale: scale_val, offset: offset_val }) self.assertLess(err, 1e-3) - y_val, mean_val, var_val = sess.run( - [y, mean, var], {t_val: x_val, - scale: scale_val, - offset: offset_val}) - y_ref, mean_ref, var_ref = self._reference_training( - x_val, scale_val, offset_val, epsilon, data_format) + y_val, mean_val, var_val = sess.run([y, mean, var], { + t_val: x_val_converted, + scale: scale_val, + offset: offset_val + }) self.assertAllClose(mean_val, mean_ref, atol=1e-3) - self.assertAllClose(y_val, y_ref, atol=1e-3) + self.assertAllClose(y_val, y_ref_converted, atol=1e-3) self.assertAllClose(var_val, var_ref, atol=1e-3) - def testLearning(self): - self._testLearning(False) + @parameterized.named_parameters( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), + ) + def testLearning(self, data_format): + self._testLearning(False, data_format) - def testLearningWithGradientChecker(self): - self._testLearning(True) + @parameterized.named_parameters( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), + ) + def testLearningWithGradientChecker(self, data_format): + self._testLearning(True, data_format) - def testGradientTraining(self): + @parameterized.named_parameters( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), + ) + def testGradientTraining(self, data_format): # TODO(b/64270657): Use gradient_checker here in addition to comparing with # this reference implementation. channel = 3 @@ -167,33 +206,48 @@ class FusedBatchNormTest(XLATestCase): mean_val = np.random.random_sample(scale_shape).astype(np.float32) var_val = np.random.random_sample(scale_shape).astype(np.float32) epsilon = 0.001 + data_format_src = "NHWC" + grad_x_ref, grad_scale_ref, grad_offset_ref = self._reference_grad( + x_val, grad_val, scale_val, mean_val, var_val, epsilon, data_format_src) with self.test_session() as sess, self.test_scope(): - grad = array_ops.placeholder(np.float32, shape=x_shape, name="grad") - x = array_ops.placeholder(np.float32, shape=x_shape, name="x") + grad_val_converted = test_utils.ConvertBetweenDataFormats( + grad_val, data_format_src, data_format) + x_val_converted = test_utils.ConvertBetweenDataFormats( + x_val, data_format_src, data_format) + grad_x_ref_converted = test_utils.ConvertBetweenDataFormats( + grad_x_ref, data_format_src, data_format) + + grad = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="grad") + x = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="x") mean = array_ops.placeholder(np.float32, shape=scale_shape, name="mean") var = array_ops.placeholder(np.float32, shape=scale_shape, name="var") scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale") grad_x, grad_scale, grad_offset, _, _ = gen_nn_ops.fused_batch_norm_grad( - grad, x, scale, mean, var, data_format="NHWC", is_training=True) + grad, x, scale, mean, var, data_format=data_format, is_training=True) grad_x_val, grad_scale_val, grad_offset_val = sess.run( [grad_x, grad_scale, grad_offset], { - grad: grad_val, - x: x_val, + grad: grad_val_converted, + x: x_val_converted, mean: mean_val, var: var_val, scale: scale_val }) - grad_x_ref, grad_scale_ref, grad_offset_ref = self._reference_grad( - x_val, grad_val, scale_val, mean_val, var_val, epsilon, "NHWC") - - self.assertAllClose(grad_x_val, grad_x_ref, atol=1e-2) + self.assertAllClose(grad_x_val, grad_x_ref_converted, atol=1e-2) self.assertAllClose(grad_scale_val, grad_scale_ref, atol=1e-2) self.assertAllClose(grad_offset_val, grad_offset_ref, atol=1e-3) - def testGradientInference(self): + @parameterized.named_parameters( + ("_data_format_NHWC", "NHWC"), + ("_data_format_NCHW", "NCHW"), + ("_data_format_HWNC", "HWNC"), + ("_data_format_HWCN", "HWCN"), + ) + def testGradientInference(self, data_format): # TODO(b/64270657): Use gradient_checker here in addition to comparing with # this reference implementation. channel = 3 @@ -204,33 +258,47 @@ class FusedBatchNormTest(XLATestCase): scale_val = np.random.random_sample(scale_shape).astype(np.float32) mean_val = np.random.random_sample(scale_shape).astype(np.float32) var_val = np.random.random_sample(scale_shape).astype(np.float32) + data_format_src = "NHWC" with self.test_session() as sess, self.test_scope(): - grad = array_ops.placeholder(np.float32, shape=x_shape, name="grad") - x = array_ops.placeholder(np.float32, shape=x_shape, name="x") + grad_val_converted = test_utils.ConvertBetweenDataFormats( + grad_val, data_format_src, data_format) + x_val_converted = test_utils.ConvertBetweenDataFormats( + x_val, data_format_src, data_format) + + grad = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="grad") + x = array_ops.placeholder( + np.float32, shape=x_val_converted.shape, name="x") mean = array_ops.placeholder(np.float32, shape=scale_shape, name="mean") var = array_ops.placeholder(np.float32, shape=scale_shape, name="var") scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale") with self.test_scope(): out = gen_nn_ops.fused_batch_norm_grad( - grad, x, scale, mean, var, data_format="NHWC", is_training=False) + grad, + x, + scale, + mean, + var, + data_format=data_format, + is_training=False) grad_x, grad_scale, grad_offset, _, _ = out ref_x, ref_scale, ref_offset, _, _ = gen_nn_ops.fused_batch_norm_grad( - grad, x, scale, mean, var, data_format="NHWC", is_training=False) + grad, x, scale, mean, var, data_format=data_format, is_training=False) grad_x_val, grad_scale_val, grad_offset_val, = sess.run( [grad_x, grad_scale, grad_offset], { - grad: grad_val, - x: x_val, + grad: grad_val_converted, + x: x_val_converted, mean: mean_val, var: var_val, scale: scale_val }) grad_x_ref, grad_scale_ref, grad_offset_ref, = sess.run( [ref_x, ref_scale, ref_offset], { - grad: grad_val, - x: x_val, + grad: grad_val_converted, + x: x_val_converted, mean: mean_val, var: var_val, scale: scale_val diff --git a/tensorflow/compiler/tests/gather_nd_op_test.py b/tensorflow/compiler/tests/gather_nd_op_test.py index 9378b1db7245c0da3e8298e7dcd972491616b0cd..23b0aed34fb460f50c241e5a920cb4f6f613b947 100644 --- a/tensorflow/compiler/tests/gather_nd_op_test.py +++ b/tensorflow/compiler/tests/gather_nd_op_test.py @@ -20,13 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import errors from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class GatherNdTest(XLATestCase): +class GatherNdTest(xla_test.XLATestCase): def _runGather(self, params, indices): with self.test_session(): diff --git a/tensorflow/compiler/tests/gather_test.py b/tensorflow/compiler/tests/gather_test.py index 1a8c4519118f69ce51ca9a5eb95a9d706c7766cc..e9c8ef7c91a728b7dfc948fd9b315e6c9102f6a3 100644 --- a/tensorflow/compiler/tests/gather_test.py +++ b/tensorflow/compiler/tests/gather_test.py @@ -136,6 +136,20 @@ class GatherTest(xla_test.XLATestCase): self.assertAllEqual( [[7]], gather.eval(feed_dict={params: [4, 7, 2], indices: [[1]]})) + def testGatherPrecision(self): + with self.test_session() as session, self.test_scope(): + data = np.array([[0, 0, 0, 0], [0, 2 * (1 + np.exp2(-8)), 0, 0], + [0, 0, 0, 0], [0.015789, 0.0985, 0.55789, 0.3842]]) + indices = np.array([1, 2, 3, 1]) + dtype = dtypes.float32 + params_np = self._buildParams(data, dtype) + params = array_ops.placeholder(dtype=dtype) + indices_tf = constant_op.constant(indices) + gather_t = array_ops.gather(params, indices_tf) + gather_val = session.run(gather_t, feed_dict={params: params_np}) + np_val = params_np[indices] + self.assertAllEqual(np_val, gather_val) + class GatherBenchmark(test.Benchmark): """Microbenchmarks for the gather op.""" diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py index 7cf953ef25ef5daf8a6d4fc9985ed8dbfb2081e5..8b01ef96db3e8ab58850df234c2e05b764be52ba 100644 --- a/tensorflow/compiler/tests/image_ops_test.py +++ b/tensorflow/compiler/tests/image_ops_test.py @@ -25,7 +25,7 @@ import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -41,7 +41,7 @@ def GenerateNumpyRandomRGB(shape): return np.random.randint(0, 256, shape) / 256. -class RGBToHSVTest(XLATestCase): +class RGBToHSVTest(xla_test.XLATestCase): def testBatch(self): # Build an arbitrary RGB image @@ -104,7 +104,7 @@ class RGBToHSVTest(XLATestCase): self.assertAllCloseAccordingToType(hsv_tf, hsv_np) -class AdjustContrastTest(XLATestCase): +class AdjustContrastTest(xla_test.XLATestCase): def _testContrast(self, x_np, y_np, contrast_factor): with self.test_session(): @@ -168,7 +168,7 @@ class AdjustContrastTest(XLATestCase): self.assertAllClose(y_tf, y_np, rtol=1e-5, atol=1e-5) -class AdjustHueTest(XLATestCase): +class AdjustHueTest(xla_test.XLATestCase): def testAdjustNegativeHue(self): x_shape = [2, 2, 3] @@ -303,7 +303,7 @@ class AdjustHueTest(XLATestCase): self._adjustHueTf(x_np, delta_h) -class AdjustSaturationTest(XLATestCase): +class AdjustSaturationTest(xla_test.XLATestCase): def _adjust_saturation(self, image, saturation_factor): image = ops.convert_to_tensor(image, name="image") @@ -403,7 +403,7 @@ class AdjustSaturationTest(XLATestCase): self.assertAllClose(y_fused, y_baseline, rtol=2e-5, atol=1e-5) -class ResizeBilinearTest(XLATestCase): +class ResizeBilinearTest(xla_test.XLATestCase): def _assertForwardOpMatchesExpected(self, image_np, diff --git a/tensorflow/compiler/tests/lrn_ops_test.py b/tensorflow/compiler/tests/lrn_ops_test.py index 69bd8f7230d4394c45764d02a88fb0ec097c5756..253b45902fba2df64e5234f135b373cd2a0a7e2a 100644 --- a/tensorflow/compiler/tests/lrn_ops_test.py +++ b/tensorflow/compiler/tests/lrn_ops_test.py @@ -22,7 +22,7 @@ import copy import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -36,7 +36,7 @@ CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0" # Local response normalization tests. The forward tests are copied from # tensorflow/python/kernel_tests/lrn_op_test.py -class LRNTest(XLATestCase): +class LRNTest(xla_test.XLATestCase): def _LRN(self, input_image, lrn_depth_radius=5, bias=1.0, alpha=1.0, beta=0.5): diff --git a/tensorflow/compiler/tests/matrix_band_part_test.py b/tensorflow/compiler/tests/matrix_band_part_test.py index 29394f9ea5139b30f88f53de0469b27e37d79195..0d9f99f8a6803ecae5f9233518a1768109161ac0 100644 --- a/tensorflow/compiler/tests/matrix_band_part_test.py +++ b/tensorflow/compiler/tests/matrix_band_part_test.py @@ -19,14 +19,14 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class MatrixBandPartTest(XLATestCase): +class MatrixBandPartTest(xla_test.XLATestCase): def _testMatrixBandPart(self, dtype, shape): with self.test_session(): diff --git a/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py index 5819b2bf2b55b9213a039c0ba82dd0bf1c738b00..2bb8a97bdaf5836a05501ab9754433e29ae34675 100644 --- a/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py +++ b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py @@ -22,7 +22,7 @@ import itertools import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -35,7 +35,7 @@ def MakePlaceholder(x): return array_ops.placeholder(dtypes.as_dtype(x.dtype), shape=x.shape) -class MatrixTriangularSolveOpTest(XLATestCase): +class MatrixTriangularSolveOpTest(xla_test.XLATestCase): # MatrixTriangularSolve defined for float64, float32, complex64, complex128 # (https://www.tensorflow.org/api_docs/python/tf/matrix_triangular_solve) diff --git a/tensorflow/compiler/tests/momentum_test.py b/tensorflow/compiler/tests/momentum_test.py index af9394e7d7dc9cf7dd009420ff9c845aec8785bd..c2592c54cf83d41f0e3bdbc1f4dc9ff276ddb078 100644 --- a/tensorflow/compiler/tests/momentum_test.py +++ b/tensorflow/compiler/tests/momentum_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -30,7 +30,7 @@ from tensorflow.python.platform import test from tensorflow.python.training import momentum as momentum_lib -class MomentumOptimizerTest(XLATestCase): +class MomentumOptimizerTest(xla_test.XLATestCase): def _update_nesterov_momentum_numpy(self, var, accum, g, lr, momentum): var += accum * lr * momentum diff --git a/tensorflow/compiler/tests/nary_ops_test.py b/tensorflow/compiler/tests/nary_ops_test.py index e4843b169b943b63346b783ddc50039030988ca5..da08225e9fc0d5a8ec21ee9961c4758fa38628b4 100644 --- a/tensorflow/compiler/tests/nary_ops_test.py +++ b/tensorflow/compiler/tests/nary_ops_test.py @@ -22,14 +22,14 @@ import unittest import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest -class NAryOpsTest(XLATestCase): +class NAryOpsTest(xla_test.XLATestCase): def _testNAry(self, op, args, expected, equality_fn=None): with self.test_session() as session: diff --git a/tensorflow/compiler/tests/nullary_ops_test.py b/tensorflow/compiler/tests/nullary_ops_test.py index 6f588d8ab562cb24f33c4c2987df22264aede027..2f9122645d3c5ccabc8130ac30a3f09cf4bc2de7 100644 --- a/tensorflow/compiler/tests/nullary_ops_test.py +++ b/tensorflow/compiler/tests/nullary_ops_test.py @@ -20,13 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import googletest -class NullaryOpsTest(XLATestCase): +class NullaryOpsTest(xla_test.XLATestCase): def _testNullary(self, op, expected): with self.test_session() as session: diff --git a/tensorflow/compiler/tests/placeholder_test.py b/tensorflow/compiler/tests/placeholder_test.py index 5e6d1313bd0336eba71fcf3658d949bd3342ae11..a75d99189b5b673261c9e48f1c5998ea0c575594 100644 --- a/tensorflow/compiler/tests/placeholder_test.py +++ b/tensorflow/compiler/tests/placeholder_test.py @@ -18,14 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest -class PlaceholderTest(XLATestCase): +class PlaceholderTest(xla_test.XLATestCase): def test_placeholder_with_default_default(self): with self.test_session() as sess, self.test_scope(): diff --git a/tensorflow/compiler/tests/pooling_ops_3d_test.py b/tensorflow/compiler/tests/pooling_ops_3d_test.py index 4eed903963a34a253ea5c409782d9a89a97a4fdf..17f860db61aeda98326a6820771d67ee948b6dda 100644 --- a/tensorflow/compiler/tests/pooling_ops_3d_test.py +++ b/tensorflow/compiler/tests/pooling_ops_3d_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -41,7 +41,7 @@ def _AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding): padding=padding) -class Pooling3DTest(XLATestCase): +class Pooling3DTest(xla_test.XLATestCase): def _VerifyValues(self, pool_func, input_sizes, window, strides, padding, expected): @@ -187,8 +187,14 @@ class Pooling3DTest(XLATestCase): padding="VALID", expected=[29.5, 32.5, 50.5, 53.5, 176.5, 179.5, 197.5, 200.5]) - def _VerifyGradient(self, pool_func, pool_grad_func, input_sizes, ksize, - strides, padding): + def _VerifyGradient(self, + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + pool_grad_grad_func=None): """Verifies the output values of the pooling gradient function. Args: @@ -198,6 +204,7 @@ class Pooling3DTest(XLATestCase): ksize: The kernel size dimensions strides: The stride dimensions padding: Padding type. + pool_grad_grad_func: Second-order gradient function, if available. """ ksize = [1] + ksize + [1] strides = [1] + strides + [1] @@ -218,6 +225,8 @@ class Pooling3DTest(XLATestCase): output_gradient_vals = np.arange( 1, output_vals.size + 1, dtype=np.float32) output_gradient_vals = output_gradient_vals.reshape(output_vals.shape) + output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32) + output_grad_grad_vals = output_grad_grad_vals.reshape(x.shape) # Use the Tensorflow CPU pooling gradient to compute the expected input # gradients. @@ -236,6 +245,22 @@ class Pooling3DTest(XLATestCase): {inputs: x, output_gradients: output_gradient_vals}) + output_grad_gradients = array_ops.placeholder( + dtypes.float32, shape=expected_input_gradient_vals.shape) + if pool_grad_grad_func is not None: + expected_grad_gradients = pool_grad_grad_func( + inputs, + outputs, + output_grad_gradients, + ksize=ksize, + strides=strides, + padding=padding, + data_format="NDHWC") + expected_grad_gradients_vals = sess.run(expected_grad_gradients, { + inputs: x, + output_grad_gradients: output_grad_grad_vals + }) + # Run the gradient op on the XLA device with self.test_scope(): outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape) @@ -246,6 +271,16 @@ class Pooling3DTest(XLATestCase): ksize=ksize, strides=strides, padding=padding) + if pool_grad_grad_func is not None: + actual_grad_gradients = pool_grad_grad_func( + inputs, + outputs, + output_grad_gradients, + ksize=ksize, + strides=strides, + padding=padding, + data_format="NDHWC") + actual = sess.run(actual_input_gradients, { inputs: x, outputs: output_vals, @@ -260,6 +295,22 @@ class Pooling3DTest(XLATestCase): atol=1e-6) self.assertShapeEqual(actual, inputs) + if pool_grad_grad_func is not None: + actual_grad_gradients_vals = sess.run( + actual_grad_gradients, { + inputs: x, + outputs: output_vals, + output_grad_gradients: output_grad_grad_vals + }) + + # Compare the Tensorflow and XLA results. + self.assertAllClose( + expected_grad_gradients_vals, + actual_grad_gradients_vals, + rtol=1e-4, + atol=1e-6) + self.assertShapeEqual(actual_grad_gradients_vals, outputs) + def testMaxPoolGradValidPadding1_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, @@ -267,7 +318,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[1, 3, 3, 3, 1], ksize=[1, 1, 1], strides=[1, 1, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradValidPadding2_1_6_3d(self): self._VerifyGradient( @@ -276,9 +328,13 @@ class Pooling3DTest(XLATestCase): input_sizes=[2, 3, 3, 6, 3], ksize=[2, 2, 2], strides=[1, 1, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradValidPadding2_1_7_3d(self): + # TODO(b/73062247): the bfloat16 implementation of MaxPool3DGradGrad does + # not have enough precision for this test case to pass if + # pool_grad_grad_func is passed. self._VerifyGradient( nn_ops.max_pool3d, gen_nn_ops.max_pool3d_grad, @@ -294,7 +350,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[2, 2, 2, 2, 3], ksize=[2, 2, 2], strides=[2, 2, 2], - padding="VALID") + padding="VALID", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradSamePadding1_1_3d(self): self._VerifyGradient( @@ -303,7 +360,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[2, 3, 2, 4, 1], ksize=[1, 1, 1], strides=[1, 1, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradSamePadding2_1_3d(self): self._VerifyGradient( @@ -312,7 +370,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[2, 3, 2, 4, 1], ksize=[2, 2, 2], strides=[1, 1, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradSamePadding2_2_3d(self): self._VerifyGradient( @@ -321,7 +380,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[2, 5, 2, 4, 3], ksize=[2, 2, 2], strides=[2, 2, 2], - padding="SAME") + padding="SAME", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testMaxPoolGradSamePadding3_1_3d(self): self._VerifyGradient( @@ -330,7 +390,8 @@ class Pooling3DTest(XLATestCase): input_sizes=[1, 3, 3, 7, 1], ksize=[3, 3, 3], strides=[1, 1, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad) def testAvgPoolGradValidPadding1_1_3d(self): self._VerifyGradient( diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py index fe270af3d636c0824621f36360ce9e7d14d8fc91..9fc94752ea660f7fb8b2c792180f01485ad04419 100644 --- a/tensorflow/compiler/tests/pooling_ops_test.py +++ b/tensorflow/compiler/tests/pooling_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -69,7 +69,7 @@ def GetTestConfigs(): return test_configs -class PoolingTest(XLATestCase): +class PoolingTest(xla_test.XLATestCase): def _VerifyOneTest(self, pool_func, input_sizes, ksize, strides, padding, data_format, expected): @@ -288,7 +288,7 @@ class PoolingTest(XLATestCase): expected=expected_output) -class PoolGradTest(XLATestCase): +class PoolGradTest(xla_test.XLATestCase): CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0" diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index f13dff96203b5480480c2a2fc9ac38ca78b7f78a..b880b2a3fea3ee72af96396bc2d61b2887e6e9b8 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -18,17 +18,20 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import math + import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops.distributions import special_math from tensorflow.python.platform import googletest -class RandomOpsTest(XLATestCase): +class RandomOpsTest(xla_test.XLATestCase): """Test cases for random-number generating operators.""" def _random_types(self): @@ -87,15 +90,52 @@ class RandomOpsTest(XLATestCase): self._testRngIsNotConstant(rng, dtypes.float32) def testTruncatedNormalIsInRange(self): - count = 10000 + count = 10000000 # TODO(b/34339814): implement inverse erf support for non-F32 types. for dtype in [dtypes.float32]: with self.test_session() as sess: with self.test_scope(): x = random_ops.truncated_normal(shape=[count], dtype=dtype, seed=42) y = sess.run(x) - self.assertTrue((y >= -2).sum() == count) - self.assertTrue((y <= 2).sum() == count) + + def normal_cdf(x): + return .5 * math.erfc(-x / math.sqrt(2)) + + def normal_pdf(x): + return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi) + + def probit(x, sess=sess): + return sess.run(special_math.ndtri(x)) + + a = -2. + b = 2. + mu = 0. + sigma = 1. + + alpha = (a - mu) / sigma + beta = (b - mu) / sigma + z = normal_cdf(beta) - normal_cdf(alpha) + + self.assertTrue((y >= a).sum() == count) + self.assertTrue((y <= b).sum() == count) + + # For more information on these calculations, see: + # Burkardt, John. "The Truncated Normal Distribution". + # Department of Scientific Computing website. Florida State University. + expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma + actual_mean = np.mean(y) + self.assertAllClose(actual_mean, expected_mean, atol=2e-4) + + expected_median = mu + probit( + (normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma + actual_median = np.median(y) + self.assertAllClose(actual_median, expected_median, atol=8e-4) + + expected_variance = sigma**2 * (1 + ( + (alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - ( + (normal_pdf(alpha) - normal_pdf(beta)) / z)**2) + actual_variance = np.var(y) + self.assertAllClose(actual_variance, expected_variance, rtol=3e-4) def testShuffle1d(self): with self.test_session() as sess: diff --git a/tensorflow/compiler/tests/reduce_ops_test.py b/tensorflow/compiler/tests/reduce_ops_test.py index 7420724bdbeab63b39542ada59328621febad895..cea2ec816f85e88b11e6e80c91c14fca9015f45c 100644 --- a/tensorflow/compiler/tests/reduce_ops_test.py +++ b/tensorflow/compiler/tests/reduce_ops_test.py @@ -22,7 +22,7 @@ import functools import itertools import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.ops import array_ops @@ -30,7 +30,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest -class ReduceOpsTest(XLATestCase): +class ReduceOpsTest(xla_test.XLATestCase): def _testReduction(self, tf_reduce_fn, @@ -156,7 +156,7 @@ class ReduceOpsTest(XLATestCase): self._testReduction(math_ops.reduce_any, np.any, np.bool, self.BOOL_DATA) -class ReduceOpPrecisionTest(XLATestCase): +class ReduceOpPrecisionTest(xla_test.XLATestCase): def _testReduceSum(self, expected_result, diff --git a/tensorflow/compiler/tests/reduce_window_test.py b/tensorflow/compiler/tests/reduce_window_test.py index e78a63465b80644d8810d9fa7433653bc4639fed..c69b6837b0f88ced844faf3713a29a1c14c8790d 100644 --- a/tensorflow/compiler/tests/reduce_window_test.py +++ b/tensorflow/compiler/tests/reduce_window_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.compiler.tf2xla.python import xla from tensorflow.python.framework import dtypes from tensorflow.python.framework import function @@ -28,7 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import googletest -class ReduceWindowTest(XLATestCase): +class ReduceWindowTest(xla_test.XLATestCase): """Test cases for xla.reduce_window.""" def _reduce_window(self, operand, init, reducer, **kwargs): diff --git a/tensorflow/compiler/tests/reverse_ops_test.py b/tensorflow/compiler/tests/reverse_ops_test.py index 18fabca28c9817fc8517595fa1694a18399f54b0..d01c676e7c2fe705344f26818350c46c30451c67 100644 --- a/tensorflow/compiler/tests/reverse_ops_test.py +++ b/tensorflow/compiler/tests/reverse_ops_test.py @@ -21,14 +21,14 @@ from __future__ import print_function import itertools import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.platform import googletest -class ReverseOpsTest(XLATestCase): +class ReverseOpsTest(xla_test.XLATestCase): def testReverseOneDim(self): shape = (7, 5, 9, 11) diff --git a/tensorflow/compiler/tests/reverse_sequence_op_test.py b/tensorflow/compiler/tests/reverse_sequence_op_test.py index 1a5d05094e53cfecd9476d7d87f023e8a02d7458..ccfa63001653537c4d1b7140e3d745c126f9034b 100644 --- a/tensorflow/compiler/tests/reverse_sequence_op_test.py +++ b/tensorflow/compiler/tests/reverse_sequence_op_test.py @@ -20,13 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class ReverseSequenceTest(XLATestCase): +class ReverseSequenceTest(xla_test.XLATestCase): def _testReverseSequence(self, x, diff --git a/tensorflow/compiler/tests/rmsprop_test.py b/tensorflow/compiler/tests/rmsprop_test.py index ecdce4f052bbe3eeae8697c02c891105103f4f69..9489fded32a7b6aada0543721a8bfe5f2d74575e 100644 --- a/tensorflow/compiler/tests/rmsprop_test.py +++ b/tensorflow/compiler/tests/rmsprop_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables @@ -28,7 +28,7 @@ from tensorflow.python.platform import test from tensorflow.python.training import rmsprop -class RmspropTest(XLATestCase): +class RmspropTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: diff --git a/tensorflow/compiler/tests/scan_ops_test.py b/tensorflow/compiler/tests/scan_ops_test.py index 3260e63b23226d736a7ddc0f21a94a8c791e0442..4292352e76ebcef7dbf41df7b857d2604a468117 100644 --- a/tensorflow/compiler/tests/scan_ops_test.py +++ b/tensorflow/compiler/tests/scan_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops @@ -69,7 +69,7 @@ def handle_options(func, x, axis, exclusive, reverse): return x -class CumsumTest(XLATestCase): +class CumsumTest(xla_test.XLATestCase): valid_dtypes = [np.float32] @@ -147,7 +147,7 @@ class CumsumTest(XLATestCase): math_ops.cumsum(input_tensor, [0]).eval() -class CumprodTest(XLATestCase): +class CumprodTest(xla_test.XLATestCase): valid_dtypes = [np.float32] diff --git a/tensorflow/compiler/tests/scatter_nd_op_test.py b/tensorflow/compiler/tests/scatter_nd_op_test.py index 638946e234daf28dc4a34e6c33fc0f78b8e8699b..f606f88545d0b6f0b52cee9b93083a6bd91169bc 100644 --- a/tensorflow/compiler/tests/scatter_nd_op_test.py +++ b/tensorflow/compiler/tests/scatter_nd_op_test.py @@ -22,7 +22,7 @@ import functools import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import errors from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -68,7 +68,7 @@ def _NumpyUpdate(indices, updates, shape): return _NumpyScatterNd(ref, indices, updates, lambda p, u: u) -class ScatterNdTest(XLATestCase): +class ScatterNdTest(xla_test.XLATestCase): def _VariableRankTest(self, np_scatter, diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py index 4a9c0e7471f9cdb2a47b54705495d2dda9748890..772c20fd424577c3e06eeae409f424b77b52aa8a 100644 --- a/tensorflow/compiler/tests/segment_reduction_ops_test.py +++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py @@ -21,26 +21,40 @@ from __future__ import print_function import functools import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest -class SegmentReductionOpsTest(XLATestCase): +class SegmentReductionOpsTest(xla_test.XLATestCase): """Test cases for segment reduction ops.""" - def UnsortedSegmentSum(self, data, indices, num_segments): + def _segmentReduction(self, op, data, indices, num_segments): with self.test_session() as sess, self.test_scope(): d = array_ops.placeholder(data.dtype, shape=data.shape) if isinstance(indices, int): i = array_ops.placeholder(np.int32, shape=[]) else: i = array_ops.placeholder(indices.dtype, shape=indices.shape) - return sess.run( - math_ops.unsorted_segment_sum(d, i, num_segments), - {d: data, - i: indices}) + return sess.run(op(d, i, num_segments), {d: data, i: indices}) + + def _unsortedSegmentSum(self, data, indices, num_segments): + return self._segmentReduction(math_ops.unsorted_segment_sum, data, indices, + num_segments) + + def _unsortedSegmentProd(self, data, indices, num_segments): + return self._segmentReduction(math_ops.unsorted_segment_prod, data, indices, + num_segments) + + def _unsortedSegmentMin(self, data, indices, num_segments): + return self._segmentReduction(math_ops.unsorted_segment_min, data, indices, + num_segments) + + def _unsortedSegmentMax(self, data, indices, num_segments): + return self._segmentReduction(math_ops.unsorted_segment_max, data, indices, + num_segments) def testUnsortedSegmentSum0DIndices1DData(self): for dtype in self.numeric_types: @@ -49,14 +63,14 @@ class SegmentReductionOpsTest(XLATestCase): [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5], [0, 0, 0, 0, 0, 0]], dtype=dtype), - self.UnsortedSegmentSum( + self._unsortedSegmentSum( np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4)) def testUnsortedSegmentSum1DIndices1DData(self): for dtype in self.numeric_types: self.assertAllClose( np.array([1, 3, 2, 9], dtype=dtype), - self.UnsortedSegmentSum( + self._unsortedSegmentSum( np.array([0, 1, 2, 3, 4, 5], dtype=dtype), np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4)) @@ -64,7 +78,7 @@ class SegmentReductionOpsTest(XLATestCase): for dtype in self.numeric_types: self.assertAllClose( np.array([6, 3, 0, 6], dtype=dtype), - self.UnsortedSegmentSum( + self._unsortedSegmentSum( np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) @@ -76,7 +90,7 @@ class SegmentReductionOpsTest(XLATestCase): dtype=dtype) indices = np.array([8, 1, 0, 3, 7], dtype=np.int32) num_segments = 10 - y = self.UnsortedSegmentSum(data, indices, num_segments) + y = self._unsortedSegmentSum(data, indices, num_segments) self.assertAllClose( np.array( [[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0], @@ -92,7 +106,7 @@ class SegmentReductionOpsTest(XLATestCase): dtype=dtype) indices = np.array([0, 1, 2, 0, 1], dtype=np.int32) num_segments = 4 - y = self.UnsortedSegmentSum(data, indices, num_segments) + y = self._unsortedSegmentSum(data, indices, num_segments) self.assertAllClose( np.array( [[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33], @@ -102,30 +116,30 @@ class SegmentReductionOpsTest(XLATestCase): def testUnsortedSegmentSum2DIndices3DData(self): for dtype in self.numeric_types: data = np.array( - [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], - [[200, 201, 202], [210, 211, 212]], [[300, 301, 302], - [310, 311, 312]]], + [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[ + 200, 201, 202 + ], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]], dtype=dtype) indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32) num_segments = 8 - y = self.UnsortedSegmentSum(data, indices, num_segments) + y = self._unsortedSegmentSum(data, indices, num_segments) self.assertAllClose( np.array( - [[210, 211, 212], [110, 111, 112], [310, 311, 312], - [100, 102, 104], [0, 0, 0.], [210, 212, 214], [300, 301, - 302], [0, 0, 0]], + [[210, 211, 212], [110, 111, 112], [310, 311, 312], [ + 100, 102, 104 + ], [0, 0, 0.], [210, 212, 214], [300, 301, 302], [0, 0, 0]], dtype=dtype), y) def testUnsortedSegmentSum1DIndices3DData(self): for dtype in self.numeric_types: data = np.array( - [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], - [[200, 201, 202], [210, 211, 212]], [[300, 301, 302], - [310, 311, 312]]], + [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[ + 200, 201, 202 + ], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]], dtype=dtype) indices = np.array([3, 0, 2, 5], dtype=np.int32) num_segments = 6 - y = self.UnsortedSegmentSum(data, indices, num_segments) + y = self._unsortedSegmentSum(data, indices, num_segments) self.assertAllClose( np.array( [[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]], @@ -138,10 +152,40 @@ class SegmentReductionOpsTest(XLATestCase): data = np.ones((4, 8, 7), dtype=dtype) indices = np.ones((3, 2), dtype=np.int32) num_segments = 4 - self.assertRaises(ValueError, - functools.partial(self.UnsortedSegmentSum, data, - indices, num_segments)) + self.assertRaises( + ValueError, + functools.partial(self._segmentReduction, + math_ops.unsorted_segment_sum, data, indices, + num_segments)) + + def testUnsortedSegmentOps1DIndices1DDataNegativeIndices(self): + """Tests for min, max, and prod ops. + + These share most of their implementation with sum, so we only test basic + functionality. + """ + for dtype in self.numeric_types: + self.assertAllClose( + np.array([8, 3, 1, 0], dtype=dtype), + self._unsortedSegmentProd( + np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), + np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) + + for dtype in self.int_types | self.float_types: + minval = dtypes.as_dtype(dtype).min + maxval = dtypes.as_dtype(dtype).max + + self.assertAllClose( + np.array([2, 3, maxval, 0], dtype=dtype), + self._unsortedSegmentMin( + np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), + np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) + self.assertAllClose( + np.array([4, 3, minval, 6], dtype=dtype), + self._unsortedSegmentMax( + np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), + np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) -if __name__ == '__main__': +if __name__ == "__main__": googletest.main() diff --git a/tensorflow/compiler/tests/slice_ops_test.py b/tensorflow/compiler/tests/slice_ops_test.py index 305ca0c6b78d3ef985deb38816f9388e7983906b..6c4890565d2083a9493abc59bd563c4dd9fdb186 100644 --- a/tensorflow/compiler/tests/slice_ops_test.py +++ b/tensorflow/compiler/tests/slice_ops_test.py @@ -18,14 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.platform import googletest -class SliceTest(XLATestCase): +class SliceTest(xla_test.XLATestCase): def test1D(self): for dtype in self.numeric_types: @@ -110,7 +110,7 @@ class SliceTest(XLATestCase): self.assertAllEqual([[[1, 1, 1, 1], [6, 5, 4, 3]]], result) -class StridedSliceTest(XLATestCase): +class StridedSliceTest(xla_test.XLATestCase): def test1D(self): for dtype in self.numeric_types: diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2ef964a1ff00a861a874135b7dfa1358a7020e --- /dev/null +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -0,0 +1,140 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for sorting operators.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.compiler.tf2xla.python import xla +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test + + +class XlaSortOpTest(xla_test.XLATestCase): + + def _assertOpOutputMatchesExpected(self, op, args, expected): + with self.test_session() as session: + with self.test_scope(): + placeholders = [ + array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape) + for arg in args + ] + feeds = {placeholders[i]: args[i] for i in range(0, len(args))} + output = op(*placeholders) + if isinstance(output, ops.Tensor): + output = [output] + + results = session.run(output, feeds) + for result, v in zip(results, expected): + self.assertAllClose(v, result, rtol=1e-3) + + def testSort(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + supported_types = set([dtypes.bfloat16.as_numpy_dtype, np.float32]) + for dtype in supported_types.intersection(self.numeric_types): + x = np.arange(101, dtype=dtype) + np.random.shuffle(x) + self._assertOpOutputMatchesExpected( + xla.sort, [x], expected=[np.arange(101, dtype=dtype)]) + + def testTopK(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + supported_types = set( + [dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32]) + for dtype in supported_types.intersection(self.numeric_types): + # Use small input size for bfloat16. Otherwise, we'll get duplicate values + # after conversion to bfloat16, so the possible resulting index array is + # no longer unique. + if dtype == dtypes.bfloat16.as_numpy_dtype: + array_size = 20 + k_options = [0, 1, 2, 10, 20] + else: + array_size = 200 * 1000 + k_options = [0, 1, 2, 10, 20, 100, 1000, 200 * 1000] + for x in [np.arange(array_size)]: + np.random.shuffle(x) + for k in k_options: + indices = x.argsort()[::-1][:k] + + def topk(v, k=k): + return nn_ops.top_k(v, k=k, sorted=True) + + self._assertOpOutputMatchesExpected( + topk, [x.astype(dtype)], + expected=[x[indices].astype(dtype), indices]) + + def testTopKZeros(self): + """Tests that positive and negative zeros sort correctly.""" + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + # Only bfloat16 is implemented. + bfloat16 = dtypes.bfloat16.as_numpy_dtype + if bfloat16 not in self.numeric_types: + return + + with self.test_session() as sess: + p = array_ops.placeholder(dtypes.bfloat16) + with self.test_scope(): + topk = nn_ops.top_k(p, k=4) + results = sess.run( + topk, + {p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)}) + self.assertAllEqual( + np.array([3., 0., 0., 0.], dtype=bfloat16), results[0]) + self.assertEqual(list([3, 0, 2, 6]), list(results[1])) + + def testTopKInfinities(self): + """Tests that positive and negative infinity sort correctly.""" + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + # Only bfloat16 is implemented. + bfloat16 = dtypes.bfloat16.as_numpy_dtype + if bfloat16 not in self.numeric_types: + return + + with self.test_session() as sess: + p = array_ops.placeholder(dtypes.bfloat16) + with self.test_scope(): + topk = nn_ops.top_k(p, k=6) + results = sess.run(topk, { + p: np.array( + [1, 2, float("inf"), -float("inf"), -1, -2], dtype=bfloat16) + }) + self.assertAllEqual( + np.array( + [float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")], + dtype=bfloat16), results[0]) + self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1])) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py index f37c34156f96761632247be4bc1b62fca54f666e..c685bc548f9f6f8f7723c6f94dfd45f5420b4a67 100644 --- a/tensorflow/compiler/tests/spacetobatch_op_test.py +++ b/tensorflow/compiler/tests/spacetobatch_op_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops @@ -68,7 +68,7 @@ def space_to_batch_direct(input_array, block_shape, paddings): return permuted_reshaped_padded.reshape(output_shape) -class SpaceToBatchTest(XLATestCase): +class SpaceToBatchTest(xla_test.XLATestCase): """Tests input-output pairs for the SpaceToBatch and BatchToSpace ops.""" def _testPad(self, inputs, paddings, block_size, outputs): @@ -149,7 +149,7 @@ class SpaceToBatchTest(XLATestCase): self._testOne(x_np, block_size, x_out) -class SpaceToBatchNDTest(XLATestCase): +class SpaceToBatchNDTest(xla_test.XLATestCase): """Tests input-output pairs for the SpaceToBatchND and BatchToSpaceND ops.""" def _testPad(self, inputs, block_shape, paddings, outputs): diff --git a/tensorflow/compiler/tests/sparse_to_dense_op_test.py b/tensorflow/compiler/tests/sparse_to_dense_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3db8101c4bfbb1b53c7318a36519612984d6f179 --- /dev/null +++ b/tensorflow/compiler/tests/sparse_to_dense_op_test.py @@ -0,0 +1,118 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.kernels.sparse_op.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.platform import test + + +def _SparseToDense(sparse_indices, + output_size, + sparse_values, + default_value, + validate_indices=True): + feed_sparse_indices = array_ops.placeholder(dtypes.int32) + feed_dict = {feed_sparse_indices: sparse_indices} + return sparse_ops.sparse_to_dense( + feed_sparse_indices, + output_size, + sparse_values, + default_value=default_value, + validate_indices=validate_indices).eval(feed_dict=feed_dict) + + +class SparseToDenseTest(xla_test.XLATestCase): + + def testInt(self): + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([1, 3], [5], 1, 0) + np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32) + self.assertAllClose(np_ans, tf_ans) + + def testFloat(self): + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0) + np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32) + self.assertAllClose(np_ans, tf_ans) + + def testSetValue(self): + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1) + np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32) + self.assertAllClose(np_ans, tf_ans) + + def testSetSingleValue(self): + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([1, 3], [5], 1, -1) + np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32) + self.assertAllClose(np_ans, tf_ans) + + def test2d(self): + # pylint: disable=bad-whitespace + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1) + np_ans = np.array([[-1, -1, -1, -1], + [-1, -1, -1, 1], + [ 1, -1, -1, -1]]).astype(np.int32) + self.assertAllClose(np_ans, tf_ans) + + def testZeroDefault(self): + with self.test_session(): + x = sparse_ops.sparse_to_dense(2, [4], 7).eval() + self.assertAllEqual(x, [0, 0, 7, 0]) + + def test3d(self): + with self.test_session(), self.test_scope(): + tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1) + np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1 + np_ans[1, 3, 0] = 1 + np_ans[2, 0, 1] = 1 + self.assertAllClose(np_ans, tf_ans) + + def testBadShape(self): + with self.test_session(), self.test_scope(): + with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"): + _SparseToDense([1, 3], [[5], [3]], 1, -1) + + def testBadValue(self): + with self.test_session(), self.test_scope(): + with self.assertRaisesOpError( + r"sparse_values has incorrect shape \[2,1\], " + r"should be \[\] or \[2\]"): + _SparseToDense([1, 3], [5], [[5], [3]], -1) + + def testBadNumValues(self): + with self.test_session(), self.test_scope(): + with self.assertRaisesOpError( + r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"): + _SparseToDense([1, 3], [5], [1, 2, 3], -1) + + def testBadDefault(self): + with self.test_session(), self.test_scope(): + with self.assertRaisesOpError("default_value should be a scalar"): + _SparseToDense([1, 3], [5], [1, 2], [0]) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py index 94342f9567ca71274609e63b0482d55637c98d51..b7dd787feff2b22a9cfb5d43a4ba6ceb6eb0b301 100644 --- a/tensorflow/compiler/tests/stack_ops_test.py +++ b/tensorflow/compiler/tests/stack_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -28,7 +28,7 @@ from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.platform import test -class StackOpTest(XLATestCase): +class StackOpTest(xla_test.XLATestCase): def testStackPushPop(self): with self.test_session(), self.test_scope(): diff --git a/tensorflow/compiler/tests/stateless_random_ops_test.py b/tensorflow/compiler/tests/stateless_random_ops_test.py index b6f8390a45d43bf7666b90e14cc6ff2f3f61947e..d162675ef840131485128414b4a29e3cd89c8761 100644 --- a/tensorflow/compiler/tests/stateless_random_ops_test.py +++ b/tensorflow/compiler/tests/stateless_random_ops_test.py @@ -22,14 +22,15 @@ import math import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.contrib import stateless from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops +from tensorflow.python.ops.distributions import special_math from tensorflow.python.platform import test -class StatelessRandomOpsTest(XLATestCase): +class StatelessRandomOpsTest(xla_test.XLATestCase): """Test cases for stateless random-number generator operators.""" def _random_types(self): @@ -122,6 +123,56 @@ class StatelessRandomOpsTest(XLATestCase): # so to avoid flakiness the seed is fixed. self.assertTrue(self._anderson_darling(y) < 2.492) + def testTruncatedNormalIsInRange(self): + # TODO(b/34339814): implement inverse erf support for non-F32 types. + for dtype in [dtypes.float32]: + with self.test_session() as sess, self.test_scope(): + seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) + n = 10000000 + x = stateless.stateless_truncated_normal( + shape=[n], seed=seed_t, dtype=dtype) + y = sess.run(x, {seed_t: [0x12345678, 0xabcdef12]}) + + def normal_cdf(x): + return .5 * math.erfc(-x / math.sqrt(2)) + + def normal_pdf(x): + return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi) + + def probit(x, sess=sess): + return sess.run(special_math.ndtri(x)) + + a = -2. + b = 2. + mu = 0. + sigma = 1. + + alpha = (a - mu) / sigma + beta = (b - mu) / sigma + z = normal_cdf(beta) - normal_cdf(alpha) + + self.assertTrue((y >= a).sum() == n) + self.assertTrue((y <= b).sum() == n) + + # For more information on these calculations, see: + # Burkardt, John. "The Truncated Normal Distribution". + # Department of Scientific Computing website. Florida State University. + expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma + actual_mean = np.mean(y) + self.assertAllClose(actual_mean, expected_mean, atol=2e-4) + + expected_median = mu + probit( + (normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma + actual_median = np.median(y) + self.assertAllClose(actual_median, expected_median, atol=8e-4) + + expected_variance = sigma**2 * (1 + ( + (alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - ( + (normal_pdf(alpha) - normal_pdf(beta)) / z)**2) + actual_variance = np.var(y) + self.assertAllClose(actual_variance, expected_variance, rtol=1e-3) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/compiler/tests/ternary_ops_test.py b/tensorflow/compiler/tests/ternary_ops_test.py index ef047005b60bd156a677050368ef67ae030d6c3a..effa5a59fee7dda543b2c409dfaa27a972a55808 100644 --- a/tensorflow/compiler/tests/ternary_ops_test.py +++ b/tensorflow/compiler/tests/ternary_ops_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_math_ops @@ -28,7 +28,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest -class TernaryOpsTest(XLATestCase): +class TernaryOpsTest(xla_test.XLATestCase): def _testTernary(self, op, a, b, c, expected): with self.test_session() as session: diff --git a/tensorflow/compiler/tests/test_utils.py b/tensorflow/compiler/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6abde18ea91f16d153a154b94effab037a911c6c --- /dev/null +++ b/tensorflow/compiler/tests/test_utils.py @@ -0,0 +1,63 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for helping test ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def ConvertBetweenDataFormats(x, data_format_src, data_format_dst): + """Converts 4D tensor between data formats.""" + + valid_data_formats = ["NHWC", "NCHW", "HWNC", "HWCN"] + if data_format_src not in valid_data_formats: + raise ValueError("data_format_src must be of %s, got %s." % + (valid_data_formats, data_format_src)) + if data_format_dst not in valid_data_formats: + raise ValueError("data_format_dst must be of %s, got %s." % + (valid_data_formats, data_format_dst)) + if len(x.shape) != 4: + raise ValueError("x must be 4D, got shape %s." % x.shape) + + if data_format_src == data_format_dst: + return x + + dim_map = {d: i for i, d in enumerate(data_format_src)} + transpose_dims = [dim_map[d] for d in data_format_dst] + return np.transpose(x, transpose_dims) + + +def PermuteDimsBetweenDataFormats(dims, data_format_src, data_format_dst): + """Get new shape for converting between data formats.""" + + valid_data_formats = ["NHWC", "NCHW", "HWNC", "HWCN"] + if data_format_src not in valid_data_formats: + raise ValueError("data_format_src must be of %s, got %s." % + (valid_data_formats, data_format_src)) + if data_format_dst not in valid_data_formats: + raise ValueError("data_format_dst must be of %s, got %s." % + (valid_data_formats, data_format_dst)) + if len(dims) != 4: + raise ValueError("dims must be of length 4, got %s." % dims) + + if data_format_src == data_format_dst: + return dims + + dim_map = {d: i for i, d in enumerate(data_format_src)} + permuted_dims = [dims[dim_map[d]] for d in data_format_dst] + return permuted_dims diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 689a4a1f4e02f5dd48f64dc94afd0fcb50df8b5b..6a7011aea6cc3f942fecf27a640b998bfc10c0de 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -23,7 +23,7 @@ import unittest import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import bitwise_ops @@ -44,11 +44,16 @@ def nhwc_to_format(x, data_format): raise ValueError("Unknown format {}".format(data_format)) -class UnaryOpsTest(XLATestCase): +class UnaryOpsTest(xla_test.XLATestCase): """Test cases for unary operators.""" - def _assertOpOutputMatchesExpected(self, op, inp, expected, - equality_test=None, rtol=1e-3, atol=1e-5): + def _assertOpOutputMatchesExpected(self, + op, + inp, + expected, + equality_test=None, + rtol=1e-3, + atol=1e-5): """Verifies that 'op' produces 'expected' when fed input 'inp' . Args: @@ -81,10 +86,10 @@ class UnaryOpsTest(XLATestCase): def testAllTypeOps(self): for dtype in self.numeric_types: self._assertOpOutputMatchesExpected( - array_ops.diag, - np.array([1, 2, 3, 4], dtype=dtype), - np.array([[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]], - dtype=dtype)) + array_ops.diag, np.array([1, 2, 3, 4], dtype=dtype), + np.array( + [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]], + dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.diag_part, np.arange(36).reshape([2, 3, 2, 3]).astype(dtype), @@ -102,8 +107,7 @@ class UnaryOpsTest(XLATestCase): expected=np.array([[-1, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( - array_ops.matrix_diag, - np.array([[1, 2], [3, 4]], dtype=dtype), + array_ops.matrix_diag, np.array([[1, 2], [3, 4]], dtype=dtype), np.array([[[1, 0], [0, 2]], [[3, 0], [0, 4]]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.matrix_diag, np.array([1, 2, 3, 4], dtype=dtype), @@ -115,10 +119,10 @@ class UnaryOpsTest(XLATestCase): np.array( [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], dtype=dtype), np.array( - [[[[1, 0, 0], [0, 2, 0], [0, 0, 3]], - [[4, 0, 0], [0, 5, 0], [0, 0, 6]]], - [[[7, 0, 0], [0, 8, 0], [0, 0, 9]], - [[10, 0, 0], [0, 11, 0], [0, 0, 12]]]], + [[[[1, 0, 0], [0, 2, 0], [0, 0, 3]], [[4, 0, 0], [0, 5, 0], [ + 0, 0, 6 + ]]], [[[7, 0, 0], [0, 8, 0], [0, 0, 9]], [[10, 0, 0], [0, 11, 0], + [0, 0, 12]]]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.matrix_diag_part, @@ -159,36 +163,30 @@ class UnaryOpsTest(XLATestCase): continue x = np.arange(-0.90, 0.90, 0.25) self._assertOpOutputMatchesExpected( - math_ops.acos, - x.astype(dtype), - expected=np.arccos(x).astype(dtype)) + math_ops.acos, x.astype(dtype), expected=np.arccos(x).astype(dtype)) self._assertOpOutputMatchesExpected( - math_ops.asin, - x.astype(dtype), - expected=np.arcsin(x).astype(dtype)) + math_ops.asin, x.astype(dtype), expected=np.arcsin(x).astype(dtype)) x = np.arange(-3, 3).reshape(1, 3, 2) self._assertOpOutputMatchesExpected( - math_ops.atan, - x.astype(dtype), - expected=np.arctan(x).astype(dtype)) + math_ops.atan, x.astype(dtype), expected=np.arctan(x).astype(dtype)) self._assertOpOutputMatchesExpected( math_ops.acosh, np.array([1, 2, 3, 4], dtype=dtype), - expected=np.array([0, 1.3169579, 1.76274717, 2.06343707], - dtype=dtype)) + expected=np.array( + [0, 1.3169579, 1.76274717, 2.06343707], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.asinh, np.array([1, 2, 3, 4], dtype=dtype), - expected=np.array([0.88137359, 1.44363548, 1.81844646, 2.09471255], - dtype=dtype)) + expected=np.array( + [0.88137359, 1.44363548, 1.81844646, 2.09471255], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.atanh, np.array([0.1, 0.2, 0.3, 0.4], dtype=dtype), - expected=np.array([0.10033535, 0.20273255, 0.3095196, 0.42364893], - dtype=dtype)) + expected=np.array( + [0.10033535, 0.20273255, 0.3095196, 0.42364893], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.ceil, @@ -198,8 +196,18 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.cosh, np.array([1, 2, 3, 4], dtype=dtype), - expected=np.array([1.54308063, 3.76219569, 10.067662, 27.30823284], - dtype=dtype)) + expected=np.array( + [1.54308063, 3.76219569, 10.067662, 27.30823284], dtype=dtype)) + + # Disable float16 testing for now + if dtype != np.float16: + x = np.arange(-10, 10, 1).astype(dtype) + with self.test_session() as session: + erf_x = session.run(math_ops.erf(x)) + erfc_x = session.run(math_ops.erfc(x)) + + self._assertOpOutputMatchesExpected(math_ops.erf, x, expected=erf_x) + self._assertOpOutputMatchesExpected(math_ops.erfc, x, expected=erfc_x) self._assertOpOutputMatchesExpected( math_ops.exp, @@ -219,8 +227,8 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.is_finite, - np.array([[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], - dtype=dtype), + np.array( + [[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[0, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=np.bool)) # Tests for tf.nn ops. @@ -261,16 +269,20 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.rint, - np.array([[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], - [0.5, 1.5, 2.5, 3.5]], dtype=dtype), - expected=np.array([[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], - dtype=dtype)) + np.array( + [[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], + [0.5, 1.5, 2.5, 3.5]], + dtype=dtype), + expected=np.array( + [[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.round, - np.array([[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], - [0.5, 1.5, 2.5, 3.5]], dtype=dtype), - expected=np.array([[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], - dtype=dtype)) + np.array( + [[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], + [0.5, 1.5, 2.5, 3.5]], + dtype=dtype), + expected=np.array( + [[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.rsqrt, @@ -279,10 +291,7 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.sigmoid, - np.array( - [[1, 1, 1, 1], - [1, 2, 3, 4]], - dtype=dtype), + np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[0.7310586, 0.7310586, 0.7310586, 0.7310586], [0.7310586, 0.880797, 0.95257413, 0.98201376]], @@ -296,8 +305,8 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.sinh, np.array([1, 2, 3, 4], dtype=dtype), - expected=np.array([1.17520119, 3.62686041, 10.01787493, 27.2899172], - dtype=dtype)) + expected=np.array( + [1.17520119, 3.62686041, 10.01787493, 27.2899172], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sqrt, @@ -307,15 +316,12 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( math_ops.tan, np.array([1, 2, 3, 4], dtype=dtype), - expected=np.array([1.55740772, -2.18503986, -0.14254654, 1.15782128], - dtype=dtype)) + expected=np.array( + [1.55740772, -2.18503986, -0.14254654, 1.15782128], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.tanh, - np.array( - [[1, 1, 1, 1], - [1, 2, 3, 4]], - dtype=dtype), + np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[0.76159418, 0.76159418, 0.76159418, 0.76159418], [0.76159418, 0.96402758, 0.99505478, 0.99932933]], @@ -323,10 +329,7 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( nn_ops.log_softmax, - np.array( - [[1, 1, 1, 1], - [1, 2, 3, 4]], - dtype=dtype), + np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[-1.3862944, -1.3862944, -1.3862944, -1.3862944], [-3.4401896, -2.4401896, -1.4401897, -0.44018969]], @@ -360,10 +363,7 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( nn_ops.softmax, - np.array( - [[1, 1, 1, 1], - [1, 2, 3, 4]], - dtype=dtype), + np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[0.25, 0.25, 0.25, 0.25], [0.032058604, 0.087144323, 0.23688284, 0.64391428]], @@ -372,8 +372,8 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( nn_ops.softsign, np.array([[-2, -1, 0, 1, 2]], dtype=dtype), - expected=np.array([[-0.66666669, -0.5, 0, 0.5, 0.66666669]], - dtype=dtype)) + expected=np.array( + [[-0.66666669, -0.5, 0, 0.5, 0.66666669]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.is_finite, @@ -382,10 +382,23 @@ class UnaryOpsTest(XLATestCase): expected=np.array( [[True, False, True], [False, True, True]], dtype=np.bool)) + def quantize_and_dequantize_v2(x): + return array_ops.quantize_and_dequantize_v2( + x, -127, 127, signed_input=True, num_bits=8) + self._assertOpOutputMatchesExpected( - lambda x: array_ops.quantize_and_dequantize_v2(x, -127, 127, True, 8), + quantize_and_dequantize_v2, np.array([-1, -0.5, 0, 0.3], dtype=dtype), - expected=np.array([-1, -64.0 / 127, 0, 38.0 / 127], dtype=dtype)) + expected=np.array([-1., -0.5, 0., 0.296875], dtype=dtype)) + + def quantize_and_dequantize_v3(x): + return array_ops.quantize_and_dequantize_v3( + x, -127, 127, num_bits=8, signed_input=True, range_given=False) + + self._assertOpOutputMatchesExpected( + quantize_and_dequantize_v3, + np.array([-1, -0.5, 0, 0.3], dtype=dtype), + expected=np.array([-1., -0.5, 0., 0.296875], dtype=dtype)) def testComplexOps(self): for dtype in self.complex_types: @@ -566,13 +579,13 @@ class UnaryOpsTest(XLATestCase): for dtype in self.float_types: self._assertOpOutputMatchesExpected( math_ops.is_inf, - np.array([[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], - dtype=dtype), + np.array( + [[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[1, 0, 0, 0, 0, 0, 0, 1, 0]], dtype=np.bool)) self._assertOpOutputMatchesExpected( math_ops.is_nan, - np.array([[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], - dtype=dtype), + np.array( + [[np.NINF, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=np.bool)) def testLogicalOps(self): @@ -589,14 +602,15 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"), - np.array([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]], - dtype=np.float32), + np.array( + [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]], dtype=np.float32), expected=np.array([10., 26.], dtype=np.float32)) def testCast(self): shapes = [[], [4], [2, 3], [2, 0, 4]] - types = (set([dtypes.bool, dtypes.int32, dtypes.float32]) | - self.complex_tf_types) + types = ( + set([dtypes.bool, dtypes.int32, dtypes.float32]) + | self.complex_tf_types) for shape in shapes: for src_type in types: for dst_type in types: @@ -638,14 +652,11 @@ class UnaryOpsTest(XLATestCase): self._assertOpOutputMatchesExpected( rank_op, dtype(7), expected=np.int32(0)) self._assertOpOutputMatchesExpected( - rank_op, np.array( - [[], []], dtype=dtype), expected=np.int32(2)) + rank_op, np.array([[], []], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( - rank_op, np.array( - [-1, 1], dtype=dtype), expected=np.int32(1)) + rank_op, np.array([-1, 1], dtype=dtype), expected=np.int32(1)) self._assertOpOutputMatchesExpected( - rank_op, np.array( - [[-1, 1]], dtype=dtype), expected=np.int32(2)) + rank_op, np.array([[-1, 1]], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( rank_op, np.array([[-1], [1], [4]], dtype=dtype), @@ -710,97 +721,97 @@ class UnaryOpsTest(XLATestCase): equality_test=self.ListsAreClose) def testDepthToSpace(self): + def make_op(data_format): + def op(x): - return array_ops.depth_to_space(x, block_size=2, - data_format=data_format) + return array_ops.depth_to_space( + x, block_size=2, data_format=data_format) + return op for dtype in self.numeric_types: for data_format in ["NCHW", "NHWC"]: self._assertOpOutputMatchesExpected( make_op(data_format), - nhwc_to_format(np.array([[[[1, 2, 3, 4]]]], dtype=dtype), - data_format), - expected=nhwc_to_format(np.array([[[[1], [2]], - [[3], [4]]]], dtype=dtype), - data_format)) + nhwc_to_format( + np.array([[[[1, 2, 3, 4]]]], dtype=dtype), data_format), + expected=nhwc_to_format( + np.array([[[[1], [2]], [[3], [4]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( - np.array([[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], - dtype=dtype), + np.array( + [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], dtype=dtype), data_format), expected=nhwc_to_format( - np.array([[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], - dtype=dtype), - data_format)) + np.array( + [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], + dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( - np.array([[[[1, 2, 3, 4], - [5, 6, 7, 8]], - [[9, 10, 11, 12], - [13, 14, 15, 16]]]], dtype=dtype), - data_format), + np.array( + [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], + [13, 14, 15, 16]]]], + dtype=dtype), data_format), expected=nhwc_to_format( - np.array([[[[1], [2], [5], [6]], - [[3], [4], [7], [8]], - [[9], [10], [13], [14]], - [[11], [12], [15], [16]]]], dtype=dtype), - data_format)) + np.array( + [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], + [[9], [10], [13], [14]], [[11], [12], [15], [16]]]], + dtype=dtype), data_format)) def testSpaceToDepth(self): + def make_op(data_format): + def op(x): - return array_ops.space_to_depth(x, block_size=2, - data_format=data_format) + return array_ops.space_to_depth( + x, block_size=2, data_format=data_format) + return op for dtype in self.numeric_types: for data_format in ["NCHW", "NHWC"]: self._assertOpOutputMatchesExpected( make_op(data_format), - nhwc_to_format(np.array([[[[1], [2]], - [[3], [4]]]], dtype=dtype), - data_format), - expected=nhwc_to_format(np.array([[[[1, 2, 3, 4]]]], dtype=dtype), - data_format)) + nhwc_to_format( + np.array([[[[1], [2]], [[3], [4]]]], dtype=dtype), data_format), + expected=nhwc_to_format( + np.array([[[[1, 2, 3, 4]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), - nhwc_to_format(np.array([[[[1, 2, 3], [4, 5, 6]], - [[7, 8, 9], [10, 11, 12]]]], dtype=dtype), - data_format), + nhwc_to_format( + np.array( + [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], + dtype=dtype), data_format), expected=nhwc_to_format( - np.array([[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], - dtype=dtype), + np.array( + [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), - nhwc_to_format(np.array([[[[1], [2], [5], [6]], - [[3], [4], [7], [8]], - [[9], [10], [13], [14]], - [[11], [12], [15], [16]]]], dtype=dtype), - data_format), + nhwc_to_format( + np.array( + [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], + [[9], [10], [13], [14]], [[11], [12], [15], [16]]]], + dtype=dtype), data_format), expected=nhwc_to_format( - np.array([[[[1, 2, 3, 4], - [5, 6, 7, 8]], - [[9, 10, 11, 12], - [13, 14, 15, 16]]]], dtype=dtype), - data_format)) + np.array( + [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], + [13, 14, 15, 16]]]], + dtype=dtype), data_format)) def _assertSoftplusMatchesExpected(self, features, dtype): features = np.array(features, dtype=dtype) zero = np.asarray(0).astype(dtype) expected = np.logaddexp(zero, features) self._assertOpOutputMatchesExpected( - nn_ops.softplus, features, expected=expected, - rtol=1e-6, - atol=9.1e-6) + nn_ops.softplus, features, expected=expected, rtol=1e-6, atol=9.1e-6) def testSoftplus(self): for dtype in self.float_types: @@ -814,9 +825,10 @@ class UnaryOpsTest(XLATestCase): one = dtype(1) ten = dtype(10) self._assertSoftplusMatchesExpected([ - log_eps, log_eps - one, log_eps + one, log_eps - ten, - log_eps + ten, -log_eps, -log_eps - one, -log_eps + one, - -log_eps - ten, -log_eps + ten], dtype) + log_eps, log_eps - one, log_eps + one, log_eps - ten, log_eps + ten, + -log_eps, -log_eps - one, -log_eps + one, -log_eps - ten, + -log_eps + ten + ], dtype) if __name__ == "__main__": diff --git a/tensorflow/compiler/tests/variable_ops_test.py b/tensorflow/compiler/tests/variable_ops_test.py index 2c09b03d5a35cde2c42d8a145781270c0c908587..dd2c252d383bca9c59033ac07e442b487e4975a6 100644 --- a/tensorflow/compiler/tests/variable_ops_test.py +++ b/tensorflow/compiler/tests/variable_ops_test.py @@ -20,12 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_state_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -36,7 +37,7 @@ from tensorflow.python.platform import googletest from tensorflow.python.training.gradient_descent import GradientDescentOptimizer -class VariableOpsTest(XLATestCase): +class VariableOpsTest(xla_test.XLATestCase): """Test cases for resource variable operators.""" def testOneWriteOneOutput(self): @@ -52,9 +53,7 @@ class VariableOpsTest(XLATestCase): with ops.control_dependencies([x]): y = v.read_value() self.assertAllClose( - np.array([[2, 1 + 2j], [4, 5]]).astype(dtype), sess.run(y, { - p: 1 - })) + np.array([[2, 1 + 2j], [4, 5]]).astype(dtype), sess.run(y, {p: 1})) def testSparseRead0DIndices(self): for dtype in self.numeric_types: @@ -103,9 +102,9 @@ class VariableOpsTest(XLATestCase): x = v.sparse_read([[2, 1], [3, 0]]) self.assertAllClose( np.array( - [[[[20, 21, 22], [23, 24j, 25]], [[10, 11, 12], [13, 14, 15]]], - [[[30, 31, 32], [33, 34, 35]], [[0, 1, 2], [3, 4, 5]]]], - ).astype(dtype), sess.run(x)) + [[[[20, 21, 22], [23, 24j, 25]], [[10, 11, 12], [13, 14, 15]] + ], [[[30, 31, 32], [33, 34, 35]], [[0, 1, 2], [3, 4, 5]]] + ],).astype(dtype), sess.run(x)) def testShape(self): for dtype in self.numeric_types: @@ -206,6 +205,206 @@ class VariableOpsTest(XLATestCase): self.assertAllClose(update, result[1]) self.assertAllClose(update, result[2]) + def testScatterAdd(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[2, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1], [7]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_add( + handle, [0], constant_op.constant([[2]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertAllEqual(sess.run(read), [[3], [7]]) + + def testScatterSub(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[2, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[4], [1]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_sub( + handle, [1], constant_op.constant([[2]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertAllEqual(sess.run(read), [[4], [-1]]) + + def testScatterMul(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_mul( + handle, [0], constant_op.constant([[5]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[5]]) + + def testScatterDiv(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_div( + handle, [0], constant_op.constant([[3]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertAllEqual(sess.run(read), [[2]]) + + def testScatterMin(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_min( + handle, [0], constant_op.constant([[3]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[3]]) + + def testScatterMax(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_max( + handle, [0], constant_op.constant([[3]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[6]]) + + def testScatterUpdate(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_update( + handle, [0], constant_op.constant([[3]], dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[3]]) + + def testScatterAddScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_add( + handle, [0], constant_op.constant(2, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[3]]) + + def testScatterSubScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_sub( + handle, [0], constant_op.constant(2, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[-1]]) + + def testScatterMulScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[1]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_mul( + handle, [0], constant_op.constant(5, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[5]]) + + def testScatterDivScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_div( + handle, [0], constant_op.constant(3, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[2]]) + + def testScatterMinScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_min( + handle, [0], constant_op.constant(3, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[3]]) + + def testScatterMaxScalar(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.int32, shape=[1, 1]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([[6]], dtype=dtypes.int32))) + sess.run( + resource_variable_ops.resource_scatter_max( + handle, [0], constant_op.constant(3, dtype=dtypes.int32))) + read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) + self.assertEqual(sess.run(read), [[6]]) + + def testScatterNdAddOps(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.float32, shape=[8]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([1] * 8, dtype=dtypes.float32))) + indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32) + updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32) + expected = np.array([1, 12, 1, 11, 10, 1, 1, 13]) + sess.run(gen_state_ops.resource_scatter_nd_add(handle, indices, updates)) + read = resource_variable_ops.read_variable_op( + handle, dtype=dtypes.float32) + self.assertAllClose(expected, sess.run(read)) + + def testScatterNdUpdateAddOps(self): + with self.test_session() as sess, self.test_scope(): + handle = resource_variable_ops.var_handle_op( + dtype=dtypes.float32, shape=[8]) + sess.run( + resource_variable_ops.assign_variable_op( + handle, constant_op.constant([1] * 8, dtype=dtypes.float32))) + indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32) + updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32) + expected = np.array([1, 11, 1, 10, 9, 1, 1, 12]) + sess.run( + gen_state_ops.resource_scatter_nd_update(handle, indices, updates)) + read = resource_variable_ops.read_variable_op( + handle, dtype=dtypes.float32) + self.assertAllClose(expected, sess.run(read)) + class StridedSliceAssignChecker(object): """Compares the results of a slice assignment using Tensorflow and numpy.""" @@ -236,12 +435,12 @@ class StridedSliceAssignChecker(object): self.test.assertAllEqual(val, valnp) -class SliceAssignTest(XLATestCase): +class SliceAssignTest(xla_test.XLATestCase): def testSliceAssign(self): for dtype in self.numeric_types: - checker = StridedSliceAssignChecker(self, [[1, 2, 3], [4, 5, 6]], - dtype=dtype) + checker = StridedSliceAssignChecker( + self, [[1, 2, 3], [4, 5, 6]], dtype=dtype) # No-op assignment checker[:] = [[10, 20, 30], [40, 50, 60]] # Checks trivial (1,1) shape tensor diff --git a/tensorflow/compiler/tests/while_test.py b/tensorflow/compiler/tests/while_test.py index f79eb27435cc954cebde4357c1d946a320f4ed75..b637cf31cfc303ebe84ce8307ef4ad8b0b5cd720 100644 --- a/tensorflow/compiler/tests/while_test.py +++ b/tensorflow/compiler/tests/while_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.compiler.tf2xla.python import xla from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -29,7 +29,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class WhileTest(XLATestCase): +class WhileTest(xla_test.XLATestCase): def testSingletonLoopHandrolled(self): # Define a function for the loop body diff --git a/tensorflow/compiler/tests/xla_device_test.py b/tensorflow/compiler/tests/xla_device_test.py index f0b010fa67f2ffb3f81fd14d4d89585f716b4890..06d977b93c28792704b910c688af510bc650d2a4 100644 --- a/tensorflow/compiler/tests/xla_device_test.py +++ b/tensorflow/compiler/tests/xla_device_test.py @@ -20,14 +20,14 @@ from __future__ import print_function import numpy as np -from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_control_flow_ops from tensorflow.python.platform import test -class XlaDeviceTest(XLATestCase): +class XlaDeviceTest(xla_test.XLATestCase): def testCopies(self): """Tests that copies onto and off XLA devices work.""" diff --git a/tensorflow/compiler/tests/xla_test.py b/tensorflow/compiler/tests/xla_test.py index e924fe1e61454aefda622a5a46a0e483d26db5c1..88827cb53bee7bb809d0163d6badcef17e59aa78 100644 --- a/tensorflow/compiler/tests/xla_test.py +++ b/tensorflow/compiler/tests/xla_test.py @@ -49,6 +49,32 @@ flags.DEFINE_string('tf_xla_flags', None, 'Value to set the TF_XLA_FLAGS environment variable to') +def parse_disabled_manifest(manifest_content): + comments_re = re.compile('#.*$') + disabled_tests = [] + disabled_method_types = [] + for l in manifest_content.splitlines(): + stripped = comments_re.sub('', l).strip() + if not stripped: + continue + entry = stripped.split(' ') + if len(entry) == 1: + disabled_tests.append(entry[0]) + elif len(entry) == 2: + disabled_method_types.append((entry[0], entry[1].strip().split(','))) + else: + raise ValueError('Bad entry in manifest file.') + + disabled_regex = '|'.join(disabled_tests) + method_types_filter = dict() + for method, types in disabled_method_types: + method_types_filter[method] = set([ + dtypes.as_dtype(types_pb2.DataType.Value(name)).as_numpy_dtype + for name in types + ]) + return disabled_regex, method_types_filter + + class XLATestCase(test.TestCase): """XLA test cases are parameterized test cases.""" @@ -85,38 +111,21 @@ class XLATestCase(test.TestCase): # Parse the manifest file, if any, into a regex identifying tests to # disable - self.disabled_regex = None - self._method_types_filter = dict() # TODO(xpan): Make it text proto if it doesn't scale. # Each line of the manifest file specifies an entry. The entry can be # 1) TestNameRegex // E.g. CumprodTest.* Or # 2) TestName TypeName // E.g. AdamOptimizerTest.testSharing DT_BFLOAT16 # The 1) disables the entire test. While 2) only filter some numeric types # so that they are not used in those tests. + self.disabled_regex = None + self._method_types_filter = {} if FLAGS.disabled_manifest is not None: - comments_re = re.compile('#.*$') - manifest_file = open(FLAGS.disabled_manifest, 'r') - disabled_tests = [] - disabled_method_types = [] - for l in manifest_file.read().splitlines(): - if not l: - continue - entry = comments_re.sub('', l).strip().split(' ') - if len(entry) == 1: - disabled_tests.append(entry[0]) - elif len(entry) == 2: - disabled_method_types.append( - (entry[0], entry[1].strip().split(','))) - else: - raise ValueError('Bad entry in manifest file.') - - self.disabled_regex = re.compile('|'.join(disabled_tests)) - for method, types in disabled_method_types: - self._method_types_filter[method] = set([ - dtypes.as_dtype(types_pb2.DataType.Value(name)).as_numpy_dtype - for name in types]) - manifest_file.close() + with open(FLAGS.disabled_manifest, 'r') as manifest_file: + disabled_regex, self._method_types_filter = ( + parse_disabled_manifest(manifest_file.read())) + if disabled_regex: + self.disabled_regex = re.compile(disabled_regex) if FLAGS.tf_xla_flags is not None: os.environ['TF_XLA_FLAGS'] = FLAGS.tf_xla_flags diff --git a/tensorflow/compiler/tests/xla_test_test.py b/tensorflow/compiler/tests/xla_test_test.py new file mode 100644 index 0000000000000000000000000000000000000000..24664451579445edaadb335c30d253ee55f003da --- /dev/null +++ b/tensorflow/compiler/tests/xla_test_test.py @@ -0,0 +1,44 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the XLATestCase test fixture base class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.platform import test + + +class XlaTestCaseTestCase(test.TestCase): + + def testManifestEmptyLineDoesNotCatchAll(self): + manifest = """ +testCaseOne +""" + disabled_regex, _ = xla_test.parse_disabled_manifest(manifest) + self.assertEqual(disabled_regex, "testCaseOne") + + def testManifestWholeLineCommentDoesNotCatchAll(self): + manifest = """# I am a comment +testCaseOne +testCaseTwo +""" + disabled_regex, _ = xla_test.parse_disabled_manifest(manifest) + self.assertEqual(disabled_regex, "testCaseOne|testCaseTwo") + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index cd57452302fcbde37d79ce760a80615a76d7ad8c..40e32f2e757c96de86414b5699b67935f4d92776 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -164,11 +164,15 @@ cc_library( "//tensorflow/compiler/tf2xla/lib:util", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:core_cpu", @@ -462,3 +466,13 @@ cc_library( "//tensorflow/core:protos_all_cc", ], ) + +tf_cc_test( + name = "xla_op_registry_test", + srcs = ["xla_op_registry_test.cc"], + deps = [ + ":xla_compiler", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 1438f6b48c4913e60b0c0a9f5c3d67fe595cbfe8..6cc95149a16a59fce8486c5d103ad09e3e262765 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -166,6 +166,27 @@ StatusOr AddNode(const NodeDef& node_def, Graph* graph) { return inserted_node; } +// Check that the graph has no cycle containing the given node. +Status CheckNoCycleContains(const Node* node, const int num_nodes) { + std::vector ready; + ready.push_back(node); + std::vector visited(num_nodes); + while (!ready.empty()) { + const Node* current_node = ready.back(); + ready.pop_back(); + visited[current_node->id()] = true; + for (const Edge* out : current_node->out_edges()) { + if (out->dst() == node) { + return errors::Internal("Detect a cycle: Node \"", node->name(), "\"(", + node->def().op(), ") feeds into itself."); + } else if (!visited[out->dst()->id()]) { + ready.push_back(out->dst()); + } + } + } + return Status::OK(); +} + StatusOr BuildArgNode(Graph* graph, DataType type, int index) { NodeDef arg_def; NodeDefBuilder builder(strings::StrCat(kArgOp, index), kArgOp); @@ -1407,6 +1428,10 @@ StatusOr FunctionalizeCond::ConvertToXlaIf( TF_RETURN_IF_ERROR( AddInputEdges(cond_arg_nodes, switch_cluster.predicate_edge, if_node)); TF_RETURN_IF_ERROR(AddOutputEdges(merge_nodes, if_node)); + // Check that the if_node doesn't feed into itself. + TF_RETURN_WITH_CONTEXT_IF_ERROR( + CheckNoCycleContains(if_node, graph_->num_node_ids()), + "ConvertToXlaIf failed."); return if_node; } @@ -1439,7 +1464,9 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, // invariant. std::vector cf_info; std::vector unreachable_nodes; - TF_RETURN_IF_ERROR(BuildControlFlowInfo(graph, &cf_info, &unreachable_nodes)); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + BuildControlFlowInfo(graph, &cf_info, &unreachable_nodes), + "FunctionalizeControlFlow failed"); if (!unreachable_nodes.empty()) { return errors::InvalidArgument( "The following nodes are unreachable from the source in the graph: ", @@ -1464,10 +1491,6 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, frame.parent = parent; frame.name = cf.frame_name; ++parent->num_children; - } else if (frame.parent != parent) { - return errors::InvalidArgument("Mismatched parent frames for ", - cf.frame->id(), ": ", parent->name, " vs ", - frame.parent->name); } if (IsEnter(node)) { @@ -1477,12 +1500,6 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, &arg.is_loop_invariant)); frame.args.push_back(arg); } else if (IsLoopCond(node)) { - if (frame.loop_cond) { - return errors::InvalidArgument( - "Loop ", cf.frame_name, - " has more than one LoopCond node: ", node->name(), " and ", - frame.loop_cond->name()); - } frame.loop_cond = node; } frame.nodes.insert(node); @@ -1514,6 +1531,16 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, worklist.push_back(frame->parent); } } + // There should be no cycle at this point, since while loops have been removed + // from graph. + // Check that the newly added XlaWhile nodes don't feed into themselves. + for (const Node* node : graph->op_nodes()) { + if (node->def().op() == "XlaWhile") { + TF_RETURN_WITH_CONTEXT_IF_ERROR( + CheckNoCycleContains(node, graph->num_node_ids()), + "FunctionalizeLoop failed."); + } + } // FunctionalizeControlFlow is invoked for every function, so the loops's // bodies and conditionals that were extracted into functions will be handled diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index 14977a908ae2b0ff7e13b634c41b6d331b4b8a36..aae2f8ee5acd6249f8b6002d94c877f18064f936 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/core/framework/op.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/graph_def_builder.h" +#include "tensorflow/core/graph/validate.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/util/equal_graph_def.h" @@ -1012,5 +1013,60 @@ TEST(FunctionalizeControlFlow, Complex) { } } +TEST(FunctionalizeControlFlow, Cycle) { + std::unique_ptr graph(new Graph(OpRegistry::Global())); + // ----------------------------------------------------- + // | | + // | v + // less -> switch_1 --> add -> merge_1 -> identity -> switch_2 + // | ^ | + // | | v + // --------> one -------------------------> add_2 ---> merge_2 + { + Scope scope = Scope::NewRootScope().ExitOnError(); + + auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32); + auto y = ops::Placeholder(scope.WithOpName("y"), DT_INT32); + auto less = ops::Less(scope.WithOpName("cond/Less"), y, x); + auto switch_1 = ops::Switch(scope.WithOpName("cond/Switch"), x, less); + auto two = + ops::Const(scope.WithOpName("cond/two") + .WithControlDependencies(switch_1.output_true), + 2); + auto mul = ops::Multiply(scope.WithOpName("cond/true/mul"), + switch_1.output_true, two); + auto one = + ops::Const(scope.WithOpName("cond/one") + .WithControlDependencies(switch_1.output_false), + 1); + auto add = ops::Add(scope.WithOpName("cond/false/add"), + switch_1.output_false, one); + + auto merge_1 = ops::Merge(scope.WithOpName("cond/Merge"), + std::initializer_list{add, mul}); + auto identity = + ops::Identity(scope.WithOpName("cond/Merge/identity"), merge_1.output); + auto switch_2 = + ops::Switch(scope.WithOpName("grad/cond/Switch"), identity, less); + auto add_2 = ops::Add(scope.WithOpName("cond_2/false/add"), + switch_2.output_false, one); + auto mul_2 = ops::Multiply(scope.WithOpName("cond_2/true/mul"), + switch_2.output_true, two); + auto merge_2 = ops::Merge(scope.WithOpName("cond_2/Merge"), + std::initializer_list{add_2, mul_2}); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + } + // No cycle before functionalize control flow. + TF_EXPECT_OK(graph::ValidateGraphHasNoCycle(*graph)); + FunctionLibraryDefinition library(OpRegistry::Global(), {}); + // switch_1 and switch_2 have the same switch depth. They are replaced by a + // single XlaIf node during FunctionalizeControlFlow, resulting in a cycle: + // less -> XlaIf <--> identity. + Status status = FunctionalizeControlFlow(graph.get(), &library); + EXPECT_FALSE(status.ok()); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "Detect a cycle")) + << status.error_message(); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 212f6f3966149ca0b2d2e012b19300e1f488f996..4900af6df17f360630abb1e64b7f144ccd4a0289 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" @@ -39,6 +40,7 @@ limitations under the License. #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/core/graph/validate.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/platform/logging.h" @@ -87,6 +89,8 @@ Status PrepareArguments(XlaOpKernelContext* ctx, Graph* graph, } } // namespace Status GraphCompiler::Compile() { + // Check that the graph has no illegal cycles. + TF_RETURN_IF_ERROR(graph::ValidateGraphHasNoCycle(*graph_)); // Maintain a mapping from node id to node outputs. using NodeOutputs = std::vector; std::vector output_registry(graph_->num_node_ids()); @@ -227,7 +231,7 @@ Status GraphCompiler::CompileFunctionalNode(Node* n, XlaContext& context = XlaContext::Get(op_context); auto* b = context.builder(); - auto output_handle = b->Call(*result.computation, handles); + auto output_handle = xla::Call(b, *result.computation, handles); // The output handle of `Call` computation is a tuple type. Unzip it so // that it can fit into future computations. int computation_output = 0; @@ -236,7 +240,7 @@ Status GraphCompiler::CompileFunctionalNode(Node* n, xla_op_context.SetConstantOutput(i, result.outputs[i].constant_value); } else { xla_op_context.SetOutput( - i, b->GetTupleElement(output_handle, computation_output)); + i, xla::GetTupleElement(output_handle, computation_output)); ++computation_output; } } diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index edd2ab6301ee891c433639ce300cde0c72929cea..a8eb7d942dfbabff3c53e2b5225c1018b01eb315 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -79,14 +79,17 @@ tf_kernel_library( "shape_util.cc", "slice_op.cc", "softmax_op.cc", + "sort_ops.cc", "spacetobatch_op.cc", "spacetodepth_op.cc", + "sparse_to_dense_op.cc", "split_op.cc", "stack_ops.cc", "stateless_random_ops.cc", "strided_slice_op.cc", "tensor_array_ops.cc", "tile_ops.cc", + "topk_op.cc", "training_ops.cc", "transpose_op.cc", "unary_ops.cc", @@ -104,6 +107,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:batch_dot", "//tensorflow/compiler/tf2xla/lib:cholesky", + "//tensorflow/compiler/tf2xla/lib:random", "//tensorflow/compiler/tf2xla/lib:scatter", "//tensorflow/compiler/tf2xla/lib:triangular_solve", "//tensorflow/compiler/tf2xla/lib:util", @@ -117,6 +121,9 @@ tf_kernel_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:math", + "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:image_ops_op_lib", diff --git a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc index 1e59868621475cf72f4cc8b14dafec2dd8cd5c95..e33532828040123243f839ab1aa655b4bbc72520 100644 --- a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -31,7 +32,7 @@ class AddNOp : public XlaOpKernel { xla::XlaOp sum = ctx->Input(0); for (int i = 1; i < ctx->num_inputs(); ++i) { - sum = ctx->builder()->Add(sum, ctx->Input(i)); + sum = xla::Add(sum, ctx->Input(i)); } ctx->SetOutput(0, sum); diff --git a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc index b0ba25b9983c3a9af26728ce4b1c263c844327db..4cfe946b2e6146f034867c06e996ffae42b90705 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc @@ -28,11 +28,10 @@ class BatchMatMulOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - auto result = BatchDot(ctx->builder(), ctx->Input(0), ctx->Input(1), + auto result = BatchDot(ctx->Input(0), ctx->Input(1), /*transpose_x=*/adj_x_, /*transpose_y=*/adj_y_, /*conjugate_x=*/adj_x_, /*conjugate_y=*/adj_y_); - OP_REQUIRES_OK(ctx, result.status()); - ctx->SetOutput(0, result.ValueOrDie()); + ctx->SetOutput(0, result); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc index 15e1815a4cf07ff50dd1431b6790d14781da590f..c4af79281d2162b1dbfb0a7881720892f4bc49d2 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { @@ -34,10 +35,11 @@ class FusedBatchNormOp : public XlaOpKernel { ctx, FormatFromString(data_format_str, &data_format_), errors::InvalidArgument("Invalid data format: ", data_format_str)); OP_REQUIRES(ctx, - (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW), + (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW || + data_format_ == FORMAT_HWNC || data_format_ == FORMAT_HWCN), errors::InvalidArgument( "Unsupported data format ", ToString(data_format_), - "; supported formats are NHWC and NCHW")); + "; supported formats are NHWC, NCHW, HWNC and HWCN")); } void Compile(XlaOpKernelContext* ctx) override { @@ -48,8 +50,6 @@ class FusedBatchNormOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(ctx->input_type(1), &scale_type)); - xla::XlaBuilder* builder = ctx->builder(); - xla::XlaOp input = ctx->Input(0); TensorShape input_shape = ctx->InputShape(0); @@ -59,30 +59,30 @@ class FusedBatchNormOp : public XlaOpKernel { // TODO(b/69928690): support mixed precision in the XLA batch normalization // operators. As a workaround, cast everything to the statistics type (which // may be more precise than the input type). - input = builder->ConvertElementType(input, scale_type); + input = xla::ConvertElementType(input, scale_type); if (is_training_) { - xla::XlaOp output = builder->BatchNormTraining( + xla::XlaOp output = xla::BatchNormTraining( input, ctx->Input(1), ctx->Input(2), epsilon_, feature_index); // In training mode, outputs the normalized value as well as the // calculated mean and variance. - ctx->SetOutput(0, builder->ConvertElementType( - builder->GetTupleElement(output, 0), input_type)); - ctx->SetOutput(1, builder->GetTupleElement(output, 1)); - ctx->SetOutput(2, builder->GetTupleElement(output, 2)); + ctx->SetOutput(0, xla::ConvertElementType(xla::GetTupleElement(output, 0), + input_type)); + ctx->SetOutput(1, xla::GetTupleElement(output, 1)); + ctx->SetOutput(2, xla::GetTupleElement(output, 2)); // Output 3 and 4 for "FusedBatchNorm" are currently marked as "reserved // space 1 & 2". They are used to pass the per-batch mean and // variance to the gradient. Here we maintain the same behavior by setting // them to the mean and variance calculated by BatchNormTraining. - ctx->SetOutput(3, builder->GetTupleElement(output, 1)); - ctx->SetOutput(4, builder->GetTupleElement(output, 2)); + ctx->SetOutput(3, xla::GetTupleElement(output, 1)); + ctx->SetOutput(4, xla::GetTupleElement(output, 2)); } else { - xla::XlaOp output = builder->BatchNormInference( + xla::XlaOp output = xla::BatchNormInference( input, ctx->Input(1), ctx->Input(2), ctx->Input(3), ctx->Input(4), epsilon_, feature_index); - ctx->SetOutput(0, builder->ConvertElementType(output, input_type)); + ctx->SetOutput(0, xla::ConvertElementType(output, input_type)); // Directly send input to output as mean and variance in inference mode. ctx->SetOutput(1, ctx->Input(3)); ctx->SetOutput(2, ctx->Input(4)); @@ -111,10 +111,11 @@ class FusedBatchNormGradOp : public XlaOpKernel { ctx, FormatFromString(data_format_str, &data_format_), errors::InvalidArgument("Invalid data format: ", data_format_str)); OP_REQUIRES(ctx, - (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW), + (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW || + data_format_ == FORMAT_HWNC || data_format_ == FORMAT_HWCN), errors::InvalidArgument( "Unsupported data format ", ToString(data_format_), - "; supported formats are NHWC and NCHW")); + "; supported formats are NHWC, NCHW, HWNC and HWCN")); } void Compile(XlaOpKernelContext* ctx) override { @@ -142,12 +143,12 @@ class FusedBatchNormGradOp : public XlaOpKernel { xla::XlaOp offset_backprop; if (is_training_) { xla::XlaOp output = - b->BatchNormGrad(activations, scale, mean, var, grad_backprop, - epsilon_, feature_index); + xla::BatchNormGrad(activations, scale, mean, var, grad_backprop, + epsilon_, feature_index); - x_backprop = b->GetTupleElement(output, 0); - scale_backprop = b->GetTupleElement(output, 1); - offset_backprop = b->GetTupleElement(output, 2); + x_backprop = xla::GetTupleElement(output, 0); + scale_backprop = xla::GetTupleElement(output, 1); + offset_backprop = xla::GetTupleElement(output, 2); } else { // Reduce over all dimensions except the feature dim. std::vector reduction_dims(input_dims - 1); @@ -164,35 +165,35 @@ class FusedBatchNormGradOp : public XlaOpKernel { auto converted = XlaHelpers::ConvertElementType(b, grad_backprop, accumulation_type); auto reduce = - b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), reduction_dims); + xla::Reduce(converted, XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), reduction_dims); offset_backprop = XlaHelpers::ConvertElementType(b, reduce, scale_dtype); // scratch1 = rsqrt(pop_var + epsilon) auto neg_half = XlaHelpers::FloatLiteral(b, scale_dtype, -0.5); - auto scratch1 = - b->Pow(b->Add(var, b->ConstantR0(epsilon_)), neg_half); + auto scratch1 = xla::Pow( + xla::Add(var, xla::ConstantR0(b, epsilon_)), neg_half); // scratch2 = sum(y_backprop * (x - mean)) auto mul = - b->Mul(grad_backprop, b->Sub(activations, mean, {feature_index})); + xla::Mul(grad_backprop, xla::Sub(activations, mean, {feature_index})); converted = XlaHelpers::ConvertElementType(b, mul, accumulation_type); reduce = - b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), reduction_dims); + xla::Reduce(converted, XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), reduction_dims); auto scratch2 = XlaHelpers::ConvertElementType(b, reduce, scale_dtype); x_backprop = - b->Mul(grad_backprop, b->Mul(scratch1, scale), {feature_index}); - scale_backprop = b->Mul(scratch1, scratch2); + xla::Mul(grad_backprop, xla::Mul(scratch1, scale), {feature_index}); + scale_backprop = xla::Mul(scratch1, scratch2); } ctx->SetOutput(0, XlaHelpers::ConvertElementType(b, x_backprop, input_dtype)); ctx->SetOutput(1, scale_backprop); ctx->SetOutput(2, offset_backprop); - ctx->SetConstantOutput(3, Tensor(scale_dtype, {})); - ctx->SetConstantOutput(4, Tensor(scale_dtype, {})); + ctx->SetConstantOutput(3, Tensor()); + ctx->SetConstantOutput(4, Tensor()); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index 642278ab994bf3cc84396f093ed56b009a1435c1..26130fd9e7fce75c6d2a5a53cfc85842cf762b35 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -45,7 +46,6 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, ", 2] instead of ", xla::ShapeUtil::HumanString(crops.shape()))); - xla::XlaBuilder* b = ctx->builder(); const int64 batch_size = input_shape[0]; // Compute the product of the block_shape values. @@ -72,7 +72,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, reshaped_shape[block_rank] = batch_size / block_num_elems; std::copy(input_shape.begin() + 1, input_shape.end(), reshaped_shape.begin() + block_rank + 1); - xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = xla::Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce `permuted` of shape // [batch / prod(block_shape), @@ -90,7 +90,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, } std::iota(permutation.begin() + 1 + block_rank * 2, permutation.end(), 1 + block_rank * 2); - xla::XlaOp permuted = b->Transpose(reshaped, permutation); + xla::XlaOp permuted = xla::Transpose(reshaped, permutation); // 3. Reshape `permuted` to produce `reshaped_permuted` of shape // [batch / prod(block_shape), @@ -110,7 +110,8 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, std::copy(remainder_shape.begin(), remainder_shape.end(), reshaped_permuted_shape.begin() + 1 + block_rank); - xla::XlaOp reshaped_permuted = b->Reshape(permuted, reshaped_permuted_shape); + xla::XlaOp reshaped_permuted = + xla::Reshape(permuted, reshaped_permuted_shape); // 4. Crop the start and end of dimensions `[1, ..., M]` of // `reshaped_permuted` according to `crops` to produce the output of shape: @@ -138,7 +139,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, " end: ", crop_end, " size ", reshaped_permuted_shape[1 + i])); } xla::XlaOp output = - b->Slice(reshaped_permuted, start_indices, end_indices, strides); + xla::Slice(reshaped_permuted, start_indices, end_indices, strides); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc index 9d677f426650ea17a49e5ab1401078f04623fe97..e9b2c0b16d39cb3b747c0316621fb01de709b12e 100644 --- a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/util/tensor_format.h" @@ -60,8 +61,7 @@ class BiasOp : public XlaOpKernel { "of the input tensor: ", bias_shape.DebugString(), " vs. ", input_shape.DebugString())); - xla::XlaOp result = - ctx->builder()->Add(ctx->Input(0), ctx->Input(1), {feature_dim}); + xla::XlaOp result = xla::Add(ctx->Input(0), ctx->Input(1), {feature_dim}); ctx->SetOutput(0, result); } @@ -109,8 +109,8 @@ class BiasAddGradOp : public XlaOpKernel { auto converted = XlaHelpers::ConvertElementType(b, ctx->Input(0), accumulation_type); auto reduce = - b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), reduce_dims); + xla::Reduce(converted, XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), reduce_dims); ctx->SetOutput(0, XlaHelpers::ConvertElementType(b, reduce, input_type(0))); } diff --git a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc index f04cde878e98002d9442e0f3ec251c5197ef7969..d6d4ae89376b67c14af8ef4f3a608fcc83b6fb59 100644 --- a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc @@ -41,18 +41,19 @@ namespace { const BCast& broadcast_helper, \ const std::vector& extend_dimensions) override { \ xla::XlaBuilder* b = ctx->builder(); \ + (void)b; \ return HLO; \ } \ }; \ REGISTER_XLA_OP(Name(#NAME), NAME##Op) -XLA_MAKE_BINARY(Add, b->Add(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Sub, b->Sub(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Mul, b->Mul(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Div, b->Div(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Add, xla::Add(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Sub, xla::Sub(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Mul, xla::Mul(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Div, xla::Div(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Atan2, b->Atan2(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Complex, b->Complex(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Atan2, xla::Atan2(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Complex, xla::Complex(lhs, rhs, extend_dimensions)); // Implementation of FloorDiv. Pseudo-code: // if ((x < 0) != (y < 0)) { @@ -67,13 +68,13 @@ static xla::XlaOp FloorDivImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x, std::tie(x, y) = XlaBinaryOp::Broadcast(b, x, y, broadcast_helper); auto zero = XlaHelpers::Zero(b, dtype); auto one = XlaHelpers::One(b, dtype); - auto different_sign = b->Ne(b->Lt(x, zero), b->Lt(y, zero)); - auto abs_x = b->Abs(x); - auto abs_y = b->Abs(y); - auto t = b->Neg(b->Sub(b->Add(abs_x, abs_y), one)); - auto result = b->Select(different_sign, b->Div(t, abs_y), b->Div(x, y)); + auto different_sign = xla::Ne(xla::Lt(x, zero), xla::Lt(y, zero)); + auto abs_x = xla::Abs(x); + auto abs_y = xla::Abs(y); + auto t = xla::Neg(xla::Sub(xla::Add(abs_x, abs_y), one)); + auto result = xla::Select(different_sign, xla::Div(t, abs_y), xla::Div(x, y)); if (DataTypeIsFloating(dtype)) { - result = b->Floor(result); + result = xla::Floor(result); } return result; } @@ -87,75 +88,78 @@ static xla::XlaOp FloorModImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x, xla::XlaOp y, const BCast& broadcast_helper) { std::tie(x, y) = XlaBinaryOp::Broadcast(b, x, y, broadcast_helper); auto zero = XlaHelpers::Zero(b, dtype); - auto same_sign = b->Eq(b->Lt(x, zero), b->Lt(y, zero)); - auto trunc_mod = b->Rem(x, y); - return b->Select(same_sign, trunc_mod, b->Rem(b->Add(trunc_mod, y), y)); + auto same_sign = xla::Eq(xla::Lt(x, zero), xla::Lt(y, zero)); + auto trunc_mod = xla::Rem(x, y); + return xla::Select(same_sign, trunc_mod, xla::Rem(xla::Add(trunc_mod, y), y)); } XLA_MAKE_BINARY(FloorMod, FloorModImpl(b, input_type(0), lhs, rhs, broadcast_helper)); -XLA_MAKE_BINARY(BitwiseAnd, b->And(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(BitwiseOr, b->Or(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(BitwiseAnd, xla::And(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(BitwiseOr, xla::Or(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(BitwiseXor, xla::Xor(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(LeftShift, b->ShiftLeft(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(LeftShift, xla::ShiftLeft(lhs, rhs, extend_dimensions)); XLA_MAKE_BINARY(RightShift, (DataTypeIsUnsigned(ctx->input_type(0)) - ? b->ShiftRightLogical(lhs, rhs, extend_dimensions) - : b->ShiftRightArithmetic(lhs, rhs, extend_dimensions))); - -XLA_MAKE_BINARY(LogicalAnd, b->And(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(LogicalOr, b->Or(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Mod, b->Rem(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Maximum, b->Max(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Minimum, b->Min(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(RealDiv, b->Div(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(ReciprocalGrad, b->Neg(b->Mul(rhs, b->Mul(lhs, lhs)))); + ? xla::ShiftRightLogical(lhs, rhs, extend_dimensions) + : xla::ShiftRightArithmetic(lhs, rhs, extend_dimensions))); + +XLA_MAKE_BINARY(LogicalAnd, xla::And(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(LogicalOr, xla::Or(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Mod, xla::Rem(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Maximum, xla::Max(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Minimum, xla::Min(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(RealDiv, xla::Div(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(ReciprocalGrad, xla::Neg(xla::Mul(rhs, xla::Mul(lhs, lhs)))); XLA_MAKE_BINARY( RsqrtGrad, - b->Mul(b->Pow(lhs, XlaHelpers::IntegerLiteral(b, input_type(0), 3)), - b->Div(rhs, XlaHelpers::IntegerLiteral(b, input_type(0), -2)), - extend_dimensions)); -XLA_MAKE_BINARY(SqrtGrad, - b->Div(b->Mul(rhs, - XlaHelpers::FloatLiteral(b, input_type(0), 0.5)), - lhs, extend_dimensions)); + xla::Mul(xla::Pow(lhs, XlaHelpers::IntegerLiteral(b, input_type(0), 3)), + xla::Div(rhs, XlaHelpers::IntegerLiteral(b, input_type(0), -2)), + extend_dimensions)); +XLA_MAKE_BINARY( + SqrtGrad, + xla::Div(xla::Mul(rhs, XlaHelpers::FloatLiteral(b, input_type(0), 0.5)), + lhs, extend_dimensions)); static xla::XlaOp Square(xla::XlaBuilder* builder, const xla::XlaOp& x) { - return builder->Mul(x, x); + return xla::Mul(x, x); } XLA_MAKE_BINARY(SquaredDifference, - Square(b, b->Sub(lhs, rhs, extend_dimensions))); + Square(b, xla::Sub(lhs, rhs, extend_dimensions))); -XLA_MAKE_BINARY(TruncateDiv, b->Div(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(TruncateMod, b->Rem(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(TruncateDiv, xla::Div(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(TruncateMod, xla::Rem(lhs, rhs, extend_dimensions)); // Comparison ops -XLA_MAKE_BINARY(Equal, b->Eq(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(NotEqual, b->Ne(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Greater, b->Gt(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(GreaterEqual, b->Ge(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(Less, b->Lt(lhs, rhs, extend_dimensions)); -XLA_MAKE_BINARY(LessEqual, b->Le(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Equal, xla::Eq(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(NotEqual, xla::Ne(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Greater, xla::Gt(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(GreaterEqual, xla::Ge(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Less, xla::Lt(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(LessEqual, xla::Le(lhs, rhs, extend_dimensions)); // Non-linear ops XLA_MAKE_BINARY(SigmoidGrad, - b->Mul(b->Mul(rhs, lhs), - b->Sub(XlaHelpers::One(b, input_type(0)), lhs))); + xla::Mul(xla::Mul(rhs, lhs), + xla::Sub(XlaHelpers::One(b, input_type(0)), lhs))); XLA_MAKE_BINARY(SoftplusGrad, - b->Div(lhs, b->Add(b->Exp(b->Neg(rhs)), - XlaHelpers::One(b, input_type(1))))); + xla::Div(lhs, xla::Add(xla::Exp(xla::Neg(rhs)), + XlaHelpers::One(b, input_type(1))))); // softsigngrad(gradients, features) = gradients / (1 + abs(features)) ** 2 XLA_MAKE_BINARY(SoftsignGrad, - b->Div(lhs, Square(b, b->Add(XlaHelpers::One(b, input_type(0)), - b->Abs(rhs))))); + xla::Div(lhs, + Square(b, xla::Add(XlaHelpers::One(b, input_type(0)), + xla::Abs(rhs))))); -XLA_MAKE_BINARY(TanhGrad, b->Mul(rhs, b->Sub(XlaHelpers::One(b, input_type(0)), - b->Mul(lhs, lhs)))); +XLA_MAKE_BINARY(TanhGrad, + xla::Mul(rhs, xla::Sub(XlaHelpers::One(b, input_type(0)), + xla::Mul(lhs, lhs)))); -XLA_MAKE_BINARY(Pow, b->Pow(lhs, rhs, extend_dimensions)); +XLA_MAKE_BINARY(Pow, xla::Pow(lhs, rhs, extend_dimensions)); #undef XLA_MAKE_BINARY @@ -168,12 +172,13 @@ class ApproximateEqualOp : public XlaOpKernel { // Computes the max of the scalar input x and 0. void Compile(XlaOpKernelContext* ctx) override { xla::XlaBuilder* b = ctx->builder(); - auto abs = b->Abs(b->Sub(ctx->Input(0), ctx->Input(1))); + auto abs = xla::Abs(xla::Sub(ctx->Input(0), ctx->Input(1))); auto abs_shape = b->GetShape(abs); OP_REQUIRES_OK(ctx, abs_shape.status()); auto abs_type = abs_shape.ValueOrDie().element_type(); - auto result = b->Lt( - abs, b->ConvertElementType(b->ConstantR0(tolerance_), abs_type)); + auto result = + xla::Lt(abs, xla::ConvertElementType( + xla::ConstantR0(b, tolerance_), abs_type)); ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc index ca9a6b40688d1e8496d1b823e20d273d519f65e8..efbdb76eaaf78904fe783a018940b1b096ec39bd 100644 --- a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { @@ -36,22 +37,22 @@ class BucketizeOp : public XlaOpKernel { const DataType dtype = context->input_type(0); xla::XlaOp input = context->Input(0); - xla::XlaOp boundaries = builder->ConstantR1(boundaries_); + xla::XlaOp boundaries = xla::ConstantR1(builder, boundaries_); // TODO(phawkins): the following behavior matches the behavior of the core // Bucketize kernel. However, comparing an int32 or int64 against float may // lead to inaccurate bucketing due to rounding. if (dtype == DT_DOUBLE) { - input = builder->ConvertElementType(input, xla::F64); - boundaries = builder->ConvertElementType(boundaries, xla::F64); + input = xla::ConvertElementType(input, xla::F64); + boundaries = xla::ConvertElementType(boundaries, xla::F64); } else { - input = builder->ConvertElementType(input, xla::F32); + input = xla::ConvertElementType(input, xla::F32); } - xla::XlaOp comparison = builder->ConvertElementType( - builder->Ge(builder->Broadcast(input, {1}), boundaries, - /*broadcast_dimensions=*/{0}), - xla::S32); - xla::XlaOp buckets = builder->Reduce( - comparison, /*init_value=*/builder->ConstantR0(0), + xla::XlaOp comparison = + xla::ConvertElementType(xla::Ge(xla::Broadcast(input, {1}), boundaries, + /*broadcast_dimensions=*/{0}), + xla::S32); + xla::XlaOp buckets = xla::Reduce( + comparison, /*init_value=*/xla::ConstantR0(builder, 0), /*computation=*/xla::CreateScalarAddComputation(xla::S32, builder), /*dimensions_to_reduce=*/{0}); context->SetOutput(0, buckets); diff --git a/tensorflow/compiler/tf2xla/kernels/cast_op.cc b/tensorflow/compiler/tf2xla/kernels/cast_op.cc index e9d98c768572c52825fa5192ecec834889f040fe..62eebf762b3e063da8ec456cc4726d3cc9b77d1d 100644 --- a/tensorflow/compiler/tf2xla/kernels/cast_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cast_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" @@ -40,14 +41,14 @@ class CastOp : public XlaOpKernel { if (src_dtype_ == dst_dtype_) { output = input; } else if (dst_dtype_ == DT_BOOL) { - output = builder->Ne(input, XlaHelpers::Zero(builder, src_dtype_)); + output = xla::Ne(input, XlaHelpers::Zero(builder, src_dtype_)); } else if (xla::primitive_util::IsComplexType(src_type_) && !xla::primitive_util::IsComplexType(dst_type_)) { // As in cast_op.h, we replicate the numpy behavior of truncating the // imaginary part. - output = builder->ConvertElementType(builder->Real(input), dst_type_); + output = xla::ConvertElementType(xla::Real(input), dst_type_); } else { - output = builder->ConvertElementType(input, dst_type_); + output = xla::ConvertElementType(input, dst_type_); } ctx->SetOutput(0, output); @@ -72,7 +73,6 @@ class BitcastOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); xla::XlaOp input = ctx->Input(0); xla::XlaOp output; @@ -92,7 +92,7 @@ class BitcastOp : public XlaOpKernel { xla::primitive_util::BitWidth(dst_type_), errors::Unimplemented( "Only bitcasts between equally sized types supported.")); - output = builder->BitcastConvertType(input, dst_type_); + output = xla::BitcastConvertType(input, dst_type_); } ctx->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc index 835a7f568945f0bee86fe2b39491c3326726e1aa..1784e712b56145bbdff5f1daa2e031b65d0774b6 100644 --- a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -65,24 +66,22 @@ class CategoricalOp : public XlaOpKernel { DataTypeToPrimitiveType(input_type(0), &uniform_xla_type)); xla::Shape uniform_shape = xla::ShapeUtil::MakeShape(uniform_xla_type, uniform_shape_array); - auto uniforms = builder->RngUniform( - XlaHelpers::Zero(builder, input_type(0)), - XlaHelpers::One(builder, input_type(0)), uniform_shape); + auto uniforms = + xla::RngUniform(XlaHelpers::Zero(builder, input_type(0)), + XlaHelpers::One(builder, input_type(0)), uniform_shape); // Use Gumbel softmax trick to generate categorical samples. // See: // https://hips.seas.harvard.edu/blog/2013/04/06/the-gumbel-max-trick-for-discrete-distributions/ // TODO(b/68769470): Switch to using a cumulative sum approach. - auto softmax_entries = - builder->Sub(logits, builder->Log(builder->Neg(builder->Log(uniforms))), - /*broadcast_dimensions=*/{0, 2}); - - TensorShape softmax_shape(uniform_shape_array); - xla::XlaOp argmax; - OP_REQUIRES_OK( - ctx, - XlaHelpers::ArgMax(builder, ctx, softmax_entries, softmax_shape, - input_type(0), output_type(0), /*axis=*/2, &argmax)); + auto softmax_entries = xla::Sub(logits, xla::Log(-xla::Log(uniforms)), + /*broadcast_dimensions=*/{0, 2}); + + xla::PrimitiveType xla_output_type; + OP_REQUIRES_OK(ctx, + DataTypeToPrimitiveType(output_type(0), &xla_output_type)); + xla::XlaOp argmax = + XlaHelpers::ArgMax(softmax_entries, xla_output_type, /*axis=*/2); ctx->SetOutput(0, argmax); } diff --git a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc index fe6651793dc763d13f4a4b0ac294ec3ecf64af8f..9fcbc86adc0967cbb7fb73da8bdabc58b60953da 100644 --- a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc @@ -24,12 +24,7 @@ class CholeskyOp : public XlaOpKernel { public: explicit CholeskyOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - auto result = Cholesky(ctx->builder(), ctx->Input(0)); - if (!result.ok()) { - ctx->SetStatus(result.status()); - return; - } - ctx->SetOutput(0, result.ValueOrDie()); + ctx->SetOutput(0, Cholesky(ctx->Input(0))); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc index a00bc912f9f40052565446c6bf9390629af9a4cd..4e6d33304c4ae08a0fd1e0a8373267a527087528 100644 --- a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { @@ -29,7 +30,6 @@ class ClipByValueOp : public XlaOpKernel { const TensorShape min_shape = ctx->InputShape(1); const TensorShape max_shape = ctx->InputShape(2); - xla::XlaBuilder* builder = ctx->builder(); auto input = ctx->Input(0); auto min = ctx->Input(1); auto max = ctx->Input(2); @@ -45,13 +45,13 @@ class ClipByValueOp : public XlaOpKernel { if (shape != min_shape) { OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(min_shape), shape_error()); - min = builder->Broadcast(min, shape.dim_sizes()); + min = xla::Broadcast(min, shape.dim_sizes()); } if (shape != max_shape) { OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(max_shape), shape_error()); - max = builder->Broadcast(max, shape.dim_sizes()); + max = xla::Broadcast(max, shape.dim_sizes()); } - ctx->SetOutput(0, builder->Clamp(min, input, max)); + ctx->SetOutput(0, xla::Clamp(min, input, max)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc index 78285affa1c399ae107a9172fb85cf257457c368..e3a32a5c0e2f93237c8c7ebeea3668b5d1ab6c23 100644 --- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -88,7 +89,7 @@ class ConcatBaseOp : public XlaOpKernel { "] = ", in_shape.DebugString())); if (in_shape.dims() == 0) { // Inputs that come in as scalars must be reshaped to 1-vectors. - input_data.push_back(ctx->builder()->Reshape(handle, {1})); + input_data.push_back(xla::Reshape(handle, {1})); } else { input_data.push_back(handle); } @@ -96,7 +97,7 @@ class ConcatBaseOp : public XlaOpKernel { } VLOG(1) << "Concat dim " << concat_dim << " equivalent to " << axis; - ctx->SetOutput(0, ctx->builder()->ConcatInDim(input_data, axis)); + ctx->SetOutput(0, xla::ConcatInDim(ctx->builder(), input_data, axis)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/const_op.cc b/tensorflow/compiler/tf2xla/kernels/const_op.cc index 59d06c654de18c9003fe0bdc706d0c2443de6d7b..f4360d8c3f6fc4007c31fdcfd7f7634de15c76d4 100644 --- a/tensorflow/compiler/tf2xla/kernels/const_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/const_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/tensor.pb.h" @@ -53,41 +54,41 @@ class ConstOp : public XlaOpKernel { switch (proto_.dtype()) { case DT_BOOL: if (proto_.bool_val_size() == 1) { - ctx->SetOutput(0, - b->Broadcast(b->ConstantR0(proto_.bool_val(0)), - shape.dim_sizes())); + ctx->SetOutput( + 0, xla::Broadcast(xla::ConstantR0(b, proto_.bool_val(0)), + shape.dim_sizes())); return; } break; case DT_FLOAT: if (proto_.float_val_size() == 1) { - ctx->SetOutput( - 0, b->Broadcast(b->ConstantR0(proto_.float_val(0)), - shape.dim_sizes())); + ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0( + b, proto_.float_val(0)), + shape.dim_sizes())); return; } break; case DT_DOUBLE: if (proto_.double_val_size() == 1) { - ctx->SetOutput( - 0, b->Broadcast(b->ConstantR0(proto_.double_val(0)), - shape.dim_sizes())); + ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0( + b, proto_.double_val(0)), + shape.dim_sizes())); return; } break; case DT_INT32: if (proto_.int_val_size() == 1) { - ctx->SetOutput(0, - b->Broadcast(b->ConstantR0(proto_.int_val(0)), - shape.dim_sizes())); + ctx->SetOutput( + 0, xla::Broadcast(xla::ConstantR0(b, proto_.int_val(0)), + shape.dim_sizes())); return; } break; case DT_INT64: if (proto_.int64_val_size() == 1) { - ctx->SetOutput( - 0, b->Broadcast(b->ConstantR0(proto_.int64_val(0)), - shape.dim_sizes())); + ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0( + b, proto_.int64_val(0)), + shape.dim_sizes())); return; } break; diff --git a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc index 627bad12f33c82e91bc3c6f3323f562bc8174056..48ac4867edcef97be001a24f42f6a35225d466c9 100644 --- a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc @@ -18,6 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -51,8 +53,8 @@ xla::XlaOp CreateExpandedZero(const TensorShape& filter_shape, DataType dtype, xla::XlaBuilder* builder) { TensorShape expanded_filter_shape = ExpandedFilterShapeForDepthwiseConvolution(filter_shape); - return builder->Broadcast(XlaHelpers::Zero(builder, dtype), - expanded_filter_shape.dim_sizes()); + return xla::Broadcast(XlaHelpers::Zero(builder, dtype), + expanded_filter_shape.dim_sizes()); } // Create a mask for depthwise convolution that will make a normal convolution @@ -95,32 +97,27 @@ xla::XlaOp CreateExpandedFilterMask(const TensorShape& filter_shape, // Create a M sized linspace and an M*N sized linspace that will be // broadcasted into perpendicular dimensions and compared. - xla::XlaOp input_feature_iota; - // DT_INT32 Iota will always return status::OK(). - TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, input_feature, - &input_feature_iota)); - xla::XlaOp expanded_feature_iota; - TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, - input_feature * depthwise_multiplier, - &expanded_feature_iota)); + xla::XlaOp input_feature_iota = xla::Iota(builder, xla::S32, input_feature); + xla::XlaOp expanded_feature_iota = + xla::Iota(builder, xla::S32, input_feature * depthwise_multiplier); // Divide the M*N sized linspace by the depthwise_multiplier to create // [0 0 1 1 2 2] in the example in the function comment. expanded_feature_iota = - builder->Div(expanded_feature_iota, - XlaHelpers::IntegerLiteral(builder, DataType::DT_INT32, - depthwise_multiplier)); + xla::Div(expanded_feature_iota, + XlaHelpers::IntegerLiteral(builder, DataType::DT_INT32, + depthwise_multiplier)); // Broadcast the N*M linspace to [H, W, ..., M, M*N]. auto expanded_feature_broadcast_dims = expanded_filter_shape.dim_sizes(); expanded_feature_broadcast_dims.pop_back(); - auto broadcasted_expanded_feature_iota = builder->Broadcast( - expanded_feature_iota, expanded_feature_broadcast_dims); + auto broadcasted_expanded_feature_iota = + xla::Broadcast(expanded_feature_iota, expanded_feature_broadcast_dims); // Compare the broadcasted linspace to the input feature linspace in the // input feature dimension to create a diagonal predicate. - return builder->Eq(broadcasted_expanded_feature_iota, input_feature_iota, - {expanded_filter_shape.dims() - 2}); + return xla::Eq(broadcasted_expanded_feature_iota, input_feature_iota, + {expanded_filter_shape.dims() - 2}); } // Expands a filter of shape [H, W, ..., M, N] to [H, W, ..., M, M*N] by adding @@ -142,16 +139,16 @@ xla::XlaOp ExpandFilterForDepthwiseConvolution(const TensorShape& filter_shape, implicit_broadcast_filter_shape.dims() - 1, depthwise_multiplier * input_feature); auto implicit_broadcast_filter = - builder->Reshape(filter, implicit_broadcast_filter_shape.dim_sizes()); + xla::Reshape(filter, implicit_broadcast_filter_shape.dim_sizes()); // Broadcast the filter to [H, W, ..., M, M*N]. auto expanded_zero = CreateExpandedZero(filter_shape, dtype, builder); - auto expanded_filter = builder->Add(implicit_broadcast_filter, expanded_zero); + auto expanded_filter = xla::Add(implicit_broadcast_filter, expanded_zero); // If the filter mask is set, choose the broadcasted filter, othwerwise, // choose zero. - return builder->Select(CreateExpandedFilterMask(filter_shape, builder), - expanded_filter, expanded_zero); + return xla::Select(CreateExpandedFilterMask(filter_shape, builder), + expanded_filter, expanded_zero); } // Inverse of ExpandFilterForDepthwiseConvolution. @@ -162,17 +159,17 @@ xla::XlaOp ContractFilterForDepthwiseBackprop(XlaOpKernelContext* ctx, xla::XlaBuilder* builder) { TensorShape expanded_filter_shape = ExpandedFilterShapeForDepthwiseConvolution(filter_shape); - auto masked_expanded_filter = builder->Select( + auto masked_expanded_filter = xla::Select( CreateExpandedFilterMask(filter_shape, builder), filter_backprop, CreateExpandedZero(filter_shape, dtype, builder)); - return builder->Reshape( + return xla::Reshape( // This reduce does not need inputs to be converted with // XlaHelpers::SumAccumulationType() since the ExpandedFilterMask with // ExpandedZero guarantees that only one element is non zero, so there // cannot be accumulated precision error. - builder->Reduce(masked_expanded_filter, XlaHelpers::Zero(builder, dtype), - *ctx->GetOrCreateAdd(dtype), - {expanded_filter_shape.dims() - 2}), + xla::Reduce(masked_expanded_filter, XlaHelpers::Zero(builder, dtype), + *ctx->GetOrCreateAdd(dtype), + {expanded_filter_shape.dims() - 2}), filter_shape.dim_sizes()); } @@ -289,8 +286,8 @@ class ConvOp : public XlaOpKernel { } xla::XlaOp conv = - b->ConvGeneralDilated(ctx->Input(0), filter, window_strides, padding, - lhs_dilation, rhs_dilation, dims); + xla::ConvGeneralDilated(ctx->Input(0), filter, window_strides, padding, + lhs_dilation, rhs_dilation, dims); ctx->SetOutput(0, conv); } @@ -435,11 +432,11 @@ class ConvBackpropInputOp : public XlaOpKernel { } // Mirror the filter in the spatial dimensions. - xla::XlaOp mirrored_weights = b->Rev(filter, kernel_spatial_dims); + xla::XlaOp mirrored_weights = xla::Rev(filter, kernel_spatial_dims); // activation gradients // = gradients (with padding and dilation) mirrored_weights - xla::XlaOp in_backprop = b->ConvGeneralDilated( + xla::XlaOp in_backprop = xla::ConvGeneralDilated( out_backprop, mirrored_weights, /*window_strides=*/ones, padding, lhs_dilation, rhs_dilation, dnums); @@ -638,8 +635,8 @@ class ConvBackpropFilterOp : public XlaOpKernel { // This is done by specifying the window dilation factors in the // convolution HLO below. auto filter_backprop = - b->ConvGeneralDilated(activations, gradients, window_strides, padding, - /*lhs_dilation=*/ones, rhs_dilation, dnums); + xla::ConvGeneralDilated(activations, gradients, window_strides, padding, + /*lhs_dilation=*/ones, rhs_dilation, dnums); if (depthwise_) { filter_backprop = ContractFilterForDepthwiseBackprop( diff --git a/tensorflow/compiler/tf2xla/kernels/cross_op.cc b/tensorflow/compiler/tf2xla/kernels/cross_op.cc index 7fcd4170fb79a574663c1abffe873d4b53f471d3..500a564f3f0489a42dbc9d5b70ae7708a7a43973 100644 --- a/tensorflow/compiler/tf2xla/kernels/cross_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cross_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -58,21 +59,21 @@ class CrossOp : public XlaOpKernel { auto in1 = ctx->Input(1); starts.back() = 0; limits.back() = 1; - auto u1 = b->Slice(in0, starts, limits, strides); - auto v1 = b->Slice(in1, starts, limits, strides); + auto u1 = xla::Slice(in0, starts, limits, strides); + auto v1 = xla::Slice(in1, starts, limits, strides); starts.back() = 1; limits.back() = 2; - auto u2 = b->Slice(in0, starts, limits, strides); - auto v2 = b->Slice(in1, starts, limits, strides); + auto u2 = xla::Slice(in0, starts, limits, strides); + auto v2 = xla::Slice(in1, starts, limits, strides); starts.back() = 2; limits.back() = 3; - auto u3 = b->Slice(in0, starts, limits, strides); - auto v3 = b->Slice(in1, starts, limits, strides); + auto u3 = xla::Slice(in0, starts, limits, strides); + auto v3 = xla::Slice(in1, starts, limits, strides); - auto s1 = b->Sub(b->Mul(u2, v3), b->Mul(u3, v2)); - auto s2 = b->Sub(b->Mul(u3, v1), b->Mul(u1, v3)); - auto s3 = b->Sub(b->Mul(u1, v2), b->Mul(u2, v1)); - auto output = b->ConcatInDim({s1, s2, s3}, in0_shape.dims() - 1); + auto s1 = xla::Sub(xla::Mul(u2, v3), xla::Mul(u3, v2)); + auto s2 = xla::Sub(xla::Mul(u3, v1), xla::Mul(u1, v3)); + auto s3 = xla::Sub(xla::Mul(u1, v2), xla::Mul(u2, v1)); + auto output = xla::ConcatInDim(b, {s1, s2, s3}, in0_shape.dims() - 1); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc index 01aa1a83e7967921f1583b3ef18ec57e452dcfea..9ff3e0222831cb4339943966810eeae451e47a2c 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc @@ -96,18 +96,16 @@ void XlaBinaryOp::Compile(XlaOpKernelContext* ctx) { // First reshape the inputs, which should be a metadata-only // operation since we are flattening the dimensions in order. - auto lhs_shaped = builder->Reshape(lhs, broadcast_helper.x_reshape()); - auto rhs_shaped = builder->Reshape(rhs, broadcast_helper.y_reshape()); + auto lhs_shaped = xla::Reshape(lhs, broadcast_helper.x_reshape()); + auto rhs_shaped = xla::Reshape(rhs, broadcast_helper.y_reshape()); // Next broadcast the necessary input dimensions. We rely on the // XLA optimizer to be smart about the fact that we are asking // it to broadcast size 1 on some of these dimensions, to avoid // adding complexity to this code. - auto lhs_broadcast = - builder->Broadcast(lhs_shaped, broadcast_helper.x_bcast()); + auto lhs_broadcast = xla::Broadcast(lhs_shaped, broadcast_helper.x_bcast()); int lhs_size = broadcast_helper.x_bcast().size(); - auto rhs_broadcast = - builder->Broadcast(rhs_shaped, broadcast_helper.y_bcast()); + auto rhs_broadcast = xla::Broadcast(rhs_shaped, broadcast_helper.y_bcast()); int rhs_size = broadcast_helper.y_bcast().size(); // Now reshape them to the correct output shape. After the @@ -122,15 +120,15 @@ void XlaBinaryOp::Compile(XlaOpKernelContext* ctx) { lhs_reorder.push_back(i); lhs_reorder.push_back(i + lhs_size); } - auto lhs_output = builder->Reshape(lhs_broadcast, lhs_reorder, - broadcast_helper.output_shape()); + auto lhs_output = + xla::Reshape(lhs_broadcast, lhs_reorder, broadcast_helper.output_shape()); std::vector rhs_reorder; for (int i = 0; i < rhs_size; ++i) { rhs_reorder.push_back(i); rhs_reorder.push_back(i + rhs_size); } - auto rhs_output = builder->Reshape(rhs_broadcast, rhs_reorder, - broadcast_helper.output_shape()); + auto rhs_output = + xla::Reshape(rhs_broadcast, rhs_reorder, broadcast_helper.output_shape()); return {lhs_output, rhs_output}; } diff --git a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc index 23243f62462c6315e359d9621823b19fc98c6218..f3149200250935629a6e4bf67bff0c048135ce3e 100644 --- a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { @@ -50,7 +51,6 @@ class DepthToSpaceOp : public XlaOpKernel { const gtl::InlinedVector input_shape = input_tensor_shape.dim_sizes(); - xla::XlaBuilder* b = ctx->builder(); xla::XlaOp input = ctx->Input(0); int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); @@ -130,7 +130,7 @@ class DepthToSpaceOp : public XlaOpKernel { ") is not divisible by square of the block size (", block_size_, ")")); - xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = xla::Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce // `permuted_reshaped` of shape: @@ -141,7 +141,7 @@ class DepthToSpaceOp : public XlaOpKernel { // input_shape[2], // block_size_, // depth / (block_size_ * block_size_)] - xla::XlaOp permuted_reshaped = b->Transpose(reshaped, transpose_order); + xla::XlaOp permuted_reshaped = xla::Transpose(reshaped, transpose_order); // 3. Reshape `permuted_reshaped` to flatten `block_shape` into the // batch dimension, producing an output tensor of shape: @@ -151,7 +151,7 @@ class DepthToSpaceOp : public XlaOpKernel { // input_shape[2] * block_size_, // depth / (block_size_ * block_size_)] // - xla::XlaOp output = b->Reshape(permuted_reshaped, output_shape); + xla::XlaOp output = xla::Reshape(permuted_reshaped, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc index 931705ba837153e1175cd9a209876ef5ec93f0fc..6dec414c53bee6b0102e229c86cfafb4072a35f0 100644 --- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc @@ -18,6 +18,9 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" @@ -25,10 +28,10 @@ namespace tensorflow { namespace { // Create a diagonal / batch diagonal matrix with 'input' on the diagonal. -xla::StatusOr CreateDiagonal( - const xla::XlaOp& input, int64 last_dim_size, - tensorflow::gtl::ArraySlice other_dims, XlaOpKernelContext* ctx, - xla::XlaBuilder* builder) { +xla::XlaOp CreateDiagonal(xla::XlaOp input, int64 last_dim_size, + gtl::ArraySlice other_dims, + xla::PrimitiveType element_type) { + xla::XlaBuilder* builder = input.builder(); // Create two matrices that have the following forms, and compare them: // // [[0, 0, 0, 0] [[0, 1, 2, 3] @@ -38,16 +41,14 @@ xla::StatusOr CreateDiagonal( // // This produces a predicate matrix of the right size, with "true" on the // diagonal. - xla::XlaOp iota; - TF_RETURN_IF_ERROR( - XlaHelpers::Iota(builder, DataType::DT_INT32, last_dim_size, &iota)); - xla::XlaOp iota_broadcast = builder->Broadcast(iota, {last_dim_size}); - xla::XlaOp mask = builder->Eq(iota_broadcast, iota, {0}); + xla::XlaOp iota = xla::Iota(builder, xla::S32, last_dim_size); + xla::XlaOp iota_broadcast = xla::Broadcast(iota, {last_dim_size}); + xla::XlaOp mask = xla::Eq(iota_broadcast, iota, {0}); // If this is a batched diagonal, broadcast the mask across the other // dimensions. if (!other_dims.empty()) { - mask = builder->Broadcast(mask, other_dims); + mask = xla::Broadcast(mask, other_dims); } // Broadcast the input, and then use the mask computed above to select the @@ -64,18 +65,15 @@ xla::StatusOr CreateDiagonal( std::vector broadcast_dims(other_dims.begin(), other_dims.end()); broadcast_dims.push_back(1LL); broadcast_dims.push_back(last_dim_size); - xla::XlaOp input_broadcast = builder->Reshape(input, broadcast_dims); + xla::XlaOp input_broadcast = xla::Reshape(input, broadcast_dims); broadcast_dims[broadcast_dims.size() - 2] = last_dim_size; - xla::PrimitiveType element_type; - TF_RETURN_IF_ERROR( - DataTypeToPrimitiveType(ctx->input_type(0), &element_type)); auto broadcast_shape = xla::ShapeUtil::MakeShape(element_type, broadcast_dims); - xla::XlaOp zeros = Zeros(builder, broadcast_shape); + xla::XlaOp zeros = xla::Zeros(builder, broadcast_shape); - input_broadcast = builder->Add(input_broadcast, zeros); - return builder->Select(mask, input_broadcast, zeros); + input_broadcast = xla::Add(input_broadcast, zeros); + return xla::Select(mask, input_broadcast, zeros); } class DiagOp : public XlaOpKernel { @@ -83,8 +81,6 @@ class DiagOp : public XlaOpKernel { explicit DiagOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - OP_REQUIRES(ctx, ctx->num_inputs() >= 1, errors::InvalidArgument("Diag op must have at an input")); const TensorShape input_shape = ctx->InputShape(0); @@ -104,19 +100,17 @@ class DiagOp : public XlaOpKernel { // Flattens the input to 1D. int64 size = input_shape.num_elements(); - input = builder->Reshape(input, {size}); + input = xla::Reshape(input, {size}); // Create an R2 with the R1 diagonal. - auto diag_or_status = - CreateDiagonal(input, size, /*other_dims=*/{}, ctx, builder); - OP_REQUIRES_OK(ctx, diag_or_status.status()); - xla::XlaOp diag = diag_or_status.ValueOrDie(); + xla::XlaOp diag = + CreateDiagonal(input, size, /*other_dims=*/{}, ctx->input_xla_type(0)); // Reshapes to the final shape. std::vector new_dims(dims.size() * 2); std::copy(dims.begin(), dims.end(), new_dims.begin()); std::copy(dims.begin(), dims.end(), new_dims.begin() + dims.size()); - diag = builder->Reshape(diag, new_dims); + diag = xla::Reshape(diag, new_dims); ctx->SetOutput(0, diag); } @@ -170,21 +164,21 @@ class DiagPartOp : public XlaOpKernel { // Flattens the input to 1D. int64 size = input_shape.num_elements(); - diag = builder->Reshape(diag, {size}); + diag = xla::Reshape(diag, {size}); // Adds padding after the last element of 'new_size'. xla::PaddingConfig config; auto* dim = config.add_dimensions(); dim->set_edge_padding_high(new_size); auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = builder->Pad(diag, zero, config); + diag = xla::Pad(diag, zero, config); // Reshapes so the diagonal is now in the first column. - diag = builder->Reshape(diag, {new_size, new_size + 1}); + diag = xla::Reshape(diag, {new_size, new_size + 1}); // Slices out the first column and reshapes to the final shape. - diag = builder->Slice(diag, {0, 0}, {new_size, 1}, {1, 1}); - diag = builder->Reshape(diag, new_dims); + diag = xla::Slice(diag, {0, 0}, {new_size, 1}, {1, 1}); + diag = xla::Reshape(diag, new_dims); ctx->SetOutput(0, diag); } @@ -197,8 +191,6 @@ class MatrixDiagOp : public XlaOpKernel { explicit MatrixDiagOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - OP_REQUIRES(ctx, ctx->num_inputs() >= 1, errors::InvalidArgument("MatrixDiag op must have at an input")); const TensorShape input_shape = ctx->InputShape(0); @@ -208,17 +200,15 @@ class MatrixDiagOp : public XlaOpKernel { errors::InvalidArgument("Expected 1 <= dims, got shape ", input_shape.DebugString())); - xla::XlaOp diag = ctx->Input(0); int last_dim = dims.size() - 1; int64 last_dim_size = input_shape.dim_size(last_dim); tensorflow::gtl::ArraySlice other_dims(dims); other_dims.pop_back(); - auto diag_or_status = - CreateDiagonal(diag, last_dim_size, other_dims, ctx, builder); - OP_REQUIRES_OK(ctx, diag_or_status.status()); - diag = diag_or_status.ValueOrDie(); + xla::XlaOp input = ctx->Input(0); + xla::XlaOp diag = CreateDiagonal(input, last_dim_size, other_dims, + ctx->input_xla_type(0)); ctx->SetOutput(0, diag); } }; @@ -265,7 +255,7 @@ class MatrixDiagPartOp : public XlaOpKernel { // Collapses the last two dimensions. std::vector flattened_dims(dims.begin(), dims.end() - 1); flattened_dims.back() *= dims.back(); - diag = builder->Reshape(diag, flattened_dims); + diag = xla::Reshape(diag, flattened_dims); // Slices or pads the last dimension to 'target_size'. int64 actual_size = flattened_dims.back(); @@ -276,13 +266,13 @@ class MatrixDiagPartOp : public XlaOpKernel { auto* dim = config.mutable_dimensions(flattened_dims.size() - 1); dim->set_edge_padding_high(target_size - actual_size); auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = builder->Pad(diag, zero, config); + diag = xla::Pad(diag, zero, config); } else if (actual_size > target_size) { std::vector start(flattened_dims.size(), 0); std::vector limits(flattened_dims.begin(), flattened_dims.end()); std::vector strides(flattened_dims.size(), 1); limits[flattened_dims.size() - 1] = target_size; - diag = builder->Slice(diag, start, limits, strides); + diag = xla::Slice(diag, start, limits, strides); } // Reshape so the target values are in the first position of the last @@ -290,18 +280,18 @@ class MatrixDiagPartOp : public XlaOpKernel { std::vector unflattened_dims(dims.begin(), dims.end()); dims[last_dim - 1] = smaller_dim_size; dims[last_dim] = last_dim_size + 1; - diag = builder->Reshape(diag, dims); + diag = xla::Reshape(diag, dims); // Slices out the first column and reshapes to the final shape. std::vector start(dims.size(), 0); std::vector limits(dims.begin(), dims.end()); std::vector strides(dims.size(), 1); limits[last_dim] = 1; - diag = builder->Slice(diag, start, limits, strides); + diag = xla::Slice(diag, start, limits, strides); // Collapses away the last dimension. dims.pop_back(); - diag = builder->Reshape(diag, dims); + diag = xla::Reshape(diag, dims); ctx->SetOutput(0, diag); } diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc index 0419de78b2ee83fd395e8bf23444fde84f30bba2..3b86ea34c9e7d943eb9c7de222e0a2be049ebc68 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc @@ -57,8 +57,8 @@ class DynamicUpdateSliceOp : public XlaOpKernel { input_shape.DebugString(), "; update shape is ", update_shape.DebugString())); - xla::XlaOp result = ctx->builder()->DynamicUpdateSlice( - ctx->Input(0), ctx->Input(1), ctx->Input(2)); + xla::XlaOp result = + xla::DynamicUpdateSlice(ctx->Input(0), ctx->Input(1), ctx->Input(2)); ctx->SetOutput(0, result); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc index dd4a16908779508380b36f43ce2306ff2f5fb8c4..958231505b50431b9bb267b0a3cc5ed56e3aeb21 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -150,8 +151,7 @@ class DynamicStitchOp : public XlaOpKernel { if (new_shape == data_shapes[input_num]) { input[input_num] = handle; } else { - input[input_num] = - ctx->builder()->Reshape(handle, new_shape.dim_sizes()); + input[input_num] = xla::Reshape(handle, new_shape.dim_sizes()); } } @@ -175,10 +175,10 @@ class DynamicStitchOp : public XlaOpKernel { // And place it in the concat list in the place indicated by // the index. to_concat[index_num] = - ctx->builder()->Slice(expression, slice_start, slice_limit, stride); + xla::Slice(expression, slice_start, slice_limit, stride); } - ctx->SetOutput(0, ctx->builder()->ConcatInDim(to_concat, 0)); + ctx->SetOutput(0, xla::ConcatInDim(ctx->builder(), to_concat, 0)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/elu_op.cc b/tensorflow/compiler/tf2xla/kernels/elu_op.cc index 493781a1e68b8906f1a7e018e5710130e2eb08b5..2c76bcee2593b820eafe09af3a52736ed8a92f86 100644 --- a/tensorflow/compiler/tf2xla/kernels/elu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/elu_op.cc @@ -34,9 +34,9 @@ class EluOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { xla::XlaBuilder* b = ctx->builder(); const auto zero = XlaHelpers::Zero(b, input_type(0)); - const auto pred = b->Gt(ctx->Input(0), zero); - const auto expm1 = b->Expm1(ctx->Input(0)); - ctx->SetOutput(0, b->Select(pred, ctx->Input(0), expm1)); + const auto pred = xla::Gt(ctx->Input(0), zero); + const auto expm1 = xla::Expm1(ctx->Input(0)); + ctx->SetOutput(0, xla::Select(pred, ctx->Input(0), expm1)); } }; @@ -51,9 +51,9 @@ class EluGradOp : public XlaOpKernel { const auto one = XlaHelpers::One(b, input_type(0)); const auto grad = ctx->Input(0); const auto activation = ctx->Input(1); - const auto exp_grad = b->Mul(grad, b->Add(activation, one)); - const auto pred = b->Gt(activation, zero); - ctx->SetOutput(0, b->Select(pred, grad, exp_grad)); + const auto exp_grad = xla::Mul(grad, xla::Add(activation, one)); + const auto pred = xla::Gt(activation, zero); + ctx->SetOutput(0, xla::Select(pred, grad, exp_grad)); } }; @@ -71,10 +71,10 @@ class SeluOp : public XlaOpKernel { 1.0507009873554804934193349852946); const auto scale_alpha = XlaHelpers::FloatLiteral(b, input_type(0), 1.7580993408473768599402175208123); - const auto pred = b->Gt(ctx->Input(0), zero); - const auto expm1 = b->Expm1(ctx->Input(0)); - ctx->SetOutput(0, b->Select(pred, b->Mul(scale, ctx->Input(0)), - b->Mul(scale_alpha, expm1))); + const auto pred = xla::Gt(ctx->Input(0), zero); + const auto expm1 = xla::Expm1(ctx->Input(0)); + ctx->SetOutput(0, xla::Select(pred, xla::Mul(scale, ctx->Input(0)), + xla::Mul(scale_alpha, expm1))); } }; @@ -92,10 +92,10 @@ class SeluGradOp : public XlaOpKernel { 1.7580993408473768599402175208123); const auto grad = ctx->Input(0); const auto activation = ctx->Input(1); - const auto lin_grad = b->Mul(grad, scale); - const auto exp_grad = b->Mul(grad, b->Add(activation, scale_alpha)); - const auto pred = b->Gt(activation, zero); - ctx->SetOutput(0, b->Select(pred, lin_grad, exp_grad)); + const auto lin_grad = xla::Mul(grad, scale); + const auto exp_grad = xla::Mul(grad, xla::Add(activation, scale_alpha)); + const auto pred = xla::Gt(activation, zero); + ctx->SetOutput(0, xla::Select(pred, lin_grad, exp_grad)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc index 6df01cabbf1d98c0299bfd808bcc6db6223c4777..65d42a302fca48c7b5f88813f80e975823f63ddf 100644 --- a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc @@ -17,6 +17,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { @@ -110,13 +112,11 @@ class ExtractImagePatchesOp : public XlaOpKernel { // Builds an identity matrix as a broadcast equality of iotas. // iota = np.arange(np.prod(ksize), depth) // filter = np.equal(np.reshape(iota, [-1, 1]), iota).astype(np.float32) - xla::XlaOp iota; - TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, - kernel_size * depth, &iota)); + xla::XlaOp iota = xla::Iota(builder, xla::S32, kernel_size * depth); - auto lhs = builder->Reshape(iota, lhs_shape); - auto filter = builder->ConvertElementType( - builder->Eq(lhs, iota, {num_spatial_dims + 1}), type); + auto lhs = xla::Reshape(iota, lhs_shape); + auto filter = xla::ConvertElementType( + xla::Eq(lhs, iota, {num_spatial_dims + 1}), type); xla::ConvolutionDimensionNumbers dims; std::vector window_strides(num_spatial_dims); @@ -148,8 +148,8 @@ class ExtractImagePatchesOp : public XlaOpKernel { } xla::XlaOp conv = - builder->ConvGeneralDilated(ctx->Input(0), filter, window_strides, - padding, lhs_dilation, rhs_dilation, dims); + xla::ConvGeneralDilated(ctx->Input(0), filter, window_strides, padding, + lhs_dilation, rhs_dilation, dims); ctx->SetOutput(0, conv); } diff --git a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc index 8f0de0a524c908b598c1a2165a462275346ad137..2fd1a34741e1c7235397f9a69dd8444b4679fa22 100644 --- a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { @@ -49,20 +50,20 @@ void XlaNudge(xla::XlaBuilder* b, const DataType data_type, const float quant_min_value, const float quant_max_value, xla::XlaOp* nudged_min, xla::XlaOp* nudged_max, xla::XlaOp* scale) { - *scale = b->Div(b->Sub(max, min), - XlaHelpers::FloatLiteral(b, data_type, - quant_max_value - quant_min_value)); + *scale = xla::Div(xla::Sub(max, min), + XlaHelpers::FloatLiteral( + b, data_type, quant_max_value - quant_min_value)); xla::XlaOp quant_min = XlaHelpers::FloatLiteral(b, data_type, quant_min_value); - xla::XlaOp zero_point_from_min = b->Sub(quant_min, b->Div(min, *scale)); + xla::XlaOp zero_point_from_min = xla::Sub(quant_min, xla::Div(min, *scale)); xla::XlaOp quant_max = XlaHelpers::FloatLiteral(b, data_type, quant_max_value); xla::XlaOp nudged_zero_point = - b->Select(b->Le(zero_point_from_min, quant_min), quant_min, - b->Select(b->Ge(zero_point_from_min, quant_max), quant_max, - b->Round(zero_point_from_min))); - *nudged_min = b->Mul(b->Sub(quant_min, nudged_zero_point), *scale); - *nudged_max = b->Mul(b->Sub(quant_max, nudged_zero_point), *scale); + xla::Select(xla::Le(zero_point_from_min, quant_min), quant_min, + xla::Select(xla::Ge(zero_point_from_min, quant_max), + quant_max, xla::Round(zero_point_from_min))); + *nudged_min = xla::Mul(xla::Sub(quant_min, nudged_zero_point), *scale); + *nudged_max = xla::Mul(xla::Sub(quant_max, nudged_zero_point), *scale); } xla::XlaOp Quantize(xla::XlaBuilder* b, const xla::XlaOp& input, @@ -71,14 +72,14 @@ xla::XlaOp Quantize(xla::XlaBuilder* b, const xla::XlaOp& input, const xla::XlaOp& nudged_input_max, const xla::XlaOp& input_scale) { xla::XlaOp one = XlaHelpers::FloatLiteral(b, data_type, 1.0f); - xla::XlaOp inv_scale = b->Div(one, input_scale); + xla::XlaOp inv_scale = xla::Div(one, input_scale); xla::XlaOp half = XlaHelpers::FloatLiteral(b, data_type, 0.5f); - xla::XlaOp clamped = b->Clamp(nudged_input_min, input, nudged_input_max); - xla::XlaOp clamped_shifted = b->Sub(clamped, nudged_input_min); + xla::XlaOp clamped = xla::Clamp(nudged_input_min, input, nudged_input_max); + xla::XlaOp clamped_shifted = xla::Sub(clamped, nudged_input_min); xla::XlaOp rounded = - b->Floor(b->Add(b->Mul(clamped_shifted, inv_scale), half)); - return b->Add(b->Mul(rounded, input_scale), nudged_input_min); + xla::Floor(xla::Add(xla::Mul(clamped_shifted, inv_scale), half)); + return xla::Add(xla::Mul(rounded, input_scale), nudged_input_min); } class FakeQuantWithMinMaxArgsOp : public XlaOpKernel { @@ -163,11 +164,11 @@ class FakeQuantWithMinMaxArgsGradOp : public XlaOpKernel { xla::XlaOp nudged_input_max = XlaHelpers::FloatLiteral(b, data_type, nudged_input_max_); - xla::XlaOp between_nudged_min_max = - b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); - xla::XlaOp zeroes = b->Broadcast(XlaHelpers::Zero(b, data_type), - gradient_shape.dim_sizes()); - xla::XlaOp output = b->Select(between_nudged_min_max, gradient, zeroes); + xla::XlaOp between_nudged_min_max = xla::And( + xla::Le(nudged_input_min, input), xla::Le(input, nudged_input_max)); + xla::XlaOp zeroes = xla::Broadcast(XlaHelpers::Zero(b, data_type), + gradient_shape.dim_sizes()); + xla::XlaOp output = xla::Select(between_nudged_min_max, gradient, zeroes); ctx->SetOutput(0, output); } @@ -249,25 +250,25 @@ class FakeQuantWithMinMaxVarsGradOp : public XlaOpKernel { XlaNudge(b, data_type, input_min, input_max, quant_min_, quant_max_, &nudged_input_min, &nudged_input_max, &input_scale); - xla::XlaOp between_nudged_min_max = - b->And(b->Le(nudged_input_min, input), b->Le(input, nudged_input_max)); + xla::XlaOp between_nudged_min_max = xla::And( + xla::Le(nudged_input_min, input), xla::Le(input, nudged_input_max)); xla::XlaOp zero = XlaHelpers::Zero(b, data_type); - xla::XlaOp zeroes = b->Broadcast(zero, gradient_shape.dim_sizes()); - xla::XlaOp output0 = b->Select(between_nudged_min_max, gradient, zeroes); + xla::XlaOp zeroes = xla::Broadcast(zero, gradient_shape.dim_sizes()); + xla::XlaOp output0 = xla::Select(between_nudged_min_max, gradient, zeroes); ctx->SetOutput(0, output0); - xla::XlaOp below_min = b->Lt(input, nudged_input_min); - xla::XlaOp select1 = b->Select(below_min, gradient, zeroes); - xla::XlaOp reduce1 = b->ReduceAll( + xla::XlaOp below_min = xla::Lt(input, nudged_input_min); + xla::XlaOp select1 = xla::Select(below_min, gradient, zeroes); + xla::XlaOp reduce1 = xla::ReduceAll( XlaHelpers::ConvertElementType(b, select1, accumulation_type), XlaHelpers::Zero(b, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type)); xla::XlaOp output1 = XlaHelpers::ConvertElementType(b, reduce1, data_type); ctx->SetOutput(1, output1); - xla::XlaOp above_max = b->Gt(input, nudged_input_max); - xla::XlaOp select2 = b->Select(above_max, gradient, zeroes); - xla::XlaOp reduce2 = b->ReduceAll( + xla::XlaOp above_max = xla::Gt(input, nudged_input_max); + xla::XlaOp select2 = xla::Select(above_max, gradient, zeroes); + xla::XlaOp reduce2 = xla::ReduceAll( XlaHelpers::ConvertElementType(b, select2, accumulation_type), XlaHelpers::Zero(b, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type)); diff --git a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc index 933924cad1c7cac2879bd4720cb21ffc33c23f50..b2b00e51e3b00fa93c258af489cf0f4a3e6e764b 100644 --- a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -62,8 +63,7 @@ class GenericFftOp : public XlaOpKernel { } } - xla::XlaBuilder* b = ctx->builder(); - xla::XlaOp fft = b->Fft(ctx->Input(0), fft_type_, fft_length); + xla::XlaOp fft = xla::Fft(ctx->Input(0), fft_type_, fft_length); ctx->SetOutput(0, fft); } diff --git a/tensorflow/compiler/tf2xla/kernels/fill_op.cc b/tensorflow/compiler/tf2xla/kernels/fill_op.cc index e4467a0fb138ed7919af62ed032c0f5abee3e4f6..95faa1d058f4c0d3fa802b157c6daba1e1adaf41 100644 --- a/tensorflow/compiler/tf2xla/kernels/fill_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/fill_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" @@ -59,11 +60,11 @@ class FillOp : public XlaOpKernel { xla::XlaOp data = ctx->Input(1); if (value_shape.dims() > 0) { CHECK_EQ(value_shape.dims(), 1); - data = ctx->builder()->Reshape(data, {}); + data = xla::Reshape(data, {}); } // Emit the actual computation, which broadcasts the scalar to the // desired shape. - auto result = ctx->builder()->Broadcast(data, broadcast); + auto result = xla::Broadcast(data, broadcast); ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index d13e25bcddae16d0cd630403219657121b80868d..5f041be5df226ed996b21844c0cf92b6dfac005c 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" @@ -75,8 +76,8 @@ Status XlaGather(const xla::XlaOp& input, const TensorShape& input_shape, out_shape.AppendShape(indices_shape_no_index_vectors); out_shape.AppendShape(input_shape_post_axis); - *gather_output = builder->Broadcast(XlaHelpers::Zero(builder, dtype), - out_shape.dim_sizes()); + *gather_output = + xla::Broadcast(XlaHelpers::Zero(builder, dtype), out_shape.dim_sizes()); return Status::OK(); } @@ -142,7 +143,7 @@ Status XlaGather(const xla::XlaOp& input, const TensorShape& input_shape, dim_numbers.add_gather_dims_to_operand_dims(i); } - *gather_output = builder->Gather(input, indices, dim_numbers, window_bounds); + *gather_output = xla::Gather(input, indices, dim_numbers, window_bounds); return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/kernels/if_op.cc b/tensorflow/compiler/tf2xla/kernels/if_op.cc index 8b9b026643cf35216a2082dfcce9270c017bd14f..f5fcf3cacdbff8297bc42fcb0cf79c2bc83a4e11 100644 --- a/tensorflow/compiler/tf2xla/kernels/if_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/if_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { @@ -48,11 +49,11 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { VLOG(1) << "Building If: " << input_types_.size() << " inputs"; - std::vector inputs(input_types_.size()); std::vector arguments(input_types_.size()); for (int i = 0; i < input_types_.size(); ++i) { XlaCompiler::Argument& arg = arguments[i]; DataType type = ctx->input_type(i + 1); + if (type == DT_RESOURCE) { XlaResource* resource; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(i + 1, &resource)); @@ -60,7 +61,6 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { arg.initialized = resource->initialized(); arg.kind = XlaCompiler::Argument::kResource; arg.resource_kind = resource->kind(); - OP_REQUIRES_OK(ctx, resource->Pack(&inputs[i], b)); arg.type = resource->type(); arg.shape = resource->shape(); @@ -79,7 +79,6 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { arg.kind = XlaCompiler::Argument::kParameter; arg.type = input_types_[i]; arg.shape = ctx->InputShape(i + 1); - inputs[i] = ctx->Input(i + 1); VLOG(2) << "Arg type: " << DataTypeString(arg.type) << " shape: " << arg.shape.DebugString(); } @@ -100,6 +99,7 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { OP_REQUIRES_OK(ctx, compiler->CompileFunction(options, else_branch_, arguments, &else_result)); + bool has_tensor_array_gradients = false; for (XlaCompiler::CompilationResult* result : {&then_result, &else_result}) { for (const XlaCompiler::ResourceUpdate& update : result->resource_updates) { XlaResource* resource; @@ -121,9 +121,21 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { for (const auto& gradient : resource->tensor_array_gradients()) { arg.tensor_array_gradients.insert(gradient.first); } + if (!resource->tensor_array_gradients().empty()) + has_tensor_array_gradients = true; } } + // Recompile the functions to update the argument shapes for tensor arrays. + if (has_tensor_array_gradients) { + then_result = {}; + OP_REQUIRES_OK(ctx, compiler->CompileFunction(options, then_branch_, + arguments, &then_result)); + else_result = {}; + OP_REQUIRES_OK(ctx, compiler->CompileFunction(options, else_branch_, + arguments, &else_result)); + } + // Check that both branches have identical input shapes. OP_REQUIRES(ctx, then_result.xla_input_shapes.size() == 1, errors::FailedPrecondition("Expected one input shape")); @@ -175,13 +187,26 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { "Mismatch in resource of then and else branch for resource ", i)); } - xla::XlaOp outputs = - b->Conditional(ctx->Input(0), b->Tuple(inputs), *then_result.computation, - b->Tuple(inputs), *else_result.computation); + int num_inputs = then_result.input_mapping.size(); + std::vector inputs(num_inputs); + for (int i = 0; i < num_inputs; ++i) { + int input_num = then_result.input_mapping[i] + 1; + if (ctx->input_type(input_num) == DT_RESOURCE) { + XlaResource* resource; + OP_REQUIRES_OK(ctx, ctx->GetResourceInput(input_num, &resource)); + OP_REQUIRES_OK(ctx, resource->Pack(&inputs[i], b)); + } else { + inputs[i] = ctx->Input(i + 1); + } + } + + xla::XlaOp outputs = xla::Conditional( + ctx->Input(0), xla::Tuple(b, inputs), *then_result.computation, + xla::Tuple(b, inputs), *else_result.computation); // Sets non-variable outputs. for (int i = 0; i < output_types_.size(); ++i) { if (ctx->input_type(i) != DT_RESOURCE) { - xla::XlaOp output_handle = b->GetTupleElement(outputs, i); + xla::XlaOp output_handle = xla::GetTupleElement(outputs, i); if (VLOG_IS_ON(2)) { LOG(INFO) << "Setting output " << i; auto shape_or = b->GetShape(output_handle); @@ -209,7 +234,7 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { OP_REQUIRES_OK(ctx, resource->SetFromPack( arguments[update.input_index].tensor_array_gradients, - b->GetTupleElement(outputs, pos), b)); + xla::GetTupleElement(outputs, pos), b)); } VLOG(2) << "If variable: pos: " << update.input_index << " name: " << resource->name() diff --git a/tensorflow/compiler/tf2xla/kernels/image_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_ops.cc index 1568b33679963c1a6630525f60560180d40b8d53..cb4caf7bcb4caaa1bf7e0e79e52bb966a8838db3 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_ops.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -32,23 +33,26 @@ std::array RGBToHSV(XlaOpKernelContext* ctx, xla::XlaBuilder* b, auto red = rgb[0]; auto green = rgb[1]; auto blue = rgb[2]; - auto value = b->Max(b->Max(red, green), blue); - auto minimum = b->Min(b->Min(red, green), blue); - auto range = b->Sub(value, minimum); - - auto zeros = b->Broadcast(zero, shape.dim_sizes()); - auto saturation = b->Select(b->Gt(value, zero), b->Div(range, value), zeros); - - auto norm = b->Div(XlaHelpers::FloatLiteral(b, dtype, 1.0 / 6.0), range); - - auto hue = b->Select(b->Eq(green, value), - b->Add(b->Mul(norm, b->Sub(blue, red)), - XlaHelpers::FloatLiteral(b, dtype, 2.0 / 6.0)), - b->Add(b->Mul(norm, b->Sub(red, green)), - XlaHelpers::FloatLiteral(b, dtype, 4.0 / 6.0))); - hue = b->Select(b->Eq(red, value), b->Mul(norm, b->Sub(green, blue)), hue); - hue = b->Select(b->Gt(range, zero), hue, zeros); - hue = b->Select(b->Lt(hue, zero), b->Add(hue, one), hue); + auto value = xla::Max(xla::Max(red, green), blue); + auto minimum = xla::Min(xla::Min(red, green), blue); + auto range = xla::Sub(value, minimum); + + auto zeros = xla::Broadcast(zero, shape.dim_sizes()); + auto saturation = + xla::Select(xla::Gt(value, zero), xla::Div(range, value), zeros); + + auto norm = xla::Div(XlaHelpers::FloatLiteral(b, dtype, 1.0 / 6.0), range); + + auto hue = + xla::Select(xla::Eq(green, value), + xla::Add(xla::Mul(norm, xla::Sub(blue, red)), + XlaHelpers::FloatLiteral(b, dtype, 2.0 / 6.0)), + xla::Add(xla::Mul(norm, xla::Sub(red, green)), + XlaHelpers::FloatLiteral(b, dtype, 4.0 / 6.0))); + hue = xla::Select(xla::Eq(red, value), xla::Mul(norm, xla::Sub(green, blue)), + hue); + hue = xla::Select(xla::Gt(range, zero), hue, zeros); + hue = xla::Select(xla::Lt(hue, zero), xla::Add(hue, one), hue); return {hue, saturation, value}; } @@ -66,15 +70,15 @@ std::array HSVToRGB(xla::XlaBuilder* b, auto four = XlaHelpers::FloatLiteral(b, dtype, 4.0); auto six = XlaHelpers::FloatLiteral(b, dtype, 6.0); - auto dh = b->Mul(hue, six); - auto dr = b->Clamp(zero, b->Sub(b->Abs(b->Sub(dh, three)), one), one); - auto dg = b->Clamp(zero, b->Sub(two, b->Abs(b->Sub(dh, two))), one); - auto db = b->Clamp(zero, b->Sub(two, b->Abs(b->Sub(dh, four))), one); - auto one_minus_s = b->Sub(one, saturation); + auto dh = xla::Mul(hue, six); + auto dr = xla::Clamp(zero, xla::Sub(xla::Abs(xla::Sub(dh, three)), one), one); + auto dg = xla::Clamp(zero, xla::Sub(two, xla::Abs(xla::Sub(dh, two))), one); + auto db = xla::Clamp(zero, xla::Sub(two, xla::Abs(xla::Sub(dh, four))), one); + auto one_minus_s = xla::Sub(one, saturation); - auto red = b->Mul(b->Add(one_minus_s, b->Mul(saturation, dr)), value); - auto green = b->Mul(b->Add(one_minus_s, b->Mul(saturation, dg)), value); - auto blue = b->Mul(b->Add(one_minus_s, b->Mul(saturation, db)), value); + auto red = xla::Mul(xla::Add(one_minus_s, xla::Mul(saturation, dr)), value); + auto green = xla::Mul(xla::Add(one_minus_s, xla::Mul(saturation, dg)), value); + auto blue = xla::Mul(xla::Add(one_minus_s, xla::Mul(saturation, db)), value); return {red, green, blue}; } @@ -97,21 +101,21 @@ class RGBToHSVOp : public XlaOpKernel { xla::XlaBuilder* b = context->builder(); xla::XlaOp input = context->Input(0); - xla::XlaOp red = - b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp green = - b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp blue = - b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, - /*dimno=*/channel_dim); + xla::XlaOp red = xla::SliceInDim(input, /*start_index=*/0, + /*limit_index=*/1, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp green = xla::SliceInDim(input, /*start_index=*/1, + /*limit_index=*/2, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp blue = xla::SliceInDim(input, /*start_index=*/2, + /*limit_index=*/3, /*stride=*/1, + /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; channel_shape.set_dim(channel_dim, 1); auto hsv = RGBToHSV(context, b, {red, green, blue}, context->input_type(0), channel_shape); - context->SetOutput(0, b->ConcatInDim(hsv, channel_dim)); + context->SetOutput(0, xla::ConcatInDim(b, hsv, channel_dim)); } }; REGISTER_XLA_OP(Name("RGBToHSV"), RGBToHSVOp); @@ -134,20 +138,20 @@ class HSVToRGBOp : public XlaOpKernel { xla::XlaBuilder* b = context->builder(); xla::XlaOp input = context->Input(0); - xla::XlaOp hue = - b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp saturation = - b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp value = - b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, - /*dimno=*/channel_dim); + xla::XlaOp hue = xla::SliceInDim(input, /*start_index=*/0, + /*limit_index=*/1, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp saturation = xla::SliceInDim(input, /*start_index=*/1, + /*limit_index=*/2, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp value = xla::SliceInDim(input, /*start_index=*/2, + /*limit_index=*/3, /*stride=*/1, + /*dimno=*/channel_dim); auto rgb = HSVToRGB(context->builder(), {hue, saturation, value}, context->input_type(0)); - context->SetOutput(0, b->ConcatInDim(rgb, channel_dim)); + context->SetOutput(0, xla::ConcatInDim(b, rgb, channel_dim)); } }; REGISTER_XLA_OP(Name("HSVToRGB"), HSVToRGBOp); @@ -182,18 +186,20 @@ class AdjustContrastOpV2 : public XlaOpKernel { const DataType accumulation_type = XlaHelpers::SumAccumulationType(type); auto converted = XlaHelpers::ConvertElementType(b, input, accumulation_type); - auto reduce = b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *context->GetOrCreateAdd(accumulation_type), - {height_dim, width_dim}); + auto reduce = xla::Reduce(converted, XlaHelpers::Zero(b, accumulation_type), + *context->GetOrCreateAdd(accumulation_type), + {height_dim, width_dim}); auto output = XlaHelpers::ConvertElementType(b, reduce, type); - output = b->Div(output, XlaHelpers::FloatLiteral(b, type, height * width)); + output = + xla::Div(output, XlaHelpers::FloatLiteral(b, type, height * width)); std::vector broadcast_dims(input_shape.dims() - 2); std::iota(broadcast_dims.begin(), broadcast_dims.end(), 0); broadcast_dims.back() = channel_dim; - output = b->Add(b->Mul(input, factor), - b->Mul(output, b->Sub(XlaHelpers::One(b, type), factor)), - broadcast_dims); + output = + xla::Add(xla::Mul(input, factor), + xla::Mul(output, xla::Sub(XlaHelpers::One(b, type), factor)), + broadcast_dims); context->SetOutput(0, output); } }; @@ -226,26 +232,26 @@ class AdjustSaturationOp : public XlaOpKernel { DataType type = context->input_type(0); - xla::XlaOp red = - b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp green = - b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp blue = - b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, - /*dimno=*/channel_dim); + xla::XlaOp red = xla::SliceInDim(input, /*start_index=*/0, + /*limit_index=*/1, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp green = xla::SliceInDim(input, /*start_index=*/1, + /*limit_index=*/2, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp blue = xla::SliceInDim(input, /*start_index=*/2, + /*limit_index=*/3, /*stride=*/1, + /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; channel_shape.set_dim(channel_dim, 1); auto hsv = RGBToHSV(context, b, {red, green, blue}, context->input_type(0), channel_shape); - hsv[1] = b->Clamp(XlaHelpers::Zero(b, type), b->Mul(hsv[1], scale), - XlaHelpers::One(b, type)); + hsv[1] = xla::Clamp(XlaHelpers::Zero(b, type), xla::Mul(hsv[1], scale), + XlaHelpers::One(b, type)); auto rgb = HSVToRGB(context->builder(), hsv, context->input_type(0)); - context->SetOutput(0, b->ConcatInDim(rgb, channel_dim)); + context->SetOutput(0, xla::ConcatInDim(b, rgb, channel_dim)); } }; REGISTER_XLA_OP(Name("AdjustSaturation"), AdjustSaturationOp); @@ -276,15 +282,15 @@ class AdjustHueOp : public XlaOpKernel { DataType type = context->input_type(0); - xla::XlaOp red = - b->SliceInDim(input, /*start_index=*/0, /*limit_index=*/1, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp green = - b->SliceInDim(input, /*start_index=*/1, /*limit_index=*/2, /*stride=*/1, - /*dimno=*/channel_dim); - xla::XlaOp blue = - b->SliceInDim(input, /*start_index=*/2, /*limit_index=*/3, /*stride=*/1, - /*dimno=*/channel_dim); + xla::XlaOp red = xla::SliceInDim(input, /*start_index=*/0, + /*limit_index=*/1, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp green = xla::SliceInDim(input, /*start_index=*/1, + /*limit_index=*/2, /*stride=*/1, + /*dimno=*/channel_dim); + xla::XlaOp blue = xla::SliceInDim(input, /*start_index=*/2, + /*limit_index=*/3, /*stride=*/1, + /*dimno=*/channel_dim); TensorShape channel_shape = input_shape; channel_shape.set_dim(channel_dim, 1); auto hsv = RGBToHSV(context, b, {red, green, blue}, context->input_type(0), @@ -294,12 +300,13 @@ class AdjustHueOp : public XlaOpKernel { auto one = XlaHelpers::One(b, type); auto& hue = hsv[0]; - hue = b->Rem(b->Add(hsv[0], delta), one); - hue = b->Select(b->Lt(hue, zero), b->Rem(b->Add(one, hue), one), hue); + hue = xla::Rem(xla::Add(hsv[0], delta), one); + hue = + xla::Select(xla::Lt(hue, zero), xla::Rem(xla::Add(one, hue), one), hue); auto rgb = HSVToRGB(context->builder(), hsv, context->input_type(0)); - context->SetOutput(0, b->ConcatInDim(rgb, channel_dim)); + context->SetOutput(0, xla::ConcatInDim(b, rgb, channel_dim)); } }; REGISTER_XLA_OP(Name("AdjustHue"), AdjustHueOp); diff --git a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc index 79d3a6979cec4c6bda92a71dcff4ddd2151367d5..d6bf92fb3df8d38909df99e11c85ede4fac2bf81 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc @@ -18,6 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/array4d.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/math/math_util.h" @@ -127,48 +129,41 @@ const int64 kMax2DKernelSize = 16; xla::XlaOp MakeBilinearResizeKernel(xla::XlaBuilder* builder, gtl::ArraySlice kernel_size, int64 channels) { - xla::XlaOp channels_iota; - // DT_INT32 Iota will always return status::OK(). - TF_CHECK_OK( - XlaHelpers::Iota(builder, DataType::DT_INT32, channels, &channels_iota)); - - auto diag = builder->ConvertElementType( - builder->Eq( - builder->Broadcast(channels_iota, {2 * kernel_size[0] - 1, + xla::XlaOp channels_iota = xla::Iota(builder, xla::S32, channels); + + auto diag = xla::ConvertElementType( + xla::Eq(xla::Broadcast(channels_iota, {2 * kernel_size[0] - 1, 2 * kernel_size[1] - 1, channels}), - channels_iota, /*broadcast_dimensions=*/{2}), + channels_iota, /*broadcast_dimensions=*/{2}), xla::PrimitiveType::F32); - return builder->Mul( - builder->Mul(diag, - builder->ConstantR1(Make1DKernel(kernel_size[1])), - /*broadcast_dimensions=*/{1}), - builder->ConstantR1(Make1DKernel(kernel_size[0])), + return xla::Mul( + xla::Mul(diag, + xla::ConstantR1(builder, Make1DKernel(kernel_size[1])), + /*broadcast_dimensions=*/{1}), + xla::ConstantR1(builder, Make1DKernel(kernel_size[0])), /*broadcast_dimensions=*/{0}); } xla::XlaOp MakeBilinearResizeKernelInDim(xla::XlaBuilder* builder, gtl::ArraySlice kernel_size, int64 channels, int64 dim) { - xla::XlaOp channels_iota; - // DT_INT32 Iota will always return status::OK(). - TF_CHECK_OK( - XlaHelpers::Iota(builder, DataType::DT_INT32, channels, &channels_iota)); - - auto diag = builder->ConvertElementType( - builder->Eq(builder->Broadcast( - channels_iota, - {dim == 0 ? (2 * kernel_size[0] - 1) : 1, - dim == 1 ? (2 * kernel_size[1] - 1) : 1, channels}), - channels_iota, /*broadcast_dimensions=*/{2}), + xla::XlaOp channels_iota = xla::Iota(builder, xla::S32, channels); + + auto diag = xla::ConvertElementType( + xla::Eq( + xla::Broadcast(channels_iota, + {dim == 0 ? (2 * kernel_size[0] - 1) : 1, + dim == 1 ? (2 * kernel_size[1] - 1) : 1, channels}), + channels_iota, /*broadcast_dimensions=*/{2}), xla::PrimitiveType::F32); if (dim == 1) { - return builder->Mul( - diag, builder->ConstantR1(Make1DKernel(kernel_size[1])), + return xla::Mul( + diag, xla::ConstantR1(builder, Make1DKernel(kernel_size[1])), /*broadcast_dimensions=*/{1}); } - return builder->Mul(diag, - builder->ConstantR1(Make1DKernel(kernel_size[0])), - /*broadcast_dimensions=*/{0}); + return xla::Mul(diag, + xla::ConstantR1(builder, Make1DKernel(kernel_size[0])), + /*broadcast_dimensions=*/{0}); } xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, @@ -208,7 +203,7 @@ xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, if (dims.kernel_size[0] * dims.kernel_size[1] < kMax2DKernelSize) { xla::XlaOp kernel = MakeBilinearResizeKernel(builder, dims.kernel_size, channels); - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( input, kernel, dims.stride, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, @@ -218,7 +213,7 @@ xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, } else { xla::XlaOp kernel0 = MakeBilinearResizeKernelInDim(builder, dims.kernel_size, channels, 0); - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( input, kernel0, {dims.stride[0], 1}, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, {0, 0}}, @@ -226,7 +221,7 @@ xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, /*rhs_dilation=*/{1, 1}, dimension_numbers); xla::XlaOp kernel1 = MakeBilinearResizeKernelInDim(builder, dims.kernel_size, channels, 1); - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( output, kernel1, {1, dims.stride[1]}, /*padding=*/ {{0, 0}, {dims.kernel_size[1] - 1, dims.kernel_size[1] - 1}}, @@ -238,8 +233,8 @@ xla::XlaOp ResizeUsingDilationAndConvolution(xla::XlaBuilder* builder, // size > 1 dimension. for (int i = 0; i < num_spatial_dims; ++i) { if (in_size[i] == 1 && out_size[i] > 1) { - output = builder->Add(output, builder->ConstantR1(out_size[i], 0), - /*broadcast_dimensions=*/{1 + i}); + output = xla::Add(output, xla::ConstantR1(builder, out_size[i], 0), + /*broadcast_dimensions=*/{1 + i}); } } return output; @@ -279,12 +274,12 @@ xla::XlaOp ResizeUsingDilationAndConvolutionGradOp(xla::XlaBuilder* builder, for (int i = 0; i < num_spatial_dims; ++i) { if (in_size[i] == 1 && grad_size[i] > 1) { kernel = - builder->Add(kernel, builder->ConstantR1(grad_size[i], 0), - /*broadcast_dimensions=*/{i}); + xla::Add(kernel, xla::ConstantR1(builder, grad_size[i], 0), + /*broadcast_dimensions=*/{i}); } } - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( grad, kernel, /*window_strides=*/dims.kernel_size, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, @@ -302,23 +297,23 @@ xla::XlaOp ResizeUsingDilationAndConvolutionGradOp(xla::XlaBuilder* builder, // gradient contributions in that dimension. if (in_size[0] == 1 && grad_size[0] > 1) { kernel0 = - builder->Add(kernel0, builder->ConstantR1(grad_size[0], 0), - /*broadcast_dimensions=*/{0}); + xla::Add(kernel0, xla::ConstantR1(builder, grad_size[0], 0), + /*broadcast_dimensions=*/{0}); } if (in_size[1] == 1 && grad_size[1] > 1) { kernel1 = - builder->Add(kernel0, builder->ConstantR1(grad_size[1], 0), - /*broadcast_dimensions=*/{1}); + xla::Add(kernel0, xla::ConstantR1(builder, grad_size[1], 0), + /*broadcast_dimensions=*/{1}); } - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( grad, kernel0, /*window_strides=*/{dims.kernel_size[0], 1}, /*padding=*/ {{dims.kernel_size[0] - 1, dims.kernel_size[0] - 1}, {0, 0}}, /*lhs_dilation=*/{dims.stride[0], 1}, /*rhs_dilation=*/{1, 1}, dimension_numbers); - output = builder->ConvGeneralDilated( + output = xla::ConvGeneralDilated( output, kernel1, /*window_strides=*/{1, dims.kernel_size[1]}, /*padding=*/ {{0, 0}, {dims.kernel_size[1] - 1, dims.kernel_size[1] - 1}}, @@ -337,7 +332,7 @@ xla::XlaOp ResizeUsingDilationAndConvolutionGradOp(xla::XlaBuilder* builder, } } if (pad_output) { - output = builder->Pad(output, builder->ConstantR0(0.0f), padding); + output = xla::Pad(output, xla::ConstantR0(builder, 0.0f), padding); } return output; } @@ -393,13 +388,13 @@ class ResizeBilinearOp : public XlaOpKernel { } } if (slice_input) { - input = b->Slice(input, {0, 0, 0, 0}, - {batch, slice_size[0], slice_size[1], channels}, - {1, 1, 1, 1}); + input = xla::Slice(input, {0, 0, 0, 0}, + {batch, slice_size[0], slice_size[1], channels}, + {1, 1, 1, 1}); } // Output is always type float. - input = b->ConvertElementType(input, xla::F32); + input = xla::ConvertElementType(input, xla::F32); // Special Case: // Instead of doing a ResizeUsingDilationAndConvolution directly, @@ -529,7 +524,7 @@ class ResizeBilinearGradOp : public XlaOpKernel { } } - output = b->ConvertElementType(output, output_type_); + output = xla::ConvertElementType(output, output_type_); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops.cc b/tensorflow/compiler/tf2xla/kernels/index_ops.cc index 36eb4c75454ed82804c40b82e5dbaec2eef0a719..f3964748587c1b31cf8b1b76643ff19a9044bf44 100644 --- a/tensorflow/compiler/tf2xla/kernels/index_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/index_ops.cc @@ -60,19 +60,15 @@ void XlaArgMinMaxOp::Compile(XlaOpKernelContext* ctx) { input_shape.DebugString())); DataType index_type = output_type(0); + xla::PrimitiveType index_xla_type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(index_type, &index_xla_type)); - xla::XlaBuilder* b = ctx->builder(); xla::XlaOp input = ctx->Input(0); - xla::XlaOp output; if (is_min_) { - OP_REQUIRES_OK(ctx, - XlaHelpers::ArgMin(b, ctx, input, input_shape, input_type(0), - index_type, axis, &output)); + output = XlaHelpers::ArgMin(input, index_xla_type, axis); } else { - OP_REQUIRES_OK(ctx, - XlaHelpers::ArgMax(b, ctx, input, input_shape, input_type(0), - index_type, axis, &output)); + output = XlaHelpers::ArgMax(input, index_xla_type, axis); } ctx->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc index 2c2d88486fda99d2380382a3e2f633f5bdc7478c..a020ebc729e4c07d1b182cc0585ba0f2bca46403 100644 --- a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc +++ b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -76,14 +77,15 @@ class ArgMaxCustomCallOp : public XlaOpKernel { // XLA passes to the function, so it is not included here. std::vector args; args.push_back(ctx->Input(0)); - args.push_back(b.ConstantLiteral( - *xla::Literal::CreateR1(input_shape.dim_sizes()))); + args.push_back(xla::ConstantLiteral( + &b, *xla::Literal::CreateR1(input_shape.dim_sizes()))); if (input_shape.dims() > 1) { // Don't bother passing the output shape and dim for the 1d case, since // the shape is always a scalar and the dim is always 0. - args.push_back(b.ConstantLiteral( - *xla::Literal::CreateR1(output_shape.dim_sizes()))); - args.push_back(b.ConstantLiteral(*xla::Literal::CreateR0(dim))); + args.push_back(xla::ConstantLiteral( + &b, *xla::Literal::CreateR1(output_shape.dim_sizes()))); + args.push_back( + xla::ConstantLiteral(&b, *xla::Literal::CreateR0(dim))); } xla::Shape xla_shape = @@ -94,10 +96,12 @@ class ArgMaxCustomCallOp : public XlaOpKernel { xla::XlaOp output; switch (input_shape.dims()) { case 1: - output = b.CustomCall("argmax_float_1d_xla_impl", args, xla_shape); + output = + xla::CustomCall(&b, "argmax_float_1d_xla_impl", args, xla_shape); break; case 2: - output = b.CustomCall("argmax_float_2d_xla_impl", args, xla_shape); + output = + xla::CustomCall(&b, "argmax_float_2d_xla_impl", args, xla_shape); break; default: OP_REQUIRES(ctx, false, diff --git a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc index 1decf7d72d72bb697477e7f841ced2a1a0d5fbe9..9e64711051d31107db1bf6f1966f9ed6f5630c34 100644 --- a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc @@ -39,12 +39,12 @@ class L2LossOp : public XlaOpKernel { const DataType accumulation_type = XlaHelpers::SumAccumulationType(dtype); auto t = XlaHelpers::ConvertElementType(b, ctx->Input(0), accumulation_type); - auto square = b->Mul(t, t); - auto reduce = b->Reduce(square, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), dims); + auto square = xla::Mul(t, t); + auto reduce = xla::Reduce(square, XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), dims); auto deconverted = XlaHelpers::ConvertElementType(b, reduce, dtype); auto two = XlaHelpers::IntegerLiteral(b, dtype, 2); - ctx->SetOutput(0, b->Div(deconverted, two)); + ctx->SetOutput(0, xla::Div(deconverted, two)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc index 0388b4c830702ea00ec69fc42c6468326c88cf38..2fb072f827906d40dcf410f0312394c4f568a28d 100644 --- a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/core/errors.h" @@ -90,8 +91,10 @@ class ListDiffOp : public XlaOpKernel { idx_output.push_back(i); } - context->SetOutput(0, context->builder()->ConstantR1(val_output)); - context->SetOutput(1, context->builder()->ConstantR1(idx_output)); + context->SetOutput(0, + xla::ConstantR1(context->builder(), val_output)); + context->SetOutput(1, + xla::ConstantR1(context->builder(), idx_output)); return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc index 39fbf98a6274918840e9e351470f04c2d80c5d01..dc934543cb2f94fbe1e8f1f865156eb082d6a127 100644 --- a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { @@ -50,8 +51,8 @@ class LRNOp : public XlaOpKernel { auto accumulation_type = XlaHelpers::SumAccumulationType(input_type(0)); auto converted = XlaHelpers::ConvertElementType(builder, input, accumulation_type); - auto squared = builder->Mul(converted, converted); - auto reduce = builder->ReduceWindow( + auto squared = xla::Mul(converted, converted); + auto reduce = xla::ReduceWindow( squared, XlaHelpers::Zero(builder, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type), /* window_dimensions = */ {1, 1, 1, depth_radius_ * 2 + 1}, @@ -59,12 +60,12 @@ class LRNOp : public XlaOpKernel { auto sqr_sum = XlaHelpers::ConvertElementType(builder, reduce, input_type(0)); - auto scale = builder->Pow( - builder->Add(builder->ConstantR0(bias_), - builder->Mul(builder->ConstantR0(alpha_), sqr_sum)), - builder->ConstantR0(-beta_)); + auto scale = xla::Pow( + xla::Add(xla::ConstantR0(builder, bias_), + xla::Mul(xla::ConstantR0(builder, alpha_), sqr_sum)), + xla::ConstantR0(builder, -beta_)); - ctx->SetOutput(0, builder->Mul(input, scale)); + ctx->SetOutput(0, xla::Mul(input, scale)); } private: @@ -138,8 +139,8 @@ class LRNGradOp : public XlaOpKernel { auto accumulation_type = XlaHelpers::SumAccumulationType(input_type(0)); auto converted = XlaHelpers::ConvertElementType(builder, in_image, accumulation_type); - auto squared = builder->Mul(converted, converted); - auto reduce = builder->ReduceWindow( + auto squared = xla::Mul(converted, converted); + auto reduce = xla::ReduceWindow( squared, XlaHelpers::Zero(builder, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type), /* window_dimensions = */ {1, 1, 1, depth_radius_ * 2 + 1}, @@ -148,17 +149,17 @@ class LRNGradOp : public XlaOpKernel { XlaHelpers::ConvertElementType(builder, reduce, input_type(0)); auto norm = - builder->Add(builder->ConstantR0(bias_), - builder->Mul(builder->ConstantR0(alpha_), sqr_sum)); + xla::Add(xla::ConstantR0(builder, bias_), + xla::Mul(xla::ConstantR0(builder, alpha_), sqr_sum)); - auto dy = builder->Mul( - builder->Mul(builder->ConstantR0(-2.0f * alpha_ * beta_), - builder->Div(out_image, norm)), + auto dy = xla::Mul( + xla::Mul(xla::ConstantR0(builder, -2.0f * alpha_ * beta_), + xla::Div(out_image, norm)), in_grads); auto converted_dy = XlaHelpers::ConvertElementType(builder, dy, accumulation_type); - auto dy_reduce = builder->ReduceWindow( + auto dy_reduce = xla::ReduceWindow( converted_dy, XlaHelpers::Zero(builder, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type), /* window_dimensions = */ {1, 1, 1, depth_radius_ * 2 + 1}, @@ -166,10 +167,10 @@ class LRNGradOp : public XlaOpKernel { auto dy_reduced = XlaHelpers::ConvertElementType(builder, dy_reduce, input_type(0)); - xla::XlaOp gradients = builder->Add( - builder->Mul(in_image, dy_reduced), - builder->Mul(in_grads, - builder->Pow(norm, builder->ConstantR0(-beta_)))); + xla::XlaOp gradients = xla::Add( + xla::Mul(in_image, dy_reduced), + xla::Mul(in_grads, + xla::Pow(norm, xla::ConstantR0(builder, -beta_)))); ctx->SetOutput(0, gradients); } diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index 6949b296f4b9afe4a0c9152c763a9ad233b9f595..844080b8cf5462da201ce7671e4f9d02fa52c861 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { @@ -70,15 +71,15 @@ class MatMulOp : public XlaOpKernel { xla::XlaOp b = ctx->Input(1); if (is_sparse_) { if (a_type_ == DT_BFLOAT16) { - a = ctx->builder()->ConvertElementType(a, xla::F32); + a = xla::ConvertElementType(a, xla::F32); } if (b_type_ == DT_BFLOAT16) { - b = ctx->builder()->ConvertElementType(b, xla::F32); + b = xla::ConvertElementType(b, xla::F32); } } - auto lhs = (transpose_a_) ? ctx->builder()->Transpose(a, {1, 0}) : a; - auto rhs = (transpose_b_) ? ctx->builder()->Transpose(b, {1, 0}) : b; - ctx->SetOutput(0, ctx->builder()->Dot(lhs, rhs)); + auto lhs = (transpose_a_) ? xla::Transpose(a, {1, 0}) : a; + auto rhs = (transpose_b_) ? xla::Transpose(b, {1, 0}) : b; + ctx->SetOutput(0, xla::Dot(lhs, rhs)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc index fbd5dc0fdad4483aadbe9bc263cc1f7a034cee09..e06c87db7adb1840606208fe15cd68a3ca4d137a 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc @@ -16,6 +16,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { @@ -50,6 +52,7 @@ class MatrixBandPartOp : public XlaOpKernel { xla::XlaOp num_upper = context->Input(2); DataType input_type = context->input_type(0); DataType index_type = context->input_type(1); + xla::PrimitiveType index_xla_type = context->input_xla_type(1); TensorShape batch_shape = input_shape; batch_shape.RemoveLastDims(2); @@ -58,33 +61,29 @@ class MatrixBandPartOp : public XlaOpKernel { // Compute 'offset', which is how many diagonals we are above/below the // diagonal. - xla::XlaOp iota_m; - OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, m, &iota_m)); + xla::XlaOp iota_m = xla::Iota(builder, index_xla_type, m); + xla::XlaOp iota_n = xla::Iota(builder, index_xla_type, n); - xla::XlaOp iota_n; - OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, n, &iota_n)); - - auto offset = builder->Sub(builder->Broadcast(iota_n, {m}), iota_m, - /*broadcast_dimensions=*/{0}); + auto offset = xla::Sub(xla::Broadcast(iota_n, {m}), iota_m, + /*broadcast_dimensions=*/{0}); // If num_lower or num_upper are negative, include all lower/upper // diagonals. auto zero_index = XlaHelpers::Zero(builder, index_type); - num_lower = builder->Select( - builder->Lt(num_lower, zero_index), - XlaHelpers::IntegerLiteral(builder, index_type, m), num_lower); - num_upper = builder->Select( - builder->Lt(num_upper, zero_index), - XlaHelpers::IntegerLiteral(builder, index_type, n), num_upper); + num_lower = xla::Select(xla::Lt(num_lower, zero_index), + XlaHelpers::IntegerLiteral(builder, index_type, m), + num_lower); + num_upper = xla::Select(xla::Lt(num_upper, zero_index), + XlaHelpers::IntegerLiteral(builder, index_type, n), + num_upper); - auto indicator = builder->And(builder->Le(builder->Neg(num_lower), offset), - builder->Le(offset, num_upper)); - indicator = builder->Broadcast(indicator, batch_shape.dim_sizes()); + auto indicator = xla::And(xla::Le(xla::Neg(num_lower), offset), + xla::Le(offset, num_upper)); + indicator = xla::Broadcast(indicator, batch_shape.dim_sizes()); auto zero_input = XlaHelpers::Zero(builder, input_type); - auto output = builder->Select( - indicator, input, - builder->Broadcast(zero_input, input_shape.dim_sizes())); + auto output = xla::Select( + indicator, input, xla::Broadcast(zero_input, input_shape.dim_sizes())); context->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc index db53f6fef8d6bf901c8281f50791ca6766c46efd..e2ab4b83cfb45b2f9a7f3aba2d2a927d10ad8b85 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc @@ -16,6 +16,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { @@ -61,14 +63,11 @@ class MatrixSetDiagOp : public XlaOpKernel { auto zero = XlaHelpers::Zero(builder, context->input_type(0)); // Create an indicator tensor that is true only on the diagonal. - xla::XlaOp iota_m; - OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, m, &iota_m)); - xla::XlaOp iota_n; - OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, n, &iota_n)); - auto indicator = builder->Eq(iota_m, - builder->Broadcast(iota_n, {m}), - /*broadcast_dimensions=*/{0}); - indicator = builder->Broadcast(indicator, batch_shape.dim_sizes()); + xla::XlaOp iota_m = xla::Iota(builder, xla::S32, m); + xla::XlaOp iota_n = xla::Iota(builder, xla::S32, n); + auto indicator = xla::Eq(iota_m, xla::Broadcast(iota_n, {m}), + /*broadcast_dimensions=*/{0}); + indicator = xla::Broadcast(indicator, batch_shape.dim_sizes()); // Broadcast diag up to the input shape. Use an implicit broadcast (Add) // because we need to broadcast on the right. @@ -77,10 +76,10 @@ class MatrixSetDiagOp : public XlaOpKernel { if (min_dim != m) { diag_broadcast_dims.back() = rank - 1; } - diag = builder->Add(diag, builder->Broadcast(zero, input_shape.dim_sizes()), - /*broadcast_dimensions=*/diag_broadcast_dims); + diag = xla::Add(diag, xla::Broadcast(zero, input_shape.dim_sizes()), + /*broadcast_dimensions=*/diag_broadcast_dims); - auto output = builder->Select(indicator, diag, input); + auto output = xla::Select(indicator, diag, input); context->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc index eaed93146460de5a6e8328432302cc75bf36a534..f4def11d08c31513aec5aad15187016a7294c2fd 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc @@ -30,13 +30,9 @@ class MatrixTriangularSolveOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { auto result = TriangularSolve( - ctx->builder(), ctx->Input(0), ctx->Input(1), /*left_side=*/true, + ctx->Input(0), ctx->Input(1), /*left_side=*/true, /*lower=*/lower_, /*transpose_a=*/adjoint_, /*conjugate_a=*/adjoint_); - if (!result.ok()) { - ctx->SetStatus(result.status()); - return; - } - ctx->SetOutput(0, result.ValueOrDie()); + ctx->SetOutput(0, result); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 7e9de3ef9b245c113cc143128fe58e7e017a361c..529959dbd90b05f8860360f70e087ef225150600 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/util/mirror_pad_mode.h" namespace tensorflow { @@ -27,21 +28,21 @@ class MirrorPadOp : public XlaOpKernel { xla::StatusOr DoMirrorPad(const xla::XlaOp& t, const xla::Shape& original_shape, - const xla::Literal& pad_literal, + const xla::LiteralSlice& pad_literal, xla::XlaBuilder* b) { xla::XlaOp accum = t; for (int64 dimno = xla::ShapeUtil::Rank(original_shape) - 1; dimno >= 0; --dimno) { - auto t_rev = b->Rev(accum, {dimno}); + auto t_rev = xla::Rev(accum, {dimno}); TF_ASSIGN_OR_RETURN(int64 lhs_padding, pad_literal.GetIntegralAsS64({dimno, 0})); TF_ASSIGN_OR_RETURN(int64 rhs_padding, pad_literal.GetIntegralAsS64({dimno, 1})); int64 dim_size = original_shape.dimensions(dimno); - auto lhs_pad = b->SliceInDim(t_rev, dim_size - 1 - lhs_padding, - dim_size - 1, 1, dimno); - auto rhs_pad = b->SliceInDim(t_rev, 1, 1 + rhs_padding, 1, dimno); - accum = b->ConcatInDim({lhs_pad, accum, rhs_pad}, dimno); + auto lhs_pad = xla::SliceInDim(t_rev, dim_size - 1 - lhs_padding, + dim_size - 1, 1, dimno); + auto rhs_pad = xla::SliceInDim(t_rev, 1, 1 + rhs_padding, 1, dimno); + accum = xla::ConcatInDim(b, {lhs_pad, accum, rhs_pad}, dimno); } return accum; } diff --git a/tensorflow/compiler/tf2xla/kernels/pack_op.cc b/tensorflow/compiler/tf2xla/kernels/pack_op.cc index aecaabb6dcf46bdd6ae3da929448d6370acb989b..3aed47de2603f3e187ad515d4db3f884da4c6cc8 100644 --- a/tensorflow/compiler/tf2xla/kernels/pack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pack_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -76,11 +77,10 @@ class PackOp : public XlaOpKernel { for (int i = 0; i < num; ++i) { // Reshape the inputs to have an extra dimension of size 1. - reshaped_inputs[i] = - ctx->builder()->Reshape(values[i], child_shape.dim_sizes()); + reshaped_inputs[i] = xla::Reshape(values[i], child_shape.dim_sizes()); } - ctx->SetOutput(0, ctx->builder()->ConcatInDim(reshaped_inputs, axis)); + ctx->SetOutput(0, xla::ConcatInDim(ctx->builder(), reshaped_inputs, axis)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 7c95475e7b1f02183e44f73f116a4aeb25f05c09..89fd610bc63349d008836c3c4e6ec8927c232a54 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" @@ -63,8 +64,8 @@ class PadOp : public XlaOpKernel { int before = pad_literal.Get({i, 0}); int after = pad_literal.Get({i, 1}); OP_REQUIRES(ctx, before >= 0 && after >= 0, - errors::InvalidArgument("Paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "Paddings must be non-negative: ", before, " ", after)); dim->set_edge_padding_low(before); dim->set_edge_padding_high(after); } @@ -74,11 +75,10 @@ class PadOp : public XlaOpKernel { if (ctx->num_inputs() == 3) { OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(ctx->InputShape(2)), errors::InvalidArgument("constant_values must be a scalar.")); - ctx->SetOutput(0, - ctx->builder()->Pad(ctx->Input(0), ctx->Input(2), config)); + ctx->SetOutput(0, xla::Pad(ctx->Input(0), ctx->Input(2), config)); } else { auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0)); - ctx->SetOutput(0, ctx->builder()->Pad(ctx->Input(0), zero, config)); + ctx->SetOutput(0, xla::Pad(ctx->Input(0), zero, config)); } } }; diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index f8e7b48a0fd94835964aea033ad33523150067b4..a81f5fddf69523619d03ea2041c40222de46174e 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -20,6 +20,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" @@ -61,6 +63,9 @@ class PoolingOp : public XlaOpKernel { Padding padding; OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding)); padding_ = (padding == VALID) ? xla::Padding::kValid : xla::Padding::kSame; + + OP_REQUIRES_OK( + ctx, DataTypeToPrimitiveType(reduction_type_, &xla_reduction_type_)); } int num_dims() const { return num_spatial_dims_ + 2; } @@ -113,8 +118,8 @@ class PoolingOp : public XlaOpKernel { xla::XlaBuilder* const b = ctx->builder(); auto input = XlaHelpers::ConvertElementType(b, ctx->Input(0), reduction_type_); - auto reduce = ctx->builder()->ReduceWindow( - input, InitValue(b), *Reduction(ctx), ksize, stride, padding_); + auto reduce = xla::ReduceWindow(input, InitValue(b), *Reduction(ctx), ksize, + stride, padding_); auto pooled = XlaHelpers::ConvertElementType(b, reduce, input_type(0)); ctx->SetOutput(0, PostProcessOutput(ctx, pooled, input_type(0), input_shape)); @@ -127,6 +132,7 @@ class PoolingOp : public XlaOpKernel { xla::Padding padding_; TensorFormat data_format_ = FORMAT_NHWC; DataType reduction_type_; + xla::PrimitiveType xla_reduction_type_; }; class MaxPoolOp : public PoolingOp { @@ -136,7 +142,7 @@ class MaxPoolOp : public PoolingOp { /*reduction_type=*/ctx->input_type(0)) {} xla::XlaOp InitValue(xla::XlaBuilder* b) override { - return XlaHelpers::MinValue(b, reduction_type_); + return xla::MinValue(b, xla_reduction_type_); } const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { @@ -190,7 +196,7 @@ static xla::XlaOp AvgPoolDivideByCount( auto divisor = XlaHelpers::IntegerLiteral(ctx->builder(), dtype, window_size); - return ctx->builder()->Div(output, divisor); + return xla::Div(output, divisor); } else { // For SAME padding, the padding shouldn't be included in the // counts. We use another ReduceWindow to find the right counts. @@ -212,18 +218,18 @@ static xla::XlaOp AvgPoolDivideByCount( // Build a matrix of all 1s, with the same width/height as the input. const DataType accumulation_type = XlaHelpers::SumAccumulationType(dtype); - auto ones = ctx->builder()->Broadcast( + auto ones = xla::Broadcast( XlaHelpers::One(ctx->builder(), accumulation_type), input_dim_sizes); // Perform a ReduceWindow with the same window size, strides, and padding // to count the number of contributions to each result element. - auto reduce = ctx->builder()->ReduceWindow( + auto reduce = xla::ReduceWindow( ones, XlaHelpers::Zero(ctx->builder(), accumulation_type), *ctx->GetOrCreateAdd(accumulation_type), window_ksize, window_stride, xla::Padding::kSame); auto counts = XlaHelpers::ConvertElementType(ctx->builder(), reduce, dtype); - return ctx->builder()->Div(output, counts, window_dims); + return xla::Div(output, counts, window_dims); } } @@ -235,7 +241,7 @@ class AvgPoolOp : public PoolingOp { XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} xla::XlaOp InitValue(xla::XlaBuilder* b) override { - return XlaHelpers::Zero(b, reduction_type_); + return xla::Zero(b, xla_reduction_type_); } const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { @@ -347,9 +353,9 @@ class MaxPoolGradOp : public XlaOpKernel { xla::XlaOp init_value = XlaHelpers::Zero(ctx->builder(), input_type(2)); auto select = CreateScalarGeComputation(element_type, ctx->builder()); auto scatter = CreateScalarAddComputation(element_type, ctx->builder()); - xla::XlaOp gradients = ctx->builder()->SelectAndScatter( - input, select, ksize_, stride_, xla_padding, out_backprop, init_value, - scatter); + xla::XlaOp gradients = + xla::SelectAndScatter(input, select, ksize_, stride_, xla_padding, + out_backprop, init_value, scatter); ctx->SetOutput(0, gradients); } @@ -485,12 +491,12 @@ class AvgPoolGradOp : public XlaOpKernel { } auto zero = XlaHelpers::Zero(b, dtype); - auto padded_gradients = b->Pad(out_backprop_div, zero, padding_config); + auto padded_gradients = xla::Pad(out_backprop_div, zero, padding_config); // in_backprop = padded_gradients ones std::vector ones(num_dims(), 1LL); auto accumulation_type = XlaHelpers::SumAccumulationType(dtype); - auto in_backprop = b->ReduceWindow( + auto in_backprop = xla::ReduceWindow( XlaHelpers::ConvertElementType(b, padded_gradients, accumulation_type), XlaHelpers::Zero(b, accumulation_type), *ctx->GetOrCreateAdd(accumulation_type), ksize_, @@ -614,58 +620,61 @@ class MaxPoolGradGradOp : public XlaOpKernel { auto b = ctx->builder(); - auto sixteen = b->ConstantR0(16); + auto sixteen = xla::ConstantR0(b, 16); // in (f32) -> round to bf16 -> f32 for correct bitwidth -> 16-high-bit u32 - auto in_hi = b->BitcastConvertType( - b->ConvertElementType(b->ConvertElementType(input, xla::BF16), - xla::F32), + auto in_hi = xla::BitcastConvertType( + xla::ConvertElementType(xla::ConvertElementType(input, xla::BF16), + xla::F32), xla::U32); - auto bp_int = b->BitcastConvertType(out_backprop, xla::U32); - auto bp_hi = b->ShiftRightLogical(bp_int, sixteen); - auto bp_lo = b->ShiftRightLogical(b->ShiftLeft(bp_int, sixteen), sixteen); - auto in_hi_bp_hi = b->Add(in_hi, bp_hi); // Want an unsigned add. - auto in_hi_bp_lo = b->Add(in_hi, bp_lo); // Want an unsigned add. - - auto init_value = XlaHelpers::MinValue(b, DT_FLOAT); + auto bp_int = xla::BitcastConvertType(out_backprop, xla::U32); + auto bp_hi = xla::ShiftRightLogical(bp_int, sixteen); + auto bp_lo = + xla::ShiftRightLogical(xla::ShiftLeft(bp_int, sixteen), sixteen); + auto in_hi_bp_hi = xla::Add(in_hi, bp_hi); // Want an unsigned add. + auto in_hi_bp_lo = xla::Add(in_hi, bp_lo); // Want an unsigned add. + + auto init_value = xla::MinValue(b, xla::F32); // We will reduce by taking the maximal value up to 16 bits (ignoring the lo // 16 bits of packed-in hi/lo backprop value). auto rb = b->CreateSubBuilder("GreaterOrEqOf_ByFirst16Bits"); { // F32 parameters to satisfy lowering type restriction for reduce opcode. const xla::Shape scalar = xla::ShapeUtil::MakeShape(xla::F32, {}); - auto lhs = rb->Parameter(0, scalar, "lhs"); - auto rhs = rb->Parameter(1, scalar, "rhs"); - auto sixteen = rb->ConstantR0(16); - auto lhs_criteria = rb->ShiftLeft( - rb->ShiftRightLogical(rb->BitcastConvertType(lhs, xla::S32), sixteen), - sixteen); - auto rhs_criteria = rb->ShiftLeft( - rb->ShiftRightLogical(rb->BitcastConvertType(rhs, xla::S32), sixteen), - sixteen); + auto lhs = xla::Parameter(rb.get(), 0, scalar, "lhs"); + auto rhs = xla::Parameter(rb.get(), 1, scalar, "rhs"); + auto sixteen = xla::ConstantR0(rb.get(), 16); + auto lhs_criteria = + xla::ShiftLeft(xla::ShiftRightLogical( + xla::BitcastConvertType(lhs, xla::S32), sixteen), + sixteen); + auto rhs_criteria = + xla::ShiftLeft(xla::ShiftRightLogical( + xla::BitcastConvertType(rhs, xla::S32), sixteen), + sixteen); // Must use a F32 comparison, because S32 would not work for negatives. - rb->Select(rb->Ge(rb->BitcastConvertType(lhs_criteria, xla::F32), - rb->BitcastConvertType(rhs_criteria, xla::F32)), - lhs, rhs); + xla::Select(xla::Ge(xla::BitcastConvertType(lhs_criteria, xla::F32), + xla::BitcastConvertType(rhs_criteria, xla::F32)), + lhs, rhs); } auto reduce = rb->BuildAndNoteError(); xla::Padding xla_padding = (padding_ == VALID) ? xla::Padding::kValid : xla::Padding::kSame; auto pooled_hi = - b->ReduceWindow(b->BitcastConvertType(in_hi_bp_hi, xla::F32), - init_value, reduce, ksize_, stride_, xla_padding); + xla::ReduceWindow(xla::BitcastConvertType(in_hi_bp_hi, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); auto pooled_lo = - b->ReduceWindow(b->BitcastConvertType(in_hi_bp_lo, xla::F32), - init_value, reduce, ksize_, stride_, xla_padding); + xla::ReduceWindow(xla::BitcastConvertType(in_hi_bp_lo, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); auto grads_hi = - b->ShiftLeft(b->BitcastConvertType(pooled_hi, xla::U32), sixteen); - auto grads_lo = b->ShiftRightLogical( - b->ShiftLeft(b->BitcastConvertType(pooled_lo, xla::U32), sixteen), + xla::ShiftLeft(xla::BitcastConvertType(pooled_hi, xla::U32), sixteen); + auto grads_lo = xla::ShiftRightLogical( + xla::ShiftLeft(xla::BitcastConvertType(pooled_lo, xla::U32), sixteen), sixteen); - auto grads = b->Add(grads_hi, grads_lo); // Want an unsigned add. + auto grads = xla::Add(grads_hi, grads_lo); // Want an unsigned add. xla::PrimitiveType element_type; OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(2), &element_type)); - ctx->SetOutput(0, b->BitcastConvertType(grads, element_type)); + ctx->SetOutput(0, xla::BitcastConvertType(grads, element_type)); } protected: @@ -694,5 +703,18 @@ REGISTER_XLA_OP(Name("MaxPoolGradGradV2") .CompileTimeConstInput("strides"), MaxPool2DGradGradOp); +class MaxPool3DGradGradOp : public MaxPoolGradGradOp { + public: + explicit MaxPool3DGradGradOp(OpKernelConstruction* ctx) + : MaxPoolGradGradOp(ctx, /*num_spatial_dims=*/3) { + string data_format; + OP_REQUIRES_OK(ctx, ctx->GetAttr("data_format", &data_format)); + OP_REQUIRES(ctx, FormatFromString(data_format, &data_format_), + errors::InvalidArgument("Invalid data format")); + } +}; +REGISTER_XLA_OP(Name("MaxPool3DGradGrad").TypeConstraint("T", DT_FLOAT), + MaxPool3DGradGradOp); + } // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc index 661cd5923e1023eaf89a6bc4f56fcc362c8bcfb6..e88221e4f400abeec59d85c1539d4f70bf515d3c 100644 --- a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc @@ -13,10 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { @@ -28,82 +31,115 @@ class QuantizeAndDequantizeOp : public XlaOpKernel { : XlaOpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("signed_input", &signed_input_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("range_given", &range_given_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits_)); - OP_REQUIRES(ctx, num_bits_ > 0 && num_bits_ < (signed_input_ ? 62 : 63), - errors::InvalidArgument("num_bits is out of range: ", num_bits_, - " with signed_input_ ", signed_input_)); } void Compile(XlaOpKernelContext* ctx) override { xla::XlaOp input = ctx->Input(0); const DataType data_type = ctx->input_type(0); - // Comments taken from semantics description at - // https://www.tensorflow.org/versions/r1.0/api_docs/cc/class/tensorflow/ops/quantize-and-dequantize - // - // ... we find m such that - // - // m = max(abs(input_min), abs(input_max)) if range_given is true, - // m = max(abs(min_elem(input)), - // abs(max_elem(input))) otherwise. + xla::PrimitiveType xla_type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(data_type, &xla_type)); + xla::XlaBuilder* b = ctx->builder(); - xla::XlaOp input_min, input_max; + + // The implementation follows + // tensorflow/core/kernels/quantize_and_dequantize_op.h closely. + xla::XlaOp min_range, max_range; if (range_given_) { - double input_min_value, input_max_value; - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsFloatScalar(1, &input_min_value)); - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsFloatScalar(2, &input_max_value)); - input_min = XlaHelpers::FloatLiteral(b, data_type, input_min_value); - input_max = XlaHelpers::FloatLiteral(b, data_type, input_max_value); + min_range = ctx->Input(1); + max_range = ctx->Input(2); } else { const xla::XlaComputation* fmax = ctx->GetOrCreateMax(data_type); const xla::XlaComputation* fmin = ctx->GetOrCreateMin(data_type); - input_min = - b->ReduceAll(input, XlaHelpers::MaxValue(b, data_type), *fmin); - input_max = - b->ReduceAll(input, XlaHelpers::MinValue(b, data_type), *fmax); + min_range = ReduceAll(input, xla::MaxValue(b, xla_type), *fmin); + max_range = ReduceAll(input, xla::MinValue(b, xla_type), *fmax); } - xla::XlaOp m = b->Max(b->Abs(input_min), b->Abs(input_max)); - - // Next, we choose our fixed-point quantization buckets, [min_fixed, - // max_fixed]. If signed_input is true, this is - // - // [min_fixed, max_fixed ] = [-((1 << (num_bits - 1)) - 1), - // (1 << (num_bits - 1)) - 1]. - // - // Otherwise, if signed_input is false, the fixed-point range is - // - // [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]. - int64 min_fixed, max_fixed; + + xla::XlaOp num_bits; + if (num_bits_ < 0) { + OP_REQUIRES( + ctx, ctx->num_inputs() == 4, + errors::Internal("Expected 4 inputs to QuantizeAndDequantize")); + num_bits = ctx->Input(3); + } else { + num_bits = xla::ConstantR0(b, num_bits_); + } + + const xla::XlaOp zero = XlaHelpers::Zero(b, data_type); + const xla::XlaOp one = XlaHelpers::One(b, data_type); + const xla::XlaOp two = XlaHelpers::FloatLiteral(b, data_type, 2.0); + const xla::XlaOp half = XlaHelpers::FloatLiteral(b, data_type, 0.5); + + // Calculate the range for the simulated integer quantization: + // e.g. [-128,127] for signed = true, num_bits = 8, + // or [0, 255] for signed = false, num_bits = 8. + // We do this in floating point for hardware that does not have 64-bit + // integer support. + xla::XlaOp min_quantized, max_quantized; if (signed_input_) { - min_fixed = -((1LL << (num_bits_ - 1)) - 1); - max_fixed = (1LL << (num_bits_ - 1)) - 1; + min_quantized = + -Pow(two, ConvertElementType(num_bits - xla::ConstantR0(b, 1), + xla_type)); + max_quantized = + Pow(two, ConvertElementType(num_bits - xla::ConstantR0(b, 1), + xla_type)) - + one; } else { - min_fixed = 0; - max_fixed = (1LL << num_bits_) - 1; + min_quantized = zero; + max_quantized = Pow(two, ConvertElementType(num_bits, xla_type)) - one; } - // From this we compute our scaling factor, s: - // - // s = (max_fixed - min_fixed) / (2 * m). - xla::XlaOp s = - b->Div(XlaHelpers::FloatLiteral(b, data_type, max_fixed - min_fixed), - b->Mul(XlaHelpers::FloatLiteral(b, data_type, 2.0), m)); + // Determine the maximum scaling factor that would scale + // [min_range, max_range] to not exceed [min_quantized, max_quantized], + // while keeping 0 unchanged. + xla::XlaOp scale_from_min_side = + Select(Gt(min_quantized * min_range, zero), min_quantized / min_range, + xla::MaxFiniteValue(b, xla_type)); + xla::XlaOp scale_from_max_side = + Select(Gt(max_quantized * max_range, zero), max_quantized / max_range, + xla::MaxFiniteValue(b, xla_type)); - // Now we can quantize and dequantize the elements of our tensor. An element - // e is transformed into e': - // - // e' = (e * s).round_to_nearest() / s. - xla::XlaOp result = b->Div(b->Round(b->Mul(input, s)), s); + // Note: Avoids changing the side of the range that determines scale. + xla::XlaOp cond = Lt(scale_from_min_side, scale_from_max_side); + xla::XlaOp scale = Select(cond, scale_from_min_side, scale_from_max_side); + xla::XlaOp inverse_scale = + Select(cond, min_range / min_quantized, max_range / max_quantized); + min_range = Select(cond, min_range, min_quantized * inverse_scale); + max_range = Select(cond, max_quantized * inverse_scale, max_range); + if (range_given_) { + // Note: The clamping here is to avoid overflow in the quantized type. + // The semantics of the op does not guarantee to clamp to the specified + // min_range and max_range - because we may have changed either min_range + // or max_range. + // No need to clamp to min_range and max_range if range_given_ == false as + // in that case they were measured from the tensor. + input = Clamp(min_range, input, max_range); + } + xla::XlaOp result = + Floor((input - min_range) * scale + half) * inverse_scale + min_range; ctx->SetOutput(0, result); } - int64 num_bits_; + protected: + int64 num_bits_ = -1; bool signed_input_; bool range_given_; }; -REGISTER_XLA_OP(Name("QuantizeAndDequantizeV2"), QuantizeAndDequantizeOp); +class QuantizeAndDequantizeV2Op : public QuantizeAndDequantizeOp { + public: + explicit QuantizeAndDequantizeV2Op(OpKernelConstruction* ctx) + : QuantizeAndDequantizeOp(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_bits", &num_bits_)); + OP_REQUIRES(ctx, num_bits_ > 0 && num_bits_ < (signed_input_ ? 62 : 63), + errors::InvalidArgument("num_bits is out of range: ", num_bits_, + " with signed_input_ ", signed_input_)); + } +}; + +REGISTER_XLA_OP(Name("QuantizeAndDequantizeV2"), QuantizeAndDequantizeV2Op); +REGISTER_XLA_OP(Name("QuantizeAndDequantizeV3"), QuantizeAndDequantizeOp); } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index ebac5c4396f90f9cee5d900d3c34499677c1a02f..9a0a7f9b9004f210adac44ed8b6e32cff131d23b 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -18,6 +18,7 @@ limitations under the License. // TODO(misard,phawkins): add tests. #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" +#include "tensorflow/compiler/tf2xla/lib/random.h" #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/shape_util.h" @@ -25,6 +26,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -45,8 +48,8 @@ class RandomUniformOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(dtype, shape, &xla_shape)); xla::XlaBuilder* b = ctx->builder(); - xla::XlaOp result = b->RngUniform(XlaHelpers::Zero(b, dtype), - XlaHelpers::One(b, dtype), xla_shape); + xla::XlaOp result = xla::RngUniform(XlaHelpers::Zero(b, dtype), + XlaHelpers::One(b, dtype), xla_shape); ctx->SetOutput(0, result); } @@ -76,32 +79,13 @@ class RandomShuffleOp : public XlaOpKernel { ctx->SetOutput(0, input); } else { // Generate the random swaps for the indices. - auto zero = builder->Broadcast( - builder->ConstantLiteral(xla::Literal::Zero(xla::S32)), - gtl::ArraySlice({n})); - auto n_maxval = builder->Broadcast(builder->ConstantR0(n), - gtl::ArraySlice({n})); auto swaps_shape = xla::ShapeUtil::MakeShape(xla::S32, {n}); - auto swaps = builder->RngUniform(zero, n_maxval, swaps_shape); + auto swaps = + xla::RngUniform(xla::ConstantR0(builder, 0), + xla::ConstantR0(builder, n), swaps_shape); // Generate range(n) as the initial value for the indices to be swapped. - auto index_init_body_fn = [&](xla::XlaOp i, - gtl::ArraySlice loop_vars, - xla::XlaBuilder* builder) - -> xla::StatusOr> { - auto indices = loop_vars[0]; - i = builder->Reshape(i, {}, {1}); - // indices[i] = i - indices = builder->DynamicUpdateSlice(indices, i, i); - return std::vector{indices}; - }; - // for i in range(n): - xla::XlaOp index_zeros = Zeros(builder, swaps_shape); - auto index_init_loop_result = - XlaForEachIndex(n, xla::S32, index_init_body_fn, {index_zeros}, - "index_init_loop", builder) - .ValueOrDie(); - auto indices = index_init_loop_result[0]; + xla::XlaOp indices = xla::Iota(builder, xla::S32, n); // Swap the indices at i and swaps[i]. auto swap_body_fn = [&](xla::XlaOp i, @@ -110,17 +94,17 @@ class RandomShuffleOp : public XlaOpKernel { -> xla::StatusOr> { auto swaps = loop_vars[0]; auto indices = loop_vars[1]; - i = builder->Reshape(i, {}, {1}); + i = xla::Reshape(i, {1}); // temp = indices[i] - auto temp = builder->DynamicSlice(indices, i, {1}); + auto temp = xla::DynamicSlice(indices, i, {1}); // swap_index = swaps[i] - auto swap_index = builder->DynamicSlice(swaps, i, {1}); + auto swap_index = xla::DynamicSlice(swaps, i, {1}); // swap_value = indices[swaps[i]] - auto swap_value = builder->DynamicSlice(indices, swap_index, {1}); + auto swap_value = xla::DynamicSlice(indices, swap_index, {1}); // indices[i] = indices[swaps[i]] - indices = builder->DynamicUpdateSlice(indices, swap_value, i); + indices = xla::DynamicUpdateSlice(indices, swap_value, i); // indices[swaps[i]] = temp - indices = builder->DynamicUpdateSlice(indices, temp, swap_index); + indices = xla::DynamicUpdateSlice(indices, temp, swap_index); return std::vector{swaps, indices}; }; // for i in range(n): @@ -170,7 +154,7 @@ class RandomUniformIntOp : public XlaOpKernel { auto minval = ctx->Input(1); auto maxval = ctx->Input(2); - ctx->SetOutput(0, ctx->builder()->RngUniform(minval, maxval, xla_shape)); + ctx->SetOutput(0, xla::RngUniform(minval, maxval, xla_shape)); } private: @@ -196,8 +180,8 @@ class RandomStandardNormalOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); // Normal distribution with a mean of 0 and a standard deviation of 1: - xla::XlaOp result = b->RngNormal(XlaHelpers::Zero(b, dtype), - XlaHelpers::One(b, dtype), xla_shape); + xla::XlaOp result = xla::RngNormal(XlaHelpers::Zero(b, dtype), + XlaHelpers::One(b, dtype), xla_shape); ctx->SetOutput(0, result); } @@ -223,58 +207,17 @@ class TruncatedNormalOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); - auto two_sd = [dtype](bool negate, xla::XlaBuilder* b) { - return XlaHelpers::FloatLiteral(b, dtype, negate ? -2.0 : 2.0); - }; - auto out_of_range_mask = [two_sd](xla::XlaOp candidate, - xla::XlaBuilder* b) { - xla::XlaOp too_large = b->Gt(candidate, two_sd(false, b)); - xla::XlaOp too_small = b->Lt(candidate, two_sd(true, b)); - return b->Or(too_large, too_small); - }; - - // The algorithm we're using is roughly: - // - // while (any(candidate < mean-2*sd || candidate > mean+2*sd)) { - // out_of_range_mask := candidate < mean-2*sd || candidate > mean+2*sd - // candidate = select(out_of_range_mask, rng_normal(), candidate) - // } - std::vector initial_values = { - // The current candidate. - b->Broadcast(XlaHelpers::Zero(b, dtype), shape.dim_sizes()), - // The to_resample mask, where 'true' identifies a location in the - // current candidate that is out of range and must be regenerated. - b->Broadcast(b->ConstantR0(true), shape.dim_sizes()), - // Is any element in the mask true? - b->ConstantR0(true)}; - auto condition = [&](gtl::ArraySlice values, - xla::XlaBuilder* b) -> xla::StatusOr { - // Continue while any element in the mask is true. - return values[2]; - }; - auto body = - [&](gtl::ArraySlice values, - xla::XlaBuilder* b) -> xla::StatusOr> { - xla::XlaOp candidate = values[0]; - xla::XlaOp to_resample = values[1]; - xla::XlaOp mean = XlaHelpers::Zero(b, dtype); - xla::XlaOp stddev = XlaHelpers::One(b, dtype); - candidate = b->Select(to_resample, b->RngNormal(mean, stddev, xla_shape), - candidate); - // Compute a new to_resample mask, and determine whether any value is - // still out of range. - to_resample = out_of_range_mask(candidate, b); - TF_ASSIGN_OR_RETURN(xla::XlaOp done, Any(to_resample, b)); - return std::vector{candidate, to_resample, done}; - }; - auto result = - XlaWhileLoop(condition, body, initial_values, "truncated_normal", b); - OP_REQUIRES_OK(ctx, result.status()); - ctx->SetOutput(0, result.ValueOrDie()[0]); + xla::XlaOp one = XlaHelpers::FloatLiteral(b, dtype, 1.0); + xla::XlaOp min_positive = + XlaHelpers::FloatLiteral(b, dtype, std::numeric_limits::min()); + auto uniform = xla::RngUniform(min_positive, one, xla_shape); + ctx->SetOutput(0, TruncatedNormal(uniform)); } }; -REGISTER_XLA_OP(Name("TruncatedNormal").CompileTimeConstInput("shape"), +REGISTER_XLA_OP(Name("TruncatedNormal") + .CompileTimeConstInput("shape") + .TypeConstraint("dtype", DT_FLOAT), TruncatedNormalOp); } // anonymous namespace diff --git a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc index 08894489ac77bbbe4ddb067c06a6d031a537697d..76bd1e62aa1efd85d6ed489b9a6d22a2bacf2a8b 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" @@ -98,10 +99,10 @@ class ReduceWindowOp : public XlaOpKernel { { std::unique_ptr cb = builder->CreateSubBuilder("wrapper"); - auto x = cb->Parameter(0, scalar_shape, "x"); - auto y = cb->Parameter(1, scalar_shape, "y"); - auto outputs = cb->Call(*reducer.computation, {x, y}); - cb->GetTupleElement(outputs, 0); + auto x = xla::Parameter(cb.get(), 0, scalar_shape, "x"); + auto y = xla::Parameter(cb.get(), 1, scalar_shape, "y"); + auto outputs = xla::Call(cb.get(), *reducer.computation, {x, y}); + xla::GetTupleElement(outputs, 0); xla::StatusOr result = cb->Build(); OP_REQUIRES_OK(context, result.status()); wrapper = std::move(result.ValueOrDie()); @@ -112,7 +113,7 @@ class ReduceWindowOp : public XlaOpKernel { padding[i] = {padding_low_[i], padding_high_[i]}; } - xla::XlaOp output = builder->ReduceWindowWithGeneralPadding( + xla::XlaOp output = xla::ReduceWindowWithGeneralPadding( context->Input(0), context->Input(1), wrapper, window_dimensions_, window_strides_, padding); context->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc index 0f425637795e9633a8e36f921000ee2f5e25813a..46fae59ad4fa30b57946671518251a7e53ac4c8c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc @@ -19,6 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" @@ -31,11 +33,11 @@ class SumOp : public XlaReductionOp { : XlaReductionOp(ctx, XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return XlaHelpers::Zero(builder, reduction_type_); + return xla::Zero(builder, xla_reduction_type_); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Add(scalar_lhs, scalar_rhs); + xla::Add(scalar_lhs, scalar_rhs); } }; @@ -48,12 +50,12 @@ class ProdOp : public XlaReductionOp { XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return XlaHelpers::One(builder, reduction_type_); + return xla::One(builder, xla_reduction_type_); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Mul(scalar_lhs, scalar_rhs); + xla::Mul(scalar_lhs, scalar_rhs); } }; @@ -66,12 +68,12 @@ class MinOp : public XlaReductionOp { : XlaReductionOp(ctx, ctx->input_type(0)) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return XlaHelpers::MaxValue(builder, reduction_type_); + return xla::MaxValue(builder, xla_reduction_type_); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Min(scalar_lhs, scalar_rhs); + xla::Min(scalar_lhs, scalar_rhs); } }; @@ -83,12 +85,12 @@ class MaxOp : public XlaReductionOp { : XlaReductionOp(ctx, ctx->input_type(0)) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return XlaHelpers::MinValue(builder, reduction_type_); + return xla::MinValue(builder, xla_reduction_type_); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Max(scalar_lhs, scalar_rhs); + xla::Max(scalar_lhs, scalar_rhs); } }; @@ -101,11 +103,11 @@ class MeanOp : public XlaReductionOp { XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return XlaHelpers::Zero(builder, reduction_type_); + return xla::Zero(builder, xla_reduction_type_); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Add(scalar_lhs, scalar_rhs); + xla::Add(scalar_lhs, scalar_rhs); } xla::XlaOp BuildFinalizer(xla::XlaBuilder* builder, @@ -113,7 +115,7 @@ class MeanOp : public XlaReductionOp { int64 num_elements_reduced) override { auto divisor = XlaHelpers::IntegerLiteral(builder, input_type(0), num_elements_reduced); - return builder->Div(reduce_output, divisor); + return reduce_output / divisor; } }; @@ -126,12 +128,12 @@ class AllOp : public XlaReductionOp { : XlaReductionOp(ctx, ctx->input_type(0)) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return builder->ConstantR0(true); + return xla::ConstantR0(builder, true); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->And(scalar_lhs, scalar_rhs); + xla::And(scalar_lhs, scalar_rhs); } }; @@ -143,12 +145,12 @@ class AnyOp : public XlaReductionOp { : XlaReductionOp(ctx, ctx->input_type(0)) {} xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { - return builder->ConstantR0(false); + return xla::ConstantR0(builder, false); } void BuildReducer(xla::XlaBuilder* builder, const xla::XlaOp& scalar_lhs, const xla::XlaOp& scalar_rhs) override { - builder->Or(scalar_lhs, scalar_rhs); + xla::Or(scalar_lhs, scalar_rhs); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h index 2ecfb854a1c8625524d4f1199af3927edd204926..8333f9b288e27efe9497306f031980c9eec7c99c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h @@ -64,6 +64,7 @@ class XlaReductionOp : public XlaOpKernel { protected: DataType reduction_type_; + xla::PrimitiveType xla_reduction_type_; }; } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 4fd5bfd03999a7f8b7bb081cc4b03aa1434d4c3d..909783ecb3c2a866136e1a09767144c91c46525c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" @@ -31,6 +32,8 @@ XlaReductionOp::XlaReductionOp(OpKernelConstruction* ctx, OP_REQUIRES_OK(ctx, ctx->MatchSignature({dt, DT_INT32}, {dt})); OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_)); + OP_REQUIRES_OK( + ctx, DataTypeToPrimitiveType(reduction_type_, &xla_reduction_type_)); } // Unless BuildFinalizer is overridden the reduction has no @@ -56,9 +59,9 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { // Evaluate the constant, reshaping to a 1-vector if it is a scalar. xla::Literal axes_literal; - OP_REQUIRES_OK(ctx, - ctx->ConstantInputReshaped( - 1, {axes_tensor_shape.num_elements()}, &axes_literal)); + OP_REQUIRES_OK( + ctx, ctx->ConstantInputReshaped(1, {axes_tensor_shape.num_elements()}, + &axes_literal)); VLOG(1) << "data shape: " << data_shape.DebugString(); VLOG(1) << "axes : " << axes_literal.ToString(); @@ -101,20 +104,20 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(reduction_type_, &type)); - auto data = b->ConvertElementType(ctx->Input(0), type); + auto data = xla::ConvertElementType(ctx->Input(0), type); // Call virtual method to get the initial value. - auto initial = b->ConvertElementType(InitialValue(b), type); + auto initial = xla::ConvertElementType(InitialValue(b), type); // Make two scalar parameters of the desired type for the lambda. - auto rx = r.Parameter(0, xla::ShapeUtil::MakeShape(type, {}), "x"); - auto ry = r.Parameter(1, xla::ShapeUtil::MakeShape(type, {}), "y"); + auto rx = xla::Parameter(&r, 0, xla::ShapeUtil::MakeShape(type, {}), "x"); + auto ry = xla::Parameter(&r, 1, xla::ShapeUtil::MakeShape(type, {}), "y"); // Call virtual method to build the reduction lambda. BuildReducer(&r, rx, ry); xla::XlaComputation reduction_computation = r.Build().ConsumeValueOrDie(); - auto reduce = b->Reduce(data, initial, reduction_computation, xla_axes); + auto reduce = xla::Reduce(data, initial, reduction_computation, xla_axes); auto deconverted = XlaHelpers::ConvertElementType(b, reduce, input_type(0)); auto finalized = BuildFinalizer(b, deconverted, num_elements_reduced); - auto result = keep_dims_ ? b->Reshape(finalized, final_shape) : finalized; + auto result = keep_dims_ ? xla::Reshape(finalized, final_shape) : finalized; ctx->SetOutput(0, result); } diff --git a/tensorflow/compiler/tf2xla/kernels/relu_op.cc b/tensorflow/compiler/tf2xla/kernels/relu_op.cc index ba7d484d53d7258edaa5bc42fa116cf16e94835b..a4ba6c748a73f161ea252e2adf4050eb5dda7df5 100644 --- a/tensorflow/compiler/tf2xla/kernels/relu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/relu_op.cc @@ -34,7 +34,7 @@ class ReluOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { xla::XlaBuilder* builder = ctx->builder(); auto zero = XlaHelpers::Zero(builder, input_type(0)); - ctx->SetOutput(0, builder->Max(zero, ctx->Input(0))); + ctx->SetOutput(0, xla::Max(zero, ctx->Input(0))); } }; @@ -46,7 +46,7 @@ class Relu6Op : public XlaOpKernel { xla::XlaBuilder* builder = ctx->builder(); auto zero = XlaHelpers::Zero(builder, input_type(0)); auto six = XlaHelpers::IntegerLiteral(builder, input_type(0), 6); - ctx->SetOutput(0, builder->Clamp(zero, ctx->Input(0), six)); + ctx->SetOutput(0, xla::Clamp(zero, ctx->Input(0), six)); } }; @@ -59,9 +59,9 @@ class ReluGradOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); const TensorShape shape = ctx->InputShape(0); const auto zero = - b->Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); - const auto pred = b->Gt(ctx->Input(1), zero); - ctx->SetOutput(0, b->Select(pred, ctx->Input(0), zero)); + xla::Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); + const auto pred = xla::Gt(ctx->Input(1), zero); + ctx->SetOutput(0, xla::Select(pred, ctx->Input(0), zero)); } }; @@ -74,12 +74,12 @@ class Relu6GradOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); const TensorShape shape = ctx->InputShape(0); const auto zero = - b->Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); - const auto six = b->Broadcast( + xla::Broadcast(XlaHelpers::Zero(b, input_type(0)), shape.dim_sizes()); + const auto six = xla::Broadcast( XlaHelpers::IntegerLiteral(b, input_type(0), 6), shape.dim_sizes()); - auto out = - b->Select(b->And(b->Lt(ctx->Input(1), six), b->Gt(ctx->Input(1), zero)), - ctx->Input(0), zero); + auto out = xla::Select( + xla::And(xla::Lt(ctx->Input(1), six), xla::Gt(ctx->Input(1), zero)), + ctx->Input(0), zero); ctx->SetOutput(0, out); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc index af4d64b159c09ed7e01017f25a2b23e58542dc3c..e0ca8dd8e27914ad60d0b97e8ac5f0b91a4fd9a6 100644 --- a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -90,8 +91,7 @@ class ReshapeOp : public XlaOpKernel { VLOG(1) << "Reshape " << input_shape.DebugString() << " " << shape.DebugString(); - ctx->SetOutput(0, - ctx->builder()->Reshape(ctx->Input(0), shape.dim_sizes())); + ctx->SetOutput(0, xla::Reshape(ctx->Input(0), shape.dim_sizes())); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/retval_op.cc b/tensorflow/compiler/tf2xla/kernels/retval_op.cc index a711278638444be01fb865561957702368b75114..5be70a4ded31a988cb77cdabe3fc8a041bc3ad16 100644 --- a/tensorflow/compiler/tf2xla/kernels/retval_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/retval_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" @@ -62,15 +63,24 @@ class RetvalOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, tc.AddConstRetval(index_, dtype_, literal)); } else { TensorShape shape = ctx->InputShape(0); - TensorShape representation_shape = - tc.is_entry_computation() - ? tc.RepresentationShape(shape, ctx->input_type(0)) - : shape; + ctx->SetStatus(is_constant.status()); + TensorShape representation_shape; + if (tc.is_entry_computation()) { + xla::StatusOr shape_or_status = + tc.RepresentationShape(shape, ctx->input_type(0)); + if (!shape_or_status.ok()) { + ctx->SetStatus(shape_or_status.status()); + return; + } else { + representation_shape = shape_or_status.ValueOrDie(); + } + } else { + representation_shape = shape; + } xla::XlaOp output = input; if (tc.is_entry_computation()) { - output = - ctx->builder()->Reshape(input, representation_shape.dim_sizes()); + output = xla::Reshape(input, representation_shape.dim_sizes()); } else { // The core from which a return value is returned depends on the // device assignment of the input to the retval. Since we can't change @@ -78,8 +88,8 @@ class RetvalOp : public XlaOpKernel { // introduce an operator here, even if the shape does not change. // TODO(b/76097077): propagate device assignments onto arguments and // return values of functions, and then reshape unconditionally. - output = ctx->builder()->GetTupleElement( - ctx->builder()->Tuple({output}), 0); + output = + xla::GetTupleElement(xla::Tuple(ctx->builder(), {output}), 0); } tc.AddRetval(index_, dtype_, shape, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc index 2872a3c4d49d0d269aa3d216887a5c32cd51f1c3..037c422258555289711b8754f2277d077d0cd6a7 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -62,7 +63,7 @@ class ReverseOp : public XlaOpKernel { } } - ctx->SetOutput(0, ctx->builder()->Rev(ctx->Input(0), dimensions)); + ctx->SetOutput(0, xla::Rev(ctx->Input(0), dimensions)); } }; @@ -100,7 +101,7 @@ class ReverseV2Op : public XlaOpKernel { x_shape.dims(), ").")); } - ctx->SetOutput(0, ctx->builder()->Rev(ctx->Input(0), axes)); + ctx->SetOutput(0, xla::Rev(ctx->Input(0), axes)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc index 5d1c05268493f4f6404c40a4092a71f1e5b3f3b9..c810456f94322acfccae18d78efa861eede4648c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc @@ -17,6 +17,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { @@ -85,103 +87,96 @@ class ReverseSequenceOp : public XlaOpKernel { auto condition_builder = builder->CreateSubBuilder("reverse_sequence_condition"); { - auto param = condition_builder->Parameter(0, tuple_shape, "param"); - auto i = condition_builder->GetTupleElement(param, 0); - condition_builder->Lt( - i, XlaHelpers::IntegerLiteral(condition_builder.get(), seq_lens_type, - batch_size)); + auto param = + xla::Parameter(condition_builder.get(), 0, tuple_shape, "param"); + auto i = xla::GetTupleElement(param, 0); + xla::Lt(i, XlaHelpers::IntegerLiteral(condition_builder.get(), + seq_lens_type, batch_size)); } auto condition = condition_builder->Build(); OP_REQUIRES_OK(context, condition.status()); auto body_builder = builder->CreateSubBuilder("reverse_sequence_body"); { - auto param = body_builder->Parameter(0, tuple_shape, "param"); - auto i = body_builder->GetTupleElement(param, 0); - auto seq_lens = body_builder->GetTupleElement(param, 1); - auto output = body_builder->GetTupleElement(param, 2); + auto param = xla::Parameter(body_builder.get(), 0, tuple_shape, "param"); + auto i = xla::GetTupleElement(param, 0); + auto seq_lens = xla::GetTupleElement(param, 1); + auto output = xla::GetTupleElement(param, 2); // seq_len is the sequence length of the current batch element (rank 1) - auto seq_len = body_builder->DynamicSlice( - seq_lens, body_builder->Reshape(i, {1}), {1}); + auto seq_len = xla::DynamicSlice(seq_lens, xla::Reshape(i, {1}), {1}); // Indices is the offset of the batch element in the input. - auto batch_element_indices = body_builder->Broadcast( - XlaHelpers::Zero(body_builder.get(), seq_lens_type), - {input_shape.dims()}); - batch_element_indices = body_builder->DynamicUpdateSlice( - batch_element_indices, body_builder->Reshape(i, {1}), - body_builder->Reshape( - XlaHelpers::IntegerLiteral(body_builder.get(), seq_lens_type, - batch_dim_), - {1})); + auto batch_element_indices = + xla::Broadcast(XlaHelpers::Zero(body_builder.get(), seq_lens_type), + {input_shape.dims()}); + batch_element_indices = xla::DynamicUpdateSlice( + batch_element_indices, xla::Reshape(i, {1}), + xla::Reshape(XlaHelpers::IntegerLiteral(body_builder.get(), + seq_lens_type, batch_dim_), + {1})); // Slice out the current batch element and pad it out in the sequence // dimension. TensorShape slice_shape = input_shape; slice_shape.set_dim(batch_dim_, 1); slice_shape.set_dim(seq_dim_, max_seq_len); - auto slice = body_builder->DynamicSlice(output, batch_element_indices, - slice_shape.dim_sizes()); + auto slice = xla::DynamicSlice(output, batch_element_indices, + slice_shape.dim_sizes()); auto padding_config = xla::MakeNoPaddingConfig(slice_shape.dims()); padding_config.mutable_dimensions(seq_dim_)->set_edge_padding_high( slice_shape.dim_size(seq_dim_)); - slice = body_builder->Pad( - slice, XlaHelpers::Zero(body_builder.get(), input_type), - padding_config); + slice = xla::Pad(slice, XlaHelpers::Zero(body_builder.get(), input_type), + padding_config); // Now slice out the reversed sequence from its actual start. // sequence_start_indices is the offset of the start of the reversed // sequence in the input. The slice will go into the padding, however, we // will mask off these elements and replace them with elements from the // original input so their values do not matter. - auto sequence_start_indices = body_builder->Broadcast( - XlaHelpers::Zero(body_builder.get(), seq_lens_type), - {slice_shape.dims()}); - sequence_start_indices = body_builder->DynamicUpdateSlice( + auto sequence_start_indices = + xla::Broadcast(XlaHelpers::Zero(body_builder.get(), seq_lens_type), + {slice_shape.dims()}); + sequence_start_indices = xla::DynamicUpdateSlice( sequence_start_indices, - body_builder->Sub(XlaHelpers::IntegerLiteral( - body_builder.get(), seq_lens_type, max_seq_len), - seq_len), - body_builder->Reshape( - XlaHelpers::IntegerLiteral(body_builder.get(), seq_lens_type, - seq_dim_), - {1})); - slice = body_builder->DynamicSlice(slice, sequence_start_indices, - slice_shape.dim_sizes()); + xla::Sub(XlaHelpers::IntegerLiteral(body_builder.get(), seq_lens_type, + max_seq_len), + seq_len), + xla::Reshape(XlaHelpers::IntegerLiteral(body_builder.get(), + seq_lens_type, seq_dim_), + {1})); + slice = xla::DynamicSlice(slice, sequence_start_indices, + slice_shape.dim_sizes()); // Shift the reversed sequence to the left. - output = body_builder->DynamicUpdateSlice(output, slice, - batch_element_indices); + output = xla::DynamicUpdateSlice(output, slice, batch_element_indices); - body_builder->Tuple( - {body_builder->Add( - i, XlaHelpers::One(body_builder.get(), seq_lens_type)), + xla::Tuple( + body_builder.get(), + {xla::Add(i, XlaHelpers::One(body_builder.get(), seq_lens_type)), seq_lens, output}); } auto body = body_builder->Build(); OP_REQUIRES_OK(context, body.status()); - auto loop_output = builder->While( + auto loop_output = xla::While( condition.ValueOrDie(), body.ValueOrDie(), - builder->Tuple({XlaHelpers::Zero(builder, seq_lens_type), seq_lens, - builder->Rev(input, {seq_dim_})})); - auto output = builder->GetTupleElement(loop_output, 2); + xla::Tuple(builder, {XlaHelpers::Zero(builder, seq_lens_type), seq_lens, + xla::Rev(input, {seq_dim_})})); + auto output = xla::GetTupleElement(loop_output, 2); // Mask out elements after the sequence length. - xla::XlaOp iota; - OP_REQUIRES_OK( - context, XlaHelpers::Iota(builder, seq_lens_type, max_seq_len, &iota)); + xla::XlaOp iota = + xla::Iota(builder, seq_lens_xla_shape.element_type(), max_seq_len); std::vector dims(input_shape.dims(), 1); dims[batch_dim_] = batch_size; - auto mask = builder->Lt(iota, builder->Reshape(seq_lens, dims), {seq_dim_}); + auto mask = xla::Lt(iota, xla::Reshape(seq_lens, dims), {seq_dim_}); // Broadcast the mask up to the input shape. - mask = - builder->Or(mask, builder->Broadcast(builder->ConstantR0(false), - input_shape.dim_sizes())); + mask = xla::Or(mask, xla::Broadcast(xla::ConstantR0(builder, false), + input_shape.dim_sizes())); - output = builder->Select(mask, output, input); + output = xla::Select(mask, output, input); context->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc index 1819fb543317eed15b2fe0518d74aba5c564697d..76924c6a01a44e7a723b8c8895e8decbdd466c79 100644 --- a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" @@ -100,7 +101,7 @@ class ScanOp : public XlaOpKernel { init = XlaHelpers::One(builder, dtype); reducer = ctx->GetOrCreateMul(dtype); } - auto output = builder->ReduceWindowWithGeneralPadding( + auto output = xla::ReduceWindowWithGeneralPadding( XlaHelpers::ConvertElementType(builder, ctx->Input(0), dtype), init, *reducer, window_dims, window_strides, padding); output = @@ -110,12 +111,12 @@ class ScanOp : public XlaOpKernel { // of all the input elements. Slice off this extra "last" element. if (exclusive_) { if (reverse_) { - output = builder->SliceInDim(output, 1, input_shape.dim_size(axis) + 1, - 1, axis); + output = + xla::SliceInDim(output, 1, input_shape.dim_size(axis) + 1, 1, axis); } else { output = - builder->SliceInDim(output, 0, input_shape.dim_size(axis), 1, axis); + xla::SliceInDim(output, 0, input_shape.dim_size(axis), 1, axis); } } ctx->SetOutput(0, output); diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc index f2c63b4f9083ad3c7dd7cf318dc22def1e99fa9f..14709bb6cbce4b3ae0f7ff859b0fa622c6eda293 100644 --- a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" @@ -103,8 +104,8 @@ class ScatterNdOp : public XlaOpKernel { updates_shape)); xla::XlaBuilder* builder = context->builder(); - auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype), - buffer_shape.dim_sizes()); + auto buffer = xla::Broadcast(XlaHelpers::Zero(builder, dtype), + buffer_shape.dim_sizes()); auto indices = context->Input(0); auto updates = context->Input(1); auto result = diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc index 664078ca16c6d5d4b57c4a8c661ad0848f30dd7d..e2ac7da2c2630725efe3dbcc51c3f3d30e7aca2c 100644 --- a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc @@ -14,20 +14,30 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/tf2xla/lib/scatter.h" +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { -class UnsortedSegmentSum : public XlaOpKernel { +class UnsortedSegmentReduce : public XlaOpKernel { public: - explicit UnsortedSegmentSum(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + explicit UnsortedSegmentReduce(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + DataType dtype; + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype)); + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(dtype, &type_)); } + // The initial value to initialize elements of the output to. + virtual xla::XlaOp InitialValue(xla::XlaBuilder* builder) = 0; + + // A function to combine two scalars with the same index (e.g., sum). + virtual xla::XlaOp Combine(xla::XlaOp a, xla::XlaOp b) = 0; + void Compile(XlaOpKernelContext* ctx) override { // output = unsorted_segment_sum(data, indices, num_segments) // Compute a tensor such that: @@ -50,28 +60,28 @@ class UnsortedSegmentSum : public XlaOpKernel { OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &num_segments)); OP_REQUIRES(ctx, data_shape.dims() >= indices_shape.dims(), - errors::InvalidArgument( - "UnsortedSegmentSum requires that indices' rank be" - " less than or equal to data's rank.")); + errors::InvalidArgument(type_string(), + " requires that indices' rank be" + " less than or equal to data's rank.")); // Validate that indices.shape is a prefix of data.shape. for (int d = 0; d < indices_shape.dims(); ++d) { - OP_REQUIRES(ctx, (data_shape.dim_size(d) == indices_shape.dim_size(d)), - errors::InvalidArgument( - "UnsortedSegmentSum requires indices shape to be prefix" - " of data_shape, but dimension ", - d, " differs ", data_shape.dim_size(d), " vs. ", - indices_shape.dim_size(d))); + OP_REQUIRES( + ctx, (data_shape.dim_size(d) == indices_shape.dim_size(d)), + errors::InvalidArgument(type_string(), + " requires indices shape to be prefix" + " of data_shape, but dimension ", + d, " differs ", data_shape.dim_size(d), + " vs. ", indices_shape.dim_size(d))); } xla::XlaBuilder* builder = ctx->builder(); TensorShape buffer_shape = data_shape; buffer_shape.RemoveDimRange(0, indices_shape.dims()); buffer_shape.InsertDim(0, num_segments); - auto buffer = builder->Broadcast(XlaHelpers::Zero(builder, dtype_), - buffer_shape.dim_sizes()); + auto buffer = + xla::Broadcast(InitialValue(builder), buffer_shape.dim_sizes()); - auto combiner = [](xla::XlaOp a, xla::XlaOp b, xla::XlaBuilder* builder) { - return builder->Add(a, b); - }; + auto combiner = [this](xla::XlaOp a, xla::XlaOp b, + xla::XlaBuilder* builder) { return Combine(a, b); }; auto result = XlaScatter(buffer, /*updates=*/data, indices, /*indices_are_vectors=*/false, combiner, builder); @@ -79,13 +89,73 @@ class UnsortedSegmentSum : public XlaOpKernel { ctx->SetOutput(0, result.ValueOrDie()); } - private: - DataType dtype_; + protected: + xla::PrimitiveType type_; +}; + +class UnsortedSegmentSum : public UnsortedSegmentReduce { + public: + explicit UnsortedSegmentSum(OpKernelConstruction* ctx) + : UnsortedSegmentReduce(ctx) {} + + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { + return xla::Zero(builder, type_); + }; + xla::XlaOp Combine(xla::XlaOp a, xla::XlaOp b) override { return a + b; }; }; REGISTER_XLA_OP( Name("UnsortedSegmentSum").CompileTimeConstInput("num_segments"), UnsortedSegmentSum); +class UnsortedSegmentProd : public UnsortedSegmentReduce { + public: + explicit UnsortedSegmentProd(OpKernelConstruction* ctx) + : UnsortedSegmentReduce(ctx) {} + + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { + return xla::One(builder, type_); + }; + xla::XlaOp Combine(xla::XlaOp a, xla::XlaOp b) override { return a * b; }; +}; + +REGISTER_XLA_OP( + Name("UnsortedSegmentProd").CompileTimeConstInput("num_segments"), + UnsortedSegmentProd); + +class UnsortedSegmentMin : public UnsortedSegmentReduce { + public: + explicit UnsortedSegmentMin(OpKernelConstruction* ctx) + : UnsortedSegmentReduce(ctx) {} + + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { + return xla::MaxFiniteValue(builder, type_); + }; + xla::XlaOp Combine(xla::XlaOp a, xla::XlaOp b) override { + return xla::Min(a, b); + }; +}; + +REGISTER_XLA_OP( + Name("UnsortedSegmentMin").CompileTimeConstInput("num_segments"), + UnsortedSegmentMin); + +class UnsortedSegmentMax : public UnsortedSegmentReduce { + public: + explicit UnsortedSegmentMax(OpKernelConstruction* ctx) + : UnsortedSegmentReduce(ctx) {} + + xla::XlaOp InitialValue(xla::XlaBuilder* builder) override { + return xla::MinFiniteValue(builder, type_); + }; + xla::XlaOp Combine(xla::XlaOp a, xla::XlaOp b) override { + return xla::Max(a, b); + }; +}; + +REGISTER_XLA_OP( + Name("UnsortedSegmentMax").CompileTimeConstInput("num_segments"), + UnsortedSegmentMax); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/select_op.cc b/tensorflow/compiler/tf2xla/kernels/select_op.cc index f9f48164d63492b057d4950abfc2ca6153e44870..5c010c9df23ba6c7732d87fa014879d93ff586ce 100644 --- a/tensorflow/compiler/tf2xla/kernels/select_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/select_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" @@ -40,8 +41,6 @@ class SelectOp : public XlaOpKernel { "'then' and 'else' must have the same size. but received: ", then_shape.DebugString(), " vs. ", else_shape.DebugString())); - xla::XlaBuilder* builder = ctx->builder(); - auto cond_handle = ctx->Input(0); auto then_handle = ctx->Input(1); auto else_handle = ctx->Input(2); @@ -69,14 +68,14 @@ class SelectOp : public XlaOpKernel { const auto dim_sizes = then_shape.dim_sizes(); gtl::ArraySlice bdims = dim_sizes; bdims.pop_front(); - cond_handle = builder->Broadcast(cond_handle, bdims); + cond_handle = xla::Broadcast(cond_handle, bdims); std::vector dim_order(then_shape.dims()); dim_order[0] = then_shape.dims() - 1; std::iota(dim_order.begin() + 1, dim_order.end(), 0); - cond_handle = builder->Transpose(cond_handle, dim_order); + cond_handle = xla::Transpose(cond_handle, dim_order); } - ctx->SetOutput(0, builder->Select(cond_handle, then_handle, else_handle)); + ctx->SetOutput(0, xla::Select(cond_handle, then_handle, else_handle)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc index 9ce01d0d44509bbcbea18afdb4210a675834bb6d..6281d6c6533f7f49a269f5c7e52226ba0f1d29f6 100644 --- a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc @@ -45,7 +45,7 @@ void SendOp::Compile(XlaOpKernelContext* ctx) { XlaCompiler* compiler = XlaContext::Get(ctx).compiler(); xla::ChannelHandle channel; OP_REQUIRES_OK(ctx, compiler->GetChannelHandle(tensor_name_, &channel)); - ctx->builder()->Send(ctx->Input(0), channel); + xla::Send(ctx->Input(0), channel); } REGISTER_XLA_OP(Name("XlaSend"), SendOp); @@ -76,7 +76,7 @@ void RecvOp::Compile(XlaOpKernelContext* ctx) { XlaCompiler* compiler = XlaContext::Get(ctx).compiler(); xla::ChannelHandle channel; OP_REQUIRES_OK(ctx, compiler->GetChannelHandle(tensor_name_, &channel)); - ctx->SetOutput(0, ctx->builder()->Recv(shape_, channel)); + ctx->SetOutput(0, xla::Recv(ctx->builder(), shape_, channel)); } REGISTER_XLA_OP(Name("XlaRecv"), RecvOp); diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc index 2c31f8d90891924f6f86a54ccf548de4df87f3bd..bc3d0bf5dfe9e5af8e50a25e27db7148e05e0cfd 100644 --- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc @@ -55,9 +55,10 @@ Status GetIntValue(int index, XlaOpKernelContext* ctx, int64* value) { // The type-specific part of the implementation of Range. template -Status CreateRangeTensor(const xla::Literal& start_literal, - const xla::Literal& limit_literal, - const xla::Literal& delta_literal, Tensor* output) { +Status CreateRangeTensor(const xla::LiteralSlice& start_literal, + const xla::LiteralSlice& limit_literal, + const xla::LiteralSlice& delta_literal, + Tensor* output) { T start = start_literal.Get({}); T limit = limit_literal.Get({}); T delta = delta_literal.Get({}); @@ -67,13 +68,13 @@ Status CreateRangeTensor(const xla::Literal& start_literal, } if (delta > 0) { if (start > limit) { - return errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit); } } else { if (start < limit) { - return errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit); } } int64 size = diff --git a/tensorflow/compiler/tf2xla/kernels/shape_op.cc b/tensorflow/compiler/tf2xla/kernels/shape_op.cc index 05354bca5bb089703fdcceb6f44648bbb98d004b..5798823cd54c66dd179e3611c0041f7c5a1ff2b5 100644 --- a/tensorflow/compiler/tf2xla/kernels/shape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/shape_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" @@ -43,7 +44,7 @@ class ShapeOp : public XlaOpKernel { DataType out_dtype_; }; -REGISTER_XLA_OP(Name("Shape"), ShapeOp); +REGISTER_XLA_OP(Name("Shape").CompilationOnly(), ShapeOp); class ShapeNOp : public XlaOpKernel { public: @@ -65,7 +66,7 @@ class ShapeNOp : public XlaOpKernel { private: DataType out_dtype_; }; -REGISTER_XLA_OP(Name("ShapeN"), ShapeNOp); +REGISTER_XLA_OP(Name("ShapeN").CompilationOnly(), ShapeNOp); class RankOp : public XlaOpKernel { public: @@ -81,7 +82,7 @@ class RankOp : public XlaOpKernel { } }; -REGISTER_XLA_OP(Name("Rank"), RankOp); +REGISTER_XLA_OP(Name("Rank").CompilationOnly(), RankOp); class SizeOp : public XlaOpKernel { public: @@ -100,7 +101,7 @@ class SizeOp : public XlaOpKernel { } }; -REGISTER_XLA_OP(Name("Size"), SizeOp); +REGISTER_XLA_OP(Name("Size").CompilationOnly(), SizeOp); class ExpandDimsOp : public XlaOpKernel { public: @@ -147,7 +148,7 @@ class ExpandDimsOp : public XlaOpKernel { dim = std::min(dim, existing_dims_size); new_shape.emplace(new_shape.begin() + dim, 1); - ctx->SetOutput(0, ctx->builder()->Reshape(ctx->Input(0), new_shape)); + ctx->SetOutput(0, xla::Reshape(ctx->Input(0), new_shape)); } }; REGISTER_XLA_OP(Name("ExpandDims").CompileTimeConstInput("dim"), ExpandDimsOp); @@ -189,10 +190,9 @@ class SqueezeOp : public XlaOpKernel { if (!wrapped_squeeze_dims.empty()) { if (wrapped_squeeze_dims.count(i) > 0) { OP_REQUIRES(ctx, existing_dim == 1, - errors::InvalidArgument("Tried to explicitly squeeze " - "dimension ", - i, " but dimension was not 1: ", - existing_dim)); + errors::InvalidArgument( + "Tried to explicitly squeeze dimension ", i, + " but dimension was not 1: ", existing_dim)); } else { // This dimension is not being squeezed. new_shape.push_back(existing_dim); @@ -205,7 +205,7 @@ class SqueezeOp : public XlaOpKernel { } } - ctx->SetOutput(0, ctx->builder()->Reshape(ctx->Input(0), new_shape)); + ctx->SetOutput(0, xla::Reshape(ctx->Input(0), new_shape)); } private: @@ -222,7 +222,7 @@ class ZerosLikeOp : public XlaOpKernel { const TensorShape input_shape = ctx->InputShape(0); auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0)); - ctx->SetOutput(0, ctx->builder()->Broadcast(zero, input_shape.dim_sizes())); + ctx->SetOutput(0, xla::Broadcast(zero, input_shape.dim_sizes())); } }; @@ -236,7 +236,7 @@ class OnesLikeOp : public XlaOpKernel { const TensorShape input_shape = ctx->InputShape(0); auto one = XlaHelpers::One(ctx->builder(), input_type(0)); - ctx->SetOutput(0, ctx->builder()->Broadcast(one, input_shape.dim_sizes())); + ctx->SetOutput(0, xla::Broadcast(one, input_shape.dim_sizes())); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc index be1e97bf26fa4cde1b741c8d0b843a85ce33a59c..1864584adee357ce35a3e8a38a4e3c58c356bfca 100644 --- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -92,8 +93,7 @@ class SliceOp : public XlaOpKernel { limits.push_back(begin[i] + size[i]); } std::vector strides(begin.size(), 1); - ctx->SetOutput( - 0, ctx->builder()->Slice(ctx->Input(0), begin, limits, strides)); + ctx->SetOutput(0, xla::Slice(ctx->Input(0), begin, limits, strides)); } else { // `begin` is not a compile-time constant. for (int i = 0; i < input_dims; ++i) { @@ -106,8 +106,7 @@ class SliceOp : public XlaOpKernel { input_shape.dim_size(i), "], but ", "got ", size[i])); } - ctx->SetOutput( - 0, ctx->builder()->DynamicSlice(ctx->Input(0), ctx->Input(1), size)); + ctx->SetOutput(0, xla::DynamicSlice(ctx->Input(0), ctx->Input(1), size)); } } }; diff --git a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc index bbf5ee8b12186a582666121b1df5d8b7d881863e..a71fbcd901e8919949db5873675a7e3e785bdf4e 100644 --- a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc @@ -15,9 +15,12 @@ limitations under the License. // XLA-specific Ops for softmax. +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -41,6 +44,7 @@ class SoftmaxOp : public XlaOpKernel { const int kClassDim = 1; const DataType type = input_type(0); + const xla::PrimitiveType xla_type = ctx->input_xla_type(0); auto logits = ctx->Input(0); xla::XlaBuilder* const b = ctx->builder(); @@ -48,24 +52,27 @@ class SoftmaxOp : public XlaOpKernel { // Find the max in each batch, resulting in a tensor of shape [batch] auto logits_max = - b->Reduce(logits, XlaHelpers::MinValue(b, type), max_func, {kClassDim}); + xla::Reduce(logits, xla::MinValue(b, xla_type), max_func, {kClassDim}); // Subtract the max in batch b from every element in batch b. Broadcasts // along the batch dimension. - auto shifted_logits = b->Sub(logits, logits_max, {kBatchDim}); - auto exp_shifted = b->Exp(shifted_logits); + auto shifted_logits = xla::Sub(logits, logits_max, {kBatchDim}); + auto exp_shifted = xla::Exp(shifted_logits); const DataType accumulation_type = XlaHelpers::SumAccumulationType(type); + xla::PrimitiveType xla_accumulation_type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(accumulation_type, + &xla_accumulation_type)); auto converted = - XlaHelpers::ConvertElementType(b, exp_shifted, accumulation_type); + xla::ConvertElementType(exp_shifted, xla_accumulation_type); auto reduce = - b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); + xla::Reduce(converted, xla::Zero(b, xla_accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); auto sum = XlaHelpers::ConvertElementType(b, reduce, type); auto softmax = log_ // softmax = shifted_logits - log(sum(exp(shifted_logits))) - ? b->Sub(shifted_logits, b->Log(sum), {kBatchDim}) + ? xla::Sub(shifted_logits, xla::Log(sum), {kBatchDim}) // softmax = exp(shifted_logits) / sum(exp(shifted_logits)) - : b->Div(exp_shifted, sum, {kBatchDim}); + : xla::Div(exp_shifted, sum, {kBatchDim}); ctx->SetOutput(0, softmax); } @@ -77,8 +84,8 @@ REGISTER_XLA_OP(Name("Softmax"), SoftmaxOp); REGISTER_XLA_OP(Name("LogSoftmax"), SoftmaxOp); std::pair CrossEntropyWithLogits( - XlaOpKernelContext* ctx, DataType type, const xla::XlaOp& logits, - const xla::XlaOp& labels) { + XlaOpKernelContext* ctx, DataType type, xla::PrimitiveType xla_type, + xla::XlaOp logits, xla::XlaOp labels) { const xla::XlaComputation& max_func = *ctx->GetOrCreateMax(type); const int kBatchDim = 0; @@ -87,43 +94,44 @@ std::pair CrossEntropyWithLogits( xla::XlaBuilder* b = ctx->builder(); // Find the max in each batch, resulting in a tensor of shape [batch] auto logits_max = - b->Reduce(logits, XlaHelpers::MinValue(b, type), max_func, {kClassDim}); + xla::Reduce(logits, xla::MinValue(b, xla_type), max_func, {kClassDim}); // Subtract the max in batch b from every element in batch b. // Broadcasts along the batch dimension. - auto shifted_logits = b->Sub(logits, logits_max, {kBatchDim}); + auto shifted_logits = xla::Sub(logits, logits_max, {kBatchDim}); // exp(logits - max_logits) - auto exp_shifted_logits = b->Exp(shifted_logits); + auto exp_shifted_logits = xla::Exp(shifted_logits); // sum_{class} (exp(logits - max_logits)) const DataType accumulation_type = XlaHelpers::SumAccumulationType(type); auto converted = XlaHelpers::ConvertElementType(b, exp_shifted_logits, accumulation_type); - auto reduce = b->Reduce(converted, XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); + auto reduce = + xla::Reduce(converted, XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); auto sum_exp = XlaHelpers::ConvertElementType(b, reduce, type); // log(sum(exp(logits - max_logits))) - auto log_sum_exp = b->Log(sum_exp); + auto log_sum_exp = xla::Log(sum_exp); // sum(-labels * // ((logits - max_logits) - log(sum(exp(logits - max_logits))))) // along classes // (The subtraction broadcasts along the batch dimension.) - auto sub = b->Sub(shifted_logits, log_sum_exp, {kBatchDim}); - auto mul = b->Mul(b->Neg(labels), sub); + auto sub = xla::Sub(shifted_logits, log_sum_exp, {kBatchDim}); + auto mul = xla::Mul(xla::Neg(labels), sub); auto sum = - b->Reduce(XlaHelpers::ConvertElementType(b, mul, accumulation_type), - XlaHelpers::Zero(b, accumulation_type), - *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); + xla::Reduce(XlaHelpers::ConvertElementType(b, mul, accumulation_type), + XlaHelpers::Zero(b, accumulation_type), + *ctx->GetOrCreateAdd(accumulation_type), {kClassDim}); auto loss = XlaHelpers::ConvertElementType(b, sum, type); // backprop: prob - labels, where // prob = exp(logits - max_logits) / sum(exp(logits - max_logits)) // (where the division broadcasts along the batch dimension) xla::XlaOp backprop = - b->Sub(b->Div(exp_shifted_logits, sum_exp, {kBatchDim}), labels); + xla::Sub(xla::Div(exp_shifted_logits, sum_exp, {kBatchDim}), labels); return {loss, backprop}; } @@ -146,12 +154,13 @@ class SoftmaxXentWithLogitsOp : public XlaOpKernel { // check that "labels" is a matrix too. const DataType type = input_type(0); + const xla::PrimitiveType xla_type = ctx->input_xla_type(0); auto logits = ctx->Input(0); auto labels = ctx->Input(1); xla::XlaOp loss, backprop; std::tie(loss, backprop) = - CrossEntropyWithLogits(ctx, type, logits, labels); + CrossEntropyWithLogits(ctx, type, xla_type, logits, labels); ctx->SetOutput(0, loss); ctx->SetOutput(1, backprop); } @@ -187,8 +196,9 @@ class SparseSoftmaxXentWithLogitsOp : public XlaOpKernel { int64 batch_size = logits_shape.dim_size(0); int64 depth = logits_shape.dim_size(1); - DataType logits_type = input_type(0); - DataType indices_type = input_type(1); + const DataType logits_type = input_type(0); + const xla::PrimitiveType xla_logits_type = ctx->input_xla_type(0); + const DataType indices_type = input_type(1); xla::XlaOp indices = ctx->Input(1); @@ -206,20 +216,18 @@ class SparseSoftmaxXentWithLogitsOp : public XlaOpKernel { // Builds a vector of {batch_size} that is 0 if the index is in range, or // NaN otherwise; then add that vector to the labels to force out-of-range // values to NaNs. - xla::XlaOp nan_or_zero = builder->Select( - builder->And( - builder->Le(XlaHelpers::Zero(builder, indices_type), indices), - builder->Lt(indices, XlaHelpers::IntegerLiteral( - builder, indices_type, depth))), - builder->Broadcast(XlaHelpers::Zero(builder, logits_type), - {batch_size}), - builder->Broadcast(XlaHelpers::FloatLiteral(builder, logits_type, NAN), - {batch_size})); - labels = builder->Add(labels, nan_or_zero, {0}); + xla::XlaOp nan_or_zero = xla::Select( + xla::And(xla::Le(XlaHelpers::Zero(builder, indices_type), indices), + xla::Lt(indices, XlaHelpers::IntegerLiteral( + builder, indices_type, depth))), + xla::Broadcast(XlaHelpers::Zero(builder, logits_type), {batch_size}), + xla::Broadcast(XlaHelpers::FloatLiteral(builder, logits_type, NAN), + {batch_size})); + labels = xla::Add(labels, nan_or_zero, {0}); xla::XlaOp loss, backprop; - std::tie(loss, backprop) = - CrossEntropyWithLogits(ctx, logits_type, ctx->Input(0), labels); + std::tie(loss, backprop) = CrossEntropyWithLogits( + ctx, logits_type, xla_logits_type, ctx->Input(0), labels); ctx->SetOutput(0, loss); ctx->SetOutput(1, backprop); } diff --git a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..faaf8964ff7c40d75a493b03e6b400632117cb45 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc @@ -0,0 +1,35 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" + +namespace tensorflow { +namespace { + +class XlaSortOp : public XlaOpKernel { + public: + explicit XlaSortOp(OpKernelConstruction* context) : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + context->SetOutput(0, xla::Sort(context->Input(0))); + } +}; + +REGISTER_XLA_OP(Name("XlaSort"), XlaSortOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc index ec077924b5b5af4a573c86c8d9aeb8623bd7f801..8a8525efa186ed4aa02c494f7505f6245677e96e 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { namespace { @@ -73,7 +74,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, "The product of the block dimensions must be positive")); xla::XlaOp padded = - b->Pad(input, XlaHelpers::Zero(b, input_dtype), padding_config); + xla::Pad(input, XlaHelpers::Zero(b, input_dtype), padding_config); // 2. Reshape `padded` to `reshaped_padded` of shape: // @@ -100,7 +101,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, std::copy(remainder_shape.begin(), remainder_shape.end(), reshaped_padded_shape.begin() + 1 + 2 * block_rank); - xla::XlaOp reshaped_padded = b->Reshape(padded, reshaped_padded_shape); + xla::XlaOp reshaped_padded = xla::Reshape(padded, reshaped_padded_shape); // 3. Permute dimensions of `reshaped_padded` to produce // `permuted_reshaped_padded` of shape: @@ -120,7 +121,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, std::iota(permutation.begin() + 1 + block_rank * 2, permutation.end(), 1 + block_rank * 2); xla::XlaOp permuted_reshaped_padded = - b->Transpose(reshaped_padded, permutation); + xla::Transpose(reshaped_padded, permutation); // 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the // batch dimension, producing an output tensor of shape: @@ -140,7 +141,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, std::copy(remainder_shape.begin(), remainder_shape.end(), output_shape.begin() + 1 + block_rank); - xla::XlaOp output = b->Reshape(permuted_reshaped_padded, output_shape); + xla::XlaOp output = xla::Reshape(permuted_reshaped_padded, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc index 4c5886ee2a0f63d609f79fc690f457d93e284e3e..47d282fe9ec664bbc424793e93f778ebb13c6877 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { @@ -50,7 +51,6 @@ class SpaceToDepthOp : public XlaOpKernel { const gtl::InlinedVector input_shape = input_tensor_shape.dim_sizes(); - xla::XlaBuilder* b = ctx->builder(); xla::XlaOp input = ctx->Input(0); int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_); @@ -135,7 +135,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[1] / block_size_, block_size_, // input_shape[2] / block_size_, block_size_, // depth] - xla::XlaOp reshaped = b->Reshape(input, reshaped_shape); + xla::XlaOp reshaped = xla::Reshape(input, reshaped_shape); // 2. Permute dimensions of `reshaped` to produce // `permuted_reshaped` of shape: @@ -145,7 +145,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[2] / block_size_, // block_size_, block_size_, // depth] - xla::XlaOp permuted_reshaped = b->Transpose(reshaped, transpose_order); + xla::XlaOp permuted_reshaped = xla::Transpose(reshaped, transpose_order); // 3. Reshape `permuted_reshaped` to flatten `block_shape` into the // batch dimension, producing an output tensor of shape: @@ -155,7 +155,7 @@ class SpaceToDepthOp : public XlaOpKernel { // input_shape[2] / block_size_, // block_size_ * block_size_ * depth] // - xla::XlaOp output = b->Reshape(permuted_reshaped, output_shape); + xla::XlaOp output = xla::Reshape(permuted_reshaped, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/sparse_to_dense_op.cc b/tensorflow/compiler/tf2xla/kernels/sparse_to_dense_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..e831dc30a9d3c27ec3b1494e7d8a6de836ff2a11 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/sparse_to_dense_op.cc @@ -0,0 +1,88 @@ +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/scatter.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" + +namespace tensorflow { +namespace { + +// Operator to convert sparse representations to dense. +class SparseToDenseOp : public XlaOpKernel { + public: + explicit SparseToDenseOp(OpKernelConstruction* context) + : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + // sparse_indices + const TensorShape indices_shape = context->InputShape(0); + OP_REQUIRES(context, indices_shape.dims() <= 2, + errors::InvalidArgument( + "sparse_indices should be a scalar, vector, or matrix, " + "got shape ", + indices_shape.DebugString())); + const int64 num_elems = + indices_shape.dims() > 0 ? indices_shape.dim_size(0) : 1; + const int64 num_dims = + indices_shape.dims() > 1 ? indices_shape.dim_size(1) : 1; + + // output_shape + TensorShape output_shape; + OP_REQUIRES_OK(context, context->ConstantInputAsShape(1, &output_shape)); + OP_REQUIRES(context, output_shape.dims() == num_dims, + errors::InvalidArgument( + "output_shape has incorrect number of elements: ", + output_shape.num_elements(), " should be: ", num_dims)); + + // sparse_values + const TensorShape sparse_values_shape = context->InputShape(2); + const int64 num_values = sparse_values_shape.num_elements(); + OP_REQUIRES( + context, + sparse_values_shape.dims() == 0 || + (sparse_values_shape.dims() == 1 && num_values == num_elems), + errors::InvalidArgument("sparse_values has incorrect shape ", + sparse_values_shape.DebugString(), + ", should be [] or [", num_elems, "]")); + + // default_value + const TensorShape default_value_shape = context->InputShape(3); + OP_REQUIRES(context, TensorShapeUtils::IsScalar(default_value_shape), + errors::InvalidArgument("default_value should be a scalar.")); + + xla::XlaOp indices = context->Input(0); + xla::XlaOp sparse_values = context->Input(2); + xla::XlaOp default_value = context->Input(3); + + if (sparse_values_shape.dims() == 0 && num_elems != 1) { + sparse_values = Broadcast(sparse_values, {num_elems}); + } + xla::XlaBuilder* builder = context->builder(); + auto buffer = Broadcast(default_value, output_shape.dim_sizes()); + + auto result = XlaScatter(buffer, sparse_values, indices, + /*indices_are_vectors=*/num_dims > 1, + /*combiner=*/{}, builder); + context->SetOutput(0, builder->ReportErrorOrReturn(result)); + } +}; + +REGISTER_XLA_OP(Name("SparseToDense").CompileTimeConstInput("output_shape"), + SparseToDenseOp); + +} // namespace + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 8958b2e7701e62d802e37a895c14b662ecf9786a..ca74cf24507e1666070751a17fb940a3ad594695 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -98,7 +99,7 @@ class SplitOp : public XlaOpKernel { // Slice out the ith split from the split dimension. begin[split_dim] = i * slice_size; limits[split_dim] = (i + 1) * slice_size; - ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides)); + ctx->SetOutput(i, xla::Slice(input, begin, limits, strides)); } } }; @@ -134,7 +135,7 @@ class SplitVOp : public XlaOpKernel { errors::InvalidArgument( "Number of ways to split should be > 0, but got ", num_split)); - // check that sizes are correct + // Check that sizes are correct. int total_split_size = 0; int neg_one_dim = -1; std::vector split_sizes_vec(num_split, -1); @@ -148,7 +149,7 @@ class SplitVOp : public XlaOpKernel { " number of elements as the output. Got ", split_size_shape.dims(), "-D and ", split_size_shape.num_elements(), " elements")); - // get the dimension of this split + // Get the dimension of this split. xla::Literal split_size_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &split_size_literal)); @@ -199,7 +200,7 @@ class SplitVOp : public XlaOpKernel { // Slice out the ith split from the split dimension. limits[split_dim] = begin[split_dim] + slice_size; - ctx->SetOutput(i, ctx->builder()->Slice(input, begin, limits, strides)); + ctx->SetOutput(i, xla::Slice(input, begin, limits, strides)); begin[split_dim] = limits[split_dim]; } } diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc index 0fb05a2be7b1034d6c2e864643b69647d622ede7..591e61b4c82836bc1995cd11c4c0314c9d854e50 100644 --- a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc @@ -144,24 +144,25 @@ class StackPushOp : public XlaOpKernel { // Initializes the Stack, if the element shape was not already known. OP_REQUIRES_OK(ctx, MaybeInitializeStack(b, resource, dtype_, elem_shape)); - xla::XlaOp ta = b->GetTupleElement(resource->value(), 0); - xla::XlaOp index = b->GetTupleElement(resource->value(), 1); + xla::XlaOp ta = xla::GetTupleElement(resource->value(), 0); + xla::XlaOp index = xla::GetTupleElement(resource->value(), 1); xla::XlaOp value = ctx->Input(1); // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0]. auto start_indices = - b->Pad(b->Reshape(index, {1}), b->ConstantR0(0), - xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); + xla::Pad(xla::Reshape(index, {1}), xla::ConstantR0(b, 0), + xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); TensorShape slice_shape = elem_shape; slice_shape.InsertDim(0, 1LL); - auto update = b->Reshape(value, slice_shape.dim_sizes()); + auto update = xla::Reshape(value, slice_shape.dim_sizes()); // TODO(phawkins): We don't check the index is in bounds --- there is no // error mechanism in XLA. - OP_REQUIRES_OK(ctx, resource->SetValue(b->Tuple( - {b->DynamicUpdateSlice(ta, update, start_indices), - b->Add(index, b->ConstantR0(1))}))); + OP_REQUIRES_OK(ctx, + resource->SetValue(xla::Tuple( + b, {xla::DynamicUpdateSlice(ta, update, start_indices), + xla::Add(index, xla::ConstantR0(b, 1))}))); ctx->SetOutput(0, value); } @@ -197,27 +198,27 @@ class StackPopOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, GetStackShape(b, resource, &stack_shape)); xla::XlaOp state = resource->value(); - xla::XlaOp ta = b->GetTupleElement(state, 0); - xla::XlaOp index = b->GetTupleElement(state, 1); + xla::XlaOp ta = xla::GetTupleElement(state, 0); + xla::XlaOp index = xla::GetTupleElement(state, 1); - index = b->Sub(index, b->ConstantR0(1)); - OP_REQUIRES_OK(ctx, resource->SetValue(b->Tuple({ta, index}))); + index = Sub(index, xla::ConstantR0(b, 1)); + OP_REQUIRES_OK(ctx, resource->SetValue(xla::Tuple(b, {ta, index}))); // start_indices of the DynamicSlice are [index, 0, 0, ..., 0]. auto start_indices = - b->Pad(b->Reshape(index, {1}), b->ConstantR0(0), - xla::MakeEdgePaddingConfig({{0, stack_shape.dims() - 1}})); + xla::Pad(xla::Reshape(index, {1}), xla::ConstantR0(b, 0), + xla::MakeEdgePaddingConfig({{0, stack_shape.dims() - 1}})); auto slice_shape = stack_shape.dim_sizes(); slice_shape[0] = 1LL; // TODO(phawkins): We don't check the index is in bounds --- there is no // error mechanism in XLA. - xla::XlaOp read = b->DynamicSlice(ta, start_indices, slice_shape); + xla::XlaOp read = xla::DynamicSlice(ta, start_indices, slice_shape); // Remove the leading '1' dimension. std::vector value_shape(slice_shape.begin() + 1, slice_shape.end()); - ctx->SetOutput(0, b->Reshape(read, value_shape)); + ctx->SetOutput(0, xla::Reshape(read, value_shape)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc index a99d4ddc7c4956f7144512a9bdf6f4c2eb0f944f..a6f5769e7b7b1e550b7908caa35289cf3030120f 100644 --- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc @@ -15,11 +15,15 @@ limitations under the License. #include +#include "tensorflow/compiler/tf2xla/lib/random.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -32,17 +36,9 @@ namespace { // Rotates a 32-bit integer 'v' left by 'distance' bits. xla::XlaOp RotateLeftS32(xla::XlaBuilder* builder, const xla::XlaOp& v, int distance) { - return builder->Or( - builder->ShiftLeft(v, builder->ConstantR0(distance)), - builder->ShiftRightLogical(v, builder->ConstantR0(32 - distance))); -} - -// TODO(b/65209188): add a primitive XOR to XLA and call it here, rather than -// building XOR out of other bitwise operators. -xla::XlaOp BitwiseXor(xla::XlaBuilder* builder, const xla::XlaOp& x, - const xla::XlaOp& y) { - return builder->Or(builder->And(x, builder->Not(y)), - builder->And(builder->Not(x), y)); + return xla::Or( + xla::ShiftLeft(v, xla::ConstantR0(builder, distance)), + xla::ShiftRightLogical(v, xla::ConstantR0(builder, 32 - distance))); } using ThreeFry2x32State = std::array; @@ -58,22 +54,22 @@ ThreeFry2x32State ThreeFry2x32(xla::XlaBuilder* builder, std::array ks; // 0x1BD11BDA is a parity constant specified by the ThreeFry2x32 algorithm. - ks[2] = builder->ConstantR0(0x1BD11BDA); + ks[2] = xla::ConstantR0(builder, 0x1BD11BDA); for (int i = 0; i < 2; ++i) { ks[i] = key[i]; x[i] = input[i]; - ks[2] = BitwiseXor(builder, ks[2], key[i]); + ks[2] = xla::Xor(ks[2], key[i]); } - x[0] = builder->Add(x[0], ks[0]); - x[1] = builder->Add(x[1], ks[1]); + x[0] = xla::Add(x[0], ks[0]); + x[1] = xla::Add(x[1], ks[1]); // Performs a single round of the Threefry2x32 algorithm, with a rotation // amount 'rotation'. auto round = [builder](ThreeFry2x32State v, int rotation) { - v[0] = builder->Add(v[0], v[1]); + v[0] = xla::Add(v[0], v[1]); v[1] = RotateLeftS32(builder, v[1], rotation); - v[1] = BitwiseXor(builder, v[0], v[1]); + v[1] = xla::Xor(v[0], v[1]); return v; }; @@ -83,36 +79,36 @@ ThreeFry2x32State ThreeFry2x32(xla::XlaBuilder* builder, x = round(x, rotations[1]); x = round(x, rotations[2]); x = round(x, rotations[3]); - x[0] = builder->Add(x[0], ks[1]); - x[1] = builder->Add(builder->Add(x[1], ks[2]), builder->ConstantR0(1)); + x[0] = xla::Add(x[0], ks[1]); + x[1] = xla::Add(xla::Add(x[1], ks[2]), xla::ConstantR0(builder, 1)); x = round(x, rotations[4]); x = round(x, rotations[5]); x = round(x, rotations[6]); x = round(x, rotations[7]); - x[0] = builder->Add(x[0], ks[2]); - x[1] = builder->Add(builder->Add(x[1], ks[0]), builder->ConstantR0(2)); + x[0] = xla::Add(x[0], ks[2]); + x[1] = xla::Add(xla::Add(x[1], ks[0]), xla::ConstantR0(builder, 2)); x = round(x, rotations[0]); x = round(x, rotations[1]); x = round(x, rotations[2]); x = round(x, rotations[3]); - x[0] = builder->Add(x[0], ks[0]); - x[1] = builder->Add(builder->Add(x[1], ks[1]), builder->ConstantR0(3)); + x[0] = xla::Add(x[0], ks[0]); + x[1] = xla::Add(xla::Add(x[1], ks[1]), xla::ConstantR0(builder, 3)); x = round(x, rotations[4]); x = round(x, rotations[5]); x = round(x, rotations[6]); x = round(x, rotations[7]); - x[0] = builder->Add(x[0], ks[1]); - x[1] = builder->Add(builder->Add(x[1], ks[2]), builder->ConstantR0(4)); + x[0] = xla::Add(x[0], ks[1]); + x[1] = xla::Add(xla::Add(x[1], ks[2]), xla::ConstantR0(builder, 4)); x = round(x, rotations[0]); x = round(x, rotations[1]); x = round(x, rotations[2]); x = round(x, rotations[3]); - x[0] = builder->Add(x[0], ks[2]); - x[1] = builder->Add(builder->Add(x[1], ks[0]), builder->ConstantR0(5)); + x[0] = xla::Add(x[0], ks[2]); + x[1] = xla::Add(xla::Add(x[1], ks[0]), xla::ConstantR0(builder, 5)); return x; } @@ -123,8 +119,8 @@ xla::XlaOp RandomUniform(xla::XlaBuilder* builder, const xla::XlaOp& seed, const TensorShape& shape, double minval, double maxval) { // Split the seed into two 32-bit scalars to form a key. - auto seed0 = builder->Reshape(builder->Slice(seed, {0}, {1}, {1}), {}); - auto seed1 = builder->Reshape(builder->Slice(seed, {1}, {2}, {1}), {}); + auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {}); + auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {}); ThreeFry2x32State key = {seed0, seed1}; const int64 size = shape.num_elements(); @@ -133,81 +129,36 @@ xla::XlaOp RandomUniform(xla::XlaBuilder* builder, const xla::XlaOp& seed, // Fill the generator inputs with unique counter values. ThreeFry2x32State inputs; - TF_CHECK_OK(XlaHelpers::Iota(builder, DT_INT32, half_size, &inputs[0])); - inputs[1] = builder->Add(inputs[0], builder->ConstantR0(half_size)); + inputs[0] = xla::Iota(builder, xla::S32, half_size); + inputs[1] = xla::Add(inputs[0], xla::ConstantR0(builder, half_size)); ThreeFry2x32State outputs = ThreeFry2x32(builder, inputs, key); if (size_is_odd) { - outputs[1] = builder->Slice(outputs[1], {0}, {half_size - 1}, {1}); + outputs[1] = xla::Slice(outputs[1], {0}, {half_size - 1}, {1}); } auto bits = - builder->Reshape(builder->ConcatInDim(outputs, 0), shape.dim_sizes()); + xla::Reshape(xla::ConcatInDim(builder, outputs, 0), shape.dim_sizes()); // Form 22 random mantissa bits, with a leading 1 bit. The leading 1 bit // forces the random bits into the mantissa. constexpr int kFloatBits = 32; constexpr int kMantissaBits = 23; - bits = builder->Or( - builder->ShiftRightLogical( - bits, builder->ConstantR0(kFloatBits - kMantissaBits)), - builder->ConstantR0(bit_cast(1.0f))); - auto floats = builder->BitcastConvertType(bits, xla::F32); + bits = xla::Or( + xla::ShiftRightLogical( + bits, xla::ConstantR0(builder, kFloatBits - kMantissaBits)), + xla::ConstantR0(builder, bit_cast(1.0f))); + auto floats = xla::BitcastConvertType(bits, xla::F32); // We have a floating point number in the range [1.0, 2.0). // Subtract 1.0f to shift to the range [0.0, 1.0) - floats = builder->Sub(floats, builder->ConstantR0(1.0f)); + floats = xla::Sub(floats, xla::ConstantR0(builder, 1.0f)); // Multiply and add to shift to the range [minval, maxval). - floats = builder->Mul(floats, builder->ConstantR0(maxval - minval)); - floats = builder->Add(floats, builder->ConstantR0(minval)); + floats = xla::Mul(floats, xla::ConstantR0(builder, maxval - minval)); + floats = xla::Add(floats, xla::ConstantR0(builder, minval)); return floats; } -// Approximation for the inverse error function from -// Giles, M., "Approximating the erfinv function". -// The approximation has the form: -// w = -log((1 - x) * (1 + x)) -// if ( w < 5 ) { -// w = w - 2.5 -// p = sum_{i=1}^n lq[i]*w^i -// } else { -// w = sqrt(w) - 3 -// p = sum_{i=1}^n gq[i]*w^i -// } -// return p*x -xla::XlaOp ErfInvF32(xla::XlaBuilder* b, const xla::XlaOp& x, - const TensorShape& shape) { - constexpr int kDegree = 9; - constexpr std::array w_less_than_5_constants = { - 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, - -4.39150654e-06f, 0.00021858087f, -0.00125372503f, - -0.00417768164f, 0.246640727f, 1.50140941f}; - constexpr std::array w_greater_than_5_constants = { - -0.000200214257f, 0.000100950558f, 0.00134934322f, - -0.00367342844f, 0.00573950773f, -0.0076224613f, - 0.00943887047f, 1.00167406f, 2.83297682f}; - - auto one = b->ConstantR0(1.0); - auto w = b->Neg(b->Log(b->Mul(b->Sub(one, x), b->Add(one, x)))); - - auto lt = b->Lt(w, b->ConstantR0(5.0)); - auto coefficient = [&](int i) { - return b->Select( - lt, - b->Broadcast(b->ConstantR0(w_less_than_5_constants[i]), - shape.dim_sizes()), - b->Broadcast(b->ConstantR0(w_greater_than_5_constants[i]), - shape.dim_sizes())); - }; - w = b->Select(lt, b->Sub(w, b->ConstantR0(2.5f)), - b->Sub(b->SqrtF32(w), b->ConstantR0(3.0f))); - auto p = coefficient(0); - for (int i = 1; i < kDegree; ++i) { - p = b->Add(coefficient(i), b->Mul(p, w)); - } - return b->Mul(p, x); -} - } // namespace class StatelessRandomUniformOp : public XlaOpKernel { @@ -259,8 +210,8 @@ class StatelessRandomNormalOp : public XlaOpKernel { RandomUniform(builder, seed, shape, std::nextafter(-1.0f, 0.0f), 1.0); // Convert uniform distribution to normal distribution by computing // sqrt(2) * erfinv(x) - auto normal = builder->Mul(builder->ConstantR0(std::sqrt(2.0)), - ErfInvF32(builder, uniform, shape)); + auto normal = + xla::ScalarLike(uniform, std::sqrt(2.0)) * xla::ErfInv(uniform); ctx->SetOutput(0, normal); } @@ -275,4 +226,35 @@ REGISTER_XLA_OP(Name("StatelessRandomNormal") .TypeConstraint("Tseed", DT_INT32), StatelessRandomNormalOp); +class StatelessTruncatedNormalOp : public XlaOpKernel { + public: + explicit StatelessTruncatedNormalOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) {} + + void Compile(XlaOpKernelContext* ctx) override { + TensorShape shape; + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &shape)); + + TensorShape seed_shape = ctx->InputShape(1); + OP_REQUIRES(ctx, seed_shape == TensorShape({2}), + errors::InvalidArgument("seed must have shape [2], not ", + seed_shape.DebugString())); + xla::XlaOp seed = ctx->Input(1); + xla::XlaBuilder* b = ctx->builder(); + + auto uniform = + RandomUniform(b, seed, shape, std::numeric_limits::min(), 1.0); + ctx->SetOutput(0, TruncatedNormal(uniform)); + } + + private: + TF_DISALLOW_COPY_AND_ASSIGN(StatelessTruncatedNormalOp); +}; + +REGISTER_XLA_OP(Name("StatelessTruncatedNormal") + .CompileTimeConstInput("shape") + .TypeConstraint("dtype", DT_FLOAT) + .TypeConstraint("Tseed", DT_INT32), + StatelessTruncatedNormalOp); + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index 55254c746e5ebaf6b468c24ab59b968bf0d6260b..c2165ccd86dfa1c119790beb20af0844fb1bbda8 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -92,12 +93,12 @@ class StridedSliceOp : public XlaOpKernel { xla::XlaOp slice = ctx->Input(0); if (!dimensions_to_reverse.empty()) { - slice = ctx->builder()->Rev(slice, dimensions_to_reverse); + slice = xla::Rev(slice, dimensions_to_reverse); } - slice = ctx->builder()->Slice(slice, slice_begin, slice_end, slice_strides); + slice = xla::Slice(slice, slice_begin, slice_end, slice_strides); - slice = ctx->builder()->Reshape(slice, final_shape.dim_sizes()); + slice = xla::Reshape(slice, final_shape.dim_sizes()); ctx->SetOutput(0, slice); } @@ -171,7 +172,7 @@ class StridedSliceGradOp : public XlaOpKernel { xla::XlaOp grad = ctx->Input(4); // Undo any new/shrink axes. - grad = ctx->builder()->Reshape(grad, processing_shape.dim_sizes()); + grad = xla::Reshape(grad, processing_shape.dim_sizes()); // Pad the input gradients. gtl::InlinedVector dimensions_to_reverse; @@ -204,9 +205,9 @@ class StridedSliceGradOp : public XlaOpKernel { } } if (!dimensions_to_reverse.empty()) { - grad = ctx->builder()->Rev(grad, dimensions_to_reverse); + grad = xla::Rev(grad, dimensions_to_reverse); } - grad = ctx->builder()->Pad(grad, zero, padding_config); + grad = xla::Pad(grad, zero, padding_config); ctx->SetOutput(0, grad); } @@ -306,17 +307,17 @@ class StridedSliceAssignOp : public XlaOpKernel { } if (!dimensions_to_reverse.empty()) { - rhs = ctx->builder()->Rev(rhs, dimensions_to_reverse); + rhs = xla::Rev(rhs, dimensions_to_reverse); } - rhs = ctx->builder()->Reshape(rhs, slice_dims); + rhs = xla::Reshape(rhs, slice_dims); if (lhs_shape.dims() == 0) { // TODO(b/38323843): DynamicUpdateSlice crashes on rank 0 inputs. Fix // and remove this workaround. lhs = rhs; } else { - lhs = ctx->builder()->DynamicUpdateSlice( - lhs, rhs, ctx->builder()->ConstantR1(slice_begin)); + lhs = xla::DynamicUpdateSlice( + lhs, rhs, xla::ConstantR1(ctx->builder(), slice_begin)); } OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, lhs)); diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc index 9adee78a1fd1fb9a12afae83197425c328b5fe7e..2f650ce3052ee4502912891cd3f60cfaec8b1d7c 100644 --- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/tf2xla/xla_resource.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" @@ -123,10 +124,9 @@ xla::XlaOp DynamicAddSlice(xla::XlaBuilder* builder, const xla::XlaOp& operand, const xla::XlaOp& update, const gtl::ArraySlice& update_dims, const xla::XlaOp& start_indices) { - xla::XlaOp current = - builder->DynamicSlice(operand, start_indices, update_dims); - xla::XlaOp sum = builder->Add(current, update); - return builder->DynamicUpdateSlice(operand, sum, start_indices); + xla::XlaOp current = xla::DynamicSlice(operand, start_indices, update_dims); + xla::XlaOp sum = xla::Add(current, update); + return xla::DynamicUpdateSlice(operand, sum, start_indices); } class TensorArrayOp : public XlaOpKernel { @@ -162,7 +162,7 @@ class TensorArrayOp : public XlaOpKernel { ta_shape.AddDim(size); ta_shape.AppendShape(shape); xla::XlaOp zero = XlaHelpers::Zero(b, dtype_); - value = b->Broadcast(zero, ta_shape.dim_sizes()); + value = xla::Broadcast(zero, ta_shape.dim_sizes()); } XlaContext& xc = XlaContext::Get(ctx); @@ -215,12 +215,12 @@ class TensorArrayWriteOp : public XlaOpKernel { // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0]. auto start_indices = - b->Pad(b->Reshape(index, {1}), b->ConstantR0(0), - xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); + xla::Pad(xla::Reshape(index, {1}), xla::ConstantR0(b, 0), + xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); TensorShape slice_shape = elem_shape; slice_shape.InsertDim(0, 1LL); - auto update = b->Reshape(value, slice_shape.dim_sizes()); + auto update = xla::Reshape(value, slice_shape.dim_sizes()); xla::XlaOp written = DynamicAddSlice(b, ta, update, slice_shape.dim_sizes(), start_indices); @@ -259,17 +259,17 @@ class TensorArrayReadOp : public XlaOpKernel { // start_indices of the DynamicSlice are [index, 0, 0, ..., 0]. auto start_indices = - b->Pad(b->Reshape(index, {1}), b->ConstantR0(0), - xla::MakeEdgePaddingConfig({{0, ta_shape.dims() - 1}})); + xla::Pad(xla::Reshape(index, {1}), xla::ConstantR0(b, 0), + xla::MakeEdgePaddingConfig({{0, ta_shape.dims() - 1}})); auto slice_shape = ta_shape.dim_sizes(); slice_shape[0] = 1LL; - xla::XlaOp read = b->DynamicSlice(ta, start_indices, slice_shape); + xla::XlaOp read = xla::DynamicSlice(ta, start_indices, slice_shape); // Remove the leading '1' dimension. std::vector value_shape(slice_shape.begin() + 1, slice_shape.end()); - ctx->SetOutput(0, b->Reshape(read, value_shape)); + ctx->SetOutput(0, xla::Reshape(read, value_shape)); } private: @@ -326,7 +326,7 @@ class TensorArrayGatherOp : public XlaOpKernel { for (auto i = 1; i < ta_shape.dims(); i++) { end[i] = ta_shape.dim_size(i); } - ctx->SetOutput(0, b->Slice(ta, begin, end, strides)); + ctx->SetOutput(0, xla::Slice(ta, begin, end, strides)); return; } } @@ -391,7 +391,7 @@ class TensorArrayScatterOp : public XlaOpKernel { } if (scatter_all_elements_in_order) { - ta = b->Add(ta, value); + ta = xla::Add(ta, value); } else { auto slice_dims = value_shape.dim_sizes(); slice_dims[0] = 1LL; @@ -407,13 +407,13 @@ class TensorArrayScatterOp : public XlaOpKernel { // Slice out part of the value. value_starts[0] = i; value_ends[0] = i + 1; - auto slice = b->Slice(value, value_starts, value_ends, value_strides); + auto slice = xla::Slice(value, value_starts, value_ends, value_strides); // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0]. - auto index = b->Slice(indices, {i}, {i + 1}, {1}); + auto index = xla::Slice(indices, {i}, {i + 1}, {1}); auto start_indices = - b->Pad(b->Reshape(index, {1}), b->ConstantR0(0), - xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); + xla::Pad(xla::Reshape(index, {1}), xla::ConstantR0(b, 0), + xla::MakeEdgePaddingConfig({{0, elem_shape.dims()}})); ta = DynamicAddSlice(b, ta, slice, slice_dims, start_indices); } } @@ -452,7 +452,7 @@ class TensorArrayConcatOp : public XlaOpKernel { auto ta_dims = ta_shape.dim_sizes(); std::vector shape(ta_dims.begin() + 1, ta_dims.end()); shape[0] *= ta_shape.dim_size(0); - ctx->SetOutput(0, b->Reshape(ta, shape)); + ctx->SetOutput(0, xla::Reshape(ta, shape)); Tensor lengths(DT_INT64, {ta_dims[0]}); auto lengths_vec = lengths.vec(); @@ -522,8 +522,8 @@ class TensorArraySplitOp : public XlaOpKernel { value_shape.DebugString(), " vs. ", ta_shape.DebugString())); - OP_REQUIRES_OK(ctx, resource->SetValue(b->Add( - ta, b->Reshape(value, ta_shape.dim_sizes())))); + OP_REQUIRES_OK(ctx, resource->SetValue(xla::Add( + ta, xla::Reshape(value, ta_shape.dim_sizes())))); ctx->SetOutput(0, flow); } diff --git a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc index e91075196bd8414939888e22b5483ad637487af6..c9e56942625a009fb3660f413a845547192460d5 100644 --- a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" @@ -93,9 +94,9 @@ class TileOp : public XlaOpKernel { if (one_dimension_is_broadcasted_without_multiple) { // Create a constant Zero the size of the output shape to leverage binary // operation broadcast semantics. - auto broadcasted_zero = ctx->builder()->Broadcast( + auto broadcasted_zero = xla::Broadcast( XlaHelpers::Zero(ctx->builder(), ctx->input_type(0)), output_shape); - ctx->SetOutput(0, ctx->builder()->Add(broadcasted_zero, input)); + ctx->SetOutput(0, xla::Add(broadcasted_zero, input)); return; } @@ -103,7 +104,7 @@ class TileOp : public XlaOpKernel { // dimension. This prepends the broadcasted dimensions, so an // input of shape [2,3,1] broadcast with multiples [5,4,3] will // end up with shape [5,4,3,2,3,1]. - auto broadcasted = ctx->builder()->Broadcast(input, multiples_array); + auto broadcasted = xla::Broadcast(input, multiples_array); // Now flatten and reshape. The broadcasted dimensions are // paired with the original dimensions so in the above example // we flatten [0,3,1,4,2,5] then reshape to [10,12,3]. @@ -112,8 +113,7 @@ class TileOp : public XlaOpKernel { flattened.push_back(i); flattened.push_back(i + output_shape.size()); } - xla::XlaOp output = - ctx->builder()->Reshape(broadcasted, flattened, output_shape); + xla::XlaOp output = xla::Reshape(broadcasted, flattened, output_shape); ctx->SetOutput(0, output); } diff --git a/tensorflow/compiler/tf2xla/kernels/topk_op.cc b/tensorflow/compiler/tf2xla/kernels/topk_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..9962f1207d65edea5eba0083436fa380921bb4fd --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/topk_op.cc @@ -0,0 +1,84 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/core/framework/kernel_def_builder.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/no_op.h" + +namespace tensorflow { +namespace { + +class TopKOp : public XlaOpKernel { + public: + explicit TopKOp(OpKernelConstruction* context) : XlaOpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("sorted", &sorted_)); + } + + void Compile(XlaOpKernelContext* context) override { + int64 k; + OP_REQUIRES_OK(context, context->ConstantInputAsIntScalar(1, &k)); + OP_REQUIRES(context, k >= 0, + errors::InvalidArgument("Need k >= 0, got ", k)); + const TensorShape input_shape = context->InputShape(0); + OP_REQUIRES(context, input_shape.dims() >= 1, + errors::InvalidArgument("input must be >= 1-D, got shape ", + input_shape.DebugString())); + OP_REQUIRES( + context, input_shape.dim_size(input_shape.dims() - 1) >= k, + errors::InvalidArgument("input must have at least k columns. Had ", + input_shape.dim_size(input_shape.dims() - 1), + ", needed ", k)); + + OP_REQUIRES( + context, input_shape.dims() == 1, + errors::Unimplemented("TopK is implemented for 1-D inputs, got shape ", + input_shape.DebugString())); + + xla::XlaBuilder* const b = context->builder(); + if (input_shape.dim_size(0) < k) { + k = input_shape.dim_size(0); + } + const xla::XlaOp input = context->Input(0); + xla::XlaOp iota_s32 = xla::Iota(b, xla::S32, input_shape.dim_size(0)); + xla::XlaOp sort_result = xla::Sort(xla::Neg(input), iota_s32); + xla::XlaOp values = + xla::Neg(xla::Slice(xla::GetTupleElement(sort_result, 0), + /*start_indices=*/{0}, + /*limit_indices=*/{k}, + /*strides=*/{1})); + xla::XlaOp indices = xla::Slice(xla::GetTupleElement(sort_result, 1), + /*start_indices=*/{0}, + /*limit_indices=*/{k}, + /*strides=*/{1}); + context->SetOutput(0, values); + context->SetOutput(1, indices); + } + + private: + bool sorted_; +}; + +REGISTER_XLA_OP(Name("TopKV2").CompileTimeConstInput("k").TypeConstraint( + "T", {DT_UINT32, DT_INT32, DT_FLOAT, DT_BFLOAT16}), + TopKOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc index 34caefa050c0d58f5f7bad557286b6ed64b996ad..68b1fce477b6ac231279c81b12c9bf4b1adb37d7 100644 --- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc @@ -16,6 +16,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" @@ -31,7 +33,6 @@ class ResourceApplyGradientDescent : public XlaOpKernel { : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { xla::XlaOp handle; - xla::XlaBuilder* b = ctx->builder(); DataType type = ctx->input_type(1); TensorShape var_shape; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &handle)); @@ -48,7 +49,7 @@ class ResourceApplyGradientDescent : public XlaOpKernel { var_shape.DebugString(), " vs ", delta_shape.DebugString())); - handle = b->Sub(handle, b->Mul(ctx->Input(1), ctx->Input(2))); + handle = handle - ctx->Input(1) * ctx->Input(2); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, handle)); } }; @@ -63,8 +64,6 @@ class ResourceApplyMomentum : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* b = ctx->builder(); - DataType type = ctx->input_type(2); TensorShape var_shape, accum_shape; @@ -97,14 +96,13 @@ class ResourceApplyMomentum : public XlaOpKernel { xla::XlaOp grad = ctx->Input(3); xla::XlaOp momentum = ctx->Input(4); - accum = b->Add(b->Mul(accum, momentum), grad); + accum = accum * momentum + grad; if (use_nesterov_) { // See https://github.com/tensorflow/tensorflow/pull/2798 for an // explanation of the reparameterization used here. - var = b->Sub( - var, b->Add(b->Mul(grad, lr), b->Mul(b->Mul(accum, momentum), lr))); + var = var - (grad * lr + accum * momentum * lr); } else { - var = b->Sub(var, b->Mul(accum, lr)); + var = var - accum * lr; } OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, var)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, accum)); @@ -121,8 +119,6 @@ class ResourceApplyAdagrad : public XlaOpKernel { explicit ResourceApplyAdagrad(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* b = ctx->builder(); - DataType type = ctx->input_type(2); TensorShape var_shape, accum_shape; @@ -149,10 +145,8 @@ class ResourceApplyAdagrad : public XlaOpKernel { xla::XlaOp lr = ctx->Input(2); xla::XlaOp grad = ctx->Input(3); - accum = b->Add(accum, b->Pow(grad, XlaHelpers::FloatLiteral(b, type, 2.0))); - var = b->Sub( - var, b->Mul(b->Mul(grad, lr), - b->Pow(accum, XlaHelpers::FloatLiteral(b, type, -0.5)))); + accum = accum + xla::Square(grad); + var = var - grad * lr * xla::Rsqrt(accum); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, var)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, accum)); } @@ -227,17 +221,12 @@ class ResourceApplyAdam : public XlaOpKernel { // variable <- variable - alpha * m_t / (sqrt(v_t) + epsilon) xla::XlaBuilder* b = ctx->builder(); - xla::XlaOp half = XlaHelpers::FloatLiteral(b, dtype_, 0.5); xla::XlaOp one = XlaHelpers::FloatLiteral(b, dtype_, 1.0); - xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype_, 2.0); - xla::XlaOp alpha = - b->Div(b->Mul(lr, b->Pow(b->Sub(one, beta2_power), half)), - b->Sub(one, beta1_power)); - m = b->Add(m, b->Mul(b->Sub(grad, m), b->Sub(one, beta1))); - v = b->Add(v, b->Mul(b->Sub(b->Pow(grad, two), v), b->Sub(one, beta2))); - var = - b->Sub(var, b->Div(b->Mul(m, alpha), b->Add(b->Pow(v, half), epsilon))); + xla::XlaOp alpha = lr * xla::Sqrt(one - beta2_power) / (one - beta1_power); + m = m + (grad - m) * (one - beta1); + v = v + (xla::Square(grad) - v) * (one - beta2); + var = var - m * alpha / (xla::Sqrt(v) + epsilon); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, m)); @@ -255,8 +244,6 @@ class ResourceApplyRMSProp : public XlaOpKernel { explicit ResourceApplyRMSProp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* b = ctx->builder(); - DataType type = ctx->input_type(3); TensorShape var_shape, ms_shape, mom_shape; @@ -320,16 +307,11 @@ class ResourceApplyRMSProp : public XlaOpKernel { // ms <- grad**2 (1 - rho) + ms * rho // // Which is the equation listed above. - xla::XlaOp new_ms = b->Add( - ms, - b->Mul(b->Sub(b->Pow(grad, XlaHelpers::FloatLiteral(b, type, 2.0)), ms), - b->Sub(XlaHelpers::FloatLiteral(b, type, 1.0), rho))); + xla::XlaOp new_ms = + ms + (xla::Square(grad) - ms) * (xla::ScalarLike(ms, 1.0) - rho); xla::XlaOp new_mom = - b->Add(b->Mul(mom, momentum), - b->Mul(b->Mul(grad, lr), - b->Pow(b->Add(new_ms, epsilon), - XlaHelpers::FloatLiteral(b, type, -0.5)))); - xla::XlaOp new_var = b->Sub(var, new_mom); + mom * momentum + grad * lr * xla::Rsqrt(new_ms + epsilon); + xla::XlaOp new_var = var - new_mom; OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, new_var)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, new_ms)); @@ -424,21 +406,18 @@ void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype, xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype, 2.0); xla::XlaOp grad_to_use; if (has_l2_shrinkage) { - grad_to_use = b->Add(grad, b->Mul(two, b->Mul(l2_shrinkage, var))); + grad_to_use = grad + two * l2_shrinkage * var; } else { grad_to_use = grad; } - xla::XlaOp new_accum = b->Add(accum, b->Pow(grad_to_use, two)); - xla::XlaOp new_accum_lr_pow = b->Pow(new_accum, b->Neg(lr_power)); - xla::XlaOp accum_lr_pow = b->Pow(accum, b->Neg(lr_power)); - linear = b->Add( - linear, - b->Sub(grad_to_use, - b->Mul(b->Div(b->Sub(new_accum_lr_pow, accum_lr_pow), lr), var))); - xla::XlaOp linear_clipped = b->Clamp(b->Neg(l1), linear, l1); - xla::XlaOp quadratic = b->Add(b->Div(new_accum_lr_pow, lr), b->Mul(two, l2)); - var = b->Div(b->Sub(linear_clipped, linear), quadratic); + xla::XlaOp new_accum = accum + xla::Square(grad_to_use); + xla::XlaOp new_accum_lr_pow = xla::Pow(new_accum, -lr_power); + xla::XlaOp accum_lr_pow = xla::Pow(accum, -lr_power); + linear = linear + grad_to_use - (new_accum_lr_pow - accum_lr_pow) / lr * var; + xla::XlaOp linear_clipped = xla::Clamp(-l1, linear, l1); + xla::XlaOp quadratic = new_accum_lr_pow / lr + two * l2; + var = (linear_clipped - linear) / quadratic; accum = new_accum; OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype, var)); @@ -478,5 +457,74 @@ class ResourceApplyFtrlV2 : public XlaOpKernel { REGISTER_XLA_OP(Name("ResourceApplyFtrlV2").TypeConstraint("T", kFloatTypes), ResourceApplyFtrlV2); +class ResourceApplyAdadelta : public XlaOpKernel { + public: + explicit ResourceApplyAdadelta(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + TensorShape var_shape, accum_shape, accum_update_shape; + xla::XlaOp var, accum, accum_update; + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &var_shape, &var)); + OP_REQUIRES_OK(ctx, + ctx->ReadVariableInput(1, dtype_, &accum_shape, &accum)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, dtype_, &accum_update_shape, + &accum_update)); + + TensorShape lr_shape = ctx->InputShape(3); + TensorShape rho_shape = ctx->InputShape(4); + TensorShape epsilon_shape = ctx->InputShape(5); + TensorShape grad_shape = ctx->InputShape(6); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), + errors::InvalidArgument("lr is not a scalar: ", + lr_shape.DebugString())); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(rho_shape), + errors::InvalidArgument("rho is not a scalar: ", + rho_shape.DebugString())); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(epsilon_shape), + errors::InvalidArgument("epsilon is not a scalar: ", + epsilon_shape.DebugString())); + + OP_REQUIRES(ctx, var_shape.IsSameSize(accum_shape), + errors::InvalidArgument( + "var and accum do not have the same shape", + var_shape.DebugString(), " ", accum_shape.DebugString())); + + OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape), + errors::InvalidArgument( + "var and grad do not have the same shape", + var_shape.DebugString(), " ", grad_shape.DebugString())); + + xla::XlaOp lr = ctx->Input(3); + xla::XlaOp rho = ctx->Input(4); + xla::XlaOp epsilon = ctx->Input(5); + xla::XlaOp grad = ctx->Input(6); + + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp neg_half = XlaHelpers::FloatLiteral(b, dtype_, -0.5); + xla::XlaOp half = XlaHelpers::FloatLiteral(b, dtype_, 0.5); + xla::XlaOp one = XlaHelpers::FloatLiteral(b, dtype_, 1.0); + xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype_, 2.0); + + accum = rho * accum + (one - rho) * xla::Pow(grad, two); + xla::XlaOp update = xla::Pow(accum_update + epsilon, half) * + xla::Pow(accum + epsilon, neg_half) * grad; + accum_update = rho * accum_update + (one - rho) * xla::Pow(update, two); + var = var - update * lr; + OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, accum)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, dtype_, accum_update)); + } + + private: + DataType dtype_; +}; +REGISTER_XLA_OP(Name("ResourceApplyAdadelta").TypeConstraint("T", kFloatTypes), + ResourceApplyAdadelta); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc index c167642174b328a968d7f7ce1f0ad6e0ab8a7a68..6c721c48fe3af45aff5cd0bd5e74e2693faf9f97 100644 --- a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/bounds_check.h" @@ -32,7 +33,8 @@ namespace { class TransposeOp : public XlaOpKernel { public: - explicit TransposeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + explicit TransposeOp(OpKernelConstruction* ctx, bool conjugate = false) + : XlaOpKernel(ctx), conjugate_(conjugate) {} void Compile(XlaOpKernelContext* ctx) override { const TensorShape input_shape = ctx->InputShape(0); @@ -78,19 +80,37 @@ class TransposeOp : public XlaOpKernel { errors::InvalidArgument(i, " is missing from 'perm' argument.")); } + xla::XlaOp transposed; // 0-D, 1-D, and identity transposes do nothing. if (dims <= 1 || is_identity) { - ctx->SetOutput(0, ctx->Input(0)); - return; + transposed = ctx->Input(0); + } else { + transposed = xla::Transpose(ctx->Input(0), transposed_order); } - ctx->SetOutput(0, - ctx->builder()->Transpose(ctx->Input(0), transposed_order)); + // Conjugate the transposed result if this is ConjugateTransposeOp. + if (conjugate_) { + ctx->SetOutput(0, xla::Conj(transposed)); + } else { + ctx->SetOutput(0, transposed); + } } + + private: + const bool conjugate_; +}; + +class ConjugateTransposeOp : public TransposeOp { + public: + explicit ConjugateTransposeOp(OpKernelConstruction* ctx) + : TransposeOp(ctx, /*conjugate=*/true) {} }; REGISTER_XLA_OP(Name("Transpose").CompileTimeConstInput("perm"), TransposeOp); +REGISTER_XLA_OP(Name("ConjugateTranspose").CompileTimeConstInput("perm"), + ConjugateTransposeOp); + // InvertPermutation frequently forms part of the gradient of Transpose. // // inv = InvertPermutationOp(T p) takes a permutation of @@ -127,7 +147,7 @@ class InvertPermutationOp : public XlaOpKernel { output[d] = i; } - ctx->SetOutput(0, ctx->builder()->ConstantR1(output)); + ctx->SetOutput(0, xla::ConstantR1(ctx->builder(), output)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 71a9fd051bfc8db09738a4bfe8ddde447895ecf0..116a020437e263f1d3d82fee5c0ea0ca4f97e634 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -16,24 +16,26 @@ limitations under the License. // Native XLA implementations of simple unary Ops #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { namespace { -// A subclass of a TlaUnaryOp must build the lambda computation that -// describes the scalar->scalar function to apply to each element of -// the input. #define XLAJIT_MAKE_UNARY(NAME, COMPUTATION) \ class NAME##Op : public XlaOpKernel { \ public: \ explicit NAME##Op(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} \ void Compile(XlaOpKernelContext* ctx) { \ xla::XlaBuilder* b = ctx->builder(); \ + (void)b; \ xla::XlaOp x = ctx->Input(0); \ xla::XlaOp y = COMPUTATION; \ ctx->SetOutput(0, y); \ @@ -41,122 +43,100 @@ namespace { }; \ REGISTER_XLA_OP(Name(#NAME), NAME##Op); -XLAJIT_MAKE_UNARY(ComplexAbs, b->Abs(x)); +XLAJIT_MAKE_UNARY(ComplexAbs, xla::Abs(x)); -XLAJIT_MAKE_UNARY(Angle, b->Atan2(b->Imag(x), b->Real(x))); +XLAJIT_MAKE_UNARY(Angle, xla::Atan2(xla::Imag(x), xla::Real(x))); -XLAJIT_MAKE_UNARY(Conj, b->Conj(x)); +XLAJIT_MAKE_UNARY(Conj, xla::Conj(x)); // Return x if x>0, otherwise -x. -XLAJIT_MAKE_UNARY(Abs, b->Abs(x)); +XLAJIT_MAKE_UNARY(Abs, xla::Abs(x)); // acos(x) = 2 * atan(sqrt(1 - x^2) / (1 + x)) -XLAJIT_MAKE_UNARY( - Acos, - b->Mul(XlaHelpers::FloatLiteral(b, input_type(0), 2.0), - b->Atan2(b->Pow(b->Sub(XlaHelpers::One(b, input_type(0)), - b->Mul(x, x)), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5)), - b->Add(XlaHelpers::One(b, input_type(0)), x)))); +XLAJIT_MAKE_UNARY(Acos, + xla::ScalarLike(x, 2.0) * + xla::Atan2(xla::Sqrt(xla::ScalarLike(x, 1.0) - x * x), + xla::ScalarLike(x, 1.0) + x)); // acosh(x) = log(x + sqrt(x^2 - 1)) // = log(x + sqrt((x+1)*(x-1))) -XLAJIT_MAKE_UNARY( - Acosh, - b->Log(b->Add(x, - b->Pow(b->Mul(b->Add(x, XlaHelpers::One(b, input_type(0))), - b->Sub(x, XlaHelpers::One(b, input_type(0)))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); +XLAJIT_MAKE_UNARY(Acosh, + xla::Log(x + xla::Sqrt((x + xla::ScalarLike(x, 1.0)) * + (x - xla::ScalarLike(x, 1.0))))); // asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) XLAJIT_MAKE_UNARY( - Asin, - b->Mul(XlaHelpers::FloatLiteral(b, input_type(0), 2.0), - b->Atan2(x, b->Add(XlaHelpers::One(b, input_type(0)), - b->Pow(b->Sub(XlaHelpers::One(b, input_type(0)), - b->Mul(x, x)), - XlaHelpers::FloatLiteral(b, input_type(0), - 0.5)))))); + Asin, xla::ScalarLike(x, 2.0) * + xla::Atan2(x, xla::ScalarLike(x, 1.0) + + xla::Sqrt(xla::ScalarLike(x, 1.0) - x * x))); // asinh(x) = log(x + sqrt(x^2 + 1)) -XLAJIT_MAKE_UNARY( - Asinh, - b->Log(b->Add(x, b->Pow(b->Add(b->Mul(x, x), - XlaHelpers::One(b, input_type(0))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); +XLAJIT_MAKE_UNARY(Asinh, + xla::Log(x + xla::Sqrt(x * x + xla::ScalarLike(x, 1.0)))); -XLAJIT_MAKE_UNARY(Atan, b->Atan2(x, XlaHelpers::One(b, input_type(0)))); +XLAJIT_MAKE_UNARY(Atan, xla::Atan2(x, xla::ScalarLike(x, 1.0))); // atanh(x) = 0.5 * log((1 + x) / (1 - x)) +XLAJIT_MAKE_UNARY(Atanh, xla::Log((xla::ScalarLike(x, 1.0) + x) / + (xla::ScalarLike(x, 1.0) - x)) * + xla::ScalarLike(x, 0.5)); +XLAJIT_MAKE_UNARY(Ceil, xla::Ceil(x)); +XLAJIT_MAKE_UNARY(Cos, xla::Cos(x)); +XLAJIT_MAKE_UNARY(Cosh, (xla::Exp(x) + xla::Exp(-x)) * xla::ScalarLike(x, 0.5)); +XLAJIT_MAKE_UNARY(Sin, xla::Sin(x)); +XLAJIT_MAKE_UNARY(Exp, xla::Exp(x)); + +XLAJIT_MAKE_UNARY(Expm1, xla::Expm1(x)); + +XLAJIT_MAKE_UNARY(Floor, xla::Floor(x)); +XLAJIT_MAKE_UNARY(IsFinite, xla::IsFinite(x)); XLAJIT_MAKE_UNARY( - Atanh, b->Mul(b->Log(b->Div(b->Add(XlaHelpers::One(b, input_type(0)), x), - b->Sub(XlaHelpers::One(b, input_type(0)), x))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))); -XLAJIT_MAKE_UNARY(Ceil, b->Ceil(x)); -XLAJIT_MAKE_UNARY(Cos, b->Cos(x)); -XLAJIT_MAKE_UNARY(Cosh, - b->Mul(b->Add(b->Exp(x), b->Exp(b->Neg(x))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))); -XLAJIT_MAKE_UNARY(Sin, b->Sin(x)); -XLAJIT_MAKE_UNARY(Exp, b->Exp(x)); - -XLAJIT_MAKE_UNARY(Expm1, b->Expm1(x)); - -XLAJIT_MAKE_UNARY(Floor, b->Floor(x)); -XLAJIT_MAKE_UNARY(IsFinite, b->IsFinite(x)); -XLAJIT_MAKE_UNARY(IsInf, b->Eq(b->Abs(x), - XlaHelpers::FloatLiteral( - b, input_type(0), - std::numeric_limits::infinity()))); -XLAJIT_MAKE_UNARY(IsNan, b->Ne(x, x)); + IsInf, + xla::Eq(xla::Abs(x), + xla::ScalarLike(x, std::numeric_limits::infinity()))); +XLAJIT_MAKE_UNARY(IsNan, xla::Ne(x, x)); // Return 1/x -XLAJIT_MAKE_UNARY(Inv, b->Div(XlaHelpers::One(b, input_type(0)), x)); -XLAJIT_MAKE_UNARY(Reciprocal, b->Div(XlaHelpers::One(b, input_type(0)), x)); -XLAJIT_MAKE_UNARY(Log, b->Log(x)); +XLAJIT_MAKE_UNARY(Inv, xla::ScalarLike(x, 1.0) / x); +XLAJIT_MAKE_UNARY(Reciprocal, xla::ScalarLike(x, 1.0) / x); +XLAJIT_MAKE_UNARY(Log, xla::Log(x)); -XLAJIT_MAKE_UNARY(Log1p, b->Log1p(x)); +XLAJIT_MAKE_UNARY(Log1p, xla::Log1p(x)); -XLAJIT_MAKE_UNARY(Invert, b->Not(x)); -XLAJIT_MAKE_UNARY(LogicalNot, b->Not(x)); -XLAJIT_MAKE_UNARY(Neg, b->Neg(x)); +XLAJIT_MAKE_UNARY(Invert, xla::Not(x)); +XLAJIT_MAKE_UNARY(LogicalNot, xla::Not(x)); +XLAJIT_MAKE_UNARY(Neg, -x); // Implements Banker's rounding: numbers that are equidistant between two // integers are rounded towards even. -static xla::XlaOp Round(xla::XlaBuilder* b, DataType dtype, - const xla::XlaOp& x) { - auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5); - auto one = XlaHelpers::FloatLiteral(b, dtype, 1.0); - auto two = XlaHelpers::FloatLiteral(b, dtype, 2.0); - - auto round_val = b->Floor(x); - auto fraction = b->Sub(x, round_val); - auto nearest_even_int = - b->Sub(round_val, b->Mul(two, b->Floor(b->Mul(half, x)))); - auto is_odd = b->Eq(nearest_even_int, one); - return b->Select( - b->Or(b->Gt(fraction, half), b->And(b->Eq(fraction, half), is_odd)), - b->Add(round_val, one), round_val); +xla::XlaOp RoundToEven(xla::XlaOp x) { + auto half = xla::ScalarLike(x, 0.5); + auto one = xla::ScalarLike(x, 1.0); + auto two = xla::ScalarLike(x, 2.0); + + auto round_val = xla::Floor(x); + auto fraction = x - round_val; + auto nearest_even_int = round_val - two * xla::Floor(half * x); + auto is_odd = xla::Eq(nearest_even_int, one); + return xla::Select(xla::Or(xla::Gt(fraction, half), + xla::And(xla::Eq(fraction, half), is_odd)), + round_val + one, round_val); } -XLAJIT_MAKE_UNARY(Rint, Round(b, input_type(0), x)); -XLAJIT_MAKE_UNARY(Round, Round(b, input_type(0), x)); +XLAJIT_MAKE_UNARY(Rint, RoundToEven(x)); +XLAJIT_MAKE_UNARY(Round, RoundToEven(x)); -XLAJIT_MAKE_UNARY(Rsqrt, - b->Pow(x, XlaHelpers::FloatLiteral(b, input_type(0), -0.5))); +XLAJIT_MAKE_UNARY(Rsqrt, xla::Rsqrt(x)); // Expresses sigmoid as a rescaled tanh: sigmoid(x) == (tanh(x/2) + 1) / 2. -static xla::XlaOp Sigmoid(xla::XlaBuilder* b, DataType dtype, - const xla::XlaOp& x) { - auto half = XlaHelpers::FloatLiteral(b, dtype, 0.5); - return b->Add(half, b->Mul(half, b->Tanh(b->Mul(half, x)))); +xla::XlaOp Sigmoid(xla::XlaOp x) { + auto half = xla::ScalarLike(x, 0.5); + return half + half * xla::Tanh(half * x); } -XLAJIT_MAKE_UNARY(Sigmoid, Sigmoid(b, input_type(0), x)); +XLAJIT_MAKE_UNARY(Sigmoid, Sigmoid(x)); // Returns 0 if x is 0, -1 if x < 0 and 1 if x > 0. -XLAJIT_MAKE_UNARY(Sign, b->Sign(x)); -XLAJIT_MAKE_UNARY(Sinh, - b->Mul(b->Sub(b->Exp(x), b->Exp(b->Neg(x))), - XlaHelpers::FloatLiteral(b, input_type(0), 0.5))); +XLAJIT_MAKE_UNARY(Sign, xla::Sign(x)); +XLAJIT_MAKE_UNARY(Sinh, (xla::Exp(x) - xla::Exp(-x)) * xla::ScalarLike(x, 0.5)); // softplus(x) = log(1 + exp(x)) // @@ -166,24 +146,48 @@ XLAJIT_MAKE_UNARY(Sinh, // // This is equivalent to: // max(x, 0) + log1p(exp(-abs(x))) -XLAJIT_MAKE_UNARY(Softplus, - b->Add(b->Max(x, XlaHelpers::Zero(b, input_type(0))), - b->Log1p(b->Exp(b->Neg(b->Abs(x)))))); +XLAJIT_MAKE_UNARY(Softplus, xla::Max(x, xla::ScalarLike(x, 0.0)) + + xla::Log1p(xla::Exp(-xla::Abs(x)))); // softsign(x) = x / (abs(x) + 1) -XLAJIT_MAKE_UNARY(Softsign, - b->Div(x, - b->Add(b->Abs(x), XlaHelpers::One(b, input_type(0))))); -XLAJIT_MAKE_UNARY(Sqrt, - b->Pow(x, XlaHelpers::FloatLiteral(b, input_type(0), 0.5))); -XLAJIT_MAKE_UNARY(Square, b->Mul(x, x)); -XLAJIT_MAKE_UNARY(Tan, b->Div(b->Sin(x), b->Cos(x))); -XLAJIT_MAKE_UNARY(Tanh, b->Tanh(x)); - -XLAJIT_MAKE_UNARY(Real, b->Real(x)); -XLAJIT_MAKE_UNARY(Imag, b->Imag(x)); +XLAJIT_MAKE_UNARY(Softsign, x / (xla::Abs(x) + xla::ScalarLike(x, 1.0))); +XLAJIT_MAKE_UNARY(Sqrt, xla::Sqrt(x)); +XLAJIT_MAKE_UNARY(Square, x* x); +XLAJIT_MAKE_UNARY(Tan, xla::Sin(x) / xla::Cos(x)); +XLAJIT_MAKE_UNARY(Tanh, xla::Tanh(x)); + +XLAJIT_MAKE_UNARY(Real, xla::Real(x)); +XLAJIT_MAKE_UNARY(Imag, xla::Imag(x)); #undef XLAJIT_MAKE_UNARY +// Erf/Erfc. For x in (-1, 1), the erf approximation is used; erfc polynomial +// is used outside of this range. +class ErfOp : public XlaOpKernel { + public: + explicit ErfOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaOp x = ctx->Input(0); + xla::XlaOp one = xla::ScalarLike(x, 1.0); + auto y = + xla::Select(xla::Gt(xla::Abs(x), one), one - xla::Erfc(x), xla::Erf(x)); + ctx->SetOutput(0, y); + } +}; +REGISTER_XLA_OP(Name("Erf"), ErfOp); + +class ErfcOp : public XlaOpKernel { + public: + explicit ErfcOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaOp x = ctx->Input(0); + xla::XlaOp one = xla::ScalarLike(x, 1.0); + auto y = + xla::Select(xla::Lt(xla::Abs(x), one), one - xla::Erf(x), xla::Erfc(x)); + ctx->SetOutput(0, y); + } +}; +REGISTER_XLA_OP(Name("Erfc"), ErfcOp); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc index f87586ba578a6138e7fb921032e1a71f8c9ac80c..0e5d58ecbaeb13571f82a1311e29dc0ba91c11ac 100644 --- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -74,10 +75,9 @@ class UnpackOp : public XlaOpKernel { for (int i = 0; i < num; ++i) { start_indices[axis] = i; limit_indices[axis] = i + 1; - auto slice = ctx->builder()->Slice(input, start_indices, limit_indices, - strides); + auto slice = xla::Slice(input, start_indices, limit_indices, strides); // Reshape to drop the 'axis' dimension. - auto result = ctx->builder()->Reshape(slice, output_shape.dim_sizes()); + auto result = xla::Reshape(slice, output_shape.dim_sizes()); ctx->SetOutput(i, result); } } diff --git a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc index a163fa0a5b34675e46d0d7c5f4e0ccb1e3fb18eb..febac8287350e32fccfd4cb5613f21b9a5fbcb95 100644 --- a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc @@ -13,9 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" #include "tensorflow/compiler/tf2xla/kernels/shape_util.h" +#include "tensorflow/compiler/tf2xla/lib/scatter.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" @@ -23,8 +23,6 @@ limitations under the License. #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/bounds_check.h" -#include "tensorflow/core/kernels/no_op.h" namespace tensorflow { namespace { @@ -35,12 +33,33 @@ class VarIsInitializedOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { XlaResource* variable; OP_REQUIRES_OK(ctx, ctx->GetResourceInput(0, &variable)); - ctx->SetOutput(0, - ctx->builder()->ConstantR0(variable->initialized())); + ctx->SetOutput( + 0, xla::ConstantR0(ctx->builder(), variable->initialized())); } }; REGISTER_XLA_OP(Name("VarIsInitializedOp"), VarIsInitializedOp); +class VariableShapeOp : public XlaOpKernel { + public: + explicit VariableShapeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("out_type", &out_dtype_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + DataType variable_dtype; + TensorShape shape; + OP_REQUIRES_OK(ctx, + ctx->GetVariableTypeAndShape(0, &variable_dtype, &shape)); + Tensor shape_constant(out_dtype_, TensorShape({shape.dims()})); + OP_REQUIRES_OK(ctx, TensorShapeToConstant(shape, &shape_constant)); + ctx->SetConstantOutput(0, shape_constant); + } + + private: + DataType out_dtype_; +}; +REGISTER_XLA_OP(Name("VariableShape"), VariableShapeOp); + class ReadVariableOp : public XlaOpKernel { public: explicit ReadVariableOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { @@ -77,7 +96,7 @@ class AssignAddVariableOp : public XlaOpKernel { xla::XlaOp handle; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, /*shape=*/nullptr, &handle)); - handle = ctx->builder()->Add(handle, ctx->Input(1)); + handle = xla::Add(handle, ctx->Input(1)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, handle)); } }; @@ -93,7 +112,7 @@ class AssignSubVariableOp : public XlaOpKernel { xla::XlaOp handle; OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, /*shape=*/nullptr, &handle)); - handle = ctx->builder()->Sub(handle, ctx->Input(1)); + handle = xla::Sub(handle, ctx->Input(1)); OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, handle)); } }; @@ -125,29 +144,152 @@ class ResourceGatherOp : public XlaOpKernel { ctx->SetOutput(0, gather); } }; -REGISTER_XLA_OP(Name("ResourceGather").TypeConstraint("dtype", kNumericTypes), - ResourceGatherOp); +REGISTER_XLA_OP(Name("ResourceGather"), ResourceGatherOp); -class VariableShapeOp : public XlaOpKernel { +class ResourceScatterOp : public XlaOpKernel { public: - explicit VariableShapeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("out_type", &out_dtype_)); + explicit ResourceScatterOp( + OpKernelConstruction* context, bool indices_are_vectors, + std::function + combiner) + : XlaOpKernel(context), + indices_are_vectors_(indices_are_vectors), + combiner_(std::move(combiner)) {} + + void Compile(XlaOpKernelContext* context) override { + xla::XlaBuilder* builder = context->builder(); + + DataType dtype = context->input_type(2); + TensorShape var_shape; + xla::XlaOp var_value; + OP_REQUIRES_OK( + context, context->ReadVariableInput(0, dtype, &var_shape, &var_value)); + + const xla::XlaOp indices = context->Input(1); + const xla::XlaOp updates = context->Input(2); + + auto result = XlaScatter(var_value, updates, indices, indices_are_vectors_, + combiner_, builder); + OP_REQUIRES_OK(context, result.status()); + OP_REQUIRES_OK(context, + context->AssignVariable(0, dtype, result.ValueOrDie())); } - void Compile(XlaOpKernelContext* ctx) override { - DataType variable_dtype; - TensorShape shape; - OP_REQUIRES_OK(ctx, - ctx->GetVariableTypeAndShape(0, &variable_dtype, &shape)); - Tensor shape_constant(out_dtype_, TensorShape({shape.dims()})); - OP_REQUIRES_OK(ctx, TensorShapeToConstant(shape, &shape_constant)); - ctx->SetConstantOutput(0, shape_constant); + private: + const bool indices_are_vectors_; + const std::function + combiner_; +}; + +class ResourceScatterAddOp : public ResourceScatterOp { + public: + explicit ResourceScatterAddOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Add(x, y); } +}; +REGISTER_XLA_OP(Name("ResourceScatterAdd"), ResourceScatterAddOp); + +class ResourceScatterSubOp : public ResourceScatterOp { + public: + explicit ResourceScatterSubOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} private: - DataType out_dtype_; + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Sub(x, y); + } }; +REGISTER_XLA_OP(Name("ResourceScatterSub"), ResourceScatterSubOp); + +class ResourceScatterMulOp : public ResourceScatterOp { + public: + explicit ResourceScatterMulOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Mul(x, y); + } +}; +REGISTER_XLA_OP(Name("ResourceScatterMul"), ResourceScatterMulOp); + +class ResourceScatterDivOp : public ResourceScatterOp { + public: + explicit ResourceScatterDivOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Div(x, y); + } +}; +REGISTER_XLA_OP(Name("ResourceScatterDiv"), ResourceScatterDivOp); + +class ResourceScatterMinOp : public ResourceScatterOp { + public: + explicit ResourceScatterMinOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Min(x, y); + } +}; +REGISTER_XLA_OP(Name("ResourceScatterMin"), ResourceScatterMinOp); + +class ResourceScatterMaxOp : public ResourceScatterOp { + public: + explicit ResourceScatterMaxOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Max(x, y); + } +}; +REGISTER_XLA_OP(Name("ResourceScatterMax"), ResourceScatterMaxOp); + +class ResourceScatterUpdateOp : public ResourceScatterOp { + public: + explicit ResourceScatterUpdateOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/false, + /*combiner=*/{}) {} +}; +REGISTER_XLA_OP(Name("ResourceScatterUpdate"), ResourceScatterUpdateOp); + +class ResourceScatterNdUpdateOp : public ResourceScatterOp { + public: + explicit ResourceScatterNdUpdateOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/true, + /*combiner=*/{}) {} +}; +REGISTER_XLA_OP(Name("ResourceScatterNdUpdate"), ResourceScatterNdUpdateOp); + +class ResourceScatterNdAddOp : public ResourceScatterOp { + public: + explicit ResourceScatterNdAddOp(OpKernelConstruction* context) + : ResourceScatterOp(context, /*indices_are_vectors=*/true, + /*combiner=*/Combine) {} + + private: + static xla::XlaOp Combine(const xla::XlaOp& x, const xla::XlaOp& y, + xla::XlaBuilder* builder) { + return xla::Add(x, y); + } +}; +REGISTER_XLA_OP(Name("ResourceScatterNdAdd"), ResourceScatterNdAddOp); -REGISTER_XLA_OP(Name("VariableShape"), VariableShapeOp); } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index 5467c5d9946846ff9f14ce9c5aac9e2be4b9d6ab..340165bac6a2a214d8f84d5a116a4197b1df2c7b 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -246,7 +246,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { } } - xla::XlaOp init = builder->Tuple(inputs); + xla::XlaOp init = xla::Tuple(builder, inputs); VLOG(1) << "Building while loop"; @@ -255,22 +255,21 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { { std::unique_ptr cb = builder->CreateSubBuilder("cond_wrapper"); - auto inputs = cb->Parameter(0, cond_input_shape, "inputs"); - auto outputs = cb->Call(*cond.computation, {inputs}); - cb->GetTupleElement(outputs, 0); + auto inputs = xla::Parameter(cb.get(), 0, cond_input_shape, "inputs"); + auto outputs = xla::Call(cb.get(), *cond.computation, {inputs}); + xla::GetTupleElement(outputs, 0); xla::StatusOr result = cb->Build(); OP_REQUIRES_OK(ctx, result.status()); cond_wrapper = std::move(result.ValueOrDie()); } - xla::XlaOp while_result = - builder->While(cond_wrapper, *body.computation, init); + xla::XlaOp while_result = xla::While(cond_wrapper, *body.computation, init); // Sets non-variable outputs. for (int i = 0; i < ctx->num_outputs(); ++i) { if (ctx->input_type(i) != DT_RESOURCE) { ctx->SetOutput(body.input_mapping[i], - builder->GetTupleElement(while_result, i)); + xla::GetTupleElement(while_result, i)); } } @@ -284,7 +283,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { OP_REQUIRES_OK(ctx, resource->SetFromPack( arguments[update.input_index].tensor_array_gradients, - builder->GetTupleElement(while_result, pos), builder)); + xla::GetTupleElement(while_result, pos), builder)); } VLOG(2) << "Loop-carried variable: pos: " << update.input_index << " name: " << resource->name() << " modified: " << update.modified diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index ee7f5d510ab7a3ce7d3bbe843c5fefd362f79b7b..dfa3c0595acbfeb35f944209b4354b357b11bf3c 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -44,12 +44,28 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) +cc_library( + name = "random", + srcs = ["random.cc"], + hdrs = ["random.h"], + deps = [ + "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:math", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/core:protos_all_cc", + ], +) + cc_library( name = "scatter", srcs = ["scatter.cc"], @@ -81,6 +97,7 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index 526694d5a0c7124e1696f34b516f3b202462bc19..f9f3a8c8cfcbcd0a2ac853360c629d90c94db8b0 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -25,91 +26,94 @@ limitations under the License. namespace tensorflow { -xla::StatusOr BatchDot(xla::XlaBuilder* builder, xla::XlaOp x, - xla::XlaOp y, bool transpose_x, - bool transpose_y, bool conjugate_x, - bool conjugate_y) { - TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); - TF_ASSIGN_OR_RETURN(xla::Shape y_shape, builder->GetShape(y)); - - // Check that both tensors have the same number of dimensions. There must be - // at least two (the batch dimensions can be empty). - if (xla::ShapeUtil::Rank(x_shape) != xla::ShapeUtil::Rank(y_shape)) { - return errors::InvalidArgument( - "Arguments to BatchedDot have different ranks: ", - xla::ShapeUtil::HumanString(x_shape), " vs. ", - xla::ShapeUtil::HumanString(y_shape)); - } - const int ndims = xla::ShapeUtil::Rank(x_shape); - if (ndims < 2) { - return errors::InvalidArgument( - "Arguments to BatchedDot must have rank >= 2: ", ndims); - } - - // The batch dimensions must be equal and the matrix dimensions must be - // valid. - std::vector batch_dimension_numbers; - for (int i = 0; i < ndims - 2; ++i) { - if (x_shape.dimensions(i) != y_shape.dimensions(i)) { +xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x, + bool transpose_y, bool conjugate_x, bool conjugate_y) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); + TF_ASSIGN_OR_RETURN(xla::Shape y_shape, builder->GetShape(y)); + + // Check that both tensors have the same number of dimensions. There must be + // at least two (the batch dimensions can be empty). + if (xla::ShapeUtil::Rank(x_shape) != xla::ShapeUtil::Rank(y_shape)) { return errors::InvalidArgument( - "Dimension ", i, " of inputs to BatchedDot must be equal: ", - xla::ShapeUtil::HumanString(x_shape), " vs ", + "Arguments to BatchedDot have different ranks: ", + xla::ShapeUtil::HumanString(x_shape), " vs. ", xla::ShapeUtil::HumanString(y_shape)); } - batch_dimension_numbers.push_back(i); - } - - int x_inner_dim = transpose_x ? (ndims - 2) : (ndims - 1); - int y_inner_dim = transpose_y ? (ndims - 1) : (ndims - 2); - if (x_shape.dimensions(x_inner_dim) != y_shape.dimensions(y_inner_dim)) { - return errors::InvalidArgument( - "Dimensions ", x_inner_dim, " and ", y_inner_dim, - " of arguments to BatchedDot must be equal: ", - xla::ShapeUtil::HumanString(x_shape), " transpose: ", transpose_x, - " vs. ", xla::ShapeUtil::HumanString(y_shape), - " transpose: ", transpose_y); - } - - // Check for zero lhs/rhs dim size. - if (xla::ShapeUtil::HasZeroElements(x_shape) || - xla::ShapeUtil::HasZeroElements(y_shape)) { - std::vector dimensions(batch_dimension_numbers.size()); - for (int i = 0; i < batch_dimension_numbers.size(); ++i) { - dimensions[i] = x_shape.dimensions(batch_dimension_numbers[i]); + const int ndims = xla::ShapeUtil::Rank(x_shape); + if (ndims < 2) { + return errors::InvalidArgument( + "Arguments to BatchedDot must have rank >= 2: ", ndims); + } + + // The batch dimensions must be equal and the matrix dimensions must be + // valid. + std::vector batch_dimension_numbers; + for (int i = 0; i < ndims - 2; ++i) { + if (x_shape.dimensions(i) != y_shape.dimensions(i)) { + return errors::InvalidArgument( + "Dimension ", i, " of inputs to BatchedDot must be equal: ", + xla::ShapeUtil::HumanString(x_shape), " vs ", + xla::ShapeUtil::HumanString(y_shape)); + } + batch_dimension_numbers.push_back(i); + } + + int x_inner_dim = transpose_x ? (ndims - 2) : (ndims - 1); + int y_inner_dim = transpose_y ? (ndims - 1) : (ndims - 2); + if (x_shape.dimensions(x_inner_dim) != y_shape.dimensions(y_inner_dim)) { + return errors::InvalidArgument( + "Dimensions ", x_inner_dim, " and ", y_inner_dim, + " of arguments to BatchedDot must be equal: ", + xla::ShapeUtil::HumanString(x_shape), " transpose: ", transpose_x, + " vs. ", xla::ShapeUtil::HumanString(y_shape), + " transpose: ", transpose_y); + } + + // Check for zero lhs/rhs dim size. + if (xla::ShapeUtil::IsZeroElementArray(x_shape) || + xla::ShapeUtil::IsZeroElementArray(y_shape)) { + std::vector dimensions(batch_dimension_numbers.size()); + for (int i = 0; i < batch_dimension_numbers.size(); ++i) { + dimensions[i] = x_shape.dimensions(batch_dimension_numbers[i]); + } + int x_outer_dim = transpose_x ? (ndims - 1) : (ndims - 2); + int y_outer_dim = transpose_y ? (ndims - 2) : (ndims - 1); + dimensions.push_back(x_shape.dimensions(x_outer_dim)); + dimensions.push_back(y_shape.dimensions(y_outer_dim)); + return xla::Broadcast( + xla::ConstantLiteral(builder, + xla::Literal::Zero(x_shape.element_type())), + dimensions); + } + + if (x_shape.element_type() == xla::C64 && conjugate_x) { + x = xla::Conj(x); + } + if (y_shape.element_type() == xla::C64 && conjugate_y) { + y = xla::Conj(y); + } + + // If there are no batch dimensions, use a regular Dot. + // TODO(b/69062148) Remove this code when Dot emitters can be passed + // dimensions to transpose directly (i.e. without requiring a Transpose + // HLO). + if (batch_dimension_numbers.empty()) { + auto lhs = transpose_x ? xla::Transpose(x, {1, 0}) : x; + auto rhs = transpose_y ? xla::Transpose(y, {1, 0}) : y; + return xla::Dot(lhs, rhs); + } + + xla::DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(x_inner_dim); + dot_dnums.add_rhs_contracting_dimensions(y_inner_dim); + for (auto batch_dimension_number : batch_dimension_numbers) { + dot_dnums.add_lhs_batch_dimensions(batch_dimension_number); + dot_dnums.add_rhs_batch_dimensions(batch_dimension_number); } - int x_outer_dim = transpose_x ? (ndims - 1) : (ndims - 2); - int y_outer_dim = transpose_y ? (ndims - 2) : (ndims - 1); - dimensions.push_back(x_shape.dimensions(x_outer_dim)); - dimensions.push_back(y_shape.dimensions(y_outer_dim)); - return builder->Broadcast( - builder->ConstantLiteral(xla::Literal::Zero(x_shape.element_type())), - dimensions); - } - - if (x_shape.element_type() == xla::C64 && conjugate_x) { - x = builder->Conj(x); - } - if (y_shape.element_type() == xla::C64 && conjugate_y) { - y = builder->Conj(y); - } - - // If there are no batch dimensions, use a regular Dot. - // TODO(b/69062148) Remove this code when Dot emitters can be passed - // dimensions to transpose directly (i.e. without requiring a Transpose HLO). - if (batch_dimension_numbers.empty()) { - auto lhs = transpose_x ? builder->Transpose(x, {1, 0}) : x; - auto rhs = transpose_y ? builder->Transpose(y, {1, 0}) : y; - return builder->Dot(lhs, rhs); - } - - xla::DotDimensionNumbers dot_dnums; - dot_dnums.add_lhs_contracting_dimensions(x_inner_dim); - dot_dnums.add_rhs_contracting_dimensions(y_inner_dim); - for (auto batch_dimension_number : batch_dimension_numbers) { - dot_dnums.add_lhs_batch_dimensions(batch_dimension_number); - dot_dnums.add_rhs_batch_dimensions(batch_dimension_number); - } - return builder->DotGeneral(x, y, dot_dnums); + return xla::DotGeneral(x, y, dot_dnums); + }); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index 1acc72033b05e73b0f5f88907df20cde5cfffbf0..d07a9486f18c0b8f26782123a8fba4ba228f71ee 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -43,10 +43,9 @@ namespace tensorflow { // It is computed as: // // output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -xla::StatusOr BatchDot(xla::XlaBuilder* builder, xla::XlaOp x, - xla::XlaOp y, bool transpose_x, - bool transpose_y, bool conjugate_x = false, - bool conjugate_y = false); +xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x = false, + bool transpose_y = false, bool conjugate_x = false, + bool conjugate_y = false); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index 3f1384bc864abd882ebba2b90acbe0b1e664687a..cc840de393ebc2983ddf7659c6c18d8136de5dd6 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -22,6 +22,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/triangular_solve.h" #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -47,179 +49,163 @@ namespace { // l[..., j+1:, j] = (a[..., j+1:, j] - np.dot(l[..., j+1:, :j], row_t)) / // l[..., j, j] // return l -xla::StatusOr CholeskyUnblocked(xla::XlaBuilder* builder, - const xla::XlaOp& a) { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int n_dims = xla::ShapeUtil::Rank(a_shape); - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); - gtl::ArraySlice major_dims(xla::AsInt64Slice(a_shape.dimensions()), - /*pos=*/0, - /*len=*/n_dims - 2); - - xla::XlaOp l = Zeros(builder, a_shape); - - // Construct the for loop body to iterate over rows. - auto body_fn = [&](xla::XlaOp i, gtl::ArraySlice loop_vars, - xla::XlaBuilder* body_builder) - -> xla::StatusOr> { - xla::Shape col_shape; - xla::Shape row_shape; - for (int64 d : major_dims) { - row_shape.add_dimensions(d); - col_shape.add_dimensions(d); - } - row_shape.add_dimensions(1); - row_shape.add_dimensions(n); - row_shape.set_element_type(a_shape.element_type()); - auto mask_zeros_row = Zeros(body_builder, row_shape); - - col_shape.add_dimensions(n); - col_shape.add_dimensions(1); - col_shape.set_element_type(a_shape.element_type()); - auto mask_zeros_col = Zeros(body_builder, col_shape); - - std::vector mask_vector(n); - std::iota(mask_vector.begin(), mask_vector.end(), 0); - auto mask_range = body_builder->ConstantR1(mask_vector); - auto mask_range_row = body_builder->Broadcast( - body_builder->Reshape(mask_range, {0}, {1, n}), major_dims); - auto mask_range_col = body_builder->Broadcast( - body_builder->Reshape(mask_range, {0}, {n, 1}), major_dims); - auto body_a = loop_vars[0]; - auto body_l = loop_vars[1]; - - // row = l[..., i, :i] - // select the whole i-th row, then mask out all columns past i-1 - auto zero = body_builder->ConstantR0(0); - TF_ASSIGN_OR_RETURN(auto l_i, DynamicSliceInMinorDims(body_builder, body_l, - {i, zero}, {1, n})); - auto row = body_builder->Select(body_builder->Ge(mask_range_row, i), - mask_zeros_row, l_i); - // a[..., i, i] - TF_ASSIGN_OR_RETURN(auto a_ii, DynamicSliceInMinorDims(body_builder, body_a, - {i, i}, {1, 1})); - // np.dot(row, np.swapaxes(row, -1, -2)) - xla::XlaOp diag_dot; - TF_ASSIGN_OR_RETURN(diag_dot, BatchDot(body_builder, row, row, - /*transpose_x=*/false, - /*transpose_y=*/true)); - // l[..., i, i] = np.sqrt(a[..., i, i] - np.dot(row, - // np.swapaxes(row, -1, -2))) - auto l_ii = body_builder->Pow( - body_builder->Sub(a_ii, diag_dot), - FloatLiteral(body_builder, a_shape.element_type(), 0.5)); - - // a[..., i+1:, i] - auto ip1 = body_builder->Add(i, body_builder->ConstantR0(1)); - // select the whole i-th column, then mask out all rows above i+1 +xla::XlaOp CholeskyUnblocked(xla::XlaOp a) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int n_dims = xla::ShapeUtil::Rank(a_shape); + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + gtl::ArraySlice major_dims(xla::AsInt64Slice(a_shape.dimensions()), + /*pos=*/0, + /*len=*/n_dims - 2); + + xla::XlaOp l = xla::ZerosLike(a); + + // Construct the for loop body to iterate over rows. + auto body_fn = [&](xla::XlaOp i, gtl::ArraySlice loop_vars, + xla::XlaBuilder* body_builder) + -> xla::StatusOr> { + xla::Shape col_shape; + xla::Shape row_shape; + for (int64 d : major_dims) { + row_shape.add_dimensions(d); + col_shape.add_dimensions(d); + } + row_shape.add_dimensions(1); + row_shape.add_dimensions(n); + row_shape.set_element_type(a_shape.element_type()); + auto mask_zeros_row = xla::Zeros(body_builder, row_shape); + + col_shape.add_dimensions(n); + col_shape.add_dimensions(1); + col_shape.set_element_type(a_shape.element_type()); + auto mask_zeros_col = xla::Zeros(body_builder, col_shape); + + std::vector mask_vector(n); + std::iota(mask_vector.begin(), mask_vector.end(), 0); + auto mask_range = xla::ConstantR1(body_builder, mask_vector); + auto mask_range_row = + xla::Broadcast(xla::Reshape(mask_range, {0}, {1, n}), major_dims); + auto mask_range_col = + xla::Broadcast(xla::Reshape(mask_range, {0}, {n, 1}), major_dims); + auto body_a = loop_vars[0]; + auto body_l = loop_vars[1]; + + // row = l[..., i, :i] + // select the whole i-th row, then mask out all columns past i-1 + auto zero = xla::ConstantR0(body_builder, 0); + auto l_i = DynamicSliceInMinorDims(body_l, {i, zero}, {1, n}); + auto row = xla::Select(xla::Ge(mask_range_row, i), mask_zeros_row, l_i); + // a[..., i, i] + auto a_ii = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); + // np.dot(row, np.swapaxes(row, -1, -2)) + auto diag_dot = BatchDot(row, row, + /*transpose_x=*/false, + /*transpose_y=*/true); + // l[..., i, i] = np.sqrt(a[..., i, i] - np.dot(row, + // np.swapaxes(row, -1, -2))) + auto l_ii = + xla::Pow(a_ii - diag_dot, + FloatLiteral(body_builder, a_shape.element_type(), 0.5)); + + // a[..., i+1:, i] + // select the whole i-th column, then mask out all rows above i+1 + auto a_0i = DynamicSliceInMinorDims(body_a, {i}, {1}); + auto a_ip1i = + xla::Select(xla::Le(mask_range_col, i), mask_zeros_col, a_0i); + + // l[..., i+1:, i] = (a[..., i+1:, i] - np.dot(l[..., i+1:, :i], r.T)) / + // l[..., i, i] + // The columns in [i, n] are zeroed out in `row`, so we just have to + // zero out rows above i+1 after the BatchDot. np.dot(l[..., :, :i], + // r.T) + auto dot = BatchDot(body_l, row, + /*transpose_x=*/false, + /*transpose_y=*/true); + // np.dot(l[..., i+1:, :i], r.T) + auto dot_ip1 = + xla::Select(xla::Le(mask_range_col, i), mask_zeros_col, dot); + + body_l = + DynamicUpdateSliceInMinorDims(body_l, (a_ip1i - dot_ip1) / l_ii, {i}); + // Assign the diagonal after the rest of the column because otherwise the + // column assign will wrap around and overwrite the diagonal assign. + body_l = DynamicUpdateSliceInMinorDims(body_l, l_ii, {i, i}); + + return std::vector{body_a, body_l}; + }; + TF_ASSIGN_OR_RETURN( - auto a_0i, DynamicSliceInMinorDims(body_builder, body_a, {i}, {1})); - auto a_ip1i = body_builder->Select(body_builder->Le(mask_range_col, i), - mask_zeros_col, a_0i); - - // l[..., i+1:, i] = (a[..., i+1:, i] - np.dot(l[..., i+1:, :i], r.T)) / - // l[..., i, i] - // The columns in [i, n] are zeroed out in `row`, so we just have to - // zero out rows above i+1 after the BatchDot. np.dot(l[..., :, :i], - // r.T) - TF_ASSIGN_OR_RETURN(auto dot, BatchDot(body_builder, body_l, row, - /*transpose_x=*/false, - /*transpose_y=*/true)); - // np.dot(l[..., i+1:, :i], r.T) - auto dot_ip1 = body_builder->Select(body_builder->Le(mask_range_col, i), - mask_zeros_col, dot); - - auto col_update = - body_builder->Div(body_builder->Sub(a_ip1i, dot_ip1), l_ii); - TF_ASSIGN_OR_RETURN(body_l, DynamicUpdateSliceInMinorDims( - body_builder, body_l, col_update, {i})); - // Assign the diagonal after the rest of the column because otherwise the - // column assign will wrap around and overwrite the diagonal assign. - TF_ASSIGN_OR_RETURN(body_l, DynamicUpdateSliceInMinorDims( - body_builder, body_l, l_ii, {i, i})); - - return std::vector{body_a, body_l}; - }; - - TF_ASSIGN_OR_RETURN( - auto cholesky_while, - XlaForEachIndex(n, xla::S32, body_fn, {a, l}, "unblocked", builder)); - - return cholesky_while[1]; + auto cholesky_while, + XlaForEachIndex(n, xla::S32, body_fn, {a, l}, "unblocked", builder)); + + return cholesky_while[1]; + }); } } // namespace -xla::StatusOr Cholesky(xla::XlaBuilder* builder, xla::XlaOp a, - int64 block_size) { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int ndims = xla::ShapeUtil::Rank(a_shape); - if (ndims < 2) { - return errors::InvalidArgument( - "Arguments to Cholesky must have rank >= 2: ", ndims); - } - - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); - if (n != xla::ShapeUtil::GetDimension(a_shape, -2)) { - return errors::InvalidArgument( - "Arguments to Cholesky must be square matrices: ", - xla::ShapeUtil::HumanString(a_shape)); - } - - if (block_size < 1) { - return errors::InvalidArgument( - "block_size argument to Cholesky must be >= 1; got ", block_size); - } - - // Blocked left-looking Cholesky factorization. - // Algorithm 1 from - // Haidar, Azzam, et al. "High-performance Cholesky factorization for GPU-only - // execution." Proceedings of General Purpose GPUs. ACM, 2017. - xla::XlaOp l = Zeros(builder, a_shape); - for (int64 i = 0; i < n; i += block_size) { - int64 k = std::min(block_size, n - i); - if (i > 0) { - // TODO(phawkins): consider implementing SYRK for the diagonal part of - // the panel. - // a[i:, i:i+k] -= np.dot(l[i:, :i], np.transpose(l[i:i+k, :i])) - TF_ASSIGN_OR_RETURN(auto lhs, - SliceInMinorDims(builder, l, {i, 0}, {n, i})); - TF_ASSIGN_OR_RETURN(auto rhs, - SliceInMinorDims(builder, l, {i, 0}, {i + k, i})); - TF_ASSIGN_OR_RETURN(auto delta, - BatchDot(builder, lhs, rhs, /*transpose_x=*/false, - /*transpose_y=*/true, /*conjugate_x=*/false, - /*conjugate_y=*/false)); - TF_ASSIGN_OR_RETURN(auto before, - SliceInMinorDims(builder, a, {i, i}, {n, i + k})); - TF_ASSIGN_OR_RETURN( - a, UpdateSliceInMinorDims(builder, a, builder->Sub(before, delta), - {i, i})); +xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int ndims = xla::ShapeUtil::Rank(a_shape); + if (ndims < 2) { + return errors::InvalidArgument( + "Arguments to Cholesky must have rank >= 2: ", ndims); + } + + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + if (n != xla::ShapeUtil::GetDimension(a_shape, -2)) { + return errors::InvalidArgument( + "Arguments to Cholesky must be square matrices: ", + xla::ShapeUtil::HumanString(a_shape)); + } + + if (block_size < 1) { + return errors::InvalidArgument( + "block_size argument to Cholesky must be >= 1; got ", block_size); } - // l[i:i+k, i:i+k] = cholesky_unblocked(a[i:i+k, i:i+k]) - TF_ASSIGN_OR_RETURN(auto x, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); - TF_ASSIGN_OR_RETURN(auto factorized, CholeskyUnblocked(builder, x)); - TF_ASSIGN_OR_RETURN(l, - UpdateSliceInMinorDims(builder, l, factorized, {i, i})); - - if (i + k < n) { - // l[i+k:, i:i+k] = trsm_right_transpose(l[i:i+k, i:i+k], a[i+k:, i:i+k]) - TF_ASSIGN_OR_RETURN(auto panel, - SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); - TF_ASSIGN_OR_RETURN(auto update, - TriangularSolve(builder, factorized, panel, - /*left_side=*/false, - /*lower=*/true, - /*transpose_a=*/true, - /*conjugate_a=*/false, - /*block_size=*/block_size)); - TF_ASSIGN_OR_RETURN( - l, UpdateSliceInMinorDims(builder, l, update, {i + k, i})); + // Blocked left-looking Cholesky factorization. + // Algorithm 1 from + // Haidar, Azzam, et al. "High-performance Cholesky factorization for + // GPU-only execution." Proceedings of General Purpose GPUs. ACM, 2017. + xla::XlaOp l = xla::ZerosLike(a); + for (int64 i = 0; i < n; i += block_size) { + int64 k = std::min(block_size, n - i); + if (i > 0) { + // TODO(phawkins): consider implementing SYRK for the diagonal part of + // the panel. + // a[i:, i:i+k] -= np.dot(l[i:, :i], np.transpose(l[i:i+k, :i])) + auto lhs = SliceInMinorDims(l, {i, 0}, {n, i}); + auto rhs = SliceInMinorDims(l, {i, 0}, {i + k, i}); + auto delta = BatchDot(lhs, rhs, /*transpose_x=*/false, + /*transpose_y=*/true); + auto before = SliceInMinorDims(a, {i, i}, {n, i + k}); + a = UpdateSliceInMinorDims(a, before - delta, {i, i}); + } + + // l[i:i+k, i:i+k] = cholesky_unblocked(a[i:i+k, i:i+k]) + auto x = SliceInMinorDims(a, {i, i}, {i + k, i + k}); + auto factorized = CholeskyUnblocked(x); + l = UpdateSliceInMinorDims(l, factorized, {i, i}); + + if (i + k < n) { + // l[i+k:, i:i+k] = + // trsm_right_transpose(l[i:i+k, i:i+k], a[i+k:, i:i+k]) + auto panel = SliceInMinorDims(a, {i + k, i}, {n, i + k}); + auto update = TriangularSolve(factorized, panel, + /*left_side=*/false, + /*lower=*/true, + /*transpose_a=*/true, + /*conjugate_a=*/false, + /*block_size=*/block_size); + l = UpdateSliceInMinorDims(l, update, {i + k, i}); + } } - } - return l; + return l; + }); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 20fca7969ece2729a44933fd3ef3f87230ab6cad..0f6e0e9d152ec5daedeb9c0e355bfb9731759094 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -30,8 +30,7 @@ namespace tensorflow { // TODO(phawkins): check for negative values on the diagonal and return an // error, instead of silently yielding NaNs. // TODO(znado): handle the complex Hermitian case -xla::StatusOr Cholesky(xla::XlaBuilder* builder, xla::XlaOp a, - int64 block_size = 256); +xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size = 256); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/random.cc b/tensorflow/compiler/tf2xla/lib/random.cc new file mode 100644 index 0000000000000000000000000000000000000000..8ff10fbd3fbf9308140af84c752a5a50bec8fd32 --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/random.cc @@ -0,0 +1,55 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/random.h" + +#include +#include + +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/status_macros.h" + +namespace tensorflow { + +xla::XlaOp TruncatedNormal(xla::XlaOp uniform) { + auto normal_cdf = [](double x) { + return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0; + }; + + const double kA = -2.0; + const double kB = 2.0; + const double kMu = 0.0; + const double kSigma = 1.0; + const double kAlpha = (kA - kMu) / kSigma; + const double kBeta = (kB - kMu) / kSigma; + const double kAlphaNormalCdf = normal_cdf(kAlpha); + const double kBetaNormalCdf = normal_cdf(kBeta); + const double kZ = kBetaNormalCdf - kAlphaNormalCdf; + + xla::XlaOp one = xla::ScalarLike(uniform, 1.0); + xla::XlaOp two = xla::ScalarLike(uniform, 2.0); + xla::XlaOp sqrt_2 = xla::ScalarLike(uniform, std::sqrt(2.0)); + xla::XlaOp z = xla::ScalarLike(uniform, kZ); + xla::XlaOp alpha_normal_cdf = xla::ScalarLike(uniform, kAlphaNormalCdf); + + auto p = alpha_normal_cdf + z * uniform; + // probit(p) = sqrt(2) * erfinv(2*p-1) + return sqrt_2 * xla::ErfInv(two * p - one); +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/random.h b/tensorflow/compiler/tf2xla/lib/random.h new file mode 100644 index 0000000000000000000000000000000000000000..2c573fd85b2783fdac13457cdb277cf988ac40c4 --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/random.h @@ -0,0 +1,35 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ +#define TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ + +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/framework/types.pb.h" + +namespace tensorflow { + +// Builds an array filled with values sampled from a truncated normal +// distribution such that no values are greater than two or less than negative +// two. +// +// The "uniform" parameter must be an array of random numbers distributed in +// (0,1). +xla::XlaOp TruncatedNormal(xla::XlaOp uniform); + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index d5a27abb2585f699ae2719cb8a6b9a829263389e..85e3d3ab85a89615cc5a01bdb4ec8f7fec30d58e 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -97,8 +98,8 @@ xla::StatusOr XlaScatter( buffer_shape_post_axes.end()); // Construct the initial values of the loop-carried Tensors. - auto flat_indices = builder->Reshape(indices, flat_indices_shape); - auto flat_updates = builder->Reshape(updates, flat_updates_shape); + auto flat_indices = xla::Reshape(indices, flat_indices_shape); + auto flat_updates = xla::Reshape(updates, flat_updates_shape); auto init = {flat_indices, flat_updates, buffer}; // Constructs the loop body. The implementation of scatter is essentially: @@ -112,46 +113,44 @@ xla::StatusOr XlaScatter( auto updates = loop_vars[1]; auto buffer = loop_vars[2]; - auto zero_index = body_builder->ConstantLiteral( - xla::Literal::Zero(indices_shape.element_type())); + auto zero_index = xla::ConstantLiteral( + body_builder, xla::Literal::Zero(indices_shape.element_type())); // Slice the i-th index from the indices array. xla::XlaOp index; - auto indices_offset = body_builder->Reshape(i, {1}); + auto indices_offset = xla::Reshape(i, {1}); if (indices_are_vectors) { - indices_offset = body_builder->Pad(indices_offset, zero_index, - xla::MakeEdgePaddingConfig({{0, 1}})); + indices_offset = xla::Pad(indices_offset, zero_index, + xla::MakeEdgePaddingConfig({{0, 1}})); - index = body_builder->DynamicSlice(indices, indices_offset, - {1, num_index_dims}); - index = body_builder->Collapse(index, {0, 1}); + index = xla::DynamicSlice(indices, indices_offset, {1, num_index_dims}); + index = xla::Collapse(index, {0, 1}); } else { - index = body_builder->DynamicSlice(indices, indices_offset, {1}); + index = xla::DynamicSlice(indices, indices_offset, {1}); } // Discard updates with negative indices, since some users expect this. - auto index_in_range = - body_builder->ReduceAll(body_builder->Le(zero_index, index), - body_builder->ConstantR0(true), - xla::CreateScalarAndComputation(body_builder)); + auto index_in_range = xla::ReduceAll( + xla::Le(zero_index, index), xla::ConstantR0(body_builder, true), + xla::CreateScalarAndComputation(body_builder)); // Make the index in bounds to prevent implementation defined behavior. - index = body_builder->Max(index, zero_index); - index = body_builder->Pad( + index = xla::Max(index, zero_index); + index = xla::Pad( index, zero_index, xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); // Slice the i-th index from the updates array. - auto updates_offset = body_builder->Reshape(i, {1}); - updates_offset = body_builder->Pad( + auto updates_offset = xla::Reshape(i, {1}); + updates_offset = xla::Pad( updates_offset, zero_index, xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); std::vector flat_updates_slice_shape({1}); flat_updates_slice_shape.insert(flat_updates_slice_shape.end(), buffer_shape_post_axes.begin(), buffer_shape_post_axes.end()); - auto update = body_builder->DynamicSlice(updates, updates_offset, - flat_updates_slice_shape); + auto update = + xla::DynamicSlice(updates, updates_offset, flat_updates_slice_shape); // Unflatten the major (iteration) dimensions of the slice to their // original shape. @@ -159,20 +158,19 @@ xla::StatusOr XlaScatter( updates_slice_shape.insert(updates_slice_shape.end(), buffer_shape_post_axes.begin(), buffer_shape_post_axes.end()); - update = body_builder->Reshape(update, updates_slice_shape); + update = xla::Reshape(update, updates_slice_shape); // Apply the update to the buffer. If there is a combiner, use it to merge // the current values with the update. - auto current_value = - body_builder->DynamicSlice(buffer, index, updates_slice_shape); + auto current_value = xla::DynamicSlice(buffer, index, updates_slice_shape); if (combiner) { update = combiner(current_value, update, body_builder); } // Use the current value instead of the update if the index is out of // bounds. - update = body_builder->Select(index_in_range, update, current_value); + update = xla::Select(index_in_range, update, current_value); // Apply the update. - buffer = body_builder->DynamicUpdateSlice(buffer, update, index); + buffer = xla::DynamicUpdateSlice(buffer, update, index); return std::vector{indices, updates, buffer}; }; diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index b4503601f94baa5a595a64c9fc81bc92d9980ac6..4f97d1277c6168b42f9da9b1681fed1470f2b5c7 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -20,6 +20,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/batch_dot.h" #include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -29,619 +31,564 @@ limitations under the License. namespace tensorflow { -xla::StatusOr TriangularSolve(xla::XlaBuilder* builder, - const xla::XlaOp& a, xla::XlaOp b, - bool left_side, bool lower, - bool transpose_a, bool conjugate_a, - int64 block_size) { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - if (xla::ShapeUtil::Rank(a_shape) != xla::ShapeUtil::Rank(b_shape)) { - return errors::InvalidArgument( - "Arguments to TriangularSolve have different ranks: ", - xla::ShapeUtil::HumanString(a_shape), " vs. ", - xla::ShapeUtil::HumanString(b_shape)); - } - const int ndims = xla::ShapeUtil::Rank(a_shape); - if (ndims < 2) { - return errors::InvalidArgument( - "Arguments to TriangularSolve must have rank >= 2: ", ndims); - } - // The batch dimensions must be equal. - std::vector batch_dimensions; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - int64 b_size = b_shape.dimensions(i); - if (a_size != b_size) { +xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, + bool lower, bool transpose_a, bool conjugate_a, + int64 block_size) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + if (xla::ShapeUtil::Rank(a_shape) != xla::ShapeUtil::Rank(b_shape)) { return errors::InvalidArgument( - "Batch dimensions of arguments to TriangularSolve must be equal: ", - xla::ShapeUtil::HumanString(a_shape), " vs ", + "Arguments to TriangularSolve have different ranks: ", + xla::ShapeUtil::HumanString(a_shape), " vs. ", xla::ShapeUtil::HumanString(b_shape)); } - batch_dimensions.push_back(a_size); - } - - if (xla::ShapeUtil::GetDimension(a_shape, -1) != - xla::ShapeUtil::GetDimension(a_shape, -2)) { - return errors::InvalidArgument( - "The 'a' arguments to TriangularSolve must be square matrices: ", - xla::ShapeUtil::HumanString(a_shape)); - } - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - if ((left_side ? m : n) != xla::ShapeUtil::GetDimension(a_shape, -1)) { - return errors::InvalidArgument( - "Arguments to TriangularSolve have incompatible matrix shapes: ", - xla::ShapeUtil::HumanString(a_shape), " vs ", - xla::ShapeUtil::HumanString(b_shape)); - } - - if (block_size < 1) { - return errors::InvalidArgument( - "block_size argument to TriangularSolve must be >= 1; got ", - block_size); - } - - std::map base_computations; - auto get_base_triangular_solve = - [&](int k) -> xla::StatusOr { - xla::XlaComputation& computation = base_computations[k]; - if (computation.IsNull()) { - std::unique_ptr sub = builder->CreateSubBuilder( - tensorflow::strings::StrCat("trsm_base_", k)); - - auto a_param = sub->Parameter( - 0, - xla::ShapeUtil::MakeShape( - b_shape.element_type(), - PrependMajorDims(sub.get(), batch_dimensions, {k, k})), - "a"); - - std::array b_lastd; - if (left_side) { - b_lastd = {k, n}; - } else { - b_lastd = {m, k}; - } - auto b_param = sub->Parameter( - 1, - xla::ShapeUtil::MakeShape( - b_shape.element_type(), - PrependMajorDims(sub.get(), batch_dimensions, b_lastd)), - "b"); - - // We use a left-looking or right-looking subroutine on the block diagonal - // in the lower=true cases, while falling back to a recursive call in - // others. The left-looking and right-looking subroutines are written with - // a While loop and so yields much faster compile times. Moreover, they - // can give higher performance on smaller (sub)problems. - if (left_side && lower) { - TF_RETURN_IF_ERROR(TriangularSolveLeftLooking(sub.get(), a_param, - b_param, transpose_a, - conjugate_a) - .status()); - } else if (!left_side && lower) { - TF_RETURN_IF_ERROR(TriangularSolveRightLooking(sub.get(), a_param, - b_param, transpose_a, - conjugate_a) - .status()); - } else { - TF_RETURN_IF_ERROR(TriangularSolve(sub.get(), a_param, b_param, - left_side, lower, transpose_a, - conjugate_a, - /*block_size=*/1) - .status()); + const int ndims = xla::ShapeUtil::Rank(a_shape); + if (ndims < 2) { + return errors::InvalidArgument( + "Arguments to TriangularSolve must have rank >= 2: ", ndims); + } + // The batch dimensions must be equal. + std::vector batch_dimensions; + for (int i = 0; i < ndims - 2; ++i) { + int64 a_size = a_shape.dimensions(i); + int64 b_size = b_shape.dimensions(i); + if (a_size != b_size) { + return errors::InvalidArgument( + "Batch dimensions of arguments to TriangularSolve must be equal: ", + xla::ShapeUtil::HumanString(a_shape), " vs ", + xla::ShapeUtil::HumanString(b_shape)); } + batch_dimensions.push_back(a_size); + } - TF_ASSIGN_OR_RETURN(computation, sub->Build()); + if (xla::ShapeUtil::GetDimension(a_shape, -1) != + xla::ShapeUtil::GetDimension(a_shape, -2)) { + return errors::InvalidArgument( + "The 'a' arguments to TriangularSolve must be square matrices: ", + xla::ShapeUtil::HumanString(a_shape)); + } + const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); + if ((left_side ? m : n) != xla::ShapeUtil::GetDimension(a_shape, -1)) { + return errors::InvalidArgument( + "Arguments to TriangularSolve have incompatible matrix shapes: ", + xla::ShapeUtil::HumanString(a_shape), " vs ", + xla::ShapeUtil::HumanString(b_shape)); } - return &computation; - }; - - xla::XlaOp output = Zeros(builder, b_shape); - - // Right-looking blocked triangular solve. - // For an explanation of the algorithm, see the TRSM discussion in: - // Goto, Kazushige, and Robert Van De Geijn. "High-performance implementation - // of the level-3 BLAS." ACM Transactions on Mathematical Software (TOMS) 35.1 - // (2008): 4. - - // In the code comments below, T = lambda x: np.swapaxes(x, -1, -2) if - // conjugate_a is False, or T = lambda x: np.conj(np.swapaxes(x, -1, -2)) if - // conjugate_a is True. - - if (!left_side && lower == transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < n; i += block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] = triangular_solve( - // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) - TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); - TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {0, i}, {m, i + k})); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = builder->Call(*solve, {a_slice, b_slice}); - } else { - TF_ASSIGN_OR_RETURN(auto a_slice_conj, - MaybeConjugate(builder, a_slice, conjugate_a)); - update = builder->Div(b_slice, a_slice_conj); - } - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {0, i})); - - // if i + k < a.shape[-1]: - // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :, i+k:] -= np.matmul(output[..., :, i:i+k], a_slice_2) - if (i + k < n) { - xla::XlaOp a_slice_2; - if (lower) { - TF_ASSIGN_OR_RETURN( - a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); - } else { - TF_ASSIGN_OR_RETURN( - a_slice_2, SliceInMinorDims(builder, a, {i, i + k}, {i + k, n})); - } - TF_ASSIGN_OR_RETURN(auto b_update, - BatchDot(builder, update, a_slice_2, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a)); - TF_ASSIGN_OR_RETURN(auto b_slice_2, - SliceInMinorDims(builder, b, {0, i + k}, {m, n})); - b_update = builder->Sub(b_slice_2, b_update); - TF_ASSIGN_OR_RETURN( - b, UpdateSliceInMinorDims(builder, b, b_update, {0, i + k})); - } + if (block_size < 1) { + return errors::InvalidArgument( + "block_size argument to TriangularSolve must be >= 1; got ", + block_size); } - } else if (left_side && lower != transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < m; i += block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] = triangular_solve( - // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) - TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); - TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = builder->Call(*solve, {a_slice, b_slice}); - } else { - TF_ASSIGN_OR_RETURN(auto a_slice_conj, - MaybeConjugate(builder, a_slice, conjugate_a)); - update = builder->Div(b_slice, a_slice_conj); - } - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {i, 0})); - - // if i + k < a.shape[-1]: - // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., i+k:, :] -= np.matmul(a_slice_2, output[..., i:i+k, :]) - if (i + k < m) { - xla::XlaOp a_slice_2; - if (lower) { - TF_ASSIGN_OR_RETURN( - a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {m, i + k})); + std::map base_computations; + auto get_base_triangular_solve = + [&](int k) -> xla::StatusOr { + xla::XlaComputation& computation = base_computations[k]; + if (computation.IsNull()) { + std::unique_ptr sub = builder->CreateSubBuilder( + tensorflow::strings::StrCat("trsm_base_", k)); + + auto a_param = xla::Parameter( + sub.get(), 0, + xla::ShapeUtil::MakeShape(b_shape.element_type(), + ConcatVectors(batch_dimensions, {k, k})), + "a"); + + std::array b_lastd; + if (left_side) { + b_lastd = {k, n}; } else { - TF_ASSIGN_OR_RETURN( - a_slice_2, SliceInMinorDims(builder, a, {i, i + k}, {i + k, m})); + b_lastd = {m, k}; + } + auto b_param = xla::Parameter( + sub.get(), 1, + xla::ShapeUtil::MakeShape(b_shape.element_type(), + ConcatVectors(batch_dimensions, b_lastd)), + "b"); + + // We use a left-looking or right-looking subroutine on the block + // diagonal in the lower=true cases, while falling back to a recursive + // call in others. The left-looking and right-looking subroutines are + // written with a While loop and so yields much faster compile times. + // Moreover, they can give higher performance on smaller (sub)problems. + if (left_side && lower) { + TriangularSolveLeftLooking(a_param, b_param, transpose_a, + conjugate_a); + } else if (!left_side && lower) { + TriangularSolveRightLooking(a_param, b_param, transpose_a, + conjugate_a); + } else { + TriangularSolve(a_param, b_param, left_side, lower, transpose_a, + conjugate_a, + /*block_size=*/1); } - TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(builder, a_slice_2, update, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false)); - TF_ASSIGN_OR_RETURN(auto b_slice_2, - SliceInMinorDims(builder, b, {i + k, 0}, {m, n})); - b_update = builder->Sub(b_slice_2, b_update); - TF_ASSIGN_OR_RETURN( - b, UpdateSliceInMinorDims(builder, b, b_update, {i + k, 0})); + TF_ASSIGN_OR_RETURN(computation, sub->Build()); } - } - } else if (!left_side && lower != transpose_a) { - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = xla::RoundUpToNearest(n, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] triangular_solve( - // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) - TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); - TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {0, i}, {m, i + k})); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = builder->Call(*solve, {a_slice, b_slice}); - } else { - TF_ASSIGN_OR_RETURN(auto a_slice_conj, - MaybeConjugate(builder, a_slice, conjugate_a)); - update = builder->Div(b_slice, a_slice_conj); - } - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {0, i})); - - // if i - k >= 0: - // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :, :i] -= np.matmul(out[..., :, i:i+k], a_slice_2) - if (i - k >= 0) { - xla::XlaOp a_slice_2; - if (lower) { - TF_ASSIGN_OR_RETURN(a_slice_2, - SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); + return &computation; + }; + + xla::XlaOp output = xla::ZerosLike(b); + + // Right-looking blocked triangular solve. + // For an explanation of the algorithm, see the TRSM discussion in: + // Goto, Kazushige, and Robert Van De Geijn. "High-performance + // implementation of the level-3 BLAS." ACM Transactions on Mathematical + // Software (TOMS) 35.1 (2008): 4. + + // In the code comments below, T = lambda x: np.swapaxes(x, -1, -2) if + // conjugate_a is False, or T = lambda x: np.conj(np.swapaxes(x, -1, -2)) if + // conjugate_a is True. + + if (!left_side && lower == transpose_a) { + // for i in range(0, a.shape[-1], block_size): + for (int64 i = 0; i < n; i += block_size) { + int64 k = std::min(block_size, n - i); + + // output[..., :, i:i+k] = triangular_solve( + // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) + auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); + auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); + xla::XlaOp update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, + get_base_triangular_solve(k)); + update = xla::Call(builder, *solve, {a_slice, b_slice}); } else { - TF_ASSIGN_OR_RETURN(a_slice_2, - SliceInMinorDims(builder, a, {0, i}, {i, i + k})); + auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); + update = b_slice / a_slice_conj; } + output = UpdateSliceInMinorDims(output, update, {0, i}); + + // if i + k < a.shape[-1]: + // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :, i+k:] -= np.matmul(output[..., :, i:i+k], a_slice_2) + if (i + k < n) { + xla::XlaOp a_slice_2; + if (lower) { + a_slice_2 = SliceInMinorDims(a, {i + k, i}, {n, i + k}); + } else { + a_slice_2 = SliceInMinorDims(a, {i, i + k}, {i + k, n}); + } + + auto b_update = BatchDot(update, a_slice_2, + /*transpose_x=*/false, + /*transpose_y=*/transpose_a, + /*conjugate_x=*/false, + /*conjugate_y=*/conjugate_a); + auto b_slice_2 = SliceInMinorDims(b, {0, i + k}, {m, n}); + b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, i + k}); + } + } - TF_ASSIGN_OR_RETURN(auto b_update, - BatchDot(builder, update, a_slice_2, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a)); - TF_ASSIGN_OR_RETURN(auto b_slice_2, - SliceInMinorDims(builder, b, {0, 0}, {m, i})); - b_update = builder->Sub(b_slice_2, b_update); - TF_ASSIGN_OR_RETURN( - b, UpdateSliceInMinorDims(builder, b, b_update, {0, 0})); + } else if (left_side && lower != transpose_a) { + // for i in range(0, a.shape[-1], block_size): + for (int64 i = 0; i < m; i += block_size) { + int64 k = std::min(block_size, m - i); + + // output[..., i:i+k, :] = triangular_solve( + // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) + auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); + auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); + xla::XlaOp update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, + get_base_triangular_solve(k)); + update = xla::Call(builder, *solve, {a_slice, b_slice}); + } else { + auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); + update = b_slice / a_slice_conj; + } + output = UpdateSliceInMinorDims(output, update, {i, 0}); + + // if i + k < a.shape[-1]: + // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., i+k:, :] -= np.matmul(a_slice_2, output[..., i:i+k, :]) + if (i + k < m) { + xla::XlaOp a_slice_2; + if (lower) { + a_slice_2 = SliceInMinorDims(a, {i + k, i}, {m, i + k}); + } else { + a_slice_2 = SliceInMinorDims(a, {i, i + k}, {i + k, m}); + } + + auto b_update = BatchDot(a_slice_2, update, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false); + auto b_slice_2 = SliceInMinorDims(b, {i + k, 0}, {m, n}); + b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {i + k, 0}); + } } - } - } else { // left_side && lower == transpose_a - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = xla::RoundUpToNearest(m, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] triangular_solve( - // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) - TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); - TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = builder->Call(*solve, {a_slice, b_slice}); - } else { - TF_ASSIGN_OR_RETURN(auto a_slice_conj, - MaybeConjugate(builder, a_slice, conjugate_a)); - update = builder->Div(b_slice, a_slice_conj); + } else if (!left_side && lower != transpose_a) { + // for i in reversed(range(0, a.shape[-1], block_size)): + const int64 last_blk_ix = + xla::RoundUpToNearest(n, block_size) - block_size; + for (int64 i = last_blk_ix; i >= 0; i -= block_size) { + int64 k = std::min(block_size, n - i); + + // output[..., :, i:i+k] triangular_solve( + // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) + auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); + auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); + xla::XlaOp update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, + get_base_triangular_solve(k)); + update = xla::Call(builder, *solve, {a_slice, b_slice}); + } else { + auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); + update = b_slice / a_slice_conj; + } + output = UpdateSliceInMinorDims(output, update, {0, i}); + + // if i - k >= 0: + // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :, :i] -= np.matmul(out[..., :, i:i+k], a_slice_2) + if (i - k >= 0) { + xla::XlaOp a_slice_2; + if (lower) { + a_slice_2 = SliceInMinorDims(a, {i, 0}, {i + k, i}); + } else { + a_slice_2 = SliceInMinorDims(a, {0, i}, {i, i + k}); + } + + auto b_update = BatchDot(update, a_slice_2, + /*transpose_x=*/false, + /*transpose_y=*/transpose_a, + /*conjugate_x=*/false, + /*conjugate_y=*/conjugate_a); + auto b_slice_2 = SliceInMinorDims(b, {0, 0}, {m, i}); + b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, 0}); + } } - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {i, 0})); - - // if i - k >= 0: - // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :i, :] -= np.matmul(a_slice_2, out[..., i:i+k, :]) - if (i - k >= 0) { - xla::XlaOp a_slice_2; - if (lower) { - TF_ASSIGN_OR_RETURN(a_slice_2, - SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); + } else { // left_side && lower == transpose_a + // for i in reversed(range(0, a.shape[-1], block_size)): + const int64 last_blk_ix = + xla::RoundUpToNearest(m, block_size) - block_size; + for (int64 i = last_blk_ix; i >= 0; i -= block_size) { + int64 k = std::min(block_size, m - i); + + // output[..., i:i+k, :] triangular_solve( + // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) + auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); + auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); + xla::XlaOp update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, + get_base_triangular_solve(k)); + update = xla::Call(builder, *solve, {a_slice, b_slice}); } else { - TF_ASSIGN_OR_RETURN(a_slice_2, - SliceInMinorDims(builder, a, {0, i}, {i, i + k})); + auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); + update = b_slice / a_slice_conj; + } + output = UpdateSliceInMinorDims(output, update, {i, 0}); + + // if i - k >= 0: + // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :i, :] -= np.matmul(a_slice_2, out[..., i:i+k, :]) + if (i - k >= 0) { + xla::XlaOp a_slice_2; + if (lower) { + a_slice_2 = SliceInMinorDims(a, {i, 0}, {i + k, i}); + } else { + a_slice_2 = SliceInMinorDims(a, {0, i}, {i, i + k}); + } + + auto b_update = BatchDot(a_slice_2, update, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false); + auto b_slice_2 = SliceInMinorDims(b, {0, 0}, {i, n}); + b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, 0}); } - - TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(builder, a_slice_2, update, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false)); - TF_ASSIGN_OR_RETURN(auto b_slice_2, - SliceInMinorDims(builder, b, {0, 0}, {i, n})); - b_update = builder->Sub(b_slice_2, b_update); - TF_ASSIGN_OR_RETURN( - b, UpdateSliceInMinorDims(builder, b, b_update, {0, 0})); } } - } - return output; + return output; + }); } -xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder, - const xla::XlaOp& a, - const xla::XlaOp& b, - bool transpose_a, - bool conjugate_a) { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - } - - // The main computation is performed in a While loop. - - // Allocate the output and set its first or last row, - // output = np.zeros_like(b) - // if transpose_a: - // output[..., m-1:, :] = b[..., m-1:, :] / a[..., m-1:, m-1:] - // else: - // output[..., :1, :] = b[..., :1, :] / a[..., :1, :1] - xla::XlaOp output = Zeros(builder, b_shape); - { - auto i = transpose_a ? m - 1 : 0; - TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + 1, i + 1})); - TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {i, 0}, {i + 1, n})); - TF_ASSIGN_OR_RETURN(auto a_slice_conj, - MaybeConjugate(builder, a_slice, conjugate_a)); - auto update = builder->Div(b_slice, a_slice_conj); - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {i, 0})); - } - - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (m-2, output, a, b) - // else: - // init = (1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = builder->ConstantR0(transpose_a ? m - 2 : 1); - auto init = builder->Tuple({init_i, output, a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i >= 0 if transpose_a else i < m - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileCond"); - { - auto i = condb->GetTupleElement( - condb->Parameter(0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"), - 0); - if (transpose_a) { - condb->Ge(i, condb->ConstantR0(0)); - } else { - condb->Lt(i, condb->ConstantR0(m)); +xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); + const int64 ndims = xla::ShapeUtil::Rank(a_shape); + + std::vector batch_dimensions; + for (int i = 0; i < ndims - 2; ++i) { + int64 a_size = a_shape.dimensions(i); + batch_dimensions.push_back(a_size); } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., i+1:, i:i+1], -1 -2) - // else: - // a_row = a[..., i:i+1, :i] - // result_row = b[..., i:i+1, :] - np.matmul(a_row, output[..., :, :]) - // output[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileBody"); - { - auto input_tuple = bodyb->Parameter(0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"); - // i, output, a, b = loop_carry - auto i = bodyb->GetTupleElement(input_tuple, 0); - auto body_out = bodyb->GetTupleElement(input_tuple, 1); - auto body_a = bodyb->GetTupleElement(input_tuple, 2); - auto body_b = bodyb->GetTupleElement(input_tuple, 3); - auto zero = bodyb->ConstantR0(0); + // The main computation is performed in a While loop. - // We'd like to implement this: - // if transpose_a: - // a_row = T(a[..., i+1:, i:i+1]) - // result_row = (b[..., i:i+1, :] - // - np.matmul(a_row, body_out[..., i+1:, :])) - // else: - // result_row = (b[..., i:i+1, :] - // - np.matmul(a[..., i:i+1, :i], body_out[..., :i, :])) - // But since we can't have intermediate array sizes depend on the loop - // counter, we instead exploit the fact that we initialized the output to - // all zeros and use that as zero-padding (doing unnecessary FLOPs). - xla::XlaOp a_row; - if (transpose_a) { - TF_ASSIGN_OR_RETURN(a_row, DynamicSliceInMinorDims(bodyb.get(), body_a, - {zero, i}, {m, 1})); - } else { - TF_ASSIGN_OR_RETURN(a_row, DynamicSliceInMinorDims(bodyb.get(), body_a, - {i, zero}, {1, m})); + // Allocate the output and set its first or last row, + // output = np.zeros_like(b) + // if transpose_a: + // output[..., m-1:, :] = b[..., m-1:, :] / a[..., m-1:, m-1:] + // else: + // output[..., :1, :] = b[..., :1, :] / a[..., :1, :1] + xla::XlaOp output = xla::ZerosLike(b); + { + auto i = transpose_a ? m - 1 : 0; + auto a_slice = SliceInMinorDims(a, {i, i}, {i + 1, i + 1}); + auto b_slice = SliceInMinorDims(b, {i, 0}, {i + 1, n}); + auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); + auto update = b_slice / a_slice_conj; + output = UpdateSliceInMinorDims(output, update, {i, 0}); } - TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(bodyb.get(), a_row, body_out, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false)); - TF_ASSIGN_OR_RETURN( - auto result_row_slice, - DynamicSliceInMinorDims(bodyb.get(), body_b, {i, zero}, {1, n})); - auto result_row = bodyb->Sub(result_row_slice, b_update); - - // body_out[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] - TF_ASSIGN_OR_RETURN(auto a_elt, DynamicSliceInMinorDims(bodyb.get(), body_a, - {i, i}, {1, 1})); - TF_ASSIGN_OR_RETURN(auto a_elt_conj, - MaybeConjugate(bodyb.get(), a_elt, conjugate_a)); - auto div_result = bodyb->Div(result_row, a_elt_conj); - TF_ASSIGN_OR_RETURN(body_out, - DynamicUpdateSliceInMinorDims(bodyb.get(), body_out, - div_result, {i, zero})); + // Construct the initial loop carry tuple, // if transpose_a: - // return (i - 1, body_out, a, b) + // init = (m-2, output, a, b) // else: - // return (i + 1, body_out, a, b) - auto next_i = bodyb->Add(i, bodyb->ConstantR0(transpose_a ? -1 : 1)); - bodyb->Tuple({next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = builder->While(cond, body, init); - return builder->GetTupleElement(triangular_solve_left_looking_while, 1); + // init = (1, output, a, b) + std::vector tuple_shapes = { + // The loop iteration counter is a scalar, incremented each iteration. + xla::ShapeUtil::MakeShape(xla::S32, {}), + // The output has the shape of b, with one row updated each iteration. + b_shape, + // The coefficient matrix a is a loop invariant. + a_shape, + // The right-hand-side matrix b is a loop invariant. + b_shape}; + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); + auto init_i = xla::ConstantR0(builder, transpose_a ? m - 2 : 1); + auto init = xla::Tuple(builder, {init_i, output, a, b}); + + // Construct the loop condition function, + // def cond_fun(loop_carry): + // i, output, a, b = loop_carry + // return i >= 0 if transpose_a else i < m + std::unique_ptr condb = + builder->CreateSubBuilder("TriangularSolveLeftLookingWhileCond"); + { + auto i = xla::GetTupleElement( + xla::Parameter(condb.get(), 0, tuple_shape, + "TriangularSolveLeftLookingWhileTuple"), + 0); + if (transpose_a) { + xla::Ge(i, xla::ConstantR0(condb.get(), 0)); + } else { + xla::Lt(i, xla::ConstantR0(condb.get(), m)); + } + } + TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); + + // Construct the loop body function, + // def body_fun(loop_carry): + // i, output, a, b = loop_carry + // if transpose_a: + // a_row = np.swapaxes(a[..., i+1:, i:i+1], -1 -2) + // else: + // a_row = a[..., i:i+1, :i] + // result_row = b[..., i:i+1, :] - np.matmul(a_row, output[..., :, :]) + // output[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] + // if transpose_a: + // return (i - 1, output, a, b) + // else: + // return (i + 1, output, a, b) + // We have to do some extra FLOPs propagating zeros in the matrix multiply + // because we can't have the size of its arguments depend on the loop + // counter. + std::unique_ptr bodyb = + builder->CreateSubBuilder("TriangularSolveLeftLookingWhileBody"); + { + auto input_tuple = xla::Parameter(bodyb.get(), 0, tuple_shape, + "TriangularSolveLeftLookingWhileTuple"); + + // i, output, a, b = loop_carry + auto i = xla::GetTupleElement(input_tuple, 0); + auto body_out = xla::GetTupleElement(input_tuple, 1); + auto body_a = xla::GetTupleElement(input_tuple, 2); + auto body_b = xla::GetTupleElement(input_tuple, 3); + auto zero = xla::ConstantR0(bodyb.get(), 0); + + // We'd like to implement this: + // if transpose_a: + // a_row = T(a[..., i+1:, i:i+1]) + // result_row = (b[..., i:i+1, :] + // - np.matmul(a_row, body_out[..., i+1:, :])) + // else: + // result_row = (b[..., i:i+1, :] + // - np.matmul(a[..., i:i+1, :i], body_out[..., :i, :])) + // But since we can't have intermediate array sizes depend on the loop + // counter, we instead exploit the fact that we initialized the output to + // all zeros and use that as zero-padding (doing unnecessary FLOPs). + xla::XlaOp a_row; + if (transpose_a) { + a_row = DynamicSliceInMinorDims(body_a, {zero, i}, {m, 1}); + } else { + a_row = DynamicSliceInMinorDims(body_a, {i, zero}, {1, m}); + } + auto b_update = BatchDot(a_row, body_out, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false); + auto result_row_slice = + DynamicSliceInMinorDims(body_b, {i, zero}, {1, n}); + auto result_row = result_row_slice - b_update; + + // body_out[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] + auto a_elt = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); + auto a_elt_conj = MaybeConjugate(a_elt, conjugate_a); + auto div_result = xla::Div(result_row, a_elt_conj); + body_out = DynamicUpdateSliceInMinorDims(body_out, div_result, {i, zero}); + + // if transpose_a: + // return (i - 1, body_out, a, b) + // else: + // return (i + 1, body_out, a, b) + auto next_i = xla::Add( + i, xla::ConstantR0(bodyb.get(), transpose_a ? -1 : 1)); + xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); + } + TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); + + // Construct the While loop and return the result, + // return while_loop(cond_fun, body_fun, init)[1] + auto triangular_solve_left_looking_while = xla::While(cond, body, init); + return xla::GetTupleElement(triangular_solve_left_looking_while, 1); + }); } -xla::StatusOr TriangularSolveRightLooking(xla::XlaBuilder* builder, - const xla::XlaOp& a, - const xla::XlaOp& b, - bool transpose_a, - bool conjugate_a) { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - } - - // The main computation is performed in a While loop. - xla::XlaOp output = Zeros(builder, b_shape); - - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (0, output, a, b) - // else: - // init = (n-1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = builder->ConstantR0(transpose_a ? 0 : n - 1); - auto init = builder->Tuple({init_i, output, a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i < n if transpose_a else i >= 0 - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileCond"); - { - auto i = condb->GetTupleElement( - condb->Parameter(0, tuple_shape, - "TriangularSolveRightLookingWhileTuple"), - 0); - if (transpose_a) { - condb->Lt(i, condb->ConstantR0(n)); - } else { - condb->Ge(i, condb->ConstantR0(0)); +xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); + const int64 ndims = xla::ShapeUtil::Rank(a_shape); + + std::vector batch_dimensions; + for (int i = 0; i < ndims - 2; ++i) { + int64 a_size = a_shape.dimensions(i); + batch_dimensions.push_back(a_size); } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., :, i:i+1], -1 -2) - // else: - // a_row = a[..., :, i:i+1] - // result_row = b[..., :, i:i+1] - np.matmul(output, a_row) - // output[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileBody"); - { - auto input_tuple = bodyb->Parameter( - 0, tuple_shape, "TriangularSolveRightLookingWhileTuple"); - - // i, output, a, b = loop_carry - auto i = bodyb->GetTupleElement(input_tuple, 0); - auto body_out = bodyb->GetTupleElement(input_tuple, 1); - auto body_a = bodyb->GetTupleElement(input_tuple, 2); - auto body_b = bodyb->GetTupleElement(input_tuple, 3); - auto zero = bodyb->ConstantR0(0); - - // We'd like to implement b[..., :, i:i+1] - np.matmul(output, a[..., :, - // i:i+1]) But since we can't have intermediate array sizes depend on the - // loop counter, we instead exploit the fact that we initialized the output - // to all zeros and use that as zero-padding (doing unnecessary FLOPs). - TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(bodyb.get(), body_out, body_a, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a)); - // result = b - np.matmul(output, a) - auto result = bodyb->Sub(body_b, b_update); - // result_row = result[..., :, i:i+1] - TF_ASSIGN_OR_RETURN( - auto result_row, - DynamicSliceInMinorDims(bodyb.get(), result, {zero, i}, {m, 1})); - - // body_out[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] - TF_ASSIGN_OR_RETURN(auto a_ii, DynamicSliceInMinorDims(bodyb.get(), body_a, - {i, i}, {1, 1})); - TF_ASSIGN_OR_RETURN(auto a_ii_conj, - MaybeConjugate(bodyb.get(), a_ii, conjugate_a)); - auto div_result = bodyb->Div(result_row, a_ii_conj); - TF_ASSIGN_OR_RETURN(body_out, - DynamicUpdateSliceInMinorDims(bodyb.get(), body_out, - div_result, {zero, i})); + // The main computation is performed in a While loop. + xla::XlaOp output = xla::ZerosLike(b); + + // Construct the initial loop carry tuple, // if transpose_a: - // return (i + 1, body_out, a, b) + // init = (0, output, a, b) // else: - // return (i - 1, body_out, a, b) - auto next_i = bodyb->Add(i, bodyb->ConstantR0(transpose_a ? 1 : -1)); - bodyb->Tuple({next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = builder->While(cond, body, init); - return builder->GetTupleElement(triangular_solve_left_looking_while, 1); + // init = (n-1, output, a, b) + std::vector tuple_shapes = { + // The loop iteration counter is a scalar, incremented each iteration. + xla::ShapeUtil::MakeShape(xla::S32, {}), + // The output has the shape of b, with one row updated each iteration. + b_shape, + // The coefficient matrix a is a loop invariant. + a_shape, + // The right-hand-side matrix b is a loop invariant. + b_shape}; + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); + auto init_i = xla::ConstantR0(builder, transpose_a ? 0 : n - 1); + auto init = xla::Tuple(builder, {init_i, output, a, b}); + + // Construct the loop condition function, + // def cond_fun(loop_carry): + // i, output, a, b = loop_carry + // return i < n if transpose_a else i >= 0 + std::unique_ptr condb = + builder->CreateSubBuilder("TriangularSolveRightLookingWhileCond"); + { + auto i = xla::GetTupleElement( + xla::Parameter(condb.get(), 0, tuple_shape, + "TriangularSolveRightLookingWhileTuple"), + 0); + if (transpose_a) { + xla::Lt(i, xla::ConstantR0(condb.get(), n)); + } else { + xla::Ge(i, xla::ConstantR0(condb.get(), 0)); + } + } + TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); + + // Construct the loop body function, + // def body_fun(loop_carry): + // i, output, a, b = loop_carry + // if transpose_a: + // a_row = np.swapaxes(a[..., :, i:i+1], -1 -2) + // else: + // a_row = a[..., :, i:i+1] + // result_row = b[..., :, i:i+1] - np.matmul(output, a_row) + // output[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] + // if transpose_a: + // return (i - 1, output, a, b) + // else: + // return (i + 1, output, a, b) + // We have to do some extra FLOPs propagating zeros in the matrix multiply + // because we can't have the size of its arguments depend on the loop + // counter. + std::unique_ptr bodyb = + builder->CreateSubBuilder("TriangularSolveRightLookingWhileBody"); + { + auto input_tuple = xla::Parameter( + bodyb.get(), 0, tuple_shape, "TriangularSolveRightLookingWhileTuple"); + + // i, output, a, b = loop_carry + auto i = xla::GetTupleElement(input_tuple, 0); + auto body_out = xla::GetTupleElement(input_tuple, 1); + auto body_a = xla::GetTupleElement(input_tuple, 2); + auto body_b = xla::GetTupleElement(input_tuple, 3); + auto zero = xla::ConstantR0(bodyb.get(), 0); + + // We'd like to implement b[..., :, i:i+1] - np.matmul(output, a[..., :, + // i:i+1]) But since we can't have intermediate array sizes depend on the + // loop counter, we instead exploit the fact that we initialized the + // output to all zeros and use that as zero-padding (doing unnecessary + // FLOPs). + auto b_update = BatchDot(body_out, body_a, + /*transpose_x=*/false, + /*transpose_y=*/transpose_a, + /*conjugate_x=*/false, + /*conjugate_y=*/conjugate_a); + // result = b - np.matmul(output, a) + auto result = body_b - b_update; + // result_row = result[..., :, i:i+1] + auto result_row = DynamicSliceInMinorDims(result, {zero, i}, {m, 1}); + + // body_out[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] + auto a_ii = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); + auto a_ii_conj = MaybeConjugate(a_ii, conjugate_a); + auto div_result = xla::Div(result_row, a_ii_conj); + body_out = DynamicUpdateSliceInMinorDims(body_out, div_result, {zero, i}); + + // if transpose_a: + // return (i + 1, body_out, a, b) + // else: + // return (i - 1, body_out, a, b) + auto next_i = xla::Add( + i, xla::ConstantR0(bodyb.get(), transpose_a ? 1 : -1)); + xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); + } + TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); + + // Construct the While loop and return the result, + // return while_loop(cond_fun, body_fun, init)[1] + auto triangular_solve_left_looking_while = xla::While(cond, body, init); + return xla::GetTupleElement(triangular_solve_left_looking_while, 1); + }); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 540c26b2473df9e7885f4e549b3e516a3d8a0d43..80c2bc4c9c38ec101db419d48db26e67e25d169b 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -57,23 +57,15 @@ namespace tensorflow { // // Uses a blocked algorithm if `block_size` is > 1; if block_size == 1 then no // blocking is used. -xla::StatusOr TriangularSolve(xla::XlaBuilder* builder, - const xla::XlaOp& a, xla::XlaOp b, - bool left_side, bool lower, - bool transpose_a, bool conjugate_a, - int64 block_size = 256); +xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, + bool lower, bool transpose_a, bool conjugate_a, + int64 block_size = 256); -xla::StatusOr TriangularSolveLeftLooking(xla::XlaBuilder* builder, - const xla::XlaOp& a, - const xla::XlaOp& b, - bool transpose_a, - bool conjugate_a); +xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, + bool transpose_a, bool conjugate_a); -xla::StatusOr TriangularSolveRightLooking(xla::XlaBuilder* builder, - const xla::XlaOp& a, - const xla::XlaOp& b, - bool transpose_a, - bool conjugate_a); +xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, + bool transpose_a, bool conjugate_a); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index 87ea4763f7c2357ae179b68ade3715b24c46432f..d5ffc1498e4b6dcfbc9f24f9b5dce58fddca8ab1 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -85,11 +85,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/false, /*lower=*/true, - /*transpose_a=*/true, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {0.5, 0.08333334, 0.04629629, 0.03367003}, @@ -107,11 +106,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerNotranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/false, /*lower=*/true, - /*transpose_a=*/false, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {-0.16414141, -0.06902357, -0.07070707, 0.36363636}, @@ -129,11 +127,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightUpperTranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/false, /*lower=*/false, - /*transpose_a=*/true, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/false, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {-0.16414141, -0.06902357, -0.07070707, 0.36363636}, @@ -151,11 +148,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightUpperNotranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/false, /*lower=*/false, - /*transpose_a=*/false, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/false, /*lower=*/false, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {0.5, 0.08333334, 0.04629629, 0.03367003}, @@ -173,11 +169,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerTranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/true, /*lower=*/true, - /*transpose_a=*/true, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {-0.89646465, -0.69444444, -0.49242424}, @@ -196,11 +191,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/true, /*lower=*/true, - /*transpose_a=*/false, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {0.5, 1.0, 1.5}, @@ -219,11 +213,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/true, /*lower=*/false, - /*transpose_a=*/true, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {0.5, 1.0, 1.5}, @@ -242,11 +235,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperNotranspose) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/true, /*lower=*/false, - /*transpose_a=*/false, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {-0.89646465, -0.69444444, -0.49242424}, @@ -267,11 +259,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTransposeConjugate) { CreateR2Parameter(AValsLowerComplex(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsRightComplex(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/false, /*lower=*/true, - /*transpose_a=*/true, /*conjugate_a=*/true, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/true, + /*block_size=*/2); xla::Array2D expected({ {0.5, complex64(0.08333333, 0.08333333), @@ -295,11 +286,10 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { CreateR2Parameter(AValsUpperComplex(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeftComplex(), 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, - /*left_side=*/true, /*lower=*/false, - /*transpose_a=*/true, /*conjugate_a=*/false, - /*block_size=*/2); - TF_ASSERT_OK(result.status()); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); xla::Array2D expected({ {0.5, 1., 1.5}, @@ -323,10 +313,9 @@ XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolveLeftLooking(&builder, a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - TF_ASSERT_OK(result.status()); + TriangularSolveLeftLooking(a, b, + /*transpose_a=*/false, + /*conjugate_a=*/false); xla::Array2D expected({ {0.5, 1.0, 1.5}, @@ -345,10 +334,9 @@ XLA_TEST_F(TriangularSolveLeftLookingTest, NonzeroUpperTriangle) { xla::XlaOp a, b; auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - auto result = TriangularSolveLeftLooking(&builder, a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - TF_ASSERT_OK(result.status()); + TriangularSolveLeftLooking(a, b, + /*transpose_a=*/false, + /*conjugate_a=*/false); xla::Array2D expected({ {0.5, 1.0, 1.5}, diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index d9ff7e6259f3fbab8957394bff5c5670a67dd0eb..fdc8bfca4932fe62a4d2a8db49f4104c3eb0cd3b 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -27,29 +28,23 @@ limitations under the License. namespace tensorflow { -xla::XlaOp Zeros(xla::XlaBuilder* builder, const xla::Shape& shape) { - return builder->Broadcast( - builder->ConstantLiteral(xla::Literal::Zero(shape.element_type())), - xla::AsInt64Slice(shape.dimensions())); -} - xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, double value) { switch (type) { case xla::F16: - return builder->ConstantR0(static_cast(value)); + return xla::ConstantR0(builder, static_cast(value)); break; case xla::BF16: - return builder->ConstantR0(static_cast(value)); + return xla::ConstantR0(builder, static_cast(value)); break; case xla::F32: - return builder->ConstantR0(static_cast(value)); + return xla::ConstantR0(builder, static_cast(value)); break; case xla::F64: - return builder->ConstantR0(value); + return xla::ConstantR0(builder, value); break; case xla::C64: - return builder->ConstantR0(value); + return xla::ConstantR0(builder, value); break; default: LOG(FATAL) << "unhandled element type " << type; @@ -107,134 +102,140 @@ xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, default: LOG(FATAL) << "unhandled element type " << type; } - return builder->ConstantLiteral(literal); + return xla::ConstantLiteral(builder, literal); } -xla::StatusOr SliceInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x, - gtl::ArraySlice start, - gtl::ArraySlice end) { - TF_RET_CHECK(start.size() == end.size()); - int64 n_minor_dims = start.size(); - - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - - const int64 n_dims = xla::ShapeUtil::Rank(shape); - TF_RET_CHECK(n_minor_dims <= n_dims); - gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), - /*pos=*/0, - /*len=*/n_dims - n_minor_dims); - - // Prepends 0s in the major dim - std::vector padded_start(n_dims, 0); - std::copy(start.begin(), start.end(), - padded_start.begin() + major_dims.size()); - - // Prepends the shape of the major dims. - std::vector padded_end(n_dims); - std::copy(major_dims.begin(), major_dims.end(), padded_end.begin()); - std::copy(end.begin(), end.end(), padded_end.begin() + major_dims.size()); - - std::vector strides(n_dims, 1); - return builder->Slice(x, padded_start, padded_end, strides); +xla::XlaOp SliceInMinorDims(xla::XlaOp x, gtl::ArraySlice start, + gtl::ArraySlice end) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_RET_CHECK(start.size() == end.size()); + int64 n_minor_dims = start.size(); + + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + + const int64 n_dims = xla::ShapeUtil::Rank(shape); + TF_RET_CHECK(n_minor_dims <= n_dims); + gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), + /*pos=*/0, + /*len=*/n_dims - n_minor_dims); + + // Prepends 0s in the major dim + std::vector padded_start(n_dims, 0); + std::copy(start.begin(), start.end(), + padded_start.begin() + major_dims.size()); + + // Prepends the shape of the major dims. + std::vector padded_end(n_dims); + std::copy(major_dims.begin(), major_dims.end(), padded_end.begin()); + std::copy(end.begin(), end.end(), padded_end.begin() + major_dims.size()); + + std::vector strides(n_dims, 1); + return xla::Slice(x, padded_start, padded_end, strides); + }); } -std::vector PrependMajorDims(xla::XlaBuilder* builder, - const gtl::ArraySlice& major_dims, - const gtl::ArraySlice& indices) { - std::vector output(indices.size() + major_dims.size()); - std::copy(major_dims.begin(), major_dims.end(), output.begin()); - std::copy(indices.begin(), indices.end(), output.begin() + major_dims.size()); +std::vector ConcatVectors(gtl::ArraySlice xs, + gtl::ArraySlice ys) { + std::vector output(xs.size() + ys.size()); + std::copy(xs.begin(), xs.end(), output.begin()); + std::copy(ys.begin(), ys.end(), output.begin() + xs.size()); return output; } -xla::StatusOr DynamicSliceInMinorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, - const std::vector& starts, - const gtl::ArraySlice& sizes) { - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(shape); - int64 n_minor_dims = starts.size(); - TF_RET_CHECK(n_minor_dims == sizes.size()); - TF_RET_CHECK(n_minor_dims <= n_dims); - gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), - /*pos=*/0, - /*len=*/n_dims - sizes.size()); - TF_ASSIGN_OR_RETURN(auto padded_starts, - PrependZerosInMajorDims(builder, x, starts)); - auto padded_sizes = PrependMajorDims(builder, major_dims, sizes); - return builder->DynamicSlice(x, padded_starts, padded_sizes); +xla::XlaOp DynamicSliceInMinorDims(xla::XlaOp x, + gtl::ArraySlice starts, + gtl::ArraySlice sizes) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + int64 n_minor_dims = starts.size(); + TF_RET_CHECK(n_minor_dims == sizes.size()); + TF_RET_CHECK(n_minor_dims <= n_dims); + gtl::ArraySlice major_dims(xla::AsInt64Slice(shape.dimensions()), + /*pos=*/0, + /*len=*/n_dims - sizes.size()); + auto padded_starts = PrependZerosInMajorDims(x, starts); + auto padded_sizes = ConcatVectors(major_dims, sizes); + return xla::DynamicSlice(x, padded_starts, padded_sizes); + }); } -xla::StatusOr UpdateSlice(xla::XlaBuilder* builder, - const xla::XlaOp& x, - const xla::XlaOp& update, - gtl::ArraySlice start) { - // TODO(phawkins): make int64 work on all backends, remove the int32 cast. - std::vector start_as_int32(start.begin(), start.end()); - auto start_constant = builder->ConstantR1(start_as_int32); - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(shape); - TF_ASSIGN_OR_RETURN(xla::Shape start_constant_shape, - builder->GetShape(start_constant)); - const int64 start_length = - xla::ShapeUtil::GetDimension(start_constant_shape, -1); - TF_RET_CHECK(start_length == n_dims); - return builder->DynamicUpdateSlice(x, update, start_constant); +xla::XlaOp UpdateSlice(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice start) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + // TODO(phawkins): make int64 work on all backends, remove the int32 cast. + std::vector start_as_int32(start.begin(), start.end()); + auto start_constant = xla::ConstantR1(builder, start_as_int32); + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + TF_ASSIGN_OR_RETURN(xla::Shape start_constant_shape, + builder->GetShape(start_constant)); + const int64 start_length = + xla::ShapeUtil::GetDimension(start_constant_shape, -1); + TF_RET_CHECK(start_length == n_dims); + return xla::DynamicUpdateSlice(x, update, start_constant); + }); } -xla::StatusOr UpdateSliceInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x, - const xla::XlaOp& update, - gtl::ArraySlice start) { - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(shape); - const int64 n_minor_dims = start.size(); - TF_RET_CHECK(n_minor_dims <= n_dims); - std::vector padded_start(n_dims, 0); - std::copy(start.begin(), start.end(), - padded_start.begin() + (n_dims - n_minor_dims)); - return UpdateSlice(builder, x, update, padded_start); +xla::XlaOp UpdateSliceInMinorDims(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice start) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + const int64 n_minor_dims = start.size(); + TF_RET_CHECK(n_minor_dims <= n_dims); + std::vector padded_start(n_dims, 0); + std::copy(start.begin(), start.end(), + padded_start.begin() + (n_dims - n_minor_dims)); + return UpdateSlice(x, update, padded_start); + }); } -xla::StatusOr DynamicUpdateSliceInMinorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, const xla::XlaOp& update, - const std::vector& starts) { - TF_ASSIGN_OR_RETURN(auto padded_starts, - PrependZerosInMajorDims(builder, x, starts)); - return builder->DynamicUpdateSlice(x, update, padded_starts); +xla::XlaOp DynamicUpdateSliceInMinorDims(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice starts) { + auto padded_starts = PrependZerosInMajorDims(x, starts); + return xla::DynamicUpdateSlice(x, update, padded_starts); } -xla::StatusOr PrependZerosInMajorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, - const std::vector& starts) { - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(shape); - auto zero = builder->Reshape(builder->ConstantR0(0), {1}); - std::vector padded_starts(n_dims, zero); - for (int i = 0; i < starts.size(); ++i) { - padded_starts[n_dims - starts.size() + i] = - builder->Reshape(starts[i], {1}); - } - return builder->ConcatInDim(padded_starts, 0); +xla::XlaOp PrependZerosInMajorDims(xla::XlaOp x, + gtl::ArraySlice starts) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + auto zero = xla::Reshape(xla::ConstantR0(builder, 0), {1}); + std::vector padded_starts(n_dims, zero); + for (int i = 0; i < starts.size(); ++i) { + padded_starts[n_dims - starts.size() + i] = xla::Reshape(starts[i], {1}); + } + return xla::ConcatInDim(builder, padded_starts, 0); + }); } -xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x) { - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - const int64 n_dims = xla::ShapeUtil::Rank(shape); - TF_RET_CHECK(n_dims >= 2); - std::vector permutation(n_dims); - std::iota(permutation.begin(), permutation.end(), 0); - std::swap(permutation[n_dims - 1], permutation[n_dims - 2]); - return builder->Transpose(x, permutation); +xla::XlaOp TransposeInMinorDims(xla::XlaOp x) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(shape); + TF_RET_CHECK(n_dims >= 2); + std::vector permutation(n_dims); + std::iota(permutation.begin(), permutation.end(), 0); + std::swap(permutation[n_dims - 1], permutation[n_dims - 2]); + return xla::Transpose(x, permutation); + }); } -xla::StatusOr MaybeConjugate(xla::XlaBuilder* builder, - const xla::XlaOp& x, bool conjugate) { - TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); - auto perform_conj = shape.element_type() == xla::C64 && conjugate; - return perform_conj ? builder->Conj(x) : x; +xla::XlaOp MaybeConjugate(xla::XlaOp x, bool conjugate) { + xla::XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + auto perform_conj = shape.element_type() == xla::C64 && conjugate; + return perform_conj ? xla::Conj(x) : x; + }); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index 3c120a2548576d6ad46870583ca65beea63507a3..6cb6c088e9d20af05193f0a3da6c2595966eb495 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -23,9 +23,6 @@ limitations under the License. namespace tensorflow { -// Returns a zero-filled tensor with shape `shape`. -xla::XlaOp Zeros(xla::XlaBuilder* builder, const xla::Shape& shape); - // Returns a floating point scalar constant of 'type' with 'value'. // If 'type' is complex, returns a real value with zero imaginary component. xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, @@ -33,7 +30,7 @@ xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, // Makes a 1D tensor [0, ..., x, y] from two tensors x and y with zeros // prepended until the array is length n_dims. -xla::XlaOp PrependZerosInMajorDims(xla::XlaBuilder* builder, +xla::XlaOp PrependZerosInMajorDims(xla::XlaOp x, gtl::ArraySlice starts); // Returns a integer scalar constant of 'type' with 'value'. @@ -41,54 +38,43 @@ xla::XlaOp PrependZerosInMajorDims(xla::XlaBuilder* builder, xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, int64 value); -// Builds a vector of zeros of length rank(x) with the last two values being +// Builds a vector of zeros of length rank(x) with the last values being // those in `starts`. -xla::StatusOr PrependZerosInMajorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, - const std::vector& starts); +xla::XlaOp PrependZerosInMajorDims(xla::XlaOp x, + gtl::ArraySlice starts); // Performs a slice in the minor dimensions of a Tensor. -xla::StatusOr SliceInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x, - gtl::ArraySlice start, - gtl::ArraySlice end); +xla::XlaOp SliceInMinorDims(xla::XlaOp x, gtl::ArraySlice start, + gtl::ArraySlice end); -// Builds a 1-d vector out of a concatenation of `major_dims` and `starts`. -std::vector PrependMajorDims(xla::XlaBuilder* builder, - const gtl::ArraySlice& major_dims, - const gtl::ArraySlice& indices); +// Returns the concatenation of `xs` and `ys`. +std::vector ConcatVectors(gtl::ArraySlice xs, + gtl::ArraySlice ys); // Performs a dynamic slice in the minor dimensions of a Tensor. -xla::StatusOr DynamicSliceInMinorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, - const std::vector& starts, const gtl::ArraySlice& sizes); +xla::XlaOp DynamicSliceInMinorDims(xla::XlaOp x, + gtl::ArraySlice starts, + gtl::ArraySlice sizes); // Updates a slice of 'x', i.e., // x[start[0], ..., start[n]] = update -xla::StatusOr UpdateSlice(xla::XlaBuilder* builder, - const xla::XlaOp& x, - const xla::XlaOp& update, - gtl::ArraySlice start); +xla::XlaOp UpdateSlice(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice start); // Updates a slice of 'x', where 'start' contains a list of minor dimensions: // x[..., start[0], ..., start[n]] = update -xla::StatusOr UpdateSliceInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x, - const xla::XlaOp& update, - gtl::ArraySlice start); +xla::XlaOp UpdateSliceInMinorDims(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice start); -xla::StatusOr DynamicUpdateSliceInMinorDims( - xla::XlaBuilder* builder, const xla::XlaOp& x, const xla::XlaOp& update, - const std::vector& starts); +xla::XlaOp DynamicUpdateSliceInMinorDims(xla::XlaOp x, xla::XlaOp update, + gtl::ArraySlice starts); // Transposes a stack of matrices `x` by swapping the last two dimensions. -xla::StatusOr TransposeInMinorDims(xla::XlaBuilder* builder, - const xla::XlaOp& x); +xla::XlaOp TransposeInMinorDims(xla::XlaOp x); // Applies a complex conjugation operation if `a` is complex and `conjugate_a` // is true, otherwise returns its argument. -xla::StatusOr MaybeConjugate(xla::XlaBuilder* builder, - const xla::XlaOp& x, bool conjugate); +xla::XlaOp MaybeConjugate(xla::XlaOp x, bool conjugate); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/util_test.cc b/tensorflow/compiler/tf2xla/lib/util_test.cc index 265b39402c832f8c810a74f281563b05afdf2b1b..7d0f2222a9aa3ef09cb8be20c5f9b26431c6498c 100644 --- a/tensorflow/compiler/tf2xla/lib/util_test.cc +++ b/tensorflow/compiler/tf2xla/lib/util_test.cc @@ -70,8 +70,7 @@ XLA_TEST_F(UtilTest, Simple2dLookup) { auto a_data = CreateR2Parameter(BValsRight(), 0, "a", &builder, &a); auto x_data = CreateR0Parameter(2, 1, "x", &builder, &x); auto y_data = CreateR0Parameter(1, 2, "y", &builder, &y); - auto result = DynamicSliceInMinorDims(&builder, a, {x, y}, {1, 1}); - TF_ASSERT_OK(result.status()); + DynamicSliceInMinorDims(a, {x, y}, {1, 1}); ComputeAndCompareR2(&builder, {{10}}, {a_data.get(), x_data.get(), y_data.get()}, @@ -86,10 +85,8 @@ XLA_TEST_F(UtilTest, Simple3dLookup) { CreateR3Parameter(BatchedAValsFull(), 0, "a", &builder, &a); auto index_data = CreateR0Parameter(1, 1, "index", &builder, &index); - TF_ASSERT_OK_AND_ASSIGN( - auto l_index, - DynamicSliceInMinorDims(&builder, a, - {index, builder.ConstantR0(0)}, {1, 4})); + DynamicSliceInMinorDims(a, {index, xla::ConstantR0(&builder, 0)}, + {1, 4}); ComputeAndCompareR3(&builder, {{{3, 6, 0, 1}}, {{24, 61, 82, 48}}}, {a_data.get(), index_data.get()}); @@ -104,8 +101,7 @@ XLA_TEST_F(UtilTest, SimpleSliceUpdate) { auto x_data = CreateR0Parameter(2, 2, "x", &builder, &x); auto y_data = CreateR0Parameter(1, 3, "y", &builder, &y); - auto result = DynamicUpdateSliceInMinorDims(&builder, a, b, {x, y}); - TF_ASSERT_OK(result.status()); + DynamicUpdateSliceInMinorDims(a, b, {x, y}); xla::Array2D expected( {{{2, 0, 1, 2}, {3, 6, 0, 1}, {4, 9, 1, -10}, {5, 8, 10, 11}}}); @@ -128,13 +124,9 @@ XLA_TEST_F(UtilTest, RowBatchDot) { // Select {{3, 6, 0, 1}, {24, 61, 82, 48}} out of BatchedAValsFull(). auto index_data = CreateR0Parameter(1, 2, "index", &builder, &index); - TF_ASSERT_OK_AND_ASSIGN( - auto l_index, - DynamicSliceInMinorDims(&builder, a, - {index, builder.ConstantR0(0)}, {1, n})); - TF_ASSERT_OK_AND_ASSIGN( - auto dot, BatchDot(&builder, l_index, row, - /*transpose_x=*/false, /*transpose_y=*/true)); + auto l_index = DynamicSliceInMinorDims( + a, {index, xla::ConstantR0(&builder, 0)}, {1, n}); + BatchDot(l_index, row, /*transpose_x=*/false, /*transpose_y=*/true); ComputeAndCompareR3(&builder, {{{33}}, {{292}}}, {a_data.get(), row_data.get(), index_data.get()}); diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc index 09ce594930efc0af47306590d76b322ac730f80f..7cc88f34d291f25814fba9f802c93117973120e7 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -39,7 +40,7 @@ xla::StatusOr> XlaWhileLoop( xla::XlaBuilder* builder) { std::vector elements(arity); for (int i = 0; i < arity; ++i) { - elements[i] = builder->GetTupleElement(tuple, i); + elements[i] = xla::GetTupleElement(tuple, i); } return elements; }; @@ -48,7 +49,8 @@ xla::StatusOr> XlaWhileLoop( std::unique_ptr cond_builder = builder->CreateSubBuilder(strings::StrCat(name, "_condition")); { - auto parameter = cond_builder->Parameter(0, tuple_shape, "parameter"); + auto parameter = + xla::Parameter(cond_builder.get(), 0, tuple_shape, "parameter"); TF_RETURN_IF_ERROR( condition_function(unpack_tuple(parameter, arity, cond_builder.get()), @@ -61,7 +63,8 @@ xla::StatusOr> XlaWhileLoop( std::unique_ptr body_builder = builder->CreateSubBuilder(strings::StrCat(name, "_body")); { - auto parameter = body_builder->Parameter(0, tuple_shape, "parameter"); + auto parameter = + xla::Parameter(body_builder.get(), 0, tuple_shape, "parameter"); TF_ASSIGN_OR_RETURN( auto result, @@ -69,11 +72,11 @@ xla::StatusOr> XlaWhileLoop( body_builder.get())); TF_RET_CHECK(result.size() == initial_values.size()); - body_builder->Tuple(result); + xla::Tuple(body_builder.get(), result); } TF_ASSIGN_OR_RETURN(auto body, body_builder->Build()); - auto outputs = builder->While(cond, body, builder->Tuple(initial_values)); + auto outputs = xla::While(cond, body, xla::Tuple(builder, initial_values)); return unpack_tuple(outputs, arity, builder); } @@ -86,9 +89,8 @@ xla::StatusOr> XlaForEachIndex( auto while_cond_fn = [&](gtl::ArraySlice values, xla::XlaBuilder* cond_builder) -> xla::StatusOr { - return cond_builder->Lt( - values[0], - IntegerLiteral(cond_builder, num_iterations_type, num_iterations)); + return xla::Lt(values[0], IntegerLiteral(cond_builder, num_iterations_type, + num_iterations)); }; auto while_body_fn = [&](gtl::ArraySlice values, xla::XlaBuilder* body_builder) @@ -97,9 +99,9 @@ xla::StatusOr> XlaForEachIndex( std::vector updated_values; updated_values.reserve(values.size()); - updated_values.push_back(body_builder->Add( - iteration, - body_builder->ConstantLiteral(xla::Literal::One(num_iterations_type)))); + updated_values.push_back(xla::Add( + iteration, xla::ConstantLiteral( + body_builder, xla::Literal::One(num_iterations_type)))); values.remove_prefix(1); TF_ASSIGN_OR_RETURN(std::vector body_outputs, @@ -112,7 +114,7 @@ xla::StatusOr> XlaForEachIndex( std::vector values; values.reserve(initial_values.size() + 1); values.push_back( - builder->ConstantLiteral(xla::Literal::Zero(num_iterations_type))); + xla::ConstantLiteral(builder, xla::Literal::Zero(num_iterations_type))); values.insert(values.end(), initial_values.begin(), initial_values.end()); TF_ASSIGN_OR_RETURN(values, XlaWhileLoop(while_cond_fn, while_body_fn, values, diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index 43e1c1e9fecec1c71db1509757251cb5d903ca49..b43405a1a407b5fa98dd740c62af91e048cc9490 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -22,21 +22,34 @@ limitations under the License. namespace tensorflow { -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) { - xla::Shape literal_shape; - TF_RETURN_IF_ERROR(TensorShapeToXLAShape( - host_tensor.dtype(), host_tensor.shape(), &literal_shape)); +Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, + xla::BorrowingLiteral* literal) { + xla::Shape xla_shape; + TF_RETURN_IF_ERROR(TensorShapeToXLAShape(host_tensor.dtype(), + host_tensor.shape(), &xla_shape)); + *literal = xla::BorrowingLiteral( + static_cast(DMAHelper::base(&host_tensor)), xla_shape); + return Status::OK(); +} - *literal = xla::Literal(literal_shape); +Status HostTensorsToBorrowingLiteralTuple( + tensorflow::gtl::ArraySlice host_tensors, + xla::BorrowingLiteral* literal) { + std::vector buf_ptrs; + buf_ptrs.reserve(host_tensors.size()); + std::vector tensor_shapes(host_tensors.size()); - // memcpy over the payload ... - // TODO(phawkins): handle string types. - size_t total_bytes = host_tensor.TotalBytes(); - if (total_bytes > 0) { - void* dst_ptr = literal->untyped_data(); - const void* src_ptr = DMAHelper::base(&host_tensor); - memcpy(dst_ptr, src_ptr, total_bytes); + for (int i = 0; i < host_tensors.size(); i++) { + // Validate runtime shapes and fail if it doesn't match the contract. + const Tensor* tensor = &host_tensors[i]; + buf_ptrs.emplace_back(static_cast(DMAHelper::base(tensor))); + TF_RETURN_IF_ERROR(TensorShapeToXLAShape(tensor->dtype(), tensor->shape(), + &tensor_shapes[i])); } + + *literal = xla::BorrowingLiteral( + buf_ptrs, xla::ShapeUtil::MakeTupleShape(tensor_shapes)); + return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index 220bec15538c36fa30abef9e729b64dbbb9f72b3..ab7e861f3336097d2ea52487092f16edb5c14531 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -22,12 +22,20 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/array_slice.h" namespace tensorflow { -// Copies 'host_tensor' to an XLA Literal. Fails if host_tensor is of an -// unsupported type. -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); +// Returns a BorrowingLiteral that utilizes the same underlying buffer owned by +// 'host_tensor'. +Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, + xla::BorrowingLiteral* literal); + +// Returns a BorrowingLiteral tuple that utilizes the same underlying buffers +// owned by 'host_tensors'. +Status HostTensorsToBorrowingLiteralTuple( + tensorflow::gtl::ArraySlice host_tensors, + xla::BorrowingLiteral* literal); // Copies 'literal' to freshly allocated 'host_tensor', which is allocated of // type . diff --git a/tensorflow/compiler/tf2xla/ops/BUILD b/tensorflow/compiler/tf2xla/ops/BUILD index bb9168fa358154f3db9dab87bacc9bf28dd16406..ace6fd1d8eeaf439509a7b75d8d986997c392e73 100644 --- a/tensorflow/compiler/tf2xla/ops/BUILD +++ b/tensorflow/compiler/tf2xla/ops/BUILD @@ -8,12 +8,7 @@ load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") cc_library( name = "xla_ops", - srcs = [ - "dynamic_slice_ops.cc", - "functional_ops.cc", - "reduce_window_op.cc", - "sendrecv_ops.cc", - ], + srcs = ["xla_ops.cc"], deps = [ "//tensorflow/core:framework", ], diff --git a/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc deleted file mode 100644 index d6c0edbb889b1751ac9d9d47d0c9534b543196ff..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/shape_inference.h" - -namespace tensorflow { - -REGISTER_OP("XlaDynamicUpdateSlice") - .Input("input: T") - .Input("update: T") - .Input("indices: Tindices") - .Output("output: T") - .Attr("T: type") - .Attr("Tindices: {int32, int64}") - .SetShapeFn(shape_inference::UnchangedShape) - .Doc(R"doc( -Wraps the XLA DynamicUpdateSlice operator, documented at - https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice -. - -XlaDynamicUpdateSlice generates a result which is the value of the `input` -operand, with a slice update overwritten at `indices`. The shape of `update` -determines the shape of the sub-array of the result which is updated. The shape -of indices must be rank == 1, with dimension size equal to the rank of `input`. - -Handling of out-of-bounds slice indices is implementation-defined. - -input: A `Tensor` of type T. -indices: A vector of indices into `input`. Must have length equal to the rank of - `input`. -update: A `Tensor` of type T. Same rank as `input`. -output: A `Tensor` of type T. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/functional_ops.cc b/tensorflow/compiler/tf2xla/ops/functional_ops.cc deleted file mode 100644 index 4a669f8e6eaf644f119f3c0a66f29d9f2c9a9d16..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/tf2xla/ops/functional_ops.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -// TODO(b/37549631) setting the While Op to always be stateful is too -// conservative. -REGISTER_OP("XlaWhile") - .Input("input: T") - .Output("output: T") - .Attr("T: list(type) >= 0") - .Attr("cond: func") - .Attr("body: func") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -output = input; While (Cond(output)) { output = Body(output) } - -input: A list of input tensors whose types are T. -output: A list of output tensors whose types are T. -cond: A function takes 'input' and returns a tensor. If the tensor is - a scalar of non-boolean, the scalar is converted to a boolean - according to the following rule: if the scalar is a numerical - value, non-zero means True and zero means False; if the scalar is - a string, non-empty means True and empty means False. If the - tensor is not a scalar, non-emptiness means True and False - otherwise. -body: A function that takes a list of tensors and returns another - list of tensors. Both lists have the same types as specified by T. -)doc"); - -// TODO(b/37549631) setting the If Op to always be stateful is too -// conservative. -REGISTER_OP("XlaIf") - .Input("cond: Tcond") - .Input("inputs: Tin") - .Output("output: Tout") - .Attr("Tcond: type") - .Attr("then_branch: func") - .Attr("else_branch: func") - .Attr("Tin: list(type) >= 0") - .Attr("Tout: list(type) >= 0") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -output = cond ? then_branch(inputs) : else_branch(inputs). - -cond: A boolean scalar. -inputs: A list of input tensors. -output: A list of tensors returned by either then_branch(inputs) or - else_branch(inputs). The input shapes of the then_branch and - else_branch must match. -then_branch: A function takes 'inputs' and returns a list of tensors, - whose types are the same as what else_branch returns. -else_branch: A function takes 'inputs' and returns a list of tensors. - whose types are the same as what then_branch returns. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc b/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc deleted file mode 100644 index d9af982adc090ea78c711fd4656ba429c53b18c9..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -REGISTER_OP("XlaReduceWindow") - .Input("input: T") - .Input("init_value: T") - .Attr("T: numbertype") - .Attr("computation: func") - .Attr("window_dimensions: list(int)") - .Attr("window_strides: list(int)") - .Attr("padding_low: list(int)") - .Attr("padding_high: list(int)") - .Output("output: T") - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -Wraps the XLA ReduceWindow operator, documented at - https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow . - -input: the input tensor -init_value: a scalar representing the initial value for the reduction -computation: a reducer function to apply -window_dimensions: the shape of the window -window_strides: the inter-window strides -padding_low: the padding to apply at the start of each input dimensions -padding_high: the padding to apply at the end of each input dimension. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc deleted file mode 100644 index 7ec7b50e905a6cbdecea4543dcb87322b5a7e844..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc +++ /dev/null @@ -1,61 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -REGISTER_OP("XlaSend") - .Input("tensor: T") - .Attr("T: type") - .Attr("tensor_name: string") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -Sends the named tensor to another XLA computation. Wraps the XLA Send operator -documented at - https://www.tensorflow.org/performance/xla/operation_semantics#send . - -tensor: The tensor to send. -tensor_name: A string key that identifies the channel. -)doc"); - -REGISTER_OP("XlaRecv") - .Output("tensor: dtype") - .Attr("dtype: type") - .Attr("tensor_name: string") - .Attr("shape: shape") - .SetIsStateful() - .SetShapeFn([](shape_inference::InferenceContext* c) { - TensorShape shape_attr; - TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_attr)); - shape_inference::ShapeHandle s; - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(shape_attr, &s)); - c->set_output(0, s); - return Status::OK(); - }) - .Doc(R"doc( -Receives the named tensor from another XLA computation. Wraps the XLA Recv -operator documented at - https://www.tensorflow.org/performance/xla/operation_semantics#recv . - -tensor: The tensor to receive. -dtype: The type of the tensor. -tensor_name: A string key that identifies the channel. -shape: The shape of the tensor. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/xla_ops.cc b/tensorflow/compiler/tf2xla/ops/xla_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..a59c77f5c3a309abe8f6fbab1e48455d54e8fae5 --- /dev/null +++ b/tensorflow/compiler/tf2xla/ops/xla_ops.cc @@ -0,0 +1,182 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("XlaDynamicUpdateSlice") + .Input("input: T") + .Input("update: T") + .Input("indices: Tindices") + .Output("output: T") + .Attr("T: type") + .Attr("Tindices: {int32, int64}") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +Wraps the XLA DynamicUpdateSlice operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice +. + +XlaDynamicUpdateSlice generates a result which is the value of the `input` +operand, with a slice update overwritten at `indices`. The shape of `update` +determines the shape of the sub-array of the result which is updated. The shape +of indices must be rank == 1, with dimension size equal to the rank of `input`. + +Handling of out-of-bounds slice indices is implementation-defined. + +input: A `Tensor` of type T. +indices: A vector of indices into `input`. Must have length equal to the rank of + `input`. +update: A `Tensor` of type T. Same rank as `input`. +output: A `Tensor` of type T. +)doc"); + +// TODO(b/37549631) setting the If Op to always be stateful is too +// conservative. +REGISTER_OP("XlaIf") + .Input("cond: Tcond") + .Input("inputs: Tin") + .Output("output: Tout") + .Attr("Tcond: type") + .Attr("then_branch: func") + .Attr("else_branch: func") + .Attr("Tin: list(type) >= 0") + .Attr("Tout: list(type) >= 0") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +output = cond ? then_branch(inputs) : else_branch(inputs). + +cond: A boolean scalar. +inputs: A list of input tensors. +output: A list of tensors returned by either then_branch(inputs) or + else_branch(inputs). The input shapes of the then_branch and + else_branch must match. +then_branch: A function takes 'inputs' and returns a list of tensors, + whose types are the same as what else_branch returns. +else_branch: A function takes 'inputs' and returns a list of tensors. + whose types are the same as what then_branch returns. +)doc"); + +REGISTER_OP("XlaRecv") + .Output("tensor: dtype") + .Attr("dtype: type") + .Attr("tensor_name: string") + .Attr("shape: shape") + .SetIsStateful() + .SetShapeFn([](shape_inference::InferenceContext* c) { + TensorShape shape_attr; + TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_attr)); + shape_inference::ShapeHandle s; + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(shape_attr, &s)); + c->set_output(0, s); + return Status::OK(); + }) + .Doc(R"doc( +Receives the named tensor from another XLA computation. Wraps the XLA Recv +operator documented at + https://www.tensorflow.org/performance/xla/operation_semantics#recv . + +tensor: The tensor to receive. +dtype: The type of the tensor. +tensor_name: A string key that identifies the channel. +shape: The shape of the tensor. +)doc"); + +REGISTER_OP("XlaReduceWindow") + .Input("input: T") + .Input("init_value: T") + .Attr("T: numbertype") + .Attr("computation: func") + .Attr("window_dimensions: list(int)") + .Attr("window_strides: list(int)") + .Attr("padding_low: list(int)") + .Attr("padding_high: list(int)") + .Output("output: T") + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +Wraps the XLA ReduceWindow operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow . + +input: the input tensor +init_value: a scalar representing the initial value for the reduction +computation: a reducer function to apply +window_dimensions: the shape of the window +window_strides: the inter-window strides +padding_low: the padding to apply at the start of each input dimensions +padding_high: the padding to apply at the end of each input dimension. +)doc"); + +REGISTER_OP("XlaSend") + .Input("tensor: T") + .Attr("T: type") + .Attr("tensor_name: string") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +Sends the named tensor to another XLA computation. Wraps the XLA Send operator +documented at + https://www.tensorflow.org/performance/xla/operation_semantics#send . + +tensor: The tensor to send. +tensor_name: A string key that identifies the channel. +)doc"); + +REGISTER_OP("XlaSort") + .Input("input: T") + .Output("output: T") + .Attr("T: type") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +Wraps the XLA Sort operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#sort +. + +Sorts a tensor. Currently only rank 1 sorts in ascending order are supported. + +input: A `Tensor` of type T. +output: A `Tensor` of type T. +)doc"); + +// TODO(b/37549631) setting the While Op to always be stateful is too +// conservative. +REGISTER_OP("XlaWhile") + .Input("input: T") + .Output("output: T") + .Attr("T: list(type) >= 0") + .Attr("cond: func") + .Attr("body: func") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +output = input; While (Cond(output)) { output = Body(output) } + +input: A list of input tensors whose types are T. +output: A list of output tensors whose types are T. +cond: A function takes 'input' and returns a tensor. If the tensor is + a scalar of non-boolean, the scalar is converted to a boolean + according to the following rule: if the scalar is a numerical + value, non-zero means True and zero means False; if the scalar is + a string, non-empty means True and empty means False. If the + tensor is not a scalar, non-emptiness means True and False + otherwise. +body: A function that takes a list of tensors and returns another + list of tensors. Both lists have the same types as specified by T. +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/python/xla.py b/tensorflow/compiler/tf2xla/python/xla.py index e5ce65bec950fdfd38c3ca5bc62ac745ef8ca4a7..2fc47dffb8f5f16f24e3beb1ff75aeed3e857c58 100644 --- a/tensorflow/compiler/tf2xla/python/xla.py +++ b/tensorflow/compiler/tf2xla/python/xla.py @@ -77,4 +77,6 @@ def reduce_window(operand, recv = gen_xla_ops.xla_recv send = gen_xla_ops.xla_send +sort = gen_xla_ops.xla_sort + while_loop = gen_xla_ops.xla_while diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 9c8e56a17e07348d3cfaaca0b5eb335295af05c3..319cbc74e96262881d32bdc9de2251b53f2b05d6 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/xla/client/client_library.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" @@ -230,10 +231,13 @@ Status XlaCompiler::XLAShapeForArgument(const XlaCompiler::Argument& arg, case XlaCompiler::Argument::kConstant: LOG(FATAL) << "Unreachable case"; case XlaCompiler::Argument::kParameter: { - TensorShape shape = - is_entry_computation - ? options_.shape_representation_fn(arg.shape, arg.type) - : arg.shape; + TensorShape shape; + if (is_entry_computation) { + TF_ASSIGN_OR_RETURN( + shape, options_.shape_representation_fn(arg.shape, arg.type)); + } else { + shape = arg.shape; + } return TensorShapeToXLAShape(arg.type, shape, xla_shape); } case XlaCompiler::Argument::kResource: { @@ -241,8 +245,9 @@ Status XlaCompiler::XLAShapeForArgument(const XlaCompiler::Argument& arg, switch (arg.resource_kind) { case XlaResource::kVariable: { - TensorShape representation_shape = - options_.shape_representation_fn(arg.shape, arg.type); + TF_ASSIGN_OR_RETURN( + TensorShape representation_shape, + options_.shape_representation_fn(arg.shape, arg.type)); return TensorShapeToXLAShape(arg.type, representation_shape, xla_shape); } @@ -338,9 +343,9 @@ Status BuildComputation( const std::vector& arg_cores, const std::vector& retvals, const std::vector>& resources, - bool return_updated_values_for_all_resources, xla::XlaBuilder* builder, - xla::XlaComputation* computation, int* num_computation_outputs, - int* num_nonconst_outputs, + bool return_updated_values_for_all_resources, bool always_return_tuple, + xla::XlaBuilder* builder, xla::XlaComputation* computation, + int* num_computation_outputs, int* num_nonconst_outputs, std::vector* outputs, std::vector* resource_updates) { std::vector elems; @@ -384,13 +389,14 @@ Status BuildComputation( const XlaCompiler::Argument& arg = args[resource->arg_num()]; const int core = arg_cores[resource->arg_num()]; DCHECK_LT(resource->arg_num(), arg_cores.size()); - bool modified = resource->value() != resource->initial_value(); + bool modified = !resource->value().IsIdenticalTo(resource->initial_value()); // TensorArray gradients were modified if their values changed or there are // any newly created gradients. for (const auto& grad : resource->tensor_array_gradients()) { - modified = modified || - grad.second->value() != grad.second->initial_value() || - arg.tensor_array_gradients.count(grad.first) == 0; + modified = + modified || + !grad.second->value().IsIdenticalTo(grad.second->initial_value()) || + arg.tensor_array_gradients.count(grad.first) == 0; } if (return_updated_values_for_all_resources || modified) { resource_updates->emplace_back(); @@ -415,7 +421,7 @@ Status BuildComputation( // create a tuple/get-tuple-element combination so that sharding // assignment will be placed on this value, which will cause the resource // update to be returned from the same device that provided the resource. - handle = builder->GetTupleElement(builder->Tuple({handle}), 0); + handle = xla::GetTupleElement(xla::Tuple(builder, {handle}), 0); elems.push_back(handle); } @@ -424,7 +430,9 @@ Status BuildComputation( *num_computation_outputs = elems.size(); // Builds the XLA computation. - builder->Tuple(elems); + if (always_return_tuple || elems.size() != 1) { + xla::Tuple(builder, elems); + } builder->ClearOpMetadata(); xla::StatusOr computation_status = builder->Build(); @@ -551,16 +559,16 @@ Status XlaCompiler::BuildArguments( } xla::XlaScopedShardingAssignment assign_tuple_sharding(builder, tuple_sharding); - tuple = builder->Parameter(0, (*input_shapes)[0], "arg_tuple"); + tuple = xla::Parameter(builder, 0, (*input_shapes)[0], "arg_tuple"); } else { - tuple = builder->Parameter(0, (*input_shapes)[0], "arg_tuple"); + tuple = xla::Parameter(builder, 0, (*input_shapes)[0], "arg_tuple"); } for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { const int core = (*arg_cores)[input_mapping->at(i)]; xla::XlaScopedShardingAssignment assign_sharding( builder, core == -1 ? tensorflow::gtl::optional() : xla::sharding_builder::AssignDevice(core)); - arg_handles[i] = builder->GetTupleElement(tuple, i); + arg_handles[i] = xla::GetTupleElement(tuple, i); } } else { for (std::vector::size_type i = 0; i < input_mapping->size(); ++i) { @@ -568,8 +576,8 @@ Status XlaCompiler::BuildArguments( xla::XlaScopedShardingAssignment assign_sharding( builder, core == -1 ? tensorflow::gtl::optional() : xla::sharding_builder::AssignDevice(core)); - arg_handles[i] = - builder->Parameter(i, (*input_shapes)[i], strings::StrCat("arg", i)); + arg_handles[i] = xla::Parameter(builder, i, (*input_shapes)[i], + strings::StrCat("arg", i)); } } @@ -600,7 +608,7 @@ Status XlaCompiler::BuildArguments( // return values of functions, and then reshape unconditionally. if (is_entry_computation) { arg_expression.set_handle( - builder->Reshape(arg_handles[i], arg.shape.dim_sizes())); + xla::Reshape(arg_handles[i], arg.shape.dim_sizes())); } else { arg_expression.set_handle(arg_handles[i]); } @@ -660,20 +668,17 @@ Status XlaCompiler::CompileSingleOp( namespace { // Check that the ops of all non-functional nodes have been registered. -string ValidateFunctionDef(const FunctionDef* fdef, +Status ValidateFunctionDef(const FunctionDef* fdef, const FunctionLibraryDefinition& flib_def) { - std::vector invalid_ops; for (const NodeDef& node : fdef->node_def()) { const string& op = node.op(); if (op == FunctionLibraryDefinition::kGradientOp || flib_def.Find(op)) { continue; } const OpDef* op_def; - if (!OpRegistry::Global()->LookUpOpDef(op, &op_def).ok()) { - invalid_ops.push_back(op); - } + TF_RETURN_IF_ERROR(OpRegistry::Global()->LookUpOpDef(op, &op_def)); } - return tensorflow::str_util::Join(invalid_ops, ", "); + return Status::OK(); } // Check that the graph doesn't have any invalid nodes (e.g. incompatible with @@ -681,35 +686,33 @@ string ValidateFunctionDef(const FunctionDef* fdef, Status ValidateGraph(const Graph* graph, const FunctionLibraryDefinition& flib_def, const DeviceType& device_type, const string& name) { - std::vector invalid_ops; + auto maybe_error = [&](const string& op, const Status& s) -> Status { + if (!s.ok()) { + return errors::InvalidArgument(strings::StrCat( + "Detected unsupported operations when trying to compile graph ", name, + " on ", device_type.type_string(), ": ", op, " (", s.error_message(), + ")")); + } + return Status::OK(); + }; + for (const Node* node : graph->nodes()) { if (node->type_string() == FunctionLibraryDefinition::kGradientOp) { continue; } const FunctionDef* fdef = flib_def.Find(node->def().op()); + Status s; if (fdef) { - string error_msg = ValidateFunctionDef(fdef, flib_def); - if (!error_msg.empty()) { - invalid_ops.push_back( - strings::StrCat(node->def().op(), ":{", error_msg, "}")); - } + s = ValidateFunctionDef(fdef, flib_def); + TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); continue; } const OpDef* op_def; - if (!OpRegistry::Global()->LookUpOpDef(node->def().op(), &op_def).ok()) { - invalid_ops.push_back(node->def().op()); - continue; - } + s = OpRegistry::Global()->LookUpOpDef(node->def().op(), &op_def); + TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); TF_RETURN_IF_ERROR(ValidateNodeDef(node->def(), *op_def)); - if (!FindKernelDef(device_type, node->def(), nullptr, nullptr).ok()) { - invalid_ops.push_back(node->def().op()); - } - } - if (!invalid_ops.empty()) { - return errors::InvalidArgument(strings::StrCat( - "Detected unsupported operations when trying to compile graph ", name, - " on ", device_type.type_string(), ":", - tensorflow::str_util::Join(invalid_ops, ", "))); + s = FindKernelDef(device_type, node->def(), nullptr, nullptr); + TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); } return Status::OK(); } @@ -767,9 +770,10 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, result->outputs.resize(context->retvals().size()); TF_RETURN_IF_ERROR(BuildComputation( args, arg_cores, context->retvals(), context->resources(), - options.return_updated_values_for_all_resources, &builder, - result->computation.get(), &num_computation_outputs, - &num_nonconst_outputs, &result->outputs, &result->resource_updates)); + options.return_updated_values_for_all_resources, + options.always_return_tuple, &builder, result->computation.get(), + &num_computation_outputs, &num_nonconst_outputs, &result->outputs, + &result->resource_updates)); VLOG(2) << "Outputs: total: " << context->retvals().size() << " nonconstant: " << num_nonconst_outputs; diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index c93850ce270502ea1df1f6469963e96e86994fa2..079c99797e1f1ec26205e33b3c7c16d3764f15ca 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_mgr.h" #include "tensorflow/core/common_runtime/function.h" @@ -52,13 +53,7 @@ class XlaContext; // (kind kResource). // // Only kParameter and initialized kResource arguments become runtime parameters -// to the generated XLA computation. The XLA computation will have run-time -// parameters in the following order: -// +---------------------+-----------------------------------------+ -// | kParameter values | Initial values of kResource arguments | -// +---------------------+-----------------------------------------+ -// Within each block, the arguments are arranged by the _Arg index from which -// they were derived. +// to the generated XLA computation. // // The run-time outputs of the XLA computation are arranged in the following // order: @@ -77,10 +72,10 @@ class XlaContext; // tensors with a different shape to their representation inside the XLA // computation. // -// In both inputs and outputs, kResource values are placed the end. When +// In computation outputs, updated kResource values are placed the end. When // emitting While loop bodies, we must ensure that the loop body has -// identical input and output signatures. By moving variable values -// to the end of the argument list and using the +// identical input and output signatures. By passing variable values +// at the end of the argument list and using the // `return_updated_values_for_all_variables` option, we can ensure that the // input and output values of resources appear at the same positions. // @@ -175,6 +170,11 @@ class XlaCompiler { // computation. bool resolve_compile_time_constants = true; + // If 'always_return_tuple' is true, then the output of a computation will + // always be a tuple. Otherwise, a single-element output will not be wrapped + // in a tuple. + bool always_return_tuple = true; + // True when compiling the entry computation, false for subcomputations // (while, call, etc.) bool is_entry_computation = true; @@ -234,7 +234,8 @@ class XlaCompiler { tf2xla::HostComputeMetadata host_compute_metadata; // Resources whose values were updated by the computation, ordered - // by return value position. Resource updates follow the non-constant + // by return value position (which is the same as the order the resources + // were passed as arguments). Resource updates follow the non-constant // results in the outputs of XLA computation. std::vector resource_updates; @@ -242,7 +243,8 @@ class XlaCompiler { std::shared_ptr computation; }; - typedef std::function + typedef std::function(const TensorShape&, + DataType)> ShapeRepresentationFn; struct Options { // Name of the compilation device to use. It must be set by the caller. diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc index 613230452b74755ce7543ec2ab82861aa0dfeb7a..07af8ef54b79b215e9e99faa161c8279488ebbf7 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc @@ -1021,8 +1021,7 @@ TEST_F(XlaCompilerTest, FunctionWithInvalidOp) { status = compiler.CompileGraph(XlaCompiler::CompileOptions(), "fill", std::move(graph), args, &result); ASSERT_FALSE(status.ok()); - EXPECT_TRUE( - str_util::StrContains(status.error_message(), "FillFn:{InvalidOp}")) + EXPECT_TRUE(str_util::StrContains(status.error_message(), "InvalidOp")) << status.error_message(); } diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 098072d33cd4eb7f7dec0ec4196b43eca0220d4a..fd39a58ce64acad12768a031c3c9d03c26c01b71 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -66,8 +66,8 @@ XlaContext::XlaContext( XlaCompiler* compiler, xla::XlaBuilder* builder, bool allow_cpu_custom_calls, bool resolve_compile_time_constants, bool is_entry_computation, - const std::function* - shape_representation_fn) + const std::function( + const TensorShape&, DataType)>* shape_representation_fn) : compiler_(compiler), builder_(builder), allow_cpu_custom_calls_(allow_cpu_custom_calls), @@ -92,7 +92,7 @@ void XlaContext::AddRetval(int retval_index, DataType type, } Status XlaContext::AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal) { + const xla::LiteralSlice& literal) { VLOG(1) << "Adding retval index " << retval_index << " with non-data-dependent tensor to XLA computation"; if (retvals_.size() <= retval_index) { @@ -119,8 +119,8 @@ Status XlaContext::CreateResource( return Status::OK(); } -TensorShape XlaContext::RepresentationShape(const TensorShape& shape, - DataType type) const { +xla::StatusOr XlaContext::RepresentationShape( + const TensorShape& shape, DataType type) const { return (*shape_representation_fn_)(shape, type); } @@ -131,9 +131,11 @@ const xla::XlaComputation* XlaContext::GetOrCreateMax(const DataType type) { xla::XlaBuilder b("max<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); - auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); - auto y = b.Parameter(1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); - b.Max(x, y); + auto x = + xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); + auto y = + xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); + xla::Max(x, y); return b.Build().ConsumeValueOrDie(); }); } @@ -145,9 +147,11 @@ const xla::XlaComputation* XlaContext::GetOrCreateMin(const DataType type) { xla::XlaBuilder b("min<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); - auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); - auto y = b.Parameter(1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); - b.Min(x, y); + auto x = + xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); + auto y = + xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); + xla::Min(x, y); return b.Build().ConsumeValueOrDie(); }); } @@ -159,9 +163,11 @@ const xla::XlaComputation* XlaContext::GetOrCreateAdd(const DataType type) { xla::XlaBuilder b("add<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); - auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); - auto y = b.Parameter(1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); - b.Add(x, y); + auto x = + xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); + auto y = + xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); + xla::Add(x, y); return b.Build().ConsumeValueOrDie(); }); } @@ -173,9 +179,11 @@ const xla::XlaComputation* XlaContext::GetOrCreateMul(const DataType type) { xla::XlaBuilder b("mul<" + type_string + ">"); xla::PrimitiveType xla_type; TF_CHECK_OK(DataTypeToPrimitiveType(type, &xla_type)); - auto x = b.Parameter(0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); - auto y = b.Parameter(1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); - b.Mul(x, y); + auto x = + xla::Parameter(&b, 0, xla::ShapeUtil::MakeShape(xla_type, {}), "x"); + auto y = + xla::Parameter(&b, 1, xla::ShapeUtil::MakeShape(xla_type, {}), "y"); + xla::Mul(x, y); return b.Build().ConsumeValueOrDie(); }); } diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 341bf6ff1f37fa7cd81f41c02a941214067b1bd1..38d8cd653cbbe5b01325d6b478589d88909bac56 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -47,8 +48,8 @@ class XlaContext : public ResourceBase { XlaContext(XlaCompiler* compiler, xla::XlaBuilder* builder, bool allow_cpu_custom_calls, bool resolve_compile_time_constants, bool is_entry_computation, - const std::function* - shape_representation_fn); + const std::function( + const TensorShape&, DataType)>* shape_representation_fn); // Virtual method defined by ResourceBase. string DebugString() override; @@ -83,7 +84,7 @@ class XlaContext : public ResourceBase { // As for Retval, but for return values that are compile-time constants. Status AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal); + const xla::LiteralSlice& literal); // Creates a resource with resource `kind` and initial value `handle`. `name` // is a descriptive name for use in error messages. See the `XlaResource` @@ -101,8 +102,8 @@ class XlaContext : public ResourceBase { // Returns the XLA shape to be used to represent a variable of TF `shape` // and `type`, or of an argument or return value of a top-level computation. - TensorShape RepresentationShape(const TensorShape& shape, - DataType type) const; + xla::StatusOr RepresentationShape(const TensorShape& shape, + DataType type) const; // Get an XLA lambda to compute Max. This is cached in the // XlaContext since it may be used by multiple Ops. There is a @@ -160,7 +161,7 @@ class XlaContext : public ResourceBase { // should be represented in XLA. Parameters/return values will be shaped // according to this function, and reshaped back to/from their declared shapes // for computations. Must be non-null. - const std::function* + const std::function(const TensorShape&, DataType)>* shape_representation_fn_; // Cache of prebuilt computations indexed by their type. diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index f1594193af09c7193f03b4685d3a7d4510d654dd..edbc5e95a8c22dd35dd7c384afdfaf80553eceaf 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -19,9 +19,13 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/literal_util.h" +#include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/framework/tensor.h" @@ -32,103 +36,71 @@ namespace tensorflow { namespace { -Status ArgMinMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, - const xla::XlaOp& input, const TensorShape& input_shape, - DataType input_type, DataType output_type, int axis, - bool is_min, xla::XlaOp* argminmax) { - xla::XlaOp init_value; - const xla::XlaComputation* reducer; - if (is_min) { - init_value = XlaHelpers::MaxValue(builder, input_type); - reducer = ctx->GetOrCreateMin(input_type); - } else { - init_value = XlaHelpers::MinValue(builder, input_type); - reducer = ctx->GetOrCreateMax(input_type); - } - - xla::PrimitiveType xla_output_type; - TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(output_type, &xla_output_type)); - - xla::XlaOp input_max = builder->Reduce(input, init_value, *reducer, - /*dimensions_to_reduce=*/{axis}); - std::vector broadcast_dims(input_shape.dims() - 1); - std::iota(broadcast_dims.begin(), broadcast_dims.begin() + axis, 0); - std::iota(broadcast_dims.begin() + axis, broadcast_dims.end(), axis + 1); - // Compute a mask that has 1s for elements equal to the maximum. - xla::XlaOp partial_mask = builder->ConvertElementType( - builder->Eq(input, input_max, broadcast_dims), xla_output_type); - - // In order to make identity elements for a bitwise And, we: - // Left shift the 1 to the leftmost bit, yielding 0x10...0 - // Arithmetic right shift the 1 back to the rightmost bit, yielding - // 0xFF...F - int32 bits_in_type = - xla::ShapeUtil::ByteSizeOfPrimitiveType(xla_output_type) * 8 - 1; - xla::XlaOp shift_amount = - XlaHelpers::IntegerLiteral(builder, output_type, bits_in_type); - xla::XlaOp full_mask = builder->ShiftRightArithmetic( - builder->ShiftLeft(partial_mask, shift_amount), shift_amount); - - // And with the vector [0, 1, 2, ...] to convert each 0xFF...F into its - // index. - xla::XlaOp iota; - - const int64 axis_size = input_shape.dim_size(axis); - TF_RETURN_IF_ERROR(XlaHelpers::Iota(builder, output_type, axis_size, &iota)); - xla::XlaOp product = - builder->And(full_mask, iota, /*broadcast_dimensions=*/{axis}); - - // If there are multiple maximum elements, choose the one with the highest - // index. - xla::XlaOp output = - builder->Reduce(product, XlaHelpers::MinValue(builder, output_type), - *ctx->GetOrCreateMax(output_type), - /*dimensions_to_reduce=*/{axis}); - *argminmax = output; - return Status::OK(); +xla::XlaOp ArgMinMax(xla::XlaOp input, xla::PrimitiveType output_type, int axis, + bool is_min) { + xla::XlaBuilder* builder = input.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape input_shape, builder->GetShape(input)); + xla::XlaOp init_value; + xla::XlaComputation reducer; + if (is_min) { + init_value = xla::MaxValue(builder, input_shape.element_type()); + reducer = + xla::CreateScalarMinComputation(input_shape.element_type(), builder); + } else { + init_value = xla::MinValue(builder, input_shape.element_type()); + reducer = + xla::CreateScalarMaxComputation(input_shape.element_type(), builder); + } + + xla::XlaOp input_max = xla::Reduce(input, init_value, reducer, + /*dimensions_to_reduce=*/{axis}); + std::vector broadcast_dims(xla::ShapeUtil::Rank(input_shape) - 1); + std::iota(broadcast_dims.begin(), broadcast_dims.begin() + axis, 0); + std::iota(broadcast_dims.begin() + axis, broadcast_dims.end(), axis + 1); + // Compute a mask that has 1s for elements equal to the maximum. + xla::XlaOp partial_mask = xla::ConvertElementType( + xla::Eq(input, input_max, broadcast_dims), output_type); + + // In order to make identity elements for a bitwise And, we: + // Left shift the 1 to the leftmost bit, yielding 0x10...0 + // Arithmetic right shift the 1 back to the rightmost bit, yielding + // 0xFF...F + int32 bits_in_type = + xla::ShapeUtil::ByteSizeOfPrimitiveType(output_type) * 8 - 1; + xla::XlaOp shift_amount = + xla::ConstantR0WithType(builder, output_type, bits_in_type); + xla::XlaOp full_mask = xla::ShiftRightArithmetic( + xla::ShiftLeft(partial_mask, shift_amount), shift_amount); + + // And with the vector [0, 1, 2, ...] to convert each 0xFF...F into its + // index. + + const int64 axis_size = xla::ShapeUtil::GetDimension(input_shape, axis); + xla::XlaOp iota = xla::Iota(builder, output_type, axis_size); + xla::XlaOp product = + xla::And(full_mask, iota, /*broadcast_dimensions=*/{axis}); + + // If there are multiple maximum elements, choose the one with the highest + // index. + return xla::Reduce(product, xla::MinValue(builder, output_type), + xla::CreateScalarMaxComputation(output_type, builder), + /*dimensions_to_reduce=*/{axis}); + }); } } // namespace -xla::XlaOp XlaHelpers::MinValue(xla::XlaBuilder* b, DataType data_type) { - xla::PrimitiveType type; - TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return b->ConstantLiteral(xla::Literal::MinValue(type)); -} - -xla::XlaOp XlaHelpers::MaxValue(xla::XlaBuilder* b, DataType data_type) { - xla::PrimitiveType type; - TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return b->ConstantLiteral(xla::Literal::MaxValue(type)); -} - xla::XlaOp XlaHelpers::Zero(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return b->ConstantLiteral(xla::Literal::Zero(type)); + return xla::ConstantLiteral(b, xla::Literal::Zero(type)); } xla::XlaOp XlaHelpers::One(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return b->ConstantLiteral(xla::Literal::One(type)); -} - -xla::XlaOp XlaHelpers::Epsilon(xla::XlaBuilder* b, DataType data_type) { - switch (data_type) { - case DT_HALF: - return b->ConstantR0( - static_cast(Eigen::NumTraits::epsilon())); - case DT_BFLOAT16: - return b->ConstantR0(bfloat16::epsilon()); - case DT_FLOAT: - return b->ConstantR0(std::numeric_limits::epsilon()); - case DT_DOUBLE: - return b->ConstantR0(std::numeric_limits::epsilon()); - default: - LOG(FATAL) << "Unsupported type in XlaHelpers::Epsilon: " - << DataTypeString(data_type); - } + return xla::ConstantLiteral(b, xla::Literal::One(type)); } xla::XlaOp XlaHelpers::IntegerLiteral(xla::XlaBuilder* b, DataType data_type, @@ -176,44 +148,14 @@ static Tensor MakeLinspaceTensor(const TensorShape& shape, int64 depth) { return linspace; } -Status XlaHelpers::ArgMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, - const xla::XlaOp& input, - const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, xla::XlaOp* argmax) { - return ArgMinMax(builder, ctx, input, input_shape, input_type, output_type, - axis, /*is_min=*/false, argmax); -} - -Status XlaHelpers::ArgMin(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, - const xla::XlaOp& input, - const TensorShape& input_shape, DataType input_type, - DataType output_type, int axis, xla::XlaOp* argmin) { - return ArgMinMax(builder, ctx, input, input_shape, input_type, output_type, - axis, /*is_min=*/true, argmin); +xla::XlaOp XlaHelpers::ArgMax(xla::XlaOp input, xla::PrimitiveType output_type, + int axis) { + return ArgMinMax(input, output_type, axis, /*is_min=*/false); } -Status XlaHelpers::Iota(xla::XlaBuilder* builder, DataType dtype, int64 size, - xla::XlaOp* iota) { - TensorShape linspace_shape({size}); - Tensor linspace; - switch (dtype) { - case DT_UINT8: - linspace = MakeLinspaceTensor(linspace_shape, size); - break; - case DT_INT32: - linspace = MakeLinspaceTensor(linspace_shape, size); - break; - case DT_INT64: - linspace = MakeLinspaceTensor(linspace_shape, size); - break; - default: - return errors::InvalidArgument("Invalid argument type ", - DataTypeString(dtype)); - } - xla::Literal linspace_literal; - TF_RETURN_IF_ERROR(HostTensorToLiteral(linspace, &linspace_literal)); - *iota = builder->ConstantLiteral(linspace_literal); - return Status::OK(); +xla::XlaOp XlaHelpers::ArgMin(xla::XlaOp input, xla::PrimitiveType output_type, + int axis) { + return ArgMinMax(input, output_type, axis, /*is_min=*/true); } Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, @@ -245,25 +187,28 @@ Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, return errors::InvalidArgument("Invalid argument type ", DataTypeString(index_type)); } - xla::Literal linspace_literal; - TF_RETURN_IF_ERROR(HostTensorToLiteral(linspace, &linspace_literal)); + + xla::BorrowingLiteral linspace_literal; + TF_RETURN_IF_ERROR(HostTensorToBorrowingLiteral(linspace, &linspace_literal)); // Broadcast the linspace constant across the indices along the new axis, // and test equality at each position. std::vector broadcast_dims(indices_shape.dims()); std::iota(broadcast_dims.begin(), broadcast_dims.begin() + axis, 0); std::iota(broadcast_dims.begin() + axis, broadcast_dims.end(), axis + 1); - xla::XlaOp one_hot_bool = builder->Eq( - indices, builder->ConstantLiteral(linspace_literal), broadcast_dims); + xla::XlaOp one_hot_bool = xla::Eq( + indices, xla::ConstantLiteral(builder, linspace_literal), broadcast_dims); // Selects the user-provided off_value and on_value values. - *one_hot = builder->Select( - one_hot_bool, builder->Broadcast(on_value, output_shape.dim_sizes()), - builder->Broadcast(off_value, output_shape.dim_sizes())); + *one_hot = xla::Select(one_hot_bool, + xla::Broadcast(on_value, output_shape.dim_sizes()), + xla::Broadcast(off_value, output_shape.dim_sizes())); return Status::OK(); } DataType XlaHelpers::SumAccumulationType(const DataType& dtype) { + // Upcast 16 bit sum reductions to 32 bit to reduce the precision loss from + // repeated floating point additions. if (dtype == DT_BFLOAT16 || dtype == DT_HALF) { return DT_FLOAT; } @@ -275,7 +220,7 @@ xla::XlaOp XlaHelpers::ConvertElementType(xla::XlaBuilder* const builder, const DataType new_element_type) { xla::PrimitiveType convert_to; TF_CHECK_OK(DataTypeToPrimitiveType(new_element_type, &convert_to)); - return builder->ConvertElementType(operand, convert_to); + return xla::ConvertElementType(operand, convert_to); } } // end namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_helpers.h b/tensorflow/compiler/tf2xla/xla_helpers.h index c3fdc5252e74363fe289eeabb2cb0d68298ee291..d6ca4ab9346593892917e8375b07a8790dc26e79 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.h +++ b/tensorflow/compiler/tf2xla/xla_helpers.h @@ -28,14 +28,6 @@ namespace tensorflow { // Helper methods for building XLA computations. class XlaHelpers { public: - // Returns a handle representing the minimum value of a scalar - // element of data_type. - static xla::XlaOp MinValue(xla::XlaBuilder* b, DataType data_type); - - // Returns a handle representing the maximum value of a scalar - // element of data_type. - static xla::XlaOp MaxValue(xla::XlaBuilder* b, DataType data_type); - // Returns a handle representing the zero value of a scalar // element of data_type. static xla::XlaOp Zero(xla::XlaBuilder* b, DataType data_type); @@ -44,10 +36,6 @@ class XlaHelpers { // element of data_type. static xla::XlaOp One(xla::XlaBuilder* b, DataType data_type); - // Returns the machine epsilon for floating-point type `data_type`, i.e., - // the difference between 1.0 and the next representable value. - static xla::XlaOp Epsilon(xla::XlaBuilder* b, DataType data_type); - // Returns a handle representing the given value of an integer scalar // element of data_type. // Note that unlike One and Zero, does not work on boolean types. @@ -65,25 +53,15 @@ class XlaHelpers { gtl::ArraySlice shape, xla::Literal* output); - // Sets `argmax` to the argmax of `input` along `axis`. `input_shape` and - // `input_dtype` are the shape and dtype of `input` respectively, and - // `output_type` is the dtype to use for `argmax`. - static Status ArgMax(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, - const xla::XlaOp& input, const TensorShape& input_shape, - DataType input_type, DataType output_type, int axis, - xla::XlaOp* argmax); - - // Sets `argmin` to the argmin of `input` along `axis`. `input_shape` and - // `input_dtype` are the shape and dtype of `input` respectively, and - // `output_type` is the dtype to use for `argmin`. - static Status ArgMin(xla::XlaBuilder* builder, XlaOpKernelContext* ctx, - const xla::XlaOp& input, const TensorShape& input_shape, - DataType input_type, DataType output_type, int axis, - xla::XlaOp* argmin); - - // Sets *iota to a rank 1 tensor with values [0, 1, 2, ...] of `dtype`. - static Status Iota(xla::XlaBuilder* builder, DataType dtype, int64 size, - xla::XlaOp* iota); + // Returns the argmax of `input` along `axis`. `output_type` is the type to + // use for the output. + static xla::XlaOp ArgMax(xla::XlaOp input, xla::PrimitiveType output_type, + int axis); + + // Returns the argmin of `input` along `axis`. `output_type` is the type to + // use for the output. + static xla::XlaOp ArgMin(xla::XlaOp input, xla::PrimitiveType output_type, + int axis); // Converts `indices` into a one-hot representation. `depth` is the size // of the new axis to add. `axis` is the position at which to add the new diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 76c68d81af4dd9ec40fe6b1c33b03a876a0c6dc6..359cb4c4670227e592ed4b8339825e7f95b16899 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -19,7 +19,11 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/literal_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/core/common_runtime/dma_helper.h" namespace tensorflow { @@ -38,8 +42,7 @@ xla::XlaBuilder* XlaOpKernelContext::builder() const { static const XlaExpression* CastExpressionFromTensor(const Tensor& tensor) { const XlaExpression* expression = reinterpret_cast(tensor.tensor_data().data()); - CHECK(expression->handle().builder() != nullptr || - expression->resource() != nullptr); + CHECK(expression->handle().valid() || expression->resource() != nullptr); VLOG(1) << "Fetched T" << expression->handle(); return expression; } @@ -48,7 +51,7 @@ static const XlaExpression* CastExpressionFromTensor(const Tensor& tensor) { static XlaExpression* CastExpressionFromUninitializedTensor(Tensor* tensor) { const XlaExpression* expression = reinterpret_cast(tensor->tensor_data().data()); - CHECK_EQ(expression->handle().builder(), nullptr); + CHECK(!expression->handle().valid()); return const_cast(expression); } @@ -67,6 +70,20 @@ TensorShape XlaOpKernelContext::InputShape(int index) { return context_->input(index).shape(); } +DataType XlaOpKernelContext::input_type(int index) const { + return context_->input(index).dtype(); +} + +xla::PrimitiveType XlaOpKernelContext::input_xla_type(int index) { + xla::PrimitiveType type; + Status status = DataTypeToPrimitiveType(input_type(index), &type); + if (!status.ok()) { + SetStatus(status); + return xla::PRIMITIVE_TYPE_INVALID; + } + return type; +} + Status XlaOpKernelContext::ConstantInput(int index, xla::Literal* constant_literal) { return ConstantInputReshaped( @@ -87,6 +104,25 @@ Status XlaOpKernelContext::ConstantInputReshaped( } const XlaExpression* expression = CastExpressionFromTensor(tensor); + auto copy_tensor_to_literal = [](const Tensor& tensor, + xla::Literal* literal) { + xla::Shape literal_shape; + TF_RETURN_IF_ERROR( + TensorShapeToXLAShape(tensor.dtype(), tensor.shape(), &literal_shape)); + + *literal = xla::Literal(literal_shape); + + // memcpy over the payload ... + // TODO(phawkins): handle string types. + size_t total_bytes = tensor.TotalBytes(); + if (total_bytes > 0) { + void* dst_ptr = literal->untyped_data(); + const void* src_ptr = DMAHelper::base(&tensor); + memcpy(dst_ptr, src_ptr, total_bytes); + } + return Status::OK(); + }; + // If the tensor has a known constant value, there is no need to invoke XLA. if (expression->has_constant_value()) { Tensor temp(tensor.dtype()); @@ -95,19 +131,21 @@ Status XlaOpKernelContext::ConstantInputReshaped( // with the enclosing Tensor. return errors::Internal("Incompatible shapes in ConstantInputReshaped."); } - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } // Make sure we treat zero-element tensors as constant. if (new_shape.num_elements() == 0) { Tensor temp(tensor.dtype(), new_shape); - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } xla::XlaOp handle = expression->handle(); if (new_shape != tensor.shape()) { // Reshape the handle to the desired shape. - handle = builder()->Reshape(handle, new_shape.dim_sizes()); + handle = xla::Reshape(handle, new_shape.dim_sizes()); } // The XLA layout is specified minor to major, and TensorFlow's minor @@ -162,7 +200,8 @@ Status XlaOpKernelContext::ConstantInputReshaped( } // Converts an int32 or int64 scalar literal to an int64. -static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { +static Status LiteralToInt64Scalar(const xla::LiteralSlice& literal, + int64* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -177,7 +216,8 @@ static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { } // Converts an float32 or float64 scalar literal to a float64. -static Status LiteralToFloat64Scalar(const xla::Literal& literal, double* out) { +static Status LiteralToFloat64Scalar(const xla::LiteralSlice& literal, + double* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -204,7 +244,7 @@ Status XlaOpKernelContext::ConstantInputAsFloatScalar(int index, double* out) { } // Converts an int32 or int64 1D literal to an int64 vector. -static Status LiteralToInt64Vector(const xla::Literal& literal, +static Status LiteralToInt64Vector(const xla::LiteralSlice& literal, std::vector* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 1) { return errors::InvalidArgument("value is not 1D"); @@ -314,13 +354,13 @@ Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, } XlaContext& xla_context = XlaContext::Get(context_); - TensorShape representation_shape = - xla_context.RepresentationShape(variable->shape(), variable->type()); + TF_ASSIGN_OR_RETURN( + TensorShape representation_shape, + xla_context.RepresentationShape(variable->shape(), variable->type())); if (representation_shape == variable->shape()) { *value = variable->value(); } else { - *value = - builder()->Reshape(variable->value(), variable->shape().dim_sizes()); + *value = xla::Reshape(variable->value(), variable->shape().dim_sizes()); } return Status::OK(); } @@ -368,10 +408,11 @@ void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) { void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) { const TensorShape& shape = constant.shape(); - xla::Literal literal; - OP_REQUIRES_OK(context_, HostTensorToLiteral(constant, &literal)); - xla::XlaOp handle = builder()->ConstantLiteral(literal); - CHECK_NE(handle.builder(), nullptr); + xla::BorrowingLiteral literal; + OP_REQUIRES_OK(context_, HostTensorToBorrowingLiteral(constant, &literal)); + + xla::XlaOp handle = xla::ConstantLiteral(builder(), literal); + CHECK(handle.valid()); // Make the Tensor that will refer to the expression. Tensor* output = nullptr; @@ -416,7 +457,7 @@ Status XlaOpKernelContext::GetResourceInput(int index, XlaResource** resource) { Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, xla::XlaOp handle) { - TF_RET_CHECK(handle.builder() != nullptr); + TF_RET_CHECK(handle.valid()); const XlaExpression* expression = CastExpressionFromTensor(context_->input(input_index)); @@ -435,10 +476,10 @@ Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, TF_RETURN_IF_ERROR(variable->SetTypeAndShape(type, shape)); XlaContext& xla_context = XlaContext::Get(context_); - TensorShape representation_shape = - xla_context.RepresentationShape(shape, type); + TF_ASSIGN_OR_RETURN(TensorShape representation_shape, + xla_context.RepresentationShape(shape, type)); if (shape != representation_shape) { - handle = builder()->Reshape(handle, representation_shape.dim_sizes()); + handle = xla::Reshape(handle, representation_shape.dim_sizes()); } return variable->SetValue(handle); } diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 667dc262ca03ca716ffbf015a78fc14c7a8b7c1a..2bde2c983d0cca05558e86a36698d6f0e097705a 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" @@ -67,7 +68,12 @@ class XlaOpKernelContext { int num_inputs() const { return context_->num_inputs(); } // Returns the type of input 'index'. - DataType input_type(int index) { return context_->input(index).dtype(); } + DataType input_type(int index) const; + + // Returns the type of input 'index' as an xla::PrimitiveType. If the type + // is not representable as an XLA type, sets an error status and returns + // xla::PRIMITIVE_TYPE_INVALID. + xla::PrimitiveType input_xla_type(int index); // Returns the shape of input 'index'. TensorShape InputShape(int index); diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc index 4692038b61f6871a8a16299fd4d11e963eb46a57..46785bc1f0a1279bfd67a55844fe238d9797382b 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.cc +++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc @@ -71,16 +71,18 @@ XlaOpRegistry::~XlaOpRegistry() = default; << " have incompatible allow_resource_types settings."; return false; } - if (!x.has_device_whitelist || !y.has_device_whitelist) { - LOG(WARNING) << "Registrations of " << x.name - << " do not both have device whitelists."; + if (!x.has_device_whitelist && !y.has_device_whitelist) { + LOG(WARNING) << "Duplicate registrations of " << x.name + << "with no device whitelists."; return false; } - for (const auto& device : x.device_whitelist) { - if (y.device_whitelist.count(device) != 0) { - LOG(WARNING) << "Multiple registrations of " << x.name << " on device " - << device; - return false; + if (x.has_device_whitelist && y.has_device_whitelist) { + for (const auto& device : x.device_whitelist) { + if (y.device_whitelist.count(device) != 0) { + LOG(WARNING) << "Multiple registrations of " << x.name << " on device " + << device; + return false; + } } } if (x.compile_time_constant_inputs != y.compile_time_constant_inputs) { @@ -157,97 +159,143 @@ void XlaOpRegistry::RegisterCompilationKernels() { registry.jit_kernels_registered_ = true; OpRegistryInterface* op_registry = OpRegistry::Global(); - for (const auto& op : registry.ops_) { - const string& op_name = op.first; - const std::unique_ptr& op_registration = op.second; - const OpDef* op_def; - Status lookup_status = op_registry->LookUpOpDef(op_name, &op_def); - if (!lookup_status.ok()) { - LOG(ERROR) << lookup_status.error_message(); - XLA_LOG_LINES( - ERROR, "Ops registered: \n" + - dynamic_cast(op_registry)->DebugString(true)); + // Order of op registration: + // The goal is to allow the co-existence of backend-specific kernels and + // generic kernels. To achieve this, we enforce the following order of + // registrations for one op: + // 1. Process op registration with device whitelists: + // this pass registers backend-specific kernels for this op. + // 2. Process op registration without device whitelists: + // this pass registers the kernels for all the other supported backends. + for (auto& ops : registry.ops_) { + const string& op_name = ops.first; + std::vector>& op_registrations = ops.second; + // Partition the op registration so that the ones with device whitelists + // precede the one without device whitelist. + std::partition(op_registrations.begin(), op_registrations.end(), + [](const std::unique_ptr& op_reg) { + return op_reg->has_device_whitelist; + }); + + // Collect a set of backend registered by ops with device whitelists. + // The op registration without whitelists will register a generic kernel + // for all other backends not in this set. + std::unordered_set whitelisted_backend; + for (auto& op_registration : op_registrations) { + if (op_registration->has_device_whitelist) { + whitelisted_backend.insert(op_registration->device_whitelist.begin(), + op_registration->device_whitelist.end()); + } } - TF_CHECK_OK(lookup_status); - std::unordered_set type_attrs; - for (const OpDef::AttrDef& attr_def : op_def->attr()) { - if (attr_def.type() == "type" || attr_def.type() == "list(type)") { - type_attrs.insert(attr_def.name()); + for (auto& op_registration : op_registrations) { + const OpDef* op_def; + Status lookup_status = op_registry->LookUpOpDef(op_name, &op_def); + if (!lookup_status.ok()) { + LOG(ERROR) << lookup_status.error_message(); + XLA_LOG_LINES( + ERROR, + "Ops registered: \n" + + dynamic_cast(op_registry)->DebugString(true)); } - } + TF_CHECK_OK(lookup_status); - // Checks there are no type constraints referring to unknown attributes. - for (const auto& constraint : op_registration->type_constraints) { - if (type_attrs.find(constraint.first) == type_attrs.end()) { - LOG(FATAL) << "Unknown type attribute " << constraint.first - << " in XLA op registration for " << op_name; + std::unordered_set type_attrs; + for (const OpDef::AttrDef& attr_def : op_def->attr()) { + if (attr_def.type() == "type" || attr_def.type() == "list(type)") { + type_attrs.insert(attr_def.name()); + } } - } - for (auto& backend : registry.backends_) { - // If the operator has a device whitelist, only register on whitelisted - // devices. - if (op_registration->has_device_whitelist && - op_registration->device_whitelist.find(backend.first) == - op_registration->device_whitelist.end()) { - continue; + // Checks there are no type constraints referring to unknown attributes. + for (const auto& constraint : op_registration->type_constraints) { + if (type_attrs.find(constraint.first) == type_attrs.end()) { + LOG(FATAL) << "Unknown type attribute " << constraint.first + << " in XLA op registration for " << op_name; + } } - std::unique_ptr kdef(new KernelDef); - kdef->set_op(op_registration->name); - kdef->set_device_type(backend.first); - - // Constrain each type attribute to the intersection of: - // a) the types supported by the backend, and - // b) the types allowed by the OpDef, and - // c) the type constraints. - for (const string& type_attr : type_attrs) { - KernelDef::AttrConstraint* attr_constraint = kdef->add_constraint(); - attr_constraint->set_name(type_attr); - auto* allowed_values = - attr_constraint->mutable_allowed_values()->mutable_list(); - - const OpDef::AttrDef& op_def_attr = *FindAttr(type_attr, *op_def); - const auto* op_def_allowed_types = - op_def_attr.has_allowed_values() - ? &op_def_attr.allowed_values().list().type() - : nullptr; - auto constraint_it = op_registration->type_constraints.find(type_attr); - const std::set* type_constraints = - constraint_it != op_registration->type_constraints.end() - ? &constraint_it->second - : nullptr; - for (DataType dtype : backend.second.supported_types) { - // Filter out types that aren't allowed by the OpDef. - if (op_def_allowed_types != nullptr && - std::find(op_def_allowed_types->begin(), - op_def_allowed_types->end(), - dtype) == op_def_allowed_types->end()) { - continue; + for (auto& backend : registry.backends_) { + // If the operator has a device whitelist, only register on whitelisted + // devices. + if (op_registration->has_device_whitelist && + op_registration->device_whitelist.find(backend.first) == + op_registration->device_whitelist.end()) { + continue; + } + + // If the operator does NOT has a device whitelist, skip all devices + // that has already been registered. + if (!op_registration->has_device_whitelist && + whitelisted_backend.find(backend.first) != + whitelisted_backend.end()) { + continue; + } + + std::unique_ptr kdef(new KernelDef); + kdef->set_op(op_registration->name); + kdef->set_device_type(backend.first); + + // Constrain each type attribute to the intersection of: + // a) the types supported by the backend, and + // b) the types allowed by the OpDef, and + // c) the type constraints. + bool unsatisfiable_type_constraint = false; + for (const string& type_attr : type_attrs) { + KernelDef::AttrConstraint* attr_constraint = kdef->add_constraint(); + attr_constraint->set_name(type_attr); + auto* allowed_values = + attr_constraint->mutable_allowed_values()->mutable_list(); + + const OpDef::AttrDef& op_def_attr = *FindAttr(type_attr, *op_def); + const auto* op_def_allowed_types = + op_def_attr.has_allowed_values() + ? &op_def_attr.allowed_values().list().type() + : nullptr; + auto constraint_it = + op_registration->type_constraints.find(type_attr); + const std::set* type_constraints = + constraint_it != op_registration->type_constraints.end() + ? &constraint_it->second + : nullptr; + for (DataType dtype : backend.second.supported_types) { + // Filter out types that aren't allowed by the OpDef. + if (op_def_allowed_types != nullptr && + std::find(op_def_allowed_types->begin(), + op_def_allowed_types->end(), + dtype) == op_def_allowed_types->end()) { + continue; + } + // Filter out types based on the type constraints. + if (type_constraints != nullptr && + type_constraints->find(dtype) == type_constraints->end()) { + continue; + } + // Passed all the filters, this type is allowed. + allowed_values->add_type(dtype); } - // Filter out types based on the type constraints. - if (type_constraints != nullptr && - type_constraints->find(dtype) == type_constraints->end()) { - continue; + if (op_registration->allow_resource_types) { + allowed_values->add_type(DT_RESOURCE); + } + // Don't build KernelDefs that have unsatisfiable type constraints. + if (allowed_values->type().empty()) { + unsatisfiable_type_constraint = true; + break; } - // Passed all the filters, this type is allowed. - allowed_values->add_type(dtype); } - if (op_registration->allow_resource_types) { - allowed_values->add_type(DT_RESOURCE); + if (unsatisfiable_type_constraint) continue; + + if (backend.second.op_filter != nullptr && + !backend.second.op_filter(kdef.get())) { + continue; } + VLOG(2) << "XLA op registration: device: " << backend.first + << " op: " << op_name; + registry.kernel_registrars_.emplace_back( + new kernel_factory::OpKernelRegistrar( + new KernelDef(*kdef), "XlaJitOp", op_registration->factory)); + backend.second.kernel_defs.push_back(std::move(kdef)); } - if (backend.second.op_filter != nullptr && - !backend.second.op_filter(kdef.get())) { - continue; - } - VLOG(2) << "XLA op registration: device: " << backend.first - << " op: " << op_name; - registry.kernel_registrars_.emplace_back( - new kernel_factory::OpKernelRegistrar( - new KernelDef(*kdef), "XlaJitOp", op_registration->factory)); - backend.second.kernel_defs.push_back(std::move(kdef)); } } } @@ -265,12 +313,12 @@ std::vector XlaOpRegistry::DeviceKernels( << "Unknown backend " << compilation_device_name; for (const std::unique_ptr& k : it->second.kernel_defs) { auto op_iter = registry.ops_.find(k->op()); - CHECK(op_iter != registry.ops_.end()); + CHECK(op_iter != registry.ops_.end() && !op_iter->second.empty()); // The test in IsCompatible ensures that if there are multiple matching // registrations for this op name, they all have the same value of // compilation_only, so only the first match needs to be tested. if (include_compilation_only_kernels || - !op_iter->second->compilation_only) { + !op_iter->second.front()->compilation_only) { kernels.push_back(k.get()); } } @@ -282,10 +330,13 @@ XlaOpRegistry::CompileTimeConstantInputs(const string& op) { XlaOpRegistry& registry = Instance(); mutex_lock lock(registry.mutex_); auto it = registry.ops_.find(op); - if (it == registry.ops_.end()) { + if (it == registry.ops_.end() || it->second.empty()) { return nullptr; } - return &it->second->compile_time_constant_inputs; + // The test in IsCompatible ensures that if there are multiple matching + // registrations for this op name, they all have the same value of + // compile_time_constant_inputs, so only the first match is returned. + return &it->second.front()->compile_time_constant_inputs; } std::vector XlaOpRegistry::BackendNames() { @@ -378,16 +429,15 @@ XlaOpRegistrar::XlaOpRegistrar( std::unique_ptr registration) { XlaOpRegistry& registry = XlaOpRegistry::Instance(); mutex_lock lock(registry.mutex_); - auto existing_ops = registry.ops_.equal_range(registration->name); - for (auto existing = existing_ops.first; existing != existing_ops.second; - ++existing) { - if (!XlaOpRegistry::IsCompatible(*existing->second, *registration)) { + auto& existing_ops = registry.ops_[registration->name]; + for (auto& existing : existing_ops) { + if (!XlaOpRegistry::IsCompatible(*existing, *registration)) { LOG(FATAL) << "XLA op registration " << registration->name << " is incompatible with existing registration of the same name."; } } - registry.ops_.emplace(registration->name, std::move(registration)); + existing_ops.emplace_back(std::move(registration)); } XlaBackendRegistrar::XlaBackendRegistrar( diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h index e255b01dd7fdcb095c7992d4352d2d9bb7d36ac3..2d4593ea4999ad6d8cd0f0e2eec9c6d69c3020b8 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.h +++ b/tensorflow/compiler/tf2xla/xla_op_registry.h @@ -203,7 +203,7 @@ class XlaOpRegistry { // Map from operator name to OpRegistrations, populated by REGISTER_XLA_OP. // Registrations present under the same key must satisfy IsCompatible above, // and this is checked during registration. - std::unordered_multimap> ops_ + std::unordered_map>> ops_ GUARDED_BY(mutex_); // Have we already registered the JIT kernels on the JIT devices? diff --git a/tensorflow/compiler/tf2xla/xla_op_registry_test.cc b/tensorflow/compiler/tf2xla/xla_op_registry_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..7b3b15b1af7636fddd4c29477cbfe6f9761f2c47 --- /dev/null +++ b/tensorflow/compiler/tf2xla/xla_op_registry_test.cc @@ -0,0 +1,119 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +// This test is to verify the correctness of XLA op registration with specific +// backend overrides. + +// A dummy backend-specific OpKernel for CPU. +class DummyCPUOp : public XlaOpKernel { + public: + explicit DummyCPUOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + ctx->SetOutput(0, ctx->Input(0)); + } +}; + +// A dummy generic OpKernel for all backends. +class DummyGenericOp : public XlaOpKernel { + public: + explicit DummyGenericOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + ctx->SetOutput(0, ctx->Input(0)); + } +}; + +REGISTER_OP("DummyDuplicateOp") + .Attr("T: {float, int32}") + .Input("input: int32") + .Output("output: int32") + .Doc(R"doc( +A dummy Op. + +input: dummy input. +output: dummy output. +)doc"); + +// Register the DummyCPUOp kernel for CPU with type INT32. +REGISTER_XLA_OP(Name("DummyDuplicateOp") + .Device(DEVICE_CPU_XLA_JIT) + .TypeConstraint("T", DT_INT32), + DummyCPUOp); +// Register the DummyGeneric kernel for all registered device (except CPU since +// it is already registered), with type FLOAT. +REGISTER_XLA_OP(Name("DummyDuplicateOp").TypeConstraint("T", DT_FLOAT), + DummyGenericOp); + +// Test the correctness of registered kernels. The kernel registered for CPU +// should have type INT32 while all other kernels should have type FLOAT. +TEST(XlaOpRegistryTest, XlaOpRegistrationWithOverride) { + XlaOpRegistry::RegisterCompilationKernels(); + auto registered_kernels = GetAllRegisteredKernels().kernel(); + for (const auto& kernels : registered_kernels) { + if (kernels.op() == "DummyDuplicateOp") { + EXPECT_EQ(kernels.constraint_size(), 1); + EXPECT_EQ(kernels.constraint(0).name(), "T"); + if (kernels.device_type() == "XLA_CPU_JIT") { + EXPECT_EQ(kernels.constraint(0).allowed_values().list().type(0), + DT_INT32); + } else { + EXPECT_EQ(kernels.constraint(0).allowed_values().list().type(0), + DT_FLOAT); + } + } + } +} + +// A dummy generic OpKernel for all backends. +class DummyInfeasibleTypeConstraintOp : public XlaOpKernel { + public: + explicit DummyInfeasibleTypeConstraintOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + LOG(FATAL) << "unreachable"; + } +}; + +REGISTER_OP("DummyInfeasibleTypeConstraintOp") + .Attr("T: {float, string}") + .Input("input: T") + .Output("output: T") + .Doc(R"doc( +A dummy Op. + +input: dummy input. +output: dummy output. +)doc"); +REGISTER_XLA_OP( + Name("DummyInfeasibleTypeConstraintOp").TypeConstraint("T", DT_STRING), + DummyInfeasibleTypeConstraintOp); + +TEST(XlaOpRegistryTest, OpWithInfeasibleTypeConstraintIsNotRegistered) { + XlaOpRegistry::RegisterCompilationKernels(); + auto registered_kernels = GetAllRegisteredKernels().kernel(); + for (const auto& kernels : registered_kernels) { + // The operator should not be registered. + EXPECT_NE(kernels.op(), "DummyInfeasibleTypeConstraintOp"); + } +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_resource.cc b/tensorflow/compiler/tf2xla/xla_resource.cc index 540c65c597f20d5bb26494e56c09ff2187cfb0db..baea8149658ec0849ebb570931ca68518ec5284e 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.cc +++ b/tensorflow/compiler/tf2xla/xla_resource.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" namespace tensorflow { @@ -89,16 +90,16 @@ Status XlaResource::SetZeroValue(xla::XlaBuilder* builder) { } switch (kind_) { case kVariable: { - value_ = builder->Broadcast(XlaHelpers::Zero(builder, type_), - shape_.dim_sizes()); + value_ = + xla::Broadcast(XlaHelpers::Zero(builder, type_), shape_.dim_sizes()); break; } case kTensorArray: { TensorShape ta_shape; ta_shape.AddDim(tensor_array_size_); ta_shape.AppendShape(shape_); - value_ = builder->Broadcast(XlaHelpers::Zero(builder, type_), - ta_shape.dim_sizes()); + value_ = xla::Broadcast(XlaHelpers::Zero(builder, type_), + ta_shape.dim_sizes()); break; } case kStack: { @@ -106,9 +107,9 @@ Status XlaResource::SetZeroValue(xla::XlaBuilder* builder) { ta_shape.AddDim(tensor_array_size_); ta_shape.AppendShape(shape_); value_ = - builder->Tuple({builder->Broadcast(XlaHelpers::Zero(builder, type_), - ta_shape.dim_sizes()), - builder->ConstantR0(0)}); + xla::Tuple(builder, {xla::Broadcast(XlaHelpers::Zero(builder, type_), + ta_shape.dim_sizes()), + xla::ConstantR0(builder, 0)}); break; } @@ -130,8 +131,8 @@ Status XlaResource::GetOrCreateTensorArrayGradient(const string& source, TensorShape ta_shape; ta_shape.AddDim(tensor_array_size_); ta_shape.AppendShape(shape_); - xla::XlaOp gradient_value = builder->Broadcast( - XlaHelpers::Zero(builder, type_), ta_shape.dim_sizes()); + xla::XlaOp gradient_value = + xla::Broadcast(XlaHelpers::Zero(builder, type_), ta_shape.dim_sizes()); gradient.reset( new XlaResource(/*kind=*/kTensorArray, /*arg_num=*/-1, /*name=*/strings::StrCat("TensorArrayGrad: ", name_), @@ -152,7 +153,7 @@ Status XlaResource::Pack(xla::XlaOp* pack, xla::XlaBuilder* builder) const { for (const auto& gradient : tensor_array_gradients_) { elems.push_back(gradient.second->value_); } - *pack = builder->Tuple(elems); + *pack = xla::Tuple(builder, elems); } return Status::OK(); } @@ -168,7 +169,7 @@ Status XlaResource::SetFromPack(const std::set& gradient_sources, } else { TF_RET_CHECK(kind_ == kTensorArray); int pos = 0; - auto v = builder->GetTupleElement(pack, pos++); + auto v = xla::GetTupleElement(pack, pos++); if (!initialized()) { initial_value_ = v; } @@ -178,7 +179,7 @@ Status XlaResource::SetFromPack(const std::set& gradient_sources, XlaResource* gradient; TF_RETURN_IF_ERROR( GetOrCreateTensorArrayGradient(source, builder, &gradient)); - auto v = builder->GetTupleElement(pack, pos++); + auto v = xla::GetTupleElement(pack, pos++); if (!gradient->initialized()) { gradient->initial_value_ = v; } diff --git a/tensorflow/compiler/tf2xla/xla_resource.h b/tensorflow/compiler/tf2xla/xla_resource.h index 9ce36d1aa7622334b2acfbe9aa85d7419c4772ed..4de18a77887496d30e3b1407ecd9042e619653af 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.h +++ b/tensorflow/compiler/tf2xla/xla_resource.h @@ -75,7 +75,7 @@ class XlaResource { const xla::XlaOp& initial_value() const { return initial_value_; } // A variable is initialized if it has a value. - bool initialized() const { return value_.builder() != nullptr; } + bool initialized() const { return value_.valid(); } // Sets the type and shape of the resource. The type and shape of a resource // must not change once the variable has been initialized. diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 1b8e516770c3e217dd7c2f26ce426895b478c2e4..03e542855ba0e3ae81e0b754eb319cadbd5079ba 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -142,30 +142,15 @@ cc_library( cc_library( name = "statusor", - srcs = ["statusor.cc"], hdrs = [ "statusor.h", - "statusor_internals.h", ], visibility = ["//visibility:public"], deps = [ ":status", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", - ], -) - -tf_cc_test( - name = "statusor_test", - size = "small", - srcs = ["statusor_test.cc"], - deps = [ - ":statusor", - ":test", - ":types", - "//tensorflow/core:lib", - "//tensorflow/core:test", - "//tensorflow/core:test_main", + "//tensorflow/stream_executor", ], ) @@ -175,6 +160,7 @@ cc_library( hdrs = [ "iterator_util.h", "map_util.h", + "overflow_util.h", "ptr_util.h", "util.h", ], @@ -250,7 +236,7 @@ cc_library( ":types", ":util", ":xla_data_proto", - "//tensorflow/core:framework_internal", + "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:regexp_internal", @@ -309,7 +295,6 @@ cc_library( ":types", ":util", ":xla_data_proto", - "//tensorflow/core:framework", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/client/compile_only_client.cc b/tensorflow/compiler/xla/client/compile_only_client.cc index dc69d2097ebe14ca0e14a39849d4fcae99024fdc..5c9abad4c3126be5e45e96c770c0679fe8606788 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.cc +++ b/tensorflow/compiler/xla/client/compile_only_client.cc @@ -24,7 +24,8 @@ namespace xla { StatusOr>> CompileOnlyClient::CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options) { + const AotCompilationOptions& options, + std::unique_ptr* metadata) { std::vector service_instances; service_instances.reserve(computations.size()); for (const AotXlaComputationInstance& instance : computations) { @@ -36,7 +37,8 @@ CompileOnlyClient::CompileAheadOfTime( service_instance.argument_layouts = instance.argument_layouts; service_instance.result_layout = instance.result_layout; } - return compiler_service_->CompileAheadOfTime(service_instances, options); + return compiler_service_->CompileAheadOfTime(service_instances, options, + metadata); } int64 CompileOnlyClient::PointerSizeForTriple(tensorflow::StringPiece triple) { diff --git a/tensorflow/compiler/xla/client/compile_only_client.h b/tensorflow/compiler/xla/client/compile_only_client.h index f9a7c31270c7a11175f47a537639a97d0c9211af..332c96503637344d56e363e19db4880c37ca9684 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.h +++ b/tensorflow/compiler/xla/client/compile_only_client.h @@ -46,13 +46,15 @@ class CompileOnlyClient : public Client { const Shape* result_layout; }; - // Compiles a list of xla computations for ahead-of-time execution. This is - // intended for use in static compilation. The |options| parameter describes - // the target for which the compiler should emit code. + // Compiles a list of xla computations for ahead-of-time execution. + // This is intended for use in static compilation. The |options| + // parameter describes the target for which the compiler should emit + // code. |metadata|, if provided, is populated during compilation. StatusOr>> CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options); + const AotCompilationOptions& options, + std::unique_ptr* metadata = nullptr); // Returns the size of a pointer in bytes for a given triple. static int64 PointerSizeForTriple(tensorflow::StringPiece triple); diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index d49d959a6c8112d3701857a70cecb24701c7b6d9..a6b9b4725324adf26a136d490cf28a89c92571c0 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -13,11 +13,18 @@ filegroup( ]), ) +load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test") +load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites") + +# Generate test_suites for all backends, named "${backend}_tests". +generate_backend_suites() + cc_library( name = "arithmetic", srcs = ["arithmetic.cc"], hdrs = ["arithmetic.h"], deps = [ + ":constants", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:types", @@ -28,6 +35,88 @@ cc_library( ], ) +cc_library( + name = "constants", + srcs = ["constants.cc"], + hdrs = ["constants.h"], + deps = [ + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + ], +) + +xla_test( + name = "constants_test", + srcs = ["constants_test.cc"], + tags = ["enable_for_xla_interpreter"], + deps = [ + ":constants", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + +cc_library( + name = "math", + srcs = ["math.cc"], + hdrs = ["math.h"], + deps = [ + ":constants", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + ], +) + +xla_test( + name = "math_test", + srcs = ["math_test.cc"], + tags = ["enable_for_xla_interpreter"], + deps = [ + ":math", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + +cc_library( + name = "numeric", + srcs = ["numeric.cc"], + hdrs = ["numeric.h"], + deps = [ + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + ], +) + +xla_test( + name = "numeric_test", + srcs = ["numeric_test.cc"], + tags = ["enable_for_xla_interpreter"], + deps = [ + ":numeric", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + cc_library( name = "testing", srcs = ["testing.cc"], diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index a1d34796ccfd86f2025eff0ecb51338eb6a9b1da..978fc40f3492cd7d9d7831c370b287bf45e6d3e0 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -42,8 +43,8 @@ XlaComputation CreateScalarComputation(const string& name, PrimitiveType type, } const Shape scalar = ShapeUtil::MakeShape(type, {}); - auto lhs = b->Parameter(0, scalar, "lhs"); - auto rhs = b->Parameter(1, scalar, "rhs"); + auto lhs = Parameter(b.get(), 0, scalar, "lhs"); + auto rhs = Parameter(b.get(), 1, scalar, "rhs"); generator(b.get(), lhs, rhs); return b->BuildAndNoteError(); } @@ -55,7 +56,7 @@ XlaComputation CreateScalarAddComputation(PrimitiveType type, return CreateScalarComputation( "add", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Add(lhs, rhs); + return Add(lhs, rhs); }); } @@ -64,17 +65,15 @@ XlaComputation CreateScalarMultiplyComputation(PrimitiveType type, return CreateScalarComputation( "mul", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Mul(lhs, rhs); + return Mul(lhs, rhs); }); } XlaComputation CreateScalarGeComputation(PrimitiveType type, XlaBuilder* builder) { - return CreateScalarComputation( - "ge", type, builder, - [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Ge(lhs, rhs); - }); + return CreateScalarComputation("ge", type, builder, + [](XlaBuilder* b, const XlaOp& lhs, + const XlaOp& rhs) { return Ge(lhs, rhs); }); } XlaComputation CreateScalarMaxComputation(PrimitiveType type, @@ -82,7 +81,7 @@ XlaComputation CreateScalarMaxComputation(PrimitiveType type, return CreateScalarComputation( "max", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Max(lhs, rhs); + return Max(lhs, rhs); }); } @@ -91,7 +90,7 @@ XlaComputation CreateScalarMinComputation(PrimitiveType type, return CreateScalarComputation( "min", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Min(lhs, rhs); + return Min(lhs, rhs); }); } @@ -99,26 +98,27 @@ XlaComputation CreateScalarAndComputation(XlaBuilder* builder) { return CreateScalarComputation( "and", PRED, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->And(lhs, rhs); + return And(lhs, rhs); }); } XlaComputation CreateScalarOrComputation(XlaBuilder* builder) { - return CreateScalarComputation( - "or", PRED, builder, - [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { - return b->Or(lhs, rhs); - }); + return CreateScalarComputation("or", PRED, builder, + [](XlaBuilder* b, const XlaOp& lhs, + const XlaOp& rhs) { return Or(lhs, rhs); }); } -StatusOr Any(const XlaOp& predicates, XlaBuilder* builder) { - auto f = builder->ConstantR0(false); - XlaComputation logical_or = CreateScalarOrComputation(builder); - TF_ASSIGN_OR_RETURN(const Shape& predicates_shape, - builder->GetShape(predicates)); - std::vector all_dimensions(ShapeUtil::Rank(predicates_shape)); - std::iota(all_dimensions.begin(), all_dimensions.end(), 0); - return builder->Reduce(predicates, f, logical_or, all_dimensions); +XlaOp Any(XlaOp predicates) { + XlaBuilder* builder = predicates.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + auto f = ConstantR0(builder, false); + XlaComputation logical_or = CreateScalarOrComputation(builder); + TF_ASSIGN_OR_RETURN(const Shape& predicates_shape, + builder->GetShape(predicates)); + std::vector all_dimensions(ShapeUtil::Rank(predicates_shape)); + std::iota(all_dimensions.begin(), all_dimensions.end(), 0); + return Reduce(predicates, f, logical_or, all_dimensions); + }); } } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index 64b6b7d63353165e45bf12d35126a7eeef9e56e4..d0b916e8c8f742406caad0571d6e99224ed81404 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -53,7 +53,7 @@ XlaComputation CreateScalarOrComputation(XlaBuilder* builder); // Returns whether any predicate in "predicates" is set. // // Note: if predicates is zero-sized, Any() vacuously returns false. -StatusOr Any(const XlaOp& predicates, XlaBuilder* builder); +XlaOp Any(XlaOp predicates); } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/constants.cc b/tensorflow/compiler/xla/client/lib/constants.cc new file mode 100644 index 0000000000000000000000000000000000000000..1686389a234659a433f1508bd3e0458793541e47 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/constants.cc @@ -0,0 +1,103 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/constants.h" + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/util.h" + +namespace xla { + +XlaOp Zero(XlaBuilder* builder, PrimitiveType type) { + return ConstantLiteral(builder, Literal::Zero(type)); +} + +XlaOp Zeros(XlaBuilder* builder, const Shape& shape) { + return Broadcast(Zero(builder, shape.element_type()), + AsInt64Slice(shape.dimensions())); +} + +XlaOp ZerosLike(XlaOp prototype) { + XlaBuilder* builder = prototype.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(prototype)); + return Zeros(builder, shape); + }); +} + +XlaOp One(XlaBuilder* builder, PrimitiveType type) { + return ConstantLiteral(builder, Literal::One(type)); +} + +XlaOp Epsilon(XlaBuilder* builder, PrimitiveType type) { + switch (type) { + case F16: + return ConstantR0( + builder, + static_cast(Eigen::NumTraits::epsilon())); + case BF16: + return ConstantR0(builder, bfloat16::epsilon()); + case F32: + return ConstantR0(builder, std::numeric_limits::epsilon()); + case F64: + return ConstantR0(builder, + std::numeric_limits::epsilon()); + default: + return builder->ReportError(InvalidArgument( + "Invalid type for Epsilon (%s).", PrimitiveType_Name(type).c_str())); + } +} + +XlaOp MinValue(XlaBuilder* builder, PrimitiveType type) { + return ConstantLiteral(builder, Literal::MinValue(type)); +} + +XlaOp MinFiniteValue(XlaBuilder* builder, PrimitiveType type) { + switch (type) { + case F16: + return ConstantR0(builder, + Eigen::NumTraits::lowest()); + case BF16: + return ConstantR0(builder, bfloat16::lowest()); + case F32: + return ConstantR0(builder, -std::numeric_limits::max()); + case F64: + return ConstantR0(builder, -std::numeric_limits::max()); + default: + return MinValue(builder, type); + } +} + +XlaOp MaxValue(XlaBuilder* builder, PrimitiveType type) { + return ConstantLiteral(builder, Literal::MaxValue(type)); +} + +XlaOp MaxFiniteValue(XlaBuilder* builder, PrimitiveType type) { + switch (type) { + case F16: + return ConstantR0(builder, + Eigen::NumTraits::highest()); + case BF16: + return ConstantR0(builder, bfloat16::highest()); + case F32: + return ConstantR0(builder, std::numeric_limits::max()); + case F64: + return ConstantR0(builder, std::numeric_limits::max()); + default: + return MaxValue(builder, type); + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/constants.h b/tensorflow/compiler/xla/client/lib/constants.h new file mode 100644 index 0000000000000000000000000000000000000000..b47f5243f008ecb2045456e4505d1a571fbed745 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/constants.h @@ -0,0 +1,124 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CONSTANTS_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CONSTANTS_H_ + +#include + +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +// Returns scalar 'value' as a scalar of 'type'. Unlike ConstantR0, 'type' is +// determined at C++ run-time, rather than C++ compile-time. +// If 'value' is floating point but 'type' is not, or if 'value' is complex but +// 'type' is not, an error will be returned. This is to catch accidental +// truncation; in such cases, use an explicit cast. +template +XlaOp ConstantR0WithType(XlaBuilder* builder, PrimitiveType type, T value) { + if (std::is_floating_point::value && + !(primitive_util::IsFloatingPointType(type) || + primitive_util::IsComplexType(type))) { + return builder->ReportError(InvalidArgument( + "Invalid cast from floating point type to %s in ConstantR0WithType.", + PrimitiveType_Name(type).c_str())); + } + if (std::is_same::value && + !primitive_util::IsComplexType(type)) { + return builder->ReportError(InvalidArgument( + "Invalid cast from complex type to %s in ConstantR0WithType.", + PrimitiveType_Name(type).c_str())); + } + switch (type) { + case F16: + return ConstantR0(builder, static_cast(value)); + case BF16: + return ConstantR0(builder, static_cast(value)); + case F32: + return ConstantR0(builder, static_cast(value)); + case F64: + return ConstantR0(builder, static_cast(value)); + case C64: + return ConstantR0(builder, static_cast(value)); + case U8: + return ConstantR0(builder, static_cast(value)); + case U32: + return ConstantR0(builder, static_cast(value)); + case U64: + return ConstantR0(builder, static_cast(value)); + case S8: + return ConstantR0(builder, static_cast(value)); + case S32: + return ConstantR0(builder, static_cast(value)); + case S64: + return ConstantR0(builder, static_cast(value)); + default: + return builder->ReportError( + InvalidArgument("Invalid type for ConstantR0WithType (%s).", + PrimitiveType_Name(type).c_str())); + } +} + +// Returns a scalar containing 'value' cast to the same run-time type as +// 'prototype'. +// If 'value' is floating point but 'prototype' is not, or if 'value' is complex +// 'prototype' is not, an error will be returned. +template +XlaOp ScalarLike(XlaOp prototype, T value) { + XlaBuilder* builder = prototype.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(prototype)); + return ConstantR0WithType(builder, shape.element_type(), value); + }); +} + +// Returns a scalar with value '0' of 'type'. +XlaOp Zero(XlaBuilder* builder, PrimitiveType type); + +// Returns a zero-filled tensor with shape `shape`. +XlaOp Zeros(XlaBuilder* builder, const Shape& shape); + +// Returns a zero-filled tensor with the same shape as `prototype`. +XlaOp ZerosLike(XlaOp prototype); + +// Returns a scalar with value '1' of 'type'. +XlaOp One(XlaBuilder* builder, PrimitiveType type); + +// Returns the machine epsilon for floating-point type `type`, i.e., +// the difference between 1.0 and the next representable value. +XlaOp Epsilon(XlaBuilder* builder, PrimitiveType type); + +// Returns the minimum representable finite or infinite value for 'type'. +// Returns '-inf' for floating-point types. +XlaOp MinValue(XlaBuilder* builder, PrimitiveType type); + +// Returns the minimum representable finite value for 'type'. For a floating +// point type, this is equal to -MaxFiniteValue(). +XlaOp MinFiniteValue(XlaBuilder* builder, PrimitiveType type); + +// Returns the maximum representable finite or infinite value for 'type'. +// Returns 'inf' for floating-point types. +XlaOp MaxValue(XlaBuilder* builder, PrimitiveType type); + +// Returns the maximum representable finite value for 'type'. +XlaOp MaxFiniteValue(XlaBuilder* builder, PrimitiveType type); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CONSTANTS_H_ diff --git a/tensorflow/compiler/xla/client/lib/constants_test.cc b/tensorflow/compiler/xla/client/lib/constants_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..f1e3439862344c01af15ec0571155ca46a579e54 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/constants_test.cc @@ -0,0 +1,159 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { +namespace { + +using ConstantsTest = ClientLibraryTestBase; + +using ::testing::HasSubstr; + +XLA_TEST_F(ConstantsTest, ConstantR0WithTypeS32) { + XlaBuilder builder(TestName()); + ConstantR0WithType(&builder, xla::S32, 4); + ComputeAndCompareR0(&builder, 4, {}); +} + +XLA_TEST_F(ConstantsTest, ConstantR0WithTypeS32DoesNotAcceptFloats) { + XlaBuilder builder(TestName()); + ConstantR0WithType(&builder, xla::S32, 4.5); + auto statusor = builder.Build(); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("Invalid cast")); +} + +XLA_TEST_F(ConstantsTest, ConstantR0WithTypeF32) { + XlaBuilder builder(TestName()); + ConstantR0WithType(&builder, xla::F32, -7); + ComputeAndCompareR0(&builder, -7, {}); + ConstantR0WithType(&builder, xla::F32, 0.5); + ComputeAndCompareR0(&builder, 0.5, {}); +} + +XLA_TEST_F(ConstantsTest, ScalarLikeS32) { + XlaBuilder builder(TestName()); + ScalarLike(ConstantR0(&builder, 42), -3); + ComputeAndCompareR0(&builder, -3, {}); +} + +XLA_TEST_F(ConstantsTest, ScalarLikeF32) { + XlaBuilder builder(TestName()); + ScalarLike(ConstantR0(&builder, 42.75), -3.2); + ComputeAndCompareR0(&builder, -3.2, {}); +} + +XLA_TEST_F(ConstantsTest, ZeroS32) { + XlaBuilder builder(TestName()); + Zero(&builder, S32); + ComputeAndCompareR0(&builder, 0, {}); +} + +XLA_TEST_F(ConstantsTest, ZeroF32) { + XlaBuilder builder(TestName()); + Zero(&builder, F32); + ComputeAndCompareR0(&builder, 0.0, {}); +} + +XLA_TEST_F(ConstantsTest, ZerosS32) { + XlaBuilder builder(TestName()); + Zeros(&builder, ShapeUtil::MakeShape(S32, {2, 2})); + ComputeAndCompareR2(&builder, {{0, 0}, {0, 0}}, {}); +} + +XLA_TEST_F(ConstantsTest, ZerosLikeF32) { + XlaBuilder builder(TestName()); + ZerosLike(ConstantR1(&builder, {1., 2., 3.})); + ComputeAndCompareR1(&builder, {0., 0., 0.}, {}); +} + +XLA_TEST_F(ConstantsTest, OneS32) { + XlaBuilder builder(TestName()); + One(&builder, S32); + ComputeAndCompareR0(&builder, 1, {}); +} + +XLA_TEST_F(ConstantsTest, OneF32) { + XlaBuilder builder(TestName()); + One(&builder, F32); + ComputeAndCompareR0(&builder, 1., {}); +} + +XLA_TEST_F(ConstantsTest, EpsilonF32) { + XlaBuilder builder(TestName()); + Epsilon(&builder, F32); + ComputeAndCompareR0(&builder, std::numeric_limits::epsilon(), + {}); +} + +XLA_TEST_F(ConstantsTest, MinFiniteValueS32) { + XlaBuilder builder(TestName()); + MinFiniteValue(&builder, S32); + ComputeAndCompareR0(&builder, std::numeric_limits::min(), {}); +} + +XLA_TEST_F(ConstantsTest, MaxFiniteValueS32) { + XlaBuilder builder(TestName()); + MaxFiniteValue(&builder, S32); + ComputeAndCompareR0(&builder, std::numeric_limits::max(), {}); +} + +XLA_TEST_F(ConstantsTest, MinFiniteValueF32) { + XlaBuilder builder(TestName()); + MinFiniteValue(&builder, F32); + ComputeAndCompareR0(&builder, -std::numeric_limits::max(), {}); +} + +XLA_TEST_F(ConstantsTest, MaxFiniteValueF32) { + XlaBuilder builder(TestName()); + MaxFiniteValue(&builder, F32); + ComputeAndCompareR0(&builder, std::numeric_limits::max(), {}); +} + +XLA_TEST_F(ConstantsTest, MinValueS32) { + XlaBuilder builder(TestName()); + MinValue(&builder, S32); + ComputeAndCompareR0(&builder, std::numeric_limits::min(), {}); +} + +XLA_TEST_F(ConstantsTest, MaxValueS32) { + XlaBuilder builder(TestName()); + MaxValue(&builder, S32); + ComputeAndCompareR0(&builder, std::numeric_limits::max(), {}); +} + +XLA_TEST_F(ConstantsTest, MinValueF32) { + XlaBuilder builder(TestName()); + MinValue(&builder, F32); + ComputeAndCompareR0(&builder, -std::numeric_limits::infinity(), + {}); +} + +XLA_TEST_F(ConstantsTest, MaxValueF32) { + XlaBuilder builder(TestName()); + MaxValue(&builder, F32); + ComputeAndCompareR0(&builder, std::numeric_limits::infinity(), + {}); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/math.cc b/tensorflow/compiler/xla/client/lib/math.cc new file mode 100644 index 0000000000000000000000000000000000000000..558755904007431cc0902d95a49627ea07f59127 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/math.cc @@ -0,0 +1,152 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/math.h" + +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" + +namespace xla { + +XlaOp Sqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, 0.5)); } + +XlaOp Rsqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, -0.5)); } + +XlaOp Square(XlaOp operand) { return Pow(operand, ScalarLike(operand, 2.0)); } + +XlaOp Reciprocal(XlaOp operand) { + return Pow(operand, ScalarLike(operand, -1.0)); +} + +namespace { + +// Polynomials for computing erf/erfc. Originally from cephes. +// Note we use float for compatibility across devices, at the cost of some +// precision for 64 bit computations. +// +// Coefficients are in descending order. +std::array kErfcPCoefficient = { + 2.46196981473530512524E-10, 5.64189564831068821977E-1, + 7.46321056442269912687E0, 4.86371970985681366614E1, + 1.96520832956077098242E2, 5.26445194995477358631E2, + 9.34528527171957607540E2, 1.02755188689515710272E3, + 5.57535335369399327526E2}; +std::array kErfcQCoefficient = { + 1.00000000000000000000E0, 1.32281951154744992508E1, + 8.67072140885989742329E1, 3.54937778887819891062E2, + 9.75708501743205489753E2, 1.82390916687909736289E3, + 2.24633760818710981792E3, 1.65666309194161350182E3, + 5.57535340817727675546E2}; +std::array kErfcRCoefficient = { + 5.64189583547755073984E-1, 1.27536670759978104416E0, + 5.01905042251180477414E0, 6.16021097993053585195E0, + 7.40974269950448939160E0, 2.97886665372100240670E0}; +std::array kErfcSCoefficient = { + 1.00000000000000000000E0, 2.26052863220117276590E0, + 9.39603524938001434673E0, 1.20489539808096656605E1, + 1.70814450747565897222E1, 9.60896809063285878198E0, + 3.36907645100081516050E0}; +std::array kErfTCoefficient = { + 9.60497373987051638749E0, 9.00260197203842689217E1, + 2.23200534594684319226E3, 7.00332514112805075473E3, + 5.55923013010394962768E4}; +std::array kErfUCoefficient = { + 1.00000000000000000000E0, 3.35617141647503099647E1, + 5.21357949780152679795E2, 4.59432382970980127987E3, + 2.26290000613890934246E4, 4.92673942608635921086E4}; +} // namespace + +// Evaluate the polynomial given coefficients and `x`. +// N.B. Coefficients should be supplied in decreasing order. +XlaOp EvaluatePolynomial(XlaOp x, + tensorflow::gtl::ArraySlice coefficients) { + XlaOp poly = ScalarLike(x, 0.0); + for (float c : coefficients) { + poly = poly * x + ScalarLike(x, c); + } + return poly; +} + +// Compute an approximation of the error function complement (1 - erf(x)). +XlaOp Erfc(XlaOp x) { + XlaOp abs_x = Abs(x); + XlaOp z = Exp(-x * x); + + XlaOp pp = EvaluatePolynomial(abs_x, kErfcPCoefficient); + XlaOp pq = EvaluatePolynomial(abs_x, kErfcQCoefficient); + XlaOp pr = EvaluatePolynomial(abs_x, kErfcRCoefficient); + XlaOp ps = EvaluatePolynomial(abs_x, kErfcSCoefficient); + + XlaOp y = Select(Lt(abs_x, ScalarLike(x, 8.0)), z * pp / pq, z * pr / ps); + + return Select(Lt(x, ScalarLike(x, 0.0)), ScalarLike(x, 2.0) - y, y); +} + +// Compute a polynomial approximation of the error function. +XlaOp Erf(XlaOp x) { + XlaOp z = x * x; + XlaOp pt = EvaluatePolynomial(z, kErfTCoefficient); + XlaOp pu = EvaluatePolynomial(z, kErfUCoefficient); + return x * pt / pu; +} + +// Approximation for the inverse error function from +// Giles, M., "Approximating the erfinv function". +// The approximation has the form: +// w = -log((1 - x) * (1 + x)) +// if ( w < 5 ) { +// w = w - 2.5 +// p = sum_{i=1}^n lq[i]*w^i +// } else { +// w = sqrt(w) - 3 +// p = sum_{i=1}^n gq[i]*w^i +// } +// return p*x +XlaOp ErfInv(XlaOp x) { + XlaBuilder* b = x.builder(); + return b->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, b->GetShape(x)); + constexpr int kDegree = 9; + constexpr std::array w_less_than_5_constants = { + 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, + -4.39150654e-06f, 0.00021858087f, -0.00125372503f, + -0.00417768164f, 0.246640727f, 1.50140941f}; + constexpr std::array w_greater_than_5_constants = { + -0.000200214257f, 0.000100950558f, 0.00134934322f, + -0.00367342844f, 0.00573950773f, -0.0076224613f, + 0.00943887047f, 1.00167406f, 2.83297682f}; + + auto one = ScalarLike(x, 1.0); + auto w = -Log((one - x) * (one + x)); + + auto lt = Lt(w, ScalarLike(x, 5.0)); + auto coefficient = [&](int i) { + return Select(lt, + Broadcast(ScalarLike(x, w_less_than_5_constants[i]), + AsInt64Slice(shape.dimensions())), + Broadcast(ScalarLike(x, w_greater_than_5_constants[i]), + AsInt64Slice(shape.dimensions()))); + }; + w = Select(lt, w - ScalarLike(x, 2.5), Sqrt(w) - ScalarLike(x, 3.0)); + auto p = coefficient(0); + for (int i = 1; i < kDegree; ++i) { + p = coefficient(i) + p * w; + } + return p * x; + }); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/math.h b/tensorflow/compiler/xla/client/lib/math.h new file mode 100644 index 0000000000000000000000000000000000000000..e7c8b50273067a979158f79aa80abc6058901040 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/math.h @@ -0,0 +1,51 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ + +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" + +namespace xla { + +// Computes the square root of 'operand'. +XlaOp Sqrt(XlaOp operand); + +// Computes the reciprocal of the square root of 'operand'. +XlaOp Rsqrt(XlaOp operand); + +// Computes the square of 'operand'. +XlaOp Square(XlaOp operand); + +// Computes the reciprocal of 'operand'. +XlaOp Reciprocal(XlaOp operand); + +// Evaluates a polynomial given coefficients and `x`. +// N.B. Coefficients should be supplied in decreasing order. +XlaOp EvaluatePolynomial(XlaOp x, + tensorflow::gtl::ArraySlice coefficients); + +// Computes an approximation of the error function complement (1 - erf(x)). +XlaOp Erfc(XlaOp x); + +// Computes an approximation of the error function. +XlaOp Erf(XlaOp x); + +// Computes an approximation of the inverse of the error function. +XlaOp ErfInv(XlaOp x); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ diff --git a/tensorflow/compiler/xla/client/lib/math_test.cc b/tensorflow/compiler/xla/client/lib/math_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..1df4e6ea42a2211c285075a3ed9159a9d603ccf5 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/math_test.cc @@ -0,0 +1,85 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { +namespace { + +class MathTest : public ClientLibraryTestBase { + public: + ErrorSpec error_spec_{0.0001}; +}; + +XLA_TEST_F(MathTest, SqrtF32) { + XlaBuilder builder(TestName()); + Literal zero_literal = Literal::Zero(PrimitiveType::F32); + + std::unique_ptr zero_data = + client_->TransferToServer(zero_literal).ConsumeValueOrDie(); + + XlaOp zero = Parameter(&builder, 0, zero_literal.shape(), "zero"); + Sqrt(zero); + + ComputeAndCompareR0(&builder, 0.0f, {zero_data.get()}, error_spec_); +} + +XLA_TEST_F(MathTest, SquareTenValues) { + XlaBuilder builder(TestName()); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Square(x); + + std::vector expected = {4.41, 6.76, 6.76, 16., 4.41, + 5.29, 25., 0.81, 5.76, 2.56}; + ComputeAndCompareR1(&builder, expected, {}, error_spec_); +} + +XLA_TEST_F(MathTest, ReciprocalTenValues) { + XlaBuilder builder(TestName()); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Reciprocal(x); + + std::vector expected = { + 0.47619048, -0.38461538, 0.38461538, -0.25, 0.47619048, + 0.43478261, -0.2, -1.11111111, -0.41666667, 0.625}; + ComputeAndCompareR1(&builder, expected, {}, error_spec_); +} + +XLA_TEST_F(MathTest, SqrtZeroes) { + XlaBuilder builder(TestName()); + auto x = ConstantR1(&builder, {0.0, -0.0}); + Sqrt(x); + + ComputeAndCompareR1(&builder, {0, 0}, {}, error_spec_); +} + +XLA_TEST_F(MathTest, SqrtSixValues) { + XlaBuilder builder(TestName()); + auto x = ConstantR1(&builder, {16.0, 1.0, 1024.0, 0.16, 0.2, 12345}); + Sqrt(x); + + std::vector expected = {4, 1, 32, 0.4, 0.4472, 111.1080}; + ComputeAndCompareR1(&builder, expected, {}, error_spec_); +} +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/numeric.cc b/tensorflow/compiler/xla/client/lib/numeric.cc new file mode 100644 index 0000000000000000000000000000000000000000..cbe9e7fdd1330164f1f9c4520c2bb81e38f4ceb9 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/numeric.cc @@ -0,0 +1,71 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/numeric.h" + +#include +#include + +namespace xla { + +namespace { + +template +XlaOp MakeIota(XlaBuilder* builder, int64 size) { + std::vector values(size); + for (int64 i = 0; i < size; ++i) { + values[i] = static_cast(i); + } + return xla::ConstantR1(builder, values); +} + +} // namespace + +XlaOp Iota(XlaBuilder* builder, PrimitiveType type, int64 size) { + switch (type) { + case S8: + return MakeIota(builder, size); + case S16: + return MakeIota(builder, size); + case S32: + return MakeIota(builder, size); + case S64: + return MakeIota(builder, size); + case U8: + return MakeIota(builder, size); + case U16: + return MakeIota(builder, size); + case U32: + return MakeIota(builder, size); + case U64: + return MakeIota(builder, size); + case BF16: + return MakeIota(builder, size); + case F16: + return MakeIota(builder, size); + case F32: + return MakeIota(builder, size); + case F64: + return MakeIota(builder, size); + case C64: + return MakeIota(builder, size); + default: + return builder->ReportError( + InvalidArgument("Unimplemented type for Iota: %s.", + PrimitiveType_Name(type).c_str())); + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/versioned_computation_handle.cc b/tensorflow/compiler/xla/client/lib/numeric.h similarity index 55% rename from tensorflow/compiler/xla/service/versioned_computation_handle.cc rename to tensorflow/compiler/xla/client/lib/numeric.h index a693c4695f0e776cf297d0ecd28d6de53bd5c0c6..2a409ae31147a4a88367422ce31c9fbcb22fdbca 100644 --- a/tensorflow/compiler/xla/service/versioned_computation_handle.cc +++ b/tensorflow/compiler/xla/client/lib/numeric.h @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -13,20 +13,18 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ -#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { -string VersionedComputationHandle::ToString() const { - return tensorflow::strings::StrCat(handle.handle(), ":v", version); -} - -std::ostream& operator<<(std::ostream& out, - const VersionedComputationHandle& versioned_handle) { - out << versioned_handle.ToString(); - return out; -} +// Returns a rank 1 tensor of `type` containing values [0, 1, 2, ...]. +XlaOp Iota(XlaBuilder* builder, PrimitiveType type, int64 size); } // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..bc8a73e9d793ef8f65c321759e03b0de75edd500 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { +namespace { + +using NumericTest = ClientLibraryTestBase; + +XLA_TEST_F(NumericTest, Iota) { + XlaBuilder builder(TestName()); + Iota(&builder, S32, 10); + + ComputeAndCompareR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {}); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index 3380af9f303b1dc2cec09aa37410ec40cdeaa526..731ad13b8d0e5d65acc316e72be9fe7d35e826a4 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -48,15 +48,15 @@ int64 DataSizeOfShape(const Shape& shape) { // Creates a XlaOp for an op what generates fake data with the given shape. XlaOp BuildFakeDataOpOnDevice(const Shape& shape, XlaBuilder* builder) { if (ShapeUtil::IsArray(shape)) { - return builder->Broadcast( - builder->ConstantLiteral(Literal::One(shape.element_type())), + return Broadcast( + ConstantLiteral(builder, Literal::One(shape.element_type())), AsInt64Slice(shape.dimensions())); } std::vector parts; for (const Shape& s : shape.tuple_shapes()) { parts.push_back(BuildFakeDataOpOnDevice(s, builder)); } - return builder->Tuple(parts); + return Tuple(builder, parts); } std::unique_ptr MakeFakeDataViaDeviceOrDie(const Shape& shape, diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index ae0308020d014e038d2f0fd7de6c5f372d6cbed1..5f9710914bd0ceff55f5b0a2db05e553ce8bd637 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -51,24 +51,17 @@ LocalExecutable::LocalExecutable(std::unique_ptr executable, Status LocalExecutable::ValidateExecutionOptions( const tensorflow::gtl::ArraySlice arguments, const ExecutableRunOptions& run_options, const Backend& backend) { - const ComputationLayout& host_computation_layout = - executable_->module_config().host_entry_computation_layout(); - const ComputationLayout& device_computation_layout = - executable_->module_config().device_entry_computation_layout(); + const ComputationLayout& computation_layout = + executable_->module_config().entry_computation_layout(); // Check argument number, shapes, and layouts. - if (arguments.size() != host_computation_layout.parameter_count()) { + if (arguments.size() != computation_layout.parameter_count()) { return InvalidArgument( "invalid number of arguments for computation: expected %d, got %zu", - host_computation_layout.parameter_count(), arguments.size()); - } - if (arguments.size() != device_computation_layout.parameter_count()) { - return InvalidArgument( - "invalid number of arguments for computation: expected %d, got %zu", - device_computation_layout.parameter_count(), arguments.size()); + computation_layout.parameter_count(), arguments.size()); } for (int i = 0; i < arguments.size(); ++i) { - if (!host_computation_layout.parameter_layout(i).MatchesLayoutInShape( + if (!computation_layout.parameter_layout(i).MatchesLayoutInShape( arguments[i]->on_host_shape())) { return InvalidParameterArgument( executable_.get(), i, @@ -76,24 +69,10 @@ Status LocalExecutable::ValidateExecutionOptions( "parameter " "%d: want %s, got %s", i, - ShapeUtil::HumanString( - host_computation_layout.parameter_layout(i).shape()) + ShapeUtil::HumanString(computation_layout.parameter_layout(i).shape()) .c_str(), ShapeUtil::HumanString(arguments[i]->on_host_shape()).c_str()); } - if (!device_computation_layout.parameter_layout(i).MatchesLayoutInShape( - arguments[i]->on_device_shape())) { - return InvalidParameterArgument( - executable_.get(), i, - "Argument does not match device shape or layout of computation " - "parameter " - "%d: want %s, got %s", - i, - ShapeUtil::HumanString( - device_computation_layout.parameter_layout(i).shape()) - .c_str(), - ShapeUtil::HumanString(arguments[i]->on_device_shape()).c_str()); - } } if (run_options.stream() != nullptr) { @@ -230,10 +209,9 @@ Status LocalExecutable::RecordResult(const ShapedBuffer* result, StatusOr> LocalExecutable::LiteralFromShapedBuffer( const ShapedBuffer& shaped_buffer) { - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - backend_->stream_executor(shaped_buffer.device_ordinal())); - return backend_->transfer_manager()->TransferLiteralFromDevice(executor, + TF_ASSIGN_OR_RETURN(auto stream, + backend_->BorrowStream(shaped_buffer.device_ordinal())); + return backend_->transfer_manager()->TransferLiteralFromDevice(stream.get(), shaped_buffer); } @@ -288,19 +266,18 @@ StatusOr LocalClient::LiteralToShapedBuffer( TF_ASSIGN_OR_RETURN(auto scoped_buffer, backend().transfer_manager()->AllocateScopedShapedBuffer( literal.shape(), allocator, device_ordinal)); - TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor, - backend().stream_executor(device_ordinal)); + TF_ASSIGN_OR_RETURN(auto stream, + mutable_backend()->BorrowStream(device_ordinal)); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - executor, literal, scoped_buffer)); + stream.get(), literal, scoped_buffer)); return std::move(scoped_buffer); } StatusOr> LocalClient::ShapedBufferToLiteral( const ShapedBuffer& shaped_buffer) { - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - backend().stream_executor(shaped_buffer.device_ordinal())); - return backend().transfer_manager()->TransferLiteralFromDevice(executor, + TF_ASSIGN_OR_RETURN(auto stream, mutable_backend()->BorrowStream( + shaped_buffer.device_ordinal())); + return backend().transfer_manager()->TransferLiteralFromDevice(stream.get(), shaped_buffer); } diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD index 507a2dc5f088e159156f0ef3d663ba2819f6a2d4..ee00a9eada8dd906c26e07a4affccdaf544f1693 100644 --- a/tensorflow/compiler/xla/client/xla_client/BUILD +++ b/tensorflow/compiler/xla/client/xla_client/BUILD @@ -1,7 +1,5 @@ # Description: # The new XLA client libraries. -# -# This is NOT YET ready to use. licenses(["notice"]) # Apache 2.0 @@ -41,6 +39,7 @@ cc_library( name = "xla_builder", srcs = ["xla_builder.cc"], hdrs = ["xla_builder.h"], + visibility = ["//visibility:public"], deps = [ ":xla_computation", "//tensorflow/compiler/xla:execution_options_util", @@ -52,6 +51,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:sharding_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:shape_inference", diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc index 5e17cc4dfb0b225712e94041970545ff19f03b98..09e7e87918a31967af4719b7f13cc49d4c97a4a9 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/sharding_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/shape_inference.h" @@ -47,6 +48,7 @@ int64 GetUniqueId() { // computation. bool CanBeRoot(HloOpcode opcode) { switch (opcode) { + case HloOpcode::kAfterAll: case HloOpcode::kSend: case HloOpcode::kSendDone: case HloOpcode::kOutfeed: @@ -59,6 +61,36 @@ bool CanBeRoot(HloOpcode opcode) { } // namespace +XlaOp operator-(const XlaOp& x) { return Neg(x); } +XlaOp operator+(const XlaOp& x, const XlaOp& y) { return Add(x, y); } +XlaOp operator-(const XlaOp& x, const XlaOp& y) { return Sub(x, y); } +XlaOp operator*(const XlaOp& x, const XlaOp& y) { return Mul(x, y); } +XlaOp operator/(const XlaOp& x, const XlaOp& y) { return Div(x, y); } +XlaOp operator%(const XlaOp& x, const XlaOp& y) { return Rem(x, y); } + +XlaOp operator~(const XlaOp& x) { return Not(x); } +XlaOp operator&(const XlaOp& x, const XlaOp& y) { return And(x, y); } +XlaOp operator|(const XlaOp& x, const XlaOp& y) { return Or(x, y); } +XlaOp operator^(const XlaOp& x, const XlaOp& y) { return Xor(x, y); } +XlaOp operator<<(const XlaOp& x, const XlaOp& y) { return ShiftLeft(x, y); } + +XlaOp operator>>(const XlaOp& x, const XlaOp& y) { + XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(x)); + if (!ShapeUtil::ElementIsIntegral(shape)) { + return InvalidArgument( + "Argument to >> operator does not have an integral type (%s).", + ShapeUtil::HumanString(shape).c_str()); + } + if (ShapeUtil::ElementIsSigned(shape)) { + return ShiftRightArithmetic(x, y); + } else { + return ShiftRightLogical(x, y); + } + }); +} + StatusOr XlaBuilder::GetShape(const XlaOp& op) const { TF_RETURN_IF_ERROR(first_error_); @@ -81,7 +113,7 @@ XlaBuilder::XlaBuilder(const string& computation_name) XlaBuilder::~XlaBuilder() {} -void XlaBuilder::NoteError(const Status& error) { +XlaOp XlaBuilder::ReportError(const Status& error) { CHECK(!error.ok()); if (die_immediately_on_error_) { LOG(FATAL) << "error building computation: " << error; @@ -91,19 +123,22 @@ void XlaBuilder::NoteError(const Status& error) { first_error_ = error; first_error_backtrace_.CreateCurrent(/*skip_count=*/1); } + return XlaOp(this); } -XlaOp XlaBuilder::NoteErrorOrReturn( - const std::function()>& op_creator) { +XlaOp XlaBuilder::ReportErrorOrReturn(const StatusOr& op) { if (!first_error_.ok()) { - return {}; + return XlaOp(this); } - auto op = op_creator(); if (!op.ok()) { - NoteError(op.status()); - return {}; + return ReportError(op.status()); } - return op.ConsumeValueOrDie(); + return op.ValueOrDie(); +} + +XlaOp XlaBuilder::ReportErrorOrReturn( + const std::function()>& op_creator) { + return ReportErrorOrReturn(op_creator()); } StatusOr XlaBuilder::GetProgramShape(int64* root_id) const { @@ -207,7 +242,7 @@ XlaComputation XlaBuilder::BuildAndNoteError() { DCHECK(parent_builder_ != nullptr); auto build_status = Build(); if (!build_status.ok()) { - parent_builder_->NoteError( + parent_builder_->ReportError( AddStatus(build_status.status(), tensorflow::strings::StrCat("error from: ", name_))); return {}; @@ -315,7 +350,7 @@ StatusOr XlaBuilder::AddBroadcastSequence(const Shape& output_shape, } XlaOp XlaBuilder::UnaryOp(HloOpcode unop, const XlaOp& operand) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), @@ -327,7 +362,7 @@ XlaOp XlaBuilder::UnaryOp(HloOpcode unop, const XlaOp& operand) { XlaOp XlaBuilder::BinaryOp( HloOpcode binop, const XlaOp& lhs, const XlaOp& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -383,7 +418,7 @@ XlaOp XlaBuilder::BinaryOp( XlaOp XlaBuilder::TernaryOp(HloOpcode triop, const XlaOp& lhs, const XlaOp& rhs, const XlaOp& ehs) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -430,7 +465,7 @@ XlaOp XlaBuilder::Mul(const XlaOp& lhs, const XlaOp& rhs, } XlaOp XlaBuilder::ConstantLiteral(const LiteralSlice& literal) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; *instr.mutable_shape() = literal.shape(); *instr.mutable_literal() = literal.ToProto(); @@ -440,7 +475,7 @@ XlaOp XlaBuilder::ConstantLiteral(const LiteralSlice& literal) { XlaOp XlaBuilder::Call(const XlaComputation& computation, tensorflow::gtl::ArraySlice operands) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; std::vector operand_shape_ptrs; TF_ASSIGN_OR_RETURN(const auto& operand_shapes, GetOperandShapes(operands)); @@ -461,7 +496,7 @@ XlaOp XlaBuilder::Call(const XlaComputation& computation, XlaOp XlaBuilder::Parameter(int64 parameter_number, const Shape& shape, const string& name) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; if (!parameter_numbers_.insert(parameter_number).second) { return InvalidArgument("parameter %lld already registered", @@ -476,7 +511,7 @@ XlaOp XlaBuilder::Parameter(int64 parameter_number, const Shape& shape, XlaOp XlaBuilder::Broadcast( const XlaOp& operand, tensorflow::gtl::ArraySlice broadcast_sizes) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( const Shape& shape, @@ -498,6 +533,14 @@ XlaOp XlaBuilder::Broadcast( }); } +XlaOp XlaBuilder::BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions) { + return ReportErrorOrReturn([&]() -> StatusOr { + return InDimBroadcast(shape, operand, broadcast_dimensions); + }); +} + StatusOr XlaBuilder::Reshape(const Shape& shape, const XlaOp& operand) { TF_RETURN_IF_ERROR(first_error_); @@ -510,7 +553,7 @@ XlaOp XlaBuilder::Slice(const XlaOp& operand, tensorflow::gtl::ArraySlice start_indices, tensorflow::gtl::ArraySlice limit_indices, tensorflow::gtl::ArraySlice strides) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -530,7 +573,7 @@ XlaOp XlaBuilder::Slice(const XlaOp& operand, XlaOp XlaBuilder::SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, int64 stride, int64 dimno) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& shape, GetShape(operand)); std::vector starts(ShapeUtil::Rank(shape), 0); std::vector limits(shape.dimensions().begin(), @@ -545,7 +588,7 @@ XlaOp XlaBuilder::SliceInDim(const XlaOp& operand, int64 start_index, XlaOp XlaBuilder::DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, tensorflow::gtl::ArraySlice slice_sizes) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -566,7 +609,7 @@ XlaOp XlaBuilder::DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, XlaOp XlaBuilder::DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, const XlaOp& start_indices) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -584,7 +627,7 @@ XlaOp XlaBuilder::DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, XlaOp XlaBuilder::ConcatInDim(tensorflow::gtl::ArraySlice operands, int64 dimension) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; std::vector operand_shape_ptrs; @@ -603,7 +646,7 @@ XlaOp XlaBuilder::ConcatInDim(tensorflow::gtl::ArraySlice operands, XlaOp XlaBuilder::Pad(const XlaOp& operand, const XlaOp& padding_value, const PaddingConfig& padding_config) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -624,7 +667,7 @@ XlaOp XlaBuilder::Pad(const XlaOp& operand, const XlaOp& padding_value, XlaOp XlaBuilder::Reshape(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions, tensorflow::gtl::ArraySlice new_sizes) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN(const Shape& shape, ShapeInference::InferReshapeShape( @@ -638,7 +681,7 @@ XlaOp XlaBuilder::Reshape(const XlaOp& operand, XlaOp XlaBuilder::Reshape(const XlaOp& operand, tensorflow::gtl::ArraySlice new_sizes) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(auto shape, GetShape(operand)); std::vector dimensions(shape.dimensions_size()); std::iota(dimensions.begin(), dimensions.end(), 0); @@ -648,7 +691,7 @@ XlaOp XlaBuilder::Reshape(const XlaOp& operand, XlaOp XlaBuilder::Collapse(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { if (dimensions.size() <= 1) { // Not collapsing anything, trivially we can return the operand versus // enqueueing a trivial reshape. @@ -690,7 +733,7 @@ XlaOp XlaBuilder::Collapse(const XlaOp& operand, } void XlaBuilder::Trace(const string& tag, const XlaOp& operand) { - NoteErrorOrReturn([&]() -> StatusOr { + ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; *instr.mutable_shape() = ShapeUtil::MakeNil(); *instr.mutable_literal() = Literal::CreateR1U8(tag)->ToProto(); @@ -704,7 +747,7 @@ XlaOp XlaBuilder::Select(const XlaOp& pred, const XlaOp& on_true, } XlaOp XlaBuilder::Tuple(tensorflow::gtl::ArraySlice elements) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; std::vector operand_shape_ptrs; TF_ASSIGN_OR_RETURN(const auto& operand_shapes, GetOperandShapes(elements)); @@ -718,7 +761,7 @@ XlaOp XlaBuilder::Tuple(tensorflow::gtl::ArraySlice elements) { } XlaOp XlaBuilder::GetTupleElement(const XlaOp& tuple_data, int64 index) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& tuple_shape, GetShape(tuple_data)); if (!ShapeUtil::IsTuple(tuple_shape)) { @@ -767,7 +810,7 @@ XlaOp XlaBuilder::Lt(const XlaOp& lhs, const XlaOp& rhs, } XlaOp XlaBuilder::Dot(const XlaOp& lhs, const XlaOp& rhs) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); DotDimensionNumbers dimension_numbers; @@ -780,7 +823,7 @@ XlaOp XlaBuilder::Dot(const XlaOp& lhs, const XlaOp& rhs) { XlaOp XlaBuilder::DotGeneral(const XlaOp& lhs, const XlaOp& rhs, const DotDimensionNumbers& dimension_numbers) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -859,7 +902,7 @@ XlaOp XlaBuilder::ConvWithGeneralDimensions( const XlaOp& lhs, const XlaOp& rhs, tensorflow::gtl::ArraySlice window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -905,7 +948,7 @@ XlaOp XlaBuilder::ConvGeneralDilated( tensorflow::gtl::ArraySlice lhs_dilation, tensorflow::gtl::ArraySlice rhs_dilation, const ConvolutionDimensionNumbers& dimension_numbers) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -992,7 +1035,7 @@ StatusOr XlaBuilder::MakeWindow( XlaOp XlaBuilder::Fft(const XlaOp& operand, const FftType fft_type, const tensorflow::gtl::ArraySlice fft_length) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1009,23 +1052,69 @@ XlaOp XlaBuilder::Fft(const XlaOp& operand, const FftType fft_type, } XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; if (!LayoutUtil::HasLayout(shape)) { return InvalidArgument("Given shape to Infeed must have a layout"); } - *instr.mutable_shape() = shape; + const Shape infeed_instruction_shape = + ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeTokenShape()}); + *instr.mutable_shape() = infeed_instruction_shape; instr.set_infeed_config(config); - return AddInstruction(std::move(instr), HloOpcode::kInfeed); + + if (ShapeUtil::IsArray(shape) && sharding() && + sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) { + // TODO(b/110793772): Support tiled array-shaped infeeds. + return InvalidArgument( + "Tiled sharding is not yet supported for array-shaped infeeds"); + } + + if (sharding() && + sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) { + return InvalidArgument( + "Replicated sharding is not yet supported for infeeds"); + } + + // The sharding is set by the client according to the data tuple shape. + // However, the shape of the infeed instruction is a tuple containing the + // data and a token. For tuple sharding type, the sharding must be changed + // to accommodate the token. + XlaOp infeed; + if (sharding() && + sharding()->type() == OpSharding::Type::OpSharding_Type_TUPLE) { + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + OpSharding infeed_instruction_sharding = *sharding(); + // Arbitrarily assign the token to device 0. + *infeed_instruction_sharding.add_tuple_shardings() = + sharding_builder::AssignDevice(0); + XlaScopedShardingAssignment scoped_sharding(this, + infeed_instruction_sharding); + TF_ASSIGN_OR_RETURN(infeed, + AddInstruction(std::move(instr), HloOpcode::kInfeed)); + } else { + TF_ASSIGN_OR_RETURN(infeed, + AddInstruction(std::move(instr), HloOpcode::kInfeed)); + } + + // The infeed instruction produces a tuple of the infed data and a token + // type. Return XLA op containing the data. + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + HloInstructionProto infeed_data; + *infeed_data.mutable_shape() = shape; + infeed_data.set_tuple_index(0); + return AddInstruction(std::move(infeed_data), HloOpcode::kGetTupleElement, + {infeed}); }); } void XlaBuilder::Outfeed(const XlaOp& operand, const Shape& shape_with_layout, const string& outfeed_config) { - NoteErrorOrReturn([&]() -> StatusOr { + ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; - *instr.mutable_shape() = ShapeUtil::MakeNil(); + *instr.mutable_shape() = ShapeUtil::MakeTokenShape(); // Check and set outfeed shape. if (!LayoutUtil::HasLayout(shape_with_layout)) { @@ -1042,14 +1131,33 @@ void XlaBuilder::Outfeed(const XlaOp& operand, const Shape& shape_with_layout, instr.set_outfeed_config(outfeed_config); - return AddInstruction(std::move(instr), HloOpcode::kOutfeed, {operand}); + TF_RETURN_IF_ERROR( + AddInstruction(std::move(instr), HloOpcode::kOutfeed, {operand}) + .status()); + + // The outfeed instruction produces a token. However, existing users expect + // a nil shape (empty tuple). This should only be relevant if the outfeed is + // the root of a computation. + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + HloInstructionProto tuple_instr; + *tuple_instr.mutable_shape() = ShapeUtil::MakeNil(); + + // The dummy tuple should have no sharding. + { + XlaScopedShardingAssignment scoped_sharding(this, OpSharding()); + TF_ASSIGN_OR_RETURN( + XlaOp empty_tuple, + AddInstruction(std::move(tuple_instr), HloOpcode::kTuple, {})); + return empty_tuple; + } }); } XlaOp XlaBuilder::CustomCall(const string& call_target_name, tensorflow::gtl::ArraySlice operands, const Shape& shape) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; if (tensorflow::str_util::StartsWith(call_target_name, "$")) { return InvalidArgument( @@ -1066,7 +1174,7 @@ XlaOp XlaBuilder::CustomCall(const string& call_target_name, XlaOp XlaBuilder::HostCompute(tensorflow::gtl::ArraySlice operands, const string& channel_name, int64 cost_estimate_ns, const Shape& shape) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; *instr.mutable_shape() = shape; instr.set_channel_name(channel_name); @@ -1120,11 +1228,9 @@ XlaOp XlaBuilder::Or(const XlaOp& lhs, const XlaOp& rhs, return BinaryOp(HloOpcode::kOr, lhs, rhs, broadcast_dimensions); } -// TODO(b/65209188): Create a dedicated lowering for Xor. XlaOp XlaBuilder::Xor(const XlaOp& lhs, const XlaOp& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { - return Or(And(Not(lhs), rhs, broadcast_dimensions), - And(lhs, Not(rhs), broadcast_dimensions)); + return BinaryOp(HloOpcode::kXor, lhs, rhs, broadcast_dimensions); } XlaOp XlaBuilder::Not(const XlaOp& operand) { @@ -1223,7 +1329,7 @@ XlaOp XlaBuilder::IsFinite(const XlaOp& operand) { XlaOp XlaBuilder::Transpose(const XlaOp& operand, tensorflow::gtl::ArraySlice permutation) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1238,7 +1344,7 @@ XlaOp XlaBuilder::Transpose(const XlaOp& operand, XlaOp XlaBuilder::Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1251,13 +1357,25 @@ XlaOp XlaBuilder::Rev(const XlaOp& operand, }); } -XlaOp XlaBuilder::Sort(const XlaOp& operand) { - return UnaryOp(HloOpcode::kSort, operand); -} - -XlaOp XlaBuilder::SqrtF32(const XlaOp& operand) { - return BinaryOp(HloOpcode::kPower, operand, ConstantR0(0.5), - /*broadcast_dimensions=*/{}); +XlaOp XlaBuilder::Sort(XlaOp keys, tensorflow::gtl::optional values) { + return ReportErrorOrReturn([&]() -> StatusOr { + HloInstructionProto instr; + std::vector operand_shape_ptrs; + TF_ASSIGN_OR_RETURN(const Shape& keys_shape, GetShape(keys)); + operand_shape_ptrs.push_back(&keys_shape); + Shape values_shape; + if (values.has_value()) { + TF_ASSIGN_OR_RETURN(values_shape, GetShape(*values)); + operand_shape_ptrs.push_back(&values_shape); + } + TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), + ShapeInference::InferVariadicOpShape( + HloOpcode::kSort, operand_shape_ptrs)); + return values.has_value() + ? AddInstruction(std::move(instr), HloOpcode::kSort, + {keys, *values}) + : AddInstruction(std::move(instr), HloOpcode::kSort, {keys}); + }); } XlaOp XlaBuilder::Pow(const XlaOp& lhs, const XlaOp& rhs, @@ -1267,7 +1385,7 @@ XlaOp XlaBuilder::Pow(const XlaOp& lhs, const XlaOp& rhs, XlaOp XlaBuilder::ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1279,7 +1397,7 @@ XlaOp XlaBuilder::ConvertElementType(const XlaOp& operand, XlaOp XlaBuilder::BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1290,16 +1408,6 @@ XlaOp XlaBuilder::BitcastConvertType(const XlaOp& operand, }); } -XlaOp XlaBuilder::SquareF32(const XlaOp& operand) { - return BinaryOp(HloOpcode::kPower, operand, ConstantR0(2.0), - /*broadcast_dimensions=*/{}); -} - -XlaOp XlaBuilder::ReciprocalF32(const XlaOp& operand) { - return BinaryOp(HloOpcode::kPower, operand, ConstantR0(-1.0), - /*broadcast_dimensions=*/{}); -} - XlaOp XlaBuilder::Neg(const XlaOp& operand) { return UnaryOp(HloOpcode::kNegate, operand); } @@ -1313,13 +1421,12 @@ XlaOp XlaBuilder::Map(tensorflow::gtl::ArraySlice operands, const XlaComputation& computation, tensorflow::gtl::ArraySlice dimensions, tensorflow::gtl::ArraySlice static_operands) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { if (!static_operands.empty()) { return Unimplemented("static_operands is not supported in Map"); } HloInstructionProto instr; - std::vector operand_shape_ptrs; TF_ASSIGN_OR_RETURN(const auto& operand_shapes, GetOperandShapes(operands)); c_transform(operand_shapes, std::back_inserter(operand_shape_ptrs), @@ -1331,16 +1438,32 @@ XlaOp XlaBuilder::Map(tensorflow::gtl::ArraySlice operands, ShapeInference::InferMapShape(operand_shape_ptrs, called_program_shape, dimensions)); + const Shape& output_shape = instr.shape(); + const int64 output_rank = ShapeUtil::Rank(output_shape); AddCalledComputation(computation, &instr); + std::vector new_operands(operands.begin(), operands.end()); + for (XlaOp& new_operand : new_operands) { + TF_ASSIGN_OR_RETURN(Shape shape, GetShape(new_operand)); + const int64 rank = ShapeUtil::Rank(shape); + if (rank != output_rank) { + TF_ASSIGN_OR_RETURN(new_operand, + InDimBroadcast(output_shape, new_operand, {})); + TF_ASSIGN_OR_RETURN(shape, GetShape(new_operand)); + } + if (!ShapeUtil::SameDimensions(output_shape, shape)) { + TF_ASSIGN_OR_RETURN(new_operand, + AddBroadcastSequence(output_shape, new_operand)); + } + } - return AddInstruction(std::move(instr), HloOpcode::kMap, operands); + return AddInstruction(std::move(instr), HloOpcode::kMap, new_operands); }); } XlaOp XlaBuilder::RngOp(RandomDistribution distribution, tensorflow::gtl::ArraySlice parameters, const Shape& shape) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; // Check the number of parameters per RNG distribution. @@ -1378,7 +1501,7 @@ XlaOp XlaBuilder::RngUniform(const XlaOp& a, const XlaOp& b, XlaOp XlaBuilder::While(const XlaComputation& condition, const XlaComputation& body, const XlaOp& init) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; // Infer shape. @@ -1400,7 +1523,7 @@ XlaOp XlaBuilder::While(const XlaComputation& condition, XlaOp XlaBuilder::Gather(const XlaOp& input, const XlaOp& gather_indices, const GatherDimensionNumbers& dimension_numbers, tensorflow::gtl::ArraySlice window_bounds) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& input_shape, GetShape(input)); @@ -1425,7 +1548,7 @@ XlaOp XlaBuilder::Conditional(const XlaOp& predicate, const XlaOp& true_operand, const XlaComputation& true_computation, const XlaOp& false_operand, const XlaComputation& false_computation) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& predicate_shape, GetShape(predicate)); @@ -1457,13 +1580,14 @@ XlaOp XlaBuilder::Reduce( const XlaOp& operand, const XlaOp& init_value, const XlaComputation& computation, tensorflow::gtl::ArraySlice dimensions_to_reduce) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN(const Shape& init_shape, GetShape(init_value)); TF_ASSIGN_OR_RETURN(const ProgramShape& called_program_shape, computation.GetProgramShape()); + TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), ShapeInference::InferReduceShape( operand_shape, init_shape, dimensions_to_reduce, @@ -1482,7 +1606,7 @@ XlaOp XlaBuilder::Reduce( XlaOp XlaBuilder::ReduceAll(const XlaOp& operand, const XlaOp& init_value, const XlaComputation& computation) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); std::vector all_dimnos(ShapeUtil::Rank(operand_shape)); std::iota(all_dimnos.begin(), all_dimnos.end(), 0); @@ -1495,7 +1619,7 @@ XlaOp XlaBuilder::ReduceWindow( const XlaComputation& computation, tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, Padding padding) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1518,7 +1642,7 @@ XlaOp XlaBuilder::ReduceWindowWithGeneralPadding( tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, tensorflow::gtl::ArraySlice> padding) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1542,7 +1666,7 @@ XlaOp XlaBuilder::ReduceWindowWithGeneralPadding( XlaOp XlaBuilder::BatchNormTraining(const XlaOp& operand, const XlaOp& scale, const XlaOp& offset, float epsilon, int64 feature_index) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1565,7 +1689,7 @@ XlaOp XlaBuilder::BatchNormInference(const XlaOp& operand, const XlaOp& scale, const XlaOp& offset, const XlaOp& mean, const XlaOp& variance, float epsilon, int64 feature_index) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1590,7 +1714,7 @@ XlaOp XlaBuilder::BatchNormGrad(const XlaOp& operand, const XlaOp& scale, const XlaOp& batch_mean, const XlaOp& batch_var, const XlaOp& grad_output, float epsilon, int64 feature_index) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1611,15 +1735,17 @@ XlaOp XlaBuilder::BatchNormGrad(const XlaOp& operand, const XlaOp& scale, }); } -XlaOp XlaBuilder::CrossReplicaSum(const XlaOp& operand) { - return NoteErrorOrReturn([&]() -> StatusOr { +XlaOp XlaBuilder::CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids) { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& shape, GetShape(operand)); const Shape& scalar_shape = ShapeUtil::MakeShape(shape.element_type(), {}); auto b = CreateSubBuilder("sum"); b->Add(b->Parameter(/*parameter_number=*/0, scalar_shape, "x"), b->Parameter(/*parameter_number=*/1, scalar_shape, "y")); TF_ASSIGN_OR_RETURN(auto computation, b->Build()); - return CrossReplicaSum(operand, computation, /*replica_group_ids=*/{}, + return CrossReplicaSum(operand, computation, replica_group_ids, /*channel_id=*/tensorflow::gtl::nullopt); }); } @@ -1628,10 +1754,9 @@ XlaOp XlaBuilder::CrossReplicaSum( const XlaOp& operand, const XlaComputation& computation, tensorflow::gtl::ArraySlice replica_group_ids, const tensorflow::gtl::optional& channel_id) { - return NoteErrorOrReturn([&]() -> StatusOr { - if (!replica_group_ids.empty() || channel_id.has_value()) { - return Unimplemented( - "replica_group_ids and channel_id and is not supported in AllReduce"); + return ReportErrorOrReturn([&]() -> StatusOr { + if (channel_id.has_value()) { + return Unimplemented("channel_id is not supported in AllReduce"); } HloInstructionProto instr; @@ -1639,6 +1764,9 @@ XlaOp XlaBuilder::CrossReplicaSum( TF_ASSIGN_OR_RETURN( *instr.mutable_shape(), ShapeInference::InferCrossReplicaSumShape({&operand_shape})); + for (int64 replica_group_id : replica_group_ids) { + instr.add_replica_group_ids(replica_group_id); + } AddCalledComputation(computation, &instr); @@ -1653,7 +1781,7 @@ XlaOp XlaBuilder::SelectAndScatter( tensorflow::gtl::ArraySlice window_strides, Padding padding, const XlaOp& source, const XlaOp& init_value, const XlaComputation& scatter) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); return SelectAndScatterWithGeneralPadding( operand, select, window_dimensions, window_strides, @@ -1670,7 +1798,7 @@ XlaOp XlaBuilder::SelectAndScatterWithGeneralPadding( tensorflow::gtl::ArraySlice> padding, const XlaOp& source, const XlaOp& init_value, const XlaComputation& scatter) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); @@ -1698,7 +1826,7 @@ XlaOp XlaBuilder::SelectAndScatterWithGeneralPadding( XlaOp XlaBuilder::ReducePrecision(const XlaOp& operand, const int exponent_bits, const int mantissa_bits) { - return NoteErrorOrReturn([&]() -> StatusOr { + return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), @@ -1712,17 +1840,25 @@ XlaOp XlaBuilder::ReducePrecision(const XlaOp& operand, const int exponent_bits, } void XlaBuilder::Send(const XlaOp& operand, const ChannelHandle& handle) { - NoteErrorOrReturn([&]() -> StatusOr { - HloInstructionProto instr; + ReportErrorOrReturn([&]() -> StatusOr { + // Send HLO takes two operands: a data operand and a token. Generate the + // token to pass into the send. + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + HloInstructionProto token_instr; + *token_instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + TF_ASSIGN_OR_RETURN(XlaOp token, AddInstruction(std::move(token_instr), + HloOpcode::kAfterAll, {})); // Send instruction produces a tuple of {aliased operand, U32 context}. + HloInstructionProto send_instr; TF_ASSIGN_OR_RETURN(const Shape& shape, GetShape(operand)); - *instr.mutable_shape() = + *send_instr.mutable_shape() = ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeShape(U32, {})}); - instr.set_channel_id(handle.handle()); - TF_ASSIGN_OR_RETURN( - XlaOp send, - AddInstruction(std::move(instr), HloOpcode::kSend, {operand})); + send_instr.set_channel_id(handle.handle()); + TF_ASSIGN_OR_RETURN(XlaOp send, + AddInstruction(std::move(send_instr), HloOpcode::kSend, + {operand, token})); HloInstructionProto send_done_instr; *send_done_instr.mutable_shape() = ShapeUtil::MakeNil(); @@ -1733,15 +1869,23 @@ void XlaBuilder::Send(const XlaOp& operand, const ChannelHandle& handle) { } XlaOp XlaBuilder::Recv(const Shape& shape, const ChannelHandle& handle) { - return NoteErrorOrReturn([&]() -> StatusOr { - HloInstructionProto instr; + return ReportErrorOrReturn([&]() -> StatusOr { + // Recv HLO takes a single token operand. Generate the token to pass into + // the Recv and RecvDone instructions. + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + HloInstructionProto token_instr; + *token_instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + TF_ASSIGN_OR_RETURN(XlaOp token, AddInstruction(std::move(token_instr), + HloOpcode::kAfterAll, {})); // Recv instruction produces a tuple of {receive buffer, U32 context}. - *instr.mutable_shape() = + HloInstructionProto recv_instr; + *recv_instr.mutable_shape() = ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeShape(U32, {})}); - instr.set_channel_id(handle.handle()); - TF_ASSIGN_OR_RETURN(XlaOp recv, - AddInstruction(std::move(instr), HloOpcode::kRecv, {})); + recv_instr.set_channel_id(handle.handle()); + TF_ASSIGN_OR_RETURN(XlaOp recv, AddInstruction(std::move(recv_instr), + HloOpcode::kRecv, {token})); HloInstructionProto recv_done_instr; *recv_done_instr.mutable_shape() = shape; @@ -1988,9 +2132,497 @@ StatusOr XlaBuilder::LookUpInstruction( return &instructions_[op.handle()]; } -XlaOp XlaBuilder::UnimplementedOp() { - NoteError(Unimplemented("Op not implemented")); - return {}; +// Enqueues a "retrieve parameter value" instruction for a parameter that was +// passed to the computation. +XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, + const string& name) { + return builder->Parameter(parameter_number, shape, name); +} + +// Enqueues a constant with the value of the given literal onto the +// computation. +XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal) { + return builder->ConstantLiteral(literal); +} + +XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes) { + return operand.builder()->Broadcast(operand, broadcast_sizes); +} + +XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions) { + return operand.builder()->BroadcastInDim(operand, shape, + broadcast_dimensions); +} + +XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config) { + return operand.builder()->Pad(operand, padding_value, padding_config); +} + +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes) { + return operand.builder()->Reshape(operand, dimensions, new_sizes); +} + +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes) { + return operand.builder()->Reshape(operand, new_sizes); +} + +XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions) { + return operand.builder()->Collapse(operand, dimensions); +} + +XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides) { + return operand.builder()->Slice(operand, start_indices, limit_indices, + strides); +} + +XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno) { + return operand.builder()->SliceInDim(operand, start_index, limit_index, + stride, dimno); +} + +XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes) { + return operand.builder()->DynamicSlice(operand, start_indices, slice_sizes); +} + +XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices) { + return operand.builder()->DynamicUpdateSlice(operand, update, start_indices); +} + +XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + int64 dimension) { + return builder->ConcatInDim(operands, dimension); +} + +void Trace(const string& tag, const XlaOp& operand) { + return operand.builder()->Trace(tag, operand); +} + +XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false) { + return pred.builder()->Select(pred, on_true, on_false); +} + +XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements) { + return builder->Tuple(elements); +} + +XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index) { + return tuple_data.builder()->GetTupleElement(tuple_data, index); +} + +XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Eq(lhs, rhs, broadcast_dimensions); +} + +XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Ne(lhs, rhs, broadcast_dimensions); +} + +XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Ge(lhs, rhs, broadcast_dimensions); +} + +XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Gt(lhs, rhs, broadcast_dimensions); +} + +XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Lt(lhs, rhs, broadcast_dimensions); +} + +XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Le(lhs, rhs, broadcast_dimensions); +} + +XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs) { + return lhs.builder()->Dot(lhs, rhs); +} + +XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers) { + return lhs.builder()->DotGeneral(lhs, rhs, dimension_numbers); +} + +XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding) { + return lhs.builder()->Conv(lhs, rhs, window_strides, padding); +} + +XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding) { + return lhs.builder()->ConvWithGeneralPadding(lhs, rhs, window_strides, + padding); +} + +XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers) { + return lhs.builder()->ConvWithGeneralDimensions(lhs, rhs, window_strides, + padding, dimension_numbers); +} + +XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers) { + return lhs.builder()->ConvGeneral(lhs, rhs, window_strides, padding, + dimension_numbers); +} + +XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers) { + return lhs.builder()->ConvGeneralDilated(lhs, rhs, window_strides, padding, + lhs_dilation, rhs_dilation, + dimension_numbers); +} + +XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length) { + return operand.builder()->Fft(operand, fft_type, fft_length); +} + +XlaOp Infeed(XlaBuilder* builder, const Shape& shape, const string& config) { + return builder->Infeed(shape, config); +} + +void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config) { + return operand.builder()->Outfeed(operand, shape_with_layout, outfeed_config); +} + +XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands) { + return builder->Call(computation, operands); +} + +XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape) { + return builder->CustomCall(call_target_name, operands, shape); +} + +XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape) { + return builder->HostCompute(operands, channel_name, cost_estimate_ns, shape); +} + +XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return real.builder()->Complex(real, imag, broadcast_dimensions); +} + +XlaOp Conj(const XlaOp& operand) { return operand.builder()->Conj(operand); } + +XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Add(lhs, rhs, broadcast_dimensions); +} + +XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Sub(lhs, rhs, broadcast_dimensions); +} + +XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Mul(lhs, rhs, broadcast_dimensions); +} + +XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Div(lhs, rhs, broadcast_dimensions); +} + +XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Rem(lhs, rhs, broadcast_dimensions); +} + +XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Max(lhs, rhs, broadcast_dimensions); +} + +XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Min(lhs, rhs, broadcast_dimensions); +} + +XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->And(lhs, rhs, broadcast_dimensions); +} + +XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Or(lhs, rhs, broadcast_dimensions); +} + +XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Xor(lhs, rhs, broadcast_dimensions); +} + +XlaOp Not(const XlaOp& operand) { return operand.builder()->Not(operand); } + +XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->ShiftLeft(lhs, rhs, broadcast_dimensions); +} + +XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->ShiftRightArithmetic(lhs, rhs, broadcast_dimensions); +} + +XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->ShiftRightLogical(lhs, rhs, broadcast_dimensions); +} + +XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce) { + return operand.builder()->Reduce(operand, init_value, computation, + dimensions_to_reduce); +} + +XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation) { + return operand.builder()->ReduceAll(operand, init_value, computation); +} + +XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding) { + return operand.builder()->ReduceWindow(operand, init_value, computation, + window_dimensions, window_strides, + padding); +} + +XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding) { + return operand.builder()->ReduceWindowWithGeneralPadding( + operand, init_value, computation, window_dimensions, window_strides, + padding); +} + +XlaOp CrossReplicaSum(const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids) { + return operand.builder()->CrossReplicaSum(operand, replica_group_ids); +} + +XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids, + const tensorflow::gtl::optional& channel_id) { + return operand.builder()->CrossReplicaSum(operand, computation, + replica_group_ids, channel_id); +} + +XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, const XlaComputation& scatter) { + return operand.builder()->SelectAndScatter(operand, select, window_dimensions, + window_strides, padding, source, + init_value, scatter); +} + +XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter) { + return operand.builder()->SelectAndScatterWithGeneralPadding( + operand, select, window_dimensions, window_strides, padding, source, + init_value, scatter); +} + +XlaOp Abs(const XlaOp& operand) { return operand.builder()->Abs(operand); } + +XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return y.builder()->Atan2(y, x, broadcast_dimensions); +} + +XlaOp Exp(const XlaOp& operand) { return operand.builder()->Exp(operand); } + +XlaOp Expm1(const XlaOp& operand) { return operand.builder()->Expm1(operand); } + +XlaOp Floor(const XlaOp& operand) { return operand.builder()->Floor(operand); } + +XlaOp Ceil(const XlaOp& operand) { return operand.builder()->Ceil(operand); } + +XlaOp Round(const XlaOp& operand) { return operand.builder()->Round(operand); } + +XlaOp Log(const XlaOp& operand) { return operand.builder()->Log(operand); } + +XlaOp Log1p(const XlaOp& operand) { return operand.builder()->Log1p(operand); } + +XlaOp Sign(const XlaOp& operand) { return operand.builder()->Sign(operand); } + +XlaOp Clz(const XlaOp& operand) { return operand.builder()->Clz(operand); } + +XlaOp Cos(const XlaOp& operand) { return operand.builder()->Cos(operand); } + +XlaOp Sin(const XlaOp& operand) { return operand.builder()->Sin(operand); } + +XlaOp Tanh(const XlaOp& operand) { return operand.builder()->Tanh(operand); } + +XlaOp Real(const XlaOp& operand) { return operand.builder()->Real(operand); } + +XlaOp Imag(const XlaOp& operand) { return operand.builder()->Imag(operand); } + +XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return lhs.builder()->Pow(lhs, rhs, broadcast_dimensions); +} + +XlaOp IsFinite(const XlaOp& operand) { + return operand.builder()->IsFinite(operand); +} + +XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type) { + return operand.builder()->ConvertElementType(operand, new_element_type); +} + +XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type) { + return operand.builder()->BitcastConvertType(operand, new_element_type); +} + +XlaOp Neg(const XlaOp& operand) { return operand.builder()->Neg(operand); } + +XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation) { + return operand.builder()->Transpose(operand, permutation); +} + +XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions) { + return operand.builder()->Rev(operand, dimensions); +} + +XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values) { + return keys.builder()->Sort(keys, std::move(values)); +} + +XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max) { + return min.builder()->Clamp(min, operand, max); +} + +XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands) { + return builder->Map(operands, computation, dimensions, static_operands); +} + +XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape) { + return mu.builder()->RngNormal(mu, sigma, shape); +} + +XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape) { + return a.builder()->RngUniform(a, b, shape); +} + +XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init) { + return init.builder()->While(condition, body, init); +} + +XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation) { + return predicate.builder()->Conditional(predicate, true_operand, + true_computation, false_operand, + false_computation); +} + +XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits) { + return operand.builder()->ReducePrecision(operand, exponent_bits, + mantissa_bits); +} + +XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds) { + return input.builder()->Gather(input, gather_indices, dimension_numbers, + window_bounds); +} + +void Send(const XlaOp& operand, const ChannelHandle& handle) { + return operand.builder()->Send(operand, handle); +} + +XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle) { + return builder->Recv(shape, handle); +} + +XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index) { + return operand.builder()->BatchNormTraining(operand, scale, offset, epsilon, + feature_index); +} + +XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index) { + return operand.builder()->BatchNormInference( + operand, scale, offset, mean, variance, epsilon, feature_index); +} + +XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index) { + return operand.builder()->BatchNormGrad(operand, scale, batch_mean, batch_var, + grad_output, epsilon, feature_index); } } // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 532cae014848e17b24ee720a3c3dc5f99c89dfe5..274aba8a31072db1e821b1834178a85288d64521 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include #include #include "tensorflow/compiler/xla/client/padding.h" @@ -46,17 +47,23 @@ class XlaBuilder; // instruction as an operand. class XlaOp { public: - XlaOp() : handle_(0), builder_(nullptr) {} - ~XlaOp() {} + XlaOp() : handle_(-1), builder_(nullptr) { + static_assert(std::is_trivially_destructible::value, + "XlaOp should be trivially destructible"); + } + ~XlaOp() = default; - const XlaBuilder* builder() const { return builder_; } + XlaBuilder* builder() const { return builder_; } - bool operator==(const XlaOp& rhs) const { - return handle_ == rhs.handle_ && builder_ == rhs.builder_; - } + // Returns true if the XlaOp represents valid, non-erroneous value. + bool valid() const { return handle_ >= 0; } + + // Returns true if the XlaOp was created by the XlaOp() constructor and + // not returned by a builder. + bool IsUninitialized() const { return builder_ == nullptr; } - bool operator!=(const XlaOp& rhs) const { - return handle_ != rhs.handle_ || builder_ != rhs.builder_; + bool IsIdenticalTo(const XlaOp& rhs) const { + return handle_ == rhs.handle_ && builder_ == rhs.builder_; } friend std::ostream& operator<<(std::ostream& out, const XlaOp& op) { @@ -65,6 +72,7 @@ class XlaOp { } private: + explicit XlaOp(XlaBuilder* builder) : handle_(-1), builder_(builder) {} XlaOp(int64 handle, XlaBuilder* builder) : handle_(handle), builder_(builder) {} @@ -72,10 +80,38 @@ class XlaOp { friend class XlaBuilder; + // < 0 means "invalid handle". int64 handle_; - XlaBuilder* builder_; // Not owned. + + // Not owned. Non-null for any handle returned by XlaBuilder, even if the + // handle is invalid. + XlaBuilder* builder_; }; +// Arithmetic operator overloads for the XlaOp type. +XlaOp operator-(const XlaOp& x); +XlaOp operator+(const XlaOp& x, const XlaOp& y); +XlaOp operator-(const XlaOp& x, const XlaOp& y); +XlaOp operator*(const XlaOp& x, const XlaOp& y); +XlaOp operator/(const XlaOp& x, const XlaOp& y); +XlaOp operator%(const XlaOp& x, const XlaOp& y); + +// Bitwise operator overloads for the XlaOp type. +XlaOp operator~(const XlaOp& x); +XlaOp operator&(const XlaOp& x, const XlaOp& y); +XlaOp operator|(const XlaOp& x, const XlaOp& y); +XlaOp operator^(const XlaOp& x, const XlaOp& y); +XlaOp operator<<(const XlaOp& x, const XlaOp& y); +// Performs a right arithmetic shift if 'x' is a signed type, otherwise performs +// a right logical shift. +XlaOp operator>>(const XlaOp& x, const XlaOp& y); + +// We don't overload the relational operators (==, !=, <, <=, >, >=) because the +// semantics might be surprising since their result types are usually 'bool'. +// Further programmers may expect == to be a structural equality. +// We also choose not to overload any of the mutating operators (e.g., +=, -=) +// because the semantics might be misleading ā€” XLA computations are immutable. + // A convenient interface for building up computations. // // Thread-compatible. @@ -122,6 +158,93 @@ class XlaBuilder { die_immediately_on_error_ = enabled; } + // Default dimension numbers used for a 2D convolution. + static constexpr int64 kConvBatchDimension = 0; + static constexpr int64 kConvFeatureDimension = 1; + static constexpr int64 kConvFirstSpatialDimension = 2; + static constexpr int64 kConvSecondSpatialDimension = 3; + static constexpr int64 kConvKernelOutputDimension = 0; + static constexpr int64 kConvKernelInputDimension = 1; + static constexpr int64 kConvKernelFirstSpatialDimension = 2; + static constexpr int64 kConvKernelSecondSpatialDimension = 3; + + // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for + // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for + // the kernel operand + // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. + static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( + int num_spatial_dims = 2); + + // Returns an error if the convolution dimension numbers have conflicts. + static Status Validate(const ConvolutionDimensionNumbers& dnum); + + // Returns a new XlaBuilder whose resultant Computation is used only by this + // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error + // behavior as the parent. + std::unique_ptr CreateSubBuilder(const string& computation_name); + + // Builds the computation with the requested operations, or returns a non-ok + // status. Note that all ops that have been enqueued will be moved to the + // computation being returned. + StatusOr Build(); + + // Builds the computation with the requested operations, or notes an error in + // the parent XlaBuilder and returns an empty computation if building failed. + // This function is intended to be used where the returned XlaComputation is + // only used by the parent XlaBuilder and hence further operation on the + // returned XlaComputation will simply be error'ed out if an error occurred + // while building this computation. If the built computation is to be used by + // a XlaBuilder other than the parent XlaBuilder then Build() should be used + // instead. + XlaComputation BuildAndNoteError(); + + // Returns a subgraph that roots on the given root. If the root is not a + // compile-time constant (see `IsConstant`), returns an error. + // + // This will copy the needed ops/computations to the subgraph. + StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; + + // Returns the first error that was encountered while building the + // computation. When an error is encountered, by default we return a vacuous + // XlaOp and inform the user of the error that occurred while + // building the computation when they make a final call to Build(). + // + // See also set_die_immediately_on_error(). + Status first_error() const { return first_error_; } + + // Returns the shape of the given op. + StatusOr GetShape(const XlaOp& op) const; + + // Returns the (inferred) result for the current computation's shape. + StatusOr GetProgramShape() const; + + // Reports an error to the builder, by + // * storing it internally and capturing a backtrace if it's the first error + // (this deferred value will be produced on the call to + // Build()/GetShape()/...) + // * dying if die_immediately_on_error_ is true. + // Returns an XlaOp with an invalid handle but a valid builder. This value can + // be returned in place of a value in APIs that return an XlaOp. + XlaOp ReportError(const Status& error); + + // A helper function that converts a StatusOr into an XlaOp. + // If the Status was an error, reports the error to builder and returns an + // invalid XlaOp handle. + XlaOp ReportErrorOrReturn(const StatusOr& op); + + // A helper function that runs a function that returns a StatusOr and + // returns an XlaOp. + XlaOp ReportErrorOrReturn(const std::function()>& op_creator); + + // Returns true if 'operand' is a compile-time constant. A compile-time + // constant does not depend on any parameters, or on stateful operators such + // as `RngNormal` or `Infeed`. + // + // This tests whether a computation is a compile-time constant without + // evaluating the computation. + StatusOr IsConstant(const XlaOp& operand) const; + + private: // Enqueues a "retrieve parameter value" instruction for a parameter that was // passed to the computation. XlaOp Parameter(int64 parameter_number, const Shape& shape, @@ -194,6 +317,27 @@ class XlaBuilder { XlaOp Broadcast(const XlaOp& operand, tensorflow::gtl::ArraySlice broadcast_sizes); + // Performs in-dimension-style broadcast. + // + // Operand specifies the input to be broadcast. "shape" is expected output + // shape. "broadcast_dimensions" are the dimensions to be broadcasting into. + // Dimension numbers in broadcast_dimensions map to individual dimensions + // of the operand, and specify what dimension of the output shape they + // should be broadcast. + // e.g. + // Say operand = [1, 2], i.e., a 1D tensor with 2 elements. + // and dimension of shape is [2,2]. + // Specifying {1} as brodcast_dimension will generate output + // [1 , 2] + // [1 , 2] + // On the other hand, specifying {0} as broadcast_dimension + // will generate output + // [1 , 1] + // [2 , 2] + XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + // Enqueues a pad operation onto the computation that pads the given value on // the edges as well as between the elements of the input. padding_config // specifies the padding amount for each dimension. @@ -342,26 +486,6 @@ class XlaBuilder { XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, const DotDimensionNumbers& dimension_numbers); - // Default dimension numbers used for a 2D convolution. - static constexpr int64 kConvBatchDimension = 0; - static constexpr int64 kConvFeatureDimension = 1; - static constexpr int64 kConvFirstSpatialDimension = 2; - static constexpr int64 kConvSecondSpatialDimension = 3; - static constexpr int64 kConvKernelOutputDimension = 0; - static constexpr int64 kConvKernelInputDimension = 1; - static constexpr int64 kConvKernelFirstSpatialDimension = 2; - static constexpr int64 kConvKernelSecondSpatialDimension = 3; - - // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for - // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for - // the kernel operand - // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. - static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( - int num_spatial_dims = 2); - - // Returns an error if the convolution dimension numbers have conflicts. - static Status Validate(const ConvolutionDimensionNumbers& dnum); - // Enqueues a convolution instruction onto the computation, which uses the // default convolution dimension numbers. XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, @@ -528,9 +652,12 @@ class XlaBuilder { tensorflow::gtl::ArraySlice window_strides, tensorflow::gtl::ArraySlice> padding); - // Returns the sum of the operand value across all replicas. All replicas - // supply one input to the sum and all replicas receive the resulting sum. - XlaOp CrossReplicaSum(const XlaOp& operand); + // Returns the sum of the operand value within each subgroup of replicas. All + // replicas supply one input to the sum and all replicas receive the resulting + // sum for each subgroup. + XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); // Enqueues an operation that do an AllReduce of the operand cross cores. Here // AllReduce means doing a reduction on the input operand cross cores and then @@ -624,16 +751,6 @@ class XlaBuilder { // Enqueues an imaginary-part instruction onto the computation. XlaOp Imag(const XlaOp& operand); - // Enqueues a float32 sqrt instruction onto the computation. - // (float32 is specified as there is an implicit float32 0.5f constant - // exponent). - XlaOp SqrtF32(const XlaOp& operand); - - // Enqueues a float32 square instruction onto the computation. - // (float32 is specified as there is an implicit float32 2.0f constant - // exponent). - XlaOp SquareF32(const XlaOp& operand); - // Enqueues a lhs^rhs computation onto the computation. XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions = {}); @@ -656,14 +773,6 @@ class XlaBuilder { XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); - // Enqueues a float32 reciprocal instruction onto the computation. - // (float32 is specified as there is an implicit float32 -1.0f constant - // exponent). - // - // TODO(b/34468990) axe F32 suffix, can be determined by reflecting on the - // shape of the operand. - XlaOp ReciprocalF32(const XlaOp& operand); - // Enqueues a negate instruction onto the computation. XlaOp Neg(const XlaOp& operand); @@ -678,7 +787,18 @@ class XlaBuilder { tensorflow::gtl::ArraySlice dimensions); // Enqueues a sort (as increasing order) instruction onto the computation. - XlaOp Sort(const XlaOp& operand); + // If only keys are provided: + // * The keys must be a rank-1 tensor (i.e. an array). + // * The result is a sorted array of keys. + // + // If both keys and values are provided: + // * The keys and the values must be rank-1 tensors with the same dimensions. + // The element types of the tensors may be different. + // * The result is a tuple that consists of a sorted array of keys as the + // first element, and an array with their corresponding values as the second + // element. + XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values = + tensorflow::gtl::nullopt); // Enqueues a clamp instruction onto the computation. XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); @@ -725,14 +845,6 @@ class XlaBuilder { // be the same as the given shape. XlaOp Recv(const Shape& shape, const ChannelHandle& handle); - // Returns true if 'operand' is a compile-time constant. A compile-time - // constant does not depend on any parameters, or on stateful operators such - // as `RngNormal` or `Infeed`. - // - // This tests whether a computation is a compile-time constant without - // evaluating the computation. - StatusOr IsConstant(const XlaOp& operand) const; - // Normalizes operand across spatial and batch dimensions for each feature. // // Returns a tuple (normalized, batch_mean, batch_var) where `normalized` @@ -771,47 +883,6 @@ class XlaBuilder { const XlaOp& grad_output, float epsilon, int64 feature_index); - // Returns a new XlaBuilder whose resultant Computation is used only by this - // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error - // behavior as the parent. - std::unique_ptr CreateSubBuilder(const string& computation_name); - - // Builds the computation with the requested operations, or returns a non-ok - // status. Note that all ops that have been enqueued will be moved to the - // computation being returned. - StatusOr Build(); - - // Builds the computation with the requested operations, or notes an error in - // the parent XlaBuilder and returns an empty computation if building failed. - // This function is intended to be used where the returned XlaComputation is - // only used by the parent XlaBuilder and hence further operation on the - // returned XlaComputation will simply be error'ed out if an error occurred - // while building this computation. If the built computation is to be used by - // a XlaBuilder other than the parent XlaBuilder then Build() should be used - // instead. - XlaComputation BuildAndNoteError(); - - // Returns a subgraph that roots on the given root. If the root is not a - // compile-time constant (see `IsConstant`), returns an error. - // - // This will copy the needed ops/computations to the subgraph. - StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; - - // Returns the first error that was encountered while building the - // computation. When an error is encountered, by default we return a vacuous - // XlaOp and inform the user of the error that occurred while - // building the computation when they make a final call to Build(). - // - // See also set_die_immediately_on_error(). - Status first_error() const { return first_error_; } - - // Returns the shape of the given op. - StatusOr GetShape(const XlaOp& op) const; - - // Returns the (inferred) result for the current computation's shape. - StatusOr GetProgramShape() const; - - private: StatusOr AddInstruction( HloInstructionProto&& instr, HloOpcode opcode, tensorflow::gtl::ArraySlice operands = {}); @@ -819,17 +890,6 @@ class XlaBuilder { void AddCalledComputation(const XlaComputation& computation, HloInstructionProto* instr); - // Notes that the error occurred by: - // * storing it internally and capturing a backtrace if it's the first error - // (this deferred value will be produced on the call to Build()) - // * dying if die_immediately_on_error_ is true - void NoteError(const Status& error); - - XlaOp NoteErrorOrReturn(const std::function()>& op_creator); - - // Helper method that creates an empty op and notes error. - XlaOp UnimplementedOp(); - StatusOr LookUpInstruction(const XlaOp& op) const; // Internal helper method that does the building for an arbitrary unary op. @@ -925,8 +985,962 @@ class XlaBuilder { bool die_immediately_on_error_ = false; XlaBuilder* parent_builder_{nullptr}; + + friend XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, + const Shape& shape, const string& name); + friend XlaOp ConstantLiteral(XlaBuilder* builder, + const LiteralSlice& literal); + template + friend XlaOp ConstantR0(XlaBuilder* builder, NativeT value); + template + friend XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); + friend XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values); + template + friend XlaOp ConstantR2( + XlaBuilder* builder, + std::initializer_list> values); + template + friend XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); + template + friend XlaOp ConstantFromArray(XlaBuilder* builder, + const Array& values); + template + friend XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); + template + friend XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); + template + friend XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); + template + friend XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); + template + friend XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); + template + friend XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + + template + friend XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + + friend XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + + friend XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + + friend XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + friend XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + friend XlaOp SliceInDim(const XlaOp& operand, int64 start_index, + int64 limit_index, int64 stride, int64 dimno); + + friend XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + + friend XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + + friend XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + int64 dimension); + + friend void Trace(const string& tag, const XlaOp& operand); + + friend XlaOp Select(const XlaOp& pred, const XlaOp& on_true, + const XlaOp& on_false); + friend XlaOp Tuple(XlaBuilder* builder, + tensorflow::gtl::ArraySlice elements); + friend XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + friend XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + friend XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneral( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config); + friend void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + friend XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + friend XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + friend XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + friend XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Conj(const XlaOp& operand); + friend XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Not(const XlaOp& operand); + friend XlaOp ShiftLeft( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + friend XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + friend XlaOp ReduceWindow( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + friend XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids, + const tensorflow::gtl::optional& channel_id); + friend XlaOp SelectAndScatter( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp Abs(const XlaOp& operand); + friend XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Exp(const XlaOp& operand); + friend XlaOp Expm1(const XlaOp& operand); + friend XlaOp Floor(const XlaOp& operand); + friend XlaOp Ceil(const XlaOp& operand); + friend XlaOp Round(const XlaOp& operand); + friend XlaOp Log(const XlaOp& operand); + friend XlaOp Log1p(const XlaOp& operand); + friend XlaOp Sign(const XlaOp& operand); + friend XlaOp Clz(const XlaOp& operand); + friend XlaOp Cos(const XlaOp& operand); + friend XlaOp Sin(const XlaOp& operand); + friend XlaOp Tanh(const XlaOp& operand); + friend XlaOp Real(const XlaOp& operand); + friend XlaOp Imag(const XlaOp& operand); + friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp IsFinite(const XlaOp& operand); + friend XlaOp ConvertElementType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp BitcastConvertType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp Neg(const XlaOp& operand); + friend XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + friend XlaOp Rev(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + friend XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values); + friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + friend XlaOp Map(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands); + friend XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, + const Shape& shape); + friend XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + friend XlaOp While(const XlaComputation& condition, + const XlaComputation& body, const XlaOp& init); + friend XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + friend XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + friend XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + friend void Send(const XlaOp& operand, const ChannelHandle& handle); + friend XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + friend XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + friend XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); +}; + +// RAII-style object: sets the current sharding assignment in builder on +// construction, and sets back to the previous assignment on destruction. +class XlaScopedShardingAssignment { + public: + XlaScopedShardingAssignment(xla::XlaBuilder* builder, + tensorflow::gtl::optional sharding) + : builder_(builder), prev_sharding_(builder->sharding()) { + SetSharding(sharding); + } + + XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; + XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = + delete; + + ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } + + private: + void SetSharding(const tensorflow::gtl::optional& sharding) { + if (sharding.has_value()) { + builder_->SetSharding(sharding.value()); + } else { + builder_->ClearSharding(); + } + } + + xla::XlaBuilder* const builder_; + tensorflow::gtl::optional prev_sharding_; }; +// Free functions for building XlaOps. The intention is that these will +// become the public API for building XlaOps rather than calling methods on +// XlaBuilder directly. + +// Enqueues a "retrieve parameter value" instruction for a parameter that was +// passed to the computation. +XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, + const string& name); + +// Enqueues a constant with the value of the given literal onto the +// computation. +XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal); + +// Enqueues a constant onto the computation. Methods are templated on the +// native host type (NativeT) which corresponds to a specific XLA +// PrimitiveType as given in the following table: +// +// Native Type PrimitiveType +// ----------------------------- +// bool PRED +// int32 S32 +// int64 S64 +// uint32 U32 +// uint64 U64 +// float F32 +// double F64 +// +// Note: not all primitive types defined in xla_data.proto have a +// corresponding native type yet. +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value); +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); +XlaOp ConstantR1(XlaBuilder* builder, const tensorflow::core::Bitmap& values); +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values); +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values); +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + +// Enqueues a rank one constant (XlaBuilder* builder, vector) onto the +// computation. The vector has size 'length' and every element has the value +// 'value'. +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + +// Adds dimensions to an array by duplicating the data in the array. +// +// The new dimensions are inserted on the left, i.e. if +// broadcast_sizes has values {a0, ..., aN} and the operand shape +// has dimensions {b0, ..., bM} then the shape of the output has +// dimensions {a0, ..., aN, b0, ..., bM}. +// +// The new dimensions index into copies of the operand, i.e. +// +// output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] +XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + +// Performs in-dimension-style broadcast. +// +// Operand specifies the input to be broadcast. "shape" is expected output +// shape. "broadcast_dimensions" are the dimensions to be broadcasting into. +// Dimension numbers in broadcast_dimensions map to individual dimensions +// of the operand, and specify what dimension of the output shape they +// should be broadcast. +// e.g. +// Say operand = [1, 2], i.e., a 1D tensor with 2 elements. +// and dimension of shape is [2,2]. +// Specifying {1} as brodcast_dimension will generate output +// [1 , 2] +// [1 , 2] +// On the other hand, specifying {0} as broadcast_dimension +// will generate output +// [1 , 1] +// [2 , 2] +XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + +// Enqueues a pad operation onto the computation that pads the given value on +// the edges as well as between the elements of the input. padding_config +// specifies the padding amount for each dimension. +XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + +// Enqueues an operation onto the computation that flattens the operand based +// on the dimension order (major/slowest-varying to minor/fastest-varying) +// given, followed by reshaping it into the shape with the given dimension +// sizes (also major to minor). Conceptually, this is a limited form of +// "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + +// Enqueues an operation onto the computation that collapses the operand, from +// first to last dimension (C order), then reshapes it to the given dimension +// sizes. Conceptually, this is a limited form of "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + +// Wrapper for Reshape. +// Enqueues an operation to collapse the provided dimensions; e.g. an +// operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to +// {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must +// be a consecutive, in-order subsequence of the operand dimensions. +// +// Note that collapsing a single dimension does nothing: +// +// {256} collapsing {0} => {256} +// {1} collapsing {0} => {1} +// +// Collapsing multiple dimensions produces a single result dimension: +// +// {256, 2} collapsing {0,1} => {512} +// {256, 2, 3} collapsing {0,1} => {512, 3} +// +// This could potentially cause data to be moved -- it provides a more +// structured form of reshaping than an arbitrary Reshape operation. +XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + +// Enqueues a slice operation onto the computation that slices the operand +// from the start indices to the limit indices; e.g. +// +// x +// [ 0 1 2 3 ] +// y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] +// [ 8 9 a b ] +// +// Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D +// range notation. +// The strides parameter determines the stride over the slice +XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + +// Enqueues a slice operation in a given dimension, taking all other +// dimensions as they are; e.g. if dimno is 1 from start_index 2 to +// limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand +// for: +// +// array[:, 2:4:1, :] +XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + +// Enqueues a slice operation onto the computation that slices the 'operand' +// from dynamic start indices which are passed in 'start_indices'. +// The size of the slice in each dimension is passed in 'slice_sizes', +// which specify the end point of exclusive slice intervals in each +// dimension [start, start + size). +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo input dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + +// Enqueues a dynamic update slice operation onto the computation, which +// updates a slice of 'operand' with 'update' at dynamic 'start_indices'. +// The shape of 'update' determines the shape of the slice of 'operand' +// which is updated. +// The indices specified in 'start_indices' specify the offset of the slice +// of 'operand' which is updated. +// +// update = {10, 11} // calculated at runtime. +// [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] +// [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] +// [7 8 9] [7 8 9 ] +// +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo update dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + +// Enqueues a concatenate instruction onto the computation. 'operands' must +// have >= 1 entry. +XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, int64 dimension); + +// Enqueue a tracing operation onto the computation; the computation will emit +// a logging message with the operand. +void Trace(const string& tag, const XlaOp& operand); + +// Enqueues a conditional-move-like select operation onto the computation; +// predicated on pred, selects between on_true and on_false. +XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); + +// Enqueues a tuple-creation instruction onto the computation. +XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements); + +// Enqueues a tuple-element-get instruction onto the computation. +XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + +// Enqueues an equal-to comparison instruction onto the computation. +XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a not-equal comparison instruction onto the computation. +XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-or-equal comparison instruction onto the computation. +XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-than comparison instruction onto the computation. +XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-than comparison instruction onto the computation. +XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-or-equal comparison instruction onto the computation. +XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a dot instruction onto the computation. +XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + +// Enqueues a general dot instruction onto the computation. +XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, which uses the +// default convolution dimension numbers. +XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration in the format returned by MakePadding(). +XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided dimension numbers configuration. +XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration as well as the dimension numbers. +XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration, dilation factors and dimension numbers. +XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues an FFT instruction onto the computation, of the given type and +// with the given FFT length. +XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + +// Enqueues an infeed instruction onto the computation, which writes data of +// the given shape to the infeed buffer of the device. +XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config = ""); + +// Enqueues an outfeed instruction onto the computation. This instruction +// generates outgoing data transfers for the given data. +// +// shape_with_layout communicates the laid out shape that we want to outfeed +// -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error +// will occur. +void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + +// Enqueues a call instruction onto the computation. +XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + +// Enqueues a custom call instruction onto the computation. +// During code generation, a call instruction is emitted which targets a +// symbol with the name |call_target_name|. The |operands| are passed to the +// call instruction. |shape| is the resultant shape. +XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + +// Enqueues a pseudo-op to represent host-side computation data-dependencies. +// During code generation, host send and receive operations will be generated +// to transfer |operands| to the host and a single result of |shape| back to +// the device. Host send/recv operations are emitted using |channel_name|. +// Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO +// instruction scheduling. +XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + +// The following methods enqueue element-wise binary arithmetic operations +// onto the computation. The shapes of the operands have to match unless one +// of the operands is a scalar, or an explicit broadcast dimension is given +// (see g3doc for more details). + +// Enqueues a complex compose instruction onto the computation. +XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a complex conjugate instruction onto the computation. +XlaOp Conj(const XlaOp& operand); + +// Enqueues an add instruction onto the computation. +XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a subtract instruction onto the computation. +XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a multiply instruction onto the computation. +XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a divide instruction onto the computation. +XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a remainder instruction onto the computation. +XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a max instruction onto the computation. +XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a min instruction onto the computation. +XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Element-wise logical operators +XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Not(const XlaOp& operand); + +XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Reduces an array among the provided dimensions, given "computation" as a +// reduction operator. +XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + +// Convenience wrapper around the above that reduces all the dimensions in the +// operand shape. +XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + +// Enqueues a windowed reduce instruction onto the computation. +XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + +// As ReduceWindow(), but the padding is given in the format +// returned by MakePadding(). +XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Returns the sum of the operand value within each subgroup of replicas. All +// replicas supply one input to the sum and all replicas receive the resulting +// sum for each subgroup. +XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); + +// Enqueues an operation that do an AllReduce of the operand cross cores. Here +// AllReduce means doing a reduction on the input operand cross cores and then +// broadcasting the reduction result to those cores. The reduction function is +// defined by `computation`, which should be a commutative computation on +// scalars, e.g., add, min, or max. The way that AllReduce is applied is +// configured by: +// +// - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all +// replicas belong to one group. Allreduce will be applied within subgroups. +// For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, +// replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. +// +// - `channel_id`: for Allreduce nodes from different models, if they have the +// same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be +// applied cross models. +// +// TODO(b/79737069): Rename this to AllReduce when it's ready to use. +XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids = {}, + const tensorflow::gtl::optional& + channel_id = tensorflow::gtl::nullopt); + +// Enqueues an operation that scatters the `source` array to the selected +// indices of each window. +XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, const XlaComputation& scatter); + +// As SelectAndScatter(), but the padding is given in the format +// returned by MakePadding(). +XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + +// Enqueues an abs instruction onto the computation. +XlaOp Abs(const XlaOp& operand); + +// Enqueues a atan2 instruction onto the computation. +XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an exp instruction onto the computation. +XlaOp Exp(const XlaOp& operand); + +// Enqueues an expm1 instruction onto the computation. +XlaOp Expm1(const XlaOp& operand); + +// Enqueues a floor instruction onto the computation. +XlaOp Floor(const XlaOp& operand); + +// Enqueues a ceil instruction onto the computation. +XlaOp Ceil(const XlaOp& operand); + +// Enqueues a round instruction onto the computation, rounding to nearest even +// with half-way cases rounding away from zero. +XlaOp Round(const XlaOp& operand); + +// Enqueues an log instruction (natural logarithm) onto the computation. +XlaOp Log(const XlaOp& operand); + +// Enqueues an log1p instruction (log(x+1)) onto the computation. +XlaOp Log1p(const XlaOp& operand); + +// Enqueues a sign instruction onto the computation. +XlaOp Sign(const XlaOp& operand); + +// Enqueues a count leading zeros instruction onto the computation. +XlaOp Clz(const XlaOp& operand); + +// Enqueues a cosine instruction onto the computation. +XlaOp Cos(const XlaOp& operand); + +// Enqueues a sine instruction onto the computation. +XlaOp Sin(const XlaOp& operand); + +// Enqueues a tanh instruction onto the computation. +XlaOp Tanh(const XlaOp& operand); + +// Enqueues a real-part instruction onto the computation. +XlaOp Real(const XlaOp& operand); + +// Enqueues an imaginary-part instruction onto the computation. +XlaOp Imag(const XlaOp& operand); + +// Enqueues a lhs^rhs computation onto the computation. +XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an operator that tests if the operand's values are finite, i.e., +// not Inf or NaN. Defined only for floating-point types. Returns an array of +// booleans with the same shape where entries are true iff the corresponding +// entry was NaN. +XlaOp IsFinite(const XlaOp& operand); + +// Enqueues a convert instruction onto the computation that changes the +// element type of the operand array to primitive_type. +XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a no-op instruction onto the computation that changes +// the element type of the operand array to primitive_type. The +// bit-widths of the source and destination element types must be +// identical. +XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a negate instruction onto the computation. +XlaOp Neg(const XlaOp& operand); + +// Enqueues a transpose instruction onto the computation. +XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + +// Enqueues a reverse instruction onto the computation. The order of the +// elements in the given dimensions is reversed (i.e., the element at index i +// is moved to index dimension_size - 1 - i). +XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions); + +// * The result is a sorted array of keys. +// +// If both keys and values are provided: +// * The keys and the values must be rank-1 tensors with the same dimensions. +// The element types of the tensors may be different. +// * The result is a tuple that consists of a sorted array of keys as the +// first element, and an array with their corresponding values as the second +// element. +XlaOp Sort(XlaOp keys, + tensorflow::gtl::optional values = tensorflow::gtl::nullopt); + +// Enqueues a clamp instruction onto the computation. +XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + +// Enqueues a map instruction onto the computation. +XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands = {}); + +// Enqueues a N(mu, sigma) random number generation instruction onto the +// computation. +XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); + +// Enqueues a U(a, b) random number generation instruction onto the +// computation. Returns values in the semi-open interval [a, b). +XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + +// Enqueues a while node onto the computation. +XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init); + +// Enqueues a conditional node onto the computation. +XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + +// Enqueues a ReducePrecision node onto the computation. +XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + +// Enqueues a Gather node onto the computation. +XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + +// Enqueues a Send node onto the computation, to send the given operand to +// a Recv instruction that shares the same channel handle. +void Send(const XlaOp& operand, const ChannelHandle& handle); + +// Enqueues a Recv node onto the computation. The data comes from a Send +// instruction that shares the same channel handle and its shape must +// be the same as the given shape. +XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// Returns a tuple (normalized, batch_mean, batch_var) where `normalized` +// is the normalized result and batch_mean and batch_var are the mean and +// variance, respectively, across batch for the operand. +XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// `BatchNormInference` is equivalent to calling `BatchNormTraining` without +// computing `mean` and `variance` for each batch inside the operation. It +// uses the input `mean` and `variance` instead as estimated values. The +// purpose of this op is to reduce latency in inference, hence the name +// `BatchNormInference`. +// +// The output has the same shape as `operand`, and contains the normalized +// values for each batch. +XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + +// Calculates the gradients of a batch norm op. +// +// The inputs `batch_mean` and `batch_var` represent the mean and variance +// across the batch. +// +// Returns a tuple of three elements: +// - grad_operand: Gradient with respect to input `operand` +// - grad_offset: Gradient with respect to input `offset` +// - grad_scale: Gradient with respect to input `scale` +XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + +// Implementation details below this point. + template XlaOp XlaBuilder::ConstantR0(NativeT value) { return ConstantLiteral(*Literal::CreateR0(value)); @@ -1002,34 +2016,93 @@ XlaOp XlaBuilder::ConstantR4FromArray4D(const Array4D& values) { return ConstantFromArray(values); } -// RAII-style object: sets the current sharding assignment in builder on -// construction, and sets back to the previous assignment on destruction. -class XlaScopedShardingAssignment { - public: - XlaScopedShardingAssignment(xla::XlaBuilder* builder, - tensorflow::gtl::optional sharding) - : builder_(builder), prev_sharding_(builder->sharding()) { - SetSharding(sharding); - } +// Free function template implementations. - XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; - XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = - delete; +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value) { + return ConstantLiteral(builder, *Literal::CreateR0(value)); +} - ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values) { + return ConstantLiteral(builder, *Literal::CreateR1(values)); +} - private: - void SetSharding(const tensorflow::gtl::optional& sharding) { - if (sharding.has_value()) { - builder_->SetSharding(sharding.value()); - } else { - builder_->ClearSharding(); - } - } +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value) { + Literal literal(ShapeUtil::MakeShape( + primitive_util::NativeToPrimitiveType(), {length})); + literal.PopulateWithValue(value); + return ConstantLiteral(builder, literal); +} - xla::XlaBuilder* const builder_; - tensorflow::gtl::optional prev_sharding_; -}; +inline XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values) { + return ConstantLiteral(builder, *Literal::CreateR1(values)); +} + +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values) { + return ConstantLiteral(builder, *Literal::CreateR2(values)); +} + +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout) { + return ConstantLiteral( + builder, *Literal::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values) { + return ConstantLiteral(builder, *Literal::CreateFromArray(values)); +} + +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout) { + return ConstantLiteral( + builder, *Literal::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values) { + return ConstantLiteral(builder, + *Literal::CreateR2FromArray2D(values)); +} + +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *Literal::CreateR3FromArray3DWithLayout(values, layout)); +} + +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values) { + return ConstantFromArray(builder, values); +} + +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout) { + return ConstantFromArrayWithLayout(builder, values, layout); +} + +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values) { + return ConstantFromArray(builder, values); +} } // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc index 2df3ea3af0d4fcfb9bc803feebd96f09042ab1f3..3b8beb2c7840e23752b5f47bbc5f55d89751884d 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc @@ -53,16 +53,86 @@ class XlaBuilderTest : public ::testing::Test { TEST_F(XlaBuilderTest, OnePlusTwo) { XlaBuilder b(TestName()); - b.Add(b.ConstantR0(1.0), b.ConstantR0(2.0)); + Add(ConstantR0(&b, 1.0), ConstantR0(&b, 2.0)); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Add(op::Constant(), op::Constant())); } +TEST_F(XlaBuilderTest, UnaryOperatorsBuildExpectedHLO) { + auto test_unary_operator = + [&](std::function op, + ::testing::Matcher matches_pattern) { + XlaBuilder b(TestName()); + op(ConstantR0(&b, 1)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, matches_pattern); + }; + test_unary_operator([](XlaOp x) { return -x; }, op::Negate(op::Constant())); + test_unary_operator([](XlaOp x) { return ~x; }, op::Not(op::Constant())); +} + +TEST_F(XlaBuilderTest, BinaryOperatorsBuildExpectedHLO) { + auto test_binary_operator = + [&](std::function op, + ::testing::Matcher matches_pattern) { + XlaBuilder b(TestName()); + op(ConstantR0(&b, 1), ConstantR0(&b, 2)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, matches_pattern); + }; + + test_binary_operator([](XlaOp x, XlaOp y) { return x + y; }, + op::Add(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x - y; }, + op::Subtract(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x * y; }, + op::Multiply(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x / y; }, + op::Divide(op::Constant(), op::Constant())); + + test_binary_operator([](XlaOp x, XlaOp y) { return x & y; }, + op::And(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x | y; }, + op::Or(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x ^ y; }, + op::Xor(op::Constant(), op::Constant())); + test_binary_operator([](XlaOp x, XlaOp y) { return x << y; }, + op::ShiftLeft(op::Constant(), op::Constant())); + test_binary_operator( + [](XlaOp x, XlaOp y) { return x >> y; }, + op::ShiftRightArithmetic(op::Constant(), op::Constant())); + + auto test_unsigned_binary_operator = + [&](std::function op, + ::testing::Matcher matches_pattern) { + XlaBuilder b(TestName()); + op(ConstantR0(&b, 1), ConstantR0(&b, 2)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, matches_pattern); + }; + test_unsigned_binary_operator( + [](XlaOp x, XlaOp y) { return x >> y; }, + op::ShiftRightLogical(op::Constant(), op::Constant())); +} + +TEST_F(XlaBuilderTest, ShiftRightOperatorOnNonIntegerProducesError) { + XlaBuilder b(TestName()); + ConstantR0(&b, 1) >> ConstantR0(&b, 2); + auto statusor = b.Build(); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT( + statusor.status().error_message(), + HasSubstr("Argument to >> operator does not have an integral type")); +} + TEST_F(XlaBuilderTest, ParamPlusConstantHasScalarBroadcast) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {3, 5}), "x"); - b.Add(x, b.ConstantR0(1.0)); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {3, 5}), "x"); + Add(x, ConstantR0(&b, 1.0)); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Add(op::Parameter(), op::Broadcast(op::Constant()))); @@ -72,9 +142,9 @@ TEST_F(XlaBuilderTest, ParamPlusParamHasBroadcast) { XlaBuilder b(TestName()); const auto& x_shape = ShapeUtil::MakeShape(S32, {2, 4, 6}); const auto& y_shape = ShapeUtil::MakeShape(S32, {2, 4}); - auto x = b.Parameter(0, x_shape, "x"); - auto y = b.Parameter(1, y_shape, "y"); - auto add = b.Add(x, y, /*broadcast_dimensions=*/{0, 1}); + auto x = Parameter(&b, 0, x_shape, "x"); + auto y = Parameter(&b, 1, y_shape, "y"); + auto add = Add(x, y, /*broadcast_dimensions=*/{0, 1}); TF_ASSERT_OK_AND_ASSIGN(auto add_shape, b.GetShape(add)); EXPECT_TRUE(ShapeUtil::Equal(add_shape, x_shape)); @@ -86,8 +156,8 @@ TEST_F(XlaBuilderTest, ParamPlusParamHasBroadcast) { TEST_F(XlaBuilderTest, XPlusX) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(S32, {1, 3, 5, 7}), "x"); - b.Add(x, x); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(S32, {1, 3, 5, 7}), "x"); + Add(x, x); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Add(op::Parameter(0), op::Parameter(0))); @@ -95,9 +165,9 @@ TEST_F(XlaBuilderTest, XPlusX) { TEST_F(XlaBuilderTest, ShapeInferenceError) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(U32, {2, 4, 6}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(U32, {2, 4}), "y"); - b.Add(x, y); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(U32, {2, 4, 6}), "x"); + auto y = Parameter(&b, 1, ShapeUtil::MakeShape(U32, {2, 4}), "y"); + Add(x, y); auto statusor = BuildHloModule(&b); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), HasSubstr("shape inference")); @@ -105,12 +175,12 @@ TEST_F(XlaBuilderTest, ShapeInferenceError) { TEST_F(XlaBuilderTest, ParameterAlreadyRegistered) { XlaBuilder b_call("add"); - b_call.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "x"); + Parameter(&b_call, 0, ShapeUtil::MakeShape(PRED, {}), "x"); XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "x"); - auto y = b.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "y"); - b.Add(x, y); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(PRED, {}), "x"); + auto y = Parameter(&b, 0, ShapeUtil::MakeShape(PRED, {}), "y"); + Add(x, y); auto statusor = BuildHloModule(&b); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -119,16 +189,16 @@ TEST_F(XlaBuilderTest, ParameterAlreadyRegistered) { TEST_F(XlaBuilderTest, Call) { XlaBuilder b_call("the_only_to_apply"); - auto p0 = b_call.Parameter(0, ShapeUtil::MakeShape(F32, {}), "p0"); - auto p1 = b_call.Parameter(1, ShapeUtil::MakeShape(F32, {}), "p1"); - b_call.Add(p0, p1); + auto p0 = Parameter(&b_call, 0, ShapeUtil::MakeShape(F32, {}), "p0"); + auto p1 = Parameter(&b_call, 1, ShapeUtil::MakeShape(F32, {}), "p1"); + Add(p0, p1); TF_ASSERT_OK_AND_ASSIGN(auto call, b_call.Build()); XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - auto one = b.ConstantR0(1); - auto two = b.ConstantR0(2); - b.Add(b.Call(call, {x, y}), b.Call(call, {one, two})); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {}), "y"); + auto one = ConstantR0(&b, 1); + auto two = ConstantR0(&b, 2); + Add(Call(&b, call, {x, y}), Call(&b, call, {one, two})); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Add(op::Call(op::Parameter(), op::Parameter()), @@ -137,9 +207,9 @@ TEST_F(XlaBuilderTest, Call) { TEST_F(XlaBuilderTest, BinopHasDegenerateBroadcast) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {1, 2, 3}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(F32, {1, 2, 1}), "y"); - b.Add(x, y); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {1, 2, 3}), "x"); + auto y = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {1, 2, 1}), "y"); + Add(x, y); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); // Expected: @@ -158,9 +228,9 @@ TEST_F(XlaBuilderTest, BinopHasDegenerateBroadcast) { TEST_F(XlaBuilderTest, BinopHasInDimAndDegenerateBroadcast) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {2, 3}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(F32, {2, 1, 4}), "y"); - b.Add(x, y, /*broadcast_dimensions=*/{0, 1}); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {2, 3}), "x"); + auto y = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {2, 1, 4}), "y"); + Add(x, y, /*broadcast_dimensions=*/{0, 1}); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); // The binary operation has in-dim broadcast and degenerate broadcast, should @@ -183,9 +253,10 @@ TEST_F(XlaBuilderTest, BinopHasInDimAndDegenerateBroadcast) { TEST_F(XlaBuilderTest, OperandFromWrongBuilder) { XlaBuilder b1("b1"); - auto p0 = b1.Parameter(0, ShapeUtil::MakeShape(F32, {}), "p0"); + auto p0 = Parameter(&b1, 0, ShapeUtil::MakeShape(F32, {}), "p0"); XlaBuilder builder("main"); - builder.Add(p0, p0); + auto p = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "p"); + Add(p, p0); auto statusor = builder.Build(); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -196,8 +267,8 @@ TEST_F(XlaBuilderTest, OperandFromWrongBuilder) { TEST_F(XlaBuilderTest, ReshapeDefaultOrder) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {2, 3, 5, 7}), "x"); - b.Reshape(x, /*new_sizes=*/{6, 35}); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {2, 3, 5, 7}), "x"); + Reshape(x, /*new_sizes=*/{6, 35}); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Reshape(op::Parameter())); @@ -205,8 +276,8 @@ TEST_F(XlaBuilderTest, ReshapeDefaultOrder) { TEST_F(XlaBuilderTest, ReshapeHasTranspose) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {2, 3, 5, 7}), "x"); - b.Reshape(x, /*dimensions=*/{3, 2, 1, 0}, /*new_sizes=*/{6, 35}); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {2, 3, 5, 7}), "x"); + Reshape(x, /*dimensions=*/{3, 2, 1, 0}, /*new_sizes=*/{6, 35}); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Reshape(op::Transpose(op::Parameter()))); @@ -214,25 +285,38 @@ TEST_F(XlaBuilderTest, ReshapeHasTranspose) { TEST_F(XlaBuilderTest, Transpose) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {5, 7}), "x"); - b.Transpose(x, /*permutation=*/{1, 0}); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {5, 7}), "x"); + Transpose(x, /*permutation=*/{1, 0}); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Transpose(op::Parameter())); } -// TODO(b/65209188): Create a dedicated lowering for Xor. -TEST_F(XlaBuilderTest, Xor) { +TEST_F(XlaBuilderTest, ReportError) { XlaBuilder b(TestName()); - auto x = b.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(PRED, {}), "y"); - b.Xor(x, y); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {5, 7}), "x"); + Add(b.ReportError(InvalidArgument("a test error")), x); + auto statusor = b.Build(); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("a test error")); +} + +TEST_F(XlaBuilderTest, ReportErrorOrReturnHandlesNonErrors) { + XlaBuilder b(TestName()); + StatusOr op(ConstantR0(&b, 1.0)); + Add(b.ReportErrorOrReturn(op), ConstantR0(&b, 2.0)); TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); auto root = module->entry_computation()->root_instruction(); - LOG(ERROR) << module->ToString(); - EXPECT_THAT(root, - op::Or(op::And(op::Not(op::Parameter(0)), op::Parameter(1)), - op::And(op::Parameter(0), op::Not(op::Parameter(1))))); + EXPECT_THAT(root, op::Add(op::Constant(), op::Constant())); +} + +TEST_F(XlaBuilderTest, ReportErrorOrReturnHandlesErrors) { + XlaBuilder b(TestName()); + StatusOr op(InvalidArgument("a test error")); + Add(b.ReportErrorOrReturn(op), ConstantR0(&b, 2.0)); + auto statusor = b.Build(); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("a test error")); } } // namespace diff --git a/tensorflow/compiler/xla/experimental/xla_sharding/BUILD b/tensorflow/compiler/xla/experimental/xla_sharding/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..a26b20c861846501c911253d89619591c37322b3 --- /dev/null +++ b/tensorflow/compiler/xla/experimental/xla_sharding/BUILD @@ -0,0 +1,18 @@ +# Description: +# Python API for shardings in XLA. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +py_library( + name = "xla_sharding", + srcs = ["xla_sharding.py"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto_py", + "//tensorflow/compiler/xla/python_api:types", + "//tensorflow/compiler/xla/python_api:xla_shape", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py b/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py new file mode 100644 index 0000000000000000000000000000000000000000..abd10b164eaef8e75ed304483861baf250c5b954 --- /dev/null +++ b/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py @@ -0,0 +1,204 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Experimental support for defining XLA shardings.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import xla_shape +from tensorflow.core.framework import attr_value_pb2 + + +class Sharding(object): + """A class to support adding sharding attributes to Ops. + + Use the factory constructors and then call apply_to_tensor: + Sharding.replicate().apply_to_tensor(tensor) + """ + + def __init__(self, proto=None): + """Do not use this constructor; use the factory functions below.""" + self._proto = proto + + @classmethod + def replicate(cls): + """Returns a replicated sharding attribute. + + This causes an op to be computed in its entirety independently on all + cores in the XLA device. + """ + return Sharding( + proto=xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED)) + + @classmethod + def assign_device(cls, core): + """Returns an AssignDevice sharding attribute. + + This causes an op to be computed in its entirety only on one core in + the XLA device. + Args: + core: The core to assign this Op to. + """ + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.MAXIMAL, + tile_assignment_dimensions=[1], + tile_assignment_devices=[core])) + + @classmethod + def tile(cls, tile_shape, tile_assignment): + """Returns a Tiled sharding attribute. + + This causes an op to be partially computed on multiple cores in the + XLA device. + + Args: + tile_shape: A xla_shape.Shape describing the tile shape that each core + will compute. + The tile shape does not need to be divisible by the tile assignment. + tile_assignment: An np.ndarray describing the topology of the tiling and + which device will compute which part of the topology. + + Raises: + TypeError: tile_assignment was not of np.array type or tile_shape was + not of xla_shape.Shape type. + + TODO(jmolloy): This concept is nefarious and is not + something we really want to expose to users (especially as the + contract for tile_assignment is very strict). + """ + if not isinstance(tile_assignment, np.ndarray): + raise TypeError('Tile assignment must be of type np.ndarray') + if not isinstance(tile_shape, xla_shape.Shape): + raise TypeError('Tile shape must be of type xla_shape.Shape') + dims = list(tile_assignment.shape) + flattened_devices = tile_assignment.reshape(-1, order='C') + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.OTHER, + tile_shape=tile_shape.message, + tile_assignment_dimensions=dims, + tile_assignment_devices=list(flattened_devices))) + + @classmethod + def split(cls, tensor, split_dimension, num_devices): + """Returns a Sharding that splits a tensor across a dimension. + + This creates a Tiled attribute, similar to tile(), but easier to use for the + common case of tiling a tensor N ways in one dimension. + + Args: + tensor: A tf.Tensor to split. + split_dimension: The dimension number to split. + num_devices: The number of cores to split `tensor` over. + + Raises: + ValueError: The tensor to split was smaller in the split dimension than + the number of devices to split over. + """ + tensor.shape.assert_is_fully_defined() + shape = tensor.shape.as_list() + if shape[split_dimension] < num_devices: + raise ValueError('Split dimension was smaller than the required number ' + 'of splits: shape=%r, dimension=%r, num_devices=%r', + shape, split_dimension, num_devices) + + tile_shape = shape + tile_shape[split_dimension] = int( + math.ceil(tile_shape[split_dimension] / num_devices)) + tile_shape_proto = xla_data_pb2.Shape( + element_type=xla_data_pb2.F32, dimensions=tile_shape) + + tile_assignment_dims = [1] * len(shape) + tile_assignment_dims[split_dimension] = num_devices + + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.OTHER, + tile_shape=tile_shape_proto, + tile_assignment_dimensions=tile_assignment_dims, + tile_assignment_devices=range(num_devices))) + + def apply_to_tensor(self, tensor): + """Applies this Sharding attribute to `tensor`.""" + if len(tensor.op.outputs) > 1: + proto = self._get_or_create_tuple_proto(tensor.op) + # We can't mutate an element of old_proto.tuple_shardings, so create + # a new proto. + tuple_shardings = list(proto.tuple_shardings) + tuple_shardings[tensor.value_index] = self._proto + proto = xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=tuple_shardings) + else: + proto = self._proto + + attr_value = attr_value_pb2.AttrValue(s=proto.SerializeToString()) + # TODO(jmolloy): This need to be seriously revisited before declaring this + # API available for public use. + # pylint: disable=protected-access + tensor.op._set_attr('_XlaSharding', attr_value) + + @property + def proto(self): + """Return the sharding protobuf of type xla_data_pb2.OpSharding.""" + return self._proto + + def _get_or_create_tuple_proto(self, op): + try: + attr = op.get_attr('_XlaSharding') + proto = xla_data_pb2.OpSharding() + proto.ParseFromString(attr) + return proto + except ValueError: + return self._create_tuple_proto(op) + + def _create_tuple_proto(self, op): + shardings = [ + xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED) + for _ in op.outputs + ] + return xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=shardings) + + +# Helpers for the above factory functions that allow easy application of +# shardings, for example: +# tensor = xla_sharding.replicate(tensor) + + +def replicate(tensor): + Sharding.replicate().apply_to_tensor(tensor) + return tensor + + +def assign_device(tensor, device): + Sharding.assign_device(device).apply_to_tensor(tensor) + return tensor + + +def tile(tensor, tile_shape, tile_assignment): + Sharding.tile(tile_shape, tile_assignment).apply_to_tensor(tensor) + return tensor + + +def split(tensor, split_dimension, num_devices): + Sharding.split(tensor, split_dimension, num_devices).apply_to_tensor(tensor) + return tensor diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc index e8f29b83291a7cb238dc25b9f4bb743fe426a162..15eeb2ea13607d43c995197f8f0e3c58abd4d94a 100644 --- a/tensorflow/compiler/xla/layout_util.cc +++ b/tensorflow/compiler/xla/layout_util.cc @@ -190,9 +190,13 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } if (!ShapeUtil::IsArray(shape)) { - return InvalidArgument( - "shape of primitive type %s should not have a layout", - PrimitiveType_Name(shape.element_type()).c_str()); + if (layout.minor_to_major_size() != 0 || + layout.padded_dimensions_size() != 0) { + return InvalidArgument( + "shape of primitive type %s should not have a non-trivial layout", + PrimitiveType_Name(shape.element_type()).c_str()); + } + return Status::OK(); } if (layout.format() == INVALID_FORMAT) { @@ -244,6 +248,12 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } } + if (layout.format() == SPARSE) { + if (!layout.padded_dimensions().empty()) { + return InvalidArgument("Sparse layout has padded dimensions"); + } + } + return Status::OK(); } diff --git a/tensorflow/compiler/xla/literal_comparison.cc b/tensorflow/compiler/xla/literal_comparison.cc index bf9679cafec72c2e9dc5796e9058c6703239c508..2125ab7c61ab5e30fe51e16994e0da4883d509c4 100644 --- a/tensorflow/compiler/xla/literal_comparison.cc +++ b/tensorflow/compiler/xla/literal_comparison.cc @@ -606,8 +606,8 @@ Status NearHelper(const LiteralSlice& expected, const LiteralSlice& actual, } // namespace Status EqualShapes(const Shape& expected, const Shape& actual) { - if (ShapeUtil::IsTuple(expected) != ShapeUtil::IsTuple(actual)) { - return InvalidArgument("tupleness-mismatch! want: %s got %s", + if (expected.element_type() != actual.element_type()) { + return InvalidArgument("element type mismatch, want: %s got %s", ShapeUtil::HumanString(expected).c_str(), ShapeUtil::HumanString(actual).c_str()); } @@ -626,7 +626,7 @@ Status EqualShapes(const Shape& expected, const Shape& actual) { return AppendStatus(result, StrCat("mismatch in tuple index", i)); } } - } else { + } else if (ShapeUtil::IsArray(expected)) { if (ShapeUtil::Rank(expected) != ShapeUtil::Rank(actual)) { return InvalidArgument("want rank of %s got rank of %s", ShapeUtil::HumanString(expected).c_str(), @@ -652,6 +652,7 @@ Status EqualShapes(const Shape& expected, const Shape& actual) { } } } + // Non-array, non-tuple shapes are trivially equivalent. return Status::OK(); } @@ -705,6 +706,9 @@ Status Equal(const LiteralSlice& expected, const LiteralSlice& actual) { } break; } + case TOKEN: + // Tokens have no on-device representation and are trivially equal. + return Status::OK(); default: LOG(FATAL) << "Unsupported primitive type in LiteralTestUtil::ExpectEqual: " diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 61afc311a702930a18be4842908f9a26b98d9a32..eeabf835ac348a5ba55699631188b0e329c98c43 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -148,8 +148,7 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { piece->emplace_back(std::move(child_piece)); } - } else { - CHECK(ShapeUtil::IsArray(shape)); + } else if (ShapeUtil::IsArray(shape)) { if (allocate_arrays) { if (LayoutUtil::IsSparseArray(shape)) { // For sparse arrays, the buffer must be of the size of the maximum @@ -165,6 +164,10 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { piece->set_buffer(new char[piece->size_bytes()]); } } + } else { + // If the shape is neither an array nor tuple, then it must be + // zero-sized. Otherwise, some memory needs to be allocated for it. + CHECK_EQ(piece->size_bytes(), 0); } } @@ -264,8 +267,8 @@ Status Literal::CopySliceFromInternal( StridedCopy(data(), linear_index(shape(), dest_base), 0, src_literal.data(), linear_index(src_literal.shape(), src_base), 0, 1); - } else if (!ShapeUtil::HasZeroElements(shape()) && - !ShapeUtil::HasZeroElements(src_literal.shape())) { + } else if (!ShapeUtil::IsZeroElementArray(shape()) && + !ShapeUtil::IsZeroElementArray(src_literal.shape())) { // Perform copy if neither src nor dest has dimensions with zero element, // otherwise it's a no-op. TF_RET_CHECK(src_base.size() == dest_base.size()); @@ -327,6 +330,10 @@ Status Literal::CopyElementFrom(const LiteralSlice& src_literal, return Status::OK(); } +/* static */ std::unique_ptr Literal::CreateToken() { + return MakeUnique(ShapeUtil::MakeTokenShape()); +} + std::vector Literal::DecomposeTuple() { CHECK(ShapeUtil::IsTuple(shape())); std::vector elements; @@ -379,7 +386,7 @@ void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest, tensorflow::gtl::ArraySlice src, const Shape& dest_shape, const Shape& src_shape) { CHECK(ShapeUtil::Compatible(dest_shape, src_shape)); - if (ShapeUtil::HasZeroElements(dest_shape)) { + if (ShapeUtil::IsZeroElementArray(dest_shape)) { return; } std::vector index(ShapeUtil::Rank(dest_shape)); @@ -1177,7 +1184,7 @@ size_t LiteralBase::Hash() const { ShapeUtil::ForEachSubshape( shape(), [&](const Shape& subshape, const ShapeIndex& index) { - if (ShapeUtil::IsTuple(subshape)) { + if (!ShapeUtil::IsArray(subshape)) { return; } @@ -1368,6 +1375,11 @@ void ToStringHelper(const LiteralBase& literal, const ShapeIndex& shape_index, return; } + if (ShapeUtil::IsToken(subshape)) { + pieces->push_back("token"); + return; + } + if (LayoutUtil::IsSparseArray(subshape)) { pieces->push_back(shape_to_string(subshape)); pieces->push_back("{"); @@ -1556,7 +1568,7 @@ string LiteralBase::ToString(bool print_layout) const { void LiteralBase::EachCellAsString( const std::function indices, const string& value)>& per_cell) const { - if (ShapeUtil::HasZeroElements(shape())) { + if (ShapeUtil::IsZeroElementArray(shape())) { return; } std::vector indices = IndexUtil::LinearIndexToMultidimensionalIndex( @@ -1962,7 +1974,7 @@ bool LiteralBase::IsAllFirst() const { // Empty shapes are not all the first element since there is no first // element. - if (ShapeUtil::HasZeroElements(piece.subshape())) { + if (ShapeUtil::IsZeroElementArray(piece.subshape())) { return false; } auto piece_is_all = [&]() { @@ -2130,6 +2142,7 @@ void LiteralBase::Piece::WriteToProto(LiteralProto* proto) const { } break; case TUPLE: + case TOKEN: // Nothing to do but assign the shape which is done above. return; default: @@ -2282,6 +2295,9 @@ StatusOr> Literal::CreateFromProto( } return Status::OK(); } + if (piece->subshape().element_type() == TOKEN) { + return Status::OK(); + } CHECK(ShapeUtil::IsArray(piece->subshape())); TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element)); @@ -2341,28 +2357,27 @@ LiteralSlice::LiteralSlice(const LiteralBase& literal, : LiteralBase(), root_piece_(&literal.piece(view_root)) {} BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) - : LiteralBase(), shape_(shape) { - CHECK(ShapeUtil::IsArray(shape_)); - CHECK_NE(src_buf_ptr, nullptr); - CHECK(LayoutUtil::HasLayout(shape_)); + : LiteralBase(), shape_(MakeUnique(shape)) { + CHECK(ShapeUtil::IsArray(*shape_)); + CHECK(LayoutUtil::HasLayout(*shape_)); root_piece_ = Piece(); root_piece_.set_buffer(const_cast(src_buf_ptr)); - root_piece_.set_subshape(&shape_); + root_piece_.set_subshape(shape_.get()); } BorrowingLiteral::BorrowingLiteral( tensorflow::gtl::ArraySlice src_buf_ptrs, const Shape& shape) - : LiteralBase(), shape_(shape) { - CHECK(ShapeUtil::IsTuple(shape_)); - CHECK(!ShapeUtil::IsNestedTuple(shape_)); - CHECK_EQ(src_buf_ptrs.size(), ShapeUtil::TupleElementCount(shape_)); + : LiteralBase(), shape_(MakeUnique(shape)) { + CHECK(ShapeUtil::IsTuple(*shape_)); + CHECK(!ShapeUtil::IsNestedTuple(*shape_)); + CHECK_EQ(src_buf_ptrs.size(), ShapeUtil::TupleElementCount(*shape_)); root_piece_ = Piece(); - root_piece_.set_subshape(&shape_); - BuildPieceSubtree(shape_, &root_piece_); + root_piece_.set_subshape(shape_.get()); + BuildPieceSubtree(*shape_, &root_piece_); for (int i = 0; i < src_buf_ptrs.size(); ++i) { - const auto& src_shape = shape_.tuple_shapes(i); + const auto& src_shape = shape_->tuple_shapes(i); CHECK(ShapeUtil::IsArray(src_shape)); root_piece_.child(i).set_buffer(const_cast(src_buf_ptrs[i])); } diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index 1e26eb7ad4098bab1e757347a23edd73390b48b5..37ca8ea9f1d158b6bce8d5688288351f55c3b3c8 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -917,6 +917,9 @@ class Literal : public LiteralBase { return MakeTupleOwned(std::move(v)); } + // Create a constant token literal. Token types have no value. + static std::unique_ptr CreateToken(); + // Returns a vector containing the tuple elements of this Literal as separate // Literals. This Literal must be tuple-shaped and can be a nested tuple. The // elements are moved into the new Literals; no data is copied. Upon return @@ -1099,8 +1102,10 @@ class BorrowingLiteral : public LiteralBase { const Piece& root_piece() const override { return root_piece_; }; Piece root_piece_; - // Shape of this literal. - const Shape shape_; + // Shape of this literal. Stored as unique_ptr so such that the (default) + // move construction of this class would be trivially correct: the pointer to + // Shape root_piece_ stores will still point to the correct address. + std::unique_ptr shape_; }; template @@ -1454,7 +1459,7 @@ void LiteralBase::EachCell( std::function indices, NativeT value)> per_cell) const { - if (ShapeUtil::HasZeroElements(shape())) { + if (ShapeUtil::IsZeroElementArray(shape())) { return; } std::vector indices(ShapeUtil::Rank(shape()), 0); diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc index f127cee0fdc126429ed423aace3b3b7764a05b2e..493d807591dd3c425293e4ee796bca3036a3088c 100644 --- a/tensorflow/compiler/xla/literal_util_test.cc +++ b/tensorflow/compiler/xla/literal_util_test.cc @@ -334,6 +334,22 @@ TEST_F(LiteralUtilTest, NonScalarEquality) { EXPECT_EQ(nil, nil); } +TEST_F(LiteralUtilTest, TokenEquality) { + auto token0 = Literal::CreateToken(); + auto token1 = Literal::CreateToken(); + auto scalar = Literal::CreateR0(1.0); + + EXPECT_EQ(*token0, *token1); + EXPECT_NE(*token0, *scalar); + + EXPECT_EQ(*Literal::MakeTuple({token0.get()}), + *Literal::MakeTuple({token0.get()})); + EXPECT_EQ(*Literal::MakeTuple({token0.get(), scalar.get()}), + *Literal::MakeTuple({token1.get(), scalar.get()})); + EXPECT_NE(*Literal::MakeTuple({token0.get(), scalar.get()}), + *Literal::MakeTuple({scalar.get(), token1.get()})); +} + TEST_F(LiteralUtilTest, DifferentLayoutEquality) { // Test equality with literals which have different layouts. auto colmajor = @@ -1431,7 +1447,7 @@ TEST_F(LiteralUtilTest, LiteralSliceOfALiteralSlice) { EXPECT_EQ(matrix_view, *Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}})); } -TEST_F(LiteralUtilTest, BorrowingLiteralFromOneBufferPtrTest) { +TEST_F(LiteralUtilTest, BorrowingLiteralFromOneBufferPtr) { std::vector int64_values = {1, 2, 3}; const Shape literal_shape = ShapeUtil::MakeShape(S64, {3}); @@ -1443,7 +1459,7 @@ TEST_F(LiteralUtilTest, BorrowingLiteralFromOneBufferPtrTest) { EXPECT_EQ(literal.Get({2}), 3); } -TEST_F(LiteralUtilTest, BorrowingLiteralFromMultipleBufferPtrsTest) { +TEST_F(LiteralUtilTest, BorrowingLiteralFromMultipleBufferPtrs) { std::vector one_two_three = {1, 2, 3}; const Shape one_two_three_shape = ShapeUtil::MakeShape(S64, {3}); diff --git a/tensorflow/compiler/xla/overflow_util.h b/tensorflow/compiler/xla/overflow_util.h new file mode 100644 index 0000000000000000000000000000000000000000..8657d3a4bfa992b9ca0619f24923fd4542eed894 --- /dev/null +++ b/tensorflow/compiler/xla/overflow_util.h @@ -0,0 +1,50 @@ +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_OVERFLOW_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_OVERFLOW_UTIL_H_ + +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +// Multiply two nonnegative int64's, returning negative for overflow +inline int64 MultiplyWithoutOverflow(const int64 x, const int64 y) { + // Multiply in uint64 rather than int64 since signed overflow is undefined. + // Negative values will wrap around to large unsigned values in the casts + // (see section 4.7 [conv.integral] of the C++14 standard). + const uint64 ux = x; + const uint64 uy = y; + const uint64 uxy = ux * uy; + + // Check if we overflow uint64, using a cheap check if both inputs are small + if (TF_PREDICT_FALSE((ux | uy) >> 32 != 0)) { + // Ensure nonnegativity. Note that negative numbers will appear "large" + // to the unsigned comparisons above. + CHECK(x >= 0 && y >= 0); + + // Otherwise, detect overflow using a division + if (ux != 0 && uxy / ux != uy) return -1; + } + + // Cast back to signed. Any negative value will signal an error. + return static_cast(uxy); +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_OVERFLOW_UTIL_H_ diff --git a/tensorflow/compiler/xla/primitive_util.cc b/tensorflow/compiler/xla/primitive_util.cc index 143c9a2366be5786b7ef2148580caeb97d67d2d8..b16147e3be71771269d8b7a18528bef3a8c72d99 100644 --- a/tensorflow/compiler/xla/primitive_util.cc +++ b/tensorflow/compiler/xla/primitive_util.cc @@ -85,5 +85,10 @@ PrimitiveType ComplexComponentType(PrimitiveType complex_type) { } } +bool IsArrayType(PrimitiveType primitive_type) { + return primitive_type != PRIMITIVE_TYPE_INVALID && primitive_type != TUPLE && + primitive_type != OPAQUE && primitive_type != TOKEN; +} + } // namespace primitive_util } // namespace xla diff --git a/tensorflow/compiler/xla/primitive_util.h b/tensorflow/compiler/xla/primitive_util.h index b26a10ade63a5dad3bf8f9f3a2a33c3c5e67bdb2..889e9a1ceca675689406d255d348c82c398563aa 100644 --- a/tensorflow/compiler/xla/primitive_util.h +++ b/tensorflow/compiler/xla/primitive_util.h @@ -133,6 +133,9 @@ bool IsUnsignedIntegralType(PrimitiveType type); bool IsIntegralType(PrimitiveType type); +// Returns true if values of the given primitive type are held in array shapes. +bool IsArrayType(PrimitiveType primitive_type); + // Returns the number of bits in the representation for a given type. int BitWidth(PrimitiveType type); diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index 83834c1ff65ea2f9989fe08279c29056d9070adb..22cc4e2436e5d3a7ed77a2b9f5515878661ef294 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -52,9 +52,9 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", - "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:framework_lite", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index f808990cadeab5fd2c4857920ee1daaac7262edd..be55d50b234442ec569c85e4f5224ad1c179bca8 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -14,13 +14,14 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/python/local_computation_builder.h" +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/platform/thread_annotations.h" namespace xla { - namespace swig { // TODO(b/34473877) Ideally XLA would support AllReduce among arbitrary sets of @@ -97,6 +98,36 @@ const ScopedShapedBuffer* LocalShapedBuffer::shaped_buffer() const { return &shaped_buffer_; } +ShapedBuffer LocalShapedBuffer::Release() { return shaped_buffer_.release(); } + +LocalShapedBufferTuple::LocalShapedBufferTuple( + std::vector elements) + : elements_(std::move(elements)) { + for (auto* element : elements_) { + DCHECK(element != nullptr); + } +} + +LocalShapedBufferTuple::~LocalShapedBufferTuple() { + for (LocalShapedBuffer* element : elements_) { + if (element != nullptr) { + delete element; + } + } +} + +StatusOr LocalShapedBufferTuple::Release(int i) { + LocalShapedBuffer* element = elements_[i]; + if (element == nullptr) { + return InvalidArgument("Attempted to release already-released element %d.", + i); + } + elements_[i] = nullptr; + return element; +} + +int LocalShapedBufferTuple::size() const { return elements_.size(); } + static StatusOr ToBuffer(LocalClient* client, int device_ordinal, const Literal& arg) { @@ -145,73 +176,73 @@ StatusOr> CompiledLocalComputation::Execute( GetReplicaCount()); for (int replica = 0; replica < GetReplicaCount(); ++replica) { - pool.Schedule([this, client, replica, &arguments, &shapes_with_layout, - &results] { - StatusOr device_ordinal_status = - client->ReplicaNumberToDeviceOrdinal(replica); - if (!device_ordinal_status.ok()) { - results[replica] = device_ordinal_status.status(); - return; - } - const int device_ordinal = device_ordinal_status.ValueOrDie(); - VLOG(3) << "Replica " << replica - << " mapped to device ordinal for execution: " - << device_ordinal; - - // Transfer arguments in - std::vector scoped_buffers; - scoped_buffers.reserve(arguments.size()); - for (int i = 0; i < arguments.size(); ++i) { - const Literal& argument = arguments[i]; - const tensorflow::gtl::optional& shape_with_layout = - shapes_with_layout[i]; - - StatusOr pushed; - if (shape_with_layout) { - std::unique_ptr relaid = - argument.Relayout(shape_with_layout.value()); - pushed = ToBuffer(client, device_ordinal, *relaid); - } else { - pushed = ToBuffer(client, device_ordinal, argument); - } - if (!pushed.ok()) { - results[replica] = pushed.status(); - return; - } - - scoped_buffers.push_back(std::move(pushed).ValueOrDie()); - } - - // Execute - std::vector argument_buffers; - argument_buffers.reserve(scoped_buffers.size()); - for (auto& buffer : scoped_buffers) { - argument_buffers.push_back(&buffer); - } - - DeviceAssignment device_assignment = - client->backend() - .computation_placer() - ->AssignDevices(GetReplicaCount(), /*computation_count=*/1) - .ConsumeValueOrDie(); - - ExecutableRunOptions options; - options.set_device_ordinal(device_ordinal); - options.set_allocator(client->backend().memory_allocator()); - options.set_intra_op_thread_pool( - client->backend().eigen_intra_op_thread_pool_device()); - options.set_device_assignment(&device_assignment); - StatusOr result_buffer_status = - executable_->Run(argument_buffers, options); - if (!result_buffer_status.ok()) { - results[replica] = result_buffer_status.status(); - return; - } - - // Transfer result out - results[replica] = client->ShapedBufferToLiteral( - std::move(result_buffer_status).ValueOrDie()); - }); + pool.Schedule( + [this, client, replica, &arguments, &shapes_with_layout, &results] { + StatusOr device_ordinal_status = + client->ReplicaNumberToDeviceOrdinal(replica); + if (!device_ordinal_status.ok()) { + results[replica] = device_ordinal_status.status(); + return; + } + const int device_ordinal = device_ordinal_status.ValueOrDie(); + VLOG(3) << "Replica " << replica + << " mapped to device ordinal for execution: " + << device_ordinal; + + // Transfer arguments in + std::vector scoped_buffers; + scoped_buffers.reserve(arguments.size()); + for (int i = 0; i < arguments.size(); ++i) { + const Literal& argument = arguments[i]; + const tensorflow::gtl::optional& shape_with_layout = + shapes_with_layout[i]; + + StatusOr pushed; + if (shape_with_layout) { + std::unique_ptr relaid = + argument.Relayout(shape_with_layout.value()); + pushed = ToBuffer(client, device_ordinal, *relaid); + } else { + pushed = ToBuffer(client, device_ordinal, argument); + } + if (!pushed.ok()) { + results[replica] = pushed.status(); + return; + } + + scoped_buffers.push_back(std::move(pushed).ValueOrDie()); + } + + // Execute + std::vector argument_buffers; + argument_buffers.reserve(scoped_buffers.size()); + for (auto& buffer : scoped_buffers) { + argument_buffers.push_back(&buffer); + } + + DeviceAssignment device_assignment = + client->backend() + .computation_placer() + ->AssignDevices(GetReplicaCount(), /*computation_count=*/1) + .ConsumeValueOrDie(); + + ExecutableRunOptions options; + options.set_device_ordinal(device_ordinal); + options.set_allocator(client->backend().memory_allocator()); + options.set_intra_op_thread_pool( + client->backend().eigen_intra_op_thread_pool_device()); + options.set_device_assignment(&device_assignment); + StatusOr result_buffer_status = + executable_->Run(argument_buffers, options); + if (!result_buffer_status.ok()) { + results[replica] = result_buffer_status.status(); + return; + } + + // Transfer result out + results[replica] = client->ShapedBufferToLiteral( + std::move(result_buffer_status).ValueOrDie()); + }); } } @@ -312,14 +343,11 @@ StatusOr LocalComputationBuilder::Build() { LocalOp LocalComputationBuilder::Parameter(int64 parameter_number, const Shape& shape, const string& name) { - return builder_.Parameter(parameter_number, shape, name); + return xla::Parameter(&builder_, parameter_number, shape, name); } -std::unique_ptr LocalComputationBuilder::GetShape( - const LocalOp& operand) { - auto result = MakeUnique(); - *result = builder_.GetShape(operand.op()).ValueOrDie(); - return result; +StatusOr LocalComputationBuilder::GetShape(const LocalOp& operand) { + return builder_.GetShape(operand.op()); } StatusOr LocalComputationBuilder::GetReturnValueShape() { @@ -328,72 +356,70 @@ StatusOr LocalComputationBuilder::GetReturnValueShape() { } LocalOp LocalComputationBuilder::Infeed(const Shape& shape) { - return builder_.Infeed(shape); + return xla::Infeed(&builder_, shape); } void LocalComputationBuilder::Outfeed(const LocalOp& operand, const Shape& shape, const string& outfeed_config) { - builder_.Outfeed(operand.op(), shape, outfeed_config); + xla::Outfeed(operand.op(), shape, outfeed_config); } LocalOp LocalComputationBuilder::ConstantLiteral(const Literal& literal) { - return builder_.ConstantLiteral(literal); + return xla::ConstantLiteral(&builder_, literal); } LocalOp LocalComputationBuilder::Broadcast( const LocalOp& operand, tensorflow::gtl::ArraySlice broadcast_sizes) { - return builder_.Broadcast(operand.op(), broadcast_sizes); + return xla::Broadcast(operand.op(), broadcast_sizes); } LocalOp LocalComputationBuilder::Pad(const LocalOp& operand, const LocalOp& padding_value, const PaddingConfig& padding_config) { - return builder_.Pad(operand.op(), padding_value.op(), padding_config); + return xla::Pad(operand.op(), padding_value.op(), padding_config); } LocalOp LocalComputationBuilder::Reshape( const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions, tensorflow::gtl::ArraySlice new_sizes) { - return builder_.Reshape(operand.op(), dimensions, new_sizes); + return xla::Reshape(operand.op(), dimensions, new_sizes); } LocalOp LocalComputationBuilder::Collapse( const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions) { - return builder_.Collapse(operand.op(), dimensions); + return xla::Collapse(operand.op(), dimensions); } LocalOp LocalComputationBuilder::CrossReplicaSum(const LocalOp& operand) { - return builder_.CrossReplicaSum(operand.op()); + return xla::CrossReplicaSum(operand.op()); } LocalOp LocalComputationBuilder::Slice( const LocalOp& operand, tensorflow::gtl::ArraySlice start_indices, tensorflow::gtl::ArraySlice limit_indices, tensorflow::gtl::ArraySlice strides) { - return builder_.Slice(operand.op(), start_indices, limit_indices, strides); + return xla::Slice(operand.op(), start_indices, limit_indices, strides); } LocalOp LocalComputationBuilder::SliceInDim(const LocalOp& operand, int64 start_index, int64 limit_index, int64 stride, int64 dimno) { - return builder_.SliceInDim(operand.op(), start_index, limit_index, stride, - dimno); + return xla::SliceInDim(operand.op(), start_index, limit_index, stride, dimno); } LocalOp LocalComputationBuilder::DynamicSlice( const LocalOp& operand, const LocalOp& start_indices, tensorflow::gtl::ArraySlice slice_sizes) { - return builder_.DynamicSlice(operand.op(), start_indices.op(), slice_sizes); + return xla::DynamicSlice(operand.op(), start_indices.op(), slice_sizes); } LocalOp LocalComputationBuilder::DynamicUpdateSlice( const LocalOp& operand, const LocalOp& update, const LocalOp& start_indices) { - return builder_.DynamicUpdateSlice(operand.op(), update.op(), - start_indices.op()); + return xla::DynamicUpdateSlice(operand.op(), update.op(), start_indices.op()); } LocalOp LocalComputationBuilder::ConcatInDim( @@ -403,7 +429,7 @@ LocalOp LocalComputationBuilder::ConcatInDim( for (const auto& op : operands) { xla_ops.push_back(op.op()); } - return builder_.ConcatInDim(xla_ops, dimension); + return xla::ConcatInDim(&builder_, xla_ops, dimension); } LocalOp LocalComputationBuilder::SelectAndScatterWithGeneralPadding( @@ -413,7 +439,7 @@ LocalOp LocalComputationBuilder::SelectAndScatterWithGeneralPadding( tensorflow::gtl::ArraySlice> padding, const LocalOp& source, const LocalOp& init_value, const LocalComputation& scatter) { - return builder_.SelectAndScatterWithGeneralPadding( + return xla::SelectAndScatterWithGeneralPadding( operand.op(), select.computation(), window_dimensions, window_strides, padding, source.op(), init_value.op(), scatter.computation()); } @@ -426,22 +452,22 @@ LocalOp LocalComputationBuilder::Tuple( xla_ops.push_back(op.op()); } - return builder_.Tuple(xla_ops); + return xla::Tuple(&builder_, xla_ops); } LocalOp LocalComputationBuilder::GetTupleElement(const LocalOp& tuple_data, int64 index) { - return builder_.GetTupleElement(tuple_data.op(), index); + return xla::GetTupleElement(tuple_data.op(), index); } LocalOp LocalComputationBuilder::Dot(const LocalOp& lhs, const LocalOp& rhs) { - return builder_.Dot(lhs.op(), rhs.op()); + return xla::Dot(lhs.op(), rhs.op()); } LocalOp LocalComputationBuilder::DotGeneral( const LocalOp& lhs, const LocalOp& rhs, const DotDimensionNumbers& dimension_numbers) { - return builder_.DotGeneral(lhs.op(), rhs.op(), dimension_numbers); + return xla::DotGeneral(lhs.op(), rhs.op(), dimension_numbers); } LocalOp LocalComputationBuilder::ConvGeneralDilated( @@ -451,14 +477,13 @@ LocalOp LocalComputationBuilder::ConvGeneralDilated( tensorflow::gtl::ArraySlice lhs_dilation, tensorflow::gtl::ArraySlice rhs_dilation, const ConvolutionDimensionNumbers& dimension_numbers) { - return builder_.ConvGeneralDilated(lhs.op(), rhs.op(), window_strides, - padding, lhs_dilation, rhs_dilation, - dimension_numbers); + return xla::ConvGeneralDilated(lhs.op(), rhs.op(), window_strides, padding, + lhs_dilation, rhs_dilation, dimension_numbers); } LocalOp LocalComputationBuilder::ConvertElementType( const LocalOp& operand, PrimitiveType new_element_type) { - return builder_.ConvertElementType(operand.op(), new_element_type); + return xla::ConvertElementType(operand.op(), new_element_type); } LocalOp LocalComputationBuilder::Call( @@ -469,46 +494,39 @@ LocalOp LocalComputationBuilder::Call( for (const auto& op : operands) { xla_ops.push_back(op.op()); } - return builder_.Call(local_computation.computation(), xla_ops); + return xla::Call(&builder_, local_computation.computation(), xla_ops); } LocalOp LocalComputationBuilder::Transpose( const LocalOp& operand, tensorflow::gtl::ArraySlice permutation) { - return builder_.Transpose(operand.op(), permutation); + return xla::Transpose(operand.op(), permutation); } LocalOp LocalComputationBuilder::Rev( const LocalOp& operand, tensorflow::gtl::ArraySlice dimensions) { - return builder_.Rev(operand.op(), dimensions); + return xla::Rev(operand.op(), dimensions); } LocalOp LocalComputationBuilder::Map( tensorflow::gtl::ArraySlice operands, const LocalComputation& local_computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands) { + tensorflow::gtl::ArraySlice dimensions) { std::vector xla_ops; xla_ops.reserve(operands.size()); for (const auto& op : operands) { xla_ops.push_back(op.op()); } - std::vector static_xla_ops; - static_xla_ops.reserve(static_operands.size()); - for (const auto& op : static_operands) { - static_xla_ops.push_back(op.op()); - } - - return builder_.Map(xla_ops, local_computation.computation(), dimensions, - static_xla_ops); + return xla::Map(&builder_, xla_ops, local_computation.computation(), + dimensions); } LocalOp LocalComputationBuilder::Reduce( const LocalOp& operand, const LocalOp& init_value, const LocalComputation& local_computation, tensorflow::gtl::ArraySlice dimensions_to_reduce) { - return builder_.Reduce(operand.op(), init_value.op(), - local_computation.computation(), dimensions_to_reduce); + return xla::Reduce(operand.op(), init_value.op(), + local_computation.computation(), dimensions_to_reduce); } LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding( @@ -517,7 +535,7 @@ LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding( tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, tensorflow::gtl::ArraySlice> padding) { - return builder_.ReduceWindowWithGeneralPadding( + return xla::ReduceWindowWithGeneralPadding( operand.op(), init_value.op(), local_computation.computation(), window_dimensions, window_strides, padding); } @@ -525,27 +543,27 @@ LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding( LocalOp LocalComputationBuilder::RngNormal(const LocalOp& mu, const LocalOp& sigma, const Shape& shape) { - return builder_.RngNormal(mu.op(), sigma.op(), shape); + return xla::RngNormal(mu.op(), sigma.op(), shape); } LocalOp LocalComputationBuilder::RngUniform(const LocalOp& a, const LocalOp& b, const Shape& shape) { - return builder_.RngUniform(a.op(), b.op(), shape); + return xla::RngUniform(a.op(), b.op(), shape); } LocalOp LocalComputationBuilder::While(const LocalComputation& condition, const LocalComputation& body, const LocalOp& init) { - return builder_.While(condition.computation(), body.computation(), init.op()); + return xla::While(condition.computation(), body.computation(), init.op()); } LocalOp LocalComputationBuilder::Conditional( const LocalOp& predicate, const LocalOp& true_operand, const LocalComputation& true_computation, const LocalOp& false_operand, const LocalComputation& false_computation) { - return builder_.Conditional( - predicate.op(), true_operand.op(), true_computation.computation(), - false_operand.op(), false_computation.computation()); + return xla::Conditional(predicate.op(), true_operand.op(), + true_computation.computation(), false_operand.op(), + false_computation.computation()); } StatusOr LocalComputationBuilder::IsConstant(const LocalOp& operand) { @@ -561,7 +579,7 @@ StatusOr LocalComputationBuilder::BuildConstantSubGraph( #define _FORWARD(method_name, return_sig, args_sig, args) \ return_sig LocalComputationBuilder::method_name args_sig { \ - return builder_.method_name args; \ + return xla::method_name args; \ } #define _FORWARD_UNOP(method_name) \ @@ -595,22 +613,25 @@ _FORWARD_BINOP(Max) _FORWARD_BINOP(Min) _FORWARD_BINOP(And) _FORWARD_BINOP(Or) +_FORWARD_BINOP(Xor) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) +_FORWARD_UNOP(Expm1) _FORWARD_UNOP(Floor) _FORWARD_UNOP(Ceil) _FORWARD_UNOP(Round) _FORWARD_UNOP(Log) +_FORWARD_UNOP(Log1p) _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) -_FORWARD_UNOP(SqrtF32) -_FORWARD_UNOP(SquareF32) +_FORWARD_UNOP(Sqrt) +_FORWARD_UNOP(Square) _FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) -_FORWARD_UNOP(ReciprocalF32) +_FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) @@ -631,6 +652,54 @@ void DeleteLocalComputation(LocalComputation* computation) { delete computation; } -} // namespace swig +StatusOr DestructureLocalShapedBufferTuple( + LocalShapedBuffer* local_shaped_buffer) { + if (!ShapeUtil::IsTuple( + local_shaped_buffer->shaped_buffer()->on_device_shape())) { + return InvalidArgument( + "Attemped to destructure a LocalShapedBuffer that did not have a tuple " + "shape; shape: %s", + ShapeUtil::HumanString( + local_shaped_buffer->shaped_buffer()->on_device_shape()) + .c_str()); + } + DeviceMemoryAllocator* allocator = + local_shaped_buffer->shaped_buffer()->memory_allocator(); + ShapedBuffer tuple_buffer = local_shaped_buffer->Release(); + + // Extract some metadata we use to construct scoped buffers. + const se::Platform* platform = tuple_buffer.platform(); + int device_ordinal = tuple_buffer.device_ordinal(); + + ShapeTree& shape_tree = tuple_buffer.buffers(); + const Shape& tuple_shape = tuple_buffer.on_device_shape(); + std::vector results; + for (int64 i = 0; i < ShapeUtil::TupleElementCount(tuple_shape); ++i) { + // Create a shaped buffer for this destructured tuple element. + const Shape& subshape = ShapeUtil::GetSubshape(tuple_shape, {i}); + VLOG(3) << "Starting tuple element " << i << " subshape: " << subshape; + ShapedBuffer shaped_buffer(subshape, subshape, platform, device_ordinal); + + ShapeUtil::ForEachSubshape( + subshape, [&](const Shape& s, const ShapeIndex& index) { + ShapeIndex original(index); + original.push_front(i); + se::DeviceMemoryBase* device_memory = + shape_tree.mutable_element(original); + shaped_buffer.set_buffer(*device_memory, index); + *device_memory = se::DeviceMemoryBase(); + }); + + VLOG(3) << "Completed tuple element: " << i; + results.push_back(new LocalShapedBuffer( + ScopedShapedBuffer(std::move(shaped_buffer), allocator))); + } + // Deallocate the root buffer. + se::DeviceMemoryBase root_buffer = tuple_buffer.root_buffer(); + TF_RETURN_IF_ERROR(allocator->Deallocate(device_ordinal, root_buffer)); + return new LocalShapedBufferTuple(std::move(results)); +} + +} // namespace swig } // namespace xla diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 9ac13b65231c932f152c1e79eb8e576cc6331fbd..690ff277e884c6f1540b12e7002248571d07fe71 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -26,7 +26,6 @@ limitations under the License. #include "tensorflow/core/lib/gtl/array_slice.h" namespace xla { - namespace swig { // Initializes the number of replicas that XLA will be initialized with (when @@ -69,10 +68,42 @@ class LocalShapedBuffer { StatusOr > ToLiteral() const; + // Transfers ownership of the encapsulated ShapedBuffer to the caller, + // analogous to std::unique_ptr::release(). + ShapedBuffer Release(); + private: ScopedShapedBuffer shaped_buffer_; }; +// Result of a tuple destructuring operation on a LocalShapedBuffer -- this +// appears to be a simpler mechanism for the time being than an alternative like +// using SWIG to transform std::vectors into Python lists of SWIG objects +// directly. +class LocalShapedBufferTuple { + public: + // Note: any LocalShapedBuffer elements that are not Release()'d will be + // deallocated in the destructor. + explicit LocalShapedBufferTuple(std::vector elements); + + ~LocalShapedBufferTuple(); + + // Releases the ith element to the caller. Further attempts to release the ith + // element will return an invalid argument error. + StatusOr Release(int i); + + // Returns the number of elements in the destructured tuple. + int size() const; + + private: + std::vector elements_; +}; + +// Destructures a tuple-valued LocalShapedBuffer into its constitutent elements +// in LocalShapedBufferTuple form. +StatusOr DestructureLocalShapedBufferTuple( + LocalShapedBuffer* local_shaped_buffer); + // Wraps a LocalExecutable produced by compiling a // LocalComputation. The Execute method forwards to that of the // underlying LocalExecutable, and additionally handles tranferring @@ -156,7 +187,7 @@ class LocalComputationBuilder { LocalOp Parameter(int64 parameter_number, const Shape& shape, const string& name); - std::unique_ptr GetShape(const LocalOp& operand); + StatusOr GetShape(const LocalOp& operand); // Returns the shape of the current return value for the computation. StatusOr GetReturnValueShape(); @@ -239,8 +270,7 @@ class LocalComputationBuilder { LocalOp Map(tensorflow::gtl::ArraySlice operands, const LocalComputation& local_computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands); + tensorflow::gtl::ArraySlice dimensions); LocalOp Reduce(const LocalOp& operand, const LocalOp& init_value, const LocalComputation& local_computation, @@ -302,22 +332,25 @@ class LocalComputationBuilder { _FORWARD_BINOP(Min) _FORWARD_BINOP(And) _FORWARD_BINOP(Or) + _FORWARD_BINOP(Xor) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) + _FORWARD_UNOP(Expm1) _FORWARD_UNOP(Floor) _FORWARD_UNOP(Ceil) _FORWARD_UNOP(Round) _FORWARD_UNOP(Log) + _FORWARD_UNOP(Log1p) _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) - _FORWARD_UNOP(SqrtF32) - _FORWARD_UNOP(SquareF32) + _FORWARD_UNOP(Sqrt) + _FORWARD_UNOP(Square) _FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) - _FORWARD_UNOP(ReciprocalF32) + _FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) @@ -336,7 +369,6 @@ void DeleteCompiledLocalComputation(CompiledLocalComputation* computation); void DeleteLocalComputation(LocalComputation* computation); } // namespace swig - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_PYTHON_LOCAL_COMPUTATION_BUILDER_H_ diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 536b93c6f9381ae5c84e65eb7ed264b5eb158a72..c44e69e6153239b39f9f8a40539a75ddffdef25d 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -200,6 +200,20 @@ tensorflow::ImportNumpy(); } } +%typemap(out) StatusOr { + if ($1.ok()) { + auto* value = $1.ValueOrDie(); + { + auto* $1 = value; + $typemap(out, xla::swig::LocalShapedBufferTuple*) + } + } else { + PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str()); + SWIG_fail; + } +} + + %typemap(out) StatusOr< std::unique_ptr > { if ($1.ok()) { std::unique_ptr value = $1.ConsumeValueOrDie(); @@ -905,6 +919,9 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalShapedBuffer; %unignore xla::swig::LocalShapedBuffer::FromLiteral; %unignore xla::swig::LocalShapedBuffer::ToLiteral; +%unignore xla::swig::LocalShapedBufferTuple; +%unignore xla::swig::LocalShapedBufferTuple::Release; +%unignore xla::swig::LocalShapedBufferTuple::size; %unignore xla::swig::CompiledLocalComputation; %unignore xla::swig::CompiledLocalComputation::Execute; %unignore xla::swig::CompiledLocalComputation::ExecuteWithShapedBuffers; @@ -971,24 +988,28 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Min; %unignore xla::swig::LocalComputationBuilder::And; %unignore xla::swig::LocalComputationBuilder::Or; +%unignore xla::swig::LocalComputationBuilder::Xor; %unignore xla::swig::LocalComputationBuilder::Not; %unignore xla::swig::LocalComputationBuilder::Abs; %unignore xla::swig::LocalComputationBuilder::Exp; +%unignore xla::swig::LocalComputationBuilder::Expm1; %unignore xla::swig::LocalComputationBuilder::Floor; %unignore xla::swig::LocalComputationBuilder::Ceil; %unignore xla::swig::LocalComputationBuilder::Round; %unignore xla::swig::LocalComputationBuilder::Log; +%unignore xla::swig::LocalComputationBuilder::Log1p; %unignore xla::swig::LocalComputationBuilder::Sign; %unignore xla::swig::LocalComputationBuilder::Cos; %unignore xla::swig::LocalComputationBuilder::Sin; %unignore xla::swig::LocalComputationBuilder::Tanh; -%unignore xla::swig::LocalComputationBuilder::SqrtF32; -%unignore xla::swig::LocalComputationBuilder::SquareF32; +%unignore xla::swig::LocalComputationBuilder::Sqrt; +%unignore xla::swig::LocalComputationBuilder::Square; %unignore xla::swig::LocalComputationBuilder::Pow; %unignore xla::swig::LocalComputationBuilder::IsFinite; -%unignore xla::swig::LocalComputationBuilder::ReciprocalF32; +%unignore xla::swig::LocalComputationBuilder::Reciprocal; %unignore xla::swig::LocalComputationBuilder::Neg; %unignore xla::swig::LocalComputationBuilder::Sort; +%unignore xla::swig::DestructureLocalShapedBufferTuple; %unignore xla::swig::DeleteLocalShapedBuffer; %unignore xla::swig::DeleteLocalComputation; %unignore xla::swig::DeleteCompiledLocalComputation; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 11611ac61287da30548c335fac977bdc255396ed..27aee634bac613a87c919a357e085ec71c7deeb1 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -89,18 +89,20 @@ _UNARY_OPS = [ 'Not', 'Abs', 'Exp', + 'Expm1', 'Floor', 'Round', 'Ceil', 'Log', + 'Log1p', 'Sign', 'Cos', 'Sin', 'Tanh', - 'SqrtF32', - 'SquareF32', + 'Sqrt', + 'Square', 'IsFinite', - 'ReciprocalF32', + 'Reciprocal', 'Neg', 'Sort', ] @@ -121,6 +123,7 @@ _BINARY_OPS = [ 'Min', 'And', 'Or', + 'Xor', 'Pow', ] @@ -184,6 +187,14 @@ class LocalBuffer(object): self._delete(self.c_local_shaped_buffer) self.c_local_shaped_buffer = None + def destructure(self): + assert self.c_local_shaped_buffer is not None + result = c_api.DestructureLocalShapedBufferTuple(self.c_local_shaped_buffer) + self.c_local_shaped_buffer = None + size = result.size() + destructured = tuple(LocalBuffer(result.Release(i)) for i in xrange(size)) + return destructured + def is_deleted(self): return self.c_local_shaped_buffer is None @@ -247,9 +258,12 @@ class Shape(object): self._dimensions == other._dimensions and self._minor_to_major == other._minor_to_major) + def __ne__(self, other): + return not self == other + def __repr__(self): return ('xla_client.Shape(_dtype={!r}, _dimensions={!r}, ' - '_is_tuple={!r}), _minor_to_major={!r}').format( + '_is_tuple={!r}, _minor_to_major={!r})').format( self._dtype, self._dimensions, self._is_tuple, self._minor_to_major) @@ -895,20 +909,19 @@ class ComputationBuilder(object): """ return self._client.Call(computation_to_apply.c_local_computation, operands) - def Map(self, operands, computation_to_apply, dimensions, static_operands=()): + def Map(self, operands, computation_to_apply, dimensions): """Enqueues a map operation onto the computation. Args: operands: an iterable of LocalOp. computation_to_apply: a Computation object. dimensions: dimensions over which to apply map the function. - static_operands: auxiliary arguments passed to the applied computation. Returns: A LocalOp representing the added Map op. """ return self._client.Map(operands, computation_to_apply.c_local_computation, - dimensions, static_operands) + dimensions) def Reduce(self, operand, init_value, computation_to_apply, dimensions): """Enqueues a reduction operation onto the computation. diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 375e720f9b433f45ad5adc329104c286184a7510..0564ddcb85ee3952f82649687e79a864999baf2c 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -157,6 +157,13 @@ class ComputationsWithConstantsTest(LocalComputationTest): c.Constant(NumpyArrayBool([True, True, False, False]))) self._ExecuteAndCompareExact(c, expected=[True, True, True, False]) + def testBooleanXor(self): + c = self._NewComputation() + c.Xor( + c.Constant(NumpyArrayBool([True, False, True, False])), + c.Constant(NumpyArrayBool([True, True, False, False]))) + self._ExecuteAndCompareExact(c, expected=[False, True, True, False]) + def testSum2DF32(self): c = self._NewComputation() c.Add( @@ -365,6 +372,55 @@ class LocalBufferTest(LocalComputationTest): with self.assertRaises(ValueError): compiled_c.ExecuteWithLocalBuffers([arg_buffer]) + def testDestructureTupleEmpty(self): + t = () + local_buffer = xla_client.LocalBuffer.from_pyval(t) + pieces = local_buffer.destructure() + self.assertTrue(local_buffer.is_deleted()) + self.assertEqual(len(pieces), 0) + + def testDestructureTupleOneArrayElement(self): + t = (np.array([1, 2, 3, 4], dtype=np.int32),) + local_buffer = xla_client.LocalBuffer.from_pyval(t) + pieces = local_buffer.destructure() + self.assertTrue(local_buffer.is_deleted()) + self.assertEqual(len(pieces), 1) + array = pieces[0] + got = array.to_py() + want = NumpyArrayS32([1, 2, 3, 4]) + np.testing.assert_equal(want, got) + + def testDestructureTupleTwoArrayElementDifferentType(self): + t = (np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32), + np.array([2, 3, 4, 5], dtype=np.int32)) + local_buffer = xla_client.LocalBuffer.from_pyval(t) + pieces = local_buffer.destructure() + self.assertTrue(local_buffer.is_deleted()) + self.assertEqual(len(pieces), 2) + array0, array1 = pieces + got = array0.to_py() + want = NumpyArrayF32([1.0, 2.0, 3.0, 4.0]) + np.testing.assert_equal(want, got) + got = array1.to_py() + want = NumpyArrayS32([2, 3, 4, 5]) + np.testing.assert_equal(want, got) + + def testDestructureTupleNested(self): + t = ((NumpyArrayF32([1.0, 2.0]), NumpyArrayS32([3, 4])), NumpyArrayS32([5])) + local_buffer = xla_client.LocalBuffer.from_pyval(t) + pieces = local_buffer.destructure() + self.assertTrue(local_buffer.is_deleted()) + self.assertEqual(len(pieces), 2) + tuple0, array1 = pieces + got = array1.to_py() + want = NumpyArrayS32([5]) + np.testing.assert_equal(want, got) + got = tuple0.to_py() + self.assertEqual(type(got), tuple) + self.assertEqual(len(got), 2) + np.testing.assert_equal(NumpyArrayF32([1.0, 2.0]), got[0]) + np.testing.assert_equal(NumpyArrayS32([3, 4]), got[1]) + class SingleOpTest(LocalComputationTest): """Tests for single ops. @@ -571,6 +627,12 @@ class SingleOpTest(LocalComputationTest): c.Exp(c.Constant(arr)) self._ExecuteAndCompareClose(c, expected=np.exp(arr)) + def testExpm1(self): + c = self._NewComputation() + arr = NumpyArrayF32([3.3, 12.1]) + c.Expm1(c.Constant(arr)) + self._ExecuteAndCompareClose(c, expected=np.expm1(arr)) + def testRound(self): c = self._NewComputation() arr = NumpyArrayF32([3.3, 12.1]) @@ -583,6 +645,12 @@ class SingleOpTest(LocalComputationTest): c.Log(c.Constant(arr)) self._ExecuteAndCompareClose(c, expected=np.log(arr)) + def testLog1p(self): + c = self._NewComputation() + arr = NumpyArrayF32([3.3, 12.1]) + c.Log1p(c.Constant(arr)) + self._ExecuteAndCompareClose(c, expected=np.log1p(arr)) + def testNeg(self): c = self._NewComputation() arr = NumpyArrayF32([3.3, 12.1]) @@ -1107,14 +1175,6 @@ class EmbeddedComputationsTest(LocalComputationTest): self._CreateBinaryDivF64Computation(), [0]) self._ExecuteAndCompareClose(c, expected=[0.2, 0.4, 0.75, 1.0]) - def DISABLED_testMapWithStaticOperands(self): - c = self._NewComputation() - factor = c.ConstantF32Scalar(3.0) - c.Map([c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0]))], - self._CreateMulF32ByParamComputation(), [0], - static_operands=[factor]) - self._ExecuteAndCompareClose(c, expected=[3.0, 6.0, 9.0, 12.0]) - def testSelectAndScatterF32(self): c = self._NewComputation() c.SelectAndScatter(c.Constant(NumpyArrayF32([[1., 2., 6.], [4., 5., 3.]])), diff --git a/tensorflow/compiler/xla/python_api/BUILD b/tensorflow/compiler/xla/python_api/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..8999cda5ef852d1246bea45a3312575ec1ac0721 --- /dev/null +++ b/tensorflow/compiler/xla/python_api/BUILD @@ -0,0 +1,36 @@ +# Description: +# Python API for XLA. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +py_library( + name = "types", + srcs = ["types.py"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto_py", + "//third_party/py/numpy", + ], +) + +py_library( + name = "xla_shape", + srcs = ["xla_shape.py"], + visibility = ["//visibility:public"], + deps = [ + ":types", + "//tensorflow/compiler/xla:xla_data_proto_py", + ], +) + +py_library( + name = "xla_literal", + srcs = ["xla_literal.py"], + visibility = ["//visibility:public"], + deps = [ + ":types", + ":xla_shape", + "//tensorflow/compiler/xla:xla_data_proto_py", + ], +) diff --git a/tensorflow/compiler/xla/python_api/types.py b/tensorflow/compiler/xla/python_api/types.py new file mode 100644 index 0000000000000000000000000000000000000000..b60f8dce92ace1b2c682374a2605b3a477936bbc --- /dev/null +++ b/tensorflow/compiler/xla/python_api/types.py @@ -0,0 +1,124 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Utilities for XLA-specific Python types.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 + +# Records corresponsence between a XLA primitive type and Python/Numpy types. +# +# primitive_type: value of type xla_data_pb2.PrimitiveType +# numpy_dtype: corresponsing Numpy "dtype" (like np.float32) +# literal_field_name: name of the field in the LiteralProto message elements +# of this type go into. +# literal_field_type: type of the field named 'literal_field_name'. +# +# TODO(eliben): figure out how to avoid knowing the extra Python type and the +# astype cast when writing into Literals. +TypeConversionRecord = collections.namedtuple('TypeConversionRecord', [ + 'primitive_type', 'numpy_dtype', 'literal_field_name', 'literal_field_type' +]) + +# Maps from XLA primitive types to TypeConversionRecord. +MAP_XLA_TYPE_TO_RECORD = { + xla_data_pb2.F16: + TypeConversionRecord( + primitive_type=xla_data_pb2.F16, + numpy_dtype=np.float16, + literal_field_name='f16s', + literal_field_type=float), + xla_data_pb2.F32: + TypeConversionRecord( + primitive_type=xla_data_pb2.F32, + numpy_dtype=np.float32, + literal_field_name='f32s', + literal_field_type=float), + xla_data_pb2.F64: + TypeConversionRecord( + primitive_type=xla_data_pb2.F64, + numpy_dtype=np.float64, + literal_field_name='f64s', + literal_field_type=float), + xla_data_pb2.S8: + TypeConversionRecord( + primitive_type=xla_data_pb2.S8, + numpy_dtype=np.int8, + literal_field_name='s8s', + literal_field_type=int), + xla_data_pb2.S16: + TypeConversionRecord( + primitive_type=xla_data_pb2.S16, + numpy_dtype=np.int16, + literal_field_name='s16s', + literal_field_type=int), + xla_data_pb2.S32: + TypeConversionRecord( + primitive_type=xla_data_pb2.S32, + numpy_dtype=np.int32, + literal_field_name='s32s', + literal_field_type=int), + xla_data_pb2.S64: + TypeConversionRecord( + primitive_type=xla_data_pb2.S64, + numpy_dtype=np.int64, + literal_field_name='s64s', + literal_field_type=int), + xla_data_pb2.U8: + TypeConversionRecord( + primitive_type=xla_data_pb2.U8, + numpy_dtype=np.uint8, + literal_field_name='s8s', + literal_field_type=int), + xla_data_pb2.U16: + TypeConversionRecord( + primitive_type=xla_data_pb2.U16, + numpy_dtype=np.uint16, + literal_field_name='s16s', + literal_field_type=int), + xla_data_pb2.U32: + TypeConversionRecord( + primitive_type=xla_data_pb2.U32, + numpy_dtype=np.uint32, + literal_field_name='s32s', + literal_field_type=int), + xla_data_pb2.U64: + TypeConversionRecord( + primitive_type=xla_data_pb2.U64, + numpy_dtype=np.uint64, + literal_field_name='s64s', + literal_field_type=int), + xla_data_pb2.PRED: + TypeConversionRecord( + primitive_type=xla_data_pb2.PRED, + numpy_dtype=np.bool, + literal_field_name='preds', + literal_field_type=bool) +} + +# Maps from Numpy dtypes to TypeConversionRecord. +# Note the conversion on the key. Numpy has a known issue wherein dtype hashing +# doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus, +# when keying by dtype in this dict, we use the string form of dtypes. +MAP_DTYPE_TO_RECORD = { + str(np.dtype(record.numpy_dtype)): record + for record in MAP_XLA_TYPE_TO_RECORD.values() +} diff --git a/tensorflow/compiler/xla/python_api/xla_literal.py b/tensorflow/compiler/xla/python_api/xla_literal.py new file mode 100644 index 0000000000000000000000000000000000000000..b040098c294ffaae92b72f678947f99289239314 --- /dev/null +++ b/tensorflow/compiler/xla/python_api/xla_literal.py @@ -0,0 +1,95 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""XLA LiteralProto utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import types +from tensorflow.compiler.xla.python_api import xla_shape + + +def ConvertLiteralToNumpyArray(literal): + """Converts a XLA literal to a Numpy array.""" + element_type = literal.shape.element_type + if element_type == xla_data_pb2.TUPLE: + return tuple( + ConvertLiteralToNumpyArray(subliteral) + for subliteral in literal.tuple_literals) + + type_record = types.MAP_XLA_TYPE_TO_RECORD[element_type] + if not literal.shape.dimensions: + return np.array( + getattr(literal, type_record.literal_field_name)[0], + type_record.numpy_dtype) + else: + # Infer the proper Numpy order from the LiteralProto's layout. The repeated + # field representing the array's content in the Literal is linearized. + # Reading is done in two steps: + # + # 1. Read the array as 1D from the LiteralProto repeated field. + # 2. Reshape the array to its proper shape, using the right order depending + # on the LiteralProto's layout. + layout_order = literal.shape.layout.minor_to_major + numpy_shape = tuple(literal.shape.dimensions) + if layout_order == range(len(literal.shape.dimensions)): + numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='F') + elif layout_order == range(len(literal.shape.dimensions) - 1, -1, -1): + numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='C') + else: + raise NotImplementedError('Unsupported layout: {0}'.format(layout_order)) + ndarray = np.array( + getattr(literal, type_record.literal_field_name), + copy=False, + dtype=type_record.numpy_dtype) + return numpy_reshaper(ndarray) + + +def _ConvertNumpyArrayToLiteral(ndarray): + """Converts a Numpy array to a XLA literal.""" + type_record = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)] + literal = xla_data_pb2.LiteralProto() + literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(ndarray).message) + + if ndarray.ndim == 0: + getattr(literal, type_record.literal_field_name).append( + np.asscalar(ndarray.astype(type_record.literal_field_type))) + else: + # Ndarrays with boolean dtypes need special type conversion with protobufs + if ndarray.dtype in {np.bool_, np.dtype('bool')}: + for element in np.nditer(ndarray): + getattr(literal, type_record.literal_field_name).append( + type_record.literal_field_type(element)) + else: + ndarray_flat = ndarray.ravel(order='A') + getattr(literal, type_record.literal_field_name).extend(ndarray_flat) + return literal + + +def ConvertNumpyArrayToLiteral(value): + """Converts a Numpy array or a nested tuple thereof to an XLA literal.""" + if isinstance(value, tuple): + literal = xla_data_pb2.LiteralProto() + literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(value).message) + for component in value: + component_literal = literal.tuple_literals.add() + component_literal.CopyFrom(ConvertNumpyArrayToLiteral(component)) + return literal + else: + return _ConvertNumpyArrayToLiteral(value) diff --git a/tensorflow/compiler/xla/python_api/xla_shape.py b/tensorflow/compiler/xla/python_api/xla_shape.py new file mode 100644 index 0000000000000000000000000000000000000000..6af28958035bbb03e7e1dbb0d0c7bb2c2f25b96d --- /dev/null +++ b/tensorflow/compiler/xla/python_api/xla_shape.py @@ -0,0 +1,155 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""XLA Shape utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import types + + +class Shape(object): + """Wraps a xla_data_pb2.Shape message with a convenient Python type. + + Provides direct access to the underlying xla_data_pb2.Shape message in the + message attribute, along with accessor wrappers to the message's fields. + Avoid direct access to .message unless interacting directly with protobuf APIs + like CopyFrom. In other words, prefer hauling the shape around in a Shape, and + only access .message when strictly required by the protobuf API. + """ + + def __init__(self, element_type, dimensions, layout=None): + """Creates a new XLA Shape. + + Args: + element_type: element type from xla_data_pb2. + dimensions: sequence of dimensions sizes (integers), or sequence + of Shapes in the case of a tuple, i.e. when element_type is + TUPLE. + layout: optional minor_to_major sequence for layout. If not given, the + default major-to-minor layout is used. + + Raises: + ValueError: if element_type is TUPLE but dimensions are not Shape objects. + """ + self.message = xla_data_pb2.Shape() + self.message.element_type = element_type + if element_type == xla_data_pb2.TUPLE: + if not all(isinstance(subshape, Shape) for subshape in dimensions): + raise ValueError( + 'XLA tuple requires sequence of Shape objects as dimensions') + self._tuple_shapes = tuple(dimensions) + for component_shape in self._tuple_shapes: + component_message = self.message.tuple_shapes.add() + component_message.CopyFrom(component_shape.message) + else: + self.message.dimensions.extend(dimensions) + if layout is None: + layout = list(reversed(range(len(dimensions)))) + self.message.layout.format = xla_data_pb2.DENSE + self.message.layout.minor_to_major.extend(layout) + + def element_type(self): + return self.message.element_type + + def is_tuple(self): + return self.element_type() == xla_data_pb2.TUPLE + + def dimensions(self): + if self.is_tuple(): + raise ValueError('Tuple shape has no dimensions. Try tuple_shapes()?') + return self.message.dimensions + + def tuple_shapes(self): + """If this is a tuple, returns its sequence of constituent Shape objects. + + Returns: + Tuple sub-shapes. + + Raises: + ValueError: if this is not a tuple. + """ + if not self.is_tuple(): + raise ValueError('tuple_shapes() called on a non-tuple shape') + return self._tuple_shapes + + def layout(self): + return self.message.layout + + @staticmethod + def from_pyval(pyval): + return CreateShapeFromNumpy(pyval) + + +def _CreateShapeFromNumpy(ndarray): # pylint: disable=invalid-name + """Create a Shape from a given Numpy array. + + Args: + ndarray: Numpy array. + + Returns: + A Shape object. + """ + element_type = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)].primitive_type + dimensions = ndarray.shape + + # Set the shape's layout based on the ordering of ndarray. + # Numpy arrays come in two orders: Fortran (column-major) and C (row-major). + if np.isfortran(ndarray): + # Column-major layout. This corresponds to a "dimension order is + # minor-to-major" layout in XLA. + layout = range(ndarray.ndim) + else: + # Row-major layout. This corresponds to a "dimension order is + # major-to-minor" layout int XLA. + layout = list(reversed(xrange(ndarray.ndim))) + + return Shape(element_type, dimensions, layout) + + +def CreateShapeFromNumpy(value): # pylint: disable=invalid-name + """Create a Shape from a Numpy array or a nested tuple structure thereof. + + Args: + value: Numpy array or (possibly nested) tuple structure that bottoms out in + Numpy arrays. + + Returns: + A Shape object. + """ + if isinstance(value, tuple): + return Shape( + xla_data_pb2.TUPLE, + [CreateShapeFromNumpy(component) for component in value]) + else: + return _CreateShapeFromNumpy(value) + + +def CreateShapeFromDtypeAndTuple(dtype, shape_tuple): # pylint: disable=invalid-name + """Create a shape from a Numpy dtype and a sequence of nonnegative integers. + + Args: + dtype: a numpy dtype, e.g. np.dtype('int32'). + shape_tuple: a sequence of nonnegative integers. + + Returns: + A Shape object. + """ + element_type = types.MAP_DTYPE_TO_RECORD[str(dtype)].primitive_type + return Shape(element_type, shape_tuple) diff --git a/tensorflow/compiler/xla/rpc/BUILD b/tensorflow/compiler/xla/rpc/BUILD index 0d56a9a477b15964ad45e798865aa8d2c7385073..0b1cec1925d4424db086f8a3f62c91ede090189c 100644 --- a/tensorflow/compiler/xla/rpc/BUILD +++ b/tensorflow/compiler/xla/rpc/BUILD @@ -39,10 +39,10 @@ tf_cc_binary( srcs = ["grpc_service_main.cc"], deps = [ ":grpc_service", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", - "@grpc//:grpc++_unsecure", ], ) @@ -54,6 +54,7 @@ tf_cc_test( ], deps = [ ":grpc_stub", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -61,7 +62,6 @@ tf_cc_test( "//tensorflow/core:lib", "//tensorflow/core:test", "//tensorflow/core:test_main", - "@grpc//:grpc++_unsecure", ], ) @@ -71,9 +71,9 @@ cc_library( hdrs = ["grpc_service.h"], deps = [ ":xla_service_proto", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/core/distributed_runtime/rpc:grpc_util", - "@grpc//:grpc++_unsecure", ], ) diff --git a/tensorflow/compiler/xla/rpc/grpc_client_test.cc b/tensorflow/compiler/xla/rpc/grpc_client_test.cc index 313f11a9a957155eb277dc02ba5d2565c87e0235..f8414468bd9e0a9faf0072c47d94d12ab11b908d 100644 --- a/tensorflow/compiler/xla/rpc/grpc_client_test.cc +++ b/tensorflow/compiler/xla/rpc/grpc_client_test.cc @@ -20,8 +20,8 @@ limitations under the License. #include #include -#include "grpc++/create_channel.h" -#include "grpc++/security/credentials.h" +#include "grpcpp/create_channel.h" +#include "grpcpp/security/credentials.h" #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" @@ -85,13 +85,13 @@ TEST_F(GRPCClientTestBase, ItsAlive) { TEST_F(GRPCClientTestBase, AxpyTenValues) { XlaBuilder builder("axpy_10"); - auto alpha = builder.ConstantR0(3.1415926535); - auto x = builder.ConstantR1( - {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); - auto y = builder.ConstantR1( - {5.0, -5.0, -4.0, 4.0, 3.0, -3.0, -2.0, 2.0, 1.0, -1.0}); - auto ax = builder.Mul(alpha, x); - auto axpy = builder.Add(ax, y); + auto alpha = ConstantR0(&builder, 3.1415926535); + auto x = ConstantR1( + &builder, {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); + auto y = ConstantR1( + &builder, {5.0, -5.0, -4.0, 4.0, 3.0, -3.0, -2.0, 2.0, 1.0, -1.0}); + auto ax = Mul(alpha, x); + Add(ax, y); std::vector expected = { 1.85840735, -1.85840735, 2.28318531, -2.28318531, -6.42477796, diff --git a/tensorflow/compiler/xla/rpc/grpc_service.h b/tensorflow/compiler/xla/rpc/grpc_service.h index 5cd573167ae8c002ad8f09e8ba3fb25c6f356564..ca1b09b648013ad45d806040c5ddcf11d9e5604e 100644 --- a/tensorflow/compiler/xla/rpc/grpc_service.h +++ b/tensorflow/compiler/xla/rpc/grpc_service.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_RPC_GRPC_SERVICE_H_ #define TENSORFLOW_COMPILER_XLA_RPC_GRPC_SERVICE_H_ -#include "grpc++/server_context.h" +#include "grpcpp/server_context.h" #include "tensorflow/compiler/xla/rpc/xla_service.grpc.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/rpc/grpc_service_main.cc b/tensorflow/compiler/xla/rpc/grpc_service_main.cc index e29908ccec80db76e3b5b856e57382c56430c379..c68c857c304138ff4318e243f66547c6acce1005 100644 --- a/tensorflow/compiler/xla/rpc/grpc_service_main.cc +++ b/tensorflow/compiler/xla/rpc/grpc_service_main.cc @@ -15,9 +15,9 @@ limitations under the License. // Basic server binary that exposes a xla::Service through a GRPC interface // on a configurable port. -#include "grpc++/security/server_credentials.h" -#include "grpc++/server.h" -#include "grpc++/server_builder.h" +#include "grpcpp/security/server_credentials.h" +#include "grpcpp/server.h" +#include "grpcpp/server_builder.h" #include "tensorflow/compiler/xla/rpc/grpc_service.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/init_main.h" diff --git a/tensorflow/compiler/xla/rpc/xla_service.proto b/tensorflow/compiler/xla/rpc/xla_service.proto index 92eb19ec0f9696974556be01a93c074846f6c23a..551ae895e05586daec0ffcd425f4950f76bdd50d 100644 --- a/tensorflow/compiler/xla/rpc/xla_service.proto +++ b/tensorflow/compiler/xla/rpc/xla_service.proto @@ -115,10 +115,6 @@ service XlaService { returns (ComputeConstantResponse) { } - // Retrieves the inferred shape for a value within a computation. - rpc GetLocalShape(GetLocalShapeRequest) returns (GetLocalShapeResponse) { - } - // Requests one or more device handles from the target. The returned device // handles can be used to specify the device on which to execute computations // or transfer data. @@ -132,18 +128,6 @@ service XlaService { returns (CreateChannelHandleResponse) { } - // Requests that the referenced computation be specialized for the provided - // arguments for subsequent execution. This permits things such as value - // specialization. - rpc Specialize(SpecializeRequest) returns (SpecializeResponse) { - } - - // Modifies the provided computation so that subsequent executions - // will compute the provided ComputationDataHandle, rather than the - // last expression enqueued on that Computation. - rpc SetReturnValue(SetReturnValueRequest) returns (SetReturnValueResponse) { - } - // Invokes the provided computation with the provided global data passed as // immutable arguments. The request contains the whole computation graph. // Returns global data output and execution timing. diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 20cc671ba393769afa1dd2c964197a87c1835504..fe99f700d23dbab799ba011b705c59d6ef7a2e52 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -32,6 +32,7 @@ tf_proto_library_py( name = "hlo_proto", # bzl adds a _py suffix only to the OSS target. srcs = ["hlo.proto"], visibility = ["//visibility:public"], + deps = ["//tensorflow/compiler/xla:xla_data_proto_py"], ) xla_proto_library( @@ -292,7 +293,6 @@ cc_library( ":hlo_proto", ":hlo_reachability", ":name_uniquer", - ":versioned_computation_handle", "//tensorflow/compiler/xla:array", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:protobuf_util", @@ -401,17 +401,6 @@ tf_cc_test( ], ) -cc_library( - name = "versioned_computation_handle", - srcs = ["versioned_computation_handle.cc"], - hdrs = ["versioned_computation_handle.h"], - deps = [ - "//tensorflow/compiler/xla:types", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:lib", - ], -) - tf_cc_test( name = "hlo_instruction_test", srcs = ["hlo_instruction_test.cc"], @@ -591,7 +580,6 @@ cc_library( ":allocation_tracker", ":backend", ":channel_tracker", - ":compilation_cache", ":compiler", ":computation_layout", ":device_memory_allocator", @@ -606,7 +594,6 @@ cc_library( ":platform_util", ":source_map_util", ":transfer_manager", - ":versioned_computation_handle", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:service_interface", @@ -641,7 +628,6 @@ cc_library( ":platform_util", ":service", ":shaped_buffer", - ":versioned_computation_handle", "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:shape_layout", "//tensorflow/compiler/xla:shape_util", @@ -762,7 +748,6 @@ cc_library( ":hlo_proto", ":pool", ":shaped_buffer", - ":versioned_computation_handle", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", @@ -864,7 +849,6 @@ cc_library( hdrs = ["channel_tracker.h"], deps = [ ":hlo", - ":versioned_computation_handle", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -1118,6 +1102,7 @@ tf_cc_test( srcs = ["hlo_scheduling_test.cc"], deps = [ ":buffer_value", + ":heap_simulator", ":hlo", ":hlo_ordering", ":hlo_scheduling", @@ -1165,6 +1150,19 @@ tf_cc_test( ], ) +cc_library( + name = "multi_output_fusion", + srcs = ["multi_output_fusion.cc"], + hdrs = ["multi_output_fusion.h"], + deps = [ + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_pass", + "//tensorflow/core:lib", + ], +) + cc_library( name = "hlo_creation_utils", srcs = ["hlo_creation_utils.cc"], @@ -1646,7 +1644,6 @@ tf_cc_test( ":hlo_cost_analysis", ":local_service", ":service", - ":versioned_computation_handle", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test_helpers", @@ -1955,6 +1952,7 @@ cc_library( hdrs = ["tuple_points_to_analysis.h"], deps = [ ":hlo", + ":hlo_dataflow_analysis", ":logical_buffer", ":logical_buffer_analysis", "//tensorflow/compiler/xla:shape_tree", @@ -1987,20 +1985,6 @@ tf_cc_test( ], ) -cc_library( - name = "compilation_cache", - srcs = ["compilation_cache.cc"], - hdrs = ["compilation_cache.h"], - deps = [ - ":executable", - ":hlo_module_config", - ":versioned_computation_handle", - "//tensorflow/compiler/xla:types", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:lib", - ], -) - cc_library( name = "layout_assignment", srcs = [ @@ -2111,6 +2095,7 @@ cc_library( hdrs = ["hlo_verifier.h"], deps = [ ":hlo", + ":hlo_casting_utils", ":hlo_pass", ":shape_inference", "//tensorflow/compiler/xla:status_macros", @@ -2142,6 +2127,7 @@ cc_library( ":buffer_liveness", ":buffer_value", ":call_graph", + ":copy_insertion", ":flatten_call_graph", ":hlo", ":hlo_dce", @@ -2149,6 +2135,7 @@ cc_library( ":hlo_scheduling", ":logical_buffer", ":tuple_points_to_analysis", + ":tuple_simplifier", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -2162,6 +2149,7 @@ tf_cc_test( name = "hlo_rematerialization_test", srcs = ["hlo_rematerialization_test.cc"], deps = [ + ":flatten_call_graph", ":hlo", ":hlo_matchers", ":hlo_ordering", @@ -2171,6 +2159,7 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", ], ) @@ -2397,7 +2386,6 @@ cc_library( ":hlo_graph_dumper", ":hlo_pass", "//tensorflow/compiler/xla:types", - "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", ], ) @@ -2414,6 +2402,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -2561,7 +2550,6 @@ cc_library( name = "hlo_tfgraph_builder", srcs = ["hlo_tfgraph_builder.cc"], hdrs = ["hlo_tfgraph_builder.h"], - visibility = ["//tensorflow/compiler/xla/tools:__pkg__"], deps = [ ":hlo", "//tensorflow/compiler/xla:literal_util", @@ -2592,6 +2580,7 @@ cc_library( hdrs = ["hlo_graph_dumper.h"], deps = [ ":hlo", + ":hlo_casting_utils", ":hlo_execution_profile", ":hlo_tfgraph_builder", "//tensorflow/compiler/xla:literal_util", diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index dc5f1b31bf8510be404491b7bceb36f73f4cbf75..1ddeb27e4041df22bd3d0ec200bcddbd09937e01 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -50,20 +50,15 @@ namespace { namespace m = match; -// Returns whether operand is a literal with the given value. -bool IsLiteralWithValue(const HloInstruction* operand, int8 value) { - return operand->opcode() == HloOpcode::kConstant && - operand->literal().IsAll(value); -} - bool IsAll(const HloInstruction* op, int8 value) { - if (IsLiteralWithValue(op, value)) { - return true; - } - if (op->opcode() == HloOpcode::kBroadcast && IsAll(op->operand(0), value)) { - return true; + switch (op->opcode()) { + case HloOpcode::kBroadcast: + return IsAll(op->operand(0), value); + case HloOpcode::kConstant: + return op->literal().IsAll(value); + default: + return false; } - return false; } // Returns whether the given transpose produces a result which is bit-wise @@ -75,21 +70,22 @@ bool TransposeIsBitcast(const HloInstruction* transpose) { transpose->dimensions()); } -// Returns true if the given reshape produces a result which is bit-wise +// Returns true if the given reshape/copy produces a result which is bit-wise // identical to its operand and thus may be replaced with a bitcast. // // This function is conservative -- even if this function returns false, the // reshape may still be a bitcast. For example, a reshape from [28x28] to [784]. -bool ReshapeIsBitcast( - const HloInstruction* reshape, +bool ReshapeOrCopyIsBitcast( + const HloInstruction* instr, const AlgebraicSimplifier::ValidBitcastCallback& valid_bitcast_callback) { - CHECK_EQ(HloOpcode::kReshape, reshape->opcode()); + CHECK(HloOpcode::kReshape == instr->opcode() || + HloOpcode::kCopy == instr->opcode()); - const HloInstruction* operand = reshape->operand(0); + const HloInstruction* operand = instr->operand(0); // Can't insert bitcasts if the compiler used a memory layout which isn't // compatible. - return ShapeUtil::ReshapeIsBitcast(operand->shape(), reshape->shape()) && - valid_bitcast_callback(operand->shape(), reshape->shape()); + return ShapeUtil::ReshapeIsBitcast(operand->shape(), instr->shape()) && + valid_bitcast_callback(operand->shape(), instr->shape()); } // AlgebraicSimplifierVisitor traverses the HLO computation and reduces certain @@ -159,9 +155,6 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault { Status HandleMap(HloInstruction* map) override; - Status HandleMaximum(HloInstruction* maximum) override; - Status HandleMinimum(HloInstruction* minimum) override; - // Returns whether algebraic simplification has occurred. const bool changed() const { return changed_; } @@ -200,8 +193,9 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault { // Helper method to perform and add reduction in a single dimension. HloInstruction* AddReduce(HloInstruction* hlo, int64 dim) { - HloInstruction* zero = computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction* zero = + computation_->AddInstruction(HloInstruction::CreateConstant( + Literal::Zero(hlo->shape().element_type()).CloneToUnique())); HloComputation* AddReduce_computation = GetOrCreateScalarAddComputation(); Shape shape = ShapeUtil::DeleteDimension(dim, hlo->shape()); return computation_->AddInstruction(HloInstruction::CreateReduce( @@ -433,7 +427,15 @@ Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy) { copy, HloInstruction::CreateUnary(copy->shape(), HloOpcode::kCopy, op)); } // All copies can be eliminated (assuming layout constraints are satisified). - ReplaceInstructionIfSameShape(copy, copy->mutable_operand(0)); + if (ReplaceInstructionIfSameShape(copy, copy->mutable_operand(0))) { + return Status::OK(); + } + + if (is_layout_sensitive_ && + ReshapeOrCopyIsBitcast(copy, valid_bitcast_callback_)) { + ReplaceWithBitcast(copy); + } + return Status::OK(); } @@ -449,7 +451,7 @@ Status AlgebraicSimplifierVisitor::HandleConcatenate( // Filter out and remove empty operands. std::vector nonempty_operands; for (HloInstruction* operand : operands) { - if (!ShapeUtil::HasZeroElements(operand->shape())) { + if (!ShapeUtil::IsZeroElementArray(operand->shape())) { nonempty_operands.push_back(operand); } } @@ -528,6 +530,10 @@ Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant) { constant, BuildTupleConstant(computation_, constant->literal())); } + if (constant->shape().element_type() == TOKEN) { + return Status::OK(); + } + // If a literal is all the same element replace it with a scalar broadcast. if (ShapeUtil::ElementsIn(constant->shape()) > 1 && constant->literal().IsAllFirst()) { @@ -563,6 +569,14 @@ Status AlgebraicSimplifierVisitor::HandleSubtract(HloInstruction* sub) { return Status::OK(); } +namespace { +template +Status InvertConstant(const HloInstruction& constant, Literal* result) { + return result->Populate([&](tensorflow::gtl::ArraySlice indices) { + return T{1.0} / constant.literal().Get(indices); + }); +} +} // namespace Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide) { Shape* shape; @@ -624,14 +638,31 @@ Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide) { // (Backends can do this transformation, but generally only if the constant is // a scalar.) if (Match(divide, m::Divide(m::NonConstant(&a), m::Constant(&b)))) { - HloInstruction* one = - computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::One(a->shape().element_type()).CloneToUnique())); - HloInstruction* inverse = computation_->AddInstruction( - HloInstruction::CreateBinary(b->shape(), HloOpcode::kDivide, one, b)); - return ReplaceWithNewInstruction( - divide, HloInstruction::CreateBinary(divide->shape(), - HloOpcode::kMultiply, a, inverse)); + Literal new_literal(b->shape()); + switch (b->shape().element_type()) { + case F16: + TF_RETURN_IF_ERROR(InvertConstant(*b, &new_literal)); + break; + case F32: + TF_RETURN_IF_ERROR(InvertConstant(*b, &new_literal)); + break; + case BF16: + TF_RETURN_IF_ERROR(InvertConstant(*b, &new_literal)); + break; + case F64: + TF_RETURN_IF_ERROR(InvertConstant(*b, &new_literal)); + break; + case C64: + TF_RETURN_IF_ERROR(InvertConstant(*b, &new_literal)); + break; + default: + return Status::OK(); + } + auto inverse = computation_->AddInstruction( + HloInstruction::CreateConstant((new_literal.CloneToUnique()))); + TF_ASSIGN_OR_RETURN(auto new_divide, + MakeBinaryHlo(HloOpcode::kMultiply, a, inverse)); + return ReplaceInstruction(divide, new_divide); } // (A / B) / (C / D) => (A / B)*(D / C) => (A * D) / (B * C) @@ -651,18 +682,18 @@ Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide) { if (Match(divide, m::Divide(m::Divide(m::Op(&a), m::Op(&b)), m::Op(&c)))) { TF_ASSIGN_OR_RETURN(auto b_times_c, MakeBinaryHlo(HloOpcode::kMultiply, b, c)); - return ReplaceWithNewInstruction( - divide, HloInstruction::CreateBinary(divide->shape(), - HloOpcode::kDivide, a, b_times_c)); + TF_ASSIGN_OR_RETURN(auto new_divide, + MakeBinaryHlo(HloOpcode::kDivide, a, b_times_c)); + return ReplaceInstruction(divide, new_divide); } // A / (B / C) => (A*C) / B if (Match(divide, m::Divide(m::Op(&a), m::Divide(m::Op(&b), m::Op(&c))))) { TF_ASSIGN_OR_RETURN(auto a_times_c, MakeBinaryHlo(HloOpcode::kMultiply, a, c)); - return ReplaceWithNewInstruction( - divide, HloInstruction::CreateBinary(divide->shape(), - HloOpcode::kDivide, a_times_c, b)); + TF_ASSIGN_OR_RETURN(auto new_divide, + MakeBinaryHlo(HloOpcode::kDivide, a_times_c, b)); + return ReplaceInstruction(divide, new_divide); } return Status::OK(); @@ -1058,9 +1089,9 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) { } // Replace a zero element dot with a broadcast of the constant 0. - if (ShapeUtil::HasZeroElements(dot->shape()) || - ShapeUtil::HasZeroElements(lhs->shape()) || - ShapeUtil::HasZeroElements(rhs->shape())) { + if (ShapeUtil::IsZeroElementArray(dot->shape()) || + ShapeUtil::IsZeroElementArray(lhs->shape()) || + ShapeUtil::IsZeroElementArray(rhs->shape())) { auto zero = computation_->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); return ReplaceWithNewInstruction( @@ -1221,9 +1252,10 @@ bool OutputIsPermutationOfOperandElements(HloInstruction* instruction, switch (instruction->opcode()) { case HloOpcode::kReshape: case HloOpcode::kReverse: - case HloOpcode::kSort: case HloOpcode::kTranspose: return true; + case HloOpcode::kSort: + return (!ShapeUtil::IsTuple(instruction->shape())); default: return false; } @@ -1392,7 +1424,7 @@ Status AlgebraicSimplifierVisitor::HandleImag(HloInstruction* imag) { } Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) { - if (ShapeUtil::HasZeroElements(pad->operand(0)->shape())) { + if (ShapeUtil::IsZeroElementArray(pad->operand(0)->shape())) { return ReplaceWithNewInstruction( pad, HloInstruction::CreateBroadcast(pad->shape(), pad->mutable_operand(1), {})); @@ -1638,7 +1670,7 @@ Status AlgebraicSimplifierVisitor::HandleReshape(HloInstruction* reshape) { // Reshape directly to empty constant if the shape contains zero-element // dimension. - if (ShapeUtil::HasZeroElements(reshape->shape())) { + if (ShapeUtil::IsZeroElementArray(reshape->shape())) { auto empty_constant = HloInstruction::CreateConstant( Literal::CreateFromShape(reshape->shape())); @@ -1672,7 +1704,7 @@ Status AlgebraicSimplifierVisitor::HandleReshape(HloInstruction* reshape) { // Make this a bitcast if possible. if (is_layout_sensitive_ && - ReshapeIsBitcast(reshape, valid_bitcast_callback_)) { + ReshapeOrCopyIsBitcast(reshape, valid_bitcast_callback_)) { ReplaceWithBitcast(reshape); return Status::OK(); } @@ -1739,7 +1771,7 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( // If any dimension of update is 0, elide the DynamicUpdateSlice. This // optimization becomes invalid should we later prefer to warn about out of // bound indices. - if (ShapeUtil::HasZeroElements(update->shape())) { + if (ShapeUtil::IsZeroElementArray(update->shape())) { return ReplaceInstruction(dynamic_update_slice, dynamic_update_slice->mutable_operand(0)); } @@ -1751,8 +1783,8 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { auto init_value = reduce->mutable_operand(1); tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); HloComputation* function = reduce->to_apply(); - if (ShapeUtil::HasZeroElements(arg->shape()) || - ShapeUtil::HasZeroElements(reduce->shape())) { + if (ShapeUtil::IsZeroElementArray(arg->shape()) || + ShapeUtil::IsZeroElementArray(reduce->shape())) { return ReplaceWithNewInstruction( reduce, HloInstruction::CreateBroadcast(reduce->shape(), init_value, {})); @@ -1783,6 +1815,37 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { return ReplaceWithNewInstruction( reduce, HloInstruction::CreateReshape(reduce->shape(), arg)); } + + // If a reduce feeds a reduce with the same computation and initial value, + // they can be combined into a single reduce. + if (arg->opcode() == HloOpcode::kReduce && + init_value->Identical(*arg->operand(1)) && + *function == *arg->to_apply()) { + // Create a new reduce with the combined reduction dimensions of both + // reduces. + std::vector arg_dims = arg->dimensions(); + std::sort(arg_dims.begin(), arg_dims.end()); + std::vector reduce_dims = reduce->dimensions(); + std::sort(reduce_dims.begin(), reduce_dims.end()); + // Transform reduce_dims to the same rank as the operand of the operand. + for (int64 arg_dim : arg_dims) { + for (int64& dim : reduce_dims) { + if (dim >= arg_dim) { + ++dim; + } + } + } + std::vector new_dimensions; + new_dimensions.reserve(arg->dimensions().size() + + reduce->dimensions().size()); + std::merge(arg_dims.begin(), arg_dims.end(), reduce_dims.begin(), + reduce_dims.end(), std::back_inserter(new_dimensions)); + return ReplaceWithNewInstruction( + reduce, + HloInstruction::CreateReduce(reduce->shape(), arg->mutable_operand(0), + init_value, new_dimensions, function)); + } + // A reshape that collapses multiple dimensions into a dimension being // reduced can just reduce all of those dimensions instead of doing a // collapsing reshape before a reduction. @@ -1832,7 +1895,7 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { Status AlgebraicSimplifierVisitor::HandleReduceWindow( HloInstruction* reduce_window) { - if (ShapeUtil::HasZeroElements(reduce_window->operand(0)->shape())) { + if (ShapeUtil::IsZeroElementArray(reduce_window->operand(0)->shape())) { return ReplaceWithNewInstruction( reduce_window, HloInstruction::CreateBroadcast(reduce_window->shape(), @@ -2028,16 +2091,15 @@ Status AlgebraicSimplifierVisitor::HandleConvolution( HloInstruction* convolution) { auto lhs = convolution->mutable_operand(0); auto rhs = convolution->mutable_operand(1); - if (ShapeUtil::HasZeroElements(lhs->shape()) || - ShapeUtil::HasZeroElements(rhs->shape())) { + if (ShapeUtil::IsZeroElementArray(lhs->shape()) || + ShapeUtil::IsZeroElementArray(rhs->shape())) { return ReplaceWithNewInstruction( convolution, HloInstruction::CreateBroadcast( convolution->shape(), - computation_->AddInstruction(HloInstruction::CreateConvert( - ShapeUtil::MakeShape(convolution->shape().element_type(), {}), - computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))))), + computation_->AddInstruction(HloInstruction::CreateConstant( + Literal::Zero(convolution->shape().element_type()) + .CloneToUnique())), {})); } const auto& window = convolution->window(); @@ -2209,68 +2271,6 @@ Status AlgebraicSimplifierVisitor::HandleMap(HloInstruction* map) { return ReplaceWithNewInstruction(map, std::move(clone)); } -Status AlgebraicSimplifierVisitor::HandleMaximum(HloInstruction* maximum) { - // Match the following tree: - // min_operand operand - // \ / - // max_operand min - // \ / - // max - // where max_operand and min_operand are scalar constants. - { - HloInstruction* min; - HloInstruction* max_operand; - HloInstruction* min_operand; - HloInstruction* operand; - - if (hlo_query::MatchBinaryInstructionOperandOpcode( - HloOpcode::kMinimum, maximum, - /*matching_operand=*/&min, - /*other_operand=*/&max_operand) && - hlo_query::MatchBinaryInstructionOperand( - hlo_query::IsScalarConstant, min, - /*matching_operand=*/&min_operand, - /*other_operand=*/&operand) && - TransformToClampIfSameShape(maximum, min, min_operand, operand, maximum, - max_operand)) { - return Status::OK(); - } - } - - return Status::OK(); -} - -Status AlgebraicSimplifierVisitor::HandleMinimum(HloInstruction* minimum) { - // Match the following tree: - // max_operand operand - // \ / - // min_operand max - // \ / - // min - // where max_operand and min_operand are scalar constants. - { - HloInstruction* max; - HloInstruction* max_operand; - HloInstruction* min_operand; - HloInstruction* operand; - - if (hlo_query::MatchBinaryInstructionOperandOpcode( - HloOpcode::kMaximum, minimum, - /*matching_operand=*/&max, - /*other_operand=*/&min_operand) && - hlo_query::MatchBinaryInstructionOperand( - hlo_query::IsScalarConstant, max, - /*matching_operand=*/&max_operand, - /*other_operand=*/&operand) && - TransformToClampIfSameShape(minimum, minimum, min_operand, operand, max, - max_operand)) { - return Status::OK(); - } - } - - return Status::OK(); -} - StatusOr AlgebraicSimplifier::Run(HloModule* module) { XLA_VLOG_LINES(2, "AlgebraicSimplifier::Run(), before:\n" + module->ToString()); diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index cda157f9fac1639d792fb55b5a5ddac56df271aa..b733f6f59eb028b2dff921722c462441251772fe 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -74,6 +74,44 @@ TEST_F(AlgebraicSimplifierTest, AddZero) { EXPECT_EQ(root, param0); } +// Test that Reduce(Reduce(A)) -> Reduce(A) +TEST_F(AlgebraicSimplifierTest, TwoReducesToOne) { + HloComputation::Builder builder(TestName()); + // Create add computation. + HloInstruction* zero = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloComputation* add_computation = nullptr; + { + HloComputation::Builder builder(TestName() + ".add"); + const Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); + HloInstruction* p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "p0")); + HloInstruction* p1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, scalar_shape, "p1")); + builder.AddInstruction( + HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); + add_computation = module().AddEmbeddedComputation(builder.Build()); + } + Shape r4f32 = ShapeUtil::MakeShape(F32, {4, 5, 6, 7}); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, r4f32, "param")); + std::vector dims0({0}); + Shape r3f32 = ShapeUtil::MakeShape(F32, {5, 6, 7}); + HloInstruction* reduce0 = builder.AddInstruction( + HloInstruction::CreateReduce(r3f32, param, zero, dims0, add_computation)); + std::vector dims1({1, 2}); + Shape r1f32 = ShapeUtil::MakeShape(F32, {5}); + builder.AddInstruction(HloInstruction::CreateReduce(r1f32, reduce0, zero, + dims1, add_computation)); + module().AddEntryComputation(builder.Build()); + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + HloInstruction* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Reduce(param, zero)); + EXPECT_EQ(root->dimensions(), std::vector({0, 2, 3})); +} + // Test that Const + A is canonicalized to A + Const. TEST_F(AlgebraicSimplifierTest, AddConstOnLHS) { Shape r0f32 = ShapeUtil::MakeShape(F32, {}); @@ -163,8 +201,11 @@ TEST_F(AlgebraicSimplifierTest, InlineTrivialMap) { HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* zero = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); - builder.AddInstruction( - HloInstruction::CreateMap(r2f32, {param0, zero}, add_computation)); + builder.AddInstruction(HloInstruction::CreateMap( + r2f32, + {param0, builder.AddInstruction( + HloInstruction::CreateBroadcast(r2f32, zero, {}))}, + add_computation)); auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); @@ -173,7 +214,7 @@ TEST_F(AlgebraicSimplifierTest, InlineTrivialMap) { non_bitcasting_callback()); ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); root = computation->root_instruction(); - EXPECT_THAT(root, op::Add(param0, zero)); + EXPECT_THAT(root, op::Add(param0, op::Broadcast(zero))); } TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) { @@ -329,17 +370,16 @@ TEST_F(AlgebraicSimplifierTest, RhsDivOfDiv) { // Test that (A/B)/(C/D) is simplified to (A*D)/(B*C). TEST_F(AlgebraicSimplifierTest, DivOfDivAndDiv) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); Shape r2f32 = ShapeUtil::MakeShape(F32, {42, 123}); HloComputation::Builder builder(TestName()); HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r0f32, "param0")); + HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r2f32, "param1")); HloInstruction* param2 = builder.AddInstruction( HloInstruction::CreateParameter(2, r2f32, "param2")); HloInstruction* param3 = builder.AddInstruction( - HloInstruction::CreateParameter(3, r0f32, "param3")); + HloInstruction::CreateParameter(3, r2f32, "param3")); HloInstruction* div0 = builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kDivide, param0, param1)); HloInstruction* div1 = builder.AddInstruction( @@ -360,8 +400,6 @@ TEST_F(AlgebraicSimplifierTest, DivOfDivAndDiv) { EXPECT_THAT( computation->root_instruction(), op::Divide(op::Multiply(param0, param3), op::Multiply(param1, param2))); - EXPECT_TRUE( - ShapeUtil::Compatible(computation->root_instruction()->shape(), r2f32)); } // Test that A/exp(B) is simplified to A*exp(-B). @@ -421,7 +459,6 @@ TEST_F(AlgebraicSimplifierTest, DivOfPower) { // Test that broadcasting is done on the right step when simplifying A/pow(B,C) // to A*pow(B,-C). TEST_F(AlgebraicSimplifierTest, DivOfBroadcastingPower) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); Shape r1f32 = ShapeUtil::MakeShape(F32, {7}); HloComputation::Builder builder(TestName()); HloInstruction* param0 = builder.AddInstruction( @@ -429,7 +466,7 @@ TEST_F(AlgebraicSimplifierTest, DivOfBroadcastingPower) { HloInstruction* param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r1f32, "param1")); HloInstruction* param2 = builder.AddInstruction( - HloInstruction::CreateParameter(2, r0f32, "param2")); + HloInstruction::CreateParameter(2, r1f32, "param2")); HloInstruction* power = builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, param1, param2)); builder.AddInstruction( @@ -446,14 +483,9 @@ TEST_F(AlgebraicSimplifierTest, DivOfBroadcastingPower) { ASSERT_THAT(computation->root_instruction(), op::Multiply(param0, op::Power(param1, op::Negate(param2)))); - - const HloInstruction* negate = - computation->root_instruction()->operand(1)->operand(1); - const Shape& negate_shape = negate->shape(); - EXPECT_EQ(0, negate_shape.dimensions_size()); } -// A / Const => A * (1 / Const) +// A / Const => A * InvertedConst TEST_F(AlgebraicSimplifierTest, DivideByConstant) { Shape r1f32 = ShapeUtil::MakeShape(F32, {3}); HloComputation::Builder builder(TestName()); @@ -472,20 +504,19 @@ TEST_F(AlgebraicSimplifierTest, DivideByConstant) { ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), - op::Multiply(param0, op::Divide(op::Constant(), constant))); + op::Multiply(param0, op::Constant())); } // pow(pow(A, X), Y) => pow(A, X*Y) TEST_F(AlgebraicSimplifierTest, PowerOfPower) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); Shape r1f32 = ShapeUtil::MakeShape(F32, {7}); HloComputation::Builder builder(TestName()); HloInstruction* base = builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "param0")); HloInstruction* exp1 = builder.AddInstruction( - HloInstruction::CreateParameter(1, r0f32, "param1")); + HloInstruction::CreateParameter(1, r1f32, "param1")); HloInstruction* exp2 = builder.AddInstruction( - HloInstruction::CreateParameter(2, r0f32, "param2")); + HloInstruction::CreateParameter(2, r1f32, "param2")); HloInstruction* inner_power = builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, base, exp1)); builder.AddInstruction(HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, @@ -502,15 +533,14 @@ TEST_F(AlgebraicSimplifierTest, PowerOfPower) { // Don't simplify pow(pow(A, X), Y) => pow(A, X*Y) if X and Y are complex // numbers. TEST_F(AlgebraicSimplifierTest, PowerOfPowerComplex) { - Shape r0c64 = ShapeUtil::MakeShape(C64, {}); Shape r1c64 = ShapeUtil::MakeShape(C64, {7}); HloComputation::Builder builder(TestName()); HloInstruction* base = builder.AddInstruction( HloInstruction::CreateParameter(0, r1c64, "param0")); HloInstruction* exp1 = builder.AddInstruction( - HloInstruction::CreateParameter(1, r0c64, "param1")); + HloInstruction::CreateParameter(1, r1c64, "param1")); HloInstruction* exp2 = builder.AddInstruction( - HloInstruction::CreateParameter(2, r0c64, "param2")); + HloInstruction::CreateParameter(2, r1c64, "param2")); HloInstruction* inner_power = builder.AddInstruction( HloInstruction::CreateBinary(r1c64, HloOpcode::kPower, base, exp1)); builder.AddInstruction(HloInstruction::CreateBinary(r1c64, HloOpcode::kPower, @@ -1121,6 +1151,33 @@ TEST_F(AlgebraicSimplifierTest, RemoveCopy) { EXPECT_THAT(computation->root_instruction(), param0); } +TEST_F(AlgebraicSimplifierTest, CopyEqualsBitcast) { + HloComputation::Builder builder(TestName()); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {1, 14, 14, 64}), "param")); + *param->mutable_shape()->mutable_layout() = + LayoutUtil::MakeLayout({0, 1, 2, 3}); + HloInstruction* copy = builder.AddInstruction(HloInstruction::CreateUnary( + ShapeUtil::MakeShape(F32, {1, 14, 14, 64}), HloOpcode::kCopy, param)); + *copy->mutable_shape()->mutable_layout() = + LayoutUtil::MakeLayout({1, 2, 0, 3}); + auto computation = module().AddEntryComputation(builder.Build()); + EXPECT_THAT(computation->root_instruction(), op::Copy(param)); + + AlgebraicSimplifier simplifier1(/*is_layout_sensitive=*/true, + non_bitcasting_callback()); + ASSERT_FALSE(simplifier1.Run(&module()).ValueOrDie()); + // Verify that the copy is not replaced. + EXPECT_THAT(computation->root_instruction(), op::Copy(param)); + + AlgebraicSimplifier simplifier2(/*is_layout_sensitive=*/true, + bitcasting_callback()); + ASSERT_TRUE(simplifier2.Run(&module()).ValueOrDie()); + // Verify that the copy is replaced. + EXPECT_THAT(computation->root_instruction(), op::Bitcast(param)); +} + // Test that unary concatenates are removed. TEST_F(AlgebraicSimplifierTest, RemoveUnaryConcatenate) { Shape r1f32 = ShapeUtil::MakeShape(F32, {100}); @@ -1351,33 +1408,6 @@ TEST_F(AlgebraicSimplifierTest, ReshapeReplacedWithBitcast) { op::Tuple(op::Bitcast(), dimensions_wrong_reshape, layout_wrong_reshape)); } -// Regression test for a bug in the reshape sinking transformation, where -// moving a reshape to a scalar led to a crash. -TEST_F(AlgebraicSimplifierTest, ReshapeToScalarNotHoistedAfterEffectiveUnary) { - HloComputation::Builder builder(TestName()); - HloInstruction* param = - builder.AddInstruction(HloInstruction::CreateParameter( - 0, ShapeUtil::MakeShape(F32, {1, 1}), "param")); - HloInstruction* reshape = builder.AddInstruction( - HloInstruction::CreateReshape(ShapeUtil::MakeShape(F32, {}), param)); - HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1., 2., 3.}))); - builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(F32, {3}), HloOpcode::kMaximum, reshape, zero)); - auto computation = module().AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Maximum(op::Reshape(param), zero)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - bitcasting_callback()); - - simplifier.Run(&module()).ValueOrDie(); - - EXPECT_THAT(computation->root_instruction(), - op::Maximum(op::Reshape(param), zero)); -} - // Regression test for a bug where if we failed to sink a reshape, we'd set the // 'changed' bit in AlgebraicSimplifier to false. TEST_F(AlgebraicSimplifierTest, FailureToSinkReshapeDoesntAffectChangedBit) { @@ -1714,7 +1744,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopPad) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1759,7 +1789,7 @@ TEST_F(AlgebraicSimplifierTest, NegativePadding) { EXPECT_THAT(computation->root_instruction(), op::Pad(param, zero)); EXPECT_TRUE(has_negative_padding(pad)); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), op::Slice(op::Pad(param, zero))); EXPECT_FALSE( @@ -1781,7 +1811,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopReshape) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1804,7 +1834,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); EXPECT_THAT(computation->root_instruction(), param); } @@ -1932,7 +1962,8 @@ TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) { b.AddInstruction(HloInstruction::CreateConvolve(out_shape, input, filter, window, dnums)); - auto module = CreateNewModule(); + // TODO(b/80488902): verify this module. + auto module = HloTestBase::CreateNewModule(); auto* computation = module->AddEntryComputation(b.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/true, @@ -2037,160 +2068,6 @@ TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) { EXPECT_EQ("NO_CHANGE", build_and_simplify()); } -// Test that max(min(A, x), y) is transformed to clamp(y, A, x) -TEST_F(AlgebraicSimplifierTest, MaxMinToClamp) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); - HloComputation::Builder builder(TestName()); - HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r0f32, "param0")); - HloInstruction* min_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); - HloInstruction* max_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); - HloInstruction* min = builder.AddInstruction(HloInstruction::CreateBinary( - r0f32, HloOpcode::kMinimum, param0, min_value)); - builder.AddInstruction( - HloInstruction::CreateBinary(r0f32, HloOpcode::kMaximum, min, max_value)); - - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Maximum(op::Minimum(param0, min_value), max_value)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - - EXPECT_THAT(computation->root_instruction(), - op::Clamp(max_value, param0, min_value)); -} - -// Test that min(max(A, x), y) is transformed to clamp(x, A, y) for scalar -// values. -TEST_F(AlgebraicSimplifierTest, MinMaxToClamp) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); - HloComputation::Builder builder(TestName()); - HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r0f32, "param0")); - HloInstruction* min_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); - HloInstruction* max_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); - HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary( - r0f32, HloOpcode::kMaximum, param0, max_value)); - builder.AddInstruction( - HloInstruction::CreateBinary(r0f32, HloOpcode::kMinimum, max, min_value)); - - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Maximum(param0, max_value), min_value)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - - EXPECT_THAT(computation->root_instruction(), - op::Clamp(max_value, param0, min_value)); -} - -// Test that min(max(A, x), y) is transformed to clamp(x, A, y) for -// broadcasted scalar values. -TEST_F(AlgebraicSimplifierTest, MinMaxWithBroadcastToClamp) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); - Shape r1f32 = ShapeUtil::MakeShape(F32, {100}); - HloComputation::Builder builder(TestName()); - HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r1f32, "param0")); - HloInstruction* min_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); - HloInstruction* max_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); - HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary( - r1f32, HloOpcode::kMaximum, param0, max_value)); - builder.AddInstruction( - HloInstruction::CreateBinary(r1f32, HloOpcode::kMinimum, max, min_value)); - - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Maximum(param0, max_value), min_value)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); - - EXPECT_THAT(computation->root_instruction(), - op::Clamp(max_value, param0, min_value)); -} - -// Test that min(max(A, non-constant1), non-constant2) is not canonicalized to -// clamp(non-constant1, A, non-constant2) -TEST_F(AlgebraicSimplifierTest, MinMaxNotToClamp) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); - HloComputation::Builder builder(TestName()); - HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r0f32, "param0")); - HloInstruction* min_value = builder.AddInstruction( - HloInstruction::CreateParameter(1, r0f32, "param1")); - HloInstruction* max_value = builder.AddInstruction( - HloInstruction::CreateParameter(2, r0f32, "param2")); - HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary( - r0f32, HloOpcode::kMaximum, param0, max_value)); - builder.AddInstruction( - HloInstruction::CreateBinary(r0f32, HloOpcode::kMinimum, max, min_value)); - - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Maximum(param0, max_value), min_value)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Maximum(param0, max_value), min_value)); -} - -// Test that min(f(max(A, constant1)), constant2) is not transformed to -// clamp(constant1, A, constant2) -TEST_F(AlgebraicSimplifierTest, MinEquationWithMaxNotToClamp) { - Shape r0f32 = ShapeUtil::MakeShape(F32, {}); - HloComputation::Builder builder(TestName()); - HloInstruction* param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, r0f32, "param0")); - HloInstruction* min_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); - HloInstruction* max_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); - HloInstruction* max = builder.AddInstruction(HloInstruction::CreateBinary( - r0f32, HloOpcode::kMaximum, param0, max_value)); - HloInstruction* fmax = builder.AddInstruction( - HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, max, max_value)); - builder.AddInstruction(HloInstruction::CreateBinary( - r0f32, HloOpcode::kMinimum, fmax, min_value)); - - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Add(op::Maximum(param0, max_value), max_value), - min_value)); - - AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, - non_bitcasting_callback()); - EXPECT_FALSE(simplifier.Run(module.get()).ValueOrDie()); - - EXPECT_THAT(computation->root_instruction(), - op::Minimum(op::Add(op::Maximum(param0, max_value), max_value), - min_value)); -} - // Test that slice(broadcast(/*scalar value*/)) simplifies to a single // broadcast. TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { @@ -2200,10 +2077,8 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { HloInstruction::CreateParameter(0, r0f32, "scalar_param")); Shape broadcast_shape = ShapeUtil::MakeShape(F32, {4, 5, 6, 7}); - HloInstruction* broadcast = - builder.AddInstruction(HloInstruction::CreateBroadcast( - broadcast_shape, scalar_param, - AsInt64Slice(broadcast_shape.dimensions()))); + HloInstruction* broadcast = builder.AddInstruction( + HloInstruction::CreateBroadcast(broadcast_shape, scalar_param, {})); Shape slice_shape = ShapeUtil::MakeShape(F32, {2, 2, 3, 3}); HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice( @@ -2219,10 +2094,10 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); // Running simplification again should not result in any further changes. - ASSERT_FALSE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_FALSE(simplifier.Run(module).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Broadcast(scalar_param)); @@ -2237,10 +2112,8 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); Shape broadcast_shape = ShapeUtil::MakeShape(F32, {4, 5, 6}); - HloInstruction* broadcast = - builder.AddInstruction(HloInstruction::CreateBroadcast( - broadcast_shape, forty_two, - AsInt64Slice(broadcast_shape.dimensions()))); + HloInstruction* broadcast = builder.AddInstruction( + HloInstruction::CreateBroadcast(broadcast_shape, forty_two, {})); HloInstruction* transpose = builder.AddInstruction(HloInstruction::CreateTranspose( @@ -2259,7 +2132,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); root = computation->root_instruction(); EXPECT_THAT(root, op::Broadcast(forty_two)); @@ -2268,7 +2141,8 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { // Test that ReduceWindow(Pad(op, x), y) can simplify to ReduceWindow(op, x). TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { - auto module = CreateNewModule(); + // TODO(b/80488902): verify this module. + auto module = HloTestBase::CreateNewModule(); HloComputation::Builder builder(TestName()); // Create operand to the pad. @@ -2349,7 +2223,8 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { // Test that ReduceWindow(Convert(Pad(op, x)), y) can simplify to // ReduceWindow(Convert(op), x). TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { - auto module = CreateNewModule(); + // TODO(b/80488902): verify this module. + auto module = HloTestBase::CreateNewModule(); HloComputation::Builder builder(TestName()); // Create operand to the pad. @@ -2444,7 +2319,7 @@ TEST_F(AlgebraicSimplifierTest, ReversalOfTrivialDimensionsToBitcast) { AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, non_bitcasting_callback()); - ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); HloInstruction* root = computation->root_instruction(); EXPECT_EQ(a, root); diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.cc b/tensorflow/compiler/xla/service/batchnorm_expander.cc index 598718c72c6941a4859063ed894c45b9c620998e..ec13fadbc75e2315d1d6ef72e24a0faca0c7de40 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander.cc +++ b/tensorflow/compiler/xla/service/batchnorm_expander.cc @@ -58,8 +58,7 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { // Runs the visitor on a computation. static bool Run(HloComputation* computation, bool rewrite_training_op, - bool rewrite_inference_op, bool rewrite_grad_op, - bool use_fusion); + bool rewrite_inference_op, bool rewrite_grad_op); // Returns whether any batch norm ops were rewritten. const bool changed() const { return changed_; } @@ -70,21 +69,14 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { explicit BatchNormExpanderVisitor(HloComputation* computation, bool rewrite_training_op, bool rewrite_inference_op, - bool rewrite_grad_op, bool use_fusion) + bool rewrite_grad_op) : computation_(computation), rewrite_training_op_(rewrite_training_op), rewrite_inference_op_(rewrite_inference_op), - rewrite_grad_op_(rewrite_grad_op), - use_fusion_(use_fusion) {} + rewrite_grad_op_(rewrite_grad_op) {} HloComputation* GetOrCreateScalarAddComputation( PrimitiveType primitive_type) { - HloComputation** scalar_add_computation = - &scalar_add_computations_[primitive_type]; - if (*scalar_add_computation) { - return *scalar_add_computation; - } - HloComputation::Builder b("scalar_add_computation"); Shape shape = ShapeUtil::MakeShape(primitive_type, {}); auto scalar_lhs = b.AddInstruction( @@ -93,71 +85,38 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { HloInstruction::CreateParameter(1, shape, "scalar_rhs")); auto scalar_op = b.AddInstruction(HloInstruction::CreateBinary( shape, HloOpcode::kAdd, scalar_lhs, scalar_rhs)); - *scalar_add_computation = - computation_->parent()->AddEmbeddedComputation(b.Build(scalar_op)); - return *scalar_add_computation; - } - - // TODO(b/80534766): Remove maps after performance issues with scalar - // broadcasts are resolved on all backends. - HloComputation* GetOrCreateScalarRsqrtComputation( - PrimitiveType primitive_type) { - HloComputation** scalar_rsqrt_computation = - &scalar_rsqrt_computations_[primitive_type]; - if (*scalar_rsqrt_computation) { - return *scalar_rsqrt_computation; - } - - HloComputation::Builder b("scalar_add_computation"); - Shape shape = ShapeUtil::MakeShape(primitive_type, {}); - auto scalar_lhs = b.AddInstruction( - HloInstruction::CreateParameter(0, shape, "scalar_lhs")); - auto scalar_rhs = b.AddInstruction(HloInstruction::CreateConvert( - shape, b.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(-0.5f))))); - auto scalar_op = b.AddInstruction(HloInstruction::CreateBinary( - shape, HloOpcode::kPower, scalar_lhs, scalar_rhs)); - *scalar_rsqrt_computation = - computation_->parent()->AddEmbeddedComputation(b.Build(scalar_op)); - return *scalar_rsqrt_computation; + return computation_->parent()->AddEmbeddedComputation(b.Build(scalar_op)); } - std::unique_ptr Rsqrt(HloInstruction* operand) { - return HloInstruction::CreateMap( - operand->shape(), {operand}, - GetOrCreateScalarRsqrtComputation(operand->shape().element_type())); - } - - HloComputation* GetOrCreateScalarMeanComputation(PrimitiveType primitive_type, - int64 element_count) { - HloComputation** scalar_mean_computation = - &scalar_mean_computations_[std::pair( - primitive_type, element_count)]; - if (*scalar_mean_computation) { - return *scalar_mean_computation; - } - - HloComputation::Builder b("scalar_add_computation"); - Shape shape = ShapeUtil::MakeShape(primitive_type, {}); - auto scalar_lhs = b.AddInstruction( - HloInstruction::CreateParameter(0, shape, "scalar_lhs")); - auto scalar_rhs = b.AddInstruction(HloInstruction::CreateConvert( - shape, b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0( - 1.0f / static_cast(element_count)))))); - auto scalar_op = b.AddInstruction(HloInstruction::CreateBinary( - shape, HloOpcode::kMultiply, scalar_lhs, scalar_rhs)); - *scalar_mean_computation = - computation_->parent()->AddEmbeddedComputation(b.Build(scalar_op)); - return *scalar_mean_computation; + std::unique_ptr Rsqrt( + HloInstruction* operand, + const std::function)>& + add_instruction) { + HloInstruction* exponent = add_instruction(HloInstruction::CreateBroadcast( + operand->shape(), + add_instruction(HloInstruction::CreateConvert( + ShapeUtil::MakeShape(operand->shape().element_type(), {}), + add_instruction(HloInstruction::CreateConstant( + Literal::CreateR0(-0.5f))))), + {})); + return HloInstruction::CreateBinary(operand->shape(), HloOpcode::kPower, + operand, exponent); } - std::unique_ptr Mean(int64 element_count, - HloInstruction* operand) { - return HloInstruction::CreateMap( - operand->shape(), {operand}, - GetOrCreateScalarMeanComputation(operand->shape().element_type(), - element_count)); + std::unique_ptr Mean( + int64 element_count, HloInstruction* operand, + const std::function)>& + add_instruction) { + HloInstruction* elem_count_recip = + add_instruction(HloInstruction::CreateBroadcast( + operand->shape(), + add_instruction(HloInstruction::CreateConvert( + ShapeUtil::MakeShape(operand->shape().element_type(), {}), + add_instruction(HloInstruction::CreateConstant( + Literal::CreateR0(1.0 / element_count))))), + {})); + return HloInstruction::CreateBinary(operand->shape(), HloOpcode::kMultiply, + operand, elem_count_recip); } // Replaces the existing HLO instruction old_instruction, with @@ -189,18 +148,9 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { bool rewrite_training_op_; bool rewrite_inference_op_; bool rewrite_grad_op_; - bool use_fusion_; // Whether rewrite has occurred. bool changed_ = false; - - // Cached computations for adding two scalars. - tensorflow::gtl::FlatMap - scalar_add_computations_; - tensorflow::gtl::FlatMap - scalar_rsqrt_computations_; - tensorflow::gtl::FlatMap, HloComputation*> - scalar_mean_computations_; }; } // namespace @@ -208,13 +158,12 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { bool BatchNormExpanderVisitor::Run(HloComputation* computation, bool rewrite_training_op, bool rewrite_inference_op, - bool rewrite_grad_op, bool use_fusion) { + bool rewrite_grad_op) { BatchNormExpanderVisitor visitor( computation, /*rewrite_training_op=*/rewrite_training_op, /*rewrite_inference_op=*/rewrite_inference_op, - /*rewrite_grad_op=*/rewrite_grad_op, - /*use_fusion=*/use_fusion); + /*rewrite_grad_op=*/rewrite_grad_op); TF_CHECK_OK(computation->Accept(&visitor)); return visitor.changed_; } @@ -290,28 +239,14 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining( feature_shape, operand_squared, zero, dimensions_without_feature, add_reduce_computation)); - // Fuse two parallel reduces together to improve performance. - if (use_fusion_ && !batch_norm->has_sharding()) { - auto tuple = add(HloInstruction::CreateTuple({sum, squared_sum})); - - auto fused = computation_->CreateFusionInstruction( - {tuple, sum, squared_sum, operand_squared}, - HloInstruction::FusionKind::kInput); - - sum = add(HloInstruction::CreateGetTupleElement(feature_shape, fused, 0)); - - squared_sum = - add(HloInstruction::CreateGetTupleElement(feature_shape, fused, 1)); - } - // E[X]. - auto mean = add(Mean(elements_per_feature_int64, sum)); + auto mean = add(Mean(elements_per_feature_int64, sum, add)); auto mean_broadcasted = add( HloInstruction::CreateBroadcast(operand_shape, mean, {feature_index})); // E[X^2]. - auto square_mean = add(Mean(elements_per_feature_int64, squared_sum)); + auto square_mean = add(Mean(elements_per_feature_int64, squared_sum, add)); // E^2[X]. auto mean_square = @@ -329,7 +264,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining( add_binary(operand_shape, HloOpcode::kAdd, var_broadcasted, epsilon); // 1 / Sqrt[Var[X] + epsilon]. - auto rsqrt_var_add_epsilon = add(Rsqrt(var_add_epsilon)); + auto rsqrt_var_add_epsilon = add(Rsqrt(var_add_epsilon, add)); // X - E[X]. auto operand_minus_mean = add_binary(operand_shape, HloOpcode::kSubtract, @@ -431,7 +366,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormInference( add_binary(operand_shape, HloOpcode::kAdd, var_broadcasted, epsilon); // 1 / Sqrt[Var[X] + epsilon]. - auto rsqrt_var_add_epsilon = add(Rsqrt(var_add_epsilon)); + auto rsqrt_var_add_epsilon = add(Rsqrt(var_add_epsilon, add)); // X - E[X]. auto operand_minus_mean = add_binary(operand_shape, HloOpcode::kSubtract, @@ -545,10 +480,12 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( // rsqrt[Var[X] + epsilon]. auto rsqrt_var_add_epsilon_broadcasted = add(Rsqrt(add_binary(activation_shape, HloOpcode::kAdd, - variance_broadcasted, epsilon_activation))); + variance_broadcasted, epsilon_activation), + add)); auto rsqrt_var_add_epsilon = add(Rsqrt( - add_binary(feature_shape, HloOpcode::kAdd, variance, epsilon_feature))); + add_binary(feature_shape, HloOpcode::kAdd, variance, epsilon_feature), + add)); // X - E[X]. auto activation_minus_mean = add_binary( @@ -573,21 +510,6 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( feature_shape, grad_output, zero, dimensions_without_feature, add_reduce_computation)); - if (use_fusion_ && !batch_norm->has_sharding()) { - auto tuple = add(HloInstruction::CreateTuple( - {sum_grad_output_times_activiation_minus_mean, grad_beta})); - - auto fused = computation_->CreateFusionInstruction( - {tuple, sum_grad_output_times_activiation_minus_mean, grad_beta}, - HloInstruction::FusionKind::kInput); - - sum_grad_output_times_activiation_minus_mean = - add(HloInstruction::CreateGetTupleElement(feature_shape, fused, 0)); - - grad_beta = - add(HloInstruction::CreateGetTupleElement(feature_shape, fused, 1)); - } - // Grad[scale] = Sum(Grad[Y] * (X - E[X]) * rsqrt[Var[X] + epsilon]). auto grad_scale = add_binary(feature_shape, HloOpcode::kMultiply, sum_grad_output_times_activiation_minus_mean, @@ -616,8 +538,8 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( add_binary(activation_shape, HloOpcode::kMultiply, scale_broadcasted, rsqrt_var_add_epsilon_broadcasted); - scale_times_rsqrt_var_add_epsilon = - add(Mean(elements_per_feature_int64, scale_times_rsqrt_var_add_epsilon)); + scale_times_rsqrt_var_add_epsilon = add( + Mean(elements_per_feature_int64, scale_times_rsqrt_var_add_epsilon, add)); auto elements_per_feature_literal = Literal::CreateR0(elements_per_feature_int64); @@ -665,8 +587,8 @@ StatusOr BatchNormExpander::Run(HloModule* module) { bool changed = false; for (auto* comp : module->MakeNonfusionComputations()) { if (BatchNormExpanderVisitor::Run(comp, rewrite_training_op_, - rewrite_inference_op_, rewrite_grad_op_, - use_fusion_)) { + rewrite_inference_op_, + rewrite_grad_op_)) { changed = true; } } diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.h b/tensorflow/compiler/xla/service/batchnorm_expander.h index 4ad987085da91684bb7891070afeefd19be4138f..7ae202c583516443a6263403fb5460d1adbabd97 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander.h +++ b/tensorflow/compiler/xla/service/batchnorm_expander.h @@ -31,11 +31,10 @@ class BatchNormExpander : public HloPassInterface { // When use_fusion is set, a multi-output fusion node is created. BatchNormExpander(bool rewrite_training_op = false, bool rewrite_inference_op = false, - bool rewrite_grad_op = false, bool use_fusion = true) + bool rewrite_grad_op = false) : rewrite_training_op_(rewrite_training_op), rewrite_inference_op_(rewrite_inference_op), - rewrite_grad_op_(rewrite_grad_op), - use_fusion_(use_fusion) {} + rewrite_grad_op_(rewrite_grad_op) {} ~BatchNormExpander() = default; tensorflow::StringPiece name() const override { return "batchnorm_expander"; } @@ -47,7 +46,6 @@ class BatchNormExpander : public HloPassInterface { bool rewrite_training_op_; bool rewrite_inference_op_; bool rewrite_grad_op_; - bool use_fusion_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc index 7fd1e733e96da95cf43d9861af6d48a1850051c8..f7b4c1405dbc8719d8fba5476e6e41d2921ea877 100644 --- a/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc @@ -235,7 +235,7 @@ TEST_F(BFloat16ConversionFoldingTest, FoldCrossReplicaSumTupleOutput) { HloInstruction* crs = builder.AddInstruction(HloInstruction::CreateCrossReplicaSum( ShapeUtil::MakeTupleShape({f32_shape, f32_shape}), {convert_a, b}, - sum)); + sum, /*replica_group_ids=*/{}, /*barrier=*/"")); HloInstruction* gte_a = builder.AddInstruction( HloInstruction::CreateGetTupleElement(f32_shape, crs, 0)); HloInstruction* gte_b = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc index 9926661dd30600b2bf20e7f137aa50d9fbfd7c82..830f26422bdc2b3bd789e7d5926bcebac815d34a 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc @@ -250,8 +250,8 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) { HloInstruction* crs = builder.AddInstruction(HloInstruction::CreateCrossReplicaSum( - ShapeUtil::MakeTupleShape({f32_shape, bf16_shape}), {a, b}, - reduction)); + ShapeUtil::MakeTupleShape({f32_shape, bf16_shape}), {a, b}, reduction, + /*replica_group_ids=*/{}, /*barrier=*/"")); HloInstruction* gte = builder.AddInstruction( HloInstruction::CreateGetTupleElement(bf16_shape, crs, 1)); diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index ed0746980f87ac2bea79c308644dc63769f9e309..ff6d5027efba813042af65a0e50e172cc0a99ff8 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -85,9 +85,9 @@ void BFloat16Propagation::RevertIfFusionInternalBF16Changes( auto root_changes_it = changes_to_bf16_.find(root); if (root_changes_it != changes_to_bf16_.end()) { - for (const auto& index : root_changes_it->second) { + for (const auto& entry : root_changes_it->second) { for (const HloValue* value : - dataflow_->GetValueSet(root, index).values()) { + dataflow_->GetValueSet(root, entry.second).values()) { changed_root_buffers.insert(value); } } @@ -204,6 +204,12 @@ void BFloat16Propagation::DetermineWhileComputationsPrecision( bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, const ShapeIndex& index) const { + // If the subshape isn't floating point then none of the users will be BF16. + const Shape& subshape = ShapeUtil::GetSubshape(hlo.shape(), index); + if (subshape.element_type() != BF16 && subshape.element_type() != F32) { + return false; + } + auto& value_set = dataflow_->GetValueSet(&hlo, index); for (const HloValue* value : value_set.values()) { if (ContainsKey(values_that_must_be_kept_as_f32_, value)) { @@ -257,23 +263,34 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, // If the op propagates precision and it outputs a BF16, then it's OK to // supply BF16 also as the input. In the backward pass, the users shapes // should have already been processed. - PrimitiveType user_output_type = PRIMITIVE_TYPE_INVALID; - if (use.instruction->opcode() == HloOpcode::kTuple || - (use.instruction->opcode() == HloOpcode::kCrossReplicaSum && - ShapeUtil::IsTuple(use.instruction->shape()))) { - ShapeIndex use_output_index{use.operand_number}; - for (int64 i : use.operand_index) { - use_output_index.push_back(i); - } - user_output_type = - OutputTypeAfterChange(use.instruction, use_output_index); - } else { - user_output_type = OutputTypeAfterChange(use.instruction, {}); - } if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision( - *use.instruction, use.operand_number) && - user_output_type == BF16) { - continue; + *use.instruction, use.operand_number)) { + if (use.instruction->opcode() == HloOpcode::kTuple || + (use.instruction->opcode() == HloOpcode::kCrossReplicaSum && + ShapeUtil::IsTuple(use.instruction->shape()))) { + ShapeIndex use_output_index{use.operand_number}; + for (int64 i : use.operand_index) { + use_output_index.push_back(i); + } + if (OutputTypeAfterChange(use.instruction, use_output_index) == + BF16) { + continue; + } + } else if (use.instruction->opcode() == HloOpcode::kGetTupleElement) { + ShapeIndex use_output_index; + for (int64 i = 1; i < use.operand_index.size(); ++i) { + use_output_index.push_back(use.operand_index[i]); + } + if (OutputTypeAfterChange(use.instruction, use_output_index) == + BF16) { + continue; + } + } else { + if (OutputTypeAfterChange(use.instruction, use.operand_index) == + BF16) { + continue; + } + } } return false; } @@ -368,6 +385,7 @@ bool BFloat16Propagation::InstructionIsCandidateForBF16Output( if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) && hlo->opcode() != HloOpcode::kTuple && hlo->opcode() != HloOpcode::kGetTupleElement && + hlo->opcode() != HloOpcode::kDomain && hlo->shape().element_type() != BF16) { for (int64 i = 0; i < hlo->operand_count(); ++i) { if (!bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, @@ -559,7 +577,7 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( void BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers( HloModule* module) { - std::list computations_topological_order = + const auto& computations_topological_order = module->MakeComputationPostOrder(); tensorflow::gtl::FlatSet resolved; for (auto comp_it = computations_topological_order.rbegin(); @@ -631,7 +649,7 @@ Status BFloat16Propagation::ResolveInconsistentFusions(HloModule* module) { subshape, converted_outputs.element(parent_index), output_index.back())); } - if (ShapeUtil::IsTuple(subshape)) { + if (!ShapeUtil::IsArray(subshape)) { continue; } if (!ShapeUtil::Compatible( @@ -742,7 +760,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module)); - std::list computations_topological_order = + const auto& computations_topological_order = module->MakeComputationPostOrder(); // The first step is a forward pass (parameters to root), where we determine // the potential candidate instructions to use bfloat16 in the outputs that @@ -784,9 +802,8 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { // Apply the changes in changes_to_bf16_. for (auto& change : changes_to_bf16_) { - auto shape = change.first->mutable_shape(); - for (const auto& index : change.second) { - auto subshape = ShapeUtil::GetMutableSubshape(shape, index); + for (const auto& entry : change.second) { + auto subshape = entry.first; CHECK_EQ(subshape->element_type(), F32); subshape->set_element_type(BF16); changed_ = true; @@ -815,8 +832,8 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { PrimitiveType BFloat16Propagation::OutputTypeAfterChange( HloInstruction* hlo, const ShapeIndex& index) const { - PrimitiveType type_on_hlo = - ShapeUtil::GetSubshape(hlo->shape(), index).element_type(); + Shape* subshape = ShapeUtil::GetMutableSubshape(hlo->mutable_shape(), index); + const PrimitiveType type_on_hlo = subshape->element_type(); if (type_on_hlo != F32) { return type_on_hlo; } @@ -824,7 +841,7 @@ PrimitiveType BFloat16Propagation::OutputTypeAfterChange( if (it == changes_to_bf16_.end()) { return type_on_hlo; } - return ContainsKey(it->second, index) ? BF16 : F32; + return ContainsKey(it->second, subshape) ? BF16 : F32; } PrimitiveType BFloat16Propagation::ValueTypeAfterChange( @@ -838,14 +855,16 @@ void BFloat16Propagation::AddToOrRemoveFromBF16ChangeSet( HloInstruction* hlo, const ShapeIndex& index, PrimitiveType target_type) { if (target_type == BF16) { auto& entry = changes_to_bf16_[hlo]; - entry.insert(index); + entry.emplace(ShapeUtil::GetMutableSubshape(hlo->mutable_shape(), index), + index); } else { CHECK_EQ(target_type, F32); auto it = changes_to_bf16_.find(hlo); if (it == changes_to_bf16_.end()) { return; } - it->second.erase(index); + it->second.erase( + ShapeUtil::GetMutableSubshape(hlo->mutable_shape(), index)); } } diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h index de0355ddfca127753f90d1899b424a8e77c9b291..02b8cad089dd8465b7af5c1014e37b77ded6949d 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.h +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h @@ -194,17 +194,11 @@ class BFloat16Propagation : public HloPassInterface { // are subject to further adjustment, then finally applied to the HLOs. This // avoids setting changed_ to true but all changes are reverted during // adjustment. - struct IndexHasher { - int64 operator()(const ShapeIndex& index) const { - int64 hash = 0; - for (int64 i : index) { - hash = tensorflow::Hash64Combine(hash, std::hash()(i)); - } - return hash; - } - }; + // + // For each HloInstruction, changes_to_bf16_ stores the affected buffers in + // the output as a map from in-place pointers to subshapes to shape indices. tensorflow::gtl::FlatMap> + tensorflow::gtl::FlatMap> changes_to_bf16_; // Whether the last processed HLO module has been changed by this pass. diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index 5e1499ee6b6ef397f95f7ed29e808d530777bd07..560910cc5ffbf74737b6f025f7da2928c9cd621b 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -150,11 +150,11 @@ TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant); EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant); EXPECT_TRUE(LiteralTestUtil::Equal( - dot->operand(0)->literal(), - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)))); + *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)), + dot->operand(0)->literal())); EXPECT_TRUE(LiteralTestUtil::Equal( - dot->operand(1)->literal(), - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)))); + *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)), + dot->operand(1)->literal())); } // Tests that BF16 can be propagated through nested tuples. @@ -742,4 +742,89 @@ TEST_F(BFloat16PropagationTest, NoopConversionRemoved) { EXPECT_EQ(add1->shape().element_type(), BF16); } +TEST_F(BFloat16PropagationTest, TupleDomain) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* a_trans = + builder.AddInstruction(HloInstruction::CreateTranspose(shape, a, {0, 1})); + HloInstruction* b_trans = + builder.AddInstruction(HloInstruction::CreateTranspose(shape, b, {0, 1})); + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({a_trans, b_trans})); + HloInstruction* domain = builder.AddInstruction( + HloInstruction::CreateDomain(tuple->shape(), tuple, nullptr, nullptr)); + HloInstruction* a_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 0)); + HloInstruction* b_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 1)); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, a_gte, b_gte)); + HloInstruction* root = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, dot, dot)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + EXPECT_EQ(computation->root_instruction(), root); + + // test BF16 propagated through domain + EXPECT_EQ(ShapeUtil::GetTupleElementShape(domain->shape(), 0).element_type(), + BF16); + EXPECT_EQ(ShapeUtil::GetTupleElementShape(domain->shape(), 1).element_type(), + BF16); + + EXPECT_TRUE(OutputsBF16(a_trans)); + EXPECT_TRUE(OutputsBF16(b_trans)); + EXPECT_TRUE(OutputsBF16(a_gte)); + EXPECT_TRUE(OutputsBF16(b_gte)); + EXPECT_FALSE(OutputsBF16(a)); + EXPECT_FALSE(OutputsBF16(b)); +} + +// Tests that bf16 is not propagated through a domain in case its input cannot +// be propagated. In the case below the input of the domain is the parameter +// tuple which cannot be propagated, so the domain instruction is not propagated +// either. +TEST_F(BFloat16PropagationTest, TupleDomainNoPropagation) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + Shape tuple_shape = ShapeUtil::MakeTupleShape({shape, shape}); + + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape, "param")); + HloInstruction* domain = builder.AddInstruction( + HloInstruction::CreateDomain(param->shape(), param, nullptr, nullptr)); + HloInstruction* a_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 0)); + HloInstruction* b_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 1)); + HloInstruction* a_trans = builder.AddInstruction( + HloInstruction::CreateTranspose(shape, a_gte, {0, 1})); + HloInstruction* b_trans = builder.AddInstruction( + HloInstruction::CreateTranspose(shape, b_gte, {0, 1})); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, a_trans, b_trans)); + HloInstruction* root = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, dot, dot)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), root); + EXPECT_TRUE(OutputsBF16(a_trans)); + EXPECT_TRUE(OutputsBF16(b_trans)); + EXPECT_FALSE(OutputsBF16(a_gte)); + EXPECT_FALSE(OutputsBF16(b_gte)); + EXPECT_FALSE(OutputsBF16(domain)); + EXPECT_FALSE(OutputsBF16(param)); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc index 07b4b14b5ec1bdbc01345091105df69368b0b2fb..8595afca7e735528d9ef29a323696c0661fe971c 100644 --- a/tensorflow/compiler/xla/service/bfloat16_support.cc +++ b/tensorflow/compiler/xla/service/bfloat16_support.cc @@ -25,6 +25,7 @@ bool BFloat16Support::SupportsBF16Operand(const HloInstruction& hlo, case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kCustomCall: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kTuple: case HloOpcode::kWhile: @@ -43,6 +44,7 @@ bool BFloat16Support::SupportsBF16Output(const HloInstruction& hlo) const { case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kCustomCall: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kTuple: case HloOpcode::kWhile: @@ -81,6 +83,7 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( case HloOpcode::kConcatenate: case HloOpcode::kConvert: case HloOpcode::kCopy: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kMaximum: case HloOpcode::kMinimum: @@ -92,6 +95,9 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( case HloOpcode::kTranspose: case HloOpcode::kTuple: return true; + case HloOpcode::kBitcast: + return hlo.shape().element_type() == + hlo.operand(0)->shape().element_type(); case HloOpcode::kDynamicSlice: return operand_index == 0; case HloOpcode::kDynamicUpdateSlice: diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 682c3865797c85eedf3949738f3372857f146c0e..afe4b2e1425f9e84320ffd5f08beceaac8168c22 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -633,7 +633,7 @@ Status BufferAssignment::ComputeSummaryStats() { if (module_sequence.size() == module_->computation_count()) { TF_ASSIGN_OR_RETURN( const int64 min_size, - MinimumMemoryForSequence(module_sequence, buffer_size_)); + HeapSimulator::MinimumMemoryForModule(module_sequence, buffer_size_)); stats_.total_fragmentation_bytes = stats_.total_allocation_bytes - min_size; } diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index 7e86c33687e595ad154361dd7018791299cc56ab..28b5a5784ff7f5d0b7fd412d1c50f3025f11bb81 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -371,11 +371,11 @@ TEST_F(BufferAssignmentTest, Basic) { // param1[100] --------------/--------/ auto builder = HloComputation::Builder(TestName()); auto paramscalar = - builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "p")); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(1, f32vec100_, "")); + HloInstruction::CreateParameter(1, f32vec100_, "p1")); auto param1 = builder.AddInstruction( - HloInstruction::CreateParameter(2, f32vec100_, "")); + HloInstruction::CreateParameter(2, f32vec100_, "p2")); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); auto add = builder.AddInstruction( @@ -418,11 +418,11 @@ TEST_F(BufferAssignmentTest, BasicUniquelyColored) { // share anything. auto builder = HloComputation::Builder(TestName()); auto paramscalar = - builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "p")); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(1, f32vec100_, "")); + HloInstruction::CreateParameter(1, f32vec100_, "p1")); auto param1 = builder.AddInstruction( - HloInstruction::CreateParameter(2, f32vec100_, "")); + HloInstruction::CreateParameter(2, f32vec100_, "p2")); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); auto add = builder.AddInstruction( @@ -477,11 +477,11 @@ TEST_F(BufferAssignmentTest, BasicPartiallyColored) { // have the color 0, which allows the mul and add to share buffers. auto builder = HloComputation::Builder(TestName()); auto paramscalar = - builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "p")); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(1, f32vec100_, "")); + HloInstruction::CreateParameter(1, f32vec100_, "p1")); auto param1 = builder.AddInstruction( - HloInstruction::CreateParameter(2, f32vec100_, "")); + HloInstruction::CreateParameter(2, f32vec100_, "p2")); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); auto add = builder.AddInstruction( @@ -547,11 +547,11 @@ TEST_F(BufferAssignmentTest, MultipleUsersForNode) { // auto builder = HloComputation::Builder(TestName()); auto paramscalar = - builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "p")); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(1, f32vec100_, "")); + HloInstruction::CreateParameter(1, f32vec100_, "p1")); auto param1 = builder.AddInstruction( - HloInstruction::CreateParameter(2, f32vec100_, "")); + HloInstruction::CreateParameter(2, f32vec100_, "p2")); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); auto add = builder.AddInstruction( @@ -601,7 +601,7 @@ TEST_F(BufferAssignmentTest, TrivialMap) { // Creates the main kernel and verifies instruction counts. auto builder = HloComputation::Builder(TestName()); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32a100x10_, "")); + HloInstruction::CreateParameter(0, f32a100x10_, "p")); auto map = builder.AddInstruction( HloInstruction::CreateMap(f32a100x10_, {param0}, map_computation)); module->AddEntryComputation(builder.Build()); @@ -654,7 +654,7 @@ TEST_F(BufferAssignmentTest, CannotReuseInputBufferOfReduce) { auto builder = HloComputation::Builder(TestName()); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32a100x10_, "")); + HloInstruction::CreateParameter(0, f32a100x10_, "p")); auto exp1 = builder.AddInstruction( HloInstruction::CreateUnary(f32a100x10_, HloOpcode::kExp, param0)); auto exp2 = builder.AddInstruction( @@ -818,7 +818,7 @@ TEST_F(BufferAssignmentTest, UnaryOpReuseChain) { // param0[100] ---> (exp) ---> (tanh) ---> (exp) ---> (neg) auto builder = HloComputation::Builder(TestName()); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32vec100_, "")); + HloInstruction::CreateParameter(0, f32vec100_, "p")); auto exp1 = builder.AddInstruction( HloInstruction::CreateUnary(f32vec100_, HloOpcode::kExp, param0)); auto tanh = builder.AddInstruction( @@ -1496,11 +1496,11 @@ TEST_F(BufferAssignmentTest, TrivialPeakBuffers) { // param1[100] --------------/--------/ auto builder = HloComputation::Builder(TestName()); auto paramscalar = - builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "p")); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(1, f32vec100_, "")); + HloInstruction::CreateParameter(1, f32vec100_, "p1")); auto param1 = builder.AddInstruction( - HloInstruction::CreateParameter(2, f32vec100_, "")); + HloInstruction::CreateParameter(2, f32vec100_, "p2")); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); auto add = builder.AddInstruction( @@ -1536,7 +1536,7 @@ TEST_F(BufferAssignmentTest, PeakBuffers) { // be {%rev, %neg, %concat}. This occurs right at the concat itself. auto builder = HloComputation::Builder(TestName()); auto param = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32vec100_, "")); + HloInstruction::CreateParameter(0, f32vec100_, "p")); auto log = builder.AddInstruction( HloInstruction::CreateUnary(f32vec100_, HloOpcode::kLog, param)); auto rev = builder.AddInstruction( @@ -1673,7 +1673,7 @@ class WhileBufferAssignmentTest : public HloTestBase { std::unique_ptr RunBufferAssignment(HloModule* module, int64 alignment = 1) { auto sequence = - CreateMemoryMinimizingSequence(*module, ByteSizeOf).ConsumeValueOrDie(); + ScheduleComputationsInModule(*module, ByteSizeOf).ConsumeValueOrDie(); return BufferAssigner::Run( module, xla::MakeUnique(module, sequence), ByteSizeOf, @@ -1874,11 +1874,15 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); - auto infeed = builder.AddInstruction(HloInstruction::CreateInfeed(r0s32, "")); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto infeed = + builder.AddInstruction(HloInstruction::CreateInfeed(r0s32, token, "")); + auto infeed_data = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(r0s32, infeed, 0)); auto cond0 = module->AddEmbeddedComputation(build_cond()); auto body0 = module->AddEmbeddedComputation(build_body()); auto while0 = builder.AddInstruction( - HloInstruction::CreateWhile(r0s32, cond0, body0, infeed)); + HloInstruction::CreateWhile(r0s32, cond0, body0, infeed_data)); auto cond1 = module->AddEmbeddedComputation(build_cond()); auto body1 = module->AddEmbeddedComputation(build_body()); @@ -1909,8 +1913,8 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { // computation, since the issue this test stresses depends on the order the // nodes are traversed during BufferAssignment. SequentialHloOrdering::HloModuleSequence sequence; - sequence[module->entry_computation()] = {infeed, while0, while1, zero, - add, while2, tuple}; + sequence[module->entry_computation()] = { + token, infeed, infeed_data, while0, while1, zero, add, while2, tuple}; TF_ASSERT_OK_AND_ASSIGN( auto assignment, BufferAssigner::Run( @@ -2103,7 +2107,7 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { RunCopyInsertion(module.get()); auto sequence = - CreateMemoryMinimizingSequence(*module, ByteSizeOf).ConsumeValueOrDie(); + ScheduleComputationsInModule(*module, ByteSizeOf).ConsumeValueOrDie(); // To trigger b/38494731, we want a specific Hlo sequence for the // root computation, so we overwrite that entry with a manually diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc index f623aef67a4f98b447a9a15634a78deb60cfe6f1..7833ebe73ba5d2412101eede1b584ce86df084e8 100644 --- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc +++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc @@ -327,11 +327,12 @@ TEST_F(BufferLivenessTest, RootInstructionIsNotLastInSequentialOrder) { builder.AddInstruction(HloInstruction::CreateParameter(0, vec_, "param")); auto add = builder.AddInstruction( HloInstruction::CreateBinary(vec_, HloOpcode::kAdd, param, param)); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto recv = builder.AddInstruction( - HloInstruction::CreateRecv(vec_, /*channel_id=*/0)); + HloInstruction::CreateRecv(vec_, token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); auto send = builder.AddInstruction( - HloInstruction::CreateSend(recv_done, /*channel_id=*/1)); + HloInstruction::CreateSend(recv_done, token, /*channel_id=*/1)); auto send_done = builder.AddInstruction(HloInstruction::CreateSendDone(send)); auto module = CreateNewModule(); diff --git a/tensorflow/compiler/xla/service/call_inliner_test.cc b/tensorflow/compiler/xla/service/call_inliner_test.cc index 738d00881dd057fc13c115006c15e8f5b6d14a1d..924348c870b9ca3d86af560a0c8359af7220427e 100644 --- a/tensorflow/compiler/xla/service/call_inliner_test.cc +++ b/tensorflow/compiler/xla/service/call_inliner_test.cc @@ -148,14 +148,16 @@ TEST_F(CallInlinerTest, CallToOutfeedComputationIsInlined) { HloComputation::Builder outfeeder(TestName() + ".outfeeder"); auto value = outfeeder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + auto token = outfeeder.AddInstruction(HloInstruction::CreateAfterAll({})); outfeeder.AddInstruction( - HloInstruction::CreateOutfeed(f32, value, /*outfeed_config=*/"")); + HloInstruction::CreateOutfeed(f32, value, token, /*outfeed_config=*/"")); auto outfeed_computation = module->AddEmbeddedComputation(outfeeder.Build()); HloComputation::Builder outer(TestName() + ".outer"); outer.AddInstruction(HloInstruction::CreateCall( - ShapeUtil::MakeNil(), /*operands=*/{}, outfeed_computation)); + outfeed_computation->root_instruction()->shape(), /*operands=*/{}, + outfeed_computation)); module->AddEntryComputation(outer.Build()); diff --git a/tensorflow/compiler/xla/service/channel_tracker.h b/tensorflow/compiler/xla/service/channel_tracker.h index 52f33a1318e91d3f5941a5d68051e4c207661bbc..fac0afd672ff3ed083aacf778dd9c4f90a2ee870 100644 --- a/tensorflow/compiler/xla/service/channel_tracker.h +++ b/tensorflow/compiler/xla/service/channel_tracker.h @@ -19,7 +19,6 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/hlo_module.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" #include "tensorflow/compiler/xla/status.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/service/compilation_cache.cc b/tensorflow/compiler/xla/service/compilation_cache.cc deleted file mode 100644 index b16907da9e9c909d2639f83895db27d724a84a7b..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/compilation_cache.cc +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/compilation_cache.h" - -#include - -#include "tensorflow/compiler/xla/types.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" -#include "tensorflow/core/lib/strings/strcat.h" -#include "tensorflow/core/platform/logging.h" - -namespace xla { - -std::shared_ptr CompilationCache::Insert( - std::unique_ptr executable, - const HloModuleConfig& module_config) { - tensorflow::mutex_lock lock(mutex_); - - CacheKey key = - BuildKey(executable->entry_computation_handle(), module_config); - VLOG(2) << "inserting cache key: " << key; - if (cache_.count(key) == 0) { - cache_.emplace(key, std::move(executable)); - } else { - // Executable already exists in the cache. This can happen if two Execute - // calls for a new computation are received simultaneously by the - // service. In this case, we discard the Executable given as a parameter and - // return what is in the cache. This is necessary because the service relies - // on the cache to keep ownership of the Executable. We only want to store - // one Executable for a given computation version and we can't discard the - // executable which is in the cache because it may be in use. - executable.reset(); - } - return cache_.at(key); -} - -std::shared_ptr CompilationCache::LookUp( - const VersionedComputationHandle& versioned_handle, - const HloModuleConfig& module_config) const { - tensorflow::mutex_lock lock(mutex_); - - CacheKey key = BuildKey(versioned_handle, module_config); - VLOG(2) << "looking up cache key: " << key; - if (cache_.count(key) == 0) { - VLOG(2) << "cache key not found: " << key; - return nullptr; - } else { - std::shared_ptr result = cache_.at(key); - VLOG(2) << "hit executable with module config: " - << result->module_config().compilation_cache_key(); - return result; - } -} - -CompilationCache::CacheKey CompilationCache::BuildKey( - const VersionedComputationHandle& versioned_handle, - const HloModuleConfig& module_config) const { - // The computation shape is represented entirely by its ProgramShape member, - // so just serialize the proto as part of the key. - return tensorflow::strings::StrCat(versioned_handle.handle.handle(), "::", - versioned_handle.version, "::", - module_config.compilation_cache_key()); -} - -} // namespace xla diff --git a/tensorflow/compiler/xla/service/compilation_cache.h b/tensorflow/compiler/xla/service/compilation_cache.h deleted file mode 100644 index 09989726ae6629aa65cb1dd84c16408a75019fa5..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/compilation_cache.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_COMPILATION_CACHE_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_COMPILATION_CACHE_H_ - -#include -#include -#include - -#include "tensorflow/compiler/xla/service/executable.h" -#include "tensorflow/compiler/xla/service/hlo_module_config.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" -#include "tensorflow/compiler/xla/types.h" -#include "tensorflow/core/platform/macros.h" -#include "tensorflow/core/platform/mutex.h" -#include "tensorflow/core/platform/thread_annotations.h" - -namespace xla { - -// A cache which stores Executables indexed by computation handle and version. -class CompilationCache { - public: - CompilationCache() {} - - // Insert the given Executable into the cache. Return a bare Executable - // pointer for the caller to use. Note: the returned pointer will *not* be the - // same as the given unique pointer if the computation already exists in the - // cache. See comments in the .cc implementation for details of this case. - // - // module_config is provided by the caller, instead of being taken from the - // executable, so that we can insert keys into the compilation cache that are - // devoid of layout (where XLA gets to choose what layout to compile). - // - // A shared_ptr is returned so the caller can keep the Executable from being - // destructed in the event that the Executable is evicted from the - // computation cache (and the cache's shared_ptr to the Executable is - // destructed). - std::shared_ptr Insert(std::unique_ptr executable, - const HloModuleConfig& module_config); - - // Lookup the Executable for the specified versioned computation in the cache. - // Return a shared_ptr to the Executable if it exists in the cache. Return - // nullptr otherwise. - std::shared_ptr LookUp( - const VersionedComputationHandle& versioned_handle, - const HloModuleConfig& module_config) const; - - protected: - mutable tensorflow::mutex mutex_; - - // Map from versioned handle with program layout to Executable built - // for that computation version and program layout. - using CacheKey = string; - - CacheKey BuildKey(const VersionedComputationHandle& versioned_handle, - const HloModuleConfig& module_config) const; - std::map> cache_ GUARDED_BY(mutex_); - - private: - TF_DISALLOW_COPY_AND_ASSIGN(CompilationCache); -}; - -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_COMPILATION_CACHE_H_ diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc index d8fdccf9bbf1c1788bb4000aa702292362446503..7426672a7a2a9102bd5ea98bd51092982e1e09b4 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.cc +++ b/tensorflow/compiler/xla/service/compile_only_service.cc @@ -63,7 +63,8 @@ CompileOnlyService::CompileOnlyService(const ServiceOptions& options, StatusOr>> CompileOnlyService::CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options) { + const AotCompilationOptions& options, + std::unique_ptr* metadata) { std::vector> hlo_modules; for (const AotXlaComputationInstance& instance : computations) { TF_RET_CHECK(instance.computation.has_program_shape()); @@ -100,7 +101,8 @@ CompileOnlyService::CompileAheadOfTime( hlo_modules.push_back(std::move(hlo_module)); } - return compiler_->CompileAheadOfTime(std::move(hlo_modules), options); + return compiler_->CompileAheadOfTime(std::move(hlo_modules), options, + metadata); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h index e6a66c202d6e0df3cb6d165e51beb25abd8ec45c..1ac950bdd66bd034dfdafa8598ec506221e99c2f 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.h +++ b/tensorflow/compiler/xla/service/compile_only_service.h @@ -53,6 +53,12 @@ class CompileOnlyService : public Service { const tensorflow::gtl::ArraySlice computations, const AotCompilationOptions& options); + StatusOr>> + CompileAheadOfTime( + const tensorflow::gtl::ArraySlice computations, + const AotCompilationOptions& options, + std::unique_ptr* metadata); + Status GetDeviceHandles(const GetDeviceHandlesRequest* arg, GetDeviceHandlesResponse* result) override { return Unimplemented("CompileOnlyService does not support devices."); diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index 6f06bba6798bdff51f10d8fe9dc524d8064ba849..6b3b9820f09803c8a04504e6c35c22de51abf04b 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -35,6 +35,27 @@ Compiler::ComputeBackendConfigs(const HloInstruction& hlo, return {}; } +std::unique_ptr +Compiler::ComputeDefaultBackendConfig(const HloInstruction& hlo, + se::StreamExecutor* executor) const { + CHECK(executor != nullptr); + return nullptr; +} + +// Define a default version where metadata is not used. +StatusOr>> +Compiler::CompileAheadOfTime( + std::vector> modules, + const AotCompilationOptions& options, + std::unique_ptr* metadata) { + if (metadata != nullptr) { + return Unimplemented( + "Populating AotCompilationMetadata is not implemented on this " + "compiler."); + } + return CompileAheadOfTime(std::move(modules), options); +} + /* static */ std::map* Compiler::GetPlatformCompilerFactories() { static auto* r = new std::map; diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index 6c52ffd800d19de83877341d41ef81eee2de7251..99abb9bae32b35652e84cddc7c38dbd97ecb5006 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -94,6 +94,19 @@ class AotCompilationOptions { DebugOptions debug_options_; }; +// Abstract superclass describing metadata produced during ahead-of-time +// compilation. +class AotCompilationMetadata { + public: + AotCompilationMetadata(const AotCompilationMetadata&) = delete; + AotCompilationMetadata& operator=(AotCompilationMetadata const&) = delete; + + virtual ~AotCompilationMetadata() = default; + + protected: + AotCompilationMetadata() = default; +}; + // Abstract compiler interface that is subclassed for compilation on a // particular platform. // @@ -166,12 +179,29 @@ class Compiler { ComputeBackendConfigs(const HloInstruction& hlo, se::StreamExecutor* executor) const; + // Returns the backend configuration that the backend chooses by default for + // the given HLO. Returns no configuration if the backend does not support + // configurations for the given HLO. + // + // The stream executor is passed in to provide information about the hardware + // that the backend configurations would be targeting. + virtual std::unique_ptr + ComputeDefaultBackendConfig(const HloInstruction& hlo, + se::StreamExecutor* executor) const; + // Compiles the HLO module for ahead-of-time execution. This is intended for // use in static compilation. virtual StatusOr>> CompileAheadOfTime(std::vector> modules, const AotCompilationOptions& options) = 0; + // Similar to CompileAheadOfTime above but AotCompilationMetadata + // has an argument that can be populated during compilation. + virtual StatusOr>> + CompileAheadOfTime(std::vector> modules, + const AotCompilationOptions& options, + std::unique_ptr* metadata); + ///// // The Compiler class also serves as a point to register compiler objects // for the various platforms. diff --git a/tensorflow/compiler/xla/service/computation_layout.h b/tensorflow/compiler/xla/service/computation_layout.h index 53c3a3f7b738687db3098acfaef1ae87860d0440..6975f387b4864bf28ea0ad23d7d4602b5b346e08 100644 --- a/tensorflow/compiler/xla/service/computation_layout.h +++ b/tensorflow/compiler/xla/service/computation_layout.h @@ -32,12 +32,21 @@ namespace xla { // mutable layouts. class ComputationLayout { public: + // Creates a new ComputationLayout with the given result layout. + explicit ComputationLayout(ShapeLayout result_layout) + : result_layout_(std::move(result_layout)) {} + // Constructs a ComputationLayout from a ProgramShape. The layouts of the // parameters and results are set to the default layout. Layouts in the // ProgramShape are ignored if ignore_layouts is true. explicit ComputationLayout(const ProgramShape& program_shape, bool ignore_layouts = true); + // Adds a new parameter layout to the computation layout. + void add_parameter_layout(ShapeLayout shape_layout) { + parameter_layouts_.push_back(std::move(shape_layout)); + } + // Returns the layout of a particular parameter. const ShapeLayout& parameter_layout(int64 param_no) const { return parameter_layouts_[param_no]; diff --git a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc index 868348547d9f5cbdc7576c7fc0697d72c3a3e557..68f6ffc6b7012b7674b8a046df71c7aed7a386fa 100644 --- a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc @@ -119,10 +119,12 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsSend) { ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* true_computation = conditional->true_computation(); + auto* token = + true_computation->AddInstruction(HloInstruction::CreateAfterAll({})); auto* send = true_computation->AddInstruction(HloInstruction::CreateSend( true_computation->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(true))), - /*channel_id=*/0)); + token, /*channel_id=*/0)); true_computation->AddInstruction(HloInstruction::CreateSendDone(send)); EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); } @@ -133,8 +135,10 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsRecv) { ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* true_computation = conditional->true_computation(); + auto* token = + true_computation->AddInstruction(HloInstruction::CreateAfterAll({})); auto* recv = true_computation->AddInstruction(HloInstruction::CreateRecv( - ShapeUtil::MakeShape(F32, {1}), /*channel_id=*/0)); + ShapeUtil::MakeShape(F32, {1}), token, /*channel_id=*/0)); true_computation->AddInstruction(HloInstruction::CreateRecvDone(recv)); EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); } @@ -144,8 +148,10 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) { auto* conditional = computation->root_instruction(); ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* false_computation = conditional->false_computation(); - false_computation->AddInstruction( - HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config")); + auto token = + false_computation->AddInstruction(HloInstruction::CreateAfterAll({})); + false_computation->AddInstruction(HloInstruction::CreateInfeed( + ShapeUtil::MakeShape(F32, {1}), token, "config")); EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); } diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index 33d8338809d4e8c7c4774f062c3dda5494543ca6..ab3d846403ef264cd732a9c01d524cd4ccf65c38 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -472,6 +472,10 @@ class CopyRemover { // between copies added around aliased operations (kWhile) guarantees // this strict order. for (const HloValue* value_a : buffer.values()) { + if (ShapeUtil::IsToken(value_a->shape())) { + // Token values have no representation and cannot interfere. + continue; + } for (const HloValue* value_b : buffer.values()) { if (value_a != value_b) { DCHECK(ordering_.LiveRangeStrictlyBefore(*value_a, *value_b, @@ -613,7 +617,10 @@ class CopyRemover { VLOG(2) << copy->name() << " is not removable"; return false; } - + if (!ShapeUtil::Equal(copy->shape(), copy->operand(0)->shape())) { + VLOG(2) << copy->name() << " is not removable (shape mismatch)"; + return false; + } const CopyNodes& copy_node = copy_map_.at(copy); ValueNode* src = copy_node.src; ValueNode* dest = copy_node.dest; @@ -947,28 +954,6 @@ class CopyRemover { BufferValueTracker buffer_value_tracker_; }; -// Try to remove as many copies from the module as possible without introducing -// live range interference. Copy instructions (identified by their unique id) in -// the set copies_to_exclude are not considered for removal. -Status RemoveUnnecessaryCopies( - const HloOrdering& ordering, - const tensorflow::gtl::FlatSet& copies_to_exclude, HloModule* module) { - TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); - CopyRemover copy_remover(*alias_analysis, ordering, module); - XLA_VLOG_LINES(3, copy_remover.ToString()); - - for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kCopy && - !ContainsKey(copies_to_exclude, instruction->unique_id())) { - TF_RETURN_IF_ERROR(copy_remover.TryElideCopy(instruction).status()); - } - } - } - return Status::OK(); -} - // Add copies to address special constraints on the roots of computations not // related to live range interference: // @@ -1065,13 +1050,23 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { HloInstruction* instruction = pair.first; const ShapeTree& indices_to_copy = pair.second; + ShapeTree copies_added(indices_to_copy.shape()); std::vector users = instruction->users(); TF_ASSIGN_OR_RETURN(HloInstruction * deep_copy, instruction->parent()->DeepCopyInstruction( - instruction, &indices_to_copy)); + instruction, &indices_to_copy, &copies_added)); for (HloInstruction* user : users) { TF_RETURN_IF_ERROR(instruction->ReplaceUseWith(user, deep_copy)); } + // Special case copies are not eligible for later copy elision passes. + indices_to_copy.ForEachElement([&](const ShapeIndex& index, bool has_copy) { + if (has_copy) { + HloInstruction* copy = *copies_added.mutable_element(index); + if (copy != nullptr) { + copy->SetCopyElisionAllowed(false); + } + } + }); if (instruction == instruction->parent()->root_instruction()) { instruction->parent()->set_root_instruction(deep_copy); } @@ -1097,6 +1092,31 @@ void MaybeDumpModule(const string& message, const HloModule& module) { } // namespace +Status RemoveUnnecessaryCopies( + const HloOrdering& ordering, HloModule* module, + const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer) { + MaybeDumpModule("after adding copies to resolve interference", *module); + + TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, + HloAliasAnalysis::Run(module, fusion_can_share_buffer)); + CopyRemover copy_remover(*alias_analysis, ordering, module); + XLA_VLOG_LINES(3, copy_remover.ToString()); + + std::unique_ptr call_graph = CallGraph::Build(module); + for (HloComputation* computation : module->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kCopy && + instruction->CopyElisionAllowed()) { + TF_RETURN_IF_ERROR(copy_remover.TryElideCopy(instruction).status()); + } + } + } + MaybeDumpModule("after removing unnecessary copies", *module); + + return Status::OK(); +} + StatusOr CopyInsertion::Run(HloModule* module) { // Copy insertion is performed in three steps: // @@ -1130,16 +1150,13 @@ StatusOr CopyInsertion::Run(HloModule* module) { "Call graph must be flattened before copy insertion."); } - // Gather Ids of existing kCopy instructions in the module. We avoid removing - // these copies (except via DCE in TupleSimplifier) because they may have been - // added for reasons not considered by copy insertion (eg, layout assignment). - // Instruction id is used instead of HloInstruction* because the pointer - // values may be recycled. - tensorflow::gtl::FlatSet existing_copies; - for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kCopy) { - existing_copies.insert(instruction->unique_id()); + int64 num_existing_copies = 0; + if (VLOG_IS_ON(1)) { + for (HloComputation* computation : module->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kCopy) { + ++num_existing_copies; + } } } } @@ -1158,13 +1175,8 @@ StatusOr CopyInsertion::Run(HloModule* module) { TF_DCHECK_OK(VerifyNoLiveRangeInterference(module)); - MaybeDumpModule("after adding copies to resolve interference", *module); - DependencyHloOrdering ordering(module); - TF_RETURN_IF_ERROR( - RemoveUnnecessaryCopies(ordering, existing_copies, module)); - - MaybeDumpModule("after removing unnecessary copies", *module); + TF_RETURN_IF_ERROR(RemoveUnnecessaryCopies(ordering, module)); TF_RETURN_IF_ERROR(AddSpecialCaseCopies(*call_graph, module)); @@ -1185,7 +1197,7 @@ StatusOr CopyInsertion::Run(HloModule* module) { } } } - VLOG(1) << "Num copies before copy-insertion: " << existing_copies.size(); + VLOG(1) << "Num copies before copy-insertion: " << num_existing_copies; VLOG(1) << "Num copies after copy-insertion: " << num_total_copies; } diff --git a/tensorflow/compiler/xla/service/copy_insertion.h b/tensorflow/compiler/xla/service/copy_insertion.h index 65e3d31e347e2cb249a072e7d06ca10c55401748..e1973db928423cb4bbad00fe34329f731b23ea09 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.h +++ b/tensorflow/compiler/xla/service/copy_insertion.h @@ -21,7 +21,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_pass_interface.h" -#include "tensorflow/core/lib/gtl/flatmap.h" namespace xla { @@ -48,6 +47,15 @@ class CopyInsertion : public HloPassInterface { public: tensorflow::StringPiece name() const override { return "copy-insertion"; } + // fusion_can_share_buffer: backend specific function that decides whether a + // fusion can share buffer with its operand. + // + // TODO(b/80315712): Find a better way to tell whether a fusion can share + // buffer. + CopyInsertion(const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer = nullptr) + : fusion_can_share_buffer_(fusion_can_share_buffer) {} + // Run the pass on the given module. Returns whether the module was changed // (copies were inserted). StatusOr Run(HloModule* module) override; @@ -62,8 +70,21 @@ class CopyInsertion : public HloPassInterface { // // TODO(b/62548313): Remove this when buffer assignment is module-scoped. static StatusOr AddCopiesForBufferAssignment(HloModule* module); + + private: + // Backend specific function that decides whether a fusion can share buffer + // with its operand. + HloDataflowAnalysis::FusionCanShareBufferFunction fusion_can_share_buffer_; }; +// Try to remove as many copies from the module as possible without introducing +// live range interference. Only copy instructions that are eligible for +// copy elision are considered for removal. +Status RemoveUnnecessaryCopies( + const HloOrdering& ordering, HloModule* module, + const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer = nullptr); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_COPY_INSERTION_H_ diff --git a/tensorflow/compiler/xla/service/copy_insertion_test.cc b/tensorflow/compiler/xla/service/copy_insertion_test.cc index 153f062d015e49db11c4c9ae0a2a61e76c020f02..7ae8799b612449ecc3c45123e769aac817d12058 100644 --- a/tensorflow/compiler/xla/service/copy_insertion_test.cc +++ b/tensorflow/compiler/xla/service/copy_insertion_test.cc @@ -125,21 +125,27 @@ TEST_F(CopyInsertionTest, SingleConstant) { } TEST_F(CopyInsertionTest, ExistingCopiesNotRemoved) { - // Verify that an kCopy instructions which exist in the pass before + // Verify that kCopy instructions which change layout and exist before // copy-insertion remain in the graph after copy-insertion. auto module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); - HloInstruction* copy_1 = builder.AddInstruction(HloInstruction::CreateUnary( - constant->shape(), HloOpcode::kCopy, constant)); - HloInstruction* copy_2 = builder.AddInstruction(HloInstruction::CreateUnary( - constant->shape(), HloOpcode::kCopy, constant)); + HloInstruction* constant = + builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR2({{0.f, 2.f}, {2.f, 4.f}}))); + auto minor_to_major = LayoutUtil::MinorToMajor(constant->shape()); + Layout reversed_layout = + LayoutUtil::MakeLayoutFromMajorToMinor(minor_to_major); + Shape copy_shape = constant->shape(); + *copy_shape.mutable_layout() = reversed_layout; + HloInstruction* copy_1 = builder.AddInstruction( + HloInstruction::CreateUnary(copy_shape, HloOpcode::kCopy, constant)); + HloInstruction* copy_2 = builder.AddInstruction( + HloInstruction::CreateUnary(copy_shape, HloOpcode::kCopy, constant)); HloInstruction* add = builder.AddInstruction(HloInstruction::CreateBinary( constant->shape(), HloOpcode::kAdd, copy_1, copy_2)); - HloInstruction* add_copy = builder.AddInstruction( - HloInstruction::CreateUnary(constant->shape(), HloOpcode::kCopy, add)); + builder.AddInstruction( + HloInstruction::CreateUnary(add->shape(), HloOpcode::kCopy, add)); module->AddEntryComputation(builder.Build()); @@ -147,12 +153,11 @@ TEST_F(CopyInsertionTest, ExistingCopiesNotRemoved) { InsertCopies(module.get()); - EXPECT_EQ(CountCopies(*module), 3); + EXPECT_EQ(CountCopies(*module), 2); - EXPECT_EQ(module->entry_computation()->root_instruction(), add_copy); - EXPECT_THAT( - module->entry_computation()->root_instruction(), - op::Copy(op::Add(op::Copy(op::Constant()), op::Copy(op::Constant())))); + EXPECT_EQ(module->entry_computation()->root_instruction(), add); + EXPECT_THAT(module->entry_computation()->root_instruction(), + op::Add(op::Copy(op::Constant()), op::Copy(op::Constant()))); } TEST_F(CopyInsertionTest, MultipleConstantsAndParameters) { @@ -1595,6 +1600,45 @@ TEST_F(CopyInsertionTest, WhileBodyWithConstantRoot) { EXPECT_THAT(condition->root_instruction(), op::Constant()); } +TEST_F(CopyInsertionTest, TokensShouldNotBeCopied) { + string module_string = R"( +HloModule TokensShouldNotBeCopied + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %after-all = token[] after-all(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %after-all) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %TokensShouldNotBeCopied () -> s32[] { + %one = s32[] constant(1) + %negative_one = s32[] negate(%one) + %init_token = token[] after-all() + %init_tuple = (s32[], token[]) tuple(s32[] %negative_one, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + HloRunner::CreateModuleFromString( + module_string, GetDebugOptionsForTest())); + InsertCopies(module.get()); + + // There should be no copies added because tokens should not be copied. + EXPECT_EQ(CountCopies(*module), 0); +} + std::unique_ptr MakeTrivialCondition(const Shape& shape) { auto builder = HloComputation::Builder("trivial_condition"); builder.AddInstruction( @@ -1636,8 +1680,7 @@ void BM_SequentialWhiles(int num_iters, int num_whiles) { for (int i = 0; i < num_iters; ++i) { HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - HloModule module("BM_SequentialWhiles", VersionedComputationHandle(), - config); + HloModule module("BM_SequentialWhiles", config); auto builder = HloComputation::Builder("BM_SequentialWhiles"); HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( @@ -1677,8 +1720,7 @@ void BM_ParallelWhiles(int num_iters, int num_whiles) { for (int i = 0; i < num_iters; ++i) { HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - HloModule module("BM_SequentialWhiles", VersionedComputationHandle(), - config); + HloModule module("BM_SequentialWhiles", config); auto builder = HloComputation::Builder("BM_ParallelWhiles"); HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( @@ -1750,8 +1792,7 @@ void BM_ManyElementTuple(int num_iters, const int num_tuple_inputs) { std::vector tuple_params(num_tuple_inputs); for (int i = 0; i < num_iters; ++i) { auto builder = HloComputation::Builder("BM_ParallelWhiles"); - HloModule module("BM_ManyElementTuple", VersionedComputationHandle(), - config); + HloModule module("BM_ManyElementTuple", config); for (int j = 0; j < num_tuple_inputs; ++j) { tuple_params[j] = builder.AddInstruction( HloInstruction::CreateParameter(j, element_shape, "")); diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 278bb1bebfa1a0d76d0268b6b6fcfa87410ceee5..3479240610a197aeed0c0a07099239e1161b1352 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -53,29 +53,6 @@ cc_library( alwayslink = True, # Contains per-platform transfer manager registration ) -cc_library( - name = "external_constant_pool", - srcs = ["external_constant_pool.cc"], - hdrs = ["external_constant_pool.h"], - deps = [ - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:util", - "//tensorflow/core:lib", - ], -) - -tf_cc_test( - name = "external_constant_pool_test", - srcs = ["external_constant_pool_test.cc"], - deps = [ - ":external_constant_pool", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla/tests:xla_internal_test_main", - "//tensorflow/core:test", - ], -) - cc_library( name = "cpu_compiler", srcs = ["cpu_compiler.cc"], @@ -151,7 +128,14 @@ cc_library( "@llvm//:target", # fixdeps: keep "@llvm//:x86_code_gen", # fixdeps: keep "@llvm//:x86_disassembler", # fixdeps: keep - ], + ] + select({ + "//tensorflow:linux_ppc64le": [ + "@llvm//:powerpc_disassembler", + "@llvm//:powerpc_code_gen", + ], + "//conditions:default": [ + ], + }), alwayslink = True, # Contains compiler registration ) @@ -168,7 +152,6 @@ cc_library( ":cpu_runtime", ":custom_call_target_registry", ":disassembler", - ":external_constant_pool", ":orc_jit_memory_mapper", ":runtime_fp16", ":runtime_conv2d", @@ -249,7 +232,6 @@ cc_library( ":cpu_options", ":cpu_runtime", ":dot_op_emitter", - ":external_constant_pool", ":ir_emission_utils", ":ir_function", ":parallel_loop_emitter", @@ -266,6 +248,7 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:elemental_ir_emitter", "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_casting_utils", "//tensorflow/compiler/xla/service:hlo_module_config", "//tensorflow/compiler/xla/service:name_uniquer", "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", @@ -898,6 +881,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", + "//tensorflow/core:lib", "@llvm//:core", "@llvm//:support", ], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 25b18eff20f901fc34343a12bfbd353ecec49cfb..55962ba70d213939ccb49cad3bdd75395cc4eaa5 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -264,12 +264,12 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile, pass.AddPass( /*rewrite_training_op=*/true, /*rewrite_inference_op=*/true, - /*rewrite_grad_op=*/true, - /*use_fusion=*/false); + /*rewrite_grad_op=*/true); pass.AddPass( /*is_layout_sensitive=*/false, [](const Shape&, const Shape&) { return false; }, /*enable_dot_strength_reduction=*/false); + pass.AddPass(); // BatchNormExpander can create zero-sized ops, so zero-sized HLO // elimination has to come after that pass. @@ -304,15 +304,19 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile, ReducePrecisionInsertion::PassTiming::AFTER_FUSION); pipeline.AddPass( - module->mutable_device_entry_computation_layout(), - &target_machine_features); + module->mutable_entry_computation_layout(), &target_machine_features); // The LayoutAssignment pass may leave behind kCopy instructions which are // duplicate or NOPs, so remove them with algebraic simplification and CSE. - pipeline.AddPass>( - /*is_layout_sensitive=*/true, - [](const Shape&, const Shape&) { return true; }, - /*enable_dot_strength_reduction=*/false); - pipeline.AddPass(/*is_layout_sensitive=*/true); + { + auto& pass = pipeline.AddPass>( + "after layout assignement"); + pass.AddPass>( + /*is_layout_sensitive=*/true, + [](const Shape&, const Shape&) { return true; }, + /*enable_dot_strength_reduction=*/false); + pass.AddPass(); + pass.AddPass(/*is_layout_sensitive=*/true); + } pipeline.AddPass(BF16, F32); // Outline ops in the entry computation into calls to subcomputations. const int max_parallelism = @@ -550,8 +554,8 @@ StatusOr> CpuCompiler::RunBackend( // and reduced memory usage (as compared to using DependencyHloOrdering). TF_ASSIGN_OR_RETURN( SequentialHloOrdering::HloModuleSequence module_sequence, - CreateMemoryMinimizingSequence(*module, BufferSizeBytesFunction(), - DFSMemoryScheduler)); + ScheduleComputationsInModule(*module, BufferSizeBytesFunction(), + DFSMemoryScheduler)); // Run buffer analysis on the HLO graph. This analysis figures out which // temporary buffers are required to run the computation. @@ -580,7 +584,7 @@ StatusOr> CpuCompiler::RunBackend( IrEmitter ir_emitter(*module, *assignment, llvm_module.get(), std::move(instruction_to_profile_idx), std::move(computation_to_profile_idx), - &target_machine_features, jit->external_constant_pool()); + &target_machine_features); for (auto embedded_computation : entry_computation->MakeEmbeddedComputationsList()) { @@ -730,7 +734,7 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, TF_ASSIGN_OR_RETURN( SequentialHloOrdering::HloModuleSequence module_sequence, - CreateMemoryMinimizingSequence(*module, BufferSizeBytesFunction())); + ScheduleComputationsInModule(*module, BufferSizeBytesFunction())); // Run buffer analysis on the HLO graph. This analysis figures out which // temporary buffers are required to run the computation. @@ -767,8 +771,7 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, IrEmitter ir_emitter(*module, *assignment, &llvm_module, std::move(instruction_to_profile_idx), std::move(computation_to_profile_idx), - &target_machine_features, - /*external_constant_pool=*/nullptr); + &target_machine_features); HloComputation* computation = module->entry_computation(); for (auto embedded_computation : computation->MakeEmbeddedComputationsList()) { diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index cf43b74c699ca8cbbef11a0abbaf4d69476f5d77..1093559892ddb9c238fd9c1f7e3d419ec7022776 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -206,8 +206,8 @@ StatusOr CpuExecutable::CreateResultShapedBuffer( tensorflow::gtl::MutableArraySlice buffers) { se::Stream* stream = run_options->stream(); ScopedShapedBuffer result_buffer( - /*on_host_shape=*/host_result_shape(), - /*on_device_shape=*/host_result_shape(), run_options->allocator(), + /*on_host_shape=*/result_shape(), + /*on_device_shape=*/result_shape(), run_options->allocator(), stream->parent()->device_ordinal()); // Move OwningDeviceMemory values which contain the array(s) of the result diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 97e10a89a209c057685709e7a5034052ff4376ed..750310c633286aa8f964c9ae5dcf847f2dc0557c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -501,8 +501,8 @@ TEST_F(OpcodeFusionTest, UnaryMapOfExp) { HloInstruction* exp = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kExp, param0)); - builder.AddInstruction(HloInstruction::CreateMap( - shape, {exp}, CreateAdderToOne(module.get()), /*static_operands=*/{})); + builder.AddInstruction( + HloInstruction::CreateMap(shape, {exp}, CreateAdderToOne(module.get()))); module->AddEntryComputation(builder.Build()); @@ -525,8 +525,8 @@ TEST_F(OpcodeFusionTest, BinaryMapOfExps) { HloInstruction* exp1 = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kExp, param1)); - builder.AddInstruction(HloInstruction::CreateMap( - shape, {exp0, exp1}, CreateMax(module.get()), /*static_operands=*/{})); + builder.AddInstruction( + HloInstruction::CreateMap(shape, {exp0, exp1}, CreateMax(module.get()))); module->AddEntryComputation(builder.Build()); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc index d97802ee45d6add3c466577d7624d9ca74e2f380..b877b295814a7e13569a1837ed3e1787f2fc3f56 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc @@ -160,9 +160,8 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor, int32 size_32 = static_cast(size); CpuInfeedBuffer* queued_buffer = new CpuInfeedBuffer(size_32); - Status s = - TransferBufferToDevice(executor, /*size=*/size, - /*source=*/source, queued_buffer->device_memory()); + Status s = executor->SynchronousMemcpyH2D( + /*host_src=*/source, /*size=*/size, queued_buffer->device_memory()); if (!s.ok()) { queued_buffer->Done(s); diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index cda623f8e87a3bce7df824d89d863616413b89c6..58228180ca55ede50c8579bbd73cfdfffc07e208 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -324,11 +324,11 @@ void ColumnMajorMatrixVectorProductEmitter::Emit() { int64 column_remainder = k() % tile_cols(); int64 column_limit = k() - column_remainder; - ksl_.For("dot.outer.tiled", - /*start=*/0, /*end=*/column_limit, /*step=*/tile_cols(), - [&](llvm::Value* column, bool is_first_column) { - EmitOuterLoopBody(column, tile_cols(), is_first_column); - }); + ksl_.ForReturnVoid("dot.outer.tiled", + /*start=*/0, /*end=*/column_limit, /*step=*/tile_cols(), + [&](llvm::Value* column, bool is_first_column) { + EmitOuterLoopBody(column, tile_cols(), is_first_column); + }); if (column_remainder != 0) { EmitOuterLoopBody(ir_builder_->getInt64(column_limit), column_remainder, @@ -341,19 +341,20 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopTiled( int64 columns, bool is_first_column) { int64 row_limit = m() - (m() % tile_rows()); - ksl_.For("dot.inner.tiled", /*start=*/0, /*end=*/row_limit, - /*step=*/tile_rows(), [&](llvm::Value* row) { - std::vector lhs_tile = - lhs_memory_tile->LoadTile(/*minor_dim_offset=*/row); - llvm::Value* accumulator = - is_first_column ? (addend_ ? vsl_.LoadVector(addend_, row) - : vsl_.GetZeroVector()) - : vsl_.LoadVector(result_, row); - for (int i = 0; i < columns; i++) { - accumulator = vsl_.MulAdd(lhs_tile[i], rhs_tile[i], accumulator); - } - vsl_.StoreVector(accumulator, result_, row); - }); + ksl_.ForReturnVoid( + "dot.inner.tiled", /*start=*/0, /*end=*/row_limit, + /*step=*/tile_rows(), [&](llvm::Value* row) { + std::vector lhs_tile = + lhs_memory_tile->LoadTile(/*minor_dim_offset=*/row); + llvm::Value* accumulator = + is_first_column ? (addend_ ? vsl_.LoadVector(addend_, row) + : vsl_.GetZeroVector()) + : vsl_.LoadVector(result_, row); + for (int i = 0; i < columns; i++) { + accumulator = vsl_.MulAdd(lhs_tile[i], rhs_tile[i], accumulator); + } + vsl_.StoreVector(accumulator, result_, row); + }); } void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( @@ -372,7 +373,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( // // initialized. // } - ksl_.For( + ksl_.ForReturnVoid( "dot.inner.epilg.outer", /*start=*/current_tile_col, /*end=*/ir_builder_->CreateAdd(columns_llvm, current_tile_col), /*step=*/1, /*peel_first_iteration=*/false, @@ -382,7 +383,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( ir_builder_->CreateMul(col, ir_builder_->getInt64(m())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); - ksl_.For( + ksl_.ForReturnVoid( "dot.inner.epilg.inner", /*start=*/row_start, /*end=*/m(), /*step=*/1, [&](llvm::Value* scalar_row) { llvm::Value* product = vsl_.Mul( @@ -390,7 +391,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( llvm::Value* setting_result_first_time = ir_builder_->CreateAnd( is_first_scalar_col, ir_builder_->getInt1(is_first_tiled_column)); - ksl_.If( + ksl_.IfReturnVoid( setting_result_first_time, /*true_block_generator=*/ [&]() { @@ -571,9 +572,10 @@ void RowMajorMatrixVectorProductEmitter::Emit() { int64 row_remainder = m() % tile_rows(); int64 row_limit = m() - row_remainder; - ksl_.For("dot.outer.tiled", - /*start=*/0, /*end=*/row_limit, /*step=*/tile_rows(), - [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); + ksl_.ForReturnVoid( + "dot.outer.tiled", + /*start=*/0, /*end=*/row_limit, /*step=*/tile_rows(), + [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); if (row_remainder != 0) { EmitOuterLoopBody(ir_builder_->getInt64(row_limit), row_remainder); @@ -585,17 +587,17 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopTiled( std::vector* vector_accumulators) { int64 column_limit = k() - (k() % tile_cols()); - ksl_.For("dot.inner.tiled", /*start=*/0, /*end=*/column_limit, - /*step=*/tile_cols(), [&](llvm::Value* col) { - std::vector lhs_tile = - lhs_memory_tile->LoadTile(/*minor_dim_offset=*/col); - llvm::Value* rhs_value = vsl_.LoadVector(rhs_, col); - for (int i = 0; i < rows; i++) { - llvm::Value* old_sum = (*vector_accumulators)[i].Get(); - (*vector_accumulators)[i].Set( - vsl_.Add(old_sum, vsl_.Mul(rhs_value, lhs_tile[i]))); - } - }); + ksl_.ForReturnVoid("dot.inner.tiled", /*start=*/0, /*end=*/column_limit, + /*step=*/tile_cols(), [&](llvm::Value* col) { + std::vector lhs_tile = + lhs_memory_tile->LoadTile(/*minor_dim_offset=*/col); + llvm::Value* rhs_value = vsl_.LoadVector(rhs_, col); + for (int i = 0; i < rows; i++) { + llvm::Value* old_sum = (*vector_accumulators)[i].Get(); + (*vector_accumulators)[i].Set(vsl_.Add( + old_sum, vsl_.Mul(rhs_value, lhs_tile[i]))); + } + }); } void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( @@ -612,14 +614,15 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( ir_builder_->getInt64(k())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); - ksl_.For("dot.inner.epilg.inner", /*start=*/column_start, /*end=*/k(), - /*step=*/1, [&](llvm::Value* scalar_col) { - llvm::Value* product = - vsl_.Mul(vsl_.LoadScalar(lhs_base_pointer, scalar_col), - vsl_.LoadScalar(rhs_, scalar_col)); - llvm::Value* old_value = (*scalar_accumulators)[r].Get(); - (*scalar_accumulators)[r].Set(vsl_.Add(old_value, product)); - }); + ksl_.ForReturnVoid( + "dot.inner.epilg.inner", /*start=*/column_start, /*end=*/k(), + /*step=*/1, [&](llvm::Value* scalar_col) { + llvm::Value* product = + vsl_.Mul(vsl_.LoadScalar(lhs_base_pointer, scalar_col), + vsl_.LoadScalar(rhs_, scalar_col)); + llvm::Value* old_value = (*scalar_accumulators)[r].Get(); + (*scalar_accumulators)[r].Set(vsl_.Add(old_value, product)); + }); } } @@ -740,7 +743,7 @@ class MatrixMatrixBlockPanelEmitter { private: // The HandleResiduesOnX helpers split the iteration space for dimension X // into a multiple of the tile size on dimension X and an epilogue. These - // helpers ultimately call into `EmitTiledReductionLoop` for emitting the + // helpers ultimately call into `EmitTiledGemm` for emitting the // tiled GEMM kernel. void HandleResiduesOnN(); @@ -750,15 +753,13 @@ class MatrixMatrixBlockPanelEmitter { llvm::Value* k_start, llvm::Value* k_end, llvm::Value* n_start, llvm::Value* n_end); - // This emits the inner reduction loop. This inner reduction loop multiplies - // a tile from the LHS of size [tile_size_m,tile_size_k] and a tile from the - // RHS of size [`tile_size_k`, vls->vector_width()] to update a tile of size - // [`tile_size_m`, vls->vector_width()] in the result. - void EmitTiledReductionLoop(VectorSupportLibrary* vsl, int64 tile_size_k, - llvm::Value* k_start, llvm::Value* k_end, - llvm::Value* n_start, llvm::Value* n_end, - int64 tile_size_m, llvm::Value* m_start, - llvm::Value* m_end); + // This emits a tiled GEMM kernel. For a detailed description see the comment + // on the implementation. + void EmitTiledGemm(VectorSupportLibrary* vsl, int64 tile_size_k, + llvm::Value* k_start, llvm::Value* k_end, + llvm::Value* n_start, llvm::Value* n_end, + int64 tile_size_m, llvm::Value* m_start, + llvm::Value* m_end); llvm::Value* GetInt64(int64 value) { return ir_builder_->getInt64(value); } @@ -819,7 +820,7 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnN() { if (n_start != dims().n()) { VectorSupportLibrary vsl(scalar_type(), 1, ir_builder_, "gebp"); - ksl_.For("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { + ksl_.ForReturnVoid("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { llvm::Value* n_i_next = ir_builder_->CreateAdd(n_i, ir_builder_->getInt64(1)); HandleResiduesOnK(&vsl, n_i, n_i_next); @@ -848,16 +849,24 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnM( VectorSupportLibrary* vsl, int64 tile_size_k, llvm::Value* k_start, llvm::Value* k_end, llvm::Value* n_start, llvm::Value* n_end) { const int64 m_end = dims().m() - dims().m() % tile_size_m(); - EmitTiledReductionLoop(vsl, tile_size_k, k_start, k_end, n_start, n_end, - tile_size_m(), GetInt64(0), GetInt64(m_end)); + EmitTiledGemm(vsl, tile_size_k, k_start, k_end, n_start, n_end, tile_size_m(), + GetInt64(0), GetInt64(m_end)); if (m_end != dims().m()) { - EmitTiledReductionLoop(vsl, tile_size_k, k_start, k_end, n_start, n_end, - dims().m() - m_end, GetInt64(m_end), - GetInt64(dims().m())); + EmitTiledGemm(vsl, tile_size_k, k_start, k_end, n_start, n_end, + dims().m() - m_end, GetInt64(m_end), GetInt64(dims().m())); } } +// The loop structure is: +// +// Iterate over dimension M as m: +// Iterate over dimension N as n: +// Iterate over dimension K as k: +// OutputTile[m,n] += Dot(LhsTile[m,k], RhsTile[k,n]) +// +// I.e. a just a tiled version of a "naive" GEMM. +// // The tiling scheme is as follows: // // Let the LHS be: @@ -919,41 +928,48 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnM( // +-------------------+-------------------+-------------------+--------- // | a0*p0+b0*q0+c0*r0 | a0*p1+b0*q1+c0*r1 | a0*p2+b0*q2+c0*r2 | ... // +-------------------+-------------------+-------------------+--------- -void MatrixMatrixBlockPanelEmitter::EmitTiledReductionLoop( +void MatrixMatrixBlockPanelEmitter::EmitTiledGemm( VectorSupportLibrary* vsl, int64 tile_size_k, llvm::Value* k_start, llvm::Value* k_end, llvm::Value* n_start, llvm::Value* n_end, int64 tile_size_m, llvm::Value* m_start, llvm::Value* m_end) { - ksl_.For("dot.m", m_start, m_end, tile_size_m, [&](llvm::Value* m_i) { - MemoryTile result_memory_tile(vsl, ir_builder_, /*matrix=*/result_, - /*matrix_size_along_minor_dim=*/dims().n(), - /*major_dim_offset=*/m_i, - /*tile_size_along_major_dim=*/tile_size_m); - MemoryTile lhs_memory_tile(vsl, ir_builder_, /*matrix=*/lhs_, - /*matrix_size_along_minor_dim=*/dims().k(), - /*major_dim_offset=*/m_i, - /*tile_size_along_major_dim=*/tile_size_m); - - ksl_.For("dot.k", k_start, k_end, tile_size_k, [&](llvm::Value* k_i) { - MemoryTile rhs_memory_tile(vsl, ir_builder_, rhs_, dims().n(), k_i, - tile_size_k); - std::vector> lhs_tile = - lhs_memory_tile.LoadBroadcastTile(k_i, tile_size_k); - ksl_.For( - "dot.n", n_start, n_end, vsl->vector_size(), [&](llvm::Value* n_i) { - std::vector rhs_tile = rhs_memory_tile.LoadTile(n_i); - std::vector result_tile = - result_memory_tile.LoadTile(n_i); - for (int64 r_m_i = 0; r_m_i < tile_size_m; r_m_i++) { - for (int64 r_k_i = 0; r_k_i < tile_size_k; r_k_i++) { - result_tile[r_m_i] = - vsl->MulAdd(lhs_tile[r_m_i][r_k_i], rhs_tile[r_k_i], - result_tile[r_m_i]); - } - } - result_memory_tile.StoreTile(result_tile, n_i); - }); - }); - }); + ksl_.ForReturnVoid( + "dot.m", m_start, m_end, tile_size_m, [&](llvm::Value* m_i) { + MemoryTile result_memory_tile( + vsl, ir_builder_, /*matrix=*/result_, + /*matrix_size_along_minor_dim=*/dims().n(), + /*major_dim_offset=*/m_i, + /*tile_size_along_major_dim=*/tile_size_m); + MemoryTile lhs_memory_tile(vsl, ir_builder_, /*matrix=*/lhs_, + /*matrix_size_along_minor_dim=*/dims().k(), + /*major_dim_offset=*/m_i, + /*tile_size_along_major_dim=*/tile_size_m); + ksl_.ForReturnVoid( + "dot.n", n_start, n_end, vsl->vector_size(), [&](llvm::Value* n_i) { + TileVariable result_tile_var(vsl, + result_memory_tile.LoadTile(n_i)); + ksl_.ForReturnVoid( + "dot.k", k_start, k_end, tile_size_k, [&](llvm::Value* k_i) { + MemoryTile rhs_memory_tile(vsl, ir_builder_, rhs_, + dims().n(), k_i, tile_size_k); + std::vector> lhs_tile = + lhs_memory_tile.LoadBroadcastTile(k_i, tile_size_k); + std::vector rhs_tile = + rhs_memory_tile.LoadTile(n_i); + std::vector result_tile = + result_tile_var.Get(); + for (int64 r_m_i = 0; r_m_i < tile_size_m; r_m_i++) { + for (int64 r_k_i = 0; r_k_i < tile_size_k; r_k_i++) { + result_tile[r_m_i] = + vsl->MulAdd(lhs_tile[r_m_i][r_k_i], rhs_tile[r_k_i], + result_tile[r_m_i]); + } + } + result_tile_var.Set(result_tile); + }); + + result_memory_tile.StoreTile(result_tile_var.Get(), n_i); + }); + }); } } // namespace @@ -1285,8 +1301,11 @@ Status DotOpEmitter::Emit() { // from messing up the vectorization. std::unique_ptr reduction_loop = loop_nest.AddLoop( 0, lhs_shape.dimensions(lhs_reduction_dimension), "reduction", - /*prevent_unrolling=*/lhs_reduction_along_minor_dimension && - rhs_reduction_along_minor_dimension); + /*unroll_mode=*/ + (lhs_reduction_along_minor_dimension && + rhs_reduction_along_minor_dimension) + ? xla::llvm_ir::UnrollMode::kNoUnroll + : xla::llvm_ir::UnrollMode::kDefaultUnroll); // The final entry in the rhs and lhs indexes is the indvar of the // reduction loop. @@ -1361,7 +1380,7 @@ Status DotOpEmitter::Emit() { // the rhs and lhs indexes with the reduction dimensions removed. The terms // from the rhs index are the lower dimensions in the index so we add them // first. - llvm_ir::IrArray::Index target_index; + llvm_ir::IrArray::Index target_index(lhs_index.GetType()); for (int dimension = 0; dimension < lhs_index.size(); ++dimension) { if (dimension != lhs_reduction_dimension) { target_index.push_back(lhs_index[dimension]); @@ -1385,10 +1404,13 @@ Status DotOpEmitter::Emit() { Status DotOpEmitter::EmitScalarDot() { // A scalar dot is just a scalar multiply. llvm::Value* result; + // Use the same index_type for all tensor accesses in the same kernel. + llvm::Type* index_type = ir_builder_->getInt64Ty(); + llvm_ir::IrArray::Index element_index(index_type); llvm::Value* lhs_value = - lhs_array_.EmitReadArrayElement(/*index=*/{}, ir_builder_); + lhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); llvm::Value* rhs_value = - rhs_array_.EmitReadArrayElement(/*index=*/{}, ir_builder_); + rhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); if (ShapeUtil::ElementIsComplex(lhs_array_.GetShape())) { #define REAL(x) ir_builder_->CreateExtractValue(x, {0}) #define IMAG(x) ir_builder_->CreateExtractValue(x, {1}) @@ -1406,7 +1428,8 @@ Status DotOpEmitter::EmitScalarDot() { } else { result = ir_builder_->CreateFMul(lhs_value, rhs_value); } - target_array_.EmitWriteArrayElement(/*index=*/{}, result, ir_builder_); + target_array_.EmitWriteArrayElement(/*index=*/element_index, result, + ir_builder_); return Status::OK(); } @@ -1608,8 +1631,8 @@ bool PotentiallyImplementedAsEigenDot( const Shape& lhs_shape = hlo.operand(0)->shape(); const Shape& rhs_shape = hlo.operand(1)->shape(); - if (ShapeUtil::HasZeroElements(lhs_shape) || - ShapeUtil::HasZeroElements(rhs_shape)) { + if (ShapeUtil::IsZeroElementArray(lhs_shape) || + ShapeUtil::IsZeroElementArray(rhs_shape)) { return false; } diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h index 2effb7fc360a47bf780fcbf9b6c9a096cb1cf41e..ed2a18976a0f1a88e7bb4632d3a63167d5c146ad 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h @@ -144,8 +144,12 @@ class DotOpEmitter { } std::tuple GetGemmTileSize() const { + // Tuned for broadwell - Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz + // + // TODO(b/80093688): Tune for other architectures and centralize this + // information in one place. const std::tuple kDefaultTileSize = - std::tuple(3, 5, 1); + std::tuple(11, 9, 1); return options::LlvmIrGemmTileSize(hlo_module_config_) .value_or(kDefaultTileSize); } diff --git a/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc b/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc deleted file mode 100644 index c56286559158758ca6db5ae097729286bde346f0..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/external_constant_pool.cc +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/external_constant_pool.h" - -#include -#include -#include - -#include "tensorflow/compiler/xla/map_util.h" -#include "tensorflow/compiler/xla/ptr_util.h" -#include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/core/lib/gtl/flatset.h" - -namespace xla { -namespace cpu { -void ExternalConstantPool::Insert(string name, const LiteralSlice& literal, - int64 alignment) { - CHECK(!ShapeUtil::IsTuple(literal.shape())); - CHECK(alignment > 0 && IsPowerOfTwo(static_cast(alignment))); - CHECK(entries_.find(name) == entries_.end()); - - const int64 literal_size = ShapeUtil::ByteSizeOf(literal.shape()); - void* raw_pointer = tensorflow::port::AlignedMalloc( - literal_size, std::max(alignment, sizeof(void*))); - CHECK(raw_pointer != nullptr) << "failed to allocate " << literal_size - << " bytes with alignment of " << alignment; - - std::memcpy(raw_pointer, literal.untyped_data(), literal_size); - entries_.emplace(std::move(name), static_cast(raw_pointer)); -} - -const uint8* ExternalConstantPool::Find(const string& name) { - auto it = entries_.find(name); - return it == entries_.end() ? nullptr : it->second.get(); -} -} // namespace cpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/external_constant_pool.h b/tensorflow/compiler/xla/service/cpu/external_constant_pool.h deleted file mode 100644 index 0677f5f0b58005079890052a426e5f48c5d09ed1..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/external_constant_pool.h +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_EXTERNAL_CONSTANT_POOL_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_EXTERNAL_CONSTANT_POOL_H_ - -#include - -#include "tensorflow/compiler/xla/literal_util.h" -#include "tensorflow/core/lib/gtl/flatmap.h" -#include "tensorflow/core/platform/mem.h" - -namespace xla { -namespace cpu { -// An ExternalConstantPool maintains a set of constants kept external to -// generated LLVM IR. These constants are accessed from the IR via globals with -// extern linkage. This current incarnation of ExternalConstantPool only -// supports the JIT CPU backend; the AOT backend is not supported. -// -// Implementation-wise, this is a simple wrapper around a map of strings to byte -// buffers. This simply implementation works in a JIT scenario. This class -// will have to become smarter if we decide to support external constant pools -// on AOT compiles in the future. -class ExternalConstantPool { - public: - // Inserts a buffer with the contents of `literal` into the constant pool with - // the name `name`. It is an error to try to insert two constants with the - // same `name` into the same constant pool. The buffer for literal is aligned - // to `aligment` bytes, and `alignment` must be a power of 2. - // - // The constant pool copies out the contents of `literal` into a buffer it - // owns -- it does not keep pointers to `literal`, or to memory owned by - // `literal`. - void Insert(string name, const LiteralSlice& literal, int64 alignment); - - // Find the constant with name `name` in this constant pool. If there isn't - // such constant, return nullptr. - const uint8* Find(const string& name); - - private: - // We need to `AlignedFree` pointers allocated into `entries_` since we - // allocate them with `AlignedMalloc`. - struct FreeDeleter { - void operator()(void* ptr) { tensorflow::port::AlignedFree(ptr); } - }; - - tensorflow::gtl::FlatMap> - entries_; -}; -} // namespace cpu -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_EXTERNAL_CONSTANT_POOL_H_ diff --git a/tensorflow/compiler/xla/service/cpu/external_constant_pool_test.cc b/tensorflow/compiler/xla/service/cpu/external_constant_pool_test.cc deleted file mode 100644 index 9290a4e5dfc03ddb86e9d82f1f0f4f9a8ceebb88..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/external_constant_pool_test.cc +++ /dev/null @@ -1,82 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/external_constant_pool.h" -#include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/core/platform/test.h" - -namespace xla { -namespace cpu { -namespace { -class ExternalConstantPoolTest : public ::testing::Test {}; - -template -T GetFromBuffer(const uint8* buffer, int64 index) { - T result; - std::memcpy(&result, buffer + index * sizeof(T), sizeof(T)); - return result; -} - -TEST(ExternalConstantPoolTest, Basic) { - ExternalConstantPool constant_pool; - EXPECT_EQ(constant_pool.Find("name-0"), nullptr); - const auto literal = Literal::CreateR2({{1, 2}, {3, 4}}); - constant_pool.Insert("name-0", *literal, 4); - const uint8* constant = constant_pool.Find("name-0"); - ASSERT_NE(constant, nullptr); - - EXPECT_EQ(GetFromBuffer(constant, 0), 1); - EXPECT_EQ(GetFromBuffer(constant, 1), 2); - EXPECT_EQ(GetFromBuffer(constant, 2), 3); - EXPECT_EQ(GetFromBuffer(constant, 3), 4); - - EXPECT_EQ(constant_pool.Find("name-1"), nullptr); -} - -TEST(ExternalConstantPoolTest, RowMinorLayout) { - ExternalConstantPool constant_pool; - EXPECT_EQ(constant_pool.Find("name-0"), nullptr); - const auto literal = Literal::CreateR2WithLayout( - {{1, 2}, {3, 4}}, LayoutUtil::MakeLayout({0, 1})); - constant_pool.Insert("name-0", *literal, 4); - const uint8* constant = constant_pool.Find("name-0"); - ASSERT_NE(constant, nullptr); - - EXPECT_EQ(GetFromBuffer(constant, 0), 1); - EXPECT_EQ(GetFromBuffer(constant, 1), 3); - EXPECT_EQ(GetFromBuffer(constant, 2), 2); - EXPECT_EQ(GetFromBuffer(constant, 3), 4); -} - -TEST(ExternalConstantPoolTest, Alignment) { - ExternalConstantPool constant_pool; - EXPECT_EQ(constant_pool.Find("name-0"), nullptr); - - for (int i = 0; i < 8; i++) { - int64 alignment = 1 << i; - string name = tensorflow::strings::StrCat("name-", i); - - const auto literal = Literal::CreateR2({{1, 2}, {3, 4}}); - constant_pool.Insert(name, *literal, alignment); - - const uint8* constant = constant_pool.Find(name); - ASSERT_NE(constant, nullptr); - EXPECT_EQ(reinterpret_cast(constant) % alignment, 0); - } -} - -} // namespace -} // namespace cpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc index b560b7531c0d24e6f670e61a15dce295d9fa2a49..1a8bedfe6afb4f096ddd4703c312b84d521a7ba5 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc @@ -64,8 +64,8 @@ bool PotentiallyImplementedAsEigenConvolution( return false; } - if (ShapeUtil::HasZeroElements(input_shape) || - ShapeUtil::HasZeroElements(kernel_shape)) { + if (ShapeUtil::IsZeroElementArray(input_shape) || + ShapeUtil::IsZeroElementArray(kernel_shape)) { return false; } // Make sure input and kernel has the same data type. diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 59223fddac2f5f7e2e85de4d37e4b6c5760ae697..6b66a4b0b7cef0058a761801815606b9440016cf 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -48,6 +48,8 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/shape_partition.h" #include "tensorflow/compiler/xla/service/cpu/simple_orc_jit.h" #include "tensorflow/compiler/xla/service/elemental_ir_emitter.h" +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" @@ -83,8 +85,7 @@ IrEmitter::IrEmitter( llvm::Module* llvm_module, std::unordered_map instruction_to_profile_idx, std::unordered_map computation_to_profile_idx, - const TargetMachineFeatures* target_machine_features, - ExternalConstantPool* external_constant_pool) + const TargetMachineFeatures* target_machine_features) : assignment_(assignment), module_(llvm_module), arch_type_(llvm::Triple(llvm_module->getTargetTriple()).getArch()), @@ -94,8 +95,7 @@ IrEmitter::IrEmitter( alias_analysis_(hlo_module, assignment, &llvm_module->getContext()), hlo_module_config_(hlo_module.config()), is_top_level_computation_(false), - target_machine_features_(*target_machine_features), - external_constant_pool_(external_constant_pool) { + target_machine_features_(*target_machine_features) { ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags( /*fast_math_enabled=*/hlo_module_config_.debug_options() .xla_enable_fast_math())); @@ -161,45 +161,18 @@ Status IrEmitter::HandleBitcast(HloInstruction* bitcast) { } llvm::Constant* IrEmitter::EmitGlobalForLiteral(const Literal& literal) { - llvm::Constant* result; - - // We avoid creating large constants in the LLVM IR since LLVM is not - // efficient for large constant arrays. We still emit "small enough" constant - // arrays into the Ir, in the off chance the LLVM optimizer can do something - // interesting with it. - // - // TODO(b/29904935): Remove the large constant pool. - const int kMaxInternalConstantSizeInBytes = 128; - if (external_constant_pool_ && - ByteSizeOf(literal.shape()) >= kMaxInternalConstantSizeInBytes) { - string global_name = tensorflow::strings::StrCat( - "constant_global_", external_global_constant_counter_++); - llvm::GlobalVariable* result_global = new llvm::GlobalVariable( - /*Module=*/*module_, - /*Type=*/IrShapeType(literal.shape()), - /*isConstant=*/true, - /*Linkage=*/llvm::GlobalValue::ExternalLinkage, - /*Initializer=*/nullptr, - /*Name=*/AsStringRef(global_name)); - result_global->setAlignment(MinimumAlignmentForShape(literal.shape())); - external_constant_pool_->Insert(global_name, literal, - MinimumAlignmentForShape(literal.shape())); - result = result_global; - } else { - llvm::Constant* initializer = - llvm_ir::ConvertLiteralToIrConstant(literal, module_); - llvm::GlobalVariable* result_global = new llvm::GlobalVariable( - /*Module=*/*module_, - /*Type=*/initializer->getType(), - /*isConstant=*/true, - /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/initializer, - /*Name=*/""); - result_global->setAlignment(MinimumAlignmentForShape(literal.shape())); - result = llvm::ConstantExpr::getBitCast( - result_global, IrShapeType(literal.shape())->getPointerTo()); - } - return result; + llvm::Constant* initializer = + llvm_ir::ConvertLiteralToIrConstant(literal, module_); + llvm::GlobalVariable* result_global = new llvm::GlobalVariable( + /*Module=*/*module_, + /*Type=*/initializer->getType(), + /*isConstant=*/true, + /*Linkage=*/llvm::GlobalValue::PrivateLinkage, + /*Initializer=*/initializer, + /*Name=*/""); + result_global->setAlignment(MinimumAlignmentForShape(literal.shape())); + return llvm::ConstantExpr::getBitCast( + result_global, IrShapeType(literal.shape())->getPointerTo()); } Status IrEmitter::HandleConstant(HloInstruction* constant) { @@ -226,10 +199,13 @@ Status IrEmitter::HandleCopy(HloInstruction* copy) { // kCopy shallow copies a tuple so just memcpy the top-level buffer. TF_RETURN_IF_ERROR(EmitTargetAddressForOp(copy)); return EmitMemcpy(*(copy->operand(0)), *copy); - } else { - // Use the elemental emitter for non-tuple shapes. + } else if (ShapeUtil::IsArray(copy->shape())) { + // Use the elemental emitter for array shapes. return DefaultAction(copy); } + return Unimplemented( + "unsupported operand type %s for copy instruction", + PrimitiveType_Name(copy->shape().element_type()).c_str()); } // Calculate the alignment of a buffer allocated for a given primitive type. @@ -318,30 +294,42 @@ Status IrEmitter::HandleSelect(HloInstruction* select) { return DefaultAction(select); } -Status IrEmitter::HandleInfeed(HloInstruction* infeed) { +Status IrEmitter::HandleInfeed(HloInstruction* instruction) { + HloInfeedInstruction* infeed = Cast(instruction); VLOG(2) << "HandleInfeed: " << infeed->ToString(); - const Shape& shape = infeed->shape(); - - // The infeed operation produces data (dequeued from the infeed queue) at this - // address, which has been provided by buffer assignment. + // The infeed operation produces a two-element tuple containing data and a + // token value. HloInfeedInstruction::infeed_shape gives us the data shape. + const Shape& data_shape = infeed->infeed_shape(); + DCHECK(ShapeUtil::Equal(data_shape, + ShapeUtil::GetTupleElementShape(infeed->shape(), 0))); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(infeed)); - llvm_ir::IrArray infeed_array = GetIrArrayFor(infeed); - if (ShapeUtil::IsTuple(shape)) { - TF_RET_CHECK(!ShapeUtil::IsNestedTuple(shape)); + // Write the tuple index table. + TF_ASSIGN_OR_RETURN(BufferAllocation::Slice data_slice, + assignment_.GetUniqueSlice(infeed, {0})); + llvm::Value* data_address = EmitTempBufferPointer(data_slice, data_shape); + TF_ASSIGN_OR_RETURN(BufferAllocation::Slice token_slice, + assignment_.GetUniqueSlice(infeed, {1})); + llvm::Value* token_address = EmitTempBufferPointer( + token_slice, ShapeUtil::GetTupleElementShape(infeed->shape(), 1)); + llvm_ir::EmitTuple(GetIrArrayFor(infeed), {data_address, token_address}, + &ir_builder_, module_); + + if (ShapeUtil::IsTuple(data_shape)) { + TF_RET_CHECK(!ShapeUtil::IsNestedTuple(data_shape)); // For a tuple, we first copy each of the internal elements to // their corresponding target locations. We then construct the // tuple outer buffer containing pointers to the internal // elements. std::vector tuple_element_addresses; - for (int64 i = 0; i < shape.tuple_shapes_size(); ++i) { + for (int64 i = 0; i < data_shape.tuple_shapes_size(); ++i) { TF_ASSIGN_OR_RETURN(BufferAllocation::Slice buffer, - assignment_.GetUniqueSlice(infeed, {i})); + assignment_.GetUniqueSlice(infeed, {0, i})); const Shape& tuple_element_shape = - ShapeUtil::GetTupleElementShape(shape, i); + ShapeUtil::GetTupleElementShape(data_shape, i); // Only the outer tuple buffer's target address is obtained from // GetEmittedValueFor, to handle the case when Infeed is the root @@ -356,11 +344,11 @@ Status IrEmitter::HandleInfeed(HloInstruction* infeed) { tuple_element_addresses.push_back(tuple_element_address); } - llvm_ir::EmitTuple(infeed_array, tuple_element_addresses, &ir_builder_, - module_); + llvm_ir::EmitTuple(llvm_ir::IrArray(data_address, data_shape), + tuple_element_addresses, &ir_builder_, module_); } else { - TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kInfeed, shape, - GetEmittedValueFor(infeed))); + TF_RETURN_IF_ERROR( + EmitXfeedTransfer(XfeedKind::kInfeed, data_shape, data_address)); } return Status::OK(); @@ -560,7 +548,8 @@ Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index input_index(index.size()); + llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), + index.size()); llvm::Value* in_bounds_condition = nullptr; for (size_t i = 0; i < index.size(); ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( @@ -691,7 +680,8 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(source_index.size()); + llvm_ir::IrArray::Index operand_index(ir_builder_.getInt64Ty(), + source_index.size()); llvm::Value* in_bounds_condition = ir_builder_.getTrue(); for (int64 i = 0; i < rank; ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( @@ -765,7 +755,7 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { // value and the current output value. SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index selected_index; + llvm_ir::IrArray::Index selected_index(source_index.GetType()); for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( selected_index_address, {ir_builder_.getInt32(i)}); @@ -1107,7 +1097,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { // We are not in the padding, so carry out the computation. int num_dims = num_spatial_dims + 2; - llvm_ir::IrArray::Index input_index(num_dims); + llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), num_dims); for (int i = 0; i < num_spatial_dims; ++i) { input_index[dnums.input_spatial_dimensions(i)] = input_spatial[i]; } @@ -1115,7 +1105,8 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { input_index[dnums.input_batch_dimension()] = batch; llvm_ir::IrArray kernel_array(GetIrArrayFor(rhs)); - llvm_ir::IrArray::Index kernel_index(num_dims); + llvm_ir::IrArray::Index kernel_index(ir_builder_.getInt64Ty(), + num_dims); for (int i = 0; i < num_spatial_dims; ++i) { kernel_index[dnums.kernel_spatial_dimensions(i)] = window.dimensions(i).window_reversal() @@ -1426,6 +1417,10 @@ IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator( return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, llvm::Value* rhs) { return ir_builder->CreateOr(lhs, rhs); }; + case HloOpcode::kXor: + return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, + llvm::Value* rhs) { return ir_builder->CreateXor(lhs, rhs); }; + case HloOpcode::kMaximum: return [root_is_floating_point, root_is_signed]( llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, @@ -1682,7 +1677,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( // } llvm_ir::ForLoopNest loop_nest(IrName(reduce), &ir_builder_); - llvm_ir::IrArray::Index array_index(reduce->shape().dimensions_size()); + llvm_ir::IrArray::Index array_index(ir_builder_.getInt64Ty(), + reduce->shape().dimensions_size()); for (int i = LayoutUtil::MinorToMajor(reduce->shape()).size() - 1; i > 0; --i) { int64 dimension = LayoutUtil::Minor(reduce->shape().layout(), i); @@ -1873,7 +1869,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { TF_RETURN_IF_ERROR(EmitTargetAddressForOp(slice)); - if (ShapeUtil::HasZeroElements(slice->shape())) { + if (ShapeUtil::IsZeroElementArray(slice->shape())) { return Status::OK(); } @@ -2066,7 +2062,7 @@ Status IrEmitter::HandlePad(HloInstruction* pad) { // Compute the output index the operand element should be assigned to. // output_index := edge_padding_low + operand_index * (interior_padding + 1) const PaddingConfig& padding_config = pad->padding_config(); - llvm_ir::IrArray::Index output_index; + llvm_ir::IrArray::Index output_index(operand_index.GetType()); for (size_t i = 0; i < operand_index.size(); ++i) { llvm::Value* offset = ir_builder_.CreateMul( operand_index[i], @@ -2528,6 +2524,13 @@ Status IrEmitter::HandleConditional(HloInstruction* conditional) { return Status::OK(); } +Status IrEmitter::HandleAfterAll(HloInstruction* gen_token) { + TF_RET_CHECK(ByteSizeOf(gen_token->shape()) == 0); + // No code to generate, but we need to emit an address for book-keeping. + TF_RETURN_IF_ERROR(EmitTargetAddressForOp(gen_token)); + return Status::OK(); +} + Status IrEmitter::FinishVisit(HloInstruction* root) { // When this method is called, we should have already emitted an IR value for // the root (return) op. The IR value holds the address of the buffer holding @@ -2809,7 +2812,10 @@ Status IrEmitter::EmitTargetAddressForOp(const HloInstruction* op) { // For the root node, we write directly to the output buffer of the // function. llvm::Argument* retval = compute_function_->result_arg(); - if (!ShapeUtil::IsNil(target_shape)) { + if ((ShapeUtil::IsArray(target_shape) && + !ShapeUtil::IsZeroElementArray(target_shape)) || + (ShapeUtil::IsTuple(target_shape) && + !ShapeUtil::IsEmptyTuple(target_shape))) { llvm::AttrBuilder attr_builder; attr_builder.addAlignmentAttr(MinimumAlignmentForShape(target_shape)); attr_builder.addDereferenceableAttr(ByteSizeOf(target_shape)); diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 32c536e18fee86cc60067ba3b25ab1eb0e4233df..3c110a320fad931e68e48236d4b4a33d0601ab5a 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -30,7 +30,6 @@ limitations under the License. #include "llvm/IR/Value.h" #include "llvm/Target/TargetMachine.h" #include "tensorflow/compiler/xla/service/buffer_assignment.h" -#include "tensorflow/compiler/xla/service/cpu/external_constant_pool.h" #include "tensorflow/compiler/xla/service/cpu/ir_function.h" #include "tensorflow/compiler/xla/service/cpu/target_machine_features.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" @@ -67,17 +66,13 @@ class IrEmitter : public DfsHloVisitorWithDefault { // index in the profiling array. // computation_to_profile_idx: the mapping from HLO computations to their // index in the profiling array. - // external_constant_pool: if non-null, points to an ExternalConstantPool - // instance into which the Ir emitter can spill - // constants. IrEmitter(const HloModule& hlo_module, const BufferAssignment& assignment, llvm::Module* llvm_module, std::unordered_map instruction_to_profile_idx, std::unordered_map computation_to_profile_idx, - const TargetMachineFeatures* target_machine, - ExternalConstantPool* external_constant_pool); + const TargetMachineFeatures* target_machine); ~IrEmitter() override; // Emit and return the given HLO computation as an LLVM IR @@ -150,6 +145,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleWhile(HloInstruction* xla_while) override; Status HandleConcatenate(HloInstruction* concatenate) override; Status HandleConditional(HloInstruction* conditional) override; + Status HandleAfterAll(HloInstruction* gen_token) override; Status FinishVisit(HloInstruction* root) override; Status Preprocess(HloInstruction* hlo) override; @@ -536,9 +532,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { const TargetMachineFeatures& target_machine_features_; - int64 external_global_constant_counter_ = 0; - ExternalConstantPool* external_constant_pool_; - struct LiteralPtrHashFunctor { size_t operator()(const Literal* literal) const { return literal->Hash(); } }; diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc index 54af40506dab48b3c2a3a44eb0b5f5fb213a32ec..59ae5acd8b7cea049f09eaf4cc98b41339973c77 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc @@ -31,13 +31,15 @@ ParallelLoopEmitter::ParallelLoopEmitter( std::vector ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { + CHECK_NE(index_type, nullptr); + CHECK(!ShapeUtil::IsTuple(shape_)); CHECK(!ShapeUtil::IsScalar(shape_)); llvm_ir::ForLoopNest loop_nest(loop_name, ir_builder_); const int64 num_dims = shape_.dimensions_size(); - llvm_ir::IrArray::Index array_index(num_dims); + llvm_ir::IrArray::Index array_index(index_type, num_dims); // Add loops from outer-most to inner-most dimensions. for (int i = LayoutUtil::MinorToMajor(shape_).size() - 1; i >= 0; --i) { diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h index 755715634aa70a822b21d25dcae20a8fe053477a..25e182a26d6f21c7eba550020cf17403aa92abf7 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h @@ -61,7 +61,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ~ParallelLoopEmitter() override = default; std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) override; + tensorflow::StringPiece loop_name, llvm::Type* index_type) override; private: const DynamicLoopBounds* dynamic_loop_bounds_; diff --git a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc index fc2efbaf9a22b02cd729da2f367d53bc15506836..36c9f743859ae2da6c4fb3fd753bd7862fe2d3ab 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_task_assignment_test.cc @@ -110,8 +110,9 @@ TEST_F(ParallelTaskAssignmentTest, InfeedOutfeedOperationNotParallelized) { const string hlo_string = R"( HloModule TestTaskParallel_infeed_outfeed ENTRY InfeedOutfeed { - infeed0 = u32[12345678,2]{1,0} infeed() - ROOT outfeed0 = u32[12345678,2]{1,0} outfeed(infeed0) + infeed0 = (u32[12345678,2]{1,0}, token[]) infeed() + infeed0.data = u32[12345678,2]{1,0} get-tuple-element((u32[12345678,2]{1,0}, token[]) infeed0), index=0 + ROOT outfeed0 = token[] outfeed(infeed0.data) } )"; diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc index 92da5f71c23d5e1450b39ea8b7bb8345f6fabb3b..f8c8dd5e93d53db8d87be0208b5cf4daac3464f1 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifdef INTEL_MKL +#if defined(INTEL_MKL) && !defined(DO_NOT_USE_ML) #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "third_party/intel_mkl_ml/include/mkl_cblas.h" #include "third_party/intel_mkl_ml/include/mkl_service.h" diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc index 167aa4adda995a259190a932a76a34ca5883444c..7e792a82b8bf28121c054332bc619d736858c729 100644 --- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc +++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc @@ -49,9 +49,9 @@ int main(int argc, char** argv) { // Build computation. xla::XlaBuilder builder(""); - auto p0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto p1 = builder.Parameter(1, param1_literal->shape(), "param1"); - auto add = builder.Add(p1, p0, {0}); + auto p0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto p1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Add(p1, p0, {0}); xla::StatusOr computation_status = builder.Build(); xla::XlaComputation computation = computation_status.ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index c4c90515ac7ec2721cb9ea48d42e3c5080e249af..be772cfb7e564cebc5725854dbf5678e5c507556 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -127,13 +127,6 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options, } llvm::JITSymbol SimpleOrcJIT::ResolveRuntimeSymbol(const std::string& name) { - if (const uint8* from_constant_pool = - external_constant_pool_.Find(string(name))) { - return llvm::JITEvaluatedSymbol( - reinterpret_cast(from_constant_pool), - llvm::JITSymbolFlags::None); - } - void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name); if (func_addr == nullptr) { return nullptr; diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h index 1851a3ee0bb97b4860605d7211a6ae70ac88686b..d74b63fcf45bd70cd18ee41f1e9714ba6a222abd 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h @@ -29,7 +29,6 @@ limitations under the License. #include "llvm/Target/TargetMachine.h" #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h" #include "tensorflow/compiler/xla/service/cpu/disassembler.h" -#include "tensorflow/compiler/xla/service/cpu/external_constant_pool.h" #include "tensorflow/compiler/xla/types.h" namespace xla { @@ -91,10 +90,6 @@ class SimpleOrcJIT { llvm::TargetMachine* target_machine() const { return target_machine_.get(); } - ExternalConstantPool* external_constant_pool() { - return &external_constant_pool_; - } - // Creates an llvm::TargetMachine suitable for JITting code that will run on // the current machine. static std::unique_ptr InferTargetMachineForJIT( @@ -112,7 +107,6 @@ class SimpleOrcJIT { std::shared_ptr symbol_resolver_; ObjLayerT object_layer_; CompileLayerT compile_layer_; - ExternalConstantPool external_constant_pool_; }; } // namespace cpu diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc index faac927027c48e44eb8ff1fcc4109fbc177fc579..1d4bf483aedef5a15ef51cf216030b76255d4ec8 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc @@ -56,7 +56,8 @@ class CpuExternalConstantsTest : public CpuCodegenTest { TEST_F(CpuExternalConstantsTest, Basic) { TestWithArray(/*rows=*/1024, /*cols=*/1024, R"( -CHECK: @constant_global_0 = external constant [1024 x [1024 x float]], align 16 +CHECK-NOT: @constant_global_0 = external constant [1024 x [1024 x float]], align 16 +CHECK: @0 = private constant [4194304 x i8] {{.*}}, align 16 )"); } @@ -65,7 +66,7 @@ TEST_F(CpuExternalConstantsTest, BasicNegative) { // to externalize it. TestWithArray(/*rows=*/4, /*cols=*/4, R"( CHECK-NOT: @constant_global_0 = external constant [16 x float], align 8 -CHECK: @0 = private constant [16 x float] {{.*}}, align 8 +CHECK: @0 = private constant [64 x i8] {{.*}}, align 8 )"); } } // namespace diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc index 23e7a3de4d8188a3add259582e11030539e154c1..783b2820e922612973632c555fc8ae01418f1754 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc @@ -96,8 +96,11 @@ TEST_F(CpuFusionTest, FuseElementwiseOpChain) { HloInstruction::CreateUnary(vshape, HloOpcode::kExp, ceil)); auto floor = builder.AddInstruction( HloInstruction::CreateUnary(vshape, HloOpcode::kFloor, exp)); - auto two = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + auto two = builder.AddInstruction(HloInstruction::CreateBroadcast( + vshape, + builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(2.0))), + {})); builder.AddInstruction( HloInstruction::CreateBinary(vshape, HloOpcode::kMultiply, two, floor)); @@ -114,9 +117,9 @@ TEST_F(CpuFusionTest, FuseElementwiseOpChain) { EXPECT_EQ(HloOpcode::kFusion, fusion_instruction->opcode()); EXPECT_EQ(HloOpcode::kMultiply, fusion_instruction->fused_expression_root()->opcode()); - // There should be 7 fused instructions: 2 parameters and the fused + // There should be 8 fused instructions: 2 parameters and the fused // operations. - EXPECT_EQ(7, fusion_instruction->fused_instruction_count()); + EXPECT_EQ(8, fusion_instruction->fused_instruction_count()); // Compile and execute the computation. auto result = ExecuteAndTransfer(std::move(module), {}); @@ -170,8 +173,11 @@ TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { HloInstruction::CreateUnary(cshape, HloOpcode::kExp, reduce)); auto floor = builder.AddInstruction( HloInstruction::CreateUnary(cshape, HloOpcode::kFloor, exp)); - auto two = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + auto two = builder.AddInstruction(HloInstruction::CreateBroadcast( + cshape, + builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(2.0))), + {})); builder.AddInstruction( HloInstruction::CreateBinary(cshape, HloOpcode::kMultiply, two, floor)); @@ -188,9 +194,9 @@ TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { EXPECT_EQ(HloOpcode::kFusion, fusion_instruction1->opcode()); EXPECT_EQ(HloOpcode::kMultiply, fusion_instruction1->fused_expression_root()->opcode()); - // There should be 5 fused instructions in the root fusion instruction: 2 + // There should be 6 fused instructions in the root fusion instruction: 2 // parameters, multiply, floor, and exp. - EXPECT_EQ(5, fusion_instruction1->fused_instruction_count()) + EXPECT_EQ(6, fusion_instruction1->fused_instruction_count()) << fusion_instruction1->fused_instructions_computation()->ToString(); auto fusion_instruction2 = reduce->operand(0); diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc index dd63b998e9b6d04981ec6f7300c883c9b23b154f..ea7e479d66fbda1bfd388fd77b25db2db56f0d65 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc @@ -47,7 +47,7 @@ class InfeedTest : public ClientLibraryTestBase { // don't use ResetDevice since it is not implemented on CPU. ASSERT_IS_OK(client_->TransferToInfeed(literal)); XlaBuilder builder(TestName()); - builder.Infeed(literal.shape()); + Infeed(&builder, literal.shape()); if (ShapeUtil::IsTuple(literal.shape())) { // TODO(b/30609564): Use ComputeAndCompareLiteral instead. ComputeAndCompareTuple(&builder, literal, {}); @@ -125,8 +125,8 @@ TEST_F(InfeedTest, DISABLED_SingleInfeedInWhile) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Gt(builder.ConstantR0(40.0f), prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Gt(ConstantR0(&builder, 40.0f), prev); condition = builder.Build().ConsumeValueOrDie(); } // Create a computation for the body: add the reduced value of the Infeed @@ -134,17 +134,16 @@ TEST_F(InfeedTest, DISABLED_SingleInfeedInWhile) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto infeed = builder.Infeed(infeed_shape); - auto addend = - builder.Reduce(infeed, builder.ConstantR0(0.0f), - CreateScalarAddComputation(F32, &builder), {0}); - builder.Add(prev, addend); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto infeed = Infeed(&builder, infeed_shape); + auto addend = Reduce(infeed, ConstantR0(&builder, 0.0f), + CreateScalarAddComputation(F32, &builder), {0}); + Add(prev, addend); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. - auto init = builder.ConstantR0(0.0f); - builder.While(condition, body, init); + auto init = ConstantR0(&builder, 0.0f); + While(condition, body, init); // Build and asynchronously launch the computation. auto computation = builder.Build().ConsumeValueOrDie(); @@ -207,8 +206,8 @@ TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.GetTupleElement(prev, 1); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + GetTupleElement(prev, 1); condition = builder.Build().ConsumeValueOrDie(); } @@ -221,27 +220,27 @@ TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) { const auto build_body = [this, &result_shape](const Shape& infeed_shape) { XlaComputation body; XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto infeed = builder.Infeed(infeed_shape); - auto addend = builder.Reduce( - builder.GetTupleElement(infeed, 0), builder.ConstantR0(0.0f), - CreateScalarAddComputation(F32, &builder), {0}); - auto result = builder.Add(builder.GetTupleElement(prev, 0), addend); - builder.Tuple({result, builder.GetTupleElement(infeed, 1)}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto infeed = Infeed(&builder, infeed_shape); + auto addend = + Reduce(GetTupleElement(infeed, 0), ConstantR0(&builder, 0.0f), + CreateScalarAddComputation(F32, &builder), {0}); + auto result = Add(GetTupleElement(prev, 0), addend); + Tuple(&builder, {result, GetTupleElement(infeed, 1)}); return builder.Build().ConsumeValueOrDie(); }; // Create the first while loop with infeed1_shape. - auto init = builder.Tuple( - {builder.ConstantR0(0.0f), builder.ConstantR0(true)}); - auto while1 = builder.While(condition, build_body(infeed1_shape), init); - auto result1 = builder.Tuple( - {builder.GetTupleElement(while1, 0), builder.ConstantR0(true)}); + auto init = Tuple(&builder, {ConstantR0(&builder, 0.0f), + ConstantR0(&builder, true)}); + auto while1 = While(condition, build_body(infeed1_shape), init); + auto result1 = Tuple( + &builder, {GetTupleElement(while1, 0), ConstantR0(&builder, true)}); // Create the second while loop with infeed2_shape. Note that the result from // the first while loop is used as the initial value. - auto while2 = builder.While(condition, build_body(infeed2_shape), result1); - builder.GetTupleElement(while2, 0); + auto while2 = While(condition, build_body(infeed2_shape), result1); + GetTupleElement(while2, 0); // Build the computation. auto computation = builder.Build().ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc index 27044b1d62027e3b83744c486cb790269e505aff..90b99c828e2fcfd77579026a39d3a6711599feee 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_literal_caching_test.cc @@ -38,7 +38,8 @@ while_body { while_cond { arg_cond = f32[2,3,2] parameter(0) - ROOT unknown = pred[] infeed() + infeed = (pred[], token[]) infeed() + ROOT unknown = pred[] get-tuple-element((pred[], token[]) infeed), index=0 } ENTRY main { @@ -49,14 +50,14 @@ ENTRY main { {{2, 1}, {2001, 3002}, {2001, 2002}}}) const_b = f32[2,3,2] while(f32[2,3,2] const_a), condition=while_cond, body=while_body - out0 = () outfeed(f32[2,3,2] const_a) - ROOT out1 = () outfeed(f32[2,3,2] const_b) + out0 = token[] outfeed(f32[2,3,2] const_a) + ROOT out1 = token[] outfeed(f32[2,3,2] const_b) } )"; string filecheck_pattern = R"( -CHECK: private constant [12 x float] -CHECK-NOT: private constant [12 x float] +CHECK: private constant [48 x i8] +CHECK-NOT: private constant [48 x i8] )"; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, @@ -84,7 +85,8 @@ while_body { while_cond { arg_cond = (f32[2,1]{1,0}, f32[1]{0}) parameter(0) - ROOT unknown = pred[] infeed() + infeed = (pred[], token[]) infeed() + ROOT unknown = pred[] get-tuple-element((pred[], token[]) infeed), index=0 } ENTRY main { @@ -98,10 +100,10 @@ ENTRY main { )"; string filecheck_pattern = R"( -CHECK: private constant [1 x float] -CHECK: private constant [2 x float] -CHECK-NOT: private constant [1 x float] -CHECK-NOT: private constant [2 x float] +CHECK: private constant [4 x i8] +CHECK: private constant [8 x i8] +CHECK-NOT: private constant [4 x i8] +CHECK-NOT: private constant [8 x i8] )"; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc index 1ee279290b6fcfe775ce9867d424b1c031f5d2bd..dac416e1c78c2f60d458480c5062f48b77d4878d 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_outfeed_test.cc @@ -32,12 +32,13 @@ ENTRY main { {{{1, 2}, {1001, 1002}, {2001, 2002}}, {{2, 1}, {2001, 3002}, {2001, 2002}}}) - ROOT out = () outfeed(f32[2,3,2] const_a) + outfeed = token[] outfeed(f32[2,3,2] const_a) + ROOT root = () tuple() } )"; string filecheck_pattern = R"( -CHECK: private constant [12 x float] +CHECK: private constant [48 x i8] )"; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc index cd1165e23812861ba9951546b7dd744529232196..c444d151858d3a152a01b99657ffae89ebc6b487 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc @@ -427,5 +427,27 @@ llvm::Value* LlvmVariable::Get() const { void LlvmVariable::Set(llvm::Value* new_value) { ir_builder_->CreateStore(new_value, alloca_); } + +TileVariable::TileVariable(VectorSupportLibrary* vector_support, + std::vector initial_value) { + for (llvm::Value* initial_vector_value : initial_value) { + storage_.emplace_back(vector_support, initial_vector_value); + } +} + +std::vector TileVariable::Get() const { + std::vector result; + c_transform(storage_, std::back_inserter(result), + [&](VectorVariable vect_var) { return vect_var.Get(); }); + return result; +} + +void TileVariable::Set(tensorflow::gtl::ArraySlice value) { + CHECK_EQ(value.size(), storage_.size()); + for (int64 i = 0, e = value.size(); i < e; i++) { + storage_[i].Set(value[i]); + } +} + } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.h b/tensorflow/compiler/xla/service/cpu/vector_support_library.h index edcaec584997b17dce30b8c46fda4abc78441064..49c2a4e2f4bae9e1672b7d2fe891301bce08bd4b 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.h +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.h @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/gtl/array_slice.h" namespace xla { namespace cpu { @@ -317,6 +318,21 @@ class ScalarVariable : public LlvmVariable { Set(initial_value); } }; + +// This wraps a set of alloca-backed stack variables that can, as a whole, store +// a tile. A "tile" is a sequence of vectors that is typically used as a 2D +// grid of scalar values (e.g. for tiled GEMMs). +class TileVariable { + public: + TileVariable(VectorSupportLibrary* vector_support, + std::vector initial_value); + + std::vector Get() const; + void Set(tensorflow::gtl::ArraySlice value); + + private: + std::vector storage_; +}; } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h index 64678d9d7450974f68817f92526519697a83683c..cb3676c5ba9b55ef4cb46dbd97f84ea9a6a6c5d0 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h @@ -183,6 +183,9 @@ class DfsHloVisitorBase { virtual Status HandleOr(HloInstructionPtr hlo) { return HandleElementwiseBinary(hlo); } + virtual Status HandleXor(HloInstructionPtr hlo) { + return HandleElementwiseBinary(hlo); + } virtual Status HandleShiftLeft(HloInstructionPtr hlo) { return HandleElementwiseBinary(hlo); } @@ -243,6 +246,8 @@ class DfsHloVisitorBase { virtual Status HandleBatchNormGrad(HloInstructionPtr hlo) = 0; + virtual Status HandleAfterAll(HloInstructionPtr token) = 0; + // Invoked to inform the visitor that the traversal has completed, and that // the root was "root". virtual Status FinishVisit(HloInstructionPtr root) = 0; diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h index 240faebe62f5cee4f61b3c36b5e8f653cfd6db8e..987c91e5ba3eb01a7535d162cbcf6441d568adae 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h @@ -188,6 +188,9 @@ class DfsHloVisitorWithDefaultBase Status HandleGather(HloInstructionPtr gather) override { return DefaultAction(gather); } + Status HandleAfterAll(HloInstructionPtr token) override { + return DefaultAction(token); + } // Invoked to inform the visitor that the traversal has completed, and that // the root was "root". diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 9a8bab353ef6b1e0b05b250d35296bc3cef8bc37..ce0951bbe1873973c7b97055aba5ba71a14ad24f 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -456,17 +456,15 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( llvm::ConstantFP::get(type, 1.0))); } case HloOpcode::kIsFinite: { - // (x == x) && abs(x) != inf + // abs(x) o!= inf, this works because the comparison returns false if + // either operand is NaN. auto type = operand_value->getType(); - auto equal_self = - ir_builder_->CreateFCmpOEQ(operand_value, operand_value); auto abs_value = llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::fabs, {operand_value}, {type}, ir_builder_); auto infinity = llvm::ConstantFP::getInfinity(type); auto not_infinite = ir_builder_->CreateFCmpONE(abs_value, infinity); - auto result_i1 = ir_builder_->CreateAnd(equal_self, not_infinite); return ir_builder_->CreateZExt( - result_i1, llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + not_infinite, llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } case HloOpcode::kNegate: return ir_builder_->CreateFNeg(operand_value); @@ -1166,6 +1164,8 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( return ir_builder_->CreateAnd(lhs_value, rhs_value); case HloOpcode::kOr: return ir_builder_->CreateOr(lhs_value, rhs_value); + case HloOpcode::kXor: + return ir_builder_->CreateXor(lhs_value, rhs_value); // Shifting out bits >= the number of bits in the type being shifted // produces a poison value in LLVM which is basically "deferred undefined @@ -1222,7 +1222,7 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( const Shape& operand_shape = hlo.operand(operand_no)->shape(); // If the operand is scalar, the source index is always {}. if (ShapeUtil::IsScalar(operand_shape)) { - return llvm_ir::IrArray::Index(); + return llvm_ir::IrArray::Index(target_index.GetType()); } // If no implicit broadcast is needed for this operand, returns the target @@ -1234,13 +1234,13 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( // If implicit broadcast is needed, the source dimensions that are broadcast // have index 0. CHECK_EQ(ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(hlo.shape())); - llvm_ir::IrArray::Index source_index; + llvm_ir::IrArray::Index source_index(target_index.GetType()); for (int64 i = 0; i < ShapeUtil::Rank(hlo.shape()); ++i) { if (hlo.shape().dimensions(i) == operand_shape.dimensions(i)) { source_index.push_back(target_index[i]); } else { CHECK_EQ(1, operand_shape.dimensions(i)); - source_index.push_back(ir_builder_->getInt64(0)); + source_index.push_back(target_index.GetConstantWithIndexType(0)); } } return source_index; @@ -1542,9 +1542,14 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // Emit IR to read dynamic start indices from hlo->operand(1). const HloInstruction* input_hlo = hlo->operand(0); const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); + // Use the same index type for all tensor accesses in the same kernel. + llvm::Type* index_type = index.GetType(); + llvm_ir::IrArray::Index slice_start_index(index_type, rank); for (int64 i = 0; i < rank; ++i) { - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + llvm_ir::IrArray::Index dim_index(1, index_typed_const(i)); TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, operand_to_generator.at(hlo->operand(1))(dim_index)); @@ -1554,17 +1559,17 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to oficially document different behavior. - start_index_value = ir_builder_->CreateSExtOrBitCast(start_index_value, - index[i]->getType()); - llvm::Value* operand_dim_size = llvm::ConstantInt::get( - start_index_value->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* output_dim_size = llvm::ConstantInt::get( - start_index_value->getType(), hlo->shape().dimensions(i)); + start_index_value = + ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + llvm::Value* operand_dim_size = + index_typed_const(input_hlo->shape().dimensions(i)); + llvm::Value* output_dim_size = + index_typed_const(hlo->shape().dimensions(i)); start_index_value = EmitIntegralMin( ir_builder_->CreateSub(operand_dim_size, output_dim_size), - EmitIntegralMax(llvm::ConstantInt::get(start_index_value->getType(), 0), - start_index_value, /*is_signed=*/true), + EmitIntegralMax(index_typed_const(0), start_index_value, + /*is_signed=*/true), /*is_signed=*/true); start_index_value->setName( @@ -1572,7 +1577,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( slice_start_index[i] = start_index_value; } - llvm_ir::IrArray::Index input_index(rank); + llvm_ir::IrArray::Index input_index(index_type, rank); for (int64 i = 0; i < rank; ++i) { // Emit IR which computes: // input_index = start_index + offset_index @@ -1596,17 +1601,18 @@ StatusOr ElementalIrEmitter::EmitElementalGather( const llvm_ir::ElementGenerator& indices_generator = operand_to_generator.at(hlo->operand(1)); + llvm::Type* index_type = index.GetType(); // This is the index into `operand` that holds the element we want to // generate. This index "unsafe" as in the components in here may be // out of bounds. - IrArray::Index unsafe_operand_index; + IrArray::Index unsafe_operand_index(index_type); // First copy in the window indices to unsafe_operand_index. for (int64 i = 0, e = operand_shape.dimensions_size(), unsafe_operand_index_dim = 0; i < e; i++) { if (c_binary_search(dim_numbers.elided_window_dims(), i)) { - unsafe_operand_index.push_back(ir_builder_->getInt64(0)); + unsafe_operand_index.push_back(index.GetConstantWithIndexType(0)); } else { unsafe_operand_index.push_back( index[dim_numbers.output_window_dims(unsafe_operand_index_dim++)]); @@ -1614,7 +1620,7 @@ StatusOr ElementalIrEmitter::EmitElementalGather( } // This is the index of the index vector in the gather_indices tensor. - IrArray::Index gather_index_index; + IrArray::Index gather_index_index(index_type); { std::vector gather_index_index_components; for (int64 i = 0, e = output_shape.dimensions_size(); i < e; i++) { @@ -1630,8 +1636,8 @@ StatusOr ElementalIrEmitter::EmitElementalGather( auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, int64 dim) { - llvm::Value* gather_dim_component_extended = ir_builder_->CreateSExtOrTrunc( - index_component, ir_builder_->getInt64Ty()); + llvm::Value* gather_dim_component_extended = + ir_builder_->CreateSExtOrTrunc(index_component, index_type); unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = ir_builder_->CreateAdd( unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)], @@ -1647,18 +1653,18 @@ StatusOr ElementalIrEmitter::EmitElementalGather( indices_shape.dimensions(dim_numbers.index_vector_dim()); for (int64 i = 0; i < index_vector_size; i++) { gather_index_index[dim_numbers.index_vector_dim()] = - ir_builder_->getInt64(i); + index.GetConstantWithIndexType(i); TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, indices_generator(gather_index_index)); add_to_unsafe_operand_index(gather_dim_component, i); } } - IrArray::Index safe_operand_index; + IrArray::Index safe_operand_index(index_type); for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { safe_operand_index.push_back(ir_builder_->CreateURem( unsafe_operand_index[i], - ir_builder_->getInt64(operand_shape.dimensions(i)))); + index.GetConstantWithIndexType(operand_shape.dimensions(i)))); } return operand_generator(safe_operand_index); @@ -1673,14 +1679,18 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( const HloInstruction* start_hlo = hlo->operand(2); // Calculate slice start/end indices. const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); - llvm_ir::IrArray::Index slice_limit_index(rank); + llvm_ir::IrArray::Index slice_start_index(index.GetType(), rank); + llvm_ir::IrArray::Index slice_limit_index(index.GetType(), rank); // Slice intersection gathers (ANDs) conditions on all ranks for which // 'input' is set to 'update' llvm::Value* slice_intersection = ir_builder_->getTrue(); for (int64 i = 0; i < rank; ++i) { - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + llvm::Type* index_type = index[0]->getType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + llvm_ir::IrArray::Index dim_index(1, index_typed_const(i)); TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, operand_to_generator.at(start_hlo)(dim_index)); @@ -1690,18 +1700,18 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to oficially document different behavior. - start_index_value = ir_builder_->CreateSExtOrBitCast(start_index_value, - index[i]->getType()); - llvm::Value* input_dim_size = llvm::ConstantInt::get( - index[i]->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* update_dim_size = llvm::ConstantInt::get( - index[i]->getType(), update_hlo->shape().dimensions(i)); - - start_index_value = EmitIntegralMin( - ir_builder_->CreateSub(input_dim_size, update_dim_size), - EmitIntegralMax(llvm::ConstantInt::get(start_index_value->getType(), 0), - start_index_value, /*is_signed=*/true), - /*is_signed=*/true); + start_index_value = + ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + llvm::Value* input_dim_size = + index_typed_const(input_hlo->shape().dimensions(i)); + llvm::Value* update_dim_size = + index_typed_const(update_hlo->shape().dimensions(i)); + + start_index_value = + EmitIntegralMin(ir_builder_->CreateSub(input_dim_size, update_dim_size), + EmitIntegralMax(index_typed_const(0), start_index_value, + /*is_signed=*/true), + /*is_signed=*/true); start_index_value->setName( AsStringRef(IrName(hlo, StrCat("start_idx", i)))); @@ -1731,7 +1741,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // Handle true BB (return data from 'update') SetToFirstInsertPoint(if_data.true_block, ir_builder_); // Compute update index for intersection case. - llvm_ir::IrArray::Index update_index(rank); + llvm_ir::IrArray::Index update_index(index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { update_index[i] = ir_builder_->CreateSub(index[i], slice_start_index[i]); } @@ -1799,7 +1809,8 @@ StatusOr ElementalIrEmitter::EmitElementalPad( SetToFirstInsertPoint(if_data.false_block, ir_builder_); TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index.GetType()))); ir_builder_->CreateStore(padding_value, ret_value_addr); SetToFirstInsertPoint(if_data.after_block, ir_builder_); @@ -1826,10 +1837,15 @@ StatusOr ElementalIrEmitter::EmitElementalDot( int64 lhs_dims = hlo->operand(0)->shape().dimensions_size(); int64 rhs_dims = hlo->operand(1)->shape().dimensions_size(); - std::unique_ptr inner_loop = llvm_ir::ForLoop::EmitForLoop( - IrName(hlo, "inner"), ir_builder_->getInt64(0), - ir_builder_->getInt64(contracted_dim_size), ir_builder_->getInt64(1), - ir_builder_); + llvm::Type* index_type = dot_result_index[0]->getType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + + std::unique_ptr inner_loop = + llvm_ir::ForLoop::EmitForLoop(IrName(hlo, "inner"), index_typed_const(0), + index_typed_const(contracted_dim_size), + index_typed_const(1), ir_builder_); SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), ir_builder_); PrimitiveType primitive_type = hlo->shape().element_type(); @@ -1848,7 +1864,7 @@ StatusOr ElementalIrEmitter::EmitElementalDot( // Given an output index [a,b,c,d,e] in the result, we compute: // sum(lhs[a,b,c,t]*rhs[d,t,e] for t in [0, T)) - IrArray::Index lhs_index, rhs_index; + IrArray::Index lhs_index(index_type), rhs_index(index_type); for (int64 i = 0; i < lhs_dims - 1; i++) { lhs_index.push_back(dot_result_index[i]); @@ -1947,6 +1963,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kMultiply: case HloOpcode::kNe: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kPower: case HloOpcode::kRemainder: case HloOpcode::kShiftLeft: diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 6df172db8e541c5cef7aab04f3d8611fc735e8b0..fd75847d0c0e737957401b8efc420d504a3c0706 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -82,7 +82,18 @@ StatusOr Executable::ExecuteOnStreamWrapper( StatusOr return_value = ExecuteOnStream(run_options, arguments, profile_ptr.get()); - TF_RETURN_IF_ERROR(return_value.status()); + if (!return_value.status().ok()) { + if (profile != nullptr) { + // Ensure the ThenStartTimer call has completed before we destroy timer. + // We already have a failure status to return, so just log this if it + // fails. + Status status = stream->BlockHostUntilDone(); + if (!status.ok()) { + LOG(ERROR) << "Failed to BlockHostUntilDone: " << status; + } + } + return return_value.status(); + } if (profile != nullptr) { VLOG(1) << "enqueueing 'stop timer' and blocking host until done..."; @@ -116,6 +127,11 @@ StatusOr Executable::ExecuteOnStreamWrapper( if (profile->compute_time_ns() == 0) { profile->set_compute_time_ns(profile->compute_and_transfer_time_ns()); } + + const int64 executable_size_in_bytes = SizeInBytes(); + if (executable_size_in_bytes != 0) { + profile->set_executable_size_in_bytes(executable_size_in_bytes); + } } if (profile_ptr != nullptr) { @@ -129,6 +145,8 @@ StatusOr Executable::ExecuteOnStreamWrapper( return return_value; } +int64 Executable::SizeInBytes() { return -1; } + Status Executable::DumpHloSnapshot() { TF_RET_CHECK(dumping_snapshot()); TF_RET_CHECK(hlo_snapshot_->has_hlo() && diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index 087bd1432945abfd860fcb8b1e92dd419598e025..98eaeee30a693211ae564a5ef3c373f0364bef11 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -28,7 +28,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/service_executable_run_options.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -89,8 +88,7 @@ class Executable { // called explicitly for other (async, for example) variants after the stream // has completed. virtual Status PopulateExecutionProfile( - HloExecutionProfile* hlo_execution_profile, - se::StreamExecutor* executor) { + HloExecutionProfile* hlo_execution_profile, se::Stream* stream) { return Status::OK(); } @@ -131,18 +129,16 @@ class Executable { const HloModuleConfig& module_config() const { return hlo_module_->config(); } - // Returns the versioned computation handle of the computation computed by - // this executable. - const VersionedComputationHandle& entry_computation_handle() const { - return hlo_module_->entry_computation_handle(); - } - // The shape (including layout) that results from this execution. This is the // shape of the DeviceMemoryBase result value in ExecuteOnStream above. - const Shape& host_result_shape() const { - return hlo_module_->config().host_entry_computation_layout().result_shape(); + const Shape& result_shape() const { + return hlo_module_->config().entry_computation_layout().result_shape(); } + // Returns the size of the executable in bytes. Returns -1 by default if the + // method is not overridden to support this kind of query. + virtual int64 SizeInBytes(); + // Dumping helpers. void set_hlo_snapshot(std::unique_ptr hlo_snapshot) { hlo_snapshot_ = std::move(hlo_snapshot); diff --git a/tensorflow/compiler/xla/service/gather_expander.cc b/tensorflow/compiler/xla/service/gather_expander.cc index 2d3e4b1fcdf6675955714cab262a8b2ca8ff4297..7cd2c9c136acac46e8e6c548c9e58b9bc8e6e0d2 100644 --- a/tensorflow/compiler/xla/service/gather_expander.cc +++ b/tensorflow/compiler/xla/service/gather_expander.cc @@ -300,7 +300,7 @@ static StatusOr PermuteGatherAndWindowDims( StatusOr GatherExpander::ExpandGather( HloInstruction* gather_instr) { - CHECK(!ShapeUtil::HasZeroElements(gather_instr->shape())); + CHECK(!ShapeUtil::IsZeroElementArray(gather_instr->shape())); HloComputation* computation = gather_instr->parent(); HloInstruction* operand = gather_instr->mutable_operand(0); @@ -369,7 +369,7 @@ StatusOr GatherExpander::Run(HloModule* module) { return inst->opcode() == HloOpcode::kGather && // Avoid expanding gather ops that produce zero sized tensors, // instead punt these to ZeroSizedHloElimination. - !ShapeUtil::HasZeroElements(inst->shape()); + !ShapeUtil::IsZeroElementArray(inst->shape()); }; std::vector gather_instrs; diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index 5ee67ccb4ae147683c7b41941670c6fc413a0d09..85e28a0dfe38415974e435106a2d0b75863f2df5 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -43,7 +43,7 @@ se::Platform::Id GenericTransferManager::PlatformId() const { } Status GenericTransferManager::WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) { TF_RET_CHECK(elements.size() == ShapeUtil::TupleElementCount(shape)); @@ -52,12 +52,24 @@ Status GenericTransferManager::WriteSingleTupleIndexTable( for (const se::DeviceMemoryBase& element : elements) { element_pointers.push_back(element.opaque()); } - return TransferBufferToDevice(executor, GetByteSizeRequirement(shape), - element_pointers.data(), region); + TF_RETURN_IF_ERROR(TransferBufferToDevice( + stream, GetByteSizeRequirement(shape), element_pointers.data(), region)); + // Ensure the buffer is transferred before we destroy element_pointers. + return stream->BlockHostUntilDone(); +} + +void GenericTransferManager::TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) { + Status status = stream->BlockHostUntilDone(); + if (!status.ok()) { + return done(status); + } + done(TransferLiteralFromDeviceInternal(stream->parent(), device_buffer)); } StatusOr> -GenericTransferManager::TransferLiteralFromDevice( +GenericTransferManager::TransferLiteralFromDeviceInternal( se::StreamExecutor* executor, const ShapedBuffer& device_buffer) { VLOG(2) << "transferring literal from device ordinal " << executor->device_ordinal() << "; device buffer: " << device_buffer; @@ -74,9 +86,8 @@ GenericTransferManager::TransferLiteralFromDevice( TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_host_shape(), [&](const Shape& subshape, const ShapeIndex& index) -> Status { - if (!ShapeUtil::IsTuple(subshape)) { - TF_RETURN_IF_ERROR(TransferBufferFromDevice( - executor, + if (ShapeUtil::IsArray(subshape)) { + TF_RETURN_IF_ERROR(executor->SynchronousMemcpyD2H( /*source=*/device_buffer.buffer(index), /*size=*/GetByteSizeRequirement(subshape), /*destination=*/ @@ -88,8 +99,8 @@ GenericTransferManager::TransferLiteralFromDevice( return std::move(literal); } -Status GenericTransferManager::TransferLiteralToDevice( - se::StreamExecutor* executor, const LiteralSlice& literal, +Status GenericTransferManager::TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, const ShapedBuffer& device_buffer) { const Shape& shape = literal.shape(); VLOG(2) << "transferring literal shape to device: " @@ -103,9 +114,10 @@ Status GenericTransferManager::TransferLiteralToDevice( TF_RET_CHECK( ShapeUtil::Compatible(literal.shape(), device_buffer.on_host_shape())); - TF_RET_CHECK(executor->device_ordinal() == device_buffer.device_ordinal()); + TF_RET_CHECK(stream->parent()->device_ordinal() == + device_buffer.device_ordinal()); - TF_RETURN_IF_ERROR(WriteTupleIndexTables(executor, device_buffer)); + TF_RETURN_IF_ERROR(WriteTupleIndexTables(stream, device_buffer)); return ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_host_shape(), @@ -121,16 +133,21 @@ Status GenericTransferManager::TransferLiteralToDevice( if (LayoutUtil::Equal(device_subshape.layout(), subliteral.shape().layout())) { source = subliteral.untyped_data(); + return TransferBufferToDevice( + stream, + /*size=*/GetByteSizeRequirement(device_subshape), source, + &device_memory); } else { // Relayout data before transferring. relayed_out_literal = subliteral.Relayout(device_subshape.layout(), /*shape_index=*/{}); source = relayed_out_literal->untyped_data(); + TF_RETURN_IF_ERROR(TransferBufferToDevice( + stream, + /*size=*/GetByteSizeRequirement(device_subshape), source, + &device_memory)); + return stream->BlockHostUntilDone(); } - return TransferBufferToDevice( - executor, - /*size=*/GetByteSizeRequirement(device_subshape), source, - &device_memory); } return Status::OK(); }); diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h index 3da9570ef7eebcdf618439f628fb4d5589993e4f..d216fe7d29e8f2e84ab4f558ee5caec32d07a70a 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.h +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h @@ -41,12 +41,13 @@ class GenericTransferManager : public TransferManager { se::Platform::Id PlatformId() const override; - StatusOr> TransferLiteralFromDevice( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) override; + void TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) override; - Status TransferLiteralToDevice(se::StreamExecutor* executor, - const LiteralSlice& literal, - const ShapedBuffer& device_buffer) override; + Status TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) override; Status TransferLiteralToInfeed(se::StreamExecutor* executor, const LiteralSlice& literal) override; @@ -64,11 +65,14 @@ class GenericTransferManager : public TransferManager { const void* source) override; Status WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) override; private: + StatusOr> TransferLiteralFromDeviceInternal( + se::StreamExecutor* executor, const ShapedBuffer& device_buffer); + // The platform this transfer manager targets. const se::Platform::Id platform_id_; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 6bd9d4c31df5b76820abcb711f910b7c468c057d..d90b0fb57d7acd24576e9e8e41316b19b6c44979 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -164,6 +164,7 @@ cc_library( "//tensorflow/compiler/xla/service:name_uniquer", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", + "//tensorflow/compiler/xla/service/llvm_ir:kernel_support_library", "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", @@ -236,6 +237,20 @@ cc_library( ], ) +cc_library( + name = "hlo_execution_profiler", + srcs = ["hlo_execution_profiler.cc"], + hdrs = ["hlo_execution_profiler.h"], + deps = [ + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_execution_profile", + "//tensorflow/compiler/xla/service:pool", + "//tensorflow/core:lib", + "//tensorflow/core:ptr_util", + "//tensorflow/core:stream_executor_no_cuda", + ], +) + cc_library( name = "gpu_executable", srcs = [ @@ -277,6 +292,7 @@ cc_library( ":backend_configs", ":buffer_allocations", ":cudnn_convolution_runner", + ":hlo_execution_profiler", ":infeed_manager", ":ir_emission_utils", ":partition_assignment", @@ -422,6 +438,35 @@ tf_cc_test( ], ) +cc_library( + name = "multi_output_fusion", + srcs = ["multi_output_fusion.cc"], + hdrs = ["multi_output_fusion.h"], + deps = [ + ":ir_emission_utils", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:multi_output_fusion", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "multi_output_fusion_test", + srcs = ["multi_output_fusion_test.cc"], + deps = [ + ":multi_output_fusion", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_matchers", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", + ], +) + cc_library( name = "gpu_copy_insertion", srcs = ["gpu_copy_insertion.cc"], @@ -522,6 +567,7 @@ cc_library( ":instruction_fusion", ":ir_emission_utils", ":ir_emitter", + ":multi_output_fusion", ":pad_insertion", ":partition_assignment", ":stream_assignment", @@ -539,7 +585,6 @@ cc_library( "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", - "//tensorflow/compiler/xla/service:gather_expander", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_constant_folding", "//tensorflow/compiler/xla/service:hlo_cse", @@ -569,7 +614,6 @@ cc_library( "//tensorflow/core:regexp_internal", "//tensorflow/core:stream_executor_no_cuda", "@llvm//:core", - "@llvm//:support", ], alwayslink = True, # Contains compiler registration ) @@ -727,6 +771,7 @@ cc_library( hdrs = ["stream_executor_util.h"], deps = [ "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:stream_executor_no_cuda", ], diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc index 77a48965e031349b045a956fd3f28c58607328e5..5e4fe1dd398dedd999e18d7ef6dfb5a4fd3bf4cb 100644 --- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/conditional_thunk.h" #include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" @@ -43,7 +44,9 @@ Status ConditionalThunk::Initialize(const GpuExecutable& executable, } Status ConditionalThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); // Copy the predicate value from device. bool predicate; se::DeviceMemoryBase predicate_address = @@ -59,10 +62,15 @@ Status ConditionalThunk::ExecuteOnStream( // Execute the true or the false computation depending on the value of the // predicate. if (predicate) { - TF_RETURN_IF_ERROR(true_thunk_.ExecuteOnStream(buffer_allocations, stream)); + profiler->StartHloComputation(); + TF_RETURN_IF_ERROR( + true_thunk_.ExecuteOnStream(buffer_allocations, stream, profiler)); + profiler->FinishHloComputation(hlo_instruction()->true_computation()); } else { + profiler->StartHloComputation(); TF_RETURN_IF_ERROR( - false_thunk_.ExecuteOnStream(buffer_allocations, stream)); + false_thunk_.ExecuteOnStream(buffer_allocations, stream, profiler)); + profiler->FinishHloComputation(hlo_instruction()->false_computation()); } return Status::OK(); diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.h b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h index ee03865d174469285a9e98b8a30fea90d997df37..aef24342c9fe182eb54b1c2beff840a76e7b8115 100644 --- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h @@ -17,6 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONDITIONAL_THUNK_H_ #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -50,7 +51,8 @@ class ConditionalThunk : public Thunk { Status Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) override; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: BufferAllocation::Slice predicate_buffer_index_; diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc index f0881124128c9b043392ffc4fa3aee2cd5b754c7..7833a4077e6c6ee4960665f37fb01a35530fd302 100644 --- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -55,7 +56,8 @@ ConvolutionThunk::ConvolutionThunk( tensor_ops_enabled_(tensor_ops_enabled) {} Status ConvolutionThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase input_data = buffer_allocations.GetDeviceAddress(input_buffer_); se::DeviceMemoryBase filter_data = @@ -68,6 +70,7 @@ Status ConvolutionThunk::ExecuteOnStream( se::dnn::AlgorithmConfig algorithm_config( se::dnn::AlgorithmDesc(algorithm_, tensor_ops_enabled_)); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); TF_RETURN_IF_ERROR(RunCudnnConvolution( convolution_kind_, input_shape_, filter_shape_, output_shape_, input_data, filter_data, output_data, scratch, window_, dim_nums_, algorithm_config, diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h index 6d845025b1aef2b0a5f147401b6db0598ba94d6d..d76ca6698dcf462c3c4961ce6a9784822af3a81f 100644 --- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h" #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/types.h" @@ -66,7 +67,8 @@ class ConvolutionThunk : public Thunk { // Does the convolution for the thunk on "stream". Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: class ScratchAllocator; diff --git a/tensorflow/compiler/xla/service/gpu/copy_thunk.cc b/tensorflow/compiler/xla/service/gpu/copy_thunk.cc index ee38c0318a878c7bcdc02afdcd146bfb4498d9a2..92e03f94c11f68082f0a8caa64f82e8533557194 100644 --- a/tensorflow/compiler/xla/service/gpu/copy_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/copy_thunk.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/copy_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" namespace xla { @@ -30,9 +31,11 @@ HostToDeviceCopyThunk::HostToDeviceCopyThunk( mem_size_(mem_size) {} Status HostToDeviceCopyThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase destination_data = buffer_allocations.GetDeviceAddress(destination_buffer_); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenMemcpy(&destination_data, source_address_, mem_size_); return Status::OK(); } @@ -47,11 +50,13 @@ DeviceToDeviceCopyThunk::DeviceToDeviceCopyThunk( mem_size_(mem_size) {} Status DeviceToDeviceCopyThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase destination_data = buffer_allocations.GetDeviceAddress(destination_buffer_); se::DeviceMemoryBase source_data = buffer_allocations.GetDeviceAddress(source_buffer_); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenMemcpy(&destination_data, source_data, mem_size_); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/copy_thunk.h b/tensorflow/compiler/xla/service/gpu/copy_thunk.h index 8b128386f61636de9ac41e856a2b00c578e05735..91564b520acae1839e0a466cf580db00bdf57e46 100644 --- a/tensorflow/compiler/xla/service/gpu/copy_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/copy_thunk.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -40,7 +41,8 @@ class HostToDeviceCopyThunk : public Thunk { HostToDeviceCopyThunk& operator=(const HostToDeviceCopyThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const void* source_address_; @@ -63,7 +65,8 @@ class DeviceToDeviceCopyThunk : public Thunk { DeviceToDeviceCopyThunk& operator=(const DeviceToDeviceCopyThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const BufferAllocation::Slice source_buffer_; diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc index db6924c742e4a949a3e939b6d6659e92c2d1e312..c77e3c81c9d38af7857ad1389d20221514bf38f1 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc @@ -126,12 +126,17 @@ Status Visitor::HandleBatchNormTraining(HloInstruction* batch_norm) { HloInstruction* variance_plus_epsilon = computation_->AddInstruction(HloInstruction::CreateBinary( inverse_stddev->shape(), HloOpcode::kPower, inverse_stddev, - computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-2))))); + computation_->AddInstruction(HloInstruction::CreateBroadcast( + inverse_stddev->shape(), + computation_->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(-2))), + {})))); HloInstruction* variance = computation_->AddInstruction(HloInstruction::CreateBinary( variance_plus_epsilon->shape(), HloOpcode::kSubtract, - variance_plus_epsilon, epsilon)); + variance_plus_epsilon, + computation_->AddInstruction(HloInstruction::CreateBroadcast( + variance_plus_epsilon->shape(), epsilon, {})))); // Repackage the results. std::unique_ptr new_tuple = HloInstruction::CreateTuple({ @@ -175,12 +180,17 @@ Status Visitor::HandleBatchNormGrad(HloInstruction* batch_norm) { HloInstruction* var_plus_epsilon = computation_->AddInstruction(HloInstruction::CreateBinary( batch_norm->operand(3)->shape(), HloOpcode::kAdd, - batch_norm->mutable_operand(3), epsilon)); + batch_norm->mutable_operand(3), + computation_->AddInstruction(HloInstruction::CreateBroadcast( + batch_norm->operand(3)->shape(), epsilon, {})))); HloInstruction* inverse_stddev = computation_->AddInstruction(HloInstruction::CreateBinary( var_plus_epsilon->shape(), HloOpcode::kPower, var_plus_epsilon, - computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-.5))))); + computation_->AddInstruction(HloInstruction::CreateBroadcast( + var_plus_epsilon->shape(), + computation_->AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR0(-.5))), + {})))); std::vector operands(batch_norm->operands().begin(), batch_norm->operands().end()); diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc index 68099fd63847ef9993f9bc7ac0e28b2939631b35..7b172812c36bb141787ef3a9285d6f7ce13e343b 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" @@ -99,13 +100,15 @@ CudnnBatchNormForwardInferenceThunk::CudnnBatchNormForwardInferenceThunk( } Status CudnnBatchNormForwardInferenceThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { dnn::BatchDescriptor operand_desc; dnn::BatchDescriptor scale_offset_desc; std::tie(operand_desc, scale_offset_desc) = MakeDescriptors(hlo_instruction()->shape(), feature_index_); se::DeviceMemory output(buffer_allocations.GetDeviceAddress(output_)); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenBatchNormalizationForward( se::DeviceMemory(buffer_allocations.GetDeviceAddress(operand_)), se::DeviceMemory(buffer_allocations.GetDeviceAddress(scale_)), @@ -123,6 +126,7 @@ Status CudnnBatchNormForwardInferenceThunk::ExecuteOnStream( /*is_training=*/false, // /*var_to_inv_var=*/nullptr, // /*inv_var_to_var=*/nullptr); + if (!stream->ok()) { return InternalError("BatchNormalizationForward call failed."); } @@ -158,7 +162,8 @@ CudnnBatchNormForwardTrainingThunk::CudnnBatchNormForwardTrainingThunk( } Status CudnnBatchNormForwardTrainingThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { dnn::BatchDescriptor operand_desc; dnn::BatchDescriptor scale_offset_desc; // The BatchNormTraining HLO outputs a tuple of three elements: output data, @@ -175,6 +180,7 @@ Status CudnnBatchNormForwardTrainingThunk::ExecuteOnStream( buffer_allocations.GetDeviceAddress(output_inv_stddev_)); se::DeviceMemory null_device_ptr(nullptr); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenBatchNormalizationForward( se::DeviceMemory(buffer_allocations.GetDeviceAddress(operand_)), se::DeviceMemory(buffer_allocations.GetDeviceAddress(scale_)), @@ -240,7 +246,8 @@ CudnnBatchNormBackwardThunk::CudnnBatchNormBackwardThunk( } Status CudnnBatchNormBackwardThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { dnn::BatchDescriptor operand_desc; dnn::BatchDescriptor scale_offset_desc; @@ -257,6 +264,7 @@ Status CudnnBatchNormBackwardThunk::ExecuteOnStream( se::DeviceMemory output_grad_offset( buffer_allocations.GetDeviceAddress(output_grad_offset_)); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenBatchNormalizationBackward( se::DeviceMemory( buffer_allocations.GetDeviceAddress(grad_output_)), diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.h b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.h index 874f85a863092ee05ae5df1f92d732318c5a0554..d2143b3952984722d136757255aa0aa60e9cab7e 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/types.h" @@ -60,7 +61,8 @@ class CudnnBatchNormForwardInferenceThunk : public Thunk { const CudnnBatchNormForwardInferenceThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: BufferAllocation::Slice operand_; @@ -90,7 +92,8 @@ class CudnnBatchNormForwardTrainingThunk : public Thunk { const CudnnBatchNormForwardTrainingThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: BufferAllocation::Slice operand_; @@ -123,7 +126,8 @@ class CudnnBatchNormBackwardThunk : public Thunk { delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: BufferAllocation::Slice operand_; diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc index e0c73aa73acb7f3313eb54fb07390cb76590433e..f9dccd287d955502858f6c24ccd4de80256fc148 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc @@ -42,8 +42,8 @@ bool CanImplementAsCudnnForwardConv(HloInstruction* conv) { } // CuDNN does not accept zero-element arguments - if (ShapeUtil::HasZeroElements(conv->operand(0)->shape()) || - ShapeUtil::HasZeroElements(conv->operand(1)->shape())) { + if (ShapeUtil::IsZeroElementArray(conv->operand(0)->shape()) || + ShapeUtil::IsZeroElementArray(conv->operand(1)->shape())) { return false; } diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index e5e2a0478a0659986ddec8d6785827b14b9efb56..27d2c3e491bfc2108cbd168d1a5e1575c2eed11f 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -53,11 +53,17 @@ using llvm_ir::IrName; using llvm_ir::SetToFirstInsertPoint; using tensorflow::strings::StrAppend; +namespace { // Returns whether operand is a floating-point literal with the given value. bool IsFPLiteralWithValue(const HloInstruction* operand, float value) { - return operand->opcode() == HloOpcode::kConstant && - operand->literal().IsAllFloat(value); + if (operand->opcode() == HloOpcode::kConstant && + operand->literal().IsAllFloat(value)) { + return true; + } + return operand->opcode() == HloOpcode::kBroadcast && + IsFPLiteralWithValue(operand->operand(0), value); } +} // namespace GpuElementalIrEmitter::GpuElementalIrEmitter( const HloModuleConfig& hlo_module_config, llvm::Module* module, @@ -370,11 +376,17 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( "reduce_window_accum_ptr", ir_builder_); { TF_ASSIGN_OR_RETURN(llvm::Value * init_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index.GetType()))); ir_builder_->CreateStore(init_value, accum_ptr); } - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_); + llvm::Type* index_type = index.GetType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return index.GetConstantWithIndexType(c); + }; + + llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -385,14 +397,14 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder_); - IrArray::Index input_index(index.size()); + IrArray::Index input_index(index_type, index.size()); llvm::Value* in_bounds = ir_builder_->getInt1(true); for (size_t i = 0; i < index.size(); ++i) { llvm::Value* stridden_index = ir_builder_->CreateNSWMul( - index[i], ir_builder_->getInt64(window.dimensions(i).stride())); + index[i], index_typed_const(window.dimensions(i).stride())); input_index[i] = ir_builder_->CreateNSWSub( ir_builder_->CreateNSWAdd(stridden_index, window_index[i]), - ir_builder_->getInt64(window.dimensions(i).padding_low())); + index_typed_const(window.dimensions(i).padding_low())); // We must check whether 0 ā‰¤ input_index[i] < bound, as otherwise // we are in the pad and so can skip the computation. This @@ -403,7 +415,7 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( in_bounds, ir_builder_->CreateICmpULT( input_index[i], - ir_builder_->getInt64(operand->shape().dimensions(i)))); + index_typed_const(operand->shape().dimensions(i)))); } llvm_ir::LlvmIfData if_data = @@ -429,11 +441,13 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( llvm::Value* accum_ptr = ir_builder()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( hlo->shape().element_type(), module_)); + llvm::Type* index_type = output_index.GetType(); TF_ASSIGN_OR_RETURN(llvm::Value * init_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index_type))); ir_builder()->CreateStore(init_value, accum_ptr); - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_); + llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); IrArray::Index input_index = loops.AddLoopsForShapeOnDimensions( operand->shape(), hlo->dimensions(), "reduction_dim"); if (!ShapeUtil::IsScalar(hlo->shape())) { diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc index e14ee6918bf148861ecccac99355fccf7ae93103..0cdddf8bcfd4e849b311bf810eda471d79dbf106 100644 --- a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -107,7 +108,8 @@ FftThunk::FftThunk(FftType fft_type, output_shape_(output_shape) {} Status FftThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { VLOG(3) << "FFT type: " << FftTypeToString(fft_type_); VLOG(3) << "Input shape: " << ShapeUtil::HumanStringWithLayout(input_shape_); VLOG(3) << "Output shape: " @@ -116,6 +118,7 @@ Status FftThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, FftScratchAllocator scratch_allocator(buffer_allocations.device_ordinal(), buffer_allocations.memory_allocator()); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); if (fft_plan_ == nullptr) { const int64 fft_rank = fft_length_.size(); CHECK_LE(fft_rank, 3); diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.h b/tensorflow/compiler/xla/service/gpu/fft_thunk.h index b0a22564f3a09bb67a3c01723f6e37c604656d45..8c53be5077b0c5a88d303c729457139c6cb800f1 100644 --- a/tensorflow/compiler/xla/service/gpu/fft_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/types.h" @@ -72,7 +73,8 @@ class FftThunk : public Thunk { // Does the FFT for the thunk on "stream". Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const se::fft::Type fft_type_; diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.cc b/tensorflow/compiler/xla/service/gpu/for_thunk.cc index b36539e0cb8d0a2f4758dd90acbdd8fc7181b8ca..4fdc55909a1afbac96aaa9bc931ed8ac6c0ae1df 100644 --- a/tensorflow/compiler/xla/service/gpu/for_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/for_thunk.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/for_thunk.h" #include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" @@ -37,11 +38,15 @@ Status ForThunk::Initialize(const GpuExecutable& executable, } Status ForThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); for (int64 i = 0; i < loop_limit_; ++i) { + profiler->StartHloComputation(); // Invoke loop body thunk sequence. - TF_RETURN_IF_ERROR( - body_thunk_sequence_->ExecuteOnStream(buffer_allocations, stream)); + TF_RETURN_IF_ERROR(body_thunk_sequence_->ExecuteOnStream(buffer_allocations, + stream, profiler)); + profiler->FinishHloComputation(hlo_instruction()->while_body()); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.h b/tensorflow/compiler/xla/service/gpu/for_thunk.h index 41ddfe0ceb1d0516c1c64feca53212a925632209..c2d39071b292c6704e9b5857a68bd8b3f3b9a914 100644 --- a/tensorflow/compiler/xla/service/gpu/for_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/for_thunk.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -39,7 +40,8 @@ class ForThunk : public Thunk { Status Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) override; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const int64 loop_limit_; diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc index 79fca43d022816645b8a07b9e806fe9cc3745e7c..dbc7754e251eb8075ab97dd2f36bbc400530fcf5 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc @@ -252,7 +252,8 @@ GemmThunk::GemmThunk(const BufferAllocation::Slice& lhs_buffer, alpha_(alpha) {} Status GemmThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { VLOG(2) << "Executing a GemmThunk"; se::DeviceMemoryBase lhs_data = @@ -352,6 +353,7 @@ Status GemmThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, alpha_, stream); }; + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); bool launch_ok; if (LayoutUtil::Minor(output_shape_.layout(), 0) == 0) { launch_ok = launch( diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h index 7a4830d64e7caef5a1170cbdbf8ab373fdaf16e2..939c7f85e35b4fcb943a25aa6346d72798432920 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -48,7 +49,8 @@ class GemmThunk : public Thunk { // Does the gemm operation for the thunk on "stream", which must be non-null. Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; // Returns true if we'll perform autotuning if run on the given stream. If // so, we want the GPU to be quiescent during autotuning, so as not to diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index b85721980715e2ce2cd7a689ab12a6cea55ba3f1..decfc40dafafe875fa02bab6695f5c54e522f267 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -36,7 +36,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" -#include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.h" @@ -52,6 +51,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emitter_context.h" #include "tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h" #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h" +#include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" #include "tensorflow/compiler/xla/service/gpu/pad_insertion.h" #include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h" @@ -159,16 +159,10 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, if (hlo_module->config().debug_options().xla_gpu_use_cudnn_batchnorm()) { pass.AddPass(); } - // TODO(kramerb): Remove use_fusion once instruction fusion can create - // multi-output fusions from the unfused expander output. pass.AddPass( /*rewrite_training_op=*/true, /*rewrite_inference_op=*/true, - /*rewrite_grad_op=*/true, - /*use_fusion=*/true); - - // Rewrite gather ops into smaller ones. - pass.AddPass(); + /*rewrite_grad_op=*/true); // BatchNormExpander can create zero-sized ops, so zero-sized HLO // elimination has to come after that pass. @@ -211,7 +205,7 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, { HloPassPipeline pipeline("layout_assignment"); pipeline.AddPass( - hlo_module->mutable_device_entry_computation_layout(), stream_exec); + hlo_module->mutable_entry_computation_layout(), stream_exec); // The LayoutAssignment pass may leave behind kCopy instructions which are // duplicate or NOPs, so remove them with algebraic simplification and CSE. @@ -261,6 +255,9 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, fusion.AddPass(/*may_duplicate=*/false); fusion.AddPass(/*may_duplicate=*/true); fusion.AddPass(); + fusion.AddPass(); + fusion.AddPass(/*is_layout_sensitive=*/true, + /*only_fusion_computations=*/true); TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status()); HloPassPipeline reduce_pipeline("reduce-precision"); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc index c5ccdd4a7dcec02ddab8a1f748659de41f6202d2..fbc1303085b579e898d2f503a341754109768567 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc @@ -52,60 +52,20 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { HloDataflowAnalysis::Run(*module)); // Make sure all operands of a library call are in memory instead of constants - // in IR. - for (HloInstruction* hlo : - module->entry_computation()->MakeInstructionPostOrder()) { - // Inserts a copy of hlo->operand(n) if it's a constant. - auto copy_operand_if_constant = [&](int64 n) -> Status { - HloInstruction* operand = hlo->mutable_operand(n); - TF_RET_CHECK(ShapeUtil::IsArray(operand->shape())); - const auto& values = dataflow->GetValueSet(operand).values(); - if (std::any_of(values.begin(), values.end(), [](const HloValue* value) { - return value->defining_instruction()->opcode() == - HloOpcode::kConstant; - })) { - TF_ASSIGN_OR_RETURN(HloInstruction * copy, FindOrInsertCopy(operand)); - TF_RETURN_IF_ERROR(hlo->ReplaceOperandWith(n, copy)); - changed = true; - } - return Status::OK(); - }; - - if (IsCustomCallToDnnBatchNorm(*hlo)) { - // The epsilon and feature_index operands to a CUDNN batchnorm op don't - // need to be materialized in memory -- in fact, they must be constants. - // These are the last two operands of all three batchnorm ops. - for (int64 i = 0; i < hlo->operand_count() - 2; ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } - } else if (ImplementedAsLibraryCall(*hlo) || - hlo->opcode() == HloOpcode::kCrossReplicaSum) { - // For all other library calls and cross-replica-sum, materialize all the - // operands into memory. (Cross-replica-sum gets its constant args - // materialized even if it's not implemented as a libcall to simplify the - // implementation. It's slower, but we can constant fold away constant - // args *anyway*, so we just need to make it work.) - for (int64 i = 0; i < hlo->operand_count(); ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } - } - } - - // Init values of while and conditional nodes cannot be constants. Insert - // copies for any constants found at the operands of these nodes. + // in IR. Also, init values of while and conditional nodes cannot be + // constants. Insert copies for any constants found at the operands of these + // nodes. tensorflow::gtl::FlatSet inserted_copies; for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() != HloOpcode::kWhile && - instruction->opcode() != HloOpcode::kConditional) { - continue; - } - for (auto operand : instruction->operands()) { + for (HloInstruction* hlo : computation->instructions()) { + // Inserts a copy of hlo->operand(n) if it's a constant. + auto copy_operand_if_constant = [&](int64 n) -> Status { + HloInstruction* operand = hlo->mutable_operand(n); // Skip the operands that have already been replaced with a copy in a // previous iteration (which is possible when a constant is used as an // operand in multiple places). if (ContainsKey(inserted_copies, operand)) { - continue; + return Status::OK(); } for (auto& pair : dataflow->GetInstructionValueSet(operand)) { const HloValueSet& value_set = pair.second; @@ -121,6 +81,47 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { } } } + return Status::OK(); + }; + + if (IsCustomCallToDnnBatchNorm(*hlo)) { + // The epsilon and feature_index operands to a CUDNN batchnorm op don't + // need to be materialized in memory -- in fact, they must be constants. + // These are the last two operands of all three batchnorm ops. + for (int64 i = 0; i < hlo->operand_count() - 2; ++i) { + TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); + } + } else if (ImplementedAsLibraryCall(*hlo) || + hlo->opcode() == HloOpcode::kCrossReplicaSum || + hlo->opcode() == HloOpcode::kWhile || + hlo->opcode() == HloOpcode::kConditional) { + // For all other library calls, cross-replica-sum, while and conditional + // ops materialize all the operands into memory. (Cross-replica-sum + // gets its constant args materialized even if it's not implemented as a + // libcall to simplify the implementation. It's slower, but we can + // constant fold away constant args *anyway*, so we just need to make it + // work.) + for (int64 i = 0; i < hlo->operand_count(); ++i) { + TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); + } + } + } + } + + if (changed) { + // Check the assumption that the epsilon and feature_index constants of the + // CUDNN batchnorm op are not shared with other ops where we would replace + // them with a copy. These custom op calls are generated with the + // CudnnBatchNormRewriter, so this would only happen if HloCSE merges them. + for (HloComputation* computation : module->computations()) { + for (HloInstruction* hlo : computation->instructions()) { + if (!IsCustomCallToDnnBatchNorm(*hlo)) { + continue; + } + for (int64 i = hlo->operand_count() - 2; i < hlo->operand_count(); + ++i) { + CHECK_EQ(hlo->operand(i)->opcode(), HloOpcode::kConstant); + } } } } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 25d8f720ea4791a4c94efcad6909cd0c113fbe70..0cad2958c72797b4d70f00676928b2b21d7a3e8d 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" -#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" @@ -41,77 +41,6 @@ namespace { using tensorflow::tracing::ScopedAnnotation; -// A helper class for profiling HLO in the course of GPU program execution. -// All of the profiling is guarded internally, to avoid the caller needing to -// have lots of conditionals sprinkled around. -class HloExecutionProfiler { - public: - // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler( - bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, - const HloComputation* computation) - : do_profile_(do_profile), - profile_(profile), - stream_(stream), - sub_streams_(sub_streams), - computation_(computation) { - if (do_profile_) { - clock_rate_ghz_ = - stream->parent()->GetDeviceDescription().clock_rate_ghz(); - execution_timer_.reset(new se::Timer(stream->parent())); - per_op_timer_.reset(new se::Timer(stream->parent())); - stream->InitTimer(execution_timer_.get()) - .ThenStartTimer(execution_timer_.get()); - stream->InitTimer(per_op_timer_.get()); - } - } - - // If profiling is enabled, sets the total cycle count on the profile from the - // execution timer. - void FinishExecution() { - CHECK(!finished_execution_) << "Call FinishExecution only once!"; - finished_execution_ = true; - if (do_profile_) { - stream_->ThenWaitFor(&sub_streams_); - stream_->ThenStopTimer(execution_timer_.get()); - stream_->BlockHostUntilDone().IgnoreError(); - profile_->set_total_cycles_executed( - *computation_, execution_timer_->Nanoseconds() * clock_rate_ghz_); - } - } - - // If profiling is enabled, starts the per-operation timer. - void StartOperation() { - if (do_profile_) { - stream_->ThenStartTimer(per_op_timer_.get()); - } - } - - // If profiling is enabled, stops the per-operation timer and records the time - // that the hlo_instruction took to execute in the profile. - void FinishOperation(const HloInstruction* hlo_instruction) { - if (do_profile_) { - stream_->ThenWaitFor(&sub_streams_); - stream_->ThenStopTimer(per_op_timer_.get()); - stream_->BlockHostUntilDone().IgnoreError(); - profile_->SetCyclesTakenBy( - hlo_instruction, per_op_timer_->Nanoseconds() * clock_rate_ghz_); - } - } - - private: - const bool do_profile_; - double clock_rate_ghz_; - HloExecutionProfile* profile_; - se::Stream* stream_; - const std::vector::SmartPtr>& sub_streams_; - const HloComputation* computation_; - std::unique_ptr execution_timer_; - std::unique_ptr per_op_timer_; - bool finished_execution_ = false; -}; - } // namespace // Implementation note: HLO profiling is always enabled for GPU executables, @@ -207,18 +136,17 @@ Status GpuExecutable::ExecuteThunks( TF_RETURN_IF_ERROR(main_stream->BlockHostUntilDone()); } - profiler.StartOperation(); VLOG(2) << "Executing the thunk for " << thunk->hlo_instruction()->ToString() << " on stream " << stream_no; - TF_RETURN_IF_ERROR(thunk->ExecuteOnStream(buffer_allocations, stream)); + TF_RETURN_IF_ERROR( + thunk->ExecuteOnStream(buffer_allocations, stream, &profiler)); if (thunk_schedule_->Depended(thunk)) { auto finish_event = MakeUnique(main_stream->parent()); finish_event->Init(); stream->ThenRecordEvent(finish_event.get()); thunk_to_finish_event[thunk] = std::move(finish_event); } - profiler.FinishOperation(thunk->hlo_instruction()); } main_stream->ThenWaitFor(&sub_streams); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc index 8bf62dde8b9948375fc493fd1a524cfa7b062502..09ef62c87f8875a5803497e8eb628769f883202a 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc @@ -51,7 +51,7 @@ HeuristicLayoutAssignment(const HloInstruction* instr, // H <=> Y // W <=> X // - // Therefore kOutputInputYX means NHWC; kBatchDepthYX means NCHW. + // Therefore kOutputInputYX and kBatchDepthYX mean NCHW. // As of today, our empirical evidence is that cudnn 7.0 is faster on V100 x // fp16 with the mostly-NHWC layout. The heuristic may change as cudnn version diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc index 7bb8df6581b49b1bf8c84a972f715e8dc119d8de..5343497c03c13a2589363da0fa33e18520220826 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc @@ -55,33 +55,28 @@ Status GpuTransferManager::TransferLiteralToInfeed( return TransferBufferToInfeed(executor, size, literal.untyped_data()); } - if (ShapeUtil::IsNestedTuple(shape)) { - return Unimplemented( - "Infeed with a nested tuple shape is not supported: %s", - ShapeUtil::HumanString(literal.shape()).c_str()); - } - // For a tuple, we transfer each of its elements to the device and // enqueue the resulting destination device addresses with the // infeed manager. std::vector buffers; - buffers.reserve(ShapeUtil::TupleElementCount(shape)); auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() { for (gpu::InfeedBuffer* b : buffers) { b->Done(); } }); - for (int64 i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { - const Shape& tuple_element_shape = - ShapeUtil::GetTupleElementShape(shape, i); - int64 tuple_element_size = GetByteSizeRequirement(tuple_element_shape); - TF_ASSIGN_OR_RETURN( - gpu::InfeedBuffer * buffer, - TransferBufferToInfeedInternal(executor, tuple_element_size, - literal.untyped_data({i}))); - buffers.push_back(buffer); - } + TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( + shape, [&](const Shape& literal_subshape, const ShapeIndex& index) { + if (ShapeUtil::IsArray(literal_subshape)) { + int64 tuple_element_size = GetByteSizeRequirement(literal_subshape); + TF_ASSIGN_OR_RETURN( + gpu::InfeedBuffer * buffer, + TransferBufferToInfeedInternal(executor, tuple_element_size, + literal.untyped_data(index))); + buffers.push_back(buffer); + } + return Status::OK(); + })); cleanup.release(); return EnqueueBuffersToInfeed(executor, buffers); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc new file mode 100644 index 0000000000000000000000000000000000000000..3e96beb575300614a04c856adbb6d742b34d11df --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc @@ -0,0 +1,115 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_execution_profile.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" +#include "tensorflow/core/util/ptr_util.h" + +namespace xla { +namespace gpu { +namespace { +void InitAndStartTimer(std::stack>* timers, + se::Stream* stream) { + timers->push(MakeUnique(stream->parent())); + stream->InitTimer(timers->top().get()).ThenStartTimer(timers->top().get()); +} + +uint64 GetCyclesTaken( + std::stack>* timers, + const std::vector::SmartPtr>& sub_streams, + se::Stream* stream, double clock_rate_ghz) { + CHECK_GT(timers->size(), 0); + stream->ThenWaitFor(&sub_streams); + stream->ThenStopTimer(timers->top().get()); + stream->BlockHostUntilDone().IgnoreError(); + double nanoseconds = timers->top()->Nanoseconds(); + timers->pop(); + return static_cast(nanoseconds * clock_rate_ghz); +} +} // namespace + +HloExecutionProfiler::HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation) + : do_profile_(do_profile), + profile_(profile), + stream_(stream), + sub_streams_(sub_streams), + computation_(computation) { + if (do_profile_) { + clock_rate_ghz_ = stream->parent()->GetDeviceDescription().clock_rate_ghz(); + InitAndStartTimer(&timers_, stream); + } +} + +void HloExecutionProfiler::FinishExecution() { + CHECK(!finished_execution_) << "Call FinishExecution only once!"; + finished_execution_ = true; + if (do_profile_) { + profile_->set_total_cycles_executed( + *computation_, + GetCyclesTaken(&timers_, sub_streams_, stream_, clock_rate_ghz_)); + } +} + +void HloExecutionProfiler::StartHloComputation() { + if (do_profile_) { + InitAndStartTimer(&timers_, stream_); + } +} + +void HloExecutionProfiler::FinishHloComputation( + const HloComputation* computation) { + if (do_profile_) { + profile_->set_total_cycles_executed( + *computation, + GetCyclesTaken(&timers_, sub_streams_, stream_, clock_rate_ghz_)); + } +} + +void HloExecutionProfiler::StartHloInstruction() { + if (do_profile_) { + InitAndStartTimer(&timers_, stream_); + } +} + +void HloExecutionProfiler::FinishHloInstruction( + const HloInstruction* hlo_instruction) { + if (do_profile_) { + profile_->SetCyclesTakenBy( + hlo_instruction, + GetCyclesTaken(&timers_, sub_streams_, stream_, clock_rate_ghz_)); + } +} + +std::unique_ptr +HloExecutionProfiler::MakeScopedInstructionProfiler( + const HloInstruction* hlo_instruction) { + return MakeUnique(this, hlo_instruction); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h new file mode 100644 index 0000000000000000000000000000000000000000..e5c655edc65a0c58bfde6c7701c8874d39c0b5d7 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h @@ -0,0 +1,106 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_execution_profile.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +class ScopedInstructionProfiler; + +// A helper class for profiling HLO in the course of GPU program execution. +// All of the profiling is guarded internally, to avoid the caller needing to +// have lots of conditionals sprinkled around. +class HloExecutionProfiler { + public: + // If profiling is enabled, start an execution timer running. + explicit HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation); + + // If profiling is enabled, sets the total cycle count on the profile from the + // execution timer. + void FinishExecution(); + + // If profiling is enabled, starts a timer for a (sub)computation. + void StartHloComputation(); + + // If profiling is enabled stops the timer for a (sub)computation and records + // the time that the computation took to execute in the profile. + void FinishHloComputation(const HloComputation* computation); + + // If profiling is enabled, starts a per-operation timer. + void StartHloInstruction(); + + // If profiling is enabled, stops the per-operation timer and records the time + // that the hlo_instruction took to execute in the profile. + void FinishHloInstruction(const HloInstruction* hlo_instruction); + + // Returns a ScopedInstructionProfiler and triggers a call to + // StartHloInstruction(). Once the returned ScopedInstructionProfiler goes + // out of scope, it triggers a call to FinishHloInstruction(). + std::unique_ptr MakeScopedInstructionProfiler( + const HloInstruction* hlo_instruction); + + private: + const bool do_profile_; + double clock_rate_ghz_; + HloExecutionProfile* profile_; + se::Stream* stream_; + const std::vector::SmartPtr>& sub_streams_; + const HloComputation* computation_; + std::stack> timers_; + bool finished_execution_ = false; +}; + +// This class can be used within the ExecuteOnStream() implementations of +// Thunks. It ensures that we always have a pair of matching +// StartHloInstruction() and FinishHloInstruction() calls to the profiler. +class ScopedInstructionProfiler { + public: + ScopedInstructionProfiler(HloExecutionProfiler* profiler, + const HloInstruction* hlo_instruction) + : profiler_(profiler), hlo_instruction_(hlo_instruction) { + if (hlo_instruction != nullptr) { + profiler->StartHloInstruction(); + } + } + ~ScopedInstructionProfiler() { + if (hlo_instruction_ != nullptr) { + profiler_->FinishHloInstruction(hlo_instruction_); + } + } + + private: + HloExecutionProfiler* profiler_; + const HloInstruction* hlo_instruction_; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc index f766f968826d960a8e86308f2395301aaa09f1ae..375709150e08996ea6a40f5e9e66a8f8d9287008 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc @@ -199,7 +199,7 @@ StatusOr> HloSchedule::Build( // concurrency by optimizing for minimal memory usage. TF_ASSIGN_OR_RETURN( schedule->thunk_launch_order_, - CreateMemoryMinimizingSequence( + ScheduleOneComputation( *entry_computation, [pointer_size](const BufferValue& buffer) { return ShapeUtil::ByteSizeOf(buffer.shape(), pointer_size); })); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc index e230d538cc2df826778e8d13eaaaf31ec81c57f0..45f0a1c645b2875cf90d2c11cfb66c3dd855d097 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc @@ -47,8 +47,7 @@ class HloScheduleTest : public HloTestBase { auto debug_options = GetDebugOptionsForTest(); debug_options.set_xla_gpu_disable_multi_streaming(false); config.set_debug_options(debug_options); - return MakeUnique("test_module", VersionedComputationHandle(), - config); + return MakeUnique("test_module", config); } HloVec RemoveHlo(const HloVec& input, diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index 061210352cf12e6802d066d311fd2cb481673f15..d420863b8569771b16a03591b6a0ddd0591f7e2e 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -137,7 +137,7 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte, } llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, - const ShapeIndex& shape_index, + ShapeIndexView shape_index, llvm::Value* ir_value) { llvm::Type* pointee_type = llvm_ir::ShapeToIrType( ShapeUtil::GetSubshape(hlo.shape(), shape_index), module_); @@ -158,7 +158,7 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, void HloToIrBindings::BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value, - const ShapeIndex& shape_index) { + ShapeIndexView shape_index) { VLOG(2) << "Binding " << hlo.ToString(); const Shape& hlo_shape = hlo.shape(); @@ -202,7 +202,7 @@ llvm_ir::IrArray HloToIrBindings::GetIrArray(const HloInstruction& hlo, << " of " << hlo.ToString(); llvm_ir::IrArray ir_array(base_ptr, ShapeUtil::GetSubshape(hlo.shape(), shape_index)); - alias_analysis_.AddAliasingInformationToIrArray(hlo, &ir_array); + alias_analysis_.AddAliasingInformationToIrArray(hlo, &ir_array, shape_index); // The GPU backend emits one kernel per top-level HLO, and LLVM views // execution of one kernel as the "whole program" executed on the GPU. diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h index 3d34311b4368d17cb074aaf33c71fc865e96387e..a86e6e78c693ac53bb2c70d88b999a4e1273ecad 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h @@ -51,7 +51,7 @@ class HloToIrBindings { // Rebinds the given HLO to the LLVM IR value that represent its address. void BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value, - const ShapeIndex& shape_index = {}); + ShapeIndexView shape_index = {}); // Unbinds all IR values that's defined in an LLVM function, e.g., function // arguments and stack variables. Global variables will be kept in bindings_. @@ -71,7 +71,7 @@ class HloToIrBindings { // A helper method that returns the base pointer of the IrArray containing the // output of "inst".at the given ShapeIndex. llvm::Value* GetBasePointer(const HloInstruction& hlo, - const ShapeIndex& shape_index = {}) const { + ShapeIndexView shape_index = {}) const { auto it = base_ptrs_.find(&hlo); CHECK(it != base_ptrs_.end()) << hlo.ToString(); return it->second.element(shape_index); @@ -97,7 +97,7 @@ class HloToIrBindings { // Returns an llvm typed ir representation of 'ir_value' based on 'hlo' shape. llvm::Value* GetTypedIrValue(const HloInstruction& hlo, - const ShapeIndex& shape_index, + ShapeIndexView shape_index, llvm::Value* ir_value); const BufferAssignment* buffer_assignment_; diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc index ea34d5b30c91e8b809e3e17a904e27e589fd6b5f..62915febb11d5defa0e44b688eacabb16a7621da 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc @@ -13,8 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" #include "tensorflow/compiler/xla/service/gpu/infeed_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" +#include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -22,29 +23,31 @@ namespace xla { namespace gpu { InfeedThunk::InfeedThunk( - tensorflow::gtl::ArraySlice tuple_element_buffers, - const BufferAllocation::Slice& destination_buffer, + const ShapeTree& infeed_slices, const HloInstruction* hlo_instruction) - : Thunk(Kind::kInfeed, hlo_instruction), - tuple_element_buffers_(tuple_element_buffers.begin(), - tuple_element_buffers.end()), - destination_buffer_(destination_buffer) {} + : Thunk(Kind::kInfeed, hlo_instruction), infeed_slices_(infeed_slices) {} Status InfeedThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { VLOG(2) << "Infeeding to GPU "; - se::DeviceMemoryBase destination_address = - buffer_allocations.GetDeviceAddress(destination_buffer_); - + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); + // First copy the infeed data which is element 0 of the infeed instruction's + // two-tuple output (the other element is a token). + se::DeviceMemoryBase data_address = + buffer_allocations.GetDeviceAddress(infeed_slices_.element({0})); InfeedManager* infeed_manager = GetOrCreateInfeedManager(); std::vector infeed_buffers; - if (ShapeUtil::IsTuple(hlo_instruction()->shape())) { - CHECK(!ShapeUtil::IsNestedTuple(hlo_instruction()->shape())); + const Shape& data_shape = + ShapeUtil::GetTupleElementShape(hlo_instruction()->shape(), 0); + if (ShapeUtil::IsTuple(data_shape)) { + CHECK(!ShapeUtil::IsNestedTuple(data_shape)); // Transfer the tuple elements first. std::vector tuple_element_addresses; - for (BufferAllocation::Slice tuple_element_buffer : - tuple_element_buffers_) { + for (int i = 0; i < ShapeUtil::TupleElementCount(data_shape); ++i) { + const BufferAllocation::Slice& tuple_element_buffer = + infeed_slices_.element({0, i}); se::DeviceMemoryBase tuple_element_address = buffer_allocations.GetDeviceAddress(tuple_element_buffer); @@ -56,15 +59,23 @@ Status InfeedThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, } // Transfer the tuple outer buffer. auto host_size = tuple_element_addresses.size() * sizeof(void*); - stream->ThenMemcpy(&destination_address, tuple_element_addresses.data(), + stream->ThenMemcpy(&data_address, tuple_element_addresses.data(), host_size); } else { InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer(); infeed_buffers.push_back(buffer); - stream->ThenMemcpy(&destination_address, *(buffer->device_memory()), + stream->ThenMemcpy(&data_address, *(buffer->device_memory()), buffer->length()); } + // Construct top-level tuple of infeed containing the data and the token. Use + // a nullptr for the token, it should never be dereferenced. + std::vector infeed_addresses = {data_address.opaque(), nullptr}; + se::DeviceMemoryBase top_level_address = + buffer_allocations.GetDeviceAddress(infeed_slices_.element({})); + stream->ThenMemcpy(&top_level_address, infeed_addresses.data(), + 2 * sizeof(void*)); + Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError("Failed to complete data transfer on stream %p: %s", diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.h b/tensorflow/compiler/xla/service/gpu/infeed_thunk.h index 93713cb12defd95bdd69cb0aa7ad7b4e37fc8fae..59487e245b78e66c45409fe712e86d3392e50580 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -32,23 +33,19 @@ namespace gpu { class InfeedThunk : public Thunk { public: // Constructs a InfeedThunk that copies data from the on-device - // infeed queue to the device buffer - // `destination_buffer`. `mem_size` is the size of the data in - // bytes. - InfeedThunk(tensorflow::gtl::ArraySlice - tuple_element_buffers, - const BufferAllocation::Slice& destination_buffer, + // infeed queue into the buffers in the given shape tree. + InfeedThunk(const ShapeTree& infeed_slices, const HloInstruction* hlo_instruction); InfeedThunk(const InfeedThunk&) = delete; InfeedThunk& operator=(const InfeedThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: - const std::vector tuple_element_buffers_; - const BufferAllocation::Slice destination_buffer_; + const ShapeTree infeed_slices_; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index 36a1b82a26d84fb557c894f0bf122aef064b052e..64ed3d748febd8281a8e602194b31c937a4a682a 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -40,6 +40,7 @@ bool IsFusile(const HloInstruction& hlo) { hlo.opcode() == HloOpcode::kDynamicSlice || hlo.opcode() == HloOpcode::kDynamicUpdateSlice || hlo.opcode() == HloOpcode::kFusion || + hlo.opcode() == HloOpcode::kGather || hlo.opcode() == HloOpcode::kPad || hlo.opcode() == HloOpcode::kReduce || hlo.opcode() == HloOpcode::kReduceWindow || @@ -77,15 +78,14 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, HloInstruction* producer = consumer->mutable_operand(operand_index); // Check if we can use output fusion for (A @ B) * alpha - if (consumer->operand_count() == 2 && - (producer->opcode() == HloOpcode::kDot || - (producer->opcode() == HloOpcode::kFusion && - producer->fused_expression_root()->opcode() == HloOpcode::kDot))) { + if (producer->opcode() == HloOpcode::kDot || + (producer->opcode() == HloOpcode::kFusion && + producer->fused_expression_root()->opcode() == HloOpcode::kDot)) { int64 other_operand_index = 1 - operand_index; - const HloInstruction* alpha = consumer->operand(other_operand_index); HloInstruction* op1 = nullptr; HloInstruction* op2 = nullptr; - if (consumer->opcode() == HloOpcode::kFusion && + if (consumer->operand_count() == 1 && + consumer->opcode() == HloOpcode::kFusion && consumer->fusion_kind() == HloInstruction::FusionKind::kLoop && Match(consumer->fused_expression_root(), match::Op() @@ -103,10 +103,12 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, op2->opcode() != HloOpcode::kBroadcast) { return false; } - if (IsIEEEFloatingPointScalarConstant(alpha)) { + if (IsIEEEFloatingPointScalarConstant(op2->operand(0))) { return true; } - } else if (consumer->opcode() == HloOpcode::kMultiply) { + } else if (consumer->operand_count() == 2 && + consumer->opcode() == HloOpcode::kMultiply) { + const HloInstruction* alpha = consumer->operand(other_operand_index); // Fuse if 'alpha' is a broadcast of a scalar constant. if (alpha->opcode() == HloOpcode::kBroadcast && alpha->dimensions().empty() && @@ -173,6 +175,14 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return false; } + // Fuse scalar constants into loop fusion nodes, this reduces the number of + // parameters and makes matching scalar broadcasts easier. + if (ShapeUtil::IsEffectiveScalar(producer->shape()) && + consumer->opcode() == HloOpcode::kFusion && + producer->opcode() == HloOpcode::kConstant) { + return true; + } + return IsFusile(*producer) && IsFusile(*consumer) && InstructionFusion::ShouldFuse(consumer, operand_index); } diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 426b1d235c3135ff61671481044beed518e2db00..1963d9eef72d41fa0a275bea98f959671fa7e737 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -168,7 +168,7 @@ TEST_F(InstructionFusionTest, BroadcastIntoReduce) { HloInstruction* root = module->entry_computation()->root_instruction(); EXPECT_THAT(root, op::Fusion()); EXPECT_THAT(root->fused_expression_root(), - op::Reduce(op::Broadcast(op::Parameter()), op::Parameter())); + op::Reduce(op::Broadcast(op::Constant()), op::Constant())); } TEST_F(InstructionFusionTest, BitcastIntoAdd) { @@ -255,7 +255,7 @@ TEST_F(InstructionFusionTest, DotOutputFusion) { EXPECT_THAT( root->fused_expression_root(), op::Multiply(op::Dot(op::Parameter(), op::Transpose(op::Parameter())), - op::Broadcast(op::Parameter()))); + op::Broadcast(op::Constant()))); } // Compute sum(1/p0), where p0 has type f32, twice. Check that the division is @@ -339,7 +339,7 @@ TEST_F(InstructionFusionTest, DotOutputFusionImpossible) { EXPECT_EQ(root->fusion_kind(), HloInstruction::FusionKind::kLoop); EXPECT_THAT(root->fused_expression_root(), op::Multiply(op::Multiply(op::Parameter(), op::Parameter()), - op::Broadcast(op::Parameter()))); + op::Broadcast(op::Constant()))); } // Counts the HLO ops with a given op code in the specified module. @@ -581,5 +581,30 @@ TEST_F(InstructionFusionTest, FuseIntoInputFusionInstruction) { << module->ToString(); } +TEST_F(InstructionFusionTest, FuseScalarConstant) { + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY FuseScalarConstant { + p0 = f32[] parameter(0) + c0 = f32[] constant(1) + add1 = f32[] add(p0, c0) + b0 = f32[2]{0} broadcast(add1), dimensions={} + c1 = f32[2]{0} constant({1, 2}) + ROOT add2 = f32[2]{0} add(b0, c1) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Add(op::Broadcast(op::Add(op::Parameter(), op::Constant())), + op::Parameter())); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 67890bfed1136796c83c7ef6912ffc1ab1b7e332..388aa35d7dceeef92dbdb6c8a3bb7fb3796a0b61 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -56,8 +56,8 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, return type_is_allowed && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape) && - !ShapeUtil::HasZeroElements(lhs_shape) && - !ShapeUtil::HasZeroElements(rhs_shape); + !ShapeUtil::IsZeroElementArray(lhs_shape) && + !ShapeUtil::IsZeroElementArray(rhs_shape); } bool DotImplementedAsGemm(const HloInstruction& dot) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 547af33e9a98c03e1429366172f9a401e385a9d1..d5e07c3afb7dcb7e7a848b8c02e413c21d8ea155 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -94,10 +94,7 @@ Status IrEmitter::HandleConstant(HloInstruction* constant) { << std::endl << " its type: " << llvm_ir::DumpToString(*global_for_const->getType()); - llvm::Constant* shape_constant = llvm::ConstantExpr::getBitCast( - global_for_const, - llvm_ir::ShapeToIrType(literal.shape(), module_)->getPointerTo()); - bindings_.BindHloToIrValue(*constant, shape_constant); + bindings_.BindHloToIrValue(*constant, global_for_const); return Status::OK(); } @@ -194,6 +191,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( HloOpcode root_opcode = computation.root_instruction()->opcode(); PrimitiveType element_type = computation.root_instruction()->shape().element_type(); + bool is_atomic_integral = element_type == S32 || element_type == U32 || + element_type == S64 || element_type == U64; llvm::Value* source = ir_builder_.CreateLoad(source_address, "source"); if (root_opcode == HloOpcode::kAdd) { // NVPTX supports atomicAdd on F32 and integer types. @@ -204,7 +203,7 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( {output_address->getType()}, &ir_builder_); return true; } - if (primitive_util::IsIntegralType(element_type)) { + if (is_atomic_integral) { // integral + integral ir_builder_.CreateAtomicRMW(llvm::AtomicRMWInst::Add, output_address, source, @@ -213,9 +212,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( } } - // NVPTX supports atomicMax and atomicMin on only integer types. - if (root_opcode == HloOpcode::kMaximum && - primitive_util::IsIntegralType(element_type)) { + // NVPTX supports atomicMax and atomicMin only on integer types. + if (root_opcode == HloOpcode::kMaximum && is_atomic_integral) { // max(integral, integral) auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Max @@ -225,8 +223,7 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( return true; } - if (root_opcode == HloOpcode::kMinimum && - primitive_util::IsIntegralType(element_type)) { + if (root_opcode == HloOpcode::kMinimum && is_atomic_integral) { // min(integral, integral) auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Min @@ -478,12 +475,15 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { const Shape& lhs_shape = lhs_instruction->shape(); const Shape& rhs_shape = rhs_instruction->shape(); + // TODO(b/110211620): Convert to use i32 index_type when it is possible. + llvm::Type* index_type = ir_builder_.getInt64Ty(); + llvm_ir::IrArray::Index element_index(index_type); if (ShapeUtil::IsScalar(lhs_shape) && ShapeUtil::IsScalar(rhs_shape)) { // If the operands are scalar, don't emit any loops. llvm::Value* lhs_value = - lhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); + lhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); llvm::Value* rhs_value = - rhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); + rhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); @@ -493,7 +493,8 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { } else { result = ir_builder_.CreateFMul(lhs_value, rhs_value); } - target_array.EmitWriteArrayElement(/*index=*/{}, result, &ir_builder_); + target_array.EmitWriteArrayElement(/*index=*/element_index, result, + &ir_builder_); return Status::OK(); } @@ -584,7 +585,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // address. The index into the target address is the concatenation of the rhs // and lhs indexes with the reduction dimensions removed. The terms from the // rhs index are the lower dimensions in the index so we add them first. - llvm_ir::IrArray::Index target_index; + llvm_ir::IrArray::Index target_index(index_type); for (size_t dimension = 0; dimension < lhs_index.size(); ++dimension) { if (dimension != lhs_reduction_dimension) { target_index.push_back(lhs_index[dimension]); @@ -610,7 +611,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { } Status IrEmitter::HandleConvolution(HloInstruction* convolution) { - if (ShapeUtil::HasZeroElements(convolution->shape())) { + if (ShapeUtil::IsZeroElementArray(convolution->shape())) { // Emit no code for an empty output. return Status::OK(); } @@ -620,7 +621,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { } Status IrEmitter::HandleFft(HloInstruction* fft) { - if (ShapeUtil::HasZeroElements(fft->shape())) { + if (ShapeUtil::IsZeroElementArray(fft->shape())) { // Emit no code for an empty output. return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc index bb47a4280541ce2806472aa9365bb0ef38c0c3b3..c9574c87a3be208915b3d6a32679553eb425d2f0 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc @@ -120,9 +120,10 @@ Status IrEmitterNested::EmitTargetElementLoop( // For MOF we give the loop emitter an array for every output it should // generate. if (hlo.IsMultiOutputFusion()) { + const int64 num_elems = ShapeUtil::TupleElementCount(hlo.shape()); std::vector target_arrays; - for (int64 i = 0, e = ShapeUtil::TupleElementCount(hlo.shape()); i != e; - ++i) { + target_arrays.reserve(num_elems); + for (int64 i = 0; i != num_elems; ++i) { target_arrays.push_back(GetIrArray(hlo, hlo, {i})); } TF_RETURN_IF_ERROR( @@ -130,6 +131,7 @@ Status IrEmitterNested::EmitTargetElementLoop( .EmitLoop()); std::vector tuple_operand_ptrs; + tuple_operand_ptrs.reserve(num_elems); for (const llvm_ir::IrArray& array : target_arrays) { tuple_operand_ptrs.push_back(array.GetBasePointer()); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index ed005f6afcc6bcd8c56a76301be67bb77ef91fb8..bdb9e77da4d4fda23cad128fc6400a1205e7d54b 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -59,6 +59,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" +#include "tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/ops.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" @@ -282,6 +283,69 @@ int ComputeMaxUnrollFactor(const HloInstruction* hlo) { // Cannot unroll. return 1; } + +// Returns the llvm type for the indices used in the kernel that contains the +// hlo instruction. Such indices include the index for the parallel loop and +// the indices for the tensors accessed by the kernel. The return type is i32 +// iff the following conditions are met: +// . The launch_size of the kernel is within the range of i32. +// . The sizes of all the tensors accessed within the kernel are within the +// range of i32. +// Otherwise, the return type is i64. +llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, + llvm::IRBuilder<>* ir_builder) { + // Find the unnested hlo instructon for which the kernel is generated for. + const HloInstruction* unnested_hlo = hlo; + const HloComputation* computation = hlo->parent(); + if (computation->IsFusionComputation()) { + unnested_hlo = computation->FusionInstruction(); + } + + auto shape_in_range = [&](const Shape& s) { + bool in_range = true; + ShapeUtil::ForEachSubshape( + s, [&](const Shape& sub_shape, const ShapeIndex& /*index*/) { + if (ShapeUtil::IsArray(sub_shape) && + !IsInt32(ShapeUtil::ElementsIn(sub_shape))) { + in_range = false; + } + }); + + return in_range; + }; + + llvm::Type* i64_ty = ir_builder->getInt64Ty(); + // Check launch dimension + if (!IsInt32(launch_size)) { + return i64_ty; + } + + // Check the size of result tensors + if (!shape_in_range(unnested_hlo->shape())) { + return i64_ty; + } + + auto hlo_shape_in_range = [&](const HloInstruction* operand) -> bool { + return shape_in_range(operand->shape()); + }; + + // Check the size of input tensors + if (!c_all_of(unnested_hlo->operands(), hlo_shape_in_range)) { + return i64_ty; + } + + // Check the size of the internal result tensors + if (unnested_hlo->opcode() == HloOpcode::kFusion) { + if (!c_all_of( + unnested_hlo->fused_instructions_computation()->instructions(), + hlo_shape_in_range)) { + return i64_ty; + } + } + + return ir_builder->getInt32Ty(); +} + } // namespace Status IrEmitterUnnested::DefaultAction(HloInstruction* hlo) { @@ -550,17 +614,16 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { if (root->opcode() == HloOpcode::kTuple) { output_shape_index = {i}; } - // TODO(kramerb): CHECK that layouts are equal. Currently this - // breaks multioutputfusion_test. The test has pre-fused - // instructions, but layout_assignment will not assign any layouts - // for instructions inside of a fused computation. It just removes - // the layouts instead. if (inst->opcode() == HloOpcode::kReduce) { - CHECK(ShapeUtil::Compatible(first_reduce->shape(), inst->shape())); - CHECK(ShapeUtil::Compatible(first_reduce->operand(0)->shape(), - inst->operand(0)->shape())); - CHECK(ShapeUtil::Compatible(first_reduce->operand(1)->shape(), - inst->operand(1)->shape())); + CHECK(IsReductionToVector(*inst)) + << "Only reductions to vector are supported"; + // Shapes, layouts and dimensions must be the same for all reduces + // inside of this fusion. + CHECK(ShapeUtil::Equal(first_reduce->shape(), inst->shape())); + CHECK(ShapeUtil::Equal(first_reduce->operand(0)->shape(), + inst->operand(0)->shape())); + CHECK(ShapeUtil::Equal(first_reduce->operand(1)->shape(), + inst->operand(1)->shape())); CHECK(first_reduce->dimensions() == inst->dimensions()); input_gens.push_back(fused_emitter.GetGenerator(inst->operand(0))); init_value_gens.push_back( @@ -568,8 +631,13 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { reducers.push_back(inst->to_apply()); reduce_output_shapes.push_back(std::move(output_shape_index)); } else { - CHECK(ShapeUtil::Compatible(first_reduce->operand(0)->shape(), - inst->shape())); + // For extra outputs we can relax shape equality to allow different + // types (with the same number of elements). Layouts still have to + // match. + CHECK(ShapeUtil::CompatibleIgnoringElementType( + first_reduce->operand(0)->shape(), inst->shape())); + CHECK(LayoutUtil::Equal(first_reduce->operand(0)->shape().layout(), + inst->shape().layout())); extra_output_gens.emplace_back(fused_emitter.GetGenerator(inst), std::move(output_shape_index)); } @@ -1001,6 +1069,20 @@ Status IrEmitterUnnested::EmitReductionToScalar( int64 num_tiles = RoundUpToNearest(CeilOfRatio(num_elems, kTileSize), kWarpSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), {num_tiles}, {0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + + llvm::Type* index_ty = GetIndexTypeForKernel( + reduce, + launch_dimensions.block_count() * launch_dimensions.threads_per_block(), + &ir_builder_); + + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; + // Check whether every thread will process a full tile's worth of elements // without reading outside the bounds of the input. If this is true, we can // skip some bounds checks in the final algorithm. @@ -1049,40 +1131,42 @@ Status IrEmitterUnnested::EmitReductionToScalar( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } llvm::Value* x_in_tiles = tile_index[0]; + x_in_tiles = ir_builder_.CreateZExtOrTrunc(x_in_tiles, index_ty); // Emit an inner for-loop that reduces the elements in the tile. auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - ir_builder_.getInt64(0), - ir_builder_.getInt64(kTileSize), - ir_builder_.getInt64(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_const(0), + index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), &ir_builder_); llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, ir_builder_.getInt64(kTileSize)), + ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize)), tile_element_loop->GetIndVarValue()); // Unless we know the tile is entirely in bounds, we have to emit a // x-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, ir_builder_.getInt64(num_elems)), + ir_builder_.CreateICmpULT(x, index_typed_const(num_elems)), "x_in_bounds", &ir_builder_); // Emit code that reads the input element and accumulates it to // the partial reduction result. llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); } + llvm_ir::IrArray::Index input_index( /*linear=*/x, input_shape, &ir_builder_); llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); @@ -1101,12 +1185,12 @@ Status IrEmitterUnnested::EmitReductionToScalar( // x_end = kTileSize + x_in_tiles * kTileSize, i.e., the location that's // immediately beyond the tile. llvm::Value* x_end = ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(kTileSize), - ir_builder_.CreateNSWMul(x_in_tiles, ir_builder_.getInt64(kTileSize))); + index_typed_const(kTileSize), + ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize))); // The tile is entirely in bound if all_threads_in_bounds or // x_end <= num_elems. llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(x_end, ir_builder_.getInt64(num_elems)), + ir_builder_.CreateICmpULE(x_end, index_typed_const(num_elems)), ir_builder_.getInt1(all_threads_in_bounds)); llvm_ir::LlvmIfData if_tile_in_bounds_data = llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); @@ -1157,9 +1241,9 @@ Status IrEmitterUnnested::EmitReductionToScalar( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm::Value* lane_id = ir_builder_.CreateURem( - x_in_tiles, ir_builder_.getInt64(kWarpSize), "lane_id"); + x_in_tiles, index_typed_const(kWarpSize), "lane_id"); llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, ir_builder_.getInt64(0)), + ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), "lane_id_is_zero", &ir_builder_); llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &ir_builder_); @@ -1181,10 +1265,6 @@ Status IrEmitterUnnested::EmitReductionToScalar( }; // Emit a parallel loop that iterates through all input tiles, one per thread. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {num_tiles}, {0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1192,7 +1272,7 @@ Status IrEmitterUnnested::EmitReductionToScalar( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); } Status IrEmitterUnnested::EmitColumnReduction( @@ -1223,6 +1303,17 @@ Status IrEmitterUnnested::EmitColumnReduction( // If the height is not a multiple of the tile size, we pad the bottom of the // input matrix. const int64 height_in_tiles = CeilOfRatio(height, kTileSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), {height_in_tiles, width}, {1, 0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + + // TODO(b/110211620): Convert to use i32 index_type when it is possible. + llvm::Type* index_ty = ir_builder_.getInt64Ty(); + + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; // for (linear_index = threadIdx.x + blockIdx.x * blockDim.x; // linear_index < height_in_tiles * width; @@ -1258,8 +1349,9 @@ Status IrEmitterUnnested::EmitColumnReduction( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); @@ -1270,24 +1362,27 @@ Status IrEmitterUnnested::EmitColumnReduction( llvm::Value* y_in_tiles = tile_index[0]; llvm::Value* x = tile_index[1]; + y_in_tiles = ir_builder_.CreateZExtOrTrunc(y_in_tiles, index_ty); + x = ir_builder_.CreateZExtOrTrunc(x, index_ty); + auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - ir_builder_.getInt64(0), - ir_builder_.getInt64(kTileSize), - ir_builder_.getInt64(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_const(0), + index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), &ir_builder_); llvm::Value* y = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(y_in_tiles, ir_builder_.getInt64(kTileSize)), + ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize)), tile_element_loop->GetIndVarValue()); + // Unless we know the tile is entirely in bounds, we have to emit a // y-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(y, ir_builder_.getInt64(height)), + ir_builder_.CreateICmpULT(y, index_typed_const(height)), "y_in_bounds", &ir_builder_); // Emit code that reads the input element and accumulates it to @@ -1337,10 +1432,10 @@ Status IrEmitterUnnested::EmitColumnReduction( // y_end = kTileSize + y_in_tiles * kTileSize, i.e., the y location that's // immediately beyond the tile. llvm::Value* y_end = ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(kTileSize), - ir_builder_.CreateNSWMul(y_in_tiles, ir_builder_.getInt64(kTileSize))); + index_typed_const(kTileSize), + ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize))); llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(y_end, ir_builder_.getInt64(height)), + ir_builder_.CreateICmpULE(y_end, index_typed_const(height)), ir_builder_.getInt1(height % kTileSize == 0)); // The tile is entirely in bound if "height" is a multiple of kTileSize or // y_end <= height. @@ -1377,10 +1472,6 @@ Status IrEmitterUnnested::EmitColumnReduction( }; // Emit a parallel loop that iterate through all input tiles. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {height_in_tiles, width}, {1, 0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1388,7 +1479,31 @@ Status IrEmitterUnnested::EmitColumnReduction( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); +} + +static std::pair ComputeTilingSchemeForReduction( + int64 depth, int64 width, int64 kWarpSize) { + constexpr int64 kTargetNumElementsPerThread = 64; + int64 x_tile_size = kTargetNumElementsPerThread; + int64 z_tile_size = 1; + + // Only tile along the x dimension with tile size kTargetNumElementsPerThread + // if doing so doesn't require a slow version of loop with bound check on each + // dimension. A more sophisticated heuristics is to enable tile along the + // x dimension with tile size kTargetNumElementsPerThread when either width is + // a factor of (kWarpSize * kTargetNumElementsPerThread) or width is big + // enough so that only a small fraction of the threads execute the slow + // version of loop with bound check. + if (width % (kWarpSize * kTargetNumElementsPerThread) != 0) { + x_tile_size = 8; + z_tile_size = 8; + while (depth % z_tile_size != 0) { + z_tile_size -= 1; + } + } + + return std::pair(x_tile_size, z_tile_size); } Status IrEmitterUnnested::EmitRowReduction( @@ -1402,7 +1517,7 @@ Status IrEmitterUnnested::EmitRowReduction( std::pair> extra_output_gens) { // A naive algorithm is: - // 1. Divide the input tensor into tiles of size 1x1xK. + // 1. Divide the x dimension of the input tensor into tiles of size 1x1xX. // 2. Partially reduces each tile to a scalar using one thread. // 3. Accumulates that scalar to the output vector using atomic operations. // @@ -1413,15 +1528,15 @@ Status IrEmitterUnnested::EmitRowReduction( // int y = linear_index / width_in_tiles % height; // int z = linear_index / (height * width_in_tiles); // float partial_result = 0; - // for (element_id_in_tile : range(kTileSize)) { - // int x = x_in_tiles * kTileSize + element_id_in_tile; + // for (element_id_in_tile : range(x_tile_size)) { + // int x = x_in_tiles * x_tile_size + element_id_in_tile; // if (x < width) - // partial_result = reducer(partial_result, input[z][y][z]); + // partial_result = reducer(partial_result, input[z][y][x]); // } // AtomicReducer(&output[y], partial_result); // } // - // Three optimizations are performed. + // Four optimizations are performed. // // 1. To coalesce global memory accesses, dilate the tile with a factor of 32 // (i.e. the warp size). For example, suppose the width is 8x32=256. Instead @@ -1448,29 +1563,46 @@ Status IrEmitterUnnested::EmitRowReduction( // element_id_in_tile, which makes the code more friendly to optimizations // such as LICM. // + // 4. When the width is too small and x_tile_size is less than the target + // number of elements per thread and use a small factor of depth as + // z_tile_size to increase the number of elements calculated by each + // partial sum. This can reduce the needed number of dynamic shfl_down and + // atomic operations. + // // for (linear_index = threadIdx.x + blockIdx.x * blockDim.x; // linear_index < depth * height * width_in_tiles; // linear_index += blockDim.x * gridDim.x) { // int x_in_tiles = linear_index % width_in_tiles; // int y = linear_index / width_in_tiles % height; - // int z = linear_index / (height * width_in_tiles); + // int z_in_tiles = linear_index / (height * width_in_tiles); // int warp_id = x_in_tiles / warpSize; // int lane_id = x_in_tiles % warpSize; // float partial_result = 0; // int x = warp_id * kTileSize * warpSize + lane_id; - // if (width % (kTileSize * warpSize) == 0 || - // x + (kTileSize - 1) * warpSize < width) { - // // The entire tile is in bounds. - // for (int element_id_in_tile = 0; element_id_in_tile < kTileSize; - // ++element_id_in_tile, x += warpSize) { - // partial_result = Reducer(partial_result, input[z][y][x]); + // if (width % (x_tile_size * warpSize) == 0 || + // x + (x_tile_size - 1) * warpSize < width) { + // // The entire x_tile is in bounds. + // for (int element_id_in_z_tile = 0; element_id_in_z_tile < z_tile_size; + // ++element_id_in_z_tile) { + // z = z_in_tiles * z_tile_size + element_id_in_z_tile; + // int tx = x; + // for (int element_id_in_x_tile = 0; + // element_id_in_x_tile < x_tile_size; + // ++element_id_in_x_tile, tx += warpSize) { + // partial_result = Reducer(partial_result, input[z][y][tx]); + // } // } // } else { // // The tile is partially in bounds. - // for (int element_id_in_tile = 0; element_id_in_tile < kTileSize; - // ++element_id_in_tile, x += warpSize) { - // if (x < width) - // partial_result = Reducer(partial_result, input[z][y][x]); + // for (int element_id_in_z_tile = 0; element_id_in_z_tile < z_tile_size; + // ++element_id_in_z_tile) { + // z = z_in_tiles * z_tile_size + element_id_in_z_tile; + // int tx = x; + // for (int element_id_in_x_tile = 0; element_id_in_x_tile < + // x_tile_size; ++element_id_in_tile, tx += warpSize) { + // if (tx < width) + // partial_result = Reducer(partial_result, input[z][y][tx]); + // } // } // } // for (shuffle_distance = 16; shuffle_distance > 0; shuffle_distance /= 2) @@ -1481,17 +1613,32 @@ Status IrEmitterUnnested::EmitRowReduction( // AtomicReducer(&output[y], partial_result); // } // - // Choose 8 as the tile size, which matches Eigen's RowReduceKernel. - constexpr int64 kTileSize = 8; + + int64 x_tile_size; + int64 z_tile_size; + std::tie(x_tile_size, z_tile_size) = + ComputeTilingSchemeForReduction(depth, width, kWarpSize); + // Round the width in tiles up to the nearest multiple of kWarpSize, so that // the use of shfl_down is valid. const int64 width_in_tiles = - RoundUpToNearest(CeilOfRatio(width, kTileSize), kWarpSize); + RoundUpToNearest(CeilOfRatio(width, x_tile_size), kWarpSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), + {depth / z_tile_size, height, width_in_tiles}, {2, 1, 0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + llvm::Type* index_ty = GetIndexTypeForKernel( + reduce, + launch_dimensions.block_count() * launch_dimensions.threads_per_block(), + &ir_builder_); - auto loop_body_emitter = - [=](const llvm_ir::IrArray::Index& tile_index) -> Status { + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; + + auto loop_body_emitter = [=](const llvm_ir::IrArray::Index& tile_index) { const int num_reduces = reducers.size(); - // Emit the loop body that reduces one tile. llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType( input_shape.element_type(), ir_emitter_context_->llvm_module()); std::vector partial_reduction_result_addresses; @@ -1499,124 +1646,151 @@ Status IrEmitterUnnested::EmitRowReduction( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } - // Emit an inner for-loop that partially reduces the elements in the given - // tile. - llvm::Value* z = tile_index[0]; + llvm::Value* z_tile = tile_index[0]; llvm::Value* y = tile_index[1]; llvm::Value* x_tile = tile_index[2]; - llvm::Value* warp_id = ir_builder_.CreateUDiv( - x_tile, ir_builder_.getInt64(kWarpSize), "warp_id"); - llvm::Value* lane_id = ir_builder_.CreateURem( - x_tile, ir_builder_.getInt64(kWarpSize), "lane_id"); - // The x-location of the last element in this tile. - // last_x = lane_id + warpSize * (kTileSize - 1 + warp_id * kTileSize); - llvm::Value* last_x = ir_builder_.CreateNSWAdd( - lane_id, - ir_builder_.CreateNSWMul( - ir_builder_.getInt64(kWarpSize), - ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(kTileSize - 1), - ir_builder_.CreateNSWMul(warp_id, - ir_builder_.getInt64(kTileSize))))); + x_tile = ir_builder_.CreateZExtOrTrunc(x_tile, index_ty); - auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { - std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - ir_builder_.getInt64(0), - ir_builder_.getInt64(kTileSize), - ir_builder_.getInt64(1), &ir_builder_); + llvm::Value* warp_id = + ir_builder_.CreateUDiv(x_tile, index_typed_const(kWarpSize), "warp_id"); + llvm::Value* lane_id = + ir_builder_.CreateURem(x_tile, index_typed_const(kWarpSize), "lane_id"); - // Emit the body of the partial reduction loop. - llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &ir_builder_); - // x = lane_id + warpSize * (element_id_in_tile + warp_id * kTileSize); - llvm::Value* x = ir_builder_.CreateNSWAdd( - lane_id, - ir_builder_.CreateNSWMul( - ir_builder_.getInt64(kWarpSize), - ir_builder_.CreateNSWAdd( - tile_element_loop->GetIndVarValue(), - ir_builder_.CreateNSWMul(warp_id, - ir_builder_.getInt64(kTileSize))))); - - // Unless we know the tile is entirely in bounds, we have to emit a - // x-in-bounds check before reading from the input. - if (!tile_in_bounds) { - llvm_ir::LlvmIfData if_x_in_bounds_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, ir_builder_.getInt64(width)), - "x_in_bounds", &ir_builder_); - - // Points ir_builder_ to the then-block. - llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, - &ir_builder_); - } + // The x-location of the last element in this z-x-tile. + // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * x_tile_size); + llvm::Value* last_x = ir_builder_.CreateNSWAdd( + lane_id, ir_builder_.CreateNSWMul( + index_typed_const(kWarpSize), + ir_builder_.CreateNSWAdd( + index_typed_const(x_tile_size - 1), + ir_builder_.CreateNSWMul( + warp_id, index_typed_const(x_tile_size))))); + + KernelSupportLibrary ksl( + &ir_builder_, + /*unroll_mode=*/xla::llvm_ir::UnrollMode::kFullyUnroll, + /*prevent_vectorization=*/false); + + // Emit a for-loop that partially reduces the elements in the given + // z-x-tile. + auto emit_z_x_tile_element_loop = [&](bool x_tile_in_bounds, + int64 x_tile_loop_bound) -> Status { + auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { + llvm::Value* z = ir_builder_.CreateNSWAdd( + z_indvar, + ir_builder_.CreateNSWMul(index_typed_const(z_tile_size), z_tile)); + TF_RETURN_IF_ERROR(ksl.For( + "x_tile", + /*start=*/index_typed_const(0), + /*end=*/index_typed_const(x_tile_loop_bound), + /*step=*/1, [&](llvm::Value* x_indvar) -> Status { + // x = lane_id + + // warpSize * (element_id_in_x_tile + warp_id * x_tile_size); + llvm::Value* x = ir_builder_.CreateNSWAdd( + lane_id, + ir_builder_.CreateNSWMul( + index_typed_const(kWarpSize), + ir_builder_.CreateNSWAdd( + x_indvar, ir_builder_.CreateNSWMul( + warp_id, llvm::ConstantInt::get( + index_ty, x_tile_size))))); + + // Unless we know the x-tile is entirely in bounds, we have to + // emit a x-in-bounds check before reading from the input. + if (!x_tile_in_bounds) { + llvm_ir::LlvmIfData if_x_in_bounds_data = + llvm_ir::EmitIfThenElse( + ir_builder_.CreateICmpULT(x, index_typed_const(width)), + "x_in_bounds", &ir_builder_); + // Points ir_builder_ to the then-block. + llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, + &ir_builder_); + } + + // Emit code that reads the input element and accumulates it + // to the partial reduction result. + llvm::Value* input_address = + ir_builder_.CreateAlloca(element_ir_type); + { + // {z,y,x} is an index to input_3d_tensor_shape + // [depth,height,width]. We need to convert that to an index + // to input_shape (the shape of the operand of "reduce"). + // This conversion is composed of a transposition from + // input_shape to normalized_input_shape and a reshape from + // normalized_input_shape to input_3d_tensor_shape. + const Shape normalized_input_shape = ShapeUtil:: + MakeShapeWithDescendingLayoutAndSamePhysicalLayout( + input_shape); + auto input_shape_min2maj = + LayoutUtil::MinorToMajor(input_shape); + const std::vector transpose_dimension_mapping( + input_shape_min2maj.rbegin(), input_shape_min2maj.rend()); + const Shape input_3d_tensor_shape = + ShapeUtil::MakeShapeWithDescendingLayout( + input_shape.element_type(), {depth, height, width}); + const llvm_ir::IrArray::Index input_3d_tensor_index( + {z, y, x}, input_3d_tensor_shape, &ir_builder_); + const llvm_ir::IrArray::Index input_index = + input_3d_tensor_index + .SourceIndexOfReshape(input_3d_tensor_shape, + normalized_input_shape, + &ir_builder_) + .SourceIndexOfTranspose( + normalized_input_shape, input_shape, + transpose_dimension_mapping, &ir_builder_); + + for (int i = 0; i != num_reduces; ++i) { + TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, + input_gens[i](input_index)); + ir_builder_.CreateStore(input_ir_value, input_address); + TF_RETURN_IF_ERROR(EmitCallToNestedComputation( + *reducers[i], + {partial_reduction_result_addresses[i], input_address}, + partial_reduction_result_addresses[i])); + } + return EmitExtraOutputsForReduce(reduce, input_index, + extra_output_gens); + } + })); + return Status::OK(); + }; - // Emit code that reads the input element and accumulates it to the - // partial reduction result. - llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); - { - // {z,y,x} is an index to input_3d_tensor_shape [depth,height,width]. We - // need to convert that to an index to input_shape (the shape of the - // operand of "reduce"). This conversion is composed of a transposition - // from input_shape to normalized_input_shape and a reshape from - // normalized_input_shape to input_3d_tensor_shape. - const Shape normalized_input_shape = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - input_shape); - auto input_shape_min2maj = LayoutUtil::MinorToMajor(input_shape); - const std::vector transpose_dimension_mapping( - input_shape_min2maj.rbegin(), input_shape_min2maj.rend()); - const Shape input_3d_tensor_shape = - ShapeUtil::MakeShapeWithDescendingLayout(input_shape.element_type(), - {depth, height, width}); - const llvm_ir::IrArray::Index input_3d_tensor_index( - {z, y, x}, input_3d_tensor_shape, &ir_builder_); - const llvm_ir::IrArray::Index input_index = - input_3d_tensor_index - .SourceIndexOfReshape(input_3d_tensor_shape, - normalized_input_shape, &ir_builder_) - .SourceIndexOfTranspose(normalized_input_shape, input_shape, - transpose_dimension_mapping, - &ir_builder_); - for (int i = 0; i != num_reduces; ++i) { - TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, - input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i], input_address}, - partial_reduction_result_addresses[i])); - } - return EmitExtraOutputsForReduce(reduce, input_index, - extra_output_gens); - } + return ksl.For("z_tile", + /*start=*/index_typed_const(0), + /*end=*/index_typed_const(z_tile_size), + /*step=*/1, emit_z_tile_element_loop); }; llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.getInt1(width % (kTileSize * kWarpSize) == 0), - ir_builder_.CreateICmpULT(last_x, ir_builder_.getInt64(width))); - llvm_ir::LlvmIfData if_tile_in_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, - &ir_builder_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, - &ir_builder_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/false)); - - // After the if-then-else statement on tile_in_bounds, emit calls to - // shfl_down that accumulate the partial reduction results of all threads - // from the warp. - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, - &ir_builder_); + ir_builder_.getInt1(width % (x_tile_size * kWarpSize) == 0), + ir_builder_.CreateICmpULT(last_x, index_typed_const(width))); + + TF_RETURN_IF_ERROR( + ksl.If(tile_in_bounds, + /*true_block_generator=*/ + [&]() -> Status { + return emit_z_x_tile_element_loop(/*x_tile_in_bounds=*/true, + x_tile_size); + }, + /*false_block_generator=*/ + [&]() -> Status { + return emit_z_x_tile_element_loop( + /*x_tile_in_bounds=*/false, + CeilOfRatio(width % (x_tile_size * kWarpSize), kWarpSize)); + })); + + // After accumulating the elements of the z_x_tile, emit calls to + // shfl_down that accumulate the partial reduction results of all + // threads in a warp. int bit_width = llvm_ir::GetSizeInBits(element_ir_type); // bitcast cannot be applied to aggregate types (even packed ones), so we // instead bitcast addresses of load/store to intN* of the same bit-width. @@ -1652,7 +1826,7 @@ Status IrEmitterUnnested::EmitRowReduction( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, ir_builder_.getInt64(0)), + ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), "lane_id_is_zero", &ir_builder_); llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &ir_builder_); @@ -1666,18 +1840,23 @@ Status IrEmitterUnnested::EmitRowReduction( reduce_output_shapes[i]), &ir_builder_), &ir_builder_, "output_element_address"); - TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( - *reducers[i], output_address, partial_reduction_result_addresses[i])); + // We don't need to emit atomic operations if there is only one tile of + // results. 'depth' is the z dimension, 'width' is the x dimension. + if (z_tile_size >= depth && x_tile_size >= width) { + TF_RETURN_IF_ERROR(EmitCallToNestedComputation( + *reducers[i], + {output_address, partial_reduction_result_addresses[i]}, + output_address)); + } else { + TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( + *reducers[i], output_address, + partial_reduction_result_addresses[i])); + } } return Status::OK(); }; // Emit a parallel loop that iterates through every input tiles. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {depth, height, width_in_tiles}, - {2, 1, 0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1685,7 +1864,7 @@ Status IrEmitterUnnested::EmitRowReduction( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); } // Figures out whether `reduce` is a row or column reduction, and which @@ -1798,9 +1977,7 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { // HandleReduce specializes reduction from a multi-dimensional array to a 1D // array. The specialized version requires an initializer thunk that // initializes the output array to the initial value of the reduce. - if (IsReductionToVector(*reduce) && - // NVPTX backend can't do atomic cmpxchg any narrower than 32 bits - 32 <= primitive_util::BitWidth(reduce->shape().element_type())) { + if (IsReductionToVector(*reduce)) { TF_ASSIGN_OR_RETURN(std::unique_ptr initializer_thunk, BuildInitializerThunk(reduce)); std::vector> thunks; @@ -1885,6 +2062,14 @@ Status IrEmitterUnnested::HandleSelectAndScatter( "Dilation for SelectAndScatter not implemented on GPU."); } + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + source->shape(), ir_emitter_context_->device_description()); + llvm::Type* index_type = GetIndexTypeForKernel( + select_and_scatter, launch_dimensions.launch_bound(), &ir_builder_); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + // kSelectAndScatter is implemented as two kernel launches: the first launch // initializes the output array to the given initial value, // and the second accumulates the "source" matrix to the @@ -1915,8 +2100,8 @@ Status IrEmitterUnnested::HandleSelectAndScatter( "selected_value_address", &ir_builder_); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder_.getInt64Ty(), ir_builder_.getInt32(rank), - "selected_index_address", &ir_builder_); + index_type, index_typed_const(rank), "selected_index_address", + &ir_builder_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); ir_builder_.CreateStore(ir_builder_.getInt1(false), @@ -1924,7 +2109,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // Create the inner loop to iterate over the window. llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), - &ir_builder_); + &ir_builder_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -1938,17 +2123,17 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(source_index.size()); + llvm_ir::IrArray::Index operand_index(index_type, source_index.size()); llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); for (int64 i = 0; i < rank; ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( - source_index[i], ir_builder_.getInt64(window.dimensions(i).stride())); + source_index[i], index_typed_const(window.dimensions(i).stride())); operand_index[i] = ir_builder_.CreateNSWSub( ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); + index_typed_const(window.dimensions(i).padding_low())); llvm::Value* index_condition = ir_builder_.CreateICmpULT( operand_index[i], - ir_builder_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + index_typed_const(ShapeUtil::GetDimension(operand->shape(), i))); in_bounds_condition = ir_builder_.CreateAnd(in_bounds_condition, index_condition); } @@ -2020,7 +2205,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // value and the current output value. llvm_ir::SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index selected_index; + llvm_ir::IrArray::Index selected_index(operand_index.GetType()); for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( selected_index_address, {ir_builder_.getInt32(i)}); @@ -2038,8 +2223,6 @@ Status IrEmitterUnnested::HandleSelectAndScatter( source_value_address); }; - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - source->shape(), ir_emitter_context_->device_description()); UpdateLaunchDimensions( launch_dimensions, // IrEmitterUnnested implements kSelectAndScatter as a SequentialThunk @@ -2050,7 +2233,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, source->shape(), launch_dimensions, &ir_builder_) - .EmitLoop(IrName(select_and_scatter)); + .EmitLoop(IrName(select_and_scatter), index_type); } Status IrEmitterUnnested::HandleWhile(HloInstruction* xla_while) { @@ -2132,6 +2315,10 @@ Status IrEmitterUnnested::HandleCrossReplicaSum(HloInstruction* crs) { return Status::OK(); } +Status IrEmitterUnnested::HandleAfterAll(HloInstruction* gen_token) { + return Status::OK(); +} + Status IrEmitterUnnested::HandleInfeed(HloInstruction* infeed) { thunk_sequence_->emplace_back(BuildInfeedThunk(infeed)); return Status::OK(); @@ -2255,11 +2442,6 @@ GetHloBufferSlices(const HloInstruction* hlo, return slices; } -Status IrEmitterUnnested::HandleGather(HloInstruction* gather) { - // TODO(b/72710576): Gather is not implemented on GPUs - return Unimplemented("Gather is not implemented on GPUs."); -} - std::unique_ptr IrEmitterUnnested::BuildKernelThunk( const HloInstruction* inst, int unroll_factor) { const BufferAssignment& buffer_assn = @@ -2385,17 +2567,14 @@ std::unique_ptr IrEmitterUnnested::BuildInfeedThunk( const HloInstruction* inst) { CHECK_EQ(HloOpcode::kInfeed, inst->opcode()); - std::vector tuple_element_buffers; - for (int64 i = 0; i < inst->shape().tuple_shapes_size(); ++i) { - BufferAllocation::Slice buffer = ir_emitter_context_->buffer_assignment() - .GetUniqueSlice(inst, {i}) - .ConsumeValueOrDie(); - tuple_element_buffers.push_back(buffer); - } - - return MakeUnique( - tuple_element_buffers, - /*destination_buffer=*/GetAllocationSlice(*inst), inst); + ShapeTree slices(inst->shape()); + slices.ForEachMutableElement( + [this, inst](const ShapeIndex& index, BufferAllocation::Slice* slice) { + *slice = ir_emitter_context_->buffer_assignment() + .GetUniqueSlice(inst, index) + .ConsumeValueOrDie(); + }); + return MakeUnique(slices, inst); } namespace { @@ -2440,7 +2619,9 @@ std::unique_ptr IrEmitterUnnested::BuildGemmThunk( if (alpha->opcode() == HloOpcode::kBroadcast) { alpha = alpha->operand(0); } - alpha = inst->operand(alpha->parameter_number()); + if (alpha->opcode() == HloOpcode::kParameter) { + alpha = inst->operand(alpha->parameter_number()); + } // TODO(b/74185543): Remove the following if block once we support fusion // with a non-constant as well. Then we will just always use the constant // on the device. @@ -2486,7 +2667,7 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( const HloInstruction* hlo, const ShapeIndex& index) { bool fused = HloOpcode::kFusion == hlo->opcode(); const HloInstruction* inst = fused ? hlo->fused_expression_root() : hlo; - const HloInstruction* init_value = [&] { + const HloInstruction* init_value_operand = [&] { switch (inst->opcode()) { case HloOpcode::kSelectAndScatter: return inst->operand(2); @@ -2506,6 +2687,7 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( } }(); + const HloInstruction* init_value = init_value_operand; if (fused && init_value->opcode() == HloOpcode::kParameter) { init_value = hlo->operand(init_value->parameter_number()); } @@ -2529,14 +2711,15 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( // If the literal is 8 or 16 bits wide, we can emit a 32-bit memset by // repeating the literal 4 or 2 times, so long as the destination buffer is // an even multiple of 32 bits long. + const Shape& output_shape = ShapeUtil::GetSubshape(hlo->shape(), index); if ((num_bytes == 1 || num_bytes == 2) && - ShapeUtil::ByteSizeOf(hlo->shape()) % 4 == 0) { + ShapeUtil::ByteSizeOf(output_shape) % 4 == 0) { uint16 pattern16; if (num_bytes == 1) { uint8 b = literal_bytes.front(); pattern16 = uint16{b} | (uint16{b} << 8); } else { - pattern16 = literal_bytes.front(); + memcpy(&pattern16, literal_bytes.data(), sizeof(pattern16)); } uint32 pattern32 = uint32{pattern16} | (uint32{pattern16} << 16); return {MakeUnique( @@ -2562,6 +2745,11 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( ir_emitter_context_->device_description()); UpdateLaunchDimensions(launch_dimensions, kernel_thunk.get(), ir_emitter_context_->llvm_module()); + // If the init_value was fused into this reduce we have to generate it first. + if (fused && init_value_operand->opcode() != HloOpcode::kParameter) { + CHECK_EQ(HloOpcode::kConstant, init_value_operand->opcode()); + TF_RETURN_IF_ERROR(HandleConstant(const_cast(init_value))); + } TF_RETURN_IF_ERROR(ParallelLoopEmitter( [=](const llvm_ir::IrArray::Index& index) { return GetIrArray(*init_value, *hlo) @@ -2753,7 +2941,9 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( if (!hlo.IsMultiOutputFusion()) { return ParallelLoopEmitter(element_generator, GetIrArray(hlo, hlo), launch_dimensions, &ir_builder_, unroll_factor) - .EmitLoop(IrName(&hlo)); + .EmitLoop(IrName(&hlo), + GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), + &ir_builder_)); } // For multiple outputs fusion, we need to emit each operand and the root. @@ -2761,10 +2951,12 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( for (int64 i = 0; i < ShapeUtil::TupleElementCount(hlo.shape()); ++i) { output_arrays.push_back(GetIrArray(hlo, hlo, {i})); } - TF_RETURN_IF_ERROR(ParallelLoopEmitter(element_generator, output_arrays, - launch_dimensions, &ir_builder_, - unroll_factor) - .EmitLoop(IrName(&hlo))); + TF_RETURN_IF_ERROR( + ParallelLoopEmitter(element_generator, output_arrays, launch_dimensions, + &ir_builder_, unroll_factor) + .EmitLoop(IrName(&hlo), + GetIndexTypeForKernel( + &hlo, launch_dimensions.launch_bound(), &ir_builder_))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index 202231b82f3877c11cf932bd00a8aac350fd0afa..819060061a9b8bcf0db4f782852b0a7c6530143c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -67,7 +67,6 @@ class IrEmitterUnnested : public IrEmitter { Status HandleDot(HloInstruction* dot) override; Status HandleFft(HloInstruction* fft) override; Status HandleFusion(HloInstruction* fusion) override; - Status HandleGather(HloInstruction* gather) override; Status HandleGetTupleElement(HloInstruction* get_tuple_element) override; Status HandleReduce(HloInstruction* reduce) override; Status HandleSelectAndScatter(HloInstruction* instruction) override; @@ -77,6 +76,7 @@ class IrEmitterUnnested : public IrEmitter { Status HandleRng(HloInstruction* random) override; Status HandleSelect(HloInstruction* select) override; Status HandleCrossReplicaSum(HloInstruction* crs) override; + Status HandleAfterAll(HloInstruction* gen_token) override; Status EmitTargetElementLoop( const HloInstruction& hlo, diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc index f56c1ce69f11ed79c8be76834269f29de93a9645..e76823ad103dfa5ba61a0d3ba81b2c028dfeb33e 100644 --- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -75,7 +76,8 @@ void KernelThunk::SetLaunchDimensions(const LaunchDimensions& launch_dims) { } Status KernelThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { // Load the kernel. se::StreamExecutor* executor = stream->parent(); LaunchDimensions launch_dimensions; @@ -100,6 +102,7 @@ Status KernelThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, VLOG(3) << " Arg: alloc #" << arg->index() << ": " << buf.opaque() << " (" << buf.size() << "B)"; } + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); if (!stream->parent()->Launch( stream, se::ThreadDim(launch_dimensions.threads_per_block()), se::BlockDim(launch_dimensions.block_count()), *kernel, diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h index 7def27e189b66747569344a3dbe5c0c446f903be..d751de50ad6671b3bf88cd4de49a8feb448e13ba 100644 --- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.h @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -62,7 +63,8 @@ class KernelThunk : public Thunk { // Executes the kernel for the thunk on "stream", which must be non-null. Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: // Buffers passed to the kernel as arguments. diff --git a/tensorflow/compiler/xla/service/gpu/memset_thunk.cc b/tensorflow/compiler/xla/service/gpu/memset_thunk.cc index d4100a898b5bb9eec382c34932c2db104c9e985b..9fd6cf7157ecd659e7eb1d2c5228eca931ff6a01 100644 --- a/tensorflow/compiler/xla/service/gpu/memset_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/memset_thunk.cc @@ -14,21 +14,27 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/gpu/memset_thunk.h" + +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/stream_executor/stream_executor.h" namespace xla { namespace gpu { Status MemzeroThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase dest_data = buffer_allocations.GetDeviceAddress(dest_); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenMemZero(&dest_data, dest_data.size()); return Status::OK(); } Status Memset32BitValueThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase dest_data = buffer_allocations.GetDeviceAddress(dest_); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); stream->ThenMemset32(&dest_data, value_, dest_data.size()); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/memset_thunk.h b/tensorflow/compiler/xla/service/gpu/memset_thunk.h index 51c332d287d139335b356fc66411b5ffaa448b5a..d1fec0bd76b8a80f4a1e1c2e818f248997da7a75 100644 --- a/tensorflow/compiler/xla/service/gpu/memset_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/memset_thunk.h @@ -17,6 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_MEMSET_THUNK_H_ #include "tensorflow/compiler/xla/service/buffer_assignment.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/status.h" @@ -36,7 +37,8 @@ class MemzeroThunk : public Thunk { : Thunk(Kind::kMemzero, hlo), dest_(dest) {} Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const BufferAllocation::Slice dest_; @@ -52,7 +54,8 @@ class Memset32BitValueThunk : public Thunk { : Thunk(Kind::kMemset32BitValue, hlo), value_(value), dest_(dest) {} Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: uint32 value_; diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc new file mode 100644 index 0000000000000000000000000000000000000000..ea661b3c2cb2c945297ac2098cd1c4009b2e966d --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -0,0 +1,263 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" + +#include +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/gtl/flatset.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace gpu { + +GpuMultiOutputFusion::GpuMultiOutputFusion() : MultiOutputFusion(INT64_MAX) {} + +bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, + HloInstruction* instr2) { + auto get_element_instr = + [&](const HloInstruction* instr) -> const HloInstruction* { + const HloInstruction* element_instr = instr; + if (instr->opcode() == HloOpcode::kFusion) { + auto fused_expression_root = instr->fused_expression_root(); + if (instr->IsMultiOutputFusion()) { + // If possible, we want to pick a reduce operand of the fusion root, + // because it has the most constraints. + for (const auto* inst : fused_expression_root->operands()) { + if (inst->opcode() == HloOpcode::kReduce) { + return inst; + } + } + return fused_expression_root->operands()[0]; + } else { + element_instr = fused_expression_root; + } + } + return element_instr; + }; + + auto get_element_shape = [&](const HloInstruction* element_instr) { + // Special handling of kReduce instructions -- the fusion + // applies to the first operand. + if (element_instr->opcode() == HloOpcode::kReduce) { + return element_instr->operand(0)->shape(); + } + return element_instr->shape(); + }; + + // The shapes in all tuple operands should agree, unless it is a reduce. + // In that case, the operand of the reduce needs to have the same shape + // as the other tuple operands, but also we need to compare the output + // shapes of the reduces. + // TODO(tjoerg): Allow differences in fp precision. + auto* element_instr_1 = get_element_instr(instr1); + auto* element_instr_2 = get_element_instr(instr2); + if (element_instr_1->opcode() == HloOpcode::kReduce && + element_instr_2->opcode() == HloOpcode::kReduce && + !ShapeUtil::Equal(element_instr_1->shape(), element_instr_2->shape())) { + return false; + } + // The elementwise output shapes must be the same (including layout). + return ShapeUtil::Equal(get_element_shape(element_instr_1), + get_element_shape(element_instr_2)); +} + +namespace { +bool IsInputFusibleReduction(HloInstruction* instr) { + if (instr->IsMultiOutputFusion()) { + for (const HloInstruction* operand : + instr->fused_expression_root()->operands()) { + if (operand->opcode() == HloOpcode::kReduce) { + CHECK(instr->fusion_kind() == HloInstruction::FusionKind::kInput) + << " Reduce multi-output fusion " << instr->ToString() + << " must be an input fusion."; + return true; + } + } + return false; + } else if (instr->opcode() == HloOpcode::kFusion) { + // The loop emitter can handle to-vector reduce fusions. Such reduce + // fusions have the fusion kind kLoop rather than kInput. We do not fuse + // to-vector reduce fusions, because the resulting fusions may no longer be + // supported by loop emitter. + return IsReductionToVector(*instr->fused_expression_root()); + } else { + return IsReductionToVector(*instr); + } +} +} // namespace + +bool GpuMultiOutputFusion::IsFusible(HloInstruction* instr) { + // We can fuse reduces and loop fusions. + return IsInputFusibleReduction(instr) || + (instr->opcode() == HloOpcode::kFusion && + instr->fusion_kind() == HloInstruction::FusionKind::kLoop); +} + +int64 GpuMultiOutputFusion::GetProfit(HloInstruction* instr1, + HloInstruction* instr2) { + tensorflow::gtl::FlatSet in_list; + for (auto instr : instr1->operands()) { + if (!IsProfitableOperand(instr)) { + continue; + } + in_list.insert(instr); + } + int64 profit = 0; + for (auto instr : instr2->operands()) { + if (!IsProfitableOperand(instr) || in_list.count(instr) == 0) { + continue; + } + profit += ShapeUtil::ByteSizeOf(instr->shape()); + } + VLOG(2) << "Fusing instr1=" << instr1->name() << " instr2=" << instr2->name() + << ", the profit is =" << profit; + return profit; +} + +bool GpuMultiOutputFusion::LegalToFuse(HloInstruction* instr1, + HloInstruction* instr2) { + if (!MultiOutputFusion::LegalToFuse(instr1, instr2)) { + return false; + } + // If we're fusing fusions only do it if the fusion kind matches. Loop fusions + // merge into bigger loop fusions and input (reduce) fusions become fusions + // with multiple reduce outputs. We could fuse reduce and loop fusions + // together too (the result being an input fusion) if we find cases where this + // improves things. + CHECK(instr1->opcode() == HloOpcode::kFusion); + if (instr2->opcode() == HloOpcode::kFusion) { + return instr1->fusion_kind() == instr2->fusion_kind(); + } + return instr1->fusion_kind() != HloInstruction::FusionKind::kLoop; +} + +bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { + bool changed = false; + RecomputeReachability(); + + tensorflow::gtl::FlatSet to_fuse; + // Keep a list of the instructions to fuse after making all the fusion + // decisions. We first aggressively add instructions to potential_fusion_list, + // then filter out instructions that will be no longer fusable because of + // reachability change. This avoids recalculating reachability on a large set + // of instructions. + std::vector> + potential_fusion_list; + std::vector> fusion_list; + std::vector instrs_to_update_reachability; + + // For each reduce or reduce multi-output fusion, try to fuse it with loop + // fusions operands. + for (HloInstruction* consumer : computation()->MakeInstructionPostOrder()) { + if (consumer->user_count() == 0) { + continue; + } + if (!IsInputFusibleReduction(consumer)) { + continue; + } + + auto consumer_operands = consumer->operands(); + for (size_t i = 0; i < consumer_operands.size(); ++i) { + HloInstruction* producer = consumer_operands[i]; + if (!producer->IsFusable()) { + continue; + } + const bool is_loop_fusion = + producer->opcode() == HloOpcode::kFusion && + producer->fusion_kind() == HloInstruction::FusionKind::kLoop; + if (!is_loop_fusion) { + continue; + } + if (!ShapesCompatibleForFusion(producer, consumer)) { + continue; + } + // If we have already decided to fuse this producer, skip it. + if (ContainsKey(to_fuse, producer)) { + continue; + } + // Do not fuse a producer if the other operands of the fusion are + // reachable from the producer, this would create a cycle. + if (c_any_of(consumer_operands, [&](HloInstruction* operand) { + return producer != operand && + reachability()->IsReachable(producer, operand); + })) { + break; + } + to_fuse.insert(producer); + potential_fusion_list.emplace_back(producer, consumer); + instrs_to_update_reachability.push_back(producer); + instrs_to_update_reachability.push_back(consumer); + break; + } + } + + // Filter out pairs that will be no longer fusable because of reachability + // change. + for (auto& fusion_pair : potential_fusion_list) { + HloInstruction* producer = fusion_pair.first; + HloInstruction* consumer = fusion_pair.second; + if (!c_any_of(consumer->operands(), [&](HloInstruction* operand) { + return producer != operand && + reachability()->IsReachable(producer, operand); + })) { + UpdateReachability(producer, consumer, instrs_to_update_reachability); + fusion_list.push_back(fusion_pair); + } + } + + for (auto fusions_to_create : fusion_list) { + HloInstruction* producer = fusions_to_create.first; + HloInstruction* consumer = fusions_to_create.second; + if (consumer->opcode() != HloOpcode::kFusion) { + // Fusing with a reduce (fusion) always results in an input fusion. + HloInstruction* input_fusion = + computation()->AddInstruction(HloInstruction::CreateFusion( + consumer->shape(), HloInstruction::FusionKind::kInput, consumer)); + VLOG(2) << "Fuse producer " << producer->name() << " and its consumer " + << consumer->name() << " into " << input_fusion->name(); + TF_CHECK_OK(computation()->ReplaceInstruction(consumer, input_fusion)); + if (producer->opcode() == HloOpcode::kFusion) { + input_fusion->MergeFusionInstructionIntoMultiOutput(producer); + } else { + input_fusion->FuseInstructionIntoMultiOutput(producer); + } + } else { + VLOG(2) << "Fuse producer " << producer->name() << " into its consumer " + << consumer->name(); + + if (producer->opcode() == HloOpcode::kFusion) { + consumer->MergeFusionInstructionIntoMultiOutput(producer); + } else { + consumer->FuseInstructionIntoMultiOutput(producer); + } + } + changed = true; + } + return changed; +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h new file mode 100644 index 0000000000000000000000000000000000000000..67ca5d49eee8508e93284b134f8410eb3a89f9ce --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h @@ -0,0 +1,56 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_MULTI_OUTPUT_FUSION_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_MULTI_OUTPUT_FUSION_H_ + +#include "tensorflow/compiler/xla/service/multi_output_fusion.h" + +namespace xla { +namespace gpu { + +// Multi-output fusion of sibling and producer-consumer instructions for the +// Jellyfish backend. +class GpuMultiOutputFusion : public MultiOutputFusion { + public: + GpuMultiOutputFusion(); + + protected: + // Test if instr1 and instr2 have the compatible shapes that can be legally + // fused. + bool ShapesCompatibleForFusion(HloInstruction* instr1, + HloInstruction* instr2) override; + + // We currently only consider reduce and reduce fusion nodes as candidates. + bool IsFusible(HloInstruction* instr) override; + + // This function estimates the amount of memory reads saved by merging + // instr1 and instr2 into one multi-output fusion instruction. For a fusion + // instruction, all the operands need to be loaded from memory. If we merge + // instr1 and instr2, common operands will not be loaded twice. The profit is + // estimated as the size of the common operands b/w instr1 and instr2. + int64 GetProfit(HloInstruction* instr1, HloInstruction* instr2) override; + + // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. + bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2) override; + + // Fuse loop fusions into reduce fusions. + bool DoProducerConsumerMultiOutputFusion() override; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_MULTI_OUTPUT_FUSION_H_ diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..979ea79243818c398b1b130254a41c95ced51830 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -0,0 +1,353 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" + +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/strings/str_util.h" + +namespace op = xla::testing::opcode_matchers; + +namespace xla { +namespace gpu { + +using InstructionFusionTest = HloTestBase; + +const char kModulePrefix[] = R"( + HloModule test_module + + scalar_add_computation { + scalar_lhs.0 = f32[] parameter(0) + scalar_rhs.0 = f32[] parameter(1) + ROOT add.0 = f32[] add(scalar_lhs.0, scalar_rhs.0) + } + scalar_mul_computation { + scalar_lhs.1 = f32[] parameter(0) + scalar_rhs.1 = f32[] parameter(1) + ROOT mul.1 = f32[] add(scalar_lhs.1, scalar_rhs.1) + })"; + +TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { + // Fusion with reduce instruction root and a sibling reduce instruction + // sharing the same input param. + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation { + p1.1 = f32[128,512,28,28]{3,2,1,0} parameter(1) + mul = f32[128,512,28,28]{3,2,1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + ROOT reduce.1 = f32[512]{0} reduce(mul, const.1), dimensions={0,2,3}, to_apply=scalar_add_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1 = f32[128,512,28,28]{3,2,1,0} parameter(1) + const.2 = f32[] constant(1) + fusion = f32[512] fusion(p0, p1), kind=kInput, calls=fused_computation + reduce.2 = f32[512]{0} reduce(p1, const.2), dimensions={0,2,3}, to_apply=scalar_add_computation + ROOT root = (f32[512]{0}, f32[512]{0}) tuple(fusion, reduce.2) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* fusion = + module->entry_computation()->root_instruction()->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce())); +} + +TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[6400]{0} parameter(1) + mul = f32[6400]{0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + ROOT reduce.1 = f32[] reduce(mul, const.1), dimensions={0}, to_apply=scalar_add_computation + } + + fused_computation_2 { + p1.2 = f32[6400]{0} parameter(1) + r1 = f32[64,100]{0,1} reshape(p1.2) + const.2 = f32[] parameter(0) + ROOT reduce.2 = f32[] reduce(r1, const.2), dimensions={1,0}, to_apply=scalar_mul_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1 = f32[6400]{0} parameter(1) + fusion.1 = f32[] fusion(p0, p1), kind=kInput, calls=fused_computation_1 + fusion.2 = f32[] fusion(p0, p1), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[], f32[]) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + +TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceOutputShapes) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[10,10]{1,0} parameter(1) + mul = f32[10,10]{1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + ROOT reduce.1 = f32[] reduce(mul, const.1), dimensions={0,1}, to_apply=scalar_add_computation + } + + fused_computation_2 { + p1.2 = f32[10,10]{1,0} parameter(1) + const.2 = f32[10]{0} parameter(0) + ROOT reduce.2 = f32[10]{0} reduce(p1.2, const.2), dimensions={0}, to_apply=scalar_mul_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1.3 = f32[10,10]{1,0} parameter(1) + fusion.1 = f32[] fusion(p0, p1.3), kind=kInput, calls=fused_computation_1 + p2 = f32[] parameter(2) + fusion.2 = f32[10]{0} fusion(p2, p1.3), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[], f32[10]{0}) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + +TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceFusions) { + // Two sibling fusions with reduce instruction roots sharing the same input + // param. + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[128,512,28,28]{3,2,1,0} parameter(1) + mul = f32[128,512,28,28]{3,2,1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + ROOT reduce.1 = f32[512]{0} reduce(mul, const.1), dimensions={0,2,3}, to_apply=scalar_add_computation + } + + fused_computation_2 { + p1.2 = f32[128,512,28,28]{3,2,1,0} parameter(1) + const.2 = f32[] parameter(0) + ROOT reduce.2 = f32[512]{0} reduce(p1.2, const.2), dimensions={0,2,3}, to_apply=scalar_add_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1 = f32[128,512,28,28]{3,2,1,0} parameter(1) + fusion.1 = f32[512] fusion(p0, p1), kind=kInput, calls=fused_computation_1 + fusion.2 = f32[512] fusion(p0, p1), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[512]{0}, f32[512]{0}) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* fusion = + module->entry_computation()->root_instruction()->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce())); +} + +TEST_F(InstructionFusionTest, + MultiOutputFusionSiblingReduceAndReduceMultiOutputFusion) { + // Multi-output fusion with two reduce instructions root and a sibling reduce + // instruction sharing the same input param. + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation (p0: f32[128,512,28,28]) -> (f32[512], f32[512]) { + const.1 = f32[] constant(1) + p0.1 = f32[128,512,28,28]{3,2,1,0} parameter(0) + mul = f32[128,512,28,28]{3,2,1,0} multiply(f32[128,512,28,28]{3,2,1,0} p0.1, f32[128,512,28,28]{3,2,1,0} p0.1) + reduce.1 = f32[512]{0} reduce(f32[128,512,28,28]{3,2,1,0} mul, f32[] const.1), dimensions={0,2,3}, to_apply=scalar_add_computation + reduce.2 = f32[512]{0} reduce(f32[128,512,28,28]{3,2,1,0} p0.1, f32[] const.1), dimensions={0,2,3}, to_apply=scalar_add_computation + ROOT tuple = (f32[512]{0}, f32[512]{0}) tuple(f32[512]{0} reduce.1, f32[512]{0} reduce.2) + } + + ENTRY entry (p0: f32[128,512,28,28]) -> (f32[512], f32[512], f32[512]) { + p0 = f32[128,512,28,28]{3,2,1,0} parameter(0) + const = f32[] constant(1) + fusion = (f32[512]{0}, f32[512]{0}) fusion(f32[128,512,28,28]{3,2,1,0} p0), kind=kInput, calls=fused_computation + get-tuple-element = f32[512]{0} get-tuple-element((f32[512]{0}, f32[512]{0}) fusion), index=0 + get-tuple-element.1 = f32[512]{0} get-tuple-element((f32[512]{0}, f32[512]{0}) fusion), index=1 + reduce.3 = f32[512]{0} reduce(p0, const), dimensions={0,2,3}, to_apply=scalar_add_computation + ROOT root = (f32[512]{0}, f32[512]{0}, f32[512]{0}) tuple(f32[512]{0} get-tuple-element, f32[512]{0} get-tuple-element.1, f32[512]{0} reduce.3) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* fusion = + module->entry_computation()->root_instruction()->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce(), op::Reduce())); +} + +TEST_F(InstructionFusionTest, + MultiOutputFusionSiblingFusionCheckAgainstReduceOperand) { + // Verify that if we already have a multi-output fusion that we prefer to pick + // a reduce op from its operands for checking shape compatibility. + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[10,10]{1,0} parameter(1) + mul = f32[10,10]{1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + reduce.1 = f32[] reduce(p1.1, const.1), dimensions={0,1}, to_apply=scalar_add_computation + ROOT tuple = (f32[10,10], f32[]) tuple(mul, reduce.1) + } + + fused_computation_2 { + p1.2 = f32[10,10]{1,0} parameter(1) + const.2 = f32[10] parameter(0) + ROOT reduce.2 = f32[10] reduce(p1.2, const.2), dimensions={0}, to_apply=scalar_mul_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1 = f32[10,10]{1,0} parameter(1) + p2 = f32[10]{0} parameter(2) + fusion.1 = (f32[10,10], f32[10]) fusion(p0, p1), kind=kInput, calls=fused_computation_1 + get-tuple-element.1 = f32[10,10] get-tuple-element((f32[10,10], f32[10]) fusion.1), index=0 + get-tuple-element.2 = f32[] get-tuple-element((f32[10,10], f32[10]) fusion.1), index=1 + fusion.2 = f32[10] fusion(p2, p1), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[10,10], f32[], f32[10]) tuple(get-tuple-element.1, get-tuple-element.2, fusion.2) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + +TEST_F(InstructionFusionTest, MultiOutputFusionTwoLoops) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p0.1 = f32[6400]{0} parameter(0) + ROOT mul = f32[6400]{0} multiply(p0.1, p0.1) + } + + fused_computation_2 { + p0.2 = f32[6400]{0} parameter(0) + const.2 = f32[] constant(1) + ROOT div = f32[6400]{0} divide(p0.2, const.2) + } + + ENTRY entry { + p0 = f32[6400]{0} parameter(0) + fusion.1 = f32[6400]{0} fusion(p0), kind=kLoop, calls=fused_computation_1 + fusion.2 = f32[6400]{0} fusion(p0), kind=kLoop, calls=fused_computation_2 + ROOT root = (f32[6400]{0}, f32[6400]{0}) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* fusion = + module->entry_computation()->root_instruction()->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Multiply(), op::Divide())); +} + +TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduce) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_add { + p0.1 = f32[2,2,2]{2,1,0} parameter(0) + p1.1 = f32[2,2,2]{2,1,0} parameter(1) + ROOT add = f32[2,2,2]{2,1,0} add(p0.1, p1.1) + } + + ENTRY reduce { + p0 = f32[2,2,2]{2,1,0} parameter(0) + p1 = f32[2,2,2]{2,1,0} parameter(1) + c0 = f32[] constant(0) + add = f32[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_add + reduce = f32[2,2]{1,0} reduce(add, c0), dimensions={2}, to_apply=scalar_add_computation + ROOT root = (f32[2,2]{1,0}, f32[2,2,2]{2,1,0}) tuple(reduce, add) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Add())); +} + +TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduceFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_select { + p1.1 = f32[2,2,2]{2,1,0} parameter(1) + c0 = f32[] constant(0) + broadcast = f32[2,2,2]{2,1,0} broadcast(f32[] c0), dimensions={} + greater-than = pred[2,2,2]{2,1,0} greater-than(f32[2,2,2]{2,1,0} p1.1, f32[2,2,2]{2,1,0} broadcast) + p0.1 = f32[2,2,2]{2,1,0} parameter(0) + ROOT select = f32[2,2,2]{2,1,0} select(pred[2,2,2]{2,1,0} greater-than, f32[2,2,2]{2,1,0} p0.1, f32[2,2,2]{2,1,0} broadcast) + } + + fused_reduce { + p0.2 = f32[2,2,2]{2,1,0} parameter(0) + c1 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(p0.2, c1), dimensions={2}, to_apply=scalar_add_computation + mul = f32[2,2,2]{2,1,0} multiply(p0.2, p0.2) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=scalar_add_computation + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(r1, r2) + } + + ENTRY reduce { + p0 = f32[2,2,2]{2,1,0} parameter(0) + p1 = f32[2,2,2]{2,1,0} parameter(1) + select = f32[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_select + fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(select), kind=kInput, calls=fused_reduce + gte0 = f32[2,2]{1,0} get-tuple-element(fusion), index=0 + gte1 = f32[2,2]{1,0} get-tuple-element(fusion), index=1 + ROOT root = (f32[2,2]{1,0}, f32[2,2]{1,0}, f32[2,2,2]{2,1,0}) tuple(gte1, gte1, select) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce(), op::Select())); +} + +TEST_F(InstructionFusionTest, ProducerConsumerFusionDoNotFuseLoopReduceFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_element_wise { + p0.1 = f32[2,2,2]{2,1,0} parameter(0) + p1.1 = f32[2,2,2]{2,1,0} parameter(1) + ROOT root = f32[2,2,2]{2,1,0} add(p0.1, p1.1) + } + + fused_reduce { + p0.2 = f32[2,2,2]{2,1,0} parameter(0) + mul = f32[2,2,2]{2,1,0} multiply(f32[2,2,2]{2,1,0} p0.2, f32[2,2,2]{2,1,0} p0.2) + c1 = f32[] constant(0) + ROOT reduce = f32[2,2]{1,0} reduce(f32[2,2,2]{2,1,0} mul, f32[] c1), dimensions={1}, to_apply=scalar_add_computation + } + + ENTRY reduce { + p0 = f32[2,2,2]{2,1,0} parameter(0) + p1 = f32[2,2,2]{2,1,0} parameter(1) + element_wise = f32[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_element_wise + fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(element_wise), kind=kLoop, calls=fused_reduce + ROOT root = (f32[2,2]{1,0}, f32[2,2,2]{2,1,0}) tuple(fusion, element_wise) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc index d8c07dc3119fb81a3ef22822acb11b7c4d5bbca5..cd833ec7bd858aabee84ac306d198e80eb112506 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc @@ -58,7 +58,7 @@ ParallelLoopEmitter::ParallelLoopEmitter( std::vector ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { // Emit the following code in LLVM IR: // linear_index = blockIdx.x * blockDim.x + threadIdx.x; // if (linear_index < num_elements) { @@ -71,14 +71,13 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( // // %nctaid.x is currently specified as 2147483647. VLOG(3) << "EmitIndexAndSetExitBasicBlock unroll_factor " << unroll_factor_; + CHECK_NE(index_type, nullptr); std::vector array_indices; - llvm::Value* block_id = llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, ir_builder_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.block_count(), static_cast(block_id)); - block_id = - ir_builder_->CreateZExt(block_id, ir_builder_->getInt64Ty(), "block_id"); + block_id = ir_builder_->CreateZExtOrTrunc(block_id, index_type, "block_id"); // Per the PTX documentation: // "It is guaranteed that [...] 0 <= %tid.x < %ntid.x" @@ -88,13 +87,15 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, ir_builder_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.threads_per_block(), static_cast(thread_id)); - thread_id = ir_builder_->CreateZExt(thread_id, ir_builder_->getInt64Ty(), - "thread_id"); + thread_id = + ir_builder_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); llvm::Value* linear_index_base = ir_builder_->CreateAdd( ir_builder_->CreateMul( block_id, - ir_builder_->getInt64(launch_dimensions_.threads_per_block()), "", + llvm::ConstantInt::get(index_type, + launch_dimensions_.threads_per_block()), + "", /*HasNUW=*/true, /*HasNSW=*/true), thread_id, "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); @@ -110,21 +111,23 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( llvm::Intrinsic::assume, {ir_builder_->CreateICmpULT( linear_index_base, - ir_builder_->getInt64(launch_dimensions_.threads_per_block() * - launch_dimensions_.block_count()), + llvm::ConstantInt::get(index_type, + launch_dimensions_.threads_per_block() * + launch_dimensions_.block_count()), "linear_index_in_range")}, {}, ir_builder_); if (unroll_factor_ > 1) { linear_index_base = ir_builder_->CreateMul( - linear_index_base, ir_builder_->getInt64(unroll_factor_), + linear_index_base, llvm::ConstantInt::get(index_type, unroll_factor_), "linear_index_base", /*HasNUW=*/true, /*HasNSW=*/true); } array_indices.emplace_back(linear_index_base, shape_, ir_builder_); for (int i = 1; i < unroll_factor_; ++i) { llvm::Value* linear_index = ir_builder_->CreateAdd( - linear_index_base, ir_builder_->getInt64(i), "linear_index", + linear_index_base, llvm::ConstantInt::get(index_type, i), + "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); array_indices.emplace_back(linear_index, shape_, ir_builder_); } @@ -132,7 +135,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( auto if_in_bounds = llvm_ir::EmitIfThenElse( ir_builder_->CreateICmpULT( linear_index_base, - ir_builder_->getInt64(ShapeUtil::ElementsIn(shape_))), + llvm::ConstantInt::get(index_type, ShapeUtil::ElementsIn(shape_))), llvm_ir::IrName(loop_name, "in_bounds"), ir_builder_, false); // Set exit_bb_ to the exit block of the if structure. diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h index 25318b3bed8bf4a2dfe3a4a974269d0405c3bfec..302e1bf1bc8e90f2eebd838f156a1552e86185ac 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h @@ -58,7 +58,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ~ParallelLoopEmitter() override = default; std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) override; + tensorflow::StringPiece loop_name, llvm::Type* index_type) override; private: // The thread and block dimension to parallelize the loop on. diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.h b/tensorflow/compiler/xla/service/gpu/partition_assignment.h index c125474edb1036090a926020f2b1e7fcf64c751a..02471129e004b4876ce20a62cade34060c65b478 100644 --- a/tensorflow/compiler/xla/service/gpu/partition_assignment.h +++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.h @@ -47,6 +47,7 @@ class LaunchDimensions { int64 block_count() const { return block_count_; } int64 threads_per_block() const { return threads_per_block_; } + int64 launch_bound() const { return block_count() * threads_per_block(); } private: int64 block_count_; diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc index 88cb10883e97ae663dc492ad088e6daf9133d7f5..dfdba7d7d9a60458e1b1c90cf9f5017b44b7b801 100644 --- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/core/lib/core/errors.h" namespace xla { @@ -33,9 +34,17 @@ Status SequentialThunk::Initialize(const GpuExecutable& executable, } Status SequentialThunk::ExecuteOnStream( - const BufferAllocations& buffer_allocations, se::Stream* stream) { + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { + // TODO(b/71544591): We need to potentially measure the total time of the + // sequential thunk. This happens for a reduce op which consists of + // SequentialThunk with a thunk that initializes the output, and another thunk + // that does the actual reduce. Right now, in this case we would only measure + // the time of the last thunk, because both thunks would have the same + // HloInstruction. for (const auto& thunk : thunks_) { - TF_RETURN_IF_ERROR(thunk->ExecuteOnStream(buffer_allocations, stream)); + TF_RETURN_IF_ERROR( + thunk->ExecuteOnStream(buffer_allocations, stream, profiler)); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h index 135f79e413dfaa27f2f2264e0daa3beb3c305e0f..3c4de1d1a6c912ba31f56c29b10ca004d1e56da6 100644 --- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -41,7 +42,8 @@ class SequentialThunk : public Thunk { Status Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) override; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: // The list of sub-thunks. diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc index 696fa7e0194032b5c78bf11383c3280a62de07fa..6f4bb0580e8dfc1dce1cca0a60cc3dd9ea600fb3 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc @@ -33,8 +33,7 @@ class StreamAssignmentTest : public HloTestBase { auto debug_options = GetDebugOptionsForTest(); debug_options.set_xla_gpu_disable_multi_streaming(false); config.set_debug_options(debug_options); - return MakeUnique("test_module", VersionedComputationHandle(), - config); + return MakeUnique("test_module", config); } // Pre-canned shapes. diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h index 8218f4fd11d3978d0ecc53fc15e287aea4b69ec3..39a6a38d001f502b2abb8de6efe2ce623b478c71 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ +#include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h index 931c0bffab850362dbd2df975657dd47d9cbd3ae..14d41033c2c7681e3262c0674be13b1f3aa83aef 100644 --- a/tensorflow/compiler/xla/service/gpu/thunk.h +++ b/tensorflow/compiler/xla/service/gpu/thunk.h @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -94,11 +95,12 @@ class Thunk { // Execute the kernel for the thunk on the given stream. This method must be // called after Initialize and can be called multiple times over Thunk's - // lifetime. Stream argument must be non-null. + // lifetime. 'stream' and 'profiler' must be non-null. // // Precondition: Initialize(stream->parent()) has been called. virtual Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) = 0; + se::Stream* stream, + HloExecutionProfiler* profiler) = 0; private: Kind kind_; diff --git a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc index 97cb04c38fbf18e516857f5269c984696ca204c3..a10e40451c1db01ce73db7b56a3a0599769fa49b 100644 --- a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc @@ -15,13 +15,15 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/tuple_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/util.h" namespace xla { namespace gpu { Status TupleThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { std::vector tuple_element_buffer_addresses; for (BufferAllocation::Slice tuple_element_buffer : tuple_element_buffers_) { tuple_element_buffer_addresses.push_back( @@ -31,6 +33,7 @@ Status TupleThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, buffer_allocations.GetDeviceAddress(dest_buffer_)); auto host_size = tuple_element_buffer_addresses.size() * sizeof(void*); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); if (!stream ->ThenMemcpy(&dest_buffer_address, tuple_element_buffer_addresses.data(), host_size) diff --git a/tensorflow/compiler/xla/service/gpu/tuple_thunk.h b/tensorflow/compiler/xla/service/gpu/tuple_thunk.h index 951f809b51937c97a6e7de0345ec58a8b66a4242..2d5735d6c40ccd26f0e527f1a02403910db4c812 100644 --- a/tensorflow/compiler/xla/service/gpu/tuple_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/tuple_thunk.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -46,7 +47,8 @@ class TupleThunk : public Thunk { TupleThunk& operator=(const TupleThunk&) = delete; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const std::vector tuple_element_buffers_; diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.cc b/tensorflow/compiler/xla/service/gpu/while_thunk.cc index 30b9640c4c75dae61e9a90da5fb10e9d4a90cd26..5e13f989c2ffb0396efc94a01783ee91725dbd44 100644 --- a/tensorflow/compiler/xla/service/gpu/while_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/while_thunk.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/while_thunk.h" #include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" @@ -43,14 +44,18 @@ Status WhileThunk::Initialize(const GpuExecutable& executable, } Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) { + se::Stream* stream, + HloExecutionProfiler* profiler) { se::DeviceMemoryBase condition_result_data = buffer_allocations.GetDeviceAddress(condition_result_buffer_index_); + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); while (true) { // Invoke thunk sequence for while 'condition' computation. - TF_RETURN_IF_ERROR( - condition_thunk_sequence_->ExecuteOnStream(buffer_allocations, stream)); + profiler->StartHloComputation(); + TF_RETURN_IF_ERROR(condition_thunk_sequence_->ExecuteOnStream( + buffer_allocations, stream, profiler)); + profiler->FinishHloComputation(hlo_instruction()->while_condition()); // Copy the result of condition computation and break the loop if 'false'. bool condition_result; @@ -66,9 +71,14 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, break; } - // Invoke thunk sequence for while 'body' computation. - TF_RETURN_IF_ERROR( - body_thunk_sequence_->ExecuteOnStream(buffer_allocations, stream)); + // We measure the time of one execution of the while body computation. The + // while body may be executed more than once, the last measurement "wins". + profiler->StartHloComputation(); + // Invoke thunk sequence for while 'body' computation, and pass on + // 'profiler' to measure the timing of the thunks in 'body_thunk_sequence_'. + TF_RETURN_IF_ERROR(body_thunk_sequence_->ExecuteOnStream(buffer_allocations, + stream, profiler)); + profiler->FinishHloComputation(hlo_instruction()->while_body()); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.h b/tensorflow/compiler/xla/service/gpu/while_thunk.h index 22176685a92df9c95b10f755b209309843c0fa3a..9270f95ee67cf0bd3ab8082452a9d8703cb4304e 100644 --- a/tensorflow/compiler/xla/service/gpu/while_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/while_thunk.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -48,7 +49,8 @@ class WhileThunk : public Thunk { Status Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) override; Status ExecuteOnStream(const BufferAllocations& buffer_allocations, - se::Stream* stream) override; + se::Stream* stream, + HloExecutionProfiler* profiler) override; private: const BufferAllocation::Slice condition_result_buffer_index_; diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index 06a5e0351b63270b61b998ca2211f480f256f759..4005fc0d114a3ec7a38dfb5edecdaeb1e8497ade 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -26,6 +26,46 @@ namespace xla { using tensorflow::gtl::FlatMap; using tensorflow::gtl::FlatSet; +/*static*/ +StatusOr HeapSimulator::MinimumMemoryForModule( + const SequentialHloOrdering::HloModuleSequence& module_sequence, + const LogicalBuffer::SizeFunction& size_function) { + if (module_sequence.empty()) { + return 0; + } + + const HloModule* module = module_sequence.begin()->first->parent(); + TF_ASSIGN_OR_RETURN(std::unique_ptr points_to_analysis, + TuplePointsToAnalysis::Run(module)); + + // The absolute minimum memory required for a given sequence of instructions + // is determined by the sequence of Alloc and Free calls on a simulated heap, + // ignoring fragmentation. We run the heap simulation on the whole module, + // rather than summing each computation, since it gives us a better lower + // bound, by minimizing the liveness of sub-computations. + TF_ASSIGN_OR_RETURN( + HeapSimulator::Result result, + HeapSimulator::Run(MakeUnique(), *module, + module_sequence, *points_to_analysis, size_function)); + return result.heap_size; +} + +/*static*/ +StatusOr HeapSimulator::MinimumMemoryForComputation( + const HloComputation& computation, + const std::vector& sequence, + const TuplePointsToAnalysis& points_to_analysis, + const LogicalBuffer::SizeFunction& size_function, + const tensorflow::gtl::FlatMap* + memory_by_computation) { + TF_ASSIGN_OR_RETURN( + HeapSimulator::Result result, + HeapSimulator::Run(MakeUnique(), computation, + sequence, points_to_analysis, size_function, + HeapSimulator::Options(), memory_by_computation)); + return result.heap_size; +} + /*static*/ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloModule& module, @@ -46,9 +86,11 @@ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloComputation& computation, const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, - const BufferValue::SizeFunction& size_fn, const Options& options) { + const BufferValue::SizeFunction& size_fn, const Options& options, + const tensorflow::gtl::FlatMap* + memory_by_computation) { HeapSimulator heap(std::move(algorithm), size_fn, options, - /*module_sequence=*/nullptr); + /*module_sequence=*/nullptr, memory_by_computation); TF_RETURN_IF_ERROR(heap.RunComputation(computation, instruction_sequence, points_to_analysis)); return heap.Finish(); @@ -188,6 +230,9 @@ Status HeapSimulator::RunComputation( // // INVARIANT: Either Alloc or ShareBuffer will be called for each buffer // that we should assign. + + // Make sure each buffer get reused at most once. + FlatSet reused_buffers; for (const BufferValue* buffer : buffers_defined_by_instruction) { if (IgnoreBuffer(buffer)) { continue; @@ -200,6 +245,9 @@ Status HeapSimulator::RunComputation( bool shared = false; if (options_.may_reuse_operand_buffers) { for (const BufferValue* operand_buffer : operand_buffers_to_free) { + if (reused_buffers.count(operand_buffer) != 0) { + continue; + } if (buffer->instruction()->IsUserOf(operand_buffer->instruction()) && buffer->instruction()->opcode() != HloOpcode::kCopy && points_to_analysis.CanShareOperandBufferWithUser( @@ -209,6 +257,7 @@ Status HeapSimulator::RunComputation( << operand_buffer->ToString(); ShareBuffer(buffer, operand_buffer, instruction); shared = true; + reused_buffers.insert(operand_buffer); break; } } @@ -219,6 +268,12 @@ Status HeapSimulator::RunComputation( Alloc(buffer, instruction); } } + // Account for the memory used by subcomputations when estimating the + // current heap size. + if (memory_by_computation_ != nullptr) { + algorithm_->AccountForSubcomputationMemory(instruction, + *memory_by_computation_); + } // If the whole module is sequential, we can save memory by running the // heap-simulation for sub-computations inline. E.g. the buffers for the @@ -286,12 +341,15 @@ Status HeapSimulator::RunComputation( HeapSimulator::HeapSimulator( std::unique_ptr algorithm, const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence) + const SequentialHloOrdering::HloModuleSequence* module_sequence, + const tensorflow::gtl::FlatMap* + memory_by_computation) : no_fragmentation_stats_(MakeUnique()), algorithm_(std::move(algorithm)), size_fn_(size_fn), options_(options), - module_sequence_(module_sequence) { + module_sequence_(module_sequence), + memory_by_computation_(memory_by_computation) { debug_trace_.set_whole_module_simulation(module_sequence_ != nullptr); } @@ -460,6 +518,26 @@ void NoFragmentationStatsHeap::Alloc(const BufferValue* buffer, int64 size) { } } +void NoFragmentationStatsHeap::AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) { + // We only count the memory usage of the largest subcomputation, instead of + // adding them all, because subcomputations won't execute in parallel. + int64 max_subcomputation_bytes = 0; + for (const auto* c : instruction->called_computations()) { + auto it = memory_by_computation.find(c); + if (it != memory_by_computation.end()) { + int64 subcomputation_bytes = it->second; + if (subcomputation_bytes > max_subcomputation_bytes) { + max_subcomputation_bytes = subcomputation_bytes; + } + } + } + max_heap_size_ = + std::max(max_heap_size_, current_heap_size_ + max_subcomputation_bytes); +} + void NoFragmentationStatsHeap::Free(const BufferValue* buffer, int64 size) { current_heap_size_ -= size; } diff --git a/tensorflow/compiler/xla/service/heap_simulator.h b/tensorflow/compiler/xla/service/heap_simulator.h index 8b2b43a37a5c41d334e5338c6a6fad160f03a51e..811a6042df9434ac3f4bed71b9c093433e25c1bb 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.h +++ b/tensorflow/compiler/xla/service/heap_simulator.h @@ -85,6 +85,23 @@ class HeapSimulator { const BufferValueFlatSet* buffers_to_assign; }; + // Returns the minimum memory required to compute an HLO module where all + // computations have been scheduled (represented by the given + // module_sequence), assuming no fragmentation. + static StatusOr MinimumMemoryForModule( + const SequentialHloOrdering::HloModuleSequence& module_sequence, + const LogicalBuffer::SizeFunction& size_function); + + // Returns the minimum memory required to compute the given computation, + // assuming no fragmentation. + static StatusOr MinimumMemoryForComputation( + const HloComputation& computation, + const std::vector& sequence, + const TuplePointsToAnalysis& points_to_analysis, + const LogicalBuffer::SizeFunction& size_function, + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); + // Run the heap simulation with the given algorithm, assuming the given // module_sequence, which must contain a topologically-consistent total // ordering of all instructions within each computation. The result is invalid @@ -111,7 +128,9 @@ class HeapSimulator { const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, const BufferValue::SizeFunction& size_fn, - const Options& options = Options()); + const Options& options = Options(), + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); private: // If 'module_sequence' is non-null, it is used to find kCall and kWhile @@ -120,7 +139,9 @@ class HeapSimulator { HeapSimulator( std::unique_ptr algorithm, const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence); + const SequentialHloOrdering::HloModuleSequence* module_sequence = nullptr, + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); ~HeapSimulator(); Status RunComputation( @@ -144,7 +165,13 @@ class HeapSimulator { const std::unique_ptr algorithm_; const BufferValue::SizeFunction size_fn_; const Options options_; + // module_sequence_ is set by buffer assignment, and memory_by_computation_ is + // set by hlo scheduling. Then, in RunComputation, we check both in order to + // handle subcomputations. It would be good to unify the handling of + // subcomputations, but it's not clear how. const SequentialHloOrdering::HloModuleSequence* module_sequence_; + const tensorflow::gtl::FlatMap* + memory_by_computation_; // In addition to Alloc and Free, the heap simulator exposes a concept of // buffer sharing. When ShareBuffer is called, instead of allocating new @@ -189,6 +216,11 @@ class HeapAlgorithm { // Alloc allocates a buffer of 'size' bytes. virtual void Alloc(const BufferValue* buffer, int64 size) = 0; + virtual void AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) {} + // Free de-allocates a previously allocated buffer. virtual void Free(const BufferValue* buffer, int64 size) = 0; @@ -207,7 +239,14 @@ class NoFragmentationStatsHeap : public HeapAlgorithm { ~NoFragmentationStatsHeap() override = default; void Alloc(const BufferValue* buffer, int64 size) override; + + void AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) override; + void Free(const BufferValue* buffer, int64 size) override; + Result Finish() override; private: diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 6271652412c2979ff926702f12722102344b0dfb..3849b565e3136924b2d2b1929353885f85b1a043 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -34,6 +34,65 @@ limitations under the License. namespace xla { namespace { +class MinimumMemoryForSequenceTest : public HloTestBase {}; + +TEST_F(MinimumMemoryForSequenceTest, MultiComputation) { + auto module = CreateNewModule(); + const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {}); + const Shape tuple_shape = + ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape}); + + auto cond_builder = HloComputation::Builder("WhileCond"); + // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element) + HloInstruction* cond_param = cond_builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape, "cond_param")); + HloInstruction* cond_iter = cond_builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0)); + HloInstruction* cond_data = cond_builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1)); + // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte) + HloInstruction* cond_lt = cond_builder.AddInstruction( + HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}), + HloOpcode::kLt, cond_iter, cond_data)); + HloComputation* cond_computation = + module->AddEmbeddedComputation(cond_builder.Build()); + + auto body_builder = HloComputation::Builder("WhileBody"); + // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element) + HloInstruction* body_param = body_builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape, "body_param")); + HloComputation* body_computation = + module->AddEmbeddedComputation(body_builder.Build()); + + auto builder = HloComputation::Builder(TestName()); + // Entry params: 8 bytes (4 bytes per param), TOTAL=8 + HloInstruction* iter = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "param_iter")); + HloInstruction* data = builder.AddInstruction( + HloInstruction::CreateParameter(1, scalar_shape, "param_data")); + // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24 + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({iter, data})); + // While: 8 bytes (4 bytes per element), TOTAL=32 + // Both cond and body use a max of 24 bytes, TOTAL=56 + HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile( + tuple_shape, cond_computation, body_computation, tuple)); + HloComputation* entry_computation = + module->AddEntryComputation(builder.Build()); + + auto size_fn = [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8); + }; + + SequentialHloOrdering::HloModuleSequence module_sequence; + module_sequence[cond_computation] = {cond_param, cond_iter, cond_data, + cond_lt}; + module_sequence[body_computation] = {body_param}; + module_sequence[entry_computation] = {iter, data, tuple, while_op}; + EXPECT_EQ(56, HeapSimulator::MinimumMemoryForModule(module_sequence, size_fn) + .ValueOrDie()); +} + const char kAlloc[] = "Alloc"; const char kFree[] = "Free"; const char kFinish[] = "Finish"; @@ -139,6 +198,11 @@ class HeapSimulatorTracker { .ConsumeValueOrDie(); } + int64 OffsetAt(const HloInstruction* instruction, const ShapeIndex& index) { + const BufferValue* buffer = BufferAt(instruction, index); + return result_.chunk_map.at(buffer).offset; + } + // Ensures the expected sequence of Alloc/Free/Finish calls was performed. void ExpectCallSequence(const CallSequence& expected) const { EXPECT_EQ(expected, actual_calls_); @@ -150,10 +214,9 @@ class HeapSimulatorTracker { const ShapeIndex& index_a, const HloInstruction* instruction_b, const ShapeIndex& index_b) { - const BufferValue* a = BufferAt(instruction_a, index_a); - const BufferValue* b = BufferAt(instruction_b, index_b); - EXPECT_EQ(result_.chunk_map[a].offset, result_.chunk_map[b].offset) - << *a << ", " << *b; + int64 offset_a = OffsetAt(instruction_a, index_a); + int64 offset_b = OffsetAt(instruction_b, index_b); + EXPECT_EQ(offset_a, offset_b); } private: @@ -252,6 +315,43 @@ TEST_F(HeapSimulatorTest, MultiplyAdd) { tracker.ExpectSharedBuffers(add, {}, mul, {}); } +TEST_F(HeapSimulatorTest, BufferReusedOnce) { + HeapSimulatorTracker tracker(TestName()); + auto builder = HloComputation::Builder(TestName()); + + HloComputation::Builder fusion_builder("fusion"); + { + HloComputation::Builder& builder = fusion_builder; + auto* a_param = builder.AddInstruction(HloInstruction::CreateParameter( + /*parameter_number=*/0, f32vec4_, "A")); + auto exp = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec4_, HloOpcode::kExp, a_param)); + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec4_, HloOpcode::kNegate, a_param)); + + builder.AddInstruction(HloInstruction::CreateTuple({exp, neg})); + } + auto fusion_computation = + tracker.module()->AddEmbeddedComputation(fusion_builder.Build()); + auto a_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32vec4_, "paramA")); + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec4_, HloOpcode::kNegate, a_param)); + auto fusion = builder.AddInstruction(HloInstruction::CreateFusion( + ShapeUtil::MakeTupleShape({f32vec4_, f32vec4_}), + HloInstruction::FusionKind::kLoop, {neg}, fusion_computation)); + tracker.module()->AddEntryComputation(builder.Build()); + + tracker.RunWholeModule({a_param, neg, fusion}); + + auto neg_buffer = tracker.OffsetAt(neg, {}); + int64 output_buffer_0 = tracker.OffsetAt(fusion, {0}); + int64 output_buffer_1 = tracker.OffsetAt(fusion, {1}); + // Only one buffer should be shared. + EXPECT_TRUE((neg_buffer == output_buffer_0) ^ + (neg_buffer == output_buffer_1)); +} + TEST_F(HeapSimulatorTest, MultiplyDot) { auto builder = HloComputation::Builder(TestName()); auto paramA = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 1f7c1cffd324ad2f4e4cdf11046c8459b8ceb6d5..d2417910606fdd13223076d33ff1bda1dd291d98 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -150,6 +150,11 @@ message HloInstructionProto { // Backend configuration for the instruction. Has backend-specific meaning. string backend_config = 43; + + // Cross Replica Sum fields. + repeated int64 replica_group_ids = 44; + int64 all_reduce_id = 45; + string cross_replica_sum_barrier = 46; } // Serialization of HloComputation. diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc index a88283ed9a6459b4fa9310e160b59c77d51f1027..e8a4b034b4396860bd5873f43003844ce92dea6c 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc @@ -452,15 +452,16 @@ string HloAliasAnalysis::ToString() const { /* static */ StatusOr> HloAliasAnalysis::Run( - HloModule* module) { + HloModule* module, const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer) { VLOG(2) << "HloAliasAnalysis::Run on module " << module->name(); XLA_VLOG_LINES(2, module->ToString()); auto alias_analysis = WrapUnique(new HloAliasAnalysis(module)); - TF_ASSIGN_OR_RETURN( - alias_analysis->dataflow_analysis_, - HloDataflowAnalysis::Run(*module, /*ssa_form=*/true, - /*bitcast_defines_value=*/false)); + TF_ASSIGN_OR_RETURN(alias_analysis->dataflow_analysis_, + HloDataflowAnalysis::Run(*module, /*ssa_form=*/true, + /*bitcast_defines_value=*/false, + fusion_can_share_buffer)); BufferValueMap buffer_map(alias_analysis->dataflow_analysis()); buffer_map.MergeAliasedBuffers(); @@ -493,6 +494,16 @@ StatusOr> HloAliasAnalysis::Run( bool HloAliasAnalysis::HasLiveRangeInterference( const HloOrdering& ordering) const { for (const HloBuffer& buffer : buffers()) { + CHECK(!buffer.values().empty()); + if (ShapeUtil::IsToken(buffer.values().front()->shape())) { + // Tokens have no on-device representation and cannot interfere. + for (const HloValue* value : buffer.values()) { + // If one of the values is a token, all values must be a token. + DCHECK(ShapeUtil::IsToken(value->shape())); + } + continue; + } + // Check that the values in the buffer are totally ordered with respect to // 'ordering'. Begin by sorting the values with respect to 'ordering' with a // tie-break using value ID. The tie-break is necessary because we need a @@ -517,7 +528,6 @@ bool HloAliasAnalysis::HasLiveRangeInterference( // a buffer and A interferes with C, then necessarily A also interferes // with B. So to check interference you only need to check interference // between A and B, and between B and C. - CHECK(!values.empty()); for (int i = 1; i < values.size(); ++i) { if (!ordering.IsDefinedBefore(*values[i - 1], *values[i])) { VLOG(1) << values[i - 1]->ToShortString() << " and " diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h index 67dfd4301b3a027a496911ecf6f06841dfd6423a..afb0c20f0cdf3eb92f72ab8bc368b4b8d723459e 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h @@ -39,7 +39,10 @@ class HloAliasAnalysis { public: // The callgraph of the given HloModule must be flattened // (xla::FlattenCallGraph) prior to running the analysis. - static StatusOr> Run(HloModule* module); + static StatusOr> Run( + HloModule* module, + const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer = nullptr); string ToString() const; diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index ed0ea39ff55dca6931fac6f93ddcddd2716ec505..34b18b0e21fbf6ce5d406cae9dbd64b9744f5a83 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -64,7 +64,7 @@ HloComputation::HloComputation( const string& name, int parameter_count, std::vector>* instructions, HloInstruction* root_instruction, HloInstruction* fusion_instruction) - : name_(name), + : name_(NameUniquer::GetSanitizedName(name)), unique_id_(-1), root_instruction_(root_instruction), fusion_instruction_(fusion_instruction) { @@ -120,6 +120,30 @@ HloInstruction* HloComputation::AddParameter( return instructions_.back().get(); } +namespace { + +// Returns the new name for a fusion parameter when we change its number. +// +// Fusion parameters are named foo.param_1, bar.param_2, etc. We are +// renumbering the parameters, so replace the final number in the name with +// the updated value. +string RenameFusionParameter(const string& original_name, int64 new_param_no) { + const string param_underscore = ".param_"; + size_t index = original_name.rfind(param_underscore); + if (index == string::npos) { + return original_name; + } + string after_param = original_name.substr(index + param_underscore.size()); + int64 numeric_suffix; + if (tensorflow::strings::safe_strto64(after_param, &numeric_suffix)) { + return StrCat(original_name.substr(0, index + param_underscore.size()), + new_param_no); + } + return original_name; +} + +} // namespace + Status HloComputation::RemoveParameter(int64 param_no) { CHECK_GE(param_no, 0); CHECK_LT(param_no, param_instructions_.size()); @@ -132,21 +156,8 @@ Status HloComputation::RemoveParameter(int64 param_no) { while (param_no < param_instructions_.size()) { param_instruction = param_instructions_[param_no]; - string param_name = param_instruction->name(); - // Fusion parameters are named foo.param_1, bar.param_2, etc. We are - // renumbering the parameters, so replace the final number in the name with - // the updated value. - const string param_underscore = ".param_"; - size_t index = param_name.rfind(param_underscore); - if (index == string::npos) { - string after_param = name().substr(index + param_underscore.size()); - int64 numeric_suffix; - if (tensorflow::strings::safe_strto64(after_param, &numeric_suffix)) { - param_name = - StrCat(param_name.substr(0, index), param_underscore, param_no); - } - } - + string param_name = + RenameFusionParameter(param_instruction->name(), param_no); HloInstruction* new_instr = AddInstructionInternal(HloInstruction::CreateParameter( param_no, param_instruction->shape(), param_name)); @@ -159,6 +170,34 @@ Status HloComputation::RemoveParameter(int64 param_no) { return Status::OK(); } +Status HloComputation::RemoveUnusedParameters() { + CHECK(IsFusionComputation()); + int64 removed = 0; + for (int64 i = 0; i < param_instructions_.size(); ++i) { + HloInstruction* param_instruction = param_instructions_[i]; + if (param_instruction->user_count() == 0 && + param_instruction != root_instruction()) { + TF_RETURN_IF_ERROR(RemoveInstruction(param_instruction)); + ++removed; + continue; + } + + if (removed > 0) { + const int64 param_no = i - removed; + string param_name = + RenameFusionParameter(param_instruction->name(), param_no); + HloInstruction* new_instr = + AddInstructionInternal(HloInstruction::CreateParameter( + param_no, param_instruction->shape(), param_name)); + TF_RETURN_IF_ERROR(param_instruction->ReplaceAllUsesWith(new_instr)); + param_instructions_[param_no] = new_instr; + TF_RETURN_IF_ERROR(RemoveInstruction(param_instruction)); + } + } + param_instructions_.resize(param_instructions_.size() - removed); + return Status::OK(); +} + bool HloComputation::IsRemovable(const HloInstruction* instruction) { // If the instruction has control predecessors or successors then we cannot // remove the instruction without violating ordering constraints (added, for @@ -234,7 +273,6 @@ Status HloComputation::RemoveInstruction(HloInstruction* instruction) { TF_RET_CHECK(instruction_iterators_.count(instruction) != 0); auto inst_it = instruction_iterators_.at(instruction); (*inst_it)->set_parent(nullptr); - instruction->DetachFromOperands(); instructions_.erase(inst_it); return Status::OK(); } @@ -264,46 +302,11 @@ void HloComputation::set_root_instruction( namespace { -// Helper class which computes the post order of an expression rooted at a -// particular instruction. -class InstructionPostOrderer : public DfsHloVisitorWithDefault { - public: - // added_instructions is the set of instructions which have already been - // accounted for in the post order in previous invocations of - // GetOrder. Without this mechanism, instructions which are predecessors of - // multiple root instructions of the computation can be added to the post - // order more than once. - static std::list GetOrder( - HloInstruction* root, - tensorflow::gtl::FlatSet* added_instructions) { - InstructionPostOrderer orderer(added_instructions); - TF_CHECK_OK(root->Accept(&orderer)); - return std::move(orderer.post_order_); - } - - private: - explicit InstructionPostOrderer( - tensorflow::gtl::FlatSet* added_instructions) - : added_instructions_(added_instructions) {} - ~InstructionPostOrderer() override {} - - Status DefaultAction(HloInstruction* hlo_instruction) override { - if (added_instructions_->count(hlo_instruction) == 0) { - post_order_.push_back(hlo_instruction); - added_instructions_->insert(hlo_instruction); - } - return Status::OK(); - } - - std::list post_order_; - tensorflow::gtl::FlatSet* added_instructions_; -}; - // Helper which builds a post order of the HLO call graph. void ComputeComputationPostOrder( HloComputation* computation, tensorflow::gtl::FlatSet* visited, - std::list* post_order) { + std::vector* post_order) { if (visited->insert(computation).second) { for (auto* instruction : computation->instructions()) { for (HloComputation* called_computation : @@ -315,49 +318,53 @@ void ComputeComputationPostOrder( } } -std::list ComputeInstructionPostOrder( - HloInstruction* root, tensorflow::gtl::FlatSet* visited) { - std::list post_order; - std::vector> dfs_stack; - dfs_stack.emplace_back(root, false); +enum State { kVisiting, kVisited }; + +void ComputeInstructionPostOrder( + std::vector* post_order, HloInstruction* root, + tensorflow::gtl::FlatMap* visited) { + std::vector dfs_stack; + dfs_stack.push_back(root); while (!dfs_stack.empty()) { const auto current = dfs_stack.back(); - if (current.second) { - dfs_stack.pop_back(); - if (!visited->insert(current.first).second) { - continue; - } - post_order.push_back(current.first); - } else { - if (visited->count(current.first)) { + auto it = visited->find(current); + if (it != visited->end()) { + if (it->second == kVisited) { + // Already visited. dfs_stack.pop_back(); continue; } - dfs_stack.back().second = true; - - // Add the operands to the stack in reverse order so the first operand is - // processed first. This will produce a more natural ordering and a nicer - // result for thigns like HLO stringification. - const auto& operands = current.first->operands(); - for (int64 i = operands.size() - 1; i >= 0; --i) { - dfs_stack.emplace_back(operands[i], false); - } + // Visit this node. + CHECK_EQ(kVisiting, it->second); + dfs_stack.pop_back(); + post_order->push_back(current); + it->second = kVisited; + continue; + } - for (HloInstruction* op : current.first->control_predecessors()) { - dfs_stack.emplace_back(op, false); - } + visited->insert({current, kVisiting}); + + // Add the operands to the stack in reverse order so the first operand is + // processed first. This will produce a more natural ordering and a nicer + // result for thigns like HLO stringification. + const auto& operands = current->operands(); + for (int64 i = operands.size() - 1; i >= 0; --i) { + dfs_stack.emplace_back(operands[i]); + } + + for (HloInstruction* op : current->control_predecessors()) { + dfs_stack.emplace_back(op); } } - return post_order; } } // namespace -std::list HloComputation::MakeInstructionPostOrder() const { - std::list post_order; - std::list trace_instructions; - tensorflow::gtl::FlatSet added_instructions; - std::vector dfs_stack; +std::vector HloComputation::MakeInstructionPostOrder() const { + std::vector post_order; + post_order.reserve(instruction_count()); + std::vector trace_instructions; + tensorflow::gtl::FlatMap visited; for (auto& instruction : instructions_) { if (instruction->opcode() == HloOpcode::kTrace) { // Trace instructions aren't handled by the DFS visitor. Add trace @@ -365,21 +372,20 @@ std::list HloComputation::MakeInstructionPostOrder() const { // users). trace_instructions.push_back(instruction.get()); } else if (instruction->users().empty()) { - post_order.splice( - post_order.end(), - ComputeInstructionPostOrder(instruction.get(), &added_instructions)); + ComputeInstructionPostOrder(&post_order, instruction.get(), &visited); } } - post_order.splice(post_order.end(), trace_instructions); + post_order.insert(post_order.end(), trace_instructions.begin(), + trace_instructions.end()); CHECK_EQ(instructions_.size(), post_order.size()) << "number of instructions does not match post order size"; return post_order; } -std::list HloComputation::MakeEmbeddedComputationsList() +std::vector HloComputation::MakeEmbeddedComputationsList() const { tensorflow::gtl::FlatSet visited; - std::list post_order; + std::vector post_order; // To avoid special handling of this computation, cast away const of // 'this'. 'this' is immediately removed from the post order after @@ -525,21 +531,7 @@ HloInstruction* HloComputation::CreateFusionInstruction( StatusOr HloComputation::DeepCopyHelper( HloInstruction* instruction, const ShapeTree* indices_to_copy, ShapeTree* copies_added, ShapeIndex* index) { - if (ShapeUtil::IsArray(instruction->shape())) { - if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { - // Use kCopy to copy array elements - HloInstruction* copy = AddInstruction(HloInstruction::CreateUnary( - instruction->shape(), HloOpcode::kCopy, instruction)); - if (copies_added != nullptr) { - *copies_added->mutable_element(*index) = copy; - } - return copy; - } else { - // Array elements which are not to be copied are passed through - // transparently. - return instruction; - } - } else if (ShapeUtil::IsTuple(instruction->shape())) { + if (ShapeUtil::IsTuple(instruction->shape())) { std::vector elements; for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape()); i++) { @@ -556,9 +548,27 @@ StatusOr HloComputation::DeepCopyHelper( index->pop_back(); } return AddInstruction(HloInstruction::CreateTuple(elements)); + } + if (ShapeUtil::IsToken(instruction->shape())) { + // Tokens have no on-device representation and cannot be copied. Pass + // through transparently. + return instruction; + } + + // Array shape. + TF_RET_CHECK(ShapeUtil::IsArray(instruction->shape())); + if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { + // Use kCopy to copy array elements + HloInstruction* copy = AddInstruction(HloInstruction::CreateUnary( + instruction->shape(), HloOpcode::kCopy, instruction)); + if (copies_added != nullptr) { + *copies_added->mutable_element(*index) = copy; + } + return copy; } else { - return FailedPrecondition( - "Can only copy array and tuple shaped instructions"); + // Elements which are not to be copied are passed through + // transparently. + return instruction; } } @@ -646,7 +656,7 @@ Status HloComputation::ReplaceInstruction(HloInstruction* old_instruction, std::unique_ptr HloComputation::ComputeReachability() const { - const std::list all = MakeInstructionPostOrder(); + const auto& all = MakeInstructionPostOrder(); auto result = MakeUnique(all); std::vector inputs; @@ -864,15 +874,6 @@ std::unique_ptr HloComputation::CloneWithReplacements( } } context->MapComputation(this, result.get()); - // We cloned the elements of 'replacements', so they're all going to be - // destroyed. HloInstructions need to be detached from their operands before - // they're destroyed, otherwise they stick around in the operands' users lists - // and cause use-after-frees. - for (auto& kv : replacements) { - if (std::unique_ptr& new_instr = kv.second) { - new_instr->DetachFromOperands(); - } - } return result; } diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index 0da4a305f3d5d694a1918fed294337100b0a27fd..c1c3e79ebc789eff0873515c5fffd11089b92043 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -113,6 +113,11 @@ class HloComputation { // instruction. Status RemoveParameter(int64 param_no); + // Remove unused parameters from the computation. + // Note this is only applicatable to the computation for the fusion + // instruction. + Status RemoveUnusedParameters(); + // Add new parameter instruction to the computation. // This should be a new parameter. Instruction will be appended to parameters // and inserted to the instruction list. @@ -199,7 +204,7 @@ class HloComputation { // Compute and return a post-order of the instructions in the computation. In // this order, definitions of values always appear before their uses. - std::list MakeInstructionPostOrder() const; + std::vector MakeInstructionPostOrder() const; // Computes and returns the reachability between HLO instructions in the // computation. The returned HloReachabilityMap is constructed such that @@ -221,7 +226,7 @@ class HloComputation { // transitively. The embedded computations are sorted such that if computation // A calls computation B (eg, via a map instruction) then A will appear after // B in the list. - std::list MakeEmbeddedComputationsList() const; + std::vector MakeEmbeddedComputationsList() const; // Creates a fusion instruction containing the given instructions. // `fusion_kind` indicates the type of the fusion, e.g., loop fusion or fusion diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc index 25469a54c48f4f5cab478aba929f1cc18de8b81f..a8f3f0e9c2dca8fb97ebc8f8c9dd80fcf7f4de4a 100644 --- a/tensorflow/compiler/xla/service/hlo_computation_test.cc +++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc @@ -371,6 +371,38 @@ TEST_F(HloComputationTest, DeepCopyTupleAtIndices) { } } +TEST_F(HloComputationTest, DeepCopyToken) { + // Test that DeepCopyInstruction properly handles tokens which should not be + // copied. + auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + auto copy = computation->DeepCopyInstruction(token).ValueOrDie(); + + // No copy should be added. + EXPECT_THAT(copy, op::AfterAll()); +} + +TEST_F(HloComputationTest, DeepCopyTokenTuple) { + // Test that DeepCopyInstruction properly handles tokens which should not be + // copied. + auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto constant = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + auto tuple = + builder.AddInstruction(HloInstruction::CreateTuple({token, constant})); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + auto copy = computation->DeepCopyInstruction(tuple).ValueOrDie(); + + // Only the array (second tuple element) should be copied. The token is passed + // through transparently. + EXPECT_THAT(copy, op::Tuple(op::GetTupleElement(tuple), + op::Copy(op::GetTupleElement(tuple)))); +} + TEST_F(HloComputationTest, CycleDetection) { // Test whether the visitor can detect cycles in the graph. auto builder = HloComputation::Builder(TestName()); @@ -385,6 +417,9 @@ TEST_F(HloComputationTest, CycleDetection) { // Add a control dependency to create a cycle. ASSERT_IS_OK(add->AddControlDependencyTo(negate)); + auto instructions = computation->MakeInstructionPostOrder(); + EXPECT_EQ(3, instructions.size()); + const auto visitor = [](HloInstruction* instruction) { return Status::OK(); }; auto visit_status = computation->Accept(visitor); ASSERT_FALSE(visit_status.ok()); diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc index 35ecd4428d0dfde2de445ea34472d2c78148c6c9..436d103f230e078e62201bff377a5bab0e62f92b 100644 --- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc +++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc @@ -51,14 +51,18 @@ StatusOr HloConstantFolding::Run(HloModule* module) { computation->root_instruction() != instruction) { continue; } - // Skip Constant, Parameter, Reduce operation. + // Skip Constant, Parameter, Reduce, and AfterAll operation. // TODO(b/35975797): Enable Reduce operation once arbitrary computation // are supported by the evaluator. // TODO(b/64407269): Enable Tuple once the timeout issue is resolved. + // TODO(b/110532604): Enable AfterAll once AfterAll requires at least one + // operand in which case constant folding will be impossible and this + // special case is not necessary. if (instruction->opcode() == HloOpcode::kParameter || instruction->opcode() == HloOpcode::kConstant || instruction->opcode() == HloOpcode::kTuple || - instruction->opcode() == HloOpcode::kReduce) { + instruction->opcode() == HloOpcode::kReduce || + instruction->opcode() == HloOpcode::kAfterAll) { continue; } // Skip instructions with non-constant operands. diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc index 94c9c7eabcc99d4cf61f535925c068a9b55ed136..8955e26d5cd1bf30f965395750f5078d070a6906 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc @@ -172,15 +172,22 @@ Status HloCostAnalysis::HandleReverse(const HloInstruction*) { return Status::OK(); } -Status HloCostAnalysis::HandleSlice(const HloInstruction*) { +Status HloCostAnalysis::HandleSlice(const HloInstruction* slice) { + current_properties_[kBytesAccessedKey] = shape_size_(slice->shape()) * 2; return Status::OK(); } -Status HloCostAnalysis::HandleDynamicSlice(const HloInstruction*) { +Status HloCostAnalysis::HandleDynamicSlice( + const HloInstruction* dynamic_slice) { + current_properties_[kBytesAccessedKey] = + shape_size_(dynamic_slice->shape()) * 2; return Status::OK(); } -Status HloCostAnalysis::HandleDynamicUpdateSlice(const HloInstruction*) { +Status HloCostAnalysis::HandleDynamicUpdateSlice( + const HloInstruction* dynamic_update_slice) { + current_properties_[kBytesAccessedKey] = + shape_size_(dynamic_update_slice->operand(1)->shape()) * 2; return Status::OK(); } @@ -386,6 +393,10 @@ Status HloCostAnalysis::HandleTranspose(const HloInstruction*) { return Status::OK(); } +Status HloCostAnalysis::HandleAfterAll(const HloInstruction*) { + return Status::OK(); +} + Status HloCostAnalysis::HandleConvolution(const HloInstruction* convolution) { auto lhs = convolution->operand(0); auto rhs = convolution->operand(1); diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h index d17678d20f2a23fd98d18b77d5fb25853901a789..44e5df587c4bf0b3004c8d624c45d42d258c3661 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h @@ -97,6 +97,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleBroadcast(const HloInstruction* broadcast) override; Status HandlePad(const HloInstruction* pad) override; Status HandleReshape(const HloInstruction* reshape) override; + Status HandleAfterAll(const HloInstruction* token) override; Status HandleTranspose(const HloInstruction* transpose) override; Status HandleWhile(const HloInstruction* xla_while) override; Status HandleConditional(const HloInstruction* conditional) override; diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc index 16fdda8a8b9ade09ea31cda1f4cf5e8ff2c0a081..9fc4c48226fa5307f5e030a612f3957756827e37 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc @@ -59,9 +59,9 @@ class HloCostAnalysisTest : public ::testing::Test { // Create a computation for a unary user function: x => exp(x + 0.5) { XlaBuilder builder("add_and_exp"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto half = builder.ConstantR0(0.5); - builder.Exp(builder.Add(x, half)); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto half = ConstantR0(&builder, 0.5); + Exp(Add(x, half)); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); add_and_exp_ = computation_status.ConsumeValueOrDie(); @@ -70,9 +70,9 @@ class HloCostAnalysisTest : public ::testing::Test { // Create a computation for a binary user function: (x, y) => x + y { XlaBuilder builder("add"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Add(x, y); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); add_ = computation_status.ConsumeValueOrDie(); @@ -81,9 +81,9 @@ class HloCostAnalysisTest : public ::testing::Test { // Create a computation for a sigmoid function: x => 1 / (1 + exp(-x)) { XlaBuilder builder("sigmoid"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto one = builder.ConstantR0(1.0); - builder.Div(one, builder.Add(one, builder.Exp(builder.Neg(x)))); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto one = ConstantR0(&builder, 1.0); + Div(one, Add(one, Exp(Neg(x)))); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); sigmoid_ = computation_status.ConsumeValueOrDie(); @@ -92,9 +92,9 @@ class HloCostAnalysisTest : public ::testing::Test { // Create a computation for a binary max function: (x, y) => max (x, y) { XlaBuilder builder("max"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Max(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Max(x, y); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); max_ = computation_status.ConsumeValueOrDie(); @@ -103,9 +103,9 @@ class HloCostAnalysisTest : public ::testing::Test { // Create a computation for a binary GT function: (x, y) => x > y { XlaBuilder builder("gt"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Gt(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Gt(x, y); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); gt_ = computation_status.ConsumeValueOrDie(); @@ -137,9 +137,9 @@ class HloCostAnalysisTest : public ::testing::Test { TEST_F(HloCostAnalysisTest, MatrixMultiply) { XlaBuilder builder("matrix_multiply"); - auto lhs = builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 5}), "lhs"); - auto rhs = builder.Parameter(1, ShapeUtil::MakeShape(F32, {5, 30}), "rhs"); - auto result = builder.Dot(lhs, rhs); + auto lhs = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10, 5}), "lhs"); + auto rhs = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {5, 30}), "rhs"); + Dot(lhs, rhs); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -159,8 +159,8 @@ TEST_F(HloCostAnalysisTest, MatrixMultiply) { TEST_F(HloCostAnalysisTest, Map) { XlaBuilder builder("map"); - auto input = builder.Parameter(0, ShapeUtil::MakeShape(F32, {10}), "in"); - auto result = builder.Map({input}, add_and_exp_, {0}); + auto input = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10}), "in"); + Map(&builder, {input}, add_and_exp_, {0}); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -176,17 +176,17 @@ TEST_F(HloCostAnalysisTest, Map) { TEST_F(HloCostAnalysisTest, Convolution) { XlaBuilder builder("convolution"); - auto input = builder.Parameter( - 0, + auto input = Parameter( + &builder, 0, ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/10, /*x_dim=*/20}), "input"); - auto kernel = builder.Parameter( - 1, + auto kernel = Parameter( + &builder, 1, ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/3, /*x_dim=*/3}), "kernel"); - auto result = builder.Conv(input, kernel, {1, 1}, Padding::kValid); + Conv(input, kernel, {1, 1}, Padding::kValid); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -206,9 +206,8 @@ TEST_F(HloCostAnalysisTest, Convolution) { TEST_F(HloCostAnalysisTest, Reduce) { XlaBuilder builder("reduce"); auto input = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); - auto result = - builder.Reduce(input, builder.ConstantR0(0.0f), add_, {1}); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); + Reduce(input, ConstantR0(&builder, 0.0f), add_, {1}); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -224,9 +223,9 @@ TEST_F(HloCostAnalysisTest, Reduce) { TEST_F(HloCostAnalysisTest, ReduceWindow) { XlaBuilder builder("reduce_window"); auto input = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); - auto result = builder.ReduceWindow(input, builder.ConstantR0(0), add_, - {4, 5}, {4, 5}, Padding::kValid); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); + ReduceWindow(input, ConstantR0(&builder, 0), add_, {4, 5}, {4, 5}, + Padding::kValid); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -241,12 +240,11 @@ TEST_F(HloCostAnalysisTest, ReduceWindow) { TEST_F(HloCostAnalysisTest, SelectAndScatter) { XlaBuilder builder("select_and_scatter"); auto operand = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10, 20}), "input"); auto source = - builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 4}), "source"); - auto result = - builder.SelectAndScatter(operand, gt_, {4, 5}, {4, 5}, Padding::kValid, - source, builder.ConstantR0(0), add_); + Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 4}), "source"); + SelectAndScatter(operand, gt_, {4, 5}, {4, 5}, Padding::kValid, source, + ConstantR0(&builder, 0), add_); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -261,7 +259,7 @@ TEST_F(HloCostAnalysisTest, SelectAndScatter) { TEST_F(HloCostAnalysisTest, Broadcast) { XlaBuilder b("broadcast"); - b.Broadcast(b.ConstantR0(42), {10, 7}); + Broadcast(ConstantR0(&b, 42), {10, 7}); auto hlo_module = BuildHloGraph(&b); HloCostAnalysis analysis(ShapeSize); ASSERT_IS_OK( @@ -273,13 +271,12 @@ TEST_F(HloCostAnalysisTest, Broadcast) { TEST_F(HloCostAnalysisTest, FullyConnectedForward) { XlaBuilder builder("fully_connected_forward"); auto input = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {10, 5}), "input"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {10, 5}), "input"); auto weight = - builder.Parameter(1, ShapeUtil::MakeShape(F32, {5, 20}), "weight"); - auto bias = builder.Parameter(2, ShapeUtil::MakeShape(F32, {20}), "bias"); + Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {5, 20}), "weight"); + auto bias = Parameter(&builder, 2, ShapeUtil::MakeShape(F32, {20}), "bias"); // sigmoid(input * weight + bias) - auto result = builder.Map( - {builder.Add(builder.Dot(input, weight), bias, {1})}, sigmoid_, {0, 1}); + Map(&builder, {Add(Dot(input, weight), bias, {1})}, sigmoid_, {0, 1}); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -297,11 +294,11 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) { HloCostAnalysis conv_analysis(ShapeSize); { XlaBuilder builder("conv_looking_matmul"); - auto lhs = builder.Parameter(0, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}), - "input"); - auto rhs = builder.Parameter(1, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}), - "weights"); - builder.Conv(lhs, rhs, {1, 1}, Padding::kSame); + auto lhs = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}), + "input"); + auto rhs = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {64, 64, 1, 1}), + "weights"); + Conv(lhs, rhs, {1, 1}, Padding::kSame); auto hlo_module = BuildHloGraph(&builder); ASSERT_IS_OK(hlo_module->entry_computation()->root_instruction()->Accept( &conv_analysis)); @@ -311,10 +308,10 @@ TEST_F(HloCostAnalysisTest, MatmulAndConvolutionCanBeTheSameComputation) { { XlaBuilder builder("matmul"); auto lhs = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {64, 64}), "input"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {64, 64}), "input"); auto rhs = - builder.Parameter(1, ShapeUtil::MakeShape(F32, {64, 64}), "weights"); - builder.Dot(lhs, rhs); + Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {64, 64}), "weights"); + Dot(lhs, rhs); auto hlo_module = BuildHloGraph(&builder); ASSERT_IS_OK(hlo_module->entry_computation()->root_instruction()->Accept( &matmul_analysis)); @@ -419,9 +416,9 @@ TEST_F(HloCostAnalysisTest, TupleCost) { HloCostAnalysis analysis(ShapeSize); { XlaBuilder builder("matmul"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {123}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {42}), "y"); - auto tuple = builder.Tuple({x, y}); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {123}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {42}), "y"); + Tuple(&builder, {x, y}); auto hlo_module = BuildHloGraph(&builder); ASSERT_IS_OK( @@ -435,21 +432,21 @@ TEST_F(HloCostAnalysisTest, TupleCost) { TEST_F(HloCostAnalysisTest, BaseDilatedConvolution) { XlaBuilder builder("BaseDilatedConvolution"); - auto input = builder.Parameter( - 0, + auto input = Parameter( + &builder, 0, ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/10, /*x_dim=*/20}), "input"); - auto kernel = builder.Parameter( - 1, + auto kernel = Parameter( + &builder, 1, ShapeUtil::MakeShape(F32, {/*p_dim=*/1, /*z_dim=*/1, /*y_dim=*/3, /*x_dim=*/3}), "kernel"); - auto result = builder.ConvGeneralDilated( - input, kernel, /*window_strides=*/{1, 1}, /*padding=*/{{1, 1}, {1, 1}}, - /*lhs_dilation=*/{3, 5}, /*rhs_dilation=*/{7, 11}, - XlaBuilder::CreateDefaultConvDimensionNumbers(2)); + ConvGeneralDilated(input, kernel, /*window_strides=*/{1, 1}, + /*padding=*/{{1, 1}, {1, 1}}, + /*lhs_dilation=*/{3, 5}, /*rhs_dilation=*/{7, 11}, + XlaBuilder::CreateDefaultConvDimensionNumbers(2)); // Run HLO cost analysis. auto hlo_module = BuildHloGraph(&builder); @@ -460,5 +457,51 @@ TEST_F(HloCostAnalysisTest, BaseDilatedConvolution) { EXPECT_EQ(analysis.flop_count(), 1472); } +TEST_F(HloCostAnalysisTest, Slice) { + // Test the analysis on a slice. + XlaBuilder builder("slice"); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "x"); + Slice(x, {0}, {1}, {1}); + auto hlo_module = BuildHloGraph(&builder); + + // Run HLO cost analysis. + HloCostAnalysis analysis(ShapeSize); + ASSERT_IS_OK( + hlo_module->entry_computation()->root_instruction()->Accept(&analysis)); + + EXPECT_EQ(analysis.bytes_accessed(), 8); +} + +TEST_F(HloCostAnalysisTest, DynamicSlice) { + // Test the analysis on a slice. + XlaBuilder builder("dynamic-slice"); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "x"); + DynamicSlice(x, ConstantR1(&builder, {1}), {1}); + auto hlo_module = BuildHloGraph(&builder); + + // Run HLO cost analysis. + HloCostAnalysis analysis(ShapeSize); + ASSERT_IS_OK( + hlo_module->entry_computation()->root_instruction()->Accept(&analysis)); + + EXPECT_EQ(analysis.bytes_accessed(), 8); +} + +TEST_F(HloCostAnalysisTest, DynamicUpdateSlice) { + // Test the analysis on a slice. + XlaBuilder builder("dynamic-update-slice"); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "x"); + DynamicUpdateSlice(x, ConstantR1(&builder, {1.0}), + ConstantR1(&builder, {1})); + auto hlo_module = BuildHloGraph(&builder); + + // Run HLO cost analysis. + HloCostAnalysis analysis(ShapeSize); + ASSERT_IS_OK( + hlo_module->entry_computation()->root_instruction()->Accept(&analysis)); + + EXPECT_EQ(analysis.bytes_accessed(), 8); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc index dab946a099fa0066a4a0d42ce29077b9de6a486e..a0ee8896230d6dcacb5a8eb607fc00ae5226cfa5 100644 --- a/tensorflow/compiler/xla/service/hlo_cse.cc +++ b/tensorflow/compiler/xla/service/hlo_cse.cc @@ -135,17 +135,18 @@ StatusOr HloCSE::Run(HloModule* module) { // instruction for each class. tensorflow::gtl::FlatSet - representatives(/*N=*/1024, &CseHash, cse_equal); - + representatives(/*N=*/computation->instruction_count() + 1, &CseHash, + cse_equal); for (auto instruction : computation->MakeInstructionPostOrder()) { // If the instruction has zero operands (constants, parameters, etc.) skip // over it. if (instruction->operand_count() == 0) { continue; } - - // Skip instructions which have side effects. - if (instruction->HasSideEffect()) { + // Skip instructions which have side effects or are a domain (which must + // not be CSE-ed). + if (instruction->HasSideEffect() || + instruction->opcode() == HloOpcode::kDomain) { continue; } diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index cc130a4900dc162d4b416116fbe879fec37136a2..8a4a9b59868eb436842c9a819ffa8d6ec2054eee 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -34,16 +34,86 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" namespace xla { +namespace { + +// We have this pattern in dynamaic update slice fusion, which should be +// supported: +// +// Parameters: p0, p1 +// Fusion +// ds = DynamicSlice(p0, p1) +// ROOT DynamicUpdateslice(p0, ds, p1) +// +// In this case, we should be able to reuse p0 and output, although p0 has +// multiple uses. +bool MultiDynamicSliceUseShareSameIndices( + tensorflow::gtl::ArraySlice uses) { + if (uses.empty()) { + return false; + } + const HloInstruction* indices = nullptr; + for (HloUse use : uses) { + auto user = use.instruction; + if (user->opcode() == HloOpcode::kDynamicUpdateSlice) { + if (indices == nullptr) { + indices = user->operand(2); + } else if (indices != user->operand(2)) { + return false; + } + if (use.operand_number != 0) { + return false; + } + } else if (user->opcode() == HloOpcode::kDynamicSlice) { + if (indices == nullptr) { + indices = user->operand(1); + } else if (indices != user->operand(1)) { + return false; + } + } else { + return false; + } + } + return true; +} + +} // namespace using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; -HloDataflowAnalysis::HloDataflowAnalysis(const HloModule& module, bool ssa_form, - bool bitcast_defines_value) +HloDataflowAnalysis::HloDataflowAnalysis( + const HloModule& module, bool ssa_form, bool bitcast_defines_value, + const FusionCanShareBufferFunction& fusion_can_share_buffer) : module_(module), ssa_form_(ssa_form), bitcast_defines_value_(bitcast_defines_value), - call_graph_(CallGraph::Build(&module)) {} + call_graph_(CallGraph::Build(&module)), + fusion_can_share_buffer_(fusion_can_share_buffer) {} + +bool HloDataflowAnalysis::AreTransitiveUsesElementwiseOrTuple( + const HloInstruction* inst) { + tensorflow::gtl::FlatSet visited; + tensorflow::gtl::InlinedVector stack; + stack.push_back(inst); + while (!stack.empty()) { + const HloInstruction* current = stack.back(); + stack.pop_back(); + visited.insert(current); + for (const HloInstruction* user : current->users()) { + // Found a user that is non-elementwise on current instruction. + for (const int64 use_index : user->OperandIndices(current)) { + if (!user->IsElementwiseOnOperand(use_index) && + user->opcode() != HloOpcode::kTuple) { + return false; + } + } + if (!visited.count(user)) { + stack.push_back(user); + } + } + } + return true; +} bool HloDataflowAnalysis::ValueIsDefinedAt(const HloInstruction* instruction, const ShapeIndex& index) const { @@ -396,6 +466,24 @@ bool HloDataflowAnalysis::UpdateCopyValueSet(HloInstruction* copy) { return changed; } +bool HloDataflowAnalysis::UpdateDomainValueSet(HloInstruction* domain) { + // Domain instructions just forward their operand. Given that domains can have + // a tuple operand, we iterate through its indexes, like for copies. + // Unlike copies though we also propagate the top-level value. + CHECK_EQ(domain->opcode(), HloOpcode::kDomain); + bool changed = false; + for (auto& pair : GetInstructionValueSet(domain)) { + const ShapeIndex& index = pair.first; + HloValueSet& value_set = pair.second; + HloValueSet& operand_value_set = GetValueSet(domain->operand(0), index); + if (value_set != operand_value_set) { + value_set = operand_value_set; + changed = true; + } + } + return changed; +} + bool HloDataflowAnalysis::UpdateGetTupleElementValueSet(HloInstruction* gte) { CHECK_EQ(gte->opcode(), HloOpcode::kGetTupleElement); bool changed = false; @@ -556,6 +644,8 @@ bool HloDataflowAnalysis::UpdateInstructionValueSet( return UpdateBitcastValueSet(instruction); case HloOpcode::kSlice: return UpdateSliceValueSet(instruction); + case HloOpcode::kDomain: + return UpdateDomainValueSet(instruction); case HloOpcode::kCopy: return UpdateCopyValueSet(instruction); case HloOpcode::kGetTupleElement: @@ -734,6 +824,7 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() { case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kGetTupleElement: + case HloOpcode::kDomain: // These instructions define no values. The values in their output // flow from their operands or from cross computation dataflow. break; @@ -787,12 +878,13 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() { /* static */ StatusOr> HloDataflowAnalysis::Run( - const HloModule& module, bool ssa_form, bool bitcast_defines_value) { + const HloModule& module, bool ssa_form, bool bitcast_defines_value, + const FusionCanShareBufferFunction& fusion_can_share_buffer) { VLOG(1) << "HloDataflowAnalysis::Run on module " << module.name(); XLA_VLOG_LINES(2, module.ToString()); - auto dataflow_analysis = WrapUnique( - new HloDataflowAnalysis(module, ssa_form, bitcast_defines_value)); + auto dataflow_analysis = WrapUnique(new HloDataflowAnalysis( + module, ssa_form, bitcast_defines_value, fusion_can_share_buffer)); TF_RETURN_IF_ERROR(dataflow_analysis->InitializeInstructionValueSets()); dataflow_analysis->Propagate(); @@ -915,6 +1007,7 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( ShapeUtil::GetSubshape(operand->shape(), operand_index); const Shape& user_subshape = ShapeUtil::GetSubshape(user->shape(), user_index); + // Check that operand and user emit the same shape and layout. if (!ShapeUtil::Equal(operand_subshape, user_subshape)) { return false; @@ -927,20 +1020,27 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( const HloValue& value = GetValueDefinedAt(fusion_param, operand_index); if (value.uses().size() != 1) { + if (MultiDynamicSliceUseShareSameIndices(value.uses())) { + return true; + } return false; } const HloUse& use = value.uses()[0]; - if (user->fusion_kind() == HloInstruction::FusionKind::kLoop && - user->fused_expression_root()->opcode() == - HloOpcode::kDynamicUpdateSlice) { - // Loop fusion with kDynamicUpdateSlice fused root. - // - // Returns true iff there is exactly one use of 'operand' at shape index - // 'operand_index', and this singleton use is the fused root at operand - // index 0. - return use.instruction == user->fused_expression_root() && - use.operand_number == 0; + if (user->fusion_kind() == HloInstruction::FusionKind::kLoop || + user->fusion_kind() == HloInstruction::FusionKind::kInput) { + if (user->fused_expression_root()->opcode() == + HloOpcode::kDynamicUpdateSlice) { + // Loop fusion with kDynamicUpdateSlice fused root. + // + // Returns true iff there is exactly one use of 'operand' at shape index + // 'operand_index', and this singleton use is the fused root at operand + // index 0. + return use.instruction == user->fused_expression_root() && + use.operand_number == 0; + } else { + return AreTransitiveUsesElementwiseOrTuple(fusion_param); + } } else if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && user->fused_expression_root()->opcode() == HloOpcode::kAdd) { // Output fusion with kAdd fused root. @@ -965,8 +1065,12 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( // index 'other_add_operand_index'). return use.instruction == user->fused_expression_root() && use.operand_number == other_add_operand_index; + } else if (fusion_can_share_buffer_ != nullptr && + fusion_can_share_buffer_(user, operand)) { + return true; } } + if (user->opcode() == HloOpcode::kDynamicUpdateSlice || user->opcode() == HloOpcode::kWhile) { // We eliminated other users in BufferLiveness::live_range_strictly_before, @@ -998,8 +1102,10 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( }) != uses.end(); return uses.size() == 2 && found_caller_use && found_elementwise_callee_use; } - // Check if 'user' is element-wise. - return user->IsElementwise(); + + // Loop fusions that contain transposing copies won't reach here as they have + // different layouts, which fails the check in the beginning of this function. + return user->IsElementwiseOnOperand(user->operand_index(operand)); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h index 9868746b6113881949e388cd2a4aa9f610b1fdb7..9fea218af0c4ac8a512bea5c187564a8219d041f 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h @@ -42,6 +42,20 @@ namespace xla { // Analysis which identifies all HLO values and their uses in an HLO module. class HloDataflowAnalysis { public: + // Different backends can have very different ways to do fusion, so we give + // backends the flexibility to decide whether an fusion instruction can share + // buffer with it's operands. If this is not specified, a default strategy + // will be used; if this is specified, it will be applied *in addition* to the + // default strategy. + // + // The first parameter of the function should be the fusion instruction, the + // second parameter should be an operand of the fusion instruction. + // + // TODO(b/80315712): Find a better way to tell whether a fusion can share + // buffer. + using FusionCanShareBufferFunction = std::function; + // Run dataflow analysis on the given module. Parameters: // // ssa_form : If true then new values are defined at the merge points of @@ -61,7 +75,10 @@ class HloDataflowAnalysis { // value of its operand. static StatusOr> Run( const HloModule& module, bool ssa_form = false, - bool bitcast_defines_value = false); + bool bitcast_defines_value = false, + const FusionCanShareBufferFunction& fusion_can_share_buffer = nullptr); + + static bool AreTransitiveUsesElementwiseOrTuple(const HloInstruction* inst); // Returns true if 'instruction' defines an HLO value at the given shape index // of its output. @@ -136,8 +153,10 @@ class HloDataflowAnalysis { const ShapeIndex& user_index) const; protected: - HloDataflowAnalysis(const HloModule& module, bool ssa_form, - bool bitcast_defines_value = false); + HloDataflowAnalysis( + const HloModule& module, bool ssa_form, + bool bitcast_defines_value = false, + const FusionCanShareBufferFunction& fusion_can_share_buffer = nullptr); // Returns a new HloValue defined at the given instruction and shape index. HloValue* NewHloValue(HloInstruction* instruction, const ShapeIndex& index, @@ -166,6 +185,7 @@ class HloDataflowAnalysis { bool UpdateCallValueSet(HloInstruction* call); bool UpdateConditionalValueSet(HloInstruction* conditional); bool UpdateCopyValueSet(HloInstruction* copy); + bool UpdateDomainValueSet(HloInstruction* domain); bool UpdateGetTupleElementValueSet(HloInstruction* gte); bool UpdateParameterValueSet(HloInstruction* parameter); bool UpdateRecvDoneValueSet(HloInstruction* recv_done); @@ -221,6 +241,10 @@ class HloDataflowAnalysis { // The Id to use for the next HloValue. HloValue::Id next_value_id_ = 0; + + // Backend specific function that decides whether a fusion can share buffer + // with its operand. + FusionCanShareBufferFunction fusion_can_share_buffer_ = nullptr; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc index 5798326dcbf65c3c34748afb02afab1dc7af9147..70254e2c1a5e2517ac96141fa0d2a570f19e2ade 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc @@ -1158,15 +1158,16 @@ TEST_P(HloDataflowAnalysisTest, SendAndSendDone) { auto builder = HloComputation::Builder(TestName()); auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param0")); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto send = builder.AddInstruction( - HloInstruction::CreateSend(param, /*channel_id=*/0)); + HloInstruction::CreateSend(param, token, /*channel_id=*/0)); auto send_done = builder.AddInstruction(HloInstruction::CreateSendDone(send)); module_->AddEntryComputation(builder.Build()); bool ssa_form = GetParam(); const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form); - EXPECT_EQ(analysis.values().size(), 4); + EXPECT_EQ(analysis.values().size(), 5); EXPECT_TRUE(analysis.ValueIsDefinedAt(param)); EXPECT_TRUE(analysis.ValueIsDefinedAt(send, /*index=*/{})); @@ -1181,15 +1182,16 @@ TEST_P(HloDataflowAnalysisTest, RecvAndRecvDone) { // Test that a RecvDone forwards its operand tuple element at {0} to the // output. auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto recv = builder.AddInstruction( - HloInstruction::CreateRecv(scalar_shape_, /*channel_id=*/0)); + HloInstruction::CreateRecv(scalar_shape_, token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); module_->AddEntryComputation(builder.Build()); bool ssa_form = GetParam(); const HloDataflowAnalysis& analysis = RunAnalysis(ssa_form); - EXPECT_EQ(analysis.values().size(), 3); + EXPECT_EQ(analysis.values().size(), 4); EXPECT_TRUE(analysis.ValueIsDefinedAt(recv, /*index=*/{})); EXPECT_TRUE(analysis.ValueIsDefinedAt(recv, /*index=*/{0})); @@ -1880,9 +1882,14 @@ class HloDataflowAnalysisTestBase : public HloTestBase { computation_ = module_->AddEntryComputation(std::move(computation)); } - void RunAnalysis() { + void RunAnalysis(const HloDataflowAnalysis::FusionCanShareBufferFunction& + fusion_can_share_buffer = nullptr) { CHECK_NOTNULL(module_.get()); - dataflow_analysis_ = HloDataflowAnalysis::Run(*module_).ConsumeValueOrDie(); + dataflow_analysis_ = + HloDataflowAnalysis::Run(*module_, /*ssa_form=*/false, + /*bitcast_defines_value=*/false, + fusion_can_share_buffer) + .ConsumeValueOrDie(); } void BuildModuleAndRunAnalysis(std::unique_ptr computation) { @@ -1974,6 +1981,114 @@ TEST_F(CanShareOperandBufferWithUserTest, ElementWiseSameShape) { dataflow_analysis_->CanShareOperandBufferWithUser(exp, {}, log, {})); } +TEST_F(CanShareOperandBufferWithUserTest, + NonElementwiseLoopFusionCantAliasOperandBuffer) { + auto builder = HloComputation::Builder(TestName()); + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, data_shape, "param0")); + + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(data_shape, HloOpcode::kNegate, param0)); + + auto reverse = builder.AddInstruction( + HloInstruction::CreateReverse(data_shape, neg, {0, 1})); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {reverse, neg}, HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_FALSE(dataflow_analysis_->CanShareOperandBufferWithUser(param0, {}, + fusion, {})); +} + +TEST_F(CanShareOperandBufferWithUserTest, + MultiOutputFusionCanAliasOperandBuffer) { + auto builder = HloComputation::Builder(TestName()); + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + + Shape in_shape = ShapeUtil::MakeShape(F32, {8}); + Shape out_shape = ShapeUtil::MakeShape(PRED, {8}); + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, in_shape, "param0")); + auto param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, in_shape, "param1")); + + auto copy0 = builder.AddInstruction( + HloInstruction::CreateUnary(in_shape, HloOpcode::kCopy, param0)); + auto copy1 = builder.AddInstruction( + HloInstruction::CreateUnary(in_shape, HloOpcode::kCopy, param1)); + + auto tuple = + builder.AddInstruction(HloInstruction::CreateTuple({copy1, copy0})); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {tuple, copy1, copy0}, HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser(param0, {}, + fusion, {0})); + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser(param0, {}, + fusion, {1})); + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser(param1, {}, + fusion, {0})); + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser(param1, {}, + fusion, {1})); +} + +TEST_F(CanShareOperandBufferWithUserTest, + ElementwiseLoopFusionCantAliasOperandBuffer) { + auto builder = HloComputation::Builder(TestName()); + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + + auto one = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + auto operand = builder.AddInstruction( + HloInstruction::CreateBroadcast(data_shape, one, {1})); + + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(data_shape, HloOpcode::kNegate, operand)); + + auto exp = builder.AddInstruction( + HloInstruction::CreateUnary(data_shape, HloOpcode::kExp, neg)); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {exp, neg}, HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser(operand, {}, + fusion, {})); +} + +TEST_F(CanShareOperandBufferWithUserTest, + CanShareOperandWhenDynamicUpdateSliceIsFedByDynamicSliceWithSameIndex) { + auto builder = HloComputation::Builder(TestName()); + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + Shape slice_shape = ShapeUtil::MakeShape(F32, {1, 2}); + + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, data_shape, "param0")); + auto index = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR1({0, 0}))); + auto ds = builder.AddInstruction( + HloInstruction::CreateDynamicSlice(slice_shape, param, index, {1, 2, 2})); + + auto dus = builder.AddInstruction( + HloInstruction::CreateDynamicUpdateSlice(data_shape, param, ds, index)); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {dus, ds, index}, HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_TRUE( + dataflow_analysis_->CanShareOperandBufferWithUser(param, {}, fusion, {})); +} + TEST_F(CanShareOperandBufferWithUserTest, ElementWiseDifferentShape) { auto builder = HloComputation::Builder(TestName()); @@ -2048,6 +2163,45 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) { fusion, {})); } +TEST_F(CanShareOperandBufferWithUserTest, + FusedDynamicUpdateSliceWithConvertCanShare) { + auto builder = HloComputation::Builder(TestName()); + + Shape data_shape = ShapeUtil::MakeShape(F32, {8}); + Shape data_shape_bf16 = ShapeUtil::MakeShape(BF16, {8}); + auto tuple = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeTupleShape({data_shape, data_shape}), "tuple")); + auto gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(data_shape, tuple, 0)); + auto gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(data_shape, tuple, 1)); + + auto convert1 = builder.AddInstruction( + HloInstruction::CreateConvert(data_shape_bf16, gte1)); + + // Create a DynamicUpdateSlice instruction of tuple element 1. + auto starts = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR1({2}))); + auto update = builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR1({2.f, 2.f, 2.f}))); + auto dynamic_update_slice = + builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( + data_shape_bf16, convert1, update, starts)); + + auto convert2 = builder.AddInstruction( + HloInstruction::CreateConvert(data_shape, dynamic_update_slice)); + builder.AddInstruction(HloInstruction::CreateTuple({gte0, convert2})); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {convert2, dynamic_update_slice, starts, update, convert1}, + HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_TRUE( + dataflow_analysis_->CanShareOperandBufferWithUser(gte1, {}, fusion, {})); +} + TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) { auto builder = HloComputation::Builder(TestName()); @@ -2136,6 +2290,33 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) { fusion, {})); } +TEST_F(CanShareOperandBufferWithUserTest, FusionCanShareBufferCustomized) { + auto builder = HloComputation::Builder(TestName()); + Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); + + auto one = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + auto operand = builder.AddInstruction( + HloInstruction::CreateBroadcast(data_shape, one, {1})); + auto mul = builder.AddInstruction(HloInstruction::CreateBinary( + data_shape, HloOpcode::kMultiply, operand, operand)); + auto two = builder.AddInstruction(HloInstruction::CreateConstant( + Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + auto add = builder.AddInstruction( + HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, mul, two)); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {add, two, mul}, HloInstruction::FusionKind::kInput); + RunAnalysis(/*fusion_can_share_buffer=*/[](const HloInstruction* fusion, + const HloInstruction*) { + return fusion->fusion_kind() == HloInstruction::FusionKind::kLoop; + }); + + EXPECT_FALSE(dataflow_analysis_->CanShareOperandBufferWithUser(operand, {}, + fusion, {})); +} + TEST_F(CanShareOperandBufferWithUserTest, WhileCanShare) { Shape data_shape = ShapeUtil::MakeShape(F32, {8}); diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc index fcd723af146e2227b8661b1a4993f1338f7de389..7d35e251ca21951036336ff1a1eb4aabc87bc5ca 100644 --- a/tensorflow/compiler/xla/service/hlo_dce.cc +++ b/tensorflow/compiler/xla/service/hlo_dce.cc @@ -41,20 +41,13 @@ StatusOr HloDCE::Run(HloModule* module) { XLA_VLOG_LINES(2, module->ToString()); for (auto* computation : module->MakeComputationPostOrder()) { - std::unordered_set live_instructions; - TF_RETURN_IF_ERROR(computation->root_instruction()->Accept( - [&live_instructions](HloInstruction* instruction) { - live_instructions.insert(instruction); - return Status::OK(); - })); - // Remove any dead roots and their dead transitive operands. Collect them // into a separate list first to avoid problems with iterating through the // computation's instruction while simultaneously removing instructions. std::vector dead_roots; for (auto* instruction : computation->instructions()) { - if (instruction->user_count() == 0 && - live_instructions.count(instruction) == 0 && + if (instruction != computation->root_instruction() && + instruction->user_count() == 0 && computation->IsRemovable(instruction) && !instruction->HasSideEffect()) { dead_roots.push_back(instruction); @@ -85,8 +78,7 @@ StatusOr HloDCE::Run(HloModule* module) { } // Remove dead computations. - std::list computations = module->MakeComputationPostOrder(); - for (auto* computation : computations) { + for (auto* computation : module->MakeComputationPostOrder()) { if (live_computations.count(computation) == 0) { TF_RETURN_IF_ERROR(module->RemoveEmbeddedComputation(computation)); changed = true; diff --git a/tensorflow/compiler/xla/service/hlo_dce_test.cc b/tensorflow/compiler/xla/service/hlo_dce_test.cc index 5a56607a665c4cbeb7b2572f182b88e890602968..f5524dc6fef3ae11e29011ad7927ee55e1701d76 100644 --- a/tensorflow/compiler/xla/service/hlo_dce_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dce_test.cc @@ -75,19 +75,20 @@ TEST_F(HloDceTest, InstructionsWithSideEffect) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); builder.AddInstruction( - HloInstruction::CreateSend(constant, /*channel_id=*/0)); + HloInstruction::CreateSend(constant, token, /*channel_id=*/0)); builder.AddInstruction(HloInstruction::CreateTuple({})); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); - EXPECT_EQ(3, computation->instruction_count()); + EXPECT_EQ(4, computation->instruction_count()); HloDCE dce; EXPECT_FALSE(dce.Run(module.get()).ValueOrDie()); - EXPECT_EQ(3, computation->instruction_count()); + EXPECT_EQ(4, computation->instruction_count()); } TEST_F(HloDceTest, DeadParameters) { @@ -234,9 +235,10 @@ TEST_F(HloDceTest, CalledComputationWithSideEffect) { { auto param = body_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param")); - - auto infeed = - body_builder.AddInstruction(HloInstruction::CreateInfeed(shape, "")); + auto token = + body_builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto infeed = body_builder.AddInstruction( + HloInstruction::CreateInfeed(shape, token, "")); body_builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, infeed)); } @@ -278,8 +280,10 @@ TEST_F(HloDceTest, CalledComputationWithNestedSideEffect) { { auto param = nested_callee_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param")); + auto token = nested_callee_builder.AddInstruction( + HloInstruction::CreateAfterAll({})); nested_callee_builder.AddInstruction( - HloInstruction::CreateOutfeed(shape, param, "")); + HloInstruction::CreateOutfeed(shape, param, token, "")); } auto nested_called_computation = module->AddEmbeddedComputation(nested_callee_builder.Build()); diff --git a/tensorflow/compiler/xla/service/hlo_domain_isolator.h b/tensorflow/compiler/xla/service/hlo_domain_isolator.h index e0c5718509dabebb7b9307bf764b0ea1ce7369a0..eded3e78eead76c4564daee119034c5031eba409 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_isolator.h +++ b/tensorflow/compiler/xla/service/hlo_domain_isolator.h @@ -26,10 +26,10 @@ limitations under the License. namespace xla { // Domain isolation is the task of placing kDomain instructions between HLO -// instructions having different shrading. A kDomain instruction is essentially +// instructions having different sharding. A kDomain instruction is essentially // used to break an HLO graph edge connecting two instructions with different // sharding. If a set of connected instructions have all the same sharding, no -// kDomain instruciton will be placed. +// kDomain instruction will be placed. class HloDomainIsolator : public HloPassInterface { public: // Creates a new kDomain instruction for the edge between the use instruction diff --git a/tensorflow/compiler/xla/service/hlo_domain_test.cc b/tensorflow/compiler/xla/service/hlo_domain_test.cc index 5553ddb153f7f1f2e6a790890c11f35e192488c4..c1412f7c68774cb3d9aefd517d19eca90d0f3a59 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_test.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_test.cc @@ -21,12 +21,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_sharding_metadata.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/core/lib/core/status_test_util.h" namespace xla { namespace { -class HloDomainTest : public HloTestBase { +class HloDomainTest : public HloVerifiedTestBase { protected: bool FindUserViaDomainPath(HloInstruction* instruction, HloInstruction* operand) const { @@ -64,11 +65,11 @@ class HloDomainTest : public HloTestBase { return false; } - StatusOr> ParseModule( - tensorflow::StringPiece hlo_string) { + StatusOr ParseModule(tensorflow::StringPiece hlo_string) { HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - return ParseHloString(hlo_string, config); + ParseAndVerifyModule(hlo_string, config); + return &module(); } }; @@ -143,32 +144,31 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "c", "a")); + EXPECT_TRUE(HasDomainEdge(module, "c", "b")); + EXPECT_TRUE(HasDomainEdge(module, "d", "a")); + EXPECT_TRUE(HasDomainEdge(module, "d", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_FALSE(HasDomainEdge(module, "c", "a")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "d", "a")); + EXPECT_FALSE(HasDomainEdge(module, "d", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); } TEST_F(HloDomainTest, CheckNoDomainAddedIfNoSharding) { @@ -186,12 +186,11 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(!isolator_changed); } @@ -202,9 +201,10 @@ HloModule Module ENTRY entry { p0 = (f32[4]) parameter(0) a = f32[4] get-tuple-element(p0), index=0 - b = (f32[4], u32[]) send(a), channel_id=1, sharding={maximal device=0} + token = token[] after-all() + b = (f32[4], u32[]) send(a, token), channel_id=1, sharding={maximal device=0} c = () send-done(b), channel_id=1, sharding={maximal device=0} - d = (f32[4], u32[]) recv(), channel_id=2, sharding={maximal device=0} + d = (f32[4], u32[]) recv(token), channel_id=2, sharding={maximal device=0} e = f32[4] recv-done(d), channel_id=2, sharding={maximal device=0} f = f32[4] add(a, e) g = f32[4] subtract(a, e) @@ -212,27 +212,26 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "b", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "f", "e")); - EXPECT_FALSE(HasDomainEdge(module.get(), "a", "p0")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "b", "a")); + EXPECT_TRUE(HasDomainEdge(module, "f", "e")); + EXPECT_FALSE(HasDomainEdge(module, "a", "p0")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "b", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "f", "e")); + EXPECT_FALSE(HasDomainEdge(module, "b", "a")); + EXPECT_FALSE(HasDomainEdge(module, "f", "e")); } TEST_F(HloDomainTest, CheckNoDomainAddedOnPureIOComputation) { @@ -240,20 +239,20 @@ TEST_F(HloDomainTest, CheckNoDomainAddedOnPureIOComputation) { HloModule Module ENTRY entry { - a = (f32[4], u32[]) recv(), channel_id=1, sharding={maximal device=-1} + token = token[] after-all(), sharding={maximal device=-1} + a = (f32[4], u32[]) recv(token), channel_id=1, sharding={maximal device=-1} b = f32[4] recv-done(a), channel_id=1, sharding={maximal device=-1} c = f32[4] add(b, b), sharding={maximal device=-1} - d = (f32[4], u32[]) send(c), channel_id=2, sharding={maximal device=-1} + d = (f32[4], u32[]) send(c, token), channel_id=2, sharding={maximal device=-1} ROOT e = () send-done(d), channel_id=2, sharding={maximal device=-1} } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_FALSE(isolator_changed); } @@ -262,24 +261,24 @@ TEST_F(HloDomainTest, CheckNormalizationOnPureIOComputation) { HloModule Module ENTRY entry { - a = (f32[4], u32[]) recv(), channel_id=1, sharding={maximal device=0} + token = token[] after-all(), sharding={maximal device=0} + a = (f32[4], u32[]) recv(token), channel_id=1, sharding={maximal device=0} b = f32[4] recv-done(a), channel_id=1, sharding={maximal device=0} c = f32[4] add(b, b) - d = (f32[4], u32[]) send(c), channel_id=2, sharding={maximal device=0} + d = (f32[4], u32[]) send(c, token), channel_id=2, sharding={maximal device=0} ROOT e = () send-done(d), channel_id=2, sharding={maximal device=0} } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_FALSE(remover_changed); - HloInstruction* add = FindInstruction(module.get(), "c"); + HloInstruction* add = FindInstruction(module, "c"); ASSERT_NE(add, nullptr); auto device = add->sharding_unique_device(); EXPECT_TRUE(device.has_value()); @@ -302,42 +301,41 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator sharding_isolator(CreateShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool sharding_isolator_changed, - sharding_isolator.Run(module.get())); + sharding_isolator.Run(module)); EXPECT_TRUE(sharding_isolator_changed); HloDomainIsolator opname_isolator(OpNameDomainCreator); TF_ASSERT_OK_AND_ASSIGN(bool opname_isolator_changed, - opname_isolator.Run(module.get())); + opname_isolator.Run(module)); EXPECT_TRUE(opname_isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "c", "a")); + EXPECT_TRUE(HasDomainEdge(module, "c", "b")); + EXPECT_TRUE(HasDomainEdge(module, "d", "a")); + EXPECT_TRUE(HasDomainEdge(module, "d", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover sharding_remover(ShardingMetadata::KindName(), NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool sharding_remover_changed, - sharding_remover.Run(module.get())); + sharding_remover.Run(module)); EXPECT_TRUE(sharding_remover_changed); HloDomainRemover opname_remover(OpNameMetadata::KindName(), OpNameDomainNormalizer); TF_ASSERT_OK_AND_ASSIGN(bool opname_remover_changed, - opname_remover.Run(module.get())); + opname_remover.Run(module)); EXPECT_TRUE(opname_remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "c")); + EXPECT_FALSE(HasDomainEdge(module, "c", "a")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "d", "a")); + EXPECT_FALSE(HasDomainEdge(module, "d", "c")); } TEST_F(HloDomainTest, CheckNormalizationOnInfeedTuple) { @@ -345,33 +343,35 @@ TEST_F(HloDomainTest, CheckNormalizationOnInfeedTuple) { HloModule Module ENTRY entry { - infeed = (f32[4], f32[4]) infeed(), - sharding={{maximal device=1}, {maximal device=0}} - gte0 = f32[4] get-tuple-element(infeed), index=0 - gte1 = f32[4] get-tuple-element(infeed), index=1 + token = token[] after-all() + infeed = ((f32[4], f32[4]), token[]) infeed(token), + sharding={{maximal device=1}, {maximal device=0}, {maximal device=0}} + infeed.data = (f32[4], f32[4]) get-tuple-element(infeed), index=0 + gte0 = f32[4] get-tuple-element(infeed.data), index=0 + gte1 = f32[4] get-tuple-element(infeed.data), index=1 copy0 = f32[4] copy(gte0) copy1 = f32[4] copy(gte1) ROOT add = f32[4] add(copy0, copy1) } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "gte0", "infeed")); - EXPECT_TRUE(HasDomainEdge(module.get(), "gte1", "infeed")); - EXPECT_FALSE(HasDomainEdge(module.get(), "copy0", "gte0")); - EXPECT_FALSE(HasDomainEdge(module.get(), "copy1", "gte1")); + EXPECT_TRUE(HasDomainEdge(module, "infeed.data", "infeed")); + EXPECT_FALSE(HasDomainEdge(module, "copy0", "gte0")); + EXPECT_FALSE(HasDomainEdge(module, "copy1", "gte1")); // Inject unassigned tuple/gte within the infeed domain, to simulate the // HLO passes adding unexpected instructions. // // infeed + // | + // infeed.data (tuple element 0 of infeed) // / \ // GTE0 GTE1 // / \ @@ -380,31 +380,36 @@ ENTRY entry { // \ / // TUPLE // | - // DOMAIN - HloInstruction* infeed = FindInstruction(module.get(), "infeed"); + HloInstruction* infeed = FindInstruction(module, "infeed"); ASSERT_NE(infeed, nullptr); - auto infeed_users = infeed->users(); - HloInstruction* new_gte0 = + HloInstruction* infeed_data = infeed->parent()->AddInstruction(HloInstruction::CreateGetTupleElement( ShapeUtil::GetTupleElementShape(infeed->shape(), 0), infeed, 0)); + + auto infeed_data_users = infeed_data->users(); + HloInstruction* new_gte0 = infeed_data->parent()->AddInstruction( + HloInstruction::CreateGetTupleElement( + ShapeUtil::GetTupleElementShape(infeed_data->shape(), 0), infeed_data, + 0)); HloInstruction* new_copy0 = - infeed->parent()->AddInstruction(HloInstruction::CreateUnary( + infeed_data->parent()->AddInstruction(HloInstruction::CreateUnary( new_gte0->shape(), HloOpcode::kCopy, new_gte0)); - HloInstruction* new_gte1 = - infeed->parent()->AddInstruction(HloInstruction::CreateGetTupleElement( - ShapeUtil::GetTupleElementShape(infeed->shape(), 1), infeed, 1)); + HloInstruction* new_gte1 = infeed_data->parent()->AddInstruction( + HloInstruction::CreateGetTupleElement( + ShapeUtil::GetTupleElementShape(infeed_data->shape(), 1), infeed_data, + 1)); HloInstruction* new_copy1 = - infeed->parent()->AddInstruction(HloInstruction::CreateUnary( + infeed_data->parent()->AddInstruction(HloInstruction::CreateUnary( new_gte1->shape(), HloOpcode::kCopy, new_gte1)); - HloInstruction* new_tuple = infeed->parent()->AddInstruction( + HloInstruction* new_tuple = infeed_data->parent()->AddInstruction( HloInstruction::CreateTuple({new_copy0, new_copy1})); - for (HloInstruction* user : infeed_users) { - TF_EXPECT_OK(infeed->ReplaceUseWith(user, new_tuple)); + for (HloInstruction* user : infeed_data_users) { + TF_EXPECT_OK(infeed_data->ReplaceUseWith(user, new_tuple)); } HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); struct Assignment { @@ -418,7 +423,7 @@ ENTRY entry { }; for (auto& assignment : assignments) { auto device = assignment.instruction->sharding_unique_device(); - EXPECT_TRUE(device.has_value()); + ASSERT_TRUE(device.has_value()); EXPECT_EQ(*device, assignment.device); } EXPECT_TRUE(new_tuple->has_sharding()); @@ -428,5 +433,26 @@ ENTRY entry { HloSharding::AssignDevice(0)})); } +// Tests that text dumps of domain instructions can be parsed back, in the +// specific case of null shardings. +TEST_F(HloDomainTest, DumpParseNullSharding) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {}); + auto sharding_md_0 = MakeUnique(nullptr); + auto sharding_md_1 = MakeUnique(nullptr); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "p")); + HloInstruction* domain = builder.AddInstruction(HloInstruction::CreateDomain( + shape, param, std::move(sharding_md_0), std::move(sharding_md_1))); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, domain, domain)); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + auto hlo_string = module->ToString(); + ASSERT_TRUE(ParseModule(hlo_string).status().ok()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_element_type_converter_test.cc b/tensorflow/compiler/xla/service/hlo_element_type_converter_test.cc index 5c5a059e0fd895f03bc26a975609b57333237faf..c170e36c73ad2bef830e528de3ec72d38683d888 100644 --- a/tensorflow/compiler/xla/service/hlo_element_type_converter_test.cc +++ b/tensorflow/compiler/xla/service/hlo_element_type_converter_test.cc @@ -57,8 +57,10 @@ TEST_F(HloElementTypeConverterTest, InfeedsOutfeedsNotConverted) { const string& hlo_string = R"( HloModule InfeedOutfeed ENTRY RoundTrip16MiBR1.v2 { - ROOT infeed = bf16[4]{0} infeed() - outfeed = () outfeed(infeed) + token = token[] after-all() + infeed = (bf16[4]{0}, token[]) infeed(token) + ROOT infeed.data = bf16[4]{0} get-tuple-element(infeed), index=0 + outfeed = token[] outfeed(infeed.data, token) } )"; auto module = CreateModuleFromHloString(hlo_string); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 1e78d775c8e172a272a03fbd1101cef365e6dc2d..e65e1af20c156f6b8fc16566ce548be6ce0d746b 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -300,12 +300,6 @@ StatusOr> HloEvaluator::EvaluateWithSubstitutions( instruction->CloneWithNewOperands(instruction->shape(), operands); auto result = Evaluate(cloned_instruction.get()); - // Clean up our cloned instructions before returning. - cloned_instruction->DetachFromOperands(); - for (auto& operand : owned_operands) { - operand->DetachFromOperands(); - } - return result; } @@ -321,7 +315,6 @@ StatusOr> HloEvaluator::EvaluateElementwiseBinaryOp( rhs_instr.get()); auto result = Evaluate(cloned_instruction.get()); - cloned_instruction->DetachFromOperands(); return result; } @@ -334,7 +327,6 @@ StatusOr> HloEvaluator::EvaluateElementwiseUnaryOp( HloInstruction::CreateUnary(operand.shape(), opcode, operand_instr.get()); auto result = Evaluate(cloned_instruction.get()); - cloned_instruction->DetachFromOperands(); return result; } @@ -372,7 +364,7 @@ Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { // The result concatenate dimension is going to be the sum of all // concatenate dimensions of the operands taking part of the operation. const Shape& reference_shape = operands[0]->shape(); - CHECK(!ShapeUtil::IsTuple(reference_shape)); + CHECK(ShapeUtil::IsArray(reference_shape)); const int64 rank = ShapeUtil::Rank(reference_shape); const int64 concat_dim = concatenate->dimensions()[0]; CHECK_GE(concat_dim, 0); @@ -383,7 +375,7 @@ Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { for (int64 i = 1; i < operands.size(); ++i) { const Shape& operand_shape = operands[i]->shape(); - CHECK(!ShapeUtil::IsTuple(operand_shape)); + CHECK(ShapeUtil::IsArray(operand_shape)); // Accumulate the concat dimension from all tensors taking part to the // operation. concat_dimensions[concat_dim] += @@ -910,6 +902,11 @@ Status HloEvaluator::HandleBroadcast(HloInstruction* broadcast) { return Status::OK(); } +Status HloEvaluator::HandleAfterAll(HloInstruction* token) { + evaluated_[token] = Literal::CreateToken(); + return Status::OK(); +} + Status HloEvaluator::HandleGetTupleElement(HloInstruction* get_tuple_element) { const auto result_shape = get_tuple_element->shape(); const int64 index = get_tuple_element->tuple_index(); @@ -1071,6 +1068,19 @@ Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) { return Status::OK(); } +Status HloEvaluator::HandleSort(HloInstruction* sort) { + if (!ShapeUtil::IsTuple(sort->shape())) { + return DefaultAction(sort); + } + // The key-value version of Sort is a special snowflake, since the output + // shape is a tuple, so its element type is not meaningful. + // + // TODO(mkuper): Do something sane here, so that we can support different key + // and value types. + return sort->Visit( + typed_visitors_.at(sort->operand(0)->shape().element_type()).get()); +} + Status HloEvaluator::Preprocess(HloInstruction* hlo) { VLOG(2) << "About to visit HLO: " << hlo->ToString(); return Status::OK(); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index b53d5644de5a17c52bdbf2593ce52f0227008a00..b330c30eeb668dfbbb6e42a401b6e93045ee50f5 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -174,6 +174,10 @@ class HloEvaluator : public DfsHloVisitorWithDefault { Status HandleBroadcast(HloInstruction* broadcast) override; + Status HandleAfterAll(HloInstruction* token) override; + + Status HandleSort(HloInstruction* sort) override; + // Returns the already-evaluated literal result for the instruction. // A Constant instruction is considered evaluated and its literal will be // returned directly without looking up the cache. diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index 84b4ead2dd28caa40b6d7830a1e1401be88b6b36..42770d848a83b2e27b87bc963d259e2b7af664a4 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -206,6 +206,15 @@ TEST_P(HloEvaluatorTest, DoesOr) { std::move(rhs)); } // Verifies that HloEvaluator evaluates a HLO instruction that performs +// element-wise or with 2 operands. +TEST_P(HloEvaluatorTest, DoesXor) { + auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); + auto expected = Literal::CreateR2({{3, 4}, {-104, 0}}); + TestBinaryOp(HloOpcode::kXor, std::move(expected), std::move(lhs), + std::move(rhs)); +} +// Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise multiply with 2 operands. TEST_P(HloEvaluatorTest, DoesMultiply) { auto lhs = Literal::CreateR2({{-1, 0}, {-100, 4}}); @@ -1248,7 +1257,7 @@ void BM_ReducePrecisely(int num_iters) { HloComputation::Builder b("BM_ReducePrecisely"); HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - HloModule module("BM_ReducePrecisely", VersionedComputationHandle(), config); + HloModule module("BM_ReducePrecisely", config); constexpr int kNumElements = 1 << 25; // float += 1 saturates at 1<<24 std::vector v(kNumElements, 1.0f); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index 13f46407e33e36bdbef4c9032630101d6c18268f..1136178e90b216960543c194348dfbceb964ca95 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -610,12 +610,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleAnd(HloInstruction* and_) { - TF_ASSIGN_OR_RETURN( - parent_->evaluated_[and_], - ElementWiseBinaryOp(and_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) { - return lhs_el && rhs_el; - })); - return Status::OK(); + return InvalidArgument("Unsupported type for And"); } template < @@ -644,12 +639,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleOr(HloInstruction* or_) { - TF_ASSIGN_OR_RETURN( - parent_->evaluated_[or_], - ElementWiseBinaryOp(or_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) { - return lhs_el || rhs_el; - })); - return Status::OK(); + return InvalidArgument("Unsupported type for Or"); } template < @@ -663,6 +653,35 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return HandleOr(or_); } + template ::value>::type* = + nullptr> + Status HandleXor(HloInstruction* xor_) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[xor_], + ElementWiseBinaryOp(xor_, [](ElementwiseT lhs_el, ElementwiseT rhs_el) { + return lhs_el ^ rhs_el; + })); + return Status::OK(); + } + + template ::value>::type* = nullptr> + Status HandleXor(HloInstruction* xor_) { + return InvalidArgument("Unsupported type for Xor"); + } + + template < + typename NativeT, + typename std::enable_if::value>::type* = nullptr> + Status HandleXor(HloInstruction* xor_) { + return InvalidArgument("Unsupported type for Xor"); + } + + Status HandleXor(HloInstruction* xor_) override { + return HandleXor(xor_); + } + template ::value && @@ -778,7 +797,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { Status HandleSelect(HloInstruction* select) override { CHECK(!ShapeUtil::IsScalar(select->operand(0)->shape())); - CHECK(!ShapeUtil::IsTuple(select->shape())); + CHECK(ShapeUtil::IsArray(select->shape())); std::function select_op = [](bool pred, ReturnT on_true, ReturnT on_false) { if (pred) { @@ -1006,83 +1025,47 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { CHECK_EQ(dnums.lhs_batch_dimensions_size(), dnums.rhs_batch_dimensions_size()); - std::vector lhs_non_contracting_dims; + DimensionVector lhs_index(lhs_rank); + DimensionVector rhs_index(rhs_rank); + + // result_index_locations[i] contains one or two pointers to the locations + // in lhs_index or rhs_index where the i'th result index should go. + tensorflow::gtl::InlinedVector, kInlineRank> + result_index_locations; + result_index_locations.reserve(lhs_rank + rhs_rank - 2); + + // The first components in the output shape are the LHS and RHS batch + // dimensions: + for (int64 i = 0; i < dnums.lhs_batch_dimensions_size(); i++) { + result_index_locations.push_back( + {&lhs_index[dnums.lhs_batch_dimensions(i)], + &rhs_index[dnums.rhs_batch_dimensions(i)]}); + } + + // Then we have the LHS and RHS non-contracting dimensions, if any: for (int64 i = 0; i < lhs_rank; i++) { - if (i != lhs_contracting_dimension) { - lhs_non_contracting_dims.push_back(i); + if (i != lhs_contracting_dimension && + !ArrayContains(AsInt64Slice(dnums.lhs_batch_dimensions()), i)) { + result_index_locations.push_back({&lhs_index[i], nullptr}); } } - - std::vector rhs_non_batch_non_contracting_dims; - tensorflow::gtl::FlatSet batch_dims_set( - dnums.rhs_batch_dimensions().begin(), - dnums.rhs_batch_dimensions().end()); for (int64 i = 0; i < rhs_rank; i++) { - if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) { - rhs_non_batch_non_contracting_dims.push_back(i); + if (i != rhs_contracting_dimension && + !ArrayContains(AsInt64Slice(dnums.rhs_batch_dimensions()), i)) { + result_index_locations.push_back({&rhs_index[i], nullptr}); } } - const int64 batch_dim_size = dnums.lhs_batch_dimensions_size(); - const int64 lhs_non_contracting_size = lhs_non_contracting_dims.size(); - - DimensionVector lhs_index(lhs_rank); - DimensionVector rhs_index(rhs_rank); auto result = MakeUnique(dot->shape()); TF_RETURN_IF_ERROR(result->Populate( [&](tensorflow::gtl::ArraySlice result_index) { ElementwiseT result_val = static_cast(0); - // Find the corresponding non-contracting indices for lhs and rhs. - // - // For `result_index`, its batch dimension, if exists, will be at the - // same dimension as the batch dimension of lhs and rhs. More - // specifically: - // - For lhs, the non-contracting dimensions, including the batch - // dimension have the same index as the `result_index`. - // - For rhs, the batch dimension is set seperately from other - // non-contracting dimensions, since these other non-contracting - // dimensions in rhs follow the non-contracting dimensions of lhs in - // the resulting index. - // - // As an example, for a resulting index: - // result_index [result_batch, result_x, result_y] - // the effecting lhs and rhs indices are: - // lhs [result_batch, lhs_non_contracting_dim, contracting_dim - // rhs [result_batch, contracting_dim, rhs_non_contracting_dim] - // `result_x` is only affected by the lhs_non_contracting_dim and - // likewise `result_y` only depends on rhs_non_contracting_dim. - // - // so we can look up the lhs and rhs indices by: - // - // lhs: - // batch index is the same as `result_batch`. - // non-contracting dimension is the same as - // result_index[lhs_non_contracting_dim] - // rhs: - // batch index: the same as `result_batch`. - // non-contracting dimension index: *not* the same as - // result_index[rhs_non_contractng_dim], since the - // non-contracting dimensions of lhs are included in the - // result_index first. Instead, the non_contracting_dim of rhs must - // be calculated as following: - // lhs_non_contracting_dimensions_size + - // (rhs_non_batch_non_contracting_dim - batch_dim_size) - 1 - // - // Note that (rhs_non_batch_contracting_dim - batch_dim_size) is - // the index offset to the result_index that only depends on - // the non_batch and non-contracting dimensions of rhs. -1 at the - // end translates size to index. - for (auto i : lhs_non_contracting_dims) { - lhs_index[i] = result_index[i]; - } - for (auto i : dnums.rhs_batch_dimensions()) { - rhs_index[i] = result_index[i]; - } - for (auto i : rhs_non_batch_non_contracting_dims) { - const int64 rhs_non_batch_non_contracting_dim = - lhs_non_contracting_size + (i - batch_dim_size) - 1; - rhs_index[i] = result_index[rhs_non_batch_non_contracting_dim]; + for (int64 i = 0; i < result_index.size(); i++) { + *result_index_locations[i].first = result_index[i]; + if (result_index_locations[i].second) { + *result_index_locations[i].second = result_index[i]; + } } // Accumulates resulting product along the contracted dimension. @@ -1103,7 +1086,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { } Status HandlePad(HloInstruction* pad) override { - CHECK(!ShapeUtil::IsTuple(pad->operand(0)->shape())); + CHECK(ShapeUtil::IsArray(pad->operand(0)->shape())); // Padding value must be scalar. CHECK(ShapeUtil::IsScalar(pad->operand(1)->shape())); CHECK_EQ(ShapeUtil::Rank(pad->operand(0)->shape()), @@ -1116,7 +1099,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { /*padding_config=*/pad->padding_config())); CHECK(ShapeUtil::Compatible(pad->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(pad->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); // Create new HLO of padded shape with padding value. @@ -1182,7 +1165,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { dynamic_slice->dynamic_slice_sizes())); TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(result_shape) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); TF_RET_CHECK( primitive_util::IsIntegralType(start_indices->shape().element_type())); @@ -1237,7 +1220,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { operand->shape(), update->shape(), start_indices->shape())); TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(result_shape) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); TF_RET_CHECK( primitive_util::IsIntegralType(start_indices->shape().element_type())); @@ -1378,6 +1361,88 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } + template ::value && + !std::is_same::value>::type* = nullptr> + Status HandleSort(HloInstruction* sort) { + auto keys = sort->operand(0); + TF_RET_CHECK(ShapeUtil::Rank(keys->shape()) == 1) + << "Sort is only supported for R1 shapes"; + + const Literal& keys_literal = parent_->GetEvaluatedLiteralFor(keys); + VLOG(3) << "HandleSort keys_literal: " << keys_literal.ToString(); + const auto& keys_data = keys_literal.data(); + + if (sort->operand_count() == 1) { + std::vector result_data(keys_data.begin(), keys_data.end()); + std::sort(result_data.begin(), result_data.end(), + [](const ReturnT& a, const ReturnT& b) { + return SafeLess(a, b); + }); + auto result_literal = MakeUnique(sort->shape()); + result_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_data)); + VLOG(3) << "HandleSort result_literal: " << result_literal->ToString(); + parent_->evaluated_[sort] = std::move(result_literal); + } else { + CHECK_EQ(sort->operand_count(), 2); + auto values = sort->operand(1); + if (values->shape().element_type() != + primitive_util::NativeToPrimitiveType()) { + return InvalidArgument( + "Evaluator requires value and key types for Sort to match"); + } + + // We need to sort and array of keys and an array of values, where the + // sorted order of the values is determined by the keys. The simplest(?) + // way to do this is to go to an array-of-pairs representation, sort the + // array using the keys, and then go back to pair-of-arrays. + const Literal& values_literal = parent_->GetEvaluatedLiteralFor(values); + VLOG(3) << "HandleSort values_literal: " << values_literal.ToString(); + const auto& values_data = values_literal.data(); + using kv_pair = std::pair; + std::vector key_value_vector; + CHECK_EQ(keys_data.size(), values_data.size()); + for (int i = 0; i < keys_data.size(); ++i) { + key_value_vector.push_back( + std::make_pair(keys_data[i], values_data[i])); + } + std::sort(key_value_vector.begin(), key_value_vector.end(), + [](const kv_pair& a, const kv_pair& b) { + return SafeLess(a.first, b.first); + }); + std::vector result_keys, result_values; + for (const auto& key_value : key_value_vector) { + result_keys.push_back(key_value.first); + result_values.push_back(key_value.second); + } + auto result_keys_literal = MakeUnique(keys->shape()); + result_keys_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_keys)); + auto result_values_literal = MakeUnique(values->shape()); + result_values_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_values)); + auto result_tuple = Literal::MakeTuple( + {result_keys_literal.get(), result_values_literal.get()}); + VLOG(3) << "HandleSort result_tuple: " << result_tuple->ToString(); + parent_->evaluated_[sort] = std::move(result_tuple); + } + return Status::OK(); + } + + template ::value || + std::is_same::value>::type* = + nullptr> + Status HandleSort(HloInstruction* sort) { + return InvalidArgument("Unsupported type for Sort"); + } + + Status HandleSort(HloInstruction* sort) override { + return HandleSort(sort); + } + Status HandleReduce(HloInstruction* reduce) override { auto arg = reduce->operand(0); auto init_value = reduce->operand(1); @@ -1393,7 +1458,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { /*to_apply=*/function->ComputeProgramShape())); TF_RET_CHECK(ShapeUtil::Compatible(reduce->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(reduce->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); const Literal& arg_literal = parent_->GetEvaluatedLiteralFor(arg); @@ -1613,7 +1678,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { ShapeUtil::Compatible(reduce_window->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanStringWithLayout(reduce_window->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanStringWithLayout(inferred_return_shape); const Literal& operand_literal = @@ -2118,6 +2183,38 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return rhs_unsigned >= lhs_size_unsigned; } + // It's UB to use std::sort with std::less, because of NaNs. Define + // "safe" less functions which are actually strict weak orders. + template ::value>::type* = + nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + return a < b; + } + + template ::value || + std::is_same::value>::type* = nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + if (std::isnan(b)) { + return !std::isnan(a); + } else { + return a < b; + } + } + + template ::value>::type* = nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + if (Eigen::half_impl::isnan(b)) { + return !Eigen::half_impl::isnan(a); + } else { + return a < b; + } + } + HloEvaluator* parent_; }; diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 61612bebd1e906d2d055e2f70de29da53275d4e8..8856723f67cf22c44e5ee482777a6a0908d1725d 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -28,6 +28,8 @@ limitations under the License. #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_tfgraph_builder.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -723,11 +725,25 @@ string HloDotDumper::DumpRootTag() { to_id, node_body, node_shape, NodeColorAttributes(color)); } +static const HloConstantInstruction* TryGetFusionParameterConstant( + const HloInstruction* instr) { + if (instr->opcode() != HloOpcode::kParameter || !instr->IsFused()) { + return nullptr; + } + const HloInstruction* fusion = instr->parent()->FusionInstruction(); + const HloInstruction* operand = fusion->operand(instr->parameter_number()); + return DynCast(operand); +} + bool HloDotDumper::ShouldMergeIntoUsers(const HloInstruction* instr) const { // If a node: // - // - is a tuple-shaped parameter, - // - is not a parameter to a fusion node, + // - is a parameter of a fusion node which is bound to a constant, + // + // or + // + // - is a tuple-shaped parameter, and + // - is not a parameter to a fusion node, and // - has at least kMinUsersToOmit users shown, and // - all of the shown users are get-tuple-elements, // @@ -735,6 +751,9 @@ bool HloDotDumper::ShouldMergeIntoUsers(const HloInstruction* instr) const { // // This helps us handle the common case where a while loop body has one big // tuple-shaped parameter. + if (TryGetFusionParameterConstant(instr) != nullptr) { + return true; + } const int kMinUsersToOmit = 3; return instr->opcode() == HloOpcode::kParameter && ShapeUtil::IsTuple(instr->shape()) && !instr->IsFused() && @@ -806,26 +825,26 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) { string HloDotDumper::GetInstructionNodeInlinedOperands( const HloInstruction* instr) { - auto stringify_constant = [](const HloInstruction* constant) { + auto stringify_constant = [](const HloConstantInstruction* constant) { const auto& shape = constant->shape(); // If the shape has a dimension of size zero, print it as e.g. // "{} (f32[42, 0, 10])". The alternative, calling Literal::ToString(), // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which // is just noise. - if (!ShapeUtil::IsTuple(shape) && ShapeUtil::HasZeroElements(shape)) { + if (ShapeUtil::IsZeroElementArray(shape)) { return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); } // Print the literal value of constants with <= K elements. optional elem_count; - if (!ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)) { + if (ShapeUtil::IsArray(shape)) { elem_count = 1; for (int64 dim : shape.dimensions()) { *elem_count *= dim; } } - if (elem_count.has_value() && *elem_count <= 8 && constant->HasLiteral()) { + if (elem_count.has_value() && *elem_count <= 8) { return Printf("%s (%s)", constant->literal().ToString(), ShapeUtil::HumanString(constant->shape())); } @@ -841,29 +860,26 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( ShapeUtil::HumanString(constant->shape())); }; - // Special case: If instr is a parameter to a fusion node, check whether the - // corresponding operand to the fusion node is a constant. - if (instr->opcode() == HloOpcode::kParameter && instr->IsFused()) { - const HloInstruction* fusion = instr->parent()->FusionInstruction(); - const HloInstruction* operand = fusion->operand(instr->parameter_number()); - if (operand->opcode() != HloOpcode::kConstant) { - return ""; - } - return StrCat("constant ", stringify_constant(operand)); - } - std::vector lines; for (int64 i = 0; i < instr->operand_count(); ++i) { const HloInstruction* operand = instr->operand(i); + const auto* constant_operand = DynCast(operand); optional operand_str; - if (operand->opcode() == HloOpcode::kConstant) { - operand_str = stringify_constant(operand); + if (constant_operand != nullptr) { + operand_str = stringify_constant(constant_operand); } else if (ShouldMergeIntoUsers(operand)) { - // Special case: If the operand is a parameter, use its parameter number - // rather than its name, because that's generally how people think of the - // node. + // Special case: If the operand is a parameter to a fusion node and it + // always has a constant value, display it like a regular constant. + // + // For other parameters, use the parameter number rather than the proper + // name, because that's generally how people think of the node. if (operand->opcode() == HloOpcode::kParameter) { - operand_str = Printf("Parameter %lld", operand->parameter_number()); + if (const HloConstantInstruction* constant = + TryGetFusionParameterConstant(operand)) { + operand_str = stringify_constant(constant); + } else { + operand_str = Printf("Parameter %lld", operand->parameter_number()); + } } else { operand_str = operand->name(); } @@ -897,11 +913,14 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { const auto kParameterColor = kOrange; // Special case: If this instruction has a parameter merged into it, paint it - // the same color as a parameter. + // the same color as a parameter. Unless the merged-in parameter is a + // parameter to a fusion node that is bound to a constant -- these aren't + // "real" parameters from the user's perspective. if (std::any_of(instr->operands().begin(), instr->operands().end(), [&](const HloInstruction* operand) { return operand->opcode() == HloOpcode::kParameter && - ShouldMergeIntoUsers(operand); + ShouldMergeIntoUsers(operand) && + TryGetFusionParameterConstant(operand) == nullptr; })) { return kParameterColor; } @@ -941,6 +960,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kNegate: case HloOpcode::kNot: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kPower: case HloOpcode::kReal: case HloOpcode::kRemainder: @@ -964,6 +984,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kBitcast: case HloOpcode::kGetTupleElement: case HloOpcode::kTrace: + case HloOpcode::kAfterAll: case HloOpcode::kTuple: return kWhite; case HloOpcode::kBroadcast: @@ -975,7 +996,6 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { } return kGreen; case HloOpcode::kConcatenate: - case HloOpcode::kCopy: case HloOpcode::kDynamicSlice: case HloOpcode::kGather: case HloOpcode::kPad: @@ -997,6 +1017,10 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { return kWhite; } return kGreen; + case HloOpcode::kCopy: + // Emphasize copy nodes, which are either physical transposes (and thus + // significant), or copies of read-only buffers (and thus dead weight). + return kGreen; case HloOpcode::kConvolution: case HloOpcode::kDot: case HloOpcode::kFft: diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc index 8e52d926d85f1ce6fabeb2dedd2f8e0fe0c2051d..68f41a1cbb4db228f5dcf8b4a6130f05e81262a8 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc @@ -121,7 +121,7 @@ TEST(HloGraphDumperTest, Constant) { HloComputation::Builder b("b"); auto instruction = b.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(-42))); - instruction->set_name("i_am_a_constant_root_instruction"); + instruction->SetAndSanitizeName("i_am_a_constant_root_instruction"); HloModuleConfig config; HloModule m(TestName(), config); HloComputation* root_computation = m.AddEntryComputation(b.Build()); diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 8d7604fae1e121b771915e0852ab44005da92fbe..5b416d965428cfd7f5cefa059560938c63401341 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -16,7 +16,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include -#include #include #include #include @@ -36,7 +35,6 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/compiler/xla/window_util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" @@ -66,26 +64,314 @@ StatusOr> HloInstruction::CreateFromProto( const auto operands = [&instruction_map, &proto](int index) { return instruction_map.at(proto.operand_ids(index)); }; + const auto all_operands = [&instruction_map, &proto]() { + std::vector result(proto.operand_ids_size()); + std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), + result.begin(), [&instruction_map](int64 operand_id) { + return instruction_map.at(operand_id); + }); + return result; + }; + const auto computations = [&computation_map, &proto](int index) { + return computation_map.at(proto.called_computation_ids(index)); + }; switch (opcode) { // Ops migrated to subclasses. case HloOpcode::kBatchNormTraining: - CHECK_EQ(proto.operand_ids_size(), 3); + TF_RET_CHECK(proto.operand_ids_size() == 3) + << "BatchNormTraining instruction should have 3 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormTraining( proto.shape(), operands(0), operands(1), operands(2), proto.epsilon(), proto.feature_index()); break; case HloOpcode::kBatchNormInference: - CHECK_EQ(proto.operand_ids_size(), 5); + TF_RET_CHECK(proto.operand_ids_size() == 5) + << "BatchNormInference instruction should have 5 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormInference( proto.shape(), operands(0), operands(1), operands(2), operands(3), operands(4), proto.epsilon(), proto.feature_index()); break; case HloOpcode::kBatchNormGrad: - CHECK_EQ(proto.operand_ids_size(), 5); + TF_RET_CHECK(proto.operand_ids_size() == 5) + << "BatchNormGrad instruction should have 5 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormGrad(proto.shape(), operands(0), operands(1), operands(2), operands(3), operands(4), proto.epsilon(), proto.feature_index()); break; + case HloOpcode::kFft: { + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Fft instruction should have 1 operand but sees " + << proto.operand_ids_size(); + std::vector fft_length(proto.fft_length().begin(), + proto.fft_length().end()); + instruction = CreateFft(proto.shape(), operands(0), proto.fft_type(), + tensorflow::gtl::ArraySlice(fft_length)); + break; + } + case HloOpcode::kSend: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Send instruction should have 2 operand but sees " + << proto.operand_ids_size(); + instruction = CreateSend(operands(0), operands(1), proto.channel_id()); + break; + case HloOpcode::kSendDone: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "SendDone instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = CreateSendDone(operands(0)); + break; + case HloOpcode::kRecv: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Recv instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = CreateRecv(proto.shape().tuple_shapes(0), operands(0), + proto.channel_id()); + break; + case HloOpcode::kRecvDone: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "RecvDone instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = CreateRecvDone(operands(0)); + break; + case HloOpcode::kReverse: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Reverse instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = CreateReverse(proto.shape(), operands(0), + std::vector(proto.dimensions().begin(), + proto.dimensions().end())); + break; + case HloOpcode::kConcatenate: + TF_RET_CHECK(proto.dimensions_size() == 1) + << "Concatenate instruction should have 1 dimension but sees " + << proto.dimensions_size(); + instruction = + CreateConcatenate(proto.shape(), all_operands(), proto.dimensions(0)); + break; + case HloOpcode::kReduce: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Reduce instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "Reduce instruction should have 1 called computation but sees " + << proto.called_computation_ids_size(); + instruction = CreateReduce(proto.shape(), operands(0), operands(1), + std::vector(proto.dimensions().begin(), + proto.dimensions().end()), + computations(0)); + break; + case HloOpcode::kTranspose: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Transpose instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = + CreateTranspose(proto.shape(), operands(0), + std::vector(proto.dimensions().begin(), + proto.dimensions().end())); + break; + case HloOpcode::kBroadcast: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Broadcast instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = + CreateBroadcast(proto.shape(), operands(0), + std::vector(proto.dimensions().begin(), + proto.dimensions().end())); + break; + case HloOpcode::kMap: + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "Map instruction should have 1 called computation but sees " + << proto.called_computation_ids_size(); + instruction = CreateMap(proto.shape(), all_operands(), computations(0)); + break; + case HloOpcode::kSlice: { + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Slice instruction should have 1 operand but sees " + << proto.operand_ids_size(); + std::vector slice_starts, slice_limits, slice_strides; + for (const HloInstructionProto::SliceDimensions& slice_dimensions : + proto.slice_dimensions()) { + slice_starts.push_back(slice_dimensions.start()); + slice_limits.push_back(slice_dimensions.limit()); + slice_strides.push_back(slice_dimensions.stride()); + } + instruction = CreateSlice(proto.shape(), operands(0), slice_starts, + slice_limits, slice_strides); + break; + } + case HloOpcode::kConstant: { + // TODO(b/110214922): Revert this to CHECK(proto.has_literal()). + if (proto.has_literal()) { + TF_ASSIGN_OR_RETURN(auto literal, + Literal::CreateFromProto(proto.literal())); + instruction = CreateConstant(std::move(literal)); + } else { + instruction = MakeUnique(proto.shape()); + } + break; + } + case HloOpcode::kTrace: { + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Trace instruction should have 1 operand but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_literal()); + TF_ASSIGN_OR_RETURN(auto literal, + Literal::CreateFromProto(proto.literal())); + instruction = CreateTrace(literal->GetR1U8AsString(), operands(0)); + break; + } + case HloOpcode::kFusion: { + // In the proto, fused computations are held exclusively within the + // HloInstructionProto and do not appear as an HloComputationProto within + // the HloModuleProto. + TF_RET_CHECK(!proto.fusion_kind().empty()); + TF_ASSIGN_OR_RETURN(FusionKind fusion_kind, + StringToFusionKind(proto.fusion_kind())); + + // Find the fused computation and set its fusion instruction. + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "Expect 1 called computation for fusion instruction but sees " + << proto.called_computation_ids_size(); + const int64 fusion_id = proto.called_computation_ids(0); + auto* fused_computation = FindPtrOrNull(computation_map, fusion_id); + TF_RET_CHECK(fused_computation != nullptr) + << "No fusion computation with id " << fusion_id; + instruction = CreateFusion(proto.shape(), fusion_kind, all_operands(), + fused_computation); + break; + } + case HloOpcode::kRng: + instruction = + CreateRng(proto.shape(), proto.distribution(), all_operands()); + break; + case HloOpcode::kParameter: + instruction = CreateParameter(proto.parameter_number(), proto.shape(), + proto.name()); + break; + case HloOpcode::kGetTupleElement: + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "GetTupleElement instruction should have 1 operand but sees " + << proto.operand_ids_size(); + instruction = CreateGetTupleElement(proto.shape(), operands(0), + proto.tuple_index()); + break; + case HloOpcode::kReducePrecision: + instruction = + CreateReducePrecision(proto.shape(), operands(0), + proto.exponent_bits(), proto.mantissa_bits()); + break; + case HloOpcode::kInfeed: { + const Shape& data_shape = + ShapeUtil::GetTupleElementShape(proto.shape(), 0); + if (proto.operand_ids_size() == 0) { + // TODO(b/80000000): Remove this when all uses of infeed are + // converted to take tokens. + instruction = CreateInfeed(data_shape, proto.infeed_config()); + } else { + CHECK_EQ(proto.operand_ids_size(), 2); + instruction = + CreateInfeed(data_shape, operands(0), proto.infeed_config()); + } + } break; + case HloOpcode::kOutfeed: + if (proto.operand_ids_size() == 1) { + // TODO(b/80000000): Remove this when all uses of outfeed are + // converted to take tokens. + instruction = CreateOutfeed(proto.outfeed_shape(), operands(0), + proto.outfeed_config()); + } else { + CHECK_EQ(proto.operand_ids_size(), 2); + instruction = CreateOutfeed(proto.outfeed_shape(), operands(0), + operands(1), proto.outfeed_config()); + } + break; + case HloOpcode::kCrossReplicaSum: { + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "CrossReplicaSum should have 1 called computation but sees " + << proto.called_computation_ids_size(); + tensorflow::gtl::optional all_reduce_id; + if (proto.all_reduce_id() > 0) { + all_reduce_id = proto.all_reduce_id(); + } + instruction = CreateCrossReplicaSum( + proto.shape(), all_operands(), computations(0), + /*replica_group_ids=*/ + std::vector(proto.replica_group_ids().begin(), + proto.replica_group_ids().end()), + /*barrier=*/proto.cross_replica_sum_barrier(), + /*all_reduce_id=*/all_reduce_id); + break; + } + case HloOpcode::kConvolution: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Convolution instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_window()); + TF_RET_CHECK(proto.has_convolution_dimension_numbers()); + instruction = + CreateConvolve(proto.shape(), operands(0), operands(1), + proto.window(), proto.convolution_dimension_numbers()); + break; + case HloOpcode::kReduceWindow: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "ReduceWindow instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "ReduceWindow should have 1 called computation but sees " + << proto.called_computation_ids_size(); + instruction = CreateReduceWindow(proto.shape(), operands(0), operands(1), + proto.window(), computations(0)); + break; + case HloOpcode::kSelectAndScatter: + TF_RET_CHECK(proto.operand_ids_size() == 3) + << "SelectAndScatter instruction should have 3 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 2) + << "SelectAndScatter should have 2 called computations but sees " + << proto.called_computation_ids_size(); + instruction = CreateSelectAndScatter( + proto.shape(), operands(0), computations(0), proto.window(), + operands(1), operands(2), computations(1)); + break; + case HloOpcode::kCustomCall: + instruction = CreateCustomCall(proto.shape(), all_operands(), + proto.custom_call_target()); + if (proto.has_window()) { + static_cast(instruction.get()) + ->set_window(proto.window()); + } + if (proto.has_convolution_dimension_numbers()) { + static_cast(instruction.get()) + ->set_convolution_dimension_numbers( + proto.convolution_dimension_numbers()); + } + break; + case HloOpcode::kHostCompute: + instruction = + CreateHostCompute(proto.shape(), all_operands(), proto.channel_name(), + proto.cost_estimate_ns()); + break; + case HloOpcode::kPad: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Pad instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_padding_config()); + instruction = CreatePad(proto.shape(), operands(0), operands(1), + proto.padding_config()); + break; + case HloOpcode::kDynamicSlice: { + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "DynamicSlice instruction should have 2 operands but sees " + << proto.operand_ids_size(); + std::vector slice_sizes(proto.dynamic_slice_sizes_size()); + c_copy(proto.dynamic_slice_sizes(), slice_sizes.begin()); + instruction = CreateDynamicSlice(proto.shape(), operands(0), operands(1), + slice_sizes); + break; + } default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); for (const int64 operand_id : proto.operand_ids()) { @@ -99,96 +385,27 @@ StatusOr> HloInstruction::CreateFromProto( TF_RETURN_IF_ERROR(instruction_map.at(predecessor_id) ->AddControlDependencyTo(instruction.get())); } + if (instruction->opcode() != HloOpcode::kFusion) { + for (const int64 computation_id : proto.called_computation_ids()) { + TF_RET_CHECK(ContainsKey(computation_map, computation_id)) + << "No computation with id " << computation_id; + instruction->called_computations_.push_back( + computation_map.at(computation_id)); + } + } break; } } - // In the proto, fused computations are held exclusively within the - // HloInstructionProto and do not appear as an HloComputationProto within the - // HloModuleProto. - if (instruction->opcode() == HloOpcode::kFusion) { - TF_RET_CHECK(!proto.fusion_kind().empty()); - TF_ASSIGN_OR_RETURN(instruction->fusion_kind_, - StringToFusionKind(proto.fusion_kind())); - - // Find the fused computation and set its fusion instruction. - TF_RET_CHECK(proto.called_computation_ids_size() == 1) - << "Expect 1 called computation for fusion instruction, but sees " - << proto.called_computation_ids_size(); - const int64 fusion_id = proto.called_computation_ids(0); - auto* fused_computation = FindPtrOrNull(computation_map, fusion_id); - TF_RET_CHECK(fused_computation != nullptr) - << "No fusion computation with id " << fusion_id; - fused_computation->SetFusionInstruction(instruction.get()); - instruction->called_computations_.push_back(fused_computation); - } else { - for (const int64 computation_id : proto.called_computation_ids()) { - TF_RET_CHECK(ContainsKey(computation_map, computation_id)) - << "No computation with id " << computation_id; - instruction->called_computations_.push_back( - computation_map.at(computation_id)); - } - } - - if (instruction->opcode() == HloOpcode::kTrace) { - TF_RET_CHECK(instruction->operands().size() == 1) - << "Trace instruction should have 1 operand but sees " - << instruction->operands().size(); - instruction->mutable_operand(0)->set_tracing(instruction.get()); - } - TF_RET_CHECK(!proto.name().empty()); - instruction->name_ = proto.name(); - + instruction->SetAndSanitizeName(proto.name()); instruction->metadata_ = proto.metadata(); instruction->backend_config_ = proto.backend_config(); - if (proto.has_literal()) { - TF_ASSIGN_OR_RETURN(instruction->literal_, - Literal::CreateFromProto(proto.literal())); - } - instruction->parameter_number_ = proto.parameter_number(); - instruction->tuple_index_ = proto.tuple_index(); - for (int64 dimension : proto.dimensions()) { - instruction->dimensions_.push_back(dimension); - } - if (proto.has_window()) { - instruction->window_ = MakeUnique(proto.window()); - } - if (proto.has_convolution_dimension_numbers()) { - instruction->convolution_dimension_numbers_ = - MakeUnique( - proto.convolution_dimension_numbers()); - } if (proto.has_dot_dimension_numbers()) { instruction->dot_dimension_numbers_ = MakeUnique(proto.dot_dimension_numbers()); } - for (const HloInstructionProto::SliceDimensions& slice_dimensions : - proto.slice_dimensions()) { - instruction->slice_starts_.push_back(slice_dimensions.start()); - instruction->slice_limits_.push_back(slice_dimensions.limit()); - instruction->slice_strides_.push_back(slice_dimensions.stride()); - } - instruction->exponent_bits_ = proto.exponent_bits(); - instruction->mantissa_bits_ = proto.mantissa_bits(); - for (int64 dynamic_slice_size : proto.dynamic_slice_sizes()) { - instruction->dynamic_slice_sizes_.push_back(dynamic_slice_size); - } - if (proto.has_padding_config()) { - instruction->padding_config_ = - MakeUnique(proto.padding_config()); - } - instruction->outfeed_config_ = proto.outfeed_config(); - instruction->distribution_ = proto.distribution(); - instruction->channel_id_ = proto.channel_id(); - instruction->infeed_config_ = proto.infeed_config(); - instruction->custom_call_target_ = proto.custom_call_target(); - instruction->outfeed_shape_ = proto.outfeed_shape(); - instruction->fft_type_ = proto.fft_type(); - for (int64 fft_len : proto.fft_length()) { - instruction->fft_length_.push_back(fft_len); - } if (proto.has_sharding()) { TF_ASSIGN_OR_RETURN(const auto& sharding, @@ -203,61 +420,34 @@ StatusOr> HloInstruction::CreateFromProto( for (int64 bound : proto.gather_window_bounds()) { instruction->gather_window_bounds_.push_back(bound); } - - instruction->channel_name_ = proto.channel_name(); - instruction->cost_estimate_ns_ = proto.cost_estimate_ns(); - return std::move(instruction); } /* static */ std::unique_ptr HloInstruction::CreateParameter( int64 parameter_number, const Shape& shape, const string& name) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kParameter, shape)); - instruction->parameter_number_ = parameter_number; - instruction->name_ = name; - return instruction; + return MakeUnique(parameter_number, shape, name); } /* static */ std::unique_ptr HloInstruction::CreateTrace( const string& tag, HloInstruction* operand) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kTrace, ShapeUtil::MakeNil())); - instruction->operands_.push_back(operand); - instruction->literal_ = Literal::CreateR1U8(tag); - operand->set_tracing(instruction.get()); - return instruction; + return MakeUnique(tag, operand); } /* static */ std::unique_ptr HloInstruction::CreateConstant( std::unique_ptr literal) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kConstant, literal->shape())); - instruction->literal_ = std::move(literal); - return instruction; + return MakeUnique(std::move(literal)); } /* static */ std::unique_ptr HloInstruction::CreateGetTupleElement(const Shape& shape, HloInstruction* operand, int64 index) { - CHECK(ShapeUtil::IsTuple(operand->shape())); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kGetTupleElement, shape)); - instruction->tuple_index_ = index; - instruction->AppendOperand(operand); - return instruction; + return MakeUnique(shape, operand, index); } /* static */ std::unique_ptr HloInstruction::CreateRng( const Shape& shape, RandomDistribution distribution, tensorflow::gtl::ArraySlice parameters) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kRng, shape)); - instruction->distribution_ = distribution; - instruction->shape_ = shape; - for (HloInstruction* param : parameters) { - instruction->AppendOperand(param); - } - return instruction; + return MakeUnique(shape, distribution, parameters); } /* static */ std::unique_ptr HloInstruction::CreateNary( @@ -299,7 +489,6 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, case HloOpcode::kReal: case HloOpcode::kSign: case HloOpcode::kSin: - case HloOpcode::kSort: case HloOpcode::kTanh: break; default: @@ -334,6 +523,7 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, case HloOpcode::kSubtract: case HloOpcode::kAnd: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kShiftLeft: case HloOpcode::kShiftRightArithmetic: case HloOpcode::kShiftRightLogical: @@ -370,45 +560,22 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, /* static */ std::unique_ptr HloInstruction::CreateMap( const Shape& shape, tensorflow::gtl::ArraySlice operands, - HloComputation* map_computation, - tensorflow::gtl::ArraySlice static_operands) { - CHECK(static_operands.empty()) << "static_operands not yet supported"; - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kMap, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->called_computations_.push_back(map_computation); - return instruction; + HloComputation* map_computation) { + return MakeUnique(shape, operands, map_computation); } /* static */ std::unique_ptr HloInstruction::CreateConvolve( const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kConvolution, shape)); - if (window_util::HasBaseDilation(window)) { - instruction->name_ = instruction->name() + "-base-dilated"; - } - if (window_util::HasWindowDilation(window)) { - instruction->name_ = instruction->name() + "-window-dilated"; - } - instruction->AppendOperand(lhs); - instruction->AppendOperand(rhs); - instruction->window_ = MakeUnique(window); - instruction->convolution_dimension_numbers_ = - MakeUnique(dimension_numbers); - return instruction; + return MakeUnique(shape, lhs, rhs, window, + dimension_numbers); } /* static */ std::unique_ptr HloInstruction::CreateFft( const Shape& shape, HloInstruction* operand, FftType fft_type, tensorflow::gtl::ArraySlice fft_length) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kFft, shape)); - instruction->AppendOperand(operand); - instruction->fft_type_ = fft_type; - instruction->fft_length_.assign(fft_length.begin(), fft_length.end()); - return instruction; + return MakeUnique(shape, operand, fft_type, fft_length); } /* static */ std::unique_ptr HloInstruction::CreateDot( @@ -441,12 +608,8 @@ HloInstruction::CreateReducePrecision(const Shape& shape, HloInstruction* operand, const int exponent_bits, const int mantissa_bits) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kReducePrecision, shape)); - instruction->AppendOperand(operand); - instruction->exponent_bits_ = exponent_bits; - instruction->mantissa_bits_ = mantissa_bits; - return instruction; + return MakeUnique( + shape, operand, exponent_bits, mantissa_bits); } /* static */ std::unique_ptr @@ -454,92 +617,77 @@ HloInstruction::CreateCrossReplicaSum( const Shape& shape, tensorflow::gtl::ArraySlice operands, HloComputation* reduce_computation, tensorflow::gtl::ArraySlice replica_group_ids, - const tensorflow::gtl::optional& channel_id) { - // TODO(b/79737069): Remove the CHECK when supported. - CHECK(replica_group_ids.empty()); - CHECK(!channel_id.has_value()); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kCrossReplicaSum, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->called_computations_.push_back(reduce_computation); - return instruction; + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id) { + return MakeUnique( + shape, operands, reduce_computation, replica_group_ids, barrier, + all_reduce_id); } /* static */ std::unique_ptr HloInstruction::CreateInfeed( - const Shape& shape, const string& config) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kInfeed, shape)); - instruction->set_infeed_config(config); - return instruction; + const Shape& infeed_shape, HloInstruction* token_operand, + const string& config) { + return MakeUnique(infeed_shape, token_operand, config); +} + +/* static */ std::unique_ptr HloInstruction::CreateInfeed( + const Shape& infeed_shape, const string& config) { + return MakeUnique(infeed_shape, config); } /* static */ std::unique_ptr HloInstruction::CreateOutfeed( - const Shape& shape, HloInstruction* operand, + const Shape& outfeed_shape, HloInstruction* operand, + HloInstruction* token_operand, tensorflow::StringPiece outfeed_config) { + return MakeUnique(outfeed_shape, operand, + token_operand, outfeed_config); +} + +/* static */ std::unique_ptr HloInstruction::CreateOutfeed( + const Shape& outfeed_shape, HloInstruction* operand, tensorflow::StringPiece outfeed_config) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kOutfeed, ShapeUtil::MakeNil())); - CHECK(ShapeUtil::Compatible(operand->shape(), shape)) - << "Outfeed shape " << shape << " must be compatible with operand shape " - << operand->shape(); - instruction->AppendOperand(operand); - instruction->outfeed_config_ = std::string(outfeed_config); - instruction->outfeed_shape_ = shape; - return instruction; + return MakeUnique(outfeed_shape, operand, + outfeed_config); } /* static */ std::unique_ptr HloInstruction::CreateSend( - HloInstruction* operand, int64 channel_id) { - // Send instruction produces a tuple of {aliased operand, U32 context}. - Shape output_shape = ShapeUtil::MakeTupleShape( - {operand->shape(), ShapeUtil::MakeShape(U32, {})}); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kSend, output_shape)); - instruction->AppendOperand(operand); - instruction->channel_id_ = channel_id; - return instruction; + HloInstruction* operand, HloInstruction* token, int64 channel_id) { + return MakeUnique(operand, token, channel_id); } /* static */ std::unique_ptr HloInstruction::CreateSendDone( HloInstruction* operand) { - CHECK(operand->opcode() == HloOpcode::kSend) + auto send_operand = DynCast(operand); + CHECK(send_operand != nullptr) << "SendDone must take the context operand from Send"; - auto instruction = WrapUnique( - new HloInstruction(HloOpcode::kSendDone, ShapeUtil::MakeNil())); - instruction->AppendOperand(operand); - instruction->channel_id_ = operand->channel_id(); - return instruction; + return MakeUnique(send_operand); } /* static */ std::unique_ptr HloInstruction::CreateRecv( - const Shape& shape, int64 channel_id) { - // Recv instruction produces a tuple of {receive buffer, U32 context}. - Shape output_shape = - ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeShape(U32, {})}); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kRecv, output_shape)); - instruction->channel_id_ = channel_id; - return instruction; + const Shape& shape, HloInstruction* token, int64 channel_id) { + return MakeUnique(shape, token, channel_id); } /* static */ std::unique_ptr HloInstruction::CreateRecvDone( HloInstruction* operand) { - CHECK(operand->opcode() == HloOpcode::kRecv) + auto recv_operand = DynCast(operand); + CHECK(recv_operand != nullptr) << "RecvDone must take the context operand from Recv"; - Shape output_shape = ShapeUtil::GetTupleElementShape(operand->shape(), 0); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kRecvDone, output_shape)); - instruction->AppendOperand(operand); - instruction->channel_id_ = operand->channel_id(); - return instruction; + return MakeUnique(recv_operand); } /* static */ std::unique_ptr HloInstruction::CreateReverse( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kReverse, shape)); - instruction->AppendOperand(operand); - instruction->dimensions_.assign(dimensions.begin(), dimensions.end()); + return MakeUnique(shape, operand, dimensions); +} + +/* static */ std::unique_ptr HloInstruction::CreateAfterAll( + tensorflow::gtl::ArraySlice operands) { + auto instruction = WrapUnique( + new HloInstruction(HloOpcode::kAfterAll, ShapeUtil::MakeTokenShape())); + for (auto operand : operands) { + instruction->AppendOperand(operand); + } return instruction; } @@ -576,30 +724,15 @@ HloInstruction::CreateCrossReplicaSum( tensorflow::gtl::ArraySlice start_indices, tensorflow::gtl::ArraySlice limit_indices, tensorflow::gtl::ArraySlice strides) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSlice, shape)); - instruction->AppendOperand(operand); - instruction->slice_starts_.assign(start_indices.begin(), start_indices.end()); - instruction->slice_limits_.assign(limit_indices.begin(), limit_indices.end()); - instruction->slice_strides_.assign(strides.begin(), strides.end()); - // For backward compatibility with old serialized computations: if there are - // no strides, assume all strides are 1. - // TODO(b/63317920): remove this code. - if (instruction->slice_strides_.empty()) { - instruction->slice_strides_ = std::vector(start_indices.size(), 1LL); - } - return instruction; + return MakeUnique(shape, operand, start_indices, + limit_indices, strides); } /* static */ std::unique_ptr HloInstruction::CreateDynamicSlice( const Shape& shape, HloInstruction* operand, HloInstruction* start_indices, tensorflow::gtl::ArraySlice slice_sizes) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kDynamicSlice, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(start_indices); - instruction->dynamic_slice_sizes_.assign(slice_sizes.begin(), - slice_sizes.end()); - return instruction; + return MakeUnique(shape, operand, start_indices, + slice_sizes); } /* static */ std::unique_ptr @@ -618,13 +751,7 @@ HloInstruction::CreateDynamicUpdateSlice(const Shape& shape, /* static */ std::unique_ptr HloInstruction::CreateConcatenate( const Shape& shape, tensorflow::gtl::ArraySlice operands, int64 dimension) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kConcatenate, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->dimensions_.push_back(dimension); - return instruction; + return MakeUnique(shape, operands, dimension); } /* static */ std::unique_ptr HloInstruction::CreateConvert( @@ -647,25 +774,15 @@ HloInstruction::CreateBitcastConvert(const Shape& shape, const Shape& shape, HloInstruction* arg, HloInstruction* init_value, tensorflow::gtl::ArraySlice dimensions_to_reduce, HloComputation* reduce_computation) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kReduce, shape)); - instruction->AppendOperand(arg); - instruction->AppendOperand(init_value); - instruction->dimensions_.assign(dimensions_to_reduce.begin(), - dimensions_to_reduce.end()); - instruction->called_computations_.push_back(reduce_computation); - return instruction; + return MakeUnique( + shape, arg, init_value, dimensions_to_reduce, reduce_computation); } /* static */ std::unique_ptr HloInstruction::CreateReduceWindow( const Shape& shape, HloInstruction* operand, HloInstruction* init_value, const Window& window, HloComputation* reduce_computation) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kReduceWindow, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(init_value); - instruction->called_computations_.push_back(reduce_computation); - instruction->window_ = MakeUnique(window); - return instruction; + return MakeUnique(shape, operand, init_value, + window, reduce_computation); } /* static */ std::unique_ptr @@ -674,8 +791,8 @@ HloInstruction::CreateBatchNormTraining(const Shape& shape, HloInstruction* scale, HloInstruction* offset, float epsilon, int64 feature_index) { - return WrapUnique(new HloBatchNormTrainingInstruction( - shape, operand, scale, offset, epsilon, feature_index)); + return MakeUnique( + shape, operand, scale, offset, epsilon, feature_index); } /* static */ std::unique_ptr @@ -683,8 +800,8 @@ HloInstruction::CreateBatchNormInference( const Shape& shape, HloInstruction* operand, HloInstruction* scale, HloInstruction* offset, HloInstruction* mean, HloInstruction* variance, float epsilon, int64 feature_index) { - return WrapUnique(new HloBatchNormInferenceInstruction( - shape, operand, scale, offset, mean, variance, epsilon, feature_index)); + return MakeUnique( + shape, operand, scale, offset, mean, variance, epsilon, feature_index); } /* static */ std::unique_ptr @@ -693,9 +810,9 @@ HloInstruction::CreateBatchNormGrad(const Shape& shape, HloInstruction* operand, HloInstruction* variance, HloInstruction* grad_output, float epsilon, int64 feature_index) { - return WrapUnique( - new HloBatchNormGradInstruction(shape, operand, scale, mean, variance, - grad_output, epsilon, feature_index)); + return MakeUnique(shape, operand, scale, mean, + variance, grad_output, epsilon, + feature_index); } /* static */ std::unique_ptr @@ -703,27 +820,15 @@ HloInstruction::CreateSelectAndScatter( const Shape& shape, HloInstruction* operand, HloComputation* select, const Window& window, HloInstruction* source, HloInstruction* init_value, HloComputation* scatter) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kSelectAndScatter, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(source); - instruction->AppendOperand(init_value); - // Select comes before scatter in the vector. - instruction->called_computations_.push_back(select); - instruction->called_computations_.push_back(scatter); - instruction->window_ = MakeUnique(window); - return instruction; + return MakeUnique( + shape, operand, select, window, source, init_value, scatter); } /* static */ std::unique_ptr HloInstruction::CreateBroadcast( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice broadcast_dimensions) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kBroadcast, shape)); - instruction->AppendOperand(operand); - instruction->dimensions_.assign(broadcast_dimensions.begin(), - broadcast_dimensions.end()); - return instruction; + return MakeUnique(shape, operand, + broadcast_dimensions); } /* static */ std::unique_ptr @@ -781,11 +886,8 @@ HloInstruction::CreateBroadcastSequence( /* static */ std::unique_ptr HloInstruction::CreatePad( const Shape& shape, HloInstruction* operand, HloInstruction* padding_value, const PaddingConfig& padding_config) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kPad, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(padding_value); - instruction->padding_config_ = MakeUnique(padding_config); - return instruction; + return MakeUnique(shape, operand, padding_value, + padding_config); } /* static */ std::unique_ptr HloInstruction::CreateReshape( @@ -802,53 +904,38 @@ HloInstruction::CreateBroadcastSequence( /* static */ std::unique_ptr HloInstruction::CreateTranspose( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions) { - CHECK_EQ(shape.dimensions().size(), dimensions.size()); - CHECK_EQ(shape.dimensions().size(), operand->shape().dimensions().size()); - CHECK(std::equal(operand->shape().dimensions().begin(), - operand->shape().dimensions().end(), - Permute(dimensions, shape.dimensions()).begin())) - << "shape: " << ShapeUtil::HumanString(shape) - << ", operand->shape(): " << ShapeUtil::HumanString(shape) - << ", dimensions: {" << Join(dimensions, ", ") << "}"; - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kTranspose, shape)); - instruction->AppendOperand(operand); - instruction->dimensions_.assign(dimensions.begin(), dimensions.end()); + return MakeUnique(shape, operand, dimensions); +} + +/* static */ std::unique_ptr HloInstruction::CreateSort( + const Shape& shape, HloInstruction* keys, HloInstruction* values) { + auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSort, shape)); + instruction->AppendOperand(keys); + if (values) { + instruction->AppendOperand(values); + } return instruction; } /* static */ std::unique_ptr HloInstruction::CreateFusion( const Shape& shape, FusionKind fusion_kind, HloInstruction* fused_root) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kFusion, shape)); - instruction->fusion_kind_ = fusion_kind; - instruction->name_ = "fusion"; - instruction->set_parent(fused_root->parent()); - instruction->set_metadata(fused_root->metadata()); - instruction->CloneAndFuseInternal(fused_root); - return instruction; + return MakeUnique(shape, fusion_kind, fused_root); } /* static */ std::unique_ptr HloInstruction::CreateFusion( const Shape& shape, FusionKind fusion_kind, tensorflow::gtl::ArraySlice operands, HloComputation* fusion_computation) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kFusion, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->fusion_kind_ = fusion_kind; - instruction->name_ = "fusion"; - instruction->called_computations_.push_back(fusion_computation); - fusion_computation->SetFusionInstruction(instruction.get()); - return instruction; + return MakeUnique(shape, fusion_kind, operands, + fusion_computation); } -void HloInstruction::set_device_sharding(int64 device) { - HloSharding device_sharding = HloSharding::AssignDevice(device); +void HloInstruction::set_single_sharding(const HloSharding& sharding) { + CHECK(!sharding.IsTuple()) << sharding; if (ShapeUtil::IsTuple(shape())) { - set_sharding(HloSharding::Tuple(device_sharding.GetAsShapeTree(shape()))); + set_sharding(HloSharding::Tuple(sharding.GetAsShapeTree(shape()))); } else { - set_sharding(device_sharding); + set_sharding(sharding); } } @@ -862,289 +949,6 @@ void HloInstruction::SetupDerivedInstruction( derived_instruction->set_metadata(metadata_); } -HloInstruction* HloInstruction::AddFusionOperand(HloInstruction* new_operand) { - CHECK_EQ(opcode(), HloOpcode::kFusion); - CHECK_EQ(operand_count(), - fused_instructions_computation()->parameter_instructions().size()); - const int64 param_no = operand_count(); - // Name the parameter after the instruction it represents in the outer - // (non-fusion) computation. - string param_name = StrCat(new_operand->name(), ".param_", param_no); - HloInstruction* fused_parameter = - fused_instructions_computation()->AddParameter( - HloInstruction::CreateParameter(param_no, new_operand->shape(), - param_name)); - AppendOperand(new_operand); - return fused_parameter; -} - -void HloInstruction::MergeFusionInstruction( - HloInstruction* instruction_to_merge) { - CHECK_EQ(opcode_, HloOpcode::kFusion); - CHECK_EQ(instruction_to_merge->opcode(), HloOpcode::kFusion); - CHECK(std::find(operands().begin(), operands().end(), instruction_to_merge) != - operands().end()); - // Clone the instruction from which to merge fused instructions. - std::unique_ptr clone = instruction_to_merge->Clone(); - // Replace uses of fused parameters with the corresponding operand of the - // fusion. Add all non-parameter fused instructions to 'unfused_instructions' - // to be merged into 'this'. This is done in reverse post order. - std::vector unfused_instructions; - auto fused_instructions = - clone->fused_instructions_computation()->MakeInstructionPostOrder(); - for (auto fused_it = fused_instructions.rbegin(); - fused_it != fused_instructions.rend(); ++fused_it) { - auto fused_instruction = *fused_it; - if (fused_instruction->opcode() == HloOpcode::kParameter) { - TF_CHECK_OK(fused_instruction->ReplaceAllUsesWith( - clone->mutable_operand(fused_instruction->parameter_number()))); - } else { - unfused_instructions.push_back(fused_instruction); - } - } - CHECK(unfused_instructions.front() == clone->fused_expression_root()); - // Replace instruction_to_merge use of 'this' with unfused_root. - TF_CHECK_OK( - instruction_to_merge->ReplaceUseWith(this, unfused_instructions.front())); - // Fuse 'unfused_instructions' into 'this'. - for (auto& instruction : unfused_instructions) { - FuseInstruction(instruction); - instruction->DetachFromOperands(); - } - CHECK_EQ(0, clone->user_count()); - clone->DetachFromOperands(); - TF_CHECK_OK(parent()->parent()->RemoveEmbeddedComputation( - clone->fused_instructions_computation())); -} - -void HloInstruction::MergeFusionInstructionIntoMultiOutput( - HloInstruction* instruction_to_merge) { - CHECK_EQ(opcode_, HloOpcode::kFusion); - CHECK_EQ(instruction_to_merge->opcode(), HloOpcode::kFusion); - // Add all non-parameter fused instructions to 'unfused_instructions' to be - // merged into 'this'. `old_to_new' maps the instructions in the fused node - // to the disaseembled fusion instructions. - // Note that we add the unfused instructions to this->parent_ computation. - // This is necessary because the unique_id needs for an instruction and - // it's only added when inserting to the computation. - tensorflow::gtl::FlatMap old_to_new; - std::vector unfused_instructions; - auto computation_to_merge = - instruction_to_merge->fused_instructions_computation(); - auto post_order = computation_to_merge->MakeInstructionPostOrder(); - for (auto rit = post_order.rbegin(); rit != post_order.rend(); ++rit) { - auto fused_instruction = *rit; - if (fused_instruction->opcode() == HloOpcode::kParameter) { - InsertOrDie(&old_to_new, fused_instruction, - instruction_to_merge->mutable_operand( - fused_instruction->parameter_number())); - continue; - } - - // Here we clone the insertion and call FuseInstructionIntoMultiOutput() - // which clones again. This can be improved. - auto cloned_instruction = - parent_->AddInstruction(fused_instruction->Clone()); - unfused_instructions.push_back(cloned_instruction); - InsertOrDie(&old_to_new, fused_instruction, cloned_instruction); - } - for (auto unfused_instruction : unfused_instructions) { - for (int64 index = 0; index < unfused_instruction->operand_count(); - index++) { - auto new_operand = - FindOrDie(old_to_new, unfused_instruction->mutable_operand(index)); - TF_CHECK_OK(unfused_instruction->ReplaceOperandWith(index, new_operand)); - } - } - - HloInstruction* unfused_root = unfused_instructions.front(); - TF_CHECK_OK(instruction_to_merge->ReplaceAllUsesWith(unfused_root)); - - TF_CHECK_OK( - instruction_to_merge->parent()->RemoveInstruction(instruction_to_merge)); - if (GetModule()) { - TF_CHECK_OK(GetModule()->RemoveEmbeddedComputation(computation_to_merge)); - } - - // Fuse the root instruction and generate multiple outputs. - FuseInstructionIntoMultiOutput(unfused_root); - TF_CHECK_OK(unfused_root->parent()->RemoveInstruction(unfused_root)); - // The rest instructions are of normal fusing. - for (int64 i = 1; i < unfused_instructions.size(); i++) { - auto instruction = unfused_instructions[i]; - FuseInstruction(instruction); - TF_CHECK_OK(instruction->parent()->RemoveInstruction(instruction)); - } -} - -HloInstruction* HloInstruction::FuseInstructionInternal( - HloInstruction* instruction_to_fuse, bool add_output) { - CHECK_EQ(opcode_, HloOpcode::kFusion); - - // When add_output is false, this fusion instruction must be a user of - // instruction_to_fuse. - if (!add_output) { - CHECK(IsUserOf(instruction_to_fuse)); - } - HloInstruction* fused_instruction = - CloneAndFuseInternal(instruction_to_fuse, add_output); - return fused_instruction; -} - -HloInstruction* HloInstruction::CloneAndFuseInternal( - HloInstruction* instruction_to_fuse, bool add_output) { - CHECK_EQ(opcode_, HloOpcode::kFusion); - CHECK(instruction_to_fuse->IsFusable()) << instruction_to_fuse->ToString(); - VLOG(3) << "CloneAndFuseInternal:\n" << instruction_to_fuse->ToString(); - HloInstruction* clone = nullptr; - if (called_computations_.empty()) { - // New fusion instruction. It should not be a multioutput instruction. - CHECK(!add_output); - auto builder = HloComputation::Builder("fused_computation", this); - builder.AddInstruction(instruction_to_fuse->Clone(/*suffix=*/"")); - called_computations_.push_back( - CHECK_NOTNULL(GetModule())->AddEmbeddedComputation(builder.Build())); - clone = fused_expression_root(); - } else { - clone = fused_instructions_computation()->AddInstruction( - instruction_to_fuse->Clone(/*suffix=*/"")); - // When add_output is false, instruction_to_fuse is necessarily an operand - // of the fusion instruction. After fusion this will no longer be the case. - // Remove the operand from the operand list and remove its corresponding - // fused parameter instruction. Renumber parameters as necessary to make - // parameter numbers consistent with their index in the - // fused_parameter_ vector. - bool in_operand_list = std::find(operands_.begin(), operands_.end(), - instruction_to_fuse) != operands_.end(); - CHECK(add_output || in_operand_list); - const std::vector& fused_parameters = - fused_instructions_computation()->parameter_instructions(); - for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { - if (instruction_to_fuse == operands_[operand_num]) { - // replace the fused parameter instruction's uses with the clone. - HloInstruction* fused_parameter = fused_parameters[operand_num]; - TF_CHECK_OK(fused_parameter->ReplaceAllUsesWith(clone)); - - // Remove the corresponding fused parameter and operand from their - // respective vectors. - TF_CHECK_OK( - fused_instructions_computation()->RemoveParameter(operand_num)); - operands_.erase(operands_.begin() + operand_num); - break; - } - } - // We've cloned instruction_to_fuse into this fusion instruction, so this - // fusion instruction is no longer a use of instruction_to_fuse. - if (in_operand_list) { - instruction_to_fuse->RemoveUser(this); - // When the instruction_to_fuse does not have other users, we don't need - // to generate a multioutput fusion instruction. - if (instruction_to_fuse->user_count() == 0) { - add_output = false; - } - } - } - - // Reread the parameters in the computation. - const std::vector& fused_parameters = - fused_instructions_computation()->parameter_instructions(); - - // Add each operand of the clone as an operand of the fusion instruction. A - // complication is that some clone operands may already be operands of the - // fusion instruction. - for (int64 operand_num = 0; operand_num < clone->operand_count(); - ++operand_num) { - HloInstruction* operand = clone->mutable_operand(operand_num); - - // See if this operand is already an operand of the fusion node. - CHECK_EQ(operands_.size(), fused_parameters.size()); - HloInstruction* fused_param = nullptr; - for (int64 i = 0; i < operands_.size(); ++i) { - if (operands_[i] == operand) { - fused_param = fused_parameters[i]; - break; - } - } - - if (fused_param == nullptr) { - // Clone's operand was not already an operand of the fusion - // instruction. Add it as an operand and add a corresponding fused - // parameter instruction. - fused_param = AddFusionOperand(operand); - } - TF_CHECK_OK(clone->ReplaceOperandWith(operand_num, fused_param)); - } - - if (add_output) { - CHECK_GT(instruction_to_fuse->user_count(), 0); - // If this is already a multioutput fusion instruction, expand the root - // tuple by 1. - HloInstruction* fused_root = fused_expression_root(); - HloInstruction::InstructionVector tuple_elements; - bool newly_created_tuple_instr = false; - if (fused_root->opcode() == HloOpcode::kTuple) { - tuple_elements = fused_root->operands(); - } else { - tuple_elements.push_back(fused_root); - newly_created_tuple_instr = true; - } - if (clone->opcode() == HloOpcode::kTuple) { - for (auto inst : clone->operands()) { - tuple_elements.push_back(inst); - } - } else { - tuple_elements.push_back(clone); - } - HloInstruction* new_root = fused_instructions_computation()->AddInstruction( - HloInstruction::CreateTuple(tuple_elements)); - fused_instructions_computation()->set_root_instruction(new_root); - shape_ = new_root->shape(); - if (fused_root->opcode() == HloOpcode::kTuple) { - TF_CHECK_OK( - fused_instructions_computation()->RemoveInstruction(fused_root)); - } - - // If this is a newly created multioutput instruction, we need to update - // the use of the original fusion instruction. - if (newly_created_tuple_instr) { - HloInstruction* new_instr = parent_->AddInstruction( - HloInstruction::CreateGetTupleElement(fused_root->shape(), this, 0)); - TF_CHECK_OK(ReplaceAllUsesWith(new_instr)); - } - int64 index = tuple_elements.size(); - if (instruction_to_fuse->opcode() == HloOpcode::kTuple) { - index -= instruction_to_fuse->operand_count(); - std::vector to_be_removed; - for (auto old_gte : instruction_to_fuse->users()) { - CHECK_EQ(old_gte->opcode(), HloOpcode::kGetTupleElement); - int64 old_tuple_index = old_gte->tuple_index(); - HloInstruction* new_gte = - parent_->AddInstruction(HloInstruction::CreateGetTupleElement( - old_gte->shape(), this, index + old_tuple_index)); - TF_CHECK_OK(old_gte->ReplaceAllUsesWith(new_gte)); - to_be_removed.push_back(old_gte); - } - for (auto old_gte : to_be_removed) { - TF_CHECK_OK(parent_->RemoveInstruction(old_gte)); - } - TF_CHECK_OK(fused_instructions_computation()->RemoveInstruction(clone)); - } else { - HloInstruction* new_gte = - parent_->AddInstruction(HloInstruction::CreateGetTupleElement( - clone->shape(), this, index - 1)); - TF_CHECK_OK(instruction_to_fuse->ReplaceAllUsesWith(new_gte)); - } - } - - VLOG(2) << "New clone:\n" << clone->ToString(); - return clone; -} - -RandomDistribution HloInstruction::random_distribution() const { - CHECK_EQ(opcode_, HloOpcode::kRng); - return distribution_; -} - bool HloInstruction::HasSideEffectNoRecurse() const { switch (opcode_) { case HloOpcode::kSend: @@ -1190,26 +994,15 @@ bool HloInstruction::HasSideEffect() const { /* static */ std::unique_ptr HloInstruction::CreateCustomCall( const Shape& shape, tensorflow::gtl::ArraySlice operands, tensorflow::StringPiece custom_call_target) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kCustomCall, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->custom_call_target_ = std::string(custom_call_target); - return instruction; + return MakeUnique(shape, operands, + custom_call_target); } /* static */ std::unique_ptr HloInstruction::CreateHostCompute( const Shape& shape, tensorflow::gtl::ArraySlice operands, tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kHostCompute, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->channel_name_ = std::string(channel_name); - instruction->cost_estimate_ns_ = cost_estimate_ns; - return instruction; + return MakeUnique(shape, operands, channel_name, + cost_estimate_ns); } /* static */ std::unique_ptr HloInstruction::CreateTuple( @@ -1287,6 +1080,35 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kBatchNormTraining: case HloOpcode::kBatchNormInference: case HloOpcode::kBatchNormGrad: + case HloOpcode::kFft: + case HloOpcode::kSend: + case HloOpcode::kSendDone: + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + case HloOpcode::kReverse: + case HloOpcode::kConcatenate: + case HloOpcode::kReduce: + case HloOpcode::kTranspose: + case HloOpcode::kBroadcast: + case HloOpcode::kMap: + case HloOpcode::kSlice: + case HloOpcode::kConstant: + case HloOpcode::kTrace: + case HloOpcode::kFusion: + case HloOpcode::kRng: + case HloOpcode::kParameter: + case HloOpcode::kGetTupleElement: + case HloOpcode::kReducePrecision: + case HloOpcode::kCrossReplicaSum: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: + case HloOpcode::kConvolution: + case HloOpcode::kCustomCall: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: + case HloOpcode::kHostCompute: + case HloOpcode::kPad: + case HloOpcode::kDynamicSlice: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1309,7 +1131,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kReal: case HloOpcode::kSign: case HloOpcode::kSin: - case HloOpcode::kSort: case HloOpcode::kTanh: CHECK_EQ(new_operands.size(), 1); clone = CreateUnary(shape, opcode_, new_operands[0]); @@ -1333,6 +1154,7 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kRemainder: case HloOpcode::kAnd: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kShiftLeft: case HloOpcode::kShiftRightArithmetic: case HloOpcode::kShiftRightLogical: @@ -1347,31 +1169,9 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( new_operands[2]); break; // Other supported ops. - case HloOpcode::kBroadcast: - CHECK_EQ(new_operands.size(), 1); - clone = CreateBroadcast(shape, new_operands[0], dimensions_); - break; case HloOpcode::kCall: clone = CreateCall(shape, new_operands, to_apply()); break; - case HloOpcode::kCustomCall: - clone = CreateCustomCall(shape, new_operands, custom_call_target_); - if (window_ != nullptr) { - clone->window_ = MakeUnique(*window_); - } - if (convolution_dimension_numbers_ != nullptr) { - clone->convolution_dimension_numbers_ = - MakeUnique( - *convolution_dimension_numbers_); - } - break; - case HloOpcode::kHostCompute: - clone = CreateHostCompute(shape, new_operands, channel_name_, - cost_estimate_ns_); - break; - case HloOpcode::kConcatenate: - clone = CreateConcatenate(shape, new_operands, dimensions(0)); - break; case HloOpcode::kConvert: CHECK_EQ(new_operands.size(), 1); clone = CreateConvert(shape, new_operands[0]); @@ -1380,85 +1180,20 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( CHECK_EQ(new_operands.size(), 1); clone = CreateBitcastConvert(shape, new_operands[0]); break; - case HloOpcode::kReducePrecision: - CHECK_EQ(new_operands.size(), 1); - clone = CreateReducePrecision(shape, new_operands[0], exponent_bits_, - mantissa_bits_); - break; - case HloOpcode::kConvolution: - CHECK_EQ(new_operands.size(), 2); - clone = CreateConvolve(shape, new_operands[0], new_operands[1], *window_, - *convolution_dimension_numbers_); - break; case HloOpcode::kDot: CHECK_EQ(new_operands.size(), 2); clone = CreateDot(shape, new_operands[0], new_operands[1], *dot_dimension_numbers_); break; - case HloOpcode::kFft: - CHECK_EQ(new_operands.size(), 1); - clone = CreateFft(shape, new_operands[0], fft_type_, fft_length_); - break; - case HloOpcode::kCrossReplicaSum: - clone = CreateCrossReplicaSum(shape, new_operands, to_apply()); - break; - case HloOpcode::kGetTupleElement: - CHECK_EQ(new_operands.size(), 1); - clone = CreateGetTupleElement(shape, new_operands[0], tuple_index()); - break; - case HloOpcode::kMap: - clone = CreateMap(shape, new_operands, to_apply()); - break; - case HloOpcode::kPad: - CHECK_EQ(new_operands.size(), 2); - clone = - CreatePad(shape, new_operands[0], new_operands[1], *padding_config_); - break; - case HloOpcode::kReduce: - CHECK_EQ(new_operands.size(), 2); - clone = CreateReduce(shape, new_operands[0], new_operands[1], dimensions_, - to_apply()); - break; - case HloOpcode::kReduceWindow: - CHECK_EQ(new_operands.size(), 2); - clone = CreateReduceWindow(shape, new_operands[0], new_operands[1], - *window_, to_apply()); - break; - case HloOpcode::kSelectAndScatter: - CHECK_EQ(new_operands.size(), 3); - clone = - CreateSelectAndScatter(shape, new_operands[0], select(), *window_, - new_operands[1], new_operands[2], scatter()); - break; - case HloOpcode::kReverse: - CHECK_EQ(new_operands.size(), 1); - clone = CreateReverse(shape, new_operands[0], dimensions_); - break; - case HloOpcode::kRng: - clone = CreateRng(shape, distribution_, new_operands); - break; case HloOpcode::kReshape: CHECK_EQ(new_operands.size(), 1); clone = CreateReshape(shape, new_operands[0]); break; - case HloOpcode::kSlice: - CHECK_EQ(new_operands.size(), 1); - clone = CreateSlice(shape, new_operands[0], slice_starts_, slice_limits_, - slice_strides_); - break; - case HloOpcode::kDynamicSlice: - clone = CreateDynamicSlice(shape, new_operands[0], new_operands[1], - dynamic_slice_sizes_); - break; case HloOpcode::kDynamicUpdateSlice: CHECK_EQ(new_operands.size(), 3); clone = CreateDynamicUpdateSlice(shape, new_operands[0], new_operands[1], new_operands[2]); break; - case HloOpcode::kTranspose: - CHECK_EQ(new_operands.size(), 1); - clone = CreateTranspose(shape, new_operands[0], dimensions_); - break; case HloOpcode::kTuple: clone = CreateTuple(new_operands); *clone->mutable_shape() = shape; @@ -1468,60 +1203,12 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( clone = CreateWhile(shape, while_condition(), while_body(), new_operands[0]); break; - case HloOpcode::kConstant: - clone = CreateConstant(literal_->CloneToUnique()); - break; - case HloOpcode::kFusion: { - HloModule* module = context != nullptr ? context->module() : GetModule(); - HloComputation* new_fused_computation = nullptr; - if (context != nullptr) { - new_fused_computation = - context->FindComputation(fused_instructions_computation()); - } - if (new_fused_computation == nullptr) { - new_fused_computation = module->AddEmbeddedComputation( - fused_instructions_computation()->Clone("clone", context)); - } - clone = CreateFusion(/*shape=*/shape, /*fusion_kind=*/fusion_kind(), - /*operands=*/new_operands, - /*fusion_computation=*/new_fused_computation); - break; - } - case HloOpcode::kParameter: - clone = CreateParameter(parameter_number_, shape, name_); - break; - case HloOpcode::kInfeed: - CHECK_EQ(new_operands.size(), 0); - clone = CreateInfeed(shape, infeed_config()); - break; - case HloOpcode::kOutfeed: - CHECK_EQ(new_operands.size(), 1); - clone = CreateOutfeed(outfeed_shape_, new_operands[0], outfeed_config()); - break; case HloOpcode::kConditional: CHECK_EQ(new_operands.size(), 3); clone = CreateConditional(shape, new_operands[0], new_operands[1], true_computation(), new_operands[2], false_computation()); break; - case HloOpcode::kSend: - CHECK_EQ(new_operands.size(), 1); - clone = CreateSend(new_operands[0], channel_id()); - break; - case HloOpcode::kSendDone: - CHECK_EQ(new_operands.size(), 1); - clone = CreateSendDone(new_operands[0]); - break; - case HloOpcode::kRecv: - CHECK_EQ(new_operands.size(), 0); - // The shape is a tuple, but CreateRecv() wants the raw data shape. - clone = - CreateRecv(ShapeUtil::GetTupleElementShape(shape, 0), channel_id()); - break; - case HloOpcode::kRecvDone: - CHECK_EQ(new_operands.size(), 1); - clone = CreateRecvDone(new_operands[0]); - break; case HloOpcode::kGather: CHECK_EQ(new_operands.size(), 2); clone = CreateGather(shape, new_operands[0], new_operands[1], @@ -1533,8 +1220,17 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( CreateDomain(shape, new_operands[0], operand_side_metadata_->Clone(), user_side_metadata_->Clone()); break; - case HloOpcode::kTrace: - LOG(FATAL) << "Not yet implemented, clone: " << HloOpcodeString(opcode_); + case HloOpcode::kAfterAll: + clone = CreateAfterAll(new_operands); + break; + case HloOpcode::kSort: + CHECK(new_operands.size() == 1 || new_operands.size() == 2) + << "Too many operands for sort: " << new_operands.size(); + HloInstruction* keys = new_operands[0]; + HloInstruction* values = + new_operands.size() == 2 ? new_operands[1] : nullptr; + clone = CreateSort(shape, keys, values); + break; } SetupDerivedInstruction(clone.get()); clone->set_parent(parent_); @@ -1550,7 +1246,29 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( return clone; } -HloInstruction::~HloInstruction() {} +HloInstruction::~HloInstruction() { + // Detach from operands. An instruction may be repeated as an operand. To + // avoid calling RemoveUser twice on the same operand, check before remove. + for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { + HloInstruction* operand = operands_[operand_num]; + if (operand == nullptr) { + continue; + } + if (operand->user_set_.find(this) != operand->user_set_.end()) { + operand->RemoveUser(this); + } + operands_[operand_num] = nullptr; + } + + // Update users. Set `nullptr` to the correpsonding operand slot for users. + for (auto& user : this->users()) { + for (int i = 0; i < user->operand_count(); ++i) { + if (user->operands_[i] == this) { + user->operands_[i] = nullptr; + } + } + } +} std::unique_ptr HloInstruction::Clone( const string& suffix, HloCloneContext* context) const { @@ -1615,40 +1333,6 @@ const HloInstruction* HloInstruction::LatestNonGteAncestor() const { return hlo; } -const Literal& HloInstruction::literal() const { - CHECK_EQ(HloOpcode::kConstant, opcode_); - return *literal_; -} - -bool HloInstruction::HasLiteral() const { return literal_ != nullptr; } - -bool HloInstruction::CanHaveDimensionsField() const { - return (opcode() == HloOpcode::kReverse || - opcode() == HloOpcode::kConcatenate || - opcode() == HloOpcode::kReduce || opcode() == HloOpcode::kBroadcast || - opcode() == HloOpcode::kTranspose); -} - -const std::vector& HloInstruction::dimensions() const { - CHECK(CanHaveDimensionsField()); - return dimensions_; -} - -int64 HloInstruction::dimensions(int64 index) const { - return dimensions()[index]; -} - -int64 HloInstruction::concatenate_dimension() const { - CHECK(opcode() == HloOpcode::kConcatenate); - CHECK_EQ(1, dimensions_.size()); - return dimensions(0); -} - -int64 HloInstruction::tuple_index() const { - CHECK_EQ(HloOpcode::kGetTupleElement, opcode_); - return tuple_index_; -} - const HloInstruction* HloInstruction::operand(int64 i) const { return operands_[i]; } @@ -1730,6 +1414,30 @@ void HloInstruction::AppendOperand(HloInstruction* operand) { operand->AddUser(this); } +void HloInstruction::RemoveOperandsAtAscendingIndices( + tensorflow::gtl::ArraySlice ascending_indices) { + if (ascending_indices.empty()) { + return; + } + int next_index = 0; + int removed_count = 0; + for (int to_remove : ascending_indices) { + while (next_index < to_remove) { + operands_[next_index - removed_count] = operands_[next_index]; + ++next_index; + } + CHECK_LT(to_remove, operands_.size()); + ++removed_count; + ++next_index; + } + while (next_index < operands_.size()) { + operands_[next_index - removed_count] = operands_[next_index]; + ++next_index; + } + CHECK_EQ(removed_count, ascending_indices.size()); + operands_.resize(operands_.size() - removed_count); +} + void HloInstruction::AddUser(HloInstruction* user) { if (!ContainsKey(user_set_, user)) { user_set_.insert(user); @@ -1737,10 +1445,6 @@ void HloInstruction::AddUser(HloInstruction* user) { } } -bool HloInstruction::IsConstant() const { - return opcode_ == HloOpcode::kConstant; -} - bool HloInstruction::HasConstantOperand() const { for (const HloInstruction* operand : operands_) { if (operand->IsConstant()) { @@ -1771,7 +1475,6 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kCopy: case HloOpcode::kCos: case HloOpcode::kDivide: - case HloOpcode::kDynamicSlice: case HloOpcode::kDynamicUpdateSlice: case HloOpcode::kEq: case HloOpcode::kExp: @@ -1787,6 +1490,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kAnd: case HloOpcode::kNot: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kLt: case HloOpcode::kMaximum: case HloOpcode::kMinimum: @@ -1803,48 +1507,19 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kShiftRightArithmetic: case HloOpcode::kShiftRightLogical: case HloOpcode::kSign: + case HloOpcode::kSort: case HloOpcode::kSin: case HloOpcode::kSubtract: case HloOpcode::kTanh: case HloOpcode::kTuple: return true; - // Broadcast, Concatenate, and Transpose need the same dimensions field. - case HloOpcode::kBroadcast: - case HloOpcode::kConcatenate: - case HloOpcode::kTranspose: - return dimensions() == other.dimensions(); - - case HloOpcode::kFusion: - return fusion_kind() == other.fusion_kind() && - eq_computations(fused_instructions_computation(), - other.fused_instructions_computation()); - // These opcodes have complex or special behavior so just return false. case HloOpcode::kDomain: - case HloOpcode::kRng: - case HloOpcode::kTrace: case HloOpcode::kWhile: + case HloOpcode::kAfterAll: return false; - case HloOpcode::kParameter: - return parameter_number() == other.parameter_number(); - - // A constant is defined by the value in the literal. - case HloOpcode::kConstant: - return literal() == other.literal(); - - // A reduce-precision operation is determined by the bit sizes. - case HloOpcode::kReducePrecision: - return exponent_bits() == other.exponent_bits() && - mantissa_bits() == other.mantissa_bits(); - - // Convolution has a window and dimensions. - case HloOpcode::kConvolution: - return protobuf_util::ProtobufEquals(window(), other.window()) && - protobuf_util::ProtobufEquals( - convolution_dimension_numbers(), - other.convolution_dimension_numbers()); // Check dot dimension numbers. case HloOpcode::kDot: return protobuf_util::ProtobufEquals(dot_dimension_numbers(), @@ -1855,91 +1530,52 @@ bool HloInstruction::IdenticalSlowPath( other.gather_dimension_numbers()) && gather_window_bounds() == other.gather_window_bounds(); - // FFT has various types & lengths. - case HloOpcode::kFft: - return fft_type() == other.fft_type() && - fft_length() == other.fft_length(); - - // Reduction results are determined by the reduction dimension and the - // reduction computation. - case HloOpcode::kReduce: - return dimensions() == other.dimensions() && - eq_computations(to_apply(), other.to_apply()); - case HloOpcode::kReduceWindow: - return eq_computations(to_apply(), other.to_apply()) && - protobuf_util::ProtobufEquals(window(), other.window()); - - // SelectAndScatter is determined by both select and scatter - // computation as well as the window configuration. - case HloOpcode::kSelectAndScatter: - return eq_computations(select(), other.select()) && - eq_computations(scatter(), other.scatter()) && - protobuf_util::ProtobufEquals(window(), other.window()); - // Remaining instructions with special values. - case HloOpcode::kGetTupleElement: - return tuple_index() == other.tuple_index(); - case HloOpcode::kPad: - return protobuf_util::ProtobufEquals(padding_config(), - other.padding_config()); - case HloOpcode::kSlice: - return slice_starts_ == other.slice_starts_ && - slice_limits_ == other.slice_limits_ && - slice_strides_ == other.slice_strides_; case HloOpcode::kCall: - case HloOpcode::kCrossReplicaSum: - case HloOpcode::kMap: return eq_computations(to_apply(), other.to_apply()); - case HloOpcode::kCustomCall: - if ((window_ == nullptr) != (other.window_ == nullptr) || - (window_ != nullptr && - !protobuf_util::ProtobufEquals(window(), other.window()))) { - return false; - } - if ((convolution_dimension_numbers_ == nullptr) != - (other.convolution_dimension_numbers_ == nullptr) || - (convolution_dimension_numbers_ != nullptr && - !protobuf_util::ProtobufEquals( - convolution_dimension_numbers(), - other.convolution_dimension_numbers()))) { - return false; - } - return custom_call_target_ == other.custom_call_target_; - case HloOpcode::kReverse: - return dimensions() == other.dimensions(); case HloOpcode::kConditional: return eq_computations(true_computation(), other.true_computation()) && eq_computations(false_computation(), other.false_computation()); - // These opcodes are not yet supported. - case HloOpcode::kInfeed: - case HloOpcode::kOutfeed: - case HloOpcode::kSort: - case HloOpcode::kRecv: - case HloOpcode::kRecvDone: - case HloOpcode::kSend: - case HloOpcode::kSendDone: - case HloOpcode::kHostCompute: - return false; - // Ops migrated to subclasses should never come to this line. // TODO(b/80131774): Remove this switch when migration is complete. case HloOpcode::kBatchNormTraining: case HloOpcode::kBatchNormInference: case HloOpcode::kBatchNormGrad: + case HloOpcode::kFft: + case HloOpcode::kSend: + case HloOpcode::kSendDone: + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + case HloOpcode::kReverse: + case HloOpcode::kConcatenate: + case HloOpcode::kReduce: + case HloOpcode::kTranspose: + case HloOpcode::kBroadcast: + case HloOpcode::kMap: + case HloOpcode::kSlice: + case HloOpcode::kConstant: + case HloOpcode::kTrace: + case HloOpcode::kFusion: + case HloOpcode::kRng: + case HloOpcode::kParameter: + case HloOpcode::kGetTupleElement: + case HloOpcode::kReducePrecision: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: + case HloOpcode::kCrossReplicaSum: + case HloOpcode::kConvolution: + case HloOpcode::kCustomCall: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: + case HloOpcode::kHostCompute: + case HloOpcode::kPad: + case HloOpcode::kDynamicSlice: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } } -bool HloInstruction::IsRank2Transpose() const { - return (opcode_ == HloOpcode::kTranspose) && - dimensions_ == std::vector({1, 0}) && - shape_.dimensions_size() == 2 && - std::equal(shape_.dimensions().begin(), shape_.dimensions().end(), - operands_[0]->shape_.dimensions().rbegin()); -} - void HloInstruction::RemoveUser(HloInstruction* user) { auto set_it = user_set_.find(user); CHECK(set_it != user_set_.end()); @@ -1969,6 +1605,10 @@ Status HloInstruction::ReplaceUseWith(HloInstruction* user, std::replace(user->operands_.begin(), user->operands_.end(), this, new_producer); new_producer->AddUser(user); + if (user->opcode() == HloOpcode::kFusion) { + TF_RETURN_IF_ERROR( + Cast(user)->DeduplicateFusionOperands()); + } return Status::OK(); } @@ -1977,6 +1617,10 @@ Status HloInstruction::ReplaceOperandWith(int64 operand_num, TF_RET_CHECK(operand_num >= 0); TF_RET_CHECK(operand_num < operand_count()); HloInstruction* old_operand = mutable_operand(operand_num); + if (old_operand == new_operand) { + return Status::OK(); + } + TF_RET_CHECK(ShapeUtil::CompatibleIgnoringFpPrecision(old_operand->shape(), new_operand->shape())) << old_operand->shape().ShortDebugString() << " is not compatible with " @@ -2007,6 +1651,10 @@ Status HloInstruction::ReplaceAllUsesWith(HloInstruction* new_producer) { std::replace(user->operands_.begin(), user->operands_.end(), this, new_producer); new_producer->AddUser(user); + if (user->opcode() == HloOpcode::kFusion) { + TF_RETURN_IF_ERROR( + Cast(user)->DeduplicateFusionOperands()); + } } } users_.clear(); @@ -2021,22 +1669,6 @@ Status HloInstruction::ReplaceAllUsesWith(HloInstruction* new_producer) { return Status::OK(); } -void HloInstruction::DetachFromOperands() { - VLOG(3) << "DetachFromOperands:\n " << ToString(); - CHECK_EQ(0, user_count()); - // An instruction may be repeated as an operand. To avoid calling RemoveUser - // twice on the same operand, keep a set of already detached operands. - std::set detached_operands; - for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { - HloInstruction* operand = operands_[operand_num]; - if (!ContainsKey(detached_operands, operand)) { - operand->RemoveUser(this); - detached_operands.insert(operand); - } - operands_[operand_num] = nullptr; - } -} - HloComputation* HloInstruction::to_apply() const { switch (opcode_) { case HloOpcode::kCall: @@ -2061,6 +1693,7 @@ void HloInstruction::set_to_apply(HloComputation* computation) { case HloOpcode::kMap: case HloOpcode::kReduceWindow: case HloOpcode::kReduce: + case HloOpcode::kCrossReplicaSum: CHECK_EQ(called_computations_.size(), 1); called_computations_[0] = computation; break; @@ -2070,16 +1703,6 @@ void HloInstruction::set_to_apply(HloComputation* computation) { } } -const string& HloInstruction::custom_call_target() const { - CHECK_EQ(opcode_, HloOpcode::kCustomCall); - return custom_call_target_; -} - -const string& HloInstruction::outfeed_config() const { - CHECK_EQ(opcode_, HloOpcode::kOutfeed); - return outfeed_config_; -} - HloComputation* HloInstruction::while_condition() const { CHECK_EQ(HloOpcode::kWhile, opcode_); return called_computations_[kConditionComputationIndex]; @@ -2106,32 +1729,6 @@ void HloInstruction::set_while_body(HloComputation* computation) { called_computations_[kBodyComputationIndex] = computation; } -HloComputation* HloInstruction::select() const { - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - return called_computations_[kSelectComputationIndex]; -} - -HloComputation* HloInstruction::scatter() const { - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - return called_computations_[kScatterComputationIndex]; -} - -void HloInstruction::set_select(HloComputation* computation) { - // Don't allow changing the computation for fused instructions so we don't - // have to recompute called_instructions for the entire fusion instruction. - CHECK(!IsFused()); - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - called_computations_[kSelectComputationIndex] = computation; -} - -void HloInstruction::set_scatter(HloComputation* computation) { - // Don't allow changing the computation for fused instructions so we don't - // have to recompute called_instructions for the entire fusion instruction. - CHECK(!IsFused()); - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - called_computations_[kScatterComputationIndex] = computation; -} - HloComputation* HloInstruction::true_computation() const { CHECK_EQ(HloOpcode::kConditional, opcode_); return called_computations_[kTrueComputationIndex]; @@ -2179,6 +1776,75 @@ string HloInstruction::ToString(const HloPrintOptions& options) const { return ToStringWithCanonicalNameMap(options, &new_map); } +bool HloInstruction::IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const { + switch (opcode_) { + // Unary elementwise operations. + case HloOpcode::kAbs: + case HloOpcode::kRoundNearestAfz: + case HloOpcode::kCeil: + case HloOpcode::kClz: + case HloOpcode::kConvert: + case HloOpcode::kBitcastConvert: + case HloOpcode::kCopy: + case HloOpcode::kCos: + case HloOpcode::kExp: + case HloOpcode::kExpm1: + case HloOpcode::kFloor: + case HloOpcode::kImag: + case HloOpcode::kIsFinite: + case HloOpcode::kLog: + case HloOpcode::kLog1p: + case HloOpcode::kNot: + case HloOpcode::kNegate: + case HloOpcode::kReal: + case HloOpcode::kReducePrecision: + case HloOpcode::kSign: + case HloOpcode::kSin: + case HloOpcode::kTanh: + CHECK_EQ(1, operand_count()); + return true; + + // Binary elementwise operations, the same as in IsElementwiseBinary(). + case HloOpcode::kAdd: + case HloOpcode::kAtan2: + case HloOpcode::kComplex: + case HloOpcode::kDivide: + case HloOpcode::kEq: + case HloOpcode::kGe: + case HloOpcode::kGt: + case HloOpcode::kLe: + case HloOpcode::kLt: + case HloOpcode::kMaximum: + case HloOpcode::kMinimum: + case HloOpcode::kMultiply: + case HloOpcode::kNe: + case HloOpcode::kPower: + case HloOpcode::kRemainder: + case HloOpcode::kSubtract: + case HloOpcode::kAnd: + case HloOpcode::kOr: + case HloOpcode::kXor: + case HloOpcode::kShiftLeft: + case HloOpcode::kShiftRightArithmetic: + case HloOpcode::kShiftRightLogical: + CHECK_EQ(2, operand_count()); + return true; + + // Ternary elementwise operations. + case HloOpcode::kSelect: + return !ShapeUtil::IsTuple(shape_); + case HloOpcode::kClamp: + return true; + + case HloOpcode::kDynamicUpdateSlice: + return operand_idx.has_value() && operand_idx.value() == 0; + + default: + return false; + } +} + string HloInstruction::ToStringWithCanonicalNameMap( const HloPrintOptions& options, CanonicalNameMap* canonical_name_map) const { @@ -2229,106 +1895,45 @@ string HloInstruction::OperandsToStringWithCanonicalNameMap( const HloPrintOptions& options, CanonicalNameMap* canonical_name_map) const { string operands; - if (opcode() == HloOpcode::kConstant) { - // For constants, show the actual value in place of an empty operand list. - // - // In HloInstruction, sometimes a constant literal is not constructed due - // to its size. Skip the printing in this case. - if (HasLiteral() && ((!ShapeUtil::IsTuple(shape()) && - ShapeUtil::ElementsIn(shape()) <= 10) || - options.print_large_constants())) { - // Literal::ToString emits multidimensional arrays over multiple - // lines. Compact this into one line by stripping out white space. - string tmp = literal().ToString(); - std::replace(tmp.begin(), tmp.end(), '\n', ' '); - std::vector v = tensorflow::str_util::Split(tmp, ' '); - bool first = true; - // Concatenate elements in "v" with spaces separating them, but ignoring - // empty entries. - for (const auto& s : v) { - if (s.empty()) { - continue; - } - StrAppend(&operands, (first ? "" : " "), s); - first = false; - } - } else { - // Do not show large constants or tuples. - operands = "{...}"; + tensorflow::gtl::ArraySlice slice(operands_); + const int64 kMaxOperandsToShowIfCompact = 4; + if (options.compact_operands() && + slice.size() > kMaxOperandsToShowIfCompact) { + slice.remove_suffix(slice.size() - kMaxOperandsToShowIfCompact); + } + operands = Join(slice, ", ", [&](string* out, HloInstruction* operand) { + // If operand is already been deleted, put `null` to the string output. + if (operand == nullptr) { + StrAppend(out, "null "); + return; } - } else if (opcode() == HloOpcode::kParameter) { - StrAppend(&operands, parameter_number_); - } else { - tensorflow::gtl::ArraySlice slice(operands_); - const int64 kMaxOperandsToShowIfCompact = 4; - if (options.compact_operands() && - slice.size() > kMaxOperandsToShowIfCompact) { - slice.remove_suffix(slice.size() - kMaxOperandsToShowIfCompact); + std::vector str; + if (options.print_operand_shape()) { + str.push_back(ShapeUtil::HumanStringWithLayout(operand->shape())); } - operands = Join(slice, ", ", [&](string* out, HloInstruction* operand) { - std::vector str; - if (options.print_operand_shape()) { - str.push_back(ShapeUtil::HumanStringWithLayout(operand->shape())); - } - // In a top-level HloInstruction::ToString() call, the operand name is not - // part of the canonical string. - if (options.canonicalize_instruction_names() && - options.is_in_nested_computation()) { - str.push_back(PrintName( - canonical_name_map->LookupOrInsert(operand->name()), options)); - } else if (!options.compact_operands()) { - str.push_back(PrintName(operand->name(), options)); - } - StrAppend(out, Join(str, " ")); - }); - const int64 remaining = operands_.size() - slice.size(); - if (slice.size() != operands_.size()) { - StrAppend(&operands, ", ...(+", remaining, ")"); + // In a top-level HloInstruction::ToString() call, the operand name is not + // part of the canonical string. + if (options.canonicalize_instruction_names() && + options.is_in_nested_computation()) { + str.push_back(PrintName( + canonical_name_map->LookupOrInsert(operand->name()), options)); + } else if (!options.compact_operands()) { + str.push_back(PrintName(operand->name(), options)); } + StrAppend(out, Join(str, " ")); + }); + const int64 remaining = operands_.size() - slice.size(); + if (slice.size() != operands_.size()) { + StrAppend(&operands, ", ...(+", remaining, ")"); } return operands; } std::vector HloInstruction::ExtraAttributesToString( const HloPrintOptions& options) const { - std::vector extra; - if (opcode() == HloOpcode::kFusion) { - extra.push_back(StrCat("kind=", xla::ToString(fusion_kind()))); - } - if (CanHaveDimensionsField()) { - extra.push_back(StrCat("dimensions={", Join(dimensions(), ","), "}")); - } - if (window_ != nullptr && window_->dimensions_size() != 0) { - extra.push_back(StrCat("window={", window_util::ToString(*window_), "}")); - } - if (padding_config_ != nullptr) { - extra.push_back( - StrCat("padding=", xla::PaddingConfigToString(*padding_config_))); - } - if (opcode() == HloOpcode::kSlice) { - std::vector bounds; - bounds.reserve(slice_starts_.size()); - const bool omit_stride = - std::all_of(slice_strides_.begin(), slice_strides_.end(), - [](int64 stride) { return stride == 1; }); - for (int i = 0; i < slice_starts_.size(); ++i) { - string stride_str = omit_stride ? "" : StrCat(":", slice_strides_[i]); - bounds.push_back(StrCat("[", slice_starts_[i], ":", slice_limits_[i], - stride_str, "]")); - } - extra.push_back(StrCat("slice={", Join(bounds, ", "), "}")); - } - if (opcode() == HloOpcode::kDynamicSlice) { - extra.push_back( - StrCat("dynamic_slice_sizes={", Join(dynamic_slice_sizes(), ","), "}")); - } + std::vector extra = ExtraAttributesToStringImpl(options); - if (convolution_dimension_numbers_ != nullptr) { - extra.push_back(StrCat( - "dim_labels=", - ConvolutionDimensionNumbersToString(*convolution_dimension_numbers_))); - } if (dot_dimension_numbers_ != nullptr) { extra.push_back(DotDimensionNumbersToString()); } @@ -2337,10 +1942,6 @@ std::vector HloInstruction::ExtraAttributesToString( extra.push_back( StrCat("window_bounds={", Join(gather_window_bounds(), ","), "}")); } - if (opcode() == HloOpcode::kFft) { - extra.push_back(StrCat("fft_type=", FftType_Name(fft_type()))); - extra.push_back(StrCat("fft_length={", Join(fft_length(), ","), "}")); - } if (options.print_subcomputation_mode() == HloPrintOptions::PrintSubcomputationMode::kNameOnly) { @@ -2396,6 +1997,7 @@ std::vector HloInstruction::ExtraAttributesToString( case HloOpcode::kMap: case HloOpcode::kReduceWindow: case HloOpcode::kReduce: + case HloOpcode::kCrossReplicaSum: extra.push_back( StrCat("to_apply=\n", to_apply()->ToString(new_options))); break; @@ -2411,14 +2013,7 @@ std::vector HloInstruction::ExtraAttributesToString( break; } } - if (opcode() == HloOpcode::kSend || opcode() == HloOpcode::kRecv || - opcode() == HloOpcode::kSendDone || opcode() == HloOpcode::kRecvDone) { - extra.push_back(StrCat("channel_id=", channel_id_)); - } - if (opcode() == HloOpcode::kGetTupleElement) { - extra.push_back(StrCat("index=", tuple_index())); - } if (has_sharding()) { extra.push_back(StrCat("sharding=", sharding().ToString())); } @@ -2431,33 +2026,11 @@ std::vector HloInstruction::ExtraAttributesToString( }), "}")); } - if (opcode() == HloOpcode::kInfeed && !infeed_config_.empty()) { - extra.push_back(StrCat("infeed_config=\"", CEscape(infeed_config_), "\"")); - } - if (opcode() == HloOpcode::kOutfeed && !outfeed_config_.empty()) { - extra.push_back( - StrCat("outfeed_config=\"", CEscape(outfeed_config_), "\"")); - } - if (opcode() == HloOpcode::kRng) { - extra.push_back( - StrCat("distribution=", RandomDistributionToString(distribution_))); - } - if (opcode() == HloOpcode::kReducePrecision) { - extra.push_back(StrCat("exponent_bits=", exponent_bits_)); - extra.push_back(StrCat("mantissa_bits=", mantissa_bits_)); - } if (operand_side_metadata_ != nullptr && user_side_metadata_ != nullptr) { extra.push_back(StrCat("domain={kind=\"", operand_side_metadata_->Kind(), "\", entry=", operand_side_metadata_->ToString(), ", exit=", user_side_metadata_->ToString(), "}")); } - // By contract, we print the custom call target even if - // options.print_subcomputation_mode() == kOff, because the call target is not - // an HloComputation. - if (opcode() == HloOpcode::kCustomCall) { - extra.push_back( - StrCat("custom_call_target=\"", CEscape(custom_call_target_), "\"")); - } return extra; } @@ -2489,31 +2062,12 @@ HloInstructionProto HloInstruction::ToProto() const { *proto.mutable_metadata() = metadata_; proto.set_backend_config(backend_config_); - if (literal_ != nullptr) { - *proto.mutable_literal() = literal_->ToProto(); - } - proto.set_parameter_number(parameter_number_); - if (opcode() == HloOpcode::kFusion) { - proto.set_fusion_kind(xla::ToString(fusion_kind())); - proto.add_called_computation_ids( - fused_instructions_computation()->unique_id()); - } else { + if (opcode() != HloOpcode::kFusion) { for (const HloComputation* computation : called_computations_) { proto.add_called_computation_ids(computation->unique_id()); } } - proto.set_tuple_index(tuple_index_); - for (int64 dimension : dimensions_) { - proto.add_dimensions(dimension); - } - if (window_ != nullptr) { - *proto.mutable_window() = *window_; - } - if (convolution_dimension_numbers_ != nullptr) { - *proto.mutable_convolution_dimension_numbers() = - *convolution_dimension_numbers_; - } if (dot_dimension_numbers_ != nullptr) { *proto.mutable_dot_dimension_numbers() = *dot_dimension_numbers_; } @@ -2525,40 +2079,11 @@ HloInstructionProto HloInstruction::ToProto() const { proto.add_gather_window_bounds(bound); } } - for (int i = 0; i < slice_starts_.size(); ++i) { - auto* slice_dimension = proto.add_slice_dimensions(); - slice_dimension->set_start(slice_starts_[i]); - slice_dimension->set_limit(slice_limits_[i]); - slice_dimension->set_stride(slice_strides_[i]); - } - proto.set_exponent_bits(exponent_bits_); - proto.set_mantissa_bits(mantissa_bits_); - for (int64 slice_size : dynamic_slice_sizes_) { - proto.add_dynamic_slice_sizes(slice_size); - } - if (padding_config_ != nullptr) { - *proto.mutable_padding_config() = *padding_config_; - } - proto.set_outfeed_config(outfeed_config_); - if (opcode() == HloOpcode::kRng) { - proto.set_distribution(distribution_); - } - proto.set_channel_id(channel_id_); - proto.set_infeed_config(infeed_config_); - proto.set_custom_call_target(custom_call_target_); - *proto.mutable_outfeed_shape() = outfeed_shape_; - proto.set_fft_type(fft_type_); - for (int64 fft_len : fft_length_) { - proto.add_fft_length(fft_len); - } if (has_sharding()) { *proto.mutable_sharding() = sharding().ToProto(); } - proto.set_channel_name(channel_name_); - proto.set_cost_estimate_ns(cost_estimate_ns_); - return proto; } @@ -2568,35 +2093,6 @@ string HloInstruction::ToCategory() const { return "data formatting"; } - if (opcode() == HloOpcode::kConvolution) { - string category = "convolution"; - if (window_util::HasBaseDilation(window())) { - category += " base-dilated"; - } - if (window_util::HasWindowDilation(window())) { - category += " window-dilated"; - } - return category; - } - - // Give transpose-dot and backwards-conv fusions the categories "dot" and - // "convolution" so they match the categories of proper kDot and kConvolution - // ops. These fusion categories are really just a way of expressing a - // particular kind of dot or conv, so they should have the same category as a - // vanilla dot/conv. - if (opcode() == HloOpcode::kFusion) { - switch (fusion_kind()) { - case FusionKind::kLoop: - return "loop fusion"; - case FusionKind::kInput: - return "input fusion"; - case FusionKind::kOutput: - return "output fusion"; - case FusionKind::kCustom: - return "custom fusion"; - } - } - if (IsElementwise()) { return "non-fusion elementwise"; } @@ -2610,12 +2106,6 @@ void HloInstruction::set_tracing(HloInstruction* trace_instruction) { trace_instruction_ = trace_instruction; } -string HloInstruction::TracingTag() const { - CHECK_EQ(HloOpcode::kTrace, opcode()); - CHECK(literal_ != nullptr); - return literal_->GetR1U8AsString(); -} - bool HloInstruction::IsFused() const { return parent_->IsFusionComputation(); } bool HloInstruction::IsFusable() const { @@ -2624,59 +2114,14 @@ bool HloInstruction::IsFusable() const { return false; } // Some kinds of instructions don't make sense to fuse. - switch (opcode_) { - case HloOpcode::kDomain: - case HloOpcode::kParameter: - return false; - // Side effecting instrutions cannot be fused. - default: - return !HasSideEffect(); - } -} - -HloComputation* HloInstruction::fused_instructions_computation() const { - CHECK_EQ(opcode_, HloOpcode::kFusion); - CHECK(!called_computations_.empty()); - auto* fused_instructions_computation = called_computations_.front(); - CHECK(fused_instructions_computation->IsFusionComputation()) - << "Computation " << fused_instructions_computation->name() - << " is not a fusion kind"; - return fused_instructions_computation; -} - -HloInstruction* HloInstruction::fused_expression_root() const { - CHECK_EQ(opcode_, HloOpcode::kFusion); - return fused_instructions_computation()->root_instruction(); -} - -HloInstruction* HloInstruction::fused_parameter(int64 parameter_number) const { - CHECK_EQ(opcode_, HloOpcode::kFusion); - return fused_instructions_computation()->parameter_instruction( - parameter_number); -} - -const std::vector& HloInstruction::fused_parameters() const { - CHECK_EQ(opcode_, HloOpcode::kFusion); - return fused_instructions_computation()->parameter_instructions(); -} - -const tensorflow::gtl::iterator_range>::const_iterator>> -HloInstruction::fused_instructions() const { - CHECK_EQ(opcode_, HloOpcode::kFusion); - const HloComputation* subcomp = fused_instructions_computation(); - return subcomp->instructions(); -} - -const tensorflow::gtl::iterator_range< - UnwrappingIterator>::iterator>> -HloInstruction::fused_instructions() { - CHECK_EQ(opcode_, HloOpcode::kFusion); - return fused_instructions_computation()->instructions(); -} - -int64 HloInstruction::fused_instruction_count() const { - return fused_instructions_computation()->instruction_count(); + switch (opcode_) { + case HloOpcode::kDomain: + case HloOpcode::kParameter: + return false; + // Side effecting instrutions cannot be fused. + default: + return !HasSideEffect(); + } } HloInstruction::HloInstruction(HloOpcode opcode, const Shape& shape) @@ -2733,6 +2178,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleAnd(this); case HloOpcode::kOr: return visitor->HandleOr(this); + case HloOpcode::kXor: + return visitor->HandleXor(this); case HloOpcode::kShiftLeft: return visitor->HandleShiftLeft(this); case HloOpcode::kShiftRightArithmetic: @@ -2857,6 +2304,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleGather(this); case HloOpcode::kDomain: return visitor->HandleDomain(this); + case HloOpcode::kAfterAll: + return visitor->HandleAfterAll(this); // These opcodes are not handled here. case HloOpcode::kTrace: @@ -3097,12 +2546,6 @@ Status HloInstruction::AcceptOrdered( return visitor->FinishVisit(this); } -const Shape& HloInstruction::outfeed_shape() const { - DCHECK_EQ(opcode_, HloOpcode::kOutfeed); - TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape_)); - return outfeed_shape_; -} - const Shape& HloInstruction::shape() const { TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape_)); return shape_; @@ -3124,87 +2567,7 @@ bool HloInstruction::IsElementwiseBinary() const { } bool HloInstruction::IsElementwise() const { - switch (opcode_) { - // Nullary elementwise operations. - case HloOpcode::kConstant: - return true; - - // Unary elementwise operations. - case HloOpcode::kAbs: - case HloOpcode::kRoundNearestAfz: - case HloOpcode::kCeil: - case HloOpcode::kClz: - case HloOpcode::kConvert: - case HloOpcode::kBitcastConvert: - case HloOpcode::kCopy: - case HloOpcode::kCos: - case HloOpcode::kExp: - case HloOpcode::kExpm1: - case HloOpcode::kFloor: - case HloOpcode::kImag: - case HloOpcode::kIsFinite: - case HloOpcode::kLog: - case HloOpcode::kLog1p: - case HloOpcode::kNot: - case HloOpcode::kNegate: - case HloOpcode::kReal: - case HloOpcode::kReducePrecision: - case HloOpcode::kSign: - case HloOpcode::kSin: - case HloOpcode::kTanh: - CHECK_EQ(1, operand_count()); - return true; - - // Binary elementwise operations, the same as in IsElementwiseBinary(). - case HloOpcode::kAdd: - case HloOpcode::kAtan2: - case HloOpcode::kComplex: - case HloOpcode::kDivide: - case HloOpcode::kEq: - case HloOpcode::kGe: - case HloOpcode::kGt: - case HloOpcode::kLe: - case HloOpcode::kLt: - case HloOpcode::kMaximum: - case HloOpcode::kMinimum: - case HloOpcode::kMultiply: - case HloOpcode::kNe: - case HloOpcode::kPower: - case HloOpcode::kRemainder: - case HloOpcode::kSubtract: - case HloOpcode::kAnd: - case HloOpcode::kOr: - case HloOpcode::kShiftLeft: - case HloOpcode::kShiftRightArithmetic: - case HloOpcode::kShiftRightLogical: - CHECK_EQ(2, operand_count()); - return true; - - // Ternary elementwise operations. - case HloOpcode::kSelect: - return !ShapeUtil::IsTuple(shape_); - case HloOpcode::kClamp: - return true; - - // Other operations. - case HloOpcode::kRng: - case HloOpcode::kMap: - return true; - case HloOpcode::kFusion: - if (fusion_kind() != FusionKind::kLoop) { - return false; - } - for (auto* fused : fused_instructions()) { - if (fused->opcode() != HloOpcode::kParameter && - !fused->IsElementwise()) { - return false; - } - } - return true; - - default: - return false; - } + return IsElementwiseImpl(tensorflow::gtl::nullopt); } bool HloInstruction::ImplicitlyBroadcastsOperand(int64 operand_idx) const { @@ -3212,54 +2575,8 @@ bool HloInstruction::ImplicitlyBroadcastsOperand(int64 operand_idx) const { return !ShapeUtil::SameDimensions(shape(), operand(operand_idx)->shape()); } -namespace { -bool IsInstructionElementwiseOnOperand(const HloInstruction* instruction, - const HloInstruction* operand) { - std::vector operand_indices = instruction->OperandIndices(operand); - return std::all_of( - operand_indices.begin(), operand_indices.end(), - [instruction](int64 operand_index) { - return instruction->IsElementwiseOnOperand(operand_index); - }); -} -} // namespace - bool HloInstruction::IsElementwiseOnOperand(int64 operand_idx) const { - // For all instructions other than kFusion, being elementwise on one of the - // operands is equivalent to being elementwise on all the operands. - if (opcode() != HloOpcode::kFusion) { - return IsElementwise(); - } - - CHECK_EQ(HloOpcode::kFusion, opcode()); - if (fusion_kind() != FusionKind::kLoop) { - return false; - } - - // A loop-fusion is elementwise on an operand if all operations (computed - // using BFS) between the operand and the fused root are elementwise. - std::deque worklist; - std::unordered_set visited; - worklist.push_back(fused_parameter(operand_idx)); - visited.insert(fused_parameter(operand_idx)); - while (!worklist.empty()) { - HloInstruction* operand = worklist.front(); - worklist.pop_front(); - for (HloInstruction* user : operand->users()) { - CHECK_GE(user->unique_id(), 0); - if (ContainsKey(visited, user)) { - continue; - } - if (user->IsElementwise() || - IsInstructionElementwiseOnOperand(user, operand)) { - worklist.push_back(user); - visited.insert(user); - } else { - return false; - } - } - } - return true; + return IsElementwiseImpl(operand_idx); } // A helper class for memoized, recursive computation of HloOpcode::kFusion @@ -3281,8 +2598,10 @@ class HloInstruction::FusionReusesParamElements { static UseKind ComputeInternal( int64 i, const HloInstruction& hlo, tensorflow::gtl::FlatMap* cache) { - if (hlo.opcode_ == HloOpcode::kParameter && hlo.parameter_number_ == i) { - return UseKind::kUse; + if (auto hlo_param = DynCast(&hlo)) { + if (hlo_param->parameter_number() == i) { + return UseKind::kUse; + } } auto p = cache->emplace(&hlo, UseKind{}); @@ -3591,30 +2910,264 @@ void HloInstruction::set_outer_dimension_partitions( outer_dimension_partitions_ = outer_dimension_partitions; } +// TODO(b/80131774): Remove these temporary methods after transition. +int64 HloInstruction::feature_index() const { + return Cast(this)->feature_index(); +} + +float HloInstruction::epsilon() const { + return Cast(this)->epsilon(); +} + +FftType HloInstruction::fft_type() const { + return Cast(this)->fft_type(); +} + +const std::vector& HloInstruction::fft_length() const { + return Cast(this)->fft_length(); +} + +int64 HloInstruction::channel_id() const { + return Cast(this)->channel_id(); +} + +int64 HloInstruction::concatenate_dimension() const { + return Cast(this)->concatenate_dimension(); +} + +bool HloInstruction::IsRank2Transpose() const { + auto transpose = DynCast(this); + return transpose != nullptr && transpose->IsRank2Transpose(); +} + +int64 HloInstruction::slice_starts(int64 dimension) const { + return Cast(this)->slice_starts(dimension); +} + +const std::vector& HloInstruction::slice_starts() const { + return Cast(this)->slice_starts(); +} + +int64 HloInstruction::slice_limits(int64 dimension) const { + return Cast(this)->slice_limits(dimension); +} + +const std::vector& HloInstruction::slice_limits() const { + return Cast(this)->slice_limits(); +} + +int64 HloInstruction::slice_strides(int64 dimension) const { + return Cast(this)->slice_strides(dimension); +} + +const std::vector& HloInstruction::slice_strides() const { + return Cast(this)->slice_strides(); +} + +bool HloInstruction::IsInPlaceSlice() const { + return Cast(this)->IsInPlaceSlice(); +} + +const Literal& HloInstruction::literal() const { + return Cast(this)->literal(); +} + +bool HloInstruction::IsConstant() const { + return DynCast(this) != nullptr; +} + void HloInstruction::RelayoutConstant(const Layout& new_layout, const ShapeIndex& shape_index) { - CHECK_EQ(opcode(), HloOpcode::kConstant); - Shape* mutable_array_subshape = - ShapeUtil::GetMutableSubshape(mutable_shape(), shape_index); - CHECK(ShapeUtil::IsArray(*mutable_array_subshape)); + Cast(this)->RelayoutConstant(new_layout, shape_index); +} + +string HloInstruction::TracingTag() const { + return Cast(this)->TracingTag(); +} + +HloInstruction* HloInstruction::AddFusionOperand(HloInstruction* new_operand) { + return Cast(this)->AddFusionOperand(new_operand); +} + +// Delegates to HloFusionInstruction::MergeFusionInstruction. +void HloInstruction::MergeFusionInstruction( + HloInstruction* instruction_to_merge) { + return Cast(this)->MergeFusionInstruction( + Cast(instruction_to_merge)); +} + +// Delegates to HloFusionInstruction::MergeFusionInstructionIntoMultiOutput. +void HloInstruction::MergeFusionInstructionIntoMultiOutput( + HloInstruction* instruction_to_merge) { + return Cast(this) + ->MergeFusionInstructionIntoMultiOutput( + Cast(instruction_to_merge)); +} + +HloInstruction* HloInstruction::FuseInstruction( + HloInstruction* instruction_to_fuse) { + return Cast(this)->FuseInstruction(instruction_to_fuse); +} + +HloInstruction* HloInstruction::FuseInstructionIntoMultiOutput( + HloInstruction* instruction_to_fuse) { + return Cast(this)->FuseInstructionIntoMultiOutput( + instruction_to_fuse); +} + +HloComputation* HloInstruction::fused_instructions_computation() const { + return Cast(this)->fused_instructions_computation(); +} + +HloInstruction* HloInstruction::fused_expression_root() const { + return Cast(this)->fused_expression_root(); +} + +const tensorflow::gtl::iterator_range>::const_iterator>> +HloInstruction::fused_instructions() const { + return Cast(this)->fused_instructions(); +} + +const tensorflow::gtl::iterator_range< + UnwrappingIterator>::iterator>> +HloInstruction::fused_instructions() { + return Cast(this)->fused_instructions(); +} + +int64 HloInstruction::fused_instruction_count() const { + return Cast(this)->fused_instruction_count(); +} + +HloInstruction* HloInstruction::fused_parameter(int64 parameter_number) const { + return Cast(this)->fused_parameter(parameter_number); +} + +const std::vector& HloInstruction::fused_parameters() const { + return Cast(this)->fused_parameters(); +} - // Normally array_subshape will always have a layout, but this invariant is - // temporarily broken in LayoutAssignment::AssignLayouts. +const bool HloInstruction::IsMultiOutputFusion() const { + const HloFusionInstruction* fusion = DynCast(this); + return fusion != nullptr && fusion->IsMultiOutputFusion(); +} + +HloInstruction::FusionKind HloInstruction::fusion_kind() const { + return Cast(this)->fusion_kind(); +} + +void HloInstruction::set_fusion_kind(FusionKind kind) { + return Cast(this)->set_fusion_kind(kind); +} + +RandomDistribution HloInstruction::random_distribution() const { + return Cast(this)->random_distribution(); +} + +int64 HloInstruction::parameter_number() const { + return Cast(this)->parameter_number(); +} + +int64 HloInstruction::tuple_index() const { + return Cast(this)->tuple_index(); +} + +int32 HloInstruction::exponent_bits() const { + return Cast(this)->exponent_bits(); +} + +int32 HloInstruction::mantissa_bits() const { + return Cast(this)->mantissa_bits(); +} + +string HloInstruction::infeed_config() const { + return Cast(this)->infeed_config(); +} + +void HloInstruction::set_infeed_config(const string& config) { + return Cast(this)->set_infeed_config(config); +} + +const Shape& HloInstruction::outfeed_shape() const { + return Cast(this)->outfeed_shape(); +} + +const string& HloInstruction::outfeed_config() const { + return Cast(this)->outfeed_config(); +} + +const std::vector& HloInstruction::replica_group_ids() const { + return Cast(this)->replica_group_ids(); +} + +string HloInstruction::cross_replica_sum_barrier() const { + return Cast(this)->cross_replica_sum_barrier(); +} + +void HloInstruction::set_cross_replica_sum_barrier(const string& barrier) { + return Cast(this)->set_cross_replica_sum_barrier( + barrier); +} + +tensorflow::gtl::optional HloInstruction::all_reduce_id() const { + return Cast(this)->all_reduce_id(); +} - if (!mutable_array_subshape->has_layout() || - !LayoutUtil::Equal(mutable_array_subshape->layout(), new_layout)) { - literal_ = literal_->Relayout(new_layout, shape_index); - *mutable_array_subshape->mutable_layout() = new_layout; +const ConvolutionDimensionNumbers& +HloInstruction::convolution_dimension_numbers() const { + if (auto convolution = DynCast(this)) { + return convolution->convolution_dimension_numbers(); + } + if (auto custom_call = DynCast(this)) { + return custom_call->convolution_dimension_numbers(); } + LOG(FATAL) << "Unimplemented method."; } -// TODO(b/80131774): Remove these temporary methods after transition. -int64 HloInstruction::feature_index() const { - return Cast(this)->feature_index(); +void HloInstruction::set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + if (auto convolution = DynCast(this)) { + convolution->set_convolution_dimension_numbers(dnums); + } else if (auto custom_call = DynCast(this)) { + custom_call->set_convolution_dimension_numbers(dnums); + } else { + LOG(FATAL) << "Unimplemented method."; + } } -float HloInstruction::epsilon() const { - return Cast(this)->epsilon(); +HloComputation* HloInstruction::select() const { + return Cast(this)->select(); +} + +HloComputation* HloInstruction::scatter() const { + return Cast(this)->scatter(); } +void HloInstruction::set_select(HloComputation* computation) { + return Cast(this)->set_select(computation); +} + +void HloInstruction::set_scatter(HloComputation* computation) { + return Cast(this)->set_scatter(computation); +} + +const string& HloInstruction::custom_call_target() const { + return Cast(this)->custom_call_target(); +} + +const string& HloInstruction::channel_name() const { + return Cast(this)->channel_name(); +} + +const PaddingConfig& HloInstruction::padding_config() const { + return Cast(this)->padding_config(); +} + +int64 HloInstruction::slice_sizes(int64 dimension) const { + return Cast(this)->slice_sizes(dimension); +} + +const std::vector& HloInstruction::dynamic_slice_sizes() const { + return Cast(this)->dynamic_slice_sizes(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index b16837eaec7fda36827095b01c15cb4f84f81333..34e7dcb43d43483f010f226f00bdf211722f2562 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -389,11 +389,10 @@ class HloInstruction { // Creates a map instruction, where the computation (given by the handle) is // applied element-wise to every element in operands (across the operands, - // at a given index) with the same `static_operands`. + // at a given index) static std::unique_ptr CreateMap( const Shape& shape, tensorflow::gtl::ArraySlice operands, - HloComputation* map_computation, - tensorflow::gtl::ArraySlice static_operands = {}); + HloComputation* map_computation); // Creates a convolution op, where rhs is the convolutional filter // and window describes how the filter is applied to lhs. @@ -435,16 +434,17 @@ class HloInstruction { // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. // - // `channel_id`: for Allreduce nodes from different models, if they have the - // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be - // applied cross models. + // `all_reduce_id`: for Allreduce nodes from different modules, if they have + // the same all_reduce_id, they will be 'Allreduce'd. If empty, Allreduce will + // not be applied cross modules. // // TODO(b/79737069): Rename this to AllReduce. static std::unique_ptr CreateCrossReplicaSum( const Shape& shape, tensorflow::gtl::ArraySlice operands, HloComputation* reduce_computation, - tensorflow::gtl::ArraySlice replica_group_ids = {}, - const tensorflow::gtl::optional& channel_id = + tensorflow::gtl::ArraySlice replica_group_ids, + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id = tensorflow::gtl::nullopt); // Creates a conversion instruction, where operand is the data to convert and @@ -458,19 +458,36 @@ class HloInstruction { const Shape& shape, HloInstruction* operand); // Creates an infeed instruction, which reads data of the given shape from the - // Infeed interface of the device. - static std::unique_ptr CreateInfeed(const Shape& shape, + // Infeed interface of the device. infeed_shape is the shape of the data + // received from the infeed *not* the shape of the infeed instruction which + // is a tuple containing the infeed_shape and the TOKEN. + static std::unique_ptr CreateInfeed( + const Shape& infeed_shape, HloInstruction* token_operand, + const string& config); + // Overload which does not require a token. + // TODO(b/80000000): Remove this overload when all uses of infeed are + // converted to take tokens. + static std::unique_ptr CreateInfeed(const Shape& infeed_shape, const string& config); - // Creates an outfeed instruction, which outputs data. + // Creates an outfeed instruction, which outputs data. outfeed_shape is the + // shape of the data being outfed *not* the shape of the outfeed instruction + // which is a TOKEN. static std::unique_ptr CreateOutfeed( - const Shape& shape, HloInstruction* operand, + const Shape& outfeed_shape, HloInstruction* operand, + HloInstruction* token_operand, tensorflow::StringPiece outfeed_config); + // Overload which does not require a token. + // TODO(b/80000000): Remove this overload when all uses of outfeed are + // converted to take tokens. + static std::unique_ptr CreateOutfeed( + const Shape& outfeed_shape, HloInstruction* operand, tensorflow::StringPiece outfeed_config); // Creates an asynchronous send instruction with the given channel id, which // initiates sending the operand data to a unique receive instruction in // another computation that has the same channel id. static std::unique_ptr CreateSend(HloInstruction* operand, + HloInstruction* token, int64 channel_id); // Blocks until data transfer for the Send instruction (operand) is complete. @@ -482,6 +499,7 @@ class HloInstruction { // which allocates resources to receive data of the given shape from a unique // send instruction in another computation that has the same channel id. static std::unique_ptr CreateRecv(const Shape& shape, + HloInstruction* token, int64 channel_id); // Blocks until data transfer for the Recv instruction (operand) is complete @@ -595,6 +613,11 @@ class HloInstruction { const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions); + // Creates a sort op, with a keys operand, and an optional values operand. + static std::unique_ptr CreateSort( + const Shape& shape, HloInstruction* keys, + HloInstruction* values = nullptr); + // Creates a while instruction, given a condition computation, a body // computation, and the initial value for the input of the computations. For // example, shape: S32, condition: i -> i < 1000, body: i -> i * 2, init: 1 @@ -664,6 +687,11 @@ class HloInstruction { const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions); + // Creates a token instruction used for joining or creating new values of + // token type which thread through side-effecting operations. + static std::unique_ptr CreateAfterAll( + tensorflow::gtl::ArraySlice operands); + // Creates an instance of GatherDimensionNumbers. static GatherDimensionNumbers MakeGatherDimNumbers( tensorflow::gtl::ArraySlice output_window_dims, @@ -802,15 +830,18 @@ class HloInstruction { // Returns whether the instruction has a constant operand. bool HasConstantOperand() const; - // Returns whether this instruction does a rank-2 transposition. - bool IsRank2Transpose() const; - // Replaces the use of this instruction in "user" with "new_producer". Note // that there might be multiple uses of this instruction in "user"; all will // be replaced. + // + // If user is a fusion instruction, this function will remove any duplicated + // operands of it which could be created due to this replacement. Status ReplaceUseWith(HloInstruction* user, HloInstruction* new_producer); // Replaces the specified operand with new_operand. + // + // This function does NOT remove duplicated operands even if this instruction + // is a fusion, so that the existing operand numbers do not change. Status ReplaceOperandWith(int64 operand_no, HloInstruction* new_operand); // Replaces all uses of this instruction with the new producer. If @@ -819,14 +850,10 @@ class HloInstruction { // // If this instruction is the root of its computation, sets the computation's // root to new_producer. - Status ReplaceAllUsesWith(HloInstruction* new_producer); - - // Detaches an instruction from its operands. That is, remove the instruction - // from each operand's user set. This should only be called prior to - // deallocating the instruction. // - // TODO(b/78305363): Make this automatic when deleting an instruction. - void DetachFromOperands(); + // If a user is a fusion instruction, this function will remove any duplicated + // operands of it which could be created due to this replacement. + Status ReplaceAllUsesWith(HloInstruction* new_producer); // Performs a postorder DFS visit using this node as the root. If // call_finish_visit is true, then DfsHloVisitor::FinishVisit is called when @@ -873,38 +900,6 @@ class HloInstruction { template Status Visit(DfsHloVisitorBase* visitor); - // Returns the literal associated with this instruction. - // - // Note: only constant and parameter opcodes have an associated literal. - const Literal& literal() const; - - // Returns whether there is literal associated with this instruction. - bool HasLiteral() const; - - // Returns the parameter number associated with this instruction. - // - // Note: only parameter opcodes have an associated parameter number. - int64 parameter_number() const { - CHECK_EQ(HloOpcode::kParameter, opcode_); - return parameter_number_; - } - - // Returns the dimension sizes or numbers associated with this instruction. - // - // Precondition: opcode() is one of: concatenate, reduce, broadcast, reshape, - // and reverse. - const std::vector& dimensions() const; - int64 dimensions(int64 index) const; - - // Accessor for the dimension in which a concatenate HLO should occur. - // Precondition: opcode() == HloOpcode::kConcatenate - int64 concatenate_dimension() const; - - // Returns the tuple index associated with this instruction. - // - // Precondition: opcode() == HloOpcode::kGetTupleElement - int64 tuple_index() const; - // Returns the first non-GetTupleElement ancestor instruction of 'hlo'. // If the first non-GTE ancestor is tuple-shaped, populates 'index' with the // (possibly nested) tuple indices used on the path from ancestor to 'hlo'. @@ -932,18 +927,6 @@ class HloInstruction { HloComputation* to_apply() const; void set_to_apply(HloComputation* to_apply); - // Returns the custom_call_target for CustomCall. - // Precondition: opcode() == HloOpcode::kCustomCall - const string& custom_call_target() const; - - // Returns the config for the Outfeed instruction. - // Precondition: opcode() == HloOpcode::kOutfeed - const string& outfeed_config() const; - - // Returns the shape for the Outfeed instruction. - // Precondition: opcode() == HloOpcode::kOutfeed - const Shape& outfeed_shape() const; - // Gets/sets the while_condition or while_body HloComputation for While. The // setters should only be called by HloModule or HloComputation methods. // @@ -953,15 +936,6 @@ class HloInstruction { void set_while_condition(HloComputation* while_condition); void set_while_body(HloComputation* while_body); - // Gets/sets the select or scatter HloComputation for SelectAndScatter. The - // setters should only be called by HloModule or HloComputation methods. - // - // Precondition: opcode() == HloOpcode::kSelectAndScatter. - HloComputation* select() const; - HloComputation* scatter() const; - void set_select(HloComputation* select); - void set_scatter(HloComputation* scatter); - // Gets/sets the true and false HloComputation for Conditional. The setters // should only be called by HloModule or HloComputation methods. // @@ -992,7 +966,7 @@ class HloInstruction { string OperandsToString(const HloPrintOptions& options) const; // Returns string representation of op-specific attributes. - virtual std::vector ExtraAttributesToString( + std::vector ExtraAttributesToString( const HloPrintOptions& options) const; // As ToString, but returns a shorter string. @@ -1003,7 +977,7 @@ class HloInstruction { // Returns a category for the HLO. This could be something like "convolution" // or "elementwise". - string ToCategory() const; + virtual string ToCategory() const; // Returns a logging instruction, if the output of this instruction is logged. // @@ -1011,105 +985,14 @@ class HloInstruction { HloInstruction* tracing() const; void set_tracing(HloInstruction* trace_instruction); - // Returns the channel id associated with the instruction. The id is - // shared between each Send/Recv pair and is globally unique to identify each - // channel. - // - // Precondition: opcode() == HloOpcode::kSend or HloOpcode::kRecv - int64 channel_id() const { return channel_id_; } - - // Returns the channel name associated with the instruction. The name is - // used to identify host Send/Recv operations. - // - // Precondition: opcode() == HloOpcode::kHostCompute - string channel_name() const { return channel_name_; } - - // Delegates to HloBatchNormInstruction::feature_index. - // TODO(b/80131774): Remove this code. - int64 feature_index() const; - - // Delegates to HloBatchNormInstruction::epsilon. - // TODO(b/80131774): Remove this code. - float epsilon() const; - - // Returns the infeed configuration string. The infeed configuration includes - // any metadata needed for the backend compiler (e.g., infeed buffer address) - // and is target-dependent. - string infeed_config() const { return infeed_config_; } - void set_infeed_config(const string& config) { infeed_config_ = config; } - - // Returns a tag to be used in tracing. - // - // Precondition: opcode() == HloOpcode::kTrace - string TracingTag() const; - - // Returns whether the instruction is a constant. - bool IsConstant() const; - // Returns true if this instruction is fused, ie contained within a fusion // instruction. bool IsFused() const; - // Returns the computation for this fused instruction. - // - // Precondition: opcode() == HloOpcode::kFusion - HloComputation* fused_instructions_computation() const; - // Returns true if this instruction can be legally fused into a fusion // instruction. bool IsFusable() const; - // Returns the root instruction of the fused expression contained within this - // fusion instruction. - // - // Precondition: opcode() == HloOpcode::kFusion - HloInstruction* fused_expression_root() const; - - // Returns the list of fused instructions inside this fusion instruction. The - // returned type is a range of HloInstruction*s. - // - // Precondition: opcode() == HloOpcode::kFusion - const tensorflow::gtl::iterator_range>::const_iterator>> - fused_instructions() const; - - const tensorflow::gtl::iterator_range< - UnwrappingIterator>::iterator>> - fused_instructions(); - - // Gets the number of instructions inside this fusion instruction. - // - // Precondition: opcode() == HloOpcode::kFusion - int64 fused_instruction_count() const; - - // Returns the fused parameter instruction in this fusion instruction - // corresponding to the given parameter number. - // - // Precondition: opcode() == HloOpcode::kFusion - HloInstruction* fused_parameter(int64 parameter_number) const; - - // Returns the vector of fused parameters inside this fusion instruction. - // - // Precondition: opcode() == HloOpcode::kFusion - const std::vector& fused_parameters() const; - - // Returns true if this instruction is a fusion instruction that generates - // multiple outputs. - const bool IsMultiOutputFusion() const { - return opcode() == HloOpcode::kFusion && - fused_expression_root()->opcode() == HloOpcode::kTuple; - } - - FusionKind fusion_kind() const { - CHECK_EQ(HloOpcode::kFusion, opcode_); - return fusion_kind_; - } - - void set_fusion_kind(FusionKind kind) { - CHECK_EQ(HloOpcode::kFusion, opcode_); - fusion_kind_ = kind; - } - // Returns the sharding applied to this operator. // REQUIRES: has_sharding() is true. const HloSharding& sharding() const { @@ -1134,8 +1017,11 @@ class HloInstruction { void set_sharding(const HloSharding& sharding) { sharding_ = MakeUnique(sharding); } + void set_single_sharding(const HloSharding& sharding); // Sets a sharding that assigns the current instruction to device. - void set_device_sharding(int64 device); + void set_device_sharding(int64 device) { + set_single_sharding(HloSharding::AssignDevice(device)); + } // Remove any sharding from this operator. void clear_sharding() { sharding_ = nullptr; } // Return true if this operator has a sharding assigned. @@ -1165,167 +1051,17 @@ class HloInstruction { // instruction. void SetupDerivedInstruction(HloInstruction* derived_instruction) const; - // Adds a new operand the fusion instruction. - HloInstruction* AddFusionOperand(HloInstruction* new_operand); - - // Merges the fused instructions from 'instruction_to_merge' into the - // fused instruction set of 'this', updating operands as necessary. - // - // Precondition: opcode() == HloOpcode::kFusion - // Predondition: 'instruction_to_merge' must be an operand of 'this'. - void MergeFusionInstruction(HloInstruction* instruction_to_merge); - - // Merges the fused instructions from instruction_to_merge into the fused - // instruction set of 'this' and generates multioutput fusion instructions. - // All the users of instruction_to_merge will be redirected to 'this' - // instruction. instruction_to_merge will be removed from its parent - // computation. - // - // Precondition: opcode() == HloOpcode::kFusion - void MergeFusionInstructionIntoMultiOutput( - HloInstruction* instruction_to_merge); - - // Fuses the given instruction in this fusion instruction. instruction_to_fuse - // is cloned and the clone is placed in the fusion - // instruction. instruction_to_fuse is unchanged. Instruction is cloned rather - // than moved to cleanly handle the case where the instruction has a use - // outside the fusion instruction. Moving such an instruction into a fusion - // instruction would violate the single-result invariant of HLO instructions - // and significantly complicate code generation. - // - // Precondition: this->opcode() == HloOpcode::kFusion - HloInstruction* FuseInstruction(HloInstruction* instruction_to_fuse) { - return FuseInstructionInternal(instruction_to_fuse); - } - - // Fuses the given instruction in this fusion instruction and generate - // multioutput fusion instruction. A clone of the instruction_to_fuse will - // be part of the output of fusion instructions. The users of - // instruction_to_fuse will be redirected to this fusion instructions. - // instruction_to_fuse will be removed from its parent computation. - // - // Precondition: this->opcode() == HloOpcode::kFusion - HloInstruction* FuseInstructionIntoMultiOutput( - HloInstruction* instruction_to_fuse) { - return FuseInstructionInternal(instruction_to_fuse, /* add_output */ true); - } - - // Returns the start index in the given dimension for a slice node. - // - // Precondition: opcode() == HloOpcode::kSlice - int64 slice_starts(int64 dimension) const { - CHECK_EQ(HloOpcode::kSlice, opcode_); - return slice_starts_[dimension]; + // TODO(b/80249101): Remove these methods once HLO scheduling and copy + // insertion are integrated, and we don't need to run a separate pass + // of copy elision anymore. + bool CopyElisionAllowed() const { + CHECK_EQ(HloOpcode::kCopy, opcode_); + return copy_elision_allowed_; } - const std::vector& slice_starts() const { return slice_starts_; } - // Returns the (exclusive) limit index in the given dimension for a slice - // node. - // - // Precondition: opcode() == HloOpcode::kSlice - int64 slice_limits(int64 dimension) const { - CHECK_EQ(HloOpcode::kSlice, opcode_); - return slice_limits_[dimension]; - } - const std::vector& slice_limits() const { - CHECK_EQ(HloOpcode::kSlice, opcode_); - return slice_limits_; - } - - // Returns the stride in the given dimension for a slice node. - // - // Precondition: opcode() == HloOpcode::kSlice - int64 slice_strides(int64 dimension) const { - CHECK_EQ(HloOpcode::kSlice, opcode_); - return slice_strides_[dimension]; - } - const std::vector& slice_strides() const { return slice_strides_; } - - // Returns the flag that describes whether a slice must be lowered into an - // offset into the original operand. - bool IsInPlaceSlice() const { return is_in_place_slice_; } - - // Sets and returns the flag that describes whether a slice must be lowered - // into an offset into the original operand. - bool SetIsInPlaceSlice(bool value) { - is_in_place_slice_ = value; - return value; - } - - // Returns the size of the slice in the given dimension for a dynamic - // slice node. - // - // Precondition: opcode() == HloOpcode::kDynamicSlice - int64 slice_sizes(int64 dimension) const { - CHECK_EQ(HloOpcode::kDynamicSlice, opcode_); - return dynamic_slice_sizes_[dimension]; - } - const std::vector& dynamic_slice_sizes() const { - CHECK_EQ(HloOpcode::kDynamicSlice, opcode_); - return dynamic_slice_sizes_; - } - - // Returns the number of exponent bits for a reduce-precision node. - // - // Precondition: opcode() == HloOpcode::kReducePrecision - int32 exponent_bits() const { - CHECK_EQ(HloOpcode::kReducePrecision, opcode_); - return exponent_bits_; - } - - // Returns the number of mantissa bits for a reduce-precision node. - // - // Precondition: opcode() == HloOpcode::kReducePrecision - int32 mantissa_bits() const { - CHECK_EQ(HloOpcode::kReducePrecision, opcode_); - return mantissa_bits_; - } - - // Returns data on the window in a windowed operation such as - // convolution. - const Window& window() const { - CHECK(window_ != nullptr); - return *window_; - } - - // Sets the window data in a windowed operation such as convolution. - void set_window(const Window& window) { - window_ = MakeUnique(window); - } - - // Returns the padding configuration for a pad node. - // - // Precondition: opcode() == HloOpcode::kPad - const PaddingConfig& padding_config() const { - CHECK(padding_config_ != nullptr); - return *padding_config_; - } - - // Returns data on the dimension numbers used for a convolution operation, - // which may be a kConvolution instruction or a kCustomCall that implements a - // convolution. - const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { - CHECK(convolution_dimension_numbers_ != nullptr); - return *convolution_dimension_numbers_; - } - - // Sets the convolution dimension numbers on this instruction. In general you - // shouldn't need to call this; instead, specify the convolution dimension - // numbers when you create the instruction. - void set_convolution_dimension_numbers( - const ConvolutionDimensionNumbers& dnums) { - convolution_dimension_numbers_ = - MakeUnique(dnums); - } - - FftType fft_type() const { - CHECK_EQ(HloOpcode::kFft, opcode_); - return fft_type_; - } - - const std::vector& fft_length() const { - CHECK_EQ(HloOpcode::kFft, opcode_); - return fft_length_; + void SetCopyElisionAllowed(bool value) { + CHECK_EQ(HloOpcode::kCopy, opcode_); + copy_elision_allowed_ = value; } // Returns data on the dimension numbers used for a dot operation. @@ -1350,11 +1086,6 @@ class HloInstruction { // Returns the dump string of the gather dimension numbers. string GatherDimensionNumbersToString() const; - // Returns the random distribution for this rng node. - // - // Precondition: opcode() == HloOpcode::kRng - RandomDistribution random_distribution() const; - // Clones the HLO instruction. The clone will have the same opcode, shape, and // operands. After creation the clone has no uses. "this" (the instruction // cloned from) is not changed. Suffix is the string to append to the name of @@ -1437,9 +1168,14 @@ class HloInstruction { std::tuple, std::vector> ReshapeMerelyInsertsOrDeletes1SizedDimensions() const; - // Gets/sets the string identifier for this instruction. + // Gets the string identifier for this instruction. const string& name() const { return name_; } - void set_name(tensorflow::StringPiece name) { name_ = std::string(name); } + + // Sets the string identifier for this instruction. Name will be sanitized to + // match the regexp "[a-zA-Z_][a-zA-Z0-9_.-]*". + void SetAndSanitizeName(const string& name) { + name_ = NameUniquer::GetSanitizedName(name); + } // Use the given NameUniquer to select a unique name for the instruction based // on the instruction's existing name. @@ -1520,13 +1256,209 @@ class HloInstruction { void set_outer_dimension_partitions( const std::vector& outer_dimension_partitions); - // Change the layout for an Constant Hlo instruction to match new_layout. For - // tuple shaped constants shape_index is the path to the internal array - // subshape whose layout needs to be changed. + // Old methods kept for smooth subclassing transition BEGIN. + // TODO(b/80131774): Remove this code. + + // Delegates to HloBatchNormInstruction::feature_index. + int64 feature_index() const; + + // Delegates to HloBatchNormInstruction::epsilon. + float epsilon() const; + + // Delegates to HloFftInstruction::fft_type. + FftType fft_type() const; + + // Delegates to HloFftInstruction::fft_length. + const std::vector& fft_length() const; + + // Delegates to HloSendRecvInstruction::channel_id. + int64 channel_id() const; + + // Returns the dimension sizes or numbers associated with this instruction. + virtual const std::vector& dimensions() const { + LOG(FATAL) << "Unimplemented method."; + } + virtual int64 dimensions(int64 index) const { + LOG(FATAL) << "Unimplemented method."; + } + + // Delegates to HloConcatenateInstruction::concatenate_dimension. + int64 concatenate_dimension() const; + + // Returns whether this instruction does a rank-2 transposition. + bool IsRank2Transpose() const; + + // Delegates to HloSliceInstruction::slice_start. + int64 slice_starts(int64 dimension) const; + const std::vector& slice_starts() const; + + // Delegates to HloSliceInstruction::slice_limits. + int64 slice_limits(int64 dimension) const; + const std::vector& slice_limits() const; + + // Delegates to HloSliceInstruction::slice_strides. + int64 slice_strides(int64 dimension) const; + const std::vector& slice_strides() const; + + // Delegates to HloSliceInstruction::IsInPlaceSlice. + bool IsInPlaceSlice() const; + + // Returns the literal associated with this instruction. + const Literal& literal() const; + + // Returns whether the instruction is a constant. + bool IsConstant() const; + + // Delegate to HloConstantInstruction::RelayoutConstant. void RelayoutConstant(const Layout& new_layout, const ShapeIndex& shape_index = {}); + // Delegates to HloTraceInstruction::TracingTag. + string TracingTag() const; + + // Delegates to HloFusionInstruction::AddFusionOperand. + HloInstruction* AddFusionOperand(HloInstruction* new_operand); + + // Delegates to HloFusionInstruction::MergeFusionInstruction. + void MergeFusionInstruction(HloInstruction* instruction_to_merge); + + // Delegates to HloFusionInstruction::MergeFusionInstructionIntoMultiOutput. + void MergeFusionInstructionIntoMultiOutput( + HloInstruction* instruction_to_merge); + + // Delegates to HloFusionInstruction::FuseInstruction. + HloInstruction* FuseInstruction(HloInstruction* instruction_to_fuse); + + // Delegates to HloFusionInstruction::FuseInstructionIntoMultiOutput. + HloInstruction* FuseInstructionIntoMultiOutput( + HloInstruction* instruction_to_fuse); + + // Delegates to HloFusionInstruction::fused_instruction. + HloComputation* fused_instructions_computation() const; + + // Delegates to HloFusionInstruction::fused_expression_root. + HloInstruction* fused_expression_root() const; + + // Delegates to HloFusionInstruction::fused_instructions. + const tensorflow::gtl::iterator_range>::const_iterator>> + fused_instructions() const; + + const tensorflow::gtl::iterator_range< + UnwrappingIterator>::iterator>> + fused_instructions(); + + // Delegates to HloFusionInstruction::fused_instruction_count. + int64 fused_instruction_count() const; + + // Delegates to HloFusionInstruction::fused_parameter. + HloInstruction* fused_parameter(int64 parameter_number) const; + + // Delegates to HloFusionInstruction::fused_parameters. + const std::vector& fused_parameters() const; + + // Returns true if this instruction is a fusion instruction that generates + // multiple outputs. + const bool IsMultiOutputFusion() const; + + // Delegates to HloFusionInstruction::fusion_kind. + FusionKind fusion_kind() const; + + // Delegates to HloFusionInstruction::set_fusion_kind. + void set_fusion_kind(FusionKind kind); + + // Delegates to HloRngInstruction::random_distribution. + RandomDistribution random_distribution() const; + + // Delegates to HloParameterInstruction::parameter_number. + int64 parameter_number() const; + + // Delegates to HloGetTupleElementInstruction::tuple_index. + int64 tuple_index() const; + + // Delegates to HloReducePrecisionInstruction::exponent_bits. + int32 exponent_bits() const; + + // Delegates to HloReducePrecisionInstruction::mantissa_bits. + int32 mantissa_bits() const; + + // Delegates to HloInfeedInstruction::infeed_config. + string infeed_config() const; + + // Delegates to HloInfeedInstruction::set_infeed_config. + void set_infeed_config(const string& config); + + // Returns the config for the Outfeed instruction. + const string& outfeed_config() const; + + // Returns the shape for the Outfeed instruction. + const Shape& outfeed_shape() const; + + // Delegates to HloAllReduceInstruction::replica_group_ids. + const std::vector& replica_group_ids() const; + + // Delegates to HloAllReduceInstruction::cross_replica_sum_barrier. + string cross_replica_sum_barrier() const; + void set_cross_replica_sum_barrier(const string& barrier); + + // Delegates to HloAllReduceInstruction::all_reduce_id. + tensorflow::gtl::optional all_reduce_id() const; + + // Returns data on the window in a windowed operation such as + // convolution. + virtual const Window& window() const { + LOG(FATAL) << "Unimplemented method."; + } + + // Sets the window data in a windowed operation such as convolution. + virtual void set_window(const Window& window) { + LOG(FATAL) << "Unimplemented method."; + } + + // Returns data on the dimension numbers used for a convolution operation, + // which may be a kConvolution instruction or a kCustomCall that implements a + // convolution. + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const; + + // Sets the convolution dimension numbers on this instruction. In general you + // shouldn't need to call this; instead, specify the convolution dimension + // numbers when you create the instruction. + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums); + + // Delegates to HloSelectAndScatterInstruction::select. + HloComputation* select() const; + + // Delegates to HloSelectAndScatterInstruction::scatter. + HloComputation* scatter() const; + + // Delegates to HloSelectAndScatterInstruction::set_select. + void set_select(HloComputation* computation); + + // Delegates to HloSelectAndScatterInstruction::set_scatter. + void set_scatter(HloComputation* computation); + + // Delegates to HloCustomCallInstruction::custom_call_target. + const string& custom_call_target() const; + + // Delegates to HloHostComputeInstruction::channel_name. + const string& channel_name() const; + + // Delegates to HloPadInstruction::padding_config. + const PaddingConfig& padding_config() const; + + // Delegates to HloDynamicSliceInstruction::slice_sizes. + int64 slice_sizes(int64 dimension) const; + + // Delegates to HloDynamicSliceInstruction::dynamic_slice_sizes. + const std::vector& dynamic_slice_sizes() const; + // Old methods kept for smooth subclassing transition END. + protected: + enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse }; + // Helper class for computing OperandElementUse for kFusion. + class FusionReusesParamElements; + // Internal constructor for a given opcode/shape, other fields must be filled // by factory methods. HloInstruction(HloOpcode opcode, const Shape& shape); @@ -1535,6 +1467,39 @@ class HloInstruction { // of the operand. void AppendOperand(HloInstruction* operand); + void RemoveOperandAt(int index) { + operands_.erase(operands_.begin() + index); + } + + // Removes a list of operands with the given indices in ascending order. + void RemoveOperandsAtAscendingIndices( + tensorflow::gtl::ArraySlice ascending_indices); + + void AppendComputation(HloComputation* computation) { + called_computations_.push_back(computation); + } + + void DetachFrom(HloInstruction* usee) { usee->RemoveUser(this); } + + void set_called_computation(int index, HloComputation* computation) { + called_computations_[index] = computation; + } + // Indices of computations in called_computations_ for instructions which call + // multiple computations. + enum { + // kWhile computations. + kBodyComputationIndex = 0, + kConditionComputationIndex = 1, + + // kSelectAndScatter computations. + kSelectComputationIndex = 0, + kScatterComputationIndex = 1, + + // kConditional computations. + kTrueComputationIndex = 0, + kFalseComputationIndex = 1, + }; + private: // Implementation for non-common logic of CloneWithNewOperands. virtual std::unique_ptr CloneWithNewOperandsImpl( @@ -1544,6 +1509,20 @@ class HloInstruction { // TODO(b/80131774): This should be pure virtual. LOG(FATAL) << "Unimplemented method."; } + + // Implementation for non-common logic of ExtraAttributesToString. + virtual std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {}; + } + + // Implementation for IsElementwise if operand_idx is nullopt and for + // IsElementwiseOnOperand if otherwise. + // + // NOTE: For all instructions other than kFusion, being elementwise on one of + // the operands is equivalent to being elementwise on all the operands. + virtual bool IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const; // Prints an instruction to a string. // // The canonical string representation needs to name operands and instruction @@ -1554,7 +1533,7 @@ class HloInstruction { CanonicalNameMap* canonical_name_map) const; // Prints an operand to a string. - string OperandsToStringWithCanonicalNameMap( + virtual string OperandsToStringWithCanonicalNameMap( const HloPrintOptions& options, CanonicalNameMap* canonical_name_map) const; @@ -1562,11 +1541,6 @@ class HloInstruction { // OperandsToStringWithCanonicalNameMap() functions. friend class HloComputation; - enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse }; - - // Helper class for computing OperandElementUse for kFusion. - class FusionReusesParamElements; - // See comments on Identical(). virtual bool IdenticalSlowPath( const HloInstruction& other, @@ -1584,38 +1558,6 @@ class HloInstruction { // Removes a user for this instruction. void RemoveUser(HloInstruction* user); - // Fuses the given instruction into this fusion instruction. When add_output - // is false (which is the default), instruction_to_fuse is cloned and the - // clone is placed in the fusion instruction. instruction_to_fuse is - // unchanged. - // - // When add_output is true, a clone of the instruction_to_fuse will be part - // of the output of fusion instructions. The users of instruction_to_fuse - // will be redirected to this fusion instructions. instruction_to_fuse will - // be removed from its parent computation. - // - // Precondition: this->opcode() == HloOpcode::kFusion - HloInstruction* FuseInstructionInternal(HloInstruction* instruction_to_fuse, - bool add_output = false); - - // Clones the given instruction_to_fuse and insert the clone into this fusion - // instruction. If add_output is true, a clone of instruction_to_fuse will - // be in the output of the this fusion instruction (part of the tuple of the - // fusion root). - // - // Precondition: opcode() == HloOpcode::kFusion - HloInstruction* CloneAndFuseInternal(HloInstruction* instruction_to_fuse, - bool add_output = false); - - // Clones a fusion instruction with a new shape and operands. - std::unique_ptr CloneFusionWithNewOperands( - const Shape& shape, tensorflow::gtl::ArraySlice operands, - HloCloneContext* context = nullptr) const; - - // Returns true if this instruction can legally have the dimensions field - // set. Used for checking precondition of dimensions field accessors. - bool CanHaveDimensionsField() const; - // Returns how this instruction uses elements of its `i`th operand. UseKind OperandElementUse(int64 i) const; @@ -1647,62 +1589,17 @@ class HloInstruction { // The computation in which this instruction is contained. HloComputation* parent_ = nullptr; - // Shape of outfeed request. - Shape outfeed_shape_; - // Result shape of this instruction. Shape shape_; - // Literal, only present for kConstant. - std::unique_ptr literal_; - - // Constant index, only present for kGetTupleElement. - int64 tuple_index_ = -1; - - // Dimensions present for some operations that require reshaping or - // broadcasting, including Reshape, Reduce, ReduceWindow, and Reverse. - std::vector dimensions_; - - // Describes the window in a windowed operation such as convolution. - std::unique_ptr window_; - - // Describes the dimension numbers used for a convolution. - std::unique_ptr convolution_dimension_numbers_; - // Describes the dimension numbers used for a dot. std::unique_ptr dot_dimension_numbers_; std::unique_ptr gather_dimension_numbers_; std::vector gather_window_bounds_; - // Describes FFT type for an FFT instruction. - FftType fft_type_ = FftType::FFT; - - // Indicates the FFT length for an FFT instruction. - std::vector fft_length_; - - // Describes the [begin, end) index range for a slice. - std::vector slice_starts_; - std::vector slice_limits_; - std::vector slice_strides_; - - // Describes whether the slice can be lowered to an offset into the operand. - bool is_in_place_slice_ = false; - - // The bit sizes for a reduce-precision operation. - int32 exponent_bits_ = 0; - int32 mantissa_bits_ = 0; - - // Describes the [start, start + size) range size for a dynamic slice - // ('start' is specified dynamically in the second operand of the operation). - std::vector dynamic_slice_sizes_; - - // The padding configuration that describes the edge padding and interior - // padding of this pad instruction. Only set for pad instructions. - std::unique_ptr padding_config_; - - // The type of the fusion. Used by kFusion only. - FusionKind fusion_kind_; + // Used to tag kCopy instructions that are eligible for copy elision. + bool copy_elision_allowed_ = true; // The sharding, if one exists. std::unique_ptr sharding_; @@ -1711,57 +1608,15 @@ class HloInstruction { std::unique_ptr operand_side_metadata_; std::unique_ptr user_side_metadata_; - // For parameter instructions this field holds the parameter number. - int64 parameter_number_ = 0; - - // Name of a global symbol to call, only present for kCustomCall. - string custom_call_target_; - - // Name to use for host send/recv channels, only present for kHostCompute. - string channel_name_; - - // Estimate of the duration of a host computation in nanoseconds. - int64 cost_estimate_ns_ = 0; - // Computations called by this instruction. std::vector called_computations_; - // Indices of computations in called_computations_ for instructions which call - // multiple computations. - enum { - // kWhile computations. - kBodyComputationIndex = 0, - kConditionComputationIndex = 1, - - // kSelectAndScatter computations. - kSelectComputationIndex = 0, - kScatterComputationIndex = 1, - - // kConditional computations. - kTrueComputationIndex = 0, - kFalseComputationIndex = 1, - }; - - // Outfeed configuration information, only present for kOutfeed. - string outfeed_config_; - // A trace instruction that consumes this instruction. // // Invariant: if trace_instruction_ != nullptr, trace_instruction has this as // an operand. HloInstruction* trace_instruction_ = nullptr; - // The distribution requested for random number generation. - // Only present for kRng. - RandomDistribution distribution_; - - // Represents a unique identifier for each Send/Recv instruction pair. - // Only present for kSend or kRecv. - int64 channel_id_ = -1; - - // The string representation of the infeed configuration. - string infeed_config_; - // The backend-specific configuration for how a backend should compile this // HLO. See the documentation on backend_config(). string backend_config_; diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index 313033ddadce6a49936f8d34d38f33e923dc2e35..d8ca99dfd12ef95ab5e1ea61093d8bf3ea97a5e2 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -342,7 +342,7 @@ TEST_F(HloInstructionTest, TrivialMap) { // Builds a parameter and feeds it to the map. HloComputation::Builder builder(TestName()); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32a100x10, "")); + HloInstruction::CreateParameter(0, f32a100x10, "p")); auto map = builder.AddInstruction( HloInstruction::CreateMap(f32a100x10, {param0}, add_f32)); module->AddEntryComputation(builder.Build()); @@ -381,7 +381,7 @@ TEST_F(HloInstructionTest, TrivialReduce) { // Builds a parameter and an initial value and feeds them to the reduce. HloComputation::Builder builder(TestName()); auto param0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, f32a100x10, "")); + HloInstruction::CreateParameter(0, f32a100x10, "p")); auto const0 = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); builder.AddInstruction( @@ -716,10 +716,11 @@ TEST_F(HloInstructionTest, PreserveOutfeedShapeThroughClone) { }))); auto shape10 = ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}); auto shape01 = ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1}); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto outfeed10 = builder.AddInstruction( - HloInstruction::CreateOutfeed(shape10, constant, "")); + HloInstruction::CreateOutfeed(shape10, constant, token, "")); auto outfeed01 = builder.AddInstruction( - HloInstruction::CreateOutfeed(shape01, constant, "")); + HloInstruction::CreateOutfeed(shape01, constant, token, "")); auto clone01 = builder.AddInstruction(outfeed01->Clone()); auto clone10 = builder.AddInstruction(outfeed10->Clone()); @@ -763,12 +764,12 @@ TEST_F(HloInstructionTest, FusionOpWithCalledComputations) { HloComputation::Builder builder(TestName()); auto constant = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); - auto map_1_x = builder.AddInstruction(HloInstruction::CreateMap( - scalar_shape, {constant}, computation_x, /*static_operands=*/{})); - auto map_2_x = builder.AddInstruction(HloInstruction::CreateMap( - scalar_shape, {map_1_x}, computation_x, /*static_operands=*/{})); - auto map_3_y = builder.AddInstruction(HloInstruction::CreateMap( - scalar_shape, {map_2_x}, computation_y, /*static_operands=*/{})); + auto map_1_x = builder.AddInstruction( + HloInstruction::CreateMap(scalar_shape, {constant}, computation_x)); + auto map_2_x = builder.AddInstruction( + HloInstruction::CreateMap(scalar_shape, {map_1_x}, computation_x)); + auto map_3_y = builder.AddInstruction( + HloInstruction::CreateMap(scalar_shape, {map_2_x}, computation_y)); auto* computation = module->AddEntryComputation(builder.Build()); auto* fusion = computation->CreateFusionInstruction( @@ -923,6 +924,40 @@ TEST_F(HloInstructionTest, IdenticalInstructions) { *HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op1, op2))); } +TEST_F(HloInstructionTest, IdenticalCallInstructions) { + const char* const hlo_string = R"( +HloModule Module + +subcomp1 (x: f32[]) -> f32[] { + x = f32[] parameter(0) + ROOT n = f32[] sine(x) +} + +subcomp2 (x: f32[]) -> f32[] { + x = f32[] parameter(0) + ROOT n = f32[] cosine(x) +} + +ENTRY entry (param: f32[]) -> (f32[], f32[], f32[]) { + p = f32[] parameter(0) + t1 = f32[] call(p), to_apply=subcomp1 + t2 = f32[] call(p), to_apply=subcomp1 + t3 = f32[] call(p), to_apply=subcomp2 + ROOT t = (f32[], f32[], f32[]) tuple(t1, t2, t3) + } +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_string)); + + auto* root = module->entry_computation()->root_instruction(); + auto* t1 = root->operand(0); + auto* t2 = root->operand(1); + auto* t3 = root->operand(2); + + EXPECT_TRUE(StructuralEqual(*t1, *t2)); + EXPECT_FALSE(StructuralEqual(*t1, *t3)); +} + TEST_F(HloInstructionTest, FunctionVisitor) { // Verify the function visitor HloInstruction::Accept visits all instructions // from a root properly given the following graph: @@ -980,6 +1015,23 @@ TEST_F(HloInstructionTest, FullyElementwise) { } } +TEST_F(HloInstructionTest, MapIsElementwise) { + auto module = CreateNewModule(); + const Shape r2f32 = ShapeUtil::MakeShapeWithLayout(F32, {10, 10}, {1, 0}); + HloComputation::Builder builder(TestName()); + HloComputation::Builder map_builder("id"); + map_builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "p0")); + auto map_computation = module->AddEmbeddedComputation(map_builder.Build()); + auto x = + builder.AddInstruction(HloInstruction::CreateParameter(0, r2f32, "x")); + auto map = builder.AddInstruction( + HloInstruction::CreateMap(r2f32, {x}, map_computation)); + module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(map->IsElementwise()); +} + TEST_F(HloInstructionTest, PartiallyElementwise) { const Shape r1f32 = ShapeUtil::MakeShape(F32, {5}); const Shape r2f32 = ShapeUtil::MakeShape(F32, {3, 5}); @@ -1119,6 +1171,40 @@ TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) { EXPECT_TRUE(StructuralEqual(*fusion, *fusion2)); } +TEST_F(HloInstructionTest, NoRedundantFusionOperandsAfterReplacingUse) { + // Fused expression: + // + // x y + // | | + // | transpose + // \ / + // dot + const Shape s = ShapeUtil::MakeShape(F32, {10, 10}); + + HloComputation::Builder builder("TransposeDot"); + HloInstruction* x = + builder.AddInstruction(HloInstruction::CreateParameter(0, s, "x")); + HloInstruction* y = + builder.AddInstruction(HloInstruction::CreateParameter(1, s, "y")); + HloInstruction* reshape = + builder.AddInstruction(HloInstruction::CreateTranspose(s, y, {1, 0})); + DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(1); + dot_dnums.add_rhs_contracting_dimensions(0); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateDot(s, x, reshape, dot_dnums)); + + auto module = CreateNewModule(); + auto* computation = module->AddEntryComputation(builder.Build()); + HloInstruction* fusion = computation->CreateFusionInstruction( + {dot, reshape}, HloInstruction::FusionKind::kLoop); + + EXPECT_TRUE(x->ReplaceAllUsesWith(y).ok()); + + EXPECT_THAT(fusion->operands(), UnorderedElementsAre(y)); + EXPECT_EQ(fusion->fused_instructions_computation()->num_parameters(), 1); +} + TEST_F(HloInstructionTest, FusionEquality) { auto module = CreateNewModule(); HloComputation::Builder builder(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index adbebb135bafb443aa27302df2b88f8a43b5ee6c..dcc1e3c8afb117b5062feff0aa8148e9012462f8 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -15,10 +15,33 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instructions.h" +#include + +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/window_util.h" +#include "tensorflow/core/lib/gtl/flatmap.h" + namespace xla { +namespace { +using ::tensorflow::str_util::CEscape; +using ::tensorflow::str_util::Join; +using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; +bool IsInstructionElementwiseOnOperand(const HloInstruction* instruction, + const HloInstruction* operand) { + std::vector operand_indices = instruction->OperandIndices(operand); + return std::all_of( + operand_indices.begin(), operand_indices.end(), + [instruction](int64 operand_index) { + return instruction->IsElementwiseOnOperand(operand_index); + }); +} +} // namespace + HloBatchNormInstruction::HloBatchNormInstruction( HloOpcode opcode, const Shape& shape, HloInstruction* operand, HloInstruction* scale, float epsilon, int64 feature_index) @@ -38,13 +61,6 @@ bool HloBatchNormInstruction::IdenticalSlowPath( epsilon() == casted_other.epsilon(); } -std::vector HloBatchNormInstruction::ExtraAttributesToString( - const HloPrintOptions& options) const { - std::vector extra = {StrCat("epsilon=", epsilon()), - StrCat("feature_index=", feature_index())}; - return extra; -} - HloInstructionProto HloBatchNormInstruction::ToProto() const { HloInstructionProto proto = HloInstruction::ToProto(); proto.set_epsilon(epsilon_); @@ -52,6 +68,12 @@ HloInstructionProto HloBatchNormInstruction::ToProto() const { return proto; } +std::vector HloBatchNormInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("epsilon=", epsilon()), + StrCat("feature_index=", feature_index())}; +} + HloBatchNormTrainingInstruction::HloBatchNormTrainingInstruction( const Shape& shape, HloInstruction* operand, HloInstruction* scale, HloInstruction* offset, float epsilon, int64 feature_index) @@ -115,4 +137,1726 @@ HloBatchNormGradInstruction::CloneWithNewOperandsImpl( new_operands[4], epsilon(), feature_index()); } +HloFftInstruction::HloFftInstruction( + const Shape& shape, HloInstruction* operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length) + : HloInstruction(HloOpcode::kFft, shape), fft_type_(fft_type) { + fft_length_.assign(fft_length.begin(), fft_length.end()); + AppendOperand(operand); +} + +HloInstructionProto HloFftInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_fft_type(fft_type_); + for (int64 fft_len : fft_length_) { + proto.add_fft_length(fft_len); + } + return proto; +} + +std::vector HloFftInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("fft_type=", FftType_Name(fft_type())), + StrCat("fft_length={", Join(fft_length(), ","), "}")}; +} + +bool HloFftInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return fft_type() == casted_other.fft_type() && + fft_length() == casted_other.fft_length(); +} + +std::unique_ptr HloFftInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], fft_type_, + fft_length_); +} + +HloSendRecvInstruction::HloSendRecvInstruction(HloOpcode opcode, + const Shape& shape, + int64 channel_id) + : HloInstruction(opcode, shape), channel_id_(channel_id) {} + +HloInstructionProto HloSendRecvInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_channel_id(channel_id_); + return proto; +} + +std::vector HloSendRecvInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("channel_id=", channel_id_)}; +} + +bool HloSendRecvInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +// Send instruction produces a tuple of {aliased operand, U32 context}. +HloSendInstruction::HloSendInstruction(HloInstruction* operand, + HloInstruction* token, int64 channel_id) + : HloSendRecvInstruction( + HloOpcode::kSend, + ShapeUtil::MakeTupleShape( + {CHECK_NOTNULL(operand)->shape(), ShapeUtil::MakeShape(U32, {})}), + channel_id) { + AppendOperand(operand); + AppendOperand(token); +} + +std::unique_ptr HloSendInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(new_operands[0], new_operands[1], + channel_id()); +} + +HloSendDoneInstruction::HloSendDoneInstruction(HloSendInstruction* operand) + : HloSendRecvInstruction(HloOpcode::kSendDone, ShapeUtil::MakeNil(), + CHECK_NOTNULL(operand)->channel_id()) { + AppendOperand(operand); +} + +std::unique_ptr +HloSendDoneInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique( + Cast(new_operands[0])); +} + +// Recv instruction produces a tuple of {receive buffer, U32 context}. +HloRecvInstruction::HloRecvInstruction(const Shape& shape, + HloInstruction* token, int64 channel_id) + : HloSendRecvInstruction( + HloOpcode::kRecv, + ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeShape(U32, {})}), + channel_id) { + AppendOperand(token); +} + +std::unique_ptr HloRecvInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique( + ShapeUtil::GetTupleElementShape(shape, 0), new_operands[0], channel_id()); +} + +HloRecvDoneInstruction::HloRecvDoneInstruction(HloRecvInstruction* operand) + : HloSendRecvInstruction( + HloOpcode::kRecvDone, + ShapeUtil::GetTupleElementShape(operand->shape(), 0), + CHECK_NOTNULL(operand)->channel_id()) { + AppendOperand(operand); +} + +std::unique_ptr +HloRecvDoneInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique( + Cast(new_operands[0])); +} + +HloAllReduceInstruction::HloAllReduceInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* reduce_computation, + tensorflow::gtl::ArraySlice replica_group_ids, + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id) + : HloInstruction(HloOpcode::kCrossReplicaSum, shape), + replica_group_ids_(replica_group_ids.begin(), replica_group_ids.end()), + cross_replica_sum_barrier_(barrier.begin(), barrier.end()), + all_reduce_id_(all_reduce_id) { + // TODO(b/79737069): Remove the CHECK when supported. + CHECK(!all_reduce_id_); + for (auto operand : operands) { + AppendOperand(operand); + } + AppendComputation(reduce_computation); +} + +HloInstructionProto HloAllReduceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 i : replica_group_ids_) { + proto.add_replica_group_ids(i); + } + // Proto3 is so sad. + if (all_reduce_id_) { + proto.set_all_reduce_id(*all_reduce_id_); + } + proto.set_cross_replica_sum_barrier(cross_replica_sum_barrier_); + return proto; +} + +std::vector HloAllReduceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& /*options*/) const { + std::vector result = { + StrCat("replica_group_ids={", Join(replica_group_ids(), ","), "}")}; + if (!cross_replica_sum_barrier().empty()) { + result.push_back(StrCat("barrier=\"", cross_replica_sum_barrier(), "\"")); + } + if (all_reduce_id_) { + result.push_back(StrCat("all_reduce_id=", *all_reduce_id_)); + } + return result; +} + +bool HloAllReduceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return replica_group_ids() == casted_other.replica_group_ids() && + eq_computations(to_apply(), casted_other.to_apply()) && + cross_replica_sum_barrier() == + casted_other.cross_replica_sum_barrier() && + all_reduce_id() == casted_other.all_reduce_id(); +} + +std::unique_ptr +HloAllReduceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* /*context*/) const { + return MakeUnique( + shape, new_operands, to_apply(), replica_group_ids(), + cross_replica_sum_barrier(), all_reduce_id()); +} + +HloReverseInstruction::HloReverseInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice dimensions) + : HloInstruction(HloOpcode::kReverse, shape), + dimensions_(dimensions.begin(), dimensions.end()) { + AppendOperand(operand); +} + +HloInstructionProto HloReverseInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloReverseInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloReverseInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return dimensions() == casted_other.dimensions(); +} + +std::unique_ptr HloReverseInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], + dimensions()); +} + +HloConcatenateInstruction::HloConcatenateInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + int64 dimension) + : HloInstruction(HloOpcode::kConcatenate, shape), dimensions_({dimension}) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +HloInstructionProto HloConcatenateInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloConcatenateInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloConcatenateInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return dimensions() == casted_other.dimensions(); +} + +std::unique_ptr +HloConcatenateInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique(shape, new_operands, + dimensions(0)); +} + +HloReduceInstruction::HloReduceInstruction( + const Shape& shape, HloInstruction* arg, HloInstruction* init_value, + tensorflow::gtl::ArraySlice dimensions_to_reduce, + HloComputation* reduce_computation) + : HloInstruction(HloOpcode::kReduce, shape), + dimensions_(dimensions_to_reduce.begin(), dimensions_to_reduce.end()) { + AppendOperand(arg); + AppendOperand(init_value); + AppendComputation(reduce_computation); +} + +HloInstructionProto HloReduceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloReduceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloReduceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + // Reduction results are determined by the reduction dimension and the + // reduction computation. + return dimensions() == casted_other.dimensions() && + eq_computations(to_apply(), casted_other.to_apply()); +} + +std::unique_ptr HloReduceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], dimensions(), to_apply()); +} + +HloTransposeInstruction::HloTransposeInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice dimensions) + : HloInstruction(HloOpcode::kTranspose, shape), + dimensions_(dimensions.begin(), dimensions.end()) { + CHECK_EQ(shape.dimensions().size(), dimensions.size()); + CHECK_EQ(shape.dimensions().size(), operand->shape().dimensions().size()); + CHECK(std::equal(operand->shape().dimensions().begin(), + operand->shape().dimensions().end(), + Permute(dimensions, shape.dimensions()).begin())) + << "shape: " << ShapeUtil::HumanString(shape) + << ", operand->shape(): " << ShapeUtil::HumanString(shape) + << ", dimensions: {" << Join(dimensions, ", ") << "}"; + AppendOperand(operand); +} + +bool HloTransposeInstruction::IsRank2Transpose() const { + return dimensions() == std::vector({1, 0}) && + shape().dimensions_size() == 2 && + std::equal(shape().dimensions().begin(), shape().dimensions().end(), + operand(0)->shape().dimensions().rbegin()); +} + +HloInstructionProto HloTransposeInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloTransposeInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloTransposeInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return dimensions() == casted_other.dimensions(); +} + +std::unique_ptr +HloTransposeInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], + dimensions()); +} + +HloBroadcastInstruction::HloBroadcastInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice broadcast_dimension) + : HloInstruction(HloOpcode::kBroadcast, shape), + dimensions_(broadcast_dimension.begin(), broadcast_dimension.end()) { + AppendOperand(operand); +} + +HloInstructionProto HloBroadcastInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloBroadcastInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloBroadcastInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return dimensions() == casted_other.dimensions(); +} + +std::unique_ptr +HloBroadcastInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], + dimensions()); +} + +HloMapInstruction::HloMapInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* map_computation) + : HloInstruction(HloOpcode::kMap, shape) { + for (auto operand : operands) { + AppendOperand(operand); + } + AppendComputation(map_computation); + // TODO(b/65689298) Remove code below once Map is generalized to accept + // arbitrary map dimensions. + dimensions_.resize(ShapeUtil::Rank(shape)); + std::iota(dimensions_.begin(), dimensions_.end(), 0); +} + +HloInstructionProto HloMapInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +bool HloMapInstruction::IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const { + if (!dimensions().empty()) { + // Check that the map is executed in elementwise compatible dimensions. + if (dimensions().size() != shape().dimensions_size()) { + return false; + } + for (int i = 0; i < dimensions().size(); ++i) { + if (dimensions()[i] != i) { + return false; + } + } + } + return true; +} + +std::vector HloMapInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloMapInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return eq_computations(to_apply(), other.to_apply()); +} + +std::unique_ptr HloMapInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique(shape, new_operands, to_apply()); +} + +HloSliceInstruction::HloSliceInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides) + : HloInstruction(HloOpcode::kSlice, shape), + slice_starts_(start_indices.begin(), start_indices.end()), + slice_limits_(limit_indices.begin(), limit_indices.end()), + slice_strides_(strides.begin(), strides.end()) { + AppendOperand(operand); + // For backward compatibility with old serialized computations: if there are + // no strides, assume all strides are 1. + // TODO(b/63317920): remove this code. + if (slice_strides_.empty()) { + slice_strides_ = std::vector(start_indices.size(), 1LL); + } +} + +HloInstructionProto HloSliceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int i = 0; i < slice_starts_.size(); ++i) { + auto* slice_dimension = proto.add_slice_dimensions(); + slice_dimension->set_start(slice_starts_[i]); + slice_dimension->set_limit(slice_limits_[i]); + slice_dimension->set_stride(slice_strides_[i]); + } + return proto; +} + +std::vector HloSliceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector bounds; + bounds.reserve(slice_starts_.size()); + const bool omit_stride = + std::all_of(slice_strides_.begin(), slice_strides_.end(), + [](int64 stride) { return stride == 1; }); + for (int i = 0; i < slice_starts_.size(); ++i) { + string stride_str = omit_stride ? "" : StrCat(":", slice_strides_[i]); + bounds.push_back( + StrCat("[", slice_starts_[i], ":", slice_limits_[i], stride_str, "]")); + } + return {StrCat("slice={", Join(bounds, ", "), "}")}; +} + +bool HloSliceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& other_slice = static_cast(other); + return slice_starts_ == other_slice.slice_starts_ && + slice_limits_ == other_slice.slice_limits_ && + slice_strides_ == other_slice.slice_strides_; +} + +std::unique_ptr HloSliceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], slice_starts_, + slice_limits_, slice_strides_); +} + +HloConstantInstruction::HloConstantInstruction(std::unique_ptr literal) + : HloInstruction(HloOpcode::kConstant, CHECK_NOTNULL(literal)->shape()), + literal_(std::move(literal)) {} + +HloConstantInstruction::HloConstantInstruction(const Shape& shape) + : HloInstruction(HloOpcode::kConstant, shape) {} + +HloInstructionProto HloConstantInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + if (literal_ != nullptr) { + *proto.mutable_literal() = literal_->ToProto(); + } + return proto; +} + +bool HloConstantInstruction::IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const { + return true; +} + +void HloConstantInstruction::RelayoutConstant(const Layout& new_layout, + const ShapeIndex& shape_index) { + Shape* mutable_array_subshape = + ShapeUtil::GetMutableSubshape(mutable_shape(), shape_index); + CHECK(ShapeUtil::IsArray(*mutable_array_subshape)); + + // Normally array_subshape will always have a layout, but this invariant is + // temporarily broken in LayoutAssignment::AssignLayouts. + + if (!mutable_array_subshape->has_layout() || + !LayoutUtil::Equal(mutable_array_subshape->layout(), new_layout)) { + literal_ = literal_->Relayout(new_layout, shape_index); + *mutable_array_subshape->mutable_layout() = new_layout; + } +} + +bool HloConstantInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& other_slice = static_cast(other); + return literal() == other_slice.literal(); +} + +std::unique_ptr +HloConstantInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique(literal_->CloneToUnique()); +} + +string HloConstantInstruction::OperandsToStringWithCanonicalNameMap( + const HloPrintOptions& options, + CanonicalNameMap* canonical_name_map) const { + string operands; + // For constants, show the actual value in place of an empty operand list. + if (literal_ != nullptr && + ((ShapeUtil::IsArray(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) || + options.print_large_constants())) { + // Literal::ToString emits multidimensional arrays over multiple + // lines. Compact this into one line by stripping out white space. + string tmp = literal().ToString(); + std::replace(tmp.begin(), tmp.end(), '\n', ' '); + std::vector v = tensorflow::str_util::Split(tmp, ' '); + bool first = true; + // Concatenate elements in "v" with spaces separating them, but ignoring + // empty entries. + for (const auto& s : v) { + if (s.empty()) { + continue; + } + StrAppend(&operands, (first ? "" : " "), s); + first = false; + } + } else { + // Do not show large constants or tuples. + operands = "{...}"; + } + return operands; +} + +HloTraceInstruction::HloTraceInstruction(const string& tag, + HloInstruction* operand) + : HloInstruction(HloOpcode::kTrace, ShapeUtil::MakeNil()), + literal_(Literal::CreateR1U8(tag)) { + AppendOperand(operand); + operand->set_tracing(this); +} + +HloInstructionProto HloTraceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_literal() = literal_->ToProto(); + return proto; +} + +bool HloTraceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return false; +} + +std::unique_ptr HloTraceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + LOG(FATAL) << "Not yet implemented, clone: " << HloOpcodeString(opcode()); +} + +HloFusionInstruction::HloFusionInstruction(const Shape& shape, + FusionKind fusion_kind, + HloInstruction* fused_root) + : HloInstruction(HloOpcode::kFusion, shape), fusion_kind_(fusion_kind) { + CHECK(fused_root != nullptr); + SetAndSanitizeName("fusion"); + set_parent(fused_root->parent()); + set_metadata(fused_root->metadata()); + CloneAndFuseInternal(fused_root); +} + +HloFusionInstruction::HloFusionInstruction( + const Shape& shape, FusionKind fusion_kind, + tensorflow::gtl::ArraySlice operands, + HloComputation* fusion_computation) + : HloInstruction(HloOpcode::kFusion, shape), fusion_kind_(fusion_kind) { + for (auto operand : operands) { + AppendOperand(operand); + } + SetAndSanitizeName("fusion"); + AppendComputation(fusion_computation); + fusion_computation->SetFusionInstruction(this); +} + +string HloFusionInstruction::ToCategory() const { + switch (fusion_kind()) { + case FusionKind::kLoop: + return "loop fusion"; + case FusionKind::kInput: + return "input fusion"; + case FusionKind::kOutput: + return "output fusion"; + case FusionKind::kCustom: + return "custom fusion"; + } +} + +HloInstructionProto HloFusionInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_fusion_kind(xla::ToString(fusion_kind())); + proto.add_called_computation_ids( + fused_instructions_computation()->unique_id()); + return proto; +} + +bool HloFusionInstruction::IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const { + if (!operand_idx.has_value()) { + for (auto* fused : fused_instructions()) { + if (fused->opcode() != HloOpcode::kParameter && !fused->IsElementwise()) { + return false; + } + } + return true; + } + // A loop-fusion is elementwise on an operand if all operations (computed + // using BFS) between the operand and the fused root are elementwise. + std::deque worklist; + std::unordered_set visited; + worklist.push_back(fused_parameter(operand_idx.value())); + visited.insert(fused_parameter(operand_idx.value())); + while (!worklist.empty()) { + HloInstruction* operand = worklist.front(); + worklist.pop_front(); + for (HloInstruction* user : operand->users()) { + CHECK_GE(user->unique_id(), 0); + if (ContainsKey(visited, user)) { + continue; + } + if (user->IsElementwise() || + IsInstructionElementwiseOnOperand(user, operand)) { + worklist.push_back(user); + visited.insert(user); + } else { + return false; + } + } + } + return true; +} + +HloInstruction* HloFusionInstruction::AddFusionOperand( + HloInstruction* new_operand) { + CHECK_EQ(operand_count(), + fused_instructions_computation()->parameter_instructions().size()); + const int64 param_no = operand_count(); + // Name the parameter after the instruction it represents in the outer + // (non-fusion) computation. + string param_name = StrCat(new_operand->name(), ".param_", param_no); + HloInstruction* fused_parameter = + fused_instructions_computation()->AddParameter( + HloInstruction::CreateParameter(param_no, new_operand->shape(), + param_name)); + AppendOperand(new_operand); + return fused_parameter; +} + +void HloFusionInstruction::MergeFusionInstruction( + HloFusionInstruction* instruction_to_merge) { + CHECK(std::find(operands().begin(), operands().end(), instruction_to_merge) != + operands().end()); + // Clone the instruction from which to merge fused instructions. + std::unique_ptr cloned = instruction_to_merge->Clone(); + HloFusionInstruction* cloned_fusion = + static_cast(cloned.get()); + // Replace uses of fused parameters with the corresponding operand of the + // fusion. Add all non-parameter fused instructions to + // 'unfused_instructions' to be merged into 'this'. This is done in reverse + // post order. + std::vector unfused_instructions; + auto fused_instructions = cloned_fusion->fused_instructions_computation() + ->MakeInstructionPostOrder(); + for (auto fused_it = fused_instructions.rbegin(); + fused_it != fused_instructions.rend(); ++fused_it) { + auto fused_instruction = *fused_it; + if (fused_instruction->opcode() == HloOpcode::kParameter) { + TF_CHECK_OK( + fused_instruction->ReplaceAllUsesWith(cloned_fusion->mutable_operand( + fused_instruction->parameter_number()))); + } else { + unfused_instructions.push_back(fused_instruction); + } + } + CHECK(unfused_instructions.front() == cloned_fusion->fused_expression_root()); + // Replace instruction_to_merge use of 'this' with unfused_root. + TF_CHECK_OK( + instruction_to_merge->ReplaceUseWith(this, unfused_instructions.front())); + // Fuse 'unfused_instructions' into 'this'. + for (auto& instruction : unfused_instructions) { + FuseInstruction(instruction); + } + CHECK_EQ(0, cloned_fusion->user_count()); + TF_CHECK_OK(parent()->parent()->RemoveEmbeddedComputation( + cloned_fusion->fused_instructions_computation())); +} + +void HloFusionInstruction::MergeFusionInstructionIntoMultiOutput( + HloFusionInstruction* instruction_to_merge) { + // Add all non-parameter fused instructions to 'unfused_instructions' to be + // merged into 'this'. `old_to_new' maps the instructions in the fused node + // to the disaseembled fusion instructions. + // Note that we add the unfused instructions to this->parent_ computation. + // This is necessary because the unique_id needs for an instruction and + // it's only added when inserting to the computation. + tensorflow::gtl::FlatMap old_to_new; + std::vector unfused_instructions; + auto computation_to_merge = + instruction_to_merge->fused_instructions_computation(); + auto post_order = computation_to_merge->MakeInstructionPostOrder(); + for (auto rit = post_order.rbegin(); rit != post_order.rend(); ++rit) { + auto fused_instruction = *rit; + if (fused_instruction->opcode() == HloOpcode::kParameter) { + InsertOrDie(&old_to_new, fused_instruction, + instruction_to_merge->mutable_operand( + fused_instruction->parameter_number())); + continue; + } + + // Here we clone the insertion and call FuseInstructionIntoMultiOutput() + // which clones again. This can be improved. + auto cloned_instruction = + parent()->AddInstruction(fused_instruction->Clone()); + unfused_instructions.push_back(cloned_instruction); + InsertOrDie(&old_to_new, fused_instruction, cloned_instruction); + } + for (auto unfused_instruction : unfused_instructions) { + for (int64 index = 0; index < unfused_instruction->operand_count(); + index++) { + auto new_operand = + FindOrDie(old_to_new, unfused_instruction->mutable_operand(index)); + TF_CHECK_OK(unfused_instruction->ReplaceOperandWith(index, new_operand)); + } + } + + HloInstruction* unfused_root = unfused_instructions.front(); + TF_CHECK_OK(instruction_to_merge->ReplaceAllUsesWith(unfused_root)); + + TF_CHECK_OK( + instruction_to_merge->parent()->RemoveInstruction(instruction_to_merge)); + if (GetModule()) { + TF_CHECK_OK(GetModule()->RemoveEmbeddedComputation(computation_to_merge)); + } + + // Fuse the root instruction and generate multiple outputs. + FuseInstructionIntoMultiOutput(unfused_root); + TF_CHECK_OK(unfused_root->parent()->RemoveInstruction(unfused_root)); + // The rest instructions are of normal fusing. + for (int64 i = 1; i < unfused_instructions.size(); i++) { + auto instruction = unfused_instructions[i]; + FuseInstruction(instruction); + TF_CHECK_OK(instruction->parent()->RemoveInstruction(instruction)); + } +} + +HloComputation* HloFusionInstruction::fused_instructions_computation() const { + CHECK(!called_computations().empty()); + auto* fused_instructions_computation = called_computations().front(); + CHECK(fused_instructions_computation->IsFusionComputation()) + << "Computation " << fused_instructions_computation->name() + << " is not a fusion kind"; + return fused_instructions_computation; +} + +HloInstruction* HloFusionInstruction::fused_expression_root() const { + return fused_instructions_computation()->root_instruction(); +} + +HloInstruction* HloFusionInstruction::fused_parameter( + int64 parameter_number) const { + return fused_instructions_computation()->parameter_instruction( + parameter_number); +} + +const std::vector& HloFusionInstruction::fused_parameters() + const { + return fused_instructions_computation()->parameter_instructions(); +} + +const tensorflow::gtl::iterator_range>::const_iterator>> +HloFusionInstruction::fused_instructions() const { + const HloComputation* subcomp = fused_instructions_computation(); + return subcomp->instructions(); +} + +const tensorflow::gtl::iterator_range< + UnwrappingIterator>::iterator>> +HloFusionInstruction::fused_instructions() { + return fused_instructions_computation()->instructions(); +} + +int64 HloFusionInstruction::fused_instruction_count() const { + return fused_instructions_computation()->instruction_count(); +} + +HloInstruction* HloFusionInstruction::FuseInstructionInternal( + HloInstruction* instruction_to_fuse, bool add_output) { + // When add_output is false, this fusion instruction must be a user of + // instruction_to_fuse. + if (!add_output) { + CHECK(IsUserOf(instruction_to_fuse)); + } + HloInstruction* fused_instruction = + CloneAndFuseInternal(instruction_to_fuse, add_output); + return fused_instruction; +} + +HloInstruction* HloFusionInstruction::CloneAndFuseInternal( + HloInstruction* instruction_to_fuse, bool add_output) { + CHECK(instruction_to_fuse->IsFusable()) << instruction_to_fuse->ToString(); + VLOG(3) << "CloneAndFuseInternal:\n" << instruction_to_fuse->ToString(); + HloInstruction* clone = nullptr; + if (called_computations().empty()) { + // New fusion instruction. It should not be a multioutput instruction. + CHECK(!add_output); + auto builder = HloComputation::Builder("fused_computation", this); + builder.AddInstruction(instruction_to_fuse->Clone(/*suffix=*/"")); + AppendComputation( + CHECK_NOTNULL(GetModule())->AddEmbeddedComputation(builder.Build())); + clone = fused_expression_root(); + } else { + clone = fused_instructions_computation()->AddInstruction( + instruction_to_fuse->Clone(/*suffix=*/"")); + // When add_output is false, instruction_to_fuse is necessarily an operand + // of the fusion instruction. After fusion this will no longer be the + // case. Remove the operand from the operand list and remove its + // corresponding fused parameter instruction. Renumber parameters as + // necessary to make parameter numbers consistent with their index in the + // fused_parameter_ vector. + bool in_operand_list = std::find(operands().begin(), operands().end(), + instruction_to_fuse) != operands().end(); + CHECK(add_output || in_operand_list); + const std::vector& fused_parameters = + fused_instructions_computation()->parameter_instructions(); + for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { + if (instruction_to_fuse == operand(operand_num)) { + // replace the fused parameter instruction's uses with the clone. + HloInstruction* fused_parameter = fused_parameters[operand_num]; + TF_CHECK_OK(fused_parameter->ReplaceAllUsesWith(clone)); + + // Remove the corresponding fused parameter and operand from their + // respective vectors. + TF_CHECK_OK( + fused_instructions_computation()->RemoveParameter(operand_num)); + RemoveOperandAt(operand_num); + break; + } + } + // We've cloned instruction_to_fuse into this fusion instruction, so this + // fusion instruction is no longer a use of instruction_to_fuse. + if (in_operand_list) { + DetachFrom(instruction_to_fuse); + // When the instruction_to_fuse does not have other users, we don't need + // to generate a multioutput fusion instruction. + if (instruction_to_fuse->user_count() == 0) { + add_output = false; + } + } + } + + // Reread the parameters in the computation. + const std::vector& fused_parameters = + fused_instructions_computation()->parameter_instructions(); + + // Add each operand of the clone as an operand of the fusion instruction. A + // complication is that some clone operands may already be operands of the + // fusion instruction. + for (int64 operand_num = 0; operand_num < clone->operand_count(); + ++operand_num) { + HloInstruction* operand = clone->mutable_operand(operand_num); + + // See if this operand is already an operand of the fusion node. + CHECK_EQ(operands().size(), fused_parameters.size()); + HloInstruction* fused_param = nullptr; + for (int64 i = 0; i < operands().size(); ++i) { + if (this->operand(i) == operand) { + fused_param = fused_parameters[i]; + break; + } + } + + if (fused_param == nullptr) { + // Clone's operand was not already an operand of the fusion + // instruction. Add it as an operand and add a corresponding fused + // parameter instruction. + fused_param = AddFusionOperand(operand); + } + TF_CHECK_OK(clone->ReplaceOperandWith(operand_num, fused_param)); + } + + if (add_output) { + CHECK_GT(instruction_to_fuse->user_count(), 0); + // If this is already a multioutput fusion instruction, expand the root + // tuple by 1. + HloInstruction* fused_root = fused_expression_root(); + HloInstruction::InstructionVector tuple_elements; + bool newly_created_tuple_instr = false; + if (fused_root->opcode() == HloOpcode::kTuple) { + tuple_elements = fused_root->operands(); + } else { + tuple_elements.push_back(fused_root); + newly_created_tuple_instr = true; + } + if (clone->opcode() == HloOpcode::kTuple) { + for (auto inst : clone->operands()) { + tuple_elements.push_back(inst); + } + } else { + tuple_elements.push_back(clone); + } + HloInstruction* new_root = fused_instructions_computation()->AddInstruction( + HloInstruction::CreateTuple(tuple_elements)); + fused_instructions_computation()->set_root_instruction(new_root); + *mutable_shape() = new_root->shape(); + if (fused_root->opcode() == HloOpcode::kTuple) { + TF_CHECK_OK( + fused_instructions_computation()->RemoveInstruction(fused_root)); + } + + // If this is a newly created multioutput instruction, we need to update + // the use of the original fusion instruction. + if (newly_created_tuple_instr) { + HloInstruction* new_instr = parent()->AddInstruction( + HloInstruction::CreateGetTupleElement(fused_root->shape(), this, 0)); + TF_CHECK_OK(ReplaceAllUsesWith(new_instr)); + } + int64 index = tuple_elements.size(); + if (instruction_to_fuse->opcode() == HloOpcode::kTuple) { + index -= instruction_to_fuse->operand_count(); + std::vector to_be_removed; + for (auto old_gte : instruction_to_fuse->users()) { + CHECK_EQ(old_gte->opcode(), HloOpcode::kGetTupleElement); + int64 old_tuple_index = old_gte->tuple_index(); + HloInstruction* new_gte = + parent()->AddInstruction(HloInstruction::CreateGetTupleElement( + old_gte->shape(), this, index + old_tuple_index)); + TF_CHECK_OK(old_gte->ReplaceAllUsesWith(new_gte)); + to_be_removed.push_back(old_gte); + } + for (auto old_gte : to_be_removed) { + TF_CHECK_OK(parent()->RemoveInstruction(old_gte)); + } + TF_CHECK_OK(fused_instructions_computation()->RemoveInstruction(clone)); + } else { + HloInstruction* new_gte = + parent()->AddInstruction(HloInstruction::CreateGetTupleElement( + clone->shape(), this, index - 1)); + TF_CHECK_OK(instruction_to_fuse->ReplaceAllUsesWith(new_gte)); + } + } + + VLOG(2) << "New clone:\n" << clone->ToString(); + return clone; +} + +std::vector HloFusionInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("kind=", xla::ToString(fusion_kind()))}; +} + +bool HloFusionInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return fusion_kind() == other.fusion_kind() && + eq_computations(fused_instructions_computation(), + other.fused_instructions_computation()); +} + +std::unique_ptr HloFusionInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + HloModule* module = context != nullptr ? context->module() : GetModule(); + HloComputation* new_fused_computation = nullptr; + if (context != nullptr) { + new_fused_computation = + context->FindComputation(fused_instructions_computation()); + } + if (new_fused_computation == nullptr) { + new_fused_computation = module->AddEmbeddedComputation( + fused_instructions_computation()->Clone("clone", context)); + } + return MakeUnique(shape, fusion_kind(), new_operands, + new_fused_computation); +} + +Status HloFusionInstruction::DeduplicateFusionOperands() { + tensorflow::gtl::FlatMap operand_indices; + std::vector operands_to_remove; + for (int i = 0; i < operand_count(); ++i) { + auto emplace_result = operand_indices.emplace(operand(i), i); + if (!emplace_result.second) { + TF_RETURN_IF_ERROR(fused_parameter(i)->ReplaceAllUsesWith( + fused_parameter(emplace_result.first->second))); + operands_to_remove.push_back(i); + } + } + if (operands_to_remove.empty()) { + return Status::OK(); + } + TF_RETURN_IF_ERROR( + fused_instructions_computation()->RemoveUnusedParameters()); + RemoveOperandsAtAscendingIndices(operands_to_remove); + return Status::OK(); +} + +HloRngInstruction::HloRngInstruction( + const Shape& shape, RandomDistribution distribution, + tensorflow::gtl::ArraySlice parameters) + : HloInstruction(HloOpcode::kRng, shape), distribution_(distribution) { + for (HloInstruction* param : parameters) { + AppendOperand(param); + } +} + +HloInstructionProto HloRngInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_distribution(distribution_); + return proto; +} + +std::vector HloRngInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("distribution=", RandomDistributionToString(distribution_))}; +} + +bool HloRngInstruction::IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const { + return true; +} + +bool HloRngInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return false; +} + +std::unique_ptr HloRngInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique(shape, distribution_, new_operands); +} + +HloParameterInstruction::HloParameterInstruction(int64 parameter_number, + const Shape& shape, + const string& name) + : HloInstruction(HloOpcode::kParameter, shape), + parameter_number_(parameter_number) { + SetAndSanitizeName(name); +} + +HloInstructionProto HloParameterInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_parameter_number(parameter_number_); + return proto; +} + +string HloParameterInstruction::OperandsToStringWithCanonicalNameMap( + const HloPrintOptions& options, + CanonicalNameMap* canonical_name_map) const { + return StrCat(parameter_number_); +} + +bool HloParameterInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return parameter_number() == casted_other.parameter_number(); +} + +std::unique_ptr +HloParameterInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique(parameter_number_, shape, name()); +} + +HloGetTupleElementInstruction::HloGetTupleElementInstruction( + const Shape& shape, HloInstruction* operand, int64 index) + : HloInstruction(HloOpcode::kGetTupleElement, shape), tuple_index_(index) { + CHECK(ShapeUtil::IsTuple(operand->shape())); + AppendOperand(operand); +} + +HloInstructionProto HloGetTupleElementInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_tuple_index(tuple_index_); + return proto; +} + +std::vector HloGetTupleElementInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("index=", tuple_index())}; +} + +bool HloGetTupleElementInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return tuple_index() == casted_other.tuple_index(); +} + +std::unique_ptr +HloGetTupleElementInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(shape, new_operands[0], + tuple_index()); +} + +HloReducePrecisionInstruction::HloReducePrecisionInstruction( + const Shape& shape, HloInstruction* operand, const int exponent_bits, + const int mantissa_bits) + : HloInstruction(HloOpcode::kReducePrecision, shape), + exponent_bits_(exponent_bits), + mantissa_bits_(mantissa_bits) { + AppendOperand(operand); +} + +HloInstructionProto HloReducePrecisionInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_exponent_bits(exponent_bits_); + proto.set_mantissa_bits(mantissa_bits_); + return proto; +} + +std::vector HloReducePrecisionInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("exponent_bits=", exponent_bits_), + StrCat("mantissa_bits=", mantissa_bits_)}; +} + +bool HloReducePrecisionInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + // A reduce-precision operation is determined by the bit sizes. + return exponent_bits() == casted_other.exponent_bits() && + mantissa_bits() == casted_other.mantissa_bits(); +} + +std::unique_ptr +HloReducePrecisionInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique( + shape, new_operands[0], exponent_bits(), mantissa_bits()); +} + +HloInfeedInstruction::HloInfeedInstruction(const Shape& infeed_shape, + HloInstruction* token_operand, + const string& config) + : HloInstruction(HloOpcode::kInfeed, + ShapeUtil::MakeTupleShape( + {infeed_shape, ShapeUtil::MakeTokenShape()})), + infeed_config_(config) { + AppendOperand(token_operand); +} + +HloInfeedInstruction::HloInfeedInstruction(const Shape& infeed_shape, + const string& config) + : HloInstruction(HloOpcode::kInfeed, + ShapeUtil::MakeTupleShape( + {infeed_shape, ShapeUtil::MakeTokenShape()})), + infeed_config_(config) {} + +HloInstructionProto HloInfeedInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_infeed_config(infeed_config_); + return proto; +} + +std::vector HloInfeedInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + if (infeed_config_.empty()) { + return {}; + } + return {StrCat("infeed_config=\"", CEscape(infeed_config_), "\"")}; +} + +bool HloInfeedInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr HloInfeedInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + if (new_operands.empty()) { + return MakeUnique(infeed_shape(), infeed_config()); + } else { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(infeed_shape(), new_operands[0], + infeed_config()); + } +} + +HloOutfeedInstruction::HloOutfeedInstruction( + const Shape& outfeed_shape, HloInstruction* operand, + HloInstruction* token_operand, tensorflow::StringPiece outfeed_config) + : HloInstruction(HloOpcode::kOutfeed, ShapeUtil::MakeTokenShape()), + outfeed_shape_(outfeed_shape), + outfeed_config_(outfeed_config.begin(), outfeed_config.end()) { + CHECK(ShapeUtil::Compatible(operand->shape(), outfeed_shape)) + << "Outfeed shape " << outfeed_shape + << " must be compatible with operand shape " << operand->shape(); + AppendOperand(operand); + AppendOperand(token_operand); +} + +HloOutfeedInstruction::HloOutfeedInstruction( + const Shape& outfeed_shape, HloInstruction* operand, + tensorflow::StringPiece outfeed_config) + : HloInstruction(HloOpcode::kOutfeed, ShapeUtil::MakeTokenShape()), + outfeed_shape_(outfeed_shape), + outfeed_config_(outfeed_config.begin(), outfeed_config.end()) { + CHECK(ShapeUtil::Compatible(operand->shape(), outfeed_shape)) + << "Outfeed shape " << outfeed_shape + << " must be compatible with operand shape " << operand->shape(); + AppendOperand(operand); +} + +HloInstructionProto HloOutfeedInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_outfeed_config(outfeed_config()); + *proto.mutable_outfeed_shape() = outfeed_shape(); + return proto; +} + +std::vector HloOutfeedInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + if (outfeed_config_.empty()) { + return {}; + } + return {StrCat("outfeed_config=\"", CEscape(outfeed_config_), "\"")}; +} + +bool HloOutfeedInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr HloOutfeedInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + if (new_operands.size() == 1) { + return MakeUnique(outfeed_shape(), new_operands[0], + outfeed_config()); + } else { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(outfeed_shape(), new_operands[0], + new_operands[1], outfeed_config()); + } +} + +HloConvolutionInstruction::HloConvolutionInstruction( + const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, + const Window& window, const ConvolutionDimensionNumbers& dimension_numbers) + : HloInstruction(HloOpcode::kConvolution, shape), + window_(window), + convolution_dimension_numbers_(dimension_numbers) { + if (window_util::HasBaseDilation(window)) { + SetAndSanitizeName(StrCat(name(), "-base-dilated")); + } + if (window_util::HasWindowDilation(window)) { + SetAndSanitizeName(StrCat(name(), "-window-dilated")); + } + AppendOperand(lhs); + AppendOperand(rhs); +} + +string HloConvolutionInstruction::ToCategory() const { + string category = "convolution"; + if (window_util::HasBaseDilation(window())) { + category += " base-dilated"; + } + if (window_util::HasWindowDilation(window())) { + category += " window-dilated"; + } + return category; +} + +HloInstructionProto HloConvolutionInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + *proto.mutable_convolution_dimension_numbers() = + convolution_dimension_numbers_; + return proto; +} + +std::vector HloConvolutionInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + extra.push_back(StrCat("dim_labels=", ConvolutionDimensionNumbersToString( + convolution_dimension_numbers_))); + return extra; +} + +bool HloConvolutionInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return protobuf_util::ProtobufEquals(window(), casted_other.window()) && + protobuf_util::ProtobufEquals( + convolution_dimension_numbers(), + casted_other.convolution_dimension_numbers()); +} + +std::unique_ptr +HloConvolutionInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(shape, new_operands[0], + new_operands[1], window(), + convolution_dimension_numbers_); +} + +HloReduceWindowInstruction::HloReduceWindowInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* init_value, + const Window& window, HloComputation* reduce_computation) + : HloInstruction(HloOpcode::kReduceWindow, shape), window_(window) { + AppendOperand(operand); + AppendOperand(init_value); + AppendComputation(reduce_computation); +} + +HloInstructionProto HloReduceWindowInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + return proto; +} + +std::vector HloReduceWindowInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + return extra; +} + +bool HloReduceWindowInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return eq_computations(to_apply(), casted_other.to_apply()) && + protobuf_util::ProtobufEquals(window(), casted_other.window()); +} + +std::unique_ptr +HloReduceWindowInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], window(), to_apply()); +} + +HloSelectAndScatterInstruction::HloSelectAndScatterInstruction( + const Shape& shape, HloInstruction* operand, HloComputation* select, + const Window& window, HloInstruction* source, HloInstruction* init_value, + HloComputation* scatter) + : HloInstruction(HloOpcode::kSelectAndScatter, shape), window_(window) { + AppendOperand(operand); + AppendOperand(source); + AppendOperand(init_value); + // Select comes before scatter in the vector. + AppendComputation(select); + AppendComputation(scatter); +} + +HloInstructionProto HloSelectAndScatterInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + return proto; +} + +std::vector HloSelectAndScatterInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + return extra; +} + +bool HloSelectAndScatterInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return eq_computations(select(), casted_other.select()) && + eq_computations(scatter(), casted_other.scatter()) && + protobuf_util::ProtobufEquals(window(), casted_other.window()); +} + +std::unique_ptr +HloSelectAndScatterInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 3); + return MakeUnique( + shape, new_operands[0], select(), window(), new_operands[1], + new_operands[2], scatter()); +} + +HloCustomCallInstruction::HloCustomCallInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece custom_call_target) + : HloInstruction(HloOpcode::kCustomCall, shape), + custom_call_target_(custom_call_target.begin(), + custom_call_target.end()) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +HloInstructionProto HloCustomCallInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + if (window_ != nullptr) { + *proto.mutable_window() = *window_; + } + if (convolution_dimension_numbers_ != nullptr) { + *proto.mutable_convolution_dimension_numbers() = + *convolution_dimension_numbers_; + } + proto.set_custom_call_target(custom_call_target_); + return proto; +} + +std::vector HloCustomCallInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_ != nullptr && window_->dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(*window_), "}")); + } + if (convolution_dimension_numbers_ != nullptr) { + extra.push_back(StrCat( + "dim_labels=", + ConvolutionDimensionNumbersToString(*convolution_dimension_numbers_))); + } + // By contract, we print the custom call target even if + // options.print_subcomputation_mode() == kOff, because the call target is not + // an HloComputation. + extra.push_back( + StrCat("custom_call_target=\"", CEscape(custom_call_target_), "\"")); + return extra; +} + +bool HloCustomCallInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + if ((window_ == nullptr) != (casted_other.window_ == nullptr) || + (window_ != nullptr && + !protobuf_util::ProtobufEquals(*window_, *casted_other.window_))) { + return false; + } + if ((convolution_dimension_numbers_ == nullptr) != + (casted_other.convolution_dimension_numbers_ == nullptr) || + (convolution_dimension_numbers_ != nullptr && + !protobuf_util::ProtobufEquals( + convolution_dimension_numbers(), + casted_other.convolution_dimension_numbers()))) { + return false; + } + return custom_call_target_ == casted_other.custom_call_target_; +} + +std::unique_ptr +HloCustomCallInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + auto cloned = MakeUnique(shape, new_operands, + custom_call_target()); + if (window_ != nullptr) { + cloned->set_window(*window_); + } + if (convolution_dimension_numbers_ != nullptr) { + cloned->set_convolution_dimension_numbers(*convolution_dimension_numbers_); + } + return std::move(cloned); +} + +HloHostComputeInstruction::HloHostComputeInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) + : HloInstruction(HloOpcode::kHostCompute, shape), + channel_name_(channel_name.begin(), channel_name.end()), + cost_estimate_ns_(cost_estimate_ns) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +HloInstructionProto HloHostComputeInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_channel_name(channel_name_); + proto.set_cost_estimate_ns(cost_estimate_ns_); + return proto; +} + +bool HloHostComputeInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr +HloHostComputeInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique( + shape, new_operands, channel_name_, cost_estimate_ns_); +} + +HloPadInstruction::HloPadInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config) + : HloInstruction(HloOpcode::kPad, shape), padding_config_(padding_config) { + AppendOperand(operand); + AppendOperand(padding_value); +} + +HloInstructionProto HloPadInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_padding_config() = padding_config_; + return proto; +} + +std::vector HloPadInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("padding=", xla::PaddingConfigToString(padding_config_))}; +} + +bool HloPadInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return protobuf_util::ProtobufEquals(padding_config(), + casted_other.padding_config()); +} + +std::unique_ptr HloPadInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(shape, new_operands[0], new_operands[1], + padding_config_); +} + +HloDynamicSliceInstruction::HloDynamicSliceInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* start_indices, + tensorflow::gtl::ArraySlice slice_sizes) + : HloInstruction(HloOpcode::kDynamicSlice, shape), + dynamic_slice_sizes_(slice_sizes.begin(), slice_sizes.end()) { + AppendOperand(operand); + AppendOperand(start_indices); +} + +HloInstructionProto HloDynamicSliceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 slice_size : dynamic_slice_sizes_) { + proto.add_dynamic_slice_sizes(slice_size); + } + return proto; +} + +std::vector HloDynamicSliceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return { + StrCat("dynamic_slice_sizes={", Join(dynamic_slice_sizes(), ","), "}")}; +} + +bool HloDynamicSliceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return true; +} + +std::unique_ptr +HloDynamicSliceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], dynamic_slice_sizes_); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 6fcd96a8c66bcb74b22a8fd5152ed3e3680ce576..df6969c410a7742a9abfff56c3d41864232a8bff 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -32,19 +32,18 @@ class HloBatchNormInstruction : public HloInstruction { // number added to the variance to avoid divide-by-zero error. float epsilon() const { return epsilon_; } - // Returns string representation of op-specific attributes. - std::vector ExtraAttributesToString( - const HloPrintOptions& options) const override; - // Returns a serialized representation of this instruction. HloInstructionProto ToProto() const override; protected: - HloBatchNormInstruction(HloOpcode opcode, const Shape& shape, - HloInstruction* operand, HloInstruction* scale, - float epsilon, int64 feature_index); + explicit HloBatchNormInstruction(HloOpcode opcode, const Shape& shape, + HloInstruction* operand, + HloInstruction* scale, float epsilon, + int64 feature_index); private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; bool IdenticalSlowPath( const HloInstruction& other, const std::function& @@ -58,9 +57,11 @@ class HloBatchNormInstruction : public HloInstruction { class HloBatchNormTrainingInstruction : public HloBatchNormInstruction { public: - HloBatchNormTrainingInstruction(const Shape& shape, HloInstruction* operand, - HloInstruction* scale, HloInstruction* offset, - float epsilon, int64 feature_index); + explicit HloBatchNormTrainingInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* scale, + HloInstruction* offset, + float epsilon, int64 feature_index); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -72,11 +73,10 @@ class HloBatchNormTrainingInstruction : public HloBatchNormInstruction { class HloBatchNormInferenceInstruction : public HloBatchNormInstruction { public: - HloBatchNormInferenceInstruction(const Shape& shape, HloInstruction* operand, - HloInstruction* scale, - HloInstruction* offset, HloInstruction* mean, - HloInstruction* variance, float epsilon, - int64 feature_index); + explicit HloBatchNormInferenceInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* scale, + HloInstruction* offset, HloInstruction* mean, HloInstruction* variance, + float epsilon, int64 feature_index); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -88,20 +88,1037 @@ class HloBatchNormInferenceInstruction : public HloBatchNormInstruction { class HloBatchNormGradInstruction : public HloBatchNormInstruction { public: - HloBatchNormGradInstruction(const Shape& shape, HloInstruction* operand, - HloInstruction* scale, HloInstruction* mean, - HloInstruction* variance, - HloInstruction* grad_output, float epsilon, - int64 feature_index); + explicit HloBatchNormGradInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* scale, + HloInstruction* mean, HloInstruction* variance, + HloInstruction* grad_output, float epsilon, int64 feature_index); + + private: + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; +}; + +class HloFftInstruction : public HloInstruction { + public: + explicit HloFftInstruction(const Shape& shape, HloInstruction* operand, + FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + FftType fft_type() const { return fft_type_; } + + const std::vector& fft_length() const { return fft_length_; } + + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Describes FFT type for an FFT instruction. + FftType fft_type_ = FftType::FFT; + + // Indicates the FFT length for an FFT instruction. + std::vector fft_length_; +}; + +class HloSendRecvInstruction : public HloInstruction { + public: + // Returns the channel id associated with the instruction. The id is + // shared between each Send/Recv pair and is globally unique to identify each + // channel. + int64 channel_id() const { return channel_id_; } + + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + protected: + explicit HloSendRecvInstruction(HloOpcode opcode, const Shape& shape, + int64 channel_id); + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Represents a unique identifier for each Send/Recv instruction pair. + int64 channel_id_; +}; + +class HloSendInstruction : public HloSendRecvInstruction { + public: + explicit HloSendInstruction(HloInstruction* operand, HloInstruction* token, + int64 channel_id); + + private: + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; +}; + +class HloSendDoneInstruction : public HloSendRecvInstruction { + public: + explicit HloSendDoneInstruction(HloSendInstruction* operand); + + private: + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; +}; + +class HloRecvInstruction : public HloSendRecvInstruction { + public: + explicit HloRecvInstruction(const Shape& shape, HloInstruction* token, + int64 channel_id); + + private: + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; +}; + +class HloRecvDoneInstruction : public HloSendRecvInstruction { + public: + explicit HloRecvDoneInstruction(HloRecvInstruction* operand); + + private: + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; +}; + +class HloAllReduceInstruction : public HloInstruction { + public: + explicit HloAllReduceInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* reduce_computation, + tensorflow::gtl::ArraySlice replica_group_ids, + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id = + tensorflow::gtl::nullopt); + + // Returns the group ids of each replica for CrossReplicaSum op. + const std::vector& replica_group_ids() const { + return replica_group_ids_; + } + + // Returns the barrier config used for the CrossReplicaSum implementation of + // each backend. + string cross_replica_sum_barrier() const { + return cross_replica_sum_barrier_; + } + void set_cross_replica_sum_barrier(string barrier) { + cross_replica_sum_barrier_ = barrier; + } + + tensorflow::gtl::optional all_reduce_id() const { + return all_reduce_id_; + } + + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The group id of each replica for CrossReplicaSum. + std::vector replica_group_ids_; + + // The string representation of the barrier config used for CrossReplicaSum. + string cross_replica_sum_barrier_; + + // For Allreduce nodes from different modules, if they have the same + // all_reduce_id, they will be 'Allreduce'd. If empty, Allreduce will not be + // applied cross modules. + tensorflow::gtl::optional all_reduce_id_; +}; + +class HloReverseInstruction : public HloInstruction { + public: + explicit HloReverseInstruction(const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice dimensions); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloConcatenateInstruction : public HloInstruction { + public: + explicit HloConcatenateInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + int64 dimension); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Accessor for the dimension in which a concatenate HLO should occur. + int64 concatenate_dimension() const { return dimensions(0); } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloReduceInstruction : public HloInstruction { + public: + explicit HloReduceInstruction( + const Shape& shape, HloInstruction* arg, HloInstruction* init_value, + tensorflow::gtl::ArraySlice dimensions_to_reduce, + HloComputation* reduce_computation); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloTransposeInstruction : public HloInstruction { + public: + explicit HloTransposeInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice dimensions); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns whether this instruction does a rank-2 transposition. + bool IsRank2Transpose() const; + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloBroadcastInstruction : public HloInstruction { + public: + explicit HloBroadcastInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice broadcast_dimension); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloMapInstruction : public HloInstruction { + public: + explicit HloMapInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* map_computation); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const override; + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + +class HloSliceInstruction : public HloInstruction { + public: + explicit HloSliceInstruction(const Shape& shape, HloInstruction* operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + HloInstructionProto ToProto() const override; + + // Returns the start index in the given dimension for a slice node. + int64 slice_starts(int64 dimension) const { return slice_starts_[dimension]; } + const std::vector& slice_starts() const { return slice_starts_; } + + // Returns the (exclusive) limit index in the given dimension for a slice + // node. + int64 slice_limits(int64 dimension) const { return slice_limits_[dimension]; } + const std::vector& slice_limits() const { return slice_limits_; } + + // Returns the stride in the given dimension for a slice node. + int64 slice_strides(int64 dimension) const { + return slice_strides_[dimension]; + } + const std::vector& slice_strides() const { return slice_strides_; } + + // Returns the flag that describes whether a slice must be lowered into an + // offset into the original operand. + bool IsInPlaceSlice() const { return is_in_place_slice_; } + + // Sets and returns the flag that describes whether a slice must be lowered + // into an offset into the original operand. + bool SetIsInPlaceSlice(bool value) { + is_in_place_slice_ = value; + return value; + } + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Describes the [begin, end) index range for a slice. + std::vector slice_starts_; + std::vector slice_limits_; + std::vector slice_strides_; + + // Describes whether the slice can be lowered to an offset into the operand. + bool is_in_place_slice_ = false; +}; + +class HloConstantInstruction : public HloInstruction { + public: + explicit HloConstantInstruction(std::unique_ptr literal); + // Used when the literal is too large and dropped. + explicit HloConstantInstruction(const Shape& shape); + // Returns the literal associated with this instruction. + const Literal& literal() const { return *literal_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + // Change the layout for an Constant Hlo instruction to match new_layout. For + // tuple shaped constants shape_index is the path to the internal array + // subshape whose layout needs to be changed. + void RelayoutConstant(const Layout& new_layout, + const ShapeIndex& shape_index = {}); private: + bool IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + string OperandsToStringWithCanonicalNameMap( + const HloPrintOptions& options, + CanonicalNameMap* canonical_name_map) const override; // Implementation for non-common logic of CloneWithNewOperands. std::unique_ptr CloneWithNewOperandsImpl( const Shape& shape, tensorflow::gtl::ArraySlice new_operands, HloCloneContext* context) const override; + // TODO(b/36360764): Remove unique_ptr wrapping. + std::unique_ptr literal_; }; +class HloTraceInstruction : public HloInstruction { + public: + explicit HloTraceInstruction(const string& tag, HloInstruction* operand); + // Returns a tag to be used in tracing. + string TracingTag() const { return literal_->GetR1U8AsString(); } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + // TODO(b/36360764): Remove unique_ptr wrapping. + std::unique_ptr literal_; +}; + +class HloFusionInstruction : public HloInstruction { + public: + explicit HloFusionInstruction(const Shape& shape, FusionKind fusion_kind, + HloInstruction* fused_root); + + explicit HloFusionInstruction( + const Shape& shape, FusionKind fusion_kind, + tensorflow::gtl::ArraySlice operands, + HloComputation* fusion_computation); + + string ToCategory() const override; + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + // Adds a new operand the fusion instruction. + HloInstruction* AddFusionOperand(HloInstruction* new_operand); + + // Merges the fused instructions from 'instruction_to_merge' into the + // fused instruction set of 'this', updating operands as necessary. + // + // Predondition: 'instruction_to_merge' must be an operand of 'this'. + void MergeFusionInstruction(HloFusionInstruction* instruction_to_merge); + + // Merges the fused instructions from instruction_to_merge into the fused + // instruction set of 'this' and generates multioutput fusion instructions. + // All the users of instruction_to_merge will be redirected to 'this' + // instruction. instruction_to_merge will be removed from its parent + // computation. + void MergeFusionInstructionIntoMultiOutput( + HloFusionInstruction* instruction_to_merge); + + // Fuses the given instruction in this fusion instruction. instruction_to_fuse + // is cloned and the clone is placed in the fusion + // instruction. instruction_to_fuse is unchanged. Instruction is cloned rather + // than moved to cleanly handle the case where the instruction has a use + // outside the fusion instruction. Moving such an instruction into a fusion + // instruction would violate the single-result invariant of HLO instructions + // and significantly complicate code generation. + HloInstruction* FuseInstruction(HloInstruction* instruction_to_fuse) { + return FuseInstructionInternal(instruction_to_fuse); + } + + // Fuses the given instruction in this fusion instruction and generate + // multioutput fusion instruction. A clone of the instruction_to_fuse will + // be part of the output of fusion instructions. The users of + // instruction_to_fuse will be redirected to this fusion instructions. + // instruction_to_fuse will be removed from its parent computation. + HloInstruction* FuseInstructionIntoMultiOutput( + HloInstruction* instruction_to_fuse) { + return FuseInstructionInternal(instruction_to_fuse, /* add_output */ true); + } + + // Returns the computation for this fused instruction. + HloComputation* fused_instructions_computation() const; + + // Returns the root instruction of the fused expression contained within this + // fusion instruction. + HloInstruction* fused_expression_root() const; + + // Returns the list of fused instructions inside this fusion instruction. The + // returned type is a range of HloInstruction*s. + const tensorflow::gtl::iterator_range>::const_iterator>> + fused_instructions() const; + + const tensorflow::gtl::iterator_range< + UnwrappingIterator>::iterator>> + fused_instructions(); + + // Gets the number of instructions inside this fusion instruction. + int64 fused_instruction_count() const; + + // Returns the fused parameter instruction in this fusion instruction + // corresponding to the given parameter number. + HloInstruction* fused_parameter(int64 parameter_number) const; + + // Returns the vector of fused parameters inside this fusion instruction. + const std::vector& fused_parameters() const; + + // Returns true if this instruction is a fusion instruction that generates + // multiple outputs. + const bool IsMultiOutputFusion() const { + return fused_expression_root()->opcode() == HloOpcode::kTuple; + } + + FusionKind fusion_kind() const { return fusion_kind_; } + + void set_fusion_kind(FusionKind kind) { fusion_kind_ = kind; } + + // If multiple operands are the same instruction, keeps only one of them. + Status DeduplicateFusionOperands(); + + private: + // Fuses the given instruction into this fusion instruction. When add_output + // is false (which is the default), instruction_to_fuse is cloned and the + // clone is placed in the fusion instruction. instruction_to_fuse is + // unchanged. + // + // When add_output is true, a clone of the instruction_to_fuse will be part + // of the output of fusion instructions. The users of instruction_to_fuse + // will be redirected to this fusion instructions. instruction_to_fuse will + // be removed from its parent computation. + HloInstruction* FuseInstructionInternal(HloInstruction* instruction_to_fuse, + bool add_output = false); + // Clones the given instruction_to_fuse and insert the clone into this fusion + // instruction. If add_output is true, a clone of instruction_to_fuse will + // be in the output of the this fusion instruction (part of the tuple of the + // fusion root). + HloInstruction* CloneAndFuseInternal(HloInstruction* instruction_to_fuse, + bool add_output = false); + + bool IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const override; + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The type of the fusion. Used by kFusion only. + FusionKind fusion_kind_; +}; + +class HloRngInstruction : public HloInstruction { + public: + explicit HloRngInstruction( + const Shape& shape, RandomDistribution distribution, + tensorflow::gtl::ArraySlice parameters); + // Returns the random distribution for this rng node. + RandomDistribution random_distribution() const { return distribution_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IsElementwiseImpl( + const tensorflow::gtl::optional& operand_idx) const override; + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The distribution requested for random number generation. + RandomDistribution distribution_; +}; + +class HloParameterInstruction : public HloInstruction { + public: + explicit HloParameterInstruction(int64 parameter_number, const Shape& shape, + const string& name); + int64 parameter_number() const { return parameter_number_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + string OperandsToStringWithCanonicalNameMap( + const HloPrintOptions& options, + CanonicalNameMap* canonical_name_map) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + int64 parameter_number_ = 0; +}; + +class HloGetTupleElementInstruction : public HloInstruction { + public: + explicit HloGetTupleElementInstruction(const Shape& shape, + HloInstruction* operand, int64 index); + // Returns the tuple index associated with this instruction. + int64 tuple_index() const { return tuple_index_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + int64 tuple_index_ = -1; +}; + +class HloReducePrecisionInstruction : public HloInstruction { + public: + explicit HloReducePrecisionInstruction(const Shape& shape, + HloInstruction* operand, + const int exponent_bits, + const int mantissa_bits); + // Returns the number of exponent bits for a reduce-precision node. + int32 exponent_bits() const { return exponent_bits_; } + // Returns the number of mantissa bits for a reduce-precision node. + int32 mantissa_bits() const { return mantissa_bits_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The bit sizes for a reduce-precision operation. + int32 exponent_bits_ = 0; + int32 mantissa_bits_ = 0; +}; + +class HloInfeedInstruction : public HloInstruction { + public: + explicit HloInfeedInstruction(const Shape& infeed_shape, + HloInstruction* token_operand, + const string& config); + // TODO(b/80000000): Remove this constructor when all uses of infeed are + // converted to take tokens. + explicit HloInfeedInstruction(const Shape& infeed_shape, + const string& config); + // Returns the infeed configuration string. The infeed configuration includes + // any metadata needed for the backend compiler (e.g., infeed buffer address) + // and is target-dependent. + string infeed_config() const { return infeed_config_; } + void set_infeed_config(const string& config) { infeed_config_ = config; } + // Returns the shape of the data received by the infeed. This is not the same + // as the shape of the infeed instruction which produces a tuple containing + // the infeed data shape and a TOKEN. + const Shape& infeed_shape() const { + TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape())); + return ShapeUtil::GetSubshape(shape(), {0}); + } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The string representation of the infeed configuration. + string infeed_config_; +}; + +class HloOutfeedInstruction : public HloInstruction { + public: + explicit HloOutfeedInstruction(const Shape& outfeed_shape, + HloInstruction* operand, + HloInstruction* token_operand, + tensorflow::StringPiece outfeed_config); + // TODO(b/80000000): Remove this constructor when all uses of outfeed are + // converted to take tokens. + explicit HloOutfeedInstruction(const Shape& outfeed_shape, + HloInstruction* operand, + tensorflow::StringPiece outfeed_config); + + // Returns the shape for the Outfeed instruction. + const Shape& outfeed_shape() const { + TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(outfeed_shape_)); + return outfeed_shape_; + } + // Returns the config for the Outfeed instruction. + const string& outfeed_config() const { return outfeed_config_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Shape of outfeed request. + Shape outfeed_shape_; + // Outfeed configuration information, only present for kOutfeed. + string outfeed_config_; +}; + +class HloConvolutionInstruction : public HloInstruction { + public: + explicit HloConvolutionInstruction( + const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, + const Window& window, + const ConvolutionDimensionNumbers& dimension_numbers); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { + return convolution_dimension_numbers_; + } + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + convolution_dimension_numbers_ = dnums; + } + string ToCategory() const override; + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; + // Describes the dimension numbers used for a convolution. + ConvolutionDimensionNumbers convolution_dimension_numbers_; +}; + +class HloReduceWindowInstruction : public HloInstruction { + public: + explicit HloReduceWindowInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* init_value, + const Window& window, + HloComputation* reduce_computation); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; +}; + +class HloSelectAndScatterInstruction : public HloInstruction { + public: + explicit HloSelectAndScatterInstruction( + const Shape& shape, HloInstruction* operand, HloComputation* select, + const Window& window, HloInstruction* source, HloInstruction* init_value, + HloComputation* scatter); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + // Gets/sets the select or scatter HloComputation for SelectAndScatter. The + // setters should only be called by HloModule or HloComputation methods. + HloComputation* select() const { + return called_computations()[kSelectComputationIndex]; + } + + HloComputation* scatter() const { + return called_computations()[kScatterComputationIndex]; + } + + void set_select(HloComputation* computation) { + // Don't allow changing the computation for fused instructions so we don't + // have to recompute called_instructions for the entire fusion instruction. + CHECK(!IsFused()); + set_called_computation(kSelectComputationIndex, computation); + } + + void set_scatter(HloComputation* computation) { + // Don't allow changing the computation for fused instructions so we don't + // have to recompute called_instructions for the entire fusion instruction. + CHECK(!IsFused()); + set_called_computation(kScatterComputationIndex, computation); + } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; +}; + +class HloCustomCallInstruction : public HloInstruction { + public: + explicit HloCustomCallInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece custom_call_target); + const Window& window() const override { + CHECK(window_ != nullptr); + return *window_; + } + + void set_window(const Window& window) override { + window_ = MakeUnique(window); + } + + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { + CHECK(convolution_dimension_numbers_ != nullptr); + return *convolution_dimension_numbers_; + } + + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + convolution_dimension_numbers_ = + MakeUnique(dnums); + } + const string& custom_call_target() const { return custom_call_target_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + // Name of a global symbol to call, only present for kCustomCall. + string custom_call_target_; + // Describes the window in a windowed operation such as convolution. + std::unique_ptr window_; + // Describes the dimension numbers used for a convolution. + std::unique_ptr convolution_dimension_numbers_; +}; + +class HloHostComputeInstruction : public HloInstruction { + public: + explicit HloHostComputeInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns); + // Returns the channel name associated with the instruction. The name is + // used to identify host Send/Recv operations. + const string& channel_name() const { return channel_name_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + // Name to use for host send/recv channels. + string channel_name_; + // Estimate of the duration of a host computation in nanoseconds. + int64 cost_estimate_ns_ = 0; +}; + +class HloPadInstruction : public HloInstruction { + public: + explicit HloPadInstruction(const Shape& shape, HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config); + // Returns the padding configuration for a pad node. + const PaddingConfig& padding_config() const { return padding_config_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The padding configuration that describes the edge padding and interior + // padding of this pad instruction. + PaddingConfig padding_config_; +}; + +class HloDynamicSliceInstruction : public HloInstruction { + public: + explicit HloDynamicSliceInstruction( + const Shape& shape, HloInstruction* operand, + HloInstruction* start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + // Old methods kept for smooth subclassing transition END. + // Returns the size of the slice in the given dimension for a dynamic + // slice node. + int64 slice_sizes(int64 dimension) const { + return dynamic_slice_sizes_[dimension]; + } + const std::vector& dynamic_slice_sizes() const { + return dynamic_slice_sizes_; + } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Describes the [start, start + size) range size for a dynamic slice + // ('start' is specified dynamically in the second operand of the operation). + std::vector dynamic_slice_sizes_; +}; } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTIONS_H_ diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index c570b420c21fed4d7828feb24ee5c7859db94a79..b57c940238f0672692e3b65827f43e2f5499502d 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -187,6 +187,7 @@ HLO_MATCHER(Exp); HLO_MATCHER(Floor); HLO_MATCHER(Fusion); HLO_MATCHER(Ge); +HLO_MATCHER(AfterAll); HLO_MATCHER(Gt); HLO_MATCHER(Infeed); HLO_MATCHER(IsFinite); @@ -195,6 +196,7 @@ HLO_MATCHER(Log); HLO_MATCHER(And); HLO_MATCHER(Not); HLO_MATCHER(Or); +HLO_MATCHER(Xor); HLO_MATCHER(Lt); HLO_MATCHER(Map); HLO_MATCHER(Maximum); diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index e63424c2dfb6c7b9e71e4cede896a8f6609fea62..39bc25ba42c2cb6a9f77e2726405311ba13b3edc 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -32,15 +32,6 @@ limitations under the License. namespace xla { -HloModule::HloModule(const string& name, - const VersionedComputationHandle& entry_computation_handle, - const HloModuleConfig& config) - : name_(NameUniquer::GetSanitizedName(name)), - config_(config), - has_entry_computation_handle_(true), - entry_computation_handle_(entry_computation_handle), - unique_id_(next_unique_module_id_++) {} - HloModule::HloModule(const string& name, const HloModuleConfig& config) : name_(NameUniquer::GetSanitizedName(name)), config_(config), @@ -67,7 +58,7 @@ HloComputation* HloModule::AddComputationInternal( // If the module configuration has no entry layout computation set, create a // default one based on the program shape. - if (!config_.has_host_entry_computation_layout()) { + if (!config_.has_entry_computation_layout()) { config_.SetDefaultComputationLayout( entry_computation_->ComputeProgramShape()); } @@ -234,21 +225,17 @@ HloModuleProto HloModule::ToProto() const { /* static */ StatusOr> HloModule::CreateFromProto( - const HloModuleProto& proto, const HloModuleConfig& module_config, - const VersionedComputationHandle& entry_computation_handle) { + const HloModuleProto& proto, const HloModuleConfig& module_config) { // The ProgramShape in the passed in module config must match the shapes of // the entry parameters and root. TF_RET_CHECK(proto.has_program_shape()) << "No program shape found in the proto"; const auto& expected_program_shape = proto.program_shape(); - TF_RET_CHECK( - expected_program_shape.parameters_size() == - module_config.device_entry_computation_layout().parameter_count()); + TF_RET_CHECK(expected_program_shape.parameters_size() == + module_config.entry_computation_layout().parameter_count()); for (int i = 0; i < expected_program_shape.parameters_size(); ++i) { const Shape& parameter_shape = - module_config.device_entry_computation_layout() - .parameter_layout(i) - .shape(); + module_config.entry_computation_layout().parameter_layout(i).shape(); TF_RET_CHECK(ShapeUtil::Compatible(expected_program_shape.parameters(i), parameter_shape)) << "HloModuleConfig has different shape for parameter " << i @@ -258,7 +245,7 @@ StatusOr> HloModule::CreateFromProto( << ", actual: " << ShapeUtil::HumanStringWithLayout(parameter_shape); } const Shape& result_shape = - module_config.device_entry_computation_layout().result_layout().shape(); + module_config.entry_computation_layout().result_layout().shape(); TF_RET_CHECK( ShapeUtil::Compatible(expected_program_shape.result(), result_shape)) << "HloModuleConfig has different result shape than the HLO module. " @@ -287,8 +274,7 @@ StatusOr> HloModule::CreateFromProto( } TF_RET_CHECK(entry != nullptr); - auto module = MakeUnique(proto.name(), entry_computation_handle, - module_config); + auto module = MakeUnique(proto.name(), module_config); // Sort the computations in the proto id's order. std::sort(computations.begin(), computations.end(), @@ -338,7 +324,7 @@ StatusOr HloModule::CreateModuleConfigFromProto( // The module config is constructed with default layouts regardless of what is // passed in via the ProgramShape. Set the layouts to the appropriate values. ComputationLayout* entry_layout = - module_config.mutable_host_entry_computation_layout(); + module_config.mutable_entry_computation_layout(); for (int64 i = 0; i < entry_layout->parameter_count(); ++i) { TF_RETURN_IF_ERROR( entry_layout->mutable_parameter_layout(i)->CopyLayoutFromShape( @@ -346,9 +332,6 @@ StatusOr HloModule::CreateModuleConfigFromProto( } TF_RETURN_IF_ERROR(entry_layout->mutable_result_layout()->CopyLayoutFromShape( program_shape.result())); - *module_config.mutable_device_entry_computation_layout() = - module_config.host_entry_computation_layout(); - return module_config; } @@ -401,7 +384,7 @@ HloInstruction* HloModule::OutlineExpressionFromComputation( // as a parameter in the new function. arguments.push_back(old_operand); *operand_slot = builder.AddInstruction(HloInstruction::CreateParameter( - parameter_count, old_operand->shape(), "")); + parameter_count, old_operand->shape(), "p")); ++parameter_count; } TF_CHECK_OK( @@ -462,7 +445,7 @@ int64 HloModule::instruction_count() const { return n; } -std::list HloModule::MakeComputationPostOrder() const { +std::vector HloModule::MakeComputationPostOrder() const { // First determine all root computations by building a set of nonroot // computations (computations which are called by an instruction in the // module). @@ -480,7 +463,7 @@ std::list HloModule::MakeComputationPostOrder() const { // order. This prevents duplication as an embedded computation may be called // from two different root computations. std::set added_computations; - std::list post_order; + std::vector post_order; for (auto& computation : computations_) { if (nonroot_computations.count(computation.get()) == 0) { for (HloComputation* embedded_computation : @@ -525,8 +508,6 @@ std::vector HloModule::MakeNonfusionComputations() const { std::unique_ptr HloModule::Clone(const string& suffix) const { VLOG(1) << "Cloning module :" << name_ << " --> " << suffix << "\n"; auto module = MakeUnique(name_ + "-" + suffix, config_); - module->entry_computation_handle_ = entry_computation_handle_; - module->has_entry_computation_handle_ = has_entry_computation_handle_; HloCloneContext context(module.get(), suffix); auto cloned_computation = entry_computation_->Clone(suffix, &context); diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index c93c74d34a95cfbb3d0d334fb1c1f40a5aad69e9..d2e726a0db63f622cd5092d56b4f746232d04aad 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -31,7 +31,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module_config.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -57,10 +56,6 @@ namespace xla { // attached to. class HloModule { public: - HloModule(const string& name, - const VersionedComputationHandle& entry_computation_handle, - const HloModuleConfig& config); - // Constructor without a versioned computation handle. This constructor should // only be used for HloModules used outside of the XLA service (eg // tests). The versioned handle is used by the service in the compilation @@ -110,24 +105,19 @@ class HloModule { return entry_computation_; } - ComputationLayout* mutable_host_entry_computation_layout() { - return config_.mutable_host_entry_computation_layout(); - } - - const ComputationLayout& host_entry_computation_layout() const { - return config_.host_entry_computation_layout(); + // Creates the ComputationLayout which describes the current status of the HLO + // module entry computation. + ComputationLayout compute_computation_layout() const { + return ComputationLayout(entry_computation()->ComputeProgramShape(), + /*ignore_layouts=*/false); } - ComputationLayout* mutable_device_entry_computation_layout() { - return config_.mutable_device_entry_computation_layout(); + ComputationLayout* mutable_entry_computation_layout() { + return config_.mutable_entry_computation_layout(); } - const ComputationLayout& device_entry_computation_layout() const { - return config_.device_entry_computation_layout(); - } - - const VersionedComputationHandle& entry_computation_handle() const { - return entry_computation_handle_; + const ComputationLayout& entry_computation_layout() const { + return config_.entry_computation_layout(); } // Gets the computations in this module. @@ -163,7 +153,7 @@ class HloModule { // Compute and return a post order of all computations in the module. The sort // is defined like so: if computation A has an instruction which calls // computation B, then A will appear after B in the sort. - std::list MakeComputationPostOrder() const; + std::vector MakeComputationPostOrder() const; // Gets the computations in this module which aren't for fusion nodes. // @@ -188,9 +178,7 @@ class HloModule { // Convert an HloModule to or from a proto. HloModuleProto ToProto() const; static StatusOr> CreateFromProto( - const HloModuleProto& proto, const HloModuleConfig& module_config, - const VersionedComputationHandle& entry_computation_handle = - VersionedComputationHandle()); + const HloModuleProto& proto, const HloModuleConfig& module_config); // Creates and returns an HloModuleConfig with an appropriate program shape // for the HLO module in the given proto. @@ -264,10 +252,6 @@ class HloModule { mutable std::mt19937_64 rng_{42}; mutable tensorflow::mutex rng_mutex_; - // Versioned handle of the entry computation of the module. - bool has_entry_computation_handle_ = false; - VersionedComputationHandle entry_computation_handle_; - // Unique name generator for computation and instruction names, which are // unique per module. NameUniquer computation_name_uniquer_{/*separator=*/"."}; diff --git a/tensorflow/compiler/xla/service/hlo_module_config.cc b/tensorflow/compiler/xla/service/hlo_module_config.cc index dae5578a3158fecb8219e518841dec1020b2ca98..07a8c798dbee072db3b75d5e99ca0dcabb5fdf6b 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.cc +++ b/tensorflow/compiler/xla/service/hlo_module_config.cc @@ -28,16 +28,14 @@ namespace xla { using tensorflow::strings::StrAppend; -HloModuleConfig::HloModuleConfig() {} - -HloModuleConfig::HloModuleConfig(const ProgramShape& program_shape) - : host_entry_computation_layout_(program_shape), - device_entry_computation_layout_(program_shape) {} +HloModuleConfig::HloModuleConfig(const ProgramShape& program_shape, + bool ignore_layouts) + : entry_computation_layout_( + ComputationLayout(program_shape, ignore_layouts)) {} void HloModuleConfig::SetDefaultComputationLayout( const ProgramShape& program_shape) { - host_entry_computation_layout_ = ComputationLayout(program_shape); - device_entry_computation_layout_ = ComputationLayout(program_shape); + entry_computation_layout_ = ComputationLayout(program_shape); } string HloModuleConfig::compilation_cache_key() const { @@ -46,18 +44,11 @@ string HloModuleConfig::compilation_cache_key() const { StrAppend(&key, "::("); std::vector params; for (const ShapeLayout& param_layout : - host_entry_computation_layout_->parameter_layouts()) { + entry_computation_layout_->parameter_layouts()) { params.push_back(param_layout.shape().DebugString()); } StrAppend(&key, tensorflow::str_util::Join(params, ", "), ") => ", - host_entry_computation_layout_->result_shape().SerializeAsString()); - for (const ShapeLayout& param_layout : - device_entry_computation_layout_->parameter_layouts()) { - params.push_back(param_layout.shape().DebugString()); - } - StrAppend( - &key, tensorflow::str_util::Join(params, ", "), ") => ", - device_entry_computation_layout_->result_shape().SerializeAsString()); + entry_computation_layout_->result_shape().SerializeAsString()); if (seed() != 0) { // TODO(b/32083678): force recompilation to reset global state. static std::atomic counter{0}; diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h index cdb0b29a2399b387bc617262032e9083ba079625..074e9c90705d432b8344aebaf3c15aeb41a59fa3 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.h +++ b/tensorflow/compiler/xla/service/hlo_module_config.h @@ -37,48 +37,34 @@ class HloModuleConfig { // ComputationLayout. The default ctor creates it without -- in this case // accessing entry_computation_layout will CHECK-fail. The ctor accepting a // ProgramShape creates a computation layout using this shape. - HloModuleConfig(); - explicit HloModuleConfig(const ProgramShape& program_shape); + // The layouts in the ProgramShape will be reset to default unless + // ignore_layouts is set to false. + HloModuleConfig() = default; - // Checks if this config has an entry computation layout already. - bool has_host_entry_computation_layout() const { - return host_entry_computation_layout_.has_value(); - } + explicit HloModuleConfig(const ProgramShape& program_shape, + bool ignore_layouts = true); - bool has_device_entry_computation_layout() const { - return device_entry_computation_layout_.has_value(); + // Checks if this config has an entry computation layout already. + bool has_entry_computation_layout() const { + return entry_computation_layout_.has_value(); } // Sets the entry computation layout for this config. If the entry computation // layout already exists, it is silently replaced. void SetDefaultComputationLayout(const ProgramShape& program_shape); - // Returns a constant reference to the on-host layout of the entry - // computation. Assumes the layout was set. - const ComputationLayout& host_entry_computation_layout() const { - CHECK(host_entry_computation_layout_.has_value()); - return *host_entry_computation_layout_; - } - - // Returns a mutable pointer to the layout of the on-host entry computation. + // Returns a constant reference to the layout of the entry computation. // Assumes the layout was set. - ComputationLayout* mutable_host_entry_computation_layout() { - CHECK(host_entry_computation_layout_.has_value()); - return &(*host_entry_computation_layout_); - } - - // Returns a constant reference to the on-device layout of the entry - // computation. Assumes the layout was set. - const ComputationLayout& device_entry_computation_layout() const { - CHECK(device_entry_computation_layout_.has_value()); - return *device_entry_computation_layout_; + const ComputationLayout& entry_computation_layout() const { + CHECK(entry_computation_layout_.has_value()); + return *entry_computation_layout_; } - // Returns a mutable pointer to the layout of the on-device entry computation. + // Returns a mutable pointer to the layout of the entry computation. // Assumes the layout was set. - ComputationLayout* mutable_device_entry_computation_layout() { - CHECK(device_entry_computation_layout_.has_value()); - return &(*device_entry_computation_layout_); + ComputationLayout* mutable_entry_computation_layout() { + CHECK(entry_computation_layout_.has_value()); + return &(*entry_computation_layout_); } // Returns whether to enable HLO-level profiling. @@ -127,8 +113,7 @@ class HloModuleConfig { private: // If you add new members, be sure to update compilation_cache_key. - tensorflow::gtl::optional host_entry_computation_layout_; - tensorflow::gtl::optional device_entry_computation_layout_; + tensorflow::gtl::optional entry_computation_layout_; // Whether this is a 'host module'. bool is_host_module_ = false; diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc index 4f1715e4cafd1a7a2d8626dc3ad386813e5c2d76..bf33640db16638803f4f8e6c66f35d6bb6e2c9fe 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -127,9 +127,14 @@ Status HloModuleGroupMetadata::VerifyCompanionSets() const { for (HloInstruction* instruction : *companions) { // Go through all the communicating instructions (send, recv) of the given // companion, and record their device. + auto it = tracked_instructions_comms_.find(instruction); + if (it == tracked_instructions_comms_.end()) { + // Companions can be added even if they have no communicating + // instructions, if they are parent of companions. + continue; + } std::unordered_set comm_devices; - for (HloInstruction* comm_instruction : - tracked_instructions_comms_.at(instruction)) { + for (HloInstruction* comm_instruction : it->second) { auto device = GetInstructionDevice(*comm_instruction); TF_RET_CHECK(device) << "Instruction " << comm_instruction->ToString() << " does not have a device"; diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc index 5a0d1e264eb5095ff53721416ebcf4842a063f97..21a9b7291acc9e0066a9061facd13ab5acbf0bac 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_util.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -277,7 +277,7 @@ Status HloModuleGroupUtil::VerifyComputations( StatusOr> HloModuleGroupUtil::ComputeReachability( tensorflow::gtl::ArraySlice computations) { - std::list post_order; + std::vector post_order; auto visit_function = [&](HloInstruction* instruction, const std::vector& instruction_group) { diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h index 1fe06ee0c0d14255b8358fb998bfd8d0b029506f..05e47a698f3b1d6345b183fb88b588a413063595 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.h +++ b/tensorflow/compiler/xla/service/hlo_opcode.h @@ -81,6 +81,7 @@ namespace xla { V(kFusion, "fusion", kHloOpcodeIsVariadic) \ V(kGather, "gather") \ V(kGe, "greater-than-or-equal-to", kHloOpcodeIsComparison) \ + V(kAfterAll, "after-all", kHloOpcodeIsVariadic) \ V(kGetTupleElement, "get-tuple-element") \ V(kGt, "greater-than", kHloOpcodeIsComparison) \ V(kHostCompute, "host-compute") \ @@ -93,6 +94,7 @@ namespace xla { V(kAnd, "and") \ V(kNot, "not") \ V(kOr, "or") \ + V(kXor, "xor") \ V(kLt, "less-than", kHloOpcodeIsComparison) \ V(kMap, "map", kHloOpcodeIsVariadic) \ V(kMaximum, "maximum") \ diff --git a/tensorflow/compiler/xla/service/hlo_opcode_test.cc b/tensorflow/compiler/xla/service/hlo_opcode_test.cc index cd2ce5c69f030c65b889d67e082a3677b8739ddb..6f3f83f63a05fafaa3f3ddcff8a7cac7cb7b06d5 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode_test.cc +++ b/tensorflow/compiler/xla/service/hlo_opcode_test.cc @@ -58,6 +58,7 @@ TEST(HloOpcodeTest, OpcodeProperties) { case HloOpcode::kConcatenate: case HloOpcode::kFusion: case HloOpcode::kMap: + case HloOpcode::kAfterAll: case HloOpcode::kTuple: EXPECT_TRUE(HloOpcodeIsVariadic(opcode)); break; diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc index dcd4725fe78e8b9b5d14437e964cb5aaf1664117..6c1e015f77a62c3e3ff7ffa5ce9dea735f46e10a 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering.cc @@ -232,6 +232,11 @@ bool HloOrdering::UseIsBeforeValueDefinition( << " and def is in FALSE computation"; return true; } + if (value.defining_instruction() == use.instruction) { + VLOG(4) << " use is conditional " << use << " and def is " + << value.ToShortString(); + return true; + } } VLOG(4) << " use is not before value"; diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h index ee526d8dd7f7e81b3a846741d3e452935f486bd2..985f3fa64d8767b0c0063ee900f7d11c3b7f6d4a 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.h +++ b/tensorflow/compiler/xla/service/hlo_ordering.h @@ -183,6 +183,10 @@ class DependencyHloOrdering : public PredecessorHloOrdering { // interference is reduced relative to DependencyHloOrdering. class SequentialHloOrdering : public HloOrdering { public: + // TODO(dimvar): HloModuleSequence is not a good name because it sounds like + // a sequence of modules, instead of a map of schedules for all computations + // in a module. We should change it at some point. + // // A sequence of instructions for each computation in the module. using HloModuleSequence = tensorflow::gtl::FlatMapnum_parameters(); p++) { const Shape& param_shape = computation->parameter_instruction(p)->shape(); - TF_CHECK_OK(module_->mutable_host_entry_computation_layout() - ->mutable_parameter_layout(p) - ->CopyLayoutFromShape(param_shape)); - TF_CHECK_OK(module_->mutable_device_entry_computation_layout() + TF_CHECK_OK(module_->mutable_entry_computation_layout() ->mutable_parameter_layout(p) ->CopyLayoutFromShape(param_shape)); } const Shape& result_shape = computation->root_instruction()->shape(); - TF_CHECK_OK(module_->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->CopyLayoutFromShape(result_shape)); - TF_CHECK_OK(module_->mutable_device_entry_computation_layout() + TF_CHECK_OK(module_->mutable_entry_computation_layout() ->mutable_result_layout() ->CopyLayoutFromShape(result_shape)); } - return true; } @@ -516,7 +509,6 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, case HloOpcode::kReal: case HloOpcode::kSign: case HloOpcode::kSin: - case HloOpcode::kSort: case HloOpcode::kTanh: { if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { @@ -545,6 +537,7 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, case HloOpcode::kRemainder: case HloOpcode::kAnd: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kShiftLeft: case HloOpcode::kShiftRightArithmetic: case HloOpcode::kShiftRightLogical: { @@ -588,13 +581,30 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, } case HloOpcode::kCrossReplicaSum: { optional to_apply; + optional> replica_group_ids; + optional barrier; + optional all_reduce_id; attrs["to_apply"] = {/*required=*/true, AttrTy::kHloComputation, &to_apply}; + attrs["replica_group_ids"] = { + /*required=*/false, AttrTy::kBracedInt64List, &replica_group_ids}; + attrs["barrier"] = {/*required=*/false, AttrTy::kString, &barrier}; + attrs["all_reduce_id"] = {/*required=*/false, AttrTy::kInt64, + &all_reduce_id}; if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } - instruction = builder->AddInstruction( - HloInstruction::CreateCrossReplicaSum(shape, operands, *to_apply)); + if (replica_group_ids) { + instruction = + builder->AddInstruction(HloInstruction::CreateCrossReplicaSum( + shape, operands, *to_apply, *replica_group_ids, + barrier ? *barrier : "", all_reduce_id)); + } else { + instruction = + builder->AddInstruction(HloInstruction::CreateCrossReplicaSum( + shape, operands, *to_apply, {}, barrier ? *barrier : "", + all_reduce_id)); + } break; } case HloOpcode::kReshape: { @@ -606,6 +616,35 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloInstruction::CreateReshape(shape, operands[0])); break; } + case HloOpcode::kAfterAll: { + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { + return false; + } + instruction = + builder->AddInstruction(HloInstruction::CreateAfterAll(operands)); + break; + } + case HloOpcode::kSort: { + auto loc = lexer_.GetLoc(); + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { + return false; + } + switch (operands.size()) { + case 1: + instruction = builder->AddInstruction( + HloInstruction::CreateSort(shape, /*keys=*/operands[0])); + break; + case 2: + instruction = builder->AddInstruction(HloInstruction::CreateSort( + shape, + /*keys=*/operands[0], /*values=*/operands[1])); + break; + default: + return Error(loc, StrCat("expects either 1 or 2 operands, but has ", + operands.size(), " operands")); + } + break; + } case HloOpcode::kTuple: { if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; @@ -631,12 +670,12 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, case HloOpcode::kRecv: { optional channel_id; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; - if (!ParseOperands(&operands, /*expected_size=*/0) || + if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { return false; } - instruction = builder->AddInstruction( - HloInstruction::CreateRecv(shape.tuple_shapes(0), *channel_id)); + instruction = builder->AddInstruction(HloInstruction::CreateRecv( + shape.tuple_shapes(0), operands[0], *channel_id)); break; } case HloOpcode::kRecvDone: { @@ -656,12 +695,12 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, case HloOpcode::kSend: { optional channel_id; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; - if (!ParseOperands(&operands, /*expected_size=*/1) || + if (!ParseOperands(&operands, /*expected_size=*/2) || !ParseAttributes(attrs)) { return false; } instruction = builder->AddInstruction( - HloInstruction::CreateSend(operands[0], *channel_id)); + HloInstruction::CreateSend(operands[0], operands[1], *channel_id)); break; } case HloOpcode::kSendDone: { @@ -777,6 +816,9 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, optional to_apply; attrs["to_apply"] = {/*required=*/true, AttrTy::kHloComputation, &to_apply}; + optional> dimensions; + attrs["dimensions"] = {/*required=*/false, AttrTy::kBracedInt64List, + &dimensions}; if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } @@ -956,23 +998,53 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, case HloOpcode::kInfeed: { optional config; attrs["infeed_config"] = {/*required=*/false, AttrTy::kString, &config}; - if (!ParseOperands(&operands, /*expected_size=*/0) || - !ParseAttributes(attrs)) { + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } - instruction = builder->AddInstruction( - HloInstruction::CreateInfeed(shape, config ? *config : "")); + // We need to know the infeed data shape to construct the infeed + // instruction. This is the zero-th element of the tuple-shaped output of + // the infeed instruction. ShapeUtil::GetTupleElementShape will check fail + // if the shape is not a non-empty tuple, so add guard so an error message + // can be emitted instead of a check fail + if (!ShapeUtil::IsTuple(shape) && !ShapeUtil::IsEmptyTuple(shape)) { + return Error(lexer_.GetLoc(), + "infeed must have a non-empty tuple shape"); + } + + if (operands.empty()) { + // TODO(b/80000000): Remove this when all uses of infeed are + // converted to take tokens. + instruction = builder->AddInstruction(HloInstruction::CreateInfeed( + ShapeUtil::GetTupleElementShape(shape, 0), config ? *config : "")); + } else if (operands.size() == 1) { + instruction = builder->AddInstruction(HloInstruction::CreateInfeed( + ShapeUtil::GetTupleElementShape(shape, 0), operands[0], + config ? *config : "")); + } else { + return Error(lexer_.GetLoc(), + "infeed must have exactly zero or one operands"); + } break; } case HloOpcode::kOutfeed: { optional config; attrs["outfeed_config"] = {/*required=*/false, AttrTy::kString, &config}; - if (!ParseOperands(&operands, /*expected_size=*/1) || - !ParseAttributes(attrs)) { + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } - instruction = builder->AddInstruction(HloInstruction::CreateOutfeed( - operands[0]->shape(), operands[0], config ? *config : "")); + if (operands.size() == 1) { + // TODO(b/80000000): Remove this when all uses of outfeed are + // converted to take tokens. + instruction = builder->AddInstruction(HloInstruction::CreateOutfeed( + operands[0]->shape(), operands[0], config ? *config : "")); + } else if (operands.size() == 2) { + instruction = builder->AddInstruction( + HloInstruction::CreateOutfeed(operands[0]->shape(), operands[0], + operands[1], config ? *config : "")); + } else { + return Error(lexer_.GetLoc(), + "outfeed must have exactly one or two operands"); + } break; } case HloOpcode::kRng: { @@ -1137,7 +1209,12 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloOpcodeString(opcode))); } - instruction->set_name(name); + instruction->SetAndSanitizeName(name); + if (instruction->name() != name) { + return Error(name_loc, + StrCat("illegal instruction name: ", name, + "; suggest renaming to: ", instruction->name())); + } // Add shared attributes like metadata to the instruction, if they were seen. if (sharding) { diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index 08068dc5042d58abe5ca97a4eac91afe2040015b..f40cd609079ba52d69acc4102d5d8187eec9d30e 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -278,10 +278,11 @@ ENTRY %WhileWithScalarS32Result.v2 () -> s32[] { R"(HloModule TwoSendRecvBothWayRecvFist_module ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> f32[] { - %recv = (f32[], u32[]) recv(), channel_id=15, sharding={maximal device=1} + %token = token[] after-all() + %recv = (f32[], u32[]) recv(token[] %token), channel_id=15, sharding={maximal device=1} ROOT %recv-done = f32[] recv-done((f32[], u32[]) %recv), channel_id=15, sharding={maximal device=1} %constant = f32[] constant(2.1), sharding={maximal device=0} - %send = (f32[], u32[]) send(f32[] %constant), channel_id=16, sharding={maximal device=0}, control-predecessors={%recv} + %send = (f32[], u32[]) send(f32[] %constant, token[] %token), channel_id=16, sharding={maximal device=0}, control-predecessors={%recv} %send-done = () send-done((f32[], u32[]) %send), channel_id=16, sharding={maximal device=0} } @@ -765,7 +766,7 @@ add_F32.v3 { ENTRY MapBinaryAdder.v3 { param0 = f32[4]{0} parameter(0) param1 = f32[4]{0} parameter(1) - ROOT map = f32[4]{0} map(param0, param1), to_apply=add_F32.v3 + ROOT map = f32[4]{0} map(param0, param1), dimensions={0}, to_apply=add_F32.v3 } )" @@ -795,10 +796,14 @@ ENTRY ReduceR3ToR2.v3 { R"(HloModule outfeed_module ENTRY InfeedToOutfeed { - infeed = (u32[3]{0}, pred[]) infeed() - outfeed = () outfeed(infeed) - ROOT infeed.1 = (u32[3]{0}, pred[]) infeed() - outfeed.1 = () outfeed(infeed.1) + token = token[] after-all() + infeed = ((u32[3]{0}, pred[]), token[]) infeed(token) + infeed.data = (u32[3]{0}, pred[]) get-tuple-element(infeed), index=0 + outfeed = token[] outfeed(infeed.data, token) + ROOT infeed.1 = ((u32[3]{0}, pred[]), token[]) infeed(token) + infeed.1.data = (u32[3]{0}, pred[]) get-tuple-element(infeed.1), index=0 + infeed.1.token = token[] get-tuple-element(infeed.1), index=1 + outfeed.1 = token[] outfeed(infeed.1.data, infeed.1.token) } )" @@ -826,6 +831,31 @@ ENTRY ReducePrecision { ROOT reduce-precision = f32[1]{0} reduce-precision(constant), exponent_bits=8, mantissa_bits=10 } +)" +}, +// Sort (Key) +{ +"SortKey", +R"(HloModule sort + +ENTRY Sort { + x = f32[1024]{0} parameter(0) + ROOT sorted = f32[1024]{0} sort(x) +} + +)" +}, +// Sort (Key, Value) +{ +"SortKeyValue", +R"(HloModule sort + +ENTRY Sort { + keys = f32[1024]{0} parameter(0) + values = s32[1024]{0} parameter(1) + ROOT sorted = (f32[1024]{0}, s32[1024]{0}) sort(keys, values) +} + )" }, // Conditional @@ -913,11 +943,29 @@ add { ENTRY CRS { input = f32[8]{0} parameter(0) - ROOT crs = f32[8]{0} cross-replica-sum(input), to_apply=add + ROOT crs = f32[8]{0} cross-replica-sum(input), replica_group_ids={}, to_apply=add } )" }, +// cross-replica-sum with subgroups +{ +"CrossReplicaSumWithSubgroups", +R"(HloModule CRS_Subgroups + +add { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(lhs, rhs) +} + +ENTRY CrossReplicaSumWithSubgroups { + input = f32[128,32]{0,1} parameter(0) + ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), replica_group_ids={0,0,1,1}, barrier="abc", to_apply=add +} + +)" +} }); // clang-format on } @@ -1174,10 +1222,11 @@ TEST_F(HloParserTest, UnexpectedAttribute) { const string original = R"(HloModule unexpected_attr_module ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> f32[] { - %recv = (f32[], u32[]) recv(), channel_id=15 + %token = token[] after-all() + %recv = (f32[], u32[]) recv(token[] %token), channel_id=15 %recv-done = f32[] recv-done((f32[], u32[]) %recv), channel_id=15 ROOT %constant = f32[] constant(2.1) - %send = (f32[], u32[]) send(f32[] %constant), channel_id=16, calls=%recv + %send = (f32[], u32[]) send(f32[] %constant, token[] %token), channel_id=16, calls=%recv %send-done = () send-done((f32[], u32[]) %send), channel_id=16 } @@ -1190,10 +1239,11 @@ TEST_F(HloParserTest, MissingAttribute) { const string original = R"(HloModule missing_attr_module ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> f32[] { - %recv = (f32[], u32[]) recv(), channel_id=15 + %token = token[] after-all() + %recv = (f32[], u32[]) recv(token[] %token), channel_id=15 %recv-done = f32[] recv-done((f32[], u32[]) %recv), channel_id=15 ROOT %constant = f32[] constant(-2.1) - %send = (f32[], u32[]) send(f32[] %constant) + %send = (f32[], u32[]) send(f32[] %constant, token[] %token) %send-done = () send-done((f32[], u32[]) %send), channel_id=16 } @@ -1206,10 +1256,11 @@ TEST_F(HloParserTest, PredecessorUndefined) { const string original = R"(HloModule pre_not_found_module ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> f32[] { - %recv = (f32[], u32[]) recv(), channel_id=15 + %token = token[] after-all() + %recv = (f32[], u32[]) recv(token[] %token), channel_id=15 %recv-done = f32[] recv-done((f32[], u32[]) %recv), channel_id=15 ROOT %constant = f32[] constant(2.1) - %send = (f32[], u32[]) send(f32[] %constant), channel_id=16, control-predecessors={%done} + %send = (f32[], u32[]) send(f32[] %constant, token[] %token), channel_id=16, control-predecessors={%done} %send-done = () send-done((f32[], u32[]) %send), channel_id=16 } @@ -1284,7 +1335,7 @@ ENTRY %Reduce (input: f32[8,16,256]) -> f32[8,16] { auto module = ParseHloString(original); TF_ASSERT_OK(module.status()); - auto program_layout = module.ValueOrDie()->host_entry_computation_layout(); + auto program_layout = module.ValueOrDie()->entry_computation_layout(); ASSERT_EQ(program_layout.parameter_count(), 1); auto param_layout = program_layout.parameter_layout(0).layout(); auto result_layout = program_layout.result_layout().layout(); @@ -1400,5 +1451,15 @@ TEST_F(HloParserTest, ParseConvolutionDimensionNumbers) { EXPECT_EQ(original, ConvolutionDimensionNumbersToString(dnums)); } +TEST_F(HloParserTest, NontupleInfeed) { + const string original = R"(HloModule nontuple_infeed: +ENTRY nontuple_infeed { + token = token[] after-all() + ROOT infeed = pred[] infeed(token) +})"; + ExpectHasSubstr(ParseHloString(original).status().error_message(), + "infeed must have a non-empty tuple shape"); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_query.cc b/tensorflow/compiler/xla/service/hlo_query.cc index d45038f1f4a2e4aa19234eec93fdc9a068a902e1..2418c19f3de7b036d7ef52d3a6db11de6316203b 100644 --- a/tensorflow/compiler/xla/service/hlo_query.cc +++ b/tensorflow/compiler/xla/service/hlo_query.cc @@ -61,7 +61,7 @@ bool AllOperandsAreConstants(const HloInstruction& instruction) { } HloInstruction* GetMatchingOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction) { for (HloInstruction* op : instruction->operands()) { if (matcher(op)) { @@ -72,7 +72,7 @@ HloInstruction* GetMatchingOperand( } bool MatchBinaryInstructionOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction, HloInstruction** matching_operand, HloInstruction** other_operand) { CHECK_EQ(instruction->operand_count(), 2); diff --git a/tensorflow/compiler/xla/service/hlo_query.h b/tensorflow/compiler/xla/service/hlo_query.h index c79347bbf9d6146943b7b787f713369cb37fadee..c0826a6aee1f693484207a86ec258c6604d92318 100644 --- a/tensorflow/compiler/xla/service/hlo_query.h +++ b/tensorflow/compiler/xla/service/hlo_query.h @@ -45,7 +45,7 @@ bool IsScalarConstant(const HloInstruction* instruction); // multiple matching operands, then the first matching operand is returned. If // there are no matching operands then nullptr is returned. HloInstruction* GetMatchingOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction); // Returns whether a binary instruction has a matching operand. Sets @@ -53,7 +53,7 @@ HloInstruction* GetMatchingOperand( // other_operand. Note: in the case where both operands match, the first operand // of the instruction is returned. bool MatchBinaryInstructionOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction, HloInstruction** matching_operand, HloInstruction** other_operand); diff --git a/tensorflow/compiler/xla/service/hlo_reachability.cc b/tensorflow/compiler/xla/service/hlo_reachability.cc index 4738e46f8aeb96a4c25d04b3246bd21f644fe3ea..01b088a957554821e65db7bf9cedf334db49728f 100644 --- a/tensorflow/compiler/xla/service/hlo_reachability.cc +++ b/tensorflow/compiler/xla/service/hlo_reachability.cc @@ -18,7 +18,7 @@ limitations under the License. namespace xla { HloReachabilityMap::HloReachabilityMap( - const std::list& instructions) + tensorflow::gtl::ArraySlice instructions) : size_(instructions.size()) { bit_vectors_.reserve(size_); for (const HloInstruction* hlo : instructions) { diff --git a/tensorflow/compiler/xla/service/hlo_reachability.h b/tensorflow/compiler/xla/service/hlo_reachability.h index 69bb2b3cee6dafe058c45b4e74e93401bea2cfc9..48215d32a8284919cce6beb1663e6a723eefc1c4 100644 --- a/tensorflow/compiler/xla/service/hlo_reachability.h +++ b/tensorflow/compiler/xla/service/hlo_reachability.h @@ -41,7 +41,8 @@ class HloReachabilityMap { public: // Sets up a graph with no edges and where the nodes correspond to the given // instructions. - explicit HloReachabilityMap(const std::list& instructions); + explicit HloReachabilityMap( + tensorflow::gtl::ArraySlice instructions); // Set the reachability set of 'instruction' to the union of the reachability // sets of 'inputs'. Upon return, IsReachable(x, instruction) where diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index bd1d9935bd37ff71064a1f8f431b2ddf9c7c789d..59a8800a7d6e9417c0e561db45341c912ad20464 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/service/buffer_value.h" +#include "tensorflow/compiler/xla/service/copy_insertion.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_dce.h" @@ -1201,7 +1202,8 @@ StatusOr HloRematerialization::RematerializeComputation( StatusOr HloRematerialization::Run( HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit_bytes, RematerializationSizes* sizes) { + int64 memory_limit_bytes, RematerializationSizes* sizes, + bool run_copy_elision) { // The sequence is constructed entirely by this method. TF_RET_CHECK(sequence->empty()); @@ -1230,12 +1232,21 @@ StatusOr HloRematerialization::Run( XLA_VLOG_LINES(3, "Before HloRematerialization:\n" + module->ToString()); // Create initial sequence of HLO instructions. - TF_ASSIGN_OR_RETURN(*sequence, CreateMemoryMinimizingSequence( + TF_ASSIGN_OR_RETURN(*sequence, ScheduleComputationsInModule( *module, [this](const BufferValue& buffer) { return size_function_(buffer.shape()); }, scheduler_algorithm_)); + if (run_copy_elision) { + // We run a separate pass of copy elision here because the sequential + // ordering from the HLO schedule allows for more copies to be eliminated. + // TODO(b/80249101): Instead of a separate copy elision pass, use the + // ordering from the HLO schedule directly for copy insertion. + SequentialHloOrdering ordering(module, *sequence); + TF_RETURN_IF_ERROR(RemoveUnnecessaryCopies(ordering, module)); + } + // Compute peak memory usage of all computations in the module called in a // sequential context. call_graph_ = CallGraph::Build(module); @@ -1338,9 +1349,10 @@ StatusOr HloRematerialization::Run( int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes) { + RematerializationSizes* sizes, bool run_copy_elision) { HloRematerialization remat(scheduler_algorithm, size_function); - return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes); + return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes, + run_copy_elision); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h index 2ee2dd0571ae8c6604e4ca722351fd48a913bda5..59b4cf5dcc761f70767ce4d7ff0959448f29939a 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.h +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h @@ -57,6 +57,12 @@ class HloRematerialization { // sizes: Optional outparam that indicates the peak memory usage of the HLO // module before/after rematerialization. // + // run_copy_elision: Enable copy elision. This pass is used to eliminate + // copies that were inserted before HLO scheduling. + // + // TODO(b/80249101): Remove the 'run_copy_elision' parameter when copy + // insertion is integrated with HLO scheduling. + // // Returns whether any instructions were rematerialized. If memory use is // already below the given limit then no instructions are rematerialized and // false is returned. @@ -68,7 +74,7 @@ class HloRematerialization { const ShapeSizeFunction& size_function, int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes = nullptr); + RematerializationSizes* sizes, bool run_copy_elision = true); protected: HloRematerialization(MemorySchedulerAlgorithm scheduler_algorithm, @@ -83,7 +89,8 @@ class HloRematerialization { // contains the memory-minimizing order in which to emit the HLO instructions. StatusOr Run(HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit, RematerializationSizes* sizes); + int64 memory_limit, RematerializationSizes* sizes, + bool run_copy_elision); // Rematerializes instructions within the given computation. 'order' is the // order in which the computation's instructions will be emitted in the diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc index 83de54f3fa56ee660b79d8c366dbc0b52f9fde87..7a46da6efe0df23129d56e16355cf66aceb68ffe 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" namespace xla { namespace { @@ -40,7 +41,8 @@ class HloRematerializationTest : public HloTestBase { // Creates and returns a computation which can benefit from // rematerialization. The computation looks like: // - // F32[] %param = {...} + // F32[1] %param = {...} + // F32[] %reshape = reshape(F32[], param) // F32[1024] %bcast = broadcast(%param) // F32[1024] %negate = negate(%bcast) // F32[2048] %concat_1 = concat({%negate, %negate}) @@ -57,9 +59,11 @@ class HloRematerializationTest : public HloTestBase { const string& suffix = "") { auto builder = HloComputation::Builder(TestName() + suffix); auto param = builder.AddInstruction( - HloInstruction::CreateParameter(0, scalar_shape_, "param")); + HloInstruction::CreateParameter(0, vec1_shape_, "param")); + auto reshape = builder.AddInstruction( + HloInstruction::CreateReshape(scalar_shape_, param)); auto bcast = builder.AddInstruction( - HloInstruction::CreateBroadcast(vec1024_shape_, param, {})); + HloInstruction::CreateBroadcast(vec1024_shape_, reshape, {})); auto negate = builder.AddInstruction( HloInstruction::CreateUnary(vec1024_shape_, HloOpcode::kNegate, bcast)); auto concat_1 = builder.AddInstruction(HloInstruction::CreateConcatenate( @@ -100,9 +104,11 @@ class HloRematerializationTest : public HloTestBase { const string& suffix = "") { auto builder = HloComputation::Builder(TestName() + suffix); auto param = builder.AddInstruction( - HloInstruction::CreateParameter(0, scalar_shape_, "param")); + HloInstruction::CreateParameter(0, vec1_shape_, "param")); + auto reshape = builder.AddInstruction( + HloInstruction::CreateReshape(scalar_shape_, param)); auto bcast = builder.AddInstruction( - HloInstruction::CreateBroadcast(vec1024_shape_, param, {})); + HloInstruction::CreateBroadcast(vec1024_shape_, reshape, {})); auto slice_1 = builder.AddInstruction( HloInstruction::CreateSlice(vec1_shape_, bcast, /*start_indices=*/{0}, /*limit_indices=*/{1}, @@ -135,6 +141,15 @@ class HloRematerializationTest : public HloTestBase { return ShapeUtil::ByteSizeOf(shape, sizeof(void*)); } + StatusOr RunHloRematerialization( + int64 memory_limit_bytes, HloModule* module, + SequentialHloOrdering::HloModuleSequence* sequence) { + TF_EXPECT_OK(verifier().Run(module).status()); + return HloRematerialization::RematerializeAndSchedule( + ByteSizeOf, memory_limit_bytes, module, DefaultMemoryScheduler, + sequence, /*sizes=*/nullptr, /*run_copy_elision=*/false); + } + // Various shapes used in the canned computations. const Shape scalar_shape_ = ShapeUtil::MakeShape(xla::F32, {}); const Shape vec1_shape_ = ShapeUtil::MakeShape(xla::F32, {1}); @@ -158,11 +173,9 @@ TEST_F(HloRematerializationTest, SingleComputation) { SequentialHloOrdering::HloModuleSequence sequence; // Computation requires 16KB without rematerialization, but uses only 12KB // with rematerialization so pick a memory limit between these values (14KB). - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/14 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/14 * 1024, + module.get(), &sequence)); EXPECT_TRUE(changed); // Root should not have changed. @@ -188,18 +201,16 @@ TEST_F(HloRematerializationTest, SingleComputationNoRematerialization) { HloComputation* computation = module->AddEntryComputation(MakeRematerializableComputation()); - EXPECT_EQ(computation->instruction_count(), 7); + EXPECT_EQ(computation->instruction_count(), 8); SequentialHloOrdering::HloModuleSequence sequence; - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/20 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/20 * 1024, + module.get(), &sequence)); // No instructions should have been materialized. EXPECT_FALSE(changed); - EXPECT_EQ(computation->instruction_count(), 7); + EXPECT_EQ(computation->instruction_count(), 8); } // Test rematerialization of a computation which calls another computation via a @@ -225,23 +236,21 @@ TEST_F(HloRematerializationTest, RematerializeAroundWhile) { module->AddEntryComputation(MakeRematerializableWhileComputation( while_cond, /*while_body=*/body_computation)); - EXPECT_EQ(entry_computation->instruction_count(), 6); - EXPECT_EQ(body_computation->instruction_count(), 7); + EXPECT_EQ(entry_computation->instruction_count(), 7); + EXPECT_EQ(body_computation->instruction_count(), 8); // The body computation uses 16KB and the entry computation uses 2KB at the // while so the peak memory use of the module is 18KB. Set the memory limit a // bit lower (17KB) to force rematerialization of the entry computation. SequentialHloOrdering::HloModuleSequence sequence; - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/17 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/17 * 1024, + module.get(), &sequence)); EXPECT_TRUE(changed); // Only the entry computation should have a rematerialized instruction added. - EXPECT_EQ(entry_computation->instruction_count(), 7); - EXPECT_EQ(body_computation->instruction_count(), 7); + EXPECT_EQ(entry_computation->instruction_count(), 8); + EXPECT_EQ(body_computation->instruction_count(), 8); } // Test rematerialization of a computation which calls another computation via a @@ -264,20 +273,18 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) { module->AddEntryComputation(MakeRematerializableWhileComputation( while_cond, /*while_body=*/body_computation)); - EXPECT_EQ(entry_computation->instruction_count(), 6); - EXPECT_EQ(body_computation->instruction_count(), 7); + EXPECT_EQ(entry_computation->instruction_count(), 7); + EXPECT_EQ(body_computation->instruction_count(), 8); SequentialHloOrdering::HloModuleSequence sequence; - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/15 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/15 * 1024, + module.get(), &sequence)); EXPECT_TRUE(changed); - // Both computations should have a rematerialized instruction added. - EXPECT_EQ(entry_computation->instruction_count(), 7); - EXPECT_EQ(body_computation->instruction_count(), 8); + // Both computations should have rematerialized instructions added. + EXPECT_EQ(entry_computation->instruction_count(), 9); + EXPECT_EQ(body_computation->instruction_count(), 9); } // Test rematerialization of a doubly nested computation. All computations @@ -303,24 +310,22 @@ TEST_F(HloRematerializationTest, RematerializeNestedComputations) { module->AddEntryComputation(MakeRematerializableWhileComputation( while_cond, /*while_body=*/middle_computation)); - EXPECT_EQ(entry_computation->instruction_count(), 6); - EXPECT_EQ(middle_computation->instruction_count(), 6); - EXPECT_EQ(inner_computation->instruction_count(), 7); + EXPECT_EQ(entry_computation->instruction_count(), 7); + EXPECT_EQ(middle_computation->instruction_count(), 7); + EXPECT_EQ(inner_computation->instruction_count(), 8); // If all computations are maximally rematerialized then peak memory usage is // ~12K so pick something slightly larger. SequentialHloOrdering::HloModuleSequence sequence; - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/13 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/13 * 1024, + module.get(), &sequence)); EXPECT_TRUE(changed); - // All computations should have a rematerialized instruction added. - EXPECT_EQ(entry_computation->instruction_count(), 7); - EXPECT_EQ(middle_computation->instruction_count(), 7); - EXPECT_EQ(inner_computation->instruction_count(), 8); + // All computations should have rematerialized instructions added. + EXPECT_EQ(entry_computation->instruction_count(), 9); + EXPECT_EQ(middle_computation->instruction_count(), 9); + EXPECT_EQ(inner_computation->instruction_count(), 9); } TEST_F(HloRematerializationTest, RngNotRematerialized) { @@ -382,10 +387,9 @@ TEST_F(HloRematerializationTest, RngNotRematerialized) { // parameter and output) and 20KB (peak memory possible with // rematerialization). TF_ASSERT_OK_AND_ASSIGN( - bool changed, HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, + bool changed, RunHloRematerialization( /*memory_limit_bytes=*/4 * ByteSizeOf(vec1024_shape_), - module.get(), DefaultMemoryScheduler, &sequence)); + module.get(), &sequence)); EXPECT_TRUE(changed); // The rng should not have been rematerialized. EXPECT_EQ(count_rngs(entry_computation), 1); @@ -476,11 +480,9 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) { // Pick a memory limit some where between 24KB (initial peak memory including // parameter and output) and 20KB (peak memory possible with // rematerialization). - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/22 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/22 * 1024, + module.get(), &sequence)); EXPECT_TRUE(changed); // The broadcast should have been rematerialized 3 times. @@ -573,11 +575,9 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) { // Pick a memory limit some where between 24KB (initial peak memory including // parameter and output) and 20KB (peak memory possible with // rematerialization). - TF_ASSERT_OK_AND_ASSIGN(bool changed, - HloRematerialization::RematerializeAndSchedule( - ByteSizeOf, - /*memory_limit_bytes=*/22 * 1024, module.get(), - DefaultMemoryScheduler, &sequence)); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( + /*memory_limit_bytes=*/22 * 1024, + module.get(), &sequence)); // Rematerialization should only occur if the rematerializable instruction has // no indirect uses. if (indirectly_used) { diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index e1f9d8efd4974055947438c8a2e15cb77d1b5c75..b2725e2918ce76248d9f2cdbb2a6e5a63226bf9a 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -98,8 +98,10 @@ StatusOr HloRunner::TransferLiteralToDevice( backend().transfer_manager()->AllocateScopedShapedBuffer( literal.shape(), backend().memory_allocator(), backend().default_device_ordinal())); + TF_ASSIGN_OR_RETURN( + auto stream, backend().BorrowStream(backend().default_stream_executor())); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - backend().default_stream_executor(), literal, buffer)); + stream.get(), literal, buffer)); return std::move(buffer); } @@ -127,8 +129,10 @@ StatusOr> HloRunner::TransferLiteralsToDevice( StatusOr> HloRunner::TransferLiteralFromDevice( const ShapedBuffer& buffer) { - return backend().transfer_manager()->TransferLiteralFromDevice( - backend().default_stream_executor(), buffer); + TF_ASSIGN_OR_RETURN( + auto stream, backend().BorrowStream(backend().default_stream_executor())); + return backend().transfer_manager()->TransferLiteralFromDevice(stream.get(), + buffer); } StatusOr> HloRunner::Execute( @@ -176,8 +180,12 @@ StatusOr HloRunner::ExecuteWithDeviceBuffers( TF_ASSIGN_OR_RETURN(std::unique_ptr executable, CreateExecutable(std::move(module), run_hlo_passes)); - return executable->ExecuteOnStreamWrapper(&service_run_options, - /*profile=*/profile, arguments); + TF_ASSIGN_OR_RETURN( + ScopedShapedBuffer retval, + executable->ExecuteOnStreamWrapper(&service_run_options, + /*profile=*/profile, arguments)); + TF_RETURN_IF_ERROR(stream.BlockHostUntilDone()); + return std::move(retval); } StatusOr HloRunner::ExecuteWithDeviceBuffers( @@ -237,7 +245,7 @@ StatusOr>> HloRunner::ExecuteReplicated( backend().transfer_manager()->AllocateScopedShapedBuffer( argument->shape(), backend().memory_allocator(), device)); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - executor, *argument, argument_buffer)); + streams.back().get(), *argument, argument_buffer)); argument_buffers.push_back(std::move(argument_buffer)); argument_buffer_ptrs[index++] = &argument_buffers.back(); } @@ -305,9 +313,10 @@ StatusOr>> HloRunner::ExecuteReplicated( std::vector> exec_results; for (int64 i = 0; i < options.num_replicas; ++i) { + TF_RETURN_IF_ERROR(streams[i]->BlockHostUntilDone()); TF_ASSIGN_OR_RETURN(std::unique_ptr literal, backend().transfer_manager()->TransferLiteralFromDevice( - streams[i]->parent(), results[i])); + streams[i].get(), results[i])); exec_results.push_back(std::move(literal)); } return std::move(exec_results); diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index 68b2cde83a2eb479d9ba71fc6eab9ac9ab1c8267..c6d3909af6103949daf4b0ab6be9b74724461e30 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -36,29 +36,6 @@ using ::tensorflow::strings::HumanReadableNumBytes; namespace xla { -StatusOr MinimumMemoryForSequence( - const SequentialHloOrdering::HloModuleSequence& module_sequence, - const LogicalBuffer::SizeFunction& size_function) { - if (module_sequence.empty()) { - return 0; - } - - const HloModule* module = module_sequence.begin()->first->parent(); - TF_ASSIGN_OR_RETURN(std::unique_ptr points_to_analysis, - TuplePointsToAnalysis::Run(module)); - - // The absolute minimum memory required for a given sequence of instructions - // is determined by the sequence of Alloc and Free calls on a simulated heap, - // ignoring fragmentation. We run the heap simulation on the whole module, - // rather than summing each computation, since it gives us a better lower - // bound, by minimizing the liveness of sub-computations. - TF_ASSIGN_OR_RETURN( - HeapSimulator::Result result, - HeapSimulator::Run(MakeUnique(), *module, - module_sequence, *points_to_analysis, size_function)); - return result.heap_size; -} - namespace { // Class implementing a list scheduler of HLO instructions which produces a @@ -398,7 +375,7 @@ int64 SumLogicalBufferSizes( return size; } -StatusOr> CreateMemoryMinimizingSequence( +StatusOr> ScheduleComputationHelper( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -416,30 +393,15 @@ StatusOr> CreateMemoryMinimizingSequence( } // namespace -StatusOr MinimumMemoryForComputation( - const HloComputation& computation, - const std::vector& sequence, - const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_function) { - TF_ASSIGN_OR_RETURN( - HeapSimulator::Result result, - HeapSimulator::Run(MakeUnique(), computation, - sequence, points_to_analysis, size_function)); - return result.heap_size; -} - StatusOr> DFSMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap& memory_by_computation) { - // This ordering is based on DFS post-order, with a heuristic to decide which - // operand to visit first. The heuristic is based on 'extra_users', which is - // simply users-1 for each instruction. By subtracting 1, we're saying that - // instructions with no users or a single user don't count; instructions with - // lots of fan-out will be visited earlier. + // These variables are a hack to prevent overflows. int64 cumulative_total_size = 0; + int64 total_hlos = computation.parent()->NumUniqueInstructionIds(); tensorflow::gtl::FlatMap extra_users; tensorflow::gtl::FlatMap total_sizes; for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) { @@ -448,6 +410,11 @@ StatusOr> DFSMemoryScheduler( total_sizes[hlo] = 0; continue; } + // This ordering is based on DFS post-order, with a heuristic to decide + // which operand to visit first. The heuristic is based on 'extra_users', + // which is simply users-1 for each instruction. By subtracting 1, we're + // saying that instructions with no users or a single user don't count; + // instructions with lots of fan-out will be visited earlier. extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1; int64 logical_buffer_size = SumLogicalBufferSizes( points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function); @@ -463,10 +430,13 @@ StatusOr> DFSMemoryScheduler( // lead to it. But computation is a DAG, so we are double-counting nodes, // which can lead to overflows for large programs. // cumulative_total_size caps the size to prevent overflows. + // Same for total_hlos: it prevents overflows on very large and branchy + // models, where the number of paths is exponential to the number of nodes. // NOTE(dimvar): this is quite ugly and should be changed. It's unclear // why we care about transitive sizes; when scheduling a node, its input // and output buffers should be all that matters, not its "history". total_sizes[hlo] = std::min(total_sizes[hlo], cumulative_total_size); + extra_users[hlo] = std::min(extra_users[hlo], total_hlos); } CHECK_EQ(extra_users.size(), computation.instruction_count()); CHECK_EQ(total_sizes.size(), computation.instruction_count()); @@ -533,29 +503,29 @@ StatusOr> DefaultMemoryScheduler( std::vector list_sequence, ListMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 list_memory, - MinimumMemoryForComputation(computation, list_sequence, - points_to_analysis, size_function)); + TF_ASSIGN_OR_RETURN(const int64 list_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, list_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory list sequence: " << HumanReadableNumBytes(list_memory); TF_ASSIGN_OR_RETURN(std::vector dfs_sequence, DFSMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 dfs_memory, - MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis, - size_function)); + TF_ASSIGN_OR_RETURN(const int64 dfs_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, dfs_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory dfs sequence: " << HumanReadableNumBytes(dfs_memory); TF_ASSIGN_OR_RETURN( std::vector post_order_sequence, PostOrderMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 post_order_memory, - MinimumMemoryForComputation(computation, post_order_sequence, - points_to_analysis, size_function)); + TF_ASSIGN_OR_RETURN(const int64 post_order_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, post_order_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory post order sequence: " << HumanReadableNumBytes(post_order_memory); @@ -576,10 +546,9 @@ StatusOr> DefaultMemoryScheduler( } } -StatusOr -CreateMemoryMinimizingSequence(const HloModule& module, - const LogicalBuffer::SizeFunction& size_function, - const MemorySchedulerAlgorithm& algorithm) { +StatusOr ScheduleComputationsInModule( + const HloModule& module, const LogicalBuffer::SizeFunction& size_function, + const MemorySchedulerAlgorithm& algorithm) { SequentialHloOrdering::HloModuleSequence sequence; TF_ASSIGN_OR_RETURN(std::unique_ptr points_to_analysis, TuplePointsToAnalysis::Run(&module)); @@ -587,12 +556,13 @@ CreateMemoryMinimizingSequence(const HloModule& module, for (const auto* computation : module.MakeComputationPostOrder()) { if (!computation->IsFusionComputation()) { TF_ASSIGN_OR_RETURN(auto one_computation_sequence, - CreateMemoryMinimizingSequence( + ScheduleComputationHelper( *computation, *points_to_analysis, size_function, algorithm, memory_by_computation)); memory_by_computation[computation] = - MinimumMemoryForComputation(*computation, one_computation_sequence, - *points_to_analysis, size_function) + HeapSimulator::MinimumMemoryForComputation( + *computation, one_computation_sequence, *points_to_analysis, + size_function, &memory_by_computation) .ValueOrDie(); sequence[computation] = std::move(one_computation_sequence); } @@ -600,15 +570,15 @@ CreateMemoryMinimizingSequence(const HloModule& module, return sequence; } -StatusOr> CreateMemoryMinimizingSequence( +StatusOr> ScheduleOneComputation( const HloComputation& computation, const LogicalBuffer::SizeFunction& size_function) { CHECK(!computation.IsFusionComputation()); TF_ASSIGN_OR_RETURN(std::unique_ptr points_to_analysis, TuplePointsToAnalysis::Run(computation.parent())); tensorflow::gtl::FlatMap empty_map; - return CreateMemoryMinimizingSequence(computation, *points_to_analysis, - size_function, nullptr, empty_map); + return ScheduleComputationHelper(computation, *points_to_analysis, + size_function, nullptr, empty_map); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h index 49b927eefd24f4e26df781dd8d2b977bedba2b80..2b33ccc8bfb895286bb3747aab0a16cf25e2cfae 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.h +++ b/tensorflow/compiler/xla/service/hlo_scheduling.h @@ -28,20 +28,6 @@ limitations under the License. namespace xla { -// Returns the minimum memory required to compute the given module sequence, -// assuming no fragmentation. -StatusOr MinimumMemoryForSequence( - const SequentialHloOrdering::HloModuleSequence& module_sequence, - const LogicalBuffer::SizeFunction& size_function); - -// Returns the minimum memory required to compute the given computation, -// assuming no fragmentation. -StatusOr MinimumMemoryForComputation( - const HloComputation& computation, - const std::vector& sequence, - const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_function); - // A memory scheduler computes an execution sequence for the HLO instructions in // 'computation' that minimizes peak memory, given a points-to analysis result // that describes buffer aliasing, together with a target-specific size function @@ -89,14 +75,13 @@ StatusOr> DefaultMemoryScheduler( // Returns an HloModuleSequence which seeks to minimize the memory required for // the computation. size_function is the function returning the number of bytes // required for a LogicalBuffer. -StatusOr -CreateMemoryMinimizingSequence(const HloModule& module, - const LogicalBuffer::SizeFunction& size_function, - const MemorySchedulerAlgorithm& algorithm = {}); +StatusOr ScheduleComputationsInModule( + const HloModule& module, const LogicalBuffer::SizeFunction& size_function, + const MemorySchedulerAlgorithm& algorithm = {}); -// Overload of above that computes the sequence for a single computation. +// Computes the schedule for a single computation. // Currently only used by the GPU backend. -StatusOr> CreateMemoryMinimizingSequence( +StatusOr> ScheduleOneComputation( const HloComputation& computation, const LogicalBuffer::SizeFunction& size_function); diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc index db7ef6f0d4bd96216ea07ccc75a51513822bf2e3..73f22f81f4e9cf597db8b184642acff2fdaaf2b0 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/service/heap_simulator.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -31,65 +32,6 @@ limitations under the License. namespace xla { namespace { -class MinimumMemoryForSequenceTest : public HloTestBase {}; - -TEST_F(MinimumMemoryForSequenceTest, MultiComputation) { - auto module = CreateNewModule(); - const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {}); - const Shape tuple_shape = - ShapeUtil::MakeTupleShape({scalar_shape, scalar_shape}); - - auto cond_builder = HloComputation::Builder("WhileCond"); - // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element) - HloInstruction* cond_param = cond_builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape, "cond_param")); - HloInstruction* cond_iter = cond_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 0)); - HloInstruction* cond_data = cond_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape, cond_param, 1)); - // Free cond_param[] (16 bytes), Alloc PRED[] (1 byte) - HloInstruction* cond_lt = cond_builder.AddInstruction( - HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}), - HloOpcode::kLt, cond_iter, cond_data)); - HloComputation* cond_computation = - module->AddEmbeddedComputation(cond_builder.Build()); - - auto body_builder = HloComputation::Builder("WhileBody"); - // Tuple param: 24 bytes (each elem has 8 byte pointer, 4 byte element) - HloInstruction* body_param = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape, "body_param")); - HloComputation* body_computation = - module->AddEmbeddedComputation(body_builder.Build()); - - auto builder = HloComputation::Builder(TestName()); - // Entry params: 8 bytes (4 bytes per param), TOTAL=8 - HloInstruction* iter = builder.AddInstruction( - HloInstruction::CreateParameter(0, scalar_shape, "param_iter")); - HloInstruction* data = builder.AddInstruction( - HloInstruction::CreateParameter(1, scalar_shape, "param_data")); - // Tuple: 16 bytes (8 bytes per pointer), TOTAL=24 - HloInstruction* tuple = - builder.AddInstruction(HloInstruction::CreateTuple({iter, data})); - // While: 8 bytes (4 bytes per element), TOTAL=32 - // Both cond and body use a max of 24 bytes, TOTAL=56 - HloInstruction* while_op = builder.AddInstruction(HloInstruction::CreateWhile( - tuple_shape, cond_computation, body_computation, tuple)); - HloComputation* entry_computation = - module->AddEntryComputation(builder.Build()); - - auto size_fn = [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8); - }; - - SequentialHloOrdering::HloModuleSequence module_sequence; - module_sequence[cond_computation] = {cond_param, cond_iter, cond_data, - cond_lt}; - module_sequence[body_computation] = {body_param}; - module_sequence[entry_computation] = {iter, data, tuple, while_op}; - EXPECT_EQ(56, - MinimumMemoryForSequence(module_sequence, size_fn).ValueOrDie()); -} - class HloSchedulingTest : public HloTestBase {}; TEST_F(HloSchedulingTest, LastUseScheduledFirst) { @@ -124,7 +66,7 @@ TEST_F(HloSchedulingTest, LastUseScheduledFirst) { TF_ASSERT_OK_AND_ASSIGN( SequentialHloOrdering::HloModuleSequence sequence, - CreateMemoryMinimizingSequence(*module, [](const BufferValue& buffer) { + ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { return ShapeUtil::ByteSizeOf(buffer.shape()); })); // Verify that all instructions are in the sequence. @@ -165,7 +107,7 @@ ENTRY root { }; TF_ASSERT_OK_AND_ASSIGN( SequentialHloOrdering::HloModuleSequence sequence, - CreateMemoryMinimizingSequence(*module, size_fn, ListMemoryScheduler)); + ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. EXPECT_EQ(module->entry_computation()->instruction_count(), sequence.at(module->entry_computation()).size()); @@ -203,7 +145,7 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { // ROOT %subtract = f32[4]{0} subtract( // f32[4]{0} %body_param, f32[1,4]{1,0} %constant.1) // } - // %SubcomputationsNotAccounted () -> f32[2,4] { + // %ListAccountsForSubcomputations () -> f32[2,4] { // %constant.3 = f32[2,4]{1,0} constant( // f32[2,4] { { 1, 2, 3, 4 }, { 1, 2, 3, 4 } }) // %transpose = f32[2,4]{1,0} transpose( @@ -269,16 +211,16 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { module->AddEntryComputation(builder.Build()); - TF_ASSERT_OK_AND_ASSIGN(SequentialHloOrdering::HloModuleSequence sequence, - CreateMemoryMinimizingSequence( - *module, - [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - }, - ListMemoryScheduler)); + auto size_fn = [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + }; + TF_ASSERT_OK_AND_ASSIGN( + SequentialHloOrdering::HloModuleSequence sequence, + ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. - EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); + auto entry_computation = module->entry_computation(); + EXPECT_EQ(entry_computation->instruction_count(), + sequence.at(entry_computation).size()); SequentialHloOrdering ordering(module.get(), sequence); // This schedule is an example of List's greedy heuristics being suboptimal. // The while_loop is more expensive than transpose, so it would have been @@ -287,6 +229,24 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { EXPECT_TRUE(ordering.ExecutesBefore(transpose, bcast)); EXPECT_TRUE(ordering.ExecutesBefore(bcast, add)); EXPECT_TRUE(ordering.ExecutesBefore(transpose, add)); + + tensorflow::gtl::FlatMap memory_by_computation; + memory_by_computation[cond_computation] = 17; + memory_by_computation[body_computation] = 16; + std::unique_ptr points_to_analysis = + TuplePointsToAnalysis::Run(module.get()).ValueOrDie(); + + // HeapSimulator doesn't account for subcomputations + EXPECT_EQ(80, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn) + .ValueOrDie()); + // HeapSimulator accounts for subcomputations. The max mem doesn't change + // because the while body isn't live during the peak. + EXPECT_EQ(80, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn, &memory_by_computation) + .ValueOrDie()); } TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) { @@ -318,12 +278,12 @@ TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) { module->AddEntryComputation(builder.Build()); TF_ASSERT_OK_AND_ASSIGN( SequentialHloOrdering::HloModuleSequence sequence, - CreateMemoryMinimizingSequence(*module, - [&TUPLE_SIZE](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf( - buffer.shape(), TUPLE_SIZE); - }, - ListMemoryScheduler)); + ScheduleComputationsInModule(*module, + [&TUPLE_SIZE](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf( + buffer.shape(), TUPLE_SIZE); + }, + ListMemoryScheduler)); // Verify that all instructions are in the sequence. EXPECT_EQ(module->entry_computation()->instruction_count(), @@ -368,7 +328,7 @@ TEST_F(HloSchedulingTest, MultiOutputFusionAccountedCorrectly) { {tuple, mul, add}, HloInstruction::FusionKind::kLoop); TF_ASSERT_OK_AND_ASSIGN(SequentialHloOrdering::HloModuleSequence sequence, - CreateMemoryMinimizingSequence( + ScheduleComputationsInModule( *module, [](const BufferValue& buffer) { return ShapeUtil::ByteSizeOf(buffer.shape(), 2); @@ -384,5 +344,70 @@ TEST_F(HloSchedulingTest, MultiOutputFusionAccountedCorrectly) { EXPECT_TRUE(ordering.ExecutesBefore(exp, fusion)); } +TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { + auto module = CreateNewModule(); + const Shape r1f32 = ShapeUtil::MakeShape(F32, {4}); + const Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 4}); + + // param != 0 + // Needs 17 bytes + auto cond_builder = HloComputation::Builder("WhileCond"); + HloInstruction* cond_param = cond_builder.AddInstruction( + HloInstruction::CreateParameter(0, r1f32, "cond_param")); + HloInstruction* zero_vector = cond_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{0, 0, 0, 0}}))); + cond_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kNe, cond_param, zero_vector)); + auto cond_computation = module->AddEmbeddedComputation(cond_builder.Build()); + + // param - 1 + // Needs 16 bytes + auto body_builder = HloComputation::Builder("WhileBody"); + HloInstruction* body_param = body_builder.AddInstruction( + HloInstruction::CreateParameter(0, r1f32, "body_param")); + HloInstruction* one_vector = body_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + body_builder.AddInstruction(HloInstruction::CreateBinary( + r1f32, HloOpcode::kSubtract, body_param, one_vector)); + auto body_computation = module->AddEmbeddedComputation(body_builder.Build()); + + auto builder = HloComputation::Builder(TestName()); + HloInstruction* while_init = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + // Creates 16 bytes, ignoring subcomputations + builder.AddInstruction(HloInstruction::CreateWhile( + r1f32, cond_computation, body_computation, while_init)); + + module->AddEntryComputation(builder.Build()); + + auto size_fn = [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + }; + TF_ASSERT_OK_AND_ASSIGN( + SequentialHloOrdering::HloModuleSequence sequence, + ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); + // Verify that all instructions are in the sequence. + auto entry_computation = module->entry_computation(); + EXPECT_EQ(entry_computation->instruction_count(), + sequence.at(entry_computation).size()); + + tensorflow::gtl::FlatMap memory_by_computation; + memory_by_computation[cond_computation] = 17; + memory_by_computation[body_computation] = 16; + std::unique_ptr points_to_analysis = + TuplePointsToAnalysis::Run(module.get()).ValueOrDie(); + + // HeapSimulator doesn't account for subcomputations + EXPECT_EQ(16, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn) + .ValueOrDie()); + // HeapSimulator accounts for subcomputations + EXPECT_EQ(33, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn, &memory_by_computation) + .ValueOrDie()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index 58224ef870096a774d5892b9aa12c38f5ff511bd..268b4727bcbed42ba71526f1d5ef5c887e941930 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -39,6 +39,34 @@ HloSharding HloSharding::Tile1D(const Shape& input_shape, int64 num_tiles) { return HloSharding(tile_shape, assignment); } +HloSharding HloSharding::Tuple(const ShapeTree& sub_shardings) { + std::vector flattened_list; + flattened_list.reserve(sub_shardings.leaf_count()); + for (const auto& index_to_sharding : sub_shardings.leaves()) { + flattened_list.push_back(index_to_sharding.second); + } + if (flattened_list.empty()) { + // Empty tuple sharding ends up having no leaves, but we want to allow + // empty tuple HLO instruction results to have sharding, so we fetch the + // root ({}) sharding value from the ShapeTree. + // A ShapeTree created with ShapeTree(shape, init) will have + // init as value at its root. + flattened_list.push_back(sub_shardings.element(ShapeIndex({}))); + } + return HloSharding(flattened_list); +} + +HloSharding HloSharding::Tuple( + const Shape& tuple_shape, + tensorflow::gtl::ArraySlice shardings) { + CHECK(ShapeUtil::IsTuple(tuple_shape)) << ShapeUtil::HumanString(tuple_shape); + std::vector flattened_list(shardings.begin(), shardings.end()); + CHECK_EQ(flattened_list.size(), RequiredLeaves(tuple_shape)) + << "Flat list has " << flattened_list.size() << ", required " + << RequiredLeaves(tuple_shape); + return HloSharding(flattened_list); +} + string HloSharding::ToString() const { if (IsTuple()) { std::vector parts; @@ -72,6 +100,29 @@ bool HloSharding::UsesDevice(int64 device) const { std::find(devices.begin(), devices.end(), device) != devices.end(); } +std::map HloSharding::UsedDevices(int64* count) const { + int64 element_count = 1; + std::map device_map; + if (IsTuple()) { + for (auto& tuple_element_sharding : tuple_elements()) { + auto unique_device = tuple_element_sharding.UniqueDevice(); + if (unique_device.ok()) { + device_map[unique_device.ValueOrDie()] += 1; + } + } + element_count = tuple_elements().size(); + } else { + auto unique_device = UniqueDevice(); + if (unique_device.ok()) { + device_map[unique_device.ValueOrDie()] += 1; + } + } + if (count != nullptr) { + *count = element_count; + } + return device_map; +} + std::vector HloSharding::TileIndexForDevice(int64 device) const { CHECK(!ShapeUtil::IsTuple(tile_shape_)); CHECK(!maximal_); @@ -123,24 +174,49 @@ std::vector HloSharding::TileLimitForDevice(int64 device) const { return index; } +int64 HloSharding::RequiredLeaves(const Shape& shape) { + // Empty tuples have no leaf nodes as far as ShapeUtil and ShapeTree are + // concerned, but they do have a single tuple_elements_ entry since we want + // to allow empty tuple results to have sharding. + return ShapeUtil::IsEmptyTuple(shape) ? 1 : ShapeUtil::GetLeafCount(shape); +} + +Status HloSharding::CheckLeafCount(const Shape& shape) const { + int64 shape_leaves = RequiredLeaves(shape); + TF_RET_CHECK(shape_leaves == tuple_elements_.size()) + << "Shape " << ShapeUtil::HumanString(shape) << " has " << shape_leaves + << " leaf nodes while this sharding has " << tuple_elements_.size(); + return Status::OK(); +} + StatusOr> HloSharding::AsShapeTree( const Shape& shape) const { if (IsTuple()) { ShapeTree result(shape, HloSharding::Replicate()); - int64 num_leaves = result.leaf_count(); - TF_RET_CHECK(num_leaves == tuple_elements_.size()) - << "Shape " << ShapeUtil::HumanString(shape) << " has " << num_leaves - << " leaf nodes while this sharding has " << tuple_elements_.size(); + TF_RETURN_IF_ERROR(CheckLeafCount(shape)); auto it = tuple_elements_.begin(); for (auto& index_to_sharding : result.leaves()) { index_to_sharding.second = *it++; } + if (ShapeUtil::IsEmptyTuple(shape)) { + // Empty tuples have no leaves, but we want to assign them a sharding + // anyway, so we use the root element sharding. + *result.mutable_element(ShapeIndex({})) = *it; + } return std::move(result); } else { return ShapeTree(shape, *this); } } +StatusOr HloSharding::GetTupleSharding(const Shape& shape) const { + if (IsTuple()) { + TF_RETURN_IF_ERROR(CheckLeafCount(shape)); + return *this; + } + return Tuple(ShapeTree(shape, *this)); +} + StatusOr HloSharding::UniqueDevice() const { if (IsTuple()) { if (tuple_elements_.empty()) { @@ -182,28 +258,12 @@ Status HloSharding::ValidateTuple(const Shape& shape, int64 num_devices) const { return tensorflow::errors::InvalidArgument( StrCat("Sharding is tuple-shaped but validation shape is not.")); } - // The easiest way to get the number of elements in a nested tuple is just to - // create a shape tree. We could call GetAsShapeTree, but that will try and - // apply our tuple_shardings_ to the shape tree, and that might cause a crash - // at this point as we haven't validated them. - ShapeTree bool_shape_tree(shape, false); - int64 num_leaves = - std::distance(bool_shape_tree.leaf_begin(), bool_shape_tree.leaf_end()); - if (num_leaves != tuple_elements_.size()) { - return tensorflow::errors::InvalidArgument( - StrCat("Validation tuple shape has ", num_leaves, - " leaf elements, but this sharding contains ", - tuple_elements_.size(), " elements.")); - } + TF_RETURN_IF_ERROR(CheckLeafCount(shape)); // Now we've validated the number of tuple elements, it's safe to request a // shape tree. ShapeTree shape_tree = GetAsShapeTree(shape); for (const auto& index_to_sharding : shape_tree.leaves()) { - if (index_to_sharding.first.empty()) { - // An empty tuple has a ShapeTree with a single leaf at the empty index. - continue; - } Status status = index_to_sharding.second.ValidateNonTuple( ShapeUtil::GetSubshape(shape, index_to_sharding.first), num_devices); if (!status.ok()) { @@ -389,6 +449,40 @@ HloSharding HloSharding::GetSubSharding(const Shape& shape, : sub_shape_tree.element(ShapeIndex({})); } +tensorflow::gtl::optional HloSharding::ExtractSingleSharding() + const { + if (!IsTuple()) { + return *this; + } + for (int64 i = 1; i < tuple_elements_.size(); ++i) { + if (tuple_elements_[0] != tuple_elements_[i]) { + return tensorflow::gtl::optional(); + } + } + return tuple_elements_.front(); +} + +size_t HloSharding::Hash() const { + if (!tuple_) { + size_t h = 0; + for (const auto& element : tuple_elements_) { + h = tensorflow::Hash64Combine(h, element.Hash()); + } + return h; + } + if (replicated_) { + return 0; + } + size_t h = 0; + for (uint32 v : tile_assignment_) { + h = tensorflow::Hash64Combine(h, std::hash{}(v)); + } + for (uint32 v : tile_shape_.dimensions()) { + h = tensorflow::Hash64Combine(h, std::hash{}(v)); + } + return h; +} + std::ostream& operator<<(std::ostream& out, const HloSharding& sharding) { out << sharding.ToString(); return out; diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index f4a0fb626f2c3e417c020cbfa2f7168359a47788..34324d2058efe804cda486600dabd8a62cb84fda 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -19,7 +19,9 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SHARDING_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SHARDING_H_ +#include #include +#include #include "tensorflow/compiler/xla/array.h" #include "tensorflow/compiler/xla/literal_util.h" @@ -70,26 +72,13 @@ class HloSharding { // Creates a new sharding for a tuple type. The given ShapeTree must have // elements for every leaf shape contained in the tuple. - static HloSharding Tuple(const ShapeTree& sub_shardings) { - std::vector flattened_list; - flattened_list.reserve( - std::distance(sub_shardings.leaf_begin(), sub_shardings.leaf_end())); - for (const auto& index_to_sharding : sub_shardings.leaves()) { - flattened_list.push_back(index_to_sharding.second); - } - return HloSharding(flattened_list); - } + static HloSharding Tuple(const ShapeTree& sub_shardings); - // Creates a new sharding for a tuple type. The requested tuple shape must not - // be nested. For nested tuples, use the ShapeTree overload. + // Creates a new sharding for a tuple type. The number of elements in + // shardings must match the number of leaf nodes in tuple_shape. For + // empty tuples, the shardings array must have one element. static HloSharding Tuple(const Shape& tuple_shape, - tensorflow::gtl::ArraySlice shardings) { - CHECK(ShapeUtil::IsTuple(tuple_shape)); - CHECK(!ShapeUtil::IsNestedTuple(tuple_shape)); - std::vector flattened_list(shardings.begin(), shardings.end()); - CHECK_EQ(flattened_list.size(), ShapeUtil::TupleElementCount(tuple_shape)); - return HloSharding(flattened_list); - } + tensorflow::gtl::ArraySlice shardings); // Create a new sharding from a protobuf OpSharding. static StatusOr FromProto(const OpSharding& proto); @@ -131,6 +120,14 @@ class HloSharding { // Returns true if the sharding defines an operation on the given device. bool UsesDevice(int64 device) const; + // Retrieves an histogram of the devices used by the sharding. The returned + // map has the device number as key, and the occurrence count as value. + // If a sharding does not have a device, it will not be incuded in the + // histogram. The count argument, if not nullptr, will receive the total + // number of elements this sharding is made of (one for array, N leaves for + // tuples). + std::map UsedDevices(int64* count) const; + // Returns the tile that should be executed on the given device. // REQUIRES: !IsTuple() std::vector TileIndexForDevice(int64 device) const; @@ -172,6 +169,18 @@ class HloSharding { // REQUIRES: IsTuple() HloSharding GetSubSharding(const Shape& shape, const ShapeIndex& index) const; + // If the current sharding is a tuple sharding, return itself as result. + // Otherwise returns a tuple sharding for the input shape, with all the leaves + // having this object sharding. + StatusOr GetTupleSharding(const Shape& shape) const; + + // Extracts the sharding that is common within the current sharding. + // If the current sharding is not a tuple sharding, the current sharding will + // be returned. If it is a tuple, and all the tuple elements are common, the + // common element will be returned. Otherwise the optional will contain no + // value. + tensorflow::gtl::optional ExtractSingleSharding() const; + bool operator==(const HloSharding& other) const { return replicated_ == other.replicated_ && maximal_ == other.maximal_ && ShapeUtil::Compatible(tile_shape_, other.tile_shape_) && @@ -180,26 +189,7 @@ class HloSharding { } bool operator!=(const HloSharding& other) const { return !(*this == other); } - size_t Hash() const { - if (!tuple_) { - size_t h = 0; - for (const auto& element : tuple_elements_) { - h = tensorflow::Hash64Combine(h, element.Hash()); - } - return h; - } - if (replicated_) { - return 0; - } - size_t h = 0; - for (uint32 v : tile_assignment_) { - h = tensorflow::Hash64Combine(h, std::hash{}(v)); - } - for (uint32 v : tile_shape_.dimensions()) { - h = tensorflow::Hash64Combine(h, std::hash{}(v)); - } - return h; - } + size_t Hash() const; struct Hasher { size_t operator()(const HloSharding& sharding) const { @@ -241,6 +231,12 @@ class HloSharding { tuple_(false), tile_shape_(), tile_assignment_({0}) {} + // device_id values: + // -2: magic number to mean unassigned device, used by spatial partitioning + // -1: the id of the host + // 0 or positive: the id of a device + // NOTE(dimvar): -1 is needed for outside compilation. It can be removed once + // we have fully switched to the side-effect tokens. explicit HloSharding(int64 device_id) : replicated_(false), maximal_(true), @@ -260,11 +256,19 @@ class HloSharding { tile_assignment_({0}), tuple_elements_(tuple_shardings) {} + // Checks that the number of elements in tuple_elements_ is consistent with + // the tuple shape passes as argument. + Status CheckLeafCount(const Shape& shape) const; + // Internal helper to validate a tuple sharding. Status ValidateTuple(const Shape& shape, int64 num_devices) const; + // Internal helper to validate a non-tuple (leaf) sharding. Status ValidateNonTuple(const Shape& shape, int64 num_devices) const; + // Returns the number of tuple_elements_ entries to fit the shape. + static int64 RequiredLeaves(const Shape& shape); + bool replicated_; bool maximal_; bool tuple_; diff --git a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc index 82cff2a4b7146c2d454feb2d90673d419ca1a54d..39036e205e76979e7da08246cd030ebd17e52f76 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc @@ -31,32 +31,22 @@ struct PassThrough { HloInstruction* operand = nullptr; }; -void SetDeviceSharding(HloInstruction* instruction, int64 device) { - VLOG(4) << " " << instruction->name() << " to device " << device; - instruction->set_device_sharding(device); -} - -tensorflow::gtl::optional ShardingUniqueDevice( - const HloSharding& sharding) { - if (sharding.IsTileMaximal()) { - auto device = sharding.UniqueDevice(); - if (device.ok()) { - return device.ValueOrDie(); - } - } - return tensorflow::gtl::optional(); +void SetSingleSharding(HloInstruction* instruction, + const HloSharding& sharding) { + VLOG(4) << " " << instruction->name() << " to " << sharding; + instruction->set_single_sharding(sharding); } bool ShardingMatches(const HloSharding& sharding1, const HloSharding& sharding2) { - auto device1 = ShardingUniqueDevice(sharding1); - if (device1) { - auto device2 = ShardingUniqueDevice(sharding2); - if (device2) { - return *device1 == *device2; + auto single_sharding1 = sharding1.ExtractSingleSharding(); + if (single_sharding1) { + auto single_sharding2 = sharding2.ExtractSingleSharding(); + if (single_sharding2) { + return *single_sharding1 == single_sharding2; } } - // Anything which is not tile maximal with unique device, gets a full sharding + // Anything which is not unique across all elements, gets a full sharding // compare. return sharding1 == sharding2; } @@ -119,21 +109,21 @@ Status FixupPassThroughDomainLinks(const DomainMetadata::Domain& domain, std::unique_ptr CloneShardingForDomain( const HloSharding& sharding) { - auto device = ShardingUniqueDevice(sharding); - if (!device) { + auto single_sharding = sharding.ExtractSingleSharding(); + if (!single_sharding) { return MakeUnique(sharding); } - return MakeUnique(HloSharding::AssignDevice(*device)); + return MakeUnique(*single_sharding); } -Status ApplyDomainDeviceSharding(const DomainMetadata::Domain& domain, - int64 device) { - VLOG(4) << "Applying device " << device << " sharding"; +Status ApplyDomainSingleSharding(const DomainMetadata::Domain& domain, + const HloSharding& sharding) { + VLOG(4) << "Applying " << sharding << " sharding"; for (HloInstruction* instruction : domain.instructions) { // We only change instructions without sharding, since otherwise we might // mess up with eventual HLO passes which has knowledge of it. if (!instruction->has_sharding()) { - SetDeviceSharding(instruction, device); + SetSingleSharding(instruction, sharding); } else { VLOG(4) << " " << instruction->name() << " already has sharding " << instruction->sharding(); @@ -186,12 +176,15 @@ StatusOr ApplyDomainShardingPass(const DomainMetadata::Domain& domain, const HloSharding* tuple_sharding = GetOperandSharding(tuple, domain, sharding); if (tuple_sharding != nullptr) { - TF_RET_CHECK(tuple_sharding->IsTuple()) << tuple->ToString(); - HloSharding sub_sharding = tuple_sharding->GetSubSharding( - tuple->shape(), {instruction->tuple_index()}); - VLOG(4) << " " << instruction->name() << " to sharding " - << sub_sharding; - instruction->set_sharding(sub_sharding); + if (tuple_sharding->IsTuple()) { + HloSharding sub_sharding = tuple_sharding->GetSubSharding( + tuple->shape(), {instruction->tuple_index()}); + VLOG(4) << " " << instruction->name() << " to sharding " + << sub_sharding; + instruction->set_sharding(sub_sharding); + } else { + SetSingleSharding(instruction, *tuple_sharding); + } ++assigned; } } else if (instruction->opcode() == HloOpcode::kTuple) { @@ -242,12 +235,29 @@ StatusOr ApplyDomainShardingPass(const DomainMetadata::Domain& domain, Status ApplyDomainSharding(const DomainMetadata::Domain& domain, const HloSharding& sharding) { - auto device = ShardingUniqueDevice(sharding); - if (device) { - // Shortcut the simple case. We have a unique device sharding, so we call - // the ApplyDomainDeviceSharding() API which will apply array or tuple - // shaped device sharding to the domain instructions. - return ApplyDomainDeviceSharding(domain, *device); + // Here is the place to call external sharding normalizers, which are + // implemented in other modules (ie, spatial partitioning). + // The signature of the external normalizer function should be something + // like: + // + // StatusOr Normalizer(const DomainMetadata::Domain&, + // const HloSharding& sharding); + // + // The function should return true if it has processed the domain + // normalization, false if domain was not one recognized by it, or an error. + // We will call the functions in order below, and fall back to local code if + // none of the external normalizers acted on the domain. + // External normalizers should not handle the cases that are already handled + // locally. + + // None of the external normalizers handled the domain sharding, try to see + // whether this is a single sharding first. + auto single_sharding = sharding.ExtractSingleSharding(); + if (single_sharding) { + // Shortcut the simple case. We have a unique sharding, so we call + // the ApplyDomainSingleSharding() API which will apply array or tuple + // shaped sharding to the domain instructions. + return ApplyDomainSingleSharding(domain, *single_sharding); } VLOG(1) << "Assigning non-trivial sharding " << sharding; for (;;) { @@ -367,7 +377,7 @@ bool ShardingMetadata::Matches(const DomainMetadata& other) const { } string ShardingMetadata::ToString() const { - return sharding_ != nullptr ? sharding_->ToString() : "None"; + return sharding_ != nullptr ? sharding_->ToString() : "{}"; } Status ShardingMetadata::NormalizeInstructions( diff --git a/tensorflow/compiler/xla/service/hlo_sharding_test.cc b/tensorflow/compiler/xla/service/hlo_sharding_test.cc index ee7133689b15348a18e6db9181199d5b25bf8143..54b7402b866361748d9eb35182b0bf486c4c9bdc 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_test.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_test.cc @@ -321,8 +321,10 @@ TEST_F(HloShardingTest, ParseHloString) { check(HloSharding::AssignDevice(2)); check(HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 1, 3, 7}), Array4D({{{{0}, {1}}}}))); - // Empty tuple. - check(HloSharding::Tuple(ShapeUtil::MakeTupleShape({}), {})); + // Empty tuple. One sharding is required for empty tuples, as we need to be + // able to assign sharding to them, even though they have no leaves. + check(HloSharding::Tuple(ShapeUtil::MakeTupleShape({}), + {HloSharding::Replicate()})); { // Non-nested tuple. auto tuple_shape = diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 9cfd8a9bf74bc69ac40b1e0974d9e084d31071c9..765245096bc37f259f09c84acce88be38882ec0a 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -15,6 +15,8 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_verifier.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -106,22 +108,57 @@ Status ShapeVerifier::HandleReducePrecision(HloInstruction* reduce_precision) { reduce_precision->mantissa_bits())); } -Status ShapeVerifier::HandleInfeed(HloInstruction*) { return Status::OK(); } +namespace { + +Status CheckIsTokenOperand(const HloInstruction* instruction, + int64 operand_no) { + const HloInstruction* token = instruction->operand(operand_no); + if (!ShapeUtil::Equal(token->shape(), ShapeUtil::MakeTokenShape())) { + return InternalError( + "Expected operand %lld to be token-shaped, actual shape is" + "%s:\n%s", + operand_no, ShapeUtil::HumanString(token->shape()).c_str(), + instruction->ToString().c_str()); + } + return Status::OK(); +} + +} // namespace + +Status ShapeVerifier::HandleInfeed(HloInstruction* instruction) { + HloInfeedInstruction* infeed = Cast(instruction); + // Infeed has an optional single token operand. + // TODO(b/80000000): Update when token is not optional. + if (infeed->operand_count() == 1) { + TF_RETURN_IF_ERROR(CheckIsTokenOperand(instruction, 0)); + } + + // The output of infeed is a tuple containing the data value and a token. + return CheckShape(infeed, + ShapeUtil::MakeTupleShape( + {infeed->infeed_shape(), ShapeUtil::MakeTokenShape()})); +} + +Status ShapeVerifier::HandleOutfeed(HloInstruction* instruction) { + HloOutfeedInstruction* outfeed = Cast(instruction); + // Outfeed has an optional token operand (operand 1). + // TODO(b/80000000): Update when token is not optional. + if (outfeed->operand_count() == 2) { + TF_RETURN_IF_ERROR(CheckIsTokenOperand(instruction, 1)); + } -Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) { // Outfeed has a separate shape field for the value which is outfed to the - // host. The shape of the instruction itself is always nil because the outfeed - // produces no HLO value in the graph. + // host. The shape of the instruction itself is always a token. if (!ShapeUtil::Compatible(outfeed->outfeed_shape(), outfeed->operand(0)->shape())) { return InternalError( - "Expected outfeed to have shape compatible with operand's shape %s, " + "Expected outfeed shape to be compatible with operand's shape %s, " "actual shape is %s:\n%s", ShapeUtil::HumanString(outfeed->operand(0)->shape()).c_str(), ShapeUtil::HumanString(outfeed->outfeed_shape()).c_str(), outfeed->ToString().c_str()); } - return CheckShape(outfeed, ShapeUtil::MakeNil()); + return CheckShape(outfeed, ShapeUtil::MakeTokenShape()); } Status ShapeVerifier::HandleHostCompute(HloInstruction*) { @@ -137,7 +174,16 @@ Status ShapeVerifier::HandleReverse(HloInstruction* reverse) { } Status ShapeVerifier::HandleSort(HloInstruction* sort) { - return CheckUnaryShape(sort); + if (sort->operand_count() == 2 && + !ShapeUtil::SameDimensions(sort->operand(0)->shape(), + sort->operand(1)->shape())) { + return InternalError( + "Expected sort to have to have the same dimensions for the keys and " + "the values. Keys shape is: %s\n, Values shape is: %s", + ShapeUtil::HumanString(sort->operand(0)->shape()).c_str(), + ShapeUtil::HumanString(sort->operand(1)->shape()).c_str()); + } + return CheckVariadicShape(sort); } Status ShapeVerifier::HandleConstant(HloInstruction* constant) { @@ -299,6 +345,7 @@ Status ShapeVerifier::HandleSend(HloInstruction* send) { const HloInstruction* send_done = send->users().front(); TF_RET_CHECK(send_done->opcode() == HloOpcode::kSendDone); TF_RETURN_IF_ERROR(CheckSameChannel(send, send_done)); + TF_RETURN_IF_ERROR(CheckIsTokenOperand(send, 1)); return CheckShape( send, ShapeUtil::MakeTupleShape( {send->operand(0)->shape(), ShapeUtil::MakeShape(U32, {})})); @@ -309,6 +356,7 @@ Status ShapeVerifier::HandleSendDone(HloInstruction* send_done) { const HloInstruction* send = send_done->operand(0); TF_RET_CHECK(send->opcode() == HloOpcode::kSend); TF_RETURN_IF_ERROR(CheckSameChannel(send, send_done)); + return CheckShape(send_done, ShapeUtil::MakeNil()); } @@ -317,6 +365,7 @@ Status ShapeVerifier::HandleRecv(HloInstruction* recv) { const HloInstruction* recv_done = recv->users().front(); TF_RET_CHECK(recv_done->opcode() == HloOpcode::kRecvDone); TF_RETURN_IF_ERROR(CheckSameChannel(recv, recv_done)); + TF_RETURN_IF_ERROR(CheckIsTokenOperand(recv, 0)); return CheckShape(recv, ShapeUtil::MakeTupleShape( {recv_done->shape(), ShapeUtil::MakeShape(U32, {})})); @@ -426,6 +475,14 @@ Status ShapeVerifier::HandleGather(HloInstruction* gather) { gather->gather_dimension_numbers(), gather->gather_window_bounds())); } +Status ShapeVerifier::HandleAfterAll(HloInstruction* token) { + std::vector operand_shapes; + for (const HloInstruction* operand : token->operands()) { + operand_shapes.push_back(&operand->shape()); + } + return CheckShape(token, ShapeInference::InferAfterAllShape(operand_shapes)); +} + Status ShapeVerifier::CheckShape(const HloInstruction* instruction, const Shape& inferred_shape) { // If allow_mixed_precision_ is false, check if there are operands with @@ -777,8 +834,7 @@ Status HloVerifier::CheckElementwiseInstruction(HloInstruction* instruction) { const Shape& out_shape = instruction->shape(); for (HloInstruction* operand : instruction->operands()) { const Shape& operand_shape = operand->shape(); - if (!ShapeUtil::IsScalar(operand_shape) && - !ShapeUtil::CompatibleIgnoringElementType(operand_shape, out_shape)) { + if (!ShapeUtil::CompatibleIgnoringElementType(operand_shape, out_shape)) { return FailedPrecondition( "Implicit broadcast is not allowed in HLO." "Found non-compatible shapes for instruction %s.\n" @@ -791,6 +847,39 @@ Status HloVerifier::CheckElementwiseInstruction(HloInstruction* instruction) { return Status::OK(); } +namespace { + +// Returns true if the given Shape has a TOKEN shape as any subshape. +bool ShapeContainsToken(const Shape& shape) { + bool contains_token = false; + ShapeUtil::ForEachSubshape( + shape, [&contains_token](const Shape& subshape, const ShapeIndex&) { + if (ShapeUtil::IsToken(subshape)) { + contains_token = true; + } + }); + return contains_token; +} + +// Verifies that all types entering and exiting the entry computation are +// legal. +Status VerifyEntryAndExitShapes(const HloModule& module) { + // Tokens cannot be passed as entry parameters. + // TODO(b/80000000): Remove this constraint. + for (int i = 0; i < module.entry_computation()->num_parameters(); ++i) { + HloInstruction* param = + module.entry_computation()->parameter_instruction(i); + if (ShapeContainsToken(param->shape())) { + return InternalError( + "Entry parameter %d is or contains a token shape: %s", i, + ShapeUtil::HumanString(param->shape()).c_str()); + } + } + return Status::OK(); +} + +} // namespace + StatusOr HloVerifier::Run(HloModule* module) { TF_RETURN_IF_ERROR(VerifyHloStructure(module)); @@ -832,7 +921,9 @@ StatusOr HloVerifier::Run(HloModule* module) { << " != " << ShapeUtil::Rank(instruction->operand(0)->shape()); } else if (instruction->opcode() == HloOpcode::kWhile) { TF_RETURN_IF_ERROR(CheckWhileInstruction(instruction)); - } else if (instruction->IsElementwise()) { + } else if (instruction->opcode() != + HloOpcode::kRng /* Rng operands are always scalar. */ + && instruction->IsElementwise()) { TF_RETURN_IF_ERROR(CheckElementwiseInstruction(instruction)); } @@ -851,6 +942,8 @@ StatusOr HloVerifier::Run(HloModule* module) { TF_RETURN_IF_ERROR(computation->Accept(shape_verifier.get())); } + TF_RETURN_IF_ERROR(VerifyEntryAndExitShapes(*module)); + return false; } diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 1392a78097aa026b2f7cffa2b0135402d3ca7ae5..da6b5d222206fe9bfcbf5157dc524ed46edaaac7 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -81,6 +81,7 @@ class ShapeVerifier : public DfsHloVisitor { HloInstruction* batch_norm_inference) override; Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override; Status HandleGather(HloInstruction* gather) override; + Status HandleAfterAll(HloInstruction* token) override; Status FinishVisit(HloInstruction*) override { return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.cc b/tensorflow/compiler/xla/service/indexed_array_analysis.cc index 8b3fa6c1572cf0ed91fc427722edcb23d8b8529d..1985d20578677ae68b244023c4640454b004bf49 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.cc @@ -28,6 +28,7 @@ namespace { using Analysis = IndexedArrayAnalysis; using UnknownArray = Analysis::UnknownArray; using ConstantArray = Analysis::ConstantArray; +using ReshapedArray = Analysis::ReshapedArray; using ScalarIndexedArray = Analysis::ScalarIndexedArray; using tensorflow::gtl::ArraySlice; using tensorflow::str_util::Join; @@ -52,6 +53,13 @@ string IndexedArrayAnalysis::ToString(Array* root, bool print_constants) { "(constant ", ShapeUtil::HumanString(root->shape()), ")"); } + case Array::kReshaped: { + ReshapedArray* reshaped_array = root->as(); + return tensorflow::strings::StrCat( + "(reshape ", ToString(reshaped_array->operand(), print_constants), + " to ", ShapeUtil::HumanString(reshaped_array->shape()), ")"); + } + case Array::kScalarIndexedConstant: case Array::kScalarIndexed: { auto* indexed_array = root->as(); @@ -239,15 +247,40 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForGather( tensorflow::gtl::ArraySlice window_bounds, Array* source, Array* indices) { if (dim_numbers.index_vector_dim() != indices->shape().dimensions_size()) { + VLOG(3) << "ComputeArrayForGather: indices are not scalar"; return nullptr; } CHECK_EQ(dim_numbers.gather_dims_to_operand_dims_size(), 1); - if (!c_binary_search(dim_numbers.elided_window_dims(), - dim_numbers.gather_dims_to_operand_dims(0))) { + + // We can also handle dim_numbers.elided_window_dims_size() == 0 here, should + // it become relevant. + + if (dim_numbers.elided_window_dims_size() != 1 || + dim_numbers.elided_window_dims(0) != + dim_numbers.gather_dims_to_operand_dims(0)) { + VLOG(3) << "ComputeArrayForGather: gather operations must elide " + "gather_dims_to_operand_dims[0] and " + "gather_dims_to_operand_dims[0] only"; return nullptr; } + // ScalarIndexedArray cannot represent gathers that "slice" along some + // dimensions -- for instance it cannot represent a gather that picks 5 [2,3] + // arrays from an array of size [7,4,6]. We check that condition down below: + + for (int64 i = 0, e = source->shape().dimensions_size(); i < e; i++) { + if (i != dim_numbers.elided_window_dims(0) && + source->shape().dimensions(i) != window_bounds[i]) { + VLOG(3) << "ComputeArrayForGather: window_bounds[" << i + << "] != source->shape().dimensions(" << i << ") -- " + << source->shape().dimensions(i) << " vs. " << window_bounds[i] + << " with dim_numbers.elided_window_dims(0) = " + << dim_numbers.elided_window_dims(0); + return nullptr; + } + } + int64 source_dim = dim_numbers.gather_dims_to_operand_dims(0); std::vector output_dims; for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { @@ -336,7 +369,11 @@ std::vector ComputeReshapePassthroughDimPairs( // result_subarray_size does not include the elements in the current // `result_dim` dimension (we multiply in result_shape[result_dim] at the // end of loop body) so candidate_operand_dim can never be zero. - CHECK_NE(candidate_operand_dim, 0); + CHECK_NE(candidate_operand_dim, 0) + << "result_dim = " << result_dim + << ", result_subarray_size = " << result_subarray_size + << ", result_shape = [" << Join(result_shape, ",") << "]" + << ", operand_shape = [" << Join(operand_shape, ",") << "]"; if (candidate_operand_dim != -1 && result_shape[result_dim] == operand_shape[candidate_operand_dim - 1]) { @@ -357,7 +394,7 @@ std::vector ComputeReshapePassthroughDimPairs( }); VLOG(3) << "For a reshape from [" << Join(operand_shape, ",") << "] to [" << Join(result_shape, ",") << "] passthrough indices are [" - << Join(result_strings, ",") << "]"; + << Join(result_strings, ",") << "] (legend: `result`->`operand`)"; } DCHECK(c_is_sorted( @@ -398,6 +435,10 @@ int64 MapPassthroughOperandDimToResultDim( int64 FindSourcePositionForPassthroughResultDim(ArraySlice operand_shape, ArraySlice result_shape, int64 source_passthrough_dim) { + VLOG(3) << "FindSourcePositionForPassthroughResultDim([" + << Join(operand_shape, ",") << "], [" << Join(result_shape, ",") + << "], " << source_passthrough_dim << ")"; + int64 indexed_source_subarray_size = std::accumulate(operand_shape.begin() + source_passthrough_dim + 1, operand_shape.end(), 1, std::multiplies()); @@ -405,15 +446,191 @@ int64 FindSourcePositionForPassthroughResultDim(ArraySlice operand_shape, return FindSuffixWithProduct(result_shape, indexed_source_subarray_size); } +Shape StripDegenerateDimensions(const Shape& shape) { + DimensionVector new_dims; + c_copy_if(shape.dimensions(), std::back_inserter(new_dims), + [](int64 dim) { return dim != 1; }); + return ShapeUtil::MakeShape(shape.element_type(), new_dims); +} }; // namespace -StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( - const Shape& shape, Array* operand) { - auto* scalar_indexed = dynamic_cast(operand); - if (!scalar_indexed) { +StatusOr +IndexedArrayAnalysis::ReshapeToRemoveDegenerateDims( + ScalarIndexedArray* operand) { + const Shape& shape = operand->shape(); + if (!ShapeUtil::HasDegenerateDimensions(shape)) { + return operand; + } + + // We only need to reshape out the degenerate dims from the indices and the + // source (except the source dim). + + const Shape& source_shape = operand->source()->shape(); + DimensionVector new_source_shape_dims; + for (int64 i = 0, e = source_shape.dimensions_size(); i < e; i++) { + if (i == operand->source_dim() || source_shape.dimensions(i) != 1) { + new_source_shape_dims.push_back(source_shape.dimensions(i)); + } + } + + Shape new_source_shape = + ShapeUtil::MakeShape(shape.element_type(), new_source_shape_dims); + Shape new_indices_shape = + StripDegenerateDimensions(operand->indices()->shape()); + + TF_ASSIGN_OR_RETURN( + Array* const new_source, + ComputeArrayForReshape(new_source_shape, operand->source())); + TF_ASSIGN_OR_RETURN( + Array* const new_indices, + ComputeArrayForReshape(new_indices_shape, operand->indices())); + + // Build the new output dims while keeping track of the degenerate dims that + // will no longer be present. + DimensionVector new_output_dims; + int64 degenerate_dims_seen = 0; + for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { + if (shape.dimensions(i) == 1) { + degenerate_dims_seen++; + } else if (ArrayContains(operand->output_dims(), i)) { + new_output_dims.push_back(i - degenerate_dims_seen); + } + } + + // Similarly, build the new source dim while keeping track of the degenerate + // dims that will no longer be present. + int64 degenerate_dims_before_source_dim = + std::count(source_shape.dimensions().begin(), + source_shape.dimensions().begin() + operand->source_dim(), 1); + int64 new_source_dim = + operand->source_dim() - degenerate_dims_before_source_dim; + + return ConstructScalarIndexedArray( + new_source, new_indices, new_source_dim, + InlinedVectorToVector(new_output_dims), + StripDegenerateDimensions(operand->shape())); +} + +StatusOr IndexedArrayAnalysis::ReshapeToAddDegenerateDims( + ScalarIndexedArray* operand, + tensorflow::gtl::ArraySlice degenerate_dims) { + if (degenerate_dims.empty()) { + return operand; + } + + CHECK(!ShapeUtil::HasDegenerateDimensions(operand->shape())); + + DimensionVector new_output_dims = [&]() { + // To make things easy we use a "scratch" buffer of bools where the i'th + // element is true iff the i'th component of the result index is an output + // index. + + gtl::InlinedVector output_dims_bitvector( + operand->shape().dimensions_size()); + for (int64 output_dim : operand->output_dims()) { + output_dims_bitvector[output_dim] = true; + } + + for (int64 degenerate_dim : degenerate_dims) { + InsertAt(&output_dims_bitvector, degenerate_dim, false); + } + + DimensionVector result; + result.reserve(operand->output_dims().size()); + for (int64 i = 0, e = output_dims_bitvector.size(); i < e; i++) { + if (output_dims_bitvector[i]) { + result.push_back(i); + } + } + + return result; + }(); + + DimensionVector new_result_shape_dims; + c_copy(operand->shape().dimensions(), + std::back_inserter(new_result_shape_dims)); + for (int64 degenerate_dim : degenerate_dims) { + InsertAt(&new_result_shape_dims, degenerate_dim, 1); + } + + DimensionVector new_source_shape_dims = new_result_shape_dims; + for (int64 output_dim : new_output_dims) { + EraseAt(&new_source_shape_dims, output_dim); + } + + int64 new_source_dim = [&]() { + for (int i = 0, e = new_source_shape_dims.size(); i < e; i++) { + int64 non_degenerate_dims_seen = 0; + if (non_degenerate_dims_seen == operand->source_dim()) { + return i; + } + if (new_source_shape_dims[new_source_dim] != 1) { + non_degenerate_dims_seen++; + } + } + LOG(FATAL) << "Did not find source dim in " << ToString(operand); + }(); + + int64 source_dim_size = + operand->source()->shape().dimensions(operand->source_dim()); + InsertAt(&new_source_shape_dims, /*index=*/new_source_dim, + /*value=*/source_dim_size); + + Shape new_source_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + new_source_shape_dims); + Shape new_result_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + new_result_shape_dims); + + TF_ASSIGN_OR_RETURN( + Array* const new_source, + ComputeArrayForReshape(new_source_shape, operand->source())); + return ConstructScalarIndexedArray( + new_source, operand->indices(), new_source_dim, + InlinedVectorToVector(new_output_dims), new_result_shape); +} + +StatusOr IndexedArrayAnalysis::FoldReshapeOfGather( + const Shape& shape, ScalarIndexedConstantArray* operand) { + VLOG(3) << "FoldReshapeOfGather(" << ToString(operand) << ")"; + + // To make things easier on ourselves, instead of directly trying to fold the + // reshape of `operand` to `shape`, we call + // `FoldReshapeOfGatherNoDegenerateDims` on shapes without degenerate dims and + // handle the degenerate dimensions here by inserting reshapes. + + TF_ASSIGN_OR_RETURN(ScalarIndexedArray* const operand_without_degenerate_dims, + ReshapeToRemoveDegenerateDims(operand)); + + Shape output_shape_without_degenerate_dims = StripDegenerateDimensions(shape); + TF_ASSIGN_OR_RETURN( + ScalarIndexedArray* const folded_reshape_without_degenerate_dims, + FoldReshapeOfGatherNoDegenerateDims( + output_shape_without_degenerate_dims, + operand_without_degenerate_dims->as())); + + if (folded_reshape_without_degenerate_dims == nullptr) { return nullptr; } + DimensionVector degenerate_result_dims; + for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { + if (shape.dimensions(i) == 1) { + degenerate_result_dims.push_back(i); + } + } + + return ReshapeToAddDegenerateDims(folded_reshape_without_degenerate_dims, + degenerate_result_dims); +} + +StatusOr +IndexedArrayAnalysis::FoldReshapeOfGatherNoDegenerateDims( + const Shape& shape, ScalarIndexedConstantArray* scalar_indexed) { + VLOG(3) << "FoldReshapeOfGatherNoDegenerateDims(" << ToString(scalar_indexed) + << ")"; + CHECK(!ShapeUtil::HasDegenerateDimensions(shape)); + CHECK(!ShapeUtil::HasDegenerateDimensions(scalar_indexed->shape())); + // Try to fold Reshape(ScalarIndexed(Const, Indices)) // => ScalarIndexed(Const', Indices) // @@ -464,7 +681,7 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( std::vector reshape_passthrough_dims = ComputeReshapePassthroughDimPairs( - /*operand_shape=*/AsInt64Slice(operand->shape().dimensions()), + /*operand_shape=*/AsInt64Slice(scalar_indexed->shape().dimensions()), /*result_shape=*/AsInt64Slice(shape.dimensions())); auto is_reshape_passthrough_operand_dim = [&](int64 operand_dim) { @@ -474,6 +691,8 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( if (!c_all_of(scalar_indexed->output_dims(), is_reshape_passthrough_operand_dim)) { + VLOG(3) << "Not all output dims are passthrough dims " + << ToString(scalar_indexed); return nullptr; } @@ -527,6 +746,11 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( // (a.k.a. isn't pass-through) than the [3,5,2] array. if (source_dim_for_new_scalar_indexed_node == -1) { + VLOG(3) << "Could not compute the source dim for the new scalar indexed " + "node: scalar_indexed_source_shape = [" + << Join(scalar_indexed_source_shape.dimensions(), ",") + << "] and new_scalar_indexed_source_shape = [" + << Join(new_scalar_indexed_source_shape, ",") << "]"; return nullptr; } @@ -534,6 +758,10 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( &new_scalar_indexed_source_shape, source_dim_for_new_scalar_indexed_node, scalar_indexed_source_shape.dimensions(scalar_indexed->source_dim())); + CHECK_EQ(c_accumulate(new_scalar_indexed_source_shape, 1l, + std::multiplies()), + ShapeUtil::ElementsIn(scalar_indexed_source_shape)); + CHECK(IsReshapePassthroughOperandDim( ComputeReshapePassthroughDimPairs( /*operand_shape=*/AsInt64Slice( @@ -564,6 +792,31 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( output_dims_for_new_scalar_indexed_node, shape); } +StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( + const Shape& shape, Array* operand) { + if (ShapeUtil::Compatible(operand->shape(), shape)) { + return operand; + } + + if (auto* scalar_indexed = + dynamic_cast(operand)) { + TF_ASSIGN_OR_RETURN(Analysis::Array * reshape_folded_into_gather, + FoldReshapeOfGather(shape, scalar_indexed)); + if (reshape_folded_into_gather) { + return reshape_folded_into_gather; + } + } + + if (auto* constant_array = dynamic_cast(operand)) { + TF_ASSIGN_OR_RETURN(Literal* const new_literal, + TakeOwnership(constant_array->literal()->Reshape( + AsInt64Slice(shape.dimensions())))); + return Construct(new_literal); + } + + return Construct(operand, shape); +} + StatusOr IndexedArrayAnalysis::ComputeArrayForElementwiseBinaryOp(HloOpcode opcode, Array* lhs, diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.h b/tensorflow/compiler/xla/service/indexed_array_analysis.h index ce92fd2919c90fa8a2fb7b796ed6f0fdaf48fe62..8684430231c1929f82508e3675f1c275c42b6149 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.h +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.h @@ -39,7 +39,13 @@ class IndexedArrayAnalysis { // Array instances are immutable once created. class Array { public: - enum Kind { kUnknown, kConstant, kScalarIndexedConstant, kScalarIndexed }; + enum Kind { + kUnknown, + kConstant, + kReshaped, + kScalarIndexedConstant, + kScalarIndexed + }; virtual Kind kind() const = 0; virtual const Shape& shape() const = 0; @@ -96,6 +102,27 @@ class IndexedArrayAnalysis { friend class IndexedArrayAnalysis; }; + // Represents an Array that is a reshape of another Array. + class ReshapedArray : public Array { + public: + Kind kind() const override { return kReshaped; } + + // The array to reshape. + Array* operand() const { return operand_; } + + // The output shape. + const Shape& shape() const override { return shape_; } + + private: + explicit ReshapedArray(Array* operand, Shape shape) + : operand_(operand), shape_(shape) {} + + Array* operand_; + const Shape shape_; + + friend class IndexedArrayAnalysis; + }; + // --------------------------------------------------------------------------- // Indexed Array Overview // --------------------------------------------------------------------------- @@ -266,6 +293,21 @@ class IndexedArrayAnalysis { ScalarIndexedArray* source, Array* indices, int64 source_dim, tensorflow::gtl::ArraySlice output_dims, Shape shape); + // Reshapes a scalar-indexed node to remove the degenerate dimensions in its + // output. The result is always a scalar-indexed node. + StatusOr ReshapeToRemoveDegenerateDims( + ScalarIndexedArray* operand); + + // Reshapes a scalar-indexed node such that the result has the degenerate + // dimensions `degenerate_dims`. The result is always a scalar-indexed node. + StatusOr ReshapeToAddDegenerateDims( + ScalarIndexedArray* operand, + tensorflow::gtl::ArraySlice degenerate_dims); + + StatusOr FoldReshapeOfGather( + const Shape& shape, ScalarIndexedConstantArray* operand); + StatusOr FoldReshapeOfGatherNoDegenerateDims( + const Shape& shape, ScalarIndexedConstantArray* scalar_indexed); StatusOr ComputeArrayForReshape(const Shape& shape, Array* operand); StatusOr ComputeArrayForElementwiseBinaryOp(HloOpcode opcode, diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc index 373556ebeba883f7dc2116bdf0ffc3274182f775..fc2befe05b18651502c42b9892e766145d85f2e8 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/compiler/xla/service/indexed_array_analysis.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/compiler/xla/tests/test_utils.h" @@ -34,6 +36,27 @@ class IndexedArrayAnalysisTest : public HloVerifiedTestBase { } private: + // Replaces seqences of whitespace with a single space. This makes the + // strings being matched against "whitespace insensitive" which lets us indent + // them for readability. + string CanonicalizeWhitespace(const string& text) { + string result; + + for (char c : text) { + if (!isspace(c)) { + result.push_back(c); + } else if (!result.empty() && result.back() != ' ') { + result.push_back(' '); + } + } + + while (!result.empty() && result.back() == ' ') { + result.pop_back(); + } + + return result; + } + void AssertArrayForRootExpressionIsImpl(const string& hlo_text, const string& root_expression, bool print_constants) { @@ -44,10 +67,10 @@ class IndexedArrayAnalysisTest : public HloVerifiedTestBase { IndexedArrayAnalysis::Array* const array_result, indexed_tensor_analysis.GetArrayFor( module().entry_computation()->root_instruction())); - string string_result = - indexed_tensor_analysis.ToString(array_result, print_constants); + string string_result = CanonicalizeWhitespace( + indexed_tensor_analysis.ToString(array_result, print_constants)); LOG(INFO) << string_result; - ASSERT_EQ(string_result, root_expression); + ASSERT_EQ(string_result, CanonicalizeWhitespace(root_expression)); } }; @@ -91,6 +114,82 @@ ENTRY main { hlo_text, "(scalar-indexed-const (constant s32[3,3]) %indices 0->[0])"); } +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed0) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3] constant(s32[3,3]{{1,2,3},{1,2,3},{1,2,3}}) + indices = s32[5,2] parameter(0) + ROOT gather = s32[5] gather(operand, indices), + output_window_dims={}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed1) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3,1] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,3] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,2}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,3,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed2) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3,1] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,2,3] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={2}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={2,3,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed3) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,2} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + TEST_F(IndexedArrayAnalysisTest, GatherOfGather_OneToOne) { string hlo_text = R"( HloModule SimpleGather @@ -273,7 +372,157 @@ ENTRY main { "(scalar-indexed-const (constant s32[3,3,4]) %indices 0->[0,3])"); } -TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNegative0) { +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather3) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,6] constant(s32[2,6]{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}) + indices = s32[1] parameter(0) + gather = s32[1,6] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,6} + ROOT reshape = s32[1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,6]) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather4) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,3]{1,0} constant(s32[2,3] { { 1, 2, 3 }, { 1, 2, 3 } }) + + i.0 = s64[1,3]{1,0} parameter(0) + g.0 = s32[1,3,3]{2,1,0} gather(operand, i.0), output_window_dims={2}, + elided_window_dims={0}, gather_dims_to_operand_dims={0}, + index_vector_dim=2, window_bounds={1,3} + + i.1 = s64[1] parameter(1) + g.1 = s32[1,1,3]{2,1,0} gather(g.0, i.1), output_window_dims={0,2}, + elided_window_dims={1}, gather_dims_to_operand_dims={1}, + index_vector_dim=1, window_bounds={1,1,3} + + ROOT reshape = s32[1,3]{1,0} reshape(g.1) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,3]) + (reshape + (scalar-indexed %i.0 %i.1 1->[1]) + to s64[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather5) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[1,6] constant(s32[1,6]{{1,2,3,4,5,6}}) + indices = s32[1] parameter(0) + gather = s32[1,6] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,6} + ROOT reshape = s32[1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[1,1,1,6]) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather6) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[1,2,6] constant(s32[1,2,6]{{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}}) + indices = s32[1] parameter(0) + gather = s32[1,1,6] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={1,1,6} + ROOT reshape = s32[1,1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,1,6] s32[2,1,1,1,6] { + { /*i0=0*/ { /*i1=0*/ { /*i2=0*/ {1, 2, 3, 4, 5, 6} } } }, + { /*i0=1*/ { /*i1=0*/ { /*i2=0*/ {1, 2, 3, 4, 5, 6} } } } }) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, + expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather7) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,6] constant(s32[2,6]{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}) + indices = s32[1,5] parameter(0) + gather = s32[1,5,6] gather(operand, indices), + output_window_dims={2}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=2, + window_bounds={1,6} + ROOT reshape = s32[1,1,5,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,6] s32[2,1,1,6] { + { /*i0=0*/ { /*i1=0*/ {1, 2, 3, 4, 5, 6} } }, + { /*i0=1*/ { /*i1=0*/ {1, 2, 3, 4, 5, 6} } } }) + (reshape %indices to s32[5]) + 0->[2]) +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, + expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold0) { string hlo_text = R"( HloModule ReshapeOfGather @@ -290,10 +539,19 @@ ENTRY main { } )"; - AssertArrayForRootExpressionIs(hlo_text, "%reshape"); + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,4]) + %indices + 0->[0,2]) + to s32[5,2,2,2,3]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); } -TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNegative1) { +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold1) { string hlo_text = R"( HloModule ReshapeOfGather @@ -313,7 +571,48 @@ ENTRY main { } )"; - AssertArrayForRootExpressionIs(hlo_text, "%reshape"); + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,5,2]) + %indices + 1->[2]) + to s32[6,7]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold2) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[3,4,1] constant(s32[3,4,1]{ + {{1},{2},{3},{4}}, + {{1},{2},{3},{4}}, + {{1},{2},{3},{4}}}) + indices = s32[5,6] parameter(0) + gather = s32[5,4,6,1] gather(operand, indices), + output_window_dims={1,3}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=2, + window_bounds={1,4,1} + ROOT reshape = s32[5,2,2,2,3,1] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,4,1]) + %indices + 0->[0,2]) + to s32[5,2,2,2,3,1]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); } TEST_F(IndexedArrayAnalysisTest, UnaryOpOfGather) { diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index 429c8503432b79f46aa0e5b1970bb565093128dd..088cc2622695c7724dae2b6cde28fecd40547445 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -83,6 +83,7 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) { case HloOpcode::kNegate: case HloOpcode::kNot: case HloOpcode::kOr: + case HloOpcode::kXor: case HloOpcode::kOutfeed: case HloOpcode::kPad: case HloOpcode::kReal: @@ -96,6 +97,7 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) { case HloOpcode::kShiftRightLogical: case HloOpcode::kSlice: case HloOpcode::kSubtract: + case HloOpcode::kAfterAll: case HloOpcode::kTranspose: case HloOpcode::kTuple: return false; @@ -236,6 +238,30 @@ InstructionFusion::ComputeGloballyUnfusable( if (EffectivelyAtMostUnary(producer)) { continue; } + + // If the total size of the inputs is less than or equal to the total size + // of the outputs for the producer then duplicating it won't increase the + // memory traffic. In that case, we do not forbid fusion of the operation + // here. + auto total_size = [](const Shape& shape) { + int64 size = 0; + ShapeUtil::ForEachSubshape( + shape, + [&size](const Shape& subshape, const ShapeIndex& shape_index) { + if (ShapeUtil::IsArray(subshape)) { + size += ShapeUtil::ElementsIn(subshape); + } + }); + return size; + }; + int64 operands_size = 0; + for (const HloInstruction* op : producer->operands()) { + operands_size += total_size(op->shape()); + } + if (operands_size <= total_size(producer->shape())) { + continue; + } + // Otherwise we will forbid fusing the op unless we can fuse it into // all of its consumers on all paths. // @@ -280,10 +306,8 @@ StatusOr InstructionFusion::Run(HloModule* module) { // map from HloInstruction* to the instruction's index in the vector. An // instruction is "removed" from the vector by setting it's element to // nullptr. - std::list post_order_list = + std::vector post_order = computation_->MakeInstructionPostOrder(); - std::vector post_order(post_order_list.begin(), - post_order_list.end()); tensorflow::gtl::FlatMap post_order_index; for (size_t i = 0; i < post_order.size(); ++i) { diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc index 21db2338995960bde00ec9c4b325e5562fc3a592..bb7231c8c868ff2fefa3e88c4be036a89ed29118 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc @@ -167,7 +167,8 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusable) { builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "1")); HloInstruction* binary1 = builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); - builder.AddInstruction(HloInstruction::CreateSend(binary1, 0)); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + builder.AddInstruction(HloInstruction::CreateSend(binary1, token, 0)); HloInstruction* unary = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, binary1)); @@ -258,7 +259,8 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) { add = f32[4,3]{1,0} add(p0, p0) abs1 = f32[4,3]{1,0} abs(add) log = f32[4,3]{1,0} log(abs1) - send = f32[4,3]{1,0} send(log), channel_id=0 + token = token[] after-all() + send = f32[4,3]{1,0} send(log, token), channel_id=0 abs2 = f32[4,3]{1,0} abs(log) ROOT root = f32[4,3]{1,0} subtract(abs2, add) })") @@ -288,7 +290,8 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) { p0 = f32[4,3]{1,0} parameter(0) add1 = f32[4,3]{1,0} add(p0, p0) log = f32[4,3]{1,0} log(p0) - send = f32[4,3]{1,0} send(log), channel_id=0 + token = token[] after-all() + send = f32[4,3]{1,0} send(log, token), channel_id=0 add2 = f32[4,3]{1,0} add(log, add1) ROOT root = f32[4,3]{1,0} subtract(add1, add2) })") @@ -321,7 +324,8 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusableRecursively) { add1 = f32[4,3]{1,0} add(p0, p0) add2 = f32[4,3]{1,0} add(add1, add1) log = f32[4,3]{1,0} log(add2) - send = f32[4,3]{1,0} send(log), channel_id=0 + token = token[] after-all() + send = f32[4,3]{1,0} send(log, token), channel_id=0 sub1 = f32[4,3]{1,0} subtract(log, add2) sub2 = f32[4,3]{1,0} subtract(add2, add1) ROOT root = (f32[4,3]{1,0}, f32[4,3]{1,0}) tuple(sub1, sub2) @@ -352,7 +356,8 @@ TEST_F(InstructionFusionTest, AllowUnaryDuplication) { builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "0")); HloInstruction* unary1 = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kFloor, param0)); - builder.AddInstruction(HloInstruction::CreateSend(unary1, 0)); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + builder.AddInstruction(HloInstruction::CreateSend(unary1, token, 0)); HloInstruction* unary2 = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, unary1)); @@ -375,7 +380,8 @@ TEST_F(InstructionFusionTest, AllowEffectiveUnaryDuplication) { builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "1")); HloInstruction* binary1 = builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); - builder.AddInstruction(HloInstruction::CreateSend(binary1, 0)); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + builder.AddInstruction(HloInstruction::CreateSend(binary1, token, 0)); HloInstruction* unary = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, binary1)); diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.cc b/tensorflow/compiler/xla/service/interpreter/compiler.cc index c1666530687f2f8407a9dcb4e271c9d95552a689..9f8f4bda875cdff5e20fa8ca8eeecaa1140e2b9c 100644 --- a/tensorflow/compiler/xla/service/interpreter/compiler.cc +++ b/tensorflow/compiler/xla/service/interpreter/compiler.cc @@ -44,7 +44,7 @@ Status InterpreterCompiler::RunHloOptimization(HloModule* hlo_module) { HloPassPipeline pipeline("Interpreter"); pipeline.AddPass( - hlo_module->mutable_device_entry_computation_layout()); + hlo_module->mutable_entry_computation_layout()); return pipeline.Run(hlo_module).status(); } diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc index 029e71058a7373b9310c6d9ffdb65f72ca28e5af..9816acf6507a0ed5391cf4f1c94ccd0f27f5227a 100644 --- a/tensorflow/compiler/xla/service/interpreter/executable.cc +++ b/tensorflow/compiler/xla/service/interpreter/executable.cc @@ -75,9 +75,9 @@ StatusOr InterpreterExecutable::ExecuteOnStream( // consumes. std::vector> arg_literals; for (int64 p = 0; p < computation->num_parameters(); ++p) { - TF_ASSIGN_OR_RETURN( - std::unique_ptr arg_literal, - transfer_manager->TransferLiteralFromDevice(executor, *arguments[p])); + TF_ASSIGN_OR_RETURN(std::unique_ptr arg_literal, + transfer_manager->TransferLiteralFromDevice( + run_options->stream(), *arguments[p])); arg_literals.push_back(std::move(arg_literal)); } @@ -96,7 +96,7 @@ StatusOr InterpreterExecutable::ExecuteOnStream( result_literal->shape(), run_options->allocator(), executor->device_ordinal())); TF_RETURN_IF_ERROR(transfer_manager->TransferLiteralToDevice( - executor, *result_literal, result)); + run_options->stream(), *result_literal, result)); uint64 end_micros = tensorflow::Env::Default()->NowMicros(); diff --git a/tensorflow/compiler/xla/service/interpreter/executor.cc b/tensorflow/compiler/xla/service/interpreter/executor.cc index 97e9fa2c8e8ecd918ffe3df2fd4e731f3b91e6db..4fb67bd0b72fc591c1ffa76ebb0513bf14ed3737 100644 --- a/tensorflow/compiler/xla/service/interpreter/executor.cc +++ b/tensorflow/compiler/xla/service/interpreter/executor.cc @@ -53,6 +53,7 @@ bool XlaInterpreterExecutor::Memcpy(Stream *stream, void *host_dst, AsExecutorStream(stream)->EnqueueTask([this, host_dst, dev_src, size]() { port::Status ok = SynchronousMemcpy(host_dst, dev_src, size); }); + AsExecutorStream(stream)->BlockUntilDone(); return true; } @@ -61,6 +62,7 @@ bool XlaInterpreterExecutor::Memcpy(Stream *stream, DeviceMemoryBase *dev_dst, AsExecutorStream(stream)->EnqueueTask([this, dev_dst, host_src, size]() { port::Status ok = SynchronousMemcpy(dev_dst, host_src, size); }); + AsExecutorStream(stream)->BlockUntilDone(); return true; } diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index 7067b6f86a0fb24fb946ad236bca9bbd48d53722..36fdfa868dfbfaf9fbf353dd6623058d518fec04 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -175,41 +175,32 @@ Status LayoutConstraints::SetBufferLayout(const Layout& layout, TF_RETURN_IF_ERROR( LayoutUtil::ValidateLayoutForShape(layout, buffer.shape())); - const BufferLayoutConstraint* curr_constraint = - GetBufferLayoutConstraint(buffer); - if (curr_constraint != nullptr) { - if (LayoutUtil::Equal(curr_constraint->layout(), layout)) { + auto iter = buffer_constraints_.find(&buffer); + if (iter != buffer_constraints_.end()) { + const BufferLayoutConstraint& curr_constraint = iter->second; + if (LayoutUtil::Equal(curr_constraint.layout(), layout)) { // New constraint matches existing constraint. Nothing to do. return Status::OK(); } - if (curr_constraint->mandatory()) { + if (curr_constraint.mandatory()) { return FailedPrecondition( "Buffer %s already has the layout constraint %s, cannot add " "incompatible constraint %s", buffer.ToString().c_str(), - LayoutUtil::HumanString(curr_constraint->layout()).c_str(), + LayoutUtil::HumanString(curr_constraint.layout()).c_str(), LayoutUtil::HumanString(layout).c_str()); } - } - - auto iter = buffer_constraints_.find(&buffer); - bool overwrite = iter != buffer_constraints_.end(); - if (!overwrite) { + iter->second = BufferLayoutConstraint(layout, buffer, mandatory, dfs); + } else { + TF_RET_CHECK(unconstrained_buffer_ids_.erase(buffer.id()) == 1) + << buffer.ToString(); iter = buffer_constraints_ .insert(std::make_pair( &buffer, BufferLayoutConstraint(layout, buffer, mandatory, dfs))) .first; - } else { - iter->second = BufferLayoutConstraint(layout, buffer, mandatory, dfs); } added_constraints_.push_back(&iter->second); - - // Remove buffer from the set of unconstrained buffers. - TF_RET_CHECK(unconstrained_buffer_ids_.count(buffer.id()) == - static_cast(!overwrite)); - unconstrained_buffer_ids_.erase(buffer.id()); - return Status::OK(); } @@ -716,7 +707,8 @@ Status CheckParameterLayout(HloInstruction* parameter, const ComputationLayout& computation_layout) { const ShapeLayout& parameter_layout = computation_layout.parameter_layout(parameter->parameter_number()); - if (!parameter_layout.MatchesLayoutInShape(parameter->shape())) { + if (parameter_layout.LayoutIsSet() && + !parameter_layout.MatchesLayoutInShape(parameter->shape())) { return InternalError( "parameter instruction %s does not match layout of computation " "shape: %s", @@ -936,14 +928,15 @@ LayoutAssignment::LayoutAssignment( ComputationLayout* entry_computation_layout, ChannelLayoutConstraints* channel_constraints) : entry_computation_layout_(entry_computation_layout), + saved_entry_computation_layout_(*entry_computation_layout), channel_layout_constraints_(channel_constraints) { + if (channel_layout_constraints_ != nullptr) { + // Save a copy of the input ChannelLayoutConstraints so that we can reset it + // if we have to undo previous operations (ClearPreviousPassSideEffects()). + channel_constraints_ = *channel_layout_constraints_; + } VLOG(1) << "Entry computation layout given to layout assignment: " << entry_computation_layout_->ToString(); - // Layouts of all parameter instructions must be set. - for (const ShapeLayout& parameter_layout : - entry_computation_layout_->parameter_layouts()) { - CHECK(parameter_layout.LayoutIsSet()); - } } std::unique_ptr LayoutAssignment::ChooseOperandLayoutFromOutputLayout( @@ -1572,6 +1565,13 @@ Status LayoutAssignment::RunOnComputation( // Propagates layouts from mandatory and backend constraints. TF_RETURN_IF_ERROR(PropagateConstraints(&constraints)); + // Prior to applying default layouts, we take note of all HLO instructions + // which lack a layout constraint. + for (LogicalBuffer::Id buffer_id : constraints.unconstrained_buffer_ids()) { + unconstrained_layout_instructions_.insert( + points_to_analysis.GetBuffer(buffer_id).instruction()); + } + // While any unconstrained buffers remain, pick an arbitrary buffer, give it a // layout and propagate the change. while (!constraints.unconstrained_buffer_ids().empty()) { @@ -1614,13 +1614,57 @@ Status LayoutAssignment::RunOnComputation( // Record the layouts assigned for any communication ops in // channel_constraints so that they are constrained for future modules. + if (channel_constraints != nullptr) { + TF_RETURN_IF_ERROR( + ConstrainChannelLayouts(computation, channel_constraints)); + } + return Status::OK(); +} + +Status LayoutAssignment::ConstrainChannelLayouts( + HloComputation* computation, + ChannelLayoutConstraints* channel_constraints) { + // We go through the kRecvDone before. These must either impose their layout, + // of find a matching one already existing (ConstrainChannel() returns + // nullptr). for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kRecvDone) { + const Layout* layout = channel_constraints->ConstrainChannel( + instruction->channel_id(), instruction->shape().layout()); + TF_RET_CHECK(layout == nullptr) + << instruction->ToString() + << " cannot constrain layout as it was set to " + << LayoutUtil::HumanString(*layout); + } + } + // After that we go through the kSend. These are likely going to have a kCopy + // as operand (otherwise we add it), so in case the constrained layout does + // not match, we can change the kCopy layout (and the kSend one as well). + for (HloInstruction* instruction : computation->MakeInstructionPostOrder()) { if (instruction->opcode() == HloOpcode::kSend) { - channel_constraints->ConstrainChannel( - instruction->channel_id(), instruction->operand(0)->shape().layout()); - } else if (instruction->opcode() == HloOpcode::kRecvDone) { - channel_constraints->ConstrainChannel(instruction->channel_id(), - instruction->shape().layout()); + HloInstruction* operand = instruction->mutable_operand(0); + const Layout* layout = channel_constraints->ConstrainChannel( + instruction->channel_id(), operand->shape().layout()); + if (layout != nullptr) { + // We found an already constrained layout which does not match the one + // the kSend wants to impose. Eitehr add a new kCopy, or use the + // existing one to marshal the correct shape. + Shape shape = operand->shape(); + *shape.mutable_layout() = *layout; + if (operand->opcode() != HloOpcode::kCopy) { + HloInstruction* copy = operand->parent()->AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kCopy, operand)); + RegisterAddedCopy(copy); + SetupCopiedInstruction(*operand, copy, {}); + TF_RETURN_IF_ERROR(instruction->ReplaceOperandWith(0, copy)); + operand = copy; + } else { + *operand->mutable_shape() = shape; + } + Shape* send_shape = + ShapeUtil::GetMutableSubshape(instruction->mutable_shape(), {0}); + *send_shape = shape; + } } } return Status::OK(); @@ -1672,13 +1716,14 @@ StatusOr LayoutAssignment::Run(HloModule* module) { // when seen from an outer instruction, which has across-computation // constraints to impose. // For example, the kWhile instruction needs to enforce the same layouts for - // the parameters and root of the bosy, as well as the condition parameters. + // the parameters and root of the body, as well as the condition parameters. // Similarly, the kConditional instruction needs to enforce the same layouts // for the root of the true and false computations. // So in the first pass, while allowing the layouts to flow to parameters and // root, we also fix up the eventually inconsistent ComputationLayout, which // will be then made mandatory by the second pass. for (int64 i = 0; i < 2; ++i) { + VLOG(5) << "Running " << (i == 0 ? "un" : "") << "constrained pass"; TF_RETURN_IF_ERROR(ClearPreviousPassSideEffects(module)); TF_ASSIGN_OR_RETURN(auto points_to_analysis, TuplePointsToAnalysis::Run(module)); @@ -1716,10 +1761,12 @@ StatusOr LayoutAssignment::Run(HloModule* module) { Status LayoutAssignment::Init() { computation_layouts_.clear(); + *entry_computation_layout_ = saved_entry_computation_layout_; return Status::OK(); } Status LayoutAssignment::ClearPreviousPassSideEffects(HloModule* module) { + VLOG(5) << "Clearing previous side effects"; // Clear all the copies which have been added, and all the related // instructions (like GTE and tuples). int64 removed_copies = 0; @@ -1737,12 +1784,14 @@ Status LayoutAssignment::ClearPreviousPassSideEffects(HloModule* module) { } } added_copies_.clear(); + unconstrained_layout_instructions_.clear(); if (removed_copies > 0) { TupleSimplifier tuple_simplifier; HloDCE dce; TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); TF_RETURN_IF_ERROR(dce.Run(module).status()); } + ResetChannelConstraints(); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index c287cca0c54ba1bb514bd8d243c137eca99b258f..b75ecb311a07b996562460fc5d6fbd8e70ac056b 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -249,25 +249,30 @@ class ChannelLayoutConstraints { // Given `shape`, apply the layout for `channel_id`. `channel_id` must already // be constrained. Shape LayoutShapeForChannel(Shape shape, int64 channel_id) const { - CHECK(IsChannelConstrained(channel_id)); - *shape.mutable_layout() = constraints_.at(channel_id); + auto it = constraints_.find(channel_id); + CHECK(it != constraints_.end()) << "Channel " << channel_id; + *shape.mutable_layout() = it->second; return shape; } // Returns the layout constraint for `channel_id`, which must already be // constrained. - Layout LayoutForChannel(int64 channel_id) const { - CHECK(IsChannelConstrained(channel_id)); - return constraints_.at(channel_id); + const Layout& LayoutForChannel(int64 channel_id) const { + auto it = constraints_.find(channel_id); + CHECK(it != constraints_.end()) << "Channel " << channel_id; + return it->second; } // Adds a new layout constraint for `channel_id`. If a constraint for - // `channel_id` already exists, this operation requires that the new layout is - // the same as the previously constrained layout. - void ConstrainChannel(int64 channel_id, const Layout& layout) { - CHECK(!IsChannelConstrained(channel_id) || - LayoutUtil::Equal(layout, constraints_[channel_id])); - constraints_[channel_id] = layout; + // `channel_id` has been added, this API returns nullptr, otherwise returns + // the layout which has already been set for the channel. + const Layout* ConstrainChannel(int64 channel_id, const Layout& layout) { + auto it = constraints_.emplace(std::make_pair(channel_id, layout)); + if (it.second) { + return nullptr; + } + return LayoutUtil::Equal(layout, it.first->second) ? nullptr + : &it.first->second; } private: @@ -427,8 +432,13 @@ class LayoutAssignment : public HloPassInterface { Status PropagateComputationLayouts(HloComputation* computation, ComputationLayout* computation_layout); + // The pointer to the ComputationLayout passed as constructor parameter. ComputationLayout* entry_computation_layout_; + // A copy of entry_computation_layout_ used to reset it to the initial values + // during the multiple passes done by the layout assignment operation. + ComputationLayout saved_entry_computation_layout_; + protected: // Sets up the copy instruction according to the characteristic (sharding, // metadata, ...) of the reference instruction. The index argument is used @@ -464,6 +474,20 @@ class LayoutAssignment : public HloPassInterface { // itself). Status AddCopyForOperand(HloInstruction* instruction, int64 operand_number); + // Apply the channel layout constraints by populating the channel_constraints + // data structure passed in at constructor time. Eventually adds copies in + // case two ends of a channel ended up with a different leyout. + Status ConstrainChannelLayouts(HloComputation* computation, + ChannelLayoutConstraints* channel_constraints); + + // Resets the input ChannelLayoutConstraints to the original copy received + // from the constructor input. + void ResetChannelConstraints() { + if (channel_layout_constraints_ != nullptr) { + *channel_layout_constraints_ = channel_constraints_; + } + } + // Map containing the layouts of all computations assigned so // far. Computations are handled in a topological sort where computations are // handled before their caller instructions so the layouts of caller @@ -474,7 +498,19 @@ class LayoutAssignment : public HloPassInterface { // here. tensorflow::gtl::FlatSet added_copies_; - ChannelLayoutConstraints* channel_layout_constraints_; + // The pointer to the channel layout constraints passed in with the + // constructor. If not nullptr, this is an input/output argument. + ChannelLayoutConstraints* channel_layout_constraints_ = nullptr; + + // A copy of the input layout constraints used to reset the above pointer in + // case we have to undo operations due to the multiple passes over the + // computations/instructions. + ChannelLayoutConstraints channel_constraints_; + + // The set of HLO instructions which lacked any layout constraint, thus + // receiving propagated default layouts. + tensorflow::gtl::FlatSet + unconstrained_layout_instructions_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index bf0448a67674f24591d866b646b98aea09ebb12c..4cd584bf8b1e133ae1528308f1f6a15e8958acf1 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -52,10 +52,18 @@ using ::testing::ElementsAre; class LayoutAssignmentTest : public HloTestBase { protected: void AssignLayouts(HloModule* module, - ComputationLayout* entry_computation_layout) { - LayoutAssignment layout_assignment(entry_computation_layout); + ComputationLayout* entry_computation_layout, + ChannelLayoutConstraints* channel_constraints = nullptr) { + LayoutAssignment layout_assignment( + entry_computation_layout, /*channel_constraints=*/channel_constraints); EXPECT_IS_OK(layout_assignment.Run(module).status()); } + + std::vector LayoutOf(HloModule* module, tensorflow::StringPiece name) { + auto minor_to_major = + FindInstruction(module, name)->shape().layout().minor_to_major(); + return std::vector(minor_to_major.begin(), minor_to_major.end()); + } }; TEST_F(LayoutAssignmentTest, ComputationLayout) { @@ -707,17 +715,10 @@ TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { LayoutUtil::MakeLayout({2, 1, 0})); AssignLayouts(module.get(), &computation_layout); - auto layout_of = [&](tensorflow::StringPiece name) { - return FindInstruction(module.get(), name) - ->shape() - .layout() - .minor_to_major(); - }; - - EXPECT_THAT(layout_of("gte0"), ElementsAre(0, 1, 2)); - EXPECT_THAT(layout_of("gte1a"), ElementsAre(1, 2, 0)); - EXPECT_THAT(layout_of("gte1b"), ElementsAre(2, 0, 1)); - EXPECT_THAT(layout_of("fresult"), ElementsAre(2, 1, 0)); + EXPECT_THAT(LayoutOf(module.get(), "gte0"), ElementsAre(0, 1, 2)); + EXPECT_THAT(LayoutOf(module.get(), "gte1a"), ElementsAre(1, 2, 0)); + EXPECT_THAT(LayoutOf(module.get(), "gte1b"), ElementsAre(2, 0, 1)); + EXPECT_THAT(LayoutOf(module.get(), "fresult"), ElementsAre(2, 1, 0)); EXPECT_THAT(FindInstruction(module.get(), "gte1") ->shape() .tuple_shapes(0) @@ -769,9 +770,13 @@ TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) { false_builder.AddInstruction( HloInstruction::CreateParameter(0, tshape, "param")); // Using infeed as layout assignment does not mess up with it. - auto infeed = - false_builder.AddInstruction(HloInstruction::CreateInfeed(xshape, "")); - false_builder.AddInstruction(HloInstruction::CreateTuple({infeed})); + auto token = + false_builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto infeed = false_builder.AddInstruction( + HloInstruction::CreateInfeed(xshape, token, "")); + auto infeed_data = false_builder.AddInstruction( + HloInstruction::CreateGetTupleElement(xshape, infeed, 0)); + false_builder.AddInstruction(HloInstruction::CreateTuple({infeed_data})); } HloComputation* false_computation = module->AddEmbeddedComputation(false_builder.Build()); @@ -816,5 +821,45 @@ TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) { "Unexpected bitcast operation seen during layout assignment")); } +TEST_F(LayoutAssignmentTest, ChannelLayoutMismatch) { + // Pin non matching layouts to parameter and root. + const char* module_str = R"( + HloModule test_module + + ENTRY entry_computation { + param = (f32[2,2]) parameter(0) + gte = f32[2,2] get-tuple-element(param), index=0 + token = token[] after-all() + recv = (f32[2,2], u32[]) recv(token), channel_id=1, sharding={maximal device=1} + ROOT recv-done = f32[2,2] recv-done(recv), channel_id=1, + sharding={maximal device=1} + send = (f32[2,2], u32[]) send(gte, token), channel_id=1, + sharding={maximal device=0} + send-done = () send-done(send), channel_id=1, sharding={maximal device=0} + } + )"; + + auto module = ParseHloString(module_str).ValueOrDie(); + ComputationLayout computation_layout( + module->entry_computation()->ComputeProgramShape()); + Shape param_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1})}); + TF_ASSERT_OK( + computation_layout.mutable_parameter_layout(0)->CopyLayoutFromShape( + param_shape)); + computation_layout.mutable_result_layout()->ResetLayout( + LayoutUtil::MakeLayout({1, 0})); + + ChannelLayoutConstraints channel_constraints; + AssignLayouts(module.get(), &computation_layout, &channel_constraints); + + EXPECT_THAT(LayoutOf(module.get(), "gte"), ElementsAre(0, 1)); + EXPECT_THAT(LayoutOf(module.get(), "recv-done"), ElementsAre(1, 0)); + EXPECT_TRUE( + ShapeUtil::Equal(ShapeUtil::GetSubshape( + FindInstruction(module.get(), "send")->shape(), {0}), + ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}))); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc index 21bca1d6beff5b2804531724b94b123d4523c173..f200a08a3cd7e33351ec4607d67d40e7ab28f3b9 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc @@ -32,7 +32,8 @@ static const BufferAllocation* kParameterAllocation = new BufferAllocation( LogicalBuffer::Color(0)); void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, - llvm_ir::IrArray* array) { + llvm_ir::IrArray* array, + const ShapeIndex& index) { BufferAllocation::Slice buffer_slice; if (hlo.opcode() == HloOpcode::kParameter) { // Parameters may alias with each other but may not alias with our temporary @@ -40,7 +41,7 @@ void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, buffer_slice = BufferAllocation::Slice(kParameterAllocation, 0, 0); } else { const std::set slices = - assignment_.GetAllSlices(&hlo, /*index=*/{}); + assignment_.GetAllSlices(&hlo, index); if (slices.empty() || slices.size() > 1) { // Skip HLOs which don't have a buffer assigned or for which the // buffer can't be determined statically. We cannot determine their @@ -137,16 +138,18 @@ llvm::MDNode* AliasAnalysis::GetNoaliasMetadataForBuffer( // 2. Operands of users of the given hlo. // 3. Operands of the given hlo. // - // This set can be increased as we need. For now only consider top-level - // buffers (index = {}) not buffers nested within the instruction's - // operands/output which are not typically touched. + // This set can be increased as we need. std::vector worklist; auto add_buffers_to_worklist = [&worklist, &assignment](const HloInstruction* instruction) { - for (const LogicalBuffer* buffer : - assignment.GetSourceBuffers(instruction, /*index=*/{})) { - worklist.push_back(buffer); - } + ShapeUtil::ForEachSubshape( + instruction->shape(), + [&](const Shape& /*shape*/, const ShapeIndex& index) { + for (const LogicalBuffer* buffer : + assignment.GetSourceBuffers(instruction, index)) { + worklist.push_back(buffer); + } + }); }; for (HloInstruction* user : hlo.users()) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h index 5244ac61e56307857aca659854647bd6c3e991d7..fe9eab93aae95557e3ee27a64c09b78f37ac2348 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h @@ -38,7 +38,8 @@ class AliasAnalysis { // Augments IrArray with aliasing information. void AddAliasingInformationToIrArray(const HloInstruction& hlo, - llvm_ir::IrArray* array); + llvm_ir::IrArray* array, + const ShapeIndex& index = {}); private: // Returns a unique alias domain for this emitter. diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index 7323abeb2077154f82828bcda3e90eb45a67138a..ea10cef49a4a9aa048b3e0ea443f052645c4912a 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -29,9 +29,9 @@ limitations under the License. namespace xla { namespace llvm_ir { -static void Delinearize(std::vector* multidim, - llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) { +void IrArray::Index::Delinearize(std::vector* multidim, + llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) const { int64 divisor = 1; const Layout& layout = shape.layout(); for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { @@ -48,10 +48,11 @@ static void Delinearize(std::vector* multidim, // useful because cuda-memcheck can't help us much in XLA: Most of our // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. - auto* quot = ir_builder->CreateUDiv(linear, ir_builder->getInt64(divisor)); + auto* quot = + ir_builder->CreateUDiv(linear, GetConstantWithIndexType(divisor)); if (i < layout.minor_to_major_size() - 1) { (*multidim)[dimension] = ir_builder->CreateURem( - quot, ir_builder->getInt64(size_of_current_dimension)); + quot, GetConstantWithIndexType(size_of_current_dimension)); } else { (*multidim)[dimension] = quot; } @@ -65,6 +66,8 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, linear_(linear), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK_NE(linear, nullptr); + index_type_ = linear->getType(); CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) << " should have a layout."; @@ -77,6 +80,13 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, linear_(linear), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + if (size()) { + index_type_ = multidim_[0]->getType(); + } else { + CHECK_NE(linear_, nullptr); + index_type_ = linear_->getType(); + } + CHECK_NE(index_type_, nullptr); CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) @@ -88,6 +98,9 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, : multidim_(multidim.begin(), multidim.end()), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK_GT(multidim_.size(), 0); + index_type_ = multidim[0]->getType(); + CHECK_NE(index_type_, nullptr); CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)); } @@ -130,15 +143,15 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( CommonFactors(AsInt64Slice(input_shape.dimensions()), AsInt64Slice(output_shape.dimensions())); std::vector source_multidim_index( - ShapeUtil::Rank(input_shape), - llvm::UndefValue::get(builder->getInt64Ty())); + ShapeUtil::Rank(input_shape), llvm::UndefValue::get(index_type_)); // We compute the source indices in each common factor from only the target // indices in the same common factor. for (ssize_t k = common_factors.size() - 2; k >= 0; --k) { llvm::Value* logical_linear_index = Index(tensorflow::gtl::ArraySlice( multidim_, common_factors[k].second, - common_factors[k + 1].second - common_factors[k].second)) + common_factors[k + 1].second - common_factors[k].second), + index_type_) .Linearize( tensorflow::gtl::ArraySlice( AsInt64Slice(output_shape.dimensions()), @@ -150,9 +163,10 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( // linear index by each dimension size. for (int64 i = common_factors[k + 1].first - 1; i >= common_factors[k].first; --i) { - llvm::Value* divisor = builder->getInt64(input_shape.dimensions(i)); + llvm::Value* divisor = + GetConstantWithIndexType(input_shape.dimensions(i)); if (input_shape.dimensions(i) == 1) { - source_multidim_index[i] = builder->getInt64(0); + source_multidim_index[i] = GetConstantWithIndexType(0); } else if (i == common_factors[k].first) { source_multidim_index[i] = logical_linear_index; } else { @@ -168,14 +182,14 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) { return Index(source_multidim_index, linear(), input_shape); } - return Index(source_multidim_index); + return Index(source_multidim_index, index_type_); } IrArray::Index IrArray::Index::SourceIndexOfSlice( const Shape& shape, tensorflow::gtl::ArraySlice starts, tensorflow::gtl::ArraySlice strides, llvm::IRBuilder<>* builder) const { - Index source_index(multidim_.size()); + Index source_index(index_type_, multidim_.size()); for (int i = 0; i < multidim_.size(); ++i) { int64 stride = strides[i]; auto type = multidim_[i]->getType(); @@ -224,11 +238,12 @@ IrArray::Index IrArray::Index::SourceIndexOfBitcast( // the physical index of the element in the buffer. This is like Linearize, // but takes the layout into account. int64 scale = 1; - llvm::Value* linear_index = builder->getInt64(0); + llvm::Value* linear_index = GetConstantWithIndexType(0); for (auto dimension : LayoutUtil::MinorToMajor(shape)) { linear_index = builder->CreateAdd( linear_index, - builder->CreateMul(multidim_[dimension], builder->getInt64(scale), "", + builder->CreateMul(multidim_[dimension], + GetConstantWithIndexType(scale), "", /*HasNUW=*/true, /*HasNSW=*/true), "", /*HasNUW=*/true, /*HasNSW=*/true); scale *= shape.dimensions(dimension); @@ -252,7 +267,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( } if (linear_ == nullptr || !LayoutUtil::HasLayout(operand_shape) || !LayoutUtil::HasLayout(shape)) { - return Index(source_index); + return Index(source_index, index_type_); } // High-level idea: we can reuse the linear index if the broadcasted // dimensions are contiguous, and this part of the operation is a bitcast. @@ -274,7 +289,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( bool contiguous_broadcast_dimensions = max_broadcasted_dimension - min_broadcasted_dimension == rank - 1; if (!contiguous_broadcast_dimensions) { - return Index(source_index); + return Index(source_index, index_type_); } // Check if the mapped dimensions are a bitcast. std::vector operand_logical_to_physical = @@ -282,7 +297,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( for (int64 i = 0; i < rank; ++i) { if (operand_logical_to_physical[i] != logical_to_physical[dimension_mapping[i]] - min_broadcasted_dimension) { - return Index(source_index); + return Index(source_index, index_type_); } } llvm::Value* linear = linear_; @@ -291,7 +306,9 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( divisor *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); } if (divisor > 1) { - linear = builder->CreateUDiv(linear, builder->getInt64(divisor)); + linear = builder->CreateUDiv( + linear, + IrArray::Index(linear->getType()).GetConstantWithIndexType(divisor)); } if (min_broadcasted_dimension > 0) { int64 mod = 1; @@ -299,7 +316,9 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( ++i) { mod *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); } - linear = builder->CreateURem(linear, builder->getInt64(mod)); + linear = builder->CreateURem( + linear, + IrArray::Index(linear->getType()).GetConstantWithIndexType(mod)); } return Index(source_index, linear, operand_shape); } @@ -309,12 +328,13 @@ llvm::Value* IrArray::Index::Linearize( llvm::IRBuilder<>* builder) const { // Each dimension is multiplied by the product of the sizes of all // earlier dimensions and added to the accumulator logical_linear_index. - llvm::Value* logical_linear_index = builder->getInt64(0); + llvm::Value* logical_linear_index = GetConstantWithIndexType(0); int64 multiplier = 1; for (ssize_t i = size() - 1; i >= 0; --i) { llvm::Value* addend = - builder->CreateMul((*this)[i], builder->getInt64(multiplier), "", + builder->CreateMul((*this)[i], GetConstantWithIndexType(multiplier), "", /*HasNUW=*/true, /*HasNSW=*/true); + addend = builder->CreateZExtOrTrunc(addend, index_type_); logical_linear_index = builder->CreateAdd(logical_linear_index, addend, "", /*HasNUW=*/true, /*HasNSW=*/true); multiplier *= dimensions[i]; @@ -349,7 +369,8 @@ llvm::Value* IrArray::EmitArrayElementAddress( // index[i] with 0. However, setting index[i] to 0 here still allows LLVM to // produce better code in some cases. auto dim = shape_->dimensions(i); - actual_index.push_back(dim == 1 ? ir_builder->getInt64(0) : index[i]); + actual_index.push_back( + dim == 1 ? llvm::ConstantInt::get(index[i]->getType(), 0) : index[i]); } // "base_ptr_" has the type of "*" @@ -357,7 +378,9 @@ llvm::Value* IrArray::EmitArrayElementAddress( // should be computed by // // getelementptr base_ptr_, 0, most major index, ..., most minor index - std::vector gep_indices(1, ir_builder->getInt64(0)); + CHECK_GT(index.size(), 0); + std::vector gep_indices( + 1, llvm::ConstantInt::get(index[0]->getType(), 0)); for (int64 i = 0; i < LayoutUtil::MinorToMajor(*shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_->layout(), i); gep_indices.push_back(actual_index[dimension]); @@ -410,7 +433,9 @@ IrArray IrArray::CastToShape(const Shape& new_shape, llvm::IRBuilder<>* ir_builder) { Index new_index = index; new_index[which_dimension] = ir_builder->CreateAdd( - index[which_dimension], ir_builder->getInt64(addend), "", /*HasNUW=*/true, + index[which_dimension], + llvm::ConstantInt::get(index[which_dimension]->getType(), addend), "", + /*HasNUW=*/true, /*HasNSW=*/true); return new_index; } diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 4c3195c29c859c9eef08e3f6531b059edbebfc47..4648c6d7ac089dbea7e660dd9889d557c8ad7318 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -53,18 +53,38 @@ class IrArray { // multidimensional index, which LLVM DCE can delete. class Index { public: - // Constructs an empty zero-dimensional index. - Index() {} - // Constructs an index of rank "size". Each dimension of the index is // initialized to "value". - explicit Index(size_t size, llvm::Value* value = nullptr) - : multidim_(size, value) {} + explicit Index(size_t size, llvm::Value* value) + : multidim_(size, value), index_type_(value->getType()) { + CHECK_NE(index_type_, nullptr); + } + + // Constructs an index of rank "size". Each dimension of the index is + // initialized to nullptr. + explicit Index(llvm::Type* index_ty, size_t size = 0) + : multidim_(size, nullptr), index_type_(index_ty) { + CHECK(index_ty->isIntegerTy()); + } // Constructs an index from multi-dimensional index "multidim". The linear // index is set to nullptr. - explicit Index(tensorflow::gtl::ArraySlice multidim) - : multidim_(multidim.begin(), multidim.end()) {} + explicit Index(tensorflow::gtl::ArraySlice multidim, + llvm::Type* index_ty = nullptr) + : multidim_(multidim.begin(), multidim.end()) { + if (size() == 0) { + index_type_ = index_ty; + } else { + index_type_ = (*this)[0]->getType(); + if (index_ty != nullptr) { + CHECK_EQ(index_type_, index_ty); + } + } + CHECK_NE(index_type_, nullptr); + CHECK(c_all_of(multidim, [&](llvm::Value* v) { + return index_type_ == v->getType(); + })); + } // Constructs an index from linear index "linear" and computes the // multi-dimensional index from "linear" and "shape". "ir_builder" is the IR @@ -154,6 +174,15 @@ class IrArray { llvm::Value* Linearize(tensorflow::gtl::ArraySlice dimensions, llvm::IRBuilder<>* builder) const; + llvm::Type* GetType() const { return index_type_; } + + llvm::Constant* GetConstantWithIndexType(int64 c) const { + // The LLVM function makes sure that the value can be represented by the + // specified type, see ConstantInt::ConstantInt(IntegerType *Ty, const + // APInt &V). + return llvm::ConstantInt::get(index_type_, c); + } + private: // Changing the multi-dimensional index invalidates the linear index. std::vector& multidim() { @@ -161,6 +190,9 @@ class IrArray { return multidim_; } + void Delinearize(std::vector* multidim, llvm::Value* linear, + const Shape& shape, llvm::IRBuilder<>* ir_builder) const; + std::vector multidim_; // These values are purely for efficiency; `multidim_` is enough to find the @@ -177,6 +209,8 @@ class IrArray { llvm::Value* linear_ = nullptr; Layout layout_; std::vector dims_; + + llvm::Type* index_type_; }; // Default constructor. Constructs an IrArray in a null status. diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc index 23d2d4e87d26f4988ebddcf20f5a27af6a7fe0d6..1f6e3c829f890d68aa251b101f0402c120a19d61 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc @@ -15,53 +15,57 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h" -#include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" namespace xla { -void KernelSupportLibrary::For( +Status KernelSupportLibrary::For( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, - const std::function& for_body_generator) { - If(ir_builder_->CreateICmpSLT(start, end), [&]() { - for_body_generator(start, /*is_first_iteration=*/true); - For(name, ir_builder_->CreateAdd(start, step), end, step, - [&](llvm::Value* iv) { for_body_generator(iv, false); }); + const std::function& for_body_generator) { + return If(ir_builder_->CreateICmpSLT(start, end), [&]() -> Status { + TF_RETURN_IF_ERROR(for_body_generator(start, /*is_first_iteration=*/true)); + return For(name, ir_builder_->CreateAdd(start, step), end, step, + [&](llvm::Value* iv) { return for_body_generator(iv, false); }); }); } -void KernelSupportLibrary::For( +Status KernelSupportLibrary::For( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, bool peel_first_iteration, - const std::function& for_body_generator) { + const std::function& + for_body_generator) { if (peel_first_iteration) { - For(name, start, end, step, true, - [&](llvm::Value* indvar, bool is_first_iteration) { - for_body_generator(indvar, ir_builder_->getInt1(is_first_iteration)); - }); + return For(name, start, end, step, true, + [&](llvm::Value* indvar, bool is_first_iteration) -> Status { + return for_body_generator( + indvar, ir_builder_->getInt1(is_first_iteration)); + }); } else { std::unique_ptr loop = llvm_ir::ForLoop::EmitForLoop( name, start, end, step, ir_builder_, - /*prevent_unrolling=*/prevent_unrolling_, + /*unroll_mode=*/unroll_mode_, /*prevent_vectorization=*/prevent_vectorization_); ir_builder_->SetInsertPoint(&loop->GetBodyBasicBlock()->back()); - for_body_generator(loop->GetIndVarValue(), - /*is_first_iteration=*/ir_builder_->CreateICmpEQ( - loop->GetIndVarValue(), start)); + TF_RETURN_IF_ERROR( + for_body_generator(loop->GetIndVarValue(), + /*is_first_iteration=*/ir_builder_->CreateICmpEQ( + loop->GetIndVarValue(), start))); llvm_ir::SetToLastInsertPoint(loop->GetExitBasicBlock(), ir_builder_); + return Status::OK(); } } -void KernelSupportLibrary::If( - llvm::Value* condition, const std::function& true_block_generator, - const std::function& false_block_generator) { +Status KernelSupportLibrary::If( + llvm::Value* condition, const std::function& true_block_generator, + const std::function& false_block_generator) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse(condition, "", ir_builder_); ir_builder_->SetInsertPoint(&if_data.true_block->back()); - true_block_generator(); + TF_RETURN_IF_ERROR(true_block_generator()); ir_builder_->SetInsertPoint(&if_data.false_block->back()); - false_block_generator(); + TF_RETURN_IF_ERROR(false_block_generator()); llvm_ir::SetToLastInsertPoint(if_data.after_block, ir_builder_); + return Status::OK(); } void KernelSupportLibrary::EmitAndCallOutlinedKernel( diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h index 64b935bbf1fb9033cd2e1259b4639cd3780be711..6f7a9d94e3b9e59b2dfe12b9673335a904ae78b6 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h @@ -21,6 +21,7 @@ limitations under the License. #include "llvm/IR/BasicBlock.h" #include "llvm/IR/IRBuilder.h" #include "llvm/IR/Value.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -30,13 +31,14 @@ namespace xla { class KernelSupportLibrary { public: // `ir_builder` is the llvm::IRBuilder instance used to generate LLVM IR. - // If `prevent_unrolling` is true then unrolling is explicitly disabled on - // every loop generated by this instance of KernelSupportLibrary. - explicit KernelSupportLibrary(llvm::IRBuilder<>* ir_builder, - bool prevent_unrolling = true, - bool prevent_vectorization = true) + // `unroll_mode` specifies the desired LLVM unrolling behavior for every loop + // generated by this instance of KernelSupportLibrary. + explicit KernelSupportLibrary( + llvm::IRBuilder<>* ir_builder, + llvm_ir::UnrollMode unroll_mode = llvm_ir::UnrollMode::kNoUnroll, + bool prevent_vectorization = true) : ir_builder_(ir_builder), - prevent_unrolling_(prevent_unrolling), + unroll_mode_(unroll_mode), prevent_vectorization_(prevent_vectorization) {} // Generates the following control flow structure: @@ -46,19 +48,41 @@ class KernelSupportLibrary { // for (i64 i = `start` + `step`; i s< `end`; i += `step`) // `for_body_generator(/*ind_var=*/,i, /*is_first_iteration=*/false)`; // } - void For( + Status For( + tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, + llvm::Value* step, + const std::function& for_body_generator); + + void ForReturnVoid( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& - for_body_generator); + for_body_generator) { + CHECK_EQ(Status::OK(), + For(name, start, end, step, + [&](llvm::Value* ind_var, bool is_first_iteration) -> Status { + for_body_generator(ind_var, is_first_iteration); + return Status::OK(); + })); + } + + Status For(tensorflow::StringPiece name, int64 start, int64 end, int64 step, + const std::function& + for_body_generator) { + return For(name, /*start=*/ir_builder_->getInt64(start), + /*end=*/ir_builder_->getInt64(end), + /*step=*/ir_builder_->getInt64(step), for_body_generator); + } - void For( + void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), + /*end=*/ir_builder_->getInt64(end), + /*step=*/ir_builder_->getInt64(step), for_body_generator); } // Generates the following control flow structure if `peel_first_iteration` is @@ -75,46 +99,102 @@ class KernelSupportLibrary { // for (i64 i = `start`; i s< `end`; i += `step`) // `for_body_generator(/*ind_var=*/,i, // /*is_first_iteration=*/,(i != `start`))`; - void For(tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, - llvm::Value* step, bool peel_first_iteration, - const std::function& - for_body_generator); - - void For(tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, - int64 step, bool peel_first_iteration, - const std::function& - for_body_generator) { - For(name, /*start=*/start, /*end=*/end, - /*step=*/ir_builder_->getInt64(step), peel_first_iteration, - for_body_generator); + Status For(tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, + llvm::Value* step, bool peel_first_iteration, + const std::function& + for_body_generator); + + void ForReturnVoid(tensorflow::StringPiece name, llvm::Value* start, + llvm::Value* end, llvm::Value* step, + bool peel_first_iteration, + const std::function& + for_body_generator) { + TF_CHECK_OK(For( + name, start, end, step, peel_first_iteration, + [&](llvm::Value* ind_var, llvm::Value* is_first_iteration) -> Status { + for_body_generator(ind_var, is_first_iteration); + return Status::OK(); + })); + } + + Status For(tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, + int64 step, bool peel_first_iteration, + const std::function& + for_body_generator) { + return For(name, /*start=*/start, /*end=*/end, + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); } - void For( + void ForReturnVoid(tensorflow::StringPiece name, llvm::Value* start, + llvm::Value* end, int64 step, bool peel_first_iteration, + const std::function& + for_body_generator) { + ForReturnVoid(name, /*start=*/start, /*end=*/end, + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); + } + + Status For( + tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, + llvm::Value* step, + const std::function& for_body_generator) { + return For(name, start, end, step, + /*peel_first_iteration=*/false, + [&](llvm::Value* indvar, llvm::Value*) -> Status { + return for_body_generator(indvar); + }); + } + + void ForReturnVoid( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - For(name, start, end, step, - /*peel_first_iteration=*/false, - [&](llvm::Value* indvar, llvm::Value*) { for_body_generator(indvar); }); + ForReturnVoid(name, start, end, step, + /*peel_first_iteration=*/false, + [&](llvm::Value* indvar, llvm::Value*) { + return for_body_generator(indvar); + }); + } + + Status For( + tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, + int64 step, + const std::function& for_body_generator) { + return For(name, start, end, llvm::ConstantInt::get(start->getType(), step), + /*peel_first_iteration=*/false, + [&](llvm::Value* indvar, llvm::Value*) -> Status { + return for_body_generator(indvar); + }); } - void For( + void ForReturnVoid( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, int64 step, const std::function& for_body_generator) { - For(name, start, end, ir_builder_->getInt64(step), - /*peel_first_iteration=*/false, - [&](llvm::Value* indvar, llvm::Value*) { for_body_generator(indvar); }); + ForReturnVoid(name, start, end, + llvm::ConstantInt::get(start->getType(), step), + for_body_generator); + } + + Status For( + tensorflow::StringPiece name, int64 start, int64 end, int64 step, + const std::function& for_body_generator) { + return For(name, /*start=*/ir_builder_->getInt64(start), + /*end=*/ir_builder_->getInt64(end), + /*step=*/ir_builder_->getInt64(step), for_body_generator); } - void For( + void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), + /*end=*/ir_builder_->getInt64(end), + /*step=*/ir_builder_->getInt64(step), for_body_generator); } // Generates the following control flow structure: @@ -123,9 +203,25 @@ class KernelSupportLibrary { // `true_block_generator()`; // else // `false_block_generator()`; - void If(llvm::Value* condition, - const std::function& true_block_generator, - const std::function& false_block_generator = []() {}); + Status If(llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = + []() -> Status { return Status::OK(); }); + + void IfReturnVoid(llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = []() { + }) { + TF_CHECK_OK(If(condition, + [&]() { + true_block_generator(); + return Status::OK(); + }, + [&]() { + false_block_generator(); + return Status::OK(); + })); + } using ArgumentVector = tensorflow::gtl::ArraySlice; @@ -183,7 +279,7 @@ class KernelSupportLibrary { private: llvm::IRBuilder<>* ir_builder_; - bool prevent_unrolling_; + llvm_ir::UnrollMode unroll_mode_; bool prevent_vectorization_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index 497b48ff227d7d1f158080529372df44b6932b24..c9ae7d3afd5cdc21157732f6d0dfa824268e86bd 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -34,7 +34,7 @@ namespace llvm_ir { ForLoop::ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix, llvm::Value* start_index, llvm::Value* end_index, - llvm::Value* step, bool prevent_unrolling, + llvm::Value* step, UnrollMode unroll_mode, bool prevent_vectorization) : prefix_(std::string(prefix)), suffix_(std::string(suffix)), @@ -42,15 +42,15 @@ ForLoop::ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix, end_index_(end_index), step_(step), insert_before_bb_(nullptr), - prevent_unrolling_(prevent_unrolling), + unroll_mode_(unroll_mode), prevent_vectorization_(prevent_vectorization) {} /* static */ std::unique_ptr ForLoop::EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, - bool prevent_unrolling, bool prevent_vectorization) { + UnrollMode unroll_mode, bool prevent_vectorization) { std::unique_ptr loop(new ForLoop(prefix, /*suffix=*/"", start_index, - end_index, step, prevent_unrolling, + end_index, step, unroll_mode, prevent_vectorization)); loop->Emit(ir_builder); return loop; @@ -97,7 +97,7 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { ir_builder->SetInsertPoint(&func->getEntryBlock(), func->getEntryBlock().getFirstInsertionPt()); llvm::Value* indvar_address = - ir_builder->CreateAlloca(ir_builder->getInt64Ty(), nullptr, + ir_builder->CreateAlloca(start_index_->getType(), nullptr, AsStringRef(GetQualifiedName("invar_address"))); // Preheader basic block. @@ -147,11 +147,12 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { std::vector ForLoop::GetLoopMetadata( llvm::IRBuilder<>* ir_builder) { const char* const kLlvmLoopUnrollDisableMDName = "llvm.loop.unroll.disable"; + const char* const kLlvmLoopUnrollFullMDName = "llvm.loop.unroll.full"; const char* const kLlvmLoopVectorizeMDName = "llvm.loop.vectorize.enable"; llvm::LLVMContext* ctx = &start_index_->getContext(); std::vector result; - if (prevent_unrolling_) { + if (unroll_mode_ == xla::llvm_ir::UnrollMode::kNoUnroll) { result.push_back(llvm::MDNode::get( *ctx, {llvm::MDString::get(*ctx, kLlvmLoopUnrollDisableMDName)})); } @@ -162,6 +163,10 @@ std::vector ForLoop::GetLoopMetadata( llvm::ConstantAsMetadata::get(ir_builder->getFalse())})); } + if (unroll_mode_ == xla::llvm_ir::UnrollMode::kFullyUnroll) { + result.push_back(llvm::MDNode::get( + *ctx, {llvm::MDString::get(*ctx, kLlvmLoopUnrollFullMDName)})); + } return result; } @@ -178,25 +183,25 @@ llvm::BasicBlock* ForLoop::CreateLoopBB(tensorflow::StringPiece name, std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, llvm::Value* start_index, llvm::Value* end_index, - bool prevent_unrolling, + UnrollMode unroll_mode, bool prevent_vectorization) { - return AddLoop(suffix, start_index, end_index, ir_builder_->getInt64(1), - prevent_unrolling, prevent_vectorization); + return AddLoop(suffix, start_index, end_index, GetConstantWithIndexType(1), + unroll_mode, prevent_vectorization); } std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, llvm::Value* start_index, llvm::Value* end_index, llvm::Value* stride, - bool prevent_unrolling, + UnrollMode unroll_mode, bool prevent_vectorization) { if (inner_loop_body_bb_ != nullptr) { // Create this loop inside the previous one. ir_builder_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt()); } std::unique_ptr loop(new ForLoop( - /*prefix=*/name_, suffix, start_index, end_index, stride, - prevent_unrolling, prevent_vectorization)); + /*prefix=*/name_, suffix, start_index, end_index, stride, unroll_mode, + prevent_vectorization)); loop->Emit(ir_builder_); if (outer_loop_preheader_bb_ == nullptr) { @@ -215,23 +220,23 @@ std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, std::unique_ptr ForLoopNest::AddLoop(int64 start_index, int64 end_index, tensorflow::StringPiece suffix, - bool prevent_unrolling, + UnrollMode unroll_mode, bool prevent_vectorization) { CHECK_LE(start_index, end_index); - return AddLoop(suffix, ir_builder_->getInt64(start_index), - ir_builder_->getInt64(end_index), prevent_unrolling, + return AddLoop(suffix, GetConstantWithIndexType(start_index), + GetConstantWithIndexType(end_index), unroll_mode, prevent_vectorization); } std::unique_ptr ForLoopNest::AddLoop(int64 start_index, int64 end_index, int64 stride, tensorflow::StringPiece suffix, - bool prevent_unrolling, + UnrollMode unroll_mode, bool prevent_vectorization) { CHECK_LE(start_index, end_index); - return AddLoop(suffix, ir_builder_->getInt64(start_index), - ir_builder_->getInt64(end_index), - ir_builder_->getInt64(stride), prevent_unrolling, + return AddLoop(suffix, GetConstantWithIndexType(start_index), + GetConstantWithIndexType(end_index), + GetConstantWithIndexType(stride), unroll_mode, prevent_vectorization); } @@ -245,7 +250,7 @@ IrArray::Index ForLoopNest::AddLoopsForShape(const Shape& shape, IrArray::Index ForLoopNest::AddLoopsForShapeOnDimensions( const Shape& shape, tensorflow::gtl::ArraySlice dimensions, tensorflow::StringPiece suffix) { - llvm_ir::IrArray::Index index(shape.dimensions_size(), nullptr); + llvm_ir::IrArray::Index index(index_type_, shape.dimensions_size()); for (int64 dimension : dimensions) { std::unique_ptr loop = AddLoop( /*start_index=*/0, diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h index d915f95db134918a173a9711936bb1e2f1ea0d95..0dd5b9d3b2656af68f76c2adfcb1f3a1385eeb91 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h @@ -34,6 +34,12 @@ limitations under the License. namespace xla { namespace llvm_ir { +enum class UnrollMode { + kDefaultUnroll, + kFullyUnroll, + kNoUnroll, +}; + // A class for constructing a for-loop in LLVM IR. class ForLoop { public: @@ -69,12 +75,13 @@ class ForLoop { // LLVM IR. If non-empty, it is prepended to the name of the induction // variable value and each basic block created for the loop. // - // If `prevent_unrolling` is true then emit metadata that directs LLVM to not - // unroll the generated loop. + // `unroll_mode` specifies the desired LLVM unrolling behavior for generated + // loop. static std::unique_ptr EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, - bool prevent_unrolling = false, bool prevent_vectorization = false); + UnrollMode unroll_mode = llvm_ir::UnrollMode::kDefaultUnroll, + bool prevent_vectorization = false); // The names of the blocks follow LLVM's conventions. Control flow amongst the // blocks for the example C code looks like: @@ -128,7 +135,7 @@ class ForLoop { ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix, llvm::Value* start_index, llvm::Value* end_index, llvm::Value* step, - bool prevent_unrolling, bool prevent_vectorization); + UnrollMode unroll_mode, bool prevent_vectorization); // Emit the loop at the insert point of the builder. void Emit(llvm::IRBuilder<>* ir_builder); @@ -161,7 +168,7 @@ class ForLoop { llvm::BasicBlock* body_bb_; llvm::BasicBlock* exit_bb_; llvm::Value* indvar_; - bool prevent_unrolling_; + UnrollMode unroll_mode_; bool prevent_vectorization_; TF_DISALLOW_COPY_AND_ASSIGN(ForLoop); @@ -170,46 +177,52 @@ class ForLoop { // A simple class for constructing nested for-loops. class ForLoopNest { public: - explicit ForLoopNest(llvm::IRBuilder<>* ir_builder) - : ForLoopNest(/*name=*/"", ir_builder) {} + explicit ForLoopNest(llvm::IRBuilder<>* ir_builder, + llvm::Type* index_ty = nullptr) + : ForLoopNest(/*name=*/"", ir_builder) { + SetIndexType(index_ty); + } - ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder) + ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder, + llvm::Type* index_ty = nullptr) : name_(std::string(name)), outer_loop_preheader_bb_(nullptr), outer_loop_exit_bb_(nullptr), inner_loop_body_bb_(nullptr), - ir_builder_(ir_builder) {} + ir_builder_(ir_builder) { + SetIndexType(index_ty); + } // Adds a loop to the nest. If no loop has been added yet then emit a loop at // the current insert point of the given builder. If one or more loops have - // been added then emit loop inside the body of the last added loop. If - // prevent_unrolling is true, then metadata is emitting directing LLVM to not - // unroll this loop. - std::unique_ptr AddLoop(tensorflow::StringPiece suffix, - llvm::Value* start_index, - llvm::Value* end_index, llvm::Value* stride, - bool prevent_unrolling = false, - bool prevent_vectorization = false); + // been added then emit loop inside the body of the last added loop. + // unroll_mode is used to emit metadata that controls LLVM unrolling. + std::unique_ptr AddLoop( + tensorflow::StringPiece suffix, llvm::Value* start_index, + llvm::Value* end_index, llvm::Value* stride, + UnrollMode unroll_mode = xla::llvm_ir::UnrollMode::kDefaultUnroll, + bool prevent_vectorization = false); // Like the above, except that it defaults to a stride of one. - std::unique_ptr AddLoop(tensorflow::StringPiece suffix, - llvm::Value* start_index, - llvm::Value* end_index, - bool prevent_unrolling = false, - bool prevent_vectorization = false); + std::unique_ptr AddLoop( + tensorflow::StringPiece suffix, llvm::Value* start_index, + llvm::Value* end_index, + UnrollMode unroll_mode = xla::llvm_ir::UnrollMode::kDefaultUnroll, + bool prevent_vectorization = false); // A convenient wrapper of the other flavor of AddLoop. The given start and // end index are constant. - std::unique_ptr AddLoop(int64 start_index, int64 end_index, - int64 stride, tensorflow::StringPiece suffix, - bool prevent_unrolling = false, - bool prevent_vectorization = false); + std::unique_ptr AddLoop( + int64 start_index, int64 end_index, int64 stride, + tensorflow::StringPiece suffix, + UnrollMode unroll_mode = xla::llvm_ir::UnrollMode::kDefaultUnroll, + bool prevent_vectorization = false); // Like the above, except that it defaults to a stride of one. - std::unique_ptr AddLoop(int64 start_index, int64 end_index, - tensorflow::StringPiece suffix, - bool prevent_unrolling = false, - bool prevent_vectorization = false); + std::unique_ptr AddLoop( + int64 start_index, int64 end_index, tensorflow::StringPiece suffix, + UnrollMode unroll_mode = xla::llvm_ir::UnrollMode::kDefaultUnroll, + bool prevent_vectorization = false); // Add loops to iterate through the indices within the specified // shape. The returned index collects the induction variables of the @@ -245,6 +258,14 @@ class ForLoopNest { llvm::BasicBlock* GetInnerLoopBodyBasicBlock() { return inner_loop_body_bb_; } private: + void SetIndexType(llvm::Type* index_ty) { + index_type_ = index_ty == nullptr ? ir_builder_->getInt64Ty() : index_ty; + } + + llvm::Constant* GetConstantWithIndexType(int64 c) const { + return llvm::ConstantInt::get(index_type_, c); + } + // Human-friendly name of the loop nest. string name_; @@ -259,6 +280,8 @@ class ForLoopNest { llvm::IRBuilder<>* ir_builder_; + llvm::Type* index_type_; + TF_DISALLOW_COPY_AND_ASSIGN(ForLoopNest); }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index ff64da87e9c9acf8a9d7ff87d3b1be7a9e9106bb..97bacc34b59118e60100e4749638d469a1ef1378 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -36,6 +36,7 @@ limitations under the License. #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/byte_order.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" @@ -193,6 +194,10 @@ llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, // An Opaque is like a void*, use i8*. case OPAQUE: return llvm::Type::getInt8PtrTy(module->getContext()); + case TOKEN: + // Tokens do not have a physical representation, but the compiler needs + // some placeholder type, so use int8*. + return llvm::Type::getInt8PtrTy(module->getContext()); default: LOG(FATAL) << "unsupported type " << element_type; } @@ -245,167 +250,14 @@ StatusOr DecodeSelfDescribingShapeConstant(const void* shape_ptr, return shape; } -namespace { - -// Recursively construct a multidimensional LLVM constant which represents the -// given literal. The minor-to-major dimension ordering in the constant matches -// that of the literal. For example, given a [2 x 3 x 4] Literal (dimension 0 -// has size 4, dimension 1 has size 3, etc) of primitive type F32 with a -// minor_to_major value of [2, 1, 0] (column major), a LLVM constant of type -// [4 x [3 x [2 x float]] will be returned. -// -// multi_index is a multidimensional index into the array. dimension_index is an -// index into the minor_to_major field in the literal shape. This determines -// which dimension is iterated over in this level of the recursion. Dimensions -// are iterated from most major down to most minor (highest dimension_index -// value down to zero). -llvm::Constant* LiteralToConstant(const Literal& literal, int64 dimension_index, - std::vector* multi_index, - llvm::Module* module) { - const Shape& shape = literal.shape(); - llvm::Type* ir_element_type = - llvm_ir::PrimitiveTypeToIrType(shape.element_type(), module); - if (dimension_index == -1) { - // Base case of the recursion. Index into the data field of the protobuf - // with the multi index. - llvm::Constant* value; - switch (shape.element_type()) { - case PRED: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U8: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case S32: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U32: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case S64: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U64: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case F32: - value = llvm::ConstantFP::get(ir_element_type, - literal.Get(*multi_index)); - break; - case BF16: - value = llvm::ConstantInt::get( - ir_element_type, - tensorflow::bit_cast(literal.Get(*multi_index))); - break; - case F16: - value = llvm::ConstantFP::get( - ir_element_type, - static_cast(literal.Get(*multi_index))); - break; - case F64: - value = llvm::ConstantFP::get(ir_element_type, - literal.Get(*multi_index)); - break; - case C64: { - complex64 x = literal.Get(*multi_index); - value = llvm::ConstantStruct::get( - static_cast(ir_element_type), - llvm::ConstantFP::get(llvm_ir::PrimitiveTypeToIrType(F32, module), - x.real()), - llvm::ConstantFP::get(llvm_ir::PrimitiveTypeToIrType(F32, module), - x.imag())); - break; - } - default: - LOG(FATAL) << "unsupported type " << shape.element_type(); - } - return value; - } - - // The dimension index starts at the one less than the rank of the array and - // decrements with each recursive call. We want to iterate through the - // dimensions in major-to-minor order as we recurse so just index into - // minor_to_major to get the dimension number for this level of the recursion. - int64 dimension = LayoutUtil::Minor(shape.layout(), dimension_index); - - // Recursively call LiteralToConstant to construct subarrays for the - // more-minor dimensions. Gather the subarrays into a vector for bundling into - // a new (higher-dimensional) ConstantArray. - std::vector elements; - for (int64 i = 0; i < shape.dimensions(dimension); ++i) { - (*multi_index)[dimension] = i; - elements.push_back( - LiteralToConstant(literal, dimension_index - 1, multi_index, module)); - } - - llvm::Type* element_type; - if (elements.empty()) { - element_type = ir_element_type; - for (int i = 0; i < dimension_index; ++i) { - int64 index = LayoutUtil::Minor(shape.layout(), i); - element_type = - llvm::ArrayType::get(element_type, shape.dimensions(index)); - } - } else { - element_type = elements[0]->getType(); - } - llvm::ArrayType* aggregate_type = - llvm::ArrayType::get(element_type, shape.dimensions(dimension)); - return llvm::ConstantArray::get(aggregate_type, elements); -} - -template -llvm::Constant* GetConstantDataArray(const Literal& literal, - llvm::Module* module) { - const T* data = static_cast(literal.untyped_data()); - int64 num_elements = literal.size_bytes() / sizeof(T); - return llvm::ConstantDataArray::get(module->getContext(), - llvm::makeArrayRef(data, num_elements)); -} - -} // namespace - llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, llvm::Module* module) { - const Shape& shape = literal.shape(); - // TODO(b/29904935): We can get rid of this switch by exposing a - // ConstantDataArray factory method that takes a llvm::Type and a StringRef. - switch (shape.element_type()) { - case U64: - return GetConstantDataArray(literal, module); - case U32: - return GetConstantDataArray(literal, module); - case U8: - return GetConstantDataArray(literal, module); - case S64: - return GetConstantDataArray(literal, module); - case S32: - return GetConstantDataArray(literal, module); - case F64: - return GetConstantDataArray(literal, module); - case F32: - return GetConstantDataArray(literal, module); - case BF16: - case F16: - return GetConstantDataArray(literal, module); - case PRED: - return GetConstantDataArray(literal, module); - // TODO(b/29904935): Also use ConstantDataArray for complex numbers. - case C64: { - int64 dimensions = ShapeUtil::Rank(shape); - std::vector multi_index(dimensions, 0); - return LiteralToConstant(literal, /*dimension_index=*/dimensions - 1, - &multi_index, module); - } - default: - LOG(FATAL) << "unsupported type " << shape.element_type(); - } + const char* data = static_cast(literal.untyped_data()); + CHECK_EQ(module->getDataLayout().isLittleEndian(), + tensorflow::port::kLittleEndian); + return llvm::ConstantDataArray::getString( + module->getContext(), llvm::StringRef(data, literal.size_bytes()), + /*AddNull=*/false); } llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc index dc2934a34c23f8229947210cacc9863d47c2ea55..e8b0605b9d75677b34f0973d88d269a5795b7629 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc @@ -90,11 +90,12 @@ LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, } std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { + CHECK_NE(index_type, nullptr); if (ShapeUtil::IsScalar(shape_)) { // No loop needed, so set exit_bb_ to nullptr. exit_bb_ = nullptr; - return {IrArray::Index()}; + return {IrArray::Index(index_type)}; } // Create loop nest with one for-loop for each dimension of the target shape. @@ -102,7 +103,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // class so emit loops in order from most-major dimension down to most-minor // dimension (of the target shape). ForLoopNest loop_nest(loop_name, ir_builder_); - IrArray::Index array_index(shape_.dimensions_size()); + IrArray::Index array_index(index_type, shape_.dimensions_size()); for (int i = 0; i < LayoutUtil::MinorToMajor(shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_.layout(), i); std::unique_ptr loop = loop_nest.AddLoop( @@ -125,9 +126,14 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( return {array_index}; } -Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name) { +Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, + llvm::Type* index_type) { + if (index_type == nullptr) { + index_type = ir_builder_->getInt64Ty(); + } + for (const IrArray::Index& array_index : - EmitIndexAndSetExitBasicBlock(loop_name)) { + EmitIndexAndSetExitBasicBlock(loop_name, index_type)) { TF_RETURN_IF_ERROR(body_emitter_(array_index)); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h index b70d28ecd3033eb26629718e50ce48f39b162273..6be1c2fba2cbd78a02865901ef8c5b7e2b2a74e6 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h @@ -65,13 +65,16 @@ class LoopEmitter { // specifies the element, will return multiple indices if the loop is // unrolled. std::vector EmitIndexAndSetExitBasicBlock() { - return EmitIndexAndSetExitBasicBlock(/*loop_name=*/""); + return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", + ir_builder_->getInt64Ty()); } + virtual std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name); + tensorflow::StringPiece loop_name, llvm::Type* index_type); // Emits a complete loop nest for every element in the given shape. - Status EmitLoop(tensorflow::StringPiece loop_name = ""); + Status EmitLoop(tensorflow::StringPiece loop_name = "", + llvm::Type* index_type = nullptr); protected: // An IR emitter that generates the loop body. diff --git a/tensorflow/compiler/xla/service/llvm_ir/ops.cc b/tensorflow/compiler/xla/service/llvm_ir/ops.cc index dacc54742c0897bbd92315f1e33a484aae56bb7f..3b298f4746d6177da52ba0227705d07fbeba5c19 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ops.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ops.cc @@ -45,7 +45,7 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // Read start indices from start_indices_generator. const int64 rank = ShapeUtil::Rank(output_shape); - IrArray::Index start_index(rank); + IrArray::Index start_index(ir_builder->getInt64Ty(), rank); for (int64 i = 0; i < rank; ++i) { IrArray::Index dim_index({ir_builder->getInt64(i)}); TF_ASSIGN_OR_RETURN(start_index[i], start_indices_generator(dim_index)); @@ -79,7 +79,7 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // // output_index[dim] = start_index[dim] + update_index[dim] // - IrArray::Index output_index(rank); + IrArray::Index output_index(start_index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { llvm::Value* start_index0 = ir_builder->CreateSExtOrBitCast( start_index[i], update_index[i]->getType()); diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 1d9c9e0678765a779ec94e578e0e6f69d46b80de..53efc30c3653879709fceae3dcdd4f679740f622 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -30,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_module_config.h" #include "tensorflow/compiler/xla/service/platform_util.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" #include "tensorflow/compiler/xla/shape_layout.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -155,7 +154,8 @@ StatusOr> LocalService::CompileExecutable( for (int i = 0; i < argument_layouts.size(); ++i) { const Shape& argument_shape = *argument_layouts[i]; - TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(argument_shape)); + TF_RETURN_IF_ERROR( + ShapeUtil::ValidateShapeWithOptionalLayout(argument_shape)); if (!ShapeUtil::Compatible(argument_shape, program_shape.parameters(i))) { tensorflow::gtl::optional metadata = ParameterMetadata(computation, /*parameter_number=*/i); @@ -179,8 +179,8 @@ StatusOr> LocalService::CompileExecutable( } } if (build_options.result_layout() != nullptr) { - TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout( - *build_options.result_layout(), program_shape.result())); + TF_RETURN_IF_ERROR(ValidateResultShape(*build_options.result_layout(), + program_shape.result())); } ExecutionOptions execution_options = @@ -190,6 +190,9 @@ StatusOr> LocalService::CompileExecutable( std::unique_ptr module_config, CreateModuleConfig(program_shape, argument_layouts, &execution_options)); + VLOG(3) << "Computation Layout: " + << module_config->entry_computation_layout().ToString(); + TF_ASSIGN_OR_RETURN( se::StreamExecutor * executor, execute_backend_->stream_executor(build_options.device_ordinal())); diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.cc b/tensorflow/compiler/xla/service/multi_output_fusion.cc new file mode 100644 index 0000000000000000000000000000000000000000..4166ef5baf9c891968b584a0c498005e9ae87784 --- /dev/null +++ b/tensorflow/compiler/xla/service/multi_output_fusion.cc @@ -0,0 +1,338 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/multi_output_fusion.h" + +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +StatusOr MultiOutputFusion::Run(HloModule* module) { + bool changed = false; + + for (auto* computation : module->MakeNonfusionComputations()) { + computation_ = computation; + RecomputeReachability(); + candidates_.clear(); + candidates_index_.clear(); + all_fusion_candidates_.clear(); + + int64 index = 0; + for (auto it : computation_->MakeInstructionPostOrder()) { + candidates_.emplace_back(it); + InsertOrDie(&candidates_index_, it, index++); + } + + // Create the initial candidate list for each Node. + for (auto& node : candidates_) { + HloInstruction* instruction = node.hlo; + int64 instruction_id = get_candidate_id(instruction); + FusionCandidate& instr_node = candidates_[instruction_id]; + if (!IsFusible(instruction)) { + continue; + } + all_fusion_candidates_.push_back(instruction); + + std::vector candidates; + tensorflow::gtl::FlatSet candidates_set; + VLOG(10) << "Looking at instruction: " << instruction->name(); + for (auto operand : instruction->operands()) { + // Filter out the non-interesting instructions -- they + // will not generate the savings. + if (!IsProfitableOperand(operand)) { + VLOG(10) << "Operand not profitable: " << operand->name(); + continue; + } + VLOG(10) << "Operand profitable: " << operand->name(); + for (auto user : operand->users()) { + VLOG(10) << "User: " << user->name(); + if (user == instruction || !IsFusible(user)) { + VLOG(10) << "User is not fusible, or is the instruction itself: " + << user->name(); + continue; + } + int64 user_id = get_candidate_id(user); + if (is_connected(instruction, user)) { + VLOG(10) << "User is connected: " << user->name(); + continue; + } + if (instruction_id < user_id && + user->opcode() == HloOpcode::kFusion) { + VLOG(10) << "User ID for user: " << user->name() << " is " + << user_id << " which is higher than " << instruction_id; + continue; + } + if (!LegalToFuse(instruction, user)) { + VLOG(10) << "User not legal to fuse: " << user->name(); + continue; + } + if (candidates_set.insert(user).second) { + VLOG(10) << "User added to candidate list: " << user->name(); + candidates.push_back(user); + } + } + } + + // Iterate over candidates rather than candidates_set to avoid + // nondeterminism. + for (auto candidate : candidates) { + int64 profit = GetProfit(instruction, candidate); + if (profit > 0) { + FusionCandidate& candidate_node = + candidates_[get_candidate_id(candidate)]; + instr_node.fusibles.emplace_back(candidate, profit); + candidate_node.fusibles.emplace_back(instruction, profit); + worklist_.emplace(instruction, candidate, profit); + } + } + } + if (Perform()) { + changed = true; + } + } + return changed; +} + +HloInstruction* MultiOutputFusion::Fuse(HloInstruction* instr1, + HloInstruction* instr2) { + HloInstruction* remaining = instr1; + HloInstruction* fused = instr2; + // Make sure that if only one of the instructions is a fusion, or if only one + // of the instructions is a multi-output fusion, it's what will be fused into. + if (fused->opcode() == HloOpcode::kFusion) { + std::swap(remaining, fused); + } + if (fused->IsMultiOutputFusion()) { + std::swap(remaining, fused); + } + + if (fused->opcode() == HloOpcode::kFusion) { + remaining->MergeFusionInstructionIntoMultiOutput(fused); + } else { + remaining->FuseInstructionIntoMultiOutput(fused); + } + return remaining; +} + +bool MultiOutputFusion::IsProfitableOperand(HloInstruction* instr) { + // kConstant instruction will not have memory reads, so it won't be a profit + // source. Skip them. + if (instr->opcode() == HloOpcode::kConstant && + ShapeUtil::IsEffectiveScalar(instr->shape())) { + return false; + } + // We don't target to fuse producer/consumer instructions -- this should + // be taken care of by the instruction_fusion pass. If instr has only + // one user, it will not have sibling instructions. We won't consider it. + if (instr->user_count() < 2) { + return false; + } + return true; +} + +void MultiOutputFusion::Update(HloInstruction* instr1, HloInstruction* instr2) { + HloInstruction* fusion = instr1; + HloInstruction* fused = instr2; + if (is_fused(instr1)) { + fusion = instr2; + fused = instr1; + } + + // Insert the newly created instruction (if any), to candidates_. + for (auto use : fusion->users()) { + if (candidates_index_.find(use) == candidates_index_.end()) { + int64 index = candidates_.size(); + candidates_.emplace_back(use); + InsertOrDie(&candidates_index_, use, index++); + } + } + FusionCandidate& fusion_node = candidates_[get_candidate_id(fusion)]; + FusionCandidate& fused_node = candidates_[get_candidate_id(fused)]; + + // Update the reachability graph. + UpdateReachability(fusion, fused, all_fusion_candidates_, + [this](HloInstruction* instr) { return is_fused(instr); }); + + // Update the fusible list for fusion. Variable new_fusibles keeps + // track of the new or changed entries. + std::vector> new_fusibles; + tensorflow::gtl::FlatSet in_list; + auto it = fusion_node.fusibles.begin(); + while (it != fusion_node.fusibles.end()) { + HloInstruction* instr = it->first; + if (is_fused(instr) || is_connected(fusion, instr)) { + it = fusion_node.fusibles.erase(it); + continue; + } + in_list.insert(instr); + int64 profit = GetProfit(instr, fusion); + if (profit > it->second) { + it->second = profit; + new_fusibles.emplace_back(instr, profit); + } + ++it; + } + + // Fused_node has been fused into fusion_node. Take the fusion candidates + // (fusibles) from fused_nodes and add them to the fusion_node's. Filter + // out those fusibles that no longer valid (or already in the list). + for (const auto& it : fused_node.fusibles) { + HloInstruction* instr = it.first; + if (instr == fusion || is_fused(instr) || is_connected(fusion, instr)) { + continue; + } + if (in_list.count(instr) > 0) { + continue; + } + int64 profit = GetProfit(instr, fusion); + fusion_node.fusibles.emplace_back(instr, profit); + new_fusibles.emplace_back(instr, profit); + } + fused_node.fusibles.clear(); + + // Update the worklist_. + for (auto it : new_fusibles) { + worklist_.emplace(fusion, it.first, it.second); + } +} + +bool MultiOutputFusion::LegalToFuse(HloInstruction* instr1, + HloInstruction* instr2) { + if (instr1 == instr2) { + return false; + } + if (instr1->opcode() != HloOpcode::kFusion) { + return false; + } + + // Fusing nodes with 0 user makes no sense and the rest of the implementation + // doesn't support it either. + if (instr1->user_count() == 0 || instr2->user_count() == 0) { + return false; + } + + // Check if the users of multioutput fusion is not a get-tuple-element. + // If this is the case, we bail out because the transformation assumes + // the users are get-tuple-element. + auto multioutput_user_is_not_gte = [](HloInstruction* instr) { + if (!instr->IsMultiOutputFusion()) { + return false; + } + for (auto user : instr->users()) { + if (user->opcode() != HloOpcode::kGetTupleElement) { + return true; + } + } + return false; + }; + if (multioutput_user_is_not_gte(instr1) || + multioutput_user_is_not_gte(instr2)) { + return false; + } + + if (is_connected(instr1, instr2)) { + return false; + } + if (!ShapesCompatibleForFusion(instr1, instr2)) { + return false; + } + + return true; +} + +void MultiOutputFusion::RecomputeReachability() { + reachability_ = computation_->ComputeReachability(); +} + +void MultiOutputFusion::UpdateReachability( + HloInstruction* instr1, HloInstruction* instr2, + tensorflow::gtl::ArraySlice instrs_to_update, + const std::function& skip) { + for (auto instr : instrs_to_update) { + if (skip != nullptr && skip(instr)) { + continue; + } + if (reachability_->IsReachable(instr2, instr) && + reachability_->IsReachable(instr1, instr)) { + // If a candidate was already reachable by both, no update needed. + continue; + } + if (reachability_->IsReachable(instr2, instr)) { + reachability_->FastSetReachabilityToUnion({instr, instr1}, instr); + } + if (reachability_->IsReachable(instr1, instr)) { + reachability_->FastSetReachabilityToUnion({instr, instr2}, instr); + } + } +} + +bool MultiOutputFusion::Perform() { + int changed = false; + // Pick the top candidate from queue and try to merge. + while (!worklist_.empty()) { + if (fuel_ <= 0) { + VLOG(2) << "No fusing: run out of fuel."; + break; + } + ToBeFused candidate = worklist_.top(); + worklist_.pop(); + + HloInstruction* instr1 = candidate.instr1; + HloInstruction* instr2 = candidate.instr2; + + if (is_fused(instr1) || is_fused(instr2)) { + continue; + } + + VLOG(1) << "Considering candidate profit_score=" << candidate.score + << "\n\t\tinstr1 = " << instr1->ToString() + << "\n\t\tinstr2 = " << instr2->ToString(); + + if (LegalToFuse(instr1, instr2)) { + VLOG(1) << "Fuse!"; + VLOG(2) << "Before multi_output_fusion:"; + VLOG(2) << "instr1: " << instr1->ToString(); + VLOG(2) << "\n" + << instr1->fused_instructions_computation()->ToString( + HloPrintOptions().set_indent_amount(1)); + VLOG(2) << "instr2: " << instr2->ToString(); + if (instr2->opcode() == HloOpcode::kFusion) { + VLOG(2) << "\n" + << instr2->fused_instructions_computation()->ToString( + HloPrintOptions().set_indent_amount(1)); + } + HloInstruction* ret = Fuse(instr1, instr2); + set_is_fused(ret == instr1 ? instr2 : instr1); + Update(instr1, instr2); + changed = true; + VLOG(2) << "After fusion, \t this: " << ret->name() << "\n" + << ret->fused_instructions_computation()->ToString( + HloPrintOptions().set_indent_amount(1)); + auto users = ret->users(); + --fuel_; + } + } + if (DoProducerConsumerMultiOutputFusion()) { + changed = true; + } + return changed; +} + +bool MultiOutputFusion::DoProducerConsumerMultiOutputFusion() { return false; } +} // namespace xla diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.h b/tensorflow/compiler/xla/service/multi_output_fusion.h new file mode 100644 index 0000000000000000000000000000000000000000..0019cd725417d81900974b462c3b05075ce3e893 --- /dev/null +++ b/tensorflow/compiler/xla/service/multi_output_fusion.h @@ -0,0 +1,169 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_MULTI_OUTPUT_FUSION_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_MULTI_OUTPUT_FUSION_H_ + +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/stringpiece.h" + +namespace xla { + +// This class implements the fusing of sibling fusion instructions that sharing +// common operands. +// It constructs the following associated data structures. +// (1) candidates_: stores the instruction and the set of instructions it can +// fuse to. +// (2) candidates_index_: maps instruction to id. +// (3) reachability_: reachability map in this computation. +// (4) all_fusion_candidates_: the vector of candidate instructions. +// (5) worklist_: a priority queue that contains pairs of instructions to be +// fused and their fusion profit scores. +// +// Function Perform() applies the optimization. It picks up the most profitable +// pair in the worklist_, check if it's legal to fuse and fuse the pair. +// After fusion, it updates the associated structure such as reachability_, +// candidates_ and worklist_. +// Note that the reachability map is updated based on the original computation. +// This works because the reachability is monotonically increasing with +// instruction fusion. +class MultiOutputFusion : public HloPassInterface { + public: + MultiOutputFusion(int64 fuel) : fuel_(fuel) {} + + tensorflow::StringPiece name() const override { + return "multi_output_fusion"; + } + + // Run multi-output fusion on the given module. Returns whether the module + // was changed. + StatusOr Run(HloModule* module) override; + + protected: + // Main entry for the optimization. Returns true if the optimization happens. + bool Perform(); + + // Test if instr1 and instr2 have the compatible shapes that can be legally + // fused. + virtual bool ShapesCompatibleForFusion(HloInstruction* instr1, + HloInstruction* instr2) = 0; + + // Whether the instruction is a candidate for fusion. + virtual bool IsFusible(HloInstruction* instr) = 0; + + // This function estimates the savings by merging instr1 and instr2 into one + // multi-output fusion instruction. + virtual int64 GetProfit(HloInstruction* instr1, HloInstruction* instr2) = 0; + + // Whether fusing the instruction can reduce memory reads. + virtual bool IsProfitableOperand(HloInstruction* instr); + + // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. + virtual bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2); + + // Fuse HloInstrctuion instr1 and instr2 and return the fused instruction. + // The other instruction is removed from its parent computation. + virtual HloInstruction* Fuse(HloInstruction* instr1, HloInstruction* instr2); + + // Recompute reachability for the current computation. + void RecomputeReachability(); + + // Returns the reachability map for the current computation. + HloReachabilityMap* reachability() const { return reachability_.get(); } + + // Returns the computation for the pass. + HloComputation* computation() const { return computation_; } + + // Update the reachability map after fusing instr1 and instr2. + void UpdateReachability( + HloInstruction* instr1, HloInstruction* instr2, + tensorflow::gtl::ArraySlice instrs_to_update, + const std::function& skip = nullptr); + + // Hook for multi-output fusion along producer-consumer edges. + // Returns whether any instructions were fused. + // + // TODO(b/80420762): Perform producer-consumer multi-output fusion in + // InstructionFusion instead. + virtual bool DoProducerConsumerMultiOutputFusion(); + + private: + // Update the internal data structures after instr1 and instr2 are fused into + // one fusion instruction. + void Update(HloInstruction* instr1, HloInstruction* instr2); + + // Optimization fuel is a compiler debugging technique that makes an + // optimization pass stop what it is doing after having made N changes to the + // program, where N is the fuel. By varying N, this can be used to find the + // first single change that makes a test fail. + int64 fuel_; + + // Computation for the pass. + HloComputation* computation_; + + // An internal data structure for each instruction in current computation. + // When an instruction is removed, member 'hlo' is set to nullptr. + struct FusionCandidate { + HloInstruction* hlo; + std::list> fusibles; + explicit FusionCandidate(HloInstruction* hlo) : hlo(hlo) {} + }; + std::vector candidates_; + + // A map that maps an instruction to the index_. + tensorflow::gtl::FlatMap candidates_index_; + + // The reachability map of current computation. + std::unique_ptr reachability_; + + // This stores all the candidate instructions in current computation. + std::vector all_fusion_candidates_; + + // The pair of candidates to be fused and the profit score. + struct ToBeFused { + HloInstruction* instr1; + HloInstruction* instr2; + int64 score; + ToBeFused(HloInstruction* instr1, HloInstruction* instr2, int64 score) + : instr1(instr1), instr2(instr2), score(score) {} + bool operator<(const ToBeFused& rhs) const { return score < rhs.score; } + }; + std::priority_queue worklist_; + + int64 get_candidate_id(HloInstruction* instr) { + return FindOrDie(candidates_index_, instr); + } + + bool is_fused(HloInstruction* instr) { + return candidates_[get_candidate_id(instr)].hlo == nullptr; + } + + void set_is_fused(HloInstruction* instr) { + candidates_[get_candidate_id(instr)].hlo = nullptr; + } + + bool is_connected(HloInstruction* instr1, HloInstruction* instr2) { + return reachability_->IsConnected(instr1, instr2); + } +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_MULTI_OUTPUT_FUSION_H_ diff --git a/tensorflow/compiler/xla/service/name_uniquer.cc b/tensorflow/compiler/xla/service/name_uniquer.cc index 3a6a7c25f4b727c7112dbcbcb4f3d892679a0011..f6e7578a89551ec2f23d4d8c8b488c3c10e0bf1c 100644 --- a/tensorflow/compiler/xla/service/name_uniquer.cc +++ b/tensorflow/compiler/xla/service/name_uniquer.cc @@ -67,22 +67,17 @@ string NameUniquer::GetUniqueName(tensorflow::StringPiece prefix) { has_numeric_suffix = true; // Remove numeric suffix from root. root = root.substr(0, separator_index); - // Update count to at least the numeric suffix value to avoid future - // colisions with this name. - generated_names_[root] = std::max(generated_names_[root], numeric_suffix); } } - int64* count = &(generated_names_[root]); - if (*count == 0) { - *count = 1; + + SequentialIdGenerator& id_generator = generated_names_[root]; + numeric_suffix = id_generator.RegisterId(numeric_suffix); + if (numeric_suffix == 0) { return has_numeric_suffix ? tensorflow::strings::StrCat(root, separator_, 0) : root; - } else { - tensorflow::strings::StrAppend(&root, separator_, *count); - // Increment lookup under old 'root' name. - (*count)++; - return root; } + tensorflow::strings::StrAppend(&root, separator_, numeric_suffix); + return root; } } // namespace xla diff --git a/tensorflow/compiler/xla/service/name_uniquer.h b/tensorflow/compiler/xla/service/name_uniquer.h index 4139c2700b25e8600182a034a8ac6f4f041c12e6..4423d6106920eaeab830bd9dc08529ff409a5161 100644 --- a/tensorflow/compiler/xla/service/name_uniquer.h +++ b/tensorflow/compiler/xla/service/name_uniquer.h @@ -17,10 +17,11 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_NAME_UNIQUER_H_ #include -#include #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/platform/macros.h" namespace xla { @@ -44,13 +45,40 @@ class NameUniquer { static string GetSanitizedName(const string& name); private: + // Used to track and generate new identifiers for the same instruction name + // root. + class SequentialIdGenerator { + public: + SequentialIdGenerator() = default; + + // Tries to register id as used identifier. If id is not already used, the + // id itself will be returned. Otherwise a new one will be generated, and + // returned. + int64 RegisterId(int64 id) { + if (used_.insert(id).second) { + return id; + } + while (!used_.insert(next_).second) { + ++next_; + } + return next_++; + } + + private: + // The next identifier to be tried. + int64 next_ = 0; + + // Set of all the identifiers which has been used. + tensorflow::gtl::FlatSet used_; + }; + // The string to use to separate the prefix of the name from the uniquing // integer value. string separator_; - // Map from name prefix to the number of names generated using that prefix - // so far. - std::unordered_map generated_names_; + // Map from name prefix to the generator data structure which tracks used + // identifiers and generates new ones. + tensorflow::gtl::FlatMap generated_names_; TF_DISALLOW_COPY_AND_ASSIGN(NameUniquer); }; diff --git a/tensorflow/compiler/xla/service/name_uniquer_test.cc b/tensorflow/compiler/xla/service/name_uniquer_test.cc index 2ec255558c4ed3695ec6c824458cbedac44dc297..3e2592c6ac626143f1421e545a31d9be91e376bc 100644 --- a/tensorflow/compiler/xla/service/name_uniquer_test.cc +++ b/tensorflow/compiler/xla/service/name_uniquer_test.cc @@ -54,12 +54,13 @@ TEST_F(NameUniquerTest, NumericSuffixes) { EXPECT_EQ("foo", uniquer.GetUniqueName("foo")); EXPECT_EQ("foo.54", uniquer.GetUniqueName("foo.54")); - EXPECT_EQ("foo.55", uniquer.GetUniqueName("foo")); + EXPECT_EQ("foo.1", uniquer.GetUniqueName("foo")); EXPECT_EQ("foo.55.1", uniquer.GetUniqueName("foo.55.1")); - EXPECT_EQ("foo.55.2", uniquer.GetUniqueName("foo.55.1")); - EXPECT_EQ("bar.0", uniquer.GetUniqueName("bar.-1000")); - EXPECT_EQ("bar.1", uniquer.GetUniqueName("bar.-2000")); - EXPECT_EQ("bar.2", uniquer.GetUniqueName("bar.1")); + EXPECT_EQ("foo.55.0", uniquer.GetUniqueName("foo.55.1")); + EXPECT_EQ("bar.1000", uniquer.GetUniqueName("bar.1000")); + EXPECT_EQ("bar.2000", uniquer.GetUniqueName("bar.2000")); + EXPECT_EQ("bar.-2000", uniquer.GetUniqueName("bar.-2000")); + EXPECT_EQ("bar.1", uniquer.GetUniqueName("bar.1")); } TEST_F(NameUniquerTest, PrefixHasSuffix) { @@ -77,12 +78,12 @@ TEST_F(NameUniquerTest, Sanitize) { EXPECT_EQ("foo.54", uniquer.GetUniqueName("foo.54")); EXPECT_EQ("foo_54", uniquer.GetUniqueName("foo_54")); EXPECT_EQ("foo_54.1", uniquer.GetUniqueName("foo_54.1")); - EXPECT_EQ("foo_55", uniquer.GetUniqueName("foo")); + EXPECT_EQ("foo_2", uniquer.GetUniqueName("foo")); // Invalid characters will be replaced with '_'. - EXPECT_EQ("bar_0", uniquer.GetUniqueName("bar<-1000")); - EXPECT_EQ("bar_1", uniquer.GetUniqueName("bar<-2000")); - EXPECT_EQ("bar_2", uniquer.GetUniqueName("bar_1")); + EXPECT_EQ("bar_1000", uniquer.GetUniqueName("bar<1000")); + EXPECT_EQ("bar_2000", uniquer.GetUniqueName("bar<2000")); + EXPECT_EQ("bar_1", uniquer.GetUniqueName("bar_1")); // Separator is only recognized in the middle of the prefix. EXPECT_EQ("_10", uniquer.GetUniqueName( @@ -93,5 +94,15 @@ TEST_F(NameUniquerTest, Sanitize) { EXPECT_EQ("foobar__1", uniquer.GetUniqueName("foobar_")); } +TEST_F(NameUniquerTest, KeepNamesInRandomOrder) { + NameUniquer uniquer("."); + + EXPECT_EQ("foo.11", uniquer.GetUniqueName("foo.11")); + EXPECT_EQ("foo.10", uniquer.GetUniqueName("foo.10")); + EXPECT_EQ("foo.1", uniquer.GetUniqueName("foo.1")); + EXPECT_EQ("foo.12", uniquer.GetUniqueName("foo.12")); + EXPECT_EQ("foo.3", uniquer.GetUniqueName("foo.3")); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/reshape_mover.cc b/tensorflow/compiler/xla/service/reshape_mover.cc index 0f26a025bf125f70199637894741540f89eae7e5..49ec38eb62c7b51c7a2d301d882cef032b288036 100644 --- a/tensorflow/compiler/xla/service/reshape_mover.cc +++ b/tensorflow/compiler/xla/service/reshape_mover.cc @@ -155,20 +155,15 @@ HloInstruction* UpdateOperand(const HloInstruction* first_reshape_operand, case HloOpcode::kConstant: { if (first_reshape_operand->opcode() == HloOpcode::kReshape) { VLOG(5) << "Adding reshape to kConstant operand"; - HloInstruction* reshape = computation->AddInstruction( + return computation->AddInstruction( HloInstruction::CreateReshape(new_shape, operand)); - operand->SetupDerivedInstruction(reshape); - return reshape; } else { CHECK(first_reshape_operand->opcode() == HloOpcode::kTranspose); VLOG(5) << "Adding transpose to kConstant operand"; std::vector inverse_permutation = InversePermutation(first_reshape_operand->dimensions()); - HloInstruction* transpose = - computation->AddInstruction(HloInstruction::CreateTranspose( - new_shape, operand, inverse_permutation)); - operand->SetupDerivedInstruction(transpose); - return transpose; + return computation->AddInstruction(HloInstruction::CreateTranspose( + new_shape, operand, inverse_permutation)); } } case HloOpcode::kRng: { diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index d01c35b99231310692f85d0f9fbf4f2c3709d44c..da3b622bfae8ac5132f9f95070ee41674e79b5b8 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -64,25 +64,25 @@ namespace { // Records the arguments used to invoke a computation in an HloSnapshot proto. Status RecordArguments( const tensorflow::gtl::ArraySlice arguments, - se::StreamExecutor* executor, TransferManager* transfer_manager, + se::Stream* stream, TransferManager* transfer_manager, HloSnapshot* module) { module->clear_arguments(); for (const ShapedBuffer* argument : arguments) { TF_ASSIGN_OR_RETURN( std::unique_ptr literal, - transfer_manager->TransferLiteralFromDevice(executor, *argument)); + transfer_manager->TransferLiteralFromDevice(stream, *argument)); *module->add_arguments() = literal->ToProto(); } return Status::OK(); } // Records the result of a computation in a HloSnapshot proto. -Status RecordResult(const ShapedBuffer& result, se::StreamExecutor* executor, +Status RecordResult(const ShapedBuffer& result, se::Stream* stream, TransferManager* transfer_manager, HloSnapshot* module) { module->clear_result(); TF_ASSIGN_OR_RETURN( std::unique_ptr literal, - transfer_manager->TransferLiteralFromDevice(executor, result)); + transfer_manager->TransferLiteralFromDevice(stream, result)); *module->mutable_result() = literal->ToProto(); return Status::OK(); } @@ -191,21 +191,17 @@ Status Service::DeconstructTuple(const DeconstructTupleRequest* arg, return Status::OK(); } -Status Service::ValidateResultShapeWithLayout(const Shape& shape_with_layout, - const Shape& result_shape) const { - if (!ShapeUtil::Compatible(shape_with_layout, result_shape)) { +Status Service::ValidateResultShape(const Shape& client_shape, + const Shape& result_shape) const { + TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(client_shape)); + if (!ShapeUtil::Compatible(client_shape, result_shape)) { return InvalidArgument( "Shape used to set computation result layout %s is not compatible " "with result shape %s", - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str(), + ShapeUtil::HumanStringWithLayout(client_shape).c_str(), ShapeUtil::HumanString(result_shape).c_str()); } - if (!LayoutUtil::HasLayout(shape_with_layout)) { - return InvalidArgument( - "Shape used to set computation result layout %s does not have layout", - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str()); - } - return ShapeUtil::ValidateShape(shape_with_layout); + return Status::OK(); } StatusOr>> @@ -248,10 +244,8 @@ StatusOr> Service::CreateModuleConfig( tensorflow::gtl::ArraySlice argument_shapes, const ExecutionOptions* execution_options) { auto config = MakeUnique(program_shape); - ComputationLayout* host_computation_layout = - config->mutable_host_entry_computation_layout(); - ComputationLayout* device_computation_layout = - config->mutable_device_entry_computation_layout(); + ComputationLayout* computation_layout = + config->mutable_entry_computation_layout(); if (program_shape.parameters_size() != argument_shapes.size()) { return InvalidArgument("computation takes %d parameters, but %zu given", program_shape.parameters_size(), @@ -268,32 +262,22 @@ StatusOr> Service::CreateModuleConfig( i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(), ShapeUtil::HumanString(*argument_shapes[i]).c_str()); } - TF_RETURN_IF_ERROR(host_computation_layout->mutable_parameter_layout(i) - ->CopyLayoutFromShape(*argument_shapes[i])); - TF_RETURN_IF_ERROR(device_computation_layout->mutable_parameter_layout(i) - ->CopyLayoutFromShape(*argument_shapes[i])); + TF_RETURN_IF_ERROR( + computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape( + *argument_shapes[i])); } if (execution_options != nullptr && execution_options->has_shape_with_output_layout()) { const auto& shape_with_output_layout = execution_options->shape_with_output_layout(); - TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout(shape_with_output_layout, - program_shape.result())); TF_RETURN_IF_ERROR( - host_computation_layout->mutable_result_layout()->CopyLayoutFromShape( - shape_with_output_layout)); + ValidateResultShape(shape_with_output_layout, program_shape.result())); TF_RETURN_IF_ERROR( - device_computation_layout->mutable_result_layout()->CopyLayoutFromShape( + computation_layout->mutable_result_layout()->CopyLayoutFromShape( shape_with_output_layout)); } else { // If the result layout is not set, then choose the default. - // TODO(b/29118294): Allow the compiler to choose a better layout in this - // case. - // TODO(b/78356948): We are forcing the default layout here. We should fix - // clients which expect a default layout, to be explicit about it, by - // passing the proper ExecutionOptions with shape_with_output_layout set. - host_computation_layout->mutable_result_layout()->SetToDefaultLayout(); - device_computation_layout->mutable_result_layout()->SetToDefaultLayout(); + computation_layout->mutable_result_layout()->SetToDefaultLayout(); } config->set_replica_count(options_.number_of_replicas()); @@ -348,8 +332,8 @@ StatusOr>> Service::BuildExecutables( module_protos[i]->entry_computation_name().c_str()); TF_RETURN_IF_ERROR( Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); - hlo_snapshots.push_back(std::move(hlo_snapshot)); } + hlo_snapshots.push_back(std::move(hlo_snapshot)); } VLOG(1) << "Computations:"; @@ -381,22 +365,6 @@ StatusOr>> Service::BuildExecutables( return std::move(executables); } -Status Service::ValidateEntryComputationLayout(HloModule* module) { - const ComputationLayout& on_device = - module->device_entry_computation_layout(); - for (int64 i = 0; i < on_device.parameter_count(); ++i) { - TF_RET_CHECK(ShapeUtil::Equal( - on_device.parameter_shape(i), - execute_backend_->transfer_manager()->HostShapeToDeviceShape( - module->host_entry_computation_layout().parameter_shape(i)))); - } - TF_RET_CHECK(ShapeUtil::Equal( - module->device_entry_computation_layout().result_shape(), - execute_backend_->transfer_manager()->HostShapeToDeviceShape( - module->host_entry_computation_layout().result_shape()))); - return Status::OK(); -} - StatusOr> Service::ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, @@ -498,7 +466,7 @@ Service::ExecuteParallelAndRegisterResult( HloExecutionProfile hlo_profile(&executable->hlo_profile_printer_data(), &executable->hlo_profile_index_map()); TF_RETURN_IF_ERROR( - executable->PopulateExecutionProfile(&hlo_profile, stream->parent())); + executable->PopulateExecutionProfile(&hlo_profile, stream)); XLA_LOG_LINES( tensorflow::INFO, hlo_profile.ToString(streams[0]->parent()->GetDeviceDescription())); @@ -692,7 +660,7 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, request.execution_options())); VLOG(3) << "ExecuteGraphParallel created HloModuleConfig computation layout: " - << module_config->host_entry_computation_layout().ToString(); + << module_config->entry_computation_layout().ToString(); // Adds to the vectors to build and execute the computations after the loop. all_arguments.push_back(replicated_arguments); @@ -721,6 +689,17 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, executable_ptrs.push_back(executable.get()); } + for (int i = 0; i < executable_ptrs.size(); i++) { + if (executable_ptrs[i]->dumping_snapshot()) { + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream( + all_executors[i][0]->device_ordinal())); + TF_RETURN_IF_ERROR(RecordArguments(all_arguments[i].front(), stream.get(), + execute_backend_->transfer_manager(), + executable_ptrs[i]->hlo_snapshot())); + } + } + // Execute the generated executables in parallel and return the device // handles for each computation's output. ExecutionProfile profile; @@ -736,6 +715,20 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, *result->add_responses() = response; } + for (int i = 0; i < executable_ptrs.size(); i++) { + if (executable_ptrs[i]->dumping_snapshot()) { + TF_ASSIGN_OR_RETURN(const ShapedBuffer* result_buffer, + allocation_tracker_.ResolveForReplica(outputs[i], 0)); + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream(all_executors[i][0])); + TF_RETURN_IF_ERROR(RecordResult(*result_buffer, stream.get(), + execute_backend_->transfer_manager(), + executable_ptrs[i]->hlo_snapshot())); + // Dump out the ith snapshot. + TF_RETURN_IF_ERROR(executable_ptrs[i]->DumpHloSnapshot()); + } + } + VLOG(1) << "successfully completed 'execute-graph-parallel' request"; return Status::OK(); } @@ -828,13 +821,15 @@ StatusOr> Service::BuildExecutable( TF_ASSIGN_OR_RETURN( module, backend->compiler()->RunHloPasses(std::move(module), executor, device_allocator)); - // Check that on-host and on-device shapes are consistent. - TF_RETURN_IF_ERROR(ValidateEntryComputationLayout(module.get())); TF_ASSIGN_OR_RETURN(std::unique_ptr executable, backend->compiler()->RunBackend( std::move(module), executor, device_allocator)); + if (!execution_directory_path.empty()) { + executable->set_hlo_snapshot(std::move(hlo_snapshot)); + } + return std::move(executable); } @@ -872,12 +867,14 @@ Status Service::ExecuteGraph(const ExecuteGraphRequest* arg, execute_backend_->default_stream_executor(), /*device_allocator=*/nullptr)); + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream( + execute_backend_->default_stream_executor())); if (executable->dumping_snapshot()) { executable->hlo_snapshot()->set_execution_platform( execute_backend_->platform()->Name()); TF_RETURN_IF_ERROR(RecordArguments( - replicated_arguments.front(), - execute_backend_->default_stream_executor(), + replicated_arguments.front(), stream.get(), execute_backend_->transfer_manager(), executable->hlo_snapshot())); } @@ -891,9 +888,9 @@ Status Service::ExecuteGraph(const ExecuteGraphRequest* arg, TF_ASSIGN_OR_RETURN( const ShapedBuffer* result_buffer, allocation_tracker_.ResolveForReplica(result->output(), 0)); - TF_RETURN_IF_ERROR(RecordResult( - *result_buffer, execute_backend_->default_stream_executor(), - execute_backend_->transfer_manager(), executable->hlo_snapshot())); + TF_RETURN_IF_ERROR(RecordResult(*result_buffer, stream.get(), + execute_backend_->transfer_manager(), + executable->hlo_snapshot())); TF_RETURN_IF_ERROR(executable->DumpHloSnapshot()); } @@ -931,14 +928,13 @@ Status Service::TransferToClient(const TransferToClientRequest* arg, return_shape = &shaped_buffer->on_host_shape(); } - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - execute_backend_->stream_executor(shaped_buffer->device_ordinal())); + TF_ASSIGN_OR_RETURN(auto stream, execute_backend_->BorrowStream( + shaped_buffer->device_ordinal())); TF_ASSIGN_OR_RETURN( std::unique_ptr result_literal, execute_backend_->transfer_manager()->TransferLiteralFromDevice( - executor, *shaped_buffer)); + stream.get(), *shaped_buffer)); if (LayoutUtil::LayoutsInShapesEqual(*return_shape, result_literal->shape())) { @@ -988,9 +984,10 @@ Status Service::TransferToServer(const TransferToServerRequest* arg, execute_backend_->transfer_manager()->AllocateScopedShapedBuffer( shape, execute_backend_->memory_allocator(), executor->device_ordinal())); + TF_ASSIGN_OR_RETURN(auto stream, execute_backend_->BorrowStream(executor)); TF_RETURN_IF_ERROR( execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, shaped_buffer)); + stream.get(), *literal, shaped_buffer)); replicated_buffers.emplace_back(std::move(shaped_buffer)); } TF_ASSIGN_OR_RETURN(*result->mutable_data(), diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index d64b2b4d0afa15f8c0cf48b19c33e51a3d011eb0..47d196fb2aaee897ce1fd3745129af10bf5b2d2d 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -26,14 +26,12 @@ limitations under the License. #include "tensorflow/compiler/xla/service/allocation_tracker.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/channel_tracker.h" -#include "tensorflow/compiler/xla/service/compilation_cache.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/executable.h" #include "tensorflow/compiler/xla/service/execution_tracker.h" #include "tensorflow/compiler/xla/service/hlo_execution_profile.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_module_config.h" -#include "tensorflow/compiler/xla/service/versioned_computation_handle.h" #include "tensorflow/compiler/xla/service_interface.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" @@ -195,9 +193,6 @@ class Service : public ServiceInterface { const ExecutionOptions& execution_options, tensorflow::gtl::ArraySlice arguments); - // Assert that host- and device-shapes are in a consistent state. - Status ValidateEntryComputationLayout(HloModule* module); - protected: friend class LocalExecutable; @@ -268,11 +263,11 @@ class Service : public ServiceInterface { // will be the result of this computation. Status ExecuteOneToN(const ExecuteGraphRequest* arg, ExecuteResponse* result); - // Convenience function which checks whether the given shape_with_layout + // Convenience function which checks whether the given client_shape // (presumably passed by the client to set the result layout) is valid for the // given computation result shape. - Status ValidateResultShapeWithLayout(const Shape& shape_with_layout, - const Shape& result_shape) const; + Status ValidateResultShape(const Shape& client_shape, + const Shape& result_shape) const; // Returns the stream executors assigned to the replicas represented by the // given device handle. Each device_handle is a virtual replicated device that @@ -297,9 +292,6 @@ class Service : public ServiceInterface { // Tracks asynchronously launched executions via the API. ExecutionTracker execution_tracker_; - // Cache containing previously built Executables. - CompilationCache compilation_cache_; - // Backend to compile and execute computations on. std::unique_ptr execute_backend_; diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index d624f548b1ba65e6f6dfd7b329e8c86ab29112a0..81f071ecc52026566a25e6a9efd174ad5e9da112 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -44,147 +44,18 @@ namespace xla { namespace { -// Return the UnaryOperation proto enum value associated with the given HLO -// opcode. -UnaryOperation OpcodeToUnaryOperation(HloOpcode opcode) { - switch (opcode) { - case HloOpcode::kAbs: - return UNOP_ABS; - case HloOpcode::kCeil: - return UNOP_CEIL; - case HloOpcode::kClz: - return UNOP_CLZ; - case HloOpcode::kCos: - return UNOP_COS; - case HloOpcode::kExp: - return UNOP_EXP; - case HloOpcode::kExpm1: - return UNOP_EXPM1; - case HloOpcode::kFloor: - return UNOP_FLOOR; - case HloOpcode::kImag: - return UNOP_IMAG; - case HloOpcode::kIsFinite: - return UNOP_IS_FINITE; - case HloOpcode::kLog: - return UNOP_LOG; - case HloOpcode::kLog1p: - return UNOP_LOG1P; - case HloOpcode::kNot: - return UNOP_NOT; - case HloOpcode::kNegate: - return UNOP_NEGATE; - case HloOpcode::kReal: - return UNOP_REAL; - case HloOpcode::kRoundNearestAfz: - return UNOP_ROUND_NEAREST_AFZ; - case HloOpcode::kSign: - return UNOP_SIGN; - case HloOpcode::kSin: - return UNOP_SIN; - case HloOpcode::kSort: - return UNOP_SORT; - case HloOpcode::kTanh: - return UNOP_TANH; - default: - LOG(FATAL) << "Unhandled opcode for conversion to unary operation: " - << opcode; - } -} - -// Return the BinaryOperation proto enum value associated with the given HLO -// opcode. -BinaryOperation OpcodeToBinaryOperation(HloOpcode opcode) { - switch (opcode) { - case HloOpcode::kAtan2: - return BINOP_ATAN2; - case HloOpcode::kComplex: - return BINOP_COMPLEX; - case HloOpcode::kMultiply: - return BINOP_MUL; - case HloOpcode::kAdd: - return BINOP_ADD; - case HloOpcode::kSubtract: - return BINOP_SUB; - case HloOpcode::kDivide: - return BINOP_DIV; - case HloOpcode::kEq: - return BINOP_EQ; - case HloOpcode::kGe: - return BINOP_GE; - case HloOpcode::kGt: - return BINOP_GT; - case HloOpcode::kLe: - return BINOP_LE; - case HloOpcode::kLt: - return BINOP_LT; - case HloOpcode::kNe: - return BINOP_NE; - case HloOpcode::kMaximum: - return BINOP_MAX; - case HloOpcode::kMinimum: - return BINOP_MIN; - case HloOpcode::kPower: - return BINOP_POW; - case HloOpcode::kRemainder: - return BINOP_REM; - case HloOpcode::kOr: - return BINOP_OR; - case HloOpcode::kAnd: - return BINOP_AND; - case HloOpcode::kShiftLeft: - return BINOP_SHIFT_LEFT; - case HloOpcode::kShiftRightArithmetic: - return BINOP_SHIFT_RIGHT_ARITHMETIC; - case HloOpcode::kShiftRightLogical: - return BINOP_SHIFT_RIGHT_LOGICAL; - default: - LOG(FATAL) << "unhandled opcode " << opcode; - } -} - -// Return the TernaryOperation proto enum value associated with the given HLO -// opcode. -TernaryOperation OpcodeToTernaryOperation(HloOpcode opcode) { - switch (opcode) { - case HloOpcode::kClamp: - return TRIOP_CLAMP; - case HloOpcode::kSelect: - return TRIOP_SELECT; - default: - LOG(FATAL) << "unhandled opcode " << opcode; - } -} - -// Return the VariadicOperation proto enum value associated with the given HLO -// opcode. -VariadicOperation OpcodeToVariadicOperation(HloOpcode opcode) { - switch (opcode) { - case HloOpcode::kTuple: - return VAROP_TUPLE; - default: - LOG(FATAL) << "unhandled opcode " << opcode; - } -} - // Returns true if no element is present in slice more than once. bool AllUnique(tensorflow::gtl::ArraySlice slice) { return std::set(slice.begin(), slice.end()).size() == slice.size(); } -Status ExpectNotTupleOrOpaque(const Shape& shape, - tensorflow::StringPiece op_type) { - if (ShapeUtil::IsTuple(shape)) { - return InvalidArgument("Expected non-tuple argument for %s, but got %s.", +Status ExpectArray(const Shape& shape, tensorflow::StringPiece op_type) { + if (!ShapeUtil::IsArray(shape)) { + return InvalidArgument("Expected array argument for %s, but got %s.", std::string(op_type).c_str(), ShapeUtil::HumanString(shape).c_str()); - } else if (ShapeUtil::IsOpaque(shape)) { - return InvalidArgument("Expected non-opaque argument for %s, but got %s.", - std::string(op_type).c_str(), - ShapeUtil::HumanString(shape).c_str()); - } else { - return Status::OK(); } + return Status::OK(); } Status VerifyReducerShape(const ProgramShape& reducer_shape, @@ -198,11 +69,11 @@ Status VerifyReducerShape(const ProgramShape& reducer_shape, } const Shape& accumulator_shape = reducer_shape.result(); - if (ShapeUtil::Rank(accumulator_shape) != 0) { + if (!ShapeUtil::IsArray(accumulator_shape) || + ShapeUtil::Rank(accumulator_shape) != 0) { return InvalidArgument( - "Reduction function must have rank 0 (rank %lld reduction function " - "given).", - ShapeUtil::Rank(accumulator_shape)); + "Reduction function must produce a scalar but has shape: %s", + ShapeUtil::HumanString(accumulator_shape).c_str()); } // Check that the accumulator can be passed in as the first argument. @@ -321,84 +192,79 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, return shape; } - return InferUnaryOpShape(OpcodeToUnaryOperation(opcode), shape); -} + TF_RETURN_IF_ERROR(ExpectArray(shape, "operand of unary operation")); -/* static */ StatusOr ShapeInference::InferUnaryOpShape( - UnaryOperation operation, const Shape& arg) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of unary operation")); - - TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(arg)); - switch (operation) { - case UNOP_FLOOR: - case UNOP_CEIL: - if (!ShapeUtil::ElementIsFloating(arg)) { + TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape)); + switch (opcode) { + case HloOpcode::kFloor: + case HloOpcode::kCeil: + if (!ShapeUtil::ElementIsFloating(shape)) { return InvalidArgument( "Expected element type in shape to be floating for floor/ceil " "operation; got %s.", - PrimitiveType_Name(arg.element_type()).c_str()); + PrimitiveType_Name(shape.element_type()).c_str()); } - return arg; - case UNOP_COS: - case UNOP_SIN: - case UNOP_EXP: - case UNOP_EXPM1: - case UNOP_LOG: - case UNOP_LOG1P: - case UNOP_TANH: - if (!ShapeUtil::ElementIsFloating(arg) && - !ShapeUtil::ElementIsComplex(arg)) { + return shape; + case HloOpcode::kCos: + case HloOpcode::kSin: + case HloOpcode::kExp: + case HloOpcode::kExpm1: + case HloOpcode::kLog: + case HloOpcode::kLog1p: + case HloOpcode::kTanh: + if (!ShapeUtil::ElementIsFloating(shape) && + !ShapeUtil::ElementIsComplex(shape)) { return InvalidArgument( "Expected element type in shape to be floating or complex for " "sin/cos/exp/log/tanh operation; got %s.", - PrimitiveType_Name(arg.element_type()).c_str()); + PrimitiveType_Name(shape.element_type()).c_str()); } - return arg; - case UNOP_REAL: - case UNOP_IMAG: - if (!ShapeUtil::ElementIsComplex(arg)) { + return shape; + case HloOpcode::kReal: + case HloOpcode::kImag: + if (!ShapeUtil::ElementIsComplex(shape)) { return InvalidArgument( "Expected element type in shape to be complex for real/imag " "operation; got %s.", - PrimitiveType_Name(arg.element_type()).c_str()); + PrimitiveType_Name(shape.element_type()).c_str()); } - return ShapeUtil::ChangeElementType(arg, F32); - case UNOP_ABS: - if (ShapeUtil::ElementIsComplex(arg)) { + return ShapeUtil::ChangeElementType(shape, F32); + case HloOpcode::kAbs: + if (ShapeUtil::ElementIsComplex(shape)) { return ShapeUtil::ChangeElementType( - arg, primitive_util::ComplexComponentType(arg.element_type())); + shape, primitive_util::ComplexComponentType(shape.element_type())); } - return arg; - case UNOP_CLZ: - case UNOP_NEGATE: - case UNOP_ROUND_NEAREST_AFZ: - case UNOP_SIGN: - case UNOP_SORT: - return arg; - - case UNOP_NOT: - if (arg.element_type() != PRED && - !primitive_util::IsIntegralType(arg.element_type())) { + return shape; + case HloOpcode::kClz: + case HloOpcode::kNegate: + case HloOpcode::kRoundNearestAfz: + case HloOpcode::kSign: + return shape; + + case HloOpcode::kNot: + if (shape.element_type() != PRED && + !primitive_util::IsIntegralType(shape.element_type())) { return InvalidArgument( "Expected pred or an integral element type in argument to Not " "operation; got %s.", - PrimitiveType_Name(arg.element_type()).c_str()); + PrimitiveType_Name(shape.element_type()).c_str()); } - return arg; + return shape; - case UNOP_IS_FINITE: - if (!ShapeUtil::ElementIsFloating(arg)) { + case HloOpcode::kIsFinite: + if (!ShapeUtil::ElementIsFloating(shape)) { return InvalidArgument( - "Expected element type in shape to be floating point for IsFinite " + "Expected element type in shape to be floating " + "point for IsFinite " "operation; got %s.", - PrimitiveType_Name(arg.element_type()).c_str()); + PrimitiveType_Name(shape.element_type()).c_str()); } - return ShapeUtil::ChangeElementType(arg, PRED); + return ShapeUtil::ChangeElementType(shape, PRED); default: return InvalidArgument( "Unknown operation for unary shape inference: \"%s\".", - UnaryOperation_Name(operation).c_str()); + HloOpcodeString(opcode).c_str()); } } @@ -415,8 +281,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const Shape* arg_shape = nullptr; PrimitiveType element_type = PRIMITIVE_TYPE_INVALID; for (const Shape* shape : arg_shapes) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*shape, "operand of concatenation")); + TF_RETURN_IF_ERROR(ExpectArray(*shape, "operand of concatenation")); if (!arg_shape) { arg_shape = shape; element_type = arg_shape->element_type(); @@ -463,6 +328,17 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, return ShapeUtil::MakeShape(element_type, new_dimensions); } +/* static */ StatusOr ShapeInference::InferAfterAllShape( + tensorflow::gtl::ArraySlice arg_shapes) { + for (const Shape* arg_shape : arg_shapes) { + if (arg_shape->element_type() != TOKEN) { + return InvalidArgument( + "Operands of token instructions must be TOKEN types."); + } + } + return ShapeUtil::MakeTokenShape(); +} + /* static */ StatusOr ShapeInference::InferConvertShape( const Shape& operand_shape, PrimitiveType new_element_type) { auto old_element_type = operand_shape.element_type(); @@ -473,12 +349,13 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } - if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { + if (!ShapeUtil::IsArray(operand_shape) || + !primitive_util::IsArrayType(new_element_type)) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "Convert does not allow tuples, so cannot convert from %s to %s.", + "Convert does not allow non-arrays, so cannot convert from %s to %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -495,7 +372,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } - if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { + if (!ShapeUtil::IsArray(operand_shape) || + !primitive_util::IsArrayType(new_element_type)) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. @@ -542,7 +420,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, /* static */ StatusOr ShapeInference::InferPadShape( const Shape& operand_shape, const Shape& padding_value_shape, const PaddingConfig& padding_config) { - if (ShapeUtil::IsTuple(operand_shape)) { + if (!ShapeUtil::IsArray(operand_shape)) { return InvalidArgument( "Pad operation does not support tuple-shape operands."); } @@ -681,8 +559,8 @@ Status ValidateDotDimensionNumbers( /* static */ StatusOr ShapeInference::InferDotOpShape( const Shape& lhs, const Shape& rhs, const DotDimensionNumbers& dimension_numbers) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(lhs, "lhs of dot")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(rhs, "rhs of dot")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of dot")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of dot")); auto fail = [lhs, rhs](const string& addendum) -> Status { string message = tensorflow::strings::Printf( @@ -768,8 +646,9 @@ Status ValidateDotDimensionNumbers( } /* static */ StatusOr -ShapeInference::InferDegenerateDimensionBroadcastShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs) { +ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, + const Shape& lhs, + const Shape& rhs) { TF_RET_CHECK(ShapeUtil::Rank(lhs) == ShapeUtil::Rank(rhs)); // The shapes have to be compatible. That is, if some dimension d has a @@ -787,7 +666,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } else { return InvalidArgument( "Binary op %s with incompatible shapes: %s and %s.", - BinaryOperation_Name(operation).c_str(), + HloOpcodeString(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); } @@ -797,8 +676,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } /* static */ StatusOr ShapeInference::InferInDimBroadcastShape( - BinaryOperation operation, const Shape& smaller_shape, - const Shape& larger_shape, + const Shape& smaller_shape, const Shape& larger_shape, tensorflow::gtl::ArraySlice broadcast_dimensions) { if (broadcast_dimensions.empty() && !ShapeUtil::IsScalar(smaller_shape)) { // Reject "magic" inference for binops on different shapes, requiring @@ -899,18 +777,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } /* static */ StatusOr ShapeInference::InferElementwiseBinaryOpShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs, + HloOpcode operation, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(lhs, "lhs of elementwise binary operation")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(rhs, "rhs of elementwise binary operation")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of elementwise binary operation")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of elementwise binary operation")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( "Binary op %s with different element types: %s and %s.", - BinaryOperation_Name(operation).c_str(), - ShapeUtil::HumanString(lhs).c_str(), + HloOpcodeString(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); } @@ -943,10 +818,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( ShapeUtil::Rank(lhs) > ShapeUtil::Rank(rhs) ? rhs : lhs; // After InDim broadcasting, perform degenerate dimensions broadcasting. - TF_ASSIGN_OR_RETURN( - Shape indim_broadcast_shape, - InferInDimBroadcastShape(operation, smaller_shape, larger_shape, - broadcast_dimensions)); + TF_ASSIGN_OR_RETURN(Shape indim_broadcast_shape, + InferInDimBroadcastShape(smaller_shape, larger_shape, + broadcast_dimensions)); return InferDegenerateDimensionBroadcastShape( operation, indim_broadcast_shape, larger_shape); @@ -955,51 +829,44 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferBinaryOpShape( HloOpcode opcode, const HloInstruction* lhs, const HloInstruction* rhs) { - return InferBinaryOpShape(OpcodeToBinaryOperation(opcode), lhs->shape(), - rhs->shape(), /*broadcast_dimensions=*/{}); + return InferBinaryOpShape(opcode, lhs->shape(), rhs->shape(), + /*broadcast_dimensions=*/{}); } /* static */ StatusOr ShapeInference::InferBinaryOpShape( HloOpcode opcode, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { - return InferBinaryOpShape(OpcodeToBinaryOperation(opcode), lhs, rhs, - broadcast_dimensions); -} - -/* static */ StatusOr ShapeInference::InferBinaryOpShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions) { VLOG(2) << tensorflow::strings::Printf( "inferring shape for <%s>(%s, %s) with broadcast_dimensions={%s}", - BinaryOperation_Name(operation).c_str(), - ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str(), + HloOpcodeString(opcode).c_str(), ShapeUtil::HumanString(lhs).c_str(), + ShapeUtil::HumanString(rhs).c_str(), Join(broadcast_dimensions, ", ").c_str()); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - lhs, tensorflow::strings::StrCat("lhs of binary operation ", - BinaryOperation_Name(operation)))); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - rhs, tensorflow::strings::StrCat("rhs of binary operation ", - BinaryOperation_Name(operation)))); - switch (operation) { - case BINOP_MAX: - case BINOP_MIN: - case BINOP_SUB: - case BINOP_ADD: - case BINOP_ATAN2: - case BINOP_POW: - case BINOP_DIV: - case BINOP_REM: - case BINOP_MUL: - case BINOP_SHIFT_LEFT: - case BINOP_SHIFT_RIGHT_ARITHMETIC: - case BINOP_SHIFT_RIGHT_LOGICAL: - return InferElementwiseBinaryOpShape(operation, lhs, rhs, + TF_RETURN_IF_ERROR( + ExpectArray(lhs, tensorflow::strings::StrCat("lhs of binary operation ", + HloOpcodeString(opcode)))); + TF_RETURN_IF_ERROR( + ExpectArray(rhs, tensorflow::strings::StrCat("rhs of binary operation ", + HloOpcodeString(opcode)))); + switch (opcode) { + case HloOpcode::kMaximum: + case HloOpcode::kMinimum: + case HloOpcode::kSubtract: + case HloOpcode::kAdd: + case HloOpcode::kAtan2: + case HloOpcode::kPower: + case HloOpcode::kDivide: + case HloOpcode::kRemainder: + case HloOpcode::kMultiply: + case HloOpcode::kShiftLeft: + case HloOpcode::kShiftRightArithmetic: + case HloOpcode::kShiftRightLogical: + return InferElementwiseBinaryOpShape(opcode, lhs, rhs, broadcast_dimensions); - case BINOP_COMPLEX: { + case HloOpcode::kComplex: { if (!ShapeUtil::ElementIsFloating(lhs)) { return InvalidArgument( "Expected element type in shape to be floating for complex compose " @@ -1007,7 +874,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( PrimitiveType_Name(lhs.element_type()).c_str()); } TF_ASSIGN_OR_RETURN(const Shape& shape, - InferElementwiseBinaryOpShape(operation, lhs, rhs, + InferElementwiseBinaryOpShape(opcode, lhs, rhs, broadcast_dimensions)); if (lhs.element_type() == F32 && rhs.element_type() == F32) { return ShapeUtil::ChangeElementType(shape, C64); @@ -1015,8 +882,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return Unimplemented("Complex component type is not implemented."); } } - case BINOP_AND: - case BINOP_OR: + case HloOpcode::kAnd: + case HloOpcode::kOr: + case HloOpcode::kXor: if (lhs.element_type() != PRED && !primitive_util::IsIntegralType(lhs.element_type())) { return InvalidArgument( @@ -1024,24 +892,24 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "got %s.", PrimitiveType_Name(lhs.element_type()).c_str()); } - return InferElementwiseBinaryOpShape(operation, lhs, rhs, + return InferElementwiseBinaryOpShape(opcode, lhs, rhs, broadcast_dimensions); - case BINOP_EQ: - case BINOP_GE: - case BINOP_GT: - case BINOP_LE: - case BINOP_LT: - case BINOP_NE: { + case HloOpcode::kEq: + case HloOpcode::kGe: + case HloOpcode::kGt: + case HloOpcode::kLe: + case HloOpcode::kLt: + case HloOpcode::kNe: { TF_ASSIGN_OR_RETURN(const Shape& shape, - InferElementwiseBinaryOpShape(operation, lhs, rhs, + InferElementwiseBinaryOpShape(opcode, lhs, rhs, broadcast_dimensions)); return ShapeUtil::ChangeElementType(shape, PRED); } default: return Unimplemented( "Binary op shape inference: %s; lhs: %s; rhs: %s is not implemented.", - BinaryOperation_Name(operation).c_str(), - lhs.ShortDebugString().c_str(), rhs.ShortDebugString().c_str()); + HloOpcodeString(opcode).c_str(), lhs.ShortDebugString().c_str(), + rhs.ShortDebugString().c_str()); } } @@ -1053,23 +921,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferTernaryOpShape( HloOpcode opcode, const Shape& lhs, const Shape& rhs, const Shape& ehs) { - return InferTernaryOpShape(OpcodeToTernaryOperation(opcode), lhs, rhs, ehs); -} - -/* static */ StatusOr ShapeInference::InferTernaryOpShape( - TernaryOperation operation, const Shape& lhs, const Shape& rhs, - const Shape& ehs) { TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(ehs)); - switch (operation) { - case TRIOP_CLAMP: + switch (opcode) { + case HloOpcode::kClamp: return InferClampShape(lhs, rhs, ehs); - case TRIOP_SELECT: + case HloOpcode::kSelect: return InferSelectShape(lhs, rhs, ehs); default: return InvalidArgument("Unknown operation %s.", - TernaryOperation_Name(operation).c_str()); + HloOpcodeString(opcode).c_str()); } } @@ -1077,6 +939,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( HloOpcode opcode, tensorflow::gtl::ArraySlice operands) { std::vector operand_shapes; + operand_shapes.reserve(operands.size()); for (const HloInstruction* operand : operands) { operand_shapes.push_back(&operand->shape()); } @@ -1086,27 +949,30 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferVariadicOpShape( HloOpcode opcode, tensorflow::gtl::ArraySlice operand_shapes) { - return InferVariadicOpShape(OpcodeToVariadicOperation(opcode), - operand_shapes); -} - -/* static */ StatusOr ShapeInference::InferVariadicOpShape( - VariadicOperation operation, - tensorflow::gtl::ArraySlice operand_shapes) { for (const Shape* shape : operand_shapes) { TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(*shape)); } - switch (operation) { - case VAROP_TUPLE: { + switch (opcode) { + case HloOpcode::kTuple: { Shape result = ShapeUtil::MakeTupleShape({}); + result.mutable_tuple_shapes()->Reserve(operand_shapes.size()); for (const Shape* shape : operand_shapes) { ShapeUtil::AppendShapeToTuple(*shape, &result); } return result; } + case HloOpcode::kSort: { + if (operand_shapes.size() == 1) { + return *operand_shapes[0]; + } else if (operand_shapes.size() == 2) { + return ShapeUtil::MakeTupleShape( + {*operand_shapes[0], *operand_shapes[1]}); + } + return InvalidArgument("Unexpected number of operands for sort"); + } default: return InvalidArgument("Unknown operation %s.", - VariadicOperation_Name(operation).c_str()); + HloOpcodeString(opcode).c_str()); } } @@ -1121,15 +987,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // All arguments must have the same shape. const Shape* arg_shape = arg_shapes[0]; for (size_t i = 1; i < arg_shapes.size(); ++i) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*arg_shapes[i], "operand of map")); + TF_RETURN_IF_ERROR(ExpectArray(*arg_shapes[i], "operand of map")); if (ShapeUtil::CompatibleIgnoringFpPrecision(*arg_shapes[i], *arg_shape)) { continue; } - if (!ShapeUtil::IsTuple(*arg_shapes[i]) && - !ShapeUtil::IsTuple(*arg_shape) && - ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shapes[i], + if (ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shapes[i], *arg_shape)) { if (ShapeUtil::IsScalar(*arg_shapes[i])) { continue; @@ -1212,11 +1075,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& operand_shape, const Shape& scale_shape, const Shape& offset_shape, int64 feature_index) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm training")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - offset_shape, "offset input of batch norm training")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - scale_shape, "scale input of batch norm training")); + ExpectArray(operand_shape, "operand of batch norm training")); + TF_RETURN_IF_ERROR( + ExpectArray(offset_shape, "offset input of batch norm training")); + TF_RETURN_IF_ERROR( + ExpectArray(scale_shape, "scale input of batch norm training")); TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) == Status::OK()); @@ -1318,11 +1181,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& offset_shape, const Shape& mean_shape, const Shape& variance_shape, int64 feature_index) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm inference")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - offset_shape, "offset input of batch norm inference")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - scale_shape, "scale input of batch norm inference")); + ExpectArray(operand_shape, "operand of batch norm inference")); + TF_RETURN_IF_ERROR( + ExpectArray(offset_shape, "offset input of batch norm inference")); + TF_RETURN_IF_ERROR( + ExpectArray(scale_shape, "scale input of batch norm inference")); TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) == Status::OK()); @@ -1465,16 +1328,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& operand_shape, const Shape& scale_shape, const Shape& mean_shape, const Shape& var_shape, const Shape& output_grad_shape, int64 feature_index) { + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of batch norm grad")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm grad")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(scale_shape, "scale input of batch norm grad")); + ExpectArray(scale_shape, "scale input of batch norm grad")); + TF_RETURN_IF_ERROR(ExpectArray(mean_shape, "mean input of batch norm grad")); + TF_RETURN_IF_ERROR(ExpectArray(var_shape, "var input of batch norm grad")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(mean_shape, "mean input of batch norm grad")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(var_shape, "var input of batch norm grad")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - output_grad_shape, "output_grad input of batch norm grad")); + ExpectArray(output_grad_shape, "output_grad input of batch norm grad")); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape)); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(mean_shape)); @@ -1623,8 +1483,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferConvolveShape( const Shape& lhs, const Shape& rhs, const Window& window, const ConvolutionDimensionNumbers& dnums) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(lhs, "lhs of convolution")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(rhs, "rhs of convolution")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of convolution")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of convolution")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( @@ -1859,7 +1719,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( tensorflow::gtl::ArraySlice operand_shapes) { for (const Shape* operand_shape : operand_shapes) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*operand_shape, "operand of cross replica sum")); + ExpectArray(*operand_shape, "operand of cross replica sum")); } if (operand_shapes.size() == 1) { return *operand_shapes[0]; @@ -1901,8 +1761,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferReduceWindowShape( const Shape& operand_shape, const Shape& init_value_shape, const Window& window, const ProgramShape& to_apply_shape) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of reduce-window")); + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of reduce-window")); TF_RETURN_IF_ERROR(VerifyReducerShape(to_apply_shape, init_value_shape, operand_shape.element_type())); return InferWindowOutputShape(operand_shape, window, @@ -1915,7 +1774,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Window& window, const Shape& source_shape, const Shape& init_value_shape, const ProgramShape& scatter_shape) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of select-and-scatter")); + ExpectArray(operand_shape, "operand of select-and-scatter")); // Check if the select function has a proper shape of (T,T) -> PRED. if (select_shape.parameters_size() != 2) { @@ -1980,7 +1839,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( Join(starts, ",").c_str(), Join(limits, ",").c_str(), Join(strides, ",").c_str()); }; - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice")); + TF_RETURN_IF_ERROR(ExpectArray(arg, "operand of slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s starts={%s} limits={%s}", ShapeUtil::HumanString(arg).c_str(), Join(starts, ", ").c_str(), @@ -2039,10 +1898,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferDynamicSliceShape( const Shape& operand_shape, const Shape& start_indices_shape, tensorflow::gtl::ArraySlice slice_sizes) { + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of dynamic slice")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of dynamic slice")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(start_indices_shape, - "start indices of dynamic slice")); + ExpectArray(start_indices_shape, "start indices of dynamic slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s at dynamic start_indices %s with slice_sizes={%s}", @@ -2100,11 +1958,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& operand_shape, const Shape& update_shape, const Shape& start_indices_shape) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of dynamic update slice")); + ExpectArray(operand_shape, "operand of dynamic update slice")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(update_shape, "update of dynamic update slice")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - start_indices_shape, "start indices of dynamic update slice")); + ExpectArray(update_shape, "update of dynamic update slice")); + TF_RETURN_IF_ERROR(ExpectArray(start_indices_shape, + "start indices of dynamic update slice")); VLOG(2) << tensorflow::strings::Printf( "updating slice of shape %s at dynamic start_indices %s with update " @@ -2172,8 +2030,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /*static */ StatusOr ShapeInference::InferReverseShape( const Shape& operand_shape, tensorflow::gtl::ArraySlice dimensions) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of reverse")); + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of reverse")); if (!AllUnique(dimensions)) { return InvalidArgument("a dimension number is duplicated in reverse"); } @@ -2303,7 +2160,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferBroadcastShape( const Shape& operand, tensorflow::gtl::ArraySlice broadcast_sizes) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "operand of broadcast")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "operand of broadcast")); for (int64 size : broadcast_sizes) { if (size < 0) { return InvalidArgument("Broadcast with negative dimension size %lld.", @@ -2322,7 +2179,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferReshapeShape( const Shape& operand, tensorflow::gtl::ArraySlice dimensions, tensorflow::gtl::ArraySlice new_sizes) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "reshape")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "reshape")); Shape inferred_shape = ShapeUtil::MakeShape(operand.element_type(), new_sizes); @@ -2354,7 +2211,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( /* static */ StatusOr ShapeInference::InferTransposeShape( const Shape& operand, tensorflow::gtl::ArraySlice dimensions) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "transpose")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "transpose")); std::vector indices(ShapeUtil::Rank(operand)); std::iota(indices.begin(), indices.end(), 0); @@ -2375,9 +2232,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // "degenerate" cases, as with binary elementwise ops. /* static */ StatusOr ShapeInference::InferClampShape( const Shape& min, const Shape& operand, const Shape& max) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(min, "clamp min")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "clamp operand")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(max, "clamp max")); + TF_RETURN_IF_ERROR(ExpectArray(min, "clamp min")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "clamp operand")); + TF_RETURN_IF_ERROR(ExpectArray(max, "clamp max")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) || !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) { return InvalidArgument("Clamp with different operand types: %s, %s, %s.", @@ -2576,9 +2433,9 @@ static Status ValidateGatherDimensionNumbers( const GatherDimensionNumbers& gather_dim_numbers, tensorflow::gtl::ArraySlice window_bounds) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(input_shape, "input tensor operand gather op")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - gather_indices_shape, "gather indices operand of gather op")); + ExpectArray(input_shape, "input tensor operand gather op")); + TF_RETURN_IF_ERROR( + ExpectArray(gather_indices_shape, "gather indices operand of gather op")); if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) { return InvalidArgument( diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h index 9da2c99b4177f08ece8daabaf2922ddd7e947a1b..ad34a2aa184e786a9825193d23f106f8a950758a 100644 --- a/tensorflow/compiler/xla/service/shape_inference.h +++ b/tensorflow/compiler/xla/service/shape_inference.h @@ -46,8 +46,6 @@ class ShapeInference { public: // Infers the shape produced by applying the given unary operation to the // given input shape. - static StatusOr InferUnaryOpShape(UnaryOperation operation, - const Shape& arg); static StatusOr InferUnaryOpShape(HloOpcode opcode, const Shape& shape); static StatusOr InferUnaryOpShape(HloOpcode opcode, @@ -55,9 +53,6 @@ class ShapeInference { // Infers the shape produced by applying the given binary operation to the // given input shapes. - static StatusOr InferBinaryOpShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); static StatusOr InferBinaryOpShape( HloOpcode opcode, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions); @@ -67,9 +62,6 @@ class ShapeInference { // Infers the shape produced by applying the given ternary operation to the // given input shapes. - static StatusOr InferTernaryOpShape(TernaryOperation operation, - const Shape& lhs, const Shape& rhs, - const Shape& ehs); static StatusOr InferTernaryOpShape(HloOpcode opcode, const Shape& lhs, const Shape& rhs, const Shape& ehs); @@ -80,9 +72,6 @@ class ShapeInference { // Infers the shape produced by applying the given variadic operation to the // given input operand shapes. - static StatusOr InferVariadicOpShape( - VariadicOperation operation, - tensorflow::gtl::ArraySlice operand_shapes); static StatusOr InferVariadicOpShape( HloOpcode opcode, tensorflow::gtl::ArraySlice operand_shapes); @@ -227,6 +216,13 @@ class ShapeInference { static StatusOr InferConcatOpShape( tensorflow::gtl::ArraySlice arg_shapes, int64 dimension); + // Infers the shape produced by a kAfterAll. Trivially this shape is always a + // TOKEN shape. However, ShapeInference serves two purposes: inferring shapes + // and checking operand shapes. This method verifies that the operand shapes + // are all TOKENs. + static StatusOr InferAfterAllShape( + tensorflow::gtl::ArraySlice arg_shapes); + // Helper that validates the given operand shape can be converted to the // target output_shape via a convert instruction -- the requirement is that // the shape is identical except for the element type. @@ -279,7 +275,7 @@ class ShapeInference { // the LHS and a single element in the RHS to produce a single output element, // even in the presence of broadcasting of one of the operands over the other. static StatusOr InferElementwiseBinaryOpShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs, + HloOpcode operation, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions); // Helper for inferring the shape of Clamp ops. @@ -295,7 +291,7 @@ class ShapeInference { // dimension broadcasting (a dimension of size 1 in one operand is broadcast // up to match the size of the dimension in the other operand). static StatusOr InferDegenerateDimensionBroadcastShape( - BinaryOperation operation, const Shape& lhs, const Shape& rhs); + HloOpcode operation, const Shape& lhs, const Shape& rhs); // Helper for inferring shapes of binary operations using "InDim" // broadcasting. This is the broadcasting used in the *InDim binary operations @@ -303,8 +299,7 @@ class ShapeInference { // lower-rank shape than larger_shape. Returns the shape that the // smaller_shape is broadcast to. static StatusOr InferInDimBroadcastShape( - BinaryOperation operation, const Shape& smaller_shape, - const Shape& larger_shape, + const Shape& smaller_shape, const Shape& larger_shape, tensorflow::gtl::ArraySlice broadcast_dimensions); TF_DISALLOW_COPY_AND_ASSIGN(ShapeInference); diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 0e61994a786b53a295ef9c9c2287b28fbf754d9b..bafe14d6f45f851924c37908d4c93bbff2dac459 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -101,8 +101,8 @@ class SelectAndScatterShapeInferenceTest : public ShapeInferenceTest { TEST_F(ShapeInferenceTest, UnaryNegateMatrix) { Shape matrix_shape = ShapeUtil::MakeShape(F32, {128, 64}); - auto inferred_status = ShapeInference::InferUnaryOpShape( - UnaryOperation::UNOP_NEGATE, matrix_shape); + auto inferred_status = + ShapeInference::InferUnaryOpShape(HloOpcode::kNegate, matrix_shape); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_shape, inferred_status.ValueOrDie())); } @@ -110,14 +110,14 @@ TEST_F(ShapeInferenceTest, UnaryNegateMatrix) { TEST_F(ShapeInferenceTest, SelectScalarPredBetweenTuples) { Shape tuple = ShapeUtil::MakeTupleShape({s32_, f32_}); auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, pred_, tuple, tuple); + HloOpcode::kSelect, pred_, tuple, tuple); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(tuple, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, SelectScalarPredBetweenArrays) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, pred_, matrix_64_48_, matrix_64_48_); + HloOpcode::kSelect, pred_, matrix_64_48_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } @@ -125,34 +125,34 @@ TEST_F(ShapeInferenceTest, SelectScalarPredBetweenArrays) { TEST_F(ShapeInferenceTest, SelectArrayPredBetweenArrays) { auto predarray = ShapeUtil::MakeShape(PRED, {64, 48}); auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, predarray, matrix_64_48_, matrix_64_48_); + HloOpcode::kSelect, predarray, matrix_64_48_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, SelectBadShapes) { auto inferred_status_error1 = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, pred_, matrix_64_48_, matrix_32_64_); + HloOpcode::kSelect, pred_, matrix_64_48_, matrix_32_64_); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), HasSubstr("Operands to select must be the same shape")); auto inferred_status_error2 = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, s32_, matrix_64_48_, matrix_64_48_); + HloOpcode::kSelect, s32_, matrix_64_48_, matrix_64_48_); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), HasSubstr("pred operand must have PRED")); auto inferred_status_error3 = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, ShapeUtil::MakeShape(PRED, {64}), - matrix_64_48_, matrix_64_48_); + HloOpcode::kSelect, ShapeUtil::MakeShape(PRED, {64}), matrix_64_48_, + matrix_64_48_); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), HasSubstr("with non-scalar predicate with dimensionality")); // Tuples have a TUPLE element type and cannot be the pred of a select. auto inferred_status_error4 = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_SELECT, ShapeUtil::MakeTupleShape({pred_, pred_}), + HloOpcode::kSelect, ShapeUtil::MakeTupleShape({pred_, pred_}), ShapeUtil::MakeTupleShape({f32_, f32_}), ShapeUtil::MakeTupleShape({f32_, f32_})); ASSERT_FALSE(inferred_status_error4.ok()); @@ -162,102 +162,98 @@ TEST_F(ShapeInferenceTest, SelectBadShapes) { TEST_F(ShapeInferenceTest, ClampAllMatrix) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, matrix_64_48_, matrix_64_48_, - matrix_64_48_); + HloOpcode::kClamp, matrix_64_48_, matrix_64_48_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampAllScalar) { - auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, f32_, f32_); + auto inferred_status = + ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, f32_, f32_, f32_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(f32_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampMinScalar) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, matrix_64_48_, matrix_64_48_); + HloOpcode::kClamp, f32_, matrix_64_48_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampMaxScalar) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, matrix_64_48_, matrix_64_48_, f32_); + HloOpcode::kClamp, matrix_64_48_, matrix_64_48_, f32_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampOperandScalar) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, matrix_64_48_, f32_, matrix_64_48_); + HloOpcode::kClamp, matrix_64_48_, f32_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampMinMatrix) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, matrix_64_48_, f32_, f32_); + HloOpcode::kClamp, matrix_64_48_, f32_, f32_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampMaxMatrix) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, f32_, matrix_64_48_); + HloOpcode::kClamp, f32_, f32_, matrix_64_48_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampOperandMatrix) { auto inferred_status = ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, matrix_64_48_, f32_); + HloOpcode::kClamp, f32_, matrix_64_48_, f32_); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(matrix_64_48_, inferred_status.ValueOrDie())); } TEST_F(ShapeInferenceTest, ClampBadShapes) { // Type mismatch - ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, s32_, f32_, f32_) - .ok()); - ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, s32_, f32_) - .ok()); - ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, f32_, s32_) - .ok()); - // Dimension mismatch ASSERT_FALSE( - ShapeInference::InferTernaryOpShape(TernaryOperation::TRIOP_CLAMP, - vector_64_, vector_32_, vector_32_) + ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, s32_, f32_, f32_) .ok()); ASSERT_FALSE( - ShapeInference::InferTernaryOpShape(TernaryOperation::TRIOP_CLAMP, - vector_32_, vector_64_, vector_32_) + ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, f32_, s32_, f32_) .ok()); ASSERT_FALSE( - ShapeInference::InferTernaryOpShape(TernaryOperation::TRIOP_CLAMP, - vector_32_, vector_32_, vector_64_) + ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, f32_, f32_, s32_) .ok()); - // Dimension mismatch, where one operand is a scalar + // Dimension mismatch ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, vector_64_, vector_32_, f32_) + HloOpcode::kClamp, vector_64_, vector_32_, vector_32_) .ok()); ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, vector_64_, f32_, vector_32_) + HloOpcode::kClamp, vector_32_, vector_64_, vector_32_) .ok()); ASSERT_FALSE(ShapeInference::InferTernaryOpShape( - TernaryOperation::TRIOP_CLAMP, f32_, vector_64_, vector_32_) + HloOpcode::kClamp, vector_32_, vector_32_, vector_64_) + .ok()); + // Dimension mismatch, where one operand is a scalar + ASSERT_FALSE(ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, + vector_64_, vector_32_, f32_) + .ok()); + ASSERT_FALSE(ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, + vector_64_, f32_, vector_32_) + .ok()); + ASSERT_FALSE(ShapeInference::InferTernaryOpShape(HloOpcode::kClamp, f32_, + vector_64_, vector_32_) .ok()); } TEST_F(ShapeInferenceTest, Complex) { auto complex_shape = [&](const Shape& lhs, const Shape& rhs, const tensorflow::gtl::ArraySlice& bcast) { - return ShapeInference::InferBinaryOpShape(BinaryOperation::BINOP_COMPLEX, - lhs, rhs, bcast); + return ShapeInference::InferBinaryOpShape(HloOpcode::kComplex, lhs, rhs, + bcast); }; // Inputs must be FP. ASSERT_FALSE(complex_shape(s32_, s32_, {}).ok()); @@ -292,8 +288,8 @@ TEST_F(ShapeInferenceTest, Complex) { } TEST_F(ShapeInferenceTest, VariadicOpTuplify) { - StatusOr result = ShapeInference::InferVariadicOpShape( - VariadicOperation::VAROP_TUPLE, {&s32_, &f32_}); + StatusOr result = + ShapeInference::InferVariadicOpShape(HloOpcode::kTuple, {&s32_, &f32_}); ASSERT_IS_OK(result.status()); ASSERT_TRUE(ShapeUtil::Equal(result.ValueOrDie(), ShapeUtil::MakeTupleShape({s32_, f32_}))); @@ -804,8 +800,8 @@ TEST_F(ShapeInferenceTest, InferConstIndexShape) { TEST_F(ShapeInferenceTest, InferPowShape) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); - auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_POW, ten_floats, f32_, {}); + auto inferred_status = ShapeInference::InferBinaryOpShape( + HloOpcode::kPower, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ten_floats, inferred_status.ValueOrDie())); } @@ -813,7 +809,7 @@ TEST_F(ShapeInferenceTest, InferPowShape) { TEST_F(ShapeInferenceTest, InferCompareShapeEq) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_EQ, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kEq, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -822,7 +818,7 @@ TEST_F(ShapeInferenceTest, InferCompareShapeEq) { TEST_F(ShapeInferenceTest, InferCompareShapeGe) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_GE, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kGe, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -831,7 +827,7 @@ TEST_F(ShapeInferenceTest, InferCompareShapeGe) { TEST_F(ShapeInferenceTest, InferCompareShapeGt) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_GT, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kGt, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -840,7 +836,7 @@ TEST_F(ShapeInferenceTest, InferCompareShapeGt) { TEST_F(ShapeInferenceTest, InferCompareShapeLe) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_LE, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kLe, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -849,7 +845,7 @@ TEST_F(ShapeInferenceTest, InferCompareShapeLe) { TEST_F(ShapeInferenceTest, InferCompareShapeLt) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_LT, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kLt, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -858,7 +854,7 @@ TEST_F(ShapeInferenceTest, InferCompareShapeLt) { TEST_F(ShapeInferenceTest, InferCompareShapeNe) { auto ten_floats = ShapeUtil::MakeShape(F32, {10}); auto inferred_status = - ShapeInference::InferBinaryOpShape(BINOP_NE, ten_floats, f32_, {}); + ShapeInference::InferBinaryOpShape(HloOpcode::kNe, ten_floats, f32_, {}); ASSERT_IS_OK(inferred_status.status()); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(PRED, {10}), inferred_status.ValueOrDie())); @@ -1111,22 +1107,22 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastMatrixVector) { const Shape vec8 = ShapeUtil::MakeShape(F32, {8}); const Shape vec16 = ShapeUtil::MakeShape(F32, {16}); - auto inferred_status_match = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, mat, vec8, {1}); + auto inferred_status_match = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, mat, vec8, {1}); ASSERT_IS_OK(inferred_status_match.status()); ASSERT_TRUE(ShapeUtil::Equal(inferred_status_match.ValueOrDie(), mat)); - auto inferred_status_mismatch = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, mat, vec8, {0}); + auto inferred_status_mismatch = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, mat, vec8, {0}); ASSERT_FALSE(inferred_status_mismatch.ok()); - inferred_status_match = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, mat, vec16, {0}); + inferred_status_match = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, mat, vec16, {0}); ASSERT_IS_OK(inferred_status_match.status()); ASSERT_TRUE(ShapeUtil::Equal(inferred_status_match.ValueOrDie(), mat)); - inferred_status_mismatch = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, mat, vec16, {1}); + inferred_status_mismatch = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, mat, vec16, {1}); ASSERT_FALSE(inferred_status_mismatch.ok()); } @@ -1138,17 +1134,17 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastCubeMatrix) { const Shape matrix16_8 = ShapeUtil::MakeShape(F32, {16, 8}); auto inferred_status_match = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, cube, matrix8_4, {1, 2}); + HloOpcode::kAdd, cube, matrix8_4, {1, 2}); ASSERT_IS_OK(inferred_status_match.status()); ASSERT_TRUE(ShapeUtil::Equal(inferred_status_match.ValueOrDie(), cube)); inferred_status_match = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, cube, matrix16_4, {0, 2}); + HloOpcode::kAdd, cube, matrix16_4, {0, 2}); ASSERT_IS_OK(inferred_status_match.status()); ASSERT_TRUE(ShapeUtil::Equal(inferred_status_match.ValueOrDie(), cube)); inferred_status_match = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, cube, matrix16_8, {0, 1}); + HloOpcode::kAdd, cube, matrix16_8, {0, 1}); ASSERT_IS_OK(inferred_status_match.status()); ASSERT_TRUE(ShapeUtil::Equal(inferred_status_match.ValueOrDie(), cube)); } @@ -1162,43 +1158,43 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { const Shape matrix8_8 = ShapeUtil::MakeShape(F32, {8, 8}); // "magical" broadcast rejected - auto inferred_status_error1 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, vec8, {}); + auto inferred_status_error1 = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, tensor, vec8, {}); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), HasSubstr("Automatic")); // broadcast_dimension out of bounds for tensor's rank - auto inferred_status_error2 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, vec8, {3}); + auto inferred_status_error2 = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, tensor, vec8, {3}); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), ContainsRegex("Broadcast dimension number .* too large")); // broadcast_dimension doesn't match corresponding dimension - auto inferred_status_error3 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, vec8, {0}); + auto inferred_status_error3 = + ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, tensor, vec8, {0}); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), HasSubstr("Broadcast dimension 0 mismatch")); // broadcast_dimensions list too long auto inferred_status_error4 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, matrix8_4, {0, 1, 2}); + HloOpcode::kAdd, tensor, matrix8_4, {0, 1, 2}); ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT(inferred_status_error4.status().error_message(), HasSubstr("broadcast_dimensions has to match")); // there's a dimension above the rank of the tensor auto inferred_status_error5 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, matrix8_4, {3, 0}); + HloOpcode::kAdd, tensor, matrix8_4, {3, 0}); ASSERT_FALSE(inferred_status_error5.ok()); ASSERT_THAT(inferred_status_error5.status().error_message(), ContainsRegex("dimension number .* too large")); // broadcasting dimensions don't match in this order auto inferred_status_error6 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor, matrix8_4, {2, 1}); + HloOpcode::kAdd, tensor, matrix8_4, {2, 1}); ASSERT_FALSE(inferred_status_error6.ok()); ASSERT_THAT(inferred_status_error6.status().error_message(), HasSubstr("dimension 0 mismatch")); @@ -1207,13 +1203,13 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { // in a proper (strictly increasing) order, even if the lower-rank array // matches the higher-rank array in many different ways. auto inferred_status_error7 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {0, 0}); + HloOpcode::kAdd, tensor8_8_8, matrix8_8, {0, 0}); ASSERT_FALSE(inferred_status_error7.ok()); ASSERT_THAT(inferred_status_error7.status().error_message(), HasSubstr("dimensions order is wrong")); auto inferred_status_error8 = ShapeInference::InferBinaryOpShape( - BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {1, 0}); + HloOpcode::kAdd, tensor8_8_8, matrix8_8, {1, 0}); ASSERT_FALSE(inferred_status_error8.ok()); ASSERT_THAT(inferred_status_error8.status().error_message(), HasSubstr("dimensions order is wrong")); @@ -1315,7 +1311,7 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT( inferred_status_error4.status().error_message(), - HasSubstr("Expected non-tuple argument for operand of concatenation")); + HasSubstr("Expected array argument for operand of concatenation")); const Shape vector_s32 = ShapeUtil::MakeShape(S32, {32}); auto inferred_status_error5 = ShapeInference::InferConcatOpShape( @@ -1391,7 +1387,7 @@ TEST_F(ShapeInferenceTest, ReverseInvalidDimension) { ShapeInference::InferReverseShape(tuple_shape, {0}); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("Expected non-tuple argument")); + HasSubstr("Expected array argument")); } TEST_F(ShapeInferenceTest, Call) { @@ -1690,7 +1686,7 @@ TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), - HasSubstr("Expected non-tuple argument for input")) + HasSubstr("Expected array argument for input")) << statusor.status(); } @@ -1704,7 +1700,7 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), - HasSubstr("Expected non-tuple argument for gather indices")) + HasSubstr("Expected array argument for gather indices")) << statusor.status(); } diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc index c4d01562c4e32225ebb984d8fcd93ec3fa86e403..4c5038a009ba5da4172129980014913f3f4418f4 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.cc +++ b/tensorflow/compiler/xla/service/transfer_manager.cc @@ -22,8 +22,12 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/notification.h" + +using ::tensorflow::strings::StrCat; namespace xla { /* static */ tensorflow::mutex @@ -36,8 +40,73 @@ TransferManager::GetPlatformTransferManagers() { return r; } +StatusOr> TransferManager::TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer) { + StatusOr> ret; + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + + tensorflow::Notification n; + TransferLiteralFromDevice(substream, device_buffer, + [&](StatusOr> arg) { + ret = std::move(arg); + n.Notify(); + }); + n.WaitForNotification(); + return ret; +} + +Status TransferManager::TransferLiteralToDevice( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + TF_RETURN_IF_ERROR( + TransferLiteralToDeviceAsync(substream, literal, device_buffer)); + return substream->BlockHostUntilDone(); +} + +StatusOr> TransferManager::TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, + const se::DeviceMemoryBase& source) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + StatusOr> ret; + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + + tensorflow::Notification n; + TransferArrayFromDevice(substream, shape, source, + [&](StatusOr> arg) { + ret = std::move(arg); + n.Notify(); + }); + n.WaitForNotification(); + return ret; +} + Status TransferManager::TransferArrayToDevice( - se::StreamExecutor* executor, const LiteralSlice& literal, + se::Stream* stream, const LiteralSlice& literal, + const se::DeviceMemoryBase& dest) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + TF_RETURN_IF_ERROR(TransferArrayToDeviceAsync(substream, literal, dest)); + return substream->BlockHostUntilDone(); +} + +Status TransferManager::TransferArrayToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, const se::DeviceMemoryBase& dest) { const Shape on_device_shape = HostShapeToDeviceShape(literal.shape()); TF_RET_CHECK(ShapeUtil::IsArray(on_device_shape)) @@ -51,28 +120,32 @@ Status TransferManager::TransferArrayToDevice( dest.size(), GetByteSizeRequirement(on_device_shape)); } ShapedBuffer shaped_buffer(/*on_host_shape=*/literal.shape(), on_device_shape, - executor->platform(), executor->device_ordinal()); + stream->parent()->platform(), + stream->parent()->device_ordinal()); shaped_buffer.set_buffer(dest, /*index=*/{}); - return TransferLiteralToDevice(executor, literal, shaped_buffer); + return TransferLiteralToDevice(stream, literal, shaped_buffer); } -StatusOr> TransferManager::TransferArrayFromDevice( - se::StreamExecutor* executor, const Shape& shape, - const se::DeviceMemoryBase& source) { - TF_RET_CHECK(ShapeUtil::Equal(HostShapeToDeviceShape(shape), shape)) - << "Shape " << ShapeUtil::HumanString(shape) - << " has a differently shaped representation on-device: " - << ShapeUtil::HumanString(HostShapeToDeviceShape(shape)); +void TransferManager::TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source, + std::function>)> done) { + if (!ShapeUtil::Equal(HostShapeToDeviceShape(shape), shape)) { + auto error = StrCat("Shape ", ShapeUtil::HumanString(shape), + " has a differently shaped representation on-device: ", + ShapeUtil::HumanString(HostShapeToDeviceShape(shape))); + return done(FailedPrecondition("%s", error.c_str())); + } if (source.size() < GetByteSizeRequirement(shape)) { - return FailedPrecondition( - "Allocation on device not large enough for array: " - "%lld < %lld", - source.size(), GetByteSizeRequirement(shape)); + return done( + FailedPrecondition("Allocation on device not large enough for array: " + "%lld < %lld", + source.size(), GetByteSizeRequirement(shape))); } ShapedBuffer shaped_buffer(/*on_host_shape=*/shape, shape, - executor->platform(), executor->device_ordinal()); + stream->parent()->platform(), + stream->parent()->device_ordinal()); shaped_buffer.set_buffer(source, /*index=*/{}); - return TransferLiteralFromDevice(executor, shaped_buffer); + return TransferLiteralFromDevice(stream, shaped_buffer, std::move(done)); } /* static */ void TransferManager::RegisterTransferManager( @@ -108,10 +181,14 @@ StatusOr> TransferManager::TransferArrayFromDevice( } Status TransferManager::WriteTupleIndexTables( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) { - VLOG(2) << "Writing tuple index tables for " << device_buffer; + se::Stream* stream, const ShapedBuffer& device_buffer) { + TF_RETURN_IF_ERROR(WriteTupleIndexTablesAsync(stream, device_buffer)); + return stream->BlockHostUntilDone(); +} - TF_RET_CHECK(executor->device_ordinal() == device_buffer.device_ordinal()); +Status TransferManager::WriteTupleIndexTablesAsync( + se::Stream* stream, const ShapedBuffer& device_buffer) { + VLOG(2) << "Writing tuple index tables for " << device_buffer; return ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_device_shape(), @@ -129,7 +206,7 @@ Status TransferManager::WriteTupleIndexTables( elements.push_back(device_buffer.buffer(element_index)); element_index.pop_back(); } - return WriteSingleTupleIndexTable(executor, elements, device_subshape, + return WriteSingleTupleIndexTable(stream, elements, device_subshape, &device_memory); } @@ -138,26 +215,20 @@ Status TransferManager::WriteTupleIndexTables( } Status TransferManager::TransferBufferFromDevice( - se::StreamExecutor* executor, const se::DeviceMemoryBase& source, - int64 size, void* destination) { + se::Stream* stream, const se::DeviceMemoryBase& source, int64 size, + void* destination) { if (source.size() < size) { return FailedPrecondition( "Source allocation on device not large enough for data tranfer: " "%lld < %lld", source.size(), size); } - auto copy_status = executor->SynchronousMemcpyD2H(source, size, destination); - if (!copy_status.ok()) { - return AddStatus( - Status(static_cast(copy_status.code()), - copy_status.error_message()), - "failed transfer from device to buffer"); - } + stream->ThenMemcpy(destination, source, size); return Status::OK(); } Status TransferManager::TransferBufferToDevice( - se::StreamExecutor* executor, int64 size, const void* source, + se::Stream* stream, int64 size, const void* source, se::DeviceMemoryBase* destination) { if (destination->size() < size) { return FailedPrecondition( @@ -165,13 +236,7 @@ Status TransferManager::TransferBufferToDevice( "%lld < %lld", destination->size(), size); } - auto copy_status = executor->SynchronousMemcpyH2D(source, size, destination); - if (!copy_status.ok()) { - return AddStatus( - Status(static_cast(copy_status.code()), - copy_status.error_message()), - "failed transfer of buffer to device"); - } + stream->ThenMemcpy(destination, source, size); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h index 43a8092b06fba0e2495bce0ee1a309c85a908273..e384359642a8fe09e0b8516e342a56259912922a 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.h +++ b/tensorflow/compiler/xla/service/transfer_manager.h @@ -52,30 +52,65 @@ class TransferManager { return host_shape; } - // Returns a literal containing the data held in the given ShapedBuffer. - // using the provided executor. The optional literal_shape will be the shape - // for the literal. The shape of the ShapedBuffer and - // DeviceShape(literal_shape) must be compatible, but need not have the same - // layout. + // Returns a literal containing the data held in the given ShapedBuffer + // using the provided executor. This operation is performed synchronously + // without waiting for any other operation on a stream to complete. + // + // This function should be avoided in favor of the asynchronous version below. virtual StatusOr> TransferLiteralFromDevice( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) = 0; + se::Stream* stream, const ShapedBuffer& device_buffer); + + // Begins transferring a literal containing the data held in the given + // ShapedBuffer using the provided executor. + // + // This operation is performed asynchronously on the given stream. It returns + // once the transfer is enqueued. 'done' is invoked with the result when + // complete. + // + // device_buffer is copied by reference and must live at least until done() is + // invoked. + virtual void TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) = 0; // Transfers the given literal into the previously allocated device memory // represented by the given ShapedBuffer using the given executor. The shape // of the ShapedBuffer and DeviceShape(literal.shape()) must be compatible, - // but need not have the same layout - virtual Status TransferLiteralToDevice(se::StreamExecutor* executor, + // but need not have the same layout. + // + // This operation is performed synchronously without waiting for any other + // operation on a stream to complete. This function should be avoided in favor + // of the asynchronous version below. + virtual Status TransferLiteralToDevice(se::Stream* stream, const LiteralSlice& literal, - const ShapedBuffer& device_buffer) = 0; + const ShapedBuffer& device_buffer); + + // Transfers the given literal into the previously allocated device memory + // represented by the given ShapedBuffer using the given executor. The shape + // of the ShapedBuffer and DeviceShape(literal.shape()) must be compatible, + // but need not have the same layout. + // + // This operation is performed asynchronously on the given stream. It returns + // once the transfer is enqueued. + virtual Status TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) = 0; // Convenience methods for transferring an array to or from the device at a // known address. This avoids having to construct a ShapedBuffer just to // transfer an array at a known address. - Status TransferArrayToDevice(se::StreamExecutor* executor, - const LiteralSlice& literal, + Status TransferArrayToDevice(se::Stream* stream, const LiteralSlice& literal, const se::DeviceMemoryBase& dest); + void TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, + const se::DeviceMemoryBase& source, + std::function>)> done); + + Status TransferArrayToDeviceAsync(se::Stream* stream, + const LiteralSlice& literal, + const se::DeviceMemoryBase& dest); StatusOr> TransferArrayFromDevice( - se::StreamExecutor* executor, const Shape& shape, + se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source); // Transfers the given literal into the Infeed interface of the device, @@ -96,8 +131,10 @@ class TransferManager { // Given an allocated ShapedBuffer, constructs the tuple index table(s) in // each buffer of the given ShapedBuffer corresponding to tuple shapes. If the // ShapedBuffer is array-shaped this method does nothing. - Status WriteTupleIndexTables(se::StreamExecutor* executor, + Status WriteTupleIndexTables(se::Stream* stream, const ShapedBuffer& device_buffer); + Status WriteTupleIndexTablesAsync(se::Stream* stream, + const ShapedBuffer& device_buffer); // Determines the byte size requirement for the given shape on the underlying // architecture. This will be used to allocate an appropriately sized memory @@ -144,7 +181,7 @@ class TransferManager { // 'destination' buffer. // // size is the size to transfer to destination in bytes. - virtual Status TransferBufferFromDevice(se::StreamExecutor* executor, + virtual Status TransferBufferFromDevice(se::Stream* stream, const se::DeviceMemoryBase& source, int64 size, void* destination); @@ -152,15 +189,15 @@ class TransferManager { // destination of the device. // // size is the size to transfer from source in bytes. - virtual Status TransferBufferToDevice(se::StreamExecutor* executor, - int64 size, const void* source, + virtual Status TransferBufferToDevice(se::Stream* stream, int64 size, + const void* source, se::DeviceMemoryBase* destination); // Writes the given device-memory pointers in 'elements' to the given region // to construct a tuple index table in the platform-specific tuple // representation. virtual Status WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) = 0; diff --git a/tensorflow/compiler/xla/service/transpose_folding.cc b/tensorflow/compiler/xla/service/transpose_folding.cc index ba16dc640e2d2974eab4fc8b134a6e33c03e3b85..49e1f873192f800056a2272f7d4f698898b0f8a1 100644 --- a/tensorflow/compiler/xla/service/transpose_folding.cc +++ b/tensorflow/compiler/xla/service/transpose_folding.cc @@ -178,7 +178,6 @@ bool FoldTransposeIntoConvolution(InstructionOperandsPair pair) { auto new_conv = HloInstruction::CreateConvolve( convolution.shape(), new_lhs, new_rhs, convolution.window(), new_dnums); - convolution.SetupDerivedInstruction(new_conv.get()); TF_CHECK_OK(convolution.parent()->ReplaceWithNewInstruction( &convolution, std::move(new_conv))); diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc index 3139801ea3130324f48d728dc6f739f709e55911..cccb8f2fbb0266bbf1f40b09170938a1e5d3e78d 100644 --- a/tensorflow/compiler/xla/service/transpose_folding_test.cc +++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc @@ -176,7 +176,7 @@ TEST_F(TransposeFoldingTest, FuseDotWithConstantOperands) { HloComputation* entry_computation = module->AddEntryComputation(builder.Build(mul)); HloInstruction* call = module->OutlineExpressionFromComputation( - {add, sub, mul}, "", entry_computation); + {add, sub, mul}, "entry", entry_computation); EXPECT_EQ(call, entry_computation->root_instruction()); HloComputation* callee_computation = call->to_apply(); // The arguments to the call should be const1, const2, and const3. diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc index bb634e6573ffceeaa66e0ac9141fe7e3a39ed602..d1e174464759dbc2c0d84c4ddac27cb21635e131 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/map_util.h" +#include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/types.h" @@ -121,7 +122,6 @@ void PointsToSet::add_tuple_source(const ShapeIndex& index, } namespace { - // Gather fusion instructions from 'instruction' into 'fusion_instructions'. void GatherFusionInstructions( HloInstruction* instruction, @@ -723,15 +723,22 @@ bool TuplePointsToAnalysis::CanShareOperandBufferWithUser( return false; } if (user->opcode() == HloOpcode::kFusion) { - if (user->fusion_kind() == HloInstruction::FusionKind::kLoop && - user->fused_expression_root()->opcode() == - HloOpcode::kDynamicUpdateSlice) { - // Loop fusion with kDynamicUpdateSlice fused root. - // - // Returns true iff there is exactly one use of 'operand' at shape index - // 'operand_index', and this singleton use is the fused root at operand - // index 0. - return HasUniqueFusedUseOfOperandAt(operand, operand_index, user, 0); + if (user->fusion_kind() == HloInstruction::FusionKind::kLoop || + user->fusion_kind() == HloInstruction::FusionKind::kInput) { + if (user->fused_expression_root()->opcode() == + HloOpcode::kDynamicUpdateSlice) { + // Loop fusion with kDynamicUpdateSlice fused root. + // + // Returns true iff there is exactly one use of 'operand' at shape index + // 'operand_index', and this singleton use is the fused root at operand + // index 0. + return HasUniqueFusedUseOfOperandAt(operand, operand_index, user, 0); + } else { + HloInstruction* fusion_param = + user->fused_parameter(user->operand_index(operand)); + return HloDataflowAnalysis::AreTransitiveUsesElementwiseOrTuple( + fusion_param); + } } else if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && user->fused_expression_root()->opcode() == HloOpcode::kAdd) { // Output fusion with kAdd fused root. @@ -789,8 +796,12 @@ bool TuplePointsToAnalysis::CanShareOperandBufferWithUser( return param_uses.size() == 1 && param_uses[0].first == callee_root && callee_root->IsElementwiseOnOperand(param_uses[0].second); } - // Check if 'user' is element-wise. - return user->IsElementwise(); + // Loop fusions that contain transposing copies won't reach here as they have + // different layouts, which fails the check in the beginning of this function. + // + // Multi-output fusion will fail the check here as tuples are not considered + // an elementwise operation. + return user->IsElementwiseOnOperand(user->operand_index(operand)); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc index f558316b05b168a6f100e8ef69adfd9dbc023102..a8f885fd864d45a8dcb9cfc465fbab0825325e94 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc @@ -318,8 +318,9 @@ TEST_F(TuplePointsToAnalysisTest, SendAndSendDone) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto send = builder.AddInstruction( - HloInstruction::CreateSend(constant, /*channel_id=*/0)); + HloInstruction::CreateSend(constant, token, /*channel_id=*/0)); auto send_done = builder.AddInstruction(HloInstruction::CreateSendDone(send)); BuildModuleAndRunAnalysis(builder.Build()); @@ -342,8 +343,9 @@ TEST_F(TuplePointsToAnalysisTest, SendAndSendDone) { TEST_F(TuplePointsToAnalysisTest, RecvAndRecvDone) { // RecvDone forwards its operand tuple element at {0} to the output. auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto recv = builder.AddInstruction(HloInstruction::CreateRecv( - ShapeUtil::MakeShape(F32, {1, 2, 3}), /*channel_id=*/0)); + ShapeUtil::MakeShape(F32, {1, 2, 3}), token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); BuildModuleAndRunAnalysis(builder.Build()); @@ -1148,5 +1150,30 @@ TEST_F(CanShareOperandBufferWithUserTest, CallToComputationWithFusionRoot) { call, {})); } +TEST_F(CanShareOperandBufferWithUserTest, LoopFusionWithElementwiseOperand) { + Shape full_shape = ShapeUtil::MakeShape(F32, {16, 32}); + Shape broadcast_shape = ShapeUtil::MakeShape(F32, {16}); + + auto builder = HloComputation::Builder(TestName() + "_fusion"); + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, full_shape, "full")); + auto param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, broadcast_shape, "small")); + auto broadcast = builder.AddInstruction( + HloInstruction::CreateBroadcast(full_shape, param1, {0})); + auto add = builder.AddInstruction(HloInstruction::CreateBinary( + full_shape, HloOpcode::kAdd, param0, broadcast)); + + BuildModule(builder.Build()); + auto fusion = computation_->CreateFusionInstruction( + {add, broadcast}, HloInstruction::FusionKind::kLoop); + RunAnalysis(); + + EXPECT_TRUE(points_to_analysis_->CanShareOperandBufferWithUser(param0, {}, + fusion, {})); + EXPECT_FALSE(points_to_analysis_->CanShareOperandBufferWithUser(param1, {}, + fusion, {})); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.cc b/tensorflow/compiler/xla/service/tuple_simplifier.cc index d668855084a884518b338cdf396a9330b9f43a2b..77bdcc9de0d830991208a1db271d009bccaf550e 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier.cc @@ -30,10 +30,17 @@ limitations under the License. namespace xla { +TupleSimplifier::TupleSimplifier(bool exclude_entry_computation) : + exclude_entry_computation_(exclude_entry_computation) {} + StatusOr TupleSimplifier::Run(HloModule* module) { // Initially add all GTE and Tuple instructions to the worklist. std::queue worklist; for (auto* computation : module->computations()) { + if (exclude_entry_computation_ && + computation == module->entry_computation()) { + continue; + } for (auto* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kTuple || instruction->opcode() == HloOpcode::kGetTupleElement) { @@ -69,7 +76,6 @@ StatusOr TupleSimplifier::Run(HloModule* module) { // Tuple // HloInstruction* top_tuple = nullptr; - HloInstruction* first_gte = nullptr; bool can_simplify = true; for (int64 operand_number = 0; operand_number < instruction->operand_count(); ++operand_number) { @@ -79,17 +85,10 @@ StatusOr TupleSimplifier::Run(HloModule* module) { can_simplify = false; break; } - if (first_gte == nullptr) { - first_gte = operand; - } else if (!first_gte->has_compatible_sharding(operand)) { - can_simplify = false; - break; - } if (top_tuple == nullptr) { top_tuple = operand->mutable_operand(0); if (!ShapeUtil::Compatible(top_tuple->shape(), - instruction->shape()) || - !instruction->has_compatible_sharding(top_tuple)) { + instruction->shape())) { can_simplify = false; break; } @@ -118,14 +117,12 @@ StatusOr TupleSimplifier::Run(HloModule* module) { HloInstruction* element_source = instruction->mutable_operand(0)->mutable_operand( instruction->tuple_index()); - if (instruction->has_compatible_sharding(element_source)) { - changed = true; - TF_RETURN_IF_ERROR(instruction->ReplaceAllUsesWith(element_source)); - for (HloInstruction* user : element_source->users()) { - if (user->opcode() == HloOpcode::kTuple || - user->opcode() == HloOpcode::kGetTupleElement) { - worklist.push(user); - } + changed = true; + TF_RETURN_IF_ERROR(instruction->ReplaceAllUsesWith(element_source)); + for (HloInstruction* user : element_source->users()) { + if (user->opcode() == HloOpcode::kTuple || + user->opcode() == HloOpcode::kGetTupleElement) { + worklist.push(user); } } } diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.h b/tensorflow/compiler/xla/service/tuple_simplifier.h index e5e9b10b5bf3f452d1bfec476b8d5c7d74c4f4e8..750950188312c5077d487f2feef0606f07839432 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.h +++ b/tensorflow/compiler/xla/service/tuple_simplifier.h @@ -27,13 +27,20 @@ namespace xla { // the module. class TupleSimplifier : public HloPassInterface { public: - TupleSimplifier() {} + TupleSimplifier() : TupleSimplifier(/*exclude_entry_computation=*/false) {} + explicit TupleSimplifier(bool exclude_entry_computation); ~TupleSimplifier() override {} tensorflow::StringPiece name() const override { return "tuple-simplifier"; } // Run tuple simplification on the given computation. Returns whether the // computation was changed. StatusOr Run(HloModule* module) override; + + private: + // When set, this pipeline stage will perform optimization of all computations + // apart from the module's entry computation. This is used by Graphcore's + // backend. + bool exclude_entry_computation_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index ca9ae91281fce5ee061d066fc3e538dbbc09f6b3..d3635eae81ec7017f9bf6a69250d10716309c9ec 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -42,6 +42,12 @@ class TupleSimplifierTest : public HloTestBase { TF_ASSERT_OK(changed_status.status()); EXPECT_EQ(change_expected, changed_status.ValueOrDie()); } + void Run(HloModule* module, bool change_expected, bool exclude_entry) { + TupleSimplifier simplifier(exclude_entry); + auto changed_status = simplifier.Run(module); + TF_ASSERT_OK(changed_status.status()); + EXPECT_EQ(change_expected, changed_status.ValueOrDie()); + } const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -211,5 +217,76 @@ TEST_F(TupleSimplifierTest, IncompatibleTuples) { EXPECT_THAT(computation->root_instruction(), tuple); } +TEST_F(TupleSimplifierTest, CanExcludeEntryComputation) { + // Verify that the root computation can be excluded + auto module = CreateNewModule(); + + HloInstruction* p0; + HloInstruction* p1; + HloComputation* c0; + HloComputation* c1; + HloComputation* entry; + + { + HloComputation::Builder builder(TestName() + "_1"); + p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c0 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_2"); + p1 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c1 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_Entry"); + HloInstruction* tuple_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* call0 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c0)); + HloInstruction* call1 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c1)); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call1, 1)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1})); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 0)); + HloInstruction* gte3 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 1)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte2, gte3})); + + entry = module->AddEntryComputation(builder.Build()); + } + + Run(module.get(), /*change_expected=*/true, /*exclude_entry=*/ true); + + EXPECT_THAT(c0->root_instruction(), p0); + EXPECT_THAT(c1->root_instruction(), p1); + EXPECT_THAT(entry->instruction_count(), 9); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/versioned_computation_handle.h b/tensorflow/compiler/xla/service/versioned_computation_handle.h deleted file mode 100644 index 5732a56caffa31dde52dff5c2775f9fde0cacfbd..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/versioned_computation_handle.h +++ /dev/null @@ -1,55 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_VERSIONED_COMPUTATION_HANDLE_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_VERSIONED_COMPUTATION_HANDLE_H_ - -#include - -#include "tensorflow/compiler/xla/types.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" - -namespace xla { - -// A data structure encapsulating a ComputationHandle and version value of that -// computation. This object is used to unambiguously refer to a particular -// computation in the service. -struct VersionedComputationHandle { - // A version value unambiguously specifying the state of the computation at a - // particular point in time as it is being built. This value is the - // ComputationDataHandle of the current root instruction. - using Version = int64; - - ComputationHandle handle; - Version version; - - string ToString() const; - bool operator==(const VersionedComputationHandle& other) const { - return (handle.handle() == other.handle.handle()) && - (version == other.version); - } - bool operator<(const VersionedComputationHandle& other) const { - return ((handle.handle() < other.handle.handle()) || - ((handle.handle() == other.handle.handle()) && - (version < other.version))); - } -}; - -std::ostream& operator<<(std::ostream& out, - const VersionedComputationHandle& versioned_handle); - -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_VERSIONED_COMPUTATION_HANDLE_H_ diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc index 8831c513eee66e36163135b732f833d46cb7eb03..23519e445ea8a5f578a54708f38059feef3280c0 100644 --- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc @@ -248,7 +248,9 @@ TEST_F(WhileLoopInvariantCodeMotionTest, TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistInstructionWithSideEffects) { auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); - Shape while_shape = ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32}); + auto token_shape = ShapeUtil::MakeTokenShape(); + Shape while_shape = + ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32, token_shape}); HloComputation* while_body = [&]() { HloComputation::Builder builder(TestName() + ".while_body"); @@ -258,25 +260,32 @@ TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistInstructionWithSideEffects) { HloInstruction::CreateGetTupleElement(scalar_s32, param, 0)); HloInstruction* gte_1 = builder.AddInstruction( HloInstruction::CreateGetTupleElement(scalar_s32, param, 1)); + HloInstruction* in_token = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(token_shape, param, 2)); + HloInstruction* out_token = builder.AddInstruction( + HloInstruction::CreateOutfeed(scalar_s32, gte_0, in_token, "")); builder.AddInstruction( - HloInstruction::CreateOutfeed(scalar_s32, gte_0, "")); - builder.AddInstruction(HloInstruction::CreateTuple({gte_0, gte_1})); + HloInstruction::CreateTuple({gte_0, gte_1, out_token})); return module().AddEmbeddedComputation(builder.Build()); }(); HloComputation::Builder builder(TestName()); + auto* scalar_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_s32, "param")); + auto* token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto* init_value = builder.AddInstruction( - HloInstruction::CreateParameter(0, while_shape, "init_value")); + HloInstruction::CreateTuple({scalar_param, scalar_param, token})); auto* while_inst = builder.AddInstruction(HloInstruction::CreateWhile( while_shape, MakeAlwaysTrueComputation(while_shape, &module()), while_body, init_value)); - + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_s32, while_inst, 0)); module().AddEntryComputation(builder.Build()); TF_ASSERT_OK_AND_ASSIGN(bool simplified_loop, WhileLoopInvariantCodeMotion{}.Run(&module())); - EXPECT_FALSE(simplified_loop); + ASSERT_FALSE(simplified_loop); EXPECT_THAT(while_inst->while_body()->instructions(), Contains(op::Outfeed())); @@ -287,7 +296,9 @@ TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistBitcastAlone) { // bitcast either. auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); auto scalar_f32 = ShapeUtil::MakeShape(F32, {}); - Shape while_shape = ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32}); + auto token_shape = ShapeUtil::MakeTokenShape(); + Shape while_shape = + ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32, token_shape}); HloComputation* while_body = [&]() { HloComputation::Builder builder(TestName() + ".while_body"); @@ -297,21 +308,29 @@ TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistBitcastAlone) { HloInstruction::CreateGetTupleElement(scalar_s32, param, 0)); HloInstruction* gte_1 = builder.AddInstruction( HloInstruction::CreateGetTupleElement(scalar_s32, param, 1)); + HloInstruction* in_token = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(token_shape, param, 2)); HloInstruction* bitcast_inst = builder.AddInstruction( HloInstruction::CreateUnary(scalar_f32, HloOpcode::kBitcast, gte_0)); + HloInstruction* out_token = builder.AddInstruction( + HloInstruction::CreateOutfeed(scalar_f32, bitcast_inst, in_token, "")); builder.AddInstruction( - HloInstruction::CreateOutfeed(scalar_f32, bitcast_inst, "")); - builder.AddInstruction(HloInstruction::CreateTuple({gte_0, gte_1})); + HloInstruction::CreateTuple({gte_0, gte_1, out_token})); return module().AddEmbeddedComputation(builder.Build()); }(); HloComputation::Builder builder(TestName()); + auto* scalar_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_s32, "param")); + auto* token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); auto* init_value = builder.AddInstruction( - HloInstruction::CreateParameter(0, while_shape, "init_value")); + HloInstruction::CreateTuple({scalar_param, scalar_param, token})); auto* while_inst = builder.AddInstruction(HloInstruction::CreateWhile( while_shape, MakeAlwaysTrueComputation(while_shape, &module()), while_body, init_value)); + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_s32, while_inst, 0)); module().AddEntryComputation(builder.Build()); diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc index 619e87caa5b6d0f6ec3c3b1489b0d4f50ef29963..3c8304921661a486f283ea8c0009db16a81531a4 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc @@ -175,9 +175,11 @@ TEST_F(WhileLoopSimplifierTest, LoopWithSendNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); + auto* token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); auto* send = while_body->AddInstruction(HloInstruction::CreateSend( while_body->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(true))), + token, /*channel_id=*/0)); while_body->AddInstruction(HloInstruction::CreateSendDone(send)); EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); @@ -190,8 +192,9 @@ TEST_F(WhileLoopSimplifierTest, LoopWithRecvNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); + auto* token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); auto* recv = while_body->AddInstruction( - HloInstruction::CreateRecv(ShapeUtil::MakeShape(F32, {1}), + HloInstruction::CreateRecv(ShapeUtil::MakeShape(F32, {1}), token, /*channel_id=*/0)); while_body->AddInstruction(HloInstruction::CreateRecvDone(recv)); EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); @@ -208,8 +211,9 @@ TEST_F(WhileLoopSimplifierTest, LoopWithInfeedNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); - while_body->AddInstruction( - HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config")); + auto token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); + while_body->AddInstruction(HloInstruction::CreateInfeed( + ShapeUtil::MakeShape(F32, {1}), token, "config")); EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); } diff --git a/tensorflow/compiler/xla/service/while_util_test.cc b/tensorflow/compiler/xla/service/while_util_test.cc index d79d3297213e832306ea4726483b0f215df0f5d3..2ccb919acf9c4e7c59a1ebaf36f42a6781068b5e 100644 --- a/tensorflow/compiler/xla/service/while_util_test.cc +++ b/tensorflow/compiler/xla/service/while_util_test.cc @@ -179,7 +179,9 @@ body { cond { param.c = (s32[], s32[]) parameter(0) - ROOT condition = pred[] infeed() + token = token[] after-all() + infeed = (pred[], token[]) infeed(token) + ROOT condition = pred[] get-tuple-element(infeed), index=0 } ENTRY main { diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc index aa40b5cb264803097f52966d6f61f1f41b6b3017..44b0ec5cd4c1d406467007fcc530e919d602c438 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc @@ -32,11 +32,11 @@ StatusOr ZeroSizedHloElimination::Run(HloModule* module) { for (HloComputation* comp : module->MakeNonfusionComputations()) { for (HloInstruction* instruction : comp->MakeInstructionPostOrder()) { if (instruction->HasSideEffect() || - ShapeUtil::IsTuple(instruction->shape())) { + !ShapeUtil::IsArray(instruction->shape())) { continue; } if (comp->IsRemovable(instruction) && - ShapeUtil::HasZeroElements(instruction->shape())) { + ShapeUtil::IsZeroElementArray(instruction->shape())) { TF_RETURN_IF_ERROR(comp->ReplaceWithNewInstruction( instruction, HloInstruction::CreateConstant( Literal::CreateFromShape(instruction->shape())))); diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc index f5331280ee9f252aa5717baab88f2c203be5c372..c6bd013a1aa59fe99f8f80197f04eb1e8a97cbb7 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc @@ -67,7 +67,9 @@ TEST_F(ZeroSizedHloEliminationTest, DoesNotEliminateParameter) { } TEST_F(ZeroSizedHloEliminationTest, DoesNotEliminateSideEffects) { - builder_.AddInstruction(HloInstruction::CreateSend(zero_sized_param_, 0)); + auto token = builder_.AddInstruction(HloInstruction::CreateAfterAll({})); + builder_.AddInstruction( + HloInstruction::CreateSend(zero_sized_param_, token, 0)); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunZeroSizedElimination()); EXPECT_FALSE(changed); } diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h index 5b14953ebb243da7b9be6eafd46160db8bc62707..4aacc87b78e2c271829cdf397cd69bfb490125b8 100644 --- a/tensorflow/compiler/xla/shape_tree.h +++ b/tensorflow/compiler/xla/shape_tree.h @@ -47,6 +47,9 @@ struct ShapeTreeNode { // Children of this node, as indices into the container's nodes_ array. std::vector children; + // Tells whether this is a leaf node. + bool is_leaf = true; + explicit ShapeTreeNode(ShapeIndex index) : ShapeTreeNode(std::move(index), T()) {} ShapeTreeNode(ShapeIndex index, T data) @@ -102,8 +105,8 @@ class ShapeTree { // Returns the data element associated with the array in the shape at the // given index (see ShapeUtil::GetSubshape for how indexes are defined). - const T& element(const ShapeIndex& index) const; - T* mutable_element(const ShapeIndex& index); + const T& element(ShapeIndexView index) const; + T* mutable_element(ShapeIndexView index); // Return the shape represented with this ShapeTree. const Shape& shape() const { return *shape_; } @@ -122,9 +125,7 @@ class ShapeTree { // Returns true if the node at the given index is a leaf node (an array // shape). - bool IsLeaf(const ShapeIndex& index) const { - return Lookup(index)->children.empty(); - } + bool IsLeaf(ShapeIndexView index) const { return Lookup(index)->is_leaf; } ShapeTree(const ShapeTree&) = default; ShapeTree& operator=(const ShapeTree&) = default; @@ -210,12 +211,12 @@ class ShapeTree { // Returns an iterator pointing to the given ShapeIndex. // REQUIRES: index must exist in the ShapeTree. - iterator find(const ShapeIndex& index) { + iterator find(ShapeIndexView index) { Node* element = Lookup(index); return iterator(&nodes_, typename std::vector::iterator(element), /*iterate_leaves_only=*/false); } - const_iterator find(const ShapeIndex& index) const { + const_iterator find(ShapeIndexView index) const { Node* element = Lookup(index); return iterator(&nodes_, typename std::vector::const_iterator(element), @@ -284,8 +285,8 @@ class ShapeTree { static Status ForEachMutableHelper(const Fn& func, std::vector* nodes); // Return the tree node at the given index. - Node* Lookup(const ShapeIndex& index); - const Node* Lookup(const ShapeIndex& index) const; + Node* Lookup(ShapeIndexView index); + const Node* Lookup(ShapeIndexView index) const; // The nodes in this shape tree. std::vector nodes_; @@ -311,16 +312,14 @@ class ShapeTreeIterator : nodes_(nodes), node_(std::move(node)), iterate_leaves_only_(iterate_leaves_only) { - while (iterate_leaves_only && node_ != nodes_->end() && - !node_->children.empty()) { + while (iterate_leaves_only && node_ != nodes_->end() && !node_->is_leaf) { ++node_; } } ShapeTreeIterator& operator++() { ++node_; - while (iterate_leaves_only_ && node_ != nodes_->end() && - !node_->children.empty()) { + while (iterate_leaves_only_ && node_ != nodes_->end() && !node_->is_leaf) { ++node_; } return *this; @@ -333,8 +332,7 @@ class ShapeTreeIterator ShapeTreeIterator& operator--() { --node_; - while (iterate_leaves_only_ && node_ > nodes_->begin() && - !node_->children.empty()) { + while (iterate_leaves_only_ && node_ > nodes_->begin() && !node_->is_leaf) { --node_; } return *this; @@ -358,7 +356,7 @@ class ShapeTreeIterator ContainerType* nodes_; IteratorType node_; // True if we should not include interior nodes in our walk. - bool iterate_leaves_only_; + const bool iterate_leaves_only_; }; template @@ -379,6 +377,7 @@ void ShapeTree::InitChildren(const Shape& shape, const T& init_value, if (ShapeUtil::IsTuple(shape)) { const int64 size = ShapeUtil::TupleElementCount(shape); node->children.reserve(size); + node->is_leaf = false; ShapeIndex shape_index = node->data.first; shape_index.push_back(0); for (int i = 0; i < size; ++i) { @@ -395,6 +394,7 @@ void ShapeTree::InitChildren(const Shape& shape, Node* node) { if (ShapeUtil::IsTuple(shape)) { const int64 size = ShapeUtil::TupleElementCount(shape); node->children.reserve(size); + node->is_leaf = false; ShapeIndex shape_index = node->data.first; shape_index.push_back(0); for (int i = 0; i < size; ++i) { @@ -463,17 +463,17 @@ ShapeTree::ShapeTree(const std::shared_ptr& shape, } template -const T& ShapeTree::element(const ShapeIndex& index) const { +const T& ShapeTree::element(ShapeIndexView index) const { return Lookup(index)->data.second; } template -T* ShapeTree::mutable_element(const ShapeIndex& index) { +T* ShapeTree::mutable_element(ShapeIndexView index) { return &Lookup(index)->data.second; } template -internal::ShapeTreeNode* ShapeTree::Lookup(const ShapeIndex& index) { +internal::ShapeTreeNode* ShapeTree::Lookup(ShapeIndexView index) { Node* node = &nodes_[0]; for (const int64 i : index) { CHECK_GE(i, 0); @@ -485,7 +485,7 @@ internal::ShapeTreeNode* ShapeTree::Lookup(const ShapeIndex& index) { template const internal::ShapeTreeNode* ShapeTree::Lookup( - const ShapeIndex& index) const { + ShapeIndexView index) const { return const_cast(this)->Lookup(index); } diff --git a/tensorflow/compiler/xla/shape_tree_test.cc b/tensorflow/compiler/xla/shape_tree_test.cc index dc5facf1581c07fbb74dfcee95025692938632bd..51de82e95746281ed6e587b545dc933b48ce1ad4 100644 --- a/tensorflow/compiler/xla/shape_tree_test.cc +++ b/tensorflow/compiler/xla/shape_tree_test.cc @@ -116,6 +116,11 @@ TEST_F(ShapeTreeTest, InitValueConstructor) { TestInitValueConstructor(nested_tuple_shape_, 10); } +TEST_F(ShapeTreeTest, EmptyTupleMustHaveNoLeaves) { + ShapeTree shape_tree{ShapeUtil::MakeTupleShape({})}; + EXPECT_EQ(0, shape_tree.leaf_count()); +} + TEST_F(ShapeTreeTest, ArrayShape) { ShapeTree shape_tree{array_shape_}; *shape_tree.mutable_element({}) = 42; diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index ce4d0079ee5eb28444509c712ec1a34037dc244a..56d24423c428d32c1c65ed7a47aab9691a846559 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -24,9 +24,11 @@ limitations under the License. #include "tensorflow/compiler/xla/index_util.h" #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/overflow_util.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/iterator_range.h" @@ -93,8 +95,11 @@ bool IsArrayPrimitiveType(PrimitiveType primitive_type) { // Recursive helper for comparing the equality of two shapes. Returns true if // the shapes are the same. If compare_layouts is true, then layouts must also // match. -bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) { - if (!ShapeUtil::SameElementType(lhs, rhs)) { +bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts, + bool ignore_fp_precision) { + if ((ignore_fp_precision && + !ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) || + (!ignore_fp_precision && !ShapeUtil::SameElementType(lhs, rhs))) { VLOG(3) << "CompareShapes: lhs element type != rhs element type"; return false; } @@ -102,7 +107,8 @@ bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) { if (ShapeUtil::IsTuple(lhs)) { return ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), [=](const Shape& l, const Shape& r) { - return CompareShapes(l, r, compare_layouts); + return CompareShapes(l, r, compare_layouts, + ignore_fp_precision); }); } else if (!ShapeUtil::IsArray(lhs)) { // Non-tuple, non-array tupes such as opaque and token types are trivially @@ -169,7 +175,8 @@ StatusOr MakeShapeWithLayoutInternal( } // namespace /* static */ bool ShapeUtil::Equal(const Shape& lhs, const Shape& rhs) { - bool equal = CompareShapes(lhs, rhs, /*compare_layouts=*/true); + bool equal = CompareShapes(lhs, rhs, /*compare_layouts=*/true, + /*ignore_fp_precision=*/false); if (!equal && VLOG_IS_ON(3)) { VLOG(3) << "ShapeUtil::Equal differ: lhs = " << lhs.ShortDebugString() << ", rhs = " << rhs.ShortDebugString(); @@ -178,6 +185,18 @@ StatusOr MakeShapeWithLayoutInternal( return equal; } +/* static */ bool ShapeUtil::EqualIgnoringFpPrecision(const Shape& lhs, + const Shape& rhs) { + bool equal = CompareShapes(lhs, rhs, /*compare_layouts=*/true, + /*ignore_fp_precision=*/true); + if (!equal && VLOG_IS_ON(3)) { + VLOG(3) << "ShapeUtil::EqualIgnoringFpPrecision differ: lhs = " + << lhs.ShortDebugString() << ", rhs = " << rhs.ShortDebugString(); + } + + return equal; +} + /* static */ int64 ShapeUtil::Rank(const Shape& shape) { CHECK(ShapeUtil::IsArray(shape)) << "Non-arrays do not have a rank, shape: " << shape; @@ -263,6 +282,7 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( tensorflow::gtl::ArraySlice shapes) { Shape result; result.set_element_type(TUPLE); + result.mutable_tuple_shapes()->Reserve(shapes.size()); for (const auto& shape : shapes) { AppendShapeToTuple(shape, &result); } @@ -363,7 +383,7 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( } /* static */ bool ShapeUtil::IsNil(const Shape& shape) { - return IsTuple(shape) ? IsEmptyTuple(shape) : HasZeroElements(shape); + return IsEmptyTuple(shape); } /* static */ int64 ShapeUtil::TupleElementCount(const Shape& shape) { @@ -379,6 +399,13 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( return shape.tuple_shapes(index); } +/* static */ int64 ShapeUtil::SubshapeCount(const Shape& shape) { + int64 n = 0; + ForEachSubshape(shape, [&](const Shape& literal_subshape, + const ShapeIndex& index) { ++n; }); + return n; +} + /* static */ Shape ShapeUtil::SliceTuple(const Shape& tuple, int64 start, int64 limit) { TF_DCHECK_OK(ValidateShapeWithOptionalLayout(tuple)); @@ -413,15 +440,26 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( std::multiplies()); } -/* static */ bool ShapeUtil::HasZeroElements(const Shape& shape) { - return ElementsIn(shape) == 0; +/* static */ int64 ShapeUtil::ElementsInRecursive(const Shape& shape) { + CHECK(IsArray(shape) || IsTuple(shape)); + if (IsArray(shape)) { + return ElementsIn(shape); + } + int64 count = 0; + for (const Shape& element_shape : shape.tuple_shapes()) { + count += ElementsInRecursive(element_shape); + } + return count; +} + +/* static */ bool ShapeUtil::IsZeroElementArray(const Shape& shape) { + return ShapeUtil::IsArray(shape) && ElementsIn(shape) == 0; } /* static */ bool ShapeUtil::IsScalarF32(const Shape& shape) { return shape.element_type() == F32 && Rank(shape) == 0; } - namespace { // Class to memoize the computation of @@ -554,12 +592,11 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { // tensorflow::StringPiece is not compatible with internal RE2 StringPiece, so // we convert in to the RE2-consumable type and then consume the corresponding // amount from our StringPiece type. + static LazyRE2 shape_pattern = { + "^(\\w*\\d*)\\[([\\d,]*)\\](?:\\s*(dense|sparse)?\\s*{([\\d,]+)})?"}; tensorflow::RegexpStringPiece s_consumable(s->data(), s->size()); - if (RE2::Consume( - &s_consumable, - "^(\\w*\\d*)\\[([\\d,]*)\\](?:\\s*(dense|sparse)?\\s*{([\\d,]+)})?", - &element_type_string, &dimensions_string, &format_string, - &layout_string)) { + if (RE2::Consume(&s_consumable, *shape_pattern, &element_type_string, + &dimensions_string, &format_string, &layout_string)) { size_t consumed = s->size() - s_consumable.size(); s->remove_prefix(consumed); auto string_to_int64 = [&s](const string& input) -> StatusOr { @@ -645,15 +682,8 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { } /* static */ bool ShapeUtil::Compatible(const Shape& lhs, const Shape& rhs) { - if (IsArray(lhs)) { - return SameElementType(lhs, rhs) && SameDimensions(lhs, rhs); - } else if (lhs.element_type() == TUPLE) { - return rhs.element_type() == TUPLE && - ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), Compatible); - } else { - // Opaque, token, etc types are vacuously compatible. - return true; - } + return CompareShapes(lhs, rhs, /*compare_layouts=*/false, + /*ignore_fp_precision=*/false); } /* static */ bool ShapeUtil::CompatibleIgnoringElementType(const Shape& lhs, @@ -855,6 +885,60 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { } } + TF_RETURN_IF_ERROR(ValidateShapeSize(shape)); + return Status::OK(); +} + +/* static */ Status ShapeUtil::ValidateShapeSize(const Shape& shape) { + VLOG(3) << "Validating shape size: " << ShapeUtil::HumanString(shape); + + if (!IsArray(shape)) { + return Status::OK(); + } + + int64 shape_size = [&shape]() { + int64 shape_size; + if (LayoutUtil::IsSparseArray(shape)) { + shape_size = LayoutUtil::MaxSparseElements(shape.layout()); + if (shape_size < 0) { + return shape_size; + } + shape_size = MultiplyWithoutOverflow(shape_size, ShapeUtil::Rank(shape)); + if (shape_size < 0) { + return shape_size; + } + shape_size = MultiplyWithoutOverflow(shape_size, sizeof(int64)); + if (shape_size < 0) { + return shape_size; + } + } + + shape_size = 1; + + // This is intentionally unconditional: even if the shape is sparse, we want + // to verify the densified version has a reasonable size. + if (shape.dimensions().empty()) { + return shape_size; + } + + for (int64 dim : shape.dimensions()) { + shape_size = MultiplyWithoutOverflow(shape_size, dim); + if (shape_size < 0) { + return shape_size; + } + } + shape_size = MultiplyWithoutOverflow( + shape_size, ByteSizeOfPrimitiveType(shape.element_type())); + + return shape_size; + }(); + + if (shape_size < 0) { + return InvalidArgument("Shape %s size may overflow int64.", + ShapeUtil::HumanString(shape).c_str()); + } + + VLOG(3) << "Shape size is valid: " << shape_size; return Status::OK(); } @@ -903,6 +987,21 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { return *return_shape; } +/* static */ StatusOr ShapeUtil::TryGetSubshape( + const Shape& shape, ShapeIndexView index) { + const Shape* return_shape = &shape; + for (auto i : index) { + if (!IsTuple(*return_shape) || i < 0 || + i >= return_shape->tuple_shapes_size()) { + return InvalidArgument( + "Shape index %s not a valid subshape index for tuple with shape %s", + index.ToString().c_str(), shape.DebugString().c_str()); + } + return_shape = &return_shape->tuple_shapes(i); + } + return return_shape; +} + /* static */ Shape* ShapeUtil::GetMutableSubshape(Shape* shape, ShapeIndexView index) { Shape* return_shape = shape; @@ -939,66 +1038,9 @@ bool ShapeUtil::IsLeafIndex(const Shape& shape, const ShapeIndex& index) { return leaves; } -/* static */ Shape ShapeUtil::StripDegenerateDimensions(const Shape& shape) { - CHECK(IsArray(shape)); - - std::vector dimension_sizes; - std::vector degenerate_dimensions; - for (int64 i = 0; i < shape.dimensions_size(); ++i) { - if (shape.dimensions(i) == 1) { - degenerate_dimensions.push_back(i); - } else { - dimension_sizes.push_back(shape.dimensions(i)); - } - } - - // Construct minor_to_major of stripped shape. The order of the non-degenerate - // dimensions should be preserved from the original shape. First, create - // vector of the non-degenerate dimensions from the original minor_to_major - // array. - std::vector minor_to_major; - for (int64 i : shape.layout().minor_to_major()) { - if (std::find(degenerate_dimensions.begin(), degenerate_dimensions.end(), - i) == degenerate_dimensions.end()) { - minor_to_major.push_back(i); - } - } - - // The dimensions in minor_to_major need to be renumbered to account for the - // degenerate dimensions which have removed. Decrement each dimension number - // once for each degenerate dimension which has a smaller number. - for (int i = 0; i < minor_to_major.size(); ++i) { - int adjustment = 0; - for (int64 dim : degenerate_dimensions) { - if (minor_to_major[i] > dim) { - adjustment++; - } - } - minor_to_major[i] -= adjustment; - } - - { - std::vector dims(minor_to_major.size()); - std::iota(dims.begin(), dims.end(), 0); - DCHECK(minor_to_major.size() == dims.size() && - std::is_permutation(minor_to_major.begin(), minor_to_major.end(), - dims.begin())); - } - Shape stripped_shape; - if (LayoutUtil::IsDenseArray(shape)) { - stripped_shape = MakeShapeWithLayout(shape.element_type(), dimension_sizes, - minor_to_major); - } else if (LayoutUtil::IsSparseArray(shape)) { - stripped_shape = - MakeShapeWithSparseLayout(shape.element_type(), dimension_sizes, - shape.layout().max_sparse_elements()); - } else { - stripped_shape = MakeShape(shape.element_type(), dimension_sizes); - } - - VLOG(10) << "Original_shape: " << HumanStringWithLayout(shape); - VLOG(10) << "Stripped_shape: " << HumanStringWithLayout(stripped_shape); - return stripped_shape; +/* static */ bool ShapeUtil::HasDegenerateDimensions(const Shape& shape) { + CHECK(ShapeUtil::IsArray(shape)); + return ArrayContains(AsInt64Slice(shape.dimensions()), 1); } namespace { diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 3853ada6ba65dbb1ac0754bcf753b4553ec260e7..5ae04451d32bd733dce55c4a56f5ebc1882d9fbd 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -62,6 +62,8 @@ class ShapeIndex { public: ShapeIndex() = default; ShapeIndex(std::initializer_list init) : indices_(init) {} + template + ShapeIndex(InputIt start, InputIt end) : indices_(start, end) {} bool empty() const { return indices_.empty(); } size_t size() const { return indices_.size(); } @@ -132,6 +134,7 @@ class ShapeIndexView { ++new_begin; return ShapeIndexView(new_begin, end_); } + ShapeIndex ToShapeIndex() const { return ShapeIndex(begin_, end_); } bool operator==(const ShapeIndexView& other) const; bool operator!=(const ShapeIndexView& other) const; @@ -172,8 +175,11 @@ class ShapeUtil { // Precondition: IsArray(shape) static int64 ElementsIn(const Shape& shape); - // Returns true if 'shape' has zero elements. - static bool HasZeroElements(const Shape& shape); + // As ElementsIn(), but recurses through tuples. + static int64 ElementsInRecursive(const Shape& shape); + + // Returns true if 'shape' is an array with zero elements. + static bool IsZeroElementArray(const Shape& shape); // Returns the number of bytes required for an allocation of shape. The // |pointer_size| parameter is used for calculating the size of tuple @@ -274,6 +280,9 @@ class ShapeUtil { // Returns whether the lhs and rhs shapes are identical protobufs. static bool Equal(const Shape& lhs, const Shape& rhs); + // As Equal, but allow one of lhs and rhs to be F16 while the other is F32. + static bool EqualIgnoringFpPrecision(const Shape& lhs, const Shape& rhs); + // Returns the rank (number of dimensions) of the given shape. // Precondition: !IsTuple(shape) static int64 Rank(const Shape& shape); @@ -333,7 +342,7 @@ class ShapeUtil { // Appends a major dimension to the shape with the given bound. static void AppendMajorDimension(int bound, Shape* shape); - // Returns an empty tuple shape. Can be used to indicate side-effects. + // Returns an empty tuple shape. Can be used as a sentinel Shape value. static Shape MakeNil() { return MakeTupleShape({}); } // Checks whether the shape is initialized. @@ -443,7 +452,7 @@ class ShapeUtil { // Returns true if shape is an empty tuple. static bool IsEmptyTuple(const Shape& shape); - // Returns true if shape is an empty tuple, or is an array with no elements. + // Returns true if shape is the nil shape (an empty tuple). static bool IsNil(const Shape& shape); // Returns the number of elements in the given tuple shape. @@ -454,6 +463,9 @@ class ShapeUtil { // Precondition: IsTuple(shape) && TupleElementCount(shape) > index static const Shape& GetTupleElementShape(const Shape& shape, int64 index); + // Returns the number of elements, recursively, in the given shape. + static int64 SubshapeCount(const Shape& shape); + // Slices tuple elements in the range [start, limit) and returns a new tuple // shape. E.g. a tuple like (f32, s32, u32) would slice via 1,3 to (s32, u32). static Shape SliceTuple(const Shape& tuple, int64 start, int64 limit); @@ -473,8 +485,11 @@ class ShapeUtil { static bool IndexIsValid(const Shape& shape, ShapeIndexView index); // GetSubshape and GetMutableSubshape return a particular nested Shape within - // the given Shape argument. + // the given Shape argument. The non-Try variants check fail if index is + // invalid. static const Shape& GetSubshape(const Shape& shape, ShapeIndexView index); + static StatusOr TryGetSubshape(const Shape& shape, + ShapeIndexView index); static Shape* GetMutableSubshape(Shape* shape, ShapeIndexView index); // Returns whether the given index in the given shape is a leaf element of the @@ -510,25 +525,9 @@ class ShapeUtil { static Status ForEachMutableSubshapeWithStatus( Shape* shape, const MutatingStatusVisitorFunction& func); - // Removes all degenerate dimensions (size one) from the given shape. The - // stripped minor_to_major preserves the relative ordering of non-degenerate - // dimensions. The stripped shape has the property that the underlying - // representation (bits in memory) for the stripped shape is the same as the - // original shape modulo padding. Examples: - // - // input shape: F32 [1, 2, 1], minor_to_major = {0, 1, 2} - // stripped shape: F32 [2], minor_to_major = {0} - // - // input shape: F32 [6, 1, 5], minor_to_major = {2, 0, 1} - // stripped shape: F32 [6, 5], minor_to_major = {1, 0} - // - // input shape: F32 [1, 7, 1, 6, 5, 1], minor_to_major = {0, 2, 5, 4, 3, 1} - // stripped shape: F32 [7, 6, 5], minor_to_major = {0, 2, 1} - // - // input shape: F32 [1, 1], minor_to_major = {0, 1} - // stripped shape: F32 [], minor_to_major = {} - // Precondition: !ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape) - static Shape StripDegenerateDimensions(const Shape& shape); + // Returns true if `shape` (which must be an array) with degenerate dimensions + // (dimensions with bound 1). + static bool HasDegenerateDimensions(const Shape& shape); // Permutes the dimensions by the given permutation, so // return_value.dimensions[permutation[i]] = argument.dimensions[i] @@ -703,6 +702,10 @@ class ShapeUtil { static size_t Hash(const Shape& shape); private: + // Validates the shape size is sane. This makes sure it's safe to do + // calculations in int64 without overflowing. + static Status ValidateShapeSize(const Shape& shape); + // Validates all of the non-layout properties of the shape -- this is a helper // used by both the layout-optional and layout-required public method. static Status ValidateShapeWithOptionalLayoutInternal(const Shape& shape); @@ -714,7 +717,7 @@ class ShapeUtil { tensorflow::gtl::ArraySlice incr, const FnType& visitor_function, bool parallel = false) { - if (ShapeUtil::HasZeroElements(shape)) { + if (ShapeUtil::IsZeroElementArray(shape)) { return Status::OK(); } CHECK_EQ(Rank(shape), base.size()); diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index ecdb6532f1d743c7dacc266eeba615e19748ee27..b6f30af381dd8d24ff28fdf7f729d6cb3df46ec9 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -172,6 +172,41 @@ TEST(ShapeUtilTest, CompatibleIdenticalShapes) { ASSERT_TRUE(ShapeUtil::Compatible(shape1, shape2)); } +TEST(ShapeUtilTest, TokenCompatibility) { + EXPECT_TRUE(ShapeUtil::Compatible(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeTokenShape())); + EXPECT_FALSE(ShapeUtil::Compatible(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShape(F32, {}))); + EXPECT_FALSE(ShapeUtil::Compatible(ShapeUtil::MakeShape(F32, {}), + ShapeUtil::MakeTokenShape())); + EXPECT_TRUE(ShapeUtil::Compatible( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeTokenShape()}), + ShapeUtil::MakeTupleShape({ShapeUtil::MakeTokenShape()}))); +} + +TEST(ShapeUtilTest, TokensEqualShapes) { + EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeTokenShape())); + EXPECT_FALSE(ShapeUtil::Equal(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShape(F32, {}))); + EXPECT_FALSE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {}), + ShapeUtil::MakeTokenShape())); + EXPECT_TRUE(ShapeUtil::Equal( + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}), + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}))); + EXPECT_FALSE(ShapeUtil::Equal( + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}), + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {1, 0})}))); +} + TEST(ShapeUtilTest, CompatibleNotIdenticalShapes) { Shape shape_1 = ShapeUtil::MakeShape(F32, {3, 2}); auto layout_1 = shape_1.mutable_layout(); @@ -207,6 +242,24 @@ TEST(ShapeUtilTest, IncompatibleDifferentElementShapes) { EXPECT_FALSE(ShapeUtil::Compatible(shape_1, shape_2)); } +TEST(ShapeUtilTest, EqualIgnoringFpPrecision) { + EXPECT_TRUE(ShapeUtil::EqualIgnoringFpPrecision( + ShapeUtil::MakeShapeWithLayout(F32, {4, 3}, {0, 1}), + ShapeUtil::MakeShapeWithLayout(F16, {4, 3}, {0, 1}))); +} + +TEST(ShapeUtilTest, UnequalIgnoringFpPrecision) { + EXPECT_FALSE(ShapeUtil::EqualIgnoringFpPrecision( + ShapeUtil::MakeShapeWithLayout(F32, {4, 3}, {0, 1}), + ShapeUtil::MakeShapeWithLayout(F16, {3, 4}, {0, 1}))); + EXPECT_FALSE(ShapeUtil::EqualIgnoringFpPrecision( + ShapeUtil::MakeShapeWithLayout(F32, {3, 4}, {0, 1}), + ShapeUtil::MakeShapeWithLayout(F16, {3, 4}, {1, 0}))); + EXPECT_FALSE(ShapeUtil::EqualIgnoringFpPrecision( + ShapeUtil::MakeShapeWithLayout(F32, {4, 3}, {0, 1}), + ShapeUtil::MakeShapeWithLayout(PRED, {4, 3}, {0, 1}))); +} + TEST(ShapeUtilTest, CompatibleTuples) { Shape tuple1 = ShapeUtil::MakeTupleShape( {ShapeUtil::MakeShape(F32, {3, 2}), ShapeUtil::MakeShape(PRED, {4, 5})}); @@ -329,6 +382,16 @@ TEST(ShapeUtilTest, ByteSizeOfWithPadding) { EXPECT_EQ(15 * 21 * 4, ShapeUtil::ByteSizeOf(shape)); } +TEST(ShapeUtilTest, NilShape) { + EXPECT_TRUE(ShapeUtil::IsNil(ShapeUtil::MakeNil())); + EXPECT_FALSE(ShapeUtil::IsNil(ShapeUtil::MakeShape(F32, {1, 2, 3}))); + EXPECT_FALSE(ShapeUtil::IsNil(ShapeUtil::MakeShape(F32, {0, 1}))); + EXPECT_FALSE(ShapeUtil::IsNil( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {})}))); + EXPECT_FALSE(ShapeUtil::IsNil( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(F32, {0})}))); +} + TEST(ShapeUtilTest, NestedTuple) { EXPECT_FALSE(ShapeUtil::IsNestedTuple(ShapeUtil::MakeTupleShape({}))); EXPECT_FALSE(ShapeUtil::IsNestedTuple( @@ -359,25 +422,30 @@ TEST(ShapeUtilTest, ElementsIn) { EXPECT_EQ(221, ShapeUtil::ElementsIn(ShapeUtil::MakeShape(S32, {13, 17}))); } -TEST(ShapeUtilTest, HasZeroElements) { - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {}))); - EXPECT_EQ(true, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {0}))); - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1, 1}))); - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {2}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {2, 1}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {3, 5}))); - EXPECT_EQ(true, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {3, 0, 5}))); - EXPECT_EQ(true, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {0, 3, 0}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1, 3, 5}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {13, 17}))); +TEST(ShapeUtilTest, IsZeroElementArray) { + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {}))); + EXPECT_TRUE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {0}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1, 1}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {2}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {2, 1}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {3, 5}))); + EXPECT_TRUE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {3, 0, 5}))); + EXPECT_TRUE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {0, 3, 0}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1, 3, 5}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {13, 17}))); + + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeNil())); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeTupleShape({}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {0, 3, 0})}))); } TEST(ShapeUtilTest, SameDimensions) { @@ -742,14 +810,15 @@ TEST(ShapeUtilTest, ReshapeIsBitcast_3x2x2_6x2_Dim1IsMostMinor) { ShapeUtil::MakeShapeWithLayout(F32, {6, 2}, {0, 1}))); } -TEST(ShapeUtilTest, StripDegenerateDimensions) { - EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::StripDegenerateDimensions( - ShapeUtil::MakeShape(F32, {3, 1, 2})), - ShapeUtil::MakeShape(F32, {3, 2}))); - EXPECT_TRUE(ShapeUtil::Equal( - ShapeUtil::StripDegenerateDimensions( - ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 1, 2}, 10)), - ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 2}, 10))); +TEST(ShapeUtilTest, HasDegenerateDimensions) { + EXPECT_TRUE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 1, 2}))); + EXPECT_TRUE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 1, 1}))); + EXPECT_FALSE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 3, 5}))); + EXPECT_FALSE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 0, 5}))); } TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) { diff --git a/tensorflow/compiler/xla/statusor.h b/tensorflow/compiler/xla/statusor.h index 0e1387c93938fa520562fcd63ac107a82b089a51..a32e2ad9851b0b5644f7e6f0f9ead6c438934c07 100644 --- a/tensorflow/compiler/xla/statusor.h +++ b/tensorflow/compiler/xla/statusor.h @@ -12,297 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ - -// StatusOr is the union of a Status object and a T object. StatusOr models -// the concept of an object that is either a value, or an error Status -// explaining why such a value is not present. To this end, StatusOr does not -// allow its Status value to be Status::OK. -// -// The primary use-case for StatusOr is as the return value of a -// function which may fail. -// -// Example client usage for a StatusOr, where T is not a pointer: -// -// StatusOr result = DoBigCalculationThatCouldFail(); -// if (result.ok()) { -// float answer = result.ValueOrDie(); -// printf("Big calculation yielded: %f", answer); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example client usage for a StatusOr: -// -// StatusOr result = FooFactory::MakeNewFoo(arg); -// if (result.ok()) { -// std::unique_ptr foo(result.ValueOrDie()); -// foo->DoSomethingCool(); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example client usage for a StatusOr>: -// -// StatusOr> result = FooFactory::MakeNewFoo(arg); -// if (result.ok()) { -// std::unique_ptr foo = std::move(result.ValueOrDie()); -// foo->DoSomethingCool(); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example factory implementation returning StatusOr: -// -// StatusOr FooFactory::MakeNewFoo(int arg) { -// if (arg <= 0) { -// return tensorflow::InvalidArgument("Arg must be positive"); -// } else { -// return new Foo(arg); -// } -// } -// -// Note that the assignment operators require that destroying the currently -// stored value cannot invalidate the argument; in other words, the argument -// cannot be an alias for the current value, or anything owned by the current -// value. #ifndef TENSORFLOW_COMPILER_XLA_STATUSOR_H_ #define TENSORFLOW_COMPILER_XLA_STATUSOR_H_ #include "tensorflow/compiler/xla/status.h" -#include "tensorflow/compiler/xla/statusor_internals.h" -#include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/statusor.h" namespace xla { -#if defined(__clang__) -// Only clang supports warn_unused_result as a type annotation. -template -class TF_MUST_USE_RESULT StatusOr; -#endif - -template -class StatusOr : private internal_statusor::StatusOrData, - private internal_statusor::TraitsBase< - std::is_copy_constructible::value, - std::is_move_constructible::value> { - template - friend class StatusOr; - - typedef internal_statusor::StatusOrData Base; - - public: - typedef T element_type; - - // Constructs a new StatusOr with Status::UNKNOWN status. This is marked - // 'explicit' to try to catch cases like 'return {};', where people think - // StatusOr> will be initialized with an empty vector, - // instead of a Status::UNKNOWN status. - explicit StatusOr(); - - // StatusOr will be copy constructible/assignable if T is copy - // constructible. - StatusOr(const StatusOr&) = default; - StatusOr& operator=(const StatusOr&) = default; - - // StatusOr will be move constructible/assignable if T is move - // constructible. - StatusOr(StatusOr&&) = default; - StatusOr& operator=(StatusOr&&) = default; - - // Conversion copy/move constructor, T must be convertible from U. - template ::value>::type* = nullptr> - StatusOr(const StatusOr& other); - template ::value>::type* = nullptr> - StatusOr(StatusOr&& other); - - // Conversion copy/move assignment operator, T must be convertible from U. - template ::value>::type* = nullptr> - StatusOr& operator=(const StatusOr& other); - template ::value>::type* = nullptr> - StatusOr& operator=(StatusOr&& other); - - // Constructs a new StatusOr with the given value. After calling this - // constructor, calls to ValueOrDie() will succeed, and calls to status() will - // return OK. - // - // NOTE: Not explicit - we want to use StatusOr as a return type - // so it is convenient and sensible to be able to do 'return T()' - // when the return type is StatusOr. - // - // REQUIRES: T is copy constructible. - StatusOr(const T& value); - - // Constructs a new StatusOr with the given non-ok status. After calling - // this constructor, calls to ValueOrDie() will CHECK-fail. - // - // NOTE: Not explicit - we want to use StatusOr as a return - // value, so it is convenient and sensible to be able to do 'return - // Status()' when the return type is StatusOr. - // - // REQUIRES: !status.ok(). This requirement is DCHECKed. - // In optimized builds, passing Status::OK() here will have the effect - // of passing tensorflow::error::INTERNAL as a fallback. - StatusOr(const Status& status); - StatusOr& operator=(const Status& status); - - // TODO(b/62186997): Add operator=(T) overloads. - - // Similar to the `const T&` overload. - // - // REQUIRES: T is move constructible. - StatusOr(T&& value); - - // RValue versions of the operations declared above. - StatusOr(Status&& status); - StatusOr& operator=(Status&& status); - - // Returns this->status().ok() - bool ok() const { return this->status_.ok(); } - - // Returns a reference to our status. If this contains a T, then - // returns Status::OK(). - const Status& status() const &; - Status status() &&; - - // Returns a reference to our current value, or CHECK-fails if !this->ok(). - // - // Note: for value types that are cheap to copy, prefer simple code: - // - // T value = statusor.ValueOrDie(); - // - // Otherwise, if the value type is expensive to copy, but can be left - // in the StatusOr, simply assign to a reference: - // - // T& value = statusor.ValueOrDie(); // or `const T&` - // - // Otherwise, if the value type supports an efficient move, it can be - // used as follows: - // - // T value = std::move(statusor).ValueOrDie(); - // - // The std::move on statusor instead of on the whole expression enables - // warnings about possible uses of the statusor object after the move. - // C++ style guide waiver for ref-qualified overloads granted in cl/143176389 - // See go/ref-qualifiers for more details on such overloads. - const T& ValueOrDie() const &; - T& ValueOrDie() &; - const T&& ValueOrDie() const &&; - T&& ValueOrDie() &&; - - T ConsumeValueOrDie() { return std::move(ValueOrDie()); } - - // Ignores any errors. This method does nothing except potentially suppress - // complaints from any tools that are checking that errors are not dropped on - // the floor. - void IgnoreError() const; -}; - -//////////////////////////////////////////////////////////////////////////////// -// Implementation details for StatusOr - -template -StatusOr::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {} - -template -StatusOr::StatusOr(const T& value) : Base(value) {} - -template -StatusOr::StatusOr(const Status& status) : Base(status) {} - -template -StatusOr& StatusOr::operator=(const Status& status) { - this->Assign(status); - return *this; -} - -template -StatusOr::StatusOr(T&& value) : Base(std::move(value)) {} - -template -StatusOr::StatusOr(Status&& status) : Base(std::move(status)) {} - -template -StatusOr& StatusOr::operator=(Status&& status) { - this->Assign(std::move(status)); - return *this; -} - -template -template ::value>::type*> -inline StatusOr::StatusOr(const StatusOr& other) - : Base(static_cast::Base&>(other)) {} - -template -template ::value>::type*> -inline StatusOr& StatusOr::operator=(const StatusOr& other) { - if (other.ok()) - this->Assign(other.ValueOrDie()); - else - this->Assign(other.status()); - return *this; -} - -template -template ::value>::type*> -inline StatusOr::StatusOr(StatusOr&& other) - : Base(static_cast::Base&&>(other)) {} - -template -template ::value>::type*> -inline StatusOr& StatusOr::operator=(StatusOr&& other) { - if (other.ok()) { - this->Assign(std::move(other).ValueOrDie()); - } else { - this->Assign(std::move(other).status()); - } - return *this; -} - -template -const Status& StatusOr::status() const & { - return this->status_; -} -template -Status StatusOr::status() && { - return ok() ? Status::OK() : std::move(this->status_); -} - -template -const T& StatusOr::ValueOrDie() const & { - this->EnsureOk(); - return this->data_; -} - -template -T& StatusOr::ValueOrDie() & { - this->EnsureOk(); - return this->data_; -} - -template -const T&& StatusOr::ValueOrDie() const && { - this->EnsureOk(); - return std::move(this->data_); -} - -template -T&& StatusOr::ValueOrDie() && { - this->EnsureOk(); - return std::move(this->data_); -} - +// Use steam_executor's StatusOr so we don't duplicate code. template -void StatusOr::IgnoreError() const { - // no-op -} +using StatusOr = ::stream_executor::port::StatusOr; } // namespace xla diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 7f6bbe6f879fd9596601f99f034a0391a71c52f8..77d398e5e2889b00c651b8c5cb2d834ec0290312 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -697,6 +697,7 @@ xla_test( "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -885,6 +886,7 @@ xla_test( "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", @@ -1203,6 +1205,22 @@ xla_test( ], ) +xla_test( + name = "token_hlo_test", + srcs = ["token_hlo_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], + deps = [ + ":client_library_test_base", + "//tensorflow/compiler/xla/service:hlo_verifier", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) + xla_test( name = "call_test", srcs = ["call_test.cc"], @@ -1232,6 +1250,7 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", "//tensorflow/compiler/xla/tests:client_library_test_base", @@ -1970,6 +1989,7 @@ xla_test( "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", + "//tensorflow/core:test", ], ) @@ -2021,6 +2041,7 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 36a706496918ac8c15780473019e2a8d098ffa22..3bdf98544affca11fd825e28d20f4903188fe920 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -51,16 +51,16 @@ class ArrayElementwiseOpTestParamCount XLA_TEST_F(ArrayElementwiseOpTest, NegConstantZeroElementF32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.Neg(a); + auto a = ConstantR1(&builder, {}); + Neg(a); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, NegConstantF32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); - builder.Neg(a); + auto a = ConstantR1(&builder, {-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); + Neg(a); ComputeAndCompareR1(&builder, {2.5f, -3.14f, -2.25f, 10.0f, -6.0f}, {}, error_spec_); @@ -68,10 +68,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantF32) { XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-1, 0, 1, 324, - std::numeric_limits::min(), - std::numeric_limits::max()}); - builder.Neg(a); + auto a = ConstantR1(&builder, + {-1, 0, 1, 324, std::numeric_limits::min(), + std::numeric_limits::max()}); + Neg(a); // -min == min for int32 due to an overflow. In C++ it is undefined behavior // to do this calculation. For XLA we have not specified that, so it @@ -84,17 +84,17 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS32) { XLA_TEST_F(ArrayElementwiseOpTest, NegConstantZeroElementC64) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.Neg(a); + auto a = ConstantR1(&builder, {}); + Neg(a); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {{-2.5f, 1.0f}, {0.0f, 3.14f}, {2.25f, -1.0f}, {-10.0f, 0.0f}}); - builder.Neg(a); + auto a = ConstantR1( + &builder, {{-2.5f, 1.0f}, {0.0f, 3.14f}, {2.25f, -1.0f}, {-10.0f, 0.0f}}); + Neg(a); ComputeAndCompareR1( &builder, {{2.5f, -1.0f}, {0.0f, -3.14f}, {-2.25f, 1.0f}, {10.0f, 0.0f}}, @@ -103,16 +103,17 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) { XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({ - -1, - 1, - 0, - 0x12345678, - static_cast(0xffffffff12345678l), - static_cast(0x8000000000000000LL), - static_cast(0x8000000000000001LL), - }); - builder.Neg(a); + auto a = + ConstantR1(&builder, { + -1, + 1, + 0, + 0x12345678, + static_cast(0xffffffff12345678l), + static_cast(0x8000000000000000LL), + static_cast(0x8000000000000001LL), + }); + Neg(a); LOG(INFO) << -static_cast(0x7FFFFFFFFFFFFFFFLL); ComputeAndCompareR1(&builder, @@ -130,8 +131,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) { XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.IsFinite(a); + auto a = ConstantR1(&builder, {}); + IsFinite(a); ComputeAndCompareR1(&builder, {}, {}); } @@ -141,21 +142,21 @@ static const float kNonCanonicalNaN = tensorflow::bit_cast(0x7FD01234); XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteScalarF32) { XlaBuilder builder(TestName()); - builder.IsFinite(builder.ConstantR0(NAN)); + IsFinite(ConstantR0(&builder, NAN)); ComputeAndCompareR0(&builder, false, {}); EXPECT_TRUE(std::isnan(kNonCanonicalNaN)); - builder.IsFinite(builder.ConstantR0(kNonCanonicalNaN)); + IsFinite(ConstantR0(&builder, kNonCanonicalNaN)); ComputeAndCompareR0(&builder, false, {}); const float inf = std::numeric_limits::infinity(); - builder.IsFinite(builder.ConstantR0(inf)); + IsFinite(ConstantR0(&builder, inf)); ComputeAndCompareR0(&builder, false, {}); - builder.IsFinite(builder.ConstantR0(-inf)); + IsFinite(ConstantR0(&builder, -inf)); ComputeAndCompareR0(&builder, false, {}); - builder.IsFinite(builder.ConstantR0(0.0f)); + IsFinite(ConstantR0(&builder, 0.0f)); ComputeAndCompareR0(&builder, true, {}); } @@ -163,9 +164,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteR1F32s) { XlaBuilder builder(TestName()); const float inf = std::numeric_limits::infinity(); EXPECT_TRUE(std::isnan(kNonCanonicalNaN)); - auto a = builder.ConstantR1( - {{NAN, 7.0f, kNonCanonicalNaN, -1.0f, inf, -inf}}); - builder.IsFinite(a); + auto a = ConstantR1(&builder, + {{NAN, 7.0f, kNonCanonicalNaN, -1.0f, inf, -inf}}); + IsFinite(a); ComputeAndCompareR1(&builder, {false, true, false, true, false, false}, {}); @@ -173,9 +174,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteR1F32s) { XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); - auto b = builder.ConstantR1({100.0f, 3.13f, 2.75f, 10.5f, -999.0f}); - builder.Add(a, b); + auto a = ConstantR1(&builder, {-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); + auto b = ConstantR1(&builder, {100.0f, 3.13f, 2.75f, 10.5f, -999.0f}); + Add(a, b); ComputeAndCompareR1(&builder, {97.5f, 6.27f, 5.0f, 0.5f, -993.0f}, {}, error_spec_); @@ -183,20 +184,20 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantF32s) { XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Add(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Add(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {{-2.5f, 0.0f}, {0.0f, 3.14f}, {2.25f, 0.0f}, {1.0f, -10.0f}}); - auto b = builder.ConstantR1( - {{100.0f, 0.0f}, {3.13f, 0.0f}, {2.75f, 1.0f}, {-2.0f, 10.5f}}); - builder.Add(a, b); + auto a = ConstantR1( + &builder, {{-2.5f, 0.0f}, {0.0f, 3.14f}, {2.25f, 0.0f}, {1.0f, -10.0f}}); + auto b = ConstantR1( + &builder, {{100.0f, 0.0f}, {3.13f, 0.0f}, {2.75f, 1.0f}, {-2.0f, 10.5f}}); + Add(a, b); ComputeAndCompareR1( &builder, {97.5f, {3.13f, 3.14f}, {5.0f, 1.0f}, {-1.0f, 0.5f}}, {}, @@ -205,9 +206,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantC64s) { XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Add(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Add(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -225,7 +226,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) { 0x8000000000000000LL, 1}; std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); - auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param"); + auto lhs_param = Parameter(&b, 0, lhs_literal->shape(), "lhs_param"); std::unique_ptr lhs_data = client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); @@ -239,11 +240,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) { 1, 0x8000000000000000LL}; std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); - auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param"); + auto rhs_param = Parameter(&b, 1, rhs_literal->shape(), "rhs_param"); std::unique_ptr rhs_data = client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); - b.Add(lhs_param, rhs_param); + Add(lhs_param, rhs_param); std::vector expected(lhs.size()); for (int64 i = 0; i < lhs.size(); ++i) { @@ -265,7 +266,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) { 0, -1}; std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); - auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param"); + auto lhs_param = Parameter(&b, 0, lhs_literal->shape(), "lhs_param"); std::unique_ptr lhs_data = client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); @@ -278,11 +279,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) { 0x7FFFFFFFFFFFFFFFLL, 0x7FFFFFFFFFFFFFFFLL}; std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); - auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param"); + auto rhs_param = Parameter(&b, 1, rhs_literal->shape(), "rhs_param"); std::unique_ptr rhs_data = client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); - auto sub = b.Sub(lhs_param, rhs_param); + Sub(lhs_param, rhs_param); std::vector expected(lhs.size()); for (int64 i = 0; i < lhs.size(); ++i) { @@ -305,23 +306,23 @@ TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) { std::unique_ptr a_literal = Literal::CreateR1({a_values}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a_constant = builder.ConstantR1(a_values); - auto a_param = builder.Parameter(0, a_literal->shape(), "a_param"); + auto a_constant = ConstantR1(&builder, a_values); + auto a_param = Parameter(&builder, 0, a_literal->shape(), "a_param"); std::unique_ptr b_literal = Literal::CreateR1({b_values}); std::unique_ptr b_data = client_->TransferToServer(*b_literal).ConsumeValueOrDie(); - auto b_constant = builder.Parameter(1, a_literal->shape(), "b_param"); - auto b_param = builder.ConstantR1(b_values); + auto b_constant = Parameter(&builder, 1, a_literal->shape(), "b_param"); + auto b_param = ConstantR1(&builder, b_values); - auto sum1 = builder.Add(a_constant, b_constant); - auto sum2 = builder.Add(a_constant, b_param); - auto sum3 = builder.Add(a_param, b_constant); - auto sum4 = builder.Add(a_param, b_param); + auto sum1 = Add(a_constant, b_constant); + auto sum2 = Add(a_constant, b_param); + auto sum3 = Add(a_param, b_constant); + auto sum4 = Add(a_param, b_param); - auto sum = builder.Add(sum1, sum2); - sum = builder.Add(sum, sum3); - sum = builder.Add(sum, sum4); + auto sum = Add(sum1, sum2); + sum = Add(sum, sum3); + sum = Add(sum, sum4); std::vector expected; for (int64 i = 0; i < count; ++i) { @@ -334,9 +335,9 @@ TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) { XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); - auto b = builder.ConstantR1({100.0f, 3.13f, 2.75f, 10.5f, -999.0f}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, {-2.5f, 3.14f, 2.25f, -10.0f, 6.0f}); + auto b = ConstantR1(&builder, {100.0f, 3.13f, 2.75f, 10.5f, -999.0f}); + Sub(a, b); ComputeAndCompareR1(&builder, {-102.5f, 0.01f, -0.5f, -20.5f, 1005.0f}, {}, error_spec_); @@ -344,38 +345,38 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantF32s) { XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Sub(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-1, 0, 2, 1000000000}); - auto b = builder.ConstantR1({-1, 2, 1, -1}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, {-1, 0, 2, 1000000000}); + auto b = ConstantR1(&builder, {-1, 2, 1, -1}); + Sub(a, b); ComputeAndCompareR1(&builder, {0, -2, 1, 1000000001}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantZeroElementS32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Sub(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {{-2.5f, 0.0f}, {0.0f, 3.14f}, {3.0f, 2.25f}}); - auto b = builder.ConstantR1( - {{0.0f, 10.0f}, {3.13f, 0.0f}, {2.75f, -0.25f}}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, + {{-2.5f, 0.0f}, {0.0f, 3.14f}, {3.0f, 2.25f}}); + auto b = ConstantR1( + &builder, {{0.0f, 10.0f}, {3.13f, 0.0f}, {2.75f, -0.25f}}); + Sub(a, b); ComputeAndCompareR1( &builder, {{-2.5f, -10.0f}, {-3.13f, 3.14f}, {0.25f, 2.5f}}, {}, @@ -384,18 +385,18 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantC64s) { XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantZeroElementC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Sub(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Sub(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); - auto b = builder.ConstantR1({10.0f, 5.1f, 1.0f, 10.0f, -6.0f}); - builder.Div(a, b); + auto a = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); + auto b = ConstantR1(&builder, {10.0f, 5.1f, 1.0f, 10.0f, -6.0f}); + Div(a, b); ComputeAndCompareR1(&builder, {-0.25f, 5.0f, 2.25f, -1.0f, -1.0f}, {}, error_spec_); @@ -403,9 +404,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantF32s) { XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Div(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Div(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -442,7 +443,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivS32s) { CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); auto divisor_data = CreateR1Parameter(divisors, 1, "divisor", &builder, &divisor); - builder.Div(dividend, divisor); + Div(dividend, divisor); ComputeAndCompareR1(&builder, quotients, {dividend_data.get(), divisor_data.get()}); @@ -454,7 +455,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivS32s) { XlaOp dividend; auto dividend_data = CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); - builder.Div(dividend, builder.ConstantR1(divisors)); + Div(dividend, ConstantR1(&builder, divisors)); ComputeAndCompareR1(&builder, quotients, {dividend_data.get()}); } @@ -467,7 +468,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivS32s) { CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); auto divisor_data = CreateR1Parameter(divisors, 1, "divisor", &builder, &divisor); - builder.Rem(dividend, divisor); + Rem(dividend, divisor); ComputeAndCompareR1(&builder, remainders, {dividend_data.get(), divisor_data.get()}); @@ -479,7 +480,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivS32s) { XlaOp dividend; auto dividend_data = CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); - builder.Rem(dividend, builder.ConstantR1(divisors)); + Rem(dividend, ConstantR1(&builder, divisors)); ComputeAndCompareR1(&builder, remainders, {dividend_data.get()}); } @@ -513,7 +514,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivU32s) { &builder, ÷nd); auto divisor_data = CreateR1Parameter(divisors, 1, "divisor", &builder, &divisor); - builder.Div(dividend, divisor); + Div(dividend, divisor); ComputeAndCompareR1(&builder, quotients, {dividend_data.get(), divisor_data.get()}); @@ -524,7 +525,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivU32s) { XlaOp dividend; auto dividend_data = CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); - builder.Div(dividend, builder.ConstantR1(divisors)); + Div(dividend, ConstantR1(&builder, divisors)); ComputeAndCompareR1(&builder, quotients, {dividend_data.get()}); } @@ -537,7 +538,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivU32s) { &builder, ÷nd); auto divisor_data = CreateR1Parameter(divisors, 1, "divisor", &builder, &divisor); - builder.Rem(dividend, divisor); + Rem(dividend, divisor); ComputeAndCompareR1(&builder, remainders, {dividend_data.get(), divisor_data.get()}); @@ -548,7 +549,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivU32s) { XlaOp dividend; auto dividend_data = CreateR1Parameter(dividends, 0, "dividend", &builder, ÷nd); - builder.Rem(dividend, builder.ConstantR1(divisors)); + Rem(dividend, ConstantR1(&builder, divisors)); ComputeAndCompareR1(&builder, remainders, {dividend_data.get()}); } @@ -556,11 +557,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivU32s) { XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {{-2.5f, 1.0f}, {-25.5f, 0.0f}, {2.0f, -1.0f}}); - auto b = builder.ConstantR1( - {{10.0f, 0.0f}, {0.0f, 1.0f}, {2.0f, -1.0f}}); - builder.Div(a, b); + auto a = ConstantR1( + &builder, {{-2.5f, 1.0f}, {-25.5f, 0.0f}, {2.0f, -1.0f}}); + auto b = ConstantR1(&builder, + {{10.0f, 0.0f}, {0.0f, 1.0f}, {2.0f, -1.0f}}); + Div(a, b); ComputeAndCompareR1( &builder, {{-0.25f, 0.1f}, {0.0f, 25.5f}, {1.0f, 0.0f}}, {}, error_spec_); @@ -568,20 +569,20 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantC64s) { XLA_TEST_F(ArrayElementwiseOpTest, DivTwoConstantZeroElementC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Div(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Div(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, RemF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f, 3.0f, 3.0f, -1.0f, -8.0f}); - auto b = builder.ConstantR1( - {10.0f, 5.1f, 1.0f, 10.0f, -6.0f, 2.0f, -2.0f, 7.0f, -4.0f}); - builder.Rem(a, b); + auto a = ConstantR1( + &builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f, 3.0f, 3.0f, -1.0f, -8.0f}); + auto b = ConstantR1( + &builder, {10.0f, 5.1f, 1.0f, 10.0f, -6.0f, 2.0f, -2.0f, 7.0f, -4.0f}); + Rem(a, b); ComputeAndCompareR1( &builder, {-2.5f, 0.0f, 0.25f, 0.0f, -0.0f, 1.0f, 1.0f, -1.0f, -0.0f}, {}, @@ -590,20 +591,20 @@ XLA_TEST_F(ArrayElementwiseOpTest, RemF32s) { XLA_TEST_F(ArrayElementwiseOpTest, RemZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Rem(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Rem(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, RemF64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {-2.5, 25.5, 2.25, -10.0, 6.0, 3.0, 3.0, -1.0, -8.0}); - auto b = builder.ConstantR1( - {10.0, 5.1, 1.0, 10.0, -6.0, 2.0, -2.0, 7.0, -4.0}); - builder.Rem(a, b); + auto a = ConstantR1( + &builder, {-2.5, 25.5, 2.25, -10.0, 6.0, 3.0, 3.0, -1.0, -8.0}); + auto b = ConstantR1( + &builder, {10.0, 5.1, 1.0, 10.0, -6.0, 2.0, -2.0, 7.0, -4.0}); + Rem(a, b); ComputeAndCompareR1( &builder, {-2.5, 0.0, 0.25, 0.0, -0.0, 1.0, 1.0, -1.0, -0.0}, {}, @@ -612,9 +613,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, RemF64s) { XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); - auto b = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); - builder.Mul(a, b); + auto a = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); + auto b = ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); + Mul(a, b); ComputeAndCompareR1(&builder, {-25.0f, 127.5f, 2.25f, -100.0f, -36.0f}, {}, error_spec_); @@ -622,9 +623,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantF32s) { XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantZeroElementF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Mul(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Mul(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -648,18 +649,18 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantS32s) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR1(a_data); - auto b = builder.ConstantR1(b_data); - builder.Mul(a, b); + auto a = ConstantR1(&builder, a_data); + auto b = ConstantR1(&builder, b_data); + Mul(a, b); ComputeAndCompareR1(&builder, expected, {}); } XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantZeroElementS32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Mul(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Mul(a, b); ComputeAndCompareR1(&builder, {}, {}); } @@ -679,20 +680,20 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantU32s) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR1(a_data); - auto b = builder.ConstantR1(b_data); - builder.Mul(a, b); + auto a = ConstantR1(&builder, a_data); + auto b = ConstantR1(&builder, b_data); + Mul(a, b); ComputeAndCompareR1(&builder, expected, {}); } XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {{-2.5f, 0.0f}, {0.0f, 25.5f}, {2.0f, -10.0f}}); - auto b = builder.ConstantR1( - {{0.0f, 10.0f}, {5.0f, 1.0f}, {10.0f, -6.0f}}); - builder.Mul(a, b); + auto a = ConstantR1( + &builder, {{-2.5f, 0.0f}, {0.0f, 25.5f}, {2.0f, -10.0f}}); + auto b = ConstantR1(&builder, + {{0.0f, 10.0f}, {5.0f, 1.0f}, {10.0f, -6.0f}}); + Mul(a, b); ComputeAndCompareR1( &builder, {{0.0f, -25.0f}, {-25.5f, 127.5f}, {-40.0f, -112.0}}, {}, @@ -701,27 +702,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantC64s) { XLA_TEST_F(ArrayElementwiseOpTest, MulTwoConstantZeroElementC64s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Mul(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Mul(a, b); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, AndPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({false, false, true, true}); - auto b = builder.ConstantR1({false, true, false, true}); - builder.And(a, b); + auto a = ConstantR1(&builder, {false, false, true, true}); + auto b = ConstantR1(&builder, {false, true, false, true}); + And(a, b); ComputeAndCompareR1(&builder, {false, false, false, true}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, AndPredR2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{false, false}, {true, true}}); - auto b = builder.ConstantR2({{false, true}, {false, true}}); - builder.And(a, b); + auto a = ConstantR2(&builder, {{false, false}, {true, true}}); + auto b = ConstantR2(&builder, {{false, true}, {false, true}}); + And(a, b); Array2D expected_array({{false, false}, {false, true}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -729,27 +730,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, AndPredR2) { XLA_TEST_F(ArrayElementwiseOpTest, AndZeroElementPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.And(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + And(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, AndS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0, -1, -8}); - auto b = builder.ConstantR1({5, -7, 12}); - builder.And(a, b); + auto a = ConstantR1(&builder, {0, -1, -8}); + auto b = ConstantR1(&builder, {5, -7, 12}); + And(a, b); ComputeAndCompareR1(&builder, {0, -7, 8}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, AndS32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{0, -5}, {-1, 5}}); - auto b = builder.ConstantR2({{1, -6}, {4, 5}}); - builder.And(a, b); + auto a = ConstantR2(&builder, {{0, -5}, {-1, 5}}); + auto b = ConstantR2(&builder, {{1, -6}, {4, 5}}); + And(a, b); Array2D expected_array({{0, -6}, {4, 5}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -757,27 +758,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, AndS32R2) { XLA_TEST_F(ArrayElementwiseOpTest, AndZeroElementS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.And(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + And(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, AndU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0, 1, 8}); - auto b = builder.ConstantR1({5, 7, 12}); - builder.And(a, b); + auto a = ConstantR1(&builder, {0, 1, 8}); + auto b = ConstantR1(&builder, {5, 7, 12}); + And(a, b); ComputeAndCompareR1(&builder, {0, 1, 8}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, AndU32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{0, 1}, {3, 8}}); - auto b = builder.ConstantR2({{1, 0}, {7, 6}}); - builder.And(a, b); + auto a = ConstantR2(&builder, {{0, 1}, {3, 8}}); + auto b = ConstantR2(&builder, {{1, 0}, {7, 6}}); + And(a, b); Array2D expected_array({{0, 0}, {3, 0}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -785,27 +786,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, AndU32R2) { XLA_TEST_F(ArrayElementwiseOpTest, AndZeroElementU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.And(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + And(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({false, false, true, true}); - auto b = builder.ConstantR1({false, true, false, true}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {false, false, true, true}); + auto b = ConstantR1(&builder, {false, true, false, true}); + Or(a, b); ComputeAndCompareR1(&builder, {false, true, true, true}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrPredR2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{false, false}, {true, true}}); - auto b = builder.ConstantR2({{false, true}, {false, true}}); - builder.Or(a, b); + auto a = ConstantR2(&builder, {{false, false}, {true, true}}); + auto b = ConstantR2(&builder, {{false, true}, {false, true}}); + Or(a, b); Array2D expected_array({{false, true}, {true, true}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -813,27 +814,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, OrPredR2) { XLA_TEST_F(ArrayElementwiseOpTest, OrZeroElementPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Or(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0, -1, 8}); - auto b = builder.ConstantR1({5, -7, 4}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {0, -1, 8}); + auto b = ConstantR1(&builder, {5, -7, 4}); + Or(a, b); ComputeAndCompareR1(&builder, {5, -1, 12}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrS32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{0, -1}, {8, 8}}); - auto b = builder.ConstantR2({{5, -7}, {4, 1}}); - builder.Or(a, b); + auto a = ConstantR2(&builder, {{0, -1}, {8, 8}}); + auto b = ConstantR2(&builder, {{5, -7}, {4, 1}}); + Or(a, b); Array2D expected_array({{5, -1}, {12, 9}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -841,27 +842,27 @@ XLA_TEST_F(ArrayElementwiseOpTest, OrS32R2) { XLA_TEST_F(ArrayElementwiseOpTest, OrZeroElementS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Or(a, b); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0, 1, 8}); - auto b = builder.ConstantR1({5, 7, 4}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {0, 1, 8}); + auto b = ConstantR1(&builder, {5, 7, 4}); + Or(a, b); ComputeAndCompareR1(&builder, {5, 7, 12}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, OrU32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{0, 1}, {8, 8}}); - auto b = builder.ConstantR2({{5, 7}, {4, 1}}); - builder.Or(a, b); + auto a = ConstantR2(&builder, {{0, 1}, {8, 8}}); + auto b = ConstantR2(&builder, {{5, 7}, {4, 1}}); + Or(a, b); Array2D expected_array({{5, 7}, {12, 9}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -869,25 +870,108 @@ XLA_TEST_F(ArrayElementwiseOpTest, OrU32R2) { XLA_TEST_F(ArrayElementwiseOpTest, OrZeroElementU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.Or(a, b); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Or(a, b); ComputeAndCompareR1(&builder, {}, {}); } +XLA_TEST_F(ArrayElementwiseOpTest, XorPredR1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {false, false, true, true}); + auto b = ConstantR1(&builder, {false, true, false, true}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {false, true, true, false}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorPredR2) { + XlaBuilder builder(TestName()); + auto a = ConstantR2(&builder, {{false, false}, {true, true}}); + auto b = ConstantR2(&builder, {{false, true}, {false, true}}); + Xor(a, b); + + Array2D expected_array({{false, true}, {true, false}}); + ComputeAndCompareR2(&builder, expected_array, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorZeroElementPredR1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorS32R1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {0, -1, 8}); + auto b = ConstantR1(&builder, {5, -7, 4}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {5, 6, 12}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorS32R2) { + XlaBuilder builder(TestName()); + auto a = ConstantR2(&builder, {{0, -1}, {8, 8}}); + auto b = ConstantR2(&builder, {{5, -7}, {4, 1}}); + Xor(a, b); + + Array2D expected_array({{5, 6}, {12, 9}}); + ComputeAndCompareR2(&builder, expected_array, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorZeroElementS32R1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorU32R1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {0, 1, 8}); + auto b = ConstantR1(&builder, {5, 7, 4}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {5, 6, 12}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorU32R2) { + XlaBuilder builder(TestName()); + auto a = ConstantR2(&builder, {{0, 1}, {8, 8}}); + auto b = ConstantR2(&builder, {{5, 7}, {4, 1}}); + Xor(a, b); + + Array2D expected_array({{5, 6}, {12, 9}}); + ComputeAndCompareR2(&builder, expected_array, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, XorZeroElementU32R1) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + Xor(a, b); + + ComputeAndCompareR1(&builder, {}, {}); +} XLA_TEST_F(ArrayElementwiseOpTest, NotPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({false, true, true, false}); - builder.Not(a); + auto a = ConstantR1(&builder, {false, true, true, false}); + Not(a); ComputeAndCompareR1(&builder, {true, false, false, true}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NotPredR2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{false, true}, {true, false}}); - builder.Not(a); + auto a = ConstantR2(&builder, {{false, true}, {true, false}}); + Not(a); Array2D expected_array({{true, false}, {false, true}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -895,24 +979,24 @@ XLA_TEST_F(ArrayElementwiseOpTest, NotPredR2) { XLA_TEST_F(ArrayElementwiseOpTest, NotZeroElementPredR1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.Not(a); + auto a = ConstantR1(&builder, {}); + Not(a); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NotS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-1, 0, 1}); - builder.Not(a); + auto a = ConstantR1(&builder, {-1, 0, 1}); + Not(a); ComputeAndCompareR1(&builder, {0, -1, -2}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NotS32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{-1, 0}, {1, 8}}); - builder.Not(a); + auto a = ConstantR2(&builder, {{-1, 0}, {1, 8}}); + Not(a); Array2D expected_array({{0, -1}, {-2, -9}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -920,24 +1004,24 @@ XLA_TEST_F(ArrayElementwiseOpTest, NotS32R2) { XLA_TEST_F(ArrayElementwiseOpTest, NotZeroElementS32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.Not(a); + auto a = ConstantR1(&builder, {}); + Not(a); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NotU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0, 4294967295}); - builder.Not(a); + auto a = ConstantR1(&builder, {0, 4294967295}); + Not(a); ComputeAndCompareR1(&builder, {4294967295, 0}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NotU32R2) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{0, 4294967295}, {1, 4294967294}}); - builder.Not(a); + auto a = ConstantR2(&builder, {{0, 4294967295}, {1, 4294967294}}); + Not(a); Array2D expected_array({{4294967295, 0}, {4294967294, 1}}); ComputeAndCompareR2(&builder, expected_array, {}); @@ -945,19 +1029,19 @@ XLA_TEST_F(ArrayElementwiseOpTest, NotU32R2) { XLA_TEST_F(ArrayElementwiseOpTest, NotZeroElementU32R1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.Not(a); + auto a = ConstantR1(&builder, {}); + Not(a); ComputeAndCompareR1(&builder, {}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftS32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({static_cast(0x12345678), - static_cast(0xF0001000), 1, 3, 77, - 1, -3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 15, 32, 100, -1}); - builder.ShiftLeft(a, b); + auto a = ConstantR1( + &builder, {static_cast(0x12345678), static_cast(0xF0001000), + 1, 3, 77, 1, -3, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 15, 32, 100, -1}); + ShiftLeft(a, b); ComputeAndCompareR1(&builder, {static_cast(0x23456780), 0x00100000, 0x4, @@ -967,11 +1051,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftS32) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticS32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({static_cast(0x92345678), - static_cast(0x10001000), 1, 3, 77, - 1, -3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 2, 32, 100, -1}); - builder.ShiftRightArithmetic(a, b); + auto a = ConstantR1( + &builder, {static_cast(0x92345678), static_cast(0x10001000), + 1, 3, 77, 1, -3, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 2, 32, 100, -1}); + ShiftRightArithmetic(a, b); ComputeAndCompareR1( &builder, @@ -982,11 +1066,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticS32) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalS32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({static_cast(0x92345678), - static_cast(0x10001000), 1, 3, 77, - 1, -3, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 5, 32, 100, -1}); - builder.ShiftRightLogical(a, b); + auto a = ConstantR1( + &builder, {static_cast(0x92345678), static_cast(0x10001000), + 1, 3, 77, 1, -3, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 5, 32, 100, -1}); + ShiftRightLogical(a, b); ComputeAndCompareR1(&builder, {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {}); @@ -994,10 +1078,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalS32) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftU32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {0x12345678, 0xF0001000, 1, 3, 77, 1, ~3u, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 15, 32, 100, ~0u}); - builder.ShiftLeft(a, b); + auto a = ConstantR1(&builder, + {0x12345678, 0xF0001000, 1, 3, 77, 1, ~3u, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 15, 32, 100, ~0u}); + ShiftLeft(a, b); ComputeAndCompareR1( &builder, {0x23456780, 0x00100000, 0x4, 0x180, 2523136, 0, 0, 0}, {}); @@ -1005,10 +1089,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftU32) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticU32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 2, 32, 100, ~0u}); - builder.ShiftRightArithmetic(a, b); + auto a = ConstantR1(&builder, + {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 2, 32, 100, ~0u}); + ShiftRightArithmetic(a, b); ComputeAndCompareR1( &builder, {0xF9234567, 0x00100010, 0, 0, 19, 0, ~0u, 0}, {}); @@ -1016,10 +1100,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticU32) { XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalU32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); - auto b = builder.ConstantR1({4, 8, 2, 7, 5, 32, 100, ~0u}); - builder.ShiftRightLogical(a, b); + auto a = ConstantR1(&builder, + {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77}); + auto b = ConstantR1(&builder, {4, 8, 2, 7, 5, 32, 100, ~0u}); + ShiftRightLogical(a, b); ComputeAndCompareR1(&builder, {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {}); @@ -1028,18 +1112,18 @@ XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalU32) { XLA_TEST_F(ArrayElementwiseOpTest, CompareEqF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 25.5f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 5.0f, 2.25f, 10.0f, NAN}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 5.0f, 2.25f, 10.0f, NAN}); + Eq(lhs, rhs); ComputeAndCompareR1(&builder, {false, false, true, false, false}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, CompareEqZeroElementF32s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Eq(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}); } @@ -1047,9 +1131,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareEqZeroElementF32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareGeF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 25.5f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, NAN}); - builder.Ge(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, NAN}); + Ge(lhs, rhs); ComputeAndCompareR1(&builder, {false, true, true, false, false}, {}); } @@ -1057,9 +1141,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGeF32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareGtF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 25.5f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, NAN}); - builder.Gt(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, NAN}); + Gt(lhs, rhs); ComputeAndCompareR1(&builder, {false, true, true, false, false}, {}); } @@ -1067,9 +1151,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGtF32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareLeF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 5.0f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, NAN}); - builder.Le(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 5.0f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, NAN}); + Le(lhs, rhs); ComputeAndCompareR1(&builder, {true, true, false, false, false}, {}); } @@ -1077,9 +1161,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareLeF32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareLtF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 25.5f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, NAN}); - builder.Lt(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, NAN}); + Lt(lhs, rhs); ComputeAndCompareR1(&builder, {true, false, false, false, false}, {}); } @@ -1088,9 +1172,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareEqS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Eq(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Eq(lhs, rhs); ComputeAndCompareR1( &builder, {true, false, false, false, true, false, false, false, true}, @@ -1099,9 +1184,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareEqS32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareEqZeroElementS32s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Eq(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}); } @@ -1109,26 +1194,26 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareEqZeroElementS32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareEqC64s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({{-2.5f, 10.0f}, - {1.0f, 25.5f}, - {2.25f, -3.0f}, - {NAN, 0.0f}, - {1.0f, 6.0f}}); - auto rhs = builder.ConstantR1({{0.0f, 10.0f}, - {1.0f, 5.0f}, - {2.25f, -3.0f}, - {10.0f, 0.0f}, - {1.0f, NAN}}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {{-2.5f, 10.0f}, + {1.0f, 25.5f}, + {2.25f, -3.0f}, + {NAN, 0.0f}, + {1.0f, 6.0f}}); + auto rhs = ConstantR1(&builder, {{0.0f, 10.0f}, + {1.0f, 5.0f}, + {2.25f, -3.0f}, + {10.0f, 0.0f}, + {1.0f, NAN}}); + Eq(lhs, rhs); ComputeAndCompareR1(&builder, {false, false, true, false, false}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, CompareEqZeroElementC64s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Eq(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}); } @@ -1138,17 +1223,17 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareNeC64s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({{-2.5f, 10.0f}, - {1.0f, 25.5f}, - {2.25f, -3.0f}, - {NAN, 0.0f}, - {1.0f, 6.0f}}); - auto rhs = builder.ConstantR1({{0.0f, 10.0f}, - {1.0f, 5.0f}, - {2.25f, -3.0f}, - {10.0f, 0.0f}, - {1.0f, NAN}}); - builder.Ne(lhs, rhs); + auto lhs = ConstantR1(&builder, {{-2.5f, 10.0f}, + {1.0f, 25.5f}, + {2.25f, -3.0f}, + {NAN, 0.0f}, + {1.0f, 6.0f}}); + auto rhs = ConstantR1(&builder, {{0.0f, 10.0f}, + {1.0f, 5.0f}, + {2.25f, -3.0f}, + {10.0f, 0.0f}, + {1.0f, NAN}}); + Ne(lhs, rhs); ComputeAndCompareR1(&builder, {true, true, false, true, true}, {}); } @@ -1158,9 +1243,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareNeF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.5f, 25.5f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({10.0f, 25.5f, 1.0f, 10.0f, NAN}); - builder.Ne(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {10.0f, 25.5f, 1.0f, 10.0f, NAN}); + Ne(lhs, rhs); ComputeAndCompareR1(&builder, {true, false, true, true, true}, {}); } @@ -1169,9 +1254,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareNeS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Ne(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Ne(lhs, rhs); ComputeAndCompareR1( &builder, {false, true, true, true, false, true, true, true, false}, {}); @@ -1181,9 +1267,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGeS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Ge(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Ge(lhs, rhs); ComputeAndCompareR1( &builder, {true, false, false, true, true, false, true, true, true}, {}); @@ -1193,9 +1280,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGtS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Gt(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Gt(lhs, rhs); ComputeAndCompareR1( &builder, {false, false, false, true, false, false, true, true, false}, @@ -1206,9 +1294,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareLeS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Le(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Le(lhs, rhs); ComputeAndCompareR1( &builder, {true, true, true, false, true, true, false, false, true}, {}); @@ -1218,9 +1307,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareLtS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({min, min, min, 0, 0, 0, max, max, max}); - auto rhs = builder.ConstantR1({min, 0, max, -1, 0, 1, min, 0, max}); - builder.Lt(lhs, rhs); + auto lhs = + ConstantR1(&builder, {min, min, min, 0, 0, 0, max, max, max}); + auto rhs = ConstantR1(&builder, {min, 0, max, -1, 0, 1, min, 0, max}); + Lt(lhs, rhs); ComputeAndCompareR1( &builder, {false, true, true, false, false, true, false, false, false}, @@ -1230,9 +1320,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareLtS32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareEqU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Eq(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Eq(lhs, rhs); ComputeAndCompareR1( &builder, {true, false, false, false, true, false, false, false, true}, @@ -1242,9 +1332,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareEqU32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareNeU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Ne(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Ne(lhs, rhs); ComputeAndCompareR1( &builder, {false, true, true, true, false, true, true, true, false}, {}); @@ -1253,9 +1343,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareNeU32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareGeU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Ge(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Ge(lhs, rhs); ComputeAndCompareR1( &builder, {true, false, false, true, true, false, true, true, true}, {}); @@ -1264,9 +1354,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGeU32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareGtU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Gt(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Gt(lhs, rhs); ComputeAndCompareR1( &builder, {false, false, false, true, false, false, true, true, false}, @@ -1276,9 +1366,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGtU32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareLeU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Le(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Le(lhs, rhs); ComputeAndCompareR1( &builder, {true, true, true, false, true, true, false, false, true}, {}); @@ -1287,9 +1377,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareLeU32s) { XLA_TEST_F(ArrayElementwiseOpTest, CompareLtU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 0, 0, 5, 5, 5, max, max, max}); - auto rhs = builder.ConstantR1({0, 1, max, 4, 5, 6, 0, 1, max}); - builder.Lt(lhs, rhs); + auto lhs = ConstantR1(&builder, {0, 0, 0, 5, 5, 5, max, max, max}); + auto rhs = ConstantR1(&builder, {0, 1, max, 4, 5, 6, 0, 1, max}); + Lt(lhs, rhs); ComputeAndCompareR1( &builder, {false, true, true, false, false, true, false, false, false}, @@ -1300,10 +1390,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); auto lhs = - builder.ConstantR1({4.0f, 2.0f, 2.0f, NAN, 6.0f, -2.0f, -2.0f}); + ConstantR1(&builder, {4.0f, 2.0f, 2.0f, NAN, 6.0f, -2.0f, -2.0f}); auto rhs = - builder.ConstantR1({2.0f, -2.0f, 3.0f, 10.0f, NAN, 3.0f, 4.0f}); - builder.Pow(lhs, rhs); + ConstantR1(&builder, {2.0f, -2.0f, 3.0f, 10.0f, NAN, 3.0f, 4.0f}); + Pow(lhs, rhs); ComputeAndCompareR1( &builder, {16.0f, 0.25f, 8.0f, NAN, NAN, -8.0f, 16.0f}, {}, error_spec_); @@ -1312,9 +1402,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowF32s) { XLA_TEST_F(ArrayElementwiseOpTest, PowNonIntegerF32s) { SetFastMathDisabled(true); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({-2.0f, -0.6f, -0.6f, 0.0f}); - auto rhs = builder.ConstantR1({0.5f, 0.6f, -0.6f, -0.6f}); - builder.Pow(lhs, rhs); + auto lhs = ConstantR1(&builder, {-2.0f, -0.6f, -0.6f, 0.0f}); + auto rhs = ConstantR1(&builder, {0.5f, 0.6f, -0.6f, -0.6f}); + Pow(lhs, rhs); ComputeAndCompareR1(&builder, {NAN, NAN, NAN, INFINITY}, {}, error_spec_); @@ -1322,9 +1412,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowNonIntegerF32s) { XLA_TEST_F(ArrayElementwiseOpTest, PowZeroElementF32s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Pow(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Pow(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -1340,10 +1430,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowSpecialF32) { std::unique_ptr param_data = client_->TransferToServer(*param_literal).ConsumeValueOrDie(); - auto sum = b.ConstantR0(0.0f); - auto param = b.Parameter(0, param_literal->shape(), "param"); + auto sum = ConstantR0(&b, 0.0f); + auto param = Parameter(&b, 0, param_literal->shape(), "param"); for (float exponent : exponents) { - sum = b.Add(sum, b.Pow(param, b.ConstantR0(exponent))); + sum = Add(sum, Pow(param, ConstantR0(&b, exponent))); } std::vector expected; @@ -1370,9 +1460,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowOfExpF32) { std::unique_ptr literal1 = Literal::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - b.Pow(b.Exp(param0), param1); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + Pow(Exp(param0), param1); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1395,9 +1485,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, LogOfPowerF32) { std::unique_ptr literal1 = Literal::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - b.Log(b.Pow(param0, param1)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + Log(Pow(param0, param1)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1420,9 +1510,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulOfExpF32) { std::unique_ptr literal1 = Literal::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - b.Mul(b.Exp(param0), b.Exp(param1)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + Mul(Exp(param0), Exp(param1)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1445,9 +1535,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivOfExpF32) { std::unique_ptr literal1 = Literal::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - b.Div(param0, b.Exp(param1)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + Div(param0, Exp(param1)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1476,10 +1566,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div3_lhs_F32) { std::unique_ptr literal2 = Literal::CreateR1(values2); std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - auto param2 = b.Parameter(2, literal2->shape(), "param2"); - b.Div(b.Div(param0, param1), param2); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + auto param2 = Parameter(&b, 2, literal2->shape(), "param2"); + Div(Div(param0, param1), param2); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1509,10 +1599,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div3_rhs_F32) { std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - auto param2 = b.Parameter(2, literal2->shape(), "param2"); - b.Div(param0, b.Div(param1, param2)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + auto param2 = Parameter(&b, 2, literal2->shape(), "param2"); + Div(param0, Div(param1, param2)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1542,10 +1632,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivOfPowerF32) { std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - auto param2 = b.Parameter(2, literal2->shape(), "param2"); - b.Div(param0, b.Pow(param1, param2)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + auto param2 = Parameter(&b, 2, literal2->shape(), "param2"); + Div(param0, Pow(param1, param2)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1580,11 +1670,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div4F32) { std::unique_ptr data3 = client_->TransferToServer(*literal3).ConsumeValueOrDie(); - auto param0 = b.Parameter(0, literal0->shape(), "param0"); - auto param1 = b.Parameter(1, literal1->shape(), "param1"); - auto param2 = b.Parameter(2, literal2->shape(), "param2"); - auto param3 = b.Parameter(3, literal3->shape(), "param2"); - b.Div(b.Div(param0, param1), b.Div(param2, param3)); + auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&b, 1, literal1->shape(), "param1"); + auto param2 = Parameter(&b, 2, literal2->shape(), "param2"); + auto param3 = Parameter(&b, 3, literal3->shape(), "param2"); + Div(Div(param0, param1), Div(param2, param3)); std::vector expected(values0.size()); for (int64 i = 0; i < values0.size(); ++i) { @@ -1604,8 +1694,8 @@ TEST_P(ArrayElementwiseOpTestParamCount, SquareManyValues) { for (int i = 0; i < count; ++i) { values.push_back(i / static_cast(count)); } - auto x = builder.ConstantR1(values); - builder.Pow(x, builder.ConstantR0(2.0f)); + auto x = ConstantR1(&builder, values); + Pow(x, ConstantR0(&builder, 2.0f)); std::vector expected; expected.reserve(values.size()); @@ -1630,8 +1720,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4D) { Array4D expected(2, 2, 2, 2, expected_vector); - auto x = builder.ConstantR4FromArray4D(values); - builder.Pow(x, builder.ConstantR0(2.0f)); + auto x = ConstantR4FromArray4D(&builder, values); + Pow(x, ConstantR0(&builder, 2.0f)); ComputeAndCompareR4(&builder, expected, {}, error_spec_); } @@ -1641,8 +1731,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4DZeroElements) { Array4D values(2, 2, 0, 2); Array4D expected(2, 2, 0, 2); - auto x = builder.ConstantR4FromArray4D(values); - builder.Pow(x, builder.ConstantR0(2.0f)); + auto x = ConstantR4FromArray4D(&builder, values); + Pow(x, ConstantR0(&builder, 2.0f)); ComputeAndCompareR4(&builder, expected, {}, error_spec_); } @@ -1650,9 +1740,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4DZeroElements) { XLA_TEST_F(ArrayElementwiseOpTest, MinF32s) { XlaBuilder builder(TestName()); SetFastMathDisabled(true); - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); - builder.Min(lhs, rhs); + auto lhs = ConstantR1(&builder, {1.0f, 1.0f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {2.0f, -5.0f, 1.0f, 10.0f, NAN}); + Min(lhs, rhs); ComputeAndCompareR1(&builder, {1.0f, -5.0f, 1.0f, NAN, NAN}, {}, error_spec_); @@ -1660,18 +1750,18 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinF32s) { XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Min(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Min(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MinF64s) { XlaBuilder builder(TestName()); SetFastMathDisabled(true); - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); - builder.Min(lhs, rhs); + auto lhs = ConstantR1(&builder, {1.0, 1.0, 2.25, NAN, 6.0}); + auto rhs = ConstantR1(&builder, {2.0, -5.0, 1.0, 10.0, NAN}); + Min(lhs, rhs); ComputeAndCompareR1(&builder, {1.0, -5.0, 1.0, NAN, NAN}, {}, error_spec_); @@ -1680,9 +1770,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinF64s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxF32s) { XlaBuilder builder(TestName()); SetFastMathDisabled(true); - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); - builder.Max(lhs, rhs); + auto lhs = ConstantR1(&builder, {1.0f, 1.0f, 2.25f, NAN, 6.0f}); + auto rhs = ConstantR1(&builder, {2.0f, -5.0f, 1.0f, 10.0f, NAN}); + Max(lhs, rhs); ComputeAndCompareR1(&builder, {2.0f, 1.0f, 2.25f, NAN, NAN}, {}, error_spec_); @@ -1690,18 +1780,18 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxF32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - builder.Max(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Max(lhs, rhs); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MaxF64s) { XlaBuilder builder(TestName()); SetFastMathDisabled(true); - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); - builder.Max(lhs, rhs); + auto lhs = ConstantR1(&builder, {1.0, 1.0, 2.25, NAN, 6.0}); + auto rhs = ConstantR1(&builder, {2.0, -5.0, 1.0, 10.0, NAN}); + Max(lhs, rhs); ComputeAndCompareR1(&builder, {2.0, 1.0, 2.25, NAN, NAN}, {}, error_spec_); @@ -1711,11 +1801,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {min, min, min, -1, -1, 0, 0, 0, 1, 1, max, max, max}); - auto y = builder.ConstantR1( - {min, max, 0, -10, 0, -1, 0, 1, 0, 10, 0, max, min}); - builder.Max(x, y); + auto x = ConstantR1( + &builder, {min, min, min, -1, -1, 0, 0, 0, 1, 1, max, max, max}); + auto y = ConstantR1( + &builder, {min, max, 0, -10, 0, -1, 0, 1, 0, 10, 0, max, min}); + Max(x, y); std::vector expected = {min, max, 0, -1, 0, 0, 0, 1, 1, 10, max, max, max}; @@ -1726,11 +1816,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinS32s) { const int32 min = std::numeric_limits::min(); const int32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {min, min, min, -1, -1, 0, 0, 0, 1, 1, max, max, max}); - auto y = builder.ConstantR1( - {min, max, 0, -10, 0, -1, 0, 1, 0, 10, 0, max, min}); - builder.Min(x, y); + auto x = ConstantR1( + &builder, {min, min, min, -1, -1, 0, 0, 0, 1, 1, max, max, max}); + auto y = ConstantR1( + &builder, {min, max, 0, -10, 0, -1, 0, 1, 0, 10, 0, max, min}); + Min(x, y); std::vector expected = {min, min, min, -10, -1, -1, 0, 0, 0, 1, 0, max, min}; @@ -1740,9 +1830,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinS32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0, 0, 1, 1, 1, max, max, max}); - auto y = builder.ConstantR1({0, 1, 0, 1, 10, 0, 234234, max}); - builder.Max(x, y); + auto x = ConstantR1(&builder, {0, 0, 1, 1, 1, max, max, max}); + auto y = ConstantR1(&builder, {0, 1, 0, 1, 10, 0, 234234, max}); + Max(x, y); std::vector expected = {0, 1, 1, 1, 10, max, max, max}; ComputeAndCompareR1(&builder, expected, {}); @@ -1751,9 +1841,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxU32s) { XLA_TEST_F(ArrayElementwiseOpTest, MinU32s) { const uint32 max = std::numeric_limits::max(); XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0, 0, 1, 1, 1, max, max, max}); - auto y = builder.ConstantR1({0, 1, 0, 1, 10, 0, 234234, max}); - builder.Min(x, y); + auto x = ConstantR1(&builder, {0, 0, 1, 1, 1, max, max, max}); + auto y = ConstantR1(&builder, {0, 1, 0, 1, 10, 0, 234234, max}); + Min(x, y); std::vector expected = {0, 0, 0, 1, 1, 0, 234234, max}; ComputeAndCompareR1(&builder, expected, {}); @@ -1761,11 +1851,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinU32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxTenF32s) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}); - auto y = builder.ConstantR1( - {-0.0, -1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0, -9.0}); - builder.Max(x, y); + auto x = ConstantR1( + &builder, {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}); + auto y = ConstantR1( + &builder, {-0.0, -1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0, -9.0}); + Max(x, y); std::vector expected = {-0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0}; @@ -1774,9 +1864,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxTenF32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxR1S1AndR1S0F32s) { XlaBuilder builder(TestName()); - auto u = builder.ConstantR1({3.5}); - auto v = builder.ConstantR1({}); - builder.Max(u, v); + auto u = ConstantR1(&builder, {3.5}); + auto v = ConstantR1(&builder, {}); + Max(u, v); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -1784,9 +1874,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxR1S1AndR1S0F32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxR1S0AndR2S0x2F32s) { for (int broadcast_dim : {0, 1}) { XlaBuilder builder(TestName()); - auto u = builder.ConstantR1({3.5}); - auto v = builder.ConstantR2FromArray2D(Array2D(0, 2)); - builder.Max(u, v, /*broadcast_dimensions=*/{broadcast_dim}); + auto u = ConstantR1(&builder, {3.5}); + auto v = ConstantR2FromArray2D(&builder, Array2D(0, 2)); + Max(u, v, /*broadcast_dimensions=*/{broadcast_dim}); ComputeAndCompareR2(&builder, Array2D(0, 2), {}, error_spec_); } @@ -1794,10 +1884,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxR1S0AndR2S0x2F32s) { XLA_TEST_F(ArrayElementwiseOpTest, Max1DAnd2DF32s) { XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - auto m = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - builder.Max(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + auto m = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + Max(v, m, /*broadcast_dimensions=*/{1}); Array2D expected({{2.0f, 3.14f, 4.0f}, {2.25f, 3.0f, 4.0f}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -1805,9 +1895,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Max1DAnd2DF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Max1DAnd2DZeroElementF32s) { XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({}); - auto m = builder.ConstantR2({{}, {}}); - builder.Max(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {}); + auto m = ConstantR2(&builder, {{}, {}}); + Max(v, m, /*broadcast_dimensions=*/{1}); Array2D expected({{}, {}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -1815,10 +1905,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Max1DAnd2DZeroElementF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Max3DAndScalarS32s) { XlaBuilder builder(TestName()); - auto scalar = builder.ConstantR0(2); + auto scalar = ConstantR0(&builder, 2); Array3D a_3d({{{3, 9, -1}, {2, -10, 3}}, {{-2, 2, 8}, {12, 10, 4}}}); - auto array = builder.ConstantR3FromArray3D(a_3d); - builder.Max(array, scalar, /*broadcast_dimensions=*/{}); + auto array = ConstantR3FromArray3D(&builder, a_3d); + Max(array, scalar, /*broadcast_dimensions=*/{}); Array3D expected({{{3, 9, 2}, {2, 2, 3}}, {{2, 2, 8}, {12, 10, 4}}}); ComputeAndCompareR3(&builder, expected, {}); @@ -1826,10 +1916,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Max3DAndScalarS32s) { XLA_TEST_F(ArrayElementwiseOpTest, Max3DAndScalarZeroElementS32s) { XlaBuilder builder(TestName()); - auto scalar = builder.ConstantR0(2); + auto scalar = ConstantR0(&builder, 2); Array3D a_3d(2, 0, 3); - auto array = builder.ConstantR3FromArray3D(a_3d); - builder.Max(array, scalar, /*broadcast_dimensions=*/{}); + auto array = ConstantR3FromArray3D(&builder, a_3d); + Max(array, scalar, /*broadcast_dimensions=*/{}); Array3D expected(2, 0, 3); ComputeAndCompareR3(&builder, expected, {}); @@ -1837,10 +1927,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Max3DAndScalarZeroElementS32s) { XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo1DF32s) { XlaBuilder builder(TestName()); - auto m = - builder.ConstantR2({{-10.4f, 64.0f, 6.0f}, {0.1f, 32.0f, 16.1f}}); - auto v = builder.ConstantR1({-10.2f, 16.4f}); - builder.Min(m, v, /*broadcast_dimensions=*/{0}); + auto m = ConstantR2(&builder, + {{-10.4f, 64.0f, 6.0f}, {0.1f, 32.0f, 16.1f}}); + auto v = ConstantR1(&builder, {-10.2f, 16.4f}); + Min(m, v, /*broadcast_dimensions=*/{0}); Array2D expected({{-10.4f, -10.2f, -10.2f}, {0.1f, 16.4f, 16.1f}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -1848,9 +1938,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo1DF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo1DZeroElementF32s) { XlaBuilder builder(TestName()); - auto m = builder.ConstantR2({{}, {}}); - auto v = builder.ConstantR1({-10.2f, 16.4f}); - builder.Min(m, v, /*broadcast_dimensions=*/{0}); + auto m = ConstantR2(&builder, {{}, {}}); + auto v = ConstantR1(&builder, {-10.2f, 16.4f}); + Min(m, v, /*broadcast_dimensions=*/{0}); Array2D expected({{}, {}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -1859,11 +1949,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo1DZeroElementF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo4DF32s) { XlaBuilder builder(TestName()); auto array2d = - builder.ConstantR2({{-12.2f, 64.3f, 6.1f}, {0.0f, 32.2f, 2.5f}}); - auto array4d = builder.ConstantR4FromArray4D( - {{{{-12.1f, 32.3f, 6.2f}}, {{0.0f, 32.5f, 3.0f}}}, - {{{-2.5f, 64.29f, 6.5f}}, {{-0.01f, 32.25f, 2.6f}}}}); - builder.Min(array2d, array4d, /*broadcast_dimensions=*/{1, 3}); + ConstantR2(&builder, {{-12.2f, 64.3f, 6.1f}, {0.0f, 32.2f, 2.5f}}); + auto array4d = ConstantR4FromArray4D( + &builder, {{{{-12.1f, 32.3f, 6.2f}}, {{0.0f, 32.5f, 3.0f}}}, + {{{-2.5f, 64.29f, 6.5f}}, {{-0.01f, 32.25f, 2.6f}}}}); + Min(array2d, array4d, /*broadcast_dimensions=*/{1, 3}); Array4D expected( {{{{-12.2f, 32.3f, 6.1f}}, {{0.0f, 32.2f, 2.5f}}}, @@ -1874,10 +1964,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo4DF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo4DZeroElementF32s) { XlaBuilder builder(TestName()); auto array2d = - builder.ConstantR2({{-12.2f, 64.3f, 6.1f}, {0.0f, 32.2f, 2.5f}}); + ConstantR2(&builder, {{-12.2f, 64.3f, 6.1f}, {0.0f, 32.2f, 2.5f}}); Array4D arg(2, 2, 0, 3); - auto array4d = builder.ConstantR4FromArray4D(arg); - builder.Min(array2d, array4d, /*broadcast_dimensions=*/{1, 3}); + auto array4d = ConstantR4FromArray4D(&builder, arg); + Min(array2d, array4d, /*broadcast_dimensions=*/{1, 3}); Array4D expected(2, 2, 0, 3); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -1885,9 +1975,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Min2DTo4DZeroElementF32s) { XLA_TEST_F(ArrayElementwiseOpTest, MinTenS32s) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); - auto y = builder.ConstantR1({9, 8, 7, 6, 5, 4, 3, 2, 1, 0}); - builder.Min(x, y); + auto x = ConstantR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + auto y = ConstantR1(&builder, {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}); + Min(x, y); std::vector expected = {0, 1, 2, 3, 4, 4, 3, 2, 1, 0}; ComputeAndCompareR1(&builder, expected, {}); @@ -1895,9 +1985,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinTenS32s) { XLA_TEST_F(ArrayElementwiseOpTest, MaxTenS32s) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); - auto y = builder.ConstantR1({9, 8, 7, 6, 5, 4, 3, 2, 1, 0}); - builder.Max(x, y); + auto x = ConstantR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + auto y = ConstantR1(&builder, {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}); + Max(x, y); std::vector expected = {9, 8, 7, 6, 5, 5, 6, 7, 8, 9}; ComputeAndCompareR1(&builder, expected, {}); @@ -1905,19 +1995,20 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxTenS32s) { XLA_TEST_F(ArrayElementwiseOpTest, RemTwoConstantS32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-3, 26, 2, -1, 1}); - auto b = builder.ConstantR1({10, 5, 1, 10, -10}); - builder.Rem(a, b); + auto a = ConstantR1(&builder, {-3, 26, 2, -1, 1}); + auto b = ConstantR1(&builder, {10, 5, 1, 10, -10}); + Rem(a, b); ComputeAndCompareR1(&builder, {-3, 1, 0, -1, 1}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, NonNanClampF32) { XlaBuilder builder(TestName()); - auto minimum = builder.ConstantR1({1.0f, -6.5f, 1.0f, 2.25f, 0.0f}); - auto argument = builder.ConstantR1({2.0f, 10.0f, -5.0f, 1.0f, 10.0f}); - auto maximum = builder.ConstantR1({3.0f, 0.5f, 25.5f, 5.0f, 123.0}); - builder.Clamp(minimum, argument, maximum); + auto minimum = ConstantR1(&builder, {1.0f, -6.5f, 1.0f, 2.25f, 0.0f}); + auto argument = + ConstantR1(&builder, {2.0f, 10.0f, -5.0f, 1.0f, 10.0f}); + auto maximum = ConstantR1(&builder, {3.0f, 0.5f, 25.5f, 5.0f, 123.0}); + Clamp(minimum, argument, maximum); ComputeAndCompareR1(&builder, {2.0f, 0.5f, 1.0f, 2.25f, 10.0f}, {}, error_spec_); @@ -1925,10 +2016,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, NonNanClampF32) { XLA_TEST_F(ArrayElementwiseOpTest, ClampF32Scalar) { XlaBuilder builder(TestName()); - auto minimum = builder.ConstantR0(0.0f); - auto argument = builder.ConstantR1({2.0f, 10.0f, -5.0f, 1.0f, 4.0f}); - auto maximum = builder.ConstantR0(5.0f); - builder.Clamp(minimum, argument, maximum); + auto minimum = ConstantR0(&builder, 0.0f); + auto argument = ConstantR1(&builder, {2.0f, 10.0f, -5.0f, 1.0f, 4.0f}); + auto maximum = ConstantR0(&builder, 5.0f); + Clamp(minimum, argument, maximum); ComputeAndCompareR1(&builder, {2.0f, 5.0f, 0.0f, 1.0f, 4.0f}, {}, error_spec_); @@ -1936,16 +2027,19 @@ XLA_TEST_F(ArrayElementwiseOpTest, ClampF32Scalar) { XLA_TEST_F(ArrayElementwiseOpTest, ClampF32ScalarVector) { XlaBuilder builder(TestName()); - auto min_scalar = builder.ConstantR0(0.0f); - auto min_vector = builder.ConstantR1({1.0f, -6.5f, 1.0f, 2.25f, 0.0f}); - auto arg_vector = builder.ConstantR1({2.0f, 10.0f, -5.0f, 1.0f, 4.0f}); - auto max_scalar = builder.ConstantR0(3.0f); - auto max_vector = builder.ConstantR1({3.0f, 0.5f, 25.5f, 5.0f, 123.0}); + auto min_scalar = ConstantR0(&builder, 0.0f); + auto min_vector = + ConstantR1(&builder, {1.0f, -6.5f, 1.0f, 2.25f, 0.0f}); + auto arg_vector = + ConstantR1(&builder, {2.0f, 10.0f, -5.0f, 1.0f, 4.0f}); + auto max_scalar = ConstantR0(&builder, 3.0f); + auto max_vector = + ConstantR1(&builder, {3.0f, 0.5f, 25.5f, 5.0f, 123.0}); // Perform clamp with broadcasted scalar and vector. - builder.Add(builder.Add(builder.Clamp(min_vector, arg_vector, max_scalar), - builder.Clamp(min_scalar, arg_vector, max_vector)), - builder.Add(builder.Clamp(min_vector, arg_vector, max_vector), - builder.Clamp(min_scalar, arg_vector, max_scalar))); + Add(Add(Clamp(min_vector, arg_vector, max_scalar), + Clamp(min_scalar, arg_vector, max_vector)), + Add(Clamp(min_vector, arg_vector, max_vector), + Clamp(min_scalar, arg_vector, max_scalar))); ComputeAndCompareR1(&builder, {8.0f, 7.0f, 2.0f, 6.5f, 14.0f}, {}, error_spec_); @@ -1953,52 +2047,52 @@ XLA_TEST_F(ArrayElementwiseOpTest, ClampF32ScalarVector) { XLA_TEST_F(ArrayElementwiseOpTest, ClampS32Vector) { XlaBuilder builder(TestName()); - auto min_vector = builder.ConstantR1({1, -6, 1, 2, 0, -5}); - auto arg_vector = builder.ConstantR1({2, 10, -5, 1, 4, 10}); - auto max_vector = builder.ConstantR1({3, 0, 25, 5, 123, -1}); - builder.Clamp(min_vector, arg_vector, max_vector); + auto min_vector = ConstantR1(&builder, {1, -6, 1, 2, 0, -5}); + auto arg_vector = ConstantR1(&builder, {2, 10, -5, 1, 4, 10}); + auto max_vector = ConstantR1(&builder, {3, 0, 25, 5, 123, -1}); + Clamp(min_vector, arg_vector, max_vector); ComputeAndCompareR1(&builder, {2, 0, 1, 2, 4, -1}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ClampS32ScalarVector) { XlaBuilder builder(TestName()); - auto min_scalar = builder.ConstantR0(0); - auto min_vector = builder.ConstantR1({1, -6, 1, 2, 0}); - auto arg_vector = builder.ConstantR1({2, 10, -5, 1, 4}); - auto max_scalar = builder.ConstantR0(3); - auto max_vector = builder.ConstantR1({3, 1, 25, 5, 123}); + auto min_scalar = ConstantR0(&builder, 0); + auto min_vector = ConstantR1(&builder, {1, -6, 1, 2, 0}); + auto arg_vector = ConstantR1(&builder, {2, 10, -5, 1, 4}); + auto max_scalar = ConstantR0(&builder, 3); + auto max_vector = ConstantR1(&builder, {3, 1, 25, 5, 123}); // Perform clamp with broadcasted scalar and vector. - builder.Add(builder.Add(builder.Clamp(min_vector, arg_vector, max_scalar), - builder.Clamp(min_scalar, arg_vector, max_vector)), - builder.Add(builder.Clamp(min_vector, arg_vector, max_vector), - builder.Clamp(min_scalar, arg_vector, max_scalar))); + Add(Add(Clamp(min_vector, arg_vector, max_scalar), + Clamp(min_scalar, arg_vector, max_vector)), + Add(Clamp(min_vector, arg_vector, max_vector), + Clamp(min_scalar, arg_vector, max_scalar))); ComputeAndCompareR1(&builder, {8, 8, 2, 6, 14}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ClampU32Vector) { XlaBuilder builder(TestName()); - auto min_vector = builder.ConstantR1({1, 2, 1, 2, 0, ~0u - 4}); - auto arg_vector = builder.ConstantR1({2, 10, 5, 1, 4, 10}); - auto max_vector = builder.ConstantR1({3, 5, 25, 5, 123, ~0u}); - builder.Clamp(min_vector, arg_vector, max_vector); + auto min_vector = ConstantR1(&builder, {1, 2, 1, 2, 0, ~0u - 4}); + auto arg_vector = ConstantR1(&builder, {2, 10, 5, 1, 4, 10}); + auto max_vector = ConstantR1(&builder, {3, 5, 25, 5, 123, ~0u}); + Clamp(min_vector, arg_vector, max_vector); ComputeAndCompareR1(&builder, {2, 5, 5, 2, 4, ~0u - 4}, {}); } XLA_TEST_F(ArrayElementwiseOpTest, ClampU32ScalarVector) { XlaBuilder builder(TestName()); - auto min_scalar = builder.ConstantR0(0); - auto min_vector = builder.ConstantR1({1, 0, 1, 2, 0}); - auto arg_vector = builder.ConstantR1({2, 10, 0, 1, 4}); - auto max_scalar = builder.ConstantR0(3); - auto max_vector = builder.ConstantR1({3, 1, 25, 5, 123}); + auto min_scalar = ConstantR0(&builder, 0); + auto min_vector = ConstantR1(&builder, {1, 0, 1, 2, 0}); + auto arg_vector = ConstantR1(&builder, {2, 10, 0, 1, 4}); + auto max_scalar = ConstantR0(&builder, 3); + auto max_vector = ConstantR1(&builder, {3, 1, 25, 5, 123}); // Perform clamp with broadcasted scalar and vector. - builder.Add(builder.Add(builder.Clamp(min_vector, arg_vector, max_scalar), - builder.Clamp(min_scalar, arg_vector, max_vector)), - builder.Add(builder.Clamp(min_vector, arg_vector, max_vector), - builder.Clamp(min_scalar, arg_vector, max_scalar))); + Add(Add(Clamp(min_vector, arg_vector, max_scalar), + Clamp(min_scalar, arg_vector, max_vector)), + Add(Clamp(min_vector, arg_vector, max_vector), + Clamp(min_scalar, arg_vector, max_scalar))); ComputeAndCompareR1(&builder, {8, 8, 2, 6, 14}, {}); } @@ -2016,9 +2110,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersF32s) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto p0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto p1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Add(p0, p1); + auto p0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto p1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Add(p0, p1); ComputeAndCompareR1(&builder, {8.3f, 4.5f, 6.7f, 11.1f}, {param0_data.get(), param1_data.get()}, @@ -2038,9 +2132,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersZeroElementF32s) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto p0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto p1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Add(p0, p1); + auto p0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto p1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Add(p0, p1); Array3D expected(0, 7, 0); ComputeAndCompareR3( @@ -2055,9 +2149,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddParameterToConstantF32s) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto a = builder.ConstantR1({1.1f, 2.2f, 3.3f, 4.4f}); - auto p = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Add(a, p); + auto a = ConstantR1(&builder, {1.1f, 2.2f, 3.3f, 4.4f}); + auto p = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Add(a, p); ComputeAndCompareR1(&builder, {2.2f, 4.4f, 6.6f, 9.9f}, {param0_data.get()}, error_spec_); @@ -2065,8 +2159,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddParameterToConstantF32s) { XLA_TEST_F(ArrayElementwiseOpTest, CosF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({3.14159f, 0.0f, 1.570796f, -0.78539f}); - builder.Cos(a); + auto a = ConstantR1(&builder, {3.14159f, 0.0f, 1.570796f, -0.78539f}); + Cos(a); ComputeAndCompareR1(&builder, {-1.0f, 1.0f, 0.0f, 0.707107f}, {}, error_spec_); @@ -2074,8 +2168,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, CosF32s) { XLA_TEST_F(ArrayElementwiseOpTest, SinF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({3.14159f, 0.0f, 1.570796f, -0.78539f}); - builder.Sin(a); + auto a = ConstantR1(&builder, {3.14159f, 0.0f, 1.570796f, -0.78539f}); + Sin(a); ComputeAndCompareR1(&builder, {0.0f, 0.0f, 1.0f, -0.707107f}, {}, error_spec_); @@ -2083,9 +2177,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, SinF32s) { XLA_TEST_F(ArrayElementwiseOpTest, Atan2F32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({0.0f, 5.0f, 0.0f, -3.0f, 2.0f, -8.0f}); - auto b = builder.ConstantR1({6.0f, 0.0f, -4.0f, 0.0f, 2.0f, 8.0f}); - builder.Atan2(a, b); + auto a = ConstantR1(&builder, {0.0f, 5.0f, 0.0f, -3.0f, 2.0f, -8.0f}); + auto b = ConstantR1(&builder, {6.0f, 0.0f, -4.0f, 0.0f, 2.0f, 8.0f}); + Atan2(a, b); ComputeAndCompareR1( &builder, @@ -2095,8 +2189,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, Atan2F32s) { XLA_TEST_F(ArrayElementwiseOpTest, TanhF32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({-2.5f, 3.14f, 2.25f}); - builder.Tanh(a); + auto a = ConstantR1(&builder, {-2.5f, 3.14f, 2.25f}); + Tanh(a); ComputeAndCompareR1(&builder, {-0.986614f, 0.996260f, 0.978026}, {}, error_spec_); @@ -2118,8 +2212,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, TanhF32sVector) { TF_ASSERT_OK_AND_ASSIGN(auto input_data, client_->TransferToServer(*input_literal)); - auto input = builder.Parameter(0, input_literal->shape(), "input"); - builder.Tanh(input); + auto input = Parameter(&builder, 0, input_literal->shape(), "input"); + Tanh(input); ComputeAndCompareR1( &builder, @@ -2164,8 +2258,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, ExpF32sVector) { TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, client_->TransferToServer(*input_literal)); - auto input = builder.Parameter(0, input_literal->shape(), "input"); - builder.Exp(input); + auto input = Parameter(&builder, 0, input_literal->shape(), "input"); + Exp(input); std::vector expected_result; int64 input_size = input_literal->shape().dimensions(0); @@ -2202,8 +2296,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, LogF32sVector) { TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, client_->TransferToServer(*input_literal)); - auto input = builder.Parameter(0, input_literal->shape(), "input"); - builder.Log(input); + auto input = Parameter(&builder, 0, input_literal->shape(), "input"); + Log(input); std::vector expected_result; int64 input_size = input_literal->shape().dimensions(0); @@ -2218,9 +2312,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, LogF32sVector) { XLA_TEST_F(ArrayElementwiseOpTest, ClzU32s) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {0, 1, 0x10, 0x10000, 0x700000, 0x12345678, 0xF2345678}); - builder.Clz(a); + auto a = ConstantR1( + &builder, {0, 1, 0x10, 0x10000, 0x700000, 0x12345678, 0xF2345678}); + Clz(a); ComputeAndCompareR1(&builder, {32, 31, 27, 15, 9, 3, 0}, {}); } @@ -2228,8 +2322,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, ClzU32s) { XLA_TEST_F(ArrayElementwiseOpTest, ClzS64s) { XlaBuilder builder(TestName()); auto a = - builder.ConstantR1({0, 1, 0x80000000, 0x7FFFFFFFF2345678ul, -1}); - builder.Clz(a); + ConstantR1(&builder, {0, 1, 0x80000000, 0x7FFFFFFFF2345678ul, -1}); + Clz(a); ComputeAndCompareR1(&builder, {64, 63, 32, 1, 0}, {}); } @@ -2241,12 +2335,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddChainFoldLeft) { // c---------------------/ XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({1.1f, 2.2f, 3.3f, 4.4f}); - auto b = builder.ConstantR1({2.1f, 3.2f, 4.3f, 5.4f}); - auto c = builder.ConstantR1({-3.3f, -15.5f, -7.7f, -29.9f}); + auto a = ConstantR1(&builder, {1.1f, 2.2f, 3.3f, 4.4f}); + auto b = ConstantR1(&builder, {2.1f, 3.2f, 4.3f, 5.4f}); + auto c = ConstantR1(&builder, {-3.3f, -15.5f, -7.7f, -29.9f}); - auto add = builder.Add(a, b); - builder.Add(add, c); + auto add = Add(a, b); + Add(add, c); ComputeAndCompareR1(&builder, {-0.1f, -10.1f, -0.1f, -20.1f}, {}, error_spec_); @@ -2259,12 +2353,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddChainFoldRight) { // a---------------------/ XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({91.1f, 2.2f, 3.3f, 4.4f}); - auto b = builder.ConstantR1({2.1f, 3.2f, 4.3f, 5.4f}); - auto c = builder.ConstantR1({-3.3f, -15.5f, -7.7f, -29.9f}); + auto a = ConstantR1(&builder, {91.1f, 2.2f, 3.3f, 4.4f}); + auto b = ConstantR1(&builder, {2.1f, 3.2f, 4.3f, 5.4f}); + auto c = ConstantR1(&builder, {-3.3f, -15.5f, -7.7f, -29.9f}); - auto add = builder.Add(b, c); - builder.Add(a, add); + auto add = Add(b, c); + Add(a, add); ComputeAndCompareR1(&builder, {89.9f, -10.1f, -0.1f, -20.1f}, {}, error_spec_); @@ -2276,12 +2370,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddWithNeg) { // b ----- (neg) ----/ XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({91.1f, 2.2f, 3.3f, 4.4f}); - auto b = builder.ConstantR1({2.1f, 3.2f, 4.3f, 5.4f}); + auto a = ConstantR1(&builder, {91.1f, 2.2f, 3.3f, 4.4f}); + auto b = ConstantR1(&builder, {2.1f, 3.2f, 4.3f, 5.4f}); - auto neg_a = builder.Neg(a); - auto neg_b = builder.Neg(b); - builder.Add(neg_a, neg_b); + auto neg_a = Neg(a); + auto neg_b = Neg(b); + Add(neg_a, neg_b); ComputeAndCompareR1(&builder, {-93.2f, -5.4f, -7.6f, -9.8f}, {}, error_spec_); @@ -2297,14 +2391,14 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddChainTwoSide) { // d -----/ XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({91.1f, 2.2f, 3.3f, 4.4f}); - auto b = builder.ConstantR1({2.1f, 3.2f, 4.3f, 5.4f}); - auto c = builder.ConstantR1({-3.3f, -15.5f, -7.7f, -29.9f}); - auto d = builder.ConstantR1({-19.0f, 10.0f, -40.0f, 20.2f}); + auto a = ConstantR1(&builder, {91.1f, 2.2f, 3.3f, 4.4f}); + auto b = ConstantR1(&builder, {2.1f, 3.2f, 4.3f, 5.4f}); + auto c = ConstantR1(&builder, {-3.3f, -15.5f, -7.7f, -29.9f}); + auto d = ConstantR1(&builder, {-19.0f, 10.0f, -40.0f, 20.2f}); - auto add_ab = builder.Add(a, b); - auto add_cd = builder.Add(c, d); - builder.Add(add_ab, add_cd); + auto add_ab = Add(a, b); + auto add_cd = Add(c, d); + Add(add_ab, add_cd); ComputeAndCompareR1(&builder, {70.9f, -0.1f, -40.1f, 0.1f}, {}, error_spec_); @@ -2312,11 +2406,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddChainTwoSide) { XLA_TEST_F(ArrayElementwiseOpTest, 2DBinaryOpF32s) { XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto b = - builder.ConstantR2({{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); - builder.Add(a, b); + auto a = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto b = ConstantR2(&builder, + {{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); + Add(a, b); Array2D expected_array( {{-4.0f, 11.28f, 43.0f}, {1.25f, -14.0f, 8.88f}}); @@ -2326,10 +2420,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, 2DBinaryOpF32s) { XLA_TEST_F(ArrayElementwiseOpTest, ScalarPlus2DF32) { // Add a scalar + matrix. XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto scalar = builder.ConstantR0(3.0f); - builder.Add(scalar, a); + auto a = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto scalar = ConstantR0(&builder, 3.0f); + Add(scalar, a); Array2D expected_array({{0.5f, 6.14f, 4.0f}, {5.25f, -7.0f, 6.33f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); @@ -2338,10 +2432,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, ScalarPlus2DF32) { XLA_TEST_F(ArrayElementwiseOpTest, 2DPlusScalarF32) { // Add a matrix + scalar. XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto scalar = builder.ConstantR0(3.0f); - builder.Add(a, scalar); + auto a = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto scalar = ConstantR0(&builder, 3.0f); + Add(a, scalar); Array2D expected_array({{0.5f, 6.14f, 4.0f}, {5.25f, -7.0f, 6.33f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); @@ -2351,13 +2445,13 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo2DF32) { // Test simple broadcasting of a R1F32 over R2F32. The vector's size matches // only dim 0 of the matrix. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({20.0f, 40.0f, 60.0f}); + auto v = ConstantR1(&builder, {20.0f, 40.0f, 60.0f}); // clang-format off - auto m = builder.ConstantR2({ + auto m = ConstantR2(&builder, { {-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); // clang-format on - builder.Add(v, m, /*broadcast_dimensions=*/{1}); + Add(v, m, /*broadcast_dimensions=*/{1}); Array2D expected_array( {{17.5f, 43.14f, 61.0f}, {22.25f, 30.0f, 63.33f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); @@ -2366,14 +2460,14 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo2DF32) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Eq) { // Test broadcasting in Eq comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({42, 73}); - auto m = builder.ConstantR2({{42, 73}, {42, 52}}); + auto v = ConstantR1(&builder, {42, 73}); + auto m = ConstantR2(&builder, {{42, 73}, {42, 52}}); // This test exercises both possible broadcast dimensions for a vector/matrix // comparison. - auto cmp_dim_0 = builder.Eq(v, m, /*broadcast_dimensions=*/{1}); - auto cmp_dim_1 = builder.Eq(v, m, /*broadcast_dimensions=*/{0}); - auto result = builder.Tuple({cmp_dim_0, cmp_dim_1}); + auto cmp_dim_0 = Eq(v, m, /*broadcast_dimensions=*/{1}); + auto cmp_dim_1 = Eq(v, m, /*broadcast_dimensions=*/{0}); + Tuple(&builder, {cmp_dim_0, cmp_dim_1}); auto expected = Literal::MakeTuple( {Literal::CreateR2({{true, true}, {true, false}}).get(), @@ -2384,9 +2478,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Eq) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Ne) { // Test broadcasting in Ne comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({42, 73}); - auto m = builder.ConstantR2({{42, 73}, {42, 52}}); - builder.Ne(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {42, 73}); + auto m = ConstantR2(&builder, {{42, 73}, {42, 52}}); + Ne(v, m, /*broadcast_dimensions=*/{1}); const string expected = R"(pred[2,2] { { 00 }, @@ -2398,9 +2492,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Ne) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Ge) { // Test broadcasting in Ge comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1, 2, 3, 4}); - auto m = builder.ConstantR2({{1, 0, 5, 6}, {42, 52, 10, 4}}); - builder.Ge(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {1, 2, 3, 4}); + auto m = ConstantR2(&builder, {{1, 0, 5, 6}, {42, 52, 10, 4}}); + Ge(v, m, /*broadcast_dimensions=*/{1}); const string expected = R"(pred[2,4] { { 1100 }, @@ -2412,9 +2506,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Ge) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Gt) { // Test broadcasting in Gt comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1, 2, 3, 4}); - auto m = builder.ConstantR2({{1, 0, 5, 6}, {42, 52, 10, 4}}); - builder.Gt(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {1, 2, 3, 4}); + auto m = ConstantR2(&builder, {{1, 0, 5, 6}, {42, 52, 10, 4}}); + Gt(v, m, /*broadcast_dimensions=*/{1}); const string expected = R"(pred[2,4] { { 0100 }, @@ -2426,9 +2520,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Gt) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Le) { // Test broadcasting in Le comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1, 2, 3, 4}); - auto m = builder.ConstantR2({{1, 0, 5, 6}, {42, 52, 10, 4}}); - builder.Le(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {1, 2, 3, 4}); + auto m = ConstantR2(&builder, {{1, 0, 5, 6}, {42, 52, 10, 4}}); + Le(v, m, /*broadcast_dimensions=*/{1}); const string expected = R"(pred[2,4] { { 1011 }, @@ -2440,9 +2534,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Le) { XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Lt) { // Test broadcasting in Lt comparison. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1, 2, 3, 4}); - auto m = builder.ConstantR2({{1, 0, 5, 6}, {42, 52, 10, 4}}); - builder.Lt(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {1, 2, 3, 4}); + auto m = ConstantR2(&builder, {{1, 0, 5, 6}, {42, 52, 10, 4}}); + Lt(v, m, /*broadcast_dimensions=*/{1}); const string expected = R"(pred[2,4] { { 0011 }, @@ -2455,9 +2549,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Mul2Dby1DF32) { // Test simple broadcasting of a R1F32 over R2F32 when the order of binary op // arguments is reversed. XlaBuilder builder(TestName()); - auto m = builder.ConstantR2({{1.5f, 2.5f, 3.5f}, {4.5f, 5.5f, 6.5f}}); - auto v = builder.ConstantR1({2.0f, 4.0f, 6.0f}); - builder.Mul(m, v, /*broadcast_dimensions=*/{1}); + auto m = + ConstantR2(&builder, {{1.5f, 2.5f, 3.5f}, {4.5f, 5.5f, 6.5f}}); + auto v = ConstantR1(&builder, {2.0f, 4.0f, 6.0f}); + Mul(m, v, /*broadcast_dimensions=*/{1}); Array2D expected_array({{3.0f, 10.0f, 21.0f}, {9.0f, 22.0f, 39.0f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); } @@ -2468,10 +2563,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add2DTo2DWithDegenerateDim1) { // m's shape in XLA notation is {3, 2} // md's shape in XLA notation is {3, 1} // The result has shape {3, 2}, where md is broadcast over m - auto m = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto md = builder.ConstantR2({{10.0f, 20.0f, 30.0f}}); - builder.Add(m, md); + auto m = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto md = ConstantR2(&builder, {{10.0f, 20.0f, 30.0f}}); + Add(m, md); Array2D expected_array( {{7.5f, 23.14f, 31.0f}, {12.25f, 10.0f, 33.33f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); @@ -2483,10 +2578,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add2DTo2DWithDegenerateDim0) { // m's shape in XLA notation is {3, 2} // md's shape in XLA notation is {1, 2} // The result has shape {3, 2}, where md is broadcast over m - auto m = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto md = builder.ConstantR2({{10.0f}, {20.0f}}); - builder.Add(m, md); + auto m = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto md = ConstantR2(&builder, {{10.0f}, {20.0f}}); + Add(m, md); Array2D expected_array( {{7.5f, 13.14f, 11.0f}, {22.25f, 10.0f, 23.33f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); @@ -2501,9 +2596,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add2DsWithDegenerateDimsOuterProduct) { // a's shape in XLA notation is {1, 4} // b's shape in XLA notation is {3, 1} // The result has shape {3, 4}. - auto a = builder.ConstantR2({{0.0f}, {10.0f}, {20.0f}, {30.0f}}); - auto b = builder.ConstantR2({{1.0f, 2.0f, 3.0f}}); - builder.Add(a, b); + auto a = ConstantR2(&builder, {{0.0f}, {10.0f}, {20.0f}, {30.0f}}); + auto b = ConstantR2(&builder, {{1.0f, 2.0f, 3.0f}}); + Add(a, b); Array2D expected_array({{1.0f, 2.0f, 3.0f}, {11.0f, 12.0f, 13.0f}, {21.0f, 22.0f, 23.0f}, @@ -2515,9 +2610,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo2DF32TwoWaysOver1) { // Add together a (2,2) array and a (2) array, using dimension 0 for // broadcasting (though there are two ways to broadcast these shapes). XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({20.0f, 40.0f}); - auto m = builder.ConstantR2({{10.0f, 50.0f}, {77.0f, 88.0f}}); - builder.Add(v, m, /*broadcast_dimensions=*/{1}); + auto v = ConstantR1(&builder, {20.0f, 40.0f}); + auto m = ConstantR2(&builder, {{10.0f, 50.0f}, {77.0f, 88.0f}}); + Add(v, m, /*broadcast_dimensions=*/{1}); Array2D expected_array({{30.0f, 90.0f}, {97.0f, 128.0f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); } @@ -2526,9 +2621,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo2DF32TwoWaysOver0) { // Add together a (2,2) array and a (2) array, using dimension 1 for // broadcasting (though there are two ways to broadcast these shapes). XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({20.0f, 40.0f}); - auto m = builder.ConstantR2({{10.0f, 50.0f}, {77.0f, 88.0f}}); - builder.Add(v, m, /*broadcast_dimensions=*/{0}); + auto v = ConstantR1(&builder, {20.0f, 40.0f}); + auto m = ConstantR2(&builder, {{10.0f, 50.0f}, {77.0f, 88.0f}}); + Add(v, m, /*broadcast_dimensions=*/{0}); Array2D expected_array({{30.0f, 70.0f}, {117.0f, 128.0f}}); ComputeAndCompareR2(&builder, expected_array, {}, error_spec_); } @@ -2538,12 +2633,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, 3DBinaryOpF32s) { XlaBuilder builder(TestName()); Array3D a_3d({{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}}, {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}}); - auto a = builder.ConstantR3FromArray3D(a_3d); + auto a = ConstantR3FromArray3D(&builder, a_3d); Array3D b_3d({{{2.0f, 4.0f}, {6.0f, 8.0f}, {10.0f, 12.0f}}, {{14.0f, 16.0f}, {18.0f, 20.0f}, {22.0f, 24.0f}}}); - auto b = builder.ConstantR3FromArray3D(b_3d); - builder.Add(a, b); + auto b = ConstantR3FromArray3D(&builder, b_3d); + Add(a, b); Array3D expected_3d( {{{3.0f, 6.0f}, {9.0f, 12.0f}, {15.0f, 18.0f}}, @@ -2565,9 +2660,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo3DTwoWaysOver2) { {11.0f, 12.0f}}, }); // clang-format on - auto a = builder.ConstantR3FromArray3D(a_3d); - auto v = builder.ConstantR1({10.0f, 20.0f}); - builder.Add(a, v, /*broadcast_dimensions=*/{2}); + auto a = ConstantR3FromArray3D(&builder, a_3d); + auto v = ConstantR1(&builder, {10.0f, 20.0f}); + Add(a, v, /*broadcast_dimensions=*/{2}); Array3D expected_3d( {{{11.0f, 22.0f}, {13.0f, 24.0f}, {15.0f, 26.0f}}, @@ -2589,9 +2684,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add1DTo3DTwoWaysOver0) { {11.0f, 12.0f}}, }); // clang-format on - auto a = builder.ConstantR3FromArray3D(a_3d); - auto v = builder.ConstantR1({10.0f, 20.0f}); - builder.Add(a, v, /*broadcast_dimensions=*/{0}); + auto a = ConstantR3FromArray3D(&builder, a_3d); + auto v = ConstantR1(&builder, {10.0f, 20.0f}); + Add(a, v, /*broadcast_dimensions=*/{0}); // clang-format off Array3D expected_3d({ @@ -2619,12 +2714,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, Add2DTo3D) { {9.0f, 10.0f}, {11.0f, 12.0f}}, }); - auto a = builder.ConstantR3FromArray3D(a_3d); - auto m = builder.ConstantR2({ + auto a = ConstantR3FromArray3D(&builder, a_3d); + auto m = ConstantR2(&builder, { {10.0f, 20.0f, 30.0f}, {40.0f, 50.0f, 60.0f}, }); - builder.Add(a, m, /*broadcast_dimensions=*/{0, 1}); + Add(a, m, /*broadcast_dimensions=*/{0, 1}); Array3D expected_3d({ {{11.0f, 12.0f}, @@ -2644,12 +2739,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, CompareGtR3F32sWithDegenerateDim2) { XlaBuilder builder(TestName()); Array3D a_3d({{{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}}, {{7.0f, 8.0f}, {9.0f, 10.0f}, {11.0f, 12.0f}}}); - auto a = builder.ConstantR3FromArray3D(a_3d); + auto a = ConstantR3FromArray3D(&builder, a_3d); Array3D b_3d({{{7.0f, 1.0f}, {3.0f, 10.0f}, {15.0f, 6.0f}}}); - auto b = builder.ConstantR3FromArray3D(b_3d); + auto b = ConstantR3FromArray3D(&builder, b_3d); - builder.Gt(a, b); + Gt(a, b); Array3D expected_3d( {{{0, 1}, {0, 0}, {0, 0}}, {{0, 1}, {1, 0}, {0, 1}}}); @@ -2684,9 +2779,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, 4DBinaryOpF32s) { } } - auto a = builder.ConstantR4FromArray4D(*operand_a_4d); - auto b = builder.ConstantR4FromArray4D(*operand_b_4d); - builder.Add(a, b); + auto a = ConstantR4FromArray4D(&builder, *operand_a_4d); + auto b = ConstantR4FromArray4D(&builder, *operand_b_4d); + Add(a, b); ComputeAndCompareR4(&builder, *expected_4d, {}, error_spec_); } @@ -2712,9 +2807,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, R4PlusR1InDim1) { } } - auto a = builder.ConstantR4FromArray4D(*operand_a_4d); - auto b = builder.ConstantR1(operand_b_1d); - builder.Add(a, b, {1}); + auto a = ConstantR4FromArray4D(&builder, *operand_a_4d); + auto b = ConstantR1(&builder, operand_b_1d); + Add(a, b, {1}); ComputeAndCompareR4(&builder, *expected_4d, {}, error_spec_); } @@ -2732,9 +2827,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, R4_16x16x2x2_Plus_R1_16) { XlaBuilder builder(TestName()); std::unique_ptr a_literal = Literal::CreateR4FromArray4DWithLayout( r4, LayoutUtil::MakeLayout({0, 1, 2, 3})); - auto a = builder.ConstantLiteral(*a_literal); - auto b = builder.ConstantR1(r1); - builder.Add(a, b, {1}); + auto a = ConstantLiteral(&builder, *a_literal); + auto b = ConstantR1(&builder, r1); + Add(a, b, {1}); for (int i0 = 0; i0 < d0; ++i0) { for (int i1 = 0; i1 < d1; ++i1) { @@ -2752,22 +2847,22 @@ XLA_TEST_F(ArrayElementwiseOpTest, R4_16x16x2x2_Plus_R1_16) { XLA_TEST_F(ArrayElementwiseOpTest, CannotAddOpaques) { XlaBuilder builder(TestName()); auto shape = ShapeUtil::MakeOpaqueShape(); - auto x = builder.Parameter(0, shape, "x"); - builder.Add(x, x); + auto x = Parameter(&builder, 0, shape, "x"); + Add(x, x); auto computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), ::testing::ContainsRegex( - "Expected non-opaque argument for lhs of binary operation")); + "Expected array argument for lhs of binary operation")); } XLA_TEST_F(ArrayElementwiseOpTest, IdentityBroadcastOfSameRankIsAllowed) { XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto b = - builder.ConstantR2({{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); - builder.Add(a, b, /*broadcast_dimensions=*/{0, 1}); + auto a = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto b = ConstantR2(&builder, + {{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); + Add(a, b, /*broadcast_dimensions=*/{0, 1}); Array2D expected_array( {{-4.0f, 11.28f, 43.0f}, {1.25f, -14.0f, 8.88f}}); @@ -2776,11 +2871,11 @@ XLA_TEST_F(ArrayElementwiseOpTest, IdentityBroadcastOfSameRankIsAllowed) { XLA_TEST_F(ArrayElementwiseOpTest, NonIdentityBroadcastOfSameRankIsDisallowed) { XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); - auto b = - builder.ConstantR2({{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); - builder.Add(a, b, /*broadcast_dimensions=*/{1, 0}); + auto a = ConstantR2(&builder, + {{-2.5f, 3.14f, 1.0f}, {2.25f, -10.0f, 3.33f}}); + auto b = ConstantR2(&builder, + {{-1.5f, 8.14f, 42.0}, {-1.0f, -4.0f, 5.55f}}); + Add(a, b, /*broadcast_dimensions=*/{1, 0}); auto computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); @@ -2797,10 +2892,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) { auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie(); auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); - auto x = builder.Parameter(0, x_literal->shape(), "x"); - auto y = builder.Parameter(1, y_literal->shape(), "y"); - auto slice = builder.Slice(x, {1}, {2}, {1}); - builder.Sub(slice, y); + auto x = Parameter(&builder, 0, x_literal->shape(), "x"); + auto y = Parameter(&builder, 1, y_literal->shape(), "y"); + auto slice = Slice(x, {1}, {2}, {1}); + Sub(slice, y); ComputeAndCompareR1(&builder, {-2, -3}, {x_data.get(), y_data.get()}, error_spec_); diff --git a/tensorflow/compiler/xla/tests/axpy_simple_test.cc b/tensorflow/compiler/xla/tests/axpy_simple_test.cc index fcd9ff55e393f64476ddd4754e0fa74427f1cb51..8d15b7841bc7298cd6865d8689cc496c0459e4b9 100644 --- a/tensorflow/compiler/xla/tests/axpy_simple_test.cc +++ b/tensorflow/compiler/xla/tests/axpy_simple_test.cc @@ -29,10 +29,10 @@ class AxpySimpleTest : public ClientLibraryTestBase {}; TEST_F(AxpySimpleTest, AxTenValues) { XlaBuilder builder("ax_10"); - auto alpha = builder.ConstantR0(3.1415926535); - auto x = builder.ConstantR1( - {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); - builder.Mul(alpha, x); + auto alpha = ConstantR0(&builder, 3.1415926535); + auto x = ConstantR1( + &builder, {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); + Mul(alpha, x); std::vector expected = { -3.14159265, 3.14159265, 6.28318531, -6.28318531, -9.42477796, @@ -42,11 +42,11 @@ TEST_F(AxpySimpleTest, AxTenValues) { XLA_TEST_F(AxpySimpleTest, AxpyZeroValues) { XlaBuilder builder("axpy_10"); - auto alpha = builder.ConstantR0(3.1415926535); - auto x = builder.ConstantR1({}); - auto y = builder.ConstantR1({}); - auto ax = builder.Mul(alpha, x); - builder.Add(ax, y); + auto alpha = ConstantR0(&builder, 3.1415926535); + auto x = ConstantR1(&builder, {}); + auto y = ConstantR1(&builder, {}); + auto ax = Mul(alpha, x); + Add(ax, y); std::vector expected = {}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -54,13 +54,13 @@ XLA_TEST_F(AxpySimpleTest, AxpyZeroValues) { TEST_F(AxpySimpleTest, AxpyTenValues) { XlaBuilder builder("axpy_10"); - auto alpha = builder.ConstantR0(3.1415926535); - auto x = builder.ConstantR1( - {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); - auto y = builder.ConstantR1( - {5.0, -5.0, -4.0, 4.0, 3.0, -3.0, -2.0, 2.0, 1.0, -1.0}); - auto ax = builder.Mul(alpha, x); - builder.Add(ax, y); + auto alpha = ConstantR0(&builder, 3.1415926535); + auto x = ConstantR1( + &builder, {-1.0, 1.0, 2.0, -2.0, -3.0, 3.0, 4.0, -4.0, -5.0, 5.0}); + auto y = ConstantR1( + &builder, {5.0, -5.0, -4.0, 4.0, 3.0, -3.0, -2.0, 2.0, 1.0, -1.0}); + auto ax = Mul(alpha, x); + Add(ax, y); TF_ASSERT_OK_AND_ASSIGN(ProgramShape shape, builder.GetProgramShape()); diff --git a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc index 22c3394e6f34bd018ffaaaa4d9d68339673c3764..8c227df7f04e79ccc332062d0889d282c0f5e40f 100644 --- a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc +++ b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc @@ -35,10 +35,10 @@ class BadRngShapeValidationTest : public ClientLibraryTestBase {}; TEST_F(BadRngShapeValidationTest, DefaultConstructedShapeCreatesError) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR0(0.0); - auto one = builder.ConstantR0(1.0); + auto zero = ConstantR0(&builder, 0.0); + auto one = ConstantR0(&builder, 1.0); Shape default_constructed; - builder.RngUniform(zero, one, default_constructed); + RngUniform(zero, one, default_constructed); StatusOr computation = builder.Build(); EXPECT_FALSE(computation.ok()); @@ -49,13 +49,13 @@ TEST_F(BadRngShapeValidationTest, DefaultConstructedShapeCreatesError) { TEST_F(BadRngShapeValidationTest, ShapeWithoutLayoutIsOk) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR0(0.0); - auto one = builder.ConstantR0(1.0); + auto zero = ConstantR0(&builder, 0.0); + auto one = ConstantR0(&builder, 1.0); Shape sans_layout; sans_layout.set_element_type(F32); sans_layout.add_dimensions(1); - builder.RngUniform(zero, one, sans_layout); + RngUniform(zero, one, sans_layout); StatusOr computation = builder.Build(); ASSERT_TRUE(computation.ok()); diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc index f3dac75a44b948c4b45b80b93e7462073010979e..217673c8cbc212958fe79b67546f28b0be091803 100644 --- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc +++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" @@ -101,9 +102,9 @@ INSTANTIATE_TEST_CASE_P(BatchNormalizationTestInstance, BatchNormalizationTest, XLA_TEST_P(BatchNormalizationTest, SubtractInZ) { XlaBuilder builder("subtract_in_z_one_sample"); - auto x = builder.ConstantLiteral(input_literal_); - auto y = builder.ConstantR1({3.14, 4.25}); - builder.Sub(x, y, /*broadcast_dimensions=*/{1}); + auto x = ConstantLiteral(&builder, input_literal_); + auto y = ConstantR1(&builder, {3.14, 4.25}); + Sub(x, y, /*broadcast_dimensions=*/{1}); Array4D expected(kSamples, kZ, kY, kX); Array2D pz({ @@ -117,8 +118,8 @@ XLA_TEST_P(BatchNormalizationTest, SubtractInZ) { XLA_TEST_P(BatchNormalizationTest, SquareTesseractElementwise) { XlaBuilder builder("square_tesseract_elementwise"); - auto x = builder.ConstantLiteral(input_literal_); - builder.SquareF32(x); + auto x = ConstantLiteral(&builder, input_literal_); + Square(x); using tensorflow::MathUtil; @@ -134,11 +135,10 @@ XLA_TEST_P(BatchNormalizationTest, SquareTesseractElementwise) { XLA_TEST_P(BatchNormalizationTest, SumToZ) { XlaBuilder builder("sum_to_z"); - auto input_activations = builder.ConstantLiteral(input_literal_); + auto input_activations = ConstantLiteral(&builder, input_literal_); XlaComputation add = CreateScalarAddComputation(F32, &builder); // Reduce all but the Z dimension. - builder.Reduce(input_activations, builder.ConstantR0(0.0f), add, - {0, 2, 3}); + Reduce(input_activations, ConstantR0(&builder, 0.0f), add, {0, 2, 3}); std::vector expected = {6, 12.6}; ComputeAndCompareR1(&builder, expected, {}, error_spec_); @@ -146,13 +146,13 @@ XLA_TEST_P(BatchNormalizationTest, SumToZ) { XLA_TEST_P(BatchNormalizationTest, SquareAndReduce) { XlaBuilder builder("square_and_reduce"); - auto input_activations = builder.ConstantLiteral(input_literal_); - auto set_means = builder.ConstantR1({2.f, 4.2f}); - auto activation_deviations = builder.Sub(input_activations, set_means, - /*broadcast_dimensions=*/{1}); + auto input_activations = ConstantLiteral(&builder, input_literal_); + auto set_means = ConstantR1(&builder, {2.f, 4.2f}); + auto activation_deviations = Sub(input_activations, set_means, + /*broadcast_dimensions=*/{1}); XlaComputation add = CreateScalarAddComputation(F32, &builder); - auto dev_squares = builder.SquareF32(activation_deviations); - builder.Reduce(dev_squares, builder.ConstantR0(0.0f), add, {0, 2, 3}); + auto dev_squares = Square(activation_deviations); + Reduce(dev_squares, ConstantR0(&builder, 0.0f), add, {0, 2, 3}); std::vector expected = {18, 0.06}; ComputeAndCompareR1(&builder, expected, {}, error_spec_); @@ -160,8 +160,8 @@ XLA_TEST_P(BatchNormalizationTest, SquareAndReduce) { XLA_TEST_P(BatchNormalizationTest, VarianceToStddev) { XlaBuilder builder("variance_to_stddev"); - auto variance = builder.ConstantR1({6.f, .02f}); - builder.SqrtF32(variance); + auto variance = ConstantR1(&builder, {6.f, .02f}); + Sqrt(variance); std::vector expected = {2.44948974f, 0.14142136f}; ComputeAndCompareR1(&builder, expected, {}, error_spec_); @@ -172,50 +172,50 @@ XLA_TEST_P(BatchNormalizationTest, VarianceToStddev) { XLA_TEST_P(BatchNormalizationTest, SpecComparisonForward) { XlaBuilder builder("batch_normalize_per_spec"); auto input_activations = - CheckShape(&builder, builder.ConstantLiteral(input_literal_), + CheckShape(&builder, ConstantLiteral(&builder, input_literal_), ShapeUtil::MakeShape(F32, {3, 2, 1, 1})); - auto gamma = builder.ConstantR1({1.0, 1.0}); - auto beta = builder.ConstantR1({0.0, 0.0}); + auto gamma = ConstantR1(&builder, {1.0, 1.0}); + auto beta = ConstantR1(&builder, {0.0, 0.0}); XlaComputation add = CreateScalarAddComputation(F32, &builder); // Reduce all dimensions except dimension 1. Shape TwoElementVectorF32 = ShapeUtil::MakeShape(F32, {2}); auto sum = CheckShape( &builder, - builder.Reduce(input_activations, builder.ConstantR0(0.0f), add, - /*dimensions_to_reduce=*/{0, 2, 3}), + Reduce(input_activations, ConstantR0(&builder, 0.0f), add, + /*dimensions_to_reduce=*/{0, 2, 3}), TwoElementVectorF32); auto input_shape = builder.GetShape(input_activations).ConsumeValueOrDie(); auto sum_shape = builder.GetShape(sum).ConsumeValueOrDie(); - auto count = builder.ConstantR0(ShapeUtil::ElementsIn(input_shape) / - ShapeUtil::ElementsIn(sum_shape)); - auto set_means = builder.Div(sum, count); + auto count = + ConstantR0(&builder, ShapeUtil::ElementsIn(input_shape) / + ShapeUtil::ElementsIn(sum_shape)); + auto set_means = Div(sum, count); const float kEpsilon = 1e-9f; - auto epsilon = builder.ConstantR0(kEpsilon); - auto epsilon2 = builder.ConstantR1({kEpsilon, kEpsilon}); - auto activation_deviations = builder.Sub(input_activations, set_means, - /*broadcast_dimensions=*/{1}); - auto dev_squares = builder.SquareF32(activation_deviations); - auto sum_of_squares = CheckShape( - &builder, - builder.Reduce(dev_squares, builder.ConstantR0(0.0f), add, - /*dimensions_to_reduce=*/{0, 2, 3}), - TwoElementVectorF32); - auto variance = builder.Div(sum_of_squares, count); - auto standard_deviation = builder.SqrtF32(variance); + auto epsilon = ConstantR0(&builder, kEpsilon); + auto epsilon2 = ConstantR1(&builder, {kEpsilon, kEpsilon}); + auto activation_deviations = Sub(input_activations, set_means, + /*broadcast_dimensions=*/{1}); + auto dev_squares = Square(activation_deviations); + auto sum_of_squares = + CheckShape(&builder, + Reduce(dev_squares, ConstantR0(&builder, 0.0f), add, + /*dimensions_to_reduce=*/{0, 2, 3}), + TwoElementVectorF32); + auto variance = Div(sum_of_squares, count); + auto standard_deviation = Sqrt(variance); auto standard_deviation_above_epsilon = - CheckShape(&builder, builder.Gt(standard_deviation, epsilon), + CheckShape(&builder, Gt(standard_deviation, epsilon), ShapeUtil::MakeShape(PRED, {2})); - auto gt_eps = builder.Select(standard_deviation_above_epsilon, - standard_deviation, epsilon2); - auto normalization_factors = builder.ReciprocalF32(gt_eps); + auto gt_eps = + Select(standard_deviation_above_epsilon, standard_deviation, epsilon2); + auto normalization_factors = Reciprocal(gt_eps); auto normalized_input_activations = - builder.Mul(activation_deviations, normalization_factors, - /*broadcast_dimensions=*/{1}); - /* auto output_activations = */ builder.Add( - builder.Mul(normalized_input_activations, gamma, - /*broadcast_dimensions=*/{1}), - beta, /*broadcast_dimensions=*/{1}); + Mul(activation_deviations, normalization_factors, + /*broadcast_dimensions=*/{1}); + /* auto output_activations = */ Add(Mul(normalized_input_activations, gamma, + /*broadcast_dimensions=*/{1}), + beta, /*broadcast_dimensions=*/{1}); Array4D expected(kSamples, kZ, kY, kX); Array2D pz({ @@ -232,15 +232,15 @@ XLA_TEST_P(BatchNormalizationTest, BasicTraining) { const int kFeatureIndex = 3; XlaBuilder builder(TestName()); - auto operand = builder.ConstantR4FromArray4D( - {{{{1.f, 2.f}}, {{3.f, 4.f}}}, {{{5.f, 6.f}}, {{7.f, 8.f}}}}); + auto operand = ConstantR4FromArray4D( + &builder, {{{{1.f, 2.f}}, {{3.f, 4.f}}}, {{{5.f, 6.f}}, {{7.f, 8.f}}}}); - auto scale = builder.ConstantR1({2.0f, 3.0f}); + auto scale = ConstantR1(&builder, {2.0f, 3.0f}); - auto offset = builder.ConstantR1({1.0f, 2.0f}); + auto offset = ConstantR1(&builder, {1.0f, 2.0f}); - builder.BatchNormTraining(operand, scale, offset, - /*epsilon=*/0.001, kFeatureIndex); + BatchNormTraining(operand, scale, offset, + /*epsilon=*/0.001, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR4({{{{-1.6f, -2.0f}}, {{0.1f, 0.6f}}}, @@ -252,19 +252,20 @@ XLA_TEST_P(BatchNormalizationTest, BasicTraining) { ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.1)); } -XLA_TEST_P(BatchNormalizationTest, BasicTrainingOnSublane) { +XLA_TEST_P(BatchNormalizationTest, BasicTrainingOnDimension2) { const int kFeatureIndex = 2; XlaBuilder builder(TestName()); - auto operand = builder.ConstantR4FromArray4D( + auto operand = ConstantR4FromArray4D( + &builder, {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}}); - auto scale = builder.ConstantR1({2.0f, 3.0f}); + auto scale = ConstantR1(&builder, {2.0f, 3.0f}); - auto offset = builder.ConstantR1({1.0f, 2.0f}); + auto offset = ConstantR1(&builder, {1.0f, 2.0f}); - builder.BatchNormTraining(operand, scale, offset, - /*epsilon=*/0.001, kFeatureIndex); + BatchNormTraining(operand, scale, offset, + /*epsilon=*/0.001, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR4({{{{-1.6f}, {-2.0f}}, {{0.1f}, {0.6f}}}, @@ -294,8 +295,8 @@ XLA_TEST_P(BatchNormalizationTest, TrainingWithFeatureOnLowDimension) { CreateR1Parameter(std::vector(260, 1.0f), /*parameter_number=*/2, "offset", &builder, &h2); - builder.BatchNormTraining(h0, h1, h2, - /*epsilon=*/1, kFeatureIndex); + BatchNormTraining(h0, h1, h2, + /*epsilon=*/1, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR3FromArray3D(Array3D(260, 2, 2, 1.0f)) @@ -327,8 +328,8 @@ XLA_TEST_P(BatchNormalizationTest, LargeEpsilonTest) { /*parameter_number=*/2, "offset", &builder, &h2); // var = 125, mean = 15, epsilon = -100 - builder.BatchNormTraining(h0, h1, h2, - /*epsilon=*/-100, kFeatureIndex); + BatchNormTraining(h0, h1, h2, + /*epsilon=*/-100, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR3FromArray3D({{{-3.0f}, {-1.0f}, {1.0f}, {3.0f}}}) @@ -346,19 +347,20 @@ XLA_TEST_P(BatchNormalizationTest, BatchNormGradBasic) { XlaBuilder builder(TestName()); auto operand = - builder.ConstantR4FromArray4D(Array4D(2, 2, 2, 1, 0.0f)); + ConstantR4FromArray4D(&builder, Array4D(2, 2, 2, 1, 0.0f)); - auto scale = builder.ConstantR1({1.0f, 1.0f}); + auto scale = ConstantR1(&builder, {1.0f, 1.0f}); - auto mean = builder.ConstantR1({0.0f, 0.0f}); + auto mean = ConstantR1(&builder, {0.0f, 0.0f}); - auto var = builder.ConstantR1({1.0f, 1.0f}); + auto var = ConstantR1(&builder, {1.0f, 1.0f}); - auto grad_output = builder.ConstantR4FromArray4D( + auto grad_output = ConstantR4FromArray4D( + &builder, {{{{1.f}, {2.f}}, {{3.f}, {4.f}}}, {{{5.f}, {6.f}}, {{7.f}, {8.f}}}}); - builder.BatchNormGrad(operand, scale, mean, var, grad_output, - /*epsilon=*/0.0, kFeatureIndex); + BatchNormGrad(operand, scale, mean, var, grad_output, + /*epsilon=*/0.0, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR4({{{{-3.f}, {-3.f}}, {{-1.f}, {-1.f}}}, @@ -518,11 +520,11 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedTrainingTests) { auto input_literal = Literal::CreateR4FromArray4D(input_array); auto input_activations = - builder.Parameter(0, input_literal->shape(), "input"); + Parameter(&builder, 0, input_literal->shape(), "input"); auto scale_activations = - builder.Parameter(1, scale_literal->shape(), "offset"); + Parameter(&builder, 1, scale_literal->shape(), "offset"); auto offset_activations = - builder.Parameter(2, offset_literal->shape(), "scale"); + Parameter(&builder, 2, offset_literal->shape(), "scale"); auto expected = Literal::MakeTuple({expected_normalized.get(), Literal::CreateR1(mean).get(), @@ -535,8 +537,8 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedTrainingTests) { std::unique_ptr offset_data = client_->TransferToServer(*offset_literal).ConsumeValueOrDie(); - builder.BatchNormTraining(input_activations, scale_activations, - offset_activations, epsilon, feature_index); + BatchNormTraining(input_activations, scale_activations, offset_activations, + epsilon, feature_index); // Run all HLO passes during this test. In particular, ClientLibraryTestBase // disables constant folding, but we want it enabled for our zero-sized tensor @@ -618,14 +620,14 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedInferencingTests) { auto input_literal = Literal::CreateR4FromArray4D(input_array); auto input_activations = - builder.Parameter(0, input_literal->shape(), "input"); + Parameter(&builder, 0, input_literal->shape(), "input"); auto scale_activations = - builder.Parameter(1, scale_literal->shape(), "offset"); + Parameter(&builder, 1, scale_literal->shape(), "offset"); auto offset_activations = - builder.Parameter(2, offset_literal->shape(), "scale"); - auto mean_activations = builder.Parameter(3, mean_literal->shape(), "mean"); + Parameter(&builder, 2, offset_literal->shape(), "scale"); + auto mean_activations = Parameter(&builder, 3, mean_literal->shape(), "mean"); auto variance_activations = - builder.Parameter(4, var_literal->shape(), "variance"); + Parameter(&builder, 4, var_literal->shape(), "variance"); Array4D expected = normalized; @@ -640,9 +642,9 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedInferencingTests) { std::unique_ptr variance_data = client_->TransferToServer(*var_literal).ConsumeValueOrDie(); - builder.BatchNormInference(input_activations, scale_activations, - offset_activations, mean_activations, - variance_activations, epsilon, feature_index); + BatchNormInference(input_activations, scale_activations, offset_activations, + mean_activations, variance_activations, epsilon, + feature_index); // Run all HLO passes during this test. In particular, ClientLibraryTestBase // disables constant folding, but we want it enabled for our zero-sized tensor @@ -807,12 +809,14 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedGradTests) { auto grad_output_literal = Literal::CreateR4FromArray4D(grad_output_array); - auto input_parameter = builder.Parameter(0, input_literal->shape(), "input"); - auto scale_parameter = builder.Parameter(1, scale_literal->shape(), "scale"); - auto mean_parameter = builder.Parameter(2, mean_literal->shape(), "mean"); - auto var_parameter = builder.Parameter(3, var_literal->shape(), "variance"); + auto input_parameter = + Parameter(&builder, 0, input_literal->shape(), "input"); + auto scale_parameter = + Parameter(&builder, 1, scale_literal->shape(), "scale"); + auto mean_parameter = Parameter(&builder, 2, mean_literal->shape(), "mean"); + auto var_parameter = Parameter(&builder, 3, var_literal->shape(), "variance"); auto grad_output_parameter = - builder.Parameter(4, grad_output_literal->shape(), "grad_output"); + Parameter(&builder, 4, grad_output_literal->shape(), "grad_output"); std::unique_ptr input_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -825,9 +829,8 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedGradTests) { std::unique_ptr grad_output_data = client_->TransferToServer(*grad_output_literal).ConsumeValueOrDie(); - builder.BatchNormGrad(input_parameter, scale_parameter, mean_parameter, - var_parameter, grad_output_parameter, epsilon, - feature_index); + BatchNormGrad(input_parameter, scale_parameter, mean_parameter, var_parameter, + grad_output_parameter, epsilon, feature_index); auto expected = Literal::MakeTuple({expected_grad_activation.get(), diff --git a/tensorflow/compiler/xla/tests/bfloat16_test.cc b/tensorflow/compiler/xla/tests/bfloat16_test.cc index ca337e78840e77377719636cd4cf33af2578210d..f40d03bea79de2a78814a0ad9f6cae6098d1449b 100644 --- a/tensorflow/compiler/xla/tests/bfloat16_test.cc +++ b/tensorflow/compiler/xla/tests/bfloat16_test.cc @@ -51,9 +51,9 @@ class Bfloat16Test : public ClientLibraryTestBase { XLA_TEST_F(Bfloat16Test, ScalarOperation) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR0(static_cast(2.0f)); - auto y = builder.ConstantR0(static_cast(1.0f)); - builder.Add(x, y); + auto x = ConstantR0(&builder, static_cast(2.0f)); + auto y = ConstantR0(&builder, static_cast(1.0f)); + Add(x, y); ComputeAndCompareR0(&builder, static_cast(3.0f), {}, error_spec_); @@ -61,8 +61,8 @@ XLA_TEST_F(Bfloat16Test, ScalarOperation) { XLA_TEST_F(Bfloat16Test, LogOperation) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR0(static_cast(4.0f)); - builder.Log(x); + auto x = ConstantR0(&builder, static_cast(4.0f)); + Log(x); ComputeAndCompareR0(&builder, static_cast(1.387f), {}, error_spec_); @@ -70,7 +70,7 @@ XLA_TEST_F(Bfloat16Test, LogOperation) { XLA_TEST_F(Bfloat16Test, NegateScalarF16) { XlaBuilder builder(TestName()); - builder.Neg(builder.ConstantR0(static_cast(2.1f))); + Neg(ConstantR0(&builder, static_cast(2.1f))); ComputeAndCompareR0(&builder, static_cast(-2.1f), {}, error_spec_); @@ -80,20 +80,20 @@ XLA_TEST_F(Bfloat16Test, BatchNormTraining) { const int kFeatureIndex = 2; XlaBuilder builder(TestName()); - auto operand = builder.ConstantR4FromArray4D( + auto operand = ConstantR4FromArray4D( + &builder, {{{{static_cast(1.f)}, {static_cast(2.f)}}, {{static_cast(3.f)}, {static_cast(4.f)}}}, {{{static_cast(5.f)}, {static_cast(6.f)}}, {{static_cast(7.f)}, {static_cast(8.f)}}}}); - auto scale = builder.ConstantR1( - {static_cast(2.0f), static_cast(3.0f)}); + auto scale = ConstantR1( + &builder, {static_cast(2.0f), static_cast(3.0f)}); - auto offset = builder.ConstantR1( - {static_cast(1.0f), static_cast(2.0f)}); + auto offset = ConstantR1( + &builder, {static_cast(1.0f), static_cast(2.0f)}); - auto tuple = builder.BatchNormTraining(operand, scale, offset, - /*epsilon=*/0.001, kFeatureIndex); + BatchNormTraining(operand, scale, offset, /*epsilon=*/0.001, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR4( @@ -117,26 +117,27 @@ XLA_TEST_F(Bfloat16Test, BatchNormGrad) { const int kFeatureIndex = 2; XlaBuilder builder(TestName()); - auto operand = builder.ConstantR4FromArray4D( - Array4D(2, 2, 2, 1, static_cast(0.0f))); + auto operand = ConstantR4FromArray4D( + &builder, Array4D(2, 2, 2, 1, static_cast(0.0f))); - auto scale = builder.ConstantR1( - {static_cast(1.0f), static_cast(1.0f)}); + auto scale = ConstantR1( + &builder, {static_cast(1.0f), static_cast(1.0f)}); - auto mean = builder.ConstantR1( - {static_cast(0.0f), static_cast(0.0f)}); + auto mean = ConstantR1( + &builder, {static_cast(0.0f), static_cast(0.0f)}); - auto var = builder.ConstantR1( - {static_cast(1.0f), static_cast(1.0f)}); + auto var = ConstantR1( + &builder, {static_cast(1.0f), static_cast(1.0f)}); - auto grad_output = builder.ConstantR4FromArray4D( + auto grad_output = ConstantR4FromArray4D( + &builder, {{{{static_cast(1.f)}, {static_cast(2.f)}}, {{static_cast(3.f)}, {static_cast(4.f)}}}, {{{static_cast(5.f)}, {static_cast(6.f)}}, {{static_cast(7.f)}, {static_cast(8.f)}}}}); - builder.BatchNormGrad(operand, scale, mean, var, grad_output, - /*epsilon=*/0.0, kFeatureIndex); + BatchNormGrad(operand, scale, mean, var, grad_output, + /*epsilon=*/0.0, kFeatureIndex); auto expected = Literal::MakeTuple( {Literal::CreateR4( diff --git a/tensorflow/compiler/xla/tests/binop_scaling_test.cc b/tensorflow/compiler/xla/tests/binop_scaling_test.cc index 48203b1d40ea69ff00a57c2c9e42620739b23d59..20cb989751ad69e2f3cf97c87c43293951f599ab 100644 --- a/tensorflow/compiler/xla/tests/binop_scaling_test.cc +++ b/tensorflow/compiler/xla/tests/binop_scaling_test.cc @@ -33,9 +33,9 @@ TEST_F(BinopScalingTest, MatrixPlusPseudoMatrixRowVector_32x4) { auto arhs = MakeLinspaceArray2D(0.0, 1.0, 1, 4); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - builder.Add(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, *alhs); + auto rhs = ConstantR2FromArray2D(&builder, *arhs); + Add(lhs, rhs); auto aexpected = ReferenceUtil::MapWithIndexArray2D( *alhs, [&](float lhs_value, int64 row, int64 col) { @@ -49,9 +49,9 @@ TEST_F(BinopScalingTest, MatrixPlusPseudoMatrixRowVector_129x129) { auto arhs = MakeLinspaceArray2D(0.0, 1.0, 1, 129); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - builder.Add(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, *alhs); + auto rhs = ConstantR2FromArray2D(&builder, *arhs); + Add(lhs, rhs); auto aexpected = ReferenceUtil::MapWithIndexArray2D( *alhs, [&](float lhs_value, int64 row, int64 col) { @@ -65,9 +65,9 @@ TEST_F(BinopScalingTest, MatrixPlusPseudoMatrixColVector_9x5) { auto arhs = MakeLinspaceArray2D(0.0, 1.0, 9, 1); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - builder.Add(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, *alhs); + auto rhs = ConstantR2FromArray2D(&builder, *arhs); + Add(lhs, rhs); auto aexpected = ReferenceUtil::MapWithIndexArray2D( *alhs, [&](float lhs_value, int64 row, int64 col) { @@ -81,9 +81,9 @@ TEST_F(BinopScalingTest, MatrixPlusPseudoMatrixColVector_129x257) { auto arhs = MakeLinspaceArray2D(0.0, 1.0, 129, 1); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - builder.Add(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, *alhs); + auto rhs = ConstantR2FromArray2D(&builder, *arhs); + Add(lhs, rhs); auto aexpected = ReferenceUtil::MapWithIndexArray2D( *alhs, [&](float lhs_value, int64 row, int64 col) { @@ -94,11 +94,12 @@ TEST_F(BinopScalingTest, MatrixPlusPseudoMatrixColVector_129x257) { TEST_F(BinopScalingTest, R0PlusR2F32) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR0(42.0); - auto rhs = builder.ConstantR2({ - {1.0, 2.0}, {3.0, 4.0}, - }); - builder.Add(lhs, rhs); + auto lhs = ConstantR0(&builder, 42.0); + auto rhs = ConstantR2(&builder, { + {1.0, 2.0}, + {3.0, 4.0}, + }); + Add(lhs, rhs); Array2D expected(2, 2); expected(0, 0) = 42.0 + 1.0; @@ -129,9 +130,9 @@ TEST_F(BinopScalingTest, R4PlusR0S32) { }); // clang-format on - auto lhs = builder.ConstantR4FromArray4D(lhs_array); - auto rhs = builder.ConstantR0(42); - builder.Add(lhs, rhs); + auto lhs = ConstantR4FromArray4D(&builder, lhs_array); + auto rhs = ConstantR0(&builder, 42); + Add(lhs, rhs); ComputeAndCompareR4(&builder, expected, {}); } diff --git a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc index bff60f25ec8f15d372d251ac313200301a04f20f..d531e8fa82e47f7bcd278f10da2c205e44db0ac1 100644 --- a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc +++ b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc @@ -43,8 +43,8 @@ class BitcastConvertTest : public ClientLibraryTestBase { TEST_F(BitcastConvertTest, ConvertR1S32ToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42, 64}); - builder.BitcastConvertType(a, S32); + auto a = ConstantR1(&builder, {42, 64}); + BitcastConvertType(a, S32); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -52,8 +52,8 @@ TEST_F(BitcastConvertTest, ConvertR1S32ToR1S32) { TEST_F(BitcastConvertTest, ConvertR1F32ToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0f, 64.0f}); - builder.BitcastConvertType(a, F32); + auto a = ConstantR1(&builder, {42.0f, 64.0f}); + BitcastConvertType(a, F32); std::vector expected = {42.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}); @@ -62,10 +62,10 @@ TEST_F(BitcastConvertTest, ConvertR1F32ToR1F32) { TEST_F(BitcastConvertTest, BitcastR1S32ToR1F32) { XlaBuilder builder(TestName()); auto a = - builder.ConstantR1({0, static_cast(0x80000000), 0x3F800000, - static_cast(0xBF800000), 0x3F000000, - static_cast(0xBF000000)}); - builder.BitcastConvertType(a, F32); + ConstantR1(&builder, {0, static_cast(0x80000000), + 0x3F800000, static_cast(0xBF800000), + 0x3F000000, static_cast(0xBF000000)}); + BitcastConvertType(a, F32); std::vector expected = {0.0f, -0.0f, 1.0f, -1.0f, 0.5f, -0.5f}; ComputeAndCompareR1(&builder, expected, {}); @@ -73,8 +73,8 @@ TEST_F(BitcastConvertTest, BitcastR1S32ToR1F32) { XLA_TEST_F(BitcastConvertTest, ConvertR1S0S32ToR1S0F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.BitcastConvertType(a, F32); + auto a = ConstantR1(&builder, {}); + BitcastConvertType(a, F32); std::vector expected = {}; ComputeAndCompareR1(&builder, expected, {}); @@ -82,8 +82,8 @@ XLA_TEST_F(BitcastConvertTest, ConvertR1S0S32ToR1S0F32) { TEST_F(BitcastConvertTest, ConvertR1F32ToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.6, 64.4}); - builder.BitcastConvertType(a, S32); + auto a = ConstantR1(&builder, {42.6, 64.4}); + BitcastConvertType(a, S32); std::vector expected = {0x422a6666, 0x4280cccd}; ComputeAndCompareR1(&builder, expected, {}); @@ -91,9 +91,9 @@ TEST_F(BitcastConvertTest, ConvertR1F32ToR1S32) { TEST_F(BitcastConvertTest, ConvertS32Extremes) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {std::numeric_limits::min(), std::numeric_limits::max()}); - builder.BitcastConvertType(a, F32); + auto a = ConstantR1(&builder, {std::numeric_limits::min(), + std::numeric_limits::max()}); + BitcastConvertType(a, F32); std::vector expected = {-0.0f, NAN}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0, 0)); @@ -102,10 +102,10 @@ TEST_F(BitcastConvertTest, ConvertS32Extremes) { TEST_F(BitcastConvertTest, ConvertMapToS32) { XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); - auto param = b->Parameter(0, ShapeUtil::MakeShape(F32, {}), "in"); - b->BitcastConvertType(param, S32); - auto a = builder.ConstantR1({42.0f, 64.0f}); - builder.Map({a}, b->BuildAndNoteError(), {0}); + auto param = Parameter(b.get(), 0, ShapeUtil::MakeShape(F32, {}), "in"); + BitcastConvertType(param, S32); + auto a = ConstantR1(&builder, {42.0f, 64.0f}); + Map(&builder, {a}, b->BuildAndNoteError(), {0}); std::vector expected = {0x42280000, 0x42800000}; ComputeAndCompareR1(&builder, expected, {}); @@ -114,10 +114,10 @@ TEST_F(BitcastConvertTest, ConvertMapToS32) { TEST_F(BitcastConvertTest, ConvertMapToF32) { XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); - auto param = b->Parameter(0, ShapeUtil::MakeShape(S32, {}), "in"); - b->BitcastConvertType(param, F32); - auto a = builder.ConstantR1({0x42280000, 0x42800000}); - builder.Map({a}, b->BuildAndNoteError(), {0}); + auto param = Parameter(b.get(), 0, ShapeUtil::MakeShape(S32, {}), "in"); + BitcastConvertType(param, F32); + auto a = ConstantR1(&builder, {0x42280000, 0x42800000}); + Map(&builder, {a}, b->BuildAndNoteError(), {0}); std::vector expected = {42.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}); @@ -130,9 +130,9 @@ TEST_F(BitcastConvertTest, ConvertMapToF32) { // the new convert should have the same element type as the old convert. TEST_F(BitcastConvertTest, ConvertReshape) { XlaBuilder builder(TestName()); - auto input = builder.ConstantR1({0x42280000}); - auto reshape = builder.Reshape(input, /*dimensions=*/{0}, /*new_sizes=*/{}); - builder.BitcastConvertType(reshape, F32); + auto input = ConstantR1(&builder, {0x42280000}); + auto reshape = Reshape(input, /*dimensions=*/{0}, /*new_sizes=*/{}); + BitcastConvertType(reshape, F32); ComputeAndCompareR0(&builder, 42.0f, {}); } diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc index 34c86e007beea1cbac04641bdbdab62dc567f13e..91aba9a8de3f1fe098e8bc8cc9d5378fa67b8385 100644 --- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc @@ -37,17 +37,17 @@ class BroadcastSimpleTest : public ClientLibraryTestBase { XlaBuilder* builder) { switch (op) { case HloOpcode::kMinimum: { - return builder->Min(lhs, rhs); + return Min(lhs, rhs); } case HloOpcode::kMaximum: { - return builder->Max(lhs, rhs); + return Max(lhs, rhs); } case HloOpcode::kMultiply: { - return builder->Mul(lhs, rhs); + return Mul(lhs, rhs); } default: { // Default to Add - return builder->Add(lhs, rhs); + return Add(lhs, rhs); } } } @@ -104,13 +104,13 @@ using ::testing::HasSubstr; XLA_TEST_F(BroadcastSimpleTest, ScalarNoOpBroadcast) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR0(1.5), {}); + Broadcast(ConstantR0(&b, 1.5), {}); ComputeAndCompareR0(&b, 1.5, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, ScalarTo2D_2x3) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR0(2.25), {2, 3}); + Broadcast(ConstantR0(&b, 2.25), {2, 3}); Array2D expected(2, 3, 2.25); ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); } @@ -122,7 +122,7 @@ XLA_TEST_F(BroadcastSimpleTest, ScalarParamTo2D_2x3) { CreateR0Parameter(2.25f, /*parameter_number=*/0, /*name=*/"src", /*builder=*/&b, /*data_handle=*/&src); - b.Broadcast(src, {2, 3}); + Broadcast(src, {2, 3}); Array2D expected(2, 3, 2.25); ComputeAndCompareR2(&b, expected, {param_data.get()}, ErrorSpec(0.0001)); @@ -130,21 +130,21 @@ XLA_TEST_F(BroadcastSimpleTest, ScalarParamTo2D_2x3) { XLA_TEST_F(BroadcastSimpleTest, ScalarTo2D_2x0) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR0(2.25), {2, 0}); + Broadcast(ConstantR0(&b, 2.25), {2, 0}); Array2D expected(2, 0); ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, ScalarTo2D_0x2) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR0(2.25), {0, 2}); + Broadcast(ConstantR0(&b, 2.25), {0, 2}); Array2D expected(0, 2); ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, 1DTo2D) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR1({1, 2, 3}), {2}); + Broadcast(ConstantR1(&b, {1, 2, 3}), {2}); Array2D expected(2, 3); expected(0, 0) = 1; @@ -156,6 +156,86 @@ XLA_TEST_F(BroadcastSimpleTest, 1DTo2D) { ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); } +XLA_TEST_F(BroadcastSimpleTest, 1DTo2D_WithDimsUsual) { + XlaBuilder b(TestName()); + BroadcastInDim(ConstantR1(&b, {1, 2}), + ShapeUtil::MakeShape(F32, {2, 2}), {1}); + + Array2D expected(2, 2); + expected(0, 0) = 1; + expected(0, 1) = 2; + expected(1, 0) = 1; + expected(1, 1) = 2; + + ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); +} + +XLA_TEST_F(BroadcastSimpleTest, 1DTo2D_WithDimsTranspose) { + XlaBuilder b(TestName()); + BroadcastInDim(ConstantR1(&b, {1, 2}), + ShapeUtil::MakeShape(F32, {2, 2}), {0}); + + Array2D expected(2, 2); + expected(0, 0) = 1; + expected(0, 1) = 1; + expected(1, 0) = 2; + expected(1, 1) = 2; + + ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); +} + +XLA_TEST_F(BroadcastSimpleTest, 2DTo3D_WithDims) { + XlaBuilder b(TestName()); + BroadcastInDim(ConstantR2(&b, {{1.0, 5.0}, {2.0, 6.0}}), + ShapeUtil::MakeShape(F32, {2, 2, 2}), {0, 1}); + + Array3D expected(2, 2, 2); + expected(0, 0, 0) = 1.0; + expected(1, 0, 0) = 2.0; + expected(0, 0, 1) = 1.0; + expected(1, 0, 1) = 2.0; + expected(0, 1, 0) = 5.0; + expected(1, 1, 0) = 6.0; + expected(1, 1, 1) = 6.0; + expected(0, 1, 1) = 5.0; + + ComputeAndCompareR3(&b, expected, {}, ErrorSpec(0.0001)); +} + +XLA_TEST_F(BroadcastSimpleTest, 2DTo3D_WithDimsNotPossibleWithBroadCast) { + XlaBuilder b(TestName()); + BroadcastInDim(ConstantR2(&b, {{1.0, 5.0}, {2.0, 6.0}}), + ShapeUtil::MakeShape(F32, {2, 2, 2}), {0, 2}); + + Array3D expected(2, 2, 2); + expected(0, 0, 0) = 1.0; + expected(1, 0, 0) = 2.0; + expected(0, 0, 1) = 5.0; + expected(1, 0, 1) = 6.0; + expected(0, 1, 0) = 1.0; + expected(1, 1, 0) = 2.0; + expected(1, 1, 1) = 6.0; + expected(0, 1, 1) = 5.0; + + ComputeAndCompareR3(&b, expected, {}, ErrorSpec(0.0001)); +} + +XLA_TEST_F(BroadcastSimpleTest, 1DTo2D_WithDimsNotPossibleWithBroadCast) { + XlaBuilder b(TestName()); + BroadcastInDim(ConstantR1(&b, {1, 2}), + ShapeUtil::MakeShape(F32, {3, 2}), {1}); + + Array2D expected(3, 2); + expected(0, 0) = 1; + expected(0, 1) = 2; + expected(1, 0) = 1; + expected(1, 1) = 2; + expected(2, 0) = 1; + expected(2, 1) = 2; + + ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); +} + // Tests implicit broadcasting of PREDs. XLA_TEST_F(BroadcastSimpleTest, BooleanAnd2DTo3D_Pred) { XlaBuilder b(TestName()); @@ -172,7 +252,7 @@ XLA_TEST_F(BroadcastSimpleTest, BooleanAnd2DTo3D_Pred) { XlaOp x, y; auto x_data = CreateR2Parameter(x_vals, 0, "x", &b, &x); auto y_data = CreateR3Parameter(y_vals, 1, "y", &b, &y); - b.And(x, y, /*broadcast_dimensions=*/{1, 2}); + And(x, y, /*broadcast_dimensions=*/{1, 2}); Array3D expected(2, 2, 1); expected(0, 0, 0) = false; @@ -185,7 +265,7 @@ XLA_TEST_F(BroadcastSimpleTest, BooleanAnd2DTo3D_Pred) { XLA_TEST_F(BroadcastSimpleTest, ZeroElement_1DTo2D) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR1({}), {2}); + Broadcast(ConstantR1(&b, {}), {2}); Array2D expected(2, 0); ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); @@ -193,7 +273,7 @@ XLA_TEST_F(BroadcastSimpleTest, ZeroElement_1DTo2D) { XLA_TEST_F(BroadcastSimpleTest, 1DToZeroElement2D) { XlaBuilder b(TestName()); - b.Broadcast(b.ConstantR1({1, 2, 3}), {0}); + Broadcast(ConstantR1(&b, {1, 2, 3}), {0}); Array2D expected(0, 3); ComputeAndCompareR2(&b, expected, {}, ErrorSpec(0.0001)); @@ -209,10 +289,10 @@ XLA_TEST_F(BroadcastSimpleTest, InDimensionAndDegenerateBroadcasting) { // dimensions. XlaBuilder b(TestName()); - b.Add(b.ConstantR2({{1.0, 5.0}}), - b.ConstantLiteral(*Literal::CreateR3( - {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), - /*broadcast_dimensions=*/{1, 2}); + Add(ConstantR2(&b, {{1.0, 5.0}}), + ConstantLiteral(&b, *Literal::CreateR3( + {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), + /*broadcast_dimensions=*/{1, 2}); auto expected = Literal::CreateR3({{{3.0, 7.0}, {4.0, 8.0}, {5.0, 9.0}}, @@ -260,9 +340,10 @@ XLA_TEST_P(BroadcastR3ImplicitTest, Doit) { MakeR3Data(spec.input_bounds, spec.minor2major_layout, &r3_implicit_shape, &r3_implicit_array, 1.0, 0.2, 56789); - auto r3_implicit_parameter = builder.Parameter(0, r3_implicit_shape, "input"); - auto r3_parameter = builder.Parameter(1, r3_shape, "input"); - XlaOp op = BuildBinOp(spec.op, r3_implicit_parameter, r3_parameter, &builder); + auto r3_implicit_parameter = + Parameter(&builder, 0, r3_implicit_shape, "input"); + auto r3_parameter = Parameter(&builder, 1, r3_shape, "input"); + BuildBinOp(spec.op, r3_implicit_parameter, r3_parameter, &builder); Array3D expected_array(spec.output_bounds[0], spec.output_bounds[1], spec.output_bounds[2]); @@ -306,7 +387,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) { auto r1 = CreateR3Parameter(r1d, 1, "r1", &b, &r1h); auto r3 = CreateR3Parameter(r3d, 0, "r3", &b, &r3h); - b.Add(r3h, r1h); + Add(r3h, r1h); auto expected = Literal::CreateR3({{{2, 3}, {4, 5}}, {{7, 8}, {9, 10}}}); @@ -317,10 +398,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR3({{{1, 2}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 4}, {4, 6}}, {{6, 8}, {8, 10}}}); @@ -330,10 +411,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_2) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR3({{{1}, {2}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1}, {2}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 3}, {5, 6}}, {{6, 7}, {9, 10}}}); @@ -343,10 +424,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_2) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR3({{{1, 2}, {3, 4}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}, {3, 4}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 4}, {6, 8}}, {{6, 8}, {10, 12}}}); @@ -356,10 +437,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR3({{{1, 2}}, {{3, 4}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + auto r1 = + ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}}, {{3, 4}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 4}, {4, 6}}, {{8, 10}, {10, 12}}}); @@ -370,10 +452,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_2) { XlaBuilder b(TestName()); auto r1 = - b.ConstantLiteral(*Literal::CreateR3({{{1}, {2}}, {{3}, {4}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + ConstantLiteral(&b, *Literal::CreateR3({{{1}, {2}}, {{3}, {4}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 3}, {5, 6}}, {{8, 9}, {11, 12}}}); @@ -383,10 +465,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_2) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1_2) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR3({{{1}}})); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1}}})); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1); auto expected = Literal::CreateR3({{{2, 3}, {4, 5}}, {{6, 7}, {8, 9}}}); @@ -509,14 +591,14 @@ XLA_TEST_P(BroadcastR2ImplicitTest, Doit) { &r2_implicit_shape2, &r2_implicit_array2, 0.8, 0.4, 56789); auto r2_implicit_parameter1 = - builder.Parameter(0, r2_implicit_shape1, "input0"); - auto r2_parameter = builder.Parameter(1, r2_shape, "input1"); + Parameter(&builder, 0, r2_implicit_shape1, "input0"); + auto r2_parameter = Parameter(&builder, 1, r2_shape, "input1"); auto r2_implicit_parameter2 = - builder.Parameter(2, r2_implicit_shape2, "input2"); + Parameter(&builder, 2, r2_implicit_shape2, "input2"); XlaOp op1 = BuildBinOp(spec.op1, r2_implicit_parameter1, r2_parameter, &builder); - XlaOp op2 = BuildBinOp(spec.op2, op1, r2_implicit_parameter2, &builder); + BuildBinOp(spec.op2, op1, r2_implicit_parameter2, &builder); Array2D expected_array(spec.output_bounds[0], spec.output_bounds[1]); @@ -544,9 +626,9 @@ INSTANTIATE_TEST_CASE_P(BroadcastR2ImplicitTestInstances, XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_0) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR2({{1, 2}})); - auto r2 = b.ConstantLiteral(*Literal::CreateR2({{1, 2}, {3, 4}})); - b.Add(r2, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}})); + auto r2 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}, {3, 4}})); + Add(r2, r1); auto expected = Literal::CreateR2({{2, 4}, {4, 6}}); @@ -555,9 +637,9 @@ XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_0) { XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_1) { XlaBuilder b(TestName()); - auto r1 = b.ConstantLiteral(*Literal::CreateR2({{1}, {2}})); - auto r2 = b.ConstantLiteral(*Literal::CreateR2({{1, 2}, {3, 4}})); - b.Add(r2, r1); + auto r1 = ConstantLiteral(&b, *Literal::CreateR2({{1}, {2}})); + auto r2 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}, {3, 4}})); + Add(r2, r1); auto expected = Literal::CreateR2({{2, 3}, {5, 6}}); @@ -566,10 +648,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_1) { XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim0) { XlaBuilder b(TestName()); - auto r1 = b.ConstantR1({10, 20}); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r3, r1, {0}); + auto r1 = ConstantR1(&b, {10, 20}); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r3, r1, {0}); auto expected = Literal::CreateR3({{{11, 12}, {13, 14}}, {{25, 26}, {27, 28}}}); @@ -579,10 +661,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim0) { XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim1) { XlaBuilder b(TestName()); - auto r1 = b.ConstantR1({10, 20}); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r1, r3, {1}); + auto r1 = ConstantR1(&b, {10, 20}); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r1, r3, {1}); auto expected = Literal::CreateR3({{{11, 12}, {23, 24}}, {{15, 16}, {27, 28}}}); @@ -592,10 +674,10 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim1) { XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim2) { XlaBuilder b(TestName()); - auto r1 = b.ConstantR1({10, 20}); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); - b.Add(r1, r3, {2}); + auto r1 = ConstantR1(&b, {10, 20}); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + Add(r1, r3, {2}); auto expected = Literal::CreateR3({{{11, 22}, {13, 24}}, {{15, 26}, {17, 28}}}); @@ -605,17 +687,17 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim2) { XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) { XlaBuilder b(TestName()); - auto r1_0 = b.ConstantR1({1000, 2000}); - auto r1_1 = b.ConstantR1({100, 200}); - auto r1_2 = b.ConstantR1({10, 20}); - auto r3 = b.ConstantLiteral( - *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + auto r1_0 = ConstantR1(&b, {1000, 2000}); + auto r1_1 = ConstantR1(&b, {100, 200}); + auto r1_2 = ConstantR1(&b, {10, 20}); + auto r3 = ConstantLiteral( + &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); for (int i = 0; i < 3; ++i) { - r3 = b.Add(r1_0, r3, {0}); - r3 = b.Add(r3, r1_1, {1}); - r3 = b.Add(r1_2, r3, {2}); + r3 = Add(r1_0, r3, {0}); + r3 = Add(r3, r1_1, {1}); + r3 = Add(r1_2, r3, {2}); } - r3 = b.Mul(r3, b.ConstantR0(-2)); + r3 = Mul(r3, ConstantR0(&b, -2)); auto expected = Literal::CreateR3( {{{-6 * 1110 - 2, -6 * 1120 - 4}, {-6 * 1210 - 6, -6 * 1220 - 8}}, @@ -626,17 +708,17 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) { XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAllWithScalarBroadcast) { XlaBuilder b(TestName()); - auto r1_0 = b.ConstantR1({1000, 2000}); - auto r1_1 = b.ConstantR1({100, 200}); - auto r1_2 = b.ConstantR1({10, 20}); - auto r0 = b.ConstantR0(3); - auto r3 = b.Broadcast(r0, {2, 2, 2}); + auto r1_0 = ConstantR1(&b, {1000, 2000}); + auto r1_1 = ConstantR1(&b, {100, 200}); + auto r1_2 = ConstantR1(&b, {10, 20}); + auto r0 = ConstantR0(&b, 3); + auto r3 = Broadcast(r0, {2, 2, 2}); for (int i = 0; i < 3; ++i) { - r3 = b.Add(r1_0, r3, {0}); - r3 = b.Add(r3, r1_1, {1}); - r3 = b.Add(r1_2, r3, {2}); + r3 = Add(r1_0, r3, {0}); + r3 = Add(r3, r1_1, {1}); + r3 = Add(r1_2, r3, {2}); } - r3 = b.Mul(r3, b.ConstantR0(-1)); + r3 = Mul(r3, ConstantR0(&b, -1)); auto expected = Literal::CreateR3( {{{-3 * 1110 - 3, -3 * 1120 - 3}, {-3 * 1210 - 3, -3 * 1220 - 3}}, @@ -650,10 +732,10 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) { // results in a shape incompatible with the lhs [2, 3, 1]. XlaBuilder b(TestName()); - b.Add(b.ConstantR2({{1.0, 5.0}, {1.0, 5.0}}), - b.ConstantLiteral(*Literal::CreateR3( - {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), - /*broadcast_dimensions=*/{1, 2}); + Add(ConstantR2(&b, {{1.0, 5.0}, {1.0, 5.0}}), + ConstantLiteral(&b, *Literal::CreateR3( + {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), + /*broadcast_dimensions=*/{1, 2}); auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); @@ -665,26 +747,26 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) { // Test invalid broadcasting with [1, 2] and [2, 3] inputs. XlaBuilder b(TestName()); - b.Add(b.ConstantR2({{1.0, 2.0}}), - b.ConstantR2({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); + Add(ConstantR2(&b, {{1.0, 2.0}}), + ConstantR2(&b, {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("op BINOP_ADD with incompatible shapes")); + HasSubstr("op add with incompatible shapes")); } XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) { // Test invalid broadcasting with [1, 2] and [2, 3] inputs. XlaBuilder b(TestName()); - b.Add(b.ConstantR2({{1.0, 2.0}}), - b.ConstantR2({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); + Add(ConstantR2(&b, {{1.0, 2.0}}), + ConstantR2(&b, {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("op BINOP_ADD with incompatible shapes")); + HasSubstr("op add with incompatible shapes")); } } // namespace diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc index 5fd33b50c94356839bbed58acd43b7d0286f4a7e..bc64a19ce22072152216a7c150fbd16480d261fb 100644 --- a/tensorflow/compiler/xla/tests/call_test.cc +++ b/tensorflow/compiler/xla/tests/call_test.cc @@ -34,7 +34,7 @@ class CallOpTest : public ClientLibraryTestBase { protected: XlaComputation CreateR0F32IdentityComputation() { XlaBuilder builder("Identity"); - builder.Parameter(0, r0f32_, "x"); + Parameter(&builder, 0, r0f32_, "x"); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -42,9 +42,9 @@ class CallOpTest : public ClientLibraryTestBase { XlaComputation CreateR1S0F32AdditionComputation() { XlaBuilder builder("Addition"); - auto x = builder.Parameter(0, r1s0f32_, "x"); - auto y = builder.Parameter(1, r1s0f32_, "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, r1s0f32_, "x"); + auto y = Parameter(&builder, 1, r1s0f32_, "y"); + Add(x, y); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -52,9 +52,9 @@ class CallOpTest : public ClientLibraryTestBase { XlaComputation CreateR1S2F32AdditionComputation() { XlaBuilder builder("Addition"); - auto x = builder.Parameter(0, r1s2f32_, "x"); - auto y = builder.Parameter(1, r1s2f32_, "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, r1s2f32_, "x"); + auto y = Parameter(&builder, 1, r1s2f32_, "y"); + Add(x, y); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -62,7 +62,7 @@ class CallOpTest : public ClientLibraryTestBase { XlaComputation CreateR0F32TupleComputation() { XlaBuilder builder("Tuple"); - builder.Tuple({builder.Parameter(0, r0f32_, "x")}); + Tuple(&builder, {Parameter(&builder, 0, r0f32_, "x")}); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -76,8 +76,8 @@ class CallOpTest : public ClientLibraryTestBase { XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR0F32IdentityComputation(); - auto constant = builder.ConstantLiteral(*Literal::CreateR0(42.0)); - builder.Call(callee, {constant}); + auto constant = ConstantLiteral(&builder, *Literal::CreateR0(42.0)); + Call(&builder, callee, {constant}); ComputeAndCompareR0(&builder, 42.0, {}, ErrorSpec(0.01f)); } @@ -85,9 +85,9 @@ XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR1S0F32AdditionComputation(); - auto x = builder.ConstantLiteral(*Literal::CreateR1({})); - auto y = builder.ConstantLiteral(*Literal::CreateR1({})); - builder.Call(callee, {x, y}); + auto x = ConstantLiteral(&builder, *Literal::CreateR1({})); + auto y = ConstantLiteral(&builder, *Literal::CreateR1({})); + Call(&builder, callee, {x, y}); ComputeAndCompareR1(&builder, {}, {}, ErrorSpec(0.01f)); } @@ -95,9 +95,9 @@ XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { XLA_TEST_F(CallOpTest, CallR1S2F32AddArray) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR1S2F32AdditionComputation(); - auto x = builder.ConstantLiteral(*Literal::CreateR1({1.0f, 2.0f})); - auto y = builder.ConstantLiteral(*Literal::CreateR1({2.0f, 3.0f})); - builder.Call(callee, {x, y}); + auto x = ConstantLiteral(&builder, *Literal::CreateR1({1.0f, 2.0f})); + auto y = ConstantLiteral(&builder, *Literal::CreateR1({2.0f, 3.0f})); + Call(&builder, callee, {x, y}); ComputeAndCompareR1(&builder, {3.0f, 5.0f}, {}, ErrorSpec(0.01f)); } @@ -105,26 +105,26 @@ XLA_TEST_F(CallOpTest, CallR1S2F32AddArray) { XLA_TEST_F(CallOpTest, CallTreeTwoDeepBranchFactorThree) { XlaBuilder builder("inner"); { - auto x = builder.Parameter(0, r0f32_, "x"); - builder.Add(x, builder.ConstantR0(1.0)); + auto x = Parameter(&builder, 0, r0f32_, "x"); + Add(x, ConstantR0(&builder, 1.0)); } TF_ASSERT_OK_AND_ASSIGN(XlaComputation inner, builder.Build()); XlaBuilder builder2("outer"); { - auto x = builder2.Parameter(0, r0f32_, "x"); - x = builder2.Call(inner, {x}); - x = builder2.Call(inner, {x}); - x = builder2.Call(inner, {x}); + auto x = Parameter(&builder2, 0, r0f32_, "x"); + x = Call(&builder2, inner, {x}); + x = Call(&builder2, inner, {x}); + x = Call(&builder2, inner, {x}); } TF_ASSERT_OK_AND_ASSIGN(XlaComputation outer, builder2.Build()); XlaBuilder builder3("outermost"); { - auto x = builder3.Parameter(0, r0f32_, "x"); - x = builder3.Call(outer, {x}); - x = builder3.Call(outer, {x}); - x = builder3.Call(outer, {x}); + auto x = Parameter(&builder3, 0, r0f32_, "x"); + x = Call(&builder3, outer, {x}); + x = Call(&builder3, outer, {x}); + x = Call(&builder3, outer, {x}); } TF_ASSERT_OK_AND_ASSIGN( @@ -138,7 +138,7 @@ XLA_TEST_F(CallOpTest, CallR0F32Tuple) { XlaComputation callee = CreateR0F32TupleComputation(); auto elem = Literal::CreateR0(42.0); auto tuple = Literal::MakeTuple({elem.get()}); - builder.Call(callee, {builder.ConstantLiteral(*elem)}); + Call(&builder, callee, {ConstantLiteral(&builder, *elem)}); ComputeAndCompareTuple(&builder, *tuple, {}, ErrorSpec(0.01f)); } diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc index 660ff0cad5666219a4a7cb1eedbed03f06e651ba..1ad57c075b22c7730ffd8d1beeab60c9d5dc7458 100644 --- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc +++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc @@ -38,9 +38,9 @@ TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) { XlaBuilder builder("add_two_params"); auto param_literal = Literal::CreateR1({1.1f, 2.2f}); - auto p0 = builder.Parameter(0, param_literal->shape(), "param0"); - auto p1 = builder.Parameter(1, param_literal->shape(), "param1"); - auto add = builder.Add(p0, p1); + auto p0 = Parameter(&builder, 0, param_literal->shape(), "param0"); + auto p1 = Parameter(&builder, 1, param_literal->shape(), "param1"); + Add(p0, p1); auto param0_data = client_->TransferToServer(*param_literal).ConsumeValueOrDie(); @@ -77,9 +77,9 @@ TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) { XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { XlaBuilder builder("add_two_params"); - auto p0 = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param0"); - auto p1 = builder.Parameter(1, ShapeUtil::MakeShape(F32, {4}), "param1"); - auto add = builder.Mul(p0, p1); + auto p0 = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "param0"); + auto p1 = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {4}), "param1"); + Mul(p0, p1); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc index bf8ed4d9fb0bc61b86ef0b5872711a122a3d416b..dafd6ebabbe6edafc1c926677b3ea00e775be010 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.cc +++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" @@ -486,11 +487,11 @@ ClientLibraryTestBase::ComputeValueAndReference( XlaComputation ClientLibraryTestBase::CreateScalarRelu() { XlaBuilder builder("relu"); auto shape = ShapeUtil::MakeShape(use_bfloat16_ ? BF16 : F32, {}); - auto z_value = builder.Parameter(0, shape, "z_value"); + auto z_value = Parameter(&builder, 0, shape, "z_value"); auto zero = use_bfloat16_ - ? builder.ConstantR0(static_cast(0.0f)) - : builder.ConstantR0(0.0f); - builder.Max(z_value, zero); + ? ConstantR0(&builder, static_cast(0.0f)) + : ConstantR0(&builder, 0.0f); + Max(z_value, zero); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -499,9 +500,9 @@ XlaComputation ClientLibraryTestBase::CreateScalarRelu() { XlaComputation ClientLibraryTestBase::CreateScalarMax() { XlaBuilder builder("max"); auto shape = ShapeUtil::MakeShape(use_bfloat16_ ? BF16 : F32, {}); - auto x = builder.Parameter(0, shape, "x"); - auto y = builder.Parameter(1, shape, "y"); - builder.Max(x, y); + auto x = Parameter(&builder, 0, shape, "x"); + auto y = Parameter(&builder, 1, shape, "y"); + Max(x, y); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -510,13 +511,13 @@ XlaComputation ClientLibraryTestBase::CreateScalarMax() { XlaComputation ClientLibraryTestBase::CreateScalarReluSensitivity() { XlaBuilder builder("relu_sensitivity"); auto shape = ShapeUtil::MakeShape(use_bfloat16_ ? BF16 : F32, {}); - auto activation = builder.Parameter(0, shape, "activation"); - auto backprop = builder.Parameter(1, shape, "backprop"); + auto activation = Parameter(&builder, 0, shape, "activation"); + auto backprop = Parameter(&builder, 1, shape, "backprop"); auto zero = use_bfloat16_ - ? builder.ConstantR0(static_cast(0.0f)) - : builder.ConstantR0(0.0f); - auto activation_gtz = builder.Gt(activation, zero); - builder.Select(activation_gtz, /*on_true=*/backprop, /*on_false=*/zero); + ? ConstantR0(&builder, static_cast(0.0f)) + : ConstantR0(&builder, 0.0f); + auto activation_gtz = Gt(activation, zero); + Select(activation_gtz, /*on_true=*/backprop, /*on_false=*/zero); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); @@ -559,8 +560,8 @@ XlaOp ClientLibraryTestBase::AddParam(const Literal& argument, XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal, XlaBuilder* builder) { - return builder->ConstantLiteral( - use_bfloat16_ ? *Literal::ConvertF32ToBF16(literal) : literal); + return ConstantLiteral( + builder, use_bfloat16_ ? *Literal::ConvertF32ToBF16(literal) : literal); } std::unique_ptr @@ -588,7 +589,7 @@ ClientLibraryTestBase::CreateParameterAndTransferLiteral( client_->TransferToServer(*param_literal, device_handle) .ConsumeValueOrDie(); *data_handle = - builder->Parameter(parameter_number, param_literal->shape(), name); + Parameter(builder, parameter_number, param_literal->shape(), name); return data; } diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h index 0499fec5898a42affa0e0a712dee10187355c13e..5361ae6783c4c103cf923ffbda066165545c39a1 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.h +++ b/tensorflow/compiler/xla/tests/client_library_test_base.h @@ -373,6 +373,13 @@ class ClientLibraryTestBase : public ::testing::Test { // The float type used in this test, BF16 or F32 according to use_bfloat16. PrimitiveType FloatType() const { return use_bfloat16_ ? BF16 : F32; } + // Executes the computation and calculates the expected reference value using + // the reference client. Returns two literals in the order of (expected, + // actual). + StatusOr, std::unique_ptr>> + ComputeValueAndReference(XlaBuilder* builder, + tensorflow::gtl::ArraySlice arguments); + Client* client_; Client* ref_client_; // To compute reference result. ExecutionOptions execution_options_; @@ -390,13 +397,6 @@ class ClientLibraryTestBase : public ::testing::Test { const string& error_message)>& verify_output, const Shape* output_with_layout = nullptr); - // Executes the computation and calculates the expected reference value using - // the reference client. Returns two literals in the order of (expected, - // actual). - StatusOr, std::unique_ptr>> - ComputeValueAndReference(XlaBuilder* builder, - tensorflow::gtl::ArraySlice arguments); - // Whether to run tests with all float-type input/output converted to // bfloat16. bool use_bfloat16_ = false; @@ -545,7 +545,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR0Parameter( } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); - *data_handle = builder->Parameter(parameter_number, literal->shape(), name); + *data_handle = Parameter(builder, parameter_number, literal->shape(), name); return data; } @@ -559,7 +559,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR1Parameter( } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); - *data_handle = builder->Parameter(parameter_number, literal->shape(), name); + *data_handle = Parameter(builder, parameter_number, literal->shape(), name); return data; } @@ -573,7 +573,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR2Parameter( } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); - *data_handle = builder->Parameter(parameter_number, literal->shape(), name); + *data_handle = Parameter(builder, parameter_number, literal->shape(), name); return data; } @@ -587,7 +587,7 @@ std::unique_ptr ClientLibraryTestBase::CreateR3Parameter( } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); - *data_handle = builder->Parameter(parameter_number, literal->shape(), name); + *data_handle = Parameter(builder, parameter_number, literal->shape(), name); return data; } diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc index 08671cf62445826649b5c97003f998ae98a59d97..831b863998f1cab31d37aa4474be45d8531075ac 100644 --- a/tensorflow/compiler/xla/tests/client_test.cc +++ b/tensorflow/compiler/xla/tests/client_test.cc @@ -43,8 +43,8 @@ XLA_TEST_F(ClientTest, ExecuteWithLayout) { std::vector> layouts = {{0, 1}, {1, 0}}; for (const std::vector& execute_layout : layouts) { for (const std::vector& transfer_layout : layouts) { - b.Add(b.ConstantR2({{1, 2}, {3, 4}}), - b.ConstantR2({{10, 20}, {30, 40}})); + Add(ConstantR2(&b, {{1, 2}, {3, 4}}), + ConstantR2(&b, {{10, 20}, {30, 40}})); TF_ASSERT_OK_AND_ASSIGN(auto computation, b.Build()); ExecutionOptions execution_options = execution_options_; @@ -72,8 +72,8 @@ XLA_TEST_F(ClientTest, ExecuteWithLayout) { XLA_TEST_F(ClientTest, ExecuteWithTupleLayout) { XlaBuilder b(TestName()); - b.Tuple({b.ConstantR2({{1, 2}, {3, 4}}), - b.ConstantR2({{10, 20}, {30, 40}})}); + Tuple(&b, {ConstantR2(&b, {{1, 2}, {3, 4}}), + ConstantR2(&b, {{10, 20}, {30, 40}})}); TF_ASSERT_OK_AND_ASSIGN(auto computation, b.Build()); @@ -117,8 +117,8 @@ XLA_TEST_F(ClientTest, DISABLED_ON_GPU(ExecuteParallel)) { client_->TransferToServer(*Literal::CreateR2({{5, 6}, {7, 8}}))); XlaBuilder b(TestName() + ".add"); - b.Add(b.Parameter(0, shape, "param_0"), - b.ConstantR2({{1, 2}, {3, 4}})); + Add(Parameter(&b, 0, shape, "param_0"), + ConstantR2(&b, {{1, 2}, {3, 4}})); TF_ASSERT_OK_AND_ASSIGN(add_with_one_arg, b.Build()); // We can't really test parallel execution on CPU since all of the cores in a diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc index 50a006964869b3e5dce431d441f7cd81af9df910..eb211dd8ff376fb0da03b3e68be1d849970d96fd 100644 --- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc +++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc @@ -77,7 +77,7 @@ class CompilationCacheTest : public ClientLibraryTestBase { // TODO(b/74197823): Disabled because there is no cache in the new design. XLA_TEST_F(CompilationCacheTest, DISABLED_ComputationCalledMultipleTimes) { XlaBuilder builder(TestName()); - builder.Neg(builder.ConstantR0(42.0)); + Neg(ConstantR0(&builder, 42.0)); XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation, {}, -42.0, /*expect_cache_hit=*/false); @@ -99,7 +99,7 @@ XLA_TEST_F(CompilationCacheTest, .ConsumeValueOrDie(); XlaBuilder builder(TestName()); - builder.Neg(builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param")); + Neg(Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "param")); XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation, {data_42.get()}, -42.0, @@ -115,16 +115,16 @@ XLA_TEST_F(CompilationCacheTest, // TODO(b/74197823): Disabled because there is no cache in the new design. XLA_TEST_F(CompilationCacheTest, DISABLED_MultipleComputations) { XlaBuilder builder_neg(TestName() + "_neg"); - builder_neg.Neg(builder_neg.ConstantR0(42.0)); + Neg(ConstantR0(&builder_neg, 42.0)); XlaComputation computation_neg = builder_neg.Build().ConsumeValueOrDie(); XlaBuilder builder_exp(TestName() + "_exp"); - builder_exp.Exp(builder_exp.ConstantR0(1.0)); + Exp(ConstantR0(&builder_exp, 1.0)); XlaComputation computation_exp = builder_exp.Build().ConsumeValueOrDie(); XlaBuilder builder_add(TestName() + "_add"); - builder_add.Add(builder_add.ConstantR0(2.0), - builder_add.ConstantR0(3.0)); + Add(ConstantR0(&builder_add, 2.0), + ConstantR0(&builder_add, 3.0)); XlaComputation computation_add = builder_add.Build().ConsumeValueOrDie(); ExecuteComputationR0F32(computation_neg, {}, -42.0, @@ -154,7 +154,7 @@ XLA_TEST_F(CompilationCacheTest, DISABLED_DifferentParameterLayouts) { client_->TransferToServer(*colmaj_array).ConsumeValueOrDie(); XlaBuilder builder(TestName()); - builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "param0"); XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecuteComputationR2F32(computation, {colmaj_handle.get()}, diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc index ba22530f1cfee56337f862c25122d399dbf0f1e4..1a396b090c615dbd829964bd68ebda74df29c71e 100644 --- a/tensorflow/compiler/xla/tests/compute_constant_test.cc +++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc @@ -99,7 +99,7 @@ TEST_F(ComputeConstantTest, ScalarInt32Literal) { for (ClientType client_type : client_types) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); - auto computation = b.ConstantR0(42); + auto computation = ConstantR0(&b, 42); EXPECT_TRUE(IsConstant(computation, &b)); auto value = ComputeConstantScalar(client, computation, &b); @@ -113,7 +113,7 @@ TEST_F(ComputeConstantTest, ScalarFloatAdd) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); auto computation = - b.Add(b.ConstantR0(42.5f), b.ConstantR0(1.5f)); + Add(ConstantR0(&b, 42.5f), ConstantR0(&b, 1.5f)); EXPECT_TRUE(IsConstant(computation, &b)); auto value = ComputeConstantScalar(client, computation, &b); @@ -127,8 +127,8 @@ TEST_F(ComputeConstantTest, ScalarRng) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); auto computation = - b.RngUniform(b.ConstantR0(1.1f), b.ConstantR0(2.1f), - ShapeUtil::MakeShape(F32, {})); + RngUniform(ConstantR0(&b, 1.1f), ConstantR0(&b, 2.1f), + ShapeUtil::MakeShape(F32, {})); EXPECT_FALSE(IsConstant(computation, &b)); auto value = ComputeConstantScalar(client, computation, &b); @@ -141,7 +141,7 @@ TEST_F(ComputeConstantTest, DirectParamMissing) { for (ClientType client_type : client_types) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); - auto computation = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param"); + auto computation = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "param"); EXPECT_FALSE(IsConstant(computation, &b)); auto value = ComputeConstantScalar(client, computation, &b); @@ -156,8 +156,8 @@ TEST_F(ComputeConstantTest, IndirectParamMissing) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); auto computation = - b.Add(b.ConstantR0(1.0f), - b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param")); + Add(ConstantR0(&b, 1.0f), + Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "param")); EXPECT_FALSE(IsConstant(computation, &b)); auto value = ComputeConstantScalar(client, computation, &b); @@ -174,18 +174,18 @@ TEST_F(ComputeConstantTest, UnrelatedParam) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); - auto param_a = b.Parameter(10, ShapeUtil::MakeShape(F32, {}), "param0"); + auto param_a = Parameter(&b, 10, ShapeUtil::MakeShape(F32, {}), "param0"); auto constant_4 = - b.Add(b.ConstantR0(2.5f), b.ConstantR0(1.5f)); - auto not_constant_a = b.Add(constant_4, param_a); + Add(ConstantR0(&b, 2.5f), ConstantR0(&b, 1.5f)); + auto not_constant_a = Add(constant_4, param_a); - auto param_b = b.Parameter(1, ShapeUtil::MakeShape(F32, {}), "param1"); + auto param_b = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {}), "param1"); auto constant_9 = - b.Mul(b.ConstantR0(2.0f), b.ConstantR0(4.5f)); - auto not_constant_b = b.Add(param_b, constant_9); + Mul(ConstantR0(&b, 2.0f), ConstantR0(&b, 4.5f)); + auto not_constant_b = Add(param_b, constant_9); - auto constant_13 = b.Add(constant_4, constant_9); - b.Add(not_constant_b, b.Add(constant_13, not_constant_a)); + auto constant_13 = Add(constant_4, constant_9); + Add(not_constant_b, Add(constant_13, not_constant_a)); EXPECT_TRUE(IsConstant(constant_13, &b)); @@ -201,7 +201,7 @@ TEST_F(ComputeConstantTest, NonScalarAdd) { XlaBuilder b(TestName()); auto computation = - b.Add(b.ConstantR1({1, 2}), b.ConstantR1({3, 4})); + Add(ConstantR1(&b, {1, 2}), ConstantR1(&b, {3, 4})); EXPECT_TRUE(IsConstant(computation, &b)); TF_ASSERT_OK_AND_ASSIGN(auto computed, @@ -216,7 +216,7 @@ TEST_F(ComputeConstantTest, IntegerDivide) { for (ClientType client_type : client_types) { Client* client = ClientOrDie(platform_, client_type); XlaBuilder b(TestName()); - auto computation = b.Div(b.ConstantR0(15), b.ConstantR0(3)); + auto computation = Div(ConstantR0(&b, 15), ConstantR0(&b, 3)); EXPECT_TRUE(IsConstant(computation, &b)); TF_ASSERT_OK_AND_ASSIGN(auto computed, @@ -237,8 +237,8 @@ XLA_TEST_F(ComputeConstantTest, Layout) { TF_ASSERT_OK_AND_ASSIGN( auto computed, ComputeConstantLiteral( client, - b.Add(b.ConstantR2({{1, 2}, {3, 4}}), - b.ConstantR2({{10, 20}, {30, 40}})), + Add(ConstantR2(&b, {{1, 2}, {3, 4}}), + ConstantR2(&b, {{10, 20}, {30, 40}})), &b, &layout_proto)); std::unique_ptr expected_literal = diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index a4c8a83eb15f7cc279b6c8f1bf1394c0afb9f7cf..1161b560b7b0756556911812666c6f4fe9179f72 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -39,7 +39,7 @@ using ::testing::HasSubstr; // Concatenate expects at least one argument. XLA_TEST_F(ConcatTest, Concat_Nothing) { XlaBuilder builder(TestName()); - builder.ConcatInDim({}, 0); + ConcatInDim(&builder, {}, 0); StatusOr computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), @@ -49,8 +49,8 @@ XLA_TEST_F(ConcatTest, Concat_Nothing) { // Concatenate with one argument works. XLA_TEST_F(ConcatTest, Concat_R1_With_Nothing) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0, 64.0}); - builder.ConcatInDim({a}, 0); + auto a = ConstantR1(&builder, {42.0, 64.0}); + ConcatInDim(&builder, {a}, 0); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -58,8 +58,8 @@ XLA_TEST_F(ConcatTest, Concat_R1_With_Nothing) { XLA_TEST_F(ConcatTest, Concat_R1_L0_With_Nothing) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.ConcatInDim({a}, 0); + auto a = ConstantR1(&builder, {}); + ConcatInDim(&builder, {a}, 0); std::vector expected = {}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -69,9 +69,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L0_With_Nothing) { // to concatenate on. XLA_TEST_F(ConcatTest, CannotConcatR0WithR0) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR0(42.0); - auto b = builder.ConstantR0(64.0); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR0(&builder, 42.0); + auto b = ConstantR0(&builder, 64.0); + ConcatInDim(&builder, {a, b}, 0); StatusOr computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), @@ -80,9 +80,9 @@ XLA_TEST_F(ConcatTest, CannotConcatR0WithR0) { XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L0) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({}); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {}); + ConcatInDim(&builder, {a, b}, 0); std::vector expected = {}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -90,9 +90,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L0) { XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - auto b = builder.ConstantR1({256.0}); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR1(&builder, {}); + auto b = ConstantR1(&builder, {256.0}); + ConcatInDim(&builder, {a, b}, 0); std::vector expected = {256}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -100,9 +100,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L1) { XLA_TEST_F(ConcatTest, Concat_R1_L2_With_R1_L0) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0, 64.0}); - auto b = builder.ConstantR1({}); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR1(&builder, {42.0, 64.0}); + auto b = ConstantR1(&builder, {}); + ConcatInDim(&builder, {a, b}, 0); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -110,9 +110,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L2_With_R1_L0) { XLA_TEST_F(ConcatTest, Concat_R1_L2_With_R1_L1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0, 64.0}); - auto b = builder.ConstantR1({256.0}); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR1(&builder, {42.0, 64.0}); + auto b = ConstantR1(&builder, {256.0}); + ConcatInDim(&builder, {a, b}, 0); std::vector expected = {42, 64, 256}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -130,9 +130,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L253_With_R1_L7) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR1(lhs); - auto b = builder.ConstantR1(rhs); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR1(&builder, lhs); + auto b = ConstantR1(&builder, rhs); + ConcatInDim(&builder, {a, b}, 0); ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); } @@ -140,9 +140,9 @@ XLA_TEST_F(ConcatTest, Concat_R1_L253_With_R1_L7) { XLA_TEST_F(ConcatTest, Concat_0x0_With_0x0) { for (int dim : {0, 1}) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2FromArray2D(Array2D(0, 0)); - auto b = builder.ConstantR2FromArray2D(Array2D(0, 0)); - builder.ConcatInDim({a, b}, dim); + auto a = ConstantR2FromArray2D(&builder, Array2D(0, 0)); + auto b = ConstantR2FromArray2D(&builder, Array2D(0, 0)); + ConcatInDim(&builder, {a, b}, dim); ComputeAndCompareR2(&builder, Array2D(0, 0), {}, ErrorSpec(0.0001)); @@ -153,9 +153,9 @@ XLA_TEST_F(ConcatTest, Concat_1x1_With_1x1_InDim0) { XlaBuilder builder(TestName()); auto a_array = CreatePatternedMatrix(1, 1); auto b_array = CreatePatternedMatrix(1, 1, /*offset=*/64.0); - auto a = builder.ConstantR2FromArray2D(*a_array); - auto b = builder.ConstantR2FromArray2D(*b_array); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR2FromArray2D(&builder, *a_array); + auto b = ConstantR2FromArray2D(&builder, *b_array); + ConcatInDim(&builder, {a, b}, 0); Array2D expected({ {0}, @@ -168,9 +168,9 @@ XLA_TEST_F(ConcatTest, Concat_1x1_With_1x1_InDim1) { XlaBuilder builder(TestName()); auto a_array = CreatePatternedMatrix(1, 1); auto b_array = CreatePatternedMatrix(1, 1, /*offset=*/64.0); - auto a = builder.ConstantR2FromArray2D(*a_array); - auto b = builder.ConstantR2FromArray2D(*b_array); - builder.ConcatInDim({a, b}, 1); + auto a = ConstantR2FromArray2D(&builder, *a_array); + auto b = ConstantR2FromArray2D(&builder, *b_array); + ConcatInDim(&builder, {a, b}, 1); Array2D expected({ {0, 64}, @@ -181,9 +181,9 @@ XLA_TEST_F(ConcatTest, Concat_1x1_With_1x1_InDim1) { XLA_TEST_F(ConcatTest, Concat2x0With2x5) { XlaBuilder builder(TestName()); auto b_array = CreatePatternedMatrix(2, 5, /*offset=*/64.0); - auto a = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto b = builder.ConstantR2FromArray2D(*b_array); - builder.ConcatInDim({a, b}, 1); + auto a = ConstantR2FromArray2D(&builder, Array2D(2, 0)); + auto b = ConstantR2FromArray2D(&builder, *b_array); + ConcatInDim(&builder, {a, b}, 1); ComputeAndCompareR2(&builder, *b_array, {}, ErrorSpec(0.0001)); } @@ -192,9 +192,9 @@ XLA_TEST_F(ConcatTest, Concat2x3With2x5) { XlaBuilder builder(TestName()); auto a_array = CreatePatternedMatrix(2, 3); auto b_array = CreatePatternedMatrix(2, 5, /*offset=*/64.0); - auto a = builder.ConstantR2FromArray2D(*a_array); - auto b = builder.ConstantR2FromArray2D(*b_array); - builder.ConcatInDim({a, b}, 1); + auto a = ConstantR2FromArray2D(&builder, *a_array); + auto b = ConstantR2FromArray2D(&builder, *b_array); + ConcatInDim(&builder, {a, b}, 1); Array2D expected({ {0, 1, 2, 64, 65, 66, 67, 68}, @@ -206,9 +206,9 @@ XLA_TEST_F(ConcatTest, Concat2x3With2x5) { XLA_TEST_F(ConcatTest, Concat3x2With0x2) { XlaBuilder builder(TestName()); auto a_array = CreatePatternedMatrix(3, 2); - auto a = builder.ConstantR2FromArray2D(*a_array); - auto b = builder.ConstantR2FromArray2D(Array2D(0, 2)); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR2FromArray2D(&builder, *a_array); + auto b = ConstantR2FromArray2D(&builder, Array2D(0, 2)); + ConcatInDim(&builder, {a, b}, 0); ComputeAndCompareR2(&builder, *a_array, {}, ErrorSpec(0.0001)); } @@ -217,9 +217,9 @@ XLA_TEST_F(ConcatTest, Concat3x2With5x2) { XlaBuilder builder(TestName()); auto a_array = CreatePatternedMatrix(3, 2); auto b_array = CreatePatternedMatrix(5, 2, /*offset=*/64.0); - auto a = builder.ConstantR2FromArray2D(*a_array); - auto b = builder.ConstantR2FromArray2D(*b_array); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR2FromArray2D(&builder, *a_array); + auto b = ConstantR2FromArray2D(&builder, *b_array); + ConcatInDim(&builder, {a, b}, 0); Array2D expected({ {0, 1}, @@ -236,9 +236,9 @@ XLA_TEST_F(ConcatTest, Concat3x2With5x2) { XLA_TEST_F(ConcatTest, Concat_R3_3x0x2_3x0x1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR3FromArray3D(Array3D(3, 0, 2)); - auto b = builder.ConstantR3FromArray3D(Array3D(3, 0, 1)); - builder.ConcatInDim({a, b}, 2); + auto a = ConstantR3FromArray3D(&builder, Array3D(3, 0, 2)); + auto b = ConstantR3FromArray3D(&builder, Array3D(3, 0, 1)); + ConcatInDim(&builder, {a, b}, 2); ComputeAndCompareR3(&builder, Array3D(3, 0, 3), {}, ErrorSpec(0.0001)); } @@ -257,9 +257,9 @@ XLA_TEST_F(ConcatTest, Concat_R3_3x1x2_3x1x1) { {{7}}, {{8}}, }); - auto a = builder.ConstantR3FromArray3D(a_array); - auto b = builder.ConstantR3FromArray3D(b_array); - builder.ConcatInDim({a, b}, 2); + auto a = ConstantR3FromArray3D(&builder, a_array); + auto b = ConstantR3FromArray3D(&builder, b_array); + ConcatInDim(&builder, {a, b}, 2); Array3D expected({ {{0, 1, 6}}, @@ -271,10 +271,10 @@ XLA_TEST_F(ConcatTest, Concat_R3_3x1x2_3x1x1) { XLA_TEST_F(ConcatTest, Concat_R1_1x1_1x1_1x1) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0}); - auto b = builder.ConstantR1({64.0}); - auto c = builder.ConstantR1({256.0}); - builder.ConcatInDim({a, b, c}, 0); + auto a = ConstantR1(&builder, {42.0}); + auto b = ConstantR1(&builder, {64.0}); + auto c = ConstantR1(&builder, {256.0}); + ConcatInDim(&builder, {a, b, c}, 0); std::vector expected = {42, 64, 256}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -300,10 +300,10 @@ XLA_TEST_F(ConcatTest, Concat_R3_3x1x2_3x1x1_3x1x1) { {{7}}, {{11}}, }); - auto a = builder.ConstantR3FromArray3D(a_array); - auto b = builder.ConstantR3FromArray3D(b_array); - auto c = builder.ConstantR3FromArray3D(c_array); - builder.ConcatInDim({a, b, c}, 2); + auto a = ConstantR3FromArray3D(&builder, a_array); + auto b = ConstantR3FromArray3D(&builder, b_array); + auto c = ConstantR3FromArray3D(&builder, c_array); + ConcatInDim(&builder, {a, b, c}, 2); Array3D expected({ {{0, 1, 2, 3}}, @@ -315,11 +315,11 @@ XLA_TEST_F(ConcatTest, Concat_R3_3x1x2_3x1x1_3x1x1) { XLA_TEST_F(ConcatTest, DoubleConcatLeftAssociative) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0}); - auto b = builder.ConstantR1({64.0}); - auto c = builder.ConstantR1({256.0}); + auto a = ConstantR1(&builder, {42.0}); + auto b = ConstantR1(&builder, {64.0}); + auto c = ConstantR1(&builder, {256.0}); // concatenated = (a concat b) concat c - builder.ConcatInDim({builder.ConcatInDim({a, b}, 0), c}, 0); + ConcatInDim(&builder, {ConcatInDim(&builder, {a, b}, 0), c}, 0); std::vector expected = {42, 64, 256}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -327,11 +327,11 @@ XLA_TEST_F(ConcatTest, DoubleConcatLeftAssociative) { XLA_TEST_F(ConcatTest, DoubleConcatRightAssociative) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0}); - auto b = builder.ConstantR1({64.0}); - auto c = builder.ConstantR1({256.0}); + auto a = ConstantR1(&builder, {42.0}); + auto b = ConstantR1(&builder, {64.0}); + auto c = ConstantR1(&builder, {256.0}); // concatenated = a concat (b concat c) - builder.ConcatInDim({a, builder.ConcatInDim({b, c}, 0)}, 0); + ConcatInDim(&builder, {a, ConcatInDim(&builder, {b, c}, 0)}, 0); std::vector expected = {42, 64, 256}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -346,9 +346,9 @@ XLA_TEST_F(ConcatTest, Concat_1x1024_With_1x1024_InDim0) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR2FromArray2D(lhs); - auto b = builder.ConstantR2FromArray2D(rhs); - builder.ConcatInDim({a, b}, 0); + auto a = ConstantR2FromArray2D(&builder, lhs); + auto b = ConstantR2FromArray2D(&builder, rhs); + ConcatInDim(&builder, {a, b}, 0); Array2D expected(2, 1024); for (int i = 0; i < 1024; ++i) { @@ -367,9 +367,9 @@ XLA_TEST_F(ConcatTest, Concat_1x1024_With_1x1024_InDim1) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR2FromArray2D(lhs); - auto b = builder.ConstantR2FromArray2D(rhs); - builder.ConcatInDim({a, b}, 1); + auto a = ConstantR2FromArray2D(&builder, lhs); + auto b = ConstantR2FromArray2D(&builder, rhs); + ConcatInDim(&builder, {a, b}, 1); Array2D expected(1, 2048); for (int i = 0; i < 1024; ++i) { @@ -392,9 +392,9 @@ XLA_TEST_F(ConcatTest, Concat_64x64_With_64x2) { } XlaBuilder builder(TestName()); - auto a = builder.ConstantR2FromArray2D(lhs); - auto b = builder.ConstantR2FromArray2D(rhs); - builder.ConcatInDim({a, b}, 1); + auto a = ConstantR2FromArray2D(&builder, lhs); + auto b = ConstantR2FromArray2D(&builder, rhs); + ConcatInDim(&builder, {a, b}, 1); Array2D expected(64, 66); for (int i0 = 0; i0 < 64; ++i0) { @@ -410,22 +410,37 @@ XLA_TEST_F(ConcatTest, CannotConcatOpaques) { XlaBuilder builder(TestName()); auto opaque_shape = ShapeUtil::MakeOpaqueShape(); auto r1f32 = xla::ShapeUtil::MakeShape(xla::F32, {1}); - auto x = builder.Parameter(0, r1f32, "x"); - auto y = builder.Parameter(1, opaque_shape, "y"); - builder.ConcatInDim({x, y}, 0); + auto x = Parameter(&builder, 0, r1f32, "x"); + auto y = Parameter(&builder, 1, opaque_shape, "y"); + ConcatInDim(&builder, {x, y}, 0); StatusOr computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT( computation_status.status().ToString(), - HasSubstr("Expected non-opaque argument for operand of concatenation")); + HasSubstr("Expected array argument for operand of concatenation")); +} + +// Show that we can't concatenate with tokens. +XLA_TEST_F(ConcatTest, CannotConcatTokens) { + XlaBuilder builder(TestName()); + auto token_shape = ShapeUtil::MakeTokenShape(); + auto r1f32 = xla::ShapeUtil::MakeShape(xla::F32, {1}); + auto x = Parameter(&builder, 0, r1f32, "x"); + auto y = Parameter(&builder, 1, token_shape, "y"); + ConcatInDim(&builder, {x, y}, 0); + StatusOr computation_status = builder.Build(); + ASSERT_FALSE(computation_status.ok()); + EXPECT_THAT( + computation_status.status().ToString(), + HasSubstr("Expected array argument for operand of concatenation")); } XLA_TEST_F(ConcatTest, ConcatSeveralBoxedPredicates) { XlaBuilder builder(TestName()); - auto p0 = builder.ConstantR1({true}); - auto p1 = builder.ConstantR1({false}); - auto p2 = builder.ConstantR1({true}); - builder.ConcatInDim({p0, p1, p2}, 0); + auto p0 = ConstantR1(&builder, {true}); + auto p1 = ConstantR1(&builder, {false}); + auto p2 = ConstantR1(&builder, {true}); + ConcatInDim(&builder, {p0, p1, p2}, 0); bool expected[] = {true, false, true}; ComputeAndCompareR1(&builder, expected, {}); @@ -433,11 +448,11 @@ XLA_TEST_F(ConcatTest, ConcatSeveralBoxedPredicates) { XLA_TEST_F(ConcatTest, ConcatSeveralR1S32s) { XlaBuilder builder(TestName()); - auto a0 = builder.ConstantR1({1}); - auto a1 = builder.ConstantR1({2, 3}); - auto a2 = builder.ConstantR1({4, 5, 6}); - auto a3 = builder.ConstantR1({7, 8, 9, 10}); - builder.ConcatInDim({a0, a1, a2, a3}, 0); + auto a0 = ConstantR1(&builder, {1}); + auto a1 = ConstantR1(&builder, {2, 3}); + auto a2 = ConstantR1(&builder, {4, 5, 6}); + auto a3 = ConstantR1(&builder, {7, 8, 9, 10}); + ConcatInDim(&builder, {a0, a1, a2, a3}, 0); std::vector expected(10); std::iota(expected.begin(), expected.end(), 1); @@ -472,7 +487,7 @@ XLA_TEST_F(ConcatTest, ConcatR3WeirdDims) { auto p1 = CreateR3Parameter(arr1, /*parameter_number=*/1, "p1", &builder, &h1); - builder.ConcatInDim({h0, h1}, 2); + ConcatInDim(&builder, {h0, h1}, 2); ComputeAndCompareR3(&builder, expected, {p0.get(), p1.get()}); } @@ -499,9 +514,9 @@ TEST_P(ConcatR2BinaryTest, DoIt) { rhs.FillUnique(1000); XlaBuilder builder(TestName()); - auto a0 = builder.ConstantR2FromArray2D(lhs); - auto a1 = builder.ConstantR2FromArray2D(rhs); - builder.ConcatInDim({a0, a1}, spec.concat_dimension); + auto a0 = ConstantR2FromArray2D(&builder, lhs); + auto a1 = ConstantR2FromArray2D(&builder, rhs); + ConcatInDim(&builder, {a0, a1}, spec.concat_dimension); std::unique_ptr> expected = ReferenceUtil::Concat2D(lhs, rhs, spec.concat_dimension); @@ -525,13 +540,13 @@ XLA_TEST_F(ConcatTest, ConcatOperandsOfSameOperand) { auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, f32_scalar, "x"); - auto y = builder.Parameter(1, f32_scalar, "y"); - auto mul = builder.Mul(x, y); - auto add1 = builder.Add(mul, builder.ConstantR1({1.f, 2.f})); - auto add2 = builder.Add(mul, builder.ConstantR1({3.f, 4.f})); - auto add3 = builder.Add(mul, builder.ConstantR1({5.f, 6.f})); - builder.ConcatInDim({add1, add2, add3}, /*dimension=*/0); + auto x = Parameter(&builder, 0, f32_scalar, "x"); + auto y = Parameter(&builder, 1, f32_scalar, "y"); + auto mul = Mul(x, y); + auto add1 = Add(mul, ConstantR1(&builder, {1.f, 2.f})); + auto add2 = Add(mul, ConstantR1(&builder, {3.f, 4.f})); + auto add3 = Add(mul, ConstantR1(&builder, {5.f, 6.f})); + ConcatInDim(&builder, {add1, add2, add3}, /*dimension=*/0); ComputeAndCompareR1(&builder, {7., 8., 9., 10., 11., 12.}, {x_data.get(), y_data.get()}, ErrorSpec(1e-4)); @@ -549,13 +564,13 @@ XLA_TEST_F(ConcatTest, ConcatBroadcastArgument) { auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie(); XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, x_literal->shape(), "x"); - auto y = builder.Parameter(1, f32_scalar, "y"); - auto z = builder.Parameter(2, f32_scalar, "z"); - auto bcast = builder.Broadcast(y, {5}); - auto bcast2 = builder.Broadcast(z, {3}); - auto concat = builder.ConcatInDim({bcast, x}, /*dimension=*/0); - builder.ConcatInDim({concat, bcast2}, /*dimension=*/0); + auto x = Parameter(&builder, 0, x_literal->shape(), "x"); + auto y = Parameter(&builder, 1, f32_scalar, "y"); + auto z = Parameter(&builder, 2, f32_scalar, "z"); + auto bcast = Broadcast(y, {5}); + auto bcast2 = Broadcast(z, {3}); + auto concat = ConcatInDim(&builder, {bcast, x}, /*dimension=*/0); + ConcatInDim(&builder, {concat, bcast2}, /*dimension=*/0); ComputeAndCompareR1( &builder, @@ -577,13 +592,13 @@ XLA_TEST_F(ConcatTest, ConcatBroadcastArgumentR3) { auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie(); XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, x_literal->shape(), "x"); - auto y = builder.Parameter(1, f32_scalar, "y"); - auto z = builder.Parameter(2, f32_scalar, "y"); - auto y_bcast = builder.Broadcast(y, {1, 5, 7}); - auto z_bcast = builder.Broadcast(z, {4, 1, 7}); - auto concat = builder.ConcatInDim({y_bcast, x}, /*dimension=*/0); - builder.ConcatInDim({concat, z_bcast}, /*dimension=*/1); + auto x = Parameter(&builder, 0, x_literal->shape(), "x"); + auto y = Parameter(&builder, 1, f32_scalar, "y"); + auto z = Parameter(&builder, 2, f32_scalar, "y"); + auto y_bcast = Broadcast(y, {1, 5, 7}); + auto z_bcast = Broadcast(z, {4, 1, 7}); + auto concat = ConcatInDim(&builder, {y_bcast, x}, /*dimension=*/0); + ConcatInDim(&builder, {concat, z_bcast}, /*dimension=*/1); Array3D y_bcast3d(1, 5, 7, 1.5f); Array3D z_bcast3d(4, 1, 7, 5.5f); auto concat0 = ReferenceUtil::Concat3D(y_bcast3d, x3d, 0); diff --git a/tensorflow/compiler/xla/tests/conditional_test.cc b/tensorflow/compiler/xla/tests/conditional_test.cc index 7ff6706935740c7d76ee5cd03eae292386760397..ee3c83039bfc13f6ad78111d92ba0f8387a3ade3 100644 --- a/tensorflow/compiler/xla/tests/conditional_test.cc +++ b/tensorflow/compiler/xla/tests/conditional_test.cc @@ -26,8 +26,8 @@ class ConditionalOpTest : public ClientLibraryTestBase { protected: XlaComputation CreateR0ConstantComputation(float value) { XlaBuilder builder("Constant"); - builder.Parameter(0, empty_tuple_, "tuple"); - builder.ConstantR0(value); + Parameter(&builder, 0, empty_tuple_, "tuple"); + ConstantR0(&builder, value); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -35,7 +35,7 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateR0IdentityComputation() { XlaBuilder builder("Identity"); - builder.Parameter(0, r0f32_, "x"); + Parameter(&builder, 0, r0f32_, "x"); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -43,8 +43,8 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateCeilComputation(const Shape& shape) { XlaBuilder builder("Ceil"); - auto param = builder.Parameter(0, shape, "param"); - builder.Ceil(param); + auto param = Parameter(&builder, 0, shape, "param"); + Ceil(param); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -60,8 +60,8 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateFloorComputation(const Shape& shape) { XlaBuilder builder("Floor"); - auto param = builder.Parameter(0, shape, "param"); - builder.Floor(param); + auto param = Parameter(&builder, 0, shape, "param"); + Floor(param); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -78,12 +78,12 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateTupleCeilComputation(const string& computation_name, const Shape& tuple_shape) { XlaBuilder builder(computation_name); - auto tuple = builder.Parameter(0, tuple_shape, "tuple"); - auto x = builder.GetTupleElement(tuple, 0); - auto y = builder.GetTupleElement(tuple, 1); - auto x_ceil = builder.Ceil(x); - auto y_ceil = builder.Ceil(y); - builder.Tuple({x_ceil, y_ceil}); + auto tuple = Parameter(&builder, 0, tuple_shape, "tuple"); + auto x = GetTupleElement(tuple, 0); + auto y = GetTupleElement(tuple, 1); + auto x_ceil = Ceil(x); + auto y_ceil = Ceil(y); + Tuple(&builder, {x_ceil, y_ceil}); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -100,12 +100,12 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateTupleFloorComputation(const string& computation_name, const Shape& tuple_shape) { XlaBuilder builder(computation_name); - auto tuple = builder.Parameter(0, tuple_shape, "tuple"); - auto x = builder.GetTupleElement(tuple, 0); - auto y = builder.GetTupleElement(tuple, 1); - auto x_floor = builder.Floor(x); - auto y_floor = builder.Floor(y); - builder.Tuple({x_floor, y_floor}); + auto tuple = Parameter(&builder, 0, tuple_shape, "tuple"); + auto x = GetTupleElement(tuple, 0); + auto y = GetTupleElement(tuple, 1); + auto x_floor = Floor(x); + auto y_floor = Floor(y); + Tuple(&builder, {x_floor, y_floor}); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -122,10 +122,10 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateTupleAddComputation(const string& computation_name, const Shape& tuple_shape) { XlaBuilder builder(computation_name); - auto tuple = builder.Parameter(0, tuple_shape, "tuple"); - auto x = builder.GetTupleElement(tuple, 0); - auto y = builder.GetTupleElement(tuple, 1); - builder.Add(x, y); + auto tuple = Parameter(&builder, 0, tuple_shape, "tuple"); + auto x = GetTupleElement(tuple, 0); + auto y = GetTupleElement(tuple, 1); + Add(x, y); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -142,10 +142,10 @@ class ConditionalOpTest : public ClientLibraryTestBase { XlaComputation CreateTupleSubComputation(const string& computation_name, const Shape& tuple_shape) { XlaBuilder builder(computation_name); - auto tuple = builder.Parameter(0, tuple_shape, "tuple"); - auto x = builder.GetTupleElement(tuple, 0); - auto y = builder.GetTupleElement(tuple, 1); - builder.Sub(x, y); + auto tuple = Parameter(&builder, 0, tuple_shape, "tuple"); + auto x = GetTupleElement(tuple, 0); + auto y = GetTupleElement(tuple, 1); + Sub(x, y); auto build_status = builder.Build(); EXPECT_IS_OK(build_status.status()); return build_status.ConsumeValueOrDie(); @@ -172,12 +172,11 @@ class ConditionalOpTest : public ClientLibraryTestBase { // Test true and false computations that do not take any parameters. XLA_TEST_F(ConditionalOpTest, Parameters0) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operands = builder.Tuple({}); + auto pred = ConstantR0(&builder, true); + auto operands = Tuple(&builder, {}); auto true_computation = CreateR0ConstantComputation(56.0f); auto false_computation = CreateR0ConstantComputation(12.0f); - builder.Conditional(pred, operands, true_computation, operands, - false_computation); + Conditional(pred, operands, true_computation, operands, false_computation); ComputeAndCompareR0(&builder, 56.0f, {}, error_spec_); } @@ -185,11 +184,11 @@ XLA_TEST_F(ConditionalOpTest, Parameters0) { // Test true and false computations that take in 1 parameter. XLA_TEST_F(ConditionalOpTest, Parameters1) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.0f); - auto operand2 = builder.ConstantR0(12.0f); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.0f); + auto operand2 = ConstantR0(&builder, 12.0f); auto identity = CreateR0IdentityComputation(); - builder.Conditional(pred, operand1, identity, operand2, identity); + Conditional(pred, operand1, identity, operand2, identity); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -198,11 +197,11 @@ XLA_TEST_F(ConditionalOpTest, Parameters1) { // that take in different arguments. XLA_TEST_F(ConditionalOpTest, DiffComputationsDiffArgs) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.4f); - auto operand2 = builder.ConstantR0(12.6f); - builder.Conditional(pred, operand1, CreateR0CeilComputation(), operand2, - CreateR0FloorComputation()); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.4f); + auto operand2 = ConstantR0(&builder, 12.6f); + Conditional(pred, operand1, CreateR0CeilComputation(), operand2, + CreateR0FloorComputation()); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -211,10 +210,10 @@ XLA_TEST_F(ConditionalOpTest, DiffComputationsDiffArgs) { // that take in the same arguments. XLA_TEST_F(ConditionalOpTest, DiffComputationsSameArg) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand = builder.ConstantR0(12.6f); - builder.Conditional(pred, operand, CreateR0CeilComputation(), operand, - CreateR0FloorComputation()); + auto pred = ConstantR0(&builder, false); + auto operand = ConstantR0(&builder, 12.6f); + Conditional(pred, operand, CreateR0CeilComputation(), operand, + CreateR0FloorComputation()); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -223,11 +222,11 @@ XLA_TEST_F(ConditionalOpTest, DiffComputationsSameArg) { // take in different arguments. XLA_TEST_F(ConditionalOpTest, SameComputationDiffArgs) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.4f); - auto operand2 = builder.ConstantR0(12.6f); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.4f); + auto operand2 = ConstantR0(&builder, 12.6f); auto floor = CreateR0FloorComputation(); - builder.Conditional(pred, operand1, floor, operand2, floor); + Conditional(pred, operand1, floor, operand2, floor); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -236,10 +235,10 @@ XLA_TEST_F(ConditionalOpTest, SameComputationDiffArgs) { // take in the same arguments. XLA_TEST_F(ConditionalOpTest, SameComputationSameArg) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand = builder.ConstantR0(12.6f); + auto pred = ConstantR0(&builder, false); + auto operand = ConstantR0(&builder, 12.6f); auto floor = CreateR0FloorComputation(); - builder.Conditional(pred, operand, floor, operand, floor); + Conditional(pred, operand, floor, operand, floor); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -248,11 +247,11 @@ XLA_TEST_F(ConditionalOpTest, SameComputationSameArg) { // and false cases. XLA_TEST_F(ConditionalOpTest, SameComputationDiffInstances) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.4f); - auto operand2 = builder.ConstantR0(12.6f); - builder.Conditional(pred, operand1, CreateR0FloorComputation(), operand2, - CreateR0FloorComputation()); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.4f); + auto operand2 = ConstantR0(&builder, 12.6f); + Conditional(pred, operand1, CreateR0FloorComputation(), operand2, + CreateR0FloorComputation()); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -261,19 +260,19 @@ XLA_TEST_F(ConditionalOpTest, SameComputationDiffInstances) { XLA_TEST_F(ConditionalOpTest, ConditionalWithCall) { Shape r0bool = ShapeUtil::MakeShape(PRED, {}); XlaBuilder inner_builder(TestName() + ".inner_conditional"); - auto pred_cond = inner_builder.Parameter(0, r0bool, "param0"); - auto true_operand = inner_builder.Parameter(1, r0f32_, "param1"); - auto false_operand = inner_builder.Parameter(2, r0f32_, "param2"); - inner_builder.Conditional(pred_cond, true_operand, CreateR0CeilComputation(), - false_operand, CreateR0FloorComputation()); + auto pred_cond = Parameter(&inner_builder, 0, r0bool, "param0"); + auto true_operand = Parameter(&inner_builder, 1, r0f32_, "param1"); + auto false_operand = Parameter(&inner_builder, 2, r0f32_, "param2"); + Conditional(pred_cond, true_operand, CreateR0CeilComputation(), false_operand, + CreateR0FloorComputation()); auto inner_builder_result = inner_builder.Build(); XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.4f); - auto operand2 = builder.ConstantR0(12.6f); - builder.Call(inner_builder_result.ConsumeValueOrDie(), - {pred, operand1, operand2}); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.4f); + auto operand2 = ConstantR0(&builder, 12.6f); + Call(&builder, inner_builder_result.ConsumeValueOrDie(), + {pred, operand1, operand2}); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -282,12 +281,12 @@ XLA_TEST_F(ConditionalOpTest, ConditionalWithCall) { // true. XLA_TEST_F(ConditionalOpTest, Parameters2TrueBranch) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operand1 = builder.ConstantR0(56.0f); - auto operand2 = builder.ConstantR0(12.0f); - auto operands = builder.Tuple({operand1, operand2}); - builder.Conditional(pred, operands, CreateR0TupleAddComputation(), operands, - CreateR0TupleSubComputation()); + auto pred = ConstantR0(&builder, true); + auto operand1 = ConstantR0(&builder, 56.0f); + auto operand2 = ConstantR0(&builder, 12.0f); + auto operands = Tuple(&builder, {operand1, operand2}); + Conditional(pred, operands, CreateR0TupleAddComputation(), operands, + CreateR0TupleSubComputation()); ComputeAndCompareR0(&builder, 68.0f, {}, error_spec_); } @@ -296,12 +295,12 @@ XLA_TEST_F(ConditionalOpTest, Parameters2TrueBranch) { // false. XLA_TEST_F(ConditionalOpTest, Parameters2FalseBranch) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(56.0f); - auto operand2 = builder.ConstantR0(12.0f); - auto operands = builder.Tuple({operand1, operand2}); - builder.Conditional(pred, operands, CreateR0TupleAddComputation(), operands, - CreateR0TupleSubComputation()); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 56.0f); + auto operand2 = ConstantR0(&builder, 12.0f); + auto operands = Tuple(&builder, {operand1, operand2}); + Conditional(pred, operands, CreateR0TupleAddComputation(), operands, + CreateR0TupleSubComputation()); ComputeAndCompareR0(&builder, 44.0f, {}, error_spec_); } @@ -310,12 +309,12 @@ XLA_TEST_F(ConditionalOpTest, Parameters2FalseBranch) { // predicate is true. XLA_TEST_F(ConditionalOpTest, Parameters2ArrayTrueBranch) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operand1 = builder.ConstantR1({24.0f, 56.0f}); - auto operand2 = builder.ConstantR1({10.0f, 11.0f}); - auto operands = builder.Tuple({operand1, operand2}); - builder.Conditional(pred, operands, CreateR1TupleAddComputation(), operands, - CreateR1TupleSubComputation()); + auto pred = ConstantR0(&builder, true); + auto operand1 = ConstantR1(&builder, {24.0f, 56.0f}); + auto operand2 = ConstantR1(&builder, {10.0f, 11.0f}); + auto operands = Tuple(&builder, {operand1, operand2}); + Conditional(pred, operands, CreateR1TupleAddComputation(), operands, + CreateR1TupleSubComputation()); ComputeAndCompareR1(&builder, {34.0f, 67.0f}, {}, error_spec_); } @@ -324,12 +323,12 @@ XLA_TEST_F(ConditionalOpTest, Parameters2ArrayTrueBranch) { // predicate is false. XLA_TEST_F(ConditionalOpTest, Parameters2ArrayFalseBranch) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operand1 = builder.ConstantR1({24.0f, 56.0f}); - auto operand2 = builder.ConstantR1({10.0f, 11.0f}); - auto operands = builder.Tuple({operand1, operand2}); - builder.Conditional(pred, operands, CreateR1TupleAddComputation(), operands, - CreateR1TupleSubComputation()); + auto pred = ConstantR0(&builder, false); + auto operand1 = ConstantR1(&builder, {24.0f, 56.0f}); + auto operand2 = ConstantR1(&builder, {10.0f, 11.0f}); + auto operands = Tuple(&builder, {operand1, operand2}); + Conditional(pred, operands, CreateR1TupleAddComputation(), operands, + CreateR1TupleSubComputation()); ComputeAndCompareR1(&builder, {14.0f, 45.0f}, {}, error_spec_); } @@ -337,11 +336,11 @@ XLA_TEST_F(ConditionalOpTest, Parameters2ArrayFalseBranch) { // Test true and false computations that return a tuple of scalars. XLA_TEST_F(ConditionalOpTest, ReturnTupleOfScalars) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operands = builder.Tuple( - {builder.ConstantR0(12.2f), builder.ConstantR0(25.6f)}); - builder.Conditional(pred, operands, CreateR0TupleCeilComputation(), operands, - CreateR0TupleFloorComputation()); + auto pred = ConstantR0(&builder, false); + auto operands = Tuple(&builder, {ConstantR0(&builder, 12.2f), + ConstantR0(&builder, 25.6f)}); + Conditional(pred, operands, CreateR0TupleCeilComputation(), operands, + CreateR0TupleFloorComputation()); ComputeAndCompareTuple( &builder, @@ -353,11 +352,12 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleOfScalars) { // Test true and false computations that return a tuple of arrays. XLA_TEST_F(ConditionalOpTest, ReturnTupleOfArrays) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operands = builder.Tuple({builder.ConstantR1({12.2f, 15.8f}), - builder.ConstantR1({25.6f, 29.2f})}); - builder.Conditional(pred, operands, CreateR1TupleCeilComputation(), operands, - CreateR1TupleFloorComputation()); + auto pred = ConstantR0(&builder, true); + auto operands = + Tuple(&builder, {ConstantR1(&builder, {12.2f, 15.8f}), + ConstantR1(&builder, {25.6f, 29.2f})}); + Conditional(pred, operands, CreateR1TupleCeilComputation(), operands, + CreateR1TupleFloorComputation()); ComputeAndCompareTuple( &builder, @@ -371,31 +371,31 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleOfArrays) { XLA_TEST_F(ConditionalOpTest, ReturnTupleofPredicateScalarArray) { XlaBuilder true_builder(TestName() + ".true"); { - true_builder.Parameter(0, empty_tuple_, "tuple"); - auto true_pred = true_builder.ConstantR0(true); - auto true_scalar = true_builder.ConstantR0(12.2f); - auto true_array = true_builder.ConstantR1({12.8f, 14.6f}); - true_builder.Tuple({true_pred, true_scalar, true_array}); + Parameter(&true_builder, 0, empty_tuple_, "tuple"); + auto true_pred = ConstantR0(&true_builder, true); + auto true_scalar = ConstantR0(&true_builder, 12.2f); + auto true_array = ConstantR1(&true_builder, {12.8f, 14.6f}); + Tuple(&true_builder, {true_pred, true_scalar, true_array}); } auto true_builder_result = true_builder.Build(); EXPECT_IS_OK(true_builder_result.status()); XlaBuilder false_builder(TestName() + ".false"); { - false_builder.Parameter(0, empty_tuple_, "tuple"); - auto false_pred = false_builder.ConstantR0(false); - auto false_scalar = false_builder.ConstantR0(25.6f); - auto false_array = false_builder.ConstantR1({26.4f, 32.6f}); - false_builder.Tuple({false_pred, false_scalar, false_array}); + Parameter(&false_builder, 0, empty_tuple_, "tuple"); + auto false_pred = ConstantR0(&false_builder, false); + auto false_scalar = ConstantR0(&false_builder, 25.6f); + auto false_array = ConstantR1(&false_builder, {26.4f, 32.6f}); + Tuple(&false_builder, {false_pred, false_scalar, false_array}); } auto false_builder_result = false_builder.Build(); EXPECT_IS_OK(false_builder_result.status()); XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operands = builder.Tuple({}); - builder.Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), - operands, false_builder_result.ConsumeValueOrDie()); + auto pred = ConstantR0(&builder, true); + auto operands = Tuple(&builder, {}); + Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), operands, + false_builder_result.ConsumeValueOrDie()); ComputeAndCompareTuple( &builder, @@ -409,36 +409,37 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleofPredicateScalarArray) { XLA_TEST_F(ConditionalOpTest, ReturnNestedTuple) { XlaBuilder true_builder(TestName() + ".true"); { - true_builder.Parameter(0, empty_tuple_, "tuple"); - auto true_constant1 = true_builder.ConstantR0(12.2f); - auto true_constant2 = true_builder.ConstantR1({12.8f, 14.6f}); - auto true_constant3 = true_builder.ConstantR1({25.4f, 29.8f}); - auto true_constant4 = true_builder.ConstantR0(35.6f); - true_builder.Tuple({true_builder.Tuple({true_constant1, true_constant2}), - true_builder.Tuple({true_constant3, true_constant4})}); + Parameter(&true_builder, 0, empty_tuple_, "tuple"); + auto true_constant1 = ConstantR0(&true_builder, 12.2f); + auto true_constant2 = ConstantR1(&true_builder, {12.8f, 14.6f}); + auto true_constant3 = ConstantR1(&true_builder, {25.4f, 29.8f}); + auto true_constant4 = ConstantR0(&true_builder, 35.6f); + Tuple(&true_builder, + {Tuple(&true_builder, {true_constant1, true_constant2}), + Tuple(&true_builder, {true_constant3, true_constant4})}); } auto true_builder_result = true_builder.Build(); EXPECT_IS_OK(true_builder_result.status()); XlaBuilder false_builder(TestName() + ".false"); { - false_builder.Parameter(0, empty_tuple_, "tuple"); - auto false_constant1 = false_builder.ConstantR0(46.6f); - auto false_constant2 = false_builder.ConstantR1({54.4f, 58.4f}); - auto false_constant3 = false_builder.ConstantR1({62.1f, 67.4f}); - auto false_constant4 = false_builder.ConstantR0(9.3f); - false_builder.Tuple( - {false_builder.Tuple({false_constant1, false_constant2}), - false_builder.Tuple({false_constant3, false_constant4})}); + Parameter(&false_builder, 0, empty_tuple_, "tuple"); + auto false_constant1 = ConstantR0(&false_builder, 46.6f); + auto false_constant2 = ConstantR1(&false_builder, {54.4f, 58.4f}); + auto false_constant3 = ConstantR1(&false_builder, {62.1f, 67.4f}); + auto false_constant4 = ConstantR0(&false_builder, 9.3f); + Tuple(&false_builder, + {Tuple(&false_builder, {false_constant1, false_constant2}), + Tuple(&false_builder, {false_constant3, false_constant4})}); } auto false_builder_result = false_builder.Build(); EXPECT_IS_OK(false_builder_result.status()); XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto operands = builder.Tuple({}); - builder.Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), - operands, false_builder_result.ConsumeValueOrDie()); + auto pred = ConstantR0(&builder, false); + auto operands = Tuple(&builder, {}); + Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), operands, + false_builder_result.ConsumeValueOrDie()); ComputeAndCompareTuple( &builder, @@ -464,8 +465,8 @@ XLA_TEST_F(ConditionalOpTest, ScalarOperandsFromExternalParams) { CreateR0Parameter(56.3f, 1, "operand1", &builder, &operand1); auto operand2_param = CreateR0Parameter(12.7f, 2, "operand2", &builder, &operand2); - builder.Conditional(pred, operand1, CreateR0CeilComputation(), operand2, - CreateR0FloorComputation()); + Conditional(pred, operand1, CreateR0CeilComputation(), operand2, + CreateR0FloorComputation()); ComputeAndCompareR0( &builder, 57.0f, @@ -484,8 +485,8 @@ XLA_TEST_F(ConditionalOpTest, ArrayOperandsFromExternalParams) { &builder, &operand1); auto operand2_param = CreateR1Parameter({10.2f, 11.6f}, 2, "operand2", &builder, &operand2); - builder.Conditional(pred, operand1, CreateR1CeilComputation(), operand2, - CreateR1FloorComputation()); + Conditional(pred, operand1, CreateR1CeilComputation(), operand2, + CreateR1FloorComputation()); ComputeAndCompareR1( &builder, {10.0f, 11.0f}, @@ -499,27 +500,25 @@ XLA_TEST_F(ConditionalOpTest, NestedConditionals) { { Shape r0bool = ShapeUtil::MakeShape(PRED, {}); Shape tuple_shape = ShapeUtil::MakeTupleShape({r0bool, r0f32_, r0f32_}); - auto param0 = inner_builder.Parameter(0, tuple_shape, "param0"); - auto pred_cond = inner_builder.GetTupleElement(param0, 0); - auto true_operand = inner_builder.GetTupleElement(param0, 1); - auto false_operand = inner_builder.GetTupleElement(param0, 2); - inner_builder.Conditional(pred_cond, true_operand, - CreateR0CeilComputation(), false_operand, - CreateR0FloorComputation()); + auto param0 = Parameter(&inner_builder, 0, tuple_shape, "param0"); + auto pred_cond = GetTupleElement(param0, 0); + auto true_operand = GetTupleElement(param0, 1); + auto false_operand = GetTupleElement(param0, 2); + Conditional(pred_cond, true_operand, CreateR0CeilComputation(), + false_operand, CreateR0FloorComputation()); } auto inner_builder_result = inner_builder.Build(); EXPECT_IS_OK(inner_builder_result.status()); XlaBuilder builder(TestName()); - auto pred1 = builder.ConstantR0(true); - auto pred2 = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(1.1f); - auto operand2 = builder.ConstantR0(12.2f); - auto operand3 = builder.ConstantR0(43.3f); - auto tuple_operand = builder.Tuple({pred2, operand1, operand2}); - builder.Conditional(pred1, tuple_operand, - inner_builder_result.ConsumeValueOrDie(), operand3, - CreateR0IdentityComputation()); + auto pred1 = ConstantR0(&builder, true); + auto pred2 = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 1.1f); + auto operand2 = ConstantR0(&builder, 12.2f); + auto operand3 = ConstantR0(&builder, 43.3f); + auto tuple_operand = Tuple(&builder, {pred2, operand1, operand2}); + Conditional(pred1, tuple_operand, inner_builder_result.ConsumeValueOrDie(), + operand3, CreateR0IdentityComputation()); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -529,23 +528,22 @@ XLA_TEST_F(ConditionalOpTest, ConditionalInNestedComputation) { { Shape r0bool = ShapeUtil::MakeShape(PRED, {}); Shape tuple_shape = ShapeUtil::MakeTupleShape({r0bool, r0f32_, r0f32_}); - auto param0 = inner_builder.Parameter(0, tuple_shape, "param0"); - auto pred_cond = inner_builder.GetTupleElement(param0, 0); - auto true_operand = inner_builder.GetTupleElement(param0, 1); - auto false_operand = inner_builder.GetTupleElement(param0, 2); - inner_builder.Conditional(pred_cond, true_operand, - CreateR0CeilComputation(), false_operand, - CreateR0FloorComputation()); + auto param0 = Parameter(&inner_builder, 0, tuple_shape, "param0"); + auto pred_cond = GetTupleElement(param0, 0); + auto true_operand = GetTupleElement(param0, 1); + auto false_operand = GetTupleElement(param0, 2); + Conditional(pred_cond, true_operand, CreateR0CeilComputation(), + false_operand, CreateR0FloorComputation()); } auto inner_builder_result = inner_builder.Build(); EXPECT_IS_OK(inner_builder_result.status()); XlaBuilder builder(TestName()); - auto pred2 = builder.ConstantR0(false); - auto operand1 = builder.ConstantR0(1.1f); - auto operand2 = builder.ConstantR0(12.2f); - auto tuple_operand = builder.Tuple({pred2, operand1, operand2}); - builder.Call(inner_builder_result.ConsumeValueOrDie(), {tuple_operand}); + auto pred2 = ConstantR0(&builder, false); + auto operand1 = ConstantR0(&builder, 1.1f); + auto operand2 = ConstantR0(&builder, 12.2f); + auto tuple_operand = Tuple(&builder, {pred2, operand1, operand2}); + Call(&builder, inner_builder_result.ConsumeValueOrDie(), {tuple_operand}); ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } @@ -553,12 +551,12 @@ XLA_TEST_F(ConditionalOpTest, ConditionalInNestedComputation) { // Test a mismatch in the shape of the true operand and true computation. XLA_TEST_F(ConditionalOpTest, ShapeMismatch) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto operand1 = builder.ConstantR0(56.0f); - auto operand2 = builder.ConstantR0(12.0f); - auto operands = builder.Tuple({operand1, operand2}); - builder.Conditional(pred, operands, CreateR1TupleAddComputation(), operands, - CreateR0TupleSubComputation()); + auto pred = ConstantR0(&builder, true); + auto operand1 = ConstantR0(&builder, 56.0f); + auto operand2 = ConstantR0(&builder, 12.0f); + auto operands = Tuple(&builder, {operand1, operand2}); + Conditional(pred, operands, CreateR1TupleAddComputation(), operands, + CreateR0TupleSubComputation()); auto result = builder.Build(); EXPECT_FALSE(result.ok()); @@ -572,40 +570,40 @@ XLA_TEST_F(ConditionalOpTest, SwappedInputsInSequentialConditionals) { XlaComputation swapper; { XlaBuilder builder(TestName() + ".swapper"); - auto param0 = builder.Parameter(0, tuple_shape, "sp0"); - auto x = builder.GetTupleElement(param0, 0); - auto y = builder.GetTupleElement(param0, 1); - builder.Tuple({y, x}); + auto param0 = Parameter(&builder, 0, tuple_shape, "sp0"); + auto x = GetTupleElement(param0, 0); + auto y = GetTupleElement(param0, 1); + Tuple(&builder, {y, x}); swapper = builder.Build().ConsumeValueOrDie(); } XlaComputation forwarder; { XlaBuilder builder(TestName() + ".forwarder"); - auto param0 = builder.Parameter(0, tuple_shape, "fp0"); - auto x = builder.GetTupleElement(param0, 0); - auto y = builder.GetTupleElement(param0, 1); - builder.Tuple({x, y}); + auto param0 = Parameter(&builder, 0, tuple_shape, "fp0"); + auto x = GetTupleElement(param0, 0); + auto y = GetTupleElement(param0, 1); + Tuple(&builder, {x, y}); forwarder = builder.Build().ConsumeValueOrDie(); } XlaComputation main; { XlaBuilder builder(TestName() + ".main"); - auto param0 = builder.Parameter(0, tuple_shape, "mp0"); - auto x = builder.GetTupleElement(param0, 0); - auto y = builder.GetTupleElement(param0, 1); - auto lt_pred = builder.Lt(x, y); - auto res = builder.Conditional(lt_pred, param0, forwarder, param0, swapper); - auto ge_pred = builder.Ge(x, y); - builder.Conditional(ge_pred, res, swapper, res, forwarder); + auto param0 = Parameter(&builder, 0, tuple_shape, "mp0"); + auto x = GetTupleElement(param0, 0); + auto y = GetTupleElement(param0, 1); + auto lt_pred = Lt(x, y); + auto res = Conditional(lt_pred, param0, forwarder, param0, swapper); + auto ge_pred = Ge(x, y); + Conditional(ge_pred, res, swapper, res, forwarder); main = builder.Build().ConsumeValueOrDie(); } auto test_swap = [&](float a, float b) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR0(a); - auto y = builder.ConstantR0(b); - auto tuple_operand = builder.Tuple({x, y}); - builder.Call(main, {tuple_operand}); + auto x = ConstantR0(&builder, a); + auto y = ConstantR0(&builder, b); + auto tuple_operand = Tuple(&builder, {x, y}); + Call(&builder, main, {tuple_operand}); ComputeAndCompareTuple( &builder, diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc index 916ffadbc798ec0dd016f45b0bc4c36233455ee7..cc5d3b11767457444d4c199943e689f082d5b199 100644 --- a/tensorflow/compiler/xla/tests/constants_test.cc +++ b/tensorflow/compiler/xla/tests/constants_test.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" @@ -39,7 +40,7 @@ class ConstantsTest : public ClientLibraryTestBase { TEST_F(ConstantsTest, ZeroCellF32) { XlaBuilder builder(TestName()); - builder.ConstantR1({}); + ConstantR1(&builder, {}); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -48,7 +49,7 @@ TEST_F(ConstantsTest, OneCellF32) { std::vector constant = {2.0}; XlaBuilder builder(TestName()); - builder.ConstantR1(constant); + ConstantR1(&builder, constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); } @@ -57,7 +58,7 @@ TEST_F(ConstantsTest, OneCellS32) { std::vector constant = {2}; XlaBuilder builder(TestName()); - builder.ConstantR1(constant); + ConstantR1(&builder, constant); ComputeAndCompareR1(&builder, constant, {}); } @@ -66,7 +67,7 @@ TEST_F(ConstantsTest, OneCellU32) { std::vector constant = {2}; XlaBuilder builder(TestName()); - builder.ConstantR1(constant); + ConstantR1(&builder, constant); ComputeAndCompareR1(&builder, constant, {}); } @@ -75,7 +76,7 @@ TEST_F(ConstantsTest, EightCells) { std::vector constant = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}; XlaBuilder builder(TestName()); - builder.ConstantR1(constant); + ConstantR1(&builder, constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); } @@ -85,14 +86,14 @@ TEST_F(ConstantsTest, SixteenCells) { 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0}; XlaBuilder builder(TestName()); - builder.ConstantR1(constant); + ConstantR1(&builder, constant); ComputeAndCompareR1(&builder, constant, {}, error_spec_); } TEST_F(ConstantsTest, Empty_0x2) { XlaBuilder builder(TestName()); - builder.ConstantR2FromArray2D(Array2D(0, 2)); + ConstantR2FromArray2D(&builder, Array2D(0, 2)); ComputeAndCompareR2(&builder, Array2D(0, 2), {}, error_spec_); } @@ -102,15 +103,15 @@ TEST_F(ConstantsTest, Small_2x2) { MakeLinspaceArray2D(100.0, 200.0, 2, 2); XlaBuilder builder(TestName()); - builder.ConstantR2FromArray2D(*constant); + ConstantR2FromArray2D(&builder, *constant); ComputeAndCompareR2(&builder, *constant, {}, error_spec_); } TEST_F(ConstantsTest, Empty_3x0x2) { XlaBuilder builder(TestName()); - auto constant = builder.ConstantLiteral( - *Literal::CreateR3FromArray3D(Array3D(3, 0, 2))); + ConstantLiteral( + &builder, *Literal::CreateR3FromArray3D(Array3D(3, 0, 2))); ComputeAndCompareR3(&builder, Array3D(3, 0, 2), {}); } @@ -125,8 +126,7 @@ TEST_F(ConstantsTest, Small_2x2x2) { {{5.f, 6.f}, // y0 {7.f, 8.f}}, // y1 }); - auto constant = - builder.ConstantLiteral(*Literal::CreateR3FromArray3D(array3d)); + ConstantLiteral(&builder, *Literal::CreateR3FromArray3D(array3d)); ComputeAndCompareR3(&builder, array3d, {}); } @@ -145,13 +145,13 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { { XlaBuilder builder(TestName()); - builder.ConstantLiteral(*input_literal); + ConstantLiteral(&builder, *input_literal); ComputeAndCompareR4(&builder, input_array, {}, error_spec_); } { XlaBuilder builder(TestName()); - builder.ConstantR4FromArray4D(input_array); + ConstantR4FromArray4D(&builder, input_array); ComputeAndCompareR4(&builder, input_array, {}, error_spec_); } } @@ -159,9 +159,9 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { // TODO(b/29263943): Support tuple constants. TEST_F(ConstantsTest, DISABLED_TupleConstant) { XlaBuilder builder(TestName()); - builder.ConstantLiteral( - *Literal::MakeTuple({Literal::CreateR2({{1.0}, {2.0}}).get(), - Literal::CreateR1({2.0, 42}).get()})); + ConstantLiteral(&builder, *Literal::MakeTuple( + {Literal::CreateR2({{1.0}, {2.0}}).get(), + Literal::CreateR1({2.0, 42}).get()})); std::unique_ptr result = ExecuteAndTransfer(&builder, {}).ConsumeValueOrDie(); @@ -172,5 +172,13 @@ TEST_F(ConstantsTest, DISABLED_TupleConstant) { {2.0, 42.0}, LiteralSlice(*result, {1}), error_spec_); } +TEST_F(ConstantsTest, Token) { + XlaBuilder builder(TestName()); + ConstantLiteral(&builder, *Literal::CreateToken()); + // TODO(b/80000000): tokens cannot be returned from computations. + Tuple(&builder, {}); + TF_ASSERT_OK(Execute(&builder, {}).status()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index 722d882471a41a75c1e5e60f8c1a151b76c7e004..292942a49e2f0c4b077dc71c9d0e730909689e3a 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -45,8 +45,8 @@ class ConvertTest : public ClientLibraryTestBase { TEST_F(ConvertTest, ConvertR1S32ToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42, 64}); - builder.ConvertElementType(a, S32); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, S32); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -54,8 +54,8 @@ TEST_F(ConvertTest, ConvertR1S32ToR1S32) { TEST_F(ConvertTest, ConvertR1F32ToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.0f, 64.0f}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {42.0f, 64.0f}); + ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -63,8 +63,8 @@ TEST_F(ConvertTest, ConvertR1F32ToR1F32) { TEST_F(ConvertTest, ConvertR1S32ToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42, 64}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -72,8 +72,8 @@ TEST_F(ConvertTest, ConvertR1S32ToR1F32) { TEST_F(ConvertTest, ConvertR1PREDToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({true, false, true}); - builder.ConvertElementType(a, S32); + auto a = ConstantR1(&builder, {true, false, true}); + ConvertElementType(a, S32); std::vector expected = {1, 0, 1}; ComputeAndCompareR1(&builder, expected, {}); @@ -81,8 +81,8 @@ TEST_F(ConvertTest, ConvertR1PREDToR1S32) { TEST_F(ConvertTest, ConvertR1PREDToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({true, false, true}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {true, false, true}); + ConvertElementType(a, F32); std::vector expected = {1., 0., 1.}; ComputeAndCompareR1(&builder, expected, {}); @@ -90,8 +90,8 @@ TEST_F(ConvertTest, ConvertR1PREDToR1F32) { XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {}); + ConvertElementType(a, F32); std::vector expected = {}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -99,8 +99,8 @@ XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { TEST_F(ConvertTest, ConvertR1F32ToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({42.6, 64.4}); - builder.ConvertElementType(a, S32); + auto a = ConstantR1(&builder, {42.6, 64.4}); + ConvertElementType(a, S32); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -146,11 +146,11 @@ XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) { static_cast(0x8000010000000000LL), }; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, F32); + ConvertElementType(arg_param, F32); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -165,11 +165,11 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) { 0x80000000, 0x80000001, 0x80000002, 0x80000003, 0x80000080, 0x80000081, 0x80000082, 0xFFFFFFFF}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, F32); + ConvertElementType(arg_param, F32); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -183,11 +183,11 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1U32) { std::vector arg{0.0f, 1.0f, 16777216.0f, 16777218.0f, 2147483647.0f, 4294967040.0f}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, U32); + ConvertElementType(arg_param, U32); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -200,11 +200,11 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000082, 0xFFFFFFFF}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, S64); + ConvertElementType(arg_param, S64); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -217,11 +217,11 @@ XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) { XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, -1, -0x1000}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, S64); + ConvertElementType(arg_param, S64); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -254,11 +254,11 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1S64) { -9223371487098961920.f, -9223370937343148032.f}; std::unique_ptr arg_literal = Literal::CreateR1({arg}); - auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param"); + auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); - builder.ConvertElementType(arg_param, S64); + ConvertElementType(arg_param, S64); std::vector expected(arg.size()); for (int64 i = 0; i < arg.size(); ++i) { @@ -269,8 +269,8 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1S64) { XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({32, 64}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {32, 64}); + ConvertElementType(a, F32); std::vector expected = {32.0, 64.0}; ComputeAndCompareR1(&builder, expected, {}); @@ -278,8 +278,8 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) { XLA_TEST_F(ConvertTest, ConvertR1U8ToR1S32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({32, 64}); - builder.ConvertElementType(a, S32); + auto a = ConstantR1(&builder, {32, 64}); + ConvertElementType(a, S32); std::vector expected = {32, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -287,8 +287,8 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1S32) { XLA_TEST_F(ConvertTest, ConvertR1U8ToR1U32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({32, 64}); - builder.ConvertElementType(a, U32); + auto a = ConstantR1(&builder, {32, 64}); + ConvertElementType(a, U32); std::vector expected = {32, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -296,8 +296,8 @@ XLA_TEST_F(ConvertTest, ConvertR1U8ToR1U32) { XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F64) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({32.0f, 64.0f}); - builder.ConvertElementType(a, F64); + auto a = ConstantR1(&builder, {32.0f, 64.0f}); + ConvertElementType(a, F64); std::vector expected = {32.0, 64.0}; ComputeAndCompareR1(&builder, expected, {}); @@ -305,8 +305,8 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F64) { XLA_TEST_F(ConvertTest, ConvertR1F64ToR1F32) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({32.0, 64.0}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {32.0, 64.0}); + ConvertElementType(a, F32); std::vector expected = {32.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}); @@ -314,9 +314,9 @@ XLA_TEST_F(ConvertTest, ConvertR1F64ToR1F32) { TEST_F(ConvertTest, ConvertS32Extremes) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1( - {std::numeric_limits::min(), std::numeric_limits::max()}); - builder.ConvertElementType(a, F32); + auto a = ConstantR1(&builder, {std::numeric_limits::min(), + std::numeric_limits::max()}); + ConvertElementType(a, F32); std::vector expected = { static_cast(std::numeric_limits::min()), @@ -327,10 +327,10 @@ TEST_F(ConvertTest, ConvertS32Extremes) { TEST_F(ConvertTest, ConvertMapToS32) { XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); - auto param = b->Parameter(0, ShapeUtil::MakeShape(F32, {}), "in"); - b->ConvertElementType(param, S32); - auto a = builder.ConstantR1({42.0f, 64.0f}); - builder.Map({a}, b->BuildAndNoteError(), {0}); + auto param = Parameter(b.get(), 0, ShapeUtil::MakeShape(F32, {}), "in"); + ConvertElementType(param, S32); + auto a = ConstantR1(&builder, {42.0f, 64.0f}); + Map(&builder, {a}, b->BuildAndNoteError(), {0}); std::vector expected = {42, 64}; ComputeAndCompareR1(&builder, expected, {}); @@ -339,10 +339,10 @@ TEST_F(ConvertTest, ConvertMapToS32) { TEST_F(ConvertTest, ConvertMapToF32) { XlaBuilder builder(TestName()); auto b = builder.CreateSubBuilder("convert"); - auto param = b->Parameter(0, ShapeUtil::MakeShape(S32, {}), "in"); - b->ConvertElementType(param, F32); - auto a = builder.ConstantR1({42, 64}); - builder.Map({a}, b->BuildAndNoteError(), {0}); + auto param = Parameter(b.get(), 0, ShapeUtil::MakeShape(S32, {}), "in"); + ConvertElementType(param, F32); + auto a = ConstantR1(&builder, {42, 64}); + Map(&builder, {a}, b->BuildAndNoteError(), {0}); std::vector expected = {42.0f, 64.0f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -355,9 +355,9 @@ TEST_F(ConvertTest, ConvertMapToF32) { // the new convert should have the same element type as the old convert. TEST_F(ConvertTest, ConvertReshape) { XlaBuilder builder(TestName()); - auto input = builder.ConstantR1({42}); - auto reshape = builder.Reshape(input, /*dimensions=*/{0}, /*new_sizes=*/{}); - builder.ConvertElementType(reshape, F32); + auto input = ConstantR1(&builder, {42}); + auto reshape = Reshape(input, /*dimensions=*/{0}, /*new_sizes=*/{}); + ConvertElementType(reshape, F32); ComputeAndCompareR0(&builder, 42.0f, {}, ErrorSpec(0.0001)); } @@ -394,10 +394,10 @@ XLA_TEST_F(ConvertTest, ConvertR1F16ToR1F32) { client_->TransferToServer(*Literal::CreateR1(input))); XlaBuilder builder(TestName()); - builder.ConvertElementType( - builder.Parameter( - 0, ShapeUtil::MakeShape(F16, {static_cast(input.size())}), - "param"), + ConvertElementType( + Parameter(&builder, 0, + ShapeUtil::MakeShape(F16, {static_cast(input.size())}), + "param"), F32); ComputeAndCompareR1(&builder, expected_output, {dot_lhs_handle.get()}); @@ -414,10 +414,10 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { client_->TransferToServer(*Literal::CreateR1(input))); XlaBuilder builder(TestName()); - builder.ConvertElementType( - builder.Parameter( - 0, ShapeUtil::MakeShape(F32, {static_cast(input.size())}), - "param"), + ConvertElementType( + Parameter(&builder, 0, + ShapeUtil::MakeShape(F32, {static_cast(input.size())}), + "param"), F16); ComputeAndCompareR1(&builder, expected_output, {dot_lhs_handle.get()}); @@ -426,28 +426,28 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { XLA_TEST_F(ConvertTest, ConvertC64ToC64) { XlaBuilder builder(TestName()); std::vector x = {{42.0f, 64.0f}}; - builder.ConvertElementType(builder.ConstantR1(x), C64); + ConvertElementType(ConstantR1(&builder, x), C64); ComputeAndCompareR1(&builder, x, {}, ErrorSpec(0.0001)); } XLA_TEST_F(ConvertTest, ConvertS64S64) { XlaBuilder builder(TestName()); std::vector x = {{-42, 64}}; - builder.ConvertElementType(builder.ConstantR1(x), S64); + ConvertElementType(ConstantR1(&builder, x), S64); ComputeAndCompareR1(&builder, x, {}); } XLA_TEST_F(ConvertTest, ConvertU64U64) { XlaBuilder builder(TestName()); std::vector x = {{42, 64}}; - builder.ConvertElementType(builder.ConstantR1(x), U64); + ConvertElementType(ConstantR1(&builder, x), U64); ComputeAndCompareR1(&builder, x, {}); } XLA_TEST_F(ConvertTest, ConvertU64S64) { XlaBuilder builder(TestName()); std::vector unsigned_x = {{42, UINT64_MAX}}; - builder.ConvertElementType(builder.ConstantR1(unsigned_x), S64); + ConvertElementType(ConstantR1(&builder, unsigned_x), S64); std::vector signed_x = {{42, -1}}; ComputeAndCompareR1(&builder, signed_x, {}); } @@ -455,11 +455,31 @@ XLA_TEST_F(ConvertTest, ConvertU64S64) { XLA_TEST_F(ConvertTest, ConvertS64U64) { XlaBuilder builder(TestName()); std::vector signed_x = {{42, -1, INT64_MIN}}; - builder.ConvertElementType(builder.ConstantR1(signed_x), U64); + ConvertElementType(ConstantR1(&builder, signed_x), U64); std::vector unsigned_x = { {42, UINT64_MAX, tensorflow::MathUtil::IPow(2, 63)}}; ComputeAndCompareR1(&builder, unsigned_x, {}); } +XLA_TEST_F(ConvertTest, ConvertBF16F32) { + XlaBuilder builder(TestName()); + + std::vector all_bfloats(1 << 16); + for (int i = 0; i < all_bfloats.size(); ++i) { + all_bfloats[i].value = i; + } + + std::vector expected(all_bfloats.size()); + for (int i = 0; i < expected.size(); ++i) { + expected[i] = (1U << 16) * i; + } + + // Exhaustively test all bf16 to f32 conversions. + xla::XlaOp all_bfloats_bf16 = ConstantR1(&builder, all_bfloats); + xla::XlaOp all_bfloats_f32 = ConvertElementType(all_bfloats_bf16, F32); + BitcastConvertType(all_bfloats_f32, U32); + ComputeAndCompareR1(&builder, expected, {}); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc index b5a42e305987df030c15d089f5877f73bb61de1b..7605ebf4c0eacd7f44e867e23dbc27c6c1bc3e93 100644 --- a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc @@ -97,10 +97,10 @@ XLA_TEST_F(ConvolutionDimensionNumbersTest, .ConsumeValueOrDie(); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(*input_array); + auto input = ConstantR4FromArray4D(&builder, *input_array); auto weight = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {4, 3, 1, 1}), "weight"); - auto conv1 = builder.Conv(input, weight, {1, 1}, Padding::kValid); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {4, 3, 1, 1}), "weight"); + auto conv1 = Conv(input, weight, {1, 1}, Padding::kValid); ConvolutionDimensionNumbers dim_nums = XlaBuilder::CreateDefaultConvDimensionNumbers(); @@ -117,8 +117,7 @@ XLA_TEST_F(ConvolutionDimensionNumbersTest, dim_nums.set_kernel_input_feature_dimension( dim_nums.kernel_output_feature_dimension()); dim_nums.set_kernel_output_feature_dimension(old_kernel_input_feature_dim); - builder.ConvWithGeneralDimensions(input, conv1, {1, 1}, Padding::kValid, - dim_nums); + ConvWithGeneralDimensions(input, conv1, {1, 1}, Padding::kValid, dim_nums); auto expected_conv1 = ReferenceUtil::ConvArray4D(*input_array, *weight_array, {1, 1}, Padding::kValid); diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc index 947959beb144e1509a77ad2f94b8493de46ba6f2..0f6d54d042dd6af6d82e1eea93a66c2e9be53639 100644 --- a/tensorflow/compiler/xla/tests/convolution_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_test.cc @@ -47,9 +47,9 @@ class ConvolutionTest : public ClientLibraryTestBase { #if XLA_TEST_BACKEND_GPU // XLA:GPU sometimes uses FFT convolution which isn't as precise as spatial // convolution. So relax the absolute error threshold. - ErrorSpec error_spec_ = ErrorSpec(1e-2); + ErrorSpec error_spec_ = ErrorSpec(1e-2, 1e-4); #else - ErrorSpec error_spec_ = ErrorSpec(1e-4); + ErrorSpec error_spec_ = ErrorSpec(1e-4, 1e-4); #endif }; @@ -89,9 +89,9 @@ class ForwardPassConvolution_3x3x256_256_OutputZ_Iota : public ConvolutionTest { ASSERT_EQ(2, arhs->height()); XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR4FromArray4D(*alhs); - auto rhs = builder.ConstantR4FromArray4D(*arhs); - builder.Conv(lhs, rhs, {1, 1}, Padding::kValid); + auto lhs = ConstantR4FromArray4D(&builder, *alhs); + auto rhs = ConstantR4FromArray4D(&builder, *arhs); + Conv(lhs, rhs, {1, 1}, Padding::kValid); ComputeAndCompare(&builder, {}, error_spec_); } @@ -109,9 +109,9 @@ class Convolve_1x1x1x2_1x1x1x2_Valid : public ConvolutionTest { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D input_data(1, 1, 1, 2); input_data.FillWithYX(Array2D({ @@ -140,9 +140,9 @@ class Convolve_1x1x4x4_1x1x2x2_Valid : public ConvolutionTest { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D input_data(1, 1, 4, 4); input_data.FillWithYX(Array2D({ @@ -174,9 +174,9 @@ class Convolve_1x1x4x4_1x1x2x2_Same : public ConvolutionTest { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D input_data(1, 1, 4, 4); input_data.FillWithYX(Array2D({ @@ -210,9 +210,9 @@ class Convolve_1x1x4x4_1x1x3x3_Same : public ConvolutionTest { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 3, 3}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D input_data(1, 1, 4, 4); input_data.FillWithYX(Array2D({{1.0f, 2.0f, 3.0f, 4.0f}, @@ -238,9 +238,9 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_Valid) { { Shape input_shape = ShapeUtil::MakeShape(F32, {1, 2, 5}); Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1}, Padding::kValid); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1}, Padding::kValid); } Array3D input({{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}}}); @@ -268,10 +268,10 @@ class Convolve1D_1x2x5_1x2x2_WithRHSDilation : public ConvolutionTest { { Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( + ConvGeneralDilated( input, filter, /*window_strides=*/{1}, /*padding=*/{{0, 0}}, /*lhs_dilation=*/{1}, /*rhs_dilation=*/{2}, /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); @@ -304,10 +304,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSDilation) { { Shape input_shape = ShapeUtil::MakeShape(F32, {1, 2, 5}); Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( + ConvGeneralDilated( input, filter, /*window_strides=*/{1}, /*padding=*/{{0, 0}}, /*lhs_dilation=*/{2}, /*rhs_dilation=*/{1}, /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); @@ -335,10 +335,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSAndRHSDilation) { { Shape input_shape = ShapeUtil::MakeShape(F32, {1, 2, 5}); Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( + ConvGeneralDilated( input, filter, /*window_strides=*/{1}, /*padding=*/{{0, 0}}, /*lhs_dilation=*/{2}, /*rhs_dilation=*/{2}, /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); @@ -369,10 +369,10 @@ class Convolve1D_1x2x5_1x2x2_WithPadding : public ConvolutionTest { { Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. - builder.ConvGeneralDilated( + ConvGeneralDilated( input, filter, /*window_strides=*/{1}, /*padding=*/{{2, 2}}, /*lhs_dilation=*/{1}, /*rhs_dilation=*/{1}, /*dimension_numbers=*/builder.CreateDefaultConvDimensionNumbers(1)); @@ -408,8 +408,8 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { Shape input_shape = ShapeUtil::MakeShape(F32, input_dims); Shape filter_shape = ShapeUtil::MakeShape(F32, filter_dims); { - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Tensorflow dimension numbers for 3D convolution. ConvolutionDimensionNumbers dnums; @@ -429,8 +429,7 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { dnums.set_kernel_input_feature_dimension(3); dnums.set_kernel_output_feature_dimension(4); - builder.ConvWithGeneralDimensions(input, filter, {1, 1, 1}, Padding::kValid, - dnums); + ConvWithGeneralDimensions(input, filter, {1, 1, 1}, Padding::kValid, dnums); } std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); @@ -475,8 +474,8 @@ class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest { Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); { - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Tensorflow dimension numbers for 2D convolution. ConvolutionDimensionNumbers dnums; @@ -493,8 +492,7 @@ class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest { dnums.set_kernel_input_feature_dimension(2); dnums.set_kernel_output_feature_dimension(3); - builder.ConvWithGeneralDimensions(input, filter, {1, 1}, Padding::kValid, - dnums); + ConvWithGeneralDimensions(input, filter, {1, 1}, Padding::kValid, dnums); } std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); @@ -541,8 +539,8 @@ XLA_TEST_P(ConvolveWithAndWithoutCanonicalization, Shape input_shape = ShapeUtil::MakeShape(F32, {4, 29}); Shape filter_shape = ShapeUtil::MakeShape(F32, {4, 10}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); ConvolutionDimensionNumbers dnums; dnums.set_input_feature_dimension(0); @@ -551,7 +549,7 @@ XLA_TEST_P(ConvolveWithAndWithoutCanonicalization, dnums.set_kernel_output_feature_dimension(1); dnums.set_output_batch_dimension(0); dnums.set_output_feature_dimension(1); - builder.ConvWithGeneralDimensions(input, filter, {}, Padding::kValid, dnums); + ConvWithGeneralDimensions(input, filter, {}, Padding::kValid, dnums); Array2D param0(4, 29); param0.FillUnique(); @@ -599,8 +597,8 @@ class Convolve1D1WindowTestBase Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); { - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); // Tensorflow dimension numbers for 1D convolution. ConvolutionDimensionNumbers dnums; @@ -614,8 +612,7 @@ class Convolve1D1WindowTestBase dnums.set_kernel_input_feature_dimension(1); dnums.set_kernel_output_feature_dimension(2); - builder.ConvWithGeneralDimensions(input, filter, {1}, Padding::kValid, - dnums); + ConvWithGeneralDimensions(input, filter, {1}, Padding::kValid, dnums); } std::vector input_elems(ShapeUtil::ElementsIn(input_shape), @@ -726,9 +723,9 @@ XLA_TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); Shape filter_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D input_data(1, 1, 1, 2); input_data.FillWithYX(Array2D({ @@ -754,9 +751,9 @@ XLA_TEST_F(ConvolutionTest, NoCudnnAlgorithmPicker) { XlaBuilder builder(TestName()); Shape input_shape = ShapeUtil::MakeShape(F32, {1, 1, 1, 2}); Shape filter_shape = ShapeUtil::MakeShape(F32, {1, 1, 1, 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto filter = builder.Parameter(1, filter_shape, "filter"); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto filter = Parameter(&builder, 1, filter_shape, "filter"); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D input_data(1, 1, 1, 2); input_data.FillIota(0); diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc index fea850dc135e33fe098aa755c6fdd93319cd2837..c31d033bb0f0e52d40251c4d7b64d52f42d29dc6 100644 --- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc @@ -55,12 +55,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Minimal) { XlaBuilder builder(TestName()); const Array4D input_array(1, 1, 1, 1, {2}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {3}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); const Array4D expected(1, 1, 1, 1, {6}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -70,12 +70,12 @@ XLA_TEST_F(ConvolutionVariantsTest, MinimalWithBatch) { XlaBuilder builder(TestName()); const Array4D input_array(5, 1, 1, 1, {1, 2, 3, 4, 5}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {2}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); const Array4D expected(5, 1, 1, 1, {2, 4, 6, 8, 10}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -86,12 +86,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Flat1x1) { Array4D input_array(2, 1, 3, 4); input_array.FillWithMultiples(1); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {2.3}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(2, 1, 3, 4); expected.FillWithMultiples(2.3); @@ -102,12 +102,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Deep1x1) { XlaBuilder builder(TestName()); Array4D input_array(1, 2, 1, 1, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(3, 2, 1, 1, {1, 2, 3, 4, 5, 6}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 3, 1, 1, {12, 34, 56}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -117,12 +117,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2in1x2) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 2, {1, 2}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 1, 1, {12}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -132,12 +132,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2in1x3) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 3, {1, 2, 3}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 1, 2, {12, 23}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -147,12 +147,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2in2x2) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 2, 2, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 2, 1, {12, 34}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -162,12 +162,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x1in2x2) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 2, 2, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 2, 1, {10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 1, 2, {13, 24}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -177,12 +177,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2in2x2) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 2, 2, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 2, 2, {1000, 100, 10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 1, 1, {1234}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -194,13 +194,13 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2in2x3WithDepthAndBatch) { Array4D input_array( 2, 2, 2, 3, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, // plane 0 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 0}); // plane 1 - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array( 2, 2, 1, 2, {1000, 100, 10, 1, 0.1, 0.01, 0.001, 0.0001}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected( 2, 2, 2, 2, @@ -213,12 +213,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1stride1x2in1x4) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 4, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {10}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 2}, Padding::kValid); + Conv(input, filter, {1, 2}, Padding::kValid); Array4D expected(1, 1, 1, 2, {10, 30}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -228,12 +228,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1stride1x2in1x5) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 5, {1, 2, 3, 4, 5}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {10}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 2}, Padding::kValid); + Conv(input, filter, {1, 2}, Padding::kValid); Array4D expected(1, 1, 1, 3, {10, 30, 50}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -243,12 +243,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x3stride1x2in1x4) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 4, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 3, {100, 10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 2}, Padding::kValid); + Conv(input, filter, {1, 2}, Padding::kValid); Array4D expected(1, 1, 1, 1, {123}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -258,12 +258,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x3stride1x2in1x5) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 5, {1, 2, 3, 4, 5}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 3, {100, 10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 2}, Padding::kValid); + Conv(input, filter, {1, 2}, Padding::kValid); Array4D expected(1, 1, 1, 2, {123, 345}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -273,12 +273,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1stride2x2in3x3) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 3, 3, {1, 2, 3, 4, 5, 6, 7, 8, 9}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 1, {10}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {2, 2}, Padding::kValid); + Conv(input, filter, {2, 2}, Padding::kValid); Array4D expected(1, 1, 2, 2, {10, 30, 70, 90}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -288,12 +288,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter3x1in1x1Padded) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 1, {1}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 3, {10, 20, 30}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D expected(1, 1, 1, 1, {20}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -303,12 +303,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter5x1in3x1Padded) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 3, {1, 2, 3}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 5, {10000, 1000, 100, 10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D expected(1, 1, 1, 3, {123, 1230, 12300}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -318,15 +318,15 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter3x3in2x2Padded) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 2, 2, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 3, 3, {10000, 0, 1000, // row 0 0, 100, 0, // row 1 10, 0, 1}); // row 2 - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D expected(1, 1, 2, 2, {104, 230, 2300, 10400}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -336,12 +336,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1in2x1WithPaddingAndDepth) { XlaBuilder builder(TestName()); Array4D input_array(1, 2, 1, 2, {1, 2, 3, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 2, 1, 1, {10, 1}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kSame); + Conv(input, filter, {1, 1}, Padding::kSame); Array4D expected(1, 1, 1, 2, {13, 24}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -351,12 +351,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2Stride1x1Input3x3) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 3, 3, {1, 2, 3, 4, 5, 6, 7, 8, 9}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 2, 2, {7, 13, 17, 23}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 2, 2, {216, 276, 396, 456}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -366,12 +366,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2Stride1x1Input1x3) { XlaBuilder builder(TestName()); Array4D input_array(1, 1, 1, 3, {1, 2, 3}); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); const Array4D filter_array(1, 1, 1, 2, {7, 13}); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 1, 1, 2, {33, 53}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -383,15 +383,15 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x1x8x8Input1x1x8x8) { std::vector input_data(64); std::iota(input_data.begin(), input_data.end(), 0.0); Array4D input_array(1, 1, 8, 8, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(128); std::fill(filter_data.begin(), filter_data.begin() + 64, 1.0); std::fill(filter_data.begin() + 64, filter_data.begin() + 128, 2.0); const Array4D filter_array(2, 1, 8, 8, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 2, 1, 1, {2016, 4032}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -403,14 +403,14 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x1x1Input16x1x1x1) { std::vector input_data(16 * 1 * 1 * 1); std::iota(input_data.begin(), input_data.end(), 1.0); Array4D input_array(16, 1, 1, 1, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * 1 * 1); std::iota(filter_data.begin(), filter_data.end(), 1.0); const Array4D filter_array(1, 1, 1, 1, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); std::vector expected_data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; @@ -432,14 +432,14 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x2x2Input16x1x2x2) { } } } - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * ky * kx); std::iota(filter_data.begin(), filter_data.end(), 1.0); const Array4D filter_array(1, 1, ky, kx, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); std::vector expected_data(bs); for (int i = 0; i < bs; ++i) { @@ -463,14 +463,14 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x2x2Input3x1x2x2) { } } } - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * ky * kx); std::iota(filter_data.begin(), filter_data.end(), 1.0); const Array4D filter_array(1, 1, ky, kx, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); std::vector expected_data = { 23, @@ -492,14 +492,14 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x8x8Input16x1x8x8) { } } } - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * 8 * 8); std::iota(filter_data.begin(), filter_data.end(), 1.0); const Array4D filter_array(1, 1, 8, 8, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); std::vector expected_data = { 19664, 21744, 23824, 25904, 27984, 30064, 32144, 34224, @@ -515,7 +515,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input1x2x8x8) { std::vector input_data(2 * 8 * 8); std::iota(input_data.begin(), input_data.end(), 0.0); Array4D input_array(1, 2, 8, 8, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(2 * 2 * 8 * 8); std::fill(filter_data.begin(), filter_data.begin() + filter_data.size() / 4, @@ -527,9 +527,9 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input1x2x8x8) { std::fill(filter_data.begin() + 3 * filter_data.size() / 4, filter_data.end(), 4.0); const Array4D filter_array(2, 2, 8, 8, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(1, 2, 1, 1, {14240, 30496}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -541,7 +541,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input2x2x8x8) { std::vector input_data(2 * 2 * 8 * 8); std::iota(input_data.begin(), input_data.end(), 0.0); Array4D input_array(2, 2, 8, 8, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(2 * 2 * 8 * 8); std::fill(filter_data.begin(), filter_data.begin() + filter_data.size() / 4, @@ -553,9 +553,9 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input2x2x8x8) { std::fill(filter_data.begin() + 3 * filter_data.size() / 4, filter_data.end(), 4.0); const Array4D filter_array(2, 2, 8, 8, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(2, 2, 1, 1, {14240, 30496, 38816, 87840}); ComputeAndCompareR4(&builder, expected, {}, error_spec_); @@ -567,7 +567,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input32x2x8x8) { std::vector input_data(32 * 2 * 8 * 8); std::iota(input_data.begin(), input_data.end(), 0.0); Array4D input_array(32, 2, 8, 8, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(2 * 2 * 8 * 8); std::fill(filter_data.begin(), filter_data.begin() + filter_data.size() / 4, @@ -579,9 +579,9 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter2x2x8x8Input32x2x8x8) { std::fill(filter_data.begin() + 3 * filter_data.size() / 4, filter_data.end(), 4.0); const Array4D filter_array(2, 2, 8, 8, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + Conv(input, filter, {1, 1}, Padding::kValid); std::vector expected_data = { 14240, 30496, 38816, 87840, 63392, 145184, 87968, @@ -613,9 +613,9 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter16x16x1x1Input16x16x1x1) { } } - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); Array4D expected(16, 16, 1, 1); for (int i0 = 0; i0 < 16; ++i0) { @@ -635,9 +635,9 @@ XLA_TEST_F(ConvolutionVariantsTest, FlatRhsDilation) { Array4D input_array(1, 1, 4, 6, input_data); Array4D filter_array(1, 1, 2, 3, {1, 10, 100, 2, 20, 200}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneralDilated( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneralDilated( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{}, /*lhs_dilation=*/{}, /*rhs_dilation=*/{2, 2}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -654,9 +654,9 @@ XLA_TEST_F(ConvolutionVariantsTest, FlatLhsDilation1D) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneralDilated( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneralDilated( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{}, /*lhs_dilation=*/{1, 2}, /*rhs_dilation=*/{}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -677,9 +677,9 @@ XLA_TEST_F(ConvolutionVariantsTest, FlatLhsDilation) { 200, 20, 2, // 300, 30, 3, // 400, 40, 4}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneralDilated( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneralDilated( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{2, 1}, /*padding=*/{{1, 0}, {0, 0}}, /*lhs_dilation=*/{3, 2}, /*rhs_dilation=*/{}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -699,9 +699,9 @@ XLA_TEST_F(ConvolutionVariantsTest, NegativePaddingOnBothEnds) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneral( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneral( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{{0, 0}, {-1, -1}}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -718,9 +718,9 @@ XLA_TEST_F(ConvolutionVariantsTest, NegativePaddingLowAndPositivePaddingHigh) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneral( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneral( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{{0, 0}, {-1, 2}}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -737,9 +737,9 @@ XLA_TEST_F(ConvolutionVariantsTest, PositivePaddingLowAndNegativePaddingHigh) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneral( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneral( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{{0, 0}, {2, -1}}, XlaBuilder::CreateDefaultConvDimensionNumbers()); @@ -756,9 +756,9 @@ XLA_TEST_F(ConvolutionVariantsTest, PositivePaddingAndDilation) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneralDilated( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneralDilated( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{{0, 0}, {3, 2}}, /*lhs_dilation=*/{1, 2}, /*rhs_dilation=*/{1, 2}, @@ -781,9 +781,9 @@ XLA_TEST_F(ConvolutionVariantsTest, NegativePaddingAndDilation) { Array4D input_array(1, 1, 1, 5, input_data); Array4D filter_array(1, 1, 1, 2, {10, 1}); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.ConvGeneralDilated( + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + ConvGeneralDilated( /*lhs=*/input, /*rhs=*/filter, /*window_strides=*/{}, /*padding=*/{{0, 0}, {-3, -2}}, /*lhs_dilation=*/{1, 2}, /*rhs_dilation=*/{1, 2}, @@ -821,9 +821,9 @@ XLA_TEST_F(ConvolutionVariantsTest, RandomData_Input1x1x2x3_Filter2x1x1x2) { Array4D filter_array(oz, iz, ky, kx, kernel_data); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); std::unique_ptr> expected = ReferenceUtil::ConvArray4D( input_array, filter_array, {1, 1}, Padding::kValid); @@ -854,9 +854,9 @@ XLA_TEST_F(ConvolutionVariantsTest, RandomData_Input1x16x1x1_Filter1x16x1x1) { Array4D filter_array(oz, iz, ky, kx, kernel_data); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); std::unique_ptr> expected = ReferenceUtil::ConvArray4D( input_array, filter_array, {1, 1}, Padding::kValid); @@ -887,9 +887,9 @@ XLA_TEST_F(ConvolutionVariantsTest, RandomData_Input16x16x1x1_Filter1x16x1x1) { Array4D filter_array(oz, iz, ky, kx, kernel_data); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); std::unique_ptr> expected = ReferenceUtil::ConvArray4D( input_array, filter_array, {1, 1}, Padding::kValid); @@ -920,9 +920,9 @@ XLA_TEST_F(ConvolutionVariantsTest, RandomData_Input16x16x1x1_Filter16x16x1x1) { Array4D filter_array(oz, iz, ky, kx, kernel_data); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); std::unique_ptr> expected = ReferenceUtil::ConvArray4D( input_array, filter_array, {1, 1}, Padding::kValid); @@ -954,9 +954,9 @@ XLA_TEST_F(ConvolutionVariantsTest, Array4D filter_array(oz, iz, ky, kx, kernel_data); XlaBuilder builder(TestName()); - auto input = builder.ConstantR4FromArray4D(input_array); - auto filter = builder.ConstantR4FromArray4D(filter_array); - builder.Conv(input, filter, {1, 1}, Padding::kValid); + auto input = ConstantR4FromArray4D(&builder, input_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); + Conv(input, filter, {1, 1}, Padding::kValid); std::unique_ptr> expected = ReferenceUtil::ConvArray4D( input_array, filter_array, {1, 1}, Padding::kValid); @@ -970,12 +970,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2x1x1Input1x2x3x1GeneralPadding) { std::vector input_data(1 * 2 * 3 * 1); std::iota(input_data.begin(), input_data.end(), 1.0); Array4D input_array(1, 2, 3, 1, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 2 * 1 * 1); std::iota(filter_data.begin(), filter_data.end(), 1.0); Array4D filter_array(1, 2, 1, 1, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); ConvolutionDimensionNumbers dnums; // NHWC input format. @@ -995,7 +995,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x2x1x1Input1x2x3x1GeneralPadding) { dnums.set_kernel_output_feature_dimension(3); // Tests padding sizes that don't correspond either to SAME or VALID padding. - builder.ConvGeneral(input, filter, {1, 1}, {{2, 1}, {2, 3}}, dnums); + ConvGeneral(input, filter, {1, 1}, {{2, 1}, {2, 3}}, dnums); std::vector expected_data = { 0, 0, 0, 0, 0, 0, 0, // @@ -1014,12 +1014,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x1x1Input1x2x3x1GeneralPadding) { std::vector input_data(1 * 2 * 3 * 1); std::iota(input_data.begin(), input_data.end(), 1.0); Array4D input_array(1, 2, 3, 1, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * 1 * 1); std::iota(filter_data.begin(), filter_data.end(), 2.0); Array4D filter_array(1, 1, 1, 1, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); ConvolutionDimensionNumbers dnums; // NHWC input format. @@ -1039,7 +1039,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x1x1Input1x2x3x1GeneralPadding) { dnums.set_kernel_output_feature_dimension(3); // Tests padding sizes that don't correspond either to SAME or VALID padding. - builder.ConvGeneral(input, filter, {1, 1}, {{2, 1}, {2, 3}}, dnums); + ConvGeneral(input, filter, {1, 1}, {{2, 1}, {2, 3}}, dnums); std::vector expected_data = { 0, 0, 0, 0, 0, 0, 0, 0, // @@ -1058,12 +1058,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x1x1Input1x2x3x1NoPadding) { std::vector input_data(1 * 2 * 3 * 1); std::iota(input_data.begin(), input_data.end(), 1.0); Array4D input_array(1, 2, 3, 1, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * 1 * 1); std::iota(filter_data.begin(), filter_data.end(), 2.0); Array4D filter_array(1, 1, 1, 1, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); ConvolutionDimensionNumbers dnums; // NHWC input format. @@ -1083,7 +1083,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x1x1Input1x2x3x1NoPadding) { dnums.set_kernel_output_feature_dimension(3); // Tests zero padding sizes. This can use matmul for computation. - builder.ConvGeneral(input, filter, {1, 1}, {{0, 0}, {0, 0}}, dnums); + ConvGeneral(input, filter, {1, 1}, {{0, 0}, {0, 0}}, dnums); std::vector expected_data = { 2, 4, 6, // @@ -1099,12 +1099,12 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x2x3Input1x2x3x2NoPadding) { std::vector input_data(1 * 2 * 3 * 2); std::iota(input_data.begin(), input_data.end(), 1.0); Array4D input_array(1, 2, 3, 2, input_data); - auto input = builder.ConstantR4FromArray4D(input_array); + auto input = ConstantR4FromArray4D(&builder, input_array); std::vector filter_data(1 * 1 * 2 * 3); std::iota(filter_data.begin(), filter_data.end(), 2.0); Array4D filter_array(1, 1, 2, 3, filter_data); - auto filter = builder.ConstantR4FromArray4D(filter_array); + auto filter = ConstantR4FromArray4D(&builder, filter_array); ConvolutionDimensionNumbers dnums; // NHWC input format. @@ -1124,7 +1124,7 @@ XLA_TEST_F(ConvolutionVariantsTest, Filter1x1x2x3Input1x2x3x2NoPadding) { dnums.set_kernel_output_feature_dimension(3); // Tests zero padding sizes. This can use matmul for computation. - builder.ConvGeneral(input, filter, {1, 1}, {{0, 0}, {0, 0}}, dnums); + ConvGeneral(input, filter, {1, 1}, {{0, 0}, {0, 0}}, dnums); std::vector expected_data = { 12, 15, 18, // @@ -1148,14 +1148,14 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardInputLowPaddingLessThanHighPadding) { XlaBuilder builder(TestName()); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{1, 2, 3})); - auto weights = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 2, /*values=*/{5, 6})); - auto mirrored_weights = builder.Rev(weights, {2, 3}); - builder.ConvWithGeneralPadding(gradients, mirrored_weights, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {1, 0}}); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{1, 2, 3})); + auto weights = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 2, /*values=*/{5, 6})); + auto mirrored_weights = Rev(weights, {2, 3}); + ConvWithGeneralPadding(gradients, mirrored_weights, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {1, 0}}); ComputeAndCompareR4(&builder, {{{{5, 16, 27}}}}, {}, error_spec_); } @@ -1167,16 +1167,16 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardInputLowPaddingGreaterThanHighPadding) { XlaBuilder builder(TestName()); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 1, /*values=*/{1})); - auto weights = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{1, 10, 100})); - auto mirrored_weights = builder.Rev(weights, {2, 3}); - builder.ConvGeneralDilated(gradients, mirrored_weights, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {0, 3}}, - /*lhs_dilation=*/{1, 3}, /*rhs_dilation=*/{}, - XlaBuilder::CreateDefaultConvDimensionNumbers()); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 1, /*values=*/{1})); + auto weights = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{1, 10, 100})); + auto mirrored_weights = Rev(weights, {2, 3}); + ConvGeneralDilated(gradients, mirrored_weights, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {0, 3}}, + /*lhs_dilation=*/{1, 3}, /*rhs_dilation=*/{}, + XlaBuilder::CreateDefaultConvDimensionNumbers()); ComputeAndCompareR4(&builder, {{{{100, 0}}}}, {}, error_spec_); } @@ -1187,14 +1187,14 @@ XLA_TEST_F(ConvolutionVariantsTest, XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding) { XlaBuilder builder(TestName()); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 1, /*values=*/{1})); - auto weights = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{1, 10, 100})); - auto mirrored_weights = builder.Rev(weights, {2, 3}); - builder.ConvWithGeneralPadding(gradients, mirrored_weights, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {1, 1}}); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 1, /*values=*/{1})); + auto weights = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{1, 10, 100})); + auto mirrored_weights = Rev(weights, {2, 3}); + ConvWithGeneralPadding(gradients, mirrored_weights, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {1, 1}}); ComputeAndCompareR4(&builder, {{{{10}}}}, {}, error_spec_); } @@ -1208,14 +1208,14 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding) { XLA_TEST_F(ConvolutionVariantsTest, BackwardInputWithNegativePaddingHigh) { XlaBuilder builder(TestName()); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{1, 2, 3})); - auto weights = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 2, /*values=*/{1, 10})); - auto mirrored_weights = builder.Rev(weights, {2, 3}); - builder.ConvWithGeneralPadding(gradients, mirrored_weights, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {0, 2}}); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{1, 2, 3})); + auto weights = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 2, /*values=*/{1, 10})); + auto mirrored_weights = Rev(weights, {2, 3}); + ConvWithGeneralPadding(gradients, mirrored_weights, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {0, 2}}); ComputeAndCompareR4(&builder, {{{{12, 23, 30, 0}}}}, {}, error_spec_); } @@ -1229,17 +1229,17 @@ XLA_TEST_F(ConvolutionVariantsTest, // weight gradients: 24,130,240 // // This pattern will be fused to backward convolution with padding=(1,2). - auto activations = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); - auto forward_conv = builder.ConvGeneralDilated( - activations, gradients, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {1, 2}}, - /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, - XlaBuilder::CreateDefaultConvDimensionNumbers()); - builder.Transpose(forward_conv, {0, 1, 2, 3}); + auto activations = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); + auto forward_conv = + ConvGeneralDilated(activations, gradients, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {1, 2}}, + /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, + XlaBuilder::CreateDefaultConvDimensionNumbers()); + Transpose(forward_conv, {0, 1, 2, 3}); ComputeAndCompareR4(&builder, {{{{24, 130, 240}}}}, {}, error_spec_); } @@ -1255,17 +1255,17 @@ XLA_TEST_F(ConvolutionVariantsTest, // This pattern will be fused to backward convolution with padding=(2,1). // Note: both (2,1) and (2,0) are valid padding for the backward convolution // because the stride is 2. - auto activations = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); - auto forward_conv = builder.ConvGeneralDilated( - activations, gradients, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {2, 0}}, - /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, - XlaBuilder::CreateDefaultConvDimensionNumbers()); - builder.Transpose(forward_conv, {0, 1, 2, 3}); + auto activations = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); + auto forward_conv = + ConvGeneralDilated(activations, gradients, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {2, 0}}, + /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, + XlaBuilder::CreateDefaultConvDimensionNumbers()); + Transpose(forward_conv, {0, 1, 2, 3}); ComputeAndCompareR4(&builder, {{{{13, 24}}}}, {}, error_spec_); } @@ -1282,17 +1282,17 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding) { // because the stride is 2. ConvolutionFolding prefers (2,2) because cuDNN // supports even padding only -- using (2,1) would need extra effort of // canonicalization. - auto activations = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); - auto gradients = builder.ConstantR4FromArray4D( - Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); - auto forward_conv = builder.ConvGeneralDilated( - activations, gradients, - /*window_strides=*/{1, 1}, - /*padding=*/{{0, 0}, {2, 1}}, - /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, - XlaBuilder::CreateDefaultConvDimensionNumbers()); - builder.Transpose(forward_conv, {0, 1, 2, 3}); + auto activations = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 4, /*values=*/{1, 2, 3, 4})); + auto gradients = ConstantR4FromArray4D( + &builder, Array4D(1, 1, 1, 3, /*values=*/{100, 10, 1})); + auto forward_conv = + ConvGeneralDilated(activations, gradients, + /*window_strides=*/{1, 1}, + /*padding=*/{{0, 0}, {2, 1}}, + /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 2}, + XlaBuilder::CreateDefaultConvDimensionNumbers()); + Transpose(forward_conv, {0, 1, 2, 3}); ComputeAndCompareR4(&builder, {{{{13, 24, 130}}}}, {}, error_spec_); } @@ -1300,14 +1300,14 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding) { XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding1D) { XlaBuilder builder(TestName()); - auto gradients = builder.ConstantR3FromArray3D( - Array3D(1, 1, 1, /*value=*/1)); + auto gradients = ConstantR3FromArray3D( + &builder, Array3D(1, 1, 1, /*value=*/1)); auto weights = - builder.ConstantR3FromArray3D(Array3D({{{1, 10, 100}}})); - auto mirrored_weights = builder.Rev(weights, {2}); - builder.ConvWithGeneralPadding(gradients, mirrored_weights, - /*window_strides=*/{1}, - /*padding=*/{{1, 1}}); + ConstantR3FromArray3D(&builder, Array3D({{{1, 10, 100}}})); + auto mirrored_weights = Rev(weights, {2}); + ConvWithGeneralPadding(gradients, mirrored_weights, + /*window_strides=*/{1}, + /*padding=*/{{1, 1}}); ComputeAndCompareR3(&builder, {{{10}}}, {}, error_spec_); } @@ -1315,17 +1315,17 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding1D) { XlaBuilder builder(TestName()); auto activations = - builder.ConstantR3FromArray3D(Array3D({{{1, 2, 3, 4}}})); + ConstantR3FromArray3D(&builder, Array3D({{{1, 2, 3, 4}}})); auto gradients = - builder.ConstantR3FromArray3D(Array3D({{{100, 10, 1}}})); + ConstantR3FromArray3D(&builder, Array3D({{{100, 10, 1}}})); auto forward_conv = - builder.ConvGeneralDilated(activations, gradients, - /*window_strides=*/{1}, - /*padding=*/{{2, 1}}, - /*lhs_dilation=*/{}, /*rhs_dilation=*/{2}, - XlaBuilder::CreateDefaultConvDimensionNumbers( - /*num_spatial_dims=*/1)); - builder.Transpose(forward_conv, {0, 1, 2}); + ConvGeneralDilated(activations, gradients, + /*window_strides=*/{1}, + /*padding=*/{{2, 1}}, + /*lhs_dilation=*/{}, /*rhs_dilation=*/{2}, + XlaBuilder::CreateDefaultConvDimensionNumbers( + /*num_spatial_dims=*/1)); + Transpose(forward_conv, {0, 1, 2}); ComputeAndCompareR3(&builder, {{{13, 24, 130}}}, {}, error_spec_); } @@ -1336,21 +1336,21 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding3D) { auto gradients_flat = Literal::CreateR1({1}); auto gradients_literal = gradients_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie(); - auto gradients = builder.ConstantLiteral(*gradients_literal); + auto gradients = ConstantLiteral(&builder, *gradients_literal); auto weights_flat = Literal::CreateR1({1, 10, 100}); auto weights_literal = weights_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); - auto weights = builder.ConstantLiteral(*weights_literal); + auto weights = ConstantLiteral(&builder, *weights_literal); auto expected_flat = Literal::CreateR1({10}); auto expected_literal = expected_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie(); - auto mirrored_weights = builder.Rev(weights, {2, 3, 4}); - builder.ConvWithGeneralPadding(gradients, mirrored_weights, - /*window_strides=*/{1, 1, 1}, - /*padding=*/{{0, 0}, {0, 0}, {1, 1}}); + auto mirrored_weights = Rev(weights, {2, 3, 4}); + ConvWithGeneralPadding(gradients, mirrored_weights, + /*window_strides=*/{1, 1, 1}, + /*padding=*/{{0, 0}, {0, 0}, {1, 1}}); ComputeAndCompareLiteral(&builder, *expected_literal, {}, error_spec_); } @@ -1360,25 +1360,25 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding3D) { auto activations_flat = Literal::CreateR1({1, 2, 3, 4}); auto activations_literal = activations_flat->Reshape({1, 1, 1, 1, 4}).ConsumeValueOrDie(); - auto activations = builder.ConstantLiteral(*activations_literal); + auto activations = ConstantLiteral(&builder, *activations_literal); auto gradients_flat = Literal::CreateR1({100, 10, 1}); auto gradients_literal = gradients_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); - auto gradients = builder.ConstantLiteral(*gradients_literal); + auto gradients = ConstantLiteral(&builder, *gradients_literal); auto expected_flat = Literal::CreateR1({13, 24, 130}); auto expected_literal = expected_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); - auto forward_conv = builder.ConvGeneralDilated( - activations, gradients, - /*window_strides=*/{1, 1, 1}, - /*padding=*/{{0, 0}, {0, 0}, {2, 1}}, - /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 1, 2}, - XlaBuilder::CreateDefaultConvDimensionNumbers( - /*num_spatial_dims=*/3)); - builder.Transpose(forward_conv, {0, 1, 2, 3, 4}); + auto forward_conv = + ConvGeneralDilated(activations, gradients, + /*window_strides=*/{1, 1, 1}, + /*padding=*/{{0, 0}, {0, 0}, {2, 1}}, + /*lhs_dilation=*/{}, /*rhs_dilation=*/{1, 1, 2}, + XlaBuilder::CreateDefaultConvDimensionNumbers( + /*num_spatial_dims=*/3)); + Transpose(forward_conv, {0, 1, 2, 3, 4}); ComputeAndCompareLiteral(&builder, *expected_literal, {}, error_spec_); } diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc index 2b3390ca98cb2922410d451c06811aa9d4ff8c0b..fef42885e516fa8c8f87756d7a953fe5f37a630f 100644 --- a/tensorflow/compiler/xla/tests/copy_test.cc +++ b/tensorflow/compiler/xla/tests/copy_test.cc @@ -248,7 +248,7 @@ XLA_TEST_F(CopyOpClientTest, Copy0x0) { auto empty = Literal::CreateFromShape(in_shape); XlaBuilder builder(TestName()); - auto param0 = builder.Parameter(0, in_shape, "input"); + Parameter(&builder, 0, in_shape, "input"); auto input_data = client_->TransferToServer(*empty).ConsumeValueOrDie(); auto actual = ExecuteAndTransfer(&builder, {input_data.get()}, &out_shape) diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc index b43d5c9ff5d75ee0e1b3c9ceb2bc295e631ac107..d1516a28b0bb3857d9aee0922a252e25a8f9d2d5 100644 --- a/tensorflow/compiler/xla/tests/custom_call_test.cc +++ b/tensorflow/compiler/xla/tests/custom_call_test.cc @@ -16,6 +16,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" @@ -135,8 +136,8 @@ class CustomCallClientAPITest : public ClientLibraryTestBase {}; // are reserved for internal use. XLA_TEST_F(CustomCallClientAPITest, IllegalCustomCallTarget) { XlaBuilder builder(TestName()); - builder.CustomCall("$illegal", /*operands=*/{}, - ShapeUtil::MakeShape(F32, {1})); + CustomCall(&builder, "$illegal", /*operands=*/{}, + ShapeUtil::MakeShape(F32, {1})); StatusOr> result = Execute(&builder, /*arguments=*/{}); diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc index bfe688e20d182d581c3e3b545ac2289413deef7c..d4b3aac85bff283515088f6e61c9d2bad11f60d3 100644 --- a/tensorflow/compiler/xla/tests/deallocation_test.cc +++ b/tensorflow/compiler/xla/tests/deallocation_test.cc @@ -48,7 +48,7 @@ class DeallocationTest : public ClientLibraryTestBase { TEST_F(DeallocationTest, DeallocateScalar) { XlaBuilder builder(TestName()); - builder.ConstantR0(42.0); + ConstantR0(&builder, 42.0); auto global_data = ExecuteAndCheckTransfer(&builder, {}); // A result can be transferred an arbitrary number of times. Add an extra @@ -66,7 +66,7 @@ TEST_F(DeallocationTest, DeallocateScalar) { TEST_F(DeallocationTest, DeallocateVector) { XlaBuilder builder(TestName()); - builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); + ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); ASSERT_IS_OK(client_->Unregister(*global_data)); @@ -79,7 +79,7 @@ TEST_F(DeallocationTest, DeallocateVector) { TEST_F(DeallocationTest, DeallocateEmptyVector) { XlaBuilder builder(TestName()); - builder.ConstantR1({}); + ConstantR1(&builder, {}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); ASSERT_IS_OK(client_->Unregister(*global_data)); @@ -92,8 +92,8 @@ TEST_F(DeallocationTest, DeallocateEmptyVector) { XLA_TEST_F(DeallocationTest, DeallocateTuple) { XlaBuilder builder(TestName()); - builder.Tuple({builder.ConstantR0(42.0), - builder.ConstantR1({1.0, 2.0, 3.0})}); + Tuple(&builder, {ConstantR0(&builder, 42.0), + ConstantR1(&builder, {1.0, 2.0, 3.0})}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); ASSERT_IS_OK(client_->Unregister(*global_data)); @@ -106,9 +106,10 @@ XLA_TEST_F(DeallocationTest, DeallocateTuple) { XLA_TEST_F(DeallocationTest, DeallocateTupleWithRepeatedElements) { XlaBuilder builder(TestName()); - auto element = builder.ConstantR0(42.0); - auto inner_tuple = builder.Tuple({builder.ConstantR0(42.0), element}); - builder.Tuple({element, inner_tuple, element}); + auto element = ConstantR0(&builder, 42.0); + auto inner_tuple = + Tuple(&builder, {ConstantR0(&builder, 42.0), element}); + Tuple(&builder, {element, inner_tuple, element}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); ASSERT_IS_OK(client_->Unregister(*global_data)); @@ -122,9 +123,9 @@ XLA_TEST_F(DeallocationTest, DeallocateTupleWithRepeatedElements) { XLA_TEST_F(DeallocationTest, DeallocateNestedTuple) { XlaBuilder builder(TestName()); auto inner_tuple = - builder.Tuple({builder.ConstantR0(42.0), - builder.ConstantR1({1.0, 2.0, 3.0})}); - builder.Tuple({inner_tuple, builder.ConstantR1({0.123, 0.456})}); + Tuple(&builder, {ConstantR0(&builder, 42.0), + ConstantR1(&builder, {1.0, 2.0, 3.0})}); + Tuple(&builder, {inner_tuple, ConstantR1(&builder, {0.123, 0.456})}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); ASSERT_IS_OK(client_->Unregister(*global_data)); diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index 12789fe66530fe03eb33316eda652336f29971ab..acba67491d25007ab774530fd7ca236a4363b6f0 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -54,9 +54,9 @@ class DeconstructTupleTest : public ClientLibraryTestBase { TEST_F(DeconstructTupleTest, DeconstructTuple) { XlaBuilder builder(TestName()); - auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); - auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); - builder.Tuple({const1, const2}); + auto const1 = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); + auto const2 = ConstantR1(&builder, {2.0, 4.0, 6.0, 8.0}); + Tuple(&builder, {const1, const2}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status = client_->DeconstructTuple(*global_data); @@ -73,9 +73,9 @@ TEST_F(DeconstructTupleTest, DeconstructTuple) { TEST_F(DeconstructTupleTest, DeconstructTupleTwice) { XlaBuilder builder(TestName()); - auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); - auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); - builder.Tuple({const1, const2}); + auto const1 = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); + auto const2 = ConstantR1(&builder, {2.0, 4.0, 6.0, 8.0}); + Tuple(&builder, {const1, const2}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status1 = client_->DeconstructTuple(*global_data); @@ -103,9 +103,9 @@ TEST_F(DeconstructTupleTest, DeconstructTupleTwice) { XLA_TEST_F(DeconstructTupleTest, DeconstructTupleRepeatedElement) { XlaBuilder builder(TestName()); - auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); - auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); - builder.Tuple({const1, const2, const2, const1}); + auto const1 = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); + auto const2 = ConstantR1(&builder, {2.0, 4.0, 6.0, 8.0}); + Tuple(&builder, {const1, const2, const2, const1}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status = client_->DeconstructTuple(*global_data); @@ -129,9 +129,9 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleRepeatedElement) { TEST_F(DeconstructTupleTest, DeconstructTupleThenDeallocate) { XlaBuilder builder(TestName()); - auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); - auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); - builder.Tuple({const1, const2, const1}); + auto const1 = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); + auto const2 = ConstantR1(&builder, {2.0, 4.0, 6.0, 8.0}); + Tuple(&builder, {const1, const2, const1}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status = client_->DeconstructTuple(*global_data); @@ -159,7 +159,7 @@ TEST_F(DeconstructTupleTest, DeconstructTupleThenDeallocate) { TEST_F(DeconstructTupleTest, DeconstructNonTuple) { XlaBuilder builder(TestName()); - builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); + ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status = client_->DeconstructTuple(*global_data); @@ -174,8 +174,8 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) { Literal::CreateR1({3.14f, -100.25f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2}), "param0"); - builder.Tuple({p}); + auto p = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "param0"); + Tuple(&builder, {p}); auto global_data = ExecuteAndCheckTransfer(&builder, {param0_data.get()}); auto result_status = client_->DeconstructTuple(*global_data); @@ -186,9 +186,9 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) { XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) { XlaBuilder builder(TestName()); - auto const1 = builder.ConstantR1({1.0, 2.0, 3.0, 4.0}); - auto const2 = builder.ConstantR1({2.0, 4.0, 6.0, 8.0}); - builder.Tuple({builder.Tuple({const1, const2}), const1}); + auto const1 = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0}); + auto const2 = ConstantR1(&builder, {2.0, 4.0, 6.0, 8.0}); + Tuple(&builder, {Tuple(&builder, {const1, const2}), const1}); auto global_data = ExecuteAndCheckTransfer(&builder, {}); auto result_status = client_->DeconstructTuple(*global_data); diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc index 085a5105aca1c173a7cbc211aebbeb5b254b0753..810947ab01b69b10b6ae60c551bd7aba10a6313d 100644 --- a/tensorflow/compiler/xla/tests/deep_graph_test.cc +++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc @@ -30,7 +30,7 @@ TEST_F(ClientLibraryTestBase, DeepGraph) { auto y_data = CreateR0Parameter(1, 1, "y", &b, &y); XlaOp z = x; for (int i = 0; i < kDepth; ++i) { - z = b.Add(z, y); + z = Add(z, y); } ComputeAndCompareR0(&b, /*expected=*/kDepth + 3, {x_data.get(), y_data.get()}); diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index 0fd846cef8095a857dd7b2c12d8afdf409e2bd66..cf2e645d472efab9ca649dbde6602fd4f205d924 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -70,9 +70,9 @@ XLA_TEST_F(DotOperationTest, DotOfInputTupleElem) { *Literal::MakeTuple({Literal::CreateR2({{1, 2}, {3, 4}}).get(), Literal::CreateR2({{5, 6}, {7, 8}}).get()}), "arg0", &builder, ¶m); - auto lhs = builder.GetTupleElement(param, 0); - auto rhs = builder.GetTupleElement(param, 1); - builder.Dot(lhs, rhs); + auto lhs = GetTupleElement(param, 0); + auto rhs = GetTupleElement(param, 1); + Dot(lhs, rhs); ComputeAndCompareLiteral(&builder, *Literal::CreateR2({{19, 22}, {43, 50}}), @@ -87,9 +87,9 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64CF64, ZeroElementVectorDot) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR1(&builder, {}); + auto rhs = ConstantR1(&builder, {}); + Dot(lhs, rhs); this->template ComputeAndCompareR0(&builder, static_cast(0.0), {}, this->error_spec_); @@ -102,9 +102,9 @@ TYPED_TEST_CASE(DotOperationTest_F16F32F64, TypesF16F32F64); XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR2FromArray2D({{3.0f, 4.0f}}); - auto rhs = builder.ConstantFromArray({3.0f, 4.0f}); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, {{3.0f, 4.0f}}); + auto rhs = ConstantFromArray(&builder, {3.0f, 4.0f}); + Dot(lhs, rhs); this->template ComputeAndCompareR1(&builder, {static_cast(25.0f)}, {}, this->error_spec_); @@ -113,9 +113,9 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR1({static_cast(2.0f)}); - auto rhs = builder.ConstantR1({static_cast(3.0f)}); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR1(&builder, {static_cast(2.0f)}); + auto rhs = ConstantR1(&builder, {static_cast(3.0f)}); + Dot(lhs, rhs); this->template ComputeAndCompareR0(&builder, static_cast(6.0f), {}, this->error_spec_); @@ -124,9 +124,9 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, VectorDot) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantFromArray({1.0f, 2.5f, 42.0f}); - auto rhs = builder.ConstantFromArray({11.0f, -1.0f, 0.5f}); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantFromArray(&builder, {1.0f, 2.5f, 42.0f}); + auto rhs = ConstantFromArray(&builder, {11.0f, -1.0f, 0.5f}); + Dot(lhs, rhs); this->template ComputeAndCompareR0(&builder, static_cast(29.5f), {}, this->error_spec_); @@ -139,9 +139,9 @@ std::vector MinorToMajorForIsRowMajor(bool row_major) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(0, 2)); + auto rhs = ConstantR2FromArray2D(&builder, Array2D(2, 0)); + Dot(lhs, rhs); this->template ComputeAndCompareR2(&builder, Array2D(0, 0), {}, this->error_spec_); @@ -150,10 +150,10 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2FromArray2D( - {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}}); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(0, 2)); + auto rhs = ConstantR2FromArray2D( + &builder, {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}}); + Dot(lhs, rhs); this->template ComputeAndCompareR2(&builder, Array2D(0, 3), {}, this->error_spec_); @@ -162,10 +162,10 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR2FromArray2D( - {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}}); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR2FromArray2D( + &builder, {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}}); + auto rhs = ConstantR2FromArray2D(&builder, Array2D(2, 0)); + Dot(lhs, rhs); this->template ComputeAndCompareR2(&builder, Array2D(3, 0), {}, this->error_spec_); @@ -174,9 +174,9 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto result = builder.Dot(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(2, 0)); + auto rhs = ConstantR2FromArray2D(&builder, Array2D(0, 2)); + Dot(lhs, rhs); this->template ComputeAndCompareR2( &builder, Array2D(2, 2, static_cast(0.0f)), {}, this->error_spec_); @@ -186,11 +186,11 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) { using T = TypeParam; XlaBuilder builder(this->TestName()); auto param0 = - builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 4}), "arg0"); + Parameter(&builder, 0, ShapeUtil::MakeShapeWithType({2, 4}), "arg0"); auto param1 = - builder.Parameter(1, ShapeUtil::MakeShapeWithType({4, 1}), "arg1"); - auto exp0 = builder.Exp(param0); - auto result = builder.Dot(exp0, param1); + Parameter(&builder, 1, ShapeUtil::MakeShapeWithType({4, 1}), "arg1"); + auto exp0 = Exp(param0); + Dot(exp0, param1); auto lhs_handle = this->client_ @@ -231,9 +231,8 @@ class SquareMatrixDot : public DotOperationTest { .ConsumeValueOrDie(); XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); + Dot(Parameter(&builder, 0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), + Parameter(&builder, 1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); Array2D expected({{15.0f, -2.0f}, {-25.0f, 34.0f}}); ComputeAndCompareR2(&builder, expected, @@ -316,26 +315,26 @@ void ParametricDotTest::TestImpl() { XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, - ShapeUtil::MakeShapeWithLayout( - prim_type, {param.m, param.k}, - MinorToMajorForIsRowMajor(param.dot_lhs_row_major)), - "dot_lhs"), - builder.Parameter(1, - ShapeUtil::MakeShapeWithLayout( - prim_type, {param.k, param.n}, - MinorToMajorForIsRowMajor(param.dot_rhs_row_major)), - "dot_rhs")); + auto result = + Dot(Parameter(&builder, 0, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.m, param.k}, + MinorToMajorForIsRowMajor(param.dot_lhs_row_major)), + "dot_lhs"), + Parameter(&builder, 1, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.k, param.n}, + MinorToMajorForIsRowMajor(param.dot_rhs_row_major)), + "dot_rhs")); if (param.has_addend) { - result = builder.Add( - result, builder.Parameter( - 2, - ShapeUtil::MakeShapeWithLayout( - prim_type, {param.m, param.n}, - MinorToMajorForIsRowMajor(param.addend_row_major)), - "addend")); + result = + Add(result, + Parameter(&builder, 2, + ShapeUtil::MakeShapeWithLayout( + prim_type, {param.m, param.n}, + MinorToMajorForIsRowMajor(param.addend_row_major)), + "addend")); } std::unique_ptr> expected; @@ -492,9 +491,8 @@ class NonsquareMatrixDot : public DotOperationTest { XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); + Dot(Parameter(&builder, 0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), + Parameter(&builder, 1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); @@ -524,9 +522,8 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) { XlaBuilder builder(TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {1, 4}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {4, 2}), "rhs")); + Dot(Parameter(&builder, 0, ShapeUtil::MakeShape(prim_type, {1, 4}), "lhs"), + Parameter(&builder, 1, ShapeUtil::MakeShape(prim_type, {4, 2}), "rhs")); Array2D expected({{30.0, -2.0}}); @@ -538,11 +535,13 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, ConcurrentMatMult) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto matrix1 = builder.ConstantR2FromArray2D({{1.0f, 2.0f}, {3.0f, 4.0f}}); - auto matrix2 = builder.ConstantR2FromArray2D({{5.0f, 6.0f}, {7.0f, 8.0f}}); - auto matrix12 = builder.Dot(matrix1, matrix2); - auto matrix21 = builder.Dot(matrix2, matrix1); - builder.Add(matrix12, matrix21); + auto matrix1 = + ConstantR2FromArray2D(&builder, {{1.0f, 2.0f}, {3.0f, 4.0f}}); + auto matrix2 = + ConstantR2FromArray2D(&builder, {{5.0f, 6.0f}, {7.0f, 8.0f}}); + auto matrix12 = Dot(matrix1, matrix2); + auto matrix21 = Dot(matrix2, matrix1); + Add(matrix12, matrix21); Array2D expected({{42.0f, 56.0f}, {74.0f, 96.0f}}); this->template ComputeAndCompareR2(&builder, expected, {}, @@ -559,29 +558,29 @@ TYPED_TEST_CASE(DotOperationTestForBatchMatMul, TypesF16F32F64); XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { using T = TypeParam; XlaBuilder builder(this->TestName()); - auto x = - builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "x"); - auto y = - builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "y"); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), + "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), + "y"); - auto x_flat = builder.Reshape(x, {0, 1, 2, 3}, {4, 2, 2}); - auto y_flat = builder.Reshape(y, {0, 1, 2, 3}, {4, 2, 2}); + auto x_flat = Reshape(x, {0, 1, 2, 3}, {4, 2, 2}); + auto y_flat = Reshape(y, {0, 1, 2, 3}, {4, 2, 2}); // Slice batches into individual matrices and multiply them. std::vector out_slices; for (int i = 0; i < 4; ++i) { // Slice off individual matrices and reshape to 2D tensors. - auto x_slice = builder.Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1}); - x_slice = builder.Reshape(x_slice, {0, 1, 2}, {2, 2}); - auto y_slice = builder.Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1}); - y_slice = builder.Reshape(y_slice, {0, 1, 2}, {2, 2}); + auto x_slice = Slice(x_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1}); + x_slice = Reshape(x_slice, {0, 1, 2}, {2, 2}); + auto y_slice = Slice(y_flat, {i, 0, 0}, {i + 1, 2, 2}, {1, 1, 1}); + y_slice = Reshape(y_slice, {0, 1, 2}, {2, 2}); - auto out = builder.Dot(x_slice, y_slice); - out = builder.Reshape(out, {0, 1}, {1, 2, 2}); + auto out = Dot(x_slice, y_slice); + out = Reshape(out, {0, 1}, {1, 2, 2}); out_slices.push_back(out); } - auto out_flat = builder.ConcatInDim(out_slices, 0); - builder.Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2}); + auto out_flat = ConcatInDim(&builder, out_slices, 0); + Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2}); auto x_data = this->client_ ->TransferToServer(*Literal::CreateR4FromArray4D( @@ -616,9 +615,9 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { XlaBuilder builder(this->TestName()); auto x = - builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2}), "x"); + Parameter(&builder, 0, ShapeUtil::MakeShapeWithType({2, 2, 2}), "x"); auto y = - builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2}), "y"); + Parameter(&builder, 1, ShapeUtil::MakeShapeWithType({2, 2, 2}), "y"); DotDimensionNumbers dnums; dnums.add_lhs_contracting_dimensions(2); @@ -626,7 +625,7 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { dnums.add_lhs_batch_dimensions(0); dnums.add_rhs_batch_dimensions(0); - auto out = builder.DotGeneral(x, y, dnums); + DotGeneral(x, y, dnums); auto x_data = this->client_ @@ -678,19 +677,21 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) { XlaBuilder builder(this->TestName()); auto prim_type = primitive_util::NativeToPrimitiveType(); - auto lhs_arg = builder.Parameter( - 0, ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}), + auto lhs_arg = Parameter( + &builder, 0, + ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}), "lhs"); - auto rhs_arg = builder.Parameter( - 1, ShapeUtil::MakeShape(prim_type, {rhs->height(), rhs->width()}), + auto rhs_arg = Parameter( + &builder, 1, + ShapeUtil::MakeShape(prim_type, {rhs->height(), rhs->width()}), "rhs"); if (transpose_lhs) { - lhs_arg = builder.Transpose(lhs_arg, {1, 0}); + lhs_arg = Transpose(lhs_arg, {1, 0}); } if (transpose_rhs) { - rhs_arg = builder.Transpose(rhs_arg, {1, 0}); + rhs_arg = Transpose(rhs_arg, {1, 0}); } - auto result = builder.Dot(lhs_arg, rhs_arg); + Dot(lhs_arg, rhs_arg); Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); VLOG(1) << "TestTransposeFolding " << transpose_lhs << " " @@ -713,15 +714,15 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}})); XlaBuilder builder(this->TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), - "rhs_arg_0"); - auto rhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), - "rhs_arg_1"); - auto rhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShape(prim_type, {1, 2}), - "rhs_arg_2"); - auto result = builder.Dot( - lhs_constant, builder.ConcatInDim({rhs_arg_0, rhs_arg_1, rhs_arg_2}, 0)); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_arg_0 = Parameter( + &builder, 0, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs_arg_0"); + auto rhs_arg_1 = Parameter( + &builder, 1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs_arg_1"); + auto rhs_arg_2 = Parameter( + &builder, 2, ShapeUtil::MakeShape(prim_type, {1, 2}), "rhs_arg_2"); + Dot(lhs_constant, + ConcatInDim(&builder, {rhs_arg_0, rhs_arg_1, rhs_arg_2}, 0)); std::unique_ptr> arg_0_value_array( new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); @@ -761,15 +762,15 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, {2.0f, 1.0f}})); XlaBuilder builder(this->TestName()); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2}), - "lhs_arg_0"); - auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 3}), - "lhs_arg_1"); - auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShapeWithType({2, 1}), - "lhs_arg_2"); - auto result = builder.Dot( - builder.ConcatInDim({lhs_arg_0, lhs_arg_1, lhs_arg_2}, 1), rhs_constant); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto lhs_arg_0 = Parameter( + &builder, 0, ShapeUtil::MakeShapeWithType({2, 2}), "lhs_arg_0"); + auto lhs_arg_1 = Parameter( + &builder, 1, ShapeUtil::MakeShapeWithType({2, 3}), "lhs_arg_1"); + auto lhs_arg_2 = Parameter( + &builder, 2, ShapeUtil::MakeShapeWithType({2, 1}), "lhs_arg_2"); + Dot(ConcatInDim(&builder, {lhs_arg_0, lhs_arg_1, lhs_arg_2}, 1), + rhs_constant); std::unique_ptr> arg_0_value_array( new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); @@ -811,16 +812,15 @@ XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSClassicMM) { // Dot result to slice from: {{114, 105, 96}, {96, 105, 114}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({1, 0}); - auto dynamic_slice = - builder.DynamicSlice(lhs_constant, start_constant, {1, 6}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {1, 0}); + auto dynamic_slice = DynamicSlice(lhs_constant, start_constant, {1, 6}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums); + DotGeneral(dynamic_slice, rhs_constant, dot_dnums); Array2D expected({{96.0, 105.0, 114.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -839,25 +839,23 @@ XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSClassicMM) { // Dot result to slice from: {{114, 105, 96}, {96, 105, 114}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({0, 1}); - auto dynamic_slice = - builder.DynamicSlice(rhs_constant, start_constant, {6, 1}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {0, 1}); + auto dynamic_slice = DynamicSlice(rhs_constant, start_constant, {6, 1}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums); + DotGeneral(lhs_constant, dynamic_slice, dot_dnums); Array2D expected({{105.0}, {105.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER( - DotOfGatherOptimizationWithConstRHSReverseMM)))) { + + DotOfGatherOptimizationWithConstRHSReverseMM) { std::unique_ptr> constant_lhs_array( new Array2D({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, @@ -870,25 +868,21 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{114, 96}, {105, 105}, {96, 114}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({0, 1}); - auto dynamic_slice = - builder.DynamicSlice(lhs_constant, start_constant, {6, 1}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {0, 1}); + auto dynamic_slice = DynamicSlice(lhs_constant, start_constant, {6, 1}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(0); dot_dnums.add_rhs_contracting_dimensions(1); - auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums); + DotGeneral(dynamic_slice, rhs_constant, dot_dnums); Array2D expected({{105.0, 105.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. -XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU(DISABLED_ON_INTERPRETER( - DotOfGatherOptimizationWithConstLHSReverseMM)))) { +XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSReverseMM) { std::unique_ptr> constant_lhs_array( new Array2D({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, @@ -901,25 +895,21 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{114, 96}, {105, 105}, {96, 114}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({1, 0}); - auto dynamic_slice = - builder.DynamicSlice(rhs_constant, start_constant, {1, 6}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {1, 0}); + auto dynamic_slice = DynamicSlice(rhs_constant, start_constant, {1, 6}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(0); dot_dnums.add_rhs_contracting_dimensions(1); - auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums); + DotGeneral(lhs_constant, dynamic_slice, dot_dnums); Array2D expected({{96.0}, {105.0}, {114.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. -XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU( - DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSRows)))) { +XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSRows) { std::unique_ptr> constant_lhs_array( new Array2D({{1.0, 2.0}, {3.0, 4.0}, @@ -937,25 +927,21 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{132, 129, 126}, {126, 129, 132}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({0, 1}); - auto dynamic_slice = - builder.DynamicSlice(lhs_constant, start_constant, {6, 1}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {0, 1}); + auto dynamic_slice = DynamicSlice(lhs_constant, start_constant, {6, 1}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(0); dot_dnums.add_rhs_contracting_dimensions(0); - auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums); + DotGeneral(dynamic_slice, rhs_constant, dot_dnums); Array2D expected({{126.0, 129.0, 132.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. -XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU( - DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSRows)))) { +XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSRows) { std::unique_ptr> constant_lhs_array( new Array2D({{1.0, 2.0}, {3.0, 4.0}, @@ -973,25 +959,21 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{132, 129, 126}, {126, 129, 132}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({0, 1}); - auto dynamic_slice = - builder.DynamicSlice(rhs_constant, start_constant, {6, 1}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {0, 1}); + auto dynamic_slice = DynamicSlice(rhs_constant, start_constant, {6, 1}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(0); dot_dnums.add_rhs_contracting_dimensions(0); - auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums); + DotGeneral(lhs_constant, dynamic_slice, dot_dnums); Array2D expected({{129.0}, {129.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. -XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU( - DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstRHSCols)))) { +XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstRHSCols) { std::unique_ptr> constant_lhs_array(new Array2D( {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}})); std::unique_ptr> constant_rhs_array( @@ -1001,25 +983,21 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{91, 168, 56}, {56, 168, 91}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({1, 0}); - auto dynamic_slice = - builder.DynamicSlice(lhs_constant, start_constant, {1, 6}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {1, 0}); + auto dynamic_slice = DynamicSlice(lhs_constant, start_constant, {1, 6}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(1); - auto result = builder.DotGeneral(dynamic_slice, rhs_constant, dot_dnums); + DotGeneral(dynamic_slice, rhs_constant, dot_dnums); Array2D expected({{56.0, 168.0, 91.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } -// TODO (b/69062148) Enable when Dot implements general contracting dimensions. -XLA_TEST_F(DotOperationTest, - DISABLED_ON_CPU(DISABLED_ON_GPU( - DISABLED_ON_INTERPRETER(DotOfGatherOptimizationWithConstLHSCols)))) { +XLA_TEST_F(DotOperationTest, DotOfGatherOptimizationWithConstLHSCols) { std::unique_ptr> constant_lhs_array(new Array2D( {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}})); std::unique_ptr> constant_rhs_array( @@ -1029,19 +1007,41 @@ XLA_TEST_F(DotOperationTest, // Dot result to slice from: {{91, 168, 56}, {56, 168, 91}} XlaBuilder builder(TestName()); - auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); - auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto start_constant = builder.ConstantR1({1, 0}); - auto dynamic_slice = - builder.DynamicSlice(rhs_constant, start_constant, {1, 6}); + auto lhs_constant = ConstantR2FromArray2D(&builder, *constant_lhs_array); + auto rhs_constant = ConstantR2FromArray2D(&builder, *constant_rhs_array); + auto start_constant = ConstantR1(&builder, {1, 0}); + auto dynamic_slice = DynamicSlice(rhs_constant, start_constant, {1, 6}); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(1); - auto result = builder.DotGeneral(lhs_constant, dynamic_slice, dot_dnums); + DotGeneral(lhs_constant, dynamic_slice, dot_dnums); Array2D expected({{168.0}, {168.0}}); ComputeAndCompareR2(&builder, expected, {}, error_spec_); } + +XLA_TEST_F(DotOperationTest, DotRank2AndRank2NonDefaultContractionDims) { + XlaBuilder builder(TestName()); + + Array2D lhs_array({{1.0f, 2.0f}, {3.0f, 4.0f}}); + auto lhs_constant = ConstantR2FromArray2D(&builder, lhs_array); + + Array2D rhs_array({{5.0f, 6.0f}, {7.0f, 8.0f}}); + auto rhs_constant = ConstantR2FromArray2D(&builder, rhs_array); + + Shape shape = ShapeUtil::MakeShape(F32, {2, 2}); + DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(0); + dot_dnums.add_rhs_contracting_dimensions(0); + DotGeneral(lhs_constant, rhs_constant, dot_dnums); + + Array2D expected({ + {26.f, 30.f}, + {38.f, 44.f}, + }); + + ComputeAndCompareR2(&builder, expected, {}, error_spec_); +} } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index 49f3a10d227f2f9edfe76405ba13498fe822f8d8..f3c258a4d4c446c465320ac16ef7c72e299a51a8 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -138,8 +138,8 @@ class DynamicSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - builder.DynamicSlice(input, starts, slice_sizes); + auto input = ConstantLiteral(&builder, input_values); + DynamicSlice(input, starts, slice_sizes); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -164,8 +164,8 @@ class DynamicSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - builder.DynamicSlice(input, starts, slice_sizes); + auto input = ConstantLiteral(&builder, input_values); + DynamicSlice(input, starts, slice_sizes); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -190,8 +190,8 @@ class DynamicSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - builder.DynamicSlice(input, starts, slice_sizes); + auto input = ConstantLiteral(&builder, input_values); + DynamicSlice(input, starts, slice_sizes); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -367,9 +367,9 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_value); - auto update = builder.ConstantLiteral(update_value); - builder.DynamicUpdateSlice(input, update, starts); + auto input = ConstantLiteral(&builder, input_value); + auto update = ConstantLiteral(&builder, update_value); + DynamicUpdateSlice(input, update, starts); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_value, {start_data.get()}); } @@ -398,9 +398,9 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - auto update = builder.ConstantLiteral(update_values); - builder.DynamicUpdateSlice(input, update, starts); + auto input = ConstantLiteral(&builder, input_values); + auto update = ConstantLiteral(&builder, update_values); + DynamicUpdateSlice(input, update, starts); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -429,9 +429,9 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - auto update = builder.ConstantLiteral(update_values); - builder.DynamicUpdateSlice(input, update, starts); + auto input = ConstantLiteral(&builder, input_values); + auto update = ConstantLiteral(&builder, update_values); + DynamicUpdateSlice(input, update, starts); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -460,9 +460,9 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { std::unique_ptr start_data = CreateR1Parameter( slice_starts, 0, "slice_starts", &builder, &starts); // Build dynamic slice computation. - auto input = builder.ConstantLiteral(input_values); - auto update = builder.ConstantLiteral(update_values); - builder.DynamicUpdateSlice(input, update, starts); + auto input = ConstantLiteral(&builder, input_values); + auto update = ConstantLiteral(&builder, update_values); + DynamicUpdateSlice(input, update, starts); // Run computation and compare against expected values. ComputeAndCompareLiteral(&builder, expected_values, {start_data.get()}); } @@ -508,8 +508,8 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { XlaOp update; std::unique_ptr update_data = CreateR3Parameter( update_values, 1, "update_values", &builder, &update); - auto starts = builder.ConstantR1({index, 0, 0}); - builder.DynamicUpdateSlice(input, update, starts); + auto starts = ConstantR1(&builder, {index, 0, 0}); + DynamicUpdateSlice(input, update, starts); // Run computation and compare against expected values. ComputeAndCompareR3(&builder, expected_values, @@ -698,14 +698,14 @@ void BM_DynamicSlice(int num_iters) { auto input_literal = Literal::CreateR4( {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}}); - auto input = builder.ConstantLiteral(*input_literal); + auto input = ConstantLiteral(&builder, *input_literal); // Create dynamic slice start indices as a parameter: shape [4] auto start_indices_shape = ShapeUtil::MakeShape(S32, {4}); auto start_indices = - builder.Parameter(0, start_indices_shape, "start_indices"); + Parameter(&builder, 0, start_indices_shape, "start_indices"); // Add DynamicSlice op to the computatation. - builder.DynamicSlice(input, start_indices, {1, 1, 1, 1}); + DynamicSlice(input, start_indices, {1, 1, 1, 1}); auto computation = builder.Build().ConsumeValueOrDie(); // Initialize and transfer parameter buffer. @@ -716,8 +716,10 @@ void BM_DynamicSlice(int num_iters) { .ConsumeValueOrDie(); auto start_indices_literal = Literal::CreateR1({0, 1, 2, 3}); + auto stream = + client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice( - executors[device_ordinal], *start_indices_literal, buffer)); + stream.get(), *start_indices_literal, buffer)); std::unique_ptr executable = client diff --git a/tensorflow/compiler/xla/tests/execution_profile_test.cc b/tensorflow/compiler/xla/tests/execution_profile_test.cc index a6ba6db5d3bf86de91f6fda022c46afee01281c2..ddc6a7db18760bf951023f0a684d78739f3e869d 100644 --- a/tensorflow/compiler/xla/tests/execution_profile_test.cc +++ b/tensorflow/compiler/xla/tests/execution_profile_test.cc @@ -34,7 +34,7 @@ XLA_TEST_F(ExecutionProfileTest, ExecuteWithExecutionProfile) { *Literal::CreateR2F32Linspace(1e0, 1e5, 256, 256))); XlaBuilder b(TestName() + ".add"); - b.Dot(b.Parameter(0, shape, "param_0"), b.Parameter(1, shape, "param_1")); + Dot(Parameter(&b, 0, shape, "param_0"), Parameter(&b, 1, shape, "param_1")); TF_ASSERT_OK_AND_ASSIGN(XlaComputation dot_product, b.Build()); ExecutionProfile execution_profile; diff --git a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc index 0a37e4d423620122f2e109343a86a964f46d778f..74cf8b213e0a03394c84008e7a2919e1a5bf1af2 100644 --- a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc +++ b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc @@ -54,7 +54,7 @@ class ExhaustiveF32ElementwiseOpTest TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, client_->TransferToServer(*input_literal)); - auto input = builder.Parameter(0, input_literal->shape(), "input"); + auto input = Parameter(&builder, 0, input_literal->shape(), "input"); enqueue_op(&builder, input); std::vector expected_result; @@ -79,8 +79,8 @@ XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, LogF32) { #endif ExhaustivelyTestF32Op( - [](XlaBuilder* builder, const XlaOp& input) { builder->Log(input); }, - std::log, known_incorrect_range); + [](XlaBuilder* builder, const XlaOp& input) { Log(input); }, std::log, + known_incorrect_range); } XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, ExpF32) { @@ -95,14 +95,14 @@ XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, ExpF32) { #endif ExhaustivelyTestF32Op( - [](XlaBuilder* builder, const XlaOp& input) { builder->Exp(input); }, - std::exp, known_incorrect_range); + [](XlaBuilder* builder, const XlaOp& input) { Exp(input); }, std::exp, + known_incorrect_range); } XLA_TEST_P(ExhaustiveF32ElementwiseOpTest, TanhF32) { ExhaustivelyTestF32Op( - [](XlaBuilder* builder, const XlaOp& input) { builder->Tanh(input); }, - std::tanh, /*known_incorrect_range=*/{0, 0}); + [](XlaBuilder* builder, const XlaOp& input) { Tanh(input); }, std::tanh, + /*known_incorrect_range=*/{0, 0}); } std::vector> CreateExhaustiveParameters() { diff --git a/tensorflow/compiler/xla/tests/floor_ceil_test.cc b/tensorflow/compiler/xla/tests/floor_ceil_test.cc index 71eb914a8e5eaef2e38b9e6e7d45b8a10ce1bd7a..30dc639f117b9871238f0bf1628502cf8bef2e0c 100644 --- a/tensorflow/compiler/xla/tests/floor_ceil_test.cc +++ b/tensorflow/compiler/xla/tests/floor_ceil_test.cc @@ -42,12 +42,12 @@ class FloorCeilTest : public ClientLibraryTestBase { LOG(INFO) << "input: {" << tensorflow::str_util::Join(expected, ", ") << "}"; XlaBuilder builder(TestName()); - auto c = builder.ConstantR1(input); + auto c = ConstantR1(&builder, input); if (f == kCeil) { - builder.Ceil(c); + Ceil(c); } else { ASSERT_EQ(kFloor, f); - builder.Floor(c); + Floor(c); } ComputeAndCompareR1(&builder, expected, /*arguments=*/{}); } @@ -55,12 +55,12 @@ class FloorCeilTest : public ClientLibraryTestBase { void TestR0F32(float input, float expected, Function f) { LOG(INFO) << "input: " << expected; XlaBuilder builder(TestName()); - auto c = builder.ConstantR0(input); + auto c = ConstantR0(&builder, input); if (f == kCeil) { - builder.Ceil(c); + Ceil(c); } else { ASSERT_EQ(kFloor, f); - builder.Floor(c); + Floor(c); } ComputeAndCompareR0(&builder, expected, /*arguments=*/{}); } diff --git a/tensorflow/compiler/xla/tests/fmax_test.cc b/tensorflow/compiler/xla/tests/fmax_test.cc index 73f029b59bc56aa6c3e86200a49fcae0fd177101..0254ae1baaa864b38c3b217a5c2026d34b7f7d12 100644 --- a/tensorflow/compiler/xla/tests/fmax_test.cc +++ b/tensorflow/compiler/xla/tests/fmax_test.cc @@ -28,11 +28,11 @@ class FmaxSimpleTest : public ClientLibraryTestBase {}; TEST_F(FmaxSimpleTest, FmaxTenValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}); - auto y = builder.ConstantR1( - {-0.0, -1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0, -9.0}); - builder.Max(x, y); + auto x = ConstantR1( + &builder, {-0.0, 1.0, 2.0, -3.0, -4.0, 5.0, 6.0, -7.0, -8.0, 9.0}); + auto y = ConstantR1( + &builder, {-0.0, -1.0, -2.0, 3.0, 4.0, -5.0, -6.0, 7.0, 8.0, -9.0}); + Max(x, y); std::vector expected = {-0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0}; diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc index e6f79b5ac55dddfbb213a36cadbee53bc9443d9d..ab470f16a32c2363e88a11a9f7d564dcf2981f42 100644 --- a/tensorflow/compiler/xla/tests/fusion_test.cc +++ b/tensorflow/compiler/xla/tests/fusion_test.cc @@ -557,8 +557,7 @@ XLA_TEST_F(FusionTest, ReshapeNegate) { *ExecuteAndTransfer(std::move(hlo_module), {}))); } -// TODO(b/64070202): Investigate failure. -XLA_TEST_F(FusionTest, DISABLED_ON_GPU(TransposeNegate)) { +XLA_TEST_F(FusionTest, TransposeNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( @@ -793,14 +792,14 @@ void BM_ParallelFusion(int num_iters) { // Create computation. XlaBuilder builder("ParallelFusion"); Shape shape0 = ShapeUtil::MakeShape(F32, {param0_dim0, param0_dim1}); - auto param0 = builder.Parameter(0, shape0, "param0"); + auto param0 = Parameter(&builder, 0, shape0, "param0"); Shape shape1 = ShapeUtil::MakeShape(F32, {param1_dim0, param1_dim1}); - auto param1 = builder.Parameter(1, shape1, "param1"); + auto param1 = Parameter(&builder, 1, shape1, "param1"); Shape shape2 = ShapeUtil::MakeShape(F32, {param2_dim0, param2_dim1}); - auto param2 = builder.Parameter(2, shape2, "param2"); + auto param2 = Parameter(&builder, 2, shape2, "param2"); - auto x = builder.Mul(param0, param1); - auto y = builder.Add(x, param2); + auto x = Mul(param0, param1); + Add(x, param2); auto computation = builder.Build().ConsumeValueOrDie(); // Transfer literals to device. diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index 143ffbdeb409d91ab6d46d386aa5ff98ebc4ae10..b8404826b161b9edbbd260d73c175cce935ace91 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -598,14 +599,14 @@ XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) { Shape operand_shape = ShapeUtil::MakeShape(S32, {3, 3}); Shape indices_shape = ShapeUtil::MakeShape(S32, {2}); - auto operand = builder.Parameter(0, operand_shape, "operand"); - auto indices = builder.Parameter(1, indices_shape, "indices"); + auto operand = Parameter(&builder, 0, operand_shape, "operand"); + auto indices = Parameter(&builder, 1, indices_shape, "indices"); GatherDimensionNumbers dim_numbers; dim_numbers.add_output_window_dims(1); dim_numbers.add_elided_window_dims(0); dim_numbers.add_gather_dims_to_operand_dims(0); dim_numbers.set_index_vector_dim(1); - builder.Gather(operand, indices, dim_numbers, {1, 3}); + Gather(operand, indices, dim_numbers, {1, 3}); std::vector expected = {}; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr operand_arg, @@ -629,8 +630,8 @@ XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) { client_->ExecuteParallel(computation_instances)); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, client_->Transfer(*(result_data[0]))); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result_literal, *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}))); + LiteralTestUtil::ExpectR2Equal({{1, 2, 3}, {7, 8, 9}}, + *result_literal); } } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/half_test.cc b/tensorflow/compiler/xla/tests/half_test.cc index 76bf47845ca045b4eede9a3b47ae5c2ce93ce577..fd8511884907ae500d8256c3250fe779f8eba83a 100644 --- a/tensorflow/compiler/xla/tests/half_test.cc +++ b/tensorflow/compiler/xla/tests/half_test.cc @@ -37,8 +37,7 @@ class HalfTestBase : public ClientLibraryTestBase { static const int kNumElements = 4; }; -using UnaryBuildFuncTy = - std::function; +using UnaryBuildFuncTy = std::function; struct UnaryOpTestParam { std::function compute_func; @@ -62,7 +61,7 @@ XLA_TEST_P(UnaryOpTest, Ops) { } UnaryBuildFuncTy build_func = GetParam().build_func; - build_func(&builder, x_opnd); + build_func(x_opnd); ComputeAndCompareR1(&builder, expected, {x_data.get()}, error_spec_); } @@ -79,18 +78,17 @@ half round_imp(half value) { INSTANTIATE_TEST_CASE_P( half, UnaryOpTest, ::testing::Values( - UnaryOpTestParam{[](half x) { return abs(x); }, &XlaBuilder::Abs}, - UnaryOpTestParam{[](half x) { return round_imp(x); }, - &XlaBuilder::Round}, - UnaryOpTestParam{[](half x) { return ceil(x); }, &XlaBuilder::Ceil}, - UnaryOpTestParam{[](half x) { return cos(x); }, &XlaBuilder::Cos}, - UnaryOpTestParam{[](half x) { return exp(x); }, &XlaBuilder::Exp}, - UnaryOpTestParam{[](half x) { return floor(x); }, &XlaBuilder::Floor}, - UnaryOpTestParam{[](half x) { return log(x); }, &XlaBuilder::Log}, - UnaryOpTestParam{[](half x) { return -x; }, &XlaBuilder::Neg}, - UnaryOpTestParam{[](half x) { return sign_imp(x); }, &XlaBuilder::Sign}, - UnaryOpTestParam{[](half x) { return sin(x); }, &XlaBuilder::Sin}, - UnaryOpTestParam{[](half x) { return tanh(x); }, &XlaBuilder::Tanh} + UnaryOpTestParam{[](half x) { return abs(x); }, &Abs}, + UnaryOpTestParam{[](half x) { return round_imp(x); }, &Round}, + UnaryOpTestParam{[](half x) { return ceil(x); }, &Ceil}, + UnaryOpTestParam{[](half x) { return cos(x); }, &Cos}, + UnaryOpTestParam{[](half x) { return exp(x); }, &Exp}, + UnaryOpTestParam{[](half x) { return floor(x); }, &Floor}, + UnaryOpTestParam{[](half x) { return log(x); }, &Log}, + UnaryOpTestParam{[](half x) { return -x; }, &Neg}, + UnaryOpTestParam{[](half x) { return sign_imp(x); }, &Sign}, + UnaryOpTestParam{[](half x) { return sin(x); }, &Sin}, + UnaryOpTestParam{[](half x) { return tanh(x); }, &Tanh} )); @@ -118,19 +116,18 @@ XLA_TEST_P(UnaryPredTest, Ops) { } UnaryBuildFuncTy build_func = GetParam().build_func; - build_func(&builder, x_opnd); + build_func(x_opnd); ComputeAndCompareR1(&builder, expected, {x_data.get()}); } INSTANTIATE_TEST_CASE_P(half, UnaryPredTest, ::testing::Values(UnaryPredTestParam{ - [](half x) { return isfinite(x); }, - &XlaBuilder::IsFinite})); + [](half x) { return isfinite(x); }, &IsFinite})); -using BinaryBuildFuncTy = std::function)>; +using BinaryBuildFuncTy = + std::function)>; struct BinaryOpTestParam { std::function compute_func; @@ -159,7 +156,7 @@ XLA_TEST_P(BinaryOpTest, Ops) { } BinaryBuildFuncTy build_func = GetParam().build_func; - build_func(&builder, x_opnd, y_opnd, {}); + build_func(x_opnd, y_opnd, {}); ComputeAndCompareR1(&builder, expected, {x_data.get(), y_data.get()}, error_spec_); @@ -173,22 +170,15 @@ half atan2_imp(half x, half y) { INSTANTIATE_TEST_CASE_P( half, BinaryOpTest, ::testing::Values( - BinaryOpTestParam{[](half x, half y) { return x + y; }, - &XlaBuilder::Add}, + BinaryOpTestParam{[](half x, half y) { return x + y; }, &Add}, BinaryOpTestParam{[](half x, half y) { return atan2_imp(x, y); }, - &XlaBuilder::Atan2}, - BinaryOpTestParam{[](half x, half y) { return x / y; }, - &XlaBuilder::Div}, - BinaryOpTestParam{[](half x, half y) { return max(x, y); }, - &XlaBuilder::Max}, - BinaryOpTestParam{[](half x, half y) { return min(x, y); }, - &XlaBuilder::Min}, - BinaryOpTestParam{[](half x, half y) { return x * y; }, - &XlaBuilder::Mul}, - BinaryOpTestParam{[](half x, half y) { return pow(x, y); }, - &XlaBuilder::Pow}, - BinaryOpTestParam{[](half x, half y) { return x - y; }, - &XlaBuilder::Sub} + &Atan2}, + BinaryOpTestParam{[](half x, half y) { return x / y; }, &Div}, + BinaryOpTestParam{[](half x, half y) { return max(x, y); }, &Max}, + BinaryOpTestParam{[](half x, half y) { return min(x, y); }, &Min}, + BinaryOpTestParam{[](half x, half y) { return x * y; }, &Mul}, + BinaryOpTestParam{[](half x, half y) { return pow(x, y); }, &Pow}, + BinaryOpTestParam{[](half x, half y) { return x - y; }, &Sub} )); @@ -221,27 +211,22 @@ XLA_TEST_P(BinaryPredTest, Ops) { } BinaryBuildFuncTy build_func = GetParam().build_func; - build_func(&builder, x_opnd, y_opnd, {}); + build_func(x_opnd, y_opnd, {}); ComputeAndCompareR1(&builder, expected, {x_data.get(), y_data.get()}); } INSTANTIATE_TEST_CASE_P( half, BinaryPredTest, - ::testing::Values(BinaryPredTestParam{[](half x, half y) { return x == y; }, - &XlaBuilder::Eq}, - BinaryPredTestParam{[](half x, half y) { return x != y; }, - &XlaBuilder::Ne}, - BinaryPredTestParam{[](half x, half y) { return x >= y; }, - &XlaBuilder::Ge}, - BinaryPredTestParam{[](half x, half y) { return x > y; }, - &XlaBuilder::Gt}, - BinaryPredTestParam{[](half x, half y) { return x <= y; }, - &XlaBuilder::Le}, - BinaryPredTestParam{[](half x, half y) { return x < y; }, - &XlaBuilder::Lt} - - )); + ::testing::Values( + BinaryPredTestParam{[](half x, half y) { return x == y; }, &Eq}, + BinaryPredTestParam{[](half x, half y) { return x != y; }, &Ne}, + BinaryPredTestParam{[](half x, half y) { return x >= y; }, &Ge}, + BinaryPredTestParam{[](half x, half y) { return x > y; }, &Gt}, + BinaryPredTestParam{[](half x, half y) { return x <= y; }, &Le}, + BinaryPredTestParam{[](half x, half y) { return x < y; }, &Lt} + + )); } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc index cf971dd61b71ad329b20b0bb7c16166126562681..4d82442f7e3630c115eff1f17544e2b892c5e7eb 100644 --- a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc +++ b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc @@ -30,9 +30,9 @@ class HloMetadataTest : public LocalClientTestBase { } void BuildAddComputation(XlaBuilder* builder) { - auto x = builder->Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder->Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder->Add(x, y); + auto x = Parameter(builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Add(x, y); } OpMetadata metadata_; diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 08ed826c80823efe0af8ce682945fe7e46d267ae..242cc5db11ff2bdf69209df7537216573d8afbf3 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -94,8 +94,7 @@ HloTestBase::HloTestBase(se::Platform* test_platform, /* static */ std::unique_ptr HloTestBase::CreateNewModule(const string& name) { - return MakeUnique(name, VersionedComputationHandle(), - GetModuleConfigForTest()); + return MakeUnique(name, GetModuleConfigForTest()); } /*static*/ DebugOptions HloTestBase::GetDebugOptionsForTest() { diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index eb3a2ea76a667a2afa2562f01d28f34384b84a21..9009d67cea6840235d63724ef76d777c8f693d33 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -66,6 +66,15 @@ namespace xla { // // For a more detailed example, see "../tests/sample_text_test.cc". class HloTestBase : public ::testing::Test { + public: + // Creates a new HLO module for a test. The module created will have + // TestName() for its name; it will also automatically populate its debug + // options from command-line flags. If you want a fresh HloModule object and + // then add HloComputations to it, it's recommended to use this method in your + // tests. + static std::unique_ptr CreateNewModule( + const string& name = TestName()); + protected: // This uses the interpreter backend as the reference backend and // automatically finds another supported backend as the test backend. If the @@ -80,14 +89,6 @@ class HloTestBase : public ::testing::Test { ~HloTestBase() override {} - // Creates a new HLO module for a test. The module created will have - // TestName() for its name; it will also automatically populate its debug - // options from command-line flags. If you want a fresh HloModule object and - // then add HloComputations to it, it's recommended to use this method in your - // tests. - static std::unique_ptr CreateNewModule( - const string& name = TestName()); - // Populates debug options from command-line flags and adjusts the options for // testing. It is recommended to use this when you need to pass in // DebugOptions, e.g. when creating a module from a string or a file. @@ -184,13 +185,9 @@ class HloTestBase : public ::testing::Test { // 'layout'. void ForceParameterLayout(HloModule* module, int64 param_no, const Layout& layout) { - ASSERT_LT( - param_no, - module->mutable_host_entry_computation_layout()->parameter_count()); - module->mutable_host_entry_computation_layout() - ->mutable_parameter_layout(param_no) - ->ResetLayout(layout); - module->mutable_device_entry_computation_layout() + ASSERT_LT(param_no, + module->mutable_entry_computation_layout()->parameter_count()); + module->mutable_entry_computation_layout() ->mutable_parameter_layout(param_no) ->ResetLayout(layout); } @@ -198,10 +195,7 @@ class HloTestBase : public ::testing::Test { // Convenience method to force the layout of the computation result in a // module. The result layout of 'module' is set to 'layout'. void ForceResultLayout(HloModule* module, const Layout& layout) { - module->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->ResetLayout(layout); - module->mutable_device_entry_computation_layout() + module->mutable_entry_computation_layout() ->mutable_result_layout() ->ResetLayout(layout); } @@ -209,10 +203,7 @@ class HloTestBase : public ::testing::Test { // Convenience method to clear the layout of the computation result in // 'module'. void ForceClearResultLayout(HloModule* module) { - module->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->Clear(); - module->mutable_device_entry_computation_layout() + module->mutable_entry_computation_layout() ->mutable_result_layout() ->Clear(); } diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc index c8a05c2e9e971d86feb6ff893fcd25c6767af99f..ad1f5b9eed8b5b140100c1fa35dc7d698e3db48b 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc @@ -41,14 +41,17 @@ void HloVerifiedTestBase::TearDown() { << "TearDown called more than once; it should be called exactly once."; tear_down_called_ = true; if (module_) { - VerifyModule(); + VerifyModule(module_.get()); + } + for (int i = 0; i < modules_.size(); ++i) { + VerifyModule(modules_.at(i).get()); } HloTestBase::TearDown(); } -void HloVerifiedTestBase::VerifyModule() { - HloVerifier verifier; - xla::StatusOr mutated = verifier.Run(module_.get()); +void HloVerifiedTestBase::VerifyModule(HloModule* module) { + HloVerifier verifier(/*allow_mixed_precision=*/true); + xla::StatusOr mutated = verifier.Run(module); if (!mutated.ok()) { ADD_FAILURE() << "HloVerifier failed: " << mutated.status(); } else { @@ -59,15 +62,20 @@ void HloVerifiedTestBase::VerifyModule() { HloModule& HloVerifiedTestBase::module() { if (!module_) { - module_ = CreateNewModule(); + module_ = HloTestBase::CreateNewModule(); } return *module_; } -void HloVerifiedTestBase::ParseAndVerifyModule( - tensorflow::StringPiece hlo_text) { +HloModule* HloVerifiedTestBase::CreateNewModule(const string& name) { + modules_.emplace_back(HloTestBase::CreateNewModule()); + return modules_.back().get(); +} + +void HloVerifiedTestBase::ParseAndVerifyModule(tensorflow::StringPiece hlo_text, + const HloModuleConfig& config) { CHECK(!module_) << "Called ParseModule when test already has a module."; - TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text)); - VerifyModule(); + TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text, config)); + VerifyModule(module_.get()); } } // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h index e5bb14a8839acbdef8fd2b79bb0f574c46ea3d40..5b28c01c369fa1ae1c7941f5c8139882c4dbed08 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h @@ -44,7 +44,8 @@ class HloVerifiedTestBase : public HloTestBase { // Returns the default HloModule, lazily creating it if necessary via // HloTestBase::CreateNewModule(). HloModule& module(); - void ParseAndVerifyModule(tensorflow::StringPiece hlo_text); + void ParseAndVerifyModule(tensorflow::StringPiece hlo_text, + const HloModuleConfig& config = HloModuleConfig()); // Sets the shape-size function used during hlo verification. If this isn't // called, a default ShapeVerifier is used instead. @@ -52,11 +53,23 @@ class HloVerifiedTestBase : public HloTestBase { shape_verifier_ = std::move(shape_verifier); } + // Creates a new module for a test, and stores it in modules_ so it can be + // verified. Intentionally hides HloTestBase::CreateNewModule, to prevent + // creation of unverified modules. + HloModule* CreateNewModule(const string& name = TestName()); + + // It is confusing to store modules created by module() and CreateNewModule() + // in different fields, but it allows us to migrate tests to + // HloVerifiedTestBase more easily, so it's a win because we can verify more + // modules. See b/80488902. private: - std::unique_ptr module_; // Lazily populated. Access via module(). + // Lazily populated. Access via module(). + std::unique_ptr module_; + // Populated by calls to CreateNewModule. + std::vector> modules_; std::unique_ptr shape_verifier_; bool tear_down_called_ = false; - void VerifyModule(); + static void VerifyModule(HloModule* module); }; } // namespace xla diff --git a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc index 2f46ee0be216d7dabf1c476d3cfb7d528f8ab6a4..082bc34136e004795ce300c66591758f47c665fe 100644 --- a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc +++ b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc @@ -124,8 +124,7 @@ class LLVMCompilerTest : public ::testing::Test { static std::unique_ptr CreateNewModule() { HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - return MakeUnique(TestName(), VersionedComputationHandle(), - config); + return MakeUnique(TestName(), config); } }; diff --git a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc index f21f83992ffb7c07dff31c68a7e9e3f7944bf512..9191be9fd905ab2e0c661042b042c8233d39e4a1 100644 --- a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc @@ -38,9 +38,9 @@ class LocalClientAllocationTest : public LocalClientTestBase { XLA_TEST_F(LocalClientAllocationTest, AddVectors) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0.0f, 1.0f, 2.0f}); - auto y = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - builder.Add(x, y); + auto x = ConstantR1(&builder, {0.0f, 1.0f, 2.0f}); + auto y = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + Add(x, y); TestAllocator* allocator = GetOrCreateAllocator(local_client_->platform()); @@ -74,9 +74,9 @@ XLA_TEST_F(LocalClientAllocationTest, RunOnDevices) { // Run a computation on every device on the system. Verify that allocation // occurs on the proper device. XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0.0f, 1.0f, 2.0f}); - auto y = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - builder.Add(x, y); + auto x = ConstantR1(&builder, {0.0f, 1.0f, 2.0f}); + auto y = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + Add(x, y); auto computation = builder.Build().ConsumeValueOrDie(); TestAllocator* allocator = GetOrCreateAllocator(local_client_->platform()); diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index a366afe8262e1f537b225e395bba9cb2fc22683a..70612e7c49d2815096cc54fd6ae796148249b4db 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -37,8 +37,8 @@ using xla::string; xla::XlaComputation Doubler() { xla::XlaBuilder builder("doubler"); auto r0f32 = xla::ShapeUtil::MakeShape(xla::F32, {}); - auto x = builder.Parameter(0, r0f32, "x"); - builder.Mul(x, builder.ConstantR0(2.0)); + auto x = xla::Parameter(&builder, 0, r0f32, "x"); + xla::Mul(x, xla::ConstantR0(&builder, 2.0)); return std::move(builder.Build().ValueOrDie()); } @@ -51,10 +51,10 @@ int main(int argc, char** argv) { xla::XlaBuilder builder("aot_test_helper"); auto opaque_shape = xla::ShapeUtil::MakeOpaqueShape(); - auto opaque_param = builder.Parameter(0, opaque_shape, "x"); + auto opaque_param = Parameter(&builder, 0, opaque_shape, "x"); auto r0f32 = xla::ShapeUtil::MakeShape(xla::F32, {}); - auto sum = builder.CustomCall("SumStructElements", {opaque_param}, r0f32); - builder.Call(Doubler(), {sum}); + auto sum = CustomCall(&builder, "SumStructElements", {opaque_param}, r0f32); + Call(&builder, Doubler(), {sum}); if (argc != 2) { LOG(FATAL) << "local_client_aot_test_helper TARGET_CPU"; diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc index 96858c00d6bbe59b673a34e7d5ca261756709596..2c6393794ef1b1558f5e651b5cb7bfa2afa961de 100644 --- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc @@ -54,7 +54,7 @@ class LocalClientExecuteTest : public LocalClientTestBase { XLA_TEST_F(LocalClientExecuteTest, Constant) { XlaBuilder builder(TestName()); - auto y = builder.ConstantR0(123.0f); + ConstantR0(&builder, 123.0f); ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {}); @@ -64,9 +64,9 @@ XLA_TEST_F(LocalClientExecuteTest, Constant) { XLA_TEST_F(LocalClientExecuteTest, AddScalars) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.ConstantR0(123.0f); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = ConstantR0(&builder, 123.0f); + Add(x, y); auto x_value = LiteralToShapedBuffer(*Literal::CreateR0(42.0f)); ScopedShapedBuffer result = @@ -77,9 +77,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddScalars) { XLA_TEST_F(LocalClientExecuteTest, AddZeroElementVectors) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {0}), "x"); - auto y = builder.ConstantR1({}); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {0}), "x"); + auto y = ConstantR1(&builder, {}); + Add(x, y); auto x_array = LiteralToShapedBuffer(*Literal::CreateR1({})); ScopedShapedBuffer result = @@ -90,9 +90,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddZeroElementVectors) { XLA_TEST_F(LocalClientExecuteTest, AddVectors) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3}), "x"); - auto y = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3}), "x"); + auto y = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + Add(x, y); auto x_array = LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); @@ -104,9 +104,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddVectors) { XLA_TEST_F(LocalClientExecuteTest, AddVectorsWithProfile) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3}), "x"); - auto y = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3}), "x"); + auto y = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + Add(x, y); auto x_array = LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); @@ -122,9 +122,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddVectorsWithProfile) { XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentInputLayouts) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); + Add(x, y); auto computation = builder.Build().ConsumeValueOrDie(); // Create x as a col-major array. @@ -155,9 +155,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentInputLayouts) { XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentOutputLayouts) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); + Add(x, y); auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( @@ -192,9 +192,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentOutputLayouts) { XLA_TEST_F(LocalClientExecuteTest, TupleResult) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); - builder.Tuple({x, y, x}); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); + Tuple(&builder, {x, y, x}); auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( @@ -209,21 +209,20 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResult) { EXPECT_EQ(3, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{10.0f, 20.0f}, {30.0f, 40.0f}}, - LiteralSlice(*result_literal, {1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {2})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{10.0f, 20.0f}, {30.0f, 40.0f}}, + LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {2})); } XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); - auto inner_tuple = builder.Tuple({x, y, x}); - builder.Tuple({inner_tuple, x}); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); + auto inner_tuple = Tuple(&builder, {x, y, x}); + Tuple(&builder, {inner_tuple, x}); auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( @@ -238,25 +237,22 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) { EXPECT_EQ(2, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, - LiteralSlice(*result_literal, {0, 0})); - LiteralTestUtil::ExpectR2Equal( - {{10.0f, 20.0f}, {30.0f, 40.0f}}, - LiteralSlice(*result_literal, {0, 1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, - LiteralSlice(*result_literal, {0, 2})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0, 0})); + LiteralTestUtil::ExpectR2Equal({{10.0f, 20.0f}, {30.0f, 40.0f}}, + LiteralSlice(*result_literal, {0, 1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0, 2})); } XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) { // Verify setting the result layout of a computation with a tuple output. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); - builder.Tuple({x, y}); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {2, 2}), "y"); + Tuple(&builder, {x, y}); auto array = LiteralToShapedBuffer( *Literal::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); @@ -273,10 +269,10 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) { options, DefaultExecutableRunOptions()); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { @@ -291,15 +287,15 @@ XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { // Computation adds the respective array and vector elements from each tuple // argument and returns the results as a tuple. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, tuple_shape0, "x"); - auto y = builder.Parameter(1, tuple_shape1, "y"); - auto x_0 = builder.GetTupleElement(x, 0); - auto x_1 = builder.GetTupleElement(x, 1); - auto y_0 = builder.GetTupleElement(y, 0); - auto y_1 = builder.GetTupleElement(y, 1); - auto array_sum = builder.Add(x_0, y_1); - auto vector_diff = builder.Sub(x_1, y_0); - builder.Tuple({array_sum, vector_diff}); + auto x = Parameter(&builder, 0, tuple_shape0, "x"); + auto y = Parameter(&builder, 1, tuple_shape1, "y"); + auto x_0 = GetTupleElement(x, 0); + auto x_1 = GetTupleElement(x, 1); + auto y_0 = GetTupleElement(y, 0); + auto y_1 = GetTupleElement(y, 1); + auto array_sum = Add(x_0, y_1); + auto vector_diff = Sub(x_1, y_0); + Tuple(&builder, {array_sum, vector_diff}); auto computation = builder.Build().ConsumeValueOrDie(); auto x_literal = Literal::MakeTuple( @@ -319,11 +315,10 @@ XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { EXPECT_EQ(2, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{56.0f, 46.0f}, {36.0f, 26.0f}}, - LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {40.0f, 71.0f, 117.0f}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{56.0f, 46.0f}, {36.0f, 26.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR1Equal({40.0f, 71.0f, 117.0f}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { @@ -338,15 +333,15 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { // Computation negates the array element and sums the two vector elements in // the nested tuple. The resulting array and vector are returned as a tuple. XlaBuilder builder(TestName()); - auto param = builder.Parameter(0, nested_tuple_shape, "param"); - auto inner_tuple = builder.GetTupleElement(param, 0); - auto inner_array = builder.GetTupleElement(inner_tuple, 0); - auto inner_vector = builder.GetTupleElement(inner_tuple, 1); - auto outer_vector = builder.GetTupleElement(param, 1); - - auto negate_array = builder.Neg(inner_array); - auto vector_sum = builder.Add(inner_vector, outer_vector); - builder.Tuple({negate_array, vector_sum}); + auto param = Parameter(&builder, 0, nested_tuple_shape, "param"); + auto inner_tuple = GetTupleElement(param, 0); + auto inner_array = GetTupleElement(inner_tuple, 0); + auto inner_vector = GetTupleElement(inner_tuple, 1); + auto outer_vector = GetTupleElement(param, 1); + + auto negate_array = Neg(inner_array); + auto vector_sum = Add(inner_vector, outer_vector); + Tuple(&builder, {negate_array, vector_sum}); auto computation = builder.Build().ConsumeValueOrDie(); auto arg_literal = Literal::MakeTuple( @@ -360,10 +355,10 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&arg_buffer}); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{-1.0, -2.0}, {-3.0, -4}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {264.0, 73.0, 133.0}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{-1.0, -2.0}, {-3.0, -4}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR1Equal({264.0, 73.0, 133.0}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { @@ -376,10 +371,10 @@ XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { ShapeUtil::MakeTupleShape({array_shape, array_shape}); XlaBuilder builder(TestName()); - auto param = builder.Parameter(0, tuple_shape, "param"); - auto element_0 = builder.GetTupleElement(param, 0); - auto element_1 = builder.GetTupleElement(param, 1); - builder.Tuple({builder.Neg(element_0), builder.Add(element_1, element_1)}); + auto param = Parameter(&builder, 0, tuple_shape, "param"); + auto element_0 = GetTupleElement(param, 0); + auto element_1 = GetTupleElement(param, 1); + Tuple(&builder, {Neg(element_0), Add(element_1, element_1)}); auto computation = builder.Build().ConsumeValueOrDie(); auto arg_literal = Literal::MakeTuple( @@ -389,18 +384,17 @@ XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { ScopedShapedBuffer result_0 = ExecuteLocallyOrDie(computation, {&arg_buffer}); std::unique_ptr result_0_literal = ShapedBufferToLiteral(result_0); - LiteralTestUtil::ExpectR2Equal( - {{-1.0, -2.0}, {-3.0, -4.0}}, - LiteralSlice(*result_0_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{22.0, 6.0}, {8.0, 10}}, LiteralSlice(*result_0_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{-1.0, -2.0}, {-3.0, -4.0}}, + LiteralSlice(*result_0_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{22.0, 6.0}, {8.0, 10}}, + LiteralSlice(*result_0_literal, {1})); ScopedShapedBuffer result_1 = ExecuteLocallyOrDie(computation, {&result_0}); std::unique_ptr result_1_literal = ShapedBufferToLiteral(result_1); - LiteralTestUtil::ExpectR2Equal( - {{1.0, 2.0}, {3.0, 4.0}}, LiteralSlice(*result_1_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{44.0, 12.0}, {16.0, 20}}, LiteralSlice(*result_1_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0, 2.0}, {3.0, 4.0}}, + LiteralSlice(*result_1_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{44.0, 12.0}, {16.0, 20}}, + LiteralSlice(*result_1_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { @@ -420,16 +414,15 @@ XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { const Shape tuple_shape = ShapeUtil::MakeTupleShape(element_shapes); XlaBuilder builder(TestName()); - auto param = builder.Parameter(0, tuple_shape, "param"); + auto param = Parameter(&builder, 0, tuple_shape, "param"); // Add each element's tuple index value to every element. std::vector result_elements; for (int i = 0; i < kElementCount; ++i) { - auto element = builder.GetTupleElement(param, i); - result_elements.push_back( - builder.Add(element, builder.ConstantR0(i))); + auto element = GetTupleElement(param, i); + result_elements.push_back(Add(element, ConstantR0(&builder, i))); } - builder.Tuple(result_elements); + Tuple(&builder, result_elements); auto computation = builder.Build().ConsumeValueOrDie(); // Feed in a tuple where each two-element vector element is {tuple_index, @@ -447,8 +440,7 @@ XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { for (int i = 0; i < kElementCount; ++i) { LiteralTestUtil::ExpectR1Near( - {2.0f * i, 0.0f}, LiteralSlice(*result_literal, {i}), - error_spec_); + {2.0f * i, 0.0f}, LiteralSlice(*result_literal, {i}), error_spec_); } } @@ -465,22 +457,22 @@ XLA_TEST_F(LocalClientExecuteTest, LargeNestedTuple) { const Shape tuple_shape = ShapeUtil::MakeTupleShape(inner_tuple_shapes); XlaBuilder builder(TestName()); - auto param = builder.Parameter(0, tuple_shape, "param"); + auto param = Parameter(&builder, 0, tuple_shape, "param"); // The computation increments each leaf value by an amount equal to the leaf's // ordinal position in a traversal of the tuple. std::vector result_elements; for (int i = 0; i < kFanout; ++i) { - auto outer_element = builder.GetTupleElement(param, i); + auto outer_element = GetTupleElement(param, i); std::vector inner_result_elements; for (int j = 0; j < kFanout; ++j) { - auto inner_element = builder.GetTupleElement(outer_element, j); - inner_result_elements.push_back(builder.Add( - inner_element, builder.ConstantR0(i * kFanout + j))); + auto inner_element = GetTupleElement(outer_element, j); + inner_result_elements.push_back( + Add(inner_element, ConstantR0(&builder, i * kFanout + j))); } - result_elements.push_back(builder.Tuple(inner_result_elements)); + result_elements.push_back(Tuple(&builder, inner_result_elements)); } - builder.Tuple(result_elements); + Tuple(&builder, result_elements); auto computation = builder.Build().ConsumeValueOrDie(); // Construct the argument to pass to the computation. @@ -520,14 +512,14 @@ XLA_TEST_F(LocalClientExecuteTest, DeepTuple) { } XlaBuilder builder(TestName()); - auto element = builder.Parameter(0, shape, "param"); + auto element = Parameter(&builder, 0, shape, "param"); for (int i = 0; i < kTupleDepth; ++i) { - element = builder.GetTupleElement(element, 0); + element = GetTupleElement(element, 0); } - auto output = builder.Add(element, builder.ConstantR0(42.0)); + auto output = Add(element, ConstantR0(&builder, 42.0)); for (int i = 0; i < kTupleDepth; ++i) { - output = builder.Tuple({output}); + output = Tuple(&builder, {output}); } auto computation = builder.Build().ConsumeValueOrDie(); @@ -547,16 +539,16 @@ XLA_TEST_F(LocalClientExecuteTest, DeepTuple) { for (int i = 0; i < kTupleDepth; ++i) { index.push_back(0); } - LiteralTestUtil::ExpectR0Equal( - 165.0, LiteralSlice(*result_literal, index)); + LiteralTestUtil::ExpectR0Equal(165.0, + LiteralSlice(*result_literal, index)); } XLA_TEST_F(LocalClientExecuteTest, InvalidNumberOfArguments) { // Test passing in an invalid number of arguments. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {3}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {3}), "y"); + Add(x, y); auto x_array = LiteralToShapedBuffer(*Literal::CreateR1({1.0f, 2.0f, 3.0f})); @@ -571,8 +563,8 @@ XLA_TEST_F(LocalClientExecuteTest, InvalidNumberOfArguments) { XLA_TEST_F(LocalClientExecuteTest, IncorrectArgumentShape) { // Test passing in an argument with the wrong shape. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3}), "x"); - builder.Neg(x); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3}), "x"); + Neg(x); auto x_array = LiteralToShapedBuffer( *Literal::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); @@ -588,8 +580,8 @@ XLA_TEST_F(LocalClientExecuteTest, IncorrectArgumentShape) { XLA_TEST_F(LocalClientExecuteTest, InvalidResultLayout) { // Test passing in an invalid result layout parameter. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); - builder.Neg(x); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2, 2}), "x"); + Neg(x); auto x_array = LiteralToShapedBuffer( *Literal::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); @@ -611,7 +603,7 @@ XLA_TEST_F(LocalClientExecuteTest, RunOnAllDeviceOrdinals) { // Try to run a trivial computation on every device on the system. If a // specific device is not supported, check that the right error is returned. XlaBuilder builder(TestName()); - builder.ConstantR0(42.0f); + ConstantR0(&builder, 42.0f); auto computation = builder.Build().ConsumeValueOrDie(); for (int d = 0; d < local_client_->device_count(); ++d) { if (!local_client_->device_ordinal_supported(d)) { @@ -638,7 +630,7 @@ XLA_TEST_F(LocalClientExecuteTest, InvalidDeviceOrdinalValues) { // Try running computations on devices with device ordinal values which do not // exist. XlaBuilder builder(TestName()); - builder.ConstantR0(42.0f); + ConstantR0(&builder, 42.0f); auto computation = builder.Build().ConsumeValueOrDie(); auto execute_status = @@ -655,7 +647,7 @@ XLA_TEST_F(LocalClientExecuteTest, InvalidDeviceOrdinalValues) { XLA_TEST_F(LocalClientExecuteTest, RunOnStream) { // Run a computation on a specific stream on each device on the system. XlaBuilder builder(TestName()); - builder.ConstantR0(42.0f); + ConstantR0(&builder, 42.0f); auto computation = builder.Build().ConsumeValueOrDie(); for (int d = 0; d < local_client_->device_count(); ++d) { @@ -691,7 +683,7 @@ XLA_TEST_F(LocalClientExecuteTest, wrong_stream.Init(); XlaBuilder builder(TestName()); - builder.ConstantR0(42.0f); + ConstantR0(&builder, 42.0f); auto execute_status = ExecuteLocally( builder.Build().ValueOrDie(), {}, DefaultExecutableBuildOptions(), DefaultExecutableRunOptions().set_stream(&wrong_stream)); @@ -708,7 +700,7 @@ XLA_TEST_F(LocalClientExecuteTest, TestAllocator allocator(wrong_platform); XlaBuilder builder(TestName()); - auto y = builder.ConstantR0(123.0f); + ConstantR0(&builder, 123.0f); auto execute_status = ExecuteLocally( builder.Build().ValueOrDie(), {}, DefaultExecutableBuildOptions(), @@ -721,7 +713,7 @@ XLA_TEST_F(LocalClientExecuteTest, XLA_TEST_F(LocalClientExecuteTest, RunOnUninitializedStream) { // Try to run a computation on a stream that has not been initialized. XlaBuilder builder(TestName()); - builder.ConstantR0(42.0f); + ConstantR0(&builder, 42.0f); LOG(INFO) << "default device = " << local_client_->default_device_ordinal(); se::StreamExecutor* executor = @@ -744,26 +736,26 @@ XLA_TEST_F(LocalClientExecuteTest, SelectBetweenTuples) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto tuple12 = builder.Tuple( - {builder.ConstantR1(vec1), builder.ConstantR1(vec2)}); - auto tuple21 = builder.Tuple( - {builder.ConstantR1(vec2), builder.ConstantR1(vec1)}); - builder.Select(builder.ConstantR0(false), tuple12, tuple21); + auto tuple12 = Tuple(&builder, {ConstantR1(&builder, vec1), + ConstantR1(&builder, vec2)}); + auto tuple21 = Tuple(&builder, {ConstantR1(&builder, vec2), + ConstantR1(&builder, vec1)}); + Select(ConstantR0(&builder, false), tuple12, tuple21); ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {}); std::unique_ptr tuple_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR1Equal( - {2.0f, 4.0f, 6.0f}, LiteralSlice(*tuple_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {1.0f, 2.0f, 3.0f}, LiteralSlice(*tuple_literal, {1})); + LiteralTestUtil::ExpectR1Equal({2.0f, 4.0f, 6.0f}, + LiteralSlice(*tuple_literal, {0})); + LiteralTestUtil::ExpectR1Equal({1.0f, 2.0f, 3.0f}, + LiteralSlice(*tuple_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3}), "x"); - auto y = builder.ConstantR1({2.0f, 3.0f, 4.0f}); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3}), "x"); + auto y = ConstantR1(&builder, {2.0f, 3.0f, 4.0f}); + Add(x, y); Shape argument_layout = ShapeUtil::MakeShapeWithLayout(F32, /*dimensions=*/{3}, {0}); @@ -779,6 +771,10 @@ XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) { ScopedShapedBuffer result = executable->Run({&x_array}, DefaultExecutableRunOptions()) .ConsumeValueOrDie(); + ASSERT_IS_OK(local_client_->mutable_backend() + ->BorrowStream(0) + .ValueOrDie() + ->BlockHostUntilDone()); LiteralTestUtil::ExpectR1Near( {2.0f, 4.0f, 6.0f}, *ShapedBufferToLiteral(result), error_spec_); @@ -848,15 +844,40 @@ XLA_TEST_F(LocalClientExecuteTest, ShapeBufferToLiteralConversion64bit) { Literal::CreateR0(123456789000LL).get()})); } +XLA_TEST_F(LocalClientExecuteTest, InfeedTest) { + XlaBuilder builder(TestName()); + const Shape shape = ShapeUtil::MakeShape(F32, {3}); + auto in = Infeed(&builder, shape); + auto constant = ConstantR1(&builder, {1.0f, 2.0f, 3.0f}); + Add(in, constant); + + std::unique_ptr result; + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + result = ShapedBufferToLiteral(ExecuteLocallyOrDie( + builder.Build().ValueOrDie(), /*arguments=*/{})); + })); + + ASSERT_IS_OK(local_client_->TransferToInfeedLocal( + *Literal::CreateR1({-5.0, 123.0, 42.0}), + local_client_->default_device_ordinal())); + + // Join the thread. + thread.reset(); + + LiteralTestUtil::ExpectR1Equal({-4.0, 125.0, 45.0}, *result); +} + // TODO(b/34359662): Support infeed/outfeed on GPU and CPU parallel. // 2017-10-18. XLA_TEST_F(LocalClientExecuteTest, DISABLED_ON_GPU(InfeedOutfeedTest)) { XlaBuilder builder(TestName()); const Shape shape = ShapeUtil::MakeShape(F32, {3}); - auto in = builder.Infeed(shape); - auto constant = builder.ConstantR1({1.0f, 2.0f, 3.0f}); - auto sum = builder.Add(in, constant); - builder.Outfeed(sum, shape, /*outfeed_config=*/""); + auto in = Infeed(&builder, shape); + auto constant = ConstantR1(&builder, {1.0f, 2.0f, 3.0f}); + auto sum = Add(in, constant); + Outfeed(sum, shape, /*outfeed_config=*/""); std::unique_ptr thread( tensorflow::Env::Default()->StartThread( @@ -891,8 +912,8 @@ void BM_LocalClientOverhead(int num_iters) { // Use a tiny add operation as the computation. XlaBuilder builder("Add"); auto shape = ShapeUtil::MakeShape(F32, {2, 3}); - auto x = builder.Parameter(0, shape, "x"); - builder.Add(x, x); + auto x = Parameter(&builder, 0, shape, "x"); + Add(x, x); auto computation = builder.Build().ConsumeValueOrDie(); auto buffer = @@ -900,8 +921,10 @@ void BM_LocalClientOverhead(int num_iters) { ->AllocateScopedShapedBuffer(shape, &allocator, /*device_ordinal=*/0) .ConsumeValueOrDie(); auto literal = Literal::CreateR2({{0, 0, 0}, {0, 0, 0}}); - ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice( - executors[device_ordinal], *literal, buffer)); + auto stream = + client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); + ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice(stream.get(), *literal, + buffer)); const int kWarmups = 2; @@ -911,11 +934,8 @@ void BM_LocalClientOverhead(int num_iters) { std::unique_ptr executable = executable_status.ConsumeValueOrDie(); - se::Stream stream(executors[client->default_device_ordinal()]); - stream.Init(); - ExecutableRunOptions run_options; - run_options.set_allocator(&allocator).set_stream(&stream); + run_options.set_allocator(&allocator).set_stream(stream.get()); for (int i = 0; i < kWarmups; ++i) { auto result = executable->Run({&buffer}, run_options); diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc index 88797a7d0a7d0567b3a380c5fb1ad0c0ee875587..c31ba0e713a45d18b60bfdb9a47545cf34220333 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.cc +++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc @@ -189,7 +189,19 @@ StatusOr LocalClientTestBase::ExecuteLocally( TF_ASSIGN_OR_RETURN( std::unique_ptr executable, local_client_->Compile(computation, argument_layouts, build_options)); - return executable->Run(arguments, run_options); + TF_ASSIGN_OR_RETURN(auto ret, executable->Run(arguments, run_options)); + + auto device_ordinal = + build_options.device_ordinal() == -1 ? 0 : build_options.device_ordinal(); + auto* stream = run_options.stream(); + if (!stream) { + stream = local_client_->mutable_backend() + ->BorrowStream(device_ordinal) + .ValueOrDie() + .get(); + } + TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); + return std::move(ret); } } // namespace xla diff --git a/tensorflow/compiler/xla/tests/log_test.cc b/tensorflow/compiler/xla/tests/log_test.cc index c0c02e584c2348f64a9d7d0800038f5ca67a2171..cdf70ee4185be2ecd9dcb2d21fbd98c2ab6cc0ad 100644 --- a/tensorflow/compiler/xla/tests/log_test.cc +++ b/tensorflow/compiler/xla/tests/log_test.cc @@ -30,8 +30,8 @@ class LogTest : public ClientLibraryTestBase {}; XLA_TEST_F(LogTest, LogZeroValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR3FromArray3D(Array3D(3, 0, 0)); - builder.Log(x); + auto x = ConstantR3FromArray3D(&builder, Array3D(3, 0, 0)); + Log(x); ComputeAndCompareR3(&builder, Array3D(3, 0, 0), {}, ErrorSpec(0.0001)); @@ -42,8 +42,8 @@ TEST_F(LogTest, LogTenValues) { 5.0, 6.0, -7.0, -8.0, 9.0}; XlaBuilder builder(TestName()); - auto x = builder.ConstantR1(input); - builder.Log(x); + auto x = ConstantR1(&builder, input); + Log(x); std::vector expected; expected.reserve(input.size()); diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 7df45bebebdd3eb2e71f27d831a8e2ac9e3b5f7c..1b3bc9d5040e1382f534e00ea2679ebbd48ceb59 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -52,9 +52,9 @@ class MapTest : public ClientLibraryTestBase { // 1.0f ---------/ XlaComputation CreateAdderToOne() { XlaBuilder mapped_builder(TestName()); - auto x = mapped_builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto one = mapped_builder.ConstantR0(1.0); - mapped_builder.Add(x, one); + auto x = Parameter(&mapped_builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto one = ConstantR0(&mapped_builder, 1.0); + Add(x, one); auto computation_status = mapped_builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -62,9 +62,9 @@ class MapTest : public ClientLibraryTestBase { XlaComputation CreateMax() { XlaBuilder b(TestName()); - auto lhs = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto rhs = b.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - b.Max(lhs, rhs); + auto lhs = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto rhs = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Max(lhs, rhs); auto computation_status = b.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -75,8 +75,8 @@ class MapTest : public ClientLibraryTestBase { template XlaComputation CreateScalarOne() { XlaBuilder mapped_builder("scalar_one"); - (void)mapped_builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - mapped_builder.ConstantR0(1); + (void)Parameter(&mapped_builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + ConstantR0(&mapped_builder, 1); auto computation_status = mapped_builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -89,9 +89,9 @@ class MapTest : public ClientLibraryTestBase { // 2.0f ---------/ XlaComputation CreateMulByTwo() { XlaBuilder mapped_builder(TestName()); - auto x = mapped_builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto two = mapped_builder.ConstantR0(2.0); - mapped_builder.Mul(x, two); + auto x = Parameter(&mapped_builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto two = ConstantR0(&mapped_builder, 2.0); + Mul(x, two); auto computation_status = mapped_builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -107,10 +107,10 @@ class MapTest : public ClientLibraryTestBase { // 1.0f ---------/ XlaComputation CreateAdderToOneTimesItself() { XlaBuilder mapped_builder(TestName()); - auto x = mapped_builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto one = mapped_builder.ConstantR0(1.0); - auto adder_to_one = mapped_builder.Add(x, one); - mapped_builder.Mul(x, adder_to_one); + auto x = Parameter(&mapped_builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto one = ConstantR0(&mapped_builder, 1.0); + auto adder_to_one = Add(x, one); + Mul(x, adder_to_one); auto computation_status = mapped_builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -125,10 +125,10 @@ class MapTest : public ClientLibraryTestBase { XlaComputation CreateMapPlusN(const XlaComputation& embedded_computation, float n) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto map = builder.Map({x}, embedded_computation, {}); - auto constant_n = builder.ConstantR0(n); - builder.Add(map, constant_n); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto map = Map(&builder, {x}, embedded_computation, {}); + auto constant_n = ConstantR0(&builder, n); + Add(map, constant_n); auto computation_status = builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -138,9 +138,9 @@ class MapTest : public ClientLibraryTestBase { // defined by (x, y) -> x > y. XlaComputation CreateGt() { XlaBuilder b("Gt"); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = b.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - b.Gt(x, y); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&b, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Gt(x, y); auto computation_status = b.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -155,11 +155,11 @@ class MapTest : public ClientLibraryTestBase { // z {R0F32} ---------------/ XlaComputation CreateTernaryAdder() { XlaBuilder mapped_builder("TernaryAdder"); - auto x = mapped_builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = mapped_builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - auto z = mapped_builder.Parameter(2, ShapeUtil::MakeShape(F32, {}), "z"); - auto xy = mapped_builder.Add(x, y); - mapped_builder.Add(xy, z); + auto x = Parameter(&mapped_builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&mapped_builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + auto z = Parameter(&mapped_builder, 2, ShapeUtil::MakeShape(F32, {}), "z"); + auto xy = Add(x, y); + Add(xy, z); auto computation_status = mapped_builder.Build(); TF_CHECK_OK(computation_status.status()); return computation_status.ConsumeValueOrDie(); @@ -173,8 +173,8 @@ TEST_F(MapTest, MapEachElemPlusOneR0) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateAdderToOne(), {}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateAdderToOne(), {}); ComputeAndCompareR0(&builder, 43.0, {param0_data.get()}, ErrorSpec(0.01f)); @@ -187,8 +187,8 @@ XLA_TEST_F(MapTest, MapEachElemPlusOneR1S0) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateAdderToOne(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateAdderToOne(), {0}); ComputeAndCompareR1(&builder, {}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -202,8 +202,8 @@ TEST_F(MapTest, MapEachElemPlusOneR1S4) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateAdderToOne(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateAdderToOne(), {0}); ComputeAndCompareR1(&builder, {3.2f, 4.3f, 5.4f, 6.5f}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -216,8 +216,8 @@ TEST_F(MapTest, MapEachF32ElementToS32Constant) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateScalarOne(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateScalarOne(), {0}); ComputeAndCompareR1(&builder, {1, 1, 1, 1}, {param0_data.get()}); } @@ -229,8 +229,8 @@ TEST_F(MapTest, MapEachF32ElementToU32Constant) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateScalarOne(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateScalarOne(), {0}); ComputeAndCompareR1(&builder, {1, 1, 1, 1}, {param0_data.get()}); } @@ -243,8 +243,8 @@ TEST_F(MapTest, MapEachElemLongerChainR1) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateAdderToOneTimesItself(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateAdderToOneTimesItself(), {0}); ComputeAndCompareR1( &builder, {9.36f, 20.91f, 0.11f, 0.24f, 999000.0f, 65535.75f}, @@ -259,9 +259,9 @@ XLA_TEST_F(MapTest, MapMultipleMapsR1S0) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - auto map1 = builder.Map({param}, CreateAdderToOne(), {0}); - builder.Map({map1}, CreateMulByTwo(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto map1 = Map(&builder, {param}, CreateAdderToOne(), {0}); + Map(&builder, {map1}, CreateMulByTwo(), {0}); ComputeAndCompareR1(&builder, {}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -276,9 +276,9 @@ TEST_F(MapTest, MapMultipleMapsR1S4) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - auto map1 = builder.Map({param}, CreateAdderToOne(), {0}); - builder.Map({map1}, CreateMulByTwo(), {0}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto map1 = Map(&builder, {param}, CreateAdderToOne(), {0}); + Map(&builder, {map1}, CreateMulByTwo(), {0}); ComputeAndCompareR1(&builder, {6.4f, 8.6f, 10.8f, 13.0f}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -292,8 +292,8 @@ TEST_F(MapTest, MapEachElemPlusOneR2) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param}, CreateAdderToOne(), {0, 1}); + auto param = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param}, CreateAdderToOne(), {0, 1}); Array2D expected_array( {{14.25f, 15.0f}, {-6.1f, -6.2f}, {-7.8f, 9.8f}}); @@ -319,10 +319,10 @@ XLA_TEST_F(MapTest, ComplexNestedMaps) { auto embed3 = CreateMapPlusN(embed1, 4.0); XlaBuilder embed4_builder("embed4"); - auto embed4_param = embed4_builder.Parameter(0, scalar_shape, "x"); - auto embed4_map_lhs = embed4_builder.Map({embed4_param}, embed2, {}); - auto embed4_map_rhs = embed4_builder.Map({embed4_param}, embed3, {}); - embed4_builder.Add(embed4_map_lhs, embed4_map_rhs); + auto embed4_param = Parameter(&embed4_builder, 0, scalar_shape, "x"); + auto embed4_map_lhs = Map(&embed4_builder, {embed4_param}, embed2, {}); + auto embed4_map_rhs = Map(&embed4_builder, {embed4_param}, embed3, {}); + Add(embed4_map_lhs, embed4_map_rhs); auto embed4_status = embed4_builder.Build(); ASSERT_IS_OK(embed4_status.status()); auto embed4 = embed4_status.ConsumeValueOrDie(); @@ -330,11 +330,11 @@ XLA_TEST_F(MapTest, ComplexNestedMaps) { auto embed5 = CreateMapPlusN(embed2, 6.0); XlaBuilder builder(TestName()); - auto constant_42 = builder.ConstantR0(42.0); - auto constant_7 = builder.ConstantR0(7.0); - auto map_42 = builder.Map({constant_42}, embed5, {}); - auto map_7 = builder.Map({constant_7}, embed4, {}); - builder.Add(map_42, map_7); + auto constant_42 = ConstantR0(&builder, 42.0); + auto constant_7 = ConstantR0(&builder, 7.0); + auto map_42 = Map(&builder, {constant_42}, embed5, {}); + auto map_7 = Map(&builder, {constant_7}, embed4, {}); + Add(map_42, map_7); ComputeAndCompareR0(&builder, 73.0, {}, ErrorSpec(0.01f)); } @@ -351,9 +351,10 @@ TEST_F(MapTest, MapBinaryAdder) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, CreateScalarAddComputation(F32, &builder), {0}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, CreateScalarAddComputation(F32, &builder), + {0}); ComputeAndCompareR1(&builder, {7.3f, 7.7, 4.3f, 0}, {param0_data.get(), param1_data.get()}, @@ -374,10 +375,10 @@ XLA_TEST_F(MapTest, AddWithMixedLayouts) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, CreateScalarAddComputation(S32, &builder), - {0, 1}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, CreateScalarAddComputation(S32, &builder), + {0, 1}); Array2D expected(2, 2); expected(0, 0) = 11; @@ -400,10 +401,10 @@ XLA_TEST_F(MapTest, AddR3_3x0x2) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, CreateScalarAddComputation(S32, &builder), - {0, 1, 2}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, CreateScalarAddComputation(S32, &builder), + {0, 1, 2}); ComputeAndCompareR3(&builder, Array3D(3, 0, 2), {param0_data.get(), param1_data.get()}); @@ -425,10 +426,10 @@ TEST_F(MapTest, MapTernaryAdder) { std::unique_ptr param2_data = client_->TransferToServer(*param2_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - auto param2 = builder.Parameter(2, param2_literal->shape(), "param2"); - builder.Map({param0, param1, param2}, CreateTernaryAdder(), {0}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + auto param2 = Parameter(&builder, 2, param2_literal->shape(), "param2"); + Map(&builder, {param0, param1, param2}, CreateTernaryAdder(), {0}); ComputeAndCompareR1( &builder, {-2.7f, -92.3f, -895.7f, -400.0f}, @@ -440,7 +441,8 @@ TEST_F(MapTest, MapGt) { // Maps (x,y) -> x > y onto two R1F32 vectors. XlaBuilder b(TestName()); auto gt = CreateGt(); - b.Map({b.ConstantR1({1, 20}), b.ConstantR1({10, 2})}, gt, {0}); + Map(&b, {ConstantR1(&b, {1, 20}), ConstantR1(&b, {10, 2})}, gt, + {0}); ComputeAndCompareR1(&b, {false, true}, {}); } @@ -449,15 +451,15 @@ TEST_F(MapTest, NestedBinaryMap) { { // max_with_square(x) = do max(x, x^2) via a map. XlaBuilder b("max_with_square"); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - b.Map({x, b.Mul(x, x)}, CreateMax(), {}); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "x"); + Map(&b, {x, Mul(x, x)}, CreateMax(), {}); auto computation_status = b.Build(); ASSERT_IS_OK(computation_status.status()); max_with_square = computation_status.ConsumeValueOrDie(); } XlaBuilder b(TestName()); - auto input = b.ConstantR1({0.1f, 0.5f, -0.5f, 1.0f, 2.0f}); - b.Map({input}, max_with_square, {0}); + auto input = ConstantR1(&b, {0.1f, 0.5f, -0.5f, 1.0f, 2.0f}); + Map(&b, {input}, max_with_square, {0}); ComputeAndCompareR1(&b, {0.1f, 0.5f, 0.25f, 1.0f, 4.0f}, {}); } @@ -468,9 +470,9 @@ TEST_F(MapTest, MapOperantionWithBuildError) { XlaBuilder builder(TestName()); auto sub_builder = builder.CreateSubBuilder("ErrorAdd"); - auto x = sub_builder->Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = sub_builder->Parameter(1, ShapeUtil::MakeShape(U16, {}), "y"); - sub_builder->Add(x, y); + auto x = Parameter(sub_builder.get(), 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(sub_builder.get(), 1, ShapeUtil::MakeShape(U16, {}), "y"); + Add(x, y); auto error_add = sub_builder->BuildAndNoteError(); std::unique_ptr param0_literal = @@ -482,16 +484,15 @@ TEST_F(MapTest, MapOperantionWithBuildError) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, error_add, {0}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, error_add, {0}); StatusOr computation_status = builder.Build(); ASSERT_TRUE(!computation_status.ok()); - EXPECT_THAT( - computation_status.status().ToString(), - ::testing::HasSubstr("error from: ErrorAdd: Binary op BINOP_ADD with " - "different element types: f32[] and u16[]")); + EXPECT_THAT(computation_status.status().ToString(), + ::testing::HasSubstr("error from: ErrorAdd: Binary op add with " + "different element types: f32[] and u16[]")); } // MapTest disables inline and algsimp. MapTestWithFullOpt runs all @@ -507,9 +508,9 @@ TEST_F(MapTestWithFullOpt, MapScalarPower) { XlaBuilder builder(TestName()); auto sub_builder = builder.CreateSubBuilder("power"); - auto x = sub_builder->Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = sub_builder->Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - sub_builder->Pow(x, y); + auto x = Parameter(sub_builder.get(), 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(sub_builder.get(), 1, ShapeUtil::MakeShape(F32, {}), "y"); + Pow(x, y); auto power = sub_builder->BuildAndNoteError(); std::unique_ptr param0_literal = Literal::CreateR0(2.0f); @@ -519,9 +520,9 @@ TEST_F(MapTestWithFullOpt, MapScalarPower) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, power, {}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, power, {}); ComputeAndCompareR0(&builder, 32.0f, {param0_data.get(), param1_data.get()}, @@ -534,9 +535,9 @@ TEST_F(MapTestWithFullOpt, MapSubtractOppositeOrder) { XlaBuilder builder(TestName()); auto sub_builder = builder.CreateSubBuilder("power"); - auto x = sub_builder->Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = sub_builder->Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - sub_builder->Sub(y, x); // note that this is y - x, not x - y + auto x = Parameter(sub_builder.get(), 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(sub_builder.get(), 1, ShapeUtil::MakeShape(F32, {}), "y"); + Sub(y, x); // note that this is y - x, not x - y auto sub_opposite = sub_builder->BuildAndNoteError(); std::unique_ptr param0_literal = Literal::CreateR0(2.0f); @@ -546,9 +547,9 @@ TEST_F(MapTestWithFullOpt, MapSubtractOppositeOrder) { std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - auto param1 = builder.Parameter(1, param1_literal->shape(), "param1"); - builder.Map({param0, param1}, sub_opposite, {}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + auto param1 = Parameter(&builder, 1, param1_literal->shape(), "param1"); + Map(&builder, {param0, param1}, sub_opposite, {}); ComputeAndCompareR0( &builder, 3.0f, {param0_data.get(), param1_data.get()}, ErrorSpec(0.01f)); @@ -560,16 +561,16 @@ TEST_F(MapTestWithFullOpt, MapSquare) { XlaBuilder builder(TestName()); auto sub_builder = builder.CreateSubBuilder("power"); - auto x = sub_builder->Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - sub_builder->Mul(x, x); + auto x = Parameter(sub_builder.get(), 0, ShapeUtil::MakeShape(F32, {}), "x"); + Mul(x, x); auto square = sub_builder->BuildAndNoteError(); std::unique_ptr param0_literal = Literal::CreateR0(10.0f); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); - builder.Map({param0}, square, {}); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); + Map(&builder, {param0}, square, {}); ComputeAndCompareR0(&builder, 100.0f, {param0_data.get()}, ErrorSpec(0.01f)); diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 27fd36e06acdc589f3a84ad561164e4a33b93506..17b1807f44a457786906afc15d8d410f6cf2d4cd 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -56,11 +56,11 @@ TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32); XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) { using T = TypeParam; XlaBuilder builder("exp_2x2"); - auto data = builder.ConstantR2FromArray2D({ - {1.0f, 0.0f}, // row 0 - {-1.0f, 0.5f}, // row 1 - }); - builder.Exp(data); + auto data = ConstantR2FromArray2D(&builder, { + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 + }); + Exp(data); std::unique_ptr expected = Literal::CreateR2FromArray2D({{2.71828f, 1.00000f}, // row 0 @@ -76,20 +76,20 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) { // add_half(x) = x + 0.5 XlaBuilder builder("add_half"); auto x_value = - builder.Parameter(0, ShapeUtil::MakeShapeWithType({}), "x_value"); - auto half = builder.ConstantR0(static_cast(0.5)); - builder.Add(x_value, half); + Parameter(&builder, 0, ShapeUtil::MakeShapeWithType({}), "x_value"); + auto half = ConstantR0(&builder, static_cast(0.5)); + Add(x_value, half); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); add_half = computation_status.ConsumeValueOrDie(); } XlaBuilder builder("map_2x2"); - auto data = builder.ConstantR2FromArray2D({ - {1.0f, 0.0f}, // row 0 - {-1.0f, 0.5f}, // row 1 - }); - auto map = builder.Map({data}, add_half, {0, 1}); + auto data = ConstantR2FromArray2D(&builder, { + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 + }); + Map(&builder, {data}, add_half, {0, 1}); std::unique_ptr expected = Literal::CreateR2FromArray2D({{1.5f, 0.5f}, // row 0 @@ -100,15 +100,15 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) { XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) { using T = TypeParam; XlaBuilder builder("max_2x2"); - auto lhs = builder.ConstantR2FromArray2D({ - {7.0f, 2.0f}, // row 0 - {3.0f, -4.0f}, // row 1 - }); - auto rhs = builder.ConstantR2FromArray2D({ - {5.0f, 6.0f}, // row 0 - {1.0f, -8.0f}, // row 1 - }); - auto max = builder.Max(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, { + {7.0f, 2.0f}, // row 0 + {3.0f, -4.0f}, // row 1 + }); + auto rhs = ConstantR2FromArray2D(&builder, { + {5.0f, 6.0f}, // row 0 + {1.0f, -8.0f}, // row 1 + }); + Max(lhs, rhs); std::unique_ptr expected = Literal::CreateR2FromArray2D({{7.0f, 6.0f}, // row 0 @@ -137,9 +137,9 @@ class TestLinspaceMaxParametric XlaBuilder builder( tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols)); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - auto max = builder.Max(lhs, rhs); + auto lhs = ConstantR2FromArray2D(&builder, *alhs); + auto rhs = ConstantR2FromArray2D(&builder, *arhs); + Max(lhs, rhs); Array2D expected(rows, cols); for (int row = 0; row < rows; ++row) { @@ -208,23 +208,23 @@ class MatOpsDotAddTest rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); XlaBuilder builder(TestName()); - auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); + auto lhs_arg = Parameter(&builder, 0, lhs_shape, "lhs"); auto lhs_mat_arg = lhs_arg; if (transpose) { - lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); + lhs_mat_arg = Transpose(lhs_mat_arg, {1, 0}); } - auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); - auto result = builder.Dot(lhs_mat_arg, rhs_arg); + auto rhs_arg = Parameter(&builder, 1, rhs_shape, "rhs"); + auto result = Dot(lhs_mat_arg, rhs_arg); Array2D expected; if (add_lhs) { - result = builder.Add(result, lhs_arg); + result = Add(result, lhs_arg); if (transpose) { expected = Array2D({{47.0f, 52.0f}, {71.0f, 78.0f}}); } else { expected = Array2D({{35.0f, 39.0f}, {81.0f, 89.0f}}); } } else { - result = builder.Add(result, rhs_arg); + result = Add(result, rhs_arg); if (transpose) { expected = Array2D({{56.0f, 61.0f}, {80.0f, 87.0f}}); } else { diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc index 0791a71aacf7614286fe964623a3172a174d4722..e576f000ef23e761d6fa818457eec2144d4bcb00 100644 --- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc +++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc @@ -33,9 +33,10 @@ class SliceTest : public ClientLibraryTestBase {}; XLA_TEST_F(SliceTest, Slice2D) { XlaBuilder builder("slice_2d"); - auto original = builder.ConstantR2( + auto original = ConstantR2( + &builder, {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, {7.0, 8.0, 9.0}, {10.0, 11.0, 12.0}}); - builder.Slice(original, {2, 1}, {4, 3}, {1, 1}); + Slice(original, {2, 1}, {4, 3}, {1, 1}); Array2D expected({{8.0f, 9.0f}, {11.0f, 12.0f}}); ComputeAndCompareR2(&builder, expected, {}, ErrorSpec(0.000001)); @@ -45,8 +46,8 @@ XLA_TEST_F(SliceTest, Slice3D) { XlaBuilder builder("slice_3d"); Array3D array_3d( {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}); - auto original = builder.ConstantR3FromArray3D(array_3d); - builder.Slice(original, {0, 0, 1}, {2, 1, 2}, {1, 1, 1}); + auto original = ConstantR3FromArray3D(&builder, array_3d); + Slice(original, {0, 0, 1}, {2, 1, 2}, {1, 1, 1}); Array3D expected_3d({{{2.0f}}, {{6.0f}}}); ComputeAndCompareR3(&builder, expected_3d, {}, ErrorSpec(0.000001)); diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 41f723edf1ff3518686231f31b61b64291b1f6bf..6597748c8d1f45391799dbe384a5afc0284de2dd 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -204,10 +204,10 @@ XLA_TEST_F(MultiOutputFusionTest, FusionNodeIsRoot) { Literal::CreateR0(1.0)), Literal::MakeTupleOwned(Literal::CreateR0(3.0), Literal::CreateR0(4))); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR0(42)))); + *Literal::MakeTupleOwned(Literal::CreateR0(42)), *result)); } XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { @@ -233,10 +233,9 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR1({1.0, 2.0, 3.0, -1.0}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::CreateR1({0.0, 4.0, 9.0, 1.0}))); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); + LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0, 1.0}, *result); } XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { @@ -267,10 +266,9 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR1({1.0, 2.0, 3.0}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::CreateR1({0.0, 4.0, 9.0}))); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); + LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0}, *result); } const char* const kScalarOps = R"( @@ -311,12 +309,12 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})))); + Literal::CreateR2({{5, 16}, {36, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -341,12 +339,12 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( - Literal::CreateR2({{6, 8}, {10, 12}}), - Literal::CreateR2({{25, 36}, {49, 64}})))); + *Literal::MakeTupleOwned(Literal::CreateR2({{6, 8}, {10, 12}}), + Literal::CreateR2({{25, 36}, {49, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -372,12 +370,13 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), - Literal::CreateR1({36, 64}), - Literal::CreateR1({66, 138})))); + *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), + Literal::CreateR1({36, 64}), + Literal::CreateR1({66, 138})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -403,14 +402,14 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}), Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})))); + Literal::CreateR2({{5, 16}, {36, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -436,14 +435,14 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR2({{6, 8}, {10, 12}}), Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), - Literal::CreateR2({{25, 36}, {49, 64}})))); + Literal::CreateR2({{25, 36}, {49, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -455,7 +454,8 @@ XLA_TEST_F(MultiOutputFusionTest, r1 = f32[2]{0} reduce(p0, c0), dimensions={0,2}, to_apply=Add mul = f32[2,2,2]{2,1,0} multiply(p0, p0) c1 = f32[] constant(5) - mul2 = f32[2,2,2]{2,1,0} multiply(p0, c1) + b1 = f32[2,2,2]{2,1,0} broadcast(c1), dimensions={} + mul2 = f32[2,2,2]{2,1,0} multiply(p0, b1) ROOT tuple = (f32[2]{0}, f32[2,2,2]{2,1,0}, f32[2,2,2]{2,1,0}) tuple(r1, mul, mul2) } @@ -469,15 +469,15 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR1({14, 22}), Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), Literal::CreateR3( - {{{5, 10}, {15, 20}}, {{25, 30}, {35, 40}}})))); + {{{5, 10}, {15, 20}}, {{25, 30}, {35, 40}}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -505,13 +505,52 @@ XLA_TEST_F(MultiOutputFusionTest, auto param = Literal::CreateR3({{{0, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); auto init1 = Literal::CreateR0(5); auto init2 = Literal::CreateR0(6); - TF_ASSERT_OK_AND_ASSIGN( - auto result, - Execute(std::move(module), {param.get(), init1.get(), init2.get()})); + std::unique_ptr result = ExecuteNoHloPasses( + std::move(module), {param.get(), init1.get(), init2.get()}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *Literal::MakeTupleOwned( + Literal::CreateR2({{167, 172}, {176, 180}}), + Literal::CreateR2({{6, 6}, {6, 8}})), + *result)); +} + +XLA_TEST_F(MultiOutputFusionTest, + DISABLED_ON_CPU(MultiOutputReduceFusionDifferentElementTypes)) { + const string testcase = tensorflow::strings::StrCat(kScalarOps, R"( + fused_reduce (p0: f16[2,2,2]) -> (f32[2,2], f32[2,2], f16[2,2,2]) { + p0 = f16[2,2,2]{2,1,0} parameter(0) + convert = f32[2,2,2]{2,1,0} convert(p0) + c0 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(convert, c0), dimensions={2}, to_apply=Add + mul = f32[2,2,2]{2,1,0} multiply(convert, convert) + c1 = f32[] constant(5) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=Max + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) + tuple(r1, r2, p0) + } + + ENTRY reduce { + p = f16[2,2,2]{2,1,0} parameter(0) + ROOT fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) fusion(p), + kind=kInput, calls=fused_reduce + })"); + auto module = + HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) + .ValueOrDie(); + auto param = Literal::CreateR3( + {{{Eigen::half(1), Eigen::half(2)}, {Eigen::half(3), Eigen::half(4)}}, + {{Eigen::half(5), Eigen::half(6)}, {Eigen::half(7), Eigen::half(8)}}}); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( - Literal::CreateR2({{167, 172}, {176, 180}}), - Literal::CreateR2({{6, 6}, {6, 8}})))); + *Literal::MakeTupleOwned( + Literal::CreateR2({{3, 7}, {11, 15}}), + Literal::CreateR2({{5, 16}, {36, 64}}), + Literal::CreateR3({{{Eigen::half(1), Eigen::half(2)}, + {Eigen::half(3), Eigen::half(4)}}, + {{Eigen::half(5), Eigen::half(6)}, + {Eigen::half(7), Eigen::half(8)}}})), + *result)); } } // namespace diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc index ce295b832d79e4f00656f2893c2ba1162693dd73..2e5081bbcb64ea9416c5a9731dba43891ecceedf 100644 --- a/tensorflow/compiler/xla/tests/pad_test.cc +++ b/tensorflow/compiler/xla/tests/pad_test.cc @@ -93,8 +93,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS0ToS0Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(0); - b.Pad(AddParam(*Literal::CreateR1({}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*Literal::CreateR1({}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); ComputeAndCompareR1(&b, {}, {}, DefaultErrorSpec()); } @@ -108,8 +108,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS0ToS5Array) { dimension->set_edge_padding_high(4); dimension->set_interior_padding(7); - b.Pad(AddParam(*Literal::CreateR1({}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*Literal::CreateR1({}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); ComputeAndCompareR1(&b, std::vector(5, 0.1), {}, DefaultErrorSpec()); } @@ -123,16 +123,16 @@ XLA_TEST_P(PadTestFloat, Pad1DS3Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(1); - b.Pad(AddParam(*Literal::CreateR1({1, 2, 3}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*Literal::CreateR1({1, 2, 3}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); std::vector expected({0.1, 0.1, 0.1, 1, 0.1, 2, 0.1, 3}); ComputeAndCompareR1(&b, expected, {}, DefaultErrorSpec()); } XLA_TEST_P(PadTestFloat, Pad4D_2x0x3x2_FloatArray) { XlaBuilder b(TestName()); - b.Pad(AddParam(Array4D(2, 0, 3, 2), &b), - AddParam(*Literal::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); + Pad(AddParam(Array4D(2, 0, 3, 2), &b), + AddParam(*Literal::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); ComputeAndCompareR4(&b, Array4D(5, 2, 3, 2, 1.5f), {}, DefaultErrorSpec()); } @@ -147,8 +147,8 @@ TEST_P(PadTestFloat, Pad4DFloat_1x1x3x2_Array) { }); input->FillWithYX(input_xy); - b.Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(1.5), &b), - r4_padding_on_dim0_dim1_); + Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(1.5), &b), + r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(2, 3, 3, 2); expected->Fill(1.5); @@ -166,8 +166,8 @@ TEST_P(PadTestFloat, Pad4DFloatArrayWithInteriorPadding) { const float pad_value = 1.5f; Array4D input(3, 2, 1, 1, {1, 2, 3, 4, 5, 6}); - b.Pad(AddParam(input, &b), AddParam(*Literal::CreateR0(pad_value), &b), - r4_padding_on_dim0_dim1_); + Pad(AddParam(input, &b), AddParam(*Literal::CreateR0(pad_value), &b), + r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(8, 5, 1, 1); expected->Fill(pad_value); @@ -208,8 +208,8 @@ TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstSmall) { auto input = Literal::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - b.Pad(AddParam(*input, &b), - AddParam(*Literal::CreateR0(pad_value), &b), padding_config); + Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(pad_value), &b), + padding_config); Array4D expected_array(1, 1, 5, 8); expected_array.Fill(pad_value); @@ -254,8 +254,8 @@ XLA_TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) { auto input = Literal::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - b.Pad(AddParam(*input, &b), - AddParam(*Literal::CreateR0(pad_value), &b), padding_config); + Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(pad_value), &b), + padding_config); Array4D expected_array(1, 25, 17, 11); expected_array.Fill(pad_value); @@ -275,8 +275,8 @@ XLA_TEST_F(PadTest, Pad4DU8Array) { }); input->FillWithYX(input_xy); - b.Pad(AddParam(*input, &b), b.ConstantR0(35), - r4_padding_on_dim0_dim1_); + Pad(AddParam(*input, &b), ConstantR0(&b, 35), + r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(2, 3, 3, 2); expected->Fill(35); @@ -294,16 +294,16 @@ XLA_TEST_F(PadTest, Pad4DPredArray) { // Since bool is currently not well supported, use Broadcast operation to // create the operand for Pad. - auto input = b.Broadcast(b.ConstantR0(true), {1, 1, 3, 2}); + auto input = Broadcast(ConstantR0(&b, true), {1, 1, 3, 2}); auto padded = - b.Pad(input, b.ConstantR0(false), r4_padding_on_dim0_dim1_); + Pad(input, ConstantR0(&b, false), r4_padding_on_dim0_dim1_); // For the same reason, use Select to convert boolean values to int32. auto zeros = MakeUnique>(2, 3, 3, 2); auto ones = MakeUnique>(2, 3, 3, 2); zeros->Fill(0); ones->Fill(1); - b.Select(padded, AddParam(*ones, &b), AddParam(*zeros, &b)); + Select(padded, AddParam(*ones, &b), AddParam(*zeros, &b)); auto expected = MakeUnique>(2, 3, 3, 2); expected->Fill(0); @@ -329,7 +329,7 @@ XLA_TEST_P(PadTestFloat, Large2DPad) { padding_config.mutable_dimensions(dim)->set_edge_padding_high(58 + 100 * dim); } - b.Pad(input, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); + Pad(input, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*ones, padding_config, 0.0f); ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); @@ -351,7 +351,7 @@ XLA_TEST_P(PadTestFloat, AllTypes2DPad) { padding_config.mutable_dimensions(1)->set_edge_padding_low(6); padding_config.mutable_dimensions(1)->set_edge_padding_high(4); padding_config.mutable_dimensions(1)->set_interior_padding(2); - b.Pad(input, AddParam(*Literal::CreateR0(3.14f), &b), padding_config); + Pad(input, AddParam(*Literal::CreateR0(3.14f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 3.14f); ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); @@ -376,7 +376,7 @@ XLA_TEST_P(PadTestFloat, High2DPad) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -403,7 +403,7 @@ XLA_TEST_P(PadTestFloat, NegativePadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -430,7 +430,7 @@ XLA_TEST_P(PadTestFloat, NegativeAndInteriorPadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding[dim]); } - b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -446,12 +446,12 @@ XLA_TEST_P(PadTestFloat, ReducePad) { XlaComputation add = CreateScalarAddComputation(FloatType(), &b); auto reduce = - b.Reduce(input, AddParam(*Literal::CreateR0(0.0), &b), add, {0}); + Reduce(input, AddParam(*Literal::CreateR0(0.0), &b), add, {0}); PaddingConfig padding_config = MakeNoPaddingConfig(3); padding_config.mutable_dimensions(0)->set_edge_padding_low(1); padding_config.mutable_dimensions(0)->set_edge_padding_high(1); - b.Pad(reduce, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); + Pad(reduce, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); Array3D expected({{{0.0, 0.0}, {0.0, 0.0}}, {{2.0, 2.0}, {2.0, 2.0}}, diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc index 838f1b4e2f0f0e0871ec717bdeefcbbc653397e3..2620063aa492902a705690d28d8124d16184d635 100644 --- a/tensorflow/compiler/xla/tests/params_test.cc +++ b/tensorflow/compiler/xla/tests/params_test.cc @@ -46,7 +46,7 @@ XLA_TEST_F(ParamsTest, ConstantR0F32Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "param0"); ComputeAndCompareR0(&builder, 3.14159f, {param0_data.get()}, ErrorSpec(0.0001f)); @@ -58,7 +58,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S0F32Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {0}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {0}), "param0"); ComputeAndCompareR1(&builder, {}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -71,7 +71,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S2F32Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "param0"); ComputeAndCompareR1(&builder, {3.14f, -100.25f}, {param0_data.get()}, ErrorSpec(0.01f)); @@ -84,8 +84,9 @@ XLA_TEST_F(ParamsTest, ConstantR1U8Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter( - 0, ShapeUtil::MakeShape(U8, {static_cast(str.size())}), "param0"); + Parameter(&builder, 0, + ShapeUtil::MakeShape(U8, {static_cast(str.size())}), + "param0"); ComputeAndCompareR1U8(&builder, str, {param0_data.get()}); } @@ -97,7 +98,7 @@ XLA_TEST_F(ParamsTest, ConstantR2_3x0_F32Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3, 0}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3, 0}), "param0"); ComputeAndCompareR2(&builder, Array2D(3, 0), {param0_data.get()}, ErrorSpec(0.01f)); @@ -110,7 +111,7 @@ XLA_TEST_F(ParamsTest, ConstantR2F32Param) { std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - auto p = builder.Parameter(0, ShapeUtil::MakeShape(F32, {3, 2}), "param0"); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {3, 2}), "param0"); Array2D expected_array( {{3.14f, -100.25f}, {7e8f, 7e-9f}, {30.3f, -100.0f}}); @@ -124,25 +125,25 @@ XLA_TEST_F(ParamsTest, TwoParameters) { std::unique_ptr literal0 = Literal::CreateR1({1, 2}); std::unique_ptr param0_data = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, literal0->shape(), "param0"); + auto param0 = Parameter(&builder, 0, literal0->shape(), "param0"); std::unique_ptr literal1 = Literal::CreateR1({10, 20}); std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param1 = builder.Parameter(1, literal1->shape(), "param1"); + auto param1 = Parameter(&builder, 1, literal1->shape(), "param1"); // Use both parameters // // {1, 2} + {10, 20} = {11, 22} - auto sum = builder.Add(param0, param1); - sum = builder.Add(param0, param1); + auto sum = Add(param0, param1); + sum = Add(param0, param1); // Use only the second parameter again, to show that it can be used // twice and to make the computation asymmetric in the two // parameters to test that the parameters are not swapped. // // {11, 22} * {10, 20} = {110, 440} - auto prod = builder.Mul(sum, param1); + Mul(sum, param1); ComputeAndCompareR1(&builder, {110, 440}, {param0_data.get(), param1_data.get()}, @@ -157,7 +158,7 @@ XLA_TEST_F(ParamsTest, MissingParameter) { client_->TransferToServer(*literal).ConsumeValueOrDie(); XlaBuilder builder(TestName()); - auto p = builder.Parameter(2, ShapeUtil::MakeShape(F32, {}), "param2"); + Parameter(&builder, 2, ShapeUtil::MakeShape(F32, {}), "param2"); auto computation_status = builder.Build(); ASSERT_NE(computation_status.status(), Status::OK()); @@ -169,12 +170,12 @@ XLA_TEST_F(ParamsTest, UnusedParameter) { std::unique_ptr literal0 = Literal::CreateR1({1, 2}); std::unique_ptr param0_data = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, literal0->shape(), "param0"); + Parameter(&builder, 0, literal0->shape(), "param0"); std::unique_ptr literal1 = Literal::CreateR1({10, 20}); std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param1 = builder.Parameter(1, literal1->shape(), "param1"); + Parameter(&builder, 1, literal1->shape(), "param1"); ComputeAndCompareR1(&builder, {10, 20}, {param0_data.get(), param1_data.get()}, @@ -194,14 +195,14 @@ XLA_TEST_F(ParamsTest, UnusedParametersInUnusedExpression) { std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - auto param0 = builder.Parameter(0, literal0->shape(), "param0"); - auto param1 = builder.Parameter(1, literal1->shape(), "param1"); - auto param2 = builder.Parameter(2, literal1->shape(), "param2"); + auto param0 = Parameter(&builder, 0, literal0->shape(), "param0"); + auto param1 = Parameter(&builder, 1, literal1->shape(), "param1"); + auto param2 = Parameter(&builder, 2, literal1->shape(), "param2"); // This add is unused. - builder.Add(param1, param2); + Add(param1, param2); - builder.Neg(param0); + Neg(param0); ComputeAndCompareR1( &builder, {-1, -2}, @@ -215,7 +216,7 @@ XLA_TEST_F(ParamsTest, HundredLargeR1Parameters) { std::vector init_value = {{0, 1}}; init_value.resize(size); - XlaOp sum_handle = builder.ConstantR1(init_value); + XlaOp sum_handle = ConstantR1(&builder, init_value); std::vector sum = {{0, 1}}; sum.resize(size); @@ -233,8 +234,8 @@ XLA_TEST_F(ParamsTest, HundredLargeR1Parameters) { std::unique_ptr literal = Literal::CreateR1(sum_value); param_data_owner.push_back( client_->TransferToServer(*literal).ConsumeValueOrDie()); - XlaOp param = builder.Parameter(i, literal->shape(), "param"); - sum_handle = builder.Add(sum_handle, param); + XlaOp param = Parameter(&builder, i, literal->shape(), "param"); + sum_handle = Add(sum_handle, param); } std::vector param_data; @@ -260,7 +261,7 @@ XLA_TEST_F(ParamsTest, XlaBuilder builder(TestName()); std::vector> param_data_owner; - XlaOp sum_handle = builder.ConstantR0(0.0f); + XlaOp sum_handle = ConstantR0(&builder, 0.0f); float target = 0.0; constexpr int kParamCount = 3000; for (int i = 0; i < kParamCount; ++i) { @@ -268,8 +269,8 @@ XLA_TEST_F(ParamsTest, std::unique_ptr literal = Literal::CreateR0(i); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); - XlaOp param = builder.Parameter(i, literal->shape(), "param"); - sum_handle = builder.Add(sum_handle, param); + XlaOp param = Parameter(&builder, i, literal->shape(), "param"); + sum_handle = Add(sum_handle, param); } std::vector param_data; @@ -291,7 +292,7 @@ XLA_TEST_F(ParamsTest, DISABLED_ON_CPU(DISABLED_ON_GPU( XlaBuilder builder(TestName()); std::vector> param_data_owner; - XlaOp sum_handle = builder.ConstantR1({0, 0}); + XlaOp sum_handle = ConstantR1(&builder, {0, 0}); int32 target = 0; constexpr int kParamCount = 3000; std::vector params; @@ -300,17 +301,17 @@ XLA_TEST_F(ParamsTest, DISABLED_ON_CPU(DISABLED_ON_GPU( std::unique_ptr literal = Literal::CreateR1({i, i}); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); - XlaOp param = builder.Parameter(i, literal->shape(), "param"); + XlaOp param = Parameter(&builder, i, literal->shape(), "param"); params.push_back(param); - sum_handle = builder.Add(sum_handle, param); + sum_handle = Add(sum_handle, param); } std::vector outputs; for (int i = 0; i < kParamCount; ++i) { - outputs.push_back(builder.Add(params[i], sum_handle)); + outputs.push_back(Add(params[i], sum_handle)); } - builder.Tuple(outputs); + Tuple(&builder, outputs); std::vector param_data; param_data.reserve(param_data_owner.size()); @@ -356,7 +357,7 @@ XLA_TEST_F(ParamsTest, std::unique_ptr literal = Literal::CreateR1({i, i}); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); - XlaOp param = builder.Parameter(i, literal->shape(), "param"); + XlaOp param = Parameter(&builder, i, literal->shape(), "param"); params.push_back(param); parameter_shapes.push_back(literal->shape()); } @@ -367,11 +368,11 @@ XLA_TEST_F(ParamsTest, param_data_owner.push_back( std::move(client_->TransferToServer(*bool_literal)).ValueOrDie()); XlaOp bool_param = - builder.Parameter(kParamCount, bool_literal->shape(), "bool_param"); + Parameter(&builder, kParamCount, bool_literal->shape(), "bool_param"); params.push_back(bool_param); parameter_shapes.push_back(bool_literal->shape()); - auto init = builder.Tuple(params); + auto init = Tuple(&builder, params); // Create a computation for the condition: while(bool_param). Shape while_shape = ShapeUtil::MakeTupleShape(parameter_shapes); @@ -379,8 +380,8 @@ XLA_TEST_F(ParamsTest, { XlaBuilder builder("condition"); auto condition_parameter = - builder.Parameter(0, while_shape, "condition_parameter"); - builder.GetTupleElement(condition_parameter, kParamCount); + Parameter(&builder, 0, while_shape, "condition_parameter"); + GetTupleElement(condition_parameter, kParamCount); condition = builder.Build().ConsumeValueOrDie(); } @@ -389,27 +390,27 @@ XLA_TEST_F(ParamsTest, XlaComputation body; { XlaBuilder builder("body"); - auto body_parameter = builder.Parameter(0, while_shape, "body_parameter"); + auto body_parameter = Parameter(&builder, 0, while_shape, "body_parameter"); std::vector updates; for (int i = 0; i < kParamCount; ++i) { - auto add = builder.Add(builder.GetTupleElement(body_parameter, i), - builder.ConstantR1({1, 1})); + auto add = Add(GetTupleElement(body_parameter, i), + ConstantR1(&builder, {1, 1})); updates.push_back(add); } // Add bool parameter. - updates.push_back(builder.GetTupleElement(body_parameter, kParamCount)); + updates.push_back(GetTupleElement(body_parameter, kParamCount)); - builder.Tuple(updates); + Tuple(&builder, updates); body = builder.Build().ConsumeValueOrDie(); } - auto loop = builder.While(condition, body, init); + auto loop = While(condition, body, init); std::vector outputs; for (int i = 0; i < kParamCount; ++i) { - outputs.push_back(builder.GetTupleElement(loop, i)); + outputs.push_back(GetTupleElement(loop, i)); } - builder.Tuple(outputs); + Tuple(&builder, outputs); std::vector param_data; param_data.reserve(param_data_owner.size()); @@ -433,10 +434,10 @@ XLA_TEST_F(ParamsTest, TupleOfR1ParametersAddedTogether) { Shape r1f32_3 = ShapeUtil::MakeShape(F32, {3}); Shape tuple_shape = ShapeUtil::MakeTupleShape({r1f32_3, r1f32_3}); - auto input = builder.Parameter(0, tuple_shape, "input"); - auto lhs = builder.GetTupleElement(input, 0); - auto rhs = builder.GetTupleElement(input, 1); - builder.Add(lhs, rhs); + auto input = Parameter(&builder, 0, tuple_shape, "input"); + auto lhs = GetTupleElement(input, 0); + auto rhs = GetTupleElement(input, 1); + Add(lhs, rhs); std::unique_ptr data = client_ @@ -457,7 +458,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_01) { std::unique_ptr literal = Literal::CreateR2WithLayout( {{1, 2}, {3, 4}}, LayoutUtil::MakeLayout({0, 1})); XlaBuilder builder(TestName()); - builder.Parameter(0, literal->shape(), "input"); + Parameter(&builder, 0, literal->shape(), "input"); std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -469,7 +470,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) { std::unique_ptr literal = Literal::CreateR2WithLayout( {{1, 3}, {2, 4}}, LayoutUtil::MakeLayout({1, 0})); XlaBuilder builder(TestName()); - builder.Parameter(0, literal->shape(), "input"); + Parameter(&builder, 0, literal->shape(), "input"); std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -478,7 +479,8 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) { XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) { std::unique_ptr literal = Literal::CreateR2({ - {1, 3}, {2, 4}, + {1, 3}, + {2, 4}, }); const Shape original = literal->shape(); { @@ -494,9 +496,9 @@ XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) { } // Use the original shape in building the computation. XlaBuilder builder(TestName()); - auto input = builder.Parameter(0, original, "input"); + auto input = Parameter(&builder, 0, original, "input"); // Use the slice operator to get an off-diagonal element. - builder.Slice(input, {0, 1}, {1, 2}, {1, 1}); + Slice(input, {0, 1}, {1, 2}, {1, 1}); std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/pred_test.cc b/tensorflow/compiler/xla/tests/pred_test.cc index 77159efb26f3b7dd4918f24305f7269a2d6ff647..5c351b2d113709105244de4aafa49d7cc535ced1 100644 --- a/tensorflow/compiler/xla/tests/pred_test.cc +++ b/tensorflow/compiler/xla/tests/pred_test.cc @@ -29,64 +29,63 @@ namespace { class PredTest : public ClientLibraryTestBase { protected: - void TestCompare( - bool lhs, bool rhs, bool expected, - XlaOp (XlaBuilder::*op)(const xla::XlaOp&, const xla::XlaOp&, - tensorflow::gtl::ArraySlice)) { + void TestCompare(bool lhs, bool rhs, bool expected, + std::function)> + op) { XlaBuilder builder(TestName()); - XlaOp lhs_op = builder.ConstantR0(lhs); - XlaOp rhs_op = builder.ConstantR0(rhs); - XlaOp result = (builder.*op)(lhs_op, rhs_op, {}); + XlaOp lhs_op = ConstantR0(&builder, lhs); + XlaOp rhs_op = ConstantR0(&builder, rhs); + op(lhs_op, rhs_op, {}); ComputeAndCompareR0(&builder, expected, {}); } }; TEST_F(PredTest, ConstantR0PredTrue) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR0(true); + ConstantR0(&builder, true); ComputeAndCompareR0(&builder, true, {}); } TEST_F(PredTest, ConstantR0PredFalse) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR0(false); + ConstantR0(&builder, false); ComputeAndCompareR0(&builder, false, {}); } TEST_F(PredTest, ConstantR0PredCompareEq) { - TestCompare(true, false, false, &XlaBuilder::Eq); + TestCompare(true, false, false, &Eq); } TEST_F(PredTest, ConstantR0PredCompareNe) { - TestCompare(true, false, true, &XlaBuilder::Ne); + TestCompare(true, false, true, &Ne); } TEST_F(PredTest, ConstantR0PredCompareLe) { - TestCompare(true, false, false, &XlaBuilder::Le); + TestCompare(true, false, false, &Le); } TEST_F(PredTest, ConstantR0PredCompareLt) { - TestCompare(true, false, false, &XlaBuilder::Lt); + TestCompare(true, false, false, &Lt); } TEST_F(PredTest, ConstantR0PredCompareGe) { - TestCompare(true, false, true, &XlaBuilder::Ge); + TestCompare(true, false, true, &Ge); } TEST_F(PredTest, ConstantR0PredCompareGt) { - TestCompare(true, false, true, &XlaBuilder::Gt); + TestCompare(true, false, true, &Gt); } TEST_F(PredTest, ConstantR1Pred) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({true, false, false, true}); + ConstantR1(&builder, {true, false, false, true}); ComputeAndCompareR1(&builder, {true, false, false, true}, {}); } TEST_F(PredTest, ConstantR2Pred) { XlaBuilder builder(TestName()); - auto a = - builder.ConstantR2({{false, true, true}, {true, false, false}}); + ConstantR2(&builder, {{false, true, true}, {true, false, false}}); const string expected = R"(pred[2,3] { { 011 }, { 100 } @@ -96,44 +95,44 @@ TEST_F(PredTest, ConstantR2Pred) { TEST_F(PredTest, AnyR1True) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({true, false}); - TF_ASSERT_OK(Any(a, &builder).status()); + auto a = ConstantR1(&builder, {true, false}); + Any(a); ComputeAndCompareR0(&builder, true, {}); } TEST_F(PredTest, AnyR1False) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({false, false}); - TF_ASSERT_OK(Any(a, &builder).status()); + auto a = ConstantR1(&builder, {false, false}); + Any(a); ComputeAndCompareR0(&builder, false, {}); } TEST_F(PredTest, AnyR1VacuouslyFalse) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR1({}); - TF_ASSERT_OK(Any(a, &builder).status()); + auto a = ConstantR1(&builder, {}); + Any(a); ComputeAndCompareR0(&builder, false, {}); } TEST_F(PredTest, AnyR2True) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({ - {false, false, false}, - {false, false, false}, - {false, false, true}, - }); - TF_ASSERT_OK(Any(a, &builder).status()); + auto a = ConstantR2(&builder, { + {false, false, false}, + {false, false, false}, + {false, false, true}, + }); + Any(a); ComputeAndCompareR0(&builder, true, {}); } TEST_F(PredTest, AnyR2False) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({ - {false, false, false}, - {false, false, false}, - {false, false, false}, - }); - TF_ASSERT_OK(Any(a, &builder).status()); + auto a = ConstantR2(&builder, { + {false, false, false}, + {false, false, false}, + {false, false, false}, + }); + Any(a); ComputeAndCompareR0(&builder, false, {}); } diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc index 1a2de6937c3e134852a730f62f7b56417cf49b28..8e163e885d0d6315341c213577a3beb0180b679a 100644 --- a/tensorflow/compiler/xla/tests/prng_test.cc +++ b/tensorflow/compiler/xla/tests/prng_test.cc @@ -53,8 +53,8 @@ template std::unique_ptr PrngTest::UniformTest( T a, T b, tensorflow::gtl::ArraySlice dims, int64 seed) { XlaBuilder builder(TestName()); - builder.RngUniform( - builder.ConstantR0(a), builder.ConstantR0(b), + RngUniform( + ConstantR0(&builder, a), ConstantR0(&builder, b), ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType(), dims)); SetSeed(seed); @@ -141,9 +141,9 @@ double PrngTest::UniformChiSquared(int32 range_size, int32 expected_count, int32 sample_size = range_size * expected_count; XlaBuilder builder(TestName()); - builder.RngUniform(builder.ConstantR0(0), - builder.ConstantR0(range_size), - ShapeUtil::MakeShape(S32, {sample_size})); + RngUniform(ConstantR0(&builder, 0), + ConstantR0(&builder, range_size), + ShapeUtil::MakeShape(S32, {sample_size})); SetSeed(seed); auto actual = @@ -184,9 +184,10 @@ XLA_TEST_F(PrngTest, MapUsingRng) { // Build a x -> (x + U[0,1)) computation. auto build_sum_rng = [this](XlaBuilder& builder) { auto b = builder.CreateSubBuilder("sum_with_rng"); - auto x = b->Parameter(0, ShapeUtil::MakeShape(F32, {}), "input"); - b->Add(x, b->RngUniform(b->ConstantR0(0), b->ConstantR0(1), - ShapeUtil::MakeShape(F32, {}))); + auto x = Parameter(b.get(), 0, ShapeUtil::MakeShape(F32, {}), "input"); + Add(x, + RngUniform(ConstantR0(b.get(), 0), ConstantR0(b.get(), 1), + ShapeUtil::MakeShape(F32, {}))); return b->BuildAndNoteError(); }; @@ -196,9 +197,9 @@ XLA_TEST_F(PrngTest, MapUsingRng) { TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr param0_data, client_->TransferToServer(*param0_literal)); - auto param0 = builder.Parameter(0, param0_literal->shape(), "param0"); + auto param0 = Parameter(&builder, 0, param0_literal->shape(), "param0"); auto fn = build_sum_rng(builder); - builder.Map({param0}, fn, {0}); + Map(&builder, {param0}, fn, {0}); TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build()); @@ -226,9 +227,8 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) { // Build a U[0,1) computation. auto build_computation = [this]() { XlaBuilder builder(TestName()); - builder.RngUniform(builder.ConstantR0(0), - builder.ConstantR0(1), - ShapeUtil::MakeShape(F32, {10})); + RngUniform(ConstantR0(&builder, 0), ConstantR0(&builder, 1), + ShapeUtil::MakeShape(F32, {10})); return builder.Build(); }; @@ -282,8 +282,8 @@ XLA_TEST_F(PrngTest, PassInGlobalRngSeed) { XLA_TEST_F(PrngTest, TenValuesN01) { XlaBuilder builder(TestName()); - builder.RngNormal(builder.ConstantR0(0), builder.ConstantR0(1), - ShapeUtil::MakeShape(F32, {10})); + RngNormal(ConstantR0(&builder, 0), ConstantR0(&builder, 1), + ShapeUtil::MakeShape(F32, {10})); SetSeed(42); ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie(); @@ -294,9 +294,9 @@ XLA_TEST_F(PrngTest, RngUniformCrash) { XlaBuilder builder(TestName()); // This used to crash XLA during LLVM IR generation for CPUs. - auto rng_uniform = builder.RngUniform(builder.ConstantR0(0), - builder.ConstantR0(1000 * 1000), - ShapeUtil::MakeShape(S32, {})); + RngUniform(ConstantR0(&builder, 0), + ConstantR0(&builder, 1000 * 1000), + ShapeUtil::MakeShape(S32, {})); SetSeed(0); ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie(); } diff --git a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc index f95e75648343aa88bd7c39de4ee9f387f2b60506..526a38e8d1dbed9cdd4a31bfbec49bc5c6bb174b 100644 --- a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc +++ b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc @@ -31,8 +31,8 @@ class QueryInferredShapeTest : public ClientLibraryTestBase {}; TEST_F(QueryInferredShapeTest, OnePlusOneShape) { XlaBuilder builder("one_plus_one"); - auto one = builder.ConstantR0(1.0); - auto result = builder.Add(one, one); + auto one = ConstantR0(&builder, 1.0); + auto result = Add(one, one); StatusOr shape_status = builder.GetShape(result); ASSERT_IS_OK(shape_status.status()); auto shape = shape_status.ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc index b311785449f1774c3bc1e4d7ad35c2866e3b4061..4c1aa121067eed465c6128ea7a34e0284f7af43e 100644 --- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc @@ -233,9 +233,9 @@ XLA_TEST_P(ReducePrecisionAccuracyTest, ReducePrecisionF32) { std::unique_ptr a_literal = Literal::CreateR1({input_values}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); - builder.ReducePrecision(a, exponent_bits, mantissa_bits); + ReducePrecision(a, exponent_bits, mantissa_bits); ComputeAndCompareR1(&builder, expected_values, {a_data.get()}); } @@ -256,15 +256,15 @@ XLA_TEST_F(ReducePrecisionInsertionTest, std::unique_ptr a_literal = Literal::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); // Abs doesn't affect resolution. - auto abs = builder.Abs(a); + auto abs = Abs(a); // Near 1.0, Log(x) approximates x - 1; this lets us confirm that the // reduce-precision operation showed up in the correct place in the // graph. - builder.Log(abs); + Log(abs); // Insert precision-reduction after the Abs(x) operation, rounding that // result to exactly 1.0f. @@ -285,11 +285,11 @@ XLA_TEST_F(ReducePrecisionInsertionTest, std::unique_ptr a_literal = Literal::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); // These two operations should be fused by any reasonable backend. - auto abs = builder.Abs(a); - builder.Neg(abs); + auto abs = Abs(a); + Neg(abs); // Add a pass after operation fusion, suffixing kAbs operations. This // should not see into the fusion nodes and thus should not affect the @@ -311,11 +311,11 @@ XLA_TEST_F(ReducePrecisionInsertionTest, std::unique_ptr a_literal = Literal::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); // These two operations should be fused by any reasonable backend. - auto abs = builder.Abs(a); - builder.Neg(abs); + auto abs = Abs(a); + Neg(abs); // Add a pass after operation fusion, suffixing kFusion operations. auto reduce_precision_pass = execution_options_.mutable_debug_options() @@ -335,11 +335,11 @@ XLA_TEST_F(ReducePrecisionInsertionTest, std::unique_ptr a_literal = Literal::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); // These two operations should be fused by any reasonable backend. - auto abs = builder.Abs(a); - builder.Neg(abs); + auto abs = Abs(a); + Neg(abs); // Add a pass suffixing fusion nodes containing kCos operations. This // should have no effect. @@ -360,11 +360,11 @@ XLA_TEST_F(ReducePrecisionInsertionTest, std::unique_ptr a_literal = Literal::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); - auto a = builder.Parameter(0, a_literal->shape(), "a"); + auto a = Parameter(&builder, 0, a_literal->shape(), "a"); // These two operations should be fused by any reasonable backend. - auto abs = builder.Abs(a); - builder.Neg(abs); + auto abs = Abs(a); + Neg(abs); // Add a pass suffixing fusion nodes containing kAbs operations. This // should see the kAbs operation within the above fusion node. diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index d671d40456a276a44b462f390c95aa4af301263a..c9f57cbb16729627a5e9ad3d49438295a286989e 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -89,9 +89,9 @@ class ReduceTest : public ClientLibraryTestBase { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {element_count}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - builder.Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0}); std::vector input_data(element_count); for (int64 i = 0; i < element_count; ++i) { @@ -118,20 +118,20 @@ class ReduceTest : public ClientLibraryTestBase { const int element_count = input_data.size(); XlaBuilder builder(TestName()); const Shape input_shape = ShapeUtil::MakeShape(S32, {element_count}); - auto input_par = builder.Parameter(0, input_shape, "input"); + auto input_par = Parameter(&builder, 0, input_shape, "input"); auto pred_values = - builder.Eq(input_par, builder.ConstantR1(element_count, 1)); + Eq(input_par, ConstantR1(&builder, element_count, 1)); XlaOp init_value; XlaComputation reduce; if (and_reduce) { - init_value = builder.ConstantR0(true); + init_value = ConstantR0(&builder, true); reduce = CreateScalarAndComputation(&builder); } else { - init_value = builder.ConstantR0(false); + init_value = ConstantR0(&builder, false); reduce = CreateScalarOrComputation(&builder); } - builder.Reduce(pred_values, init_value, reduce, - /*dimensions_to_reduce=*/{0}); + Reduce(pred_values, init_value, reduce, + /*dimensions_to_reduce=*/{0}); std::unique_ptr input_literal = Literal::CreateR1(input_data); std::unique_ptr input_global_data = @@ -156,21 +156,21 @@ class ReduceTest : public ClientLibraryTestBase { int64 major = 0) { XlaBuilder builder(TestName()); const Shape input_shape = ShapeUtil::MakeShape(U8, {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto input_pred = builder.Eq(input, builder.ConstantR0(1)); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto input_pred = Eq(input, ConstantR0(&builder, 1)); XlaOp init_value; XlaComputation reduce_op; if (and_reduce) { - init_value = builder.ConstantR0(true); + init_value = ConstantR0(&builder, true); reduce_op = CreateScalarAndComputation(&builder); } else { - init_value = builder.ConstantR0(false); + init_value = ConstantR0(&builder, false); reduce_op = CreateScalarOrComputation(&builder); } - builder.Reduce(input_pred, init_value, reduce_op, - /*dimensions_to_reduce=*/{0}); + Reduce(input_pred, init_value, reduce_op, + /*dimensions_to_reduce=*/{0}); Array2D input_data(rows, cols); input_data.FillRandom(0, 1); @@ -202,9 +202,9 @@ class ReduceTest : public ClientLibraryTestBase { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - builder.Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0, 1}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0, 1}); Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); @@ -230,9 +230,9 @@ class ReduceTest : public ClientLibraryTestBase { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - builder.Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + Reduce(input, zero, add_f32, /*dimensions_to_reduce=*/{0}); Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); @@ -287,10 +287,10 @@ class ReduceTest : public ClientLibraryTestBase { XlaComputation reduction_function = reduction_function_generator(&builder); const Shape input_shape = ShapeUtil::MakeShape( xla::primitive_util::NativeToPrimitiveType(), {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(initial_value); - builder.Reduce(input, zero, reduction_function, - /*dimensions_to_reduce=*/{0}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, initial_value); + Reduce(input, zero, reduction_function, + /*dimensions_to_reduce=*/{0}); Array2D input_data(rows, cols); input_data.FillUnique(initial_value); @@ -442,10 +442,10 @@ XLA_TEST_F(ReduceTest, ReduceElementwiseR2_111x50_To_R1) { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - auto log_ = builder.Log(input); - builder.Reduce(log_, zero, add_f32, /*dimensions_to_reduce=*/{0}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + auto log_ = Log(input); + Reduce(log_, zero, add_f32, /*dimensions_to_reduce=*/{0}); Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); @@ -473,11 +473,11 @@ XLA_TEST_F(ReduceTest, TransposeAndReduceElementwiseR2_111x50_To_R1) { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {rows, cols}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - auto log_ = builder.Log(input); - auto transpose = builder.Transpose(log_, {1, 0}); - builder.Reduce(transpose, zero, add_f32, /*dimensions_to_reduce=*/{1}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + auto log_ = Log(input); + auto transpose = Transpose(log_, {1, 0}); + Reduce(transpose, zero, add_f32, /*dimensions_to_reduce=*/{1}); Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); @@ -505,10 +505,10 @@ XLA_TEST_F(ReduceTest, TransposeAndReduceR3_12x111x50_To_R2) { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {12, 111, 50}); - XlaOp input = builder.Parameter(0, input_shape, "input"); - XlaOp zero = builder.ConstantR0(0.0); - XlaOp transpose = builder.Transpose(input, /*permutation=*/{1, 0, 2}); - builder.Reduce(transpose, zero, add_f32, /*dimensions_to_reduce=*/{0}); + XlaOp input = Parameter(&builder, 0, input_shape, "input"); + XlaOp zero = ConstantR0(&builder, 0.0); + XlaOp transpose = Transpose(input, /*permutation=*/{1, 0, 2}); + Reduce(transpose, zero, add_f32, /*dimensions_to_reduce=*/{0}); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, MakeFakeLiteral(input_shape)); @@ -522,11 +522,11 @@ XLA_TEST_F(ReduceTest, Reshape_111x2x25Reduce_111x50_To_R1) { XlaBuilder builder(TestName()); XlaComputation add_f32 = CreateScalarAddComputation(F32, &builder); const Shape input_shape = ShapeUtil::MakeShape(F32, {rows, 2, cols / 2}); - auto input = builder.Parameter(0, input_shape, "input"); - auto zero = builder.ConstantR0(0.0); - auto log_ = builder.Tanh(input); - auto reshape = builder.Reshape(log_, {rows, cols}); - builder.Reduce(reshape, zero, add_f32, /*dimensions_to_reduce=*/{0}); + auto input = Parameter(&builder, 0, input_shape, "input"); + auto zero = ConstantR0(&builder, 0.0); + auto log_ = Tanh(input); + auto reshape = Reshape(log_, {rows, cols}); + Reduce(reshape, zero, add_f32, /*dimensions_to_reduce=*/{0}); Array3D input_data(rows, 2, cols / 2); input_data.FillRandom(3.14f, 0.04); @@ -568,9 +568,9 @@ void PrintTo(const BoundsLayout& spec, std::ostream* os) { XLA_TEST_F(ReduceTest, AddReduce2DScalarToR0) { XlaBuilder builder(TestName()); auto add = CreateScalarAddComputation(F32, &builder); - auto scalar = builder.ConstantR0(42.0); - auto broadcasted = builder.Broadcast(scalar, {500, 500}); - builder.Reduce(broadcasted, builder.ConstantR0(0.0f), add, {0, 1}); + auto scalar = ConstantR0(&builder, 42.0); + auto broadcasted = Broadcast(scalar, {500, 500}); + Reduce(broadcasted, ConstantR0(&builder, 0.0f), add, {0, 1}); float expected = 42.0f * static_cast(500 * 500); ComputeAndCompareR0(&builder, expected, {}, ErrorSpec(0.0001)); @@ -580,9 +580,9 @@ XLA_TEST_F(ReduceTest, AddReduce2DScalarToR0) { XLA_TEST_F(ReduceTest, MaxReduce2DScalarToR0) { XlaBuilder builder(TestName()); auto max = CreateScalarMaxComputation(F32, &builder); - auto scalar = builder.ConstantR0(42.0); - auto broadcasted = builder.Broadcast(scalar, {500, 500}); - builder.Reduce(broadcasted, builder.ConstantR0(0.0f), max, {0, 1}); + auto scalar = ConstantR0(&builder, 42.0); + auto broadcasted = Broadcast(scalar, {500, 500}); + Reduce(broadcasted, ConstantR0(&builder, 0.0f), max, {0, 1}); float expected = 42.0f; ComputeAndCompareR0(&builder, expected, {}, ErrorSpec(0.0001)); @@ -595,8 +595,8 @@ XLA_TEST_F(ReduceTest, MaxReduce2DToR0) { Array2D input(300, 250); input.FillRandom(214.0f); auto input_literal = Literal::CreateR2FromArray2D(input); - builder.Reduce(builder.ConstantLiteral(*input_literal), - builder.ConstantR0(FLT_MIN), max, {0, 1}); + Reduce(ConstantLiteral(&builder, *input_literal), + ConstantR0(&builder, FLT_MIN), max, {0, 1}); auto input_max = FLT_MIN; input.Each( [&](int64, int64, float* v) { input_max = std::max(input_max, *v); }); @@ -610,8 +610,8 @@ XLA_TEST_F(ReduceTest, MinReduce2DToR0) { Array2D input(150, 130); input.FillRandom(214.0f); auto input_literal = Literal::CreateR2FromArray2D(input); - builder.Reduce(builder.ConstantLiteral(*input_literal), - builder.ConstantR0(FLT_MAX), min, {0, 1}); + Reduce(ConstantLiteral(&builder, *input_literal), + ConstantR0(&builder, FLT_MAX), min, {0, 1}); auto input_min = FLT_MAX; input.Each( @@ -625,10 +625,9 @@ XLA_TEST_F(ReduceTest, UnsignedInt_MinReduce) { auto min = CreateScalarMinComputation(U32, &builder); auto input_literal = Literal::CreateR2FromArray2D(input); auto initial_value = - builder.ConstantR0(std::numeric_limits::max()); + ConstantR0(&builder, std::numeric_limits::max()); - builder.Reduce(builder.ConstantLiteral(*input_literal), initial_value, min, - {0, 1}); + Reduce(ConstantLiteral(&builder, *input_literal), initial_value, min, {0, 1}); ComputeAndCompareR0(&builder, 1, {}); } @@ -638,19 +637,18 @@ XLA_TEST_F(ReduceTest, UnsignedInt_MaxReduce) { auto max = CreateScalarMaxComputation(U32, &builder); auto input_literal = Literal::CreateR2FromArray2D(input); auto initial_value = - builder.ConstantR0(std::numeric_limits::min()); + ConstantR0(&builder, std::numeric_limits::min()); - builder.Reduce(builder.ConstantLiteral(*input_literal), initial_value, max, - {0, 1}); + Reduce(ConstantLiteral(&builder, *input_literal), initial_value, max, {0, 1}); ComputeAndCompareR0(&builder, 2, {}); } // Reduces a matrix among dimension 1. XLA_TEST_F(ReduceTest, Reduce2DAmong1) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_2d_); + auto m = ConstantLiteral(&builder, *literal_2d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {1}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {1}); std::vector expected = {6.f, 15.f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -659,9 +657,9 @@ XLA_TEST_F(ReduceTest, Reduce2DAmong1) { XLA_TEST_F(ReduceTest, Reduce2DAmong0and1) { // Reduce a matrix among dimensions 0 and 1 (sum it up to a scalar). XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_2d_); + auto m = ConstantLiteral(&builder, *literal_2d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {0, 1}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {0, 1}); ComputeAndCompareR0(&builder, 21.0f, {}, ErrorSpec(0.0001, 1e-4)); } @@ -669,9 +667,9 @@ XLA_TEST_F(ReduceTest, Reduce2DAmong0and1) { // Tests 2D matrix ReduceToRow operation. XLA_TEST_F(ReduceTest, Reduce2DAmongY) { XlaBuilder builder("reduce_among_y"); - auto m = builder.ConstantLiteral(*literal_2d_); + auto m = ConstantLiteral(&builder, *literal_2d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {0}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {0}); std::vector expected = {5.f, 7.f, 9.f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -679,9 +677,9 @@ XLA_TEST_F(ReduceTest, Reduce2DAmongY) { XLA_TEST_F(ReduceTest, ReduceR3AmongDims_1_2) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {1, 2}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {1, 2}); std::vector expected = {21.f, 21.f, 21.f, 21.f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -689,9 +687,9 @@ XLA_TEST_F(ReduceTest, ReduceR3AmongDims_1_2) { XLA_TEST_F(ReduceTest, ReduceR3AmongDims_0_1) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {0, 1}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {0, 1}); std::vector expected = {20.f, 28.f, 36.f}; ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); @@ -699,9 +697,9 @@ XLA_TEST_F(ReduceTest, ReduceR3AmongDims_0_1) { XLA_TEST_F(ReduceTest, ReduceR3ToR0) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {0, 1, 2}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {0, 1, 2}); float expected = 21.0f * 4.0; ComputeAndCompareR0(&builder, expected, {}, ErrorSpec(0.0001)); @@ -709,9 +707,9 @@ XLA_TEST_F(ReduceTest, ReduceR3ToR0) { XLA_TEST_F(ReduceTest, ReduceR3AmongDim0) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {0}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {0}); // clang-format off Array2D expected({ @@ -724,9 +722,9 @@ XLA_TEST_F(ReduceTest, ReduceR3AmongDim0) { XLA_TEST_F(ReduceTest, ReduceR3AmongDim1) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {1}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {1}); // clang-format off Array2D expected({ @@ -741,9 +739,9 @@ XLA_TEST_F(ReduceTest, ReduceR3AmongDim1) { XLA_TEST_F(ReduceTest, ReduceR3AmongDim2) { XlaBuilder builder(TestName()); - auto m = builder.ConstantLiteral(*literal_3d_); + auto m = ConstantLiteral(&builder, *literal_3d_); auto add = CreateScalarAddComputation(F32, &builder); - builder.Reduce(m, builder.ConstantR0(0.0f), add, {2}); + Reduce(m, ConstantR0(&builder, 0.0f), add, {2}); // clang-format off Array2D expected({ @@ -827,10 +825,10 @@ XLA_TEST_P(ReduceR3ToR2Test, ReduceR3ToR2) { client_->TransferToServer(*input_literal).ConsumeValueOrDie(); auto input_activations = - builder.Parameter(0, input_literal->shape(), "input"); + Parameter(&builder, 0, input_literal->shape(), "input"); XlaComputation add = CreateScalarAddComputation(F32, &builder); - auto sum = builder.Reduce(input_activations, builder.ConstantR0(0.0f), - add, GetParam().reduce_dims); + Reduce(input_activations, ConstantR0(&builder, 0.0f), add, + GetParam().reduce_dims); auto expected = ReferenceUtil::Reduce3DTo2D(input_array, 0.0f, GetParam().reduce_dims, @@ -871,14 +869,14 @@ XLA_TEST_F(ReduceTest, DISABLED_ON_GPU(OperationOnConstantAsInitValue)) { XlaBuilder builder(TestName()); XlaComputation max_f32 = CreateScalarMaxComputation(F32, &builder); - auto a = builder.ConstantR0(2.0f); - auto a2 = builder.Abs(a); + auto a = ConstantR0(&builder, 2.0f); + auto a2 = Abs(a); std::unique_ptr b_literal = Literal::CreateR1({1.0f, 4.0f}); std::unique_ptr b_data = client_->TransferToServer(*b_literal).ConsumeValueOrDie(); - auto b = builder.Parameter(0, b_literal->shape(), "b"); - auto max = builder.Reduce(b, a2, max_f32, {0}); + auto b = Parameter(&builder, 0, b_literal->shape(), "b"); + Reduce(b, a2, max_f32, {0}); ComputeAndCompareR0(&builder, 4.0f, {b_data.get()}); } @@ -900,13 +898,13 @@ class ReduceInitializerTest : public ReduceTest { XlaComputation max_fn = CreateScalarMaxComputation( primitive_util::NativeToPrimitiveType(), &builder); - auto init = builder.ConstantR0(initializer); + auto init = ConstantR0(&builder, initializer); std::vector input_arr(num_elems, std::numeric_limits::lowest()); auto input_literal = Literal::CreateR1(input_arr); auto input_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); - builder.Reduce(builder.Parameter(0, input_literal->shape(), "input"), init, - max_fn, {0}); + Reduce(Parameter(&builder, 0, input_literal->shape(), "input"), init, + max_fn, {0}); ComputeAndCompareR0(&builder, initializer, {input_data.get()}); } @@ -939,15 +937,15 @@ XLA_TEST_F(ReduceInitializerTest, U64InitializerBigValue) { XLA_TEST_F(ReduceTest, ReduceIdentity) { XlaBuilder builder(TestName()); Shape single_float = ShapeUtil::MakeShape(F32, {}); - builder.Parameter(0, single_float, "lhs-unused"); - builder.Parameter(1, single_float, "rhs-used"); + Parameter(&builder, 0, single_float, "lhs-unused"); + Parameter(&builder, 1, single_float, "rhs-used"); auto computation_status = builder.Build(); TF_ASSERT_OK(computation_status.status()); Shape operand_shape = ShapeUtil::MakeShape(F32, {1}); - builder.Reduce(builder.Parameter(0, operand_shape, "operand"), - builder.Parameter(1, single_float, "init"), - computation_status.ValueOrDie(), {0}); + Reduce(Parameter(&builder, 0, operand_shape, "operand"), + Parameter(&builder, 1, single_float, "init"), + computation_status.ValueOrDie(), {0}); float operand[] = {42.0f}; float init = 58.5f; diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 266760e8202fddc48792ac66dda334255e428808..741974480c6a862a7794aa6257f131a5893e963d 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -72,9 +72,9 @@ class ReduceWindowTest : public ::testing::WithParamInterface, Padding padding) { auto init = CreateConstantFromLiteral(*Literal::CreateR0(0.0f), &builder_); - builder_.ReduceWindow(input, init, - CreateScalarAddComputation(FloatType(), &builder_), - window_dimensions, window_strides, padding); + ReduceWindow(input, init, + CreateScalarAddComputation(FloatType(), &builder_), + window_dimensions, window_strides, padding); } void ReduceWindowMax(const XlaOp& input, @@ -82,9 +82,9 @@ class ReduceWindowTest : public ::testing::WithParamInterface, tensorflow::gtl::ArraySlice window_strides, Padding padding) { auto init = CreateConstantFromLiteral(Literal::MinValue(F32), &builder_); - builder_.ReduceWindow(input, init, - CreateScalarMaxComputation(FloatType(), &builder_), - window_dimensions, window_strides, padding); + ReduceWindow(input, init, + CreateScalarMaxComputation(FloatType(), &builder_), + window_dimensions, window_strides, padding); } void ReduceWindowMin(const XlaOp& input, @@ -92,9 +92,9 @@ class ReduceWindowTest : public ::testing::WithParamInterface, tensorflow::gtl::ArraySlice window_strides, Padding padding) { auto init = CreateConstantFromLiteral(Literal::MaxValue(F32), &builder_); - builder_.ReduceWindow(input, init, - CreateScalarMinComputation(FloatType(), &builder_), - window_dimensions, window_strides, padding); + ReduceWindow(input, init, + CreateScalarMinComputation(FloatType(), &builder_), + window_dimensions, window_strides, padding); } XlaBuilder builder_; @@ -106,10 +106,10 @@ TEST_P(ReduceWindowTest, MismatchedRanksGivesErrorStatus) { const auto init_value = CreateConstantFromLiteral(*Literal::CreateR0(0), &builder_); TF_ASSERT_OK(builder_.first_error()); - builder_.ReduceWindow(input, init_value, - CreateScalarAddComputation(FloatType(), &builder_), - /*window_dimensions=*/{1, 2}, - /*window_strides=*/{1}, Padding::kValid); + ReduceWindow(input, init_value, + CreateScalarAddComputation(FloatType(), &builder_), + /*window_dimensions=*/{1, 2}, + /*window_strides=*/{1}, Padding::kValid); ASSERT_EQ(builder_.first_error().code(), tensorflow::error::INVALID_ARGUMENT) << builder_.first_error(); ASSERT_THAT(builder_.first_error().error_message(), @@ -122,10 +122,9 @@ TEST_P(ReduceWindowTest, R0ReduceWindow) { CreateConstantFromLiteral(*Literal::CreateR0(42.0), &builder_); const auto init = CreateConstantFromLiteral(*Literal::CreateR0(1.0), &builder_); - builder_.ReduceWindow(input, init, - CreateScalarAddComputation(FloatType(), &builder_), - /*window_dimensions=*/{}, - /*window_strides=*/{}, Padding::kSame); + ReduceWindow(input, init, CreateScalarAddComputation(FloatType(), &builder_), + /*window_dimensions=*/{}, + /*window_strides=*/{}, Padding::kSame); ComputeAndCompareLiteral(&builder_, *Literal::CreateR0(43.0), {}, ErrorSpec(0.00001)); } @@ -306,13 +305,13 @@ XLA_TEST_P(ReduceWindowTest, NonstandardReduceFunction) { Padding padding = Padding::kValid; const Shape scalar = ShapeUtil::MakeShape(FloatType(), {}); auto b = builder_.CreateSubBuilder("unusual"); - auto lhs = b->Parameter(0, scalar, "lhs"); - auto rhs = b->Parameter(1, scalar, "rhs"); - b->Min(b->Add(lhs, rhs), - CreateConstantFromLiteral(*Literal::CreateR0(8.0f), b.get())); + auto lhs = Parameter(b.get(), 0, scalar, "lhs"); + auto rhs = Parameter(b.get(), 1, scalar, "rhs"); + Min(Add(lhs, rhs), + CreateConstantFromLiteral(*Literal::CreateR0(8.0f), b.get())); XlaComputation reduce_fn = b->BuildAndNoteError(); - builder_.ReduceWindow( + ReduceWindow( input, CreateConstantFromLiteral(*Literal::CreateR0(0.0f), &builder_), reduce_fn, @@ -542,7 +541,7 @@ TEST_P(ReduceWindowTest, R2ReduceWindowInceptionFromBroadcast) { TEST_P(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) { Array2D input_array(6, 4, 1.0f); - XlaOp input = builder_.Broadcast( + XlaOp input = Broadcast( CreateConstantFromLiteral(Literal::One(F32), &builder_), {6, 4}); Padding padding = Padding::kSame; @@ -627,7 +626,7 @@ class R4ReduceWindowTest : public ReduceWindowTestBase, auto computation = param.reducer == kAdd ? CreateScalarAddComputation(FloatType(), &b) : CreateScalarMaxComputation(FloatType(), &b); - b.ReduceWindowWithGeneralPadding( + ReduceWindowWithGeneralPadding( /*operand=*/parameter, /*init_value=*/init_value, /*computation=*/computation, @@ -968,11 +967,11 @@ TEST_P(R3ReduceWindowTest, Add) { &b, ¶meter); auto init_value = CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); - b.ReduceWindow(/*operand=*/parameter, - /*init_value=*/init_value, - /*computation=*/CreateScalarAddComputation(FloatType(), &b), - /*window_dimensions=*/param.window_bounds, - /*window_strides=*/param.strides, /*padding=*/param.padding); + ReduceWindow(/*operand=*/parameter, + /*init_value=*/init_value, + /*computation=*/CreateScalarAddComputation(FloatType(), &b), + /*window_dimensions=*/param.window_bounds, + /*window_strides=*/param.strides, /*padding=*/param.padding); auto expected = ReferenceUtil::ReduceWindow3DAdd( /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds, @@ -1109,7 +1108,7 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, : CreateScalarMaxComputation(FloatType(), &b); auto init_value = CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); - b.ReduceWindowWithGeneralPadding( + ReduceWindowWithGeneralPadding( /*operand=*/parameter, /*init_value=*/init_value, /*computation=*/computation, @@ -1306,7 +1305,7 @@ TEST_P(R1ReduceWindowTest, DoIt) { : CreateScalarMaxComputation(FloatType(), &b); auto init_value = CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); - b.ReduceWindowWithGeneralPadding( + ReduceWindowWithGeneralPadding( /*operand=*/parameter, /*init_value=*/init_value, /*computation=*/computation, diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc index 36d763b0f7f4267ede076c0b25cfaf9654e96e0d..bebd814fa8b863428750dc12a93d1ef5ad7e6685 100644 --- a/tensorflow/compiler/xla/tests/replay_test.cc +++ b/tensorflow/compiler/xla/tests/replay_test.cc @@ -39,8 +39,8 @@ class ReplayTest : public ClientLibraryTestBase {}; TEST_F(ReplayTest, TwoPlusTwoReplay) { // Make 2+2 computation. XlaBuilder builder(TestName()); - auto two = builder.ConstantR0(2); - builder.Add(two, two); + auto two = ConstantR0(&builder, 2); + Add(two, two); XlaComputation computation = builder.Build().ConsumeValueOrDie(); // Serialize it out. @@ -70,9 +70,9 @@ TEST_F(ReplayTest, TwoPlusTwoReplay) { XLA_TEST_F(ReplayTest, XPlusYReplayWithParameters) { // Make computation. XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(S32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(S32, {}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(S32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(S32, {}), "y"); + Add(x, y); XlaComputation computation = builder.Build().ConsumeValueOrDie(); // Serialize it out. @@ -111,13 +111,13 @@ TEST_F(ReplayTest, MapPlusTwoOverR1) { // As above, but with map(+2) over some constant array. XlaBuilder plus_two_builder("plus two"); auto input = - plus_two_builder.Parameter(0, ShapeUtil::MakeShape(S32, {}), "input"); - plus_two_builder.Add(input, plus_two_builder.ConstantR0(2)); + Parameter(&plus_two_builder, 0, ShapeUtil::MakeShape(S32, {}), "input"); + Add(input, ConstantR0(&plus_two_builder, 2)); XlaComputation plus_two = plus_two_builder.Build().ConsumeValueOrDie(); XlaBuilder mapper_builder(TestName()); - auto original = mapper_builder.ConstantR1({1, 2, 3}); - mapper_builder.Map({original}, plus_two, {0}); + auto original = ConstantR1(&mapper_builder, {1, 2, 3}); + Map(&mapper_builder, {original}, plus_two, {0}); XlaComputation computation = mapper_builder.Build().ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/reshape_motion_test.cc b/tensorflow/compiler/xla/tests/reshape_motion_test.cc index da1b588ec41cef711412367e89b2a9b1029bca71..5812fe442b25da1b7e34494d00fe8025d29b2802 100644 --- a/tensorflow/compiler/xla/tests/reshape_motion_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_motion_test.cc @@ -44,11 +44,11 @@ using ReshapeMotionTest = ClientLibraryTestBase; TEST_F(ReshapeMotionTest, ElementwiseOfReshapesWithNonSameInputShapes) { XlaBuilder builder(TestName()); - auto a = builder.ConstantR2({{2, 3, 5}, {7, 11, 13}}); - auto b = builder.ConstantR2({{17, 19}, {23, 29}, {31, 37}}); - auto c = builder.Reshape(a, {6}); - auto d = builder.Reshape(b, {6}); - auto e = builder.Mul(c, d); + auto a = ConstantR2(&builder, {{2, 3, 5}, {7, 11, 13}}); + auto b = ConstantR2(&builder, {{17, 19}, {23, 29}, {31, 37}}); + auto c = Reshape(a, {6}); + auto d = Reshape(b, {6}); + Mul(c, d); ComputeAndCompareR1(&builder, {34, 57, 115, 203, 341, 481}, {}); } diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc index a4580cd71d46ad0a0186eddd51291f9c322b6f49..d3d6c3c7d703161e433740acbbd58d51ba1434af 100644 --- a/tensorflow/compiler/xla/tests/reshape_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_test.cc @@ -59,7 +59,7 @@ XLA_TEST_P(ReshapeTest, CollapseTrivial1x1) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -72,7 +72,7 @@ XLA_TEST_P(ReshapeTest, CollapseTrivialR1EmptyDims) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{}); + Collapse(/*operand=*/parameter, /*dimensions=*/{}); auto expected_literal = Literal::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -85,7 +85,7 @@ XLA_TEST_P(ReshapeTest, CollapseTrivialR1OnlyDim) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0}); auto expected_literal = Literal::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -101,8 +101,8 @@ XLA_TEST_P(ReshapeTest, SingleElementArrayToScalar) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); - auto reshape = builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, - /*new_sizes=*/{}); + auto reshape = Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, + /*new_sizes=*/{}); auto new_shape = builder.GetShape(reshape).ConsumeValueOrDie(); auto expected_literal = Literal::CreateR0(1.0f); @@ -117,34 +117,28 @@ XLA_TEST_P(ReshapeTest, ScalarToSingleElementArray) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *param0_literal, "param0", &builder, ¶meter); - auto a = builder.Neg(parameter); - builder.Reshape(/*operand=*/a, /*dimensions=*/{}, /*new_sizes=*/{1}); + auto a = Neg(parameter); + Reshape(/*operand=*/a, /*dimensions=*/{}, /*new_sizes=*/{1}); auto expected_literal = Literal::CreateR1({-1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Trivial0x3)) { +XLA_TEST_P(ReshapeTest, Trivial0x3) { XlaBuilder builder(TestName()); Array2D input_array(0, 3); auto input_literal = Literal::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-05-15 -// with an incorrect result rank. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Trivial0x3WithParameter)) { +XLA_TEST_P(ReshapeTest, Trivial0x3WithParameter) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = @@ -152,23 +146,20 @@ XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Trivial0x3WithParameter)) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *param0_literal, "param0", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Trivial3x0)) { +XLA_TEST_P(ReshapeTest, Trivial3x0) { XlaBuilder builder(TestName()); Array2D input_array(3, 0); auto input_literal = Literal::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -181,7 +172,7 @@ XLA_TEST_P(ReshapeTest, Trivial1x3) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({1.0f, 2.0f, 3.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -194,25 +185,21 @@ XLA_TEST_P(ReshapeTest, Trivial3x1) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); + Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); auto expected_literal = Literal::CreateR1({1.0f, 2.0f, 3.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -// // Splits an empty vector into an empty matrix. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(R1ToR2_0_To_2x0)) { +XLA_TEST_P(ReshapeTest, R1ToR2_0_To_2x0) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateR1({}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0}, - /*new_sizes=*/{2, 0}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0}, + /*new_sizes=*/{2, 0}); auto expected_literal = Literal::CreateR2({{}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -226,27 +213,23 @@ XLA_TEST_P(ReshapeTest, R1ToR2_6_To_2x3) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0}, - /*new_sizes=*/{2, 3}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0}, + /*new_sizes=*/{2, 3}); auto expected_literal = Literal::CreateR2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -// // Transposes a 2x0 array to a 0x2 array. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Reshape0x2To2x0)) { +XLA_TEST_P(ReshapeTest, Reshape0x2To2x0) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateFromArray(Array2D(0, 2)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, - /*new_sizes=*/{2, 0}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, + /*new_sizes=*/{2, 0}); auto expected_literal = Literal::CreateR2({{}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -260,8 +243,8 @@ XLA_TEST_P(ReshapeTest, ReshapeRowToCol) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, - /*new_sizes=*/{3, 1}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, + /*new_sizes=*/{3, 1}); auto expected = ReferenceUtil::TransposeArray2D(*simple); auto expected_literal = Literal::CreateFromArray(*expected); @@ -277,8 +260,8 @@ XLA_TEST_P(ReshapeTest, TransposeAsReshape) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, - /*new_sizes=*/{3, 4}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, + /*new_sizes=*/{3, 4}); auto expected = ReferenceUtil::TransposeArray2D(*a4x3); auto expected_literal = Literal::CreateFromArray(*expected); @@ -286,18 +269,14 @@ XLA_TEST_P(ReshapeTest, TransposeAsReshape) { zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -// // Transposes a 0x4 array with XlaBuilder::Transpose. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(Transpose0x4)) { +XLA_TEST_P(ReshapeTest, Transpose0x4) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateFromArray(Array2D(0, 4)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Transpose(parameter, {1, 0}); + Transpose(parameter, {1, 0}); auto expected_literal = Literal::CreateR2({{}, {}, {}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -311,7 +290,7 @@ XLA_TEST_P(ReshapeTest, Transpose4x3) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Transpose(parameter, {1, 0}); + Transpose(parameter, {1, 0}); auto expected = ReferenceUtil::TransposeArray2D(*a4x3); auto expected_literal = Literal::CreateFromArray(*expected); @@ -319,36 +298,29 @@ XLA_TEST_P(ReshapeTest, Transpose4x3) { zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -// // Reshapes an empty 2-dimensional array with dimensions that are not just a // rearrangement of the originals (split), but no reordering (no shuffle). -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(ReshapeSplitNoShuffleZeroElements)) { +XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffleZeroElements) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateFromArray(Array2D(6, 0)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, - /*new_sizes=*/{2, 3, 0, 0}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, + /*new_sizes=*/{2, 3, 0, 0}); auto expected_literal = Literal::CreateFromArray(Array4D(2, 3, 0, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(ReshapeR4ToR2ZeroElements)) { +XLA_TEST_P(ReshapeTest, ReshapeR4ToR2ZeroElements) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateFromArray(Array4D(2, 3, 4, 0)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, - /*new_sizes=*/{24, 0}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, + /*new_sizes=*/{24, 0}); auto expected_literal = Literal::CreateFromArray(Array2D(24, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -363,8 +335,8 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffle) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, - /*new_sizes=*/{2, 6}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, + /*new_sizes=*/{2, 6}); auto expected = MakeLinspaceArray2D(1.0f, 12.0f, 2, 6); auto expected_literal = Literal::CreateFromArray(*expected); @@ -372,18 +344,14 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffle) { zero_error_spec_); } -// TODO(b/29185393): Make this work with the GPU backend. The GPU backend -// does not handle zero-sized shapes correctly. Failed last on 2017-11-30 -// with an incorrect result rank. -// -XLA_TEST_P(ReshapeTest, DISABLED_ON_GPU(ReshapeSplitAndShuffleZeroElements)) { +XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffleZeroElements) { XlaBuilder builder(TestName()); auto input_literal = Literal::CreateFromArray(Array2D(0, 6)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, - /*new_sizes=*/{3, 0}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, + /*new_sizes=*/{3, 0}); auto expected_literal = Literal::CreateFromArray(Array2D(3, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); @@ -398,8 +366,8 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffle) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, - /*new_sizes=*/{2, 6}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, + /*new_sizes=*/{2, 6}); Array2D expected({{1.0f, 4.0f, 7.0f, 10.0f, 2.0f, 5.0f}, {8.0f, 11.0f, 3.0f, 6.0f, 9.0f, 12.0f}}); auto expected_literal = Literal::CreateFromArray(expected); @@ -424,8 +392,8 @@ XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_012) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, - /*new_sizes=*/{24}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, + /*new_sizes=*/{24}); auto expected_literal = Literal::CreateR1( {10, 11, 12, 15, 16, 17, 20, 21, 22, 25, 26, 27, 30, 31, 32, 35, 36, 37, 40, 41, 42, 45, 46, 47}); @@ -439,8 +407,8 @@ XLA_TEST_P(ReshapeTest, DocR3_R2_Collapse_012_Refine_83) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, - /*new_sizes=*/{8, 3}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, + /*new_sizes=*/{8, 3}); auto expected_literal = Literal::CreateR2({{10, 11, 12}, {15, 16, 17}, {20, 21, 22}, @@ -459,8 +427,8 @@ XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_120) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, - /*new_sizes=*/{24}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, + /*new_sizes=*/{24}); auto expected_literal = Literal::CreateR1( {10, 20, 30, 40, 11, 21, 31, 41, 12, 22, 32, 42, 15, 25, 35, 45, 16, 26, 36, 46, 17, 27, 37, 47}); @@ -474,8 +442,8 @@ XLA_TEST_P(ReshapeTest, DocR3_R2_Collapse_120_Refine_83) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, - /*new_sizes=*/{8, 3}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, + /*new_sizes=*/{8, 3}); auto expected_literal = Literal::CreateR2({{10, 20, 30}, {40, 11, 21}, {31, 41, 12}, @@ -494,8 +462,8 @@ XLA_TEST_P(ReshapeTest, DocR3_R3_Collapse_120_Refine_262) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, - /*new_sizes=*/{2, 6, 2}); + Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, + /*new_sizes=*/{2, 6, 2}); auto expected_literal = Literal::CreateR3( {{{10, 20}, {30, 40}, {11, 21}, {31, 41}, {12, 22}, {32, 42}}, {{15, 25}, {35, 45}, {16, 26}, {36, 46}, {17, 27}, {37, 47}}}); @@ -527,7 +495,7 @@ XLA_TEST_P(ReshapeTest, FullyConnectedCollapse) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Collapse(/*operand=*/parameter, /*dimensions=*/{1, 2, 3}); + Collapse(/*operand=*/parameter, /*dimensions=*/{1, 2, 3}); auto expected_literal = Literal::CreateR2( {{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, @@ -552,8 +520,8 @@ XLA_TEST_P(ReshapeTest, FullyConnectedCollapseDesugared) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, - /*new_sizes=*/{2, 4}); + Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, + /*new_sizes=*/{2, 4}); auto expected_literal = Literal::CreateR2({{0, 1, 2, 3}, {4, 5, 6, 7}}); @@ -575,7 +543,7 @@ XLA_TEST_P(ReshapeTest, ToScalar) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, input_literal, "input", &b, ¶meter); - b.Reshape(parameter, dimensions, {}); + Reshape(parameter, dimensions, {}); auto expected_literal = Literal::CreateR0(83.0f); ComputeAndCompareLiteral(&b, *expected_literal, {input.get()}, @@ -589,7 +557,7 @@ XLA_TEST_P(ReshapeTest, BadDimensions) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &b, ¶meter); - b.Reshape(parameter, {}, {}); + Reshape(parameter, {}, {}); EXPECT_THAT( ExecuteToString(&b, {}), ::testing::HasSubstr("not a permutation of the operand dimensions")); @@ -601,7 +569,7 @@ XLA_TEST_P(ReshapeTest, BadNewSizes) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &b, ¶meter); - b.Reshape(parameter, {1}, {}); + Reshape(parameter, {1}, {}); EXPECT_THAT(ExecuteToString(&b, {}), ::testing::HasSubstr("mismatched element counts")); } @@ -637,7 +605,7 @@ XLA_TEST_P(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) { auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 8}); + Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 8}); Array2D expected_array({ {0, 1, 2, 3, 100, 101, 102, 103}, @@ -671,7 +639,7 @@ XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1}, /*new_sizes=*/{3, 2, 1, 4}); + Reshape(parameter, /*dimensions=*/{0, 1}, /*new_sizes=*/{3, 2, 1, 4}); // clang-format off auto expected_literal = Literal::CreateR4({ @@ -698,7 +666,7 @@ XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4_Dimensions_10) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{1, 0}, /*new_sizes=*/{3, 2, 1, 4}); + Reshape(parameter, /*dimensions=*/{1, 0}, /*new_sizes=*/{3, 2, 1, 4}); // clang-format off auto expected_literal = Literal::CreateR4({ @@ -728,7 +696,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x1x1_To_2x1) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 1}); + Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 1}); std::unique_ptr expected = Literal::ReshapeSlice({2, 1}, {1, 0}, *input_literal); @@ -750,7 +718,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x4x1_To_4x2) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{4, 2}); + Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{4, 2}); std::unique_ptr expected = Literal::ReshapeSlice({4, 2}, {1, 0}, *input_literal); @@ -773,8 +741,8 @@ XLA_TEST_P(ReshapeTest, R4ToR2_5x10x2x3_To_5x60_Dimensions_0213) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 2, 1, 3}, - /*new_sizes=*/{5, 60}); + Reshape(parameter, /*dimensions=*/{0, 2, 1, 3}, + /*new_sizes=*/{5, 60}); Array2D expected_array(5, 60); input.Each([&](tensorflow::gtl::ArraySlice indices, float* cell) { @@ -800,8 +768,8 @@ XLA_TEST_P(ReshapeTest, NoopReshape) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{3, 0, 1, 2}, - /*new_sizes=*/{7, 2, 3, 5}); + Reshape(parameter, /*dimensions=*/{3, 0, 1, 2}, + /*new_sizes=*/{7, 2, 3, 5}); XlaComputation computation = builder.Build().ConsumeValueOrDie(); ExecutionOptions execution_options = execution_options_; @@ -833,8 +801,8 @@ XLA_TEST_P(ReshapeTest, R4ToR4Reshape_Trivial) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *literal_1x2x3x4, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, - /*new_sizes=*/{1, 2, 3, 4}); + Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, + /*new_sizes=*/{1, 2, 3, 4}); ComputeAndCompareLiteral(&builder, *literal_1x2x3x4, {input.get()}); } @@ -848,8 +816,8 @@ XLA_TEST_P(ReshapeTest, R4ToR4Reshape) { XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *literal_1x2x3x4, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{1, 3, 2, 0}, - /*new_sizes=*/{2, 4, 3, 1}); + Reshape(parameter, /*dimensions=*/{1, 3, 2, 0}, + /*new_sizes=*/{2, 4, 3, 1}); // clang-format off auto expected_2x4x3x1 = Literal::CreateR4( @@ -882,8 +850,8 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeSimple) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, - /*new_sizes=*/new_bounds); + Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, + /*new_sizes=*/new_bounds); std::unique_ptr expected = Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) @@ -911,8 +879,8 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, - /*new_sizes=*/new_bounds); + Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, + /*new_sizes=*/new_bounds); std::unique_ptr expected = Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) @@ -940,8 +908,8 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, - /*new_sizes=*/new_bounds); + Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, + /*new_sizes=*/new_bounds); std::unique_ptr expected = Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) @@ -970,8 +938,8 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, - /*new_sizes=*/new_bounds); + Reshape(parameter, /*dimensions=*/{0, 1, 3, 2}, + /*new_sizes=*/new_bounds); std::unique_ptr expected = Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) @@ -999,8 +967,8 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeTrivialR2) { XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( 0, *input_literal, "input", &builder, ¶meter); - builder.Reshape(parameter, /*dimensions=*/{1, 0, 2, 3}, - /*new_sizes=*/new_bounds); + Reshape(parameter, /*dimensions=*/{1, 0, 2, 3}, + /*new_sizes=*/new_bounds); std::unique_ptr expected = Literal::ReshapeSlice(new_bounds, {1, 0, 2, 3}, *input_literal) diff --git a/tensorflow/compiler/xla/tests/reverse_test.cc b/tensorflow/compiler/xla/tests/reverse_test.cc index e7bd142dc9ddefbd8bebfb77d72218d662645c31..662bc42224851ac19c690129f525953e6d410a55 100644 --- a/tensorflow/compiler/xla/tests/reverse_test.cc +++ b/tensorflow/compiler/xla/tests/reverse_test.cc @@ -87,7 +87,7 @@ TEST_P(FloatReverseTest, Reverses) { XlaBuilder builder(TestName()); auto a = AddParam(*input_literal, &builder); - builder.Rev(a, spec.reversal); + Rev(a, spec.reversal); std::unique_ptr expected = input_literal->CloneToUnique(); std::vector output_indices(spec.input_dims.size()); @@ -127,7 +127,7 @@ XLA_TEST_F(ReverseTest, Reverse4DU8ArrayOnDim23) { }}); // clang-format on - b.Rev(b.ConstantR4FromArray4D(input), {0, 3}); + Rev(ConstantR4FromArray4D(&b, input), {0, 3}); // clang-format off Array4D expected({{ @@ -163,7 +163,7 @@ TEST_F(ReverseTest, Reverse4DFloatArrayOnDim01) { }); // clang-format on - b.Rev(b.ConstantR4FromArray4D(input), {0, 1}); + Rev(ConstantR4FromArray4D(&b, input), {0, 1}); // clang-format off Array4D expected({ diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 308d3fc78a51e63c0e3db8c0cda18caf11f665bd..3afd8c8fc88a3879cc524c2d1680e8b176b55f81 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -44,74 +44,75 @@ class ScalarComputationsTest : public ClientLibraryTestBase { protected: // A template for building and running a binary comparison test. template - void TestCompare( - NativeT lhs, NativeT rhs, bool expected, - XlaOp (XlaBuilder::*op)(const XlaOp&, const XlaOp&, - tensorflow::gtl::ArraySlice)) { + void TestCompare(NativeT lhs, NativeT rhs, bool expected, + std::function)> + op) { XlaBuilder builder(TestName()); - XlaOp lhs_op = builder.ConstantR0(lhs); - XlaOp rhs_op = builder.ConstantR0(rhs); - XlaOp result = (builder.*op)(lhs_op, rhs_op, {}); + XlaOp lhs_op = ConstantR0(&builder, lhs); + XlaOp rhs_op = ConstantR0(&builder, rhs); + op(lhs_op, rhs_op, {}); ComputeAndCompareR0(&builder, expected, {}); } template void TestMinMax(NativeT lhs, NativeT rhs, NativeT expected, - XlaOp (XlaBuilder::*op)(const XlaOp&, const XlaOp&, - tensorflow::gtl::ArraySlice)) { + std::function)> + op) { XlaBuilder builder(TestName()); - XlaOp lhs_op = builder.ConstantR0(lhs); - XlaOp rhs_op = builder.ConstantR0(rhs); - XlaOp result = (builder.*op)(lhs_op, rhs_op, {}); + XlaOp lhs_op = ConstantR0(&builder, lhs); + XlaOp rhs_op = ConstantR0(&builder, rhs); + op(lhs_op, rhs_op, {}); ComputeAndCompareR0(&builder, expected, {}); } }; XLA_TEST_F(ScalarComputationsTest, ReturnScalarF32) { XlaBuilder builder(TestName()); - builder.ConstantR0(2.1f); + ConstantR0(&builder, 2.1f); ComputeAndCompareR0(&builder, 2.1f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, NegateScalarF32) { XlaBuilder builder(TestName()); - builder.Neg(builder.ConstantR0(2.1f)); + Neg(ConstantR0(&builder, 2.1f)); ComputeAndCompareR0(&builder, -2.1f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, NegateScalarS32) { XlaBuilder builder(TestName()); - builder.Neg(builder.ConstantR0(2)); + Neg(ConstantR0(&builder, 2)); ComputeAndCompareR0(&builder, -2, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsF32) { XlaBuilder builder(TestName()); - builder.Add(builder.ConstantR0(2.1f), builder.ConstantR0(5.5f)); + Add(ConstantR0(&builder, 2.1f), ConstantR0(&builder, 5.5f)); ComputeAndCompareR0(&builder, 7.6f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsS32) { XlaBuilder builder(TestName()); - builder.Add(builder.ConstantR0(2), builder.ConstantR0(5)); + Add(ConstantR0(&builder, 2), ConstantR0(&builder, 5)); ComputeAndCompareR0(&builder, 7, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU32) { XlaBuilder builder(TestName()); - builder.Add(builder.ConstantR0(35), builder.ConstantR0(57)); + Add(ConstantR0(&builder, 35), ConstantR0(&builder, 57)); ComputeAndCompareR0(&builder, 92, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU8) { XlaBuilder builder(TestName()); - builder.Add(builder.ConstantR0(35), builder.ConstantR0(57)); + Add(ConstantR0(&builder, 35), ConstantR0(&builder, 57)); ComputeAndCompareR0(&builder, 92, {}); } @@ -120,7 +121,7 @@ XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsU64) { XlaBuilder builder(TestName()); const uint64 a = static_cast(1) << 63; const uint64 b = a + 1; - builder.Add(builder.ConstantR0(a), builder.ConstantR0(b)); + Add(ConstantR0(&builder, a), ConstantR0(&builder, b)); ComputeAndCompareR0(&builder, a + b, {}); } @@ -129,37 +130,36 @@ XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsS64) { XlaBuilder builder(TestName()); const int64 a = static_cast(1) << 62; const int64 b = a - 1; - builder.Add(builder.ConstantR0(a), builder.ConstantR0(b)); + Add(ConstantR0(&builder, a), ConstantR0(&builder, b)); ComputeAndCompareR0(&builder, a + b, {}); } XLA_TEST_F(ScalarComputationsTest, AddTwoScalarsF64) { XlaBuilder builder(TestName()); - builder.Add(builder.ConstantR0(0.25), - builder.ConstantR0(3.5)); + Add(ConstantR0(&builder, 0.25), ConstantR0(&builder, 3.5)); ComputeAndCompareR0(&builder, 3.75, {}); } XLA_TEST_F(ScalarComputationsTest, SubtractTwoScalarsF32) { XlaBuilder builder(TestName()); - builder.Sub(builder.ConstantR0(2.1f), builder.ConstantR0(5.5f)); + Sub(ConstantR0(&builder, 2.1f), ConstantR0(&builder, 5.5f)); ComputeAndCompareR0(&builder, -3.4f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, SubtractTwoScalarsS32) { XlaBuilder builder(TestName()); - builder.Sub(builder.ConstantR0(2), builder.ConstantR0(5)); + Sub(ConstantR0(&builder, 2), ConstantR0(&builder, 5)); ComputeAndCompareR0(&builder, -3, {}); } XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { XlaBuilder builder(TestName()); - auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a"); - builder.ConvertElementType(a, F32); + auto a = Parameter(&builder, 0, ShapeUtil::MakeShape(S64, {}), "a"); + ConvertElementType(a, F32); int64 value = 3LL << 35; std::unique_ptr a_literal = Literal::CreateR0(value); @@ -171,9 +171,8 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32) { XlaBuilder builder(TestName()); - builder.Mul(builder.Mul(builder.ConstantR0(2.1f), - builder.ConstantR0(5.5f)), - builder.ConstantR0(0.5f)); + Mul(Mul(ConstantR0(&builder, 2.1f), ConstantR0(&builder, 5.5f)), + ConstantR0(&builder, 0.5f)); ComputeAndCompareR0(&builder, 5.775f, {}, error_spec_); } @@ -190,7 +189,7 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsS32) { for (int32 x : data) { for (int32 y : data) { XlaBuilder builder(TestName()); - builder.Mul(builder.ConstantR0(x), builder.ConstantR0(y)); + Mul(ConstantR0(&builder, x), ConstantR0(&builder, y)); // Signed integer overflow is undefined behavior in C++. Convert the input // integers to unsigned, perform the multiplication unsigned, and convert @@ -209,7 +208,7 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsU32) { for (uint32 x : data) { for (uint32 y : data) { XlaBuilder builder(TestName()); - builder.Mul(builder.ConstantR0(x), builder.ConstantR0(y)); + Mul(ConstantR0(&builder, x), ConstantR0(&builder, y)); uint32 expected = x * y; ComputeAndCompareR0(&builder, expected, {}); @@ -219,9 +218,8 @@ XLA_TEST_F(ScalarComputationsTest, MulTwoScalarsU32) { XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsS32) { XlaBuilder builder(TestName()); - builder.Mul( - builder.Mul(builder.ConstantR0(2), builder.ConstantR0(5)), - builder.ConstantR0(1)); + Mul(Mul(ConstantR0(&builder, 2), ConstantR0(&builder, 5)), + ConstantR0(&builder, 1)); ComputeAndCompareR0(&builder, 10, {}); } @@ -239,10 +237,10 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { std::unique_ptr c_data = client_->TransferToServer(*c_literal).ConsumeValueOrDie(); - XlaOp a = builder.Parameter(0, a_literal->shape(), "a"); - XlaOp b = builder.Parameter(1, b_literal->shape(), "b"); - XlaOp c = builder.Parameter(2, c_literal->shape(), "c"); - builder.Mul(builder.Mul(a, b), c); + XlaOp a = Parameter(&builder, 0, a_literal->shape(), "a"); + XlaOp b = Parameter(&builder, 1, b_literal->shape(), "b"); + XlaOp c = Parameter(&builder, 2, c_literal->shape(), "c"); + Mul(Mul(a, b), c); ComputeAndCompareR0(&builder, 5.775f, {a_data.get(), b_data.get(), c_data.get()}, @@ -251,14 +249,14 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { XLA_TEST_F(ScalarComputationsTest, DivideTwoScalarsF32) { XlaBuilder builder(TestName()); - builder.Div(builder.ConstantR0(5.0f), builder.ConstantR0(2.5f)); + Div(ConstantR0(&builder, 5.0f), ConstantR0(&builder, 2.5f)); ComputeAndCompareR0(&builder, 2.0f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, RemTwoScalarsF32) { XlaBuilder builder(TestName()); - builder.Rem(builder.ConstantR0(2.5f), builder.ConstantR0(5.0f)); + Rem(ConstantR0(&builder, 2.5f), ConstantR0(&builder, 5.0f)); ComputeAndCompareR0(&builder, 2.5f, {}, error_spec_); } @@ -281,8 +279,8 @@ class DivS32Test : public ClientLibraryTestBase, XLA_TEST_P(DivS32Test, DivideTwoScalarsS32) { DivS32Params p = GetParam(); XlaBuilder builder(TestName()); - builder.Div(builder.ConstantR0(p.dividend), - builder.ConstantR0(p.divisor)); + Div(ConstantR0(&builder, p.dividend), + ConstantR0(&builder, p.divisor)); ComputeAndCompareR0(&builder, p.quotient, {}); } @@ -290,8 +288,8 @@ XLA_TEST_P(DivS32Test, DivideTwoScalarsS32) { XLA_TEST_P(DivS32Test, RemainderTwoScalarsS32) { DivS32Params p = GetParam(); XlaBuilder builder(TestName()); - builder.Rem(builder.ConstantR0(p.dividend), - builder.ConstantR0(p.divisor)); + Rem(ConstantR0(&builder, p.dividend), + ConstantR0(&builder, p.divisor)); ComputeAndCompareR0(&builder, p.remainder, {}); } @@ -305,7 +303,7 @@ XLA_TEST_P(DivS32Test, DivideTwoScalarsNonConstS32) { CreateR0Parameter(p.dividend, 0, "dividend", &builder, ÷nd); auto divisord = CreateR0Parameter(p.divisor, 1, "divisor", &builder, &divisor); - builder.Div(dividend, divisor); + Div(dividend, divisor); ComputeAndCompareR0(&builder, p.quotient, {dividendd.get(), divisord.get()}); @@ -320,7 +318,7 @@ XLA_TEST_P(DivS32Test, RemainderTwoScalarsNonConstDivisorS32) { CreateR0Parameter(p.dividend, 0, "dividend", &builder, ÷nd); auto divisord = CreateR0Parameter(p.divisor, 1, "divisor", &builder, &divisor); - builder.Rem(dividend, divisor); + Rem(dividend, divisor); ComputeAndCompareR0(&builder, p.remainder, {dividendd.get(), divisord.get()}); @@ -367,10 +365,10 @@ XLA_TEST_F(ScalarComputationsTest, DivU32s) { XlaBuilder builder(TestName()); XlaOp dividend = - builder.Parameter(0, ShapeUtil::MakeShape(U32, {}), "dividend"); + Parameter(&builder, 0, ShapeUtil::MakeShape(U32, {}), "dividend"); XlaOp divisor = - builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor"); - builder.Div(dividend, divisor); + Parameter(&builder, 1, ShapeUtil::MakeShape(U32, {}), "divisor"); + Div(dividend, divisor); TF_ASSERT_OK_AND_ASSIGN(div_computation, builder.Build()); } @@ -408,10 +406,10 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { XlaBuilder builder(TestName()); XlaOp dividend = - builder.Parameter(0, ShapeUtil::MakeShape(U32, {}), "dividend"); + Parameter(&builder, 0, ShapeUtil::MakeShape(U32, {}), "dividend"); XlaOp divisor = - builder.Parameter(1, ShapeUtil::MakeShape(U32, {}), "divisor"); - builder.Rem(dividend, divisor); + Parameter(&builder, 1, ShapeUtil::MakeShape(U32, {}), "divisor"); + Rem(dividend, divisor); TF_ASSERT_OK_AND_ASSIGN(rem_computation, builder.Build()); } @@ -439,8 +437,8 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { XLA_TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) { XlaBuilder builder(TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(S32, {}), "x"); - builder.Rem(x, builder.ConstantR0(80000)); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(S32, {}), "x"); + Rem(x, ConstantR0(&builder, 80000)); std::unique_ptr literal = Literal::CreateR0(87919); TF_ASSERT_OK_AND_ASSIGN(auto input_data, client_->TransferToServer(*literal)); @@ -451,15 +449,15 @@ XLA_TEST_F(ScalarComputationsTest, DivideTwoScalarsU32) { XlaBuilder builder(TestName()); // This verifies 0xFFFFFFFE / 2 = 0x7FFFFFFF. If XLA incorrectly treated U32 // as S32, it would output -2 / 2 = -1 (0xFFFFFFFF). - builder.Div(builder.ConstantR0(0xFFFFFFFE), - builder.ConstantR0(2)); + Div(ConstantR0(&builder, 0xFFFFFFFE), + ConstantR0(&builder, 2)); ComputeAndCompareR0(&builder, 0x7FFFFFFF, {}); } XLA_TEST_F(ScalarComputationsTest, RemTwoScalarsU32) { XlaBuilder builder(TestName()); - builder.Rem(builder.ConstantR0(11), builder.ConstantR0(3)); + Rem(ConstantR0(&builder, 11), ConstantR0(&builder, 3)); ComputeAndCompareR0(&builder, 2, {}); } @@ -468,7 +466,7 @@ XLA_TEST_F(ScalarComputationsTest, AndBool) { for (bool x : {false, true}) { for (bool y : {false, true}) { XlaBuilder builder(TestName()); - builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); + And(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x && y, {}); } @@ -479,7 +477,7 @@ XLA_TEST_F(ScalarComputationsTest, AndS32) { for (int32 x : {0, 8}) { for (int32 y : {1, -16}) { XlaBuilder builder(TestName()); - builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); + And(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x & y, {}); } @@ -490,7 +488,7 @@ XLA_TEST_F(ScalarComputationsTest, AndU32) { for (uint32 x : {0, 8}) { for (uint32 y : {1, 16}) { XlaBuilder builder(TestName()); - builder.And(builder.ConstantR0(x), builder.ConstantR0(y)); + And(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x & y, {}); } @@ -501,7 +499,7 @@ XLA_TEST_F(ScalarComputationsTest, OrBool) { for (bool x : {false, true}) { for (bool y : {false, true}) { XlaBuilder builder(TestName()); - builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); + Or(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x || y, {}); } @@ -512,7 +510,7 @@ XLA_TEST_F(ScalarComputationsTest, OrS32) { for (int32 x : {0, 8}) { for (int32 y : {1, -16}) { XlaBuilder builder(TestName()); - builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); + Or(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x | y, {}); } @@ -523,7 +521,7 @@ XLA_TEST_F(ScalarComputationsTest, OrU32) { for (uint32 x : {0, 8}) { for (uint32 y : {1, 16}) { XlaBuilder builder(TestName()); - builder.Or(builder.ConstantR0(x), builder.ConstantR0(y)); + Or(ConstantR0(&builder, x), ConstantR0(&builder, y)); ComputeAndCompareR0(&builder, x | y, {}); } @@ -533,7 +531,7 @@ XLA_TEST_F(ScalarComputationsTest, OrU32) { XLA_TEST_F(ScalarComputationsTest, NotBool) { for (bool x : {false, true}) { XlaBuilder builder(TestName()); - builder.Not(builder.ConstantR0(x)); + Not(ConstantR0(&builder, x)); ComputeAndCompareR0(&builder, !x, {}); } @@ -542,7 +540,7 @@ XLA_TEST_F(ScalarComputationsTest, NotBool) { XLA_TEST_F(ScalarComputationsTest, NotS32) { for (int32 x : {-1, 0, 1}) { XlaBuilder builder(TestName()); - builder.Not(builder.ConstantR0(x)); + Not(ConstantR0(&builder, x)); ComputeAndCompareR0(&builder, ~x, {}); } @@ -551,7 +549,7 @@ XLA_TEST_F(ScalarComputationsTest, NotS32) { XLA_TEST_F(ScalarComputationsTest, NotU32) { for (uint32 x : {0, 1, 2}) { XlaBuilder builder(TestName()); - builder.Not(builder.ConstantR0(x)); + Not(ConstantR0(&builder, x)); ComputeAndCompareR0(&builder, ~x, {}); } @@ -559,18 +557,18 @@ XLA_TEST_F(ScalarComputationsTest, NotU32) { XLA_TEST_F(ScalarComputationsTest, SelectScalarTrue) { XlaBuilder builder(TestName()); - builder.Select(builder.ConstantR0(true), // The predicate. - builder.ConstantR0(123.0f), // The value on true. - builder.ConstantR0(42.0f)); // The value on false. + Select(ConstantR0(&builder, true), // The predicate. + ConstantR0(&builder, 123.0f), // The value on true. + ConstantR0(&builder, 42.0f)); // The value on false. ComputeAndCompareR0(&builder, 123.0f, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, SelectScalarFalse) { XlaBuilder builder(TestName()); - builder.Select(builder.ConstantR0(false), // The predicate. - builder.ConstantR0(123.0f), // The value on true. - builder.ConstantR0(42.0f)); // The value on false. + Select(ConstantR0(&builder, false), // The predicate. + ConstantR0(&builder, 123.0f), // The value on true. + ConstantR0(&builder, 42.0f)); // The value on false. ComputeAndCompareR0(&builder, 42.0f, {}, error_spec_); } @@ -579,313 +577,311 @@ XLA_TEST_F(ScalarComputationsTest, SelectScalarFalse) { // templatized comparison tests. XLA_TEST_F(ScalarComputationsTest, CompareGtScalar) { XlaBuilder builder(TestName()); - builder.Gt(builder.ConstantR0(2.0f), builder.ConstantR0(1.0f)); + Gt(ConstantR0(&builder, 2.0f), ConstantR0(&builder, 1.0f)); ComputeAndCompareR0(&builder, true, {}); } // S32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqS32Greater) { - TestCompare(2, 1, false, &XlaBuilder::Eq); + TestCompare(2, 1, false, &Eq); } XLA_TEST_F(ScalarComputationsTest, CompareEqS32Equal) { - TestCompare(3, 3, true, &XlaBuilder::Eq); + TestCompare(3, 3, true, &Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeS32) { - TestCompare(2, 1, true, &XlaBuilder::Ne); + TestCompare(2, 1, true, &Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeS32) { - TestCompare(2, 1, true, &XlaBuilder::Ge); + TestCompare(2, 1, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtS32) { - TestCompare(1, 5, false, &XlaBuilder::Gt); + TestCompare(1, 5, false, &Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeS32) { - TestCompare(2, 1, false, &XlaBuilder::Le); + TestCompare(2, 1, false, &Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtS32) { - TestCompare(9, 7, false, &XlaBuilder::Lt); + TestCompare(9, 7, false, &Lt); TestCompare(std::numeric_limits::min(), - std::numeric_limits::max(), true, &XlaBuilder::Lt); + std::numeric_limits::max(), true, &Lt); } // U32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqU32False) { - TestCompare(2, 1, false, &XlaBuilder::Eq); + TestCompare(2, 1, false, &Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeU32) { - TestCompare(2, 1, true, &XlaBuilder::Ne); + TestCompare(2, 1, true, &Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeU32Greater) { - TestCompare(2, 1, true, &XlaBuilder::Ge); + TestCompare(2, 1, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeU32Equal) { - TestCompare(3, 3, true, &XlaBuilder::Ge); + TestCompare(3, 3, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtU32) { - TestCompare(1, 5, false, &XlaBuilder::Gt); - TestCompare(5, 5, false, &XlaBuilder::Gt); - TestCompare(5, 1, true, &XlaBuilder::Gt); + TestCompare(1, 5, false, &Gt); + TestCompare(5, 5, false, &Gt); + TestCompare(5, 1, true, &Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeU32) { - TestCompare(2, 1, false, &XlaBuilder::Le); + TestCompare(2, 1, false, &Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtU32) { - TestCompare(9, 7, false, &XlaBuilder::Lt); - TestCompare(0, std::numeric_limits::max(), true, - &XlaBuilder::Lt); + TestCompare(9, 7, false, &Lt); + TestCompare(0, std::numeric_limits::max(), true, &Lt); } // F32 comparisons. XLA_TEST_F(ScalarComputationsTest, CompareEqF32False) { - TestCompare(2.0, 1.3, false, &XlaBuilder::Eq); + TestCompare(2.0, 1.3, false, &Eq); } XLA_TEST_F(ScalarComputationsTest, CompareNeF32) { - TestCompare(2.0, 1.3, true, &XlaBuilder::Ne); + TestCompare(2.0, 1.3, true, &Ne); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32Greater) { - TestCompare(2.0, 1.9, true, &XlaBuilder::Ge); + TestCompare(2.0, 1.9, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32Equal) { - TestCompare(3.5, 3.5, true, &XlaBuilder::Ge); + TestCompare(3.5, 3.5, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGtF32) { - TestCompare(1.0, 5.2, false, &XlaBuilder::Gt); + TestCompare(1.0, 5.2, false, &Gt); } XLA_TEST_F(ScalarComputationsTest, CompareLeF32) { - TestCompare(2.0, 1.2, false, &XlaBuilder::Le); + TestCompare(2.0, 1.2, false, &Le); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32) { - TestCompare(9.0, 7.2, false, &XlaBuilder::Lt); + TestCompare(9.0, 7.2, false, &Lt); } // F32 comparisons with exceptional values. The test names encode the // left/right operands at the end, and use Minf and Mzero for -inf and -0.0. XLA_TEST_F(ScalarComputationsTest, CompareLtF32MinfMzero) { - TestCompare(-INFINITY, -0.0, true, &XlaBuilder::Lt); + TestCompare(-INFINITY, -0.0, true, &Lt); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32MzeroZero) { // Comparisons of 0.0 to -0.0 consider them equal in IEEE 754. - TestCompare(-0.0, 0.0, false, &XlaBuilder::Lt); + TestCompare(-0.0, 0.0, false, &Lt); } XLA_TEST_F(ScalarComputationsTest, CompareLtF32ZeroInf) { - TestCompare(0.0, INFINITY, true, &XlaBuilder::Lt); + TestCompare(0.0, INFINITY, true, &Lt); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32MinfMzero) { - TestCompare(-INFINITY, -0.0, false, &XlaBuilder::Ge); + TestCompare(-INFINITY, -0.0, false, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32MzeroZero) { // Comparisons of 0.0 to -0.0 consider them equal in IEEE 754. - TestCompare(-0.0, 0.0, true, &XlaBuilder::Ge); + TestCompare(-0.0, 0.0, true, &Ge); } XLA_TEST_F(ScalarComputationsTest, CompareGeF32ZeroInf) { - TestCompare(0.0, INFINITY, false, &XlaBuilder::Ge); + TestCompare(0.0, INFINITY, false, &Ge); } XLA_TEST_F(ScalarComputationsTest, ExpScalar) { XlaBuilder builder(TestName()); - builder.Exp(builder.ConstantR0(2.0f)); + Exp(ConstantR0(&builder, 2.0f)); ComputeAndCompareR0(&builder, 7.3890562, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, LogScalar) { XlaBuilder builder("log"); - builder.Log(builder.ConstantR0(2.0f)); + Log(ConstantR0(&builder, 2.0f)); ComputeAndCompareR0(&builder, 0.6931471, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, TanhScalar) { XlaBuilder builder(TestName()); - builder.Tanh(builder.ConstantR0(2.0f)); + Tanh(ConstantR0(&builder, 2.0f)); ComputeAndCompareR0(&builder, 0.96402758, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, TanhDoubleScalar) { XlaBuilder builder(TestName()); - builder.Tanh(builder.ConstantR0(2.0)); + Tanh(ConstantR0(&builder, 2.0)); ComputeAndCompareR0(&builder, 0.96402758, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, PowScalar) { XlaBuilder builder(TestName()); - builder.Pow(builder.ConstantR0(2.0f), builder.ConstantR0(3.0f)); + Pow(ConstantR0(&builder, 2.0f), ConstantR0(&builder, 3.0f)); ComputeAndCompareR0(&builder, 8.0, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighS32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(-1), // The lower bound. - builder.ConstantR0(5), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, -1), // The lower bound. + ConstantR0(&builder, 5), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, 3, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleS32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(-1), // The lower bound. - builder.ConstantR0(2), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, -1), // The lower bound. + ConstantR0(&builder, 2), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, 2, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowS32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(-1), // The lower bound. - builder.ConstantR0(-5), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, -1), // The lower bound. + ConstantR0(&builder, -5), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, -1, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighU32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(1), // The lower bound. - builder.ConstantR0(5), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, 1), // The lower bound. + ConstantR0(&builder, 5), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, 3, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleU32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(1), // The lower bound. - builder.ConstantR0(2), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, 1), // The lower bound. + ConstantR0(&builder, 2), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, 2, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowU32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(1), // The lower bound. - builder.ConstantR0(0), // The operand to be clamped. - builder.ConstantR0(3)); // The upper bound. + Clamp(ConstantR0(&builder, 1), // The lower bound. + ConstantR0(&builder, 0), // The operand to be clamped. + ConstantR0(&builder, 3)); // The upper bound. ComputeAndCompareR0(&builder, 1, {}); } XLA_TEST_F(ScalarComputationsTest, ClampScalarHighF32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. - builder.ConstantR0(5.0f), // The operand to be clamped. - builder.ConstantR0(3.0f)); // The upper bound. + Clamp(ConstantR0(&builder, 2.0f), // The lower bound. + ConstantR0(&builder, 5.0f), // The operand to be clamped. + ConstantR0(&builder, 3.0f)); // The upper bound. ComputeAndCompareR0(&builder, 3.0, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, ClampScalarMiddleF32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. - builder.ConstantR0(2.5f), // The operand to be clamped. - builder.ConstantR0(3.0f)); // The upper bound. + Clamp(ConstantR0(&builder, 2.0f), // The lower bound. + ConstantR0(&builder, 2.5f), // The operand to be clamped. + ConstantR0(&builder, 3.0f)); // The upper bound. ComputeAndCompareR0(&builder, 2.5, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, ClampScalarLowF32) { XlaBuilder builder(TestName()); - builder.Clamp(builder.ConstantR0(2.0f), // The lower bound. - builder.ConstantR0(-5.0f), // The operand to be clamped. - builder.ConstantR0(3.0f)); // The upper bound. + Clamp(ConstantR0(&builder, 2.0f), // The lower bound. + ConstantR0(&builder, -5.0f), // The operand to be clamped. + ConstantR0(&builder, 3.0f)); // The upper bound. ComputeAndCompareR0(&builder, 2.0, {}, error_spec_); } XLA_TEST_F(ScalarComputationsTest, MinS32Above) { - TestMinMax(10, 3, 3, &XlaBuilder::Min); + TestMinMax(10, 3, 3, &Min); } XLA_TEST_F(ScalarComputationsTest, MinS32Below) { - TestMinMax(-100, 3, -100, &XlaBuilder::Min); + TestMinMax(-100, 3, -100, &Min); } XLA_TEST_F(ScalarComputationsTest, MaxS32Above) { - TestMinMax(10, 3, 10, &XlaBuilder::Max); + TestMinMax(10, 3, 10, &Max); } XLA_TEST_F(ScalarComputationsTest, MaxS32Below) { - TestMinMax(-100, 3, 3, &XlaBuilder::Max); + TestMinMax(-100, 3, 3, &Max); } XLA_TEST_F(ScalarComputationsTest, MinU32Above) { const uint32 large = std::numeric_limits::max(); - TestMinMax(large, 3, 3, &XlaBuilder::Min); + TestMinMax(large, 3, 3, &Min); } XLA_TEST_F(ScalarComputationsTest, MinU32Below) { - TestMinMax(0, 5, 0, &XlaBuilder::Min); + TestMinMax(0, 5, 0, &Min); } XLA_TEST_F(ScalarComputationsTest, MaxU32Above) { const uint32 large = std::numeric_limits::max(); - TestMinMax(large, 3, large, &XlaBuilder::Max); + TestMinMax(large, 3, large, &Max); } XLA_TEST_F(ScalarComputationsTest, MaxU32Below) { - TestMinMax(0, 5, 5, &XlaBuilder::Max); + TestMinMax(0, 5, 5, &Max); } XLA_TEST_F(ScalarComputationsTest, MinF32Above) { - TestMinMax(10.1f, 3.1f, 3.1f, &XlaBuilder::Min); + TestMinMax(10.1f, 3.1f, 3.1f, &Min); } XLA_TEST_F(ScalarComputationsTest, MinF32Below) { - TestMinMax(-100.1f, 3.1f, -100.1f, &XlaBuilder::Min); + TestMinMax(-100.1f, 3.1f, -100.1f, &Min); } XLA_TEST_F(ScalarComputationsTest, MinPropagatesNan) { SetFastMathDisabled(true); - TestMinMax(NAN, 3.1f, NAN, &XlaBuilder::Min); - TestMinMax(-3.1f, NAN, NAN, &XlaBuilder::Min); + TestMinMax(NAN, 3.1f, NAN, &Min); + TestMinMax(-3.1f, NAN, NAN, &Min); } XLA_TEST_F(ScalarComputationsTest, MaxF32Above) { - TestMinMax(10.1f, 3.1f, 10.1f, &XlaBuilder::Max); + TestMinMax(10.1f, 3.1f, 10.1f, &Max); } XLA_TEST_F(ScalarComputationsTest, MaxF32Below) { - TestMinMax(-100.1f, 3.1f, 3.1f, &XlaBuilder::Max); + TestMinMax(-100.1f, 3.1f, 3.1f, &Max); } XLA_TEST_F(ScalarComputationsTest, MaxPropagatesNan) { SetFastMathDisabled(true); - TestMinMax(NAN, 3.1f, NAN, &XlaBuilder::Max); - TestMinMax(-3.1f, NAN, NAN, &XlaBuilder::Max); + TestMinMax(NAN, 3.1f, NAN, &Max); + TestMinMax(-3.1f, NAN, NAN, &Max); } XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { // Compute the expression (1 * (3 - 1) * (7 + 0) - 4) / 20. XlaBuilder b(TestName()); - b.Div( - b.Sub(b.Mul(b.ConstantR0(1), - b.Mul(b.Sub(b.ConstantR0(3), b.ConstantR0(1)), - b.Add(b.ConstantR0(7), b.ConstantR0(0)))), - b.ConstantR0(4)), - b.ConstantR0(20)); + Div(Sub(Mul(ConstantR0(&b, 1), + Mul(Sub(ConstantR0(&b, 3), ConstantR0(&b, 1)), + Add(ConstantR0(&b, 7), ConstantR0(&b, 0)))), + ConstantR0(&b, 4)), + ConstantR0(&b, 20)); ComputeAndCompareR0(&b, 0.5, {}, error_spec_); } @@ -893,30 +889,18 @@ XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionS32) { // Compute the expression 1 * (3 - 1) * (7 + 0) - 4. XlaBuilder b(TestName()); - b.Sub(b.Mul(b.ConstantR0(1), - b.Mul(b.Sub(b.ConstantR0(3), b.ConstantR0(1)), - b.Add(b.ConstantR0(7), b.ConstantR0(0)))), - b.ConstantR0(4)); + Sub(Mul(ConstantR0(&b, 1), + Mul(Sub(ConstantR0(&b, 3), ConstantR0(&b, 1)), + Add(ConstantR0(&b, 7), ConstantR0(&b, 0)))), + ConstantR0(&b, 4)); ComputeAndCompareR0(&b, 10, {}); } -XLA_TEST_F(ScalarComputationsTest, SqrtF320) { - XlaBuilder builder(TestName()); - Literal zero_literal = Literal::Zero(PrimitiveType::F32); - - std::unique_ptr zero_data = - client_->TransferToServer(zero_literal).ConsumeValueOrDie(); - - XlaOp zero = builder.Parameter(0, zero_literal.shape(), "zero"); - builder.SqrtF32(zero); - - ComputeAndCompareR0(&builder, 0.0f, {zero_data.get()}, error_spec_); -} XLA_TEST_F(ScalarComputationsTest, RoundScalar) { XlaBuilder builder(TestName()); - builder.Round(builder.ConstantR0(1.4f)); + Round(ConstantR0(&builder, 1.4f)); ComputeAndCompareR0(&builder, 1.0f, {}, error_spec_); } diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index 7015e5a6a31f506d30c2629d7735482cf354455a..0a173fbbbd5cb5e5005728331561008b8b29af26 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -73,16 +73,16 @@ XLA_TEST_P(SelectAndScatterTest, ParamTest) { auto operand_shape = GetParam().operand_shape; Array o(operand_shape); o.FillRandom(1.5f); - auto operand = builder_.ConstantFromArray(o); + auto operand = ConstantFromArray(&builder_, o); auto source_shape = GetParam().source_shape; Array s(source_shape); s.FillRandom(12.0f); - auto source = builder_.ConstantFromArray(s); + auto source = ConstantFromArray(&builder_, s); - builder_.SelectAndScatter(operand, ge_f32_, GetParam().window_dimensions, - GetParam().window_strides, GetParam().padding_type, - source, builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, GetParam().window_dimensions, + GetParam().window_strides, GetParam().padding_type, source, + ConstantR0(&builder_, 0.0f), add_f32_); ComputeAndCompare(&builder_, {}, ErrorSpec(1e-5)); } @@ -197,110 +197,110 @@ INSTANTIATE_TEST_CASE_P( // Test for F32 1D array, with a zero-element input. XLA_TEST_F(SelectAndScatterTest, R1S0F32) { - const auto operand = builder_.ConstantR1({}); - const auto source = builder_.ConstantR1({}); - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3}, - /*window_strides=*/{3}, Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + const auto operand = ConstantR1(&builder_, {}); + const auto source = ConstantR1(&builder_, {}); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3}, + /*window_strides=*/{3}, Padding::kValid, source, + ConstantR0(&builder_, 0.0f), add_f32_); ComputeAndCompareR1(&builder_, {}, {}, ErrorSpec(1e-7)); } // Test for F32 1D array, when windows do not overlap. XLA_TEST_F(SelectAndScatterTest, R1F32) { const auto operand = - builder_.ConstantR1({1.f, 9.f, 3.f, 7.f, 5.f, 6.f}); - const auto source = builder_.ConstantR1({34.f, 42.f}); + ConstantR1(&builder_, {1.f, 9.f, 3.f, 7.f, 5.f, 6.f}); + const auto source = ConstantR1(&builder_, {34.f, 42.f}); const std::vector expected = {0.f, 34.f, 0.f, 42.f, 0.f, 0.f}; - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3}, - /*window_strides=*/{3}, Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3}, + /*window_strides=*/{3}, Padding::kValid, source, + ConstantR0(&builder_, 0.0f), add_f32_); ComputeAndCompareR1(&builder_, expected, {}, ErrorSpec(1e-7)); } // Test for S32 1D array, when windows do not overlap and the init value is 1. XLA_TEST_F(SelectAndScatterTest, R1S32) { - const auto operand = builder_.ConstantR1({-1, 0, 6, 4, -4, 10}); - const auto source = builder_.ConstantR1({-10, 20}); + const auto operand = ConstantR1(&builder_, {-1, 0, 6, 4, -4, 10}); + const auto source = ConstantR1(&builder_, {-10, 20}); const std::vector expected = {1, 1, -9, 1, 1, 21}; - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{3}, - /*window_strides=*/{3}, Padding::kValid, source, - builder_.ConstantR0(1), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{3}, + /*window_strides=*/{3}, Padding::kValid, source, + ConstantR0(&builder_, 1), add_s32_); ComputeAndCompareR1(&builder_, expected, {}); } // Test for S32 1D array, when windows overlap with each other. XLA_TEST_F(SelectAndScatterTest, R1S32OverlappingWindow) { - const auto operand = builder_.ConstantR1({1, 9, 3, 7, 5, 6}); - const auto source = builder_.ConstantR1({34, 42, 53, 19}); + const auto operand = ConstantR1(&builder_, {1, 9, 3, 7, 5, 6}); + const auto source = ConstantR1(&builder_, {34, 42, 53, 19}); const std::vector expected = {0, 76, 0, 72, 0, 0}; - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{3}, - /*window_strides=*/{1}, Padding::kValid, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{3}, + /*window_strides=*/{1}, Padding::kValid, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR1(&builder_, expected, {}); } // Test for S32 2D array, when windows do not overlap. XLA_TEST_F(SelectAndScatterTest, R2S32) { const auto operand = - builder_.ConstantR2({{7, 2, 5, 3, 10, 2}, {3, 8, 9, 3, 4, 2}}); - const auto source = builder_.ConstantR2({{2, 6}}); + ConstantR2(&builder_, {{7, 2, 5, 3, 10, 2}, {3, 8, 9, 3, 4, 2}}); + const auto source = ConstantR2(&builder_, {{2, 6}}); Array2D expected({{0, 0, 0, 0, 6, 0}, {0, 0, 2, 0, 0, 0}}); - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 3}, - /*window_strides=*/{2, 3}, Padding::kValid, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 3}, + /*window_strides=*/{2, 3}, Padding::kValid, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR2(&builder_, expected, {}); } // Test for tie breaking rule in ge_f32_. When a tie is present, the operand // that has the lower lexicographical order (smaller index) should be chosen. XLA_TEST_F(SelectAndScatterTest, R2F32Tie) { - const auto operand = builder_.ConstantR2( - {{0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}}); - const auto source = builder_.ConstantR2( - {{1.0f, 2.0f, 3.0f}, {4.f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}}); + const auto operand = ConstantR2( + &builder_, {{0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}}); + const auto source = ConstantR2( + &builder_, {{1.0f, 2.0f, 3.0f}, {4.f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}}); Array2D expected( {{12.f, 9.f, 0.f}, {15.f, 9.f, 0.f}, {0.f, 0.f, 0.f}}); - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3, 3}, - /*window_strides=*/{1, 1}, Padding::kSame, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3, 3}, + /*window_strides=*/{1, 1}, Padding::kSame, source, + ConstantR0(&builder_, 0.0f), add_f32_); ComputeAndCompareR2(&builder_, expected, {}, ErrorSpec(1e-7)); } // Similar to SelectAndScatterTest.R2S32 but the input is transposed. XLA_TEST_F(SelectAndScatterTest, ReshapeR2S32) { - const auto operand = builder_.ConstantR2( - {{7, 3}, {2, 8}, {5, 9}, {3, 3}, {10, 4}, {2, 2}}); + const auto operand = ConstantR2( + &builder_, {{7, 3}, {2, 8}, {5, 9}, {3, 3}, {10, 4}, {2, 2}}); const auto reshape = - builder_.Reshape(operand, /*dimensions=*/{1, 0}, /*new_sizes=*/{2, 6}); - const auto source = builder_.ConstantR2({{2, 6}}); + Reshape(operand, /*dimensions=*/{1, 0}, /*new_sizes=*/{2, 6}); + const auto source = ConstantR2(&builder_, {{2, 6}}); Array2D expected({{0, 0, 0, 0, 6, 0}, {0, 0, 2, 0, 0, 0}}); - builder_.SelectAndScatter(reshape, ge_s32_, /*window_dimensions=*/{2, 3}, - /*window_strides=*/{2, 3}, Padding::kValid, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(reshape, ge_s32_, /*window_dimensions=*/{2, 3}, + /*window_strides=*/{2, 3}, Padding::kValid, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR2(&builder_, expected, {}); } // Test for S32 2D array, when windows overlap with each other. XLA_TEST_F(SelectAndScatterTest, R2S32OverlappingWindow) { const auto operand = - builder_.ConstantR2({{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); - const auto source = builder_.ConstantR2({{2, 6, 4}}); + ConstantR2(&builder_, {{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); + const auto source = ConstantR2(&builder_, {{2, 6, 4}}); Array2D expected({{0, 0, 0, 0, 0}, {0, 0, 12, 0, 0}}); - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 3}, - /*window_strides=*/{1, 1}, Padding::kValid, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 3}, + /*window_strides=*/{1, 1}, Padding::kValid, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR2(&builder_, expected, {}); } // Test for S32 2D array, when the padding is Padding::kSAME. XLA_TEST_F(SelectAndScatterTest, R2S32SamePadding) { const auto operand = - builder_.ConstantR2({{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); - const auto source = builder_.ConstantR2({{2, 6, 4}}); + ConstantR2(&builder_, {{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); + const auto source = ConstantR2(&builder_, {{2, 6, 4}}); Array2D expected({{0, 0, 0, 0, 4}, {0, 2, 6, 0, 0}}); - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 2}, - /*window_strides=*/{2, 2}, Padding::kSame, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 2}, + /*window_strides=*/{2, 2}, Padding::kSame, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR2(&builder_, expected, {}); } @@ -308,25 +308,26 @@ XLA_TEST_F(SelectAndScatterTest, R2S32SamePadding) { // with each other. XLA_TEST_F(SelectAndScatterTest, R2S32SamePaddingOverlappingWindow) { const auto operand = - builder_.ConstantR2({{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); + ConstantR2(&builder_, {{7, 2, 5, 3, 8}, {3, 8, 9, 3, 4}}); const auto source = - builder_.ConstantR2({{2, 6, 4, 7, 1}, {3, 5, 8, 9, 10}}); + ConstantR2(&builder_, {{2, 6, 4, 7, 1}, {3, 5, 8, 9, 10}}); Array2D expected({{0, 0, 0, 0, 8}, {0, 5, 23, 0, 19}}); - builder_.SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 2}, - /*window_strides=*/{1, 1}, Padding::kSame, source, - builder_.ConstantR0(0), add_s32_); + SelectAndScatter(operand, ge_s32_, /*window_dimensions=*/{2, 2}, + /*window_strides=*/{1, 1}, Padding::kSame, source, + ConstantR0(&builder_, 0), add_s32_); ComputeAndCompareR2(&builder_, expected, {}); } XLA_TEST_F(SelectAndScatterTest, R2F32OverlappingR2Source) { - const auto operand = builder_.ConstantR2( - {{1.5f, 2.5f, 1.5f}, {3.5f, 1.5f, 3.5f}, {4.5f, 2.5f, 4.5f}}); - const auto source = builder_.ConstantR2({{1.0f, 2.0f}, {3.0f, 4.0f}}); + const auto operand = ConstantR2( + &builder_, {{1.5f, 2.5f, 1.5f}, {3.5f, 1.5f, 3.5f}, {4.5f, 2.5f, 4.5f}}); + const auto source = + ConstantR2(&builder_, {{1.0f, 2.0f}, {3.0f, 4.0f}}); Array2D expected( {{0.0f, 0.0f, 0.0f}, {1.0f, 0.0f, 2.0f}, {3.0f, 0.0f, 4.0f}}); - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{2, 2}, - /*window_strides=*/{1, 1}, Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{2, 2}, + /*window_strides=*/{1, 1}, Padding::kValid, source, + ConstantR0(&builder_, 0.0f), add_f32_); ComputeAndCompareR2(&builder_, expected, {}, ErrorSpec(1e-7)); } @@ -342,16 +343,16 @@ TEST_F(SelectAndScatterTest, R4F32Valid) { {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f}}; Array4D o(4, 6, 15, 220); o.FillWithPZ(pzo); - auto operand = builder_.ConstantR4FromArray4D(o); + auto operand = ConstantR4FromArray4D(&builder_, o); Array4D e(4, 6, 15, 220); e.FillWithPZ(pze); Array4D s(2, 2, 15, 220); s.FillWithPZ(pzs); - auto source = builder_.ConstantR4FromArray4D(s); + auto source = ConstantR4FromArray4D(&builder_, s); s.FillWithPZ(pzs); - builder_.SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 3, 1, 1}, - Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 3, 1, 1}, + Padding::kValid, source, ConstantR0(&builder_, 0.0f), + add_f32_); ComputeAndCompareR4(&builder_, e, {}, ErrorSpec(1e-7)); } @@ -367,16 +368,16 @@ TEST_F(SelectAndScatterTest, R4F32Overlap) { {0.0f, 0.0f, 0.0f, 1.0f, 0.0f}}; Array4D o(4, 5, 17, 128); o.FillWithPZ(pzo); - auto operand = builder_.ConstantR4FromArray4D(o); + auto operand = ConstantR4FromArray4D(&builder_, o); Array4D e(4, 5, 17, 128); e.FillWithPZ(pze); Array4D s(2, 2, 17, 128); s.FillWithPZ(pzs); - auto source = builder_.ConstantR4FromArray4D(s); + auto source = ConstantR4FromArray4D(&builder_, s); s.FillWithPZ(pzs); - builder_.SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 2, 1, 1}, - Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 2, 1, 1}, + Padding::kValid, source, ConstantR0(&builder_, 0.0f), + add_f32_); ComputeAndCompareR4(&builder_, e, {}, ErrorSpec(1e-7)); } @@ -392,16 +393,16 @@ TEST_F(SelectAndScatterTest, R4F32OverlapSmall) { {0.0f, 0.0f, 0.0f, 1.0f, 0.0f}}; Array4D o(4, 5, 1, 1); o.FillWithPZ(pzo); - auto operand = builder_.ConstantR4FromArray4D(o); + auto operand = ConstantR4FromArray4D(&builder_, o); Array4D e(4, 5, 1, 1); e.FillWithPZ(pze); Array4D s(2, 2, 1, 1); s.FillWithPZ(pzs); - auto source = builder_.ConstantR4FromArray4D(s); + auto source = ConstantR4FromArray4D(&builder_, s); s.FillWithPZ(pzs); - builder_.SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 2, 1, 1}, - Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 2, 1, 1}, + Padding::kValid, source, ConstantR0(&builder_, 0.0f), + add_f32_); ComputeAndCompareR4(&builder_, e, {}, ErrorSpec(1e-7)); } @@ -414,39 +415,39 @@ TEST_F(SelectAndScatterTest, R4F32RefValidFixedSmall) { Array2D pzs = {{2.0f, 6.0f}, {3.0f, 1.0f}}; Array4D o(4, 6, 4, 4); o.FillWithPZ(pzo); - auto operand = builder_.ConstantR4FromArray4D(o); + auto operand = ConstantR4FromArray4D(&builder_, o); Array4D s(2, 2, 4, 4); s.FillWithPZ(pzs); - auto source = builder_.ConstantR4FromArray4D(s); + auto source = ConstantR4FromArray4D(&builder_, s); s.FillWithPZ(pzs); - builder_.SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 3, 1, 1}, - Padding::kValid, source, - builder_.ConstantR0(0.0f), add_f32_); + SelectAndScatter(operand, ge_f32_, {2, 3, 1, 1}, {2, 3, 1, 1}, + Padding::kValid, source, ConstantR0(&builder_, 0.0f), + add_f32_); auto e = ReferenceUtil::SelectAndScatter4DGePlus(o, s, 0.0f, {2, 3, 1, 1}, {2, 3, 1, 1}, false); ComputeAndCompareR4(&builder_, *e, {}, ErrorSpec(1e-7)); } XLA_TEST_F(SelectAndScatterTest, R1F32OverlappingWindowMaxScatter) { - const auto operand = builder_.ConstantR1({1, 2, 3, 100, 3, 2, 1}); - const auto source = builder_.ConstantR1({34, 42, 53, 19}); + const auto operand = ConstantR1(&builder_, {1, 2, 3, 100, 3, 2, 1}); + const auto source = ConstantR1(&builder_, {34, 42, 53, 19}); const std::vector expected = {0, 0, 0, 53, 0, 0, 0}; - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{4}, - /*window_strides=*/{1}, Padding::kValid, source, - builder_.ConstantR0(0), max_f32_); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{4}, + /*window_strides=*/{1}, Padding::kValid, source, + ConstantR0(&builder_, 0), max_f32_); ComputeAndCompareR1(&builder_, expected, {}, ErrorSpec(1e-7)); } XLA_TEST_F(SelectAndScatterTest, R1F32OverlappingWindowMinScatter) { - const auto operand = builder_.ConstantR1({1, 2, 3, 100, 3, 2, 1}); - const auto source = builder_.ConstantR1({34, 42, 53, 19}); + const auto operand = ConstantR1(&builder_, {1, 2, 3, 100, 3, 2, 1}); + const auto source = ConstantR1(&builder_, {34, 42, 53, 19}); const float max_float = std::numeric_limits::max(); const std::vector expected = {max_float, max_float, max_float, 19, max_float, max_float, max_float}; - builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{4}, - /*window_strides=*/{1}, Padding::kValid, source, - builder_.ConstantR0(max_float), min_f32_); + SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{4}, + /*window_strides=*/{1}, Padding::kValid, source, + ConstantR0(&builder_, max_float), min_f32_); ComputeAndCompareR1(&builder_, expected, {}, ErrorSpec(1e-7)); } diff --git a/tensorflow/compiler/xla/tests/select_test.cc b/tensorflow/compiler/xla/tests/select_test.cc index 72707f224446c7585d1d90ac6681a7b38c41d5f1..59409ab26e1c19a8271318c18e19caa7b8ddc3b7 100644 --- a/tensorflow/compiler/xla/tests/select_test.cc +++ b/tensorflow/compiler/xla/tests/select_test.cc @@ -35,50 +35,52 @@ class SelectTest : public ClientLibraryTestBase { TEST_F(SelectTest, SelectScalarF32True) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto on_true = builder.ConstantR0(123.0f); - auto on_false = builder.ConstantR0(42.0f); - auto result = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, true); + auto on_true = ConstantR0(&builder, 123.0f); + auto on_false = ConstantR0(&builder, 42.0f); + Select(pred, on_true, on_false); ComputeAndCompareR0(&builder, 123.0f, {}, error_spec_); } TEST_F(SelectTest, SelectScalarS32True) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto on_true = builder.ConstantR0(-42); - auto on_false = builder.ConstantR0(42); - auto result = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, true); + auto on_true = ConstantR0(&builder, -42); + auto on_false = ConstantR0(&builder, 42); + Select(pred, on_true, on_false); ComputeAndCompareR0(&builder, -42, {}); } TEST_F(SelectTest, SelectScalarF32False) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto on_true = builder.ConstantR0(123.0f); - auto on_false = builder.ConstantR0(42.0f); - auto result = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, false); + auto on_true = ConstantR0(&builder, 123.0f); + auto on_false = ConstantR0(&builder, 42.0f); + Select(pred, on_true, on_false); ComputeAndCompareR0(&builder, 42.0f, {}, error_spec_); } XLA_TEST_F(SelectTest, SelectR1S0F32WithConstantR1S0PRED) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR1({}); - auto on_true = builder.ConstantR1({}); - auto on_false = builder.ConstantR1({}); - auto select = builder.Select(pred, on_true, on_false); + auto pred = ConstantR1(&builder, {}); + auto on_true = ConstantR1(&builder, {}); + auto on_false = ConstantR1(&builder, {}); + Select(pred, on_true, on_false); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } TEST_F(SelectTest, SelectR1F32WithConstantR1PRED) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR1({false, true, false, true, false}); - auto on_true = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); - auto on_false = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); - auto select = builder.Select(pred, on_true, on_false); + auto pred = ConstantR1(&builder, {false, true, false, true, false}); + auto on_true = + ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); + auto on_false = + ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); + Select(pred, on_true, on_false); ComputeAndCompareR1(&builder, {10.0f, 25.5f, 1.0f, -10.0f, -6.0f}, {}, error_spec_); @@ -88,12 +90,12 @@ XLA_TEST_F(SelectTest, SelectR1S0F32WithCmpR1S0S32s) { // Similar to SelectR1S0F32WithConstantR1S0PRED, except that the pred vector // is not a constant, but rather the result of comparing two other vectors. XlaBuilder builder(TestName()); - auto v1 = builder.ConstantR1({}); - auto v2 = builder.ConstantR1({}); - auto cmp = builder.Eq(v1, v2); - auto on_true = builder.ConstantR1({}); - auto on_false = builder.ConstantR1({}); - auto select = builder.Select(cmp, on_true, on_false); + auto v1 = ConstantR1(&builder, {}); + auto v2 = ConstantR1(&builder, {}); + auto cmp = Eq(v1, v2); + auto on_true = ConstantR1(&builder, {}); + auto on_false = ConstantR1(&builder, {}); + Select(cmp, on_true, on_false); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -102,12 +104,14 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1S32s) { // Similar to SelectR1F32WithConstantR1PRED, except that the pred vector is // not a constant, but rather the result of comparing two other vectors. XlaBuilder builder(TestName()); - auto v1 = builder.ConstantR1({1, 2, 3, 4, 5}); - auto v2 = builder.ConstantR1({9, 2, 9, 4, 9}); - auto cmp = builder.Eq(v1, v2); - auto on_true = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); - auto on_false = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); - auto select = builder.Select(cmp, on_true, on_false); + auto v1 = ConstantR1(&builder, {1, 2, 3, 4, 5}); + auto v2 = ConstantR1(&builder, {9, 2, 9, 4, 9}); + auto cmp = Eq(v1, v2); + auto on_true = + ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); + auto on_false = + ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); + Select(cmp, on_true, on_false); ComputeAndCompareR1(&builder, {10.0f, 25.5f, 1.0f, -10.0f, -6.0f}, {}, error_spec_); @@ -116,12 +120,14 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1S32s) { TEST_F(SelectTest, SelectR1F32WithCmpR1F32s) { // Similar to SelectR1F32WithCmpR1S32s, except "gt"-comparing two R1F32s. XlaBuilder builder(TestName()); - auto v1 = builder.ConstantR1({1.0f, 2.0f, 3.0f, 4.0f, 5.0f}); - auto v2 = builder.ConstantR1({-1.0f, -2.0f, 13.0f, 14.0f, 4.4f}); - auto cmp = builder.Gt(v1, v2); - auto on_true = builder.ConstantR1({-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); - auto on_false = builder.ConstantR1({10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); - auto select = builder.Select(cmp, on_true, on_false); + auto v1 = ConstantR1(&builder, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f}); + auto v2 = ConstantR1(&builder, {-1.0f, -2.0f, 13.0f, 14.0f, 4.4f}); + auto cmp = Gt(v1, v2); + auto on_true = + ConstantR1(&builder, {-2.5f, 25.5f, 2.25f, -10.0f, 6.0f}); + auto on_false = + ConstantR1(&builder, {10.0f, 5.0f, 1.0f, 10.0f, -6.0f}); + Select(cmp, on_true, on_false); ComputeAndCompareR1(&builder, {-2.5f, 25.5f, 1.0f, 10.0f, 6.0f}, {}, error_spec_); @@ -140,8 +146,8 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsSmall) { {21.0f, 22.0f, 23.0f, 24.0f}, /*parameter_number=*/1, /*name=*/"v2", /*builder=*/&builder, /*data_handle=*/&v2); - auto cmp = builder.Gt(v1, v2); - auto select = builder.Select(cmp, v1, v2); + auto cmp = Gt(v1, v2); + Select(cmp, v1, v2); ComputeAndCompareR1(&builder, {41.0f, 22.0f, 23.0f, 84.0f}, {param0_data.get(), param1_data.get()}, error_spec_); @@ -181,8 +187,8 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32sFromParamsLarge) { CreateR1Parameter(v2vec, /*parameter_number=*/1, /*name=*/"v2", /*builder=*/&builder, /*data_handle=*/&v2); - auto cmp = builder.Gt(v1, v2); - auto select = builder.Select(cmp, v1, v2); + auto cmp = Gt(v1, v2); + Select(cmp, v1, v2); ComputeAndCompareR1(&builder, expected_vec, {param0_data.get(), param1_data.get()}, error_spec_); @@ -192,14 +198,14 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1S32ToScalar) { // "gt"-compares a R1S32 with a S32 scalar, and uses the resulting R1PRED to // select between two R1F32s. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1, -1, 2, -2}); - auto s = builder.ConstantR0(0); - auto cmp = builder.Gt(v, s); + auto v = ConstantR1(&builder, {1, -1, 2, -2}); + auto s = ConstantR0(&builder, 0); + auto cmp = Gt(v, s); - auto on_true = builder.ConstantR1({11.0f, 22.0f, 33.0f, 44.0f}); + auto on_true = ConstantR1(&builder, {11.0f, 22.0f, 33.0f, 44.0f}); auto on_false = - builder.ConstantR1({-111.0f, -222.0f, -333.0f, -444.0f}); - auto select = builder.Select(cmp, on_true, on_false); + ConstantR1(&builder, {-111.0f, -222.0f, -333.0f, -444.0f}); + Select(cmp, on_true, on_false); ComputeAndCompareR1(&builder, {11.0f, -222.0f, 33.0f, -444.0f}, {}, error_spec_); @@ -209,14 +215,14 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32ToScalar) { // "gt"-compares a R1F32 with a F32 scalar, and uses the resulting R1PRED to // select between two R1F32s. XlaBuilder builder(TestName()); - auto v = builder.ConstantR1({1.0f, 2.0f, 3.0f, 4.0f}); - auto s = builder.ConstantR0(2.5f); - auto cmp = builder.Gt(v, s); + auto v = ConstantR1(&builder, {1.0f, 2.0f, 3.0f, 4.0f}); + auto s = ConstantR0(&builder, 2.5f); + auto cmp = Gt(v, s); - auto on_true = builder.ConstantR1({11.0f, 22.0f, 33.0f, 44.0f}); + auto on_true = ConstantR1(&builder, {11.0f, 22.0f, 33.0f, 44.0f}); auto on_false = - builder.ConstantR1({-111.0f, -222.0f, -333.0f, -444.0f}); - auto select = builder.Select(cmp, on_true, on_false); + ConstantR1(&builder, {-111.0f, -222.0f, -333.0f, -444.0f}); + Select(cmp, on_true, on_false); ComputeAndCompareR1(&builder, {-111.0f, -222.0f, 33.0f, 44.0f}, {}, error_spec_); @@ -225,10 +231,10 @@ TEST_F(SelectTest, SelectR1F32WithCmpR1F32ToScalar) { XLA_TEST_F(SelectTest, SelectR1S0F32WithScalarPredicate) { for (bool which : {false, true}) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(which); - auto on_true = builder.ConstantR1({}); - auto on_false = builder.ConstantR1({}); - auto select = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, which); + auto on_true = ConstantR1(&builder, {}); + auto on_false = ConstantR1(&builder, {}); + Select(pred, on_true, on_false); ComputeAndCompareR1(&builder, {}, {}, error_spec_); } @@ -236,20 +242,20 @@ XLA_TEST_F(SelectTest, SelectR1S0F32WithScalarPredicate) { TEST_F(SelectTest, SelectR1F32WithScalarPredicateTrue) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(true); - auto on_true = builder.ConstantR1({-2.5f, 25.5f}); - auto on_false = builder.ConstantR1({10.0f, 5.0f}); - auto select = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, true); + auto on_true = ConstantR1(&builder, {-2.5f, 25.5f}); + auto on_false = ConstantR1(&builder, {10.0f, 5.0f}); + Select(pred, on_true, on_false); ComputeAndCompareR1(&builder, {-2.5f, 25.5f}, {}, error_spec_); } TEST_F(SelectTest, SelectR1F32WithScalarPredicateFalse) { XlaBuilder builder(TestName()); - auto pred = builder.ConstantR0(false); - auto on_true = builder.ConstantR1({-2.5f, 25.5f}); - auto on_false = builder.ConstantR1({10.0f, 5.0f}); - auto select = builder.Select(pred, on_true, on_false); + auto pred = ConstantR0(&builder, false); + auto on_true = ConstantR1(&builder, {-2.5f, 25.5f}); + auto on_false = ConstantR1(&builder, {10.0f, 5.0f}); + Select(pred, on_true, on_false); ComputeAndCompareR1(&builder, {10.0f, 5.0f}, {}, error_spec_); } diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc index 5653bf11a7364bf9ed79bcb6b53f7db31f454803..3e5c01d6d47cc3f3b7d46ce300fe26c5ec9e63fa 100644 --- a/tensorflow/compiler/xla/tests/slice_test.cc +++ b/tensorflow/compiler/xla/tests/slice_test.cc @@ -42,8 +42,8 @@ TEST_F(SliceTest, Slice3x3x3_To_3x3x1_F32) { values.FillIota(0); XlaBuilder builder(TestName()); - auto original = builder.ConstantR3FromArray3D(values); - builder.Slice(original, {0, 0, 0}, {3, 3, 1}, {1, 1, 1}); + auto original = ConstantR3FromArray3D(&builder, values); + Slice(original, {0, 0, 0}, {3, 3, 1}, {1, 1, 1}); Array3D expected{ {{0.0}, {3.0}, {6.0}}, {{9.0}, {12.0}, {15.0}}, {{18.0}, {21.0}, {24.0}}}; @@ -55,8 +55,8 @@ TEST_F(SliceTest, Slice3x3x3_To_3x1x3_F32) { values.FillIota(0); XlaBuilder builder(TestName()); - auto original = builder.ConstantR3FromArray3D(values); - builder.Slice(original, {0, 0, 0}, {3, 1, 3}, {1, 1, 1}); + auto original = ConstantR3FromArray3D(&builder, values); + Slice(original, {0, 0, 0}, {3, 1, 3}, {1, 1, 1}); Array3D expected{ {{0.0, 1.0, 2.0}}, {{9.0, 10.0, 11.0}}, {{18.0, 19.0, 20.0}}}; @@ -68,8 +68,8 @@ TEST_F(SliceTest, Slice3x3x3_To_1x3x3_F32) { values.FillIota(0); XlaBuilder builder(TestName()); - auto original = builder.ConstantR3FromArray3D(values); - builder.Slice(original, {0, 0, 0}, {1, 3, 3}, {1, 1, 1}); + auto original = ConstantR3FromArray3D(&builder, values); + Slice(original, {0, 0, 0}, {1, 3, 3}, {1, 1, 1}); Array3D expected{ {{{0.0, 1.0, 2.0}, {3.0, 4.0, 5.0}, {6.0, 7.0, 8.0}}}}; @@ -78,24 +78,24 @@ TEST_F(SliceTest, Slice3x3x3_To_1x3x3_F32) { XLA_TEST_F(SliceTest, Slice0x0to0x0F32) { XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(Array2D(0, 0)); - builder.Slice(original, {0, 0}, {0, 0}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, Array2D(0, 0)); + Slice(original, {0, 0}, {0, 0}, {1, 1}); ComputeAndCompareR2(&builder, Array2D(0, 0), {}); } XLA_TEST_F(SliceTest, Slice0x20to0x5F32) { XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(Array2D(0, 20)); - builder.Slice(original, {0, 15}, {0, 20}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, Array2D(0, 20)); + Slice(original, {0, 15}, {0, 20}, {1, 1}); ComputeAndCompareR2(&builder, Array2D(0, 5), {}); } XLA_TEST_F(SliceTest, Slice3x0to2x0F32) { XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(Array2D(3, 0)); - builder.Slice(original, {1, 0}, {3, 0}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, Array2D(3, 0)); + Slice(original, {1, 0}, {3, 0}, {1, 1}); ComputeAndCompareR2(&builder, Array2D(2, 0), {}); } @@ -109,8 +109,8 @@ XLA_TEST_F(SliceTest, SliceQuadrantOf256x256) { } XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(values); - builder.Slice(original, {128, 128}, {256, 256}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, values); + Slice(original, {128, 128}, {256, 256}, {1, 1}); Array2D expected(128, 128); for (int row = 0; row < 128; ++row) { @@ -127,8 +127,8 @@ TEST_F(SliceTest, Slice_1x4096_To_1x1024) { std::iota(values.data(), values.data() + 4096, 0.0); XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(values); - builder.Slice(original, {0, 3072}, {1, 4096}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, values); + Slice(original, {0, 3072}, {1, 4096}, {1, 1}); Array2D expected(1, 1024); std::iota(expected.data(), expected.data() + 1024, 3072.0); @@ -148,8 +148,8 @@ TEST_F(SliceTest, Slice_16x4_To_16x2) { } } XlaBuilder builder(TestName()); - auto original = builder.ConstantR2FromArray2D(values); - builder.Slice(original, {0, 0}, {16, 2}, {1, 1}); + auto original = ConstantR2FromArray2D(&builder, values); + Slice(original, {0, 0}, {16, 2}, {1, 1}); ComputeAndCompareR2(&builder, expected, {}, ErrorSpec(0.000001)); } @@ -160,8 +160,8 @@ TEST_F(SliceTest, SliceR4ThreeDimsMiddleMinor) { auto expected = ReferenceUtil::Slice4D( values, {{1, 0, 8, 0}}, {{2, 2, 16, 128}}, /*strides=*/{{1, 1, 1, 1}}); XlaBuilder builder(TestName()); - auto original = builder.ConstantR4FromArray4D(values); - builder.Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1}); + auto original = ConstantR4FromArray4D(&builder, values); + Slice(original, {1, 0, 8, 0}, {2, 2, 16, 128}, {1, 1, 1, 1}); ComputeAndCompareR4(&builder, *expected, {}, ErrorSpec(0.000001)); } @@ -173,8 +173,8 @@ XLA_TEST_F(SliceTest, StridedSliceR4WithOutputLayout) { auto expected_literal = Literal::CreateR4FromArray4DWithLayout( *expected, LayoutUtil::MakeLayout({0, 1, 2, 3})); XlaBuilder builder(TestName()); - auto original = builder.ConstantR4FromArray4D(values); - builder.Slice(original, {0, 0, 0, 0}, {2, 4, 6, 8}, {1, 1, 2, 1}); + auto original = ConstantR4FromArray4D(&builder, values); + Slice(original, {0, 0, 0, 0}, {2, 4, 6, 8}, {1, 1, 2, 1}); ComputeAndCompareLiteral(&builder, *expected_literal, {}, ErrorSpec(0.000001), &expected_literal->shape()); } @@ -200,9 +200,9 @@ class SliceR1Test : public ClientLibraryTestBase, auto literal = Literal::CreateR1(input); XlaBuilder builder(TestName()); - auto original = builder.Parameter(0, literal->shape(), "p0"); - builder.Slice(original, {spec.slice_start}, {spec.slice_limit}, - {spec.slice_stride}); + auto original = Parameter(&builder, 0, literal->shape(), "p0"); + Slice(original, {spec.slice_start}, {spec.slice_limit}, + {spec.slice_stride}); // Ditto. tensorflow::gtl::InlinedVector expected; @@ -372,8 +372,8 @@ XLA_TEST_P(SliceR2Test, DoIt) { input, LayoutUtil::MakeLayout(spec.layout)); XlaBuilder builder(TestName()); - auto a = builder.Parameter(0, literal->shape(), "p0"); - builder.Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides); + auto a = Parameter(&builder, 0, literal->shape(), "p0"); + Slice(a, spec.slice_starts, spec.slice_limits, spec.slice_strides); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr arg, client_->TransferToServer(*literal)); @@ -465,11 +465,10 @@ class SliceR4Test : public ClientLibraryTestBase, XlaBuilder builder(TestName()); auto literal = Literal::CreateR4FromArray4DWithLayout( values, LayoutUtil::MakeLayout(spec.input_layout)); - auto parameter = builder.Parameter(0, literal->shape(), "p0"); + auto parameter = Parameter(&builder, 0, literal->shape(), "p0"); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr arg, client_->TransferToServer(*literal)); - builder.Slice(parameter, spec.slice_starts, spec.slice_limits, - spec.slice_strides); + Slice(parameter, spec.slice_starts, spec.slice_limits, spec.slice_strides); ComputeAndCompareR4(&builder, *expected, {arg.get()}, ErrorSpec(0.000001)); } }; diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc index dd7c541733634213606b5a7983b59bb1f14bf75c..20c7c30878a2821915d47bcf9fa1cc53907df9da 100644 --- a/tensorflow/compiler/xla/tests/test_utils.cc +++ b/tensorflow/compiler/xla/tests/test_utils.cc @@ -161,6 +161,9 @@ StatusOr> MakeFakeLiteralInternal( })); break; } + // Token requires no data. + case TOKEN: + break; default: return Unimplemented("Unsupported type for fake literal generation: %s", ShapeUtil::HumanString(shape).c_str()); @@ -270,14 +273,22 @@ StatusOr> CreateLiteralForConstrainedUses( switch (use->opcode()) { case HloOpcode::kDynamicSlice: case HloOpcode::kDynamicUpdateSlice: - if (needs_index != nullptr && - !ShapeUtil::Equal(needs_index->shape(), use->shape())) { - return Unimplemented( - "Conflicting operand generation slice index constraints\n"); + if (needs_index != nullptr) { + auto needs_index_shape = needs_index->shape(); + auto use_shape = use->shape(); + if (needs_index->opcode() == HloOpcode::kDynamicSlice) { + needs_index_shape = needs_index->operand(0)->shape(); + } + if (use->opcode() == HloOpcode::kDynamicSlice) { + use_shape = use->operand(0)->shape(); + } + if (!ShapeUtil::Equal(needs_index_shape, use_shape)) { + return Unimplemented( + "Conflicting operand generation slice index constraints\n"); + } } needs_index = use; break; - case HloOpcode::kReduce: case HloOpcode::kReduceWindow: needs_constant = use; diff --git a/tensorflow/compiler/xla/tests/test_utils_test.cc b/tensorflow/compiler/xla/tests/test_utils_test.cc index 59afd28a80c0fbf3df38457cd05961c883769856..8f424ae81f592bfd8accd8decb8fc363f7561c73 100644 --- a/tensorflow/compiler/xla/tests/test_utils_test.cc +++ b/tensorflow/compiler/xla/tests/test_utils_test.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/local_client_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -31,16 +32,16 @@ XLA_TEST_F(TestUtilsTest, UnusedParam) { XlaBuilder builder(TestName()); // Make the reduction lambda. Shape single_float = ShapeUtil::MakeShape(F32, {}); - builder.Parameter(0, single_float, "unused"); - builder.Parameter(1, single_float, "used"); + Parameter(&builder, 0, single_float, "unused"); + Parameter(&builder, 1, single_float, "used"); auto computation_status = builder.Build(); TF_ASSERT_OK(computation_status.status()); // Make the reduction. Shape pair_float = ShapeUtil::MakeShape(F32, {2}); - builder.Reduce(builder.Parameter(0, pair_float, "operand"), - builder.Parameter(1, single_float, "init"), - computation_status.ValueOrDie(), {0}); + Reduce(Parameter(&builder, 0, pair_float, "operand"), + Parameter(&builder, 1, single_float, "init"), + computation_status.ValueOrDie(), {0}); computation_status = builder.Build(); TF_ASSERT_OK(computation_status.status()); @@ -53,5 +54,23 @@ XLA_TEST_F(TestUtilsTest, UnusedParam) { TF_ASSERT_OK(MakeFakeArguments(&module).status()); } +XLA_TEST_F(TestUtilsTest, Token) { + auto module = ParseHloString( + R"(HloModule outfeed_module + + ENTRY InfeedToOutfeed { + token = token[] parameter(0) + infeed = ((u32[3]{0}, pred[]), token[]) infeed(token) + infeed.data = (u32[3]{0}, pred[]) get-tuple-element(infeed), index=0 + outfeed = token[] outfeed(infeed.data, token) + ROOT infeed.1 = ((u32[3]{0}, pred[]), token[]) infeed(token) + infeed.1.data = (u32[3]{0}, pred[]) get-tuple-element(infeed.1), index=0 + infeed.1.token = token[] get-tuple-element(infeed.1), index=1 + outfeed.1 = token[] outfeed(infeed.1.data, infeed.1.token) + })") + .ValueOrDie(); + TF_ASSERT_OK(MakeFakeArguments(module.get()).status()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/token_hlo_test.cc b/tensorflow/compiler/xla/tests/token_hlo_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..e9008fa48aa7d0158bd2221791be23c128859098 --- /dev/null +++ b/tensorflow/compiler/xla/tests/token_hlo_test.cc @@ -0,0 +1,206 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/hlo_verifier.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +class TokenHloTest : public HloTestBase {}; + +XLA_TEST_F(TokenHloTest, SingleTokenInstruction) { + std::unique_ptr module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + builder.AddInstruction(HloInstruction::CreateAfterAll({})); + + module->AddEntryComputation(builder.Build()); + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {})); + EXPECT_TRUE(LiteralTestUtil::Equal(*result, *Literal::CreateToken())); +} + +XLA_TEST_F(TokenHloTest, TokenTree) { + std::unique_ptr module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + auto token0 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token1 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token2 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + builder.AddInstruction( + HloInstruction::CreateAfterAll({token0, token0, token1, token2})); + + module->AddEntryComputation(builder.Build()); + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {})); + EXPECT_TRUE(LiteralTestUtil::Equal(*result, *Literal::CreateToken())); +} + +XLA_TEST_F(TokenHloTest, InvalidTokenShapedEntryParameter) { + std::unique_ptr module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "p0")); + builder.AddInstruction( + HloInstruction::CreateParameter(1, ShapeUtil::MakeTokenShape(), "p1")); + builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42))); + module->AddEntryComputation(builder.Build()); + + Status status = HloVerifier().Run(module.get()).status(); + ASSERT_IS_NOT_OK(status); + EXPECT_THAT( + status.error_message(), + ::testing::HasSubstr("Entry parameter 1 is or contains a token shape")); +} + +XLA_TEST_F(TokenHloTest, InvalidTupleTokenShapedEntryParameter) { + std::unique_ptr module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + builder.AddInstruction(HloInstruction::CreateParameter( + 0, + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {1, 2, 3}), ShapeUtil::MakeTokenShape()}), + "param")); + module->AddEntryComputation(builder.Build()); + + Status status = HloVerifier().Run(module.get()).status(); + ASSERT_IS_NOT_OK(status); + EXPECT_THAT( + status.error_message(), + ::testing::HasSubstr("Entry parameter 0 is or contains a token shape")); +} + +XLA_TEST_F(TokenHloTest, InvalidOperandToTokenInstruction) { + std::unique_ptr module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "p0")); + builder.AddInstruction(HloInstruction::CreateAfterAll({param})); + builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(123))); + module->AddEntryComputation(builder.Build()); + + Status status = HloVerifier().Run(module.get()).status(); + ASSERT_IS_NOT_OK(status); + EXPECT_THAT(status.error_message(), + ::testing::HasSubstr( + "Operands of token instructions must be TOKEN types")); +} + +XLA_TEST_F(TokenHloTest, TokenInWhileLoop) { + // Thread a token around a while loop. Token is created and consumed by a + // AfterAll instruction in the while body. + string module_string = R"( +HloModule TokenInWhileLoop + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %after-all = token[] after-all(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %after-all) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %TokenInWhileLoop () -> s32[] { + %zero = s32[] constant(0) + %init_token = token[] after-all() + %init_tuple = (s32[], token[]) tuple(s32[] %zero, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + + DebugOptions debug_options = GetDebugOptionsForTest(); + // Module DCE pass removes the generate token instructions. + debug_options.add_xla_disable_hlo_passes("hlo-module-dce"); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + + EXPECT_TRUE(RunAndCompare(std::move(module), error_spec_)); +} + +XLA_TEST_F(TokenHloTest, TokenInConditional) { + string module_string = R"( +HloModule TokenInConditional + +%True (param.1: token[]) -> (s32[], token[]) { + %param.1 = token[] parameter(0) + %forty_two = s32[] constant(42) + ROOT %tuple = (s32[], token[]) tuple(s32[] %forty_two, token[] %param.1) +} + +%False (param.2: s32[]) -> (s32[], token[]) { + %param.2 = s32[] parameter(0) + %new_token = token[] after-all() + ROOT %tuple = (s32[], token[]) tuple(s32[] %param.2, token[] %new_token) +} + +ENTRY %TokenInConditional (param.3: pred[]) -> s32[] { + %param.3 = pred[] parameter(0) + %init_token = token[] after-all() + %seven = s32[] constant(7) + %cond = (s32[], token[]) conditional(pred[] %param.3, token[] %init_token, s32[] %seven), true_computation=True, false_computation=False + ROOT %root = s32[] get-tuple-element((s32[], token[]) %cond), index=0 +} +)"; + + DebugOptions debug_options = GetDebugOptionsForTest(); + // Module DCE pass removes the generate token instructions. + debug_options.add_xla_disable_hlo_passes("hlo-module-dce"); + + { + // True case. + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + auto arg = Literal::CreateR0(true); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {arg.get()})); + EXPECT_EQ(42, result->Get({})); + } + + { + // False case. + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + auto arg = Literal::CreateR0(false); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {arg.get()})); + EXPECT_EQ(7, result->Get({})); + } +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/transfer_manager_test.cc b/tensorflow/compiler/xla/tests/transfer_manager_test.cc index 0063e7ad415e9b6718c164f415ced6fb76cbf44a..86babb58c9d4515935a5904e04e8fea1074a2812 100644 --- a/tensorflow/compiler/xla/tests/transfer_manager_test.cc +++ b/tensorflow/compiler/xla/tests/transfer_manager_test.cc @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" +#include "tensorflow/core/platform/test_benchmark.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -41,7 +42,12 @@ class TransferManagerTest : public LocalClientTestBase { TransferManagerTest() : shape_size_fn_([this](const Shape& shape) { return transfer_manager_->GetByteSizeRequirement(shape); - }) {} + }) { + stream_ptr_ = local_client_->mutable_backend() + ->BorrowStream(stream_executor_) + .ValueOrDie(); + stream_ = stream_ptr_.get(); + } ~TransferManagerTest() override = default; @@ -53,6 +59,10 @@ class TransferManagerTest : public LocalClientTestBase { .ValueOrDie(); } + protected: + Backend::StreamPtr stream_ptr_; + se::Stream* stream_; + private: std::function shape_size_fn_; }; @@ -63,11 +73,11 @@ XLA_TEST_F(TransferManagerTest, TransferR0U32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR0Equal(42, *result); } @@ -79,11 +89,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1F32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR1Equal({1.25f, 2.5f, -17.0f, -20.125f}, *result); @@ -97,11 +107,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1LargeF32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR1Equal(test_vector, *result); } @@ -113,11 +123,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1U8) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_EQ(result->GetR1U8AsString(), test_string); } @@ -129,11 +139,11 @@ XLA_TEST_F(TransferManagerTest, TransferR2F32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR2Equal( {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, *result); @@ -149,11 +159,11 @@ XLA_TEST_F(TransferManagerTest, // Round trip literal through device. Set the on-device layout to something // different than the literal layout. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_FALSE( LayoutUtil::Equal(result->shape().layout(), literal->shape().layout())); @@ -169,11 +179,11 @@ XLA_TEST_F(TransferManagerTest, TransferTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -183,11 +193,11 @@ XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -203,11 +213,11 @@ XLA_TEST_F(TransferManagerTest, TransferNestedTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -218,11 +228,11 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValue) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -237,14 +247,162 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValueInTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } +XLA_TEST_F(TransferManagerTest, TransferTokenFromDevice) { + // "Copy" a token from the device. The token has no physical representation so + // no copying is actually performed, but it shouldn't fail. + // TODO(b/110532604): Add transferring the token to device when this is + // supported. + auto device_buffer = AllocateDeviceBuffer(ShapeUtil::MakeTokenShape()); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); + EXPECT_TRUE(LiteralTestUtil::Equal(*Literal::CreateToken(), *result)); +} + +XLA_TEST_F(TransferManagerTest, MultiStreamRoundTripSoak) { + const int64 kIterationCount = 5000; + std::unique_ptr literal1 = Literal::MakeTuple( + {Literal::CreateR0(123.0f).get(), + Literal::MakeTuple( + {Literal::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), + Literal::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) + .get(), + Literal::CreateR1({-10.0f, 123.0f}).get()}); + std::unique_ptr literal2 = Literal::MakeTuple( + {Literal::CreateR0(456.0f).get(), + Literal::MakeTuple( + {Literal::CreateR2({{5.0f, 7.0f}, {9.0f, 4.0f}}).get(), + Literal::CreateR1({44.0f, -11.0f, 3333333.3f}).get()}) + .get(), + Literal::CreateR1({-98.0f, 153.0f}).get()}); + + auto device_buffer1 = AllocateDeviceBuffer(literal1->shape()); + auto device_buffer2 = AllocateDeviceBuffer(literal2->shape()); + + auto stream1 = stream_; + auto stream2 = stream_->GetOrCreateSubStream(); + + std::unique_ptr result1, result2; + + // Round trip literals through device in multiple streams asynchronously. + for (int i = 0; i < kIterationCount; ++i) { + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream1, *literal1, + device_buffer1)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream2, *literal2, + device_buffer2)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr this_result1, + transfer_manager_->TransferLiteralFromDevice(stream1, device_buffer1)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr this_result2, + transfer_manager_->TransferLiteralFromDevice(stream2, device_buffer2)); + result1 = std::move(this_result1); + result2 = std::move(this_result2); + } + + EXPECT_TRUE(LiteralTestUtil::Equal(*literal1, *result1)); + EXPECT_TRUE(LiteralTestUtil::Equal(*literal2, *result2)); +} + +class TransferDeviceToHostBenchmark : public TransferManagerTest { + public: + using TransferManagerTest::TransferManagerTest; + ~TransferDeviceToHostBenchmark() override {} + + void Run(int iters, int num_tuple_elements, int array_size) { + tensorflow::testing::StopTiming(); + SetUp(); + + std::vector> tuple_elements; + for (int i = 0; i < num_tuple_elements; ++i) { + tuple_elements.push_back( + Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + } + std::unique_ptr literal = + Literal::MakeTupleOwned(std::move(tuple_elements)); + auto device_buffer = AllocateDeviceBuffer(literal->shape()); + TF_CHECK_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + tensorflow::testing::StartTiming(); + for (int i = 0; i < iters; ++i) { + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); + } + tensorflow::testing::StopTiming(); + TearDown(); + } + + void TestBody() override {} +}; + +class TransferHostToDeviceBenchmark : public TransferManagerTest { + public: + using TransferManagerTest::TransferManagerTest; + ~TransferHostToDeviceBenchmark() override {} + + void Run(int iters, int num_tuple_elements, int array_size) { + tensorflow::testing::StopTiming(); + SetUp(); + + std::vector> tuple_elements; + for (int i = 0; i < num_tuple_elements; ++i) { + tuple_elements.push_back( + Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + } + std::unique_ptr literal = + Literal::MakeTupleOwned(std::move(tuple_elements)); + auto device_buffer = AllocateDeviceBuffer(literal->shape()); + tensorflow::testing::StartTiming(); + for (int i = 0; i < iters; ++i) { + TF_CHECK_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + } + tensorflow::testing::StopTiming(); + TearDown(); + } + + void TestBody() override {} +}; + +void BM_TransferDeviceToHost(int iters, int num_tuple_elements, + int array_size) { + TransferDeviceToHostBenchmark bm; + bm.Run(iters, num_tuple_elements, array_size); +} + +void BM_TransferHostToDevice(int iters, int num_tuple_elements, + int array_size) { + TransferHostToDeviceBenchmark bm; + bm.Run(iters, num_tuple_elements, array_size); +} + +BENCHMARK(BM_TransferHostToDevice) + ->ArgPair(1, 256) + ->ArgPair(1, 257) + ->ArgPair(100, 256) + ->ArgPair(100, 257); + +BENCHMARK(BM_TransferDeviceToHost) + ->ArgPair(1, 256) + ->ArgPair(1, 257) + ->ArgPair(100, 256) + ->ArgPair(100, 257); + +int main(int argc, char** argv) { + ::testing::InitGoogleTest(&argc, argv); + tensorflow::testing::RunBenchmarks(); + return RUN_ALL_TESTS(); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/transpose_test.cc b/tensorflow/compiler/xla/tests/transpose_test.cc index fe1e3da7eca00e128377e6e56af877868aafa836..6ebb4324f8d20ed9f8886d92b0513441685ed19b 100644 --- a/tensorflow/compiler/xla/tests/transpose_test.cc +++ b/tensorflow/compiler/xla/tests/transpose_test.cc @@ -38,34 +38,35 @@ class TransposeTest : public ClientLibraryTestBase { XLA_TEST_F(TransposeTest, Transpose0x0) { XlaBuilder builder("Transpose"); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 0)); - auto result = builder.Transpose(lhs, {1, 0}); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(0, 0)); + Transpose(lhs, {1, 0}); ComputeAndCompareR2(&builder, Array2D(0, 0), {}, error_spec_); } XLA_TEST_F(TransposeTest, Transpose0x42) { XlaBuilder builder("Transpose"); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 42)); - auto result = builder.Transpose(lhs, {1, 0}); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(0, 42)); + Transpose(lhs, {1, 0}); ComputeAndCompareR2(&builder, Array2D(42, 0), {}, error_spec_); } XLA_TEST_F(TransposeTest, Transpose7x0) { XlaBuilder builder("Transpose"); - auto lhs = builder.ConstantR2FromArray2D(Array2D(7, 0)); - auto result = builder.Transpose(lhs, {1, 0}); + auto lhs = ConstantR2FromArray2D(&builder, Array2D(7, 0)); + Transpose(lhs, {1, 0}); ComputeAndCompareR2(&builder, Array2D(0, 7), {}, error_spec_); } TEST_F(TransposeTest, Transpose2x2) { XlaBuilder builder("Transpose"); - auto lhs = builder.ConstantR2({ - {1.0, 2.0}, {3.0, 4.0}, - }); - auto result = builder.Transpose(lhs, {1, 0}); + auto lhs = ConstantR2(&builder, { + {1.0, 2.0}, + {3.0, 4.0}, + }); + Transpose(lhs, {1, 0}); Array2D expected({{1.0f, 3.0f}, {2.0f, 4.0f}}); @@ -74,16 +75,18 @@ TEST_F(TransposeTest, Transpose2x2) { XLA_TEST_F(TransposeTest, Transpose0x2x3_2x3x0) { XlaBuilder builder("Transpose"); - auto operand = builder.ConstantR3FromArray3D(Array3D(0, 2, 3)); - auto result = builder.Transpose(operand, {1, 2, 0}); + auto operand = + ConstantR3FromArray3D(&builder, Array3D(0, 2, 3)); + Transpose(operand, {1, 2, 0}); ComputeAndCompareR3(&builder, Array3D(2, 3, 0), {}); } TEST_F(TransposeTest, Transpose1x2x3_2x3x1) { XlaBuilder builder("Transpose"); - auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); - auto result = builder.Transpose(operand, {1, 2, 0}); + auto operand = + ConstantR3FromArray3D(&builder, {{{1, 2, 3}, {4, 5, 6}}}); + Transpose(operand, {1, 2, 0}); Array3D expected({{{1}, {2}, {3}}, {{4}, {5}, {6}}}); @@ -92,8 +95,9 @@ TEST_F(TransposeTest, Transpose1x2x3_2x3x1) { TEST_F(TransposeTest, Transpose1x2x3_3x2x1) { XlaBuilder builder("Transpose"); - auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); - auto result = builder.Transpose(operand, {2, 1, 0}); + auto operand = + ConstantR3FromArray3D(&builder, {{{1, 2, 3}, {4, 5, 6}}}); + Transpose(operand, {2, 1, 0}); Array3D expected({{{1}, {4}}, {{2}, {5}}, {{3}, {6}}}); @@ -102,8 +106,9 @@ TEST_F(TransposeTest, Transpose1x2x3_3x2x1) { TEST_F(TransposeTest, Transpose1x2x3_1x2x3) { XlaBuilder builder("Transpose"); - auto operand = builder.ConstantR3FromArray3D({{{1, 2, 3}, {4, 5, 6}}}); - auto result = builder.Transpose(operand, {0, 1, 2}); + auto operand = + ConstantR3FromArray3D(&builder, {{{1, 2, 3}, {4, 5, 6}}}); + Transpose(operand, {0, 1, 2}); Array3D expected({{{1, 2, 3}, {4, 5, 6}}}); @@ -116,9 +121,9 @@ TEST_F(TransposeTest, MultiTranspose3x2) { for (int transposes = 0; transposes <= 10; ++transposes) { XlaBuilder builder("Transpose"); - auto computed = builder.ConstantR2FromArray2D(input); + auto computed = ConstantR2FromArray2D(&builder, input); for (int i = 0; i < transposes; ++i) { - computed = builder.Transpose(computed, {1, 0}); + computed = Transpose(computed, {1, 0}); } const Array2D& expected = transposes % 2 == 0 ? input : transposed; ComputeAndCompareR2(&builder, expected, {}, error_spec_); @@ -130,8 +135,8 @@ TEST_F(TransposeTest, Small_1x1) { auto aoperand = MakeLinspaceArray2D(0.0, 1.0, 1, 1); XlaBuilder builder("transpose_1x1"); - auto operand = builder.ConstantR2FromArray2D(*aoperand); - builder.Transpose(operand, {1, 0}); + auto operand = ConstantR2FromArray2D(&builder, *aoperand); + Transpose(operand, {1, 0}); auto expected = ReferenceUtil::TransposeArray2D(*aoperand); ComputeAndCompareR2(&builder, *expected, {}, ErrorSpec(1e-4)); @@ -142,8 +147,8 @@ TEST_F(TransposeTest, Small_2x2) { auto aoperand = MakeLinspaceArray2D(0.0, 4.0, 2, 2); XlaBuilder builder("transpose_2x2"); - auto operand = builder.ConstantR2FromArray2D(*aoperand); - builder.Transpose(operand, {1, 0}); + auto operand = ConstantR2FromArray2D(&builder, *aoperand); + Transpose(operand, {1, 0}); auto expected = ReferenceUtil::TransposeArray2D(*aoperand); ComputeAndCompareR2(&builder, *expected, {}, ErrorSpec(1e-4)); @@ -162,8 +167,8 @@ void TransposeTest::TestTransposeConstant021(size_t n1, size_t n2, size_t n3) { } XlaBuilder builder(TestName()); - auto operand = builder.ConstantR3FromArray3D(aoperand); - builder.Transpose(operand, {0, 2, 1}); + auto operand = ConstantR3FromArray3D(&builder, aoperand); + Transpose(operand, {0, 2, 1}); ComputeAndCompareR3(&builder, expected, {}); } diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index 41189231b90e842292830a932cf381af60456d4c..ec11508891d13f8032a1ebec388c756cf6d752c7 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -54,7 +54,7 @@ XLA_TEST_F(TupleTest, TupleConstant) { Literal::CreateR1(constant_vector).get(), Literal::CreateR2(constant_matrix).get()}); - builder.ConstantLiteral(*value); + ConstantLiteral(&builder, *value); ComputeAndCompareTuple(&builder, *value, {}, error_spec_); } @@ -68,7 +68,7 @@ XLA_TEST_F(TupleTest, TupleScalarConstant) { Literal::MakeTuple({Literal::CreateR0(constant_scalar1).get(), Literal::CreateR0(constant_scalar2).get()}); - builder.ConstantLiteral(*value); + ConstantLiteral(&builder, *value); ComputeAndCompareTuple(&builder, *value, {}, error_spec_); } @@ -82,9 +82,9 @@ XLA_TEST_F(TupleTest, TupleCreate) { {1.1f, 2.2f, 3.5f}, // row 0 {4.8f, 5.0f, 6.7f}, // row 1 }; - builder.Tuple({builder.ConstantR0(constant_scalar), - builder.ConstantR1(constant_vector), - builder.ConstantR2(constant_matrix)}); + Tuple(&builder, {ConstantR0(&builder, constant_scalar), + ConstantR1(&builder, constant_vector), + ConstantR2(&builder, constant_matrix)}); auto expected = Literal::MakeTuple({Literal::CreateR0(constant_scalar).get(), @@ -97,8 +97,8 @@ XLA_TEST_F(TupleTest, TupleCreate) { XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) { XlaBuilder builder(TestName()); - builder.Tuple( - {builder.ConstantR0(7.0), builder.ConstantR1({})}); + Tuple(&builder, + {ConstantR0(&builder, 7.0), ConstantR1(&builder, {})}); auto expected = Literal::MakeTuple({Literal::CreateR0(7.0).get(), Literal::CreateR1({}).get()}); @@ -108,7 +108,7 @@ XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) { // Tests the creation of an empty tuple. XLA_TEST_F(TupleTest, EmptyTupleCreate) { XlaBuilder builder(TestName()); - builder.Tuple({}); + Tuple(&builder, {}); auto expected = Literal::MakeTuple({}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -121,9 +121,10 @@ XLA_TEST_F(TupleTest, GetTupleElement) { {1.f, 2.f, 3.f}, // row 0 {4.f, 5.f, 6.f}, // row 1 }; - auto tuple_data = builder.Tuple({builder.ConstantR1(constant_vector), - builder.ConstantR2(constant_matrix)}); - builder.GetTupleElement(tuple_data, 1); + auto tuple_data = + Tuple(&builder, {ConstantR1(&builder, constant_vector), + ConstantR2(&builder, constant_matrix)}); + GetTupleElement(tuple_data, 1); ComputeAndCompareR2(&builder, Array2D(constant_matrix), {}, error_spec_); } @@ -131,17 +132,18 @@ XLA_TEST_F(TupleTest, GetTupleElement) { // Trivial test for extracting a tuple element with GetTupleElement. XLA_TEST_F(TupleTest, GetTupleElementWithZeroElements) { XlaBuilder builder(TestName()); - auto tuple_data = builder.Tuple( - {builder.ConstantR1({}), - builder.ConstantR2FromArray2D(Array2D(0, 101))}); - builder.GetTupleElement(tuple_data, 1); + auto tuple_data = + Tuple(&builder, + {ConstantR1(&builder, {}), + ConstantR2FromArray2D(&builder, Array2D(0, 101))}); + GetTupleElement(tuple_data, 1); ComputeAndCompareR2(&builder, Array2D(0, 101), {}, error_spec_); } XLA_TEST_F(TupleTest, GetTupleElementOfNonTupleFailsGracefully) { XlaBuilder builder(TestName()); - auto value = builder.ConstantR1({4.5f}); - builder.GetTupleElement(value, 1); + auto value = ConstantR1(&builder, {4.5f}); + GetTupleElement(value, 1); auto result_status = builder.Build(); EXPECT_FALSE(result_status.ok()); EXPECT_THAT( @@ -158,14 +160,15 @@ XLA_TEST_F(TupleTest, AddTupleElements) { {1.f, 2.f, 3.f}, // row 0 {4.f, 5.f, 6.f}, // row 1 }; - auto tuple_data = builder.Tuple({builder.ConstantR1(constant_vector), - builder.ConstantR2(constant_matrix)}); - auto vector_element = builder.GetTupleElement(tuple_data, 0); - auto matrix_element = builder.GetTupleElement(tuple_data, 1); + auto tuple_data = + Tuple(&builder, {ConstantR1(&builder, constant_vector), + ConstantR2(&builder, constant_matrix)}); + auto vector_element = GetTupleElement(tuple_data, 0); + auto matrix_element = GetTupleElement(tuple_data, 1); auto vector_shape = builder.GetShape(vector_element).ConsumeValueOrDie(); auto matrix_shape = builder.GetShape(matrix_element).ConsumeValueOrDie(); - builder.Add(matrix_element, vector_element, - /*broadcast_dimensions=*/{1}); + Add(matrix_element, vector_element, + /*broadcast_dimensions=*/{1}); Array2D expected({ {2.f, 4.f, 6.f}, // row 0 @@ -185,10 +188,11 @@ XLA_TEST_F(TupleTest, TupleGTEToTuple) { {1.f, 2.f, 3.f}, // row 0 {4.f, 5.f, 6.f}, // row 1 }; - auto tuple_data = builder.Tuple({builder.ConstantR1(constant_vector), - builder.ConstantR2(constant_matrix)}); - builder.Tuple({builder.GetTupleElement(tuple_data, 1), - builder.GetTupleElement(tuple_data, 0)}); + auto tuple_data = + Tuple(&builder, {ConstantR1(&builder, constant_vector), + ConstantR2(&builder, constant_matrix)}); + Tuple(&builder, + {GetTupleElement(tuple_data, 1), GetTupleElement(tuple_data, 0)}); auto expected = Literal::MakeTuple({Literal::CreateR2(constant_matrix).get(), Literal::CreateR1(constant_vector).get()}); @@ -206,11 +210,11 @@ XLA_TEST_F(TupleTest, SelectBetweenPredTuples) { std::unique_ptr v2_data = CreateR0Parameter(1.0f, /*parameter_number=*/1, /*name=*/"v2", /*builder=*/&b, /*data_handle=*/&v2); - auto v1_gt = b.Gt(v1, v2); // false - auto v2_gt = b.Gt(v2, v1); // true - auto v1_v2 = b.Tuple({v1_gt, v2_gt}); // {false, true} - auto v2_v1 = b.Tuple({v2_gt, v1_gt}); // {true, false} - b.Select(direction ? v1_gt : v2_gt, v1_v2, v2_v1); + auto v1_gt = Gt(v1, v2); // false + auto v2_gt = Gt(v2, v1); // true + auto v1_v2 = Tuple(&b, {v1_gt, v2_gt}); // {false, true} + auto v2_v1 = Tuple(&b, {v2_gt, v1_gt}); // {true, false} + Select(direction ? v1_gt : v2_gt, v1_v2, v2_v1); auto expected = Literal::MakeTuple({Literal::CreateR0(direction).get(), Literal::CreateR0(!direction).get()}); @@ -243,22 +247,23 @@ XLA_TEST_F(TupleTest, TupleGTEToTupleToGTEAdd) { {1.f, 2.f, 3.f}, // row 0 {4.f, 5.f, 6.f}, // row 1 }; - auto tuple_data = builder.Tuple({builder.ConstantR1(constant_vector), - builder.ConstantR2(constant_matrix)}); - auto new_tuple01 = builder.Tuple({builder.GetTupleElement(tuple_data, 0), - builder.GetTupleElement(tuple_data, 1)}); - auto new_tuple10 = builder.Tuple({builder.GetTupleElement(tuple_data, 1), - builder.GetTupleElement(tuple_data, 0)}); - auto vector_from_01 = builder.GetTupleElement(new_tuple01, 0); - auto vector_from_10 = builder.GetTupleElement(new_tuple10, 1); - auto matrix_from_01 = builder.GetTupleElement(new_tuple01, 1); - auto matrix_from_10 = builder.GetTupleElement(new_tuple10, 0); - - auto addvectors = builder.Add(vector_from_01, vector_from_10); - auto addmatrices = builder.Add(matrix_from_01, matrix_from_10); - - builder.Add(addmatrices, addvectors, - /*broadcast_dimensions=*/{1}); + auto tuple_data = + Tuple(&builder, {ConstantR1(&builder, constant_vector), + ConstantR2(&builder, constant_matrix)}); + auto new_tuple01 = Tuple(&builder, {GetTupleElement(tuple_data, 0), + GetTupleElement(tuple_data, 1)}); + auto new_tuple10 = Tuple(&builder, {GetTupleElement(tuple_data, 1), + GetTupleElement(tuple_data, 0)}); + auto vector_from_01 = GetTupleElement(new_tuple01, 0); + auto vector_from_10 = GetTupleElement(new_tuple10, 1); + auto matrix_from_01 = GetTupleElement(new_tuple01, 1); + auto matrix_from_10 = GetTupleElement(new_tuple10, 0); + + auto addvectors = Add(vector_from_01, vector_from_10); + auto addmatrices = Add(matrix_from_01, matrix_from_10); + + Add(addmatrices, addvectors, + /*broadcast_dimensions=*/{1}); Array2D expected({ {4.f, 8.f, 12.f}, // row 0 @@ -273,12 +278,12 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesOnFalse) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto tuple12 = builder.Tuple( - {builder.ConstantR1(vec1), builder.ConstantR1(vec2)}); - auto tuple21 = builder.Tuple( - {builder.ConstantR1(vec2), builder.ConstantR1(vec1)}); + auto tuple12 = Tuple(&builder, {ConstantR1(&builder, vec1), + ConstantR1(&builder, vec2)}); + auto tuple21 = Tuple(&builder, {ConstantR1(&builder, vec2), + ConstantR1(&builder, vec1)}); - builder.Select(builder.ConstantR0(false), tuple12, tuple21); + Select(ConstantR0(&builder, false), tuple12, tuple21); auto expected = Literal::MakeTuple({Literal::CreateR1(vec2).get(), Literal::CreateR1(vec1).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); @@ -292,22 +297,22 @@ XLA_TEST_F(TupleTest, TuplesInAMap) { // Need to put a select in there to prevent HLO-level optimizations from // optimizing out the tuples. XlaBuilder b("sort_square"); - auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto x2 = b.Mul(x, x); - auto x_smaller_tuple = b.Tuple({x, x2}); - auto x2_smaller_tuple = b.Tuple({x2, x}); - auto sorted = b.Select(b.Lt(x, x2), x_smaller_tuple, x2_smaller_tuple); - auto smaller = b.GetTupleElement(sorted, 0); - auto greater = b.GetTupleElement(sorted, 1); - b.Add(greater, b.Mul(b.ConstantR0(100.0f), smaller)); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto x2 = Mul(x, x); + auto x_smaller_tuple = Tuple(&b, {x, x2}); + auto x2_smaller_tuple = Tuple(&b, {x2, x}); + auto sorted = Select(Lt(x, x2), x_smaller_tuple, x2_smaller_tuple); + auto smaller = GetTupleElement(sorted, 0); + auto greater = GetTupleElement(sorted, 1); + Add(greater, Mul(ConstantR0(&b, 100.0f), smaller)); auto computation_status = b.Build(); ASSERT_IS_OK(computation_status.status()); tuple_computation = computation_status.ConsumeValueOrDie(); } XlaBuilder b(TestName()); - auto input = b.ConstantR1({-1.0f, 1.0f, 2.1f}); - b.Map({input}, tuple_computation, {0}); + auto input = ConstantR1(&b, {-1.0f, 1.0f, 2.1f}); + Map(&b, {input}, tuple_computation, {0}); ComputeAndCompareR1(&b, {-99.0f, 101.0f, 214.41f}, {}, error_spec_); } @@ -317,12 +322,12 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesOnTrue) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto tuple12 = builder.Tuple( - {builder.ConstantR1(vec1), builder.ConstantR1(vec2)}); - auto tuple21 = builder.Tuple( - {builder.ConstantR1(vec2), builder.ConstantR1(vec1)}); + auto tuple12 = Tuple(&builder, {ConstantR1(&builder, vec1), + ConstantR1(&builder, vec2)}); + auto tuple21 = Tuple(&builder, {ConstantR1(&builder, vec2), + ConstantR1(&builder, vec1)}); - builder.Select(builder.ConstantR0(true), tuple12, tuple21); + Select(ConstantR0(&builder, true), tuple12, tuple21); auto expected = Literal::MakeTuple({Literal::CreateR1(vec1).get(), Literal::CreateR1(vec2).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); @@ -335,14 +340,13 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesElementResult) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto tuple12 = builder.Tuple( - {builder.ConstantR1(vec1), builder.ConstantR1(vec2)}); - auto tuple21 = builder.Tuple( - {builder.ConstantR1(vec2), builder.ConstantR1(vec1)}); + auto tuple12 = Tuple(&builder, {ConstantR1(&builder, vec1), + ConstantR1(&builder, vec2)}); + auto tuple21 = Tuple(&builder, {ConstantR1(&builder, vec2), + ConstantR1(&builder, vec1)}); - auto select = - builder.Select(builder.ConstantR0(false), tuple12, tuple21); - builder.GetTupleElement(select, 0); + auto select = Select(ConstantR0(&builder, false), tuple12, tuple21); + GetTupleElement(select, 0); ComputeAndCompareR1(&builder, vec2, {}, error_spec_); } @@ -371,19 +375,16 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesCascaded) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto pred_tuple = builder.Tuple( - {builder.ConstantR0(true), builder.ConstantR0(false)}); - auto tuple12 = builder.Tuple( - {builder.ConstantR1(vec1), builder.ConstantR1(vec2)}); - auto tuple21 = builder.Tuple( - {builder.ConstantR1(vec2), builder.ConstantR1(vec1)}); + auto pred_tuple = Tuple(&builder, {ConstantR0(&builder, true), + ConstantR0(&builder, false)}); + auto tuple12 = Tuple(&builder, {ConstantR1(&builder, vec1), + ConstantR1(&builder, vec2)}); + auto tuple21 = Tuple(&builder, {ConstantR1(&builder, vec2), + ConstantR1(&builder, vec1)}); - auto select1 = - builder.Select(builder.GetTupleElement(pred_tuple, 0), tuple12, tuple21); - auto select2 = - builder.Select(builder.GetTupleElement(pred_tuple, 1), tuple21, select1); - builder.Add(builder.GetTupleElement(select2, 0), - builder.GetTupleElement(select2, 1)); + auto select1 = Select(GetTupleElement(pred_tuple, 0), tuple12, tuple21); + auto select2 = Select(GetTupleElement(pred_tuple, 1), tuple21, select1); + Add(GetTupleElement(select2, 0), GetTupleElement(select2, 1)); ComputeAndCompareR1(&builder, {3.f, 6.f, 9.f}, {}, error_spec_); } @@ -395,12 +396,12 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesReuseConstants) { std::initializer_list vec1 = {1.f, 2.f, 3.f}; std::initializer_list vec2 = {2.f, 4.f, 6.f}; - auto c1 = builder.ConstantR1(vec1); - auto c2 = builder.ConstantR1(vec2); - auto tuple12 = builder.Tuple({c1, c2}); - auto tuple21 = builder.Tuple({c2, c1}); + auto c1 = ConstantR1(&builder, vec1); + auto c2 = ConstantR1(&builder, vec2); + auto tuple12 = Tuple(&builder, {c1, c2}); + auto tuple21 = Tuple(&builder, {c2, c1}); - builder.Select(builder.ConstantR0(false), tuple12, tuple21); + Select(ConstantR0(&builder, false), tuple12, tuple21); auto expected = Literal::MakeTuple({Literal::CreateR1(vec2).get(), Literal::CreateR1(vec1).get()}); @@ -409,9 +410,9 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesReuseConstants) { XLA_TEST_F(TupleTest, NestedTuples) { XlaBuilder builder(TestName()); - auto inner_tuple = builder.Tuple( - {builder.ConstantR1({1.0, 2.0}), builder.ConstantR0(42.0)}); - builder.Tuple({inner_tuple, builder.ConstantR1({22.0, 44.0})}); + auto inner_tuple = Tuple(&builder, {ConstantR1(&builder, {1.0, 2.0}), + ConstantR0(&builder, 42.0)}); + Tuple(&builder, {inner_tuple, ConstantR1(&builder, {22.0, 44.0})}); auto expected_v1 = Literal::CreateR1({1.0, 2.0}); auto expected_s = Literal::CreateR0(42.0); @@ -432,10 +433,10 @@ XLA_TEST_F(TupleTest, GetTupleElementOfNestedTuple) { Shape outer_tuple_shape = ShapeUtil::MakeTupleShape({inner_tuple_shape, data_shape}); - auto input = builder.Parameter(0, outer_tuple_shape, "input"); - auto gte0 = builder.GetTupleElement(input, 0); - auto gte1 = builder.GetTupleElement(gte0, 1); - builder.Add(gte1, builder.ConstantR1({10.0, 11.0, 12.0})); + auto input = Parameter(&builder, 0, outer_tuple_shape, "input"); + auto gte0 = GetTupleElement(input, 0); + auto gte1 = GetTupleElement(gte0, 1); + Add(gte1, ConstantR1(&builder, {10.0, 11.0, 12.0})); std::unique_ptr data = client_ @@ -463,16 +464,16 @@ XLA_TEST_F(TupleTest, ComplexTuples) { Shape c64r2 = ShapeUtil::MakeShape(C64, {3, 2}); Shape arg0_shape = ShapeUtil::MakeTupleShape( {c64r0, ShapeUtil::MakeTupleShape({c64r1, c64r2})}); - auto input0 = builder.Parameter(0, arg0_shape, "input0"); - auto t0 = builder.GetTupleElement(input0, 0); - auto t1 = builder.GetTupleElement(input0, 1); - auto t10 = builder.GetTupleElement(t1, 0); - auto t11 = builder.GetTupleElement(t1, 1); - auto sum = builder.Add(builder.Add(t10, t11, {1}), t0); - auto input1 = builder.Parameter(1, c64r1, "input1"); - auto prod = builder.Mul(input1, sum, {1}); - builder.Tuple({builder.Tuple({prod, sum}), - builder.ConstantR0({123, 456})}); + auto input0 = Parameter(&builder, 0, arg0_shape, "input0"); + auto t0 = GetTupleElement(input0, 0); + auto t1 = GetTupleElement(input0, 1); + auto t10 = GetTupleElement(t1, 0); + auto t11 = GetTupleElement(t1, 1); + auto sum = Add(Add(t10, t11, {1}), t0); + auto input1 = Parameter(&builder, 1, c64r1, "input1"); + auto prod = Mul(input1, sum, {1}); + Tuple(&builder, {Tuple(&builder, {prod, sum}), + ConstantR0(&builder, {123, 456})}); } std::unique_ptr arg0 = @@ -532,8 +533,8 @@ XLA_TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) { auto param = Literal::MakeTupleOwned(Literal::CreateR1({1, 2, 3})); auto result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, - *Literal::MakeTupleOwned(Literal::CreateR2({{1, 2, 3}})))); + *Literal::MakeTupleOwned(Literal::CreateR2({{1, 2, 3}})), + *result)); } } // namespace diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc index c3abe22797f5eaa76ced2ad8534bd68c32983e60..929b1ca7fb93c545265bf85fec1ed7dc845405b2 100644 --- a/tensorflow/compiler/xla/tests/unary_op_test.cc +++ b/tensorflow/compiler/xla/tests/unary_op_test.cc @@ -38,8 +38,8 @@ class UnaryOpTest : public ClientLibraryTestBase { template void AbsSize0TestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1({}); - auto abs = builder.Abs(arg); + auto arg = ConstantR1(&builder, {}); + Abs(arg); if (primitive_util::NativeToPrimitiveType() == C64) { ComputeAndCompareR1(&builder, {}, {}); @@ -51,8 +51,8 @@ class UnaryOpTest : public ClientLibraryTestBase { template void AbsTestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1({-2, 25, 0, -123, inf(), -inf()}); - auto abs = builder.Abs(arg); + auto arg = ConstantR1(&builder, {-2, 25, 0, -123, inf(), -inf()}); + Abs(arg); ComputeAndCompareR1(&builder, {2, 25, 0, 123, inf(), inf()}, {}); } @@ -60,9 +60,9 @@ class UnaryOpTest : public ClientLibraryTestBase { template void SignTestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1( - {-2, 25, 0, static_cast(-0.0), -123, inf(), -inf()}); - auto sign = builder.Sign(arg); + auto arg = ConstantR1( + &builder, {-2, 25, 0, static_cast(-0.0), -123, inf(), -inf()}); + Sign(arg); ComputeAndCompareR1(&builder, {-1, 1, 0, 0, -1, 1, -1}, {}); } @@ -70,10 +70,10 @@ class UnaryOpTest : public ClientLibraryTestBase { template void SignAbsTestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1({-2, 25, 0, -123}); - auto sign = builder.Sign(arg); - auto abs = builder.Abs(arg); - builder.Sub(builder.Mul(sign, abs), arg); + auto arg = ConstantR1(&builder, {-2, 25, 0, -123}); + auto sign = Sign(arg); + auto abs = Abs(arg); + Sub(Mul(sign, abs), arg); ComputeAndCompareR1(&builder, {0, 0, 0, 0}, {}); } @@ -92,13 +92,13 @@ int64 UnaryOpTest::inf() { template <> void UnaryOpTest::AbsTestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1({{-2, 0}, - {0, 25}, - {0, 0}, - {-0.3f, 0.4f}, - {0, inf()}, - {-inf(), 0}}); - auto abs = builder.Abs(arg); + auto arg = ConstantR1(&builder, {{-2, 0}, + {0, 25}, + {0, 0}, + {-0.3f, 0.4f}, + {0, inf()}, + {-inf(), 0}}); + Abs(arg); std::unique_ptr expected = Literal::CreateR1({2, 25, 0, 0.5, inf(), inf()}); @@ -108,9 +108,10 @@ void UnaryOpTest::AbsTestHelper() { template <> void UnaryOpTest::SignTestHelper() { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1( + auto arg = ConstantR1( + &builder, {{-2, 0}, {0, 25}, {0, 0}, {static_cast(-0.0), 0}, {-1, 1}}); - auto sign = builder.Sign(arg); + Sign(arg); std::unique_ptr expected = Literal::CreateR1( {{-1, 0}, {0, 1}, {0, 0}, {0, 0}, {-std::sqrt(0.5f), std::sqrt(0.5f)}}); @@ -121,10 +122,10 @@ template <> void UnaryOpTest::SignAbsTestHelper() { XlaBuilder builder(TestName()); auto arg = - builder.ConstantR1({{-2, 0}, {0, 25}, {0, 0}, {-0.4, 0.3}}); - auto sign = builder.Sign(arg); - auto abs = builder.Abs(arg); - builder.Sub(builder.Mul(sign, builder.ConvertElementType(abs, C64)), arg); + ConstantR1(&builder, {{-2, 0}, {0, 25}, {0, 0}, {-0.4, 0.3}}); + auto sign = Sign(arg); + auto abs = Abs(arg); + Sub(Mul(sign, ConvertElementType(abs, C64)), arg); std::unique_ptr expected = Literal::CreateR1({0, 0, 0, 0}); @@ -145,34 +146,31 @@ XLA_TEST_F(UnaryOpTest, AbsTestR1) { XLA_TEST_F(UnaryOpTest, AbsTestR0) { XlaBuilder builder(TestName()); - auto argi = builder.ConstantR0(-5); - auto absi = builder.Abs(argi); - auto argf = builder.ConstantR0(-3.0f); - auto absf = builder.Abs(argf); - auto argf0 = builder.ConstantR0(-0.0f); - auto absf0 = builder.Abs(argf0); - auto argc = builder.ConstantR0({-0.3f, 0.4f}); - auto absc = builder.Abs(argc); - builder.Add(builder.Add(absc, absf0), - builder.Add(absf, builder.ConvertElementType(absi, F32))); + auto argi = ConstantR0(&builder, -5); + auto absi = Abs(argi); + auto argf = ConstantR0(&builder, -3.0f); + auto absf = Abs(argf); + auto argf0 = ConstantR0(&builder, -0.0f); + auto absf0 = Abs(argf0); + auto argc = ConstantR0(&builder, {-0.3f, 0.4f}); + auto absc = Abs(argc); + Add(Add(absc, absf0), Add(absf, ConvertElementType(absi, F32))); ComputeAndCompareR0(&builder, 8.5f, {}); } XLA_TEST_F(UnaryOpTest, SignTestR0) { XlaBuilder builder(TestName()); - auto argi = builder.ConstantR0(-5); - auto sgni = builder.Sign(argi); // -1 - auto argf = builder.ConstantR0(-4.0f); - auto sgnf = builder.Sign(argf); // -1 - auto argf0 = builder.ConstantR0(-0.0f); - auto sgnf0 = builder.Sign(argf0); // 0 - auto argc = builder.ConstantR0({-.3, .4}); - auto sgnc = builder.Sign(argc); // (-.6, .8) - builder.Add(sgnc, builder.ConvertElementType( - builder.Add(builder.Add(sgnf0, sgnf), - builder.ConvertElementType(sgni, F32)), - C64)); + auto argi = ConstantR0(&builder, -5); + auto sgni = Sign(argi); // -1 + auto argf = ConstantR0(&builder, -4.0f); + auto sgnf = Sign(argf); // -1 + auto argf0 = ConstantR0(&builder, -0.0f); + auto sgnf0 = Sign(argf0); // 0 + auto argc = ConstantR0(&builder, {-.3, .4}); + auto sgnc = Sign(argc); // (-.6, .8) + Add(sgnc, ConvertElementType( + Add(Add(sgnf0, sgnf), ConvertElementType(sgni, F32)), C64)); std::unique_ptr expected = Literal::CreateR0({-2.6f, 0.8f}); @@ -194,9 +192,9 @@ XLA_TEST_F(UnaryOpTest, SignAbsTestR1) { XLA_TEST_F(UnaryOpTest, UnsignedAbsTestR1) { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1( - {2, 25, 0, 123, std::numeric_limits::max()}); - auto abs = builder.Abs(arg); + auto arg = ConstantR1( + &builder, {2, 25, 0, 123, std::numeric_limits::max()}); + Abs(arg); ComputeAndCompareR1( &builder, {2, 25, 0, 123, std::numeric_limits::max()}, {}); @@ -204,37 +202,37 @@ XLA_TEST_F(UnaryOpTest, UnsignedAbsTestR1) { XLA_TEST_F(UnaryOpTest, UnsignedSignTestR1) { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR1( - {2, 25, 0, 123, std::numeric_limits::max()}); - auto sign = builder.Sign(arg); + auto arg = ConstantR1( + &builder, {2, 25, 0, 123, std::numeric_limits::max()}); + Sign(arg); ComputeAndCompareR1(&builder, {1, 1, 0, 1, 1}, {}); } XLA_TEST_F(UnaryOpTest, SignAbsTestR2) { XlaBuilder builder(TestName()); - auto arg = builder.ConstantR2({{1.0, -2.0}, {-3.0, 4.0}}); - auto sign = builder.Sign(arg); - auto abs = builder.Abs(arg); - builder.Sub(builder.Mul(sign, abs), arg); + auto arg = ConstantR2(&builder, {{1.0, -2.0}, {-3.0, 4.0}}); + auto sign = Sign(arg); + auto abs = Abs(arg); + Sub(Mul(sign, abs), arg); ComputeAndCompareR2(&builder, {{0, 0}, {0, 0}}, {}); } XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToS32) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 1}); - auto rhs = builder.ConstantR1({1, 1}); - builder.ConvertElementType(builder.Eq(lhs, rhs), S32); + auto lhs = ConstantR1(&builder, {0, 1}); + auto rhs = ConstantR1(&builder, {1, 1}); + ConvertElementType(Eq(lhs, rhs), S32); ComputeAndCompareR1(&builder, {0, 1}, {}); } XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToF32) { XlaBuilder builder(TestName()); - auto lhs = builder.ConstantR1({0, 1}); - auto rhs = builder.ConstantR1({1, 1}); - builder.ConvertElementType(builder.Eq(lhs, rhs), F32); + auto lhs = ConstantR1(&builder, {0, 1}); + auto rhs = ConstantR1(&builder, {1, 1}); + ConvertElementType(Eq(lhs, rhs), F32); ComputeAndCompareR1(&builder, {0.0, 1.0}, {}); } diff --git a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc index 82d301983fc7885ef5c1c1ed05b74fc017bb7727..ea3aba6df1d3fbd492a23b280309322b8524c0bf 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc @@ -46,7 +46,7 @@ class VecOpsReduceTest : public ClientLibraryTestBase { {{1.0, 2.0, 3.0}, // } plane 2 in dim 0 {4.0, 5.0, 6.0}}}); // clang-format on - return builder_.ConstantR3FromArray3D(x3d); + return ConstantR3FromArray3D(&builder_, x3d); } XlaBuilder builder_; @@ -56,11 +56,10 @@ class VecOpsReduceTest : public ClientLibraryTestBase { TEST_F(VecOpsReduceTest, AddReduceR1F32) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); - auto x = builder_.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0}); + auto x = ConstantR1( + &builder_, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0}); ComputeAndCompareR0(&builder_, -4.2f, {}, errspec_); } @@ -71,10 +70,9 @@ TEST_F(VecOpsReduceTest, AddReduceBigR1F32) { std::vector input(3000); std::iota(input.begin(), input.end(), 100.0f); - auto x = builder_.ConstantR1(input); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0}); + auto x = ConstantR1(&builder_, input); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0}); float expected = std::accumulate(input.begin(), input.end(), 0.0f); ComputeAndCompareR0(&builder_, expected, {}, errspec_); @@ -83,11 +81,10 @@ TEST_F(VecOpsReduceTest, AddReduceBigR1F32) { TEST_F(VecOpsReduceTest, MaxReduceR1F32) { auto max_reducer = CreateScalarMax(); - auto x = builder_.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto max_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), max_reducer, - /*dimensions_to_reduce=*/{0}); + auto x = ConstantR1( + &builder_, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Reduce(x, ConstantR0(&builder_, 0.0f), max_reducer, + /*dimensions_to_reduce=*/{0}); ComputeAndCompareR0(&builder_, 2.6f, {}, errspec_); } @@ -95,11 +92,10 @@ TEST_F(VecOpsReduceTest, MaxReduceR1F32) { TEST_F(VecOpsReduceTest, MaxReduceR1F32WithNontrivialInit) { auto max_reducer = CreateScalarMax(); - auto x = builder_.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto max_reduce = - builder_.Reduce(x, builder_.ConstantR0(4.0f), max_reducer, - /*dimensions_to_reduce=*/{0}); + auto x = ConstantR1( + &builder_, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Reduce(x, ConstantR0(&builder_, 4.0f), max_reducer, + /*dimensions_to_reduce=*/{0}); ComputeAndCompareR0(&builder_, 4.0f, {}, errspec_); } @@ -108,15 +104,14 @@ TEST_F(VecOpsReduceTest, AddReduceR2F32Dim1) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); // clang-format off - auto x = builder_.ConstantR2({ + auto x = ConstantR2(&builder_, { {1.0, 2.0, 3.0}, // | dim 0 {4.0, 5.0, 6.0}}); // | // ------ dim 1 ---------- // clang-format on - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{1}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{1}); ComputeAndCompareR1(&builder_, {6.0, 15.0}, {}, errspec_); } @@ -125,13 +120,12 @@ TEST_F(VecOpsReduceTest, AddReduceR2F32Dim0) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); // clang-format off - auto x = builder_.ConstantR2({ + auto x = ConstantR2(&builder_, { {1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}}); // clang-format on - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0}); ComputeAndCompareR1(&builder_, {5.0, 7.0, 9.0}, {}, errspec_); } @@ -139,9 +133,8 @@ TEST_F(VecOpsReduceTest, AddReduceR2F32Dim0) { TEST_F(VecOpsReduceTest, AddReduceR3F32Dim2) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{2}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{2}); Array2D expected_array({{6.0f, 15.0f}, {6.0f, 15.0f}, {6.0f, 15.0f}}); @@ -151,9 +144,8 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32Dim2) { TEST_F(VecOpsReduceTest, AddReduceR3F32Dim1) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{1}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{1}); Array2D expected_array( {{5.0f, 7.0f, 9.0f}, {5.0f, 7.0f, 9.0f}, {5.0f, 7.0f, 9.0f}}); @@ -164,9 +156,8 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32Dim1) { TEST_F(VecOpsReduceTest, AddReduceR3F32Dim0) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0}); Array2D expected_array({{3.0f, 6.0f, 9.0f}, {12.0f, 15.0f, 18.0f}}); @@ -176,9 +167,8 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32Dim0) { TEST_F(VecOpsReduceTest, AddReduceR3F32Dims1and2) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{1, 2}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{1, 2}); ComputeAndCompareR1(&builder_, {21.0, 21.0, 21.0}, {}, errspec_); } @@ -186,9 +176,8 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32Dims1and2) { XLA_TEST_F(VecOpsReduceTest, AddReduceR3F32Dims0and2) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0, 2}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0, 2}); ComputeAndCompareR1(&builder_, {18.0, 45.0}, {}, errspec_); } @@ -196,9 +185,8 @@ XLA_TEST_F(VecOpsReduceTest, AddReduceR3F32Dims0and2) { TEST_F(VecOpsReduceTest, AddReduceR3F32Dims0and1) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0, 1}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0, 1}); ComputeAndCompareR1(&builder_, {15.0, 21.0, 27.0}, {}, errspec_); } @@ -206,9 +194,8 @@ TEST_F(VecOpsReduceTest, AddReduceR3F32Dims0and1) { TEST_F(VecOpsReduceTest, AddReduceR3F32AllDims) { auto sum_reducer = CreateScalarAddComputation(F32, &builder_); auto x = BuildSampleConstantCube(); - auto add_reduce = - builder_.Reduce(x, builder_.ConstantR0(0.0f), sum_reducer, - /*dimensions_to_reduce=*/{0, 1, 2}); + Reduce(x, ConstantR0(&builder_, 0.0f), sum_reducer, + /*dimensions_to_reduce=*/{0, 1, 2}); ComputeAndCompareR0(&builder_, 63.0, {}, errspec_); } diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc index 5cce7a2bf82c1a8403536a91e67910f949ef185a..79bae22dac9599a38c73ea1dc2e6b4856395ff79 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc @@ -50,9 +50,9 @@ class VecOpsSimpleTest : public ClientLibraryTestBase { XLA_TEST_F(VecOpsSimpleTest, ExpTenValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto exp = builder.Exp(x); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Exp(x); std::vector expected = {8.1662, 7.4274e-02, 13.4637, 1.8316e-02, 8.1662, 9.9742, 6.7379e-03, 4.0657e-01, @@ -69,8 +69,8 @@ XLA_TEST_F(VecOpsSimpleTest, ExpManyValues) { for (int i = 0; i < count; ++i) { exponents.push_back(i / static_cast(count)); } - auto x = builder.ConstantR1(exponents); - auto exp = builder.Exp(x); + auto x = ConstantR1(&builder, exponents); + Exp(x); std::vector expected; expected.reserve(exponents.size()); @@ -98,8 +98,8 @@ XLA_TEST_F(VecOpsSimpleTest, ExpIn4D) { Array4D expected(2, 2, 2, 2, expected_vector); - auto x = builder.ConstantR4FromArray4D(exponents); - auto exp = builder.Exp(x); + auto x = ConstantR4FromArray4D(&builder, exponents); + Exp(x); ComputeAndCompareR4(&builder, expected, {}, ErrorSpec(/*aabs=*/1e-2, /*arel=*/1e-3)); @@ -107,9 +107,9 @@ XLA_TEST_F(VecOpsSimpleTest, ExpIn4D) { XLA_TEST_F(VecOpsSimpleTest, NegateTenFloatValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - builder.Neg(x); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Neg(x); std::vector expected = {-2.1, 2.6, -2.6, 4.0, -2.1, -2.3, 5.0, 0.9, 2.4, -1.6}; @@ -118,8 +118,8 @@ XLA_TEST_F(VecOpsSimpleTest, NegateTenFloatValues) { XLA_TEST_F(VecOpsSimpleTest, NegateTenInt32Values) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({2, -2, 12, -4, 5, 20, -15, 0, -2, 1}); - builder.Neg(x); + auto x = ConstantR1(&builder, {2, -2, 12, -4, 5, 20, -15, 0, -2, 1}); + Neg(x); std::vector expected = {-2, 2, -12, 4, -5, -20, 15, 0, 2, -1}; ComputeAndCompareR1(&builder, expected, {}); @@ -127,59 +127,19 @@ XLA_TEST_F(VecOpsSimpleTest, NegateTenInt32Values) { XLA_TEST_F(VecOpsSimpleTest, NegateUint32Values) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {0, 1, 42, static_cast(-1), static_cast(-12)}); - builder.Neg(x); + auto x = ConstantR1( + &builder, {0, 1, 42, static_cast(-1), static_cast(-12)}); + Neg(x); std::vector expected = {0, static_cast(-1), static_cast(-42), 1, 12}; ComputeAndCompareR1(&builder, expected, {}); } -XLA_TEST_F(VecOpsSimpleTest, SquareTenValues) { - XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - builder.SquareF32(x); - - std::vector expected = {4.41, 6.76, 6.76, 16., 4.41, - 5.29, 25., 0.81, 5.76, 2.56}; - ComputeAndCompareR1(&builder, expected, {}, error_spec_); -} - -XLA_TEST_F(VecOpsSimpleTest, ReciprocalTenValues) { - XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - builder.ReciprocalF32(x); - - std::vector expected = { - 0.47619048, -0.38461538, 0.38461538, -0.25, 0.47619048, - 0.43478261, -0.2, -1.11111111, -0.41666667, 0.625}; - ComputeAndCompareR1(&builder, expected, {}, error_spec_); -} - -XLA_TEST_F(VecOpsSimpleTest, SqrtZeroes) { - XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({0.0, -0.0}); - auto exp = builder.SqrtF32(x); - - ComputeAndCompareR1(&builder, {0, 0}, {}, error_spec_); -} - -XLA_TEST_F(VecOpsSimpleTest, SqrtSixValues) { - XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({16.0, 1.0, 1024.0, 0.16, 0.2, 12345}); - auto exp = builder.SqrtF32(x); - - std::vector expected = {4, 1, 32, 0.4, 0.4472, 111.1080}; - ComputeAndCompareR1(&builder, expected, {}, error_spec_); -} - XLA_TEST_F(VecOpsSimpleTest, InvSqrtSevenValues) { XlaBuilder builder(TestName()); - auto x = - builder.ConstantR1({16.0, 1.0, 1024.0, 0.16, 0.2, 12345, 1.2345}); - auto exp = builder.Pow(x, builder.ConstantR0(-.5f)); + auto x = ConstantR1(&builder, + {16.0, 1.0, 1024.0, 0.16, 0.2, 12345, 1.2345}); + Pow(x, ConstantR0(&builder, -.5f)); std::vector expected = {.25, 1, .03125, 2.5, 2.23607, .009000, .900025}; @@ -191,11 +151,11 @@ XLA_TEST_F(VecOpsSimpleTest, AddTenValuesViaMap) { XlaBuilder builder(TestName()); auto add = CreateScalarAddComputation(F32, &builder); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto y = builder.ConstantR1( - {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); - auto max = builder.Map({x, y}, add, {0}); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + auto y = ConstantR1( + &builder, {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); + Map(&builder, {x, y}, add, {0}); std::vector expected = {1.7, -3.2, -0.4, -3.8, 5.9, 0.1, -6.8, 4., -1., 2.2}; @@ -204,11 +164,11 @@ XLA_TEST_F(VecOpsSimpleTest, AddTenValuesViaMap) { XLA_TEST_F(VecOpsSimpleTest, MaxTenValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto y = builder.ConstantR1( - {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); - auto max = builder.Max(x, y); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + auto y = ConstantR1( + &builder, {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); + Max(x, y); std::vector expected = {2.1, -0.6, 2.6, 0.2, 3.8, 2.3, -1.8, 4.9, 1.4, 1.6}; @@ -227,7 +187,7 @@ XLA_TEST_F(VecOpsSimpleTest, MaxTenValuesFromParams) { {21.0f, 22.0f, 23.0f, 24.0f}, /*parameter_number=*/1, /*name=*/"v2", /*builder=*/&builder, /*data_handle=*/&v2); - auto max = builder.Max(v1, v2); + Max(v1, v2); ComputeAndCompareR1(&builder, {41.0f, 22.0f, 23.0f, 84.0f}, {param0_data.get(), param1_data.get()}, error_spec_); @@ -267,7 +227,7 @@ XLA_TEST_F(VecOpsSimpleTest, Max15000ValuesFromParams) { CreateR1Parameter(v2vec, /*parameter_number=*/1, /*name=*/"v2", /*builder=*/&builder, /*data_handle=*/&v2); - auto max = builder.Max(v1, v2); + Max(v1, v2); ComputeAndCompareR1(&builder, expected_vec, {param0_data.get(), param1_data.get()}, error_spec_); @@ -275,10 +235,10 @@ XLA_TEST_F(VecOpsSimpleTest, Max15000ValuesFromParams) { XLA_TEST_F(VecOpsSimpleTest, MaxTenValuesWithScalar) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto y = builder.ConstantR0(0); - auto max = builder.Max(x, y); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + auto y = ConstantR0(&builder, 0); + Max(x, y); std::vector expected = {2.1, 0.0, 2.6, 0.0, 2.1, 2.3, 0.0, 0.0, 0.0, 1.6}; @@ -287,11 +247,11 @@ XLA_TEST_F(VecOpsSimpleTest, MaxTenValuesWithScalar) { XLA_TEST_F(VecOpsSimpleTest, MinTenValues) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto y = builder.ConstantR1( - {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); - auto min = builder.Min(x, y); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + auto y = ConstantR1( + &builder, {-0.4, -0.6, -3.0, 0.2, 3.8, -2.2, -1.8, 4.9, 1.4, 0.6}); + Min(x, y); std::vector expected = {-0.4, -2.6, -3.0, -4.0, 2.1, -2.2, -5.0, -0.9, -2.4, 0.6}; @@ -300,11 +260,11 @@ XLA_TEST_F(VecOpsSimpleTest, MinTenValues) { XLA_TEST_F(VecOpsSimpleTest, MinMaxTenValues) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR0(0); - auto one = builder.ConstantR0(1); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); - auto clamp = builder.Min(builder.Max(x, zero), one); + auto zero = ConstantR0(&builder, 0); + auto one = ConstantR0(&builder, 1); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); + Min(Max(x, zero), one); std::vector expected = {1.0, 0.0, 1.0, 0.3, 1.0, 0.9, 0.0, 0.1, 0.0, 0.6}; @@ -313,11 +273,11 @@ XLA_TEST_F(VecOpsSimpleTest, MinMaxTenValues) { XLA_TEST_F(VecOpsSimpleTest, ClampTenValuesConstant) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR0(0); - auto one = builder.ConstantR0(1); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); - auto clamp = builder.Clamp(zero, x, one); + auto zero = ConstantR0(&builder, 0); + auto one = ConstantR0(&builder, 1); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); + Clamp(zero, x, one); std::vector expected = {1.0, 0.0, 1.0, 0.3, 1.0, 0.9, 0.0, 0.1, 0.0, 0.6}; @@ -326,10 +286,10 @@ XLA_TEST_F(VecOpsSimpleTest, ClampTenValuesConstant) { XLA_TEST_F(VecOpsSimpleTest, ClampTwoValuesConstant) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR1({0.0f, 0.0f}); - auto one = builder.ConstantR1({1.0f, 1.0f}); - auto x = builder.ConstantR1({2.1, -2.6}); - auto clamp = builder.Clamp(zero, x, one); + auto zero = ConstantR1(&builder, {0.0f, 0.0f}); + auto one = ConstantR1(&builder, {1.0f, 1.0f}); + auto x = ConstantR1(&builder, {2.1, -2.6}); + Clamp(zero, x, one); std::vector expected = {1.0, 0.0}; ComputeAndCompareR1(&builder, expected, {}); @@ -337,11 +297,11 @@ XLA_TEST_F(VecOpsSimpleTest, ClampTwoValuesConstant) { XLA_TEST_F(VecOpsSimpleTest, ClampTenValuesConstantNonzeroLower) { XlaBuilder builder(TestName()); - auto one = builder.ConstantR0(1); - auto two = builder.ConstantR0(2); - auto x = builder.ConstantR1( - {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); - auto clamp = builder.Clamp(one, x, two); + auto one = ConstantR0(&builder, 1); + auto two = ConstantR0(&builder, 2); + auto x = ConstantR1( + &builder, {2.1, -2.6, 2.6, 0.3, 3.1, 0.9, -5.0, 0.1, -2.4, 0.6}); + Clamp(one, x, two); std::vector expected = {2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0}; @@ -350,10 +310,10 @@ XLA_TEST_F(VecOpsSimpleTest, ClampTenValuesConstantNonzeroLower) { XLA_TEST_F(VecOpsSimpleTest, ClampValuesConstantS64) { XlaBuilder builder(TestName()); - auto zero = builder.ConstantR0(0); - auto one = builder.ConstantR0(10); - auto x = builder.ConstantR1({-3, 3, 9, 13}); - auto clamp = builder.Clamp(zero, x, one); + auto zero = ConstantR0(&builder, 0); + auto one = ConstantR0(&builder, 10); + auto x = ConstantR1(&builder, {-3, 3, 9, 13}); + Clamp(zero, x, one); std::vector expected = {0, 3, 9, 10}; ComputeAndCompareR1(&builder, expected, {}); @@ -365,9 +325,9 @@ XLA_TEST_F(VecOpsSimpleTest, MapTenValues) { // add_half(x) = x + 0.5 XlaBuilder builder("add_half"); auto x_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value"); - auto half = builder.ConstantR0(0.5); - builder.Add(x_value, half); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x_value"); + auto half = ConstantR0(&builder, 0.5); + Add(x_value, half); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); add_half = computation_status.ConsumeValueOrDie(); @@ -378,9 +338,9 @@ XLA_TEST_F(VecOpsSimpleTest, MapTenValues) { // clamp(y) = clamp<0,5>(y) XlaBuilder builder("clamp"); auto y_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "y_value"); - auto zero = builder.ConstantR0(0.0); - auto clamped = builder.Clamp(zero, y_value, builder.ConstantR0(5)); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "y_value"); + auto zero = ConstantR0(&builder, 0.0); + Clamp(zero, y_value, ConstantR0(&builder, 5)); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); clamp = computation_status.ConsumeValueOrDie(); @@ -391,13 +351,13 @@ XLA_TEST_F(VecOpsSimpleTest, MapTenValues) { // mult_relu_add(z) = clamp(add_half(2 * max(z, 0))) XlaBuilder builder("mult_relu_add"); auto z_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "z_value"); - auto zero = builder.ConstantR0(0.0); - auto two = builder.ConstantR0(2.0); - auto max = builder.Max(z_value, zero); - auto mult = builder.Mul(two, max); - auto inner = builder.Map({mult}, add_half, {}); - builder.Map({inner}, clamp, {}); + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "z_value"); + auto zero = ConstantR0(&builder, 0.0); + auto two = ConstantR0(&builder, 2.0); + auto max = Max(z_value, zero); + auto mult = Mul(two, max); + auto inner = Map(&builder, {mult}, add_half, {}); + Map(&builder, {inner}, clamp, {}); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); mult_relu_add = computation_status.ConsumeValueOrDie(); @@ -405,9 +365,9 @@ XLA_TEST_F(VecOpsSimpleTest, MapTenValues) { XlaBuilder builder("map10"); { - auto x = builder.ConstantR1( - {2.1, -21.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); - auto activations = builder.Map({x}, mult_relu_add, {0}); + auto x = ConstantR1( + &builder, {2.1, -21.6, 2.6, -4.0, 2.1, 2.3, -5.0, -0.9, -2.4, 1.6}); + Map(&builder, {x}, mult_relu_add, {0}); } std::vector expected = {4.7, 0.5, 5.0, 0.5, 4.7, @@ -417,9 +377,9 @@ XLA_TEST_F(VecOpsSimpleTest, MapTenValues) { XLA_TEST_F(VecOpsSimpleTest, RemainderTenValuesS32) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({-5, -4, -3, -2, -1, 0, 1, 2, 3, 4}); - auto y = builder.ConstantR0(3); - builder.Rem(x, y); + auto x = ConstantR1(&builder, {-5, -4, -3, -2, -1, 0, 1, 2, 3, 4}); + auto y = ConstantR0(&builder, 3); + Rem(x, y); std::vector expected = {-2, -1, 0, -2, -1, 0, 1, 2, 0, 1}; ComputeAndCompareR1(&builder, expected, {}); @@ -427,9 +387,9 @@ XLA_TEST_F(VecOpsSimpleTest, RemainderTenValuesS32) { XLA_TEST_F(VecOpsSimpleTest, VectorPredicateEqual) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({false, true}); - auto y = builder.ConstantR1({true, false}); - builder.Eq(x, y); + auto x = ConstantR1(&builder, {false, true}); + auto y = ConstantR1(&builder, {true, false}); + Eq(x, y); std::array expected = {{false, false}}; ComputeAndCompareR1(&builder, expected, {}); @@ -437,9 +397,9 @@ XLA_TEST_F(VecOpsSimpleTest, VectorPredicateEqual) { XLA_TEST_F(VecOpsSimpleTest, VectorPredicateNotEqual) { XlaBuilder builder(TestName()); - auto x = builder.ConstantR1({false, true}); - auto y = builder.ConstantR1({true, false}); - builder.Ne(x, y); + auto x = ConstantR1(&builder, {false, true}); + auto y = ConstantR1(&builder, {true, false}); + Ne(x, y); std::array expected = {{true, true}}; ComputeAndCompareR1(&builder, expected, {}); diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index c463f3eac55e5b8ab32dc52d5a38e7840241bc58..bbd67cd8d7c433550deefc38ce28b2b732d354aa 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -55,8 +55,8 @@ TEST_F(WhileTest, WhileWithScalarS32Result) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Gt(builder.ConstantR0(5), prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Gt(ConstantR0(&builder, 5), prev); condition = builder.Build().ConsumeValueOrDie(); } @@ -64,16 +64,16 @@ TEST_F(WhileTest, WhileWithScalarS32Result) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR0(1); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR0(&builder, 1); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.ConstantR0(0); - builder.While(condition, body, init); + auto init = ConstantR0(&builder, 0); + While(condition, body, init); ComputeAndCompareR0(&builder, 5, {}); } @@ -91,8 +91,8 @@ TEST_F(WhileTest, WhileWithScalarS64Result) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Gt(builder.ConstantR0(5), prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Gt(ConstantR0(&builder, 5), prev); condition = builder.Build().ConsumeValueOrDie(); } @@ -100,16 +100,16 @@ TEST_F(WhileTest, WhileWithScalarS64Result) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR0(1); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR0(&builder, 1); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.ConstantR0(0); - builder.While(condition, body, init); + auto init = ConstantR0(&builder, 0); + While(condition, body, init); ComputeAndCompareR0(&builder, 5, {}); } @@ -122,8 +122,8 @@ TEST_F(WhileTest, WhileWithScalarResultNonConstInit) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Gt(builder.ConstantR0(5), prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Gt(ConstantR0(&builder, 5), prev); condition = builder.Build().ConsumeValueOrDie(); } @@ -131,18 +131,18 @@ TEST_F(WhileTest, WhileWithScalarResultNonConstInit) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR0(1); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR0(&builder, 1); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.Reduce(builder.ConstantR1(2, 1), - builder.ConstantR0(0), - CreateScalarAddComputation(S32, &builder), {0}); - builder.While(condition, body, init); + auto init = + Reduce(ConstantR1(&builder, 2, 1), ConstantR0(&builder, 0), + CreateScalarAddComputation(S32, &builder), {0}); + While(condition, body, init); ComputeAndCompareR0(&builder, 5, {}); } @@ -154,8 +154,8 @@ TEST_F(WhileTest, WhileWithPredicateResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Ne(builder.ConstantR0(true), prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Ne(ConstantR0(&builder, true), prev); condition = builder.Build().ConsumeValueOrDie(); } @@ -163,16 +163,16 @@ TEST_F(WhileTest, WhileWithPredicateResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Or(prev, builder.ConstantR0(true)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Or(prev, ConstantR0(&builder, true)); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.Ne(builder.ConstantR0(false), - builder.ConstantR0(true)); - builder.While(condition, body, init); + auto init = + Ne(ConstantR0(&builder, false), ConstantR0(&builder, true)); + While(condition, body, init); ComputeAndCompareR0(&builder, true, {}); } @@ -184,17 +184,16 @@ TEST_F(WhileTest, WhileWithPredicateResult) { // while (result.sum() < 15.5f) { // result = result + vector(0); // } -// TODO(b/29185393): does not terminate on CPU. -TEST_F(WhileTest, DISABLED_WhileWithEmptyVectorResult) { +TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithEmptyVectorResult)) { Shape result_shape = ShapeUtil::MakeShape(F32, {0}); // Create a computation for the reduction. XlaComputation add; { XlaBuilder builder("add"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Add(x, y); add = builder.Build().ConsumeValueOrDie(); } @@ -203,10 +202,10 @@ TEST_F(WhileTest, DISABLED_WhileWithEmptyVectorResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto sum = builder.Reduce(prev, builder.ConstantR0(0.0f), add, - /*dimensions_to_reduce=*/{0}); - builder.Gt(builder.ConstantR0(15.5f), sum); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto sum = Reduce(prev, ConstantR0(&builder, 0.0f), add, + /*dimensions_to_reduce=*/{0}); + Gt(ConstantR0(&builder, 15.5f), sum); condition = builder.Build().ConsumeValueOrDie(); } @@ -215,16 +214,16 @@ TEST_F(WhileTest, DISABLED_WhileWithEmptyVectorResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR1({}); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR1(&builder, {}); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.ConstantR1({}); - auto result = builder.While(condition, body, init); + auto init = ConstantR1(&builder, {}); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -246,9 +245,9 @@ TEST_F(WhileTest, WhileWithVectorResult) { XlaComputation add; { XlaBuilder builder("add"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Add(x, y); add = builder.Build().ConsumeValueOrDie(); } @@ -257,10 +256,10 @@ TEST_F(WhileTest, WhileWithVectorResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto sum = builder.Reduce(prev, builder.ConstantR0(0.0f), add, - /*dimensions_to_reduce=*/{0}); - builder.Gt(builder.ConstantR0(15.5f), sum); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto sum = Reduce(prev, ConstantR0(&builder, 0.0f), add, + /*dimensions_to_reduce=*/{0}); + Gt(ConstantR0(&builder, 15.5f), sum); condition = builder.Build().ConsumeValueOrDie(); } @@ -269,16 +268,16 @@ TEST_F(WhileTest, WhileWithVectorResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR1(8, 0.125f); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR1(&builder, 8, 0.125f); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.ConstantR1(8, 0.f); - auto result = builder.While(condition, body, init); + auto init = ConstantR1(&builder, 8, 0.f); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -306,9 +305,9 @@ TEST_F(WhileTest, WhileWithVectorResultIntoTuple) { XlaComputation add; { XlaBuilder builder("add"); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); - builder.Add(x, y); + auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {}), "y"); + Add(x, y); add = builder.Build().ConsumeValueOrDie(); } @@ -317,10 +316,10 @@ TEST_F(WhileTest, WhileWithVectorResultIntoTuple) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto sum = builder.Reduce(prev, builder.ConstantR0(0.0f), add, - /*dimensions_to_reduce=*/{0}); - builder.Gt(builder.ConstantR0(15.5f), sum); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto sum = Reduce(prev, ConstantR0(&builder, 0.0f), add, + /*dimensions_to_reduce=*/{0}); + Gt(ConstantR0(&builder, 15.5f), sum); condition = builder.Build().ConsumeValueOrDie(); } @@ -329,20 +328,20 @@ TEST_F(WhileTest, WhileWithVectorResultIntoTuple) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR1(8, 0.125f); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR1(&builder, 8, 0.125f); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.ConstantR1(8, 0.f); - auto result = builder.While(condition, body, init); + auto init = ConstantR1(&builder, 8, 0.f); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - builder.Tuple({result}); + Tuple(&builder, {result}); // Individual elements with increase by 1/8 each time through the loop, so // the sum will increase by 1.0. It will first be >15.5 when the elements @@ -366,9 +365,9 @@ TEST_F(WhileTest, WhileWithPermutationAndTupleResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(N), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, N), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -377,22 +376,23 @@ TEST_F(WhileTest, WhileWithPermutationAndTupleResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto w1 = builder.GetTupleElement(prev, 1); - auto w2 = builder.GetTupleElement(prev, 2); - auto w3 = builder.GetTupleElement(prev, 3); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), w3, w1, w2}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto w1 = GetTupleElement(prev, 1); + auto w2 = GetTupleElement(prev, 2); + auto w3 = GetTupleElement(prev, 3); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), w3, w1, w2}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(3, 1.f), - builder.ConstantR1(3, 2.f), builder.ConstantR1(3, 3.f)}); - auto result = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 3, 1.f), + ConstantR1(&builder, 3, 2.f), + ConstantR1(&builder, 3, 3.f)}); + auto result = While(condition, body, init); VLOG(2) << "result = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -419,9 +419,9 @@ TEST_F(WhileTest, WhileWithPermutationAndVectorResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(N), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, N), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -430,26 +430,27 @@ TEST_F(WhileTest, WhileWithPermutationAndVectorResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto w1 = builder.GetTupleElement(prev, 1); - auto w2 = builder.GetTupleElement(prev, 2); - auto w3 = builder.GetTupleElement(prev, 3); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), w3, w1, w2}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto w1 = GetTupleElement(prev, 1); + auto w2 = GetTupleElement(prev, 2); + auto w3 = GetTupleElement(prev, 3); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), w3, w1, w2}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(3, 1.f), - builder.ConstantR1(3, 2.f), builder.ConstantR1(3, 3.f)}); - auto xla_while = builder.While(condition, body, init); - - auto add12 = builder.Add(builder.GetTupleElement(xla_while, 1), - builder.GetTupleElement(xla_while, 2)); - auto result = builder.Add(add12, builder.GetTupleElement(xla_while, 3)); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 3, 1.f), + ConstantR1(&builder, 3, 2.f), + ConstantR1(&builder, 3, 3.f)}); + auto xla_while = While(condition, body, init); + + auto add12 = + Add(GetTupleElement(xla_while, 1), GetTupleElement(xla_while, 2)); + auto result = Add(add12, GetTupleElement(xla_while, 3)); VLOG(2) << "result = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -474,9 +475,9 @@ TEST_F(WhileTest, WhileWithTupleResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(5), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, 5), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -486,21 +487,21 @@ TEST_F(WhileTest, WhileWithTupleResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto weights = builder.GetTupleElement(prev, 1); - auto input = builder.ConstantR1(10, 1.f); - auto new_weights = builder.Add(weights, input); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_weights}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto weights = GetTupleElement(prev, 1); + auto input = ConstantR1(&builder, 10, 1.f); + auto new_weights = Add(weights, input); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), new_weights}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(10, 0.f)}); - auto result = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 10, 0.f)}); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -524,9 +525,9 @@ TEST_F(WhileTest, WhileWithPredicateTupleResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(5), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, 5), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -535,21 +536,20 @@ TEST_F(WhileTest, WhileWithPredicateTupleResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto pred = builder.GetTupleElement(prev, 1); - auto new_pred = builder.Or(pred, builder.ConstantR0(true)); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_pred}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto pred = GetTupleElement(prev, 1); + auto new_pred = Or(pred, ConstantR0(&builder, true)); + Tuple(&builder, {Add(iteration, ConstantR0(&builder, 1)), new_pred}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple({builder.ConstantR0(0), - builder.Ne(builder.ConstantR0(false), - builder.ConstantR0(true))}); - auto result = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + Ne(ConstantR0(&builder, false), + ConstantR0(&builder, true))}); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -571,9 +571,9 @@ TEST_F(WhileTest, WhileWithTupleConstantScalarResult) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(5), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, 5), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -583,18 +583,18 @@ TEST_F(WhileTest, WhileWithTupleConstantScalarResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Tuple({builder.Add(iteration, builder.ConstantR0(1)), - builder.ConstantR0(7)}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Tuple(&builder, {Add(iteration, ConstantR0(&builder, 1)), + ConstantR0(&builder, 7)}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR0(7)}); - auto result = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR0(&builder, 7)}); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -632,9 +632,9 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) { const int c1 = 5; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c1)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c1)); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } @@ -642,9 +642,9 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) { const int c2 = 7; { XlaBuilder builder("condition2"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c2)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c2)); TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build()); } @@ -654,43 +654,43 @@ TEST_F(WhileTest, TwoWhileWithTupleResult) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto weights = builder.GetTupleElement(prev, 1); - auto input = builder.ConstantR1(10, 1.f); - auto new_weights = builder.Add(weights, input); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_weights}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto weights = GetTupleElement(prev, 1); + auto input = ConstantR1(&builder, 10, 1.f); + auto new_weights = Add(weights, input); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), new_weights}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } XlaComputation body2; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto weights = builder.GetTupleElement(prev, 1); - auto input = builder.ConstantR1(10, 1.f); - auto new_weights = builder.Add(weights, input); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_weights}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto weights = GetTupleElement(prev, 1); + auto input = ConstantR1(&builder, 10, 1.f); + auto new_weights = Add(weights, input); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), new_weights}); TF_ASSERT_OK_AND_ASSIGN(body2, builder.Build()); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(10, 0.f)}); - auto while1 = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 10, 0.f)}); + auto while1 = While(condition, body, init); - auto while2 = builder.While(condition2, body2, while1); + auto while2 = While(condition2, body2, while1); - auto while_result1 = builder.GetTupleElement(while1, 1); - auto while_result2 = builder.GetTupleElement(while2, 1); + auto while_result1 = GetTupleElement(while1, 1); + auto while_result2 = GetTupleElement(while2, 1); VLOG(2) << "while_result2 = " << ShapeUtil::HumanString( builder.GetShape(while_result2).ConsumeValueOrDie()); - auto result = builder.Add(while_result1, while_result2); + auto result = Add(while_result1, while_result2); VLOG(2) << "result = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -711,9 +711,9 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) { const int c1 = 5; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c1)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c1)); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } @@ -721,9 +721,9 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) { const int c2 = 7; { XlaBuilder builder("condition2"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c2)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c2)); TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build()); } @@ -733,30 +733,30 @@ TEST_F(WhileTest, TwoWhileLoopsAndSharedBody) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto weights = builder.GetTupleElement(prev, 1); - auto input = builder.ConstantR1(10, 1.f); - auto new_weights = builder.Add(weights, input); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_weights}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto weights = GetTupleElement(prev, 1); + auto input = ConstantR1(&builder, 10, 1.f); + auto new_weights = Add(weights, input); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), new_weights}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(10, 0.f)}); - auto while1 = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 10, 0.f)}); + auto while1 = While(condition, body, init); - auto while2 = builder.While(condition2, body, while1); + auto while2 = While(condition2, body, while1); - auto while_result1 = builder.GetTupleElement(while1, 1); - auto while_result2 = builder.GetTupleElement(while2, 1); + auto while_result1 = GetTupleElement(while1, 1); + auto while_result2 = GetTupleElement(while2, 1); VLOG(2) << "while_result2 = " << ShapeUtil::HumanString( builder.GetShape(while_result2).ConsumeValueOrDie()); - auto result = builder.Add(while_result1, while_result2); + auto result = Add(while_result1, while_result2); VLOG(2) << "result = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -778,9 +778,9 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) { const int c1 = 5; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c1)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c1)); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } @@ -788,9 +788,9 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) { const int c2 = 7; { XlaBuilder builder("condition2"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(c2)); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, c2)); TF_ASSERT_OK_AND_ASSIGN(condition2, builder.Build()); } @@ -800,29 +800,29 @@ TEST_F(WhileTest, DISABLED_ON_GPU(WhileLoopsWithSharedBodyAndInit)) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - auto weights = builder.GetTupleElement(prev, 1); - auto input = builder.ConstantR1(10, 1.f); - auto new_weights = builder.Add(weights, input); - builder.Tuple( - {builder.Add(iteration, builder.ConstantR0(1)), new_weights}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + auto weights = GetTupleElement(prev, 1); + auto input = ConstantR1(&builder, 10, 1.f); + auto new_weights = Add(weights, input); + Tuple(&builder, + {Add(iteration, ConstantR0(&builder, 1)), new_weights}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(10, 0.f)}); - auto while1 = builder.While(condition, body, init); - auto while2 = builder.While(condition2, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 10, 0.f)}); + auto while1 = While(condition, body, init); + auto while2 = While(condition2, body, init); - auto while_result1 = builder.GetTupleElement(while1, 1); - auto while_result2 = builder.GetTupleElement(while2, 1); + auto while_result1 = GetTupleElement(while1, 1); + auto while_result2 = GetTupleElement(while2, 1); VLOG(2) << "while_result2 = " << ShapeUtil::HumanString( builder.GetShape(while_result2).ConsumeValueOrDie()); - auto result = builder.Add(while_result1, while_result2); + auto result = Add(while_result1, while_result2); VLOG(2) << "result = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -844,9 +844,9 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Gt(builder.ConstantR0(5), iteration); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Gt(ConstantR0(&builder, 5), iteration); condition = builder.Build().ConsumeValueOrDie(); } @@ -856,29 +856,28 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); + auto prev = Parameter(&builder, 0, result_shape, "prev"); // TupleElement 0 - auto iteration = builder.GetTupleElement(prev, 0); - auto out0 = builder.Add(iteration, builder.ConstantR0(1)); + auto iteration = GetTupleElement(prev, 0); + auto out0 = Add(iteration, ConstantR0(&builder, 1)); // TupleElement 1 - auto input = builder.GetTupleElement(prev, 1); + auto input = GetTupleElement(prev, 1); // Update. - auto update = builder.ConvertElementType(builder.Broadcast(out0, {2}), F32); + auto update = ConvertElementType(Broadcast(out0, {2}), F32); // Starts = iteration * 2; - auto starts = builder.Reshape( - builder.Mul(iteration, builder.ConstantR0(2)), {1}); + auto starts = Reshape(Mul(iteration, ConstantR0(&builder, 2)), {1}); // UpdateSlice. - auto out1 = builder.DynamicUpdateSlice(input, update, starts); + auto out1 = DynamicUpdateSlice(input, update, starts); - builder.Tuple({out0, out1}); + Tuple(&builder, {out0, out1}); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder("while"); - auto init = builder.Tuple( - {builder.ConstantR0(0), builder.ConstantR1(10, 0.f)}); - auto result = builder.While(condition, body, init); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), + ConstantR1(&builder, 10, 0.f)}); + auto result = While(condition, body, init); VLOG(2) << "while = " << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); @@ -913,10 +912,9 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) { // Create a computation for the condition: repeat for count iterations. auto build_condition = [this, v6s32](int count) { XlaBuilder builder(TestName()); - auto prev = builder.Reshape( - builder.Slice(builder.Parameter(0, v6s32, "prev"), {0}, {1}, {1}), {0}, - {}); - builder.Gt(builder.ConstantR0(count), prev); + auto prev = Reshape( + Slice(Parameter(&builder, 0, v6s32, "prev"), {0}, {1}, {1}), {0}, {}); + Gt(ConstantR0(&builder, count), prev); return builder.Build().ConsumeValueOrDie(); }; @@ -924,22 +922,22 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, v6s32, "prev"); - auto inc = builder.ConcatInDim( - {builder.ConstantR1({1}), - builder.RngUniform(builder.ConstantR0(0), - builder.ConstantR0(100), - ShapeUtil::MakeShape(S32, {5}))}, - 0); - builder.Add(inc, prev); + auto prev = Parameter(&builder, 0, v6s32, "prev"); + auto inc = ConcatInDim(&builder, + {ConstantR1(&builder, {1}), + RngUniform(ConstantR0(&builder, 0), + ConstantR0(&builder, 100), + ShapeUtil::MakeShape(S32, {5}))}, + 0); + Add(inc, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. auto while_loop = [this, &body, build_condition](int count) { XlaBuilder builder(TestName()); - auto init = builder.ConstantR1({0, 0, 0, 0, 0, 0}); - builder.While(build_condition(count), body, init); + auto init = ConstantR1(&builder, {0, 0, 0, 0, 0, 0}); + While(build_condition(count), body, init); return builder.Build(); }; @@ -958,26 +956,23 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithTupleElement) { auto element_shape = ShapeUtil::MakeShape(F32, {2}); XlaBuilder outer("outer"); - auto p = outer.Parameter(0, element_shape, "param"); - auto t = outer.Tuple({p, outer.ConstantR1({1, 1})}); + auto p = Parameter(&outer, 0, element_shape, "param"); + auto t = Tuple(&outer, {p, ConstantR1(&outer, {1, 1})}); TF_ASSERT_OK_AND_ASSIGN(Shape tuple_shape, outer.GetShape(t)); XlaBuilder cond("cond"); - auto cond_t = cond.Parameter(0, tuple_shape, "t"); - TF_ASSERT_OK(Any(cond.Eq(cond.GetTupleElement(cond_t, 0), - cond.ConstantR1({42, 42})), - &cond) - .status()); + auto cond_t = Parameter(&cond, 0, tuple_shape, "t"); + Any(Eq(GetTupleElement(cond_t, 0), ConstantR1(&cond, {42, 42}))); XlaBuilder body("body"); - auto body_t = body.Parameter(0, tuple_shape, "t"); - auto e = body.GetTupleElement(body_t, 1); - body.Tuple({e, e}); + auto body_t = Parameter(&body, 0, tuple_shape, "t"); + auto e = GetTupleElement(body_t, 1); + Tuple(&body, {e, e}); TF_ASSERT_OK_AND_ASSIGN(auto cond_computation, cond.Build()); TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); - outer.While(cond_computation, body_computation, t); + While(cond_computation, body_computation, t); auto expected_element = Literal::CreateR1({1, 1}); auto expected = @@ -993,20 +988,19 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithBroadcast) { auto element_shape = ShapeUtil::MakeShape(F32, {2}); XlaBuilder outer("outer"); - auto p = outer.Parameter(0, element_shape, "param"); + auto p = Parameter(&outer, 0, element_shape, "param"); XlaBuilder cond("cond"); - auto cond_t = cond.Parameter(0, element_shape, "t"); - TF_ASSERT_OK( - Any(cond.Eq(cond_t, cond.ConstantR1({42, 42})), &cond).status()); + auto cond_t = Parameter(&cond, 0, element_shape, "t"); + Any(Eq(cond_t, ConstantR1(&cond, {42, 42}))); XlaBuilder body("body"); - auto body_t = body.Parameter(0, element_shape, "t"); - auto e = body.Broadcast(body.ConstantR0(1.0), {2}); + Parameter(&body, 0, element_shape, "t"); + Broadcast(ConstantR0(&body, 1.0), {2}); TF_ASSERT_OK_AND_ASSIGN(auto cond_computation, cond.Build()); TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); - outer.While(cond_computation, body_computation, p); + While(cond_computation, body_computation, p); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, @@ -1019,21 +1013,20 @@ TEST_F(WhileTest, WhileThatTurnsScalarParameterToTupleElement) { auto element_shape = ShapeUtil::MakeShape(F32, {}); XlaBuilder outer("outer"); - auto p = outer.Parameter(0, element_shape, "param"); + auto p = Parameter(&outer, 0, element_shape, "param"); XlaBuilder cond("cond"); - auto cond_t = cond.Parameter(0, element_shape, "t"); - cond.Eq(cond_t, cond.ConstantR0(42)); + auto cond_t = Parameter(&cond, 0, element_shape, "t"); + Eq(cond_t, ConstantR0(&cond, 42)); XlaBuilder body("body"); - auto body_t = body.Parameter(0, element_shape, "t"); - auto tuple = - body.Tuple({body_t, body.Add(body_t, body.ConstantR0(1))}); - auto e = body.GetTupleElement(tuple, 1); + auto body_t = Parameter(&body, 0, element_shape, "t"); + auto tuple = Tuple(&body, {body_t, Add(body_t, ConstantR0(&body, 1))}); + GetTupleElement(tuple, 1); TF_ASSERT_OK_AND_ASSIGN(auto cond_computation, cond.Build()); TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); - outer.While(cond_computation, body_computation, p); + While(cond_computation, body_computation, p); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, @@ -1056,25 +1049,23 @@ TEST_F(WhileTest, WhileWithMixedTupleElements) { XlaBuilder outer("outer"); auto p = - outer.Tuple({outer.ConstantR0(0), - outer.Parameter(0, ShapeUtil::MakeShape(S32, {}), "t")}); + Tuple(&outer, {ConstantR0(&outer, 0), + Parameter(&outer, 0, ShapeUtil::MakeShape(S32, {}), "t")}); XlaBuilder cond("cond"); - auto params = cond.Parameter(0, result_shape, "prev"); - auto cond_t = cond.Add(cond.GetTupleElement(params, 1), - cond.GetTupleElement(params, 0)); - cond.Lt(cond_t, cond.ConstantR0(30)); + auto params = Parameter(&cond, 0, result_shape, "prev"); + auto cond_t = Add(GetTupleElement(params, 1), GetTupleElement(params, 0)); + Lt(cond_t, ConstantR0(&cond, 30)); XlaBuilder body("body"); - auto body_t = body.Parameter(0, result_shape, "t"); + auto body_t = Parameter(&body, 0, result_shape, "t"); - auto tuple = body.Tuple( - {body.Add(body.GetTupleElement(body_t, 0), body.ConstantR0(1)), - body.Add(body.GetTupleElement(body_t, 1), body.ConstantR0(1))}); + Tuple(&body, {Add(GetTupleElement(body_t, 0), ConstantR0(&body, 1)), + Add(GetTupleElement(body_t, 1), ConstantR0(&body, 1))}); TF_ASSERT_OK_AND_ASSIGN(auto cond_computation, cond.Build()); TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); - outer.While(cond_computation, body_computation, p); + While(cond_computation, body_computation, p); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, @@ -1105,9 +1096,9 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { XlaComputation inner_condition; { XlaBuilder builder("inner_condition"); - auto params = builder.Parameter(0, inner_result_shape, "prev"); - auto i = builder.GetTupleElement(params, 0); - builder.Lt(i, builder.ConstantR0(7)); + auto params = Parameter(&builder, 0, inner_result_shape, "prev"); + auto i = GetTupleElement(params, 0); + Lt(i, ConstantR0(&builder, 7)); inner_condition = builder.Build().ConsumeValueOrDie(); } @@ -1116,8 +1107,8 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { XlaComputation outer_condition; { XlaBuilder builder("outer_condition"); - auto prev = builder.Parameter(0, outer_result_shape, "prev"); - builder.Lt(prev, builder.ConstantR0(30)); + auto prev = Parameter(&builder, 0, outer_result_shape, "prev"); + Lt(prev, ConstantR0(&builder, 30)); outer_condition = builder.Build().ConsumeValueOrDie(); } @@ -1126,12 +1117,12 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { XlaComputation inner_body; { XlaBuilder builder("inner_body"); - auto params = builder.Parameter(0, inner_result_shape, "prev"); - auto i = builder.GetTupleElement(params, 0); - auto result = builder.GetTupleElement(params, 1); - i = builder.Add(builder.ConstantR0(1), i); - result = builder.Add(builder.ConstantR0(2), result); - builder.Tuple({i, result}); + auto params = Parameter(&builder, 0, inner_result_shape, "prev"); + auto i = GetTupleElement(params, 0); + auto result = GetTupleElement(params, 1); + i = Add(ConstantR0(&builder, 1), i); + result = Add(ConstantR0(&builder, 2), result); + Tuple(&builder, {i, result}); inner_body = builder.Build().ConsumeValueOrDie(); } @@ -1139,17 +1130,17 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { XlaComputation outer_body; { XlaBuilder builder("outer_body"); - auto prev = builder.Parameter(0, outer_result_shape, "prev"); - auto init = builder.Tuple({builder.ConstantR0(0), prev}); - auto result = builder.While(inner_condition, inner_body, init); - builder.GetTupleElement(result, 1); + auto prev = Parameter(&builder, 0, outer_result_shape, "prev"); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), prev}); + auto result = While(inner_condition, inner_body, init); + GetTupleElement(result, 1); outer_body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.ConstantR0(0); - builder.While(outer_condition, outer_body, init); + auto init = ConstantR0(&builder, 0); + While(outer_condition, outer_body, init); ComputeAndCompareR0(&builder, 42, {}); } @@ -1167,8 +1158,8 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) { XlaComputation condition_callee; { XlaBuilder builder("condition_callee"); - auto prev = builder.Parameter(0, result_shape, "prev"); - builder.Tuple({builder.Gt(builder.ConstantR0(5), prev)}); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + Tuple(&builder, {Gt(ConstantR0(&builder, 5), prev)}); condition_callee = builder.Build().ConsumeValueOrDie(); } @@ -1176,9 +1167,9 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto result = builder.Call(condition_callee, {prev}); - builder.GetTupleElement(result, 0); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto result = Call(&builder, condition_callee, {prev}); + GetTupleElement(result, 0); condition = builder.Build().ConsumeValueOrDie(); } @@ -1186,16 +1177,16 @@ TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, result_shape, "prev"); - auto input = builder.ConstantR0(1); - builder.Add(input, prev); + auto prev = Parameter(&builder, 0, result_shape, "prev"); + auto input = ConstantR0(&builder, 1); + Add(input, prev); body = builder.Build().ConsumeValueOrDie(); } // Create a While node with computations for the condition and the body. XlaBuilder builder(TestName()); - auto init = builder.ConstantR0(0); - builder.While(condition, body, init); + auto init = ConstantR0(&builder, 0); + While(condition, body, init); ComputeAndCompareR0(&builder, 5, {}); } @@ -1210,30 +1201,30 @@ TEST_F(WhileTest, WhileWithLoopInvariantOperation) { XlaComputation condition; { XlaBuilder builder("condition"); - auto state = builder.Parameter(0, while_shape, "state"); - builder.Gt(builder.ConstantR0(5), builder.GetTupleElement(state, 0)); + auto state = Parameter(&builder, 0, while_shape, "state"); + Gt(ConstantR0(&builder, 5), GetTupleElement(state, 0)); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } XlaComputation body; { XlaBuilder builder("body"); - auto state = builder.Parameter(0, while_shape, "state"); - auto indvar = builder.GetTupleElement(state, 0); - auto input_0 = builder.GetTupleElement(state, 1); - auto input_1 = builder.GetTupleElement(state, 2); - auto output = builder.Tanh(builder.Dot(input_0, input_1)); - auto indvar_next = builder.Add(indvar, builder.ConstantR0(1)); - builder.Tuple({indvar_next, input_0, input_1, output}); + auto state = Parameter(&builder, 0, while_shape, "state"); + auto indvar = GetTupleElement(state, 0); + auto input_0 = GetTupleElement(state, 1); + auto input_1 = GetTupleElement(state, 2); + auto output = Tanh(Dot(input_0, input_1)); + auto indvar_next = Add(indvar, ConstantR0(&builder, 1)); + Tuple(&builder, {indvar_next, input_0, input_1, output}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } XlaBuilder builder(TestName()); - auto matrix_input = builder.Parameter(0, matrix_shape, "matrix"); - auto init = builder.Tuple( - {builder.ConstantR0(0), matrix_input, matrix_input, matrix_input}); - auto while_instruction = builder.While(condition, body, init); - builder.GetTupleElement(while_instruction, 3); + auto matrix_input = Parameter(&builder, 0, matrix_shape, "matrix"); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), matrix_input, + matrix_input, matrix_input}); + auto while_instruction = While(condition, body, init); + GetTupleElement(while_instruction, 3); TF_ASSERT_OK_AND_ASSIGN(auto param_value, client_->TransferToServer(*Literal::CreateR2( @@ -1264,9 +1255,9 @@ void BM_WhileLoop(int num_iters) { XlaComputation condition; { XlaBuilder builder("condition"); - auto prev = builder.Parameter(0, loop_state_shape, "prev"); - auto iteration = builder.GetTupleElement(prev, 0); - builder.Lt(iteration, builder.ConstantR0(loop_limit)); + auto prev = Parameter(&builder, 0, loop_state_shape, "prev"); + auto iteration = GetTupleElement(prev, 0); + Lt(iteration, ConstantR0(&builder, loop_limit)); condition = builder.Build().ConsumeValueOrDie(); } @@ -1274,29 +1265,29 @@ void BM_WhileLoop(int num_iters) { XlaComputation body; { XlaBuilder builder("body"); - auto prev = builder.Parameter(0, loop_state_shape, "prev"); + auto prev = Parameter(&builder, 0, loop_state_shape, "prev"); // TupleElement 0 - auto iteration = builder.GetTupleElement(prev, 0); - auto out0 = builder.Add(iteration, builder.ConstantR0(1)); + auto iteration = GetTupleElement(prev, 0); + auto out0 = Add(iteration, ConstantR0(&builder, 1)); // TupleElement 1 - auto input = builder.GetTupleElement(prev, 1); + auto input = GetTupleElement(prev, 1); // Update. - auto one = builder.ConstantR0(1.0); - auto update = builder.Broadcast(one, {1, 1024, 1024}); + auto one = ConstantR0(&builder, 1.0); + auto update = Broadcast(one, {1, 1024, 1024}); // Starts = iteration * 2; - auto starts = builder.ConstantR1({0, 0, 0}); + auto starts = ConstantR1(&builder, {0, 0, 0}); // UpdateSlice. - auto out1 = builder.DynamicUpdateSlice(input, update, starts); - builder.Tuple({out0, out1}); + auto out1 = DynamicUpdateSlice(input, update, starts); + Tuple(&builder, {out0, out1}); body = builder.Build().ConsumeValueOrDie(); } // Create a While instruction. XlaBuilder builder("while"); - auto zero = builder.ConstantR0(0.0); - auto input = builder.Broadcast(zero, {seq_len, 1024, 1024}); - auto init = builder.Tuple({builder.ConstantR0(0), input}); - builder.While(condition, body, init); + auto zero = ConstantR0(&builder, 0.0); + auto input = Broadcast(zero, {seq_len, 1024, 1024}); + auto init = Tuple(&builder, {ConstantR0(&builder, 0), input}); + While(condition, body, init); auto computation = builder.Build().ConsumeValueOrDie(); std::unique_ptr executable = diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 3c9a01653c67203cbc962a3d3d967142f7a2102c..7dba058d407758b42365c3b6883e5e0891e1ab6c 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -128,20 +128,23 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, se::StreamExecutor* executor = backend->default_stream_executor(); DeviceMemoryAllocator* allocator = backend->memory_allocator(); auto* transfer_manager = backend->transfer_manager(); + TF_ASSERT_OK_AND_ASSIGN( + Backend::StreamPtr stream_ptr, + backend->BorrowStream(backend->default_device_ordinal())); TF_ASSERT_OK_AND_ASSIGN( ScopedShapedBuffer lhs_arg, transfer_manager->AllocateScopedShapedBuffer( lhs_arg_shape, allocator, backend->default_device_ordinal())); TF_ASSERT_OK(transfer_manager->TransferLiteralToDevice( - executor, *Literal::CreateFromShape(lhs_arg_shape), lhs_arg)); + stream_ptr.get(), *Literal::CreateFromShape(lhs_arg_shape), lhs_arg)); TF_ASSERT_OK_AND_ASSIGN( ScopedShapedBuffer rhs_arg, transfer_manager->AllocateScopedShapedBuffer( rhs_arg_shape, allocator, backend->default_device_ordinal())); TF_ASSERT_OK(transfer_manager->TransferLiteralToDevice( - executor, *Literal::CreateFromShape(rhs_arg_shape), rhs_arg)); + stream_ptr.get(), *Literal::CreateFromShape(rhs_arg_shape), rhs_arg)); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr local_executable, @@ -153,9 +156,6 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, &executable->hlo_profile_printer_data(), &executable->hlo_profile_index_map()); - TF_ASSERT_OK_AND_ASSIGN( - Backend::StreamPtr stream_ptr, - backend->BorrowStream(backend->default_device_ordinal())); ExecutableRunOptions exec_run_options; exec_run_options.set_stream(stream_ptr.get()); exec_run_options.set_allocator(backend->memory_allocator()); @@ -168,6 +168,7 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, auto execution_result, executable->ExecuteOnStream(&run_options, {&lhs_arg, &rhs_arg}, &hlo_execution_profile)); + TF_ASSERT_OK(stream_ptr->BlockHostUntilDone()); (void)execution_result; *profile_output = @@ -187,9 +188,9 @@ XLA_TEST_F(HloProfileTest, ProfileSingleComputation) { ClientLibrary::GetOrCreateLocalClient(platform)); XlaBuilder builder(TestName()); - auto result = builder.Tanh(builder.Add( - builder.Parameter(0, ShapeUtil::MakeShape(F32, {m, k}), "dot_lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(F32, {k, n}), "dot_rhs"))); + Tanh(Add( + Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {m, k}), "dot_lhs"), + Parameter(&builder, 1, ShapeUtil::MakeShape(F32, {k, n}), "dot_rhs"))); TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build()); @@ -239,9 +240,7 @@ XLA_TEST_F(HloProfileTest, ProfileSingleComputation) { EXPECT_TRUE(HasTrops(tanh_profile)); } -// TODO(b/71544591): The GPU backend does not record cycles spent in on Hlo -// instructions "interior" to while nodes. -XLA_TEST_F(HloProfileTest, DISABLED_ON_GPU(ProfileWhileComputation)) { +XLA_TEST_F(HloProfileTest, ProfileWhileComputation) { const int64 size = 256; Shape matrix_shape = ShapeUtil::MakeShape(F32, {size, size}); Shape while_result_shape = @@ -255,30 +254,30 @@ XLA_TEST_F(HloProfileTest, DISABLED_ON_GPU(ProfileWhileComputation)) { XlaComputation condition; { XlaBuilder builder("condition"); - auto state = builder.Parameter(0, while_result_shape, "state"); - auto iteration = builder.GetTupleElement(state, 0); - builder.Gt(builder.ConstantR0(5), iteration); + auto state = Parameter(&builder, 0, while_result_shape, "state"); + auto iteration = GetTupleElement(state, 0); + Gt(ConstantR0(&builder, 5), iteration); TF_ASSERT_OK_AND_ASSIGN(condition, builder.Build()); } XlaComputation body; { XlaBuilder builder("body"); - auto state = builder.Parameter(0, while_result_shape, "state"); - auto matrix = builder.GetTupleElement(state, 1); - auto next_iteration = builder.Add(builder.GetTupleElement(state, 0), - builder.ConstantR0(1)); - builder.Tuple({next_iteration, builder.Add(matrix, matrix)}); + auto state = Parameter(&builder, 0, while_result_shape, "state"); + auto matrix = GetTupleElement(state, 1); + auto next_iteration = + Add(GetTupleElement(state, 0), ConstantR0(&builder, 1)); + Tuple(&builder, {next_iteration, Add(matrix, matrix)}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } XlaBuilder builder(TestName()); auto initial_while_state = - builder.Tuple({builder.ConstantR0(0), - builder.Parameter(0, matrix_shape, "initial_value")}); - auto while_result = builder.While(condition, body, initial_while_state); - builder.Add(builder.GetTupleElement(while_result, 1), - builder.Parameter(1, matrix_shape, "other_value")); + Tuple(&builder, {ConstantR0(&builder, 0), + Parameter(&builder, 0, matrix_shape, "initial_value")}); + auto while_result = While(condition, body, initial_while_state); + Add(GetTupleElement(while_result, 1), + Parameter(&builder, 1, matrix_shape, "other_value")); TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build()); @@ -336,8 +335,11 @@ static std::pair AddXlaHloProfileFlag(int argc, char** argv) { new_argv[argc] = strdup("--xla_hlo_profile"); // Fusion can change the Hlo instructions that show up in the final Hlo - // executable, so block it here. - new_argv[argc + 1] = strdup("--xla_disable_hlo_passes=fusion"); + // executable, so block it here. Also block the WhileLoopInvariantCodeMotion + // pass, otherwise a while loop is transformed and we could not match the + // original name in the ProfileWhileComputation test. + new_argv[argc + 1] = strdup( + "--xla_disable_hlo_passes=fusion,while-loop-invariant-code-motion"); return {argc + 2, new_argv}; } diff --git a/tensorflow/compiler/xla/tests/xla_internal_test_main.cc b/tensorflow/compiler/xla/tests/xla_internal_test_main.cc index a9f2915b458b1816926de727b3da21982d06f6c0..a075195618c42aaa11f7b1c17730e67889a2c308 100644 --- a/tensorflow/compiler/xla/tests/xla_internal_test_main.cc +++ b/tensorflow/compiler/xla/tests/xla_internal_test_main.cc @@ -49,6 +49,7 @@ GTEST_API_ int main(int argc, char** argv) { } // Unfortunately Google's internal benchmark infrastructure has a // different API than Tensorflow's. + testing::InitGoogleTest(&argc, argv); #if defined(PLATFORM_GOOGLE) base::SetFlag(&FLAGS_benchmarks, pattern); RunSpecifiedBenchmarks(); diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD index ff5340ee3fac51288eef43962ac6427cab64bc54..e4a052c8f1c0009619c3a94606f6384d04006e4e 100644 --- a/tensorflow/compiler/xla/tools/BUILD +++ b/tensorflow/compiler/xla/tools/BUILD @@ -85,6 +85,7 @@ cc_library( "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client/lib:testing", + "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service/gpu:infeed_manager", "//tensorflow/compiler/xla/tests:test_utils", diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc index be094b7890aab08c55686c4785e01ff2ffba7cc2..3a7917cf3043de8a77f189f011bdeb3e8d2ddf3c 100644 --- a/tensorflow/compiler/xla/tools/replay_computation.cc +++ b/tensorflow/compiler/xla/tools/replay_computation.cc @@ -24,6 +24,9 @@ limitations under the License. // passing --use_fake_data on the command line. If the real data is available // in the proto and --use_fake_data is false, the real data is used. // +// Input can be a binary HloSnapshot proto, a binary HloProto proto, or a +// textual HLO string. +// // The output format is: // // file_path: computation_name :: type:literal_str @@ -43,6 +46,7 @@ limitations under the License. #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -170,6 +174,11 @@ StatusOr ReplayComputation(const HloSnapshot& module, client->Compile(computation, argument_layouts, ExecutableBuildOptions()) .ValueOrDie(); + // Do not attmept to run the executable, if num_runs is less than 1. + if (opts.num_runs < 1) { + return Cancelled("Cancelled after compilation since --num_runs < 1."); + } + // Run the computation num_runs times, and return the result from the last // execution. StreamExecutorMemoryAllocator allocator( @@ -187,33 +196,50 @@ StatusOr ReplayComputation(const HloSnapshot& module, << static_cast(profile.compute_time_ns()) / 1e9 << "s"; } - // Check that --num_runs > 0, otherwise *result below will fail with an - // unhelpful error (because the loop didn't run any iterations). - CHECK_GT(opts.num_runs, 0) << "--num_runs must be > 0"; TF_ASSIGN_OR_RETURN(std::unique_ptr result_literal, client->ShapedBufferToLiteral(*result)); return std::move(*result_literal); } +StatusOr ParseInputFile(const string& filename, + const Options& opts) { + tensorflow::Env* env = tensorflow::Env::Default(); + HloSnapshot snapshot; + if (tensorflow::ReadBinaryProto(env, filename, &snapshot).ok()) { + return snapshot; + } + CHECK(opts.use_fake_data) + << "Without --use_fake_data, you must pass an HloSnapshot -- HloProto " + "and textual HLO don't carry real data."; + fprintf(stderr, "%s: is not HloSnapshot. Trying HloProto.\n", + filename.c_str()); + + if (tensorflow::ReadBinaryProto(env, filename, snapshot.mutable_hlo()).ok()) { + return snapshot; + } + fprintf(stderr, "%s: is not HloProto. Trying HLO text.\n", filename.c_str()); + string contents; + TF_RETURN_IF_ERROR(tensorflow::ReadFileToString(env, filename, &contents)); + StatusOr> module = ParseHloString(contents); + if (module.ok()) { + *snapshot.mutable_hlo()->mutable_hlo_module() = + module.ValueOrDie()->ToProto(); + return snapshot; + } + fprintf(stderr, "%s: is not HLO text. Nothing left to try.\n", + filename.c_str()); + return InvalidArgument("Could not parse %s.", filename.c_str()); +} + int RealMain(tensorflow::gtl::ArraySlice args, const Options& opts) { LocalClient* client = ClientLibrary::LocalClientOrDie(); - tensorflow::Env* env = tensorflow::Env::Default(); int exit_status = EXIT_SUCCESS; for (char* arg : args) { - HloSnapshot snapshot; - auto status = tensorflow::ReadBinaryProto(env, arg, &snapshot); - if (!status.ok()) { - fprintf(stderr, "%s: is not HloSnapshot. Trying HloProto.\n", arg); - status = tensorflow::ReadBinaryProto(env, arg, snapshot.mutable_hlo()); - if (!status.ok()) { - fprintf(stderr, "%s: is not HloSnapshot or HloProto: %s.\n", arg, - status.ToString().c_str()); - continue; - } - CHECK(opts.use_fake_data) - << "HloProto input must be handled with --use_fake_data"; + StatusOr maybe_snapshot = ParseInputFile(arg, opts); + if (!maybe_snapshot.ok()) { + continue; } - + HloSnapshot snapshot = std::move(maybe_snapshot).ValueOrDie(); StatusOr result_status = ReplayComputation(snapshot, client, opts); if (!result_status.ok()) { fprintf(stderr, "%s: error: %s\n", arg, diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index b4f45cc972d3d397ddff8e8d9163d1fef387392f..b23b968aae6ed8d6fb2b9f61ea5db2690eb5246c 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/lib/math/math_util.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -533,12 +534,24 @@ c_count_if(const C& c, Pred&& pred) { return std::count_if(std::begin(c), std::end(c), std::forward(pred)); } +// Determines whether `value` is present in `c`. +template +bool c_linear_search(const C& c, T&& value) { + auto last = std::end(c); + return std::find(std::begin(c), last, std::forward(value)) != last; +} + template int64 FindIndex(const C& c, Value&& value) { auto it = c_find(c, std::forward(value)); return std::distance(c.begin(), it); } +template +bool ArrayContains(tensorflow::gtl::ArraySlice c, const T& value) { + return c_find(c, value) != c.end(); +} + template void InsertAt(C* c, int64 index, Value&& value) { c->insert(c->begin() + index, std::forward(value)); @@ -549,6 +562,12 @@ void EraseAt(C* c, int64 index) { c->erase(c->begin() + index); } +template +std::vector InlinedVectorToVector( + const tensorflow::gtl::InlinedVector& inlined_vector) { + return std::vector(inlined_vector.begin(), inlined_vector.end()); +} + // Returns true if `x` fits in 32-bits. template bool IsInt32(T x) { diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index 53ba120d21a9e16904d8c709617fc0eda6be63c4..6f07e4606bef015214f2c564515c8258a906205b 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -225,14 +225,6 @@ message ExecutionOptions { repeated DeviceHandle device_handles = 5; } -message SnapshotComputationRequest { - ComputationHandle computation = 1; -} - -message LoadComputationSnapshotResponse { - ComputationHandle computation = 1; -} - message GetDeviceHandlesRequest { int64 device_count = 1; } @@ -291,11 +283,6 @@ message ResetDeviceRequest { message ResetDeviceResponse { } -message ComputationStatsRequest { - ComputationHandle computation = 1; - DebugOptions debug_options = 2; -} - message ComputationGraphStatsRequest { HloModuleProto computation = 1; DebugOptions debug_options = 2; @@ -305,14 +292,6 @@ message ComputationStatsResponse { ComputationStats stats = 1; } -message ComputationRequest { - string name = 1; -} - -message ComputationResponse { - ComputationHandle computation = 1; -} - message CreateChannelHandleRequest { } @@ -327,24 +306,6 @@ message UnregisterRequest { message UnregisterResponse { } -message SetReturnValueRequest { - ComputationHandle computation = 1; - ComputationDataHandle operand = 2; -} - -message SetReturnValueResponse { -} - -message ExecuteRequest { - reserved 3, 4; - - ComputationHandle computation = 1; - repeated GlobalDataHandle arguments = 2; - - // Options that affect how XLA compiles and runs code to service this request. - ExecutionOptions execution_options = 5; -} - message ExecuteGraphRequest { HloModuleProto computation = 1; repeated GlobalDataHandle arguments = 2; @@ -353,10 +314,6 @@ message ExecuteGraphRequest { ExecutionOptions execution_options = 3; } -message ExecuteParallelRequest { - repeated ExecuteRequest requests = 1; -} - message ExecuteGraphParallelRequest { repeated ExecuteGraphRequest requests = 1; } @@ -370,21 +327,6 @@ message ExecuteParallelResponse { repeated ExecuteResponse responses = 1; } -message ExecuteAsyncRequest { - reserved 3, 4; - - ComputationHandle computation = 1; - repeated GlobalDataHandle arguments = 2; - - // Options that affect how XLA compiles and runs code to service this request. - ExecutionOptions execution_options = 6; -} - -message ExecuteAsyncResponse { - // A handle to the execution launched asynchronously. - ExecutionHandle execution = 1; -} - message WaitForExecutionRequest { ExecutionHandle execution = 1; } @@ -394,31 +336,13 @@ message WaitForExecutionResponse { ExecutionProfile profile = 2; } -message IsConstantRequest { - ComputationHandle computation = 1; - ComputationDataHandle operand = 2; - int64 num_parameters = 3; -} - -message IsConstantResponse { - bool is_constant = 1; -} - -message ComputeConstantRequest { - ComputationHandle computation = 1; - ComputationDataHandle operand = 2; - Layout output_layout = 3; - repeated LiteralProto parameters = 4; -} - message ComputeConstantGraphRequest { HloModuleProto computation = 1; Layout output_layout = 2; } message ComputeConstantResponse { - // A LiteralProto is returned directly for this request, instead of a - // ComputationDataHandle. + // A LiteralProto is returned directly for this request. LiteralProto literal = 1; } @@ -460,14 +384,6 @@ message LoadDataResponse { int64 nanoseconds = 5; } -message SpecializeRequest { - ComputationHandle computation = 1; - repeated GlobalDataHandle arguments = 2; -} - -message SpecializeResponse { -} - message GetShapeRequest { GlobalDataHandle data = 1; } @@ -476,14 +392,6 @@ message GetShapeResponse { Shape shape = 1; } -message GetComputationShapeRequest { - ComputationHandle computation = 1; -} - -message GetComputationShapeResponse { - ProgramShape program_shape = 1; -} - message UnpackRequest { GlobalDataHandle data = 1; } diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 6bdfb0179cd6a5e4eaee20cd877bd976e0e173c3..c7472173a705b7a6e1bee2f5221f23db0a77991d 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -274,12 +274,9 @@ message ExecutionProfile { // for the input data transfer since the memory is initialized with the proper // values before the execution. int64 compute_and_transfer_time_ns = 5; -} -// Handle given to a user that represents a computation that the user builds up -// before execution. -message ComputationHandle { - int64 handle = 1; + // The size of the binary code in the executable. + int64 executable_size_in_bytes = 6; } // Handle given to a user that represents an execution that the user launched @@ -295,13 +292,6 @@ message GlobalDataHandle { int64 handle = 1; } -// Handle given to a user that represents a data result in a computation. -// This is used to pass to subsequent computations that depends upon the data as -// an operand. -message ComputationDataHandle { - int64 handle = 1; -} - // Handle given to a user that represents a replicated virtual device. Each // replicated device represents N physical devices for execution where N is the // number of replicas. @@ -441,44 +431,6 @@ message GatherDimensionNumbers { int64 index_vector_dim = 4; } -// Operation requests that are all collected as a tagged union with a oneof -// field in OpRequest. - -message ConstantRequest { - LiteralProto literal = 2; -} - -message GetTupleElementRequest { - ComputationDataHandle operand = 2; - int64 index = 3; -} - -message SliceRequest { - ComputationDataHandle operand = 2; - repeated int64 start_indices = 3; - repeated int64 limit_indices = 4; - repeated int64 strides = 5; -} - -message DynamicSliceRequest { - // Operand from which to slice at dynamic 'start_indices'. - ComputationDataHandle operand = 2; - // Dynamically computed 'start_indices' for slice operation. - ComputationDataHandle start_indices = 3; - // Slice sizes for each dimension (note that indices calculations are computed - // modulo dimension sizes to avoid out-of-bound array accesses). - repeated int64 slice_sizes = 4; -} - -message DynamicUpdateSliceRequest { - // Operand on which slice 'update' is to be applied. - ComputationDataHandle operand = 2; - // The slice update to apply to 'operand'. - ComputationDataHandle update = 3; - // Dynamically computed start indices for the update slice operation. - ComputationDataHandle start_indices = 4; -} - message ConvolutionDimensionNumbers { // The number of the dimension that represents batch in the input. int64 input_batch_dimension = 7; @@ -516,13 +468,6 @@ message ConvolutionDimensionNumbers { // Next = 13 }; -message ConvolveRequest { - ComputationDataHandle lhs = 2; - ComputationDataHandle rhs = 3; // This is the filter/kernel. - Window window = 4; // Describes the filter/kernel. - ConvolutionDimensionNumbers dimension_numbers = 5; -} - enum FftType { FFT = 0; // Forward FFT; complex in, complex out. IFFT = 1; // Inverse FFT; complex in, complex out. @@ -531,56 +476,6 @@ enum FftType { // fft_length real out } -message FftRequest { - FftType fft_type = 1; - repeated int64 fft_length = 2; // Multivalent for higher-order FFT. - ComputationDataHandle operand = 3; -} - -message InfeedRequest { - // The shape of the data returned by reading the device's infeed buffer. - Shape shape = 2; - - // Additional infeed configuration for the backend. - bytes config = 3; -} - -message OutfeedRequest { - // The shape of the data returned by reading the device's outfeed buffer. - Shape shape = 1; - - // Operand to the Outfeed. Supports tuple. - ComputationDataHandle operand = 2; - - // Backend-specific information for how to perform the outfeed. - bytes outfeed_config = 3; -} - -message CallRequest { - ComputationHandle to_apply = 2; - repeated ComputationDataHandle operands = 3; -} - -message CustomCallRequest { - string call_target_name = 2; - repeated ComputationDataHandle operands = 3; - Shape shape = 4; -} - -message HostComputeRequest { - // Operand to the HostCompute. Supports tuple. - repeated ComputationDataHandle operands = 1; - - // Name used to identify HostSend/Recv channels. - string channel_name = 2; - - // Cost estimate in nanoseconds. - int64 cost_estimate_ns = 3; - - // The shape of any data returned by host. - Shape shape = 4; -} - message DotDimensionNumbers { // The dimension numbers that represent the 'lhs' contracting dimensions. repeated int64 lhs_contracting_dimensions = 1; @@ -592,297 +487,6 @@ message DotDimensionNumbers { repeated int64 rhs_batch_dimensions = 4; }; -message DotRequest { - ComputationDataHandle lhs = 2; - ComputationDataHandle rhs = 3; - DotDimensionNumbers dimension_numbers = 4; -} - -message MapRequest { - repeated ComputationDataHandle operands = 2; - ComputationHandle to_apply = 3; - repeated ComputationDataHandle static_operands = 4; - // The dimensions over which to map. - // Example mapping a Dot operation along the batch dimension 0: - // operand0.shape = [2, 2, 2], operand1.shape = [2,2,3] - // Map({operand0, operand1}, Dot, {0}) - repeated int64 dimensions = 5; -} - -message ReduceRequest { - // Operand to the reduction. - ComputationDataHandle operand = 2; - - // Initial value for the reduction. This must be consistent with the result - // shape of to_apply. - ComputationDataHandle init_value = 3; - - // The dimensions to reduce over. - repeated int64 dimensions = 4; - - // The computation to apply in the reduction. - ComputationHandle to_apply = 5; -} - -message ReduceWindowRequest { - ComputationDataHandle operand = 2; - ComputationDataHandle init_value = 3; - Window window = 4; - ComputationHandle to_apply = 5; -} - -message BatchNormTrainingRequest { - ComputationDataHandle operand = 1; - ComputationDataHandle scale = 2; - ComputationDataHandle offset = 3; - float epsilon = 4; - int64 feature_index = 5; -} - -message BatchNormInferenceRequest { - ComputationDataHandle operand = 1; - ComputationDataHandle scale = 2; - ComputationDataHandle offset = 3; - ComputationDataHandle mean = 4; - ComputationDataHandle variance = 5; - float epsilon = 6; - int64 feature_index = 7; -} - -message BatchNormGradRequest { - ComputationDataHandle operand = 1; - ComputationDataHandle scale = 2; - ComputationDataHandle mean = 3; - ComputationDataHandle variance = 4; - ComputationDataHandle grad_output = 5; - float epsilon = 6; - int64 feature_index = 7; -} - -message CrossReplicaSumRequest { - ComputationDataHandle operand = 2; -} - -message SelectAndScatterRequest { - // Operand array on which the windows slide. - ComputationDataHandle operand = 2; - - // Source array for the data to scatter. - ComputationDataHandle source = 3; - - // Initial scalar value for each element in the output. - ComputationDataHandle init_value = 4; - - // Window configuration. - Window window = 5; - - // Binary function used to select an element from each window. - ComputationHandle select = 6; - - // Binary function used to combine each scattered value from source with the - // current output value at the selected location. - ComputationHandle scatter = 7; -} - -message ReverseRequest { - ComputationDataHandle operand = 2; - repeated int64 dimensions = 3; -} - -message BroadcastRequest { - ComputationDataHandle operand = 2; - repeated int64 broadcast_sizes = 3; -} - -message PadRequest { - ComputationDataHandle operand = 2; - ComputationDataHandle padding_value = 3; - PaddingConfig padding_config = 4; -} - -message ReshapeRequest { - ComputationDataHandle operand = 2; - - // The dimension order for collapse (from fastest-changing to slowest). - repeated int64 dimensions = 3; - - // The new dimension sizes (from dimension 0 to n-1). - repeated int64 new_sizes = 4; -} - -message TransposeRequest { - ComputationDataHandle operand = 2; - - // The permutation of the operand's dimensions (in the range 0 to n-1). - repeated int64 dimensions = 3; -} - -message ParameterRequest { - Shape shape = 2; - int64 parameter = 3; - string name = 4; -} - -message GetLocalShapeRequest { - ComputationHandle computation = 1; - ComputationDataHandle operand = 2; -} - -message GetLocalShapeResponse { - Shape shape = 1; -} - -message TraceRequest { - string tag = 2; - ComputationDataHandle operand = 3; -} - -message ConvertRequest { - ComputationDataHandle operand = 2; - PrimitiveType new_element_type = 3; -} - -message ConcatenateRequest { - repeated ComputationDataHandle operands = 2; - // The dimension in which we concatenate; e.g. if you had dimension arrays of - // [4, 1] and [5, 1], you'd concatenate in dimension 0 to produce a [9, 1]. - // Attempting to concatenate those in dimension 1 would produce an error, as - // 4 != 5 (and there is no ragged array support). - int64 dimension = 3; -} - -message ConditionalRequest { - ComputationDataHandle predicate = 2; - ComputationDataHandle true_operand = 3; - ComputationHandle true_computation = 4; - ComputationDataHandle false_operand = 5; - ComputationHandle false_computation = 6; -} - -message WhileRequest { - ComputationHandle condition = 2; - ComputationHandle body = 3; - ComputationDataHandle init = 4; -} - -enum UnaryOperation { - UNOP_INVALID = 0; - - // Elementwise, logical negation on booleans and bitwise negation on ints. - UNOP_NOT = 1; - - // Elementwise, computes e^x. - UNOP_EXP = 2; - - // Elementwise, computes -x. - UNOP_NEGATE = 3; - - // Puts the elements in the operand into sorted order. - UNOP_SORT = 4; - - // Elementwise, computes tanh(x). - UNOP_TANH = 5; - - // Elementwise, computes the natural logarithm of x. - UNOP_LOG = 6; - - // Elementwise, computes the floor of x. - UNOP_FLOOR = 7; - - // Elementwise, computes the ceil of x. - UNOP_CEIL = 8; - - // Elementwise, computes the abs of x. - UNOP_ABS = 9; - - // Elementwise, computes the sign of x. - UNOP_SIGN = 10; - - // Elementwise, tests if values are finite (not NaN or inf) - UNOP_IS_FINITE = 11; - - // Elementwise, computes the cosine of x. - UNOP_COS = 12; - - // Elementwise, computes the sine of x. - UNOP_SIN = 13; - - // Elementwise, rounds x to nearest integral value, rounding half-way cases - // away from zero. - UNOP_ROUND_NEAREST_AFZ = 14; - - // Elementwise, extract real component of complex x. - UNOP_REAL = 15; - - // Elementwise, extract real component of complex x. - UNOP_IMAG = 16; - - // Elementwise, computes clz(x). - UNOP_CLZ = 17; - - // Elementwise, computes exp(x)-1. - UNOP_EXPM1 = 18; - - // Elementwise, computes log(x+1). - UNOP_LOG1P = 19; -} - -message UnaryOpRequest { - UnaryOperation unop = 2; - ComputationDataHandle operand = 3; -} - -enum BinaryOperation { - BINOP_INVALID = 0; - - // Arithmetic operations. - BINOP_ADD = 1; - BINOP_DIV = 2; - BINOP_MUL = 3; - BINOP_SUB = 4; - - // Comparison operators. - BINOP_EQ = 5; - BINOP_GE = 6; - BINOP_GT = 7; - BINOP_LE = 8; - BINOP_LT = 9; - BINOP_NE = 10; - - // Element-wise maximum. - BINOP_MAX = 14; - - // Element-wise minimum. - BINOP_MIN = 15; - - // Raises the left-hand-side to the right-hand-side power. - BINOP_POW = 16; - - // Remainder operation. - BINOP_REM = 17; - - // Element-wise, logical operators on booleans and bitwise operators on ints. - BINOP_AND = 18; - BINOP_OR = 19; - - BINOP_SHIFT_LEFT = 20; - BINOP_SHIFT_RIGHT_ARITHMETIC = 21; - BINOP_SHIFT_RIGHT_LOGICAL = 22; - - // Complex from real, imag. - BINOP_COMPLEX = 23; - - // Computes the 4-quadrant arctangent of the y, x input arguments. - BINOP_ATAN2 = 24; -} - -message BinaryOpRequest { - BinaryOperation binop = 2; - ComputationDataHandle lhs = 3; - ComputationDataHandle rhs = 4; - repeated int64 broadcast_dimensions = 5; -} - enum RandomDistribution { RNG_INVALID = 0; @@ -897,67 +501,6 @@ enum RandomDistribution { // Next: 4 } -message RngRequest { - RandomDistribution distribution = 2; - repeated ComputationDataHandle parameter = 3; - Shape shape = 4; -} - -enum TernaryOperation { - TRIOP_INVALID = 0; - - // Given a predicate and two operands, selects operand0 if the predicate is - // true and operand1 if the predicate is false. - TRIOP_SELECT = 1; - - // Given a min, max and an operand returns the operand if between min and max, - // else returns min if operand is less than min or max if operand is greater - // than max. - TRIOP_CLAMP = 3; -} - -message TernaryOpRequest { - TernaryOperation triop = 2; - ComputationDataHandle lhs = 3; - ComputationDataHandle rhs = 4; - ComputationDataHandle ehs = 5; -} - -enum VariadicOperation { - VAROP_INVALID = 0; - - // Creates a tuple from its operands. - VAROP_TUPLE = 1; -} - -message VariadicOpRequest { - VariadicOperation varop = 2; - repeated ComputationDataHandle operands = 3; -} - -message ReducePrecisionRequest { - ComputationDataHandle operand = 1; - int32 exponent_bits = 2; - int32 mantissa_bits = 3; -} - -message SendRequest { - ComputationDataHandle operand = 1; - ChannelHandle channel_handle = 2; -} - -message RecvRequest { - Shape shape = 1; - ChannelHandle channel_handle = 2; -} - -message GatherRequest { - ComputationDataHandle input = 1; - ComputationDataHandle gather_indices = 2; - GatherDimensionNumbers dimension_numbers = 3; - repeated int64 window_bounds = 4; -} - message OpSharding { enum Type { // This sharding is replicated across all devices (implies maximal, @@ -988,59 +531,3 @@ message OpSharding { // to. repeated OpSharding tuple_shardings = 5; } - -message OpRequest { - ComputationHandle computation = 1; - OpMetadata metadata = 33; - OpSharding sharding = 40; - - oneof op { - BinaryOpRequest binary_op_request = 2; - BroadcastRequest broadcast_request = 3; - CallRequest call_request = 4; - ConcatenateRequest concatenate_request = 5; - ConstantRequest constant_request = 6; - ConvertRequest convert_request = 7; - ConvolveRequest convolve_request = 8; - CrossReplicaSumRequest cross_replica_sum_request = 9; - CustomCallRequest custom_call_request = 10; - DotRequest dot_request = 43; - DynamicSliceRequest dynamic_slice_request = 11; - DynamicUpdateSliceRequest dynamic_update_slice_request = 12; - GetTupleElementRequest get_tuple_element_request = 13; - InfeedRequest infeed_request = 14; - MapRequest map_request = 15; - PadRequest pad_request = 16; - ParameterRequest parameter_request = 17; - ReducePrecisionRequest reduce_precision_request = 36; - ReduceRequest reduce_request = 18; - ReduceWindowRequest reduce_window_request = 19; - ReshapeRequest reshape_request = 20; - ReverseRequest reverse_request = 21; - RngRequest rng_request = 22; - SelectAndScatterRequest select_and_scatter_request = 23; - SliceRequest slice_request = 24; - TernaryOpRequest ternary_op_request = 25; - TraceRequest trace_request = 26; - TransposeRequest transpose_request = 34; - UnaryOpRequest unary_op_request = 27; - VariadicOpRequest variadic_op_request = 28; - WhileRequest while_request = 29; - SendRequest send_request = 30; - RecvRequest recv_request = 31; - OutfeedRequest outfeed_request = 32; - BatchNormTrainingRequest batch_norm_training_request = 35; - BatchNormGradRequest batch_norm_grad_request = 37; - BatchNormInferenceRequest batch_norm_inference_request = 38; - FftRequest fft_request = 41; - ConvertRequest bitcast_convert_request = 42; - ConditionalRequest conditional_request = 44; - HostComputeRequest host_compute_request = 45; - GatherRequest gather_request = 46; - // Next: 47 - } -} - -message OpResponse { - ComputationDataHandle output = 1; -} diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 50b1ae5cc3cba2d6ac89c4415a3419ffdf7aec93..c039624daa65174b0550ff6a304947e37cf58e1d 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -9,6 +9,7 @@ load("//third_party/mpi:mpi.bzl", "if_mpi") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") load("//tensorflow:tensorflow.bzl", "if_not_windows") +load("//tensorflow:tensorflow.bzl", "if_not_windows_cuda") py_library( name = "contrib_py", @@ -26,14 +27,12 @@ py_library( "//tensorflow/contrib/bayesflow:bayesflow_py", "//tensorflow/contrib/boosted_trees:init_py", "//tensorflow/contrib/checkpoint/python:checkpoint", - "//tensorflow/contrib/cloud:cloud_py", "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_py", "//tensorflow/contrib/coder:coder_py", "//tensorflow/contrib/compiler:compiler_py", "//tensorflow/contrib/autograph", "//tensorflow/contrib/constrained_optimization", - "//tensorflow/contrib/control_flow", "//tensorflow/contrib/copy_graph:copy_graph_py", "//tensorflow/contrib/crf:crf_py", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_py", @@ -46,7 +45,6 @@ py_library( "//tensorflow/contrib/factorization:factorization_py", "//tensorflow/contrib/feature_column:feature_column_py", "//tensorflow/contrib/framework:framework_py", - "//tensorflow/contrib/fused_conv:fused_conv_py", "//tensorflow/contrib/gan", "//tensorflow/contrib/graph_editor:graph_editor_py", "//tensorflow/contrib/grid_rnn:grid_rnn_py", @@ -115,6 +113,7 @@ py_library( "//tensorflow/contrib/training:training_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:util", + "//tensorflow/python/estimator:estimator_py", ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([ "//tensorflow/contrib/tensorrt:init_py", ]) + select({ @@ -123,7 +122,17 @@ py_library( "//tensorflow/contrib/kafka", ], "//conditions:default": [], - }) + if_not_windows([ + }) + select({ + "//tensorflow:with_aws_support_windows_override": [], + "//tensorflow:with_aws_support": [ + "//tensorflow/contrib/kinesis", + ], + "//conditions:default": [], + }) + if_not_windows_cuda([ + "//tensorflow/contrib/fused_conv:fused_conv_py", # unresolved symbols, need to export more symbols + ]) + if_not_windows([ + "//tensorflow/contrib/bigtable", # depends on bigtable + "//tensorflow/contrib/cloud:cloud_py", # doesn't compile on Windows "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code ]), @@ -154,6 +163,12 @@ cc_library( "//tensorflow/contrib/kafka:dataset_kernels", ], "//conditions:default": [], + }) + select({ + "//tensorflow:with_aws_support_windows_override": [], + "//tensorflow:with_aws_support": [ + "//tensorflow/contrib/kinesis:dataset_kernels", + ], + "//conditions:default": [], }), ) @@ -183,5 +198,11 @@ cc_library( "//tensorflow/contrib/kafka:dataset_ops_op_lib", ], "//conditions:default": [], + }) + select({ + "//tensorflow:with_aws_support_windows_override": [], + "//tensorflow:with_aws_support": [ + "//tensorflow/contrib/kinesis:dataset_ops_op_lib", + ], + "//conditions:default": [], }), ) diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index ad8c40395c2cdcc5e4288e04bb2115bd3627cdc9..ded05da71877566781a5fb6d0c21e1c8d43de9ed 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -25,12 +25,12 @@ import os from tensorflow.contrib import batching from tensorflow.contrib import bayesflow from tensorflow.contrib import checkpoint -from tensorflow.contrib import cloud +if os.name != "nt": + from tensorflow.contrib import cloud from tensorflow.contrib import cluster_resolver from tensorflow.contrib import coder from tensorflow.contrib import compiler from tensorflow.contrib import constrained_optimization -from tensorflow.contrib import control_flow from tensorflow.contrib import copy_graph from tensorflow.contrib import crf from tensorflow.contrib import cudnn_rnn diff --git a/tensorflow/contrib/android/BUILD b/tensorflow/contrib/android/BUILD index c10179ba8b290b6209f5567d6323df4bcf711585..f0b1c92cf7e4b760381da38febd9682ce2a4f27c 100644 --- a/tensorflow/contrib/android/BUILD +++ b/tensorflow/contrib/android/BUILD @@ -1,6 +1,8 @@ # Description: # JNI-based Java inference interface for TensorFlow. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/autograph/BUILD b/tensorflow/contrib/autograph/BUILD index 30dd846893c30b9205972bd5216cc1871ab03d76..ad700ac4a0342e2a7bc07a6ecf6710cea892e296 100644 --- a/tensorflow/contrib/autograph/BUILD +++ b/tensorflow/contrib/autograph/BUILD @@ -23,9 +23,9 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/autograph/impl", + "//tensorflow/contrib/autograph/lang", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/utils", - "@gast_archive//:gast", - "@six_archive//:six", + "//tensorflow/python:util", ], ) diff --git a/tensorflow/contrib/autograph/CONTRIBUTING.md b/tensorflow/contrib/autograph/CONTRIBUTING.md index a4aec8c74a9ad1418072471a5d3cde8c3b968a38..06fb7b03d5dbbfd2fcb6d6a2ecfe5c817f94a469 100644 --- a/tensorflow/contrib/autograph/CONTRIBUTING.md +++ b/tensorflow/contrib/autograph/CONTRIBUTING.md @@ -1,4 +1,4 @@ -# How to Contribute +# How to contribute We'd love to have your patches and contributions! Here are some guidelines. In general, we follow the [TensorFlow contributing guidelines](../../CONTRIBUTING.md), but have some [AutoGraph-specific style guidelines](STYLE_GUIDE.md). More details below. @@ -46,3 +46,50 @@ bazel test --config=opt --copt=-O3 --copt=-march=native \ ``` from the root of the `tensorflow` repository. For more details see the [main TensorFlow Contributing File](../../CONTRIBUTING.md) + +## Developer info + +### Module structure + +The graph below describes the dependencies between AutoGraph modules (not to be mistaken with the directory structure for these modules, which is flat): + +```dot +digraph d_modules { + autograph [style=filled]; + converters; + core; + impl; + lang; + operators; + + autograph -> impl + autograph -> lang + + impl -> converters + impl -> core + impl -> operators + + lang -> operators + + converters -> core + converters -> lang +} +``` + +`autograph` is the sole user-visible module. + +A short description of the modules: + + * `autograph`: the main module imported by the user and by the generated code; only contains declarations + * `impl`: high level code and the implementation of the api frontend + * `core`: base classes for the AutoGraph source code transformation logic; see in particular `converter.py` + * `lang`: special user-visible functions that serve as extensions to the Python language + * `converters`: collection of source code transformation modules specialized for particular AutoGraph features + * `operators`: collection of operators that AutoGraph overloads; these correspond to Python operators as well as Python syntactic structures, like control flow + +There are two additional modules, `pyct` and `utils`. These are independent of AutoGraph: + + * `pyct`: a general purpose Python source code transformation library + * `utils`: the kitchen sync; deprecated + +Note: we have a long term plan to factor out an implementation of `impl` and `converters` that is independent of autograph, into a general purpose Python operator overloading library. diff --git a/tensorflow/contrib/autograph/LIMITATIONS.md b/tensorflow/contrib/autograph/LIMITATIONS.md new file mode 100644 index 0000000000000000000000000000000000000000..d8b1cb7616ac348981bf2b69d6e2fd8d8a6e6b78 --- /dev/null +++ b/tensorflow/contrib/autograph/LIMITATIONS.md @@ -0,0 +1,50 @@ +# Capabilities and Limitations + +TF AutoGraph converts Eager Python code into TensorFlow graph-mode code. For example, users write code with `if` and `while` and AutoGraph automatically converts it into the equivalent `tf.cond`, and `tf.while_loop`. + +Python is a large language, so hoping to convert arbitrary Python code directly to TF graphs is overly ambitious. However, the Python code written to metaprogram TF graphs is in practice a restricted subset. We aim to support as much of this subset as possible. The table below lays out what we currently handle, what we hope to support, and what we have no plans to support. + +# Python Language Support Status + +Note: as more complex features in TensorFlow are made more accessible using AutoGraph, we expect to come across use cases that haven't been tried before, some of which might reveal rare bugs. If we do find any such bugs, we may add additional restrictions for the affected configurations, until those bugs are resolved. + + Construct | Supported now? | Plan to support? | Notes + :--------- | :--------------: | :----------------: | :----- +If statement | Yes | | Converts to `tf.cond`. If variables are created in one branch that donā€™t exist in another, which is inexpressible in TF, we throw a clear error. +For statement | Yes | | We will specialize `for` loops with unknown and known lengths, as well as for loops over TF datasets. Converts to `tf.while_loop`, with an additional `maximum_iterations` hint, if that is known. Creating variables inside the loop that are used later outside the loop is not supported, as the loop may have no iterations. +While statement | Yes | | Converts to `tf.while_loop`. Creating variables inside the loop is not supported, as the loop may have no iterations. +Continue and break | Yes | | Converts to boolean flags and extra predicates in loop tests. +Composition of control flow | Yes | | Arbitrary composition of `if`, `while`, `for`, `break`, and `continue`, along with other supported language elements, is supported and tested. +Iterators | Some | Yes | Not all iterators supported, but we plan to support everything that can be desugared, such as `enumerate` and `zip`. +Multiple return values | Yes | | We desugar them into variables, boolean flags and conditionals so that the function has a single return value at the end, and provide a clear error if we are unable to do so. +Print expression | Yes | | Wrapped in `PyFunc`, and given proper control dependencies. Optional support for using tf.Log when py_func is undesirable exists. +Static function calls | Yes | | Non-recursive function calls +Nested call trees | Yes | | For example, `f` calls `g` which calls `h`, all of which need conversion. +Recursive function calls | No | Maybe | Based on available support in TF. Currently `function.Defun` is the best candidate, but it is not reentrant. +Python built-ins | Some | Yes | `print`, `len`, `range`, `xrange`, `int`, `float` are supported, and we plan to support or clearly error on all [Python built-ins](https://docs.python.org/3/library/functions.html). +List operations | Yes | | We convert list creation, append, pop and indexing to their TF TensorArray equivalents. However, we do need some extra type hints to fully convert correctly. We hope to remove this limitation. +Function variables | Yes | | e.g. `f_new = f_orig; f_new()` +Lambda functions | No | Yes | Planned feature. +Classes | Yes | | Classes can be converted all at once, or method-by-method. Some limitations exist around static and class methods. +Subclasses | Yes | | Subclassing library objects like tf.keras.Model is also supported. +Dynamic types | Some | | `o = C1() if foo else C2(); o.bar()`. Some scenarios where types are data-dependent may not be supported. We will raise a meaningful error in that case. +Dynamic code / exec | No | | +Reflection | No | | +Try / Except | No | No | No current sane TF equivalent. +Global variables | Restricted | | In general, we only support read-only access to arguments or variables defined outside the converted code. A few exceptions include TensorFlow library code. +Functions with side effects | Some | | Side effects are allowed, under certain circumstances. +Collections | Some | Yes | We currently support lists. There are currently no TF equivalents of dictionaries or tuples. +List Comprehensions | Yes | | We desugar `ListComp` into the appropriate combination of `For` and `If` statements. Other comprehensions are currently very low priority. +Custom context managers | No | Yes | Currently low priority. Left unconverted currently. +Generators | No | Maybe | Could be achievable using queues; very low priority. +Assertions | Yes | | As `tf.Assert` +Deletion | Yes | Maybe | Currently unconverted. If new semanti cs are required for `del`, we are able to add it in. +Inline imports | No | Yes | For example, `import numpy as np; np.eye(3)`. Currently low priority. +Async | No | No | + +## Extra capabilities + + - We liberally add name scopes to generated functions + - Operations get decent default names everywhere (planned) + - Statements that have no output values are given correct control dependencies. For example, `for i in range(n): print(i)` will have control dependencies to ensure the `print` statements are executed serially. + diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 674859bed4ec157d5d5b33b6fc015c930e54b392..7e26f4711851138c1834f881621ebfa227a85821 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -4,7 +4,7 @@ IMPORTANT: AutoGraph is alpha software, and under active development. Expect rou AutoGraph is a Python to TensorFlow compiler. -With AutoGraph, you can write [Eager style](https://www.tensorflow.org/programmers_guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. +With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. For example, this Python function: @@ -120,3 +120,15 @@ You can use the functional API to inspect the generated code as well: print(ag.to_code(f)) # Output: ``` + +## Filing bugs and feature requests + +### Reporting a bug + + - If AutoGraph-generated code is compiling and running, but producing an incorrect result, send us a minimal reproduction case that includes the original Eager code, the inputs and if possible, the outputs or the error message. + - If AutoGraph-generated code is compiling, but not running, send us a minimal reproduction case that includes the original Eager code, the inputs and if possible, the outputs or the error message. + - If AutoGraph-generated code is not compiling, send us two minimal pieces of code. First, the Eager code that you would like to write, and second, the Graph code that you would like AutoGraph to have generated for you. + +### Requesting a feature + +If youā€™d like AutoGraph to convert a feature of Python or TF that we currently donā€™t handle, please let us know by filing a bug. Weā€™ll make it as easy as possible to interact with us through there. diff --git a/tensorflow/contrib/autograph/STYLE_GUIDE.md b/tensorflow/contrib/autograph/STYLE_GUIDE.md index 866e5f583a34570dfddc733f57561ed1d2b7c5bf..7e6b0cc27dd1cf8c0f459a0a34f98092728342a2 100644 --- a/tensorflow/contrib/autograph/STYLE_GUIDE.md +++ b/tensorflow/contrib/autograph/STYLE_GUIDE.md @@ -20,7 +20,17 @@ Naming conventions: Below are AutoGraph-specific conventions. In the event of conflict, it supercedes all previous conventions. -1. __Citations in Docstrings.__ Write a `#### References` subsection at the +1. __Types in docstrings.__ Use [PEP 484][https://www.python.org/dev/peps/pep-0484/] + notation to describe the type for args, return values and attributes. + + Example: + + ``` + Args: + foo: Dict[str, List[int]], a dictionary of sorts + ``` + +2. __Citations in Docstrings.__ Write a `#### References` subsection at the bottom of any docstring with citations. Use ICLRā€™s bibliography style to write references; for example, order entries by the first author's last name. Add a link to the paper if the publication is open source (ideally, @@ -60,12 +70,12 @@ it supercedes all previous conventions. https://arxiv.org/abs/1803.04386 ``` -2. Avoid LaTeX in docstrings. +3. Avoid LaTeX in docstrings. * It is not rendered in many (if not most) editors and can be hard to read for both LaTeX experts and non-experts. -3. Write docstring and comment math using ASCII friendly notation; python using +4. Write docstring and comment math using ASCII friendly notation; python using operators. E.g., `x**2` better than `x^2`, `x[i, j]` better than `x_{i,j}`, `sum{ f(x[i]) : i=1...n }` better than `\sum_{i=1}^n f(x_i)` `int{sin(x) dx: x in [0, 2 pi]}` better than `\int_0^{2\pi} sin(x) dx`. diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 79d73af98097aea418f2116aee40b2572b418ef7..361cf2d77c7e46912d5bff5881df2ffa897c5179 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -30,7 +30,9 @@ from tensorflow.contrib.autograph.impl.api import do_not_convert from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import to_code from tensorflow.contrib.autograph.impl.api import to_graph -from tensorflow.contrib.autograph.impl.special_functions import stack +from tensorflow.contrib.autograph.lang.directives import set_element_type +from tensorflow.contrib.autograph.lang.directives import set_loop_options +from tensorflow.contrib.autograph.lang.special_functions import stack from tensorflow.contrib.autograph.pyct.transformer import AutographParseError from tensorflow.python.util.all_util import remove_undocumented @@ -42,8 +44,11 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', - # Special functions and overloaded operators + # Overloaded operators 'operators', + # Python language "extensions" + 'set_element_type', + 'set_loop_options', 'stack', # Exceptions 'AutographParseError', diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index 8f9bffa55e44e4942bb3845945b3d440c7957cc9..b2e2e27673dafe290cef40a9fe0a834bfe1ea61f 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -31,29 +31,17 @@ py_library( "name_scopes.py", "side_effect_guards.py", "single_return.py", + "slices.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ - "@gast_archive//:gast", - ], -) - -py_library( - name = "test_lib", - srcs = [ - "converter_test_base.py", - ], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:__subpackages__"], - deps = [ - ":converters", - "//tensorflow/contrib/autograph/operators", + "//tensorflow/contrib/autograph/core", + "//tensorflow/contrib/autograph/lang", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/pyct/static_analysis", - "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:util", "@gast_archive//:gast", - "@six_archive//:six", ], ) @@ -63,7 +51,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -73,7 +62,8 @@ py_test( srcs = ["break_statements_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -84,7 +74,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -96,7 +87,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl", "//tensorflow/python:client_testlib", ], @@ -107,7 +99,8 @@ py_test( srcs = ["continue_statements_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -117,7 +110,8 @@ py_test( srcs = ["control_flow_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -126,8 +120,13 @@ py_test( name = "decorators_test", srcs = ["decorators_test.py"], srcs_version = "PY2AND3", + tags = [ + "no_pip", + "no_windows", + ], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -136,7 +135,8 @@ py_test( name = "name_scopes_test", srcs = ["name_scopes_test.py"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], @@ -147,7 +147,8 @@ py_test( srcs = ["list_comprehension_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -157,7 +158,8 @@ py_test( srcs = ["lists_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -167,7 +169,8 @@ py_test( srcs = ["logical_expressions_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -182,7 +185,8 @@ py_test( "notap", ], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -192,7 +196,8 @@ py_test( srcs = ["single_return_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], @@ -203,7 +208,20 @@ py_test( srcs = ["ifexp_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "slices_test", + srcs = ["slices_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], diff --git a/tensorflow/contrib/autograph/converters/asserts.py b/tensorflow/contrib/autograph/converters/asserts.py index 3b0db677ce5e417e7afea8d8fe4121a0352bb6d7..e664a403a5fb800e7d0dddfa5695330927aaf4e0 100644 --- a/tensorflow/contrib/autograph/converters/asserts.py +++ b/tensorflow/contrib/autograph/converters/asserts.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class AssertsTransformer(transformer.Base): +class AssertsTransformer(converter.Base): """Transforms Print nodes to Call so they can be handled as functions.""" def visit_Assert(self, node): @@ -45,5 +45,5 @@ class AssertsTransformer(transformer.Base): raise NotImplementedError('can only convert string messages for now.') -def transform(node, context): - return AssertsTransformer(context).visit(node) +def transform(node, ctx): + return AssertsTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/asserts_test.py b/tensorflow/contrib/autograph/converters/asserts_test.py index cc913febe8d0f411588af69b87ec52ce58f4469c..2cd0e626bc4552bd40bc94b890fdcc7efcafb3f3 100644 --- a/tensorflow/contrib/autograph/converters/asserts_test.py +++ b/tensorflow/contrib/autograph/converters/asserts_test.py @@ -21,11 +21,11 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.converters import asserts -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class AssertsTest(converter_test_base.TestCase): +class AssertsTest(converter_testing.TestCase): def test_transform(self): diff --git a/tensorflow/contrib/autograph/converters/break_statements.py b/tensorflow/contrib/autograph/converters/break_statements.py index 775d92c1d9f8bc35d1eda62f3f3ef7ee43414779..a990e359a2a25a57ee2a4f8a866350633f3b9ea8 100644 --- a/tensorflow/contrib/autograph/converters/break_statements.py +++ b/tensorflow/contrib/autograph/converters/break_statements.py @@ -18,9 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -29,7 +29,7 @@ BREAK_USED = 'break_used' CONTROL_VAR_NAME = 'control_var_name' -class BreakStatementTransformer(transformer.Base): +class BreakStatementTransformer(converter.Base): """Canonicalizes break statements into additional conditionals.""" def visit_Break(self, node): @@ -67,7 +67,7 @@ class BreakStatementTransformer(transformer.Base): def visit_While(self, node): scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - break_var = self.context.namer.new_symbol('break_', scope.referenced) + break_var = self.ctx.namer.new_symbol('break_', scope.referenced) node.test = self.visit(node.test) node.body, break_used = self._track_body(node.body, break_var) @@ -97,7 +97,7 @@ class BreakStatementTransformer(transformer.Base): def visit_For(self, node): scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - break_var = self.context.namer.new_symbol('break_', scope.referenced) + break_var = self.ctx.namer.new_symbol('break_', scope.referenced) node.target = self.visit(node.target) node.iter = self.visit(node.iter) @@ -137,5 +137,5 @@ class BreakStatementTransformer(transformer.Base): return node -def transform(node, context): - return BreakStatementTransformer(context).visit(node) +def transform(node, ctx): + return BreakStatementTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/break_statements_test.py b/tensorflow/contrib/autograph/converters/break_statements_test.py index 1af59e9b5260fe0d3a3ef72c7a003dc451e230f3..dcff1c54c2f9300d58d217517e108d634ae85fb4 100644 --- a/tensorflow/contrib/autograph/converters/break_statements_test.py +++ b/tensorflow/contrib/autograph/converters/break_statements_test.py @@ -19,11 +19,11 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import break_statements -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class BreakCanonicalizationTest(converter_test_base.TestCase): +class BreakCanonicalizationTest(converter_testing.TestCase): def test_basic_while(self): diff --git a/tensorflow/contrib/autograph/converters/builtin_functions.py b/tensorflow/contrib/autograph/converters/builtin_functions.py index 231e4ee35a72f51845a476d9f605986ac73b4676..b26c52294c2d1c11ce14d8a2903f7f88079a703f 100644 --- a/tensorflow/contrib/autograph/converters/builtin_functions.py +++ b/tensorflow/contrib/autograph/converters/builtin_functions.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class BuiltinFunctionTransformer(transformer.Base): +class BuiltinFunctionTransformer(converter.Base): """Handles builtin functions. This transformer only covers functions that are translated into a @@ -68,5 +68,5 @@ class BuiltinFunctionTransformer(transformer.Base): return self.visit(function_call) -def transform(node, context): - return BuiltinFunctionTransformer(context).visit(node) +def transform(node, ctx): + return BuiltinFunctionTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/builtin_functions_test.py b/tensorflow/contrib/autograph/converters/builtin_functions_test.py index 30272409df322560b04ba75b3e1cb6f9ad5ff0af..e9000e518ce14f9e0ea486d5b3e374439b8c78ca 100644 --- a/tensorflow/contrib/autograph/converters/builtin_functions_test.py +++ b/tensorflow/contrib/autograph/converters/builtin_functions_test.py @@ -23,13 +23,13 @@ import sys import six from tensorflow.contrib.autograph.converters import builtin_functions -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class BuiltinFunctionsTest(converter_test_base.TestCase): +class BuiltinFunctionsTest(converter_testing.TestCase): def test_len(self): diff --git a/tensorflow/contrib/autograph/converters/call_trees.py b/tensorflow/contrib/autograph/converters/call_trees.py index b6ecdcb7809b1ad7e7461324cb6a110ef4180609..a36b3d77a9233daed864c616306b2ad27f582a38 100644 --- a/tensorflow/contrib/autograph/converters/call_trees.py +++ b/tensorflow/contrib/autograph/converters/call_trees.py @@ -26,12 +26,12 @@ from collections import namedtuple import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import inspect_utils from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.util import tf_inspect @@ -45,6 +45,9 @@ KNOWN_NUMPY_FUNCTIONS = { } +# TODO(mdan): Get rid of these interfaces. Can now depend directly on Namer. + + class FunctionNamer(object): """Describes the interface for CallTreeTransformer's namer.""" @@ -76,20 +79,18 @@ class FunctionNamer(object): raise NotImplementedError() -class CallTreeTransformer(transformer.Base): - """Transforms the call tree by renaming transformed symbols.""" +# TODO(mdan): Rename to CallsTransformer. - def __init__(self, context, uncompiled_modules, nocompile_decorators): - super(CallTreeTransformer, self).__init__(context) - self.uncompiled_modules = uncompiled_modules - self.nocompile_decorators = nocompile_decorators + +class CallTreeTransformer(converter.Base): + """Transforms the call tree by renaming transformed symbols.""" def _resolve_name(self, node): """Used to resolve decorator info.""" if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): - return self.context.namespace.get(node.id) + return self.ctx.namespace.get(node.id) if isinstance(node, gast.Attribute): parent = self._resolve_name(node.value) if parent is not None: @@ -119,12 +120,12 @@ class CallTreeTransformer(transformer.Base): """Determines whether an entity should be compiled in the context.""" # TODO(mdan): Needs cleanup. We should remove the use of fqn altogether. module_name = fqn[0] - for mod in self.uncompiled_modules: + for mod in self.ctx.program.uncompiled_modules: if module_name.startswith(mod[0] + '.'): return False for i in range(1, len(fqn)): - if fqn[:i] in self.uncompiled_modules: + if fqn[:i] in self.ctx.program.uncompiled_modules: return False # Check for local decorations @@ -140,7 +141,7 @@ class CallTreeTransformer(transformer.Base): if hasattr(target_entity, '__pyct_is_compile_decorator'): return False - if target_entity in self.nocompile_decorators: + if target_entity in self.ctx.program.autograph_decorators: return False # Inspect the target function decorators. If any include a @convert @@ -159,7 +160,7 @@ class CallTreeTransformer(transformer.Base): for dec in target_node.decorator_list: decorator_fn = self._resolve_name(dec) if (decorator_fn is not None and - decorator_fn in self.nocompile_decorators): + decorator_fn in self.ctx.program.autograph_decorators): return False return True @@ -174,7 +175,7 @@ class CallTreeTransformer(transformer.Base): return node if anno.hasanno(node, 'is_constructor'): - new_name = self.context.namer.compiled_class_name( + new_name = self.ctx.namer.compiled_class_name( target_fqn, live_entity=target_entity) do_rename = True else: @@ -183,7 +184,7 @@ class CallTreeTransformer(transformer.Base): else: # Fallback - not reliable. owner_type = inspect_utils.getmethodclass(target_entity) - new_name, do_rename = self.context.namer.compiled_function_name( + new_name, do_rename = self.ctx.namer.compiled_function_name( target_fqn, live_entity=target_entity, owner_type=owner_type) if do_rename: @@ -264,15 +265,16 @@ class CallTreeTransformer(transformer.Base): return node def visit_Call(self, node): - # If the function is wrapped by one of the marker decorators, + # If the function call is wrapped by one of the marker decorators, # consider it graph ready. if anno.hasanno(node.func, 'live_val'): target_entity = anno.getanno(node.func, 'live_val') - if target_entity in self.nocompile_decorators: + if target_entity in self.ctx.program.autograph_decorators: if len(node.args) < 1: raise ValueError( 'Found call to decorator function "%s", but it had no arguments. ' - 'A decorator needs at least an argument.') + 'A decorator needs at least one positional argument.' % + target_entity) anno.setanno(node.args[0], 'graph_ready', True) self.generic_visit(node) @@ -309,27 +311,20 @@ class CallTreeTransformer(transformer.Base): # ensure that they return the correct value. return node - if self.context.recursive: + if self.ctx.program.recursive: node = self._insert_dynamic_conversion(node) return node -def transform(node, context, uncompiled_modules, nocompile_decorators): +def transform(node, ctx): """Transform function call to the compiled counterparts. Args: - node: AST to transform. - context: An EntityContext object. - uncompiled_modules: set of string tuples, each tuple represents the fully - qualified name of a package containing functions that will not be - compiled. - nocompile_decorators: A tuple containing decorators to be stripped from - functions during conversion. + node: AST + ctx: EntityContext Returns: A tuple (node, new_names): node: The transformed AST new_names: set(string), containing any newly-generated names """ - t = CallTreeTransformer(context, uncompiled_modules, nocompile_decorators) - node = t.visit(node) - return node + return CallTreeTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/call_trees_test.py b/tensorflow/contrib/autograph/converters/call_trees_test.py index 303dd54a4ee49de27fad0c5cdc2d6274abfe0fa8..27d8281b856f505062ceacc8ad50c8cbc2ce6c81 100644 --- a/tensorflow/contrib/autograph/converters/call_trees_test.py +++ b/tensorflow/contrib/autograph/converters/call_trees_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.autograph.converters import call_trees -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -29,7 +29,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class CallTreesTest(converter_test_base.TestCase): +class CallTreesTest(converter_testing.TestCase): def test_basic(self): @@ -43,7 +43,7 @@ class CallTreesTest(converter_test_base.TestCase): return test_fn_1(a) + 1 node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 @@ -60,7 +60,7 @@ class CallTreesTest(converter_test_base.TestCase): return f() + 3 node = self.parse_and_analyze(test_fn_2, {}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: # 10 = 7 (from the mock) + 3 (from test_fn_2) @@ -78,9 +78,9 @@ class CallTreesTest(converter_test_base.TestCase): node = self.parse_and_analyze( TestClass.test_fn_2, {'TestClass': TestClass}, - namer=converter_test_base.FakeNoRenameNamer(), + namer=converter_testing.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: tc = TestClass() @@ -92,7 +92,7 @@ class CallTreesTest(converter_test_base.TestCase): setattr(a, 'foo', 'bar') node = self.parse_and_analyze(test_fn, {'setattr': setattr}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: with self.test_session() as sess: @@ -115,7 +115,7 @@ class CallTreesTest(converter_test_base.TestCase): return np.random.binomial(2, 0.5) node = self.parse_and_analyze(test_fn, {'np': np}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node, dtypes.int64) as result: result.np = np @@ -130,13 +130,13 @@ class CallTreesTest(converter_test_base.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self.parse_and_analyze(test_fn, { - 'math_ops': math_ops, - 'constant_op': constant_op - }) - node = call_trees.transform(node, self.ctx, - set(((math_ops.__name__,), - (constant_op.__name__,))), ()) + node = self.parse_and_analyze( + test_fn, { + 'math_ops': math_ops, + 'constant_op': constant_op + }, + arg_types=set(((math_ops.__name__,), (constant_op.__name__,)))) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: result.math_ops = math_ops diff --git a/tensorflow/contrib/autograph/converters/continue_statements.py b/tensorflow/contrib/autograph/converters/continue_statements.py index 0417817a77e706fc0ce805f7391bea600f5fbb2d..958bde0a58764e705c35ab73ce879b2c11ce7cdc 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements.py +++ b/tensorflow/contrib/autograph/converters/continue_statements.py @@ -18,9 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -31,7 +31,7 @@ GUARD_CREATED = 'guard_created' CREATE_GUARD_NEXT = 'create_guard_next' -class ContinueCanonicalizationTransformer(transformer.Base): +class ContinueCanonicalizationTransformer(converter.Base): """Canonicalizes continue statements into additional conditionals.""" def visit_Continue(self, node): @@ -85,7 +85,7 @@ class ContinueCanonicalizationTransformer(transformer.Base): def _visit_loop_body(self, node, nodes): self.enter_local_scope() scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - continue_var = self.context.namer.new_symbol('continue_', scope.referenced) + continue_var = self.ctx.namer.new_symbol('continue_', scope.referenced) self.set_local(CONTROL_VAR_NAME, continue_var) nodes = self.visit_block(nodes, after_visit=self._postprocess_statement) @@ -135,5 +135,5 @@ class ContinueCanonicalizationTransformer(transformer.Base): return node -def transform(node, namer): - return ContinueCanonicalizationTransformer(namer).visit(node) +def transform(node, ctx): + return ContinueCanonicalizationTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/continue_statements_test.py b/tensorflow/contrib/autograph/converters/continue_statements_test.py index bcbb316d7459aa5a25bb0bd128cd6e359a393288..2ce1837972c50bbc4921487a290f5cb2f782b5f3 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements_test.py +++ b/tensorflow/contrib/autograph/converters/continue_statements_test.py @@ -19,11 +19,11 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import continue_statements -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class ContinueCanonicalizationTest(converter_test_base.TestCase): +class ContinueCanonicalizationTest(converter_testing.TestCase): def test_basic_continue(self): diff --git a/tensorflow/contrib/autograph/converters/control_flow.py b/tensorflow/contrib/autograph/converters/control_flow.py index d7ddbe8a04f64848d6ec21155d8d85f60e19d276..f4a87106279d5658ecaa90a577cbe741711ba22e 100644 --- a/tensorflow/contrib/autograph/converters/control_flow.py +++ b/tensorflow/contrib/autograph/converters/control_flow.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import cfg from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -45,9 +45,8 @@ class SymbolNamer(object): raise NotImplementedError() -class ControlFlowTransformer(transformer.Base): +class ControlFlowTransformer(converter.Base): """Transforms control flow structures like loops an conditionals.""" - def _create_cond_branch(self, body_name, aliased_orig_names, aliased_new_names, body, returns): if aliased_orig_names: @@ -141,10 +140,10 @@ class ControlFlowTransformer(transformer.Base): aliased_orelse_orig_names = tuple(orelse_scope.modified - orelse_scope.created) aliased_body_new_names = tuple( - self.context.namer.new_symbol(s.ssf(), body_scope.referenced) + self.ctx.namer.new_symbol(s.ssf(), body_scope.referenced) for s in aliased_body_orig_names) aliased_orelse_new_names = tuple( - self.context.namer.new_symbol(s.ssf(), orelse_scope.referenced) + self.ctx.namer.new_symbol(s.ssf(), orelse_scope.referenced) for s in aliased_orelse_orig_names) alias_body_map = dict(zip(aliased_body_orig_names, aliased_body_new_names)) @@ -165,9 +164,8 @@ class ControlFlowTransformer(transformer.Base): else: results = gast.Tuple([s.ast() for s in modified], None) - body_name = self.context.namer.new_symbol('if_true', body_scope.referenced) - orelse_name = self.context.namer.new_symbol('if_false', - orelse_scope.referenced) + body_name = self.ctx.namer.new_symbol('if_true', body_scope.referenced) + orelse_name = self.ctx.namer.new_symbol('if_false', orelse_scope.referenced) if modified: def build_returns(aliased_names, alias_map, scope): @@ -235,7 +233,7 @@ class ControlFlowTransformer(transformer.Base): raise ValueError('cannot convert while loop: no outputs') state_ssf = [ - self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state + self.ctx.namer.new_symbol(s.ssf(), all_referenced) for s in state ] ssf_map = { name: ssf @@ -267,11 +265,9 @@ class ControlFlowTransformer(transformer.Base): state=state, state_ssf=state_ssf, state_ast_tuple=state_ast_tuple, - test_name=self.context.namer.new_symbol('loop_test', - body_scope.referenced), + test_name=self.ctx.namer.new_symbol('loop_test', body_scope.referenced), test=test, - body_name=self.context.namer.new_symbol('loop_body', - body_scope.referenced), + body_name=self.ctx.namer.new_symbol('loop_body', body_scope.referenced), body=node_body, extra_deps=tuple(s.ast() for s in cond_closure), ) @@ -288,7 +284,7 @@ class ControlFlowTransformer(transformer.Base): state = list(body_closure) state_ssf = [ - self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state + self.ctx.namer.new_symbol(s.ssf(), all_referenced) for s in state ] ssf_map = { name: ssf @@ -326,17 +322,16 @@ class ControlFlowTransformer(transformer.Base): state_ast_tuple=state_ast_tuple, iter_=node.iter, iterate=node.target, - extra_test_name=self.context.namer.new_symbol('extra_test', - all_referenced), + extra_test_name=self.ctx.namer.new_symbol('extra_test', all_referenced), extra_test_expr=extra_test, - body_name=self.context.namer.new_symbol('loop_body', all_referenced), + body_name=self.ctx.namer.new_symbol('loop_body', all_referenced), body=node_body) return node -def transform(node, context): - cfg.run_analyses(node, cfg.Liveness(context)) - cfg.run_analyses(node, cfg.Defined(context)) - node = ControlFlowTransformer(context).visit(node) +def transform(node, ctx): + cfg.run_analyses(node, cfg.Liveness(ctx.info)) + cfg.run_analyses(node, cfg.Defined(ctx.info)) + node = ControlFlowTransformer(ctx).visit(node) return node diff --git a/tensorflow/contrib/autograph/converters/control_flow_test.py b/tensorflow/contrib/autograph/converters/control_flow_test.py index 9d23d9b5b7e8e8480e04fccc1c8c81799abf382b..735eb92a0dd06ee7fd621b92b1a8f894e09cee4a 100644 --- a/tensorflow/contrib/autograph/converters/control_flow_test.py +++ b/tensorflow/contrib/autograph/converters/control_flow_test.py @@ -19,7 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import control_flow -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -27,7 +27,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test -class ControlFlowTest(converter_test_base.TestCase): +class ControlFlowTest(converter_testing.TestCase): def test_simple_while(self): diff --git a/tensorflow/contrib/autograph/converters/decorators.py b/tensorflow/contrib/autograph/converters/decorators.py index 92445f31746cf94856ea43893f99a2ba60355fb5..3471bd11d6073f57a2703b438df95a60f19e8e0c 100644 --- a/tensorflow/contrib/autograph/converters/decorators.py +++ b/tensorflow/contrib/autograph/converters/decorators.py @@ -24,19 +24,14 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import pretty_printer +from tensorflow.python.util import tf_inspect -class DecoratorsTransformer(gast.NodeTransformer): +class DecoratorsTransformer(converter.Base): """Converts or removes decorators.""" - def __init__(self, remove_decorators): - self.remove_decorators = remove_decorators - self.additional_dependencies = set() - - # pylint:disable=invalid-name - def visit_FunctionDef(self, node): self.generic_visit(node) kept_decorators = [] @@ -58,31 +53,53 @@ class DecoratorsTransformer(gast.NodeTransformer): # This is currently verified by tests. continue - if not anno.hasanno(dec_func, 'live_val'): - raise ValueError( - 'Could not resolve decorator: %s' % pretty_printer.fmt(dec_func)) - + original_dec = anno.getanno(dec_func, anno.Basic.QN) dec_value = anno.getanno(dec_func, 'live_val') - if dec_value not in self.remove_decorators: - kept_decorators.append((dec, dec_value)) - for _, dec_value in kept_decorators: - if dec_value.__module__ == '__main__': + if dec_value in self.ctx.program.autograph_decorators: + # AutoGraph decorators do not need to be preserved. + continue + + # When using foo.bar.baz, we only really need to grab foo and import + # that. + dec_support_node = dec_func + while isinstance(dec_support_node, gast.Attribute): + dec_support_node = dec_support_node.value + + if not anno.hasanno(dec_support_node, 'live_val'): raise ValueError( - 'decorator "%s" was not allowed because it is declared ' - 'in the module "%s". To fix this, declare it in a separate ' - 'module that we can import it from.' % (dec_value, - dec_value.__module__)) + 'could not resolve symbol "%s" when looking up decorator "%s"' % + (anno.getanno(dec_support_node, anno.Basic.QN), original_dec)) + + dec_support = anno.getanno(dec_support_node, 'live_val') + # The tuple contains: + # * the AST that represents the decorator + # * the entity supporting the decorator (i.e., what we need to import) + # * the name of the module that needs to be imported for this decorator + # to properly resolve. + # Examples: + # for foo.bar, the tuple is (, , 'foo') + # for baz, the tuple is (, , 'baz') + kept_decorators.append((dec, dec_support, + anno.getanno(dec_support_node, anno.Basic.QN))) + + for _, dec_support, name in kept_decorators: + if tf_inspect.ismodule(dec_support): + self.ctx.program.additional_imports.add( + 'import %s as %s' % (dec_support.__name__, name)) else: - self.additional_dependencies.add(dec_value) - - node.decorator_list = [dec for dec, _ in kept_decorators] + if dec_support.__module__ == '__main__': + raise ValueError( + 'decorator "%s" was not allowed because it is declared ' + 'in the module "%s". To fix this, declare it in a separate ' + 'module that we can import it from.' % (dec_support, + dec_support.__module__)) + self.ctx.program.additional_imports.add( + 'from %s import %s' % (dec_support.__module__, name)) + + node.decorator_list = [dec for dec, _, _ in kept_decorators] return node - # pylint:enable=invalid-name - -def transform(node, remove_decorators): - transformer = DecoratorsTransformer(remove_decorators) - node = transformer.visit(node) - return node, transformer.additional_dependencies +def transform(node, ctx): + return DecoratorsTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/decorators_test.py b/tensorflow/contrib/autograph/converters/decorators_test.py index 9c01f689127dbedad7669c65b03e7da071b2d64d..d41c7fde2474803a438100e7e00ce8e9f675de45 100644 --- a/tensorflow/contrib/autograph/converters/decorators_test.py +++ b/tensorflow/contrib/autograph/converters/decorators_test.py @@ -20,9 +20,10 @@ from __future__ import print_function from functools import wraps -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import decorators +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.platform import test @@ -39,28 +40,35 @@ def simple_decorator(f): return lambda a: f(a) + 1 -def self_removing_decorator(removing_wrapper): +def self_transform_decorator(transform): + def decorator(f): @wraps(f) def wrapper(*args): # This removing wrapper is defined in the test below. This setup is so - # intricate just to simulate how we use the transformer in practice. - transformed_f = removing_wrapper(f, (self_removing_decorator,)) + # intricate in order to simulate how we use the transformer in practice. + transformed_f = transform(f, (self_transform_decorator,)) return transformed_f(*args) + 1 return wrapper return decorator -class DecoratorsTest(converter_test_base.TestCase): +class DecoratorsTest(converter_testing.TestCase): - def _remover_wrapper(self, f, remove_decorators): + def _transform(self, f, autograph_decorators): namespace = { - 'self_removing_decorator': self_removing_decorator, - 'simple_decorator': simple_decorator + 'self_transform_decorator': self_transform_decorator, + 'simple_decorator': simple_decorator, + 'converter_testing': converter_testing, } - node = self.parse_and_analyze(f, namespace) - node, _ = decorators.transform(node, remove_decorators=remove_decorators) - result, _ = compiler.ast_to_object(node) + node = self.parse_and_analyze( + f, + namespace, + recursive=False, + autograph_decorators=autograph_decorators) + node = decorators.transform(node, self.ctx) + import_line = '\n'.join(self.ctx.program.additional_imports) + result, _ = compiler.ast_to_object(node, source_prefix=import_line) return getattr(result, f.__name__) def test_noop(self): @@ -69,15 +77,14 @@ class DecoratorsTest(converter_test_base.TestCase): return a node = self.parse_and_analyze(test_fn, {}) - node, deps = decorators.transform(node, remove_decorators=()) + node = decorators.transform(node, self.ctx) result, _ = compiler.ast_to_object(node) - self.assertFalse(deps) self.assertEqual(1, result.test_fn(1)) def test_function(self): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(a): return a @@ -88,7 +95,7 @@ class DecoratorsTest(converter_test_base.TestCase): class TestClass(object): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(self, a): return a @@ -101,38 +108,39 @@ class DecoratorsTest(converter_test_base.TestCase): # Note that reversing the order of this two doesn't work. @classmethod - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(cls, a): return a # 2 = 1 (a) + 1 (decorator applied exactly once) self.assertEqual(2, TestClass.test_fn(1)) - def test_nested_decorators(self): + def test_nested_decorators_local(self): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(a): @simple_decorator def inner_fn(b): return b + 11 return inner_fn(a) - with self.assertRaises(ValueError): + # Expected to fail because simple_decorator cannot be imported. + with self.assertRaises(transformer.AutographParseError): test_fn(1) - # TODO(mdan): Uncomment this test once converter_test_base is updated. - # (can't do it now because it has unrelated pending changes) - # def test_nested_decorators(self): - # - # @self_removing_decorator(self._remover_wrapper) - # def test_fn(a): - # @imported_decorator - # def inner_fn(b): - # return b + 11 - # return inner_fn(a) - # - # # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn) - # self.assertEqual(14, test_fn(1)) + def test_nested_decorators_imported(self): + + @self_transform_decorator(self._transform) + def test_fn(a): + + @converter_testing.imported_decorator + def inner_fn(b): + return b + 11 + + return inner_fn(a) + + # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn) + self.assertEqual(14, test_fn(1)) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/ifexp.py b/tensorflow/contrib/autograph/converters/ifexp.py index 616d222762e09feeba1809f119d915dfbe522283..e996138498ab2b7efa76671d8cc67fd4c6a9d9b8 100644 --- a/tensorflow/contrib/autograph/converters/ifexp.py +++ b/tensorflow/contrib/autograph/converters/ifexp.py @@ -18,11 +18,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class IfExp(transformer.Base): +class IfExp(converter.Base): """Canonicalizes all IfExp nodes into plain conditionals.""" def visit_IfExp(self, node): @@ -34,16 +34,16 @@ class IfExp(transformer.Base): return desugared_ifexp -def transform(node, context): +def transform(node, ctx): """Desugar IfExp nodes into plain conditionals. Args: - node: an AST node to transform - context: a context object + node: ast.AST, the node to transform + ctx: converter.EntityContext Returns: new_node: an AST with no IfExp nodes, only conditionals. """ - node = IfExp(context).visit(node) + node = IfExp(ctx).visit(node) return node diff --git a/tensorflow/contrib/autograph/converters/ifexp_test.py b/tensorflow/contrib/autograph/converters/ifexp_test.py index ac6849dcb4bd7dacd84bb205f5c65395d8c2f51e..cdd5a2f591edc1138df1c165577ed375131ddf09 100644 --- a/tensorflow/contrib/autograph/converters/ifexp_test.py +++ b/tensorflow/contrib/autograph/converters/ifexp_test.py @@ -19,12 +19,12 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import ifexp +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class IfExpTest(converter_test_base.TestCase): +class IfExpTest(converter_testing.TestCase): def compiled_fn(self, test_fn, *args): node = self.parse_and_analyze(test_fn, {}) diff --git a/tensorflow/contrib/autograph/converters/list_comprehension.py b/tensorflow/contrib/autograph/converters/list_comprehension.py index d7f292015164e047d054c5d1fb0b391e960bb73d..c4a13ee822ab84706df83256d9e9684c3f7dacba 100644 --- a/tensorflow/contrib/autograph/converters/list_comprehension.py +++ b/tensorflow/contrib/autograph/converters/list_comprehension.py @@ -31,17 +31,14 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class ListCompCanonicalizationTransformer(transformer.Base): +class ListCompCanonicalizationTransformer(converter.Base): """NodeTransformer to canonicalize list comprehensions.""" - def __init__(self, context): - super(ListCompCanonicalizationTransformer, self).__init__(context) - def make_update_list_node(self, list_, elt): return templates.replace('list_.append(elt)', list_=list_, elt=elt)[0] @@ -76,5 +73,5 @@ class ListCompCanonicalizationTransformer(transformer.Base): return make_list + loop_body -def transform(node, context): - return ListCompCanonicalizationTransformer(context).visit(node) +def transform(node, ctx): + return ListCompCanonicalizationTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/list_comprehension_test.py b/tensorflow/contrib/autograph/converters/list_comprehension_test.py index 4758671f5ec83c26cfa54be0ef68f5f564094f6c..2bbee93412ce3174a14f3d60af9435dcf3b82cc6 100644 --- a/tensorflow/contrib/autograph/converters/list_comprehension_test.py +++ b/tensorflow/contrib/autograph/converters/list_comprehension_test.py @@ -18,12 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import list_comprehension +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class ListCompTest(converter_test_base.TestCase): +class ListCompTest(converter_testing.TestCase): def test_basic(self): diff --git a/tensorflow/contrib/autograph/converters/lists.py b/tensorflow/contrib/autograph/converters/lists.py index b49521b2c328f418828a5e92890aa1b169384b70..d77a04479826779b8aa859d70f2f7ff51138f841 100644 --- a/tensorflow/contrib/autograph/converters/lists.py +++ b/tensorflow/contrib/autograph/converters/lists.py @@ -32,85 +32,196 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -from tensorflow.python.framework import dtypes +from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno -class ListTransformer(transformer.Base): +# Tags for local state. +POP_USES = 'pop_uses' + + +class ListTransformer(converter.Base): """Converts lists and related operations to their TF counterpart.""" - def _empty_list(self, node): - if not anno.hasanno(node, 'element_type'): - raise NotImplementedError( - 'type inference for empty lists is not yet supported; ' - 'use set_element_type(, ) to continue') - dtype = anno.getanno(node, 'element_type') - if not isinstance(dtype, dtypes.DType): - # TODO(mdan): Allow non-TF dtypes? - # That would be consistent with the dynamic dispatch pattern, but - # we must make sure that doesn't become confusing. - raise NotImplementedError('element type "%s" not yet supported' % dtype) - - dtype_name = dtype.name - # TODO(mdan): Does it ever make sense not to use tensor lists? + def visit_List(self, node): + node = self.generic_visit(node) template = """ - tf.TensorArray(tf.dtype_name, size=0, dynamic_size=True) + ag__.new_list(elements) """ - return templates.replace_as_expression(template, dtype_name=dtype_name) + return templates.replace_as_expression(template, elements=node) - def _pre_populated_list(self, node): - raise NotImplementedError('pre-populated lists') + def _replace_append_call(self, node): + assert len(node.args) == 1 + assert isinstance(node.func, gast.Attribute) + template = """ + target = ag__.list_append(target, element) + """ + return templates.replace( + template, + target=node.func.value, + element=node.args[0]) + + def _replace_pop_call(self, node): + # Expressions that use pop() are converted to a statement + expression. + # + # For example: + # + # print(target.pop()) + # + # ... is converted to: + # + # target, target_pop = ag__.list_pop(target) + # print(target_pop) + # + # Here, we just generate the variable name and swap it in, + # and _generate_pop_operation will handle the rest. + # + # Multiple uses of pop() are allowed: + # + # print(tartget.pop(), target.pop()) + # print(tartget.pop().pop()) + # + assert isinstance(node.func, gast.Attribute) + scope = anno.getanno(node, NodeAnno.ARGS_SCOPE) + target_node = node.func.value + + # Attempt to use a related name if can get one. Otherwise use something + # generic. + if anno.hasanno(target_node, anno.Basic.QN): + target_name = anno.getanno(target_node, anno.Basic.QN).ssf() + else: + target_name = 'list' + pop_var_name = self.ctx.namer.new_symbol(target_name, scope.referenced) + + pop_uses = self.get_local(POP_USES, []) + pop_uses.append((node, pop_var_name)) + self.set_local(POP_USES, pop_uses) + + return templates.replace_as_expression('var_name', var_name=pop_var_name) + + def _replace_stack_call(self, node): + assert len(node.args) == 1 + dtype = anno.getanno( + node.args[0], + 'element_type', + default=templates.replace_as_expression('None')) + template = """ + ag__.list_stack( + target, + opts=ag__.ListStackOpts( + element_dtype=dtype, + original_call=orig_call)) + """ + return templates.replace_as_expression( + template, + dtype=dtype, + target=node.args[0], + orig_call=node.func) - def visit_Expr(self, node): + def visit_Call(self, node): node = self.generic_visit(node) - if isinstance(node.value, gast.Call): - call_node = node.value - - if not anno.hasanno(call_node.func, anno.Basic.QN): - return node - qn = anno.getanno(call_node.func, anno.Basic.QN) - - if qn.qn[-1] == 'append' and (len(call_node.args) == 1): - template = """ - target = ag__.utils.dynamic_list_append(target, element) - """ - node = templates.replace( - template, - target=qn.parent.ast(), - element=call_node.args[0]) + + # TODO(mdan): This is insufficient if target is a function argument. + # In the case of function arguments, we need to add the list to the + # function's return value, because it is being modified. + # TODO(mdan): Checking just the name is brittle, can it be improved? + if isinstance(node.func, gast.Attribute): + func_name = node.func.attr + if func_name == 'append' and (len(node.args) == 1): + node = self._replace_append_call(node) + elif func_name == 'pop' and (len(node.args) <= 1): + node = self._replace_pop_call(node) + elif func_name == 'stack' and (len(node.args) == 1): + node = self._replace_stack_call(node) + return node - def _replace_list_constructors(self, targets, values): - for target in targets: - if (isinstance(target, (gast.Tuple, gast.List)) and - isinstance(values, (gast.Tuple, gast.List))): - n_targets = len(target.elts) - for i in range(n_targets): - target_el, value_el = target.elts[i], values.elts[i] - values.elts[i] = self._replace_list_constructors( - (target_el,), value_el) - return values - if isinstance(values, gast.List): - if values.elts: - return self._pre_populated_list(values) - else: - return self._empty_list(values) - return values - - def visit_Assign(self, node): - node = self.generic_visit(node) + def _generate_pop_operation(self, original_call_node, pop_var_name): + assert isinstance(original_call_node.func, gast.Attribute) + + if original_call_node.args: + pop_element = original_call_node.args[0] + else: + pop_element = parser.parse_expression('None') + # The call will be something like "target.pop()", and the dtype is hooked to + # target, hence the func.value. + dtype = anno.getanno( + original_call_node.func.value, + 'element_type', + default=templates.replace_as_expression('None')) + shape = anno.getanno( + original_call_node.func.value, + 'element_shape', + default=templates.replace_as_expression('None')) + + template = """ + target, pop_var_name = ag__.list_pop( + target, element, + opts=ag__.ListPopOpts(element_dtype=dtype, element_shape=shape)) + """ + return templates.replace( + template, + target=original_call_node.func.value, + pop_var_name=pop_var_name, + element=pop_element, + dtype=dtype, + shape=shape) + + def _postprocess_statement(self, node): + """Inserts any separate pop() calls that node may use.""" + pop_uses = self.get_local(POP_USES, None) + if pop_uses: + replacements = [] + for original_call_node, pop_var_name in pop_uses: + replacements.extend( + self._generate_pop_operation(original_call_node, pop_var_name)) + replacements.append(node) + node = replacements + self.exit_local_scope() + return node, None + + # TODO(mdan): Should we have a generic visit_block instead? + # Right now it feels that a visit_block would add too much magic that's + # hard to follow. + + def _visit_and_process_block(self, block): + return self.visit_block( + block, + before_visit=self.enter_local_scope, + after_visit=self._postprocess_statement) + + def visit_FunctionDef(self, node): + node.args = self.generic_visit(node.args) + node.decorator_list = self.visit_block(node.decorator_list) + node.body = self._visit_and_process_block(node.body) + return node + + def visit_For(self, node): + node.target = self.visit(node.target) + node.body = self._visit_and_process_block(node.body) + node.orelse = self._visit_and_process_block(node.orelse) + return node + + def visit_While(self, node): + node.test = self.visit(node.test) + node.body = self._visit_and_process_block(node.body) + node.orelse = self._visit_and_process_block(node.orelse) + return node + + def visit_If(self, node): + node.test = self.visit(node.test) + node.body = self._visit_and_process_block(node.body) + node.orelse = self._visit_and_process_block(node.orelse) + return node - # Only convert lists when they are assigned to a variable, e.g.: - # l = [] - # TODO(mdan): A similar pattern exists in type_info.py - # We should add a generic "unpack_assignment" function to the base - # transformer, that has the same effect as applying some logic to the SSA - # form. - node.value = self._replace_list_constructors(node.targets, node.value) + def visit_With(self, node): + node.items = self.visit_block(node.items) + node.body = self._visit_and_process_block(node.body) return node -def transform(node, context): - return ListTransformer(context).visit(node) +def transform(node, ctx): + return ListTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/lists_test.py b/tensorflow/contrib/autograph/converters/lists_test.py index 74c6dc64f197f75eb3e66c01fb078467e8e8ea89..ea04097b28deedd705164bd95ab62dba3e3c7834 100644 --- a/tensorflow/contrib/autograph/converters/lists_test.py +++ b/tensorflow/contrib/autograph/converters/lists_test.py @@ -19,77 +19,129 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import lists +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import dtypes -from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import list_ops from tensorflow.python.platform import test -class ListTest(converter_test_base.TestCase): +class ListTest(converter_testing.TestCase): - def test_empty_annotated_list(self): + def test_empty_list(self): def test_fn(): - l = [] - utils.set_element_type(l, dtypes.int32) - l.append(1) - return l + return [] - node = self.parse_and_analyze(test_fn, {'dtypes': dtypes, 'utils': utils}) + node = self.parse_and_analyze(test_fn, {}) node = lists.transform(node, self.ctx) - with self.compiled(node, tensor_array_ops.TensorArray, - dtypes.int32) as result: - # TODO(mdan): Attach these additional modules automatically. - result.utils = utils - result.dtypes = dtypes + with self.compiled(node) as result: + tl = result.test_fn() + # Empty tensor lists cannot be evaluated or stacked. + self.assertTrue(isinstance(tl, ops.Tensor)) + self.assertEqual(tl.dtype, dtypes.variant) + + def test_initialized_list(self): + + def test_fn(): + return [1, 2, 3] + + node = self.parse_and_analyze(test_fn, {}) + node = lists.transform(node, self.ctx) + + with self.compiled(node) as result: with self.test_session() as sess: - self.assertAllEqual([1], sess.run(result.test_fn().stack())) + tl = result.test_fn() + r = list_ops.tensor_list_stack(tl, dtypes.int32) + self.assertAllEqual(sess.run(r), [1, 2, 3]) - def test_empty_annotated_lists_unpacked(self): + def test_list_append(self): def test_fn(): - l, m = [], [] - utils.set_element_type(l, dtypes.int32) - utils.set_element_type(m, dtypes.int32) - l.append(1) - m.append(2) - return l, m + l = [1] + l.append(2) + l.append(3) + return l - node = self.parse_and_analyze(test_fn, {'dtypes': dtypes, 'utils': utils}) + node = self.parse_and_analyze(test_fn, {}) node = lists.transform(node, self.ctx) - with self.compiled(node, tensor_array_ops.TensorArray, - dtypes.int32) as result: + with self.compiled(node) as result: + with self.test_session() as sess: + tl = result.test_fn() + r = list_ops.tensor_list_stack(tl, dtypes.int32) + self.assertAllEqual(sess.run(r), [1, 2, 3]) + + def test_list_pop(self): + + def test_fn(): + l = [1, 2, 3] + utils.set_element_type(l, dtypes.int32, ()) + s = l.pop() + return s, l + + node = self.parse_and_analyze( + test_fn, + { + 'utils': utils, + 'dtypes': dtypes + }, + include_type_analysis=True, + ) + node = lists.transform(node, self.ctx) + + with self.compiled(node) as result: result.utils = utils result.dtypes = dtypes with self.test_session() as sess: - res_l, res_m = result.test_fn() - self.assertEqual([1], sess.run(res_l.stack())) - self.assertEqual([2], sess.run(res_m.stack())) + ts, tl = result.test_fn() + r = list_ops.tensor_list_stack(tl, dtypes.int32) + self.assertAllEqual(sess.run(r), [1, 2]) + self.assertAllEqual(sess.run(ts), 3) + + def test_double_list_pop(self): - def test_empty_annotated_lists_list_unpacked(self): + def test_fn(l): + s = l.pop().pop() + return s + + node = self.parse_and_analyze(test_fn, {}) + node = lists.transform(node, self.ctx) + + with self.compiled(node) as result: + test_input = [1, 2, [1, 2, 3]] + # TODO(mdan): Pass a list of lists of tensor when we fully support that. + # For now, we just pass a regular Python list of lists just to verify that + # the two pop calls are sequenced properly. + self.assertAllEqual(result.test_fn(test_input), 3) + + def test_list_stack(self): + + tf = None # Will be replaced with a mock. def test_fn(): - [l, m] = [], [] + l = [1, 2, 3] utils.set_element_type(l, dtypes.int32) - utils.set_element_type(m, dtypes.int32) - l.append(1) - m.append(2) - return l, m - - node = self.parse_and_analyze(test_fn, {'dtypes': dtypes, 'utils': utils}) + return tf.stack(l) + + node = self.parse_and_analyze( + test_fn, + { + 'utils': utils, + 'dtypes': dtypes + }, + include_type_analysis=True, + ) node = lists.transform(node, self.ctx) - with self.compiled(node, tensor_array_ops.TensorArray, - dtypes.int32) as result: + with self.compiled(node, array_ops.stack, dtypes.int32) as result: result.utils = utils result.dtypes = dtypes with self.test_session() as sess: - res_l, res_m = result.test_fn() - self.assertEqual([1], sess.run(res_l.stack())) - self.assertEqual([2], sess.run(res_m.stack())) + self.assertAllEqual(sess.run(result.test_fn()), [1, 2, 3]) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/logical_expressions.py b/tensorflow/contrib/autograph/converters/logical_expressions.py index 3a795a315a3c2aa08ac1577a204102755b6e849c..16eb1f0e3f8ad34e615931882ab2896db485f457 100644 --- a/tensorflow/contrib/autograph/converters/logical_expressions.py +++ b/tensorflow/contrib/autograph/converters/logical_expressions.py @@ -23,10 +23,10 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer # TODO(mdan): Properly extrack boolean ops according to lazy eval rules. @@ -39,11 +39,11 @@ from tensorflow.contrib.autograph.pyct import transformer SAFE_BOOLEAN_OPERAND = 'SAFE_BOOLEAN_OPERAND' -class LogicalExpressionTransformer(transformer.Base): +class LogicalExpressionTransformer(converter.Base): """Converts logical expressions to corresponding TF calls.""" - def __init__(self, context): - super(LogicalExpressionTransformer, self).__init__(context) + def __init__(self, ctx): + super(LogicalExpressionTransformer, self).__init__(ctx) # TODO(mdan): Look into replacing with bitwise operators instead. # TODO(mdan): Skip replacing if the function is trivial. self.op_mapping = { @@ -128,5 +128,5 @@ class LogicalExpressionTransformer(transformer.Base): return right -def transform(node, context): - return LogicalExpressionTransformer(context).visit(node) +def transform(node, ctx): + return LogicalExpressionTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/logical_expressions_test.py b/tensorflow/contrib/autograph/converters/logical_expressions_test.py index 2814060c4d831e4dddacb3dcbcbe1db42160db20..48186024a9da7b41fa7ff9a8ab18f3477ba09c8f 100644 --- a/tensorflow/contrib/autograph/converters/logical_expressions_test.py +++ b/tensorflow/contrib/autograph/converters/logical_expressions_test.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import logical_expressions +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class GradientsFunctionTest(converter_test_base.TestCase): +class GradientsFunctionTest(converter_testing.TestCase): def test_equals(self): diff --git a/tensorflow/contrib/autograph/converters/name_scopes.py b/tensorflow/contrib/autograph/converters/name_scopes.py index dfee529abaa8c14d9b408819b32c5199500a2c2f..dd6c6bf960c52d094a16d4cd72fa84f65b9322a1 100644 --- a/tensorflow/contrib/autograph/converters/name_scopes.py +++ b/tensorflow/contrib/autograph/converters/name_scopes.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class FunctionNameScopeTransformer(transformer.Base): +class FunctionNameScopeTransformer(converter.Base): """Wrap a function body with a `name_scope` of the function name.""" def _name_for_current_scope(self): @@ -70,5 +70,5 @@ class FunctionNameScopeTransformer(transformer.Base): return node -def transform(node, context): - return FunctionNameScopeTransformer(context).visit(node) +def transform(node, ctx): + return FunctionNameScopeTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/name_scopes_test.py b/tensorflow/contrib/autograph/converters/name_scopes_test.py index 17692cbd880dbc1db4bb40ad7345e27907499f9d..444d0bcd469f35689d078debe3622f930dbac723 100644 --- a/tensorflow/contrib/autograph/converters/name_scopes_test.py +++ b/tensorflow/contrib/autograph/converters/name_scopes_test.py @@ -18,14 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import name_scopes +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.platform import test -class FunctionNameScopeTransformer(converter_test_base.TestCase): +class FunctionNameScopeTransformer(converter_testing.TestCase): def test_basic(self): diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards.py b/tensorflow/contrib/autograph/converters/side_effect_guards.py index 3bcb2d3c42c6e0663c8f78523199a364b6ac231f..b808604f0ab2d42f41a560035ab046ff782a3431 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards.py @@ -36,11 +36,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -59,14 +59,9 @@ class SymbolNamer(object): raise NotImplementedError() -class SideEffectGuardTransformer(transformer.Base): +class SideEffectGuardTransformer(converter.Base): """Adds control dependencies to functions with side effects.""" - def __init__(self, context): - super(SideEffectGuardTransformer, self).__init__(context) - - # pylint:disable=invalid-name - def _visit_and_reindent(self, nodes): new_nodes = [] current_dest = new_nodes @@ -149,7 +144,7 @@ class SideEffectGuardTransformer(transformer.Base): s for s in guarded_args if s not in args_scope.parent.modified) aliased_new_names = tuple( qual_names.QN( - self.context.namer.new_symbol( + self.ctx.namer.new_symbol( s.ssf(), args_scope.parent.referenced)) for s in need_alias) alias_map = dict(zip(need_alias, aliased_new_names)) if len(guarded_args) == 1: @@ -183,8 +178,6 @@ class SideEffectGuardTransformer(transformer.Base): (node.body, alias_map)) return node - # pylint:enable=invalid-name - -def transform(node, context): - return SideEffectGuardTransformer(context).visit(node) +def transform(node, ctx): + return SideEffectGuardTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py index ce0ce33243a1352107eb8121050ee76474869809..a7ad8efed4c88e15ce9dc14cb02e5e035602013d 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py @@ -18,8 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import side_effect_guards +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops @@ -29,7 +29,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test -class SideEffectGuardsTest(converter_test_base.TestCase): +class SideEffectGuardsTest(converter_testing.TestCase): def test_side_effect_on_return_only_variable(self): diff --git a/tensorflow/contrib/autograph/converters/single_return.py b/tensorflow/contrib/autograph/converters/single_return.py index bcc9ca9dfeb00ef2d2e60edf6a1abfba19a1bad7..a351cd81b82f7fb32f62ac1579355ace0501759d 100644 --- a/tensorflow/contrib/autograph/converters/single_return.py +++ b/tensorflow/contrib/autograph/converters/single_return.py @@ -20,21 +20,21 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno # TODO(mdan): Move this logic into transformer_base. -class BodyVisitor(transformer.Base): +class BodyVisitor(converter.Base): """Walks breadth- or depth-first the list-of-nodes bodies of AST nodes.""" - def __init__(self, context, depth_first=False): + def __init__(self, ctx, depth_first=False): + super(BodyVisitor, self).__init__(ctx) self.depth_first = depth_first self.changes_made = False - super(BodyVisitor, self).__init__(context) def visit_nodelist(self, nodelist): for node in nodelist: @@ -144,13 +144,13 @@ def contains_return(node): return False -class LiftReturn(transformer.Base): +class LiftReturn(converter.Base): """Move return statements out of If and With blocks.""" - def __init__(self, context): + def __init__(self, ctx): + super(LiftReturn, self).__init__(ctx) self.changes_made = False self.common_return_name = None - super(LiftReturn, self).__init__(context) def visit_If(self, node): # Depth-first traversal of if statements @@ -195,8 +195,8 @@ class LiftReturn(transformer.Base): last_return_name = self.common_return_name body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) referenced_names = body_scope.referenced - self.common_return_name = self.context.namer.new_symbol( - 'return_', referenced_names) + self.common_return_name = self.ctx.namer.new_symbol('return_', + referenced_names) node = self.generic_visit(node) self.common_return_name = last_return_name return node @@ -265,7 +265,7 @@ class DetectReturnInFunctionDef(gast.NodeVisitor): 'Each function definition should contain at least one return.') -def transform(node, context): +def transform(node, ctx): """Ensure a function has only a single return. This transforms an AST node with multiple returns successively into containing @@ -280,8 +280,8 @@ def transform(node, context): this is an error. Args: - node: an AST node to transform - context: a context object + node: ast.AST + ctx: converter.EntityContext Returns: new_node: an AST with a single return value @@ -301,10 +301,10 @@ def transform(node, context): while True: # Try to lift all returns out of if statements and with blocks - lr = LiftReturn(context) + lr = LiftReturn(ctx) node = lr.visit(node) changes_made = lr.changes_made - fe = FoldElse(context) + fe = FoldElse(ctx) node = fe.visit(node) changes_made = changes_made or fe.changes_made diff --git a/tensorflow/contrib/autograph/converters/single_return_test.py b/tensorflow/contrib/autograph/converters/single_return_test.py index d483005a09537ea8227814f65aa7e6402c853f60..1f0de4310e370235a4a7bfeaa61bd519a81aff47 100644 --- a/tensorflow/contrib/autograph/converters/single_return_test.py +++ b/tensorflow/contrib/autograph/converters/single_return_test.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import single_return +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework.ops import name_scope from tensorflow.python.platform import test -class SingleReturnTest(converter_test_base.TestCase): +class SingleReturnTest(converter_testing.TestCase): def compiled_fn(self, test_fn, *args): node = self.parse_and_analyze(test_fn, {}) diff --git a/tensorflow/contrib/autograph/converters/slices.py b/tensorflow/contrib/autograph/converters/slices.py new file mode 100644 index 0000000000000000000000000000000000000000..3f5fc57125a8b65faf1e3a377d7984ff05b3245c --- /dev/null +++ b/tensorflow/contrib/autograph/converters/slices.py @@ -0,0 +1,83 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converter for slice operations.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import templates + + +class SliceTransformer(converter.Base): + """Converts slicing operations to their TF counterpart. + + Currently, relying on the default slice operator that Tensor uses is + insufficient, because TensorArray and tensor lists use dedicated index read + and write functions. + """ + + def _process_single_assignment(self, target, value): + if not isinstance(target, gast.Subscript): + return None + + template = """ + target = ag__.set_item(target, key, item) + """ + return templates.replace( + template, target=target.value, key=target.slice, item=value) + + def visit_Assign(self, node): + node = self.generic_visit(node) + # TODO(mdan): Support unpackings and multiple assignments. + if len(node.targets) != 1: + raise NotImplementedError('multiple assignment') + replacement = self._process_single_assignment(node.targets[0], node.value) + if replacement is not None: + return replacement + return node + + def visit_Subscript(self, node): + node = self.generic_visit(node) + if not isinstance(node.slice, gast.Index): + # TODO(mdan): It might make more sense to wave them through. + raise NotImplementedError('non-index slice') + + if not isinstance(node.ctx, gast.Load): + # Index writes are handled at a higher level, one at which the rvalue is + # also available. + return node + + dtype = anno.getanno( + node.value, + 'element_type', + default=templates.replace_as_expression('None')) + + template = """ + ag__.get_item( + target, + key, + opts=ag__.GetItemOpts(element_dtype=dtype)) + """ + return templates.replace_as_expression( + template, target=node.value, key=node.slice, dtype=dtype) + + +def transform(node, ctx): + return SliceTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/slices_test.py b/tensorflow/contrib/autograph/converters/slices_test.py new file mode 100644 index 0000000000000000000000000000000000000000..df9a4c8bab66f24374605b45bc90bc2730431323 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/slices_test.py @@ -0,0 +1,59 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slices module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.converters import slices +from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import list_ops +from tensorflow.python.platform import test + + +class SliceTest(converter_testing.TestCase): + + def test_index_access(self): + + def test_fn(l): + utils.set_element_type(l, dtypes.int32) + return l[1] + + node = self.parse_and_analyze( + test_fn, + { + 'utils': utils, + 'dtypes': dtypes + }, + include_type_analysis=True, + ) + node = slices.transform(node, self.ctx) + + with self.compiled(node, dtypes.int32) as result: + result.utils = utils + result.dtypes = dtypes + with self.test_session() as sess: + tl = list_ops.tensor_list_from_tensor( + [1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32)) + y = result.test_fn(tl) + self.assertEqual(2, sess.run(y)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/core/BUILD b/tensorflow/contrib/autograph/core/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..833f9dced81bd651244d281322c830bb1c88b259 --- /dev/null +++ b/tensorflow/contrib/autograph/core/BUILD @@ -0,0 +1,59 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "core", + srcs = [ + "config.py", + "converter.py", + "naming.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/pyct/static_analysis", + "//tensorflow/contrib/autograph/utils", + ], +) + +py_library( + name = "test_lib", + srcs = [ + "converter_testing.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + ":core", + "//tensorflow/contrib/autograph/operators", + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/pyct/static_analysis", + "//tensorflow/contrib/autograph/utils", + "@gast_archive//:gast", + "@six_archive//:six", + ], +) + +py_test( + name = "naming_test", + srcs = ["naming_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":core", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/core/annos.py b/tensorflow/contrib/autograph/core/annos.py new file mode 100644 index 0000000000000000000000000000000000000000..b8937ce36a9631739ab3d7e65a4dad4124406a00 --- /dev/null +++ b/tensorflow/contrib/autograph/core/annos.py @@ -0,0 +1,39 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Annotations specific to AutoGraph.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from enum import Enum + + +class NoValue(Enum): + + def __repr__(self): + return self.name + + +class NodeAnno(NoValue): + """Additional annotations used by AutoGraph converters. + + These are in addition to the basic annotations declared in pyct/anno.py and + pyct/static_analysis/annos.py. + """ + + # The directives collection - see directives.py + DIRECTIVES = ( + 'Dict depicting static directive calls. See the directives converter.') diff --git a/tensorflow/contrib/autograph/impl/config.py b/tensorflow/contrib/autograph/core/config.py similarity index 100% rename from tensorflow/contrib/autograph/impl/config.py rename to tensorflow/contrib/autograph/core/config.py diff --git a/tensorflow/contrib/autograph/core/converter.py b/tensorflow/contrib/autograph/core/converter.py new file mode 100644 index 0000000000000000000000000000000000000000..54e6aa0f3bbb9059e044861362407cb5050240b4 --- /dev/null +++ b/tensorflow/contrib/autograph/core/converter.py @@ -0,0 +1,210 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converter construction support. + +This module contains a base class for all converters, as well as supporting +structures. These structures are referred to as contexts. + +The class hierarchy is as follows: + + + [extends] converter.Base + [extends] transformer.Base + [extends] gast.nodeTransformer + [uses] transfomer.SourceInfo + [uses] converter.EntityContext + [uses] converter.ProgramContext + [uses] transfomer.SourceInfo + +converter.Base is a specialization of transformer.Base for AutoGraph. It's a +very lightweight subclass that adds a `ctx` attribute holding the corresponding +EntityContext object (see below). Note that converters are not reusable, and +`visit` will raise an error if called more than once. + +converter.EntityContext contains mutable state associated with an entity that +the converter processes. + +converter.ProgramContext contains mutable state across related entities. For +example, when converting several functions that call one another, the +ProgramContext should be shared across these entities. + +Below is the overal flow at conversion: + + program_ctx = ProgramContext(, , ...) + while : + entity, source_info = + entity_ctx = EntityContext(program_ctx, source_info) + for : + converter = ConverterClass(entity_ctx) + + # May update entity_ctx and program_ctx + entity = converter.visit(entity) + + + +Note that pyct contains a small number of transformers used for static analysis. +These implement transformer.Base, rather than converter.Base, to avoid a +dependency on AutoGraph. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import naming +from tensorflow.contrib.autograph.pyct import transformer + +# TODO(mdan): These contexts can be refactored into first class objects. +# For example, we could define Program and Entity abstractions that hold on +# to the actual entity and have conversion methods. + + +class ProgramContext(object): + """ProgramContext keeps track of converting function hierarchies. + + This object is mutable, and is updated during conversion. Not thread safe. + + Attributes: + recursive: bool, whether to recursively convert any functions that the + decorator function may call. + autograph_decorators: Tuple[Callable, ...], decorator functions that belong + to AutoGraph. These require special treatment. + dependency_cache: Dict[Any, ast.AST], the original entities mapped to their + converted AST + additional_imports: Set[Any], additional entities which for any reason + cannot be attached after loading and need to be explicitly imported + in the generated code + name_map: Dict[str, str], map of original entity name to the name of + their converted counterparts + autograph_module: Module, a reference to the autograph module. This + needs to be specified by the caller to avoid circular dependencies. + uncompiled_modules: Set[Tuple[str, ...]], with each tuple representing the + fully qualified name of a package containing functions that will not be + compiled. + required_imports: str, containing an import statement on each line. These + are all the imports necessary for the compiled code to run, in addition + to the closures of each entity, which are attached dynamically. + """ + + def __init__( + self, + recursive, + autograph_decorators, + partial_types, + autograph_module, + uncompiled_modules, + ): + self.recursive = recursive + self.autograph_decorators = autograph_decorators + self.partial_types = partial_types if partial_types else () + self.autograph_module = autograph_module + self.uncompiled_modules = uncompiled_modules + + # Required to output dependencies in discovery order, which should match + # the reverse dependency order. + self.dependency_cache = collections.OrderedDict() + self.additional_imports = set() + self.name_map = {} + + @property + def required_imports(self): + """Returns a block containing all imports required by the converted code.""" + # TODO(mdan): Check that these don't clobber one another. + return '\n'.join(config.COMPILED_IMPORT_STATEMENTS + + tuple(self.additional_imports)) + + def new_namer(self, namespace): + return naming.Namer(namespace, self.recursive, self.name_map, + self.partial_types) + + def update_name_map(self, namer): + """Updates renamed_calls based on the recent activity from the namer. + + Whenever we convert a new entity, any references to other entities are being + renamed to match their soon-to-be-converted counterparts. The namer keeps + track of these renames. When conversion is complete, we copy those renames + so that when those referenced entities are being converted, their new name + matches. + + Args: + namer: naming.Namer + + Raises: + ValueError: when an entity was renamed twice and to different names. + """ + # TODO(mdan): Have call_trees do this directly. + # This is done so indirectly, via the namer, for historic reasons. But + # now we can have the converter that does the rename record the new name + # as well and skip this step altogether. + for o, name in namer.renamed_calls.items(): + if o in self.name_map: + if self.name_map[o] != name: + raise ValueError( + 'Calls to %s were converted using multiple names (%s). This is ' + 'possible when an entity with one of these names already ' + 'existed. To fix, avoid using any of these names.' % + (o, (name, self.name_map[o]))) + else: + self.name_map[o] = name + + def add_to_cache(self, original_entity, converted_ast): + self.dependency_cache[original_entity] = converted_ast + + +class EntityContext(object): + """Tracks the conversion of a single entity. + + This object is mutable, and is updated during conversion. Not thread safe. + + Attributes: + namer: Namer + info: transformer.EntityInfo + program: ProgramContext + """ + + def __init__(self, namer, entity_info, program_ctx): + self.namer = namer + self.info = entity_info + self.program = program_ctx + + +class Base(transformer.Base): + """All converters should inherit from this class. + + Attributes: + ctx: EntityContext + """ + + def __init__(self, ctx): + super(Base, self).__init__(ctx.info) + self.ctx = ctx # Keeping this short because it's used frequently. + + self._used = False + self._ast_depth = 0 + + def visit(self, node): + if not self._ast_depth: + if self._used: + raise ValueError('converter objects cannot be reused') + self._used = True + + self._ast_depth += 1 + try: + return super(Base, self).visit(node) + finally: + self._ast_depth -= 1 diff --git a/tensorflow/contrib/autograph/converters/converter_test_base.py b/tensorflow/contrib/autograph/core/converter_testing.py similarity index 80% rename from tensorflow/contrib/autograph/converters/converter_test_base.py rename to tensorflow/contrib/autograph/core/converter_testing.py index 41c2e71702e7e3ee3811a2cbee27c8c988eb3a5c..0e46aacc1216d2dbd9d34ad0e72ca8251094bddc 100644 --- a/tensorflow/contrib/autograph/converters/converter_test_base.py +++ b/tensorflow/contrib/autograph/core/converter_testing.py @@ -23,17 +23,24 @@ import imp from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import compiler -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import pretty_printer from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info from tensorflow.python.platform import test +def imported_decorator(f): + return lambda a: f(a) + 1 + + +# TODO(mdan): We might be able to use the real namer here. class FakeNamer(object): """A fake namer that uses a global counter to generate unique names.""" @@ -114,23 +121,32 @@ class TestCase(test.TestCase): arg_types=None, include_type_analysis=True, owner_type=None, - recursive=True): + recursive=True, + autograph_decorators=()): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=namer or FakeNamer(), + + if namer is None: + namer = FakeNamer() + program_ctx = converter.ProgramContext( + recursive=recursive, + autograph_decorators=autograph_decorators, + partial_types=None, + autograph_module=None, + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + entity_info = transformer.EntityInfo( source_code=source, - source_file=None, + source_file='', namespace=namespace, arg_values=None, arg_types=arg_types, - owner_type=owner_type, - recursive=recursive, - type_annotation_func=utils.set_element_type) + owner_type=owner_type) + ctx = converter.EntityContext(namer, entity_info, program_ctx) + node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) if include_type_analysis: - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) self.ctx = ctx return node diff --git a/tensorflow/contrib/autograph/impl/naming.py b/tensorflow/contrib/autograph/core/naming.py similarity index 100% rename from tensorflow/contrib/autograph/impl/naming.py rename to tensorflow/contrib/autograph/core/naming.py diff --git a/tensorflow/contrib/autograph/impl/naming_test.py b/tensorflow/contrib/autograph/core/naming_test.py similarity index 98% rename from tensorflow/contrib/autograph/impl/naming_test.py rename to tensorflow/contrib/autograph/core/naming_test.py index 73fc0894655cb49e4f61bf8ca51995b06feb3072..d2bebd0478b1074e421b5da1427a0dbaf91b6c9f 100644 --- a/tensorflow/contrib/autograph/impl/naming_test.py +++ b/tensorflow/contrib/autograph/core/naming_test.py @@ -18,7 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.impl import naming +from tensorflow.contrib.autograph.core import naming from tensorflow.python.platform import test diff --git a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb index d62390494b78c415212ba91ac914cdfee324f971..0702273fac15da61a72d66d8344a5add32ad12a6 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb @@ -570,7 +570,7 @@ " autograph.utils.set_element_type(numbers, tf.int32)\n", " for i in range(n):\n", " numbers.append(i)\n", - " return numbers.stack() # Stack the list so that it can be used as a Tensor\n", + " return autograph.stack(numbers) # Stack the list so that it can be used as a Tensor\n", "\n", "\n", "tf_f = autograph.to_graph(f)\n", @@ -648,7 +648,7 @@ " if not is_prime:\n", " continue\n", " primes.append(i)\n", - " all_primes = primes.stack()\n", + " all_primes = autograph.stack(primes)\n", "\n", " print('The prime numbers less than', n, 'are:')\n", " print(all_primes)\n", @@ -953,8 +953,9 @@ " train_accuracies.append(step_train_accuracy)\n", " test_accuracies.append(step_test_accuracy)\n", " i += 1\n", - " return (train_losses.stack(), test_losses.stack(), train_accuracies.stack(),\n", - " test_accuracies.stack())" + " return (autograph.stack(train_losses), autograph.stack(test_losses),\n", + " autograph.stack(train_accuracies),\n", + " autograph.stack(test_accuracies))" ], "execution_count": 0, "outputs": [] @@ -1236,7 +1237,7 @@ " cell_output, (state, output) = cell.call(ch, (state, output))\n", " hidden_outputs.append(cell_output)\n", " i += 1\n", - " hidden_outputs = hidden_outputs.stack()\n", + " hidden_outputs = autograph.stack(hidden_outputs)\n", " if training:\n", " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n", " return hidden_outputs\n", diff --git a/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb b/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb index 324b23c24b5a7970d7f20ed955839ba1cf1774fc..44532cb078f9bd1578172f8a7d8a4b55cd21a7cb 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb @@ -190,7 +190,6 @@ " self.upper_cell = tf.contrib.rnn.LSTMBlockCell(128)\n", " self.relu_layer = tf.layers.Dense(3, activation=tf.nn.relu)\n", "\n", - "\n", " def _rnn_layer(self, chars, cell, batch_size, training):\n", " \"\"\"A single RNN layer.\n", "\n", @@ -203,13 +202,12 @@ " Returns:\n", " A Tensor of shape (max_sequence_length, batch_size, output_size).\n", " \"\"\"\n", - " hidden_outputs = []\n", - " autograph.utils.set_element_type(hidden_outputs, tf.float32)\n", + " hidden_outputs = tf.TensorArray(tf.float32, 0, True)\n", " state, output = cell.zero_state(batch_size, tf.float32)\n", " for ch in chars:\n", " cell_output, (state, output) = cell.call(ch, (state, output))\n", " hidden_outputs.append(cell_output)\n", - " hidden_outputs = hidden_outputs.stack()\n", + " hidden_outputs = autograph.stack(hidden_outputs)\n", " if training:\n", " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n", " return hidden_outputs\n", @@ -223,7 +221,7 @@ "\n", "\n", " def call(self, inputs, training=False):\n", - " \"\"\"The RNN model code. Uses Eager and \n", + " \"\"\"The RNN model code. Uses Eager.\n", "\n", " The model consists of two RNN layers (made by lower_cell and upper_cell),\n", " followed by a fully connected layer with ReLU activation.\n", @@ -243,7 +241,8 @@ " seq = self._rnn_layer(seq, self.upper_cell, batch_size, training)\n", "\n", " # Grab just the end-of-sequence from each output.\n", - " indices = tf.stack([length - 1, range(batch_size)], axis=1)\n", + " indices = (length - 1, range(batch_size))\n", + " indices = tf.stack(indices, 1)\n", " sequence_ends = tf.gather_nd(seq, indices)\n", " return self.relu_layer(sequence_ends)\n", "\n", @@ -381,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 107, "metadata": { "colab": { "autoexec": { @@ -392,9 +391,9 @@ }, "colab_type": "code", "executionInfo": { - "elapsed": 10604, + "elapsed": 5454, "status": "ok", - "timestamp": 1524095272039, + "timestamp": 1529952160455, "user": { "displayName": "", "photoUrl": "", @@ -403,7 +402,7 @@ "user_tz": 240 }, "id": "2pg1AfbxBJQq", - "outputId": "9c924b4f-06e1-4538-976c-a3e1ddac5660", + "outputId": "4aef3052-f7c7-4bb1-a0a2-73fef2e96efb", "slideshow": { "slide_type": "-" } @@ -413,7 +412,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eval loss at step 100: 0.0674834\n" + "Eval loss at step 100: 0.0705221\n" ] } ], @@ -423,8 +422,8 @@ " 'learning_rate': 0.01,\n", "}\n", "\n", - "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/train.csv\"\n", - "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/test.csv\"\n", + "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/train.csv\"\n", + "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/test.csv\"\n", "data_dir = \"tmp/rnn/data\"\n", "\n", "regressor = tf.estimator.Estimator(\n", @@ -457,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 108, "metadata": { "colab": { "autoexec": { @@ -468,9 +467,9 @@ }, "colab_type": "code", "executionInfo": { - "elapsed": 7990, + "elapsed": 3432, "status": "ok", - "timestamp": 1524095280105, + "timestamp": 1529952163923, "user": { "displayName": "", "photoUrl": "", @@ -479,7 +478,7 @@ "user_tz": 240 }, "id": "dxHex2tUN_10", - "outputId": "2b889e5a-b9ed-4645-bf03-d98f26c72101", + "outputId": "1ff438f2-b045-4f4e-86a0-4dae7503f6b2", "slideshow": { "slide_type": "slide" } @@ -491,12 +490,12 @@ "\u003clink rel=stylesheet type=text/css href='/nbextensions/google.colab/tabbar.css'\u003e\u003c/link\u003e" ], "text/plain": [ - "\u003cIPython.core.display.HTML at 0x7f3f36aa6cd0\u003e" + "\u003cIPython.core.display.HTML at 0x7fcd7222a110\u003e" ] }, "metadata": { "tags": [ - "outputarea_id1" + "outputarea_id3" ] }, "output_type": "display_data" @@ -507,12 +506,12 @@ "\u003cscript src='/nbextensions/google.colab/tabbar_main.min.js'\u003e\u003c/script\u003e" ], "text/plain": [ - "\u003cIPython.core.display.HTML at 0x7f3eca67f7d0\u003e" + "\u003cIPython.core.display.HTML at 0x7fcd7222a8d0\u003e" ] }, "metadata": { "tags": [ - "outputarea_id1" + "outputarea_id3" ] }, "output_type": "display_data" @@ -520,15 +519,15 @@ { "data": { "text/html": [ - "\u003cdiv id=\"id1\"\u003e\u003c/div\u003e" + "\u003cdiv id=\"id3\"\u003e\u003c/div\u003e" ], "text/plain": [ - "\u003cIPython.core.display.HTML at 0x7f3eca67f8d0\u003e" + "\u003cIPython.core.display.HTML at 0x7fcd7222a050\u003e" ] }, "metadata": { "tags": [ - "outputarea_id1" + "outputarea_id3" ] }, "output_type": "display_data" @@ -536,16 +535,16 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa22-4362-11e8-91ec-c8d3ffb5fbe0\"] = colab_lib.createTabBar({\"contentBorder\": [\"0px\"], \"elementId\": \"id1\", \"borderColor\": [\"#a7a7a7\"], \"contentHeight\": [\"initial\"], \"tabNames\": [\"RNN Colorbot\"], \"location\": \"top\", \"initialSelection\": 0});\n", - "//# sourceURL=js_71b9087b6d" + "window[\"8a03307e-78a7-11e8-99f9-c8d3ffb5fbe0\"] = colab_lib.createTabBar({\"contentBorder\": [\"0px\"], \"elementId\": \"id3\", \"contentHeight\": [\"initial\"], \"tabNames\": [\"RNN Colorbot\"], \"location\": \"top\", \"initialSelection\": 0, \"borderColor\": [\"#a7a7a7\"]});\n", + "//# sourceURL=js_dc5d7f2784" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67f950\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222a190\u003e" ] }, "metadata": { "tags": [ - "outputarea_id1" + "outputarea_id3" ] }, "output_type": "display_data" @@ -553,16 +552,16 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa23-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_e390445f33" + "window[\"8a03307f-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id3\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_be7950150b" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67f990\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222ac90\u003e" ] }, "metadata": { "tags": [ - "outputarea_id1" + "outputarea_id3" ] }, "output_type": "display_data" @@ -570,17 +569,17 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa24-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", - "//# sourceURL=js_241dd76d85" + "window[\"8a033080-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", + "//# sourceURL=js_d0c3bd4eaa" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fc50\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222aad0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -588,17 +587,17 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa25-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n", - "//# sourceURL=js_60c64e3d50" + "window[\"8a033081-78a7-11e8-99f9-c8d3ffb5fbe0\"] = document.querySelector(\"#id3_content_0\");\n", + "//# sourceURL=js_f10f6eba86" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fd90\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222aed0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -606,17 +605,17 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa26-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"e8ddfa25-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n", - "//# sourceURL=js_14ea437cbd" + "window[\"8a033082-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"8a033081-78a7-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_ff29697179" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fe10\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222abd0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -624,17 +623,17 @@ { "data": { "application/javascript": [ - "window[\"e8ddfa27-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_09294c2226" + "window[\"8a033083-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id3\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_ff85295dc7" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fcd0\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222ab90\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -642,17 +641,17 @@ { "data": { "application/javascript": [ - "window[\"ec965514-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"e8ddfa24-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n", - "//# sourceURL=js_e5e8266997" + "window[\"8b18d8dc-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"8a033080-78a7-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_ed7aabfedb" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fe10\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222a110\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -660,17 +659,17 @@ { "data": { "application/javascript": [ - "window[\"ec965515-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", - "//# sourceURL=js_07a097f0ee" + "window[\"8b18d8dd-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", + "//# sourceURL=js_c86f8feaf4" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fc90\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222acd0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -678,17 +677,17 @@ { "data": { "application/javascript": [ - "window[\"ec965516-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n", - "//# sourceURL=js_790d669ca8" + "window[\"8b18d8de-78a7-11e8-99f9-c8d3ffb5fbe0\"] = document.querySelector(\"#id3_content_0\");\n", + "//# sourceURL=js_4d0fde6662" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67f8d0\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222ae50\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -696,17 +695,17 @@ { "data": { "application/javascript": [ - "window[\"ec965517-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec965516-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n", - "//# sourceURL=js_d30df771f0" + "window[\"8b18d8df-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"8b18d8de-78a7-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_3f66d52720" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fd90\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222a210\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -714,32 +713,32 @@ { "data": { "application/javascript": [ - "window[\"ec965518-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_8a43a2da4b" + "window[\"8b18d8e0-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id3\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_375f5ae6d7" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fc50\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd7222a310\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" }, { "data": { - "image/png": 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"data": { "application/javascript": [ - "window[\"ec96551a-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", - "//# sourceURL=js_2d99e0ac17" + "window[\"8b18d8e2-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", + "//# sourceURL=js_518a0f26fe" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67fe50\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6ec90\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -784,17 +783,17 @@ { "data": { "application/javascript": [ - "window[\"ec96551b-4362-11e8-91ec-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n", - "//# sourceURL=js_5c19462e32" + "window[\"8b18d8e3-78a7-11e8-99f9-c8d3ffb5fbe0\"] = document.querySelector(\"#id3_content_0\");\n", + "//# sourceURL=js_17eb3ff612" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b55dd0\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6eb50\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -802,17 +801,17 @@ { "data": { "application/javascript": [ - "window[\"ec96551c-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec96551b-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n", - "//# sourceURL=js_b9c8b7567b" + "window[\"8b18d8e4-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"8b18d8e3-78a7-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_99da807c8e" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b55a50\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6eb90\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -820,17 +819,17 @@ { "data": { "application/javascript": [ - "window[\"ec96551d-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_fd05186348" + "window[\"8b18d8e5-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id3\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_dee01cb4b6" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b55810\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e610\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -838,16 +837,16 @@ { "data": { "text/html": [ - "\u003cdiv class=id_888646481 style=\"margin-right:10px; display:flex;align-items:center;\"\u003e\u003cspan style=\"margin-right: 3px;\"\u003e\u003c/span\u003e\u003c/div\u003e" + "\u003cdiv class=id_853612217 style=\"margin-right:10px; display:flex;align-items:center;\"\u003e\u003cspan style=\"margin-right: 3px;\"\u003e\u003c/span\u003e\u003c/div\u003e" ], "text/plain": [ - "\u003cIPython.core.display.HTML at 0x7f3f32414810\u003e" + "\u003cIPython.core.display.HTML at 0x7fcd7222aa10\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -856,17 +855,17 @@ { "data": { "application/javascript": [ - "window[\"ec96551e-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 span\");\n", - "//# sourceURL=js_efef96e882" + "window[\"8b18d8e6-78a7-11e8-99f9-c8d3ffb5fbe0\"] = jQuery(\".id_853612217 span\");\n", + "//# sourceURL=js_8c378be329" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b55710\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e990\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -875,17 +874,17 @@ { "data": { "application/javascript": [ - "window[\"ec96551f-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ec96551e-4362-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n", - "//# sourceURL=js_6eca889864" + "window[\"8b18d8e7-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"8b18d8e6-78a7-11e8-99f9-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n", + "//# sourceURL=js_f0b946600c" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3eca67f990\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e310\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -894,17 +893,17 @@ { "data": { "application/javascript": [ - "window[\"ed8ea972-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 input\");\n", - "//# sourceURL=js_f02070cc60" + "window[\"8b18d8e9-78a7-11e8-99f9-c8d3ffb5fbe0\"] = jQuery(\".id_853612217 input\");\n", + "//# sourceURL=js_9e21b1373a" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b553d0\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6ea90\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -913,17 +912,17 @@ { "data": { "application/javascript": [ - "window[\"ed8ea973-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ed8ea972-4362-11e8-91ec-c8d3ffb5fbe0\"].remove();\n", - "//# sourceURL=js_ed9faba660" + "window[\"8b18d8ea-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"8b18d8e9-78a7-11e8-99f9-c8d3ffb5fbe0\"].remove();\n", + "//# sourceURL=js_a7764968c6" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31a95450\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e5d0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -932,17 +931,17 @@ { "data": { "application/javascript": [ - "window[\"ed8ea974-4362-11e8-91ec-c8d3ffb5fbe0\"] = jQuery(\".id_888646481 span\");\n", - "//# sourceURL=js_f3458d7074" + "window[\"8b18d8eb-78a7-11e8-99f9-c8d3ffb5fbe0\"] = jQuery(\".id_853612217 span\");\n", + "//# sourceURL=js_74279d3ff0" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31a95250\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e890\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -951,17 +950,17 @@ { "data": { "application/javascript": [ - "window[\"ed8ea975-4362-11e8-91ec-c8d3ffb5fbe0\"] = window[\"ed8ea974-4362-11e8-91ec-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n", - "//# sourceURL=js_3ffd97bd6f" + "window[\"8b18d8ec-78a7-11e8-99f9-c8d3ffb5fbe0\"] = window[\"8b18d8eb-78a7-11e8-99f9-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n", + "//# sourceURL=js_82b6c34cdb" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31a953d0\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e8d0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1", + "id3_content_0", + "outputarea_id3", "user_output" ] }, @@ -970,17 +969,17 @@ { "data": { "application/javascript": [ - "window[\"ed8ea976-4362-11e8-91ec-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"ec96551a-4362-11e8-91ec-c8d3ffb5fbe0\"]);\n", - "//# sourceURL=js_7f73e8bcca" + "window[\"8b18d8ed-78a7-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"8b18d8e2-78a7-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_ff6144734a" ], "text/plain": [ - "\u003cIPython.core.display.Javascript at 0x7f3f31b55710\u003e" + "\u003cIPython.core.display.Javascript at 0x7fcd08e6e8d0\u003e" ] }, "metadata": { "tags": [ - "id1_content_0", - "outputarea_id1" + "id3_content_0", + "outputarea_id3" ] }, "output_type": "display_data" @@ -1043,28 +1042,6 @@ "kind": "local" }, "name": "RNN Colorbot using Keras and Estimators", - "provenance": [ - { - "file_id": "1CtzefX39ffFibX_BqE6cRbT0UW_DdVKl", - "timestamp": 1523579810961 - }, - { - "file_id": "1DcfimonWU11tmyivKBGVrbpAl3BIOaRG", - "timestamp": 1523016192637 - }, - { - "file_id": "1wCZUh73zTNs1jzzYjqoxMIdaBWCdKJ2K", - "timestamp": 1522238054357 - }, - { - "file_id": "1_HpC-RrmIv4lNaqeoslUeWaX8zH5IXaJ", - "timestamp": 1521743157199 - }, - { - "file_id": "1mjO2fQ2F9hxpAzw2mnrrUkcgfb7xSGW-", - "timestamp": 1520522344607 - } - ], "version": "0.3.2", "views": {} }, diff --git a/tensorflow/contrib/autograph/impl/BUILD b/tensorflow/contrib/autograph/impl/BUILD index 91ae0b9b82c6f649c3c80b91ef894b2221cdc962..a5438592c30021eac7183b65ccc10c36d220bc57 100644 --- a/tensorflow/contrib/autograph/impl/BUILD +++ b/tensorflow/contrib/autograph/impl/BUILD @@ -18,19 +18,19 @@ py_library( name = "impl", srcs = [ "api.py", - "config.py", "conversion.py", - "naming.py", - "special_functions.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ "//tensorflow/contrib/autograph/converters", + "//tensorflow/contrib/autograph/core", "//tensorflow/contrib/autograph/operators", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/pyct/static_analysis", "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:platform", + "//tensorflow/python:util", "@gast_archive//:gast", "@six_archive//:six", ], @@ -60,23 +60,3 @@ py_test( "@gast_archive//:gast", ], ) - -py_test( - name = "naming_test", - srcs = ["naming_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":impl", - "//tensorflow/python:client_testlib", - ], -) - -py_test( - name = "special_functions_test", - srcs = ["special_functions_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":impl", - "//tensorflow/python:client_testlib", - ], -) diff --git a/tensorflow/contrib/autograph/impl/api.py b/tensorflow/contrib/autograph/impl/api.py index 24f87b2c14da4a3523f1e580d4362cbd3679a2cd..c7401c7df126b73ca22cdaf74a2f1fd6149d7545 100644 --- a/tensorflow/contrib/autograph/impl/api.py +++ b/tensorflow/contrib/autograph/impl/api.py @@ -27,14 +27,15 @@ import gast import six # pylint:enable=g-bad-import-order -from tensorflow.contrib.autograph.impl import config +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.impl import conversion from tensorflow.contrib.autograph.pyct import compiler from tensorflow.contrib.autograph.pyct import inspect_utils -from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.utils import builtins from tensorflow.contrib.autograph.utils import py_func from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect # TODO(mdan): Properly document the type hints. @@ -70,6 +71,8 @@ def convert(recursive=False, verbose=False, arg_types=None): def wrapper(*args, **kwargs): return converted_call(f, recursive, verbose, arg_types, *args, **kwargs) + wrapper = tf_decorator.make_decorator(f, wrapper) + # Sometimes the decorator is just desugared, making it impossible to detect. # This attribute makes detection easier. setattr(wrapper, '__pyct_is_compile_decorator', True) @@ -230,20 +233,20 @@ def to_graph(e, A function with a signature identical to `o`, but which when executed it creates TF a graph that has the same functionality as the original entity. """ - conversion_map = conversion.ConversionMap( + program_ctx = converter.ProgramContext( recursive=recursive, - nocompile_decorators=(convert, do_not_convert, converted_call), + autograph_decorators=(convert, do_not_convert, converted_call), partial_types=partial_types, - api_module=tf_inspect.getmodule(to_graph)) - _, name, namespace = conversion.entity_to_graph(e, conversion_map, arg_values, + autograph_module=tf_inspect.getmodule(to_graph), + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + _, name, namespace = conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) module = gast.Module([]) - for import_line in config.COMPILED_IMPORT_STATEMENTS: - module.body.extend(parser.parse_str(import_line).body) - for dep in reversed(conversion_map.dependency_cache.values()): + for dep in reversed(program_ctx.dependency_cache.values()): module.body.append(dep) - compiled_node, compiled_src = compiler.ast_to_object(module) + compiled_node, compiled_src = compiler.ast_to_object( + module, source_prefix=program_ctx.required_imports) # The compiled code should see everything the entry entity saw. # TODO(mdan): This might not work well if the call tree spans modules? @@ -280,17 +283,16 @@ def to_code(e, Returns: String. """ - conversion_map = conversion.ConversionMap( + program_ctx = converter.ProgramContext( recursive=recursive, - nocompile_decorators=(convert, do_not_convert, converted_call), + autograph_decorators=(convert, do_not_convert, converted_call), partial_types=partial_types, - api_module=tf_inspect.getmodule(to_graph)) - conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) + autograph_module=tf_inspect.getmodule(to_graph), + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) - imports = '\n'.join(config.COMPILED_IMPORT_STATEMENTS) code = '\n'.join( compiler.ast_to_source(dep, indentation) - for dep in reversed(tuple( - six.itervalues(conversion_map.dependency_cache)))) + for dep in reversed(tuple(six.itervalues(program_ctx.dependency_cache)))) - return imports + '\n\n' + code + return program_ctx.required_imports + '\n\n' + code diff --git a/tensorflow/contrib/autograph/impl/api_test.py b/tensorflow/contrib/autograph/impl/api_test.py index a7737b7f448131b1c54951efa719b481e1f4d0c9..994309333209586001c9369322ec3ddeee0a508e 100644 --- a/tensorflow/contrib/autograph/impl/api_test.py +++ b/tensorflow/contrib/autograph/impl/api_test.py @@ -21,12 +21,13 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config from tensorflow.contrib.autograph.impl import api -from tensorflow.contrib.autograph.impl import config from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.utils import py_func from tensorflow.python.framework import constant_op from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect tf = utils.fake_tf() @@ -154,6 +155,22 @@ class ApiTest(test.TestCase): constant_op.constant(-2)) self.assertListEqual([0, 1], sess.run(x).tolist()) + def test_decorator_preserves_argspec(self): + + class TestClass(object): + + def called_member(self, a): + if a < 0: + a = -a + return a + + called_member_converted = api.convert()(called_member) + + tc = TestClass() + self.assertListEqual( + list(tf_inspect.getfullargspec(tc.called_member)), + list(tf_inspect.getfullargspec(tc.called_member_converted))) + def test_convert_call_site_decorator(self): class TestClass(object): diff --git a/tensorflow/contrib/autograph/impl/conversion.py b/tensorflow/contrib/autograph/impl/conversion.py index 55a30dc127957b2a9caa053db843380c94bacfbf..776d19f672ebbd6b88985dda157434f2046d87e7 100644 --- a/tensorflow/contrib/autograph/impl/conversion.py +++ b/tensorflow/contrib/autograph/impl/conversion.py @@ -12,13 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""High level conversion support.""" +"""Core conversion logic, serves as main point of access.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -import collections import imp import gast @@ -38,77 +37,23 @@ from tensorflow.contrib.autograph.converters import logical_expressions from tensorflow.contrib.autograph.converters import name_scopes from tensorflow.contrib.autograph.converters import side_effect_guards from tensorflow.contrib.autograph.converters import single_return -from tensorflow.contrib.autograph.impl import config -from tensorflow.contrib.autograph.impl import naming +from tensorflow.contrib.autograph.converters import slices +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import ast_util -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import inspect_utils from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info -from tensorflow.contrib.autograph.utils import type_hints from tensorflow.python.util import tf_inspect # TODO(mdan): Might we not need any renaming at all? -class ConversionMap(object): - """ConversionMap keeps track of converting function hierarchies. - - This object is mutable, and is updated as functions are converted. - - Attributes: - recursive: Whether to recursively convert any functions that the decorator - function may call. - nocompile_decorators: tuple of decorator functions that toggle compilation - off. - dependency_cache: dict[object]: ast; maps original entities to their - converted AST - additional_imports: set(object); additional entities which for any reason - cannot be attached after loading and need to be explicitly imported - in the generated code - name_map: dict[string]: string; maps original entities to the name of - their converted counterparts - api_module: A reference to the api module. The reference needs to be passed - to avoid circular dependencies. - """ - - # TODO(mdan): Rename to ConversionContext, and pull in additional flags. - - def __init__(self, recursive, nocompile_decorators, partial_types, - api_module): - self.recursive = recursive - self.nocompile_decorators = nocompile_decorators - self.partial_types = partial_types if partial_types else () - # Required to output dependencies in discovery order, which should match - # the reverse dependency order. - self.dependency_cache = collections.OrderedDict() - self.additional_imports = set() - self.name_map = {} - self.api_module = api_module - - def new_namer(self, namespace): - return naming.Namer(namespace, self.recursive, self.name_map, - self.partial_types) - - def update_name_map(self, namer): - for o, name in namer.renamed_calls.items(): - if o in self.name_map: - if self.name_map[o] != name: - raise ValueError( - 'Calls to %s were converted using multiple names (%s). This is ' - 'possible when an entity with one of these names already ' - 'existed. To fix, avoid using any of these names.') - else: - self.name_map[o] = name - - def add_to_cache(self, original_entity, converted_ast): - self.dependency_cache[original_entity] = converted_ast - - def is_whitelisted_for_graph(o): """Check whether an entity is whitelisted for use in graph mode. @@ -127,7 +72,7 @@ def is_whitelisted_for_graph(o): return False -def entity_to_graph(o, conversion_map, arg_values, arg_types): +def entity_to_graph(o, program_ctx, arg_values, arg_types): """Compile a Python entity into equivalent TensorFlow. The function will also recursively compile all the entities that `o` @@ -138,7 +83,7 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): Args: o: A Python entity. - conversion_map: A ConversionMap object. + program_ctx: A ProgramContext object. arg_values: A dict containing value hints for symbols like function parameters. arg_types: A dict containing type hints for symbols like function @@ -156,7 +101,7 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): ValueError: if the entity type is not supported. """ if tf_inspect.isclass(o): - node, name, ns = class_to_graph(o, conversion_map) + node, name, ns = class_to_graph(o, program_ctx) elif tf_inspect.isfunction(o): # TODO(mdan): This is not a reliable mechanism. # The most reliable way is to check the source code, the AST will contain @@ -166,36 +111,35 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): 'lambda functions are not yet supported; declare the function' ' using def instead: %s' % o) else: - node, name, ns = function_to_graph(o, conversion_map, arg_values, - arg_types) + node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types) elif tf_inspect.ismethod(o): - node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types) + node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types) else: raise ValueError( 'Entity "%s" has unsupported type "%s". Only functions and classes are ' 'supported for now.' % (o, type(o))) - conversion_map.add_to_cache(o, node) - if conversion_map.recursive: + program_ctx.add_to_cache(o, node) + if program_ctx.recursive: while True: candidate = None - for obj in conversion_map.name_map.keys(): - if obj not in conversion_map.dependency_cache: + for obj in program_ctx.name_map.keys(): + if obj not in program_ctx.dependency_cache: candidate = obj break if candidate is None: break if (hasattr(candidate, 'im_class') and - getattr(candidate, 'im_class') not in conversion_map.partial_types): + getattr(candidate, 'im_class') not in program_ctx.partial_types): # Class members are converted with their objects, unless they're # only converted partially. continue - entity_to_graph(candidate, conversion_map, {}, {}) + entity_to_graph(candidate, program_ctx, {}, {}) return node, name, ns -def class_to_graph(c, conversion_map): +def class_to_graph(c, program_ctx): """Specialization of `entity_to_graph` for classes.""" converted_members = {} method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m) @@ -210,7 +154,7 @@ def class_to_graph(c, conversion_map): continue node, _, namespace = function_to_graph( m, - conversion_map=conversion_map, + program_ctx=program_ctx, arg_values={}, arg_types={'self': (c.__name__, c)}, owner_type=c) @@ -219,14 +163,14 @@ def class_to_graph(c, conversion_map): else: class_namespace.update(namespace) converted_members[m] = node - namer = conversion_map.new_namer(class_namespace) + namer = program_ctx.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) # TODO(mdan): This needs to be explained more thoroughly. # Process any base classes: if the sueprclass if of a whitelisted type, an # absolute import line is generated. Otherwise, it is marked for conversion # (as a side effect of the call to namer.compiled_class_name() followed by - # conversion_map.update_name_map(namer)). + # program_ctx.update_name_map(namer)). output_nodes = [] renames = {} bases = [] @@ -246,7 +190,7 @@ def class_to_graph(c, conversion_map): alias = namer.compiled_class_name(base.__name__, base) bases.append(alias) renames[qual_names.QN(base.__name__)] = qual_names.QN(alias) - conversion_map.update_name_map(namer) + program_ctx.update_name_map(namer) # Generate the definition of the converted class. output_nodes.append( @@ -278,14 +222,14 @@ def _add_reserved_symbol(namespace, name, entity): ag_internal = None -def _add_self_references(namespace, api_module): +def _add_self_references(namespace, autograph_module): """Adds namespace references to the module that exposes the api itself.""" global ag_internal if ag_internal is None: # Craft a module that exposes parts of the external API as well as certain # internal modules. ag_internal = imp.new_module('autograph') - ag_internal.converted_call = api_module.converted_call + ag_internal.converted_call = autograph_module.converted_call ag_internal.utils = utils # TODO(mdan): Add safeguards against name clashes. # We don't want to create a submodule because we want the operators to be @@ -295,27 +239,24 @@ def _add_self_references(namespace, api_module): _add_reserved_symbol(namespace, 'ag__', ag_internal) -def function_to_graph(f, conversion_map, arg_values, arg_types, - owner_type=None): +def function_to_graph(f, program_ctx, arg_values, arg_types, owner_type=None): """Specialization of `entity_to_graph` for callable functions.""" node, source = parser.parse_entity(f) node = node.body[0] namespace = inspect_utils.getnamespace(f) - _add_self_references(namespace, conversion_map.api_module) - namer = conversion_map.new_namer(namespace) + _add_self_references(namespace, program_ctx.autograph_module) + namer = program_ctx.new_namer(namespace) - ctx = context.EntityContext( - namer=namer, + entity_info = transformer.EntityInfo( source_code=source, source_file='', namespace=namespace, arg_values=arg_values, arg_types=arg_types, - owner_type=owner_type, - recursive=conversion_map.recursive, - type_annotation_func=type_hints.set_element_type) - node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators) + owner_type=owner_type) + context = converter.EntityContext(namer, entity_info, program_ctx) + node = node_to_graph(node, context) # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type) @@ -325,29 +266,28 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, raise NotImplementedError('Strange corner case. Send us offending code!') node.name = new_name - conversion_map.update_name_map(namer) + program_ctx.update_name_map(namer) # TODO(mdan): Use this at compilation. - conversion_map.additional_imports.update(deps) return node, new_name, namespace -def _static_analysis_pass(node, ctx): +def _apply_transformer(node, context, converter_module): + # TODO(mdan): Clear static analysis here. node = qual_names.resolve(node) - node = activity.resolve(node, ctx, None) - node = live_values.resolve(node, ctx, config.PYTHON_LITERALS) - node = type_info.resolve(node, ctx) + node = activity.resolve(node, context.info, None) + node = live_values.resolve(node, context.info, config.PYTHON_LITERALS) + node = type_info.resolve(node, context.info) + node = converter_module.transform(node, context) return node -def node_to_graph(node, ctx, nocompile_decorators): +def node_to_graph(node, context): """Convert Python code to equivalent TF graph mode code. Args: - node: A Python AST node representing the code to convert. - ctx: An EntityContext object. - nocompile_decorators: A tuple containing decorators to be stripped from - functions during conversion. + node: AST, the code to convert. + context: converter.EntityContext Returns: A tuple (node, deps): @@ -357,53 +297,26 @@ def node_to_graph(node, ctx, nocompile_decorators): """ # TODO(mdan): Verify arguments for correctness. - # TODO(mdan): Factor out common elements. - # These include: - # * code move between blocks - # * visiting blocks in transformers - - # Certain steps, especially canonicalization, insert new symbols into the - # tree, which must be accounted. Although less efficient, it is most robust - # to re-run the analysis. - - node = _static_analysis_pass(node, ctx) - - # TODO(mdan): Clean this up. - # Some intermediate analyses are not required, and some comments got orphaned. - + node = _apply_transformer(node, context, ifexp) # Past this point, line numbers are no longer accurate so we ignore the # source. # TODO(mdan): Is it feasible to reconstruct intermediate source code? - ctx.source_code = None - node = ifexp.transform(node, ctx) - node, deps = decorators.transform(node, nocompile_decorators) - node = break_statements.transform(node, ctx) - node = _static_analysis_pass(node, ctx) - - node = asserts.transform(node, ctx) - + context.info.source_code = None + node = _apply_transformer(node, context, decorators) + node = _apply_transformer(node, context, break_statements) + node = _apply_transformer(node, context, asserts) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. - node = continue_statements.transform(node, ctx) - ctx.namespace['len'] = len - - node = _static_analysis_pass(node, ctx) - node = single_return.transform(node, ctx) - - node = _static_analysis_pass(node, ctx) - node = lists.transform(node, ctx) - node = builtin_functions.transform(node, ctx) - - node = _static_analysis_pass(node, ctx) - node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES, - nocompile_decorators) - node = control_flow.transform(node, ctx) - - # control_flow may create new symbols and change scopes. - node = _static_analysis_pass(node, ctx) - node = logical_expressions.transform(node, ctx) - node = side_effect_guards.transform(node, ctx) - node = name_scopes.transform(node, ctx) - - return node, deps + node = _apply_transformer(node, context, continue_statements) + context.info.namespace['len'] = len + node = _apply_transformer(node, context, single_return) + node = _apply_transformer(node, context, lists) + node = _apply_transformer(node, context, slices) + node = _apply_transformer(node, context, builtin_functions) + node = _apply_transformer(node, context, call_trees) + node = _apply_transformer(node, context, control_flow) + node = _apply_transformer(node, context, logical_expressions) + node = _apply_transformer(node, context, side_effect_guards) + node = _apply_transformer(node, context, name_scopes) + return node diff --git a/tensorflow/contrib/autograph/impl/conversion_test.py b/tensorflow/contrib/autograph/impl/conversion_test.py index bc61498b5422f5e130bbfeef935d0a796b4f5922..f5279298afdcd406a9a6762e58367cea8ca63141 100644 --- a/tensorflow/contrib/autograph/impl/conversion_test.py +++ b/tensorflow/contrib/autograph/impl/conversion_test.py @@ -21,6 +21,8 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.impl import api from tensorflow.contrib.autograph.impl import conversion from tensorflow.python.framework import constant_op @@ -30,8 +32,13 @@ from tensorflow.python.platform import test class ConversionTest(test.TestCase): - def _simple_conversion_map(self): - return conversion.ConversionMap(True, (), (), api) + def _simple_program_ctx(self): + return converter.ProgramContext( + recursive=True, + autograph_decorators=(), + partial_types=(), + autograph_module=api, + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) def test_is_whitelisted_for_graph(self): @@ -44,16 +51,16 @@ class ConversionTest(test.TestCase): def test_entity_to_graph_unsupported_types(self): with self.assertRaises(ValueError): - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph('dummy', conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph('dummy', program_ctx, None, None) def test_entity_to_graph_callable(self): b = 2 def f(a): return a + b - conversion_map = self._simple_conversion_map() - ast, name, ns = conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + ast, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) self.assertTrue(isinstance(ast, gast.FunctionDef), ast) self.assertEqual('tf__f', name) self.assertTrue(ns['b'] is b) @@ -66,18 +73,17 @@ class ConversionTest(test.TestCase): def f(a): return g(a) - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(f, program_ctx, None, None) - self.assertTrue(f in conversion_map.dependency_cache) - self.assertTrue(g in conversion_map.dependency_cache) - self.assertEqual('tf__f', conversion_map.dependency_cache[f].name) + self.assertTrue(f in program_ctx.dependency_cache) + self.assertTrue(g in program_ctx.dependency_cache) + self.assertEqual('tf__f', program_ctx.dependency_cache[f].name) # need the extra .body[0] in order to step past the with tf.name_scope('f') # that is added automatically self.assertEqual( - 'tf__g', - conversion_map.dependency_cache[f].body[0].body[0].value.func.id) - self.assertEqual('tf__g', conversion_map.dependency_cache[g].name) + 'tf__g', program_ctx.dependency_cache[f].body[0].body[0].value.func.id) + self.assertEqual('tf__g', program_ctx.dependency_cache[g].name) def test_entity_to_graph_class_hierarchy(self): @@ -104,16 +110,15 @@ class ConversionTest(test.TestCase): def baz(self): return self.y - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(TestSubclass, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(TestSubclass, program_ctx, None, None) - self.assertTrue(TestBase in conversion_map.dependency_cache) - self.assertTrue(TestSubclass in conversion_map.dependency_cache) + self.assertTrue(TestBase in program_ctx.dependency_cache) + self.assertTrue(TestSubclass in program_ctx.dependency_cache) self.assertEqual('TfTestBase', - conversion_map.dependency_cache[TestBase].body[-1].name) - self.assertEqual( - 'TfTestSubclass', - conversion_map.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestBase].body[-1].name) + self.assertEqual('TfTestSubclass', + program_ctx.dependency_cache[TestSubclass].body[-1].name) def test_entity_to_graph_class_hierarchy_whitelisted(self): @@ -126,24 +131,23 @@ class ConversionTest(test.TestCase): def call(self, x): return 3 * x - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(TestSubclass, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(TestSubclass, program_ctx, None, None) - self.assertTrue(TestSubclass in conversion_map.dependency_cache) - self.assertFalse(training.Model in conversion_map.dependency_cache) + self.assertTrue(TestSubclass in program_ctx.dependency_cache) + self.assertFalse(training.Model in program_ctx.dependency_cache) self.assertEqual( 'Model', - conversion_map.dependency_cache[TestSubclass].body[0].names[0].name) - self.assertEqual( - 'TfTestSubclass', - conversion_map.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass].body[0].names[0].name) + self.assertEqual('TfTestSubclass', + program_ctx.dependency_cache[TestSubclass].body[-1].name) def test_entity_to_graph_lambda(self): f = lambda a: a with self.assertRaises(NotImplementedError): - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(f, program_ctx, None, None) def test_ag_module_cached(self): def callee(): @@ -152,11 +156,11 @@ class ConversionTest(test.TestCase): def caller(a): return a() - conversion_map = self._simple_conversion_map() - _, _, callee_ns = conversion.entity_to_graph( - callee, conversion_map, None, None) - _, _, caller_ns = conversion.entity_to_graph( - caller, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + _, _, callee_ns = conversion.entity_to_graph(callee, program_ctx, None, + None) + _, _, caller_ns = conversion.entity_to_graph(caller, program_ctx, None, + None) self.assertTrue(callee_ns['ag__'] is caller_ns['ag__']) diff --git a/tensorflow/contrib/autograph/lang/BUILD b/tensorflow/contrib/autograph/lang/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..77a2184e229003a3403cbe3bf116ad2570274a1b --- /dev/null +++ b/tensorflow/contrib/autograph/lang/BUILD @@ -0,0 +1,40 @@ +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "lang", + srcs = [ + "directives.py", + "special_functions.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/contrib/autograph/operators", + ], +) + +py_test( + name = "special_functions_test", + srcs = ["special_functions_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":lang", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/lang/directives.py b/tensorflow/contrib/autograph/lang/directives.py new file mode 100644 index 0000000000000000000000000000000000000000..aabe5d99394a0cb921196d1c6a6b2a9496ea7545 --- /dev/null +++ b/tensorflow/contrib/autograph/lang/directives.py @@ -0,0 +1,68 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Directives are special no-op functions that serve as compilation markers. + +They provide static information like type hints, compilation and TensorFlow +overrides. + +These serve as annotations in the compiled code, allowing the user some control +over the compilation process. They have no functional role at runtime. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +UNSPECIFIED = object() + + +def set_element_type(entity, dtype, shape=UNSPECIFIED): + """Indicates that the entity is expected hold items of specified type/shape. + + The staged TensorFlow ops will reflect and assert this data type. Ignored + otherwise. + + Args: + entity: The entity to annotate. + dtype: TensorFlow dtype value to assert for entity. + shape: Optional shape to assert for entity. + """ + del entity + del dtype + del shape + + +def set_loop_options( + parallel_iterations=UNSPECIFIED, + back_prop=UNSPECIFIED, + swap_memory=UNSPECIFIED, + maximum_iterations=UNSPECIFIED): + """Specifies additional arguments to be passed to the enclosing while_loop. + + The parameters apply to and only to the immediately enclosing loop. It only + has effect if the loop is staged as a TF while_loop; otherwise the parameters + have no effect. + + Args: + parallel_iterations: See tf.while_loop. + back_prop: See tf.while_loop. + swap_memory: See tf.while_loop. + maximum_iterations: See tf.while_loop. + """ + del parallel_iterations + del back_prop + del swap_memory + del maximum_iterations diff --git a/tensorflow/contrib/autograph/impl/special_functions.py b/tensorflow/contrib/autograph/lang/special_functions.py similarity index 62% rename from tensorflow/contrib/autograph/impl/special_functions.py rename to tensorflow/contrib/autograph/lang/special_functions.py index b7a8177c44c88217560fb7f72c77d3ac1aa0c9ec..11135295a7966bc5d693676fcc71fe43791f2e99 100644 --- a/tensorflow/contrib/autograph/impl/special_functions.py +++ b/tensorflow/contrib/autograph/lang/special_functions.py @@ -26,23 +26,34 @@ from __future__ import print_function from tensorflow.contrib.autograph.operators import data_structures -def stack(list_or_tensor, element_dtype=None): - """Stacks the input, if it admits the notion of stacking. No-op otherwise. +def stack(list_or_tensor, element_dtype=None, strict=True): + """Stacks the input, if it admits the notion of stacking. For example, a list of tensors can be stacked into a larger tensor. This function is similar to tf.stack, but it accepts non-lists and lists of non-tensors as arguments. In the latter case, the function does nothing. Args: - list_or_tensor: Any entity. - element_dtype: Optional dtype for the elements in the list. Required if the - input is stackable, and the list is untyped. + list_or_tensor: Any + element_dtype: tf.DType, optional dtypedtype for the elements in the list. + Required if the input is stackable, and the list is untyped. + strict: bool, if True an error is raised if the input is not stackable. + Otherwise the function is a no-op. Returns: - If the input is stackable, a new object representing the stacked inputs. - Otherwise it returns list_or_tensor unchanged. + Any, if the input is stackable, the result will be a tf.Tensor. Otherwise, + if strict=False, the result will be list_or_tensor. + + Raises: + ValueError: if strict=True and the input is not stackable. """ + if strict: + def raise_error(x): + raise ValueError('%s must be stackable when strict=True' % x) + original_call = raise_error + else: + original_call = lambda x: x return data_structures.list_stack( list_or_tensor, data_structures.ListStackOpts( - element_dtype=element_dtype, original_call=lambda x: x)) + element_dtype=element_dtype, original_call=original_call)) diff --git a/tensorflow/contrib/autograph/impl/special_functions_test.py b/tensorflow/contrib/autograph/lang/special_functions_test.py similarity index 81% rename from tensorflow/contrib/autograph/impl/special_functions_test.py rename to tensorflow/contrib/autograph/lang/special_functions_test.py index 9b52d2a59b5a3e3c92f11343197379c773ecc828..a49cb6407517b634e0f1259fccda03d4ed18e83f 100644 --- a/tensorflow/contrib/autograph/impl/special_functions_test.py +++ b/tensorflow/contrib/autograph/lang/special_functions_test.py @@ -18,7 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.impl import special_functions +from tensorflow.contrib.autograph.lang import special_functions from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util @@ -29,14 +29,18 @@ from tensorflow.python.platform import test class SpecialFunctionsTest(test.TestCase): def test_basic(self): - self.assertEqual(special_functions.stack(1), 1) - self.assertListEqual(special_functions.stack([1, 2, 3]), [1, 2, 3]) + self.assertEqual(special_functions.stack(1, strict=False), 1) + self.assertListEqual( + special_functions.stack([1, 2, 3], strict=False), [1, 2, 3]) # TODO(mdan): This should probably forward to tf.stack. self.assertTrue( isinstance( special_functions.stack( [constant_op.constant(1), - constant_op.constant(2)]), list)) + constant_op.constant(2)], strict=False), list)) + + with self.assertRaises(ValueError): + special_functions.stack([1, 2, 3]) t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor( diff --git a/tensorflow/contrib/autograph/operators/BUILD b/tensorflow/contrib/autograph/operators/BUILD index 0c6ab65505ee03e19588adae73d3134399a34b65..332d5dab19e7ade1531b564fbdef2fa0dc2d09d5 100644 --- a/tensorflow/contrib/autograph/operators/BUILD +++ b/tensorflow/contrib/autograph/operators/BUILD @@ -28,7 +28,15 @@ py_library( visibility = ["//tensorflow:__subpackages__"], deps = [ "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:array_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:list_ops", "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:tensor_util", + "//tensorflow/python:variables", "//tensorflow/python/data/ops:dataset_ops", ], ) diff --git a/tensorflow/contrib/autograph/operators/control_flow.py b/tensorflow/contrib/autograph/operators/control_flow.py index 671c9ccc13eaa887522cfc248a6d56d7ab9719ca..988df70157170ed0a9ece33976e871e6f7693bbc 100644 --- a/tensorflow/contrib/autograph/operators/control_flow.py +++ b/tensorflow/contrib/autograph/operators/control_flow.py @@ -51,7 +51,7 @@ def for_stmt(iter_, extra_test, body, init_state): Args: iter_: The entity being iterated over. extra_test: Callable with the state as arguments, and boolean return type. - An additionnal loop condition. + An additional loop condition. body: Callable with the iterate and the state as arguments, and state as return type. The actual loop body. init_state: Tuple containing the initial state. diff --git a/tensorflow/contrib/autograph/pyct/BUILD b/tensorflow/contrib/autograph/pyct/BUILD index 989b821e53a5cefbe39095e669f9a9e0bec65b8a..a49a4ed05ca99a5c9784cfc132784890e63a94de 100644 --- a/tensorflow/contrib/autograph/pyct/BUILD +++ b/tensorflow/contrib/autograph/pyct/BUILD @@ -22,8 +22,8 @@ py_library( "__init__.py", "anno.py", "ast_util.py", + "cfg.py", "compiler.py", - "context.py", "inspect_utils.py", "parser.py", "pretty_printer.py", @@ -38,6 +38,8 @@ py_library( "@gast_archive//:gast", "@six_archive//:six", "@termcolor_archive//:termcolor", + # TODO(mdan): Remove this dependency. + "//tensorflow/python:util", ], ) @@ -62,6 +64,17 @@ py_test( ], ) +py_test( + name = "cfg_test", + srcs = ["cfg_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":pyct", + "//tensorflow/python:client_testlib", + "@gast_archive//:gast", + ], +) + py_test( name = "compiler_test", srcs = ["compiler_test.py"], diff --git a/tensorflow/contrib/autograph/pyct/cfg.py b/tensorflow/contrib/autograph/pyct/cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..666328781f683c9457f6892c0a26088c33ba94a7 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/cfg.py @@ -0,0 +1,733 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Control flow graph (CFG) structure for Python AST representation. + +The CFG is a digraph with edges representing valid control flow. Each +node is associated with exactly one AST node, but not all AST nodes may have +a corresponding CFG counterpart. + +Once built, the CFG itself is immutable, but the values it holds need not be; +they are usually annotated with information extracted by walking the graph. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +from enum import Enum + +# pylint:disable=g-bad-import-order +import gast +# pylint:enable=g-bad-import-order + +from tensorflow.contrib.autograph.pyct import compiler + + +class Node(object): + """A node in the CFG. + + Although new instances of this class are mutable, the objects that a user + finds in the CFG are typically not. + + The nodes represent edges in the CFG graph, and maintain pointers to allow + efficient walking in both forward and reverse order. The following property + holds for all nodes: "child in node.next" iff "node in child.prev". + + Attributes: + next: FrozenSet[Node, ...], the nodes that follow this node, in control + flow order + prev: FrozenSet[Node, ...], the nodes that precede this node, in reverse + control flow order + ast_node: ast.AST, the AST node corresponding to this CFG node + """ + + def __init__(self, next_, prev, ast_node): + self.next = next_ + self.prev = prev + self.ast_node = ast_node + + def freeze(self): + self.next = frozenset(self.next) + self.prev = frozenset(self.prev) + + def __repr__(self): + return compiler.ast_to_source(self.ast_node).strip() + + +class Graph( + collections.namedtuple('Graph', ['entry', 'exit', 'error', 'index'])): + """A Control Flow Graph. + + The CFG maintains an index to allow looking up a CFG node by the AST node to + which it is associated. The index can also be enumerated in top-down, depth + first order. + + Walking the graph in forward or reverse order is supported by double + parent-child links. + + Note: the error nodes are not wired to their corresponding finally guards, + because these are shared, and wiring them would create a reverse path from + normal control flow into the error nodes, which we want to avoid. + + Attributes: + entry: Node, the entry node + exit: FrozenSet[Node, ...], the exit nodes + error: FrozenSet[Node, ...], nodes that exit due to an explicitly raised + error (errors propagated from function calls are not accounted) + index: Dict[ast.Node, Node], mapping AST nodes to the respective CFG + node + """ + + def __repr__(self): + result = 'digraph CFG {\n' + for node in self.index.values(): + result += ' %s [label="%s"];\n' % (id(node), node) + for node in self.index.values(): + if node.next: + result += ' %s -> {%s};\n' % (id(node), ', '.join( + repr(id(n)) for n in node.next)) + result += '}' + return result + + +class _WalkMode(Enum): + FORWARD = 1 + REVERSE = 2 + + +class GraphVisitor(object): + """Base class for a CFG visitors. + + This implementation is not thread safe. + + The visitor has some facilities to simplify dataflow analyses. In particular, + it allows revisiting the nodes at the decision of the subclass. This can be + used to visit the graph until the state reaches a fixed point. + + For more details on dataflow analysis, see + https://www.seas.harvard.edu/courses/cs252/2011sp/slides/Lec02-Dataflow.pdf + + Note: the literature generally suggests visiting successor nodes only when the + state of the current node changed, regardless of whether that successor has + ever been visited. This implementation visits every successor at least once. + + Attributes: + graph: Graph + in_: Dict[Node, Any], stores node-keyed state during a visit + out: Dict[Node, Any], stores node-keyed state during a visit + """ + + def reset(self): + self.in_ = { + node: self.init_state(node) for node in self.graph.index.values() + } + self.out = { + node: self.init_state(node) for node in self.graph.index.values() + } + + def init_state(self, node): + """State initialization function. Optional to overload. + + An in/out state slot will be created for each node in the graph. Subclasses + may overload this to control what that is initialized to. + + Args: + node: Node + """ + del node + return None + + def visit_node(self, node): + """Visitor function. + + Args: + node: Node + Returns: + bool, whether the node should be revisited; subclasses can visit every + reachable node exactly once by always returning False + """ + raise NotImplementedError('Subclasses must implement this.') + + def _visit_internal(self, mode): + """Visits the CFG, depth-first.""" + assert mode in (_WalkMode.FORWARD, _WalkMode.REVERSE) + if mode == _WalkMode.FORWARD: + open_ = [self.graph.entry] + elif mode == _WalkMode.REVERSE: + open_ = list(self.graph.exit) + closed = set() + self.reset() + + while open_: + node = open_.pop(0) + closed.add(node) + + should_revisit = self.visit_node(node) + + if mode == _WalkMode.FORWARD: + children = node.next + elif mode == _WalkMode.REVERSE: + children = node.prev + + for next_ in children: + if should_revisit or next_ not in closed: + open_.append(next_) + + def visit_forward(self, graph): + self.graph = graph + self._visit_internal(_WalkMode.FORWARD) + + def visit_reverse(self, graph): + self.graph = graph + self._visit_internal(_WalkMode.REVERSE) + + +class GraphBuilder(object): + """Builder that constructs a CFG from a given AST. + + This GraphBuilder facilitates constructing the DAG that forms the CFG when + nodes + are supplied in lexical order (i.e., top-down, depth first). Under these + conditions, it supports building patterns found in typical structured + programs. + + This builder ignores the flow generated by exceptions, which are assumed to + always be catastrophic and present purely for diagnostic purposes (e.g. to + print debug information). Statements like raise and try/catch sections are + allowed and will generate control flow edges, but ordinaty statements are + assumed not to raise exceptions. + + Finally sections are also correctly interleaved between break/continue/return + nodes and their subsequent statements. + + Important concepts: + * nodes - nodes refer refer to CFG nodes; AST nodes are qualified explicitly + * leaf set - since the graph is constructed gradually, a leaf set maintains + the CFG nodes that will precede the node that the builder expects to + receive next; when an ordinary node is added, it is connected to the + existing leaves and it in turn becomes the new leaf + * jump nodes - nodes that should generate edges other than what + ordinary nodes would; these correspond to break, continue and return + statements + * sections - logical delimiters for subgraphs that require special + edges; there are various types of nodes, each admitting various + types of jump nodes; sections are identified by their corresponding AST + node + """ + + # TODO(mdan): Perhaps detail this in a markdown doc. + # TODO(mdan): Add exception support. + + def __init__(self, parent_ast_node): + self.reset() + self.parent = parent_ast_node + + def reset(self): + """Resets the state of this factory.""" + self.head = None + self.errors = set() + self.node_index = collections.OrderedDict() + + # TODO(mdan): Too many primitives. Use classes. + self.leaves = set() + + self.finally_sections = {} + self.finally_section_subgraphs = {} # Values are [begin_node, exit_nodes] + # Whether the guard section can be reached from the statement that precedes + # it. + self.finally_section_has_direct_flow = {} + # Finally sections that await their first node. + self.pending_finally_sections = set() + + # Exit jumps keyed by the section they affect. + self.exits = {} + + # The entry of loop sections, keyed by the section. + self.section_entry = {} + # Continue jumps keyed by the section they affect. + self.continues = {} + + # The entry of conditional sections, keyed by the section. + self.cond_entry = {} + # Lists of leaf nodes corresponding to each branch in the section. + self.cond_leaves = {} + + def _connect_nodes(self, first, second): + """Connects nodes to signify that control flows from first to second. + + Args: + first: Union[Set[Node, ...], Node] + second: Node + """ + if isinstance(first, Node): + first.next.add(second) + second.prev.add(first) + else: + for node in first: + self._connect_nodes(node, second) + + def _add_new_node(self, ast_node): + """Grows the graph by adding a CFG node following the current leaves.""" + if ast_node is self.node_index: + raise ValueError('%s added twice' % ast_node) + node = Node(next_=set(), prev=set(), ast_node=ast_node) + self.node_index[ast_node] = node + + if self.head is None: + self.head = node + + for leaf in self.leaves: + self._connect_nodes(leaf, node) + + # If any finally section awaits its first node, populate it. + for section_id in self.pending_finally_sections: + self.finally_section_subgraphs[section_id][0] = node + self.pending_finally_sections = set() + + return node + + def add_ordinary_node(self, ast_node): + """Grows the graph by adding an ordinary CFG node. + + Ordinary nodes are followed by the next node, in lexical order, that is, + they become the new leaf set. + + Args: + ast_node: ast.AST + Returns: + Node + """ + node = self._add_new_node(ast_node) + self.leaves = set((node,)) + return node + + def _add_jump_node(self, ast_node, guards): + """Grows the graph by adding a jump node. + + Jump nodes are added to the current leaf set, and the leaf set becomes + empty. If the jump node is the last in a cond section, then it may be added + back to the leaf set by a separate mechanism. + + Args: + ast_node: ast.AST + guards: Tuple[ast.AST, ...], the finally sections active for this node + Returns: + Node + """ + node = self._add_new_node(ast_node) + self.leaves = set() + # The guards themselves may not yet be complete, and will be wired later. + self.finally_sections[node] = guards + return node + + def _connect_jump_to_finally_sections(self, node): + """Connects a jump node to the finally sections protecting it.""" + cursor = set((node,)) + for guard_section_id in self.finally_sections[node]: + guard_begin, guard_ends = self.finally_section_subgraphs[guard_section_id] + self._connect_nodes(cursor, guard_begin) + cursor = guard_ends + del self.finally_sections[node] + # TODO(mdan): Should garbage-collect finally_section_subgraphs. + return cursor + + def add_exit_node(self, ast_node, section_id, guards): + """Grows the graph by adding an exit node. + + This node becomes an exit for the current section. + + Args: + ast_node: ast.AST + section_id: Hashable, the node for which ast_node should be considered + to be an exit node + guards: Tuple[ast.AST, ...], the finally sections that guard ast_node + """ + node = self._add_jump_node(ast_node, guards) + self.exits[section_id].add(node) + + def add_continue_node(self, ast_node, section_id, guards): + """Grows the graph by adding a reentry node. + + This node causes control flow to go back to the loop section's entry. + + Args: + ast_node: ast.AST + section_id: Hashable, the node for which ast_node should be considered + to be an exit node + guards: Tuple[ast.AST, ...], the finally sections that guard ast_node + """ + node = self._add_jump_node(ast_node, guards) + self.continues[section_id].add(node) + + def add_error_node(self, ast_node, guards): + """Grows the graph by adding an error node. + + This node becomes an exit for the entire graph. + + Args: + ast_node: ast.AST + guards: Tuple[ast.AST, ...], the finally sections that guard ast_node + """ + node = self._add_jump_node(ast_node, guards) + self.errors.add(node) + self.leaves = set() + + def enter_section(self, section_id): + """Enters a regular section. + + Regular sections admit exit jumps, which end the section. + + Args: + section_id: Hashable, the same node that will be used in calls to the + ast_node arg passed to add_exit_node + """ + assert section_id not in self.exits + self.exits[section_id] = set() + + def exit_section(self, section_id): + """Exits a regular section.""" + + # Exits are jump nodes, which may be protected. + for exit_ in self.exits[section_id]: + self.leaves |= self._connect_jump_to_finally_sections(exit_) + + del self.exits[section_id] + + def enter_loop_section(self, section_id, entry_node): + """Enters a loop section. + + Loop sections define an entry node. The end of the section always flows back + to the entry node. These admit continue jump nodes which also flow to the + entry node. + + Args: + section_id: Hashable, the same node that will be used in calls to the + ast_node arg passed to add_continue_node + entry_node: ast.AST, the entry node into the loop (e.g. the test node + for while loops) + """ + assert section_id not in self.section_entry + assert section_id not in self.continues + self.continues[section_id] = set() + node = self.add_ordinary_node(entry_node) + self.section_entry[section_id] = node + + def exit_loop_section(self, section_id): + """Exits a loop section.""" + self._connect_nodes(self.leaves, self.section_entry[section_id]) + + # continues are jump nodes, which may be protected. + for reentry in self.continues[section_id]: + guard_ends = self._connect_jump_to_finally_sections(reentry) + self._connect_nodes(guard_ends, self.section_entry[section_id]) + + # Loop nodes always loop back. + self.leaves = set((self.section_entry[section_id],)) + + del self.continues[section_id] + del self.section_entry[section_id] + + def enter_cond_section(self, section_id): + """Enters a conditional section. + + Conditional sections define an entry node, and one or more branches. + + Args: + section_id: Hashable, the same node that will be used in calls to the + section_id arg passed to new_cond_branch + """ + + assert section_id not in self.cond_entry + assert section_id not in self.cond_leaves + self.cond_leaves[section_id] = [] + + def new_cond_branch(self, section_id): + """Begins a new branch in a cond section.""" + assert section_id in self.cond_leaves + + if section_id in self.cond_entry: + # Subsequent splits move back to the split point, and memorize the + # current leaves. + self.cond_leaves[section_id].append(self.leaves) + self.leaves = self.cond_entry[section_id] + else: + # If this is the first time we split a section, just remember the split + # point. + self.cond_entry[section_id] = self.leaves + + def exit_cond_section(self, section_id): + """Exits a conditional section.""" + for split in self.cond_leaves[section_id]: + self.leaves |= split + del self.cond_entry[section_id] + del self.cond_leaves[section_id] + + def enter_finally_section(self, section_id): + """Enters a finally section.""" + # TODO(mdan): This, not the caller, should track the active sections. + self.finally_section_subgraphs[section_id] = [None, None] + if self.leaves: + self.finally_section_has_direct_flow[section_id] = True + else: + self.finally_section_has_direct_flow[section_id] = False + self.pending_finally_sections.add(section_id) + + def exit_finally_section(self, section_id): + """Exits a finally section.""" + assert section_id not in self.pending_finally_sections, 'Empty finally?' + self.finally_section_subgraphs[section_id][1] = self.leaves + # If the guard can only be reached by a jump, then it will not flow + # into the statement that follows it. + if not self.finally_section_has_direct_flow[section_id]: + self.leaves = set() + del self.finally_section_has_direct_flow[section_id] + + def build(self): + """Returns the CFG accumulated so far and resets the builder. + + Returns: + Graph + """ + # Freeze the nodes. + for node in self.node_index.values(): + node.freeze() + + result = Graph( + entry=self.head, + exit=self.leaves, + error=self.errors, + index=self.node_index) + + # Reset the state. + self.reset() + + return result + + +class AstToCfg(gast.NodeVisitor): + """Converts an AST to CFGs. + + A separate CFG will be constructed for each function. + """ + + # TODO(mdan): Figure out how to deal with closures. + + def __init__(self): + super(AstToCfg, self).__init__() + + self.builder_stack = [] + self.builder = None + self.cfgs = {} + + self.lexical_scopes = [] + + def _enter_lexical_scope(self, node): + self.lexical_scopes.append(node) + + def _exit_lexical_scope(self, node): + leaving_node = self.lexical_scopes.pop() + assert node == leaving_node + + def _get_enclosing_scopes(self, include, stop_at): + included = [] + for node in reversed(self.lexical_scopes): + if isinstance(node, include): + included.append(node) + if isinstance(node, stop_at): + return node, included + return None, included + + def _process_basic_statement(self, node): + self.generic_visit(node) + self.builder.add_ordinary_node(node) + + def _process_exit_statement(self, node, *exits_nodes_of_type): + # Note: this is safe because we process functions separately. + try_node, guards = self._get_enclosing_scopes( + include=(gast.Try,), + stop_at=tuple(exits_nodes_of_type), + ) + if try_node is None: + raise ValueError( + '%s that is not enclosed by any of %s' % (node, exits_nodes_of_type)) + self.builder.add_exit_node(node, try_node, guards) + + def _process_continue_statement(self, node, *loops_to_nodes_of_type): + # Note: this is safe because we process functions separately. + try_node, guards = self._get_enclosing_scopes( + include=(gast.Try,), + stop_at=tuple(loops_to_nodes_of_type), + ) + if try_node is None: + raise ValueError('%s that is not enclosed by any of %s' % + (node, loops_to_nodes_of_type)) + self.builder.add_continue_node(node, try_node, guards) + + def visit_FunctionDef(self, node): + self.builder_stack.append(self.builder) + self.builder = GraphBuilder(node) + + self._enter_lexical_scope(node) + self.builder.enter_section(node) + + self._process_basic_statement(node.args) + for stmt in node.body: + self.visit(stmt) + + self.builder.exit_section(node) + self._exit_lexical_scope(node) + + self.cfgs[node] = self.builder.build() + self.builder = self.builder_stack.pop() + + def visit_Lambda(self, node): + # TODO(mdan): Treat like FunctionDef? That would be a separate CFG. + raise NotImplementedError() + + def visit_Return(self, node): + self._process_exit_statement(node, gast.FunctionDef) + + def visit_Expr(self, node): + self._process_basic_statement(node) + + def visit_Assign(self, node): + self._process_basic_statement(node) + + def visit_AnnAssign(self, node): + self._process_basic_statement(node) + + def visit_AugAssign(self, node): + self._process_basic_statement(node) + + def visit_Print(self, node): + self._process_basic_statement(node) + + def visit_Raise(self, node): + try_node, guards = self._get_enclosing_scopes( + include=(gast.Try,), + stop_at=(gast.FunctionDef,), + ) + if try_node is None: + raise ValueError('%s that is not enclosed by any FunctionDef' % node) + self.builder.add_error_node(node, try_node, guards) + + def visit_Assert(self, node): + # Ignoring the effect of exceptions. + self._process_basic_statement(node) + + def visit_Delete(self, node): + self._process_basic_statement(node) + + def visit_If(self, node): + # No need to track ifs as lexical scopes, for now. + # Lexical scopes are generally tracked in order to be able to resolve the + # targets of jump statements like break/continue/etc. Since there is no + # statement that can interrupt a conditional, we don't need to track their + # lexical scope. That may change in the future. + + self.builder.enter_cond_section(node) + self._process_basic_statement(node.test) + + self.builder.new_cond_branch(node) + for stmt in node.body: + self.visit(stmt) + + self.builder.new_cond_branch(node) + for stmt in node.orelse: + self.visit(stmt) + + self.builder.exit_cond_section(node) + + def visit_While(self, node): + self._enter_lexical_scope(node) + + self.builder.enter_section(node) + + self.builder.enter_loop_section(node, node.test) + for stmt in node.body: + self.visit(stmt) + self.builder.exit_loop_section(node) + + # Note: although the orelse is technically part of the loop node, + # the statements inside it don't affect the loop itself. For example, a + # break in the loop's orelse will not affect the loop itself. + self._exit_lexical_scope(node) + + for stmt in node.orelse: + self.visit(stmt) + + self.builder.exit_section(node) + + def visit_For(self, node): + self._enter_lexical_scope(node) + + self.builder.enter_section(node) + + # TODO(mdan): Strictly speaking, this should be node.target + node.iter. + # A blind dataflow analysis would have to process both node.target and + # node.iter to properly process read and write access. + self.builder.enter_loop_section(node, node.iter) + for stmt in node.body: + self.visit(stmt) + self.builder.exit_loop_section(node) + + # Note: although the orelse is technically part of the loop node, + # they don't count as loop bodies. For example, a break in the loop's + # orelse will affect the parent loop, not the current one. + self._exit_lexical_scope(node) + + for stmt in node.orelse: + self.visit(stmt) + + self.builder.exit_section(node) + + def visit_Break(self, node): + self._process_exit_statement(node, gast.While, gast.For) + + def visit_Continue(self, node): + self._process_continue_statement(node, gast.While, gast.For) + + def visit_Try(self, node): + self._enter_lexical_scope(node) + + for stmt in node.body: + self.visit(stmt) + # Unlike loops, the orelse is a simple continuation of the body. + for stmt in node.orelse: + self.visit(stmt) + + if node.handlers: + # TODO(mdan): Should we still support bare try/except? Might be confusing. + raise NotImplementedError('exceptions are not yet supported') + + self._exit_lexical_scope(node) + + self.builder.enter_finally_section(node) + for stmt in node.finalbody: + self.visit(stmt) + self.builder.exit_finally_section(node) + + def visit_With(self, node): + # TODO(mdan): Mark the context manager's exit call as exit guard. + self._process_basic_statement(node.items) + for stmt in node.body: + self.visit(stmt) + + +def build(node): + builder = AstToCfg() + builder.visit(node) + return builder.cfgs diff --git a/tensorflow/contrib/autograph/pyct/cfg_test.py b/tensorflow/contrib/autograph/pyct/cfg_test.py new file mode 100644 index 0000000000000000000000000000000000000000..00afadd5212a3aba8f25cd9a6f111d292635bbce --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/cfg_test.py @@ -0,0 +1,790 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for cfg module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.python.platform import test + + +class CountingVisitor(cfg.GraphVisitor): + + def __init__(self): + self.counts = {} + + def visit_node(self, node): + self.counts[node.ast_node] = self.counts.get(node.ast_node, 0) + 1 + return False # visit only once + + +class GraphVisitorTest(test.TestCase): + + def _build_cfg(self, fn): + node, _ = parser.parse_entity(fn) + cfgs = cfg.build(node) + return cfgs, node + + def test_basic_coverage_forward(self): + + def test_fn(a): + while a > 0: + a = 1 + break + return a # pylint:disable=unreachable + a = 2 + + graphs, node = self._build_cfg(test_fn) + graph, = graphs.values() + visitor = CountingVisitor() + visitor.visit_forward(graph) + fn_node = node.body[0] + + self.assertEqual(visitor.counts[fn_node.args], 1) + self.assertEqual(visitor.counts[fn_node.body[0].test], 1) + self.assertEqual(visitor.counts[fn_node.body[0].body[0]], 1) + self.assertEqual(visitor.counts[fn_node.body[0].body[1]], 1) + # The return node should be unreachable in forward direction. + self.assertTrue(fn_node.body[0].body[2] not in visitor.counts) + self.assertEqual(visitor.counts[fn_node.body[1]], 1) + + def test_basic_coverage_reverse(self): + + def test_fn(a): + while a > 0: + a = 1 + break + return a # pylint:disable=unreachable + a = 2 + + graphs, node = self._build_cfg(test_fn) + graph, = graphs.values() + visitor = CountingVisitor() + visitor.visit_reverse(graph) + fn_node = node.body[0] + + self.assertEqual(visitor.counts[fn_node.args], 1) + self.assertEqual(visitor.counts[fn_node.body[0].test], 1) + self.assertEqual(visitor.counts[fn_node.body[0].body[0]], 1) + self.assertEqual(visitor.counts[fn_node.body[0].body[1]], 1) + self.assertTrue(visitor.counts[fn_node.body[0].body[2]], 1) + self.assertEqual(visitor.counts[fn_node.body[1]], 1) + + +class AstToCfgTest(test.TestCase): + + def _build_cfg(self, fn): + node, _ = parser.parse_entity(fn) + cfgs = cfg.build(node) + return cfgs + + def _repr_set(self, node_set): + return set(repr(n) for n in node_set) + + def _as_set(self, elements): + if elements is None: + return frozenset() + elif isinstance(elements, str): + return frozenset((elements,)) + else: + return frozenset(elements) + + def assertGraphMatches(self, graph, edges): + """Tests whether the CFG contains the specified edges.""" + for prev, node_repr, next_ in edges: + matched = False + for cfg_node in graph.index.values(): + if repr(cfg_node) == node_repr: + if (self._as_set(prev) == set(map(repr, cfg_node.prev)) and + self._as_set(next_) == set(map(repr, cfg_node.next))): + matched = True + break + if not matched: + self.fail( + 'match failed for node "%s" in graph:\n%s' % (node_repr, graph)) + + def test_straightline(self): + + def test_fn(a): + a += 1 + a = 2 + a = 3 + return + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a', 'a += 1'), + ('a += 1', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', 'return'), + ('a = 3', 'return', None), + ), + ) + + def test_straightline_no_return(self): + + def test_fn(a, b): + a = b + 1 + a += max(a) + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a, b', 'a = b + 1'), + ('a = b + 1', 'a += max(a)', None), + ), + ) + + def test_unreachable_code(self): + + def test_fn(a): + return + a += 1 # pylint:disable=unreachable + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a', 'return'), + ('a', 'return', None), + (None, 'a += 1', None), + ), + ) + + def test_branch_straightline(self): + + def test_fn(a): + if a > 0: + a = 1 + else: + a += -1 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a', '(a > 0)'), + ('(a > 0)', 'a = 1', None), + ('(a > 0)', 'a += -1', None), + ), + ) + + def test_branch_nested(self): + + def test_fn(a): + if a > 0: + if a > 1: + a = 1 + else: + a = 2 + else: + if a > 2: + a = 3 + else: + a = 4 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a', '(a > 0)'), + ('a', '(a > 0)', ('(a > 1)', '(a > 2)')), + ('(a > 0)', '(a > 1)', ('a = 1', 'a = 2')), + ('(a > 1)', 'a = 1', None), + ('(a > 1)', 'a = 2', None), + ('(a > 0)', '(a > 2)', ('a = 3', 'a = 4')), + ('(a > 2)', 'a = 3', None), + ('(a > 2)', 'a = 4', None), + ), + ) + + def test_branch_straightline_semi(self): + + def test_fn(a): + if a > 0: + a = 1 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (None, 'a', '(a > 0)'), + ('a', '(a > 0)', 'a = 1'), + ('(a > 0)', 'a = 1', None), + ), + ) + + def test_branch_return(self): + + def test_fn(a): + if a > 0: + return + else: + a = 1 + a = 2 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', '(a > 0)', ('return', 'a = 1')), + ('(a > 0)', 'a = 1', 'a = 2'), + ('(a > 0)', 'return', None), + ('a = 1', 'a = 2', None), + ), + ) + + def test_branch_return_minimal(self): + + def test_fn(a): + if a > 0: + return + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', '(a > 0)', 'return'), + ('(a > 0)', 'return', None), + ), + ) + + def test_while_straightline(self): + + def test_fn(a): + while a > 0: + a = 1 + a = 2 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), '(a > 0)', ('a = 1', 'a = 2')), + ('(a > 0)', 'a = 1', '(a > 0)'), + ('(a > 0)', 'a = 2', None), + ), + ) + + def test_while_else_straightline(self): + + def test_fn(a): + while a > 0: + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), '(a > 0)', ('a = 1', 'a = 2')), + ('(a > 0)', 'a = 1', '(a > 0)'), + ('(a > 0)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_while_else_continue(self): + + def test_fn(a): + while a > 0: + if a > 1: + continue + else: + a = 0 + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'continue', 'a = 1'), '(a > 0)', ('(a > 1)', 'a = 2')), + ('(a > 0)', '(a > 1)', ('continue', 'a = 0')), + ('(a > 1)', 'continue', '(a > 0)'), + ('a = 0', 'a = 1', '(a > 0)'), + ('(a > 0)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_while_else_break(self): + + def test_fn(a): + while a > 0: + if a > 1: + break + a = 1 + else: + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), '(a > 0)', ('(a > 1)', 'a = 2')), + ('(a > 0)', '(a > 1)', ('break', 'a = 1')), + ('(a > 1)', 'break', 'a = 3'), + ('(a > 1)', 'a = 1', '(a > 0)'), + ('(a > 0)', 'a = 2', 'a = 3'), + (('break', 'a = 2'), 'a = 3', None), + ), + ) + + def test_while_else_return(self): + + def test_fn(a): + while a > 0: + if a > 1: + return + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), '(a > 0)', ('(a > 1)', 'a = 2')), + ('(a > 0)', '(a > 1)', ('return', 'a = 1')), + ('(a > 1)', 'return', None), + ('(a > 1)', 'a = 1', '(a > 0)'), + ('(a > 0)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_while_nested_straightline(self): + + def test_fn(a): + while a > 0: + while a > 1: + a = 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), '(a > 0)', ('(a > 1)', 'a = 3')), + (('(a > 0)', 'a = 1'), '(a > 1)', ('a = 1', 'a = 2')), + ('(a > 1)', 'a = 1', '(a > 1)'), + ('(a > 1)', 'a = 2', '(a > 0)'), + ('(a > 0)', 'a = 3', None), + ), + ) + + def test_while_nested_continue(self): + + def test_fn(a): + while a > 0: + while a > 1: + if a > 3: + continue + a = 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), '(a > 0)', ('(a > 1)', 'a = 3')), + (('(a > 0)', 'continue', 'a = 1'), '(a > 1)', ('(a > 3)', 'a = 2')), + ('(a > 1)', '(a > 3)', ('continue', 'a = 1')), + ('(a > 3)', 'continue', '(a > 1)'), + ('(a > 3)', 'a = 1', '(a > 1)'), + ('(a > 1)', 'a = 2', '(a > 0)'), + ('(a > 0)', 'a = 3', None), + ), + ) + + def test_while_nested_break(self): + + def test_fn(a): + while a > 0: + while a > 1: + if a > 2: + break + a = 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), '(a > 0)', ('(a > 1)', 'a = 3')), + (('(a > 0)', 'a = 1'), '(a > 1)', ('(a > 2)', 'a = 2')), + ('(a > 1)', '(a > 2)', ('break', 'a = 1')), + ('(a > 2)', 'break', 'a = 2'), + ('(a > 2)', 'a = 1', '(a > 1)'), + (('(a > 1)', 'break'), 'a = 2', '(a > 0)'), + ('(a > 0)', 'a = 3', None), + ), + ) + + def test_for_straightline(self): + + def test_fn(a): + for a in range(0, a): + a = 1 + a = 2 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), 'range(0, a)', ('a = 1', 'a = 2')), + ('range(0, a)', 'a = 1', 'range(0, a)'), + ('range(0, a)', 'a = 2', None), + ), + ) + + def test_for_else_straightline(self): + + def test_fn(a): + for a in range(0, a): + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), 'range(0, a)', ('a = 1', 'a = 2')), + ('range(0, a)', 'a = 1', 'range(0, a)'), + ('range(0, a)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_for_else_continue(self): + + def test_fn(a): + for a in range(0, a): + if a > 1: + continue + else: + a = 0 + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'continue', 'a = 1'), 'range(0, a)', ('(a > 1)', 'a = 2')), + ('range(0, a)', '(a > 1)', ('continue', 'a = 0')), + ('(a > 1)', 'continue', 'range(0, a)'), + ('(a > 1)', 'a = 0', 'a = 1'), + ('a = 0', 'a = 1', 'range(0, a)'), + ('range(0, a)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_for_else_break(self): + + def test_fn(a): + for a in range(0, a): + if a > 1: + break + a = 1 + else: + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), 'range(0, a)', ('(a > 1)', 'a = 2')), + ('range(0, a)', '(a > 1)', ('break', 'a = 1')), + ('(a > 1)', 'break', 'a = 3'), + ('(a > 1)', 'a = 1', 'range(0, a)'), + ('range(0, a)', 'a = 2', 'a = 3'), + (('break', 'a = 2'), 'a = 3', None), + ), + ) + + def test_for_else_return(self): + + def test_fn(a): + for a in range(0, a): + if a > 1: + return + a = 1 + else: # pylint:disable=useless-else-on-loop + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), 'range(0, a)', ('(a > 1)', 'a = 2')), + ('range(0, a)', '(a > 1)', ('return', 'a = 1')), + ('(a > 1)', 'return', None), + ('(a > 1)', 'a = 1', 'range(0, a)'), + ('range(0, a)', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_for_nested_straightline(self): + + def test_fn(a): + for a in range(0, a): + for b in range(1, a): + b += 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), 'range(0, a)', ('range(1, a)', 'a = 3')), + (('range(0, a)', 'b += 1'), 'range(1, a)', ('b += 1', 'a = 2')), + ('range(1, a)', 'b += 1', 'range(1, a)'), + ('range(1, a)', 'a = 2', 'range(0, a)'), + ('range(0, a)', 'a = 3', None), + ), + ) + + def test_for_nested_continue(self): + + def test_fn(a): + for a in range(0, a): + for b in range(1, a): + if a > 3: + continue + b += 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), 'range(0, a)', ('range(1, a)', 'a = 3')), + (('range(0, a)', 'continue', 'b += 1'), 'range(1, a)', + ('(a > 3)', 'a = 2')), + ('range(1, a)', '(a > 3)', ('continue', 'b += 1')), + ('(a > 3)', 'continue', 'range(1, a)'), + ('(a > 3)', 'b += 1', 'range(1, a)'), + ('range(1, a)', 'a = 2', 'range(0, a)'), + ('range(0, a)', 'a = 3', None), + ), + ) + + def test_for_nested_break(self): + + def test_fn(a): + for a in range(0, a): + for b in range(1, a): + if a > 2: + break + b += 1 + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 2'), 'range(0, a)', ('range(1, a)', 'a = 3')), + (('range(0, a)', 'b += 1'), 'range(1, a)', ('(a > 2)', 'a = 2')), + ('range(1, a)', '(a > 2)', ('break', 'b += 1')), + ('(a > 2)', 'break', 'a = 2'), + ('(a > 2)', 'b += 1', 'range(1, a)'), + (('range(1, a)', 'break'), 'a = 2', 'range(0, a)'), + ('range(0, a)', 'a = 3', None), + ), + ) + + def test_complex(self): + + def test_fn(a): + b = 0 + while a > 0: + for b in range(0, a): + if a > 2: + break + if a > 3: + if a > 4: + continue + else: + max(a) + break + b += 1 + else: # for b in range(0, a): + return a + a = 2 + for a in range(1, a): + return b + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('b = 0', 'a = 2'), '(a > 0)', ('range(0, a)', 'range(1, a)')), + ( + ('(a > 0)', 'continue', 'b += 1'), + 'range(0, a)', + ('(a > 2)', 'return a'), + ), + ('range(0, a)', '(a > 2)', ('(a > 3)', 'break')), + ('(a > 2)', 'break', 'a = 2'), + ('(a > 2)', '(a > 3)', ('(a > 4)', 'b += 1')), + ('(a > 3)', '(a > 4)', ('continue', 'max(a)')), + ('(a > 4)', 'max(a)', 'break'), + ('max(a)', 'break', 'a = 2'), + ('(a > 4)', 'continue', 'range(0, a)'), + ('(a > 3)', 'b += 1', 'range(0, a)'), + ('range(0, a)', 'return a', None), + ('break', 'a = 2', '(a > 0)'), + ('(a > 0)', 'range(1, a)', ('return b', 'a = 3')), + ('range(1, a)', 'return b', None), + ('range(1, a)', 'a = 3', None), + ), + ) + + def test_finally_straightline(self): + + def test_fn(a): + try: + a += 1 + finally: + a = 2 + a = 3 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', 'a += 1', 'a = 2'), + ('a += 1', 'a = 2', 'a = 3'), + ('a = 2', 'a = 3', None), + ), + ) + + def test_return_finally(self): + + def test_fn(a): + try: + return a + finally: + a = 1 + a = 2 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', 'return a', 'a = 1'), + ('return a', 'a = 1', None), + (None, 'a = 2', None), + ), + ) + + def test_break_finally(self): + + def test_fn(a): + while a > 0: + try: + break + finally: + a = 1 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', '(a > 0)', 'break'), + ('(a > 0)', 'break', 'a = 1'), + ('break', 'a = 1', None), + ), + ) + + def test_continue_finally(self): + + def test_fn(a): + while a > 0: + try: + continue + finally: + a = 1 + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + (('a', 'a = 1'), '(a > 0)', 'continue'), + ('(a > 0)', 'continue', 'a = 1'), + ('continue', 'a = 1', '(a > 0)'), + ), + ) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/BUILD b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..ca1441cf6f8bb034c95b37fcdd9e8158d1db2e39 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD @@ -0,0 +1,38 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "common_transformers", + srcs = [ + "anf.py", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/contrib/autograph/pyct", + "@gast_archive//:gast", + ], +) + +py_test( + name = "anf_test", + srcs = ["anf_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":common_transformers", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py new file mode 100644 index 0000000000000000000000000000000000000000..cc039986c219db1febfe610a5078e26eeb2d5a83 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py @@ -0,0 +1,57 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Conversion to A-normal form.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import transformer + + +class DummyGensym(object): + """A dumb gensym that suffixes a stem by sequential numbers from 1000.""" + + def __init__(self, entity_info): + del entity_info + # A proper implementation needs to account for: + # * entity_info.namespace + # * all the symbols defined in the AST + # * the symbols generated so far + self._idx = 0 + + def new_name(self, stem): + self._idx += 1 + return stem + '_' + str(1000 + self._idx) + + +class AnfTransformer(transformer.Base): + """Performs the actual conversion.""" + + # TODO(mdan): Link to a reference. + # TODO(mdan): Implement. + + def __init__(self, entity_info): + """Creates a transformer. + + Args: + entity_info: transformer.EntityInfo + """ + super(AnfTransformer, self).__init__(entity_info) + self._gensym = DummyGensym(entity_info) + + +def transform(node, entity_info): + return AnfTransformer(entity_info).visit(node) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py new file mode 100644 index 0000000000000000000000000000000000000000..81983a5ecb7b8c6216285409f854e27b7154a08b --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py @@ -0,0 +1,53 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for anf module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.common_transformers import anf +from tensorflow.python.platform import test + + +class AnfTransformerTest(test.TestCase): + + def _simple_source_info(self): + return transformer.EntityInfo( + source_code=None, + source_file=None, + namespace=None, + arg_values=None, + arg_types=None, + owner_type=None) + + def test_basic(self): + + def test_function(): + a = 0 + return a + + node, _ = parser.parse_entity(test_function) + node = anf.transform(node, self._simple_source_info()) + result, _ = compiler.ast_to_object(node) + + self.assertEqual(test_function(), result.test_function()) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/context.py b/tensorflow/contrib/autograph/pyct/context.py deleted file mode 100644 index b34015cfd2888f0dbeb6492b9e7335d561bf4763..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/pyct/context.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Conversion context containers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -class EntityContext(object): - """Contains information about an entity, like source code. - - In general, objects of this class should be considered immutable. - - Attributes: - namer: Namer that matches the contract of all converters. - source_code: The entity's source code. - source_file: The entity's source file. - namespace: Dict[str->*], containing symbols visible to the entity - (excluding parameters). - arg_values: Dict[str->*], containing parameter values, if known. - arg_types: Dict[str->*], containing parameter types, if known. - owner_type: The surrounding class type of the function, if present. - """ - - # TODO(mdan): Remove the default and update tests. - def __init__(self, namer, source_code, source_file, namespace, arg_values, - arg_types, owner_type, recursive, type_annotation_func=None): - self.namer = namer - self.source_code = source_code - self.source_file = source_file - self.namespace = namespace - self.arg_values = {} if arg_values is None else arg_values - self.arg_types = {} if arg_types is None else arg_types - self.owner_type = owner_type - self.recursive = recursive - self.type_annotation_func = type_annotation_func diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD index 8064a967cd389e88d3febbeb21cac87b0fef9e18..bcf2dacec2062704805f1d72ec27a243159d13c1 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD @@ -27,6 +27,7 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/utils", "@gast_archive//:gast", ], ) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py index fdbd349af9d3325af114a7206d89617134278f14..bc22be0a270bbc9c361aea6d6d9c255ea51796e8 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py @@ -21,9 +21,9 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.qual_names import QN from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -112,18 +112,16 @@ class ActivityAnalyzerTest(test.TestCase): def _parse_and_analyze(self, test_fn): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace={}, arg_values=None, arg_types=None, - owner_type=None, - recursive=True) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - return node, ctx + node = activity.resolve(node, entity_info) + return node, entity_info def test_local_markers(self): diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py index ad97fdfa8e78d1fd4c38724612d83519c6609cce..4acc4ed66a62b0ccd407d39b1abda00c4c88a9a1 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py @@ -276,9 +276,9 @@ class Forward(object): taken). """ - def __init__(self, label, context, transfer_fn=operator.or_): + def __init__(self, label, source_info, transfer_fn=operator.or_): self.transfer_fn = transfer_fn - self.context = context + self.source_info = source_info self.out_label = label + '_out' self.in_label = label + '_in' self.gen_label = label + '_gen' @@ -286,7 +286,7 @@ class Forward(object): # TODO(alexbw): see if we can simplify by visiting breadth-first def visit(self, node): - """Depth-first walking the CFG, applying dataflow information propagtion.""" + """Depth-first walking the CFG, applying dataflow info propagation.""" # node.value is None only for the exit CfgNode. if not node.value: return @@ -399,18 +399,18 @@ class Liveness(Backward): later in the program. """ - def __init__(self, context): - super(Liveness, self).__init__('live', context) + def __init__(self, source_info): + super(Liveness, self).__init__('live', source_info) def get_gen_kill(self, node, _): # A variable's parents are live if it is live # e.g. x is live if x.y is live. This means gen needs to return # all parents of a variable (if it's an Attribute or Subscript). # This doesn't apply to kill (e.g. del x.y doesn't affect liveness of x) - gen = activity.get_read(node.value, self.context) + gen = activity.get_read(node.value, self.source_info) gen = functools.reduce(lambda left, right: left | right.support_set, gen, gen) - kill = activity.get_updated(node.value, self.context) + kill = activity.get_updated(node.value, self.source_info) return gen, kill @@ -420,11 +420,11 @@ class ReachingDefinitions(Forward): Each statement is annotated with a set of (variable, definition) pairs. """ - def __init__(self, context): - super(ReachingDefinitions, self).__init__('definitions', context) + def __init__(self, source_info): + super(ReachingDefinitions, self).__init__('definitions', source_info) def get_gen_kill(self, node, incoming): - definitions = activity.get_updated(node.value, self.context) + definitions = activity.get_updated(node.value, self.source_info) gen = frozenset((id_, node.value) for id_ in definitions) kill = frozenset(def_ for def_ in incoming if def_[0] in definitions) return gen, kill @@ -437,9 +437,10 @@ class Defined(Forward): be defined at that point. """ - def __init__(self, context): - super(Defined, self).__init__('defined', context, transfer_fn=operator.and_) + def __init__(self, source_info): + super(Defined, self).__init__( + 'defined', source_info, transfer_fn=operator.and_) def get_gen_kill(self, node, _): - gen = activity.get_updated(node.value, self.context) + gen = activity.get_updated(node.value, self.source_info) return gen, frozenset() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py index fc07fa3447b23c0595a5893329de8a2d7055ca15..428ebbedca85f9b94b4b1db0f3b36a334126196b 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py @@ -23,29 +23,26 @@ import functools import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import cfg from tensorflow.python.platform import test class CFGTest(test.TestCase): - def _parse_and_analyze(self, test_fn, namespace, arg_types=None): - arg_types = arg_types or {} + def _parse_and_analyze(self, test_fn): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, - namespace=namespace, + namespace={}, arg_values=None, - arg_types=arg_types, - owner_type=None, - recursive=True) + arg_types=None, + owner_type=None) node = qual_names.resolve(node) - return node, ctx + return node, entity_info def _check_anno_matches(self, node, anno_name, var_names): if isinstance(var_names, str): @@ -73,7 +70,7 @@ class CFGTest(test.TestCase): x = x return x - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) body = node.body[0].body # Only the argument reaches the expression @@ -106,7 +103,7 @@ class CFGTest(test.TestCase): y = 2 # pylint: disable=unused-variable return x - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body # only x is for sure defined at the end @@ -116,7 +113,7 @@ class CFGTest(test.TestCase): self._check_anno_matches(if_body[0], 'defined_out', ('x', 'y')) def _get_live_annotated_fnbody(self, f): - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Liveness(ctx)) body = node.body[0].body return body @@ -226,7 +223,7 @@ class CFGTest(test.TestCase): return g(x) - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body @@ -253,7 +250,7 @@ class CFGTest(test.TestCase): return g() # y is not defined here - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body self.assertEqual( @@ -282,7 +279,7 @@ class CFGTest(test.TestCase): return x, y for f in (for_orelse, while_orelse): - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) body = node.body[0].body return_node = body[-1] diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py index 53ae15459097baff918432a493edd7360ebf209d..9ccb98f79adbe5410a7554548ee75ab95345962d 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py @@ -39,7 +39,7 @@ class LiveValueResolver(transformer.Base): def visit_ClassDef(self, node): self.generic_visit(node) - anno.setanno(node, 'live_val', self.context.namespace[node.name]) + anno.setanno(node, 'live_val', self.entity_info.namespace[node.name]) return node def visit_Name(self, node): @@ -55,8 +55,8 @@ class LiveValueResolver(transformer.Base): if not symbol_is_local and not symbol_is_param: if node.id in self.literals: anno.setanno(node, 'live_val', self.literals[node.id]) - elif node.id in self.context.namespace: - obj = self.context.namespace[node.id] + elif node.id in self.entity_info.namespace: + obj = self.entity_info.namespace[node.id] anno.setanno(node, 'live_val', obj) if hasattr(obj, '__name__'): anno.setanno(node, 'fqn', (obj.__name__,)) @@ -80,8 +80,8 @@ class LiveValueResolver(transformer.Base): # TODO(mdan): Use type annotations as fallback. if not symbol_is_modified: - if node.id in self.context.arg_values: - obj = self.context.arg_values[node.id] + if node.id in self.entity_info.arg_values: + obj = self.entity_info.arg_values[node.id] anno.setanno(node, 'live_val', obj) anno.setanno(node, 'fqn', (obj.__class__.__name__,)) return node diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py index 69e428bde109ed43c3cdda1a94970a832dc47852..38af79277779f77ffe31c2f6e26ae88f3e1a7ae9 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py @@ -21,9 +21,9 @@ from __future__ import print_function import six from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info @@ -39,22 +39,19 @@ class LiveValuesResolverTest(test.TestCase): literals=None, arg_types=None): literals = literals or {} - arg_types = arg_types or {} node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace=namespace, arg_values=None, arg_types=arg_types, - owner_type=None, - recursive=True) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, literals) - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, literals) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, literals) + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, literals) return node def test_literals(self): diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py index d6555dc7e0b3d49b3befa7326b28387509c83006..a229c288a83e516fc02f3af8df2046c5365e569c 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py @@ -17,8 +17,8 @@ This analyzer uses known live values to further infer object types. This may include for instance constructed objects and object member functions. -In addition, the analyzer will also process annotations for TF (staged) type -annotations. +In addition, the analyzer also handles user annotations made in the code (for +example, the autograph.set_element_type function). Requires annotations generated by LiveValuesResolver. """ @@ -43,7 +43,9 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.util import tf_inspect @@ -51,6 +53,7 @@ from tensorflow.python.util import tf_inspect # TODO(mdan): Remove the duplication between this and activity.py. # In particular, the symbol definitions we track here could as well be tracked # there because they follow the same rules for visibility. +# TODO(mdan): Use a CFG based Defined analysis instead. class Scope(object): """Tracks symbol value references. @@ -134,37 +137,40 @@ class TypeInfoResolver(transformer.Base): node.orelse = self._visit_block(node.orelse) return node - def _process_function_arg(self, arg_name): - str_name = str(arg_name) - type_holder = arg_name.ast() - self.scope.setval(arg_name, type_holder) - if len(self.enclosing_entities) == 1 and str_name in self.context.arg_types: + def _process_function_arg(self, arg_node): + qn = anno.getanno(arg_node, anno.Basic.QN) + arg_name = str(qn) + self.scope.setval(qn, arg_node) + if (len(self.enclosing_entities) == 1 and + arg_name in self.entity_info.arg_types): # Forge a node to hold the type information, so that method calls on # it can resolve the type. - type_string, type_obj = self.context.arg_types[str_name] - anno.setanno(type_holder, 'type', type_obj) - anno.setanno(type_holder, 'type_fqn', tuple(type_string.split('.'))) + type_string, type_obj = self.entity_info.arg_types[arg_name] + anno.setanno(arg_node, 'type', type_obj) + anno.setanno(arg_node, 'type_fqn', tuple(type_string.split('.'))) def visit_arg(self, node): - self._process_function_arg(anno.getanno(node.arg, anno.Basic.QN)) + self._process_function_arg(node.arg) return node def visit_Name(self, node): self.generic_visit(node) - qn = anno.getanno(node, anno.Basic.QN) if isinstance(node.ctx, gast.Param): - self._process_function_arg(qn) - elif isinstance(node.ctx, gast.Load) and self.scope.hasval(qn): - # E.g. if we had - # a = b - # then for future references to `a` we should have definition = `b` - definition = self.scope.getval(qn) - if anno.hasanno(definition, 'type'): - anno.setanno(node, 'type', anno.getanno(definition, 'type')) - anno.setanno(node, 'type_fqn', anno.getanno(definition, 'type_fqn')) - if anno.hasanno(definition, 'element_type'): - anno.setanno(node, 'element_type', - anno.getanno(definition, 'element_type')) + self._process_function_arg(node) + elif isinstance(node.ctx, gast.Load): + qn = anno.getanno(node, anno.Basic.QN) + if self.scope.hasval(qn): + # E.g. if we had + # a = b + # then for future references to `a` we should have definition = `b` + definition = self.scope.getval(qn) + anno.copyanno(definition, node, 'type') + anno.copyanno(definition, node, 'type_fqn') + anno.setanno(node, 'definition', definition) + + # TODO(mdan): Remove this when the directives module is in. + anno.copyanno(definition, node, 'element_type') + anno.copyanno(definition, node, 'element_shape') return node def _process_variable_assignment(self, target, value): @@ -204,30 +210,27 @@ class TypeInfoResolver(transformer.Base): node.targets, node.value, self._process_variable_assignment) return node + # TODO(mdan): Remove as soon as the new directives module is ready. def visit_Call(self, node): if anno.hasanno(node.func, 'live_val'): # Symbols targeted by the "set_type" marker function are assigned the data # type that it specified. - if (anno.getanno(node.func, 'live_val') is - self.context.type_annotation_func): + if anno.getanno(node.func, 'live_val') is utils.set_element_type: - if len(node.args) != 2: - raise ValueError('"%s" must have exactly two parameters' + if len(node.args) < 2 or len(node.args) > 3: + raise ValueError('"%s" must have either two or three parameters' % self.context.type_annotation_func) - target_arg, type_arg = node.args - if not anno.hasanno(target_arg, anno.Basic.QN): - raise ValueError('the first argument of "%s" must by a symbol' - % self.context.type_annotation_func) - if isinstance(type_arg, gast.Str): - element_type = type_arg.s - elif isinstance(type_arg, gast.Num): - element_type = type_arg.n + if len(node.args) == 2: + target_arg, type_arg = node.args + shape_arg = parser.parse_expression('None') else: - if not anno.hasanno(type_arg, 'live_val'): - raise ValueError( - 'the second argument of "%s" must be statically resolvable' % - self.context.type_annotation_func) - element_type = anno.getanno(type_arg, 'live_val') + target_arg, type_arg, shape_arg = node.args + if not anno.hasanno(target_arg, anno.Basic.QN): + raise ValueError('the first argument of "%s" must by a symbol' % + utils.set_element_type) + # TODO(mdan): This is vulnerable to symbol renaming. + element_type = type_arg + element_shape = shape_arg target_symbol = anno.getanno(target_arg, anno.Basic.QN) # Find the definition of this symbol and annotate it with the given @@ -235,7 +238,9 @@ class TypeInfoResolver(transformer.Base): # to receive the same type annotation. definition = self.scope.getval(target_symbol) anno.setanno(node, 'element_type', element_type) + anno.setanno(node, 'element_shape', element_shape) anno.setanno(definition, 'element_type', element_type) + anno.setanno(definition, 'element_shape', element_shape) # TODO(mdan): Should we update references between definition and here? return self.generic_visit(node) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py index 95cbf5ca79a5045f5e050b735390dcfb668b5bb2..32b1148ab21809514bc09a31e26f0219017bd088 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py @@ -18,11 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info @@ -62,21 +61,18 @@ class TypeInfoResolverTest(test.TestCase): namespace, arg_types=None): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace=namespace, arg_values=None, arg_types=arg_types, - owner_type=None, - recursive=True, - type_annotation_func=utils.set_element_type) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) return node def test_constructor_detection(self): @@ -147,7 +143,7 @@ class TypeInfoResolverTest(test.TestCase): opt.minimize(0) node = self._parse_and_analyze( - test_fn, {'training': training}, + test_fn, {}, arg_types={ 'opt': (training.GradientDescentOptimizer.__name__, training.GradientDescentOptimizer) @@ -180,35 +176,6 @@ class TypeInfoResolverTest(test.TestCase): method_call = node.body[0].body[1].value.func self.assertFalse(anno.hasanno(method_call, 'live_val')) - def test_type_annotation(self): - - class Foo(object): - pass - - def test_fn(): - f = [] - f = utils.set_element_type(f, Foo) - return f - - node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) - f_def = node.body[0].body[0].value - self.assertEqual(anno.getanno(f_def, 'element_type'), Foo) - f_ref = node.body[0].body[1].value - self.assertEqual(anno.getanno(f_ref, 'element_type'), Foo) - - def test_type_annotation_args(self): - - class Foo(object): - pass - - def test_fn(f): - utils.set_element_type(f, Foo) - return f - - node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) - f_ref = node.body[0].body[1].value - self.assertEqual(anno.getanno(f_ref, 'element_type'), Foo) - def test_nested_unpacking(self): class Foo(object): @@ -223,32 +190,13 @@ class TypeInfoResolverTest(test.TestCase): node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'Bar': Bar}) a, b, c = node.body[0].body[1].value.elts - self.assertEquals(Foo, anno.getanno(a, 'type')) - self.assertEquals(Bar, anno.getanno(b, 'type')) - self.assertEquals(Foo, anno.getanno(c, 'type')) + self.assertEquals(anno.getanno(a, 'type'), Foo) + self.assertEquals(anno.getanno(b, 'type'), Bar) + self.assertEquals(anno.getanno(c, 'type'), Foo) self.assertFalse(anno.hasanno(a, 'live_val')) self.assertFalse(anno.hasanno(b, 'live_val')) self.assertFalse(anno.hasanno(c, 'live_val')) - def test_inner_scope(self): - - def test_fn(): - a = [] - utils.set_element_type(a, 1) - for _ in a: - b = [] - utils.set_element_type(b, 2) - return a, b - - node = self._parse_and_analyze(test_fn, {'utils': utils}) - a, b = node.body[0].body[2].body[2].value.elts - self.assertEquals(1, anno.getanno(a, 'element_type')) - self.assertEquals(2, anno.getanno(b, 'element_type')) - self.assertFalse(anno.hasanno(a, 'type')) - self.assertFalse(anno.hasanno(b, 'type')) - self.assertFalse(anno.hasanno(a, 'live_val')) - self.assertFalse(anno.hasanno(b, 'live_val')) - if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index 60bca8b38dcf62b4e997379d075cfc45511a894f..76558118308c31a2c1a770cad814e96abd6a6063 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -32,15 +32,40 @@ class AutographParseError(SyntaxError): pass -def try_ast_to_source(node): - try: - return compiler.ast_to_source(node) - except AssertionError: - return '' +# TODO(mdan): Use namedtuple. +class EntityInfo(object): + """Contains information about a Python entity. Immutable. + + Examples of entities include functions and classes. + + Attributes: + source_code: The entity's source code. + source_file: The entity's source file. + namespace: Dict[str, ], containing symbols visible to the entity + (excluding parameters). + arg_values: dict[str->*], containing parameter values, if known. + arg_types: dict[str->*], containing parameter types, if known. + owner_type: The surrounding class type of the function, if present. + """ + + # TODO(mdan): Remove the default and update tests. + def __init__(self, source_code, source_file, namespace, arg_values, arg_types, + owner_type): + self.source_code = source_code + self.source_file = source_file + self.namespace = namespace + self.arg_values = {} if arg_values is None else arg_values + self.arg_types = {} if arg_types is None else arg_types + self.owner_type = owner_type class Base(gast.NodeTransformer): - """Base class for specialized transformers. + """Base class for general-purpose code transformers transformers. + + This is an extension of ast.NodeTransformer that provides a few additional + functions, like state tracking within the scope of arbitrary node, helpers + for processing code blocks, debugging, mapping of transformed code to + original code, and others. Scope-local state tracking: to keep state across nodes, at the level of (possibly nested) scopes, use enter/exit_local_scope and set/get_local. @@ -48,15 +73,17 @@ class Base(gast.NodeTransformer): when they are not properly paired. """ - def __init__(self, context): + # TODO(mdan): Document all extra features. + + def __init__(self, entity_info): """Initialize the transformer. Subclasses should call this. Args: - context: An EntityContext. + entity_info: An EntityInfo object. """ self._lineno = 0 self._col_offset = 0 - self.context = context + self.entity_info = entity_info self._enclosing_entities = [] # A stack that allows keeping mutable, scope-local state where scopes may be @@ -191,7 +218,7 @@ class Base(gast.NodeTransformer): # TODO(mdan): Once we have error tracing, we may be able to just go to SSA. def apply_to_single_assignments(self, targets, values, apply_fn): - """Applies a fuction to each individual assignment. + """Applies a function to each individual assignment. This function can process a possibly-unpacked (e.g. a, b = c, d) assignment. It tries to break down the unpacking if possible. In effect, it has the same @@ -219,7 +246,7 @@ class Base(gast.NodeTransformer): targets field of an ast.Assign node. values: an AST node. apply_fn: a function of a single argument, which will be called with the - respective nodes of each single assignment. The signaure is + respective nodes of each single assignment. The signature is apply_fn(target, value), no return value. """ if not isinstance(targets, (list, tuple)): @@ -237,9 +264,15 @@ class Base(gast.NodeTransformer): # TODO(mdan): Look into allowing to rewrite the AST here. apply_fn(target, values) + def _get_source(self, node): + try: + return compiler.ast_to_source(node) + except AssertionError: + return '' + def visit(self, node): - source_code = self.context.source_code - source_file = self.context.source_file + source_code = self.entity_info.source_code + source_file = self.entity_info.source_file did_enter_function = False local_scope_size_at_entry = len(self._local_scope_state) @@ -275,7 +308,7 @@ class Base(gast.NodeTransformer): except (ValueError, AttributeError, KeyError, NotImplementedError) as e: msg = '%s: %s\nOffending source:\n%s\n\nOccurred at node:\n%s' % ( - e.__class__.__name__, str(e), try_ast_to_source(node), + e.__class__.__name__, str(e), self._get_source(node), pretty_printer.fmt(node, color=False)) if source_code: line = source_code.splitlines()[self._lineno - 1] diff --git a/tensorflow/contrib/autograph/pyct/transformer_test.py b/tensorflow/contrib/autograph/pyct/transformer_test.py index f110e79605945e908e8a49112cf758ec29fa1b11..baf04653ae862b0159fb50a1c67fa675ceb74b9a 100644 --- a/tensorflow/contrib/autograph/pyct/transformer_test.py +++ b/tensorflow/contrib/autograph/pyct/transformer_test.py @@ -21,7 +21,6 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.platform import test @@ -29,16 +28,14 @@ from tensorflow.python.platform import test class TransformerTest(test.TestCase): - def _context_for_testing(self): - return context.EntityContext( - namer=None, + def _simple_source_info(self): + return transformer.EntityInfo( source_code=None, source_file=None, namespace=None, arg_values=None, arg_types=None, - owner_type=None, - recursive=False) + owner_type=None) def test_entity_scope_tracking(self): @@ -55,7 +52,7 @@ class TransformerTest(test.TestCase): anno.setanno(node, 'enclosing_entities', self.enclosing_entities) return self.generic_visit(node) - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def test_function(): a = 0 @@ -118,7 +115,7 @@ class TransformerTest(test.TestCase): def visit_For(self, node): return self._annotate_result(node) - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def test_function(a): """Docstring.""" @@ -157,7 +154,7 @@ class TransformerTest(test.TestCase): self.exit_local_scope() return node - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def no_exit(a): if a > 0: @@ -196,7 +193,7 @@ class TransformerTest(test.TestCase): z = y return z - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) node, _ = parser.parse_entity(test_function) node = tr.visit(node) diff --git a/tensorflow/contrib/batching/__init__.py b/tensorflow/contrib/batching/__init__.py index 44fa5f42a73bfb1bf008f6f4eafd14913c88dcfa..1e503a097a7b72d9244b0a1cf57747c4b4122c81 100644 --- a/tensorflow/contrib/batching/__init__.py +++ b/tensorflow/contrib/batching/__init__.py @@ -14,6 +14,7 @@ # ============================================================================== """Ops and modules related to batch. +@@batch_function_v1 @@batch_function """ from __future__ import absolute_import diff --git a/tensorflow/contrib/batching/python/ops/batch_ops.py b/tensorflow/contrib/batching/python/ops/batch_ops.py index 921d6917a4e478c3e60771fdc3ae99febc33d2e3..47b80bdf4ad88ebce3603a14ea2aa3cbe5bd345f 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.ops import gen_batch_ops # go/tf-wildcard-import @@ -83,6 +84,70 @@ def batch_function(num_batch_threads, SparseTensor is not supported. The return value of the decorated function must be a Tensor or a list/tuple of Tensors. + Args: + num_batch_threads: Number of scheduling threads for processing batches + of work. Determines the number of batches processed in parallel. + max_batch_size: Batch sizes will never be bigger than this. + batch_timeout_micros: Maximum number of microseconds to wait before + outputting an incomplete batch. + allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, + does nothing. Otherwise, supplies a list of batch sizes, causing the op + to pad batches up to one of those sizes. The entries must increase + monotonically, and the final entry must equal max_batch_size. + grad_timeout_micros: The timeout to use for the gradient. See the + documentation of the unbatch op for more details. Defaults to 60s. + unbatch_timeout_micros: The timeout to use for unbatching. See the + documentation of the unbatch op for more details. Defaults to 60s. + max_enqueued_batches: The maximum depth of the batch queue. Defaults to 10. + + Returns: + The decorated function will return the unbatched computation output Tensors. + """ + + def decorator(fn): # pylint: disable=missing-docstring + + def decorated(*args): # pylint: disable=missing-docstring + types = [arg.dtype for arg in args] + + @function.Defun(*types) + def computation(*computation_args): + return fn(*computation_args) + + with ops.name_scope("batch") as name: + for a in args: + if not isinstance(a, ops.Tensor): + raise ValueError("All arguments to functions decorated with " + "`batch_function` are supposed to be Tensors; " + "found %s" % repr(a)) + return gen_batch_ops.batch_function( + num_batch_threads=num_batch_threads, + max_batch_size=max_batch_size, + batch_timeout_micros=batch_timeout_micros, + allowed_batch_sizes=allowed_batch_sizes, + max_enqueued_batches=max_enqueued_batches, + shared_name=name, + f=computation, + in_tensors=list(args), + captured_tensors=computation.captured_inputs, + Tout=[o.type for o in computation.definition.signature.output_arg]) + + return decorated + + return decorator + + +def batch_function_v1(num_batch_threads, + max_batch_size, + batch_timeout_micros, + allowed_batch_sizes=None, + grad_timeout_micros=60 * 1000 * 1000, + unbatch_timeout_micros=60 * 1000 * 1000, + max_enqueued_batches=10): + """Batches the computation done by the decorated function. + + This is the older version of batch_function(). Please use the former instead + of this. + Args: num_batch_threads: Number of scheduling threads for processing batches of work. Determines the number of batches processed in parallel. diff --git a/tensorflow/contrib/batching/python/ops/batch_ops_test.py b/tensorflow/contrib/batching/python/ops/batch_ops_test.py index ea8339334f9b5e58a35dc9edf314a220e4c9868c..78468145469df216344bc00f116add250dc51dd3 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops_test.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops_test.py @@ -188,12 +188,62 @@ class BatchOpsTest(test.TestCase): self.assertEqual(thread_results[0], [2]) self.assertEqual(main_results[0], [3]) + def testBasicUnbatchV1Decorated(self): + """Tests that the batch_function_v1 decorator works.""" + with self.test_session() as sess: + @batch_ops.batch_function_v1(1, 10, 100000) + def computation(in_t): + return in_t + 1 + + inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1]) + result = computation(inp) + thread_results = [] + + def worker(): + thread_results.extend(sess.run([result], feed_dict={inp: [1]})) + + worker_thread = threading.Thread(target=worker) + worker_thread.start() + main_results = sess.run([result], feed_dict={inp: [2]}) + worker_thread.join() + self.assertEqual(thread_results[0], [2]) + self.assertEqual(main_results[0], [3]) + def testBasicUnbatchDecorated(self): """Tests that the batch_function decorator works.""" with self.test_session() as sess: + # TODO(apassos): Removing this line causes test flakiness! Ideally should + # be investigated. + default_inp = array_ops.placeholder_with_default(2, shape=[]) # pylint: disable=unused-variable + @batch_ops.batch_function(1, 10, 100000) def computation(in_t): return in_t + 1 + + inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1]) + result = computation(inp) + thread_results = [] + + def worker(): + thread_results.extend(sess.run([result], feed_dict={inp: [1]})) + + worker_thread = threading.Thread(target=worker) + worker_thread.start() + main_results = sess.run([result], feed_dict={inp: [2]}) + worker_thread.join() + self.assertEqual(thread_results[0], [2]) + self.assertEqual(main_results[0], [3]) + + def testBatchDecoratedWithCapturedInput(self): + """Tests that the batch_function decorator works.""" + with self.test_session() as sess: + captured_inp0 = array_ops.placeholder_with_default(2, shape=[]) + captured_inp1 = array_ops.placeholder_with_default(1, shape=[]) + + @batch_ops.batch_function(1, 10, 100000) + def computation(in_t): + return in_t + captured_inp0 - captured_inp1 + inp = array_ops.placeholder(dtype=dtypes.int32, shape=[1]) result = computation(inp) thread_results = [] diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py index d9e23646d8334014f1bef0d0744df9310b59909f..9e6a146f67796466202cc5074ddd25e4c2b083a6 100644 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py +++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py @@ -29,7 +29,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib -from tensorflow.python.ops.distributions import gamma as gamma_lib from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.platform import test @@ -256,50 +255,6 @@ class ExpectationTest(test.TestCase): gradq_approx_kl_normal_normal_, rtol=0.01, atol=0.) - def test_docstring_example_gamma(self): - with self.test_session() as sess: - num_draws = int(1e5) - concentration_p = constant_op.constant(1.) - concentration_q = constant_op.constant(2.) - p = gamma_lib.Gamma(concentration=concentration_p, rate=1.) - q = gamma_lib.Gamma(concentration=concentration_q, rate=3.) - approx_kl_gamma_gamma = monte_carlo_lib.expectation( - f=lambda x: p.log_prob(x) - q.log_prob(x), - samples=p.sample(num_draws, seed=42), - log_prob=p.log_prob, - use_reparametrization=(p.reparameterization_type - == distribution_lib.FULLY_REPARAMETERIZED)) - exact_kl_gamma_gamma = kullback_leibler.kl_divergence(p, q) - [exact_kl_gamma_gamma_, approx_kl_gamma_gamma_] = sess.run([ - exact_kl_gamma_gamma, approx_kl_gamma_gamma]) - self.assertEqual( - False, - p.reparameterization_type == distribution_lib.FULLY_REPARAMETERIZED) - self.assertAllClose(exact_kl_gamma_gamma_, approx_kl_gamma_gamma_, - rtol=0.01, atol=0.) - - # Compare gradients. (Not present in `docstring`.) - gradp = lambda fp: gradients_impl.gradients(fp, concentration_p)[0] - gradq = lambda fq: gradients_impl.gradients(fq, concentration_q)[0] - [ - gradp_exact_kl_gamma_gamma_, - gradq_exact_kl_gamma_gamma_, - gradp_approx_kl_gamma_gamma_, - gradq_approx_kl_gamma_gamma_, - ] = sess.run([ - gradp(exact_kl_gamma_gamma), - gradq(exact_kl_gamma_gamma), - gradp(approx_kl_gamma_gamma), - gradq(approx_kl_gamma_gamma), - ]) - # Notice that variance (i.e., `rtol`) is higher when using score-trick. - self.assertAllClose(gradp_exact_kl_gamma_gamma_, - gradp_approx_kl_gamma_gamma_, - rtol=0.05, atol=0.) - self.assertAllClose(gradq_exact_kl_gamma_gamma_, - gradq_approx_kl_gamma_gamma_, - rtol=0.03, atol=0.) - if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py index 5770bcdd706723394bb06196d24aeb32b8b8491a..68fa415eeaf1d1ae7c2ecf1be1c300eddbfa4e69 100644 --- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py +++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py @@ -12,10 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Monte Carlo integration and helpers. - -See the @{$python/contrib.bayesflow.monte_carlo} guide. -""" +"""Monte Carlo integration and helpers.""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py index 032b859d469ee5039e08e4af4c2f4ebf35c2ff19..68ead2f7609ca987180fe8973cf902f1e56b8388 100644 --- a/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py +++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py @@ -192,7 +192,7 @@ def _logspace_mean(log_values): def expectation(f, samples, log_prob=None, use_reparametrization=True, axis=0, keep_dims=False, name=None): - """Computes the Monte-Carlo approximation of \\(E_p[f(X)]\\). + r"""Computes the Monte-Carlo approximation of \\(E_p[f(X)]\\). This function computes the Monte-Carlo approximation of an expectation, i.e., diff --git a/tensorflow/contrib/bigtable/BUILD b/tensorflow/contrib/bigtable/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..5c15d21e35557ba5ff25d9d943aae2809eddba4a --- /dev/null +++ b/tensorflow/contrib/bigtable/BUILD @@ -0,0 +1,196 @@ +# Cloud Bigtable client for TensorFlow + +package( + default_visibility = ["//tensorflow:internal"], +) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") +load( + "//tensorflow:tensorflow.bzl", + "tf_copts", + "tf_custom_op_library", + "tf_gen_op_libs", + "tf_gen_op_wrapper_py", + "tf_kernel_library", + "tf_cc_test", + "tf_py_test", +) + +tf_custom_op_py_library( + name = "bigtable", + srcs = ["__init__.py"] + glob(["python/ops/*.py"]), + dso = [ + ":python/ops/_bigtable.so", + ], + kernels = [ + ":bigtable_kernels", + ":bigtable_ops_op_lib", + ], + srcs_version = "PY2AND3", + deps = [ + ":bigtable_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform", + "//tensorflow/python:util", + "//tensorflow/python/data", + ], +) + +tf_custom_op_library( + name = "python/ops/_bigtable.so", + srcs = [ + "kernels/bigtable_kernels.cc", + "kernels/bigtable_lookup_dataset_op.cc", + "kernels/bigtable_prefix_key_dataset_op.cc", + "kernels/bigtable_range_key_dataset_op.cc", + "kernels/bigtable_scan_dataset_op.cc", + "ops/bigtable_ops.cc", + ], + deps = [ + ":bigtable_lib_cc", + "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", + ], +) + +tf_gen_op_wrapper_py( + name = "bigtable_ops", + deps = [":bigtable_ops_op_lib"], +) + +tf_gen_op_libs( + op_lib_names = [ + "bigtable_ops", + "bigtable_test_ops", + ], +) + +tf_kernel_library( + name = "bigtable_kernels", + srcs = [ + "kernels/bigtable_kernels.cc", + "kernels/bigtable_lookup_dataset_op.cc", + "kernels/bigtable_prefix_key_dataset_op.cc", + "kernels/bigtable_range_key_dataset_op.cc", + "kernels/bigtable_scan_dataset_op.cc", + ], + deps = [ + ":bigtable_lib_cc", + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", + ], +) + +# A library for use in the bigtable kernels. +cc_library( + name = "bigtable_lib_cc", + srcs = ["kernels/bigtable_lib.cc"], + hdrs = ["kernels/bigtable_lib.h"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", + ], +) + +cc_library( + name = "bigtable_test_client", + srcs = ["kernels/test_kernels/bigtable_test_client.cc"], + hdrs = ["kernels/test_kernels/bigtable_test_client.h"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "@com_github_googleapis_googleapis//:bigtable_protos", + "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", + "@com_googlesource_code_re2//:re2", + ], +) + +tf_cc_test( + name = "bigtable_test_client_test", + srcs = ["kernels/test_kernels/bigtable_test_client_test.cc"], + tags = ["manual"], + deps = [ + ":bigtable_test_client", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", + ], +) + +tf_gen_op_wrapper_py( + name = "bigtable_test_ops", + deps = [":bigtable_test_ops_op_lib"], +) + +tf_custom_op_library( + name = "python/kernel_tests/_bigtable_test.so", + srcs = [ + "kernels/test_kernels/bigtable_test_client_op.cc", + "ops/bigtable_test_ops.cc", + ], + deps = [ + ":bigtable_lib_cc", + ":bigtable_test_client", + "@com_googlesource_code_re2//:re2", + ], +) + +# Don't use tf_kernel_library because it prevents access to strings/stringprintf.h +cc_library( + name = "bigtable_test_kernels", + srcs = [ + "kernels/test_kernels/bigtable_test_client_op.cc", + ], + copts = tf_copts(), + linkstatic = 1, + deps = [ + ":bigtable_lib_cc", + ":bigtable_test_client", + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@com_googlesource_code_re2//:re2", + ], + alwayslink = 1, +) + +tf_custom_op_py_library( + name = "bigtable_test_py", + dso = [ + ":python/kernel_tests/_bigtable_test.so", + ], + kernels = [ + ":bigtable_test_kernels", + ":bigtable_test_ops_op_lib", + ], + srcs_version = "PY2AND3", + deps = [ + ":bigtable_test_ops", + # "//tensorflow/contrib/util:util_py", + # "//tensorflow/python:framework_for_generated_wrappers", + # "//tensorflow/python:platform", + # "//tensorflow/python:util", + # "//tensorflow/python/data", + ], +) + +tf_py_test( + name = "bigtable_ops_test", + size = "small", + srcs = ["python/kernel_tests/bigtable_ops_test.py"], + additional_deps = [ + ":bigtable", + ":bigtable_test_py", + "//tensorflow/core:protos_all_py", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform", + "//tensorflow/python:util", + ], + tags = ["manual"], +) diff --git a/tensorflow/contrib/bigtable/README.md b/tensorflow/contrib/bigtable/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ef3c60069e8a97f7a13457156d20f3f7a4f7eccb --- /dev/null +++ b/tensorflow/contrib/bigtable/README.md @@ -0,0 +1,10 @@ +# Bigtable # + +[Google Cloud Bigtable](https://cloud.google.com/bigtable/) is a high +performance storage system that can store and serve training data. This contrib +package contains an experimental integration with TensorFlow. + +> **Status: Highly experimental.** The current implementation is very much in +> flux. Please use at your own risk! :-) + + diff --git a/tensorflow/contrib/bigtable/__init__.py b/tensorflow/contrib/bigtable/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7df054637cdab32f2dd6201dd3488a90495e1cf5 --- /dev/null +++ b/tensorflow/contrib/bigtable/__init__.py @@ -0,0 +1,39 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Cloud Bigtable Client for TensorFlow. + +This contrib package allows TensorFlow to interface directly with Cloud Bigtable +for high-speed data loading. + +@@BigtableClient +@@BigTable + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable +from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient + +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + 'BigTable', + 'BigtableClient', +] + +remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc new file mode 100644 index 0000000000000000000000000000000000000000..8a7309e870711570f708f52d7b6bd8858d04db29 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc @@ -0,0 +1,313 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" + +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/lib/core/threadpool.h" + +namespace tensorflow { + +namespace { + +class BigtableClientOp : public OpKernel { + public: + explicit BigtableClientOp(OpKernelConstruction* ctx) : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("project_id", &project_id_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("instance_id", &instance_id_)); + OP_REQUIRES(ctx, !project_id_.empty(), + errors::InvalidArgument("project_id must be non-empty")); + OP_REQUIRES(ctx, !instance_id_.empty(), + errors::InvalidArgument("instance_id must be non-empty")); + } + + ~BigtableClientOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + + void Compute(OpKernelContext* ctx) override LOCKS_EXCLUDED(mu_) { + mutex_lock l(mu_); + if (!initialized_) { + ResourceMgr* mgr = ctx->resource_manager(); + OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def())); + BigtableClientResource* resource; + OP_REQUIRES_OK( + ctx, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, ctx]( + BigtableClientResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + std::shared_ptr client = + google::cloud::bigtable::CreateDefaultDataClient( + project_id_, instance_id_, + google::cloud::bigtable::ClientOptions()); + *ret = new BigtableClientResource(project_id_, instance_id_, + std::move(client)); + return Status::OK(); + })); + core::ScopedUnref resource_cleanup(resource); + initialized_ = true; + } + OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( + ctx, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + string project_id_; + string instance_id_; + + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableClient").Device(DEVICE_CPU), + BigtableClientOp); + +class BigtableTableOp : public OpKernel { + public: + explicit BigtableTableOp(OpKernelConstruction* ctx) : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("table_name", &table_)); + OP_REQUIRES(ctx, !table_.empty(), + errors::InvalidArgument("table_name must be non-empty")); + } + + ~BigtableTableOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + + void Compute(OpKernelContext* ctx) override LOCKS_EXCLUDED(mu_) { + mutex_lock l(mu_); + if (!initialized_) { + ResourceMgr* mgr = ctx->resource_manager(); + OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def())); + + BigtableClientResource* client_resource; + OP_REQUIRES_OK( + ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &client_resource)); + core::ScopedUnref unref_client(client_resource); + + BigtableTableResource* resource; + OP_REQUIRES_OK( + ctx, mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, client_resource](BigtableTableResource** ret) { + *ret = new BigtableTableResource(client_resource, table_); + return Status::OK(); + })); + initialized_ = true; + } + OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( + ctx, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + string table_; // Note: this is const after construction. + + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableTable").Device(DEVICE_CPU), + BigtableTableOp); + +class ToBigtableOp : public AsyncOpKernel { + public: + explicit ToBigtableOp(OpKernelConstruction* ctx) + : AsyncOpKernel(ctx), + thread_pool_(new thread::ThreadPool( + ctx->env(), ThreadOptions(), + strings::StrCat("to_bigtable_op_", SanitizeThreadSuffix(name())), + /* num_threads = */ 1, /* low_latency_hint = */ false)) {} + + void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override { + // The call to `iterator->GetNext()` may block and depend on an + // inter-op thread pool thread, so we issue the call from the + // owned thread pool. + thread_pool_->Schedule([this, ctx, done]() { + const Tensor* column_families_tensor; + OP_REQUIRES_OK_ASYNC( + ctx, ctx->input("column_families", &column_families_tensor), done); + OP_REQUIRES_ASYNC( + ctx, column_families_tensor->dims() == 1, + errors::InvalidArgument("`column_families` must be a vector."), done); + + const Tensor* columns_tensor; + OP_REQUIRES_OK_ASYNC(ctx, ctx->input("columns", &columns_tensor), done); + OP_REQUIRES_ASYNC(ctx, columns_tensor->dims() == 1, + errors::InvalidArgument("`columns` must be a vector."), + done); + OP_REQUIRES_ASYNC( + ctx, + columns_tensor->NumElements() == + column_families_tensor->NumElements(), + errors::InvalidArgument("len(column_families) != len(columns)"), + done); + + std::vector column_families; + column_families.reserve(column_families_tensor->NumElements()); + std::vector columns; + columns.reserve(column_families_tensor->NumElements()); + for (uint64 i = 0; i < column_families_tensor->NumElements(); ++i) { + column_families.push_back(column_families_tensor->flat()(i)); + columns.push_back(columns_tensor->flat()(i)); + } + + DatasetBase* dataset; + OP_REQUIRES_OK_ASYNC( + ctx, GetDatasetFromVariantTensor(ctx->input(1), &dataset), done); + + IteratorContext iter_ctx = dataset::MakeIteratorContext(ctx); + std::unique_ptr iterator; + OP_REQUIRES_OK_ASYNC( + ctx, + dataset->MakeIterator(&iter_ctx, "ToBigtableOpIterator", &iterator), + done); + + int64 timestamp_int; + OP_REQUIRES_OK_ASYNC( + ctx, ParseScalarArgument(ctx, "timestamp", ×tamp_int), + done); + OP_REQUIRES_ASYNC(ctx, timestamp_int >= -1, + errors::InvalidArgument("timestamp must be >= -1"), + done); + std::chrono::milliseconds timestamp(timestamp_int); + + BigtableTableResource* resource; + OP_REQUIRES_OK_ASYNC( + ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &resource), done); + core::ScopedUnref resource_cleanup(resource); + + std::vector components; + components.reserve(dataset->output_dtypes().size()); + bool end_of_sequence = false; + do { + ::google::cloud::bigtable::BulkMutation mutation; + // TODO(saeta): Make # of mutations configurable. + for (uint64 i = 0; i < 100 && !end_of_sequence; ++i) { + OP_REQUIRES_OK_ASYNC( + ctx, iterator->GetNext(&iter_ctx, &components, &end_of_sequence), + done); + if (!end_of_sequence) { + OP_REQUIRES_OK_ASYNC( + ctx, + CreateMutation(std::move(components), column_families, columns, + timestamp, &mutation), + done); + } + components.clear(); + } + grpc::Status mutation_status; + std::vector<::google::cloud::bigtable::FailedMutation> failures = + resource->table().BulkApply(std::move(mutation), mutation_status); + if (!failures.empty()) { + for (const auto& failure : failures) { + LOG(ERROR) << "Failure applying mutation on row (" + << failure.original_index() + << "): " << failure.mutation().row_key() + << " - error: " << failure.status().error_message() + << " (Details: " << failure.status().error_details() + << ")."; + } + } + OP_REQUIRES_ASYNC( + ctx, failures.empty() && mutation_status.ok(), + errors::Unknown("Failure while writing to BigTable: ", + mutation_status.error_code(), " - ", + mutation_status.error_message(), " (", + mutation_status.error_details(), + "), # of mutation failures: ", failures.size(), + ". See the log for the specific error details."), + done); + } while (!end_of_sequence); + done(); + }); + } + + private: + static string SanitizeThreadSuffix(string suffix) { + string clean; + for (int i = 0; i < suffix.size(); ++i) { + const char ch = suffix[i]; + if ((ch >= 'a' && ch <= 'z') || (ch >= 'A' && ch <= 'Z') || + (ch >= '0' && ch <= '9') || ch == '_' || ch == '-') { + clean += ch; + } else { + clean += '_'; + } + } + return clean; + } + + Status CreateMutation( + std::vector tensors, const std::vector& column_families, + const std::vector& columns, std::chrono::milliseconds timestamp, + ::google::cloud::bigtable::BulkMutation* bulk_mutation) { + if (tensors.size() != column_families.size() + 1) { + return errors::InvalidArgument( + "Iterator produced a set of Tensors shorter than expected"); + } + ::google::cloud::bigtable::SingleRowMutation mutation( + std::move(tensors[0].scalar()())); + for (size_t i = 1; i < tensors.size(); ++i) { + if (!TensorShapeUtils::IsScalar(tensors[i].shape())) { + return errors::Internal("Output tensor ", i, " was not a scalar"); + } + mutation.emplace_back(::google::cloud::bigtable::SetCell( + column_families[i - 1], columns[i - 1], timestamp, + std::move(tensors[i].scalar()()))); + } + bulk_mutation->emplace_back(std::move(mutation)); + return Status::OK(); + } + + template + Status ParseScalarArgument(OpKernelContext* ctx, + const StringPiece& argument_name, T* output) { + const Tensor* argument_t; + TF_RETURN_IF_ERROR(ctx->input(argument_name, &argument_t)); + if (!TensorShapeUtils::IsScalar(argument_t->shape())) { + return errors::InvalidArgument(argument_name, " must be a scalar"); + } + *output = argument_t->scalar()(); + return Status::OK(); + } + + std::unique_ptr thread_pool_; +}; + +REGISTER_KERNEL_BUILDER(Name("DatasetToBigtable").Device(DEVICE_CPU), + ToBigtableOp); + +} // namespace + +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc new file mode 100644 index 0000000000000000000000000000000000000000..2514575f30831bdcfab87eba07511fd309e8b1c2 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc @@ -0,0 +1,45 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" + +namespace tensorflow { + +Status GrpcStatusToTfStatus(const ::grpc::Status& status) { + if (status.ok()) { + return Status::OK(); + } + auto grpc_code = status.error_code(); + if (status.error_code() == ::grpc::StatusCode::ABORTED || + status.error_code() == ::grpc::StatusCode::UNAVAILABLE || + status.error_code() == ::grpc::StatusCode::OUT_OF_RANGE) { + grpc_code = ::grpc::StatusCode::INTERNAL; + } + return Status( + static_cast<::tensorflow::error::Code>(status.error_code()), + strings::StrCat("Error reading from BigTable: ", status.error_message(), + " (Details: ", status.error_details(), ")")); +} + +string RegexFromStringSet(const std::vector& strs) { + CHECK(!strs.empty()) << "The list of strings to turn into a regex was empty."; + std::unordered_set uniq(strs.begin(), strs.end()); + if (uniq.size() == 1) { + return *uniq.begin(); + } + return str_util::Join(uniq, "|"); +} + +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.h b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h new file mode 100644 index 0000000000000000000000000000000000000000..12d8256dea72e443826675765369ac6daa99a0ca --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h @@ -0,0 +1,142 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_LIB_H_ +#define TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_LIB_H_ + +// Note: we use bigtable/client/internal/table.h as this is the no-exception API + +#include "google/cloud/bigtable/data_client.h" +#include "google/cloud/bigtable/internal/table.h" +#include "tensorflow/core/framework/dataset.h" +#include "tensorflow/core/framework/resource_mgr.h" + +namespace tensorflow { + +Status GrpcStatusToTfStatus(const ::grpc::Status& status); + +string RegexFromStringSet(const std::vector& strs); + +class BigtableClientResource : public ResourceBase { + public: + BigtableClientResource( + string project_id, string instance_id, + std::shared_ptr client) + : project_id_(std::move(project_id)), + instance_id_(std::move(instance_id)), + client_(std::move(client)) {} + + std::shared_ptr get_client() { + return client_; + } + + string DebugString() override { + return strings::StrCat("BigtableClientResource(project_id: ", project_id_, + ", instance_id: ", instance_id_, ")"); + } + + private: + const string project_id_; + const string instance_id_; + std::shared_ptr client_; +}; + +class BigtableTableResource : public ResourceBase { + public: + BigtableTableResource(BigtableClientResource* client, string table_name) + : client_(client), + table_name_(std::move(table_name)), + table_(client->get_client(), table_name_) { + client_->Ref(); + } + + ~BigtableTableResource() override { client_->Unref(); } + + ::google::cloud::bigtable::noex::Table& table() { return table_; } + + string DebugString() override { + return strings::StrCat( + "BigtableTableResource(client: ", client_->DebugString(), + ", table: ", table_name_, ")"); + } + + private: + BigtableClientResource* client_; // Ownes one ref. + const string table_name_; + ::google::cloud::bigtable::noex::Table table_; +}; + +// BigtableReaderDatasetIterator is an abstract class for iterators from +// datasets that are "readers" (source datasets, not transformation datasets) +// that read from Bigtable. +template +class BigtableReaderDatasetIterator : public DatasetIterator { + public: + explicit BigtableReaderDatasetIterator( + const typename DatasetIterator::Params& params) + : DatasetIterator(params), iterator_(nullptr, false) {} + + Status GetNextInternal(IteratorContext* ctx, std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(EnsureIteratorInitialized()); + if (iterator_ == reader_->end()) { + grpc::Status status = reader_->Finish(); + if (status.ok()) { + *end_of_sequence = true; + return Status::OK(); + } + return GrpcStatusToTfStatus(status); + } + *end_of_sequence = false; + google::cloud::bigtable::Row& row = *iterator_; + Status s = ParseRow(ctx, row, out_tensors); + // Ensure we always advance. + ++iterator_; + return s; + } + + protected: + virtual ::google::cloud::bigtable::RowRange MakeRowRange() = 0; + virtual ::google::cloud::bigtable::Filter MakeFilter() = 0; + virtual Status ParseRow(IteratorContext* ctx, + const ::google::cloud::bigtable::Row& row, + std::vector* out_tensors) = 0; + + private: + Status EnsureIteratorInitialized() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + if (reader_) { + return Status::OK(); + } + + auto rows = MakeRowRange(); + auto filter = MakeFilter(); + + // Note: the this in `this->dataset()` below is necessary due to namespace + // name conflicts. + reader_.reset(new ::google::cloud::bigtable::RowReader( + this->dataset()->table()->table().ReadRows(rows, filter))); + iterator_ = reader_->begin(); + return Status::OK(); + } + + mutex mu_; + std::unique_ptr<::google::cloud::bigtable::RowReader> reader_ GUARDED_BY(mu_); + ::google::cloud::bigtable::RowReader::iterator iterator_ GUARDED_BY(mu_); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_LIB_H_ diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..9e49fa35db4b2cd2c8991100a28a5b9c55f01ffe --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc @@ -0,0 +1,221 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtableLookupDatasetOp : public UnaryDatasetOpKernel { + public: + using UnaryDatasetOpKernel::UnaryDatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase* input, + DatasetBase** output) override { + BigtableTableResource* table; + OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 1), &table)); + + std::vector column_families; + std::vector columns; + OP_REQUIRES_OK(ctx, ParseVectorArgument(ctx, "column_families", + &column_families)); + OP_REQUIRES_OK(ctx, ParseVectorArgument(ctx, "columns", &columns)); + OP_REQUIRES( + ctx, column_families.size() == columns.size(), + errors::InvalidArgument("len(columns) != len(column_families)")); + + const uint64 num_outputs = columns.size() + 1; + std::vector output_shapes; + output_shapes.reserve(num_outputs); + DataTypeVector output_types; + output_types.reserve(num_outputs); + for (uint64 i = 0; i < num_outputs; ++i) { + output_shapes.push_back({}); + output_types.push_back(DT_STRING); + } + + *output = + new Dataset(ctx, input, table, std::move(column_families), + std::move(columns), output_types, std::move(output_shapes)); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, const DatasetBase* input, + BigtableTableResource* table, + std::vector column_families, + std::vector columns, + const DataTypeVector& output_types, + std::vector output_shapes) + : GraphDatasetBase(ctx), + input_(input), + table_(table), + column_families_(std::move(column_families)), + columns_(std::move(columns)), + output_types_(output_types), + output_shapes_(std::move(output_shapes)), + filter_(MakeFilter(column_families_, columns_)) { + table_->Ref(); + input_->Ref(); + } + + ~Dataset() override { + table_->Unref(); + input_->Unref(); + } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtableLookupDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + return output_types_; + } + + const std::vector& output_shapes() const override { + return output_shapes_; + } + + string DebugString() const override { + return "BigtableLookupDatasetOp::Dataset"; + } + + private: + static ::google::cloud::bigtable::Filter MakeFilter( + const std::vector& column_families, + const std::vector& columns) { + string column_family_regex = RegexFromStringSet(column_families); + string column_regex = RegexFromStringSet(columns); + + return ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1), + ::google::cloud::bigtable::Filter::FamilyRegex(column_family_regex), + ::google::cloud::bigtable::Filter::ColumnRegex(column_regex)); + } + + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + Status Initialize(IteratorContext* ctx) override { + return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_); + } + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); // Sequence requests. + std::vector input_tensors; + TF_RETURN_IF_ERROR( + input_impl_->GetNext(ctx, &input_tensors, end_of_sequence)); + if (*end_of_sequence) { + return Status::OK(); + } + if (input_tensors.size() != 1) { + return errors::InvalidArgument( + "Upstream iterator (", dataset()->input_->DebugString(), + ") did not produce a single `tf.string` `tf.Tensor`. It " + "produced ", + input_tensors.size(), " tensors."); + } + if (input_tensors[0].NumElements() == 0) { + return errors::InvalidArgument("Upstream iterator (", + dataset()->input_->DebugString(), + ") return an empty set of keys."); + } + if (input_tensors[0].NumElements() == 1) { + // Single key lookup. + ::grpc::Status status; + auto pair = dataset()->table_->table().ReadRow( + input_tensors[0].scalar()(), dataset()->filter_, status); + if (!status.ok()) { + return GrpcStatusToTfStatus(status); + } + if (!pair.first) { + return errors::DataLoss("Row key '", + input_tensors[0].scalar()(), + "' not found."); + } + TF_RETURN_IF_ERROR(ParseRow(ctx, pair.second, out_tensors)); + } else { + // Batched get. + return errors::Unimplemented( + "BigtableLookupDataset doesn't yet support batched retrieval."); + } + return Status::OK(); + } + + private: + Status ParseRow(IteratorContext* ctx, + const ::google::cloud::bigtable::Row& row, + std::vector* out_tensors) { + out_tensors->reserve(dataset()->columns_.size() + 1); + Tensor row_key_tensor(ctx->allocator({}), DT_STRING, {}); + row_key_tensor.scalar()() = string(row.row_key()); + out_tensors->emplace_back(std::move(row_key_tensor)); + + if (row.cells().size() > 2 * dataset()->columns_.size()) { + LOG(WARNING) << "An excessive number of columns (" + << row.cells().size() + << ") were retrieved when reading row: " + << row.row_key(); + } + + for (uint64 i = 0; i < dataset()->columns_.size(); ++i) { + Tensor col_tensor(ctx->allocator({}), DT_STRING, {}); + bool found_column = false; + for (auto cell_itr = row.cells().begin(); + !found_column && cell_itr != row.cells().end(); ++cell_itr) { + if (cell_itr->family_name() == dataset()->column_families_[i] && + string(cell_itr->column_qualifier()) == + dataset()->columns_[i]) { + col_tensor.scalar()() = string(cell_itr->value()); + found_column = true; + } + } + if (!found_column) { + return errors::DataLoss("Column ", dataset()->column_families_[i], + ":", dataset()->columns_[i], + " not found in row: ", row.row_key()); + } + out_tensors->emplace_back(std::move(col_tensor)); + } + return Status::OK(); + } + + mutex mu_; + std::unique_ptr input_impl_ GUARDED_BY(mu_); + }; + + const DatasetBase* const input_; + BigtableTableResource* table_; + const std::vector column_families_; + const std::vector columns_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + const ::google::cloud::bigtable::Filter filter_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableLookupDataset").Device(DEVICE_CPU), + BigtableLookupDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..e960719614a1c7c6c4af53ea924aef214a09b24d --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc @@ -0,0 +1,104 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtablePrefixKeyDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + string prefix; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "prefix", &prefix)); + + BigtableTableResource* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + + *output = new Dataset(ctx, resource, std::move(prefix)); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, BigtableTableResource* table, + string prefix) + : GraphDatasetBase(ctx), table_(table), prefix_(std::move(prefix)) { + table_->Ref(); + } + + ~Dataset() override { table_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtablePrefixKeyDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}}); + return *shapes; + } + + string DebugString() const override { + return "BigtablePrefixKeyDatasetOp::Dataset"; + } + + BigtableTableResource* table() const { return table_; } + + private: + class Iterator : public BigtableReaderDatasetIterator { + public: + explicit Iterator(const Params& params) + : BigtableReaderDatasetIterator(params) {} + + ::google::cloud::bigtable::RowRange MakeRowRange() override { + return ::google::cloud::bigtable::RowRange::Prefix(dataset()->prefix_); + } + ::google::cloud::bigtable::Filter MakeFilter() override { + return ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::CellsRowLimit(1), + ::google::cloud::bigtable::Filter::StripValueTransformer()); + } + Status ParseRow(IteratorContext* ctx, + const ::google::cloud::bigtable::Row& row, + std::vector* out_tensors) override { + Tensor output_tensor(ctx->allocator({}), DT_STRING, {}); + output_tensor.scalar()() = string(row.row_key()); + out_tensors->emplace_back(std::move(output_tensor)); + return Status::OK(); + } + }; + + BigtableTableResource* const table_; + const string prefix_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtablePrefixKeyDataset").Device(DEVICE_CPU), + BigtablePrefixKeyDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..96d3565d9b90e72f9e25e69e91f1931c982714cd --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc @@ -0,0 +1,112 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtableRangeKeyDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + string start_key; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "start_key", &start_key)); + string end_key; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "end_key", &end_key)); + + BigtableTableResource* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + + *output = + new Dataset(ctx, resource, std::move(start_key), std::move(end_key)); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, BigtableTableResource* table, + string start_key, string end_key) + : GraphDatasetBase(ctx), + table_(table), + start_key_(std::move(start_key)), + end_key_(std::move(end_key)) { + table_->Ref(); + } + + ~Dataset() override { table_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtableRangeKeyDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}}); + return *shapes; + } + + string DebugString() const override { + return "BigtableRangeKeyDatasetOp::Dataset"; + } + + BigtableTableResource* table() const { return table_; } + + private: + class Iterator : public BigtableReaderDatasetIterator { + public: + explicit Iterator(const Params& params) + : BigtableReaderDatasetIterator(params) {} + + ::google::cloud::bigtable::RowRange MakeRowRange() override { + return ::google::cloud::bigtable::RowRange::Range(dataset()->start_key_, + dataset()->end_key_); + } + ::google::cloud::bigtable::Filter MakeFilter() override { + return ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::CellsRowLimit(1), + ::google::cloud::bigtable::Filter::StripValueTransformer()); + } + Status ParseRow(IteratorContext* ctx, + const ::google::cloud::bigtable::Row& row, + std::vector* out_tensors) override { + Tensor output_tensor(ctx->allocator({}), DT_STRING, {}); + output_tensor.scalar()() = string(row.row_key()); + out_tensors->emplace_back(std::move(output_tensor)); + return Status::OK(); + } + }; + + BigtableTableResource* const table_; + const string start_key_; + const string end_key_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableRangeKeyDataset").Device(DEVICE_CPU), + BigtableRangeKeyDatasetOp); +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..13cb8681679ec1541b74a20474665f770790201f --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc @@ -0,0 +1,219 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtableScanDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + string prefix; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "prefix", &prefix)); + string start_key; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "start_key", &start_key)); + string end_key; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "end_key", &end_key)); + + OP_REQUIRES(ctx, !(prefix.empty() && start_key.empty()), + errors::InvalidArgument( + "Either prefix or start_key must be specified")); + OP_REQUIRES(ctx, prefix.empty() || start_key.empty(), + errors::InvalidArgument( + "Only one of prefix and start_key can be provided")); + if (!prefix.empty()) { + OP_REQUIRES(ctx, end_key.empty(), + errors::InvalidArgument( + "If prefix is specified, end_key must be empty.")); + } + + std::vector column_families; + std::vector columns; + OP_REQUIRES_OK(ctx, ParseVectorArgument(ctx, "column_families", + &column_families)); + OP_REQUIRES_OK(ctx, ParseVectorArgument(ctx, "columns", &columns)); + OP_REQUIRES( + ctx, column_families.size() == columns.size(), + errors::InvalidArgument("len(columns) != len(column_families)")); + OP_REQUIRES(ctx, !column_families.empty(), + errors::InvalidArgument("`column_families` is empty")); + + float probability = 0; + OP_REQUIRES_OK( + ctx, ParseScalarArgument(ctx, "probability", &probability)); + OP_REQUIRES( + ctx, probability > 0 && probability <= 1, + errors::InvalidArgument( + "Probability outside the range of (0, 1]. Got: ", probability)); + + BigtableTableResource* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + + const uint64 num_outputs = columns.size() + 1; + std::vector output_shapes; + output_shapes.reserve(num_outputs); + DataTypeVector output_types; + output_types.reserve(num_outputs); + for (uint64 i = 0; i < num_outputs; ++i) { + output_shapes.push_back({}); + output_types.push_back(DT_STRING); + } + + *output = new Dataset(ctx, resource, std::move(prefix), + std::move(start_key), std::move(end_key), + std::move(column_families), std::move(columns), + probability, output_types, std::move(output_shapes)); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, BigtableTableResource* table, + string prefix, string start_key, string end_key, + std::vector column_families, + std::vector columns, float probability, + const DataTypeVector& output_types, + std::vector output_shapes) + : GraphDatasetBase(ctx), + table_(table), + prefix_(std::move(prefix)), + start_key_(std::move(start_key)), + end_key_(std::move(end_key)), + column_families_(std::move(column_families)), + columns_(std::move(columns)), + column_family_regex_(RegexFromStringSet(column_families_)), + column_regex_(RegexFromStringSet(columns_)), + probability_(probability), + output_types_(output_types), + output_shapes_(std::move(output_shapes)) { + table_->Ref(); + } + + ~Dataset() override { table_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtableScanDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + return output_types_; + } + + const std::vector& output_shapes() const override { + return output_shapes_; + } + + string DebugString() const override { + return "BigtableScanDatasetOp::Dataset"; + } + + BigtableTableResource* table() const { return table_; } + + private: + class Iterator : public BigtableReaderDatasetIterator { + public: + explicit Iterator(const Params& params) + : BigtableReaderDatasetIterator(params) {} + + ::google::cloud::bigtable::RowRange MakeRowRange() override { + if (!dataset()->prefix_.empty()) { + DCHECK(dataset()->start_key_.empty()); + return ::google::cloud::bigtable::RowRange::Prefix( + dataset()->prefix_); + } else { + DCHECK(!dataset()->start_key_.empty()) + << "Both prefix and start_key were empty!"; + return ::google::cloud::bigtable::RowRange::Range( + dataset()->start_key_, dataset()->end_key_); + } + } + ::google::cloud::bigtable::Filter MakeFilter() override { + // TODO(saeta): Investigate optimal ordering here. + return ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1), + ::google::cloud::bigtable::Filter::FamilyRegex( + dataset()->column_family_regex_), + ::google::cloud::bigtable::Filter::ColumnRegex( + dataset()->column_regex_), + dataset()->probability_ != 1.0 + ? ::google::cloud::bigtable::Filter::RowSample( + dataset()->probability_) + : ::google::cloud::bigtable::Filter::PassAllFilter()); + } + Status ParseRow(IteratorContext* ctx, + const ::google::cloud::bigtable::Row& row, + std::vector* out_tensors) override { + out_tensors->reserve(dataset()->columns_.size() + 1); + Tensor row_key_tensor(ctx->allocator({}), DT_STRING, {}); + row_key_tensor.scalar()() = string(row.row_key()); + out_tensors->emplace_back(std::move(row_key_tensor)); + + if (row.cells().size() > 2 * dataset()->columns_.size()) { + LOG(WARNING) << "An excessive number of columns (" + << row.cells().size() + << ") were retrieved when reading row: " + << row.row_key(); + } + + for (uint64 i = 0; i < dataset()->columns_.size(); ++i) { + Tensor col_tensor(ctx->allocator({}), DT_STRING, {}); + bool found_column = false; + for (auto cell_itr = row.cells().begin(); + !found_column && cell_itr != row.cells().end(); ++cell_itr) { + if (cell_itr->family_name() == dataset()->column_families_[i] && + string(cell_itr->column_qualifier()) == + dataset()->columns_[i]) { + col_tensor.scalar()() = string(cell_itr->value()); + found_column = true; + } + } + if (!found_column) { + return errors::InvalidArgument( + "Column ", dataset()->column_families_[i], ":", + dataset()->columns_[i], " not found in row: ", row.row_key()); + } + out_tensors->emplace_back(std::move(col_tensor)); + } + return Status::OK(); + } + }; + + BigtableTableResource* table_; + const string prefix_; + const string start_key_; + const string end_key_; + const std::vector column_families_; + const std::vector columns_; + const string column_family_regex_; + const string column_regex_; + const float probability_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableScanDataset").Device(DEVICE_CPU), + BigtableScanDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc new file mode 100644 index 0000000000000000000000000000000000000000..0f107f169cfa1e9c9158be270323e09250388724 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc @@ -0,0 +1,367 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h" + +#include "google/bigtable/v2/data.pb.h" +#include "google/protobuf/wrappers.pb.h" +#include "re2/re2.h" +#include "tensorflow/core/lib/strings/stringprintf.h" +#include "tensorflow/core/util/ptr_util.h" +// #include "util/task/codes.pb.h" + +namespace tensorflow { +namespace { + +void UpdateRow(const ::google::bigtable::v2::Mutation& mut, + std::map* row) { + if (mut.has_set_cell()) { + auto col = + strings::Printf("%s:%s", mut.set_cell().family_name().c_str(), + string(mut.set_cell().column_qualifier()).c_str()); + (*row)[col] = string(mut.set_cell().value()); + } else if (mut.has_delete_from_column()) { + auto col = strings::Printf( + "%s:%s", mut.delete_from_column().family_name().c_str(), + string(mut.delete_from_column().column_qualifier()).c_str()); + row->erase(col); + } else if (mut.has_delete_from_family()) { + auto itr = row->lower_bound(mut.delete_from_family().family_name()); + auto prefix = + strings::Printf("%s:", mut.delete_from_family().family_name().c_str()); + while (itr != row->end() && itr->first.substr(0, prefix.size()) == prefix) { + row->erase(itr); + } + } else if (mut.has_delete_from_row()) { + row->clear(); + } else { + LOG(ERROR) << "Unknown mutation: " << mut.ShortDebugString(); + } +} + +} // namespace + +class SampleRowKeysResponse : public grpc::ClientReaderInterface< + google::bigtable::v2::SampleRowKeysResponse> { + public: + explicit SampleRowKeysResponse(BigtableTestClient* client) + : client_(client) {} + + bool NextMessageSize(uint32_t* sz) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + *sz = 10000; // A sufficiently high enough value to not worry about. + return true; + } + + bool Read(google::bigtable::v2::SampleRowKeysResponse* resp) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + sent_first_message_ = true; + + mutex_lock l2(client_->mu_); + *resp = google::bigtable::v2::SampleRowKeysResponse(); + resp->set_row_key(client_->table_.rows.begin()->first); + resp->set_offset_bytes(0); + return true; + } + + grpc::Status Finish() override { return grpc::Status::OK; } + + void WaitForInitialMetadata() override {} // Do nothing. + + private: + mutex mu_; + bool sent_first_message_ GUARDED_BY(mu_) = false; + BigtableTestClient* client_; // Not owned. +}; + +class ReadRowsResponse : public grpc::ClientReaderInterface< + google::bigtable::v2::ReadRowsResponse> { + public: + ReadRowsResponse(BigtableTestClient* client, + google::bigtable::v2::ReadRowsRequest const& request) + : client_(client), request_(request) {} + + bool NextMessageSize(uint32_t* sz) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + *sz = 10000000; // A sufficiently high enough value to not worry about. + return true; + } + + bool Read(google::bigtable::v2::ReadRowsResponse* resp) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + sent_first_message_ = true; + RowFilter filter = MakeRowFilter(); + + mutex_lock l2(client_->mu_); + *resp = google::bigtable::v2::ReadRowsResponse(); + // Send all contents in first response. + for (auto itr = client_->table_.rows.begin(); + itr != client_->table_.rows.end(); ++itr) { + if (filter.AllowRow(itr->first)) { + ::google::bigtable::v2::ReadRowsResponse_CellChunk* chunk = nullptr; + bool sent_first = false; + for (auto col_itr = itr->second.columns.begin(); + col_itr != itr->second.columns.end(); ++col_itr) { + if (filter.AllowColumn(col_itr->first)) { + chunk = resp->add_chunks(); + if (!sent_first) { + sent_first = true; + chunk->set_row_key(itr->first); + } + auto colon_idx = col_itr->first.find(":"); + CHECK(colon_idx != string::npos) + << "No ':' found in: " << col_itr->first; + chunk->mutable_family_name()->set_value( + string(col_itr->first, 0, colon_idx)); + chunk->mutable_qualifier()->set_value( + string(col_itr->first, ++colon_idx)); + if (!filter.strip_values) { + chunk->set_value(col_itr->second); + } + if (filter.only_one_column) { + break; + } + } + } + if (sent_first) { + // We are sending this row, so set the commit flag on the last chunk. + chunk->set_commit_row(true); + } + } + } + return true; + } + + grpc::Status Finish() override { return grpc::Status::OK; } + + void WaitForInitialMetadata() override {} // Do nothing. + + private: + struct RowFilter { + std::set row_set; + std::vector> row_ranges; + double row_sample = 0.0; // Note: currently ignored. + std::unique_ptr col_filter; + bool strip_values = false; + bool only_one_column = false; + + bool AllowRow(const string& row) { + if (row_set.find(row) != row_set.end()) { + return true; + } + for (const auto& range : row_ranges) { + if (range.first <= row && range.second > row) { + return true; + } + } + return false; + } + + bool AllowColumn(const string& col) { + if (col_filter) { + return RE2::FullMatch(col, *col_filter); + } else { + return true; + } + } + }; + + RowFilter MakeRowFilter() { + RowFilter filter; + for (auto i = request_.rows().row_keys().begin(); + i != request_.rows().row_keys().end(); ++i) { + filter.row_set.insert(string(*i)); + } + for (auto i = request_.rows().row_ranges().begin(); + i != request_.rows().row_ranges().end(); ++i) { + if (i->start_key_case() != + google::bigtable::v2::RowRange::kStartKeyClosed || + i->end_key_case() != google::bigtable::v2::RowRange::kEndKeyOpen) { + LOG(WARNING) << "Skipping row range that cannot be processed: " + << i->ShortDebugString(); + continue; + } + filter.row_ranges.emplace_back(std::make_pair( + string(i->start_key_closed()), string(i->end_key_open()))); + } + if (request_.filter().has_chain()) { + string family_filter; + string qualifier_filter; + for (auto i = request_.filter().chain().filters().begin(); + i != request_.filter().chain().filters().end(); ++i) { + switch (i->filter_case()) { + case google::bigtable::v2::RowFilter::kFamilyNameRegexFilter: + family_filter = i->family_name_regex_filter(); + break; + case google::bigtable::v2::RowFilter::kColumnQualifierRegexFilter: + qualifier_filter = i->column_qualifier_regex_filter(); + break; + case google::bigtable::v2::RowFilter::kCellsPerColumnLimitFilter: + if (i->cells_per_column_limit_filter() != 1) { + LOG(ERROR) << "Unexpected cells_per_column_limit_filter: " + << i->cells_per_column_limit_filter(); + } + break; + case google::bigtable::v2::RowFilter::kStripValueTransformer: + filter.strip_values = i->strip_value_transformer(); + break; + case google::bigtable::v2::RowFilter::kRowSampleFilter: + LOG(INFO) << "Ignoring row sample directive."; + break; + case google::bigtable::v2::RowFilter::kPassAllFilter: + break; + case google::bigtable::v2::RowFilter::kCellsPerRowLimitFilter: + filter.only_one_column = true; + break; + default: + LOG(WARNING) << "Ignoring unknown filter type: " + << i->ShortDebugString(); + } + } + if (family_filter.empty() || qualifier_filter.empty()) { + LOG(WARNING) << "Missing regex!"; + } else { + string regex = strings::Printf("%s:%s", family_filter.c_str(), + qualifier_filter.c_str()); + filter.col_filter.reset(new RE2(regex)); + } + } else { + LOG(WARNING) << "Read request did not have a filter chain specified: " + << request_.filter().DebugString(); + } + return filter; + } + + mutex mu_; + bool sent_first_message_ GUARDED_BY(mu_) = false; + BigtableTestClient* client_; // Not owned. + const google::bigtable::v2::ReadRowsRequest request_; +}; + +class MutateRowsResponse : public grpc::ClientReaderInterface< + google::bigtable::v2::MutateRowsResponse> { + public: + explicit MutateRowsResponse(size_t num_successes) + : num_successes_(num_successes) {} + + bool NextMessageSize(uint32_t* sz) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + *sz = 10000000; // A sufficiently high enough value to not worry about. + return true; + } + + bool Read(google::bigtable::v2::MutateRowsResponse* resp) override { + mutex_lock l(mu_); + if (sent_first_message_) { + return false; + } + sent_first_message_ = true; + *resp = google::bigtable::v2::MutateRowsResponse(); + for (size_t i = 0; i < num_successes_; ++i) { + auto entry = resp->add_entries(); + entry->set_index(i); + } + return true; + } + + grpc::Status Finish() override { return grpc::Status::OK; } + + void WaitForInitialMetadata() override {} // Do nothing. + + private: + const size_t num_successes_; + + mutex mu_; + bool sent_first_message_ = false; +}; + +grpc::Status BigtableTestClient::MutateRow( + grpc::ClientContext* context, + google::bigtable::v2::MutateRowRequest const& request, + google::bigtable::v2::MutateRowResponse* response) { + mutex_lock l(mu_); + auto* row = &table_.rows[string(request.row_key())]; + for (int i = 0; i < request.mutations_size(); ++i) { + UpdateRow(request.mutations(i), &row->columns); + } + *response = google::bigtable::v2::MutateRowResponse(); + return grpc::Status::OK; +} +grpc::Status BigtableTestClient::CheckAndMutateRow( + grpc::ClientContext* context, + google::bigtable::v2::CheckAndMutateRowRequest const& request, + google::bigtable::v2::CheckAndMutateRowResponse* response) { + return grpc::Status(grpc::StatusCode::UNIMPLEMENTED, + "CheckAndMutateRow not implemented."); +} +grpc::Status BigtableTestClient::ReadModifyWriteRow( + grpc::ClientContext* context, + google::bigtable::v2::ReadModifyWriteRowRequest const& request, + google::bigtable::v2::ReadModifyWriteRowResponse* response) { + return grpc::Status(grpc::StatusCode::UNIMPLEMENTED, + "ReadModifyWriteRow not implemented."); +} +std::unique_ptr< + grpc::ClientReaderInterface> +BigtableTestClient::ReadRows( + grpc::ClientContext* context, + google::bigtable::v2::ReadRowsRequest const& request) { + return MakeUnique(this, request); +} + +std::unique_ptr< + grpc::ClientReaderInterface> +BigtableTestClient::SampleRowKeys( + grpc::ClientContext* context, + google::bigtable::v2::SampleRowKeysRequest const& request) { + return MakeUnique(this); +} +std::unique_ptr< + grpc::ClientReaderInterface> +BigtableTestClient::MutateRows( + grpc::ClientContext* context, + google::bigtable::v2::MutateRowsRequest const& request) { + mutex_lock l(mu_); + for (auto i = request.entries().begin(); i != request.entries().end(); ++i) { + auto* row = &table_.rows[string(i->row_key())]; + for (auto mut = i->mutations().begin(); mut != i->mutations().end(); + ++mut) { + UpdateRow(*mut, &row->columns); + } + } + return MakeUnique(request.entries_size()); +} + +std::shared_ptr BigtableTestClient::Channel() { + LOG(WARNING) << "Call to InMemoryDataClient::Channel(); this will likely " + "cause a crash!"; + return nullptr; +} +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h new file mode 100644 index 0000000000000000000000000000000000000000..dac2b16a216d26f02684c7401ed2ddaa4b7baddb --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h @@ -0,0 +1,87 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_TEST_KERNELS_BIGTABLE_TEST_CLIENT_H_ +#define TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_TEST_KERNELS_BIGTABLE_TEST_CLIENT_H_ + +#include "google/cloud/bigtable/data_client.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/mutex.h" + +namespace tensorflow { + +class BigtableTestClient : public ::google::cloud::bigtable::DataClient { + public: + std::string const& project_id() const override { return project_id_; } + std::string const& instance_id() const override { return instance_id_; } + void reset() override { + mutex_lock l(mu_); + table_ = Table(); + } + + grpc::Status MutateRow( + grpc::ClientContext* context, + google::bigtable::v2::MutateRowRequest const& request, + google::bigtable::v2::MutateRowResponse* response) override; + + grpc::Status CheckAndMutateRow( + grpc::ClientContext* context, + google::bigtable::v2::CheckAndMutateRowRequest const& request, + google::bigtable::v2::CheckAndMutateRowResponse* response) override; + + grpc::Status ReadModifyWriteRow( + grpc::ClientContext* context, + google::bigtable::v2::ReadModifyWriteRowRequest const& request, + google::bigtable::v2::ReadModifyWriteRowResponse* response) override; + + std::unique_ptr< + grpc::ClientReaderInterface> + ReadRows(grpc::ClientContext* context, + google::bigtable::v2::ReadRowsRequest const& request) override; + std::unique_ptr< + grpc::ClientReaderInterface> + SampleRowKeys( + grpc::ClientContext* context, + google::bigtable::v2::SampleRowKeysRequest const& request) override; + + std::unique_ptr< + grpc::ClientReaderInterface> + MutateRows(grpc::ClientContext* context, + google::bigtable::v2::MutateRowsRequest const& request) override; + + std::shared_ptr Channel() override; + + private: + friend class SampleRowKeysResponse; + friend class ReadRowsResponse; + friend class MutateRowsResponse; + + struct Row { + string row_key; + std::map columns; + }; + struct Table { + std::map rows; + }; + + mutex mu_; + const std::string project_id_ = "testproject"; + const std::string instance_id_ = "testinstance"; + Table table_ GUARDED_BY(mu_); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_TEST_KERNELS_BIGTABLE_TEST_CLIENT_H_ diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_op.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..fa3e587b90147bd519586eef0cfb5e048b1b75be --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_op.cc @@ -0,0 +1,78 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/lib/strings/stringprintf.h" + +namespace tensorflow { + +namespace { + +class BigtableTestClientOp : public OpKernel { + public: + explicit BigtableTestClientOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + ~BigtableTestClientOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + void Compute(OpKernelContext* ctx) override LOCKS_EXCLUDED(mu_) { + mutex_lock l(mu_); + if (!initialized_) { + ResourceMgr* mgr = ctx->resource_manager(); + OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def())); + BigtableClientResource* resource; + OP_REQUIRES_OK( + ctx, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, ctx](BigtableClientResource** ret) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + std::shared_ptr client( + new BigtableTestClient()); + // Note: must make explicit copies to sequence + // them before the move of client. + string project_id = client->project_id(); + string instance_id = client->instance_id(); + *ret = new BigtableClientResource(std::move(project_id), + std::move(instance_id), + std::move(client)); + return Status::OK(); + })); + initialized_ = true; + } + OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( + ctx, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableTestClient").Device(DEVICE_CPU), + BigtableTestClientOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..d6b396471941eaa0ca1c13a7386503ed3861e087 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc @@ -0,0 +1,290 @@ +/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h" +#include "google/cloud/bigtable/internal/table.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +void WriteCell(const string& row, const string& family, const string& column, + const string& value, + ::google::cloud::bigtable::noex::Table* table) { + ::google::cloud::bigtable::SingleRowMutation mut(row); + mut.emplace_back(::google::cloud::bigtable::SetCell(family, column, value)); + table->Apply(std::move(mut)); +} + +TEST(BigtableTestClientTest, EmptyRowRead) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + ::google::cloud::bigtable::RowSet rowset; + rowset.Append("r1"); + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1)); + auto rows = table.ReadRows(std::move(rowset), filter); + EXPECT_EQ(rows.begin(), rows.end()) << "Some rows were returned in response!"; + EXPECT_TRUE(rows.Finish().ok()) << "Error reading rows."; +} + +TEST(BigtableTestClientTest, SingleRowWriteAndRead) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + + ::google::cloud::bigtable::RowSet rowset("r1"); + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1)); + auto rows = table.ReadRows(std::move(rowset), filter); + auto itr = rows.begin(); + EXPECT_NE(itr, rows.end()) << "No rows were returned in response!"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v1"); + + ++itr; + EXPECT_EQ(itr, rows.end()); + EXPECT_TRUE(rows.Finish().ok()); +} + +TEST(BigtableTestClientTest, MultiRowWriteAndSingleRowRead) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + + ::google::cloud::bigtable::RowSet rowset("r1"); + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1)); + auto rows = table.ReadRows(std::move(rowset), filter); + auto itr = rows.begin(); + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v1"); + + ++itr; + EXPECT_EQ(itr, rows.end()) << "Extra rows in the response."; + EXPECT_TRUE(rows.Finish().ok()); +} + +TEST(BigtableTestClientTest, MultiRowWriteAndRead) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + + ::google::cloud::bigtable::RowSet rowset("r1", "r2", "r3"); + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1)); + auto rows = table.ReadRows(std::move(rowset), filter); + auto itr = rows.begin(); + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v1"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r2"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v2"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r3"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v3"); + + ++itr; + EXPECT_EQ(itr, rows.end()) << "Extra rows in the response."; + EXPECT_TRUE(rows.Finish().ok()); +} + +TEST(BigtableTestClientTest, MultiRowWriteAndPrefixRead) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1)); + auto rows = + table.ReadRows(::google::cloud::bigtable::RowRange::Prefix("r"), filter); + auto itr = rows.begin(); + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v1"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r2"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v2"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r3"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v3"); + + ++itr; + EXPECT_EQ(itr, rows.end()) << "Extra rows in the response."; + EXPECT_TRUE(rows.Finish().ok()); +} + +TEST(BigtableTestClientTest, ColumnFiltering) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + + // Extra cells + WriteCell("r1", "f2", "c1", "v1", &table); + WriteCell("r2", "f2", "c1", "v2", &table); + WriteCell("r3", "f1", "c2", "v3", &table); + + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1), + ::google::cloud::bigtable::Filter::FamilyRegex("f1"), + ::google::cloud::bigtable::Filter::ColumnRegex("c1")); + auto rows = + table.ReadRows(::google::cloud::bigtable::RowRange::Prefix("r"), filter); + auto itr = rows.begin(); + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v1"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r2"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v2"); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r3"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), "v3"); + + ++itr; + EXPECT_EQ(itr, rows.end()) << "Extra rows in the response."; + EXPECT_TRUE(rows.Finish().ok()); +} + +TEST(BigtableTestClientTest, RowKeys) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + + // Extra cells + WriteCell("r1", "f2", "c1", "v1", &table); + WriteCell("r2", "f2", "c1", "v2", &table); + WriteCell("r3", "f1", "c2", "v3", &table); + + auto filter = ::google::cloud::bigtable::Filter::Chain( + ::google::cloud::bigtable::Filter::Latest(1), + ::google::cloud::bigtable::Filter::CellsRowLimit(1), + ::google::cloud::bigtable::Filter::StripValueTransformer()); + auto rows = + table.ReadRows(::google::cloud::bigtable::RowRange::Prefix("r"), filter); + auto itr = rows.begin(); + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r1"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), ""); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r2"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), ""); + + ++itr; + + EXPECT_NE(itr, rows.end()) << "Missing rows"; + EXPECT_EQ(itr->row_key(), "r3"); + EXPECT_EQ(itr->cells().size(), 1); + EXPECT_EQ(itr->cells()[0].family_name(), "f1"); + EXPECT_EQ(itr->cells()[0].column_qualifier(), "c1"); + EXPECT_EQ(itr->cells()[0].value(), ""); + + ++itr; + EXPECT_EQ(itr, rows.end()) << "Extra rows in the response."; + EXPECT_TRUE(rows.Finish().ok()); +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/ops/bigtable_ops.cc b/tensorflow/contrib/bigtable/ops/bigtable_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..17ecc3dcb24f35a80cbc904ea11df3eff3fce6b9 --- /dev/null +++ b/tensorflow/contrib/bigtable/ops/bigtable_ops.cc @@ -0,0 +1,88 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" + +namespace tensorflow { + +// TODO(saeta): Add support for setting ClientOptions values. +REGISTER_OP("BigtableClient") + .Attr("project_id: string") + .Attr("instance_id: string") + .Attr("container: string = ''") + .Attr("shared_name: string = ''") + .Output("client: resource") + .SetShapeFn(shape_inference::ScalarShape); + +// TODO(saeta): Add support for Application Profiles. +// See https://cloud.google.com/bigtable/docs/app-profiles for more info. +REGISTER_OP("BigtableTable") + .Input("client: resource") + .Attr("table_name: string") + .Attr("container: string = ''") + .Attr("shared_name: string = ''") + .Output("table: resource") + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("DatasetToBigtable") + .Input("table: resource") + .Input("input_dataset: variant") + .Input("column_families: string") + .Input("columns: string") + .Input("timestamp: int64") + .SetShapeFn(shape_inference::NoOutputs); + +REGISTER_OP("BigtableLookupDataset") + .Input("keys_dataset: variant") + .Input("table: resource") + .Input("column_families: string") + .Input("columns: string") + .Output("handle: variant") + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("BigtablePrefixKeyDataset") + .Input("table: resource") + .Input("prefix: string") + .Output("handle: variant") + .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked + // stateful to inhibit constant folding. + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("BigtableRangeKeyDataset") + .Input("table: resource") + .Input("start_key: string") + .Input("end_key: string") + .Output("handle: variant") + .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked + // stateful to inhibit constant folding. + .SetShapeFn(shape_inference::ScalarShape); + +// TODO(saeta): Support continuing despite bad data (e.g. empty string, or +// skip incomplete row.) +REGISTER_OP("BigtableScanDataset") + .Input("table: resource") + .Input("prefix: string") + .Input("start_key: string") + .Input("end_key: string") + .Input("column_families: string") + .Input("columns: string") + .Input("probability: float") + .Output("handle: variant") + .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked + // stateful to inhibit constant folding. + .SetShapeFn(shape_inference::ScalarShape); + +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/ops/bigtable_test_ops.cc b/tensorflow/contrib/bigtable/ops/bigtable_test_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..f7d02458f63d547000f00b184b3d5e3c5007fb72 --- /dev/null +++ b/tensorflow/contrib/bigtable/ops/bigtable_test_ops.cc @@ -0,0 +1,27 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" + +namespace tensorflow { + +REGISTER_OP("BigtableTestClient") + .Attr("container: string = ''") + .Attr("shared_name: string = ''") + .Output("client: resource") + .SetShapeFn(shape_inference::ScalarShape); + +} // namespace tensorflow diff --git a/tensorflow/python/training/checkpointable/data_structures_base.py b/tensorflow/contrib/bigtable/python/kernel_tests/__init__.py similarity index 71% rename from tensorflow/python/training/checkpointable/data_structures_base.py rename to tensorflow/contrib/bigtable/python/kernel_tests/__init__.py index f1b2cf105b81490ea12e0a667f53fb02d45135c9..292d8f4e51abbbd89d68b47febd86b7297bb8ed2 100644 --- a/tensorflow/python/training/checkpointable/data_structures_base.py +++ b/tensorflow/contrib/bigtable/python/kernel_tests/__init__.py @@ -1,4 +1,3 @@ -"""A trivial base class to avoid circular imports for isinstance checks.""" # Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,15 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== + +"""This module contains tests for the bigtable integration.""" + from __future__ import absolute_import from __future__ import division from __future__ import print_function - - -from tensorflow.python.training.checkpointable import base as checkpointable_lib - - -class CheckpointableDataStructureBase(checkpointable_lib.CheckpointableBase): - """Base class for data structures which contain checkpointable objects.""" - - pass diff --git a/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d33a66f2dfbecd0dc1082fd98973660ce9a93931 --- /dev/null +++ b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py @@ -0,0 +1,132 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Bigtable Ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib import bigtable +from tensorflow.contrib.bigtable.ops import gen_bigtable_ops +from tensorflow.contrib.bigtable.ops import gen_bigtable_test_ops +from tensorflow.contrib.util import loader +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import resource_loader +from tensorflow.python.platform import test +from tensorflow.python.util import compat + +_bigtable_so = loader.load_op_library( + resource_loader.get_path_to_datafile("_bigtable_test.so")) + + +class BigtableOpsTest(test.TestCase): + COMMON_ROW_KEYS = ["r1", "r2", "r3"] + COMMON_VALUES = ["v1", "v2", "v3"] + + def setUp(self): + self._client = gen_bigtable_test_ops.bigtable_test_client() + table = gen_bigtable_ops.bigtable_table(self._client, "testtable") + self._table = bigtable.BigTable("testtable", None, table) + + def _makeSimpleDataset(self): + output_rows = dataset_ops.Dataset.from_tensor_slices(self.COMMON_ROW_KEYS) + output_values = dataset_ops.Dataset.from_tensor_slices(self.COMMON_VALUES) + return dataset_ops.Dataset.zip((output_rows, output_values)) + + def _writeCommonValues(self, sess): + output_ds = self._makeSimpleDataset() + write_op = self._table.write(output_ds, ["cf1"], ["c1"]) + sess.run(write_op) + + def runReadKeyTest(self, read_ds): + itr = read_ds.make_initializable_iterator() + n = itr.get_next() + expected = list(self.COMMON_ROW_KEYS) + expected.reverse() + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + for i in range(3): + output = sess.run(n) + want = expected.pop() + self.assertEqual( + compat.as_bytes(want), compat.as_bytes(output), + "Unequal at step %d: want: %s, got: %s" % (i, want, output)) + + def testReadPrefixKeys(self): + self.runReadKeyTest(self._table.keys_by_prefix_dataset("r")) + + def testReadRangeKeys(self): + self.runReadKeyTest(self._table.keys_by_range_dataset("r1", "r4")) + + def runScanTest(self, read_ds): + itr = read_ds.make_initializable_iterator() + n = itr.get_next() + expected_keys = list(self.COMMON_ROW_KEYS) + expected_keys.reverse() + expected_values = list(self.COMMON_VALUES) + expected_values.reverse() + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + for i in range(3): + output = sess.run(n) + want = expected_keys.pop() + self.assertEqual( + compat.as_bytes(want), compat.as_bytes(output[0]), + "Unequal keys at step %d: want: %s, got: %s" % (i, want, output[0])) + want = expected_values.pop() + self.assertEqual( + compat.as_bytes(want), compat.as_bytes(output[1]), + "Unequal values at step: %d: want: %s, got: %s" % (i, want, + output[1])) + + def testScanPrefixStringCol(self): + self.runScanTest(self._table.scan_prefix("r", cf1="c1")) + + def testScanPrefixListCol(self): + self.runScanTest(self._table.scan_prefix("r", cf1=["c1"])) + + def testScanRangeStringCol(self): + self.runScanTest(self._table.scan_range("r1", "r4", cf1="c1")) + + def testScanRangeListCol(self): + self.runScanTest(self._table.scan_range("r1", "r4", cf1=["c1"])) + + def testLookup(self): + ds = self._table.keys_by_prefix_dataset("r") + ds = ds.apply(self._table.lookup_columns(cf1="c1")) + itr = ds.make_initializable_iterator() + n = itr.get_next() + expected_keys = list(self.COMMON_ROW_KEYS) + expected_values = list(self.COMMON_VALUES) + expected_tuples = zip(expected_keys, expected_values) + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + for i, elem in enumerate(expected_tuples): + output = sess.run(n) + self.assertEqual( + compat.as_bytes(elem[0]), compat.as_bytes(output[0]), + "Unequal keys at step %d: want: %s, got: %s" % + (i, compat.as_bytes(elem[0]), compat.as_bytes(output[0]))) + self.assertEqual( + compat.as_bytes(elem[1]), compat.as_bytes(output[1]), + "Unequal values at step %d: want: %s, got: %s" % + (i, compat.as_bytes(elem[1]), compat.as_bytes(output[1]))) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/bigtable/python/ops/__init__.py b/tensorflow/contrib/bigtable/python/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..36d75b0d7068a650347a5e17f4727a5432d8752f --- /dev/null +++ b/tensorflow/contrib/bigtable/python/ops/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""This module contains the Python API for the Cloud Bigtable integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function diff --git a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py new file mode 100644 index 0000000000000000000000000000000000000000..a54e020ed770ed24f6ede1aac5ed4674a41b0e52 --- /dev/null +++ b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py @@ -0,0 +1,480 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""The Python API for TensorFlow's Bigtable integration. + +TensorFlow has support for reading from and writing to Cloud Bigtable. To use +the Bigtable TensorFlow integration, first create a BigtableClient (which +configures your connection to Cloud Bigtable), and then open a Table. The Table +object then allows you to create numerous @{tf.data.Dataset}s to read data, or +write a @{tf.data.Dataset} object to the underlying Bigtable Table. + +For background on Google Cloud Bigtable, see: https://cloud.google.com/bigtable. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from six import iteritems + +from tensorflow.contrib.bigtable.ops import gen_bigtable_ops +from tensorflow.contrib.util import loader +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.platform import resource_loader + +_bigtable_so = loader.load_op_library( + resource_loader.get_path_to_datafile("_bigtable.so")) + + +class BigtableClient(object): + """BigtableClient is the entrypoint for interacting with Cloud Bigtable in TF. + + BigtableClient encapsulates a connection to Cloud Bigtable, and exposes the + `table` method to open a Bigtable Table. + """ + + def __init__(self, project_id, instance_id): + """Creates a BigtableClient that can be used to open connections to tables. + + Args: + project_id: A string representing the GCP project id to connect to. + instance_id: A string representing the Bigtable instance to connect to. + """ + self._project_id = project_id + self._instance_id = instance_id + self._resource = gen_bigtable_ops.bigtable_client(project_id, instance_id) + + def table(self, name, snapshot=None): + """Opens a table and returns a `BigTable` object. + + Args: + name: A `tf.string` `tf.Tensor` name of the table to open. + snapshot: Either a `tf.string` `tf.Tensor` snapshot id, or `True` to + request the creation of a snapshot. (Note: currently unimplemented.) + + Returns: + A `BigTable` python object representing the operations available on the + table. + """ + # TODO(saeta): Implement snapshot functionality. + table = gen_bigtable_ops.bigtable_table(self._resource, name) + return BigTable(name, snapshot, table) + + +class BigTable(object): + """BigTable is the entrypoint for reading and writing data in Cloud Bigtable. + + This BigTable class is the python representation of the Cloud Bigtable table + within TensorFlow. Methods on this class allow data to be read from and + written to the Cloud Bigtable service in flexible and high performance + manners. + """ + + # TODO(saeta): Investigate implementing tf.contrib.lookup.LookupInterface. + # TODO(saeta): Consider variant tensors instead of resources (while supporting + # connection pooling). + + def __init__(self, name, snapshot, resource): + self._name = name + self._snapshot = snapshot + self._resource = resource + + def lookup_columns(self, *args, **kwargs): + """Retrieves the values of columns for a dataset of keys. + + Example usage: + ``` + table = bigtable_client.table("my_table") + key_dataset = table.get_keys_prefix("imagenet") + images = key_dataset.apply(table.lookup_columns(("cf1", "image"), + ("cf2", "label"), + ("cf2", "boundingbox"))) + training_data = images.map(parse_and_crop, num_parallel_calls=64).batch(128) + ``` + + Alternatively, you can use keyword arguments to specify the columns to + capture. Example (same as above, rewritten): + ``` + table = bigtable_client.table("my_table") + key_dataset = table.get_keys_prefix("imagenet") + images = key_dataset.apply(table.lookup_columns( + cf1="image", cf2=("label", "boundingbox"))) + training_data = images.map(parse_and_crop, num_parallel_calls=64).batch(128) + ``` + + Note: certain kwargs keys are reserved, and thus some column families cannot + be identified using the kwargs syntax. Instead, please use the args syntax. + This list includes: + - 'name' + This list can change at any time. + + Args: + *args: A list of tuples containing (column family, column name) pairs. + **kwargs: Column families and + + Returns: + A function that can be passed to `tf.data.Dataset.apply` to retrieve the + values of columns for the rows. + """ + table = self # Capture self + normalized = args + if normalized is None: + normalized = [] + if isinstance(normalized, tuple): + normalized = list(normalized) + for key, value in iteritems(kwargs): + if key == "name": + continue + if isinstance(value, str): + normalized.append((key, value)) + continue + for col in value: + normalized.append((key, col)) + + def _apply_fn(dataset): + # TODO(saeta): Verify dataset's types are correct! + return _BigtableLookupDataset(dataset, table, normalized) + + return _apply_fn + + def keys_by_range_dataset(self, start, end): + """Retrieves all row keys between start and end. + + Note: it does NOT retrieve the values of columns. + + Args: + start: The start row key. The row keys for rows after start (inclusive) + will be retrieved. + end: (Optional.) The end row key. Rows up to (but not including) end will + be retrieved. If end is None, all subsequent row keys will be retrieved. + + Returns: + A @{tf.data.Dataset} containing `tf.string` Tensors corresponding to all + of the row keys between `start` and `end`. + """ + # TODO(saeta): Make inclusive / exclusive configurable? + if end is None: + end = "" + return _BigtableRangeKeyDataset(self, start, end) + + def keys_by_prefix_dataset(self, prefix): + """Retrieves the row keys matching a given prefix. + + Args: + prefix: All row keys that begin with `prefix` in the table will be + retrieved. + + Returns: + A @{tf.data.Dataset}. containing `tf.string` Tensors corresponding to all + of the row keys matching that prefix. + """ + return _BigtablePrefixKeyDataset(self, prefix) + + def scan_prefix(self, prefix, probability=None, columns=None, **kwargs): + """Retrieves row (including values) from the Bigtable service. + + Rows with row-key prefixed by `prefix` will be retrieved. + + Specifying the columns to retrieve for each row is done by either using + kwargs or in the columns parameter. To retrieve values of the columns "c1", + and "c2" from the column family "cfa", and the value of the column "c3" + from column family "cfb", the following datasets (`ds1`, and `ds2`) are + equivalent: + + ``` + table = # ... + ds1 = table.scan_prefix("row_prefix", columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) + ds2 = table.scan_prefix("row_prefix", cfa=["c1", "c2"], cfb="c3") + ``` + + Note: only the latest value of a cell will be retrieved. + + Args: + prefix: The prefix all row keys muat match to be retrieved for prefix- + based scans. + probability: Probabilistically sample rows. + columns: The columns to read. Note: most commonly, they are expressed as + kwargs. Use the columns value if you are using column families that are + reserved. The value of columns and kwargs are merged. Columns is a list + of tuples of strings ("column_family", "column_qualifier"). + **kwargs: The column families and columns to read. Keys are treated as + column_families, and values can be either lists of strings, or strings + that are treated as the column qualifier (column name). + + Returns: + A @{tf.data.Dataset} returning the row keys and the cell contents. + + Raises: + ValueError: If the configured probability is unexpected. + """ + if probability is None: + probability = 1.0 + if isinstance(probability, float) and (probability <= 0.0 or + probability > 1.0): + raise ValueError("probability must be in the range (0, 1].") + + normalized = columns + if normalized is None: + normalized = [] + if isinstance(normalized, tuple): + normalized = list(normalized) + for key, value in iteritems(kwargs): + if key == "name": + continue + if isinstance(value, str): + normalized.append((key, value)) + continue + for col in value: + normalized.append((key, col)) + + return _BigtableScanDataset(self, prefix, "", "", normalized, probability) + + def scan_range(self, start, end, probability=None, columns=None, **kwargs): + """Retrieves rows (including values) from the Bigtable service. + + Rows with row-keys between `start` and `end` will be retrieved. + + Specifying the columns to retrieve for each row is done by either using + kwargs or in the columns parameter. To retrieve values of the columns "c1", + and "c2" from the column family "cfa", and the value of the column "c3" + from column family "cfb", the following datasets (`ds1`, and `ds2`) are + equivalent: + + ``` + table = # ... + ds1 = table.scan_range("row_start", "row_end", columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) + ds2 = table.scan_range("row_start", "row_end", cfa=["c1", "c2"], cfb="c3") + ``` + + Note: only the latest value of a cell will be retrieved. + + Args: + start: The start of the range when scanning by range. + end: (Optional.) The end of the range when scanning by range. + probability: Probabilistically sample rows. + columns: The columns to read. Note: most commonly, they are expressed as + kwargs. Use the columns value if you are using column families that are + reserved. The value of columns and kwargs are merged. Columns is a list + of tuples of strings ("column_family", "column_qualifier"). + **kwargs: The column families and columns to read. Keys are treated as + column_families, and values can be either lists of strings, or strings + that are treated as the column qualifier (column name). + + Returns: + A @{tf.data.Dataset} returning the row keys and the cell contents. + + Raises: + ValueError: If the configured probability is unexpected. + """ + if probability is None: + probability = 1.0 + if isinstance(probability, float) and (probability <= 0.0 or + probability > 1.0): + raise ValueError("probability must be in the range (0, 1].") + + normalized = columns + if normalized is None: + normalized = [] + if isinstance(normalized, tuple): + normalized = list(normalized) + for key, value in iteritems(kwargs): + if key == "name": + continue + if isinstance(value, str): + normalized.append((key, value)) + continue + for col in value: + normalized.append((key, col)) + + return _BigtableScanDataset(self, "", start, end, normalized, probability) + + def write(self, dataset, column_families, columns, timestamp=None): + """Writes a dataset to the table. + + Args: + dataset: A @{tf.data.Dataset} to be written to this table. It must produce + a list of number-of-columns+1 elements, all of which must be strings. + The first value will be used as the row key, and subsequent values will + be used as cell values for the corresponding columns from the + corresponding column_families and columns entries. + column_families: A @{tf.Tensor} of `tf.string`s corresponding to the + column names to store the dataset's elements into. + columns: A `tf.Tensor` of `tf.string`s corresponding to the column names + to store the dataset's elements into. + timestamp: (Optional.) An int64 timestamp to write all the values at. + Leave as None to use server-provided timestamps. + + Returns: + A @{tf.Operation} that can be run to perform the write. + + Raises: + ValueError: If there are unexpected or incompatible types, or if the + number of columns and column_families does not match the output of + `dataset`. + """ + if timestamp is None: + timestamp = -1 # Bigtable server provided timestamp. + for tensor_type in nest.flatten(dataset.output_types): + if tensor_type != dtypes.string: + raise ValueError("Not all elements of the dataset were `tf.string`") + for shape in nest.flatten(dataset.output_shapes): + if not shape.is_compatible_with(tensor_shape.scalar()): + raise ValueError("Not all elements of the dataset were scalars") + if len(column_families) != len(columns): + raise ValueError("len(column_families) != len(columns)") + if len(nest.flatten(dataset.output_types)) != len(columns) + 1: + raise ValueError("A column name must be specified for every component of " + "the dataset elements. (e.g.: len(columns) != " + "len(dataset.output_types))") + return gen_bigtable_ops.dataset_to_bigtable( + self._resource, + dataset._as_variant_tensor(), # pylint: disable=protected-access + column_families, + columns, + timestamp) + + +class _BigtableKeyDataset(dataset_ops.Dataset): + """_BigtableKeyDataset is an abstract class representing the keys of a table. + """ + + def __init__(self, table): + """Constructs a _BigtableKeyDataset. + + Args: + table: a Bigtable class. + """ + super(_BigtableKeyDataset, self).__init__() + self._table = table + + @property + def output_classes(self): + return ops.Tensor + + @property + def output_shapes(self): + return tensor_shape.TensorShape([]) + + @property + def output_types(self): + return dtypes.string + + +class _BigtablePrefixKeyDataset(_BigtableKeyDataset): + """_BigtablePrefixKeyDataset represents looking up keys by prefix. + """ + + def __init__(self, table, prefix): + super(_BigtablePrefixKeyDataset, self).__init__(table) + self._prefix = prefix + + def _as_variant_tensor(self): + return gen_bigtable_ops.bigtable_prefix_key_dataset( + table=self._table._resource, # pylint: disable=protected-access + prefix=self._prefix) + + +class _BigtableRangeKeyDataset(_BigtableKeyDataset): + """_BigtableRangeKeyDataset represents looking up keys by range. + """ + + def __init__(self, table, start, end): + super(_BigtableRangeKeyDataset, self).__init__(table) + self._start = start + self._end = end + + def _as_variant_tensor(self): + return gen_bigtable_ops.bigtable_range_key_dataset( + table=self._table._resource, # pylint: disable=protected-access + start_key=self._start, + end_key=self._end) + + +class _BigtableLookupDataset(dataset_ops.Dataset): + """_BigtableLookupDataset represents a dataset that retrieves values for keys. + """ + + def __init__(self, dataset, table, normalized): + self._num_outputs = len(normalized) + 1 # 1 for row key + self._dataset = dataset + self._table = table + self._normalized = normalized + self._column_families = [i[0] for i in normalized] + self._columns = [i[1] for i in normalized] + + @property + def output_classes(self): + return tuple([ops.Tensor] * self._num_outputs) + + @property + def output_shapes(self): + return tuple([tensor_shape.TensorShape([])] * self._num_outputs) + + @property + def output_types(self): + return tuple([dtypes.string] * self._num_outputs) + + def _as_variant_tensor(self): + # pylint: disable=protected-access + return gen_bigtable_ops.bigtable_lookup_dataset( + keys_dataset=self._dataset._as_variant_tensor(), + table=self._table._resource, + column_families=self._column_families, + columns=self._columns) + + +class _BigtableScanDataset(dataset_ops.Dataset): + """_BigtableScanDataset represents a dataset that retrieves keys and values. + """ + + def __init__(self, table, prefix, start, end, normalized, probability): + self._table = table + self._prefix = prefix + self._start = start + self._end = end + self._column_families = [i[0] for i in normalized] + self._columns = [i[1] for i in normalized] + self._probability = probability + self._num_outputs = len(normalized) + 1 # 1 for row key + + @property + def output_classes(self): + return tuple([ops.Tensor] * self._num_outputs) + + @property + def output_shapes(self): + return tuple([tensor_shape.TensorShape([])] * self._num_outputs) + + @property + def output_types(self): + return tuple([dtypes.string] * self._num_outputs) + + def _as_variant_tensor(self): + return gen_bigtable_ops.bigtable_scan_dataset( + table=self._table._resource, # pylint: disable=protected-access + prefix=self._prefix, + start_key=self._start, + end_key=self._end, + column_families=self._column_families, + columns=self._columns, + probability=self._probability) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD index 8cff1a3bb1d11aff6a264636291a7149b40de516..ef0e80cd0997bc0e95cd0d150e87db144a2dde44 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD +++ b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD @@ -15,8 +15,9 @@ py_library( srcs = ["__init__.py"], srcs_version = "PY2AND3", deps = [ - "custom_export_strategy", + ":custom_export_strategy", ":custom_loss_head", + ":distillation_loss", ":estimator", ":model", ":trainer_hooks", @@ -144,6 +145,7 @@ py_library( srcs = ["dnn_tree_combined_estimator.py"], srcs_version = "PY2AND3", deps = [ + ":distillation_loss", ":estimator_utils", ":trainer_hooks", "//tensorflow/contrib/boosted_trees:gbdt_batch", @@ -156,6 +158,17 @@ py_library( ], ) +py_library( + name = "distillation_loss", + srcs = ["distillation_loss.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/learn", + "//tensorflow/python:math_ops", + "//tensorflow/python:nn", + ], +) + py_test( name = "dnn_tree_combined_estimator_test", size = "medium", diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/distillation_loss.py b/tensorflow/contrib/boosted_trees/estimator_batch/distillation_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..9aacc5534329d1302b25dcfab678f9adb8f773f6 --- /dev/null +++ b/tensorflow/contrib/boosted_trees/estimator_batch/distillation_loss.py @@ -0,0 +1,75 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utill functions for distillation loss. + +The distillation loss_fn will be called with the following: + +Args: + dnn_logits: Tensor of logits from the dnn, treated as the "target". This will + be the output of a call to tf.stop_gradient(). + tree_logits: Tensor of logits from the tree, treated as the "predictions". + example_weights: Tensor of example weights, or a single scalar. + +Returns: + A scalar indicating the reduced loss for that batch of examples. + +Note: we calls the loss_fn defined in contrib head, which is computing two +losses, first one for training and second one for reporting. We only take the +first one here. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.learn.python.learn.estimators import head as head_lib +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn + + +def _logits_to_label_for_tree(logits, n_classes): + if n_classes == 2: + return math_ops.sigmoid(logits) + else: + return nn.softmax(logits) + + +def create_dnn_to_tree_squared_loss_fn(n_classes): + """Returns a squared loss function for dnn to tree distillation.""" + + def _dnn_to_tree_squared_loss(dnn_logits, tree_logits, example_weights): + return head_lib._mean_squared_loss( # pylint: disable=protected-access + labels=_logits_to_label_for_tree(dnn_logits, n_classes), + logits=_logits_to_label_for_tree(tree_logits, n_classes), + weights=example_weights)[0] + + return _dnn_to_tree_squared_loss + + +def create_dnn_to_tree_cross_entropy_loss_fn(n_classes): + """Returns a cross entropy loss function for dnn to tree distillation.""" + + def _dnn_to_tree_cross_entropy_loss(dnn_logits, tree_logits, example_weights): + if n_classes == 2: + return head_lib._log_loss_with_two_classes( # pylint: disable=protected-access + labels=_logits_to_label_for_tree(dnn_logits, n_classes), + logits=tree_logits, + weights=example_weights)[0] + else: + return head_lib._softmax_cross_entropy_loss( # pylint: disable=protected-access + labels=_logits_to_label_for_tree(dnn_logits, n_classes), + logits=tree_logits, + weights=example_weights)[0] + + return _dnn_to_tree_cross_entropy_loss diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py index 758754feac31f1d2cf10e69d7a9a6d288931c900..7eb429b636a5193a124dd9b0c020dae6cac910cb 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py @@ -24,7 +24,9 @@ from __future__ import division from __future__ import print_function import six + from tensorflow.contrib import layers +from tensorflow.contrib.boosted_trees.estimator_batch import distillation_loss from tensorflow.contrib.boosted_trees.estimator_batch import estimator_utils from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks from tensorflow.contrib.boosted_trees.python.ops import model_ops @@ -35,11 +37,13 @@ from tensorflow.contrib.learn.python.learn.estimators import head as head_lib from tensorflow.contrib.learn.python.learn.estimators import model_fn from tensorflow.python.feature_column import feature_column as feature_column_lib from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import nn from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary from tensorflow.python.training import training_util @@ -77,6 +81,7 @@ def _dnn_tree_combined_model_fn(features, predict_with_tree_only=False, tree_feature_columns=None, tree_center_bias=False, + dnn_to_tree_distillation_param=None, use_core_versions=False): """DNN and GBDT combined model_fn. @@ -117,6 +122,13 @@ def _dnn_tree_combined_model_fn(features, set to True, these features are in addition to dnn_feature_columns. tree_center_bias: Whether a separate tree should be created for first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. use_core_versions: Whether feature columns and loss are from the core (as opposed to contrib) version of tensorflow. @@ -132,6 +144,12 @@ def _dnn_tree_combined_model_fn(features, if not dnn_feature_columns: raise ValueError("dnn_feature_columns must be specified") + if dnn_to_tree_distillation_param: + if not predict_with_tree_only: + logging.warning("update predict_with_tree_only to True since distillation" + "is specified.") + predict_with_tree_only = True + # Build DNN Logits. dnn_parent_scope = "dnn" dnn_partitioner = dnn_input_layer_partitioner or ( @@ -225,6 +243,25 @@ def _dnn_tree_combined_model_fn(features, def _tree_train_op_fn(loss): """Returns the op to optimize the loss.""" + if dnn_to_tree_distillation_param: + loss_weight, loss_fn = dnn_to_tree_distillation_param + weight_tensor = head_lib._weight_tensor( # pylint: disable=protected-access + features, head.weight_column_name) + dnn_logits_fixed = array_ops.stop_gradient(dnn_logits) + + if loss_fn is None: + # we create the loss_fn similar to the head loss_fn for + # multi_class_head used previously as the default one. + n_classes = 2 if head.logits_dimension == 1 else head.logits_dimension + loss_fn = distillation_loss.create_dnn_to_tree_cross_entropy_loss_fn( + n_classes) + + dnn_to_tree_distillation_loss = loss_weight * loss_fn( + dnn_logits_fixed, tree_logits, weight_tensor) + summary.scalar("dnn_to_tree_distillation_loss", + dnn_to_tree_distillation_loss) + loss += dnn_to_tree_distillation_loss + update_op = gbdt_model.train(loss, predictions_dict, labels) with ops.control_dependencies( [update_op]), (ops.colocate_with(global_step)): @@ -232,7 +269,13 @@ def _dnn_tree_combined_model_fn(features, return update_op if predict_with_tree_only: - tree_train_logits = tree_logits + if mode == model_fn.ModeKeys.TRAIN or mode == model_fn.ModeKeys.INFER: + tree_train_logits = tree_logits + else: + tree_train_logits = control_flow_ops.cond( + global_step > dnn_steps_to_train, + lambda: tree_logits, + lambda: dnn_logits) else: tree_train_logits = dnn_logits + tree_logits @@ -325,6 +368,7 @@ class DNNBoostedTreeCombinedClassifier(estimator.Estimator): predict_with_tree_only=False, tree_feature_columns=None, tree_center_bias=False, + dnn_to_tree_distillation_param=None, use_core_versions=False): """Initializes a DNNBoostedTreeCombinedClassifier instance. @@ -372,6 +416,13 @@ class DNNBoostedTreeCombinedClassifier(estimator.Estimator): set to True, these features are in addition to dnn_feature_columns. tree_center_bias: Whether a separate tree should be created for first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. use_core_versions: Whether feature columns and loss are from the core (as opposed to contrib) version of tensorflow. """ @@ -403,6 +454,7 @@ class DNNBoostedTreeCombinedClassifier(estimator.Estimator): predict_with_tree_only=predict_with_tree_only, tree_feature_columns=tree_feature_columns, tree_center_bias=tree_center_bias, + dnn_to_tree_distillation_param=dnn_to_tree_distillation_param, use_core_versions=use_core_versions) super(DNNBoostedTreeCombinedClassifier, self).__init__( @@ -436,6 +488,7 @@ class DNNBoostedTreeCombinedRegressor(estimator.Estimator): predict_with_tree_only=False, tree_feature_columns=None, tree_center_bias=False, + dnn_to_tree_distillation_param=None, use_core_versions=False): """Initializes a DNNBoostedTreeCombinedRegressor instance. @@ -483,6 +536,13 @@ class DNNBoostedTreeCombinedRegressor(estimator.Estimator): set to True, these features are in addition to dnn_feature_columns. tree_center_bias: Whether a separate tree should be created for first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. use_core_versions: Whether feature columns and loss are from the core (as opposed to contrib) version of tensorflow. """ @@ -519,6 +579,7 @@ class DNNBoostedTreeCombinedRegressor(estimator.Estimator): predict_with_tree_only=predict_with_tree_only, tree_feature_columns=tree_feature_columns, tree_center_bias=tree_center_bias, + dnn_to_tree_distillation_param=dnn_to_tree_distillation_param, use_core_versions=use_core_versions) super(DNNBoostedTreeCombinedRegressor, self).__init__( @@ -553,6 +614,7 @@ class DNNBoostedTreeCombinedEstimator(estimator.Estimator): predict_with_tree_only=False, tree_feature_columns=None, tree_center_bias=False, + dnn_to_tree_distillation_param=None, use_core_versions=False): """Initializes a DNNBoostedTreeCombinedEstimator instance. @@ -595,6 +657,13 @@ class DNNBoostedTreeCombinedEstimator(estimator.Estimator): set to True, these features are in addition to dnn_feature_columns. tree_center_bias: Whether a separate tree should be created for first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. use_core_versions: Whether feature columns and loss are from the core (as opposed to contrib) version of tensorflow. """ @@ -620,6 +689,7 @@ class DNNBoostedTreeCombinedEstimator(estimator.Estimator): predict_with_tree_only=predict_with_tree_only, tree_feature_columns=tree_feature_columns, tree_center_bias=tree_center_bias, + dnn_to_tree_distillation_param=dnn_to_tree_distillation_param, use_core_versions=use_core_versions) super(DNNBoostedTreeCombinedEstimator, self).__init__( diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py index f495edc62f0909880c170ccb4cf5d11e3f20f55c..9b7acfa664b0398216b5a7fb904960d8363929d6 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py @@ -131,6 +131,30 @@ class DNNBoostedTreeCombinedTest(test_util.TensorFlowTestCase): classifier.fit(input_fn=_train_input_fn, steps=15) classifier.evaluate(input_fn=_eval_input_fn, steps=1) + def testFitAndEvaluateWithDistillation(self): + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + classifier = estimator.DNNBoostedTreeCombinedClassifier( + dnn_hidden_units=[1], + dnn_feature_columns=[feature_column.real_valued_column("x")], + tree_learner_config=learner_config, + num_trees=1, + tree_examples_per_layer=3, + n_classes=2, + model_dir=model_dir, + config=config, + dnn_steps_to_train=10, + dnn_input_layer_to_tree=False, + tree_feature_columns=[feature_column.real_valued_column("x")], + dnn_to_tree_distillation_param=(1, None)) + + classifier.fit(input_fn=_train_input_fn, steps=15) + classifier.evaluate(input_fn=_eval_input_fn, steps=1) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py index 56ff00b39062d57c813633c98c765e077dd4c262..1b7f59ea4218355a13f1df7264352bd68503bd19 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py @@ -37,6 +37,7 @@ class BaseSplitHandler(object): gradient_shape, hessian_shape, multiclass_strategy, + loss_uses_sum_reduction=False, name=None): """Constructor for BaseSplitHandler. @@ -51,6 +52,8 @@ class BaseSplitHandler(object): gradient_shape: A TensorShape, containing shape of gradients. hessian_shape: A TensorShape, containing shape of hessians. multiclass_strategy: Strategy describing how to treat multiclass problems. + loss_uses_sum_reduction: A scalar boolean tensor that specifies whether + SUM or MEAN reduction was used for the loss. name: An optional handler name. """ self._l1_regularization = l1_regularization @@ -62,6 +65,7 @@ class BaseSplitHandler(object): self._multiclass_strategy = multiclass_strategy self._hessian_shape = hessian_shape self._gradient_shape = gradient_shape + self._loss_uses_sum_reduction = loss_uses_sum_reduction def scheduled_reads(self): """Returns the list of `ScheduledOp`s required for update_stats.""" diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py index 9f78ab20242800fd8af7ad049d5970fbe26ec0ea..bf686237ff696dadad9713d26bf784d7442b80d0 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py @@ -23,6 +23,7 @@ from tensorflow.contrib.boosted_trees.python.ops import split_handler_ops from tensorflow.contrib.boosted_trees.python.ops import stats_accumulator_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops @@ -44,6 +45,7 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): hessian_shape, multiclass_strategy, init_stamp_token=0, + loss_uses_sum_reduction=False, name=None): """Initialize the internal state for this split handler. @@ -62,6 +64,8 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): multiclass_strategy: Strategy describing how to treat multiclass problems. init_stamp_token: A tensor containing an scalar for initial stamp of the stamped objects. + loss_uses_sum_reduction: A scalar boolean tensor that specifies whether + SUM or MEAN reduction was used for the loss. name: An optional handler name. """ super(EqualitySplitHandler, self).__init__( @@ -73,6 +77,7 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): gradient_shape=gradient_shape, hessian_shape=hessian_shape, multiclass_strategy=multiclass_strategy, + loss_uses_sum_reduction=loss_uses_sum_reduction, name=name) self._stats_accumulator = stats_accumulator_ops.StatsAccumulator( init_stamp_token, @@ -173,6 +178,11 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): # pair. num_minibatches, partition_ids, feature_ids, gradients, hessians = ( self._stats_accumulator.flush(stamp_token, next_stamp_token)) + # For sum_reduction, we don't need to divide by number of minibatches. + + num_minibatches = control_flow_ops.cond( + ops.convert_to_tensor(self._loss_uses_sum_reduction), + lambda: math_ops.to_int64(1), lambda: num_minibatches) partition_ids, gains, split_infos = ( split_handler_ops.build_categorical_equality_splits( num_minibatches=num_minibatches, @@ -187,7 +197,7 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): tree_complexity_regularization=self._tree_complexity_regularization, min_node_weight=self._min_node_weight, bias_feature_id=_BIAS_FEATURE_ID, - multiclass_strategy=self._multiclass_strategy,)) + multiclass_strategy=self._multiclass_strategy)) # There are no warm-up rounds needed in the equality column handler. So we # always return ready. are_splits_ready = constant_op.constant(True) diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py index 0b65eba2a76273a81f1464ed7639f0c0760e0050..ef253e7cec4e8a96b360ced32b59398c2e2c9680 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler_test.py @@ -90,7 +90,17 @@ class EqualitySplitHandlerTest(test_util.TensorFlowTestCase): empty_hessians, example_weights, is_active=array_ops.constant([True, True])) - with ops.control_dependencies([update_1]): + update_2 = split_handler.update_stats_sync( + 0, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + + with ops.control_dependencies([update_1, update_2]): are_splits_ready, partitions, gains, splits = ( split_handler.make_splits(0, 1, class_id)) are_splits_ready, partitions, gains, splits = (sess.run( @@ -159,6 +169,129 @@ class EqualitySplitHandlerTest(test_util.TensorFlowTestCase): self.assertEqual(1, split_node.feature_id) + def testGenerateFeatureSplitCandidatesSumReduction(self): + with self.test_session() as sess: + # The data looks like the following: + # Example | Gradients | Partition | Feature ID | + # i0 | (0.2, 0.12) | 0 | 1,2 | + # i1 | (-0.5, 0.07) | 0 | | + # i2 | (1.2, 0.2) | 0 | 2 | + # i3 | (4.0, 0.13) | 1 | 1 | + gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0]) + hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13]) + partition_ids = [0, 0, 0, 1] + indices = [[0, 0], [0, 1], [2, 0], [3, 0]] + values = array_ops.constant([1, 2, 2, 1], dtype=dtypes.int64) + + gradient_shape = tensor_shape.scalar() + hessian_shape = tensor_shape.scalar() + class_id = -1 + + split_handler = categorical_split_handler.EqualitySplitHandler( + l1_regularization=0.1, + l2_regularization=1, + tree_complexity_regularization=0, + min_node_weight=0, + sparse_int_column=sparse_tensor.SparseTensor(indices, values, [4, 1]), + feature_column_group_id=0, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, + multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS, + init_stamp_token=0, + loss_uses_sum_reduction=True) + resources.initialize_resources(resources.shared_resources()).run() + + empty_gradients, empty_hessians = get_empty_tensors( + gradient_shape, hessian_shape) + example_weights = array_ops.ones([4, 1], dtypes.float32) + + update_1 = split_handler.update_stats_sync( + 0, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + update_2 = split_handler.update_stats_sync( + 0, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_1, update_2]): + are_splits_ready, partitions, gains, splits = ( + split_handler.make_splits(0, 1, class_id)) + are_splits_ready, partitions, gains, splits = ( + sess.run([are_splits_ready, partitions, gains, splits])) + self.assertTrue(are_splits_ready) + self.assertAllEqual([0, 1], partitions) + + # Check the split on partition 0. + # -(0.4 + 2.4 - 0.1) / (0.24 + 0.4 + 1) + expected_left_weight = -1.6463414634146338 + + # (0.4 + 2.4 - 0.1) ** 2 / (0.24 + 0.4 + 1) + expected_left_gain = 4.445121951219511 + + # -(-1 + 0.1) / (0.14 + 1) + expected_right_weight = 0.789473684211 + + # (-1 + 0.1) ** 2 / (0.14 + 1) + expected_right_gain = 0.710526315789 + + # (0.4 + -1 + 2.4 - 0.1) ** 2 / (0.24 + 0.14 + 0.4 + 1) + expected_bias_gain = 1.6235955056179772 + + split_info = split_info_pb2.SplitInfo() + split_info.ParseFromString(splits[0]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.categorical_id_binary_split + + self.assertEqual(0, split_node.feature_column) + + self.assertEqual(2, split_node.feature_id) + + self.assertAllClose( + expected_left_gain + expected_right_gain - expected_bias_gain, gains[0], + 0.00001) + + self.assertAllClose([expected_left_weight], left_child.value, 0.00001) + + self.assertAllClose([expected_right_weight], right_child.value, 0.00001) + + # Check the split on partition 1. + # (-8 + 0.1) / (0.26 + 1) + expected_left_weight = -6.26984126984 + # (-8 + 0.1) ** 2 / (0.26 + 1) + expected_left_gain = 49.5317460317 + expected_right_weight = 0 + expected_right_gain = 0 + # (-8 + 0.1) ** 2 / (0.26 + 1) + expected_bias_gain = 49.5317460317 + + # Verify candidate for partition 1, there's only one active feature here + # so zero gain is expected. + split_info = split_info_pb2.SplitInfo() + split_info.ParseFromString(splits[1]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.categorical_id_binary_split + self.assertAllClose(0.0, gains[1], 0.00001) + + self.assertAllClose([expected_left_weight], left_child.value, 0.00001) + + self.assertAllClose([expected_right_weight], right_child.value, 0.00001) + + self.assertEqual(0, split_node.feature_column) + + self.assertEqual(1, split_node.feature_id) + def testGenerateFeatureSplitCandidatesMulticlass(self): with self.test_session() as sess: # Batch size is 4, 2 gradients per each instance. diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py index 409a2d8f46c331c13aec10542c4967d50575e94a..df0bec1fe363e07bbff6b059e86076239bd605e9 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py @@ -99,6 +99,7 @@ class InequalitySplitHandler(base_split_handler.BaseSplitHandler): hessian_shape, multiclass_strategy, init_stamp_token=0, + loss_uses_sum_reduction=False, name=None): """Initialize the internal state for this split handler. @@ -117,6 +118,8 @@ class InequalitySplitHandler(base_split_handler.BaseSplitHandler): multiclass_strategy: Strategy describing how to treat multiclass problems. init_stamp_token: A tensor containing an scalar for initial stamp of the stamped objects. + loss_uses_sum_reduction: A scalar boolean tensor that specifies whether + SUM or MEAN reduction was used for the loss. name: An optional handler name. """ super(InequalitySplitHandler, self).__init__( @@ -128,7 +131,8 @@ class InequalitySplitHandler(base_split_handler.BaseSplitHandler): feature_column_group_id=feature_column_group_id, gradient_shape=gradient_shape, hessian_shape=hessian_shape, - multiclass_strategy=multiclass_strategy) + multiclass_strategy=multiclass_strategy, + loss_uses_sum_reduction=loss_uses_sum_reduction) self._stats_accumulator = stats_accumulator_ops.StatsAccumulator( init_stamp_token, gradient_shape, @@ -160,6 +164,7 @@ class DenseSplitHandler(InequalitySplitHandler): hessian_shape, multiclass_strategy, init_stamp_token=0, + loss_uses_sum_reduction=False, name=None): """Initialize the internal state for this split handler. @@ -179,6 +184,8 @@ class DenseSplitHandler(InequalitySplitHandler): multiclass_strategy: Strategy describing how to treat multiclass problems. init_stamp_token: A tensor containing an scalar for initial stamp of the stamped objects. + loss_uses_sum_reduction: A scalar boolean tensor that specifies whether + SUM or MEAN reduction was used for the loss. name: An optional handler name. """ super(DenseSplitHandler, self).__init__( @@ -193,7 +200,8 @@ class DenseSplitHandler(InequalitySplitHandler): name=name, gradient_shape=gradient_shape, hessian_shape=hessian_shape, - multiclass_strategy=multiclass_strategy) + multiclass_strategy=multiclass_strategy, + loss_uses_sum_reduction=loss_uses_sum_reduction) self._dense_float_column = dense_float_column # Register dense_make_stats_update function as an Op to the graph. g = ops.get_default_graph() @@ -255,15 +263,15 @@ class DenseSplitHandler(InequalitySplitHandler): next_stamp_token, self._multiclass_strategy, class_id, self._feature_column_group_id, self._l1_regularization, self._l2_regularization, self._tree_complexity_regularization, - self._min_node_weight)) + self._min_node_weight, self._loss_uses_sum_reduction)) return are_splits_ready, partition_ids, gains, split_infos -def _make_dense_split(quantile_accumulator_handle, stats_accumulator_handle, - stamp_token, next_stamp_token, multiclass_strategy, - class_id, feature_column_id, l1_regularization, - l2_regularization, tree_complexity_regularization, - min_node_weight, is_multi_dimentional): +def _make_dense_split( + quantile_accumulator_handle, stats_accumulator_handle, stamp_token, + next_stamp_token, multiclass_strategy, class_id, feature_column_id, + l1_regularization, l2_regularization, tree_complexity_regularization, + min_node_weight, is_multi_dimentional, loss_uses_sum_reduction): """Function that builds splits for a dense feature column.""" # Get the bucket boundaries are_splits_ready, buckets = ( @@ -291,7 +299,10 @@ def _make_dense_split(quantile_accumulator_handle, stats_accumulator_handle, num_minibatches, partition_ids, bucket_ids, gradients, hessians = ( gen_stats_accumulator_ops.stats_accumulator_scalar_flush( stats_accumulator_handle, stamp_token, next_stamp_token)) - + # For sum_reduction, we don't need to divide by number of minibatches. + num_minibatches = control_flow_ops.cond(loss_uses_sum_reduction, + lambda: math_ops.to_int64(1), + lambda: num_minibatches) # Put quantile and stats accumulator flushing in the dependency path. with ops.control_dependencies([flush_quantiles, partition_ids]): are_splits_ready = array_ops.identity(are_splits_ready) @@ -329,6 +340,7 @@ class SparseSplitHandler(InequalitySplitHandler): hessian_shape, multiclass_strategy, init_stamp_token=0, + loss_uses_sum_reduction=False, name=None): """Initialize the internal state for this split handler. @@ -348,6 +360,8 @@ class SparseSplitHandler(InequalitySplitHandler): multiclass_strategy: Strategy describing how to treat multiclass problems. init_stamp_token: A tensor containing an scalar for initial stamp of the stamped objects. + loss_uses_sum_reduction: A scalar boolean tensor that specifies whether + SUM or MEAN reduction was used for the loss. name: An optional handler name. """ super(SparseSplitHandler, self).__init__( @@ -362,6 +376,7 @@ class SparseSplitHandler(InequalitySplitHandler): hessian_shape=hessian_shape, multiclass_strategy=multiclass_strategy, init_stamp_token=init_stamp_token, + loss_uses_sum_reduction=loss_uses_sum_reduction, name=name) self._sparse_float_column = sparse_float_column @@ -424,15 +439,15 @@ class SparseSplitHandler(InequalitySplitHandler): next_stamp_token, self._multiclass_strategy, class_id, self._feature_column_group_id, self._l1_regularization, self._l2_regularization, self._tree_complexity_regularization, - self._min_node_weight)) + self._min_node_weight, self._loss_uses_sum_reduction)) return are_splits_ready, partition_ids, gains, split_infos -def _make_sparse_split(quantile_accumulator_handle, stats_accumulator_handle, - stamp_token, next_stamp_token, multiclass_strategy, - class_id, feature_column_id, l1_regularization, - l2_regularization, tree_complexity_regularization, - min_node_weight, is_multi_dimentional): +def _make_sparse_split( + quantile_accumulator_handle, stats_accumulator_handle, stamp_token, + next_stamp_token, multiclass_strategy, class_id, feature_column_id, + l1_regularization, l2_regularization, tree_complexity_regularization, + min_node_weight, is_multi_dimentional, loss_uses_sum_reduction): """Function that builds splits for a sparse feature column.""" # Get the bucket boundaries are_splits_ready, buckets = ( @@ -460,7 +475,9 @@ def _make_sparse_split(quantile_accumulator_handle, stats_accumulator_handle, num_minibatches, partition_ids, bucket_ids, gradients, hessians = ( gen_stats_accumulator_ops.stats_accumulator_scalar_flush( stats_accumulator_handle, stamp_token, next_stamp_token)) - + num_minibatches = control_flow_ops.cond(loss_uses_sum_reduction, + lambda: math_ops.to_int64(1), + lambda: num_minibatches) # Put quantile and stats accumulator flushing in the dependency path. with ops.control_dependencies([flush_quantiles, partition_ids]): are_splits_ready = array_ops.identity(are_splits_ready) @@ -498,17 +515,18 @@ def _specialize_make_split(func, is_multi_dimentional): dtypes.float32, dtypes.float32, dtypes.float32, + dtypes.bool, noinline=True) def f(quantile_accumulator_handle, stats_accumulator_handle, stamp_token, next_stamp_token, multiclass_strategy, class_id, feature_column_id, l1_regularization, l2_regularization, tree_complexity_regularization, - min_node_weight): + min_node_weight, loss_uses_sum_reduction): """Function that builds splits for a sparse feature column.""" - return func( - quantile_accumulator_handle, stats_accumulator_handle, stamp_token, - next_stamp_token, multiclass_strategy, class_id, feature_column_id, - l1_regularization, l2_regularization, tree_complexity_regularization, - min_node_weight, is_multi_dimentional) + return func(quantile_accumulator_handle, stats_accumulator_handle, + stamp_token, next_stamp_token, multiclass_strategy, class_id, + feature_column_id, l1_regularization, l2_regularization, + tree_complexity_regularization, min_node_weight, + is_multi_dimentional, loss_uses_sum_reduction) return f diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py index 2f2c2302113bf59d6a065d5005c934dc76c2148d..d59732cf92eb85e88732ac5a17dccf475ae5342f 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py @@ -182,6 +182,144 @@ class DenseSplitHandlerTest(test_util.TensorFlowTestCase): self.assertAllClose(0.52, split_node.threshold, 0.00001) + def testGenerateFeatureSplitCandidatesLossUsesSumReduction(self): + with self.test_session() as sess: + # The data looks like the following: + # Example | Gradients | Partition | Dense Quantile | + # i0 | (0.2, 0.12) | 0 | 1 | + # i1 | (-0.5, 0.07) | 0 | 1 | + # i2 | (1.2, 0.2) | 0 | 0 | + # i3 | (4.0, 0.13) | 1 | 1 | + dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52]) + gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0]) + hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13]) + partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32) + class_id = -1 + + gradient_shape = tensor_shape.scalar() + hessian_shape = tensor_shape.scalar() + split_handler = ordinal_split_handler.DenseSplitHandler( + l1_regularization=0.2, + l2_regularization=2., + tree_complexity_regularization=0., + min_node_weight=0., + epsilon=0.001, + num_quantiles=10, + feature_column_group_id=0, + dense_float_column=dense_column, + init_stamp_token=0, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, + multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS, + loss_uses_sum_reduction=True) + resources.initialize_resources(resources.shared_resources()).run() + + empty_gradients, empty_hessians = get_empty_tensors( + gradient_shape, hessian_shape) + example_weights = array_ops.ones([4, 1], dtypes.float32) + + update_1 = split_handler.update_stats_sync( + 0, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_1]): + are_splits_ready = split_handler.make_splits( + np.int64(0), np.int64(1), class_id)[0] + + with ops.control_dependencies([are_splits_ready]): + update_2 = split_handler.update_stats_sync( + 1, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + update_3 = split_handler.update_stats_sync( + 1, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_2, update_3]): + are_splits_ready2, partitions, gains, splits = ( + split_handler.make_splits(np.int64(1), np.int64(2), class_id)) + are_splits_ready, are_splits_ready2, partitions, gains, splits = ( + sess.run([ + are_splits_ready, are_splits_ready2, partitions, gains, splits + ])) + + # During the first iteration, inequality split handlers are not going to + # have any splits. Make sure that we return not_ready in that case. + self.assertFalse(are_splits_ready) + self.assertTrue(are_splits_ready2) + + self.assertAllEqual([0, 1], partitions) + + # Check the split on partition 0. + # -(2.4 - 0.2) / (0.4 + 2) + expected_left_weight = -0.91666 + + # expected_left_weight * -(2.4 - 0.2) + expected_left_gain = 2.016666666666666 + + # -(-1 + 0.4 + 0.2) / (0.38 + 2) + expected_right_weight = 0.1680672 + + # expected_right_weight * -(-1 + 0.4 + 0.2) + expected_right_gain = 0.0672268907563025 + + # (0.2 + -0.5 + 1.2 - 0.1) ** 2 / (0.12 + 0.07 + 0.2 + 1) + expected_bias_gain = 0.9208633093525178 + + split_info = split_info_pb2.SplitInfo() + split_info.ParseFromString(splits[0]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.dense_float_binary_split + self.assertAllClose( + expected_left_gain + expected_right_gain - expected_bias_gain, gains[0], + 0.00001) + + self.assertAllClose([expected_left_weight], left_child.value, 0.00001) + + self.assertAllClose([expected_right_weight], right_child.value, 0.00001) + + self.assertEqual(0, split_node.feature_column) + + self.assertAllClose(0.3, split_node.threshold, 0.00001) + + # Check the split on partition 1. + # (-8 + 0.2) / (0.26 + 2) + expected_left_weight = -3.4513274336283186 + expected_right_weight = 0 + + # Verify candidate for partition 1, there's only one active bucket here + # so zero gain is expected. + split_info = split_info_pb2.SplitInfo() + split_info.ParseFromString(splits[1]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.dense_float_binary_split + self.assertAllClose(0.0, gains[1], 0.00001) + + self.assertAllClose([expected_left_weight], left_child.value, 0.00001) + + self.assertAllClose([expected_right_weight], right_child.value, 0.00001) + + self.assertEqual(0, split_node.feature_column) + + self.assertAllClose(0.52, split_node.threshold, 0.00001) + def testGenerateFeatureSplitCandidatesMulticlassFullHessian(self): with self.test_session() as sess: dense_column = array_ops.constant([0.52, 0.52, 0.3, 0.52]) @@ -798,6 +936,139 @@ class SparseSplitHandlerTest(test_util.TensorFlowTestCase): self.assertAllClose(0.52, split_node.split.threshold) + def testGenerateFeatureSplitCandidatesLossUsesSumReduction(self): + with self.test_session() as sess: + # The data looks like the following: + # Example | Gradients | Partition | Sparse Quantile | + # i0 | (0.2, 0.12) | 0 | 1 | + # i1 | (-0.5, 0.07) | 0 | N/A | + # i2 | (1.2, 0.2) | 0 | 0 | + # i3 | (4.0, 0.13) | 1 | 1 | + gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0]) + hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13]) + example_partitions = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32) + indices = array_ops.constant([[0, 0], [2, 0], [3, 0]], dtype=dtypes.int64) + values = array_ops.constant([0.52, 0.3, 0.52]) + sparse_column = sparse_tensor.SparseTensor(indices, values, [4, 1]) + + gradient_shape = tensor_shape.scalar() + hessian_shape = tensor_shape.scalar() + class_id = -1 + + split_handler = ordinal_split_handler.SparseSplitHandler( + l1_regularization=0.0, + l2_regularization=4.0, + tree_complexity_regularization=0.0, + min_node_weight=0.0, + epsilon=0.01, + num_quantiles=2, + feature_column_group_id=0, + sparse_float_column=sparse_column, + init_stamp_token=0, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, + multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS, + loss_uses_sum_reduction=True) + resources.initialize_resources(resources.shared_resources()).run() + + empty_gradients, empty_hessians = get_empty_tensors( + gradient_shape, hessian_shape) + example_weights = array_ops.ones([4, 1], dtypes.float32) + + update_1 = split_handler.update_stats_sync( + 0, + example_partitions, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_1]): + are_splits_ready = split_handler.make_splits( + np.int64(0), np.int64(1), class_id)[0] + with ops.control_dependencies([are_splits_ready]): + update_2 = split_handler.update_stats_sync( + 1, + example_partitions, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + update_3 = split_handler.update_stats_sync( + 1, + example_partitions, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_2, update_3]): + are_splits_ready2, partitions, gains, splits = ( + split_handler.make_splits(np.int64(1), np.int64(2), class_id)) + are_splits_ready, are_splits_ready2, partitions, gains, splits = ( + sess.run([ + are_splits_ready, are_splits_ready2, partitions, gains, splits + ])) + + # During the first iteration, inequality split handlers are not going to + # have any splits. Make sure that we return not_ready in that case. + self.assertFalse(are_splits_ready) + self.assertTrue(are_splits_ready2) + + self.assertAllEqual([0, 1], partitions) + # Check the split on partition 0. + # -(0.4 + 2.4) / (0.24 + 0.4 + 4) + expected_left_weight = -0.603448275862069 + # (0.4 + 2.4) ** 2 / (0.24 + 0.4 + 4) + expected_left_gain = 1.689655172413793 + # 1 / (0.14 + 4) + expected_right_weight = 0.24154589371980678 + # 1 ** 2 / (0.14 + 4) + expected_right_gain = 0.24154589371980678 + # (0.4 + 2.4 - 1) ** 2 / (0.24 + 0.4 + 0.14 + 4) + expected_bias_gain = 0.6778242677824265 + + split_info = split_info_pb2.SplitInfo() + split_info.ParseFromString(splits[0]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.sparse_float_binary_split_default_right + self.assertAllClose( + expected_left_gain + expected_right_gain - expected_bias_gain, gains[0]) + + self.assertAllClose([expected_left_weight], left_child.value) + + self.assertAllClose([expected_right_weight], right_child.value) + + self.assertEqual(0, split_node.split.feature_column) + + self.assertAllClose(0.52, split_node.split.threshold) + + # Check the split on partition 1. + expected_left_weight = -1.8779342723004695 + expected_right_weight = 0 + + # Verify candidate for partition 1, there's only one active bucket here + # so zero gain is expected. + split_info.ParseFromString(splits[1]) + left_child = split_info.left_child.vector + right_child = split_info.right_child.vector + split_node = split_info.split_node.sparse_float_binary_split_default_left + + self.assertAllClose(0.0, gains[1]) + + self.assertAllClose([expected_left_weight], left_child.value) + + self.assertAllClose([expected_right_weight], right_child.value) + + self.assertEqual(0, split_node.split.feature_column) + + self.assertAllClose(0.52, split_node.split.threshold) + def testGenerateFeatureSplitCandidatesMulticlassFullHessian(self): with self.test_session() as sess: # Batch is 4, 2 classes diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index 47698d45c81478f2b694aaadc603f742c44d5351..1ee7f2395ea2ad71a7d380a1cc8f9a77bd4782b3 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -46,6 +46,7 @@ from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables +from tensorflow.python.ops.losses import losses from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary from tensorflow.python.training import device_setter @@ -61,6 +62,13 @@ USED_HANDLERS_MASK = "used_handlers_mask" LEAF_INDEX = "leaf_index" _FEATURE_NAME_TEMPLATE = "%s_%d" +# Keys in Training state. +GBDTTrainingState = collections.namedtuple("GBDTTrainingState", [ + "num_layer_examples", "num_layer_steps", "num_layers", "active_tree", + "active_layer", "continue_centering", "bias_stats_accumulator", + "steps_accumulator", "handlers" +]) + def _get_column_by_index(tensor, indices): """Returns columns from a 2-D tensor by index.""" @@ -276,6 +284,7 @@ class GradientBoostedDecisionTreeModel(object): learner_config, features, logits_dimension, + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS, feature_columns=None, use_core_columns=False, output_leaf_index=False): @@ -292,7 +301,10 @@ class GradientBoostedDecisionTreeModel(object): learner_config: A learner config. features: `dict` of `Tensor` objects. logits_dimension: An int, the dimension of logits. + loss_reduction: Either `SUM_OVER_NONZERO_WEIGHTS` (mean) or `SUM`. feature_columns: A list of feature columns. + use_core_columns: A boolean specifying whether core feature columns are + used. output_leaf_index: A boolean variable indicating whether to output leaf index into predictions dictionary. @@ -315,6 +327,13 @@ class GradientBoostedDecisionTreeModel(object): self._center_bias = center_bias self._examples_per_layer = examples_per_layer + # Check loss reduction value. + if (loss_reduction != losses.Reduction.SUM and + loss_reduction != losses.Reduction.SUM_OVER_NONZERO_WEIGHTS): + raise ValueError( + "Invalid loss reduction is provided: %s." % loss_reduction) + self._loss_reduction = loss_reduction + # Fill in the defaults. if (learner_config.multi_class_strategy == learner_pb2.LearnerConfig.MULTI_CLASS_STRATEGY_UNSPECIFIED): @@ -325,6 +344,19 @@ class GradientBoostedDecisionTreeModel(object): learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) + if logits_dimension == 1 or learner_config.multi_class_strategy == ( + learner_pb2.LearnerConfig.TREE_PER_CLASS): + self._gradient_shape = tensor_shape.scalar() + self._hessian_shape = tensor_shape.scalar() + else: + self._gradient_shape = tensor_shape.TensorShape([logits_dimension]) + if (learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.FULL_HESSIAN): + self._hessian_shape = tensor_shape.TensorShape( + ([logits_dimension, logits_dimension])) + else: + # Diagonal hessian strategy. + self._hessian_shape = tensor_shape.TensorShape(([logits_dimension])) if (learner_config.growing_mode == learner_pb2.LearnerConfig.GROWING_MODE_UNSPECIFIED): learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER @@ -359,6 +391,7 @@ class GradientBoostedDecisionTreeModel(object): sparse_int_values, sparse_int_shapes) = extract_features( features, self._feature_columns, use_core_columns) logging.info("Active Feature Columns: " + str(fc_names)) + logging.info("Learner config: " + str(learner_config)) self._fc_names = fc_names self._dense_floats = dense_floats self._sparse_float_indices = sparse_float_indices @@ -522,17 +555,30 @@ class GradientBoostedDecisionTreeModel(object): return self._predict_and_return_dict(self._ensemble_handle, ensemble_stamp, mode) - def train(self, loss, predictions_dict, labels): - """Grows a new tree and adds it to the ensemble. + def _get_class_id(self, predictions_dict): + # Handle different multiclass strategies. + if (self._learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.TREE_PER_CLASS and + self._logits_dimension != 1): + # Choose the class for which the tree is built (one vs rest). + return math_ops.to_int32( + predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) + return constant_op.constant(-1, dtype=dtypes.int32) + + def update_stats(self, loss, predictions_dict): + """Update the accumulators with stats from this batch. Args: loss: A scalar tensor representing average loss of examples. predictions_dict: Dictionary of Rank 2 `Tensor` representing information about predictions per example. - labels: Rank 2 `Tensor` representing labels per example. Returns: - An op that adds a new tree to the ensemble. + Three values: + - An op that adds a new tree to the ensemble, and + - An op that increments the stamp but removes all the trees and resets + the handlers. This can be used to reset the state of the ensemble. + - A dict containing the training state. Raises: ValueError: if inputs are not valid. @@ -556,13 +602,10 @@ class GradientBoostedDecisionTreeModel(object): aggregation_method=None)[0] strategy = self._learner_config.multi_class_strategy - class_id = constant_op.constant(-1, dtype=dtypes.int32) + class_id = self._get_class_id(predictions_dict) # Handle different multiclass strategies. if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS: # We build one vs rest trees. - gradient_shape = tensor_shape.scalar() - hessian_shape = tensor_shape.scalar() - if self._logits_dimension == 1: # We have only 1 score, gradients is of shape [batch, 1]. hessians = gradients_impl.gradients( @@ -579,11 +622,6 @@ class GradientBoostedDecisionTreeModel(object): hessian_list = self._diagonal_hessian(gradients, predictions) # Assemble hessian list into a tensor. hessians = array_ops.stack(hessian_list, axis=1) - - # Choose the class for which the tree is built (one vs rest). - class_id = math_ops.to_int32( - predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) - # Use class id tensor to get the column with that index from gradients # and hessians. squeezed_gradients = array_ops.squeeze( @@ -592,15 +630,10 @@ class GradientBoostedDecisionTreeModel(object): _get_column_by_index(hessians, class_id)) else: # Other multiclass strategies. - gradient_shape = tensor_shape.TensorShape([self._logits_dimension]) - if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN: - hessian_shape = tensor_shape.TensorShape( - ([self._logits_dimension, self._logits_dimension])) hessian_list = self._full_hessian(gradients, predictions) else: # Diagonal hessian strategy. - hessian_shape = tensor_shape.TensorShape(([self._logits_dimension])) hessian_list = self._diagonal_hessian(gradients, predictions) squeezed_gradients = gradients @@ -608,7 +641,7 @@ class GradientBoostedDecisionTreeModel(object): squeezed_hessians = hessians # Get the weights for each example for quantiles calculation, - weights = self._get_weights(hessian_shape, squeezed_hessians) + weights = self._get_weights(self._hessian_shape, squeezed_hessians) # Create all handlers ensuring resources are evenly allocated across PS. fc_name_idx = 0 @@ -622,6 +655,8 @@ class GradientBoostedDecisionTreeModel(object): self._learner_config.regularization.tree_complexity, dtypes.float32) min_node_weight = constant_op.constant( self._learner_config.constraints.min_node_weight, dtypes.float32) + loss_uses_sum_reduction = self._loss_reduction == losses.Reduction.SUM + loss_uses_sum_reduction = constant_op.constant(loss_uses_sum_reduction) epsilon = 0.01 num_quantiles = 100 strategy_tensor = constant_op.constant(strategy) @@ -635,15 +670,18 @@ class GradientBoostedDecisionTreeModel(object): l2_regularization=l2_regularization, tree_complexity_regularization=tree_complexity_regularization, min_node_weight=min_node_weight, - feature_column_group_id=dense_float_column_idx, + feature_column_group_id=constant_op.constant( + dense_float_column_idx), epsilon=epsilon, num_quantiles=num_quantiles, dense_float_column=self._dense_floats[dense_float_column_idx], name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, - init_stamp_token=init_stamp_token)) + init_stamp_token=init_stamp_token, + loss_uses_sum_reduction=loss_uses_sum_reduction, + )) fc_name_idx += 1 # Create handlers for sparse float columns. @@ -655,7 +693,8 @@ class GradientBoostedDecisionTreeModel(object): l2_regularization=l2_regularization, tree_complexity_regularization=tree_complexity_regularization, min_node_weight=min_node_weight, - feature_column_group_id=sparse_float_column_idx, + feature_column_group_id=constant_op.constant( + sparse_float_column_idx), epsilon=epsilon, num_quantiles=num_quantiles, sparse_float_column=sparse_tensor.SparseTensor( @@ -663,10 +702,11 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_float_values[sparse_float_column_idx], self._sparse_float_shapes[sparse_float_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, - init_stamp_token=init_stamp_token)) + init_stamp_token=init_stamp_token, + loss_uses_sum_reduction=loss_uses_sum_reduction)) fc_name_idx += 1 # Create handlers for sparse int columns. @@ -678,32 +718,20 @@ class GradientBoostedDecisionTreeModel(object): l2_regularization=l2_regularization, tree_complexity_regularization=tree_complexity_regularization, min_node_weight=min_node_weight, - feature_column_group_id=sparse_int_column_idx, + feature_column_group_id=constant_op.constant( + sparse_int_column_idx), sparse_int_column=sparse_tensor.SparseTensor( self._sparse_int_indices[sparse_int_column_idx], self._sparse_int_values[sparse_int_column_idx], self._sparse_int_shapes[sparse_int_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, - init_stamp_token=init_stamp_token)) + init_stamp_token=init_stamp_token, + loss_uses_sum_reduction=loss_uses_sum_reduction)) fc_name_idx += 1 - # Create steps accumulator. - steps_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=tensor_shape.scalar(), - hessian_shape=tensor_shape.scalar(), - name="StepsAccumulator") - - # Create bias stats accumulator. - bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, - name="BiasAccumulator") - # Create ensemble stats variables. num_layer_examples = variables.Variable( initial_value=array_ops.zeros([], dtypes.int64), @@ -725,7 +753,23 @@ class GradientBoostedDecisionTreeModel(object): initial_value=array_ops.zeros([], dtypes.int64), name="active_layer", trainable=False) - + # Variable that becomes false once bias centering is done. + continue_centering = variables.Variable( + initial_value=self._center_bias, + name="continue_centering", + trainable=False) + # Create bias stats accumulator. + bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, + name="BiasAccumulator") + # Create steps accumulator. + steps_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=tensor_shape.scalar(), + hessian_shape=tensor_shape.scalar(), + name="StepsAccumulator") # Create ensemble stats summaries. summary.scalar("layer_stats/num_examples", num_layer_examples) summary.scalar("layer_stats/num_steps", num_layer_steps) @@ -734,16 +778,13 @@ class GradientBoostedDecisionTreeModel(object): # Update bias stats. stats_update_ops = [] - continue_centering = variables.Variable( - initial_value=self._center_bias, - name="continue_centering", - trainable=False) + stats_update_ops.append( control_flow_ops.cond( continue_centering, - self._make_update_bias_stats_fn(ensemble_stamp, predictions, - gradients, bias_stats_accumulator), - control_flow_ops.no_op)) + self._make_update_bias_stats_fn( + ensemble_stamp, predictions, gradients, + bias_stats_accumulator), control_flow_ops.no_op)) # Update handler stats. handler_reads = collections.OrderedDict() @@ -800,8 +841,8 @@ class GradientBoostedDecisionTreeModel(object): lambda: active_handlers)) # Prepare empty gradients and hessians when handlers are not ready. - empty_hess_shape = [1] + hessian_shape.as_list() - empty_grad_shape = [1] + gradient_shape.as_list() + empty_hess_shape = [1] + self._hessian_shape.as_list() + empty_grad_shape = [1] + self._gradient_shape.as_list() empty_gradients = constant_op.constant( [], dtype=dtypes.float32, shape=empty_grad_shape) @@ -823,34 +864,86 @@ class GradientBoostedDecisionTreeModel(object): per_handler_updates, ensemble_stamp, worker_device) for update in update_results.values(): stats_update_ops += update + + training_state = GBDTTrainingState( + num_layer_examples=num_layer_examples, + num_layer_steps=num_layer_steps, + num_layers=num_layers, + active_tree=active_tree, + active_layer=active_layer, + continue_centering=continue_centering, + bias_stats_accumulator=bias_stats_accumulator, + steps_accumulator=steps_accumulator, + handlers=handlers) + + reset_op = control_flow_ops.no_op() + if self._is_chief: + # Advance the ensemble stamp to throw away staggered workers. + stamp_token, _ = model_ops.tree_ensemble_serialize(self._ensemble_handle) + next_stamp_token = stamp_token + 1 + + reset_ops = [] + for handler in handlers: + reset_ops.append(handler.make_splits(stamp_token, next_stamp_token, 0)) + if self._center_bias: + reset_ops.append( + bias_stats_accumulator.flush(stamp_token, next_stamp_token)) + reset_ops.append(steps_accumulator.flush(stamp_token, next_stamp_token)) + reset_ops.append(self._finalized_trees.assign(0).op) + reset_ops.append(self._attempted_trees.assign(0).op) + reset_ops.append( + model_ops.tree_ensemble_deserialize( + self._ensemble_handle, + stamp_token=next_stamp_token, + tree_ensemble_config="", + name="reset_gbdt")) + + reset_op = control_flow_ops.group([reset_ops]) + + return stats_update_ops, reset_op, training_state + + def increment_step_counter_and_maybe_update_ensemble(self, predictions_dict, + training_state): + """Increments number of visited examples and grows the ensemble. + + If the number of visited examples reaches the target examples_per_layer, + ensemble is updated. + + Args: + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + training_state: `dict` returned by update_stats. + + Returns: + An op that updates the counters and potientially grows the ensemble. + """ + batch_size = math_ops.cast( + array_ops.shape(predictions_dict[PREDICTIONS])[0], dtypes.float32) + ensemble_stamp = predictions_dict[ENSEMBLE_STAMP] # Accumulate a step after updating stats. - batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) - with ops.control_dependencies(stats_update_ops): - add_step_op = steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], - [batch_size], [1.0]) - # Determine learning rate. - learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( - "tuner") - if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": - tuner = getattr(self._learner_config.learning_rate_tuner, - learning_rate_tuner) - learning_rate = tuner.learning_rate - else: - # TODO(nponomareva, soroush) do the line search. - raise ValueError("Line search learning rate is not yet supported.") + steps_accumulator = training_state.steps_accumulator + num_layer_examples = training_state.num_layer_examples + num_layer_steps = training_state.num_layer_steps + active_layer = training_state.active_layer + add_step_op = steps_accumulator.add( + ensemble_stamp, [0], [[0, 0]], [batch_size], [1.0]) # After adding the step, decide if further processing is needed. ensemble_update_ops = [add_step_op] + class_id = self._get_class_id(predictions_dict) + with ops.control_dependencies([add_step_op]): if self._is_chief: dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED] # Get accumulated steps and examples for the current layer. - _, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize() + _, _, _, _, acc_examples, acc_steps = ( + steps_accumulator.serialize()) acc_examples = math_ops.cast(acc_examples[0], dtypes.int64) acc_steps = math_ops.cast(acc_steps[0], dtypes.int64) - ensemble_update_ops.append(num_layer_examples.assign(acc_examples)) + ensemble_update_ops.append( + num_layer_examples.assign(acc_examples)) ensemble_update_ops.append(num_layer_steps.assign(acc_steps)) # Determine whether we need to update tree ensemble. examples_per_layer = self._examples_per_layer @@ -859,18 +952,172 @@ class GradientBoostedDecisionTreeModel(object): ensemble_update_ops.append( control_flow_ops.cond( acc_examples >= examples_per_layer, - self._make_update_ensemble_fn( - ensemble_stamp, steps_accumulator, bias_stats_accumulator, - continue_centering, learning_rate, handlers, num_layers, - active_tree, active_layer, dropout_seed, class_id), + self.make_update_ensemble_fn(ensemble_stamp, training_state, + dropout_seed, class_id), control_flow_ops.no_op)) - # Calculate the loss to be reported. # Note, the loss is calculated from the prediction considering dropouts, so # that the value might look staggering over steps when the dropout ratio is # high. eval_loss might be referred instead in the aspect of convergence. return control_flow_ops.group(*ensemble_update_ops) + def make_update_ensemble_fn(self, ensemble_stamp, training_state, + dropout_seed, class_id): + """A method to create the function which updates the tree ensemble.""" + # Determine learning rate. + learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( + "tuner") + if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": + tuner = getattr(self._learner_config.learning_rate_tuner, + learning_rate_tuner) + learning_rate = tuner.learning_rate + else: + # TODO(nponomareva, soroush) do the line search. + raise ValueError("Line search learning rate is not yet supported.") + + def _update_ensemble(): + """A method to update the tree ensemble.""" + # Get next stamp token. + next_ensemble_stamp = ensemble_stamp + 1 + # Finalize bias stats. + _, _, _, bias_grads, bias_hess = ( + training_state.bias_stats_accumulator.flush(ensemble_stamp, + next_ensemble_stamp)) + + # Finalize handler splits. + are_splits_ready_list = [] + partition_ids_list = [] + gains_list = [] + split_info_list = [] + + for handler in training_state.handlers: + (are_splits_ready, + partition_ids, gains, split_info) = handler.make_splits( + ensemble_stamp, next_ensemble_stamp, class_id) + are_splits_ready_list.append(are_splits_ready) + partition_ids_list.append(partition_ids) + gains_list.append(gains) + split_info_list.append(split_info) + # Stack all the inputs to one tensor per type. + # This is a workaround for the slowness of graph building in tf.cond. + # See (b/36554864). + split_sizes = array_ops.reshape( + array_ops.shape_n(partition_ids_list), [len(partition_ids_list)]) + partition_ids = array_ops.concat(partition_ids_list, axis=0) + gains = array_ops.concat(gains_list, axis=0) + split_infos = array_ops.concat(split_info_list, axis=0) + + # Determine if all splits are ready. + are_all_splits_ready = math_ops.reduce_all( + array_ops.stack( + are_splits_ready_list, axis=0, name="stack_handler_readiness")) + + # Define bias centering update operation. + def _center_bias_fn(): + # Center tree ensemble bias. + delta_updates = array_ops.where(bias_hess > 0, -bias_grads / bias_hess, + array_ops.zeros_like(bias_grads)) + center_bias = training_ops.center_tree_ensemble_bias( + tree_ensemble_handle=self._ensemble_handle, + stamp_token=ensemble_stamp, + next_stamp_token=next_ensemble_stamp, + delta_updates=delta_updates, + learner_config=self._learner_config_serialized) + return training_state.continue_centering.assign(center_bias) + + # Define ensemble growing operations. + def _grow_ensemble_ready_fn(): + # Grow the ensemble given the current candidates. + sizes = array_ops.unstack(split_sizes) + partition_ids_list = list(array_ops.split(partition_ids, sizes, axis=0)) + gains_list = list(array_ops.split(gains, sizes, axis=0)) + split_info_list = list(array_ops.split(split_infos, sizes, axis=0)) + return training_ops.grow_tree_ensemble( + tree_ensemble_handle=self._ensemble_handle, + stamp_token=ensemble_stamp, + next_stamp_token=next_ensemble_stamp, + learning_rate=learning_rate, + partition_ids=partition_ids_list, + gains=gains_list, + splits=split_info_list, + learner_config=self._learner_config_serialized, + dropout_seed=dropout_seed, + center_bias=self._center_bias) + + def _grow_ensemble_not_ready_fn(): + # Don't grow the ensemble, just update the stamp. + return training_ops.grow_tree_ensemble( + tree_ensemble_handle=self._ensemble_handle, + stamp_token=ensemble_stamp, + next_stamp_token=next_ensemble_stamp, + learning_rate=0, + partition_ids=[], + gains=[], + splits=[], + learner_config=self._learner_config_serialized, + dropout_seed=dropout_seed, + center_bias=self._center_bias) + + def _grow_ensemble_fn(): + # Conditionally grow an ensemble depending on whether the splits + # from all the handlers are ready. + return control_flow_ops.cond(are_all_splits_ready, + _grow_ensemble_ready_fn, + _grow_ensemble_not_ready_fn) + + # Update ensemble. + update_ops = [are_all_splits_ready] + if self._center_bias: + update_model = control_flow_ops.cond(training_state.continue_centering, + _center_bias_fn, _grow_ensemble_fn) + else: + update_model = _grow_ensemble_fn() + update_ops.append(update_model) + + # Update ensemble stats. + with ops.control_dependencies([update_model]): + stats = training_ops.tree_ensemble_stats( + self._ensemble_handle, stamp_token=next_ensemble_stamp) + update_ops.append(self._finalized_trees.assign(stats.num_trees)) + update_ops.append(self._attempted_trees.assign(stats.attempted_trees)) + update_ops.append(training_state.num_layers.assign(stats.num_layers)) + update_ops.append(training_state.active_tree.assign(stats.active_tree)) + update_ops.append( + training_state.active_layer.assign(stats.active_layer)) + + # Flush step stats. + update_ops.extend( + training_state.steps_accumulator.flush(ensemble_stamp, + next_ensemble_stamp)) + return control_flow_ops.group(*update_ops, name="update_ensemble") + + return _update_ensemble + + def get_number_of_trees_tensor(self): + return self._finalized_trees, self._attempted_trees + + def train(self, loss, predictions_dict, labels): + """Updates the accumalator stats and grows the ensemble. + + Args: + loss: A scalar tensor representing average loss of examples. + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + labels: Rank 2 `Tensor` representing labels per example. Has no effect + on the training and is only kept for backward compatibility. + + Returns: + An op that adds a new tree to the ensemble. + + Raises: + ValueError: if inputs are not valid. + """ + del labels # unused; kept for backward compatibility. + update_op, _, training_state = self.update_stats(loss, predictions_dict) + with ops.control_dependencies(update_op): + return self.increment_step_counter_and_maybe_update_ensemble( + predictions_dict, training_state) + def _get_weights(self, hessian_shape, hessians): """Derives weights to be used based on hessians and multiclass strategy.""" if hessian_shape == tensor_shape.scalar(): @@ -986,127 +1233,3 @@ class GradientBoostedDecisionTreeModel(object): return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") return _update_bias_stats - - def _make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, - bias_stats_accumulator, continue_centering, - learning_rate, handlers, num_layers, active_tree, - active_layer, dropout_seed, class_id): - """A method to create the function which updates the tree ensemble.""" - - def _update_ensemble(): - """A method to update the tree ensemble.""" - # Get next stamp token. - next_ensemble_stamp = ensemble_stamp + 1 - # Finalize bias stats. - _, _, _, bias_grads, bias_hess = bias_stats_accumulator.flush( - ensemble_stamp, next_ensemble_stamp) - - # Finalize handler splits. - are_splits_ready_list = [] - partition_ids_list = [] - gains_list = [] - split_info_list = [] - - for handler in handlers: - (are_splits_ready, - partition_ids, gains, split_info) = handler.make_splits( - ensemble_stamp, next_ensemble_stamp, class_id) - are_splits_ready_list.append(are_splits_ready) - partition_ids_list.append(partition_ids) - gains_list.append(gains) - split_info_list.append(split_info) - # Stack all the inputs to one tensor per type. - # This is a workaround for the slowness of graph building in tf.cond. - # See (b/36554864). - split_sizes = array_ops.reshape( - array_ops.shape_n(partition_ids_list), [len(partition_ids_list)]) - partition_ids = array_ops.concat(partition_ids_list, axis=0) - gains = array_ops.concat(gains_list, axis=0) - split_infos = array_ops.concat(split_info_list, axis=0) - - # Determine if all splits are ready. - are_all_splits_ready = math_ops.reduce_all( - array_ops.stack( - are_splits_ready_list, axis=0, name="stack_handler_readiness")) - - # Define bias centering update operation. - def _center_bias_fn(): - # Center tree ensemble bias. - delta_updates = array_ops.where(bias_hess > 0, -bias_grads / bias_hess, - array_ops.zeros_like(bias_grads)) - center_bias = training_ops.center_tree_ensemble_bias( - tree_ensemble_handle=self._ensemble_handle, - stamp_token=ensemble_stamp, - next_stamp_token=next_ensemble_stamp, - delta_updates=delta_updates, - learner_config=self._learner_config_serialized) - return continue_centering.assign(center_bias) - - # Define ensemble growing operations. - def _grow_ensemble_ready_fn(): - # Grow the ensemble given the current candidates. - sizes = array_ops.unstack(split_sizes) - partition_ids_list = list(array_ops.split(partition_ids, sizes, axis=0)) - gains_list = list(array_ops.split(gains, sizes, axis=0)) - split_info_list = list(array_ops.split(split_infos, sizes, axis=0)) - return training_ops.grow_tree_ensemble( - tree_ensemble_handle=self._ensemble_handle, - stamp_token=ensemble_stamp, - next_stamp_token=next_ensemble_stamp, - learning_rate=learning_rate, - partition_ids=partition_ids_list, - gains=gains_list, - splits=split_info_list, - learner_config=self._learner_config_serialized, - dropout_seed=dropout_seed, - center_bias=self._center_bias) - - def _grow_ensemble_not_ready_fn(): - # Don't grow the ensemble, just update the stamp. - return training_ops.grow_tree_ensemble( - tree_ensemble_handle=self._ensemble_handle, - stamp_token=ensemble_stamp, - next_stamp_token=next_ensemble_stamp, - learning_rate=0, - partition_ids=[], - gains=[], - splits=[], - learner_config=self._learner_config_serialized, - dropout_seed=dropout_seed, - center_bias=self._center_bias) - - def _grow_ensemble_fn(): - # Conditionally grow an ensemble depending on whether the splits - # from all the handlers are ready. - return control_flow_ops.cond(are_all_splits_ready, - _grow_ensemble_ready_fn, - _grow_ensemble_not_ready_fn) - - # Update ensemble. - update_ops = [are_all_splits_ready] - if self._center_bias: - update_model = control_flow_ops.cond(continue_centering, - _center_bias_fn, _grow_ensemble_fn) - else: - update_model = _grow_ensemble_fn() - update_ops.append(update_model) - - # Update ensemble stats. - with ops.control_dependencies([update_model]): - stats = training_ops.tree_ensemble_stats( - self._ensemble_handle, stamp_token=next_ensemble_stamp) - update_ops.append(self._finalized_trees.assign(stats.num_trees)) - update_ops.append(self._attempted_trees.assign(stats.attempted_trees)) - update_ops.append(num_layers.assign(stats.num_layers)) - update_ops.append(active_tree.assign(stats.active_tree)) - update_ops.append(active_layer.assign(stats.active_layer)) - - # Flush step stats. - update_ops.extend( - steps_accumulator.flush(ensemble_stamp, next_ensemble_stamp)) - return control_flow_ops.group(*update_ops, name="update_ensemble") - - return _update_ensemble - - def get_number_of_trees_tensor(self): - return self._finalized_trees, self._attempted_trees diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py index e3d4397fadcbaf148f7f6cfaca13e850639786cf..f7867d882d6813a8701065ad0ce8d27f8bb9c301 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py @@ -29,6 +29,7 @@ from tensorflow.contrib.layers.python.layers import feature_column as feature_co from tensorflow.contrib.learn.python.learn.estimators import model_fn from tensorflow.python.feature_column import feature_column_lib as core_feature_column from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops @@ -1560,6 +1561,301 @@ class GbdtTest(test_util.TensorFlowTestCase): self.assertEquals(output.growing_metadata.num_layers_attempted, 2) + def testResetModelBeforeAndAfterSplit(self): + """Tests whether resetting works.""" + with self.test_session(): + # First build a small tree and train it to verify training works. + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + features = {} + features["dense_float"] = array_ops.ones([4, 1], dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = model_ops.tree_ensemble_stamp_token(ensemble_handle) + + predictions_dict = { + "predictions": predictions, + "predictions_no_dropout": predictions, + "partition_ids": partition_ids, + "ensemble_stamp": ensemble_stamp, + "num_trees": 12, + "max_tree_depth": 4, + } + + labels = array_ops.ones([4, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + loss = math_ops.reduce_mean(_squared_loss(labels, weights, predictions)) + + # Create train op. + update_op, reset_op, training_state = gbdt_model.update_stats( + loss, predictions_dict) + with ops.control_dependencies(update_op): + train_op = gbdt_model.increment_step_counter_and_maybe_update_ensemble( + predictions_dict, training_state) + + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + original_stamp = ensemble_stamp.eval() + expected_tree = """ + nodes { + dense_float_binary_split { + threshold: 1.0 + left_id: 1 + right_id: 2 + } + node_metadata { + gain: 0 + } + } + nodes { + leaf { + vector { + value: 0.25 + } + } + } + nodes { + leaf { + vector { + value: 0.0 + } + } + }""" + + def _train_once_and_check(expect_split): + stamp = ensemble_stamp.eval() + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(stamp_token.eval(), stamp + 1) + if expect_split: + # State of the ensemble after a split occurs. + self.assertEquals(len(output.trees), 1) + self.assertProtoEquals(expected_tree, output.trees[0]) + else: + # State of the ensemble after a single accumulation but before any + # splitting occurs + self.assertEquals(len(output.trees), 0) + self.assertProtoEquals(""" + growing_metadata { + num_trees_attempted: 1 + num_layers_attempted: 1 + }""", output) + + def _run_reset(): + stamp_before_reset = ensemble_stamp.eval() + reset_op.run() + stamp_after_reset = ensemble_stamp.eval() + self.assertNotEquals(stamp_after_reset, stamp_before_reset) + + _, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertProtoEquals("", output) + + return stamp_after_reset + + # Exit after one train_op, so no new layer are created but the handlers + # contain enough information to split on the next call to train. + _train_once_and_check(expect_split=False) + self.assertEquals(ensemble_stamp.eval(), original_stamp + 1) + + # Reset the handlers so it still requires two training calls to split. + stamp_after_reset = _run_reset() + + _train_once_and_check(expect_split=False) + _train_once_and_check(expect_split=True) + self.assertEquals(ensemble_stamp.eval(), stamp_after_reset + 2) + + # This time, test that the reset_op works right after splitting. + stamp_after_reset = _run_reset() + + # Test that after resetting, the tree can be trained as normal. + _train_once_and_check(expect_split=False) + _train_once_and_check(expect_split=True) + self.assertEquals(ensemble_stamp.eval(), stamp_after_reset + 2) + + def testResetModelNonChief(self): + """Tests the reset function on a non-chief worker.""" + with self.test_session(): + # Create ensemble with one bias node. + ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() + text_format.Merge( + """ + trees { + nodes { + leaf { + vector { + value: 0.25 + } + } + } + } + tree_weights: 1.0 + tree_metadata { + num_tree_weight_updates: 1 + num_layers_grown: 1 + is_finalized: false + }""", ensemble_config) + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, + tree_ensemble_config=ensemble_config.SerializeToString(), + name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + features = {} + features["dense_float"] = array_ops.ones([4, 1], dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=False, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = model_ops.tree_ensemble_stamp_token(ensemble_handle) + + predictions_dict = { + "predictions": predictions, + "predictions_no_dropout": predictions, + "partition_ids": partition_ids, + "ensemble_stamp": ensemble_stamp + } + + labels = array_ops.ones([4, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + loss = math_ops.reduce_mean(_squared_loss(labels, weights, predictions)) + + # Create reset op. + _, reset_op, _ = gbdt_model.update_stats( + loss, predictions_dict) + + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # Reset op doesn't do anything because this is a non-chief worker. + reset_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 1) + self.assertEquals(len(output.tree_weights), 1) + self.assertEquals(stamp_token.eval(), 0) + + def testResetModelWithCenterBias(self): + """Tests the reset function running on chief with bias centering.""" + with self.test_session(): + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + features["dense_float"] = array_ops.ones([4, 1], dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=True, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = model_ops.tree_ensemble_stamp_token(ensemble_handle) + + predictions_dict = { + "predictions": predictions, + "predictions_no_dropout": predictions, + "partition_ids": partition_ids, + "ensemble_stamp": ensemble_stamp, + "num_trees": 12, + } + + labels = array_ops.ones([4, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + loss = math_ops.reduce_mean(_squared_loss(labels, weights, predictions)) + + # Create train op. + update_op, reset_op, training_state = gbdt_model.update_stats( + loss, predictions_dict) + with ops.control_dependencies(update_op): + train_op = gbdt_model.increment_step_counter_and_maybe_update_ensemble( + predictions_dict, training_state) + + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect bias to be centered. + def train_and_check(): + train_op.run() + _, serialized = model_ops.tree_ensemble_serialize(ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + expected_tree = """ + nodes { + leaf { + vector { + value: 0.25 + } + } + }""" + self.assertEquals(len(output.trees), 1) + self.assertAllEqual(output.tree_weights, [1.0]) + self.assertProtoEquals(expected_tree, output.trees[0]) + + train_and_check() + self.assertEquals(ensemble_stamp.eval(), 1) + + reset_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 0) + self.assertEquals(len(output.tree_weights), 0) + self.assertEquals(stamp_token.eval(), 2) + + train_and_check() + self.assertEquals(ensemble_stamp.eval(), 3) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py index 8ae493ba998bd882b5ef946f927ec1882d91f61d..2fbaa31d5e19b58c335cd0a894e1db9af2c34d08 100644 --- a/tensorflow/contrib/checkpoint/__init__.py +++ b/tensorflow/contrib/checkpoint/__init__.py @@ -16,10 +16,13 @@ Visualization and inspection: @@dot_graph_from_checkpoint +@@list_objects @@object_metadata Managing dependencies: +@@capture_dependencies @@Checkpointable +@@CheckpointableBase @@CheckpointableObjectGraph @@NoDependency @@split_dependency @@ -38,13 +41,15 @@ from tensorflow.contrib.checkpoint.python.containers import UniqueNameTracker from tensorflow.contrib.checkpoint.python.split_dependency import split_dependency from tensorflow.contrib.checkpoint.python.visualize import dot_graph_from_checkpoint from tensorflow.core.protobuf.checkpointable_object_graph_pb2 import CheckpointableObjectGraph -from tensorflow.python.training.checkpointable.base import Checkpointable -from tensorflow.python.training.checkpointable.base import NoDependency +from tensorflow.python.training.checkpointable.base import CheckpointableBase from tensorflow.python.training.checkpointable.data_structures import List from tensorflow.python.training.checkpointable.data_structures import Mapping +from tensorflow.python.training.checkpointable.data_structures import NoDependency +from tensorflow.python.training.checkpointable.tracking import Checkpointable +from tensorflow.python.training.checkpointable.util import capture_dependencies +from tensorflow.python.training.checkpointable.util import list_objects from tensorflow.python.training.checkpointable.util import object_metadata from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(module_name=__name__) - diff --git a/tensorflow/contrib/checkpoint/python/containers_test.py b/tensorflow/contrib/checkpoint/python/containers_test.py index 3717d7f583ffdc205a279d45df60cddbc5cbf08e..ac85c7be803cd4c2f8ba19d3ef887a3c65a15933 100644 --- a/tensorflow/contrib/checkpoint/python/containers_test.py +++ b/tensorflow/contrib/checkpoint/python/containers_test.py @@ -26,13 +26,14 @@ from tensorflow.python.keras import layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test -from tensorflow.python.training.checkpointable import base as checkpointable -from tensorflow.python.training.checkpointable import util as checkpointable_utils +from tensorflow.python.training.checkpointable import data_structures +from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util class UniqueNameTrackerTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNames(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -48,11 +49,11 @@ class UniqueNameTrackerTests(test.TestCase): slots.track(y, "y") self.evaluate((x1.initializer, x2.initializer, x3.initializer, y.initializer)) - save_root = checkpointable_utils.Checkpoint(slots=slots) + save_root = util.Checkpoint(slots=slots) save_path = save_root.save(checkpoint_prefix) - restore_slots = checkpointable.Checkpointable() - restore_root = checkpointable_utils.Checkpoint( + restore_slots = tracking.Checkpointable() + restore_root = util.Checkpoint( slots=restore_slots) status = restore_root.restore(save_path) restore_slots.x = resource_variable_ops.ResourceVariable(0.) @@ -65,9 +66,9 @@ class UniqueNameTrackerTests(test.TestCase): self.assertEqual(4., self.evaluate(restore_slots.x_1_1)) self.assertEqual(5., self.evaluate(restore_slots.y)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExample(self): - class SlotManager(checkpointable.Checkpointable): + class SlotManager(tracking.Checkpointable): def __init__(self): self.slotdeps = containers.UniqueNameTracker() @@ -79,15 +80,15 @@ class UniqueNameTrackerTests(test.TestCase): resource_variable_ops.ResourceVariable(4.), "y")) slots.append(slotdeps.track( resource_variable_ops.ResourceVariable(5.), "x")) - self.slots = slots + self.slots = data_structures.NoDependency(slots) manager = SlotManager() self.evaluate([v.initializer for v in manager.slots]) - checkpoint = checkpointable_utils.Checkpoint(slot_manager=manager) + checkpoint = util.Checkpoint(slot_manager=manager) checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") save_path = checkpoint.save(checkpoint_prefix) - metadata = checkpointable_utils.object_metadata(save_path) + metadata = util.object_metadata(save_path) dependency_names = [] for node in metadata.nodes: for child in node.children: @@ -97,7 +98,7 @@ class UniqueNameTrackerTests(test.TestCase): dependency_names, ["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayers(self): tracker = containers.UniqueNameTracker() tracker.track(layers.Dense(3), "dense") diff --git a/tensorflow/contrib/checkpoint/python/split_dependency_test.py b/tensorflow/contrib/checkpoint/python/split_dependency_test.py index 69dc0b9be2d5548852c37552a64a0d31c9557b43..00a805af25d5d0ea723db5d015fb12bf45c53857 100644 --- a/tensorflow/contrib/checkpoint/python/split_dependency_test.py +++ b/tensorflow/contrib/checkpoint/python/split_dependency_test.py @@ -23,8 +23,9 @@ from tensorflow.python.eager import test from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops -from tensorflow.python.training.checkpointable import base as checkpointable -from tensorflow.python.training.checkpointable import util as checkpointable_utils +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util def _split_variable_closure(variable): @@ -43,7 +44,7 @@ def _combine_variable_closure(variable): return _consume_restore_buffer_fn -class SaveTensorSlicesAsDeps(checkpointable.CheckpointableBase): +class SaveTensorSlicesAsDeps(base.CheckpointableBase): def __init__(self): self.combined = resource_variable_ops.ResourceVariable([0., 0., 0., 0.]) @@ -58,14 +59,14 @@ class SaveTensorSlicesAsDeps(checkpointable.CheckpointableBase): self._track_checkpointable(dep, name=name) -class HasRegularDeps(checkpointable.Checkpointable): +class HasRegularDeps(tracking.Checkpointable): def __init__(self): self.first_half = resource_variable_ops.ResourceVariable([0., 0.]) self.second_half = resource_variable_ops.ResourceVariable([0., 0.]) -class OnlyOneDep(checkpointable.Checkpointable): +class OnlyOneDep(tracking.Checkpointable): def __init__(self): self.first_half = resource_variable_ops.ResourceVariable([0., 0.]) @@ -73,9 +74,9 @@ class OnlyOneDep(checkpointable.Checkpointable): class SplitTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestoreSplitDep(self): - save_checkpoint = checkpointable_utils.Checkpoint( + save_checkpoint = util.Checkpoint( dep=SaveTensorSlicesAsDeps()) self.evaluate(save_checkpoint.dep.combined.assign([1., 2., 3., 4.])) checkpoint_directory = self.get_temp_dir() @@ -83,7 +84,7 @@ class SplitTests(test.TestCase): save_path = save_checkpoint.save(checkpoint_prefix) regular_deps = HasRegularDeps() - regular_restore_checkpoint = checkpointable_utils.Checkpoint( + regular_restore_checkpoint = util.Checkpoint( dep=regular_deps) regular_restore_checkpoint.restore( save_path).assert_consumed().run_restore_ops() @@ -91,7 +92,7 @@ class SplitTests(test.TestCase): self.assertAllEqual([3., 4.], self.evaluate(regular_deps.second_half)) one_dep = OnlyOneDep() - one_dep_restore_checkpoint = checkpointable_utils.Checkpoint(dep=one_dep) + one_dep_restore_checkpoint = util.Checkpoint(dep=one_dep) status = one_dep_restore_checkpoint.restore(save_path) with self.assertRaises(AssertionError): # Missing the second dependency. @@ -99,7 +100,7 @@ class SplitTests(test.TestCase): status.run_restore_ops() self.assertAllEqual([1., 2.], self.evaluate(one_dep.first_half)) - restore_checkpoint = checkpointable_utils.Checkpoint() + restore_checkpoint = util.Checkpoint() status = restore_checkpoint.restore(save_path) restore_checkpoint.dep = SaveTensorSlicesAsDeps() status.assert_consumed().run_restore_ops() diff --git a/tensorflow/contrib/cloud/BUILD b/tensorflow/contrib/cloud/BUILD index 42ba368531468b789a87429f88ca84937f9b909d..523a9efcf05f5d32589f6e1734f866bf8b4b9cdc 100644 --- a/tensorflow/contrib/cloud/BUILD +++ b/tensorflow/contrib/cloud/BUILD @@ -50,6 +50,7 @@ py_library( deps = [ ":gen_bigquery_reader_ops", ":gen_gcs_config_ops", + "//tensorflow/contrib/bigtable", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:io_ops", "//tensorflow/python:util", @@ -74,3 +75,14 @@ tf_py_test( ], tags = ["manual"], ) + +tf_py_test( + name = "gcs_config_ops_test", + size = "small", + srcs = ["python/ops/gcs_config_ops_test.py"], + additional_deps = [ + ":cloud_py", + "//tensorflow/python:client_testlib", + ], + tags = ["manual"], +) diff --git a/tensorflow/contrib/cloud/README.md b/tensorflow/contrib/cloud/README.md new file mode 100644 index 0000000000000000000000000000000000000000..134ce057f4334096b4fbbec29cc85f0ea42c9f86 --- /dev/null +++ b/tensorflow/contrib/cloud/README.md @@ -0,0 +1,18 @@ +# Cloud # + +## BigTable ## + +[Google Cloud BigTable](https://cloud.google.com/bigtable/) is a high +performance storage system that can store and serve training data. This contrib +package contains an experimental integration with TensorFlow. + +> **Status: Highly experimental.** The current implementation is very much in +> flux. Please use at your own risk! :-) + + + +## Cloud Storage (GCS) ## + +The Google Cloud Storage ops allow the user to configure the GCS File System. + + diff --git a/tensorflow/contrib/cloud/__init__.py b/tensorflow/contrib/cloud/__init__.py index a6e13ea3ae938444b9ead0772e52fb8797a847da..af81106a6848bfd8c91108b56c8150d47c3eb501 100644 --- a/tensorflow/contrib/cloud/__init__.py +++ b/tensorflow/contrib/cloud/__init__.py @@ -18,17 +18,27 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# pylint: disable=line-too-long,wildcard-import +import os + +# pylint: disable=line-too-long,wildcard-import,g-import-not-at-top from tensorflow.contrib.cloud.python.ops.bigquery_reader_ops import * from tensorflow.contrib.cloud.python.ops.gcs_config_ops import * -# pylint: enable=line-too-long,wildcard-import + +if os.name != 'nt': + from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable + from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient + +del os from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'BigQueryReader', - 'ConfigureColabSession', - 'ConfigureGcs', + 'BigTable', + 'BigtableClient', + 'BlockCacheParams', + 'configure_colab_session', + 'configure_gcs', 'ConfigureGcsHook', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/cloud/kernels/BUILD b/tensorflow/contrib/cloud/kernels/BUILD index 40160706f70e8fa8323005dd183770ed51c8c415..1311063ec023bdaa2588d6f1c826bf900f7dea09 100644 --- a/tensorflow/contrib/cloud/kernels/BUILD +++ b/tensorflow/contrib/cloud/kernels/BUILD @@ -79,6 +79,7 @@ tf_kernel_library( srcs = ["gcs_config_ops.cc"], visibility = ["//tensorflow:internal"], deps = [ + "//tensorflow/contrib/cloud:gcs_config_ops_op_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/platform/cloud:curl_http_request", diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py index 8c8c5acb31af69b4f738a13c6548cdd31947d71a..95e7e744d34391a511cdba7702aad369b8d9d9c0 100644 --- a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py @@ -120,13 +120,18 @@ class ConfigureGcsHook(training.SessionRunHook): def begin(self): if self._credentials: self._credentials_placeholder = array_ops.placeholder(dtypes.string) - self._credentials_ops = gen_gcs_config_ops.gcs_configure_credentials( + self._credentials_op = gen_gcs_config_ops.gcs_configure_credentials( self._credentials_placeholder) + else: + self._credentials_op = None + if self._block_cache: self._block_cache_op = gen_gcs_config_ops.gcs_configure_block_cache( max_cache_size=self._block_cache.max_bytes, block_size=self._block_cache.block_size, max_staleness=self._block_cache.max_staleness) + else: + self._block_cache_op = None def after_create_session(self, session, coord): del coord diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9b6c056d6c8adfa50b95aefb8e9740631327a572 --- /dev/null +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py @@ -0,0 +1,44 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the gcs_config_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.cloud.python.ops import gcs_config_ops +from tensorflow.python.platform import test + + +class GcsConfigOpsTest(test.TestCase): + + def testSetBlockCache(self): + cfg = gcs_config_ops.BlockCacheParams(max_bytes=1024*1024*1024) + with self.test_session() as sess: + gcs_config_ops.configure_gcs(sess, block_cache=cfg) + + def testConfigureGcsHook(self): + creds = {'client_id': 'fake_client', + 'refresh_token': 'fake_token', + 'client_secret': 'fake_secret', + 'type': 'authorized_user'} + hook = gcs_config_ops.ConfigureGcsHook(credentials=creds) + hook.begin() + with self.test_session() as sess: + sess.run = lambda _, feed_dict=None, options=None, run_metadata=None: None + hook.after_create_session(sess, None) + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py index a5a9630a4aa382fb13d8fc88e575e094e575cc87..8f521ffee4d31e090c13bac98290656d6e1d330e 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -36,6 +36,7 @@ except ImportError: _GKE_ENV_VARIABLE = 'KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' +_ENDPOINTS_SEPARATOR = ',' _DEFAULT_ENV_VARIABLE = 'TPU_NAME' _DISCOVERY_SERVICE_URL_ENV_VARIABLE = 'TPU_API_DISCOVERY_URL' @@ -69,8 +70,8 @@ class TPUClusterResolver(ClusterResolver): return _GKE_ENV_VARIABLE in os.environ @staticmethod - def _gkeMaster(): - return os.environ[_GKE_ENV_VARIABLE].split(',')[0] + def _gkeEndpoints(): + return os.environ[_GKE_ENV_VARIABLE] @staticmethod def _envVarFallback(): @@ -143,7 +144,7 @@ class TPUClusterResolver(ClusterResolver): # When using GKE with Cloud TPUs, the env variable will be set. if tpu is None: if in_gke: - tpu = self._gkeMaster() + tpu = self._gkeEndpoints() else: tpu = self._envVarFallback() @@ -214,7 +215,7 @@ class TPUClusterResolver(ClusterResolver): ValueError: If none of the TPUs specified exists. """ if not self._shouldResolve(): - return self._tpu + return self._tpu.split(compat.as_bytes(_ENDPOINTS_SEPARATOR))[0] job_tasks = self.cluster_spec().job_tasks(self._job_name) if not job_tasks: @@ -256,6 +257,10 @@ class TPUClusterResolver(ClusterResolver): request = self._service.projects().locations().nodes().get(name=full_name) response = request.execute() + if 'state' in response and response['state'] != 'READY': + raise RuntimeError('TPU "%s" is not yet ready; state: "%s"' % + (self._tpu, response['state'])) + if 'health' in response and response['health'] != 'HEALTHY': raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, response['health'])) @@ -276,8 +281,12 @@ class TPUClusterResolver(ClusterResolver): # Case 3. return None # Case 2. - cluster_spec = {self._job_name: [self._tpu[len( - compat.as_bytes('grpc://')):]]} + cluster_spec = { + self._job_name: [ + x[len(compat.as_bytes('grpc://')):] + for x in self._tpu.split(compat.as_bytes(_ENDPOINTS_SEPARATOR)) + ] + } if self._coordinator_address: # {1, 2}.a diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py index 5fac55fd027fa2d100621e08a09e05cdb3a1b941..ad4f6432630be44a7de6e778f55f1fb7fd66f307 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py @@ -158,6 +158,50 @@ class TPUClusterResolverTest(test.TestCase): """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testUnhealthyCloudTpu(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'ipAddress': '10.1.2.3', + 'port': '8470', + 'health': 'UNHEALTHY' + } + } + + tpu_cluster_resolver = TPUClusterResolver( + project=None, + zone=None, + tpu='test-tpu-1', + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + with self.assertRaises(RuntimeError): + tpu_cluster_resolver.cluster_spec() + + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testNotReadyCloudTpu(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'ipAddress': '10.1.2.3', + 'port': '8470', + 'state': 'CREATING' + } + } + + tpu_cluster_resolver = TPUClusterResolver( + project=None, + zone=None, + tpu='test-tpu-1', + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + with self.assertRaises(RuntimeError): + tpu_cluster_resolver.cluster_spec() + def testSimpleSuccessfulRetrieval(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { @@ -358,13 +402,61 @@ class TPUClusterResolverTest(test.TestCase): compat.as_bytes('/bns/foo/bar'), tpu_cluster_resolver.master()) self.assertEqual(None, tpu_cluster_resolver.cluster_spec()) - def testGkeEnvironment(self): + def testGkeEnvironmentForDonut(self): os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] = 'grpc://10.120.27.5:8470' - self.assertTrue('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' in os.environ) + + self.assertIn('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS', os.environ) + self.assertTrue(TPUClusterResolver._inGke()) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(TPUClusterResolver._gkeEndpoints())) + + tpu_cluster_resolver = TPUClusterResolver() + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(tpu_cluster_resolver.master())) + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'worker' + tasks { key: 0 value: '10.120.27.5:8470' } + } + """ + self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + + del os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] + + def testGkeEnvironmentForPod(self): + os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] = ('grpc://10.120.27.5:8470,' + 'grpc://10.120.27.6:8470,' + 'grpc://10.120.27.7:8470,' + 'grpc://10.120.27.8:8470') + + self.assertIn('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS', os.environ) self.assertTrue(TPUClusterResolver._inGke()) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470,' + 'grpc://10.120.27.6:8470,' + 'grpc://10.120.27.7:8470,' + 'grpc://10.120.27.8:8470'), + compat.as_bytes(TPUClusterResolver._gkeEndpoints())) + + tpu_cluster_resolver = TPUClusterResolver() self.assertEqual( compat.as_bytes('grpc://10.120.27.5:8470'), - compat.as_bytes(TPUClusterResolver._gkeMaster())) + compat.as_bytes(tpu_cluster_resolver.master())) + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'worker' + tasks { key: 0 value: '10.120.27.5:8470' } + tasks { key: 1 value: '10.120.27.6:8470' } + tasks { key: 2 value: '10.120.27.7:8470' } + tasks { key: 3 value: '10.120.27.8:8470' } + } + """ + self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + del os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] def testDiscoveryUrl(self): diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 0708d6b7b9f0ba549aea091a265f42890e50d223..a0a5b0e00c1979ebf8850408785135b9ceac7d2a 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -18,7 +18,16 @@ cmake_policy(SET CMP0022 NEW) # Options option(tensorflow_VERBOSE "Enable for verbose output" OFF) + +if(WIN32) +# BoringSSL is disabled for windows as it currently doesn't build with +# MSBuild. (Ninja is required.) option(tensorflow_ENABLE_SSL_SUPPORT "Enable boringssl support" OFF) +else() +# BoringSSL is enabled for gRPC. +option(tensorflow_ENABLE_SSL_SUPPORT "Enable boringssl support" ON) +endif() + option(tensorflow_ENABLE_GRPC_SUPPORT "Enable gRPC support" ON) option(tensorflow_ENABLE_HDFS_SUPPORT "Enable HDFS support" OFF) option(tensorflow_ENABLE_JEMALLOC_SUPPORT "Enable jemalloc support" OFF) @@ -290,17 +299,20 @@ include_directories( ${double_conversion_INCLUDE_DIR} ) -if(tensorflow_ENABLE_SSL_SUPPORT) - include(boringssl) - list(APPEND tensorflow_EXTERNAL_LIBRARIES ${boringssl_STATIC_LIBRARIES}) - list(APPEND tensorflow_EXTERNAL_DEPENDENCIES boringssl) - include_directories(${boringssl_INCLUDE_DIR}) -endif() if(tensorflow_ENABLE_GRPC_SUPPORT) + if(tensorflow_ENABLE_SSL_SUPPORT) + include(boringssl) + include_directories(${boringssl_INCLUDE_DIR}) + endif() include(grpc) + include_directories(${GRPC_INCLUDE_DIRS}) + # Place boringssl after grpc as grpc depends on boringssl. list(APPEND tensorflow_EXTERNAL_LIBRARIES ${grpc_STATIC_LIBRARIES}) list(APPEND tensorflow_EXTERNAL_DEPENDENCIES grpc) - include_directories(${GRPC_INCLUDE_DIRS}) + if(tensorflow_ENABLE_SSL_SUPPORT) + list(APPEND tensorflow_EXTERNAL_LIBRARIES ${boringssl_STATIC_LIBRARIES}) + list(APPEND tensorflow_EXTERNAL_DEPENDENCIES boringssl) + endif() endif() if(tensorflow_ENABLE_JEMALLOC_SUPPORT) include(jemalloc) @@ -327,40 +339,14 @@ endif() # MKL Support if (tensorflow_ENABLE_MKL_SUPPORT) add_definitions(-DINTEL_MKL -DEIGEN_USE_VML) - if (WIN32) - find_path(MKL_HOME_PLATFORM mkl - PATHS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ - $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ - PATH_SUFFIXES windows) - set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) - set(MKL_LINK_DIRS - ${MKL_HOME_PLATFORM}/mkl/lib/intel64 - ${MKL_HOME_PLATFORM}/tbb/lib/intel64/vc_mt - ${MKL_HOME_PLATFORM}/compiler/lib/intel64 - ${MKL_HOME_PLATFORM}/mkl/tools/builder/lib) - set(MKL_REDIST_DLL_DIRS - ${MKL_HOME_PLATFORM}/redist/intel64/mkl - ${MKL_HOME_PLATFORM}/redist/intel64/tbb/vc_mt - ${MKL_HOME_PLATFORM}/redist/intel64/compiler) - list(APPEND tensorflow_EXTERNAL_LIBRARIES - mkl_intel_lp64_dll mkl_sequential_dll mkl_core_dll mkl_rt mkl_cdll_intel64) - endif() - if (UNIX) - # Fix me: complete the path on linux - find_path(MKL_HOME_PLATFORM mkl - HINTS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ - $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ - PATH_SUFFIXES linux) - set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) - set(MKL_LINK_DIRS) # incompleted - set(MKL_REDIST_SO_DIRS) # incompleted - endif() - include_directories(${MKL_INCLUDE_DIRS}) - link_directories(${MKL_LINK_DIRS}) + include(mkl) + list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkl_STATIC_LIBRARIES}) + list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkl_copy_shared_to_destination) + include_directories(${mkl_INCLUDE_DIRS}) if (tensorflow_ENABLE_MKLDNN_SUPPORT) include(mkldnn) list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkldnn_STATIC_LIBRARIES}) - list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkldnn) + list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkldnn_copy_shared_to_destination) include_directories(${mkldnn_INCLUDE_DIRS}) else (tensorflow_ENABLE_MKLDNN_SUPPORT) add_definitions(-DINTEL_MKL_ML) diff --git a/tensorflow/contrib/cmake/external/boringssl.cmake b/tensorflow/contrib/cmake/external/boringssl.cmake index 3c4bb01e24fd121c9d0fc3594cc25de37af0e8a1..fbb14b2515a656f1dfc0e3f63ac367e9b7738a23 100644 --- a/tensorflow/contrib/cmake/external/boringssl.cmake +++ b/tensorflow/contrib/cmake/external/boringssl.cmake @@ -17,7 +17,7 @@ include (ExternalProject) set(boringssl_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/boringssl/src/boringssl/include) #set(boringssl_EXTRA_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/boringssl/src) set(boringssl_URL https://boringssl.googlesource.com/boringssl) -set(boringssl_TAG ee7aa02) +set(boringssl_TAG 7f8c553d7f4db0a6ce727f2986d41bf8fe8ec4bf) set(boringssl_BUILD ${CMAKE_BINARY_DIR}/boringssl/src/boringssl-build) #set(boringssl_LIBRARIES ${boringssl_BUILD}/obj/so/libboringssl.so) set(boringssl_STATIC_LIBRARIES diff --git a/tensorflow/contrib/cmake/external/double_conversion.cmake b/tensorflow/contrib/cmake/external/double_conversion.cmake index 527ccdc8d887cb4c2e7d2412c99a8bc682568472..5c5adaf5798289fba1c5d0b3f9e0489dc242043e 100644 --- a/tensorflow/contrib/cmake/external/double_conversion.cmake +++ b/tensorflow/contrib/cmake/external/double_conversion.cmake @@ -16,15 +16,15 @@ include (ExternalProject) set(double_conversion_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/double_conversion/src/double_conversion) set(double_conversion_URL https://github.com/google/double-conversion.git) -set(double_conversion_TAG 5664746) +set(double_conversion_TAG 3992066a95b823efc8ccc1baf82a1cfc73f6e9b8) set(double_conversion_BUILD ${double_conversion_INCLUDE_DIR}) set(double_conversion_LIBRARIES ${double_conversion_BUILD}/double-conversion/libdouble-conversion.so) set(double_conversion_INCLUDES ${double_conversion_BUILD}) if(WIN32) - set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/double-conversion/$(Configuration)/double-conversion.lib) + set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/$(Configuration)/double-conversion.lib) else() - set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/double-conversion/libdouble-conversion.a) + set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/libdouble-conversion.a) endif() set(double_conversion_HEADERS diff --git a/tensorflow/contrib/cmake/external/grpc.cmake b/tensorflow/contrib/cmake/external/grpc.cmake index 693dc7cd673233b889b35a3f3170b57581da9a9f..b1e64aa55c80ad59cfdc0f4767c0282b4f73367f 100644 --- a/tensorflow/contrib/cmake/external/grpc.cmake +++ b/tensorflow/contrib/cmake/external/grpc.cmake @@ -20,6 +20,10 @@ set(GRPC_BUILD ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc) set(GRPC_TAG d184fa229d75d336aedea0041bd59cb93e7e267f) if(WIN32) + # We use unsecure gRPC because boringssl does not build on windows + set(grpc_TARGET grpc++_unsecure) + set(grpc_DEPENDS protobuf zlib) + set(grpc_SSL_PROVIDER NONE) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") set(grpc_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc++_unsecure.lib @@ -32,9 +36,12 @@ if(WIN32) ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/gpr.lib) endif() else() + set(grpc_TARGET grpc++) + set(grpc_DEPENDS boringssl protobuf zlib) + set(grpc_SSL_PROVIDER module) set(grpc_STATIC_LIBRARIES - ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a - ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc_unsecure.a + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++.a + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libaddress_sorting.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/third_party/cares/cares/lib/libcares.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgpr.a) @@ -44,13 +51,13 @@ add_definitions(-DGRPC_ARES=0) ExternalProject_Add(grpc PREFIX grpc - DEPENDS protobuf zlib + DEPENDS ${grpc_DEPENDS} GIT_REPOSITORY ${GRPC_URL} GIT_TAG ${GRPC_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" BUILD_IN_SOURCE 1 BUILD_BYPRODUCTS ${grpc_STATIC_LIBRARIES} - BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc++_unsecure + BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target ${grpc_TARGET} COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc_cpp_plugin INSTALL_COMMAND "" CMAKE_CACHE_ARGS @@ -59,7 +66,7 @@ ExternalProject_Add(grpc -DPROTOBUF_INCLUDE_DIRS:STRING=${PROTOBUF_INCLUDE_DIRS} -DPROTOBUF_LIBRARIES:STRING=${protobuf_STATIC_LIBRARIES} -DZLIB_ROOT:STRING=${ZLIB_INSTALL} - -DgRPC_SSL_PROVIDER:STRING=NONE + -DgRPC_SSL_PROVIDER:STRING=${grpc_SSL_PROVIDER} ) # grpc/src/core/ext/census/tracing.c depends on the existence of openssl/rand.h. diff --git a/tensorflow/contrib/cmake/external/mkl.cmake b/tensorflow/contrib/cmake/external/mkl.cmake new file mode 100644 index 0000000000000000000000000000000000000000..a172e3a41a283359b9a8c823ddcb2b1973b5b3cc --- /dev/null +++ b/tensorflow/contrib/cmake/external/mkl.cmake @@ -0,0 +1,68 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +include (ExternalProject) + +# NOTE: Different from mkldnn.cmake, this file is meant to download mkl libraries +set(mkl_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/include) +set(mkl_BIN_DIRS ${CMAKE_CURRENT_BINARY_DIR}/mkl/bin) +set(mkl_WIN mklml_win_2018.0.3.20180406.zip) # match for v0.14 +set(mkl_MAC mklml_mac_2018.0.3.20180406.tgz) +set(mkl_LNX mklml_lnx_2018.0.3.20180406.tgz) +set(mkl_TAG v0.14) +set(mkl_URL https://github.com/intel/mkl-dnn/releases) + +if (WIN32) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_WIN}) + list(APPEND mkl_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/mklml.lib) + list(APPEND mkl_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5md.lib) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/mklml.dll) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5md.dll) +elseif (UNIX) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_LNX}) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5.so) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libmklml_gnu.so) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libmklml_intel.so) +elseif (APPLE) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_MAC}) + #TODO need more information +endif () + +ExternalProject_Add(mkl + PREFIX mkl + URL ${mkl_DOWNLOAD_URL} + DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" + UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "") + +# put mkl dynamic libraries in one bin directory +add_custom_target(mkl_create_destination_dir + COMMAND ${CMAKE_COMMAND} -E make_directory ${mkl_BIN_DIRS} + DEPENDS mkl) + +add_custom_target(mkl_copy_shared_to_destination DEPENDS mkl_create_destination_dir) + +foreach(dll_file ${mkl_SHARED_LIBRARIES}) + add_custom_command(TARGET mkl_copy_shared_to_destination PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${dll_file} ${mkl_BIN_DIRS}) +endforeach() diff --git a/tensorflow/contrib/cmake/external/mkldnn.cmake b/tensorflow/contrib/cmake/external/mkldnn.cmake index a639fdee367f060d4c8a79267803da6ffe3dc503..8123ee1f393ab8e3a52f13915ea2a65decc188d9 100644 --- a/tensorflow/contrib/cmake/external/mkldnn.cmake +++ b/tensorflow/contrib/cmake/external/mkldnn.cmake @@ -22,8 +22,11 @@ set(mkldnn_TAG 3063b2e4c943983f6bf5f2fb9a490d4a998cd291) if(WIN32) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release/mkldnn.lib) + set(mkldnn_SHARED_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release/mkldnn.dll) + set(mkldnn_BUILD ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release) else() set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/mkldnn.lib) + set(mkldnn_SHARED_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/mkldnn.dll) endif() else() set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/libmkldnn.a) @@ -31,6 +34,7 @@ endif() ExternalProject_Add(mkldnn PREFIX mkldnn + DEPENDS mkl GIT_REPOSITORY ${mkldnn_URL} GIT_TAG ${mkldnn_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" @@ -40,5 +44,11 @@ ExternalProject_Add(mkldnn CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF - -DMKLINC:STRING=${MKL_INCLUDE_DIRS} + -DMKLINC:STRING=${mkl_INCLUDE_DIRS} ) + +# since mkldnn depends on mkl, copy the mkldnn.dll together with mklml.dll to mkl_bin_dirs +add_custom_target(mkldnn_copy_shared_to_destination DEPENDS mkldnn) + +add_custom_command(TARGET mkldnn_copy_shared_to_destination PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${mkldnn_SHARED_LIBRARIES} ${mkl_BIN_DIRS}) diff --git a/tensorflow/contrib/cmake/external/nsync.cmake b/tensorflow/contrib/cmake/external/nsync.cmake index b9d1dd88d4c2d3c9141ba56e14911e06b4d33f7c..eba3bcfc79efe87d0a45c979c5accfa1b6511ed0 100644 --- a/tensorflow/contrib/cmake/external/nsync.cmake +++ b/tensorflow/contrib/cmake/external/nsync.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(nsync_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/nsync/public) set(nsync_URL https://github.com/google/nsync) -set(nsync_TAG 0559ce013feac8db639ee1bf776aca0325d28777) +set(nsync_TAG 1.20.0) set(nsync_BUILD ${CMAKE_CURRENT_BINARY_DIR}/nsync/src/nsync) set(nsync_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/nsync/install) diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake index ab464bc99a43138130bb2758ae28ecef29805c31..f56fb35a0f71250f00b84e5cf94a24682bda6c82 100644 --- a/tensorflow/contrib/cmake/external/protobuf.cmake +++ b/tensorflow/contrib/cmake/external/protobuf.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src) set(PROTOBUF_URL https://github.com/google/protobuf.git) -set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9) +set(PROTOBUF_TAG v3.6.0) if(WIN32) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 015cb73bbd93bb77f6748a364b263d99eb305c27..a5eba5a8c94d6ddfa820ae371841f764b628c4b5 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -35,6 +35,7 @@ tensorflow/python/keras tensorflow/python/keras/applications tensorflow/python/keras/datasets tensorflow/python/keras/engine +tensorflow/python/keras/estimator tensorflow/python/keras/layers tensorflow/python/keras/preprocessing tensorflow/python/keras/utils @@ -85,6 +86,8 @@ tensorflow/contrib/batching/python/ops tensorflow/contrib/bayesflow tensorflow/contrib/bayesflow/python tensorflow/contrib/bayesflow/python/ops +# tensorflow/contrib/bigtable/python +# tensorflow/contrib/bigtable/python/ops tensorflow/contrib/boosted_trees tensorflow/contrib/boosted_trees/estimator_batch tensorflow/contrib/boosted_trees/kernels @@ -115,8 +118,6 @@ tensorflow/contrib/coder/python/ops tensorflow/contrib/compiler tensorflow/contrib/constrained_optimization tensorflow/contrib/constrained_optimization/python -tensorflow/contrib/control_flow -tensorflow/contrib/control_flow/python tensorflow/contrib/copy_graph tensorflow/contrib/copy_graph/python tensorflow/contrib/copy_graph/python/util @@ -131,6 +132,7 @@ tensorflow/contrib/data tensorflow/contrib/data/kernels tensorflow/contrib/data/python tensorflow/contrib/data/python/kernel_tests +tensorflow/contrib/data/python/kernel_tests/serialization tensorflow/contrib/data/python/ops tensorflow/contrib/decision_trees tensorflow/contrib/decision_trees/proto @@ -238,6 +240,8 @@ tensorflow/contrib/keras/api/keras/wrappers/scikit_learn tensorflow/contrib/kernel_methods tensorflow/contrib/kernel_methods/python tensorflow/contrib/kernel_methods/python/mappers +tensorflow/contrib/kinesis/python +tensorflow/contrib/kinesis/python/ops tensorflow/contrib/kfac tensorflow/contrib/kfac/examples tensorflow/contrib/kfac/python diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake index 2e0a2fcef4cbdc50f0521296c4a25a864dbd8b77..7a30eb94f54b18a2a517615a315e23e09e1170d0 100644 --- a/tensorflow/contrib/cmake/tf_c.cmake +++ b/tensorflow/contrib/cmake/tf_c.cmake @@ -36,16 +36,3 @@ add_dependencies( tf_cc_while_loop tf_core_lib tf_protos_cc) - -if(tensorflow_BUILD_PYTHON_BINDINGS) - add_library(tf_c_python_api OBJECT - "${tensorflow_source_dir}/tensorflow/c/python_api.cc" - "${tensorflow_source_dir}/tensorflow/c/python_api.h" - ) - add_dependencies( - tf_c_python_api - tf_c - tf_core_lib - tf_core_framework - tf_protos_cc) -endif() diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake index dac84ccb0dbf4848329e35a6e9bcf6213d8c0e55..872b016d2b6c1b8fb5875c9568a1b7b6201507c0 100644 --- a/tensorflow/contrib/cmake/tf_core_framework.cmake +++ b/tensorflow/contrib/cmake/tf_core_framework.cmake @@ -49,43 +49,48 @@ function(RELATIVE_PROTOBUF_GENERATE_CPP SRCS HDRS ROOT_DIR) set(${HDRS} ${${HDRS}} PARENT_SCOPE) endfunction() -if(NOT WIN32) - function(RELATIVE_PROTOBUF_GENERATE_GRPC_CPP SRCS HDRS ROOT_DIR) - if(NOT ARGN) - message(SEND_ERROR "Error: RELATIVE_PROTOBUF_GENERATE_GRPC_CPP() called without any proto files") - return() +function(RELATIVE_PROTOBUF_GENERATE_GRPC_CPP SRCS HDRS ROOT_DIR) + if(NOT ARGN) + message(SEND_ERROR "Error: RELATIVE_PROTOBUF_GENERATE_GRPC_CPP() called without any proto files") + return() + endif() + + set(${SRCS}) + set(${HDRS}) + foreach(FIL ${ARGN}) + set(ABS_FIL ${ROOT_DIR}/${FIL}) + get_filename_component(FIL_WE ${FIL} NAME_WE) + get_filename_component(FIL_DIR ${ABS_FIL} PATH) + file(RELATIVE_PATH REL_DIR ${ROOT_DIR} ${FIL_DIR}) + + list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc") + list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h") + list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc") + list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h") + + # We adust the path of the gRPC code generation accordingly. + if(WIN32) + set(GRPC_PROTOC_PLUGIN_PATH ${GRPC_BUILD}/Release/grpc_cpp_plugin.exe) + else() + set(GRPC_PROTOC_PLUGIN_PATH ${GRPC_BUILD}/grpc_cpp_plugin) endif() - set(${SRCS}) - set(${HDRS}) - foreach(FIL ${ARGN}) - set(ABS_FIL ${ROOT_DIR}/${FIL}) - get_filename_component(FIL_WE ${FIL} NAME_WE) - get_filename_component(FIL_DIR ${ABS_FIL} PATH) - file(RELATIVE_PATH REL_DIR ${ROOT_DIR} ${FIL_DIR}) - - list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc") - list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h") - list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc") - list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h") - - add_custom_command( - OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h" - COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} - ARGS --grpc_out ${CMAKE_CURRENT_BINARY_DIR} --cpp_out ${CMAKE_CURRENT_BINARY_DIR} --plugin protoc-gen-grpc=${GRPC_BUILD}/grpc_cpp_plugin -I ${ROOT_DIR} ${ABS_FIL} -I ${PROTOBUF_INCLUDE_DIRS} - DEPENDS ${ABS_FIL} protobuf grpc - COMMENT "Running C++ protocol buffer grpc compiler on ${FIL}" - VERBATIM ) - endforeach() - - set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE) - set(${SRCS} ${${SRCS}} PARENT_SCOPE) - set(${HDRS} ${${HDRS}} PARENT_SCOPE) - endfunction() -endif() + add_custom_command( + OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h" + COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} + ARGS --grpc_out ${CMAKE_CURRENT_BINARY_DIR} --cpp_out ${CMAKE_CURRENT_BINARY_DIR} --plugin=protoc-gen-grpc=${GRPC_PROTOC_PLUGIN_PATH} -I ${ROOT_DIR} ${ABS_FIL} -I ${PROTOBUF_INCLUDE_DIRS} + DEPENDS ${ABS_FIL} protobuf grpc + COMMENT "Running C++ protocol buffer grpc compiler on ${FIL}" + VERBATIM ) + endforeach() + + set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE) + set(${SRCS} ${${SRCS}} PARENT_SCOPE) + set(${HDRS} ${${HDRS}} PARENT_SCOPE) +endfunction() function(RELATIVE_PROTOBUF_TEXT_GENERATE_CPP SRCS HDRS ROOT_DIR) if(NOT ARGN) @@ -125,6 +130,7 @@ endfunction() file(GLOB_RECURSE tf_protos_cc_srcs RELATIVE ${tensorflow_source_dir} "${tensorflow_source_dir}/tensorflow/core/*.proto" + "${tensorflow_source_dir}/tensorflow/compiler/xla/*.proto" "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto" "${tensorflow_source_dir}/tensorflow/contrib/tpu/proto/*.proto" ) @@ -174,17 +180,14 @@ RELATIVE_PROTOBUF_TEXT_GENERATE_CPP(PROTO_TEXT_SRCS PROTO_TEXT_HDRS ${tensorflow_source_dir} ${tf_proto_text_srcs} ) -if(WIN32) - add_library(tf_protos_cc ${PROTO_SRCS} ${PROTO_HDRS}) -else() - file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir} - "${tensorflow_source_dir}/tensorflow/core/debug/*.proto" - ) - RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS - ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs} - ) - add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS} ${PROTO_SRCS} ${PROTO_HDRS}) -endif() +file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir} + "${tensorflow_source_dir}/tensorflow/core/debug/*.proto" + "${tensorflow_source_dir}/tensorflow/core/protobuf/master_service.proto" +) +RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS + ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs} +) +add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS} ${PROTO_SRCS} ${PROTO_HDRS}) ######################################################## # tf_core_lib library @@ -233,15 +236,6 @@ if(WIN32) list(APPEND tf_core_lib_srcs ${tf_core_platform_windows_srcs}) endif(WIN32) -if(tensorflow_ENABLE_SSL_SUPPORT) - # Cloud libraries require boringssl. - file(GLOB tf_core_platform_cloud_srcs - "${tensorflow_source_dir}/tensorflow/core/platform/cloud/*.h" - "${tensorflow_source_dir}/tensorflow/core/platform/cloud/*.cc" - ) - list(APPEND tf_core_lib_srcs ${tf_core_platform_cloud_srcs}) -endif() - if (tensorflow_ENABLE_HDFS_SUPPORT) list(APPEND tf_core_platform_hdfs_srcs "${tensorflow_source_dir}/tensorflow/core/platform/hadoop/hadoop_file_system.cc" diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake index 2d76bf530a2100b2afa80a16a5d64b6ec51ffc68..844f62649d970506f1b4b4c5718fab8d1f0856e1 100644 --- a/tensorflow/contrib/cmake/tf_core_kernels.cmake +++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake @@ -134,14 +134,13 @@ if(tensorflow_BUILD_CONTRIB_KERNELS) list(APPEND tf_core_kernels_srcs ${tf_contrib_kernels_srcs}) endif(tensorflow_BUILD_CONTRIB_KERNELS) -if(NOT tensorflow_ENABLE_SSL_SUPPORT) - # Cloud libraries require boringssl. - file(GLOB tf_core_kernels_cloud_srcs - "${tensorflow_source_dir}/tensorflow/contrib/cloud/kernels/*.h" - "${tensorflow_source_dir}/tensorflow/contrib/cloud/kernels/*.cc" - ) +# Cloud libraries require curl and boringssl. +# Curl is not supported yet anyway so we remove for now. +file(GLOB tf_core_kernels_cloud_srcs + "${tensorflow_source_dir}/tensorflow/contrib/cloud/kernels/*.h" + "${tensorflow_source_dir}/tensorflow/contrib/cloud/kernels/*.cc" +) list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_cloud_srcs}) -endif() file(GLOB_RECURSE tf_core_kernels_exclude_srcs "${tensorflow_source_dir}/tensorflow/core/kernels/*test*.h" diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 1959ad028a06f3c1ff6a658d656155541891fd13..e3b59001bcb4f081eb2db3443ee9ad714c822ac8 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -456,6 +456,18 @@ add_custom_command( COMMENT "Running SWIG to generate Python wrappers" VERBATIM ) +add_library(tf_c_python_api OBJECT + "${tensorflow_source_dir}/tensorflow/c/python_api.cc" + "${tensorflow_source_dir}/tensorflow/c/python_api.h" +) +add_dependencies( + tf_c_python_api + tf_c + tf_core_lib + tf_core_framework + tf_protos_cc + tf_python_protos_cc) + set (pywrap_tensorflow_internal_src "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.h" "${tensorflow_source_dir}/tensorflow/core/profiler/internal/print_model_analysis.cc" @@ -742,30 +754,113 @@ endforeach(api_init_file) set(api_init_list_file "${tensorflow_source_dir}/api_init_files_list.txt") file(WRITE "${api_init_list_file}" "${api_init_files}") +# Run create_python_api.py to generate __init__.py files. + +### TODO +# In order to download and compile MKL/MKL-DNN automatically in cmake script, mkl-built libraries should be added to system path +# to be loaded by python executor. However `add_custom_command` has an issue with `COMMAND ${CMAKE_COMMAND} -E env PATH=`, where +# arguments of multiple paths (such as D:/;D:/mkl) will be parsed in to seperate string without semicolon and that command fail to +# recongnize paths. As CUDA isn't built with MKL, the MKL built directory is the only path to this command to work around that issue. +# To not override the CUDA and system path in other circumstances, `if-else` branch used here to handle this problem, +# and should be removed if the path issue can be resolved. +### + +if (tensorflow_ENABLE_MKL_SUPPORT) + # add mkl dist dlls to system path for python + # TODO: In current cmake version, PY_RUNTIME_ENV behaves strange with multiple paths, + # so we have to specify only one path in it to work around the issue. We need this if/else + # to protect overwriting CUDA environments + set(PY_RUNTIME_ENV ${mkl_BIN_DIRS}) + add_custom_command( + OUTPUT ${api_init_files} + DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops + + # tensorflow/__init__.py depends on files generated in this step. So, remove it while + # this step is running since the files aren't there yet. + COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py + + # Run create_python_api.py to generate API init files. + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python PATH=${PY_RUNTIME_ENV} ${PYTHON_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" + "--package=tensorflow.python" + "--apiname=tensorflow" + "${api_init_list_file}" + + COMMENT "Generating __init__.py files for Python API." + WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" + VERBATIM + ) +else (tensorflow_ENABLE_MKL_SUPPORT) + add_custom_command( + OUTPUT ${api_init_files} + DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops + + # tensorflow/__init__.py depends on files generated in this step. So, remove it while + # this step is running since the files aren't there yet. + COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py + + # Run create_python_api.py to generate API init files. + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" + "--package=tensorflow.python" + "--apiname=tensorflow" + "${api_init_list_file}" + + COMMENT "Generating __init__.py files for Python API." + WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" + ) +endif (tensorflow_ENABLE_MKL_SUPPORT) + +add_custom_target(tf_python_api SOURCES ${api_init_files}) +add_dependencies(tf_python_api tf_python_ops) + +# TODO(mikecase): This can be removed once tf.estimator is moved +# out of TensorFlow. +######################################################## +# Generate API __init__.py files for tf.estimator. +######################################################## + +# Parse tensorflow/tools/api/generator/BUILD to get list of generated files. +FILE(READ ${tensorflow_source_dir}/tensorflow/tools/api/generator/api_gen.bzl api_generator_BUILD_text) +STRING(REGEX MATCH "# BEGIN GENERATED ESTIMATOR FILES.*# END GENERATED ESTIMATOR FILES" api_init_files_text ${api_generator_BUILD_text}) +string(REPLACE "# BEGIN GENERATED ESTIMATOR FILES" "" api_init_files_text ${api_init_files_text}) +string(REPLACE "# END GENERATED ESTIMATOR FILES" "" api_init_files_text ${api_init_files_text}) +string(REPLACE "," ";" api_init_files_list ${api_init_files_text}) + +set(api_init_files "") +foreach(api_init_file ${api_init_files_list}) + string(STRIP "${api_init_file}" api_init_file) + if(api_init_file) + string(REPLACE "\"" "" api_init_file "${api_init_file}") # Remove quotes + list(APPEND api_init_files "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/estimator/api/${api_init_file}") + endif() +endforeach(api_init_file) +set(estimator_api_init_list_file "${tensorflow_source_dir}/estimator_api_init_files_list.txt") +file(WRITE "${estimator_api_init_list_file}" "${api_init_files}") + # Run create_python_api.py to generate __init__.py files. add_custom_command( OUTPUT ${api_init_files} DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops - # tensorflow/__init__.py depends on files generated in this step. So, remove it while - # this step is running since the files aren't there yet. - COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py - # Run create_python_api.py to generate API init files. COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" - "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" - "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" - "${api_init_list_file}" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/estimator/api" + "--package=tensorflow.python.estimator" + "--apiname=estimator" + "${estimator_api_init_list_file}" COMMENT "Generating __init__.py files for Python API." WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" ) -add_custom_target(tf_python_api SOURCES ${api_init_files}) -add_dependencies(tf_python_api tf_python_ops) - - +add_custom_target(estimator_python_api SOURCES ${api_init_files}) +add_dependencies(estimator_python_api tf_python_ops) ############################################################ # Build a PIP package containing the TensorFlow runtime. ############################################################ @@ -776,6 +871,7 @@ add_dependencies(tf_python_build_pip_package tf_python_touchup_modules tf_python_ops tf_python_api + estimator_python_api tf_extension_ops) # Fix-up Python files that were not included by the add_python_module() macros. diff --git a/tensorflow/contrib/cmake/tf_shared_lib.cmake b/tensorflow/contrib/cmake/tf_shared_lib.cmake index 38f40452b533fdc0dba6ac686a0ff43a2ef13cb8..fdf522f1fd90ffc64acbe82381ef57a389645d61 100644 --- a/tensorflow/contrib/cmake/tf_shared_lib.cmake +++ b/tensorflow/contrib/cmake/tf_shared_lib.cmake @@ -145,3 +145,8 @@ install(DIRECTORY ${tensorflow_source_dir}/third_party/eigen3/ # unsupported Eigen directory install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/eigen/src/eigen/unsupported/Eigen/ DESTINATION include/unsupported/Eigen) +# mkl +if (tensorflow_ENABLE_MKL_SUPPORT) + install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/include/ + DESTINATION include/mkl) +endif (tensorflow_ENABLE_MKL_SUPPORT) diff --git a/tensorflow/contrib/cmake/tf_stream_executor.cmake b/tensorflow/contrib/cmake/tf_stream_executor.cmake index 9a37b681194d4ef82b27a0160dd969f733ecad67..6d634cb1709910f366c7ca538d28bd802b2a7c63 100644 --- a/tensorflow/contrib/cmake/tf_stream_executor.cmake +++ b/tensorflow/contrib/cmake/tf_stream_executor.cmake @@ -64,8 +64,6 @@ file(GLOB tf_stream_executor_srcs if (tensorflow_ENABLE_GPU) file(GLOB tf_stream_executor_gpu_srcs "${tensorflow_source_dir}/tensorflow/stream_executor/cuda/*.cc" - "${tensorflow_source_dir}/tensorflow/compiler/xla/statusor.h" - "${tensorflow_source_dir}/tensorflow/compiler/xla/statusor.cc" ) if (NOT tensorflow_BUILD_CC_TESTS) file(GLOB tf_stream_executor_gpu_tests @@ -76,11 +74,11 @@ if (tensorflow_ENABLE_GPU) list(APPEND tf_stream_executor_srcs ${tf_stream_executor_gpu_srcs}) endif() -#file(GLOB_RECURSE tf_stream_executor_test_srcs -# "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.cc" -# "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.h" -#) -#list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) +file(GLOB_RECURSE tf_stream_executor_test_srcs + "${tensorflow_source_dir}/tensorflow/stream_executor/*test.cc" + "${tensorflow_source_dir}/tensorflow/stream_executor/lib/*test.h" +) +list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) if (NOT WIN32) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lgomp") diff --git a/tensorflow/contrib/coder/python/layers/entropybottleneck.py b/tensorflow/contrib/coder/python/layers/entropybottleneck.py index 0fbe3081af0b4de7f116918b3f49efe91a2d83bd..0c997bd4fdfa4233117c9fec2c4397301b1c8cb9 100644 --- a/tensorflow/contrib/coder/python/layers/entropybottleneck.py +++ b/tensorflow/contrib/coder/python/layers/entropybottleneck.py @@ -28,7 +28,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.keras import engine +from tensorflow.python.keras.engine import base_layer from tensorflow.python.ops import array_ops from tensorflow.python.ops import functional_ops from tensorflow.python.ops import init_ops @@ -40,7 +40,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.summary import summary -class EntropyBottleneck(engine.Layer): +class EntropyBottleneck(base_layer.Layer): """Entropy bottleneck layer. This layer can be used to model the entropy (the amount of information @@ -262,7 +262,7 @@ class EntropyBottleneck(engine.Layer): self._range_coder_precision = int(range_coder_precision) self._data_format = data_format self._channel_axis(2) # trigger ValueError early - self.input_spec = engine.InputSpec(min_ndim=2) + self.input_spec = base_layer.InputSpec(min_ndim=2) @property def init_scale(self): @@ -357,7 +357,7 @@ class EntropyBottleneck(engine.Layer): channels = input_shape[channel_axis].value if channels is None: raise ValueError("The channel dimension of the inputs must be defined.") - self.input_spec = engine.InputSpec( + self.input_spec = base_layer.InputSpec( ndim=input_shape.ndims, axes={channel_axis: channels}) filters = (1,) + self.filters + (1,) scale = self.init_scale ** (1 / (len(self.filters) + 1)) diff --git a/tensorflow/contrib/constrained_optimization/README.md b/tensorflow/contrib/constrained_optimization/README.md index c65a150464efc1e77419040f66f36fc6756325aa..cb1dd7d836ae11700b2ffaaff4fda5b7f943f87d 100644 --- a/tensorflow/contrib/constrained_optimization/README.md +++ b/tensorflow/contrib/constrained_optimization/README.md @@ -46,7 +46,7 @@ document. Imagine that we want to constrain the recall of a binary classifier to be at least 90%. Since the recall is proportional to the number of true positive classifications, which itself is a sum of indicator functions, this constraint -is non-differentible, and therefore cannot be used in a problem that will be +is non-differentiable, and therefore cannot be used in a problem that will be optimized using a (stochastic) gradient-based algorithm. For this and similar problems, TFCO supports so-called *proxy constraints*, diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py index 04014ab4aebd6d9cd70653c53f9361320e803329..3791dae8d7f6b03bc1115bca97811dfc4775c45b 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py @@ -169,8 +169,8 @@ def _project_stochastic_matrix_wrt_euclidean_norm(matrix): del old_inactive # Needed by the condition, but not the body. iteration += 1 scale = (1.0 - standard_ops.reduce_sum( - matrix, axis=0, keep_dims=True)) / standard_ops.maximum( - 1.0, standard_ops.reduce_sum(inactive, axis=0, keep_dims=True)) + matrix, axis=0, keepdims=True)) / standard_ops.maximum( + 1.0, standard_ops.reduce_sum(inactive, axis=0, keepdims=True)) matrix += scale * inactive new_inactive = standard_ops.to_float(matrix > 0) matrix *= new_inactive @@ -206,10 +206,10 @@ def _project_log_stochastic_matrix_wrt_kl_divergence(log_matrix): # For numerical reasons, make sure that the largest matrix element is zero # before exponentiating. - log_matrix -= standard_ops.reduce_max(log_matrix, axis=0, keep_dims=True) + log_matrix -= standard_ops.reduce_max(log_matrix, axis=0, keepdims=True) log_matrix -= standard_ops.log( standard_ops.reduce_sum( - standard_ops.exp(log_matrix), axis=0, keep_dims=True)) + standard_ops.exp(log_matrix), axis=0, keepdims=True)) return log_matrix diff --git a/tensorflow/contrib/control_flow/BUILD b/tensorflow/contrib/control_flow/BUILD deleted file mode 100644 index e8036d63aeeac224b226899c036416a06b4ffe65..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/control_flow/BUILD +++ /dev/null @@ -1,53 +0,0 @@ -# New implementations of control flow ops - -licenses(["notice"]) # Apache 2.0 - -package(default_visibility = ["//visibility:public"]) - -load("//tensorflow:tensorflow.bzl", "tf_py_test") - -py_library( - name = "control_flow", - srcs = ["__init__.py"], - srcs_version = "PY2AND3", - deps = [ - ":cond_v2", - ], -) - -py_library( - name = "cond_v2", - srcs = ["python/cond_v2.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:c_api_util", - "//tensorflow/python:framework_ops", - "//tensorflow/python:function", - "//tensorflow/python:function_def_to_graph", - "//tensorflow/python:functional_ops_gen", - "//tensorflow/python:gradients", - "//tensorflow/python:pywrap_tensorflow", - "//tensorflow/python:util", - ], -) - -tf_py_test( - name = "cond_v2_test", - size = "small", - srcs = ["python/cond_v2_test.py"], - additional_deps = [ - ":cond_v2", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework", - "//tensorflow/python:framework_ops", - "//tensorflow/python:gradients", - "//tensorflow/python:training", - ], - grpc_enabled = True, -) diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py deleted file mode 100644 index dcecefb520ee4bee276f1682f6a90550ffa7e547..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ /dev/null @@ -1,156 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""Tests for cond_v2.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.control_flow.python import cond_v2 -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import math_ops -from tensorflow.python.platform import test -from tensorflow.python.training import saver - - -class NewCondTest(test.TestCase): - - def _testCond(self, true_fn, false_fn, train_vals): - pred = array_ops.placeholder(dtypes.bool, name="pred") - - expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") - actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual") - - expected_grad = gradients_impl.gradients(expected, train_vals) - actual_grad = gradients_impl.gradients(actual, train_vals) - - with self.test_session() as sess: - expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( - (expected, actual, expected_grad, actual_grad), {pred: True}) - self.assertEqual(expected_val, actual_val) - self.assertEqual(expected_grad_val, actual_grad_val) - - expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( - (expected, actual, expected_grad, actual_grad), {pred: False}) - self.assertEqual(expected_val, actual_val) - self.assertEqual(expected_grad_val, actual_grad_val) - - def testBasic(self): - x = constant_op.constant(1.0, name="x") - y = constant_op.constant(2.0, name="y") - - def true_fn(): - return x * 2.0 - - def false_fn(): - return y * 3.0 - - self._testCond(true_fn, false_fn, [x]) - self._testCond(true_fn, false_fn, [x, y]) - self._testCond(true_fn, false_fn, [y]) - - def testBasic2(self): - x = constant_op.constant(1.0, name="x") - y = constant_op.constant(2.0, name="y") - - def true_fn(): - return x * y * 2.0 - - def false_fn(): - return 2.0 - - self._testCond(true_fn, false_fn, [x]) - self._testCond(true_fn, false_fn, [x, y]) - self._testCond(true_fn, false_fn, [y]) - - def testSecondDerivative(self): - pred = array_ops.placeholder(dtypes.bool, name="pred") - x = constant_op.constant(3.0, name="x") - - def true_fn(): - return math_ops.pow(x, 3) - - def false_fn(): - return x - - cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") - cond_grad = gradients_impl.gradients(cond, [x]) - cond_grad_grad = gradients_impl.gradients(cond_grad, [x]) - - with self.test_session() as sess: - # d[x^3]/dx = 3x^2 - true_val = sess.run(cond_grad, {pred: True}) - self.assertEqual(true_val, [27.0]) - # d[x]/dx = 1 - false_val = sess.run(cond_grad, {pred: False}) - self.assertEqual(false_val, [1.0]) - - true_val = sess.run(cond_grad_grad, {pred: True}) - # d2[x^3]/dx2 = 6x - self.assertEqual(true_val, [18.0]) - false_val = sess.run(cond_grad_grad, {pred: False}) - # d2[x]/dx2 = 0 - self.assertEqual(false_val, [0.0]) - - def testGradientOfDeserializedCond(self): - with ops.Graph().as_default(): - pred = array_ops.placeholder(dtypes.bool, name="pred") - x = constant_op.constant(3.0, name="x") - ops.add_to_collection("x", x) - - def true_fn(): - return math_ops.pow(x, 3) - - def false_fn(): - return x - - ops.add_to_collection("pred", pred) - cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") - for c in cond: - ops.add_to_collection("cond", c) - meta_graph = saver.export_meta_graph() - - with ops.Graph().as_default() as g: - saver.import_meta_graph(meta_graph) - x = ops.get_collection("x")[0] - pred = ops.get_collection("pred")[0] - cond = ops.get_collection("cond") - cond_grad = gradients_impl.gradients(cond, [x], name="cond_grad") - cond_grad_grad = gradients_impl.gradients( - cond_grad, [x], name="cond_grad_grad") - with self.test_session(graph=g) as sess: - # d[x^3]/dx = 3x^2 - true_val = sess.run(cond_grad, {pred: True}) - self.assertEqual(true_val, [27.0]) - # d[x]/dx = 1 - false_val = sess.run(cond_grad, {pred: False}) - self.assertEqual(false_val, [1.0]) - - true_val = sess.run(cond_grad_grad, {pred: True}) - # d2[x^3]/dx2 = 6x - self.assertEqual(true_val, [18.0]) - false_val = sess.run(cond_grad_grad, {pred: False}) - # d2[x]/dx2 = 0 - self.assertEqual(false_val, [0.0]) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py index 8285ea04926d3a24e9c22bd6d69eb7a48f5e3a85..252ea1560d7f5be3799686d6d91ae9a6d262ac0a 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py @@ -768,7 +768,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLSTMCheckpointableSingleLayer(self): num_units = 2 direction = CUDNN_RNN_UNIDIRECTION @@ -781,7 +781,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGRUCheckpointableSingleLayer(self): num_units = 2 direction = CUDNN_RNN_UNIDIRECTION @@ -826,7 +826,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCudnnCompatibleLSTMCheckpointablMultiLayer(self): num_units = 2 num_layers = 3 diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py index 8822a7523f6b168f569e29970c9c29f2eb3614fc..748d7cd011f32fdebd781176b560b9b7498f327e 100644 --- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py +++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py @@ -33,7 +33,7 @@ from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import saver -from tensorflow.python.training.checkpointable import base as checkpointable_lib +from tensorflow.python.training.checkpointable import tracking as checkpointable_lib CUDNN_RNN_UNIDIRECTION = "unidirectional" CUDNN_RNN_BIDIRECTION = "bidirectional" diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 1af1ed08b53ee04367eb316d5c9caa0216f2e88d..156538b4e01bf1a1ccca0fca1e309b1d37b6dbc0 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -20,12 +20,15 @@ be used in conjunction with the @{tf.data.Dataset} API. Note that the guarantees as `tf.data`, but we will provide deprecation advice in advance of removing existing functionality. -See the @{$datasets$Importing Data} Programmer's Guide for an overview. +See @{$guide/datasets$Importing Data} for an overview. @@Counter @@CheckpointInputPipelineHook @@CsvDataset +@@RandomDataset +@@Reducer @@SqlDataset +@@TFRecordWriter @@assert_element_shape @@batch_and_drop_remainder @@ -33,11 +36,15 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@choose_from_datasets @@dense_to_sparse_batch @@enumerate_dataset + +@@get_single_element +@@group_by_reducer @@group_by_window @@ignore_errors @@make_batched_features_dataset @@make_csv_dataset @@make_saveable_from_iterator + @@map_and_batch @@padded_batch_and_drop_remainder @@parallel_interleave @@ -50,8 +57,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@sliding_window_batch @@sloppy_interleave @@unbatch - -@@get_single_element +@@unique """ from __future__ import absolute_import @@ -71,13 +77,17 @@ from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset from tensorflow.contrib.data.python.ops.error_ops import ignore_errors from tensorflow.contrib.data.python.ops.get_single_element import get_single_element from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length +from tensorflow.contrib.data.python.ops.grouping import group_by_reducer from tensorflow.contrib.data.python.ops.grouping import group_by_window +from tensorflow.contrib.data.python.ops.grouping import Reducer +from tensorflow.contrib.data.python.ops.interleave_ops import choose_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave from tensorflow.contrib.data.python.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator from tensorflow.contrib.data.python.ops.prefetching_ops import prefetch_to_device +from tensorflow.contrib.data.python.ops.random_ops import RandomDataset from tensorflow.contrib.data.python.ops.readers import CsvDataset from tensorflow.contrib.data.python.ops.readers import make_batched_features_dataset from tensorflow.contrib.data.python.ops.readers import make_csv_dataset @@ -87,6 +97,8 @@ from tensorflow.contrib.data.python.ops.resampling import rejection_resample from tensorflow.contrib.data.python.ops.scan_ops import scan from tensorflow.contrib.data.python.ops.shuffle_ops import shuffle_and_repeat from tensorflow.contrib.data.python.ops.sliding import sliding_window_batch +from tensorflow.contrib.data.python.ops.unique import unique +from tensorflow.contrib.data.python.ops.writers import TFRecordWriter # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/data/kernels/csv_dataset_op.cc b/tensorflow/contrib/data/kernels/csv_dataset_op.cc index e88ad3dc32003ece2b8810661cd4db374196561c..4657807785d58727d34f37172bd30c56a5b7cde6 100644 --- a/tensorflow/contrib/data/kernels/csv_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/csv_dataset_op.cc @@ -236,7 +236,7 @@ class CSVDatasetOp : public DatasetOpKernel { size_t num_parsed = 0; size_t num_selected_parsed = 0; - Status result = Status::OK(); + Status result; while (!end_of_record) { // Read till we reach \n, \r or EOF bool include = @@ -329,6 +329,7 @@ class CSVDatasetOp : public DatasetOpKernel { size_t start = pos_; pos_++; // Starting quotation mark + Status parse_result; while (true) { // Each iter reads 1 char, filling buffer if necessary if (pos_ >= buffer_.size()) { Status s = SaveAndFillBuffer(&earlier_pieces, &start, include); @@ -351,8 +352,9 @@ class CSVDatasetOp : public DatasetOpKernel { if (errors::IsOutOfRange(s)) { // This was the last field. We are done *end_of_record = true; - return QuotedFieldToOutput(ctx, StringPiece(), out_tensors, - earlier_pieces, include); + parse_result.Update(QuotedFieldToOutput( + ctx, StringPiece(), out_tensors, earlier_pieces, include)); + return parse_result; } else if (!s.ok()) { return s; } @@ -361,20 +363,24 @@ class CSVDatasetOp : public DatasetOpKernel { char next = buffer_[pos_]; pos_++; if (next == dataset()->delim_) { - return QuotedFieldToOutput( + parse_result.Update(QuotedFieldToOutput( ctx, StringPiece(&buffer_[start], pos_ - 1 - start), - out_tensors, earlier_pieces, include); + out_tensors, earlier_pieces, include)); + return parse_result; } else if (next == '\n' || next == '\r') { *end_of_record = true; - Status s = QuotedFieldToOutput( + parse_result.Update(QuotedFieldToOutput( ctx, StringPiece(&buffer_[start], pos_ - 1 - start), - out_tensors, earlier_pieces, include); + out_tensors, earlier_pieces, include)); if (next == '\r') SkipNewLineIfNecessary(); - return s; + return parse_result; } else if (next != '"') { - return errors::InvalidArgument( - "Quote inside a string has to be escaped by another quote"); + // Take note of the error, but keep going to end of field. + include = false; // So we don't get funky errors when trying to + // unescape the quotes. + parse_result.Update(errors::InvalidArgument( + "Quote inside a string has to be escaped by another quote")); } } else { @@ -454,6 +460,8 @@ class CSVDatasetOp : public DatasetOpKernel { EXCLUSIVE_LOCKS_REQUIRED(mu_) { std::vector earlier_pieces; size_t start = pos_; + Status parse_result; + while (true) { // Each iter reads 1 char, filling buffer if necessary if (pos_ >= buffer_.size()) { Status s = SaveAndFillBuffer(&earlier_pieces, &start, include); @@ -461,9 +469,10 @@ class CSVDatasetOp : public DatasetOpKernel { if (errors::IsOutOfRange(s)) { // Whatever we have is the last field of the last record *end_of_record = true; - return UnquotedFieldToOutput( + parse_result.Update(UnquotedFieldToOutput( ctx, StringPiece(&buffer_[start], pos_ - start), out_tensors, - earlier_pieces, include); + earlier_pieces, include)); + return parse_result; } else if (!s.ok()) { return s; // Surface all other errors to caller } @@ -472,66 +481,33 @@ class CSVDatasetOp : public DatasetOpKernel { char ch = buffer_[pos_]; if (ch == dataset()->delim_) { - Status s = UnquotedFieldToOutput( + parse_result.Update(UnquotedFieldToOutput( ctx, StringPiece(&buffer_[start], pos_ - start), out_tensors, - earlier_pieces, include); + earlier_pieces, include)); pos_++; - return s; + return parse_result; } if (ch == '\n' || ch == '\r') { // need special case to skip over first \n of record if the line // breaks are \r\n - Status s = UnquotedFieldToOutput( + parse_result.Update(UnquotedFieldToOutput( ctx, StringPiece(&buffer_[start], pos_ - start), out_tensors, - earlier_pieces, include); + earlier_pieces, include)); *end_of_record = true; pos_++; if (ch == '\r') SkipNewLineIfNecessary(); - return s; + return parse_result; } if (dataset()->use_quote_delim_ && ch == '"') { - // Advance pos_ to the next field anyway so that we can ignore - // errors gracefully if required. The caller of this will be able to - // call ParseOneField and continue with the rest of the record. - AdvanceToNextField(end_of_record); - return errors::InvalidArgument( - "Unquoted fields cannot have quotes inside"); + // Take note of the error, but keep going to end of field. + parse_result.Update(errors::InvalidArgument( + "Unquoted fields cannot have quotes inside")); } // Otherwise, go to next character pos_++; } } - // Advances pos_ to the start of the next field, as delimited by delim, - // CRLF, or EOF, ignoring errors, and not keeping track of characters in - // the current field. - void AdvanceToNextField(bool* end_of_record) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - while (true) { - if (pos_ >= buffer_.size()) { - Status s = FillBuffer(&buffer_); - pos_ = 0; - if (!s.ok()) { - *end_of_record = true; - return; - } - } - - char ch = buffer_[pos_]; - pos_++; - - if (ch == dataset()->delim_) { - return; - } - - if (ch == '\n' || ch == '\r') { - *end_of_record = true; - if (ch == '\r') SkipNewLineIfNecessary(); - return; - } - } - } - Status FillBuffer(string* result) EXCLUSIVE_LOCKS_REQUIRED(mu_) { result->clear(); Status s = input_stream_->ReadNBytes(dataset()->buffer_size_, result); diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index a2bfce03620a1482f5b21cbf23c66833bc5cd480..b3d464d7165d53cf198072e06214f7d5e982073d 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -40,7 +40,8 @@ class FunctionBufferingResource : public ResourceBase { const NameAttrList& func, int64 buffer_size, const string& source_device, const string& target_device, - const std::vector& func_args) + const std::vector& func_args, + const DataTypeVector& output_types) : lib_(lib), pflr_(std::move(pflr)), func_(func), @@ -48,6 +49,7 @@ class FunctionBufferingResource : public ResourceBase { source_device_(source_device), target_device_(target_device), func_args_(func_args), + output_types_(output_types), handle_(kInvalidHandle), is_buffering_(false), end_of_sequence_(false), @@ -176,6 +178,13 @@ class FunctionBufferingResource : public ResourceBase { AllocatorAttributes arg_alloc_attr; arg_alloc_attr.set_on_host(true); opts.args_alloc_attrs.push_back(arg_alloc_attr); + for (const auto& dtype : output_types_) { + AllocatorAttributes ret_alloc_attrs; + if (DataTypeAlwaysOnHost(dtype)) { + ret_alloc_attrs.set_on_host(true); + } + opts.rets_alloc_attrs.push_back(ret_alloc_attrs); + } if (opts.source_device != target_device_) { opts.remote_execution = true; } @@ -233,6 +242,7 @@ class FunctionBufferingResource : public ResourceBase { const string source_device_; const string target_device_; const std::vector func_args_; + const DataTypeVector output_types_; FunctionLibraryRuntime::Handle handle_ GUARDED_BY(mu_); std::deque buffer_ GUARDED_BY(mu_); std::deque requests_ GUARDED_BY(mu_); @@ -250,6 +260,7 @@ class FunctionBufferResourceHandleOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("buffer_size", &buffer_size_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("container", &container_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &name_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); } ~FunctionBufferResourceHandleOp() override { @@ -269,18 +280,20 @@ class FunctionBufferResourceHandleOp : public OpKernel { std::vector func_args; func_args.push_back(*string_arg); + const string& source_device = ctx->device()->name(); + // Obtain and canonicalize target_device. const Tensor* target_arg; OP_REQUIRES_OK(ctx, ctx->input("target_device", &target_arg)); - const string& target_device = - DeviceNameUtils::CanonicalizeDeviceName(target_arg->scalar()()); + string target_device; + OP_REQUIRES_OK(ctx, DeviceNameUtils::CanonicalizeDeviceName( + target_arg->scalar()(), source_device, + &target_device)); FunctionLibraryRuntime* lib = ctx->function_library(); OP_REQUIRES(ctx, lib != nullptr, errors::Internal("No function library is provided.")); - const string& source_device = ctx->device()->name(); - mutex_lock l(mu_); if (!initialized_) { OP_REQUIRES_OK(ctx, cinfo_.Init(ctx->resource_manager(), def())); @@ -297,7 +310,7 @@ class FunctionBufferResourceHandleOp : public OpKernel { this](FunctionBufferingResource** ptr) { *ptr = new FunctionBufferingResource( clone_lib, std::move(pflr), func_, buffer_size_, - source_device, target_device, func_args); + source_device, target_device, func_args, output_types_); return Status::OK(); })); core::ScopedUnref s(buffer); @@ -319,6 +332,7 @@ class FunctionBufferResourceHandleOp : public OpKernel { int64 buffer_size_; string container_; string name_; + DataTypeVector output_types_; }; REGISTER_KERNEL_BUILDER(Name("FunctionBufferingResource") diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc index 3dfc3741c2b040dd5be3223c24f0715ba3be4248..141706f393b076d9f55898ca4bdbe7438f7c3625 100644 --- a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/lib/core/threadpool.h" +#include "tensorflow/core/util/work_sharder.h" namespace tensorflow { namespace { @@ -24,19 +25,32 @@ namespace { class ThreadPoolResource : public ResourceBase { public: ThreadPoolResource(Env* env, const ThreadOptions& thread_options, - const string& name, int num_threads, bool low_latency_hint) - : thread_pool_(env, thread_options, name, num_threads, low_latency_hint) { - } + const string& name, int num_threads, bool low_latency_hint, + int max_intra_op_parallelism) + : thread_pool_(env, thread_options, name, num_threads, low_latency_hint), + max_intra_op_parallelism_(max_intra_op_parallelism) {} // Schedules fn() for execution in the pool of threads. void Schedule(std::function fn) { - thread_pool_.Schedule(std::move(fn)); + if (max_intra_op_parallelism_ < 0) { + thread_pool_.Schedule(std::move(fn)); + } else { + thread_pool_.Schedule(std::bind( + [this](std::function bound_fn) { + // TODO(mrry): Consider moving this thread-local configuration to + // the threads themselves. + ScopedPerThreadMaxParallelism scope(max_intra_op_parallelism_); + bound_fn(); + }, + std::move(fn))); + } } string DebugString() override { return "ThreadPoolResource"; } private: thread::ThreadPool thread_pool_; + const int max_intra_op_parallelism_; }; // Creates a handle to a ThreadPool resource. Note that we don't use @@ -48,6 +62,8 @@ class ThreadPoolHandleOp : public OpKernel { explicit ThreadPoolHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("display_name", &display_name_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("num_threads", &num_threads_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("max_intra_op_parallelism", + &max_intra_op_parallelism_)); OP_REQUIRES( ctx, num_threads_ > 0, errors::InvalidArgument("`num_threads` must be greater than zero.")); @@ -78,7 +94,7 @@ class ThreadPoolHandleOp : public OpKernel { EXCLUSIVE_LOCKS_REQUIRED(mu_) { *ret = new ThreadPoolResource( ctx->env(), {}, display_name_, - num_threads_, + num_threads_, max_intra_op_parallelism_, false /* low_latency_hint */); return Status::OK(); })); @@ -95,6 +111,7 @@ class ThreadPoolHandleOp : public OpKernel { bool initialized_ GUARDED_BY(mu_) = false; string display_name_; int num_threads_; + int max_intra_op_parallelism_; }; class ThreadPoolDatasetOp : public UnaryDatasetOpKernel { diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc index f271d269ab1b9339de4657e459dcbbd462890f0a..8413fcaf872f49f654c6a1327a14d5c44bdd815a 100644 --- a/tensorflow/contrib/data/ops/dataset_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -104,6 +104,7 @@ REGISTER_OP("FunctionBufferingResource") .Attr("container: string") .Attr("f: func") .Attr("buffer_size: int") + .Attr("output_types: list(type)") .SetShapeFn(shape_inference::UnknownShape) .Doc(R"doc( Creates a resource that fills up a buffer by making function calls. @@ -117,6 +118,7 @@ container: If non-empty, this resource is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this resource will be shared under the given name across multiple sessions. +output_types: The type list for the return values. )doc"); REGISTER_OP("FunctionBufferingResourceGetNext") @@ -158,6 +160,7 @@ REGISTER_OP("ThreadPoolHandle") .Output("handle: resource") .SetShapeFn(shape_inference::ScalarShape) .Attr("num_threads: int") + .Attr("max_intra_op_parallelism: int = 1") .Attr("display_name: string") .Attr("container: string = ''") .Attr("shared_name: string = ''") @@ -166,6 +169,8 @@ Creates a custom thread pool with the given number of threads. handle: A resource that can be consumed by one or more ThreadPoolDataset ops. num_threads: The number of threads in the thread pool. +max_intra_op_parallelism: The maximum degree of parallelism to use within + operations that execute on this threadpool. display_name: A human-readable name for the threads that may be visible in some visualizations. )doc"); diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index ba707d8d6e466561442f48e5dd7e8bdee20fb0f7..c9435eadcd08c2ca85746ee593f091cdaab66aae 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -4,7 +4,7 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -load("//tensorflow:tensorflow.bzl", "cuda_py_test", "py_test", "tf_py_test") +load("//tensorflow:tensorflow.bzl", "cuda_py_test", "py_test") py_test( name = "batch_dataset_op_test", @@ -16,20 +16,23 @@ py_test( "no_pip", ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:batching", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", "//tensorflow/python:script_ops", + "//tensorflow/python:session", "//tensorflow/python:sparse_tensor", "//tensorflow/python:string_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) @@ -39,7 +42,6 @@ py_test( srcs = ["bucketing_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:grouping", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", @@ -48,24 +50,33 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", "//tensorflow/python:string_ops", "//tensorflow/python:tensor_shape", + "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], ) py_test( - name = "concatenate_dataset_op_test", + name = "csv_dataset_op_test", size = "small", - srcs = ["concatenate_dataset_op_test.py"], + srcs = ["csv_dataset_op_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - ":dataset_serialization_test", + "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/contrib/data/python/ops:readers", "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:tensor_shape", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/util:nest", + "//tensorflow/python:framework_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:platform", + "//tensorflow/python:platform_test", + "//tensorflow/python:session", + "//tensorflow/python/data/ops:readers", "//third_party/py/numpy", ], ) @@ -80,104 +91,44 @@ py_test( "nomac", # b/62040583 ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:batching", - "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python:session", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:tensor_shape", + "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/util:nest", - "//third_party/py/numpy", ], ) -py_library( - name = "dataset_serialization_test", - srcs = [ - "dataset_serialization_test_base.py", - ], +py_test( + name = "directed_interleave_dataset_test", + size = "medium", + srcs = ["directed_interleave_dataset_test.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/contrib/data/python/ops:interleave_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:iterator_ops", - "//third_party/py/numpy", - ], -) - -py_test( - name = "csv_dataset_op_test", - size = "small", - srcs = ["csv_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:error_ops", - "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/python:random_seed", + "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], ) py_test( - name = "filter_dataset_op_test", + name = "get_single_element_test", size = "small", - srcs = ["filter_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = [ - "no_pip", - "optonly", - ], + srcs = ["get_single_element_test.py"], deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:functional_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -tf_py_test( - name = "flat_map_dataset_op_test", - size = "medium", - srcs = ["flat_map_dataset_op_test.py"], - additional_deps = [ - ":dataset_serialization_test", - "//third_party/py/numpy", - "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:get_single_element", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:function", - "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", - "//tensorflow/python:session", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", ], - grpc_enabled = True, - tags = ["no_pip"], ) py_test( @@ -192,10 +143,8 @@ py_test( "notap", ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:interleave_ops", "//tensorflow/python:array_ops", - "//tensorflow/python:client", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -203,43 +152,28 @@ py_test( "//tensorflow/python:script_ops", "//tensorflow/python:sparse_ops", "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", + "@six_archive//:six", ], ) py_test( - name = "directed_interleave_dataset_test", - size = "medium", - srcs = ["directed_interleave_dataset_test.py"], + name = "iterator_ops_test", + size = "small", + srcs = ["iterator_ops_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:interleave_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -tf_py_test( - name = "get_single_element_test", - size = "small", - srcs = ["get_single_element_test.py"], - additional_deps = [ - "//third_party/py/numpy", - "//tensorflow/contrib/data/python/ops:get_single_element", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python:array_ops", + "//tensorflow/contrib/data/python/ops:iterator_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_test_lib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:model_fn", ], ) @@ -254,27 +188,14 @@ py_test( "optonly", ], deps = [ - ":dataset_serialization_test", + "//tensorflow/contrib/data/python/ops:batching", "//tensorflow/contrib/data/python/ops:error_ops", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", - "//tensorflow/python:function", - "//tensorflow/python:functional_ops", "//tensorflow/python:io_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", - "//tensorflow/python:script_ops", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", "//tensorflow/python:util", - "//tensorflow/python:variable_scope", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], @@ -286,23 +207,30 @@ py_test( srcs = ["optimize_dataset_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:optimization", - "//tensorflow/python:platform", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", "//tensorflow/python/data/ops:dataset_ops", ], ) -py_test( - name = "prefetch_dataset_op_test", +cuda_py_test( + name = "prefetching_ops_test", size = "small", - srcs = ["prefetch_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/python:platform", + srcs = ["prefetching_ops_test.py"], + additional_deps = [ + "//tensorflow/contrib/data/python/ops:prefetching_ops", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:function", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", ], ) @@ -312,46 +240,60 @@ py_test( srcs = ["range_dataset_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:counter", "//tensorflow/contrib/data/python/ops:enumerate_ops", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", - "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:io_ops", - "//tensorflow/python:parsing_ops", "//tensorflow/python:tensor_shape", - "//tensorflow/python:variables", "//tensorflow/python/data/ops:dataset_ops", ], ) +py_library( + name = "reader_dataset_ops_test_base", + testonly = 1, + srcs = [ + "reader_dataset_ops_test_base.py", + ], + srcs_version = "PY2AND3", + visibility = [ + "//tensorflow/contrib/data/python/kernel_tests:__pkg__", + "//tensorflow/contrib/data/python/kernel_tests/serialization:__pkg__", + ], + deps = [ + "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:lib", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:util", + "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", + ], +) + py_test( name = "reader_dataset_ops_test", size = "medium", srcs = ["reader_dataset_ops_test.py"], - shard_count = 4, srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", + ":reader_dataset_ops_test_base", "//tensorflow/contrib/data/python/ops:readers", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", - "//tensorflow/python:lib", "//tensorflow/python:parsing_ops", "//tensorflow/python:string_ops", - "//tensorflow/python:util", - "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", "//third_party/py/numpy", ], ) @@ -378,6 +320,7 @@ py_test( "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", + "@six_archive//:six", ], ) @@ -388,13 +331,14 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:scan_ops", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/eager:context", "//third_party/py/numpy", @@ -402,55 +346,55 @@ py_test( ) py_test( - name = "sequence_dataset_op_test", + name = "shuffle_dataset_op_test", size = "medium", - srcs = ["sequence_dataset_op_test.py"], + srcs = ["shuffle_dataset_op_test.py"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_pip", + "optonly", + ], deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", + "//tensorflow/contrib/data/python/ops:shuffle_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], ) py_test( - name = "serialization_integration_test", + name = "slide_dataset_op_test", size = "small", - srcs = ["serialization_integration_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], + srcs = ["slide_dataset_op_test.py"], deps = [ - "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/contrib/data/python/ops:sliding", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) -py_test( - name = "shuffle_dataset_op_test", - size = "medium", - srcs = ["shuffle_dataset_op_test.py"], +py_library( + name = "sql_dataset_op_test_base", + srcs = ["sql_dataset_op_test_base.py"], srcs_version = "PY2AND3", - tags = ["no_pip"], + visibility = [ + "//tensorflow/contrib/data/python/kernel_tests:__pkg__", + "//tensorflow/contrib/data/python/kernel_tests/serialization:__pkg__", + ], deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:shuffle_ops", + "//tensorflow/contrib/data/python/ops:readers", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - "//third_party/py/numpy", + "@org_sqlite//:python", ], ) @@ -459,14 +403,12 @@ py_test( size = "small", srcs = ["sql_dataset_op_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:readers", - "//tensorflow/python:array_ops", + ":sql_dataset_op_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "@org_sqlite//:python", ], ) @@ -477,11 +419,15 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", + ":reader_dataset_ops_test_base", "//tensorflow/contrib/data/python/ops:stats_ops", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) @@ -495,8 +441,12 @@ py_test( "//tensorflow/contrib/data/python/ops:threadpool", "//tensorflow/contrib/data/python/ops:unique", "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:script_ops", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) @@ -507,87 +457,27 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:unique", - "//tensorflow/contrib/stateless", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", ], ) py_test( - name = "zip_dataset_op_test", - size = "small", - srcs = ["zip_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -cuda_py_test( - name = "prefetching_ops_test", - size = "small", - srcs = ["prefetching_ops_test.py"], - additional_deps = [ - "//tensorflow/contrib/data/python/ops:prefetching_ops", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:function", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - ], -) - -tf_py_test( - name = "slide_dataset_op_test", - size = "small", - srcs = ["slide_dataset_op_test.py"], - additional_deps = [ - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/contrib/data/python/ops:sliding", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//third_party/py/numpy", - ], -) - -tf_py_test( name = "writer_ops_test", size = "small", srcs = ["writer_ops_test.py"], - additional_deps = [ + deps = [ "//tensorflow/contrib/data/python/ops:writers", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:io_ops", "//tensorflow/python:lib", - "//tensorflow/python:tensor_shape", "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:readers", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py index b5fbc45ad3d8d262c1c79b5723ffeb38ff6a34c2..af97fbf87aee5f7005f9d266ba9b1b6cf109a2ec 100644 --- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py @@ -20,9 +20,9 @@ from __future__ import print_function import math import time +from absl.testing import parameterized import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops @@ -40,7 +40,7 @@ from tensorflow.python.platform import test from tensorflow.python.util import compat -class BatchDatasetTest(test.TestCase): +class BatchDatasetTest(test.TestCase, parameterized.TestCase): def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) @@ -427,9 +427,13 @@ class BatchDatasetTest(test.TestCase): self.assertEqual([None], dataset.output_shapes[1][0].as_list()) self.assertEqual([None, 30], dataset.output_shapes[1][1].as_list()) - def _testMapAndBatchDatasetHelper(self, - num_parallel_calls=None, - num_parallel_batches=None): + @parameterized.named_parameters( + ("default", None, None), + ("sequential_calls", 1, None), + ("parallel_calls", 2, None), + ("parallel_batches", None, 10), + ) + def testMapAndBatch(self, num_parallel_calls, num_parallel_batches): """Test a dataset that maps a TF function across its input elements.""" # The pipeline is TensorSliceDataset -> # RepeatDataset(count) -> MapAndBatchDataset(square_3, batch_size). @@ -500,19 +504,11 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.InvalidArgumentError): sess.run(init_op, feed_dict={count: 14, batch_size: 0}) - def testMapAndBatch(self): - return self._testMapAndBatchDatasetHelper() - - def testMapAndBatchWithParallelBatches(self): - return self._testMapAndBatchDatasetHelper(num_parallel_batches=10) - - def testMapAndBatchWithSequentialCalls(self): - return self._testMapAndBatchDatasetHelper(num_parallel_calls=1) - - def testMapAndBatchWithParallelCalls(self): - return self._testMapAndBatchDatasetHelper(num_parallel_calls=2) - - def _testMapAndBatchPartialBatchHelper(self, drop_remainder=False): + @parameterized.named_parameters( + ("even", False), + ("uneven", True), + ) + def testMapAndBatchPartialBatch(self, drop_remainder): iterator = ( dataset_ops.Dataset.range(10).apply( batching.map_and_batch( @@ -532,12 +528,6 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) - def testMapAndBatchPartialBatch(self): - return self._testMapAndBatchPartialBatchHelper() - - def testMapAndBatchPartialBatchDropRemainder(self): - return self._testMapAndBatchPartialBatchHelper(drop_remainder=True) - def testMapAndBatchYieldsPartialBatch(self): iterator = (dataset_ops.Dataset.range(10) .apply(batching.map_and_batch( @@ -614,7 +604,7 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - def testMapAndBatchDatasetFails(self): + def testMapAndBatchFails(self): """Test a dataset that maps a TF function across its input elements.""" dataset = dataset_ops.Dataset.from_tensors( array_ops.check_numerics( @@ -628,7 +618,7 @@ class BatchDatasetTest(test.TestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): sess.run(init_op, feed_dict={batch_size: 14}) - def testMapAndBatchDatasetShapeMismatch(self): + def testMapAndBatchShapeMismatch(self): """Test a dataset that maps a TF function across its input elements.""" def generator(): @@ -651,173 +641,79 @@ class BatchDatasetTest(test.TestCase): "number of elements does not match"): sess.run(get_next) + def testMapAndBatchImplicitDispose(self): + # Tests whether a map and batch dataset will be cleaned up correctly when + # the pipeline does not run it until exhaustion. + # The pipeline is TensorSliceDataset -> RepeatDataset(1000) -> + # MapAndBatchDataset(f=square_3, batch_size=100). + components = (np.arange(1000), + np.array([[1, 2, 3]]) * np.arange(1000)[:, np.newaxis], + np.array(37.0) * np.arange(1000)) -class BatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): - components = ( - np.arange(tensor_slice_len), - np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(tensor_slice_len)) + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) - return dataset_ops.Dataset.from_tensor_slices(components).batch(batch_size) + dataset = dataset_ops.Dataset.from_tensor_slices(components).repeat( + 1000).apply(batching.map_and_batch(_map_fn, batch_size=100)) + dataset = dataset.prefetch(5) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() - def testCore(self): - tensor_slice_len = 8 - batch_size = 2 - num_outputs = tensor_slice_len // batch_size - self.run_core_tests( - lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), - lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), - num_outputs) + with self.test_session() as sess: + for _ in range(3): + sess.run(get_next) - def _build_dataset_dense_to_sparse(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.fill([x], x)).apply( - batching.dense_to_sparse_batch(4, [12])) + @parameterized.parameters(0, 5, 10, 90, 95, 99) + def testMapAndBatchOutOfRangeError(self, threshold): - def testDenseToSparseBatchDatasetCore(self): - components = np.random.randint(5, size=(40,)).astype(np.int32) - diff_comp = np.random.randint(2, size=(100,)).astype(np.int32) - - num_outputs = len(components) // 4 - self.run_core_tests(lambda: self._build_dataset_dense_to_sparse(components), - lambda: self._build_dataset_dense_to_sparse(diff_comp), - num_outputs) - - def _sparse(self, i): - return sparse_tensor.SparseTensorValue( - indices=[[0]], values=(i * [1]), dense_shape=[1]) + def raising_py_fn(i): + if i >= threshold: + raise StopIteration() + else: + return i - def _build_dataset_sparse(self, batch_size=5): - return dataset_ops.Dataset.range(10).map(self._sparse).batch(batch_size) - - def testSparseCore(self): - self.run_core_tests(self._build_dataset_sparse, - lambda: self._build_dataset_sparse(2), 2) - - def _build_dataset_nested_sparse(self): - return dataset_ops.Dataset.range(10).map(self._sparse).batch(5).batch(2) - - def testNestedSparseCore(self): - self.run_core_tests(self._build_dataset_nested_sparse, None, 1) + iterator = ( + dataset_ops.Dataset.range(100).apply( + batching.map_and_batch( + lambda x: script_ops.py_func(raising_py_fn, [x], dtypes.int64), + batch_size=10)).make_one_shot_iterator()) + get_next = iterator.get_next() + with self.test_session() as sess: + for i in range(threshold // 10): + self.assertAllEqual([i * 10 + j for j in range(10)], sess.run(get_next)) + if threshold % 10 != 0: + self.assertAllEqual( + [threshold // 10 * 10 + j for j in range(threshold % 10)], + sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) -class UnbatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): + @parameterized.parameters( + (False, dtypes.bool), + (-42, dtypes.int8), + (-42, dtypes.int16), + (-42, dtypes.int32), + (-42, dtypes.int64), + (42, dtypes.uint8), + (42, dtypes.uint16), + (42.0, dtypes.float16), + (42.0, dtypes.float32), + (42.0, dtypes.float64), + (b"hello", dtypes.string), + ) + def testMapAndBatchTypes(self, element, dtype): + def gen(): + yield element + + dataset = dataset_ops.Dataset.from_generator(gen, dtype).repeat(100).apply( + batching.map_and_batch(lambda x: x, batch_size=10)) + + get_next = dataset.make_one_shot_iterator().get_next() - def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): - components = ( - np.arange(tensor_slice_len), - np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(tensor_slice_len)) - - return dataset_ops.Dataset.from_tensor_slices(components).batch( - batch_size).apply(batching.unbatch()) - - def testCore(self): - tensor_slice_len = 8 - batch_size = 2 - num_outputs = tensor_slice_len - self.run_core_tests( - lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), - lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), - num_outputs) - - -class MapAndBatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testNumParallelBatches(self): - range_size = 11 - num_repeats = 2 - batch_size = 5 - total_outputs = range_size * num_repeats - num_outputs_drop_remainder = total_outputs // batch_size - num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) - num_parallel_batches = 2 - - def build_ds(range_start, drop_remainder=False): - - def _map_fn(x): - return math_ops.square(x) - - return dataset_ops.Dataset.range( - range_start, range_start + range_size).repeat(num_repeats).apply( - batching.map_and_batch( - map_func=_map_fn, - batch_size=batch_size, - num_parallel_batches=num_parallel_batches, - drop_remainder=drop_remainder)) - - self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), - num_outputs_keep_remainder) - self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), - num_outputs_drop_remainder) - - def testNumParallelCalls(self): - range_size = 11 - num_repeats = 2 - batch_size = 5 - total_outputs = range_size * num_repeats - num_outputs_drop_remainder = total_outputs // batch_size - num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) - num_parallel_calls = 7 - - def build_ds(range_start, drop_remainder=False): - - def _map_fn(x): - return math_ops.square(x) - - return dataset_ops.Dataset.range( - range_start, range_start + range_size).repeat(num_repeats).apply( - batching.map_and_batch( - map_func=_map_fn, - batch_size=batch_size, - num_parallel_calls=num_parallel_calls, - drop_remainder=drop_remainder)) - - self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), - num_outputs_keep_remainder) - self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), - num_outputs_drop_remainder) - - -class PaddedBatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testPaddedBatch(self): - - def build_dataset(seq_lens): - return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( - lambda x: array_ops.fill([x], x)).padded_batch( - 4, padded_shapes=[-1]) - - seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) - seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) - self.run_core_tests(lambda: build_dataset(seq_lens1), - lambda: build_dataset(seq_lens2), 8) - - def testPaddedBatchNonDefaultPadding(self): - - def build_dataset(seq_lens): - - def fill_tuple(x): - filled = array_ops.fill([x], x) - return (filled, string_ops.as_string(filled)) - - padded_shape = [-1] - return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( - fill_tuple).padded_batch( - 4, - padded_shapes=(padded_shape, padded_shape), - padding_values=(-1, "")) - - seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) - seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) - self.run_core_tests(lambda: build_dataset(seq_lens1), - lambda: build_dataset(seq_lens2), 8) + with self.test_session() as sess: + for _ in range(10): + self.assertAllEqual([element for _ in range(10)], sess.run(get_next)) class RestructuredDatasetTest(test.TestCase): diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index bd3e034211c4aa454e4f8f6b09f14935d7a3b35c..2022c1f2bdd09cdf43a993b3666335ce468a40ba 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -21,7 +21,6 @@ import random import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import grouping from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -68,7 +67,7 @@ class GroupByReducerTest(test.TestCase): reducer = grouping.Reducer( init_func=lambda _: (0.0, 0.0), reduce_func=reduce_fn, - finalize_func=lambda x: x[0]) + finalize_func=lambda x, _: x) for i in range(1, 11): dataset = dataset_ops.Dataset.range(2 * i).apply( grouping.group_by_reducer( @@ -121,7 +120,7 @@ class GroupByReducerTest(test.TestCase): reducer = grouping.Reducer( init_func=lambda x: ([0], 1), reduce_func=reduce_fn, - finalize_func=lambda x: x) + finalize_func=lambda x, y: (x, y)) for i in range(1, 11): dataset = dataset_ops.Dataset.from_tensors(np.int64(0)).repeat(i).apply( @@ -176,37 +175,27 @@ class GroupByReducerTest(test.TestCase): dataset.apply( grouping.group_by_reducer(lambda _: "wrong", reducer)) + def testTuple(self): + def init_fn(_): + return np.array([], dtype=np.int64), np.int64(0) -class GroupByReducerSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): + def reduce_fn(state, value): + s1, s2 = state + v1, v2 = value + return array_ops.concat([s1, [v1]], 0), s2 + v2 - def _build_dataset(self, components): - reducer = grouping.Reducer( - init_func=lambda _: np.int64(0), - reduce_func=lambda x, y: x + y, - finalize_func=lambda x: x) + def finalize_fn(s1, s2): + return s1, s2 - return dataset_ops.Dataset.from_tensor_slices(components).apply( - grouping.group_by_reducer(lambda x: x % 5, reducer)) - - def testCoreGroupByReducer(self): - components = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64) - self.verify_unused_iterator( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_init_before_restore( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_multiple_breaks( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_reset_restored_iterator( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_restore_in_empty_graph( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - diff_components = np.array([5, 4, 3, 2, 1, 0], dtype=np.int64) - self.verify_restore_in_modified_graph( - lambda: self._build_dataset(components), - lambda: self._build_dataset(diff_components), - 5, - verify_exhausted=True) + reducer = grouping.Reducer(init_fn, reduce_fn, finalize_fn) + dataset = dataset_ops.Dataset.zip( + (dataset_ops.Dataset.range(10), dataset_ops.Dataset.range(10))).apply( + grouping.group_by_reducer(lambda x, y: np.int64(0), reducer)) + get_next = dataset.make_one_shot_iterator().get_next() + with self.test_session() as sess: + x, y = sess.run(get_next) + self.assertAllEqual(x, np.asarray([x for x in range(10)])) + self.assertEqual(y, 45) class GroupByWindowTest(test.TestCase): @@ -353,34 +342,6 @@ class GroupByWindowTest(test.TestCase): self.assertEqual(len(components), sum(counts)) -class GroupByWindowSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply( - grouping.group_by_window(lambda x: x % 3, lambda _, xs: xs.batch(4), 4)) - - def testCoreGroupByWindow(self): - components = np.array( - [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0], dtype=np.int64) - self.verify_unused_iterator( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_init_before_restore( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_multiple_breaks( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_reset_restored_iterator( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_restore_in_empty_graph( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - diff_components = np.array([0, 0, 0, 1, 1, 1], dtype=np.int64) - self.verify_restore_in_modified_graph( - lambda: self._build_dataset(components), - lambda: self._build_dataset(diff_components), - 12, - verify_exhausted=False) - - # NOTE(mrry): These tests are based on the tests in bucket_ops_test.py. # Currently, they use a constant batch size, though should be made to use a # different batch size per key. @@ -655,7 +616,44 @@ class BucketBySequenceLength(test.TestCase): batch_sizes = batch_sizes[:-1] self.assertEqual(sum(batch_sizes_val), sum(batch_sizes)) self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val)) - self.assertEqual(sorted(boundaries), sorted(lengths_val)) + self.assertEqual([boundary - 1 for boundary in sorted(boundaries)], + sorted(lengths_val)) + + def testPadToBoundaryNoExtraneousPadding(self): + + boundaries = [3, 7, 11] + batch_sizes = [2, 2, 2, 2] + lengths = range(1, 11) + + def element_gen(): + for length in lengths: + yield ([1] * length,) + + element_len = lambda element: array_ops.shape(element)[0] + dataset = dataset_ops.Dataset.from_generator( + element_gen, (dtypes.int64,), ([None],)).apply( + grouping.bucket_by_sequence_length( + element_len, boundaries, batch_sizes, + pad_to_bucket_boundary=True)) + batch, = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + batches = [] + for _ in range(5): + batches.append(sess.run(batch)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(batch) + + self.assertAllEqual(batches[0], [[1, 0], + [1, 1]]) + self.assertAllEqual(batches[1], [[1, 1, 1, 0, 0, 0], + [1, 1, 1, 1, 0, 0]]) + self.assertAllEqual(batches[2], [[1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 1]]) + self.assertAllEqual(batches[3], [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]) + self.assertAllEqual(batches[4], [[1, 1, 1, 1, 1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) def testTupleElements(self): diff --git a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py index 74b90ec7d1617d221888d1e1c56cf594c367ddf9..df115175f5046803ada036563be1ca802f7ad0cd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py @@ -33,7 +33,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.ops import gen_parsing_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import googletest from tensorflow.python.platform import test @@ -76,7 +76,7 @@ class CsvDatasetOpTest(test.TestCase): filenames = self.setup_files(inputs) dataset_expected = core_readers.TextLineDataset(filenames) dataset_expected = dataset_expected.map( - lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) + lambda l: parsing_ops.decode_csv(l, **kwargs)) dataset_actual = readers.CsvDataset(filenames, **kwargs) return (dataset_actual, dataset_expected) @@ -162,9 +162,28 @@ class CsvDatasetOpTest(test.TestCase): expected_err_re='Unquoted fields cannot have quotes inside', record_defaults=record_defaults) + def testCsvDataset_errWithUnescapedQuotes(self): + record_defaults = [['']] * 3 + inputs = [['"a"b","c","d"']] + self._test_dataset( + inputs, + expected_err_re= + 'Quote inside a string has to be escaped by another quote', + record_defaults=record_defaults) + + def testCsvDataset_ignoreErrWithUnescapedQuotes(self): + record_defaults = [['']] * 3 + inputs = [['1,"2"3",4', '1,"2"3",4",5,5', 'a,b,"c"d"', 'e,f,g']] + filenames = self.setup_files(inputs) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + dataset = readers.CsvDataset(filenames, record_defaults=record_defaults) + dataset = dataset.apply(error_ops.ignore_errors()) + self._verify_output_or_err(sess, dataset, [['e', 'f', 'g']]) + def testCsvDataset_ignoreErrWithUnquotedQuotes(self): record_defaults = [['']] * 3 - inputs = [['1,2"3,4', 'a,b,c"d', 'e,f,g']] + inputs = [['1,2"3,4', 'a,b,c"d', '9,8"7,6,5', 'e,f,g']] filenames = self.setup_files(inputs) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: @@ -562,7 +581,7 @@ class CsvDatasetBenchmark(test.Benchmark): num_cols = self._num_cols[i] kwargs = {'record_defaults': [[0.0]] * num_cols} dataset = core_readers.TextLineDataset(self._filenames[i]).repeat() - dataset = dataset.map(lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop + dataset = dataset.map(lambda l: parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop self._runBenchmark(dataset, num_cols, 'csv_float_map_decode_csv') self._tearDown() @@ -572,7 +591,7 @@ class CsvDatasetBenchmark(test.Benchmark): num_cols = self._num_cols[i] kwargs = {'record_defaults': [['']] * num_cols} dataset = core_readers.TextLineDataset(self._filenames[i]).repeat() - dataset = dataset.map(lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop + dataset = dataset.map(lambda l: parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop self._runBenchmark(dataset, num_cols, 'csv_strings_map_decode_csv') self._tearDown() diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py index a842502cc6fe3605dde0be5f50cf46e3e37d7ed4..a2ab3de52e8e512e3cba399f7a1725e5570cfd01 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py @@ -17,14 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.framework import dtypes -from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -70,63 +66,5 @@ class DatasetConstructorTest(test.TestCase): # pylint: enable=protected-access -class DatasetConstructorSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_tensor_dataset(self, variable_array): - components = (variable_array, np.array([1, 2, 3]), np.array(37.0)) - - return dataset_ops.Dataset.from_tensors(components) - - def testFromTensorsCore(self): - # Equal length components - arr = np.array(1) - num_outputs = 1 - diff_arr = np.array(2) - self.run_core_tests(lambda: self._build_tensor_dataset(arr), - lambda: self._build_tensor_dataset(diff_arr), - num_outputs) - - def _build_tensor_slices_dataset(self, components): - return dataset_ops.Dataset.from_tensor_slices(components) - - def testFromTensorSlicesCore(self): - # Equal length components - components = (np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 22), - np.array([37.0, 38.0, 39.0, 40.0])) - - diff_comp = (np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[5], [6], [7], [8]]), 22), - np.array([1.0, 2.0, 3.0, 4.0])) - - dict_components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]} - - self.run_core_tests(lambda: self._build_tensor_slices_dataset(components), - lambda: self._build_tensor_slices_dataset(diff_comp), 4) - self.run_core_tests( - lambda: self._build_tensor_slices_dataset(dict_components), None, 3) - - def _build_sparse_tensor_slice_dataset(self, slices): - indices = np.array( - [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))], - dtype=np.int64) - values = np.array([val for s in slices for val in s], dtype=np.float64) - dense_shape = np.array( - [len(slices), max(len(s) for s in slices) + 1], dtype=np.int64) - sparse_components = sparse_tensor.SparseTensor(indices, values, dense_shape) - return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components) - - def testFromSparseTensorSlicesCore(self): - slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []] - diff_slices = [[1., 2.], [2.], [2., 3., 4.], [], [], []] - - self.run_core_tests( - lambda: self._build_sparse_tensor_slice_dataset(slices), - lambda: self._build_sparse_tensor_slice_dataset(diff_slices), - 9, - sparse_tensors=True) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py index 34b6a080c0aae7dfc228746139acc52cea4e6f28..9b1857de1a96c8f71788a1bf5085ef0605417fe7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py @@ -19,7 +19,6 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors @@ -34,8 +33,8 @@ class DirectedInterleaveDatasetTest(test.TestCase): input_datasets = [ dataset_ops.Dataset.from_tensors(i).repeat(100) for i in range(10) ] - dataset = interleave_ops.DirectedInterleaveDataset(selector_dataset, - input_datasets) + dataset = interleave_ops._DirectedInterleaveDataset(selector_dataset, + input_datasets) iterator = dataset.make_initializable_iterator() next_element = iterator.get_next() @@ -144,24 +143,5 @@ class DirectedInterleaveDatasetTest(test.TestCase): ], choice_dataset=dataset_ops.Dataset.from_tensors([1.0])) -class SampleFromDatasetsSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, probs, num_samples): - dataset = interleave_ops.sample_from_datasets( - [ - dataset_ops.Dataset.from_tensors(i).repeat(None) - for i in range(len(probs)) - ], - probs, - seed=1813) - return dataset.take(num_samples) - - def testSerializationCore(self): - self.run_core_tests( - lambda: self._build_dataset([0.5, 0.5], 100), - lambda: self._build_dataset([0.25, 0.25, 0.25, 0.25], 1000), 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py index bee561e3e23a2ab6f314894caa21785347e6ca8b..44c3325a3db84bb844b7f860a7c925982f1e3d6a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py @@ -22,10 +22,8 @@ import math import threading import time -import numpy as np from six.moves import zip_longest -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes @@ -38,132 +36,6 @@ from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test -class InterleaveDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, input_values, cycle_length, block_length): - repeat_count = 2 - return dataset_ops.Dataset.from_tensor_slices(input_values).repeat( - repeat_count).interleave( - lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x), - cycle_length, block_length) - - def testSerializationCore(self): - input_values = np.array([4, 5, 6], dtype=np.int64) - num_outputs = np.sum(input_values) * 2 - # cycle_length > 1, block_length > 1 - cycle_length = 2 - block_length = 3 - # pylint: disable=g-long-lambda - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - lambda: self._build_iterator_graph( - input_values, cycle_length * 2, block_length * 1), - num_outputs) - # cycle_length = 1 - cycle_length = 1 - block_length = 3 - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - None, num_outputs) - # block_length = 1 - cycle_length = 2 - block_length = 1 - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - None, num_outputs) - # pylint: enable=g-long-lambda - - def testSparseCore(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _interleave_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - def _build_dataset(): - return dataset_ops.Dataset.range(10).map(_map_fn).interleave( - _interleave_fn, cycle_length=1) - - self.run_core_tests(_build_dataset, None, 20) - - -class ParallelInterleaveDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def setUp(self): - self.input_values = np.array([4, 5, 6], dtype=np.int64) - self.num_repeats = 2 - self.num_outputs = np.sum(self.input_values) * 2 - - def _build_ds(self, cycle_length, block_length, sloppy=False): - return (dataset_ops.Dataset.from_tensor_slices( - self.input_values).repeat(self.num_repeats).apply( - interleave_ops.parallel_interleave( - lambda x: dataset_ops.Dataset.range(10 * x, 11 * x), - cycle_length, block_length, sloppy))) - - def testSerializationCore(self): - # cycle_length > 1, block_length > 1 - cycle_length = 2 - block_length = 3 - self.run_core_tests( - lambda: self._build_ds(cycle_length, block_length), - lambda: self._build_ds(cycle_length * 2, block_length * 1), - self.num_outputs) - # cycle_length = 1 - cycle_length = 1 - block_length = 3 - self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), - None, self.num_outputs) - # block_length = 1 - cycle_length = 2 - block_length = 1 - self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), - None, self.num_outputs) - - def testSerializationWithSloppy(self): - break_points = self.gen_break_points(self.num_outputs, 10) - expected_outputs = np.repeat( - np.concatenate([np.arange(10 * x, 11 * x) for x in self.input_values]), - self.num_repeats).tolist() - - def run_test(cycle_length, block_length): - actual = self.gen_outputs( - lambda: self._build_ds(cycle_length, block_length, True), - break_points, self.num_outputs) - self.assertSequenceEqual(sorted(actual), expected_outputs) - - # cycle_length > 1, block_length > 1 - run_test(2, 3) - # cycle_length = 1 - run_test(1, 3) - # block_length = 1 - run_test(2, 1) - - def testSparseCore(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _interleave_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - def _build_dataset(): - return dataset_ops.Dataset.range(10).map(_map_fn).apply( - interleave_ops.parallel_interleave(_interleave_fn, 1)) - - self.run_core_tests(_build_dataset, None, 20) - - class ParallelInterleaveDatasetTest(test.TestCase): def setUp(self): diff --git a/tensorflow/contrib/data/python/ops/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py similarity index 100% rename from tensorflow/contrib/data/python/ops/iterator_ops_test.py rename to tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py index 8d4042927970cab2f5a518fc0da49b38444dbcdf..a075dfd8b56079c7b2509bb5795521b8b9eb3127 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py @@ -17,24 +17,21 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import itertools import os +import time import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.data.python.ops import error_ops +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import io_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -143,229 +140,82 @@ class MapDatasetTest(test.TestCase): sess.run(get_next) -class MapDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): +class MapDatasetBenchmark(test.Benchmark): - def setUp(self): - self._tensor_slice_len = 7 - self._num_epochs = 14 - self._num_outputs = self._tensor_slice_len * self._num_epochs + def benchmarkMapAndBatch(self): + small = itertools.product([1, 4], [1, 4], [1, 4], [16, 64], [100]) + large = itertools.product([16, 64], [16, 64], [16, 64], [256, 1024], [10]) - def _build_ds(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) + num_iters = 100 - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) + def benchmark(series): - return ( - dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(self._num_epochs)) - - def testSaveRestoreCore(self): - self.run_core_tests( - self._build_ds, - lambda: self._build_ds(multiplier=15.0), - self._num_outputs) - - def testSaveStatefulFunction(self): - - def _build_ds(): - - def _map_fn(x): - return random_ops.random_uniform( - (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - - return dataset_ops.Dataset.range(100).map(_map_fn) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureVariableInMapFn(self): - - def _build_ds(): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda _: counter_var.assign_add(1))) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureConstantInMapFn(self): - - def _build_ds(): - constant_var = constant_op.constant(5) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda x: x + constant_var)) - - self.run_core_tests(_build_ds, None, 10) - - def testCaptureDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return dataset_ops.Dataset.range(num_outputs).map(defun_fn) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testBuildDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - - @function.Defun(dtypes.int32) - def defun_fn_deep(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - - return dataset_ops.Dataset.range(num_outputs).map(defun_fn) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testSparseCore(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0]]), - values=(i * np.array([1])), - dense_shape=np.array([1, 1])) - - def _build_ds(num_outputs): - return dataset_ops.Dataset.range(num_outputs).map(_sparse) - - num_outputs = 10 - self.run_core_tests(lambda: _build_ds(num_outputs), - lambda: _build_ds(int(num_outputs / 2)), num_outputs) - - -class ParallelMapDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def setUp(self): - self._tensor_slice_len = 7 - self._num_epochs = 1 - self._num_outputs = self._tensor_slice_len * self._num_epochs - - def _build_ds(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return (dataset_ops.Dataset.from_tensor_slices(components).map( - _map_fn, num_parallel_calls=3).repeat(self._num_epochs)) - - def _build_ds_with_prefetch(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return (dataset_ops.Dataset.from_tensor_slices(components).map( - _map_fn, num_parallel_calls=3).repeat(self._num_epochs).prefetch(5)) - - def testSaveRestoreCore(self): - for ds_fn in [self._build_ds, self._build_ds_with_prefetch]: - self.run_core_tests( - ds_fn, - lambda: ds_fn(multiplier=15.0), - self._num_outputs) - - def testSaveStatefulFunction(self): - - def _build_ds(): - - def _map_fn(x): - return random_ops.random_uniform( - (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - - return dataset_ops.Dataset.range(100).map( - _map_fn, num_parallel_calls=2).prefetch(2) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureVariableInMapFn(self): - - def _build_ds(): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda _: counter_var.assign_add(1), - num_parallel_calls=2).prefetch(2)) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureConstantInMapFn(self): - - def _build_ds(): - constant_var = constant_op.constant(5) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) - - self.run_core_tests(_build_ds, None, 10) - - def testCaptureDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return dataset_ops.Dataset.range(num_outputs).map( - defun_fn, num_parallel_calls=2).prefetch(2) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testBuildDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - - @function.Defun(dtypes.int32) - def defun_fn_deep(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - - return dataset_ops.Dataset.range(num_outputs).map( - defun_fn, num_parallel_calls=2).prefetch(2) - - self.run_core_tests(_build_ds, None, num_outputs) - - -class IgnoreErrorsSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_ds(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.check_numerics(x, "message")).apply( - error_ops.ignore_errors()) - - def testIgnoreErrorsCore(self): - components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) - diff_components = np.array([1., 2., 3., np.nan]).astype(np.float32) - num_outputs = 4 - self.run_core_tests(lambda: self._build_ds(components), - lambda: self._build_ds(diff_components), num_outputs) + for num_calls, inter_op, element_size, batch_size, num_steps in series: + dataset = dataset_ops.Dataset.from_tensors( + np.random.randint(100, size=element_size)).repeat().map( + lambda x: x, + num_parallel_calls=num_calls).batch(batch_size=batch_size) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + fused_dataset = dataset_ops.Dataset.from_tensors( + np.random.randint(100, size=element_size)).repeat(None).apply( + batching.map_and_batch( + lambda x: x, + num_parallel_calls=num_calls, + batch_size=batch_size)) + fused_iterator = fused_dataset.make_one_shot_iterator() + fused_get_next = fused_iterator.get_next() + + fused_deltas = [] + with session.Session( + config=config_pb2.ConfigProto( + inter_op_parallelism_threads=inter_op)) as sess: + + for _ in range(5): + sess.run(fused_get_next) + for _ in range(num_iters): + start = time.time() + for _ in range(num_steps): + sess.run(fused_get_next) + end = time.time() + fused_deltas.append(end - start) + + chained_deltas = [] + with session.Session( + config=config_pb2.ConfigProto( + inter_op_parallelism_threads=inter_op)) as sess: + for _ in range(5): + sess.run(get_next) + for _ in range(num_iters): + start = time.time() + for _ in range(num_steps): + sess.run(get_next) + end = time.time() + chained_deltas.append(end - start) + + chained_wall_time = np.median(chained_deltas) / num_iters + fused_wall_time = np.median(fused_deltas) / num_iters + print( + "batch size: %d, num parallel calls: %d, inter-op parallelism: %d, " + "element size: %d, chained wall time: %f, fused wall time: %f" % + (batch_size, num_calls, inter_op, element_size, chained_wall_time, + fused_wall_time)) + + self.report_benchmark( + iters=num_iters, + wall_time=chained_wall_time, + name="chained_batch_size_%d_num_calls_%d_inter_op_%d_elem_size_%d" + % (batch_size, num_calls, inter_op, element_size)) + + self.report_benchmark( + iters=num_iters, + wall_time=fused_wall_time, + name="fused_batch_size_%d_num_calls_%d_inter_op_%d_elem_size_%d" + % (batch_size, num_calls, inter_op, element_size)) + + benchmark(small) + benchmark(large) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py index 30f1847dcddbfaf379ef2b09185f7a8db4aaeae2..e35be8a23f3706bd170c09b967b4f419fc9a626e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py @@ -17,7 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import optimization from tensorflow.core.framework import graph_pb2 from tensorflow.python.data.ops import dataset_ops @@ -73,17 +72,5 @@ class OptimizeDatasetTest(test.TestCase): sess.run(get_next) -class OptimizeDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testCore(self): - - def build_dataset(num_elements, batch_size): - return dataset_ops.Dataset.range(num_elements).map(lambda x: x * x).batch( - batch_size).apply(optimization.optimize(["map_and_batch_fusion"])) - - self.run_core_tests(lambda: build_dataset(200, 10), None, 20) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py index b08132cd72254326d965907a1fdafb8a820926a1..40a8e4667678710251a25f906a917ca1eadd21c2 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py @@ -68,6 +68,7 @@ class PrefetchingKernelsOpsTest(test.TestCase): with ops.device(device1): buffer_resource_handle = prefetching_ops.function_buffering_resource( f=_remote_fn, + output_types=[dtypes.float32], target_device=target, string_arg=ds_iterator_handle, buffer_size=3, @@ -201,6 +202,49 @@ class PrefetchingKernelsOpsTest(test.TestCase): sess.run(destroy_op) + def testStringsGPU(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + device0 = "/job:localhost/replica:0/task:0/cpu:0" + device1 = "/job:localhost/replica:0/task:0/gpu:0" + + ds = dataset_ops.Dataset.from_tensor_slices(["a", "b", "c"]) + ds_iterator = ds.make_one_shot_iterator() + ds_iterator_handle = ds_iterator.string_handle() + + @function.Defun(dtypes.string) + def _remote_fn(h): + remote_iterator = iterator_ops.Iterator.from_string_handle( + h, ds.output_types, ds.output_shapes) + return remote_iterator.get_next() + + target = constant_op.constant(device0) + with ops.device(device1): + buffer_resource_handle = prefetching_ops.function_buffering_resource( + f=_remote_fn, + output_types=[dtypes.string], + target_device=target, + string_arg=ds_iterator_handle, + buffer_size=3, + shared_name="strings") + + with ops.device(device1): + prefetch_op = prefetching_ops.function_buffering_resource_get_next( + function_buffer_resource=buffer_resource_handle, + output_types=[dtypes.string]) + destroy_op = resource_variable_ops.destroy_resource_op( + buffer_resource_handle, ignore_lookup_error=True) + + with self.test_session() as sess: + self.assertEqual([b"a"], sess.run(prefetch_op)) + self.assertEqual([b"b"], sess.run(prefetch_op)) + self.assertEqual([b"c"], sess.run(prefetch_op)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(prefetch_op) + + sess.run(destroy_op) + class PrefetchToDeviceTest(test.TestCase): @@ -235,6 +279,36 @@ class PrefetchToDeviceTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) + def testPrefetchToSameDevice(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.prefetch_to_device( + "/job:localhost/replica:0/task:0/device:CPU:0")) + + # NOTE(mrry): This device block creates the "host" dataset and iterator on + # /cpu:0, and ensures that the prefetching is across devices. In typical use + # this would not be necessary, because the GPU device would not support any + # of the dataset-related ops. + with ops.device("/cpu:0"): + iterator = device_dataset.make_one_shot_iterator() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + next_element = iterator.get_next() + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + with self.test_session() as sess: + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + def testPrefetchDictToDevice(self): host_dataset = dataset_ops.Dataset.range(10).map(lambda x: {"a": x}) device_dataset = host_dataset.apply( diff --git a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py index 80e1cb0041024b68bd5268b5de5d69c88c839896..592642da0cfd84e50cb20d9b2e534411faf927e8 100644 --- a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py @@ -17,21 +17,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import counter from tensorflow.contrib.data.python.ops import enumerate_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import gen_dataset_ops -from tensorflow.python.ops import io_ops -from tensorflow.python.ops import parsing_ops -from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -81,88 +73,5 @@ class RangeDatasetTest(test.TestCase): self.assertEqual(-2, sess.run(negative_get_next)) -class RangeDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _iterator_checkpoint_prefix_local(self): - return os.path.join(self.get_temp_dir(), "iterator") - - def _save_op(self, iterator_resource): - iterator_state_variant = gen_dataset_ops.serialize_iterator( - iterator_resource) - save_op = io_ops.write_file( - self._iterator_checkpoint_prefix_local(), - parsing_ops.serialize_tensor(iterator_state_variant)) - return save_op - - def _restore_op(self, iterator_resource): - iterator_state_variant = parsing_ops.parse_tensor( - io_ops.read_file(self._iterator_checkpoint_prefix_local()), - dtypes.variant) - restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource, - iterator_state_variant) - return restore_op - - def testSaveRestore(self): - - def _build_graph(start, stop): - iterator = dataset_ops.Dataset.range(start, - stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - save_op = self._save_op(iterator._iterator_resource) - restore_op = self._restore_op(iterator._iterator_resource) - return init_op, get_next, save_op, restore_op - - # Saving and restoring in different sessions. - start = 2 - stop = 10 - break_point = 5 - with ops.Graph().as_default() as g: - init_op, get_next, save_op, _ = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) - for i in range(start, break_point): - self.assertEqual(i, sess.run(get_next)) - sess.run(save_op) - - with ops.Graph().as_default() as g: - init_op, get_next, _, restore_op = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(init_op) - sess.run(restore_op) - for i in range(break_point, stop): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Saving and restoring in same session. - with ops.Graph().as_default() as g: - init_op, get_next, save_op, restore_op = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) - for i in range(start, break_point): - self.assertEqual(i, sess.run(get_next)) - sess.run(save_op) - sess.run(restore_op) - for i in range(break_point, stop): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def _build_range_dataset(self, start, stop): - return dataset_ops.Dataset.range(start, stop) - - def testRangeCore(self): - start = 2 - stop = 10 - stop_1 = 8 - self.run_core_tests(lambda: self._build_range_dataset(start, stop), - lambda: self._build_range_dataset(start, stop_1), - stop - start) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py index e0237198b7d47eb98eeffe88d28bf9681b2722c6..9df403ef50e459d94b8edf3f651c7c95baf3ec42 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py @@ -17,426 +17,24 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import gzip import os -import zlib import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base from tensorflow.contrib.data.python.ops import readers -from tensorflow.core.example import example_pb2 -from tensorflow.core.example import feature_pb2 -from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.lib.io import python_io -from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import string_ops from tensorflow.python.platform import test -from tensorflow.python.util import compat -class TextLineDatasetTestBase(test.TestCase): - - def _lineText(self, f, l): - return compat.as_bytes("%d: %d" % (f, l)) - - def _createFiles(self, - num_files, - num_lines, - crlf=False, - compression_type=None): - filenames = [] - for i in range(num_files): - fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i) - filenames.append(fn) - contents = [] - for j in range(num_lines): - contents.append(self._lineText(i, j)) - # Always include a newline after the record unless it is - # at the end of the file, in which case we include it - if j + 1 != num_lines or i == 0: - contents.append(b"\r\n" if crlf else b"\n") - contents = b"".join(contents) - - if not compression_type: - with open(fn, "wb") as f: - f.write(contents) - elif compression_type == "GZIP": - with gzip.GzipFile(fn, "wb") as f: - f.write(contents) - elif compression_type == "ZLIB": - contents = zlib.compress(contents) - with open(fn, "wb") as f: - f.write(contents) - else: - raise ValueError("Unsupported compression_type", compression_type) - - return filenames - - -class TextLineDatasetSerializationTest( - TextLineDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, test_filenames, compression_type=None): - return core_readers.TextLineDataset( - test_filenames, compression_type=compression_type, buffer_size=10) - - def testTextLineCore(self): - compression_types = [None, "GZIP", "ZLIB"] - num_files = 5 - lines_per_file = 5 - num_outputs = num_files * lines_per_file - for compression_type in compression_types: - test_filenames = self._createFiles( - num_files, - lines_per_file, - crlf=True, - compression_type=compression_type) - # pylint: disable=cell-var-from-loop - self.run_core_tests( - lambda: self._build_iterator_graph(test_filenames, compression_type), - lambda: self._build_iterator_graph(test_filenames), num_outputs) - # pylint: enable=cell-var-from-loop - - -class FixedLengthRecordReaderTestBase(test.TestCase): - - def setUp(self): - super(FixedLengthRecordReaderTestBase, self).setUp() - self._num_files = 2 - self._num_records = 7 - self._header_bytes = 5 - self._record_bytes = 3 - self._footer_bytes = 2 - - def _record(self, f, r): - return compat.as_bytes(str(f * 2 + r) * self._record_bytes) - - def _createFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) - filenames.append(fn) - with open(fn, "wb") as f: - f.write(b"H" * self._header_bytes) - for j in range(self._num_records): - f.write(self._record(i, j)) - f.write(b"F" * self._footer_bytes) - return filenames - - -class FixedLengthRecordDatasetSerializationTest( - FixedLengthRecordReaderTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, num_epochs, compression_type=None): - filenames = self._createFiles() - return core_readers.FixedLengthRecordDataset( - filenames, self._record_bytes, self._header_bytes, - self._footer_bytes).repeat(num_epochs) - - def testFixedLengthRecordCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), - lambda: self._build_iterator_graph(num_epochs * 2), - num_outputs) - - -class TFRecordDatasetTestBase(test.TestCase): - - def setUp(self): - super(TFRecordDatasetTestBase, self).setUp() - self._num_files = 2 - self._num_records = 7 - - self.test_filenames = self._createFiles() - - self.filenames = array_ops.placeholder(dtypes.string, shape=[None]) - self.num_epochs = array_ops.placeholder_with_default( - constant_op.constant(1, dtypes.int64), shape=[]) - self.compression_type = array_ops.placeholder_with_default("", shape=[]) - self.batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_dataset = core_readers.TFRecordDataset( - self.filenames, self.compression_type).repeat(self.num_epochs) - batch_dataset = repeat_dataset.batch(self.batch_size) - - iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) - self.init_op = iterator.make_initializer(repeat_dataset) - self.init_batch_op = iterator.make_initializer(batch_dataset) - self.get_next = iterator.get_next() - - def _record(self, f, r): - return compat.as_bytes("Record %d of file %d" % (r, f)) - - def _createFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) - filenames.append(fn) - writer = python_io.TFRecordWriter(fn) - for j in range(self._num_records): - writer.write(self._record(i, j)) - writer.close() - return filenames - - -class TFRecordDatasetSerializationTest( - TFRecordDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, - num_epochs, - batch_size=1, - compression_type=None, - buffer_size=None): - filenames = self._createFiles() - if compression_type is "ZLIB": - zlib_files = [] - for i, fn in enumerate(filenames): - with open(fn, "rb") as f: - cdata = zlib.compress(f.read()) - zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) - with open(zfn, "wb") as f: - f.write(cdata) - zlib_files.append(zfn) - filenames = zlib_files - - elif compression_type is "GZIP": - gzip_files = [] - for i, fn in enumerate(self.test_filenames): - with open(fn, "rb") as f: - gzfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) - with gzip.GzipFile(gzfn, "wb") as gzf: - gzf.write(f.read()) - gzip_files.append(gzfn) - filenames = gzip_files - - return core_readers.TFRecordDataset( - filenames, compression_type, - buffer_size=buffer_size).repeat(num_epochs).batch(batch_size) - - def testTFRecordWithoutBufferCore(self): - num_epochs = 5 - batch_size = num_epochs - num_outputs = num_epochs * self._num_files * self._num_records // batch_size - # pylint: disable=g-long-lambda - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, batch_size, - buffer_size=0), - lambda: self._build_iterator_graph(num_epochs * 2, batch_size), - num_outputs) - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, buffer_size=0), None, - num_outputs * batch_size) - # pylint: enable=g-long-lambda - - def testTFRecordWithBufferCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), - lambda: self._build_iterator_graph(num_epochs * 2), - num_outputs) - - def testTFRecordWithCompressionCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, compression_type="ZLIB"), - lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, compression_type="GZIP"), - lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) - - -def _interleave(iterators, cycle_length): - pending_iterators = iterators - open_iterators = [] - num_open = 0 - for i in range(cycle_length): - if pending_iterators: - open_iterators.append(pending_iterators.pop(0)) - num_open += 1 - - while num_open: - for i in range(min(cycle_length, len(open_iterators))): - if open_iterators[i] is None: - continue - try: - yield next(open_iterators[i]) - except StopIteration: - if pending_iterators: - open_iterators[i] = pending_iterators.pop(0) - else: - open_iterators[i] = None - num_open -= 1 - - -class ReadBatchFeaturesTest(test.TestCase): - - def setUp(self): - super(ReadBatchFeaturesTest, self).setUp() - self._num_files = 2 - self._num_records = 7 - self.test_filenames = self._createFiles() - - def _read_batch_features(self, - filenames, - num_epochs, - batch_size, - reader_num_threads=1, - parser_num_threads=1, - shuffle=False, - shuffle_seed=None, - drop_final_batch=False): - self.filenames = filenames - self.num_epochs = num_epochs - self.batch_size = batch_size - - return readers.make_batched_features_dataset( - file_pattern=self.filenames, - batch_size=self.batch_size, - features={ - "file": parsing_ops.FixedLenFeature([], dtypes.int64), - "record": parsing_ops.FixedLenFeature([], dtypes.int64), - "keywords": parsing_ops.VarLenFeature(dtypes.string) - }, - reader=core_readers.TFRecordDataset, - num_epochs=self.num_epochs, - shuffle=shuffle, - shuffle_seed=shuffle_seed, - reader_num_threads=reader_num_threads, - parser_num_threads=parser_num_threads, - drop_final_batch=drop_final_batch).make_one_shot_iterator( - ).get_next() - - def _record(self, f, r): - example = example_pb2.Example( - features=feature_pb2.Features( - feature={ - "file": - feature_pb2.Feature( - int64_list=feature_pb2.Int64List(value=[f])), - "record": - feature_pb2.Feature( - int64_list=feature_pb2.Int64List(value=[r])), - "keywords": - feature_pb2.Feature( - bytes_list=feature_pb2.BytesList( - value=self._get_keywords(f, r))) - })) - return example.SerializeToString() - - def _get_keywords(self, f, r): - num_keywords = 1 + (f + r) % 2 - keywords = [] - for index in range(num_keywords): - keywords.append(compat.as_bytes("keyword%d" % index)) - return keywords - - def _createFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) - filenames.append(fn) - writer = python_io.TFRecordWriter(fn) - for j in range(self._num_records): - writer.write(self._record(i, j)) - writer.close() - return filenames - - def _run_actual_batch(self, outputs, sess): - file_op = outputs["file"] - keywords_indices_op = outputs["keywords"].indices - keywords_values_op = outputs["keywords"].values - keywords_dense_shape_op = outputs["keywords"].dense_shape - record_op = outputs["record"] - return sess.run([ - file_op, keywords_indices_op, keywords_values_op, - keywords_dense_shape_op, record_op - ]) - - def _next_actual_batch(self, sess): - return self._run_actual_batch(self.outputs, sess) - - def _next_expected_batch(self, - file_indices, - batch_size, - num_epochs, - cycle_length=1): - - def _next_record(file_indices): - for j in file_indices: - for i in range(self._num_records): - yield j, i - - def _next_record_interleaved(file_indices, cycle_length): - return _interleave([_next_record([i]) for i in file_indices], - cycle_length) - - file_batch = [] - keywords_batch_indices = [] - keywords_batch_values = [] - keywords_batch_max_len = 0 - record_batch = [] - batch_index = 0 - for _ in range(num_epochs): - if cycle_length == 1: - next_records = _next_record(file_indices) - else: - next_records = _next_record_interleaved(file_indices, cycle_length) - for record in next_records: - f = record[0] - r = record[1] - file_batch.append(f) - record_batch.append(r) - keywords = self._get_keywords(f, r) - keywords_batch_values.extend(keywords) - keywords_batch_indices.extend( - [[batch_index, i] for i in range(len(keywords))]) - batch_index += 1 - keywords_batch_max_len = max(keywords_batch_max_len, len(keywords)) - if len(file_batch) == batch_size: - yield [ - file_batch, keywords_batch_indices, keywords_batch_values, - [batch_size, keywords_batch_max_len], record_batch - ] - file_batch = [] - keywords_batch_indices = [] - keywords_batch_values = [] - keywords_batch_max_len = 0 - record_batch = [] - batch_index = 0 - if file_batch: - yield [ - file_batch, keywords_batch_indices, keywords_batch_values, - [len(file_batch), keywords_batch_max_len], record_batch - ] - - def _verify_records(self, - sess, - batch_size, - file_index=None, - num_epochs=1, - interleave_cycle_length=1): - if file_index is not None: - file_indices = [file_index] - else: - file_indices = range(self._num_files) - - for expected_batch in self._next_expected_batch( - file_indices, batch_size, num_epochs, interleave_cycle_length): - actual_batch = self._next_actual_batch(sess) - for i in range(len(expected_batch)): - self.assertAllEqual(expected_batch[i], actual_batch[i]) +class ReadBatchFeaturesTest( + reader_dataset_ops_test_base.ReadBatchFeaturesTestBase): def testRead(self): for batch_size in [1, 2]: @@ -444,33 +42,33 @@ class ReadBatchFeaturesTest(test.TestCase): with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: # Basic test: read from file 0. - self.outputs = self._read_batch_features( + self.outputs = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, - batch_size=batch_size) - self._verify_records(sess, batch_size, 0, num_epochs=num_epochs) + batch_size=batch_size).make_one_shot_iterator().get_next() + self.verify_records(sess, batch_size, 0, num_epochs=num_epochs) with self.assertRaises(errors.OutOfRangeError): self._next_actual_batch(sess) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: # Basic test: read from file 1. - self.outputs = self._read_batch_features( + self.outputs = self.make_batch_feature( filenames=self.test_filenames[1], num_epochs=num_epochs, - batch_size=batch_size) - self._verify_records(sess, batch_size, 1, num_epochs=num_epochs) + batch_size=batch_size).make_one_shot_iterator().get_next() + self.verify_records(sess, batch_size, 1, num_epochs=num_epochs) with self.assertRaises(errors.OutOfRangeError): self._next_actual_batch(sess) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: # Basic test: read from both files. - self.outputs = self._read_batch_features( + self.outputs = self.make_batch_feature( filenames=self.test_filenames, num_epochs=num_epochs, - batch_size=batch_size) - self._verify_records(sess, batch_size, num_epochs=num_epochs) + batch_size=batch_size).make_one_shot_iterator().get_next() + self.verify_records(sess, batch_size, num_epochs=num_epochs) with self.assertRaises(errors.OutOfRangeError): self._next_actual_batch(sess) @@ -504,18 +102,18 @@ class ReadBatchFeaturesTest(test.TestCase): # Test that shuffling with same seed produces the same result. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: - outputs1 = self._read_batch_features( + outputs1 = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, batch_size=batch_size, shuffle=True, - shuffle_seed=5) - outputs2 = self._read_batch_features( + shuffle_seed=5).make_one_shot_iterator().get_next() + outputs2 = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, batch_size=batch_size, shuffle=True, - shuffle_seed=5) + shuffle_seed=5).make_one_shot_iterator().get_next() for _ in range(total_records // batch_size): batch1 = self._run_actual_batch(outputs1, sess) batch2 = self._run_actual_batch(outputs2, sess) @@ -525,18 +123,18 @@ class ReadBatchFeaturesTest(test.TestCase): # Test that shuffling with different seeds produces a different order. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: - outputs1 = self._read_batch_features( + outputs1 = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, batch_size=batch_size, shuffle=True, - shuffle_seed=5) - outputs2 = self._read_batch_features( + shuffle_seed=5).make_one_shot_iterator().get_next() + outputs2 = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, batch_size=batch_size, shuffle=True, - shuffle_seed=15) + shuffle_seed=15).make_one_shot_iterator().get_next() all_equal = True for _ in range(total_records // batch_size): batch1 = self._run_actual_batch(outputs1, sess) @@ -552,13 +150,14 @@ class ReadBatchFeaturesTest(test.TestCase): for parser_num_threads in [2, 4]: with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: - self.outputs = self._read_batch_features( + self.outputs = self.make_batch_feature( filenames=self.test_filenames, num_epochs=num_epochs, batch_size=batch_size, reader_num_threads=reader_num_threads, - parser_num_threads=parser_num_threads) - self._verify_records( + parser_num_threads=parser_num_threads).make_one_shot_iterator( + ).get_next() + self.verify_records( sess, batch_size, num_epochs=num_epochs, @@ -571,11 +170,11 @@ class ReadBatchFeaturesTest(test.TestCase): for num_epochs in [1, 10]: with ops.Graph().as_default(): # Basic test: read from file 0. - self.outputs = self._read_batch_features( + self.outputs = self.make_batch_feature( filenames=self.test_filenames[0], num_epochs=num_epochs, batch_size=batch_size, - drop_final_batch=True) + drop_final_batch=True).make_one_shot_iterator().get_next() for _, tensor in self.outputs.items(): if isinstance(tensor, ops.Tensor): # Guard against SparseTensor. self.assertEqual(tensor.shape[0], batch_size) @@ -1069,7 +668,30 @@ class MakeCsvDatasetTest(test.TestCase): self.assertFalse(all_equal) -class MakeTFRecordDatasetTest(TFRecordDatasetTestBase): +class MakeTFRecordDatasetTest( + reader_dataset_ops_test_base.TFRecordDatasetTestBase): + + def _interleave(self, iterators, cycle_length): + pending_iterators = iterators + open_iterators = [] + num_open = 0 + for i in range(cycle_length): + if pending_iterators: + open_iterators.append(pending_iterators.pop(0)) + num_open += 1 + + while num_open: + for i in range(min(cycle_length, len(open_iterators))): + if open_iterators[i] is None: + continue + try: + yield next(open_iterators[i]) + except StopIteration: + if pending_iterators: + open_iterators[i] = pending_iterators.pop(0) + else: + open_iterators[i] = None + num_open -= 1 def _next_expected_batch(self, file_indices, @@ -1085,8 +707,8 @@ class MakeTFRecordDatasetTest(TFRecordDatasetTestBase): yield j, i def _next_record_interleaved(file_indices, cycle_length): - return _interleave([_next_record([i]) for i in file_indices], - cycle_length) + return self._interleave([_next_record([i]) for i in file_indices], + cycle_length) record_batch = [] batch_index = 0 diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..e63bc4c72049c61aa40314ffebe5c4366a818d46 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py @@ -0,0 +1,331 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base class for testing reader datasets.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os +import zlib + +from tensorflow.contrib.data.python.ops import readers +from tensorflow.core.example import example_pb2 +from tensorflow.core.example import feature_pb2 +from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.lib.io import python_io +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.platform import test +from tensorflow.python.util import compat + + +class FixedLengthRecordDatasetTestBase(test.TestCase): + """Base class for setting up and testing FixedLengthRecordDataset.""" + + def setUp(self): + super(FixedLengthRecordDatasetTestBase, self).setUp() + self._num_files = 2 + self._num_records = 7 + self._header_bytes = 5 + self._record_bytes = 3 + self._footer_bytes = 2 + + def _record(self, f, r): + return compat.as_bytes(str(f * 2 + r) * self._record_bytes) + + def _createFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) + filenames.append(fn) + with open(fn, "wb") as f: + f.write(b"H" * self._header_bytes) + for j in range(self._num_records): + f.write(self._record(i, j)) + f.write(b"F" * self._footer_bytes) + return filenames + + +class ReadBatchFeaturesTestBase(test.TestCase): + """Base class for setting up and testing `make_batched_feature_dataset`.""" + + def setUp(self): + super(ReadBatchFeaturesTestBase, self).setUp() + self._num_files = 2 + self._num_records = 7 + self.test_filenames = self._createFiles() + + def make_batch_feature(self, + filenames, + num_epochs, + batch_size, + reader_num_threads=1, + parser_num_threads=1, + shuffle=False, + shuffle_seed=None, + drop_final_batch=False): + self.filenames = filenames + self.num_epochs = num_epochs + self.batch_size = batch_size + + return readers.make_batched_features_dataset( + file_pattern=self.filenames, + batch_size=self.batch_size, + features={ + "file": parsing_ops.FixedLenFeature([], dtypes.int64), + "record": parsing_ops.FixedLenFeature([], dtypes.int64), + "keywords": parsing_ops.VarLenFeature(dtypes.string) + }, + reader=core_readers.TFRecordDataset, + num_epochs=self.num_epochs, + shuffle=shuffle, + shuffle_seed=shuffle_seed, + reader_num_threads=reader_num_threads, + parser_num_threads=parser_num_threads, + drop_final_batch=drop_final_batch) + + def _record(self, f, r): + example = example_pb2.Example( + features=feature_pb2.Features( + feature={ + "file": + feature_pb2.Feature( + int64_list=feature_pb2.Int64List(value=[f])), + "record": + feature_pb2.Feature( + int64_list=feature_pb2.Int64List(value=[r])), + "keywords": + feature_pb2.Feature( + bytes_list=feature_pb2.BytesList( + value=self._get_keywords(f, r))) + })) + return example.SerializeToString() + + def _get_keywords(self, f, r): + num_keywords = 1 + (f + r) % 2 + keywords = [] + for index in range(num_keywords): + keywords.append(compat.as_bytes("keyword%d" % index)) + return keywords + + def _sum_keywords(self, num_files): + sum_keywords = 0 + for i in range(num_files): + for j in range(self._num_records): + sum_keywords += 1 + (i + j) % 2 + return sum_keywords + + def _createFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) + filenames.append(fn) + writer = python_io.TFRecordWriter(fn) + for j in range(self._num_records): + writer.write(self._record(i, j)) + writer.close() + return filenames + + def _run_actual_batch(self, outputs, sess): + file_op = outputs["file"] + keywords_indices_op = outputs["keywords"].indices + keywords_values_op = outputs["keywords"].values + keywords_dense_shape_op = outputs["keywords"].dense_shape + record_op = outputs["record"] + return sess.run([ + file_op, keywords_indices_op, keywords_values_op, + keywords_dense_shape_op, record_op + ]) + + def _next_actual_batch(self, sess): + return self._run_actual_batch(self.outputs, sess) + + def _interleave(self, iterators, cycle_length): + pending_iterators = iterators + open_iterators = [] + num_open = 0 + for i in range(cycle_length): + if pending_iterators: + open_iterators.append(pending_iterators.pop(0)) + num_open += 1 + + while num_open: + for i in range(min(cycle_length, len(open_iterators))): + if open_iterators[i] is None: + continue + try: + yield next(open_iterators[i]) + except StopIteration: + if pending_iterators: + open_iterators[i] = pending_iterators.pop(0) + else: + open_iterators[i] = None + num_open -= 1 + + def _next_expected_batch(self, + file_indices, + batch_size, + num_epochs, + cycle_length=1): + + def _next_record(file_indices): + for j in file_indices: + for i in range(self._num_records): + yield j, i + + def _next_record_interleaved(file_indices, cycle_length): + return self._interleave([_next_record([i]) for i in file_indices], + cycle_length) + + file_batch = [] + keywords_batch_indices = [] + keywords_batch_values = [] + keywords_batch_max_len = 0 + record_batch = [] + batch_index = 0 + for _ in range(num_epochs): + if cycle_length == 1: + next_records = _next_record(file_indices) + else: + next_records = _next_record_interleaved(file_indices, cycle_length) + for record in next_records: + f = record[0] + r = record[1] + file_batch.append(f) + record_batch.append(r) + keywords = self._get_keywords(f, r) + keywords_batch_values.extend(keywords) + keywords_batch_indices.extend( + [[batch_index, i] for i in range(len(keywords))]) + batch_index += 1 + keywords_batch_max_len = max(keywords_batch_max_len, len(keywords)) + if len(file_batch) == batch_size: + yield [ + file_batch, keywords_batch_indices, keywords_batch_values, + [batch_size, keywords_batch_max_len], record_batch + ] + file_batch = [] + keywords_batch_indices = [] + keywords_batch_values = [] + keywords_batch_max_len = 0 + record_batch = [] + batch_index = 0 + if file_batch: + yield [ + file_batch, keywords_batch_indices, keywords_batch_values, + [len(file_batch), keywords_batch_max_len], record_batch + ] + + def verify_records(self, + sess, + batch_size, + file_index=None, + num_epochs=1, + interleave_cycle_length=1): + if file_index is not None: + file_indices = [file_index] + else: + file_indices = range(self._num_files) + + for expected_batch in self._next_expected_batch( + file_indices, batch_size, num_epochs, interleave_cycle_length): + actual_batch = self._next_actual_batch(sess) + for i in range(len(expected_batch)): + self.assertAllEqual(expected_batch[i], actual_batch[i]) + + +class TextLineDatasetTestBase(test.TestCase): + """Base class for setting up and testing TextLineDataset.""" + + def _lineText(self, f, l): + return compat.as_bytes("%d: %d" % (f, l)) + + def _createFiles(self, + num_files, + num_lines, + crlf=False, + compression_type=None): + filenames = [] + for i in range(num_files): + fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i) + filenames.append(fn) + contents = [] + for j in range(num_lines): + contents.append(self._lineText(i, j)) + # Always include a newline after the record unless it is + # at the end of the file, in which case we include it + if j + 1 != num_lines or i == 0: + contents.append(b"\r\n" if crlf else b"\n") + contents = b"".join(contents) + + if not compression_type: + with open(fn, "wb") as f: + f.write(contents) + elif compression_type == "GZIP": + with gzip.GzipFile(fn, "wb") as f: + f.write(contents) + elif compression_type == "ZLIB": + contents = zlib.compress(contents) + with open(fn, "wb") as f: + f.write(contents) + else: + raise ValueError("Unsupported compression_type", compression_type) + + return filenames + + +class TFRecordDatasetTestBase(test.TestCase): + """Base class for setting up and testing TFRecordDataset.""" + + def setUp(self): + super(TFRecordDatasetTestBase, self).setUp() + self._num_files = 2 + self._num_records = 7 + + self.test_filenames = self._createFiles() + + self.filenames = array_ops.placeholder(dtypes.string, shape=[None]) + self.num_epochs = array_ops.placeholder_with_default( + constant_op.constant(1, dtypes.int64), shape=[]) + self.compression_type = array_ops.placeholder_with_default("", shape=[]) + self.batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = core_readers.TFRecordDataset( + self.filenames, self.compression_type).repeat(self.num_epochs) + batch_dataset = repeat_dataset.batch(self.batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + self.init_op = iterator.make_initializer(repeat_dataset) + self.init_batch_op = iterator.make_initializer(batch_dataset) + self.get_next = iterator.get_next() + + def _record(self, f, r): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _createFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) + filenames.append(fn) + writer = python_io.TFRecordWriter(fn) + for j in range(self._num_records): + writer.write(self._record(i, j)) + writer.close() + return filenames diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index bdc003a8a5bd646e1d5c598befa2694da512d0a9..c5cfddb72b56a1bcffc80c0dd34994def3ee45cd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -17,10 +17,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np -from six.moves import xrange # pylint: disable=redefined-builtin import time + from absl.testing import parameterized +import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.data.python.ops import resampling from tensorflow.python.data.ops import dataset_ops diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py index eb2ceff893543f710d4f0246adf4e6367a2deeb0..42cada0b97bcd9ab755297e8b1f0667766f7999e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py @@ -21,7 +21,6 @@ import itertools import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import scan_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.eager import context @@ -64,7 +63,7 @@ class ScanDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFibonacci(self): iterator = dataset_ops.Dataset.from_tensors(1).repeat(None).apply( scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1])) @@ -168,18 +167,5 @@ class ScanDatasetTest(test.TestCase): scan_ops.scan(constant_op.constant(1, dtype=dtypes.int32), _scan_fn)) -class ScanDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, num_elements): - return dataset_ops.Dataset.from_tensors(1).repeat(num_elements).apply( - scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1]))) - - def testScanCore(self): - num_output = 5 - self.run_core_tests(lambda: self._build_dataset(num_output), - lambda: self._build_dataset(2), num_output) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..686788522acdf1c5e91132c38bdc81d10d2a0cc2 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD @@ -0,0 +1,526 @@ +package(default_visibility = ["//tensorflow:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + +py_library( + name = "dataset_serialization_test_base", + srcs = [ + "dataset_serialization_test_base.py", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:lookup_ops", + "//tensorflow/python:platform", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:training", + "//tensorflow/python:util", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:iterator_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "batch_dataset_serialization_test", + size = "medium", + srcs = ["batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "cache_dataset_serialization_test", + size = "small", + srcs = ["cache_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "concatenate_dataset_serialization_test", + size = "small", + srcs = ["concatenate_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "dataset_constructor_serialization_test", + size = "medium", + srcs = ["dataset_constructor_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "filter_dataset_serialization_test", + size = "medium", + srcs = ["filter_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "fixed_length_record_dataset_serialization_test", + size = "medium", + srcs = ["fixed_length_record_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "flat_map_dataset_serialization_test", + size = "medium", + srcs = ["flat_map_dataset_serialization_test.py"], + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "group_by_reducer_serialization_test", + size = "medium", + srcs = ["group_by_reducer_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:grouping", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "group_by_window_serialization_test", + size = "medium", + srcs = ["group_by_window_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:grouping", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "ignore_errors_serialization_test", + size = "small", + srcs = ["ignore_errors_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "interleave_dataset_serialization_test", + size = "medium", + srcs = ["interleave_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "map_and_batch_dataset_serialization_test", + size = "medium", + srcs = ["map_and_batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:client_testlib", + "//tensorflow/python:math_ops", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "map_dataset_serialization_test", + size = "medium", + srcs = ["map_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "optimize_dataset_serialization_test", + size = "small", + srcs = ["optimize_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:optimization", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "padded_batch_dataset_serialization_test", + size = "medium", + srcs = ["padded_batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:string_ops", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "parallel_interleave_dataset_serialization_test", + size = "medium", + srcs = ["parallel_interleave_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:interleave_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "parallel_map_dataset_serialization_test", + size = "medium", + srcs = ["parallel_map_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "prefetch_dataset_serialization_test", + size = "small", + srcs = ["prefetch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "range_dataset_serialization_test", + size = "small", + srcs = ["range_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:io_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sample_from_datasets_serialization_test", + size = "medium", + srcs = ["sample_from_datasets_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:interleave_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "scan_dataset_serialization_test", + size = "small", + srcs = ["scan_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:scan_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sequence_dataset_serialization_test", + size = "medium", + srcs = ["sequence_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "serialization_integration_test", + size = "small", + srcs = ["serialization_integration_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "shuffle_and_repeat_dataset_serialization_test", + size = "medium", + srcs = ["shuffle_and_repeat_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:shuffle_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "shuffle_dataset_serialization_test", + size = "medium", + srcs = ["shuffle_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sql_dataset_serialization_test", + size = "small", + srcs = ["sql_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:sql_dataset_op_test_base", + "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + ], +) + +py_test( + name = "stats_dataset_serialization_test", + size = "medium", + srcs = ["stats_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:stats_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "textline_dataset_serialization_test", + size = "medium", + srcs = ["textline_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "tf_record_dataset_serialization_test", + size = "medium", + srcs = ["tf_record_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "unbatch_dataset_serialization_test", + size = "medium", + srcs = ["unbatch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "unique_dataset_serialization_test", + size = "small", + srcs = ["unique_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:unique", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "zip_dataset_serialization_test", + size = "small", + srcs = ["zip_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..af87d8b6083de268fafd4346d2871f14e0f4e7c9 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py @@ -0,0 +1,83 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the BatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class BatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): + components = ( + np.arange(tensor_slice_len), + np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(tensor_slice_len)) + + return dataset_ops.Dataset.from_tensor_slices(components).batch(batch_size) + + def testCore(self): + tensor_slice_len = 8 + batch_size = 2 + num_outputs = tensor_slice_len // batch_size + self.run_core_tests( + lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), + lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), + num_outputs) + + def _build_dataset_dense_to_sparse(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).map( + lambda x: array_ops.fill([x], x)).apply( + batching.dense_to_sparse_batch(4, [12])) + + def testDenseToSparseBatchDatasetCore(self): + components = np.random.randint(5, size=(40,)).astype(np.int32) + diff_comp = np.random.randint(2, size=(100,)).astype(np.int32) + + num_outputs = len(components) // 4 + self.run_core_tests(lambda: self._build_dataset_dense_to_sparse(components), + lambda: self._build_dataset_dense_to_sparse(diff_comp), + num_outputs) + + def _sparse(self, i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + def _build_dataset_sparse(self, batch_size=5): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(batch_size) + + def testSparseCore(self): + self.run_core_tests(self._build_dataset_sparse, + lambda: self._build_dataset_sparse(2), 2) + + def _build_dataset_nested_sparse(self): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(5).batch(2) + + def testNestedSparseCore(self): + self.run_core_tests(self._build_dataset_nested_sparse, None, 1) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a1100893c7384b0e2bd9fcfdaa8d3698b95d28 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py @@ -0,0 +1,190 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the CacheDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import errors +from tensorflow.python.platform import test + + +class CacheDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self.range_size = 10 + self.num_repeats = 3 + self.num_outputs = self.range_size * self.num_repeats + self.cache_file_prefix = 'test' + + def ds_fn(self): + return dataset_ops.Dataset.range(self.range_size).cache( + os.path.join(self.get_temp_dir(), + self.cache_file_prefix)).repeat(self.num_repeats) + + def expected_outputs(self): + return list(range(self.range_size)) * self.num_repeats + + def testCheckpointBeforeOneEpoch(self): + # Generate 5 entries from iterator and save checkpoint. + outputs = self.gen_outputs(self.ds_fn, [], 5, verify_exhausted=False) + self.assertSequenceEqual(outputs, range(5)) + + # Restore from checkpoint and produce the rest of the elements from the + # iterator. + outputs.extend( + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 5, + ckpt_saved=True, + verify_exhausted=False)) + self.assertSequenceEqual(outputs, self.expected_outputs()) + + def testCheckpointBeforeOneEpochThenRunFewSteps(self): + # Generate 8 entries from iterator but save checkpoint after producing + # 5. + outputs = self.gen_outputs( + self.ds_fn, [5], + 8, + verify_exhausted=False, + save_checkpoint_at_end=False) + self.assertSequenceEqual(outputs, range(8)) + + # Restoring from checkpoint and running GetNext should return a + # `AlreadExistsError` now because the lockfile already exists. + with self.assertRaises(errors.AlreadyExistsError): + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 5, + ckpt_saved=True, + verify_exhausted=False) + + def testCheckpointAfterOneEpoch(self): + # Generate 15 entries from iterator and save checkpoint. + outputs = self.gen_outputs(self.ds_fn, [], 15, verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) + list(range(5))) + + # Restore from checkpoint and produce the rest of the elements from the + # iterator. + outputs.extend( + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 15, + ckpt_saved=True, + verify_exhausted=False)) + self.assertSequenceEqual(outputs, self.expected_outputs()) + + def testCheckpointAfterOneEpochThenRunFewSteps(self): + # Generate 18 entries from iterator but save checkpoint after producing + # 15. + outputs = self.gen_outputs( + self.ds_fn, [15], + 18, + verify_exhausted=False, + save_checkpoint_at_end=False) + self.assertSequenceEqual(outputs, list(range(10)) + list(range(8))) + + outputs = list(range(10)) + list(range(5)) + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 15, + ckpt_saved=True, + verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) * 3) + + def testCheckpointBeforeOneEpochButRunCompleteEpoch(self): + # Generate 13 entries from iterator but save checkpoint after producing + # 5. + outputs = self.gen_outputs( + self.ds_fn, [5], + 13, + verify_exhausted=False, + save_checkpoint_at_end=False) + self.assertSequenceEqual(outputs, list(range(10)) + list(range(3))) + + # Since we ran for more than one epoch, the cache was completely written. + # The ckpt was saved when the iterator was in cache-write mode. Test that + # the iterator falls back to read mode after restoring if the cache has + # been completely written. + + outputs = list(range(5)) + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 5, + ckpt_saved=True, + verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) * 3) + + def testCheckpointUnusedWriterIterator(self): + # Checkpoint before get_next is called even once. + outputs = self.gen_outputs(self.ds_fn, [], 0, verify_exhausted=False) + self.assertSequenceEqual(outputs, []) + + outputs = self.gen_outputs( + self.ds_fn, [], + self.num_outputs, + ckpt_saved=True, + verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) * 3) + + def testCheckpointUnusedMidwayWriterIterator(self): + # Produce 5 elements and checkpoint. + outputs = self.gen_outputs(self.ds_fn, [], 5, verify_exhausted=False) + self.assertSequenceEqual(outputs, range(5)) + + # Restore from checkpoint, then produce no elements and checkpoint. + outputs.extend( + self.gen_outputs( + self.ds_fn, [], 0, ckpt_saved=True, verify_exhausted=False)) + self.assertSequenceEqual(outputs, range(5)) + + # Restore from checkpoint and produce rest of the elements. + outputs.extend( + self.gen_outputs( + self.ds_fn, [], + self.num_outputs - 5, + ckpt_saved=True, + verify_exhausted=False)) + self.assertSequenceEqual(outputs, list(range(10)) * 3) + + def testUnusedCheckpointError(self): + # Produce 5 elements and save ckpt. + outputs = self.gen_outputs(self.ds_fn, [], 5, verify_exhausted=False) + self.assertSequenceEqual(outputs, range(5)) + + # Since the complete cache has not been written, a new iterator which does + # not restore the checkpoint will throw an error since there is a partial + # cache shard. + with self.assertRaises(errors.AlreadyExistsError): + outputs = self.gen_outputs( + self.ds_fn, [], self.num_outputs, verify_exhausted=False) + + def testIgnoreCheckpointIfCacheWritten(self): + # Produce 15 elements and save ckpt. This will write the complete cache. + outputs = self.gen_outputs(self.ds_fn, [], 15, verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) + list(range(5))) + + # Build the iterator again but do not restore from ckpt. Since the cache + # has already been written we should be able to use it. + outputs = self.gen_outputs( + self.ds_fn, [], self.num_outputs, verify_exhausted=False) + self.assertSequenceEqual(outputs, list(range(10)) * 3) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py similarity index 92% rename from tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py index 17f2980157ddd0350dafd1d745cbb9b64e65f7c5..96f13d75a31b6762b35062e6cf8c0cdb4d61d2c5 100644 --- a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the ConcatenateDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2139b5c33db69a7ffbdebee74e5824928004b407 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py @@ -0,0 +1,95 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the dataset constructors serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.platform import test + + +class FromTensorsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_tensor_dataset(self, variable_array): + components = (variable_array, np.array([1, 2, 3]), np.array(37.0)) + + return dataset_ops.Dataset.from_tensors(components) + + def testFromTensorsCore(self): + # Equal length components + arr = np.array(1) + num_outputs = 1 + diff_arr = np.array(2) + self.run_core_tests(lambda: self._build_tensor_dataset(arr), + lambda: self._build_tensor_dataset(diff_arr), + num_outputs) + + +class FromTensorSlicesSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_tensor_slices_dataset(self, components): + return dataset_ops.Dataset.from_tensor_slices(components) + + def testFromTensorSlicesCore(self): + # Equal length components + components = (np.tile(np.array([[1], [2], [3], [4]]), 20), + np.tile(np.array([[12], [13], [14], [15]]), 22), + np.array([37.0, 38.0, 39.0, 40.0])) + + diff_comp = (np.tile(np.array([[1], [2], [3], [4]]), 20), + np.tile(np.array([[5], [6], [7], [8]]), 22), + np.array([1.0, 2.0, 3.0, 4.0])) + + dict_components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]} + + self.run_core_tests(lambda: self._build_tensor_slices_dataset(components), + lambda: self._build_tensor_slices_dataset(diff_comp), 4) + self.run_core_tests( + lambda: self._build_tensor_slices_dataset(dict_components), None, 3) + + +class FromSparseTensorSlicesSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_sparse_tensor_slice_dataset(self, slices): + indices = np.array( + [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))], + dtype=np.int64) + values = np.array([val for s in slices for val in s], dtype=np.float64) + dense_shape = np.array( + [len(slices), max(len(s) for s in slices) + 1], dtype=np.int64) + sparse_components = sparse_tensor.SparseTensor(indices, values, dense_shape) + return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components) + + def testFromSparseTensorSlicesCore(self): + slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []] + diff_slices = [[1., 2.], [2.], [2., 3., 4.], [], [], []] + + self.run_core_tests( + lambda: self._build_sparse_tensor_slice_dataset(slices), + lambda: self._build_sparse_tensor_slice_dataset(diff_slices), + 9, + sparse_tensors=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_serialization_test_base.py similarity index 97% rename from tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/dataset_serialization_test_base.py index 78ecce8f7daaf84002ae78d8d77820755b967d89..393f08850b1865180a8b94e9209b2445b54c8b69 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_serialization_test_base.py @@ -467,7 +467,8 @@ class DatasetSerializationTestBase(test.TestCase): ckpt_saved=False, init_before_restore=False, sparse_tensors=False, - verify_exhausted=True): + verify_exhausted=True, + save_checkpoint_at_end=True): """Generates elements from input dataset while stopping at break points. Produces `num_outputs` outputs and saves the state of the iterator in the @@ -490,6 +491,10 @@ class DatasetSerializationTestBase(test.TestCase): sparse_tensors: Whether dataset is built from SparseTensor(s). verify_exhausted: Whether to verify that the iterator has been exhausted after producing `num_outputs` elements. + save_checkpoint_at_end: Whether to save a checkpoint after producing all + outputs. If False, checkpoints are saved each break point but not at the + end. Note that checkpoints overwrite each other so there is always only + a single checkpoint available. Defaults to True. Returns: A list of `num_outputs` items. @@ -526,8 +531,9 @@ class DatasetSerializationTestBase(test.TestCase): if i == len(break_points) and verify_exhausted: with self.assertRaises(errors.OutOfRangeError): sess.run(get_next_op) - self._save(sess, saver) - ckpt_saved = True + if save_checkpoint_at_end or i < len(break_points): + self._save(sess, saver) + ckpt_saved = True return outputs diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py similarity index 91% rename from tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py index b572d6ed770fc0fe0f852359baf343c55966eddd..7c170078a11aadce9e5730437e4c25209bd58edb 100644 --- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the FilterDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import math_ops @@ -35,7 +35,7 @@ class FilterDatasetSerializationTest( def testFilterCore(self): div = 3 - num_outputs = np.sum([x % 3 is not 2 for x in range(100)]) + num_outputs = np.sum([x % 3 != 2 for x in range(100)]) self.run_core_tests(lambda: self._build_filter_range_graph(div), lambda: self._build_filter_range_graph(div * 2), num_outputs) diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..34392d88d4505175c4562e23d5f0c4116e00b022 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py @@ -0,0 +1,45 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the FixedLengthRecordDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class FixedLengthRecordDatasetSerializationTest( + reader_dataset_ops_test_base.FixedLengthRecordDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, num_epochs, compression_type=None): + filenames = self._createFiles() + return core_readers.FixedLengthRecordDataset( + filenames, self._record_bytes, self._header_bytes, + self._footer_bytes).repeat(num_epochs) + + def testFixedLengthRecordCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), + lambda: self._build_iterator_graph(num_epochs * 2), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py similarity index 96% rename from tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py index f3feecef32e587045be25056815315136a883ca7..16051ffd3fd1e1e7ff419f28109df7bc1f165257 100644 --- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the FlatMapDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..571e0899bbc1f856d66f85c4f6f3ac78aa0b1368 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py @@ -0,0 +1,61 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the GroupByReducer serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import grouping +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class GroupByReducerSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, components): + reducer = grouping.Reducer( + init_func=lambda _: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + + return dataset_ops.Dataset.from_tensor_slices(components).apply( + grouping.group_by_reducer(lambda x: x % 5, reducer)) + + def testCoreGroupByReducer(self): + components = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64) + self.verify_unused_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_init_before_restore( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_multiple_breaks( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_reset_restored_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_restore_in_empty_graph( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + diff_components = np.array([5, 4, 3, 2, 1, 0], dtype=np.int64) + self.verify_restore_in_modified_graph( + lambda: self._build_dataset(components), + lambda: self._build_dataset(diff_components), + 5, + verify_exhausted=True) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f86af4084ef61c2f20dbe2fb388a20287676f39d --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py @@ -0,0 +1,57 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the GroupByWindow serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import grouping +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class GroupByWindowSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply( + grouping.group_by_window(lambda x: x % 3, lambda _, xs: xs.batch(4), 4)) + + def testCoreGroupByWindow(self): + components = np.array( + [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0], dtype=np.int64) + self.verify_unused_iterator( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_init_before_restore( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_multiple_breaks( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_reset_restored_iterator( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_restore_in_empty_graph( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + diff_components = np.array([0, 0, 0, 1, 1, 1], dtype=np.int64) + self.verify_restore_in_modified_graph( + lambda: self._build_dataset(components), + lambda: self._build_dataset(diff_components), + 12, + verify_exhausted=False) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..65ae9923b8f64dddcd54afc53e2fa67bc770fc2a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py @@ -0,0 +1,46 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the IgnoreErrors input pipeline ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import error_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class IgnoreErrorsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_ds(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).map( + lambda x: array_ops.check_numerics(x, "message")).apply( + error_ops.ignore_errors()) + + def testIgnoreErrorsCore(self): + components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) + diff_components = np.array([1., 2., 3., np.nan]).astype(np.float32) + num_outputs = 4 + self.run_core_tests(lambda: self._build_ds(components), + lambda: self._build_ds(diff_components), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ac3892fe81a1c0d325ddc5f501c2caed4b53f5d5 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py @@ -0,0 +1,86 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the InterleaveDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops +from tensorflow.python.platform import test + + +class InterleaveDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, input_values, cycle_length, block_length): + repeat_count = 2 + return dataset_ops.Dataset.from_tensor_slices(input_values).repeat( + repeat_count).interleave( + lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x), + cycle_length, block_length) + + def testSerializationCore(self): + input_values = np.array([4, 5, 6], dtype=np.int64) + num_outputs = np.sum(input_values) * 2 + # cycle_length > 1, block_length > 1 + cycle_length = 2 + block_length = 3 + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + lambda: self._build_iterator_graph( + input_values, cycle_length * 2, block_length * 1), + num_outputs) + # cycle_length = 1 + cycle_length = 1 + block_length = 3 + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + None, num_outputs) + # block_length = 1 + cycle_length = 2 + block_length = 1 + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + None, num_outputs) + # pylint: enable=g-long-lambda + + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _interleave_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_dataset(): + return dataset_ops.Dataset.range(10).map(_map_fn).interleave( + _interleave_fn, cycle_length=1) + + self.run_core_tests(_build_dataset, None, 20) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c9cd211328fa595c0ce0efe3509e8ba9dc06af80 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py @@ -0,0 +1,88 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the MapAndBatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class MapAndBatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testNumParallelBatches(self): + range_size = 11 + num_repeats = 2 + batch_size = 5 + total_outputs = range_size * num_repeats + num_outputs_drop_remainder = total_outputs // batch_size + num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) + num_parallel_batches = 2 + + def build_ds(range_start, drop_remainder=False): + + def _map_fn(x): + return math_ops.square(x) + + return dataset_ops.Dataset.range( + range_start, range_start + range_size).repeat(num_repeats).apply( + batching.map_and_batch( + map_func=_map_fn, + batch_size=batch_size, + num_parallel_batches=num_parallel_batches, + drop_remainder=drop_remainder)) + + self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), + num_outputs_keep_remainder) + self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), + num_outputs_drop_remainder) + + def testNumParallelCalls(self): + range_size = 11 + num_repeats = 2 + batch_size = 5 + total_outputs = range_size * num_repeats + num_outputs_drop_remainder = total_outputs // batch_size + num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) + num_parallel_calls = 7 + + def build_ds(range_start, drop_remainder=False): + + def _map_fn(x): + return math_ops.square(x) + + return dataset_ops.Dataset.range( + range_start, range_start + range_size).repeat(num_repeats).apply( + batching.map_and_batch( + map_func=_map_fn, + batch_size=batch_size, + num_parallel_calls=num_parallel_calls, + drop_remainder=drop_remainder)) + + self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), + num_outputs_keep_remainder) + self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), + num_outputs_drop_remainder) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ab783e5cce95ed63fe64c273abb3846121c7a274 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py @@ -0,0 +1,140 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the MapDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import function +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test + + +class MapDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self._tensor_slice_len = 7 + self._num_epochs = 14 + self._num_outputs = self._tensor_slice_len * self._num_epochs + + def _build_ds(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return ( + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + .repeat(self._num_epochs)) + + def testSaveRestoreCore(self): + self.run_core_tests( + self._build_ds, + lambda: self._build_ds(multiplier=15.0), + self._num_outputs) + + def testSaveStatefulFunction(self): + + def _build_ds(): + + def _map_fn(x): + return random_ops.random_uniform( + (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) + + return dataset_ops.Dataset.range(100).map(_map_fn) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureVariableInMapFn(self): + + def _build_ds(): + counter_var = variable_scope.get_variable( + "counter", (), dtypes.int32, use_resource=True) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda _: counter_var.assign_add(1))) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureConstantInMapFn(self): + + def _build_ds(): + constant_var = constant_op.constant(5) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda x: x + constant_var)) + + self.run_core_tests(_build_ds, None, 10) + + def testCaptureDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testBuildDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + + @function.Defun(dtypes.int32) + def defun_fn_deep(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) + + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testSparseCore(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=np.array([[0, 0]]), + values=(i * np.array([1])), + dense_shape=np.array([1, 1])) + + def _build_ds(num_outputs): + return dataset_ops.Dataset.range(num_outputs).map(_sparse) + + num_outputs = 10 + self.run_core_tests(lambda: _build_ds(num_outputs), + lambda: _build_ds(int(num_outputs / 2)), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c03495e34e73018bf9832bf77cdcf038449488 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py @@ -0,0 +1,39 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the OptimizeDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import optimization +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class OptimizeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testCore(self): + + def build_dataset(num_elements, batch_size): + return dataset_ops.Dataset.range(num_elements).map(lambda x: x * x).batch( + batch_size).apply(optimization.optimize(["map_and_batch_fusion"])) + + self.run_core_tests(lambda: build_dataset(200, 10), None, 20) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9ac42a461afcb6803a0e033892e74fb84d1e5e58 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py @@ -0,0 +1,66 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the PaddedBatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import string_ops +from tensorflow.python.platform import test + + +class PaddedBatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testPaddedBatch(self): + + def build_dataset(seq_lens): + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + lambda x: array_ops.fill([x], x)).padded_batch( + 4, padded_shapes=[-1]) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + def testPaddedBatchNonDefaultPadding(self): + + def build_dataset(seq_lens): + + def fill_tuple(x): + filled = array_ops.fill([x], x) + return (filled, string_ops.as_string(filled)) + + padded_shape = [-1] + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + fill_tuple).padded_batch( + 4, + padded_shapes=(padded_shape, padded_shape), + padding_values=(-1, "")) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..1f8a584df902180aa7ab020b47ecc749912a3a3a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py @@ -0,0 +1,101 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ParallelInterleaveDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops +from tensorflow.python.platform import test + + +class ParallelInterleaveDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self.input_values = np.array([4, 5, 6], dtype=np.int64) + self.num_repeats = 2 + self.num_outputs = np.sum(self.input_values) * 2 + + def _build_ds(self, cycle_length, block_length, sloppy=False): + return (dataset_ops.Dataset.from_tensor_slices( + self.input_values).repeat(self.num_repeats).apply( + interleave_ops.parallel_interleave( + lambda x: dataset_ops.Dataset.range(10 * x, 11 * x), + cycle_length, block_length, sloppy))) + + def testSerializationCore(self): + # cycle_length > 1, block_length > 1 + cycle_length = 2 + block_length = 3 + self.run_core_tests( + lambda: self._build_ds(cycle_length, block_length), + lambda: self._build_ds(cycle_length * 2, block_length * 1), + self.num_outputs) + # cycle_length = 1 + cycle_length = 1 + block_length = 3 + self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), + None, self.num_outputs) + # block_length = 1 + cycle_length = 2 + block_length = 1 + self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), + None, self.num_outputs) + + def testSerializationWithSloppy(self): + break_points = self.gen_break_points(self.num_outputs, 10) + expected_outputs = np.repeat( + np.concatenate([np.arange(10 * x, 11 * x) for x in self.input_values]), + self.num_repeats).tolist() + + def run_test(cycle_length, block_length): + actual = self.gen_outputs( + lambda: self._build_ds(cycle_length, block_length, True), + break_points, self.num_outputs) + self.assertSequenceEqual(sorted(actual), expected_outputs) + + # cycle_length > 1, block_length > 1 + run_test(2, 3) + # cycle_length = 1 + run_test(1, 3) + # block_length = 1 + run_test(2, 1) + + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _interleave_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_dataset(): + return dataset_ops.Dataset.range(10).map(_map_fn).apply( + interleave_ops.parallel_interleave(_interleave_fn, 1)) + + self.run_core_tests(_build_dataset, None, 20) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3fb7605be1f230cef4cdae30aa672842a678edf7 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py @@ -0,0 +1,139 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ParallelMapDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import function +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test + + +class ParallelMapDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self._tensor_slice_len = 7 + self._num_epochs = 1 + self._num_outputs = self._tensor_slice_len * self._num_epochs + + def _build_ds(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return (dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn, num_parallel_calls=3).repeat(self._num_epochs)) + + def _build_ds_with_prefetch(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return (dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn, num_parallel_calls=3).repeat(self._num_epochs).prefetch(5)) + + def testSaveRestoreCore(self): + for ds_fn in [self._build_ds, self._build_ds_with_prefetch]: + self.run_core_tests( + ds_fn, + lambda: ds_fn(multiplier=15.0), + self._num_outputs) + + def testSaveStatefulFunction(self): + + def _build_ds(): + + def _map_fn(x): + return random_ops.random_uniform( + (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) + + return dataset_ops.Dataset.range(100).map( + _map_fn, num_parallel_calls=2).prefetch(2) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureVariableInMapFn(self): + + def _build_ds(): + counter_var = variable_scope.get_variable( + "counter", (), dtypes.int32, use_resource=True) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda _: counter_var.assign_add(1), + num_parallel_calls=2).prefetch(2)) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureConstantInMapFn(self): + + def _build_ds(): + constant_var = constant_op.constant(5) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) + + self.run_core_tests(_build_ds, None, 10) + + def testCaptureDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testBuildDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + + @function.Defun(dtypes.int32) + def defun_fn_deep(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) + + return dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) + + self.run_core_tests(_build_ds, None, num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py similarity index 90% rename from tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py index 3d120a3071ef730f21221e3291d8c84385b51aa3..c802402461216de33e7d3232ba38063c27f33557 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the PrefetchDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..e4f5b6cf5db788ad2fd09b7e93d0ae5ebb530a11 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py @@ -0,0 +1,118 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the RangeDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.ops import io_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +class RangeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _iterator_checkpoint_prefix_local(self): + return os.path.join(self.get_temp_dir(), "iterator") + + def _save_op(self, iterator_resource): + iterator_state_variant = gen_dataset_ops.serialize_iterator( + iterator_resource) + save_op = io_ops.write_file( + self._iterator_checkpoint_prefix_local(), + parsing_ops.serialize_tensor(iterator_state_variant)) + return save_op + + def _restore_op(self, iterator_resource): + iterator_state_variant = parsing_ops.parse_tensor( + io_ops.read_file(self._iterator_checkpoint_prefix_local()), + dtypes.variant) + restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource, + iterator_state_variant) + return restore_op + + def testSaveRestore(self): + + def _build_graph(start, stop): + iterator = dataset_ops.Dataset.range(start, + stop).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + save_op = self._save_op(iterator._iterator_resource) + restore_op = self._restore_op(iterator._iterator_resource) + return init_op, get_next, save_op, restore_op + + # Saving and restoring in different sessions. + start = 2 + stop = 10 + break_point = 5 + with ops.Graph().as_default() as g: + init_op, get_next, save_op, _ = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(variables.global_variables_initializer()) + sess.run(init_op) + for i in range(start, break_point): + self.assertEqual(i, sess.run(get_next)) + sess.run(save_op) + + with ops.Graph().as_default() as g: + init_op, get_next, _, restore_op = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(init_op) + sess.run(restore_op) + for i in range(break_point, stop): + self.assertEqual(i, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Saving and restoring in same session. + with ops.Graph().as_default() as g: + init_op, get_next, save_op, restore_op = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(variables.global_variables_initializer()) + sess.run(init_op) + for i in range(start, break_point): + self.assertEqual(i, sess.run(get_next)) + sess.run(save_op) + sess.run(restore_op) + for i in range(break_point, stop): + self.assertEqual(i, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def _build_range_dataset(self, start, stop): + return dataset_ops.Dataset.range(start, stop) + + def testRangeCore(self): + start = 2 + stop = 10 + stop_1 = 8 + self.run_core_tests(lambda: self._build_range_dataset(start, stop), + lambda: self._build_range_dataset(start, stop_1), + stop - start) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb35ea624c22ad0a9561d774c86247119c4c837 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py @@ -0,0 +1,46 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the SampleFromDatasets serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class SampleFromDatasetsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, probs, num_samples): + dataset = interleave_ops.sample_from_datasets( + [ + dataset_ops.Dataset.from_tensors(i).repeat(None) + for i in range(len(probs)) + ], + probs, + seed=1813) + return dataset.take(num_samples) + + def testSerializationCore(self): + self.run_core_tests( + lambda: self._build_dataset([0.5, 0.5], 100), + lambda: self._build_dataset([0.25, 0.25, 0.25, 0.25], 1000), 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..af9ef48c0f3b92f61c097410ef4dfd787292e76a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py @@ -0,0 +1,40 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ScanDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import scan_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class ScanDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, num_elements): + return dataset_ops.Dataset.from_tensors(1).repeat(num_elements).apply( + scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1]))) + + def testScanCore(self): + num_output = 5 + self.run_core_tests(lambda: self._build_dataset(num_output), + lambda: self._build_dataset(2), num_output) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py similarity index 91% rename from tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py index d0cb203a3afd2775756c8542a1e86faedc5cee53..2afebca0f5849c640044830fff05ebff131e0875 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py @@ -12,19 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the sequence datasets serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test -class SequenceDatasetSerializationTest( +class SkipDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def _build_skip_dataset(self, count): @@ -52,6 +52,10 @@ class SequenceDatasetSerializationTest( 'Shape must be rank 0 but is rank 1'): self.run_core_tests(lambda: self._build_skip_dataset([1, 2]), None, 0) + +class TakeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + def _build_take_dataset(self, count): components = (np.arange(10),) return dataset_ops.Dataset.from_tensor_slices(components).take(count) @@ -79,6 +83,10 @@ class SequenceDatasetSerializationTest( 'Shape must be rank 0 but is rank 1'): self.run_core_tests(lambda: self._build_take_dataset([1, 2]), None, 0) + +class RepeatDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + def _build_repeat_dataset(self, count, take_count=3): components = (np.arange(10),) return dataset_ops.Dataset.from_tensor_slices(components).take( @@ -117,5 +125,5 @@ class SequenceDatasetSerializationTest( None, 0) -if __name__ == "__main__": +if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py similarity index 96% rename from tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py index 0a6b74dc3eb80a6168117beed06935737198cecb..992d996a485de94ad55305552e42c7fbc92ec64b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Integration test for input pipeline serialization.""" +"""Integration test for dataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -26,7 +26,7 @@ from tensorflow.python.platform import test from tensorflow.python.training import saver as saver_lib -class MultipleInputPipelinesTest(test.TestCase): +class SerializationIntegrationTest(test.TestCase): def _build_input_pipeline(self, name, num_outputs): with ops.name_scope(name): diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f199ec835ef1c72e2c3f8b3b1cc4f5fe6ea0b6f4 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py @@ -0,0 +1,39 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ShuffleAndRepeatDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import shuffle_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class ShuffleAndRepeatSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_ds(self, seed): + return dataset_ops.Dataset.range(20).apply( + shuffle_ops.shuffle_and_repeat(buffer_size=5, count=5, seed=seed)) + + def testCore(self): + self.run_core_tests(lambda: self._build_ds(10), lambda: self._build_ds(20), + 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d46c762aaaadc4314a10acc5aeb7ace7df5002a8 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py @@ -0,0 +1,148 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ShuffleDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import iterator_ops as contrib_iterator_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.training import saver as saver_lib + + +class ShuffleDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_shuffle_dataset( + self, + range_limit=10, + num_repeats=5, + buffer_size=5, + seed=None, + reshuffle_each_iteration=None, + ): + return dataset_ops.Dataset.range(range_limit).shuffle( + buffer_size, + seed=seed, + reshuffle_each_iteration=reshuffle_each_iteration).repeat(num_repeats) + + def testShuffleCore(self): + + seed = 55 + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + # pylint: disable=cell-var-from-loop + # pylint: disable=g-long-lambda + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + self.run_core_tests( + lambda: self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=seed, + reshuffle_each_iteration=reshuffle_each_iteration), + lambda: self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=10, + reshuffle_each_iteration=reshuffle_each_iteration), + num_outputs) + # pylint: enable=cell-var-from-loop + # pylint: enable=g-long-lambda + + def testNonDeterministicSeeding(self): + + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + + def ds_fn(): + # pylint: disable=cell-var-from-loop + return self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=None, # Iterator seeds are generated non-deterministically. + reshuffle_each_iteration=reshuffle_each_iteration) + # pylint: enable=cell-var-from-loop + + # We checkpoint the initial state of the Dataset so that we can restore + # the seeds in the next run. Since the seeding is non-deterministic + # the dataset gets initialized with different seeds each time. + expected = self.gen_outputs( + ds_fn, + break_points=[0], + num_outputs=num_outputs, + ckpt_saved=False, + verify_exhausted=False, + save_checkpoint_at_end=False) + actual = self.gen_outputs( + ds_fn, + break_points=self.gen_break_points(num_outputs), + num_outputs=num_outputs, + ckpt_saved=True, + verify_exhausted=False) + self.match(expected, actual) + + def testMultipleIterators(self): + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + + def ds_fn(): + # pylint: disable=cell-var-from-loop + return self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=None, # Iterator seeds are generated non-deterministically. + reshuffle_each_iteration=reshuffle_each_iteration) + # pylint: enable=cell-var-from-loop + + with ops.Graph().as_default() as g: + ds = ds_fn() + iterators = [ds.make_one_shot_iterator(), ds.make_one_shot_iterator()] + get_next_ops = [it.get_next() for it in iterators] + saveables = [ + contrib_iterator_ops.make_saveable_from_iterator(it) + for it in iterators + ] + for saveable in saveables: + ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable) + saver = saver_lib.Saver(allow_empty=True) + with self.test_session(graph=g) as sess: + self._save(sess, saver) + expected = [sess.run(get_next_ops) for _ in range(num_outputs)] + self._restore(saver, sess) + actual = [sess.run(get_next_ops) for _ in range(num_outputs)] + self.match(expected, actual) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..93b26ed58a065de2074906528a0f49d696a813ff --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py @@ -0,0 +1,53 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the SqlDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.kernel_tests import sql_dataset_op_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class SqlDatasetSerializationTest( + sql_dataset_op_test_base.SqlDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, num_repeats): + data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") + driver_name = array_ops.placeholder_with_default( + array_ops.constant("sqlite", dtypes.string), shape=[]) + query = ("SELECT first_name, last_name, motto FROM students ORDER BY " + "first_name DESC") + output_types = (dtypes.string, dtypes.string, dtypes.string) + return readers.SqlDataset(driver_name, data_source_name, query, + output_types).repeat(num_repeats) + + def testSQLSaveable(self): + num_repeats = 4 + num_outputs = num_repeats * 2 + self.run_core_tests(lambda: self._build_dataset(num_repeats), + lambda: self._build_dataset(num_repeats // 2), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..14cd3e9c4a72cc7832f9bb1cb49c72a8a7cb2dcd --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py @@ -0,0 +1,95 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the StatsDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import stats_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +# TODO(shivaniagrawal): Can not checkpoint input_pipeline with the +# transformation `stats_ops.set_stats_aggregator`, since we don't support +# serializing StatsAggregator yet. +class StatsDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset_bytes_stats(self, num_elements): + return dataset_ops.Dataset.range(num_elements).map( + lambda x: array_ops.tile([x], ops.convert_to_tensor([x]))).apply( + stats_ops.bytes_produced_stats("bytes_produced")) + + def test_bytes_produced_stats_invalid_tag_shape(self): + with self.assertRaisesRegexp( + ValueError, "Shape must be rank 0 but is rank 1"): + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: dataset_ops.Dataset.range(100).apply( + stats_ops.bytes_produced_stats(["bytes_produced"])), + None, 100) + # pylint: enable=g-long-lambda + + def testBytesStatsDatasetSaveableCore(self): + num_outputs = 100 + self.run_core_tests( + lambda: self._build_dataset_bytes_stats(num_outputs), + lambda: self._build_dataset_bytes_stats(num_outputs // 10), num_outputs) + + def _build_dataset_latency_stats(self, num_elements, tag="record_latency"): + return dataset_ops.Dataset.range(num_elements).apply( + stats_ops.latency_stats(tag)) + + def _build_dataset_multiple_tags(self, + num_elements, + tag1="record_latency", + tag2="record_latency_2"): + return dataset_ops.Dataset.range(num_elements).apply( + stats_ops.latency_stats(tag1)).apply(stats_ops.latency_stats(tag2)) + + def test_latency_stats_invalid_tag_shape(self): + with self.assertRaisesRegexp( + ValueError, "Shape must be rank 0 but is rank 1"): + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: dataset_ops.Dataset.range(100).apply( + stats_ops.latency_stats(["record_latency", "record_latency_2"])), + None, 100) + # pylint: enable=g-long-lambda + + def testLatencyStatsDatasetSaveableCore(self): + num_outputs = 100 + + self.run_core_tests( + lambda: self._build_dataset_latency_stats(num_outputs), + lambda: self._build_dataset_latency_stats(num_outputs // 10), + num_outputs) + + self.run_core_tests(lambda: self._build_dataset_multiple_tags(num_outputs), + None, num_outputs) + + tag1 = "record_latency" + tag2 = "record_latency" + self.run_core_tests( + lambda: self._build_dataset_multiple_tags(num_outputs, tag1, tag2), + None, num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2483787f44f913199e3f2aa46d181d609a4a9a8f --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py @@ -0,0 +1,53 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the TextLineDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class TextLineDatasetSerializationTest( + reader_dataset_ops_test_base.TextLineDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, test_filenames, compression_type=None): + return core_readers.TextLineDataset( + test_filenames, compression_type=compression_type, buffer_size=10) + + def testTextLineCore(self): + compression_types = [None, "GZIP", "ZLIB"] + num_files = 5 + lines_per_file = 5 + num_outputs = num_files * lines_per_file + for compression_type in compression_types: + test_filenames = self._createFiles( + num_files, + lines_per_file, + crlf=True, + compression_type=compression_type) + # pylint: disable=cell-var-from-loop + self.run_core_tests( + lambda: self._build_iterator_graph(test_filenames, compression_type), + lambda: self._build_iterator_graph(test_filenames), num_outputs) + # pylint: enable=cell-var-from-loop + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..55a6257a274cd7f78e3818943627cfa09a185fd7 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py @@ -0,0 +1,99 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the TFRecordDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os +import zlib + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class TFRecordDatasetSerializationTest( + reader_dataset_ops_test_base.TFRecordDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, + num_epochs, + batch_size=1, + compression_type=None, + buffer_size=None): + filenames = self._createFiles() + if compression_type == "ZLIB": + zlib_files = [] + for i, fn in enumerate(filenames): + with open(fn, "rb") as f: + cdata = zlib.compress(f.read()) + zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) + with open(zfn, "wb") as f: + f.write(cdata) + zlib_files.append(zfn) + filenames = zlib_files + + elif compression_type == "GZIP": + gzip_files = [] + for i, fn in enumerate(self.test_filenames): + with open(fn, "rb") as f: + gzfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) + with gzip.GzipFile(gzfn, "wb") as gzf: + gzf.write(f.read()) + gzip_files.append(gzfn) + filenames = gzip_files + + return core_readers.TFRecordDataset( + filenames, compression_type, + buffer_size=buffer_size).repeat(num_epochs).batch(batch_size) + + def testTFRecordWithoutBufferCore(self): + num_epochs = 5 + batch_size = num_epochs + num_outputs = num_epochs * self._num_files * self._num_records // batch_size + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, batch_size, + buffer_size=0), + lambda: self._build_iterator_graph(num_epochs * 2, batch_size), + num_outputs) + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, buffer_size=0), None, + num_outputs * batch_size) + # pylint: enable=g-long-lambda + + def testTFRecordWithBufferCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), + lambda: self._build_iterator_graph(num_epochs * 2), + num_outputs) + + def testTFRecordWithCompressionCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, compression_type="ZLIB"), + lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, compression_type="GZIP"), + lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a5a8a20dd7a9f891b07351570006636ca34bd0 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py @@ -0,0 +1,51 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the UnbatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class UnbatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): + components = ( + np.arange(tensor_slice_len), + np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(tensor_slice_len)) + + return dataset_ops.Dataset.from_tensor_slices(components).batch( + batch_size).apply(batching.unbatch()) + + def testCore(self): + tensor_slice_len = 8 + batch_size = 2 + num_outputs = tensor_slice_len + self.run_core_tests( + lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), + lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..22f15b88464a770207dc7c6f0387d73ea3d5c2e4 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py @@ -0,0 +1,40 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the UniqueDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import unique +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class UniqueDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testUnique(self): + + def build_dataset(num_elements, unique_elem_range): + return dataset_ops.Dataset.range(num_elements).map( + lambda x: x % unique_elem_range).apply(unique.unique()) + + self.run_core_tests(lambda: build_dataset(200, 100), + lambda: build_dataset(40, 100), 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py similarity index 92% rename from tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py index e39fa957f0bbb9d3671274d5f58b993e8399814b..340a6ff72e6813c3743d3d83a72ac12d4a392b66 100644 --- a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the ZipDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py index bcc644c0971854d948025009dc7add2fea214048..3c11d7a97fc9a4b2b8b19a8e82ad5e9037d6bbcd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py @@ -19,7 +19,6 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors @@ -27,60 +26,25 @@ from tensorflow.python.framework import ops from tensorflow.python.platform import test -class ShuffleDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_shuffle_dataset( - self, - range_limit=10, - num_repeats=5, - buffer_size=5, - seed=None, - reshuffle_each_iteration=None, - ): - return dataset_ops.Dataset.range(range_limit).shuffle( - buffer_size, - seed=seed, - reshuffle_each_iteration=reshuffle_each_iteration).repeat(num_repeats) - - def testShuffleCore(self): - - seed = 55 - range_limit = 10 - num_repeats = 5 - num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 8, 10, 25, 50] - reshuffle_each_iteration = False - # pylint: disable=cell-var-from-loop - # pylint: disable=g-long-lambda - for buffer_size in buffer_sizes: - self.run_core_tests( - lambda: self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=seed, - reshuffle_each_iteration=reshuffle_each_iteration), - lambda: self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=10, - reshuffle_each_iteration=reshuffle_each_iteration), - num_outputs) - # pylint: enable=cell-var-from-loop - # pylint: enable=g-long-lambda - - -class ShuffleAndRepeatTest( - dataset_serialization_test_base.DatasetSerializationTestBase): +class ShuffleAndRepeatTest(test.TestCase): def _build_ds(self, seed, count=5, num_elements=20): return dataset_ops.Dataset.range(num_elements).apply( shuffle_ops.shuffle_and_repeat(buffer_size=5, count=count, seed=seed)) + def _gen_outputs(self, ds_fn, num_outputs, verify_exhausted=True): + get_next = ds_fn().make_one_shot_iterator().get_next() + outputs = [] + with self.test_session() as sess: + for _ in range(num_outputs): + outputs.append(sess.run(get_next)) + if verify_exhausted: + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + return outputs + def testCorrectOutput(self): - output = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output = self._gen_outputs(lambda: self._build_ds(10), 100) self.assertSequenceEqual( sorted(output), sorted( np.array([range(20) for _ in range(5)]).flatten())) @@ -89,53 +53,53 @@ class ShuffleAndRepeatTest( def testReshuffling(self): # Check that the output orders of different epochs are indeed different. - output = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output = self._gen_outputs(lambda: self._build_ds(10), 100) for i in range(4): epoch1 = output[i * 20:(i + 1) * 20] epoch2 = output[(i + 1) * 20:(i + 2) * 20] self.assertNotEqual(epoch1, epoch2) def testSameOrderForSameSeeds(self): - output1 = self.gen_outputs(lambda: self._build_ds(10), [], 100) - output2 = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output1 = self._gen_outputs(lambda: self._build_ds(10), 100) + output2 = self._gen_outputs(lambda: self._build_ds(10), 100) self.assertEqual(output1, output2) def testDifferentOrderForDifferentSeeds(self): - output1 = self.gen_outputs(lambda: self._build_ds(10), [], 100) - output2 = self.gen_outputs(lambda: self._build_ds(20), [], 100) + output1 = self._gen_outputs(lambda: self._build_ds(10), 100) + output2 = self._gen_outputs(lambda: self._build_ds(20), 100) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testCountNone(self): - output1 = self.gen_outputs( - lambda: self._build_ds(10, count=None), [], 100, verify_exhausted=False) - output2 = self.gen_outputs( - lambda: self._build_ds(20, count=None), [], 100, verify_exhausted=False) + output1 = self._gen_outputs( + lambda: self._build_ds(10, count=None), 100, verify_exhausted=False) + output2 = self._gen_outputs( + lambda: self._build_ds(20, count=None), 100, verify_exhausted=False) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testCountMinusOne(self): - output1 = self.gen_outputs( - lambda: self._build_ds(10, count=-1), [], 100, verify_exhausted=False) - output2 = self.gen_outputs( - lambda: self._build_ds(20, count=-1), [], 100, verify_exhausted=False) + output1 = self._gen_outputs( + lambda: self._build_ds(10, count=-1), 100, verify_exhausted=False) + output2 = self._gen_outputs( + lambda: self._build_ds(20, count=-1), 100, verify_exhausted=False) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testInfiniteOutputs(self): # Asserting the iterator is exhausted after producing 100 items should fail. with self.assertRaises(AssertionError): - self.gen_outputs(lambda: self._build_ds(10, count=None), [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=None), 100) with self.assertRaises(AssertionError): - self.gen_outputs(lambda: self._build_ds(10, count=-1), [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=-1), 100) def testInfiniteEmpty(self): with self.assertRaises(errors.OutOfRangeError): - self.gen_outputs(lambda: self._build_ds(10, count=None, num_elements=0), - [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=None, num_elements=0), + 100) with self.assertRaises(errors.OutOfRangeError): - self.gen_outputs(lambda: self._build_ds(10, count=-1, num_elements=0), [], - 100) + self._gen_outputs(lambda: self._build_ds(10, count=-1, num_elements=0), + 100) def testLargeBufferSize(self): with ops.Graph().as_default() as g: @@ -146,17 +110,5 @@ class ShuffleAndRepeatTest( sess.run(get_next_op) -class ShuffleAndRepeatSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_ds(self, seed): - return dataset_ops.Dataset.range(20).apply( - shuffle_ops.shuffle_and_repeat(buffer_size=5, count=5, seed=seed)) - - def testCore(self): - self.run_core_tests(lambda: self._build_ds(10), lambda: self._build_ds(20), - 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py index 33c48e20bea53b88d69a59e715af38b22dd2cbd4..5590a4bf783d12b0d0710c0130b0b1df921c9baa 100644 --- a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py @@ -58,6 +58,7 @@ class SlideDatasetTest(test.TestCase): [t.shape.as_list() for t in get_next]) with self.test_session() as sess: + # stride < window_size. # Slide over a finite input, where the window_size divides the # total number of elements. sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7}) @@ -71,11 +72,9 @@ class SlideDatasetTest(test.TestCase): result_component[j]) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # Slide over a finite input, where the window_size does not # divide the total number of elements. sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9}) - num_batches = (20 * 7 - 17) // 9 + 1 for i in range(num_batches): result = sess.run(get_next) @@ -86,6 +85,41 @@ class SlideDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + # stride == window_size. + sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 14}) + num_batches = 20 * 7 // 14 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(14): + self.assertAllEqual(component[(i*14 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # stride > window_size. + sess.run(init_op, feed_dict={count: 20, window_size: 10, stride: 14}) + num_batches = 20 * 7 // 14 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(10): + self.assertAllEqual(component[(i*14 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + # Drop the last batch which is smaller than window_size. + sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 19}) + num_batches = (20 * 7 - 7) // 19 # = 19 * 7 // 19 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(14): + self.assertAllEqual(component[(i*19 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + # Slide over a finite input, which is less than window_size, # should fail straight away. sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4}) @@ -108,10 +142,6 @@ class SlideDatasetTest(test.TestCase): # Invalid stride should be an initialization time error. with self.assertRaises(errors.InvalidArgumentError): sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0}) - with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 3}) - with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 5}) def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py index 4148addf2878c99f47ebe1454edf69ad7f38dfbc..2c2cfbebff5d3eba00f120467102b4185d81ab24 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py @@ -18,83 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -import sqlite3 - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import readers +from tensorflow.contrib.data.python.kernel_tests import sql_dataset_op_test_base from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class SqlDatasetTestBase(test.TestCase): - - def _createSqlDataset(self, output_types, num_repeats=1): - dataset = readers.SqlDataset(self.driver_name, self.data_source_name, - self.query, output_types).repeat(num_repeats) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - return init_op, get_next - - def setUp(self): - self.data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") - self.driver_name = array_ops.placeholder_with_default( - array_ops.constant("sqlite", dtypes.string), shape=[]) - self.query = array_ops.placeholder(dtypes.string, shape=[]) - - conn = sqlite3.connect(self.data_source_name) - c = conn.cursor() - c.execute("DROP TABLE IF EXISTS students") - c.execute("DROP TABLE IF EXISTS people") - c.execute("DROP TABLE IF EXISTS townspeople") - c.execute( - "CREATE TABLE IF NOT EXISTS students (id INTEGER NOT NULL PRIMARY KEY, " - "first_name VARCHAR(100), last_name VARCHAR(100), motto VARCHAR(100), " - "school_id VARCHAR(100), favorite_nonsense_word VARCHAR(100), " - "desk_number INTEGER, income INTEGER, favorite_number INTEGER, " - "favorite_big_number INTEGER, favorite_negative_number INTEGER, " - "favorite_medium_sized_number INTEGER, brownie_points INTEGER, " - "account_balance INTEGER, registration_complete INTEGER)") - c.executemany( - "INSERT INTO students (first_name, last_name, motto, school_id, " - "favorite_nonsense_word, desk_number, income, favorite_number, " - "favorite_big_number, favorite_negative_number, " - "favorite_medium_sized_number, brownie_points, account_balance, " - "registration_complete) " - "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", - [("John", "Doe", "Hi!", "123", "n\0nsense", 9, 0, 2147483647, - 9223372036854775807, -2, 32767, 0, 0, 1), - ("Jane", "Moe", "Hi again!", "1000", "nonsense\0", 127, -20000, - -2147483648, -9223372036854775808, -128, -32768, 255, 65535, 0)]) - c.execute( - "CREATE TABLE IF NOT EXISTS people (id INTEGER NOT NULL PRIMARY KEY, " - "first_name VARCHAR(100), last_name VARCHAR(100), state VARCHAR(100))") - c.executemany( - "INSERT INTO PEOPLE (first_name, last_name, state) VALUES (?, ?, ?)", - [("Benjamin", "Franklin", "Pennsylvania"), ("John", "Doe", - "California")]) - c.execute( - "CREATE TABLE IF NOT EXISTS townspeople (id INTEGER NOT NULL PRIMARY " - "KEY, first_name VARCHAR(100), last_name VARCHAR(100), victories " - "FLOAT, accolades FLOAT, triumphs FLOAT)") - c.executemany( - "INSERT INTO townspeople (first_name, last_name, victories, " - "accolades, triumphs) VALUES (?, ?, ?, ?, ?)", - [("George", "Washington", 20.00, - 1331241.321342132321324589798264627463827647382647382643874, - 9007199254740991.0), - ("John", "Adams", -19.95, - 1331241321342132321324589798264627463827647382647382643874.0, - 9007199254740992.0)]) - conn.commit() - conn.close() - - -class SqlDatasetTest(SqlDatasetTestBase): +class SqlDatasetTest(sql_dataset_op_test_base.SqlDatasetTestBase): # Test that SqlDataset can read from a database table. def testReadResultSet(self): @@ -656,27 +586,5 @@ class SqlDatasetTest(SqlDatasetTestBase): sess.run(get_next) -class SqlDatasetSerializationTest( - SqlDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, num_repeats): - data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") - driver_name = array_ops.placeholder_with_default( - array_ops.constant("sqlite", dtypes.string), shape=[]) - query = ("SELECT first_name, last_name, motto FROM students ORDER BY " - "first_name DESC") - output_types = (dtypes.string, dtypes.string, dtypes.string) - return readers.SqlDataset(driver_name, data_source_name, query, - output_types).repeat(num_repeats) - - def testSQLSaveable(self): - num_repeats = 4 - num_outputs = num_repeats * 2 - self.run_core_tests(lambda: self._build_dataset(num_repeats), - lambda: self._build_dataset(num_repeats // 2), - num_outputs) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..1f5c725a9269e80311f3e73c51c28ab80e7c4815 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py @@ -0,0 +1,96 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base class for testing SqlDataset.""" + + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import sqlite3 + +from tensorflow.contrib.data.python.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class SqlDatasetTestBase(test.TestCase): + """Base class for setting up and testing SqlDataset.""" + + def _createSqlDataset(self, output_types, num_repeats=1): + dataset = readers.SqlDataset(self.driver_name, self.data_source_name, + self.query, output_types).repeat(num_repeats) + iterator = dataset.make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + return init_op, get_next + + def setUp(self): + self.data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") + self.driver_name = array_ops.placeholder_with_default( + array_ops.constant("sqlite", dtypes.string), shape=[]) + self.query = array_ops.placeholder(dtypes.string, shape=[]) + + conn = sqlite3.connect(self.data_source_name) + c = conn.cursor() + c.execute("DROP TABLE IF EXISTS students") + c.execute("DROP TABLE IF EXISTS people") + c.execute("DROP TABLE IF EXISTS townspeople") + c.execute( + "CREATE TABLE IF NOT EXISTS students (id INTEGER NOT NULL PRIMARY KEY, " + "first_name VARCHAR(100), last_name VARCHAR(100), motto VARCHAR(100), " + "school_id VARCHAR(100), favorite_nonsense_word VARCHAR(100), " + "desk_number INTEGER, income INTEGER, favorite_number INTEGER, " + "favorite_big_number INTEGER, favorite_negative_number INTEGER, " + "favorite_medium_sized_number INTEGER, brownie_points INTEGER, " + "account_balance INTEGER, registration_complete INTEGER)") + c.executemany( + "INSERT INTO students (first_name, last_name, motto, school_id, " + "favorite_nonsense_word, desk_number, income, favorite_number, " + "favorite_big_number, favorite_negative_number, " + "favorite_medium_sized_number, brownie_points, account_balance, " + "registration_complete) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", + [("John", "Doe", "Hi!", "123", "n\0nsense", 9, 0, 2147483647, + 9223372036854775807, -2, 32767, 0, 0, 1), + ("Jane", "Moe", "Hi again!", "1000", "nonsense\0", 127, -20000, + -2147483648, -9223372036854775808, -128, -32768, 255, 65535, 0)]) + c.execute( + "CREATE TABLE IF NOT EXISTS people (id INTEGER NOT NULL PRIMARY KEY, " + "first_name VARCHAR(100), last_name VARCHAR(100), state VARCHAR(100))") + c.executemany( + "INSERT INTO PEOPLE (first_name, last_name, state) VALUES (?, ?, ?)", + [("Benjamin", "Franklin", "Pennsylvania"), ("John", "Doe", + "California")]) + c.execute( + "CREATE TABLE IF NOT EXISTS townspeople (id INTEGER NOT NULL PRIMARY " + "KEY, first_name VARCHAR(100), last_name VARCHAR(100), victories " + "FLOAT, accolades FLOAT, triumphs FLOAT)") + c.executemany( + "INSERT INTO townspeople (first_name, last_name, victories, " + "accolades, triumphs) VALUES (?, ?, ?, ?, ?)", + [("George", "Washington", 20.00, + 1331241.321342132321324589798264627463827647382647382643874, + 9007199254740991.0), + ("John", "Adams", -19.95, + 1331241321342132321324589798264627463827647382647382643874.0, + 9007199254740992.0)]) + conn.commit() + conn.close() + + diff --git a/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py index 5c74ed6ae7210e8e22efb6e8fdb773397459ce1e..b4945685c1d1062bf416b73f1541f351adf45604 100644 --- a/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py @@ -19,7 +19,7 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base from tensorflow.contrib.data.python.ops import stats_ops from tensorflow.core.framework import summary_pb2 from tensorflow.python.data.ops import dataset_ops @@ -29,7 +29,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class StatsDatasetTest(test.TestCase): +class StatsDatasetTestBase(test.TestCase): def _assertSummaryHasCount(self, summary_str, tag, expected_value): summary_proto = summary_pb2.Summary() @@ -49,6 +49,9 @@ class StatsDatasetTest(test.TestCase): return self.fail("Expected tag %r not found in summary %r" % (tag, summary_proto)) + +class StatsDatasetTest(StatsDatasetTestBase): + def testBytesProduced(self): stats_aggregator = stats_ops.StatsAggregator() dataset = dataset_ops.Dataset.range(100).map( @@ -193,68 +196,44 @@ class StatsDatasetTest(test.TestCase): self._assertSummaryHasCount(sess.run(summary_t), "record_latency", 200.0) -class StatsDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset_bytes_stats(self, num_elements): - return dataset_ops.Dataset.range(num_elements).map( - lambda x: array_ops.tile([x], ops.convert_to_tensor([x]))).apply( - stats_ops.bytes_produced_stats("bytes_produced")) - - def test_bytes_produced_stats_invalid_tag_shape(self): - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 1'): - self.run_core_tests( - lambda: dataset_ops.Dataset.range(100).apply( - stats_ops.bytes_produced_stats(["bytes_produced"])), - None, 100) - - def testBytesStatsDatasetSaveableCore(self): - num_outputs = 100 - self.run_core_tests( - lambda: self._build_dataset_bytes_stats(num_outputs), - lambda: self._build_dataset_bytes_stats(num_outputs // 10), num_outputs) +class FeatureStatsDatasetTest( + StatsDatasetTestBase, + reader_dataset_ops_test_base.ReadBatchFeaturesTestBase): - def _build_dataset_latency_stats(self, num_elements, tag="record_latency"): - return dataset_ops.Dataset.range(num_elements).apply( - stats_ops.latency_stats(tag)) - - def _build_dataset_multiple_tags(self, - num_elements, - tag1="record_latency", - tag2="record_latency_2"): - return dataset_ops.Dataset.range(num_elements).apply( - stats_ops.latency_stats(tag1)).apply(stats_ops.latency_stats(tag2)) - - def test_latency_stats_invalid_tag_shape(self): - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 1'): - self.run_core_tests( - lambda: dataset_ops.Dataset.range(100).apply( - stats_ops.latency_stats(["record_latency", "record_latency_2"])), - None, 100) - - def testLatencyStatsDatasetSaveableCore(self): - num_outputs = 100 - - self.run_core_tests( - lambda: self._build_dataset_latency_stats(num_outputs), - lambda: self._build_dataset_latency_stats(num_outputs // 10), - num_outputs) - - self.run_core_tests(lambda: self._build_dataset_multiple_tags(num_outputs), - None, num_outputs) + def testFeaturesStats(self): + num_epochs = 5 + total_records = num_epochs * self._num_records + batch_size = 2 + stats_aggregator = stats_ops.StatsAggregator() + dataset = self.make_batch_feature( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5, + drop_final_batch=True).apply( + stats_ops.set_stats_aggregator(stats_aggregator)) + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() + summary_t = stats_aggregator.get_summary() - tag1 = "record_latency" - tag2 = "record_latency" - self.run_core_tests( - lambda: self._build_dataset_multiple_tags(num_outputs, tag1, tag2), - None, num_outputs) + with self.test_session() as sess: + sess.run(iterator.initializer) + for _ in range(total_records // batch_size): + sess.run(next_element) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + self._assertSummaryHasCount( + sess.run(summary_t), "record_stats:features", total_records) + self._assertSummaryHasCount( + sess.run(summary_t), "record_stats:feature-values", total_records) + self._assertSummaryHasSum( + sess.run(summary_t), "record_stats:features", total_records * 3) + self._assertSummaryHasSum( + sess.run(summary_t), "record_stats:feature-values", + self._sum_keywords(1) * num_epochs + 2 * total_records) -# TODO(shivaniagrawal): Can not checkpoint input_pipeline with the -# transformation `stats_ops.set_stats_aggregator`, since we don't support -# serializing StatsAggregator yet. if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py index 9167cb3379bba5cb1ba76a96549395c45dca9e35..0486e2bce20e9dcf81dcb5ac49fe5b397e44bf0c 100644 --- a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import threading +from absl.testing import parameterized import numpy as np from tensorflow.contrib.data.python.ops import threadpool @@ -30,9 +31,11 @@ from tensorflow.python.ops import script_ops from tensorflow.python.platform import test -class OverrideThreadpoolDatasetTest(test.TestCase): +class OverrideThreadpoolDatasetTest(test.TestCase, parameterized.TestCase): - def testNumThreads(self): + @parameterized.parameters((1, None), (2, None), (4, None), (8, None), + (16, None), (4, -1), (4, 0), (4, 1), (4, 4)) + def testNumThreads(self, num_threads, max_intra_op_parallelism): def get_thread_id(_): # Python creates a dummy thread object to represent the current @@ -42,35 +45,35 @@ class OverrideThreadpoolDatasetTest(test.TestCase): # identifier that maps one-to-one with the underlying OS thread. return np.array(threading.current_thread().ident).astype(np.int64) - for num_threads in [1, 2, 4, 8, 16]: + dataset = ( + dataset_ops.Dataset.range(1000).map( + lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), + num_parallel_calls=32).apply(unique.unique())) - dataset = ( - dataset_ops.Dataset.range(1000).map( - lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), - num_parallel_calls=32).apply(unique.unique())) + dataset = threadpool.override_threadpool( + dataset, + threadpool.PrivateThreadPool( + num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, + display_name="private_thread_pool_%d" % num_threads)) - dataset = threadpool.override_threadpool( - dataset, - threadpool.PrivateThreadPool( - num_threads, display_name="private_thread_pool_%d" % num_threads)) + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() - iterator = dataset.make_initializable_iterator() - next_element = iterator.get_next() - - with self.test_session() as sess: - sess.run(iterator.initializer) - thread_ids = [] - try: - while True: - thread_ids.append(sess.run(next_element)) - except errors.OutOfRangeError: - pass - self.assertEqual(len(thread_ids), len(set(thread_ids))) - self.assertGreater(len(thread_ids), 0) - # NOTE(mrry): We don't control the thread pool scheduling, and - # so cannot guarantee that all of the threads in the pool will - # perform work. - self.assertLessEqual(len(thread_ids), num_threads) + with self.test_session() as sess: + sess.run(iterator.initializer) + thread_ids = [] + try: + while True: + thread_ids.append(sess.run(next_element)) + except errors.OutOfRangeError: + pass + self.assertEqual(len(thread_ids), len(set(thread_ids))) + self.assertGreater(len(thread_ids), 0) + # NOTE(mrry): We don't control the thread pool scheduling, and + # so cannot guarantee that all of the threads in the pool will + # perform work. + self.assertLessEqual(len(thread_ids), num_threads) if __name__ == "__main__": diff --git a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py index 3c436f7a0b45a13109960e87dd97ca56b10bb871..d79a842e7a5d816e2e6a52fc83acbd6b260cf64b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py @@ -17,7 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import unique from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes @@ -79,18 +78,5 @@ class UniqueDatasetTest(test.TestCase): ]) -class UniqueSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testUnique(self): - - def build_dataset(num_elements, unique_elem_range): - return dataset_ops.Dataset.range(num_elements).map( - lambda x: x % unique_elem_range).apply(unique.unique()) - - self.run_core_tests(lambda: build_dataset(200, 100), - lambda: build_dataset(40, 100), 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 086661adb7603345be09a4c710d4fb2b170ac8f9..02408145625b7e751541e7b87dc4fd5da4f7cad9 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -49,26 +49,6 @@ py_library( ], ) -py_test( - name = "iterator_ops_test", - size = "small", - srcs = ["iterator_ops_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":iterator_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", - ], -) - py_library( name = "random_ops", srcs = [ @@ -96,8 +76,10 @@ py_library( srcs_version = "PY2AND3", deps = [ ":batching", + ":gen_dataset_ops", ":interleave_ops", ":shuffle_ops", + ":stats_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", @@ -106,12 +88,12 @@ py_library( "//tensorflow/python:math_ops", "//tensorflow/python:parsing_ops", "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", "//tensorflow/python:string_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:readers", + "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//third_party/py/numpy", ], @@ -142,6 +124,7 @@ py_library( "//tensorflow/python:tensor_shape", "//tensorflow/python:tensor_util", "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", ], diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index b9393de4e90ae2597045b29070934b94e18cfcbd..7350d595f5f6b64d062dcc5ebc69d7e85d3f7b22 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -19,6 +19,7 @@ from __future__ import print_function from tensorflow.contrib.framework import with_shape from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import convert from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes @@ -29,6 +30,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import math_ops +from tensorflow.python.util import deprecation def dense_to_sparse_batch(batch_size, row_shape): @@ -75,17 +77,17 @@ def dense_to_sparse_batch(batch_size, row_shape): """ def _apply_fn(dataset): - return DenseToSparseBatchDataset(dataset, batch_size, row_shape) + return _DenseToSparseBatchDataset(dataset, batch_size, row_shape) return _apply_fn -class UnbatchDataset(dataset_ops.Dataset): +class _UnbatchDataset(dataset_ops.Dataset): """A dataset that splits the elements of its input into multiple elements.""" def __init__(self, input_dataset): """See `unbatch()` for more details.""" - super(UnbatchDataset, self).__init__() + super(_UnbatchDataset, self).__init__() flat_shapes = nest.flatten(input_dataset.output_shapes) if any(s.ndims == 0 for s in flat_shapes): raise ValueError("Cannot unbatch an input with scalar components.") @@ -101,10 +103,7 @@ class UnbatchDataset(dataset_ops.Dataset): def _as_variant_tensor(self): return gen_dataset_ops.unbatch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): @@ -145,7 +144,7 @@ def unbatch(): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" if not sparse.any_sparse(dataset.output_classes): - return UnbatchDataset(dataset) + return _UnbatchDataset(dataset) # NOTE(mrry): We must ensure that any SparseTensors in `dataset` # are normalized to the rank-1 dense representation, so that the @@ -171,12 +170,12 @@ def unbatch(): dataset.output_shapes, dataset.output_classes, allow_unsafe_cast=True) - return UnbatchDataset(restructured_dataset) + return _UnbatchDataset(restructured_dataset) return _apply_fn -def filter_irregular_batches(batch_size): +def _filter_irregular_batches(batch_size): """Transformation that filters out batches that are not of size batch_size.""" def _apply_fn(dataset): @@ -218,6 +217,8 @@ def filter_irregular_batches(batch_size): return _apply_fn +@deprecation.deprecated( + None, "Use `tf.data.Dataset.batch(..., drop_remainder=True)`.") def batch_and_drop_remainder(batch_size): """A batching transformation that omits the final small batch (if present). @@ -250,12 +251,16 @@ def batch_and_drop_remainder(batch_size): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" + # TODO(jsimsa): Switch to using `batch(..., drop_remainder=True)` any time + # after 6/30/2018. batched = dataset.batch(batch_size) - return filter_irregular_batches(batch_size)(batched) + return _filter_irregular_batches(batch_size)(batched) return _apply_fn +@deprecation.deprecated( + None, "Use `tf.data.Dataset.padded_batch(..., drop_remainder=True)`.") def padded_batch_and_drop_remainder(batch_size, padded_shapes, padding_values=None): @@ -284,19 +289,21 @@ def padded_batch_and_drop_remainder(batch_size, def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" + # TODO(jsimsa): Switch to using `padded_batch(..., drop_remainder=True)` + # any time after 6/30/2018. batched = dataset.padded_batch( batch_size, padded_shapes=padded_shapes, padding_values=padding_values) - return filter_irregular_batches(batch_size)(batched) + return _filter_irregular_batches(batch_size)(batched) return _apply_fn -class DenseToSparseBatchDataset(dataset_ops.Dataset): +class _DenseToSparseBatchDataset(dataset_ops.Dataset): """A `Dataset` that batches ragged dense elements into `tf.SparseTensor`s.""" def __init__(self, input_dataset, batch_size, row_shape): """See `Dataset.dense_to_sparse_batch()` for more details.""" - super(DenseToSparseBatchDataset, self).__init__() + super(_DenseToSparseBatchDataset, self).__init__() if not isinstance(input_dataset.output_types, dtypes.DType): raise TypeError("DenseToSparseDataset requires an input whose elements " "have a single component, whereas the input has %r." % @@ -309,11 +316,8 @@ class DenseToSparseBatchDataset(dataset_ops.Dataset): return gen_dataset_ops.dense_to_sparse_batch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._batch_size, - row_shape=dataset_ops._partial_shape_to_tensor(self._row_shape), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + row_shape=convert.partial_shape_to_tensor(self._row_shape), + **dataset_ops.flat_structure(self)) @property def output_classes(self): @@ -490,10 +494,7 @@ class _MapAndBatchDataset(dataset_ops.MapDataset): batch_size=self._batch_size_t, num_parallel_calls=self._num_parallel_calls_t, drop_remainder=self._drop_remainder_t, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property diff --git a/tensorflow/contrib/data/python/ops/error_ops.py b/tensorflow/contrib/data/python/ops/error_ops.py index 6c21e489f7c35484ebacd465e3b46d6920df5933..d46d96c461ad4cc0ac25a8ddc285cec23d09c682 100644 --- a/tensorflow/contrib/data/python/ops/error_ops.py +++ b/tensorflow/contrib/data/python/ops/error_ops.py @@ -20,8 +20,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse def ignore_errors(): @@ -48,26 +46,23 @@ def ignore_errors(): """ def _apply_fn(dataset): - return IgnoreErrorsDataset(dataset) + return _IgnoreErrorsDataset(dataset) return _apply_fn -class IgnoreErrorsDataset(dataset_ops.Dataset): +class _IgnoreErrorsDataset(dataset_ops.Dataset): """A `Dataset` that silently ignores errors when computing its input.""" def __init__(self, input_dataset): """See `Dataset.ignore_errors()` for details.""" - super(IgnoreErrorsDataset, self).__init__() + super(_IgnoreErrorsDataset, self).__init__() self._input_dataset = input_dataset def _as_variant_tensor(self): return gen_dataset_ops.ignore_errors_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index 3a07df572748e464284f580d67e3a664e71acdfe..0f4cd8e20c5727a5bcfa1dce4dadbfa8f90bd551 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -64,10 +64,7 @@ def get_single_element(dataset): nested_ret = nest.pack_sequence_as( dataset.output_types, gen_dataset_ops.dataset_to_single_element( dataset._as_variant_tensor(), # pylint: disable=protected-access - output_types=nest.flatten(sparse.as_dense_types( - dataset.output_types, dataset.output_classes)), - output_shapes=nest.flatten(sparse.as_dense_shapes( - dataset.output_shapes, dataset.output_classes)))) + **dataset_ops.flat_structure(dataset))) return sparse.deserialize_sparse_tensors( nested_ret, dataset.output_types, dataset.output_shapes, dataset.output_classes) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index ea229b5b27b117984e508fa4edc6f1cf713008b4..5d9640a7680c29d8cc087c6fbb66f1e78778d7a2 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -21,12 +21,9 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops @@ -58,7 +55,7 @@ def group_by_reducer(key_func, reducer): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return GroupByReducerDataset(dataset, key_func, reducer) + return _GroupByReducerDataset(dataset, key_func, reducer) return _apply_fn @@ -116,8 +113,8 @@ def group_by_window(key_func, def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return GroupByWindowDataset(dataset, key_func, reduce_func, - window_size_func) + return _GroupByWindowDataset(dataset, key_func, reduce_func, + window_size_func) return _apply_fn @@ -152,9 +149,9 @@ def bucket_by_sequence_length(element_length_func, @{tf.data.Dataset.padded_batch}. Defaults to padding with 0. pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown size to maximum length in batch. If `True`, will pad dimensions with - unknown size to bucket boundary, and caller must ensure that the source - `Dataset` does not contain any elements with length longer than - `max(bucket_boundaries)`. + unknown size to bucket boundary minus 1 (i.e., the maximum length in each + bucket), and caller must ensure that the source `Dataset` does not contain + any elements with length longer than `max(bucket_boundaries)`. Returns: A `Dataset` transformation function, which can be passed to @@ -206,7 +203,7 @@ def bucket_by_sequence_length(element_length_func, none_filler = None if pad_to_bucket_boundary: err_msg = ("When pad_to_bucket_boundary=True, elements must have " - "length <= max(bucket_boundaries).") + "length < max(bucket_boundaries).") check = check_ops.assert_less( bucket_id, constant_op.constant(len(bucket_batch_sizes) - 1, @@ -216,7 +213,7 @@ def bucket_by_sequence_length(element_length_func, boundaries = constant_op.constant(bucket_boundaries, dtype=dtypes.int64) bucket_boundary = boundaries[bucket_id] - none_filler = bucket_boundary + none_filler = bucket_boundary - 1 shapes = make_padded_shapes( padded_shapes or grouped_dataset.output_shapes, none_filler=none_filler) @@ -230,39 +227,12 @@ def bucket_by_sequence_length(element_length_func, return _apply_fn -class _VariantDataset(dataset_ops.Dataset): - """A Dataset wrapper for a tf.variant-typed function argument.""" - - def __init__(self, dataset_variant, output_types, output_shapes, - output_classes): - super(_VariantDataset, self).__init__() - self._dataset_variant = dataset_variant - self._output_types = output_types - self._output_shapes = output_shapes - self._output_classes = output_classes - - def _as_variant_tensor(self): - return self._dataset_variant - - @property - def output_classes(self): - return self._output_classes - - @property - def output_shapes(self): - return self._output_shapes - - @property - def output_types(self): - return self._output_types - - -class GroupByReducerDataset(dataset_ops.Dataset): +class _GroupByReducerDataset(dataset_ops.Dataset): """A `Dataset` that groups its input and performs a reduction.""" def __init__(self, input_dataset, key_func, reducer): """See `group_by_reducer()` for details.""" - super(GroupByReducerDataset, self).__init__() + super(_GroupByReducerDataset, self).__init__() self._input_dataset = input_dataset @@ -273,67 +243,27 @@ class GroupByReducerDataset(dataset_ops.Dataset): def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_key_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - # pylint: disable=protected-access - if dataset_ops._should_unpack_args(nested_args): - ret = key_func(*nested_args) - # pylint: enable=protected-access - else: - ret = key_func(nested_args) - ret = ops.convert_to_tensor(ret) - if ret.dtype != dtypes.int64 or ret.get_shape() != tensor_shape.scalar(): - raise ValueError( - "`key_func` must return a single tf.int64 tensor. " - "Got type=%s and shape=%s" % (ret.dtype, ret.get_shape())) - return ret - - self._key_func = tf_key_func - self._key_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + key_func, "tf.contrib.data.group_by_reducer()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`key_func` must return a single tf.int64 tensor. " + "Got type=%s and shape=%s" + % (wrapped_func.output_types, wrapped_func.output_shapes)) + self._key_func = wrapped_func.function def _make_init_func(self, init_func): """Make wrapping Defun for init_func.""" - - @function.Defun(dtypes.int64) - def tf_init_func(key): - """A wrapper for Defun that facilitates shape inference.""" - key.set_shape([]) - ret = init_func(key) - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._state_classes = sparse.get_classes(ret) - self._state_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._state_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._init_func = tf_init_func - self._init_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + init_func, "tf.contrib.data.group_by_reducer()", + input_classes=ops.Tensor, input_shapes=tensor_shape.scalar(), + input_types=dtypes.int64) + self._init_func = wrapped_func.function + self._state_classes = wrapped_func.output_classes + self._state_shapes = wrapped_func.output_shapes + self._state_types = wrapped_func.output_types def _make_reduce_func(self, reduce_func, input_dataset): """Make wrapping Defun for reduce_func.""" @@ -343,83 +273,47 @@ class GroupByReducerDataset(dataset_ops.Dataset): need_to_rerun = True while need_to_rerun: - # Create a list in which `tf_reduce_func` will store the new shapes. - flat_new_state_shapes = [] - - @function.Defun(*(nest.flatten( - sparse.as_dense_types( - self._state_types, self._state_classes)) + nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes)))) - def tf_reduce_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - for arg, shape in zip( - args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes)) - + nest.flatten( - sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes))): - arg.set_shape(shape) - - pivot = len(nest.flatten(self._state_shapes)) - nested_state_args = nest.pack_sequence_as(self._state_types, - args[:pivot]) - nested_state_args = sparse.deserialize_sparse_tensors( - nested_state_args, self._state_types, self._state_shapes, - self._state_classes) - nested_input_args = nest.pack_sequence_as(input_dataset.output_types, - args[pivot:]) - nested_input_args = sparse.deserialize_sparse_tensors( - nested_input_args, input_dataset.output_types, - input_dataset.output_shapes, input_dataset.output_classes) - - ret = reduce_func(nested_state_args, nested_input_args) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - # Extract shape information from the returned values. - flat_new_state = nest.flatten(ret) - flat_new_state_shapes.extend([t.get_shape() for t in flat_new_state]) - - # Extract and validate type information from the returned values. - for t, dtype in zip(flat_new_state, nest.flatten(self._state_types)): - if t.dtype != dtype: - raise TypeError( - "The element types for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_types, - nest.pack_sequence_as(self._state_types, - [t.dtype for t in flat_new_state]))) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, - [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - # Use the private method that will execute `tf_reduce_func` but delay - # adding it to the graph in case we need to rerun the function. - tf_reduce_func._create_definition_if_needed() # pylint: disable=protected-access - + wrapped_func = dataset_ops.StructuredFunctionWrapper( + reduce_func, "tf.contrib.data.group_by_reducer()", + input_classes=(self._state_classes, input_dataset.output_classes), + input_shapes=(self._state_shapes, input_dataset.output_shapes), + input_types=(self._state_types, input_dataset.output_types), + add_to_graph=False) + + # Extract and validate class information from the returned values. + for new_state_class, state_class in zip( + nest.flatten(wrapped_func.output_classes), + nest.flatten(self._state_classes)): + if not issubclass(new_state_class, state_class): + raise TypeError( + "The element classes for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_classes, wrapped_func.output_classes)) + + # Extract and validate type information from the returned values. + for new_state_type, state_type in zip( + nest.flatten(wrapped_func.output_types), + nest.flatten(self._state_types)): + if new_state_type != state_type: + raise TypeError( + "The element types for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_types, wrapped_func.output_types)) + + # Extract shape information from the returned values. flat_state_shapes = nest.flatten(self._state_shapes) + flat_new_state_shapes = nest.flatten(wrapped_func.output_shapes) weakened_state_shapes = [ - old.most_specific_compatible_shape(new) - for old, new in zip(flat_state_shapes, flat_new_state_shapes) + original.most_specific_compatible_shape(new) + for original, new in zip(flat_state_shapes, flat_new_state_shapes) ] need_to_rerun = False - for old_shape, weakened_shape in zip(flat_state_shapes, - weakened_state_shapes): - if old_shape.ndims is not None and ( + for original_shape, weakened_shape in zip(flat_state_shapes, + weakened_state_shapes): + if original_shape.ndims is not None and ( weakened_shape.ndims is None or - old_shape.as_list() != weakened_shape.as_list()): + original_shape.as_list() != weakened_shape.as_list()): need_to_rerun = True break @@ -427,50 +321,19 @@ class GroupByReducerDataset(dataset_ops.Dataset): self._state_shapes = nest.pack_sequence_as(self._state_shapes, weakened_state_shapes) - self._reduce_func = tf_reduce_func + self._reduce_func = wrapped_func.function self._reduce_func.add_to_graph(ops.get_default_graph()) def _make_finalize_func(self, finalize_func): """Make wrapping Defun for finalize_func.""" - - @function.Defun(*(nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes)))) - def tf_finalize_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - for arg, shape in zip( - args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes))): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - - ret = finalize_func(nested_args) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._finalize_func = tf_finalize_func - self._finalize_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + finalize_func, "tf.contrib.data.group_by_reducer()", + input_classes=self._state_classes, input_shapes=self._state_shapes, + input_types=self._state_types) + self._finalize_func = wrapped_func.function + self._output_classes = wrapped_func.output_classes + self._output_shapes = wrapped_func.output_shapes + self._output_types = wrapped_func.output_types @property def output_classes(self): @@ -495,18 +358,15 @@ class GroupByReducerDataset(dataset_ops.Dataset): init_func=self._init_func, reduce_func=self._reduce_func, finalize_func=self._finalize_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) -class GroupByWindowDataset(dataset_ops.Dataset): +class _GroupByWindowDataset(dataset_ops.Dataset): """A `Dataset` that groups its input and performs a windowed reduction.""" def __init__(self, input_dataset, key_func, reduce_func, window_size_func): """See `group_by_window()` for details.""" - super(GroupByWindowDataset, self).__init__() + super(_GroupByWindowDataset, self).__init__() self._input_dataset = input_dataset @@ -516,74 +376,48 @@ class GroupByWindowDataset(dataset_ops.Dataset): def _make_window_size_func(self, window_size_func): """Make wrapping Defun for window_size_func.""" - - @function.Defun(dtypes.int64) - def tf_window_size_func(key): - key.set_shape([]) - window_size = ops.convert_to_tensor( - window_size_func(key), dtype=dtypes.int64) - if window_size.dtype != dtypes.int64: - raise ValueError( - "`window_size_func` must return a single tf.int64 tensor.") - return window_size - - self._window_size_func = tf_window_size_func - self._window_size_func.add_to_graph(ops.get_default_graph()) + def window_size_func_wrapper(key): + return ops.convert_to_tensor(window_size_func(key), dtype=dtypes.int64) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + window_size_func_wrapper, "tf.contrib.data.group_by_window()", + input_classes=ops.Tensor, input_shapes=tensor_shape.scalar(), + input_types=dtypes.int64) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`window_size_func` must return a single tf.int64 scalar tensor.") + self._window_size_func = wrapped_func.function def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_key_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - # pylint: disable=protected-access - if dataset_ops._should_unpack_args(nested_args): - ret = key_func(*nested_args) - # pylint: enable=protected-access - else: - ret = key_func(nested_args) - ret = ops.convert_to_tensor(ret, dtype=dtypes.int64) - if ret.dtype != dtypes.int64: - raise ValueError("`key_func` must return a single tf.int64 tensor.") - return ret - - self._key_func = tf_key_func - self._key_func.add_to_graph(ops.get_default_graph()) + def key_func_wrapper(*args): + return ops.convert_to_tensor(key_func(*args), dtype=dtypes.int64) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + key_func_wrapper, "tf.contrib.data.group_by_window()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`key_func` must return a single tf.int64 scalar tensor.") + self._key_func = wrapped_func.function def _make_reduce_func(self, reduce_func, input_dataset): """Make wrapping Defun for reduce_func.""" - - @function.Defun(dtypes.int64, dtypes.variant) - def tf_reduce_func(key, window_dataset_variant): - """A wrapper for Defun that facilitates shape inference.""" - key.set_shape([]) - window_dataset = _VariantDataset( - window_dataset_variant, input_dataset.output_types, - input_dataset.output_shapes, input_dataset.output_classes) - if not isinstance(window_dataset, dataset_ops.Dataset): - raise TypeError("`window_dataset` must return a `Dataset` object.") - output_dataset = reduce_func(key, window_dataset) - if not isinstance(output_dataset, dataset_ops.Dataset): - raise TypeError("`reduce_func` must return a `Dataset` object.") - self._output_classes = output_dataset.output_classes - self._output_types = output_dataset.output_types - self._output_shapes = output_dataset.output_shapes - return output_dataset._as_variant_tensor() # pylint: disable=protected-access - - self._reduce_func = tf_reduce_func - self._reduce_func.add_to_graph(ops.get_default_graph()) + nested_dataset = dataset_ops._NestedDatasetComponent(input_dataset) # pylint: disable=protected-access + wrapped_func = dataset_ops.StructuredFunctionWrapper( + reduce_func, "tf.contrib.data.reduce_by_window()", + input_classes=(ops.Tensor, nested_dataset), + input_shapes=(tensor_shape.scalar(), nested_dataset), + input_types=(dtypes.int64, nested_dataset), + experimental_nested_dataset_support=True) + if not isinstance( + wrapped_func.output_classes, dataset_ops._NestedDatasetComponent): # pylint: disable=protected-access + raise TypeError("`reduce_func` must return a `Dataset` object.") + self._output_classes = wrapped_func.output_classes.output_classes + self._output_types = wrapped_func.output_types.output_types + self._output_shapes = wrapped_func.output_shapes.output_shapes + self._reduce_func = wrapped_func.function @property def output_classes(self): @@ -606,10 +440,7 @@ class GroupByWindowDataset(dataset_ops.Dataset): key_func=self._key_func, reduce_func=self._reduce_func, window_size_func=self._window_size_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) class Reducer(object): diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index be66fbac50753c8f54b62dd615ee60804f4cf20d..bcc959594a6b311a3c60bb4696ac97be5c448756 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -24,7 +24,6 @@ from tensorflow.contrib.data.python.ops import random_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -154,7 +153,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1): return _apply_fn -class DirectedInterleaveDataset(dataset_ops.Dataset): +class _DirectedInterleaveDataset(dataset_ops.Dataset): """A substitute for `Dataset.interleave()` on a fixed list of datasets.""" def __init__(self, selector_input, data_inputs): @@ -171,10 +170,7 @@ class DirectedInterleaveDataset(dataset_ops.Dataset): return gen_dataset_ops.directed_interleave_dataset( self._selector_input._as_variant_tensor(), [data_input._as_variant_tensor() for data_input in self._data_inputs], - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property @@ -240,7 +236,7 @@ def sample_from_datasets(datasets, weights=None, seed=None): selector_input = dataset_ops.Dataset.zip( (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset) - return DirectedInterleaveDataset(selector_input, datasets) + return _DirectedInterleaveDataset(selector_input, datasets) def choose_from_datasets(datasets, choice_dataset): @@ -284,4 +280,4 @@ def choose_from_datasets(datasets, choice_dataset): and choice_dataset.output_classes == ops.Tensor): raise TypeError("`choice_dataset` must be a dataset of scalar " "`tf.int64` tensors.") - return DirectedInterleaveDataset(choice_dataset, datasets) + return _DirectedInterleaveDataset(choice_dataset, datasets) diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index cad41bce2961f29a7591fe3d382d1ab35a6b38b4..cf896572262929add5ac34d4fc8e4192c1049da3 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -19,8 +19,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops @@ -41,17 +39,17 @@ def optimize(optimizations=None): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return OptimizeDataset(dataset, optimizations) + return _OptimizeDataset(dataset, optimizations) return _apply_fn -class OptimizeDataset(dataset_ops.Dataset): +class _OptimizeDataset(dataset_ops.Dataset): """A `Dataset` that acts as an identity, and applies optimizations.""" def __init__(self, input_dataset, optimizations): """See `optimize()` for details.""" - super(OptimizeDataset, self).__init__() + super(_OptimizeDataset, self).__init__() self._input_dataset = input_dataset if optimizations is None: optimizations = [] @@ -62,10 +60,7 @@ class OptimizeDataset(dataset_ops.Dataset): return gen_dataset_ops.optimize_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._optimizations, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index e4c9f8b58a2a4390004b0ad318163526b443d44f..21fc17102e16a1f98f2c2e8aa0aeec89989edf67 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -32,15 +32,32 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops as core_gen_dataset_ops -# TODO(rohanj): Add a python class that constructs resource in the __init__ -# method and provides a get_next() that calls the prefetch op. def function_buffering_resource(string_arg, target_device, f, buffer_size, + output_types, container="", shared_name=None, name=None): + """Creates a FunctionBufferingResource. + + A FunctionBufferingResource fills up a buffer by calling a function `f` on + `target_device`. `f` should take in only a single string argument as input. + + Args: + string_arg: The single string argument to the function. + target_device: The device to run `f` on. + f: The function to be executed. + buffer_size: Size of the buffer to be populated. + output_types: The output types generated by the function. + container: (Optional) string. Defaults to "". + shared_name: (Optional) string. + name: (Optional) string to name the op. + + Returns: + Handle to a FunctionBufferingResource. + """ if shared_name is None: shared_name = "" return gen_dataset_ops.function_buffering_resource( @@ -50,7 +67,8 @@ def function_buffering_resource(string_arg, f=f, buffer_size=buffer_size, container=container, - name=name) + name=name, + output_types=output_types) def function_buffering_resource_get_next(function_buffer_resource, @@ -123,7 +141,10 @@ class _PrefetchToDeviceIterator(object): target_device=iterator_device, string_arg=input_iterator_handle, buffer_size=buffer_size, - shared_name=shared_name) + shared_name=shared_name, + output_types=nest.flatten( + sparse.as_dense_types(self._input_dataset.output_types, + self._input_dataset.output_classes))) if not self._one_shot: reset_op = function_buffering_resource_reset(self._buffering_resource) @@ -212,6 +233,7 @@ class _PrefetchToDeviceEagerIterator(iterator_ops.EagerIterator): with ops.device(device): self._buffering_resource = function_buffering_resource( f=_prefetch_fn, + output_types=self._flat_output_types, target_device=gen_dataset_ops.iterator_get_device(self._resource), string_arg=input_iterator_handle, buffer_size=buffer_size, diff --git a/tensorflow/contrib/data/python/ops/random_ops.py b/tensorflow/contrib/data/python/ops/random_ops.py index 28ef5e50f39dd7d1b6f124e58e068fc968ddd6dc..e670c4c8354f4067eb21c9b1fce708147c162967 100644 --- a/tensorflow/contrib/data/python/ops/random_ops.py +++ b/tensorflow/contrib/data/python/ops/random_ops.py @@ -18,9 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest from tensorflow.python.data.util import random_seed -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -39,10 +37,7 @@ class RandomDataset(dataset_ops.Dataset): return gen_dataset_ops.random_dataset( seed=self._seed, seed2=self._seed2, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/readers.py b/tensorflow/contrib/data/python/ops/readers.py index f938153f5f8c8becc5877a667117fd6facd3e428..83095c7ba1c6465d18490e5197f71bf7f1fe2497 100644 --- a/tensorflow/contrib/data/python/ops/readers.py +++ b/tensorflow/contrib/data/python/ops/readers.py @@ -26,6 +26,7 @@ from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.data.python.ops import gen_dataset_ops as contrib_gen_dataset_ops from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.contrib.data.python.ops import shuffle_ops +from tensorflow.contrib.data.python.ops import stats_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.data.util import convert @@ -754,6 +755,8 @@ def make_batched_features_dataset(file_pattern, dataset = _maybe_shuffle_and_repeat( dataset, num_epochs, shuffle, shuffle_buffer_size, shuffle_seed) + dataset = dataset.apply(stats_ops.feature_stats("record_stats")) + if drop_final_batch: dataset = dataset.apply(batching.batch_and_drop_remainder(batch_size)) else: diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index bad6edd5147d832228c412919f1e6e782aafc40f..182a5c6ff36fcda8c9e2c522cce07bed0c2daec9 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -291,4 +291,4 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) - return a_i, m \ No newline at end of file + return a_i, m diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index e911ad0fa0541f2d8b991d66182dd002c2ecaab0..ea9dcfe68fa2630d915323fa295031af7d48cdfb 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -22,7 +22,6 @@ import collections from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse -from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import gen_dataset_ops @@ -67,102 +66,45 @@ class _ScanDataset(dataset_ops.Dataset): need_to_rerun = True while need_to_rerun: - # Create a list in which `tf_scan_func` will store the new shapes. - flat_new_state_shapes = [] - - @function.Defun(*(nest.flatten( - sparse.as_dense_types( - self._state_types, self._state_classes)) + nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes)))) - def tf_scan_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the state and input_dataset. - for arg, shape in zip( - args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes)) - + nest.flatten( - sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes))): - arg.set_shape(shape) - - pivot = len(nest.flatten(self._state_shapes)) - print(self._state_classes) - nested_state_args = nest.pack_sequence_as(self._state_types, - args[:pivot]) - nested_state_args = sparse.deserialize_sparse_tensors( - nested_state_args, self._state_types, self._state_shapes, - self._state_classes) - print(input_dataset.output_classes) - nested_input_args = nest.pack_sequence_as(input_dataset.output_types, - args[pivot:]) - nested_input_args = sparse.deserialize_sparse_tensors( - nested_input_args, input_dataset.output_types, - input_dataset.output_shapes, input_dataset.output_classes) - - ret = scan_func(nested_state_args, nested_input_args) - if not isinstance(ret, collections.Sequence) or len(ret) != 2: - raise TypeError("The scan function must return a pair comprising the " - "new state and the output value.") - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - new_state, output_value = ret - - # Extract and validate class information from the returned values. - for t, clazz in zip( - nest.flatten(new_state), nest.flatten(self._state_classes)): - if not isinstance(t, clazz): - raise TypeError( - "The element classes for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_classes, - nest.pack_sequence_as( - self._state_types, - [type(t) for t in nest.flatten(new_state)]))) - self._output_classes = sparse.get_classes(output_value) - - # Extract shape information from the returned values. - flat_new_state_shapes.extend( - [t.get_shape() for t in nest.flatten(new_state)]) - self._output_shapes = nest.pack_sequence_as( - output_value, [t.get_shape() for t in nest.flatten(output_value)]) - - # Extract and validate type information from the returned values. - for t, dtype in zip( - nest.flatten(new_state), nest.flatten(self._state_types)): - if t.dtype != dtype: - raise TypeError( - "The element types for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_types, - nest.pack_sequence_as( - self._state_types, - [t.dtype for t in nest.flatten(new_state)]))) - self._output_types = nest.pack_sequence_as( - output_value, [t.dtype for t in nest.flatten(output_value)]) - - # Serialize any sparse tensors. - new_state = nest.pack_sequence_as(new_state, [ - t for t in nest.flatten(sparse.serialize_sparse_tensors(new_state)) - ]) - output_value = nest.pack_sequence_as(output_value, [ - t for t in nest.flatten( - sparse.serialize_sparse_tensors(output_value)) - ]) - return nest.flatten(new_state) + nest.flatten(output_value) - - # Use the private method that will execute `tf_scan_func` but delay - # adding it to the graph in case we need to rerun the function. - tf_scan_func._create_definition_if_needed() # pylint: disable=protected-access + wrapped_func = dataset_ops.StructuredFunctionWrapper( + scan_func, "tf.contrib.data.scan()", + input_classes=(self._state_classes, input_dataset.output_classes), + input_shapes=(self._state_shapes, input_dataset.output_shapes), + input_types=(self._state_types, input_dataset.output_types), + add_to_graph=False) + if not ( + isinstance(wrapped_func.output_types, collections.Sequence) and + len(wrapped_func.output_types) == 2): + raise TypeError("The scan function must return a pair comprising the " + "new state and the output value.") + + new_state_classes, self._output_classes = wrapped_func.output_classes + + # Extract and validate class information from the returned values. + for new_state_class, state_class in zip( + nest.flatten(new_state_classes), + nest.flatten(self._state_classes)): + if not issubclass(new_state_class, state_class): + raise TypeError( + "The element classes for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_classes, new_state_classes)) + + # Extract and validate type information from the returned values. + new_state_types, self._output_types = wrapped_func.output_types + for new_state_type, state_type in zip( + nest.flatten(new_state_types), nest.flatten(self._state_types)): + if new_state_type != state_type: + raise TypeError( + "The element types for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_types, new_state_types)) + + # Extract shape information from the returned values. + new_state_shapes, self._output_shapes = wrapped_func.output_shapes flat_state_shapes = nest.flatten(self._state_shapes) + flat_new_state_shapes = nest.flatten(new_state_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) for original, new in zip(flat_state_shapes, flat_new_state_shapes) @@ -178,12 +120,10 @@ class _ScanDataset(dataset_ops.Dataset): break if need_to_rerun: - # NOTE(mrry): `self._output_shapes` will be overwritten when we rerun - # `tf_scan_func`. self._state_shapes = nest.pack_sequence_as(self._state_shapes, weakened_state_shapes) - self._scan_func = tf_scan_func + self._scan_func = wrapped_func.function self._scan_func.add_to_graph(ops.get_default_graph()) def _as_variant_tensor(self): @@ -193,10 +133,7 @@ class _ScanDataset(dataset_ops.Dataset): nest.flatten(sparse.serialize_sparse_tensors(self._initial_state)), self._scan_func.captured_inputs, f=self._scan_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py index f35795abd38000b13cec0f08596e2ff66e86286c..d7f8a73fe3d67bb83e44e962832ce34c116aef66 100644 --- a/tensorflow/contrib/data/python/ops/shuffle_ops.py +++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py @@ -18,9 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest from tensorflow.python.data.util import random_seed -from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -56,10 +54,7 @@ class _ShuffleAndRepeatDataset(dataset_ops.Dataset): count=self._count, seed=self._seed, seed2=self._seed2, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py index 19cc3cb89fc5c494f79ce1d25ed57c92099c8bd2..3f3c5ca17cf6ae22a719ed1d593d98eec37413fb 100644 --- a/tensorflow/contrib/data/python/ops/sliding.py +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -19,7 +19,6 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -43,10 +42,7 @@ class _SlideDataset(dataset_ops.Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access window_size=self._window_size, stride=self._stride, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): @@ -90,7 +86,7 @@ def sliding_window_batch(window_size, stride=1): elements in the sliding window. stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the steps moving the sliding window forward for one iteration. The default - is `1`. It must be in `[1, window_size)`. + is `1`. It must be positive. Returns: A `Dataset` transformation function, which can be passed to diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 3cbaab5affd7397213b0fbb6b0682db92b99d591..97931f75bd37d9e45864fe477c6e1620b5e4f193 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -18,13 +18,13 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. class StatsAggregator(object): """A stateful resource that aggregates statistics from one or more iterators. @@ -97,10 +97,7 @@ class _SetStatsAggregatorDataset(dataset_ops.Dataset): return gen_dataset_ops.set_stats_aggregator_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._stats_aggregator._resource, # pylint: disable=protected-access - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): @@ -115,7 +112,8 @@ class _SetStatsAggregatorDataset(dataset_ops.Dataset): return self._input_dataset.output_classes -# TODO(shivaniagrawal): Expose these methods in `tf.contrib.data`. +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def set_stats_aggregator(stats_aggregator): """Set the given stats_aggregator for aggregating the input dataset stats. @@ -133,6 +131,8 @@ def set_stats_aggregator(stats_aggregator): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def bytes_produced_stats(tag): """Records the number of bytes produced by each element of the input dataset. @@ -155,6 +155,8 @@ def bytes_produced_stats(tag): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def latency_stats(tag): """Records the latency of producing each element of the input dataset. @@ -176,6 +178,29 @@ def latency_stats(tag): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. +def feature_stats(tag): + """Records the features stats from `Example` records of the input dataset. + + To consume the statistics, associate a `StatsAggregator` with the output + dataset. + + Args: + tag: String. All statistics recorded by the returned transformation will be + associated with the given `tag`. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + + def _apply_fn(dataset): + return _StatsDataset(dataset, gen_dataset_ops.feature_stats_dataset, tag) + + return _apply_fn + + class _StatsDataset(dataset_ops.Dataset): """A `Dataset` that acts as an identity, and also records statistics.""" @@ -189,10 +214,7 @@ class _StatsDataset(dataset_ops.Dataset): return self._op_function( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._tag, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index 56f67e1766bbaff680bdff6b939df0c3ba68c679..9af1e784ffb4f6d71da25f09d60343b649c5079b 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -22,8 +22,6 @@ import threading from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.eager import context from tensorflow.python.ops import resource_variable_ops @@ -39,22 +37,28 @@ def _generate_shared_name(prefix): return "{}{}".format(prefix, uid) +# TODO(b/73383364): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. class PrivateThreadPool(object): """A stateful resource that represents a private thread pool.""" - def __init__(self, num_threads, display_name=None): + def __init__(self, num_threads, display_name=None, + max_intra_op_parallelism=1): """Creates a `PrivateThreadPool` with the given number of threads.""" if context.executing_eagerly(): shared_name = _generate_shared_name("privatethreadpool") self._resource = gen_dataset_ops.thread_pool_handle( num_threads=num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, display_name=display_name, shared_name=shared_name) self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device=context.context().device_name) else: self._resource = gen_dataset_ops.thread_pool_handle( - num_threads=num_threads, display_name=display_name) + num_threads=num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, + display_name=display_name) class _ThreadPoolDataset(dataset_ops.Dataset): @@ -69,10 +73,7 @@ class _ThreadPoolDataset(dataset_ops.Dataset): return gen_dataset_ops.thread_pool_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._thread_pool._resource, # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): @@ -87,6 +88,8 @@ class _ThreadPoolDataset(dataset_ops.Dataset): return self._input_dataset.output_classes +# TODO(b/73383364): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def override_threadpool(dataset, thread_pool): """Returns a new dataset that uses the given thread pool for its operations. diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py index 765ef3f9b6d42c9d7af3ce4916731d37d65c9260..e0ce0a4ef15f6b9181bce92fb4d73bf1fab2e66c 100644 --- a/tensorflow/contrib/data/python/ops/unique.py +++ b/tensorflow/contrib/data/python/ops/unique.py @@ -20,8 +20,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes @@ -44,17 +42,17 @@ def unique(): """ def _apply_fn(dataset): - return UniqueDataset(dataset) + return _UniqueDataset(dataset) return _apply_fn -class UniqueDataset(dataset_ops.Dataset): +class _UniqueDataset(dataset_ops.Dataset): """A `Dataset` contains the unique elements from its input.""" def __init__(self, input_dataset): """See `unique()` for details.""" - super(UniqueDataset, self).__init__() + super(_UniqueDataset, self).__init__() self._input_dataset = input_dataset if input_dataset.output_types not in (dtypes.int32, dtypes.int64, dtypes.string): @@ -65,10 +63,7 @@ class UniqueDataset(dataset_ops.Dataset): def _as_variant_tensor(self): return gen_dataset_ops.unique_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index 9624abd1997b36c4424f525366c658fe24b25f3a..eba0dd0ea330e29db0ea8e68ee14767fcb8ddad0 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -77,6 +77,7 @@ py_library( "//tensorflow/python:device_util", "//tensorflow/python:distribute", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", "//tensorflow/python:pywrap_tensorflow", "//tensorflow/python:training", "//tensorflow/python:variable_scope", @@ -312,7 +313,6 @@ cuda_py_test( tags = [ "multi_and_single_gpu", "no_pip", - "noguitar", # TODO(b/109653107): test is flaky. ], ) @@ -587,7 +587,25 @@ cuda_py_test( ], tags = [ "multi_and_single_gpu", - "noguitar", "notsan", ], ) + +cuda_py_test( + name = "metrics_v1_test", + srcs = ["metrics_v1_test.py"], + additional_deps = [ + ":combinations", + "@absl_py//absl/testing:parameterized", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:math_ops", + "//tensorflow/python:metrics", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/eager:test", + ], + tags = [ + "multi_and_single_gpu", + "no_pip", + ], +) diff --git a/tensorflow/contrib/distribute/python/combinations.py b/tensorflow/contrib/distribute/python/combinations.py index ba03b14deb9a3897dae29382ce601c0319f84735..9a8ea4aa48b8cf4c5906f18d8bddacc224e0b644 100644 --- a/tensorflow/contrib/distribute/python/combinations.py +++ b/tensorflow/contrib/distribute/python/combinations.py @@ -321,10 +321,6 @@ default_strategy = NamedDistribution( one_device_strategy = NamedDistribution( "OneDeviceCPU", lambda: one_device_lib.OneDeviceStrategy("/cpu:0"), required_gpus=None) -tpu_strategy_single_iteration = NamedDistribution( - "TPUSingleIteration", - lambda: tpu_lib.TPUStrategy(iterations_per_step=1), - required_tpu=True) tpu_strategy = NamedDistribution("TPU", tpu_lib.TPUStrategy, required_tpu=True) # Note that we disable prefetching for testing since prefetching makes # the input non-deterministic. diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index f8ae8b9712c392fa948c8598dd123cdea01d9866..b0baf0dad1d55eafac5338d1eb43465927e428a1 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -28,11 +28,12 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope as vs from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import device_util -def _validate_destinations(destinations): +def validate_destinations(destinations): if not isinstance(destinations, (value_lib.DistributedValues, six.string_types, list)): raise ValueError("destinations must be one of a `DistributedValues` object," @@ -55,7 +56,7 @@ def _validate_value_destination_pairs(value_destination_pairs): # TODO(yuefengz): consider calling this function in the caller of CrossTowerOps. -def _get_devices_from(destinations): +def get_devices_from(destinations): if isinstance(destinations, value_lib.DistributedValues): return list(destinations.devices) elif isinstance(destinations, six.string_types): @@ -65,7 +66,7 @@ def _get_devices_from(destinations): def _devices_match(left, right): - return set(_get_devices_from(left)) == set(_get_devices_from(right)) + return set(get_devices_from(left)) == set(get_devices_from(right)) def _all_devices_match(value_destination_pairs): @@ -80,7 +81,7 @@ def _all_devices_match(value_destination_pairs): def _simple_broadcast(value, destinations): index = {} - devices = _get_devices_from(destinations) + devices = get_devices_from(destinations) for d in devices: index[d] = cross_tower_utils.copy_tensor_or_indexed_slices_to_device( value, d) @@ -88,7 +89,7 @@ def _simple_broadcast(value, destinations): def _simple_reduce(per_device_value, reduce_to_device, accumulation_fn, - method_string): + aggregation): # pylint: disable=g-missing-docstring all_values = [] count = 0 @@ -112,11 +113,12 @@ def _simple_reduce(per_device_value, reduce_to_device, accumulation_fn, with context.context().device_policy(context.DEVICE_PLACEMENT_SILENT): reduced = cross_tower_utils.aggregate_tensors_or_indexed_slices( all_values, accumulation_fn) - if method_string == "mean": + if aggregation == vs.VariableAggregation.MEAN: reduced = cross_tower_utils.divide_by_n_tensors_or_indexed_slices( reduced, count) - elif method_string != "sum": - raise ValueError("`method_string` must be 'sum' or 'mean'") + elif aggregation != vs.VariableAggregation.SUM: + raise ValueError("`aggregation` must be VariableAggregation.SUM " + "or VariableAggregation.MEAN.") return reduced @@ -126,14 +128,15 @@ class CrossTowerOps(object): def __init__(self): pass - def reduce(self, method_string, per_device_value, destinations=None): + def reduce(self, aggregation, per_device_value, destinations=None): """Reduce `per_device_value` to `destinations`. - It runs the reduction operation defined by `method_string` and put the + It runs the reduction operation defined by `aggregation` and put the result on `destinations`. Args: - method_string: either 'sum' or 'mean' specifying the reduction method. + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. per_device_value: a PerDevice object. destinations: the reduction destinations. @@ -146,17 +149,18 @@ class CrossTowerOps(object): if not isinstance(per_device_value, value_lib.PerDevice): raise ValueError("`per_device_value` must be a `PerDevice` object.") if destinations is not None: - _validate_destinations(destinations) - return self._reduce(method_string, per_device_value, destinations) + validate_destinations(destinations) + return self._reduce(aggregation, per_device_value, destinations) - def batch_reduce(self, method_string, value_destination_pairs): + def batch_reduce(self, aggregation, value_destination_pairs): """Reduce PerDevice objects in a batch. Reduce each first element in `value_destination_pairs` to each second element which indicates the destinations. Args: - method_string: either 'sum' or 'mean' specifying the reduction method. + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. value_destination_pairs: a list or a tuple of tuples of PerDevice objects and destinations. If a destination is None, then the destinations are set to match the devices of the input PerDevice object. @@ -173,9 +177,9 @@ class CrossTowerOps(object): "tuples of PerDevice objects and destinations") for _, d in value_destination_pairs: if d is not None: - _validate_destinations(d) + validate_destinations(d) - return self._batch_reduce(method_string, value_destination_pairs) + return self._batch_reduce(aggregation, value_destination_pairs) def broadcast(self, tensor, destinations): """Broadcast the `tensor` to destinations. @@ -187,14 +191,14 @@ class CrossTowerOps(object): Returns: a Mirrored object. """ - _validate_destinations(destinations) + validate_destinations(destinations) return self._broadcast(tensor, destinations) - def _reduce(self, method_string, per_device_value, destinations): + def _reduce(self, aggregation, per_device_value, destinations): raise NotImplementedError( "_reduce method must be implemented in descendants.") - def _batch_reduce(self, method_string, value_destination_pairs): + def _batch_reduce(self, aggregation, value_destination_pairs): raise NotImplementedError( "_batch_reduce method must be implemented in descendants.") @@ -220,16 +224,18 @@ class ReductionToOneDeviceCrossTowerOps(CrossTowerOps): self.accumulation_fn = accumulation_fn super(ReductionToOneDeviceCrossTowerOps, self).__init__() - def _reduce(self, method_string, per_device_value, destinations): - devices = _get_devices_from(destinations or per_device_value) + def _reduce(self, aggregation, per_device_value, destinations): + devices = get_devices_from(destinations or per_device_value) reduce_to_device = self.reduce_to_device or devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, - self.accumulation_fn, method_string) + self.accumulation_fn, aggregation) return self.broadcast(reduced, devices) - def _batch_reduce(self, method_string, value_destination_pairs): - return [self._reduce(method_string, t, destinations=v) - for t, v in value_destination_pairs] + def _batch_reduce(self, aggregation, value_destination_pairs): + return [ + self._reduce(aggregation, t, destinations=v) + for t, v in value_destination_pairs + ] def _group_value_by_device(per_device_values): @@ -260,18 +266,19 @@ def _group_value_by_device(per_device_values): return grouped -def _ungroup_and_make_mirrored(grouped_reduced, destinations, method_string): +def _ungroup_and_make_mirrored(grouped_reduced, destinations, aggregation): """Ungroup results from all-reduce and make Mirrored objects. Each all-reduce result will be divided by the number of destinations before - Mirrored objects are created if method_string is "mean". + Mirrored objects are created if aggregation is "mean". Args: grouped_reduced: a list of lists, each sublist has components for each device, paired with a None. It is the result from cross_tower_utils.aggregate_gradients_using*. destinations: a list of device strings for returned Mirrored objects. - method_string: "mean" or "sum". + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. Returns: a list of Mirrored objects. @@ -279,7 +286,7 @@ def _ungroup_and_make_mirrored(grouped_reduced, destinations, method_string): index = [{} for _ in range(len(grouped_reduced[0]))] for d, per_device_reduced in enumerate(grouped_reduced): for i, (v, _) in enumerate(per_device_reduced): - if method_string == "mean": + if aggregation == vs.VariableAggregation.MEAN: index[i][destinations[d]] = v / len(destinations) else: index[i][destinations[d]] = v @@ -488,32 +495,32 @@ class AllReduceCrossTowerOps(CrossTowerOps): self._agg_small_grads_max_group = agg_small_grads_max_group super(AllReduceCrossTowerOps, self).__init__() - def _reduce(self, method_string, per_device_value, destinations): + def _reduce(self, aggregation, per_device_value, destinations): contains_indexed_slices = cross_tower_utils.contains_indexed_slices( per_device_value) if ((destinations is None or _devices_match(per_device_value, destinations)) and not context.executing_eagerly() and not contains_indexed_slices): - return self._batch_all_reduce(method_string, [per_device_value])[0] + return self._batch_all_reduce(aggregation, [per_device_value])[0] else: if contains_indexed_slices: logging.log_first_n( logging.WARN, "Efficient allreduce is not supported for IndexedSlices.", 10) - devices = _get_devices_from(destinations or per_device_value) + devices = get_devices_from(destinations or per_device_value) reduce_to_device = devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, - math_ops.add_n, method_string) + math_ops.add_n, aggregation) return self.broadcast(reduced, devices) - def _batch_reduce(self, method_string, value_destination_pairs): + def _batch_reduce(self, aggregation, value_destination_pairs): all_devices_match = _all_devices_match(value_destination_pairs) contains_indexed_slices = cross_tower_utils.contains_indexed_slices( value_destination_pairs) if (all_devices_match and not context.executing_eagerly() and not contains_indexed_slices): - return self._batch_all_reduce(method_string, + return self._batch_all_reduce(aggregation, [v[0] for v in value_destination_pairs]) else: if not all_devices_match: @@ -521,11 +528,11 @@ class AllReduceCrossTowerOps(CrossTowerOps): "destinations are different.") return [ - self._reduce(method_string, t, destinations=v) + self._reduce(aggregation, t, destinations=v) for t, v in value_destination_pairs ] - def _batch_all_reduce(self, method_string, per_device_values): + def _batch_all_reduce(self, aggregation, per_device_values): """All reduce algorithm in a batch.""" logging.info( "batch_all_reduce invoked for batches size = %d with " @@ -536,7 +543,7 @@ class AllReduceCrossTowerOps(CrossTowerOps): destinations = per_device_values[0].devices grouped = _group_value_by_device(per_device_values) - device_grad_packs, self._tensor_packer = _pack_tensors( + device_grad_packs, tensor_packer = _pack_tensors( grouped, self._num_packs, self._agg_small_grads_max_bytes, self._agg_small_grads_max_group) @@ -554,9 +561,9 @@ class AllReduceCrossTowerOps(CrossTowerOps): cross_tower_utils.aggregate_gradients_using_hierarchical_copy( destinations, device_grad_packs)) - reduced = _unpack_tensors(reduced, self._tensor_packer) + reduced = _unpack_tensors(reduced, tensor_packer) return _ungroup_and_make_mirrored(reduced, per_device_values[0].devices, - method_string) + aggregation) AllReduceSpecTuple = collections.namedtuple("AllReduceSpecTuple", @@ -635,7 +642,7 @@ class MultiWorkerAllReduce(AllReduceCrossTowerOps): validate_and_complete_spec(spec) for spec in all_reduce_spec ] - def _batch_all_reduce(self, method_string, per_device_values): + def _batch_all_reduce(self, aggregation, per_device_values): """All reduce algorithm in a batch.""" logging.info( "distributed batch_all_reduce invoked for batches size = %d with " @@ -665,13 +672,13 @@ class MultiWorkerAllReduce(AllReduceCrossTowerOps): (this_grads, remaining_grads) = cross_tower_utils.split_grads_by_size( spec_tuple.limit, remaining_grads) if this_grads: - device_grad_packs, self._tensor_packer = _pack_tensors( + device_grad_packs, tensor_packer = _pack_tensors( this_grads, self._num_packs, self._agg_small_grads_max_bytes, self._agg_small_grads_max_group) range_agg_grads = cross_tower_utils.sum_gradients_all_reduce( self._worker_devices, device_grad_packs, len(self._worker_devices), spec_tuple.alg, spec_tuple.shards, range(self._num_gpus_per_worker)) - range_agg_grads = _unpack_tensors(range_agg_grads, self._tensor_packer) + range_agg_grads = _unpack_tensors(range_agg_grads, tensor_packer) if not aggregated_grads: aggregated_grads = range_agg_grads @@ -682,7 +689,7 @@ class MultiWorkerAllReduce(AllReduceCrossTowerOps): assert not remaining_grads return _ungroup_and_make_mirrored(aggregated_grads, destinations, - method_string) + aggregation) _dgx1_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7], diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops_test.py b/tensorflow/contrib/distribute/python/cross_tower_ops_test.py index fed5505d92ef2544215069736c166a67d6141708..6a780ff60ffcd59d416278bfde6d005d7ad37a68 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops_test.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops_test.py @@ -32,11 +32,12 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import device_util def _make_per_device(values, devices): - devices = cross_tower_ops_lib._get_devices_from(devices) + devices = cross_tower_ops_lib.get_devices_from(devices) assert len(values) == len(devices) index = {} for d, v in zip(devices, values): @@ -53,7 +54,7 @@ def _fake_mirrored(value, devices): All components of the returned Mirrored have the same objects, which is not true in reality. """ - devices = cross_tower_ops_lib._get_devices_from(devices) + devices = cross_tower_ops_lib.get_devices_from(devices) return value_lib.Mirrored( {d: v for d, v in zip(devices, [value] * len(devices))}) @@ -93,7 +94,7 @@ class CrossTowerOpsTestBase(test.TestCase, parameterized.TestCase): self._assert_values_equal(l, r) else: self.assertEqual(type(left), type(right)) - self.assertEqual(left.devices, right.devices) + self.assertEqual(set(left.devices), set(right.devices)) if isinstance(list(left._index.values())[0], ops.IndexedSlices): for (d, v) in left._index.items(): self._assert_indexed_slices_equal(v, right._index[d]) @@ -129,32 +130,45 @@ class CrossTowerOpsTestBase(test.TestCase, parameterized.TestCase): # test reduce() for destinations in all_destinations: self._assert_values_equal( - cross_tower_ops.reduce("mean", per_device, destinations=destinations), + cross_tower_ops.reduce( + vs.VariableAggregation.MEAN, + per_device, + destinations=destinations), _fake_mirrored(mean, destinations or per_device)) self._assert_values_equal( cross_tower_ops.reduce( - "mean", per_device_2, destinations=destinations), + vs.VariableAggregation.MEAN, + per_device_2, + destinations=destinations), _fake_mirrored(mean_2, destinations or per_device)) self._assert_values_equal( - cross_tower_ops.reduce("sum", per_device, destinations=destinations), + cross_tower_ops.reduce( + vs.VariableAggregation.SUM, per_device, + destinations=destinations), _fake_mirrored(mean * len(devices), destinations or per_device)) self._assert_values_equal( cross_tower_ops.reduce( - "sum", per_device_2, destinations=destinations), + vs.VariableAggregation.SUM, + per_device_2, + destinations=destinations), _fake_mirrored(mean_2 * len(devices), destinations or per_device)) # test batch_reduce() for d1, d2 in itertools.product(all_destinations, all_destinations): self._assert_values_equal( - cross_tower_ops.batch_reduce( - "mean", [(per_device, d1), (per_device_2, d2)]), - [_fake_mirrored(mean, d1 or per_device), - _fake_mirrored(mean_2, d2 or per_device_2)]) + cross_tower_ops.batch_reduce(vs.VariableAggregation.MEAN, + [(per_device, d1), (per_device_2, d2)]), + [ + _fake_mirrored(mean, d1 or per_device), + _fake_mirrored(mean_2, d2 or per_device_2) + ]) self._assert_values_equal( - cross_tower_ops.batch_reduce( - "sum", [(per_device, d1), (per_device_2, d2)]), - [_fake_mirrored(mean * len(devices), d1 or per_device), - _fake_mirrored(mean_2 * len(devices), d2 or per_device_2)]) + cross_tower_ops.batch_reduce(vs.VariableAggregation.SUM, + [(per_device, d1), (per_device_2, d2)]), + [ + _fake_mirrored(mean * len(devices), d1 or per_device), + _fake_mirrored(mean_2 * len(devices), d2 or per_device_2) + ]) # test broadcast() for destinations in all_destinations: @@ -255,8 +269,8 @@ class SingleWorkerCrossTowerOpsTest(CrossTowerOpsTestBase): t0 = _make_indexed_slices([[1., 2.]], [1], [5, 2], devices[0]) t1 = _make_indexed_slices([[3., 4.], [5., 6.]], [1, 3], [5, 2], devices[1]) per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1}) - result = cross_tower_ops_lib._simple_reduce(per_device, devices[0], - math_ops.add_n, "sum") + result = cross_tower_ops_lib._simple_reduce( + per_device, devices[0], math_ops.add_n, vs.VariableAggregation.SUM) # Test that the result is semantically equal to both the concatenated # IndexedSlices with and without duplicate indices. @@ -267,21 +281,22 @@ class SingleWorkerCrossTowerOpsTest(CrossTowerOpsTestBase): self._assert_indexed_slices_equal(total_with_dups, result) self._assert_indexed_slices_equal(total_without_dups, result) - @combinations.generate(combinations.combine( - cross_tower_ops_instance=[ - combinations.NamedObject( - "ReductionToOneDeviceCrossTowerOps", - cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()), - combinations.NamedObject( - "AllReduceCrossTowerOps", - cross_tower_ops_lib.AllReduceCrossTowerOps()) - ], - method_string=["sum", "mean"], - batch_reduce=[True, False], - mode=["graph", "eager"], - required_gpus=1)) - def testIndexedSlicesAllReduce(self, cross_tower_ops_instance, - method_string, batch_reduce): + @combinations.generate( + combinations.combine( + cross_tower_ops_instance=[ + combinations.NamedObject( + "ReductionToOneDeviceCrossTowerOps", + cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()), + combinations.NamedObject( + "AllReduceCrossTowerOps", + cross_tower_ops_lib.AllReduceCrossTowerOps()) + ], + aggregation=[vs.VariableAggregation.SUM, vs.VariableAggregation.MEAN], + batch_reduce=[True, False], + mode=["graph", "eager"], + required_gpus=1)) + def testIndexedSlicesAllReduce(self, cross_tower_ops_instance, aggregation, + batch_reduce): devices = ["/cpu:0", "/gpu:0"] dense_shape = [5, 2] t0 = _make_indexed_slices([[1., 2.]], [1], dense_shape, devices[0]) @@ -290,20 +305,19 @@ class SingleWorkerCrossTowerOpsTest(CrossTowerOpsTestBase): per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1}) if batch_reduce: - result = cross_tower_ops_instance.batch_reduce(method_string, + result = cross_tower_ops_instance.batch_reduce(aggregation, [(per_device, devices)]) else: - result = cross_tower_ops_instance.reduce(method_string, per_device, - devices) + result = cross_tower_ops_instance.reduce(aggregation, per_device, devices) total_indices_with_dups = [1, 1, 3] total_indices_without_dups = [1, 3] - if method_string == "sum": + if aggregation == vs.VariableAggregation.SUM: total_values_with_dups = [[1., 2.], [3., 4.], [5., 6.]] total_values_without_dups = [[4., 6.], [5., 6.]] else: - assert method_string == "mean" + assert aggregation == vs.VariableAggregation.MEAN total_values_with_dups = [[0.5, 1.], [1.5, 2.], [2.5, 3.]] total_values_without_dups = [[2., 3.], [2.5, 3.]] diff --git a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py index 4ef8db681503dcef8c72f641455dbb999cef05cf..d25964fa41adc7b1c9164a4ffe49c4c5532f76ac 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py +++ b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py @@ -38,7 +38,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.evaluate(ops.convert_to_tensor(left)), self.evaluate(ops.convert_to_tensor(right))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateTensors(self): t0 = constant_op.constant([[1., 2.], [0, 0], [3., 4.]]) t1 = constant_op.constant([[0., 0.], [5, 6], [7., 8.]]) @@ -46,7 +46,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): result = cross_tower_utils.aggregate_tensors_or_indexed_slices([t0, t1]) self._assert_values_equal(total, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateIndexedSlices(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -57,7 +57,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.assertIsInstance(result, ops.IndexedSlices) self._assert_values_equal(total, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDivideTensor(self): t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]]) n = 2 @@ -65,7 +65,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): result = cross_tower_utils.divide_by_n_tensors_or_indexed_slices(t, n) self._assert_values_equal(expected, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDivideIndexedSlices(self): t = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -75,13 +75,13 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.assertIsInstance(result, ops.IndexedSlices) self._assert_values_equal(expected, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testIsIndexedSlices(self): t = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices(t)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_List(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -89,7 +89,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): constant_op.constant([[0., 0.], [5, 6], [7., 8.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices([t0, t1])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_Tuple(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -97,7 +97,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): constant_op.constant([[0., 0.], [5, 6], [7., 8.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices((t0, t1))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_PerDevice(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -106,7 +106,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): per_device = value_lib.PerDevice({"/gpu:0": t0, "/cpu:0": t1}) self.assertTrue(cross_tower_utils.contains_indexed_slices(per_device)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_PerDeviceMapOutput(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) diff --git a/tensorflow/contrib/distribute/python/metrics_v1_test.py b/tensorflow/contrib/distribute/python/metrics_v1_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6bf143098c1bba64d47efce1bfface7682683d --- /dev/null +++ b/tensorflow/contrib/distribute/python/metrics_v1_test.py @@ -0,0 +1,438 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for V1 metrics.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from absl.testing import parameterized + +from tensorflow.contrib.data.python.ops import batching +from tensorflow.contrib.distribute.python import combinations +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.eager import test +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics +from tensorflow.python.ops import variables + + +def _labeled_dataset_fn(): + # First four batches of x: labels, predictions -> (labels == predictions) + # 0: 0, 0 -> True; 1: 1, 1 -> True; 2: 2, 2 -> True; 3: 3, 0 -> False + # 4: 4, 1 -> False; 5: 0, 2 -> False; 6: 1, 0 -> False; 7: 2, 1 -> False + # 8: 3, 2 -> False; 9: 4, 0 -> False; 10: 0, 1 -> False; 11: 1, 2 -> False + # 12: 2, 0 -> False; 13: 3, 1 -> False; 14: 4, 2 -> False; 15: 0, 0 -> True + return dataset_ops.Dataset.range(1000).map( + lambda x: {"labels": x % 5, "predictions": x % 3}).batch(4) + + +def _boolean_dataset_fn(): + # First four batches of labels, predictions: {TP, FP, TN, FN} + # with a threshold of 0.5: + # T, T -> TP; F, T -> FP; T, F -> FN + # F, F -> TN; T, T -> TP; F, T -> FP + # T, F -> FN; F, F -> TN; T, T -> TP + # F, T -> FP; T, F -> FN; F, F -> TN + return dataset_ops.Dataset.from_tensor_slices({ + "labels": [True, False, True, False], + "predictions": [True, True, False, False]}).repeat().batch(3) + + +def _threshold_dataset_fn(): + # First four batches of labels, predictions: {TP, FP, TN, FN} + # with a threshold of 0.5: + # True, 1.0 -> TP; False, .75 -> FP; True, .25 -> FN + # False, 0.0 -> TN; True, 1.0 -> TP; False, .75 -> FP + # True, .25 -> FN; False, 0.0 -> TN; True, 1.0 -> TP + # False, .75 -> FP; True, .25 -> FN; False, 0.0 -> TN + return dataset_ops.Dataset.from_tensor_slices({ + "labels": [True, False, True, False], + "predictions": [1.0, 0.75, 0.25, 0.]}).repeat().batch(3) + + +def _regression_dataset_fn(): + return dataset_ops.Dataset.from_tensor_slices({ + "labels": [1., .5, 1., 0.], + "predictions": [1., .75, .25, 0.]}).repeat() + + +def all_combinations(): + return combinations.combine( + distribution=[combinations.default_strategy, + combinations.one_device_strategy, + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.mirrored_strategy_with_two_gpus], + mode=["graph"]) + + +# TODO(josh11b): Test metrics.recall_at_top_k, metrics.average_precision_at_k, +# metrics.precision_at_k +class MetricsV1Test(test.TestCase, parameterized.TestCase): + + def _test_metric(self, distribution, dataset_fn, metric_fn, expected_fn): + with ops.Graph().as_default(), distribution.scope(): + iterator = distribution.distribute_dataset( + dataset_fn).make_one_shot_iterator() + value, update = distribution.call_for_each_tower( + metric_fn, iterator.get_next()) + update = distribution.group(update) + self.evaluate(variables.local_variables_initializer()) + # TODO(josh11b): Once we switch to using a global batch size for input, + # replace "distribution.num_towers" with "1". + batches_per_update = distribution.num_towers + + # Update variables using the first `num_towers` batches. + self.evaluate(update) + self.assertAllClose(expected_fn(batches_per_update), self.evaluate(value), + 0.001, msg="After first update") + + # Update variables using the second `num_towers` batches. + self.evaluate(update) + self.assertAllClose(expected_fn(2 * batches_per_update), + self.evaluate(value), + 0.001, + msg="After second update") + + if batches_per_update == 1: # Consume 4 input batches + self.evaluate(update) + self.assertAllClose(expected_fn(3 * batches_per_update), + self.evaluate(value), + 0.001, + msg="After third update") + self.evaluate(update) + self.assertAllClose(expected_fn(4 * batches_per_update), + self.evaluate(value), + 0.001, + msg="After fourth update") + + @combinations.generate(all_combinations()) + def testMean(self, distribution): + def _dataset_fn(): + return dataset_ops.Dataset.range(1000).map(math_ops.to_float).batch(4) + + def _expected_fn(num_batches): + # Mean(0..3) = 1.5, Mean(0..7) = 3.5, Mean(0..11) = 5.5, etc. + return num_batches * 2 - 0.5 + + self._test_metric(distribution, _dataset_fn, metrics.mean, _expected_fn) + + @combinations.generate(all_combinations()) + def testAccuracy(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.accuracy(labels, predictions) + + def _expected_fn(num_batches): + return [3./4, 3./8, 3./12, 4./16][num_batches - 1] + + self._test_metric( + distribution, _labeled_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testMeanPerClassAccuracy(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.mean_per_class_accuracy( + labels, predictions, num_classes=5) + + def _expected_fn(num_batches): + mean = lambda x: sum(x) / len(x) + return [mean([1., 1., 1., 0., 0.]), + mean([0.5, 0.5, 0.5, 0., 0.]), + mean([1./3, 1./3, 0.5, 0., 0.]), + mean([0.5, 1./3, 1./3, 0., 0.])][num_batches - 1] + + self._test_metric( + distribution, _labeled_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testMeanIOU(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.mean_iou( + labels, predictions, num_classes=5) + + def _expected_fn(num_batches): + mean = lambda x: sum(x) / len(x) + return [mean([1./2, 1./1, 1./1, 0.]), # no class 4 in first batch + mean([1./4, 1./4, 1./3, 0., 0.]), + mean([1./6, 1./6, 1./5, 0., 0.]), + mean([2./8, 1./7, 1./7, 0., 0.])][num_batches - 1] + + self._test_metric( + distribution, _labeled_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testMeanTensor(self, distribution): + def _dataset_fn(): + dataset = dataset_ops.Dataset.range(1000).map(math_ops.to_float) + # Want to produce a fixed, known shape, so drop remainder when batching. + dataset = dataset.apply(batching.batch_and_drop_remainder(4)) + return dataset + + def _expected_fn(num_batches): + # Mean(0, 4, ..., 4 * num_batches - 4) == 2 * num_batches - 2 + # Mean(1, 5, ..., 4 * num_batches - 3) == 2 * num_batches - 1 + # Mean(2, 6, ..., 4 * num_batches - 2) == 2 * num_batches + # Mean(3, 7, ..., 4 * num_batches - 1) == 2 * num_batches + 1 + first = 2. * num_batches - 2. + return [first, first + 1., first + 2., first + 3.] + + self._test_metric( + distribution, _dataset_fn, metrics.mean_tensor, _expected_fn) + + @combinations.generate(all_combinations()) + def testAUCROC(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.auc(labels, predictions, num_thresholds=8, curve="ROC", + summation_method="careful_interpolation") + + def _expected_fn(num_batches): + return [0.5, 7./9, 0.8, 0.75][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testAUCPR(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.auc(labels, predictions, num_thresholds=8, curve="PR", + summation_method="careful_interpolation") + + def _expected_fn(num_batches): + return [0.797267, 0.851238, 0.865411, 0.797267][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testFalseNegatives(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.false_negatives(labels, predictions) + + def _expected_fn(num_batches): + return [1., 1., 2., 3.][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testFalseNegativesAtThresholds(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.false_negatives_at_thresholds(labels, predictions, [.5]) + + def _expected_fn(num_batches): + return [[1.], [1.], [2.], [3.]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testTrueNegatives(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.true_negatives(labels, predictions) + + def _expected_fn(num_batches): + return [0., 1., 2., 3.][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testTrueNegativesAtThresholds(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.true_negatives_at_thresholds(labels, predictions, [.5]) + + def _expected_fn(num_batches): + return [[0.], [1.], [2.], [3.]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testFalsePositives(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.false_positives(labels, predictions) + + def _expected_fn(num_batches): + return [1., 2., 2., 3.][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testFalsePositivesAtThresholds(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.false_positives_at_thresholds(labels, predictions, [.5]) + + def _expected_fn(num_batches): + return [[1.], [2.], [2.], [3.]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testTruePositives(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.true_positives(labels, predictions) + + def _expected_fn(num_batches): + return [1., 2., 3., 3.][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testTruePositivesAtThresholds(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.true_positives_at_thresholds(labels, predictions, [.5]) + + def _expected_fn(num_batches): + return [[1.], [2.], [3.], [3.]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testPrecision(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.precision(labels, predictions) + + def _expected_fn(num_batches): + return [0.5, 0.5, 0.6, 0.5][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testPrecisionAtThreshold(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.precision_at_thresholds(labels, predictions, [0.5]) + + def _expected_fn(num_batches): + return [[0.5], [0.5], [0.6], [0.5]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testRecall(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.recall(labels, predictions) + + def _expected_fn(num_batches): + return [0.5, 2./3, 0.6, 0.5][num_batches - 1] + + self._test_metric( + distribution, _boolean_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testRecallAtThreshold(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.recall_at_thresholds(labels, predictions, [0.5]) + + def _expected_fn(num_batches): + return [[0.5], [2./3], [0.6], [0.5]][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testMeanSquaredError(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.mean_squared_error(labels, predictions) + + def _expected_fn(num_batches): + return [0., 1./32, 0.208333, 0.15625][num_batches - 1] + + self._test_metric( + distribution, _regression_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testRootMeanSquaredError(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.root_mean_squared_error(labels, predictions) + + def _expected_fn(num_batches): + return [0., 0.176777, 0.456435, 0.395285][num_batches - 1] + + self._test_metric( + distribution, _regression_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testSensitivityAtSpecificity(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.sensitivity_at_specificity(labels, predictions, 0.8) + + def _expected_fn(num_batches): + return [0.5, 2./3, 0.6, 0.5][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + @combinations.generate(all_combinations()) + def testSpecificityAtSensitivity(self, distribution): + def _metric_fn(x): + labels = x["labels"] + predictions = x["predictions"] + return metrics.specificity_at_sensitivity(labels, predictions, 0.95) + + def _expected_fn(num_batches): + return [0., 1./3, 0.5, 0.5][num_batches - 1] + + self._test_metric( + distribution, _threshold_dataset_fn, _metric_fn, _expected_fn) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index 5c056a7c73def2f1fb4bbe0df4d3f82fdabda3df..aeeb9553e6044a0a928936597400e582e0329b95 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -56,6 +56,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): is_tpu=[True])) def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): model_fn, dataset_fn, layer = minimize_loss_example( optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss) @@ -84,8 +88,8 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) if is_tpu: with self.test_session() as sess: @@ -111,6 +115,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): is_tpu=[True])) def testOptimizerInsideModelFn(self, distribution, optimizer_fn, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + created_variables = [] trainable_variables = [] @@ -186,7 +194,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): # towers will re-execute UPDATE_OPS of previous towers. update_ops_in_cross_tower_mode=[True])) + combinations.combine( - distribution=[combinations.tpu_strategy_single_iteration], + distribution=[combinations.tpu_strategy], optimizer_fn=[ combinations.gradient_descent_optimizer_v1_fn, combinations.gradient_descent_optimizer_v2_fn @@ -198,6 +206,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): renorm, is_tpu, update_ops_in_cross_tower_mode): """Verifies that moving mean updates are reduced across towers.""" + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): num_towers = len(distribution.worker_devices) model_fn, dataset_fn, batchnorm = batchnorm_example( @@ -242,7 +254,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - moving_means = self.evaluate(distribution.fetch(batchnorm.moving_mean)) + moving_means = self.evaluate(batchnorm.moving_mean) # We make sure that the moving_mean is updated as if the sample mean is # calculated over all towers. @@ -279,12 +291,16 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.combine( - distribution=[combinations.tpu_strategy_single_iteration], + distribution=[combinations.tpu_strategy], is_tpu=[True], mode=["graph"], use_callable_loss=[True, False]))) def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction, use_callable_loss, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): all_vars = [] @@ -329,7 +345,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): v = all_vars[0] self.assertTrue(all([v is vi for vi in all_vars[1:]])) - weight = numpy.squeeze(self.evaluate(distribution.fetch(v))) + weight = numpy.squeeze(self.evaluate(v)) # Our model is: # predict = x * w # loss = (predict - y)^2 diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index cef0a2907b85d230606eb530a0e94549b6b95e53..dcbc6b0878b89cbb5b9779de315429e6f9478d15 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -104,9 +104,39 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): colocate_with = kwargs.pop("colocate_with", None) devices = self._get_devices_from(colocate_with) - tower_local = kwargs.pop("tower_local_reduce_method", None) - if tower_local is not None: + # Get synchronization value + synchronization = kwargs.get( + "synchronization", variable_scope.VariableSynchronization.ON_WRITE) + if synchronization == variable_scope.VariableSynchronization.NONE: + raise ValueError("`NONE` variable synchronization mode is not " + "supported with `Mirrored` distribution strategy. Please" + " change the `synchronization` for variable: " + + kwargs["name"]) + elif synchronization == variable_scope.VariableSynchronization.ON_READ: + # Variables that are to be synced on read are tower local. + is_tower_local = True kwargs["trainable"] = False + elif (synchronization == variable_scope.VariableSynchronization.ON_WRITE or + synchronization == variable_scope.VariableSynchronization.AUTO): + # `AUTO` synchronization for `MirroredStrategy` is `ON_WRITE`. + is_tower_local = False + else: + raise ValueError("Invalid variable synchronization mode: " + + synchronization + " for variable: " + kwargs["name"]) + + # Get aggregation value + aggregation = kwargs.pop("aggregation", + variable_scope.VariableAggregation.NONE) + if aggregation not in [ + variable_scope.VariableAggregation.NONE, + variable_scope.VariableAggregation.SUM, + variable_scope.VariableAggregation.MEAN + ]: + raise ValueError("Invalid variable aggregation mode: " + aggregation + + " for variable: " + kwargs["name"]) + + # Ignore user-specified caching device, not needed for mirrored variables. + kwargs.pop("caching_device", None) # TODO(josh11b,apassos): It would be better if variable initialization # was never recorded on the tape instead of having to do this manually @@ -136,11 +166,11 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): assert not isinstance(v, values.DistributedVariable) index[d] = v - if tower_local is None: - result = values.MirroredVariable(index, index[devices[0]]) + if is_tower_local: + result = values.TowerLocalVariable(index, index[devices[0]], + aggregation) else: - result = values.TowerLocalVariable( - index, index[devices[0]], tower_local) + result = values.MirroredVariable(index, index[devices[0]], aggregation) if not context.executing_eagerly(): g = ops.get_default_graph() @@ -282,8 +312,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def map(self, map_over, fn, *args, **kwargs): # TODO(josh11b): In eager mode, use one thread per device. index = {} - i = 0 - for m in map_over: + for i, m in enumerate(map_over): d = self._devices[i % len(self._devices)] with ops.device(d): l = index.get(d, []) @@ -306,27 +335,46 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()) return self._cross_tower_ops - def _reduce(self, method_string, value, destinations): - if len(self._devices) == 1 and not isinstance(value, values.PerDevice): - value = values.PerDevice({self._devices[0]: value}) - assert isinstance(value, values.PerDevice) + def _reduce(self, aggregation, value, destinations): + assert not isinstance(value, values.Mirrored) + if not isinstance(value, values.PerDevice): + if value == 0: + return 0 + if aggregation == variable_scope.VariableAggregation.MEAN: + return self._broadcast(value, destinations) + + cross_tower_ops_lib.validate_destinations(destinations) + if len(self._devices) == 1: + if destinations: + # TODO(anjalisridhar): Moves these methods to a device utility file? + devices = cross_tower_ops_lib.get_devices_from(destinations) + if len(devices) == 1: + with ops.device(devices[0]): + return array_ops.identity(value) + else: + value_updates = {} + for d in devices: + with ops.device(d): + value_updates[d] = array_ops.identity(value) + return values.Mirrored(value_updates) + raise ValueError("A non PerDevice value cannot be reduced with the given " + "aggregation.") return self._get_cross_tower_ops().reduce( - method_string, value, destinations=destinations) + aggregation, value, destinations=destinations) - def _batch_reduce(self, method_string, value_destination_pairs): - return self._get_cross_tower_ops().batch_reduce(method_string, + def _batch_reduce(self, aggregation, value_destination_pairs): + return self._get_cross_tower_ops().batch_reduce(aggregation, value_destination_pairs) def _update(self, var, fn, *args, **kwargs): - # TODO(josh11b): Also support TowerLocalVariables here? If so, args and - # kwargs don't need to be mirrored. - assert isinstance(var, values.MirroredVariable) # TODO(josh11b): In eager mode, use one thread per device. + assert isinstance(var, values.DistributedVariable) updates = {} for d, v in var._index.items(): # pylint: disable=protected-access name = "update_%d" % self._device_index.get(d) with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name): + # If args and kwargs are not mirrored, the value is returned as is. updates[d] = fn(v, *values.select_device_mirrored(d, args), **values.select_device_mirrored(d, kwargs)) @@ -343,32 +391,12 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): **values.select_device_mirrored(d, kwargs)) return values.regroup(updates, values.Mirrored) - def _fetch(self, val, destination, fn): - """Return a copy of `val` or `fn(val)` on `destination`.""" - if isinstance(val, values.TowerLocalVariable): - val = self.reduce(val.reduce_method, val, destinations=destination) - with ops.device(destination): - return fn(self.unwrap(val)[0]) - - assert isinstance(val, values.Mirrored), ( - "val = %s (type %s)" % (val, val.__class__.__name__)) - if val.on_device(destination): - with ops.device(destination): - # Use an identity here to make sure we are returning a tensor - # instead of e.g. a variable object. - return array_ops.identity(fn(val.get(destination))) - device = None - for d in self._devices: - if val.on_device(d): - device = d - break - assert device is not None, ( - "Could not find destination %s in list of devices %s." % - (destination, val.devices)) - with ops.device(device): - v = fn(val.get(device)) - with ops.device(destination): - return array_ops.identity(v) + def read_var(self, tower_local_var): + """Read the aggregate value of a tower-local variable.""" + if isinstance(tower_local_var, values.TowerLocalVariable): + return tower_local_var._get_cross_tower() # pylint: disable=protected-access + assert isinstance(tower_local_var, values.Mirrored) + return array_ops.identity(tower_local_var.get()) def _unwrap(self, val): if isinstance(val, values.DistributedValues): diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index bccd278847e3c87080af3cb15665e7a0d802d8fb..b597bce035493891c3f492bca04abda60c6e8e22 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -32,12 +32,14 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.layers import core +from tensorflow.python.ops import math_ops from tensorflow.python.ops import rnn from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import distribute as distribute_lib + GPU_TEST = "test_gpu" in sys.argv[0] @@ -83,13 +85,13 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase): self.skipTest("Not GPU test") self.assertEqual(2, self._get_distribution_strategy().num_towers) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): if not GPU_TEST: self.skipTest("Not GPU test") self._test_call_and_merge_exceptions(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRunRegroupError(self): def run_fn(device_id): @@ -101,7 +103,7 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase): with dist.scope(), self.assertRaises(AssertionError): dist.call_for_each_tower(run_fn, dist.worker_device_index) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceToCpu(self): if not GPU_TEST: self.skipTest("Not GPU test") @@ -112,12 +114,35 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase): dist = self._get_distribution_strategy() with dist.scope(): result = dist.call_for_each_tower(run_fn, dist.worker_device_index) - reduced = dist.reduce("sum", result, destinations="/device:CPU:0") + reduced = dist.reduce( + variable_scope.VariableAggregation.SUM, + result, + destinations="/device:CPU:0") unwrapped = dist.unwrap(reduced) self.assertEqual(1, len(unwrapped)) expected = sum(range(len(dist.worker_devices))) self.assertEqual(expected, self.evaluate(unwrapped[0])) + @test_util.run_in_graph_and_eager_modes() + def testReduceToMultipleDestinations(self): + if not GPU_TEST: + self.skipTest("Not GPU test") + + devices = ["/device:GPU:0"] + if GPU_TEST: + self.assertGreater(context.num_gpus(), 0) + print(self.id().split(".")[-1], "devices:", ", ".join(devices)) + + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + reduced = dist.reduce( + variable_scope.VariableAggregation.SUM, + 1.0, + destinations=["/device:CPU:0", "/device:GPU:0"]) + unwrapped = dist.unwrap(reduced) + self.assertEqual(2, len(unwrapped)) + self.assertEqual(1.0, self.evaluate(unwrapped[0])) + class MirroredStrategyVariableCreationTest(test.TestCase): @@ -263,19 +288,69 @@ class MirroredStrategyVariableCreationTest(test.TestCase): self.assertIsInstance(bias, values.MirroredVariable) self.assertEquals("common/dense" + suffix + "/bias:0", bias.name) + @test_util.run_in_graph_and_eager_modes(config=config) + def testWithVariableAndVariableScope(self): + self._skip_eager_if_gpus_less_than(1) + + def model_fn(): + v0 = variable_scope.variable(1.0, name="var0", aggregation=None) + with variable_scope.variable_scope("common"): + v1 = variable_scope.variable(1.0, name="var1") + # This will pause the current thread, and execute the other thread. + distribute_lib.get_tower_context().merge_call(lambda _: _) + v2 = variable_scope.variable( + 1.0, + name="var2", + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + v3 = variable_scope.variable( + 1.0, + name="var3", + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation=variable_scope.VariableAggregation.MEAN) + + return v0, v1, v2, v3 + + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + v = variable_scope.variable(1.0, name="var-main0") + self.assertEquals("var-main0:0", v.name) + + result = dist.call_for_each_tower(model_fn, run_concurrently=False) + self.assertEquals(4, len(result)) + v0, v1, v2, v3 = result + self.assertIsInstance(v0, values.MirroredVariable) + self.assertEquals("var0:0", v0.name) + self.assertIsInstance(v1, values.MirroredVariable) + self.assertEquals("common/var1:0", v1.name) + self.assertIsInstance(v2, values.TowerLocalVariable) + self.assertEquals("common/var2:0", v2.name) + self.assertEquals(variable_scope.VariableAggregation.SUM, v2.aggregation) + self.assertIsInstance(v3, values.MirroredVariable) + self.assertEquals("common/var3:0", v3.name) + self.assertEquals(variable_scope.VariableAggregation.MEAN, v3.aggregation) + @test_util.run_in_graph_and_eager_modes(config=config) def testWithGetVariableAndVariableScope(self): self._skip_eager_if_gpus_less_than(1) def model_fn(): - v0 = variable_scope.get_variable("var-thread0", [1]) + v0 = variable_scope.get_variable("var0", [1]) with variable_scope.variable_scope("common"): - v1 = variable_scope.get_variable("var-thread1", [1]) + v1 = variable_scope.get_variable("var1", [1]) # This will pause the current thread, and execute the other thread. distribute_lib.get_tower_context().merge_call(lambda _: _) - v2 = variable_scope.get_variable("var-thread2", [1]) + v2 = variable_scope.get_variable( + "var2", [1], + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + v3 = variable_scope.get_variable( + "var3", [1], + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation=variable_scope.VariableAggregation.MEAN) - return v0, v1, v2 + return v0, v1, v2, v3 devices = ["/device:CPU:0", "/device:GPU:0"] dist = mirrored_strategy.MirroredStrategy(devices) @@ -285,14 +360,89 @@ class MirroredStrategyVariableCreationTest(test.TestCase): self.assertEquals("main/var-main0:0", v.name) result = dist.call_for_each_tower(model_fn, run_concurrently=False) - self.assertEquals(3, len(result)) - v0, v1, v2 = result + self.assertEquals(4, len(result)) + v0, v1, v2, v3 = result self.assertIsInstance(v0, values.MirroredVariable) - self.assertEquals("main/var-thread0:0", v0.name) + self.assertEquals("main/var0:0", v0.name) self.assertIsInstance(v1, values.MirroredVariable) - self.assertEquals("main/common/var-thread1:0", v1.name) - self.assertIsInstance(v2, values.MirroredVariable) - self.assertEquals("main/common/var-thread2:0", v2.name) + self.assertEquals("main/common/var1:0", v1.name) + self.assertIsInstance(v2, values.TowerLocalVariable) + self.assertEquals("main/common/var2:0", v2.name) + self.assertEquals(variable_scope.VariableAggregation.SUM, + v2.aggregation) + self.assertIsInstance(v3, values.MirroredVariable) + self.assertEquals("main/common/var3:0", v3.name) + self.assertEquals(variable_scope.VariableAggregation.MEAN, + v3.aggregation) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testNoneSynchronizationWithGetVariable(self): + self._skip_eager_if_gpus_less_than(1) + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + with self.assertRaisesRegexp( + ValueError, "`NONE` variable synchronization mode is not " + "supported with `Mirrored` distribution strategy. Please change " + "the `synchronization` for variable: v"): + variable_scope.get_variable( + "v", [1], + synchronization=variable_scope.VariableSynchronization.NONE) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testNoneSynchronizationWithVariable(self): + self._skip_eager_if_gpus_less_than(1) + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + with self.assertRaisesRegexp( + ValueError, "`NONE` variable synchronization mode is not " + "supported with `Mirrored` distribution strategy. Please change " + "the `synchronization` for variable: v"): + variable_scope.variable( + 1.0, + name="v", + synchronization=variable_scope.VariableSynchronization.NONE) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testInvalidSynchronizationWithVariable(self): + self._skip_eager_if_gpus_less_than(1) + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + with self.assertRaisesRegexp( + ValueError, "Invalid variable synchronization mode: Invalid for " + "variable: v"): + variable_scope.variable(1.0, name="v", synchronization="Invalid") + + @test_util.run_in_graph_and_eager_modes(config=config) + def testInvalidAggregationWithGetVariable(self): + self._skip_eager_if_gpus_less_than(1) + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + with self.assertRaisesRegexp( + ValueError, "Invalid variable aggregation mode: invalid for " + "variable: v"): + variable_scope.get_variable( + "v", [1], + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation="invalid") + + @test_util.run_in_graph_and_eager_modes(config=config) + def testInvalidAggregationWithVariable(self): + self._skip_eager_if_gpus_less_than(1) + devices = ["/device:CPU:0", "/device:GPU:0"] + dist = mirrored_strategy.MirroredStrategy(devices) + with dist.scope(): + with self.assertRaisesRegexp( + ValueError, "Invalid variable aggregation mode: invalid for " + "variable: v"): + variable_scope.variable( + 1.0, + name="v", + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation="invalid") @test_util.run_in_graph_and_eager_modes(config=config) def testThreeDevices(self): @@ -337,12 +487,16 @@ class MirroredStrategyVariableCreationTest(test.TestCase): all_v_sum = {} all_v_mean = {} + components_sum = {} + components_mean = {} def model_fn(device_id): tower_context = distribute_lib.get_tower_context() - with tower_context.tower_local_var_scope("sum"): + with tower_context.tower_local_var_scope( + variable_scope.VariableAggregation.SUM): v_sum = variable_scope.variable(1.0) - with tower_context.tower_local_var_scope("mean"): + with tower_context.tower_local_var_scope( + variable_scope.VariableAggregation.MEAN): v_mean = variable_scope.variable(4.0) self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) self.assertTrue(isinstance(v_mean, values.TowerLocalVariable)) @@ -350,21 +504,33 @@ class MirroredStrategyVariableCreationTest(test.TestCase): v_mean.assign(6.0 * device_id)] all_v_sum[device_id] = v_sum all_v_mean[device_id] = v_mean - return updates, v_sum, v_mean + c_sum = v_sum.get() + c_mean = v_mean.get() + components_sum[device_id] = c_sum + components_mean[device_id] = c_mean + self.assertIsNot(v_sum, c_sum) + self.assertIsNot(v_mean, c_mean) + return updates, v_sum, v_mean, c_sum, c_mean dist = mirrored_strategy.MirroredStrategy( ["/device:GPU:0", "/device:CPU:0"]) with dist.scope(): # Create "sum" and "mean" versions of TowerLocalVariables. - ret_ops, ret_v_sum, ret_v_mean = dist.call_for_each_tower( - model_fn, dist.worker_device_index, run_concurrently=False) + ret_ops, ret_v_sum, ret_v_mean, regrouped_sum, regrouped_mean = ( + dist.call_for_each_tower( + model_fn, dist.worker_device_index, run_concurrently=False)) # Should see the same wrapping instance in all towers. self.assertIs(all_v_sum[0], ret_v_sum) self.assertIs(all_v_mean[0], ret_v_mean) - for i in range(1, dist.num_towers): - self.assertIs(all_v_sum[0], all_v_sum[1]) - self.assertIs(all_v_mean[0], all_v_mean[1]) + self.assertIs(all_v_sum[0], all_v_sum[1]) + self.assertIs(all_v_mean[0], all_v_mean[1]) + + # Regroup should recover the same wrapper. + self.assertIs(ret_v_sum, regrouped_sum) + self.assertIs(ret_v_mean, regrouped_mean) + self.assertIsNot(components_sum[0], components_sum[1]) + self.assertIsNot(components_mean[0], components_mean[1]) # Apply updates self.evaluate(variables.global_variables_initializer()) @@ -385,14 +551,13 @@ class MirroredStrategyVariableCreationTest(test.TestCase): # Without get(device), should return the value you get by # applying the reduction across all towers (whether you use - # fetch(), get(), or nothing). - self.assertEqual(expected_sum, self.evaluate(dist.fetch(ret_v_sum))) - self.assertEqual(expected_mean, self.evaluate(dist.fetch(ret_v_mean))) + # read_var(), get(), or nothing). + self.assertEqual(expected_sum, self.evaluate(dist.read_var(ret_v_sum))) + self.assertEqual(expected_mean, self.evaluate(dist.read_var(ret_v_mean))) self.assertEqual(expected_sum, self.evaluate(ret_v_sum.get())) self.assertEqual(expected_mean, self.evaluate(ret_v_mean.get())) - if not context.executing_eagerly(): - self.assertEqual(expected_sum, self.evaluate(ret_v_sum)) - self.assertEqual(expected_mean, self.evaluate(ret_v_mean)) + self.assertEqual(expected_sum, self.evaluate(ret_v_sum)) + self.assertEqual(expected_mean, self.evaluate(ret_v_mean)) # NOTE(priyag): Names and name scopes are ignored in eager, hence we are not # testing this in eager mode. @@ -530,6 +695,232 @@ class MirroredStrategyVariableCreationTest(test.TestCase): _, v1 = dist.unwrap(v) self.assertStartsWith(v1.name, "tower_1/") + @test_util.run_in_graph_and_eager_modes(config=config) + def testTowerLocalVariableUpdate(self): + with context.graph_mode(): + + def model_fn(): + tower_context = distribute_lib.get_tower_context() + with tower_context.tower_local_var_scope( + variable_scope.VariableAggregation.SUM): + v_sum = variable_scope.variable(1.0) + self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:GPU:1"]) + + def update(var, value): + return var.assign(value) + + with dist.scope(): + ret_v_sum = dist.call_for_each_tower(model_fn, run_concurrently=False) + update_ops = dist.unwrap(dist.update(ret_v_sum, update, 5.0)) + + # Initialize variables. + self.evaluate(variables.global_variables_initializer()) + # Assert that the aggregated value of the tower local vars is the sum of + # the individual values before running the update ops. + self.assertEquals(1.0, self.evaluate( + ret_v_sum.get(dist._devices[0]).read_value())) + self.assertEquals(2.0, self.evaluate(ret_v_sum)) + + # Apply updates. + self.evaluate(update_ops) + # Assert that the aggregated value of the tower local vars is the sum of + # the individual values after running the update ops. + self.assertEquals(5.0, self.evaluate( + ret_v_sum.get(dist._devices[0]).read_value())) + self.assertEquals(10.0, self.evaluate(ret_v_sum)) + + +class MirroredVariableUpdateTest(test.TestCase): + # The following tests check assign, assign_add and assign_sub on Mirrored + # variables in tower and cross tower context. + config = config_pb2.ConfigProto() + config.allow_soft_placement = True + + def _skip_eager_if_gpus_less_than(self, num_gpus): + if context.num_gpus() < num_gpus and context.executing_eagerly(): + self.skipTest("Enough GPUs not available for this test in eager mode.") + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignMirroredVarTowerContextWithoutAggregationType(self): + # Test that we always have an aggregation type set on the mirrored variable + # if we assign to it in tower mode. + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + v = variable_scope.variable(1.0, name="foo") + return v + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + + def model_fn(): + return mirrored_var.assign(5.0) + + with self.assertRaisesRegexp( + ValueError, "You must specify an aggregation method to update a " + "MirroredVariable in Tower Context."): + self.evaluate(dist.unwrap(dist.call_for_each_tower(model_fn))) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignMirroredVarTowerContextWithSum(self): + # Test that we don't reduce a non-per-device value with the "sum" + # aggregation type. + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + v = variable_scope.variable( + 1.0, name="foo", aggregation=variable_scope.VariableAggregation.SUM) + return v + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + + def model_fn(): + return mirrored_var.assign(5.0) + + with self.assertRaisesRegexp( + ValueError, "A non PerDevice value cannot be reduced with the given " + "aggregation."): + self.evaluate(dist.unwrap(dist.call_for_each_tower(model_fn))) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignMirroredVarCrossTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable(1.0, name="foo") + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(1.0, self.evaluate(mirrored_var)) + mirrored_var_result = self.evaluate(mirrored_var.assign(6.0)) + self.assertEquals(6.0, mirrored_var_result) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignMirroredVarTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable( + 1.0, name="foo", aggregation=variable_scope.VariableAggregation.MEAN) + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(1.0, self.evaluate(mirrored_var)) + + def model_fn(): + value = math_ops.cast(distribute_lib.get_tower_context().tower_id, + mirrored_var.dtype) + return mirrored_var.assign(value) + + self.evaluate(dist.unwrap(dist.call_for_each_tower( + model_fn, run_concurrently=False))) + self.assertEquals(0.5, self.evaluate(mirrored_var)) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignAddMirroredVarCrossTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable(1.0, name="foo") + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(1.0, self.evaluate(mirrored_var)) + mirrored_var_result = self.evaluate(mirrored_var.assign_add(6.0)) + self.assertEquals(7.0, mirrored_var_result) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignAddMirroredVarTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable( + 1.0, name="foo", aggregation=variable_scope.VariableAggregation.MEAN) + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(1.0, self.evaluate(mirrored_var)) + + def model_fn(): + value = math_ops.cast(distribute_lib.get_tower_context().tower_id, + mirrored_var.dtype) + return mirrored_var.assign_add(value) + + self.evaluate(dist.unwrap(dist.call_for_each_tower( + model_fn, run_concurrently=False))) + self.assertEquals(1.5, self.evaluate(mirrored_var)) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignSubMirroredVarCrossTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable(5.0, name="foo") + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(5.0, self.evaluate(mirrored_var)) + mirrored_var_result = self.evaluate(mirrored_var.assign_sub(2.0)) + self.assertEquals(3.0, mirrored_var_result) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignSubMirroredVarTowerContext(self): + self._skip_eager_if_gpus_less_than(1) + def var_fn(): + return variable_scope.variable( + 5.0, name="foo", aggregation=variable_scope.VariableAggregation.MEAN) + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn, run_concurrently=False) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(5.0, self.evaluate(mirrored_var)) + + def model_fn(): + value = math_ops.cast(distribute_lib.get_tower_context().tower_id, + mirrored_var.dtype) + return mirrored_var.assign_sub(value) + + self.evaluate(dist.unwrap(dist.call_for_each_tower( + model_fn, run_concurrently=False))) + self.assertEquals(4.5, self.evaluate(mirrored_var)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py index 61cbe6df813bb28bf8baa83d9e28ffafc4f0cbb8..a066adf1246ecd9ab8bd6a85be1f1e9be2c35b17 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py @@ -47,7 +47,7 @@ class MirroredOneCPUDistributionTest(strategy_test_lib.DistributionTestBase): def testTowerId(self): self._test_tower_id(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): self._test_call_and_merge_exceptions(self._get_distribution_strategy()) diff --git a/tensorflow/contrib/distribute/python/monitor_test.py b/tensorflow/contrib/distribute/python/monitor_test.py index 4fdb9bf69b4f6ad76b79fd298f5303f24a1bd455..2892ce439494320a115b8eae0025a132841c4a8f 100644 --- a/tensorflow/contrib/distribute/python/monitor_test.py +++ b/tensorflow/contrib/distribute/python/monitor_test.py @@ -52,11 +52,11 @@ class MonitorTest(test.TestCase, parameterized.TestCase): self.assertEqual(1, len(layer.trainable_variables)) mirrored_weight_variable = layer.trainable_variables[0] - start_error = self.evaluate(distribution.fetch(mirrored_weight_variable)) + start_error = self.evaluate(mirrored_weight_variable) start_error = abs(numpy.array(start_error) - 1) monitor.run_steps(9) - end_error = self.evaluate(distribution.fetch(mirrored_weight_variable)) + end_error = self.evaluate(mirrored_weight_variable) end_error = abs(numpy.array(end_error) - 1) self.assertGreaterEqual(start_error, end_error) diff --git a/tensorflow/contrib/distribute/python/one_device_strategy.py b/tensorflow/contrib/distribute/python/one_device_strategy.py index 09b6d4a515ab46879520f304cd5ef60469512380..dbd3514aec7d40d9a04dba4bcbc5c14be639aa33 100644 --- a/tensorflow/contrib/distribute/python/one_device_strategy.py +++ b/tensorflow/contrib/distribute/python/one_device_strategy.py @@ -24,6 +24,7 @@ from tensorflow.contrib.distribute.python import values from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import distribute as distribute_lib @@ -43,11 +44,6 @@ class OneDeviceStrategy(distribute_lib.DistributionStrategy): self._default_device = device def _create_variable(self, next_creator, *args, **kwargs): - # No need to distinguish tower-local variables when not mirroring, - # we just enforce that they are not trainable. - if kwargs.pop("tower_local_reduce_method", None) is not None: - kwargs["trainable"] = False - colocate_with = kwargs.pop("colocate_with", None) if colocate_with is None: with ops.device(self._device): @@ -80,15 +76,15 @@ class OneDeviceStrategy(distribute_lib.DistributionStrategy): with ops.device(self._device): return values.MapOutput([fn(m, *args, **kwargs) for m in map_over]) - def _reduce(self, method_string, value, destinations): + def _reduce(self, aggregation, value, destinations): if not isinstance(value, values.MapOutput): return value l = value.get() assert l with ops.device(self._device): - if method_string == "sum": + if aggregation == vs.VariableAggregation.SUM: return math_ops.add_n(l) - elif method_string == "mean": + elif aggregation == vs.VariableAggregation.MEAN: return math_ops.add_n(l) / len(l) else: assert False @@ -102,12 +98,9 @@ class OneDeviceStrategy(distribute_lib.DistributionStrategy): with ops.device(self._device), distribute_lib.UpdateContext(self._device): return fn(*args, **kwargs) - def _fetch(self, val, destination, fn): - """Return a copy of `val` or `fn(val)` on `destination`.""" - with ops.device(self._device): - v = fn(val) - with ops.device(destination): - return array_ops.identity(v) + def read_var(self, tower_local_var): + """Read the aggregate value of a tower-local variable.""" + return array_ops.identity(tower_local_var) def _unwrap(self, value): return [value] diff --git a/tensorflow/contrib/distribute/python/one_device_strategy_test.py b/tensorflow/contrib/distribute/python/one_device_strategy_test.py index 7aad8a953cbedd30b48739416e74b3dc164dc4cd..4fdc0f72e6745b7ef25c591157955f214e0b2c79 100644 --- a/tensorflow/contrib/distribute/python/one_device_strategy_test.py +++ b/tensorflow/contrib/distribute/python/one_device_strategy_test.py @@ -44,7 +44,7 @@ class OneDeviceStrategyTest(strategy_test_lib.DistributionTestBase): def testTowerId(self): self._test_tower_id(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): self._test_call_and_merge_exceptions(self._get_distribution_strategy()) diff --git a/tensorflow/contrib/distribute/python/optimizer_v2_test.py b/tensorflow/contrib/distribute/python/optimizer_v2_test.py index abd3a65ac4e19ece6b69b9834f4218fde55b60c2..a2d736e42271ab1627240949b99088ed3f0746f6 100644 --- a/tensorflow/contrib/distribute/python/optimizer_v2_test.py +++ b/tensorflow/contrib/distribute/python/optimizer_v2_test.py @@ -59,8 +59,8 @@ class MinimizeLossOptimizerV2Test(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) diff --git a/tensorflow/contrib/distribute/python/prefetching_ops_v2.py b/tensorflow/contrib/distribute/python/prefetching_ops_v2.py index 7b3670b45aba801cf8c18e04bfea03e23eb67184..24cdc627a35f4455cb92484566dc13fa1bbaf2cc 100644 --- a/tensorflow/contrib/distribute/python/prefetching_ops_v2.py +++ b/tensorflow/contrib/distribute/python/prefetching_ops_v2.py @@ -89,6 +89,9 @@ class _PrefetchToDeviceIterator(object): with ops.device(device): buffer_resource_handle = prefetching_ops.function_buffering_resource( f=_prefetch_fn, + output_types=data_nest.flatten( + sparse.as_dense_types(self._input_dataset.output_types, + self._input_dataset.output_classes)), target_device=target_device, string_arg=input_iterator_handle, buffer_size=buffer_size, diff --git a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py index a0b452fc2d445d1cf7dbf5e8fe0e29edef516207..2a9ab51fcfd29a8ae5b37b5c513415af29b277dc 100644 --- a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py +++ b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py @@ -46,7 +46,7 @@ class CanonicalizeVariableNameTest(test.TestCase): class SharedVariableCreatorTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSharedVariable(self): shared_variable_store = {} diff --git a/tensorflow/contrib/distribute/python/step_fn_test.py b/tensorflow/contrib/distribute/python/step_fn_test.py index 75c5ec9659d193e77d219ba79977615d58841d64..2ee94d8f70868c07ca217dd4d433585458efa8d8 100644 --- a/tensorflow/contrib/distribute/python/step_fn_test.py +++ b/tensorflow/contrib/distribute/python/step_fn_test.py @@ -50,8 +50,8 @@ class SingleLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) diff --git a/tensorflow/contrib/distribute/python/strategy_test_lib.py b/tensorflow/contrib/distribute/python/strategy_test_lib.py index 2b4ad9f146bc1d6a987fbeecbb05122946137154..baed0ebaae8a3f41c55f309d28203b363336dd16 100644 --- a/tensorflow/contrib/distribute/python/strategy_test_lib.py +++ b/tensorflow/contrib/distribute/python/strategy_test_lib.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.layers import core from tensorflow.python.ops import array_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import optimizer @@ -106,13 +107,14 @@ class DistributionTestBase(test.TestCase): before_list = [] after_list = [] for g, v in g_v: - fetched = d.fetch(v) + fetched = d.read_var(v) before_list.append(fetched) # control_dependencies irrelevant but harmless in eager execution with ops.control_dependencies([fetched]): - g = d.reduce("sum", g, destinations=v) + g = d.reduce( + variable_scope.VariableAggregation.SUM, g, destinations=v) with ops.control_dependencies(d.unwrap(d.update(v, update, g))): - after_list.append(d.fetch(v)) + after_list.append(d.read_var(v)) return before_list, after_list for i in range(10): @@ -159,12 +161,13 @@ class DistributionTestBase(test.TestCase): before_list = [] after_list = [] for g, v in g_v: - fetched = d.fetch(v) + fetched = d.read_var(v) before_list.append(fetched) with ops.control_dependencies([fetched]): - g = d.reduce("sum", g, destinations=v) + g = d.reduce( + variable_scope.VariableAggregation.SUM, g, destinations=v) with ops.control_dependencies(d.unwrap(d.update(v, update, g))): - after_list.append(d.fetch(v)) + after_list.append(d.read_var(v)) return before_list, after_list before_out, after_out = step() @@ -184,7 +187,7 @@ class DistributionTestBase(test.TestCase): with d.scope(): map_in = [constant_op.constant(i) for i in range(10)] map_out = d.map(map_in, lambda x, y: x * y, 2) - observed = d.fetch(d.reduce("sum", map_out)) + observed = d.reduce(variable_scope.VariableAggregation.SUM, map_out) expected = 90 # 2 * (0 + 1 + ... + 9) self.assertEqual(expected, observed.numpy()) diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py index 75441786a615fc0d87b4c4b0b45b9384d678c1d3..bc53898539d76320e331784f9a717be9491365e1 100644 --- a/tensorflow/contrib/distribute/python/tpu_strategy.py +++ b/tensorflow/contrib/distribute/python/tpu_strategy.py @@ -21,104 +21,126 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import itertools - from tensorflow.contrib import tpu from tensorflow.contrib.distribute.python import one_device_strategy from tensorflow.contrib.distribute.python import values from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import nest class TPUStrategy(one_device_strategy.OneDeviceStrategy): """Experimental TPU distribution strategy implementation.""" - def __init__(self, - num_cores_per_host=2, - iterations_per_step=2): + def __init__(self, num_cores_per_host=2): # TODO(isaprykin): Generalize the defaults. They are currently tailored for # the unit test. super(TPUStrategy, self).__init__('/cpu:0') # TODO(isaprykin): Auto-detect number of cores and hosts. self._num_cores_per_host = num_cores_per_host - # TODO(isaprykin): This might have to be per-call. - self._iterations_per_step = iterations_per_step + # TODO(priyag): This should not be hardcoded here. + self._host = '/task:0/device:CPU:0' def distribute_dataset(self, dataset_fn): - return values.PerIterationDataset( - self._call_dataset_fn(dataset_fn), self._iterations_per_step, - self._num_cores_per_host) - - def _call_for_each_tower(self, fn, *args, **kwargs): - kwargs.pop('run_concurrently', None) - - inputs = {'args': args, 'kwargs': kwargs} - flat_inputs = nest.flatten(inputs) - - feed_mask = [isinstance(f, values.PerIteration) for f in flat_inputs] - - feeds = lambda: itertools.compress(flat_inputs, feed_mask) - shapes = [f.get_shape() for f in feeds()] + # TODO(priyag): Perhaps distribute across cores here. + return self._call_dataset_fn(dataset_fn) + + # TODO(priyag): Deal with OutOfRange errors. + # TODO(sourabhbajaj): Remove the initial_loop_values parameter when we have + # a mechanism to infer the outputs of `fn`. Pending b/110550782. + def _run_steps_on_dataset(self, fn, iterator, iterations, + initial_loop_values=None): + # Enqueue ops + shapes = nest.flatten(iterator.output_shapes) if any([not s.is_fully_defined() for s in shapes]): raise ValueError( 'TPU currently requires fully defined shapes. Either use ' 'set_shape() on the input tensors or use ' 'dataset.apply(map_and_batch(..., drop_remainder=True)).') - types = [f.get_dtype() for f in feeds()] - - def infeed_input(i): - """Get input, split it and then enqueue.""" - iteration_inputs = [f.get(i) for f in feeds()] - infeed_inputs = [[inputs_per_core[core_id] - for inputs_per_core in iteration_inputs] - for core_id in range(self._num_cores_per_host)] - - infeed_ops = [] - for core_id, infeed_input in enumerate(infeed_inputs): - infeed_ops.append( + types = nest.flatten(iterator.output_types) + + def enqueue_ops_fn(): + """Enqueue ops for one iteration.""" + control_deps = [] + sharded_inputs = [] + with ops.device(self._host): + for _ in range(self._num_cores_per_host): + # Use control dependencies to ensure a deterministic ordering. + with ops.control_dependencies(control_deps): + inputs = nest.flatten(iterator.get_next()) + control_deps.extend(inputs) + sharded_inputs.append(inputs) + + enqueue_ops = [] + for core_id, shard_input in enumerate(sharded_inputs): + enqueue_ops.append( tpu_ops.infeed_enqueue_tuple( - inputs=infeed_input, shapes=shapes, device_ordinal=core_id)) + inputs=shard_input, shapes=shapes, device_ordinal=core_id)) + return enqueue_ops - with ops.control_dependencies(infeed_ops): + def enqueue_ops_loop_body(i): + with ops.control_dependencies(enqueue_ops_fn()): return i + 1 - with ops.device('/task:0/device:CPU:0'): + with ops.device(self._host): enqueue_ops = control_flow_ops.while_loop( - lambda i: i < self._iterations_per_step, - infeed_input, [constant_op.constant(0)], + lambda i: i < iterations, + enqueue_ops_loop_body, + [constant_op.constant(0)], parallel_iterations=1) - def dequeueing_fn(*args, **kwargs): - """Dequeue input arguments and supply them to `fn`.""" - del args, kwargs + # Dequeue ops + def dequeue_fn(): dequeued = tpu.infeed_dequeue_tuple(dtypes=types, shapes=shapes) - dequeued = iter(dequeued) + return nest.pack_sequence_as(iterator.output_shapes, dequeued) - fn_inputs = [] - for inp, is_feed in zip(flat_inputs, feed_mask): - if is_feed: - fn_inputs.append(next(dequeued)) - else: - fn_inputs.append(inp) + # Wrap `fn` for repeat. + if initial_loop_values is None: + initial_loop_values = [] + ctx = values.MultiStepContext(initial_loop_values) + def run_fn(*args, **kwargs): + del args, kwargs + fn_result = fn(ctx, dequeue_fn()) + if ctx.last_step_outputs is None: + ctx.last_step_outputs = [] + with ops.control_dependencies([fn_result]): + return array_ops.identity(ctx.last_step_outputs) + + # Repeat + # TODO(sourabhbajaj): The input to while loop should be based on the output + # type of the step_fn + def iterate_on_tpu(): + return tpu.repeat(iterations, run_fn, [initial_loop_values]) - fn_inputs = nest.pack_sequence_as(inputs, fn_inputs) - return fn(*fn_inputs['args'], **fn_inputs['kwargs']) + # Re-write and distribute computation. + # TODO(sourabhbajaj): Convert the output to PerDevice variable and + # implement support for that in reduce. + last_step_tensor_outputs = tpu.batch_parallel( + iterate_on_tpu, [], num_shards=self._num_cores_per_host) - def iterate_on_tpu(): - return tpu.repeat(self._iterations_per_step, dequeueing_fn, []) + # Take index [0] of last_step_tensor_outputs as we wrapped + # initial_loop_values in a list in the `repeat` call. + return (control_flow_ops.group(last_step_tensor_outputs, enqueue_ops), + last_step_tensor_outputs[0], ctx) + def _call_for_each_tower(self, fn, *args, **kwargs): + kwargs.pop('run_concurrently', None) with one_device_strategy._OneDeviceTowerContext(self): # pylint: disable=protected-access - tpu_result = tpu.batch_parallel( - iterate_on_tpu, [], num_shards=self._num_cores_per_host) + return fn(*args, **kwargs) + + def get_initialization_ops(self): + return [tpu.initialize_system()] - return control_flow_ops.group(tpu_result, enqueue_ops) + def get_finalize_ops(self): + return [tpu.shutdown_system()] - def _reduce(self, method_string, value, destinations): + def _reduce(self, aggregation, value, destinations): del destinations # TPU is graph mode only. Rely on implicit Send/Recv. - if method_string == 'mean': + if aggregation == vs.VariableAggregation.MEAN: # TODO(jhseu): Revisit once we support model-parallelism. value *= (1. / self._num_cores_per_host) return tpu_ops.cross_replica_sum(value) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 9572ade8e497fa13a7ca0746399d3e0237ee79fd..b36ac563d29fc9157873796a845fefba3651edda 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -23,10 +23,8 @@ from __future__ import print_function import collections import weakref - import six -from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.distribute.python import input_ops from tensorflow.contrib.distribute.python import prefetching_ops_v2 from tensorflow.python.eager import context @@ -35,6 +33,8 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import device_util from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import saver @@ -43,7 +43,7 @@ from tensorflow.python.util import nest # pylint: disable=line-too-long -# TODO(josh11b): Should device values be strings or DeviceSpec objects +# TODO(josh11b): Should device values be strings or DeviceSpec objects? # Not sure DeviceSpec objects are usable as a dict key. class DistributedValues(object): """Holds a map from device to values. Either PerDevice or Mirrored.""" @@ -163,9 +163,16 @@ class PerDevice(DistributedValues): pass -class Mirrored(DistributedValues): +# Note that unlike PerDevice, Mirrored values inherit from +# DistributedDelegate and so can be used directly in cross-tower mode. +class Mirrored(DistributedDelegate): """Holds a map from device to values which are kept in sync.""" - pass + + def _get_cross_tower(self): + device = device_util.canonicalize(device_util.current()) + if device in self._index: + return self._index[device] + return list(self._index.values())[0] def _assign_on_device(device, variable, tensor): @@ -186,6 +193,10 @@ class DistributedVariable(DistributedDelegate): # Child class must set self._primary_var before calling # super(...).__init__(index). self._common_name = self._primary_var.name.split(":")[0] + # Use a weakref to make it easy to map from the contained values + # to the container without introducing a reference cycle. + for v in six.itervalues(index): + v._distributed_container = weakref.ref(self) # pylint: disable=protected-access super(DistributedVariable, self).__init__(index) @property @@ -238,35 +249,9 @@ class DistributedVariable(DistributedDelegate): pass -# Register a conversion function which reads the value of the variable, -# allowing instances of the class to be used as tensors. -def _tensor_conversion(var, dtype=None, name=None, as_ref=False): - # Try to avoid assignments to and other mutations of MirroredVariable - # state except through a DistributionStrategy.update() call. - assert not as_ref - return ops.internal_convert_to_tensor( - var.get(), dtype=dtype, name=name, as_ref=as_ref) - - -ops.register_tensor_conversion_function(DistributedVariable, _tensor_conversion) ops.register_dense_tensor_like_type(DistributedVariable) -class _MirroredSaveable(saver.BaseSaverBuilder.ResourceVariableSaveable): - """Class for defining how to restore a MirroredVariable.""" - - def __init__(self, mirrored_variable, primary_variable, name): - self._mirrored_variable = mirrored_variable - super(_MirroredSaveable, self).__init__(primary_variable, "", name) - - def restore(self, restored_tensors, restored_shapes): - """Restore the same value into all variables.""" - tensor, = restored_tensors - return control_flow_ops.group([ - _assign_on_device(d, v, tensor) - for d, v in six.iteritems(self._mirrored_variable._index)]) # pylint: disable=protected-access - - def _get_update_device(): """Validate we are in update/update_non_slot() and return current device. @@ -287,34 +272,85 @@ def _get_update_device(): return device +class _MirroredSaveable(saver.BaseSaverBuilder.ResourceVariableSaveable): + """Class for defining how to restore a MirroredVariable.""" + + def __init__(self, mirrored_variable, primary_variable, name): + self._mirrored_variable = mirrored_variable + super(_MirroredSaveable, self).__init__(primary_variable, "", name) + + def restore(self, restored_tensors, restored_shapes): + """Restore the same value into all variables.""" + tensor, = restored_tensors + return control_flow_ops.group([ + _assign_on_device(d, v, tensor) + for d, v in six.iteritems(self._mirrored_variable._index)]) # pylint: disable=protected-access + + class MirroredVariable(DistributedVariable, Mirrored, checkpointable.CheckpointableBase): """Holds a map from device to variables whose values are kept in sync.""" - def __init__(self, index, primary_var): + def __init__(self, index, primary_var, aggregation): # Use a weakref to make it easy to map from the contained values # to the container without introducing a reference cycle. for v in six.itervalues(index): v._mirrored_container = weakref.ref(self) # pylint: disable=protected-access self._primary_var = primary_var + self._aggregation = aggregation super(MirroredVariable, self).__init__(index) - # We use _get_update_device() for the assign* methods to enforce - # that we are in an update() function. The arguments to update() are - # automatically unwrapped so the update() function would normally - # see regular variables, not MirroredVariables. However, the update - # function can still operate on wrapped MirroredVariables through - # object members, captured arguments, etc. This is more likely in an + # The arguments to update() are automatically unwrapped so the update() + # function would normally see regular variables, not MirroredVariables. + # However, the update function can still operate on wrapped MirroredVariables + # through object members, captured arguments, etc. This is more likely in an # update_non_slot() function (like OptimizerV2._finish), which can # update several non-slot variables in one call. + def _assign_func(self, *args, **kwargs): + f = kwargs.pop("f") + if distribute_lib.get_cross_tower_context(): + update_device = distribute_lib.get_update_device() + # We are calling update on the mirrored variable in cross tower context. + if update_device is not None: + # We are calling an assign function on the mirrored variable in cross + # tower context. + v = self.get(device=update_device) + return f(v, *args, **kwargs) + + return distribute_lib.get_distribution_strategy().update( + self, f, *args, **kwargs) + else: + # We are calling an assign function on the mirrored variable in tower + # context. + # We reduce the value we want to assign/add/sub. More details about how we + # handle the different use cases can be found in the _reduce method. + # We call the function on each of the mirrored variables with the reduced + # value. + if self._aggregation == vs.VariableAggregation.NONE: + raise ValueError("You must specify an aggregation method to update a " + "MirroredVariable in Tower Context.") + + def merge_fn(strategy, value): + return strategy.update( + self, f, + strategy.reduce( + aggregation=self._aggregation, value=value, destinations=self)) + + return distribute_lib.get_tower_context().merge_call(merge_fn, *args, + **kwargs) + def assign_sub(self, *args, **kwargs): - return self.get(device=_get_update_device()).assign_sub(*args, **kwargs) + return self._assign_func(f=state_ops.assign_sub, *args, **kwargs) def assign_add(self, *args, **kwargs): - return self.get(device=_get_update_device()).assign_add(*args, **kwargs) + return self._assign_func(f=state_ops.assign_add, *args, **kwargs) def assign(self, *args, **kwargs): - return self.get(device=_get_update_device()).assign(*args, **kwargs) + return self._assign_func(f=state_ops.assign, *args, **kwargs) + + @property + def aggregation(self): + return self._aggregation def _get_cross_tower(self): device = device_util.canonicalize(device_util.current()) @@ -342,6 +378,20 @@ class MirroredVariable(DistributedVariable, Mirrored, return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} +# Register a conversion function which reads the value of the variable, +# allowing instances of the class to be used as tensors. +def _tensor_conversion_mirrored(var, dtype=None, name=None, as_ref=False): + # Try to avoid assignments to and other mutations of MirroredVariable + # state except through a DistributionStrategy.update() call. + assert not as_ref + return ops.internal_convert_to_tensor( + var.get(), dtype=dtype, name=name, as_ref=as_ref) + + +ops.register_tensor_conversion_function(MirroredVariable, + _tensor_conversion_mirrored) + + class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): """Class for defining how to restore a TowerLocalVariable.""" @@ -350,7 +400,7 @@ class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): # We use a callable so that we don't have to evaluate this expression # in the case where we are trying to restore instead of save. def tensor(): - return distribute_lib.get_distribution_strategy().fetch( + return distribute_lib.get_distribution_strategy().read_var( tower_local_variable) spec = saver.BaseSaverBuilder.SaveSpec( tensor=tensor, @@ -365,7 +415,7 @@ class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): # To preserve the sum across save and restore, we have to divide the # total across all devices when restoring a variable that was summed # when saving. - if self._tower_local_variable.reduce_method == "sum": + if self._tower_local_variable.aggregation == vs.VariableAggregation.SUM: tensor *= 1. / len(self._tower_local_variable.devices) return control_flow_ops.group([ _assign_on_device(d, v, tensor) @@ -382,9 +432,9 @@ class TowerLocalVariable(DistributedVariable, PerDevice, checkpointable.CheckpointableBase): """Holds a map from device to variables whose values are reduced on save.""" - def __init__(self, index, primary_var, reduce_method): + def __init__(self, index, primary_var, aggregation): self._primary_var = primary_var - self._reduce_method = reduce_method + self._aggregation = aggregation super(TowerLocalVariable, self).__init__(index) def assign_sub(self, *args, **kwargs): @@ -400,14 +450,14 @@ class TowerLocalVariable(DistributedVariable, PerDevice, return self.get().assign(*args, **kwargs) @property - def reduce_method(self): - return self._reduce_method + def aggregation(self): + return self._aggregation def _get_cross_tower(self): all_components = tuple(self._index.values()) # TODO(josh11b): Use a strategy-specific method. total = math_ops.add_n(all_components) - if self._reduce_method == "mean": + if self._aggregation == vs.VariableAggregation.MEAN: return total * (1./ len(all_components)) return total @@ -431,6 +481,17 @@ class TowerLocalVariable(DistributedVariable, PerDevice, return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} +# Register a conversion function for TowerLocalVariable which allows as_ref to +# be true. +def _tensor_conversion_tower_local(var, dtype=None, name=None, as_ref=False): + return ops.internal_convert_to_tensor( + var.get(), dtype=dtype, name=name, as_ref=as_ref) + + +ops.register_tensor_conversion_function(TowerLocalVariable, + _tensor_conversion_tower_local) + + def _devices_match(d1, d2): return device_util.canonicalize(d1) == device_util.canonicalize(d2) @@ -478,40 +539,40 @@ def regroup(per_device, wrap_class=PerDevice): same_id = False break # Consider three cases where same_id is true: - # * If v0 is a MirroredVariable (and same_id means it is the same - # across all devices), we want to return it. We check - # MirroredVariable specifically since it can look like it - # has a _mirrored_container member since its members do. - # * If v0 is a member of a mirrored variable, in which case - # hasattr(v0, "_mirrored_container") is true, we want to - # return the MirroredVariable that contains it using the - # _mirrored_container logic below. This case can trigger + # * If v0 is a DistributedVariable (a MirroredVariable or + # TowerLocalVariable, and same_id means it is the same across all + # devices), we want to return it. We check DistributedVariable + # specifically since it can look like it has a + # _distributed_container member since its members do. + # * If v0 is a member of a distributed variable, in which case + # hasattr(v0, "_distributed_container") is true, we want to + # return the DistributedVariable that contains it using the + # _distributed_container logic below. This case can trigger # same_id when there is only one device. # * In any other situation, same_id means we return v0. - if same_id and (isinstance(v0, MirroredVariable) or - not hasattr(v0, "_mirrored_container")): + if same_id and (isinstance(v0, DistributedVariable) or + not hasattr(v0, "_distributed_container")): return v0 # Detect the case where each device has a parallel component of the - # same MirroredVariable. In this case we want to return the - # containing MirroredVariable, after a bunch of sanity checking. - # In particular, each component should have the same container, - # and the devices of the variables should match the keys of the - # per-device dictionary. - # TODO(josh11b): Do we need similar logic for TowerLocalVariables? - if hasattr(v0, "_mirrored_container"): + # same MirroredVariable (or TowerLocalVariable). In this case we + # want to return the containing MirroredVariable, after a bunch of + # sanity checking. In particular, each component should have the + # same container, and the devices of the variables should match the + # keys of the per-device dictionary. + if hasattr(v0, "_distributed_container"): # pylint: disable=protected-access assert not isinstance(v0, MirroredVariable), ( "ids = %s, items = %s" % ([id(v[1]) for v in items], items)) assert _devices_match(v0.device, items[0][0]), ( "v0.device = %s, items = %s" % (v0.device, items)) - mirrored_container = v0._mirrored_container() - assert mirrored_container is not None + distributed_container = v0._distributed_container() + assert distributed_container is not None for d, v in items[1:]: assert _devices_match(v.device, d), ( "v.device = %s, d = %s, items = %s" % (v.device, d, items)) - assert mirrored_container is v._mirrored_container() - return mirrored_container + assert distributed_container is v._distributed_container() + return distributed_container # pylint: enable=protected-access return wrap_class(per_device) @@ -593,8 +654,7 @@ class PerDeviceDataset(object): # TODO(priyag): If dropping remainder is not appropriate, find another # approach to distributing the dataset when not possible to divide evenly. # Possibly not an issue when we start using PartitionedDataset. - self._dataset = dataset.apply( - batching.batch_and_drop_remainder(len(devices))) + self._dataset = dataset.batch(len(devices), drop_remainder=True) def make_one_shot_iterator(self): """Get a one time use iterator for the distributed PerDeviceDataset.""" @@ -805,3 +865,71 @@ class MapOutput(object): def get(self): return self._l + + +class MultiStepContext(object): + """A context object that can be used to capture things when running steps. + + This context object is useful when running multiple steps at a time using the + `run_steps_on_dataset` API. For e.g. it allows the user's step function to + specify which outputs to emit at what frequency. Currently it only supports + capturing output from the last step, but will soon be augmented to support + other use cases such as output each N steps. + """ + + def __init__(self, initial_loop_values=None): + """Initializes an output context. + + Args: + initial_loop_values: Initial values passed to the run steps + while loop. The only purpose is to verify the shapes and types + when the actual output is set. This will be removed once we + automatically infer the output shapes and types (and do not need to + check for user error in specifying them manually). + Returns: + A context object. + """ + self._last_step_outputs = None + self._non_tensor_outputs = None + self._initial_loop_values = initial_loop_values + + @property + def last_step_outputs(self): + """Return the last step's outputs.""" + return self._last_step_outputs + + @last_step_outputs.setter + def last_step_outputs(self, outputs): + """Set the last step's outputs.""" + self._verify_structure_shapes_types(outputs, self._initial_loop_values) + self._last_step_outputs = outputs + + @property + def non_tensor_outputs(self): + """Return the non tensor outputs.""" + return self._non_tensor_outputs + + @non_tensor_outputs.setter + def non_tensor_outputs(self, outputs): + """Set any non tensor outputs.""" + self._non_tensor_outputs = outputs + + def _verify_structure_shapes_types(self, left, right): + """Verify that the structure, shapes and types of left are same as right.""" + nest.assert_same_structure(left, right) + flat_left = nest.flatten(left) + flat_right = nest.flatten(right) + assert len(flat_left) == len(flat_right), ( + "Length of left {} and right {} should be same.". + format(len(flat_left), len(flat_right))) + + for o, i in zip(flat_left, flat_right): + # TODO(priyag): Add checks for other types like IndexedSlices. + if isinstance(o, ops.Tensor): + assert isinstance(i, ops.Tensor) + assert o.shape == i.shape, ( + "Shape {} of left {} doesn't match shape {} of right {}.". + format(o.shape, o, i.shape, i)) + assert o.dtype == i.dtype, ( + "Dtype {} of left {} doesn't match dtype {} of right {}.". + format(o.dtype, o, i.dtype, i)) diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index 1c95758d96aba47e9581dde6411763e98b99a968..8e44f2fea16ac851c124b573948ee14ec0640556 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -82,7 +82,7 @@ class DistributedValuesTest(test.TestCase): class DistributedDelegateTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetAttr(self): with ops.device("/device:CPU:0"): @@ -97,7 +97,7 @@ class DistributedDelegateTest(test.TestCase): with self.assertRaises(AttributeError): _ = v.y - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOperatorOverride(self): with ops.device("/device:CPU:0"): v = values.DistributedDelegate({"/device:CPU:0": 7, "/device:GPU:0": 8}) @@ -158,7 +158,8 @@ def _make_mirrored(): v.append(variable_scope.get_variable( name=n, initializer=init, use_resource=True)) index[d] = v[-1] - mirrored = values.MirroredVariable(index, v[0]) + mirrored = values.MirroredVariable(index, v[0], + variable_scope.VariableAggregation.SUM) return v, devices, mirrored @@ -277,7 +278,8 @@ class RegroupAndSelectDeviceTest(test.TestCase): v = variable_scope.get_variable( name="v", initializer=1., use_resource=True) index = {d: v} - mirrored = values.MirroredVariable(index, v) + mirrored = values.MirroredVariable(index, v, + variable_scope.VariableAggregation.SUM) result = values.regroup(index) self.assertIs(mirrored, result) @@ -363,7 +365,7 @@ class PerDeviceDatasetTest(test.TestCase): self._test_iterator_no_prefetch(devices, dataset, expected_values) self._test_iterator_with_prefetch(devices, dataset, expected_values) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOneDevice(self): devices = ["/device:CPU:0"] dataset = dataset_ops.Dataset.range(10) @@ -581,7 +583,8 @@ class MirroredVariableTest(test.TestCase): v = variable_scope.get_variable( name="v", initializer=[1.], use_resource=True) index = {"/job:foo/device:CPU:0": v} - mirrored = values.MirroredVariable(index, v) + mirrored = values.MirroredVariable(index, v, + variable_scope.VariableAggregation.MEAN) self.assertEquals(v.name, mirrored.name) self.assertEquals(v.dtype, mirrored.dtype) @@ -716,7 +719,9 @@ class MirroredVariableTest(test.TestCase): with ops.device("/device:GPU:0"): v = variable_scope.get_variable( name="v", initializer=1., use_resource=True) - mirrored = values.MirroredVariable({"/device:GPU:0": v}, v) + mirrored = values.MirroredVariable({ + "/device:GPU:0": v + }, v, variable_scope.VariableAggregation.MEAN) sess.run(variables_lib.global_variables_initializer()) sess.run({"complicated": mirrored}) @@ -746,24 +751,27 @@ class TowerLocalVariableTest(test.TestCase): if context.num_gpus() < 1 and context.executing_eagerly(): self.skipTest("A GPU is not available for this test in eager mode.") - v, tower_local = _make_tower_local("sum") + v, tower_local = _make_tower_local(variable_scope.VariableAggregation.SUM) self.assertEquals(v[0].name, tower_local.name) self.assertEquals(v[0].dtype, tower_local.dtype) self.assertEquals(v[0].shape, tower_local.shape) - self.assertEquals("sum", tower_local.reduce_method) + self.assertEquals(variable_scope.VariableAggregation.SUM, + tower_local.aggregation) @test_util.run_in_graph_and_eager_modes(config=config) def testVariableOnAnotherDevice(self): v = variable_scope.get_variable( name="v", initializer=[1.], use_resource=True) index = {"/job:foo/device:CPU:0": v} - tower_local = values.TowerLocalVariable(index, v, "mean") + tower_local = values.TowerLocalVariable( + index, v, variable_scope.VariableAggregation.MEAN) self.assertEquals(v.name, tower_local.name) self.assertEquals(v.dtype, tower_local.dtype) self.assertEquals(v.shape, tower_local.shape) - self.assertEquals("mean", tower_local.reduce_method) + self.assertEquals(variable_scope.VariableAggregation.MEAN, + tower_local.aggregation) def _assign_tower_local(self, devices, v, new): for d, var, n in zip(devices, v, new): @@ -789,7 +797,7 @@ class TowerLocalVariableTest(test.TestCase): self.skipTest("A GPU is not available for this test in eager mode.") with self.test_session() as sess: - v, tower_local = _make_tower_local("sum") + v, tower_local = _make_tower_local(variable_scope.VariableAggregation.SUM) # Overwrite the initial values. self._assign_tower_local(_devices, v, [3., 4.]) @@ -812,7 +820,8 @@ class TowerLocalVariableTest(test.TestCase): self.skipTest("A GPU is not available for this test in eager mode.") with self.test_session() as sess: - v, tower_local = _make_tower_local("mean") + v, tower_local = _make_tower_local( + variable_scope.VariableAggregation.MEAN) # Overwrite the initial values. self._assign_tower_local(_devices, v, [3., 4.]) @@ -831,7 +840,8 @@ class TowerLocalVariableTest(test.TestCase): def _save_tower_local_mean(self): """Save variables with mirroring, returns save_path.""" with self.test_session(graph=ops.Graph()) as sess: - v, tower_local = _make_tower_local("mean") + v, tower_local = _make_tower_local( + variable_scope.VariableAggregation.MEAN) # Overwrite the initial values. self._assign_tower_local(_devices, v, [3., 4.]) @@ -893,7 +903,8 @@ class TowerLocalVariableTest(test.TestCase): def _restore_tower_local_mean(self, save_path): """Restore to variables with mirroring in a fresh graph.""" with self.test_session(graph=ops.Graph()) as sess: - v, tower_local = _make_tower_local("mean") + v, tower_local = _make_tower_local( + variable_scope.VariableAggregation.MEAN) # Overwrite the initial values. self._assign_tower_local(_devices, v, [7., 8.]) @@ -907,7 +918,7 @@ class TowerLocalVariableTest(test.TestCase): def _restore_tower_local_sum(self, save_path): """Restore to variables with mirroring in a fresh graph.""" with self.test_session(graph=ops.Graph()) as sess: - v, tower_local = _make_tower_local("sum") + v, tower_local = _make_tower_local(variable_scope.VariableAggregation.SUM) # Overwrite the initial values. self._assign_tower_local(_devices, v, [7., 8.]) @@ -966,6 +977,18 @@ class TowerLocalVariableTest(test.TestCase): save_path = self._save_normal() self._restore_tower_local_sum(save_path) + def testTensorConversion(self): + with context.graph_mode(): + _, tower_local = _make_tower_local(variable_scope.VariableAggregation.SUM) + converted = ops.internal_convert_to_tensor(tower_local, as_ref=False) + self.assertIsInstance(converted, ops.Tensor) + self.assertEqual(converted.dtype, tower_local.dtype) + + converted = ops.internal_convert_to_tensor(tower_local, as_ref=True) + # Resources variable are converted to tensors as well when as_ref is True. + self.assertIsInstance(converted, ops.Tensor) + self.assertEqual(converted.dtype, tower_local.dtype) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD index 23d9dbcd91a25e7cbb5d6cfea5d63ba8412f4255..ad00d1734dd14ed846522a33d888a5387cb25cc6 100644 --- a/tensorflow/contrib/distributions/BUILD +++ b/tensorflow/contrib/distributions/BUILD @@ -16,6 +16,13 @@ load("//tensorflow:tensorflow.bzl", "cuda_py_test") py_library( name = "bijectors_py", srcs = glob(["python/ops/bijectors/*.py"]), + deprecation = ("TensorFlow Distributions has migrated to " + + "TensorFlow Probability " + + "(https://github.com/tensorflow/probability). " + + "Deprecated copies remaining in tf.contrib.distributions " + + "are unmaintained, unsupported, and will be removed by " + + "late 2018. You should update all usage of " + + "`tf.contrib.distributions` to `tfp.distributions`."), srcs_version = "PY2AND3", deps = [ "//tensorflow/contrib/linalg:linalg_py", @@ -42,6 +49,13 @@ py_library( py_library( name = "distributions_py", srcs = ["__init__.py"] + glob(["python/ops/*.py"]), + deprecation = ("TensorFlow Distributions has migrated to " + + "TensorFlow Probability " + + "(https://github.com/tensorflow/probability). " + + "Deprecated copies remaining in tf.contrib.distributions " + + "are unmaintained, unsupported, and will be removed by " + + "late 2018. You should update all usage of " + + "`tf.contrib.distributions` to `tfp.distributions`."), srcs_version = "PY2AND3", deps = [ ":bijectors_py", @@ -940,6 +954,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "fill_triangular_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/fill_triangular_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "gumbel_test", size = "small", @@ -1118,6 +1151,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "scale_tril_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/scale_tril_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "sigmoid_test", size = "small", @@ -1235,6 +1287,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "transform_diagonal_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/transform_diagonal_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "weibull_test", size = "small", diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py index 802538ba97578ce6cfe7e3555963ecd2fd014a66..5cec93c4df2e970f203253be6342bb292f296eb0 100644 --- a/tensorflow/contrib/distributions/__init__.py +++ b/tensorflow/contrib/distributions/__init__.py @@ -13,8 +13,6 @@ # limitations under the License. # ============================================================================== """Classes representing statistical distributions and ops for working with them. - -See the @{$python/contrib.distributions} guide. """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py index e281e81bdf0698c1f7b2f60fb27783dd1351773f..d1ce273499c8a646c0757844c91a785fa8d56ce4 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py @@ -61,6 +61,28 @@ class CholeskyOuterProductBijectorTest(test.TestCase): atol=0., rtol=1e-7) + def testNoBatchStaticJacobian(self): + x = np.eye(2) + bijector = bijectors.CholeskyOuterProduct() + + # The Jacobian matrix is 2 * tf.eye(2), which has jacobian determinant 4. + self.assertAllClose( + np.log(4), + self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=2))) + + def testNoBatchDynamicJacobian(self): + x = np.eye(2) + bijector = bijectors.CholeskyOuterProduct() + x_pl = array_ops.placeholder(dtypes.float32) + + with self.test_session(): + log_det_jacobian = bijector.forward_log_det_jacobian(x_pl, event_ndims=2) + + # The Jacobian matrix is 2 * tf.eye(2), which has jacobian determinant 4. + self.assertAllClose( + np.log(4), + log_det_jacobian.eval({x_pl: x})) + def testNoBatchStatic(self): x = np.array([[1., 0], [2, 1]]) # np.linalg.cholesky(y) y = np.array([[1., 2], [2, 5]]) # np.matmul(x, x.T) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3530e142e4d1545e80a3b1bf1e8ddbf7819ba58a --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py @@ -0,0 +1,98 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for FillTriangular bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class FillTriangularBijectorTest(test.TestCase): + """Tests the correctness of the FillTriangular bijector.""" + + @test_util.run_in_graph_and_eager_modes + def testBijector(self): + x = np.float32(np.array([1., 2., 3.])) + y = np.float32(np.array([[3., 0.], + [2., 1.]])) + + b = bijectors.FillTriangular() + + y_ = self.evaluate(b.forward(x)) + self.assertAllClose(y, y_) + + x_ = self.evaluate(b.inverse(y)) + self.assertAllClose(x, x_) + + fldj = self.evaluate(b.forward_log_det_jacobian(x, event_ndims=1)) + self.assertAllClose(fldj, 0.) + + ildj = self.evaluate(b.inverse_log_det_jacobian(y, event_ndims=2)) + self.assertAllClose(ildj, 0.) + + @test_util.run_in_graph_and_eager_modes + def testShape(self): + x_shape = tensor_shape.TensorShape([5, 4, 6]) + y_shape = tensor_shape.TensorShape([5, 4, 3, 3]) + + b = bijectors.FillTriangular(validate_args=True) + + x = array_ops.ones(shape=x_shape, dtype=dtypes.float32) + y_ = b.forward(x) + self.assertAllEqual(y_.shape.as_list(), y_shape.as_list()) + x_ = b.inverse(y_) + self.assertAllEqual(x_.shape.as_list(), x_shape.as_list()) + + y_shape_ = b.forward_event_shape(x_shape) + self.assertAllEqual(y_shape_.as_list(), y_shape.as_list()) + x_shape_ = b.inverse_event_shape(y_shape) + self.assertAllEqual(x_shape_.as_list(), x_shape.as_list()) + + y_shape_tensor = self.evaluate( + b.forward_event_shape_tensor(x_shape.as_list())) + self.assertAllEqual(y_shape_tensor, y_shape.as_list()) + x_shape_tensor = self.evaluate( + b.inverse_event_shape_tensor(y_shape.as_list())) + self.assertAllEqual(x_shape_tensor, x_shape.as_list()) + + @test_util.run_in_graph_and_eager_modes + def testShapeError(self): + + b = bijectors.FillTriangular(validate_args=True) + + x_shape_bad = tensor_shape.TensorShape([5, 4, 7]) + with self.assertRaisesRegexp(ValueError, "is not a triangular number"): + b.forward_event_shape(x_shape_bad) + with self.assertRaisesOpError("is not a triangular number"): + self.evaluate(b.forward_event_shape_tensor(x_shape_bad.as_list())) + + y_shape_bad = tensor_shape.TensorShape([5, 4, 3, 2]) + with self.assertRaisesRegexp(ValueError, "Matrix must be square"): + b.inverse_event_shape(y_shape_bad) + with self.assertRaisesOpError("Matrix must be square"): + self.evaluate(b.inverse_event_shape_tensor(y_shape_bad.as_list())) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py index 18397035571561731698b06d90e20dc74e3cf83c..85d604e34ac25cf94b601470b7f166d9d414a8e3 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import test class MatrixInverseTriLBijectorTest(test.TestCase): """Tests the correctness of the Y = inv(tril) transformation.""" - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputesCorrectValues(self): inv = bijectors.MatrixInverseTriL(validate_args=True) self.assertEqual("matrix_inverse_tril", inv.name) @@ -51,7 +51,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOneByOneMatrix(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[5.]], dtype=np.float32) @@ -70,7 +70,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testZeroByZeroMatrix(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.eye(0, dtype=np.float32) @@ -89,7 +89,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBatch(self): # Test batch computation with input shape (2, 1, 2, 2), i.e. batch shape # (2, 1). @@ -114,7 +114,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertAllClose(expected_fldj_, fldj_, atol=0., rtol=1e-3) self.assertAllClose(-expected_fldj_, ildj_, atol=0., rtol=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputRankTooLow(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([0.1], dtype=np.float32) @@ -149,7 +149,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): ## square_error_msg): ## inv.inverse_log_det_jacobian(x_, event_ndims=2).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputNotLowerTriangular(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[1., 2.], @@ -169,7 +169,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): triangular_error_msg): inv.inverse_log_det_jacobian(x_, event_ndims=2).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputSingular(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[1., 0.], diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py index a5f5219588fb3be67beb797ba68ed8148e9e9fd2..cb42331a21a6acdd5244c311a7def5359bb6c574 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py @@ -36,7 +36,7 @@ class OrderedBijectorTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorVector(self): with self.test_session(): ordered = Ordered() @@ -82,7 +82,7 @@ class OrderedBijectorTest(test.TestCase): atol=0., rtol=1e-7) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShapeGetters(self): with self.test_session(): x = tensor_shape.TensorShape([4]) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d5b3367f9a31a9c602e0b138e617db68834b8229 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py @@ -0,0 +1,69 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for ScaleTriL bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test + + +class ScaleTriLBijectorTest(test.TestCase): + """Tests the correctness of the ScaleTriL bijector.""" + + def setUp(self): + self._rng = np.random.RandomState(42) + + def testComputesCorrectValues(self): + shift = 1.61803398875 + x = np.float32(np.array([-1, .5, 2])) + y = np.float32(np.array([[np.exp(2) + shift, 0.], + [.5, np.exp(-1) + shift]])) + + b = bijectors.ScaleTriL(diag_bijector=bijectors.Exp(), + diag_shift=shift) + + y_ = self.evaluate(b.forward(x)) + self.assertAllClose(y, y_) + + x_ = self.evaluate(b.inverse(y)) + self.assertAllClose(x, x_) + + @test_util.run_in_graph_and_eager_modes + def testInvertible(self): + + # Generate random inputs from an unconstrained space, with + # event size 6 to specify 3x3 triangular matrices. + batch_shape = [2, 1] + x = np.float32(np.random.randn(*(batch_shape + [6]))) + b = bijectors.ScaleTriL(diag_bijector=bijectors.Softplus(), + diag_shift=3.14159) + y = self.evaluate(b.forward(x)) + self.assertAllEqual(y.shape, batch_shape + [3, 3]) + + x_ = self.evaluate(b.inverse(y)) + self.assertAllClose(x, x_) + + fldj = self.evaluate(b.forward_log_det_jacobian(x, event_ndims=1)) + ildj = self.evaluate(b.inverse_log_det_jacobian(y, event_ndims=2)) + self.assertAllClose(fldj, -ildj) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py index 2ac06fce55b448a5f3da7ccb7f8766b5b1404ad7..d0098c3c105626da1da5855710169069ebeffbd9 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py @@ -40,7 +40,7 @@ class SoftsignBijectorTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorBounds(self): bijector = Softsign(validate_args=True) with self.test_session(): @@ -54,7 +54,7 @@ class SoftsignBijectorTest(test.TestCase): with self.assertRaisesOpError("less than 1"): bijector.inverse_log_det_jacobian(3., event_ndims=0).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorForwardInverse(self): bijector = Softsign(validate_args=True) self.assertEqual("softsign", bijector.name) @@ -64,7 +64,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(y, self.evaluate(bijector.forward(x))) self.assertAllClose(x, self.evaluate(bijector.inverse(y))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorLogDetJacobianEventDimsZero(self): bijector = Softsign(validate_args=True) y = self._rng.rand(2, 10) @@ -74,7 +74,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(ildj, self.evaluate( bijector.inverse_log_det_jacobian(y, event_ndims=0))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorForwardInverseEventDimsOne(self): bijector = Softsign(validate_args=True) self.assertEqual("softsign", bijector.name) @@ -83,7 +83,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(y, self.evaluate(bijector.forward(x))) self.assertAllClose(x, self.evaluate(bijector.inverse(y))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorLogDetJacobianEventDimsOne(self): bijector = Softsign(validate_args=True) y = self._rng.rand(2, 10) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py new file mode 100644 index 0000000000000000000000000000000000000000..efc9f266d1fb6bcc53ae318e218b0697825c0155 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py @@ -0,0 +1,66 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for TransformDiagonal bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test + + +class TransformDiagonalBijectorTest(test.TestCase): + """Tests correctness of the TransformDiagonal bijector.""" + + def setUp(self): + self._rng = np.random.RandomState(42) + + @test_util.run_in_graph_and_eager_modes + def testBijector(self): + x = np.float32(np.random.randn(3, 4, 4)) + + y = x.copy() + for i in range(x.shape[0]): + np.fill_diagonal(y[i, :, :], np.exp(np.diag(x[i, :, :]))) + + exp = bijectors.Exp() + b = bijectors.TransformDiagonal(diag_bijector=exp) + + y_ = self.evaluate(b.forward(x)) + self.assertAllClose(y, y_) + + x_ = self.evaluate(b.inverse(y)) + self.assertAllClose(x, x_) + + fldj = self.evaluate(b.forward_log_det_jacobian(x, event_ndims=2)) + ildj = self.evaluate(b.inverse_log_det_jacobian(y, event_ndims=2)) + self.assertAllEqual( + fldj, + self.evaluate(exp.forward_log_det_jacobian( + np.array([np.diag(x_mat) for x_mat in x]), + event_ndims=1))) + self.assertAllEqual( + ildj, + self.evaluate(exp.inverse_log_det_jacobian( + np.array([np.diag(y_mat) for y_mat in y]), + event_ndims=1))) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py index 31d24aa9ea09007b8db40e4869371b1f62639ac7..181c46d2e52552e641bc59c0fe94743f1af42845 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py @@ -29,7 +29,9 @@ from tensorflow.contrib.distributions.python.ops import mvn_diag from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import categorical from tensorflow.python.ops.distributions import normal from tensorflow.python.ops.linalg import linear_operator_diag @@ -540,5 +542,51 @@ class PadDynamicTest(_PadTest, test.TestCase): return False +class TestMoveDimension(test.TestCase): + + @test_util.run_in_graph_and_eager_modes + def test_move_dimension_static_shape(self): + + x = random_ops.random_normal(shape=[200, 30, 4, 1, 6]) + + x_perm = distribution_util.move_dimension(x, 1, 1) + self.assertAllEqual(x_perm.shape.as_list(), [200, 30, 4, 1, 6]) + + x_perm = distribution_util.move_dimension(x, 0, 3) + self.assertAllEqual(x_perm.shape.as_list(), [30, 4, 1, 200, 6]) + + x_perm = distribution_util.move_dimension(x, 0, -2) + self.assertAllEqual(x_perm.shape.as_list(), [30, 4, 1, 200, 6]) + + x_perm = distribution_util.move_dimension(x, 4, 2) + self.assertAllEqual(x_perm.shape.as_list(), [200, 30, 6, 4, 1]) + + @test_util.run_in_graph_and_eager_modes + def test_move_dimension_dynamic_shape(self): + + x_ = random_ops.random_normal(shape=[200, 30, 4, 1, 6]) + x = array_ops.placeholder_with_default(input=x_, shape=None) + + x_perm = distribution_util.move_dimension(x, 1, 1) + self.assertAllEqual(self.evaluate(array_ops.shape(x_perm)), + [200, 30, 4, 1, 6]) + + x_perm = distribution_util.move_dimension(x, 0, 3) + self.assertAllEqual(self.evaluate(array_ops.shape(x_perm)), + [30, 4, 1, 200, 6]) + + x_perm = distribution_util.move_dimension(x, 0, -2) + self.assertAllEqual(self.evaluate(array_ops.shape(x_perm)), + [30, 4, 1, 200, 6]) + + x_perm = distribution_util.move_dimension(x, 4, 2) + self.assertAllEqual(self.evaluate(array_ops.shape(x_perm)), + [200, 30, 6, 4, 1]) + + x_perm = distribution_util.move_dimension(x, -1, 2) + self.assertAllEqual(self.evaluate(array_ops.shape(x_perm)), + [200, 30, 6, 4, 1]) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD b/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD index 03e26b198ea02ad1bef8bcd2f6076078ecd7df0b..42ecea034d77430924bd6f597bf42ec3f64fec92 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD +++ b/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD @@ -34,7 +34,10 @@ py_test( name = "correlation_matrix_volumes_test", size = "medium", srcs = ["correlation_matrix_volumes_test.py"], - tags = ["no_pip"], + tags = [ + "no_pip", + "optonly", + ], deps = [ ":correlation_matrix_volumes_py", # For statistical testing diff --git a/tensorflow/contrib/distributions/python/ops/autoregressive.py b/tensorflow/contrib/distributions/python/ops/autoregressive.py index 11ca90c4833d84b092f0b43a8f5404e3a11450cd..bb9b8043b2233b2109f51b5dde188d088fdb0d39 100644 --- a/tensorflow/contrib/distributions/python/ops/autoregressive.py +++ b/tensorflow/contrib/distributions/python/ops/autoregressive.py @@ -23,6 +23,7 @@ import numpy as np from tensorflow.python.framework import ops from tensorflow.python.ops.distributions import distribution as distribution_lib from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class Autoregressive(distribution_lib.Distribution): @@ -107,6 +108,14 @@ class Autoregressive(distribution_lib.Distribution): https://arxiv.org/abs/1606.05328 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, distribution_fn, sample0=None, diff --git a/tensorflow/contrib/distributions/python/ops/batch_reshape.py b/tensorflow/contrib/distributions/python/ops/batch_reshape.py index 4714caad69ee4341d259f6677decdd5842931834..519077bc9ab1063a1135486cfae34656f3f68157 100644 --- a/tensorflow/contrib/distributions/python/ops/batch_reshape.py +++ b/tensorflow/contrib/distributions/python/ops/batch_reshape.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib +from tensorflow.python.util import deprecation __all__ = [ @@ -71,6 +72,14 @@ class BatchReshape(distribution_lib.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, distribution, batch_shape, @@ -352,6 +361,14 @@ class BatchReshape(distribution_lib.Distribution): return runtime_assertions +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def calculate_reshape(original_shape, new_shape, validate=False, name=None): """Calculates the reshaped dimensions (replacing up to one -1 in reshape).""" batch_shape_static = tensor_util.constant_value_as_shape(new_shape) @@ -384,6 +401,14 @@ def calculate_reshape(original_shape, new_shape, validate=False, name=None): return expanded_new_shape, batch_shape_static, validations +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def validate_init_args_statically(distribution, batch_shape): """Helper to __init__ which makes or raises assertions.""" if batch_shape.shape.ndims is not None: diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py index 4965381ef33e14cef0e0339341d50c943d412d8f..e141f8b5c6423bd6cce4d09da6f49d55b3e25a24 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py @@ -24,6 +24,7 @@ @@CholeskyOuterProduct @@ConditionalBijector @@Exp +@@FillTriangular @@Gumbel @@Identity @@Inline @@ -36,12 +37,14 @@ @@PowerTransform @@RealNVP @@Reshape +@@ScaleTriL @@Sigmoid @@SinhArcsinh @@SoftmaxCentered @@Softplus @@Softsign @@Square +@@TransformDiagonal @@Weibull @@masked_autoregressive_default_template @@ -64,6 +67,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.chain import * from tensorflow.contrib.distributions.python.ops.bijectors.cholesky_outer_product import * from tensorflow.contrib.distributions.python.ops.bijectors.conditional_bijector import * from tensorflow.contrib.distributions.python.ops.bijectors.exp import * +from tensorflow.contrib.distributions.python.ops.bijectors.fill_triangular import * from tensorflow.contrib.distributions.python.ops.bijectors.gumbel import * from tensorflow.contrib.distributions.python.ops.bijectors.inline import * from tensorflow.contrib.distributions.python.ops.bijectors.invert import * @@ -75,12 +79,14 @@ from tensorflow.contrib.distributions.python.ops.bijectors.permute import * from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import * from tensorflow.contrib.distributions.python.ops.bijectors.real_nvp import * from tensorflow.contrib.distributions.python.ops.bijectors.reshape import * +from tensorflow.contrib.distributions.python.ops.bijectors.scale_tril import * from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import * from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import * from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import * from tensorflow.contrib.distributions.python.ops.bijectors.softplus import * from tensorflow.contrib.distributions.python.ops.bijectors.softsign import * from tensorflow.contrib.distributions.python.ops.bijectors.square import * +from tensorflow.contrib.distributions.python.ops.bijectors.transform_diagonal import * from tensorflow.python.ops.distributions.bijector import * from tensorflow.python.ops.distributions.identity_bijector import Identity diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/absolute_value.py b/tensorflow/contrib/distributions/python/ops/bijectors/absolute_value.py index c9e31d7712f09f6c4b4cc6ae51a34c42a19c291d..4d6a46e7358933fdf512f49eae2673f35953c90a 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/absolute_value.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/absolute_value.py @@ -23,6 +23,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ "AbsoluteValue", @@ -70,6 +71,14 @@ class AbsoluteValue(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="absolute_value"): """Instantiates the `AbsoluteValue` bijector. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine.py index b4c2939eb914d50475ba6b1c1e979a804090f641..25f29452c3949600b8a4153a8585dd7269bd3b2b 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/affine.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -36,6 +37,14 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _as_tensor(x, name): """Convenience to convert to `Tensor` or leave as `None`.""" return None if x is None else ops.convert_to_tensor(x, name=name) @@ -97,6 +106,14 @@ class Affine(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, shift=None, scale_identity_multiplier=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_linear_operator.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_linear_operator.py index 59f9742d576a7804f401d3a47ba31ae61d6c6e54..91301f15ad87e133777371b346864ecf7b964f27 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_linear_operator.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_linear_operator.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops.distributions import bijector from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.util import deprecation __all__ = [ @@ -88,6 +89,14 @@ class AffineLinearOperator(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, shift=None, scale=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py index cd792e2c8cf48602daf9fb5eb56b8c34bac050c7..460d906231bd30f8cec4fe21d42afe7b2a05805e 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -52,6 +53,14 @@ class AffineScalar(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, shift=None, scale=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py index 224cec8a63dba53a528490117efac890312fe8d5..f19f147dd645b4f805f1905899b44293284d4225 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py @@ -27,6 +27,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -34,6 +35,14 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _undo_batch_normalization(x, mean, variance, @@ -128,6 +137,14 @@ class BatchNormalization(bijector.Bijector): Processing Systems_, 2017. https://arxiv.org/abs/1705.07057 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, batchnorm_layer=None, training=True, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/chain.py b/tensorflow/contrib/distributions/python/ops/bijectors/chain.py index 16f959560ce0f171035b3ef0bd80b16dae1cc654..910774ea5bb4106a948567144c46c6db23a2c6e0 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/chain.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/chain.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -31,10 +32,26 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _use_static_shape(input_tensor, ndims): return input_tensor.shape.is_fully_defined() and isinstance(ndims, int) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _compute_min_event_ndims(bijector_list, compute_forward=True): """Computes the min_event_ndims associated with the give list of bijectors. @@ -142,6 +159,14 @@ class Chain(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, bijectors=None, validate_args=False, name=None): """Instantiates `Chain` bijector. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py index 268c8d03426d435dc38412ac1bd05c674bd05d2b..3e1e4fc82971b71792d193ea8518dd402e4a4d9d 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py @@ -27,6 +27,7 @@ from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = [ @@ -69,6 +70,14 @@ class CholeskyOuterProduct(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="cholesky_outer_product"): """Instantiates the `CholeskyOuterProduct` bijector. @@ -173,7 +182,20 @@ class CholeskyOuterProduct(bijector.Bijector): axis=-1) fldj = p_float * np.log(2.) + sum_weighted_log_diag - return fldj + # We finally need to undo adding an extra column in non-scalar cases + # where there is a single matrix as input. + if x.get_shape().ndims is not None: + if x.get_shape().ndims == 2: + fldj = array_ops.squeeze(fldj, axis=-1) + return fldj + + shape = array_ops.shape(fldj) + maybe_squeeze_shape = array_ops.concat([ + shape[:-1], + distribution_util.pick_vector( + math_ops.equal(array_ops.rank(x), 2), + np.array([], dtype=np.int32), shape[-1:])], 0) + return array_ops.reshape(fldj, maybe_squeeze_shape) def _make_columnar(self, x): """Ensures non-scalar input has at least one column. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/exp.py b/tensorflow/contrib/distributions/python/ops/bijectors/exp.py index 9fc1bbf052b419d07a9db149b990c2b80190d72b..07627e1e45eae6b63d830b2adf036bdc3b1d2895 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/exp.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/exp.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.distributions.python.ops.bijectors import power_transform +from tensorflow.python.util import deprecation __all__ = [ @@ -47,6 +48,14 @@ class Exp(power_transform.PowerTransform): over the event space. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="exp"): diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/fill_triangular.py b/tensorflow/contrib/distributions/python/ops/bijectors/fill_triangular.py new file mode 100644 index 0000000000000000000000000000000000000000..31a9ca27e519bc312813668bf621a875838f12a0 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/fill_triangular.py @@ -0,0 +1,165 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""FillTriangular bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.distributions import bijector +from tensorflow.python.ops.distributions import util as dist_util +from tensorflow.python.util import deprecation + + +__all__ = [ + "FillTriangular", +] + + +class FillTriangular(bijector.Bijector): + """Transforms vectors to triangular. + + Triangular matrix elements are filled in a clockwise spiral. + + Given input with shape `batch_shape + [d]`, produces output with + shape `batch_shape + [n, n]`, where + `n = (-1 + sqrt(1 + 8 * d))/2`. + This follows by solving the quadratic equation + `d = 1 + 2 + ... + n = n * (n + 1)/2`. + + #### Example + + ```python + b = tfb.FillTriangular(upper=False) + b.forward([1, 2, 3, 4, 5, 6]) + # ==> [[4, 0, 0], + # [6, 5, 0], + # [3, 2, 1]] + + b = tfb.FillTriangular(upper=True) + b.forward([1, 2, 3, 4, 5, 6]) + # ==> [[1, 2, 3], + # [0, 5, 6], + # [0, 0, 4]] + + ``` + """ + + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) + def __init__(self, + upper=False, + validate_args=False, + name="fill_triangular"): + """Instantiates the `FillTriangular` bijector. + + Args: + upper: Python `bool` representing whether output matrix should be upper + triangular (`True`) or lower triangular (`False`, default). + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + """ + self._upper = upper + super(FillTriangular, self).__init__( + forward_min_event_ndims=1, + inverse_min_event_ndims=2, + validate_args=validate_args, + name=name) + + def _forward(self, x): + return dist_util.fill_triangular(x, upper=self._upper) + + def _inverse(self, y): + return dist_util.fill_triangular_inverse(y, upper=self._upper) + + def _forward_log_det_jacobian(self, x): + return array_ops.zeros_like(x[..., 0]) + + def _inverse_log_det_jacobian(self, y): + return array_ops.zeros_like(y[..., 0, 0]) + + def _forward_event_shape(self, input_shape): + batch_shape, d = input_shape[:-1], input_shape[-1].value + if d is None: + n = None + else: + n = vector_size_to_square_matrix_size(d, self.validate_args) + return batch_shape.concatenate([n, n]) + + def _inverse_event_shape(self, output_shape): + batch_shape, n1, n2 = (output_shape[:-2], + output_shape[-2].value, + output_shape[-1].value) + if n1 is None or n2 is None: + m = None + elif n1 != n2: + raise ValueError("Matrix must be square. (saw [{}, {}])".format(n1, n2)) + else: + m = n1 * (n1 + 1) / 2 + return batch_shape.concatenate([m]) + + def _forward_event_shape_tensor(self, input_shape_tensor): + batch_shape, d = input_shape_tensor[:-1], input_shape_tensor[-1] + n = vector_size_to_square_matrix_size(d, self.validate_args) + return array_ops.concat([batch_shape, [n, n]], axis=0) + + def _inverse_event_shape_tensor(self, output_shape_tensor): + batch_shape, n = output_shape_tensor[:-2], output_shape_tensor[-1] + if self.validate_args: + is_square_matrix = check_ops.assert_equal( + n, output_shape_tensor[-2], message="Matrix must be square.") + with ops.control_dependencies([is_square_matrix]): + n = array_ops.identity(n) + d = math_ops.cast(n * (n + 1) / 2, output_shape_tensor.dtype) + return array_ops.concat([batch_shape, [d]], axis=0) + + +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) +def vector_size_to_square_matrix_size(d, validate_args, name=None): + """Convert a vector size to a matrix size.""" + if isinstance(d, (float, int, np.generic, np.ndarray)): + n = (-1 + np.sqrt(1 + 8 * d)) / 2. + if float(int(n)) != n: + raise ValueError("Vector length is not a triangular number.") + return int(n) + else: + with ops.name_scope(name, "vector_size_to_square_matrix_size", [d]) as name: + n = (-1. + math_ops.sqrt(1 + 8. * math_ops.to_float(d))) / 2. + if validate_args: + with ops.control_dependencies([check_ops.assert_equal( + math_ops.to_float(math_ops.to_int32(n)), n, + message="Vector length is not a triangular number")]): + n = array_ops.identity(n) + return math_ops.cast(n, d.dtype) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/gumbel.py b/tensorflow/contrib/distributions/python/ops/bijectors/gumbel.py index e656a258e56e71898ecb719dd2af876f158cf799..71e562a927a30a17d695b81c566f981db7553ad9 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/gumbel.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/gumbel.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ "Gumbel", @@ -45,6 +46,14 @@ class Gumbel(bijector.Bijector): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=0., scale=1., diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/inline.py b/tensorflow/contrib/distributions/python/ops/bijectors/inline.py index 2bde956d1345129285acae4684256c5ac828b9a1..1504bd27204f728c0cb519159230e945128c4740 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/inline.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/inline.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -43,6 +44,14 @@ class Inline(bijector.Bijector): The above example is equivalent to the `Bijector` `Exp()`. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, forward_fn=None, inverse_fn=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/invert.py b/tensorflow/contrib/distributions/python/ops/bijectors/invert.py index 84a3289ba2160ed22a2bc7030dd612ba9ca6f6df..a648676d4b1956e5c27f67a71e6bd93d0d7fc97d 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/invert.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/invert.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ "Invert", @@ -40,6 +41,14 @@ class Invert(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, bijector, validate_args=False, name=None): """Creates a `Bijector` which swaps the meaning of `inverse` and `forward`. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py index 97000c17262d3efdef10274711364c2bc2083bd4..33b75a04d34fdd01bc0d854d4e5b9c45a737b122 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ "Kumaraswamy", @@ -44,6 +45,14 @@ class Kumaraswamy(bijector.Bijector): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, concentration1=None, concentration0=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py index 83667b0e80cfcc1c4f0617cdc739221f24439665..b8f2a4b2c731bdaee78692c036fb9f2fba4e3760 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py @@ -33,6 +33,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import template as template_ops from tensorflow.python.ops import variable_scope as variable_scope_lib from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -186,6 +187,14 @@ class MaskedAutoregressiveFlow(bijector.Bijector): Processing Systems_, 2017. https://arxiv.org/abs/1705.07057 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, shift_and_log_scale_fn, is_constant_jacobian=False, @@ -296,6 +305,14 @@ MASK_INCLUSIVE = "inclusive" MASK_EXCLUSIVE = "exclusive" +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _gen_slices(num_blocks, n_in, n_out, mask_type=MASK_EXCLUSIVE): """Generate the slices for building an autoregressive mask.""" # TODO(b/67594795): Better support of dynamic shape. @@ -313,6 +330,14 @@ def _gen_slices(num_blocks, n_in, n_out, mask_type=MASK_EXCLUSIVE): return slices +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _gen_mask(num_blocks, n_in, n_out, @@ -327,6 +352,14 @@ def _gen_mask(num_blocks, return mask +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def masked_dense(inputs, units, num_blocks=None, @@ -399,6 +432,14 @@ def masked_dense(inputs, return layer.apply(inputs) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def masked_autoregressive_default_template( hidden_layers, shift_only=False, @@ -515,6 +556,14 @@ def masked_autoregressive_default_template( "masked_autoregressive_default_template", _fn) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _clip_by_value_preserve_grad(x, clip_value_min, clip_value_max, name=None): """Clips input while leaving gradient unaltered.""" with ops.name_scope(name, "clip_by_value_preserve_grad", diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/matrix_inverse_tril.py b/tensorflow/contrib/distributions/python/ops/bijectors/matrix_inverse_tril.py index 71903f705232f0c5e5e0b3271550b4ef938c4f9d..49e6192f067edec4890dcfa107876a5104c14dd4 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/matrix_inverse_tril.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/matrix_inverse_tril.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -55,6 +56,14 @@ class MatrixInverseTriL(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="matrix_inverse_tril"): """Instantiates the `MatrixInverseTriL` bijector. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py index 3f03592f314cc13e8a9ea7e2ae18c5bb1f14e74f..fb393218b6b47764f45b5055bbf15cc17aba219e 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -57,6 +58,14 @@ class Ordered(bijector.Bijector): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="ordered"): super(Ordered, self).__init__( forward_min_event_ndims=1, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/permute.py b/tensorflow/contrib/distributions/python/ops/bijectors/permute.py index 12a16a3f2ba3da53077307fd97d3f10d99b2c81f..f182a1adcbb6b11af2376cd271f903d50e50f1a0 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/permute.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/permute.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -74,6 +75,14 @@ class Permute(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, permutation, validate_args=False, name=None): """Creates the `Permute` bijector. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/power_transform.py b/tensorflow/contrib/distributions/python/ops/bijectors/power_transform.py index 71f123f2a998458edaa9c8da07ea2932f62625ca..16264fe728a334db347304500767ce5876f9db7e 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/power_transform.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/power_transform.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -41,6 +42,14 @@ class PowerTransform(bijector.Bijector): This bijector is equivalent to the `Exp` bijector when `c=0`. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, power=0., validate_args=False, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/real_nvp.py b/tensorflow/contrib/distributions/python/ops/bijectors/real_nvp.py index 66e8a5b9b356867424d1d47efaf848fc6903c371..773ae2446118051a61636bc21de6b81dfacda746 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/real_nvp.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/real_nvp.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import template as template_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -126,6 +127,14 @@ class RealNVP(bijector.Bijector): Processing Systems_, 2017. https://arxiv.org/abs/1705.07057 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, num_masked, shift_and_log_scale_fn, @@ -228,6 +237,14 @@ class RealNVP(bijector.Bijector): return math_ops.reduce_sum(log_scale, axis=-1) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def real_nvp_default_template( hidden_layers, shift_only=False, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/reshape.py b/tensorflow/contrib/distributions/python/ops/bijectors/reshape.py index 5497c422e4d51e259435692dac722f801e8844ac..c8282229a30fabff0c4c267d0bdfcdbce4f5f3d9 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/reshape.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/reshape.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -36,10 +37,26 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _static_ndims_from_shape(shape): return shape.shape.with_rank_at_least(1)[0].value +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _ndims_from_shape(shape): return array_ops.shape(shape)[0] @@ -86,6 +103,14 @@ class Reshape(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, event_shape_out, event_shape_in=(-1,), validate_args=False, name=None): """Creates a `Reshape` bijector. diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/scale_tril.py b/tensorflow/contrib/distributions/python/ops/bijectors/scale_tril.py new file mode 100644 index 0000000000000000000000000000000000000000..6fbe8665781211ca803feb8bf5a8c04fb0b969e8 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/scale_tril.py @@ -0,0 +1,123 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""ScaleTriL bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.distributions.python.ops.bijectors import affine_scalar +from tensorflow.contrib.distributions.python.ops.bijectors import chain +from tensorflow.contrib.distributions.python.ops.bijectors import fill_triangular +from tensorflow.contrib.distributions.python.ops.bijectors import softplus +from tensorflow.contrib.distributions.python.ops.bijectors import transform_diagonal +from tensorflow.python.util import deprecation + +__all__ = [ + "ScaleTriL", +] + + +class ScaleTriL(chain.Chain): + """Transforms unconstrained vectors to TriL matrices with positive diagonal. + + This is implemented as a simple `tfb.Chain` of `tfb.FillTriangular` + followed by `tfb.TransformDiagonal`, and provided mostly as a + convenience. The default setup is somewhat opinionated, using a + Softplus transformation followed by a small shift (`1e-5`) which + attempts to avoid numerical issues from zeros on the diagonal. + + #### Examples + + ```python + tfb = tf.contrib.distributions.bijectors + b = tfb.ScaleTriL( + diag_bijector=tfb.Exp(), + diag_shift=None) + b.forward(x=[0., 0., 0.]) + # Result: [[1., 0.], + # [0., 1.]] + b.inverse(y=[[1., 0], + [.5, 2]]) + # Result: [log(2), .5, log(1)] + + # Define a distribution over PSD matrices of shape `[3, 3]`, + # with `1 + 2 + 3 = 6` degrees of freedom. + dist = tfd.TransformedDistribution( + tfd.Normal(tf.zeros(6), tf.ones(6)), + tfb.Chain([tfb.CholeskyOuterProduct(), tfb.ScaleTriL()])) + + # Using an identity transformation, ScaleTriL is equivalent to + # tfb.FillTriangular. + b = tfb.ScaleTriL( + diag_bijector=tfb.Identity(), + diag_shift=None) + + # For greater control over initialization, one can manually encode + # pre- and post- shifts inside of `diag_bijector`. + b = tfb.ScaleTriL( + diag_bijector=tfb.Chain([ + tfb.AffineScalar(shift=1e-3), + tfb.Softplus(), + tfb.AffineScalar(shift=0.5413)]), # softplus_inverse(1.) + # = log(expm1(1.)) = 0.5413 + diag_shift=None) + ``` + """ + + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) + def __init__(self, + diag_bijector=None, + diag_shift=1e-5, + validate_args=False, + name="scale_tril"): + """Instantiates the `ScaleTriL` bijector. + + Args: + diag_bijector: `Bijector` instance, used to transform the output diagonal + to be positive. + Default value: `None` (i.e., `tfb.Softplus()`). + diag_shift: Float value broadcastable and added to all diagonal entries + after applying the `diag_bijector`. Setting a positive + value forces the output diagonal entries to be positive, but + prevents inverting the transformation for matrices with + diagonal entries less than this value. + Default value: `1e-5` (i.e., no shift is applied). + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + Default value: `False` (i.e., arguments are not validated). + name: Python `str` name given to ops managed by this object. + Default value: `scale_tril`. + """ + + if diag_bijector is None: + diag_bijector = softplus.Softplus(validate_args=validate_args) + + if diag_shift is not None: + diag_bijector = chain.Chain([affine_scalar.AffineScalar(shift=diag_shift), + diag_bijector]) + + super(ScaleTriL, self).__init__( + [transform_diagonal.TransformDiagonal(diag_bijector=diag_bijector), + fill_triangular.FillTriangular()], + validate_args=validate_args, + name=name) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sigmoid.py b/tensorflow/contrib/distributions/python/ops/bijectors/sigmoid.py index 5df8c886315ff75cdc884e3b9b4665fb64bb109d..194b318fce31a13f84e7b664b58cebb24fc9a264 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/sigmoid.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/sigmoid.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -31,6 +32,14 @@ __all__ = [ class Sigmoid(bijector.Bijector): """Bijector which computes `Y = g(X) = 1 / (1 + exp(-X))`.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="sigmoid"): super(Sigmoid, self).__init__( forward_min_event_ndims=0, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py index 2a32e8abcde940b0056b0faf2955ec1b3bd71803..241fba2cb7ec33b7b02c1ca79051f1b826d7d2aa 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/sinh_arcsinh.py @@ -26,12 +26,21 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ "SinhArcsinh", ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _sqrtx2p1(x): """Implementation of `sqrt(1 + x**2)` which is stable despite large `x`.""" return array_ops.where( @@ -88,6 +97,14 @@ class SinhArcsinh(bijector.Bijector): `Y approx 0.5 X**tailweight e**(sign(X) skewness * tailweight)`. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, skewness=None, tailweight=None, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py index f52b91550edff7390d8094a4508d862674e85d59..20ee0d340833d5c5275e2ab52a89dcdf7198add1 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -60,6 +61,14 @@ class SoftmaxCentered(bijector.Bijector): makes the (forward) image non-open and the theorem does not directly apply. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="softmax_centered"): diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/softplus.py b/tensorflow/contrib/distributions/python/ops/bijectors/softplus.py index 96a938c803418ff818f9c531754b47ba1eb8667a..3df84ef8b04c2c8f6be91ecd1c972ad1484b4285 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/softplus.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/softplus.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import bijector from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = [ @@ -80,6 +81,14 @@ class Softplus(bijector.Bijector): "hinge_softness": ( "Nonzero floating point `Tensor`. Controls the softness of what " "would otherwise be a kink at the origin. Default is 1.0")}) + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, hinge_softness=None, validate_args=False, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/softsign.py b/tensorflow/contrib/distributions/python/ops/bijectors/softsign.py index b4a658c171b8313358754228aabbfa4bf93fd84d..f96a4bb01de59a21107b9e7c14f929e13e358ac9 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/softsign.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/softsign.py @@ -22,6 +22,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -51,6 +52,14 @@ class Softsign(bijector.Bijector): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="softsign"): super(Softsign, self).__init__( forward_min_event_ndims=0, diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/square.py b/tensorflow/contrib/distributions/python/ops/bijectors/square.py index 2ccfdc95970e387e708603e2614ad29fb6a18db3..294460a80f6209797831ea361e64efe677f71e59 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/square.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/square.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -49,6 +50,14 @@ class Square(bijector.Bijector): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, validate_args=False, name="square"): """Instantiates the `Square` bijector. @@ -81,4 +90,3 @@ class Square(bijector.Bijector): is_valid = check_ops.assert_non_negative( t, message="All elements must be non-negative.") return control_flow_ops.with_dependencies([is_valid], t) - diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/transform_diagonal.py b/tensorflow/contrib/distributions/python/ops/bijectors/transform_diagonal.py new file mode 100644 index 0000000000000000000000000000000000000000..9b7a3b026b8dcc31bed49c489d77b9c184f463cb --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/transform_diagonal.py @@ -0,0 +1,111 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""TransformDiagonal bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.ops import array_ops +from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation + +__all__ = [ + "TransformDiagonal", +] + + +class TransformDiagonal(bijector.Bijector): + """Applies a Bijector to the diagonal of a matrix. + + #### Example + + ```python + b = tfb.TransformDiagonal(diag_bijector=tfb.Exp()) + + b.forward([[1., 0.], + [0., 1.]]) + # ==> [[2.718, 0.], + [0., 2.718]] + ``` + + """ + + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) + def __init__(self, + diag_bijector, + validate_args=False, + name="transform_diagonal"): + """Instantiates the `TransformDiagonal` bijector. + + Args: + diag_bijector: `Bijector` instance used to transform the diagonal. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + """ + self._diag_bijector = diag_bijector + super(TransformDiagonal, self).__init__( + forward_min_event_ndims=2, + inverse_min_event_ndims=2, + validate_args=validate_args, + name=name) + + def _forward(self, x): + diag = self._diag_bijector.forward(array_ops.matrix_diag_part(x)) + return array_ops.matrix_set_diag(x, diag) + + def _inverse(self, y): + diag = self._diag_bijector.inverse(array_ops.matrix_diag_part(y)) + return array_ops.matrix_set_diag(y, diag) + + def _forward_log_det_jacobian(self, x): + # We formulate the Jacobian with respect to the flattened matrices + # `vec(x)` and `vec(y)`. Suppose for notational convenience that + # the first `n` entries of `vec(x)` are the diagonal of `x`, and + # the remaining `n**2-n` entries are the off-diagonals in + # arbitrary order. Then the Jacobian is a block-diagonal matrix, + # with the Jacobian of the diagonal bijector in the first block, + # and the identity Jacobian for the remaining entries (since this + # bijector acts as the identity on non-diagonal entries): + # + # J_vec(x) (vec(y)) = + # ------------------------------- + # | J_diag(x) (diag(y)) 0 | n entries + # | | + # | 0 I | n**2-n entries + # ------------------------------- + # n n**2-n + # + # Since the log-det of the second (identity) block is zero, the + # overall log-det-jacobian is just the log-det of first block, + # from the diagonal bijector. + # + # Note that for elementwise operations (exp, softplus, etc) the + # first block of the Jacobian will itself be a diagonal matrix, + # but our implementation does not require this to be true. + return self._diag_bijector.forward_log_det_jacobian( + array_ops.matrix_diag_part(x), event_ndims=1) + + def _inverse_log_det_jacobian(self, y): + return self._diag_bijector.inverse_log_det_jacobian( + array_ops.matrix_diag_part(y), event_ndims=1) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/weibull.py b/tensorflow/contrib/distributions/python/ops/bijectors/weibull.py index a22560fe80298b762795e7b0e7aea2db55823065..8903a70d98ae144731b12047e5074d0450b59378 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/weibull.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/weibull.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector +from tensorflow.python.util import deprecation __all__ = [ @@ -47,6 +48,14 @@ class Weibull(bijector.Bijector): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, scale=1., concentration=1., diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py index e4944beedcbca09b5eabd4daf1445ce4503b1c80..b349e5966dd750fdf96c0b211dce02658c9400b7 100644 --- a/tensorflow/contrib/distributions/python/ops/binomial.py +++ b/tensorflow/contrib/distributions/python/ops/binomial.py @@ -27,6 +27,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation _binomial_sample_note = """ @@ -42,6 +43,14 @@ to integer values. """ +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _bdtr(k, n, p): """The binomial cumulative distribution function. @@ -130,6 +139,14 @@ class Binomial(distribution.Distribution): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, total_count, logits=None, diff --git a/tensorflow/contrib/distributions/python/ops/cauchy.py b/tensorflow/contrib/distributions/python/ops/cauchy.py index 23b6a83c17d58652001543047febeebabba0c69f..cb5223b0557080e10bf24c3e1cb432f15fd5e7e3 100644 --- a/tensorflow/contrib/distributions/python/ops/cauchy.py +++ b/tensorflow/contrib/distributions/python/ops/cauchy.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution +from tensorflow.python.util import deprecation __all__ = [ "Cauchy", @@ -92,6 +93,14 @@ class Cauchy(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale, diff --git a/tensorflow/contrib/distributions/python/ops/chi2.py b/tensorflow/contrib/distributions/python/ops/chi2.py index 686ae1ba74641e2b7b76667e512fa6453477a8da..e9a7b39070f3d76693ad54852ed0847a0980d2a6 100644 --- a/tensorflow/contrib/distributions/python/ops/chi2.py +++ b/tensorflow/contrib/distributions/python/ops/chi2.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import gamma +from tensorflow.python.util import deprecation __all__ = [ @@ -63,6 +64,14 @@ class Chi2(gamma.Gamma): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, validate_args=False, @@ -114,6 +123,14 @@ class Chi2(gamma.Gamma): class Chi2WithAbsDf(Chi2): """Chi2 with parameter transform `df = floor(abs(df))`.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, validate_args=False, diff --git a/tensorflow/contrib/distributions/python/ops/deterministic.py b/tensorflow/contrib/distributions/python/ops/deterministic.py index c44c76a133817640449ba126bb8ca25abadba5e6..ad853ee293f86565c1af601214522f53d936b70a 100644 --- a/tensorflow/contrib/distributions/python/ops/deterministic.py +++ b/tensorflow/contrib/distributions/python/ops/deterministic.py @@ -32,6 +32,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution +from tensorflow.python.util import deprecation __all__ = [ "Deterministic", @@ -43,6 +44,14 @@ __all__ = [ class _BaseDeterministic(distribution.Distribution): """Base class for Deterministic distributions.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, atol=None, @@ -203,6 +212,14 @@ class Deterministic(_BaseDeterministic): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, atol=None, @@ -308,6 +325,14 @@ class VectorDeterministic(_BaseDeterministic): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, atol=None, diff --git a/tensorflow/contrib/distributions/python/ops/distribution_util.py b/tensorflow/contrib/distributions/python/ops/distribution_util.py index 289e1d50e1146a641c0cc433ece3465aed73b1c2..6959b3e8775d2dd488b4ee3252d143ef376d58f9 100644 --- a/tensorflow/contrib/distributions/python/ops/distribution_util.py +++ b/tensorflow/contrib/distributions/python/ops/distribution_util.py @@ -21,12 +21,19 @@ from __future__ import print_function from tensorflow.contrib import linalg from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib + +# The following two lines are redundant, in a sense. The first enables +# good coding practice *within* this file (`util.prefer_static_value` +# rather than `prefer_static_value`). The second ensures that users +# also get the core utils when they import this file. +from tensorflow.python.ops.distributions import util from tensorflow.python.ops.distributions.util import * # pylint: disable=wildcard-import @@ -484,3 +491,75 @@ def pad_mixture_dimensions(x, mixture_distribution, categorical_distribution, def static_value(x): """Returns the static value of a `Tensor` or `None`.""" return tensor_util.constant_value(ops.convert_to_tensor(x)) + + +def move_dimension(x, source_idx, dest_idx): + """Move a single tensor dimension within its shape. + + This is a special case of `tf.transpose()`, which applies + arbitrary permutations to tensor dimensions. + + Args: + x: Tensor of rank `ndims`. + source_idx: Integer index into `x.shape` (negative indexing is + supported). + dest_idx: Integer index into `x.shape` (negative indexing is + supported). + + Returns: + x_perm: Tensor of rank `ndims`, in which the dimension at original + index `source_idx` has been moved to new index `dest_idx`, with + all other dimensions retained in their original order. + + Example: + + ```python + x = tf.placeholder(shape=[200, 30, 4, 1, 6]) + x_perm = _move_dimension(x, 1, 1) # no-op + x_perm = _move_dimension(x, 0, 3) # result shape [30, 4, 1, 200, 6] + x_perm = _move_dimension(x, 0, -2) # equivalent to previous + x_perm = _move_dimension(x, 4, 2) # result shape [200, 30, 6, 4, 1] + ``` + """ + ndims = util.prefer_static_rank(x) + if isinstance(source_idx, int): + dtype = dtypes.int32 + else: + dtype = dtypes.as_dtype(source_idx.dtype) + + # Handle negative indexing. Since ndims might be dynamic, this makes + # source_idx and dest_idx also possibly dynamic. + if source_idx < 0: + source_idx = ndims + source_idx + if dest_idx < 0: + dest_idx = ndims + dest_idx + + # Construct the appropriate permutation of dimensions, depending + # whether the source is before or after the destination. + def move_left_permutation(): + return util.prefer_static_value( + array_ops.concat([ + math_ops.range(0, dest_idx, dtype=dtype), + [source_idx], + math_ops.range(dest_idx, source_idx, dtype=dtype), + math_ops.range(source_idx+1, ndims, dtype=dtype)], axis=0)) + + def move_right_permutation(): + return util.prefer_static_value( + array_ops.concat([ + math_ops.range(0, source_idx, dtype=dtype), + math_ops.range(source_idx+1, dest_idx+1, dtype=dtype), + [source_idx], + math_ops.range(dest_idx+1, ndims, dtype=dtype)], axis=0)) + + def x_permuted(): + return array_ops.transpose( + x, perm=smart_cond.smart_cond(source_idx < dest_idx, + move_right_permutation, + move_left_permutation)) + + # One final conditional to handle the special case where source + # and destination indices are equal. + return smart_cond.smart_cond(math_ops.equal(source_idx, dest_idx), + lambda: x, + x_permuted) diff --git a/tensorflow/contrib/distributions/python/ops/estimator.py b/tensorflow/contrib/distributions/python/ops/estimator.py index 98edd337fe02ffbf53c6ecd9ebda9424231ea2fe..bdec6527d5378d6e86aa8e6279cc6ee672083e56 100644 --- a/tensorflow/contrib/distributions/python/ops/estimator.py +++ b/tensorflow/contrib/distributions/python/ops/estimator.py @@ -23,6 +23,7 @@ from tensorflow.contrib.learn.python.learn.estimators.head import _RegressionHea from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.util import deprecation __all__ = [ @@ -30,6 +31,14 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def estimator_head_distribution_regression(make_distribution_fn, label_dimension=1, logits_dimension=None, @@ -77,6 +86,14 @@ def estimator_head_distribution_regression(make_distribution_fn, class _DistributionRegressionHead(_RegressionHead): """Creates a _RegressionHead instance from an arbitrary `Distribution`.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, make_distribution_fn, label_dimension, diff --git a/tensorflow/contrib/distributions/python/ops/geometric.py b/tensorflow/contrib/distributions/python/ops/geometric.py index e1e42ee95d200df30c2c8a53a89cb5b7e9c4d17c..d62f024aa2a081f0ec231015af1f26a8851518e9 100644 --- a/tensorflow/contrib/distributions/python/ops/geometric.py +++ b/tensorflow/contrib/distributions/python/ops/geometric.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class Geometric(distribution.Distribution): @@ -55,6 +56,14 @@ class Geometric(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, logits=None, probs=None, diff --git a/tensorflow/contrib/distributions/python/ops/gumbel.py b/tensorflow/contrib/distributions/python/ops/gumbel.py index 9d94fd11c62ce6ecd3d7daee35447bece2b4b2fb..acdea4d61d3ada7e9f4f0aa7bc58c5643db2802b 100644 --- a/tensorflow/contrib/distributions/python/ops/gumbel.py +++ b/tensorflow/contrib/distributions/python/ops/gumbel.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution +from tensorflow.python.util import deprecation class _Gumbel(distribution.Distribution): @@ -96,6 +97,14 @@ class _Gumbel(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale, diff --git a/tensorflow/contrib/distributions/python/ops/half_normal.py b/tensorflow/contrib/distributions/python/ops/half_normal.py index 9c96254d1c0a593b955231132330931ff5f4ad07..b02c4031069191592b8acc1a90313450f98af6d7 100644 --- a/tensorflow/contrib/distributions/python/ops/half_normal.py +++ b/tensorflow/contrib/distributions/python/ops/half_normal.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import special_math +from tensorflow.python.util import deprecation __all__ = [ @@ -85,6 +86,14 @@ class HalfNormal(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, scale, validate_args=False, diff --git a/tensorflow/contrib/distributions/python/ops/independent.py b/tensorflow/contrib/distributions/python/ops/independent.py index cd6eaa8407477b4ed92f169bc0d2d80644d7c956..0672702b96c1eb81c176774554df3f5922a0319e 100644 --- a/tensorflow/contrib/distributions/python/ops/independent.py +++ b/tensorflow/contrib/distributions/python/ops/independent.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib from tensorflow.python.ops.distributions import kullback_leibler +from tensorflow.python.util import deprecation class Independent(distribution_lib.Distribution): @@ -94,6 +95,14 @@ class Independent(distribution_lib.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__( self, distribution, reinterpreted_batch_ndims=None, validate_args=False, name=None): @@ -258,6 +267,14 @@ class Independent(distribution_lib.Distribution): @kullback_leibler.RegisterKL(Independent, Independent) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _kl_independent(a, b, name="kl_independent"): """Batched KL divergence `KL(a || b)` for Independent distributions. diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py index 208057b34db2881b5c9c2adb102d02a87a333007..70d050d7a647b38928ddb1c788db0e6957ac0f03 100644 --- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py +++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py @@ -32,6 +32,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = [ @@ -95,6 +96,14 @@ class InverseGamma(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, concentration, rate, @@ -274,6 +283,14 @@ class InverseGamma(distribution.Distribution): class InverseGammaWithSoftplusConcentrationRate(InverseGamma): """`InverseGamma` with softplus of `concentration` and `rate`.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, concentration, rate, diff --git a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py index 0ff989fc952c6fb3f54dad9a943eb36a0494a3be..e3712dd84e36609d6bba4a5a39866046c0c8d1d8 100644 --- a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py +++ b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import special_math_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.distributions import uniform +from tensorflow.python.util import deprecation __all__ = [ "Kumaraswamy", @@ -40,6 +41,14 @@ _kumaraswamy_sample_note = """Note: `x` must have dtype `self.dtype` and be in `[0, 1].` It must have a shape compatible with `self.batch_shape()`.""" +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _harmonic_number(x): """Compute the harmonic number from its analytic continuation. @@ -123,6 +132,14 @@ class Kumaraswamy(transformed_distribution.TransformedDistribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, concentration1=None, concentration0=None, diff --git a/tensorflow/contrib/distributions/python/ops/logistic.py b/tensorflow/contrib/distributions/python/ops/logistic.py index 27aa863440574eb0cdb5c7ae326e877d472999ad..02e3bad51ee48188acf83cb09359861c9e6932c7 100644 --- a/tensorflow/contrib/distributions/python/ops/logistic.py +++ b/tensorflow/contrib/distributions/python/ops/logistic.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution +from tensorflow.python.util import deprecation class Logistic(distribution.Distribution): @@ -91,6 +92,14 @@ class Logistic(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale, diff --git a/tensorflow/contrib/distributions/python/ops/mixture.py b/tensorflow/contrib/distributions/python/ops/mixture.py index bfb53a06c011cec60cf5b2132e4b1106128a1ece..3b7114ef067c0aaede23fff04c40d1dc6e830f1c 100644 --- a/tensorflow/contrib/distributions/python/ops/mixture.py +++ b/tensorflow/contrib/distributions/python/ops/mixture.py @@ -32,6 +32,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import categorical from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class Mixture(distribution.Distribution): @@ -66,6 +67,14 @@ class Mixture(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, cat, components, diff --git a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py index 112eefd3691815ead19d59bc3aef5909b27ed169..8ffee940d03c9a5204f2ac6f7acd9ea482adae1a 100644 --- a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py +++ b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class MixtureSameFamily(distribution.Distribution): @@ -95,6 +96,14 @@ class MixtureSameFamily(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, mixture_distribution, components_distribution, @@ -321,6 +330,14 @@ class MixtureSameFamily(distribution.Distribution): return x +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _outer_squared_difference(x, y): """Convenience function analogous to tf.squared_difference.""" z = x - y diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag.py b/tensorflow/contrib/distributions/python/ops/mvn_diag.py index d2beb2aff0481eb4ec3a3abbf44fad5efff8eedd..cd0c282ba6cebf784261a4e821f36ce4eed98fe0 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_diag.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_diag.py @@ -22,6 +22,7 @@ from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.contrib.distributions.python.ops import mvn_linear_operator as mvn_linop from tensorflow.python.framework import ops from tensorflow.python.ops import nn +from tensorflow.python.util import deprecation __all__ = [ @@ -134,6 +135,14 @@ class MultivariateNormalDiag( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_diag=None, @@ -218,6 +227,14 @@ class MultivariateNormalDiag( class MultivariateNormalDiagWithSoftplusScale(MultivariateNormalDiag): """MultivariateNormalDiag with `diag_stddev = softplus(diag_stddev)`.""" + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale_diag, diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py index 5117379b047f5e510a8a1a5490ddf76ee93d9d74..d8401801f21afbe8fd042053c6a38a31a2539438 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py @@ -22,6 +22,7 @@ from tensorflow.contrib import linalg from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.contrib.distributions.python.ops import mvn_linear_operator as mvn_linop from tensorflow.python.framework import ops +from tensorflow.python.util import deprecation __all__ = [ @@ -141,6 +142,14 @@ class MultivariateNormalDiagPlusLowRank( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_diag=None, diff --git a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py index 57f47db50c496f1e3e80d8177560b1bab594eb56..dbc4c1b3dc956641f3e38ffafe3a3410bd3e2097 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py @@ -24,6 +24,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import linalg_ops +from tensorflow.python.util import deprecation __all__ = [ @@ -112,6 +113,14 @@ class MultivariateNormalFullCovariance(mvn_tril.MultivariateNormalTriL): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, covariance_matrix=None, diff --git a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py index 6a0383db02555274239ee0b1845f24a705270d84..efe5a6d0d99ca8fa9e0274049423bb3c4eef2d6f 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py @@ -27,6 +27,7 @@ from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.ops.distributions import normal from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.linalg import linalg +from tensorflow.python.util import deprecation __all__ = [ @@ -133,6 +134,14 @@ class MultivariateNormalLinearOperator( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale=None, @@ -266,6 +275,14 @@ class MultivariateNormalLinearOperator( @kullback_leibler.RegisterKL(MultivariateNormalLinearOperator, MultivariateNormalLinearOperator) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _kl_brute_force(a, b, name=None): """Batched KL divergence `KL(a || b)` for multivariate Normals. diff --git a/tensorflow/contrib/distributions/python/ops/mvn_tril.py b/tensorflow/contrib/distributions/python/ops/mvn_tril.py index c809ef3c1cb5b8b9cd892b98d81e57710807d0aa..d9110947ecdbba1a63669573f46db17b02e512ab 100644 --- a/tensorflow/contrib/distributions/python/ops/mvn_tril.py +++ b/tensorflow/contrib/distributions/python/ops/mvn_tril.py @@ -22,6 +22,7 @@ from tensorflow.contrib import linalg from tensorflow.contrib.distributions.python.ops import mvn_linear_operator as mvn_linop from tensorflow.python.framework import ops from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = [ @@ -134,6 +135,14 @@ class MultivariateNormalTriL( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_tril=None, diff --git a/tensorflow/contrib/distributions/python/ops/negative_binomial.py b/tensorflow/contrib/distributions/python/ops/negative_binomial.py index 2bd11e24b315e044624344580108a232d1b6da89..6acfc5746a0cc20e916de81b71f90e08d8d91ad5 100644 --- a/tensorflow/contrib/distributions/python/ops/negative_binomial.py +++ b/tensorflow/contrib/distributions/python/ops/negative_binomial.py @@ -27,6 +27,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class NegativeBinomial(distribution.Distribution): @@ -51,6 +52,14 @@ class NegativeBinomial(distribution.Distribution): * `n!` is the factorial of `n`. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, total_count, logits=None, diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py index 3e44c10fab726ad1299cc852a5e1391fecb8b390..214c6dca4a7f2b4cd6242e1b7ca78be9eeffb851 100644 --- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class OneHotCategorical(distribution.Distribution): @@ -83,6 +84,14 @@ class OneHotCategorical(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__( self, logits=None, @@ -226,13 +235,21 @@ class OneHotCategorical(distribution.Distribution): return x return control_flow_ops.with_dependencies([ check_ops.assert_non_positive(x), - distribution_util.assert_close( + check_ops.assert_near( array_ops.zeros([], dtype=self.dtype), math_ops.reduce_logsumexp(x, axis=[-1])), ], x) @kullback_leibler.RegisterKL(OneHotCategorical, OneHotCategorical) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _kl_categorical_categorical(a, b, name=None): """Calculate the batched KL divergence KL(a || b) with a, b OneHotCategorical. diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py index 04de8106ee0c06f4bc888964e053eb3123f3dab3..3d055085cc7386e57a71aa310458b7666bb9a396 100644 --- a/tensorflow/contrib/distributions/python/ops/poisson.py +++ b/tensorflow/contrib/distributions/python/ops/poisson.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = [ "Poisson", @@ -65,6 +66,14 @@ class Poisson(distribution.Distribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, rate=None, log_rate=None, diff --git a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py index 7b10ba998f0ceac37571524ce858bbd4c87455fe..7a7ad1be35b80ff0f000181ea0778ab282a8220f 100644 --- a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py +++ b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py @@ -33,6 +33,7 @@ from tensorflow.python.ops.distributions import categorical as categorical_lib from tensorflow.python.ops.distributions import distribution as distribution_lib from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.ops.distributions import transformed_distribution as transformed_lib +from tensorflow.python.util import deprecation __all__ = [ @@ -42,6 +43,14 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def quadrature_scheme_lognormal_gauss_hermite( loc, scale, quadrature_size, validate_args=False, name=None): # pylint: disable=unused-argument @@ -85,6 +94,14 @@ def quadrature_scheme_lognormal_gauss_hermite( return grid, probs +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def quadrature_scheme_lognormal_quantiles( loc, scale, quadrature_size, validate_args=False, name=None): @@ -214,6 +231,14 @@ class PoissonLogNormalQuadratureCompound(distribution_lib.Distribution): validate_args=True) """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale, @@ -417,6 +442,14 @@ class PoissonLogNormalQuadratureCompound(distribution_lib.Distribution): axis=[-2, -1]) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def concat_vectors(*args): """Concatenates input vectors, statically if possible.""" args_ = [distribution_util.static_value(x) for x in args] diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py index 5ac6c34b538016af376f53aa5a889e78c1f65f5f..ef3bdfa75fcaa8df17db1238ceadadf788601356 100644 --- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py +++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py @@ -27,10 +27,19 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distributions from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation __all__ = ["QuantizedDistribution"] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def _logsum_expbig_minus_expsmall(big, small): """Stable evaluation of `Log[exp{big} - exp{small}]`. @@ -228,6 +237,14 @@ class QuantizedDistribution(distributions.Distribution): https://arxiv.org/abs/1711.10433 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, distribution, low=None, diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py index 4182ca2b56ea80dba71787b006a1652e0f979694..7e1f64dc425e6a576bfbe1bb456901fddfac26e1 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py @@ -19,15 +19,16 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.distributions.python.ops import logistic +from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import Sigmoid # Bijectors must be directly imported because `remove_undocumented` prevents # individual file imports. -from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import Sigmoid from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class RelaxedBernoulli(transformed_distribution.TransformedDistribution): @@ -131,6 +132,14 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution): Gumbel-Softmax. 2016. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, temperature, logits=None, diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py index 5414f347cd65e2d3327d1934cbc7a91e7f780fc5..25aaac379a7c54c832bdcf962e16f339522d61fc 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class ExpRelaxedOneHotCategorical(distribution.Distribution): @@ -125,6 +126,14 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution): A Continuous Relaxation of Discrete Random Variables. 2016. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__( self, temperature, @@ -290,7 +299,7 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution): return x return control_flow_ops.with_dependencies([ check_ops.assert_non_positive(x), - distribution_util.assert_close( + check_ops.assert_near( array_ops.zeros([], dtype=self.dtype), math_ops.reduce_logsumexp(x, axis=[-1])), ], x) @@ -368,6 +377,14 @@ class RelaxedOneHotCategorical( A Continuous Relaxation of Discrete Random Variables. 2016. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__( self, temperature, diff --git a/tensorflow/contrib/distributions/python/ops/shape.py b/tensorflow/contrib/distributions/python/ops/shape.py index 6a7f28713acefd2285b07a212e2e47a6db1ae5e1..4f348be2806aa3ade7c1ea2a7bc68ca26db6447f 100644 --- a/tensorflow/contrib/distributions/python/ops/shape.py +++ b/tensorflow/contrib/distributions/python/ops/shape.py @@ -27,6 +27,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util import deprecation class _DistributionShape(object): @@ -166,6 +167,14 @@ class _DistributionShape(object): "free," i.e., during graph construction. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, batch_ndims=None, event_ndims=None, diff --git a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py index a764544932cea8a624820153e383595fec9d7fc6..a9d0fb4ccfb1803873f7fe17089f3e7c7f10f4b7 100644 --- a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py +++ b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops.distributions import normal from tensorflow.python.ops.distributions import transformed_distribution +from tensorflow.python.util import deprecation __all__ = [ "SinhArcsinh", @@ -94,6 +95,14 @@ class SinhArcsinh(transformed_distribution.TransformedDistribution): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc, scale, diff --git a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py index 8d4914e16cd3748e81e3d9b3be8b35f64a1c6f0d..ece03fe4aab3cc3046e0958d883ca9388517b94b 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py +++ b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py @@ -40,6 +40,7 @@ from tensorflow.python.ops.linalg import linear_operator_diag as linop_diag_lib from tensorflow.python.ops.linalg import linear_operator_full_matrix as linop_full_lib from tensorflow.python.ops.linalg import linear_operator_identity as linop_identity_lib from tensorflow.python.ops.linalg import linear_operator_lower_triangular as linop_tril_lib +from tensorflow.python.util import deprecation __all__ = [ @@ -49,6 +50,14 @@ __all__ = [ ] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def quadrature_scheme_softmaxnormal_gauss_hermite( normal_loc, normal_scale, quadrature_size, validate_args=False, name=None): @@ -111,6 +120,14 @@ def quadrature_scheme_softmaxnormal_gauss_hermite( return grid, probs +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def quadrature_scheme_softmaxnormal_quantiles( normal_loc, normal_scale, quadrature_size, validate_args=False, name=None): @@ -318,6 +335,14 @@ class VectorDiffeomixture(distribution_lib.Distribution): https://arxiv.org/abs/1801.03080 """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, mix_loc, temperature, @@ -779,6 +804,14 @@ class VectorDiffeomixture(distribution_lib.Distribution): return array_ops.reshape(p, shape=expand_shape) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def maybe_check_quadrature_param(param, name, validate_args): """Helper which checks validity of `loc` and `scale` init args.""" with ops.name_scope(name="check_" + name, values=[param]): @@ -812,6 +845,14 @@ def maybe_check_quadrature_param(param, name, validate_args): return param +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def determine_batch_event_shapes(grid, endpoint_affine): """Helper to infer batch_shape and event_shape.""" with ops.name_scope(name="determine_batch_event_shapes"): @@ -850,6 +891,14 @@ def determine_batch_event_shapes(grid, endpoint_affine): return batch_shape, batch_shape_tensor, event_shape, event_shape_tensor +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def interpolate_loc(grid, loc): """Helper which interpolates between two locs.""" if len(loc) != 2: @@ -876,6 +925,14 @@ def interpolate_loc(grid, loc): return [x[..., k] for k in range(deg)] # list(shape:[B, e]) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def interpolate_scale(grid, scale): """Helper which interpolates between two scales.""" if len(scale) != 2: @@ -892,6 +949,14 @@ def interpolate_scale(grid, scale): ])[0] for q in range(deg)] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def linop_scale(w, op): # We assume w > 0. (This assumption only relates to the is_* attributes.) with ops.name_scope("linop_scale", values=[w]): @@ -927,6 +992,14 @@ def linop_scale(w, op): "Unsupported Linop type ({})".format(type(op).__name__)) +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def concat_vectors(*args): """Concatenates input vectors, statically if possible.""" args_ = [distribution_util.static_value(x) for x in args] @@ -935,6 +1008,14 @@ def concat_vectors(*args): return [val for vec in args_ for val in vec] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def add(x, y): """Adds inputs; interprets `None` as zero.""" if x is None: @@ -944,11 +1025,27 @@ def add(x, y): return x + y +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def vec_osquare(x): """Computes the outer-product of a (batch of) vector, i.e., x.T x.""" return x[..., :, array_ops.newaxis] * x[..., array_ops.newaxis, :] +@deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def softmax(x, axis, name=None): """Equivalent to tf.nn.softmax but works around b/70297725.""" with ops.name_scope(name, "softmax", [x, axis]): diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py index a75b3f3df1f2867f214f47051fa358b79a52a35e..73356a3625c9a1aa15af5b6c1cf2ccb0c514b39a 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py +++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.contrib.distributions.python.ops import vector_exponential_linear_operator as vector_exponential_linop from tensorflow.python.framework import ops +from tensorflow.python.util import deprecation __all__ = [ @@ -116,6 +117,14 @@ class VectorExponentialDiag( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_diag=None, diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py index a7d4c55be93f6190ae4d6976030190f27dcfe48f..9a47b4855763a25b484ad04a3415d191f19256f7 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py +++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import exponential from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.linalg import linalg +from tensorflow.python.util import deprecation __all__ = ["VectorExponentialLinearOperator"] @@ -138,6 +139,14 @@ class VectorExponentialLinearOperator( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale=None, diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py index 4a53e7a621f27382d2995798f724392d34459670..e68ddc569c95ff63760b4b2f6d7a92f17240a558 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py +++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.contrib.distributions.python.ops import vector_laplace_linear_operator as vector_laplace_linop from tensorflow.python.framework import ops +from tensorflow.python.util import deprecation __all__ = [ @@ -151,6 +152,14 @@ class VectorLaplaceDiag( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_diag=None, diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py index 0566e04fece6f9ca0de6903ce5c424eccbc003cd..3923161a332a77e4eaab8d65d96fd8c278c872ec 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py +++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import laplace from tensorflow.python.ops.distributions import transformed_distribution from tensorflow.python.ops.linalg import linalg +from tensorflow.python.util import deprecation __all__ = [ @@ -154,6 +155,14 @@ class VectorLaplaceLinearOperator( """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale=None, diff --git a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py index bb33cd0762a368eb7e53f1623ede9231e80f0b14..49ffff24caec8d6c525f65f06796d10548d5ec40 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py +++ b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops.distributions import normal from tensorflow.python.ops.distributions import transformed_distribution +from tensorflow.python.util import deprecation __all__ = [ "VectorSinhArcsinhDiag", @@ -95,6 +96,14 @@ class VectorSinhArcsinhDiag(transformed_distribution.TransformedDistribution): ``` """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, loc=None, scale_diag=None, diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py index 21f84dcbdea8b422dd45fadeac1bb8b2804c551f..f289b39e51aff36780541a0545ed9e6cfe21dd4e 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py +++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops.distributions import student_t from tensorflow.python.ops.distributions import transformed_distribution +from tensorflow.python.util import deprecation class _VectorStudentT(transformed_distribution.TransformedDistribution): @@ -121,6 +122,14 @@ class _VectorStudentT(transformed_distribution.TransformedDistribution): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, loc=None, diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py index 88d4280759da7ca685056f4d41cf8dc51393c9f3..f1accaaa4c920344608015c792a2c3606de1337f 100644 --- a/tensorflow/contrib/distributions/python/ops/wishart.py +++ b/tensorflow/contrib/distributions/python/ops/wishart.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.distributions import distribution +from tensorflow.python.util import deprecation __all__ = [ "WishartCholesky", @@ -73,6 +74,14 @@ class _WishartLinearOperator(distribution.Distribution): this class. """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, scale_operator, @@ -501,6 +510,14 @@ class WishartCholesky(_WishartLinearOperator): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, scale, @@ -617,6 +634,14 @@ class WishartFull(_WishartLinearOperator): """ + @deprecation.deprecated( + "2018-10-01", + "The TensorFlow Distributions library has moved to " + "TensorFlow Probability " + "(https://github.com/tensorflow/probability). You " + "should update all references to use `tfp.distributions` " + "instead of `tf.contrib.distributions`.", + warn_once=True) def __init__(self, df, scale, diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md index 4384431e7b9c3e6ef259391fa9efa5a35d23c86a..86d203452e24d6d73f3ebb17b989867905a61382 100644 --- a/tensorflow/contrib/eager/README.md +++ b/tensorflow/contrib/eager/README.md @@ -44,7 +44,7 @@ Installation instructions at https://www.tensorflow.org/install/ For an introduction to eager execution in TensorFlow, see: -- [User Guide](https://www.tensorflow.org/programmers_guide/eager) ([source](../../docs_src/programmers_guide/eager.md)) +- [User Guide](https://www.tensorflow.org/guide/eager) ([source](../../docs_src/guide/eager.md)) - Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb) - Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb) - Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb) diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index adf92c27ea0a27c5741bcdd175b277462cb28d02..58c548d798178a2848006cbf301f7d5cb2143f24 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -102,6 +102,7 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): with ops.device(self._device): self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long string_arg=iter_string_handle, + output_types=self._flat_output_types, f=remote_fn, target_device=target, buffer_size=10, diff --git a/tensorflow/contrib/eager/python/examples/BUILD b/tensorflow/contrib/eager/python/examples/BUILD index 1d9371c7ac405dbf0ec40210270b90f2cf9b9a25..12155a459c29c353c57679c407e7dda25047a35c 100644 --- a/tensorflow/contrib/eager/python/examples/BUILD +++ b/tensorflow/contrib/eager/python/examples/BUILD @@ -11,8 +11,12 @@ py_library( "//tensorflow/contrib/eager/python/examples/l2hmc:neural_nets", "//tensorflow/contrib/eager/python/examples/linear_regression", "//tensorflow/contrib/eager/python/examples/resnet50", + "//tensorflow/contrib/eager/python/examples/revnet", + "//tensorflow/contrib/eager/python/examples/revnet:config", "//tensorflow/contrib/eager/python/examples/rnn_colorbot", "//tensorflow/contrib/eager/python/examples/rnn_ptb", + "//tensorflow/contrib/eager/python/examples/sagan", + "//tensorflow/contrib/eager/python/examples/sagan:config", "//tensorflow/contrib/eager/python/examples/spinn:data", ], ) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..54ebcad8e929c3195099121a290dd7c0651e5c9f --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -0,0 +1,909 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "nmt_with_attention.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [ + { + "file_id": "1C4fpM7_7IL8ZzF7Gc5abywqQjeQNS2-U", + "timestamp": 1527858391290 + }, + { + "file_id": "1pExo6aUuw0S6MISFWoinfJv0Ftm9V4qv", + "timestamp": 1527776041613 + } + ], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "AOpGoE2T-YXS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Neural Machine Translation with Attention\n", + "\n", + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on Github
" + ] + }, + { + "metadata": { + "id": "CiwtNgENbx2g", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example that assumes some knowledge of sequence to sequence models.\n", + "\n", + "After training the model in this notebook, you will be able to input a Spanish sentence, such as *\"Āætodavia estan en casa?\"*, and return the English translation: *\"are you still at home?\"*\n", + "\n", + "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", + "\n", + "\"spanish-english\n", + "\n", + "Note: This example takes approximately 10 mintues to run on a single P100 GPU." + ] + }, + { + "metadata": { + "id": "tnxXKDjq3jEL", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow >= 1.9 and enable eager execution\n", + "import tensorflow as tf\n", + "\n", + "tf.enable_eager_execution()\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import unicodedata\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import time\n", + "\n", + "print(tf.__version__)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wfodePkj3jEa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Download and prepare the dataset\n", + "\n", + "We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:\n", + "\n", + "```\n", + "May I borrow this book?\tĀæPuedo tomar prestado este libro?\n", + "```\n", + "\n", + "There are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:\n", + "\n", + "1. Add a *start* and *end* token to each sentence.\n", + "2. Clean the sentences by removing special characters.\n", + "3. Create a word index and reverse word index (dictionaries mapping from word ā†’ id and id ā†’ word).\n", + "4. Pad each sentence to a maximum length." + ] + }, + { + "metadata": { + "id": "kRVATYOgJs1b", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Download the file\n", + "path_to_zip = tf.keras.utils.get_file(\n", + " 'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip', \n", + " extract=True)\n", + "\n", + "path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rd0jw-eC3jEh", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Converts the unicode file to ascii\n", + "def unicode_to_ascii(s):\n", + " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", + " if unicodedata.category(c) != 'Mn')\n", + "\n", + "\n", + "def preprocess_sentence(w):\n", + " w = unicode_to_ascii(w.lower().strip())\n", + " \n", + " # creating a space between a word and the punctuation following it\n", + " # eg: \"he is a boy.\" => \"he is a boy .\" \n", + " # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n", + " w = re.sub(r\"([?.!,Āæ])\", r\" \\1 \", w)\n", + " w = re.sub(r'[\" \"]+', \" \", w)\n", + " \n", + " # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n", + " w = re.sub(r\"[^a-zA-Z?.!,Āæ]+\", \" \", w)\n", + " \n", + " w = w.rstrip().strip()\n", + " \n", + " # adding a start and an end token to the sentence\n", + " # so that the model know when to start and stop predicting.\n", + " w = ' ' + w + ' '\n", + " return w" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OHn4Dct23jEm", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# 1. Remove the accents\n", + "# 2. Clean the sentences\n", + "# 3. Return word pairs in the format: [ENGLISH, SPANISH]\n", + "def create_dataset(path, num_examples):\n", + " lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n", + " \n", + " word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n", + " \n", + " return word_pairs" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9xbqO7Iie9bb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# This class creates a word -> index mapping (e.g,. \"dad\" -> 5) and vice-versa \n", + "# (e.g., 5 -> \"dad\") for each language,\n", + "class LanguageIndex():\n", + " def __init__(self, lang):\n", + " self.lang = lang\n", + " self.word2idx = {}\n", + " self.idx2word = {}\n", + " self.vocab = set()\n", + " \n", + " self.create_index()\n", + " \n", + " def create_index(self):\n", + " for phrase in self.lang:\n", + " self.vocab.update(phrase.split(' '))\n", + " \n", + " self.vocab = sorted(self.vocab)\n", + " \n", + " self.word2idx[''] = 0\n", + " for index, word in enumerate(self.vocab):\n", + " self.word2idx[word] = index + 1\n", + " \n", + " for word, index in self.word2idx.items():\n", + " self.idx2word[index] = word" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eAY9k49G3jE_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def max_length(tensor):\n", + " return max(len(t) for t in tensor)\n", + "\n", + "\n", + "def load_dataset(path, num_examples):\n", + " # creating cleaned input, output pairs\n", + " pairs = create_dataset(path, num_examples)\n", + "\n", + " # index language using the class defined above \n", + " inp_lang = LanguageIndex(sp for en, sp in pairs)\n", + " targ_lang = LanguageIndex(en for en, sp in pairs)\n", + " \n", + " # Vectorize the input and target languages\n", + " \n", + " # Spanish sentences\n", + " input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]\n", + " \n", + " # English sentences\n", + " target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]\n", + " \n", + " # Calculate max_length of input and output tensor\n", + " # Here, we'll set those to the longest sentence in the dataset\n", + " max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)\n", + " \n", + " # Padding the input and output tensor to the maximum length\n", + " input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor, \n", + " maxlen=max_length_inp,\n", + " padding='post')\n", + " \n", + " target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor, \n", + " maxlen=max_length_tar, \n", + " padding='post')\n", + " \n", + " return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "GOi42V79Ydlr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Limit the size of the dataset to experiment faster (optional)\n", + "\n", + "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" + ] + }, + { + "metadata": { + "id": "cnxC7q-j3jFD", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Try experimenting with the size of that dataset\n", + "num_examples = 30000\n", + "input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4QILQkOs3jFG", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Creating training and validation sets using an 80-20 split\n", + "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", + "\n", + "# Show length\n", + "len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rgCLkfv5uO3d", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Create a tf.data dataset" + ] + }, + { + "metadata": { + "id": "TqHsArVZ3jFS", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "BUFFER_SIZE = len(input_tensor_train)\n", + "BATCH_SIZE = 64\n", + "embedding_dim = 256\n", + "units = 1024\n", + "vocab_inp_size = len(inp_lang.word2idx)\n", + "vocab_tar_size = len(targ_lang.word2idx)\n", + "\n", + "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", + "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TNfHIF71ulLu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Write the encoder and decoder model\n", + "\n", + "Here, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", + "\n", + "\"attention\n", + "\n", + "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n", + "\n", + "Here are the equations that are implemented:\n", + "\n", + "\"attention\n", + "\"attention\n", + "\n", + "We're using *Bahdanau attention*. Lets decide on notation before writing the simplified form:\n", + "\n", + "* FC = Fully connected (dense) layer\n", + "* EO = Encoder output\n", + "* H = hidden state\n", + "* X = input to the decoder\n", + "\n", + "And the pseudo-code:\n", + "\n", + "* `score = FC(tanh(FC(EO) + FC(H)))`\n", + "* `attention weights = softmax(score, axis = 1)`. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. `Max_length` is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + "* `context vector = sum(attention weights * EO, axis = 1)`. Same reason as above for choosing axis as 1.\n", + "* `embedding output` = The input to the decoder X is passed through an embedding layer.\n", + "* `merged vector = concat(embedding output, context vector)`\n", + "* This merged vector is then given to the GRU\n", + " \n", + "The shapes of all the vectors at each step have been specified in the comments in the code:" + ] + }, + { + "metadata": { + "id": "avyJ_4VIUoHb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def gru(units):\n", + " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", + " # the code automatically does that.\n", + " if tf.test.is_gpu_available():\n", + " return tf.keras.layers.CuDNNGRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " return tf.keras.layers.GRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "nZ2rI24i3jFg", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Encoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n", + " super(Encoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.enc_units = enc_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.enc_units)\n", + " \n", + " def call(self, x, hidden):\n", + " x = self.embedding(x)\n", + " output, state = self.gru(x, initial_state = hidden) \n", + " return output, state\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.enc_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yJ_B3mhW3jFk", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Decoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n", + " super(Decoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.dec_units = dec_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.dec_units)\n", + " self.fc = tf.keras.layers.Dense(vocab_size)\n", + " \n", + " # used for attention\n", + " self.W1 = tf.keras.layers.Dense(self.dec_units)\n", + " self.W2 = tf.keras.layers.Dense(self.dec_units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + " \n", + " def call(self, x, hidden, enc_output):\n", + " # enc_output shape == (batch_size, max_length, hidden_size)\n", + " \n", + " # hidden shape == (batch_size, hidden size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n", + " # we are doing this to perform addition to calculate the score\n", + " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", + " \n", + " # score shape == (batch_size, max_length, hidden_size)\n", + " score = tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n", + " \n", + " # attention_weights shape == (batch_size, max_length, 1)\n", + " # we get 1 at the last axis because we are applying score to self.V\n", + " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", + " \n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * enc_output\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + " \n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + " \n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + " \n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + " \n", + " # output shape == (batch_size * max_length, hidden_size)\n", + " output = tf.reshape(output, (-1, output.shape[2]))\n", + " \n", + " # output shape == (batch_size * max_length, vocab)\n", + " x = self.fc(output)\n", + " \n", + " return x, state, attention_weights\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.dec_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "P5UY8wko3jFp", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\n", + "decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_ch_71VbIRfK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Define the optimizer and the loss function" + ] + }, + { + "metadata": { + "id": "WmTHr5iV3jFr", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "optimizer = tf.train.AdamOptimizer()\n", + "\n", + "\n", + "def loss_function(real, pred):\n", + " mask = 1 - np.equal(real, 0)\n", + " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", + " return tf.reduce_mean(loss_)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hpObfY22IddU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Training\n", + "\n", + "1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.\n", + "2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.\n", + "3. The decoder returns the *predictions* and the *decoder hidden state*.\n", + "4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "5. Use *teacher forcing* to decide the next input to the decoder.\n", + "6. *Teacher forcing* is the technique where the *target word* is passed as the *next input* to the decoder.\n", + "7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate." + ] + }, + { + "metadata": { + "id": "ddefjBMa3jF0", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "EPOCHS = 10\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + " \n", + " hidden = encoder.initialize_hidden_state()\n", + " total_loss = 0\n", + " \n", + " for (batch, (inp, targ)) in enumerate(dataset):\n", + " loss = 0\n", + " \n", + " with tf.GradientTape() as tape:\n", + " enc_output, enc_hidden = encoder(inp, hidden)\n", + " \n", + " dec_hidden = enc_hidden\n", + " \n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']] * BATCH_SIZE, 1) \n", + " \n", + " # Teacher forcing - feeding the target as the next input\n", + " for t in range(1, targ.shape[1]):\n", + " # passing enc_output to the decoder\n", + " predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n", + " \n", + " loss += loss_function(targ[:, t], predictions)\n", + " \n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(targ[:, t], 1)\n", + " \n", + " total_loss += (loss / int(targ.shape[1]))\n", + " \n", + " variables = encoder.variables + decoder.variables\n", + " \n", + " gradients = tape.gradient(loss, variables)\n", + " \n", + " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", + "\n", + " if batch % 100 == 0:\n", + " print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n", + " batch,\n", + " loss.numpy() / int(targ.shape[1])))\n", + " \n", + " print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n", + " total_loss/len(input_tensor)))\n", + " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mU3Ce8M6I3rz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Translate\n", + "\n", + "* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the *end token*.\n", + "* And store the *attention weights for every time step*.\n", + "\n", + "Note: The encoder output is calculated only once for one input." + ] + }, + { + "metadata": { + "id": "EbQpyYs13jF_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " attention_plot = np.zeros((max_length_targ, max_length_inp))\n", + " \n", + " sentence = preprocess_sentence(sentence)\n", + "\n", + " inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]\n", + " inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')\n", + " inputs = tf.convert_to_tensor(inputs)\n", + " \n", + " result = ''\n", + "\n", + " hidden = [tf.zeros((1, units))]\n", + " enc_out, enc_hidden = encoder(inputs, hidden)\n", + "\n", + " dec_hidden = enc_hidden\n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']], 0)\n", + "\n", + " for t in range(max_length_targ):\n", + " predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)\n", + " \n", + " # storing the attention weigths to plot later on\n", + " attention_weights = tf.reshape(attention_weights, (-1, ))\n", + " attention_plot[t] = attention_weights.numpy()\n", + "\n", + " predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n", + "\n", + " result += targ_lang.idx2word[predicted_id] + ' '\n", + "\n", + " if targ_lang.idx2word[predicted_id] == '':\n", + " return result, sentence, attention_plot\n", + " \n", + " # the predicted ID is fed back into the model\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " return result, sentence, attention_plot" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "s5hQWlbN3jGF", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# function for plotting the attention weights\n", + "def plot_attention(attention, sentence, predicted_sentence):\n", + " fig = plt.figure(figsize=(10,10))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " ax.matshow(attention, cmap='viridis')\n", + " \n", + " fontdict = {'fontsize': 14}\n", + " \n", + " ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n", + " ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n", + "\n", + " plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sl9zUHzg3jGI", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n", + " \n", + " print('Input: {}'.format(sentence))\n", + " print('Predicted translation: {}'.format(result))\n", + " \n", + " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", + " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WrAM0FDomq3E", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zSx2iM36EZQZ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('esta es mi vida.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A3LLCx3ZE0Ls", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('Āætodavia estan en casa?', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DUQVLVqUE1YW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# wrong translation\n", + "translate('trata de averiguarlo.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RTe5P5ioMJwN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Next steps\n", + "\n", + "* [Download a different dataset](http://www.manythings.org/anki/) to experiment with translations, for example, English to German, or English to French.\n", + "* Experiment with training on a larger dataset, or using more epochs\n" + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb index bfcc7feb075c403d024772e0d715339d58877a51..d268cbcd9171b0f4a4f2ab27ad958374e521685b 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb @@ -9,7 +9,7 @@ "source": [ "# Eager Execution Tutorial: Importing Data\n", "\n", - "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/programmers_guide/datasets) to build pipelines to feed data to your program. It covers:\n", + "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n", "\n", "* Creating a `Dataset`.\n", "* Iteration over a `Dataset` with eager execution enabled.\n", @@ -18,7 +18,7 @@ "\n", "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n", "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n", - "As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled." + "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled." ] }, { @@ -63,7 +63,7 @@ "source": [ "# Step 1: Create a source `Dataset`\n", "\n", - "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [Programmer's Guide](https://www.google.com/url?sa=D\u0026q=https%3A%2F%2Fwww.tensorflow.org%2Fprogrammers_guide%2Fdatasets%23reading_input_data) for more information." + "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information." ] }, { diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb index 4fe3a0e3f3d431684973a9251aa3d92bf2010444..5749f22ac58e0a012ed7e3fec4dfe2913d3f8273 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb @@ -68,7 +68,7 @@ "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensionss is often unnecessary, as it can be inferred\n", + "# The number of input dimensions is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" @@ -267,7 +267,7 @@ " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." ] }, { diff --git a/tensorflow/contrib/eager/python/examples/resnet50/BUILD b/tensorflow/contrib/eager/python/examples/resnet50/BUILD index 0c0e28dd95c68dc300384a128eb5aa2208f63a0d..68a84d5fbb4f13e4ebe0d71e3f5caebe97e2101c 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/BUILD +++ b/tensorflow/contrib/eager/python/examples/resnet50/BUILD @@ -51,5 +51,6 @@ cuda_py_test( "noasan", "nomsan", "notsan", + "optonly", ], ) diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..81c9facfb5f00c45c8f26c1cd4284b98fb73dd23 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -0,0 +1,115 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +# Model +py_library( + name = "ops", + srcs = ["ops.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "config", + srcs = ["config.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "blocks", + srcs = ["blocks.py"], + srcs_version = "PY2AND3", + deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "revnet", + srcs = ["revnet.py"], + srcs_version = "PY2AND3", + deps = [ + ":blocks", + "//tensorflow:tensorflow_py", + ], +) + +# Tests +cuda_py_test( + name = "ops_test", + size = "large", + srcs = ["ops_test.py"], + additional_deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +cuda_py_test( + name = "blocks_test", + size = "large", + srcs = ["blocks_test.py"], + additional_deps = [ + ":blocks", + "//tensorflow:tensorflow_py", + ], + tags = [ + "optonly", + ], +) + +cuda_py_test( + name = "revnet_test", + size = "large", + srcs = ["revnet_test.py"], + additional_deps = [ + ":blocks_test", + ":config", + ":revnet", + "//tensorflow:tensorflow_py", + ], + tags = [ + "no_pip", + "optonly", + ], +) + +# Training +py_library( + name = "cifar_input", + srcs = ["cifar_input.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_binary( + name = "cifar_tfrecords", + srcs = ["cifar_tfrecords.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_binary( + name = "main", + srcs = ["main.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":config", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/README.md b/tensorflow/contrib/eager/python/examples/revnet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..21fc44febc8abdc30daad1b35d8434b083360bdf --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/README.md @@ -0,0 +1,45 @@ +# RevNet with TensorFlow eager execution + +This folder contains an TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran both in eager and graph mode. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the step of reconstructing the outputs. This saves us from using `tf.stop_gradient` and makes the model run faster. + +## Content + +- `revnet.py`: The RevNet model. +- `blocks.py`: The relevant reversible blocks. +- `cifar_tfrecords.py`: Script to generate the TFRecords for both CIFAR-10 and CIFAR-100. +- `cifar_input.py`: Script to read from TFRecords and generate dataset objects with the `tf.data` API. +- `config.py`: Configuration file for network architectures and training hyperparameters. +- `main.py`: Main training and evaluation script. +- `ops.py`: Auxiliary downsampling operation. + +## To run +- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly` +or `tf-nightly-gpu` pip package in order to access the eager execution feature. + +- First run + +```bash +python cifar_tfrecords.py --data_dir ${PWD}/cifar +``` +to download the cifar dataset and convert them +to TFRecords. This produces TFRecord files for both CIFAR-10 and CIFAR-100. + +- To train a model run + +```bash +python main.py --data_dir ${PWD}/cifar +``` + +- Optional arguments for `main.py` include + - `train_dir`: Directory to store eventfiles and checkpoints. + - `restore`: Restore the latest checkpoint. + - `validate`: Use validation set for training monitoring. + - `manual_grad`: Use the manually defined gradient map given by the authors. + - `dataset`: Use either `cifar-10` or `cifar-100` + +## Performance +- With the current implementation, RevNet-38 achieves >92% on CIFAR-10 and >71% on CIFAR-100. + +## Reference +The Reversible Residual Network: Backpropagation Without Storing Activations. +Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse. Neural Information Processing Systems (NIPS), 2017. diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..306096e9f8c4da0ed7f893ae75067cd24e7274b1 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -0,0 +1,357 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Building blocks with manual backward gradient computation. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import ops + + +class RevBlock(tf.keras.Model): + """Single reversible block containing several `_Residual` blocks. + + Each `_Residual` block in turn contains two _ResidualInner blocks, + corresponding to the `F`/`G` functions in the paper. + """ + + def __init__(self, + n_res, + filters, + strides, + input_shape, + batch_norm_first=False, + data_format="channels_first", + bottleneck=False, + fused=True, + dtype=tf.float32): + """Initialize RevBlock. + + Args: + n_res: number of residual blocks + filters: list/tuple of integers for output filter sizes of each residual + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(RevBlock, self).__init__() + self.blocks = tf.contrib.checkpoint.List() + for i in range(n_res): + curr_batch_norm_first = batch_norm_first and i == 0 + curr_strides = strides if i == 0 else (1, 1) + block = _Residual( + filters, + curr_strides, + input_shape, + batch_norm_first=curr_batch_norm_first, + data_format=data_format, + bottleneck=bottleneck, + fused=fused, + dtype=dtype) + self.blocks.append(block) + + if data_format == "channels_first": + input_shape = (filters, input_shape[1] // curr_strides[0], + input_shape[2] // curr_strides[1]) + else: + input_shape = (input_shape[0] // curr_strides[0], + input_shape[1] // curr_strides[1], filters) + + def call(self, h, training=True): + """Apply reversible block to inputs.""" + + for block in self.blocks: + h = block(h, training=training) + return h + + def backward_grads_and_vars(self, x, y, dy, training=True): + """Apply reversible block backward to outputs.""" + + grads_all = [] + vars_all = [] + + for i in reversed(range(len(self.blocks))): + block = self.blocks[i] + if i == 0: + # First block usually contains downsampling that can't be reversed + with tf.GradientTape() as tape: + x = tf.identity(x) + tape.watch(x) + y = block(x, training=training) + + grads_combined = tape.gradient( + y, [x] + block.trainable_variables, output_gradients=dy) + dy = grads_combined[0] + grads_all += grads_combined[1:] + vars_all += block.trainable_variables + else: + y, dy, grads, vars_ = block.backward_grads_and_vars( + y, dy, training=training) + grads_all += grads + vars_all += vars_ + + return dy, grads_all, vars_all + + +class _Residual(tf.keras.Model): + """Single residual block contained in a _RevBlock. Each `_Residual` object has + two _ResidualInner objects, corresponding to the `F` and `G` functions in the + paper. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + bottleneck=False, + fused=True, + dtype=tf.float32): + super(_Residual, self).__init__() + + self.filters = filters + self.strides = strides + self.axis = 1 if data_format == "channels_first" else 3 + if data_format == "channels_first": + f_input_shape = (input_shape[0] // 2,) + input_shape[1:] + g_input_shape = (filters // 2, input_shape[1] // strides[0], + input_shape[2] // strides[1]) + else: + f_input_shape = input_shape[:2] + (input_shape[2] // 2,) + g_input_shape = (input_shape[0] // strides[0], + input_shape[1] // strides[1], filters // 2) + + factory = _BottleneckResidualInner if bottleneck else _ResidualInner + self.f = factory( + filters=filters // 2, + strides=strides, + input_shape=f_input_shape, + batch_norm_first=batch_norm_first, + data_format=data_format, + fused=fused, + dtype=dtype) + self.g = factory( + filters=filters // 2, + strides=(1, 1), + input_shape=g_input_shape, + batch_norm_first=batch_norm_first, + data_format=data_format, + fused=fused, + dtype=dtype) + + def call(self, x, training=True, concat=True): + """Apply residual block to inputs.""" + + x1, x2 = tf.split(x, num_or_size_splits=2, axis=self.axis) + f_x2 = self.f(x2, training=training) + x1_down = ops.downsample( + x1, self.filters // 2, self.strides, axis=self.axis) + x2_down = ops.downsample( + x2, self.filters // 2, self.strides, axis=self.axis) + y1 = f_x2 + x1_down + g_y1 = self.g(y1, training=training) + y2 = g_y1 + x2_down + if not concat: # For correct backward grads + return y1, y2 + + return tf.concat([y1, y2], axis=self.axis) + + def backward_grads_and_vars(self, y, dy, training=True): + """Manually compute backward gradients given input and output grads.""" + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) + + with tf.GradientTape(persistent=True) as tape: + y = tf.identity(y) + tape.watch(y) + y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) + z1 = y1 + gz1 = self.g(z1, training=training) + x2 = y2 - gz1 + fx2 = self.f(x2, training=training) + x1 = z1 - fx2 + + grads_combined = tape.gradient( + gz1, [z1] + self.g.trainable_variables, output_gradients=dy2) + dz1 = dy1 + grads_combined[0] + dg = grads_combined[1:] + dx1 = dz1 + + grads_combined = tape.gradient( + fx2, [x2] + self.f.trainable_variables, output_gradients=dz1) + dx2 = dy2 + grads_combined[0] + df = grads_combined[1:] + + del tape + + grads = df + dg + vars_ = self.f.trainable_variables + self.g.trainable_variables + + x = tf.concat([x1, x2], axis=self.axis) + dx = tf.concat([dx1, dx2], axis=self.axis) + + return x, dx, grads, vars_ + + +def _BottleneckResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Single bottleneck residual inner function contained in _Resdual. + + Corresponds to the `F`/`G` functions in the paper. + Suitable for training on ImageNet dataset. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + + Returns: + A keras model + """ + + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + return model + + +def _ResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Single residual inner function contained in _ResdualBlock. + + Corresponds to the `F`/`G` functions in the paper. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + + Returns: + A keras model + """ + + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + return model diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d74785c8fe1c170ee95172974141c1cfe18b9502 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py @@ -0,0 +1,304 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic building blocks used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import blocks + + +def compute_degree(g1, g2, eps=1e-7): + """Compute the degree between two vectors using their usual inner product.""" + + def _dot(u, v): + return tf.reduce_sum(u * v) + + g1_norm = tf.sqrt(_dot(g1, g1)) + g2_norm = tf.sqrt(_dot(g2, g2)) + if g1_norm.numpy() == 0 and g2_norm.numpy() == 0: + cosine = 1. - eps + else: + g1_norm = 1. if g1_norm.numpy() == 0 else g1_norm + g2_norm = 1. if g2_norm.numpy() == 0 else g2_norm + cosine = _dot(g1, g2) / g1_norm / g2_norm + # Restrict to arccos range + cosine = tf.minimum(tf.maximum(cosine, eps - 1.), 1. - eps) + degree = tf.acos(cosine) * 180. / 3.141592653589793 + + return degree + + +def _validate_block_call_channels_last(block_factory, test): + """Generic testing function for `channels_last` data format. + + Completes a set of tests varying data format, stride, and batch normalization + configured train vs test time. + Args: + block_factory: constructor of one of blocks.InitBlock, blocks.FinalBlock, + blocks._ResidualInner + test: tf.test.TestCase object + """ + with tf.device("/cpu:0"): # NHWC format + input_shape = (8, 8, 128) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + block = block_factory( + filters=128, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 8, 8, 128)) + test.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = block_factory( + filters=128, + strides=(2, 2), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 4, 4, 128)) + test.assertNotAllClose(y_tr, y_ev) + + +def _validate_block_call_channels_first(block_factory, test): + """Generic testing function for `channels_first` data format. + + Completes a set of tests varying data format, stride, and batch normalization + configured train vs test time. + Args: + block_factory: constructor of one of blocks.InitBlock, blocks.FinalBlock, + blocks._ResidualInner + test: tf.test.TestCase object + """ + if not tf.test.is_gpu_available(): + test.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (128, 8, 8) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride of 1 + block = block_factory(filters=128, strides=(1, 1), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 128, 8, 8)) + test.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = block_factory(filters=128, strides=(2, 2), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 128, 4, 4)) + test.assertNotAllClose(y_tr, y_ev) + + +class RevBlockTest(tf.test.TestCase): + + def test_call_channels_first(self): + """Test `call` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (128, 8, 8) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride of 1 + block = blocks.RevBlock( + n_res=3, filters=128, strides=(1, 1), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 128, 8, 8)) + self.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = blocks.RevBlock( + n_res=3, filters=128, strides=(2, 2), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, [16, 128, 4, 4]) + self.assertNotAllClose(y_tr, y_ev) + + def test_call_channels_last(self): + """Test `call` function with `channels_last` data format.""" + with tf.device("/cpu:0"): # NHWC format + input_shape = (8, 8, 128) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + block = blocks.RevBlock( + n_res=3, + filters=128, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 8, 8, 128)) + self.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = blocks.RevBlock( + n_res=3, + filters=128, + strides=(2, 2), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 4, 4, 128)) + self.assertNotAllClose(y_tr, y_ev) + + def _check_grad_angle(self, grads, grads_true, atol=1e0): + """Check the angle between two list of vectors are all close.""" + for g1, g2 in zip(grads, grads_true): + degree = compute_degree(g1, g2) + self.assertLessEqual(degree, atol) + + def test_backward_grads_and_vars_channels_first(self): + """Test `backward` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + # Stride 1 + input_shape = (128, 8, 8) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy = tf.random_normal(shape=data_shape, dtype=tf.float64) + block = blocks.RevBlock( + n_res=3, + filters=128, + strides=(1, 1), + input_shape=input_shape, + fused=False, + dtype=tf.float64) + with tf.GradientTape() as tape: + tape.watch(x) + y = block(x, training=True) + # Compute grads from reconstruction + dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + # Compute true grads + grads = tape.gradient(y, [x] + vars_, output_gradients=dy) + dx_true, dw_true = grads[0], grads[1:] + self.assertAllClose(dx_true, dx) + self.assertAllClose(dw_true, dw) + self._check_grad_angle(dx_true, dx) + self._check_grad_angle(dw_true, dw) + + # Stride 2 + x = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy = tf.random_normal(shape=(16, 128, 4, 4), dtype=tf.float64) + block = blocks.RevBlock( + n_res=3, + filters=128, + strides=(2, 2), + input_shape=input_shape, + fused=False, + dtype=tf.float64) + with tf.GradientTape() as tape: + tape.watch(x) + y = block(x, training=True) + # Compute grads from reconstruction + dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + # Compute true grads + grads = tape.gradient(y, [x] + vars_, output_gradients=dy) + dx_true, dw_true = grads[0], grads[1:] + self.assertAllClose(dx_true, dx) + self.assertAllClose(dw_true, dw) + self._check_grad_angle(dx_true, dx) + self._check_grad_angle(dw_true, dw) + + +class _ResidualTest(tf.test.TestCase): + + def test_call(self): + """Test `call` function. + + Varying downsampling and data format options. + """ + + _validate_block_call_channels_first(blocks._Residual, self) + _validate_block_call_channels_last(blocks._Residual, self) + + def test_backward_grads_and_vars_channels_first(self): + """Test `backward_grads` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (128, 8, 8) + data_shape = (16,) + input_shape + # Use double precision for testing + x_true = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy = tf.random_normal(shape=data_shape, dtype=tf.float64) + residual = blocks._Residual( + filters=128, + strides=(1, 1), + input_shape=input_shape, + fused=False, + dtype=tf.float64) + + with tf.GradientTape() as tape: + x_true = tf.identity(x_true) + tape.watch(x_true) + y = residual(x_true, training=True) + + # Gradients computed due to reversibility + x, dx, dw, vars_ = residual.backward_grads_and_vars( + y, dy=dy, training=True) + + # True gradients computed by the tape + grads = tape.gradient(y, [x_true] + vars_, output_gradients=dy) + dx_true, dw_true = grads[0], grads[1:] + + self.assertAllClose(x_true, x) + self.assertAllClose(dx_true, dx) + self.assertAllClose(dw_true, dw) + + +class _ResidualInnerTest(tf.test.TestCase): + + def test_call(self): + """Test `call` function.""" + + _validate_block_call_channels_first(blocks._ResidualInner, self) + _validate_block_call_channels_last(blocks._ResidualInner, self) + + +class _BottleneckResidualInner(tf.test.TestCase): + + def test_call(self): + """Test `call` function.""" + + _validate_block_call_channels_first(blocks._BottleneckResidualInner, self) + _validate_block_call_channels_last(blocks._BottleneckResidualInner, self) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py new file mode 100644 index 0000000000000000000000000000000000000000..b6d4c35bfd21f9d651c4f059c019cf2e585da8b2 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -0,0 +1,116 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script for reading and loading CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import tensorflow as tf + +# Global constants describing the CIFAR data set. +IMAGE_HEIGHT = 32 +IMAGE_WIDTH = 32 +NUM_CHANNEL = 3 + + +def get_ds_from_tfrecords(data_dir, + split, + data_aug=True, + batch_size=100, + epochs=None, + shuffle=True, + data_format="channels_first", + num_parallel_calls=12, + prefetch=0, + div255=True, + dtype=tf.float32): + """Returns a tf.train.Dataset object from reading tfrecords. + + Args: + data_dir: Directory of tfrecords + split: "train", "validation", or "test" + data_aug: Apply data augmentation if True + batch_size: Batch size of dataset object + epochs: Number of epochs to repeat the dataset; default `None` means + repeating indefinitely + shuffle: Shuffle the dataset if True + data_format: `channels_first` or `channels_last` + num_parallel_calls: Number of threads for dataset preprocess + prefetch: Buffer size for prefetch + div255: Divide the images by 255 if True + dtype: Data type of images + Returns: + A tf.train.Dataset object + + Raises: + ValueError: Unknown split + """ + + if split not in ["train", "validation", "test", "train_all"]: + raise ValueError("Unknown split {}".format(split)) + + def _parser(serialized_example): + """Parses a single tf.Example into image and label tensors.""" + features = tf.parse_single_example( + serialized_example, + features={ + "image": tf.FixedLenFeature([], tf.string), + "label": tf.FixedLenFeature([], tf.int64), + }) + image = tf.decode_raw(features["image"], tf.uint8) + # Initially reshaping to [H, W, C] does not work + image = tf.reshape(image, [NUM_CHANNEL, IMAGE_HEIGHT, IMAGE_WIDTH]) + # This is needed for `tf.image.resize_image_with_crop_or_pad` + image = tf.transpose(image, [1, 2, 0]) + + image = tf.cast(image, dtype) + label = tf.cast(features["label"], tf.int32) + + if data_aug: + image = tf.image.resize_image_with_crop_or_pad(image, IMAGE_HEIGHT + 4, + IMAGE_WIDTH + 4) + image = tf.random_crop(image, [IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNEL]) + image = tf.image.random_flip_left_right(image) + + if data_format == "channels_first": + image = tf.transpose(image, [2, 0, 1]) + + if div255: + image /= 255. + + return image, label + + filename = os.path.join(data_dir, split + ".tfrecords") + dataset = tf.data.TFRecordDataset(filename) + dataset = dataset.repeat(epochs) + dataset = dataset.map(_parser, num_parallel_calls=num_parallel_calls) + dataset = dataset.prefetch(prefetch) + + if shuffle: + # Find the right size according to the split + size = { + "train": 40000, + "validation": 10000, + "test": 10000, + "train_all": 50000 + }[split] + dataset = dataset.shuffle(size) + + dataset = dataset.batch(batch_size) + + return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py new file mode 100644 index 0000000000000000000000000000000000000000..377844ad8fbca92629a4d71f5df2aab67b570c3c --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py @@ -0,0 +1,154 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Read CIFAR data from pickled numpy arrays and writes TFRecords. + +Generates tf.train.Example protos and writes them to TFRecord files from the +python version of the CIFAR dataset downloaded from +https://www.cs.toronto.edu/~kriz/cifar.html. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import tarfile + +from absl import flags +from six.moves import cPickle as pickle +from six.moves import urllib +import tensorflow as tf + +BASE_URL = 'https://www.cs.toronto.edu/~kriz/' +CIFAR_FILE_NAMES = ['cifar-10-python.tar.gz', 'cifar-100-python.tar.gz'] +CIFAR_DOWNLOAD_URLS = [BASE_URL + name for name in CIFAR_FILE_NAMES] +CIFAR_LOCAL_FOLDERS = ['cifar-10', 'cifar-100'] +EXTRACT_FOLDERS = ['cifar-10-batches-py', 'cifar-100-python'] + + +def download_and_extract(data_dir, file_name, url): + """Download CIFAR if not already downloaded.""" + filepath = os.path.join(data_dir, file_name) + if tf.gfile.Exists(filepath): + return filepath + if not tf.gfile.Exists(data_dir): + tf.gfile.MakeDirs(data_dir) + + urllib.request.urlretrieve(url, filepath) + tarfile.open(os.path.join(filepath), 'r:gz').extractall(data_dir) + return filepath + + +def _int64_feature(value): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +def _get_file_names(folder): + """Returns the file names expected to exist in the input_dir.""" + assert folder in ['cifar-10', 'cifar-100'] + + file_names = {} + if folder == 'cifar-10': + file_names['train'] = ['data_batch_%d' % i for i in range(1, 5)] + file_names['validation'] = ['data_batch_5'] + file_names['train_all'] = ['data_batch_%d' % i for i in range(1, 6)] + file_names['test'] = ['test_batch'] + else: + file_names['train_all'] = ['train'] + file_names['test'] = ['test'] + # Split in `convert_to_tfrecord` function + file_names['train'] = ['train'] + file_names['validation'] = ['train'] + return file_names + + +def read_pickle_from_file(filename): + with tf.gfile.Open(filename, 'rb') as f: + if sys.version_info >= (3, 0): + data_dict = pickle.load(f, encoding='bytes') + else: + data_dict = pickle.load(f) + return data_dict + + +def convert_to_tfrecord(input_files, output_file, folder): + """Converts files with pickled data to TFRecords.""" + assert folder in ['cifar-10', 'cifar-100'] + + print('Generating %s' % output_file) + with tf.python_io.TFRecordWriter(output_file) as record_writer: + for input_file in input_files: + data_dict = read_pickle_from_file(input_file) + data = data_dict[b'data'] + try: + labels = data_dict[b'labels'] + except KeyError: + labels = data_dict[b'fine_labels'] + + if folder == 'cifar-100' and input_file.endswith('train.tfrecords'): + data = data[:40000] + labels = labels[:40000] + elif folder == 'cifar-100' and input_file.endswith( + 'validation.tfrecords'): + data = data[40000:] + labels = labels[40000:] + + num_entries_in_batch = len(labels) + + for i in range(num_entries_in_batch): + example = tf.train.Example( + features=tf.train.Features( + feature={ + 'image': _bytes_feature(data[i].tobytes()), + 'label': _int64_feature(labels[i]) + })) + record_writer.write(example.SerializeToString()) + + +def main(_): + for file_name, url, folder, extract_folder in zip( + CIFAR_FILE_NAMES, CIFAR_DOWNLOAD_URLS, CIFAR_LOCAL_FOLDERS, + EXTRACT_FOLDERS): + print('Download from {} and extract.'.format(url)) + data_dir = os.path.join(FLAGS.data_dir, folder) + download_and_extract(data_dir, file_name, url) + file_names = _get_file_names(folder) + input_dir = os.path.join(data_dir, extract_folder) + + for mode, files in file_names.items(): + input_files = [os.path.join(input_dir, f) for f in files] + output_file = os.path.join(data_dir, mode + '.tfrecords') + try: + os.remove(output_file) + except OSError: + pass + convert_to_tfrecord(input_files, output_file, folder) + + print('Done!') + + +if __name__ == '__main__': + FLAGS = flags.FLAGS + flags.DEFINE_string( + 'data_dir', + default=None, + help='Directory to download, extract and store TFRecords.') + + tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3d93fa955a29718fdec52b04500c41f77351dd8d --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -0,0 +1,140 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Configuration in format of tf.contrib.training.HParams. +Supports CIFAR-10, CIFAR-100, and ImageNet datasets. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +tfe = tf.contrib.eager + + +def get_hparams_cifar_38(): + """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" + + config = tf.contrib.training.HParams() + config.add_hparam("init_filters", 32) + config.add_hparam("init_kernel", 3) + config.add_hparam("init_stride", 1) + config.add_hparam("n_classes", 10) + config.add_hparam("n_rev_blocks", 3) + config.add_hparam("n_res", [3, 3, 3]) + config.add_hparam("filters", [32, 64, 112]) + config.add_hparam("strides", [1, 2, 2]) + config.add_hparam("batch_size", 100) + config.add_hparam("bottleneck", False) + config.add_hparam("fused", True) + config.add_hparam("init_max_pool", False) + if tfe.num_gpus() > 0: + config.add_hparam("input_shape", (3, 32, 32)) + config.add_hparam("data_format", "channels_first") + else: + config.add_hparam("input_shape", (32, 32, 3)) + config.add_hparam("data_format", "channels_last") + + # Training details + config.add_hparam("weight_decay", 2e-4) + config.add_hparam("momentum", .9) + config.add_hparam("lr_decay_steps", [40000, 60000]) + config.add_hparam("lr_list", [1e-1, 1e-2, 1e-3]) + config.add_hparam("max_train_iter", 80000) + config.add_hparam("seed", 1234) + config.add_hparam("shuffle", True) + config.add_hparam("log_every", 500) + config.add_hparam("save_every", 500) + config.add_hparam("dtype", tf.float32) + config.add_hparam("eval_batch_size", 1000) + config.add_hparam("div255", True) + # This is imprecise, when training with validation set, + # we only have 40k images in training data + config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) + + return config + + +def get_hparams_cifar_110(): + config = get_hparams_cifar_38() + config.filters = [32, 64, 128] + config.n_res = [9, 9, 9] + + return config + + +def get_hparams_cifar_164(): + config = get_hparams_cifar_38() + config.filters = [32, 64, 128] + config.n_res = [9, 9, 9] + config.use_bottleneck = True + # Due to bottleneck residual blocks + filters = [f * 4 for f in config.filters] + config.filters = filters + + return config + + +def get_hparams_imagenet_56(): + """RevNet-56 configurations for ImageNet.""" + + config = tf.contrib.training.HParams() + config.add_hparam("init_filters", 128) + config.add_hparam("init_kernel", 7) + config.add_hparam("init_stride", 2) + config.add_hparam("n_classes", 1000) + config.add_hparam("n_rev_blocks", 4) + config.add_hparam("n_res", [2, 2, 2, 2]) + config.add_hparam("filters", [128, 256, 512, 832]) + config.add_hparam("strides", [1, 2, 2, 2]) + config.add_hparam("batch_size", 16) + config.add_hparam("bottleneck", True) + config.add_hparam("fused", True) + config.add_hparam("init_max_pool", True) + if tf.test.is_gpu_available(): + config.add_hparam("input_shape", (3, 224, 224)) + config.add_hparam("data_format", "channels_first") + else: + config.add_hparam("input_shape", (224, 224, 3)) + config.add_hparam("data_format", "channels_last") + + # Training details + config.add_hparam("weight_decay", 1e-4) + config.add_hparam("momentum", .9) + config.add_hparam("lr_decay_steps", [160000, 320000, 480000]) + config.add_hparam("lr_list", [1e-1, 1e-2, 1e-3, 1e-4]) + config.add_hparam("max_train_iter", 600000) + config.add_hparam("seed", 1234) + config.add_hparam("shuffle", True) + config.add_hparam("log_every", 50) + config.add_hparam("save_every", 50) + config.add_hparam("dtype", tf.float32) + config.add_hparam("eval_batch_size", 1000) + config.add_hparam("div255", True) + # TODO(lxuechen): Update this according to ImageNet data + config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) + # Due to bottleneck residual blocks + filters = [f * 4 for f in config.filters] + config.filters = filters + + return config diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py new file mode 100644 index 0000000000000000000000000000000000000000..e2f43b03f90ef6db01db1f85943e10ce8c9b582a --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -0,0 +1,256 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Eager execution workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import config as config_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +tfe = tf.contrib.eager + + +def main(_): + """Eager execution workflow with RevNet trained on CIFAR-10.""" + config = get_config() + ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(config) + model = revnet.RevNet(config=config) + global_step = tf.train.get_or_create_global_step() # Ensure correct summary + global_step.assign(1) + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + checkpointer = tf.train.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=global_step) + + if FLAGS.train_dir: + summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) + if FLAGS.restore: + latest_path = tf.train.latest_checkpoint(FLAGS.train_dir) + checkpointer.restore(latest_path) + print("Restored latest checkpoint at path:\"{}\" " + "with global_step: {}".format(latest_path, global_step.numpy())) + sys.stdout.flush() + + if FLAGS.manual_grad: + print("Using manual gradients.") + else: + print("Not using manual gradients.") + sys.stdout.flush() + + for x, y in ds_train: + train_one_iter(model, x, y, optimizer, global_step=global_step) + + if global_step.numpy() % config.log_every == 0: + it_train = ds_train_one_shot.make_one_shot_iterator() + it_test = ds_test.make_one_shot_iterator() + acc_train, loss_train = evaluate(model, it_train) + acc_test, loss_test = evaluate(model, it_test) + + if FLAGS.validate: + it_validation = ds_validation.make_one_shot_iterator() + acc_validation, loss_validation = evaluate(model, it_validation) + print("Iter {}, " + "training set accuracy {:.4f}, loss {:.4f}; " + "validation set accuracy {:.4f}, loss {:4.f}" + "test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_train, loss_train, acc_validation, + loss_validation, acc_test, loss_test)) + else: + print("Iter {}, " + "training set accuracy {:.4f}, loss {:.4f}; " + "test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_train, loss_train, acc_test, + loss_test)) + sys.stdout.flush() + + if FLAGS.train_dir: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Training accuracy", acc_train) + tf.contrib.summary.scalar("Test accuracy", acc_test) + tf.contrib.summary.scalar("Training loss", loss_train) + tf.contrib.summary.scalar("Test loss", loss_test) + if FLAGS.validate: + tf.contrib.summary.scalar("Validation accuracy", acc_validation) + tf.contrib.summary.scalar("Validation loss", loss_validation) + + if global_step.numpy() % config.save_every == 0 and FLAGS.train_dir: + saved_path = checkpointer.save( + file_prefix=os.path.join(FLAGS.train_dir, "ckpt")) + print("Saved checkpoint at path: \"{}\" " + "with global_step: {}".format(saved_path, global_step.numpy())) + sys.stdout.flush() + + +def get_config(): + """Return configuration.""" + print("Config: {}".format(FLAGS.config)) + sys.stdout.flush() + config = { + "revnet-38": config_.get_hparams_cifar_38(), + "revnet-110": config_.get_hparams_cifar_110(), + "revnet-164": config_.get_hparams_cifar_164(), + }[FLAGS.config] + + if FLAGS.dataset == "cifar-100": + config.n_classes = 100 + + return config + + +def get_datasets(config): + """Return dataset.""" + if FLAGS.data_dir is None: + raise ValueError("No supplied data directory") + if not os.path.exists(FLAGS.data_dir): + raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) + if FLAGS.dataset not in ["cifar-10", "cifar-100"]: + raise ValueError("Unknown dataset {}".format(FLAGS.dataset)) + + print("Training on {} dataset.".format(FLAGS.dataset)) + sys.stdout.flush() + data_dir = os.path.join(FLAGS.data_dir, FLAGS.dataset) + if FLAGS.validate: + # 40k Training set + ds_train = cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split="train", + data_aug=True, + batch_size=config.batch_size, + epochs=config.epochs, + shuffle=config.shuffle, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.batch_size) + # 10k Training set + ds_validation = cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split="validation", + data_aug=False, + batch_size=config.eval_batch_size, + epochs=1, + shuffle=False, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.eval_batch_size) + else: + # 50k Training set + ds_train = cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split="train_all", + data_aug=True, + batch_size=config.batch_size, + epochs=config.epochs, + shuffle=config.shuffle, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.batch_size) + ds_validation = None + + # Always compute loss and accuracy on whole training and test set + ds_train_one_shot = cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split="train_all", + data_aug=False, + batch_size=config.eval_batch_size, + epochs=1, + shuffle=False, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.eval_batch_size) + + ds_test = cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split="test", + data_aug=False, + batch_size=config.eval_batch_size, + epochs=1, + shuffle=False, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.eval_batch_size) + + return ds_train, ds_train_one_shot, ds_validation, ds_test + + +def train_one_iter(model, inputs, labels, optimizer, global_step=None): + """Train for one iteration.""" + if FLAGS.manual_grad: + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + else: # For correctness validation + with tf.GradientTape() as tape: + logits, _ = model(inputs, training=True) + loss = model.compute_loss(logits=logits, labels=labels) + tf.logging.info("Logits are placed on device: {}".format(logits.device)) + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) + + return loss.numpy() + + +def evaluate(model, iterator): + """Compute accuracy with the given dataset iterator.""" + mean_loss = tfe.metrics.Mean() + accuracy = tfe.metrics.Accuracy() + for x, y in iterator: + logits, _ = model(x, training=False) + loss = model.compute_loss(logits=logits, labels=y) + accuracy( + labels=tf.cast(y, tf.int64), + predictions=tf.argmax(logits, axis=1, output_type=tf.int64)) + mean_loss(loss) + + return accuracy.result().numpy(), mean_loss.result().numpy() + + +if __name__ == "__main__": + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_boolean( + "restore", + default=False, + help="[Optional] Restore the latest checkpoint from `train_dir` if True") + flags.DEFINE_boolean( + "validate", + default=False, + help="[Optional] Use the validation set or not for hyperparameter search") + flags.DEFINE_boolean( + "manual_grad", + default=False, + help="[Optional] Use manual gradient graph to save memory") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_string( + "config", default="revnet-38", help="[Optional] Architecture of network.") + FLAGS = flags.FLAGS + tf.enable_eager_execution() + tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/ops.py b/tensorflow/contrib/eager/python/examples/revnet/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..9ed5d363e6c8bffd817357c006abee7ac0d1dbba --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/ops.py @@ -0,0 +1,70 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Customized basic operations. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + + +def downsample(x, filters, strides, axis=1): + """Downsample feature map with avg pooling, if filter size doesn't match.""" + + def pad_strides(strides, axis=1): + """Convert length 2 to length 4 strides. + + Needed since `tf.layers.Conv2D` uses length 2 strides, whereas operations + such as `tf.nn.avg_pool` use length 4 strides. + + Args: + strides: length 2 list/tuple strides for height and width + axis: integer specifying feature dimension according to data format + Returns: + length 4 strides padded with 1 on batch and channel dimension + """ + + assert len(strides) == 2 + + if axis == 1: + return [1, 1, strides[0], strides[1]] + return [1, strides[0], strides[1], 1] + + assert len(x.shape) == 4 and (axis == 1 or axis == 3) + + data_format = "NCHW" if axis == 1 else "NHWC" + strides_ = pad_strides(strides, axis=axis) + + if strides[0] > 1: + x = tf.nn.avg_pool( + x, strides_, strides_, padding="VALID", data_format=data_format) + + in_filter = x.shape[axis] + out_filter = filters + + if in_filter < out_filter: + pad_size = [(out_filter - in_filter) // 2, (out_filter - in_filter) // 2] + if axis == 1: + x = tf.pad(x, [[0, 0], pad_size, [0, 0], [0, 0]]) + else: + x = tf.pad(x, [[0, 0], [0, 0], [0, 0], pad_size]) + # In case `tape.gradient(x, [x])` produces a list of `None` + return x + 0. diff --git a/tensorflow/contrib/eager/python/examples/revnet/ops_test.py b/tensorflow/contrib/eager/python/examples/revnet/ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc2641faf5a5d26262de683e52e36b1f42b3a7b --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/ops_test.py @@ -0,0 +1,80 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic ops used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import ops +tfe = tf.contrib.eager + + +class OpsTest(tf.test.TestCase): + + def test_downsample(self): + """Test `possible_down_sample` function with mock object.""" + + batch_size = 100 + # NHWC format + x = tf.random_normal(shape=[batch_size, 32, 32, 3]) + # HW doesn't change but number of features increased + y = ops.downsample(x, filters=5, strides=(1, 1), axis=3) + self.assertEqual(y.shape, [batch_size, 32, 32, 5]) + # Feature map doesn't change but HW reduced + y = ops.downsample(x, filters=3, strides=(2, 2), axis=3) + self.assertEqual(y.shape, [batch_size, 16, 16, 3]) + # Number of feature increased and HW reduced + y = ops.downsample(x, filters=5, strides=(2, 2), axis=3) + self.assertEqual(y.shape, [batch_size, 16, 16, 5]) + + # Test gradient flow + x = tf.random_normal(shape=[batch_size, 32, 32, 3]) + with tfe.GradientTape() as tape: + tape.watch(x) + y = ops.downsample(x, filters=3, strides=(1, 1)) + self.assertEqual(y.shape, x.shape) + dy = tf.random_normal(shape=[batch_size, 3, 32, 32]) + grad, = tape.gradient(y, [x], output_gradients=[dy]) + self.assertEqual(grad.shape, x.shape) + + # Default NCHW format + if tf.test.is_gpu_available(): + x = tf.random_normal(shape=[batch_size, 3, 32, 32]) + # HW doesn't change but feature map reduced + y = ops.downsample(x, filters=5, strides=(1, 1)) + self.assertEqual(y.shape, [batch_size, 5, 32, 32]) + # Feature map doesn't change but HW reduced + y = ops.downsample(x, filters=3, strides=(2, 2)) + self.assertEqual(y.shape, [batch_size, 3, 16, 16]) + # Both feature map and HW reduced + y = ops.downsample(x, filters=5, strides=(2, 2)) + self.assertEqual(y.shape, [batch_size, 5, 16, 16]) + + # Test gradient flow + x = tf.random_normal(shape=[batch_size, 3, 32, 32]) + with tfe.GradientTape() as tape: + tape.watch(x) + y = ops.downsample(x, filters=3, strides=(1, 1)) + self.assertEqual(y.shape, x.shape) + dy = tf.random_normal(shape=[batch_size, 3, 32, 32]) + grad, = tape.gradient(y, [x], output_gradients=[dy]) + self.assertEqual(grad.shape, x.shape) + + +if __name__ == '__main__': + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py new file mode 100644 index 0000000000000000000000000000000000000000..af0d20fa729836b12036d5d54a9b5b0b68d719d2 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -0,0 +1,301 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Code for main model. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import operator + +import six +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import blocks + + +class RevNet(tf.keras.Model): + """RevNet that depends on all the blocks.""" + + def __init__(self, config): + """Initialize RevNet with building blocks. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(RevNet, self).__init__() + self.axis = 1 if config.data_format == "channels_first" else 3 + self.config = config + + self._init_block = self._construct_init_block() + self._block_list = self._construct_intermediate_blocks() + self._final_block = self._construct_final_block() + + def _construct_init_block(self): + init_block = tf.keras.Sequential( + [ + tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape, + dtype=self.config.dtype), + tf.keras.layers.BatchNormalization( + axis=self.axis, + fused=self.config.fused, + dtype=self.config.dtype), + tf.keras.layers.Activation("relu"), + ], + name="init") + if self.config.init_max_pool: + init_block.add( + tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format, + dtype=self.config.dtype)) + return init_block + + def _construct_final_block(self): + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + + final_block = tf.keras.Sequential( + [ + tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused, + dtype=self.config.dtype), + tf.keras.layers.Activation("relu"), + tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format, dtype=self.config.dtype), + tf.keras.layers.Dense( + self.config.n_classes, dtype=self.config.dtype) + ], + name="final") + return final_block + + def _construct_intermediate_blocks(self): + # Precompute input shape after initial block + stride = self.config.init_stride + if self.config.init_max_pool: + stride *= 2 + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (self.config.init_filters, w // stride, h // stride) + else: + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // stride, h // stride, self.config.init_filters) + + # Aggregate intermediate blocks + block_list = tf.contrib.checkpoint.List() + for i in range(self.config.n_rev_blocks): + # RevBlock configurations + n_res = self.config.n_res[i] + filters = self.config.filters[i] + if filters % 2 != 0: + raise ValueError("Number of output filters must be even to ensure" + "correct partitioning of channels") + stride = self.config.strides[i] + strides = (self.config.strides[i], self.config.strides[i]) + + # Add block + rev_block = blocks.RevBlock( + n_res, + filters, + strides, + input_shape, + batch_norm_first=(i != 0), # Only skip on first block + data_format=self.config.data_format, + bottleneck=self.config.bottleneck, + fused=self.config.fused, + dtype=self.config.dtype) + block_list.append(rev_block) + + # Precompute input shape for the next block + if self.config.data_format == "channels_first": + w, h = input_shape[1], input_shape[2] + input_shape = (filters, w // stride, h // stride) + else: + w, h = input_shape[0], input_shape[1] + input_shape = (w // stride, h // stride, filters) + + return block_list + + def call(self, inputs, training=True): + """Forward pass.""" + + if training: + saved_hidden = [inputs] + + h = self._init_block(inputs, training=training) + if training: + saved_hidden.append(h) + + for block in self._block_list: + h = block(h, training=training) + if training: + saved_hidden.append(h) + + logits = self._final_block(h, training=training) + + return (logits, saved_hidden) if training else (logits, None) + + def compute_loss(self, logits, labels): + """Compute cross entropy loss.""" + + if self.config.dtype == tf.float32 or self.config.dtype == tf.float16: + cross_ent = tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=labels) + else: + # `sparse_softmax_cross_entropy_with_logits` does not have a GPU kernel + # for float64, int32 pairs + labels = tf.one_hot( + labels, depth=self.config.n_classes, axis=1, dtype=self.config.dtype) + cross_ent = tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=labels) + + return tf.reduce_mean(cross_ent) + + def compute_gradients(self, inputs, labels, training=True, l2_reg=True): + """Manually computes gradients. + + When eager execution is enabled, this method also SILENTLY updates the + running averages of batch normalization when `training` is set to True. + + Args: + inputs: Image tensor, either NHWC or NCHW, conforming to `data_format` + labels: One-hot labels for classification + training: Use the mini-batch stats in batch norm if set to True + l2_reg: Apply l2 regularization + + Returns: + list of tuples each being (grad, var) for optimizer to use + """ + + # Run forward pass to record hidden states; avoid updating running averages + vars_and_vals = self.get_moving_stats() + _, saved_hidden = self.call(inputs, training=training) + self.restore_moving_stats(vars_and_vals) + + grads_all = [] + vars_all = [] + + # Manually backprop through last block + x = saved_hidden[-1] + with tf.GradientTape() as tape: + x = tf.identity(x) + tape.watch(x) + # Running stats updated below + logits = self._final_block(x, training=training) + loss = self.compute_loss(logits, labels) + + grads_combined = tape.gradient(loss, + [x] + self._final_block.trainable_variables) + dy, grads_ = grads_combined[0], grads_combined[1:] + grads_all += grads_ + vars_all += self._final_block.trainable_variables + + # Manually backprop through intermediate blocks + for block in reversed(self._block_list): + y = saved_hidden.pop() + x = saved_hidden[-1] + dy, grads, vars_ = block.backward_grads_and_vars( + x, y, dy, training=training) + grads_all += grads + vars_all += vars_ + + # Manually backprop through first block + saved_hidden.pop() + x = saved_hidden.pop() + assert not saved_hidden # Cleared after backprop + + with tf.GradientTape() as tape: + x = tf.identity(x) + # Running stats updated below + y = self._init_block(x, training=training) + + grads_all += tape.gradient( + y, self._init_block.trainable_variables, output_gradients=dy) + vars_all += self._init_block.trainable_variables + + # Apply weight decay + if l2_reg: + grads_all = self._apply_weight_decay(grads_all, vars_all) + + return grads_all, vars_all, loss + + def _apply_weight_decay(self, grads, vars_): + """Update gradients to reflect weight decay.""" + # Don't decay bias + return [ + g + self.config.weight_decay * v if v.name.endswith("kernel:0") else g + for g, v in zip(grads, vars_) + ] + + def get_moving_stats(self): + """Get moving averages of batch normalization. + + This is needed to avoid updating the running average twice in one iteration. + + Returns: + A dictionary mapping variables for batch normalization moving averages + to their current values. + """ + vars_and_vals = {} + + def _is_moving_var(v): + n = v.name + return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") + + for v in filter(_is_moving_var, self.variables): + vars_and_vals[v] = v.read_value() + + return vars_and_vals + + def restore_moving_stats(self, vars_and_vals): + """Restore moving averages of batch normalization. + + This is needed to avoid updating the running average twice in one iteration. + + Args: + vars_and_vals: The dictionary mapping variables to their previous values. + """ + for var_, val in six.iteritems(vars_and_vals): + var_.assign(val) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py new file mode 100644 index 0000000000000000000000000000000000000000..b2ac4b67c926951672996df5564b9b57def0ea13 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -0,0 +1,332 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic building blocks used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gc +import time + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import blocks_test +from tensorflow.contrib.eager.python.examples.revnet import config as config_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +from tensorflow.python.client import device_lib +tfe = tf.contrib.eager + + +def train_one_iter(model, inputs, labels, optimizer, global_step=None): + """Train for one iteration.""" + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + + return loss + + +class RevNetTest(tf.test.TestCase): + + def setUp(self): + super(RevNetTest, self).setUp() + config = config_.get_hparams_cifar_38() + # Reconstruction could cause numerical error, use double precision for tests + config.dtype = tf.float64 + config.fused = False # Fused batch norm does not support tf.float64 + shape = (config.batch_size,) + config.input_shape + self.model = revnet.RevNet(config=config) + self.x = tf.random_normal(shape=shape, dtype=tf.float64) + self.t = tf.random_uniform( + shape=[config.batch_size], + minval=0, + maxval=config.n_classes, + dtype=tf.int64) + self.config = config + + def tearDown(self): + del self.model + del self.x + del self.t + del self.config + super(RevNetTest, self).tearDown() + + def test_call(self): + """Test `call` function.""" + + y, _ = self.model(self.x, training=False) + self.assertEqual(y.shape, [self.config.batch_size, self.config.n_classes]) + + def _check_grad_angle_combined(self, grads, grads_true): + """Verify that the reconstructed gradients has correct direction. + + Due to numerical imprecision, the magnitude may be slightly different. + Yet according to the paper, the angle should be roughly the same. + + Args: + grads: list of gradients from reconstruction + grads_true: list of true gradients + """ + + def _combine(gs): + return [tf.reshape(g, [-1]) for g in gs] + + g1_all = tf.concat(_combine(grads), axis=0) + g2_all = tf.concat(_combine(grads_true), axis=0) + + self.assertEqual(len(g1_all.shape), 1) + self.assertEqual(len(g2_all.shape), 1) + + degree = blocks_test.compute_degree(g1_all, g2_all) + self.assertLessEqual(degree, 1e0) + + def test_compute_gradients(self): + """Test `compute_gradients` function.""" + self.model(self.x, training=False) # Initialize model + grads, vars_, loss = self.model.compute_gradients( + inputs=self.x, labels=self.t, training=True, l2_reg=True) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + self.assertEqual(len(grads), len(vars_)) + for grad, var in zip(grads, vars_): + self.assertEqual(grad.shape, var.shape) + + # Compare against the true gradient computed by the tape + with tf.GradientTape() as tape: + logits, _ = self.model(self.x, training=True) + loss_true = self.model.compute_loss(logits=logits, labels=self.t) + grads_true = tape.gradient(loss_true, vars_) + self.assertAllClose(loss, loss_true) + self.assertAllClose(grads, grads_true, rtol=1e-4, atol=1e-4) + self._check_grad_angle_combined(grads, grads_true) + + def test_call_defun(self): + """Test `call` function with defun.""" + y, _ = tfe.defun(self.model.call)(self.x, training=False) + self.assertEqual(y.shape, [self.config.batch_size, self.config.n_classes]) + + def test_compute_gradients_defun(self): + """Test `compute_gradients` function with defun.""" + compute_gradients = tfe.defun(self.model.compute_gradients) + grads, vars_, _ = compute_gradients(self.x, self.t, training=True) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + self.assertEqual(len(grads), len(vars_)) + for grad, var in zip(grads, vars_): + if grad is not None: + self.assertEqual(grad.shape, var.shape) + + def test_training_graph(self): + """Test model training in graph mode.""" + with tf.Graph().as_default(): + config = config_.get_hparams_cifar_38() + x = tf.random_normal( + shape=(self.config.batch_size,) + self.config.input_shape) + t = tf.random_uniform( + shape=(self.config.batch_size,), + minval=0, + maxval=self.config.n_classes, + dtype=tf.int32) + global_step = tfe.Variable(0., trainable=False) + model = revnet.RevNet(config=config) + model(x) + updates = model.get_updates_for(x) + + x_ = tf.identity(x) + grads_all, vars_all, _ = model.compute_gradients(x_, t, training=True) + optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) + with tf.control_dependencies(updates): + train_op = optimizer.apply_gradients( + zip(grads_all, vars_all), global_step=global_step) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for _ in range(1): + sess.run(train_op) + + +# Benchmark related +def device_and_data_format(): + return ("/gpu:0", + "channels_first") if tf.test.is_gpu_available() else ("/cpu:0", + "channels_last") + + +def random_batch(batch_size, config): + shape = (batch_size,) + config.input_shape + images = tf.random_uniform(shape) + labels = tf.random_uniform( + [batch_size], minval=0, maxval=config.n_classes, dtype=tf.int32) + + return images, labels + + +class MockIterator(object): + + def __init__(self, tensors): + self._tensors = [tf.identity(x) for x in tensors] + + def next(self): + return self._tensors + + +class RevNetBenchmark(tf.test.Benchmark): + """Eager and graph benchmarks for RevNet.""" + + def _train_batch_sizes(self): + """Shamelessly copied from `resnet50_test.py`. + + Note: This is targeted towards ImageNet. CIFAR-10 should allow more + aggressive batch sizes. + + Returns: + A tuple of possible batch sizes + """ + for device in device_lib.list_local_devices(): + if tf.DeviceSpec.from_string(device.name).device_type == "GPU": + if "K20" in device.physical_device_desc: + return (16,) + if "P100" in device.physical_device_desc: + return (16, 32, 64) + if tf.DeviceSpec.from_string(device.name).device_type == "TPU": + return (32,) + return (16, 32) + + def _force_device_sync(self): + """Shamelessly copied from `resnet50_test.py`.""" + tf.constant(1.).cpu() + + def _report(self, label, start, num_iters, device, batch_size, data_format): + avg_time = (time.time() - start) / num_iters + dev = tf.DeviceSpec.from_string(device).device_type.lower() + name = "%s_%s_batch_%d_%s" % (label, dev, batch_size, data_format) + extras = {"examples_per_sec": batch_size / avg_time} + self.report_benchmark( + iters=num_iters, wall_time=avg_time, name=name, extras=extras) + + def _benchmark_eager_apply(self, + label, + device_and_format, + defun=False, + execution_mode=None, + compiled=False): + config = config_.get_hparams_imagenet_56() + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + model = revnet.RevNet(config=config) + if defun: + model.call = tfe.defun(model.call, compiled=compiled) + batch_size = 64 + num_burn = 5 + num_iters = 10 + with tf.device(device): + images, _ = random_batch(batch_size, config) + for _ in range(num_burn): + model(images, training=False) + if execution_mode: + tfe.async_wait() + gc.collect() + start = time.time() + for _ in range(num_iters): + model(images, training=False) + if execution_mode: + tfe.async_wait() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_apply_sync(self): + self._benchmark_eager_apply( + "eager_apply_sync", device_and_data_format(), defun=False) + + def benchmark_eager_apply_async(self): + self._benchmark_eager_apply( + "eager_apply_async", + device_and_data_format(), + defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_call_defun(self): + self._benchmark_eager_apply( + "eager_apply_with_defun", device_and_data_format(), defun=True) + + def _benchmark_eager_train(self, + label, + make_iterator, + device_and_format, + defun=False, + execution_mode=None, + compiled=False): + config = config_.get_hparams_imagenet_56() + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + for batch_size in self._train_batch_sizes(): + (images, labels) = random_batch(batch_size, config) + model = revnet.RevNet(config=config) + optimizer = tf.train.GradientDescentOptimizer(0.1) + if defun: + model.call = tfe.defun(model.call) + + num_burn = 3 + num_iters = 10 + with tf.device(device): + iterator = make_iterator((images, labels)) + for _ in range(num_burn): + (images, labels) = iterator.next() + train_one_iter(model, images, labels, optimizer) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + gc.collect() + + start = time.time() + for _ in range(num_iters): + (images, labels) = iterator.next() + train_one_iter(model, images, labels, optimizer) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_train_sync(self): + self._benchmark_eager_train( + "eager_train_sync", MockIterator, device_and_data_format(), defun=False) + + def benchmark_eager_train_async(self): + self._benchmark_eager_train( + "eager_train_async", + MockIterator, + device_and_data_format(), + defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_train_defun(self): + self._benchmark_eager_train( + "eager_train", MockIterator, device_and_data_format(), defun=False) + + def benchmark_eager_train_datasets_with_defun(self): + + def make_iterator(tensors): + with tf.device("/device:CPU:0"): + ds = tf.data.Dataset.from_tensors(tensors).repeat() + return tfe.Iterator(ds) + + self._benchmark_eager_train( + "eager_train_dataset_with_defun", + make_iterator, + device_and_data_format(), + defun=True) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/sagan/BUILD b/tensorflow/contrib/eager/python/examples/sagan/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..b470a41d815ce650731680065cc7341f844e3fdc --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/BUILD @@ -0,0 +1,59 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +# Model +py_library( + name = "config", + srcs = ["config.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "ops", + srcs = ["ops.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "sagan", + srcs = ["sagan.py"], + srcs_version = "PY2AND3", + deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +# Tests +cuda_py_test( + name = "ops_test", + size = "small", + srcs = ["ops_test.py"], + additional_deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +cuda_py_test( + name = "sagan_test", + size = "large", + srcs = ["sagan_test.py"], + additional_deps = [ + ":config", + ":sagan", + "//tensorflow:tensorflow_py", + ], + tags = [ + "optonly", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/sagan/config.py b/tensorflow/contrib/eager/python/examples/sagan/config.py new file mode 100644 index 0000000000000000000000000000000000000000..1967bbd867447d9deaf9a7cb3b22a38889276a50 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/config.py @@ -0,0 +1,72 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Configuration in format of tf.contrib.training.HParams. +Supports default 128x128 ImageNet. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +tfe = tf.contrib.eager + + +def get_hparams_imagenet(): + """Configurations to train SAGAN on 128x128 ImageNet dataset.""" + config = tf.contrib.training.HParams() + if tf.test.is_gpu_available(): + config.add_hparam("image_shape", (3, 128, 128)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (512, 4, 4)) + else: + config.add_hparam("image_shape", (128, 128, 3)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (4, 4, 512)) + + config.add_hparam("latent_dim", 128) + config.add_hparam("update_g_once_every", 1) + config.add_hparam("batch_size", 64) + config.add_hparam("d_init_filters", 32) + config.add_hparam("num_upsamples", 5) + # (512, 4, 4) -> (3, 128, 128) + return config + + +def get_hparams_mock(): + """Configurations of smaller networks for testing.""" + config = tf.contrib.training.HParams() + if tf.test.is_gpu_available(): + config.add_hparam("image_shape", (3, 16, 16)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (32, 2, 2)) + else: + config.add_hparam("image_shape", (16, 16, 3)) + config.add_hparam("data_format", "channels_last") + config.add_hparam("g_init_shape", (2, 2, 32)) + + config.add_hparam("latent_dim", 16) + config.add_hparam("update_g_once_every", 1) + config.add_hparam("batch_size", 2) + config.add_hparam("d_init_filters", 4) + config.add_hparam("num_upsamples", 3) + # (32, 2, 2) -> (3, 16, 16) + return config diff --git a/tensorflow/contrib/eager/python/examples/sagan/ops.py b/tensorflow/contrib/eager/python/examples/sagan/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..9a03cab1d12fc16baa7343f72ac58ccd39f698bc --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/ops.py @@ -0,0 +1,71 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Auxiliary operations. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + + +def flatten_hw(x, data_format="channels_first"): + """Flatten the input tensor across height and width dimensions.""" + if data_format == "channels_last": + x = tf.transpose(x, perm=[0, 3, 1, 2]) # Convert to `channels_first` + + old_shape = tf.shape(x) + new_shape = [old_shape[0], old_shape[2] * old_shape[3], old_shape[1]] + + return tf.reshape(x, new_shape) + + +def broaden_hw(x, h, w, c, data_format="channels_first"): + """Broaden dimension so that output has height and width.""" + if data_format == "channels_first": + shape = [-1, c, h, w] + else: + shape = [-1, h, w, c] + + return tf.reshape(x, shape) + + +class BroadenHW(tf.keras.layers.Layer): + """Wrapper class so that `broaden_hw` can be used in `tf.keras.Sequential`.""" + + def __init__(self, h, w, c, data_format="channels_first"): + super(BroadenHW, self).__init__() + self.h = h + self.w = w + self.c = c + self.data_format = data_format + + def call(self, x): + return broaden_hw( + x, h=self.h, w=self.w, c=self.c, data_format=self.data_format) + + def compute_output_shape(self, input_shape): + input_shape = tf.TensorShape(input_shape).as_list() + if self.data_format == "channels_first": + output_shape = (input_shape[0], self.c, self.h, self.w) + else: + output_shape = (input_shape[0], self.h, self.w, self.c) + + return tf.TensorShape(output_shape) diff --git a/tensorflow/contrib/eager/python/examples/sagan/ops_test.py b/tensorflow/contrib/eager/python/examples/sagan/ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3454985904215b59d27fc4b76ccb4a8c2c2eff00 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/ops_test.py @@ -0,0 +1,59 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for auxiliary operations.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import ops + + +class OpsTest(tf.test.TestCase): + + def test_flatten_hw(self): + """Test `flatten_hw` function with mock object.""" + + batch_size = 1 + # Default NCHW format + if tf.test.is_gpu_available(): + x = tf.random_normal(shape=(batch_size, 3, 4, 4)) + y = ops.flatten_hw(x, data_format="channels_first") + self.assertEqual(y.shape, (batch_size, 4 * 4, 3)) + + # NHWC format + x = tf.random_normal(shape=(batch_size, 4, 4, 3)) + y = ops.flatten_hw(x, data_format="channels_last") + self.assertEqual(y.shape, (batch_size, 4 * 4, 3)) + + def test_broaden_hw(self): + """Test `broaden_hw` function with mock object.""" + + batch_size = 1 + # NHWC format + x = tf.random_normal(shape=[batch_size, 4 * 4 * 16]) + y = ops.broaden_hw(x, h=4, w=4, c=16, data_format="channels_last") + self.assertEqual(y.shape, (batch_size, 4, 4, 16)) + + # Default NCHW format + if tf.test.is_gpu_available(): + y = ops.broaden_hw(x, h=4, w=4, c=16, data_format="channels_first") + self.assertEqual(y.shape, (batch_size, 16, 4, 4)) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/sagan/sagan.py b/tensorflow/contrib/eager/python/examples/sagan/sagan.py new file mode 100644 index 0000000000000000000000000000000000000000..561be36c911d7145e2d4a5ed12eccd8ceb054f45 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/sagan.py @@ -0,0 +1,232 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Code for main model. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import ops +tfe = tf.contrib.eager + + +class SelfAttentionModule(tf.keras.Model): + """Self-attention module composed of convolutional layers.""" + + def __init__(self, + attention_features, + original_features, + data_format="channels_first"): + """Initialize the module. + + Args: + attention_features: Number of filters for the attention computation. + original_features: Number of filters of the original Tensor. + data_format: Either 'channels_first' or 'channels_last' + """ + super(SelfAttentionModule, self).__init__() + self.data_format = data_format + # Matrix multiplication implemented as 2D Convolution + self.f = tf.keras.layers.Conv2D( + filters=attention_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.g = tf.keras.layers.Conv2D( + filters=attention_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.h = tf.keras.layers.Conv2D( + filters=original_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.scale = tfe.Variable(0., trainable=True) + + def call(self, x): + f = self.f(x) + g = self.g(x) + h = self.h(x) + + f_flatten = ops.flatten_hw(f, data_format=self.data_format) + g_flatten = ops.flatten_hw(g, data_format=self.data_format) + h_flatten = ops.flatten_hw(h, data_format=self.data_format) + + s = tf.matmul(g_flatten, f_flatten, transpose_b=True) + b = tf.nn.softmax(s, axis=-1) + o = tf.matmul(b, h_flatten) + y = self.scale * tf.reshape(o, tf.shape(x)) + x + + return y + + def compute_output_shape(self, input_shape): + return input_shape + + +class SAGAN(tf.contrib.checkpoint.Checkpointable): + """Self-attention generative adversarial network.""" + + def __init__(self, config): + """Initialize the model. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(SAGAN, self).__init__() + self.config = config + self.generator = self._construct_generator() + self.discriminator = self._construct_discriminator() + + def _construct_generator(self): + """Construct generator.""" + # TODO(lxuechen): Add spectral normalization for WGAN + axis = 1 if self.config.data_format == "channels_first" else 3 + + generator = tf.keras.Sequential() + generator.add( + tf.keras.layers.InputLayer(input_shape=(self.config.latent_dim,))) + generator.add( + tf.keras.layers.Dense( + units=np.prod(self.config.g_init_shape), activation=tf.nn.relu)) + + if self.config.data_format == "channels_first": + c, h, w = self.config.g_init_shape + else: + h, w, c = self.config.g_init_shape + + # Reshape to NHWC/NCHW + generator.add( + ops.BroadenHW(h=h, w=w, c=c, data_format=self.config.data_format)) + + filters_list = [c // 2**p for p in range(1, self.config.num_upsamples + 1)] + filters_list[-1] = 3 # Standard RGB images + + for filters in filters_list[:len(filters_list) // 2]: + generator.add( + tf.keras.layers.Conv2DTranspose( + filters=filters, + kernel_size=4, + strides=(2, 2), + use_bias=False, + padding="SAME", + data_format=self.config.data_format)) + generator.add(tf.keras.layers.BatchNormalization(axis=axis)) + generator.add(tf.keras.layers.Activation("relu")) + + # pylint: disable=undefined-loop-variable + generator.add( + SelfAttentionModule( + original_features=filters, + attention_features=filters // 8, + data_format=self.config.data_format)) + # pylint: enable=undefined-loop-variable + + for filters in filters_list[len(filters_list) // 2:]: + generator.add( + tf.keras.layers.Conv2DTranspose( + filters=filters, + kernel_size=4, + strides=(2, 2), + use_bias=False, + padding="SAME", + data_format=self.config.data_format)) + if filters == 3: + # Assume Image rescaled to [-1, 1] + generator.add(tf.keras.layers.Activation("tanh")) + else: + generator.add(tf.keras.layers.BatchNormalization(axis=axis)) + generator.add(tf.keras.layers.Activation("relu")) + + return generator + + def _construct_discriminator(self): + """Construct discriminator.""" + # TODO(lxuechen): Add spectral normalization for WGAN + discriminator = tf.keras.Sequential() + discriminator.add( + tf.keras.layers.InputLayer(input_shape=self.config.image_shape)) + + filters_list = [ + self.config.d_init_filters * 2**p + for p in range(self.config.num_upsamples) + ] + + for filters in filters_list[:(len(filters_list) + 1) // 2]: + discriminator.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=4, + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format)) + discriminator.add(tf.keras.layers.LeakyReLU(alpha=.1)) + + # pylint: disable=undefined-loop-variable + discriminator.add( + SelfAttentionModule( + original_features=filters, + attention_features=filters // 8, + data_format=self.config.data_format)) + # pylint: enable=undefined-loop-variable + + for filters in filters_list[(len(filters_list) + 1) // 2:]: + discriminator.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=4, + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format)) + discriminator.add(tf.keras.layers.LeakyReLU(alpha=.1)) + + discriminator.add(tf.keras.layers.Flatten()) + discriminator.add(tf.keras.layers.Dense(units=1)) + + return discriminator + + def compute_loss_and_grads(self, real_images, noise, training=True): + """Compute loss and gradients for both generator and discriminator.""" + # TODO(lxuechen): Add gradient penalty for discriminator + with tf.GradientTape() as g_tape, tf.GradientTape() as d_tape: + real_logits = self.discriminator(real_images, training=training) + + fake_images = self.generator.call(noise, training=training) + fake_logits = self.discriminator.call(fake_images) + + g_loss = self.compute_g_loss(fake_logits) + d_loss = self.compute_d_loss(fake_logits, real_logits) + + g_grads = g_tape.gradient(g_loss, self.generator.trainable_variables) + d_grads = d_tape.gradient(d_loss, self.discriminator.trainable_variables) + + return g_loss, d_loss, g_grads, d_grads + + def compute_g_loss(self, fake_logits): + return -tf.reduce_mean(fake_logits) # Hinge loss + + def compute_d_loss(self, fake_logits, real_logits): + # Hinge loss + real_loss = tf.reduce_mean(tf.nn.relu(1. - real_logits)) + fake_loss = tf.reduce_mean(tf.nn.relu(1. + fake_logits)) + return real_loss + fake_loss diff --git a/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py b/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py new file mode 100644 index 0000000000000000000000000000000000000000..18345945108111b57c5401c26b7dca0bfc8f8316 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py @@ -0,0 +1,101 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for self-attention generative adversarial network.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import config as config_ +from tensorflow.contrib.eager.python.examples.sagan import sagan +tfe = tf.contrib.eager + + +class SAGANTest(tf.test.TestCase): + + def setUp(self): + super(SAGANTest, self).setUp() + config = config_.get_hparams_mock() + self.noise_shape = (config.batch_size, config.latent_dim) + self.logits_shape = (config.batch_size, 1) + self.images_shape = (config.batch_size,) + config.image_shape + + self.model = sagan.SAGAN(config=config) + self.noise = tf.random_normal(shape=self.noise_shape) + self.real_images = tf.random_normal(shape=self.images_shape) + self.config = config + + def tearDown(self): + del self.model + del self.noise + del self.real_images + super(SAGANTest, self).tearDown() + + def test_generator_call(self): + """Test `generator.__call__` function.""" + fake_images = self.model.generator(self.noise, training=False) + self.assertEqual(fake_images.shape, self.images_shape) + + def test_generator_call_defun(self): + """Test `generator.__call__` function with defun.""" + call_ = tfe.defun(self.model.generator.__call__) + fake_images = call_(self.noise, training=False) + self.assertEqual(fake_images.shape, self.images_shape) + + def test_discriminator_call(self): + """Test `discriminator.__call__` function.""" + real_logits = self.model.discriminator(self.real_images) + self.assertEqual(real_logits.shape, self.logits_shape) + + def test_discriminator_call_defun(self): + """Test `discriminator.__call__` function with defun.""" + call_ = tfe.defun(self.model.discriminator.__call__) + real_logits = call_(self.real_images) + self.assertEqual(real_logits.shape, self.logits_shape) + + def test_compute_loss_and_grads(self): + """Test `compute_loss_and_grads` function.""" + g_loss, d_loss, g_grads, d_grads = self.model.compute_loss_and_grads( + self.real_images, self.noise, training=False) + self.assertEqual(g_loss.shape, ()) + self.assertEqual(d_loss.shape, ()) + self.assertTrue(isinstance(g_grads, list)) + self.assertTrue(isinstance(d_grads, list)) + g_vars = self.model.generator.trainable_variables + d_vars = self.model.discriminator.trainable_variables + + self.assertEqual(len(g_grads), len(g_vars)) + self.assertEqual(len(d_grads), len(d_vars)) + + def test_compute_loss_and_grads_defun(self): + """Test `compute_loss_and_grads` function with defun.""" + compute_loss_and_grads = tfe.defun(self.model.compute_loss_and_grads) + g_loss, d_loss, g_grads, d_grads = compute_loss_and_grads( + self.real_images, self.noise, training=False) + self.assertEqual(g_loss.shape, ()) + self.assertEqual(d_loss.shape, ()) + self.assertTrue(isinstance(g_grads, list)) + self.assertTrue(isinstance(d_grads, list)) + g_vars = self.model.generator.trainable_variables + d_vars = self.model.discriminator.trainable_variables + + self.assertEqual(len(g_grads), len(g_vars)) + self.assertEqual(len(d_grads), len(d_vars)) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb b/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3e7abe952d63610b14967d41be0a36430fcd29c6 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb @@ -0,0 +1,282 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TFE Workshop: control flow", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/gist/alextp/664b2f8700485ff6801f4d26293bd567/tfe-workshop-control-flow.ipynb)" + ] + }, + { + "metadata": { + "id": "9BpQzh9BvJlj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37 + }, + "outputId": "0b336886-8204-4815-89fa-5291a49d5784" + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "tf.enable_eager_execution()" + ], + "execution_count": 1, + "outputs": [] + }, + { + "metadata": { + "id": "0roIB19GvOjI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Eager execution basics\n", + "\n", + "When eager execution is enabled TensorFlow immediately executes operations, and Tensors are always available. " + ] + }, + { + "metadata": { + "id": "jeO8F-V-vN24", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "aeb3bdec-50b7-440d-93d8-5a171f091081" + }, + "cell_type": "code", + "source": [ + "t = tf.constant([[1, 2], [3, 4]])\n", + "t" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "Y17RwSFxvlDL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "cfcc10c7-707b-4997-99b3-a5f382c5166b" + }, + "cell_type": "code", + "source": [ + "tf.matmul(t, t)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "Dab1bS3TvmRE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8a624f3d-a658-4359-c586-1c5f6bf4c8b7" + }, + "cell_type": "code", + "source": [ + "# It's also possible to have Python control flow which depends on the value of tensors.\n", + "if t[0, 0] > 0.5:\n", + " print(\"T is bigger\")\n", + "else:\n", + " print(\"T is smaller\")" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "T is bigger\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "dPgptJcGwIon", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c4f27f2b-0848-4475-dde5-2534dac65a5c" + }, + "cell_type": "code", + "source": [ + "# Tensors are also usable as numpy arrays\n", + "np.prod(t)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "24" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "p3DTfQXnwXzj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Exercise\n", + "\n", + "The algorithm for bisecting line search is a pretty simple way to find a zero of a continuous scalar function in an interval [a,b] where f(a) and f(b) have different signs. Simply evaluate f((a+b)/2), and narrow the interval by replacing either a or b with (a+b)/2 such that the function when applied on the boundary of the interval still has different signs.\n", + "\n", + "Implement a python function `bisecting_line_search(f, a, b, epsilon)` which returns a value such that `tf.abs(f(value)) < epsilon`.\n", + "\n", + "One thing to keep in mind: python's `==` opertor is not overloaded on Tensors, so you need to use `tf.equal` to compare for equality." + ] + }, + { + "metadata": { + "id": "6eq0YuI6ykm5", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Example test harness to get you going\n", + "\n", + "def test_f(x):\n", + " return x - 0.1234\n", + "def bisecting_line_search(f, a, b, epsilon):\n", + " # Return x such that f(x) <= epsilon.\n", + " pass\n", + "a = tf.constant(0.0)\n", + "b = tf.constant(1.0)\n", + "epsilon = tf.constant(0.001)\n", + "x = bisecting_line_search(test_f, a, b, epsilon)\n", + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "LcMmEfd_xvej", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "outputId": "f402aa50-8ce3-4416-f755-8bbcd1af7809" + }, + "cell_type": "code", + "source": [ + "#@title Double-click to see the solution\n", + "\n", + "def bisecting_line_search(f, a, b, epsilon):\n", + " f_a = f(a)\n", + " f_b = f(b)\n", + " probe = (a + b) / 2\n", + " f_probe = f(probe)\n", + " while tf.abs(f_probe) > epsilon:\n", + " if tf.equal(tf.sign(f_probe), tf.sign(f_a)):\n", + " a = probe\n", + " f_a = f_probe\n", + " else:\n", + " b = probe\n", + " f_b = f_probe\n", + " probe = (a + b) / 2\n", + " f_probe = f(probe)\n", + " print(\"new probe\", probe)\n", + " return probe\n", + "\n", + "bisecting_line_search(test_f, 0., 1., 0.001)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "('new probe', 0.25)\n", + "('new probe', 0.125)\n", + "('new probe', 0.0625)\n", + "('new probe', 0.09375)\n", + "('new probe', 0.109375)\n", + "('new probe', 0.1171875)\n", + "('new probe', 0.12109375)\n", + "('new probe', 0.123046875)\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.123046875" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb b/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4f1410e00bb986f68f3c4c8494aa97bf66284510 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb @@ -0,0 +1,1018 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TFE Workshop: Models.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/gist/alextp/5cfcffd408bd5103f5ae747bc97ab0b5/tfe-workshop-models.ipynb)" + ] + }, + { + "metadata": { + "id": "BMxv1O6Q0SJL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "8be9c556-ac7f-4142-e35e-19dc2b097121" + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ], + "execution_count": 1, + "outputs": [] + }, + { + "metadata": { + "id": "lE1vJhxp0WR9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Variables\n", + "\n", + "TensorFlow variables are useful to store the state in your program. They are integrated with other parts of the API (taking gradients, checkpointing, graph functions)." + ] + }, + { + "metadata": { + "id": "C4ztQNgc0VpW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8b63ae1f-2670-49c0-a31b-8cf7fc4194a1" + }, + "cell_type": "code", + "source": [ + "# Creating variables\n", + "v = tfe.Variable(1.0)\n", + "v" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "H0daItGg1IAp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e47d5aab-16a1-4e29-c27d-7fbc0b94b5d3" + }, + "cell_type": "code", + "source": [ + "v.assign_add(1.0)\n", + "v" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "BJvBzcIG1hyK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Layers: common sets of useful operations\n", + "\n", + "Most of the time when writing code for machine learning models you want to operate at a higher level of abstraction than individual operations and manipulation of individual variables.\n", + "\n", + "Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers.\n", + "\n", + "TensorFlow includes the full [Keras](https://keras.io) API in the tf.keras package, and the Keras layers are very useful when building your own models.\n" + ] + }, + { + "metadata": { + "id": "iSQTS3QW1YQQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "c5d8aa10-dcad-44f7-f0eb-0faf5249fd7e" + }, + "cell_type": "code", + "source": [ + "# In the tf.keras.layers package, layers are objects. To construct a layer,\n", + "# simply construct the object. Most layers take as a first argument the number\n", + "# of output dimensions / channels.\n", + "layer = tf.keras.layers.Dense(100)\n", + "\n", + "# The number of input dimensions is often unnecessary, as it can be inferred\n", + "# the first time the layer is used, but it can be provided if you want to \n", + "# specify it manually, which is useful in some complex models.\n", + "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))\n" + ], + "execution_count": 4, + "outputs": [] + }, + { + "metadata": { + "id": "nRuUogoS1liV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "c352ce79-d519-45e4-a12e-1eaba76871a2" + }, + "cell_type": "code", + "source": [ + "layer(tf.zeros([2, 2]))" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "JH4Kf4ka1mht", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "c34e2378-f83d-42c5-d30a-ebe55620368a" + }, + "cell_type": "code", + "source": [ + "layer.variables" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "DSI4NF0_1vn-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),\n", + "Conv2D, LSTM, BatchNormalization, Dropout, and many others." + ] + }, + { + "metadata": { + "id": "hMgDBftJ12Bp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Models: composing layers\n", + "\n", + "Many interesting layer-like things in machine learning models are implemented by composing existing layers. For example, each residual block in a resnet is a composition of convolutions, batch normalizations, and a shortcut.\n", + "\n", + "The main class used when creating a layer-like thing which contains other layers is tf.keras.Model. Implementing one is done by inheriting from tf.keras.Model.\n" + ] + }, + { + "metadata": { + "id": "K3gVY6gj1nbe", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 190 + }, + "outputId": "6e9be0c4-960e-46c2-cdd9-7e94ad09d46b" + }, + "cell_type": "code", + "source": [ + "class ResnetIdentityBlock(tf.keras.Model):\n", + " def __init__(self, kernel_size, filters):\n", + " super(ResnetIdentityBlock, self).__init__(name='')\n", + " filters1, filters2, filters3 = filters\n", + "\n", + " self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))\n", + " self.bn2a = tf.keras.layers.BatchNormalization()\n", + "\n", + " self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')\n", + " self.bn2b = tf.keras.layers.BatchNormalization()\n", + "\n", + " self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))\n", + " self.bn2c = tf.keras.layers.BatchNormalization()\n", + "\n", + " def call(self, input_tensor, training=False):\n", + " x = self.conv2a(input_tensor)\n", + " x = self.bn2a(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = self.conv2b(x)\n", + " x = self.bn2b(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = self.conv2c(x)\n", + " x = self.bn2c(x, training=training)\n", + "\n", + " x += input_tensor\n", + " return tf.nn.relu(x)\n", + " \n", + "block = ResnetIdentityBlock(1, [1, 2, 3])\n", + "print(block(tf.zeros([1, 2, 3, 3])))\n", + "print([x.name for x in block.variables])" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[[[0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]]\n", + "\n", + " [[0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]]]], shape=(1, 2, 3, 3), dtype=float32)\n", + "['resnet_identity_block/conv2d/kernel:0', 'resnet_identity_block/conv2d/bias:0', 'resnet_identity_block/batch_normalization/gamma:0', 'resnet_identity_block/batch_normalization/beta:0', 'resnet_identity_block/conv2d_1/kernel:0', 'resnet_identity_block/conv2d_1/bias:0', 'resnet_identity_block/batch_normalization_1/gamma:0', 'resnet_identity_block/batch_normalization_1/beta:0', 'resnet_identity_block/conv2d_2/kernel:0', 'resnet_identity_block/conv2d_2/bias:0', 'resnet_identity_block/batch_normalization_2/gamma:0', 'resnet_identity_block/batch_normalization_2/beta:0', 'resnet_identity_block/batch_normalization/moving_mean:0', 'resnet_identity_block/batch_normalization/moving_variance:0', 'resnet_identity_block/batch_normalization_1/moving_mean:0', 'resnet_identity_block/batch_normalization_1/moving_variance:0', 'resnet_identity_block/batch_normalization_2/moving_mean:0', 'resnet_identity_block/batch_normalization_2/moving_variance:0']\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "LPXhHUIc1-sO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential" + ] + }, + { + "metadata": { + "id": "5pXgzNAU17xk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "03b7eaf8-9b35-482b-bcf0-a99af6c2c6a4" + }, + "cell_type": "code", + "source": [ + " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.Conv2D(2, 1, \n", + " padding='same'),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.Conv2D(3, (1, 1)),\n", + " tf.keras.layers.BatchNormalization()])\n", + "my_seq(tf.zeros([1, 2, 3, 3]))\n" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "MZrns6p22GEQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Exercise!\n", + "\n", + "Make a simple convolutional neural network model, useful for things such as MNIST which don't need too many parameters. A sequence of two or three convolutions with small output channels (say, 32 and 64) plus one or two fully connected layers is probably enough.\n", + "\n", + "The input shape should be [batch_size, 28, 28, 1]." + ] + }, + { + "metadata": { + "id": "8CAUa3KNN916", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "97c0ff3c-c962-4c13-eee8-406101465761" + }, + "cell_type": "code", + "source": [ + "# TODO: Implement a convolutional model as described above, and assign it to\n", + "# model.\n", + "model = tf.keras.Sequential([\n", + " \n", + "])" + ], + "execution_count": 9, + "outputs": [] + }, + { + "metadata": { + "id": "vLDDduR32E82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "09bb1d43-b4c6-44b5-916e-0d2903d10cf4" + }, + "cell_type": "code", + "source": [ + "#@title Click to see the answer\n", + "\n", + "max_pool = tf.keras.layers.MaxPooling2D(\n", + " (2, 2), (2, 2), padding='same')\n", + " # The model consists of a sequential chain of layers, so tf.keras.Sequential\n", + " # (a subclass of tf.keras.Model) makes for a compact description.\n", + "model = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.Conv2D(\n", + " 32,\n", + " 5,\n", + " padding='same',\n", + " activation=tf.nn.relu),\n", + " max_pool,\n", + " tf.keras.layers.Conv2D(\n", + " 64,\n", + " 5,\n", + " padding='same',\n", + " activation=tf.nn.relu),\n", + " max_pool,\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(1024, activation=tf.nn.relu),\n", + " tf.keras.layers.Dropout(0.4),\n", + " tf.keras.layers.Dense(10)\n", + " ])\n", + "\n", + "model(tf.zeros([1, 28, 28, 1]))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "H_CKVBroik4M", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Stop here for now" + ] + }, + { + "metadata": { + "id": "_yRwuE6MMmzC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Training\n", + "\n", + "When eager execution is enabled, you can write Pythonic training loops. Simply\n", + "\n", + "1. load your data into a `tf.data.Dataset`, which lets you construct functional pipelines for processing, shuffling, and batching your data,\n", + "2. iterate over the dataset using a Python `for` loop, and\n", + "3. perform an optimization step in the body of your `for` loop.\n", + "\n", + "This workflow is exemplified in the following exercise." + ] + }, + { + "metadata": { + "id": "gj0-EkTc_Xt1", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "## Exercise!\n", + "\n", + "In this exercise, you'll train the convolutional model you implemented for the previous exericse on the MNIST dataset. " + ] + }, + { + "metadata": { + "id": "WOGm9HHn_byR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "bbccc7ad-33cd-446e-bcda-f358c7547e1b" + }, + "cell_type": "code", + "source": [ + "#@title Utilities for downloading MNIST data (double-click to show code)\n", + "import gzip\n", + "import os\n", + "import tempfile\n", + "from six.moves import urllib\n", + "import shutil\n", + "\n", + "import numpy as np\n", + "\n", + "def read32(bytestream):\n", + " \"\"\"Read 4 bytes from bytestream as an unsigned 32-bit integer.\"\"\"\n", + " dt = np.dtype(np.uint32).newbyteorder('>')\n", + " return np.frombuffer(bytestream.read(4), dtype=dt)[0]\n", + "\n", + "\n", + "def check_image_file_header(filename):\n", + " \"\"\"Validate that filename corresponds to images for the MNIST dataset.\"\"\"\n", + " with tf.gfile.Open(filename, 'rb') as f:\n", + " magic = read32(f)\n", + " read32(f) # num_images, unused\n", + " rows = read32(f)\n", + " cols = read32(f)\n", + " if magic != 2051:\n", + " raise ValueError('Invalid magic number %d in MNIST file %s' % (magic,\n", + " f.name))\n", + " if rows != 28 or cols != 28:\n", + " raise ValueError(\n", + " 'Invalid MNIST file %s: Expected 28x28 images, found %dx%d' %\n", + " (f.name, rows, cols))\n", + "\n", + "\n", + "def check_labels_file_header(filename):\n", + " \"\"\"Validate that filename corresponds to labels for the MNIST dataset.\"\"\"\n", + " with tf.gfile.Open(filename, 'rb') as f:\n", + " magic = read32(f)\n", + " read32(f) # num_items, unused\n", + " if magic != 2049:\n", + " raise ValueError('Invalid magic number %d in MNIST file %s' % (magic,\n", + " f.name))\n", + " \n", + "def download(directory, filename):\n", + " \"\"\"Download (and unzip) a file from the MNIST dataset if not already done.\"\"\"\n", + " filepath = os.path.join(directory, filename)\n", + " if tf.gfile.Exists(filepath):\n", + " return filepath\n", + " if not tf.gfile.Exists(directory):\n", + " tf.gfile.MakeDirs(directory)\n", + " # CVDF mirror of http://yann.lecun.com/exdb/mnist/\n", + " url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'\n", + " _, zipped_filepath = tempfile.mkstemp(suffix='.gz')\n", + " print('Downloading %s to %s' % (url, zipped_filepath))\n", + " urllib.request.urlretrieve(url, zipped_filepath)\n", + " with gzip.open(zipped_filepath, 'rb') as f_in, \\\n", + " tf.gfile.Open(filepath, 'wb') as f_out:\n", + " shutil.copyfileobj(f_in, f_out)\n", + " os.remove(zipped_filepath)\n", + " return filepath\n", + "\n", + "\n", + "def dataset(directory, images_file, labels_file):\n", + " \"\"\"Download and parse MNIST dataset.\"\"\"\n", + "\n", + " images_file = download(directory, images_file)\n", + " labels_file = download(directory, labels_file)\n", + "\n", + " check_image_file_header(images_file)\n", + " check_labels_file_header(labels_file)\n", + "\n", + " def decode_image(image):\n", + " # Normalize from [0, 255] to [0.0, 1.0]\n", + " image = tf.decode_raw(image, tf.uint8)\n", + " image = tf.cast(image, tf.float32)\n", + " image = tf.reshape(image, [28, 28, 1])\n", + " return image / 255.0\n", + "\n", + " def decode_label(label):\n", + " label = tf.decode_raw(label, tf.uint8) # tf.string -> [tf.uint8]\n", + " label = tf.reshape(label, []) # label is a scalar\n", + " return tf.to_int32(label)\n", + "\n", + " images = tf.data.FixedLengthRecordDataset(\n", + " images_file, 28 * 28, header_bytes=16).map(decode_image)\n", + " labels = tf.data.FixedLengthRecordDataset(\n", + " labels_file, 1, header_bytes=8).map(decode_label)\n", + " return tf.data.Dataset.zip((images, labels))\n", + "\n", + "\n", + "def get_training_data(directory):\n", + " \"\"\"tf.data.Dataset object for MNIST training data.\"\"\"\n", + " return dataset(directory, 'train-images-idx3-ubyte',\n", + " 'train-labels-idx1-ubyte').take(1024)\n", + "\n", + "def get_test_data(directory):\n", + " \"\"\"tf.data.Dataset object for MNIST test data.\"\"\"\n", + " return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')" + ], + "execution_count": 11, + "outputs": [] + }, + { + "metadata": { + "id": "4ejmJ2dv_f0R", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "274c0381-e505-4e69-f910-3def6f8572a7" + }, + "cell_type": "code", + "source": [ + "# Don't forget to run the cell above!\n", + "training_data = get_training_data(\"/tmp/mnist/train\")\n", + "test_data = get_test_data(\"/tmp/mnist/test\")" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz to /tmp/tmp4ull1xwa.gz\n", + "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz to /tmp/tmp1eikhj1v.gz\n", + "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz to /tmp/tmpcp8xah9c.gz\n", + "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz to /tmp/tmpqww_1e74.gz\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "TANpFS6GKLMC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Fill in the implementation of `train_one_epoch` below and run the cell to train your model. " + ] + }, + { + "metadata": { + "id": "btKL0Ss9_rmC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "56858516-86fc-424a-f00d-6f088f98bf9b" + }, + "cell_type": "code", + "source": [ + "EPOCHS = 5\n", + "optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.5)\n", + "\n", + "def loss_fn(logits, labels):\n", + " return tf.reduce_mean(\n", + " tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=tf.squeeze(logits), labels=labels))\n", + "\n", + "def train_one_epoch(model, training_data, optimizer):\n", + " # TODO: Implement an optimization step and return the average loss.\n", + " #\n", + " # Hint: Use `tf.GradientTape` to compute the gradient of the loss, and use\n", + " # `optimizer.apply_gradients` to update the model's variables, which are\n", + " # accessible as `model.variables`\n", + " average_loss = tfe.metrics.Mean('loss')\n", + " for images, labels in training_data.shuffle(buffer_size=10000).batch(64):\n", + " pass\n", + " return average_loss.result()\n", + "\n", + "for epoch in range(EPOCHS):\n", + " loss = train_one_epoch(model, training_data, optimizer)\n", + " print(\"Average loss after epoch %d: %.4f\" % (epoch, loss))" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Average loss after epoch 0: 2.2847\n", + "Average loss after epoch 1: 2.2305\n", + "Average loss after epoch 2: 2.1334\n", + "Average loss after epoch 3: 1.9115\n", + "Average loss after epoch 4: 1.4285\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yAOFupJN_htg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "67e711e4-76c9-4e3f-bb49-a14955dba03a" + }, + "cell_type": "code", + "source": [ + "#@title Double-click to see a solution.\n", + "EPOCHS = 5\n", + "optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.5)\n", + "\n", + "def _loss_fn(logits, labels):\n", + " return tf.reduce_mean(\n", + " tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=tf.squeeze(logits), labels=labels))\n", + "\n", + "def _train_one_epoch(model, training_data):\n", + " average_loss = tfe.metrics.Mean(\"loss\")\n", + " for images, labels in training_data.shuffle(buffer_size=10000).batch(64):\n", + " with tf.GradientTape() as tape:\n", + " logits = model(images, training=True)\n", + " loss = _loss_fn(logits, labels)\n", + " average_loss(loss)\n", + " gradients = tape.gradient(loss, model.variables)\n", + " optimizer.apply_gradients(zip(gradients, model.variables))\n", + " return average_loss.result()\n", + " \n", + "for epoch in range(EPOCHS):\n", + " loss = _train_one_epoch(model, training_data)\n", + " print(\"Average loss after epoch %d: %.4f\" % (epoch, loss))" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Average loss after epoch 0: 1.0563\n", + "Average loss after epoch 1: 0.8013\n", + "Average loss after epoch 2: 0.6306\n", + "Average loss after epoch 3: 0.5543\n", + "Average loss after epoch 4: 0.5037\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "uDy1DrYA_2Jz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Run the below cell to qualitatively evaluate your model. Note how eager execution interoperates seamlessly with `matplotlib`." + ] + }, + { + "metadata": { + "id": "vR7rMtpu_3nB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1752 + }, + "outputId": "b212aefa-f4b3-425c-f34d-2491429fa521" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "sampled_data = test_data.batch(1).shuffle(buffer_size=10000).take(5)\n", + "for image, label in sampled_data:\n", + " plt.figure()\n", + " plt.imshow(tf.reshape(image, (28, 28)))\n", + " plt.show()\n", + " logits = model(image, training=False)\n", + " prediction = tf.argmax(logits, axis=1, output_type=tf.int64)\n", + " print(\"Prediction: %d\" % prediction)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Prediction: 5\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Prediction: 6\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4SJizeJtNaAs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Profiling\n", + "\n", + "If you want to drill down into the performance characteristics of your code, you can use native Python profilers like [`cProfile`](https://docs.python.org/3/library/profile.html). In the next exercise, you'll do just that." + ] + }, + { + "metadata": { + "id": "_2v0QnG8__PJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Exercise!\n", + "\n", + "This exercise does not require coding. If you have not completed the training exercise, replace `train_one_epoch` below with `_train_one_epoch`.\n", + "\n", + "Run the below cell and inspect the printed profiles. What parts of the code appear to be hotspots or\n", + "bottlenecks? How does sorting the profile by total time compare to sorting it\n", + "by cumulative time?\n", + "\n" + ] + }, + { + "metadata": { + "id": "IFypaYbG_9fB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 714 + }, + "outputId": "d9c3596b-a165-4edd-fc6b-53ccd0d01d19" + }, + "cell_type": "code", + "source": [ + "import cProfile\n", + "import pstats\n", + "\n", + "cProfile.run(\"train_one_epoch(model, training_data, optimizer)\", \"training_profile\")\n", + "\n", + "stats = pstats.Stats(\"training_profile\").strip_dirs().sort_stats(\"tottime\")\n", + "stats.print_stats(10)\n", + "\n", + "stats.sort_stats(\"cumtime\").print_stats(10)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Thu Jun 7 12:25:04 2018 training_profile\n", + "\n", + " 92209 function calls (91817 primitive calls) in 3.446 seconds\n", + "\n", + " Ordered by: internal time\n", + " List reduced from 672 to 10 due to restriction <10>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1080 2.552 0.002 2.552 0.002 {built-in method _pywrap_tensorflow_internal.TFE_Py_FastPathExecute}\n", + " 83 0.753 0.009 0.753 0.009 {built-in method _pywrap_tensorflow_internal.TFE_Py_Execute}\n", + " 16 0.006 0.000 1.019 0.064 network.py:736(_run_internal_graph)\n", + " 16 0.005 0.000 2.253 0.141 {built-in method _pywrap_tensorflow_internal.TFE_Py_TapeGradient}\n", + " 2321 0.004 0.000 0.007 0.000 abc.py:178(__instancecheck__)\n", + " 288 0.004 0.000 0.009 0.000 inspect.py:2092(_signature_from_function)\n", + " 878 0.004 0.000 0.005 0.000 ops.py:5936(__enter__)\n", + " 288 0.004 0.000 0.016 0.000 inspect.py:1079(getfullargspec)\n", + " 11006 0.003 0.000 0.005 0.000 {built-in method builtins.isinstance}\n", + " 768 0.003 0.000 0.008 0.000 {built-in method _pywrap_tensorflow_internal.Flatten}\n", + "\n", + "\n", + "Thu Jun 7 12:25:04 2018 training_profile\n", + "\n", + " 92209 function calls (91817 primitive calls) in 3.446 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 672 to 10 due to restriction <10>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 3.446 3.446 {built-in method builtins.exec}\n", + " 1 0.000 0.000 3.446 3.446 :1()\n", + " 1 0.001 0.001 3.446 3.446 :9(train_one_epoch)\n", + " 1080 2.552 0.002 2.552 0.002 {built-in method _pywrap_tensorflow_internal.TFE_Py_FastPathExecute}\n", + " 16 0.000 0.000 2.255 0.141 backprop.py:739(gradient)\n", + " 16 0.000 0.000 2.253 0.141 imperative_grad.py:31(imperative_grad)\n", + " 16 0.005 0.000 2.253 0.141 {built-in method _pywrap_tensorflow_internal.TFE_Py_TapeGradient}\n", + " 400 0.002 0.000 2.246 0.006 backprop.py:145(grad_fn)\n", + " 400 0.002 0.000 2.239 0.006 backprop.py:95(_magic_gradient_function)\n", + " 32 0.001 0.000 1.601 0.050 nn_grad.py:497(_Conv2DGrad)\n", + "\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "id": "8ixpnyCNNTI4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/workshop/3_inspecting.ipynb b/tensorflow/contrib/eager/python/examples/workshop/3_inspecting.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..64d19ec5c9bfccd07eabb21ce8fbb62b21f23efa --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/workshop/3_inspecting.ipynb @@ -0,0 +1,443 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Debugging \"graph-first\" models with eager execution", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/gist/alextp/9568ab40f6ed6f9a3ba4736f6aef6127/debugging-graph-first-models-with-eager-execution.ipynb)" + ] + }, + { + "metadata": { + "id": "mm-t0GuIu1Dt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This colab uses eager execution and the Python debugger to modify the execution of a translation model. This combination lets you quickly explore counterfactuals when researching and designing modifications to a model.\n", + "\n", + "The model, Transformer from [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor), was originally written with graph building in mind. Executing it eagerly can still be helpful!" + ] + }, + { + "metadata": { + "id": "gxb1DvIDg4sv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#@title License (double click to show)\n", + "# Copyright 2018 The TensorFlow Authors.\n", + "\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Gx3HA9N1ui64", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37 + }, + "outputId": "f6986f34-f3e1-44e1-c902-2eb33081acad" + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "import pdb\n", + "tfe = tf.contrib.eager\n", + "\n", + "tf.enable_eager_execution()" + ], + "execution_count": 1, + "outputs": [] + }, + { + "metadata": { + "id": "3LkOm2ct-Lmc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37 + }, + "outputId": "2edc74d9-6bc0-4e78-ab4e-83bf96099ef4" + }, + "cell_type": "code", + "source": [ + "!pip install -q -U tensor2tensor\n", + "from tensor2tensor.models import transformer" + ], + "execution_count": 2, + "outputs": [] + }, + { + "metadata": { + "id": "1Z3oMsqV0zB6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "outputId": "0a8186ee-c688-457f-c9f6-9a6c1477a93b" + }, + "cell_type": "code", + "source": [ + "#@title Create a tensor2tensor translation model, fetch a checkpoint (double click to show)\n", + "from tensor2tensor import problems\n", + "from tensor2tensor.utils import trainer_lib\n", + "from tensor2tensor.utils import registry\n", + "\n", + "import numpy as np\n", + "import os\n", + "\n", + "# Setup some directories\n", + "data_dir = os.path.expanduser(\"~/t2t/data\")\n", + "tmp_dir = os.path.expanduser(\"~/t2t/tmp\")\n", + "train_dir = os.path.expanduser(\"~/t2t/train\")\n", + "checkpoint_dir = os.path.expanduser(\"~/t2t/checkpoints\")\n", + "tf.gfile.MakeDirs(data_dir)\n", + "tf.gfile.MakeDirs(tmp_dir)\n", + "tf.gfile.MakeDirs(train_dir)\n", + "tf.gfile.MakeDirs(checkpoint_dir)\n", + "gs_data_dir = \"gs://tensor2tensor-data\"\n", + "gs_ckpt_dir = \"gs://tensor2tensor-checkpoints/\"\n", + "\n", + "# Fetch the problem\n", + "ende_problem = problems.problem(\"translate_ende_wmt32k\")\n", + "\n", + "# Copy the vocab file locally so we can encode inputs and decode model outputs\n", + "# All vocabs are stored on GCS\n", + "vocab_name = \"vocab.ende.32768\"\n", + "vocab_file = os.path.join(gs_data_dir, vocab_name)\n", + "!gsutil cp {vocab_file} {data_dir}\n", + "\n", + "# Get the encoders from the problem\n", + "encoders = ende_problem.feature_encoders(data_dir)\n", + "\n", + "# Setup helper functions for encoding and decoding\n", + "def encode(input_str, output_str=None):\n", + " \"\"\"Input str to features dict, ready for inference\"\"\"\n", + " inputs = encoders[\"inputs\"].encode(input_str) + [1] # add EOS id\n", + " batch_inputs = tf.reshape(inputs, [1, -1, 1]) # Make it 3D.\n", + " return {\"inputs\": batch_inputs}\n", + "\n", + "def decode(integers):\n", + " \"\"\"List of ints to str\"\"\"\n", + " integers = list(np.squeeze(integers))\n", + " if 1 in integers:\n", + " integers = integers[:integers.index(1)]\n", + " return encoders[\"inputs\"].decode(np.squeeze(integers))\n", + "\n", + "# Copy the pretrained checkpoint locally\n", + "ckpt_name = \"transformer_ende_test\"\n", + "gs_ckpt = os.path.join(gs_ckpt_dir, ckpt_name)\n", + "!gsutil -q cp -R {gs_ckpt} {checkpoint_dir}\n", + "checkpoint_path = tf.train.latest_checkpoint(\n", + " os.path.join(checkpoint_dir, ckpt_name))\n", + "\n", + "# Create hparams and the model\n", + "model_name = \"transformer\"\n", + "hparams_set = \"transformer_base\"\n", + "\n", + "hparams = trainer_lib.create_hparams(hparams_set, data_dir=data_dir, problem_name=\"translate_ende_wmt32k\")\n", + "\n", + "# NOTE: Only create the model once when restoring from a checkpoint; it's a\n", + "# Layer and so subsequent instantiations will have different variable scopes\n", + "# that will not match the checkpoint.\n", + "translate_model = registry.model(model_name)(hparams, tf.estimator.ModeKeys.EVAL)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Copying gs://tensor2tensor-data/vocab.ende.32768...\n", + "/ [1 files][316.4 KiB/316.4 KiB] \n", + "Operation completed over 1 objects/316.4 KiB. \n", + "INFO:tensorflow:Setting T2TModel mode to 'eval'\n", + "INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0\n", + "INFO:tensorflow:Setting hparams.symbol_dropout to 0.0\n", + "INFO:tensorflow:Setting hparams.attention_dropout to 0.0\n", + "INFO:tensorflow:Setting hparams.dropout to 0.0\n", + "INFO:tensorflow:Setting hparams.relu_dropout to 0.0\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4IblPXLGjuCl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We've created a Transformer model and fetched an existing training checkpoint. It hasn't created variables yet, and we want to load them from the checkpoint before they're used (restore-on-create) so the first run of the model outputs the correct value. The `tfe.restore_variables_on_create` API looks up variables by name on creation and restores their values." + ] + }, + { + "metadata": { + "id": "o3MWxcAqJoqG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "fbc1b1bf-ffbe-4621-b3cb-5eb855fec3a8" + }, + "cell_type": "code", + "source": [ + "with tfe.restore_variables_on_create(checkpoint_path):\n", + " model_output = translate_model.infer(encode(\"Eager execution\"))\n", + "print(decode(model_output[\"outputs\"]))" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "INFO:tensorflow:Greedy Decoding\n", + "Hinrichtung\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "xk5HV9Hhu9zO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Using global variable names can get somewhat fragile, so for new code we recommend the object-based `tf.keras.Model.save_weights` or `tf.train.Checkpoint`. However, these require some small code changes to work with existing graph building code.\n", + "\n", + "The Transformer model translates \"Eager execution\" in English to \"Hinrichtung\" in German, which refers to capital punishment rather than getting things done. Transformer first encodes the English, then decodes to German. We'll add a debugging hook at the start of the decode phase (once the encodings have been finalized) and see if we can correct the translation." + ] + }, + { + "metadata": { + "id": "GUGwbYvXZ9-7", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "previous_fast_decode = transformer.fast_decode\n", + "def debug_fn(*args, **kwargs):\n", + " pdb.set_trace()\n", + " return previous_fast_decode(*args, **kwargs) # \"step\" in pdb to step in\n", + "transformer.fast_decode = debug_fn # Add our debugging hook to Transformer" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "f61HlvECxJn0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Now that we've \"monkey patched\" the model, we'll drop into a debugger just before decoding starts. In most cases it'd be simpler to add the `pdb.set_trace()` call to the code directly, but in this case we're working with prepackaged library code.\n", + "\n", + "First, let's find an encoding which represents the correct sense of \"execution\". Then we'll patch part of that encoding into the encoding of \"Eager execution\" to fix the translation. Feel free to poke around with the debugger (e.g. print a Tensor's value), but your main task is to save the encodings by assigning them to an attribute of the function:\n", + "\n", + "```\n", + "(running the next cell drops you into a pdb shell)\n", + "step\n", + "fast_decode.previous_encoding = encoder_output\n", + "continue\n", + "\n", + "```\n", + "\n", + "You can type `next` (or `n`) a few times before `continue` to watch the decoding ops run." + ] + }, + { + "metadata": { + "id": "dX4CPOGSpZrb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 179 + }, + "outputId": "6de38c31-836f-40ef-b701-e42908172619" + }, + "cell_type": "code", + "source": [ + "model_output = translate_model.infer(encode(\"Immediate running\"))\n", + "print(decode(model_output[\"outputs\"]))" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "> (4)debug_fn()\n", + "-> return previous_fast_decode(*args, **kwargs) # \"step\" in pdb to step in\n", + "(Pdb) step\n", + "--Call--\n", + "> /usr/local/lib/python2.7/dist-packages/tensor2tensor/models/transformer.py(427)fast_decode()\n", + "-> def fast_decode(encoder_output,\n", + "(Pdb) fast_decode.previous_encoding = encoder_output\n", + "(Pdb) continue\n", + "Sofortige DurchfĆ¼hrung\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "-ZEZciV4FpLo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Now we have an encoding saved which gets the correct sense for \"execution\"." + ] + }, + { + "metadata": { + "id": "QeC_oDVqHD_v", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 179 + }, + "outputId": "253c9af1-003e-46bd-8bf5-db968cf6a8cf" + }, + "cell_type": "code", + "source": [ + "# Assumes you followed the pdb instructions above!\n", + "transformer.fast_decode.previous_encoding" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "bC9JjeDcHEav", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's replace part of the encoding for \"Eager execution\" with the encoding of \"Immediate running\".\n", + "\n", + "Again we'll drop into a pdb shell. This time we'll run some TensorFlow operations to patch the encodings while the model is running.\n", + "\n", + "```\n", + "(running the next cell again drops you into a pdb shell)\n", + "step\n", + "encoder_output = tf.concat([fast_decode.previous_encoding[:, :3], encoder_output[:, 3:]], axis=1)\n", + "continue\n", + "```" + ] + }, + { + "metadata": { + "id": "t2as_Kn1h65G", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 179 + }, + "outputId": "5b4e546e-3bb4-4761-c545-467b631e3ffe" + }, + "cell_type": "code", + "source": [ + "model_output = translate_model.infer(encode(\"Eager execution\"))\n", + "print(decode(model_output[\"outputs\"]))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "> (4)debug_fn()\n", + "-> return previous_fast_decode(*args, **kwargs) # \"step\" in pdb to step in\n", + "(Pdb) step\n", + "--Call--\n", + "> /usr/local/lib/python2.7/dist-packages/tensor2tensor/models/transformer.py(427)fast_decode()\n", + "-> def fast_decode(encoder_output,\n", + "(Pdb) encoder_output = tf.concat([fast_decode.previous_encoding[:, :3], encoder_output[:, 3:]], axis=1)\n", + "(Pdb) continue\n", + "sofortige AusfĆ¼hrung\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "rK6tYZ23I2cm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We get a different decoding, with the correct sense of \"execution\". Likely we're keeping just the encoding of \"tion\" from \"Eager execution\", so no great breakthrough in translation modeling.\n", + "\n", + "Similarly it's possible to modify attention vectors, or change words during decoding to help debug a beam search." + ] + }, + { + "metadata": { + "id": "Nb-4ipYNRWxA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This colab was adapted from the [Tensor2Tensor colab](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb). Credit to Ankur Taly for its concept." + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md index 2d2aba6908b168e0bf63f4706b6344cbb4ca82bd..23f33d0230b0b9fa906636a9df4e046c6873d90b 100644 --- a/tensorflow/contrib/eager/python/g3doc/guide.md +++ b/tensorflow/contrib/eager/python/g3doc/guide.md @@ -4,8 +4,8 @@ Eager execution is a feature that makes TensorFlow execute operations immediately: concrete values are returned, instead of creating a computational graph that is executed later. -A user guide is available: https://www.tensorflow.org/programmers_guide/eager -([source file](../../../../docs_src/programmers_guide/eager.md)) +A user guide is available: https://www.tensorflow.org/guide/eager +([source file](../../../../docs_src/guide/eager.md)) We welcome feedback through [GitHub issues](https://github.com/tensorflow/tensorflow/labels/comp:eager). diff --git a/tensorflow/contrib/eager/python/metrics.py b/tensorflow/contrib/eager/python/metrics.py index 3e3100427376ddd480b50d967cf53e7831aaefb2..04b7b1165e19612be2fa878f83effbe814fc5c46 100644 --- a/tensorflow/contrib/eager/python/metrics.py +++ b/tensorflow/contrib/eager/python/metrics.py @@ -22,5 +22,6 @@ from __future__ import print_function from tensorflow.contrib.eager.python.metrics_impl import * from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['Accuracy', 'Mean', 'Metric'] +_allowed_symbols = ['Accuracy', 'Mean', 'Metric', 'CategoricalAccuracy', + 'BinaryAccuracy', 'SparseAccuracy'] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index c947ed9dcc415670a820f8a5cd9eaaf07334cfc3..efa6ba062631500bd7cd16620ebec23d15b93b62 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -345,9 +345,14 @@ class Mean(Metric): class Accuracy(Mean): - """Calculates how often `predictions` matches `labels`.""" + """Calculates how often `predictions` matches `labels`. + Attributes: + name: name of the accuracy object + dtype: data type of the tensor + """ def __init__(self, name=None, dtype=dtypes.float64): + """Inits Accuracy class with name and dtype.""" super(Accuracy, self).__init__(name=name, dtype=dtype) def call(self, labels, predictions, weights=None): @@ -377,3 +382,146 @@ class Accuracy(Mean): if weights is None: return labels, predictions return labels, predictions, weights + + +class CategoricalAccuracy(Mean): + """Calculates how often `predictions` matches `labels`. + + This class is compatible with `tf.keras.losses.categorical_crossentropy`, + `tf.nn.softmax_cross_entropy_with_logits_v2`, + `tf.losses.softmax_cross_entropy`. + + Attributes: + name: name of the accuracy object. + dtype: data type of tensor. + """ + + def __init__(self, name=None, dtype=dtypes.float64): + """Inits CategoricalAccuracy with name and dtype.""" + super(CategoricalAccuracy, self).__init__(name=name, dtype=dtype) + + def call(self, labels, predictions, weights=None): + """Accumulate accuracy statistics. + + `labels` and `predictions` should have the same shape. + As argmax is being done here, labels and predictions type + can be different. + + Args: + labels: One-hot Tensor. + predictions: Tensor with the logits or probabilities for each example. + weights: Optional weighting of each example. Defaults to 1. + + Returns: + The arguments, for easy chaining. + """ + check_ops.assert_equal( + array_ops.shape(labels), array_ops.shape(predictions), + message="Shapes of labels and predictions are unequal") + labels = math_ops.argmax(labels, axis=-1) + predictions = math_ops.argmax(predictions, axis=-1) + matches = math_ops.equal(labels, predictions) + matches = math_ops.cast(matches, dtypes.float64) + super(CategoricalAccuracy, self).call(matches, weights=weights) + if weights is None: + return labels, predictions + return labels, predictions, weights + + +class BinaryAccuracy(Mean): + """Calculates how often `predictions` matches `labels`. + + This class is compatible with `tf.keras.losses.binary_crossentropy`, + `tf.losses.sigmoid_cross_entropy`, + `tf.nn.sigmoid_cross_entropy_with_logits`. + If there is more than one label, this will become multi-label classification. + + Attributes: + name: name of the accuracy object. + threshold: Used for rounding off the predictions. + If the predictions are, + 1. probabilities then set the threshold to 0.5. + 2. logits then set the threshold to 0. + You can set the threshold appropriately, + to trade off with precision and recall. + dtype: data type of tensor. + """ + + def __init__(self, threshold, name=None, dtype=dtypes.float64): + """Inits BinaryAccuracy with name, threshold and dtype.""" + + super(BinaryAccuracy, self).__init__(name=name, dtype=dtype) + self.threshold = threshold + + def call(self, labels, predictions, weights=None): + """Accumulate accuracy statistics. + + `labels` and `predictions` should have the same shape and type. + + Args: + labels: Binary Tensor(containing 0 or 1). + predictions: Tensor with probabilities or logits. + weights: Optional weighting of each example. Defaults to 1. + + Returns: + The arguments, for easy chaining. + """ + check_ops.assert_equal( + array_ops.shape(labels), array_ops.shape(predictions), + message="Shapes of labels and predictions are unequal") + predictions = ops.convert_to_tensor(predictions) + predictions = predictions > self.threshold + matches = math_ops.equal(labels, predictions) + matches = math_ops.cast(matches, dtypes.float64) + super(BinaryAccuracy, self).call(matches, weights=weights) + if weights is None: + return labels, predictions + return labels, predictions, weights + + +class SparseAccuracy(Mean): + """Calculates how often `predictions` matches `labels`. + + This class is compatible with + `tf.keras.losses.sparse_categorical_crossentropy`, + `tf.nn.sparse_softmax_cross_entropy_with_logits`, + `tf.losses.sparse_softmax_cross_entropy`. + + Attributes: + name: name of the accuracy object + dtype: data type of tensor. + """ + + def __init__(self, name=None, dtype=dtypes.float64): + """Inits SparseAccuracy with name and dtype.""" + + super(SparseAccuracy, self).__init__(name=name, dtype=dtype) + + def call(self, labels, predictions, weights=None): + """Accumulate accuracy statistics. + + `labels` and `predictions` should have the same shape except the + predictions must have one additional trailing dimension equal to the + number of classes(you want to predict). + + Type of labels and predictions can be different. + + Args: + labels: Tensor of shape (batch_size, ) containing integers + predictions: Tensor with the logits or probabilities for each example. + weights: Optional weighting of each example. Defaults to 1. + + Returns: + The arguments, for easy chaining. + """ + check_ops.assert_equal( + array_ops.shape(labels), array_ops.shape(predictions)[0], + message="First axis of labels and predictions is unequal") + predictions = math_ops.argmax(predictions, axis=-1) + labels = math_ops.cast(labels, dtypes.int64) + matches = math_ops.equal(labels, predictions) + matches = math_ops.cast(matches, dtypes.float64) + super(SparseAccuracy, self).call(matches, weights=weights) + if weights is None: + return labels, predictions + return labels, predictions, weights diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index 02ee05487515b81bfae70d02c1dfdb6d816b77c7..20d938d492bf78fab852c638ba675d7ee6ed9073 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -118,6 +118,39 @@ class MetricsTest(test.TestCase): self.assertEqual(dtypes.float64, m.dtype) self.assertEqual(dtypes.float64, m.result().dtype) + def testCategoricalAccuracy(self): + m = metrics.CategoricalAccuracy() + m([[1, 0, 0, 0], [0, 1, 0, 0]], + [[0.6, 0.1, 0.25, 0.05], [0.4, 0.05, 0.45, 0.0]]) # 1/2 correct + m([[0, 0, 0, 1]], [[0.25, 0.95, 0.25, 0.0]]) # 0/1 correct + m([[1, 0, 0, 0], [0, 1, 0, 0]], + [[0.99, 0.01, 0.0, 0.0], [0.35, 0.35, 0.3, 0.0]]) # 1/2 correct + self.assertEqual(2.0/5, m.result().numpy()) + self.assertEqual(dtypes.float64, m.dtype) + self.assertEqual(dtypes.float64, m.result().dtype) + + def testBinaryAccuracy(self): + m = metrics.BinaryAccuracy(threshold=0) + # as threshold is 0 hence the predictions are logits + m([[0, 0, 0, 0]], + [[-4.2, 4.5, 1.2, -1.1]]) # 2/4 correct + m([[0, 1]], [[-5.3, 11.65]]) # 2/2 correct + m([[0, 1], [1, 1]], + [[-5.3, 11.65], [-10.32, 56.38]]) # 3/4 correct + self.assertEqual(7.0/10, m.result().numpy()) + self.assertEqual(dtypes.float64, m.dtype) + self.assertEqual(dtypes.float64, m.result().dtype) + + def testSparseAccuracy(self): + m = metrics.SparseAccuracy() + m([0, 2], + [[0.6, 0.1, 0.25, 0.05], [0.4, 0.05, 0.45, 0.0]]) # 2/2 correct + m([1], [[0.25, 0.95, 0.25, 0.0]]) # 1/1 correct + m([0, 3], [[0.99, 0.01, 0.0, 0.0], [0.35, 0.35, 0.3, 0.0]]) # 1/2 correct + self.assertEqual(4.0/5, m.result().numpy()) + self.assertEqual(dtypes.float64, m.dtype) + self.assertEqual(dtypes.float64, m.result().dtype) + def testAccuracyDifferentShapes(self): m = metrics.Accuracy() with self.assertRaises(errors.InvalidArgumentError): @@ -173,7 +206,7 @@ class MetricsTest(test.TestCase): sess.run(accumulate, feed_dict={p: 7}) self.assertAllEqual(m.result().eval(), 7) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphAndEagerTensor(self): m = metrics.Mean() inputs = ops.convert_to_tensor([1.0, 2.0]) @@ -221,7 +254,7 @@ class MetricsTest(test.TestCase): self.assertAllEqual(m2.result().eval(), 2.0) self.assertAllEqual(m1.result().eval(), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py index c92bd15b253b67a3301cd562046a4467e1bf877d..240f213c602395b8589d39c3ecd90b602ffa9848 100644 --- a/tensorflow/contrib/eager/python/network_test.py +++ b/tensorflow/contrib/eager/python/network_test.py @@ -126,7 +126,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual([[17.0], [34.0]], self.evaluate(result)) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkSaveRestoreAlreadyBuilt(self): net = MyNetwork(name="abcd") with self.assertRaisesRegexp( @@ -138,7 +138,7 @@ class NetworkTest(test.TestCase): self._save_modify_load_network_built(net, global_step=10) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestoreDefaultGlobalStep(self): net = MyNetwork(name="abcd") net(constant_op.constant([[2.0]])) @@ -149,7 +149,7 @@ class NetworkTest(test.TestCase): self.assertIn("abcd-4242", save_path) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkSaveAndRestoreIntoUnbuilt(self): save_dir = self.get_temp_dir() net1 = MyNetwork() @@ -166,7 +166,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual(self.evaluate(net1.variables[0]), self.evaluate(net2.variables[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkMatchesLayerVariableNames(self): zero = constant_op.constant([[0.]]) layer_one = core.Dense(1, use_bias=False) @@ -193,7 +193,7 @@ class NetworkTest(test.TestCase): self.assertEqual("two_layer_net/" + layer_two.variables[0].name, net.second.variables[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadIntoUnbuiltSharedLayer(self): class Owner(network.Network): @@ -272,7 +272,7 @@ class NetworkTest(test.TestCase): network.restore_network_checkpoint( load_into, save_path, map_func=_restore_map_func) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRestoreIntoSubNetwork(self): class Parent(network.Network): @@ -327,7 +327,7 @@ class NetworkTest(test.TestCase): # The checkpoint is incompatible. network.restore_network_checkpoint(save_into_parent, checkpoint) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCustomMapCollisionErrors(self): class Parent(network.Network): @@ -372,7 +372,7 @@ class NetworkTest(test.TestCase): network.restore_network_checkpoint( loader, checkpoint, map_func=lambda n: "foo") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDefaultMapCollisionErrors(self): one = constant_op.constant([[1.]]) @@ -571,7 +571,7 @@ class NetworkTest(test.TestCase): expected_start="my_network_1/dense/", actual=outside_net_after.trainable_weights[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVariableScopeStripping(self): with variable_scope.variable_scope("scope1"): with variable_scope.variable_scope("scope2"): @@ -596,7 +596,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual([[42.]], self.evaluate(restore_net.variables[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerNamesRespected(self): class ParentNetwork(network.Network): @@ -677,7 +677,7 @@ class NetworkTest(test.TestCase): self.assertStartsWith(expected_start="my_network_1/dense/", actual=net2.trainable_weights[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableAnonymous(self): # The case where no explicit names are specified. We make up unique names, @@ -721,7 +721,7 @@ class NetworkTest(test.TestCase): self.assertEqual("my_network", net2.first.name) self.assertEqual("my_network_1", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicit(self): # We have explicit network names and everything is globally unique. @@ -750,7 +750,7 @@ class NetworkTest(test.TestCase): self.assertEqual("first_unique_child_name", net.first.name) self.assertEqual("second_unique_child_name", net.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerNetworkNameInteractions(self): # Same base name as core.Dense; Networks and non-Network Layers with the @@ -801,7 +801,7 @@ class NetworkTest(test.TestCase): actual=net.trainable_weights[4].name) self.assertEqual("mixed_layer_network", net.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitCollisions(self): # We have explicit network names and they are unique within the layer @@ -831,7 +831,7 @@ class NetworkTest(test.TestCase): self.assertEqual("nonunique_name", net.first.name) self.assertEqual("second_unique_child_name", net.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitWithAnonymousParent(self): # A parent network is instantiated multiple times with explicitly named @@ -873,7 +873,7 @@ class NetworkTest(test.TestCase): self.assertEqual("first_unique_child_name", net2.first.name) self.assertEqual("second_unique_child_name", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitSameLayerCollisions(self): # We have explicit network names and they are _not_ unique within the layer @@ -891,7 +891,7 @@ class NetworkTest(test.TestCase): with self.assertRaisesRegexp(ValueError, "nonunique_name"): ParentNetwork() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAnonymousVariableSharing(self): # Two "owned" Networks @@ -989,7 +989,7 @@ class NetworkTest(test.TestCase): self.assertEqual("my_network", net4.first.name) self.assertEqual("my_network", net4.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRecursiveLayerRenaming(self): core.Dense(1) # Under default Layer naming, would change subsequent names. @@ -1041,7 +1041,7 @@ class NetworkTest(test.TestCase): self.assertEqual("dense", net.second.first.name) self.assertEqual("dense_1", net.second.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallInDifferentOrderThanConstruct(self): shared_network = MyNetwork() @@ -1091,7 +1091,7 @@ class NetworkTest(test.TestCase): self.assertTrue(net2.first is net1.first) self.assertEqual("my_network", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerCallInDifferentOrderThanConstruct(self): # Same idea as testCallInDifferentOrderThanConstruct, but this time with a # non-Network Layer shared between two Networks rather than a @@ -1144,7 +1144,7 @@ class NetworkTest(test.TestCase): self.assertTrue(net2.first is net1.first) self.assertEqual("dense", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerAlreadyBuilt(self): one = constant_op.constant([[1.]]) core.Dense(1, use_bias=False) # pre-built layers use global naming diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index fee9db46fa4f79d7dd613436726e8ddad51faf1c..ca6430253b67d825290b6a376ba3f29b3ae67577 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -68,6 +68,7 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@async_clear_error @@run_test_in_graph_and_eager_modes +@@run_all_tests_in_graph_and_eager_modes @@DEVICE_PLACEMENT_EXPLICIT @@DEVICE_PLACEMENT_WARN @@ -121,7 +122,7 @@ from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Vari from tensorflow.python.ops.variable_scope import EagerVariableStore from tensorflow.python.ops import script_ops from tensorflow.python.ops import template -from tensorflow.python.training.checkpointable.base import Checkpointable +from tensorflow.python.training.checkpointable.tracking import Checkpointable from tensorflow.python.training.checkpointable.util import CheckpointableSaver from tensorflow.python.training.checkpointable.util import Checkpoint from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 1937ffb583bc727df76470d072b35fb3c9acaa88..30d297a5fb2dd2f844093d790d051a79105984dd 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -117,7 +117,7 @@ py_library( py_test( name = "dnn_test", - size = "small", + size = "medium", srcs = ["python/estimator/dnn_test.py"], srcs_version = "PY2AND3", tags = [ diff --git a/tensorflow/contrib/estimator/python/estimator/dnn.py b/tensorflow/contrib/estimator/python/estimator/dnn.py index 7ff25b95c079c7e06d29e874bcaa0d2c13e7167e..9efa8f474d865a36788cba40a15404bf0b30a17e 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn.py @@ -53,6 +53,25 @@ class DNNEstimator(estimator.Estimator): l1_regularization_strength=0.001 )) + # Or estimator using an optimizer with a learning rate decay. + estimator = DNNEstimator( + head=tf.contrib.estimator.multi_label_head(n_classes=3), + feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb], + hidden_units=[1024, 512, 256], + optimizer=lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + + # Or estimator with warm-starting from a previous checkpoint. + estimator = DNNEstimator( + head=tf.contrib.estimator.multi_label_head(n_classes=3), + feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb], + hidden_units=[1024, 512, 256], + warm_start_from="/path/to/checkpoint/dir") + # Input builders def input_fn_train: # returns x, y pass @@ -92,7 +111,9 @@ class DNNEstimator(estimator.Estimator): activation_fn=nn.relu, dropout=None, input_layer_partitioner=None, - config=None): + config=None, + warm_start_from=None, + batch_norm=False): """Initializes a `DNNEstimator` instance. Args: @@ -107,8 +128,9 @@ class DNNEstimator(estimator.Estimator): model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to Adagrad optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to Adagrad optimizer. activation_fn: Activation function applied to each layer. If `None`, will use `tf.nn.relu`. dropout: When not `None`, the probability we will drop out a given @@ -116,6 +138,12 @@ class DNNEstimator(estimator.Estimator): input_layer_partitioner: Optional. Partitioner for input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20. config: `RunConfig` object to configure the runtime settings. + warm_start_from: A string filepath to a checkpoint to warm-start from, or + a `WarmStartSettings` object to fully configure warm-starting. If the + string filepath is provided instead of a `WarmStartSettings`, then all + weights are warm-started, and it is assumed that vocabularies and Tensor + names are unchanged. + batch_norm: Whether to use batch normalization after each hidden layer. """ def _model_fn(features, labels, mode, config): return dnn_lib._dnn_model_fn( # pylint: disable=protected-access @@ -129,6 +157,8 @@ class DNNEstimator(estimator.Estimator): activation_fn=activation_fn, dropout=dropout, input_layer_partitioner=input_layer_partitioner, - config=config) + config=config, + batch_norm=batch_norm) super(DNNEstimator, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py index ccaf1128bf23af734f7a5722a4dd8c1f0304fab7..894a2954987a4af760d3c08fc6f30405010150c5 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py @@ -53,12 +53,19 @@ class DNNLinearCombinedEstimator(estimator.Estimator): dnn_hidden_units=[1000, 500, 100], dnn_optimizer=tf.train.ProximalAdagradOptimizer(...)) - # To apply L1 and L2 regularization, you can set optimizers as follows: + # To apply L1 and L2 regularization, you can set dnn_optimizer to: tf.train.ProximalAdagradOptimizer( learning_rate=0.1, l1_regularization_strength=0.001, l2_regularization_strength=0.001) - # It is same for FtrlOptimizer. + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. # Input builders def input_fn_train: # returns x, y @@ -116,12 +123,16 @@ class DNNLinearCombinedEstimator(estimator.Estimator): used by linear part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the linear part of the model. Defaults to FTRL optimizer. + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. dnn_feature_columns: An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the deep part of the model. Defaults to Adagrad optimizer. + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. dnn_hidden_units: List of hidden units per layer. All layers are fully connected. dnn_activation_fn: Activation function applied to each layer. If None, diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_test.py b/tensorflow/contrib/estimator/python/estimator/dnn_test.py index 75e3107670d658e55ce23d983e47311f1c180104..050b0428bf7b685229e12561cfb0682d931299d2 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn_test.py @@ -38,7 +38,7 @@ from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache -def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): +def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg """Returns a DNNEstimator that uses regression_head.""" return dnn.DNNEstimator( head=head_lib.regression_head( @@ -48,6 +48,12 @@ def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): *args, **kwargs) +def _dnn_estimator_classifier_fn(n_classes=3, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg + """Returns a DNNEstimator that uses multi_class_head.""" + return dnn.DNNEstimator(head=head_lib.multi_class_head(n_classes=n_classes), + *args, **kwargs) + + class DNNEstimatorEvaluateTest( dnn_testing_utils.BaseDNNRegressorEvaluateTest, test.TestCase): @@ -75,6 +81,15 @@ class DNNEstimatorTrainTest( self, _dnn_estimator_fn) +class DNNEstimatorWarmStartingTest(dnn_testing_utils.BaseDNNWarmStartingTest, + test.TestCase): + + def __init__(self, methodName='runTest'): # pylint: disable=invalid-name + test.TestCase.__init__(self, methodName) + dnn_testing_utils.BaseDNNWarmStartingTest.__init__( + self, _dnn_estimator_classifier_fn, _dnn_estimator_fn) + + class DNNEstimatorIntegrationTest(test.TestCase): def setUp(self): diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index b798769d2cfde69e9e0b8d65882a07d038cbb994..c9d86ef4ab89950b0c7b0414ba60d9e0a1cbe476 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -529,11 +529,13 @@ def multi_label_head(n_classes, applications, the shape is `[batch_size, n_classes]`. Labels can be: + * A multi-hot tensor of shape `[D0, D1, ... DN, n_classes]` * An integer `SparseTensor` of class indices. The `dense_shape` must be `[D0, D1, ... DN, ?]` and the values within `[0, n_classes)`. * If `label_vocabulary` is given, a string `SparseTensor`. The `dense_shape` - must be `[D0, D1, ... DN, ?]` and the values within `label_vocabulary`. + must be `[D0, D1, ... DN, ?]` and the values within `label_vocabulary` or a + multi-hot tensor of shape `[D0, D1, ... DN, n_classes]`. If `weight_column` is specified, weights must be of shape `[D0, D1, ... DN]`, or `[D0, D1, ... DN, 1]`. diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index b2b57fa06ba818d4455871fe57dde5ce287b39a2..7b884402d4650636bc9fe053994246aabb9c312d 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -568,6 +568,33 @@ class MultiLabelHead(test.TestCase): expected_loss=expected_loss, expected_metrics=expected_metrics) + def test_eval_with_label_vocabulary_with_multi_hot_input(self): + n_classes = 2 + head = head_lib.multi_label_head( + n_classes, label_vocabulary=['class0', 'class1']) + logits = np.array([[-1., 1.], [-1.5, 1.5]], dtype=np.float32) + labels_multi_hot = np.array([[1, 0], [1, 1]], dtype=np.int64) + # loss = labels * -log(sigmoid(logits)) + + # (1 - labels) * -log(1 - sigmoid(logits)) + # Sum over examples, divide by batch_size. + expected_loss = 0.5 * np.sum( + _sigmoid_cross_entropy(labels=labels_multi_hot, logits=logits)) + keys = metric_keys.MetricKeys + expected_metrics = { + # Average loss over examples. + keys.LOSS_MEAN: expected_loss, + # auc and auc_pr cannot be reliably calculated for only 4 samples, but + # this assert tests that the algorithm remains consistent. + keys.AUC: 0.3333, + keys.AUC_PR: 0.7639, + } + self._test_eval( + head=head, + logits=logits, + labels=labels_multi_hot, + expected_loss=expected_loss, + expected_metrics=expected_metrics) + def test_eval_with_thresholds(self): n_classes = 2 thresholds = [0.25, 0.5, 0.75] diff --git a/tensorflow/contrib/estimator/python/estimator/linear.py b/tensorflow/contrib/estimator/python/estimator/linear.py index 3bf4abe83d54504d55de73b63f369cceaf149dd2..b960b16f1ba6b1bf8046c922e21ac1ed136c599e 100644 --- a/tensorflow/contrib/estimator/python/estimator/linear.py +++ b/tensorflow/contrib/estimator/python/estimator/linear.py @@ -39,6 +39,18 @@ class LinearEstimator(estimator.Estimator): feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b]) + # Or estimator using an optimizer with a learning rate decay. + estimator = LinearEstimator( + head=tf.contrib.estimator.multi_label_head(n_classes=3), + feature_columns=[categorical_column_a, + categorical_feature_a_x_categorical_feature_b], + optimizer=lambda: tf.train.FtrlOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + # Or estimator using the FTRL optimizer with regularization. estimator = LinearEstimator( head=tf.contrib.estimator.multi_label_head(n_classes=3), @@ -99,8 +111,9 @@ class LinearEstimator(estimator.Estimator): model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to FTRL optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to FTRL optimizer. config: `RunConfig` object to configure the runtime settings. partitioner: Optional. Partitioner for input layer. """ diff --git a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc index bb9b835889b1b5e36d6f470b51834d4c6bb3d493..7fcae5ad8e1536530e2d039e1d14df4e192c4fa3 100644 --- a/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc +++ b/tensorflow/contrib/factorization/kernels/wals_solver_ops.cc @@ -62,10 +62,11 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { public: explicit WALSComputePartialLhsAndRhsOp(OpKernelConstruction* context) : OpKernel(context) { - OP_REQUIRES_OK(context, context->MatchSignature( - {DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, - DT_INT64, DT_FLOAT, DT_INT64, DT_BOOL}, - {DT_FLOAT, DT_FLOAT})); + OP_REQUIRES_OK(context, + context->MatchSignature( + {DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64, + DT_FLOAT, DT_FLOAT, DT_INT64, DT_BOOL}, + {DT_FLOAT, DT_FLOAT})); } void Compute(OpKernelContext* context) override { @@ -75,8 +76,9 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { const Tensor& input_weights = context->input(3); const Tensor& input_indices = context->input(4); const Tensor& input_values = context->input(5); - const Tensor& input_block_size = context->input(6); - const Tensor& input_is_transpose = context->input(7); + const Tensor& entry_weights = context->input(6); + const Tensor& input_block_size = context->input(7); + const Tensor& input_is_transpose = context->input(8); OP_REQUIRES(context, TensorShapeUtils::IsMatrix(factors.shape()), InvalidArgument("Input factors should be a matrix.")); @@ -89,13 +91,33 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { InvalidArgument("Input input_weights should be a vector.")); OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices.shape()), InvalidArgument("Input input_indices should be a matrix.")); + OP_REQUIRES( + context, input_indices.dim_size(1) == 2, + InvalidArgument("Input input_indices should have shape (?, 2).")); OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values.shape()), InvalidArgument("Input input_values should be a vector")); + OP_REQUIRES(context, TensorShapeUtils::IsVector(entry_weights.shape()), + InvalidArgument("Input entry_weights should be a vector")); + OP_REQUIRES(context, input_indices.dim_size(0) == input_values.dim_size(0), + InvalidArgument("Input input_values' length should match the " + "first dimension of Input input_indices ")); OP_REQUIRES(context, TensorShapeUtils::IsScalar(input_block_size.shape()), InvalidArgument("Input input_block_size should be a scalar.")); OP_REQUIRES( context, TensorShapeUtils::IsScalar(input_is_transpose.shape()), InvalidArgument("Input input_is_transpose should be a scalar.")); + OP_REQUIRES( + context, + ((input_weights.dim_size(0) > 0 && + factor_weights.dim_size(0) == factors.dim_size(0) && + entry_weights.dim_size(0) == 0) || + (input_weights.dim_size(0) == 0 && factor_weights.dim_size(0) == 0 && + entry_weights.dim_size(0) == input_indices.dim_size(0))), + InvalidArgument("To specify the weights for observed entries, either " + "(1) entry_weights must be set or (2) input_weights " + "and factor_weights must be set, but not both.")); + // TODO(yifanchen): Deprecate the support of input_weights and + // factor_weights. const int64 factor_dim = factors.dim_size(1); const int64 factors_size = factors.dim_size(0); @@ -105,6 +127,7 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { const auto& input_weights_vec = input_weights.vec(); const float w_0 = unobserved_weights.scalar()(); const auto& input_values_vec = input_values.vec(); + const auto& entry_weights_vec = entry_weights.vec(); ConstEigenMatrixFloatMap factors_mat(factors.matrix().data(), factor_dim, factors_size); @@ -134,6 +157,8 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { return is_transpose ? indices_mat(0, i) : indices_mat(1, i); }; + const bool use_entry_weights = entry_weights_vec.size() > 0; + // TODO(rmlarsen): In principle, we should be using the SparseTensor class // and machinery for iterating over groups, but the fact that class // SparseTensor makes a complete copy of the matrix makes me reluctant to @@ -195,6 +220,8 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { // map using the hash of the thread id as the key. // // TODO(jpoulson): Switch to try_emplace once C++17 is supported + // TODO(b/72952120): Check whether the 3 lock-unlock pairs can be + // consolidated into just one. map_mutex.lock(); const auto key_count = factor_batch_map.count(id_hash); map_mutex.unlock(); @@ -213,6 +240,8 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { CHECK_LE(shard.second, perm.size()); CHECK_LE(shard.first, shard.second); const int64 input_index = get_input_index(perm[shard.first]); + const float input_weight = + use_entry_weights ? 1.0 : input_weights_vec(input_index); // Accumulate the rhs and lhs terms in the normal equations // for the non-zero elements in the row or column of the sparse matrix // corresponding to input_index. @@ -228,7 +257,8 @@ class WALSComputePartialLhsAndRhsOp : public OpKernel { const int64 factor_index = get_factor_index(i); const float input_value = input_values_vec(i); const float weight = - input_weights_vec(input_index) * factor_weights_vec(factor_index); + use_entry_weights ? entry_weights_vec(i) + : input_weight * factor_weights_vec(factor_index); CHECK_GE(weight, 0); factor_batch.col(num_batched) = factors_mat.col(factor_index) * std::sqrt(weight); diff --git a/tensorflow/contrib/factorization/ops/factorization_ops.cc b/tensorflow/contrib/factorization/ops/factorization_ops.cc index 11ea36946e92769cd6901eb998a20148250ef7ce..1d31bd38c824f24e9a70c0f69da129f5ddc18985 100644 --- a/tensorflow/contrib/factorization/ops/factorization_ops.cc +++ b/tensorflow/contrib/factorization/ops/factorization_ops.cc @@ -25,20 +25,33 @@ REGISTER_OP("WALSComputePartialLhsAndRhs") .Input("input_weights: float32") .Input("input_indices: int64") .Input("input_values: float32") + .Input("entry_weights: float32") .Input("input_block_size: int64") .Input("input_is_transpose: bool") .Output("partial_lhs: float32") .Output("partial_rhs: float32") .SetShapeFn(shape_inference::UnknownShape) .Doc(R"( -Computes the partial left-hand side and right-hand side of WALS update. +Computes the partial left-hand side and right-hand side of WALS update. For +observed entry input_indices[i]=[m, n] with value input_values[i]=v, the weight +should be specified either through (1) entry_weights[i] or (2) through +input_weights[m] * factor_weights[n] (if input_is_transpose is false) or +input_weights[n] * factor_weights[m] (if input_is_transpose is true). Note it is +not allowed to have both (1) and (2) specified at the same time: when one +approach is used, the input tensors related to the other approach must be kept +completely empty. factors: Matrix of size m * k. -factor_weights: Vector of size m. Corresponds to column weights +factor_weights: Vector of size m. Corresponds to column weights. Should be empty + if entry_weights is used. unobserved_weights: Scalar. Weight for unobserved input entries. -input_weights: Vector of size n. Corresponds to row weights. +input_weights: Vector of size n. Corresponds to row weights. Should be empty if + entry_weights is used. input_indices: Indices for the input SparseTensor. input_values: Values for the input SparseTensor. +entry_weights: If not empty, this must be same length as input_vaues and is used + as the per-entry non-zero weight. If this is used, input_weights and + factor_weights must be empty. input_block_size: Scalar. Number of rows spanned by input. input_is_transpose: If true, logically transposes the input for processing. partial_lhs: 3-D tensor with size input_block_size x k x k. diff --git a/tensorflow/contrib/factorization/python/kernel_tests/wals_solver_ops_test.py b/tensorflow/contrib/factorization/python/kernel_tests/wals_solver_ops_test.py index ba30fd997700f461b6afffa13cf371c598d3332e..6c2f1d46084d701beac1e3a99e3ad66bae57eda5 100644 --- a/tensorflow/contrib/factorization/python/kernel_tests/wals_solver_ops_test.py +++ b/tensorflow/contrib/factorization/python/kernel_tests/wals_solver_ops_test.py @@ -55,7 +55,41 @@ class WalsSolverOpsTest(test.TestCase): rhs_matrix] = gen_factorization_ops.wals_compute_partial_lhs_and_rhs( self._column_factors, self._column_weights, self._unobserved_weights, self._row_weights, sparse_block.indices, sparse_block.values, - sparse_block.dense_shape[0], False) + [], + input_block_size=sparse_block.dense_shape[0], + input_is_transpose=False) + self.assertAllClose(lhs_tensor.eval(), [[ + [0.014800, 0.017000, 0.019200], + [0.017000, 0.019600, 0.022200], + [0.019200, 0.022200, 0.025200], + ], [ + [0.0064000, 0.0080000, 0.0096000], + [0.0080000, 0.0100000, 0.0120000], + [0.0096000, 0.0120000, 0.0144000], + ], [ + [0.0099000, 0.0126000, 0.0153000], + [0.0126000, 0.0162000, 0.0198000], + [0.0153000, 0.0198000, 0.0243000], + ], [ + [0.058800, 0.067200, 0.075600], + [0.067200, 0.076800, 0.086400], + [0.075600, 0.086400, 0.097200], + ]]) + self.assertAllClose(rhs_matrix.eval(), [[0.019300, 0.023000, 0.026700], + [0.061600, 0.077000, 0.092400], + [0.160400, 0.220000, 0.279600], + [0.492800, 0.563200, 0.633600]]) + + def testWalsSolverLhsEntryWeights(self): + sparse_block = SparseBlock3x3() + with self.test_session(): + [lhs_tensor, + rhs_matrix] = gen_factorization_ops.wals_compute_partial_lhs_and_rhs( + self._column_factors, [], self._unobserved_weights, + [], sparse_block.indices, sparse_block.values, + [0.01, 0.03, 0.04, 0.03, 0.06, 0.12], + input_block_size=sparse_block.dense_shape[0], + input_is_transpose=False) self.assertAllClose(lhs_tensor.eval(), [[ [0.014800, 0.017000, 0.019200], [0.017000, 0.019600, 0.022200], diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops.py b/tensorflow/contrib/factorization/python/ops/factorization_ops.py index 8f73274c2a0ebbdc41ce6a647a8a5650694c9a23..7ab70fbcfd7324961b61526a08daab7e393630e9 100644 --- a/tensorflow/contrib/factorization/python/ops/factorization_ops.py +++ b/tensorflow/contrib/factorization/python/ops/factorization_ops.py @@ -943,6 +943,7 @@ class WALSModel(object): row_weights_slice, new_sp_input.indices, new_sp_input.values, + [], num_rows, transpose_input, name="wals_compute_partial_lhs_rhs")) diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index b588f75efe9d0bbf8213a89978a627c0a0ccf554..05bcdac2caa77062f9a8a44a948d2897b439ea1f 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -95,7 +95,7 @@ def sequence_input_layer( Raises: ValueError: If any of the `feature_columns` is the wrong type. """ - feature_columns = fc._clean_feature_columns(feature_columns) + feature_columns = fc._normalize_feature_columns(feature_columns) for c in feature_columns: if not isinstance(c, fc._SequenceDenseColumn): raise ValueError( diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index 89b5f4c4137f6c42417f539a578fd8b11f8b235d..45d7b740462ca21139e2e93e34b43668f1e08a94 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -110,7 +110,7 @@ class SequenceInputLayerTest(test.TestCase): expected_sequence_length, sequence_length.eval(session=sess)) def test_embedding_column_with_non_sequence_categorical(self): - """Tests that error is raised for non-sequence categorical column.""" + """Tests that error is raised for non-sequence embedding column.""" vocabulary_size = 3 sparse_input = sparse_tensor.SparseTensorValue( # example 0, ids [2] @@ -132,6 +132,107 @@ class SequenceInputLayerTest(test.TestCase): features={'aaa': sparse_input}, feature_columns=[embedding_column_a]) + def test_shared_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + + def _get_initializer(embedding_dimension, embedding_values): + + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 3., 4.], [0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 5., 6.], [3., 4., 1., 2.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + # Test that columns are reordered alphabetically. + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_b, categorical_column_a], + dimension=embedding_dimension, + initializer=_get_initializer(embedding_dimension, embedding_values)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + feature_columns=shared_embedding_columns) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_bbb_shared_embedding/embedding_weights:0',), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_shared_embedding_column_with_non_sequence_categorical(self): + """Tests that error is raised for non-sequence shared embedding column.""" + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = fc.categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = fc.categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + with self.assertRaisesRegexp( + ValueError, + r'In embedding_column: aaa_shared_embedding\. categorical_column must ' + r'be of type _SequenceCategoricalColumn to use sequence_input_layer\.'): + _, _ = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b + }, + feature_columns=shared_embedding_columns) + def test_indicator_column(self): vocabulary_size_a = 3 sparse_input_a = sparse_tensor.SparseTensorValue( @@ -578,6 +679,182 @@ class SequenceEmbeddingColumnTest(test.TestCase): expected_sequence_length, sequence_length.eval(session=sess)) +class SequenceSharedEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [0, 2] + # example 2, ids [0] + # example 3, ids [] + indices=((0, 0), (1, 0), (1, 1), (2, 0)), + values=(1, 0, 2, 0), + dense_shape=(4, 2)) + + expected_lookups_a = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + expected_lookups_b = [ + # example 0, ids [1] + [[3., 5.], [0., 0.]], + # example 1, ids [0, 2] + [[1., 2.], [7., 11.]], + # example 2, ids [0] + [[1., 2.], [0., 0.]], + # example 3, ids [] + [[0., 0.], [0., 0.]], + ] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], + dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[0] + embedding_lookup_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[0] + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual(('embedding_weights:0',), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual( + expected_lookups_a, embedding_lookup_a.eval(session=sess)) + self.assertAllEqual( + expected_lookups_b, embedding_lookup_b.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length_a = [1, 2] + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [0, 2] + # example 1, ids [1] + indices=((0, 0), (0, 1), (1, 0)), + values=(0, 2, 1), + dense_shape=(2, 2)) + expected_sequence_length_b = [2, 1] + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[1] + sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[1] + + with monitored_session.MonitoredSession() as sess: + sequence_length_a = sess.run(sequence_length_a) + self.assertAllEqual(expected_sequence_length_a, sequence_length_a) + self.assertEqual(np.int64, sequence_length_a.dtype) + sequence_length_b = sess.run(sequence_length_b) + self.assertAllEqual(expected_sequence_length_b, sequence_length_b) + self.assertEqual(np.int64, sequence_length_b.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length_a = [0, 1, 2, 0, 1, 0] + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [] + # example 2, ids [] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [0, 1] + indices=((0, 0), (4, 0), (5, 0), (5, 1)), + values=(2, 1, 0, 1), + dense_shape=(6, 2)) + expected_sequence_length_b = [1, 0, 0, 0, 1, 2] + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[1] + sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[1] + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length_a, sequence_length_a.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length_b, sequence_length_b.eval(session=sess)) + + class SequenceIndicatorColumnTest(test.TestCase): def test_get_sequence_dense_tensor(self): diff --git a/tensorflow/contrib/framework/python/ops/critical_section_test.py b/tensorflow/contrib/framework/python/ops/critical_section_test.py index df7d7e9dae80722569efccbc9cc0d1b75e90cf03..34fd5018af125335845540dedfdffc984ba02313 100644 --- a/tensorflow/contrib/framework/python/ops/critical_section_test.py +++ b/tensorflow/contrib/framework/python/ops/critical_section_test.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import tf_logging as logging class CriticalSectionTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCreateCriticalSection(self): cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") @@ -53,7 +53,7 @@ class CriticalSectionTest(test.TestCase): self.assertAllClose([2.0 * i for i in range(num_concurrent)], sorted(r_value)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCriticalSectionWithControlFlow(self): for outer_cond in [False, True]: for inner_cond in [False, True]: @@ -109,7 +109,7 @@ class CriticalSectionTest(test.TestCase): with self.assertRaisesOpError("Error"): self.evaluate(r) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCreateCriticalSectionFnReturnsOp(self): cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") @@ -332,7 +332,7 @@ class CriticalSectionTest(test.TestCase): self.evaluate(v.initializer) self.assertEqual(10, self.evaluate(out)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInsideFunction(self): cs = critical_section_ops.CriticalSection() v = resource_variable_ops.ResourceVariable(1) diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index 40ae01bfcce1dde580e6a5f6d9c8ec1aa1abb83f..e8e318001972934c7d2154bc14744823a3ba09f9 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -712,7 +712,8 @@ class VariableDeviceChooser(object): num_tasks=0, job_name='ps', device_type='CPU', - device_index=0): + device_index=0, + replica=None): """Initialize VariableDeviceChooser. Usage: @@ -733,12 +734,15 @@ class VariableDeviceChooser(object): self._job_name = job_name self._device_type = device_type self._device_index = device_index + self._replica = replica self._num_tasks = num_tasks self._next_task_id = 0 def __call__(self, op): - device_spec = tf_device.DeviceSpec(device_type=self._device_type, - device_index=self._device_index) + device_spec = tf_device.DeviceSpec( + replica=self._replica, + device_type=self._device_type, + device_index=self._device_index) if self._num_tasks > 0: task_id = self._next_task_id self._next_task_id = (self._next_task_id + 1) % self._num_tasks diff --git a/tensorflow/contrib/framework/python/ops/variables_test.py b/tensorflow/contrib/framework/python/ops/variables_test.py index 37ea6eb12aba7d25656f19cbbc86475c1228d916..7e0c7dbec1d9266b53a169fe83b88d1e3af77d04 100644 --- a/tensorflow/contrib/framework/python/ops/variables_test.py +++ b/tensorflow/contrib/framework/python/ops/variables_test.py @@ -506,6 +506,35 @@ class VariablesTest(test.TestCase): self.assertDeviceEqual(e.device, '/job:ps/task:1/cpu:0') self.assertDeviceEqual(e.initial_value.device, '/cpu:99') + def testVariableWithVariableDeviceChooserWithReplica(self): + + with ops.Graph().as_default(): + device_fn = variables_lib2.VariableDeviceChooser(replica=3, num_tasks=2) + with arg_scope([variables_lib2.variable], device=device_fn): + a = variables_lib2.variable('a', []) + b = variables_lib2.variable('b', []) + c = variables_lib2.variable('c', [], device='cpu:12') + d = variables_lib2.variable('d', []) + with ops.device('cpu:99'): + e_init = constant_op.constant(12) + e = variables_lib2.variable('e', initializer=e_init) + # The values below highlight how the VariableDeviceChooser puts initial + # values on the same device as the variable job. + self.assertDeviceEqual(a.device, '/job:ps/replica:3/task:0/cpu:0') + self.assertEqual(a.initial_value.op.colocation_groups(), + a.op.colocation_groups()) + self.assertDeviceEqual(b.device, '/job:ps/replica:3/task:1/cpu:0') + self.assertEqual(b.initial_value.op.colocation_groups(), + b.op.colocation_groups()) + self.assertDeviceEqual(c.device, '/cpu:12') + self.assertEqual(c.initial_value.op.colocation_groups(), + c.op.colocation_groups()) + self.assertDeviceEqual(d.device, '/job:ps/replica:3/task:0/cpu:0') + self.assertEqual(d.initial_value.op.colocation_groups(), + d.op.colocation_groups()) + self.assertDeviceEqual(e.device, '/job:ps/replica:3/task:1/cpu:0') + self.assertDeviceEqual(e.initial_value.device, '/cpu:99') + def testVariableGPUPlacement(self): with ops.Graph().as_default(): @@ -930,8 +959,8 @@ class AssignFromCheckpointTest(test.TestCase): return saver.save(sess, checkpoint_dir, global_step=global_step) def testLoadExistingVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'load_existing_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_existing_variables')) init_value0 = 10.0 init_value1 = 20.0 @@ -944,8 +973,8 @@ class AssignFromCheckpointTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -960,8 +989,8 @@ class AssignFromCheckpointTest(test.TestCase): # Tests restoring PartitionedVariables and tests using a dictionary # of lists as the assign_from_checkpoint() var_list param. def testLoadPartitionedVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'load_partitioned_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_partitioned_variables')) init_value0 = np.array([[10.0, 11.0], [12.0, 13.0]]) init_value1 = np.array([20.0]) # Partitioned into 1 part, edge case. @@ -974,15 +1003,14 @@ class AssignFromCheckpointTest(test.TestCase): partitioner = partitioned_variables.variable_axis_size_partitioner(2) var0 = variables_lib2.variable( 'var0', shape=init_value0.shape, partitioner=partitioner) - var0full = variables_lib2.variable( - 'var0full', shape=init_value0.shape) + var0full = variables_lib2.variable('var0full', shape=init_value0.shape) var1 = variables_lib2.variable( 'var1', shape=init_value1.shape, partitioner=partitioner) # Convert var0 and var1 into a list of underlying variables. vars_to_restore = {'var0': list(var0) + [var0full], 'var1': list(var1)} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -992,16 +1020,18 @@ class AssignFromCheckpointTest(test.TestCase): # Request and test the variable values. PartitionedVariables can't # be evaled so we wrap them in an identity. - self.assertTrue(np.array_equal( - init_value0, array_ops.identity(var0).eval())) - self.assertTrue(np.array_equal( - init_value0, var0full.eval())) - self.assertTrue(np.array_equal( - init_value1, array_ops.identity(var1).eval())) + self.assertTrue( + np.array_equal(init_value0, + array_ops.identity(var0).eval())) + self.assertTrue(np.array_equal(init_value0, var0full.eval())) + self.assertTrue( + np.array_equal(init_value1, + array_ops.identity(var1).eval())) def testRaisesValueErrorIfAVariableIsntFound(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'raises_value_error_if_var_isnt_found')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'raises_value_error_if_var_isnt_found')) init_value0 = 10.0 init_value1 = 20.0 @@ -1019,8 +1049,9 @@ class AssignFromCheckpointTest(test.TestCase): variables_lib2.assign_from_checkpoint(model_path, vars_to_restore) def testInitFromCheckpointWithScopes(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'init_from_checkpoint_with_scopes')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'init_from_checkpoint_with_scopes')) init_value0 = np.asarray( [1.0, 3.0, 9.0], dtype=np.float32).reshape((1, 3, 1)) @@ -1038,8 +1069,8 @@ class AssignFromCheckpointTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=init_value1.shape) vars_to_restore = {'layer0/v0': var0, 'layer1/v1': var1} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1081,8 +1112,8 @@ class AssignFromCheckpointFnTest(test.TestCase): return saver.save(sess, checkpoint_dir, global_step=global_step) def testLoadExistingVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'load_existing_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_existing_variables')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1097,8 +1128,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1111,8 +1142,9 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testLoadExistingVariablesDifferentShapeDefaultDoesNotAllowReshape(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'load_existing_vars_no_reshape')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'load_existing_vars_no_reshape')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1127,8 +1159,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1138,9 +1170,10 @@ class AssignFromCheckpointFnTest(test.TestCase): init_fn(sess) def testLoadExistingVariablesDifferentShapeAllowReshape(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), - 'load_existing_variables_different_shape_allow_reshape')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join( + self.get_temp_dir(), + 'load_existing_variables_different_shape_allow_reshape')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1169,8 +1202,8 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testNotFoundError(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'not_found_error')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'not_found_error')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1186,8 +1219,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var2 = variables_lib2.variable('my_var2', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1, 'v2': var2} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1197,8 +1230,8 @@ class AssignFromCheckpointFnTest(test.TestCase): init_fn(sess) def testMissingVariablesList(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'missing_variables_list')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'missing_variables_list')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1228,8 +1261,8 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testMissingVariablesDict(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'missing_variables_dict')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'missing_variables_dict')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1279,9 +1312,8 @@ class ZeroInitializerOpTest(test.TestCase): def testZeroInitializer(self): for dtype in (dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64): for use_init in (False, True): - self._testZeroInitializer( - [10, 20], array_ops.ones( - [10, 20], dtype=dtype), use_init) + self._testZeroInitializer([10, 20], array_ops.ones( + [10, 20], dtype=dtype), use_init) class ZeroVarInitializerOpTest(test.TestCase): diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py index ff903a78cc36c1965b7655aa902501b1943637a8..d1441e1eb2aae0fb7d1771110f969bf727ebbb14 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_impl.py @@ -24,6 +24,7 @@ from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python import train as tfgan_train from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator.canned import head +from tensorflow.python.estimator.export import export_output from tensorflow.python.framework import ops from tensorflow.python.ops import metrics as metrics_lib @@ -102,9 +103,20 @@ class GANHead(head._Head): # pylint: disable=protected-access name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. """ + + if not callable(generator_loss_fn): + raise TypeError('generator_loss_fn must be callable.') + if not callable(discriminator_loss_fn): + raise TypeError('discriminator_loss_fn must be callable.') + if not use_loss_summaries in [True, False, None]: + raise ValueError('use_loss_summaries must be True, False or None.') + if get_hooks_fn is not None and not callable(get_hooks_fn): + raise TypeError('get_hooks_fn must be callable.') + if name is not None and not isinstance(name, str): + raise TypeError('name must be string.') + if get_hooks_fn is None: get_hooks_fn = tfgan_train.get_sequential_train_hooks() - # TODO(joelshor): Validate inputs. if use_loss_summaries in [True, False]: generator_loss_fn = functools.partial( @@ -182,7 +194,10 @@ class GANHead(head._Head): # pylint: disable=protected-access if mode == model_fn_lib.ModeKeys.PREDICT: return model_fn_lib.EstimatorSpec( mode=model_fn_lib.ModeKeys.PREDICT, - predictions=gan_model.generated_data) + predictions=gan_model.generated_data, + export_outputs={ + 'predict': export_output.PredictOutput(gan_model.generated_data) + }) elif mode == model_fn_lib.ModeKeys.EVAL: gan_loss = self.create_loss( features=None, mode=mode, logits=gan_model, labels=None) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py index 6587f1fc600b94d27f7c12b44ca2136d0be5a8c5..5309d87765694fa476dae006105e842420a7c437 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py @@ -26,8 +26,11 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test +from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import training +_DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + def dummy_loss(gan_model, add_summaries=True): # pylint:disable=unused-argument return math_ops.reduce_sum(gan_model.discriminator_real_outputs - @@ -71,13 +74,15 @@ class GANHeadTest(test.TestCase): return {} def _test_modes_helper(self, mode): - self.gan_head.create_estimator_spec( + return self.gan_head.create_estimator_spec( features=None, mode=mode, logits=get_gan_model()) def test_modes_predict(self): - self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) + spec = self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) + self.assertItemsEqual((_DEFAULT_SERVING_KEY, 'predict'), + spec.export_outputs.keys()) def test_modes_eval(self): self._test_modes_helper(model_fn_lib.ModeKeys.EVAL) diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 81e70ae30a4c72dbcedd1aabfe758ecca4c8b366..1435e19109ca2f3bbd6ce70e6e5f26a92dfc2713 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -34,8 +34,9 @@ limitations under the License. #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/dma_helper.h" #if GOOGLE_CUDA +#include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#include "tensorflow/core/common_runtime/gpu/process_state.h" +#include "tensorflow/core/common_runtime/process_state.h" #endif // GOOGLE_CUDA #include "tensorflow/core/framework/allocator_registry.h" #include "tensorflow/core/lib/core/status.h" @@ -274,7 +275,7 @@ Status GdrMemoryManager::Init() { Allocator* allocators[] = { #if GOOGLE_CUDA - ProcessState::singleton()->GetCUDAHostAllocator(0), + GPUProcessState::singleton()->GetCUDAHostAllocator(0), ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA cpu_allocator(), @@ -308,7 +309,8 @@ Status GdrMemoryManager::Init() { if (IsGDRAvailable()) { // Note we don't free allocated GPU memory so there is no free visitor int32_t bus_id = TryToReadNumaNode(listening_->verbs->device) + 1; - ProcessState::singleton()->AddGPUAllocVisitor(bus_id, cuda_alloc_visitor); + GPUProcessState::singleton()->AddGPUAllocVisitor(bus_id, + cuda_alloc_visitor); LOG(INFO) << "Instrumenting GPU allocator with bus_id " << bus_id; } #endif // GOOGLE_CUDA @@ -430,7 +432,7 @@ void GdrMemoryManager::TransportOptionsFromTensor( #if GOOGLE_CUDA if (!on_host) { - Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0); + Allocator* alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0); Tensor* host_copy = new Tensor(alloc, tensor.dtype(), tensor.shape()); GPUUtil::CopyGPUTensorToCPU( device, device_context, &tensor, host_copy, @@ -532,7 +534,7 @@ void GdrMemoryManager::TensorFromTransportOptions( Tensor host_copy; #if GOOGLE_CUDA if (mr == nullptr && !on_host) { - Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0); + Allocator* alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0); host_copy = Tensor(alloc, tensor->dtype(), tensor->shape()); buffer = DMAHelper::buffer(&host_copy); addr = buffer->data(); diff --git a/tensorflow/contrib/gdr/gdr_server_lib.cc b/tensorflow/contrib/gdr/gdr_server_lib.cc index 1f9dd0decb84cf9b7b703f18c061d3c0c7a1cb25..9025c992a4467f521d6d8d514e6a5e92f5492947 100644 --- a/tensorflow/contrib/gdr/gdr_server_lib.cc +++ b/tensorflow/contrib/gdr/gdr_server_lib.cc @@ -57,7 +57,7 @@ Status GdrServer::Init() { new GdrWorker(env, remote_memory_manager_.get())); }; TF_RETURN_IF_ERROR( - GrpcServer::Init(nullptr, rendezvous_mgr_func, worker_func)); + GrpcServer::Init(nullptr, rendezvous_mgr_func, nullptr, worker_func)); return remote_memory_manager_->Init(); } diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc index c2e32da133b32c8fe169302668031af8bace2c22..022e17d13963a14f81d76e683d13060d1f3f8a7e 100644 --- a/tensorflow/contrib/image/kernels/image_ops.cc +++ b/tensorflow/contrib/image/kernels/image_ops.cc @@ -35,6 +35,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; template struct FillProjectiveTransform; template struct FillProjectiveTransform; template struct FillProjectiveTransform; +template struct FillProjectiveTransform; template struct FillProjectiveTransform; template struct FillProjectiveTransform; @@ -99,6 +100,7 @@ class ImageProjectiveTransform : public OpKernel { TF_CALL_uint8(REGISTER); TF_CALL_int32(REGISTER); TF_CALL_int64(REGISTER); +TF_CALL_half(REGISTER); TF_CALL_float(REGISTER); TF_CALL_double(REGISTER); diff --git a/tensorflow/contrib/image/kernels/image_ops.h b/tensorflow/contrib/image/kernels/image_ops.h index ad501330617be89c87a0e94ab6e8773a6e1eecf6..209aa24548443bb10c13cd506b8c93c23cfff4a4 100644 --- a/tensorflow/contrib/image/kernels/image_ops.h +++ b/tensorflow/contrib/image/kernels/image_ops.h @@ -21,6 +21,7 @@ limitations under the License. #define EIGEN_USE_THREADS #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" + #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/platform/types.h" @@ -58,6 +59,11 @@ class ProjectiveGenerator { ? transforms_.data() : &transforms_.data()[transforms_.dimension(1) * coords[0]]; float projection = transform[6] * output_x + transform[7] * output_y + 1.f; + if (projection == 0) { + // Return the fill value (0) for infinite coordinates, + // which are outside the input image + return T(0); + } const float input_x = (transform[0] * output_x + transform[1] * output_y + transform[2]) / projection; @@ -105,21 +111,21 @@ class ProjectiveGenerator { // f(x, y_floor) = (x_ceil - x) / (x_ceil - x_floor) * f(x_floor, y_floor) // + (x - x_floor) / (x_ceil - x_floor) * f(x_ceil, y_floor) const float value_yfloor = - (x_ceil - x) * read_with_fill_value(batch, DenseIndex(y_floor), - DenseIndex(x_floor), channel, - fill_value) + - (x - x_floor) * read_with_fill_value(batch, DenseIndex(y_floor), - DenseIndex(x_ceil), channel, - fill_value); + (x_ceil - x) * static_cast(read_with_fill_value( + batch, DenseIndex(y_floor), DenseIndex(x_floor), + channel, fill_value)) + + (x - x_floor) * static_cast(read_with_fill_value( + batch, DenseIndex(y_floor), DenseIndex(x_ceil), + channel, fill_value)); // f(x, y_ceil) = (x_ceil - x) / (x_ceil - x_floor) * f(x_floor, y_ceil) // + (x - x_floor) / (x_ceil - x_floor) * f(x_ceil, y_ceil) const float value_yceil = - (x_ceil - x) * read_with_fill_value(batch, DenseIndex(y_ceil), - DenseIndex(x_floor), channel, - fill_value) + - (x - x_floor) * read_with_fill_value(batch, DenseIndex(y_ceil), - DenseIndex(x_ceil), channel, - fill_value); + (x_ceil - x) * static_cast(read_with_fill_value( + batch, DenseIndex(y_ceil), DenseIndex(x_floor), + channel, fill_value)) + + (x - x_floor) * static_cast(read_with_fill_value( + batch, DenseIndex(y_ceil), DenseIndex(x_ceil), + channel, fill_value)); // f(x, y) = (y_ceil - y) / (y_ceil - y_floor) * f(x, y_floor) // + (y - y_floor) / (y_ceil - y_floor) * f(x, y_ceil) return T((y_ceil - y) * value_yfloor + (y - y_floor) * value_yceil); diff --git a/tensorflow/contrib/image/ops/image_ops.cc b/tensorflow/contrib/image/ops/image_ops.cc index ebdcaea7abae2a967786831b62b331897aa3f6a3..e59f1bf8443732a4b84fe7461439e3d0ee7dd158 100644 --- a/tensorflow/contrib/image/ops/image_ops.cc +++ b/tensorflow/contrib/image/ops/image_ops.cc @@ -29,7 +29,7 @@ using shape_inference::ShapeHandle; REGISTER_OP("ImageProjectiveTransform") .Input("images: dtype") .Input("transforms: float32") - .Attr("dtype: {uint8, int32, int64, float32, float64}") + .Attr("dtype: {uint8, int32, int64, float16, float32, float64}") .Attr("interpolation: string") .Output("transformed_images: dtype") .SetShapeFn([](InferenceContext* c) { diff --git a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py index b50177ae5651fbc15f292e11031411c2074357ec..62a22dcf3411fb160b3c432bbdd67303697f7262 100644 --- a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py +++ b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py @@ -30,7 +30,8 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest _DTYPES = set( - [dtypes.uint8, dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]) + [dtypes.uint8, dtypes.int32, dtypes.int64, + dtypes.float16, dtypes.float32, dtypes.float64]) class ImageOpsTest(test_util.TensorFlowTestCase): @@ -127,6 +128,23 @@ class ImageOpsTest(test_util.TensorFlowTestCase): [0, 1, 0, 1], [0, 1, 1, 1]]) + def test_extreme_projective_transform(self): + for dtype in _DTYPES: + with self.test_session(): + image = constant_op.constant( + [[1, 0, 1, 0], + [0, 1, 0, 1], + [1, 0, 1, 0], + [0, 1, 0, 1]], dtype=dtype) + transformation = constant_op.constant([1, 0, 0, 0, 1, 0, -1, 0], + dtypes.float32) + image_transformed = image_ops.transform(image, transformation) + self.assertAllEqual(image_transformed.eval(), + [[1, 0, 0, 0], + [0, 0, 0, 0], + [1, 0, 0, 0], + [0, 0, 0, 0]]) + def test_bilinear(self): with self.test_session(): image = constant_op.constant( diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py index cd984c80543886be1f682933e2e003bd3374e425..86b0ffe9a0f2236d5ac7d5f846e7b5d2615c9b09 100644 --- a/tensorflow/contrib/image/python/ops/image_ops.py +++ b/tensorflow/contrib/image/python/ops/image_ops.py @@ -33,7 +33,8 @@ _image_ops_so = loader.load_op_library( resource_loader.get_path_to_datafile("_image_ops.so")) _IMAGE_DTYPES = set( - [dtypes.uint8, dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]) + [dtypes.uint8, dtypes.int32, dtypes.int64, + dtypes.float16, dtypes.float32, dtypes.float64]) ops.RegisterShape("ImageConnectedComponents")(common_shapes.call_cpp_shape_fn) ops.RegisterShape("ImageProjectiveTransform")(common_shapes.call_cpp_shape_fn) diff --git a/tensorflow/contrib/integrate/python/ops/odes.py b/tensorflow/contrib/integrate/python/ops/odes.py index b4a99867ed46897f60be3f230838c3f576d5455e..61f78febfc07bb4e677259366a81c16b2b585244 100644 --- a/tensorflow/contrib/integrate/python/ops/odes.py +++ b/tensorflow/contrib/integrate/python/ops/odes.py @@ -28,7 +28,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import functional_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import tensor_array_ops @@ -279,13 +278,27 @@ def _assert_increasing(t): return ops.control_dependencies([assert_increasing]) -def _check_input_types(t, y0): +def _check_input_types(y0, t, dt=None): if not (y0.dtype.is_floating or y0.dtype.is_complex): raise TypeError('`y0` must have a floating point or complex floating ' 'point dtype') if not t.dtype.is_floating: raise TypeError('`t` must have a floating point dtype') + if dt is not None and not dt.dtype.is_floating: + raise TypeError('`dt` must have a floating point dtype') + + +def _check_input_sizes(t, dt): + if len(t.get_shape().as_list()) > 1: + raise ValueError('t must be a 1D tensor') + + if len(dt.get_shape().as_list()) > 1: + raise ValueError('t must be a 1D tensor') + + if t.get_shape()[0] != dt.get_shape()[0] + 1: + raise ValueError('t and dt have incompatible lengths, must be N and N-1') + def _dopri5(func, y0, @@ -510,7 +523,7 @@ def odeint(func, # avoiding the need to pack/unpack in user functions. y0 = ops.convert_to_tensor(y0, name='y0') t = ops.convert_to_tensor(t, preferred_dtype=dtypes.float64, name='t') - _check_input_types(t, y0) + _check_input_types(y0, t) error_dtype = abs(y0).dtype rtol = ops.convert_to_tensor(rtol, dtype=error_dtype, name='rtol') @@ -530,24 +543,74 @@ def odeint(func, class _FixedGridIntegrator(six.with_metaclass(abc.ABCMeta)): """Base class for fixed-grid ODE integrators.""" - def integrate(self, evol_func, y0, time_grid): - time_delta_grid = time_grid[1:] - time_grid[:-1] - - scan_func = self._make_scan_func(evol_func) + def integrate(self, evol_func, y0, time_grid, dt_grid, steps_on_intervals): + """Returns integrated values of differential equation on the `time grid`. + + Numerically integrates differential equation defined via time derivative + evaluator `evol_func` using fixed time steps specified in dt_grid. + + Args: + evol_func: Callable, evaluates time derivative of y at a given time. + y0: N-D Tensor holds initial values of the solution. + time_grid: 1-D Tensor holding the time points at which the solution + will be recorded, must have a floating dtype. + dt_grid: 1-D Tensor holds fixed time steps to be used on time_grid + intervals. Must be a floating dtype and have one less element than that + of the time_grid. + steps_on_intervals: 1-D Tensor of integer dtype, must have the same size + as dt_grid. Specifies number of steps needed for every interval. Assumes + steps_on_intervals * dt_grid == time intervals. + + Returns: + (N+1)-D tensor, where the first dimension corresponds to different + time points. Contains the solved value of y for each desired time point in + `t`, with the initial value `y0` being the first element along the first + dimension. + """ - y_grid = functional_ops.scan(scan_func, (time_grid[:-1], time_delta_grid), - y0) - return array_ops.concat([[y0], y_grid], axis=0) + iteration_func = self._make_iteration_func(evol_func, dt_grid) + integrate_interval = self._make_interval_integrator(iteration_func, + steps_on_intervals) - def _make_scan_func(self, evol_func): + num_times = array_ops.size(time_grid) + current_time = time_grid[0] + solution_array = tensor_array_ops.TensorArray(y0.dtype, num_times) + solution_array = solution_array.write(0, y0) - def scan_func(y, t_and_dt): - t, dt = t_and_dt + solution_array, _, _, _ = control_flow_ops.while_loop( + lambda _, __, ___, i: i < num_times, + integrate_interval, + (solution_array, y0, current_time, 1) + ) + solution_array = solution_array.stack() + solution_array.set_shape(time_grid.get_shape().concatenate(y0.get_shape())) + return solution_array + + def _make_iteration_func(self, evol_func, dt_grid): + """Returns a function that builds operations of a single time step.""" + + def iteration_func(y, t, dt_step, interval_step): + """Performs a single time step advance.""" + dt = dt_grid[interval_step - 1] dy = self._step_func(evol_func, t, dt, y) dy = math_ops.cast(dy, dtype=y.dtype) - return y + dy + return y + dy, t + dt, dt_step + 1, interval_step + + return iteration_func + + def _make_interval_integrator(self, iteration_func, interval_sizes): + """Returns a function that builds operations for interval integration.""" - return scan_func + def integrate_interval(solution_array, y, t, interval_num): + """Integrates y with fixed time step on interval `interval_num`.""" + y, t, _, _ = control_flow_ops.while_loop( + lambda _, __, j, interval_num: j < interval_sizes[interval_num - 1], + iteration_func, + (y, t, 0, interval_num) + ) + return solution_array.write(interval_num, y), y, t, interval_num + 1 + + return integrate_interval @abc.abstractmethod def _step_func(self, evol_func, t, dt, y): @@ -555,6 +618,7 @@ class _FixedGridIntegrator(six.with_metaclass(abc.ABCMeta)): class _MidpointFixedGridIntegrator(_FixedGridIntegrator): + """Fixed grid integrator implementing midpoint scheme.""" def _step_func(self, evol_func, t, dt, y): dt_cast = math_ops.cast(dt, y.dtype) @@ -563,6 +627,7 @@ class _MidpointFixedGridIntegrator(_FixedGridIntegrator): class _RK4FixedGridIntegrator(_FixedGridIntegrator): + """Fixed grid integrator implementing RK4 scheme.""" def _step_func(self, evol_func, t, dt, y): k1 = evol_func(y, t) @@ -575,7 +640,7 @@ class _RK4FixedGridIntegrator(_FixedGridIntegrator): return math_ops.add_n([k1, 2 * k2, 2 * k3, k4]) * (dt_cast / 6) -def odeint_fixed(func, y0, t, method='rk4', name=None): +def odeint_fixed(func, y0, t, dt=None, method='rk4', name=None): """ODE integration on a fixed grid (with no step size control). Useful in certain scenarios to avoid the overhead of adaptive step size @@ -590,6 +655,14 @@ def odeint_fixed(func, y0, t, method='rk4', name=None): `y`. The initial time point should be the first element of this sequence, and each time must be larger than the previous time. May have any floating point dtype. + dt: 0-D or 1-D Tensor providing time step suggestion to be used on time + integration intervals in `t`. 1-D Tensor should provide values + for all intervals, must have 1 less element than that of `t`. + If given a 0-D Tensor, the value is interpreted as time step suggestion + same for all intervals. If passed None, then time step is set to be the + t[1:] - t[:-1]. Defaults to None. The actual step size is obtained by + insuring an integer number of steps per interval, potentially reducing the + time step. method: One of 'midpoint' or 'rk4'. name: Optional name for the resulting operation. @@ -602,16 +675,29 @@ def odeint_fixed(func, y0, t, method='rk4', name=None): Raises: ValueError: Upon caller errors. """ - with ops.name_scope(name, 'odeint_fixed', [y0, t]): + with ops.name_scope(name, 'odeint_fixed', [y0, t, dt]): t = ops.convert_to_tensor(t, preferred_dtype=dtypes.float64, name='t') y0 = ops.convert_to_tensor(y0, name='y0') - _check_input_types(t, y0) + + intervals = t[1:] - t[:-1] + if dt is None: + dt = intervals + dt = ops.convert_to_tensor(dt, preferred_dtype=dtypes.float64, name='dt') + + steps_on_intervals = math_ops.ceil(intervals / dt) + dt = intervals / steps_on_intervals + steps_on_intervals = math_ops.cast(steps_on_intervals, dtype=dtypes.int32) + + _check_input_types(y0, t, dt) + _check_input_sizes(t, dt) with _assert_increasing(t): with ops.name_scope(method): if method == 'midpoint': - return _MidpointFixedGridIntegrator().integrate(func, y0, t) + return _MidpointFixedGridIntegrator().integrate(func, y0, t, dt, + steps_on_intervals) elif method == 'rk4': - return _RK4FixedGridIntegrator().integrate(func, y0, t) + return _RK4FixedGridIntegrator().integrate(func, y0, t, dt, + steps_on_intervals) else: raise ValueError('method not supported: {!s}'.format(method)) diff --git a/tensorflow/contrib/integrate/python/ops/odes_test.py b/tensorflow/contrib/integrate/python/ops/odes_test.py index 3ec01212d25ca8dc6e13f340177a5e85138868d5..c7b4e2faa84e1a87cb1904b22eb0008ab1ee4be6 100644 --- a/tensorflow/contrib/integrate/python/ops/odes_test.py +++ b/tensorflow/contrib/integrate/python/ops/odes_test.py @@ -242,40 +242,56 @@ class InterpolationTest(test.TestCase): class OdeIntFixedTest(test.TestCase): - def _test_integrate_sine(self, method): + def _test_integrate_sine(self, method, t, dt=None): def evol_func(y, t): del t return array_ops.stack([y[1], -y[0]]) y0 = [0., 1.] - time_grid = np.linspace(0., 10., 200) - y_grid = odes.odeint_fixed(evol_func, y0, time_grid, method=method) + y_grid = odes.odeint_fixed(evol_func, y0, t, dt, method=method) with self.test_session() as sess: y_grid_array = sess.run(y_grid) np.testing.assert_allclose( - y_grid_array[:, 0], np.sin(time_grid), rtol=1e-2, atol=1e-2) + y_grid_array[:, 0], np.sin(t), rtol=1e-2, atol=1e-2) - def _test_integrate_gaussian(self, method): + def _test_integrate_gaussian(self, method, t, dt=None): def evol_func(y, t): return -math_ops.cast(t, dtype=y.dtype) * y[0] y0 = [1.] - time_grid = np.linspace(0., 2., 100) - y_grid = odes.odeint_fixed(evol_func, y0, time_grid, method=method) + y_grid = odes.odeint_fixed(evol_func, y0, t, dt, method=method) with self.test_session() as sess: y_grid_array = sess.run(y_grid) np.testing.assert_allclose( - y_grid_array[:, 0], np.exp(-time_grid**2 / 2), rtol=1e-2, atol=1e-2) + y_grid_array[:, 0], np.exp(-t**2 / 2), rtol=1e-2, atol=1e-2) + + def _test_integrate_sine_all(self, method): + uniform_time_grid = np.linspace(0., 10., 200) + non_uniform_time_grid = np.asarray([0.0, 0.4, 4.7, 5.2, 7.0]) + uniform_dt = 0.02 + non_uniform_dt = np.asarray([0.01, 0.001, 0.05, 0.03]) + self._test_integrate_sine(method, uniform_time_grid) + self._test_integrate_sine(method, non_uniform_time_grid, uniform_dt) + self._test_integrate_sine(method, non_uniform_time_grid, non_uniform_dt) + + def _test_integrate_gaussian_all(self, method): + uniform_time_grid = np.linspace(0., 2., 100) + non_uniform_time_grid = np.asarray([0.0, 0.1, 0.7, 1.2, 2.0]) + uniform_dt = 0.01 + non_uniform_dt = np.asarray([0.01, 0.001, 0.1, 0.03]) + self._test_integrate_gaussian(method, uniform_time_grid) + self._test_integrate_gaussian(method, non_uniform_time_grid, uniform_dt) + self._test_integrate_gaussian(method, non_uniform_time_grid, non_uniform_dt) def _test_everything(self, method): - self._test_integrate_sine(method) - self._test_integrate_gaussian(method) + self._test_integrate_sine_all(method) + self._test_integrate_gaussian_all(method) def test_midpoint(self): self._test_everything('midpoint') @@ -283,6 +299,21 @@ class OdeIntFixedTest(test.TestCase): def test_rk4(self): self._test_everything('rk4') + def test_dt_size_exceptions(self): + times = np.linspace(0., 2., 100) + dt = np.ones(99) * 0.01 + dt_wrong_length = np.asarray([0.01, 0.001, 0.1, 0.03]) + dt_wrong_dim = np.expand_dims(np.linspace(0., 2., 99), axis=0) + times_wrong_dim = np.expand_dims(np.linspace(0., 2., 100), axis=0) + with self.assertRaises(ValueError): + self._test_integrate_gaussian('midpoint', times, dt_wrong_length) + + with self.assertRaises(ValueError): + self._test_integrate_gaussian('midpoint', times, dt_wrong_dim) + + with self.assertRaises(ValueError): + self._test_integrate_gaussian('midpoint', times_wrong_dim, dt) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/keras/api/keras/layers/__init__.py b/tensorflow/contrib/keras/api/keras/layers/__init__.py index 938c881fcbe18623fa18c21c112375f9914f887b..3327a9f9a613bfb56e6a25af0fe1c0ca18609035 100644 --- a/tensorflow/contrib/keras/api/keras/layers/__init__.py +++ b/tensorflow/contrib/keras/api/keras/layers/__init__.py @@ -20,10 +20,10 @@ from __future__ import print_function # Generic layers. # pylint: disable=g-bad-import-order -from tensorflow.python.keras.engine import Input -from tensorflow.python.keras.engine import InputLayer -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer +from tensorflow.python.keras.engine.input_layer import Input +from tensorflow.python.keras.engine.input_layer import InputLayer # Advanced activations. from tensorflow.python.keras.layers.advanced_activations import LeakyReLU diff --git a/tensorflow/contrib/kinesis/BUILD b/tensorflow/contrib/kinesis/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..25443d0ad47aa7d503f905eb34000488b62f22c6 --- /dev/null +++ b/tensorflow/contrib/kinesis/BUILD @@ -0,0 +1,113 @@ +package(default_visibility = ["//tensorflow:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load( + "//tensorflow:tensorflow.bzl", + "tf_custom_op_library", + "tf_custom_op_py_library", + "tf_gen_op_libs", + "tf_gen_op_wrapper_py", + "tf_kernel_library", + "tf_py_test", +) + +py_library( + name = "kinesis", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_ops", + ], +) + +tf_custom_op_library( + name = "_dataset_ops.so", + srcs = ["ops/dataset_ops.cc"], + deps = [":dataset_kernels"], +) + +tf_gen_op_libs( + op_lib_names = ["dataset_ops"], +) + +cc_library( + name = "dataset_kernels", + srcs = [ + "kernels/kinesis_dataset_ops.cc", + ], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//tensorflow/core/platform/s3:aws_crypto", + "//third_party/eigen3", + "@aws", + "@protobuf_archive//:protobuf_headers", + ], + alwayslink = 1, +) + +py_library( + name = "dataset_ops", + srcs = [ + "python/ops/kinesis_dataset_ops.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":kinesis_op_loader", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/util:nest", + ], +) + +tf_gen_op_wrapper_py( + name = "gen_dataset_ops", + out = "python/ops/gen_dataset_ops.py", + deps = ["//tensorflow/contrib/kinesis:dataset_ops_op_lib"], +) + +tf_kernel_library( + name = "dataset_ops_kernels", + deps = [ + ":dataset_kernels", + "//tensorflow/core:framework", + ], + alwayslink = 1, +) + +tf_custom_op_py_library( + name = "kinesis_op_loader", + srcs = ["python/ops/kinesis_op_loader.py"], + dso = ["//tensorflow/contrib/kinesis:_dataset_ops.so"], + kernels = [ + ":dataset_ops_kernels", + "//tensorflow/contrib/kinesis:dataset_ops_op_lib", + ], + srcs_version = "PY2AND3", + deps = [ + ":gen_dataset_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:platform", + ], +) + +tf_py_test( + name = "kinesis_test", + srcs = ["python/kernel_tests/kinesis_test.py"], + additional_deps = [ + ":kinesis", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], + tags = [ + "manual", + "no_windows", + "notap", + ], +) diff --git a/tensorflow/contrib/control_flow/__init__.py b/tensorflow/contrib/kinesis/__init__.py similarity index 82% rename from tensorflow/contrib/control_flow/__init__.py rename to tensorflow/contrib/kinesis/__init__.py index 582af2cf10a3d92dd8611b0f2826625e3acfb099..3824b8ae7532ab97a5ebf01ab66ece6476c87d42 100644 --- a/tensorflow/contrib/control_flow/__init__.py +++ b/tensorflow/contrib/kinesis/__init__.py @@ -12,20 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +"""Kinesis Dataset. -"""New implementations of TF control flow ops. - -@@cond_v2 +@@KinesisDataset """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# pylint: disable=unused-import -from tensorflow.contrib.control_flow.python.cond_v2 import cond_v2 -# pylint: enable=unused-import +from tensorflow.contrib.kinesis.python.ops.kinesis_dataset_ops import KinesisDataset from tensorflow.python.util.all_util import remove_undocumented +_allowed_symbols = [ + "KinesisDataset", +] + remove_undocumented(__name__) diff --git a/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc b/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..3212279c4c50efb92acc712b82cb3e1a22c76870 --- /dev/null +++ b/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc @@ -0,0 +1,359 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "tensorflow/core/framework/dataset.h" +#include "tensorflow/core/platform/s3/aws_crypto.h" + +namespace tensorflow { +namespace { + +Aws::Client::ClientConfiguration* InitializeDefaultClientConfig() { + static Aws::Client::ClientConfiguration config; + const char* endpoint = getenv("KINESIS_ENDPOINT"); + if (endpoint) { + config.endpointOverride = Aws::String(endpoint); + } + const char* region = getenv("AWS_REGION"); + if (region) { + config.region = Aws::String(region); + } else { + // Load config file (e.g., ~/.aws/config) only if AWS_SDK_LOAD_CONFIG + // is set with a truthy value. + const char* load_config_env = getenv("AWS_SDK_LOAD_CONFIG"); + string load_config = + load_config_env ? str_util::Lowercase(load_config_env) : ""; + if (load_config == "true" || load_config == "1") { + Aws::String config_file; + // If AWS_CONFIG_FILE is set then use it, otherwise use ~/.aws/config. + const char* config_file_env = getenv("AWS_CONFIG_FILE"); + if (config_file_env) { + config_file = config_file_env; + } else { + const char* home_env = getenv("HOME"); + if (home_env) { + config_file = home_env; + config_file += "/.aws/config"; + } + } + Aws::Config::AWSConfigFileProfileConfigLoader loader(config_file); + // Load the configuration. If successful, get the region. + // If the load is not successful, then generate a warning. + if (loader.Load()) { + auto profiles = loader.GetProfiles(); + if (!profiles["default"].GetRegion().empty()) { + config.region = profiles["default"].GetRegion(); + } + } else { + LOG(WARNING) << "Failed to load the profile in " << config_file << "."; + } + } + } + const char* use_https = getenv("KINESIS_USE_HTTPS"); + if (use_https) { + if (use_https[0] == '0') { + config.scheme = Aws::Http::Scheme::HTTP; + } else { + config.scheme = Aws::Http::Scheme::HTTPS; + } + } + const char* verify_ssl = getenv("KINESIS_VERIFY_SSL"); + if (verify_ssl) { + if (verify_ssl[0] == '0') { + config.verifySSL = false; + } else { + config.verifySSL = true; + } + } + const char* connect_timeout = getenv("KINESIS_CONNECT_TIMEOUT_MSEC"); + if (connect_timeout) { + int64 timeout; + + if (strings::safe_strto64(connect_timeout, &timeout)) { + config.connectTimeoutMs = timeout; + } + } + const char* request_timeout = getenv("KINESIS_REQUEST_TIMEOUT_MSEC"); + if (request_timeout) { + int64 timeout; + + if (strings::safe_strto64(request_timeout, &timeout)) { + config.requestTimeoutMs = timeout; + } + } + + return &config; +} + +Aws::Client::ClientConfiguration& GetDefaultClientConfig() { + static Aws::Client::ClientConfiguration* config = + InitializeDefaultClientConfig(); + return *config; +} + +static mutex mu(LINKER_INITIALIZED); +static unsigned count(0); +void AwsInitAPI() { + mutex_lock lock(mu); + count++; + if (count == 1) { + Aws::SDKOptions options; + options.cryptoOptions.sha256Factory_create_fn = []() { + return Aws::MakeShared(AWSCryptoAllocationTag); + }; + options.cryptoOptions.sha256HMACFactory_create_fn = []() { + return Aws::MakeShared(AWSCryptoAllocationTag); + }; + Aws::InitAPI(options); + } +} +void AwsShutdownAPI() { + mutex_lock lock(mu); + count--; + if (count == 0) { + Aws::SDKOptions options; + Aws::ShutdownAPI(options); + } +} +void ShutdownClient(Aws::Kinesis::KinesisClient* client) { + if (client != nullptr) { + delete client; + AwsShutdownAPI(); + } +} +} +class KinesisDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + std::string stream = ""; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "stream", &stream)); + std::string shard = ""; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "shard", &shard)); + bool read_indefinitely = true; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "read_indefinitely", + &read_indefinitely)); + int64 interval = -1; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "interval", &interval)); + OP_REQUIRES(ctx, (interval > 0), + errors::InvalidArgument( + "Interval value should be large than 0, got ", interval)); + *output = new Dataset(ctx, stream, shard, read_indefinitely, interval); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, const string& stream, const string& shard, + const bool read_indefinitely, const int64 interval) + : GraphDatasetBase(ctx), + stream_(stream), + shard_(shard), + read_indefinitely_(read_indefinitely), + interval_(interval) {} + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::Kinesis")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}}); + return *shapes; + } + + string DebugString() const override { return "KinesisDatasetOp::Dataset"; } + + protected: + Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Node** output) const override { + Node* stream = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(stream_, &stream)); + Node* shard = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(shard_, &shard)); + Node* read_indefinitely = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(read_indefinitely_, &read_indefinitely)); + Node* interval = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(interval_, &interval)); + TF_RETURN_IF_ERROR(b->AddDataset( + this, {stream, shard, read_indefinitely, interval}, output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params), + client_(nullptr, ShutdownClient) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + if (iterator_ == "") { + TF_RETURN_IF_ERROR(SetupStreamsLocked()); + } + do { + Aws::Kinesis::Model::GetRecordsRequest request; + auto outcome = client_->GetRecords( + request.WithShardIterator(iterator_).WithLimit(1)); + if (!outcome.IsSuccess()) { + return errors::Unknown(outcome.GetError().GetExceptionName(), ": ", + outcome.GetError().GetMessage()); + } + if (outcome.GetResult().GetRecords().size() == 0) { + // If no records were returned then nothing is available at the + // moment. + if (!dataset()->read_indefinitely_) { + *end_of_sequence = true; + return Status::OK(); + } + // Continue the loop after a period of time. + ctx->env()->SleepForMicroseconds(dataset()->interval_); + continue; + } + if (outcome.GetResult().GetRecords().size() != 1) { + return errors::Unknown("invalid number of records ", + outcome.GetResult().GetRecords().size(), + " returned"); + } + + iterator_ = outcome.GetResult().GetNextShardIterator(); + + const auto& data = outcome.GetResult().GetRecords()[0].GetData(); + StringPiece value( + reinterpret_cast(data.GetUnderlyingData()), + data.GetLength()); + Tensor value_tensor(ctx->allocator({}), DT_STRING, {}); + value_tensor.scalar()() = std::string(value); + out_tensors->emplace_back(std::move(value_tensor)); + + *end_of_sequence = false; + return Status::OK(); + } while (true); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + return errors::Unimplemented("SaveInternal is currently not supported"); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + return errors::Unimplemented( + "RestoreInternal is currently not supported"); + } + + private: + // Sets up Kinesis streams to read from. + Status SetupStreamsLocked() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + AwsInitAPI(); + client_.reset( + new Aws::Kinesis::KinesisClient(GetDefaultClientConfig())); + + Aws::Kinesis::Model::DescribeStreamRequest request; + auto outcome = client_->DescribeStream( + request.WithStreamName(dataset()->stream_.c_str())); + if (!outcome.IsSuccess()) { + return errors::Unknown(outcome.GetError().GetExceptionName(), ": ", + outcome.GetError().GetMessage()); + } + Aws::String shard; + Aws::String sequence; + if (dataset()->shard_ == "") { + if (outcome.GetResult().GetStreamDescription().GetShards().size() != + 1) { + return errors::InvalidArgument( + "shard has to be provided unless the stream only have one " + "shard, there are ", + outcome.GetResult().GetStreamDescription().GetShards().size(), + " shards in stream ", dataset()->stream_); + } + shard = outcome.GetResult() + .GetStreamDescription() + .GetShards()[0] + .GetShardId(); + sequence = outcome.GetResult() + .GetStreamDescription() + .GetShards()[0] + .GetSequenceNumberRange() + .GetStartingSequenceNumber(); + } else { + for (const auto& entry : + outcome.GetResult().GetStreamDescription().GetShards()) { + if (entry.GetShardId() == dataset()->shard_.c_str()) { + shard = entry.GetShardId(); + sequence = + entry.GetSequenceNumberRange().GetStartingSequenceNumber(); + break; + } + } + if (shard == "") { + return errors::InvalidArgument("no shard ", dataset()->shard_, + " in stream ", dataset()->stream_); + } + } + + Aws::Kinesis::Model::GetShardIteratorRequest iterator_request; + auto iterator_outcome = client_->GetShardIterator( + iterator_request.WithStreamName(dataset()->stream_.c_str()) + .WithShardId(shard) + .WithShardIteratorType( + Aws::Kinesis::Model::ShardIteratorType::AT_SEQUENCE_NUMBER) + .WithStartingSequenceNumber(sequence)); + if (!iterator_outcome.IsSuccess()) { + return errors::Unknown(iterator_outcome.GetError().GetExceptionName(), + ": ", + iterator_outcome.GetError().GetMessage()); + } + iterator_ = iterator_outcome.GetResult().GetShardIterator(); + return Status::OK(); + } + + mutex mu_; + Aws::String iterator_ GUARDED_BY(mu_); + std::unique_ptr + client_ GUARDED_BY(mu_); + }; + + const std::string stream_; + const std::string shard_; + const bool read_indefinitely_; + const int64 interval_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("KinesisDataset").Device(DEVICE_CPU), + KinesisDatasetOp); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kinesis/ops/dataset_ops.cc b/tensorflow/contrib/kinesis/ops/dataset_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..54204513cf22519ecfb5fa45748250ee0f4aac7a --- /dev/null +++ b/tensorflow/contrib/kinesis/ops/dataset_ops.cc @@ -0,0 +1,42 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("KinesisDataset") + .Input("stream: string") + .Input("shard: string") + .Input("read_indefinitely: bool") + .Input("interval: int64") + .Output("handle: variant") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that emits the messages of one or more Kinesis topics. + +stream: A `tf.string` tensor containing the name of the stream. +shard: A `tf.string` tensor containing the id of the shard. +read_indefinitely: If `True`, the Kinesis dataset will keep retry + again on `EOF` after the `interval` period. If `False`, then + the dataset will stop on `EOF`. The default value is `True`. +interval: The interval for the Kinesis Client to wait before + it tries to get records again (in millisecond). +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kinesis/python/kernel_tests/kinesis_test.py b/tensorflow/contrib/kinesis/python/kernel_tests/kinesis_test.py new file mode 100644 index 0000000000000000000000000000000000000000..7289b45c50fa92455b4c317b8a039ca414fa585e --- /dev/null +++ b/tensorflow/contrib/kinesis/python/kernel_tests/kinesis_test.py @@ -0,0 +1,139 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. +# ============================================================================== +"""Tests for KinesisDataset. +NOTE: boto3 is needed and the test has to be invoked manually: +``` +$ bazel test -s --verbose_failures --config=opt \ + --action_env=AWS_ACCESS_KEY_ID=XXXXXX \ + --action_env=AWS_SECRET_ACCESS_KEY=XXXXXX \ + //tensorflow/contrib/kinesis:kinesis_test +``` +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import boto3 + +from tensorflow.contrib.kinesis.python.ops import kinesis_dataset_ops +from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class KinesisDatasetTest(test.TestCase): + + def testKinesisDatasetOneShard(self): + client = boto3.client('kinesis', region_name='us-east-1') + + # Setup the Kinesis with 1 shard. + stream_name = "tf_kinesis_test_1" + client.create_stream(StreamName=stream_name, ShardCount=1) + # Wait until stream exists, default is 10 * 18 seconds. + client.get_waiter('stream_exists').wait(StreamName=stream_name) + for i in range(10): + data = "D" + str(i) + client.put_record( + StreamName=stream_name, Data=data, PartitionKey="TensorFlow" + str(i)) + + stream = array_ops.placeholder(dtypes.string, shape=[]) + num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) + batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = kinesis_dataset_ops.KinesisDataset( + stream, read_indefinitely=False).repeat(num_epochs) + batch_dataset = repeat_dataset.batch(batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + init_op = iterator.make_initializer(repeat_dataset) + init_batch_op = iterator.make_initializer(batch_dataset) + get_next = iterator.get_next() + + with self.test_session() as sess: + # Basic test: read from shard 0 of stream 1. + sess.run(init_op, feed_dict={stream: stream_name, num_epochs: 1}) + for i in range(10): + self.assertEqual("D" + str(i), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + client.delete_stream(StreamName=stream_name) + # Wait until stream deleted, default is 10 * 18 seconds. + client.get_waiter('stream_not_exists').wait(StreamName=stream_name) + + def testKinesisDatasetTwoShards(self): + client = boto3.client('kinesis', region_name='us-east-1') + + # Setup the Kinesis with 2 shards. + stream_name = "tf_kinesis_test_2" + client.create_stream(StreamName=stream_name, ShardCount=2) + # Wait until stream exists, default is 10 * 18 seconds. + client.get_waiter('stream_exists').wait(StreamName=stream_name) + + for i in range(10): + data = "D" + str(i) + client.put_record( + StreamName=stream_name, Data=data, PartitionKey="TensorFlow" + str(i)) + response = client.describe_stream(StreamName=stream_name) + shard_id_0 = response["StreamDescription"]["Shards"][0]["ShardId"] + shard_id_1 = response["StreamDescription"]["Shards"][1]["ShardId"] + + stream = array_ops.placeholder(dtypes.string, shape=[]) + shard = array_ops.placeholder(dtypes.string, shape=[]) + num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) + batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = kinesis_dataset_ops.KinesisDataset( + stream, shard, read_indefinitely=False).repeat(num_epochs) + batch_dataset = repeat_dataset.batch(batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + init_op = iterator.make_initializer(repeat_dataset) + init_batch_op = iterator.make_initializer(batch_dataset) + get_next = iterator.get_next() + + data = list() + with self.test_session() as sess: + # Basic test: read from shard 0 of stream 2. + sess.run( + init_op, feed_dict={ + stream: stream_name, shard: shard_id_0, num_epochs: 1}) + with self.assertRaises(errors.OutOfRangeError): + # Use range(11) to guarantee the OutOfRangeError. + for i in range(11): + data.append(sess.run(get_next)) + + # Basic test: read from shard 1 of stream 2. + sess.run( + init_op, feed_dict={ + stream: stream_name, shard: shard_id_1, num_epochs: 1}) + with self.assertRaises(errors.OutOfRangeError): + # Use range(11) to guarantee the OutOfRangeError. + for i in range(11): + data.append(sess.run(get_next)) + + data.sort() + self.assertEqual(data, ["D" + str(i) for i in range(10)]) + + client.delete_stream(StreamName=stream_name) + # Wait until stream deleted, default is 10 * 18 seconds. + client.get_waiter('stream_not_exists').wait(StreamName=stream_name) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/kinesis/python/ops/kinesis_dataset_ops.py b/tensorflow/contrib/kinesis/python/ops/kinesis_dataset_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..ca2df95ba4f20ec5fa58ff13530096e6e065f4fe --- /dev/null +++ b/tensorflow/contrib/kinesis/python/ops/kinesis_dataset_ops.py @@ -0,0 +1,96 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Kinesis Dataset.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kinesis.python.ops import kinesis_op_loader # pylint: disable=unused-import +from tensorflow.contrib.kinesis.python.ops import gen_dataset_ops +from tensorflow.python.data.ops.dataset_ops import Dataset +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape + + +class KinesisDataset(Dataset): + """A Kinesis Dataset that consumes the message. + + Kinesis is a managed service provided by AWS for data streaming. + This dataset reads messages from Kinesis with each message presented + as a `tf.string`. + + For example, we can construct and use the KinesisDataset as follows: + ```python + dataset = tf.contrib.kinesis.KinesisDataset( + "kinesis_stream_name", read_indefinitely=False) + next = dataset.make_one_shot_iterator().get_next() + with tf.Session() as sess: + while True: + try: + print(sess.run(nxt)) + except tf.errors.OutOfRangeError: + break + ``` + + Since Kinesis is a data streaming service, data may not be available + at the time it is being read. The argument `read_indefinitely` is + used to control the behavior in this situation. If `read_indefinitely` + is `True`, then `KinesisDataset` will keep retrying to retrieve data + from the stream. If `read_indefinitely` is `False`, an `OutOfRangeError` + is returned immediately instead. + """ + + def __init__(self, + stream, + shard="", + read_indefinitely=True, + interval=100000): + """Create a KinesisDataset. + + Args: + stream: A `tf.string` tensor containing the name of the stream. + shard: A `tf.string` tensor containing the id of the shard. + read_indefinitely: If `True`, the Kinesis dataset will keep retry + again on `EOF` after the `interval` period. If `False`, then + the dataset will stop on `EOF`. The default value is `True`. + interval: The interval for the Kinesis Client to wait before + it tries to get records again (in millisecond). + """ + super(KinesisDataset, self).__init__() + self._stream = ops.convert_to_tensor( + stream, dtype=dtypes.string, name="stream") + self._shard = ops.convert_to_tensor( + shard, dtype=dtypes.string, name="shard") + self._read_indefinitely = ops.convert_to_tensor( + read_indefinitely, dtype=dtypes.bool, name="read_indefinitely") + self._interval = ops.convert_to_tensor( + interval, dtype=dtypes.int64, name="interval") + + def _as_variant_tensor(self): + return gen_dataset_ops.kinesis_dataset( + self._stream, self._shard, self._read_indefinitely, self._interval) + + @property + def output_classes(self): + return ops.Tensor + + @property + def output_shapes(self): + return tensor_shape.scalar() + + @property + def output_types(self): + return dtypes.string diff --git a/tensorflow/contrib/kinesis/python/ops/kinesis_op_loader.py b/tensorflow/contrib/kinesis/python/ops/kinesis_op_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..c9ce9f3646200a777cdbdf34b37626154ca730bb --- /dev/null +++ b/tensorflow/contrib/kinesis/python/ops/kinesis_op_loader.py @@ -0,0 +1,24 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python helper for loading kinesis ops and kernels.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader + +_dataset_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("../../_dataset_ops.so")) diff --git a/tensorflow/contrib/labeled_tensor/python/ops/ops.py b/tensorflow/contrib/labeled_tensor/python/ops/ops.py index 3ba1026383ef146adb32197ae41b5c251155bf46..2ede5daee74223e812cc29e9708b1989b698fb4e 100644 --- a/tensorflow/contrib/labeled_tensor/python/ops/ops.py +++ b/tensorflow/contrib/labeled_tensor/python/ops/ops.py @@ -652,7 +652,8 @@ def map_fn(fn, labeled_tensor, name=None): tensor_lt = core.LabeledTensor(tensor, original_axes) return fn(tensor_lt).tensor - map_op = functional_ops.map_fn(tf_fn, labeled_tensor.tensor) + map_op = functional_ops.map_fn( + tf_fn, labeled_tensor.tensor, dtype=first_map_lt.dtype) map_lt = core.LabeledTensor(map_op, final_axes) return core.identity(map_lt, name=scope) diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py index dd2395f8c9748dadbecfe47df5511874d5f848ea..7ede193029d2d95fa4953b4c417a1e86ebb4a42e 100644 --- a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py +++ b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py @@ -21,7 +21,6 @@ from __future__ import print_function import itertools import math -import sys import numpy as np diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py index 06060b99e7e58787994f20f037ffa451abbc7459..a85cff4f7098e9a5eedca1b0c8c0cb42e172d90a 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py @@ -683,11 +683,12 @@ def parse_feature_columns_from_sequence_examples( the serialized proto. Returns: - A tuple consisting of: - context_features: a dict mapping `FeatureColumns` from - `context_feature_columns` to their parsed `Tensors`/`SparseTensor`s. - sequence_features: a dict mapping `FeatureColumns` from - `sequence_feature_columns` to their parsed `Tensors`/`SparseTensor`s. + A tuple consisting of (context_features, sequence_features) + + * context_features: a dict mapping `FeatureColumns` from + `context_feature_columns` to their parsed `Tensors`/`SparseTensor`s. + * sequence_features: a dict mapping `FeatureColumns` from + `sequence_feature_columns` to their parsed `Tensors`/`SparseTensor`s. """ # Sequence example parsing requires a single (scalar) example. try: diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index b6d63c9640611abdda65f1205f544ee505dae1f0..beeabd6b65631cad88efd10d5faee1917e162e41 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -2664,6 +2664,7 @@ def separable_convolution2d( normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), + pointwise_initializer=None, weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, @@ -2705,7 +2706,9 @@ def separable_convolution2d( `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. - weights_initializer: An initializer for the weights. + weights_initializer: An initializer for the depthwise weights. + pointwise_initializer: An initializer for the pointwise weights. + default set to None, means use weights_initializer. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. @@ -2737,6 +2740,9 @@ def separable_convolution2d( custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) + if pointwise_initializer is None: + pointwise_initializer = weights_initializer + df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') if num_outputs is not None: @@ -2752,7 +2758,7 @@ def separable_convolution2d( depth_multiplier=depth_multiplier, use_bias=not normalizer_fn and biases_initializer, depthwise_initializer=weights_initializer, - pointwise_initializer=weights_initializer, + pointwise_initializer=pointwise_initializer, bias_initializer=biases_initializer, depthwise_regularizer=weights_regularizer, pointwise_regularizer=weights_regularizer, diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py index 541da9061732ad271f6d5456446a9c30b81e58dd..f8a3709ee57a32734afa7ac8133271c75d152b2c 100644 --- a/tensorflow/contrib/learn/python/learn/experiment.py +++ b/tensorflow/contrib/learn/python/learn/experiment.py @@ -505,7 +505,7 @@ class Experiment(object): eval_result = None last_warning_time = 0 while (not predicate_fn or predicate_fn( - eval_result, checkpoint_path=previous_path if eval_result else None)): + eval_result, checkpoint_path=previous_path)): # Exit if we have already reached number of steps to train. if self._has_training_stopped(eval_result): logging.info("Exiting continuous eval, global_step=%s >= " diff --git a/tensorflow/contrib/learn/python/learn/experiment_test.py b/tensorflow/contrib/learn/python/learn/experiment_test.py index d10927a0cdd5c67c8d2a8e569153235ee175ec4d..fb16c94c29660e2777942ea9cf30da51dbf90571 100644 --- a/tensorflow/contrib/learn/python/learn/experiment_test.py +++ b/tensorflow/contrib/learn/python/learn/experiment_test.py @@ -500,7 +500,7 @@ class ExperimentTest(test.TestCase): noop_hook = _NoopHook() def _predicate_fn(eval_result, checkpoint_path): - self.assertEqual(not eval_result, + self.assertEqual(eval_result is None, checkpoint_path is None) return est.eval_count < 3 # pylint: disable=cell-var-from-loop diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index 9c804d27854b8004d34c65691b48ca2b0d3bbf7c..73f5c1448d91c573efed34c6aaaf5c28feac6555 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -128,6 +128,7 @@ cc_library( hdrs = [ "allocation.h", "context.h", + "context_util.h", "error_reporter.h", "graph_info.h", "interpreter.h", @@ -184,6 +185,7 @@ cc_test( deps = [ ":framework", ":string_util", + "//tensorflow/contrib/lite/kernels:builtin_ops", "//tensorflow/contrib/lite/kernels:kernel_util", "//tensorflow/contrib/lite/kernels/internal:tensor_utils", "//tensorflow/contrib/lite/schema:schema_fbs", diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile index cc8a8035d1dadeec98886ba1dae4cdf403f26de4..a616138d3321d43f66a2b430f7df609a13b9caf6 100644 --- a/tensorflow/contrib/lite/Makefile +++ b/tensorflow/contrib/lite/Makefile @@ -17,7 +17,29 @@ else endif endif -ARCH := $(shell if [[ $(shell uname -m) =~ i[345678]86 ]]; then echo x86_32; else echo $(shell uname -m); fi) +HOST_ARCH := $(shell if [[ $(shell uname -m) =~ i[345678]86 ]]; then echo x86_32; else echo $(shell uname -m); fi) + +# Self-hosting +TARGET_ARCH := ${HOST_ARCH} + +# Cross compiling +ifeq ($(CROSS),rpi) + TARGET_ARCH := armv7l + TARGET_TOOLCHAIN_PREFIX := arm-linux-gnueabihf- +endif + +ifeq ($(CROSS),riscv) + TARGET_ARCH := riscv + TARGET_TOOLCHAIN_PREFIX := riscv32-unknown-elf- +endif +ifeq ($(CROSS),stm32f7) + TARGET_ARCH := armf7 + TARGET_TOOLCHAIN_PREFIX := arm-none-eabi- +endif +ifeq ($(CROSS),stm32f1) + TARGET_ARCH := armm1 + TARGET_TOOLCHAIN_PREFIX := arm-none-eabi- +endif # Where compiled objects are stored. OBJDIR := $(MAKEFILE_DIR)/gen/obj/ @@ -25,11 +47,46 @@ BINDIR := $(MAKEFILE_DIR)/gen/bin/ LIBDIR := $(MAKEFILE_DIR)/gen/lib/ GENDIR := $(MAKEFILE_DIR)/gen/obj/ +LIBS := +ifeq ($(TARGET_ARCH),x86_64) + CXXFLAGS += -fPIC -DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -pthread # -msse4.2 +endif + +ifeq ($(TARGET_ARCH),armv7l) + CXXFLAGS += -mfpu=neon -pthread -fPIC + LIBS += -ldl +endif + +ifeq ($(TARGET_ARCH),riscv) +# CXXFLAGS += -march=gap8 + CXXFLAGS += -DTFLITE_MCU + LIBS += -ldl + BUILD_TYPE := micro +endif + +ifeq ($(TARGET_ARCH),armf7) + CXXFLAGS += -DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -DTFLITE_MCU + CXXFLAGS += -fno-rtti -fmessage-length=0 -fno-exceptions -fno-builtin -ffunction-sections -fdata-sections + CXXFLAGS += -funsigned-char -MMD + CXXFLAGS += -mcpu=cortex-m7 -mthumb -mfpu=fpv5-sp-d16 -mfloat-abi=softfp + CXXFLAGS += '-std=gnu++11' '-fno-rtti' '-Wvla' '-c' '-Wall' '-Wextra' '-Wno-unused-parameter' '-Wno-missing-field-initializers' '-fmessage-length=0' '-fno-exceptions' '-fno-builtin' '-ffunction-sections' '-fdata-sections' '-funsigned-char' '-MMD' '-fno-delete-null-pointer-checks' '-fomit-frame-pointer' '-Os' + LIBS += -ldl + BUILD_TYPE := micro +endif +ifeq ($(TARGET_ARCH),armm1) + CXXFLAGS += -DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -mcpu=cortex-m1 -mthumb -DTFLITE_MCU + CXXFLAGS += -fno-rtti -fmessage-length=0 -fno-exceptions -fno-builtin -ffunction-sections -fdata-sections + CXXFLAGS += -funsigned-char -MMD + LIBS += -ldl +endif + # Settings for the host compiler. -CXX := $(CC_PREFIX)gcc -CXXFLAGS := --std=c++11 -O3 -DNDEBUG -CC := $(CC_PREFIX)gcc -CCFLAGS := -O3 -DNDEBUG +CXX := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}g++ +CXXFLAGS += --std=c++11 -O3 -DNDEBUG +CCFLAGS := ${CXXFLAGS} +CC := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}gcc +AR := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}ar +CFLAGS := LDOPTS := LDOPTS += -L/usr/local/lib ARFLAGS := -r @@ -48,7 +105,7 @@ INCLUDES := \ # override local versions in the source tree. INCLUDES += -I/usr/local/include -LIBS := \ +LIBS += \ -lstdc++ \ -lpthread \ -lm \ @@ -70,6 +127,12 @@ LIB_PATH := $(LIBDIR)$(LIB_NAME) # A small example program that shows how to link against the library. MINIMAL_PATH := $(BINDIR)minimal +# Benchmark static library and binary +BENCHMARK_LIB_NAME := benchmark-lib.a +BENCHMARK_BINARY_NAME := benchmark_model +BENCHMARK_LIB := $(LIBDIR)$(BENCHMARK_LIB_NAME) +BENCHMARK_BINARY := $(BINDIR)$(BENCHMARK_BINARY_NAME) + MINIMAL_SRCS := \ tensorflow/contrib/lite/examples/minimal/minimal.cc MINIMAL_OBJS := $(addprefix $(OBJDIR), \ @@ -78,19 +141,29 @@ $(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MINIMAL_SRCS)))) # What sources we want to compile, must be kept in sync with the main Bazel # build files. +PROFILER_SRCS := \ + tensorflow/contrib/lite/profiling/time.cc +PROFILE_SUMMARIZER_SRCS := \ + tensorflow/contrib/lite/profiling/profile_summarizer.cc \ + tensorflow/core/util/stats_calculator.cc + CORE_CC_ALL_SRCS := \ $(wildcard tensorflow/contrib/lite/*.cc) \ +$(wildcard tensorflow/contrib/lite/*.c) +ifneq ($(BUILD_TYPE),micro) +CORE_CC_ALL_SRCS += \ $(wildcard tensorflow/contrib/lite/kernels/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/optimized/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/reference/*.cc) \ -$(wildcard tensorflow/contrib/lite/*.c) \ +$(PROFILER_SRCS) \ $(wildcard tensorflow/contrib/lite/kernels/*.c) \ $(wildcard tensorflow/contrib/lite/kernels/internal/*.c) \ $(wildcard tensorflow/contrib/lite/kernels/internal/optimized/*.c) \ $(wildcard tensorflow/contrib/lite/kernels/internal/reference/*.c) \ $(wildcard tensorflow/contrib/lite/downloads/farmhash/src/farmhash.cc) \ $(wildcard tensorflow/contrib/lite/downloads/fft2d/fftsg.c) +endif # Remove any duplicates. CORE_CC_ALL_SRCS := $(sort $(CORE_CC_ALL_SRCS)) CORE_CC_EXCLUDE_SRCS := \ @@ -100,6 +173,11 @@ $(wildcard tensorflow/contrib/lite/*/*/*test.cc) \ $(wildcard tensorflow/contrib/lite/*/*/*/*test.cc) \ $(wildcard tensorflow/contrib/lite/kernels/test_util.cc) \ $(MINIMAL_SRCS) +ifeq ($(BUILD_TYPE),micro) +CORE_CC_EXCLUDE_SRCS += \ +tensorflow/contrib/lite/model.cc \ +tensorflow/contrib/lite/nnapi_delegate.cc +endif # Filter out all the excluded files. TF_LITE_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS)) # File names of the intermediate files target compilation generates. @@ -107,18 +185,33 @@ TF_LITE_CC_OBJS := $(addprefix $(OBJDIR), \ $(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(TF_LITE_CC_SRCS)))) LIB_OBJS := $(TF_LITE_CC_OBJS) +# Benchmark sources +BENCHMARK_SRCS_DIR := tensorflow/contrib/lite/tools/benchmark +BENCHMARK_ALL_SRCS := $(TFLITE_CC_SRCS) \ + $(wildcard $(BENCHMARK_SRCS_DIR)/*.cc) \ + $(PROFILE_SUMMARIZER_SRCS) + +BENCHMARK_SRCS := $(filter-out \ + $(wildcard $(BENCHMARK_SRCS_DIR)/*_test.cc), \ + $(BENCHMARK_ALL_SRCS)) + +BENCHMARK_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(BENCHMARK_SRCS)))) + # For normal manually-created TensorFlow C++ source files. $(OBJDIR)%.o: %.cc @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@ - # For normal manually-created TensorFlow C++ source files. $(OBJDIR)%.o: %.c @mkdir -p $(dir $@) $(CC) $(CCFLAGS) $(INCLUDES) -c $< -o $@ # The target that's compiled if there's no command-line arguments. -all: $(LIB_PATH) $(MINIMAL_PATH) +all: $(LIB_PATH) $(MINIMAL_PATH) $(BENCHMARK_BINARY) + +# The target that's compiled for micro-controllers +micro: $(LIB_PATH) # Gathers together all the objects we've compiled into a single '.a' archive. $(LIB_PATH): $(LIB_OBJS) @@ -131,6 +224,21 @@ $(MINIMAL_PATH): $(MINIMAL_OBJS) $(LIB_PATH) -o $(MINIMAL_PATH) $(MINIMAL_OBJS) \ $(LIBFLAGS) $(LIB_PATH) $(LDFLAGS) $(LIBS) + +$(BENCHMARK_LIB) : $(LIB_PATH) $(BENCHMARK_OBJS) + @mkdir -p $(dir $@) + $(AR) $(ARFLAGS) $(BENCHMARK_LIB) $(LIB_OBJS) $(BENCHMARK_OBJS) + +benchmark_lib: $(BENCHMARK_LIB) +$(info $(BENCHMARK_BINARY)) +$(BENCHMARK_BINARY) : $(BENCHMARK_LIB) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(BENCHMARK_BINARY) \ + $(LIBFLAGS) $(BENCHMARK_LIB) $(LDFLAGS) $(LIBS) + +benchmark: $(BENCHMARK_BINARY) + # Gets rid of all generated files. clean: rm -rf $(MAKEFILE_DIR)/gen diff --git a/tensorflow/contrib/lite/allocation.cc b/tensorflow/contrib/lite/allocation.cc index a4772731ecda92431c412672610a39c188dabf27..c42622ff02fc2837b61b35f19e834276c0518d1e 100644 --- a/tensorflow/contrib/lite/allocation.cc +++ b/tensorflow/contrib/lite/allocation.cc @@ -14,7 +14,9 @@ limitations under the License. ==============================================================================*/ #include +#ifndef TFLITE_MCU #include +#endif #include #include #include @@ -27,10 +29,13 @@ limitations under the License. #include "tensorflow/contrib/lite/allocation.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/error_reporter.h" +#ifndef TFLITE_MCU #include "tensorflow/contrib/lite/nnapi_delegate.h" +#endif namespace tflite { +#ifndef TFLITE_MCU MMAPAllocation::MMAPAllocation(const char* filename, ErrorReporter* error_reporter) : Allocation(error_reporter), mmapped_buffer_(MAP_FAILED) { @@ -111,6 +116,7 @@ MemoryAllocation::MemoryAllocation(const void* ptr, size_t num_bytes, buffer_ = ptr; buffer_size_bytes_ = num_bytes; } +#endif MemoryAllocation::~MemoryAllocation() {} diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc index 4f836d367747e06de682b5764206d33f6e2fb983..4257e754ad5c30e17ec8ba8d5c6e69b5c5bcd728 100644 --- a/tensorflow/contrib/lite/arena_planner.cc +++ b/tensorflow/contrib/lite/arena_planner.cc @@ -31,16 +31,17 @@ struct AllocationInfo { // The tensor index to be allocated or deallocated. int tensor; // Whether to allocate or deallocate - enum { ALLOC, DEALLOC } type; + enum Type { ALLOC, DEALLOC } type; }; ArenaPlanner::ArenaPlanner(TfLiteContext* context, - std::unique_ptr graph_info) + std::unique_ptr graph_info, + bool preserve_inputs) : context_(context), graph_info_(std::move(graph_info)), arena_(kDefaultArenaAlignment), - persistent_arena_(kDefaultArenaAlignment) {} - + persistent_arena_(kDefaultArenaAlignment), + preserve_inputs_(preserve_inputs) {} ArenaPlanner::~ArenaPlanner() {} int64_t ArenaPlanner::BasePointer(TfLiteAllocationType type) { @@ -67,6 +68,33 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { // Keeps track of references to each tensor. std::vector refcounts(graph_info_->num_tensors(), 0); + // `allocated` and `deallocated` are technically list of boolean values. + // We're saving the compiled binary size by using `vector`. + std::vector allocated(graph_info_->num_tensors(), false); + std::vector deallocated(graph_info_->num_tensors(), false); + + auto allocate = [this, &allocated, &deallocated](int node, + int tensor) -> TfLiteStatus { + if (allocated[tensor]) { + return kTfLiteOk; + } + TF_LITE_ENSURE(context_, !deallocated[tensor]); + alloc_queue_.push_back({node, tensor, AllocationInfo::ALLOC}); + allocated[tensor] = true; + return kTfLiteOk; + }; + + auto deallocate = [this, &allocated, &deallocated]( + int node, int tensor) -> TfLiteStatus { + if (!allocated[tensor]) { + // Do not enqueue a DEALLOC if the tensor is never allocated. + // This happened with the constant tensors. + return kTfLiteOk; + } + TF_LITE_ENSURE(context_, !deallocated[tensor]); + alloc_queue_.push_back({node, tensor, AllocationInfo::DEALLOC}); + return kTfLiteOk; + }; // There will be an entry in alloc_queue_ for the allocation of each tensor // and another for their deallocation. @@ -79,6 +107,32 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { refcounts[tensor_index]++; } + // Variable tensors should are also never overwritten and need to be alive all + // the time. + for (int tensor_index : graph_info_->variables()) { + refcounts[tensor_index]++; + } + + // Queue all graph inputs for allocation. If preserve_inputs_ is true, make + // sure they never be overwritten. + for (int tensor_index : graph_info_->inputs()) { + if (tensor_index != kOptionalTensor) { + if (preserve_inputs_) { + refcounts[tensor_index]++; + } + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); + } + } + + // Queue all graph variable tensors for allocation. + for (int tensor_index : graph_info_->variables()) { + if (tensor_index != kOptionalTensor) { + // Increase the reference count for input tensors by one, so it will + // never be deallocated. + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); + } + } + // Count references to node input tensors. for (int i = 0; i < graph_info_->num_nodes(); ++i) { const TfLiteNode& node = graph_info_->node(i); @@ -94,10 +148,9 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { // Queue all graph inputs for allocation. for (int tensor_index : graph_info_->inputs()) { if (tensor_index != kOptionalTensor) { - alloc_queue_.push_back({0, tensor_index, AllocationInfo::ALLOC}); + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); } } - // Go through the graph in execution order. for (int i = 0; i < graph_info_->num_nodes(); ++i) { const TfLiteNode& node = graph_info_->node(i); @@ -106,7 +159,7 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { TfLiteIntArray* node_outputs = node.outputs; for (int j = 0; j < node_outputs->size; ++j) { int tensor_index = node_outputs->data[j]; - alloc_queue_.push_back({i, tensor_index, AllocationInfo::ALLOC}); + TF_LITE_ENSURE_STATUS(allocate(i, tensor_index)); } // Then update the ref-counts of the node's inputs, and if necessary queue @@ -117,7 +170,7 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { if (tensor_index != kOptionalTensor) { refcounts[tensor_index]--; if (refcounts[tensor_index] == 0) { - alloc_queue_.push_back({i, tensor_index, AllocationInfo::DEALLOC}); + TF_LITE_ENSURE_STATUS(deallocate(i, tensor_index)); } } } diff --git a/tensorflow/contrib/lite/arena_planner.h b/tensorflow/contrib/lite/arena_planner.h index e9d0fbc5a9b5aec06e28da8757466b25f40da2f5..1d84950e91bc48fd1c1a7e5b2d9063e20dea0718 100644 --- a/tensorflow/contrib/lite/arena_planner.h +++ b/tensorflow/contrib/lite/arena_planner.h @@ -43,8 +43,11 @@ struct AllocationInfo; class ArenaPlanner : public MemoryPlanner { public: // Ownership of 'context' is not taken and it must remain util the - // ArenaPlanner is destroyed. - ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info); + // ArenaPlanner is destroyed. If 'preserve_inputs' is true the inputs to the + // graph will not share memory with any other tensor, effectively preserving + // them until the end of inference. + ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info, + bool preserve_inputs); ~ArenaPlanner() override; ArenaPlanner(const ArenaPlanner&) = delete; ArenaPlanner& operator=(const ArenaPlanner&) = delete; @@ -100,6 +103,8 @@ class ArenaPlanner : public MemoryPlanner { // Raw memory buffer that is allocated for persistent tensors that are // declared as kTfLiteArenaRwPersistent. SimpleMemoryArena persistent_arena_; + + bool preserve_inputs_; }; } // namespace tflite diff --git a/tensorflow/contrib/lite/arena_planner_test.cc b/tensorflow/contrib/lite/arena_planner_test.cc index a8a8755e2c9e81474f2ff9cd2b85c0eb3d5c3441..f5bd1932f976f5c7d0f0d14bbaf9ca3807dfd3b0 100644 --- a/tensorflow/contrib/lite/arena_planner_test.cc +++ b/tensorflow/contrib/lite/arena_planner_test.cc @@ -100,12 +100,18 @@ class TestGraph { std::vector* tensors() { return &tensors_; } const std::vector& inputs() { return inputs_; } const std::vector& outputs() { return outputs_; } + const std::vector& variables() { return variables_; } + + void SetVariables(const std::vector& variables) { + variables_ = variables; + } private: std::vector nodes_; std::vector tensors_; std::vector inputs_; std::vector outputs_; + std::vector variables_; }; // The GraphInfo for a TestGraph. @@ -123,6 +129,9 @@ class TestGraphInfo : public GraphInfo { } const std::vector& inputs() const override { return graph_->inputs(); } const std::vector& outputs() const override { return graph_->outputs(); } + const std::vector& variables() const override { + return graph_->variables(); + } private: TestGraph* graph_; @@ -142,11 +151,12 @@ void ReportError(TfLiteContext* context, const char* format, ...) { class ArenaPlannerTest : public ::testing::Test { protected: - void SetGraph(TestGraph* graph) { + void SetGraph(TestGraph* graph, bool preserve_inputs = false) { graph_ = graph; context_.ReportError = ReportError; planner_.reset(new ArenaPlanner( - &context_, std::unique_ptr(new TestGraphInfo(graph)))); + &context_, std::unique_ptr(new TestGraphInfo(graph)), + preserve_inputs)); CHECK(planner_->ResetAllocations() == kTfLiteOk); CHECK(planner_->PlanAllocations() == kTfLiteOk); } @@ -209,11 +219,8 @@ TEST_F(ArenaPlannerTest, ZeroSizedTensors) { TestGraph graph({1}, {{{1}, {2}, {}}}, {2}); (*graph.tensors())[1].bytes = 0; SetGraph(&graph); - // TODO(ahentz): this is currently broken because the arena finds two - // allocations with the same offset and returns an error. - ASSERT_FALSE(planner_->ExecuteAllocations(0, 10) == kTfLiteOk); - // EXPECT_EQ(GetOffset(1), 0); - // EXPECT_EQ(GetOffset(2), GetOffsetAfter(1)); + ASSERT_EQ(planner_->ExecuteAllocations(0, 10), kTfLiteOk); + EXPECT_EQ((*graph_->tensors())[1].data.raw, nullptr); } TEST_F(ArenaPlannerTest, SimpleGraph) { @@ -237,6 +244,30 @@ TEST_F(ArenaPlannerTest, SimpleGraph) { EXPECT_EQ(GetOffset(3), 0); } +TEST_F(ArenaPlannerTest, SimpleGraphInputsPreserved) { + TestGraph graph({0, 1}, + { + /* in, out, tmp */ + {{0, 1}, {2}, {}}, // First op + {{2, 0}, {4, 5}, {}}, // Second op + {{4, 5}, {3}, {}} // Third op + }, + {3}); + SetGraph(&graph, /*preserve_inputs=*/true); + Execute(0, 10); + + // Alloc(+) and dealloc(-) order: +0 +1 +2 +4 +5 -2 +3 -4 -5 + EXPECT_EQ(GetOffset(0), 0); + EXPECT_EQ(GetOffset(1), GetOffsetAfter(0)); + EXPECT_EQ(GetOffset(2), GetOffsetAfter(1)); + EXPECT_EQ(GetOffset(4), GetOffsetAfter(2)); + EXPECT_EQ(GetOffset(5), GetOffsetAfter(4)); + // Because we are keeping the inputs alive until the end (due to + // preserve_inputs=true), the output tensor will not be able to use that + // space. It will end up using the same are as tensor #2. + EXPECT_EQ(GetOffset(3), GetOffsetAfter(1)); +} + TEST_F(ArenaPlannerTest, SimpleGraphWithTemporary) { TestGraph graph({0, 1}, { @@ -309,13 +340,15 @@ TEST_F(ArenaPlannerTest, SimpleGraphWithPersistentTensor) { { /* in, out, tmp */ {{0, 1}, {2}, {}}, // First op - {{2, 0}, {4}, {5}}, // Second op, with temporary + {{2, 0}, {4}, {5}}, // Second op, with persistent {{4, -1}, {3}, {}} // Third op, with optional }, {3}); // Make #1 persistent so it goes into its own arena. (*graph.tensors())[1].allocation_type = kTfLiteArenaRwPersistent; + // The only use case for kTfLiteArenaRwPersistent is variable tensor now. + graph.SetVariables({1}); SetGraph(&graph); Execute(0, 10); diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 13d9a463fb9516cd96fa638ebad84ddccf1b59f2..5543acc1f5dabaa8a54ec4d1f2027bc66a00f6db 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -201,7 +201,7 @@ def generated_test_models(): "concat", "constant", "control_dep", - # "conv", + "conv", "depthwiseconv", "div", "equal", @@ -214,12 +214,14 @@ def generated_test_models(): "global_batch_norm", "greater", "greater_equal", + "sum", "l2norm", "l2_pool", "less", "less_equal", "local_response_norm", "log_softmax", + "log", "lstm", "max_pool", "maximum", @@ -231,11 +233,14 @@ def generated_test_models(): "pad", "padv2", # "prelu", + "pow", "relu", "relu1", "relu6", "reshape", "resize_bilinear", + "rsqrt", + "shape", "sigmoid", "sin", "slice", @@ -244,6 +249,7 @@ def generated_test_models(): "space_to_depth", "sparse_to_dense", "split", + "sqrt", "squeeze", "strided_slice", "strided_slice_1d_exhaustive", diff --git a/tensorflow/contrib/lite/build_ios_universal_lib.sh b/tensorflow/contrib/lite/build_ios_universal_lib.sh index 9f398f4a9f3dcafd7bd49fd5d95e9991b8b36b75..e9531aef19f04adf719156aa3e874dc5ce6e2b04 100755 --- a/tensorflow/contrib/lite/build_ios_universal_lib.sh +++ b/tensorflow/contrib/lite/build_ios_universal_lib.sh @@ -19,22 +19,23 @@ set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$SCRIPT_DIR/../../.." -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=x86_64 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_x86_64/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=i386 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_i386/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=armv7 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_armv7/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=armv7s -j 8 \ -$SCRIPT_DIR/gen/lib/ios_armv7s/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=arm64 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_arm64/libtensorflow-lite.a +# Build library for supported architectures and packs them in a fat binary. +make_library() { + for arch in x86_64 i386 armv7 armv7s arm64 + do + make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=${arch} \ + -j 8 \ + $SCRIPT_DIR/gen/lib/ios_${arch}/${1} + done + lipo \ + tensorflow/contrib/lite/gen/lib/ios_x86_64/${1} \ + tensorflow/contrib/lite/gen/lib/ios_i386/${1} \ + tensorflow/contrib/lite/gen/lib/ios_armv7/${1} \ + tensorflow/contrib/lite/gen/lib/ios_armv7s/${1} \ + tensorflow/contrib/lite/gen/lib/ios_arm64/${1} \ + -create \ + -output tensorflow/contrib/lite/gen/lib/${1} +} -lipo \ -tensorflow/contrib/lite/gen/lib/ios_x86_64/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_i386/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_armv7/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_armv7s/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_arm64/libtensorflow-lite.a \ --create \ --output tensorflow/contrib/lite/gen/lib/libtensorflow-lite.a +make_library libtensorflow-lite.a +make_library benchmark-lib.a diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index c1cc4476fbd45fa6b3f5b3a1ed2cba39cc2ad54b..cda889bf502a535eac4249bbae645359cdb2135d 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -92,8 +92,17 @@ typedef struct { TfLiteFusedActivation activation; } TfLiteSequenceRNNParams; +typedef enum { + kTfLiteFullyConnectedWeightsFormatDefault = 0, + kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8 = 1, +} TfLiteFullyConnectedWeightsFormat; + typedef struct { + // Parameters for FullyConnected version 1 or above. TfLiteFusedActivation activation; + + // Parameters for FullyConnected version 2 or above. + TfLiteFullyConnectedWeightsFormat weights_format; } TfLiteFullyConnectedParams; typedef enum { @@ -215,7 +224,7 @@ typedef struct { typedef struct { bool keep_dims; -} TfLiteMeanParams; +} TfLiteReducerParams; typedef struct { int num_splits; @@ -250,6 +259,10 @@ typedef struct { bool validate_indices; } TfLiteSparseToDenseParams; +typedef struct { + TfLiteType out_type; +} TfLiteShapeParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 7b10b69f438536709f47ac8bd5cb2f8e27d0a1aa..a44e9182302d19acd1e1c183ed388531eec11d93 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ // DO NOT EDIT MANUALLY: This file is automatically generated by -// `schema_builtin_ops_header_generator.py`. +// `schema/builtin_ops_header/generator.cc`. #ifdef __cplusplus extern "C" { @@ -98,6 +98,12 @@ typedef enum { kTfLiteBuiltinExpandDims = 70, kTfLiteBuiltinEqual = 71, kTfLiteBuiltinNotEqual = 72, + kTfLiteBuiltinLog = 73, + kTfLiteBuiltinSum = 74, + kTfLiteBuiltinSqrt = 75, + kTfLiteBuiltinRsqrt = 76, + kTfLiteBuiltinShape = 77, + kTfLiteBuiltinPow = 78, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/context.c b/tensorflow/contrib/lite/context.c index 5c6f5e72a47180cd98be46f60cfa8eaf28197806..7f2aa316f4a9a265b14a216a6ffa53c7f0757426 100644 --- a/tensorflow/contrib/lite/context.c +++ b/tensorflow/contrib/lite/context.c @@ -76,7 +76,7 @@ void TfLiteTensorFree(TfLiteTensor* t) { void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, TfLiteQuantizationParams quantization, char* buffer, size_t size, TfLiteAllocationType allocation_type, - const void* allocation, TfLiteTensor* tensor) { + const void* allocation, bool is_variable, TfLiteTensor* tensor) { TfLiteTensorFree(tensor); tensor->type = type; tensor->name = name; @@ -86,6 +86,7 @@ void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, tensor->bytes = size; tensor->allocation_type = allocation_type; tensor->allocation = allocation; + tensor->is_variable = is_variable; } void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) { diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index 4eb66cc225eb04923be9aaa445a335ad822c8a6f..4f260ad40a7f2ac597cabad9fe38a264ede5b1b3 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -138,6 +138,8 @@ typedef enum { kTfLiteInt64 = 4, kTfLiteString = 5, kTfLiteBool = 6, + kTfLiteInt16 = 7, + kTfLiteComplex64 = 8, } TfLiteType; // Parameters for asymmetric quantization. Quantized values can be converted @@ -148,7 +150,7 @@ typedef struct { int32_t zero_point; } TfLiteQuantizationParams; -// A union of points that points to memory for a given tensor. +// A union of pointers that points to memory for a given tensor. typedef union { int* i32; int64_t* i64; @@ -157,6 +159,8 @@ typedef union { const char* raw_const; uint8_t* uint8; bool* b; + int16_t* i16; + _Complex float* c64; } TfLitePtrUnion; // Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped @@ -223,6 +227,9 @@ typedef struct { // delegate buffer. // WARNING: This is an // experimental interface that is subject to change. bool data_is_stale; + + // True if the tensor is a variable. + bool is_variable; } TfLiteTensor; // Free data memory of tensor `t`; @@ -235,9 +242,11 @@ void TfLiteTensorFree(TfLiteTensor* t); void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, TfLiteQuantizationParams quantization, char* buffer, size_t size, TfLiteAllocationType allocation_type, - const void* allocation, TfLiteTensor* tensor); + const void* allocation, bool is_variable, + TfLiteTensor* tensor); -// Resize the allocated data of a (dynamic) tensor. +// Resize the allocated data of a (dynamic) tensor. Tensors with allocation +// types other than kTfLiteDynamic will be ignored. void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); // A structure representing an instance of a node. @@ -368,6 +377,14 @@ typedef struct _TfLiteRegistration { // Returns kTfLiteOk on success. TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + // profiling_string is called during summarization of profiling information + // in order to group executions together. Providing a value here will cause a + // given op to appear multiple times is the profiling report. This is + // particularly useful for custom ops that can perform significantly + // different calculations depending on their `user-data`. + const char* (*profiling_string)(const TfLiteContext* context, + const TfLiteNode* node); + // Builtin codes. If this kernel refers to a builtin this is the code // of the builtin. This is so we can do marshaling to other frameworks like // NN API. diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index 0731d14419d2dec2ea5efa48ef5d4b7728af635f..fd798c209e5112235cf6e351e231d4096006a8b0 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -26,6 +26,10 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h" +#ifdef __ANDROID__ +#include +#endif + namespace tflite { namespace { @@ -37,6 +41,32 @@ namespace { return kTfLiteError; \ } +namespace { +int32_t GetAndroidSdkVersion() { +#ifdef __ANDROID__ + const char* sdkProp = "ro.build.version.sdk"; + char sdkVersion[PROP_VALUE_MAX]; + int length = __system_property_get(sdkProp, sdkVersion); + if (length != 0) { + for (int i = 0; i < length; ++i) { + int digit = sdkVersion[i] - '0'; + if (digit < 0 || digit > 9) { + // Non-numeric SDK version, assume it's higher then expected; + return std::numeric_limits::max(); + } + } + return atoi(sdkVersion); + } +#endif // __ANDROID__ + return 0; +} + +constexpr int32_t kMinSdkVersionForNNAPI = 27; +constexpr int32_t kMinSdkVersionForNNAPI11 = 28; +static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion(); + +} // namespace + // RAII NN API Model Destructor for use with std::unique_ptr struct NNFreeModel { void operator()(ANeuralNetworksModel* model) { @@ -71,7 +101,7 @@ class OperandMapping { // Add a new mapping from `tflite_index` and return the NN API tensor index. int add_new_ann_tensor_index(int tflite_index) { if (tflite_index >= lite_tensor_to_ann_tensor_.size()) { - lite_tensor_to_ann_tensor_.resize(tflite_index + 1); + lite_tensor_to_ann_tensor_.resize(tflite_index + 1, -1); } int new_tensor_index = next_ann_tensor_index_++; lite_tensor_to_ann_tensor_[tflite_index] = new_tensor_index; @@ -98,14 +128,28 @@ class NNAPIOpBuilder { operand_mapping_(tensor_mapping), nn_model_(nn_model) {} - TfLiteStatus AddScalarInt32Operand(int value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; - CHECK_NN(context_, - ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); - int ann_operand = operand_mapping_->add_new_non_tensor_operand(); - CHECK_NN(context_, ANeuralNetworksModel_setOperandValue( - nn_model_, ann_operand, &value, sizeof(int32_t))); - augmented_inputs_.push_back(ann_operand); + TfLiteStatus AddScalarInt32Operand(int32_t value) { + return AddScalarOperand(value, ANEURALNETWORKS_INT32); + } + + TfLiteStatus AddScalarFloat32Operand(float value) { + return AddScalarOperand(value, ANEURALNETWORKS_FLOAT32); + } + + TfLiteStatus AddVectorInt32Operand(const int32_t* values, + uint32_t num_values) { + return AddVectorOperand(values, num_values, + ANEURALNETWORKS_TENSOR_INT32); + } + + TfLiteStatus AddPoolingParams(void* data) { + auto builtin = reinterpret_cast(data); + AddScalarInt32Operand(builtin->padding); + AddScalarInt32Operand(builtin->stride_width); + AddScalarInt32Operand(builtin->stride_height); + AddScalarInt32Operand(builtin->filter_width); + AddScalarInt32Operand(builtin->filter_height); + AddScalarInt32Operand(builtin->activation); return kTfLiteOk; } @@ -149,7 +193,6 @@ class NNAPIOpBuilder { return kTfLiteOk; case kTfLiteFloat32: nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - scale = 0.f; break; case kTfLiteUInt8: nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; @@ -158,8 +201,8 @@ class NNAPIOpBuilder { break; case kTfLiteInt32: nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = 0.f; - zeroPoint = 0; + scale = tensor->params.scale; + zeroPoint = tensor->params.zero_point; break; default: context_->ReportError(context_, "Logic error in NN API Delegate.\n"); @@ -192,12 +235,39 @@ class NNAPIOpBuilder { augmented_inputs_.data(), static_cast(augmented_outputs_.size()), augmented_outputs_.data())); - augmented_outputs_.clear(); + augmented_inputs_.clear(); augmented_outputs_.clear(); return kTfLiteOk; } private: + template + TfLiteStatus AddScalarOperand(T value, int32_t nn_type) { + ANeuralNetworksOperandType operand_type{.type = nn_type}; + CHECK_NN(context_, + ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); + int ann_operand = operand_mapping_->add_new_non_tensor_operand(); + CHECK_NN(context_, ANeuralNetworksModel_setOperandValue( + nn_model_, ann_operand, &value, sizeof(T))); + augmented_inputs_.push_back(ann_operand); + return kTfLiteOk; + } + + template + TfLiteStatus AddVectorOperand(const T* values, uint32_t num_values, + int32_t nn_type) { + ANeuralNetworksOperandType operand_type{ + .type = nn_type, .dimensionCount = 1, .dimensions = &num_values}; + CHECK_NN(context_, + ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); + int ann_operand = operand_mapping_->add_new_non_tensor_operand(); + CHECK_NN(context_, + ANeuralNetworksModel_setOperandValue( + nn_model_, ann_operand, values, sizeof(T) * num_values)); + augmented_inputs_.push_back(ann_operand); + return kTfLiteOk; + } + // TfLiteContext for error handling. Must be named context for macros to // work. TfLiteContext* context_; @@ -227,29 +297,161 @@ class NNAPIDelegateKernel { // Return a function that knows how to translate a node into its operands // when called. You can use this function to see if a node is supported // (i.e. that MappingFn is not nullptr). - MappingFn Map(TfLiteContext* context, int builtin_code, TfLiteNode* node) { + MappingFn Map(TfLiteContext* context, int builtin_code, int version, + TfLiteNode* node) { switch (builtin_code) { case kTfLiteBuiltinAdd: - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); - return ANEURALNETWORKS_ADD; - }; + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_ADD; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinMul: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_MUL; + }; + } else { + return nullptr; + } break; case kTfLiteBuiltinAveragePool2d: - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_AVERAGE_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinMaxPool2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_MAX_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinL2Pool2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_L2_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinConv2d: + if (version == 1) { auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->padding); - builder->AddScalarInt32Operand(builtin->stride_width); - builder->AddScalarInt32Operand(builtin->stride_height); - builder->AddScalarInt32Operand(builtin->filter_width); - builder->AddScalarInt32Operand(builtin->filter_height); - builder->AddScalarInt32Operand(builtin->activation); - return ANEURALNETWORKS_AVERAGE_POOL_2D; - }; + reinterpret_cast(node->builtin_data); + if (builtin->dilation_width_factor != 1 || + builtin->dilation_height_factor != 1 || node->inputs->size != 3) { + // NNAPI does not support dilated Conv2D. + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->padding); + builder->AddScalarInt32Operand(builtin->stride_width); + builder->AddScalarInt32Operand(builtin->stride_height); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_CONV_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDepthwiseConv2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->padding); + builder->AddScalarInt32Operand(builtin->stride_width); + builder->AddScalarInt32Operand(builtin->stride_height); + builder->AddScalarInt32Operand(builtin->depth_multiplier); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_DEPTHWISE_CONV_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinFullyConnected: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_FULLY_CONNECTED; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSoftmax: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarFloat32Operand(builtin->beta); + return ANEURALNETWORKS_SOFTMAX; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinReshape: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RESHAPE; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSqueeze: + // Squeeze requires NNAPI1.1. + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + // Note that we add the squeeze dimensions even if the dimensions + // were unspecified (empty), as NNAPI requires the operand. + builder->AddVectorInt32Operand( + builtin->squeeze_dims, + static_cast(builtin->num_squeeze_dims)); + return ANEURALNETWORKS_SQUEEZE; + }; + } else { + return nullptr; + } break; default: return nullptr; @@ -292,10 +494,14 @@ class NNAPIDelegateKernel { int relative_input_index = 0; for (auto absolute_input_index : TfLiteIntArrayView(node->inputs)) { TfLiteTensor* tensor = &context->tensors[absolute_input_index]; - CHECK_NN(context, ANeuralNetworksExecution_setInput( - execution, relative_input_index, nullptr, - tensor->data.raw, tensor->bytes)); - relative_input_index++; + // TODO(miaowang): make sure the delegation works with dequantized weights + // as intermediate tensors. + if (tensor->allocation_type != kTfLiteMmapRo) { + CHECK_NN(context, ANeuralNetworksExecution_setInput( + execution, relative_input_index, nullptr, + tensor->data.raw, tensor->bytes)); + relative_input_index++; + } } // Set the output tensor buffers. @@ -345,8 +551,8 @@ class NNAPIDelegateKernel { TF_LITE_ENSURE_STATUS(builder.AddTensorInput(input_index)); } // Get op type and operands - int nn_op_type = - Map(context, reg->builtin_code, node)(context, &builder, node); + int nn_op_type = Map(context, reg->builtin_code, reg->version, node)( + context, &builder, node); // Map outputs to NN API tensor indices. for (auto output_index : TfLiteIntArrayView(node->outputs)) { TF_LITE_ENSURE_STATUS(builder.AddTensorOutput(output_index)); @@ -368,8 +574,12 @@ class NNAPIDelegateKernel { std::vector outputs; outputs.reserve(output_tensors->size); // Make the TensorFlow lite inputs and outputs to ann_indices. - for (int i : TfLiteIntArrayView(input_tensors)) - inputs.push_back(operand_mapping_.lite_index_to_ann(i)); + for (int i : TfLiteIntArrayView(input_tensors)) { + // Constant tensors are not NNAPI inputs. + if (context->tensors[i].allocation_type != kTfLiteMmapRo) { + inputs.push_back(operand_mapping_.lite_index_to_ann(i)); + } + } for (int i : TfLiteIntArrayView(output_tensors)) outputs.push_back(operand_mapping_.lite_index_to_ann(i)); // Tell ANN to declare inputs/outputs @@ -392,7 +602,9 @@ TfLiteDelegate* NnApiDelegate() { .Prepare = [](TfLiteContext* context, TfLiteDelegate* delegate) -> TfLiteStatus { // Do not check nodes_ if NN API is unavailable. - if (!NNAPIExists()) return kTfLiteOk; + if (kAndroidSdkVersion < kMinSdkVersionForNNAPI || !NNAPIExists()) { + return kTfLiteOk; + } std::vector supported_nodes(1); // We don't care about all nodes_, we only care about ones in the @@ -400,6 +612,7 @@ TfLiteDelegate* NnApiDelegate() { TfLiteIntArray* plan; TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan)); int total_supported_nodes = 0; + // Check for every node if it is supported // TODO(b/80625235): Fix this to do more careful checking of versioning. for (int node_index : TfLiteIntArrayView(plan)) { @@ -408,7 +621,8 @@ TfLiteDelegate* NnApiDelegate() { TF_LITE_ENSURE_STATUS(context->GetNodeAndRegistration( context, node_index, &node, ®istration)); NNAPIDelegateKernel dummy_kernel; - if (dummy_kernel.Map(context, registration->builtin_code, node)) { + if (dummy_kernel.Map(context, registration->builtin_code, + registration->version, node)) { supported_nodes.push_back(node_index); } total_supported_nodes += 1; diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index ff2e721423f07889f36746a2889afcc3369f28fc..aad10c9ce730a2e90481a123a1e3e323cfb2bd42 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -21,8 +21,12 @@ limitations under the License. namespace tflite { namespace { +using ::testing::ElementsAre; using ::testing::ElementsAreArray; +// TODO(b/110368244): figure out how to share the existing tests in kernels/ but +// with the delegation on. Also, add more unit tests to improve code coverage. + class FloatAddOpModel : public SingleOpModel { public: FloatAddOpModel(const TensorData& input1, const TensorData& input2, @@ -72,6 +76,596 @@ TEST(NNAPIDelegate, AddWithRelu) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.4, 1.0, 1.3})); } +class FloatMulOpModel : public SingleOpModel { + public: + FloatMulOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_MUL, BuiltinOptions_MulOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, MulWithNoActivation) { + FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4}))); +} + +class FloatPoolingOpModel : public SingleOpModel { + public: + FloatPoolingOpModel(BuiltinOperator type, const TensorData& input, + int filter_width, int filter_height, + const TensorData& output) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + output_ = AddOutput(output); + + SetBuiltinOp( + type, BuiltinOptions_Pool2DOptions, + CreatePool2DOptions(builder_, Padding_VALID, 2, 2, filter_width, + filter_height, ActivationFunctionType_NONE) + .Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, AveragePoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2.75, 5.75})); +} + +TEST(NNAPIDelegate, MaxPoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_MAX_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({6, 10})); +} + +TEST(NNAPIDelegate, L2PoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_L2_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3.5, 6.5})); +} + +class BaseConvolutionOpModel : public SingleOpModel { + public: + BaseConvolutionOpModel( + const TensorData& input, const TensorData& filter, + const TensorData& output, int stride_width = 2, int stride_height = 2, + enum Padding padding = Padding_VALID, + enum ActivationFunctionType activation = ActivationFunctionType_NONE, + int dilation_width_factor = 1, int dilation_height_factor = 1) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + filter_ = AddInput(filter); + + int bias_size = GetShape(filter_)[0]; + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {bias_size}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(filter_); + TensorData bias{TensorType_INT32, {bias_size}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + if (input.type != TensorType_FLOAT32) { + // The following is required by quantized inference. It is the unittest's + // responsibility to make sure the output scale falls into the correct + // range. + CHECK_LT(GetScale(input_) * GetScale(filter_), GetScale(output_)); + } + + SetBuiltinOp(BuiltinOperator_CONV_2D, BuiltinOptions_Conv2DOptions, + CreateConv2DOptions( + builder_, padding, stride_width, stride_height, activation, + dilation_width_factor, dilation_height_factor) + .Union()); + + BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)}); + } + + protected: + int input_; + int filter_; + int bias_; + int output_; +}; + +class ConvolutionOpModel : public BaseConvolutionOpModel { + public: + using BaseConvolutionOpModel::BaseConvolutionOpModel; + + void SetFilter(std::initializer_list f) { PopulateTensor(filter_, f); } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +class QuantizedConvolutionOpModel : public BaseConvolutionOpModel { + public: + using BaseConvolutionOpModel::BaseConvolutionOpModel; + + void SetInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + + void SetFilter(std::initializer_list data) { + QuantizeAndPopulate(filter_, data); + } + + void SetBias(std::initializer_list data) { + QuantizeAndPopulate(bias_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } +}; + +// In this tests we set the input and output scales so that the results +// match exactly the 'non-quantized' version. +TEST(NNAPIDelegate, SimpleTestQuantized) { + QuantizedConvolutionOpModel m({TensorType_UINT8, {2, 2, 4, 1}, -63.5, 64}, + {TensorType_UINT8, {3, 2, 2, 1}, -63.5, 64}, + {TensorType_UINT8, {}, -127, 128}); + m.SetInput({ + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }); + m.SetFilter({ + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }); + m.SetBias({1, 2, 3}); + + m.Invoke(); + + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + }, + 1e-5))); + // For good measure, let's also verify the quantized values: + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 145, 129, 132, // + 145, 129, 132, // + 144, 131, 130, // + 164, 131, 130, // + })); +} + +TEST(NNAPIDelegate, Conv2DWithNoActivation) { + ConvolutionOpModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, + {TensorType_FLOAT32, {3, 2, 2, 1}}, + {TensorType_FLOAT32, {}}); + + m.SetInput({ + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }); + m.SetFilter({ + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }); + m.SetBias({1, 2, 3}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + })); +} + +class DepthwiseConvolutionOpModel : public SingleOpModel { + public: + DepthwiseConvolutionOpModel(const TensorData& input, const TensorData& filter, + const TensorData& output) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + filter_ = AddInput(filter); + + int bias_size = GetShape(filter_)[3]; + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {bias_size}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(filter_); + TensorData bias{TensorType_INT32, {bias_size}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + + int input_depth = GetShape(input_)[3]; + int output_depth = GetShape(filter_)[3]; + int depth_mul = output_depth / input_depth; + + SetBuiltinOp( + BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOptions_DepthwiseConv2DOptions, + CreateDepthwiseConv2DOptions(builder_, Padding_VALID, 1, 1, depth_mul, + ActivationFunctionType_NONE) + .Union()); + + BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)}); + } + + void SetFilter(std::initializer_list f) { PopulateTensor(filter_, f); } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int filter_; + int bias_; + int output_; +}; + +TEST(NNAPIDelegate, DepthwiseConv2DWithNoActivation) { + DepthwiseConvolutionOpModel m({TensorType_FLOAT32, {1, 3, 2, 2}}, + {TensorType_FLOAT32, {1, 2, 2, 4}}, + {TensorType_FLOAT32, {}}); + + m.SetInput({ + 1, 2, 7, 8, // column 1 + 3, 4, 9, 10, // column 2 + 5, 6, 11, 12, // column 3 + }); + m.SetFilter({ + 1, 2, 3, 4, // + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }); + m.SetBias({1, 2, 3, 4}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 71, -34, 99, -20, // + 91, -26, 127, -4, // + })); +} + +class FloatFullyConnectedOpModel : public SingleOpModel { + public: + FloatFullyConnectedOpModel(int units, int batches, const TensorData& input, + const TensorData& output = {TensorType_FLOAT32}) + : batches_(batches), units_(units) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + int total_input_size = 1; + for (int i = 0; i < input.shape.size(); ++i) { + total_input_size *= input.shape[i]; + } + input_size_ = total_input_size / batches_; + + input_ = AddInput(input); + weights_ = + AddInput({input.type, {units_, input_size_}, input.min, input.max}); + + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {units_}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(weights_); + TensorData bias{TensorType_INT32, {units_}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + + SetBuiltinOp( + BuiltinOperator_FULLY_CONNECTED, BuiltinOptions_FullyConnectedOptions, + CreateFullyConnectedOptions(builder_, ActivationFunctionType_RELU) + .Union()); + BuildInterpreter({GetShape(input_), GetShape(weights_), GetShape(bias_)}); + } + + int input_size() { return input_size_; } + int num_units() { return units_; } + int num_batches() { return batches_; } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetWeights(std::initializer_list f) { + PopulateTensor(weights_, f); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int weights_; + int bias_; + int output_; + + int batches_; + int units_; + int input_size_; +}; + +TEST(NNAPIDelegate, FullyConnectedSimpleTest) { + FloatFullyConnectedOpModel m(/*units=*/3, /*batches=*/2, + /*input=*/{TensorType_FLOAT32, {2, 10}}); + m.SetWeights({ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + }); + m.SetBias({1, 2, 3}); + + m.SetInput({ + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(24, 25, 26, 58, 59, 60)); +} + +class SoftmaxOpModel : public SingleOpModel { + public: + SoftmaxOpModel(int batches, int size, float beta) + : batches_(batches), input_size_(size), beta_(beta) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_SOFTMAX, BuiltinOptions_SoftmaxOptions, + CreateSoftmaxOptions(builder_, beta_).Union()); + BuildInterpreter({{batches_, input_size_}}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; + + int batches_; + int input_size_; + float beta_; +}; + +TEST(NNAPIDelegate, SoftmaxSimpleTest) { + SoftmaxOpModel m(/*batches=*/2, /*size=*/5, /*beta=*/1.0); + m.SetInput({ + 1.0, 2.0, 3.0, 4.0, 5.0, // b = 0 + -1.0, -2.0, -3.0, -4.0, -5.0, // b = 0 + }); + + m.Invoke(); + + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647, + 0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231}, + 1e-6))); +} + +class ReshapeOpModel : public SingleOpModel { + public: + ReshapeOpModel(std::initializer_list input_shape, + std::initializer_list new_shape) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(TensorType_FLOAT32); + new_shape_ = AddInput(TensorType_INT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_RESHAPE, BuiltinOptions_ReshapeOptions, + CreateReshapeOptions(builder_, builder_.CreateVector(new_shape)) + .Union()); + BuildInterpreter({input_shape, {static_cast(new_shape.size())}}); + PopulateTensor(new_shape_, new_shape); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int new_shape_; + int output_; +}; + +TEST(NNAPIDelegate, ReshapeSimpleTest) { + ReshapeOpModel m({1, 2, 4, 1}, {2, 2, 2}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2})); +} + +class SqueezeOpModel : public SingleOpModel { + public: + SqueezeOpModel(const TensorData& input, const TensorData& output, + std::initializer_list axis) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp( + BuiltinOperator_SQUEEZE, BuiltinOptions_SqueezeOptions, + CreateSqueezeOptions(builder_, builder_.CreateVector(axis)) + .Union()); + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int new_shape_; + int output_; +}; + +TEST(NNAPIDelegate, SqueezeSimpleTest) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SqueezeOpModel m({TensorType_FLOAT32, {1, 24, 1}}, {TensorType_FLOAT32, {24}}, + {}); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({24})); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0})); +} + +TEST(NNAPIDelegate, SqueezeWithAxisTest) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SqueezeOpModel m({TensorType_FLOAT32, {1, 24, 1}}, {TensorType_FLOAT32, {24}}, + {2}); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 24})); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/examples/android/BUILD b/tensorflow/contrib/lite/examples/android/BUILD index 57000072561303e8457f61b1ebe95d382fc01f10..4d2437e7d3714e1b8b427b0c6197b295c0355b07 100644 --- a/tensorflow/contrib/lite/examples/android/BUILD +++ b/tensorflow/contrib/lite/examples/android/BUILD @@ -1,6 +1,8 @@ # Description: # TensorFlow camera demo app for Android. +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 @@ -24,28 +26,29 @@ cc_library( android_binary( name = "tflite_demo", srcs = glob([ - "src/**/*.java", + "app/src/main/java/**/*.java", ]), # Package assets from assets dir as well as all model targets. # Remove undesired models (and corresponding Activities in source) # to reduce APK size. assets = [ - "//tensorflow/contrib/lite/examples/android/assets:labels_mobilenet_quant_v1_224.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:labels_mobilenet_quant_v1_224.txt", "@tflite_mobilenet//:mobilenet_quant_v1_224.tflite", "@tflite_conv_actions_frozen//:conv_actions_frozen.tflite", - "//tensorflow/contrib/lite/examples/android/assets:conv_actions_labels.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:conv_actions_labels.txt", "@tflite_mobilenet_ssd//:mobilenet_ssd.tflite", - "//tensorflow/contrib/lite/examples/android/assets:box_priors.txt", - "//tensorflow/contrib/lite/examples/android/assets:coco_labels_list.txt", + "@tflite_mobilenet_ssd_quant//:detect.tflite", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:box_priors.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:coco_labels_list.txt", ], assets_dir = "", custom_package = "org.tensorflow.lite.demo", inline_constants = 1, - manifest = "AndroidManifest.xml", + manifest = "app/src/main/AndroidManifest.xml", nocompress_extensions = [ ".tflite", ], - resource_files = glob(["res/**"]), + resource_files = glob(["app/src/main/res/**"]), tags = [ "manual", "notap", @@ -55,31 +58,3 @@ android_binary( "//tensorflow/contrib/lite/java:tensorflowlite", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - "gradleBuild/**", - "libs/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) - -filegroup( - name = "java_files", - srcs = glob(["src/**/*.java"]), -) - -filegroup( - name = "resource_files", - srcs = glob(["res/**"]), -) - -exports_files(["AndroidManifest.xml"]) diff --git a/tensorflow/contrib/lite/examples/android/android.iml b/tensorflow/contrib/lite/examples/android/android.iml new file mode 100644 index 0000000000000000000000000000000000000000..f0a5ac2bf4cdfb7c98f5704310fbf2f16e9065a2 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/android.iml @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/tensorflow/contrib/lite/examples/android/app/build.gradle b/tensorflow/contrib/lite/examples/android/app/build.gradle new file mode 100644 index 0000000000000000000000000000000000000000..1ffb9dd377730bb3dc872cbf1548fa29ffaa0949 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/build.gradle @@ -0,0 +1,60 @@ +apply plugin: 'com.android.application' + +android { + compileSdkVersion 26 + buildToolsVersion '26.0.2' + defaultConfig { + applicationId "org.tensorflow.lite.demo" + minSdkVersion 15 + targetSdkVersion 26 + versionCode 1 + versionName "1.0" + testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner" + + // Remove this block. + jackOptions { + enabled true + } + } + lintOptions { + abortOnError false + } + buildTypes { + release { + minifyEnabled false + proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' + } + } + aaptOptions { + noCompress "tflite" + } + + compileOptions { + sourceCompatibility JavaVersion.VERSION_1_8 + targetCompatibility JavaVersion.VERSION_1_8 + } +} + +repositories { + maven { + url 'https://google.bintray.com/tensorflow' + } +} + +// import DownloadModels task +project.ext.ASSET_DIR = projectDir.toString() + '/src/main/assets' +project.ext.TMP_DIR = project.buildDir.toString() + '/downloads' + +// Download default models; if you wish to use your own models then +// place them in the "assets" directory and comment out this line. +apply from: "download-models.gradle" + +dependencies { + compile fileTree(dir: 'libs', include: ['*.jar']) + androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { + exclude group: 'com.android.support', module: 'support-annotations' + }) + compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' + + testCompile 'junit:junit:4.12' +} diff --git a/tensorflow/contrib/lite/examples/android/app/download-models.gradle b/tensorflow/contrib/lite/examples/android/app/download-models.gradle new file mode 100644 index 0000000000000000000000000000000000000000..c100e37c16f38a65f7b1f64a3f6e3eaa1477e8eb --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/download-models.gradle @@ -0,0 +1,74 @@ +/* + * download-models.gradle + * Downloads model files from ${MODEL_URL} into application's asset folder + * Input: + * project.ext.TMP_DIR: absolute path to hold downloaded zip files + * project.ext.ASSET_DIR: absolute path to save unzipped model files + * Output: + * 3 model files will be downloaded into given folder of ext.ASSET_DIR + */ +// hard coded model files +// LINT.IfChange + +def models = ['conv_actions_tflite.zip', + 'mobilenet_ssd_tflite_v1.zip', + 'mobilenet_v1_224_android_quant_2017_11_08.zip', + 'coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip'] +// LINT.ThenChange(//tensorflow/contrib/lite/examples/android/BUILD) + +// Root URL for model archives +def MODEL_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite' + +buildscript { + repositories { + jcenter() + } + dependencies { + classpath 'de.undercouch:gradle-download-task:3.2.0' + } +} + +import de.undercouch.gradle.tasks.download.Download +task downloadFile(type: Download){ + for (f in models) { + def modelUrl = MODEL_URL + "/" + f + println "Downloading ${f} from ${modelUrl}" + src modelUrl + } + + dest new File(project.ext.TMP_DIR) + overwrite true +} + +task extractModels(type: Copy) { + for (f in models) { + def localFile = f.split("/")[-1] + from zipTree(project.ext.TMP_DIR + '/' + localFile) + } + + into file(project.ext.ASSET_DIR) + fileMode 0644 + exclude '**/LICENSE' + + def needDownload = false + for (f in models) { + def localFile = f.split("/")[-1] + if (!(new File(project.ext.TMP_DIR + '/' + localFile)).exists()) { + needDownload = true + } + } + + if (needDownload) { + dependsOn downloadFile + } +} + +tasks.whenTaskAdded { task -> + if (task.name == 'assembleDebug') { + task.dependsOn 'extractModels' + } + if (task.name == 'assembleRelease') { + task.dependsOn 'extractModels' + } +} + diff --git a/tensorflow/contrib/lite/examples/android/AndroidManifest.xml b/tensorflow/contrib/lite/examples/android/app/src/main/AndroidManifest.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/AndroidManifest.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/AndroidManifest.xml diff --git a/tensorflow/contrib/lite/examples/android/assets/BUILD b/tensorflow/contrib/lite/examples/android/app/src/main/assets/BUILD similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/BUILD rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/BUILD diff --git a/tensorflow/contrib/lite/examples/android/assets/box_priors.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/box_priors.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/box_priors.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/box_priors.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/coco_labels_list.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/coco_labels_list.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/coco_labels_list.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/coco_labels_list.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/conv_actions_labels.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/conv_actions_labels.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/conv_actions_labels.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/conv_actions_labels.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/labels_mobilenet_quant_v1_224.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/labels_mobilenet_quant_v1_224.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/labels_mobilenet_quant_v1_224.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/labels_mobilenet_quant_v1_224.txt diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/AutoFitTextureView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/AutoFitTextureView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/AutoFitTextureView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/AutoFitTextureView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraConnectionFragment.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraConnectionFragment.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/Classifier.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/Classifier.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/Classifier.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/Classifier.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ClassifierActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ClassifierActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/DetectorActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java similarity index 96% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/DetectorActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java index de997e454a1e33254cb7c2c932ca79d0072539fa..87160f6b3fb8c0d24e5df131d9becbb3eb6e2980 100644 --- a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/DetectorActivity.java +++ b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java @@ -1,5 +1,5 @@ /* - * Copyright 2016 The TensorFlow Authors. All Rights Reserved. + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -50,9 +50,10 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable // Configuration values for the prepackaged SSD model. private static final int TF_OD_API_INPUT_SIZE = 300; - private static final String TF_OD_API_MODEL_FILE = "mobilenet_ssd.tflite"; + private static final boolean TF_OD_API_IS_QUANTIZED = true; + private static final String TF_OD_API_MODEL_FILE = "detect.tflite"; private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/coco_labels_list.txt"; - + // Which detection model to use: by default uses Tensorflow Object Detection API frozen // checkpoints. private enum DetectorMode { @@ -107,7 +108,11 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable try { detector = TFLiteObjectDetectionAPIModel.create( - getAssets(), TF_OD_API_MODEL_FILE, TF_OD_API_LABELS_FILE, TF_OD_API_INPUT_SIZE); + getAssets(), + TF_OD_API_MODEL_FILE, + TF_OD_API_LABELS_FILE, + TF_OD_API_INPUT_SIZE, + TF_OD_API_IS_QUANTIZED); cropSize = TF_OD_API_INPUT_SIZE; } catch (final IOException e) { LOGGER.e("Exception initializing classifier!", e); diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/LegacyCameraConnectionFragment.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/LegacyCameraConnectionFragment.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/OverlayView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/OverlayView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/OverlayView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/OverlayView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognitionScoreView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognitionScoreView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognitionScoreView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognitionScoreView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognizeCommands.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognizeCommands.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognizeCommands.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognizeCommands.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ResultsView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ResultsView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ResultsView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ResultsView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/SpeechActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/SpeechActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/SpeechActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteImageClassifier.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteImageClassifier.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java similarity index 50% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java index bfb4a0a04bc90566736864bf62340d1032961858..9eb21de9d03e387d3c25b38171e154a358dc81ce 100644 --- a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java +++ b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java @@ -25,15 +25,14 @@ import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; import java.nio.MappedByteBuffer; import java.nio.channels.FileChannel; import java.util.ArrayList; -import java.util.Comparator; import java.util.HashMap; import java.util.List; import java.util.Map; -import java.util.PriorityQueue; -import java.util.StringTokenizer; import java.util.Vector; import org.tensorflow.demo.env.Logger; import org.tensorflow.lite.Interpreter; @@ -46,32 +45,35 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { private static final Logger LOGGER = new Logger(); // Only return this many results. - private static final int NUM_RESULTS = 1917; - private static final int NUM_CLASSES = 91; - - private static final float Y_SCALE = 10.0f; - private static final float X_SCALE = 10.0f; - private static final float H_SCALE = 5.0f; - private static final float W_SCALE = 5.0f; - + private static final int NUM_DETECTIONS = 10; + private boolean isModelQuantized; + // Float model + private static final float IMAGE_MEAN = 128.0f; + private static final float IMAGE_STD = 128.0f; + // Number of threads in the java app + private static final int NUM_THREADS = 4; // Config values. private int inputSize; - - private final float[][] boxPriors = new float[4][NUM_RESULTS]; - // Pre-allocated buffers. private Vector labels = new Vector(); private int[] intValues; + // outputLocations: array of shape [Batchsize, NUM_DETECTIONS,4] + // contains the location of detected boxes private float[][][] outputLocations; - private float[][][] outputClasses; - - float[][][][] img; + // outputClasses: array of shape [Batchsize, NUM_DETECTIONS] + // contains the classes of detected boxes + private float[][] outputClasses; + // outputScores: array of shape [Batchsize, NUM_DETECTIONS] + // contains the scores of detected boxes + private float[][] outputScores; + // numDetections: array of shape [Batchsize] + // contains the number of detected boxes + private float[] numDetections; + + private ByteBuffer imgData; private Interpreter tfLite; - private float expit(final float x) { - return (float) (1. / (1. + Math.exp(-x))); - } /** Memory-map the model file in Assets. */ private static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename) @@ -84,77 +86,24 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength); } - private void loadCoderOptions( - final AssetManager assetManager, final String locationFilename, final float[][] boxPriors) - throws IOException { - // Try to be intelligent about opening from assets or sdcard depending on prefix. - final String assetPrefix = "file:///android_asset/"; - InputStream is; - if (locationFilename.startsWith(assetPrefix)) { - is = assetManager.open(locationFilename.split(assetPrefix, -1)[1]); - } else { - is = new FileInputStream(locationFilename); - } - - final BufferedReader reader = new BufferedReader(new InputStreamReader(is)); - - for (int lineNum = 0; lineNum < 4; ++lineNum) { - String line = reader.readLine(); - final StringTokenizer st = new StringTokenizer(line, ", "); - int priorIndex = 0; - while (st.hasMoreTokens()) { - final String token = st.nextToken(); - try { - final float number = Float.parseFloat(token); - boxPriors[lineNum][priorIndex++] = number; - } catch (final NumberFormatException e) { - // Silently ignore. - } - } - if (priorIndex != NUM_RESULTS) { - throw new RuntimeException( - "BoxPrior length mismatch: " + priorIndex + " vs " + NUM_RESULTS); - } - } - - LOGGER.i("Loaded box priors!"); - } - - void decodeCenterSizeBoxes(float[][][] predictions) { - for (int i = 0; i < NUM_RESULTS; ++i) { - float ycenter = predictions[0][i][0] / Y_SCALE * boxPriors[2][i] + boxPriors[0][i]; - float xcenter = predictions[0][i][1] / X_SCALE * boxPriors[3][i] + boxPriors[1][i]; - float h = (float) Math.exp(predictions[0][i][2] / H_SCALE) * boxPriors[2][i]; - float w = (float) Math.exp(predictions[0][i][3] / W_SCALE) * boxPriors[3][i]; - - float ymin = ycenter - h / 2.f; - float xmin = xcenter - w / 2.f; - float ymax = ycenter + h / 2.f; - float xmax = xcenter + w / 2.f; - - predictions[0][i][0] = ymin; - predictions[0][i][1] = xmin; - predictions[0][i][2] = ymax; - predictions[0][i][3] = xmax; - } - } - /** * Initializes a native TensorFlow session for classifying images. * * @param assetManager The asset manager to be used to load assets. * @param modelFilename The filepath of the model GraphDef protocol buffer. * @param labelFilename The filepath of label file for classes. + * @param inputSize The size of image input + * @param isQuantized Boolean representing model is quantized or not */ public static Classifier create( final AssetManager assetManager, final String modelFilename, final String labelFilename, - final int inputSize) throws IOException { + final int inputSize, + final boolean isQuantized) + throws IOException { final TFLiteObjectDetectionAPIModel d = new TFLiteObjectDetectionAPIModel(); - d.loadCoderOptions(assetManager, "file:///android_asset/box_priors.txt", d.boxPriors); - InputStream labelsInput = null; String actualFilename = labelFilename.split("file:///android_asset/")[1]; labelsInput = assetManager.open(actualFilename); @@ -175,12 +124,23 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { throw new RuntimeException(e); } + d.isModelQuantized = isQuantized; // Pre-allocate buffers. - d.img = new float[1][inputSize][inputSize][3]; - + int numBytesPerChannel; + if (isQuantized) { + numBytesPerChannel = 1; // Quantized + } else { + numBytesPerChannel = 4; // Floating point + } + d.imgData = ByteBuffer.allocateDirect(1 * d.inputSize * d.inputSize * 3 * numBytesPerChannel); + d.imgData.order(ByteOrder.nativeOrder()); d.intValues = new int[d.inputSize * d.inputSize]; - d.outputLocations = new float[1][NUM_RESULTS][4]; - d.outputClasses = new float[1][NUM_RESULTS][NUM_CLASSES]; + + d.tfLite.setNumThreads(NUM_THREADS); + d.outputLocations = new float[1][NUM_DETECTIONS][4]; + d.outputClasses = new float[1][NUM_DETECTIONS]; + d.outputScores = new float[1][NUM_DETECTIONS]; + d.numDetections = new float[1]; return d; } @@ -196,25 +156,37 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); + imgData.rewind(); for (int i = 0; i < inputSize; ++i) { for (int j = 0; j < inputSize; ++j) { - int pixel = intValues[j * inputSize + i]; - img[0][j][i][2] = (float) (pixel & 0xFF) / 128.0f - 1.0f; - img[0][j][i][1] = (float) ((pixel >> 8) & 0xFF) / 128.0f - 1.0f; - img[0][j][i][0] = (float) ((pixel >> 16) & 0xFF) / 128.0f - 1.0f; + int pixelValue = intValues[i * inputSize + j]; + if (isModelQuantized) { + // Quantized model + imgData.put((byte) ((pixelValue >> 16) & 0xFF)); + imgData.put((byte) ((pixelValue >> 8) & 0xFF)); + imgData.put((byte) (pixelValue & 0xFF)); + } else { // Float model + imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + } } } Trace.endSection(); // preprocessBitmap // Copy the input data into TensorFlow. Trace.beginSection("feed"); - outputLocations = new float[1][NUM_RESULTS][4]; - outputClasses = new float[1][NUM_RESULTS][NUM_CLASSES]; + outputLocations = new float[1][NUM_DETECTIONS][4]; + outputClasses = new float[1][NUM_DETECTIONS]; + outputScores = new float[1][NUM_DETECTIONS]; + numDetections = new float[1]; - Object[] inputArray = {img}; + Object[] inputArray = {imgData}; Map outputMap = new HashMap<>(); outputMap.put(0, outputLocations); outputMap.put(1, outputClasses); + outputMap.put(2, outputScores); + outputMap.put(3, numDetections); Trace.endSection(); // Run the inference call. @@ -222,56 +194,26 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { tfLite.runForMultipleInputsOutputs(inputArray, outputMap); Trace.endSection(); - decodeCenterSizeBoxes(outputLocations); - - // Find the best detections. - final PriorityQueue pq = - new PriorityQueue( - 1, - new Comparator() { - @Override - public int compare(final Recognition lhs, final Recognition rhs) { - // Intentionally reversed to put high confidence at the head of the queue. - return Float.compare(rhs.getConfidence(), lhs.getConfidence()); - } - }); - - // Scale them back to the input size. - for (int i = 0; i < NUM_RESULTS; ++i) { - float topClassScore = -1000f; - int topClassScoreIndex = -1; - - // Skip the first catch-all class. - for (int j = 1; j < NUM_CLASSES; ++j) { - float score = expit(outputClasses[0][i][j]); - - if (score > topClassScore) { - topClassScoreIndex = j; - topClassScore = score; - } - } - - if (topClassScore > 0.001f) { - final RectF detection = - new RectF( - outputLocations[0][i][1] * inputSize, - outputLocations[0][i][0] * inputSize, - outputLocations[0][i][3] * inputSize, - outputLocations[0][i][2] * inputSize); - - pq.add( - new Recognition( - "" + i, - labels.get(topClassScoreIndex), - outputClasses[0][i][topClassScoreIndex], - detection)); - } - } - - final ArrayList recognitions = new ArrayList(); - for (int i = 0; i < Math.min(pq.size(), 10); ++i) { - Recognition recog = pq.poll(); - recognitions.add(recog); + // Show the best detections. + // after scaling them back to the input size. + final ArrayList recognitions = new ArrayList<>(NUM_DETECTIONS); + for (int i = 0; i < NUM_DETECTIONS; ++i) { + final RectF detection = + new RectF( + outputLocations[0][i][1] * inputSize, + outputLocations[0][i][0] * inputSize, + outputLocations[0][i][3] * inputSize, + outputLocations[0][i][2] * inputSize); + // SSD Mobilenet V1 Model assumes class 0 is background class + // in label file and class labels start from 1 to number_of_classes+1, + // while outputClasses correspond to class index from 0 to number_of_classes + int labelOffset = 1; + recognitions.add( + new Recognition( + "" + i, + labels.get((int) outputClasses[0][i] + labelOffset), + outputScores[0][i], + detection)); } Trace.endSection(); // "recognizeImage" return recognitions; diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/AssetUtils.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/AssetUtils.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/AssetUtils.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/AssetUtils.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/BorderedText.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/BorderedText.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/BorderedText.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/BorderedText.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/ImageUtils.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/ImageUtils.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/ImageUtils.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/ImageUtils.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Logger.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Logger.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Logger.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Logger.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Size.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Size.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Size.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Size.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/SplitTimer.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/SplitTimer.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/SplitTimer.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/SplitTimer.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/MultiBoxTracker.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/MultiBoxTracker.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/ObjectTracker.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/ObjectTracker.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/ObjectTracker.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/ObjectTracker.java diff --git a/tensorflow/contrib/lite/examples/android/res/animator/color_animation.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/animator/color_animation.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/animator/color_animation.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/animator/color_animation.xml diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/tile.9.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/tile.9.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/tile.9.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/tile.9.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable/border.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable/border.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable/border.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable/border.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/activity_camera.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_camera.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/activity_camera.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_camera.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/activity_speech.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_speech.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/activity_speech.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_speech.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_stylize.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_stylize.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_stylize.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_stylize.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_tracking.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_tracking.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_tracking.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_tracking.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/list_text_item.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/list_text_item.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/list_text_item.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/list_text_item.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-dimens.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-dimens.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-dimens.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-dimens.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v11/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v11/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v11/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v11/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v14/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v14/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v14/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v14/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v21/base-colors.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-colors.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v21/base-colors.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-colors.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v21/base-template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v21/base-template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/attrs.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/attrs.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/attrs.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/attrs.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/base-strings.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/base-strings.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/base-strings.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/base-strings.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/colors.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/colors.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/colors.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/colors.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/strings.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/strings.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/strings.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/strings.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/template-dimens.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-dimens.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/template-dimens.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-dimens.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/build.gradle b/tensorflow/contrib/lite/examples/android/build.gradle index 0d4de358156a5d139e35cc542b8d36ab24e763b9..a47fa4bbf6730c7d1269737564381c8464224713 100644 --- a/tensorflow/contrib/lite/examples/android/build.gradle +++ b/tensorflow/contrib/lite/examples/android/build.gradle @@ -1,52 +1,23 @@ -apply plugin: 'com.android.application' +// Top-level build file where you can add configuration options common to all sub-projects/modules. -android { - compileSdkVersion 26 - buildToolsVersion "26.0.1" - defaultConfig { - applicationId "org.tensorflow.lite.demo" - minSdkVersion 15 - targetSdkVersion 26 - versionCode 1 - versionName "1.0" - testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" - - // Remove this block. - jackOptions { - enabled true - } - } - lintOptions { - abortOnError false - } - buildTypes { - release { - minifyEnabled false - proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' - } - } - aaptOptions { - noCompress "tflite" +buildscript { + repositories { + jcenter() } + dependencies { + classpath 'com.android.tools.build:gradle:3.0.1' - compileOptions { - sourceCompatibility JavaVersion.VERSION_1_8 - targetCompatibility JavaVersion.VERSION_1_8 + // NOTE: Do not place your application dependencies here; they belong + // in the individual module build.gradle files } } -repositories { - maven { - url 'https://google.bintray.com/tensorflow' +allprojects { + repositories { + jcenter() } } -dependencies { - compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', { - exclude group: 'com.android.support', module: 'support-annotations' - }) - compile 'org.tensorflow:tensorflow-lite:+' - - testCompile 'junit:junit:4.12' +task clean(type: Delete) { + delete rootProject.buildDir } diff --git a/tensorflow/contrib/lite/examples/android/settings.gradle b/tensorflow/contrib/lite/examples/android/settings.gradle new file mode 100644 index 0000000000000000000000000000000000000000..e7b4def49cb53d9aa04228dd3edb14c9e635e003 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/settings.gradle @@ -0,0 +1 @@ +include ':app' diff --git a/tensorflow/contrib/lite/examples/label_image/BUILD b/tensorflow/contrib/lite/examples/label_image/BUILD index 9322e186a280e932a2441ab16ac8579d9ab67ee2..c61445114ecc6dfbe4f2b6ab666b28a8aa746be3 100644 --- a/tensorflow/contrib/lite/examples/label_image/BUILD +++ b/tensorflow/contrib/lite/examples/label_image/BUILD @@ -53,19 +53,18 @@ cc_library( ], ) -# TODO(ahentz): Test disabled as it has a memory leek from read_bmp -# cc_test( -# name = "label_image_test", -# srcs = [ -# "get_top_n.h", -# "get_top_n_impl.h", -# "label_image_test.cc", -# ], -# data = [ -# "testdata/grace_hopper.bmp", -# ], -# deps = [ -# ":bitmap_helpers", -# "//testing/base/public:gunit", -# ], -# ) +cc_test( + name = "label_image_test", + srcs = [ + "get_top_n.h", + "get_top_n_impl.h", + "label_image_test.cc", + ], + data = [ + "testdata/grace_hopper.bmp", + ], + deps = [ + ":bitmap_helpers", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.cc b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.cc index 0b38cd38c83927c65d251b9356301b6bef7521f2..2735d1f5ea4e2a104f71a3a6f874d9acb2f48142 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.cc +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.cc @@ -28,8 +28,9 @@ limitations under the License. namespace tflite { namespace label_image { -uint8_t* decode_bmp(const uint8_t* input, int row_size, uint8_t* const output, - int width, int height, int channels, bool top_down) { +std::vector decode_bmp(const uint8_t* input, int row_size, int width, + int height, int channels, bool top_down) { + std::vector output(height * width * channels); for (int i = 0; i < height; i++) { int src_pos; int dst_pos; @@ -66,12 +67,11 @@ uint8_t* decode_bmp(const uint8_t* input, int row_size, uint8_t* const output, } } } - return output; } -uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, - int* channels, Settings* s) { +std::vector read_bmp(const std::string& input_bmp_name, int* width, + int* height, int* channels, Settings* s) { int begin, end; std::ifstream file(input_bmp_name, std::ios::in | std::ios::binary); @@ -87,14 +87,15 @@ uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, if (s->verbose) LOG(INFO) << "len: " << len << "\n"; - const uint8_t* img_bytes = new uint8_t[len]; + std::vector img_bytes(len); file.seekg(0, std::ios::beg); - file.read((char*)img_bytes, len); + file.read(reinterpret_cast(img_bytes.data()), len); const int32_t header_size = - *(reinterpret_cast(img_bytes + 10)); - *width = *(reinterpret_cast(img_bytes + 18)); - *height = *(reinterpret_cast(img_bytes + 22)); - const int32_t bpp = *(reinterpret_cast(img_bytes + 28)); + *(reinterpret_cast(img_bytes.data() + 10)); + *width = *(reinterpret_cast(img_bytes.data() + 18)); + *height = *(reinterpret_cast(img_bytes.data() + 22)); + const int32_t bpp = + *(reinterpret_cast(img_bytes.data() + 28)); *channels = bpp / 8; if (s->verbose) @@ -110,10 +111,9 @@ uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, bool top_down = (*height < 0); // Decode image, allocating tensor once the image size is known - uint8_t* output = new uint8_t[abs(*height) * *width * *channels]; const uint8_t* bmp_pixels = &img_bytes[header_size]; - return decode_bmp(bmp_pixels, row_size, output, *width, abs(*height), - *channels, top_down); + return decode_bmp(bmp_pixels, row_size, *width, abs(*height), *channels, + top_down); } } // namespace label_image diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h index 97343dde6b31694e5b2de20b35a7083fb8fe4a0e..5fc75b1f7274c14d49e4a26d6ce4902c037afa6b 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h @@ -22,8 +22,8 @@ limitations under the License. namespace tflite { namespace label_image { -uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, - int* channels, Settings* s); +std::vector read_bmp(const std::string& input_bmp_name, int* width, + int* height, int* channels, Settings* s); template void resize(T* out, uint8_t* in, int image_height, int image_width, diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index 966fcd2a31fd4d4ff2c3e91633550a8effa81ee8..86d7d1cc4a625243791d5e7d5b746526a58efb6d 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -138,8 +138,8 @@ void RunInference(Settings* s) { int image_width = 224; int image_height = 224; int image_channels = 3; - uint8_t* in = read_bmp(s->input_bmp_name, &image_width, &image_height, - &image_channels, s); + std::vector in = read_bmp(s->input_bmp_name, &image_width, + &image_height, &image_channels, s); int input = interpreter->inputs()[0]; if (s->verbose) LOG(INFO) << "input: " << input << "\n"; @@ -168,12 +168,12 @@ void RunInference(Settings* s) { switch (interpreter->tensor(input)->type) { case kTfLiteFloat32: s->input_floating = true; - resize(interpreter->typed_tensor(input), in, image_height, - image_width, image_channels, wanted_height, wanted_width, - wanted_channels, s); + resize(interpreter->typed_tensor(input), in.data(), + image_height, image_width, image_channels, wanted_height, + wanted_width, wanted_channels, s); break; case kTfLiteUInt8: - resize(interpreter->typed_tensor(input), in, + resize(interpreter->typed_tensor(input), in.data(), image_height, image_width, image_channels, wanted_height, wanted_width, wanted_channels, s); break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image_test.cc b/tensorflow/contrib/lite/examples/label_image/label_image_test.cc index ce35483f76e8f40ced79e1ee30774c62d0eba94e..de7de21f7741d3d46cb96e793e8bc4bfb21384fe 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image_test.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image_test.cc @@ -27,20 +27,20 @@ namespace label_image { TEST(LabelImageTest, GraceHopper) { std::string lena_file = - "tensorflow/contrib/lite/examples/label_image/testdata/grace_hopper.bmp"; + "tensorflow/contrib/lite/examples/label_image/testdata/" + "grace_hopper.bmp"; int height, width, channels; Settings s; - uint8_t *data; - - data = read_bmp(lena_file, &width, &height, &channels, &s); + std::vector input = + read_bmp(lena_file, &width, &height, &channels, &s); ASSERT_EQ(height, 606); ASSERT_EQ(width, 517); ASSERT_EQ(channels, 3); - uint8_t *out = new uint8_t[606 * 517 * 3]; - downsize(out, data, 606, 517, 3, 214, 214, 3, &s); - ASSERT_EQ(out[0], 0x15); - ASSERT_EQ(out[214 * 214 * 3 - 1], 0x12); + std::vector output(606 * 517 * 3); + resize(output.data(), input.data(), 606, 517, 3, 214, 214, 3, &s); + ASSERT_EQ(output[0], 0x15); + ASSERT_EQ(output[214 * 214 * 3 - 1], 0x11); } TEST(LabelImageTest, GetTopN) { diff --git a/tensorflow/contrib/lite/examples/minimal/BUILD b/tensorflow/contrib/lite/examples/minimal/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..b403628d6c457ce3fb67eac3675fd7bb9187deab --- /dev/null +++ b/tensorflow/contrib/lite/examples/minimal/BUILD @@ -0,0 +1,27 @@ +# Description: +# TensorFlow Lite minimal example. + +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "tf_cc_binary") +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_linkopts") + +tf_cc_binary( + name = "minimal", + srcs = [ + "minimal.cc", + ], + linkopts = tflite_linkopts() + select({ + "//tensorflow:android": [ + "-pie", # Android 5.0 and later supports only PIE + "-lm", # some builtin ops, e.g., tanh, need -lm + ], + "//conditions:default": [], + }), + deps = [ + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], +) diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 8b0ace96ccaf06ac1cbdc2ea95ac6e92ef886993..8b65cde7b79fde19280ad778ea874c64b01d169a 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/contrib/lite/model.h" +#include #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" -#include +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/contrib/lite/optional_debug_tools.h" // This is an example that is minimal to read a model // from disk and perform inference. There is no data being loaded @@ -29,14 +30,13 @@ limitations under the License. using namespace tflite; -#define TFLITE_MINIMAL_CHECK(x) \ - if(!(x)) { \ - fprintf(stderr, "Error at %s:%d\n", __FILE__, __LINE__); \ - exit(1); \ +#define TFLITE_MINIMAL_CHECK(x) \ + if (!(x)) { \ + fprintf(stderr, "Error at %s:%d\n", __FILE__, __LINE__); \ + exit(1); \ } - -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { if(argc != 2) { fprintf(stderr, "minimal \n"); return 1; @@ -44,8 +44,8 @@ int main(int argc, char *argv[]) { const char* filename = argv[1]; // Load model - std::unique_ptr model - = tflite::FlatBufferModel::BuildFromFile(filename); + std::unique_ptr model = + tflite::FlatBufferModel::BuildFromFile(filename); TFLITE_MINIMAL_CHECK(model != nullptr); // Build the interpreter @@ -57,12 +57,16 @@ int main(int argc, char *argv[]) { // Allocate tensor buffers. TFLITE_MINIMAL_CHECK(interpreter->AllocateTensors() == kTfLiteOk); + printf("=== Pre-invoke Interpreter State ===\n"); + tflite::PrintInterpreterState(interpreter.get()); // Fill input buffers // TODO(user): Insert code to fill input tensors // Run inference TFLITE_MINIMAL_CHECK(interpreter->Invoke() == kTfLiteOk); + printf("\n\n=== Post-invoke Interpreter State ===\n"); + tflite::PrintInterpreterState(interpreter.get()); // Read output buffers // TODO(user): Insert getting data out code. diff --git a/tensorflow/contrib/lite/g3doc/apis.md b/tensorflow/contrib/lite/g3doc/apis.md index 50cc146a87ee9ab94aea6a92fb2fb5c531f83369..a591a353dd8f0ac94ecaa3f12e1aa1c57566ef69 100644 --- a/tensorflow/contrib/lite/g3doc/apis.md +++ b/tensorflow/contrib/lite/g3doc/apis.md @@ -7,6 +7,9 @@ no surprise that the APIs try to avoid unnecessary copies at the expense of convenience. Similarly, consistency with TensorFlow APIs was not an explicit goal and some variance is to be expected. +There is also a Python API for TensorFlow Lite described +[here](../toco/g3doc/python_api.md#interpreter). + ## C++ In order to run the inference model in TensorFlow Lite, one has to load the diff --git a/tensorflow/contrib/lite/g3doc/benchmarks.md b/tensorflow/contrib/lite/g3doc/benchmarks.md new file mode 100644 index 0000000000000000000000000000000000000000..29b087bea7aab1fcbc87ef764795f01e87b0bf9e --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/benchmarks.md @@ -0,0 +1,178 @@ +# Performance Benchmark numbers + +This document contains the performance benchmark numbers for running a few well +known models on some Android and iOS devices. + +The benchmark numbers were generated by running the [TFLite benchmark +binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark) +on Android and running the [iOS benchmark +app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios) +on iOS. + +# Android benchmarks + +When running Android benchmarks, the CPU affinity is set to use big cores on the +device to reduce variance (see +[details](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#reducing-variance-between-runs-on-android)). + +Models are assumed to have been downloaded from the link, unzipped and pushed to +`/data/local/tmp/tflite_models` folder. The benchmark binary is built according +to instructions listed +[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#on-android). +and is assumed to have been pushed to `/data/local/tmp`. + +The following command was used to run the benchmark: + +``` +adb shell taskset ${CPU_MASK} /data/local/tmp/benchmark_model \ + --num_threads=1 \ + --graph=/data/local/tmp/tflite_models/${GRAPH} \ + --warmup_runs=1 \ + --num_runs=50 \ + --use_nnapi=false +``` + +where `${GRAPH}` is the name of model and `${CPU_MASK}` is the CPU affinity +chosen according to the following table: + +Device | CPU_MASK | +-------| ---------- +Pixel 2 | f0 | +Pixel xl | 0c | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model NameDevice Mean inference time (std dev)
+ Mobilenet_1.0_224(float) + Pixel 2 166.5 ms (2.6 ms)
Pixel xl 122.9 ms (1.8 ms)
+ Mobilenet_1.0_224 (quant) + Pixel 2 69.5 ms (0.9 ms)
Pixel xl 78.9 ms (2.2 ms)
+ NASNet mobile + Pixel 2 273.8 ms (3.5 ms)
Pixel xl 210.8 ms (4.2 ms)
+ SqueezeNet + Pixel 2 234.0 ms (2.1 ms)
Pixel xl 158.0 ms (2.1 ms)
+ Inception_ResNet_V2 + Pixel 2 2846.0 ms (15.0 ms)
Pixel xl 1973.0 ms (15.0 ms)
+ Inception_V4 + Pixel 2 3180.0 ms (11.7 ms)
Pixel xl 2262.0 ms (21.0 ms)
+ +# iOS benchmarks + +For running iOS benchmarks, the [benchmark +app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios) +was modified to include the appropriate model and `benchmark_params.json` was +modified to set `num_threads` to 1. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model NameDevice Mean inference time (std dev)
+ Mobilenet_1.0_224(float) + iPhone 8 32.2 ms (0.8 ms)
+ Mobilenet_1.0_224 (quant) + iPhone 8 24.4 ms (0.8 ms)
+ NASNet mobile + iPhone 8 60.3 ms (0.6 ms)
+ SqueezeNet + iPhone 8 44.3 (0.7 ms)
+ Inception_ResNet_V2 + iPhone 8562.4 ms (18.2 ms)
+ Inception_V4 + iPhone 8 661.0 ms (29.2 ms)
diff --git a/tensorflow/contrib/lite/g3doc/ops_versioning.md b/tensorflow/contrib/lite/g3doc/ops_versioning.md new file mode 100644 index 0000000000000000000000000000000000000000..bd2f797e6c5b05f52bec9fc34f1b8011aca70330 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/ops_versioning.md @@ -0,0 +1,206 @@ +# TensorFlow Lite Ops Versioning + +This document describes TensorFlow Lite's op versioning schema. Op +versioning enables developers to add new functionalities and parameters into +existing ops. In addition, it guarantees the following: + +* Backward compatibility: New TensorFlow Lite implementation should + handle an old model file. +* Forward compatibility: Old TensorFlow Lite implementation should + handle a new model file produced by new version of TOCO, as long as no new + features are used. +* Forward in-compatibility detection: If an old TensorFlow Lite implementation + reads a new model that contains a new version of an op which isn't + supported, it should report the error. + +## Example: Adding Dilation into Convolution + +The remainder of this document explains op versioning in TFLite by showing how +to add dilation parameters to the convolution operation. + +Knowledge of dilation is not required to understand this document. Note that: + +* 2 new integer parameters will be added: `dilation_width_factor` and + `dilation_height_factor`. +* Old convolution kernels that don't support dilation are equivalent to + setting the dilation factors to 1. + +### Change FlatBuffer Schema + +To add new parameters into an op, change the options table in +`lite/schema/schema.fbs`. + +For example, the options table of convolution looks like this: + +``` +table Conv2DOptions { + padding:Padding; + stride_w:int; + stride_h:int; + fused_activation_function:ActivationFunctionType; +} +``` + +When adding new parameters: + +* Add comments indicating which parameters are supported by which version. +* When the new implementation gets the default values for newly added + parameters, it should work exactly the same as the old implementation. + +The table will be like this after the new parameters are added: + +``` +table Conv2DOptions { + // Parameters supported by version 1: + padding:Padding; + stride_w:int; + stride_h:int; + fused_activation_function:ActivationFunctionType; + + // Parameters supported by version 2: + dilation_width_factor:int = 1; + dilation_height_factor:int = 1; +} +``` + +### Change C Structures and Kernel Implementation + +In TensorFlow Lite, the kernel implementation is decoupled from +FlatBuffer definition. The kernels read the parameter from C structures defined +in `lite/builtin_op_data.h`. + +The original convolution parameter is as follows: + +``` +typedef struct { + TfLitePadding padding; + int stride_width; + int stride_height; + TfLiteFusedActivation activation; +} TfLiteConvParams; +``` + +As with the FlatBuffer schema, add comments indicating which parameters are +supported starting from which version. The result is seen below: + +``` +typedef struct { + // Parameters supported by version 1: TfLitePadding padding; int + stride_width; + int stride_height; + TfLiteFusedActivation activation; + + // Parameters supported by version 2: + int dilation_width_factor; + int dilation_height_factor; +} TfLiteConvParams; +``` + +Please also change the kernel implementation to read the newly added parameters +from the C structures. The details are omitted here. + +### Change the FlatBuffer Reading Code + +The logic to read FlatBuffer and produce C structure is in `lite/model.cc`. + +Update the file to handle the new parameters, as shown below: + +``` +case BuiltinOperator_CONV_2D: { + TfLiteConvParams* params = MallocPOD(); + if (auto* conv_params = op->builtin_options_as_Conv2DOptions()) { + params->padding = parse_padding(conv_params->padding()); + params->stride_width = conv_params->stride_w(); + params->stride_height = conv_params->stride_h(); + params->activation = + parse_activation(conv_params->fused_activation_function()); + params->dilation_width_factor = conv_params->dilation_width_factor(); + params->dilation_height_factor = conv_params->dilation_height_factor(); + } + *builtin_data = reinterpret_cast(params); + break; +} +``` + +It's not required to check the op version here. When the new implementation +reads an old model file where dilation factors are missing, it will use 1 as +the default value, and the new kernel will work consistently with the old +kernel. + +### Change Kernel Registration + +The MutableOpResolver (defined in `lite/op_resolver.h`) provides a few functions +to register op kernels. The minimum and maximum version are 1 by default: + +``` +void AddBuiltin(tflite::BuiltinOperator op, TfLiteRegistration* registration, + int min_version = 1, int max_version = 1); +void AddCustom(const char* name, TfLiteRegistration* registration, + int min_version = 1, int max_version = 1); +``` + +The built-in ops are registered in `lite/kernels/register.cc`. In this example, +we implemented a new op kernel which can handle `Conv2D` version 1 and 2, so we +need to change this line: + +``` +AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D()); +``` + +to: + +``` +AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D(), 1, 2); +``` + +### Change TOCO TFLite exporter + +The last step is to make TOCO populate the minimum version that's required to +execute the op. In this example, it means: + +* Populate version=1 when dilation factors are all 1. +* Populate version=2 otherwise. + +To do this, you need to override `GetVersion` function for the operator class in +`lite/toco/tflite/operator.cc`. + +For ops with only one version, the `GetVersion` function is defined as: + +``` +int GetVersion(const Operator& op) const override { return 1; } +``` + +When supporting multiple versions, check the parameters and determine the +version for the op, as shown in the following example: + +``` +int GetVersion(const Operator& op) const override { + const auto& conv_op = static_cast(op); + if (conv_op.dilation_width_factor != 1 || + conv_op.dilation_height_factor != 1) { + return 2; + } + return 1; +} +``` + +### Delegation Implementation + +TensorFlow Lite provides a delegation API which enables delegating ops to +hardware backends. In Delegate's `Prepare` function, check if the version +is supported for every node in Delegation code. + +``` +const int kMinVersion = 1; +TfLiteNode* node; +TfLiteRegistration; +context->GetNodeAndRegistration(context, node_index, &node, ®istration); + +if (registration->version > kMinVersion) { + // Reject the node if the version isn't supported. +} +``` + +This is required even if the delegation only supports version 1 ops, so the +delegation can detect incompatibility when getting a higher version op. + diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index a991a239d92d847af2ae17acab472dd823ad236f..dcd17bbeabda08eaf86f8d5ac7f26cea0d3719a3 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -429,6 +429,17 @@ Outputs { } ``` +**LOG** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: a tensor equivalent to log(input) +} +``` + **LOG_SOFTMAX** ``` @@ -573,6 +584,31 @@ Options { } ``` +**RSQRT** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: result of computing element-wise reciprocal square root of the input tensor +} +``` + +**SHAPE** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: a 1D tensor representing the shape of the input tensor +} +Options { + out_type: the output type of the op (int32 or int64). Defaults to int32. +} +``` + **SLICE** ``` @@ -659,6 +695,17 @@ Options { } ``` +**SQRT** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: result of computing element-wise square root of the input tensor +} +``` + **SQUEEZE** ``` @@ -731,6 +778,18 @@ Outputs { } ``` +**POW** + +``` +Inputs { + 0: a tensor + 1: a tensor +} +Outputs { + 0: elementwise pow of the input tensors +} +``` + And these are TensorFlow Lite operations that are present but not ready for custom models yet: diff --git a/tensorflow/contrib/lite/graph_info.h b/tensorflow/contrib/lite/graph_info.h index 313af5fb7574b42bcdd53b4baad06e4ccfb34053..77268d7aebe9ebfb33b9f35b319d34e6de8324ee 100644 --- a/tensorflow/contrib/lite/graph_info.h +++ b/tensorflow/contrib/lite/graph_info.h @@ -46,6 +46,9 @@ class GraphInfo { // Returns the indices of the output tensors. virtual const std::vector& outputs() const = 0; + + // Returns the indices of the variable tensors. + virtual const std::vector& variables() const = 0; }; // Represents a subgraph of a TensorFlow Lite graph. diff --git a/tensorflow/contrib/lite/graph_info_test.cc b/tensorflow/contrib/lite/graph_info_test.cc index ea38b43993fef71c6820c7a978351d92d5420287..89a8f36b416b5dec54c1e374cdcdae3ab9ab0cde 100644 --- a/tensorflow/contrib/lite/graph_info_test.cc +++ b/tensorflow/contrib/lite/graph_info_test.cc @@ -45,6 +45,7 @@ class SimpleTestGraph : public GraphInfo { TfLiteTensor* tensor(size_t index) override { return &tensors_[index]; } const std::vector& inputs() const override { return inputs_; } const std::vector& outputs() const override { return outputs_; } + const std::vector& variables() const override { return variables_; } void AddNode(const std::vector& inputs, const std::vector& outputs) { @@ -67,6 +68,7 @@ class SimpleTestGraph : public GraphInfo { std::vector tensors_; std::vector inputs_; std::vector outputs_; + std::vector variables_; }; // Partition a graph to generate a list of subgraphs. This wraps the API call diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index ebb0aedc2001a86b7fcff67ef8703b5e4a845818..3089a4c568a181de2fb88a6cf91d2e816380808b 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -22,17 +22,25 @@ limitations under the License. #include "tensorflow/contrib/lite/arena_planner.h" #include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/context_util.h" #include "tensorflow/contrib/lite/error_reporter.h" #include "tensorflow/contrib/lite/graph_info.h" +#ifndef TFLITE_MCU #include "tensorflow/contrib/lite/kernels/eigen_support.h" #include "tensorflow/contrib/lite/kernels/gemm_support.h" +#endif #include "tensorflow/contrib/lite/memory_planner.h" +#ifndef TFLITE_MCU #include "tensorflow/contrib/lite/nnapi_delegate.h" +#endif #include "tensorflow/contrib/lite/profiling/profiler.h" #include "tensorflow/contrib/lite/schema/schema_generated.h" #include "tensorflow/contrib/lite/util.h" namespace tflite { +#ifdef TFLITE_MCU +class NNAPIDelegate {}; +#endif namespace { @@ -53,6 +61,19 @@ void SetForbiddenContextFunction(FunctionType* func) { *func = reinterpret_cast(ForbiddenContextFunction); } +// Returns true if at least one tensor in the given list is kTfLiteDynamic. +template +bool HasDynamicTensorImpl(const TfLiteContext& context, + const TensorIntArray& int_array) { + for (int i : int_array) { + const TfLiteTensor& tensor = context.tensors[i]; + if (tensor.allocation_type == kTfLiteDynamic) { + return true; + } + } + return false; +} + } // namespace // A trivial implementation of GraphInfo around the Interpreter. @@ -82,6 +103,9 @@ class InterpreterInfo : public GraphInfo { const std::vector& outputs() const override { return interpreter_->outputs(); } + const std::vector& variables() const override { + return interpreter_->variables(); + } public: Interpreter* interpreter_; @@ -302,6 +326,13 @@ TfLiteStatus Interpreter::SetOutputs(std::vector outputs) { return kTfLiteOk; } +TfLiteStatus Interpreter::SetVariables(std::vector variables) { + TF_LITE_ENSURE_OK(&context_, CheckTensorIndices("variables", variables.data(), + variables.size())); + variables_ = std::move(variables); + return kTfLiteOk; +} + TfLiteStatus Interpreter::CheckTensorIndices(const char* label, const int* indices, int length) { // Making sure kOptionalTensor is not re-defined to something other than -1. @@ -334,6 +365,9 @@ TfLiteStatus Interpreter::BytesRequired(TfLiteType type, const int* dims, case kTfLiteFloat32: *bytes = sizeof(float) * count; break; + case kTfLiteInt16: + *bytes = sizeof(int16_t) * count; + break; case kTfLiteInt32: *bytes = sizeof(int32_t) * count; break; @@ -346,32 +380,58 @@ TfLiteStatus Interpreter::BytesRequired(TfLiteType type, const int* dims, case kTfLiteBool: *bytes = sizeof(bool) * count; break; + case kTfLiteComplex64: + *bytes = sizeof(std::complex) * count; + break; default: - ReportError( - &context_, - "Only float32, int32, int64, uint8, bool supported currently."); + ReportError(&context_, + "Only float32, int16, int32, int64, uint8, bool, complex64 " + "supported currently."); return kTfLiteError; } return kTfLiteOk; } TfLiteStatus Interpreter::AllocateTensors() { - next_execution_plan_index_to_prepare_ = 0; - if (memory_planner_) { - TF_LITE_ENSURE_STATUS(memory_planner_->ResetAllocations()); - } - if (!consistent_) { ReportError(&context_, "AllocateTensors() called on inconsistent model."); return kTfLiteError; } + // Explicit (re)allocation is necessary if nodes have been changed or tensors + // have been resized. For inputs marked as dynamic, we can't short-circuit the + // allocation as the client may have done the resize manually. + if (state_ != kStateUninvokable && !HasDynamicTensorImpl(context_, inputs_)) { + return kTfLiteOk; + } + + next_execution_plan_index_to_prepare_ = 0; + if (memory_planner_) { + TF_LITE_ENSURE_STATUS(memory_planner_->ResetAllocations()); + } + TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); - if (state_ == kStateUninvokable) { - state_ = kStateInvokable; + + state_ = kStateInvokable; + return kTfLiteOk; +} + +// TODO(ycling): Consider to provide other functions to initialize variable +// tensors to non-zero values. +TfLiteStatus Interpreter::ResetVariableTensorsToZero() { + for (auto& tensor : tensors_) { + if (!tensor.is_variable) { + continue; + } + + // Variable tensors have to be `kTfLiteArenaRwPersistent`, and must be + // allocated after the initial `PrepareOpsAndTensors()` is called. + TF_LITE_ENSURE_EQ(&context_, tensor.allocation_type, + kTfLiteArenaRwPersistent); + TF_LITE_ENSURE(&context_, tensor.data.raw != nullptr); + + memset(tensor.data.raw, 0, tensor.bytes); } - TF_LITE_ENSURE(&context_, state_ == kStateInvokable || - state_ == kStateInvokableAndImmutable); return kTfLiteOk; } @@ -445,26 +505,26 @@ TfLiteStatus Interpreter::ResizeInputTensor(int tensor_index, "ResizeInputTensor is disallowed when graph is immutable."); return kTfLiteError; } - state_ = kStateUninvokable; // TODO(aselle): All bounds checks can be implemented as one-sided bounds // checks by casting to unsigned for efficiency. Profile before doing this. TF_LITE_ENSURE(&context_, tensor_index < context_.tensors_size && tensor_index >= 0); - TfLiteIntArray* dims_lite = ConvertVectorToTfLiteIntArray(dims); - return ResizeTensorImpl(&context_.tensors[tensor_index], dims_lite); + TfLiteTensor* tensor = &context_.tensors[tensor_index]; + + // Short-circuit the state change if the dimensions don't change, avoiding + // unnecessary (re)allocations. + if (EqualArrayAndTfLiteIntArray(tensor->dims, dims.size(), dims.data())) { + return kTfLiteOk; + } + + state_ = kStateUninvokable; + return ResizeTensorImpl(tensor, ConvertVectorToTfLiteIntArray(dims)); } -// Returns true if at least one tensor in the given list is kTfLiteDynamic. bool HasDynamicTensor(const TfLiteContext& context, - const TfLiteIntArray* tensors) { - for (int i = 0; i < tensors->size; ++i) { - const TfLiteTensor& tensor = context.tensors[tensors->data[i]]; - if (tensor.allocation_type == kTfLiteDynamic) { - return true; - } - } - return false; + const TfLiteIntArray* int_array) { + return HasDynamicTensorImpl(context, TfLiteIntArrayView{int_array}); } TfLiteStatus Interpreter::PrepareOpsStartingAt( @@ -495,7 +555,8 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt( TfLiteStatus Interpreter::PrepareOpsAndTensors() { if (!memory_planner_) { memory_planner_.reset(new ArenaPlanner( - &context_, std::unique_ptr(new InterpreterInfo(this)))); + &context_, std::unique_ptr(new InterpreterInfo(this)), + /*preserve_inputs=*/true)); memory_planner_->PlanAllocations(); } @@ -521,6 +582,7 @@ TfLiteStatus Interpreter::Invoke() { } TfLiteStatus status = kTfLiteOk; +#ifndef TFLITE_MCU if (nnapi_delegate_) { if (next_execution_plan_index_to_prepare_ == execution_plan_.size()) { TF_LITE_ENSURE_OK(&context_, nnapi_delegate_->Invoke(this)); @@ -534,6 +596,7 @@ TfLiteStatus Interpreter::Invoke() { return kTfLiteError; } } +#endif // Invocations are always done in node order. // Note that calling Invoke repeatedly will cause the original memory plan to @@ -572,9 +635,17 @@ TfLiteStatus Interpreter::Invoke() { } EnsureTensorsVectorCapacity(); + tensor_resized_since_op_invoke_ = false; if (OpInvoke(registration, &node) == kTfLiteError) { status = kTfLiteError; } + + // Force execution prep for downstream ops if the latest op triggered the + // resize of a dynamic tensor. + if (tensor_resized_since_op_invoke_ && + HasDynamicTensor(context_, node.outputs)) { + next_execution_plan_index_to_prepare_ = execution_plan_index + 1; + } } if (!allow_buffer_handle_output_) { @@ -687,7 +758,7 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( state_ = kStateUninvokable; TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), quantization, const_cast(buffer), bytes, - kTfLiteMmapRo, allocation, &tensor); + kTfLiteMmapRo, allocation, false, &tensor); } return kTfLiteOk; } @@ -698,7 +769,7 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( // to Interpreter. TfLiteStatus Interpreter::SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, const size_t rank, - const int* dims, TfLiteQuantizationParams quantization) { + const int* dims, TfLiteQuantizationParams quantization, bool is_variable) { if (state_ == kStateInvokableAndImmutable) { ReportError( &context_, @@ -716,11 +787,23 @@ TfLiteStatus Interpreter::SetTensorParametersReadWrite( TF_LITE_ENSURE_OK(&context_, BytesRequired(type, dims, rank, &required_bytes)); } + + TfLiteAllocationType allocation_type = kTfLiteArenaRw; + if (type == kTfLiteString) { + if (is_variable) { + // We don't have a real use case for string variable tensor. + ReportError(&context_, "String variable tensor isn't supported."); + return kTfLiteError; + } + allocation_type = kTfLiteDynamic; + } else if (is_variable) { + allocation_type = kTfLiteArenaRwPersistent; + } + TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), quantization, - /*buffer=*/nullptr, required_bytes, - type == kTfLiteString ? kTfLiteDynamic : kTfLiteArenaRw, - nullptr, &context_.tensors[tensor_index]); + /*buffer=*/nullptr, required_bytes, allocation_type, + nullptr, is_variable, &context_.tensors[tensor_index]); return kTfLiteOk; } @@ -736,7 +819,10 @@ TfLiteStatus Interpreter::ResizeTensorImpl(TfLiteTensor* tensor, TfLiteIntArray* new_size) { // Note that in theory we could resize kTfLiteArenaRwPersistent tensors too. if (tensor->allocation_type == kTfLiteArenaRw || - tensor->allocation_type == kTfLiteDynamic) { + tensor->allocation_type == kTfLiteDynamic || + tensor->allocation_type == kTfLiteArenaRwPersistent) { + tensor_resized_since_op_invoke_ |= + TfLiteIntArrayEqual(tensor->dims, new_size) == 0; if (tensor->type != kTfLiteString) { size_t bytesRequired; TfLiteStatus status = BytesRequired(tensor->type, new_size->data, @@ -767,6 +853,7 @@ TfLiteStatus Interpreter::ResizeTensorImpl(TfLiteTensor* tensor, } void Interpreter::UseNNAPI(bool enable) { +#ifndef TFLITE_MCU // TODO(aselle): This is a workaround for finding if NNAPI exists. // We also need to make sure getLibraryHandle() is renamed to be NNAPI // prefixed. @@ -776,6 +863,7 @@ void Interpreter::UseNNAPI(bool enable) { } else if (!nnapi_delegate_) { nnapi_delegate_.reset(new NNAPIDelegate); } +#endif } void Interpreter::SetNumThreads(int num_threads) { @@ -783,8 +871,10 @@ void Interpreter::SetNumThreads(int num_threads) { // TODO(ahentz): find a way to avoid this. It causes gemmlowp and eigen to // be required in order to compile the framework. +#ifndef TFLITE_MCU gemm_support::SetNumThreads(&context_, num_threads); eigen_support::SetNumThreads(&context_, num_threads); +#endif } TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, @@ -828,9 +918,10 @@ TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, TF_LITE_ENSURE_OK(&context_, status); if (!allow_dynamic_tensors) { + // Reset the state to force tensor/op reallocation. + state_ = kStateUninvokable; TF_LITE_ENSURE_OK(&context_, AllocateTensors()); - TF_LITE_ENSURE(&context_, state_ == kStateInvokable || - state_ == kStateInvokableAndImmutable); + TF_LITE_ENSURE_EQ(&context_, state_, kStateInvokable); // After using a delegate which doesn't support dynamic tensors, make the // entire graph immutable. state_ = kStateInvokableAndImmutable; diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 7315d8360680ca0d3c405dc80b593762275815ee..033b8ee5fabc416fd5936b7ff69697235cd9e7e7 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -17,6 +17,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_INTERPRETER_H_ #define TENSORFLOW_CONTRIB_LITE_INTERPRETER_H_ +#include #include #include #include @@ -39,6 +40,10 @@ constexpr TfLiteType typeToTfLiteType() { return kTfLiteInt32; } template <> +constexpr TfLiteType typeToTfLiteType() { + return kTfLiteInt16; +} +template <> constexpr TfLiteType typeToTfLiteType() { return kTfLiteInt64; } @@ -54,6 +59,10 @@ template <> constexpr TfLiteType typeToTfLiteType() { return kTfLiteBool; } +template <> +constexpr TfLiteType typeToTfLiteType>() { + return kTfLiteComplex64; +} // Forward declare since NNAPIDelegate uses Interpreter. class NNAPIDelegate; @@ -118,6 +127,11 @@ class Interpreter { // interpreter. TfLiteStatus SetOutputs(std::vector outputs); + // Provide a list of tensor indexes that are variable tensors. + // Each index is bound check and this modifies the consistent_ flag of the + // interpreter. + TfLiteStatus SetVariables(std::vector variables); + // Adds a node with the given parameters and returns the index of the new // node in `node_index` (optionally). Interpreter will take ownership of // `builtin_data` and destroy it with `free`. Ownership of 'init_data' @@ -160,13 +174,15 @@ class Interpreter { // to Interpreter. inline TfLiteStatus SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, - const std::vector& dims, TfLiteQuantizationParams quantization) { + const std::vector& dims, TfLiteQuantizationParams quantization, + bool is_variable = false) { return SetTensorParametersReadWrite(tensor_index, type, name, dims.size(), - dims.data(), quantization); + dims.data(), quantization, is_variable); } TfLiteStatus SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, const size_t rank, - const int* dims, TfLiteQuantizationParams quantization); + const int* dims, TfLiteQuantizationParams quantization, + bool is_variable = false); // Functions to access tensor data @@ -182,6 +198,9 @@ class Interpreter { // Read only access to list of outputs. const std::vector& outputs() const { return outputs_; } + // Read only access to list of variable tensors. + const std::vector& variables() const { return variables_; } + // Return the name of a given output. The given index must be between 0 and // outputs().size(). const char* GetOutputName(int index) const { @@ -379,6 +398,17 @@ class Interpreter { allow_buffer_handle_output_ = allow_buffer_handle_output; } + // Reset all variable tensors to zero. + // WARNING: This is an experimental API and subject to change. + TfLiteStatus ResetVariableTensorsToZero(); + + // Retrieve an operator's description of its work, for profiling purposes. + const char* OpProfilingString(const TfLiteRegistration& op_reg, + const TfLiteNode* node) const { + if (op_reg.profiling_string == nullptr) return nullptr; + return op_reg.profiling_string(&context_, node); + } + private: // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. @@ -541,6 +571,9 @@ class Interpreter { // interpreter. std::vector outputs_; + // Array of indices representing the tensors that are variable tensors. + std::vector variables_; + // The error reporter delegate that tflite will forward queries errors to. ErrorReporter* error_reporter_; @@ -572,6 +605,11 @@ class Interpreter { bool allow_buffer_handle_output_ = false; + // Tracking bit for whether a tensor was resized in the course of an op + // invocation. This is a useful hint to ensure that dynamic tensor outputs + // trigger downstream reallocation after op invocation. + bool tensor_resized_since_op_invoke_ = false; + // Profiler for this interpreter instance. profiling::Profiler* profiler_; }; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index 453c1ada1cf6263be14a3b170f209e3a30580cc3..4f7fb36696131f1ef1bacc694c7e4dc7b340f779 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -23,6 +23,12 @@ limitations under the License. #include "tensorflow/contrib/lite/testing/util.h" namespace tflite { +namespace ops { +namespace builtin { +TfLiteRegistration* Register_PADV2(); +TfLiteRegistration* Register_NEG(); +} // namespace builtin +} // namespace ops namespace { // Make an interpreter that has no tensors and no nodes @@ -106,10 +112,9 @@ TEST(BasicInterpreter, CheckAllocate) { TfLiteType type; size_t size; } cases[] = { - {kTfLiteFloat32, sizeof(float)}, - {kTfLiteInt32, sizeof(int32_t)}, - {kTfLiteUInt8, sizeof(uint8_t)}, - {kTfLiteInt64, sizeof(int64_t)}, + {kTfLiteFloat32, sizeof(float)}, {kTfLiteInt32, sizeof(int32_t)}, + {kTfLiteUInt8, sizeof(uint8_t)}, {kTfLiteInt64, sizeof(int64_t)}, + {kTfLiteInt16, sizeof(int16_t)}, }; for (auto test : cases) { @@ -134,6 +139,7 @@ TEST(BasicInterpreter, CheckResize) { const int32_t int32s[] = {-3, -4}; const uint8_t uint8s[] = {3, 4}; const int64_t int64s[] = {6, -7}; + const int16_t int16s[] = {8, -9}; struct { TfLiteType type; @@ -144,6 +150,7 @@ TEST(BasicInterpreter, CheckResize) { {kTfLiteInt32, sizeof(int32_t), reinterpret_cast(int32s)}, {kTfLiteUInt8, sizeof(uint8_t), reinterpret_cast(uint8s)}, {kTfLiteInt64, sizeof(int64_t), reinterpret_cast(int64s)}, + {kTfLiteInt16, sizeof(int16_t), reinterpret_cast(int16s)}, }; for (auto test : cases) { @@ -179,10 +186,8 @@ TEST(BasicInterpreter, CheckAlignment) { struct { TfLiteType type; } cases[] = { - {kTfLiteFloat32}, - {kTfLiteInt32}, - {kTfLiteUInt8}, - {kTfLiteInt64}, + {kTfLiteFloat32}, {kTfLiteInt32}, {kTfLiteUInt8}, + {kTfLiteInt64}, {kTfLiteInt16}, }; for (auto test : cases) { @@ -211,7 +216,7 @@ TEST(BasicInterpreter, CheckArenaAllocation) { TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; std::vector sizes{2048, 4096, 1023, 2047, 1021, - 2047, 1023, 2046, 1021, 2048}; + 2047, 1023, 2046, 0, 2048}; for (int i = 0; i < sizes.size(); ++i) { interpreter.SetTensorParametersReadWrite(i, kTfLiteUInt8, "", {sizes[i]}, quant); @@ -226,31 +231,16 @@ TEST(BasicInterpreter, CheckArenaAllocation) { ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); - ASSERT_EQ(interpreter.tensor(0)->data.raw, interpreter.tensor(4)->data.raw); - ASSERT_EQ(interpreter.tensor(1)->data.raw, interpreter.tensor(7)->data.raw); - - ASSERT_LT(interpreter.tensor(4)->data.raw, interpreter.tensor(1)->data.raw); - ASSERT_LT(interpreter.tensor(6)->data.raw, interpreter.tensor(1)->data.raw); ASSERT_LT(interpreter.tensor(0)->data.raw, interpreter.tensor(1)->data.raw); - - ASSERT_LT(interpreter.tensor(0)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(1)->data.raw, interpreter.tensor(3)->data.raw); + ASSERT_LT(interpreter.tensor(1)->data.raw, interpreter.tensor(2)->data.raw); ASSERT_LT(interpreter.tensor(2)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(4)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(6)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(7)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(8)->data.raw, interpreter.tensor(3)->data.raw); - ASSERT_LT(interpreter.tensor(9)->data.raw, interpreter.tensor(3)->data.raw); - - ASSERT_LT(interpreter.tensor(0)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(1)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(2)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(3)->data.raw, interpreter.tensor(5)->data.raw); + ASSERT_LT(interpreter.tensor(3)->data.raw, interpreter.tensor(4)->data.raw); ASSERT_LT(interpreter.tensor(4)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(6)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(7)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(8)->data.raw, interpreter.tensor(5)->data.raw); - ASSERT_LT(interpreter.tensor(9)->data.raw, interpreter.tensor(5)->data.raw); + ASSERT_LT(interpreter.tensor(5)->data.raw, interpreter.tensor(7)->data.raw); + ASSERT_EQ(interpreter.tensor(6)->data.raw, interpreter.tensor(2)->data.raw); + // #7 is the one with the largest pointer. + ASSERT_EQ(interpreter.tensor(8)->data.raw, nullptr); + ASSERT_EQ(interpreter.tensor(9)->data.raw, interpreter.tensor(5)->data.raw); } TEST(BasicInterpreter, BufferAccess) { @@ -286,6 +276,57 @@ TEST(BasicInterpreter, NoOpInterpreter) { ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); } +TEST(BasicInterpreter, RedundantAllocateTensors) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(1), kTfLiteOk); + ASSERT_EQ(interpreter.SetInputs({0}), kTfLiteOk); + + ASSERT_EQ(interpreter.SetTensorParametersReadWrite( + 0, kTfLiteFloat32, "", {3}, TfLiteQuantizationParams()), + kTfLiteOk); + + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + const auto data_raw = interpreter.tensor(0)->data.raw; + ASSERT_NE(data_raw, nullptr); + + // A redundant allocation request should have no impact. + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + ASSERT_EQ(interpreter.tensor(0)->data.raw, data_raw); +} + +TEST(BasicInterpreter, RedundantAllocateTensorsWithDynamicInputs) { + Interpreter interpreter; + TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + ASSERT_EQ(interpreter.AddTensors(2), kTfLiteOk); + interpreter.SetInputs({0}); + interpreter.SetOutputs({1}); + interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, ®); + + ASSERT_EQ(interpreter.SetTensorParametersReadWrite( + 0, kTfLiteFloat32, "", {3}, TfLiteQuantizationParams()), + kTfLiteOk); + ASSERT_EQ(interpreter.SetTensorParametersReadWrite( + 1, kTfLiteFloat32, "", {3}, TfLiteQuantizationParams()), + kTfLiteOk); + + // Configure the input tensor as dynamic. + interpreter.tensor(0)->data.raw = nullptr; + interpreter.tensor(0)->allocation_type = kTfLiteDynamic; + + ASSERT_EQ(interpreter.ResizeInputTensor(interpreter.inputs()[0], {1, 2, 3}), + kTfLiteOk); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + ASSERT_NE(interpreter.tensor(1)->data.raw, nullptr); + + // Reset the output tensor's buffer. + interpreter.tensor(1)->data.raw = nullptr; + + // A redundant allocation request should be honored, as the input tensor + // was marked dynamic. + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + ASSERT_NE(interpreter.tensor(1)->data.raw, nullptr); +} + TEST(BasicInterpreter, ResizingTensors) { Interpreter interpreter; ASSERT_EQ(interpreter.AddTensors(1), kTfLiteOk); @@ -314,6 +355,18 @@ TEST(BasicInterpreter, ResizingTensors) { EXPECT_EQ(tensor->bytes, 8 * sizeof(float)); ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + ASSERT_EQ(interpreter.ResizeInputTensor(t, {}), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 1 * sizeof(float)); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + + ASSERT_EQ(interpreter.ResizeInputTensor(t, {0}), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 0); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + + ASSERT_EQ(interpreter.ResizeInputTensor(t, {1, 2, 0}), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 0); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + // TODO(ahentz): We shouldn't have to force reallocation, but // ResizeInputTensor doesn't realloc dynamic tensors. Also note that // TfLiteTensorRealloc(tensor->bytes, tensor) is a no-op. @@ -331,6 +384,37 @@ TEST(BasicInterpreter, ResizingTensors) { tensor->data.f[15] = 0.123f; } +TEST(BasicInterpreter, NoopResizingTensors) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(1), kTfLiteOk); + ASSERT_EQ(interpreter.SetInputs({0}), kTfLiteOk); + ASSERT_EQ(interpreter.SetOutputs({0}), kTfLiteOk); + + ASSERT_EQ(interpreter.SetTensorParametersReadWrite( + 0, kTfLiteFloat32, "", {3}, TfLiteQuantizationParams()), + kTfLiteOk); + + int t = interpreter.inputs()[0]; + TfLiteTensor* tensor = interpreter.tensor(t); + + ASSERT_EQ(interpreter.ResizeInputTensor(t, {1, 2, 3}), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 6 * sizeof(float)); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + tensor->data.f[5] = 0.123f; + + // Resizing to the same size should not trigger re-allocation. + ASSERT_EQ(interpreter.ResizeInputTensor(t, {1, 2, 3}), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 6 * sizeof(float)); + ASSERT_NE(tensor->data.raw, nullptr); + ASSERT_EQ(tensor->data.f[5], 0.123f); + + // Explicitly allocating should be a no-op, as no resize was performed. + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + EXPECT_EQ(tensor->bytes, 6 * sizeof(float)); + ASSERT_NE(tensor->data.raw, nullptr); + ASSERT_EQ(tensor->data.f[5], 0.123f); +} + TEST(BasicInterpreter, OneOpInterpreter) { Interpreter interpreter; ASSERT_EQ(interpreter.AddTensors(2), kTfLiteOk); @@ -603,6 +687,59 @@ TEST(BasicInterpreter, TestUnsupportedDelegateFunctions) { EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteError); } +TEST(BasicInterpreter, DynamicTensorsResizeDescendants) { + // Assemble a graph with a node that has dynamically sized output (via the + // pad op), followed by a node with a standard element-wise op (negate). + Interpreter interpreter; + interpreter.AddTensors(4); + interpreter.SetInputs({0, 1}); + interpreter.SetOutputs({3}); + TfLiteQuantizationParams quant; + interpreter.SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {2, 2, 1, 1}, + quant); + interpreter.SetTensorParametersReadWrite(1, kTfLiteInt32, "", {4, 2}, quant); + interpreter.SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {}, quant); + interpreter.SetTensorParametersReadWrite(3, kTfLiteFloat32, "", {}, quant); + + TfLiteRegistration* pad_op = tflite::ops::builtin::Register_PADV2(); + TfLiteRegistration* neg_op = tflite::ops::builtin::Register_NEG(); + interpreter.AddNodeWithParameters({0, 1}, {2}, nullptr, 0, nullptr, pad_op); + interpreter.AddNodeWithParameters({2}, {3}, nullptr, 0, nullptr, neg_op); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + + // Configure [[2,2],[4,4]] padding and execute the graph. + interpreter.typed_tensor(1)[0] = 2; + interpreter.typed_tensor(1)[1] = 2; + interpreter.typed_tensor(1)[2] = 2; + interpreter.typed_tensor(1)[3] = 2; + interpreter.typed_tensor(1)[4] = 0; + interpreter.typed_tensor(1)[5] = 0; + interpreter.typed_tensor(1)[6] = 0; + interpreter.typed_tensor(1)[7] = 0; + ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); + + // Both the output and intermediate tensor sizes should reflect the output + // from the dynamic pad operation. + ASSERT_EQ(interpreter.tensor(2)->bytes, sizeof(float) * 6 * 6); + ASSERT_EQ(interpreter.tensor(3)->bytes, sizeof(float) * 6 * 6); + + // Now configure [[4,4],[6,6]] padding and execute the graph. + interpreter.typed_tensor(1)[0] = 4; + interpreter.typed_tensor(1)[1] = 4; + interpreter.typed_tensor(1)[2] = 6; + interpreter.typed_tensor(1)[3] = 6; + interpreter.typed_tensor(1)[4] = 0; + interpreter.typed_tensor(1)[5] = 0; + interpreter.typed_tensor(1)[6] = 0; + interpreter.typed_tensor(1)[7] = 0; + ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); + + // Again, the output and intermediate tensor sizes should reflect the *new* + // resize from the latest pad operation. + ASSERT_EQ(interpreter.tensor(2)->bytes, sizeof(float) * 10 * 14); + ASSERT_EQ(interpreter.tensor(3)->bytes, sizeof(float) * 10 * 14); +} + TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) { Interpreter interpreter; ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), diff --git a/tensorflow/contrib/lite/java/aar_with_jni.bzl b/tensorflow/contrib/lite/java/aar_with_jni.bzl index 4450bc9085555b3416f51bac07ea94a1240e919c..db837cf29edfc0ffe9950ffedc02cca1389b0fdf 100644 --- a/tensorflow/contrib/lite/java/aar_with_jni.bzl +++ b/tensorflow/contrib/lite/java/aar_with_jni.bzl @@ -1,5 +1,7 @@ """Generate zipped aar file including different variants of .so in jni folder.""" +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + def aar_with_jni(name, android_library): # Generate dummy AndroidManifest.xml for dummy apk usage # (dummy apk is generated by _dummy_app_for_so target below) @@ -19,7 +21,7 @@ EOF # Generate dummy apk including .so files and later we extract out # .so files and throw away the apk. - native.android_binary( + android_binary( name = name + "_dummy_app_for_so", manifest = name + "_generated_AndroidManifest.xml", custom_package = "dummy.package.for.so", diff --git a/tensorflow/contrib/lite/java/demo/README.md b/tensorflow/contrib/lite/java/demo/README.md index 2e818f728ef208d30b0eeb27ffd7e3fa0c7c1a2d..e3cea19e1683ac2680521bce66d1328e4b2caf1c 100644 --- a/tensorflow/contrib/lite/java/demo/README.md +++ b/tensorflow/contrib/lite/java/demo/README.md @@ -1,5 +1,14 @@ # TF Lite Android App +## Building in Android Studio with TensorFlow Lite AAR from JCenter. +The build.gradle is configured to use TensorFlow Lite's nightly build. + +If you see a build error related to compatibility with Tensorflow Lite's Java API (example: method X is +undefined for type Interpreter), there has likely been a backwards compatible +change to the API. You will need to pull new app code that's compatible with the +nightly build and may need to first wait a few days for our external and internal +code to merge. + ## Building from Source with Bazel 1. Follow the [Bazel steps for the TF Demo App](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#bazel): diff --git a/tensorflow/contrib/lite/java/demo/app/build.gradle b/tensorflow/contrib/lite/java/demo/app/build.gradle index b76eaad8bb91224805d16b3d6f7c3274c9feb90c..49868c5a7566c8c537ac2ae9e0a4acc2c872ecbf 100644 --- a/tensorflow/contrib/lite/java/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/demo/app/build.gradle @@ -5,11 +5,12 @@ android { buildToolsVersion "26.0.1" defaultConfig { applicationId "android.example.com.tflitecamerademo" - minSdkVersion 15 + // Required by Camera2 API. + minSdkVersion 21 targetSdkVersion 26 versionCode 1 versionName "1.0" - testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" + testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner" // Remove this block. jackOptions { @@ -43,7 +44,7 @@ repositories { dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', { + androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { exclude group: 'com.android.support', module: 'support-annotations' }) compile 'com.android.support:appcompat-v7:25.2.0' @@ -52,7 +53,43 @@ dependencies { compile 'com.android.support:support-annotations:25.3.1' compile 'com.android.support:support-v13:25.2.0' - compile 'org.tensorflow:tensorflow-lite:+' + compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' testCompile 'junit:junit:4.12' } + +def modelDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip" +def localCache = "build/intermediates/mobilenet_v1_224_android_quant_2017_11_08.zip" +def targetFolder = "src/main/assets" + +task downloadModel(type: DownloadUrlTask) { + doFirst { + println "Downloading ${modelDownloadUrl}" + } + sourceUrl = "${modelDownloadUrl}" + target = file("${localCache}") +} + +task unzipModel(type: Copy, dependsOn: 'downloadModel') { + doFirst { + println "Unzipping ${localCache}" + } + from zipTree("${localCache}") + into "${targetFolder}" +} + +// Ensure the model file is downloaded and extracted before every build +preBuild.dependsOn unzipModel + +class DownloadUrlTask extends DefaultTask { + @Input + String sourceUrl + + @OutputFile + File target + + @TaskAction + void download() { + ant.get(src: sourceUrl, dest: target) + } +} diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/BUILD b/tensorflow/contrib/lite/java/demo/app/src/main/BUILD index d6fbef9cc938993b283103984307ab51e609dd6e..220d6c2159b56f6349e93132418fa0f6c69d1ab3 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/BUILD +++ b/tensorflow/contrib/lite/java/demo/app/src/main/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:private"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/ovic/BUILD b/tensorflow/contrib/lite/java/ovic/BUILD index 362d93636f72205ddcda6d97fa9fae376ff211f1..f232b00045cf1df6a31ada80af4cc5885a4c0099 100644 --- a/tensorflow/contrib/lite/java/ovic/BUILD +++ b/tensorflow/contrib/lite/java/ovic/BUILD @@ -1,6 +1,8 @@ # Description: # OVIC Benchmarker Java API. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/ovic/demo/app/BUILD b/tensorflow/contrib/lite/java/ovic/demo/app/BUILD index 83974f4b337baedebaf9c9ffc0a03501418a3e36..a8d751ade26adc358e130138381eab9956f2d848 100644 --- a/tensorflow/contrib/lite/java/ovic/demo/app/BUILD +++ b/tensorflow/contrib/lite/java/ovic/demo/app/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + # Sample app for OVIC benchmarking. licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle b/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle index c5d19bad89a93988a6830a17fe2fb4a60e2fb00f..3f32d62e5c08419c6413fffe09b64356edcac836 100644 --- a/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle @@ -9,7 +9,7 @@ android { targetSdkVersion 26 versionCode 1 versionName "1.0" - testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" + testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner" // Remove this block. jackOptions { @@ -43,7 +43,7 @@ repositories { dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', { + androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { exclude group: 'com.android.support', module: 'support-annotations' }) compile 'com.android.support:appcompat-v7:25.2.0' diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java index fd1f0ffa68eeca7b5866b146ecaa1f9216ef377d..4e22a68bf2e5e9cdc7783ffd829e124023a05479 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java @@ -135,7 +135,8 @@ public final class Interpreter implements AutoCloseable { * including int, float, long, and byte. {@link ByteBuffer} is the preferred way to pass large * input data. When {@link ByteBuffer} is used, its content should remain unchanged until * model inference is done. - * @param output a multidimensional array of output data. + * @param output a multidimensional array of output data, or a {@link ByteBuffer} of primitive + * types including int, float, long, and byte. */ public void run(@NonNull Object input, @NonNull Object output) { Object[] inputs = {input}; @@ -155,8 +156,9 @@ public final class Interpreter implements AutoCloseable { * primitive types including int, float, long, and byte. {@link ByteBuffer} is the preferred * way to pass large input data. When {@link ByteBuffer} is used, its content should remain * unchanged until model inference is done. - * @param outputs a map mapping output indices to multidimensional arrays of output data. It only - * needs to keep entries for the outputs to be used. + * @param outputs a map mapping output indices to multidimensional arrays of output data or {@link + * ByteBuffer}s of primitive types including int, float, long, and byte. It only needs to keep + * entries for the outputs to be used. */ public void runForMultipleInputsOutputs( @NonNull Object[] inputs, @NonNull Map outputs) { diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 2ae6c516b03ef4292667bbd944c73d2eeaf82db3..80de88b6a1cd75b033e116f76f5612ee66e48f03 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -311,8 +311,30 @@ final class NativeInterpreterWrapper implements AutoCloseable { return DataType.fromNumber(type).toStringName(); } + /** + * Gets the quantization zero point of an output. + * + * @throws IllegalArgumentExeption if the output index is invalid. + */ + int getOutputQuantizationZeroPoint(int index) { + return getOutputQuantizationZeroPoint(interpreterHandle, index); + } + + /** + * Gets the quantization scale of an output. + * + * @throws IllegalArgumentExeption if the output index is invalid. + */ + float getOutputQuantizationScale(int index) { + return getOutputQuantizationScale(interpreterHandle, index); + } + private static native int getOutputDataType(long interpreterHandle, int outputIdx); + private static native int getOutputQuantizationZeroPoint(long interpreterHandle, int outputIdx); + + private static native float getOutputQuantizationScale(long interpreterHandle, int outputIdx); + private static final int ERROR_BUFFER_SIZE = 512; private long errorHandle; diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java index 09e887aae3339e9f114c07d689c0d7b5e2fc384b..b2a3e04c55d86a33307e48571d50a72e0fa461ac 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java @@ -15,6 +15,8 @@ limitations under the License. package org.tensorflow.lite; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; import java.util.Arrays; /** @@ -29,8 +31,21 @@ final class Tensor { return new Tensor(nativeHandle); } - /** Reads Tensor content into an array. */ + /** + * Copies the contents of the tensor to {@code dst} and returns {@code dst}. + * + * @param dst the destination buffer, either an explicitly-typed array or a {@link ByteBuffer}. + * @throws IllegalArgumentException if {@code dst} is not compatible with the tensor (for example, + * mismatched data types or shapes). + * @throws BufferOverflowException If {@code dst} is a ByteBuffer with insufficient space for the + * data in this tensor. + */ T copyTo(T dst) { + if (dst instanceof ByteBuffer) { + ByteBuffer dstByteBuffer = (ByteBuffer) dst; + dstByteBuffer.put(buffer()); + return dst; + } if (NativeInterpreterWrapper.dataTypeOf(dst) != dtype) { throw new IllegalArgumentException( String.format( @@ -60,6 +75,12 @@ final class Tensor { this.shapeCopy = shape(nativeHandle); } + private ByteBuffer buffer() { + return buffer(nativeHandle).order(ByteOrder.nativeOrder()); + } + + private static native ByteBuffer buffer(long handle); + private static native int dtype(long handle); private static native int[] shape(long handle); diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc index 1fb6997fb9ba180e9a3f3a89a6d177086440c0d7..31f7b58fbc30cab9e6cb813094ea4b2627ba5cba 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -561,6 +561,38 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( return static_cast(type); } +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationZeroPoint( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + const int idx = static_cast(output_idx); + if (output_idx < 0 || output_idx >= interpreter->outputs().size()) { + throwException(env, kIllegalArgumentException, + "Failed to get %d-th output out of %d outputs", output_idx, + interpreter->outputs().size()); + return 0; + } + TfLiteTensor* target = interpreter->tensor(interpreter->outputs()[idx]); + return static_cast(target->params.zero_point); +} + +JNIEXPORT jfloat JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationScale( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 1.0f; + const int idx = static_cast(output_idx); + if (output_idx < 0 || output_idx >= interpreter->outputs().size()) { + throwException(env, kIllegalArgumentException, + "Failed to get %d-th output out of %d outputs", output_idx, + interpreter->outputs().size()); + return 1.0f; + } + TfLiteTensor* target = interpreter->tensor(interpreter->outputs()[idx]); + return static_cast(target->params.scale); +} + JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h index eaa765cb343e9764bd0ef018d636a76f4b8a13e4..128ece49811a112684dac7b36810e920eeeb7351 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h @@ -152,6 +152,28 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( JNIEnv* env, jclass clazz, jlong handle, jint output_idx); +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: + * Signature: (JI)I + * + * Gets output quantization zero point. + */ +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationZeroPoint( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: + * Signature: (JI)F + * + * Gets output quantization scale. + */ +JNIEXPORT jfloat JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationScale( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx); + /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: diff --git a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc index 9e9387da86ebde7d711a7ce967461e370c95bc3e..08b4d042803708830221d5e25fe4463366a4c99a 100644 --- a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc @@ -203,6 +203,16 @@ size_t writeMultiDimensionalArray(JNIEnv* env, jobject src, TfLiteType type, } } +JNIEXPORT jobject JNICALL Java_org_tensorflow_lite_Tensor_buffer(JNIEnv* env, + jclass clazz, + jlong handle) { + TfLiteTensor* tensor = convertLongToTensor(env, handle); + if (tensor == nullptr) return nullptr; + + return env->NewDirectByteBuffer(static_cast(tensor->data.raw), + static_cast(tensor->bytes)); +} + JNIEXPORT void JNICALL Java_org_tensorflow_lite_Tensor_readMultiDimensionalArray(JNIEnv* env, jclass clazz, diff --git a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h index 3a4910dcc3a719fbb9f365dae693423de768349c..9ba95d9ac402662e6de69e3da8a60a6e841f97d6 100644 --- a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h @@ -24,8 +24,17 @@ extern "C" { #endif // __cplusplus /* - * Class: org_tensorflow_lite_TfLiteTensor - * Method: + * Class: org_tensorflow_lite_Tensor + * Method: buffer + * Signature: (J)Ljava/nio/ByteBuffer; + */ +JNIEXPORT jobject JNICALL Java_org_tensorflow_lite_Tensor_buffer(JNIEnv* env, + jclass clazz, + jlong handle); + +/* + * Class: org_tensorflow_lite_Tensor + * Method: dtype * Signature: (J)I */ JNIEXPORT jint JNICALL Java_org_tensorflow_lite_Tensor_dtype(JNIEnv* env, @@ -33,8 +42,8 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_lite_Tensor_dtype(JNIEnv* env, jlong handle); /* - * Class: org_tensorflow_lite_TfLiteTensor - * Method: + * Class: org_tensorflow_lite_Tensor + * Method: shape * Signature: (J)[I */ JNIEXPORT jintArray JNICALL Java_org_tensorflow_lite_Tensor_shape(JNIEnv* env, @@ -42,8 +51,8 @@ JNIEXPORT jintArray JNICALL Java_org_tensorflow_lite_Tensor_shape(JNIEnv* env, jlong handle); /* - * Class: org_tensorflow_lite_TfLiteTensor - * Method: + * Class: org_tensorflow_lite_Tensor + * Method: readMultiDimensionalArray * Signature: (JLjava/lang/Object;) */ JNIEXPORT void JNICALL diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index 82007a6ab5be3492495125b1c20ed155907ae5a0..e6deadffe2d7a110ff742b05a5bf06fa1bc67de9 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -164,6 +164,24 @@ public final class InterpreterTest { interpreter.close(); } + @Test + public void testRunWithByteBufferOutput() { + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + ByteBuffer parsedOutput = + ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); + try (Interpreter interpreter = new Interpreter(MODEL_FILE)) { + interpreter.run(fourD, parsedOutput); + } + float[] outputOneD = { + parsedOutput.getFloat(0), parsedOutput.getFloat(4), parsedOutput.getFloat(8) + }; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + } + @Test public void testMobilenetRun() { // Create a gray image. diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java index 7c00d3196fd001a288d77d4e01f0b30978d72afe..029e5853e2f843fc38eeca0ffa9bb3a82390093b 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java @@ -41,6 +41,9 @@ public final class NativeInterpreterWrapperTest { private static final String BYTE_MODEL_PATH = "tensorflow/contrib/lite/java/src/testdata/uint8.bin"; + private static final String QUANTIZED_MODEL_PATH = + "tensorflow/contrib/lite/java/src/testdata/quantized.bin"; + private static final String INVALID_MODEL_PATH = "tensorflow/contrib/lite/java/src/testdata/invalid_model.bin"; @@ -108,6 +111,27 @@ public final class NativeInterpreterWrapperTest { wrapper.close(); } + @Test + public void testRunWithBufferOutput() { + try (NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH)) { + float[] oneD = {1.23f, -6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + Tensor[] outputs = wrapper.run(inputs); + assertThat(outputs).hasLength(1); + ByteBuffer parsedOutput = + ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); + outputs[0].copyTo(parsedOutput); + float[] outputOneD = { + parsedOutput.getFloat(0), parsedOutput.getFloat(4), parsedOutput.getFloat(8) + }; + float[] expected = {3.69f, -19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + } + } + @Test public void testRunWithInputsOfSameDims() { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); @@ -536,4 +560,16 @@ public final class NativeInterpreterWrapperTest { assertThat(wrapper.getOutputDataType(0)).contains("byte"); wrapper.close(); } + + @Test + public void testGetOutputQuantizationParams() { + try (NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH)) { + assertThat(wrapper.getOutputQuantizationZeroPoint(0)).isEqualTo(0); + assertThat(wrapper.getOutputQuantizationScale(0)).isWithin(1e-6f).of(0.0f); + } + try (NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(QUANTIZED_MODEL_PATH)) { + assertThat(wrapper.getOutputQuantizationZeroPoint(0)).isEqualTo(127); + assertThat(wrapper.getOutputQuantizationScale(0)).isWithin(1e-6f).of(0.25f); + } + } } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java index 94b6632bb8dd7117bf4074da1939bd23ce732efd..dd9d37eedafaa8250f5f926375edcf7cb3b730a0 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java @@ -18,6 +18,9 @@ package org.tensorflow.lite; import static com.google.common.truth.Truth.assertThat; import static org.junit.Assert.fail; +import java.nio.BufferOverflowException; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -70,6 +73,32 @@ public final class TensorTest { assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); } + @Test + public void testCopyToByteBuffer() { + Tensor tensor = Tensor.fromHandle(nativeHandle); + ByteBuffer parsedOutput = + ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); + tensor.copyTo(parsedOutput); + assertThat(parsedOutput.position()).isEqualTo(2 * 8 * 8 * 3 * 4); + float[] outputOneD = { + parsedOutput.getFloat(0), parsedOutput.getFloat(4), parsedOutput.getFloat(8) + }; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + } + + @Test + public void testCopyToInvalidByteBuffer() { + Tensor tensor = Tensor.fromHandle(nativeHandle); + ByteBuffer parsedOutput = ByteBuffer.allocateDirect(3 * 4).order(ByteOrder.nativeOrder()); + try { + tensor.copyTo(parsedOutput); + fail(); + } catch (BufferOverflowException e) { + // Expected. + } + } + @Test public void testCopyToWrongType() { Tensor tensor = Tensor.fromHandle(nativeHandle); diff --git a/tensorflow/contrib/lite/java/src/testdata/quantized.bin b/tensorflow/contrib/lite/java/src/testdata/quantized.bin new file mode 100644 index 0000000000000000000000000000000000000000..4062088cdf717e8752490de5c9acff35fd6af54f Binary files /dev/null and b/tensorflow/contrib/lite/java/src/testdata/quantized.bin differ diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD index b524246d436858bbf506809a38cead2897f78d93..af1d99ef41e6413d8ef2c6f478aaa8f9e3931ff8 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD @@ -1,6 +1,8 @@ # Description: # Internal helper function to test TF Lite API. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index cf5d0b4ce9cb3c516c185f31fea12db70a2c3bdb..27b8a16e1522de4d31b2870e6130fb3281941a05 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -142,6 +142,7 @@ cc_library( "conv.cc", "depthwise_conv.cc", "dequantize.cc", + "detection_postprocess.cc", "div.cc", "elementwise.cc", "embedding_lookup.cc", @@ -157,16 +158,18 @@ cc_library( "lsh_projection.cc", "lstm.cc", "maximum_minimum.cc", - "mean.cc", "mfcc.cc", "mul.cc", "neg.cc", "pad.cc", "pooling.cc", + "pow.cc", + "reduce.cc", "register.cc", "reshape.cc", "resize_bilinear.cc", "select.cc", + "shape.cc", "skip_gram.cc", "slice.cc", "space_to_batch_nd.cc", @@ -246,6 +249,20 @@ tf_cc_test( ], ) +tf_cc_test( + name = "detection_postprocess_test", + size = "small", + srcs = ["detection_postprocess_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + "@flatbuffers", + ], +) + tf_cc_test( name = "activations_test", size = "small", @@ -554,9 +571,9 @@ tf_cc_test( ) tf_cc_test( - name = "mean_test", + name = "reduce_test", size = "small", - srcs = ["mean_test.cc"], + srcs = ["reduce_test.cc"], tags = ["tflite_not_portable_ios"], deps = [ ":builtin_ops", @@ -947,6 +964,7 @@ tf_cc_test( ":builtin_ops", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_absl//absl/memory", "@com_google_googletest//:gtest", ], ) @@ -979,6 +997,34 @@ tf_cc_test( ], ) +tf_cc_test( + name = "shape_test", + size = "small", + srcs = ["shape_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "pow_test", + size = "small", + srcs = ["pow_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index add36b46c0b8a4deab1e842d50194c8b99a3a20c..99f81c4a8a78ab0b2a24955d77f25ed09da13b84 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -84,6 +84,38 @@ TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) { &data->input_left_shift); data->input_range_radius = CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } else if (input->type == kTfLiteInt16) { + static constexpr int kInputIntegerBits = 3; + static constexpr int kOutputFractionalBits = 15; + + // These operators are implemented in fixed-point arithmetic, + // which intrinsically wants symmetric ranges (zero_point==0) + // and power-of-two scales (power-of-two is abbreviated below as POT). + // While more general support would be possible by means of rescaling, + // that would add some overhead and some loss of accuracy and wouldn't + // be used at the moment as current quantized LSTM applications are + // happy with symmetric, power-of-two-scales quantization. So we just + // implement that narrow case only for now. + + TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input_scale_log2_rounded; + TF_LITE_ENSURE(context, + CheckedLog2(input->params.scale, &input_scale_log2_rounded)); + + int output_scale_log2_rounded; + TF_LITE_ENSURE( + context, CheckedLog2(output->params.scale, &output_scale_log2_rounded)); + TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded, + -kOutputFractionalBits); + + data->input_left_shift = + (15 - kInputIntegerBits) + input_scale_log2_rounded; + // Support for shifts is limited until we have a parameterized version of + // SaturatingRoundingMultiplyByPOT(). + TF_LITE_ENSURE(context, data->input_left_shift >= 0); + TF_LITE_ENSURE(context, data->input_left_shift <= 1); } return context->ResizeTensor(context, output, @@ -114,6 +146,30 @@ TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) { &data->input_left_shift); data->input_range_radius = CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } else if (input->type == kTfLiteInt16) { + static constexpr int kInputIntegerBits = 3; + static constexpr int kOutputFractionalBits = 15; + + // See comments in TanhPrepare about requiring zero_point==0 + // and a power-of-two ("POT") scale. + + TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input_scale_log2_rounded; + TF_LITE_ENSURE(context, + CheckedLog2(input->params.scale, &input_scale_log2_rounded)); + + int output_scale_log2_rounded; + TF_LITE_ENSURE( + context, CheckedLog2(output->params.scale, &output_scale_log2_rounded)); + TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded, + -kOutputFractionalBits); + + data->input_left_shift = + (15 - kInputIntegerBits) + input_scale_log2_rounded; + // The int16 logistic implementation does not support shifting of the input. + TF_LITE_ENSURE_EQ(context, data->input_left_shift, 0); } return context->ResizeTensor(context, output, @@ -250,12 +306,19 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = std::tanh(*in); return kTfLiteOk; } break; + case kTfLiteInt16: { + optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), + data->input_left_shift, + GetTensorData(output), + GetTensorShape(output)); + return kTfLiteOk; + } break; case kTfLiteUInt8: { - optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), + optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); return kTfLiteOk; } break; default: @@ -280,12 +343,18 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = 1.f / (1.f + std::exp(-*in)); break; } + case kTfLiteInt16: { + optimized_ops::Logistic( + GetTensorData(input), GetTensorShape(input), + GetTensorData(output), GetTensorShape(output)); + break; + } case kTfLiteUInt8: { optimized_ops::Logistic( - GetTensorData(input), GetTensorDims(input), + GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, - GetTensorData(output), GetTensorDims(output)); + GetTensorData(output), GetTensorShape(output)); break; } default: @@ -341,26 +410,26 @@ void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, const int batch_size = input->dims->data[0]; const int input_size = input->dims->data[1]; optimized_ops::Softmax(GetTensorData(input), - GetTensorDims({batch_size, 1, 1, input_size}), + GetTensorShape({batch_size, 1, 1, input_size}), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims({batch_size, 1, 1, input_size})); + GetTensorShape({batch_size, 1, 1, input_size})); } // Takes a 4D tensor and perform softmax along the forth dimension. void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), params->beta, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { @@ -415,8 +484,8 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { switch (input->type) { case kTfLiteFloat32: optimized_ops::LogSoftmax( - GetTensorData(input), GetTensorDims(input), - GetTensorData(output), GetTensorDims(output)); + GetTensorData(input), GetTensorShape(input), + GetTensorData(output), GetTensorShape(output)); return kTfLiteOk; default: context->ReportError(context, "Only float32 supported currently., got %d", diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 50a84edd475c8051a563cf8ed9fc03099829b786..587e1303da6afed1fc711100f457f1bf62b0b7e1 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -75,23 +75,42 @@ class FloatActivationsOpModel : public BaseActivationsOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; -// TODO(ahentz): I don't quite understand the tradeoffs in the quantized -// implementation of sigmoid and software, but a tolerance of twice the output -// scale seems reasonable. We might want to change this if we have a better -// theoretical bound. +// Our fixed-point math function implementations have roughly 12 bits of +// accuracy, when specialized to 16-bit fixed-point arithmetic. +// That is purely an implementation compromise, it would have been possible +// to get closer to 16 bits of accuracy but that would be more expensive, +// and not needed for our purposes as ultimately the output is either +// immediately down-quantized to 8 bits, or will typically be at the output +// of the surrounding LSTM cell. +// So we can require roughly 2^-12 accuracy when the output is 16-bit, and +// we can more or less expect the full 2^-8 accuracy when the output is 8-bit. +// +// However, the representable output interval is often [-1, 1] (it has to be +// for tanh, and even for logistic, when we implement it in fixed-point, we +// typically have to do so on such a symmetric interval, e.g. ARM NEON only +// has signed fixed-point arithmetic (SQRDMULH)). As the width of [-1, 1] +// is 2, our representable values are often diluted by a factor of 2, whence +// the factor of 2 below. const float kQuantizedTolerance = 2 * (1. / 256); +const float kQuantizedToleranceInt16 = 2 * (1. / 4096); class QuantizedActivationsOpModel : public BaseActivationsOpModel { public: using BaseActivationsOpModel::BaseActivationsOpModel; + template void SetInput(std::initializer_list data) { - QuantizeAndPopulate(input_, data); + QuantizeAndPopulate(input_, data); } - std::vector GetOutput() { return ExtractVector(output_); } + template + + std::vector GetOutput() { + return ExtractVector(output_); + } + template std::vector GetDequantizedOutput() { - return Dequantize(ExtractVector(output_), - GetScale(output_), GetZeroPoint(output_)); + return Dequantize(ExtractVector(output_), GetScale(output_), + GetZeroPoint(output_)); } }; @@ -152,24 +171,47 @@ TEST(FloatActivationsOpTest, Tanh) { } TEST(QuantizedActivationsOpTest, Tanh) { + const float kMin = -1; + const float kMax = 127.f / 128.f; QuantizedActivationsOpModel m( BuiltinOperator_TANH, - /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -8, 8}, - /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, -1, 1}); - m.SetInput({ + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ 0, -6, 2, 4, // -4, -2, 8, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { 0.0, -0.999987, 0.964027, 0.999329, // - -0.996078, -0.96402, 0.99999, 0.76159, // + -0.999329, -0.96402, 0.99999, 0.76159, // }, - 4 * (1. / 256)))); - EXPECT_THAT(m.GetOutput(), - ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 226})); + kQuantizedTolerance))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 225})); +} + +TEST(QuantizedActivationsOpTest, TanhInt16) { + const float kMin = -1; + const float kMax = 32767.f / 32768.f; + QuantizedActivationsOpModel m( + BuiltinOperator_TANH, + /*input=*/{TensorType_INT16, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_INT16, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ + 0, -6, 2, 4, // + -4, -2, 8, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.0, -0.999987, 0.964027, 0.999329, // + -0.999329, -0.96402, 0.99999, 0.76159, // + }, + kQuantizedToleranceInt16))); } TEST(FloatActivationsOpTest, Sigmoid) { @@ -190,22 +232,43 @@ TEST(QuantizedActivationsOpTest, Sigmoid) { QuantizedActivationsOpModel m( BuiltinOperator_LOGISTIC, /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // 3, -2, 10, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { 0.5, 0.002473, 0.880797, 0.982014, // 0.952574, 0.119203, 0.999955, 0.731059, // }, kQuantizedTolerance))); - EXPECT_THAT(m.GetOutput(), + EXPECT_THAT(m.GetOutput(), ElementsAreArray({128, 1, 227, 251, 244, 32, 255, 188})); } +TEST(QuantizedActivationsOpTest, SigmoidInt16) { + const float kMin = -1; + const float kMax = 32767.f / 32768.f; + QuantizedActivationsOpModel m( + BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_INT16, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_INT16, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }, + kQuantizedToleranceInt16))); +} + TEST(FloatActivationsOpTest, Softmax4D) { FloatActivationsOpModel m(0.1, /*input=*/{TensorType_FLOAT32, {1, 2, 1, 4}}); @@ -241,12 +304,12 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { QuantizedActivationsOpModel m( 0.1, /*input=*/{TensorType_UINT8, {1, 2, 1, 4}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // depth = 0 3, -2, 10, 1, // depth = 1 }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { .23463, .12877, .28658, .35003, // @@ -258,21 +321,22 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { QuantizedActivationsOpModel m2( 0.1, /*input=*/{TensorType_UINT8, {4, 1, 1, 2}, -10, 10}); - m2.SetInput({ + m2.SetInput({ 0, -6, // 2, 4, // 3, -2, // 10, 1, // }); m2.Invoke(); - EXPECT_THAT(m2.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( - { - 0.645656, 0.354344, // - 0.450166, 0.549834, // - 0.622459, 0.377541, // - 0.710949, 0.28905, // - }, - kQuantizedTolerance))); + EXPECT_THAT(m2.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.645656, 0.354344, // + 0.450166, 0.549834, // + 0.622459, 0.377541, // + 0.710949, 0.28905, // + }, + kQuantizedTolerance))); } TEST(FloatActivationsOpTest, Softmax2D) { @@ -309,12 +373,12 @@ TEST(FloatActivationsOpTest, Softmax2D) { TEST(QuantizedActivationsOpTest, Softmax2D) { QuantizedActivationsOpModel m(0.1, /*input=*/{TensorType_UINT8, {2, 4}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // 3, -2, 10, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { .23463, .12877, .28658, .35003, // @@ -325,21 +389,22 @@ TEST(QuantizedActivationsOpTest, Softmax2D) { // Same input, but a different shape. QuantizedActivationsOpModel m2(0.1, /*input=*/{TensorType_UINT8, {4, 2}, -10, 10}); - m2.SetInput({ + m2.SetInput({ 0, -6, // 2, 4, // 3, -2, // 10, 1, // }); m2.Invoke(); - EXPECT_THAT(m2.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( - { - 0.645656, 0.354344, // - 0.450166, 0.549834, // - 0.622459, 0.377541, // - 0.710949, 0.28905, // - }, - kQuantizedTolerance))); + EXPECT_THAT(m2.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.645656, 0.354344, // + 0.450166, 0.549834, // + 0.622459, 0.377541, // + 0.710949, 0.28905, // + }, + kQuantizedTolerance))); } // This contains the same test values as the Softmax test, but reference answer diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index 7ca1e35489cba3b5d2567bc04e532fedf8a527a7..f44d531cbfa9ed41f881380752558555aab97b4d 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -39,6 +39,23 @@ constexpr int kOutputTensor = 0; struct OpData { bool requires_broadcast; + + // These fields are used in both the general 8-bit -> 8bit quantized path, + // and the special 16-bit -> 16bit quantized path + int input1_shift; + int input2_shift; + int32 output_activation_min; + int32 output_activation_max; + + // These fields are used only in the general 8-bit -> 8bit quantized path + int32 input1_multiplier; + int32 input2_multiplier; + int32 output_multiplier; + int output_shift; + int left_shift; + int32 input1_offset; + int32 input2_offset; + int32 output_offset; }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -52,6 +69,7 @@ void Free(TfLiteContext* context, void* buffer) { } TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); OpData* data = reinterpret_cast(node->user_data); TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); @@ -74,89 +92,169 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size = TfLiteIntArrayCopy(input1->dims); } + if (output->type == kTfLiteUInt8) { + // 8bit -> 8bit general quantized path, with general rescalings + data->input1_offset = -input1->params.zero_point; + data->input2_offset = -input2->params.zero_point; + data->output_offset = output->params.zero_point; + data->left_shift = 20; + const double twice_max_input_scale = + 2 * std::max(input1->params.scale, input2->params.scale); + const double real_input1_multiplier = + input1->params.scale / twice_max_input_scale; + const double real_input2_multiplier = + input2->params.scale / twice_max_input_scale; + const double real_output_multiplier = + twice_max_input_scale / + ((1 << data->left_shift) * output->params.scale); + + QuantizeMultiplierSmallerThanOneExp( + real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); + data->input1_shift *= -1; + + QuantizeMultiplierSmallerThanOneExp( + real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); + data->input2_shift *= -1; + + QuantizeMultiplierSmallerThanOneExp( + real_output_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; + + CalculateActivationRangeUint8(params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + + } else if (output->type == kTfLiteInt16) { + // 16bit -> 16bit special quantized path, supporting only a rather + // narrow case of quantization parameters: zero_points must all be 0 + // ("symmetric quantization") and scales must be power-of-two (which + // we abbreviate as "POT" below). The intended use case for this path + // is in LSTM cells, where, due to the constraints of implementing + // some of the math in these LSTM cells in fixed-point arithmetic, + // we need to have such symmetric, power-of-two quantization + // (Fixed-point formats are inherently symmetric, power-of-two). + TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, input2->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input1_scale_log2_rounded; + bool input1_scale_is_pot = + CheckedLog2(input1->params.scale, &input1_scale_log2_rounded); + TF_LITE_ENSURE(context, input1_scale_is_pot); + + int input2_scale_log2_rounded; + bool input2_scale_is_pot = + CheckedLog2(input2->params.scale, &input2_scale_log2_rounded); + TF_LITE_ENSURE(context, input2_scale_is_pot); + + int output_scale_log2_rounded; + bool output_scale_is_pot = + CheckedLog2(output->params.scale, &output_scale_log2_rounded); + TF_LITE_ENSURE(context, output_scale_is_pot); + + data->input1_shift = output_scale_log2_rounded - input1_scale_log2_rounded; + data->input2_shift = output_scale_log2_rounded - input2_scale_log2_rounded; + + // Shifting of one input is supported. The graph quantization should ensure + // that the other input matches the output. + TF_LITE_ENSURE(context, data->input1_shift == 0 || data->input2_shift == 0); + TF_LITE_ENSURE(context, data->input1_shift >= 0); + TF_LITE_ENSURE(context, data->input2_shift >= 0); + + CalculateActivationRangeQuantized(context, params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + } + return context->ResizeTensor(context, output, output_size); } template -void EvalAddFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); -#define TF_LITE_ADD(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) - if (kernel_type == kReference) { - if (data->requires_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd); +void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, + const OpData* data, const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { +#define TF_LITE_ADD(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output_activation_min, output_activation_max, \ + GetTensorData(output), GetTensorDims(output)) + if (output->type == kTfLiteInt32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_ADD(reference_ops, BroadcastAdd, int32_t); + } else { + TF_LITE_ADD(reference_ops, Add, int32_t); + } } else { - TF_LITE_ADD(reference_ops, Add); + if (data->requires_broadcast) { + TF_LITE_ADD(optimized_ops, BroadcastAdd, int32_t); + } else { + TF_LITE_ADD(optimized_ops, Add, int32_t); + } } - } else { - if (data->requires_broadcast) { - TF_LITE_ADD(optimized_ops, BroadcastAdd); + } else if (output->type == kTfLiteFloat32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_ADD(reference_ops, BroadcastAdd, float); + } else { + TF_LITE_ADD(reference_ops, Add, float); + } } else { - TF_LITE_ADD(optimized_ops, Add); + if (data->requires_broadcast) { + TF_LITE_ADD(optimized_ops, BroadcastAdd, float); + } else { + TF_LITE_ADD(optimized_ops, Add, float); + } } } #undef TF_LITE_ADD } template -void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - auto input1_offset = -input1->params.zero_point; - auto input2_offset = -input2->params.zero_point; - auto output_offset = output->params.zero_point; - const int left_shift = 20; - const double twice_max_input_scale = - 2 * std::max(input1->params.scale, input2->params.scale); - const double real_input1_multiplier = - input1->params.scale / twice_max_input_scale; - const double real_input2_multiplier = - input2->params.scale / twice_max_input_scale; - const double real_output_multiplier = - twice_max_input_scale / ((1 << left_shift) * output->params.scale); - - int32 input1_multiplier; - int input1_shift; - QuantizeMultiplierSmallerThanOne(real_input1_multiplier, &input1_multiplier, - &input1_shift); - int32 input2_multiplier; - int input2_shift; - QuantizeMultiplierSmallerThanOne(real_input2_multiplier, &input2_multiplier, - &input2_shift); - int32 output_multiplier; - int output_shift; - QuantizeMultiplierSmallerThanOne(real_output_multiplier, &output_multiplier, - &output_shift); - - int32 output_activation_min, output_activation_max; - CalculateActivationRangeUint8(params->activation, output, - &output_activation_min, &output_activation_max); - -#define TF_LITE_ADD(type, opname) \ - type::opname(left_shift, GetTensorData(input1), \ - GetTensorDims(input1), input1_offset, input1_multiplier, \ - input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, input2_multiplier, \ - input2_shift, output_offset, output_multiplier, output_shift, \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); - // The quantized version of Add doesn't support activations, so we - // always use BroadcastAdd. - if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops, BroadcastAdd); - } else { - TF_LITE_ADD(optimized_ops, BroadcastAdd); - } +TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteAddParams* params, const OpData* data, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteTensor* output) { + if (output->type == kTfLiteUInt8) { +#define TF_LITE_ADD(type, opname) \ + type::opname( \ + data->left_shift, GetTensorData(input1), GetTensorDims(input1), \ + data->input1_offset, data->input1_multiplier, data->input1_shift, \ + GetTensorData(input2), GetTensorDims(input2), \ + data->input2_offset, data->input2_multiplier, data->input2_shift, \ + data->output_offset, data->output_multiplier, data->output_shift, \ + data->output_activation_min, data->output_activation_max, \ + GetTensorData(output), GetTensorDims(output)); + // The quantized version of Add doesn't support activations, so we + // always use BroadcastAdd. + if (kernel_type == kReference) { + TF_LITE_ADD(reference_ops, BroadcastAdd); + } else { + TF_LITE_ADD(optimized_ops, BroadcastAdd); + } +#undef TF_LITE_ADD + } else if (output->type == kTfLiteInt16) { +#define TF_LITE_ADD(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + data->input1_shift, GetTensorData(input2), \ + GetTensorDims(input2), data->input2_shift, \ + data->output_activation_min, data->output_activation_max, \ + GetTensorData(output), GetTensorDims(output)); + // The quantized version of Add doesn't support activations, so we + // always use BroadcastAdd. + if (kernel_type == kReference) { + TF_LITE_ADD(reference_ops, Add); + } else { + TF_LITE_ADD(optimized_ops, Add); + } #undef TF_LITE_ADD + } + + return kTfLiteOk; } template @@ -168,15 +266,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - if (output->type == kTfLiteFloat32) { - EvalAddFloat(context, node, params, data, input1, input2, - output); - } else if (output->type == kTfLiteUInt8) { - EvalAddQuantized(context, node, params, data, input1, input2, - output); + if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) { + EvalAdd(context, node, params, data, input1, input2, output); + } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + TF_LITE_ENSURE_OK(context, + EvalAddQuantized(context, node, params, data, + input1, input2, output)); } else { context->ReportError(context, - "Inputs and outputs not all float|uint8 types."); + "Inputs and outputs not all float|uint8|int16 types."); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/add_test.cc b/tensorflow/contrib/lite/kernels/add_test.cc index 956d05bed5162f6ce59705d59aad77ff056dda77..0b5844321133de103919de76d367574f018a6698 100644 --- a/tensorflow/contrib/lite/kernels/add_test.cc +++ b/tensorflow/contrib/lite/kernels/add_test.cc @@ -52,6 +52,13 @@ class FloatAddOpModel : public BaseAddOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; +class IntegerAddOpModel : public BaseAddOpModel { + public: + using BaseAddOpModel::BaseAddOpModel; + + std::vector GetOutput() { return ExtractVector(output_); } +}; + class QuantizedAddOpModel : public BaseAddOpModel { public: using BaseAddOpModel::BaseAddOpModel; @@ -60,15 +67,26 @@ class QuantizedAddOpModel : public BaseAddOpModel { return Dequantize(ExtractVector(output_), GetScale(output_), GetZeroPoint(output_)); } + + std::vector GetDequantizedOutputInt16() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } }; // for quantized Add, the error shouldn't exceed 2*step -float GetTolerance(int min, int max) { +float GetTolerance(float min, float max) { float kQuantizedStep = (max - min) / 255.0; float kQuantizedTolerance = 2.0 * kQuantizedStep; return kQuantizedTolerance; } +float GetToleranceInt16(float min, float max) { + float kQuantizedStep = (max - min) / 32767.f; + float kQuantizedTolerance = 2.0 * kQuantizedStep; + return kQuantizedTolerance; +} + TEST(FloatAddOpModel, NoActivation) { FloatAddOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, @@ -122,6 +140,57 @@ TEST(FloatAddOpModel, WithBroadcast) { } } +TEST(IntegerAddOpModel, NoActivation) { + IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 4, 10, 13})); +} + +TEST(IntegerAddOpModel, ActivationRELU_N1_TO_1) { + IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_RELU_N1_TO_1); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1})); +} + +TEST(IntegerAddOpModel, VariousInputShapes) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerAddOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5, 11, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 04, 10, 13, 22, 21})) + << "With shape number " << i; + } +} + +TEST(IntegerAddOpModel, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerAddOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, // always a scalar + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-19, 3, 8, 9, 12, 21}))) + << "With shape number " << i; + } +} + TEST(QuantizedAddOpModel, QuantizedTestsNoActivation) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); std::vector> inputs1 = { @@ -144,6 +213,31 @@ TEST(QuantizedAddOpModel, QuantizedTestsNoActivation) { } } +TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt16) { + const float kMin = -1.f; + const float kMax = 32767.f / 32768.f; + float kQuantizedTolerance = GetToleranceInt16(kMin, kMax); + std::vector> inputs1 = { + {0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, 0.7, 0.3}}; + std::vector> inputs2 = { + {0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, -0.8, 0.5}}; + std::vector> results = { + {0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}}; + for (int i = 0; i < inputs1.size(); ++i) { + QuantizedAddOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {}, kMin, kMax}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), inputs1[i]); + m.QuantizeAndPopulate(m.input2(), inputs2[i]); + m.Invoke(); + EXPECT_THAT( + m.GetDequantizedOutputInt16(), + ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance))) + << "With test number " << i; + } +} + TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); std::vector> inputs1 = {{-0.8, 0.2, 0.9, 0.7}, diff --git a/tensorflow/contrib/lite/kernels/cast.cc b/tensorflow/contrib/lite/kernels/cast.cc index 60770ca0aa8b85d9710d26beca3d4d603da5db2f..8dd48af57fd1bd9ef21256410d6bede6b7baa566 100644 --- a/tensorflow/contrib/lite/kernels/cast.cc +++ b/tensorflow/contrib/lite/kernels/cast.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include #include +#include #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" @@ -53,6 +54,20 @@ void copyCast(const FromT* in, ToT* out, int num_elements) { [](FromT a) { return static_cast(a); }); } +template +void copyCast(const std::complex* in, ToT* out, int num_elements) { + std::transform(in, in + num_elements, out, [](std::complex a) { + return static_cast(std::real(a)); + }); +} + +template <> +void copyCast(const std::complex* in, std::complex* out, + int num_elements) { + std::transform(in, in + num_elements, out, + [](std::complex a) { return a; }); +} + template TfLiteStatus copyToTensor(const FromT* in, TfLiteTensor* out, int num_elements) { @@ -72,6 +87,10 @@ TfLiteStatus copyToTensor(const FromT* in, TfLiteTensor* out, case kTfLiteBool: copyCast(in, out->data.b, num_elements); break; + case kTfLiteComplex64: + copyCast(in, reinterpret_cast*>(out->data.c64), + num_elements); + break; default: // Unsupported type. return kTfLiteError; @@ -95,6 +114,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return copyToTensor(input->data.f, output, num_elements); case kTfLiteBool: return copyToTensor(input->data.b, output, num_elements); + case kTfLiteComplex64: + return copyToTensor( + reinterpret_cast*>(input->data.c64), output, + num_elements); default: // Unsupported type. return kTfLiteError; diff --git a/tensorflow/contrib/lite/kernels/cast_test.cc b/tensorflow/contrib/lite/kernels/cast_test.cc index 53e20007378392467356ab29ecb8b217bb7a9e89..954f998206563a38c74a1382092851cfbee1013b 100644 --- a/tensorflow/contrib/lite/kernels/cast_test.cc +++ b/tensorflow/contrib/lite/kernels/cast_test.cc @@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" @@ -73,6 +75,71 @@ TEST(CastOpModel, CastBoolToFloat) { ElementsAreArray({1.f, 1.0f, 0.f, 1.0f, 0.0f, 1.0f})); } +TEST(CastOpModel, CastComplex64ToFloat) { + CastOpModel m({TensorType_COMPLEX64, {2, 3}}, {TensorType_FLOAT32, {2, 3}}); + m.PopulateTensor>( + m.input(), + {std::complex(1.0f, 11.0f), std::complex(2.0f, 12.0f), + std::complex(3.0f, 13.0f), std::complex(4.0f, 14.0f), + std::complex(5.0f, 15.0f), std::complex(6.0f, 16.0f)}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f})); +} + +TEST(CastOpModel, CastFloatToComplex64) { + CastOpModel m({TensorType_FLOAT32, {2, 3}}, {TensorType_COMPLEX64, {2, 3}}); + m.PopulateTensor(m.input(), {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}); + m.Invoke(); + EXPECT_THAT( + m.ExtractVector>(m.output()), + ElementsAreArray( + {std::complex(1.0f, 0.0f), std::complex(2.0f, 0.0f), + std::complex(3.0f, 0.0f), std::complex(4.0f, 0.0f), + std::complex(5.0f, 0.0f), std::complex(6.0f, 0.0f)})); +} + +TEST(CastOpModel, CastComplex64ToInt) { + CastOpModel m({TensorType_COMPLEX64, {2, 3}}, {TensorType_INT32, {2, 3}}); + m.PopulateTensor>( + m.input(), + {std::complex(1.0f, 11.0f), std::complex(2.0f, 12.0f), + std::complex(3.0f, 13.0f), std::complex(4.0f, 14.0f), + std::complex(5.0f, 15.0f), std::complex(6.0f, 16.0f)}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({1, 2, 3, 4, 5, 6})); +} + +TEST(CastOpModel, CastIntToComplex64) { + CastOpModel m({TensorType_INT32, {2, 3}}, {TensorType_COMPLEX64, {2, 3}}); + m.PopulateTensor(m.input(), {1, 2, 3, 4, 5, 6}); + m.Invoke(); + EXPECT_THAT( + m.ExtractVector>(m.output()), + ElementsAreArray( + {std::complex(1.0f, 0.0f), std::complex(2.0f, 0.0f), + std::complex(3.0f, 0.0f), std::complex(4.0f, 0.0f), + std::complex(5.0f, 0.0f), std::complex(6.0f, 0.0f)})); +} + +TEST(CastOpModel, CastComplex64ToComplex64) { + CastOpModel m({TensorType_COMPLEX64, {2, 3}}, {TensorType_COMPLEX64, {2, 3}}); + m.PopulateTensor>( + m.input(), + {std::complex(1.0f, 11.0f), std::complex(2.0f, 12.0f), + std::complex(3.0f, 13.0f), std::complex(4.0f, 14.0f), + std::complex(5.0f, 15.0f), std::complex(6.0f, 16.0f)}); + m.Invoke(); + EXPECT_THAT( + m.ExtractVector>(m.output()), + ElementsAreArray( + {std::complex(1.0f, 11.0f), std::complex(2.0f, 12.0f), + std::complex(3.0f, 13.0f), std::complex(4.0f, 14.0f), + std::complex(5.0f, 15.0f), + std::complex(6.0f, 16.0f)})); +} + } // namespace } // namespace tflite int main(int argc, char** argv) { diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index ee42e5cdc838fac4bf9a3de15b7e95e001588907..0321b2e2a0088bdb09b2c3c61827be8064fe939b 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -134,7 +134,9 @@ static TfLiteStatus AllocateTemporaryTensorsIfRequired(TfLiteContext* context, // optimized_ops.h, in order to avoid a DCHECK(!im2col_data). data->need_im2col = (params->stride_width != 1 || params->stride_height != 1 || - filter_width != 1 || filter_height != 1); + params->dilation_width_factor != 1 || + params->dilation_height_factor != 1 || filter_width != 1 || + filter_height != 1); // If we're using the optimized multithreaded EigenTensor implementation of // convolution, it expects the filter weights to be transposed compared to // the normal TF Lite buffer format. Typical TF Lite weights are @@ -177,9 +179,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_STATUS(AllocateTemporaryTensorsIfRequired(context, node)); - bool hasBias = node->inputs->size == 3; + bool has_bias = node->inputs->size == 3; // Check number of inputs/outputs - TF_LITE_ENSURE(context, hasBias || node->inputs->size == 2); + TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; TfLiteTensor* input = &context->tensors[node->inputs->data[0]]; @@ -202,9 +204,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // TODO(ahentz): At this point the optimized versions require 'bias'. We can // either change that or document that convolution requires it. - TF_LITE_ENSURE(context, hasBias); + TF_LITE_ENSURE(context, has_bias); - if (hasBias) { + if (has_bias) { bias = &context->tensors[node->inputs->data[2]]; if (data_type == kTfLiteUInt8) { TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteInt32); @@ -224,29 +226,30 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Matching GetWindowedOutputSize in TensorFlow. auto padding = params->padding; - auto computeOutSize = [padding](int imageSize, int filterSize, int stride, - int dilationRate) -> int { - int effectiveFilterSize = (filterSize - 1) * dilationRate + 1; + auto compute_out_size = [padding](int image_size, int filter_size, int stride, + int dilation_rate) -> int { + int effective_filter_size = (filter_size - 1) * dilation_rate + 1; return padding == kTfLitePaddingSame - ? (imageSize + stride - 1) / stride + ? (image_size + stride - 1) / stride : padding == kTfLitePaddingValid - ? (imageSize - effectiveFilterSize + stride) / stride + ? (image_size - effective_filter_size + stride) / stride : 0; }; - int outWidth = computeOutSize(width, filter_width, params->stride_width, - params->dilation_width_factor); - int outHeight = computeOutSize(height, filter_height, params->stride_height, - params->dilation_height_factor); + int out_width = compute_out_size(width, filter_width, params->stride_width, + params->dilation_width_factor); + int out_height = + compute_out_size(height, filter_height, params->stride_height, + params->dilation_height_factor); data->padding.height = ComputePadding(params->stride_height, params->dilation_height_factor, - height, filter_height, outHeight); + height, filter_height, out_height); data->padding.width = ComputePadding(params->stride_width, params->dilation_width_factor, width, - filter_width, outWidth); + filter_width, out_width); - TF_LITE_ENSURE(context, hasBias); + TF_LITE_ENSURE(context, has_bias); // Note that quantized inference requires that all tensors have their // parameters set. This is usually done during quantized training. @@ -255,8 +258,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( context, input, filter, bias, output, &real_multiplier)); TF_LITE_ENSURE(context, real_multiplier < 1.0); - QuantizeMultiplierSmallerThanOne(real_multiplier, &data->output_multiplier, - &data->output_shift); + QuantizeMultiplierSmallerThanOneExp( + real_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; CalculateActivationRangeUint8(params->activation, output, &data->output_activation_min, &data->output_activation_max); @@ -264,8 +268,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArray* output_size = TfLiteIntArrayCreate(4); output_size->data[0] = batches; - output_size->data[1] = outHeight; - output_size->data[2] = outWidth; + output_size->data[1] = out_height; + output_size->data[2] = out_width; output_size->data[3] = channels_out; auto output_status = context->ResizeTensor(context, output, output_size); @@ -305,18 +309,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* hwcn_weights = &context->tensors[node->temporaries->data[data->hwcn_weights_index]]; hwcn_weights->type = data_type; - hwcn_weights->allocation_type = kTfLiteDynamic; - // Make sure we release any previous allocations before we reallocate. - // TODO(petewarden): Persistent arenas would be a better fit for this, but - // they aren't fully implemented yet. - if (hwcn_weights->data.raw) { - free(hwcn_weights->data.raw); - hwcn_weights->data.raw = nullptr; - } + hwcn_weights->allocation_type = kTfLiteArenaRwPersistent; - // Note that hwcn_weights_status is a kTfLiteDynamic tensor, and - // ResizeTensor will actually allocate space for it. The would be more - // efficient if we placed hwcn_weights_status in the persistent arena. auto hwcn_weights_status = context->ResizeTensor(context, hwcn_weights, hwcn_weights_size); if (hwcn_weights_status != kTfLiteOk) return hwcn_weights_status; @@ -378,8 +372,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, TfLiteTensor* filter, TfLiteTensor* bias, TfLiteTensor* im2col, TfLiteTensor* hwcn_weights, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); KernelType effective_kernel_type; if (((kernel_type == kMultithreadOptimized) || (kernel_type == kCblasOptimized)) && @@ -455,9 +449,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; TfLiteTensor* input = &context->tensors[node->inputs->data[0]]; TfLiteTensor* filter = &context->tensors[node->inputs->data[1]]; - bool hasBias = node->inputs->size == 3; + bool has_bias = node->inputs->size == 3; TfLiteTensor* bias = - hasBias ? &context->tensors[node->inputs->data[2]] : nullptr; + has_bias ? &context->tensors[node->inputs->data[2]] : nullptr; TfLiteTensor* im2col = data->need_im2col ? &context->tensors[node->temporaries->data[data->im2col_index]] diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc index a308de055f49eddba99d02e264fad11409a799f4..16e5f1d065d8ea6d187c5e368d6c9385fe62514b 100644 --- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc +++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc @@ -173,8 +173,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input, const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); void (*depthwise_conv)(const float*, const Dims<4>&, const float*, const Dims<4>&, const float*, const Dims<4>&, int, int, diff --git a/tensorflow/contrib/lite/kernels/detection_postprocess.cc b/tensorflow/contrib/lite/kernels/detection_postprocess.cc new file mode 100644 index 0000000000000000000000000000000000000000..0c532cac5a9f59c8b09ff9aefc294e243561f027 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/detection_postprocess.cc @@ -0,0 +1,591 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace custom { +namespace detection_postprocess { + +// Input tensors +constexpr int kInputTensorBoxEncodings = 0; +constexpr int kInputTensorClassPredictions = 1; +constexpr int kInputTensorAnchors = 2; + +// Output tensors +constexpr int kOutputTensorDetectionBoxes = 0; +constexpr int kOutputTensorDetectionClasses = 1; +constexpr int kOutputTensorDetectionScores = 2; +constexpr int kOutputTensorNumDetections = 3; + +constexpr size_t kNumCoordBox = 4; +constexpr size_t kBatchSize = 1; + +// Object Detection model produces axis-aligned boxes in two formats: +// BoxCorner represents the upper right (xmin, ymin) and +// lower left corner (xmax, ymax). +// CenterSize represents the center (xcenter, ycenter), height and width. +// BoxCornerEncoding and CenterSizeEncoding are related as follows: +// ycenter = y / y_scale * anchor.h + anchor.y; +// xcenter = x / x_scale * anchor.w + anchor.x; +// half_h = 0.5*exp(h/ h_scale)) * anchor.h; +// half_w = 0.5*exp(w / w_scale)) * anchor.w; +// ymin = ycenter - half_h +// ymax = ycenter + half_h +// xmin = xcenter - half_w +// xmax = xcenter + half_w +struct BoxCornerEncoding { + float ymin; + float xmin; + float ymax; + float xmax; +}; + +struct CenterSizeEncoding { + float y; + float x; + float h; + float w; +}; +// We make sure that the memory allocations are contiguous with static assert. +static_assert(sizeof(BoxCornerEncoding) == sizeof(float) * kNumCoordBox, + "Size of BoxCornerEncoding is 4 float values"); +static_assert(sizeof(CenterSizeEncoding) == sizeof(float) * kNumCoordBox, + "Size of CenterSizeEncoding is 4 float values"); + +struct OpData { + int max_detections; + int max_classes_per_detection; + float non_max_suppression_score_threshold; + float intersection_over_union_threshold; + int num_classes; + CenterSizeEncoding scale_values; + // Indices of Temporary tensors + int decoded_boxes_index; + int scores_index; + int active_candidate_index; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* op_data = new OpData; + const uint8_t* buffer_t = reinterpret_cast(buffer); + const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap(); + op_data->max_detections = m["max_detections"].AsInt32(); + op_data->max_classes_per_detection = m["max_classes_per_detection"].AsInt32(); + op_data->non_max_suppression_score_threshold = + m["nms_score_threshold"].AsFloat(); + op_data->intersection_over_union_threshold = m["nms_iou_threshold"].AsFloat(); + op_data->num_classes = m["num_classes"].AsInt32(); + op_data->scale_values.y = m["y_scale"].AsFloat(); + op_data->scale_values.x = m["x_scale"].AsFloat(); + op_data->scale_values.h = m["h_scale"].AsFloat(); + op_data->scale_values.w = m["w_scale"].AsFloat(); + context->AddTensors(context, 1, &op_data->decoded_boxes_index); + context->AddTensors(context, 1, &op_data->scores_index); + context->AddTensors(context, 1, &op_data->active_candidate_index); + return op_data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +// TODO(chowdhery): Add to kernel_util.h +TfLiteStatus SetTensorSizes(TfLiteContext* context, TfLiteTensor* tensor, + std::initializer_list values) { + TfLiteIntArray* size = TfLiteIntArrayCreate(values.size()); + int index = 0; + for (int v : values) { + size->data[index] = v; + ++index; + } + return context->ResizeTensor(context, tensor, size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* op_data = reinterpret_cast(node->user_data); + // Inputs: box_encodings, scores, anchors + TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* input_class_predictions = + GetInput(context, node, kInputTensorClassPredictions); + const TfLiteTensor* input_anchors = + GetInput(context, node, kInputTensorAnchors); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_box_encodings), 3); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_class_predictions), 3); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_anchors), 2); + // number of detected boxes + const int num_detected_boxes = + op_data->max_detections * op_data->max_classes_per_detection; + + // Outputs: detection_boxes, detection_scores, detection_classes, + // num_detections + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 4); + // Output Tensor detection_boxes: size is set to (1, num_detected_boxes, 4) + TfLiteTensor* detection_boxes = + GetOutput(context, node, kOutputTensorDetectionBoxes); + detection_boxes->type = kTfLiteFloat32; + SetTensorSizes(context, detection_boxes, + {kBatchSize, num_detected_boxes, kNumCoordBox}); + + // Output Tensor detection_classes: size is set to (1, num_detected_boxes) + TfLiteTensor* detection_classes = + GetOutput(context, node, kOutputTensorDetectionClasses); + detection_classes->type = kTfLiteFloat32; + SetTensorSizes(context, detection_classes, {kBatchSize, num_detected_boxes}); + + // Output Tensor detection_scores: size is set to (1, num_detected_boxes) + TfLiteTensor* detection_scores = + GetOutput(context, node, kOutputTensorDetectionScores); + detection_scores->type = kTfLiteFloat32; + SetTensorSizes(context, detection_scores, {kBatchSize, num_detected_boxes}); + + // Output Tensor num_detections: size is set to 1 + TfLiteTensor* num_detections = + GetOutput(context, node, kOutputTensorNumDetections); + num_detections->type = kTfLiteFloat32; + // TODO (chowdhery): Make it a scalar when available + SetTensorSizes(context, num_detections, {1}); + + // Temporary tensors + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(3); + node->temporaries->data[0] = op_data->decoded_boxes_index; + node->temporaries->data[1] = op_data->scores_index; + node->temporaries->data[2] = op_data->active_candidate_index; + + // decoded_boxes + TfLiteTensor* decoded_boxes = &context->tensors[op_data->decoded_boxes_index]; + decoded_boxes->type = kTfLiteFloat32; + decoded_boxes->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, decoded_boxes, + {input_box_encodings->dims->data[1], kNumCoordBox}); + + // scores + TfLiteTensor* scores = &context->tensors[op_data->scores_index]; + scores->type = kTfLiteFloat32; + scores->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, scores, + {input_class_predictions->dims->data[1], + input_class_predictions->dims->data[2]}); + + // active_candidate + TfLiteTensor* active_candidate = + &context->tensors[op_data->active_candidate_index]; + active_candidate->type = kTfLiteUInt8; + active_candidate->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, active_candidate, + {input_box_encodings->dims->data[1]}); + + return kTfLiteOk; +} + +class Dequantizer { + public: + Dequantizer(int zero_point, float scale) + : zero_point_(zero_point), scale_(scale) {} + float operator()(uint8 x) { + return (static_cast(x) - zero_point_) * scale_; + } + + private: + int zero_point_; + float scale_; +}; + +void DequantizeBoxEncodings(const TfLiteTensor* input_box_encodings, int idx, + float quant_zero_point, float quant_scale, + CenterSizeEncoding* box_centersize) { + const uint8* boxes = + GetTensorData(input_box_encodings) + kNumCoordBox * idx; + Dequantizer dequantize(quant_zero_point, quant_scale); + box_centersize->y = dequantize(boxes[0]); + box_centersize->x = dequantize(boxes[1]); + box_centersize->h = dequantize(boxes[2]); + box_centersize->w = dequantize(boxes[3]); +} + +template +T ReInterpretTensor(const TfLiteTensor* tensor) { + // TODO (chowdhery): check float + const float* tensor_base = tensor->data.f; + return reinterpret_cast(tensor_base); +} + +template +T ReInterpretTensor(TfLiteTensor* tensor) { + // TODO (chowdhery): check float + float* tensor_base = tensor->data.f; + return reinterpret_cast(tensor_base); +} + +TfLiteStatus DecodeCenterSizeBoxes(TfLiteContext* context, TfLiteNode* node, + OpData* op_data) { + // Parse input tensor boxencodings + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[0], kBatchSize); + const int num_boxes = input_box_encodings->dims->data[1]; + TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[2], kNumCoordBox); + const TfLiteTensor* input_anchors = + GetInput(context, node, kInputTensorAnchors); + + // Decode the boxes to get (ymin, xmin, ymax, xmax) based on the anchors + CenterSizeEncoding box_centersize; + CenterSizeEncoding scale_values = op_data->scale_values; + CenterSizeEncoding anchor; + for (int idx = 0; idx < num_boxes; ++idx) { + switch (input_box_encodings->type) { + // Quantized + case kTfLiteUInt8: + DequantizeBoxEncodings( + input_box_encodings, idx, + static_cast(input_box_encodings->params.zero_point), + static_cast(input_box_encodings->params.scale), + &box_centersize); + DequantizeBoxEncodings( + input_anchors, idx, + static_cast(input_anchors->params.zero_point), + static_cast(input_anchors->params.scale), &anchor); + break; + // Float + case kTfLiteFloat32: + box_centersize = ReInterpretTensor( + input_box_encodings)[idx]; + anchor = + ReInterpretTensor(input_anchors)[idx]; + break; + default: + // Unsupported type. + return kTfLiteError; + } + + float ycenter = box_centersize.y / scale_values.y * anchor.h + anchor.y; + float xcenter = box_centersize.x / scale_values.x * anchor.w + anchor.x; + float half_h = + 0.5f * static_cast(std::exp(box_centersize.h / scale_values.h)) * + anchor.h; + float half_w = + 0.5f * static_cast(std::exp(box_centersize.w / scale_values.w)) * + anchor.w; + TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + auto& box = ReInterpretTensor(decoded_boxes)[idx]; + box.ymin = ycenter - half_h; + box.xmin = xcenter - half_w; + box.ymax = ycenter + half_h; + box.xmax = xcenter + half_w; + } + return kTfLiteOk; +} + +void DecreasingPartialArgSort(const float* values, int num_values, + int num_to_sort, int* indices) { + std::iota(indices, indices + num_values, 0); + std::partial_sort( + indices, indices + num_to_sort, indices + num_values, + [&values](const int i, const int j) { return values[i] > values[j]; }); +} + +void SelectDetectionsAboveScoreThreshold(const std::vector& values, + const float threshold, + std::vector* keep_values, + std::vector* keep_indices) { + for (int i = 0; i < values.size(); i++) { + if (values[i] >= threshold) { + keep_values->emplace_back(values[i]); + keep_indices->emplace_back(i); + } + } +} + +bool ValidateBoxes(const TfLiteTensor* decoded_boxes, const int num_boxes) { + for (int i = 0; i < num_boxes; ++i) { + // ymax>=ymin, xmax>=xmin + auto& box = ReInterpretTensor(decoded_boxes)[i]; + if (box.ymin >= box.ymax || box.xmin >= box.xmax) { + return false; + } + } + return true; +} + +float ComputeIntersectionOverUnion(const TfLiteTensor* decoded_boxes, + const int i, const int j) { + auto& box_i = ReInterpretTensor(decoded_boxes)[i]; + auto& box_j = ReInterpretTensor(decoded_boxes)[j]; + const float area_i = (box_i.ymax - box_i.ymin) * (box_i.xmax - box_i.xmin); + const float area_j = (box_j.ymax - box_j.ymin) * (box_j.xmax - box_j.xmin); + if (area_i <= 0 || area_j <= 0) return 0.0; + const float intersection_ymin = std::max(box_i.ymin, box_j.ymin); + const float intersection_xmin = std::max(box_i.xmin, box_j.xmin); + const float intersection_ymax = std::min(box_i.ymax, box_j.ymax); + const float intersection_xmax = std::min(box_i.xmax, box_j.xmax); + const float intersection_area = + std::max(intersection_ymax - intersection_ymin, 0.0) * + std::max(intersection_xmax - intersection_xmin, 0.0); + return intersection_area / (area_i + area_j - intersection_area); +} + +// NonMaxSuppressionSingleClass() is O(n^2) pairwise comparison between boxes +// It assumes all boxes are good in beginning and sorts based on the scores. +// If lower-scoring box has too much overlap with a higher-scoring box, +// we get rid of the lower-scoring box. +TfLiteStatus NonMaxSuppressionSingleClassHelper( + TfLiteContext* context, TfLiteNode* node, OpData* op_data, + const std::vector& scores, std::vector* selected) { + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + const int num_boxes = input_box_encodings->dims->data[1]; + const int max_detections = op_data->max_detections; + const float non_max_suppression_score_threshold = + op_data->non_max_suppression_score_threshold; + const float intersection_over_union_threshold = + op_data->intersection_over_union_threshold; + // Maximum detections should be positive. + TF_LITE_ENSURE(context, (max_detections >= 0)); + // intersection_over_union_threshold should be positive + // and should be less than 1. + TF_LITE_ENSURE(context, (intersection_over_union_threshold > 0.0f) && + (intersection_over_union_threshold <= 1.0f)); + // Validate boxes + TF_LITE_ENSURE(context, ValidateBoxes(decoded_boxes, num_boxes)); + + // threshold scores + std::vector keep_indices; + // TODO (chowdhery): Remove the dynamic allocation and replace it + // with temporaries, esp for std::vector + std::vector keep_scores; + SelectDetectionsAboveScoreThreshold( + scores, non_max_suppression_score_threshold, &keep_scores, &keep_indices); + + int num_scores_kept = keep_scores.size(); + std::vector sorted_indices; + sorted_indices.resize(num_scores_kept); + DecreasingPartialArgSort(keep_scores.data(), num_scores_kept, num_scores_kept, + sorted_indices.data()); + + const int num_boxes_kept = num_scores_kept; + const int output_size = std::min(num_boxes_kept, max_detections); + selected->clear(); + TfLiteTensor* active_candidate = + &context->tensors[op_data->active_candidate_index]; + TF_LITE_ENSURE(context, (active_candidate->dims->data[0]) == num_boxes); + int num_active_candidate = num_boxes_kept; + uint8_t* active_box_candidate = (active_candidate->data.uint8); + for (int row = 0; row < num_boxes_kept; row++) { + active_box_candidate[row] = 1; + } + + for (int i = 0; i < num_boxes_kept; ++i) { + if (num_active_candidate == 0 || selected->size() >= output_size) break; + if (active_box_candidate[i] == 1) { + selected->push_back(keep_indices[sorted_indices[i]]); + active_box_candidate[i] = 0; + num_active_candidate--; + } else { + continue; + } + for (int j = i + 1; j < num_boxes_kept; ++j) { + if (active_box_candidate[j] == 1) { + float intersection_over_union = ComputeIntersectionOverUnion( + decoded_boxes, keep_indices[sorted_indices[i]], + keep_indices[sorted_indices[j]]); + + if (intersection_over_union > intersection_over_union_threshold) { + active_box_candidate[j] = 0; + num_active_candidate--; + } + } + } + } + return kTfLiteOk; +} + +// This function implements a fast version of Non Maximal Suppression for +// multiple classes where +// 1) we keep the top-k scores for each anchor and +// 2) during NMS, each anchor only uses the highest class score for sorting. +// 3) Compared to standard NMS, the worst runtime of this version is O(N^2) +// instead of O(KN^2) where N is the number of anchors and K the number of +// classes. +TfLiteStatus NonMaxSuppressionMultiClassFastHelper(TfLiteContext* context, + TfLiteNode* node, + OpData* op_data, + const float* scores) { + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + + TfLiteTensor* detection_boxes = + GetOutput(context, node, kOutputTensorDetectionBoxes); + TfLiteTensor* detection_classes = + GetOutput(context, node, kOutputTensorDetectionClasses); + TfLiteTensor* detection_scores = + GetOutput(context, node, kOutputTensorDetectionScores); + TfLiteTensor* num_detections = + GetOutput(context, node, kOutputTensorNumDetections); + + const int num_boxes = input_box_encodings->dims->data[1]; + const int num_classes = op_data->num_classes; + const int max_categories_per_anchor = op_data->max_classes_per_detection; + // The row index offset is 1 if background class is included and 0 otherwise. + const int label_offset = 1; + TF_LITE_ENSURE(context, (label_offset != -1)); + TF_LITE_ENSURE(context, (max_categories_per_anchor > 0)); + const int num_classes_with_background = num_classes + label_offset; + const int num_categories_per_anchor = + std::min(max_categories_per_anchor, num_classes); + std::vector max_scores; + max_scores.resize(num_boxes); + std::vector sorted_class_indices; + sorted_class_indices.resize(num_boxes * num_classes); + for (int row = 0; row < num_boxes; row++) { + const float* box_scores = + scores + row * num_classes_with_background + label_offset; + int* class_indices = sorted_class_indices.data() + row * num_classes; + DecreasingPartialArgSort(box_scores, num_classes, num_categories_per_anchor, + class_indices); + max_scores[row] = box_scores[class_indices[0]]; + } + // Perform non-maximal suppression on max scores + std::vector selected; + NonMaxSuppressionSingleClassHelper(context, node, op_data, max_scores, + &selected); + // Allocate output tensors + int output_box_index = 0; + for (const auto& selected_index : selected) { + const float* box_scores = + scores + selected_index * num_classes_with_background + label_offset; + const int* class_indices = + sorted_class_indices.data() + selected_index * num_classes; + + for (int col = 0; col < num_categories_per_anchor; ++col) { + int box_offset = num_categories_per_anchor * output_box_index + col; + // detection_boxes + ReInterpretTensor(detection_boxes)[box_offset] = + ReInterpretTensor( + decoded_boxes)[selected_index]; + // detection_classes + detection_classes->data.f[box_offset] = class_indices[col]; + // detection_scores + detection_scores->data.f[box_offset] = box_scores[class_indices[col]]; + output_box_index++; + } + } + num_detections->data.f[0] = output_box_index; + return kTfLiteOk; +} + +void DequantizeClassPredictions(const TfLiteTensor* input_class_predictions, + const int num_boxes, + const int num_classes_with_background, + const TfLiteTensor* scores) { + float quant_zero_point = + static_cast(input_class_predictions->params.zero_point); + float quant_scale = static_cast(input_class_predictions->params.scale); + Dequantizer dequantize(quant_zero_point, quant_scale); + const uint8* scores_quant = GetTensorData(input_class_predictions); + for (int idx = 0; idx < num_boxes * num_classes_with_background; ++idx) { + scores->data.f[idx] = dequantize(scores_quant[idx]); + } +} + +TfLiteStatus NonMaxSuppressionMultiClass(TfLiteContext* context, + TfLiteNode* node, OpData* op_data) { + // Get the input tensors + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* input_class_predictions = + GetInput(context, node, kInputTensorClassPredictions); + const int num_boxes = input_box_encodings->dims->data[1]; + const int num_classes = op_data->num_classes; + TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[0], + kBatchSize); + TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[1], num_boxes); + const int num_classes_with_background = + input_class_predictions->dims->data[2]; + + TF_LITE_ENSURE(context, (num_classes_with_background == num_classes + 1)); + + const TfLiteTensor* scores; + switch (input_class_predictions->type) { + case kTfLiteUInt8: { + TfLiteTensor* temporary_scores = &context->tensors[op_data->scores_index]; + DequantizeClassPredictions(input_class_predictions, num_boxes, + num_classes_with_background, temporary_scores); + scores = temporary_scores; + } break; + case kTfLiteFloat32: + scores = input_class_predictions; + break; + default: + // Unsupported type. + return kTfLiteError; + } + NonMaxSuppressionMultiClassFastHelper(context, node, op_data, + GetTensorData(scores)); + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + // TODO(chowdhery): Generalize for any batch size + TF_LITE_ENSURE(context, (kBatchSize == 1)); + auto* op_data = reinterpret_cast(node->user_data); + // These two functions correspond to two blocks in the Object Detection model. + // In future, we would like to break the custom op in two blocks, which is + // currently not feasible because we would like to input quantized inputs + // and do all calculations in float. Mixed quantized/float calculations are + // currently not supported in TFLite. + + // This fills in temporary decoded_boxes + // by transforming input_box_encodings and input_anchors from + // CenterSizeEncodings to BoxCornerEncoding + DecodeCenterSizeBoxes(context, node, op_data); + // This fills in the output tensors + // by choosing effective set of decoded boxes + // based on Non Maximal Suppression, i.e. selecting + // highest scoring non-overlapping boxes. + NonMaxSuppressionMultiClass(context, node, op_data); + + return kTfLiteOk; +} +} // namespace detection_postprocess + +TfLiteRegistration* Register_DETECTION_POSTPROCESS() { + static TfLiteRegistration r = {detection_postprocess::Init, + detection_postprocess::Free, + detection_postprocess::Prepare, + detection_postprocess::Eval}; + return &r; +} + +} // namespace custom +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/detection_postprocess_test.cc b/tensorflow/contrib/lite/kernels/detection_postprocess_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4e0f8484a328d7d1668afd096ad3d08204fbb4a1 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/detection_postprocess_test.cc @@ -0,0 +1,235 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace ops { +namespace custom { + +TfLiteRegistration* Register_DETECTION_POSTPROCESS(); + +namespace { + +using ::testing::ElementsAre; +using ::testing::ElementsAreArray; + +class BaseDetectionPostprocessOpModel : public SingleOpModel { + public: + BaseDetectionPostprocessOpModel(const TensorData& input1, + const TensorData& input2, + const TensorData& input3, + const TensorData& output1, + const TensorData& output2, + const TensorData& output3, + const TensorData& output4) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + input3_ = AddInput(input3); + output1_ = AddOutput(output1); + output2_ = AddOutput(output2); + output3_ = AddOutput(output3); + output4_ = AddOutput(output4); + + flexbuffers::Builder fbb; + fbb.Map([&]() { + fbb.Int("max_detections", 3); + fbb.Int("max_classes_per_detection", 1); + fbb.Float("nms_score_threshold", 0.0); + fbb.Float("nms_iou_threshold", 0.5); + fbb.Int("num_classes", 2); + fbb.Float("y_scale", 10.0); + fbb.Float("x_scale", 10.0); + fbb.Float("h_scale", 5.0); + fbb.Float("w_scale", 5.0); + }); + fbb.Finish(); + SetCustomOp("TFLite_Detection_PostProcess", fbb.GetBuffer(), + Register_DETECTION_POSTPROCESS); + BuildInterpreter({GetShape(input1_), GetShape(input2_), GetShape(input3_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + int input3() { return input3_; } + + template + void SetInput1(std::initializer_list data) { + PopulateTensor(input1_, data); + } + + template + void SetInput2(std::initializer_list data) { + PopulateTensor(input2_, data); + } + + template + void SetInput3(std::initializer_list data) { + PopulateTensor(input3_, data); + } + + template + std::vector GetOutput1() { + return ExtractVector(output1_); + } + + template + std::vector GetOutput2() { + return ExtractVector(output2_); + } + + template + std::vector GetOutput3() { + return ExtractVector(output3_); + } + + template + std::vector GetOutput4() { + return ExtractVector(output4_); + } + + std::vector GetOutputShape1() { return GetTensorShape(output1_); } + std::vector GetOutputShape2() { return GetTensorShape(output2_); } + std::vector GetOutputShape3() { return GetTensorShape(output3_); } + std::vector GetOutputShape4() { return GetTensorShape(output4_); } + + protected: + int input1_; + int input2_; + int input3_; + int output1_; + int output2_; + int output3_; + int output4_; +}; + +TEST(DetectionPostprocessOpTest, FloatTest) { + BaseDetectionPostprocessOpModel m( + {TensorType_FLOAT32, {1, 6, 4}}, {TensorType_FLOAT32, {1, 6, 3}}, + {TensorType_FLOAT32, {6, 4}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}); + + // six boxes in center-size encoding + m.SetInput1({0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, + 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}); + // class scores - two classes with background + m.SetInput2({0., .9, .8, 0., .75, .72, 0., .6, .5, 0., .93, .95, 0., + .5, .4, 0., .3, .2}); + // six anchors in center-size encoding + m.SetInput3({0.5, 0.5, 1.0, 1.0, 0.5, 0.5, 1.0, 1.0, + 0.5, 0.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, + 0.5, 10.5, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}); + // Same boxes in box-corner encoding: + // { 0.0, 0.0, 1.0, 1.0, + // 0.0, 0.1, 1.0, 1.1, + // 0.0, -0.1, 1.0, 0.9, + // 0.0, 10.0, 1.0, 11.0, + // 0.0, 10.1, 1.0, 11.1, + // 0.0, 100.0, 1.0, 101.0} + m.Invoke(); + // detection_boxes + // in center-size + std::vector output_shape1 = m.GetOutputShape1(); + EXPECT_THAT(output_shape1, ElementsAre(1, 3, 4)); + EXPECT_THAT( + m.GetOutput1(), + ElementsAreArray(ArrayFloatNear( + {0.0, 10.0, 1.0, 11.0, 0.0, 0.0, 1.0, 1.0, 0.0, 100.0, 1.0, 101.0}, + 1e-1))); + // detection_classes + std::vector output_shape2 = m.GetOutputShape2(); + EXPECT_THAT(output_shape2, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput2(), + ElementsAreArray(ArrayFloatNear({1, 0, 0}, 1e-1))); + // detection_scores + std::vector output_shape3 = m.GetOutputShape3(); + EXPECT_THAT(output_shape3, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput3(), + ElementsAreArray(ArrayFloatNear({0.95, 0.9, 0.3}, 1e-1))); + // num_detections + std::vector output_shape4 = m.GetOutputShape4(); + EXPECT_THAT(output_shape4, ElementsAre(1)); + EXPECT_THAT(m.GetOutput4(), + ElementsAreArray(ArrayFloatNear({3.0}, 1e-1))); +} + +TEST(DetectionPostprocessOpTest, QuantizedTest) { + BaseDetectionPostprocessOpModel m( + {TensorType_UINT8, {1, 6, 4}, -1.0, 1.0}, + {TensorType_UINT8, {1, 6, 3}, 0.0, 1.0}, + {TensorType_UINT8, {6, 4}, 0.0, 100.5}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}); + // six boxes in center-size encoding + std::vector> inputs1 = { + {0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}}; + m.QuantizeAndPopulate(m.input1(), inputs1[0]); + // class scores - two classes with background + std::vector> inputs2 = { + {0., .9, .8, 0., .75, .72, 0., .6, .5, 0., .93, .95, 0., .5, .4, 0., .3, + .2}}; + m.QuantizeAndPopulate(m.input2(), inputs2[0]); + // six anchors in center-size encoding + std::vector> inputs3 = { + {0.5, 0.5, 1.0, 1.0, 0.5, 0.5, 1.0, 1.0, 0.5, 0.5, 1.0, 1.0, + 0.5, 10.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}}; + m.QuantizeAndPopulate(m.input3(), inputs3[0]); + m.Invoke(); + // detection_boxes + // in center-size + std::vector output_shape1 = m.GetOutputShape1(); + EXPECT_THAT(output_shape1, ElementsAre(1, 3, 4)); + EXPECT_THAT( + m.GetOutput1(), + ElementsAreArray(ArrayFloatNear( + {0.0, 10.0, 1.0, 11.0, 0.0, 0.0, 1.0, 1.0, 0.0, 100.0, 1.0, 101.0}, + 3e-1))); + // detection_classes + std::vector output_shape2 = m.GetOutputShape2(); + EXPECT_THAT(output_shape2, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput2(), + ElementsAreArray(ArrayFloatNear({1, 0, 0}, 1e-1))); + // detection_scores + std::vector output_shape3 = m.GetOutputShape3(); + EXPECT_THAT(output_shape3, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput3(), + ElementsAreArray(ArrayFloatNear({0.95, 0.9, 0.3}, 1e-1))); + // num_detections + std::vector output_shape4 = m.GetOutputShape4(); + EXPECT_THAT(output_shape4, ElementsAre(1)); + EXPECT_THAT(m.GetOutput4(), + ElementsAreArray(ArrayFloatNear({3.0}, 1e-1))); +} +} // namespace +} // namespace custom +} // namespace ops +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/div.cc b/tensorflow/contrib/lite/kernels/div.cc index d264821e30cf622ff5d3d8ad513add46caa9e7ae..bc5c3783fd63451fd6d600df2d8e93f740c68e95 100644 --- a/tensorflow/contrib/lite/kernels/div.cc +++ b/tensorflow/contrib/lite/kernels/div.cc @@ -83,8 +83,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); #define TF_LITE_DIV(type, opname) \ type::opname(GetTensorData(input1), GetTensorDims(input1), \ GetTensorData(input2), GetTensorDims(input2), \ diff --git a/tensorflow/contrib/lite/kernels/elementwise.cc b/tensorflow/contrib/lite/kernels/elementwise.cc index 0bd504695074011efd946f4c4d1f8d4854e82730..59bab3c4ecd20bf938919ca606a5933f3112f233 100644 --- a/tensorflow/contrib/lite/kernels/elementwise.cc +++ b/tensorflow/contrib/lite/kernels/elementwise.cc @@ -23,7 +23,7 @@ namespace ops { namespace builtin { namespace elementwise { -TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) { +TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); const TfLiteTensor* input = GetInput(context, node, 0); @@ -35,7 +35,8 @@ TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArrayCopy(input->dims)); } -TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { +inline TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, + float float_func(float)) { const TfLiteTensor* input = GetInput(context, node, 0); TfLiteTensor* output = GetOutput(context, node, 0); switch (input->type) { @@ -44,7 +45,7 @@ TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { const float* in = GetTensorData(input); const float* in_end = in + elements; float* out = output->data.f; - for (; in < in_end; in++, out++) *out = std::sin(*in); + for (; in < in_end; in++, out++) *out = float_func(*in); return kTfLiteOk; } default: { @@ -55,14 +56,48 @@ TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { } } +TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, std::sin); +} + +TfLiteStatus LogEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, std::log); +} + +TfLiteStatus SqrtEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, std::sqrt); +} + +TfLiteStatus RsqrtEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, [](float f) { return 1.f / std::sqrt(f); }); +} + } // namespace elementwise TfLiteRegistration* Register_SIN() { - static TfLiteRegistration r = {nullptr, nullptr, elementwise::SinPrepare, + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, elementwise::SinEval}; return &r; } +TfLiteRegistration* Register_LOG() { + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, + elementwise::LogEval}; + return &r; +} + +TfLiteRegistration* Register_SQRT() { + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, + elementwise::SqrtEval}; + return &r; +} + +TfLiteRegistration* Register_RSQRT() { + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, + elementwise::RsqrtEval}; + return &r; +} + } // namespace builtin } // namespace ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/elementwise_test.cc b/tensorflow/contrib/lite/kernels/elementwise_test.cc index 412ffb04b90fbc24d232d25d2a86ce639752c3e8..ce4c602ee5c788d67701af3ecd3e023f2b25aae7 100644 --- a/tensorflow/contrib/lite/kernels/elementwise_test.cc +++ b/tensorflow/contrib/lite/kernels/elementwise_test.cc @@ -24,12 +24,13 @@ namespace { using ::testing::ElementsAreArray; -class SinOpModel : public SingleOpModel { +class ElementWiseOpModel : public SingleOpModel { public: - SinOpModel(std::initializer_list input_shape) { + ElementWiseOpModel(BuiltinOperator op, + std::initializer_list input_shape) { input_ = AddInput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); - SetBuiltinOp(BuiltinOperator_SIN, BuiltinOptions_NONE, 0); + SetBuiltinOp(op, BuiltinOptions_NONE, 0); BuildInterpreter({input_shape}); } @@ -42,7 +43,7 @@ class SinOpModel : public SingleOpModel { }; TEST(ElementWise, Sin) { - SinOpModel m({1, 1, 4, 1}); + ElementWiseOpModel m(BuiltinOperator_SIN, {1, 1, 4, 1}); m.PopulateTensor(m.input(), {0, 3.1415926, -3.1415926, 1}); m.Invoke(); EXPECT_THAT(m.ExtractVector(m.output()), @@ -50,6 +51,33 @@ TEST(ElementWise, Sin) { EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); } +TEST(ElementWise, Log) { + ElementWiseOpModel m(BuiltinOperator_LOG, {1, 1, 4, 1}); + m.PopulateTensor(m.input(), {1, 3.1415926, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray(ArrayFloatNear({0, 1.14473, 0, 0}))); + EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); +} + +TEST(ElementWise, Sqrt) { + ElementWiseOpModel m(BuiltinOperator_SQRT, {1, 1, 4, 1}); + m.PopulateTensor(m.input(), {0, 1, 2, 4}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray(ArrayFloatNear({0, 1, 1.41421, 2}))); + EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); +} + +TEST(ElementWise, Rsqrt) { + ElementWiseOpModel m(BuiltinOperator_RSQRT, {1, 1, 4, 1}); + m.PopulateTensor(m.input(), {1, 2, 4, 9}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray(ArrayFloatNear({1, 0.7071, 0.5, 0.33333}))); + EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup.cc b/tensorflow/contrib/lite/kernels/embedding_lookup.cc index 7539c0b30ded921df957217bebdc7b20ea4b40b4..0ba170a4da7b7f0d7afa8b425027b03185d3a559 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup.cc @@ -24,7 +24,8 @@ limitations under the License. // Output: // Output.dim[0] == Tensor[0].dim[0], num of lookups // Output.dim[1] == Tensor[1].dim[1], num of items per row -// Each item in output is a raw bytes copy of corresponding item in input. +// Each item in output is a raw bytes copy of the corresponding item in input, +// or a dequantized value in the case of a uint8 input. // When indices are out of bound, the ops will not succeed. // @@ -69,11 +70,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return context->ResizeTensor(context, output, outputSize); } -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TfLiteTensor* output = GetOutput(context, node, 0); - const TfLiteTensor* lookup = GetInput(context, node, 0); - const TfLiteTensor* value = GetInput(context, node, 1); - +TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, + const TfLiteTensor* lookup, const TfLiteTensor* value, + TfLiteTensor* output) { const int row_size = SizeOfDimension(value, 0); const int row_bytes = value->bytes / row_size; @@ -91,6 +90,52 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +TfLiteStatus EvalHybrid(TfLiteContext* context, TfLiteNode* node, + const TfLiteTensor* lookup, const TfLiteTensor* value, + TfLiteTensor* output) { + const int row_size = SizeOfDimension(value, 0); + const double scaling_factor = value->params.scale; + + // col_size after we flatten tensor into 2D. + int col_size = 1; + for (int i = 1; i < NumDimensions(value); i++) { + col_size *= SizeOfDimension(value, i); + } + + for (int i = 0; i < SizeOfDimension(lookup, 0); i++) { + int idx = lookup->data.i32[i]; + if (idx >= row_size || idx < 0) { + context->ReportError(context, "Embedding Lookup: index out of bounds."); + return kTfLiteError; + } else { + // Dequantize embedding values. + // TODO(alanchiao): refactor scalar multiply into separate function + // for ease of adding a neon equivalent if ever necessary. + for (int j = 0; j < col_size; j++) { + output->data.f[j + i * col_size] = + value->data.uint8[j + idx * col_size] * scaling_factor; + } + } + } + + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const TfLiteTensor* lookup = GetInput(context, node, 0); + const TfLiteTensor* value = GetInput(context, node, 1); + TfLiteTensor* output = GetOutput(context, node, 0); + switch (value->type) { + case kTfLiteFloat32: + return EvalFloat(context, node, lookup, value, output); + case kTfLiteUInt8: + return EvalHybrid(context, node, lookup, value, output); + default: + context->ReportError(context, "Type not currently supported."); + return kTfLiteError; + } +} + } // namespace embedding_lookup TfLiteRegistration* Register_EMBEDDING_LOOKUP() { diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc b/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc index 9b501878f196216a61568bfa36e6615f4dd07478..04657fd86323ef1c58d069c06097c7665f55cc87 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc @@ -7,13 +7,14 @@ You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License +for the specific language governing permissions and limitations under the +License. ==============================================================================*/ // Unit test for TFLite Lookup op. +#include #include #include @@ -29,12 +30,13 @@ namespace { using ::testing::ElementsAreArray; -class EmbeddingLookupOpModel : public SingleOpModel { +class BaseEmbeddingLookupOpModel : public SingleOpModel { public: - EmbeddingLookupOpModel(std::initializer_list index_shape, - std::initializer_list weight_shape) { + BaseEmbeddingLookupOpModel(std::initializer_list index_shape, + std::initializer_list weight_shape, + TensorType weight_type = TensorType_FLOAT32) { input_ = AddInput(TensorType_INT32); - weight_ = AddInput(TensorType_FLOAT32); + weight_ = AddInput(weight_type); output_ = AddOutput(TensorType_FLOAT32); SetBuiltinOp(BuiltinOperator_EMBEDDING_LOOKUP, BuiltinOptions_NONE, 0); BuildInterpreter({index_shape, weight_shape}); @@ -44,6 +46,18 @@ class EmbeddingLookupOpModel : public SingleOpModel { PopulateTensor(input_, data); } + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int weight_; + int output_; +}; + +class EmbeddingLookupOpModel : public BaseEmbeddingLookupOpModel { + public: + using BaseEmbeddingLookupOpModel::BaseEmbeddingLookupOpModel; + void Set3DWeightMatrix(const std::function& function) { TfLiteTensor* tensor = interpreter_->tensor(weight_); int rows = tensor->dims->data[0]; @@ -57,20 +71,25 @@ class EmbeddingLookupOpModel : public SingleOpModel { } } } +}; - std::vector GetOutput() { return ExtractVector(output_); } +class HybridEmbeddingLookupOpModel : public BaseEmbeddingLookupOpModel { + public: + HybridEmbeddingLookupOpModel(std::initializer_list index_shape, + std::initializer_list weight_shape) + : BaseEmbeddingLookupOpModel(index_shape, weight_shape, + TensorType_UINT8) {} - private: - int input_; - int weight_; - int output_; + void SetWeight(std::initializer_list data) { + SymmetricQuantizeAndPopulate(weight_, data); + } }; // TODO(ahentz): write more tests that exercise the details of the op, such as // lookup errors and variable input shapes. TEST(EmbeddingLookupOpTest, SimpleTest) { EmbeddingLookupOpModel m({3}, {3, 2, 4}); - m.PopulateTensor(0, {1, 0, 2}); + m.SetInput({1, 0, 2}); m.Set3DWeightMatrix( [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; }); @@ -84,6 +103,69 @@ TEST(EmbeddingLookupOpTest, SimpleTest) { }))); } +TEST(HybridEmbeddingLookupHybridOpTest, Simple2DTest) { + HybridEmbeddingLookupOpModel m({3}, {3, 8}); + m.SetInput({1, 0, 2}); + m.SetWeight({ + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + { + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }, + 7.41e-03))); +} + +TEST(HybridEmbeddingLookupHybridOpTest, Simple3DTest) { + HybridEmbeddingLookupOpModel m({3}, {3, 2, 4}); + m.SetInput({1, 0, 2}); + m.SetWeight({ + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + { + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }, + 7.41e-03))); +} + +TEST(HybridEmbeddingLookupHybridOpTest, Simple4DTest) { + HybridEmbeddingLookupOpModel m({3}, {3, 2, 2, 2}); + m.SetInput({1, 0, 2}); + m.SetWeight({ + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + { + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }, + 7.41e-03))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/expand_dims_test.cc b/tensorflow/contrib/lite/kernels/expand_dims_test.cc index b755e8ce293442813b26ec3177162a3c95af2f89..50dc860e5a83f185abc70a844abdbc974f7bc4e7 100644 --- a/tensorflow/contrib/lite/kernels/expand_dims_test.cc +++ b/tensorflow/contrib/lite/kernels/expand_dims_test.cc @@ -39,7 +39,7 @@ class ExpandDimsOpModel : public SingleOpModel { void SetInputFloat(std::initializer_list data) { PopulateTensor(input_, data); } - void SetAxis(int axis) { PopulateTensor(axis_, {axis}); } + void SetAxis(int axis) { PopulateTensor(axis_, {axis}); } std::vector GetValuesFloat() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } @@ -51,7 +51,7 @@ class ExpandDimsOpModel : public SingleOpModel { TEST(ExpandDimsOpTest, DifferentAxis) { ExpandDimsOpModel m({2, 2}, TensorType_FLOAT32); - const auto values = {-1.f, 1.f, -2.f, 2.f}; + std::initializer_list values = {-1.f, 1.f, -2.f, 2.f}; m.SetInputFloat(values); m.SetAxis(0); m.Invoke(); diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index 989920622dff1fe246efb920e0d18efa5f8e9215..3b203dd480f95c5dc70a69aafce0bac6ab2cbc06 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -63,6 +63,7 @@ constexpr int kInputTensor = 0; constexpr int kWeightsTensor = 1; constexpr int kBiasTensor = 2; constexpr int kOutputTensor = 0; +constexpr int kShuffledInputWorkspaceTensor = 1; constexpr int kScratchBufferTensor = 1; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -87,7 +88,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check we have all the inputs and outputs we need. TF_LITE_ENSURE_EQ(context, node->inputs->size, 3); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); + // Shuffled formats need a workspace to store the shuffled input activations. + const int expected_outputs_count = + params->weights_format == kTfLiteFullyConnectedWeightsFormatDefault ? 1 + : 2; + TF_LITE_ENSURE_EQ(context, node->outputs->size, expected_outputs_count); const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor); @@ -105,7 +110,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int batch_size = input_size / filter->dims->data[1]; const int num_units = filter->dims->data[0]; - TF_LITE_ASSERT_EQ(input_size, batch_size * filter->dims->data[1]); + TF_LITE_ENSURE_EQ(context, input_size, batch_size * filter->dims->data[1]); if (bias) { TF_LITE_ENSURE_EQ(context, NumElements(bias), SizeOfDimension(filter, 0)); } @@ -118,11 +123,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( context, input, filter, bias, output, &real_multiplier)); TF_LITE_ENSURE(context, real_multiplier < 1.0); - QuantizeMultiplierSmallerThanOne(real_multiplier, &data->output_multiplier, - &data->output_shift); - CalculateActivationRangeUint8(params->activation, output, - &data->output_activation_min, - &data->output_activation_max); + QuantizeMultiplierSmallerThanOneExp( + real_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; + TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( + context, params->activation, output, &data->output_activation_min, + &data->output_activation_max)); } // If we have to perform on-the-fly quantization (with quantized weights and @@ -277,44 +283,101 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t input_offset = -input->params.zero_point; int32_t filter_offset = -filter->params.zero_point; int32_t output_offset = output->params.zero_point; -#define TF_LITE_FULLY_CONNECTED(type) \ +#define TF_LITE_FULLY_CONNECTED(type, output_data_type) \ type::FullyConnected( \ GetTensorData(input), GetTensorDims(input), input_offset, \ GetTensorData(filter), GetTensorDims(filter), filter_offset, \ GetTensorData(bias), GetTensorDims(bias), output_offset, \ data->output_multiplier, data->output_shift, \ data->output_activation_min, data->output_activation_max, \ - GetTensorData(output), GetTensorDims(output), gemm_context) + GetTensorData(output), GetTensorDims(output), \ + gemm_context) if (kernel_type == kReference) { - TF_LITE_FULLY_CONNECTED(reference_ops); - } else if (kernel_type == kPie) { - if (input->type == kTfLiteFloat32) { - // Pie currently only supports quantized models and float inputs/outputs. - TfLiteTensor* input_quantized = - &context->tensors[node->temporaries->data[0]]; - return EvalPieQuantized(context, node, params, data, input, filter, bias, - input_quantized, output); - } else { - // TODO(ahentz): we don't have a quantized version of the PIE kernels, so - // we just defer to the MINI ones. - TF_LITE_FULLY_CONNECTED(optimized_ops); + switch (output->type) { + case kTfLiteUInt8: + TF_LITE_FULLY_CONNECTED(reference_ops, uint8_t); + break; + case kTfLiteInt16: + TF_LITE_FULLY_CONNECTED(reference_ops, int16_t); + break; + default: + context->ReportError( + context, + "Quantized FullyConnected expects output data type uint8 or int16"); + return kTfLiteError; } + } else if (kernel_type == kPie && input->type == kTfLiteFloat32) { + // Pie currently only supports quantized models and float inputs/outputs. + TfLiteTensor* input_quantized = + &context->tensors[node->temporaries->data[0]]; + return EvalPieQuantized(context, node, params, data, input, filter, bias, + input_quantized, output); } else { - TF_LITE_FULLY_CONNECTED(optimized_ops); + switch (output->type) { + case kTfLiteUInt8: + TF_LITE_FULLY_CONNECTED(optimized_ops, uint8_t); + break; + case kTfLiteInt16: + TF_LITE_FULLY_CONNECTED(optimized_ops, int16_t); + break; + default: + context->ReportError( + context, + "Quantized FullyConnected expects output data type uint8 or int16"); + return kTfLiteError; + } } #undef TF_LITE_FULLY_CONNECTED return kTfLiteOk; } +template +TfLiteStatus EvalShuffledQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteFullyConnectedParams* params, + OpData* data, const TfLiteTensor* input, + const TfLiteTensor* filter, + const TfLiteTensor* bias, + TfLiteTensor* output, + TfLiteTensor* shuffled_input_workspace) { + gemmlowp::GemmContext* gemm_context = gemm_support::GetFromContext(context); + + // TODO(b/110697972) decide more consistently if / how / where we want + // to perform this kind of runtime data type checks. + if (input->type != kTfLiteUInt8 || filter->type != kTfLiteUInt8 || + bias->type != kTfLiteInt32 || output->type != kTfLiteInt16 || + shuffled_input_workspace->type != kTfLiteUInt8) { + context->ReportError(context, "Unexpected data type"); + return kTfLiteError; + } + +#define TF_LITE_SHUFFLED_FULLY_CONNECTED(type) \ + type::ShuffledFullyConnected( \ + GetTensorData(input), GetTensorDims(input), \ + GetTensorData(filter), GetTensorDims(filter), \ + GetTensorData(bias), GetTensorDims(bias), \ + data->output_multiplier, data->output_shift, \ + data->output_activation_min, data->output_activation_max, \ + GetTensorData(output), GetTensorDims(output), \ + GetTensorData(shuffled_input_workspace), gemm_context) + if (kernel_type == kReference) { + TF_LITE_SHUFFLED_FULLY_CONNECTED(reference_ops); + } else { + TF_LITE_SHUFFLED_FULLY_CONNECTED(optimized_ops); + } +#undef TF_LITE_SHUFFLED_FULLY_CONNECTED + + return kTfLiteOk; +} + template TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, TfLiteFullyConnectedParams* params, OpData* data, const TfLiteTensor* input, const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); #define TF_LITE_FULLY_CONNECTED(type) \ type::FullyConnected(GetTensorData(input), GetTensorDims(input), \ GetTensorData(filter), GetTensorDims(filter), \ @@ -351,8 +414,22 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return EvalFloat(context, node, params, data, input, filter, bias, output); case kTfLiteUInt8: - return EvalQuantized(context, node, params, data, input, - filter, bias, output); + if (params->weights_format == + kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8) { + TfLiteTensor* shuffled_input_workspace = + GetOutput(context, node, kShuffledInputWorkspaceTensor); + return EvalShuffledQuantized(context, node, params, data, + input, filter, bias, output, + shuffled_input_workspace); + } else if (params->weights_format == + kTfLiteFullyConnectedWeightsFormatDefault) { + return EvalQuantized(context, node, params, data, input, + filter, bias, output); + } else { + context->ReportError(context, + "Unhandled fully-connected weights format"); + return kTfLiteError; + } default: context->ReportError(context, "Type %d not currently supported.", filter->type); diff --git a/tensorflow/contrib/lite/kernels/fully_connected_test.cc b/tensorflow/contrib/lite/kernels/fully_connected_test.cc index 05dd028b484c09bdf90a09fab1238f48e8a9ddab..ec949056971ccb5f7a6f93fa9f236a93625ca6ad 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected_test.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected_test.cc @@ -15,6 +15,7 @@ limitations under the License. // Unit test for TFLite FULLY_CONNECTED op. #include +#include #include #include @@ -133,9 +134,12 @@ static float fully_connected_golden_output[] = { class BaseFullyConnectedOpModel : public SingleOpModel { public: // TODO(ahentz): test different activation types too. - BaseFullyConnectedOpModel(TfLiteRegistration* registration, int units, - int batches, const TensorData& input, - const TensorData& output = {TensorType_FLOAT32}) + BaseFullyConnectedOpModel( + TfLiteRegistration* registration, int units, int batches, + const TensorData& input, const TensorData& output = {TensorType_FLOAT32}, + ActivationFunctionType activation_func = ActivationFunctionType_RELU, + FullyConnectedOptionsWeightsFormat weights_format = + FullyConnectedOptionsWeightsFormat_DEFAULT) : batches_(batches), units_(units) { int total_input_size = 1; for (int i = 0; i < input.shape.size(); ++i) { @@ -159,10 +163,13 @@ class BaseFullyConnectedOpModel : public SingleOpModel { } output_ = AddOutput(output); + if (weights_format != FullyConnectedOptionsWeightsFormat_DEFAULT) { + AddOutput({TensorType_UINT8, input.shape}); + } SetBuiltinOp( BuiltinOperator_FULLY_CONNECTED, BuiltinOptions_FullyConnectedOptions, - CreateFullyConnectedOptions(builder_, ActivationFunctionType_RELU) + CreateFullyConnectedOptions(builder_, activation_func, weights_format) .Union()); resolver_ = absl::make_unique( BuiltinOperator_FULLY_CONNECTED, registration); @@ -188,13 +195,11 @@ class FloatFullyConnectedOpModel : public BaseFullyConnectedOpModel { public: using BaseFullyConnectedOpModel::BaseFullyConnectedOpModel; - void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + void SetBias(const std::vector& f) { PopulateTensor(bias_, f); } - void SetWeights(std::initializer_list f) { - PopulateTensor(weights_, f); - } + void SetWeights(const std::vector& f) { PopulateTensor(weights_, f); } - void SetInput(std::initializer_list data) { + void SetInput(const std::vector& data) { PopulateTensor(input_, data); } void SetInput(int offset, float* begin, float* end) { @@ -208,20 +213,50 @@ class QuantizedFullyConnectedOpModel : public BaseFullyConnectedOpModel { public: using BaseFullyConnectedOpModel::BaseFullyConnectedOpModel; - void SetBias(std::initializer_list data) { + void SetBias(const std::vector& data) { QuantizeAndPopulate(bias_, data); } - void SetWeights(std::initializer_list data) { + void SetWeights(const std::vector& data) { QuantizeAndPopulate(weights_, data); } - void SetInput(std::initializer_list data) { + void ShuffleAndSetWeights(const std::vector& data, int input_depth, + int output_depth) { + std::vector shuffled_data(data.size()); + CHECK_EQ(input_depth % 16, 0); + CHECK_EQ(output_depth % 4, 0); + float* shuffled_data_ptr = shuffled_data.data(); + for (int block_o = 0; block_o < output_depth; block_o += 4) { + for (int block_i = 0; block_i < input_depth; block_i += 16) { + for (int o = 0; o < 4; o++) { + for (int i = 0; i < 16; i++) { + *shuffled_data_ptr++ = + data[(block_o + o) * input_depth + block_i + i]; + } + } + } + } + TfLiteTensor* t = interpreter_->tensor(weights_); + auto quantized_data = + Quantize(shuffled_data, t->params.scale, t->params.zero_point); + for (uint8_t& q : quantized_data) { + q ^= 0x80; + } + PopulateTensor(weights_, 0, quantized_data.data(), + quantized_data.data() + quantized_data.size()); + } + void SetInput(const std::vector& data) { QuantizeAndPopulate(input_, data); } - std::vector GetOutput() { return ExtractVector(output_); } + template + std::vector GetOutput() { + return ExtractVector(output_); + } + + template std::vector GetDequantizedOutput() { - return Dequantize(ExtractVector(output_), - GetScale(output_), GetZeroPoint(output_)); + return Dequantize(ExtractVector(output_), GetScale(output_), + GetZeroPoint(output_)); } }; @@ -256,12 +291,12 @@ class HybridFullyConnectedOpModel : public SingleOpModel { ops::builtin::Register_FULLY_CONNECTED_PIE()); BuildInterpreter({GetShape(input_), GetShape(weights_), GetShape(bias_)}); } - void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } - void SetWeights(std::initializer_list data) { + void SetBias(const std::vector& f) { PopulateTensor(bias_, f); } + void SetWeights(const std::vector& data) { SymmetricQuantizeAndPopulate(weights_, data); } - void SetInput(std::initializer_list f) { PopulateTensor(input_, f); } + void SetInput(const std::vector& f) { PopulateTensor(input_, f); } std::vector GetOutput() { return ExtractVector(output_); } int input_size() { return input_size_; } @@ -340,6 +375,24 @@ TEST_P(FloatFullyConnectedOpTest, SimpleTest) { EXPECT_THAT(m.GetOutput(), ElementsAre(24, 25, 26, 58, 59, 60)); } +TEST_P(FloatFullyConnectedOpTest, SimpleTest2) { + FloatFullyConnectedOpModel m(GetRegistration(), /*units=*/1, /*batches=*/2, + /*input=*/{TensorType_FLOAT32, {2, 2}}); + m.SetWeights({ + 2, 4, // u = 0 + }); + m.SetBias({1}); + + m.SetInput({ + 1, 2, // b = 0 + 2, 1, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(11, 9)); +} + TEST_P(QuantizedFullyConnectedOpTest, SimpleTestQuantized) { QuantizedFullyConnectedOpModel m( GetRegistration(), /*units=*/3, /*batches*/ 2, @@ -350,7 +403,7 @@ TEST_P(QuantizedFullyConnectedOpTest, SimpleTestQuantized) { m.SetWeights({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 2 }); m.SetBias({1, 2, 3}); @@ -361,11 +414,136 @@ TEST_P(QuantizedFullyConnectedOpTest, SimpleTestQuantized) { m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear({ - 24, 25, 26, // - 58, 59, 60, // - }))); - EXPECT_THAT(m.GetOutput(), ElementsAre(151, 152, 153, 185, 186, 187)); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 24, 25, 26, // + 58, 59, 60, // + }))); + EXPECT_THAT(m.GetOutput(), + ElementsAre(151, 152, 153, 185, 186, 187)); +} + +void SimpleTestQuantizedInt16OutputCase( + TfLiteRegistration* registration, int input_depth, int output_depth, + int batches, FullyConnectedOptionsWeightsFormat weights_format) { + const uint8_t kWeightsZeroPoint = 128; + const float kWeightsScale = 1.f / 128.f; + const uint8_t kInputZeroPoint = 128; + const float kInputScale = 1.f / 128.f; + const float kInputMin = (0 - kInputZeroPoint) * kInputScale; + const float kInputMax = (255 - kInputZeroPoint) * kInputScale; + // Output ranges in [-8..8] encoded as int16 + const float kOutputScale = 8.f / 32768.f; + const float kOutputMin = -32768 * kOutputScale; + const float kOutputMax = 32767 * kOutputScale; + + QuantizedFullyConnectedOpModel m( + registration, output_depth, batches, + /*input=*/ + {TensorType_UINT8, {batches, input_depth}, kInputMin, kInputMax}, + /*output=*/{TensorType_INT16, {}, kOutputMin, kOutputMax}, + /*activation_func=*/ActivationFunctionType_NONE, weights_format); + + std::mt19937 random_engine; + std::uniform_int_distribution weights_dist; + + std::vector weights_data(input_depth * output_depth); + for (auto& w : weights_data) { + uint8_t q = weights_dist(random_engine); + w = (q - kWeightsZeroPoint) * kWeightsScale; + } + + // Based on weights_format, enforce any shape requirement for that format/path + // and set the (possibly shuffled) weights. + switch (weights_format) { + case FullyConnectedOptionsWeightsFormat_DEFAULT: + m.SetWeights(weights_data); + break; + case FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8: + // The shuffled path currently supports only a restrictive subset of + // shapes, described by the following assertions: + CHECK_EQ(input_depth % 16, 0); + CHECK_EQ(output_depth % 4, 0); + CHECK(batches == 1 || batches == 4); + m.ShuffleAndSetWeights(weights_data, input_depth, output_depth); + break; + default: + LOG(FATAL) << "Unhandled weights format"; + } + + std::uniform_int_distribution input_dist; + std::vector input_data(input_depth * batches); + for (auto& i : input_data) { + uint8_t q = input_dist(random_engine); + i = (q - kInputZeroPoint) * kInputScale; + } + + std::vector bias_data(output_depth); + // As the output ranges in [-8, 8], it's reasonable to have bias values + // in [-1, 1], this won't result in too much saturation. + std::uniform_real_distribution bias_dist(-1.f, 1.f); + for (auto& b : bias_data) { + b = bias_dist(random_engine); + } + + m.SetBias(bias_data); + m.SetInput(input_data); + + m.Invoke(); + + std::vector expected_output_data(output_depth * batches); + for (int b = 0; b < batches; b++) { + for (int o = 0; o < output_depth; o++) { + float accum = bias_data[o]; + for (int i = 0; i < input_depth; i++) { + accum += + input_data[b * input_depth + i] * weights_data[o * input_depth + i]; + } + accum = std::min(accum, kOutputMax); + accum = std::max(accum, kOutputMin); + expected_output_data[b * output_depth + o] = accum; + } + } + + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear(expected_output_data, 3e-4f))); +} + +TEST_P(QuantizedFullyConnectedOpTest, + SimpleTestQuantizedInt16OutputDefaultWeights) { + for (int input_depth : {1, 3, 10, 100}) { + for (int output_depth : {1, 3, 10, 100}) { + for (int batch : {1, 3, 10, 100}) { + SimpleTestQuantizedInt16OutputCase( + GetRegistration(), input_depth, output_depth, batch, + FullyConnectedOptionsWeightsFormat_DEFAULT); + } + } + } +} + +TEST_P(QuantizedFullyConnectedOpTest, + SimpleTestQuantizedInt16OutputShuffled4x16Int8Weights) { + // The shuffled weights block shape is 4x16. The shape of the weights matrix + // is: rows = output_depth, cols = input_depth. It must be a multiple of 4x16. + // This means that output_depth must be a multiple of 4, and input_deth must + // be a multiple of 16. + for (int input_depth_numblocks : {1, 3}) { + for (int output_depth_numblocks : {1, 3}) { + int input_depth = 16 * input_depth_numblocks; + int output_depth = 4 * output_depth_numblocks; + // The fast shuffled path is currently supporting only batch sizes of 1 + // and 4. The idea is that the whole point of that path is to go as fast + // as possible for small batch size, which requires fully specializing + // it for each batch size, and for larger batch sizes the generic + // gemmlowp-based implementation is fast enough. + for (int batch : {1, 4}) { + SimpleTestQuantizedInt16OutputCase( + GetRegistration(), input_depth, output_depth, batch, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8); + } + } + } } TEST(HybridFullyConnectedOpTest, SimpleTestQuantized) { @@ -396,11 +574,11 @@ TEST(HybridFullyConnectedOpTest, SimpleTestQuantized) { /*max_abs_error=*/1.3f))); } -TEST(FloatFullyConnectedOpTest, SimpleTest4DInput) { +TEST_P(FloatFullyConnectedOpTest, SimpleTest4DInput) { // Note that it is not required that the first dimension be the number of // batches. All we care is that the input can be evenly distributed in // batches. In this case, we need the input to have multiples of '2'. - FloatFullyConnectedOpModel m(ops::builtin::Register_FULLY_CONNECTED_PIE(), + FloatFullyConnectedOpModel m(GetRegistration(), /*units=*/3, /*batches=*/2, /*input=*/{TensorType_FLOAT32, {4, 1, 5, 1}}); m.SetWeights({ @@ -444,11 +622,13 @@ TEST_P(QuantizedFullyConnectedOpTest, SimpleTest4dInputQuantized) { m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear({ - 24, 25, 26, // - 58, 59, 60, // - }))); - EXPECT_THAT(m.GetOutput(), ElementsAre(151, 152, 153, 185, 186, 187)); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 24, 25, 26, // + 58, 59, 60, // + }))); + EXPECT_THAT(m.GetOutput(), + ElementsAre(151, 152, 153, 185, 186, 187)); } INSTANTIATE_TEST_CASE_P( diff --git a/tensorflow/contrib/lite/kernels/gather.cc b/tensorflow/contrib/lite/kernels/gather.cc index 6a2341461f2c627c78bd4783ee27579b59b5fde3..2b2a9e662051287fd1e3dbe8978f4689dc731064 100644 --- a/tensorflow/contrib/lite/kernels/gather.cc +++ b/tensorflow/contrib/lite/kernels/gather.cc @@ -40,10 +40,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = GetOutput(context, node, kOutputTensor); // Only INT32 positions are supported. TF_LITE_ENSURE_EQ(context, positions->type, kTfLiteInt32); - // Check that input and output types match. - TF_LITE_ENSURE_EQ(context, input->type, output->type); - // TODO(mgubin): only 0D or 1D positions are currently supported. - TF_LITE_ENSURE(context, NumDimensions(positions) <= 1); + // Assign to output the input type. + output->type = input->type; // TODO(mgubin): Only default axis == 0 is supported. TF_LITE_ENSURE_EQ(context, params->axis, 0); // Check conditions for different types. @@ -102,6 +100,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_GATHER(int32_t, int32_t); break; case kTfLiteString: { + // TODO(mgubin): Currently support only for 1D output tensors. DynamicBuffer buffer; const int32* indexes = positions->data.i32; const int num_strings = GetStringCount(input); diff --git a/tensorflow/contrib/lite/kernels/gather_test.cc b/tensorflow/contrib/lite/kernels/gather_test.cc index cdadbeda1884ba0186846826dd16be6ff69878d9..1d4292955cced59a47e0500833a86113cb9d3eb8 100644 --- a/tensorflow/contrib/lite/kernels/gather_test.cc +++ b/tensorflow/contrib/lite/kernels/gather_test.cc @@ -96,6 +96,15 @@ TEST(GatherOpTest, Test0DIndexWith0DResult) { EXPECT_TRUE(m.GetOutputShape().empty()); } +TEST(GatherOpTest, Test2DIndexWith2DResult) { + GatherOpModel m({3}, TensorType_FLOAT32, {1, 2}); + m.SetInputFloat({1.0, 2.0, 3.0}); + m.SetPositions({1, 0}); + m.Invoke(); + EXPECT_THAT(m.GetOutputFloat(), ElementsAreArray(ArrayFloatNear({2.0, 1.0}))); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); +} + TEST(FloatGatherOpTest, Duplicate) { GatherOpModel m({1, 2, 2}, TensorType_FLOAT32, {2}); m.SetInputFloat({-2.0, 0.2, 0.7, 0.8}); diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 0a5223b23529ef80b251d5144a94c5969c5cc02c..7962fcbc9d6c839ea11d7355e955239194442e03 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -176,6 +176,40 @@ cc_library( }), ) +cc_library( + name = "legacy_optimized_base", + srcs = [], + hdrs = [ + "common.h", + "optimized/depthwiseconv_float.h", + "optimized/depthwiseconv_uint8.h", + "optimized/depthwiseconv_uint8_3x3_filter.h", + "optimized/legacy_optimized_ops.h", + "optimized/optimized_ops.h", + ], + copts = tflite_copts(), + deps = [ + ":quantization_util", + ":strided_slice_logic", + ":types", + ":legacy_reference_base", + ":round", + "//third_party/eigen3", + "@gemmlowp", + "//tensorflow/contrib/lite:builtin_op_data", + ] + select({ + ":haswell": tflite_deps_intel, + ":ios_x86_64": tflite_deps_intel, + ":k8": tflite_deps_intel, + ":x86": tflite_deps_intel, + ":x86_64": tflite_deps_intel, + ":darwin": tflite_deps_intel, + ":darwin_x86_64": tflite_deps_intel, + ":freebsd": tflite_deps_intel, + "//conditions:default": [], + }), +) + cc_library( name = "optimized", hdrs = [ @@ -273,6 +307,37 @@ cc_library( }), ) +cc_library( + name = "legacy_reference_base", + srcs = [], + hdrs = [ + "common.h", + "reference/depthwiseconv_float.h", + "reference/depthwiseconv_uint8.h", + "reference/legacy_reference_ops.h", + "reference/reference_ops.h", + ], + deps = [ + ":quantization_util", + ":round", + ":strided_slice_logic", + ":types", + "//third_party/eigen3", + "@gemmlowp", + "//tensorflow/contrib/lite:builtin_op_data", + ] + select({ + ":haswell": tflite_deps_intel, + ":ios_x86_64": tflite_deps_intel, + ":k8": tflite_deps_intel, + ":x86": tflite_deps_intel, + ":x86_64": tflite_deps_intel, + ":darwin": tflite_deps_intel, + ":darwin_x86_64": tflite_deps_intel, + ":freebsd": tflite_deps_intel, + "//conditions:default": [], + }), +) + cc_library( name = "reference", hdrs = ["tensor.h"], @@ -474,8 +539,9 @@ cc_test( ) cc_test( - name = "resize_bilinear_float_test", - srcs = ["resize_bilinear_float_test.cc"], + name = "resize_bilinear_test", + srcs = ["resize_bilinear_test.cc"], + tags = ["tflite_not_portable"], deps = [ ":optimized_base", ":reference_base", diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 6e621839753b5dacd96f2615b47e878dbe1de683..a0e382edb6efe467c7b16624cf1760b0d1c6d760 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -350,7 +350,7 @@ void LstmStep( for (int b = 0; b < n_batch; ++b) { product_scaling_factors[b] = - scaling_factors[b] * input_to_cell_weights_scale; + scaling_factors[b] * input_to_output_weights_scale; } tensor_utils::MatrixBatchVectorMultiplyAccumulate( input_to_output_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, @@ -409,14 +409,14 @@ void LstmStep( } // Save quantization and matmul computation for all zero input. - const bool is_cell_state_all_zeros = + bool is_cell_state_all_zeros = tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); // For each batch and cell: update input gate. if (!use_cifg) { if (use_peephole && !is_cell_state_all_zeros) { VectorMultiply(cell_to_input_weights_ptr, n_cell, - 1. / cell_to_input_weights_scale, recovered_cell_weights); + cell_to_input_weights_scale, recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, input_gate_scratch); @@ -428,7 +428,7 @@ void LstmStep( // For each batch and cell: update forget gate. if (use_peephole && !is_cell_state_all_zeros) { VectorMultiply(cell_to_forget_weights_ptr, n_cell, - 1. / cell_to_forget_weights_scale, recovered_cell_weights); + cell_to_forget_weights_scale, recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, forget_gate_scratch); @@ -455,10 +455,12 @@ void LstmStep( params->cell_clip, cell_state_ptr); } + is_cell_state_all_zeros = + tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); // For each batch and cell: update the output gate. if (use_peephole && !is_cell_state_all_zeros) { VectorMultiply(cell_to_output_weights_ptr, n_cell, - 1. / cell_to_output_weights_scale, recovered_cell_weights); + cell_to_output_weights_scale, recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, output_gate_scratch); diff --git a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc index b7531ea2e202cd6fe012e0fa675380775016d38f..d2f1103e14b40b81c59c8053bcdbee30c85e5c78 100644 --- a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunLogSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, - float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, + uint8* reference_output_data) { + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float LogSoftmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::LogSoftmax(reference_dequant_data.data(), dims_common, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::LogSoftmax(reference_dequant_data.data(), shape_common, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for LogSoftmax, under which 255 represents 0, // and -16 gets nudged up to 0. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunLogSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -99,15 +101,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the LogSoftmax and compares against the float reference implementation // and the quantized reference implementation. -void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, - int stride, float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneLogSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_logsoftmax_output(buffer_size); std::vector reference_float_logsoftmax_output(buffer_size); std::vector reference_quant_logsoftmax_output(buffer_size); - RunLogSoftmaxFloatReference(input_data, dims_common, input_offset, + RunLogSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_logsoftmax_output.data()); @@ -116,32 +118,33 @@ void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, int32 reverse_scaling_divisor; int reverse_scaling_right_shift; static const int kScaledDiffIntegerBits = 5; - tflite::PreprocessLogSoftmaxScaling( + tflite::PreprocessLogSoftmaxScalingExp( beta, input_scale, kScaledDiffIntegerBits, &input_beta_multiplier, &input_beta_left_shift, &reverse_scaling_divisor, &reverse_scaling_right_shift); + reverse_scaling_right_shift *= -1; // diff_min has a negative value, and is used to limit the maximum magnitude // of the diffs, which are <= 0. const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::LogSoftmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::LogSoftmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - optimized_logsoftmax_output.data(), dims_common); + optimized_logsoftmax_output.data(), shape_common); reference_ops::LogSoftmax( - input_data, dims_common, input_beta_multiplier, input_beta_left_shift, + input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - reference_quant_logsoftmax_output.data(), dims_common); + reference_quant_logsoftmax_output.data(), shape_common); CheckOutputData(optimized_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_logsoftmax_output.data(), - reference_quant_logsoftmax_output.data(), dims_common, + reference_quant_logsoftmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -164,13 +167,13 @@ bool TryOneUniformLogSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); static constexpr float beta = 1.0f; - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -202,14 +205,14 @@ bool TryOneSkyscraperLogSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h index a7b0d805a3acd35b592a35ba4266dfff4eb992cd..0ce64f8c70d76f970df610f47947580a1efde720 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h @@ -26,7 +26,7 @@ namespace optimized_ops { // Enable for arm64 except for the Nvidia Linux 4 Tegra (L4T) running on // Jetson TX-2. This compiler does not support the offsetof() macro. #if defined(__aarch64__) && !defined(GOOGLE_L4T) - +#include // clang-format gets confused with this file and ends up formatting lines to // be larger than 80 characters. Turn off here and back on at the end of the // file. @@ -3242,6 +3242,7 @@ inline void DepthwiseConv3x3Filter( int32 output_shift, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label(__PRETTY_FUNCTION__); DepthwiseConvParams params; params.input_depth = ArraySize(input_dims, 0); params.input_width = ArraySize(input_dims, 1); diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..7816752132761d9523ffc1f45b3740c0817ed402 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -0,0 +1,324 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ + +#include +#include + +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { +namespace optimized_ops { + +// Unoptimized reference ops: +using reference_ops::Relu1; +using reference_ops::Relu6; + +inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { + return RuntimeShape( + {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); +} + +template +void L2Normalization(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, uint8* output_data, + const Dims<4>& output_dims) { + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); +} + +} // namespace optimized_ops +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index 38ad32c734a2286c7d23162810625169a4d8df43..5ba7e2af9b8f2beeee151e219997b68f5c7a6bce 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -162,7 +162,7 @@ void NeonMatrixBatchVectorMultiplyAccumulate( int batch, row, col; for (batch = 0; batch < n_batch; ++batch) { - const float batch_scaling_factor_inv = 1.0 / scaling_factors[batch]; + const float batch_scaling_factor = scaling_factors[batch]; // Copy the vector data to an aligned vector. memcpy(aligned_vec, vectors + batch * m_cols, sizeof(int8) * m_cols); // Compute dot-product for every column. @@ -232,7 +232,7 @@ void NeonMatrixBatchVectorMultiplyAccumulate( int32 neon_sum = vgetq_lane_s64(pairwiseAdded, 0) + vgetq_lane_s64(pairwiseAdded, 1); - *result += ((neon_sum + postable_sum) * batch_scaling_factor_inv); + *result += ((neon_sum + postable_sum) * batch_scaling_factor); } // for row } // for batch @@ -418,13 +418,14 @@ void NeonSymmetricQuantizeFloats(const float* values, const int size, *scaling_factor = 1; return; } - *scaling_factor = kScale / range; + *scaling_factor = range / kScale; + const float scaling_factor_inv = 1.0f / *scaling_factor; const int postamble_start = size - (size & (2 * kFloatWeightsPerNeonLane - 1)); // Vectorized constants. - const float32x4_t q_factor_f32x4 = vmovq_n_f32(*scaling_factor); + const float32x4_t q_factor_f32x4 = vmovq_n_f32(scaling_factor_inv); const float32x4_t point5_f32x4 = vmovq_n_f32(0.5); const float32x4_t zero_f32x4 = vmovq_n_f32(0.0); const int32x4_t scale_i32x4 = vmovq_n_s32(kScale); @@ -476,7 +477,7 @@ void NeonSymmetricQuantizeFloats(const float* values, const int size, for (int i = postamble_start; i < size; ++i) { const int32 quantized_value = - static_cast(TfLiteRound(*scaling_factor * values[i])); + static_cast(TfLiteRound(scaling_factor_inv * values[i])); quantized_values[i] = std::min(kScale, std::max(-kScale, quantized_value)); } } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 0ce781db59a2cff0e0c199244b867fddf98804d6..8597707b24325588b1b4dc4f4ac68ccfa9cecd36 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -40,16 +40,30 @@ namespace tflite { namespace optimized_ops { // Unoptimized reference ops: +using reference_ops::ArgMax; using reference_ops::BroadcastGreater; using reference_ops::BroadcastGreaterEqual; using reference_ops::BroadcastLess; using reference_ops::BroadcastLessEqual; +using reference_ops::Concatenation; +using reference_ops::DepthConcatenation; +using reference_ops::Dequantize; +using reference_ops::Div; +using reference_ops::FakeQuant; +using reference_ops::Gather; using reference_ops::Greater; using reference_ops::GreaterEqual; using reference_ops::Less; using reference_ops::LessEqual; +using reference_ops::Mean; using reference_ops::RankOneSelect; +using reference_ops::Relu1; +using reference_ops::Relu6; +using reference_ops::ReluX; using reference_ops::Select; +using reference_ops::SpaceToBatchND; +using reference_ops::StridedSlice; +using reference_ops::Transpose; // TODO(b/80247582) Remove this constant. // This will be phased out as the shifts are revised with more thought. Use of a @@ -72,6 +86,12 @@ using VectorMap = typename std::conditional< Eigen::Dynamic, 1>>, Eigen::Map>>::type; +template +VectorMap MapAsVector(Scalar* data, const RuntimeShape& shape) { + const int size = shape.FlatSize(); + return VectorMap(data, size, 1); +} + template VectorMap MapAsVector(Scalar* data, const Dims& dims) { const int size = FlatSize(dims); @@ -88,6 +108,23 @@ using MatrixMap = typename std::conditional< Eigen::Dynamic, Eigen::Dynamic>>, Eigen::Map>>::type; +template +MatrixMap MapAsMatrixWithLastDimAsRows(Scalar* data, + const RuntimeShape& shape) { + const int dims_count = shape.DimensionsCount(); + const int rows = shape.Dims(dims_count - 1); + const int cols = FlatSizeSkipDim(shape, dims_count - 1); + return MatrixMap(data, rows, cols); +} + +template +MatrixMap MapAsMatrixWithFirstDimAsCols(Scalar* data, + const RuntimeShape& shape) { + const int cols = shape.Dims(0); + const int rows = FlatSizeSkipDim(shape, 0); + return MatrixMap(data, rows, cols); +} + template MatrixMap MapAsMatrixWithFirstDimAsRows(Scalar* data, const Dims& dims) { @@ -134,16 +171,9 @@ template MatrixMap MapAsMatrixWithGivenNumberOfRows(Scalar* data, const Dims& dims, int rows) { - int cols = 1; - bool matched_rows = false; - for (int d = 0; d < N; d++) { - cols *= dims.sizes[d]; - if (cols == rows) { - matched_rows = true; - cols = 1; - } - } - TFLITE_DCHECK(matched_rows); + const int flatsize = FlatSize(dims); + TFLITE_DCHECK((flatsize % rows) == 0); + const int cols = flatsize / rows; return MatrixMap(data, rows, cols); } @@ -1082,10 +1112,10 @@ struct GemmlowpOutputPipeline { gemmlowp::OutputStageQuantizeDownInt32ToUint8ScaleByFixedPoint, gemmlowp::OutputStageClamp, gemmlowp::OutputStageSaturatingCastToUint8> Pipeline; - static Pipeline Make(const int32* bias_data, int output_rows, - int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, - int32 output_activation_max) { + static Pipeline MakeExp(const int32* bias_data, int output_rows, + int32 output_offset, int32 output_multiplier, + int output_left_shift, int32 output_activation_min, + int32 output_activation_max) { ColVectorMap bias_vector(bias_data, output_rows); gemmlowp::OutputStageBiasAddition bias_addition_stage; bias_addition_stage.bias_vector = bias_vector; @@ -1093,7 +1123,7 @@ struct GemmlowpOutputPipeline { quantize_down_stage; quantize_down_stage.result_offset_after_shift = output_offset; quantize_down_stage.result_fixedpoint_multiplier = output_multiplier; - quantize_down_stage.result_shift = output_shift; + quantize_down_stage.result_shift = -output_left_shift; gemmlowp::OutputStageClamp clamp_stage; clamp_stage.min = output_activation_min; clamp_stage.max = output_activation_max; @@ -1146,8 +1176,8 @@ inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, input_data, filter_cols, batches, filter_cols); gemmlowp::MatrixMap output_matrix( output_data, output_rows, batches, output_rows); - const auto& output_pipeline = GemmlowpOutputPipeline::Make( - bias_data, output_rows, output_offset, output_multiplier, output_shift, + const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( + bias_data, output_rows, output_offset, output_multiplier, -output_shift, output_activation_min, output_activation_max); gemmlowp::GemmWithOutputPipeline( @@ -1256,11 +1286,11 @@ void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, } // Internal function doing the actual arithmetic work for -// ExperimentalShuffledFullyConnected. +// ShuffledFullyConnected. // May be called either directly by it (single-threaded case) or may be used // as the 'task' for worker threads to run (multi-threaded case, see -// ExperimentalShuffledFullyConnectedWorkerTask below). -inline void ExperimentalShuffledFullyConnectedWorkerImpl( +// ShuffledFullyConnectedWorkerTask below). +inline void ShuffledFullyConnectedWorkerImpl( const uint8* shuffled_input_workspace_data, const int8* shuffled_weights_data, int batches, int output_depth, int output_stride, int accum_depth, const int32* bias_data, @@ -1534,14 +1564,16 @@ inline void ExperimentalShuffledFullyConnectedWorkerImpl( #endif } -// Wraps ExperimentalShuffledFullyConnectedWorkerImpl into a Task class +// Wraps ShuffledFullyConnectedWorkerImpl into a Task class // to allow using gemmlowp's threadpool. -struct ExperimentalShuffledFullyConnectedWorkerTask : gemmlowp::Task { - ExperimentalShuffledFullyConnectedWorkerTask( - const uint8* input_data, const int8* shuffled_weights_data, int batches, - int output_depth, int output_stride, int accum_depth, - const int32* bias_data, int32 output_multiplier, int output_shift, - int16* output_data) +struct ShuffledFullyConnectedWorkerTask : gemmlowp::Task { + ShuffledFullyConnectedWorkerTask(const uint8* input_data, + const int8* shuffled_weights_data, + int batches, int output_depth, + int output_stride, int accum_depth, + const int32* bias_data, + int32 output_multiplier, int output_shift, + int16* output_data) : input_data_(input_data), shuffled_weights_data_(shuffled_weights_data), batches_(batches), @@ -1554,7 +1586,7 @@ struct ExperimentalShuffledFullyConnectedWorkerTask : gemmlowp::Task { output_data_(output_data) {} void Run() override { - ExperimentalShuffledFullyConnectedWorkerImpl( + ShuffledFullyConnectedWorkerImpl( input_data_, shuffled_weights_data_, batches_, output_depth_, output_stride_, accum_depth_, bias_data_, output_multiplier_, output_shift_, output_data_); @@ -1572,15 +1604,14 @@ struct ExperimentalShuffledFullyConnectedWorkerTask : gemmlowp::Task { int16* output_data_; }; -inline void ExperimentalShuffledFullyConnected( +inline void ShuffledFullyConnected( const uint8* input_data, const Dims<4>& input_dims, const uint8* shuffled_weights_data, const Dims<4>& weights_dims, const int32* bias_data, const Dims<4>& bias_dims, int32 output_multiplier, int output_shift, int32 output_activation_min, int32 output_activation_max, int16* output_data, const Dims<4>& output_dims, uint8* shuffled_input_workspace_data, gemmlowp::GemmContext* gemm_context) { - gemmlowp::ScopedProfilingLabel label( - "ExperimentalShuffledFullyConnected/8bit"); + gemmlowp::ScopedProfilingLabel label("ShuffledFullyConnected/8bit"); (void)gemm_context; // only used in optimized code. TFLITE_DCHECK_EQ(output_activation_min, -32768); TFLITE_DCHECK_EQ(output_activation_max, 32767); @@ -1664,7 +1695,7 @@ inline void ExperimentalShuffledFullyConnected( if (thread_count == 1) { // Single-thread case: do the computation on the current thread, don't // use a threadpool - ExperimentalShuffledFullyConnectedWorkerImpl( + ShuffledFullyConnectedWorkerImpl( shuffled_input_workspace_data, int8_shuffled_weights_data, batches, output_depth, output_depth, accum_depth, bias_data, output_multiplier, output_shift, output_data); @@ -1679,7 +1710,7 @@ inline void ExperimentalShuffledFullyConnected( int row_start = 0; for (int i = 0; i < thread_count; i++) { int row_end = std::min(output_depth, row_start + kRowsPerWorker); - tasks[i] = new ExperimentalShuffledFullyConnectedWorkerTask( + tasks[i] = new ShuffledFullyConnectedWorkerTask( shuffled_input_workspace_data, int8_shuffled_weights_data + row_start * accum_depth, batches, row_end - row_start, output_depth, accum_depth, bias_data + row_start, @@ -1821,8 +1852,8 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, // Use dimensions M and N to construct dims for indexing directly into im2col Dims<4> im2col_dims; - im2col_dims.sizes[0] = col_dims.strides[3]; - im2col_dims.sizes[1] = row_dims.strides[3]; + im2col_dims.sizes[0] = FlatSize(col_dims); + im2col_dims.sizes[1] = FlatSize(row_dims); im2col_dims.sizes[2] = 1; im2col_dims.sizes[3] = 1; ComputeStrides(&im2col_dims); @@ -1831,8 +1862,8 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { - // Each row is an output pixel. Arrange the input data into this row in - // an order we can conveniently multiply with the filter data. + // Each im2col row is an output pixel. Arrange the input data in this + // row in an order we can conveniently multiply with the filter data. int row_offset = Offset(row_dims, out_x, out_y, batch, 0); const int in_x_origin = (out_x * stride_width) - pad_width; const int in_y_origin = (out_y * stride_height) - pad_height; @@ -1848,7 +1879,7 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, T* dst = im2col_data + Offset(im2col_dims, col_offset, row_offset, 0, 0); if ((in_x >= 0) && (in_x < input_width)) { - // Filter pixel is within the input, copy the data. + // Filter pixel is within the input, copy the input data. T const* src = input_data + Offset(input_dims, 0, in_x, in_y, batch); memcpy(dst, src, input_depth * sizeof(T)); @@ -1858,7 +1889,7 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, } } } else { - // Filter row is outside the input, zero out the entire im2col row. + // Filter row is outside the input, zero out the entire filter row. int col_offset = Offset(col_dims, 0, 0, filter_y, 0); T* dst = im2col_data + Offset(im2col_dims, col_offset, row_offset, 0, 0); @@ -1922,7 +1953,7 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, (void)im2col_dims; gemmlowp::ScopedProfilingLabel label("Conv"); - // A float set to 0x00000000h == 0.0f + // NB: static_cast(0x00000000h) == 0.0f const uint8 float_zero_byte = 0x00; const float* gemm_input_data = nullptr; const Dims<4>* gemm_input_dims = nullptr; @@ -2084,8 +2115,8 @@ inline void Conv(const uint8* input_data, const Dims<4>& input_dims, gemm_input_data, gemm_input_rows, gemm_input_cols); gemmlowp::MatrixMap output_matrix( output_data, output_rows, output_cols); - const auto& output_pipeline = GemmlowpOutputPipeline::Make( - bias_data, output_rows, output_offset, output_multiplier, output_shift, + const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( + bias_data, output_rows, output_offset, output_multiplier, -output_shift, output_activation_min, output_activation_max); gemmlowp::GemmWithOutputPipeline( @@ -2242,8 +2273,8 @@ void ConvAsGemm(const uint8* input_data, const Dims<4>& input_dims, input_data, filter_cols, output_cols, filter_cols); gemmlowp::MatrixMap output_matrix( output_data, output_rows, output_cols, output_rows); - const auto& output_pipeline = GemmlowpOutputPipeline::Make( - bias_data, output_rows, output_offset, output_multiplier, output_shift, + const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( + bias_data, output_rows, output_offset, output_multiplier, -output_shift, output_activation_min, output_activation_max); gemmlowp::GemmWithOutputPipeline( @@ -2330,48 +2361,25 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Relu (not fused)"); - const auto input = MapAsVector(input_data, input_dims); - auto output = MapAsVector(output_data, output_dims); + const auto input = MapAsVector(input_data, input_shape); + auto output = MapAsVector(output_data, output_shape); output = input.cwiseMax(0.0f); } -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); - const int flat_size = MatchingFlatSize(input_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float upper = 1; - const float lower = -1; - const float clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); - const int flat_size = MatchingFlatSize(input_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float upper = 6; - const float lower = 0; - const float clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - template -void L2Normalization(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +void L2Normalization(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Normalization"); static_assert(Ac == FusedActivationFunctionType::kNone, ""); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { float squared_l2_norm = 0; for (int c = 0; c < depth; ++c) { @@ -2387,8 +2395,9 @@ void L2Normalization(const float* input_data, const Dims<4>& input_dims, } } -inline void GetInvSqrtQuantizedMultiplier(int32 input, int32* output_inv_sqrt, - int* output_shift) { +inline void GetInvSqrtQuantizedMultiplierExp(int32 input, + int32* output_inv_sqrt, + int* output_shift) { *output_shift = 11; while (input >= (1 << 29)) { input /= 4; @@ -2430,31 +2439,35 @@ inline void GetInvSqrtQuantizedMultiplier(int32 input, int32* output_inv_sqrt, *output_inv_sqrt <<= -*output_shift; *output_shift = 0; } + *output_shift *= kReverseShift; } -inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, +inline void L2Normalization(const uint8* input_data, + const RuntimeShape& input_shape, int32 input_zero_point, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Normalization/8bit"); - TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); for (int i = 0; i < outer_size; ++i) { int32 square_l2_norm = 0; for (int c = 0; c < depth; c++) { + // Note that input_data advances by depth in the second pass below. int32 diff = input_data[c] - input_zero_point; square_l2_norm += diff * diff; } int32 inv_l2norm_multiplier; int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, - &inv_l2norm_shift); + GetInvSqrtQuantizedMultiplierExp(square_l2_norm, &inv_l2norm_multiplier, + &inv_l2norm_shift); for (int c = 0; c < depth; c++) { int32 diff = *input_data - input_zero_point; int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( - 128 * diff, inv_l2norm_multiplier, kReverseShift * inv_l2norm_shift); + 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); int32 unclamped_output_val = 128 + rescaled_diff; int32 output_val = std::min(255, std::max(0, unclamped_output_val)); *output_data = static_cast(output_val); @@ -2663,25 +2676,13 @@ inline void Add(int left_shift, const uint8* input1_data, output_activation_max, output_data); } -template inline void Add(const int16* input1_data, const Dims<4>& input1_dims, int input1_shift, const int16* input2_data, const Dims<4>& input2_dims, int input2_shift, int16 output_activation_min, int16 output_activation_max, int16* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Add/Int16"); - // This is a copy of the reference implementation. We do not currently have a - // properly optimized version. - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); @@ -2707,6 +2708,42 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } +inline void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32 output_activation_min, int32 output_activation_max, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + + const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] + input2_data[i], output_activation_min, + output_activation_max); + } +} + +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, + input2_shift, output_activation_min, output_activation_max, output_data, + output_dims); +} + template void Add(const int32* input1_data, const Dims<4>& input1_dims, const int32* input2_data, const Dims<4>& input2_dims, @@ -3207,19 +3244,6 @@ inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, output_data, output_dims); } -// TODO(aselle): This is not actually optimized yet. -inline void Div(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); - for (int i = 0; i < flat_size; i++) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] / input2_data[i], output_activation_min, - output_activation_max); - } -} - // TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then @@ -3385,105 +3409,6 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, } } -template -void Concatenation(int concat_dim, const Scalar* const* input_data, - const Dims<4>* const* input_dims, int inputs_count, - Scalar* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Concatenation"); - int concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - for (int j = 0; j < 4; j++) { - if (j != concat_dim) { - MatchingArraySize(*input_dims[i], j, output_dims, j); - } - } - concat_size += ArraySize(*input_dims[i], concat_dim); - } - TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - // for now we dont have a model with a Concatenation - // with fused activation function. - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); - int outer_size = 1; - for (int i = concat_dim + 1; i < 4; i++) { - outer_size *= output_dims.sizes[i]; - } - Scalar* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = - input_dims[i]->sizes[concat_dim] * input_dims[i]->strides[concat_dim]; - memcpy(output_ptr, input_data[i] + k * copy_size, - copy_size * sizeof(Scalar)); - output_ptr += copy_size; - } - } -} - -// TODO(prabhumk): This is the same as the reference implementation. -// TODO(prabhumk): The quantized implementation of concatentation isn't fully -// quantized as it takes scale as a floating point value. This should be fixed -// when optimizng this routine further. -inline void Concatenation(int concat_dim, const uint8* const* input_data, - const Dims<4>* const* input_dims, - const int32* input_zeropoint, - const float* input_scale, int inputs_count, - uint8* output_data, const Dims<4>& output_dims, - const int32 output_zeropoint, - const float output_scale) { - // The arguments input_zeropoint and input_scale are expected to be an array - // that have the quantization parameters for all the inputs to the concat - // operator. - gemmlowp::ScopedProfilingLabel label("Concatenation"); - TFLITE_DCHECK_GT(inputs_count, 1); - int concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - for (int j = 0; j < 4; j++) { - if (j != concat_dim) { - MatchingArraySize(*input_dims[i], j, output_dims, j); - } - } - concat_size += ArraySize(*input_dims[i], concat_dim); - } - TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - int outer_size = 1; - for (int i = concat_dim + 1; i < 4; i++) { - outer_size *= output_dims.sizes[i]; - } - const float inverse_output_scale = 1.f / output_scale; - uint8* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = - input_dims[i]->sizes[concat_dim] * input_dims[i]->strides[concat_dim]; - const uint8* input_ptr = input_data[i] + k * copy_size; - if (input_zeropoint[i] == output_zeropoint && - input_scale[i] == output_scale) { - memcpy(output_ptr, input_ptr, copy_size); - } else { - const float scale = input_scale[i] * inverse_output_scale; - const float bias = -input_zeropoint[i] * scale; - for (int j = 0; j < copy_size; ++j) { - const int32_t value = - static_cast(round(input_ptr[j] * scale + bias)) + - output_zeropoint; - output_ptr[j] = - static_cast(std::max(std::min(255, value), 0)); - } - } - output_ptr += copy_size; - } - } -} - -template -void DepthConcatenation(const Scalar* const* input_data, - const Dims<4>* const* input_dims, int inputs_count, - Scalar* output_data, const Dims<4>& output_dims) { - Concatenation(0, input_data, input_dims, inputs_count, - output_data, output_dims); -} - inline void LstmCell(const float* input_data, const Dims<4>& input_dims, const float* prev_activ_data, const Dims<4>& prev_activ_dims, const float* weights_data, @@ -3846,23 +3771,25 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // TODO(benoitjacob) make this a proper reference impl without Eigen! - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // TODO(benoitjacob) get rid of the dynamic memory allocation here! Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -3900,9 +3827,9 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -3910,44 +3837,23 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -3967,11 +3873,12 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, uint16 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -4002,7 +3909,7 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON #define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ @@ -4043,54 +3950,23 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Prefill the output to minimum representable float value out_mat.setConstant(std::numeric_limits::lowest()); for (int b = 0; b < batches; ++b) { @@ -4123,9 +3999,9 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -4133,41 +4009,21 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -4185,11 +4041,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, uint8 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -4215,7 +4072,7 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON for (; channel <= depth - 16; channel += 16) { @@ -4242,53 +4099,23 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Pool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // Actually carry out L2 Pool. Code is written in forward mode: we go through // the input values once, and write to all the pooled regions that it maps to. - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); Eigen::VectorXf in_square(in_mat.rows()); Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -4330,28 +4157,6 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -4397,14 +4202,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Softmax"); - MatchingFlatSize(input_dims, output_dims); + MatchingFlatSize(input_shape, output_shape); - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Compute the exponential first, removing the max coefficient for numerical // stability. out_mat = (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * beta; @@ -4416,10 +4221,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, out_mat.array().rowwise() *= scale; } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -4433,8 +4238,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPoint0 = gemmlowp::FixedPoint; gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int b = 0; b < outer_size; ++b) { const uint8* input_data_ptr = input_data + b * depth; @@ -4624,11 +4432,14 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, // TODO(myenik): This is the same as the reference implementation, not actually // optimized yet. -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const float* block_input_data = input_data + i * depth; @@ -4769,11 +4580,11 @@ log_x_for_x_greater_than_or_equal_to_1( } // Currently just a copy of the reference code. -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax/Uint8"); // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as @@ -4788,8 +4599,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const uint8* block_input_data = input_data + i * depth; @@ -4853,21 +4667,21 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().unaryExpr(Eigen::internal::scalar_sigmoid_op()); } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Uint8"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; #ifdef USE_NEON @@ -4999,10 +4813,10 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { } @@ -5059,21 +4873,21 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().tanh(); } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // Note that this is almost the exact same code as in Logistic(). gemmlowp::ScopedProfilingLabel label("Tanh"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; int32_t output_zero_point = 128; @@ -5214,16 +5028,16 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); int c = 0; const int16* input_data_ptr = input_data; @@ -5314,49 +5128,6 @@ inline void Tanh(const int16* input_data, const Dims<4>& input_dims, } } -inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, - int32 zero_point, double scale, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Dequantize"); - const int flat_size = MatchingFlatSize(output_dims, input_dims); - for (int i = 0; i < flat_size; ++i) { - int32 val = input_data[i]; - float result = static_cast(scale * (val - zero_point)); - output_data[i] = result; - } -} - -inline void FakeQuant(const float* input_data, const Dims<4>& input_dims, - float rmin, float rmax, int num_bits, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("FakeQuant"); - - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - TFLITE_DCHECK_LE(rmin, 0.0f); - TFLITE_DCHECK_GE(rmax, 0.0f); - TFLITE_DCHECK_LT(rmin, rmax); - - // Code matches tensorflow's FakeQuantWithMinMaxArgsFunctor. - int quant_min = 0; - int quant_max = (1 << num_bits) - 1; - float nudged_min, nudged_max, nudged_scale; - NudgeQuantizationRange(rmin, rmax, quant_min, quant_max, &nudged_min, - &nudged_max, &nudged_scale); - const float inv_nudged_scale = 1.0f / nudged_scale; - - const int flat_size = MatchingFlatSize(output_dims, input_dims); - for (int i = 0; i < flat_size; ++i) { - const float src_val = input_data[i]; - const float clamped = std::min(nudged_max, std::max(nudged_min, src_val)); - const float clamped_shifted = clamped - nudged_min; - const float dst_val = - TfLiteRound(clamped_shifted * inv_nudged_scale) * nudged_scale + - nudged_min; - output_data[i] = dst_val; - } -} - template inline void Cast(const SrcT* input_data, const Dims<4>& input_dims, DstT* output_data, const Dims<4>& output_dims) { @@ -5374,26 +5145,6 @@ inline void Floor(const float* input_data, const Dims<4>& input_dims, output_map.array() = Eigen::floor(input_map.array()); } -template -inline void Gather(const T* input_data, const Dims<4>& input_dims, - int input_rank, const int32* coords_data, - const Dims<4>& coords_dims, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Gather"); - - TFLITE_DCHECK(coords_dims.sizes[0] == output_dims.sizes[input_rank - 1]); - int stride = input_dims.strides[input_rank - 1]; - T* out = output_data; - - for (int i = 0; i < coords_dims.sizes[0]; i++) { - TFLITE_DCHECK_GE(coords_data[i], 0); - TFLITE_DCHECK_LT(coords_data[i], input_dims.sizes[input_rank - 1]); - const T* in = input_data + coords_data[i] * stride; - memcpy(out, in, sizeof(T) * stride); - out += stride; - } -} - #ifdef USE_NEON inline void ResizeBilinearKernel(const float* input_ptr, int32 depth, float scale, float* output_ptr) { @@ -5722,6 +5473,46 @@ inline void ResizeBilinearGeneric(const float* input_data, } } +template +inline void ResizeBilinearGenericSmallChannel( + const T* input_data, const Dims<4>& input_dims, T* output_data, + const Dims<4>& output_dims, int32 batches, int32 input_height, + int32 input_width, int32 depth, int32 output_height, int32 output_width, + float height_scale, float width_scale) { + memset(output_data, 0, + batches * output_height * output_width * depth * sizeof(T)); + + T* output_ptr = &output_data[0]; + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < output_height; ++y) { + float input_y = y * height_scale; + int32 y0 = static_cast(std::floor(input_y)); + int32 y1 = std::min(y0 + 1, input_height - 1); + for (int x = 0; x < output_width; ++x) { + float input_x = x * width_scale; + int32 x0 = static_cast(input_x); + int32 x1 = std::min(x0 + 1, input_width - 1); + + int32 input_offset[4] = { + Offset(input_dims, 0, x0, y0, b), Offset(input_dims, 0, x1, y0, b), + Offset(input_dims, 0, x0, y1, b), Offset(input_dims, 0, x1, y1, b)}; + float scale[4] = {(1 - (input_y - y0)) * (1 - (input_x - x0)), + (1 - (input_y - y0)) * (input_x - x0), + (input_y - y0) * (1 - (input_x - x0)), + (input_y - y0) * (input_x - x0)}; + + for (int d = 0; d < depth; d++) { + const T* input_ptr = &input_data[d]; + *output_ptr++ = static_cast(input_ptr[input_offset[0]] * scale[0] + + input_ptr[input_offset[1]] * scale[1] + + input_ptr[input_offset[2]] * scale[2] + + input_ptr[input_offset[3]] * scale[3]); + } + } + } + } +} + inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, const int32* output_size_data, const Dims<4>& output_size_dims, float* output_data, @@ -5762,6 +5553,41 @@ inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, } } +// TODO(prabhumk): This is not a real quantized bilinear. It does not use int8 +// or int16 arithmetic. +inline void ResizeBilinear(const uint8* input_data, const Dims<4>& input_dims, + const int32* output_size_data, + const Dims<4>& output_size_dims, uint8* output_data, + const Dims<4>& output_dims, bool align_corners) { + gemmlowp::ScopedProfilingLabel label("ResizeBilinear"); + int32 batches = MatchingArraySize(input_dims, 3, output_dims, 3); + int32 input_height = ArraySize(input_dims, 2); + int32 input_width = ArraySize(input_dims, 1); + int32 depth = MatchingArraySize(input_dims, 0, output_dims, 0); + + TFLITE_DCHECK_EQ(ArraySize(output_size_dims, 3), 1); + TFLITE_DCHECK_EQ(ArraySize(output_size_dims, 2), 1); + TFLITE_DCHECK_EQ(ArraySize(output_size_dims, 1), 1); + TFLITE_DCHECK_EQ(ArraySize(output_size_dims, 0), 2); + int32 output_height = output_size_data[Offset(output_size_dims, 0, 0, 0, 0)]; + int32 output_width = output_size_data[Offset(output_size_dims, 1, 0, 0, 0)]; + + float height_scale = + (align_corners && output_height > 1) + ? (static_cast(input_height - 1) / (output_height - 1)) + : (static_cast(input_height) / output_height); + + float width_scale = + (align_corners && output_width > 1) + ? (static_cast(input_width - 1) / (output_width - 1)) + : (static_cast(input_width) / output_width); + + ResizeBilinearGenericSmallChannel( + input_data, input_dims, output_data, output_dims, batches, input_height, + input_width, depth, output_height, output_width, height_scale, + width_scale); +} + // legacy, for compatibility with old checked-in code inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, const int32* output_size_data, @@ -5771,53 +5597,13 @@ inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, output_data, output_dims, /*align_corners=*/false); } -template -inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, - const int32* block_shape_data, - const Dims<4>& block_shape_dims, - const int32* paddings_data, - const Dims<4>& paddings_dims, T* output_data, +// legacy, for compatibility with old checked-in code +inline void ResizeBilinear(const uint8* input_data, const Dims<4>& input_dims, + const int32* output_size_data, + const Dims<4>& output_size_dims, uint8* output_data, const Dims<4>& output_dims) { - // Unoptimized - Straight copy from reference ops. - gemmlowp::ScopedProfilingLabel label("SpaceToBatchND"); - - const int output_batch_size = ArraySize(output_dims, 3); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int input_batch_size = ArraySize(input_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int depth = ArraySize(input_dims, 0); - const int block_shape_height = block_shape_data[0]; - const int block_shape_width = block_shape_data[1]; - const int padding_top = paddings_data[0]; - const int padding_left = paddings_data[2]; - - for (int out_b = 0; out_b < output_batch_size; ++out_b) { - int input_batch = out_b % input_batch_size; - int shift_w = (out_b / input_batch_size) % block_shape_width; - int shift_h = (out_b / input_batch_size) / block_shape_width; - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - T* out = output_data + Offset(output_dims, 0, out_w, out_h, out_b); - if (out_h * block_shape_height + shift_h < padding_top || - out_h * block_shape_height + shift_h >= - padding_top + input_height || - out_w * block_shape_width + shift_w < padding_left || - out_w * block_shape_width + shift_w >= padding_left + input_width) { - memset(out, 0, depth * sizeof(T)); - } else { - const T* in = - input_data + - Offset(input_dims, 0, - (out_w * block_shape_width + shift_w) - padding_left, - (out_h * block_shape_height + shift_h) - padding_top, - input_batch); - memcpy(out, in, depth * sizeof(T)); - } - } - } - } + ResizeBilinear(input_data, input_dims, output_size_data, output_size_dims, + output_data, output_dims, /*align_corners=*/false); } // Helper methods for BatchToSpaceND. @@ -6022,54 +5808,6 @@ inline void Pad(const T* input_data, const Dims<4>& input_dims, output_dims, 0); } -// UNOPTIMIZED COPY of StridedSlice from reference_ops.h. -template -inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, - int begin_mask, int end_mask, - const std::vector& start_indices, - const std::vector& stop_indices, - const std::vector& strides, T* output_data, - const Dims<4>& output_dims) { - TFLITE_DCHECK_EQ(start_indices.size(), 4); - TFLITE_DCHECK_EQ(stop_indices.size(), 4); - TFLITE_DCHECK_EQ(strides.size(), 4); - const int start_b = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 3); - const int stop_b = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 3); - const int start_h = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 2); - const int stop_h = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 2); - const int start_w = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 1); - const int stop_w = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 1); - const int start_d = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 0); - const int stop_d = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 0); - - T* out_ptr = output_data; - for (int in_b = start_b; - !strided_slice::LoopCondition(in_b, stop_b, strides[3]); - in_b += strides[3]) { - for (int in_h = start_h; - !strided_slice::LoopCondition(in_h, stop_h, strides[2]); - in_h += strides[2]) { - for (int in_w = start_w; - !strided_slice::LoopCondition(in_w, stop_w, strides[1]); - in_w += strides[1]) { - for (int in_d = start_d; - !strided_slice::LoopCondition(in_d, stop_d, strides[0]); - in_d += strides[0]) { - *out_ptr++ = input_data[Offset(input_dims, in_d, in_w, in_h, in_b)]; - } - } - } - } -} - template inline void Slice(const T* input_data, const Dims<4>& input_dims, const std::vector& begin, const std::vector& size, @@ -6104,41 +5842,6 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims, } } -template -inline void Mean(const T* input_data, const Dims<4>& input_dims, - const std::vector& reduction_indices, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Mean"); - const int output_batch = ArraySize(output_dims, 3); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int output_depth = ArraySize(output_dims, 0); - - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - - // The current implementation only supports simultaneous reduction over - // width and height. - TFLITE_DCHECK_EQ(reduction_indices.size(), 2); - TFLITE_DCHECK((reduction_indices[0] == 1 && reduction_indices[1] == 2) || - (reduction_indices[0] == 2 && reduction_indices[1] == 1)); - TFLITE_DCHECK_EQ(output_height, 1); - TFLITE_DCHECK_EQ(output_width, 1); - - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - float value = 0; - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - value += input_data[Offset(input_dims, out_d, in_w, in_h, out_b)]; - } - } - output_data[Offset(output_dims, out_d, 0, 0, out_b)] = - value / (input_width * input_height); - } - } -} - template void GenericBroadcastSub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, const Dims<4>& input2_dims, @@ -6218,130 +5921,84 @@ void TensorFlowMaximum(const T* input1_data, const Dims<4>& input1_dims, output_map.array() = input1_map.array().max(max_value); } -template -void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, - T2* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("ArgMax"); - - // The current ArgMax implemention can only determine the index of the maximum - // value in the last dimension. So the axis argument is ignored. - - // For ArgMax, the number of output dimensions = (number of input dimensions - - // 1). For the sake of simplicity, the output dimensions are equal to the - // input dimensions here. We enforce the constraint that the last dimension - // must always be 1. - TFLITE_DCHECK_EQ(ArraySize(output_dims, 0), 1); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = ArraySize(input_dims, 0); - for (int i = 0; i < outer_size; ++i) { - auto max_value = *input_data; - ++input_data; - int max_index = 0; - for (int d = 1; d < depth; ++d) { - const auto& curr_value = *input_data; - if (curr_value > max_value) { - max_value = curr_value; - max_index = d; - } - ++input_data; - } - *output_data = max_index; - ++output_data; - } -} - template -void Transpose(const T* input, const Dims<4>& input_dims, T* output, - const Dims<4>& output_dims, const int* permuted_axes) { - int out_sizes[4]; - // Compute the inverse permutation array so we can do an output centered - // transpose. Also, check to make sure output_dims is matching input_dims. - for (int k = 0; k < 4; k++) { - out_sizes[k] = - MatchingArraySize(input_dims, permuted_axes[k], output_dims, k); - } - - // Naive transpose loop (iterate on output index and compute input index). - int o[4]; // loop index (on output). - int i[4]; - for (o[3] = 0; o[3] < out_sizes[3]; o[3]++) { - i[permuted_axes[3]] = o[3]; - for (o[2] = 0; o[2] < out_sizes[2]; o[2]++) { - i[permuted_axes[2]] = o[2]; - for (o[1] = 0; o[1] < out_sizes[1]; o[1]++) { - i[permuted_axes[1]] = o[1]; - for (o[0] = 0; o[0] < out_sizes[0]; o[0]++) { - i[permuted_axes[0]] = o[0]; - output[Offset(output_dims, o)] = input[Offset(input_dims, i)]; - } - } - } - } -} +void TransposeIm2col(const T* input_data, const Dims<4>& input_dims, + const Dims<4>& filter_dims, int stride_width, + int stride_height, int pad_width, int pad_height, + const Dims<4>& output_dims, uint8 zero_byte, + T* im2col_data) { + gemmlowp::ScopedProfilingLabel label("TransposeIm2col"); + TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(filter_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + TFLITE_DCHECK(im2col_data); -inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, - const float* filter_data, const Dims<4>& filter_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("TransposeConv"); - // THIS FUNCTION IS A COPY FROM reference_ops.h. - // To optimize, start by using the conv code with transposed weights for the - // case of stride_height = stride_width = 1. const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); - const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); const int input_height = ArraySize(input_dims, 2); const int input_width = ArraySize(input_dims, 1); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); const int filter_height = ArraySize(filter_dims, 2); const int filter_width = ArraySize(filter_dims, 1); const int output_height = ArraySize(output_dims, 2); const int output_width = ArraySize(output_dims, 1); + MatchingArraySize(output_dims, 0, filter_dims, 0); // output_depth - // Although transpose convolution simplifies to convolution with transposed - // weights for strides of 1, non-unitary striding complicates matters. To - // keep this reference implementation as clear as possible, we use a "scatter" - // access pattern, where we loop through all the input elements, computing - // their influence on the output, rather than looping through the output - // elements in the typical "gather" access pattern of a conv. We therefore - // must initialize the output array to zero. - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] = - 0.0f; - } - } - } - } + // Construct the MxN sized im2col matrix. + // The rows M, are sub-ordered B x H x W + Dims<4> row_dims; + row_dims.sizes[0] = output_width; + row_dims.sizes[1] = output_height; + row_dims.sizes[2] = batches; + row_dims.sizes[3] = 1; + ComputeStrides(&row_dims); - // Loop through input elements one at a time. + // The columns, N, are sub-ordered Kh x Kw x Din + Dims<4> col_dims; + col_dims.sizes[0] = input_depth; + col_dims.sizes[1] = filter_width; + col_dims.sizes[2] = filter_height; + col_dims.sizes[3] = 1; + ComputeStrides(&col_dims); + + // Use dimensions M and N to construct dims for indexing directly into im2col + Dims<4> im2col_dims; + im2col_dims.sizes[0] = FlatSize(col_dims); + im2col_dims.sizes[1] = FlatSize(row_dims); + im2col_dims.sizes[2] = 1; + im2col_dims.sizes[3] = 1; + ComputeStrides(&im2col_dims); + + // Build the im2col matrix by looping through all the input pixels, + // computing their influence on the output, rather than looping through all + // the output pixels. We therefore must initialize the im2col array to zero. + // This is potentially inefficient because we subsequently overwrite bytes + // set here. However, in practice memset is very fast and costs negligible. + memset(im2col_data, zero_byte, FlatSize(im2col_dims) * sizeof(T)); + + // Loop through the output batches for (int batch = 0; batch < batches; ++batch) { + // Loop through input pixels one at a time. for (int in_y = 0; in_y < input_height; ++in_y) { for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + // Loop through the output pixels it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int out_y = out_y_origin + filter_y; + // Is output pixel within height bounds? + if ((out_y >= 0) && (out_y < output_height)) { for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - float input_value = input_data[Offset(input_dims, in_channel, - in_x, in_y, batch)]; - float filter_value = - filter_data[Offset(filter_dims, out_channel, filter_x, - filter_y, in_channel)]; - output_data[Offset(output_dims, out_channel, out_x, out_y, - batch)] += input_value * filter_value; - } + const int out_x = out_x_origin + filter_x; + // Is output pixel within width bounds? + if ((out_x >= 0) && (out_x < output_width)) { + // Copy the input elements of this pixel + T const* src = + input_data + Offset(input_dims, 0, in_x, in_y, batch); + T* dst = im2col_data + + Offset(im2col_dims, + Offset(col_dims, 0, filter_x, filter_y, 0), + Offset(row_dims, out_x, out_y, batch, 0), 0, 0); + memcpy(dst, src, input_depth * sizeof(T)); } } } @@ -6351,6 +6008,31 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, } } +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims, float* im2col_data, + const Dims<4>& im2col_dims) { + gemmlowp::ScopedProfilingLabel label("TransposeConv"); + + // Note we could use transposed weights with forward conv for unstrided + // cases. But we are already getting good performance with this code as-is. + TFLITE_DCHECK(im2col_data); + TransposeIm2col(input_data, input_dims, filter_dims, stride_width, + stride_height, pad_width, pad_height, output_dims, 0, + im2col_data); + + const auto im2col_matrix_map = + MapAsMatrixWithFirstDimAsRows(im2col_data, im2col_dims); + const auto filter_matrix_map = + MapAsMatrixWithLastDimAsCols(filter_data, filter_dims); + auto output_matrix_map = + MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + + Gemm(filter_matrix_map.transpose(), im2col_matrix_map, &output_matrix_map); +} + } // namespace optimized_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc index b0951aac8cbb98a181d9dcaef88770fadfc74f62..e224980493aa11f642da103ee7d7377b6c4b1da0 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc @@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ + #include #include #include @@ -48,15 +49,15 @@ void QuantizeMultiplierGreaterThanOne(double double_multiplier, TFLITE_CHECK_GE(*left_shift, 0); } -void QuantizeMultiplierSmallerThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* right_shift) { +void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, + int32_t* quantized_multiplier, + int* left_shift) { TFLITE_CHECK_LT(double_multiplier, 1.); TFLITE_CHECK_GT(double_multiplier, 0.); int shift; QuantizeMultiplier(double_multiplier, quantized_multiplier, &shift); TFLITE_CHECK_LE(shift, 0); - *right_shift = -shift; + *left_shift = shift; } void PreprocessSoftmaxScaling(double beta, double input_scale, @@ -78,20 +79,21 @@ void PreprocessSoftmaxScaling(double beta, double input_scale, quantized_multiplier, left_shift); } -void PreprocessLogSoftmaxScaling(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, int* left_shift, - int32_t* reverse_scaling_divisor, - int* reverse_scaling_right_shift) { +void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, + int input_integer_bits, + int32_t* quantized_multiplier, + int* left_shift, + int32_t* reverse_scaling_divisor, + int* reverse_scaling_left_shift) { PreprocessSoftmaxScaling(beta, input_scale, input_integer_bits, quantized_multiplier, left_shift); // Also calculate what amounts to the inverse scaling factor for the input. const double real_reverse_scaling_divisor = (1 << (31 - *left_shift)) / static_cast(*quantized_multiplier); - tflite::QuantizeMultiplierSmallerThanOne(real_reverse_scaling_divisor, - reverse_scaling_divisor, - reverse_scaling_right_shift); + tflite::QuantizeMultiplierSmallerThanOneExp(real_reverse_scaling_divisor, + reverse_scaling_divisor, + reverse_scaling_left_shift); } int CalculateInputRadius(int input_integer_bits, int input_left_shift) { @@ -125,4 +127,16 @@ void NudgeQuantizationRange(const float min, const float max, *nudged_max = (quant_max_float - nudged_zero_point) * (*scale); } +bool CheckedLog2(const float x, int* log2_result) { + // Using TfLiteRound instead of std::round and std::log instead of + // std::log2 to work around these fuctions being missing in a toolchain + // used in some TensorFlow tests as of May 2018. + const float x_log2 = std::log(x) * (1.0f / std::log(2.0f)); + const float x_log2_rounded = TfLiteRound(x_log2); + const float x_log2_fracpart = x_log2 - x_log2_rounded; + + *log2_result = static_cast(x_log2_rounded); + return std::abs(x_log2_fracpart) < 1e-3; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.h b/tensorflow/contrib/lite/kernels/internal/quantization_util.h index 4a217515f142b2451ebd61e423871b95cdc09748..525857a2e6f73276d0a6e64770947169033c7667 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.h +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.h @@ -167,9 +167,9 @@ IntOut SafeCast(FloatIn x) { // this is intended as a RIGHT-shift. // // Restricted to the case where the multiplier < 1 (and non-negative). -void QuantizeMultiplierSmallerThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* right_shift); +void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, + int32_t* quantized_multiplier, + int* left_shift); // Decompose a double multiplier into a Q0.31 int32 representation of its // significand, and shift representation of its exponent. @@ -197,11 +197,12 @@ void PreprocessSoftmaxScaling(double beta, double input_scale, int input_integer_bits, int32_t* quantized_multiplier, int* left_shift); // Like PreprocessSoftmaxScaling, but inverse scaling factors also calculated. -void PreprocessLogSoftmaxScaling(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, int* left_shift, - int32_t* reverse_scaling_divisor, - int* reverse_scaling_right_shift); +void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, + int input_integer_bits, + int32_t* quantized_multiplier, + int* left_shift, + int32_t* reverse_scaling_divisor, + int* reverse_scaling_left_shift); // Calculate the largest input that will result in a within-bounds intermediate // result within MultiplyByQuantizedMultiplierGreaterThanOne. In other words, // it must not overflow before we reduce the value by multiplication by the @@ -217,6 +218,11 @@ void NudgeQuantizationRange(const float min, const float max, const int quant_min, const int quant_max, float* nudged_min, float* nudged_max, float* scale); +// If x is approximately a power of two (with any positive or negative +// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise +// returns false. +bool CheckedLog2(const float x, int* log2_result); + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc index 2d74b3d3849812a2dc95fabcd680aa280c99ca55..94773b47d3817d7ed7240f74545ad04e7fa4bd52 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc @@ -196,21 +196,21 @@ TEST(QuantizationUtilTest, ChooseQuantizationParamsInvalidRange) { EXPECT_DEATH(ChooseQuantizationParams(10.0, -30.0), ""); } -TEST(QuantizationUtilTest, QuantizeMultiplierSmallerThanOne) { +TEST(QuantizationUtilTest, QuantizeMultiplierSmallerThanOneExp) { auto quantize = [](double d) { int32_t q; int s; - QuantizeMultiplierSmallerThanOne(d, &q, &s); + QuantizeMultiplierSmallerThanOneExp(d, &q, &s); return std::pair{q, s}; }; EXPECT_DEATH(quantize(-0.1), ""); EXPECT_DEATH(quantize(0.0), ""); - EXPECT_THAT(quantize(0.25), Pair(1073741824, 1)); + EXPECT_THAT(quantize(0.25), Pair(1073741824, -1)); // Around 0.5 we can see the change in exponent and how we try hard to // void hitting max int32. - EXPECT_THAT(quantize(0.50 - 5e-9), Pair(2147483627, 1)); + EXPECT_THAT(quantize(0.50 - 5e-9), Pair(2147483627, -1)); EXPECT_THAT(quantize(0.50 - 1e-10), Pair(1073741824, 0)); EXPECT_THAT(quantize(0.50), Pair(1073741824, 0)); diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..878b2441b4f2828a014673f5bd80fb8aa29514db --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -0,0 +1,332 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ + +#include +#include + +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { + +namespace reference_ops { + +inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { + return RuntimeShape( + {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); +} + +template +void L2Normalization(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, uint8* output_data, + const Dims<4>& output_dims) { + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu1(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu1(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu6(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu6(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); +} + +} // namespace reference_ops +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index f8c6f341f7e61529bbbac592f9caf115f6121e0c..ccf112c990f3b5cba755a9b29aadd5aa82104849 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -51,10 +51,11 @@ void PortableSymmetricQuantizeFloats(const float* values, const int size, *scaling_factor = 1; return; } - *scaling_factor = kScale / range; + *scaling_factor = range / kScale; + const float scaling_factor_inv = 1.0f / *scaling_factor; for (int i = 0; i < size; ++i) { const int32_t quantized_value = - static_cast(TfLiteRound(*scaling_factor * values[i])); + static_cast(TfLiteRound(values[i] * scaling_factor_inv)); // Clamp: just in case some odd numeric offset. quantized_values[i] = std::min(kScale, std::max(-kScale, quantized_value)); } @@ -85,7 +86,7 @@ void PortableMatrixBatchVectorMultiplyAccumulate( float* __restrict__ result, int result_stride) { int batch, row, col; for (batch = 0; batch < n_batch; ++batch, vectors += m_cols) { - const float batch_scaling_factor_inv = 1.0 / scaling_factors[batch]; + const float batch_scaling_factor = scaling_factors[batch]; // Get the address of the first row. const int8_t* row_ptr = matrix; for (row = 0; row < m_rows; ++row, result += result_stride) { @@ -98,7 +99,7 @@ void PortableMatrixBatchVectorMultiplyAccumulate( for (col = 0; col < m_cols; ++col, ++row_ptr) { dotprod += (*row_ptr) * (vectors[col]); } // for col - *result += (dotprod * batch_scaling_factor_inv); + *result += (dotprod * batch_scaling_factor); } // for row } // for batch } diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index af7bc8b91ae08323299a0eda6a8b7720bd7310ae..9357e7407eb83fe8ea3486dfdde8742fc6323ee9 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -697,7 +697,7 @@ inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, } } -inline void ExperimentalShuffledFullyConnected( +inline void ShuffledFullyConnected( const uint8* input_data, const Dims<4>& input_dims, const uint8* shuffled_weights_data, const Dims<4>& weights_dims, const int32* bias_data, const Dims<4>& bias_dims, int32 output_multiplier, @@ -914,9 +914,9 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float lower = 0; @@ -925,9 +925,10 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu1(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 1; @@ -937,9 +938,10 @@ inline void Relu1(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu6(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 6; @@ -949,12 +951,28 @@ inline void Relu6(const float* input_data, const Dims<4>& input_dims, } } +inline void ReluX(uint8 min_value, uint8 max_value, const uint8* input_data, + const RuntimeShape& input_shape, uint8* output_data, + const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Quantized ReluX (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + const uint8 val = input_data[i]; + const uint8 clamped = + val > max_value ? max_value : val < min_value ? min_value : val; + output_data[i] = clamped; + } +} + template -void L2Normalization(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +void L2Normalization(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { static_assert(Ac == FusedActivationFunctionType::kNone, ""); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { float squared_l2_norm = 0; for (int c = 0; c < depth; ++c) { @@ -968,8 +986,9 @@ void L2Normalization(const float* input_data, const Dims<4>& input_dims, } } -inline void GetInvSqrtQuantizedMultiplier(int32 input, int32* output_inv_sqrt, - int* output_shift) { +inline void GetInvSqrtQuantizedMultiplierExp(int32 input, + int32* output_inv_sqrt, + int* output_shift) { *output_shift = 11; while (input >= (1 << 29)) { input /= 4; @@ -1011,42 +1030,45 @@ inline void GetInvSqrtQuantizedMultiplier(int32 input, int32* output_inv_sqrt, *output_inv_sqrt <<= -*output_shift; *output_shift = 0; } + *output_shift *= kReverseShift; } -inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, +inline void L2Normalization(const uint8* input_data, + const RuntimeShape& input_shape, int32 input_zero_point, uint8* output_data, - const Dims<4>& output_dims) { - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); for (int i = 0; i < outer_size; ++i) { int32 square_l2_norm = 0; for (int c = 0; c < depth; c++) { - int32 diff = - input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + int32 diff = input_data[depth * i + c] - input_zero_point; square_l2_norm += diff * diff; } int32 inv_l2norm_multiplier; int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, - &inv_l2norm_shift); + GetInvSqrtQuantizedMultiplierExp(square_l2_norm, &inv_l2norm_multiplier, + &inv_l2norm_shift); for (int c = 0; c < depth; c++) { - int32 diff = - input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + int32 diff = input_data[depth * i + c] - input_zero_point; int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( - 128 * diff, inv_l2norm_multiplier, kReverseShift * inv_l2norm_shift); + 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); int32 unclamped_output_val = 128 + rescaled_diff; int32 output_val = std::min(255, std::max(0, unclamped_output_val)); - output_data[Offset(output_dims, c, i, 0, 0)] = - static_cast(output_val); + output_data[depth * i + c] = static_cast(output_val); } } } -inline void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { +template +inline void Add(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( @@ -1128,22 +1150,12 @@ inline void Add(int left_shift, const uint8* input1_data, } } -template inline void Add(const int16* input1_data, const Dims<4>& input1_dims, int input1_shift, const int16* input2_data, const Dims<4>& input2_dims, int input2_shift, int16 output_activation_min, int16 output_activation_max, int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); @@ -1169,6 +1181,28 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, + input2_shift, output_activation_min, output_activation_max, output_data, + output_dims); +} + // TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then @@ -1749,7 +1783,6 @@ template void Concatenation(int concat_dim, const Scalar* const* input_data, const Dims<4>* const* input_dims, int inputs_count, Scalar* output_data, const Dims<4>& output_dims) { - TFLITE_DCHECK_GT(inputs_count, 1); int concat_size = 0; for (int i = 0; i < inputs_count; i++) { for (int j = 0; j < 4; j++) { @@ -1760,7 +1793,9 @@ void Concatenation(int concat_dim, const Scalar* const* input_data, concat_size += ArraySize(*input_dims[i], concat_dim); } TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + // For now we don't have a model with a Concatenation with fused activation. + TFLITE_DCHECK_EQ(Ac, FusedActivationFunctionType::kNone); int outer_size = 1; for (int i = concat_dim + 1; i < 4; i++) { outer_size *= output_dims.sizes[i]; @@ -2238,18 +2273,21 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2273,12 +2311,12 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; total += - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } const float average = total / filter_count; - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(average, output_activation_min, output_activation_max); } @@ -2287,42 +2325,22 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2345,14 +2363,15 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, ++filter_x) { const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; - acc += input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } acc = (acc + filter_count / 2) / filter_count; acc = std::max(acc, output_activation_min); acc = std::min(acc, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(acc); } } @@ -2360,50 +2379,19 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2427,13 +2415,13 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; const float val = - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; sum_squares += val * val; filter_count++; } } const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(l2pool_result, output_activation_min, output_activation_max); } @@ -2442,40 +2430,19 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2499,10 +2466,10 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(max, output_activation_min, output_activation_max); } @@ -2511,42 +2478,22 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); TFLITE_DCHECK_GE(output_activation_min, 0); TFLITE_DCHECK_LE(output_activation_max, 255); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2570,12 +2517,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } max = std::max(max, output_activation_min); max = std::min(max, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(max); } } @@ -2583,38 +2530,6 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -2638,11 +2553,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2667,10 +2585,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, } } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2683,8 +2601,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2745,10 +2666,13 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2888,11 +2812,11 @@ log_x_for_x_greater_than_or_equal_to_1( input_val); } -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2906,8 +2830,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2971,9 +2898,9 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -2982,11 +2909,11 @@ inline void Logistic(const float* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); + uint8* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3020,9 +2947,9 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. @@ -3038,9 +2965,9 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -3049,12 +2976,12 @@ inline void Tanh(const float* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { const int32 output_zero_point = 128; - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3089,15 +3016,15 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); // F0 uses 0 integer bits, range [-1, 1]. // This is the return type of math functions such as tanh, logistic, @@ -3202,9 +3129,10 @@ inline void Gather(const T* input_data, const Dims<4>& input_dims, } } -inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, +template +inline void ResizeBilinear(const T* input_data, const Dims<4>& input_dims, const int32* output_size_data, - const Dims<4>& output_size_dims, float* output_data, + const Dims<4>& output_size_dims, T* output_data, const Dims<4>& output_dims, bool align_corners) { int32 batches = MatchingArraySize(input_dims, 3, output_dims, 3); int32 input_height = ArraySize(input_dims, 2); @@ -3236,15 +3164,15 @@ inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, int32 x0 = static_cast(std::floor(input_x)); int32 x1 = std::min(x0 + 1, input_width - 1); for (int c = 0; c < depth; ++c) { - float interpolation = input_data[Offset(input_dims, c, x0, y0, b)] * - (1 - (input_y - y0)) * - (1 - (input_x - x0)) + - input_data[Offset(input_dims, c, x0, y1, b)] * - (input_y - y0) * (1 - (input_x - x0)) + - input_data[Offset(input_dims, c, x1, y0, b)] * - (1 - (input_y - y0)) * (input_x - x0) + - input_data[Offset(input_dims, c, x1, y1, b)] * - (input_y - y0) * (input_x - x0); + T interpolation = + static_cast(input_data[Offset(input_dims, c, x0, y0, b)] * + (1 - (input_y - y0)) * (1 - (input_x - x0)) + + input_data[Offset(input_dims, c, x0, y1, b)] * + (input_y - y0) * (1 - (input_x - x0)) + + input_data[Offset(input_dims, c, x1, y0, b)] * + (1 - (input_y - y0)) * (input_x - x0) + + input_data[Offset(input_dims, c, x1, y1, b)] * + (input_y - y0) * (input_x - x0)); output_data[Offset(output_dims, c, x, y, b)] = interpolation; } } @@ -3257,8 +3185,18 @@ inline void ResizeBilinear(const float* input_data, const Dims<4>& input_dims, const int32* output_size_data, const Dims<4>& output_size_dims, float* output_data, const Dims<4>& output_dims) { - ResizeBilinear(input_data, input_dims, output_size_data, output_size_dims, - output_data, output_dims, /*align_corners=*/false); + ResizeBilinear(input_data, input_dims, output_size_data, + output_size_dims, output_data, output_dims, + /*align_corners=*/false); +} + +inline void ResizeBilinear(const uint8* input_data, const Dims<4>& input_dims, + const int32* output_size_data, + const Dims<4>& output_size_dims, uint8* output_data, + const Dims<4>& output_dims) { + ResizeBilinear(input_data, input_dims, output_size_data, + output_size_dims, output_data, output_dims, + /*align_corners=*/false); } template @@ -3418,7 +3356,7 @@ inline void Pad(const T* input_data, const Dims<4>& input_dims, template inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, - int begin_mask, int end_mask, + int begin_mask, int end_mask, int shrink_axis_mask, const std::vector& start_indices, const std::vector& stop_indices, const std::vector& strides, T* output_data, @@ -3430,20 +3368,24 @@ inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, TFLITE_DCHECK_EQ(strides.size(), 4); const int start_b = strided_slice::StartForAxis(begin_mask, start_indices, strides, input_dims.sizes, 3); - const int stop_b = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 3); + const int stop_b = + strided_slice::StopForAxis(end_mask, shrink_axis_mask, stop_indices, + strides, input_dims.sizes, 3, start_b); const int start_h = strided_slice::StartForAxis(begin_mask, start_indices, strides, input_dims.sizes, 2); - const int stop_h = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 2); + const int stop_h = + strided_slice::StopForAxis(end_mask, shrink_axis_mask, stop_indices, + strides, input_dims.sizes, 2, start_h); const int start_w = strided_slice::StartForAxis(begin_mask, start_indices, strides, input_dims.sizes, 1); - const int stop_w = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 1); + const int stop_w = + strided_slice::StopForAxis(end_mask, shrink_axis_mask, stop_indices, + strides, input_dims.sizes, 1, start_w); const int start_d = strided_slice::StartForAxis(begin_mask, start_indices, strides, input_dims.sizes, 0); - const int stop_d = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 0); + const int stop_d = + strided_slice::StopForAxis(end_mask, shrink_axis_mask, stop_indices, + strides, input_dims.sizes, 0, start_d); T* out_ptr = output_data; for (int in_b = start_b; @@ -3506,8 +3448,6 @@ inline void Exp(const T* input_data, const size_t num_elements, } // A generic reduce method that can be used for reduce_sum, reduce_mean, etc. -// It takes a reducer function as input and returns false when numeric overflow -// is detected. // This method iterates through input data and reduce elements along the // dimensions given in axis. template @@ -3515,8 +3455,7 @@ inline bool Reduce(const In* input_data, const int* input_dims, const int* output_dims, const int input_num_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, - Out reducer(Out current, const In in, bool* overflow), - Out* output_data) { + Out reducer(Out current, const In in), Out* output_data) { // Reset input iterator. TFLITE_DCHECK(input_num_dims > 0); for (int idx = 0; idx < input_num_dims; ++idx) { @@ -3528,10 +3467,8 @@ inline bool Reduce(const In* input_data, const int* input_dims, ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, input_iter, num_axis, axis); - bool overflow = false; - output_data[output_offset] = reducer(output_data[output_offset], - input_data[input_offset], &overflow); - if (overflow) return false; + output_data[output_offset] = + reducer(output_data[output_offset], input_data[input_offset]); } while (NextIndex(input_num_dims, input_dims, input_iter)); return true; } @@ -3566,7 +3503,7 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, Out* output_data) { - auto reducer = [](Out current, const In in, bool* overflow) -> Out { + auto reducer = [](Out current, const In in) -> Out { const Out actual_in = static_cast(in); return current + actual_in; }; @@ -3575,6 +3512,39 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, output_data); } +// Computes the sum of elements across dimensions given in axis. +template +inline bool Sum(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int num_axis_dimensions, bool keep_dims, + int* temp_index, int* resolved_axis) { + // Reset output data. + size_t num_outputs = 1; + for (int idx = 0; idx < output_num_dims; ++idx) { + size_t current = static_cast(output_dims[idx]); + // Overflow prevention. + if (num_outputs > std::numeric_limits::max() / current) { + return false; + } + num_outputs *= current; + } + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = T(); + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + return ReduceSumImpl(input_data, input_dims, output_dims, + input_num_dims, output_num_dims, resolved_axis, + num_resolved_axis, temp_index, output_data); +} + // Computes the mean of elements across dimensions given in axis. // It does so in two stages, first calculates the sum of elements along the axis // then divides it by the number of element in axis. @@ -3777,7 +3747,7 @@ void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, template void Transpose(const T* input, const Dims<4>& input_dims, T* output, - const Dims<4>& output_dims, int* permuted_axes) { + const Dims<4>& output_dims, const int* permuted_axes) { int out_sizes[4]; // Compute the inverse permutation array so we can do an output centered // transpose. Also, check to make sure output_dims is matching input_dims. @@ -3808,10 +3778,11 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, const float* filter_data, const Dims<4>& filter_dims, int stride_width, int stride_height, int pad_width, int pad_height, float* output_data, - const Dims<4>& output_dims) { + const Dims<4>& output_dims, float* /*im2col_data*/, + const Dims<4>& /*im2col_dims*/) { const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); - const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 0); + const int output_depth = MatchingArraySize(filter_dims, 3, output_dims, 0); const int input_height = ArraySize(input_dims, 2); const int input_width = ArraySize(input_dims, 1); const int filter_height = ArraySize(filter_dims, 2); @@ -3826,7 +3797,8 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, // computing their influence on the output, rather than looping through the // output elements in the typical "gather" access pattern of a conv. We // therefore must initialize the output array to zero. - for (int i = 0; i < FlatSize(output_dims); i++) { + const int num_elements = FlatSize(output_dims); + for (int i = 0; i < num_elements; i++) { output_data[i] = 0.0f; } @@ -3851,8 +3823,8 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, float input_value = input_data[Offset(input_dims, in_channel, in_x, in_y, batch)]; float filter_value = - filter_data[Offset(filter_dims, out_channel, filter_x, - filter_y, in_channel)]; + filter_data[Offset(filter_dims, in_channel, filter_x, + filter_y, out_channel)]; output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] += input_value * filter_value; } @@ -4115,6 +4087,36 @@ inline void SparseToDense(const std::vector>& indices, } } +template +inline void Pow(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = std::pow(input1_data[i], input2_data[i]); + } +} + +template +inline void BroadcastPow(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + for (int b = 0; b < ArraySize(output_dims, 3); ++b) { + for (int y = 0; y < ArraySize(output_dims, 2); ++y) { + for (int x = 0; x < ArraySize(output_dims, 1); ++x) { + for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + output_data[Offset(output_dims, c, x, y, b)] = + std::pow(input1_data[SubscriptToIndex(desc1, c, x, y, b)], + input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + } + } + } + } +} + } // namespace reference_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/resize_bilinear_float_test.cc b/tensorflow/contrib/lite/kernels/internal/resize_bilinear_test.cc similarity index 60% rename from tensorflow/contrib/lite/kernels/internal/resize_bilinear_float_test.cc rename to tensorflow/contrib/lite/kernels/internal/resize_bilinear_test.cc index c1c50dff4d2a966bff70853701334f599ee03849..3d8765f11b2941ef5871c7db8e3582e506713aa6 100644 --- a/tensorflow/contrib/lite/kernels/internal/resize_bilinear_float_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/resize_bilinear_test.cc @@ -24,9 +24,10 @@ limitations under the License. namespace tflite { namespace { +template void TestOneResizeBilinear(int batch, int depth, int input_width, int input_height, int output_width, - int output_height) { + int output_height, float error_threshold) { Dims<4> input_dims_inference = MakeDimsForInference(depth, input_width, input_height, batch); Dims<4> output_dims_inference = @@ -36,14 +37,15 @@ void TestOneResizeBilinear(int batch, int depth, int input_width, const int output_buffer_size = RequiredBufferSizeForDims(output_dims_inference); - std::vector input_data(input_buffer_size, 0); - std::vector reference_output_data(output_buffer_size, 0); + std::vector input_data(input_buffer_size, 0); + std::vector reference_output_data(output_buffer_size, 0); // Initialize the output data with something other than zero, so we can catch // issue with kernels failing to initialize the output. - std::vector output_data(output_buffer_size, 3.1415); + std::vector output_data(output_buffer_size, 3); - const float input_amplitude = 1.f; - FillRandom(&input_data, -input_amplitude, input_amplitude); + const T min_amplitude = static_cast(0); + const T max_amplitude = static_cast(255); + FillRandom(&input_data, min_amplitude, max_amplitude); Dims<4> output_size_dims = MakeDimsForInference(2, 1, 1, 1); std::vector output_size_data = {output_height, output_width}; @@ -58,14 +60,46 @@ void TestOneResizeBilinear(int batch, int depth, int input_width, double sum_diff = 0; float max_abs_val = 0; for (int i = 0; i < output_buffer_size; i++) { - sum_diff += std::abs(output_data[i] - reference_output_data[i]); - max_abs_val = std::max(max_abs_val, std::abs(reference_output_data[i])); + sum_diff += std::abs(static_cast(output_data[i]) - + static_cast(reference_output_data[i])); + max_abs_val = std::max( + max_abs_val, std::abs(static_cast(reference_output_data[i]))); } if (sum_diff != 0.f) { const float mean_diff = static_cast(sum_diff / output_buffer_size); const float relative_error = std::abs(mean_diff) / max_abs_val; - ASSERT_LT(relative_error, 1e-5f); + ASSERT_LT(relative_error, error_threshold); + } +} + +TEST(ResizeBilinear, TestResizeBilinear8Bit) { + const int kTestsToRun = 100 * 1000; + for (int i = 0; i < kTestsToRun; i++) { + const int batch = ExponentialRandomPositiveInt(0.9f, 3, 20); + const int depth = ExponentialRandomPositiveInt(0.9f, 6, 50); + const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); + const int input_height = ExponentialRandomPositiveInt(0.9f, 20, 200); + const int output_width = ExponentialRandomPositiveInt(0.9f, 20, 200); + const int output_height = ExponentialRandomPositiveInt(0.9f, 20, 200); + + TestOneResizeBilinear(batch, depth, input_width, input_height, + output_width, output_height, 0.025); + } +} + +TEST(ResizeBilinear2x2, TestResizeBilinear8Bit) { + const int kTestsToRun = 100 * 1000; + for (int i = 0; i < kTestsToRun; i++) { + const int batch = ExponentialRandomPositiveInt(0.9f, 3, 20); + const int depth = ExponentialRandomPositiveInt(0.9f, 6, 50); + const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); + const int input_height = ExponentialRandomPositiveInt(0.9f, 20, 200); + const int output_width = input_width * 2; + const int output_height = input_height * 2; + + TestOneResizeBilinear(batch, depth, input_width, input_height, + output_width, output_height, 1e-5); } } @@ -79,8 +113,8 @@ TEST(ResizeBilinear, TestResizeBilinear) { const int output_width = ExponentialRandomPositiveInt(0.9f, 20, 200); const int output_height = ExponentialRandomPositiveInt(0.9f, 20, 200); - TestOneResizeBilinear(batch, depth, input_width, input_height, output_width, - output_height); + TestOneResizeBilinear(batch, depth, input_width, input_height, + output_width, output_height, 1e-5); } } @@ -94,8 +128,8 @@ TEST(ResizeBilinear2x2, TestResizeBilinear) { const int output_width = input_width * 2; const int output_height = input_height * 2; - TestOneResizeBilinear(batch, depth, input_width, input_height, output_width, - output_height); + TestOneResizeBilinear(batch, depth, input_width, input_height, + output_width, output_height, 1e-5); } } } // namespace diff --git a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc index d781a7b642036f3c5ddaa366f257fe26511c83c3..a7dad3c14e60fac9da9c0bcfd5d1d4c8f10b71c7 100644 --- a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, float beta, + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float Softmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::Softmax(reference_dequant_data.data(), dims_common, beta, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::Softmax(reference_dequant_data.data(), shape_common, beta, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for Softmax, under which 256 represents 1, but // we limit this to 255. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -91,15 +93,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the Softmax and compares against the float reference implementation and // the quantized reference implementation. -void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, int stride, - float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_softmax_output(buffer_size); std::vector reference_float_softmax_output(buffer_size); std::vector reference_quant_softmax_output(buffer_size); - RunSoftmaxFloatReference(input_data, dims_common, input_offset, input_scale, + RunSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_softmax_output.data()); int32 input_beta_multiplier; @@ -113,21 +115,21 @@ void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - optimized_softmax_output.data(), dims_common); - reference_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_softmax_output.data(), shape_common); + reference_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - reference_quant_softmax_output.data(), dims_common); + reference_quant_softmax_output.data(), shape_common); CheckOutputData(optimized_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_softmax_output.data(), - reference_quant_softmax_output.data(), dims_common, + reference_quant_softmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -150,13 +152,13 @@ bool TryOneUniformSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); const float beta = 1.0f + ExponentialRandomPositiveFloat(0.9f, 2, 10); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -188,14 +190,14 @@ bool TryOneSkyscraperSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h b/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h index ef77371bf65cc975dfa35275c8daa32de112a249..5994fad5c73df1dde6e33ba46dbd6e0802ea61be 100644 --- a/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h +++ b/tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h @@ -74,12 +74,22 @@ inline int StartForAxis(int begin_mask, // size 4, this function would return 4 as the stop, because it is one past the // "real" indices of 0, 1, 2 & 3. template -inline int StopForAxis(int end_mask, std::vector const& stop_indices, +inline int StopForAxis(int end_mask, int shrink_axis_mask, + std::vector const& stop_indices, std::vector const& strides, - int const* input_shape, int axis) { + int const* input_shape, int axis, int start_for_axis) { // Begin with the specified index + const bool shrink_axis = shrink_axis_mask & (1 << axis); int stop = stop_indices[axis]; + // When shrinking an axis, the end position does not matter (and can be + // incorrect when negative indexing is used, see Issue #19260). Always use + // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has + // already been adjusted for negative indices. + if (shrink_axis) { + stop = start_for_axis + 1; + } + // end_mask override if (end_mask & (1 << axis)) { if (strides[axis] > 0) { @@ -93,7 +103,7 @@ inline int StopForAxis(int end_mask, std::vector const& stop_indices, } // Handle negative indices - int axis_size = input_shape[axis]; + const int axis_size = input_shape[axis]; if (stop < 0) { stop += axis_size; } diff --git a/tensorflow/contrib/lite/kernels/internal/tensor.h b/tensorflow/contrib/lite/kernels/internal/tensor.h index ce887cea8b794b4b0cfd31722581cf9327be625e..ee2af5b46046c9e8bdc5816d5b6e9e9100cdc240 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor.h @@ -15,6 +15,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_TENSOR_H_ #define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_TENSOR_H_ +#include #include #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" @@ -34,6 +35,11 @@ inline uint8_t* GetTensorData(TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.uint8 : nullptr; } +template <> +inline int16_t* GetTensorData(TfLiteTensor* tensor) { + return tensor != nullptr ? tensor->data.i16 : nullptr; +} + template <> inline int32_t* GetTensorData(TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.i32 : nullptr; @@ -49,6 +55,13 @@ inline bool* GetTensorData(TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.b : nullptr; } +template <> +inline std::complex* GetTensorData(TfLiteTensor* tensor) { + return tensor != nullptr + ? reinterpret_cast*>(tensor->data.c64) + : nullptr; +} + template inline const T* GetTensorData(const TfLiteTensor* tensor); @@ -62,6 +75,11 @@ inline const uint8_t* GetTensorData(const TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.uint8 : nullptr; } +template <> +inline const int16_t* GetTensorData(const TfLiteTensor* tensor) { + return tensor != nullptr ? tensor->data.i16 : nullptr; +} + template <> inline const int32_t* GetTensorData(const TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.i32 : nullptr; @@ -77,6 +95,13 @@ inline const bool* GetTensorData(const TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.b : nullptr; } +template <> +inline const std::complex* GetTensorData(const TfLiteTensor* tensor) { + return tensor != nullptr + ? reinterpret_cast*>(tensor->data.c64) + : nullptr; +} + inline int RemapDim(int max_dimensions, int d) { return max_dimensions - d - 1; } @@ -114,6 +139,19 @@ inline Dims<4> GetTensorDims(const TfLiteTensor* tensor) { return GetTensorDims(dims->data, dims->size); } +inline RuntimeShape GetTensorShape(std::vector data) { + return RuntimeShape(data.size(), data.data()); +} + +inline RuntimeShape GetTensorShape(const TfLiteTensor* tensor) { + if (tensor == nullptr) { + return RuntimeShape(); + } + + auto* dims = tensor->dims; + return RuntimeShape(dims->size, dims->data); +} + // A list of tensors in a format that can be used by kernels like split and // concatenation. template diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc index 14ee528394b6872d9e79969db0e431658277f56b..aa0d49ae4db6b4952b5864166f4a13459763cf44 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc @@ -63,7 +63,8 @@ TEST(uKernels, SymmetricQuantizeFloatsTest) { EXPECT_EQ(min, -640); EXPECT_EQ(max, 1000); - EXPECT_NEAR(scaling_factor, 0.127, 1e-6); // EQ won't work due to fpoint. + // EQ won't work due to fpoint. + EXPECT_NEAR(scaling_factor, 1000 / 127.0, 1e-6); EXPECT_THAT(output, testing::ElementsAreArray({-81, -81, -80, 1, 0, -1, -1, 0, 127})); } @@ -95,7 +96,7 @@ TEST(uKernels, SymmetricQuantizeFloatsAllAlmostZeroTest) { EXPECT_NEAR(min, -9e-05, 1e-6); EXPECT_NEAR(max, 0.0002, 1e-6); - EXPECT_EQ(scaling_factor, 635000); + EXPECT_NEAR(scaling_factor, 1.57e-6, 1e-6); EXPECT_THAT(output, testing::ElementsAreArray({-6, 19, -4, -57, 1, 25, 6, 127, 0})); } diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 0c7fb7a76a5075652e705e65f5379596dfa77c78..fa2420713fea4faa3596251a95c2ed9606878b98 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -25,6 +25,67 @@ namespace tflite { enum class FusedActivationFunctionType : uint8 { kNone, kRelu6, kRelu1, kRelu }; enum class PaddingType { kNone, kSame, kValid }; +// This enumeration allows for non-default formats for the weights array +// of a fully-connected operator, allowing the use of special optimized +// runtime paths. +enum class FullyConnectedWeightsFormat : uint8 { + // Default format (flat 2D layout, the inner contiguous dimension + // is input_depth, the outer non-contiguous dimension is output_depth) + kDefault, + // Summary: optimized layout for fast CPU runtime implementation, + // aimed specifically at ARM CPUs at the moment, and specialized for + // 8-bit quantized layers. + // + // The use case we're concerned with here is: 8-bit quantization, + // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in + // a key application that drove this), very small batch size (e.g. 1 -- 4). + // + // Even with 8-bit quantization of weights, the performance of memory + // accesses to the weights can become the dominant issue when + // the batch size is small, so each weight value is used in only a few + // arithmetic ops, i.e. the fully-connected node has a low arithmetic + // intensity. The specific issues that arise are of three kinds: + // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory + // bound. That's the "good" issue to run into. + // (2) One may run into sub-optimal pre-fetching: the data hasn't been + // prefetched into the cache by the time we need it. + // (3) One may run into cache aliasing: multiple values that are + // pre-fetched, alias each other in the L1 cache (which typically + // has only 4-way set associativity in ARM CPUs) and thus evict + // each other before we get to using them. + // + // The point of this shuffling is to avoid issues (2) and (3) so that + // we get as fast as possible given only the hard constraint (1). + // This is achieved by turning the difficulty into a solution: the + // difficulty, that each value loaded from memory is used only in + // one kernel iteration, making this operation memory-intensive, hints at + // the solution, of shuffling the weights so that they are stored in the + // exact order as the kernel needs to load them, so that the memory + // accesses made by the kernel are trivial. This solves (2) because the + // trivial memory access pattern allows the CPU's automatic prefetching + // to perform very well (no need even for preload instructions), and this + // solves (3) because the values being loaded concurrently are now + // contiguous in the address space, thus don't alias each other in the cache. + // + // On ARM, we typically want our kernel to process a 4x16 block of weights + // at a time, because: + // - 16 is the number of bytes in a NEON register. + // - 4 is how many rows we need to handle concurrently in the kernel in + // order to have sufficient mutual independence of instructions to + // maximize arithmetic throughput. + // + // Finally, the 'Int8' part in the name refers to the fact that this + // weights format has each weights value encoded as a signed int8 value, + // even if the data type of the weights buffer is uint8. This is intended + // to save runtime kernels the effort to have to XOR the top bit of these + // bytes before using them in signed arithmetic, see this file for more + // explanations on the 'signed int8 trick' in matrix multiplication kernels: + // + // tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc + // + kShuffled4x16Int8, +}; + // Quantization parameters, determining the mapping of quantized values // to real values (i.e. determining how quantized values are mathematically // interpreted). @@ -65,6 +126,10 @@ class RuntimeShape { ReplaceWith(dimensions_count, dims_data); } + RuntimeShape(const std::initializer_list init_list) : size_(0) { + BuildFrom(init_list); + } + ~RuntimeShape() { if (size_ > kMaxSmallSize) { delete[] dims_pointer_; @@ -121,6 +186,10 @@ class RuntimeShape { } } + inline void BuildFrom(const std::initializer_list init_list) { + BuildFrom>(init_list); + } + // Returns the total count of elements, that is the size when flattened into a // vector. inline int FlatSize() const { @@ -142,6 +211,22 @@ class RuntimeShape { }; }; +// Converts inference-style shape to legacy tflite::Dims<4>. +inline tflite::Dims<4> ToRuntimeDims(const tflite::RuntimeShape& array_shape) { + tflite::Dims<4> result; + const int dimensions_count = array_shape.DimensionsCount(); + TFLITE_CHECK_LE(dimensions_count, 4); + int cum_prod = 1; + for (int i = 0; i < 4; i++) { + const int new_dim = + (i < dimensions_count) ? array_shape.Dims(dimensions_count - 1 - i) : 1; + result.sizes[i] = new_dim; + result.strides[i] = cum_prod; + cum_prod *= new_dim; + } + return result; +} + // Gets next index to iterate through a multidimensional array. inline bool NextIndex(const int num_dims, const int* dims, int* current) { TFLITE_DCHECK_GT(num_dims, 0); @@ -194,6 +279,15 @@ inline size_t ReducedOutputOffset(const int num_dims, const int* dims, return offset; } +inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < shape.Dims(0)); + TFLITE_DCHECK(i1 >= 0 && i1 < shape.Dims(1)); + TFLITE_DCHECK(i2 >= 0 && i2 < shape.Dims(2)); + TFLITE_DCHECK(i3 >= 0 && i3 < shape.Dims(3)); + const int* dims_data = shape.DimsData(); + return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; +} + inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); TFLITE_DCHECK(i1 >= 0 && i1 < dims.sizes[1]); @@ -208,6 +302,9 @@ inline int Offset(const Dims<4>& dims, int* index) { } // Get array size, DCHECKing that the dim index is in range. +// +// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() +// already performs this check. template int ArraySize(const Dims& array, int index) { TFLITE_DCHECK(index >= 0 && index < N); @@ -229,6 +326,21 @@ int MatchingArraySize(const ArrayType1& array1, int index1, return MatchingArraySize(array1, index1, args...); } +// Get common shape dim, DCHECKing that they all agree. +inline int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return shape1.Dims(index1); +} + +template +int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2, Args... args) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return MatchingDim(shape1, index1, args...); +} + +// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). template inline int FlatSize(const Dims& dims) { int flat_size = 1; @@ -243,6 +355,50 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { return FlatSize(dims); } +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return shape.FlatSize(); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); +} + // Flat size calculation, checking that dimensions match with one or more other // arrays. template @@ -269,7 +425,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2); + return MatchingFlatSize(dims, check_dims_1, check_dims_2); } template @@ -280,7 +436,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2, check_dims_3); + return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); } // Data is required to be contiguous, and so many operators can use either the @@ -348,6 +504,72 @@ inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, check_dims_3); } +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { + const int dims_count = shape.DimensionsCount(); + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); + const auto* dims_data = shape.DimsData(); + int flat_size = 1; + for (int i = 0; i < dims_count; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims_data[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return FlatSizeSkipDim(shape, skip_dim); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, + check_shape_3); +} + template bool IsPackedWithoutStrides(const Dims& dims) { int expected_stride = 1; diff --git a/tensorflow/contrib/lite/kernels/kernel_util.cc b/tensorflow/contrib/lite/kernels/kernel_util.cc index 184028427fb193aa99cf155961c16eda1298e326..08f942c933552aa6ca7369550c928efba9e2e93e 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.cc +++ b/tensorflow/contrib/lite/kernels/kernel_util.cc @@ -43,12 +43,11 @@ TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, return kTfLiteOk; } -void CalculateActivationRangeUint8(TfLiteFusedActivation activation, - TfLiteTensor* output, int32_t* act_min, - int32_t* act_max) { - const int32_t qmin = std::numeric_limits::min(); - const int32_t qmax = std::numeric_limits::max(); - +namespace { +void CalculateActivationRangeQuantizedImpl(TfLiteFusedActivation activation, + int32_t qmin, int32_t qmax, + TfLiteTensor* output, + int32_t* act_min, int32_t* act_max) { const auto scale = output->params.scale; const auto zero_point = output->params.zero_point; @@ -70,23 +69,38 @@ void CalculateActivationRangeUint8(TfLiteFusedActivation activation, *act_max = qmax; } } - -void CalculateActivationRangeFloat(TfLiteFusedActivation activation, - float* activation_min, - float* activation_max) { - if (activation == kTfLiteActRelu) { - *activation_min = 0.f; - *activation_max = std::numeric_limits::max(); - } else if (activation == kTfLiteActRelu6) { - *activation_min = 0.f; - *activation_max = 6.f; - } else if (activation == kTfLiteActRelu1) { - *activation_min = -1.f; - *activation_max = 1.f; +} // namespace + +TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, + TfLiteFusedActivation activation, + TfLiteTensor* output, + int32_t* act_min, + int32_t* act_max) { + int32_t qmin = 0; + int32_t qmax = 0; + if (output->type == kTfLiteUInt8) { + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + } else if (output->type == kTfLiteInt16) { + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); } else { - *activation_min = std::numeric_limits::lowest(); - *activation_max = std::numeric_limits::max(); + TF_LITE_ENSURE(context, false); } + + CalculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, act_min, + act_max); + return kTfLiteOk; +} + +void CalculateActivationRangeUint8(TfLiteFusedActivation activation, + TfLiteTensor* output, int32_t* act_min, + int32_t* act_max) { + const int32_t qmin = std::numeric_limits::min(); + const int32_t qmax = std::numeric_limits::max(); + + CalculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, act_min, + act_max); } bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2) { diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index 82cded36f2ed2777daccafee5890f47c0d7254e8..c8ce3c917d5bf66e01fbae95c18dfe97b3c84bae 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -15,6 +15,8 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_ #define TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_ +#include + #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" @@ -86,14 +88,35 @@ TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, TfLiteTensor* output, double* multiplier); -// Calculates the useful range of an activation layer given its activation -// tensor. +// Calculates the useful quantized range of an activation layer given its +// activation tensor. +TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, + TfLiteFusedActivation activation, + TfLiteTensor* output, + int32_t* act_min, + int32_t* act_max); void CalculateActivationRangeUint8(TfLiteFusedActivation activation, TfLiteTensor* output, int32_t* act_min, int32_t* act_max); -void CalculateActivationRangeFloat(TfLiteFusedActivation activation, - float* activation_min, - float* activation_max); +// Calculates the useful range of an activation layer given its activation +// tensor.a +template +void CalculateActivationRange(TfLiteFusedActivation activation, + T* activation_min, T* activation_max) { + if (activation == kTfLiteActRelu) { + *activation_min = 0; + *activation_max = std::numeric_limits::max(); + } else if (activation == kTfLiteActRelu6) { + *activation_min = 0; + *activation_max = 6; + } else if (activation == kTfLiteActRelu1) { + *activation_min = -1; + *activation_max = 1; + } else { + *activation_min = std::numeric_limits::lowest(); + *activation_max = std::numeric_limits::max(); + } +} // Return true if the given tensors have the same shape. bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2); diff --git a/tensorflow/contrib/lite/kernels/l2norm.cc b/tensorflow/contrib/lite/kernels/l2norm.cc index 3205c1cc52724207904621a5870636841ef379fe..a7b54c6b842332feb2d9e7179e79ae054bd23bb9 100644 --- a/tensorflow/contrib/lite/kernels/l2norm.cc +++ b/tensorflow/contrib/lite/kernels/l2norm.cc @@ -70,8 +70,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (output->type == kTfLiteFloat32) { #define TF_LITE_L2NORM(type) \ type::L2Normalization( \ - GetTensorData(input), GetTensorDims(input), \ - GetTensorData(output), GetTensorDims(output)) + GetTensorData(input), GetTensorShape(input), \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2NORM(reference_ops); @@ -81,10 +81,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } #undef TF_LITE_L2NORM } else if (output->type == kTfLiteUInt8) { -#define TF_LITE_L2NORM(type) \ - type::L2Normalization(GetTensorData(input), GetTensorDims(input), \ - input->params.zero_point, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_L2NORM(type) \ + type::L2Normalization(GetTensorData(input), GetTensorShape(input), \ + input->params.zero_point, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2NORM(reference_ops); diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc index 62820a2f5113cb6ae252386aaf3842135383b79f..9a8d35e82cbc3a7e55246e6c06599b2838d1ee67 100644 --- a/tensorflow/contrib/lite/kernels/log_softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc @@ -90,10 +90,9 @@ TEST(LogSoftmaxOpTest, CompareWithTFmini) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::LogSoftmax(input_buffer, input_dims, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::LogSoftmax(input_buffer, input_shape, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index eb26a02455ce2afccaa081a72d93a9ceeca746cc..3577ae6caa1e02ce2e5db2e8054ba9c2fccbe93e 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/gemm_support.h" #include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" #include "tensorflow/contrib/lite/kernels/internal/tensor.h" @@ -37,14 +38,17 @@ namespace builtin { namespace lstm { struct OpData { - // Which kernel type to use. Full kernel (18-inputs) or basic kernel - // (5-inputs). + // Which kernel type to use. Full kernel (18 or 20 inputs) or basic kernel + // (5 inputs). TfLiteLSTMKernelType kernel_type; - // Only used by full kernel. + + // These fields are only used by full kernel. + int activation_state_tensor_index; + int cell_state_tensor_index; int scratch_tensor_index; }; -// For full inputs kernel (18-inputs). +// For full inputs kernel (18 or 20 inputs). namespace full { // Input Tensors of size {n_batch, n_input} @@ -78,7 +82,16 @@ constexpr int kProjectionWeightsTensor = 16; // Optional // Projection bias tensor of size {n_output} constexpr int kProjectionBiasTensor = 17; // Optional +// If the node has 20 inputs, the following 2 tensors are used as state tensors. +// These are defined as variable tensors, and will be modified by this op. +constexpr int kInputActivationStateTensor = 18; +constexpr int kInputCellStateTensor = 19; + // Output tensors. +// * If the node has 18 inputs, these 2 tensors are used as state tensors. +// * If the node has 20 inputs, these 2 tensors are ignored. +// TODO(ycling): Make the 2 output state tensors optional, and propagate the +// state to output tensors when the 2 tensors present. constexpr int kOutputStateTensor = 0; constexpr int kCellStateTensor = 1; constexpr int kOutputTensor = 2; @@ -246,10 +259,31 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { OpData* op_data = reinterpret_cast(node->user_data); - // Check we have all the inputs and outputs we need. - TF_LITE_ENSURE_EQ(context, node->inputs->size, 18); TF_LITE_ENSURE_EQ(context, node->outputs->size, 3); + // True if the node is using input variable state tensors. It means: + // * The state tensors are defined as inputs. In this case it would be the + // 19th and 20th input tensors. + // * Otherwise, the output tensors are used to store states. + bool use_input_variable_states; + if (node->inputs->size == 20) { + use_input_variable_states = true; + op_data->activation_state_tensor_index = + node->inputs->data[kInputActivationStateTensor]; + op_data->cell_state_tensor_index = + node->inputs->data[kInputCellStateTensor]; + } else if (node->inputs->size == 18) { + use_input_variable_states = false; + op_data->activation_state_tensor_index = + node->outputs->data[kOutputStateTensor]; + op_data->cell_state_tensor_index = node->outputs->data[kCellStateTensor]; + } else { + context->ReportError( + context, "The LSTM Full kernel expects 18 or 20 inputs. Got %d inputs", + node->inputs->size); + return kTfLiteError; + } + // Inferring batch size, number of outputs and number of cells from the // input tensors. const TfLiteTensor* input = GetInput(context, node, kInputTensor); @@ -274,34 +308,47 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check that input tensor dimensions matches with each other. CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); - // Get the pointer to output, output_state and cell_state tensors. + // Get the pointer to output, activation_state and cell_state tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - // Resize the output, output_state and cell_state tensors. + TfLiteTensor* activation_state = + &context->tensors[op_data->activation_state_tensor_index]; + TfLiteTensor* cell_state = + &context->tensors[op_data->cell_state_tensor_index]; + + if (use_input_variable_states) { + // Check the shape of input state tensors. + // These tensor may be 1D or 2D. It's fine as long as the total size is + // correct. + TF_LITE_ENSURE_EQ(context, NumElements(activation_state), + n_batch * n_output); + TF_LITE_ENSURE_EQ(context, NumElements(cell_state), n_batch * n_cell); + } else { + // If the state tensors are outputs, this function takes the + // responsibility to resize the state tensors. + TfLiteIntArray* activation_state_size = TfLiteIntArrayCreate(2); + activation_state_size->data[0] = n_batch; + activation_state_size->data[1] = n_output; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, activation_state, + activation_state_size)); + + TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); + cell_size->data[0] = n_batch; + cell_size->data[1] = n_cell; + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, cell_state, cell_size)); + // Mark state tensors as persistent tensors. + activation_state->allocation_type = kTfLiteArenaRwPersistent; + cell_state->allocation_type = kTfLiteArenaRwPersistent; + } + + // Resize the output tensors. TfLiteIntArray* output_size = TfLiteIntArrayCreate(2); output_size->data[0] = n_batch; output_size->data[1] = n_output; TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output, output_size)); - TfLiteIntArray* output_state_size = TfLiteIntArrayCreate(2); - output_state_size->data[0] = n_batch; - output_state_size->data[1] = n_output; - TF_LITE_ENSURE_OK( - context, context->ResizeTensor(context, output_state, output_state_size)); - - TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); - cell_size->data[0] = n_batch; - cell_size->data[1] = n_cell; - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, cell_state, cell_size)); - - // Mark state tensors as persistent tensors. - output_state->allocation_type = kTfLiteArenaRwPersistent; - cell_state->allocation_type = kTfLiteArenaRwPersistent; - // The weights are of consistent type, so it suffices to check one. // TODO(mirkov): create a utility/macro for this check, so all Ops can use it. const bool is_hybrid_op = (input_to_output_weights->type == kTfLiteUInt8 && @@ -337,7 +384,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { if (is_hybrid_op) { // Allocate temporary tensors to store quantized values of input, - // output_state and cell_state tensors. + // activation_state and cell_state tensors. node->temporaries->data[1] = op_data->scratch_tensor_index + 1; TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); input_quantized->type = kTfLiteUInt8; @@ -348,17 +395,17 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { input_quantized_size)); } node->temporaries->data[2] = op_data->scratch_tensor_index + 2; - TfLiteTensor* output_state_quantized = + TfLiteTensor* activation_state_quantized = GetTemporary(context, node, /*index=*/2); - output_state_quantized->type = kTfLiteUInt8; - output_state_quantized->allocation_type = kTfLiteArenaRw; - if (!TfLiteIntArrayEqual(output_state_quantized->dims, - output_state->dims)) { - TfLiteIntArray* output_state_quantized_size = - TfLiteIntArrayCopy(output_state->dims); - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, output_state_quantized, - output_state_quantized_size)); + activation_state_quantized->type = kTfLiteUInt8; + activation_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(activation_state_quantized->dims, + activation_state->dims)) { + TfLiteIntArray* activation_state_quantized_size = + TfLiteIntArrayCopy(activation_state->dims); + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, activation_state_quantized, + activation_state_quantized_size)); } node->temporaries->data[3] = op_data->scratch_tensor_index + 3; TfLiteTensor* cell_state_quantized = @@ -438,7 +485,7 @@ TfLiteStatus EvalFloat( const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* activation_state, TfLiteTensor* cell_state, TfLiteTensor* output) { const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; @@ -499,7 +546,7 @@ TfLiteStatus EvalFloat( const float* cell_bias_ptr = cell_bias->data.f; const float* output_gate_bias_ptr = output_gate_bias->data.f; - float* output_state_ptr = output_state->data.f; + float* activation_state_ptr = activation_state->data.f; float* cell_state_ptr = cell_state->data.f; float* output_ptr_batch = output->data.f; @@ -512,8 +559,8 @@ TfLiteStatus EvalFloat( cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, output_ptr_batch); + activation_state_ptr, cell_state_ptr, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, output_ptr_batch); return kTfLiteOk; } @@ -536,9 +583,9 @@ TfLiteStatus EvalHybrid( const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, - TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, - TfLiteTensor* output_state, TfLiteTensor* cell_state, - TfLiteTensor* output) { + TfLiteTensor* activation_state_quantized, + TfLiteTensor* cell_state_quantized, TfLiteTensor* activation_state, + TfLiteTensor* cell_state, TfLiteTensor* output) { const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; // n_cell and n_output will be the same size when there is no projection. @@ -639,15 +686,15 @@ TfLiteStatus EvalHybrid( const float* cell_bias_ptr = cell_bias->data.f; const float* output_gate_bias_ptr = output_gate_bias->data.f; - float* output_state_ptr = output_state->data.f; + float* activation_state_ptr = activation_state->data.f; float* cell_state_ptr = cell_state->data.f; float* output_ptr_batch = output->data.f; // Temporary storage for quantized values and scaling factors. int8_t* quantized_input_ptr = reinterpret_cast(input_quantized->data.uint8); - int8_t* quantized_output_state_ptr = - reinterpret_cast(output_state_quantized->data.uint8); + int8_t* quantized_activation_state_ptr = + reinterpret_cast(activation_state_quantized->data.uint8); int8_t* quantized_cell_state_ptr = reinterpret_cast(cell_state_quantized->data.uint8); float* scaling_factors_ptr = scaling_factors->data.f; @@ -672,14 +719,16 @@ TfLiteStatus EvalHybrid( input_gate_scratch, forget_gate_scratch, cell_scratch, output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, recovered_cell_weights_ptr, quantized_input_ptr, - quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, - cell_state_ptr, output_ptr_batch); + quantized_activation_state_ptr, quantized_cell_state_ptr, + activation_state_ptr, cell_state_ptr, output_ptr_batch); return kTfLiteOk; } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const auto* params = reinterpret_cast(node->builtin_data); + OpData* op_data = reinterpret_cast(node->user_data); + const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* input_to_input_weights = @@ -723,8 +772,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Index the scratch buffers pointers to the global scratch buffer. TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); + TfLiteTensor* activation_state = + &context->tensors[op_data->activation_state_tensor_index]; + TfLiteTensor* cell_state = + &context->tensors[op_data->cell_state_tensor_index]; + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); // TODO(mirkov): add a check that weights are all uint8s or all floats. @@ -738,11 +790,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, projection_weights, projection_bias, params, - scratch_buffer, output_state, cell_state, output); + scratch_buffer, activation_state, cell_state, output); } case kTfLiteUInt8: { TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); - TfLiteTensor* output_state_quantized = + TfLiteTensor* activation_state_quantized = GetTemporary(context, node, /*index=*/2); TfLiteTensor* cell_state_quantized = GetTemporary(context, node, /*index=*/3); @@ -760,8 +812,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, projection_weights, projection_bias, params, scratch_buffer, scaling_factors, prod_scaling_factors, recovered_cell_weights, - input_quantized, output_state_quantized, cell_state_quantized, - output_state, cell_state, output); + input_quantized, activation_state_quantized, cell_state_quantized, + activation_state, cell_state, output); } default: context->ReportError(context, "Type %d is not currently supported.", @@ -805,13 +857,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, node->inputs->size == kInputNum); TF_LITE_ENSURE(context, node->outputs->size == kOutputNum); - // Only Float32 is supported currently. - // TODO(ycling): Implement quantize uint8 support. - for (int index = 0; index < node->inputs->size; ++index) { - TfLiteTensor* tensor = &context->tensors[node->inputs->data[index]]; - TF_LITE_ENSURE_EQ(context, tensor->type, kTfLiteFloat32); - } - const TfLiteTensor* input = GetInput(context, node, kInputData); const TfLiteTensor* prev_activation = GetInput(context, node, kInputPrevActivation); @@ -821,15 +866,23 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, input->dims->size, 2); const int num_batches = input->dims->data[0]; + const int input_depth = input->dims->data[1]; TF_LITE_ENSURE_EQ(context, prev_activation->dims->size, 2); TF_LITE_ENSURE_EQ(context, prev_activation->dims->data[0], num_batches); + const int activation_depth = prev_activation->dims->data[1]; + const int total_depth = input_depth + activation_depth; TF_LITE_ENSURE_EQ(context, weights->dims->size, 2); + TF_LITE_ENSURE_EQ(context, weights->dims->data[0], 4 * activation_depth); + TF_LITE_ENSURE_EQ(context, weights->dims->data[1], total_depth); + TF_LITE_ENSURE_EQ(context, bias->dims->size, 1); + TF_LITE_ENSURE_EQ(context, bias->dims->data[0], 4 * activation_depth); TF_LITE_ENSURE_EQ(context, prev_state->dims->size, 2); TF_LITE_ENSURE_EQ(context, prev_state->dims->data[0], num_batches); + TF_LITE_ENSURE_EQ(context, prev_state->dims->data[1], activation_depth); TfLiteTensor* activation_out = GetOutput(context, node, kOutputActivation); TfLiteTensor* state_out = GetOutput(context, node, kOutputState); @@ -843,14 +896,15 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, state_out, TfLiteIntArrayCopy(prev_state->dims))); + TfLiteIntArray* concat_temp_size = TfLiteIntArrayCreate(2); concat_temp_size->data[0] = num_batches; - concat_temp_size->data[1] = weights->dims->data[1]; + concat_temp_size->data[1] = total_depth; TF_LITE_ENSURE_OK( context, context->ResizeTensor(context, concat_temp, concat_temp_size)); TfLiteIntArray* activation_temp_size = TfLiteIntArrayCreate(2); activation_temp_size->data[0] = num_batches; - activation_temp_size->data[1] = weights->dims->data[0]; + activation_temp_size->data[1] = 4 * activation_depth; TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, activation_temp, activation_temp_size)); @@ -876,18 +930,73 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* activation_temp = GetOutput(context, node, kOutputActivationTemp); - optimized_ops::LstmCell( - // Inputs. - GetTensorData(input), GetTensorDims(input), - GetTensorData(prev_activation), GetTensorDims(prev_activation), - GetTensorData(weights), GetTensorDims(weights), - GetTensorData(bias), GetTensorDims(bias), - GetTensorData(prev_state), GetTensorDims(prev_state), - // Outputs. - GetTensorData(state_out), GetTensorDims(state_out), - GetTensorData(activation_out), GetTensorDims(activation_out), - GetTensorData(concat_temp), GetTensorDims(concat_temp), - GetTensorData(activation_temp), GetTensorDims(activation_temp)); + if (input->type == kTfLiteFloat32 && + prev_activation->type == kTfLiteFloat32 && + weights->type == kTfLiteFloat32 && bias->type == kTfLiteFloat32 && + prev_state->type == kTfLiteFloat32 && state_out->type == kTfLiteFloat32 && + activation_out->type == kTfLiteFloat32 && + concat_temp->type == kTfLiteFloat32 && + activation_temp->type == kTfLiteFloat32) { + optimized_ops::LstmCell( + // Inputs. + GetTensorData(input), GetTensorDims(input), + GetTensorData(prev_activation), GetTensorDims(prev_activation), + GetTensorData(weights), GetTensorDims(weights), + GetTensorData(bias), GetTensorDims(bias), + GetTensorData(prev_state), GetTensorDims(prev_state), + // Outputs. + GetTensorData(state_out), GetTensorDims(state_out), + GetTensorData(activation_out), GetTensorDims(activation_out), + GetTensorData(concat_temp), GetTensorDims(concat_temp), + GetTensorData(activation_temp), GetTensorDims(activation_temp)); + } else if (input->type == kTfLiteUInt8 && + prev_activation->type == kTfLiteUInt8 && + weights->type == kTfLiteUInt8 && bias->type == kTfLiteInt32 && + prev_state->type == kTfLiteInt16 && + state_out->type == kTfLiteInt16 && + activation_out->type == kTfLiteUInt8 && + concat_temp->type == kTfLiteUInt8 && + activation_temp->type == kTfLiteInt16) { + gemmlowp::GemmContext* gemm_context = gemm_support::GetFromContext(context); + int state_scale_log2_rounded; + if (!CheckedLog2(state_out->params.scale, &state_scale_log2_rounded)) { + context->ReportError( + context, + "The internal state of a LSTM cell must have a power-of-two scale."); + return kTfLiteError; + } + const int state_integer_bits = 15 + state_scale_log2_rounded; + if (state_integer_bits != 4) { + context->ReportError(context, + "The only case of quantized LstmCell currently " + "supported is with StateIntegerBits==4"); + return kTfLiteError; + } + + double real_accum_multiplier = 4096 * bias->params.scale; + int32 accum_multiplier; + int accum_shift; + tflite::QuantizeMultiplier(real_accum_multiplier, &accum_multiplier, + &accum_shift); + optimized_ops::LstmCell<4>( + // Inputs. + GetTensorData(input), GetTensorDims(input), + GetTensorData(prev_activation), GetTensorDims(prev_activation), + GetTensorData(weights), GetTensorDims(weights), + GetTensorData(bias), GetTensorDims(bias), + GetTensorData(prev_state), GetTensorDims(prev_state), + // Outputs. + GetTensorData(state_out), GetTensorDims(state_out), + GetTensorData(activation_out), GetTensorDims(activation_out), + GetTensorData(concat_temp), GetTensorDims(concat_temp), + GetTensorData(activation_temp), GetTensorDims(activation_temp), + weights->params.zero_point, accum_multiplier, accum_shift, + gemm_context); + } else { + context->ReportError(context, + "Unsupported combination of data types for LstmCell"); + return kTfLiteError; + } // TODO(ycling): Investigate if this copy can be avoided with the 5-inputs // LSTM kernel. @@ -901,6 +1010,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace basic void* Init(TfLiteContext* context, const char* buffer, size_t length) { + gemm_support::IncrementUsageCounter(context); + const auto* params = reinterpret_cast(buffer); switch (params->kernel_type) { case kTfLiteLSTMFullKernel: @@ -910,6 +1021,8 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { } } void Free(TfLiteContext* context, void* buffer) { + gemm_support::DecrementUsageCounter(context); + delete reinterpret_cast(buffer); } diff --git a/tensorflow/contrib/lite/kernels/lstm_test.cc b/tensorflow/contrib/lite/kernels/lstm_test.cc index 6da29a4a923f16f7b5ad382f51cfd820783504cd..0b7c56133e3cbb3d85f75657b6141620a8019e61 100644 --- a/tensorflow/contrib/lite/kernels/lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/lstm_test.cc @@ -97,6 +97,12 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } + // Adding the 2 input state tensors. + input_activation_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}}, true); + input_cell_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}}, true); + output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -227,6 +233,8 @@ class LSTMOpModel : public SingleOpModel { int projection_weights_; int projection_bias_; + int input_activation_state_; + int input_cell_state_; int output_; int output_state_; @@ -352,14 +360,6 @@ class BaseLstmTest : public ::testing::Test { } EXPECT_THAT(lstm->GetOutput(), ElementsAreArray(ArrayFloatNear(expected, tolerance))); - for (int i = 0; i < num_outputs; ++i) { - std::cout << lstm->GetOutput()[i] << ", "; - } - std::cout << std::endl; - for (int i = 0; i < num_outputs; ++i) { - std::cout << expected[i] << ", "; - } - std::cout << std::endl; } } }; diff --git a/tensorflow/contrib/lite/kernels/maximum_minimum_test.cc b/tensorflow/contrib/lite/kernels/maximum_minimum_test.cc index 0752aa1804722accb1f88910fe013ffd632a4503..fd4d5367c5a6369b5ffeeea30a910262bc0796a9 100644 --- a/tensorflow/contrib/lite/kernels/maximum_minimum_test.cc +++ b/tensorflow/contrib/lite/kernels/maximum_minimum_test.cc @@ -126,10 +126,10 @@ TEST(MaximumOpTest, FloatWithBroadcastTest) { TEST(MaximumOpTest, Int32WithBroadcastTest) { std::initializer_list data1 = {1, 0, -1, -2, 3, 11}; std::initializer_list data2 = {2}; - TestModel(BuiltinOperator_MAXIMUM, {TensorType_INT32, {3, 1, 2}}, + TestModel(BuiltinOperator_MAXIMUM, {TensorType_INT32, {3, 1, 2}}, {TensorType_INT32, {1}}, {TensorType_INT32, {3, 1, 2}}, data1, data2, {2, 2, 2, 2, 3, 11}); - TestModel(BuiltinOperator_MINIMUM, {TensorType_INT32, {3, 1, 2}}, + TestModel(BuiltinOperator_MINIMUM, {TensorType_INT32, {3, 1, 2}}, {TensorType_INT32, {1}}, {TensorType_INT32, {3, 1, 2}}, data1, data2, {1, 0, -1, -2, 2, 2}); } diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc index 62f4e94a386fbbc6987e8a6dc1a9a47ce3349cbb..1f72f3a3c7af4f9e042c9b2ac09252fab5de1a4f 100644 --- a/tensorflow/contrib/lite/kernels/mul.cc +++ b/tensorflow/contrib/lite/kernels/mul.cc @@ -39,6 +39,14 @@ constexpr int kOutputTensor = 0; struct OpData { bool requires_broadcast; + + // Parameters used in the quantized paths where the output is 8bit + int32 output_activation_min; + int32 output_activation_max; + + // Parameters used in all quantized paths + int32_t output_multiplier; + int output_shift; }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -52,6 +60,7 @@ void Free(TfLiteContext* context, void* buffer) { } TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); OpData* data = reinterpret_cast(node->user_data); TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); @@ -62,7 +71,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = GetOutput(context, node, kOutputTensor); TF_LITE_ENSURE_EQ(context, input1->type, input2->type); - output->type = input2->type; data->requires_broadcast = !HaveSameShapes(input1, input2); @@ -74,6 +82,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size = TfLiteIntArrayCopy(input1->dims); } + if (output->type == kTfLiteUInt8) { + CalculateActivationRangeUint8(params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + } + + if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + double real_multiplier = + input1->params.scale * input2->params.scale / output->params.scale; + QuantizeMultiplierSmallerThanOneExp( + real_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; + } + return context->ResizeTensor(context, output, output_size); } @@ -83,8 +105,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); #define TF_LITE_MUL(type, opname) \ type::opname(GetTensorData(input1), GetTensorDims(input1), \ GetTensorData(input2), GetTensorDims(input2), \ @@ -107,41 +129,60 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, } template -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - auto input1_offset = -input1->params.zero_point; - auto input2_offset = -input2->params.zero_point; - auto output_offset = output->params.zero_point; - - int32_t output_multiplier; - int output_shift; - - double real_multiplier = - input1->params.scale * input2->params.scale / output->params.scale; - QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier, - &output_shift); - - int32 output_activation_min, output_activation_max; - CalculateActivationRangeUint8(params->activation, output, - &output_activation_min, &output_activation_max); - -#define TF_LITE_MUL(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - input1_offset, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, output_offset, \ - output_multiplier, output_shift, output_activation_min, \ - output_activation_max, GetTensorData(output), \ +TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteMulParams* params, const OpData* data, + const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { + if (input1->type == kTfLiteUInt8 && input2->type == kTfLiteUInt8 && + output->type == kTfLiteUInt8) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + -input1->params.zero_point, GetTensorData(input2), \ + GetTensorDims(input2), -input2->params.zero_point, \ + output->params.zero_point, data->output_multiplier, \ + data->output_shift, data->output_activation_min, \ + data->output_activation_max, GetTensorData(output), \ GetTensorDims(output)); - // The quantized version of Mul doesn't support activations, so we - // always use BroadcastMul. - if (kernel_type == kReference) { - TF_LITE_MUL(reference_ops, BroadcastMul); + // The quantized version of Mul doesn't support activations, so we + // always use BroadcastMul. + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, BroadcastMul); + } else { + TF_LITE_MUL(optimized_ops, BroadcastMul); + } +#undef TF_LITE_MUL + } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 && + output->type == kTfLiteInt16) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + GetTensorData(output), GetTensorDims(output)); + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, Mul); + } else { + TF_LITE_MUL(optimized_ops, Mul); + } +#undef TF_LITE_MUL + } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 && + output->type == kTfLiteUInt8) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output->params.zero_point, data->output_activation_min, \ + data->output_activation_max, GetTensorData(output), \ + GetTensorDims(output)); + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, Mul); + } else { + TF_LITE_MUL(optimized_ops, Mul); + } +#undef TF_LITE_MUL } else { - TF_LITE_MUL(optimized_ops, BroadcastMul); + context->ReportError( + context, "Unsupported combination of input and output types in Mul."); + return kTfLiteError; } -#undef TF_LITE_MUL + return kTfLiteOk; } template @@ -155,12 +196,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (output->type == kTfLiteFloat32) { EvalFloat(context, node, params, data, input1, input2, output); - } else if (output->type == kTfLiteUInt8) { - EvalQuantized(context, node, params, data, input1, input2, - output); + } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + TF_LITE_ENSURE_OK( + context, EvalQuantized(context, node, params, data, input1, + input2, output)); } else { context->ReportError( - context, "Mul only supports FLOAT32 and quantized UINT8 now, got %d.", + context, + "Mul only supports FLOAT32 and quantized UINT8 and INT16 now, got %d.", output->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc index f1a30f82634631ba8320421d5b36ffe446f443fa..43d56e50d2686ff2624f36a0c5d8e43279a572cc 100644 --- a/tensorflow/contrib/lite/kernels/mul_test.cc +++ b/tensorflow/contrib/lite/kernels/mul_test.cc @@ -58,6 +58,9 @@ class FloatMulOpModel : public BaseMulOpModel { const float kQuantizedStep = 2.0 / 255.0; const float kQuantizedTolerance = 2.0 * kQuantizedStep + kQuantizedStep * kQuantizedStep; +const float kQuantizedStepInt16 = 2.0 / 32767.0; +const float kQuantizedToleranceInt16 = + 2.0 * kQuantizedStepInt16 + kQuantizedStepInt16 * kQuantizedStepInt16; class QuantizedMulOpModel : public BaseMulOpModel { public: @@ -67,6 +70,11 @@ class QuantizedMulOpModel : public BaseMulOpModel { return Dequantize(ExtractVector(output_), GetScale(output_), GetZeroPoint(output_)); } + + std::vector GetDequantizedOutputInt16() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } }; TEST(FloatMulOpTest, NoActivation) { @@ -138,6 +146,38 @@ TEST(QuantizedMulOpTest, NoActivation) { kQuantizedTolerance))); } +TEST(QuantizedMulOpTest, NoActivationInt16) { + const float kMin = -1.f; + const float kMax = 32767.f / 32768.f; + QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {}, kMin, kMax}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-0.8, 0.2, 0.9, 0.7}); + m.QuantizeAndPopulate(m.input2(), {0.6, 0.4, 0.9, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutputInt16(), + ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56}, + kQuantizedToleranceInt16))); +} + +TEST(QuantizedMulOpTest, NoActivationInt16WithUint8Output) { + const float kMinInt16 = -1.f; + const float kMaxInt16 = 32767.f / 32768.f; + const float kMinUint8 = -1.f; + const float kMaxUint8 = 127.f / 128.f; + QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16}, + {TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16}, + {TensorType_UINT8, {}, kMinUint8, kMaxUint8}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-0.8, 0.2, 0.9, 0.7}); + m.QuantizeAndPopulate(m.input2(), {0.6, 0.4, 0.9, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56}, + kQuantizedTolerance))); +} + // for quantized Mul, the error shouldn't exceed 2*step float GetTolerance(int min, int max) { float kQuantizedStep = (max - min) / 255.0; diff --git a/tensorflow/contrib/lite/kernels/neg_test.cc b/tensorflow/contrib/lite/kernels/neg_test.cc index 3c95ac8cc2727fdeff5f39aa2fe30eb6129a6022..3d3594c60bbe1684dff7b1816f5f8a715b1abc60 100644 --- a/tensorflow/contrib/lite/kernels/neg_test.cc +++ b/tensorflow/contrib/lite/kernels/neg_test.cc @@ -58,9 +58,9 @@ TEST(NegOpModel, NegFloat) { TEST(NegOpModel, NegInt32) { NegOpModel m({TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 3}}); - m.SetInput({-2, -1, 0, 1, 2, 3}); + m.SetInput({-2, -1, 0, 1, 2, 3}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 1, 0, -1, -2, -3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 1, 0, -1, -2, -3})); } TEST(NegOpModel, NegInt64) { diff --git a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc index bcad58406af1cdd466e410a06011641692194be4..1c728a473326564a85a5e7d3d72718265979e29a 100644 --- a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc +++ b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc @@ -95,6 +95,12 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } + // Adding the 2 input state tensors. + input_activation_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}}, true); + input_cell_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}}, true); + output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -228,6 +234,8 @@ class LSTMOpModel : public SingleOpModel { int projection_weights_; int projection_bias_; + int input_activation_state_; + int input_cell_state_; int output_; int output_state_; diff --git a/tensorflow/contrib/lite/kernels/pad.cc b/tensorflow/contrib/lite/kernels/pad.cc index 83668cb4ca87e9eb53ab4ba9e88f91e3315594de..4be8c243c17c533e8c7d5aa7bb50c9d790b06995 100644 --- a/tensorflow/contrib/lite/kernels/pad.cc +++ b/tensorflow/contrib/lite/kernels/pad.cc @@ -128,7 +128,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // TODO(nupurgarg): Change kernel implementation to use padding arrays in // forward order (depth, width, height, batch). // Build paddings in order of int[] = {batch, height, width, depth} to match - // kernel implementation of Pad in referenced_ops.h and optimized_ops.h. + // kernel implementation of Pad in reference_ops.h and optimized_ops.h. for (int idx = op_context.dims - 1; idx >= 0; --idx) { before_padding.push_back(paddings_data[idx * 2]); after_padding.push_back(paddings_data[idx * 2 + 1]); diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 311e9b8399726d758182e1f084a890d6f10e57ce..7240fe04ccdadfb7b9703c3f2775c4b3502bd1d9 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -80,24 +80,24 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { // Matching GetWindowedOutputSize in TensorFlow. auto padding = params->padding; - auto computeOutSize = [padding](int imageSize, int filterSize, - int stride) -> int { + auto compute_out_size = [padding](int image_size, int filter_size, + int stride) -> int { return padding == kTfLitePaddingSame - ? (imageSize + stride - 1) / stride + ? (image_size + stride - 1) / stride : padding == kTfLitePaddingValid - ? (imageSize - filterSize + stride) / stride + ? (image_size - filter_size + stride) / stride : 0; }; - int outWidth = - computeOutSize(width, params->filter_width, params->stride_width); - int outHeight = - computeOutSize(height, params->filter_height, params->stride_height); + int out_width = + compute_out_size(width, params->filter_width, params->stride_width); + int out_height = + compute_out_size(height, params->filter_height, params->stride_height); data->padding.height = ComputePadding(params->stride_height, 1, height, - params->filter_height, outHeight); + params->filter_height, out_height); data->padding.width = ComputePadding(params->stride_width, 1, width, - params->filter_width, outWidth); + params->filter_width, out_width); if (input->type == kTfLiteUInt8) { if (pool_type == kAverage || pool_type == kMax) { @@ -111,12 +111,12 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { } } - TfLiteIntArray* outputSize = TfLiteIntArrayCreate(4); - outputSize->data[0] = batches; - outputSize->data[1] = outHeight; - outputSize->data[2] = outWidth; - outputSize->data[3] = channels_out; - return context->ResizeTensor(context, output, outputSize); + TfLiteIntArray* output_size = TfLiteIntArrayCreate(4); + output_size->data[0] = batches; + output_size->data[1] = out_height; + output_size->data[2] = out_width; + output_size->data[3] = channels_out; + return context->ResizeTensor(context, output, output_size); } template @@ -124,14 +124,15 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, TfLitePoolParams* params, OpData* data, const TfLiteTensor* input, TfLiteTensor* output) { float activation_min, activation_max; - CalculateActivationRangeFloat(params->activation, &activation_min, - &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) + CalculateActivationRange(params->activation, &activation_min, + &activation_max); +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -148,13 +149,13 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorDims(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -168,14 +169,15 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, TfLitePoolParams* params, OpData* data, const TfLiteTensor* input, TfLiteTensor* output) { float activation_min, activation_max; - CalculateActivationRangeFloat(params->activation, &activation_min, - &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) + CalculateActivationRange(params->activation, &activation_min, + &activation_max); +#define TF_LITE_MAX_POOL(type) \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -193,12 +195,12 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); #define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorDims(input), \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ params->stride_width, params->stride_height, \ data->padding.width, data->padding.height, \ params->filter_width, params->filter_height, activation_min, \ activation_max, GetTensorData(output), \ - GetTensorDims(output)) + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -212,14 +214,15 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, TfLitePoolParams* params, OpData* data, const TfLiteTensor* input, TfLiteTensor* output) { float activation_min, activation_max; - CalculateActivationRangeFloat(params->activation, &activation_min, - &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) + CalculateActivationRange(params->activation, &activation_min, + &activation_max); +#define TF_LITE_L2_POOL(type) \ + type::L2Pool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { diff --git a/tensorflow/contrib/lite/kernels/pow.cc b/tensorflow/contrib/lite/kernels/pow.cc new file mode 100644 index 0000000000000000000000000000000000000000..4a539c47a8fbe392e0e6542ab8ffb9065b550485 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pow.cc @@ -0,0 +1,143 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace pow { +namespace { + +// Input/output tensor index. +constexpr int kInputTensor1 = 0; +constexpr int kInputTensor2 = 1; +constexpr int kOutputTensor = 0; + +// Op data for pow op. +struct OpData { + bool requires_broadcast; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->requires_broadcast = false; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + OpData* data = reinterpret_cast(node->user_data); + + const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); + const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + TF_LITE_ENSURE_EQ(context, input1->type, input2->type); + + const TfLiteType type = input1->type; + if (type != kTfLiteInt32 && type != kTfLiteFloat32) { + context->ReportError(context, "Unsupported data type %d.", type); + return kTfLiteError; + } + output->type = type; + + data->requires_broadcast = !HaveSameShapes(input1, input2); + + TfLiteIntArray* output_size = nullptr; + if (data->requires_broadcast) { + TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( + context, input1, input2, &output_size)); + } else { + output_size = TfLiteIntArrayCopy(input1->dims); + } + + return context->ResizeTensor(context, output, output_size); +} + +template +void PowImpl(const TfLiteTensor* input1, const TfLiteTensor* input2, + TfLiteTensor* output, bool requires_broadcast) { + if (requires_broadcast) { + reference_ops::BroadcastPow(GetTensorData(input1), GetTensorDims(input1), + GetTensorData(input2), GetTensorDims(input2), + GetTensorData(output), + GetTensorDims(output)); + } else { + reference_ops::Pow(GetTensorData(input1), GetTensorDims(input1), + GetTensorData(input2), GetTensorDims(input2), + GetTensorData(output), GetTensorDims(output)); + } +} + +TfLiteStatus CheckValue(TfLiteContext* context, const TfLiteTensor* input) { + const int64_t num_elements = NumElements(input); + const int32_t* data = GetTensorData(input); + for (int i = 0; i < num_elements; ++i) { + if (data[i] < 0) { + context->ReportError(context, + "POW does not support negative value for int32."); + return kTfLiteError; + } + } + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + + const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); + const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + switch (output->type) { + case kTfLiteInt32: { + // TensorFlow does not support negative for int32. + TF_LITE_ENSURE_OK(context, CheckValue(context, input2)); + PowImpl(input1, input2, output, data->requires_broadcast); + break; + } + case kTfLiteFloat32: { + PowImpl(input1, input2, output, data->requires_broadcast); + break; + } + default: { + context->ReportError(context, "Unsupported data type: %d", output->type); + return kTfLiteError; + } + } + return kTfLiteOk; +} + +} // namespace +} // namespace pow + +TfLiteRegistration* Register_POW() { + static TfLiteRegistration r = {pow::Init, pow::Free, pow::Prepare, pow::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/pow_test.cc b/tensorflow/contrib/lite/kernels/pow_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..474d323bc3a1a0f224aa0575a5bbd35394aa2f53 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pow_test.cc @@ -0,0 +1,117 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAre; +using ::testing::ElementsAreArray; + +template +class PowOpModel : public SingleOpModel { + public: + PowOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_POW, BuiltinOptions_PowOptions, + CreatePowOptions(builder_).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input1_; + int input2_; + int output_; +}; + +TEST(PowOpModel, Simple) { + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {12, 2, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 1}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); + EXPECT_THAT(model.GetOutput(), ElementsAre(12, 4, 343, 8)); +} + +TEST(PowOpModel, NegativeAndZeroValue) { + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {0, 2, -7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 0}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); + EXPECT_THAT(model.GetOutput(), ElementsAre(0, 4, -343, 1)); +} + +TEST(PowOpModel, Float) { + PowOpModel model({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}); + model.PopulateTensor(model.input1(), {0.3, 0.4, 0.7, 5.8}); + model.PopulateTensor(model.input2(), {0.5, 2.7, 3.1, 3.2}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0.5477226, 0.08424846, 0.33098164, 277.313}, 1e-3))); +} + +TEST(PowOpModel, NegativeFloatTest) { + PowOpModel model({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}); + model.PopulateTensor(model.input1(), {0.3, 0.4, 0.7, 5.8}); + model.PopulateTensor(model.input2(), {0.5, -2.7, 3.1, -3.2}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0.5477226, 11.869653, 0.33098164, 0.003606}, 1e-3))); +} + +TEST(PowOpModel, BroadcastTest) { + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1}}, {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {12, 2, 7, 8}); + model.PopulateTensor(model.input2(), {4}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); + EXPECT_THAT(model.GetOutput(), ElementsAre(20736, 16, 2401, 4096)); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/reduce.cc similarity index 72% rename from tensorflow/contrib/lite/kernels/mean.cc rename to tensorflow/contrib/lite/kernels/reduce.cc index 03e5db24de3f3c2d4e17df21bc0b592a02078d6b..31c331a8c61ded203af9ff2ae127cb6f985e2932 100644 --- a/tensorflow/contrib/lite/kernels/mean.cc +++ b/tensorflow/contrib/lite/kernels/reduce.cc @@ -25,21 +25,21 @@ limitations under the License. namespace tflite { namespace ops { namespace builtin { -namespace mean { +namespace reduce { -// This file has reference implementation of Mean. +// This file has reference implementation of reduce_* operators. enum KernelType { kReference, }; -struct MeanContext { - MeanContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); axis = GetInput(context, node, 1); output = GetOutput(context, node, 0); } - TfLiteMeanParams* params; + TfLiteReducerParams* params; const TfLiteTensor* input; const TfLiteTensor* axis; TfLiteTensor* output; @@ -58,7 +58,7 @@ void Free(TfLiteContext* context, void* buffer) { } // Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext* context, MeanContext* op_context, +TfLiteStatus ResizeTempAxis(TfLiteContext* context, OpContext* op_context, TfLiteTensor* resolved_axis) { TfLiteIntArray* axis_size = TfLiteIntArrayCreate(1); axis_size->data[0] = static_cast(NumElements(op_context->axis)); @@ -66,7 +66,7 @@ TfLiteStatus ResizeTempAxis(TfLiteContext* context, MeanContext* op_context, } // Resizes the temp tensor that stores temp sum of reduced elements. -TfLiteStatus ResizeTempSum(TfLiteContext* context, MeanContext* op_context, +TfLiteStatus ResizeTempSum(TfLiteContext* context, OpContext* op_context, TfLiteTensor* temp_sum) { TfLiteIntArray* size = TfLiteIntArrayCreate(1); size->data[0] = static_cast(NumElements(op_context->output)); @@ -74,8 +74,7 @@ TfLiteStatus ResizeTempSum(TfLiteContext* context, MeanContext* op_context, } // Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext* context, - MeanContext* op_context) { +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, OpContext* op_context) { size_t num_axis = NumElements(op_context->axis); const TfLiteIntArray* input_dims = op_context->input->dims; int input_num_dims = NumDimensions(op_context->input); @@ -140,7 +139,7 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, // Initializes temp tensors to store index and resolved axis. TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node, - MeanContext* op_context) { + OpContext* op_context) { // Creates a temp index to iterate through input data. int* scratch_tensor_index = reinterpret_cast(node->user_data); TfLiteIntArrayFree(node->temporaries); @@ -180,33 +179,44 @@ TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node, return kTfLiteOk; } -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { +TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - MeanContext op_context(context, node); + OpContext op_context(context, node); TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); - TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2); // Leaves work to Eval if axis is not constant; else resizes output. if (!IsConstantTensor(op_context.axis)) { SetTensorToDynamic(op_context.output); SetTensorToDynamic(resolved_axis); - SetTensorToDynamic(temp_sum); return kTfLiteOk; } resolved_axis->allocation_type = kTfLiteArenaRw; TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + return kTfLiteOk; +} + +TfLiteStatus PrepareMean(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_OK(context, PrepareSimple(context, node)); + + // reduce_mean requires a buffer to store intermediate sum result. + OpContext op_context(context, node); + TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2); + if (!IsConstantTensor(op_context.axis)) { + SetTensorToDynamic(temp_sum); + return kTfLiteOk; + } temp_sum->allocation_type = kTfLiteArenaRw; return ResizeTempSum(context, &op_context, temp_sum); } template -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - MeanContext op_context(context, node); +TfLiteStatus EvalMean(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); int num_axis = static_cast(NumElements(op_context.axis)); TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); @@ -255,16 +265,75 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { #undef TF_LITE_MEAN return kTfLiteOk; } -} // namespace mean + +template +TfLiteStatus EvalSum(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + int num_axis = static_cast(NumElements(op_context.axis)); + TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); + TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + +#define TF_LITE_SUM(kernel_type, data_type) \ + kernel_type::Sum<>( \ + GetTensorData(op_context.input), \ + op_context.input->dims->data, op_context.input->dims->size, \ + GetTensorData(op_context.output), \ + op_context.output->dims->data, op_context.output->dims->size, \ + GetTensorData(op_context.axis), num_axis, \ + op_context.params->keep_dims, GetTensorData(temp_index), \ + GetTensorData(resolved_axis)) + + if (kernel_type == kReference) { + switch (op_context.input->type) { + case kTfLiteFloat32: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, float)); + break; + case kTfLiteInt32: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, int)); + break; + case kTfLiteInt64: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, int64_t)); + break; + case kTfLiteUInt8: + TF_LITE_ENSURE_EQ(context, op_context.input->params.scale, + op_context.output->params.scale); + TF_LITE_ENSURE_EQ(context, op_context.input->params.zero_point, + op_context.output->params.zero_point); + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, uint8_t)); + break; + default: + return kTfLiteError; + } + } +#undef TF_LITE_SUM + return kTfLiteOk; +} + +} // namespace reduce TfLiteRegistration* Register_MEAN_REF() { - static TfLiteRegistration r = {mean::Init, mean::Free, mean::Prepare, - mean::Eval}; + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareMean, + reduce::EvalMean}; + return &r; +} + +TfLiteRegistration* Register_SUM_REF() { + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareSimple, + reduce::EvalSum}; return &r; } // TODO(kanlig): add optimized implementation of Mean. TfLiteRegistration* Register_MEAN() { return Register_MEAN_REF(); } +TfLiteRegistration* Register_SUM() { return Register_SUM_REF(); } } // namespace builtin } // namespace ops diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/reduce_test.cc similarity index 53% rename from tensorflow/contrib/lite/kernels/mean_test.cc rename to tensorflow/contrib/lite/kernels/reduce_test.cc index 79c9957f76fdb994be0a71f2e90b883435de4815..9e946822c686f6f20505d60b6161239624c94696 100644 --- a/tensorflow/contrib/lite/kernels/mean_test.cc +++ b/tensorflow/contrib/lite/kernels/reduce_test.cc @@ -23,7 +23,7 @@ namespace { using ::testing::ElementsAreArray; -class BaseMeanOpModel : public SingleOpModel { +class BaseOpModel : public SingleOpModel { public: void SetAxis(std::initializer_list data) { PopulateTensor(axis_, data); } @@ -53,7 +53,7 @@ class BaseMeanOpModel : public SingleOpModel { }; // Model for the tests case where axis is a const tensor. -class MeanOpConstModel : public BaseMeanOpModel { +class MeanOpConstModel : public BaseOpModel { public: MeanOpConstModel(const TensorData& input, const TensorData& output, std::initializer_list axis_shape, @@ -61,26 +61,59 @@ class MeanOpConstModel : public BaseMeanOpModel { input_ = AddInput(input); axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); output_ = AddOutput(output); - SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, - CreateMeanOptions(builder_, keep_dims).Union()); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); BuildInterpreter({GetShape(input_)}); } }; // Model for the tests case where axis is a dynamic tensor. -class MeanOpDynamicModel : public BaseMeanOpModel { +class MeanOpDynamicModel : public BaseOpModel { public: MeanOpDynamicModel(const TensorData& input, const TensorData& output, const TensorData& axis, bool keep_dims) { input_ = AddInput(input); axis_ = AddInput(axis); output_ = AddOutput(output); - SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, - CreateMeanOptions(builder_, keep_dims).Union()); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); BuildInterpreter({GetShape(input_)}); } }; +// Model for the tests case where axis is a const tensor. +class SumOpConstModel : public BaseOpModel { + public: + SumOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Model for the tests case where axis is a dynamic tensor. +class SumOpDynamicModel : public BaseOpModel { + public: + SumOpDynamicModel(const TensorData& input, const TensorData& output, + const TensorData& axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddInput(axis); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// for quantized Add, the error shouldn't exceed step +float GetTolerance(int min, int max) { return (max - min) / 255.0; } + +// Tests for reduce_mean TEST(ConstFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, @@ -149,8 +182,6 @@ TEST(DynamicFloatMeanOpTest, Scale) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); } -// for quantized Add, the error shouldn't exceed step -float GetTolerance(int min, int max) { return (max - min) / 255.0; } TEST(ConstUint8MeanOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); @@ -209,6 +240,135 @@ TEST(DynamicUint8MeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({9.2815, 0.3695}, kQuantizedTolerance))); } +// Tests for reduce_sum + +TEST(ConstFloatSumOpTest, NotKeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({144, 156}))); +} + +TEST(ConstFloatSumOpTest, KeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({84, 100, 116}))); +} + +TEST(DynamicFloatSumOpTest, NotKeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, + false); + std::initializer_list axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({144, 156}))); +} + +TEST(DynamicFloatSumOpTest, KeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true); + std::initializer_list axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({84, 100, 116}))); +} + +TEST(DynamicFloatSumOpTest, Scale) { + std::initializer_list data = {9.527}; + SumOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +TEST(ConstUint8SumOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + SumOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({-0.823529, -0.815686}, kQuantizedTolerance))); +} + +TEST(ConstUint8SumOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + SumOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({-0.407843, -0.313726, 0.0941177}, + kQuantizedTolerance))); +} + +TEST(DynamicUint8SumOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-5.0, 2.0); + std::initializer_list data = {1.3, -4.8, -3.6, 0.24}; + SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0}, + {TensorType_UINT8, {2}, -5.0, 2.0}, + {TensorType_INT32, {1}}, false); + std::initializer_list axis = {1}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({1.48235, 1.64706}, kQuantizedTolerance))); +} + +TEST(DynamicUint8SumOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-10.0, 12.0); + std::initializer_list data = {11.14, -0.14, 7.423, 0.879}; + SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0}, + {TensorType_UINT8, {2}, -10.0, 12.0}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT( + m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({6.47059, 10.698}, kQuantizedTolerance))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 6c68bb2f31003e08585b2fa3df0efe6d291ddb36..0ca08cd8f38216549b4383ebaacbf4c54442cd97 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -22,6 +22,7 @@ namespace custom { TfLiteRegistration* Register_AUDIO_SPECTROGRAM(); TfLiteRegistration* Register_MFCC(); +TfLiteRegistration* Register_DETECTION_POSTPROCESS(); } // namespace custom @@ -73,6 +74,7 @@ TfLiteRegistration* Register_SQUEEZE(); TfLiteRegistration* Register_STRIDED_SLICE(); TfLiteRegistration* Register_EXP(); TfLiteRegistration* Register_TOPK_V2(); +TfLiteRegistration* Register_LOG(); TfLiteRegistration* Register_LOG_SOFTMAX(); TfLiteRegistration* Register_CAST(); TfLiteRegistration* Register_DEQUANTIZE(); @@ -87,6 +89,7 @@ TfLiteRegistration* Register_LESS_EQUAL(); TfLiteRegistration* Register_FLOOR(); TfLiteRegistration* Register_TILE(); TfLiteRegistration* Register_NEG(); +TfLiteRegistration* Register_SUM(); TfLiteRegistration* Register_SELECT(); TfLiteRegistration* Register_SLICE(); TfLiteRegistration* Register_SIN(); @@ -95,6 +98,10 @@ TfLiteRegistration* Register_EXPAND_DIMS(); TfLiteRegistration* Register_SPARSE_TO_DENSE(); TfLiteRegistration* Register_EQUAL(); TfLiteRegistration* Register_NOT_EQUAL(); +TfLiteRegistration* Register_SQRT(); +TfLiteRegistration* Register_RSQRT(); +TfLiteRegistration* Register_SHAPE(); +TfLiteRegistration* Register_POW(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -116,7 +123,9 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, Register_EMBEDDING_LOOKUP_SPARSE()); - AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED()); + AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED(), + /* min_version */ 1, + /* max_version */ 2); AddBuiltin(BuiltinOperator_LSH_PROJECTION, Register_LSH_PROJECTION()); AddBuiltin(BuiltinOperator_HASHTABLE_LOOKUP, Register_HASHTABLE_LOOKUP()); AddBuiltin(BuiltinOperator_SOFTMAX, Register_SOFTMAX()); @@ -150,6 +159,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); AddBuiltin(BuiltinOperator_EXP, Register_EXP()); AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); + AddBuiltin(BuiltinOperator_LOG, Register_LOG()); AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); AddBuiltin(BuiltinOperator_CAST, Register_CAST()); AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); @@ -168,16 +178,23 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_SIN, Register_SIN()); AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); AddBuiltin(BuiltinOperator_TILE, Register_TILE()); + AddBuiltin(BuiltinOperator_SUM, Register_SUM()); AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS()); AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL()); + AddBuiltin(BuiltinOperator_SQRT, Register_SQRT()); + AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); + AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); + AddBuiltin(BuiltinOperator_POW, Register_POW()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. AddCustom("Mfcc", tflite::ops::custom::Register_MFCC()); AddCustom("AudioSpectrogram", tflite::ops::custom::Register_AUDIO_SPECTROGRAM()); + AddCustom("TFLite_Detection_PostProcess", + tflite::ops::custom::Register_DETECTION_POSTPROCESS()); } } // namespace builtin diff --git a/tensorflow/contrib/lite/kernels/register.h b/tensorflow/contrib/lite/kernels/register.h index b928f1b302580d52f708bbf85dfcfc0f79ff1e69..940718d67e70b7206227b891ea529cb9e9619161 100644 --- a/tensorflow/contrib/lite/kernels/register.h +++ b/tensorflow/contrib/lite/kernels/register.h @@ -32,4 +32,4 @@ class BuiltinOpResolver : public MutableOpResolver { } // namespace ops } // namespace tflite -#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_BUILTIN_KERNELS_H +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_REGISTER_H_ diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear.cc b/tensorflow/contrib/lite/kernels/resize_bilinear.cc index f2092eaa36db32ebbc959ac23365bb13dd034e68..86c4cd3ee88013ca4174f444d0388bc036d9cde6 100644 --- a/tensorflow/contrib/lite/kernels/resize_bilinear.cc +++ b/tensorflow/contrib/lite/kernels/resize_bilinear.cc @@ -61,12 +61,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4); TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1); - // TODO(ahentz): Our current implementations only support float32. - TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32); // ResizeBilinear creates a float tensor even when the input is made of // integers. - output->type = kTfLiteFloat32; + output->type = input->type; if (!IsConstantTensor(size)) { SetTensorToDynamic(output); @@ -90,17 +88,24 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } if (output->type == kTfLiteFloat32) { -#define TF_LITE_RESIZE_BILINEAR(type) \ - type::ResizeBilinear(GetTensorData(input), GetTensorDims(input), \ - GetTensorData(size), GetTensorDims(size), \ - GetTensorData(output), GetTensorDims(output), \ +#define TF_LITE_RESIZE_BILINEAR(type, datatype) \ + type::ResizeBilinear(GetTensorData(input), GetTensorDims(input), \ + GetTensorData(size), GetTensorDims(size), \ + GetTensorData(output), GetTensorDims(output), \ params->align_corners) if (kernel_type == kReference) { - TF_LITE_RESIZE_BILINEAR(reference_ops); + TF_LITE_RESIZE_BILINEAR(reference_ops, float); } if (kernel_type == kGenericOptimized || kernel_type == kNeonOptimized) { - TF_LITE_RESIZE_BILINEAR(optimized_ops); + TF_LITE_RESIZE_BILINEAR(optimized_ops, float); + } + } else if (output->type == kTfLiteUInt8) { + if (kernel_type == kReference) { + TF_LITE_RESIZE_BILINEAR(reference_ops, uint8_t); + } + if (kernel_type == kGenericOptimized || kernel_type == kNeonOptimized) { + TF_LITE_RESIZE_BILINEAR(optimized_ops, uint8_t); } #undef TF_LITE_RESIZE_BILINEAR } else { diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc index 4e03f3820a5c14ee1692c553db61e385716b1723..10caffea03ebcec7862df1627541ac3d076b04e4 100644 --- a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc +++ b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc @@ -22,6 +22,7 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using uint8 = std::uint8_t; class ResizeBilinearOpModel : public SingleOpModel { public: @@ -34,7 +35,7 @@ class ResizeBilinearOpModel : public SingleOpModel { } else { size_ = AddInput({TensorType_INT32, {2}}); } - output_ = AddOutput(TensorType_FLOAT32); // Always float. + output_ = AddOutput(input.type); SetBuiltinOp(BuiltinOperator_RESIZE_BILINEAR, BuiltinOptions_ResizeBilinearOptions, CreateResizeBilinearOptions(builder_).Union()); @@ -45,12 +46,16 @@ class ResizeBilinearOpModel : public SingleOpModel { } } - void SetInput(std::initializer_list data) { + template + void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } void SetSize(std::initializer_list data) { PopulateTensor(size_, data); } - std::vector GetOutput() { return ExtractVector(output_); } + template + std::vector GetOutput() { + return ExtractVector(output_); + } private: int input_; @@ -60,60 +65,121 @@ class ResizeBilinearOpModel : public SingleOpModel { TEST(ResizeBilinearOpTest, HorizontalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 1, 2, 1}}); - m.SetInput({3, 6}); + m.SetInput({3, 6}); m.SetSize({1, 3}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 1, 2, 1}}, {1, 3}); - const_m.SetInput({3, 6}); + const_m.SetInput({3, 6}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); +} + +TEST(ResizeBilinearOpTest, HorizontalResize8Bit) { + ResizeBilinearOpModel m({TensorType_UINT8, {1, 1, 2, 1}}); + m.SetInput({3, 6}); + m.SetSize({1, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); + + ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 1, 2, 1}}, {1, 3}); + const_m.SetInput({3, 6}); const_m.Invoke(); - EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); } TEST(ResizeBilinearOpTest, VerticalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 1, 1}}); - m.SetInput({3, 9}); + m.SetInput({3, 9}); m.SetSize({3, 1}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 1, 1}}, {3, 1}); - const_m.SetInput({3, 9}); + const_m.SetInput({3, 9}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); +} + +TEST(ResizeBilinearOpTest, VerticalResize8Bit) { + ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 1, 1}}); + m.SetInput({3, 9}); + m.SetSize({3, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); + + ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 1, 1}}, {3, 1}); + const_m.SetInput({3, 9}); const_m.Invoke(); - EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); } TEST(ResizeBilinearOpTest, TwoDimensionalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}); - m.SetInput({ + m.SetInput({ 3, 6, // 9, 12 // }); m.SetSize({3, 3}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 5, 6, // - 7, 9, 10, // - 9, 11, 12, // - }))); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 1}}, {3, 3}); - const_m.SetInput({ + const_m.SetInput({ 3, 6, // 9, 12 // }); const_m.Invoke(); - EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 5, 6, // - 7, 9, 10, // - 9, 11, 12, // - }))); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); +} + +TEST(ResizeBilinearOpTest, TwoDimensionalResize8Bit) { + ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 1}}); + m.SetInput({ + 3, 6, // + 9, 12 // + }); + m.SetSize({3, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); + + ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 1}}, {3, 3}); + const_m.SetInput({ + 3, 6, // + 9, 12 // + }); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); } TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches) { ResizeBilinearOpModel m({TensorType_FLOAT32, {2, 2, 2, 1}}); - m.SetInput({ + m.SetInput({ 3, 6, // 9, 12, // 4, 10, // @@ -121,60 +187,123 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches) { }); m.SetSize({3, 3}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 5, 6, // - 7, 9, 10, // - 9, 11, 12, // - 4, 8, 10, // - 8, 12, 14, // - 10, 14, 16, // - }))); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + 4, 8, 10, // + 8, 12, 14, // + 10, 14, 16, // + }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {2, 2, 2, 1}}, {3, 3}); - const_m.SetInput({ + const_m.SetInput({ 3, 6, // 9, 12, // 4, 10, // 10, 16 // }); const_m.Invoke(); - EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 5, 6, // - 7, 9, 10, // - 9, 11, 12, // - 4, 8, 10, // - 8, 12, 14, // - 10, 14, 16, // - }))); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + 4, 8, 10, // + 8, 12, 14, // + 10, 14, 16, // + }))); } TEST(ResizeBilinearOpTest, ThreeDimensionalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 2}}); - m.SetInput({ + m.SetInput({ 3, 4, 6, 10, // 9, 10, 12, 16, // }); m.SetSize({3, 3}); m.Invoke(); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 14, 12, 16, // - }))); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 4, 5, 8, 6, 10, // + 7, 8, 9, 12, 10, 14, // + 9, 10, 11, 14, 12, 16, // + }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 2}}, {3, 3}); - const_m.SetInput({ + const_m.SetInput({ 3, 4, 6, 10, // 9, 10, 12, 16, // }); const_m.Invoke(); - EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 14, 12, 16, // - }))); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 4, 5, 8, 6, 10, // + 7, 8, 9, 12, 10, 14, // + 9, 10, 11, 14, 12, 16, // + }))); +} + +TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { + ResizeBilinearOpModel m({TensorType_UINT8, {2, 2, 2, 1}}); + m.SetInput({ + 3, 6, // + 9, 12, // + 4, 10, // + 10, 16 // + }); + m.SetSize({3, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + 4, 8, 10, // + 8, 12, 14, // + 10, 13, 16, // + }))); + + ResizeBilinearOpModel const_m({TensorType_UINT8, {2, 2, 2, 1}}, {3, 3}); + const_m.SetInput({ + 3, 6, // + 9, 12, // + 4, 10, // + 10, 16 // + }); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + 4, 8, 10, // + 8, 12, 14, // + 10, 13, 16, // + }))); } +TEST(ResizeBilinearOpTest, ThreeDimensionalResize8Bit) { + ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 2}}); + m.SetInput({ + 3, 4, 6, 10, // + 9, 10, 12, 16, // + }); + m.SetSize({3, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 4, 5, 8, 6, 10, // + 7, 8, 9, 12, 10, 14, // + 9, 10, 11, 13, 12, 16, // + }))); + + ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 2}}, {3, 3}); + const_m.SetInput({ + 3, 4, 6, 10, // + 9, 10, 12, 16, // + }); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 4, 5, 8, 6, 10, // + 7, 8, 9, 12, 10, 14, // + 9, 10, 11, 13, 12, 16, // + }))); +} } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/select_test.cc b/tensorflow/contrib/lite/kernels/select_test.cc index cfe24a5fc92765747d1c75bc3e6964b959e2205d..4664b9acb444747167f991944ddc120e9941ccd6 100644 --- a/tensorflow/contrib/lite/kernels/select_test.cc +++ b/tensorflow/contrib/lite/kernels/select_test.cc @@ -88,11 +88,11 @@ TEST(SelectOpTest, SelectUInt8) { TensorType_UINT8); model.PopulateTensor(model.input1(), {false, true, false, false}); - model.PopulateTensor(model.input2(), {1, 2, 3, 4}); - model.PopulateTensor(model.input3(), {5, 6, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 4}); + model.PopulateTensor(model.input3(), {5, 6, 7, 8}); model.Invoke(); - EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8})); EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4})); } @@ -101,11 +101,11 @@ TEST(SelectOpTest, SelectInt32) { TensorType_INT32); model.PopulateTensor(model.input1(), {false, true, false, false}); - model.PopulateTensor(model.input2(), {1, 2, 3, 4}); - model.PopulateTensor(model.input3(), {5, 6, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 4}); + model.PopulateTensor(model.input3(), {5, 6, 7, 8}); model.Invoke(); - EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8})); EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4})); } @@ -113,11 +113,11 @@ TEST(SelectOpTest, RankOneSelectInt32) { SelectOpModel model({2}, {2, 1, 2, 1}, {2, 1, 2, 1}, TensorType_INT32); model.PopulateTensor(model.input1(), {false, true}); - model.PopulateTensor(model.input2(), {1, 2, 3, 4}); - model.PopulateTensor(model.input3(), {5, 6, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 4}); + model.PopulateTensor(model.input3(), {5, 6, 7, 8}); model.Invoke(); - EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 3, 4})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 3, 4})); EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 2, 1})); } @@ -125,11 +125,11 @@ TEST(SelectOpTest, RankZeroSelectInt32) { SelectOpModel model({1}, {1, 2, 2, 1}, {1, 2, 2, 1}, TensorType_INT32); model.PopulateTensor(model.input1(), {false}); - model.PopulateTensor(model.input2(), {1, 2, 3, 4}); - model.PopulateTensor(model.input3(), {5, 6, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 4}); + model.PopulateTensor(model.input3(), {5, 6, 7, 8}); model.Invoke(); - EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 7, 8})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 6, 7, 8})); EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 2, 2, 1})); } diff --git a/tensorflow/contrib/lite/kernels/shape.cc b/tensorflow/contrib/lite/kernels/shape.cc new file mode 100644 index 0000000000000000000000000000000000000000..dbcd2ef004f490f00193153be7a2cfda83e73c24 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/shape.cc @@ -0,0 +1,93 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace shape { + +constexpr int kInputTensor = 0; +constexpr int kOutputTensor = 0; + +template +void ExtractShape(const TfLiteTensor* input, OutType* output_data) { + for (int i = 0; i < NumDimensions(input); ++i) { + output_data[i] = SizeOfDimension(input, i); + } +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + auto* params = reinterpret_cast(node->builtin_data); + switch (params->out_type) { + case kTfLiteInt32: + output->type = kTfLiteInt32; + break; + case kTfLiteInt64: + output->type = kTfLiteInt64; + break; + default: + context->ReportError(context, "Unknown shape output data type: %d", + params->out_type); + return kTfLiteError; + } + + // Shape always produces a 1-dimensional output tensor, where each output + // element is the length of the corresponding input tensor's dimension. + TfLiteIntArray* output_size = TfLiteIntArrayCreate(1); + output_size->data[0] = NumDimensions(input); + return context->ResizeTensor(context, output, output_size); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + TFLITE_DCHECK_EQ(NumDimensions(output), 1); + TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input)); + + switch (output->type) { + case kTfLiteInt32: + ExtractShape(input, GetTensorData(output)); + break; + case kTfLiteInt64: + ExtractShape(input, GetTensorData(output)); + break; + default: + return kTfLiteError; + } + + return kTfLiteOk; +} + +} // namespace shape + +TfLiteRegistration* Register_SHAPE() { + static TfLiteRegistration r = {nullptr, nullptr, shape::Prepare, shape::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/shape_test.cc b/tensorflow/contrib/lite/kernels/shape_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..27b48f4e992a8f02d56815bd1bd9074f5b41f400 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/shape_test.cc @@ -0,0 +1,95 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +template +class ShapeOpModel : public SingleOpModel { + public: + ShapeOpModel(std::initializer_list input_shape, TensorType input_type, + TensorType output_type) { + input_ = AddInput(input_type); + output_ = AddOutput(output_type); + SetBuiltinOp(BuiltinOperator_SHAPE, BuiltinOptions_ShapeOptions, + CreateShapeOptions(builder_, output_type).Union()); + BuildInterpreter({input_shape}); + } + + TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); } + + int input() { return input_; } + + int32_t GetOutputSize() { return GetTensorSize(output_); } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int output_; +}; + +TEST(ShapeOpTest, OutTypeInt) { + ShapeOpModel model({1, 3, 1, 3, 5}, TensorType_FLOAT32, + TensorType_INT32); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5})); +} + +TEST(ShapeOpTest, OutTypeInt64) { + ShapeOpModel model({1, 3, 1, 3, 5}, TensorType_FLOAT32, + TensorType_INT64); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5})); +} + +TEST(ShapeOpTest, ScalarTensor) { + ShapeOpModel model({}, TensorType_FLOAT32, TensorType_INT32); + model.Invoke(); + + EXPECT_EQ(model.GetOutputSize(), 0); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({0})); +} + +TEST(ShapeOpTest, EmptyTensor) { + ShapeOpModel model({1, 0}, TensorType_FLOAT32, TensorType_INT32); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/softmax_test.cc b/tensorflow/contrib/lite/kernels/softmax_test.cc index 6c5338ff0fd26337c9adc8e0b94a0a88edfde37f..727822f6beaa8a63ca8f1b57ba4993d2e59f7e0b 100644 --- a/tensorflow/contrib/lite/kernels/softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/softmax_test.cc @@ -92,10 +92,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), @@ -120,10 +119,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaNotEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), diff --git a/tensorflow/contrib/lite/kernels/split.cc b/tensorflow/contrib/lite/kernels/split.cc index 43387df9ceb4d54a2784c3fa4718a95262948729..b14448604123253bac9c50c21f047891721ab122 100644 --- a/tensorflow/contrib/lite/kernels/split.cc +++ b/tensorflow/contrib/lite/kernels/split.cc @@ -76,8 +76,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits); auto input_type = op_context.input->type; - TF_LITE_ENSURE(context, - input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8); + TF_LITE_ENSURE(context, input_type == kTfLiteFloat32 || + input_type == kTfLiteUInt8 || + input_type == kTfLiteInt16); for (int i = 0; i < NumOutputs(node); ++i) { GetOutput(context, node, i)->type = input_type; } @@ -137,9 +138,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_SPLIT(uint8_t); break; } + case kTfLiteInt16: { + TF_LITE_SPLIT(int16_t); + break; + } default: context->ReportError( - context, "Only float32 and uint8 are currently supported, got %d.", + context, + "Only float32, uint8 and int16 are currently supported, got %d.", op_context.input->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc index 725dd8105ab9506d5203ed38a11f8e06abdab603..bed2117f9ae3a64e963478eb03b46f0547f4c05f 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice.cc @@ -121,10 +121,19 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, int32_t begin = GetBeginValueAtIndex(op_context, idx); int32_t end = GetEndValueAtIndex(op_context, idx); + // When shrinking an axis, the end position does not matter (and can be + // incorrect when negative indexing is used, see Issue #19260). Always use + // begin + 1 to generate a length 1 slice, since begin has + // already been adjusted for negative indices by GetBeginValueAtIndex. + const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx); + if (shrink_axis) { + end = begin + 1; + } + // This is valid for both positive and negative strides int32_t dim_shape = ceil((end - begin) / static_cast(stride)); dim_shape = dim_shape < 0 ? 0 : dim_shape; - if (!(op_context->params->shrink_axis_mask & (1 << idx))) { + if (!shrink_axis) { output_shape_vector.push_back(dim_shape); } } @@ -204,13 +213,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { int begin_mask = ReverseMaskBits(op_context.params->begin_mask, op_context.dims); int end_mask = ReverseMaskBits(op_context.params->end_mask, op_context.dims); - -#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ - kernel_type::StridedSlice(GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), begin_mask, \ - end_mask, starts, stops, strides, \ - GetTensorData(op_context.output), \ - GetTensorDims(op_context.output)) + int shrink_axis_mask = + ReverseMaskBits(op_context.params->shrink_axis_mask, op_context.dims); + +#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ + kernel_type::StridedSlice( \ + GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), begin_mask, end_mask, shrink_axis_mask, \ + starts, stops, strides, GetTensorData(op_context.output), \ + GetTensorDims(op_context.output)) switch (op_context.input->type) { case kTfLiteFloat32: diff --git a/tensorflow/contrib/lite/kernels/strided_slice_test.cc b/tensorflow/contrib/lite/kernels/strided_slice_test.cc index cc39179bc705aa1083e74b06f8f7f3fb45e9f616..c5d4f9affb46c82b4dec15bc0653d7315d132335 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice_test.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice_test.cc @@ -21,7 +21,6 @@ limitations under the License. namespace tflite { namespace { -using ::int32; using ::testing::ElementsAreArray; template data) { PopulateTensor(input_, data); } - void SetBegin(std::initializer_list data) { - PopulateTensor(begin_, data); + void SetBegin(std::initializer_list data) { + PopulateTensor(begin_, data); } - void SetEnd(std::initializer_list data) { - PopulateTensor(end_, data); + void SetEnd(std::initializer_list data) { + PopulateTensor(end_, data); } - void SetStrides(std::initializer_list data) { - PopulateTensor(strides_, data); + void SetStrides(std::initializer_list data) { + PopulateTensor(strides_, data); } std::vector GetOutput() { @@ -384,6 +383,45 @@ TEST(StridedSliceOpTest, In1D_ShrinkAxisMask1) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({2})); } +TEST(StridedSliceOpTest, In1D_ShrinkAxisMask1_NegativeSlice) { + // This is equivalent to tf.range(4)[-1]. + StridedSliceOpModel<> m({4}, {1}, {1}, {1}, 0, 0, 0, 0, 1); + m.SetInput({0, 1, 2, 3}); + m.SetBegin({-1}); + m.SetEnd({0}); + m.SetStrides({1}); + + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); +} + +TEST(StridedSliceOpTest, In2D_ShrinkAxis3_NegativeSlice) { + // This is equivalent to tf.range(4)[:, tf.newaxis][-2, -1]. + StridedSliceOpModel<> m({4, 1}, {2}, {2}, {2}, 0, 0, 0, 0, 3); + m.SetInput({0, 1, 2, 3}); + m.SetBegin({-2, -1}); + m.SetEnd({-1, 0}); + m.SetStrides({1, 1}); + + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2})); +} + +TEST(StridedSliceOpTest, In2D_ShrinkAxis2_BeginEndAxis1_NegativeSlice) { + // This is equivalent to tf.range(4)[:, tf.newaxis][:, -1]. + StridedSliceOpModel<> m({4, 1}, {2}, {2}, {2}, 1, 1, 0, 0, 2); + m.SetInput({0, 1, 2, 3}); + m.SetBegin({0, -1}); + m.SetEnd({0, 0}); + m.SetStrides({1, 1}); + + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 1, 2, 3})); +} + TEST(StridedSliceOpTest, In1D_BeginMaskShrinkAxisMask1) { StridedSliceOpModel<> m({4}, {1}, {1}, {1}, 1, 0, 0, 0, 1); m.SetInput({1, 2, 3, 4}); @@ -395,17 +433,6 @@ TEST(StridedSliceOpTest, In1D_BeginMaskShrinkAxisMask1) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); } -TEST(StridedSliceOpTest, In1D_NegativeBeginNegativeStrideShrinkAxisMask1) { - StridedSliceOpModel<> m({4}, {1}, {1}, {1}, 0, 0, 0, 0, 1); - m.SetInput({1, 2, 3, 4}); - m.SetBegin({-2}); - m.SetEnd({-3}); - m.SetStrides({-1}); - m.Invoke(); - EXPECT_TRUE(m.GetOutputShape().empty()); - EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); -} - TEST(StridedSliceOpTest, In2D_ShrinkAxisMask1) { StridedSliceOpModel<> m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 1); m.SetInput({1, 2, 3, 4, 5, 6}); @@ -538,7 +565,7 @@ TEST(StridedSliceOpTest, RunTwice) { } TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis1Uint8) { - StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 1); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); m.SetBegin({0, 0, 0}); diff --git a/tensorflow/contrib/lite/kernels/sub.cc b/tensorflow/contrib/lite/kernels/sub.cc index bdcaab8e2fa8a3342e0958635ec77a15a7238ccf..1247525d416e8166a9e2e1d67c7907c00b0f6723 100644 --- a/tensorflow/contrib/lite/kernels/sub.cc +++ b/tensorflow/contrib/lite/kernels/sub.cc @@ -83,8 +83,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(params->activation, &output_activation_min, - &output_activation_max); + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); #define TF_LITE_SUB(type, opname) \ type::opname(GetTensorData(input1), GetTensorDims(input1), \ GetTensorData(input2), GetTensorDims(input2), \ @@ -126,16 +126,19 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, int32 input1_multiplier; int input1_shift; - QuantizeMultiplierSmallerThanOne(real_input1_multiplier, &input1_multiplier, - &input1_shift); + QuantizeMultiplierSmallerThanOneExp(real_input1_multiplier, + &input1_multiplier, &input1_shift); + input1_shift *= -1; int32 input2_multiplier; int input2_shift; - QuantizeMultiplierSmallerThanOne(real_input2_multiplier, &input2_multiplier, - &input2_shift); + QuantizeMultiplierSmallerThanOneExp(real_input2_multiplier, + &input2_multiplier, &input2_shift); + input2_shift *= -1; int32 output_multiplier; int output_shift; - QuantizeMultiplierSmallerThanOne(real_output_multiplier, &output_multiplier, - &output_shift); + QuantizeMultiplierSmallerThanOneExp(real_output_multiplier, + &output_multiplier, &output_shift); + output_shift *= -1; int32 output_activation_min, output_activation_max; CalculateActivationRangeUint8(params->activation, output, diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc index 308860c299e9d74729d35b760e0f605437872c92..22eebdd4ceb16aeabc5e799c708f7236b3e2be37 100644 --- a/tensorflow/contrib/lite/kernels/svdf.cc +++ b/tensorflow/contrib/lite/kernels/svdf.cc @@ -12,6 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ + +// SVDF op that compresses a fully connected op via low-rank matrix +// factorization. See https://research.google.com/pubs/archive/43813.pdf for +// details. #include #include #include @@ -32,6 +36,67 @@ namespace ops { namespace builtin { namespace svdf { +namespace { + +struct OpData { + int scratch_tensor_index; + bool float_weights_time_initialized; +}; + +static inline void ApplyTimeWeightsBiasAndActivation( + int batch_size, int memory_size, int num_filters, int num_units, int rank, + const TfLiteTensor* weights_time, const TfLiteTensor* bias, + TfLiteFusedActivation activation, TfLiteTensor* state, + TfLiteTensor* scratch, TfLiteTensor* output) { + // Compute matmul(state, weights_time). + // The right most column is used to save temporary output (with the size of + // num_filters). This is achieved by starting at state->data.f and having the + // stride equal to memory_size. + for (int b = 0; b < batch_size; ++b) { + float* state_ptr_batch = state->data.f + b * memory_size * num_filters; + float* scratch_ptr_batch = scratch->data.f + b * num_filters; + tensor_utils::BatchVectorBatchVectorDotProduct( + weights_time->data.f, state_ptr_batch, memory_size, num_filters, + scratch_ptr_batch, /*result_stride=*/1); + } + + // Initialize output with bias if provided. + if (bias) { + tensor_utils::VectorBatchVectorAssign(bias->data.f, num_units, batch_size, + output->data.f); + } else { + tensor_utils::ZeroVector(output->data.f, batch_size * num_units); + } + + // Reduction sum. + for (int b = 0; b < batch_size; ++b) { + float* output_ptr_batch = output->data.f + b * num_units; + float* scratch_ptr_batch = scratch->data.f + b * num_filters; + tensor_utils::ReductionSumVector(scratch_ptr_batch, output_ptr_batch, + num_units, rank); + } + + // Apply activation. + for (int b = 0; b < batch_size; ++b) { + float* output_ptr_batch = output->data.f + b * num_units; + tensor_utils::ApplyActivationToVector(output_ptr_batch, num_units, + activation, output_ptr_batch); + } + + // Left shift the state to make room for next cycle's activation. + // TODO(alanchiao): explore collapsing this into a single loop. + for (int b = 0; b < batch_size; ++b) { + float* state_ptr_batch = state->data.f + b * memory_size * num_filters; + for (int f = 0; f < num_filters; ++f) { + tensor_utils::VectorShiftLeft(state_ptr_batch, memory_size, + /*shift_value=*/0.0); + state_ptr_batch += memory_size; + } + } +} + +} // namespace + constexpr int kInputTensor = 0; constexpr int kWeightsFeatureTensor = 1; constexpr int kWeightsTimeTensor = 2; @@ -40,29 +105,34 @@ constexpr int kStateTensor = 0; constexpr int kOutputTensor = 1; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* scratch_tensor_index = new int; - context->AddTensors(context, 1, scratch_tensor_index); - return scratch_tensor_index; + auto* op_data = new OpData; + op_data->float_weights_time_initialized = false; + context->AddTensors(context, /*tensors_to_add=*/4, + &op_data->scratch_tensor_index); + return op_data; } void Free(TfLiteContext* context, void* buffer) { - delete reinterpret_cast(buffer); + delete reinterpret_cast(buffer); } TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - int* scratch_tensor_index = reinterpret_cast(node->user_data); + const auto* params = reinterpret_cast(node->builtin_data); + OpData* op_data = reinterpret_cast(node->user_data); + int scratch_tensor_index = op_data->scratch_tensor_index; // Check we have all the inputs and outputs we need. TF_LITE_ENSURE_EQ(context, node->inputs->size, 4); TF_LITE_ENSURE_EQ(context, node->outputs->size, 2); - TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]]; + const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* weights_feature = GetInput(context, node, kWeightsFeatureTensor); const TfLiteTensor* weights_time = GetInput(context, node, kWeightsTimeTensor); + TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); + // Check all the parameters of tensor match within themselves and match the // input configuration. const int rank = params->rank; @@ -103,10 +173,18 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output, output_size_array)); + // The weights are of consistent type, so it suffices to check one. + const bool is_hybrid_op = + (input->type == kTfLiteFloat32 && weights_feature->type == kTfLiteUInt8); + // Resize scratch. TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(1); - node->temporaries->data[0] = *scratch_tensor_index; + if (is_hybrid_op) { + node->temporaries = TfLiteIntArrayCreate(4); + } else { + node->temporaries = TfLiteIntArrayCreate(1); + } + node->temporaries->data[0] = scratch_tensor_index; TfLiteIntArray* scratch_size_array = TfLiteIntArrayCreate(2); scratch_size_array->data[0] = batch_size; @@ -118,24 +196,56 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, scratch_size_array)); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* weights_feature = - GetInput(context, node, kWeightsFeatureTensor); - const TfLiteTensor* weights_time = - GetInput(context, node, kWeightsTimeTensor); + if (is_hybrid_op) { + // Tell interpreter to allocate temporary tensors to store quantized values + // of input tensors. + node->temporaries->data[1] = scratch_tensor_index + 1; + TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); + input_quantized->type = kTfLiteUInt8; + input_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) { + TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims); + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized, + input_quantized_size)); + } - TfLiteTensor* state = GetOutput(context, node, kStateTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TfLiteTensor* scratch = GetTemporary(context, node, /*index=*/0); + // Tell interpreter to allocate temporary tensors to store scaling factors. + node->temporaries->data[2] = scratch_tensor_index + 2; + TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/2); + scaling_factors->type = kTfLiteFloat32; + scaling_factors->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1); + scaling_factors_size->data[0] = batch_size; + if (!TfLiteIntArrayEqual(scaling_factors->dims, scaling_factors_size)) { + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors, + scaling_factors_size)); + } - const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); + // Used to store dequantized weights_time matrix for hybrid computation + // of matmul(state, weights_time), which occurs in floating point. + node->temporaries->data[3] = scratch_tensor_index + 3; + TfLiteTensor* float_weights_time = GetTemporary(context, node, /*index=*/3); + float_weights_time->type = kTfLiteFloat32; + // Persistent so that we can compute the dequantized weights only once. + float_weights_time->allocation_type = kTfLiteArenaRwPersistent; + if (!TfLiteIntArrayEqual(float_weights_time->dims, weights_time->dims)) { + TfLiteIntArray* float_weights_time_size = + TfLiteIntArrayCopy(weights_time->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, float_weights_time, + float_weights_time_size)); + } + } + return kTfLiteOk; +} +TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, + const TfLiteTensor* input, + const TfLiteTensor* weights_feature, + const TfLiteTensor* weights_time, + const TfLiteTensor* bias, const TfLiteSVDFParams* params, + TfLiteTensor* scratch, TfLiteTensor* state, + TfLiteTensor* output) { const int rank = params->rank; const int batch_size = input->dims->data[0]; const int input_size = input->dims->data[1]; @@ -146,67 +256,151 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Clear the activation (state left most column). // TODO(ghodrat): Add a test which initialize state with invalid values in // left most column and make sure it passes. - for (int b = 0; b < batch_size; b++) { + for (int b = 0; b < batch_size; ++b) { float* state_ptr_batch = state->data.f + b * memory_size * num_filters; - for (int c = 0; c < num_filters; c++) { + for (int c = 0; c < num_filters; ++c) { float* state_ptr = state_ptr_batch + c * memory_size; state_ptr[memory_size - 1] = 0.0; } } // Compute conv1d(inputs, weights_feature). - // The state left most column is used to save current cycle activation. This + // The state right most column is used to save current cycle activation. This // is achieved by starting at state->data.f[memory_size - 1] and having the // stride equal to memory_size. tensor_utils::MatrixBatchVectorMultiplyAccumulate( weights_feature->data.f, num_filters, input_size, input->data.f, batch_size, &state->data.f[memory_size - 1], memory_size); - // Compute matmul(state, weights_time). - // The right most column is used to save temporary output (with the size of - // num_filters). This is achieved by starting at state->data.f and having the - // stride equal to memory_size. - for (int b = 0; b < batch_size; b++) { + ApplyTimeWeightsBiasAndActivation(batch_size, memory_size, num_filters, + num_units, rank, weights_time, bias, + params->activation, state, scratch, output); + return kTfLiteOk; +} + +TfLiteStatus EvalHybrid( + TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input, + const TfLiteTensor* weights_feature, const TfLiteTensor* weights_time, + const TfLiteTensor* bias, const TfLiteSVDFParams* params, + TfLiteTensor* scratch, TfLiteTensor* scaling_factors, + TfLiteTensor* input_quantized, TfLiteTensor* state, TfLiteTensor* output) { + const int rank = params->rank; + const int batch_size = input->dims->data[0]; + const int input_size = input->dims->data[1]; + const int num_filters = weights_feature->dims->data[0]; + const int num_units = num_filters / rank; + const int memory_size = weights_time->dims->data[1]; + + // Initialize the pointer to input. + const float* input_ptr_batch = input->data.f; + + // Initialize the pointer to storage for quantized values and + // scaling factors. + int8_t* quantized_input_ptr_batch = + reinterpret_cast(input_quantized->data.uint8); + + float* scaling_factors_ptr = scaling_factors->data.f; + + // Other initializations. + const int8_t* weights_feature_ptr = + reinterpret_cast(weights_feature->data.uint8); + const float weights_feature_scale = weights_feature->params.scale; + + // Clear the activation (state left most column). + // TODO(ghodrat): Add a test which initialize state with invalid values in + // left most column and make sure it passes. + for (int b = 0; b < batch_size; ++b) { float* state_ptr_batch = state->data.f + b * memory_size * num_filters; - float* scratch_ptr_batch = scratch->data.f + b * num_filters; - tensor_utils::BatchVectorBatchVectorDotProduct( - weights_time->data.f, state_ptr_batch, memory_size, num_filters, - scratch_ptr_batch, /*result_stride=*/1); + for (int c = 0; c < num_filters; ++c) { + float* state_ptr = state_ptr_batch + c * memory_size; + state_ptr[memory_size - 1] = 0.0; + } } - // Initialize output with bias if provided. - if (bias) { - tensor_utils::VectorBatchVectorAssign(bias->data.f, num_units, batch_size, - output->data.f); - } else { - tensor_utils::ZeroVector(output->data.f, batch_size * num_units); - } + if (!tensor_utils::IsZeroVector(input_ptr_batch, batch_size * input_size)) { + // Quantize input from float to int8. + float unused_min, unused_max; + for (int b = 0; b < batch_size; ++b) { + const int offset = b * input_size; + tensor_utils::SymmetricQuantizeFloats( + input_ptr_batch + offset, input_size, + quantized_input_ptr_batch + offset, &unused_min, &unused_max, + &scaling_factors_ptr[b]); + scaling_factors_ptr[b] *= weights_feature_scale; + } - // Reduction sum - for (int b = 0; b < batch_size; b++) { - float* output_ptr_batch = output->data.f + b * num_units; - float* scratch_ptr_batch = scratch->data.f + b * num_filters; - tensor_utils::ReductionSumVector(scratch_ptr_batch, output_ptr_batch, - num_units, rank); + // Compute conv1d(inputs, weights_feature). + // The state right most column is used to save current cycle activation. + // This is achieved by starting at state->data.f[memory_size - 1] and having + // the stride equal to memory_size. + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + weights_feature_ptr, num_filters, input_size, quantized_input_ptr_batch, + scaling_factors_ptr, batch_size, &state->data.f[memory_size - 1], + memory_size); } - // Apply activation. - for (int b = 0; b < batch_size; b++) { - float* output_ptr_batch = output->data.f + b * num_units; - tensor_utils::ApplyActivationToVector(output_ptr_batch, num_units, - params->activation, output_ptr_batch); - } + // TODO(alanchiao): can optimize hybrid case ~5% by unrolling loop in applying + // time weights so that the inner loop multiplies eight elements at a time. + ApplyTimeWeightsBiasAndActivation(batch_size, memory_size, num_filters, + num_units, rank, weights_time, bias, + params->activation, state, scratch, output); + return kTfLiteOk; +} - // Right shift the state. - for (int b = 0; b < batch_size; b++) { - float* state_ptr_batch = state->data.f + b * memory_size * num_filters; - for (int f = 0; f < num_filters; f++) { - tensor_utils::VectorShiftLeft(state_ptr_batch, memory_size, - /*shift_value=*/0.0); - state_ptr_batch += memory_size; +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + OpData* op_data = reinterpret_cast(node->user_data); + + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + const TfLiteTensor* weights_feature = + GetInput(context, node, kWeightsFeatureTensor); + const TfLiteTensor* weights_time = + GetInput(context, node, kWeightsTimeTensor); + const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); + + TfLiteTensor* scratch = GetTemporary(context, node, /*index=*/0); + + TfLiteTensor* state = GetOutput(context, node, kStateTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + switch (weights_feature->type) { + case kTfLiteFloat32: { + return EvalFloat(context, node, input, weights_feature, weights_time, + bias, params, scratch, state, output); + break; } + case kTfLiteUInt8: { + TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); + TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/2); + TfLiteTensor* float_weights_time = + GetTemporary(context, node, /*index=*/3); + + // Dequantize weights time. + // TODO(alanchiao): this dequantization initialization only needs to + // happen once per model and should theoretically be placed in either Init + // or Prepare. However, TFLite doesn't allocate float_weights_time until + // the Eval function. + // TODO(alanchiao): refactor logic out into dequantize function. + if (!op_data->float_weights_time_initialized) { + const float dequantization_scale = weights_time->params.scale; + const int8_t* weights_time_ptr = + reinterpret_cast(weights_time->data.uint8); + for (int i = 0; i < NumElements(float_weights_time); ++i) { + float_weights_time->data.f[i] = + weights_time_ptr[i] * dequantization_scale; + } + op_data->float_weights_time_initialized = true; + } + return EvalHybrid(context, node, input, weights_feature, + float_weights_time, bias, params, scratch, + scaling_factors, input_quantized, state, output); + break; + } + default: + context->ReportError(context, "Type %d not currently supported.", + weights_feature->type); + return kTfLiteError; } - return kTfLiteOk; } } // namespace svdf diff --git a/tensorflow/contrib/lite/kernels/svdf_test.cc b/tensorflow/contrib/lite/kernels/svdf_test.cc index 0f166dc69b95f3459388135b3a6c4d9b73a31cb4..5af3ff85004ce43c5b75c6f12761f121c0d8deca 100644 --- a/tensorflow/contrib/lite/kernels/svdf_test.cc +++ b/tensorflow/contrib/lite/kernels/svdf_test.cc @@ -126,17 +126,20 @@ static float svdf_golden_output_rank_2[] = { }; // Derived class of SingleOpModel, which is used to test SVDF TFLite op. -class SVDFOpModel : public SingleOpModel { +class BaseSVDFOpModel : public SingleOpModel { public: - SVDFOpModel(int batches, int units, int input_size, int memory_size, int rank) + BaseSVDFOpModel(int batches, int units, int input_size, int memory_size, + int rank, + TensorType weights_feature_type = TensorType_FLOAT32, + TensorType weights_time_type = TensorType_FLOAT32) : batches_(batches), units_(units), input_size_(input_size), memory_size_(memory_size), rank_(rank) { input_ = AddInput(TensorType_FLOAT32); - weights_feature_ = AddInput(TensorType_FLOAT32); - weights_time_ = AddInput(TensorType_FLOAT32); + weights_feature_ = AddInput(weights_feature_type); + weights_time_ = AddInput(weights_time_type); bias_ = AddNullInput(); state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -182,7 +185,7 @@ class SVDFOpModel : public SingleOpModel { int num_units() { return units_; } int num_batches() { return batches_; } - private: + protected: int input_; int weights_feature_; int weights_time_; @@ -197,7 +200,61 @@ class SVDFOpModel : public SingleOpModel { int rank_; }; -TEST(SVDFOpTest, BlackBoxTestRank1) { +class SVDFOpModel : public BaseSVDFOpModel { + public: + using BaseSVDFOpModel::BaseSVDFOpModel; +}; + +class HybridSVDFOpModel : public BaseSVDFOpModel { + public: + HybridSVDFOpModel(int batches, int units, int input_size, int memory_size, + int rank) + : BaseSVDFOpModel(batches, units, input_size, memory_size, rank, + TensorType_UINT8, TensorType_UINT8) {} + + void SetWeightsFeature(std::initializer_list f) { + SymmetricQuantizeAndPopulate(weights_feature_, f); + } + + void SetWeightsTime(std::initializer_list f) { + SymmetricQuantizeAndPopulate(weights_time_, f); + } +}; + +class SVDFOpTest : public ::testing::Test { + protected: + void VerifyGoldens(float golden_input[], float golden_output[], + int golden_size, BaseSVDFOpModel* svdf, + float tolerance = 1e-5) { + const int svdf_num_batches = svdf->num_batches(); + const int svdf_input_size = svdf->input_size(); + const int svdf_num_units = svdf->num_units(); + const int input_sequence_size = + golden_size / sizeof(float) / (svdf_input_size * svdf_num_batches); + // Going over each input batch, setting the input tensor, invoking the SVDF + // op and checking the output with the expected golden values. + for (int i = 0; i < input_sequence_size; i++) { + float* batch_start = + golden_input + i * svdf_input_size * svdf_num_batches; + float* batch_end = batch_start + svdf_input_size * svdf_num_batches; + svdf->SetInput(0, batch_start, batch_end); + + svdf->Invoke(); + + const float* golden_start = + golden_output + i * svdf_num_units * svdf_num_batches; + const float* golden_end = + golden_start + svdf_num_units * svdf_num_batches; + std::vector expected; + expected.insert(expected.end(), golden_start, golden_end); + + EXPECT_THAT(svdf->GetOutput(), + ElementsAreArray(ArrayFloatNear(expected, tolerance))); + } + } +}; + +TEST_F(SVDFOpTest, BlackBoxTestRank1) { SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, /*memory_size=*/10, /*rank=*/1); svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, @@ -218,31 +275,11 @@ TEST(SVDFOpTest, BlackBoxTestRank1) { -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); svdf.ResetState(); - const int svdf_num_batches = svdf.num_batches(); - const int svdf_input_size = svdf.input_size(); - const int svdf_num_units = svdf.num_units(); - const int input_sequence_size = - sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches); - // Going over each input batch, setting the input tensor, invoking the SVDF op - // and checking the output with the expected golden values. - for (int i = 0; i < input_sequence_size; i++) { - float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches; - float* batch_end = batch_start + svdf_input_size * svdf_num_batches; - svdf.SetInput(0, batch_start, batch_end); - - svdf.Invoke(); - - float* golden_start = - svdf_golden_output_rank_1 + i * svdf_num_units * svdf_num_batches; - float* golden_end = golden_start + svdf_num_units * svdf_num_batches; - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - - EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); - } + VerifyGoldens(svdf_input, svdf_golden_output_rank_1, sizeof(svdf_input), + &svdf); } -TEST(SVDFOpTest, BlackBoxTestRank2) { +TEST_F(SVDFOpTest, BlackBoxTestRank2) { SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, /*memory_size=*/10, /*rank=*/2); svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, @@ -278,28 +315,75 @@ TEST(SVDFOpTest, BlackBoxTestRank2) { 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); svdf.ResetState(); - const int svdf_num_batches = svdf.num_batches(); - const int svdf_input_size = svdf.input_size(); - const int svdf_num_units = svdf.num_units(); - const int input_sequence_size = - sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches); - // Going over each input batch, setting the input tensor, invoking the SVDF op - // and checking the output with the expected golden values. - for (int i = 0; i < input_sequence_size; i++) { - float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches; - float* batch_end = batch_start + svdf_input_size * svdf_num_batches; - svdf.SetInput(0, batch_start, batch_end); - - svdf.Invoke(); - - float* golden_start = - svdf_golden_output_rank_2 + i * svdf_num_units * svdf_num_batches; - float* golden_end = golden_start + svdf_num_units * svdf_num_batches; - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - - EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); - } + VerifyGoldens(svdf_input, svdf_golden_output_rank_2, sizeof(svdf_input), + &svdf); +} + +TEST_F(SVDFOpTest, BlackBoxTestHybridRank1) { + HybridSVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, + /*memory_size=*/10, /*rank=*/1); + svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, + 0.22197971, 0.12416199, 0.27901134, 0.27557442, + 0.3905206, -0.36137494, -0.06634006, -0.10640851}); + + svdf.SetWeightsTime( + {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, + 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, + + 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, + -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, + + -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, + 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, + + -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, + -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); + + svdf.ResetState(); + VerifyGoldens(svdf_input, svdf_golden_output_rank_1, sizeof(svdf_input), + &svdf, + /*tolerance=*/0.002945); +} + +TEST_F(SVDFOpTest, BlackBoxTestHybridRank2) { + HybridSVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, + /*memory_size=*/10, /*rank=*/2); + svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, + 0.12416199, 0.15785322, 0.27901134, 0.3905206, + 0.21931258, -0.36137494, -0.10640851, 0.31053296, + -0.36118156, -0.0976817, -0.36916667, 0.22197971, + 0.15294972, 0.38031587, 0.27557442, 0.39635518, + -0.21580373, -0.06634006, -0.02702999, 0.27072677}); + + svdf.SetWeightsTime( + {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, + 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, + + 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, + -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, + + -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, + 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, + + -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, + -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, + + -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, + 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, + + -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, + 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, + + -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, + -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, + + 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, + 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); + + svdf.ResetState(); + VerifyGoldens(svdf_input, svdf_golden_output_rank_2, sizeof(svdf_input), + &svdf, + /*tolerance=*/0.00625109); } } // namespace diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc index d23ec201b41887b0682242687fc938d76d058c44..9156917140b5af6c0f38c878ab77fef7f93b049a 100644 --- a/tensorflow/contrib/lite/kernels/test_util.cc +++ b/tensorflow/contrib/lite/kernels/test_util.cc @@ -32,8 +32,8 @@ std::vector> ArrayFloatNear(const std::vector& values, return matchers; } -int SingleOpModel::AddInput(const TensorData& t) { - int id = AddTensor(t, {}); +int SingleOpModel::AddInput(const TensorData& t, bool is_variable) { + int id = AddTensor(t, {}, is_variable); inputs_.push_back(id); return id; } @@ -120,6 +120,7 @@ void SingleOpModel::BuildInterpreter( CHECK(interpreter_->AllocateTensors() == kTfLiteOk) << "Cannot allocate tensors"; + interpreter_->ResetVariableTensorsToZero(); } void SingleOpModel::Invoke() { CHECK(interpreter_->Invoke() == kTfLiteOk); } diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index db80c0082c394a2cb2f9388d3db5bd1a7cbe6266..bedbe93ae65662647f6a0fb0c9c6a6a921e148bb 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -126,8 +126,10 @@ class SingleOpModel { SingleOpModel& operator=(const SingleOpModel&) = delete; // Add a TensorType input tensor and return its index. - int AddInput(TensorType type) { return AddInput(TensorData{type}); } - int AddInput(const TensorData& t); + int AddInput(TensorType type, bool is_variable = false) { + return AddInput(TensorData{type}, is_variable); + } + int AddInput(const TensorData& t, bool is_variable = false); // Templated version of AddConstInput(). template @@ -146,20 +148,18 @@ class SingleOpModel { int AddOutput(const TensorData& t); template - void QuantizeAndPopulate(int index, std::initializer_list data) { + void QuantizeAndPopulate(int index, const std::vector& data) { TfLiteTensor* t = interpreter_->tensor(index); auto q = Quantize(data, t->params.scale, t->params.zero_point); PopulateTensor(index, 0, q.data(), q.data() + q.size()); } - void SymmetricQuantizeAndPopulate(int index, - std::initializer_list data) { + void SymmetricQuantizeAndPopulate(int index, const std::vector& data) { TfLiteTensor* t = interpreter_->tensor(index); - std::vector values(data); - const int length = values.size(); + const int length = data.size(); std::vector q(length); float min, max, scaling_factor; - tensor_utils::SymmetricQuantizeFloats(values.data(), length, q.data(), &min, + tensor_utils::SymmetricQuantizeFloats(data.data(), length, q.data(), &min, &max, &scaling_factor); // Update quantization params. t->params.scale = scaling_factor; @@ -196,8 +196,22 @@ class SingleOpModel { } // Populate the tensor given its index. + // TODO(b/110696148) clean up and merge with vector-taking variant below. + template + void PopulateTensor(int index, const std::initializer_list& data) { + T* v = interpreter_->typed_tensor(index); + CHECK(v) << "No tensor with index '" << index << "'."; + for (T f : data) { + *v = f; + ++v; + } + } + + // Populate the tensor given its index. + // TODO(b/110696148) clean up and merge with initializer_list-taking variant + // above. template - void PopulateTensor(int index, std::initializer_list data) { + void PopulateTensor(int index, const std::vector& data) { T* v = interpreter_->typed_tensor(index); CHECK(v) << "No tensor with index '" << index << "'."; for (T f : data) { @@ -260,7 +274,8 @@ class SingleOpModel { } template - int AddTensor(TensorData t, std::initializer_list data) { + int AddTensor(TensorData t, std::initializer_list data, + bool is_variable = false) { int id = tensors_.size(); // This is slightly different depending on whether we are adding a @@ -277,6 +292,9 @@ class SingleOpModel { } else if (t.type == TensorType_INT32) { std::tie(t.scale, t.zero_point) = QuantizationParams(t.min, t.max); + } else if (t.type == TensorType_INT16) { + std::tie(t.scale, t.zero_point) = + QuantizationParams(t.min, t.max); } else { LOG(FATAL) << "No support for the requested quantized type"; } @@ -309,7 +327,7 @@ class SingleOpModel { tensors_.push_back(CreateTensor(builder_, builder_.CreateVector(t.shape), t.type, /*buffer=*/buffer_id, - /*name=*/0, q_params)); + /*name=*/0, q_params, is_variable)); tensor_data_[id] = t; diff --git a/tensorflow/contrib/lite/kernels/test_util_test.cc b/tensorflow/contrib/lite/kernels/test_util_test.cc index 1e10e89061213b6fcabd404310893dd97a51d83f..236580347254d336609a3081736f54e069b5cb5a 100644 --- a/tensorflow/contrib/lite/kernels/test_util_test.cc +++ b/tensorflow/contrib/lite/kernels/test_util_test.cc @@ -22,22 +22,22 @@ using ::testing::ElementsAreArray; TEST(TestUtilTest, QuantizeVector) { std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; - auto q_data = Quantize(data, /*scale=*/1.0, /*zero_point=*/0); - std::vector expected = {0, 0, 0, 1, 1, 255}; + auto q_data = Quantize(data, /*scale=*/1.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 1, 1, 255}; EXPECT_THAT(q_data, ElementsAreArray(expected)); } TEST(TestUtilTest, QuantizeVectorScalingDown) { std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; - auto q_data = Quantize(data, /*scale=*/10.0, /*zero_point=*/0); - std::vector expected = {0, 0, 0, 0, 0, 100}; + auto q_data = Quantize(data, /*scale=*/10.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 0, 0, 100}; EXPECT_THAT(q_data, ElementsAreArray(expected)); } TEST(TestUtilTest, QuantizeVectorScalingUp) { std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; - auto q_data = Quantize(data, /*scale=*/0.1, /*zero_point=*/0); - std::vector expected = {0, 0, 0, 5, 10, 255}; + auto q_data = Quantize(data, /*scale=*/0.1, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 5, 10, 255}; EXPECT_THAT(q_data, ElementsAreArray(expected)); } diff --git a/tensorflow/contrib/lite/kernels/tile_test.cc b/tensorflow/contrib/lite/kernels/tile_test.cc index a134a75d56ae03a5d03a3cdf632146474b863971..4f78c224e54f0c71bc6622134a1c8e4142c22daa 100644 --- a/tensorflow/contrib/lite/kernels/tile_test.cc +++ b/tensorflow/contrib/lite/kernels/tile_test.cc @@ -38,27 +38,27 @@ class TileOpModel : public SingleOpModel { PopulateTensor(input_, data); } - void SetInputUInt8(std::initializer_list data) { - PopulateTensor(input_, data); + void SetInputUInt8(std::initializer_list data) { + PopulateTensor(input_, data); } - void SetInputInt32(std::initializer_list data) { - PopulateTensor(input_, data); + void SetInputInt32(std::initializer_list data) { + PopulateTensor(input_, data); } void SetInputInt64(std::initializer_list data) { PopulateTensor(input_, data); } - void SetMultipliers(std::initializer_list data) { - PopulateTensor(multipliers_, data); + void SetMultipliers(std::initializer_list data) { + PopulateTensor(multipliers_, data); } std::vector GetOutputFloat() { return ExtractVector(output_); } - std::vector GetOutputUInt8() { return ExtractVector(output_); } + std::vector GetOutputUInt8() { return ExtractVector(output_); } - std::vector GetOutputInt32() { return ExtractVector(output_); } + std::vector GetOutputInt32() { return ExtractVector(output_); } std::vector GetOutputInt64() { return ExtractVector(output_); diff --git a/tensorflow/contrib/lite/kernels/topk_v2.cc b/tensorflow/contrib/lite/kernels/topk_v2.cc index fb0e49c90c41747f9b7e53570276c8b8045030fd..2dd760bbfebd1faa8b7ff9158bc1a1b1d4647525 100644 --- a/tensorflow/contrib/lite/kernels/topk_v2.cc +++ b/tensorflow/contrib/lite/kernels/topk_v2.cc @@ -56,11 +56,13 @@ TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) { output_values_shape->data[num_dimensions - 1] = k; TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes); TfLiteTensor* output_values = GetOutput(context, node, kOutputValues); + // Force output types. + output_indexes->type = kTfLiteInt32; + output_values->type = input->type; auto resize_tensor = [context](TfLiteTensor* tensor, TfLiteIntArray* new_size, TfLiteIntArray* delete_on_error) { TfLiteStatus status = context->ResizeTensor(context, tensor, new_size); if (status != kTfLiteOk) { - TfLiteIntArrayFree(new_size); if (delete_on_error != nullptr) { TfLiteIntArrayFree(delete_on_error); } diff --git a/tensorflow/contrib/lite/kernels/topk_v2_test.cc b/tensorflow/contrib/lite/kernels/topk_v2_test.cc index 212f8acc76d4afba56933029175f69b34ea87a3e..2abb89b617742b33b9280b15ad379422c5c9b207 100644 --- a/tensorflow/contrib/lite/kernels/topk_v2_test.cc +++ b/tensorflow/contrib/lite/kernels/topk_v2_test.cc @@ -42,32 +42,32 @@ class TopKV2OpModel : public SingleOpModel { PopulateTensor(input_, data); } - void SetInputUInt8(std::initializer_list data) { - PopulateTensor(input_, data); + void SetInputUInt8(std::initializer_list data) { + PopulateTensor(input_, data); } - void SetInputInt32(std::initializer_list data) { - PopulateTensor(input_, data); + void SetInputInt32(std::initializer_list data) { + PopulateTensor(input_, data); } void SetInputInt64(std::initializer_list data) { PopulateTensor(input_, data); } - std::vector GetIndexes() { - return ExtractVector(output_indexes_); + std::vector GetIndexes() { + return ExtractVector(output_indexes_); } std::vector GetValuesFloat() { return ExtractVector(output_values_); } - std::vector GetValuesUInt8() { - return ExtractVector(output_values_); + std::vector GetValuesUInt8() { + return ExtractVector(output_values_); } - std::vector GetValuesInt32() { - return ExtractVector(output_values_); + std::vector GetValuesInt32() { + return ExtractVector(output_values_); } std::vector GetValuesInt64() { @@ -119,7 +119,7 @@ TEST(TopKV2OpTest, VectorFloat) { EXPECT_THAT(m.GetValuesFloat(), ElementsAreArray(ArrayFloatNear({0.8, 0.2}))); } -// Check that uint8 works. +// Check that uint8_t works. TEST(TopKV2OpTest, TypeUint8) { TopKV2OpModel m({2, 3}, TensorType_UINT8, 2); m.SetInputUInt8({1, 2, 3, 251, 250, 249}); @@ -128,7 +128,7 @@ TEST(TopKV2OpTest, TypeUint8) { EXPECT_THAT(m.GetValuesUInt8(), ElementsAreArray({3, 2, 251, 250})); } -// Check that int32 works. +// Check that int32_t works. TEST(TopKV2OpTest, TypeInt32) { TopKV2OpModel m({2, 3}, TensorType_INT32, 2); m.SetInputInt32({1, 2, 3, 10251, 10250, 10249}); diff --git a/tensorflow/contrib/lite/kernels/transpose_conv.cc b/tensorflow/contrib/lite/kernels/transpose_conv.cc index 3c99661029ed1ac881536f83519dcec355c60d50..7182374a6f2ec39c670e02e6fda9b967ae0a5b43 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/eigen_support.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" #include "tensorflow/contrib/lite/kernels/internal/tensor.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" @@ -38,9 +39,35 @@ constexpr int kWeightsTensor = 1; constexpr int kDataInputTensor = 2; constexpr int kOutputTensor = 0; -TfLiteStatus ResizeOutputShape(TfLiteContext* context, - const TfLiteTensor* output_shape, - TfLiteTensor* output) { +const int kTensorNotAllocated = -1; + +struct OpData { + // IDs are the arbitrary identifiers used by TF Lite to identify and access + // memory buffers. + int im2col_id = kTensorNotAllocated; + + // im2col is the only temporary currently tracked, therefore always index 0. + // If more temporaries are added, they should be properly tracked. + int32_t im2col_index = 0; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + // This is a builtin op, so we don't use the contents in 'buffer', if any. + // Instead, we allocate a new object to use as scratch space for im2col, and + // to carry information from Prepare() to Eval(). + auto* data = new OpData; + eigen_support::IncrementUsageCounter(context); + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + eigen_support::DecrementUsageCounter(context); + delete reinterpret_cast(buffer); +} + +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + const TfLiteTensor* output_shape, + TfLiteTensor* output) { // Currently only support int32 for output shape. if (output_shape->type != kTfLiteInt32) { context->ReportError(context, "Output shape is %d, not int32.", @@ -56,15 +83,60 @@ TfLiteStatus ResizeOutputShape(TfLiteContext* context, return context->ResizeTensor(context, output, output_shape_array); } +// Allocate temporary im2col tensor. +static TfLiteStatus AllocateIm2colTensor(TfLiteContext* context, + TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + if (data->im2col_id == kTensorNotAllocated) { + context->AddTensors(context, 1, &data->im2col_id); + } + + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[data->im2col_index] = data->im2col_id; + + return kTfLiteOk; +} + +TfLiteStatus ResizeIm2ColTensor(TfLiteContext* context, + const TfLiteTensor* output_shape, + const TfLiteTensor* weights, + const TfLiteTensor* input, + TfLiteTensor* im2col) { + if (output_shape->type != kTfLiteInt32) { + context->ReportError(context, "im2col shape is %d, not int32.", + output_shape->type); + return kTfLiteError; + } + TF_LITE_ENSURE_EQ(context, NumElements(output_shape), 4); + TfLiteIntArray* im2col_shape_array = TfLiteIntArrayCreate(4); + im2col_shape_array->data[0] = output_shape->data.i32[0]; + im2col_shape_array->data[1] = output_shape->data.i32[1]; + im2col_shape_array->data[2] = output_shape->data.i32[2]; + const int input_depth = SizeOfDimension(input, 3); + const int filter_width = SizeOfDimension(weights, 1); + const int filter_height = SizeOfDimension(weights, 2); + im2col_shape_array->data[3] = input_depth * filter_height * filter_width; + + im2col->type = input->type; + im2col->allocation_type = kTfLiteArenaRw; + return context->ResizeTensor(context, im2col, im2col_shape_array); +} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TF_LITE_ENSURE_STATUS(AllocateIm2colTensor(context, node)); + const TfLiteTensor* output_shape = GetInput(context, node, kOutputShapeTensor); const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor); const TfLiteTensor* input = GetInput(context, node, kDataInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + OpData* user_data = reinterpret_cast(node->user_data); + TfLiteTensor* im2col = + &context->tensors[node->temporaries->data[user_data->im2col_index]]; TF_LITE_ENSURE_EQ(context, NumDimensions(output_shape), 1); TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4); @@ -79,13 +151,17 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Ensure that weights and inputs have the same channel dimension. // Note: TOCO will reorder weights in the following format: OHWI. TF_LITE_ENSURE_EQ(context, SizeOfDimension(input, 3), - SizeOfDimension(weights, 0)); - - if (!IsConstantTensor(output_shape)) { + SizeOfDimension(weights, 3)); + + if (IsConstantTensor(output_shape)) { + TF_LITE_ENSURE_STATUS(ResizeOutputTensor(context, output_shape, output)); + TF_LITE_ENSURE_STATUS( + ResizeIm2ColTensor(context, output_shape, weights, input, im2col)); + } else { + // Defer resizing until Eval(). SetTensorToDynamic(output); - return kTfLiteOk; } - return ResizeOutputShape(context, output_shape, output); + return kTfLiteOk; } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { @@ -94,13 +170,19 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor); const TfLiteTensor* input = GetInput(context, node, kDataInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - + OpData* user_data = reinterpret_cast(node->user_data); + TfLiteTensor* im2col = + &context->tensors[node->temporaries->data[user_data->im2col_index]]; const auto* params = reinterpret_cast(node->builtin_data); if (IsDynamicTensor(output)) { TF_LITE_ENSURE_OK(context, - ResizeOutputShape(context, output_shape, output)); + ResizeOutputTensor(context, output_shape, output)); + } + if (IsDynamicTensor(im2col)) { + TF_LITE_ENSURE_OK(context, ResizeIm2ColTensor(context, output_shape, + weights, input, im2col)); } // Get height and width of the output image. @@ -123,7 +205,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTensorData(input), GetTensorDims(input), GetTensorData(weights), GetTensorDims(weights), stride_width, stride_height, padding_size.width, padding_size.height, - GetTensorData(output), GetTensorDims(output)); + GetTensorData(output), GetTensorDims(output), + GetTensorData(im2col), GetTensorDims(im2col)); break; default: context->ReportError(context, "Type %d, not currently supported.", @@ -136,8 +219,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace transpose_conv TfLiteRegistration* Register_TRANSPOSE_CONV() { - static TfLiteRegistration r = {nullptr, nullptr, transpose_conv::Prepare, - transpose_conv::Eval}; + static TfLiteRegistration r = {transpose_conv::Init, transpose_conv::Free, + transpose_conv::Prepare, transpose_conv::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc index 52be08934997f484337e4a3592bc7af832601695..c741df19dee09b140954d0c110800cbd849c2f11 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include #include +#include "absl/memory/memory.h" #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/kernels/test_util.h" @@ -24,9 +25,49 @@ namespace { using ::testing::ElementsAreArray; +class ConstTransposeConvOpModel : public SingleOpModel { + // Just to be extra confusing, transpose_conv has an _input_ named + // "output_shape". This input sets the shape of the output tensor of the op. + // In this version of the test class, "output_shape" is a constant that must + // be specified in the constructor. + public: + ConstTransposeConvOpModel(TfLiteRegistration* registration, + std::initializer_list input_shape, + std::initializer_list filter_shape, + std::initializer_list output_shape_data, + Padding padding, int stride_w, int stride_h) { + output_shape_ = AddConstInput(TensorType_INT32, output_shape_data, + {static_cast(output_shape_data.size())}); + filter_ = AddInput(TensorType_FLOAT32); + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions, + CreateTransposeConvOptions(builder_, padding, stride_w, stride_h) + .Union()); + resolver_ = absl::make_unique( + BuiltinOperator_TRANSPOSE_CONV, registration); + BuildInterpreter({{4}, filter_shape, input_shape}); + } + + int output_shape() { return output_shape_; } + int filter() { return filter_; } + int input() { return input_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int output_shape_; + int filter_; + int input_; + int output_; +}; + class TransposeConvOpModel : public SingleOpModel { public: - TransposeConvOpModel(std::initializer_list input_shape, + TransposeConvOpModel(TfLiteRegistration* registration, + std::initializer_list input_shape, std::initializer_list filter_shape, Padding padding, int stride_w, int stride_h) { output_shape_ = AddInput(TensorType_INT32); @@ -37,6 +78,8 @@ class TransposeConvOpModel : public SingleOpModel { BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions, CreateTransposeConvOptions(builder_, padding, stride_w, stride_h) .Union()); + resolver_ = absl::make_unique( + BuiltinOperator_TRANSPOSE_CONV, registration); BuildInterpreter({{4}, filter_shape, input_shape}); } @@ -54,6 +97,15 @@ class TransposeConvOpModel : public SingleOpModel { int output_; }; +const auto kKernelMap = new std::map({}); + +class TransposeConvOpTest : public SingleOpTest { + protected: + const std::map& GetKernelMap() override { + return *kKernelMap; + } +}; + // Test case: // output = tf.nn.conv2d_backprop_input( // tf.constant([ 1, 4, 4, 1 ]), @@ -61,8 +113,9 @@ class TransposeConvOpModel : public SingleOpModel { // tf.constant(np.arange(1, 17), shape=[ 1, 4, 4, 1 ], dtype=tf.float32), // [1, 1, 1, 1 ], // "SAME") -TEST(TransposeConvOpModelTest, SimpleTest) { - TransposeConvOpModel m({1, 4, 4, 1}, {1, 3, 3, 1}, Padding_SAME, 1, 1); +TEST_P(TransposeConvOpTest, SimpleTest) { + TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 3, 3, 1}, + Padding_SAME, 1, 1); m.PopulateTensor(m.output_shape(), {1, 4, 4, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); m.PopulateTensor( @@ -75,6 +128,21 @@ TEST(TransposeConvOpModelTest, SimpleTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1})); } +// Test case: Same as above, but with a const "output_shape" +TEST_P(TransposeConvOpTest, ConstSimpleTest) { + ConstTransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 4, 4, 1}, + {1, 3, 3, 1}, Padding_SAME, 1, 1); + m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); + m.PopulateTensor( + m.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({29, 62, 83, 75, 99, 192, 237, 198, 207, 372, + 417, 330, 263, 446, 485, 365})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1})); +} + // Test case: // filter = tf.constant(np.arange(1, 19), // shape=[ 3, 3, 1, 2 ], @@ -87,11 +155,12 @@ TEST(TransposeConvOpModelTest, SimpleTest) { // "SAME") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[18, 1]) -TEST(TransposeConvOpModelTest, TwoFiltersTest) { - TransposeConvOpModel m({1, 4, 4, 2}, {2, 3, 3, 1}, Padding_SAME, 1, 1); +TEST_P(TransposeConvOpTest, TwoFiltersTest) { + TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2}, + Padding_SAME, 1, 1); m.PopulateTensor(m.output_shape(), {1, 4, 4, 1}); - m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, - 8, 10, 12, 14, 16, 18}); + m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); m.PopulateTensor( m.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, @@ -116,11 +185,12 @@ TEST(TransposeConvOpModelTest, TwoFiltersTest) { // "VALID") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[1, 18]) -TEST(TransposeConvOpModelTest, PaddingValidTest) { - TransposeConvOpModel m({1, 4, 4, 2}, {2, 3, 3, 1}, Padding_VALID, 1, 1); +TEST_P(TransposeConvOpTest, PaddingValidTest) { + TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2}, + Padding_VALID, 1, 1); m.PopulateTensor(m.output_shape(), {1, 6, 6, 1}); - m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, - 8, 10, 12, 14, 16, 18}); + m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); m.PopulateTensor( m.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, @@ -146,8 +216,9 @@ TEST(TransposeConvOpModelTest, PaddingValidTest) { // tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32), // [1, 2, 2, 1 ], // "VALID") -TEST(TransposeConvOpModelTest, StrideValidTest) { - TransposeConvOpModel m({1, 2, 2, 1}, {1, 3, 3, 1}, Padding_VALID, 2, 2); +TEST_P(TransposeConvOpTest, StrideValidTest) { + TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {1, 3, 3, 1}, + Padding_VALID, 2, 2); m.PopulateTensor(m.output_shape(), {1, 5, 5, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); m.PopulateTensor(m.input(), {1, 2, 3, 4}); @@ -170,11 +241,30 @@ TEST(TransposeConvOpModelTest, StrideValidTest) { // tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32), // [1, 2, 2, 1 ], // "VALID") -TEST(TransposeConvOpModelTest, MultiChannelTest) { - TransposeConvOpModel m({1, 2, 2, 1}, {1, 3, 3, 2}, Padding_VALID, 2, 2); +TEST_P(TransposeConvOpTest, MultiChannelTest) { + TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1}, + Padding_VALID, 2, 2); m.PopulateTensor(m.output_shape(), {1, 5, 5, 2}); - m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); + m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, + 8, 10, 12, 14, 16, 18}); + m.PopulateTensor(m.input(), {1, 2, 3, 4}); + m.Invoke(); + + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray({1, 2, 3, 4, 7, 10, 6, 8, 10, 12, 7, 8, 9, + 10, 25, 28, 18, 20, 22, 24, 16, 20, 24, 28, 62, 72, + 42, 48, 54, 60, 21, 24, 27, 30, 61, 68, 36, 40, 44, + 48, 39, 42, 45, 48, 103, 110, 60, 64, 68, 72})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 5, 5, 2})); +} + +// Test case: Same as above, but with a const "output_shape" +TEST_P(TransposeConvOpTest, ConstMultiChannelTest) { + ConstTransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1}, + {1, 5, 5, 2}, Padding_VALID, 2, 2); + m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, + 8, 10, 12, 14, 16, 18}); m.PopulateTensor(m.input(), {1, 2, 3, 4}); m.Invoke(); @@ -199,8 +289,9 @@ TEST(TransposeConvOpModelTest, MultiChannelTest) { // "SAME") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[-1]) -TEST(TransposeConvOpModelTest, AccuracyTest) { - TransposeConvOpModel m({1, 1, 2, 1}, {1, 3, 3, 1}, Padding_SAME, 3, 3); +TEST_P(TransposeConvOpTest, AccuracyTest) { + TransposeConvOpModel m(GetRegistration(), {1, 1, 2, 1}, {1, 3, 3, 1}, + Padding_SAME, 3, 3); m.PopulateTensor(m.output_shape(), {1, 3, 4, 1}); m.PopulateTensor(m.filter(), {9, 5, 6, 9, 8, 5, 3, 1, 4}); m.PopulateTensor(m.input(), {323, 521}); @@ -212,6 +303,10 @@ TEST(TransposeConvOpModelTest, AccuracyTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 4, 1})); } +INSTANTIATE_TEST_CASE_P( + TransposeConvOpTest, TransposeConvOpTest, + ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap))); + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 1c28123a24edd9886476bf8e9ea3ba4c692baa2b..32daf2bb02d5f63391cc5ba45654acd4acfbfe56 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -70,9 +70,21 @@ constexpr int kOutputStateTensor = 0; constexpr int kCellStateTensor = 1; constexpr int kOutputTensor = 2; +// Temporary tensors +enum TemporaryTensor { + kScratchBuffer = 0, + kInputQuantized = 1, + kOutputStateQuantized = 2, + kCellStateQuantized = 3, + kScalingFactors = 4, + kProductScalingFactors = 5, + kRecoveredCellWeights = 6, + kNumTemporaryTensors = 7 +}; + void* Init(TfLiteContext* context, const char* buffer, size_t length) { auto* scratch_tensor_index = new int; - context->AddTensors(context, 1, scratch_tensor_index); + context->AddTensors(context, kNumTemporaryTensors, scratch_tensor_index); return scratch_tensor_index; } @@ -84,7 +96,7 @@ void Free(TfLiteContext* context, void* buffer) { TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TfLiteNode* node, int n_input, int n_output, int n_cell) { - auto* params = reinterpret_cast(node->builtin_data); + const auto* params = reinterpret_cast(node->builtin_data); // Making sure clipping parameters have valid values. // == 0 means no clipping @@ -242,6 +254,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Inferring batch size, number of outputs and sequence length and // number of cells from the input tensors. const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); TF_LITE_ENSURE(context, input->dims->size > 1); const int max_time = input->dims->data[0]; const int n_batch = input->dims->data[1]; @@ -288,86 +301,156 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, cell_state, cell_size)); - // Create a scratch buffer tensor. + // Mark state tensors as persistent tensors. + output_state->allocation_type = kTfLiteArenaRwPersistent; + cell_state->allocation_type = kTfLiteArenaRwPersistent; + + // The weights are of consistent type, so it suffices to check one. + // TODO(mirkov): create a utility/macro for this check, so all Ops can use it. + const bool is_hybrid_op = (input_to_output_weights->type == kTfLiteUInt8 && + input->type == kTfLiteFloat32); + TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(1); + if (is_hybrid_op) { + node->temporaries = TfLiteIntArrayCreate(kNumTemporaryTensors); + } else { + node->temporaries = TfLiteIntArrayCreate(1); + } node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); + + // Create a scratch buffer tensor. + TfLiteTensor* scratch_buffer = GetTemporary(context, node, kScratchBuffer); scratch_buffer->type = input->type; scratch_buffer->allocation_type = kTfLiteArenaRw; - // Mark state tensors as persistent tensors. - output_state->allocation_type = kTfLiteArenaRwPersistent; - cell_state->allocation_type = kTfLiteArenaRwPersistent; - const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); const bool use_cifg = (input_to_input_weights == nullptr); + TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); + scratch_buffer_size->data[0] = n_batch; if (use_cifg) { - TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); - scratch_buffer_size->data[0] = n_batch; // Reserving space for Cell, Forget, Output gates scratch_buffer_size->data[1] = n_cell * 3; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, - scratch_buffer_size)); } else { - TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); - scratch_buffer_size->data[0] = n_batch; // Reserving space for Input, Cell, Forget, Output gates scratch_buffer_size->data[1] = n_cell * 4; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, - scratch_buffer_size)); + } + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, + scratch_buffer_size)); + + if (is_hybrid_op) { + // Allocate temporary tensors to store quantized values of input, + // output_state and cell_state tensors. + node->temporaries->data[kInputQuantized] = + *scratch_tensor_index + kInputQuantized; + TfLiteTensor* input_quantized = + GetTemporary(context, node, kInputQuantized); + input_quantized->type = kTfLiteUInt8; + input_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) { + TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims); + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized, + input_quantized_size)); + } + node->temporaries->data[kOutputStateQuantized] = + *scratch_tensor_index + kOutputStateQuantized; + TfLiteTensor* output_state_quantized = + GetTemporary(context, node, kOutputStateQuantized); + output_state_quantized->type = kTfLiteUInt8; + output_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(output_state_quantized->dims, + output_state->dims)) { + TfLiteIntArray* output_state_quantized_size = + TfLiteIntArrayCopy(output_state->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, output_state_quantized, + output_state_quantized_size)); + } + node->temporaries->data[kCellStateQuantized] = + *scratch_tensor_index + kCellStateQuantized; + TfLiteTensor* cell_state_quantized = + GetTemporary(context, node, kCellStateQuantized); + cell_state_quantized->type = kTfLiteUInt8; + cell_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(cell_state_quantized->dims, cell_state->dims)) { + TfLiteIntArray* cell_state_quantized_size = + TfLiteIntArrayCopy(cell_state->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, cell_state_quantized, + cell_state_quantized_size)); + } + + // Allocate temporary tensors to store scaling factors and product scaling + // factors. The latter is a convenience storage which allows to quantize + // a vector once (which produces the scaling factors) and multiply it with + // different matrices (which requires multiplying the scaling factors with + // the scaling factor of the matrix). + node->temporaries->data[kScalingFactors] = + *scratch_tensor_index + kScalingFactors; + TfLiteTensor* scaling_factors = + GetTemporary(context, node, kScalingFactors); + scaling_factors->type = kTfLiteFloat32; + scaling_factors->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1); + scaling_factors_size->data[0] = n_batch; + if (!TfLiteIntArrayEqual(scaling_factors->dims, scaling_factors_size)) { + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors, + scaling_factors_size)); + } + node->temporaries->data[kProductScalingFactors] = + *scratch_tensor_index + kProductScalingFactors; + TfLiteTensor* prod_scaling_factors = + GetTemporary(context, node, kProductScalingFactors); + prod_scaling_factors->type = kTfLiteFloat32; + prod_scaling_factors->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* prod_scaling_factors_size = TfLiteIntArrayCreate(1); + prod_scaling_factors_size->data[0] = n_batch; + if (!TfLiteIntArrayEqual(prod_scaling_factors->dims, + prod_scaling_factors_size)) { + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, prod_scaling_factors, + prod_scaling_factors_size)); + } + + // Allocate a temporary tensor to store the recovered cell weights. Since + // this is used for diagonal matrices, only need to store n_cell values. + node->temporaries->data[kRecoveredCellWeights] = + *scratch_tensor_index + kRecoveredCellWeights; + TfLiteTensor* recovered_cell_weights = + GetTemporary(context, node, kRecoveredCellWeights); + recovered_cell_weights->type = kTfLiteFloat32; + recovered_cell_weights->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* recovered_cell_weights_size = TfLiteIntArrayCreate(1); + recovered_cell_weights_size->data[0] = n_cell; + if (!TfLiteIntArrayEqual(recovered_cell_weights->dims, + recovered_cell_weights_size)) { + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, recovered_cell_weights, + recovered_cell_weights_size)); + } } return kTfLiteOk; } // The LSTM Op engine. -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - - const TfLiteTensor* input_to_input_weights = - GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); - const TfLiteTensor* input_to_forget_weights = - GetInput(context, node, kInputToForgetWeightsTensor); - const TfLiteTensor* input_to_cell_weights = - GetInput(context, node, kInputToCellWeightsTensor); - const TfLiteTensor* input_to_output_weights = - GetInput(context, node, kInputToOutputWeightsTensor); - - const TfLiteTensor* recurrent_to_input_weights = - GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); - const TfLiteTensor* recurrent_to_forget_weights = - GetInput(context, node, kRecurrentToForgetWeightsTensor); - const TfLiteTensor* recurrent_to_cell_weights = - GetInput(context, node, kRecurrentToCellWeightsTensor); - const TfLiteTensor* recurrent_to_output_weights = - GetInput(context, node, kRecurrentToOutputWeightsTensor); - - const TfLiteTensor* cell_to_input_weights = - GetOptionalInputTensor(context, node, kCellToInputWeightsTensor); - const TfLiteTensor* cell_to_forget_weights = - GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor); - const TfLiteTensor* cell_to_output_weights = - GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor); - - const TfLiteTensor* input_gate_bias = - GetOptionalInputTensor(context, node, kInputGateBiasTensor); - const TfLiteTensor* forget_gate_bias = - GetInput(context, node, kForgetGateBiasTensor); - const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor); - const TfLiteTensor* output_gate_bias = - GetInput(context, node, kOutputGateBiasTensor); - - const TfLiteTensor* projection_weights = - GetOptionalInputTensor(context, node, kProjectionWeightsTensor); - const TfLiteTensor* projection_bias = - GetOptionalInputTensor(context, node, kProjectionBiasTensor); - - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - +TfLiteStatus EvalFloat( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { const int max_time = input->dims->data[0]; const int n_batch = input->dims->data[1]; const int n_input = input->dims->data[2]; @@ -380,8 +463,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const bool use_cifg = (input_to_input_weights == nullptr); const bool use_peephole = (cell_to_output_weights != nullptr); - // Index the scratch buffers pointers to the global scratch buffer. - TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); float* input_gate_scratch = nullptr; float* cell_scratch = nullptr; float* forget_gate_scratch = nullptr; @@ -432,6 +513,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { float* output_state_ptr = output_state->data.f; float* cell_state_ptr = cell_state->data.f; + // Feed the sequence into the LSTM step-by-step. for (int t = 0; t < max_time; t++) { const float* input_ptr_batch = input->data.f + t * n_batch * n_input; float* output_ptr_batch = output->data.f + t * n_batch * n_output; @@ -452,6 +534,262 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +TfLiteStatus EvalHybrid( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, + TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, + TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, + TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { + const int max_time = input->dims->data[0]; + const int n_batch = input->dims->data[1]; + const int n_input = input->dims->data[2]; + // n_cell and n_output will be the same size when there is no projection. + const int n_cell = input_to_output_weights->dims->data[0]; + const int n_output = recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existence of only one to get the condition. + const bool use_cifg = (input_to_input_weights == nullptr); + const bool use_peephole = (cell_to_output_weights != nullptr); + + float* input_gate_scratch = nullptr; + float* cell_scratch = nullptr; + float* forget_gate_scratch = nullptr; + float* output_gate_scratch = nullptr; + if (use_cifg) { + cell_scratch = scratch_buffer->data.f; + forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + } else { + input_gate_scratch = scratch_buffer->data.f; + cell_scratch = scratch_buffer->data.f + n_cell * n_batch; + forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + int8_t* input_to_input_weights_ptr = nullptr; + float input_to_input_weights_scale = 1.0f; + int8_t* recurrent_to_input_weights_ptr = nullptr; + float recurrent_to_input_weights_scale = 1.0f; + float* input_gate_bias_ptr = nullptr; + if (!use_cifg) { + input_to_input_weights_ptr = + reinterpret_cast(input_to_input_weights->data.uint8); + recurrent_to_input_weights_ptr = + reinterpret_cast(recurrent_to_input_weights->data.uint8); + input_gate_bias_ptr = input_gate_bias->data.f; + input_to_input_weights_scale = input_to_input_weights->params.scale; + recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; + } + + int8_t* cell_to_input_weights_ptr = nullptr; + int8_t* cell_to_forget_weights_ptr = nullptr; + int8_t* cell_to_output_weights_ptr = nullptr; + float cell_to_input_weights_scale = 1.0f; + float cell_to_forget_weights_scale = 1.0f; + float cell_to_output_weights_scale = 1.0f; + if (use_peephole) { + if (!use_cifg) { + cell_to_input_weights_ptr = + reinterpret_cast(cell_to_input_weights->data.uint8); + cell_to_input_weights_scale = cell_to_input_weights->params.scale; + } + cell_to_forget_weights_ptr = + reinterpret_cast(cell_to_forget_weights->data.uint8); + cell_to_output_weights_ptr = + reinterpret_cast(cell_to_output_weights->data.uint8); + cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; + cell_to_output_weights_scale = cell_to_output_weights->params.scale; + } + + const int8_t* projection_weights_ptr = + (projection_weights == nullptr) + ? nullptr + : reinterpret_cast(projection_weights->data.uint8); + float projection_weights_scale = + (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const int8_t* input_to_forget_weights_ptr = + reinterpret_cast(input_to_forget_weights->data.uint8); + const float input_to_forget_weights_scale = + input_to_forget_weights->params.scale; + const int8_t* input_to_cell_weights_ptr = + reinterpret_cast(input_to_cell_weights->data.uint8); + const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; + const int8_t* input_to_output_weights_ptr = + reinterpret_cast(input_to_output_weights->data.uint8); + const float input_to_output_weights_scale = + input_to_output_weights->params.scale; + const int8_t* recurrent_to_forget_weights_ptr = + reinterpret_cast(recurrent_to_forget_weights->data.uint8); + const float recurrent_to_forget_weights_scale = + recurrent_to_forget_weights->params.scale; + const int8_t* recurrent_to_cell_weights_ptr = + reinterpret_cast(recurrent_to_cell_weights->data.uint8); + const float recurrent_to_cell_weights_scale = + recurrent_to_cell_weights->params.scale; + const int8_t* recurrent_to_output_weights_ptr = + reinterpret_cast(recurrent_to_output_weights->data.uint8); + const float recurrent_to_output_weights_scale = + recurrent_to_output_weights->params.scale; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + + // Temporary storage for quantized values and scaling factors. + int8_t* quantized_input_ptr = + reinterpret_cast(input_quantized->data.uint8); + int8_t* quantized_output_state_ptr = + reinterpret_cast(output_state_quantized->data.uint8); + int8_t* quantized_cell_state_ptr = + reinterpret_cast(cell_state_quantized->data.uint8); + float* scaling_factors_ptr = scaling_factors->data.f; + float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; + float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; + + // Feed the sequence into the LSTM step-by-step. + for (int t = 0; t < max_time; t++) { + const float* input_ptr_batch = input->data.f + t * n_batch * n_input; + float* output_ptr_batch = output->data.f + t * n_batch * n_output; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, + input_to_input_weights_scale, input_to_forget_weights_ptr, + input_to_forget_weights_scale, input_to_cell_weights_ptr, + input_to_cell_weights_scale, input_to_output_weights_ptr, + input_to_output_weights_scale, recurrent_to_input_weights_ptr, + recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, + recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, + recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, + recurrent_to_output_weights_scale, cell_to_input_weights_ptr, + cell_to_input_weights_scale, cell_to_forget_weights_ptr, + cell_to_forget_weights_scale, cell_to_output_weights_ptr, + cell_to_output_weights_scale, input_gate_bias_ptr, forget_gate_bias_ptr, + cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, + projection_weights_scale, projection_bias_ptr, params, n_batch, n_cell, + n_input, n_output, input_gate_scratch, forget_gate_scratch, + cell_scratch, output_gate_scratch, scaling_factors_ptr, + prod_scaling_factors_ptr, recovered_cell_weights_ptr, + quantized_input_ptr, quantized_output_state_ptr, + quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, + output_ptr_batch); + } + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + + const TfLiteTensor* input_to_input_weights = + GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); + const TfLiteTensor* input_to_forget_weights = + GetInput(context, node, kInputToForgetWeightsTensor); + const TfLiteTensor* input_to_cell_weights = + GetInput(context, node, kInputToCellWeightsTensor); + const TfLiteTensor* input_to_output_weights = + GetInput(context, node, kInputToOutputWeightsTensor); + + const TfLiteTensor* recurrent_to_input_weights = + GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); + const TfLiteTensor* recurrent_to_forget_weights = + GetInput(context, node, kRecurrentToForgetWeightsTensor); + const TfLiteTensor* recurrent_to_cell_weights = + GetInput(context, node, kRecurrentToCellWeightsTensor); + const TfLiteTensor* recurrent_to_output_weights = + GetInput(context, node, kRecurrentToOutputWeightsTensor); + + const TfLiteTensor* cell_to_input_weights = + GetOptionalInputTensor(context, node, kCellToInputWeightsTensor); + const TfLiteTensor* cell_to_forget_weights = + GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor); + const TfLiteTensor* cell_to_output_weights = + GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor); + + const TfLiteTensor* input_gate_bias = + GetOptionalInputTensor(context, node, kInputGateBiasTensor); + const TfLiteTensor* forget_gate_bias = + GetInput(context, node, kForgetGateBiasTensor); + const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor); + const TfLiteTensor* output_gate_bias = + GetInput(context, node, kOutputGateBiasTensor); + + const TfLiteTensor* projection_weights = + GetOptionalInputTensor(context, node, kProjectionWeightsTensor); + const TfLiteTensor* projection_bias = + GetOptionalInputTensor(context, node, kProjectionBiasTensor); + + // Index the scratch buffers pointers to the global scratch buffer. + TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); + + TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); + TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + switch (input_to_output_weights->type) { + case kTfLiteFloat32: { + return EvalFloat(input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, + cell_to_output_weights, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, + projection_weights, projection_bias, params, + scratch_buffer, output_state, cell_state, output); + } + case kTfLiteUInt8: { + TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); + TfLiteTensor* output_state_quantized = + GetTemporary(context, node, /*index=*/2); + TfLiteTensor* cell_state_quantized = + GetTemporary(context, node, /*index=*/3); + TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/4); + TfLiteTensor* prod_scaling_factors = + GetTemporary(context, node, /*index=*/5); + TfLiteTensor* recovered_cell_weights = + GetTemporary(context, node, /*index=*/6); + return EvalHybrid( + input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, + input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, + projection_weights, projection_bias, params, scratch_buffer, + scaling_factors, prod_scaling_factors, recovered_cell_weights, + input_quantized, output_state_quantized, cell_state_quantized, + output_state, cell_state, output); + } + default: + context->ReportError(context, "Type %d is not currently supported.", + input_to_output_weights->type); + return kTfLiteError; + } + return kTfLiteOk; +} } // namespace unidirectional_sequence_lstm TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_LSTM() { diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc index 5881ced7c7a616ef2c24db60892cbbf9eec7c42e..de38bdef6fd1b019c7790a664b29cd45d29e5dcc 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm_test.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ // Unit test for TFLite Sequential LSTM op. -#include #include #include @@ -37,7 +36,8 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { bool use_peephole, bool use_projection_weights, bool use_projection_bias, float cell_clip, float proj_clip, - const std::vector>& input_shapes) + const std::vector>& input_shapes, + const TensorType& weights_type = TensorType_FLOAT32) : n_batch_(n_batch), n_input_(n_input), n_cell_(n_cell), @@ -48,31 +48,31 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { if (use_cifg) { input_to_input_weights_ = AddNullInput(); } else { - input_to_input_weights_ = AddInput(TensorType_FLOAT32); + input_to_input_weights_ = AddInput(weights_type); } - input_to_forget_weights_ = AddInput(TensorType_FLOAT32); - input_to_cell_weights_ = AddInput(TensorType_FLOAT32); - input_to_output_weights_ = AddInput(TensorType_FLOAT32); + input_to_forget_weights_ = AddInput(weights_type); + input_to_cell_weights_ = AddInput(weights_type); + input_to_output_weights_ = AddInput(weights_type); if (use_cifg) { recurrent_to_input_weights_ = AddNullInput(); } else { - recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32); + recurrent_to_input_weights_ = AddInput(weights_type); } - recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32); - recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32); - recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32); + recurrent_to_forget_weights_ = AddInput(weights_type); + recurrent_to_cell_weights_ = AddInput(weights_type); + recurrent_to_output_weights_ = AddInput(weights_type); if (use_peephole) { if (use_cifg) { cell_to_input_weights_ = AddNullInput(); } else { - cell_to_input_weights_ = AddInput(TensorType_FLOAT32); + cell_to_input_weights_ = AddInput(weights_type); } - cell_to_forget_weights_ = AddInput(TensorType_FLOAT32); - cell_to_output_weights_ = AddInput(TensorType_FLOAT32); + cell_to_forget_weights_ = AddInput(weights_type); + cell_to_output_weights_ = AddInput(weights_type); } else { cell_to_input_weights_ = AddNullInput(); cell_to_forget_weights_ = AddNullInput(); @@ -89,7 +89,7 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { output_gate_bias_ = AddInput(TensorType_FLOAT32); if (use_projection_weights) { - projection_weights_ = AddInput(TensorType_FLOAT32); + projection_weights_ = AddInput(weights_type); if (use_projection_bias) { projection_bias_ = AddInput(TensorType_FLOAT32); } else { @@ -196,8 +196,9 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { zero_buffer.get() + zero_buffer_size); } - void SetInput(int offset, float* begin, float* end) { - PopulateTensor(input_, offset, begin, end); + void SetInput(int offset, const float* begin, const float* end) { + PopulateTensor(input_, offset, const_cast(begin), + const_cast(end)); } std::vector GetOutput() { return ExtractVector(output_); } @@ -208,7 +209,7 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { int num_batches() { return n_batch_; } int sequence_length() { return sequence_length_; } - private: + protected: int input_; int input_to_input_weights_; int input_to_forget_weights_; @@ -243,7 +244,183 @@ class UnidirectionalLSTMOpModel : public SingleOpModel { int sequence_length_; }; -TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { +// The hybrid model has quantized weights. +class HybridUnidirectionalLSTMOpModel : public UnidirectionalLSTMOpModel { + public: + HybridUnidirectionalLSTMOpModel( + int n_batch, int n_input, int n_cell, int n_output, int sequence_length, + bool use_cifg, bool use_peephole, bool use_projection_weights, + bool use_projection_bias, float cell_clip, float proj_clip, + const std::vector>& input_shapes) + : UnidirectionalLSTMOpModel( + n_batch, n_input, n_cell, n_output, sequence_length, use_cifg, + use_peephole, use_projection_weights, use_projection_bias, + cell_clip, proj_clip, input_shapes, TensorType_UINT8) {} + + void SetInputToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_input_weights_, f); + } + + void SetInputToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_forget_weights_, f); + } + + void SetInputToCellWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_cell_weights_, f); + } + + void SetInputToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_output_weights_, f); + } + + void SetRecurrentToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_input_weights_, f); + } + + void SetRecurrentToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_forget_weights_, f); + } + + void SetRecurrentToCellWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_cell_weights_, f); + } + + void SetRecurrentToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_output_weights_, f); + } + + void SetCellToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_input_weights_, f); + } + + void SetCellToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_forget_weights_, f); + } + + void SetCellToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_output_weights_, f); + } + + void SetProjectionWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(projection_weights_, f); + } +}; + +class BaseLstmTest : public ::testing::Test { + protected: + // Weights of the LSTM model. Some are optional. + std::initializer_list input_to_input_weights_; + std::initializer_list input_to_cell_weights_; + std::initializer_list input_to_forget_weights_; + std::initializer_list input_to_output_weights_; + std::initializer_list input_gate_bias_; + std::initializer_list cell_gate_bias_; + std::initializer_list forget_gate_bias_; + std::initializer_list output_gate_bias_; + std::initializer_list recurrent_to_input_weights_; + std::initializer_list recurrent_to_cell_weights_; + std::initializer_list recurrent_to_forget_weights_; + std::initializer_list recurrent_to_output_weights_; + std::initializer_list cell_to_input_weights_; + std::initializer_list cell_to_forget_weights_; + std::initializer_list cell_to_output_weights_; + std::initializer_list projection_weights_; + + // LSTM input is stored as num_batch x num_inputs vector. + std::vector> lstm_input_; + // LSTM output is stored as num_batch x num_outputs vector. + std::vector> lstm_golden_output_; + + // Compares output up to tolerance to the result of the lstm given the input. + void VerifyGoldens(const std::vector>& input, + const std::vector>& output, + UnidirectionalLSTMOpModel* lstm, float tolerance = 1e-5) { + const int num_batches = input.size(); + EXPECT_GT(num_batches, 0); + const int num_inputs = lstm->num_inputs(); + EXPECT_GT(num_inputs, 0); + const int input_sequence_size = input[0].size() / num_inputs; + EXPECT_GT(input_sequence_size, 0); + // Feed the whole sequence as input. + for (int i = 0; i < input_sequence_size; ++i) { + for (int b = 0; b < num_batches; ++b) { + const float* batch_start = input[b].data() + i * num_inputs; + const float* batch_end = batch_start + num_inputs; + + lstm->SetInput(((i * num_batches) + b) * lstm->num_inputs(), + batch_start, batch_end); + } + } + + lstm->Invoke(); + + const int num_outputs = lstm->num_outputs(); + EXPECT_GT(num_outputs, 0); + std::vector expected; + for (int i = 0; i < input_sequence_size; ++i) { + for (int b = 0; b < num_batches; ++b) { + const float* golden_start_batch = output[b].data() + i * num_outputs; + const float* golden_end_batch = golden_start_batch + num_outputs; + + expected.insert(expected.end(), golden_start_batch, golden_end_batch); + } + } + + EXPECT_THAT(lstm->GetOutput(), + ElementsAreArray(ArrayFloatNear(expected, tolerance))); + } +}; + +class NoCifgNoPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = {-0.45018822, -0.02338299, -0.0870589, + -0.34550029, 0.04266912, -0.15680569, + -0.34856534, 0.43890524}; + input_to_cell_weights_ = {-0.50013041, 0.1370284, 0.11810488, 0.2013163, + -0.20583314, 0.44344562, 0.22077113, -0.29909778}; + input_to_forget_weights_ = {0.09701663, 0.20334584, -0.50592935, + -0.31343272, -0.40032279, 0.44781327, + 0.01387155, -0.35593212}; + input_to_output_weights_ = {-0.25065863, -0.28290087, 0.04613829, + 0.40525138, 0.44272184, 0.03897077, + -0.1556896, 0.19487578}; + input_gate_bias_ = {0., 0., 0., 0.}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_input_weights_ = { + -0.0063535, -0.2042388, 0.31454784, -0.35746509, + 0.28902304, 0.08183324, -0.16555229, 0.02286911, + -0.13566875, 0.03034258, 0.48091322, -0.12528998, + 0.24077177, -0.51332325, -0.33502164, 0.10629296}; + + recurrent_to_cell_weights_ = { + -0.3407414, 0.24443203, -0.2078532, 0.26320225, + 0.05695659, -0.00123841, -0.4744786, -0.35869038, + -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064}; + + recurrent_to_forget_weights_ = { + -0.48684245, -0.06655136, 0.42224967, 0.2112639, + 0.27654213, 0.20864892, -0.07646349, 0.45877004, + 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004}; + + recurrent_to_output_weights_ = { + 0.43385774, -0.17194885, 0.2718237, 0.09215671, + 0.24107647, -0.39835793, 0.18212086, 0.01301402, + 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.02973187, 0.1229473, 0.20885126, -0.15358765, + -0.03716109, 0.12507336, 0.41193449, -0.20860538, + -0.15053082, 0.09120187, 0.24278517, -0.12222792}}; + } +}; + +TEST_F(NoCifgNoPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 1; const int n_input = 2; // n_cell and n_output have the same size when there is no projection. @@ -252,9 +429,11 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { const int sequence_length = 3; UnidirectionalLSTMOpModel lstm( - n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false, - /*use_peephole=*/false, /*use_projection_weights=*/false, - /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/false, /*use_peephole=*/false, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, { {sequence_length, n_batch, n_input}, // input tensor @@ -281,77 +460,138 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589, - -0.34550029, 0.04266912, -0.15680569, - -0.34856534, 0.43890524}); + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163, - -0.20583314, 0.44344562, 0.22077113, - -0.29909778}); + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935, - -0.31343272, -0.40032279, 0.44781327, - 0.01387155, -0.35593212}); + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} - lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829, - 0.40525138, 0.44272184, 0.03897077, -0.1556896, - 0.19487578}); +TEST_F(NoCifgNoPeepholeNoProjectionNoClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + const int sequence_length = 3; - lstm.SetInputGateBias({0., 0., 0., 0.}); + HybridUnidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/false, /*use_peephole=*/false, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor - lstm.SetCellBias({0., 0., 0., 0.}); + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor - lstm.SetForgetGateBias({1., 1., 1., 1.}); + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor - lstm.SetOutputGateBias({0., 0., 0., 0.}); + {0}, // cell_to_input_weight tensor + {0}, // cell_to_forget_weight tensor + {0}, // cell_to_output_weight tensor - lstm.SetRecurrentToInputWeights( - {-0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, - -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, - -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296}); + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor - lstm.SetRecurrentToCellWeights( - {-0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, - -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, - -0.46367589, 0.26016325, -0.03894562, -0.16368064}); + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); - lstm.SetRecurrentToForgetWeights( - {-0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, - -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, - 0.28053468, 0.01560611, -0.20127171, -0.01140004}); + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetRecurrentToOutputWeights( - {0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, - 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, - -0.51818722, -0.15390486, 0.0468148, 0.39922136}); + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - // Input should have n_input * sequence_length many values. - static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; - static float lstm_golden_output[] = {-0.02973187, 0.1229473, 0.20885126, - -0.15358765, -0.03716109, 0.12507336, - 0.41193449, -0.20860538, -0.15053082, - 0.09120187, 0.24278517, -0.12222792}; + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - float* batch0_start = lstm_input; - float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, + /*tolerance=*/0.0157651); +} - lstm.SetInput(0, batch0_start, batch0_end); +class CifgPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_cell_weights_ = {-0.49770179, -0.27711356, -0.09624726, + 0.05100781, 0.04717243, 0.48944736, + -0.38535351, -0.17212132}; - lstm.Invoke(); + input_to_forget_weights_ = {-0.55291498, -0.42866567, 0.13056988, + -0.3633365, -0.22755712, 0.28253698, + 0.24407166, 0.33826375}; - float* golden_start = lstm_golden_output; - float* golden_end = - golden_start + lstm.num_outputs() * lstm.sequence_length(); - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); -} + input_to_output_weights_ = {0.10725588, -0.02335852, -0.55932593, + -0.09426838, -0.44257352, 0.54939759, + 0.01533556, 0.42751634}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_cell_weights_ = { + 0.54066205, -0.32668582, -0.43562764, -0.56094903, + 0.42957711, 0.01841056, -0.32764608, -0.33027974, + -0.10826075, 0.20675004, 0.19069612, -0.03026325, + -0.54532051, 0.33003211, 0.44901288, 0.21193194}; + + recurrent_to_forget_weights_ = { + -0.13832897, -0.0515101, -0.2359007, -0.16661474, + -0.14340827, 0.36986142, 0.23414481, 0.55899, + 0.10798943, -0.41174671, 0.17751795, -0.34484994, + -0.35874045, -0.11352962, 0.27268326, 0.54058349}; + + recurrent_to_output_weights_ = { + 0.41613156, 0.42610586, -0.16495961, -0.5663873, + 0.30579174, -0.05115908, -0.33941799, 0.23364776, + 0.11178309, 0.09481031, -0.26424935, 0.46261835, + 0.50248802, 0.26114327, -0.43736315, 0.33149987}; + + cell_to_forget_weights_ = {0.47485286, -0.51955009, -0.24458408, + 0.31544167}; + cell_to_output_weights_ = {-0.17135078, 0.82760304, 0.85573703, + -0.77109635}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.36444446, -0.00352185, 0.12886585, -0.05163646, + -0.42312205, -0.01218222, 0.24201041, -0.08124574, + -0.358325, -0.04621704, 0.21641694, -0.06471302}}; + } +}; -TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { +TEST_F(CifgPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 1; const int n_input = 2; // n_cell and n_output have the same size when there is no projection. @@ -360,9 +600,11 @@ TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { const int sequence_length = 3; UnidirectionalLSTMOpModel lstm( - n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/true, - /*use_peephole=*/true, /*use_projection_weights=*/false, - /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, { {sequence_length, n_batch, n_input}, // input tensor @@ -389,71 +631,690 @@ TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, - 0.04717243, 0.48944736, -0.38535351, - -0.17212132}); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988, - -0.3633365, -0.22755712, 0.28253698, 0.24407166, - 0.33826375}); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593, - -0.09426838, -0.44257352, 0.54939759, - 0.01533556, 0.42751634}); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} + +TEST_F(CifgPeepholeNoProjectionNoClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + const int sequence_length = 3; + + HybridUnidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor + + {0, 0}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor - lstm.SetCellBias({0., 0., 0., 0.}); + {0, 0}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor - lstm.SetForgetGateBias({1., 1., 1., 1.}); + {0}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor - lstm.SetOutputGateBias({0., 0., 0., 0.}); + {0}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor - lstm.SetRecurrentToCellWeights( - {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711, - 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004, - 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288, - 0.21193194}); + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); - lstm.SetRecurrentToForgetWeights( - {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827, - 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795, - -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349}); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetRecurrentToOutputWeights( - {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908, - -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835, - 0.50248802, 0.26114327, -0.43736315, 0.33149987}); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - lstm.SetCellToForgetWeights( - {0.47485286, -0.51955009, -0.24458408, 0.31544167}); - lstm.SetCellToOutputWeights( - {-0.17135078, 0.82760304, 0.85573703, -0.77109635}); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); - static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; - static float lstm_golden_output[] = {-0.36444446, -0.00352185, 0.12886585, - -0.05163646, -0.42312205, -0.01218222, - 0.24201041, -0.08124574, -0.358325, - -0.04621704, 0.21641694, -0.06471302}; + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - float* batch0_start = lstm_input; - float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length(); - - lstm.SetInput(0, batch0_start, batch0_end); - - lstm.Invoke(); - - float* golden_start = lstm_golden_output; - float* golden_end = - golden_start + lstm.num_outputs() * lstm.sequence_length(); - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, /*tolerance=*/0.03573); } -TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { +class NoCifgPeepholeProjectionClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = { + 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, + 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, + -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, + -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, + -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, + -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, + -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, + 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, + 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, + 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, + -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, + 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, + -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, + -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, + -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, + 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, + -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, + -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, + -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, + -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}; + + input_to_forget_weights_ = { + -0.0018401089, -0.004852237, 0.03698424, 0.014181704, + 0.028273236, -0.016726194, -0.05249759, -0.10204261, + 0.00861066, -0.040979505, -0.009899187, 0.01923892, + -0.028177269, -0.08535103, -0.14585495, 0.10662567, + -0.01909731, -0.017883534, -0.0047269356, -0.045103323, + 0.0030784295, 0.076784775, 0.07463696, 0.094531395, + 0.0814421, -0.12257899, -0.033945758, -0.031303465, + 0.045630626, 0.06843887, -0.13492945, -0.012480007, + -0.0811829, -0.07224499, -0.09628791, 0.045100946, + 0.0012300825, 0.013964662, 0.099372394, 0.02543059, + 0.06958324, 0.034257296, 0.0482646, 0.06267997, + 0.052625068, 0.12784666, 0.07077897, 0.025725935, + 0.04165009, 0.07241905, 0.018668644, -0.037377294, + -0.06277783, -0.08833636, -0.040120605, -0.011405586, + -0.007808335, -0.010301386, -0.005102167, 0.027717464, + 0.05483423, 0.11449111, 0.11289652, 0.10939839, + 0.13396506, -0.08402166, -0.01901462, -0.044678304, + -0.07720565, 0.014350063, -0.11757958, -0.0652038, + -0.08185733, -0.076754324, -0.092614375, 0.10405491, + 0.052960336, 0.035755895, 0.035839386, -0.012540553, + 0.036881298, 0.02913376, 0.03420159, 0.05448447, + -0.054523353, 0.02582715, 0.02327355, -0.011857179, + -0.0011980024, -0.034641717, -0.026125094, -0.17582615, + -0.15923657, -0.27486774, -0.0006143371, 0.0001771948, + -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}; + + input_to_cell_weights_ = { + -0.04580283, -0.09549462, -0.032418985, -0.06454633, + -0.043528453, 0.043018587, -0.049152344, -0.12418144, + -0.078985475, -0.07596889, 0.019484362, -0.11434962, + -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, + -0.025034338, -0.0028890965, 0.048929527, 0.06235075, + 0.10665918, -0.032036792, -0.08505916, -0.10843358, + -0.13002433, -0.036816437, -0.02130134, -0.016518239, + 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, + -0.10652836, -0.1037554, -0.13056071, -0.03266643, + -0.033702414, -0.006473424, -0.04611692, 0.014419339, + -0.025174323, 0.0396852, 0.081777506, 0.06157468, + 0.10210095, -0.009658194, 0.046511717, 0.03603906, + 0.0069369148, 0.015960095, -0.06507666, 0.09551598, + 0.053568836, 0.06408714, 0.12835667, -0.008714329, + -0.20211966, -0.12093674, 0.029450472, 0.2849013, + -0.029227901, 0.1164364, -0.08560263, 0.09941786, + -0.036999565, -0.028842626, -0.0033637602, -0.017012902, + -0.09720865, -0.11193351, -0.029155117, -0.017936034, + -0.009768936, -0.04223324, -0.036159635, 0.06505112, + -0.021742892, -0.023377212, -0.07221364, -0.06430552, + 0.05453865, 0.091149814, 0.06387331, 0.007518393, + 0.055960953, 0.069779344, 0.046411168, 0.10509911, + 0.07463894, 0.0075130584, 0.012850982, 0.04555431, + 0.056955688, 0.06555285, 0.050801456, -0.009862683, + 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}; + + input_to_output_weights_ = { + -0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, + -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, + 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, + -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, + -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, + 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, + -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, + -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, + -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, + -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, + 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, + 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, + 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, + -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, + 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, + 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, + -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, + 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, + -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, + -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}; + + input_gate_bias_ = {0.02234832, 0.14757581, 0.18176508, 0.10380666, + 0.053110216, -0.06928846, -0.13942584, -0.11816189, + 0.19483899, 0.03652339, -0.10250295, 0.036714908, + -0.18426876, 0.036065217, 0.21810818, 0.02383196, + -0.043370757, 0.08690144, -0.04444982, 0.00030581196}; + + forget_gate_bias_ = {0.035185695, -0.042891346, -0.03032477, 0.23027696, + 0.11098921, 0.15378423, 0.09263801, 0.09790885, + 0.09508917, 0.061199076, 0.07665568, -0.015443159, + -0.03499149, 0.046190713, 0.08895977, 0.10899629, + 0.40694186, 0.06030037, 0.012413437, -0.06108739}; + + cell_gate_bias_ = {-0.024379363, 0.0055531194, 0.23377132, 0.033463873, + -0.1483596, -0.10639995, -0.091433935, 0.058573797, + -0.06809782, -0.07889636, -0.043246906, -0.09829136, + -0.4279842, 0.034901652, 0.18797937, 0.0075234566, + 0.016178843, 0.1749513, 0.13975595, 0.92058027}; + + output_gate_bias_ = {0.046159424, -0.0012809046, 0.03563469, 0.12648113, + 0.027195795, 0.35373217, -0.018957434, 0.008907322, + -0.0762701, 0.12018895, 0.04216877, 0.0022856654, + 0.040952638, 0.3147856, 0.08225149, -0.057416286, + -0.14995944, -0.008040261, 0.13208859, 0.029760877}; + + recurrent_to_input_weights_ = { + -0.001374326, -0.078856036, 0.10672688, 0.029162422, + -0.11585556, 0.02557986, -0.13446963, -0.035785314, + -0.01244275, 0.025961924, -0.02337298, -0.044228926, + -0.055839065, -0.046598054, -0.010546039, 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-0.036132768, -0.06426278, -0.05108664, + 0.13221376, 0.009441198, -0.16715929, 0.15859416, + -0.040437475, 0.050779544, -0.022187516, 0.012166504, + 0.027685808, -0.07675938, -0.0055694645, -0.09444123, + 0.0046453946, 0.050794356, 0.10770313, -0.20790008, + -0.07149004, -0.11425117, 0.008225835, -0.035802525, + 0.14374903, 0.15262283, 0.048710253, 0.1847461, + -0.007487823, 0.11000021, -0.09542012, 0.22619456, + -0.029149994, 0.08527916, 0.009043713, 0.0042746216, + 0.016261552, 0.022461696, 0.12689082, -0.043589946, + -0.12035478, -0.08361797, -0.050666027, -0.1248618, + -0.1275799, -0.071875185, 0.07377272, 0.09944291, + -0.18897448, -0.1593054, -0.06526116, -0.040107165, + -0.004618631, -0.067624845, -0.007576253, 0.10727444, + 0.041546922, -0.20424393, 0.06907816, 0.050412357, + 0.00724631, 0.039827548, 0.12449835, 0.10747581, + 0.13708383, 0.09134148, -0.12617786, -0.06428341, + 0.09956831, 0.1208086, -0.14676677, -0.0727722, + 0.1126304, 0.010139365, 0.015571211, -0.038128063, + 0.022913318, -0.042050496, 0.16842307, -0.060597885, + 0.10531834, -0.06411776, -0.07451711, -0.03410368, + -0.13393489, 0.06534304, 0.003620307, 0.04490757, + 0.05970546, 0.05197996, 0.02839995, 0.10434969, + -0.013699693, -0.028353551, -0.07260381, 0.047201227, + -0.024575593, -0.036445823, 0.07155557, 0.009672501, + -0.02328883, 0.009533515, -0.03606021, -0.07421458, + -0.028082801, -0.2678904, -0.13221288, 0.18419984, + -0.13012612, -0.014588381, -0.035059117, -0.04824723, + 0.07830115, -0.056184657, 0.03277091, 0.025466874, + 0.14494097, -0.12522776, -0.098633975, -0.10766018, + -0.08317623, 0.08594209, 0.07749552, 0.039474737, + 0.1776665, -0.07409566, -0.0477268, 0.29323658, + 0.10801441, 0.1154011, 0.013952499, 0.10739139, + 0.10708251, -0.051456142, 0.0074137426, -0.10430189, + 0.10034707, 0.045594677, 0.0635285, -0.0715442, + -0.089667566, -0.10811871, 0.00026344223, 0.08298446, + -0.009525053, 0.006585689, -0.24567553, -0.09450807, + 0.09648481, 0.026996298, -0.06419476, -0.04752702, + -0.11063944, -0.23441927, -0.17608605, -0.052156363, + 0.067035615, 0.19271925, -0.0032889997, -0.043264326, + 0.09663576, -0.057112187, -0.10100678, 0.0628376, + 0.04447668, 0.017961001, -0.10094388, -0.10190601, + 0.18335468, 0.10494553, -0.052095775, -0.0026118709, + 0.10539724, -0.04383912, -0.042349473, 0.08438151, + -0.1947263, 0.02251204, 0.11216432, -0.10307853, + 0.17351969, -0.039091777, 0.08066188, -0.00561982, + 0.12633002, 0.11335965, -0.0088127935, -0.019777594, + 0.06864014, -0.059751723, 0.016233567, -0.06894641, + -0.28651384, -0.004228674, 0.019708522, -0.16305895, + -0.07468996, -0.0855457, 0.099339016, -0.07580735, + -0.13775392, 0.08434318, 0.08330512, -0.12131499, + 0.031935584, 0.09180414, -0.08876437, -0.08049874, + 0.008753825, 0.03498998, 0.030215185, 0.03907079, + 0.089751154, 0.029194152, -0.03337423, -0.019092513, + 0.04331237, 0.04299654, -0.036394123, -0.12915532, + 0.09793732, 0.07512415, -0.11319543, -0.032502122, + 0.15661901, 0.07671967, -0.005491124, -0.19379048, + -0.218606, 0.21448623, 0.017840758, 0.1416943, + -0.07051762, 0.19488361, 0.02664691, -0.18104725, + -0.09334311, 0.15026465, -0.15493552, -0.057762887, + -0.11604192, -0.262013, -0.01391798, 0.012185008, + 0.11156489, -0.07483202, 0.06693364, -0.26151478, + 0.046425626, 0.036540434, -0.16435726, 0.17338543, + -0.21401681, -0.11385144, -0.08283257, -0.069031075, + 0.030635102, 0.010969227, 0.11109743, 0.010919218, + 0.027526086, 0.13519906, 0.01891392, -0.046839405, + -0.040167913, 0.017953383, -0.09700955, 0.0061885654, + -0.07000971, 0.026893595, -0.038844477, 0.14543656}; + + lstm_input_ = { + {// Batch0: 4 (input_sequence_size) * 5 (n_input) + 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, // step 0 + 0.596268, 0.998386, 0.568695, 0.864524, 0.571277, // step 1 + 0.073204, 0.296072, 0.743333, 0.069199, 0.045348, // step 2 + 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, // step 3 + + {// Batch1: 4 (input_sequence_size) * 5 (n_input) + 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, // step 0 + 0.642421, 0.524260, 0.134799, 0.003639, 0.162482, // step 1 + 0.640394, 0.930399, 0.050782, 0.432485, 0.988078, // step 2 + 0.082922, 0.563329, 0.865614, 0.333232, 0.259916} // step 3 + }; + + lstm_golden_output_ = { + {// Batch0: 4 (input_sequence_size) * 16 (n_output) + -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, + -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, + -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, + 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, + -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, + -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, + 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, + 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, + 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, + 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, + -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, + -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, + 0.0286833, 0.00824207, 0.0264887, 0.0305169}, + {// Batch1: 4 (input_sequence_size) * 16 (n_output) + -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, + -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, + 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, + 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, + -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, + -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, + 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, + 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, + 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, + 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, + -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, + -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, + 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; + } +}; + +TEST_F(NoCifgPeepholeProjectionClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 2; const int n_input = 5; const int n_cell = 20; @@ -461,8 +1322,9 @@ TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { const int sequence_length = 4; UnidirectionalLSTMOpModel lstm( - n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false, - /*use_peephole=*/true, /*use_projection_weights=*/true, + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/false, /*use_peephole=*/true, + /*use_projection_weights=*/true, /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, { @@ -491,588 +1353,99 @@ TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToInputWeights( - {0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, - 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, - -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, - -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, - -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, - -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, - -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, - 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, - 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, - 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, - -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, - 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, - -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, - -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, - -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, - 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, - -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, - -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, - -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, - -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}); - - lstm.SetInputToForgetWeights( - {-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236, - -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505, - -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495, - 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323, - 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421, - -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887, - -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791, - 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059, - 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068, - 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905, - 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605, - -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464, - 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506, - -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063, - -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375, - 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553, - 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353, - 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717, - -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371, - 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}); - - lstm.SetInputToCellWeights( - {-0.04580283, -0.09549462, -0.032418985, -0.06454633, - -0.043528453, 0.043018587, -0.049152344, -0.12418144, - -0.078985475, -0.07596889, 0.019484362, -0.11434962, - -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, - -0.025034338, -0.0028890965, 0.048929527, 0.06235075, - 0.10665918, -0.032036792, -0.08505916, -0.10843358, - -0.13002433, -0.036816437, -0.02130134, -0.016518239, - 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, - -0.10652836, -0.1037554, -0.13056071, -0.03266643, - -0.033702414, -0.006473424, -0.04611692, 0.014419339, - -0.025174323, 0.0396852, 0.081777506, 0.06157468, - 0.10210095, -0.009658194, 0.046511717, 0.03603906, - 0.0069369148, 0.015960095, -0.06507666, 0.09551598, - 0.053568836, 0.06408714, 0.12835667, -0.008714329, - -0.20211966, -0.12093674, 0.029450472, 0.2849013, - -0.029227901, 0.1164364, -0.08560263, 0.09941786, - -0.036999565, -0.028842626, -0.0033637602, -0.017012902, - -0.09720865, -0.11193351, -0.029155117, -0.017936034, - -0.009768936, -0.04223324, -0.036159635, 0.06505112, - -0.021742892, -0.023377212, -0.07221364, -0.06430552, - 0.05453865, 0.091149814, 0.06387331, 0.007518393, - 0.055960953, 0.069779344, 0.046411168, 0.10509911, - 0.07463894, 0.0075130584, 0.012850982, 0.04555431, - 0.056955688, 0.06555285, 0.050801456, -0.009862683, - 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}); - - lstm.SetInputToOutputWeights( - {-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, - -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, - 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, - -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, - -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, - 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, - -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, - -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, - -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, - -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, - 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, - 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, - 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, - -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, - 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, - 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, - -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, - 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, - -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, - -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}); - - lstm.SetInputGateBias( - {0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216, - -0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339, - -0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818, - 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196}); - - lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696, - 0.11098921, 0.15378423, 0.09263801, 0.09790885, - 0.09508917, 0.061199076, 0.07665568, -0.015443159, - -0.03499149, 0.046190713, 0.08895977, 0.10899629, - 0.40694186, 0.06030037, 0.012413437, -0.06108739}); - - lstm.SetCellBias({-0.024379363, 0.0055531194, 0.23377132, 0.033463873, - -0.1483596, -0.10639995, -0.091433935, 0.058573797, - -0.06809782, -0.07889636, -0.043246906, -0.09829136, - -0.4279842, 0.034901652, 0.18797937, 0.0075234566, - 0.016178843, 0.1749513, 0.13975595, 0.92058027}); - - lstm.SetOutputGateBias( - {0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795, - 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895, - 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149, - -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877}); - - lstm.SetRecurrentToInputWeights( - {-0.001374326, -0.078856036, 0.10672688, 0.029162422, - -0.11585556, 0.02557986, -0.13446963, -0.035785314, - -0.01244275, 0.025961924, -0.02337298, -0.044228926, - -0.055839065, -0.046598054, -0.010546039, -0.06900766, - 0.027239809, 0.022582639, -0.013296484, -0.05459212, - 0.08981, -0.045407712, 0.08682226, -0.06867011, - -0.14390695, -0.02916037, 0.000996957, 0.091420636, - 0.14283475, -0.07390571, -0.06402044, 0.062524505, - -0.093129106, 0.04860203, -0.08364217, -0.08119002, - 0.009352075, 0.22920375, 0.0016303885, 0.11583097, - -0.13732095, 0.012405723, -0.07551853, 0.06343048, - 0.12162708, -0.031923793, -0.014335606, 0.01790974, - -0.10650317, -0.0724401, 0.08554849, -0.05727212, - 0.06556731, -0.042729504, -0.043227166, 0.011683251, - -0.013082158, -0.029302018, -0.010899579, -0.062036745, - -0.022509435, -0.00964907, -0.01567329, 0.04260106, - -0.07787477, -0.11576462, 0.017356863, 0.048673786, - -0.017577527, -0.05527947, -0.082487635, -0.040137455, - -0.10820036, -0.04666372, 0.022746278, -0.07851417, - 0.01068115, 0.032956902, 0.022433773, 0.0026891115, - 0.08944216, -0.0685835, 0.010513544, 0.07228705, - 0.02032331, -0.059686817, -0.0005566496, -0.086984694, - 0.040414046, -0.1380399, 0.094208956, -0.05722982, - 0.012092817, -0.04989123, -0.086576, -0.003399834, - -0.04696032, -0.045747425, 0.10091314, 0.048676282, - -0.029037097, 0.031399418, -0.0040285117, 0.047237843, - 0.09504992, 0.041799378, -0.049185462, -0.031518843, - -0.10516937, 0.026374253, 0.10058866, -0.0033195973, - -0.041975245, 0.0073591834, 0.0033782164, -0.004325073, - -0.10167381, 0.042500053, -0.01447153, 0.06464186, - -0.017142897, 0.03312627, 0.009205989, 0.024138335, - -0.011337001, 0.035530265, -0.010912711, 0.0706555, - -0.005894094, 0.051841937, -0.1401738, -0.02351249, - 0.0365468, 0.07590991, 0.08838724, 0.021681072, - -0.10086113, 0.019608743, -0.06195883, 0.077335775, - 0.023646897, -0.095322326, 0.02233014, 0.09756986, - -0.048691444, -0.009579111, 0.07595467, 0.11480546, - -0.09801813, 0.019894179, 0.08502348, 0.004032281, - 0.037211012, 0.068537936, -0.048005626, -0.091520436, - -0.028379958, -0.01556313, 0.06554592, -0.045599163, - -0.01672207, -0.020169014, -0.011877351, -0.20212261, - 0.010889619, 0.0047078193, 0.038385306, 0.08540671, - -0.017140968, -0.0035865551, 0.016678626, 0.005633034, - 0.015963363, 0.00871737, 0.060130805, 0.028611384, - 0.10109069, -0.015060172, -0.07894427, 0.06401885, - 0.011584063, -0.024466386, 0.0047652307, -0.09041358, - 0.030737216, -0.0046374933, 0.14215417, -0.11823516, - 0.019899689, 0.006106124, -0.027092824, 0.0786356, - 0.05052217, -0.058925, -0.011402121, -0.024987547, - -0.0013661642, -0.06832946, -0.015667673, -0.1083353, - -0.00096863037, -0.06988685, -0.053350925, -0.027275559, - -0.033664223, -0.07978348, -0.025200296, -0.017207067, - -0.058403496, -0.055697463, 0.005798788, 0.12965427, - -0.062582195, 0.0013350133, -0.10482091, 0.0379771, - 0.072521195, -0.0029455067, -0.13797039, -0.03628521, - 0.013806405, -0.017858358, -0.01008298, -0.07700066, - -0.017081132, 0.019358726, 0.0027079724, 0.004635139, - 0.062634714, -0.02338735, -0.039547626, -0.02050681, - 0.03385117, -0.083611414, 0.002862572, -0.09421313, - 0.058618143, -0.08598433, 0.00972939, 0.023867095, - -0.053934585, -0.023203006, 0.07452513, -0.048767887, - -0.07314807, -0.056307215, -0.10433547, -0.06440842, - 0.04328182, 0.04389765, -0.020006588, -0.09076438, - -0.11652589, -0.021705797, 0.03345259, -0.010329105, - -0.025767034, 0.013057034, -0.07316461, -0.10145612, - 0.06358255, 0.18531723, 0.07759293, 0.12006465, - 0.1305557, 0.058638252, -0.03393652, 0.09622831, - -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845, - -0.005644518, 0.06857898, -0.12598175, -0.035084512, - 0.03156317, -0.12794146, -0.031963028, 0.04692781, - 0.030070418, 0.0071660685, -0.095516115, -0.004643372, - 0.040170413, -0.062104587, -0.0037324072, 0.0554317, - 0.08184801, -0.019164372, 0.06791302, 0.034257166, - -0.10307039, 0.021943003, 0.046745934, 0.0790918, - -0.0265588, -0.007824208, 0.042546265, -0.00977924, - -0.0002440307, -0.017384544, -0.017990116, 0.12252321, - -0.014512694, -0.08251313, 0.08861942, 0.13589665, - 0.026351685, 0.012641483, 0.07466548, 0.044301085, - -0.045414884, -0.051112458, 0.03444247, -0.08502782, - -0.04106223, -0.028126027, 0.028473156, 0.10467447}); - - lstm.SetRecurrentToForgetWeights( - {-0.057784554, -0.026057621, -0.068447545, -0.022581743, - 0.14811787, 0.10826372, 0.09471067, 0.03987225, - -0.0039523416, 0.00030638507, 0.053185795, 0.10572994, - 0.08414449, -0.022036452, -0.00066928595, -0.09203576, - 0.032950465, -0.10985798, -0.023809856, 0.0021431844, - -0.02196096, -0.00326074, 0.00058621005, -0.074678116, - -0.06193199, 0.055729095, 0.03736828, 0.020123724, - 0.061878487, -0.04729229, 0.034919553, -0.07585433, - -0.04421272, -0.044019096, 0.085488975, 0.04058006, - -0.06890133, -0.030951202, -0.024628663, -0.07672815, - 0.034293607, 0.08556707, -0.05293577, -0.033561368, - -0.04899627, 0.0241671, 0.015736353, -0.095442444, - -0.029564252, 0.016493602, -0.035026584, 0.022337519, - -0.026871363, 0.004780428, 0.0077918363, -0.03601621, - 0.016435321, -0.03263031, -0.09543275, -0.047392778, - 0.013454138, 0.028934088, 0.01685226, -0.086110644, - -0.046250615, -0.01847454, 0.047608484, 0.07339695, - 0.034546845, -0.04881143, 0.009128804, -0.08802852, - 0.03761666, 0.008096139, -0.014454086, 0.014361001, - -0.023502491, -0.0011840804, -0.07607001, 0.001856849, - -0.06509276, -0.006021153, -0.08570962, -0.1451793, - 0.060212336, 0.055259194, 0.06974018, 0.049454916, - -0.027794661, -0.08077226, -0.016179763, 0.1169753, - 0.17213494, -0.0056326236, -0.053934924, -0.0124349, - -0.11520337, 0.05409887, 0.088759385, 0.0019655675, - 0.0042065294, 0.03881498, 0.019844765, 0.041858196, - -0.05695512, 0.047233116, 0.038937137, -0.06542224, - 0.014429736, -0.09719407, 0.13908425, -0.05379757, - 0.012321099, 0.082840554, -0.029899208, 0.044217527, - 0.059855383, 0.07711018, -0.045319796, 0.0948846, - -0.011724666, -0.0033288454, -0.033542685, -0.04764985, - -0.13873616, 0.040668588, 0.034832682, -0.015319203, - -0.018715994, 0.046002675, 0.0599172, -0.043107376, - 0.0294216, -0.002314414, -0.022424703, 0.0030315618, - 0.0014641669, 0.0029166266, -0.11878115, 0.013738511, - 0.12375372, -0.0006038222, 0.029104086, 0.087442465, - 0.052958444, 0.07558703, 0.04817258, 0.044462286, - -0.015213451, -0.08783778, -0.0561384, -0.003008196, - 0.047060397, -0.002058388, 0.03429439, -0.018839769, - 0.024734668, 0.024614193, -0.042046934, 0.09597743, - -0.0043254104, 0.04320769, 0.0064070094, -0.0019131786, - -0.02558259, -0.022822596, -0.023273505, -0.02464396, - -0.10991725, -0.006240552, 0.0074488563, 0.024044557, - 0.04383914, -0.046476185, 0.028658995, 0.060410924, - 0.050786525, 0.009452605, -0.0073054377, -0.024810238, - 0.0052906186, 0.0066939713, -0.0020913032, 0.014515517, - 0.015898481, 0.021362653, -0.030262267, 0.016587038, - -0.011442813, 0.041154444, -0.007631438, -0.03423484, - -0.010977775, 0.036152758, 0.0066366293, 0.11915515, - 0.02318443, -0.041350313, 0.021485701, -0.10906167, - -0.028218046, -0.00954771, 0.020531068, -0.11995105, - -0.03672871, 0.024019798, 0.014255957, -0.05221243, - -0.00661567, -0.04630967, 0.033188973, 0.10107534, - -0.014027541, 0.030796422, -0.10270911, -0.035999842, - 0.15443139, 0.07684145, 0.036571592, -0.035900835, - -0.0034699554, 0.06209149, 0.015920248, -0.031122351, - -0.03858649, 0.01849943, 0.13872518, 0.01503974, - 0.069941424, -0.06948533, -0.0088794185, 0.061282158, - -0.047401894, 0.03100163, -0.041533746, -0.10430945, - 0.044574402, -0.01425562, -0.024290353, 0.034563623, - 0.05866852, 0.023947537, -0.09445152, 0.035450947, - 0.02247216, -0.0042998926, 0.061146557, -0.10250651, - 0.020881841, -0.06747029, 0.10062043, -0.0023941975, - 0.03532124, -0.016341697, 0.09685456, -0.016764693, - 0.051808182, 0.05875331, -0.04536488, 0.001626336, - -0.028892258, -0.01048663, -0.009793449, -0.017093895, - 0.010987891, 0.02357273, -0.00010856845, 0.0099760275, - -0.001845119, -0.03551521, 0.0018358806, 0.05763657, - -0.01769146, 0.040995963, 0.02235177, -0.060430344, - 0.11475477, -0.023854522, 0.10071741, 0.0686208, - -0.014250481, 0.034261297, 0.047418304, 0.08562733, - -0.030519066, 0.0060542435, 0.014653856, -0.038836084, - 0.04096551, 0.032249358, -0.08355519, -0.026823482, - 0.056386515, -0.010401743, -0.028396193, 0.08507674, - 0.014410365, 0.020995233, 0.17040324, 0.11511526, - 0.02459721, 0.0066619175, 0.025853224, -0.023133837, - -0.081302024, 0.017264642, -0.009585969, 0.09491168, - -0.051313367, 0.054532815, -0.014298593, 0.10657464, - 0.007076659, 0.10964551, 0.0409152, 0.008275321, - -0.07283536, 0.07937492, 0.04192024, -0.1075027}); - - lstm.SetRecurrentToCellWeights( - {-0.037322544, 0.018592842, 0.0056175636, -0.06253426, - 0.055647098, -0.05713207, -0.05626563, 0.005559383, - 0.03375411, -0.025757805, -0.088049285, 0.06017052, - -0.06570978, 0.007384076, 0.035123326, -0.07920549, - 0.053676967, 0.044480428, -0.07663568, 0.0071805613, - 0.08089997, 0.05143358, 0.038261272, 0.03339287, - -0.027673481, 0.044746667, 0.028349208, 0.020090483, - -0.019443132, -0.030755889, -0.0040000007, 0.04465846, - -0.021585021, 0.0031670958, 0.0053199246, -0.056117613, - -0.10893326, 0.076739706, -0.08509834, -0.027997585, - 0.037871376, 0.01449768, -0.09002357, -0.06111149, - -0.046195522, 0.0422062, -0.005683705, -0.1253618, - -0.012925729, -0.04890792, 0.06985068, 0.037654128, - 0.03398274, -0.004781977, 0.007032333, -0.031787455, - 0.010868644, -0.031489216, 0.09525667, 0.013939797, - 0.0058680447, 0.0167067, 0.02668468, -0.04797466, - -0.048885044, -0.12722108, 0.035304096, 0.06554885, - 0.00972396, -0.039238118, -0.05159735, -0.11329045, - 0.1613692, -0.03750952, 0.06529313, -0.071974665, - -0.11769596, 0.015524369, -0.0013754242, -0.12446318, - 0.02786344, -0.014179351, 0.005264273, 0.14376344, - 0.015983658, 0.03406988, -0.06939408, 0.040699873, - 0.02111075, 0.09669095, 0.041345075, -0.08316494, - -0.07684199, -0.045768797, 0.032298047, -0.041805092, - 0.0119405, 0.0061010392, 0.12652606, 0.0064572375, - -0.024950314, 0.11574242, 0.04508852, -0.04335324, - 0.06760663, -0.027437469, 0.07216407, 0.06977076, - -0.05438599, 0.034033038, -0.028602652, 0.05346137, - 0.043184172, -0.037189785, 0.10420091, 0.00882477, - -0.054019816, -0.074273005, -0.030617684, -0.0028467078, - 0.024302477, -0.0038869337, 0.005332455, 0.0013399826, - 0.04361412, -0.007001822, 0.09631092, -0.06702025, - -0.042049985, -0.035070654, -0.04103342, -0.10273396, - 0.0544271, 0.037184782, -0.13150354, -0.0058036847, - -0.008264958, 0.042035464, 0.05891794, 0.029673764, - 0.0063542654, 0.044788733, 0.054816857, 0.062257513, - -0.00093483756, 0.048938446, -0.004952862, -0.007730018, - -0.04043371, -0.017094059, 0.07229206, -0.023670016, - -0.052195564, -0.025616996, -0.01520939, 0.045104615, - -0.007376126, 0.003533447, 0.006570588, 0.056037236, - 0.12436656, 0.051817212, 0.028532185, -0.08686856, - 0.11868599, 0.07663395, -0.07323171, 0.03463402, - -0.050708205, -0.04458982, -0.11590894, 0.021273347, - 0.1251325, -0.15313013, -0.12224372, 0.17228661, - 0.023029093, 0.086124025, 0.006445803, -0.03496501, - 0.028332196, 0.04449512, -0.042436164, -0.026587414, - -0.006041347, -0.09292539, -0.05678812, 0.03897832, - 0.09465633, 0.008115513, -0.02171956, 0.08304309, - 0.071401566, 0.019622514, 0.032163795, -0.004167056, - 0.02295182, 0.030739572, 0.056506045, 0.004612461, - 0.06524936, 0.059999723, 0.046395954, -0.0045512207, - -0.1335546, -0.030136576, 0.11584653, -0.014678886, - 0.0020118146, -0.09688814, -0.0790206, 0.039770417, - -0.0329582, 0.07922767, 0.029322514, 0.026405897, - 0.04207835, -0.07073373, 0.063781224, 0.0859677, - -0.10925287, -0.07011058, 0.048005477, 0.03438226, - -0.09606514, -0.006669445, -0.043381985, 0.04240257, - -0.06955775, -0.06769346, 0.043903265, -0.026784198, - -0.017840602, 0.024307009, -0.040079936, -0.019946516, - 0.045318738, -0.12233574, 0.026170589, 0.0074471775, - 0.15978073, 0.10185836, 0.10298046, -0.015476589, - -0.039390966, -0.072174534, 0.0739445, -0.1211869, - -0.0347889, -0.07943156, 0.014809798, -0.12412325, - -0.0030663363, 0.039695457, 0.0647603, -0.08291318, - -0.018529687, -0.004423833, 0.0037507233, 0.084633216, - -0.01514876, -0.056505352, -0.012800942, -0.06994386, - 0.012962922, -0.031234352, 0.07029052, 0.016418684, - 0.03618972, 0.055686004, -0.08663945, -0.017404709, - -0.054761406, 0.029065743, 0.052404847, 0.020238016, - 0.0048197987, -0.0214882, 0.07078733, 0.013016777, - 0.06262858, 0.009184685, 0.020785125, -0.043904778, - -0.0270329, -0.03299152, -0.060088247, -0.015162964, - -0.001828936, 0.12642565, -0.056757294, 0.013586685, - 0.09232601, -0.035886683, 0.06000002, 0.05229691, - -0.052580316, -0.082029596, -0.010794592, 0.012947712, - -0.036429964, -0.085508935, -0.13127148, -0.017744139, - 0.031502828, 0.036232427, -0.031581745, 0.023051167, - -0.05325106, -0.03421577, 0.028793324, -0.034633752, - -0.009881397, -0.043551125, -0.018609839, 0.0019097115, - -0.008799762, 0.056595087, 0.0022273948, 0.055752404}); - - lstm.SetRecurrentToOutputWeights({ - 0.025825322, -0.05813119, 0.09495884, -0.045984812, -0.01255415, - -0.0026479573, -0.08196161, -0.054914974, -0.0046604523, -0.029587349, - -0.044576716, -0.07480124, -0.082868785, 0.023254942, 0.027502948, - -0.0039728214, -0.08683098, -0.08116779, -0.014675607, -0.037924774, - -0.023314456, -0.007401714, -0.09255757, 0.029460307, -0.08829125, - -0.005139627, -0.08989442, -0.0555066, 0.13596267, -0.025062224, - -0.048351806, -0.03850004, 0.07266485, -0.022414139, 0.05940088, - 0.075114764, 0.09597592, -0.010211725, -0.0049794707, -0.011523867, - -0.025980417, 0.072999895, 0.11091378, -0.081685916, 0.014416728, - 0.043229222, 0.034178585, -0.07530371, 0.035837382, -0.085607, - -0.007721233, -0.03287832, -0.043848954, -0.06404588, -0.06632928, - -0.073643476, 0.008214239, -0.045984086, 0.039764922, 0.03474462, - 0.060612556, -0.080590084, 0.049127717, 0.04151091, -0.030063879, - 0.008801774, -0.023021035, -0.019558564, 0.05158114, -0.010947698, - -0.011825728, 0.0075720972, 0.0699727, -0.0039981045, 0.069350146, - 0.08799282, 0.016156472, 0.035502106, 0.11695009, 0.006217345, - 0.13392477, -0.037875112, 0.025745004, 0.08940699, -0.00924166, - 0.0046702605, -0.036598757, -0.08811812, 0.10522024, -0.032441203, - 0.008176899, -0.04454919, 0.07058152, 0.0067963637, 0.039206743, - 0.03259838, 0.03725492, -0.09515802, 0.013326398, -0.052055415, - -0.025676316, 0.03198509, -0.015951829, -0.058556724, 0.036879618, - 0.043357447, 0.028362012, -0.05908629, 0.0059240665, -0.04995891, - -0.019187413, 0.0276265, -0.01628143, 0.0025863599, 0.08800015, - 0.035250366, -0.022165963, -0.07328642, -0.009415526, -0.07455109, - 0.11690406, 0.0363299, 0.07411125, 0.042103454, -0.009660886, - 0.019076364, 0.018299393, -0.046004917, 0.08891175, 0.0431396, - -0.026327137, -0.051502608, 0.08979574, -0.051670972, 0.04940282, - -0.07491107, -0.021240504, 0.022596184, -0.034280192, 0.060163025, - -0.058211457, -0.051837247, -0.01349775, -0.04639988, -0.035936575, - -0.011681591, 0.064818054, 0.0073146066, -0.021745546, -0.043124277, - -0.06471268, -0.07053354, -0.029321948, -0.05330136, 0.016933719, - -0.053782392, 0.13747959, -0.1361751, -0.11569455, 0.0033329215, - 0.05693899, -0.053219706, 0.063698, 0.07977434, -0.07924483, - 0.06936997, 0.0034815092, -0.007305279, -0.037325785, -0.07251102, - -0.033633437, -0.08677009, 0.091591336, -0.14165086, 0.021752775, - 0.019683983, 0.0011612234, -0.058154266, 0.049996935, 0.0288841, - -0.0024567875, -0.14345716, 0.010955264, -0.10234828, 0.1183656, - -0.0010731248, -0.023590032, -0.072285876, -0.0724771, -0.026382286, - -0.0014920527, 0.042667855, 0.0018776858, 0.02986552, 0.009814309, - 0.0733756, 0.12289186, 0.018043943, -0.0458958, 0.049412545, - 0.033632483, 0.05495232, 0.036686596, -0.013781798, -0.010036754, - 0.02576849, -0.08307328, 0.010112348, 0.042521734, -0.05869831, - -0.071689695, 0.03876447, -0.13275425, -0.0352966, -0.023077697, - 0.10285965, 0.084736146, 0.15568255, -0.00040734606, 0.027835453, - -0.10292561, -0.032401145, 0.10053256, -0.026142767, -0.08271222, - -0.0030240538, -0.016368777, 0.1070414, 0.042672627, 0.013456989, - -0.0437609, -0.022309763, 0.11576483, 0.04108048, 0.061026827, - -0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949, - 0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819, - -0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954, - 0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228, - -0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001, - -0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939, - -0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556, - -0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718, - 0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893, - 0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974, - -0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485, - 0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856, - 0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853, - -0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019, - 0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024, - 0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994, - 0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621, - }); - - lstm.SetCellToInputWeights( - {0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458, - -0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174, - -0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047, - 0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175}); - - lstm.SetCellToForgetWeights( - {-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276, - -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766, - -0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774, - 0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355}); - - lstm.SetCellToOutputWeights( - {0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764, - -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544, - -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817, - 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733}); - - lstm.SetProjectionWeights( - {-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832, - 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683, - -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931, - -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476, - 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067, - 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787, - 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588, - 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285, - -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949, - -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768, - -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929, - 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504, - 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946, - 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117, - 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253, - 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456, - -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552, - 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797, - -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272, - 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165, - -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922, - -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548, - 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786, - -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722, - 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318, - -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776, - -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307, - 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969, - -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593, - -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515, - -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288, - 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723, - 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097, - -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209, - 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, - 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, - 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, - 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, - 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, - -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, - -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, - 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, - -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, - -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, - 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, - 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, - 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, - -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, - -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, - -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, - 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, - -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, - 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, - 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, - -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, - -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, - -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, - 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, - -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, - -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, - -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, - 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, - 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, - 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656}); - - static float lstm_input[][20] = { - {// Batch0: 4 (input_sequence_size) * 5 (n_input) - 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386, - 0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199, - 0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, - - {// Batch1: 4 (input_sequence_size) * 5 (n_input) - 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260, - 0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485, - 0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}}; - - static float lstm_golden_output[][64] = { - {// Batch0: 4 (input_sequence_size) * 16 (n_output) - -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, - -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, - -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, - 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, - -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, - -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, - 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, - 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, - 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, - 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, - -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, - -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, - 0.0286833, 0.00824207, 0.0264887, 0.0305169}, - {// Batch1: 4 (input_sequence_size) * 16 (n_output) - -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, - -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, - 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, - 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, - -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, - -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, - 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, - 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, - 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, - 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, - -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, - -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, - 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToInputWeights(cell_to_input_weights_); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + lstm.SetProjectionWeights(projection_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - for (int i = 0; i < lstm.sequence_length(); i++) { - float* batch0_start = lstm_input[0] + i * lstm.num_inputs(); - float* batch0_end = batch0_start + lstm.num_inputs(); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} - lstm.SetInput(2 * i * lstm.num_inputs(), batch0_start, batch0_end); +TEST_F(NoCifgPeepholeProjectionClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 20; + const int n_output = 16; + const int sequence_length = 4; - float* batch1_start = lstm_input[1] + i * lstm.num_inputs(); - float* batch1_end = batch1_start + lstm.num_inputs(); - lstm.SetInput((2 * i + 1) * lstm.num_inputs(), batch1_start, batch1_end); - } + HybridUnidirectionalLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, sequence_length, + /*use_cifg=*/false, /*use_peephole=*/true, + /*use_projection_weights=*/true, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {sequence_length, n_batch, n_input}, // input tensor - lstm.Invoke(); + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor - std::vector expected; - for (int i = 0; i < lstm.sequence_length(); i++) { - float* golden_start_batch0 = lstm_golden_output[0] + i * lstm.num_outputs(); - float* golden_end_batch0 = golden_start_batch0 + lstm.num_outputs(); - float* golden_start_batch1 = lstm_golden_output[1] + i * lstm.num_outputs(); - float* golden_end_batch1 = golden_start_batch1 + lstm.num_outputs(); - expected.insert(expected.end(), golden_start_batch0, golden_end_batch0); - expected.insert(expected.end(), golden_start_batch1, golden_end_batch1); - } - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {n_cell}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {n_output, n_cell}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToInputWeights(cell_to_input_weights_); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + lstm.SetProjectionWeights(projection_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, /*tolerance=*/0.00467); } } // namespace diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index d78b6eae90f17a1c6775ba43647ae67720038207..c448fb71db204494042192d6a75ac4d600467e47 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -45,6 +45,9 @@ TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, case TensorType_FLOAT32: *type = kTfLiteFloat32; break; + case TensorType_INT16: + *type = kTfLiteInt16; + break; case TensorType_INT32: *type = kTfLiteInt32; break; @@ -60,6 +63,9 @@ TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, case TensorType_BOOL: *type = kTfLiteBool; break; + case TensorType_COMPLEX64: + *type = kTfLiteComplex64; + break; default: error_reporter->Report("Unimplemented data type %s (%d) in tensor\n", EnumNameTensorType(tensor_type), tensor_type); @@ -322,12 +328,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = nullptr; switch (op_type) { - case BuiltinOperator_CALL: - // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are - // ok for now, since there is no call implementation either. - break; - case BuiltinOperator_CUSTOM: - break; case BuiltinOperator_CONV_2D: { TfLiteConvParams* params = MallocPOD(); if (auto* conv_params = op->builtin_options_as_Conv2DOptions()) { @@ -343,21 +343,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_TANH: - case BuiltinOperator_LOGISTIC: - case BuiltinOperator_RELU: - case BuiltinOperator_RELU_N1_TO_1: - case BuiltinOperator_RELU6: - case BuiltinOperator_CONCAT_EMBEDDINGS: - case BuiltinOperator_EXP: - case BuiltinOperator_TOPK_V2: - case BuiltinOperator_LOG_SOFTMAX: - case BuiltinOperator_DEQUANTIZE: - case BuiltinOperator_PRELU: - case BuiltinOperator_FLOOR: - case BuiltinOperator_NEG: - case BuiltinOperator_SIN: - break; case BuiltinOperator_CAST: { TfLiteCastParams* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_CastOptions()) { @@ -445,9 +430,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_EMBEDDING_LOOKUP: - // no-op. - break; case BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: { TfLiteEmbeddingLookupSparseParams* params = MallocPOD(); @@ -465,6 +447,18 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, op->builtin_options_as_FullyConnectedOptions()) { params->activation = parse_activation( fully_connected_params->fused_activation_function()); + switch (fully_connected_params->weights_format()) { + case FullyConnectedOptionsWeightsFormat_DEFAULT: + params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault; + break; + case FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8: + params->weights_format = + kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8; + break; + default: + error_reporter->Report("Unhandled fully-connected weights format."); + return kTfLiteError; + } } *builtin_data = reinterpret_cast(params); break; @@ -579,12 +573,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_PAD: { - break; - } - case BuiltinOperator_PADV2: { - break; - } case BuiltinOperator_RESHAPE: { auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_ReshapeOptions()) { @@ -624,18 +612,10 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_SPACE_TO_BATCH_ND: { - break; - } - case BuiltinOperator_BATCH_TO_SPACE_ND: { - break; - } - case BuiltinOperator_TRANSPOSE: { - break; - } - case BuiltinOperator_MEAN: { - auto* params = MallocPOD(); - if (auto* schema_params = op->builtin_options_as_MeanOptions()) { + case BuiltinOperator_MEAN: + case BuiltinOperator_SUM: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_ReducerOptions()) { params->keep_dims = schema_params->keep_dims(); } *builtin_data = reinterpret_cast(params); @@ -672,10 +652,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_MAXIMUM: - case BuiltinOperator_MINIMUM: { - break; - } case BuiltinOperator_ARG_MAX: { auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_ArgMaxOptions()) { @@ -685,18 +661,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_GREATER: - case BuiltinOperator_GREATER_EQUAL: - case BuiltinOperator_LESS: - case BuiltinOperator_LESS_EQUAL: - case BuiltinOperator_EQUAL: - case BuiltinOperator_NOT_EQUAL: - case BuiltinOperator_SELECT: { - break; - } - case BuiltinOperator_SLICE: { - break; - } case BuiltinOperator_TRANSPOSE_CONV: { TfLiteTransposeConvParams* params = MallocPOD(); @@ -719,15 +683,63 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_SHAPE: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_ShapeOptions()) { + ConvertTensorType(schema_params->out_type(), ¶ms->out_type, + error_reporter); + } + *builtin_data = static_cast(params); + break; + } case BuiltinOperator_DELEGATE: { // TODO(ycling): Revisit when supporting saving delegated models. error_reporter->Report("DELEGATE op shouldn't exist in model."); return kTfLiteError; } + + // Below are the ops with no builtin_data strcture. + case BuiltinOperator_BATCH_TO_SPACE_ND: + // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are + // ok for now, since there is no call implementation either. + case BuiltinOperator_CALL: + case BuiltinOperator_CONCAT_EMBEDDINGS: + case BuiltinOperator_CUSTOM: + case BuiltinOperator_DEQUANTIZE: + case BuiltinOperator_EMBEDDING_LOOKUP: + case BuiltinOperator_EQUAL: + case BuiltinOperator_EXP: case BuiltinOperator_EXPAND_DIMS: - case BuiltinOperator_TILE: { + case BuiltinOperator_FLOOR: + case BuiltinOperator_GREATER: + case BuiltinOperator_GREATER_EQUAL: + case BuiltinOperator_LESS: + case BuiltinOperator_LESS_EQUAL: + case BuiltinOperator_LOG: + case BuiltinOperator_LOGISTIC: + case BuiltinOperator_LOG_SOFTMAX: + case BuiltinOperator_MAXIMUM: + case BuiltinOperator_MINIMUM: + case BuiltinOperator_NEG: + case BuiltinOperator_NOT_EQUAL: + case BuiltinOperator_PAD: + case BuiltinOperator_PADV2: + case BuiltinOperator_PRELU: + case BuiltinOperator_RELU: + case BuiltinOperator_RELU6: + case BuiltinOperator_RELU_N1_TO_1: + case BuiltinOperator_RSQRT: + case BuiltinOperator_SELECT: + case BuiltinOperator_SIN: + case BuiltinOperator_SLICE: + case BuiltinOperator_SPACE_TO_BATCH_ND: + case BuiltinOperator_SQRT: + case BuiltinOperator_TANH: + case BuiltinOperator_TILE: + case BuiltinOperator_TOPK_V2: + case BuiltinOperator_TRANSPOSE: + case BuiltinOperator_POW: break; - } } return kTfLiteOk; } @@ -749,7 +761,7 @@ TfLiteStatus InterpreterBuilder::ParseNodes( } const TfLiteRegistration* registration = - flatbuffer_op_index_to_registration_[op->opcode_index()]; + flatbuffer_op_index_to_registration_[index]; if (registration == nullptr) { error_reporter_->Report("Skipping op for opcode_index %d\n", index); status = kTfLiteError; @@ -868,7 +880,16 @@ TfLiteStatus InterpreterBuilder::ParseTensors( const char* buffer_ptr; TF_LITE_ENSURE_STATUS(get_readonly_data(&buffer_ptr, &buffer_size)); + bool is_variable = tensor->is_variable(); if (buffer_ptr) { + if (is_variable) { + error_reporter_->Report( + "Tensor %d is a variable tensor with buffer. " + "It's not supported now.\n", + i); + status = kTfLiteError; + } + if (interpreter->SetTensorParametersReadOnly( i, type, get_name(tensor), dims, quantization, buffer_ptr, buffer_size, allocation_) != kTfLiteOk) { @@ -877,8 +898,9 @@ TfLiteStatus InterpreterBuilder::ParseTensors( status = kTfLiteError; } } else { - if (interpreter->SetTensorParametersReadWrite( - i, type, get_name(tensor), dims, quantization) != kTfLiteOk) { + if (interpreter->SetTensorParametersReadWrite(i, type, get_name(tensor), + dims, quantization, + is_variable) != kTfLiteOk) { error_reporter_->Report("Tensor %d is invalidly specified in schema.\n", i); status = kTfLiteError; @@ -962,6 +984,15 @@ TfLiteStatus InterpreterBuilder::operator()( if (ParseTensors(buffers, tensors, interpreter->get()) != kTfLiteOk) return cleanup_and_error(); + std::vector variables; + for (int i = 0; i < (*interpreter)->tensors_size(); ++i) { + auto* tensor = (*interpreter)->tensor(i); + if (tensor->is_variable) { + variables.push_back(i); + } + } + (**interpreter).SetVariables(std::move(variables)); + return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD b/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD index f8767b443a2aa64b666c3b6bfb7db30cc0be62ea..f18a2ca07a5f66b760e96a6d9a57db8d6c26b7b9 100644 --- a/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD +++ b/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 605ce7d6fc1e4375409070f710e20de0c3e1352f..905c0919cb690012c2feba2cca821aa43fb2ddff 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -29,27 +29,46 @@ limitations under the License. namespace tflite { -// TODO(aselle): FATAL leaves resources hanging. -void FATAL(const char* format, ...) { +void logError(const char* format, ...) { + // TODO(mikie): use android logging, stderr is not captured for Java + // applications va_list args; va_start(args, format); vfprintf(stderr, format, args); va_end(args); + fprintf(stderr, "\n"); fflush(stderr); - exit(1); } +#define FATAL(...) \ + logError(__VA_ARGS__); \ + exit(1); + // TODO(aselle): Change the error model to use status codes. -#define CHECK_TFLITE_SUCCESS(x) \ - if (x != kTfLiteOk) { \ - FATAL("Aborting since tflite returned failure."); \ +#define CHECK_TFLITE_SUCCESS(x) \ + if (x != kTfLiteOk) { \ + FATAL("Aborting since tflite returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ } -#define CHECK_NN(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - FATAL("Aborting since tflite returned failure."); \ +#define CHECK_NN(x) \ + if (x != ANEURALNETWORKS_NO_ERROR) { \ + FATAL("Aborting since NNAPI returned failure nnapi_delegate.cc:%d", \ + __LINE__); \ } +#define RETURN_ERROR_IF_NN_FAILED(x) \ + if (x != ANEURALNETWORKS_NO_ERROR) { \ + logError( \ + "Returning error since NNAPI returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ + return kTfLiteError; \ + } + +// Tracking of NNAPI operand ids +static const int64_t kOperandIdNotSet = -1; +static const int64_t kOperandNotNeeded = -2; + namespace { int32_t GetAndroidSdkVersion() { @@ -104,21 +123,16 @@ NNAPIDelegate::~NNAPIDelegate() { } // Adds the tensors of the interpreter to the NN API model. -// Returns the number of operands added. -uint32_t addTensorOperands(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, - const std::vector& skip_list) { +TfLiteStatus addTensorOperands(tflite::Interpreter* interpreter, + ANeuralNetworksModel* nn_model, + uint32_t* no_of_operands_added, + std::vector* nnapi_ids) { uint32_t next_id = 0; for (size_t i = 0; i < interpreter->tensors_size(); i++) { - // skip temporaries tensors. - bool shouldSkip = false; - for (auto skip_idx : skip_list) { - if (i == skip_idx) { - shouldSkip = true; - break; - } - } - if (shouldSkip) continue; + // Skip temporaries and RNN back-edges. + if ((*nnapi_ids)[i] == kOperandNotNeeded) continue; + + (*nnapi_ids)[i] = int64_t(next_id); int32_t nn_type = 0; // NNAPI requires 32-bit float scale to be zero, tflite doesn't care @@ -144,7 +158,18 @@ uint32_t addTensorOperands(tflite::Interpreter* interpreter, zeroPoint = tensor->params.zero_point; break; default: - FATAL("Unsupported type."); + logError("Unsupported tensor type %d", tensor->type); + return kTfLiteError; + } + if (tensor->dims->size == 0) { + logError("NNAPI doesn't support tensors with rank 0 (index %d name %s)", + i, tensor->name); + return kTfLiteError; + } + if (tensor->dims->size > 4) { + logError("NNAPI doesn't support tensors with rank > 4 (index %d name %s)", + i, tensor->name); + return kTfLiteError; } // TODO(aselle): Note, many of these are intermediate results. Do I need // to ever specify these sizes. I am currently below doing setValue @@ -154,36 +179,53 @@ uint32_t addTensorOperands(tflite::Interpreter* interpreter, ANeuralNetworksOperandType operand_type{ nn_type, static_cast(tensor->dims->size), reinterpret_cast(tensor->dims->data), scale, zeroPoint}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); + RETURN_ERROR_IF_NN_FAILED( + ANeuralNetworksModel_addOperand(nn_model, &operand_type)); // TODO(aselle): Based on Michael's suggestion, limiting this to read // only memory if (tensor->allocation_type == kTfLiteMmapRo) { if (const NNAPIAllocation* alloc = dynamic_cast( static_cast(tensor->allocation))) { - CHECK_NN(ANeuralNetworksModel_setOperandValueFromMemory( - nn_model, next_id, alloc->memory(), alloc->offset(tensor->data.raw), - tensor->bytes)); + RETURN_ERROR_IF_NN_FAILED( + ANeuralNetworksModel_setOperandValueFromMemory( + nn_model, next_id, alloc->memory(), + alloc->offset(tensor->data.raw), tensor->bytes)); } else { - CHECK_NN(ANeuralNetworksModel_setOperandValue( + RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_setOperandValue( nn_model, next_id, tensor->data.raw, tensor->bytes)); } } else if (tensor->bytes == 0) { // These size 0 tensors are optional tensors reserved. - CHECK_NN( + RETURN_ERROR_IF_NN_FAILED( ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); } ++next_id; } - return next_id; + *no_of_operands_added = next_id; + return kTfLiteOk; +} + +void MapAndAddTensorIds(const int* from_ids_buf, size_t from_ids_count, + std::vector* into, + const std::vector& map) { + for (size_t i = 0; i < from_ids_count; i++) { + int from_id = from_ids_buf[i]; + if (from_id == kOptionalTensor) { + into->push_back(from_id); + } else { + into->push_back(map[from_id]); + } + } } // Adds the operations and their parameters to the NN API model. // 'next-id' is the operand ID of the next operand of the model. -void AddOpsAndParams(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, uint32_t next_id, - std::vector* model_state_inputs, - std::vector* model_state_outputs) { +TfLiteStatus AddOpsAndParams( + tflite::Interpreter* interpreter, ANeuralNetworksModel* nn_model, + uint32_t next_id, std::vector* model_state_inputs, + std::vector* model_state_outputs, + const std::vector& tensor_id_to_nnapi_id) { for (size_t i = 0; i < interpreter->nodes_size(); i++) { const auto* node_and_registration = interpreter->node_and_registration(i); const TfLiteNode& node = node_and_registration->first; @@ -192,10 +234,11 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, static_cast(registration.builtin_code); // Add the parameters. - std::vector augmented_inputs( - node.inputs->data, node.inputs->data + node.inputs->size); - std::vector augmented_outputs( - node.outputs->data, node.outputs->data + node.outputs->size); + std::vector augmented_inputs, augmented_outputs; + MapAndAddTensorIds(node.inputs->data, node.inputs->size, &augmented_inputs, + tensor_id_to_nnapi_id); + MapAndAddTensorIds(node.outputs->data, node.outputs->size, + &augmented_outputs, tensor_id_to_nnapi_id); auto add_scalar_int32 = [&nn_model, &augmented_inputs, &next_id](int value) { @@ -215,6 +258,17 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, augmented_inputs.push_back(next_id++); }; + auto add_vector_int32 = [&](const int* values, uint32_t num_values) { + ANeuralNetworksOperandType operand_type{ + .type = ANEURALNETWORKS_TENSOR_INT32, + .dimensionCount = 1, + .dimensions = &num_values}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) + CHECK_NN(ANeuralNetworksModel_setOperandValue( + nn_model, next_id, values, sizeof(int32_t) * num_values)); + augmented_inputs.push_back(next_id++); + }; + // Handle state tensors of RNN, LSTM, SVDF. // For each state_out tensor, a corresponding state_in operand needs to be // created for NNAPI. @@ -233,39 +287,54 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, model_state_outputs->push_back(tensor_id); next_id++; }; + auto check_and_add_activation = [&add_scalar_int32](int activation) { + if (activation > kTfLiteActRelu6) { + FATAL("NNAPI only supports RELU, RELU1 and RELU6 activations"); + } + add_scalar_int32(activation); + }; - auto add_add_params = [&add_scalar_int32]() { add_scalar_int32(0); }; + auto add_add_params = [&add_scalar_int32](void* data) { + auto* builtin = reinterpret_cast(data); + if (builtin->activation > kTfLiteActRelu6) { + FATAL("NNAPI only supports RELU, RELU1 and RELU6 activations"); + } + add_scalar_int32(builtin->activation); + }; - auto add_pooling_params = [&add_scalar_int32](void* data) { + auto add_pooling_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { auto builtin = reinterpret_cast(data); add_scalar_int32(builtin->padding); add_scalar_int32(builtin->stride_width); add_scalar_int32(builtin->stride_height); add_scalar_int32(builtin->filter_width); add_scalar_int32(builtin->filter_height); - add_scalar_int32(builtin->activation); + check_and_add_activation(builtin->activation); }; - auto add_convolution_params = [&add_scalar_int32](void* data) { + auto add_convolution_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { auto builtin = reinterpret_cast(data); add_scalar_int32(builtin->padding); add_scalar_int32(builtin->stride_width); add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->activation); + check_and_add_activation(builtin->activation); }; - auto add_depthwise_conv_params = [&add_scalar_int32](void* data) { + auto add_depthwise_conv_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { auto builtin = reinterpret_cast(data); add_scalar_int32(builtin->padding); add_scalar_int32(builtin->stride_width); add_scalar_int32(builtin->stride_height); add_scalar_int32(builtin->depth_multiplier); - add_scalar_int32(builtin->activation); + check_and_add_activation(builtin->activation); }; - auto add_fully_connected_params = [&add_scalar_int32](void* data) { + auto add_fully_connected_params = [&check_and_add_activation](void* data) { auto builtin = reinterpret_cast(data); - add_scalar_int32(builtin->activation); + check_and_add_activation(builtin->activation); }; auto add_concatenation_params = [&add_scalar_int32](void* data) { @@ -297,6 +366,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, // LSTM in NNAPI requires scratch tensor as an output operand. auto add_lstm_scratch_tensor_float32 = [interpreter, &node, &nn_model, &next_id, &augmented_outputs]() { + if (node.temporaries->size == 0) return; int scratch_buffer_index = node.temporaries->data[0]; const TfLiteTensor* tensor = interpreter->tensor(scratch_buffer_index); ANeuralNetworksOperandType operand_type{ @@ -309,7 +379,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, }; auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast(data); + auto builtin = reinterpret_cast(data); add_scalar_int32(builtin->keep_dims); }; @@ -324,6 +394,14 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, add_scalar_int32(builtin->activation); }; + auto add_squeeze_params = [&](void* data) { + const auto* builtin = reinterpret_cast(data); + // Note that we add the squeeze dimensions even if the dimensions were + // unspecified (empty), as NNAPI requires the operand. + add_vector_int32(builtin->squeeze_dims, + static_cast(builtin->num_squeeze_dims)); + }; + // Handle optional input tensors. auto add_optional_tensors = [&nn_model, &augmented_inputs, &next_id](int nn_type) { @@ -345,11 +423,11 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, switch (builtin) { case tflite::BuiltinOperator_ADD: nn_op_type = ANEURALNETWORKS_ADD; - add_add_params(); + add_add_params(node.builtin_data); break; case tflite::BuiltinOperator_MUL: nn_op_type = ANEURALNETWORKS_MUL; - add_add_params(); + add_add_params(node.builtin_data); break; case tflite::BuiltinOperator_AVERAGE_POOL_2D: add_pooling_params(node.builtin_data); @@ -363,7 +441,14 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, add_pooling_params(node.builtin_data); nn_op_type = ANEURALNETWORKS_L2_POOL_2D; break; - case tflite::BuiltinOperator_CONV_2D: + case tflite::BuiltinOperator_CONV_2D: { + auto builtin = reinterpret_cast(node.builtin_data); + if (builtin->dilation_width_factor != 1 || + builtin->dilation_height_factor != 1 || node.inputs->size != 3) { + logError("NNAPI does not support dilated Conv2D."); + return kTfLiteError; + } + } add_convolution_params(node.builtin_data); nn_op_type = ANEURALNETWORKS_CONV_2D; break; @@ -407,6 +492,10 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; break; case tflite::BuiltinOperator_LSTM: { + if (node.inputs->size + /* no of params */ 3 != 21) { + logError("NNAPI only supports 21-input LSTMs"); + return kTfLiteError; + } duplicate_state_tensor_float32( node.outputs->data[/*kOutputStateTensor*/ 0]); duplicate_state_tensor_float32( @@ -445,10 +534,19 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_DIV: nnapi_version = 11; // require NNAPI 1.1 nn_op_type = ANEURALNETWORKS_DIV; + check_and_add_activation( + reinterpret_cast(node.builtin_data)->activation); break; case tflite::BuiltinOperator_SUB: nnapi_version = 11; // require NNAPI 1.1 nn_op_type = ANEURALNETWORKS_SUB; + check_and_add_activation( + reinterpret_cast(node.builtin_data)->activation); + break; + case tflite::BuiltinOperator_SQUEEZE: + nnapi_version = 11; // requires NNAPI 1.1 + add_squeeze_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_SQUEEZE; break; case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: case tflite::BuiltinOperator_LSH_PROJECTION: @@ -471,7 +569,6 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_TOPK_V2: case tflite::BuiltinOperator_TRANSPOSE: case tflite::BuiltinOperator_SPLIT: - case tflite::BuiltinOperator_SQUEEZE: case tflite::BuiltinOperator_STRIDED_SLICE: case tflite::BuiltinOperator_EXP: case tflite::BuiltinOperator_LOG_SOFTMAX: @@ -490,18 +587,24 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_SELECT: case tflite::BuiltinOperator_SLICE: case tflite::BuiltinOperator_SIN: + case tflite::BuiltinOperator_LOG: case tflite::BuiltinOperator_TRANSPOSE_CONV: case tflite::BuiltinOperator_TILE: case tflite::BuiltinOperator_EXPAND_DIMS: case tflite::BuiltinOperator_SPARSE_TO_DENSE: case tflite::BuiltinOperator_EQUAL: case tflite::BuiltinOperator_NOT_EQUAL: - FATAL("Op code %d is currently not delegated to NNAPI", builtin); - nn_op_type = -1; // set to invalid + case tflite::BuiltinOperator_SUM: + case tflite::BuiltinOperator_SQRT: + case tflite::BuiltinOperator_RSQRT: + case tflite::BuiltinOperator_SHAPE: + case tflite::BuiltinOperator_POW: + logError("Op code %d is currently not delegated to NNAPI", builtin); + return kTfLiteError; break; case tflite::BuiltinOperator_CUSTOM: - FATAL("Custom operations are not supported when using NNAPI."); - nn_op_type = -1; // set to invalid + logError("Custom operations are not supported when using NNAPI."); + return kTfLiteError; break; } @@ -510,47 +613,70 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, } // Add the operation. - CHECK_NN(ANeuralNetworksModel_addOperation( + RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_addOperation( nn_model, nn_op_type, static_cast(augmented_inputs.size()), augmented_inputs.data(), static_cast(augmented_outputs.size()), reinterpret_cast(augmented_outputs.data()))); } + return kTfLiteOk; } TfLiteStatus NNAPIDelegate::BuildGraph(Interpreter* interpreter) { - // TODO(aselle): This is not correct. need to handle resize invalidation. - if (nn_model_ && nn_compiled_model_) return kTfLiteOk; + if (nn_model_ && nn_compiled_model_) return model_status_; + // TODO(aselle): This is not correct. need to handle resize invalidation. if (!nn_model_) { CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); - // Find all the temporary tensors and put them in a skip_list. - std::vector skip_list; + // Find which tensors should be added to NNAPI. TFLite has temporaries + // and RNN back-edges which are are not valid for NNAPI. We look through all + // inputs and outputs and mark the mapping in tensor_id_to_nnapi_id with + // kOperandIdNotSet. addTensorOperands will replace those with the + // corresponding NNAPI operand ids and skip kOperandNotNeeded entries. + std::vector tensor_id_to_nnapi_id(interpreter->tensors_size(), + kOperandNotNeeded); + auto set_ids_to_not_set = [&tensor_id_to_nnapi_id](const int* buf, + size_t count) { + for (int j = 0; j < count; j++) { + auto tensor_id = buf[j]; + if (tensor_id != kOptionalTensor) { + tensor_id_to_nnapi_id[tensor_id] = kOperandIdNotSet; + } + } + }; for (size_t i = 0; i < interpreter->nodes_size(); i++) { const auto* node_and_registration = interpreter->node_and_registration(i); const TfLiteNode& node = node_and_registration->first; - if (node.temporaries != nullptr) { - for (int j = 0; j < node.temporaries->size; j++) { - skip_list.push_back(static_cast(node.temporaries->data[j])); - } - } + set_ids_to_not_set(node.inputs->data, node.inputs->size); + set_ids_to_not_set(node.outputs->data, node.outputs->size); } - - uint32_t next_id = addTensorOperands(interpreter, nn_model_, skip_list); - AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_); - - std::vector augmented_inputs = interpreter->inputs(); - std::vector augmented_outputs = interpreter->outputs(); - - // All state tensors input/output need to be treated as model input/output. + set_ids_to_not_set(interpreter->inputs().data(), + interpreter->inputs().size()); + set_ids_to_not_set(interpreter->outputs().data(), + interpreter->outputs().size()); + + uint32_t next_id = 0; + RETURN_ERROR_IF_NN_FAILED(addTensorOperands( + interpreter, nn_model_, &next_id, &tensor_id_to_nnapi_id)); + RETURN_ERROR_IF_NN_FAILED( + AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, + &model_states_outputs_, tensor_id_to_nnapi_id)); + + std::vector augmented_inputs; + MapAndAddTensorIds(interpreter->inputs().data(), + interpreter->inputs().size(), &augmented_inputs, + tensor_id_to_nnapi_id); augmented_inputs.insert(augmented_inputs.end(), model_states_inputs_.begin(), model_states_inputs_.end()); - augmented_outputs.insert(augmented_outputs.end(), - model_states_outputs_.begin(), - model_states_outputs_.end()); + std::vector augmented_outputs; + MapAndAddTensorIds(interpreter->outputs().data(), + interpreter->outputs().size(), &augmented_outputs, + tensor_id_to_nnapi_id); + MapAndAddTensorIds(model_states_outputs_.data(), + model_states_outputs_.size(), &augmented_outputs, + tensor_id_to_nnapi_id); CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( nn_model_, static_cast(augmented_inputs.size()), @@ -568,7 +694,13 @@ TfLiteStatus NNAPIDelegate::BuildGraph(Interpreter* interpreter) { TfLiteStatus NNAPIDelegate::Invoke(Interpreter* interpreter) { if (!nn_model_) { - TF_LITE_ENSURE_STATUS(BuildGraph(interpreter)); + model_status_ = BuildGraph(interpreter); + if (model_status_ != kTfLiteOk) { + logError("Failed to build graph for NNAPI"); + } + } + if (model_status_ != kTfLiteOk) { + return model_status_; } ANeuralNetworksExecution* execution = nullptr; diff --git a/tensorflow/contrib/lite/nnapi_delegate.h b/tensorflow/contrib/lite/nnapi_delegate.h index 94dea4f9b23f208fddbacd3c77d889ea753a8a1d..8dc7d38a303f51b7ccefefd8c9d2990b443e6827 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.h +++ b/tensorflow/contrib/lite/nnapi_delegate.h @@ -59,14 +59,16 @@ class NNAPIDelegate { ANeuralNetworksModel* nn_model_ = nullptr; // The NN API compilation handle ANeuralNetworksCompilation* nn_compiled_model_ = nullptr; + // Model status + TfLiteStatus model_status_ = kTfLiteOk; // List of state tensors for LSTM, RNN, SVDF. // NN API does not allow ops to maintain states across multiple // invocations. We need to manually create state input tensors from // corresponding state output tensors of TFLite operations, and map them // correctly. - std::vector model_states_inputs_; - std::vector model_states_outputs_; + std::vector model_states_inputs_; // holds NNAPI operand ids + std::vector model_states_outputs_; // holds TFLite tensor ids }; } // namespace tflite diff --git a/tensorflow/contrib/lite/optional_debug_tools.cc b/tensorflow/contrib/lite/optional_debug_tools.cc index dfdd80ea8a42af683632be1d7e8ab0062847077d..f1f025f777c987c5ee47bdea457a973896b9bb82 100644 --- a/tensorflow/contrib/lite/optional_debug_tools.cc +++ b/tensorflow/contrib/lite/optional_debug_tools.cc @@ -50,6 +50,10 @@ const char* TensorTypeName(TfLiteType type) { return "kTfLiteString"; case kTfLiteBool: return "kTfLiteBool"; + case kTfLiteInt16: + return "kTfLiteInt16"; + case kTfLiteComplex64: + return "kTfLiteComplex64"; } return "(invalid)"; } @@ -82,13 +86,13 @@ void PrintInterpreterState(Interpreter* interpreter) { for (int tensor_index = 0; tensor_index < interpreter->tensors_size(); tensor_index++) { TfLiteTensor* tensor = interpreter->tensor(tensor_index); - printf("Tensor %3d %10s %15s %10zu bytes (%4.1f MB) ", tensor_index, - TensorTypeName(tensor->type), AllocTypeName(tensor->allocation_type), - tensor->bytes, float(tensor->bytes) / float(1 << 20)); + printf("Tensor %3d %-20s %10s %15s %10zu bytes (%4.1f MB) ", tensor_index, + tensor->name, TensorTypeName(tensor->type), + AllocTypeName(tensor->allocation_type), tensor->bytes, + (static_cast(tensor->bytes) / (1 << 20))); PrintTfLiteIntVector(tensor->dims); - printf("\n"); } - + printf("\n"); for (int node_index = 0; node_index < interpreter->nodes_size(); node_index++) { const std::pair* node_and_reg = @@ -104,7 +108,4 @@ void PrintInterpreterState(Interpreter* interpreter) { } } -// Prints a dump of what tensors and what nodes are in the interpreter. -TfLiteStatus ValidateInterpreterState(const Interpreter* interpreter); - } // namespace tflite diff --git a/tensorflow/contrib/lite/optional_debug_tools.h b/tensorflow/contrib/lite/optional_debug_tools.h index 1b6998cda382782b974bea3d18ffb6217e8f780c..7fb4b8d8b7ae87cc6e8dd8503c8a4ce0cef2ce8d 100644 --- a/tensorflow/contrib/lite/optional_debug_tools.h +++ b/tensorflow/contrib/lite/optional_debug_tools.h @@ -24,9 +24,6 @@ namespace tflite { // Prints a dump of what tensors and what nodes are in the interpreter. void PrintInterpreterState(Interpreter* interpreter); -// Prints a dump of what tensors and what nodes are in the interpreter. -TfLiteStatus ValidateInterpreterState(const Interpreter* interpreter); - } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_DEBUG_TOOLS_H_ diff --git a/tensorflow/contrib/lite/profiling/BUILD b/tensorflow/contrib/lite/profiling/BUILD index c31189f2b1f1ad6e3d8e2f5fcae9b6c2ef8eaf23..a162b87b8f98576ec7c3b2623d1d34f2baef6cce 100644 --- a/tensorflow/contrib/lite/profiling/BUILD +++ b/tensorflow/contrib/lite/profiling/BUILD @@ -2,9 +2,11 @@ package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") + common_copts = [ "-Wall", -] +] + tflite_copts() cc_library( name = "profiler", @@ -36,12 +38,14 @@ cc_library( name = "time", srcs = ["time.cc"], hdrs = ["time.h"], + copts = common_copts, ) cc_library( name = "profile_summarizer", srcs = ["profile_summarizer.cc"], hdrs = ["profile_summarizer.h"], + copts = common_copts, deps = [ ":profiler", "//tensorflow/contrib/lite:framework", @@ -53,6 +57,7 @@ cc_library( cc_test( name = "profile_summarizer_test", srcs = ["profile_summarizer_test.cc"], + copts = common_copts, deps = [ ":profile_summarizer", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/profiling/profile_summarizer.cc b/tensorflow/contrib/lite/profiling/profile_summarizer.cc index 6f2c9cd2b39a1d6be77a10b18658665874067d87..c37a0965884a803e82da536f73a8f32a28691651 100644 --- a/tensorflow/contrib/lite/profiling/profile_summarizer.cc +++ b/tensorflow/contrib/lite/profiling/profile_summarizer.cc @@ -78,18 +78,30 @@ OperatorDetails GetOperatorDetails(const tflite::Interpreter& interpreter, } else { op_name = tflite::EnumNamesBuiltinOperator()[code]; } + const char* profiling_string = + interpreter.OpProfilingString(node_reg->second, &node_reg->first); OperatorDetails details; details.name = op_name; + if (profiling_string) { + details.name += ":" + string(profiling_string); + } details.inputs = GetTensorNames(interpreter, inputs); details.outputs = GetTensorNames(interpreter, outputs); return details; } +tensorflow::StatSummarizerOptions GetProfileSummarizerOptions() { + auto options = tensorflow::StatSummarizerOptions(); + options.show_summary = true; + options.show_memory = false; + return options; +} + } // namespace ProfileSummarizer::ProfileSummarizer() - : stats_calculator_(new ::tensorflow::StatsCalculator( - tensorflow::StatSummarizerOptions())) {} + : stats_calculator_( + new ::tensorflow::StatsCalculator(GetProfileSummarizerOptions())) {} void ProfileSummarizer::ProcessProfiles( const std::vector& profile_stats, diff --git a/tensorflow/contrib/lite/profiling/profile_summarizer_test.cc b/tensorflow/contrib/lite/profiling/profile_summarizer_test.cc index 35cf780713b93db559f86dcaf62e1ac004b5049a..67a5eecfa05379c7a721e7d669fcd02602e5e369 100644 --- a/tensorflow/contrib/lite/profiling/profile_summarizer_test.cc +++ b/tensorflow/contrib/lite/profiling/profile_summarizer_test.cc @@ -31,6 +31,7 @@ namespace profiling { namespace { +#ifdef TFLITE_PROFILING_ENABLED TfLiteStatus SimpleOpEval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input1 = tflite::GetInput(context, node, /*index=*/0); const TfLiteTensor* input2 = tflite::GetInput(context, node, /*index=*/1); @@ -42,20 +43,35 @@ TfLiteStatus SimpleOpEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +const char* SimpleOpProfilingString(const TfLiteContext* context, + const TfLiteNode* node) { + return "Profile"; +} + TfLiteRegistration* RegisterSimpleOp() { + static TfLiteRegistration registration = { + nullptr, nullptr, nullptr, + SimpleOpEval, nullptr, tflite::BuiltinOperator_CUSTOM, + "SimpleOpEval", 1}; + return ®istration; +} + +TfLiteRegistration* RegisterSimpleOpWithProfilingDetails() { static TfLiteRegistration registration = {nullptr, nullptr, nullptr, SimpleOpEval, + SimpleOpProfilingString, tflite::BuiltinOperator_CUSTOM, "SimpleOpEval", 1}; return ®istration; } +#endif class SimpleOpModel : public SingleOpModel { public: - void Init(); + void Init(const std::function& registration); tflite::Interpreter* GetInterpreter() { return interpreter_.get(); } void SetInputs(int32_t x, int32_t y) { PopulateTensor(inputs_[0], {x}); @@ -68,11 +84,12 @@ class SimpleOpModel : public SingleOpModel { int output_; }; -void SimpleOpModel::Init() { +void SimpleOpModel::Init( + const std::function& registration) { inputs_[0] = AddInput({TensorType_INT32, {1}}); inputs_[1] = AddInput({TensorType_INT32, {1}}); output_ = AddOutput({TensorType_INT32, {}}); - SetCustomOp("SimpleAdd", {}, RegisterSimpleOp); + SetCustomOp("SimpleAdd", {}, registration); BuildInterpreter({GetShape(inputs_[0]), GetShape(inputs_[1])}); } @@ -86,7 +103,28 @@ TEST(ProfileSummarizerTest, Empty) { TEST(ProfileSummarizerTest, Interpreter) { Profiler profiler; SimpleOpModel m; - m.Init(); + m.Init(RegisterSimpleOp); + auto interpreter = m.GetInterpreter(); + interpreter->SetProfiler(&profiler); + profiler.StartProfiling(); + m.SetInputs(1, 2); + m.Invoke(); + // 3 = 1 + 2 + EXPECT_EQ(m.GetOutput(), 3); + profiler.StopProfiling(); + ProfileSummarizer summarizer; + auto events = profiler.GetProfileEvents(); + EXPECT_EQ(1, events.size()); + summarizer.ProcessProfiles(profiler.GetProfileEvents(), *interpreter); + auto output = summarizer.GetOutputString(); + // TODO(shashishekhar): Add a better test here. + ASSERT_TRUE(output.find("SimpleOpEval") != std::string::npos) << output; +} + +TEST(ProfileSummarizerTest, InterpreterPlusProfilingDetails) { + Profiler profiler; + SimpleOpModel m; + m.Init(RegisterSimpleOpWithProfilingDetails); auto interpreter = m.GetInterpreter(); interpreter->SetProfiler(&profiler); profiler.StartProfiling(); @@ -101,8 +139,10 @@ TEST(ProfileSummarizerTest, Interpreter) { summarizer.ProcessProfiles(profiler.GetProfileEvents(), *interpreter); auto output = summarizer.GetOutputString(); // TODO(shashishekhar): Add a better test here. - ASSERT_TRUE(output.find("SimpleOp") != std::string::npos) << output; + ASSERT_TRUE(output.find("SimpleOpEval:Profile") != std::string::npos) + << output; } + #endif } // namespace diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 7e6ff6c0a8314e71a64f27916a6189f229b81ab4..27909a9458f6b09f96cb556a5254f01e54f46e05 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -57,8 +57,9 @@ py_library( ":interpreter", ":lite_constants", ":op_hint", - "//tensorflow/contrib/saved_model:saved_model_py", "//tensorflow/python:graph_util", + "//tensorflow/python/saved_model:constants", + "//tensorflow/python/saved_model:loader", "//tensorflow/python/tools:freeze_graph_lib", ], ) diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py index 08f3f8bf32981f2ef0c66f0ce312b28e9d90b260..0ea2630f711727787332f207bdff6383aac8097c 100644 --- a/tensorflow/contrib/lite/python/convert.py +++ b/tensorflow/contrib/lite/python/convert.py @@ -25,7 +25,6 @@ import tempfile as _tempfile from tensorflow.contrib.lite.python import lite_constants from tensorflow.contrib.lite.toco import model_flags_pb2 as _model_flags_pb2 from tensorflow.contrib.lite.toco import toco_flags_pb2 as _toco_flags_pb2 -from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.platform import resource_loader as _resource_loader from tensorflow.python.util.lazy_loader import LazyLoader @@ -111,35 +110,35 @@ def tensor_name(x): return x.name.split(":")[0] -def toco_convert(input_data, - input_tensors, - output_tensors, - inference_type=lite_constants.FLOAT, - inference_input_type=None, - input_format=lite_constants.TENSORFLOW_GRAPHDEF, - output_format=lite_constants.TFLITE, - quantized_input_stats=None, - default_ranges_stats=None, - drop_control_dependency=True, - reorder_across_fake_quant=False, - allow_custom_ops=False, - change_concat_input_ranges=False, - quantize_weights=False): - """Convert a model using TOCO from `input_format` to `output_format`. +def build_toco_convert_protos(input_tensors, + output_tensors, + inference_type=lite_constants.FLOAT, + inference_input_type=None, + input_format=lite_constants.TENSORFLOW_GRAPHDEF, + output_format=lite_constants.TFLITE, + quantized_input_stats=None, + default_ranges_stats=None, + drop_control_dependency=True, + reorder_across_fake_quant=False, + allow_custom_ops=False, + change_concat_input_ranges=False, + quantize_weights=False, + dump_graphviz_dir=None, + dump_graphviz_video=False): + """Builds protocol buffers describing a conversion of a model using TOCO. Typically this is to convert from TensorFlow GraphDef to TFLite, in which case the default `input_format` and `output_format` are sufficient. Args: - input_data: Input data (i.e. often `sess.graph_def`). input_tensors: List of input tensors. Type and shape are computed using `foo.get_shape()` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). - inference_type: Target data type of arrays in the output file. Currently - must be `{FLOAT, QUANTIZED_UINT8}`. (default FLOAT) - inference_input_type: Target data type of input arrays. Allows for a - different type for input arrays in the case of quantization. Currently - must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) + inference_type: Target data type of real-number arrays in the output file. + Must be `{FLOAT, QUANTIZED_UINT8}`. (default FLOAT) + inference_input_type: Target data type of real-number input arrays. Allows + for a different type for input arrays in the case of quantization. + Must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) input_format: Type of data to read Currently must be `{TENSORFLOW_GRAPHDEF}`. (default TENSORFLOW_GRAPHDEF) output_format: Output file format. Currently must be `{TFLITE, @@ -170,10 +169,16 @@ def toco_convert(input_data, weights followed by dequantize operations. Computation is still done in float, but reduces model size (at the cost of accuracy and latency). (default False) + dump_graphviz_dir: Full filepath of folder to dump the graphs at various + stages of processing GraphViz .dot files. Preferred over + --output_format=GRAPHVIZ_DOT in order to keep the requirements of the + output file. (default None) + dump_graphviz_video: Boolean indicating whether to dump the graph after + every graph transformation. (default False) Returns: - The converted data. For example if TFLite was the destination, then - this will be a tflite flatbuffer in a bytes array. + model_flags, toco_flags: two protocol buffers describing the conversion + process. Raises: ValueError: If the input tensor type is unknown @@ -193,39 +198,50 @@ def toco_convert(input_data, if default_ranges_stats: toco.default_ranges_min = default_ranges_stats[0] toco.default_ranges_max = default_ranges_stats[1] + if dump_graphviz_dir: + toco.dump_graphviz_dir = dump_graphviz_dir + toco.dump_graphviz_include_video = dump_graphviz_video model = _model_flags_pb2.ModelFlags() model.change_concat_input_ranges = change_concat_input_ranges for idx, input_tensor in enumerate(input_tensors): - if input_tensor.dtype == _dtypes.float32: - tflite_input_type = lite_constants.FLOAT - elif input_tensor.dtype == _dtypes.int32: - tflite_input_type = lite_constants.INT32 - elif input_tensor.dtype == _dtypes.int64: - tflite_input_type = lite_constants.INT64 - elif input_tensor.dtype == _dtypes.uint8: - tflite_input_type = lite_constants.QUANTIZED_UINT8 - # TODO(aselle): Insert strings when they are available - else: - raise ValueError("Tensors %s not known type %r" % (input_tensor.name, - input_tensor.dtype)) - input_array = model.input_arrays.add() - if inference_type == lite_constants.QUANTIZED_UINT8: - if tflite_input_type == lite_constants.FLOAT: - tflite_input_type = lite_constants.QUANTIZED_UINT8 input_array.mean_value, input_array.std_value = quantized_input_stats[idx] - input_array.name = tensor_name(input_tensor) input_array.shape.dims.extend(map(int, input_tensor.get_shape())) for output_tensor in output_tensors: model.output_arrays.append(tensor_name(output_tensor)) + return model, toco + + +def toco_convert(input_data, input_tensors, output_tensors, *args, **kwargs): + """"Convert a model using TOCO. + + Typically this function is used to convert from TensorFlow GraphDef to TFLite. + Conversion can be customized by providing arguments that are forwarded to + `build_toco_convert_protos` (see documentation for details). + + Args: + input_data: Input data (i.e. often `sess.graph_def`), + input_tensors: List of input tensors. Type and shape are computed using + `foo.get_shape()` and `foo.dtype`. + output_tensors: List of output tensors (only .name is used from this). + *args: See `build_toco_convert_protos`, + **kwargs: See `build_toco_convert_protos`. + + Returns: + The converted data. For example if TFLite was the destination, then + this will be a tflite flatbuffer in a bytes array. - # TODO(aselle): Consider handling the case of allowing quantized - # inputs to be converted to float (via the toco.inference_input_type field). - data = toco_convert_protos(model.SerializeToString(), - toco.SerializeToString(), + Raises: + Defined in `build_toco_convert_protos`. + """ + model_flags, toco_flags = build_toco_convert_protos(input_tensors, + output_tensors, + *args, **kwargs) + data = toco_convert_protos(model_flags.SerializeToString(), + toco_flags.SerializeToString(), input_data.SerializeToString()) return data diff --git a/tensorflow/contrib/lite/python/convert_saved_model.py b/tensorflow/contrib/lite/python/convert_saved_model.py index 5dad49f1ed29f3bd57b1b120808ef645adee760c..1553464b9fe30f596c151bcc67efe891bb913ba3 100644 --- a/tensorflow/contrib/lite/python/convert_saved_model.py +++ b/tensorflow/contrib/lite/python/convert_saved_model.py @@ -19,13 +19,12 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.lite.python.convert import tensor_name -from tensorflow.contrib.saved_model.python.saved_model import reader -from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils from tensorflow.core.framework import types_pb2 from tensorflow.python.client import session from tensorflow.python.framework import graph_util as tf_graph_util from tensorflow.python.framework import ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import loader @@ -58,21 +57,8 @@ def _get_meta_graph_def(saved_model_dir, tag_set): Raises: ValueError: No valid MetaGraphDef for given tag_set. """ - saved_model = reader.read_saved_model(saved_model_dir) - tag_sets = [] - result_meta_graph_def = None - for meta_graph_def in saved_model.meta_graphs: - meta_graph_tag_set = set(meta_graph_def.meta_info_def.tags) - tag_sets.append(meta_graph_tag_set) - if meta_graph_tag_set == tag_set: - result_meta_graph_def = meta_graph_def - logging.info("The given saved_model contains the following tags: %s", - tag_sets) - if result_meta_graph_def is not None: - return result_meta_graph_def - else: - raise ValueError("No valid MetaGraphDef for this tag_set '{}'. Possible " - "values are '{}'. ".format(tag_set, tag_sets)) + with session.Session(graph=ops.Graph()) as sess: + return loader.load(sess, tag_set, saved_model_dir) def _get_signature_def(meta_graph, signature_key): @@ -97,9 +83,7 @@ def _get_signature_def(meta_graph, signature_key): raise ValueError("No '{}' in the SavedModel\'s SignatureDefs. Possible " "values are '{}'.".format(signature_key, ",".join(signature_def_keys))) - signature_def = signature_def_utils.get_signature_def_by_key( - meta_graph, signature_key) - return signature_def + return signature_def_map[signature_key] def _get_inputs_outputs(signature_def): @@ -247,6 +231,7 @@ def freeze_saved_model(saved_model_dir, input_arrays, input_shapes, ValueError: SavedModel doesn't contain a MetaGraphDef identified by tag_set. signature_key is not in the MetaGraphDef. + assets/ directory is in the MetaGraphDef. input_shapes does not match the length of input_arrays. input_arrays or output_arrays are not valid. """ @@ -255,9 +240,13 @@ def freeze_saved_model(saved_model_dir, input_arrays, input_shapes, signature_def = _get_signature_def(meta_graph, signature_key) inputs, outputs = _get_inputs_outputs(signature_def) + # Check SavedModel for assets directory. + collection_def = meta_graph.collection_def + if constants.ASSETS_KEY in collection_def: + raise ValueError("SavedModels with assets/ directory are not supported.") + graph = ops.Graph() with session.Session(graph=graph) as sess: - # TODO(nupurgarg): Throw ValueError if SavedModel has assets/ directory. loader.load(sess, meta_graph.meta_info_def.tags, saved_model_dir) # Gets input and output tensors. diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 779bda4c9d05fd056d6a262412fdcf0d47e7c57c..fd908234254185e0a0639618e936ca8ff58631da 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys from tensorflow.python.util.lazy_loader import LazyLoader # Lazy load since some of the performance benchmark skylark rules @@ -64,9 +65,38 @@ class Interpreter(object): raise ValueError('Can\'t both provide `model_path` and `model_content`') def allocate_tensors(self): + self._ensure_safe() if not self._interpreter.AllocateTensors(): raise ValueError('Failed to allocate tensors') + def _safe_to_run(self): + """Returns true if there exist no numpy array buffers. + + This means it is safe to run tflite calls that may destroy internally + allocated memory. This works, because in the wrapper.cc we have made + the numpy base be the self._interpreter. + """ + # NOTE, our tensor() call in cpp will use _interpreter as a base pointer. + # If this environment is the only _interpreter, then the ref count should be + # 2 (1 in self and 1 in temporary of sys.getrefcount). + return sys.getrefcount(self._interpreter) == 2 + + def _ensure_safe(self): + """Makes sure no numpy arrays pointing to internal buffers are active. + + This should be called from any function that will call a function on + _interpreter that may reallocate memory e.g. invoke(), ... + + Raises: + RuntimeError: If there exist numpy objects pointing to internal memory + then we throw. + """ + if not self._safe_to_run(): + raise RuntimeError("""There is at least 1 reference to internal data + in the interpreter in the form of a numpy array or slice. Be sure to + only hold the function returned from tensor() if you are using raw + data access.""") + def _get_tensor_details(self, tensor_index): """Gets tensor details. @@ -109,7 +139,10 @@ class Interpreter(object): ] def set_tensor(self, tensor_index, value): - """Sets the value of the input. + """Sets the value of the input tensor. Note this copies data in `value`. + + If you want to avoid copying, you can use the `tensor()` function to get a + numpy buffer pointing to the input buffer in the tflite interpreter. Args: tensor_index: Tensor index of tensor to set. This value can be gotten from @@ -133,6 +166,7 @@ class Interpreter(object): Raises: ValueError: If the interpreter could not resize the input tensor. """ + self._ensure_safe() if not self._interpreter.ResizeInputTensor(input_index, tensor_size): raise ValueError('Failed to resize input') @@ -147,7 +181,7 @@ class Interpreter(object): ] def get_tensor(self, tensor_index): - """Sets the value of the input. + """Gets the value of the input tensor. Note this makes a copy so prefer `tensor()`. Args: tensor_index: Tensor index of tensor to get. This value can be gotten from @@ -158,6 +192,60 @@ class Interpreter(object): """ return self._interpreter.GetTensor(tensor_index) + def tensor(self, tensor_index): + """Returns function that gives a numpy view of the current tensor buffer. + + This allows reading and writing to this tensors w/o copies. This more + closely mirrors the C++ Interpreter class interface's tensor() member, hence + the name. Be careful to not hold these output references through calls + to `allocate_tensors()` and `invoke()`. + + Usage: + + interpreter.allocate_tensors() + input = interpreter.tensor(interpreter.get_input_details()[0]["index"]) + output = interpreter.tensor(interpreter.get_output_details()[0]["index"]) + for i in range(10): + input().fill(3.) + interpreter.invoke() + print("inference %s" % output) + + Notice how this function avoids making a numpy array directly. This is + because it is important to not hold actual numpy views to the data longer + than necessary. If you do, then the interpreter can no longer be invoked, + because it is possible the interpreter would resize and invalidate the + referenced tensors. The NumPy API doesn't allow any mutability of the + the underlying buffers. + + WRONG: + + input = interpreter.tensor(interpreter.get_input_details()[0]["index"])() + output = interpreter.tensor(interpreter.get_output_details()[0]["index"])() + interpreter.allocate_tensors() # This will throw RuntimeError + for i in range(10): + input.fill(3.) + interpreter.invoke() # this will throw RuntimeError since input,output + + Args: + tensor_index: Tensor index of tensor to get. This value can be gotten from + the 'index' field in get_output_details. + + Returns: + A function that can return a new numpy array pointing to the internal + TFLite tensor state at any point. It is safe to hold the function forever, + but it is not safe to hold the numpy array forever. + """ + return lambda: self._interpreter.tensor(self._interpreter, tensor_index) + def invoke(self): + """Invoke the interpreter. + + Be sure to set the input sizes, allocate tensors and fill values before + calling this. + + Raises: + ValueError: When the underlying interpreter fails raise ValueError. + """ + self._ensure_safe() if not self._interpreter.Invoke(): raise ValueError('Failed to invoke TFLite model') diff --git a/tensorflow/contrib/lite/python/interpreter_test.py b/tensorflow/contrib/lite/python/interpreter_test.py index f802edf020db8a9d4e7bb890aadaae7e34e983a8..5f1fa26c3b7f76309a6f1f80aa3c1e4889781528 100644 --- a/tensorflow/contrib/lite/python/interpreter_test.py +++ b/tensorflow/contrib/lite/python/interpreter_test.py @@ -91,5 +91,61 @@ class InterpreterTest(test_util.TensorFlowTestCase): self.assertTrue((expected_output == output_data).all()) +class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase): + + def setUp(self): + self.interpreter = interpreter_wrapper.Interpreter( + model_path=resource_loader.get_path_to_datafile( + 'testdata/permute_float.tflite')) + self.interpreter.allocate_tensors() + self.input0 = self.interpreter.get_input_details()[0]['index'] + self.initial_data = np.array([[-1., -2., -3., -4.]], np.float32) + + def testTensorAccessor(self): + """Check that tensor returns a reference.""" + array_ref = self.interpreter.tensor(self.input0) + np.copyto(array_ref(), self.initial_data) + self.assertAllEqual(array_ref(), self.initial_data) + self.assertAllEqual( + self.interpreter.get_tensor(self.input0), self.initial_data) + + def testGetTensorAccessor(self): + """Check that get_tensor returns a copy.""" + self.interpreter.set_tensor(self.input0, self.initial_data) + array_initial_copy = self.interpreter.get_tensor(self.input0) + new_value = np.add(1., array_initial_copy) + self.interpreter.set_tensor(self.input0, new_value) + self.assertAllEqual(array_initial_copy, self.initial_data) + self.assertAllEqual(self.interpreter.get_tensor(self.input0), new_value) + + def testBase(self): + self.assertTrue(self.interpreter._safe_to_run()) + _ = self.interpreter.tensor(self.input0) + self.assertTrue(self.interpreter._safe_to_run()) + in0 = self.interpreter.tensor(self.input0)() + self.assertFalse(self.interpreter._safe_to_run()) + in0b = self.interpreter.tensor(self.input0)() + self.assertFalse(self.interpreter._safe_to_run()) + # Now get rid of the buffers so that we can evaluate. + del in0 + del in0b + self.assertTrue(self.interpreter._safe_to_run()) + + def testBaseProtectsFunctions(self): + in0 = self.interpreter.tensor(self.input0)() + # Make sure we get an exception if we try to run an unsafe operation + with self.assertRaisesRegexp( + RuntimeError, 'There is at least 1 reference'): + _ = self.interpreter.allocate_tensors() + # Make sure we get an exception if we try to run an unsafe operation + with self.assertRaisesRegexp( + RuntimeError, 'There is at least 1 reference'): + _ = self.interpreter.invoke() + # Now test that we can run + del in0 # this is our only buffer reference, so now it is safe to change + in0safe = self.interpreter.tensor(self.input0) + _ = self.interpreter.allocate_tensors() + del in0safe # make sure in0Safe is held but lint doesn't complain + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD index 12ab38847dc0f838ae2c6bf80ed80805285e4b8b..634c2a1e1f5005208b4eea5c853a43cccf4d244c 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD @@ -14,7 +14,7 @@ cc_library( "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:builtin_ops", "//tensorflow/core:lib", - "//tensorflow/python:numpy_lib", + "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", "@com_google_absl//absl/memory", ], diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index 5f304ad45d400b13e20bda8184b5b40cfe13f6c2..5554d08fa08fdc6ddcb042d12f979164a144e337 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -21,7 +21,14 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/core/platform/logging.h" -#include "tensorflow/python/lib/core/numpy.h" + +// Disallow Numpy 1.7 deprecated symbols. +#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + +#include + +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" #if PY_MAJOR_VERSION >= 3 #define PY_TO_CPPSTRING PyBytes_AsStringAndSize @@ -35,6 +42,13 @@ namespace tflite { namespace interpreter_wrapper { namespace { + +// Calls PyArray's initialization to initialize all the API pointers. Note that +// this usage implies only this translation unit can use the pointers. See +// tensorflow/python/core/numpy.cc for a strategy if we ever need to extend +// this further. +void ImportNumpy() { import_array1(); } + std::unique_ptr CreateInterpreter( const tflite::FlatBufferModel* model, const tflite::ops::builtin::BuiltinOpResolver& resolver) { @@ -42,7 +56,7 @@ std::unique_ptr CreateInterpreter( return nullptr; } - tensorflow::ImportNumpy(); + ImportNumpy(); std::unique_ptr interpreter; tflite::InterpreterBuilder(*model, resolver)(&interpreter); @@ -68,6 +82,8 @@ int TfLiteTypeToPyArrayType(TfLiteType tf_lite_type) { return NPY_FLOAT32; case kTfLiteInt32: return NPY_INT32; + case kTfLiteInt16: + return NPY_INT16; case kTfLiteUInt8: return NPY_UINT8; case kTfLiteInt64: @@ -76,6 +92,8 @@ int TfLiteTypeToPyArrayType(TfLiteType tf_lite_type) { return NPY_OBJECT; case kTfLiteBool: return NPY_BOOL; + case kTfLiteComplex64: + return NPY_COMPLEX64; case kTfLiteNoType: return -1; } @@ -90,6 +108,8 @@ TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { return kTfLiteFloat32; case NPY_INT32: return kTfLiteInt32; + case NPY_INT16: + return kTfLiteInt16; case NPY_UINT8: return kTfLiteUInt8; case NPY_INT64: @@ -100,6 +120,8 @@ TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { case NPY_STRING: case NPY_UNICODE: return kTfLiteString; + case NPY_COMPLEX64: + return kTfLiteComplex64; } LOG(ERROR) << "Unknown PyArray dtype " << pyarray_type; return kTfLiteNoType; @@ -284,47 +306,93 @@ bool InterpreterWrapper::SetTensor(int i, PyObject* value) { return true; } -PyObject* InterpreterWrapper::GetTensor(int i) const { - if (!interpreter_) { +namespace { + +PyObject* CheckGetTensorArgs(Interpreter* interpreter, int tensor_index, + TfLiteTensor** tensor, int* type_num) { + if (!interpreter) { LOG(ERROR) << "Invalid interpreter."; Py_INCREF(Py_None); return Py_None; } - if (i >= interpreter_->tensors_size()) { - LOG(ERROR) << "Invalid tensor index: " << i << " exceeds max tensor index " - << interpreter_->inputs().size(); + if (tensor_index >= interpreter->tensors_size() || tensor_index < 0) { + LOG(ERROR) << "Invalid tensor index: " << tensor_index + << " exceeds max tensor index " << interpreter->inputs().size(); Py_INCREF(Py_None); return Py_None; } - const TfLiteTensor* output_tensor = interpreter_->tensor(i); - const int tensor_size = output_tensor->bytes; - if (tensor_size <= 0) { + *tensor = interpreter->tensor(tensor_index); + if ((*tensor)->bytes == 0) { LOG(ERROR) << "Invalid tensor size"; Py_INCREF(Py_None); return Py_None; } - int type_num = TfLiteTypeToPyArrayType(output_tensor->type); - if (type_num == -1) { - LOG(ERROR) << "Unknown tensor type " << output_tensor->type; + *type_num = TfLiteTypeToPyArrayType((*tensor)->type); + if (*type_num == -1) { + LOG(ERROR) << "Unknown tensor type " << (*tensor)->type; + Py_INCREF(Py_None); + return Py_None; + } + + if (!(*tensor)->data.raw) { + LOG(ERROR) << "Tensor data is null."; Py_INCREF(Py_None); return Py_None; } - void* data = malloc(tensor_size); - memcpy(data, output_tensor->data.raw, tensor_size); + return nullptr; +} + +} // namespace - const TfLiteIntArray* output_dims = output_tensor->dims; - std::vector dims(output_dims->data, - output_dims->data + output_dims->size); +PyObject* InterpreterWrapper::GetTensor(int i) const { + // Sanity check accessor + TfLiteTensor* tensor = nullptr; + int type_num = 0; + if (PyObject* pynone_or_nullptr = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { + return pynone_or_nullptr; + } + std::vector dims(tensor->dims->data, + tensor->dims->data + tensor->dims->size); + // Make a buffer copy but we must tell Numpy It owns that data or else + // it will leak. + void* data = malloc(tensor->bytes); + if (!data) { + LOG(ERROR) << "Malloc to copy tensor failed."; + Py_INCREF(Py_None); + return Py_None; + } + memcpy(data, tensor->data.raw, tensor->bytes); PyObject* np_array = PyArray_SimpleNewFromData(dims.size(), dims.data(), type_num, data); - + PyArray_ENABLEFLAGS(reinterpret_cast(np_array), + NPY_ARRAY_OWNDATA); return PyArray_Return(reinterpret_cast(np_array)); } +PyObject* InterpreterWrapper::tensor(PyObject* base_object, int i) { + // Sanity check accessor + TfLiteTensor* tensor = nullptr; + int type_num = 0; + if (PyObject* pynone_or_nullptr = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { + return pynone_or_nullptr; + } + + std::vector dims(tensor->dims->data, + tensor->dims->data + tensor->dims->size); + PyArrayObject* np_array = + reinterpret_cast(PyArray_SimpleNewFromData( + dims.size(), dims.data(), type_num, tensor->data.raw)); + Py_INCREF(base_object); // SetBaseObject steals, so we need to add. + PyArray_SetBaseObject(np_array, base_object); + return PyArray_Return(np_array); +} + InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( const char* model_path) { std::unique_ptr model = diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 01320af7a9ea3a652020e2b42300da6081ff68e5..681448be20cfc013a0c4d02a6aa549744b976077 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -19,7 +19,9 @@ limitations under the License. #include #include +// Place `` before to avoid build failures in macOS. #include +#include // We forward declare TFLite classes here to avoid exposing them to SWIG. namespace tflite { @@ -56,6 +58,9 @@ class InterpreterWrapper { PyObject* TensorQuantization(int i) const; bool SetTensor(int i, PyObject* value); PyObject* GetTensor(int i) const; + // Returns a reference to tensor index i as a numpy array. The base_object + // should be the interpreter object providing the memory. + PyObject* tensor(PyObject* base_object, int i); private: InterpreterWrapper(std::unique_ptr model); diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 477faabdb90a6652495400f23a7d98fdc8aa5169..29a1487c1f468055dde85ef6c2657a50f3d2f32b 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -22,6 +22,7 @@ EXPERIMENTAL: APIs here are unstable and likely to change without notice. @@Interpreter @@OpHint @@convert_op_hints_to_stubs +@@build_toco_convert_protos @@FLOAT @@QUANTIZED_UINT8 @@ -38,6 +39,7 @@ from six import PY3 from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants +from tensorflow.contrib.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import from tensorflow.contrib.lite.python.convert import tensor_name from tensorflow.contrib.lite.python.convert import toco_convert from tensorflow.contrib.lite.python.convert import toco_convert_protos # pylint: disable=unused-import @@ -48,12 +50,14 @@ from tensorflow.contrib.lite.python.interpreter import Interpreter # pylint: di from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import from tensorflow.contrib.lite.python.op_hint import OpHint # pylint: disable=unused-import from tensorflow.core.framework import graph_pb2 as _graph_pb2 +from tensorflow.python import keras as _keras from tensorflow.python.client import session as _session from tensorflow.python.framework import graph_util as tf_graph_util from tensorflow.python.framework.importer import import_graph_def from tensorflow.python.ops.variables import global_variables_initializer from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants +# from tensorflow.python.util.all_util import remove_undocumented class TocoConverter(object): @@ -64,11 +68,11 @@ class TocoConverter(object): Attributes: - inference_type: Target data type of arrays in the output file. Currently - must be `{FLOAT, QUANTIZED_UINT8}`. (default FLOAT) - inference_input_type: Target data type of input arrays. Allows for a - different type for input arrays in the case of quantization. Currently - must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) + inference_type: Target data type of real-number arrays in the output file. + Must be `{FLOAT, QUANTIZED_UINT8}`. (default FLOAT) + inference_input_type: Target data type of real-number input arrays. Allows + for a different type for input arrays in the case of quantization. + Must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: Dict of strings representing input tensor names @@ -98,6 +102,12 @@ class TocoConverter(object): weights followed by dequantize operations. Computation is still done in float, but reduces model size (at the cost of accuracy and latency). (default False) + dump_graphviz_dir: Full filepath of folder to dump the graphs at various + stages of processing GraphViz .dot files. Preferred over + --output_format=GRAPHVIZ_DOT in order to keep the requirements of the + output file. (default None) + dump_graphviz_video: Boolean indicating whether to dump the graph after + every graph transformation. (default False) Example usage: @@ -122,7 +132,7 @@ class TocoConverter(object): Args: - graph_def: TensorFlow GraphDef. + graph_def: Frozen TensorFlow GraphDef. input_tensors: List of input tensors. Type and shape are computed using `foo.get_shape()` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). @@ -140,6 +150,8 @@ class TocoConverter(object): self.change_concat_input_ranges = False self.allow_custom_ops = False self.quantize_weights = False + self.dump_graphviz_dir = None + self.dump_graphviz_video = False @classmethod def from_session(cls, sess, input_tensors, output_tensors): @@ -166,7 +178,7 @@ class TocoConverter(object): """Creates a TocoConverter class from a file containing a frozen GraphDef. Args: - graph_def_file: Full filepath of file containing TensorFlow GraphDef. + graph_def_file: Full filepath of file containing frozen GraphDef. input_arrays: List of input tensors to freeze graph with. output_arrays: List of output tensors to freeze graph with. input_shapes: Dict of strings representing input tensor names to list of @@ -215,7 +227,7 @@ class TocoConverter(object): # Check if graph is frozen. if not _is_frozen_graph(sess): - raise ValueError("Please freeze the graph using freeze_graph.py") + raise ValueError("Please freeze the graph using freeze_graph.py.") # Create TocoConverter class. return cls(sess.graph_def, input_tensors, output_tensors) @@ -258,6 +270,48 @@ class TocoConverter(object): return cls( graph_def=result[0], input_tensors=result[1], output_tensors=result[2]) + @classmethod + def from_keras_model_file(cls, + model_file, + input_arrays=None, + input_shapes=None, + output_arrays=None): + """Creates a TocoConverter class from a tf.keras model file. + + Args: + model_file: Full filepath of HDF5 file containing the tf.keras model. + input_arrays: List of input tensors to freeze graph with. Uses input + arrays from SignatureDef when none are provided. (default None) + input_shapes: Dict of strings representing input tensor names to list of + integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). + Automatically determined when input shapes is None (e.g., {"foo" : + None}). (default None) + output_arrays: List of output tensors to freeze graph with. Uses output + arrays from SignatureDef when none are provided. (default None) + + Returns: + TocoConverter class. + """ + _keras.backend.clear_session() + _keras.backend.set_learning_phase(False) + keras_model = _keras.models.load_model(model_file) + sess = _keras.backend.get_session() + + # Get input and output tensors. + if input_arrays: + input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays) + else: + input_tensors = keras_model.inputs + + if output_arrays: + output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays) + else: + output_tensors = keras_model.outputs + set_tensor_shapes(input_tensors, input_shapes) + + graph_def = _freeze_graph(sess, output_tensors) + return cls(graph_def, input_tensors, output_tensors) + def convert(self): """Converts a TensorFlow GraphDef based on instance variables. @@ -316,7 +370,9 @@ class TocoConverter(object): reorder_across_fake_quant=self.reorder_across_fake_quant, change_concat_input_ranges=self.change_concat_input_ranges, allow_custom_ops=self.allow_custom_ops, - quantize_weights=self.quantize_weights) + quantize_weights=self.quantize_weights, + dump_graphviz_dir=self.dump_graphviz_dir, + dump_graphviz_video=self.dump_graphviz_video) return result def get_input_arrays(self): @@ -353,7 +409,7 @@ def _is_frozen_graph(sess): Bool. """ for op in sess.graph.get_operations(): - if op.type.startswith("Variable"): + if op.type.startswith("Variable") or op.type.endswith("VariableOp"): return False return True @@ -378,3 +434,5 @@ def _freeze_graph(sess, output_tensors): output_arrays) else: return sess.graph_def + +# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py index bbb00021f95b311f28124ef1bb1eb463b4985d80..ca2af5aaed3ee4f4fce5f0d31eaa61df0e11f364 100644 --- a/tensorflow/contrib/lite/python/lite_test.py +++ b/tensorflow/contrib/lite/python/lite_test.py @@ -19,11 +19,13 @@ from __future__ import division from __future__ import print_function import os +import tempfile import numpy as np from tensorflow.contrib.lite.python import lite from tensorflow.contrib.lite.python import lite_constants from tensorflow.contrib.lite.python.interpreter import Interpreter +from tensorflow.python import keras from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -220,6 +222,7 @@ class FromSessionTest(test_util.TensorFlowTestCase): self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) self.assertEqual((0., 0.), output_details[0]['quantization']) + # TODO(nupurgarg): Verify value of contents in GraphViz. def testGraphviz(self): in_tensor = array_ops.placeholder( shape=[1, 16, 16, 3], dtype=dtypes.float32) @@ -232,8 +235,42 @@ class FromSessionTest(test_util.TensorFlowTestCase): graphviz_output = converter.convert() self.assertTrue(graphviz_output) + # TODO(nupurgarg): Verify value of contents in GraphViz. + def testDumpGraphviz(self): + in_tensor = array_ops.placeholder( + shape=[1, 16, 16, 3], dtype=dtypes.float32) + out_tensor = in_tensor + in_tensor + sess = session.Session() + + # Convert model and ensure model is not None. + converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor]) + graphviz_dir = self.get_temp_dir() + converter.dump_graphviz_dir = graphviz_dir + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + # Ensure interpreter is able to allocate and check graphviz data. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + num_items_graphviz = len(os.listdir(graphviz_dir)) + self.assertTrue(num_items_graphviz) + + # Convert model and ensure model is not None. + converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor]) + graphviz_dir = self.get_temp_dir() + converter.dump_graphviz_dir = graphviz_dir + converter.dump_graphviz_video = True + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + # Ensure graphviz folder has more data after using video flag. + num_items_graphviz_video = len(os.listdir(graphviz_dir)) + self.assertTrue(num_items_graphviz_video > num_items_graphviz) + def testInferenceInputType(self): - in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3], dtype=dtypes.uint8) + in_tensor = array_ops.placeholder( + shape=[1, 16, 16, 3], dtype=dtypes.float32) out_tensor = in_tensor + in_tensor sess = session.Session() @@ -252,14 +289,13 @@ class FromSessionTest(test_util.TensorFlowTestCase): self.assertEqual('Placeholder', input_details[0]['name']) self.assertEqual(np.uint8, input_details[0]['dtype']) self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all()) - self.assertEqual((0., 0.), input_details[0]['quantization']) + self.assertEqual((1., 0.), input_details[0]['quantization']) output_details = interpreter.get_output_details() self.assertEqual(1, len(output_details)) self.assertEqual('add', output_details[0]['name']) - self.assertEqual(np.uint8, output_details[0]['dtype']) + self.assertEqual(np.float32, output_details[0]['dtype']) self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) - self.assertEqual((0., 0.), input_details[0]['quantization']) def testDefaultRangesStats(self): in_tensor = array_ops.placeholder( @@ -401,7 +437,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): with self.assertRaises(ValueError) as error: lite.TocoConverter.from_frozen_graph(graph_def_file, ['Placeholder'], ['add']) - self.assertEqual('Please freeze the graph using freeze_graph.py', + self.assertEqual('Please freeze the graph using freeze_graph.py.', str(error.exception)) def testPbtxt(self): @@ -584,5 +620,279 @@ class FromSavedModelTest(test_util.TensorFlowTestCase): self.assertTrue(tflite_model) +class FromKerasFile(test_util.TensorFlowTestCase): + + def setUp(self): + keras.backend.clear_session() + + def _getSequentialModel(self): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(), + metrics=[keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + model.predict(x) + + try: + fd, keras_file = tempfile.mkstemp('.h5') + keras.models.save_model(model, keras_file) + finally: + os.close(fd) + return keras_file + + def testSequentialModel(self): + """Test a Sequential tf.keras model with default inputs.""" + keras_file = self._getSequentialModel() + + converter = lite.TocoConverter.from_keras_model_file(keras_file) + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + os.remove(keras_file) + + # Check values from converted model. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('dense_input', input_details[0]['name']) + self.assertEqual(np.float32, input_details[0]['dtype']) + self.assertTrue(([1, 3] == input_details[0]['shape']).all()) + self.assertEqual((0., 0.), input_details[0]['quantization']) + + output_details = interpreter.get_output_details() + self.assertEqual(1, len(output_details)) + self.assertEqual('time_distributed/Reshape_1', output_details[0]['name']) + self.assertEqual(np.float32, output_details[0]['dtype']) + self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all()) + self.assertEqual((0., 0.), output_details[0]['quantization']) + + def testSequentialModelInputArray(self): + """Test a Sequential tf.keras model testing input arrays argument.""" + keras_file = self._getSequentialModel() + + # Invalid input array raises error. + with self.assertRaises(ValueError) as error: + lite.TocoConverter.from_keras_model_file( + keras_file, input_arrays=['invalid-input']) + self.assertEqual("Invalid tensors 'invalid-input' were found.", + str(error.exception)) + + # Valid input array. + converter = lite.TocoConverter.from_keras_model_file( + keras_file, input_arrays=['dense_input']) + tflite_model = converter.convert() + os.remove(keras_file) + self.assertTrue(tflite_model) + + def testSequentialModelInputShape(self): + """Test a Sequential tf.keras model testing input shapes argument.""" + keras_file = self._getSequentialModel() + + # Passing in shape of invalid input array has no impact as long as all input + # arrays have a shape. + converter = lite.TocoConverter.from_keras_model_file( + keras_file, input_shapes={'invalid-input': [2, 3]}) + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + # Passing in shape of valid input array. + converter = lite.TocoConverter.from_keras_model_file( + keras_file, input_shapes={'dense_input': [2, 3]}) + tflite_model = converter.convert() + os.remove(keras_file) + self.assertTrue(tflite_model) + + # Check input shape from converted model. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('dense_input', input_details[0]['name']) + self.assertTrue(([2, 3] == input_details[0]['shape']).all()) + + def testSequentialModelOutputArray(self): + """Test a Sequential tf.keras model testing output arrays argument.""" + keras_file = self._getSequentialModel() + + # Invalid output array raises error. + with self.assertRaises(ValueError) as error: + lite.TocoConverter.from_keras_model_file( + keras_file, output_arrays=['invalid-output']) + self.assertEqual("Invalid tensors 'invalid-output' were found.", + str(error.exception)) + + # Valid output array. + converter = lite.TocoConverter.from_keras_model_file( + keras_file, output_arrays=['time_distributed/Reshape_1']) + tflite_model = converter.convert() + os.remove(keras_file) + self.assertTrue(tflite_model) + + def testFunctionalModel(self): + """Test a Functional tf.keras model with default inputs.""" + inputs = keras.layers.Input(shape=(3,), name='input') + x = keras.layers.Dense(2)(inputs) + output = keras.layers.Dense(3)(x) + + model = keras.models.Model(inputs, output) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(), + metrics=[keras.metrics.categorical_accuracy]) + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + model.predict(x) + fd, keras_file = tempfile.mkstemp('.h5') + keras.models.save_model(model, keras_file) + + # Convert to TFLite model. + converter = lite.TocoConverter.from_keras_model_file(keras_file) + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + os.close(fd) + os.remove(keras_file) + + # Check values from converted model. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('input', input_details[0]['name']) + self.assertEqual(np.float32, input_details[0]['dtype']) + self.assertTrue(([1, 3] == input_details[0]['shape']).all()) + self.assertEqual((0., 0.), input_details[0]['quantization']) + + output_details = interpreter.get_output_details() + self.assertEqual(1, len(output_details)) + self.assertEqual('dense_1/BiasAdd', output_details[0]['name']) + self.assertEqual(np.float32, output_details[0]['dtype']) + self.assertTrue(([1, 3] == output_details[0]['shape']).all()) + self.assertEqual((0., 0.), output_details[0]['quantization']) + + def testFunctionalModelMultipleInputs(self): + """Test a Functional tf.keras model with multiple inputs and outputs.""" + a = keras.layers.Input(shape=(3,), name='input_a') + b = keras.layers.Input(shape=(3,), name='input_b') + dense = keras.layers.Dense(4, name='dense') + c = dense(a) + d = dense(b) + e = keras.layers.Dropout(0.5, name='dropout')(c) + + model = keras.models.Model([a, b], [d, e]) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(), + metrics=[keras.metrics.mae], + loss_weights=[1., 0.5]) + + input_a_np = np.random.random((10, 3)) + input_b_np = np.random.random((10, 3)) + output_d_np = np.random.random((10, 4)) + output_e_np = np.random.random((10, 4)) + model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np]) + + model.predict([input_a_np, input_b_np], batch_size=5) + fd, keras_file = tempfile.mkstemp('.h5') + keras.models.save_model(model, keras_file) + + # Convert to TFLite model. + converter = lite.TocoConverter.from_keras_model_file(keras_file) + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + os.close(fd) + os.remove(keras_file) + + # Check values from converted model. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(2, len(input_details)) + self.assertEqual('input_a', input_details[0]['name']) + self.assertEqual(np.float32, input_details[0]['dtype']) + self.assertTrue(([1, 3] == input_details[0]['shape']).all()) + self.assertEqual((0., 0.), input_details[0]['quantization']) + + self.assertEqual('input_b', input_details[1]['name']) + self.assertEqual(np.float32, input_details[1]['dtype']) + self.assertTrue(([1, 3] == input_details[1]['shape']).all()) + self.assertEqual((0., 0.), input_details[1]['quantization']) + + output_details = interpreter.get_output_details() + self.assertEqual(2, len(output_details)) + self.assertEqual('dense_1/BiasAdd', output_details[0]['name']) + self.assertEqual(np.float32, output_details[0]['dtype']) + self.assertTrue(([1, 4] == output_details[0]['shape']).all()) + self.assertEqual((0., 0.), output_details[0]['quantization']) + + self.assertEqual('dropout/Identity', output_details[1]['name']) + self.assertEqual(np.float32, output_details[1]['dtype']) + self.assertTrue(([1, 4] == output_details[1]['shape']).all()) + self.assertEqual((0., 0.), output_details[1]['quantization']) + + def testFunctionalSequentialModel(self): + """Test a Functional tf.keras model containing a Sequential model.""" + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + model = keras.models.Model(model.input, model.output) + + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(), + metrics=[keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + model.predict(x) + + model.predict(x) + fd, keras_file = tempfile.mkstemp('.h5') + keras.models.save_model(model, keras_file) + + # Convert to TFLite model. + converter = lite.TocoConverter.from_keras_model_file(keras_file) + tflite_model = converter.convert() + self.assertTrue(tflite_model) + + os.close(fd) + os.remove(keras_file) + + # Check values from converted model. + interpreter = Interpreter(model_content=tflite_model) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('dense_input', input_details[0]['name']) + self.assertEqual(np.float32, input_details[0]['dtype']) + self.assertTrue(([1, 3] == input_details[0]['shape']).all()) + self.assertEqual((0., 0.), input_details[0]['quantization']) + + output_details = interpreter.get_output_details() + self.assertEqual(1, len(output_details)) + self.assertEqual('time_distributed/Reshape_1', output_details[0]['name']) + self.assertEqual(np.float32, output_details[0]['dtype']) + self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all()) + self.assertEqual((0., 0.), output_details[0]['quantization']) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py index 2b7ad29a27c121829a3fbcb41bfdec161ee6a6c9..9bd1f4f76ee693414a8515a5bd2567001b53e2ea 100644 --- a/tensorflow/contrib/lite/python/tflite_convert.py +++ b/tensorflow/contrib/lite/python/tflite_convert.py @@ -23,19 +23,15 @@ import os import sys from tensorflow.contrib.lite.python import lite +from tensorflow.contrib.lite.python import lite_constants from tensorflow.contrib.lite.toco import toco_flags_pb2 as _toco_flags_pb2 from tensorflow.contrib.lite.toco import types_pb2 as _types_pb2 from tensorflow.python.platform import app -def _parse_array(values): +def _parse_array(values, type_fn=str): if values: - return values.split(",") - - -def _parse_int_array(values): - if values: - return [int(val) for val in values.split(",")] + return [type_fn(val) for val in values.split(",") if val] def _parse_set(values): @@ -57,7 +53,8 @@ def _get_toco_converter(flags): input_shapes = None if flags.input_shapes: input_shapes_list = [ - _parse_int_array(shape) for shape in flags.input_shapes.split(":") + _parse_array(shape, type_fn=int) + for shape in flags.input_shapes.split(":") ] input_shapes = dict(zip(input_arrays, input_shapes_list)) output_arrays = _parse_array(flags.output_arrays) @@ -77,6 +74,9 @@ def _get_toco_converter(flags): converter_kwargs["saved_model_dir"] = flags.saved_model_dir converter_kwargs["tag_set"] = _parse_set(flags.saved_model_tag_set) converter_kwargs["signature_key"] = flags.saved_model_signature_key + elif flags.keras_model_file: + converter_fn = lite.TocoConverter.from_keras_model_file + converter_kwargs["model_file"] = flags.keras_model_file return converter_fn(**converter_kwargs) @@ -103,9 +103,9 @@ def _convert_model(flags): if flags.mean_values and flags.std_dev_values: input_arrays = converter.get_input_arrays() - std_dev_values = _parse_int_array(flags.std_dev_values) - mean_values = _parse_int_array(flags.mean_values) - quant_stats = zip(mean_values, std_dev_values) + std_dev_values = _parse_array(flags.std_dev_values, type_fn=int) + mean_values = _parse_array(flags.mean_values, type_fn=int) + quant_stats = list(zip(mean_values, std_dev_values)) if ((not flags.input_arrays and len(input_arrays) > 1) or (len(input_arrays) != len(quant_stats))): raise ValueError("Mismatching --input_arrays, --std_dev_values, and " @@ -114,9 +114,10 @@ def _convert_model(flags): "--input_arrays must be present when specifying " "--std_dev_values and --mean_values with multiple input " "tensors in order to map between names and " - "values".format(",".join(input_arrays))) + "values.".format(",".join(input_arrays))) converter.quantized_input_stats = dict(zip(input_arrays, quant_stats)) - if flags.default_ranges_min and flags.default_ranges_max: + if (flags.default_ranges_min is not None) and (flags.default_ranges_max is + not None): converter.default_ranges_stats = (flags.default_ranges_min, flags.default_ranges_max) @@ -129,7 +130,14 @@ def _convert_model(flags): if flags.allow_custom_ops: converter.allow_custom_ops = flags.allow_custom_ops if flags.quantize_weights: + if flags.inference_type == lite_constants.QUANTIZED_UINT8: + raise ValueError("--quantized_weights is not supported with " + "--inference_type=QUANTIZED_UINT8") converter.quantize_weights = flags.quantize_weights + if flags.dump_graphviz_dir: + converter.dump_graphviz_dir = flags.dump_graphviz_dir + if flags.dump_graphviz_video: + converter.dump_graphviz_vode = flags.dump_graphviz_video # Convert model. output_data = converter.convert() @@ -161,8 +169,12 @@ def _check_flags(flags, unparsed): output = "" for flag in unparsed: output += _get_message_unparsed(flag, "--input_file", "--graph_def_file") + output += _get_message_unparsed(flag, "--savedmodel_directory", + "--saved_model_dir") output += _get_message_unparsed(flag, "--std_value", "--std_dev_values") output += _get_message_unparsed(flag, "--batch_size", "--input_shapes") + output += _get_message_unparsed(flag, "--dump_graphviz", + "--dump_graphviz_dir") if output: raise ValueError(output) @@ -187,10 +199,13 @@ def _check_flags(flags, unparsed): raise ValueError("--std_dev_values, --mean_values must have the same " "number of items") - if bool(flags.default_ranges_min) != bool(flags.default_ranges_max): + if (flags.default_ranges_min is None) != (flags.default_ranges_max is None): raise ValueError("--default_ranges_min and --default_ranges_max must be " "used together") + if flags.dump_graphviz_video and not flags.dump_graphviz: + raise ValueError("--dump_graphviz_video must be used with --dump_graphviz") + def run_main(_): """Main in toco_convert.py.""" @@ -210,29 +225,33 @@ def run_main(_): input_file_group.add_argument( "--graph_def_file", type=str, - help="Full filepath of file containing TensorFlow GraphDef.") + help="Full filepath of file containing frozen TensorFlow GraphDef.") input_file_group.add_argument( "--saved_model_dir", type=str, help="Full filepath of directory containing the SavedModel.") + input_file_group.add_argument( + "--keras_model_file", + type=str, + help="Full filepath of HDF5 file containing tf.Keras model.") # Model format flags. parser.add_argument( "--output_format", - type=str, + type=str.upper, choices=["TFLITE", "GRAPHVIZ_DOT"], help="Output file format.") parser.add_argument( "--inference_type", - type=str, + type=str.upper, choices=["FLOAT", "QUANTIZED_UINT8"], - help="Target data type of arrays in the output file.") + help="Target data type of real-number arrays in the output file.") parser.add_argument( "--inference_input_type", - type=str, + type=str.upper, choices=["FLOAT", "QUANTIZED_UINT8"], - help=("Target data type of input arrays. Allows for a different type for " - "input arrays in the case of quantization.")) + help=("Target data type of real-number input arrays. Allows for a " + "different type for input arrays in the case of quantization.")) # Input and output arrays flags. parser.add_argument( @@ -266,12 +285,12 @@ def run_main(_): "--std_dev_values", type=str, help=("Standard deviation of training data for each input tensor, " - "comma-separated. Used for quantization. (default None)")) + "comma-separated integers. Used for quantization. (default None)")) parser.add_argument( "--mean_values", type=str, - help=("Mean of training data for each input tensor, comma-separated. " - "Used for quantization. (default None)")) + help=("Mean of training data for each input tensor, comma-separated " + "integers. Used for quantization. (default None)")) parser.add_argument( "--default_ranges_min", type=int, @@ -322,6 +341,20 @@ def run_main(_): "provide these to the TensorFlow Lite runtime with a custom " "resolver. (default False)")) + # Logging flags. + parser.add_argument( + "--dump_graphviz_dir", + type=str, + help=("Full filepath of folder to dump the graphs at various stages of " + "processing GraphViz .dot files. Preferred over --output_format=" + "GRAPHVIZ_DOT in order to keep the requirements of the output " + "file.")) + parser.add_argument( + "--dump_graphviz_video", + action="store_true", + help=("Boolean indicating whether to dump the graph after every graph " + "transformation")) + tflite_flags, unparsed = parser.parse_known_args(args=sys.argv[1:]) try: _check_flags(tflite_flags, unparsed) diff --git a/tensorflow/contrib/lite/schema/BUILD b/tensorflow/contrib/lite/schema/BUILD index 9717a4a1a496b888348514584888e62c4e3703b4..f095151cae835aa202ff4c9f43e175246f54f1cf 100644 --- a/tensorflow/contrib/lite/schema/BUILD +++ b/tensorflow/contrib/lite/schema/BUILD @@ -65,6 +65,7 @@ cc_test( ], tags = [ "tflite_not_portable_android", + "tflite_not_portable_ios", ], deps = [ "//tensorflow/core:lib_platform", diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc index 64ab0a9fe2f01a732af91ed4052e44cf8c38f89b..9dc8daa227dd68ccde2efa4013ac4465a72e6bb0 100644 --- a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc @@ -39,7 +39,7 @@ limitations under the License. #define TENSORFLOW_CONTRIB_LITE_BUILTIN_OPS_H_ // DO NOT EDIT MANUALLY: This file is automatically generated by -// `schema_builtin_ops_header_generator.py`. +// `schema/builtin_ops_header/generator.cc`. #ifdef __cplusplus extern "C" { diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index d12a96df1c70ddaa6ae11f1ee809662314db89b0..15fb8bbdb8f100201750faf706eb45b697319dfb 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -34,6 +34,8 @@ enum TensorType : byte { INT64 = 4, STRING = 5, BOOL = 6, + INT16 = 7, + COMPLEX64 = 8, } // Parameters for converting a quantized tensor back to float. Given a @@ -63,6 +65,8 @@ table Tensor { buffer:uint; name:string; // For debugging and importing back into tensorflow. quantization:QuantizationParameters; // Optional. + + is_variable:bool = false; } // A list of builtin operators. Builtin operators are slightly faster than custom @@ -150,6 +154,12 @@ enum BuiltinOperator : byte { EXPAND_DIMS = 70, EQUAL = 71, NOT_EQUAL = 72, + LOG = 73, + SUM=74, + SQRT = 75, + RSQRT = 76, + SHAPE = 77, + POW = 78, } // Options for the builtin operators. @@ -180,7 +190,7 @@ union BuiltinOptions { BatchToSpaceNDOptions, SpaceToBatchNDOptions, TransposeOptions, - MeanOptions, + ReducerOptions, SubOptions, DivOptions, SqueezeOptions, @@ -208,6 +218,8 @@ union BuiltinOptions { ExpandDimsOptions, EqualOptions, NotEqualOptions, + ShapeOptions, + PowOptions, } enum Padding : byte { SAME, VALID } @@ -285,9 +297,18 @@ table BidirectionalSequenceRNNOptions { fused_activation_function:ActivationFunctionType; } +enum FullyConnectedOptionsWeightsFormat: byte { + DEFAULT = 0, + SHUFFLED4x16INT8 = 1, +} + // An implementation of TensorFlow fully_connected (a.k.a Dense) layer. table FullyConnectedOptions { + // Parameters for FullyConnected version 1 or above. fused_activation_function:ActivationFunctionType; + + // Parameters for FullyConnected version 2 or above. + weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT; } table SoftmaxOptions { @@ -407,7 +428,7 @@ table TransposeOptions { table ExpOptions { } -table MeanOptions { +table ReducerOptions { keep_dims: bool; } @@ -488,6 +509,14 @@ table EqualOptions { table NotEqualOptions { } +table ShapeOptions { + // Optional output type of the operation (int32 or int64). Defaults to int32. + out_type : TensorType; +} + +table PowOptions { +} + // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a // builtin, or a string if the operator is custom. table OperatorCode { @@ -519,6 +548,16 @@ table Operator { builtin_options:BuiltinOptions; custom_options:[ubyte]; custom_options_format:CustomOptionsFormat; + + // A list of booleans indicating the input tensors which are being mutated by + // this operator.(e.g. used by RNN and LSTM). + // For example, if the "inputs" array refers to 5 tensors and the second and + // fifth are mutable variables, then this list will contain + // [false, true, false, false, true]. + // + // If the list is empty, no variable is mutated in this operator. + // The list either has the same length as `inputs`, or is empty. + mutating_variable_inputs:[bool]; } // The root type, defining a subgraph, which typically represents an entire diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 8ddd2f14388cb78e23abf98b31485c254aad3e5c..fe0ff9a7a5ba0764475f4a7c14cd875b3cdb2aa8 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -127,8 +127,8 @@ struct TransposeOptionsT; struct ExpOptions; struct ExpOptionsT; -struct MeanOptions; -struct MeanOptionsT; +struct ReducerOptions; +struct ReducerOptionsT; struct SqueezeOptions; struct SqueezeOptionsT; @@ -193,6 +193,12 @@ struct EqualOptionsT; struct NotEqualOptions; struct NotEqualOptionsT; +struct ShapeOptions; +struct ShapeOptionsT; + +struct PowOptions; +struct PowOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -216,11 +222,13 @@ enum TensorType { TensorType_INT64 = 4, TensorType_STRING = 5, TensorType_BOOL = 6, + TensorType_INT16 = 7, + TensorType_COMPLEX64 = 8, TensorType_MIN = TensorType_FLOAT32, - TensorType_MAX = TensorType_BOOL + TensorType_MAX = TensorType_COMPLEX64 }; -inline TensorType (&EnumValuesTensorType())[7] { +inline TensorType (&EnumValuesTensorType())[9] { static TensorType values[] = { TensorType_FLOAT32, TensorType_FLOAT16, @@ -228,7 +236,9 @@ inline TensorType (&EnumValuesTensorType())[7] { TensorType_UINT8, TensorType_INT64, TensorType_STRING, - TensorType_BOOL + TensorType_BOOL, + TensorType_INT16, + TensorType_COMPLEX64 }; return values; } @@ -242,6 +252,8 @@ inline const char **EnumNamesTensorType() { "INT64", "STRING", "BOOL", + "INT16", + "COMPLEX64", nullptr }; return names; @@ -325,11 +337,17 @@ enum BuiltinOperator { BuiltinOperator_EXPAND_DIMS = 70, BuiltinOperator_EQUAL = 71, BuiltinOperator_NOT_EQUAL = 72, + BuiltinOperator_LOG = 73, + BuiltinOperator_SUM = 74, + BuiltinOperator_SQRT = 75, + BuiltinOperator_RSQRT = 76, + BuiltinOperator_SHAPE = 77, + BuiltinOperator_POW = 78, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_NOT_EQUAL + BuiltinOperator_MAX = BuiltinOperator_POW }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[72] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[78] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -402,7 +420,13 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[72] { BuiltinOperator_TILE, BuiltinOperator_EXPAND_DIMS, BuiltinOperator_EQUAL, - BuiltinOperator_NOT_EQUAL + BuiltinOperator_NOT_EQUAL, + BuiltinOperator_LOG, + BuiltinOperator_SUM, + BuiltinOperator_SQRT, + BuiltinOperator_RSQRT, + BuiltinOperator_SHAPE, + BuiltinOperator_POW }; return values; } @@ -482,6 +506,12 @@ inline const char **EnumNamesBuiltinOperator() { "EXPAND_DIMS", "EQUAL", "NOT_EQUAL", + "LOG", + "SUM", + "SQRT", + "RSQRT", + "SHAPE", + "POW", nullptr }; return names; @@ -520,7 +550,7 @@ enum BuiltinOptions { BuiltinOptions_BatchToSpaceNDOptions = 24, BuiltinOptions_SpaceToBatchNDOptions = 25, BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_MeanOptions = 27, + BuiltinOptions_ReducerOptions = 27, BuiltinOptions_SubOptions = 28, BuiltinOptions_DivOptions = 29, BuiltinOptions_SqueezeOptions = 30, @@ -548,11 +578,13 @@ enum BuiltinOptions { BuiltinOptions_ExpandDimsOptions = 52, BuiltinOptions_EqualOptions = 53, BuiltinOptions_NotEqualOptions = 54, + BuiltinOptions_ShapeOptions = 55, + BuiltinOptions_PowOptions = 56, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_NotEqualOptions + BuiltinOptions_MAX = BuiltinOptions_PowOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[57] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -581,7 +613,7 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { BuiltinOptions_BatchToSpaceNDOptions, BuiltinOptions_SpaceToBatchNDOptions, BuiltinOptions_TransposeOptions, - BuiltinOptions_MeanOptions, + BuiltinOptions_ReducerOptions, BuiltinOptions_SubOptions, BuiltinOptions_DivOptions, BuiltinOptions_SqueezeOptions, @@ -608,7 +640,9 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { BuiltinOptions_TileOptions, BuiltinOptions_ExpandDimsOptions, BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions + BuiltinOptions_NotEqualOptions, + BuiltinOptions_ShapeOptions, + BuiltinOptions_PowOptions }; return values; } @@ -642,7 +676,7 @@ inline const char **EnumNamesBuiltinOptions() { "BatchToSpaceNDOptions", "SpaceToBatchNDOptions", "TransposeOptions", - "MeanOptions", + "ReducerOptions", "SubOptions", "DivOptions", "SqueezeOptions", @@ -670,6 +704,8 @@ inline const char **EnumNamesBuiltinOptions() { "ExpandDimsOptions", "EqualOptions", "NotEqualOptions", + "ShapeOptions", + "PowOptions", nullptr }; return names; @@ -788,8 +824,8 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; }; -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MeanOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; }; template<> struct BuiltinOptionsTraits { @@ -900,6 +936,14 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -1139,13 +1183,13 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_TransposeOptions ? reinterpret_cast(value) : nullptr; } - MeanOptionsT *AsMeanOptions() { - return type == BuiltinOptions_MeanOptions ? - reinterpret_cast(value) : nullptr; + ReducerOptionsT *AsReducerOptions() { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; } - const MeanOptionsT *AsMeanOptions() const { - return type == BuiltinOptions_MeanOptions ? - reinterpret_cast(value) : nullptr; + const ReducerOptionsT *AsReducerOptions() const { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; } SubOptionsT *AsSubOptions() { return type == BuiltinOptions_SubOptions ? @@ -1363,6 +1407,22 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_NotEqualOptions ? reinterpret_cast(value) : nullptr; } + ShapeOptionsT *AsShapeOptions() { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + const ShapeOptionsT *AsShapeOptions() const { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + PowOptionsT *AsPowOptions() { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } + const PowOptionsT *AsPowOptions() const { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -1470,6 +1530,35 @@ inline const char *EnumNameLSHProjectionType(LSHProjectionType e) { return EnumNamesLSHProjectionType()[index]; } +enum FullyConnectedOptionsWeightsFormat { + FullyConnectedOptionsWeightsFormat_DEFAULT = 0, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, + FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 +}; + +inline FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] { + static FullyConnectedOptionsWeightsFormat values[] = { + FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 + }; + return values; +} + +inline const char **EnumNamesFullyConnectedOptionsWeightsFormat() { + static const char *names[] = { + "DEFAULT", + "SHUFFLED4x16INT8", + nullptr + }; + return names; +} + +inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) { + const size_t index = static_cast(e); + return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; +} + enum LSTMKernelType { LSTMKernelType_FULL = 0, LSTMKernelType_BASIC = 1, @@ -1668,9 +1757,11 @@ struct TensorT : public flatbuffers::NativeTable { uint32_t buffer; std::string name; std::unique_ptr quantization; + bool is_variable; TensorT() : type(TensorType_FLOAT32), - buffer(0) { + buffer(0), + is_variable(false) { } }; @@ -1681,7 +1772,8 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_TYPE = 6, VT_BUFFER = 8, VT_NAME = 10, - VT_QUANTIZATION = 12 + VT_QUANTIZATION = 12, + VT_IS_VARIABLE = 14 }; const flatbuffers::Vector *shape() const { return GetPointer *>(VT_SHAPE); @@ -1698,6 +1790,9 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const QuantizationParameters *quantization() const { return GetPointer(VT_QUANTIZATION); } + bool is_variable() const { + return GetField(VT_IS_VARIABLE, 0) != 0; + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) && @@ -1708,6 +1803,7 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { verifier.Verify(name()) && VerifyOffset(verifier, VT_QUANTIZATION) && verifier.VerifyTable(quantization()) && + VerifyField(verifier, VT_IS_VARIABLE) && verifier.EndTable(); } TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -1733,6 +1829,9 @@ struct TensorBuilder { void add_quantization(flatbuffers::Offset quantization) { fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); } + void add_is_variable(bool is_variable) { + fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); + } explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -1751,12 +1850,14 @@ inline flatbuffers::Offset CreateTensor( TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, flatbuffers::Offset name = 0, - flatbuffers::Offset quantization = 0) { + flatbuffers::Offset quantization = 0, + bool is_variable = false) { TensorBuilder builder_(_fbb); builder_.add_quantization(quantization); builder_.add_name(name); builder_.add_buffer(buffer); builder_.add_shape(shape); + builder_.add_is_variable(is_variable); builder_.add_type(type); return builder_.Finish(); } @@ -1767,14 +1868,16 @@ inline flatbuffers::Offset CreateTensorDirect( TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr, - flatbuffers::Offset quantization = 0) { + flatbuffers::Offset quantization = 0, + bool is_variable = false) { return tflite::CreateTensor( _fbb, shape ? _fbb.CreateVector(*shape) : 0, type, buffer, name ? _fbb.CreateString(name) : 0, - quantization); + quantization, + is_variable); } flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -2508,22 +2611,29 @@ flatbuffers::Offset CreateBidirectionalSequence struct FullyConnectedOptionsT : public flatbuffers::NativeTable { typedef FullyConnectedOptions TableType; ActivationFunctionType fused_activation_function; + FullyConnectedOptionsWeightsFormat weights_format; FullyConnectedOptionsT() - : fused_activation_function(ActivationFunctionType_NONE) { + : fused_activation_function(ActivationFunctionType_NONE), + weights_format(FullyConnectedOptionsWeightsFormat_DEFAULT) { } }; struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef FullyConnectedOptionsT NativeTableType; enum { - VT_FUSED_ACTIVATION_FUNCTION = 4 + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_WEIGHTS_FORMAT = 6 }; ActivationFunctionType fused_activation_function() const { return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); } + FullyConnectedOptionsWeightsFormat weights_format() const { + return static_cast(GetField(VT_WEIGHTS_FORMAT, 0)); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + VerifyField(verifier, VT_WEIGHTS_FORMAT) && verifier.EndTable(); } FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -2537,6 +2647,9 @@ struct FullyConnectedOptionsBuilder { void add_fused_activation_function(ActivationFunctionType fused_activation_function) { fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); } + void add_weights_format(FullyConnectedOptionsWeightsFormat weights_format) { + fbb_.AddElement(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast(weights_format), 0); + } explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -2551,8 +2664,10 @@ struct FullyConnectedOptionsBuilder { inline flatbuffers::Offset CreateFullyConnectedOptions( flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) { + ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, + FullyConnectedOptionsWeightsFormat weights_format = FullyConnectedOptionsWeightsFormat_DEFAULT) { FullyConnectedOptionsBuilder builder_(_fbb); + builder_.add_weights_format(weights_format); builder_.add_fused_activation_function(fused_activation_function); return builder_.Finish(); } @@ -3819,16 +3934,16 @@ inline flatbuffers::Offset CreateExpOptions( flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -struct MeanOptionsT : public flatbuffers::NativeTable { - typedef MeanOptions TableType; +struct ReducerOptionsT : public flatbuffers::NativeTable { + typedef ReducerOptions TableType; bool keep_dims; - MeanOptionsT() + ReducerOptionsT() : keep_dims(false) { } }; -struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MeanOptionsT NativeTableType; +struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReducerOptionsT NativeTableType; enum { VT_KEEP_DIMS = 4 }; @@ -3840,38 +3955,38 @@ struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_KEEP_DIMS) && verifier.EndTable(); } - MeanOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; -struct MeanOptionsBuilder { +struct ReducerOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_keep_dims(bool keep_dims) { - fbb_.AddElement(MeanOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); + fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); } - explicit MeanOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); } - MeanOptionsBuilder &operator=(const MeanOptionsBuilder &); - flatbuffers::Offset Finish() { + ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &); + flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); + auto o = flatbuffers::Offset(end); return o; } }; -inline flatbuffers::Offset CreateMeanOptions( +inline flatbuffers::Offset CreateReducerOptions( flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) { - MeanOptionsBuilder builder_(_fbb); + ReducerOptionsBuilder builder_(_fbb); builder_.add_keep_dims(keep_dims); return builder_.Finish(); } -flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SqueezeOptionsT : public flatbuffers::NativeTable { typedef SqueezeOptions TableType; @@ -4903,6 +5018,100 @@ inline flatbuffers::Offset CreateNotEqualOptions( flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct ShapeOptionsT : public flatbuffers::NativeTable { + typedef ShapeOptions TableType; + TensorType out_type; + ShapeOptionsT() + : out_type(TensorType_FLOAT32) { + } +}; + +struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ShapeOptionsT NativeTableType; + enum { + VT_OUT_TYPE = 4 + }; + TensorType out_type() const { + return static_cast(GetField(VT_OUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUT_TYPE) && + verifier.EndTable(); + } + ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ShapeOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_out_type(TensorType out_type) { + fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); + } + explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateShapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + TensorType out_type = TensorType_FLOAT32) { + ShapeOptionsBuilder builder_(_fbb); + builder_.add_out_type(out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PowOptionsT : public flatbuffers::NativeTable { + typedef PowOptions TableType; + PowOptionsT() { + } +}; + +struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PowOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PowOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + PowOptionsBuilder &operator=(const PowOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePowOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + PowOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -5001,6 +5210,7 @@ struct OperatorT : public flatbuffers::NativeTable { BuiltinOptionsUnion builtin_options; std::vector custom_options; CustomOptionsFormat custom_options_format; + std::vector mutating_variable_inputs; OperatorT() : opcode_index(0), custom_options_format(CustomOptionsFormat_FLEXBUFFERS) { @@ -5016,7 +5226,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_BUILTIN_OPTIONS_TYPE = 10, VT_BUILTIN_OPTIONS = 12, VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16 + VT_CUSTOM_OPTIONS_FORMAT = 16, + VT_MUTATING_VARIABLE_INPUTS = 18 }; uint32_t opcode_index() const { return GetField(VT_OPCODE_INDEX, 0); @@ -5112,8 +5323,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const TransposeOptions *builtin_options_as_TransposeOptions() const { return builtin_options_type() == BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; } - const MeanOptions *builtin_options_as_MeanOptions() const { - return builtin_options_type() == BuiltinOptions_MeanOptions ? static_cast(builtin_options()) : nullptr; + const ReducerOptions *builtin_options_as_ReducerOptions() const { + return builtin_options_type() == BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; } const SubOptions *builtin_options_as_SubOptions() const { return builtin_options_type() == BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; @@ -5196,12 +5407,21 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const NotEqualOptions *builtin_options_as_NotEqualOptions() const { return builtin_options_type() == BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; } + const ShapeOptions *builtin_options_as_ShapeOptions() const { + return builtin_options_type() == BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; + } + const PowOptions *builtin_options_as_PowOptions() const { + return builtin_options_type() == BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } CustomOptionsFormat custom_options_format() const { return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); } + const flatbuffers::Vector *mutating_variable_inputs() const { + return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_OPCODE_INDEX) && @@ -5215,6 +5435,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.Verify(custom_options()) && VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT) && + VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && + verifier.Verify(mutating_variable_inputs()) && verifier.EndTable(); } OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -5326,8 +5548,8 @@ template<> inline const TransposeOptions *Operator::builtin_options_as inline const MeanOptions *Operator::builtin_options_as() const { - return builtin_options_as_MeanOptions(); +template<> inline const ReducerOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReducerOptions(); } template<> inline const SubOptions *Operator::builtin_options_as() const { @@ -5438,6 +5660,14 @@ template<> inline const NotEqualOptions *Operator::builtin_options_as inline const ShapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ShapeOptions(); +} + +template<> inline const PowOptions *Operator::builtin_options_as() const { + return builtin_options_as_PowOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -5462,6 +5692,9 @@ struct OperatorBuilder { void add_custom_options_format(CustomOptionsFormat custom_options_format) { fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); } + void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { + fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); + } explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -5482,8 +5715,10 @@ inline flatbuffers::Offset CreateOperator( BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, flatbuffers::Offset> custom_options = 0, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, + flatbuffers::Offset> mutating_variable_inputs = 0) { OperatorBuilder builder_(_fbb); + builder_.add_mutating_variable_inputs(mutating_variable_inputs); builder_.add_custom_options(custom_options); builder_.add_builtin_options(builtin_options); builder_.add_outputs(outputs); @@ -5502,7 +5737,8 @@ inline flatbuffers::Offset CreateOperatorDirect( BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, const std::vector *custom_options = nullptr, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, + const std::vector *mutating_variable_inputs = nullptr) { return tflite::CreateOperator( _fbb, opcode_index, @@ -5511,7 +5747,8 @@ inline flatbuffers::Offset CreateOperatorDirect( builtin_options_type, builtin_options, custom_options ? _fbb.CreateVector(*custom_options) : 0, - custom_options_format); + custom_options_format, + mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0); } flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -5882,6 +6119,7 @@ inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t { auto _e = buffer(); _o->buffer = _e; }; { auto _e = name(); if (_e) _o->name = _e->str(); }; { auto _e = quantization(); if (_e) _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); }; + { auto _e = is_variable(); _o->is_variable = _e; }; } inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -5897,13 +6135,15 @@ inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder & auto _buffer = _o->buffer; auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; + auto _is_variable = _o->is_variable; return tflite::CreateTensor( _fbb, _shape, _type, _buffer, _name, - _quantization); + _quantization, + _is_variable); } inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { @@ -6207,6 +6447,7 @@ inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const fl (void)_o; (void)_resolver; { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }; + { auto _e = weights_format(); _o->weights_format = _e; }; } inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -6218,9 +6459,11 @@ inline flatbuffers::Offset CreateFullyConnectedOptions(fl (void)_o; struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _fused_activation_function = _o->fused_activation_function; + auto _weights_format = _o->weights_format; return tflite::CreateFullyConnectedOptions( _fbb, - _fused_activation_function); + _fused_activation_function, + _weights_format); } inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { @@ -6827,28 +7070,28 @@ inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferB _fbb); } -inline MeanOptionsT *MeanOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MeanOptionsT(); +inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ReducerOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void MeanOptions::UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; { auto _e = keep_dims(); _o->keep_dims = _e; }; } -inline flatbuffers::Offset MeanOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMeanOptions(_fbb, _o, _rehasher); +inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReducerOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MeanOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _keep_dims = _o->keep_dims; - return tflite::CreateMeanOptions( + return tflite::CreateReducerOptions( _fbb, _keep_dims); } @@ -7378,6 +7621,55 @@ inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::F _fbb); } +inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ShapeOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = out_type(); _o->out_type = _e; }; +} + +inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateShapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _out_type = _o->out_type; + return tflite::CreateShapeOptions( + _fbb, + _out_type); +} + +inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new PowOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePowOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreatePowOptions( + _fbb); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -7426,6 +7718,7 @@ inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_functi { auto _e = builtin_options(); if (_e) _o->builtin_options.value = BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); }; { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->custom_options[_i] = _e->Get(_i); } } }; { auto _e = custom_options_format(); _o->custom_options_format = _e; }; + { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } }; } inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -7443,6 +7736,7 @@ inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuild auto _builtin_options = _o->builtin_options.Pack(_fbb); auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; auto _custom_options_format = _o->custom_options_format; + auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; return tflite::CreateOperator( _fbb, _opcode_index, @@ -7451,7 +7745,8 @@ inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuild _builtin_options_type, _builtin_options, _custom_options, - _custom_options_format); + _custom_options_format, + _mutating_variable_inputs); } inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { @@ -7668,8 +7963,8 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(obj); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } case BuiltinOptions_SubOptions: { @@ -7780,6 +8075,14 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -7902,8 +8205,8 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(obj); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } case BuiltinOptions_SubOptions: { @@ -8014,6 +8317,14 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -8124,9 +8435,9 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(value); - return CreateMeanOptions(_fbb, ptr, _rehasher).Union(); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); } case BuiltinOptions_SubOptions: { auto ptr = reinterpret_cast(value); @@ -8236,6 +8547,14 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + return CreatePowOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -8346,8 +8665,8 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new TransposeOptionsT(*reinterpret_cast(u.value)); break; } - case BuiltinOptions_MeanOptions: { - value = new MeanOptionsT(*reinterpret_cast(u.value)); + case BuiltinOptions_ReducerOptions: { + value = new ReducerOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SubOptions: { @@ -8458,6 +8777,14 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new NotEqualOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_ShapeOptions: { + value = new ShapeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PowOptions: { + value = new PowOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -8595,8 +8922,8 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(value); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); delete ptr; break; } @@ -8735,6 +9062,16 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; diff --git a/tensorflow/contrib/lite/simple_memory_arena.cc b/tensorflow/contrib/lite/simple_memory_arena.cc index 2f2004f56bcad5b56f9dd6d4bc824ec14d79e795..4eaf6f1bfe76efc1e6737d03d58be9bc87bb849d 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.cc +++ b/tensorflow/contrib/lite/simple_memory_arena.cc @@ -36,6 +36,12 @@ TfLiteStatus SimpleMemoryArena::Allocate(TfLiteContext* context, ArenaAlloc* new_alloc) { TF_LITE_ENSURE(context, alignment < arena_alignment_); + if (size == 0) { + new_alloc->offset = 0; + new_alloc->size = 0; + return kTfLiteOk; + } + size_t current_top = 0; if (!allocs_.empty()) { @@ -75,6 +81,10 @@ TfLiteStatus SimpleMemoryArena::Allocate(TfLiteContext* context, TfLiteStatus SimpleMemoryArena::Deallocate(TfLiteContext* context, const ArenaAlloc& alloc) { + if (alloc.size == 0) { + return kTfLiteOk; + } + int erased_allocs_count = 0; auto it = allocs_.begin(); while (it != allocs_.end()) { @@ -122,7 +132,11 @@ TfLiteStatus SimpleMemoryArena::ResolveAlloc(TfLiteContext* context, char** output_ptr) { TF_LITE_ENSURE(context, committed_); TF_LITE_ENSURE(context, output_ptr != nullptr); - *output_ptr = underlying_buffer_aligned_ptr_ + alloc.offset; + if (alloc.size == 0) { + *output_ptr = nullptr; + } else { + *output_ptr = underlying_buffer_aligned_ptr_ + alloc.offset; + } return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/simple_memory_arena.h b/tensorflow/contrib/lite/simple_memory_arena.h index 5faf78b59e3755d22e4e866d433e622baa6c66c1..f738315cf2f91403f9dcb6fa9e66b40bd70495aa 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.h +++ b/tensorflow/contrib/lite/simple_memory_arena.h @@ -39,7 +39,8 @@ struct ArenaAlloc { // This small class is responsible for allocating, deallocating and reusing // dynamic memory from a common underlying buffer. The arena can be used in // scenarios when the pattern of memory allocations and deallocations is -// repetitive, e.g. running NN inference in multiple iterations. +// repetitive, e.g. running NN inference in multiple iterations. Note that +// zero-sized allocations are explicitly allowed, and will resolve to null. class SimpleMemoryArena { public: explicit SimpleMemoryArena(size_t arena_alignment) diff --git a/tensorflow/contrib/lite/simple_memory_arena_test.cc b/tensorflow/contrib/lite/simple_memory_arena_test.cc index 4444f642eb75c563c57762d095e454ac63d836c6..60d4d5e768aeda958574422e1c36a7cc2f6a1429 100644 --- a/tensorflow/contrib/lite/simple_memory_arena_test.cc +++ b/tensorflow/contrib/lite/simple_memory_arena_test.cc @@ -43,6 +43,47 @@ TEST(SimpleMemoryArenaTest, BasicArenaOperations) { EXPECT_EQ(allocs[5].offset, 1024); } +TEST(SimpleMemoryArenaTest, BasicZeroAlloc) { + TfLiteContext context; + SimpleMemoryArena arena(64); + ArenaAlloc alloc; + + // Zero-sized allocs should have a 0 offset and size. + ASSERT_EQ(arena.Allocate(&context, 32, 0, &alloc), kTfLiteOk); + EXPECT_EQ(alloc.offset, 0); + EXPECT_EQ(alloc.size, 0); + + // Deallocation of zero-sized allocs should always succeed (even redundantly). + ASSERT_EQ(arena.Deallocate(&context, alloc), kTfLiteOk); + ASSERT_EQ(arena.Deallocate(&context, alloc), kTfLiteOk); + + // The zero-sized alloc should resolve to null. + char* resolved_ptr = nullptr; + ASSERT_EQ(arena.Commit(&context), kTfLiteOk); + ASSERT_EQ(arena.ResolveAlloc(&context, alloc, &resolved_ptr), kTfLiteOk); + EXPECT_EQ(resolved_ptr, nullptr); +} + +TEST(SimpleMemoryArenaTest, InterleavedZeroAlloc) { + TfLiteContext context; + SimpleMemoryArena arena(64); + ArenaAlloc allocs[4]; + + // Interleave some zero and non-zero-sized allocations and deallocations. + ASSERT_EQ(arena.Allocate(&context, 32, 2047, &allocs[0]), kTfLiteOk); + ASSERT_EQ(arena.Allocate(&context, 32, 0, &allocs[1]), kTfLiteOk); + ASSERT_EQ(arena.Allocate(&context, 32, 1023, &allocs[2]), kTfLiteOk); + ASSERT_EQ(arena.Deallocate(&context, allocs[1]), kTfLiteOk); + ASSERT_EQ(arena.Deallocate(&context, allocs[2]), kTfLiteOk); + ASSERT_EQ(arena.Allocate(&context, 32, 2047, &allocs[3]), kTfLiteOk); + + // Deallocation of a zero-sized alloc should not impact the allocator offsets. + EXPECT_EQ(allocs[0].offset, 0); + EXPECT_EQ(allocs[1].offset, 0); + EXPECT_EQ(allocs[2].offset, 2048); + EXPECT_EQ(allocs[3].offset, 2048); +} + TEST(SimpleMemoryArenaTest, TestAfterClear) { TfLiteContext context; SimpleMemoryArena arena(64); diff --git a/tensorflow/contrib/lite/string_util.cc b/tensorflow/contrib/lite/string_util.cc index a89776b29f895fe82ee71efe00c0949c58c109df..a316a40b62d89189da43768d448acdf5bbeca129 100644 --- a/tensorflow/contrib/lite/string_util.cc +++ b/tensorflow/contrib/lite/string_util.cc @@ -105,7 +105,7 @@ void DynamicBuffer::WriteToTensor(TfLiteTensor* tensor) { dims->data[0] = offset_.size() - 1; // Store number of strings. TfLiteTensorReset(tensor->type, tensor->name, dims, tensor->params, tensor_buffer, bytes, kTfLiteDynamic, tensor->allocation, - tensor); + tensor->is_variable, tensor); } int GetStringCount(const char* raw_buffer) { diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 80e4c5a4dde4702229887593afc5ffeef339176d..789bc695f8e9f8721edeb3b3a3f2af59b36adeed 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -20,11 +20,15 @@ load( size = "large", srcs = ["generated_examples_zip_test.cc"], args = [ - "--zip_file_path=$(location :zip_%s)" % test_name, - # TODO(angerson) We may be able to add an external unzip binary instead - # of relying on an existing one for OSS builds. - "--unzip_binary_path=/usr/bin/unzip", - ], + ] + select({ + "//tensorflow:android": [], + "//conditions:default": [ + "--zip_file_path=$(location :zip_%s)" % test_name, + # TODO(angerson) We may be able to add an external unzip binary instead + # of relying on an existing one for OSS builds. + "--unzip_binary_path=/usr/bin/unzip", + ], + }), data = [ ":zip_%s" % test_name, ], @@ -168,6 +172,7 @@ cc_test( data = ["//tensorflow/contrib/lite:testdata/multi_add.bin"], tags = [ "tflite_not_portable_android", + "tflite_not_portable_ios", ], deps = [ ":tflite_driver", diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 723b6ae057e2db5479784811d404b92d30cb0d14..50237ed79232cff0be7ae8c5b125ac1ee7fdf520 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -94,8 +94,8 @@ KNOWN_BUGS = { r"sigmoid.*input_shape=\[\]": "67645668", # Concat doesn't work with a single input tensor r"concat.*num_tensors=1": "67378344", - # Transposition in MatMul is not supported. - r"fully_connected.*transpose_.=True": "67586970", + # Transposition in MatMul is not fully supported. + "fully_connected.*transpose_a=True": "67586970", # Softmax graphs are too complex. r"softmax.*dim=0": "67749831", # BatchToSpaceND only supports 4D tensors. @@ -137,7 +137,7 @@ def toco_options(data_types, Returns: the options in a string. """ - shape_str = ":".join([",".join(str(y) for y in x) for x in shapes]) + shape_str = ":".join([",".join(str(y) for y in x) for x in shapes if x]) inference_type = "FLOAT" # TODO(ahentz): if we get multi-input quantization to work we need this # to change @@ -705,7 +705,7 @@ def make_constant_tests(zip_path): def make_binary_op_tests(zip_path, binary_operator): - """Make a set of tests to do add with and without broadcast.""" + """Make a set of tests to do binary ops with and without broadcast.""" # These parameters are split because we don't support broadcasting. test_parameters = [{ @@ -834,6 +834,12 @@ def make_mean_tests(zip_path): return make_reduce_tests(tf.reduce_mean)(zip_path) +def make_sum_tests(zip_path): + """Make a set of tests to do sum.""" + + return make_reduce_tests(tf.reduce_sum)(zip_path) + + def make_exp_tests(zip_path): """Make a set of tests to do exp.""" @@ -984,6 +990,10 @@ def make_mul_tests(zip_path): make_binary_op_tests(zip_path, tf.multiply) +def make_pow_tests(zip_path): + make_binary_op_tests(zip_path, tf.pow) + + def make_gather_tests(zip_path): """Make a set of tests to do gather.""" @@ -1315,6 +1325,12 @@ def make_fully_connected_tests(zip_path): "transpose_a": [False], "transpose_b": [False], "constant_filter": [True, False], + }, { + "shape1": [[40, 37]], + "shape2": [[40, 37]], + "transpose_a": [False], + "transpose_b": [True], + "constant_filter": [True, False], }] def build_graph(parameters): @@ -1539,6 +1555,32 @@ def make_reshape_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_shape_tests(zip_path): + """Make a set of tests to do shape.""" + + test_parameters = [{ + "input_dtype": [tf.float32, tf.int32], + "input_shape": [[], [0], [1, 1, 1, 3], [2, 3, 4, 5], [5, 5], [10]], + "out_type": [tf.int32, tf.int64], + }] + + def build_graph(parameters): + """Build the topk op testing graph.""" + # Note that we intentionally leave out the shape from the input placeholder + # to prevent the Shape operation from being optimized out during conversion. + input_value = tf.placeholder(dtype=parameters["input_dtype"], name="input") + out = tf.shape(input_value, out_type=parameters["out_type"]) + return [input_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_value = create_tensor_data(parameters["input_dtype"], + parameters["input_shape"]) + return [input_value], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value]))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_resize_bilinear_tests(zip_path): """Make a set of tests to do resize_bilinear.""" @@ -2420,30 +2462,54 @@ def make_neg_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def _make_elementwise_tests(op): + """Make a set of tests to do element-wise operations.""" + + def f(zip_path): + """Actual function that generates examples.""" + test_parameters = [{ + "input_dtype": [tf.float32], + "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], + }] + + def build_graph(parameters): + """Build the unary op testing graph.""" + input_value = tf.placeholder( + dtype=parameters["input_dtype"], + name="input1", + shape=parameters["input_shape"]) + out = op(input_value) + return [input_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_value = create_tensor_data(parameters["input_dtype"], + parameters["input_shape"]) + return [input_value], sess.run( + outputs, feed_dict={inputs[0]: input_value}) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + return f + + def make_sin_tests(zip_path): """Make a set of tests to do sin.""" + return _make_elementwise_tests(tf.sin)(zip_path) - test_parameters = [{ - "input_dtype": [tf.float32], - "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], - }] - def build_graph(parameters): - """Build the sin op testing graph.""" - input_value = tf.placeholder( - dtype=parameters["input_dtype"], - name="input1", - shape=parameters["input_shape"]) - out = tf.sin(input_value) - return [input_value], [out] +def make_log_tests(zip_path): + """Make a set of tests to do log.""" + return _make_elementwise_tests(tf.log)(zip_path) - def build_inputs(parameters, sess, inputs, outputs): - input_value = create_tensor_data(parameters["input_dtype"], - parameters["input_shape"]) - return [input_value], sess.run( - outputs, feed_dict={inputs[0]: input_value}) - make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_sqrt_tests(zip_path): + """Make a set of tests to do sqrt.""" + return _make_elementwise_tests(tf.sqrt)(zip_path) + + +def make_rsqrt_tests(zip_path): + """Make a set of tests to do 1/sqrt.""" + return _make_elementwise_tests(tf.rsqrt)(zip_path) def make_where_tests(zip_path): diff --git a/tensorflow/contrib/lite/testing/generate_testspec.cc b/tensorflow/contrib/lite/testing/generate_testspec.cc index c0c861ff6da2fc144b9303dfdd48f19794cebeca..c1092e4d25567f0374e3cd5a27bde32419d3db19 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.cc +++ b/tensorflow/contrib/lite/testing/generate_testspec.cc @@ -25,7 +25,7 @@ namespace testing { template void GenerateCsv(const std::vector& shape, float min, float max, string* out) { - auto random_float = [](int min, int max) { + auto random_float = [](float min, float max) { static unsigned int seed; return min + (max - min) * static_cast(rand_r(&seed)) / RAND_MAX; }; @@ -37,16 +37,10 @@ void GenerateCsv(const std::vector& shape, float min, float max, *out = Join(data.data(), data.size(), ","); } -bool GenerateTestSpecFromTensorflowModel( - std::iostream& stream, const string& tensorflow_model_path, - const string& tflite_model_path, const std::vector& input_layer, +std::vector GenerateInputValues( + const std::vector& input_layer, const std::vector& input_layer_type, - const std::vector& input_layer_shape, - const std::vector& output_layer) { - CHECK_EQ(input_layer.size(), input_layer_type.size()); - CHECK_EQ(input_layer.size(), input_layer_shape.size()); - - // Generate inputs. + const std::vector& input_layer_shape) { std::vector input_values; input_values.resize(input_layer.size()); for (int i = 0; i < input_layer.size(); i++) { @@ -73,9 +67,22 @@ bool GenerateTestSpecFromTensorflowModel( default: fprintf(stderr, "Unsupported type %d (%s) when generating testspec.\n", type, input_layer_type[i].c_str()); - return false; + input_values.clear(); + return input_values; } } + return input_values; +} + +bool GenerateTestSpecFromTensorflowModel( + std::iostream& stream, const string& tensorflow_model_path, + const string& tflite_model_path, int num_invocations, + const std::vector& input_layer, + const std::vector& input_layer_type, + const std::vector& input_layer_shape, + const std::vector& output_layer) { + CHECK_EQ(input_layer.size(), input_layer_type.size()); + CHECK_EQ(input_layer.size(), input_layer_shape.size()); // Invoke tensorflow model. TfDriver runner(input_layer, input_layer_type, input_layer_shape, @@ -91,39 +98,51 @@ bool GenerateTestSpecFromTensorflowModel( return false; } - for (int i = 0; i < input_values.size(); i++) { - runner.SetInput(i, input_values[i]); - if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; - return false; - } - } - - runner.Invoke(); - if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; - return false; - } - - // Write test spec. + // Write first part of test spec, defining model and input shapes. stream << "load_model: " << tflite_model_path << "\n"; stream << "reshape {\n"; for (const auto& shape : input_layer_shape) { stream << " input: \"" << shape << "\"\n"; } stream << "}\n"; - stream << "invoke {\n"; - for (const auto& value : input_values) { - stream << " input: \"" << value << "\"\n"; - } - for (int i = 0; i < output_layer.size(); i++) { - stream << " output: \"" << runner.ReadOutput(i) << "\"\n"; + + // Generate inputs. + for (int i = 0; i < num_invocations; ++i) { + // Note that the input values are random, so each invocation will have a + // different set. + std::vector input_values = + GenerateInputValues(input_layer, input_layer_type, input_layer_shape); + if (input_values.empty()) return false; + + // Run TensorFlow. + for (int j = 0; j < input_values.size(); j++) { + runner.SetInput(j, input_values[j]); + if (!runner.IsValid()) { + cerr << runner.GetErrorMessage() << endl; + return false; + } + } + + runner.Invoke(); if (!runner.IsValid()) { cerr << runner.GetErrorMessage() << endl; return false; } + + // Write second part of test spec, with inputs and outputs. + stream << "invoke {\n"; + for (const auto& value : input_values) { + stream << " input: \"" << value << "\"\n"; + } + for (int j = 0; j < output_layer.size(); j++) { + stream << " output: \"" << runner.ReadOutput(j) << "\"\n"; + if (!runner.IsValid()) { + cerr << runner.GetErrorMessage() << endl; + return false; + } + } + stream << "}\n"; } - stream << "}\n"; return true; } diff --git a/tensorflow/contrib/lite/testing/generate_testspec.h b/tensorflow/contrib/lite/testing/generate_testspec.h index 6e31a853c3f7f82a89126ff83af784ffd418741a..bfaf5e7ec89bbdd85b68a7dc45d7686e143e5d3d 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.h +++ b/tensorflow/contrib/lite/testing/generate_testspec.h @@ -30,13 +30,15 @@ namespace testing { // stream: mutable iostream that contains the contents of test spec. // tensorflow_model_path: path to TensorFlow model. // tflite_model_path: path to tflite_model_path that the test spec runs +// num_invocations: how many pairs of inputs and outputs will be generated. // against. input_layer: names of input tensors. Example: input1 // input_layer_type: datatypes of input tensors. Example: float // input_layer_shape: shapes of input tensors, separated by comma. example: // 1,3,4 output_layer: names of output tensors. Example: output bool GenerateTestSpecFromTensorflowModel( std::iostream& stream, const string& tensorflow_model_path, - const string& tflite_model_path, const std::vector& input_layer, + const string& tflite_model_path, int num_invocations, + const std::vector& input_layer, const std::vector& input_layer_type, const std::vector& input_layer_shape, const std::vector& output_layer); diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index e85020448a572650c6a70d8b4dcb4e73faf0f8c8..c4e20312d891be6f659845fe4fc66e085955b81b 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -36,7 +36,13 @@ bool FLAGS_ignore_known_bugs = true; // TODO(b/71769302) zip_files_dir should have a more accurate default, if // possible string* FLAGS_zip_file_path = new string("./"); +#ifndef __ANDROID__ string* FLAGS_unzip_binary_path = new string("/usr/bin/unzip"); +#else +string* FLAGS_unzip_binary_path = new string("/system/bin/unzip"); +#endif +bool FLAGS_use_nnapi = false; +bool FLAGS_ignore_unsupported_nnapi = false; } // namespace // TensorFlow system environment for file system called. @@ -47,9 +53,6 @@ tensorflow::Env* env = tensorflow::Env::Default(); // Key is a substring of the test name and value is a bug number. // TODO(ahentz): make sure we clean this list up frequently. std::map kBrokenTests = { - // Add only supports float32. (and "constant" tests use Add) - {R"(^\/add_a.*int32)", "68808744"}, - {R"(^\/constant.*int32)", "68808744"}, {R"(^\/mul.*int32)", "68808744"}, {R"(^\/div.*int32)", "68808744"}, {R"(^\/sub.*int32)", "68808744"}, @@ -212,7 +215,7 @@ TEST_P(OpsTest, RunZipTests) { std::ifstream tflite_stream(tflite_test_case); ASSERT_TRUE(tflite_stream.is_open()) << tflite_test_case; - tflite::testing::TfLiteDriver test_driver(/*use_nnapi=*/true); + tflite::testing::TfLiteDriver test_driver(FLAGS_use_nnapi); test_driver.SetModelBaseDir(tflite_dir); string bug_number; @@ -223,16 +226,21 @@ TEST_P(OpsTest, RunZipTests) { } bool result = tflite::testing::ParseAndRunTests(&tflite_stream, &test_driver); + string message = test_driver.GetErrorMessage(); if (bug_number.empty()) { - EXPECT_TRUE(result) << test_driver.GetErrorMessage(); + if (FLAGS_use_nnapi && FLAGS_ignore_unsupported_nnapi && !result) { + EXPECT_EQ(message, string("Failed to invoke interpreter")) << message; + } else { + EXPECT_TRUE(result) << message; + } } else { if (FLAGS_ignore_known_bugs) { EXPECT_FALSE(result) << "Test was expected to fail but is now passing; " "you can mark http://b/" << bug_number << " as fixed! Yay!"; } else { - EXPECT_TRUE(result) << test_driver.GetErrorMessage() - << ": Possibly due to http://b/" << bug_number; + EXPECT_TRUE(result) << message << ": Possibly due to http://b/" + << bug_number; } } } @@ -273,7 +281,13 @@ int main(int argc, char** argv) { "Required: Location of the test zip file."), tensorflow::Flag("unzip_binary_path", tflite::testing::FLAGS_unzip_binary_path, - "Required: Location of a suitable unzip binary.")}; + "Required: Location of a suitable unzip binary."), + tensorflow::Flag("use_nnapi", &tflite::testing::FLAGS_use_nnapi, + "Whether to enable the NNAPI delegate"), + tensorflow::Flag("ignore_unsupported_nnapi", + &tflite::testing::FLAGS_ignore_unsupported_nnapi, + "Don't fail tests just because delegation to NNAPI " + "is not possible")}; bool success = tensorflow::Flags::Parse(&argc, argv, flags); if (!success || (argc == 2 && !strcmp(argv[1], "--helpfull"))) { fprintf(stderr, "%s", tensorflow::Flags::Usage(argv[0], flags).c_str()); @@ -281,6 +295,8 @@ int main(int argc, char** argv) { } ::tflite::LogToStderr(); + // TODO(mikie): googletest arguments do not work - maybe the tensorflow flags + // parser removes them? ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); } diff --git a/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc index 5afa0f800cdaa8bf70a11cb6e2ac64ace8138e79..f2c49fe389763110279b3dd1e4f13b1522de0460 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc +++ b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc @@ -20,12 +20,29 @@ int main(int argc, char** argv) { ::tflite::testing::DiffOptions options = ::tflite::testing::ParseTfliteDiffFlags(&argc, argv); if (options.tensorflow_model.empty()) return 1; + int failure_count = 0; - for (int i = 0; i < 100; i++) { - if (!tflite::testing::RunDiffTest(options)) { + for (int i = 0; i < options.num_runs_per_pass; i++) { + if (!tflite::testing::RunDiffTest(options, /*num_invocations=*/1)) { ++failure_count; } } - fprintf(stderr, "Num errors: %d\n", failure_count); + int failures_in_first_pass = failure_count; + + if (failure_count == 0) { + // Let's try again with num_invocations > 1 to make sure we can do multiple + // invocations without resetting the interpreter. + for (int i = 0; i < options.num_runs_per_pass; i++) { + if (!tflite::testing::RunDiffTest(options, /*num_invocations=*/2)) { + ++failure_count; + } + } + } + + fprintf(stderr, "Num errors in single-inference pass: %d\n", + failures_in_first_pass); + fprintf(stderr, "Num errors in multi-inference pass : %d\n", + failure_count - failures_in_first_pass); + return failure_count != 0 ? 1 : 0; } diff --git a/tensorflow/contrib/lite/testing/tflite_diff_flags.h b/tensorflow/contrib/lite/testing/tflite_diff_flags.h index 706108ed73bb3fd9bd784cffffe322d6981433e6..7a57e8d3fba29cd106eb038992bb5ed12bb457ae 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_flags.h +++ b/tensorflow/contrib/lite/testing/tflite_diff_flags.h @@ -30,6 +30,7 @@ DiffOptions ParseTfliteDiffFlags(int* argc, char** argv) { string input_layer_type; string input_layer_shape; string output_layer; + int32_t num_runs_per_pass = 100; } values; std::vector flags = { @@ -49,6 +50,8 @@ DiffOptions ParseTfliteDiffFlags(int* argc, char** argv) { tensorflow::Flag("output_layer", &values.output_layer, "Names of output tensors, separated by comma. Example " "output_1,output_2"), + tensorflow::Flag("num_runs_per_pass", &values.num_runs_per_pass, + "Number of full runs in each pass."), }; bool no_inputs = *argc == 1; @@ -63,7 +66,8 @@ DiffOptions ParseTfliteDiffFlags(int* argc, char** argv) { Split(values.input_layer, ","), Split(values.input_layer_type, ","), Split(values.input_layer_shape, ":"), - Split(values.output_layer, ",")}; + Split(values.output_layer, ","), + values.num_runs_per_pass}; } } // namespace testing diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.cc b/tensorflow/contrib/lite/testing/tflite_diff_util.cc index f601d3752ddb5df9f2b5ac73d9bc303efaade4a5..19f34c0a51e442804bf2824adc3a1d8bde1eb4b0 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_util.cc +++ b/tensorflow/contrib/lite/testing/tflite_diff_util.cc @@ -25,13 +25,14 @@ limitations under the License. namespace tflite { namespace testing { -bool RunDiffTest(const DiffOptions& options) { +bool RunDiffTest(const DiffOptions& options, int num_invocations) { std::stringstream tflite_stream; if (!GenerateTestSpecFromTensorflowModel( tflite_stream, options.tensorflow_model, options.tflite_model, - options.input_layer, options.input_layer_type, - options.input_layer_shape, options.output_layer)) + num_invocations, options.input_layer, options.input_layer_type, + options.input_layer_shape, options.output_layer)) { return false; + } TfLiteDriver tflite_driver(/*use_nnapi=*/true); tflite_driver.LoadModel(options.tflite_model); return tflite::testing::ParseAndRunTests(&tflite_stream, &tflite_driver); diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.h b/tensorflow/contrib/lite/testing/tflite_diff_util.h index 326fa6c3e28000dee9b6eb9cc5b3a6c5c87e28d0..4ab2f230fdcdfe4616ab1706aa41f0e806665f66 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_util.h +++ b/tensorflow/contrib/lite/testing/tflite_diff_util.h @@ -40,10 +40,14 @@ struct DiffOptions { // Names of output tensors. // Example output_1,output_2 std::vector output_layer; + // Number of full runs (from building interpreter to checking outputs) in + // each of the passes. The first pass has a single inference, while the + // second pass does multiple inferences back to back. + int num_runs_per_pass; }; // Run a single TensorFLow Lite diff test with a given options. -bool RunDiffTest(const DiffOptions& options); +bool RunDiffTest(const DiffOptions& options, int num_invocations); } // namespace testing } // namespace tflite diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc index fc28faf52405b300dc6e4f0aab33122bb5e98f12..4d08fb545801521213890a4f5a9b010de57b27cd 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.cc +++ b/tensorflow/contrib/lite/testing/tflite_driver.cc @@ -163,6 +163,7 @@ void TfLiteDriver::LoadModel(const string& bin_file_path) { Invalidate("Failed build interpreter"); return; } + interpreter_->UseNNAPI(use_nnapi_); must_allocate_tensors_ = true; } @@ -284,9 +285,11 @@ bool TfLiteDriver::CheckResults() { } void TfLiteDriver::ResetLSTMStateTensors() { - // This is a workaround for initializing state tensors for LSTM. - // TODO(ycling): Refactoring and find a better way to initialize state - // tensors. Maybe write the reset instructions into the test data. + interpreter_->ResetVariableTensorsToZero(); + + // Below is a workaround for initializing state tensors for LSTM. + // TODO(ycling): Remove the code below after nobody is using the 18-inputs + // definition. for (auto node_index : interpreter_->execution_plan()) { const auto& node_and_reg = interpreter_->node_and_registration(node_index); const auto& node = node_and_reg->first; @@ -296,19 +299,12 @@ void TfLiteDriver::ResetLSTMStateTensors() { const auto* params = reinterpret_cast(node.builtin_data); if (params->kernel_type == kTfLiteLSTMFullKernel && - node.outputs->size >= 2) { + node.inputs->size == 18 && node.outputs->size >= 2) { // The first 2 outputs of LSTM are state tensors. for (int i = 0; i < 2; ++i) { int node_index = node.outputs->data[i]; ResetTensor(node_index); } - } else if (params->kernel_type == kTfLiteLSTMBasicKernel && - node.inputs->size == 5) { - // The 2th and 5th inputs are state tensors. - for (int i : {1, 4}) { - int node_index = node.inputs->data[i]; - ResetTensor(node_index); - } } } } diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 7ea4f32ef694f3b0dc9c030b9440268ac79848aa..209dce56cbdfbbff5884aa9961bd29e9cf98f49d 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -143,7 +143,6 @@ cc_library( ":toco_graphviz_dump_options", ":toco_port", ":types_proto_cc", - "//tensorflow/cc/saved_model:tag_constants", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "@com_google_absl//absl/strings", @@ -169,41 +168,6 @@ cc_library( ], ) -cc_library( - name = "toco_saved_model", - srcs = [ - "toco_saved_model.cc", - ], - hdrs = [ - "toco_saved_model.h", - ], - visibility = ["//visibility:public"], - deps = [ - ":model_cmdline_flags", - ":model_flags_proto_cc", - ":toco_flags_proto_cc", - ":types_proto_cc", - "//tensorflow/cc/tools:freeze_saved_model", - "//tensorflow/core:protos_all_cc", - "@com_google_absl//absl/strings", - ], -) - -tf_cc_test( - name = "toco_saved_model_test", - srcs = ["toco_saved_model_test.cc"], - deps = [ - ":model_cmdline_flags", - ":toco_cmdline_flags", - ":toco_saved_model", - "//tensorflow/cc:cc_ops", - "//tensorflow/cc:scope", - "//tensorflow/core:test", - "@com_google_absl//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - cc_library( name = "graph_transformations", srcs = [ @@ -213,6 +177,7 @@ cc_library( "graph_transformations/convert_squeeze_to_reshape.cc", "graph_transformations/convert_trivial_addn_to_add.cc", "graph_transformations/convert_trivial_stack_to_reshape.cc", + "graph_transformations/convert_trivial_tile_to_concat.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", "graph_transformations/create_im2col_arrays.cc", "graph_transformations/dequantize.cc", @@ -220,10 +185,10 @@ cc_library( "graph_transformations/drop_im2col_arrays.cc", "graph_transformations/ensure_bias_vectors.cc", "graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc", - "graph_transformations/experimental_shuffle_fc_weights.cc", "graph_transformations/fuse_activation_functions.cc", "graph_transformations/fuse_binary_into_following_affine.cc", "graph_transformations/fuse_binary_into_preceding_affine.cc", + "graph_transformations/fuse_broadcast_into_following_binary.cc", "graph_transformations/graph_transformations.cc", "graph_transformations/hardcode_min_max.cc", "graph_transformations/identify_dilated_conv.cc", @@ -237,6 +202,7 @@ cc_library( "graph_transformations/lstm_utils.cc", "graph_transformations/make_initial_dequantize_operator.cc", "graph_transformations/merge_reshape_into_preceding_transpose.cc", + "graph_transformations/move_binary_operator_before_reshape.cc", "graph_transformations/propagate_activation_function_into_constants.cc", "graph_transformations/propagate_array_data_types.cc", "graph_transformations/propagate_default_min_max.cc", @@ -293,8 +259,8 @@ cc_library( "graph_transformations/resolve_tensorflow_matmul.cc", "graph_transformations/resolve_tensorflow_merge.cc", "graph_transformations/resolve_tensorflow_switch.cc", - "graph_transformations/resolve_tensorflow_tile.cc", "graph_transformations/resolve_transpose_attributes.cc", + "graph_transformations/shuffle_fc_weights.cc", "graph_transformations/unfuse_activation_functions.cc", "graph_transformations/unpartition_embedding_lookup.cc", "graph_transformations/unroll_batch_matmul.cc", @@ -374,6 +340,7 @@ tf_cc_test( ":toco_tooling", "//tensorflow/core:framework", "//tensorflow/core:graph", + "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "@com_google_googletest//:gtest_main", ], @@ -411,6 +378,7 @@ tf_cc_test( deps = [ ":model", ":tooling_util", + "//tensorflow/core:lib", "@com_google_googletest//:gtest_main", ], ) @@ -428,7 +396,6 @@ tf_cc_binary( ":toco_cmdline_flags", ":toco_flags_proto_cc", ":toco_port", - ":toco_saved_model", ":toco_tooling", ":types_proto_cc", "//tensorflow/core:lib", diff --git a/tensorflow/contrib/lite/toco/README.md b/tensorflow/contrib/lite/toco/README.md index 522e260ad2a14c5f8e080c0a0f538f4192b7ed2d..2db6a627ab59604a99cafe3b38df08b70092d989 100644 --- a/tensorflow/contrib/lite/toco/README.md +++ b/tensorflow/contrib/lite/toco/README.md @@ -17,11 +17,12 @@ Usage information is given in these documents: Once an application developer has a trained TensorFlow model, TOCO will accept that model and generate a TensorFlow Lite [FlatBuffer](https://google.github.io/flatbuffers/) file. TOCO currently supports -[SavedModels](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators) -and frozen graphs (models generated via -[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)). -The TensorFlow Lite FlatBuffer file can be shipped to client devices, generally -mobile devices, where the TensorFlow Lite interpreter handles them on-device. -This flow is represented in the diagram below. +[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators), +frozen graphs (models generated via +[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)), +and `tf.Keras` model files. The TensorFlow Lite FlatBuffer file can be shipped +to client devices, generally mobile devices, where the TensorFlow Lite +interpreter handles them on-device. This flow is represented in the diagram +below. ![drawing](g3doc/toco_landscape.svg) diff --git a/tensorflow/contrib/lite/toco/args.h b/tensorflow/contrib/lite/toco/args.h index 9f5ca66d050f0ead9b8856c77dba8d9bbd182d10..aef35ad490656c09a7d7314aa033bc985b3af661 100644 --- a/tensorflow/contrib/lite/toco/args.h +++ b/tensorflow/contrib/lite/toco/args.h @@ -21,13 +21,13 @@ limitations under the License. #include #include #include +#include "tensorflow/contrib/lite/toco/toco_port.h" #if defined(PLATFORM_GOOGLE) #include "strings/split.h" +#include "strings/strip.h" #endif #include "absl/strings/numbers.h" #include "absl/strings/str_split.h" -#include "tensorflow/cc/saved_model/tag_constants.h" -#include "tensorflow/contrib/lite/toco/toco_port.h" #include "tensorflow/contrib/lite/toco/toco_types.h" namespace toco { @@ -145,8 +145,10 @@ class Arg final { } string outer_member_copy = outer_member; absl::StripAsciiWhitespace(&outer_member); - if (!TryStripPrefixString(outer_member, "{", &outer_member)) return false; - if (!TryStripSuffixString(outer_member, "}", &outer_member)) return false; + if (!strings::TryStripPrefixString(outer_member, "{", &outer_member)) + return false; + if (!strings::TryStripSuffixString(outer_member, "}", &outer_member)) + return false; const std::vector inner_fields_vector = absl::StrSplit(outer_member, ','); @@ -223,7 +225,7 @@ struct ParsedTocoFlags { Arg output_file; Arg input_format = Arg("TENSORFLOW_GRAPHDEF"); Arg output_format = Arg("TFLITE"); - Arg savedmodel_tagset = Arg(tensorflow::kSavedModelTagServe); + Arg savedmodel_tagset; // TODO(aselle): command_line_flags doesn't support doubles Arg default_ranges_min = Arg(0.); Arg default_ranges_max = Arg(0.); diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index 8913b5c3ea962725ef2bed73e670e8f0b988a591..6877fb237c0514a972589ac0301647104f5ed7ed 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -146,6 +146,7 @@ NodeProperties GetPropertiesForArray(const Model& model, NodeProperties node_properties; node_properties.color = GetColorForArray(model, array_name); node_properties.label = absl::StrReplaceAll(array_name, {{"/", "/\\n"}}); + node_properties.log2_buffer_size = 0.0f; // Append array shape to the label. auto& array = model.GetArray(array_name); @@ -165,9 +166,12 @@ NodeProperties GetPropertiesForArray(const Model& model, } node_properties.label += "]"; - int buffer_size = RequiredBufferSizeForShape(array.shape()); - node_properties.log2_buffer_size = - std::log2(static_cast(buffer_size)); + int buffer_size = 0; + if (IsValid(array.shape())) { + buffer_size = RequiredBufferSizeForShape(array.shape()); + node_properties.log2_buffer_size = + std::log2(static_cast(buffer_size)); + } if (array.buffer) { const auto& array = model.GetArray(array_name); @@ -200,8 +204,6 @@ NodeProperties GetPropertiesForArray(const Model& model, AppendF(&node_properties.label, "}"); } } - } else { - node_properties.log2_buffer_size = 0.0f; } if (array.minmax) { @@ -225,7 +227,7 @@ NodeProperties GetPropertiesForArray(const Model& model, NodeProperties GetPropertiesForOperator(const Operator& op) { NodeProperties node_properties; - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { node_properties.label = static_cast(op).tensorflow_op; } else { diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 76ce1c58025ce84219ed6a8a0b6f2ea6e18e037c..6be6b25f9318deb08bd427d5e3166909fae8f3ea 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -145,7 +145,7 @@ void ConvertFloatTensorConst(const string& name, const Shape& input_shape, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -162,7 +162,7 @@ void ConvertFloatTensorConst(const string& name, const Shape& input_shape, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -178,7 +178,7 @@ void ConvertFloatTensorConst(const Model& model, const string& name, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -199,7 +199,7 @@ void ConvertFloatTensorConst(const Model& model, const string& name, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -222,7 +222,7 @@ void ConvertIntTensorConst(const Model& model, const string& name, } CHECK(model.HasArray(name)); const auto& array = model.GetArray(name); - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -245,7 +245,7 @@ void CreateIntTensorConst(const string& name, const std::vector& data, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -268,7 +268,7 @@ void CreateMatrixShapeTensorConst(const string& name, int rows, int cols, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -286,7 +286,7 @@ void CreateDummyConcatDimTensorConst(const string& name, int dim, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -301,7 +301,7 @@ void CreateReshapeShapeTensorConst(const string& name, if (HasAlreadyExportedConst(name, *tensorflow_graph)) { return; } - auto* const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); const_op->set_op("Const"); const_op->set_name(name); (*const_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -341,7 +341,7 @@ void ConvertConvOperator(const Model& model, const ConvOperator& src_op, conv_output += "/conv"; } - auto* conv2d_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* conv2d_op = tensorflow_graph->add_node(); conv2d_op->set_op("Conv2D"); conv2d_op->set_name(conv_output); *conv2d_op->add_input() = src_op.inputs[0]; @@ -377,7 +377,7 @@ void ConvertConvOperator(const Model& model, const ConvOperator& src_op, (*conv2d_op->mutable_attr())["padding"].set_s(padding); if (has_bias) { - auto* biasadd_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node(); biasadd_op->set_op("BiasAdd"); biasadd_op->set_name(src_op.outputs[0]); biasadd_op->add_input(conv_output); @@ -409,7 +409,7 @@ void ConvertDepthwiseConvOperator(const Model& model, conv_output += "/conv"; } - auto* dc2d_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* dc2d_op = tensorflow_graph->add_node(); dc2d_op->set_op("DepthwiseConv2dNative"); dc2d_op->set_name(conv_output); *dc2d_op->add_input() = src_op.inputs[0]; @@ -457,7 +457,7 @@ void ConvertDepthwiseConvOperator(const Model& model, (*dc2d_op->mutable_attr())["padding"].set_s(padding); if (has_bias) { - auto* biasadd_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node(); biasadd_op->set_op("BiasAdd"); biasadd_op->set_name(src_op.outputs[0]); biasadd_op->add_input(conv_output); @@ -482,7 +482,7 @@ void ConvertDepthwiseConvOperator(const Model& model, void ConvertTransposeConvOperator(const Model& model, const TransposeConvOperator& src_op, GraphDef* tensorflow_graph) { - auto* conv2d_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* conv2d_op = tensorflow_graph->add_node(); conv2d_op->set_op("Conv2DBackpropInput"); conv2d_op->set_name(src_op.outputs[0]); *conv2d_op->add_input() = src_op.inputs[0]; @@ -494,7 +494,7 @@ void ConvertTransposeConvOperator(const Model& model, const auto& weights_array = model.GetArray(weights_array_name); CHECK(weights_array.buffer->type == ArrayDataType::kFloat); ConvertFloatTensorConst(model, weights_array_name, AxesOrder::kOHWI, - AxesOrder::kHWIO, tensorflow_graph); + AxesOrder::kHWOI, tensorflow_graph); auto& strides = (*conv2d_op->mutable_attr())["strides"]; strides.mutable_list()->add_i(1); strides.mutable_list()->add_i(src_op.stride_height); @@ -514,7 +514,7 @@ void ConvertTransposeConvOperator(const Model& model, void ConvertDepthToSpaceOperator(const Model& model, const DepthToSpaceOperator& src_op, GraphDef* tensorflow_graph) { - auto* op = tensorflow_graph->add_node(); + tensorflow::NodeDef* op = tensorflow_graph->add_node(); op->set_op("DepthToSpace"); op->set_name(src_op.outputs[0]); *op->add_input() = src_op.inputs[0]; @@ -525,7 +525,7 @@ void ConvertDepthToSpaceOperator(const Model& model, void ConvertSpaceToDepthOperator(const Model& model, const SpaceToDepthOperator& src_op, GraphDef* tensorflow_graph) { - auto* op = tensorflow_graph->add_node(); + tensorflow::NodeDef* op = tensorflow_graph->add_node(); op->set_op("SpaceToDepth"); op->set_name(src_op.outputs[0]); *op->add_input() = src_op.inputs[0]; @@ -546,7 +546,7 @@ void ConvertFullyConnectedOperator(const Model& model, CHECK_EQ(fc_weights_shape.dimensions_count(), 2); CreateMatrixShapeTensorConst(reshape_shape, fc_weights_shape.dims(1), -1, tensorflow_graph); - auto* reshape_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node(); reshape_op->set_op("Reshape"); reshape_op->set_name(reshape_output); reshape_op->add_input(src_op.inputs[0]); @@ -568,7 +568,7 @@ void ConvertFullyConnectedOperator(const Model& model, const string transpose_perm = AvailableArrayName(model, transpose_output + "/perm"); CreateIntTensorConst(transpose_perm, {1, 0}, {2}, tensorflow_graph); - auto transpose_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* transpose_op = tensorflow_graph->add_node(); transpose_op->set_op("Transpose"); transpose_op->set_name(transpose_output); *transpose_op->add_input() = src_op.inputs[1]; @@ -577,7 +577,7 @@ void ConvertFullyConnectedOperator(const Model& model, GetTensorFlowDataType(model, src_op.inputs[1])); (*transpose_op->mutable_attr())["Tperm"].set_type(DT_INT32); - auto* matmul_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* matmul_op = tensorflow_graph->add_node(); matmul_op->set_op("MatMul"); matmul_op->set_name(matmul_output); *matmul_op->add_input() = reshape_output; @@ -590,7 +590,7 @@ void ConvertFullyConnectedOperator(const Model& model, // Add the bias, if it exists. if (has_bias) { - auto* biasadd_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node(); biasadd_op->set_op("BiasAdd"); biasadd_op->set_name(src_op.outputs[0]); biasadd_op->add_input(matmul_output); @@ -615,7 +615,7 @@ void ConvertFullyConnectedOperator(const Model& model, void ConvertAddOperator(const Model& model, const AddOperator& src_op, GraphDef* tensorflow_graph) { - auto* add_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* add_op = tensorflow_graph->add_node(); add_op->set_op("Add"); add_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -626,7 +626,7 @@ void ConvertAddOperator(const Model& model, const AddOperator& src_op, void ConvertAddNOperator(const Model& model, const AddNOperator& src_op, GraphDef* tensorflow_graph) { - auto* add_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* add_op = tensorflow_graph->add_node(); add_op->set_op("AddN"); add_op->set_name(src_op.outputs[0]); for (const auto& input : src_op.inputs) { @@ -638,7 +638,7 @@ void ConvertAddNOperator(const Model& model, const AddNOperator& src_op, void ConvertMulOperator(const Model& model, const MulOperator& src_op, GraphDef* tensorflow_graph) { - auto* add_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* add_op = tensorflow_graph->add_node(); add_op->set_op("Mul"); add_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -649,7 +649,7 @@ void ConvertMulOperator(const Model& model, const MulOperator& src_op, void ConvertReluOperator(const ReluOperator& src_op, GraphDef* tensorflow_graph) { - auto* relu_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* relu_op = tensorflow_graph->add_node(); relu_op->set_op("Relu"); relu_op->set_name(src_op.outputs[0]); *relu_op->add_input() = src_op.inputs[0]; @@ -662,7 +662,7 @@ void ConvertRelu1Operator(const Relu1Operator& src_op, const string min_bounds = src_op.outputs[0] + "/min_bounds"; const string max_output = src_op.outputs[0] + "/max_output"; - auto* max_bounds_const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* max_bounds_const_op = tensorflow_graph->add_node(); max_bounds_const_op->set_op("Const"); max_bounds_const_op->set_name(max_bounds); (*max_bounds_const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -671,7 +671,7 @@ void ConvertRelu1Operator(const Relu1Operator& src_op, max_bounds_const_op_tensor->set_dtype(DT_FLOAT); max_bounds_const_op_tensor->add_float_val(-1.0f); - auto* min_bounds_const_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* min_bounds_const_op = tensorflow_graph->add_node(); min_bounds_const_op->set_op("Const"); min_bounds_const_op->set_name(min_bounds); (*min_bounds_const_op->mutable_attr())["dtype"].set_type(DT_FLOAT); @@ -680,14 +680,14 @@ void ConvertRelu1Operator(const Relu1Operator& src_op, min_bounds_const_op_tensor->set_dtype(DT_FLOAT); min_bounds_const_op_tensor->add_float_val(1.0f); - auto* max_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* max_op = tensorflow_graph->add_node(); max_op->set_op("Maximum"); max_op->set_name(max_output); *max_op->add_input() = src_op.inputs[0]; *max_op->add_input() = max_bounds; (*max_op->mutable_attr())["T"].set_type(DT_FLOAT); - auto* min_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* min_op = tensorflow_graph->add_node(); min_op->set_op("Minimum"); min_op->set_name(src_op.outputs[0]); *min_op->add_input() = max_output; @@ -697,7 +697,7 @@ void ConvertRelu1Operator(const Relu1Operator& src_op, void ConvertRelu6Operator(const Relu6Operator& src_op, GraphDef* tensorflow_graph) { - auto* relu_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* relu_op = tensorflow_graph->add_node(); relu_op->set_op("Relu6"); relu_op->set_name(src_op.outputs[0]); *relu_op->add_input() = src_op.inputs[0]; @@ -705,7 +705,7 @@ void ConvertRelu6Operator(const Relu6Operator& src_op, } void ConvertLogOperator(const LogOperator& src_op, GraphDef* tensorflow_graph) { - auto* op = tensorflow_graph->add_node(); + tensorflow::NodeDef* op = tensorflow_graph->add_node(); op->set_op("Log"); op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -715,7 +715,7 @@ void ConvertLogOperator(const LogOperator& src_op, GraphDef* tensorflow_graph) { void ConvertLogisticOperator(const LogisticOperator& src_op, GraphDef* tensorflow_graph) { - auto* relu_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* relu_op = tensorflow_graph->add_node(); relu_op->set_op("Sigmoid"); relu_op->set_name(src_op.outputs[0]); *relu_op->add_input() = src_op.inputs[0]; @@ -724,7 +724,7 @@ void ConvertLogisticOperator(const LogisticOperator& src_op, void ConvertTanhOperator(const TanhOperator& src_op, GraphDef* tensorflow_graph) { - auto* tanh_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* tanh_op = tensorflow_graph->add_node(); tanh_op->set_op("Tanh"); tanh_op->set_name(src_op.outputs[0]); *tanh_op->add_input() = src_op.inputs[0]; @@ -735,8 +735,7 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, GraphDef* tensorflow_graph) { string softmax_input; Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); - if (providing_op != nullptr && - providing_op->type == OperatorType::kTensorFlowReshape) { + if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) { softmax_input = src_op.inputs[0]; } else { // Insert a reshape operator that reduces the dimensions down to the 2 that @@ -745,7 +744,7 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, const string softmax_size = src_op.outputs[0] + "/softmax_insert_size"; softmax_input = reshape_output; - auto* reshape_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node(); reshape_op->set_op("Reshape"); reshape_op->set_name(reshape_output); *reshape_op->add_input() = src_op.inputs[0]; @@ -762,7 +761,7 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, CreateReshapeShapeTensorConst(softmax_size, shape_data, tensorflow_graph); } - auto* softmax_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* softmax_op = tensorflow_graph->add_node(); softmax_op->set_op("Softmax"); softmax_op->set_name(src_op.outputs[0]); *softmax_op->add_input() = softmax_input; @@ -776,8 +775,7 @@ void ConvertLogSoftmaxOperator(const Model& model, GraphDef* tensorflow_graph) { string softmax_input; Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); - if (providing_op != nullptr && - providing_op->type == OperatorType::kTensorFlowReshape) { + if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) { softmax_input = src_op.inputs[0]; } else { // Insert a reshape operator that reduces the dimensions down to the 2 that @@ -787,7 +785,7 @@ void ConvertLogSoftmaxOperator(const Model& model, const string softmax_size = src_op.outputs[0] + "/log_softmax_insert_size"; softmax_input = reshape_output; - auto* reshape_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node(); reshape_op->set_op("Reshape"); reshape_op->set_name(reshape_output); *reshape_op->add_input() = src_op.inputs[0]; @@ -804,7 +802,7 @@ void ConvertLogSoftmaxOperator(const Model& model, CreateReshapeShapeTensorConst(softmax_size, shape_data, tensorflow_graph); } - auto* log_softmax_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* log_softmax_op = tensorflow_graph->add_node(); log_softmax_op->set_op("LogSoftmax"); log_softmax_op->set_name(src_op.outputs[0]); *log_softmax_op->add_input() = softmax_input; @@ -819,7 +817,7 @@ void ConvertL2NormalizationOperator(const L2NormalizationOperator& src_op, const string rsqrt_output = src_op.outputs[0] + "/rsqrt"; const string rsqrt_tiled_output = src_op.outputs[0] + "/rsqrt_tiled"; - auto* sum_reduction_indices_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sum_reduction_indices_op = tensorflow_graph->add_node(); sum_reduction_indices_op->set_op("Const"); sum_reduction_indices_op->set_name(sum_reduction_indices); (*sum_reduction_indices_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -833,26 +831,26 @@ void ConvertL2NormalizationOperator(const L2NormalizationOperator& src_op, sum_reduction_indices_tensor->add_int_val(0); sum_reduction_indices_tensor->add_int_val(1); - auto* square_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* square_op = tensorflow_graph->add_node(); square_op->set_op("Square"); square_op->set_name(square_output); *square_op->add_input() = src_op.inputs[0]; (*square_op->mutable_attr())["T"].set_type(DT_FLOAT); - auto* sum_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sum_op = tensorflow_graph->add_node(); sum_op->set_op("Sum"); sum_op->set_name(sum_output); *sum_op->add_input() = square_output; *sum_op->add_input() = sum_reduction_indices; (*sum_op->mutable_attr())["T"].set_type(DT_FLOAT); - auto* rsqrt_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* rsqrt_op = tensorflow_graph->add_node(); rsqrt_op->set_op("Rsqrt"); rsqrt_op->set_name(rsqrt_output); *rsqrt_op->add_input() = sum_output; (*rsqrt_op->mutable_attr())["T"].set_type(DT_FLOAT); - auto* mul_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* mul_op = tensorflow_graph->add_node(); mul_op->set_op("Mul"); mul_op->set_name(src_op.outputs[0]); *mul_op->add_input() = src_op.inputs[0]; @@ -863,7 +861,7 @@ void ConvertL2NormalizationOperator(const L2NormalizationOperator& src_op, void ConvertLocalResponseNormalizationOperator( const LocalResponseNormalizationOperator& src_op, GraphDef* tensorflow_graph) { - auto* lrn_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* lrn_op = tensorflow_graph->add_node(); lrn_op->set_op("LRN"); lrn_op->set_name(src_op.outputs[0]); *lrn_op->add_input() = src_op.inputs[0]; @@ -875,7 +873,7 @@ void ConvertLocalResponseNormalizationOperator( void ConvertFakeQuantOperator(const FakeQuantOperator& src_op, GraphDef* tensorflow_graph) { - auto* fakequant_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* fakequant_op = tensorflow_graph->add_node(); fakequant_op->set_op("FakeQuantWithMinMaxArgs"); fakequant_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -890,7 +888,7 @@ void ConvertFakeQuantOperator(const FakeQuantOperator& src_op, void ConvertMaxPoolOperator(const MaxPoolOperator& src_op, GraphDef* tensorflow_graph) { - auto* maxpool_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* maxpool_op = tensorflow_graph->add_node(); maxpool_op->set_op("MaxPool"); maxpool_op->set_name(src_op.outputs[0]); *maxpool_op->add_input() = src_op.inputs[0]; @@ -918,7 +916,7 @@ void ConvertMaxPoolOperator(const MaxPoolOperator& src_op, void ConvertAveragePoolOperator(const AveragePoolOperator& src_op, GraphDef* tensorflow_graph) { - auto* avgpool_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* avgpool_op = tensorflow_graph->add_node(); avgpool_op->set_op("AvgPool"); avgpool_op->set_name(src_op.outputs[0]); *avgpool_op->add_input() = src_op.inputs[0]; @@ -947,7 +945,7 @@ void ConvertAveragePoolOperator(const AveragePoolOperator& src_op, void ConvertConcatenationOperator(const Model& model, const ConcatenationOperator& src_op, GraphDef* tensorflow_graph) { - auto* dc_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* dc_op = tensorflow_graph->add_node(); dc_op->set_op("ConcatV2"); dc_op->set_name(src_op.outputs[0]); const string dummy_axis = src_op.outputs[0] + "/axis"; @@ -965,7 +963,7 @@ void ConvertConcatenationOperator(const Model& model, void ConvertTensorFlowReshapeOperator(const Model& model, const TensorFlowReshapeOperator& src_op, GraphDef* tensorflow_graph) { - auto* reshape_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node(); reshape_op->set_op("Reshape"); reshape_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -987,7 +985,7 @@ void ConvertL2PoolOperator(const L2PoolOperator& src_op, const string square_output = src_op.outputs[0] + "/square"; const string avgpool_output = src_op.outputs[0] + "/avgpool"; - auto* square_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* square_op = tensorflow_graph->add_node(); square_op->set_op("Square"); square_op->set_name(square_output); *square_op->add_input() = src_op.inputs[0]; @@ -1002,7 +1000,7 @@ void ConvertL2PoolOperator(const L2PoolOperator& src_op, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } - auto* avgpool_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* avgpool_op = tensorflow_graph->add_node(); avgpool_op->set_op("AvgPool"); avgpool_op->set_name(avgpool_output); *avgpool_op->add_input() = square_output; @@ -1020,7 +1018,7 @@ void ConvertL2PoolOperator(const L2PoolOperator& src_op, ksize.mutable_list()->add_i(src_op.kwidth); ksize.mutable_list()->add_i(1); - auto* sqrt_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sqrt_op = tensorflow_graph->add_node(); sqrt_op->set_op("Sqrt"); sqrt_op->set_name(src_op.outputs[0]); *sqrt_op->add_input() = avgpool_output; @@ -1029,7 +1027,7 @@ void ConvertL2PoolOperator(const L2PoolOperator& src_op, void ConvertSquareOperator(const TensorFlowSquareOperator& src_op, GraphDef* tensorflow_graph) { - auto* square_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* square_op = tensorflow_graph->add_node(); square_op->set_op("Square"); square_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1039,7 +1037,7 @@ void ConvertSquareOperator(const TensorFlowSquareOperator& src_op, void ConvertSqrtOperator(const TensorFlowSqrtOperator& src_op, GraphDef* tensorflow_graph) { - auto* sqrt_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sqrt_op = tensorflow_graph->add_node(); sqrt_op->set_op("Sqrt"); sqrt_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1047,10 +1045,23 @@ void ConvertSqrtOperator(const TensorFlowSqrtOperator& src_op, (*sqrt_op->mutable_attr())["T"].set_type(DT_FLOAT); } +void ConvertRsqrtOperator(const Model& model, + const TensorFlowRsqrtOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* rsqrt_op = tensorflow_graph->add_node(); + rsqrt_op->set_op("Rsqrt"); + rsqrt_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 1); + *rsqrt_op->add_input() = src_op.inputs[0]; + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); + (*rsqrt_op->mutable_attr())["T"].set_type(data_type); +} + void ConvertSplitOperator(const Model& model, const TensorFlowSplitOperator& src_op, GraphDef* tensorflow_graph) { - auto* split_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* split_op = tensorflow_graph->add_node(); split_op->set_op("Split"); split_op->set_name(src_op.outputs[0]); for (const auto& input : src_op.inputs) { @@ -1071,7 +1082,7 @@ void ConvertSplitOperator(const Model& model, void ConvertCastOperator(const Model& model, const CastOperator& src_op, GraphDef* tensorflow_graph) { - auto* cast_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* cast_op = tensorflow_graph->add_node(); cast_op->set_op("Cast"); cast_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1085,7 +1096,7 @@ void ConvertCastOperator(const Model& model, const CastOperator& src_op, void ConvertFloorOperator(const Model& model, const FloorOperator& src_op, GraphDef* tensorflow_graph) { - auto* floor_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* floor_op = tensorflow_graph->add_node(); floor_op->set_op("Floor"); floor_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1095,7 +1106,7 @@ void ConvertFloorOperator(const Model& model, const FloorOperator& src_op, void ConvertGatherOperator(const Model& model, const GatherOperator& src_op, GraphDef* tensorflow_graph) { - auto* gather_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* gather_op = tensorflow_graph->add_node(); gather_op->set_op("Gather"); gather_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1103,13 +1114,14 @@ void ConvertGatherOperator(const Model& model, const GatherOperator& src_op, *gather_op->add_input() = src_op.inputs[1]; (*gather_op->mutable_attr())["Tindices"].set_type(DT_INT32); - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*gather_op->mutable_attr())["Tparams"].set_type(params_type); } void ConvertArgMaxOperator(const Model& model, const ArgMaxOperator& src_op, GraphDef* tensorflow_graph) { - auto* argmax_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* argmax_op = tensorflow_graph->add_node(); argmax_op->set_op("ArgMax"); argmax_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1126,7 +1138,7 @@ void ConvertArgMaxOperator(const Model& model, const ArgMaxOperator& src_op, void ConvertTransposeOperator(const Model& model, const TransposeOperator& src_op, GraphDef* tensorflow_graph) { - auto* transpose_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* transpose_op = tensorflow_graph->add_node(); transpose_op->set_op("Transpose"); transpose_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1141,7 +1153,7 @@ void ConvertTransposeOperator(const Model& model, void ConvertTensorFlowShapeOperator(const Model& model, const TensorFlowShapeOperator& src_op, GraphDef* tensorflow_graph) { - auto* shape_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* shape_op = tensorflow_graph->add_node(); shape_op->set_op("Shape"); shape_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1154,7 +1166,7 @@ void ConvertTensorFlowShapeOperator(const Model& model, void ConvertRankOperator(const Model& model, const RankOperator& src_op, GraphDef* tensorflow_graph) { - auto* rank_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* rank_op = tensorflow_graph->add_node(); rank_op->set_op("Rank"); rank_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); @@ -1165,7 +1177,7 @@ void ConvertRankOperator(const Model& model, const RankOperator& src_op, void ConvertRangeOperator(const Model& model, const RangeOperator& src_op, GraphDef* tensorflow_graph) { - auto* range_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* range_op = tensorflow_graph->add_node(); range_op->set_op("Range"); range_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); @@ -1178,7 +1190,7 @@ void ConvertRangeOperator(const Model& model, const RangeOperator& src_op, void ConvertStackOperator(const Model& model, const StackOperator& src_op, GraphDef* tensorflow_graph) { - auto* stack_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* stack_op = tensorflow_graph->add_node(); stack_op->set_op("Stack"); stack_op->set_name(src_op.outputs[0]); for (const auto& input : src_op.inputs) { @@ -1191,7 +1203,7 @@ void ConvertStackOperator(const Model& model, const StackOperator& src_op, void ConvertFillOperator(const Model& model, const FillOperator& src_op, GraphDef* tensorflow_graph) { - auto* fill_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* fill_op = tensorflow_graph->add_node(); fill_op->set_op("Fill"); fill_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1205,7 +1217,7 @@ void ConvertFillOperator(const Model& model, const FillOperator& src_op, void ConvertFloorDivOperator(const Model& model, const FloorDivOperator& src_op, GraphDef* tensorflow_graph) { - auto* floor_div_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* floor_div_op = tensorflow_graph->add_node(); floor_div_op->set_op("FloorDiv"); floor_div_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1218,7 +1230,7 @@ void ConvertFloorDivOperator(const Model& model, const FloorDivOperator& src_op, void ConvertExpandDimsOperator(const Model& model, const ExpandDimsOperator& src_op, GraphDef* tensorflow_graph) { - auto* expand_dims_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* expand_dims_op = tensorflow_graph->add_node(); expand_dims_op->set_op("ExpandDims"); expand_dims_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1233,7 +1245,7 @@ void ConvertExpandDimsOperator(const Model& model, void ConvertResizeBilinearOperator(const Model& model, const ResizeBilinearOperator& src_op, GraphDef* tensorflow_graph) { - auto* resize_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* resize_op = tensorflow_graph->add_node(); resize_op->set_op("ResizeBilinear"); resize_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1283,7 +1295,7 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // works the same since the tensor has the same underlying data layout. const string axis_output = concat_output + "/axis"; CreateDummyConcatDimTensorConst(axis_output, axis, tensorflow_graph); - auto* concat_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* concat_op = tensorflow_graph->add_node(); concat_op->set_op("ConcatV2"); concat_op->set_name(concat_output); *concat_op->add_input() = src_op.inputs[LstmCellOperator::DATA_INPUT]; @@ -1311,7 +1323,7 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // Fully connected matrix multiply const string matmul_output = base + "MatMul"; - auto* matmul_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* matmul_op = tensorflow_graph->add_node(); matmul_op->set_op("MatMul"); matmul_op->set_name(matmul_output); *matmul_op->add_input() = concat_output; @@ -1340,7 +1352,7 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // Add biases string biasadd_output = base + "BiasAdd"; - auto* biasadd_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node(); biasadd_op->set_op("BiasAdd"); biasadd_op->set_name(biasadd_output); biasadd_op->add_input(matmul_output); @@ -1353,7 +1365,7 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // The dimension is the same as the concatenation dimension CreateDummyConcatDimTensorConst(split_dim_output, axis, tensorflow_graph); string split_output = base + "split"; - auto* split_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* split_op = tensorflow_graph->add_node(); split_op->set_op("Split"); split_op->set_name(split_output); *split_op->add_input() = split_dim_output; @@ -1363,21 +1375,21 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, // Activation functions and memory computations const string tanh_0_output = base + "Tanh"; - auto* tanh_0_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* tanh_0_op = tensorflow_graph->add_node(); tanh_0_op->set_op("Tanh"); tanh_0_op->set_name(tanh_0_output); *tanh_0_op->add_input() = split_output + ":1"; (*tanh_0_op->mutable_attr())["T"].set_type(DT_FLOAT); const string sigmoid_1_output = base + "Sigmoid_1"; - auto* logistic_1_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* logistic_1_op = tensorflow_graph->add_node(); logistic_1_op->set_op("Sigmoid"); logistic_1_op->set_name(sigmoid_1_output); *logistic_1_op->add_input() = split_output; (*logistic_1_op->mutable_attr())["T"].set_type(DT_FLOAT); const string mul_1_output = base + "mul_1"; - auto* mul_1_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* mul_1_op = tensorflow_graph->add_node(); mul_1_op->set_op("Mul"); mul_1_op->set_name(mul_1_output); *mul_1_op->add_input() = sigmoid_1_output; @@ -1385,21 +1397,21 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, (*mul_1_op->mutable_attr())["T"].set_type(DT_FLOAT); const string sigmoid_0_output = base + "Sigmoid"; - auto* logistic_2_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* logistic_2_op = tensorflow_graph->add_node(); logistic_2_op->set_op("Sigmoid"); logistic_2_op->set_name(sigmoid_0_output); *logistic_2_op->add_input() = split_output + ":2"; (*logistic_2_op->mutable_attr())["T"].set_type(DT_FLOAT); const string sigmoid_2_output = base + "Sigmoid_2"; - auto* logistic_3_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* logistic_3_op = tensorflow_graph->add_node(); logistic_3_op->set_op("Sigmoid"); logistic_3_op->set_name(sigmoid_2_output); *logistic_3_op->add_input() = split_output + ":3"; (*logistic_3_op->mutable_attr())["T"].set_type(DT_FLOAT); const string mul_0_output = base + "mul"; - auto* mul_0_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* mul_0_op = tensorflow_graph->add_node(); mul_0_op->set_op("Mul"); mul_0_op->set_name(mul_0_output); *mul_0_op->add_input() = src_op.inputs[LstmCellOperator::PREV_STATE_INPUT]; @@ -1407,7 +1419,7 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, (*mul_0_op->mutable_attr())["T"].set_type(DT_FLOAT); const string add_1_output = src_op.outputs[LstmCellOperator::STATE_OUTPUT]; - auto* add_1_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* add_1_op = tensorflow_graph->add_node(); add_1_op->set_op("Add"); add_1_op->set_name(add_1_output); *add_1_op->add_input() = mul_0_output; @@ -1415,14 +1427,14 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, (*add_1_op->mutable_attr())["T"].set_type(DT_FLOAT); const string tanh_1_output = base + "Tanh_1"; - auto* tanh_1_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* tanh_1_op = tensorflow_graph->add_node(); tanh_1_op->set_op("Tanh"); tanh_1_op->set_name(tanh_1_output); *tanh_1_op->add_input() = add_1_output; (*tanh_1_op->mutable_attr())["T"].set_type(DT_FLOAT); const string mul_2_output = src_op.outputs[LstmCellOperator::ACTIV_OUTPUT]; - auto* mul_2_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* mul_2_op = tensorflow_graph->add_node(); mul_2_op->set_op("Mul"); mul_2_op->set_name(mul_2_output); *mul_2_op->add_input() = tanh_1_output; @@ -1433,14 +1445,15 @@ void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op, void ConvertSpaceToBatchNDOperator(const Model& model, const SpaceToBatchNDOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("SpaceToBatchND"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); *new_op->add_input() = src_op.inputs[0]; *new_op->add_input() = src_op.inputs[1]; *new_op->add_input() = src_op.inputs[2]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); (*new_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32); (*new_op->mutable_attr())["Tpaddings"].set_type(DT_INT32); @@ -1449,14 +1462,15 @@ void ConvertSpaceToBatchNDOperator(const Model& model, void ConvertBatchToSpaceNDOperator(const Model& model, const BatchToSpaceNDOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("BatchToSpaceND"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); *new_op->add_input() = src_op.inputs[0]; *new_op->add_input() = src_op.inputs[1]; *new_op->add_input() = src_op.inputs[2]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); (*new_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32); (*new_op->mutable_attr())["Tcrops"].set_type(DT_INT32); @@ -1464,18 +1478,19 @@ void ConvertBatchToSpaceNDOperator(const Model& model, void ConvertPadOperator(const Model& model, const PadOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("Pad"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *new_op->add_input() = src_op.inputs[0]; *new_op->add_input() = src_op.inputs[1]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); // Create the params tensor. - auto* params_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* params_op = tensorflow_graph->add_node(); params_op->set_op("Const"); params_op->set_name(src_op.inputs[1]); (*params_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -1494,7 +1509,7 @@ void ConvertPadOperator(const Model& model, const PadOperator& src_op, void ConvertPadV2Operator(const Model& model, const PadV2Operator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("PadV2"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1502,11 +1517,12 @@ void ConvertPadV2Operator(const Model& model, const PadV2Operator& src_op, *new_op->add_input() = src_op.inputs[1]; *new_op->add_input() = src_op.inputs[2]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); // Create the params tensor. - auto* params_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* params_op = tensorflow_graph->add_node(); params_op->set_op("Const"); params_op->set_name(src_op.inputs[1]); (*params_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -1525,7 +1541,7 @@ void ConvertPadV2Operator(const Model& model, const PadV2Operator& src_op, void CreateSliceInput(const string& input_name, const std::vector& values, GraphDef* tensorflow_graph) { - auto* params_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* params_op = tensorflow_graph->add_node(); params_op->set_op("Const"); params_op->set_name(input_name); (*params_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -1542,7 +1558,7 @@ void CreateSliceInput(const string& input_name, const std::vector& values, void ConvertStridedSliceOperator(const Model& model, const StridedSliceOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("StridedSlice"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 4); @@ -1551,7 +1567,8 @@ void ConvertStridedSliceOperator(const Model& model, *new_op->add_input() = src_op.inputs[2]; *new_op->add_input() = src_op.inputs[3]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); (*new_op->mutable_attr())["Index"].set_type(DT_INT32); @@ -1569,7 +1586,7 @@ void ConvertStridedSliceOperator(const Model& model, void ConvertSliceOperator(const Model& model, const SliceOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("Slice"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); @@ -1577,7 +1594,8 @@ void ConvertSliceOperator(const Model& model, const SliceOperator& src_op, *new_op->add_input() = src_op.inputs[1]; *new_op->add_input() = src_op.inputs[2]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); (*new_op->mutable_attr())["Index"].set_type(DT_INT32); @@ -1588,14 +1606,15 @@ void ConvertSliceOperator(const Model& model, const SliceOperator& src_op, void ConvertMeanOperator(const Model& model, const MeanOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("Mean"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *new_op->add_input() = src_op.inputs[0]; *new_op->add_input() = src_op.inputs[1]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); if (src_op.keep_dims) { @@ -1603,7 +1622,7 @@ void ConvertMeanOperator(const Model& model, const MeanOperator& src_op, } // Create the params tensor. - auto* params_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* params_op = tensorflow_graph->add_node(); params_op->set_op("Const"); params_op->set_name(src_op.inputs[1]); (*params_op->mutable_attr())["dtype"].set_type(DT_INT32); @@ -1619,13 +1638,14 @@ void ConvertMeanOperator(const Model& model, const MeanOperator& src_op, void ConvertSqueezeOperator(const Model& model, const SqueezeOperator& src_op, GraphDef* tensorflow_graph) { - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("Squeeze"); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 1); *new_op->add_input() = src_op.inputs[0]; - const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType params_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); if (!src_op.squeeze_dims.empty()) { @@ -1638,58 +1658,79 @@ void ConvertSqueezeOperator(const Model& model, const SqueezeOperator& src_op, void ConvertSubOperator(const Model& model, const SubOperator& src_op, GraphDef* tensorflow_graph) { - auto* sub_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); sub_op->set_op("Sub"); sub_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *sub_op->add_input() = src_op.inputs[0]; *sub_op->add_input() = src_op.inputs[1]; - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*sub_op->mutable_attr())["T"].set_type(data_type); } void ConvertTensorFlowMinimumOperator(const Model& model, const TensorFlowMinimumOperator& src_op, GraphDef* tensorflow_graph) { - auto* sub_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); sub_op->set_op("Minimum"); sub_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *sub_op->add_input() = src_op.inputs[0]; *sub_op->add_input() = src_op.inputs[1]; - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*sub_op->mutable_attr())["T"].set_type(data_type); } void ConvertTensorFlowMaximumOperator(const Model& model, const TensorFlowMaximumOperator& src_op, GraphDef* tensorflow_graph) { - auto* sub_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); sub_op->set_op("Maximum"); sub_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *sub_op->add_input() = src_op.inputs[0]; *sub_op->add_input() = src_op.inputs[1]; - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*sub_op->mutable_attr())["T"].set_type(data_type); } void ConvertSelectOperator(const Model& model, const SelectOperator& src_op, GraphDef* tensorflow_graph) { - auto* sub_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); sub_op->set_op("Select"); sub_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); *sub_op->add_input() = src_op.inputs[0]; *sub_op->add_input() = src_op.inputs[1]; *sub_op->add_input() = src_op.inputs[2]; - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[1]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[1]); (*sub_op->mutable_attr())["T"].set_type(data_type); } +void ConvertTileOperator(const Model& model, + const TensorFlowTileOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* tile_op = tensorflow_graph->add_node(); + tile_op->set_op("Tile"); + tile_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *tile_op->add_input() = src_op.inputs[0]; + *tile_op->add_input() = src_op.inputs[1]; + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); + (*tile_op->mutable_attr())["T"].set_type(data_type); + const tensorflow::DataType multiples_data_type = + GetTensorFlowDataType(model, src_op.inputs[1]); + (*tile_op->mutable_attr())["Tmultiples"].set_type(multiples_data_type); +} + void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op, GraphDef* tensorflow_graph) { - auto* topk_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* topk_op = tensorflow_graph->add_node(); topk_op->set_op("TOPKV2"); topk_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); @@ -1702,12 +1743,13 @@ void ConvertRandomUniformOperator(const Model& model, const RandomUniformOperator& src_op, GraphDef* tensorflow_graph) { CHECK(tensorflow_graph != nullptr); - auto* new_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); new_op->set_op("RandomUniform"); CHECK_EQ(src_op.inputs.size(), 1); new_op->set_name(src_op.outputs[0]); *new_op->add_input() = src_op.inputs[0]; - const auto shape_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType shape_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(shape_type); (*new_op->mutable_attr())["dtype"].set_type( GetTensorFlowDataType(src_op.dtype)); @@ -1718,13 +1760,14 @@ void ConvertRandomUniformOperator(const Model& model, void ConvertComparisonOperator(const Model& model, const Operator& src_op, const char* op_name, GraphDef* tensorflow_graph) { - auto* comparison_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* comparison_op = tensorflow_graph->add_node(); comparison_op->set_op(op_name); comparison_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *comparison_op->add_input() = src_op.inputs[0]; *comparison_op->add_input() = src_op.inputs[1]; - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*comparison_op->mutable_attr())["T"].set_type(data_type); } @@ -1732,21 +1775,37 @@ void ConvertSparseToDenseOperator(const Model& model, const SparseToDenseOperator& src_op, const char* op_name, GraphDef* tensorflow_graph) { - auto* sparse_to_dense_op = tensorflow_graph->add_node(); + tensorflow::NodeDef* sparse_to_dense_op = tensorflow_graph->add_node(); sparse_to_dense_op->set_op(op_name); sparse_to_dense_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 4); for (int i = 0; i < 4; ++i) { *sparse_to_dense_op->add_input() = src_op.inputs[i]; } - const auto data_type = GetTensorFlowDataType(model, src_op.inputs[3]); + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[3]); (*sparse_to_dense_op->mutable_attr())["T"].set_type(data_type); - const auto index_type = GetTensorFlowDataType(model, src_op.inputs[0]); + const tensorflow::DataType index_type = + GetTensorFlowDataType(model, src_op.inputs[0]); (*sparse_to_dense_op->mutable_attr())["Tindices"].set_type(index_type); (*sparse_to_dense_op->mutable_attr())["Tindices"].set_b( src_op.validate_indices); } +void ConvertPowOperator(const Model& model, const PowOperator& src_op, + const char* op_name, GraphDef* tensorflow_graph) { + tensorflow::NodeDef* pow_op = tensorflow_graph->add_node(); + pow_op->set_op(op_name); + pow_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + for (int i = 0; i < 2; ++i) { + *pow_op->add_input() = src_op.inputs[i]; + } + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); + (*pow_op->mutable_attr())["T"].set_type(data_type); +} + void ConvertOperator(const Model& model, const Operator& src_op, GraphDef* tensorflow_graph) { if (src_op.fused_activation_function != FusedActivationFunctionType::kNone) { @@ -1827,20 +1886,24 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertConcatenationOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowReshape) { + } else if (src_op.type == OperatorType::kReshape) { ConvertTensorFlowReshapeOperator( model, static_cast(src_op), tensorflow_graph); } else if (src_op.type == OperatorType::kL2Pool) { ConvertL2PoolOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSquare) { + } else if (src_op.type == OperatorType::kSquare) { ConvertSquareOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSqrt) { + } else if (src_op.type == OperatorType::kSqrt) { ConvertSqrtOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSplit) { + } else if (src_op.type == OperatorType::kRsqrt) { + ConvertRsqrtOperator(model, + static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kSplit) { ConvertSplitOperator(model, static_cast(src_op), tensorflow_graph); @@ -1884,11 +1947,11 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kSub) { ConvertSubOperator(model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowMinimum) { + } else if (src_op.type == OperatorType::kMinimum) { ConvertTensorFlowMinimumOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowMaximum) { + } else if (src_op.type == OperatorType::kMaximum) { ConvertTensorFlowMaximumOperator( model, static_cast(src_op), tensorflow_graph); @@ -1907,7 +1970,7 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kTranspose) { ConvertTransposeOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowShape) { + } else if (src_op.type == OperatorType::kShape) { ConvertTensorFlowShapeOperator( model, static_cast(src_op), tensorflow_graph); @@ -1938,21 +2001,28 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertRandomUniformOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowEqual) { + } else if (src_op.type == OperatorType::kEqual) { ConvertComparisonOperator(model, src_op, "Equal", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowNotEqual) { + } else if (src_op.type == OperatorType::kNotEqual) { ConvertComparisonOperator(model, src_op, "NotEqual", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowGreater) { + } else if (src_op.type == OperatorType::kGreater) { ConvertComparisonOperator(model, src_op, "Greater", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowGreaterEqual) { + } else if (src_op.type == OperatorType::kGreaterEqual) { ConvertComparisonOperator(model, src_op, "GreaterEqual", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowLess) { + } else if (src_op.type == OperatorType::kLess) { ConvertComparisonOperator(model, src_op, "Less", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowLessEqual) { + } else if (src_op.type == OperatorType::kLessEqual) { ConvertComparisonOperator(model, src_op, "LessEqual", tensorflow_graph); } else if (src_op.type == OperatorType::kSelect) { ConvertSelectOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTile) { + ConvertTileOperator(model, + static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kPow) { + ConvertPowOperator(model, static_cast(src_op), "Pow", + tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } @@ -1960,7 +2030,7 @@ void ConvertOperator(const Model& model, const Operator& src_op, void AddPlaceholder(const string& name, ArrayDataType type, GraphDef* tensorflow_graph) { - auto* placeholder = tensorflow_graph->add_node(); + tensorflow::NodeDef* placeholder = tensorflow_graph->add_node(); placeholder->set_op("Placeholder"); switch (type) { case ArrayDataType::kBool: @@ -1989,7 +2059,7 @@ void AddPlaceholder(const string& name, ArrayDataType type, void AddPlaceholderForRNNState(const Model& model, const string& name, int size, GraphDef* tensorflow_graph) { - auto* placeholder = tensorflow_graph->add_node(); + tensorflow::NodeDef* placeholder = tensorflow_graph->add_node(); placeholder->set_op("Placeholder"); placeholder->set_name(name); (*placeholder->mutable_attr())["dtype"].set_type(DT_FLOAT); diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index 8e93f02ef109f7bccd07ce54baff3d0bb4ae50c7..18b7848db86e553ec645fa87298420012b5f753f 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -9,57 +9,56 @@ complemented by the following documents: Table of contents: -* [Convert a TensorFlow SavedModel to TensorFlow Lite](#savedmodel) -* [Convert a TensorFlow GraphDef to TensorFlow Lite for float - inference](#graphdef-float) +* [Command-line tools](#tools) + * [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9) +* [Basic examples](#basic) + * [Convert a TensorFlow GraphDef](#graphdef) + * [Convert a TensorFlow SavedModel](#savedmodel) + * [Convert a tf.keras model](#keras) * [Quantization](#quantization) - * [Convert a TensorFlow GraphDef to TensorFlow Lite for quantized - inference](#graphdef-quant) + * [Convert a TensorFlow GraphDef for quantized inference](#graphdef-quant) * [Use "dummy-quantization" to try out quantized inference on a float graph](#dummy-quant) * [Specifying input and output arrays](#specifying-input-and-output-arrays) - * [Multiple output arrays](#multiple-output-arrays) * [Multiple input arrays](#multiple-input-arrays) + * [Multiple output arrays](#multiple-output-arrays) * [Specifying subgraphs](#specifying-subgraphs) -* [Other conversions supported by TOCO](#other-conversions) - * [Optimize a TensorFlow GraphDef](#optimize-graphdef) - * [Convert a TensorFlow Lite FlatBuffer back into TensorFlow GraphDef - format](#to-graphdef) -* [Logging](#logging) - * [Graph "video" logging](#graph-video-logging) * [Graph visualizations](#graph-visualizations) * [Using --output_format=GRAPHVIZ_DOT](#using-output-formatgraphviz-dot) * [Using --dump_graphviz](#using-dump-graphviz) + * [Graph "video" logging](#graph-video-logging) * [Legend for the graph visualizations](#graphviz-legend) -## Convert a TensorFlow SavedModel to TensorFlow Lite +## Command-line tools -The follow example converts a basic TensorFlow SavedModel into a Tensorflow Lite -FlatBuffer to perform floating-point inference. +There are two approaches to running TOCO via command line. -``` -bazel run --config=opt \ - third_party/tensorflow/contrib/lite/toco:toco -- \ - --savedmodel_directory=/tmp/saved_model \ - --output_file=/tmp/foo.tflite -``` +* `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool + `tflite_convert` will be installed as part of the Python package. All of the + examples below use `tflite_convert` for simplicity. + * Example: `tflite --output_file=...` +* `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow + repository](https://www.tensorflow.org/install/install_sources#clone_the_tensorflow_repository) + and use `bazel`. This is the recommended approach for converting models that + utilize new features that were not supported by TOCO in TensorFlow 1.9. + * Example: `bazel run + //tensorflow/contrib/lite/python:tflite_convert -- + --output_file=...` -[SavedModel](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators) -has fewer required flags than frozen graphs (described [below](#graphdef-float)) -due to access to additional data contained within the SavedModel. The values for -`--input_arrays` and `--output_arrays` are an aggregated, alphabetized list of -the inputs and outputs in the -[SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within the -[MetaGraphDef](https://www.tensorflow.org/programmers_guide/saved_model#apis_to_build_and_load_a_savedmodel) -specified by `--savedmodel_tagset`. The value for `input_shapes` is -automatically determined from the MetaGraphDef whenever possible. The default -value for `--inference_type` for SavedModels is `FLOAT`. +### Converting models prior to TensorFlow 1.9. -There is currently no support for MetaGraphDefs without a SignatureDef or for -MetaGraphDefs that use the [`assets/` -directory](https://www.tensorflow.org/programmers_guide/saved_model#structure_of_a_savedmodel_directory). +The recommended approach for using TOCO prior to TensorFlow 1.9 is the [Python +API](python_api.md#pre-tensorflow-1.9). If a command line tool is desired, the +`toco` command line tool was available in TensorFlow 1.7. Enter `toco --help` in +Terminal for additional details on the command-line flags available. There were +no command line tools in TensorFlow 1.8. + +## Basic examples + +The following section shows examples of how to convert a basic float-point model +from each of the supported data formats into a TensorFlow Lite FlatBuffers. -## Convert a TensorFlow GraphDef to TensorFlow Lite for float inference +### Convert a TensorFlow GraphDef The follow example converts a basic TensorFlow GraphDef (frozen by [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)) @@ -69,19 +68,54 @@ graphs contain the variables stored in Checkpoint files as Const ops. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ +tflite_convert \ --output_file=/tmp/foo.tflite \ - --inference_type=FLOAT \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 +``` + +The value for `input_shapes` is automatically determined whenever possible. + +### Convert a TensorFlow SavedModel + +The follow example converts a basic TensorFlow SavedModel into a Tensorflow Lite +FlatBuffer to perform floating-point inference. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --saved_model_dir=/tmp/saved_model +``` + +[SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators) +has fewer required flags than frozen graphs due to access to additional data +contained within the SavedModel. The values for `--input_arrays` and +`--output_arrays` are an aggregated, alphabetized list of the inputs and outputs +in the [SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within +the +[MetaGraphDef](https://www.tensorflow.org/guide/saved_model#apis_to_build_and_load_a_savedmodel) +specified by `--saved_model_tag_set`. As with the GraphDef, the value for +`input_shapes` is automatically determined whenever possible. + +There is currently no support for MetaGraphDefs without a SignatureDef or for +MetaGraphDefs that use the [`assets/` +directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory). + +### Convert a tf.Keras model + +The following example converts a `tf.keras` model into a TensorFlow Lite +Flatbuffer. The `tf.keras` file must contain both the model and the weights. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --keras_model_file=/tmp/keras_model.h5 ``` ## Quantization -### Convert a TensorFlow GraphDef to TensorFlow Lite for quantized inference +### Convert a TensorFlow GraphDef for quantized inference TOCO is compatible with fixed point quantization models described [here](https://www.tensorflow.org/performance/quantization). These are float @@ -95,18 +129,14 @@ The following command generates a quantized TensorFlow Lite FlatBuffer from a "quantized" TensorFlow GraphDef. ``` -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/some_quantized_graph.pb \ +tflite_convert \ --output_file=/tmp/foo.tflite \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TFLITE \ + --graph_def_file=/tmp/some_quantized_graph.pb \ --inference_type=QUANTIZED_UINT8 \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 \ - --mean_value=128 \ - --std_value=127 + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ + --mean_values=128 \ + --std_dev_values=127 ``` ### Use \"dummy-quantization\" to try out quantized inference on a float graph @@ -124,45 +154,20 @@ a reasonable guess is that most activation ranges should be contained in [0, 6]. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ +tflite_convert \ --output_file=/tmp/foo.cc \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TFLITE \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ --inference_type=QUANTIZED_UINT8 \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ --default_ranges_min=0 \ --default_ranges_max=6 \ - --mean_value=127.5 \ - --std_value=127.5 + --mean_values=128 \ + --std_dev_values=127 ``` ## Specifying input and output arrays -### Multiple output arrays - -The flag `output_arrays` takes in a comma-separated list of output arrays as -seen in the example below. This is useful for models or subgraphs with multiple -outputs. - -``` -curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ - | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \ - --output_file=/tmp/foo.tflite \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TFLITE \ - --inference_type=FLOAT \ - --input_shape=1,224,224,3 \ - --input_array=input \ - --output_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu -``` - ### Multiple input arrays The flag `input_arrays` takes in a comma-separated list of input arrays as seen @@ -172,21 +177,33 @@ inputs. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \ +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ --output_file=/tmp/foo.tflite \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TFLITE \ - --inference_type=FLOAT \ --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \ --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \ - --output_array=InceptionV1/Logits/Predictions/Reshape_1 + --output_arrays=InceptionV1/Logits/Predictions/Reshape_1 ``` Note that `input_shapes` is provided as a colon-separated list. Each input shape corresponds to the input array at the same position in the respective list. +### Multiple output arrays + +The flag `output_arrays` takes in a comma-separated list of output arrays as +seen in the example below. This is useful for models or subgraphs with multiple +outputs. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ + --output_file=/tmp/foo.tflite \ + --input_arrays=input \ + --output_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu +``` + ### Specifying subgraphs Any array in the input file can be specified as an input or output array in @@ -201,115 +218,57 @@ GraphDef. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \ +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ --output_file=/tmp/foo.pb \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TENSORFLOW_GRAPHDEF \ --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \ --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \ - --output_array=InceptionV1/InceptionV1/Mixed_3b/concat_v2 + --output_arrays=InceptionV1/InceptionV1/Mixed_3b/concat_v2 ``` -Note that the final representation of an on-device inference workload (say, in -TensorFlow Lite FlatBuffers format) tends to have coarser granularity than the -very fine granularity of the TensorFlow GraphDef representation. For example, -while a fully-connected layer is typically represented as at least four separate -ops in TensorFlow GraphDef (Reshape, MatMul, BiasAdd, Relu...), it is typically -represented as a single "fused" op (FullyConnected) in the converter's optimized -representation and in the final on-device representation (e.g. in TensorFlow -Lite FlatBuffer format). As the level of granularity gets coarser, some +Note that the final representation in TensorFlow Lite FlatBuffers tends to have +coarser granularity than the very fine granularity of the TensorFlow GraphDef +representation. For example, while a fully-connected layer is typically +represented as at least four separate ops in TensorFlow GraphDef (Reshape, +MatMul, BiasAdd, Relu...), it is typically represented as a single "fused" op +(FullyConnected) in the converter's optimized representation and in the final +on-device representation. As the level of granularity gets coarser, some intermediate arrays (say, the array between the MatMul and the BiasAdd in the -TensorFlow GraphDef) are dropped. When specifying intermediate arrays as -`--input_arrays` / `--output_arrays`, it is desirable (and often required) to -specify arrays that are meant to survive in the final form of the graph, after -fusing. These are typically the outputs of activation functions (since -everything in each layer until the activation function tends to get fused). - -## Other conversions supported by TOCO - -The converter accepts both TENSORFLOW_GRAPHDEF and TFLITE file formats as both -`--input_format` and `--output_format`. This means that conversion to and from -any supported format is possible. - -### Optimize a TensorFlow GraphDef - -Same-format "conversions" can be used to optimize and simplify a graph or be -used to [get a subgraph](#specifying-subgraphs) of a graph. The flag -`--inference_type` is not required because TensorFlow graphs, including those -containing the -[`FakeQuant*`](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization) -ops are always float graphs. - -``` -curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ - | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ - --output_file=/tmp/foo.pb \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TENSORFLOW_GRAPHDEF \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 -``` +TensorFlow GraphDef) are dropped. -### Convert a TensorFlow Lite FlatBuffer back into TensorFlow GraphDef format - -The converter supports file format conversions from TensorFlow Lite, back into -TensorFlow GraphDef format. - -``` -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/foo.tflite \ - --output_file=/tmp/foo.pb \ - --input_format=TFLITE \ - --output_format=TENSORFLOW_GRAPHDEF \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 -``` +When specifying intermediate arrays as `--input_arrays` and `--output_arrays`, +it is desirable (and often required) to specify arrays that are meant to survive +in the final form of the graph, after fusing. These are typically the outputs of +activation functions (since everything in each layer until the activation +function tends to get fused). ## Logging -### Graph "video" logging - -When `--dump_graphviz=` is used (see the section on [graph -visualizations](#graph-visualizations)), one may additionally pass -`--dump_graphviz_video`, which causes a graph visualization to be dumped after -each individual graph transformation. This results in thousands of files. -Typically, one would then bisect into these files to understand when a given -change was introduced in the graph. ## Graph visualizations TOCO can export a graph to the GraphViz Dot format for easy visualization via -either the `--output_format` flag or the `--dump_graphviz` flag. The subsections -below outline the use cases for each. +either the `--output_format` flag or the `--dump_graphviz_dir` flag. The +subsections below outline the use cases for each. ### Using `--output_format=GRAPHVIZ_DOT` The first way to get a graphviz rendering is to pass `GRAPHVIZ_DOT` into `--output_format`. This results in a plausible visualization of the graph. This -reduces the requirements that normally exist during conversion between other -input and output formats. For example, this may be useful if conversion from -TENSORFLOW_GRAPHDEF to TFLITE is failing. +reduces the requirements that exist during conversion between other input and +output formats. This may be useful if conversion from TENSORFLOW_GRAPHDEF to +TFLITE is failing. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ +tflite_convert \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ --output_file=/tmp/foo.dot \ - --input_format=TENSORFLOW_GRAPHDEF \ --output_format=GRAPHVIZ_DOT \ --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 ``` The resulting `.dot` file can be rendered into a PDF as follows: @@ -330,49 +289,35 @@ Example PDF files are viewable online in the next section. ### Using `--dump_graphviz` -The second way to get a graphviz rendering is to pass the `--dump_graphviz=` +The second way to get a graphviz rendering is to pass the `--dump_graphviz_dir` flag, specifying a destination directory to dump GraphViz rendering to. Unlike -the previous approach, this one allows you to keep your real command-line (with -your real `--output_format` and other flags) unchanged, just appending a -`--dump_graphviz=` flag to it. This provides a visualization of the actual graph -during a specific conversion process. +the previous approach, this one retains the original output format. This +provides a visualization of the actual graph resulting from a specific +conversion process. ``` curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ | tar xzv -C /tmp -bazel run --config=opt \ - //tensorflow/contrib/lite/toco:toco -- \ - --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ +tflite_convert \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ --output_file=/tmp/foo.tflite \ - --input_format=TENSORFLOW_GRAPHDEF \ - --output_format=TFLITE \ - --inference_type=FLOAT \ - --input_shape=1,128,128,3 \ - --input_array=input \ - --output_array=MobilenetV1/Predictions/Reshape_1 \ - --dump_graphviz=/tmp -``` - -This generates a few files in the destination directory, here `/tmp`. The two -most important files are: - -``` -/tmp/toco_AT_IMPORT.dot -/tmp/toco_AFTER_TRANSFORMATIONS.dot + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ + --dump_graphviz_dir=/tmp ``` -`toco_AT_IMPORT.dot` represents the graph as it was imported from -`--input_file`, before any transformation was applied to it (besides some -transformations that are applied immediately while importing). This tends to be -a complex visualization with limited information, but is useful especially in -situations where a conversion command fails (this file is generated even if the -conversion subsequently fails). +This generates a few files in the destination directory. The two most important +files are `toco_AT_IMPORT.dot` and `/tmp/toco_AFTER_TRANSFORMATIONS.dot`. +`toco_AT_IMPORT.dot` represents the original graph containing only the +transformations done at import time. This tends to be a complex visualization +with limited information about each node. It is useful in situations where a +conversion command fails. `toco_AFTER_TRANSFORMATIONS.dot` represents the graph after all transformations -were applied to it, just before it was exported to the `--output_file`. -Typically, this is a much smaller graph with more information about each node. +were applied to it, just before it is exported. Typically, this is a much +smaller graph with more information about each node. -Again, these can be rendered to PDFs: +As before, these can be rendered to PDFs: ``` dot -Tpdf -O /tmp/toco_*.dot @@ -383,6 +328,14 @@ Sample output files can be seen here: * [toco_AT_IMPORT.dot.pdf](https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AT_IMPORT.dot.pdf) * [toco_AFTER_TRANSFORMATIONS.dot.pdf](https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AFTER_TRANSFORMATIONS.dot.pdf). +### Graph "video" logging + +When `--dump_graphviz_dir` is used, one may additionally pass +`--dump_graphviz_video`. This causes a graph visualization to be dumped after +each individual graph transformation, resulting in thousands of files. +Typically, one would then bisect into these files to understand when a given +change was introduced in the graph. + ### Legend for the graph visualizations * Operators are red square boxes with the following hues of red: diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md index 8085ae07489816c38677ff792e7ac71f1a75fa71..decc8a45a40ffba2a27320ce8391b1916391d744 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md @@ -1,7 +1,8 @@ # TensorFlow Lite Optimizing Converter command-line glossary -This page is complete reference of command-line flags. It is complemented by the -following other documents: +This page is complete reference of command-line flags used by TOCO's command +line starting from TensorFlow 1.9 up until the most recent build of TensorFlow. +It is complemented by the following other documents: * [README](../README.md) * [Command-line examples](cmdline_examples.md) @@ -16,116 +17,81 @@ Table of contents: ## High-level flags -The following high level flags specify the location of the input and output +The following high level flags specify the details of the input and output files. The flag `--output_file` is always required. Additionally, either -`--input_file` or `--savedmodel_directory` is required. - -* `--savedmodel_directory`. Type: string. Specifies the full path to the - directory containing the SavedModel. -* `--savedmodel_tagset`. Type: string. Default: +`--graph_def_file`, `--saved_model_dir` or `--keras_model_file` is required. + +* `--output_file`. Type: string. Specifies the full path of the output file. +* `--graph_def_file`. Type: string. Specifies the full path of the input + GraphDef file frozen using + [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py). +* `--saved_model_dir`. Type: string. Specifies the full path to the directory + containing the SavedModel. +* `--keras_model_file`. Type: string. Specifies the full path of the HDF5 file + containing the tf.keras model. +* `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of + the output file. Allowed values: + * `TFLITE`: TensorFlow Lite FlatBuffer format. + * `GRAPHVIZ_DOT`: GraphViz `.dot` format containg a visualization of the + graph after graph transformations. + * Note that passing `GRAPHVIZ_DOT` to `--output_format` leads to loss + of TFLite specific transformations. Therefore, the resulting + visualization may not reflect the final set of graph + transformations. To get a final visualization with all graph + transformations use `--dump_graphviz` instead. + +The following flags specify optional parameters when using SavedModels. + +* `--saved_model_tag_set`. Type: string. Default: [kSavedModelTagServe](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h). Specifies a comma-separated set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be specified. -* `--input_file`. Type: string. Specifies the path of the input file. This may - be either an absolute or a relative path. -* `--output_file`. Type: string. Specifies the path of the output file. - -The following high level flags specify the types of the input and output files: - -* `--input_format`. Type: string. Default: `TENSORFLOW_GRAPHDEF`. Specifies - the format of the input file. Allowed values: - * `TENSORFLOW_GRAPHDEF` — The TensorFlow GraphDef format. Both - binary and text proto formats are allowed. - * `TFLITE` — The TensorFlow Lite FlatBuffers format. -* `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of - the output file. Allowed values: - * `TENSORFLOW_GRAPHDEF` — The TensorFlow GraphDef format. Always - produces a file in binary (not text) proto format. - * `TFLITE` — The TensorFlow Lite FlatBuffers format. - * Whether a float or quantized TensorFlow Lite file will be produced - depends on the `--inference_type` flag. - * `GRAPHVIZ_DOT` — The GraphViz `.dot` format. This asks the - converter to generate a reasonable graphical representation of the graph - after simplification by a generic set of transformation. - * A typical `dot` command line to view the resulting graph might look - like: `dot -Tpdf -O file.dot`. - * Note that since passing this `--output_format` means losing the - information of which output format you actually care about, and - since the converter's transformations depend on the specific output - format, the resulting visualization may not fully reflect what you - would get on the actual output format that you are using. To avoid - that concern, and generally to get a visualization of exactly what - you get in your actual output format as opposed to just a merely - plausible visualization of a model, consider using `--dump_graphviz` - instead and keeping your true `--output_format`. +* `--saved_model_signature_key`. Type: string. Default: + [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants). + Specifies the key identifying the SignatureDef containing inputs and + outputs. ## Model flags *Model flags* provide additional information about the model stored in the input file. -* `--output_array`. Type: string. Specifies a single array as the output - activations. Incompatible with `--output_arrays`. -* `--output_arrays`. Type: comma-separated list of strings. Specifies a list - of arrays as the output activations, for models with multiple outputs. - Incompatible with `--output_array`. -* `--input_array`. Type: string. Specifies a single array as the input - activations. Incompatible with `--input_arrays`. -* `--input_arrays`. Type: comma-separated list of strings. Specifies a list of - arrays as the input activations, for models with multiple inputs. - Incompatible with `--input_array`. -* `--batch_size`. Type: integer. Default: 1. Specifies the batch size for the - model. Replaces the first dimension of an input size array if undefined. Use - only with SavedModels when neither `--input_shape` nor `input_shapes` flags - are specified. Incompatible with GraphDefs. - -When `--input_array` is used, the following flags are available to provide -additional information about the single input array: - -* `--input_shape`. Type: comma-separated list of integers. Specifies the shape - of the input array, in TensorFlow convention: starting with the outer-most - dimension (the dimension corresponding to the largest offset stride in the - array layout), ending with the inner-most dimension (the dimension along - which array entries are typically laid out contiguously in memory). - * For example, a typical vision model might pass - `--input_shape=1,60,80,3`, meaning a batch size of 1 (no batching), an - input image height of 60, an input image width of 80, and an input image - depth of 3, for the typical case where the input image is a RGB bitmap - (3 channels, depth=3) stored by horizontal scanlines (so 'width' is the - next innermost dimension after 'depth'). -* `--mean_value` and `--std_value`. Type: floating-point. The decimal point - character is always the dot (`.`) regardless of the locale. These specify - the (de-)quantization parameters of the input array, when it is quantized. - * The meaning of mean_value and std_value is as follows: each quantized - value in the quantized input array will be interpreted as a mathematical - real number (i.e. as an input activation value) according to the - following formula: +* `--input_arrays`. Type: comma-separated list of strings. Specifies the list + of names of input activation tensors. +* `--output_arrays`. Type: comma-separated list of strings. Specifies the list + of names of output activation tensors. + +The following flags define properties of the input tensors. Each item in the +`--input_arrays` flag should correspond to each item in the following flags +based on index. + +* `--input_shapes`. Type: colon-separated list of comma-separated lists of + integers. Each comma-separated list of integers gives the shape of one of + the input arrays specified in [TensorFlow + convention](https://www.tensorflow.org/versions/r1.2/programmers_guide/dims_types#shape). + * Example: `--input_shapes=1,60,80,3` for a typical vision model means a + batch size of 1, an input image height of 60, an input image width of + 80, and an input image depth of 3 (representing RGB channels). + * Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means "foo" + has a shape of [2, 3] and "bar" has a shape of [4, 5, 6]. +* `--std_dev_values`, `--mean_values`. Type: comma-separated list of integers. + These specify the (de-)quantization parameters of the input array, when it + is quantized. + * The meaning of `mean_values` and `std_dev_values` is as follows: each + quantized value in the quantized input array will be interpreted as a + mathematical real number (i.e. as an input activation value) according + to the following formula: * `real_value = (quantized_input_value - mean_value) / std_value`. * When performing float inference (`--inference_type=FLOAT`) on a quantized input, the quantized input would be immediately dequantized by the inference code according to the above formula, before proceeding with float inference. * When performing quantized inference - (`--inference_type=QUANTIZED_UINT8`), no dequantization is ever to be - performed by the inference code; however, the quantization parameters of - all arrays, including those of the input arrays as specified by - mean_value and std_value, all participate in the determination of the - fixed-point multipliers used in the quantized inference code. - -When `--input_arrays` is used, the following flags are available to provide -additional information about the multiple input arrays: - -* `--input_shapes`. Type: colon-separated list of comma-separated lists of - integers. Each comma-separated list of integer gives the shape of one of the - input arrays specified in `--input_arrays`, in the same order. See - `--input_shape` for details. - * Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means that - there are two input arrays. The first one, "foo", has shape [2,3]. The - second one, "bar", has shape [4,5,6]. -* `--mean_values`, `--std_values`. Type: comma-separated lists of - floating-point numbers. Each number gives the corresponding value for one of - the input arrays specified in `--input_arrays`, in the same order. See - `--mean_value`, `--std_value` for details. + (`--inference_type=QUANTIZED_UINT8`), no dequantization is performed by + the inference code. However, the quantization parameters of all arrays, + including those of the input arrays as specified by `mean_value` and + `std_dev_value`, determine the fixed-point multipliers used in the + quantized inference code. ## Transformation flags @@ -133,21 +99,13 @@ additional information about the multiple input arrays: the graph, i.e. they specify requested properties that the output file should have. -* `--inference_type`. Type: string. Sets the type of real-number arrays in the - output file, that is, controls the representation (quantization) of real - numbers in the output file, except for input arrays, which are controlled by - `--inference_input_type`. - - This flag only impacts real-number arrays. By "real-number" we mean float - arrays, and quantized arrays. This excludes plain integer arrays, strings - arrays, and every other data type. +* `--inference_type`. Type: string. Default: `FLOAT`. Data type of all + real-number arrays in the output file except for input arrays (defined by + `--inference_input_type`). Must be `{FLOAT, QUANTIZED_UINT8}`. - For real-number arrays, the impact of this flag is to allow the output file - to choose a different real-numbers representation (quantization) from what - the input file used. For any other types of arrays, changing the data type - would not make sense. - - Specifically: + This flag only impacts real-number arrays including float and quantized + arrays. This excludes all other data types including plain integer arrays + and string arrays. Specifically: * If `FLOAT`, then real-numbers arrays will be of type float in the output file. If they were quantized in the input file, then they get @@ -155,66 +113,54 @@ have. * If `QUANTIZED_UINT8`, then real-numbers arrays will be quantized as uint8 in the output file. If they were float in the input file, then they get quantized. - * If not set, then all real-numbers arrays retain the same type in the - output file as they have in the input file. - -* `--inference_input_type`. Type: string. Similar to inference_type, but - allows to control specifically the quantization of input arrays, separately - from other arrays. - - If not set, then the value of `--inference_type` is implicitly used, i.e. by - default input arrays are quantized like other arrays. - - Like `--inference_type`, this only affects real-number arrays. By - "real-number" we mean float arrays, and quantized arrays. This excludes - plain integer arrays, strings arrays, and every other data type. - - The typical use for this flag is for vision models taking a bitmap as input, - typically with uint8 channels, yet still requiring floating-point inference. - For such image models, the uint8 input is quantized, i.e. the uint8 values - are interpreted as real numbers, and the quantization parameters used for - such input arrays are their `mean_value`, `std_value` parameters. - -* `--default_ranges_min`, `--default_ranges_max`. Type: floating-point. The - decimal point character is always the dot (`.`) regardless of the locale. - These flags enable what is called "dummy quantization". If defined, their - effect is to define fallback (min, max) range values for all arrays that do - not have a properly specified (min, max) range in the input file, thus - allowing to proceed with quantization of non-quantized or - incorrectly-quantized input files. This enables easy performance prototyping - ("how fast would my model run if I quantized it?") but should never be used - in production as the resulting quantized arithmetic is inaccurate. - -* `--drop_fake_quant`. Type: boolean. Default: false. Causes fake-quantization - nodes to be dropped from the graph. This may be used to recover a plain - float graph from a fake-quantized graph. - -* `--reorder_across_fake_quant`. Type: boolean. Default: false. Normally, - fake-quantization nodes must be strict boundaries for graph transformations, - in order to ensure that quantized inference has the exact same arithmetic - behavior as quantized training --- which is the whole point of quantized - training and of FakeQuant nodes in the first place. However, that entails - subtle requirements on where exactly FakeQuant nodes must be placed in the - graph. Some quantized graphs have FakeQuant nodes at unexpected locations, - that prevent graph transformations that are necessary in order to generate a - well-formed quantized representation of these graphs. Such graphs should be - fixed, but as a temporary work-around, setting this - reorder_across_fake_quant flag allows the converter to perform necessary - graph transformations on them, at the cost of no longer faithfully matching - inference and training arithmetic. - -* `--quantize_weights`. Type: boolean. Default: false. Store weights as - quantized weights followed by dequantize operations. Computation is still - done in float, but reduces model size (at the cost of accuracy and latency). + +* `--inference_input_type`. Type: string. Data type of a real-number input + array in the output file. By default the `--inference_type` is used as type + of all of the input arrays. Flag is primarily intended for generating a + float-point graph with a quantized input array. A Dequantized operator is + added immediately after the input array. Must be `{FLOAT, QUANTIZED_UINT8}`. + + The flag is typically used for vision models taking a bitmap as input but + requiring floating-point inference. For such image models, the uint8 input + is quantized and the quantization parameters used for such input arrays are + their `mean_value` and `std_dev_value` parameters. + +* `--default_ranges_min`, `--default_ranges_max`. Type: floating-point. + Default value for the (min, max) range values used for all arrays without a + specified range. Allows user to proceed with quantization of non-quantized + or incorrectly-quantized input files. These flags produce models with low + accuracy. They are intended for easy experimentation with quantization via + "dummy quantization". + +* `--drop_control_dependency`. Type: boolean. Default: True. Indicates whether + to drop control dependencies silently. This is due to TensorFlow Lite not + supporting control dependencies. + +* `--reorder_across_fake_quant`. Type: boolean. Default: False. Indicates + whether to reorder FakeQuant nodes in unexpected locations. Used when the + location of the FakeQuant nodes is preventing graph transformations + necessary to convert the graph. Results in a graph that differs from the + quantized training graph, potentially causing differing arithmetic behavior. + +* `--allow_custom_ops`. Type: string. Default: False. Indicates whether to + allow custom operations. When false, any unknown operation is an error. When + true, custom ops are created for any op that is unknown. The developer will + need to provide these to the TensorFlow Lite runtime with a custom resolver. + +* `--quantize_weights`. Type: boolean. Default: False. Indicates whether to + store weights as quantized weights followed by dequantize operations. + Computation is still done in float, but reduces model size (at the cost of + accuracy and latency). ## Logging flags -The following flags allow to generate graph visualizations of the actual graph -at various points during transformations: +The following flags generate graph visualizations of the graph as +[GraphViz](https://www.graphviz.org/) `.dot` files at various points during +graph transformations: -* `--dump_graphviz=/path` enables dumping of the graphs at various stages of - processing as GraphViz `.dot` files. Generally preferred over - `--output_format=GRAPHVIZ_DOT` as this allows you to keep your actually - relevant `--output_format`. -* `--dump_graphviz_video` enables dumping of the graph after every single - graph transformation (for debugging purposes). +* `--dump_graphviz_dir`. Type: string. Specifies the full path of the + directory to output GraphViz `.dot` files. Outputs the graph immediately + after reading in the graph and after all of the transformations have been + completed. +* `--dump_graphviz_video`. Type: boolean. Outputs GraphViz after every graph + transformation. Requires `--dump_graphviz_dir` to be specified. diff --git a/tensorflow/contrib/lite/toco/g3doc/python_api.md b/tensorflow/contrib/lite/toco/g3doc/python_api.md index a7841a685528fb18bb08f1943278339a2daec16a..3799eac0a1181afe3b63d2f8651745c2ec61f5e0 100644 --- a/tensorflow/contrib/lite/toco/g3doc/python_api.md +++ b/tensorflow/contrib/lite/toco/g3doc/python_api.md @@ -15,11 +15,15 @@ Table of contents: * [Exporting a GraphDef from tf.Session](#basic-graphdef-sess) * [Exporting a GraphDef from file](#basic-graphdef-file) * [Exporting a SavedModel](#basic-savedmodel) + * [Exporting a tf.keras File](#basic-keras-file) * [Complex examples](#complex) * [Exporting a quantized GraphDef](#complex-quant) * [TensorFlow Lite Python interpreter](#interpreter) * [Using the interpreter from a model file](#interpreter-file) * [Using the interpreter from model data](#interpreter-data) +* [Additional instructions](#additional-instructions) + * [Build from source code](#latest-package) + * [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9) ## High-level overview @@ -31,15 +35,17 @@ designing a model that can be targeted to devices with mobile. ## API -The API for converting TensorFlow models to TensorFlow Lite is -`tf.contrib.lite.TocoConverter`. The API for calling the Python intepreter is +The API for converting TensorFlow models to TensorFlow Lite as of TensorFlow 1.9 +is `tf.contrib.lite.TocoConverter`. The API for calling the Python intepreter is `tf.contrib.lite.Interpreter`. `TocoConverter` provides class methods based on the original format of the model. `TocoConverter.from_session()` is available for GraphDefs. -`TocoConverter.from_saved_model()` is available for SavedModels. Example usages -for simple float-point models are shown in [Basic Examples](#basic). Examples -usages for more complex models is shown in [Complex Examples](#complex). +`TocoConverter.from_saved_model()` is available for SavedModels. +`TocoConverter.from_keras_model_file()` is available for `tf.Keras` files. +Example usages for simple float-point models are shown in [Basic +Examples](#basic). Examples usages for more complex models is shown in [Complex +Examples](#complex). **NOTE**: Currently, `TocoConverter` will cause a fatal error to the Python interpreter when the conversion fails. This will be remedied as soon as @@ -111,6 +117,51 @@ For more complex SavedModels, the optional parameters that can be passed into `output_arrays`, `tag_set` and `signature_key`. Details of each parameter are available by running `help(tf.contrib.lite.TocoConverter)`. +### Exporting a tf.keras File + +The following example shows how to convert a `tf.keras` model into a TensorFlow +Lite FlatBuffer. + +```python +import tensorflow as tf + +converter = tf.contrib.lite.TocoConverter.from_keras_model_file("keras_model.h5") +tflite_model = converter.convert() +open("converted_model.tflite", "wb").write(tflite_model) +``` + +The `tf.keras` file must contain both the model and the weights. A comprehensive +example including model construction can be seen below. + +```python +import numpy as np +import tensorflow as tf + +# Generate tf.keras model. +model = tf.keras.models.Sequential() +model.add(tf.keras.layers.Dense(2, input_shape=(3,))) +model.add(tf.keras.layers.RepeatVector(3)) +model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3))) +model.compile(loss=tf.keras.losses.MSE, + optimizer=tf.keras.optimizers.RMSprop(lr=0.0001), + metrics=[tf.keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + +x = np.random.random((1, 3)) +y = np.random.random((1, 3, 3)) +model.train_on_batch(x, y) +model.predict(x) + +# Save tf.keras model in HDF5 format. +keras_file = "keras_model.h5" +tf.keras.models.save_model(model, keras_file) + +# Convert to TensorFlow Lite model. +converter = tf.contrib.lite.TocoConverter.from_keras_model_file(keras_file) +tflite_model = converter.convert() +open("converted_model.tflite", "wb").write(tflite_model) +``` + ## Complex examples For models where the default value of the attributes is not sufficient, the @@ -200,3 +251,18 @@ with tf.Session() as sess: interpreter = tf.contrib.lite.Interpreter(model_content=tflite_model) interpreter.allocate_tensors() ``` + +## Additional instructions + +### Build from source code + +In order to run the latest version of the TOCO Python API, clone the TensorFlow +repository, configure the installation, and build and install the pip package. +Detailed instructions are available +[here](https://www.tensorflow.org/install/install_sources). + +### Converting models prior to TensorFlow 1.9. + +To use TOCO in TensorFlow 1.7 and TensorFlow 1.8, use the `toco_convert` +function. Run `help(tf.contrib.lite.toco_convert)` to get details about accepted +parameters. diff --git a/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg index a47c088991299159be39bc490149720dae43eb53..262e13a591b998c4f38f0a9f44a5b385f612df90 100644 --- a/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg +++ b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg @@ -1 +1 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc index 0fffab574ddd8ad75ec07ae4442f363a36ed289e..1ea83abf8eb1b49f649e81def29857094cd0c2d7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc @@ -38,6 +38,16 @@ bool ConvertPureConvToDepthwise::Run(Model* model, std::size_t op_index) { // Depthwise conv does not support dilation return false; } + auto& input_array = model->GetArray(conv_op->inputs[0]); + if (!input_array.has_shape()) { + // Shapes not propagated yet + return false; + } + if (input_array.shape().dims(3) != 1) { + // Not a pure convolution: Conv does accumulation across the depth + // dimension. + return false; + } auto& weights_array = model->GetArray(conv_op->inputs[1]); if (!weights_array.buffer) { // Yield until the weights are resolved as a constant array. @@ -46,11 +56,6 @@ bool ConvertPureConvToDepthwise::Run(Model* model, std::size_t op_index) { if (weights_array.data_type != ArrayDataType::kFloat) { return false; } - if (weights_array.shape().dims(3) != 1) { - // Not a pure convolution: Conv does accumulation across the depth - // dimension. - return false; - } // At this point we know we have a pure conv. Rewrite it as DepthwiseConv. AddMessageF( "%s is purely convolutional (input/weights depth is 1), replacing it by " diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc new file mode 100644 index 0000000000000000000000000000000000000000..b689be07926ecd9be4cc317735dc88eb90950e13 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc @@ -0,0 +1,94 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ConvertTrivialTileToConcat::Run(Model* model, std::size_t op_index) { + auto tile_it = model->operators.begin() + op_index; + if (tile_it->get()->type != OperatorType::kTile) { + return false; + } + auto* tile_op = static_cast(tile_it->get()); + + const auto& input_array = model->GetArray(tile_op->inputs[0]); + const auto& multiples_array = model->GetArray(tile_op->inputs[1]); + const auto& output_array = model->GetArray(tile_op->outputs[0]); + if (!input_array.has_shape() || !multiples_array.has_shape() || + !output_array.has_shape()) { + // Yield until PropagateFixedSizes has been run on this op. + return false; + } + // Note: We can assume we have error checked inputs in PropagateFixedSizes. + + if (!multiples_array.buffer) { + // Yield until the multiples is constant. + return false; + } + std::vector const& multiples = + multiples_array.GetBuffer().data; + + // We can simplify the tile if only a single dimension is being multiplied. + // It then just becomes a concat along that dimension. + int non_one_dims = 0; + int concat_axis = 0; + for (int i = 0; i < multiples.size(); ++i) { + if (multiples[i] != 1) { + ++non_one_dims; + concat_axis = i; + } + } + if (non_one_dims != 1) { + // The tile is non-trivial. Good luck. + AddMessageF("Tile %s is non-trivial (has more than one multiply dimension)", + LogName(*tile_op)); + return false; + } + + // The tile is like a concat. + AddMessageF("Simplifying %s to a Concat along a single axis %d", + LogName(*tile_op), concat_axis); + + auto* concat_op = new ConcatenationOperator; + + // Copy input and output. + // Note that we multiply out the input by the number of times requested. + for (int i = 0; i < multiples[concat_axis]; ++i) { + concat_op->inputs.push_back(tile_op->inputs[0]); + } + concat_op->axis = concat_axis; + concat_op->outputs = tile_op->outputs; + + // Delete multiples array if unused. + if (IsDiscardableArray(*model, tile_op->inputs[1]) && + CountOpsWithInput(*model, tile_op->inputs[1]) == 1) { + model->EraseArray(tile_op->inputs[1]); + } + + // Replace the operator in the graph. + const auto concat_it = model->operators.emplace(tile_it, concat_op); + tile_it = concat_it + 1; + CHECK_EQ(tile_it->get(), tile_op); + model->operators.erase(tile_it); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc b/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc index 076415ece8c1039caa32e947fe54ab3e101bec9e..1e68cd678bce6c27f1852a5ae0c13362d8938cdd 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc @@ -25,17 +25,12 @@ limitations under the License. namespace toco { -bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { - auto conv_it = model->operators.begin() + op_index; - if (conv_it->get()->type != OperatorType::kConv) { - return false; - } - auto* conv_op = static_cast(conv_it->get()); - if (conv_op->outputs.size() == 2) { +bool ProcessConvOperator(Model* model, ConvOperator* op) { + if (op->outputs.size() == 2) { // We already have an im2col array return false; } - const auto& weights_array = model->GetArray(conv_op->inputs[1]); + const auto& weights_array = model->GetArray(op->inputs[1]); if (!weights_array.has_shape()) { // We need to yield until weights dims have been resolved, because // from the weights dims we determine whether an im2col array is @@ -45,25 +40,52 @@ bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { const auto& weights_shape = weights_array.shape(); const int kheight = weights_shape.dims(1); const int kwidth = weights_shape.dims(2); - if (kwidth == 1 && kheight == 1 && conv_op->stride_width == 1 && - conv_op->stride_height == 1) { - // 1x1 unstrided conv does not need an im2col array. + if (kwidth == 1 && kheight == 1 && op->stride_width == 1 && + op->stride_height == 1 && op->dilation_width_factor == 1 && + op->dilation_height_factor == 1) { + // 1x1 unstrided undilated conv does not need an im2col array. return false; } // Create the im2col array. - CHECK_EQ(conv_op->outputs.size(), 1); + CHECK_EQ(op->outputs.size(), 1); const string& im2col_array_name = - AvailableArrayName(*model, conv_op->inputs[0] + "_im2col"); + AvailableArrayName(*model, op->inputs[0] + "_im2col"); model->GetOrCreateArray(im2col_array_name); - conv_op->outputs.push_back(im2col_array_name); - AddMessageF( - "Created an im2col array for %s, with %dx%d kernel and stride_width=%d, " - "stride_height=%d", - LogName(*conv_op), kwidth, kheight, conv_op->stride_width, - conv_op->stride_height); + op->outputs.push_back(im2col_array_name); return true; } +bool ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { + if (op->outputs.size() == 2) { + // We already have an im2col array + return false; + } + + // Always create an im2col array for transpose_conv. + CHECK_EQ(op->outputs.size(), 1); + const string& im2col_array_name = AvailableArrayName( + *model, op->inputs[TransposeConvOperator::DATA_INPUT] + "_im2col"); + model->GetOrCreateArray(im2col_array_name); + op->outputs.push_back(im2col_array_name); + + return true; +} + +bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { + auto it = model->operators.begin() + op_index; + auto* op = it->get(); + + switch (op->type) { + case OperatorType::kConv: + return ProcessConvOperator(model, static_cast(op)); + case OperatorType::kTransposeConv: + return ProcessTransposeConvOperator( + model, static_cast(op)); + default: + return false; + } +} + } // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc index 498c864bde6d656c8318e981204cb42cb3a4d03f..2c7ffe488477ef1a544dfe6f36a6e0d1ac40aa96 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc @@ -111,7 +111,7 @@ bool DequantizeArray(const string& array_name, auto* op_outputting_array = GetOpWithOutput(*model, array_name); if (op_outputting_array) { - if (op_outputting_array->type == OperatorType::kTensorFlowReshape) { + if (op_outputting_array->type == OperatorType::kReshape) { return true; } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/ensure_bias_vectors.cc b/tensorflow/contrib/lite/toco/graph_transformations/ensure_bias_vectors.cc index 708ecf6e0a96811ab274fbb25f748f562cd3afad..e80ed036b311cfc586c40ece410ef6a6432a0cd9 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/ensure_bias_vectors.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/ensure_bias_vectors.cc @@ -26,17 +26,38 @@ namespace toco { namespace { +int GetOutputDepthFromWeights(const Model& model, const Operator& op) { + const string& weights_name = op.inputs[1]; + const auto& weights_shape = model.GetArray(weights_name).shape(); + if (op.type == OperatorType::kConv || + op.type == OperatorType::kFullyConnected) { + return weights_shape.dims(0); + } + if (op.type == OperatorType::kDepthwiseConv) { + return weights_shape.dims(3); + } + LOG(FATAL) << "Unhandled operator type"; + return 0; +} + bool ProcessLinearOperator(Model* model, Operator* op) { if (op->inputs.size() >= 3) { return false; } const string& output_name = op->outputs[0]; + const string& weights_name = op->inputs[1]; + if (!model->GetArray(weights_name).has_shape()) { + return false; + } + const int depth = GetOutputDepthFromWeights(*model, *op); const string& bias_name = AvailableArrayName(*model, output_name + "_bias"); op->inputs.push_back(bias_name); DCHECK_EQ(op->inputs.size(), 3); auto& bias_array = model->GetOrCreateArray(bias_name); bias_array.data_type = ArrayDataType::kFloat; - + bias_array.mutable_shape()->mutable_dims()->push_back(depth); + auto& bias_buffer = bias_array.GetMutableBuffer(); + bias_buffer.data.resize(depth, 0.f); return true; } } // namespace diff --git a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc index 394fa349e2663e2806344f27a96a5132a2d4a810..75642bbc37be6b3140e5b79a463ca70b5786d772 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc @@ -122,7 +122,7 @@ bool EnsureUint8WeightsSafeForFastInt8Kernels::Run(Model* model, case OperatorType::kFullyConnected: { weights_index = 1; const auto& fc_op = static_cast(op); - CHECK(!fc_op.experimental_shuffled_weights) + CHECK(fc_op.weights_format == FullyConnectedWeightsFormat::kDefault) << "This graph transformation expects to run before FC weights get " "shuffled."; break; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc new file mode 100644 index 0000000000000000000000000000000000000000..874d8def571fbce4219de15285c8df6fd2487a9a --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc @@ -0,0 +1,102 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +// Returns true if the given op is strictly a broadcasting operation. +// This is commonly seen as a Concat of the same input multiple times, and is +// often generated from Tile ops that were converted via the +// convert_trivial_tile_to_concat transformation. +bool IsBroadcastingOp(const Model& model, Operator* op) { + // Concatenation of identical inputs is usually a broadcast. + if (op->type == OperatorType::kConcatenation) { + // Verify that all inputs are the same. + for (int i = 1; i < op->inputs.size(); ++i) { + if (op->inputs[i] != op->inputs[0]) { + return false; + } + } + return true; + } + + // There are other things we could look for (Stack/etc) when needed. + return false; +} + +} // namespace + +// Finds an operation that looks like a broadcast (concat of the same sources +// along the last dimension) and drops it by relying on the ability of certain +// binary ops to perform an implicit broadcast. +bool FuseBroadcastIntoFollowingBinary::Run(Model* model, std::size_t op_index) { + const auto binary_it = model->operators.begin() + op_index; + auto* binary_op = binary_it->get(); + + // Test for binary ops of types that we know how to resolve + if (binary_op->inputs.size() != 2) { + return false; + } + if (binary_op->type != OperatorType::kAdd && + binary_op->type != OperatorType::kMul && + binary_op->type != OperatorType::kSub && + binary_op->type != OperatorType::kDiv) { + return false; + } + + // NOTE: either of these ops may be nullptr if the input array is constant. + Operator* const op[2] = { + GetOpWithOutput(*model, binary_op->inputs[0]), + GetOpWithOutput(*model, binary_op->inputs[1]), + }; + + // Check whether either input is a broadcast-like concat. + bool is_op_0_broadcast = op[0] && IsBroadcastingOp(*model, op[0]); + bool is_op_1_broadcast = op[1] && IsBroadcastingOp(*model, op[1]); + if (!is_op_0_broadcast && !is_op_1_broadcast) { + // Neither input is a broadcast-looking thing. + AddMessageF("Neither input looks broadcasty"); + return false; + } else if (is_op_0_broadcast && is_op_1_broadcast) { + AddMessageF( + "Unable to fuse broadcast into %s as both inputs (%s, %s) are " + "broadcasts", + LogName(*binary_op), op[0] ? LogName(*op[0]) : "(?)", + op[1] ? LogName(*op[1]) : "(?)"); + return false; + } + int broadcast_index = is_op_0_broadcast ? 0 : 1; + + // Just pull out the input of the broadcast op and pass it directly to the + // binary op. + AddMessageF("Fusing broadcast op %s into the following binary %s", + LogName(*op[broadcast_index]), LogName(*binary_op)); + binary_op->inputs[broadcast_index] = op[broadcast_index]->inputs[0]; + + // We leave the broadcast op in; it'll get cleaned up if it's not used later. + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index 1bc7557d46cfa5e1b27468d2da271e75fd491d36..8cd1298bcacd7b9c1379ccb4532885f686484278 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -117,12 +117,14 @@ DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) DECLARE_GRAPH_TRANSFORMATION(ConvertSqueezeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialStackToReshape) +DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTileToConcat) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertReorderAxes) DECLARE_GRAPH_TRANSFORMATION(EnsureBiasVectors) DECLARE_GRAPH_TRANSFORMATION(FuseActivationFunctions) DECLARE_GRAPH_TRANSFORMATION(FuseBinaryIntoFollowingAffine) DECLARE_GRAPH_TRANSFORMATION(FuseBinaryIntoPrecedingAffine) +DECLARE_GRAPH_TRANSFORMATION(FuseBroadcastIntoFollowingBinary) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Normalization) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Pool) DECLARE_GRAPH_TRANSFORMATION(IdentifyLstmCell) @@ -133,6 +135,7 @@ DECLARE_GRAPH_TRANSFORMATION(IdentifyRelu1) DECLARE_GRAPH_TRANSFORMATION(IdentifyPRelu) DECLARE_GRAPH_TRANSFORMATION(IdentifyDilatedConv) DECLARE_GRAPH_TRANSFORMATION(MakeInitialDequantizeOperator) +DECLARE_GRAPH_TRANSFORMATION(MoveBinaryOperatorBeforeReshape) DECLARE_GRAPH_TRANSFORMATION(PropagateActivationFunctionIntoConstants) DECLARE_GRAPH_TRANSFORMATION(PropagateArrayDataTypes) DECLARE_GRAPH_TRANSFORMATION(PropagateFakeQuantNumBits); @@ -165,7 +168,6 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowMatMul) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowMerge) DECLARE_GRAPH_TRANSFORMATION(ResolveSqueezeAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowSwitch) -DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowTile) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantConcatenation) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantReshape) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantTranspose) @@ -191,7 +193,7 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantGather) DECLARE_GRAPH_TRANSFORMATION(ResolveMultiplyByZero) DECLARE_GRAPH_TRANSFORMATION(Dequantize) DECLARE_GRAPH_TRANSFORMATION(UnpartitionEmbeddingLookup) -DECLARE_GRAPH_TRANSFORMATION(ExperimentalShuffleFCWeights) +DECLARE_GRAPH_TRANSFORMATION(ShuffleFCWeights) class PropagateDefaultMinMax : public GraphTransformation { public: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc index d63ee7c9519d169a2f44ec1afe81125217db8976..2f1bb8f0ad6374243e5a094701eef54cd086151a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc @@ -133,24 +133,20 @@ bool HardcodeMinMaxForConcatenation(Model* model, Operator* op) { } bool HardcodeMinMaxForSplit(Model* model, Operator* op) { - for (const auto& output : op->outputs) { - if (model->GetArray(output).minmax) { - LOG(WARNING) << "Skipping min-max setting for " << LogName(*op) - << " because output " << output << " already has min-max."; - return false; - } - } // Data is in second input. auto& input_array = model->GetArray(op->inputs[1]); if (!input_array.minmax) { return false; - } else { - for (const auto& output : op->outputs) { - auto& array = model->GetArray(output); + } + bool changed = false; + for (const auto& output : op->outputs) { + auto& array = model->GetArray(output); + if (!array.minmax || !(array.GetMinMax() == input_array.GetMinMax())) { + changed = true; array.GetOrCreateMinMax() = *input_array.minmax; } - return true; } + return changed; } // The output of average or max pooling is within the same range as its input. @@ -232,6 +228,14 @@ bool HardcodeMinMaxForOutput(Model* model, Operator* op, double min, return true; } +bool MinMaxApproximatelyEqual(const MinMax& minmax1, const MinMax& minmax2) { + const double magnitude = + std::min(minmax1.max - minmax1.min, minmax2.max - minmax2.min); + const double tolerated = 1e-6 * magnitude; + return std::abs(minmax1.min - minmax2.min) < tolerated && + std::abs(minmax1.max - minmax2.max) < tolerated; +} + // Propagates MinMax from any of the listed arrays, to all others. // If multiple of these arrays have MinMax, then these are required // to agree with each other. @@ -254,7 +258,7 @@ bool PropagateMinMaxAmongArrays(Model* model, for (const string& array_name : array_names) { auto& array = model->GetArray(array_name); if (array.minmax) { - CHECK(*array.minmax == *reference_minmax) + CHECK(MinMaxApproximatelyEqual(*array.minmax, *reference_minmax)) << "Both the following arrays have minmax, and they disagree: " << reference_array_name << " (" << reference_minmax->min << "," << reference_minmax->max << ") and " << array_name << " (" @@ -353,7 +357,7 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForConcatenation(model, op); break; - case OperatorType::kTensorFlowSplit: + case OperatorType::kSplit: changed = HardcodeMinMaxForSplit(model, op); break; @@ -362,9 +366,11 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForAverageOrMaxPool(model, op); break; + case OperatorType::kResizeBilinear: + case OperatorType::kSlice: case OperatorType::kStridedSlice: case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kPad: case OperatorType::kGather: case OperatorType::kTranspose: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc index ae3301f467de5714230e731b4bab87ddc1637201..d49857cfc22ecaf5feb06b39a42187f8adb61d50 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc @@ -90,12 +90,13 @@ bool IdentifyDilatedConv::Run(Model* model, std::size_t op_index) { } // Conv Op - ConvOperator* conv_op = dynamic_cast( - has_expand_op ? GetOpWithInput(*model, post_stb_op->outputs[0]) - : GetOpWithInput(*model, stb_op->outputs[0])); - if (!conv_op || conv_op->type != OperatorType::kConv) { + const string& input_of_conv_op = + has_expand_op ? post_stb_op->outputs[0] : stb_op->outputs[0]; + auto* conv_base_op = GetOpWithInput(*model, input_of_conv_op); + if (conv_base_op->type != OperatorType::kConv) { return false; } + auto* conv_op = static_cast(conv_base_op); if (conv_op->inputs.size() != 2) { // The conv op must only have weights, no bias. return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc index 419a0776a6b987a18df059d3c1d4bf4370cd24d8..b78efd7fc3602dc2d6e03fd28d694c344b61c17c 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc @@ -44,10 +44,9 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { const auto* div_or_mul_op = div_it->get(); OperatorType expected_op_type_producing_div_or_mul_input; if (div_or_mul_op->type == OperatorType::kDiv) { - expected_op_type_producing_div_or_mul_input = OperatorType::kTensorFlowSqrt; + expected_op_type_producing_div_or_mul_input = OperatorType::kSqrt; } else if (div_or_mul_op->type == OperatorType::kMul) { - expected_op_type_producing_div_or_mul_input = - OperatorType::kTensorFlowRsqrt; + expected_op_type_producing_div_or_mul_input = OperatorType::kRsqrt; } else { return false; } @@ -75,8 +74,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { Operator* add_op = nullptr; Operator* op_producing_add_input = nullptr; if (op_producing_sqrt_or_rsqrt_input->type == OperatorType::kAdd || - op_producing_sqrt_or_rsqrt_input->type == - OperatorType::kTensorFlowMaximum) { + op_producing_sqrt_or_rsqrt_input->type == OperatorType::kMaximum) { add_op = op_producing_sqrt_or_rsqrt_input; bool add_can_be_removed = false; CHECK_EQ(op_producing_sqrt_or_rsqrt_input->inputs.size(), 2); @@ -113,7 +111,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { Operator* sum_op = add_op ? op_producing_add_input : op_producing_sqrt_or_rsqrt_input; - if (sum_op->type != OperatorType::kTensorFlowSum) { + if (sum_op->type != OperatorType::kSum) { AddMessageF( "Giving up trying to identify L2Normalization subgraph: " "expected Sum op, got %s", @@ -122,7 +120,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { } Operator* square_op = GetOpWithOutput(*model, sum_op->inputs[0]); - if (square_op->type != OperatorType::kTensorFlowSquare) { + if (square_op->type != OperatorType::kSquare) { AddMessageF( "Giving up trying to identify L2Normalization subgraph: " "expected Square op, got %s", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc index e4d52476c649de53b3ab663f53ce7a5538dbb5ab..705e73779b7f74698149d5e9e56f69a371326ceb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc @@ -41,7 +41,7 @@ std::vector>::iterator FindOperator( bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { const auto sqrt_it = model->operators.begin() + op_index; const auto* sqrt_op = sqrt_it->get(); - if (sqrt_op->type != OperatorType::kTensorFlowSqrt) { + if (sqrt_op->type != OperatorType::kSqrt) { return false; } @@ -52,6 +52,13 @@ bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { const Operator* square_op; Operator* prev_to_sqrt_op = GetOpWithOutput(*model, sqrt_op->inputs[0]); + if (prev_to_sqrt_op == nullptr) { + AddMessageF( + "Giving up trying to identify L2Pool subgraph: " + "expected AveragePool op, but Sqrt op has no preceding op"); + return false; + } + if (prev_to_sqrt_op->type != OperatorType::kAveragePool) { AddMessageF( "Giving up trying to identify L2Pool subgraph: " @@ -65,7 +72,7 @@ bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { square_op = GetOpWithOutput(*model, avpool_op->inputs[0]); CHECK_EQ(square_op->inputs.size(), 1); - if (square_op->type != OperatorType::kTensorFlowSquare) { + if (square_op->type != OperatorType::kSquare) { AddMessageF( "Giving up trying to identify L2Pool subgraph: " "expected Square op, got %s", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc index e9842524c829b839b97b3453a36c41efe186efbb..c0b014b45eb1df25173ce3ca3fa488b0655c3c76 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc @@ -35,19 +35,24 @@ std::vector>::iterator FindOperator( return it; } -bool GetStateArrayForBackEdge(const Model& model, - const string& back_edge_source_array, - string* state_array = nullptr) { - for (const auto& rnn_state : model.flags.rnn_states()) { - if (back_edge_source_array == rnn_state.back_edge_source_array()) { - // Found LSTM cell output - if (state_array) { - *state_array = rnn_state.state_array(); - } - return true; +bool ValidateSourceOp(const Model& model, const string& array_name, + OperatorType op_type, Operator** source_op) { + if (op_type == OperatorType::kNone) { + CHECK(!source_op); + } else { + CHECK(source_op); + *source_op = GetOpWithOutput(model, array_name); + if (*source_op == nullptr) { + return false; + } + + // Check that first operator, if connected, is of correct type + if ((*source_op)->type != op_type) { + return false; } } - return false; + + return true; } // Returns true if the given operator has exactly 1 input, and is connected to @@ -62,24 +67,10 @@ bool MatchOperatorInputs(const Operator& op, const Model& model, } // Check if first input is disconnected/connected to an operator - Operator* x = GetOpWithOutput(model, op.inputs[0]); - if ((op_type == OperatorType::kNone) && (x != nullptr)) { - return false; - } - if ((op_type != OperatorType::kNone) && (x == nullptr)) { + if (!ValidateSourceOp(model, op.inputs[0], op_type, connected_op)) { return false; } - // Check that first operator, if connected, is of correct type - if ((x != nullptr) && (x->type != op_type)) { - return false; - } - - // Successfully matched. Optionally return matching input operators. - if (connected_op) { - *connected_op = x; - } - return true; } @@ -96,40 +87,15 @@ bool MatchOperatorInputs(const Operator& op, const Model& model, } // Check if first input is disconnected/connected to an operator - Operator* x = GetOpWithOutput(model, op.inputs[0]); - if ((a_op_type == OperatorType::kNone) && (x != nullptr)) { - return false; - } - if ((a_op_type != OperatorType::kNone) && (x == nullptr)) { - return false; - } - - // Check that first operator, if connected, is of correct type - if ((x != nullptr) && (x->type != a_op_type)) { + if (!ValidateSourceOp(model, op.inputs[0], a_op_type, a_op)) { return false; } // Check if second input is disconnected/connected to an operator - Operator* y = GetOpWithOutput(model, op.inputs[1]); - if ((b_op_type == OperatorType::kNone) && (y != nullptr)) { - return false; - } - if ((b_op_type != OperatorType::kNone) && (y == nullptr)) { + if (!ValidateSourceOp(model, op.inputs[1], b_op_type, b_op)) { return false; } - // Check that second operator, if connected, is of correct type - if ((y != nullptr) && (y->type != b_op_type)) { - return false; - } - - // Successfully matched. Optionally return matching input operators. - if (a_op != nullptr) { - *a_op = x; - } - if (b_op != nullptr) { - *b_op = y; - } return true; } @@ -147,57 +113,20 @@ bool MatchOperatorInputs(const Operator& op, const Model& model, } // Check if first input is disconnected/connected to an operator - Operator* x = GetOpWithOutput(model, op.inputs[0]); - if ((a_op_type == OperatorType::kNone) && (x != nullptr)) { - return false; - } - if ((a_op_type != OperatorType::kNone) && (x == nullptr)) { - return false; - } - - // Check that first operator, if connected, is of correct type - if ((x != nullptr) && (x->type != a_op_type)) { + if (!ValidateSourceOp(model, op.inputs[0], a_op_type, a_op)) { return false; } // Check if second input is disconnected/connected to an operator - Operator* y = GetOpWithOutput(model, op.inputs[1]); - if ((b_op_type == OperatorType::kNone) && (y != nullptr)) { - return false; - } - if ((b_op_type != OperatorType::kNone) && (y == nullptr)) { - return false; - } - - // Check that second operator, if connected, is of correct type - if ((y != nullptr) && (y->type != b_op_type)) { + if (!ValidateSourceOp(model, op.inputs[1], b_op_type, b_op)) { return false; } // Check if third input is disconnected/connected to an operator - Operator* z = GetOpWithOutput(model, op.inputs[2]); - if ((c_op_type == OperatorType::kNone) && (z != nullptr)) { - return false; - } - if ((c_op_type != OperatorType::kNone) && (z == nullptr)) { - return false; - } - - // Check that third operator, if connected, is of correct type - if ((z != nullptr) && (z->type != c_op_type)) { + if (!ValidateSourceOp(model, op.inputs[2], c_op_type, c_op)) { return false; } - // Successfully matched. Optionally return matching input operators. - if (a_op != nullptr) { - *a_op = x; - } - if (b_op != nullptr) { - *b_op = y; - } - if (c_op != nullptr) { - *c_op = z; - } return true; } @@ -231,11 +160,6 @@ bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { &state_combine_add)) { return false; } - string prev_state; - if (!GetStateArrayForBackEdge(*model, state_output_tanh->inputs[0], - &prev_state)) { - return false; - } // State forget & remember addition Operator *state_forget_mul, *state_remember_mul; @@ -244,9 +168,7 @@ bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { &state_remember_mul)) { return false; } - if (state_forget_mul->inputs[0] != prev_state) { - return false; - } + const string prev_state = state_forget_mul->inputs[0]; // State forget gate Operator* state_forget_sig; @@ -266,26 +188,26 @@ bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { // State remember "information" activation function Operator* fc_output_split; - if (!MatchOperatorInputs(*state_info_tanh, *model, - OperatorType::kTensorFlowSplit, &fc_output_split)) { + if (!MatchOperatorInputs(*state_info_tanh, *model, OperatorType::kSplit, + &fc_output_split)) { return false; } // State remember gate activation function Operator* tmp; - if (!MatchOperatorInputs(*state_remember_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*state_remember_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } // State forget gate activation function - if (!MatchOperatorInputs(*state_forget_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*state_forget_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } // Fully connected output activation function - if (!MatchOperatorInputs(*fc_output_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*fc_output_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } @@ -306,8 +228,8 @@ bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { return false; } - if (static_cast(fully_connected) - ->experimental_shuffled_weights) { + if (static_cast(fully_connected)->weights_format != + FullyConnectedWeightsFormat::kDefault) { // Not yet implemented: experimental shuffled weights in fused LSTM cell. return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc index e6e3dfa1de9c9fdd5e759fd547d11a7b8c95d837..46d1fce50e5d6e2a74cf5461d731e46469dde5bf 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc @@ -74,6 +74,12 @@ bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { lstm_cell_op->inputs[kInputTensor] = curr_op->inputs[LstmCellOperator::ACTIV_OUTPUT]; + // Previous states. + lstm_cell_op->inputs[kInputActivationStateTensor] = + curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT]; + lstm_cell_op->inputs[kInputCellStateTensor] = + curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT]; + // Get original weight tensor and decompose 1 tensor to 8 sub tensors. Array& kernel = model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]); @@ -160,10 +166,6 @@ bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { // Erase curr lstm op being replaced. DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT], model); DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::BIASES_INPUT], model); - DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT], - model); - DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT], - model); model->operators.erase(FindOp(*model, curr_op)); return true; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc index bddb563206f763a756685d196836fa41825cf045..94820a016622a12654e91967737e05fc91ed404c 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc @@ -60,24 +60,22 @@ bool IdentifyRelu1::Run(Model* model, std::size_t op_index) { // Follow sequences of min+max and max+min. First get the leading op. const auto op_it = model->operators.begin() + op_index; const auto* op_0 = op_it->get(); - if (op_0->type != OperatorType::kTensorFlowMinimum && - op_0->type != OperatorType::kTensorFlowMaximum) { + if (op_0->type != OperatorType::kMinimum && + op_0->type != OperatorType::kMaximum) { return false; } // Get the paired op and ensure it's the counter to the first. const auto* op_1 = GetOpWithInput(*model, op_0->outputs[0]); if (!op_1 || - (op_1->type != OperatorType::kTensorFlowMinimum && - op_1->type != OperatorType::kTensorFlowMaximum) || + (op_1->type != OperatorType::kMinimum && + op_1->type != OperatorType::kMaximum) || op_0->type == op_1->type) { return false; } - const auto* min_op = - op_0->type == OperatorType::kTensorFlowMinimum ? op_0 : op_1; - const auto* max_op = - op_0->type == OperatorType::kTensorFlowMaximum ? op_0 : op_1; + const auto* min_op = op_0->type == OperatorType::kMinimum ? op_0 : op_1; + const auto* max_op = op_0->type == OperatorType::kMaximum ? op_0 : op_1; if (min_op->inputs.size() != 2 || max_op->inputs.size() != 2) { return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h index 1c32a781698ec78003ebbf9caff28557924323e5..6d8603a1133a7478647b8bcc49ea1eceba28df31 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h @@ -47,10 +47,14 @@ enum ExtendedLstmCellInputs { kOutputGateBiasTensor = 15, kProjectionWeightsTensor = 16, // Optional kProjectionBiasTensor = 17, // Optional - kExtendedLstmInputCount = 18 + kInputActivationStateTensor = 18, + // The op can handle 18 inputs or 20 inputs. + kInputCellStateTensor = 19, + kExtendedLstmInputCount = 20, }; enum ExtendedLstmCellOutputs { + // TODO(ycling): Make the 2 output state tensors optional. kOutputStateTensor = 0, kCellStateTensor = 1, kOutputTensor = 2, diff --git a/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc index 5065004093434475172a39efdcfd26c10c49148b..95bc7f7d4b8b517c1cc5a73b3e85bbd985ce460f 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc @@ -106,7 +106,7 @@ bool MergeReshapeIntoPrecedingTranspose::Run(Model* model, std::size_t op_index) { auto it = model->operators.begin() + op_index; auto* reshape_op = ConvertOperator( - it->get(), OperatorType::kTensorFlowReshape); + it->get(), OperatorType::kReshape); if (reshape_op == nullptr) { return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/move_binary_operator_before_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/move_binary_operator_before_reshape.cc new file mode 100644 index 0000000000000000000000000000000000000000..7f44c65285bdef6ba314b16122fdd550bfa47e6a --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/move_binary_operator_before_reshape.cc @@ -0,0 +1,178 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +namespace { + +bool IsTailOfShape(const Shape& tail, const Shape& shape) { + // Return true if 'tail' dimensions are the same as the ending dimensions of + // 'shape'. + + int shape_end = shape.dimensions_count() - 1; + int tail_end = tail.dimensions_count() - 1; + + if (tail_end > shape_end) { + // tail cannot be longer than shape. + return false; + } + + // Walk dimensions back to front and compare + for (int i = 0; i <= tail_end; i++) { + if (shape.dims(shape_end - i) != tail.dims(tail_end - i)) { + return false; + } + } + return true; +} + +} // namespace + +// If a binary operator is doing a broadcast operation from a constant array, +// and the constant array shape is the tail of both the other input shape, and a +// subsequent reshape op's output shape, we can swap their order. Since we +// prefer to have reshape ops after mathematic ops, this can allow for the +// collapsing of some reshapes. The WaveNet model in particular benefits from +// this transformation. +// +// Note we are testing for one particular case of a broader set of possible +// binary-reshape op transformations. This transformation could be generalized. +bool MoveBinaryOperatorBeforeReshape::Run(Model* model, std::size_t op_index) { + const auto binary_it = model->operators.begin() + op_index; + Operator* binary_op = binary_it->get(); + if (binary_op->type != OperatorType::kAdd && + binary_op->type != OperatorType::kMul && + binary_op->type != OperatorType::kSub && + binary_op->type != OperatorType::kDiv && + binary_op->type != OperatorType::kFloorDiv && + binary_op->type != OperatorType::kFloorMod && + binary_op->type != OperatorType::kMinimum && + binary_op->type != OperatorType::kMaximum && + binary_op->type != OperatorType::kLess && + binary_op->type != OperatorType::kLessEqual && + binary_op->type != OperatorType::kGreater && + binary_op->type != OperatorType::kGreaterEqual) { + return false; + } + + // BINARY OP INPUT CHECKS + CHECK_EQ(binary_op->inputs.size(), 2); + const bool input_is_const[2] = { + IsConstantParameterArray(*model, binary_op->inputs[0]), + IsConstantParameterArray(*model, binary_op->inputs[1]), + }; + if (!input_is_const[0] && !input_is_const[1]) { + // To limit our scope, we require one constant input. Though there's no + // reason this transformation wouldn't work with all variable inputs. + return false; + } + if (input_is_const[0] && input_is_const[1]) { + // Both inputs are constants. Leave this for constants propagation. + return false; + } + const int constant_input_idx = input_is_const[0] ? 0 : 1; + const int variable_input_idx = input_is_const[0] ? 1 : 0; + CHECK(input_is_const[constant_input_idx]); + CHECK(!input_is_const[variable_input_idx]); + + const auto& variable_input_array = + model->GetArray(binary_op->inputs[variable_input_idx]); + if (!variable_input_array.has_shape()) { + AddMessageF( + "Not moving %s because it's non-constant input shape is not resolved.", + LogName(*binary_op)); + return false; + } + if (!IsTailOfShape( + model->GetArray(binary_op->inputs[constant_input_idx]).shape(), + model->GetArray(binary_op->inputs[variable_input_idx]).shape())) { + // Constant array shape must be the latter part of the variable shape. + return false; + } + + // RESHAPE OP CHECKS + auto reshape_it = + FindOpWithOutput(*model, binary_op->inputs[variable_input_idx]); + if (reshape_it == model->operators.end()) { + AddMessageF("Not moving %s because it's variable input is not connected.", + LogName(*binary_op)); + return false; + } + Operator* reshape_op = reshape_it->get(); + if (reshape_op->type != OperatorType::kReshape) { + AddMessageF("Not moving %s because the preceding %s is not a reshape op", + LogName(*binary_op), LogName(*reshape_op)); + return false; + } + const auto& reshape_input_array = model->GetArray(reshape_op->inputs[0]); + if (!reshape_input_array.has_shape()) { + AddMessageF( + "Not moving %s because it's non-constant input shape is not resolved " + "yet", + LogName(*binary_op)); + return false; + } + if (!IsTailOfShape( + model->GetArray(binary_op->inputs[constant_input_idx]).shape(), + model->GetArray(reshape_op->outputs[0]).shape())) { + // Constant array shape must be the latter part of the binary op output + // shape. + return false; + } + + // EXTRA CHECKS ON CONNECTING ARRAY + for (const string& output_array : model->flags.output_arrays()) { + if (binary_op->inputs[variable_input_idx] == output_array) { + AddMessageF( + "Not moving %s because the output of reshape op %s is an output op.", + LogName(*binary_op), LogName(*reshape_op)); + return false; + } + } + int count_ops_consuming_output = + CountOpsWithInput(*model, binary_op->inputs[variable_input_idx]); + DCHECK_GE(count_ops_consuming_output, 1); + if (count_ops_consuming_output > 1) { + AddMessageF( + "Not moving %s because the output of reshape op %s is consumed by " + "another op", + LogName(*binary_op), LogName(*reshape_op)); + return false; + } + + // SWAP ORDER OF BINARY AND RESHAPE OPS + AddMessageF("Moving op %s before reshape op %s", LogName(*binary_op), + LogName(*reshape_op)); + + // Swap op input and outputs + std::iter_swap(reshape_op->inputs.begin(), + binary_op->inputs.begin() + variable_input_idx); + std::iter_swap(reshape_op->outputs.begin(), binary_op->outputs.begin()); + + // Swap operator ordering + std::iter_swap(binary_it, reshape_it); + + // Clear binary output shape so it will be re-propagated + model->GetArray(binary_op->outputs[0]).clear_shape(); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index 92d283ca2cc7069f4b80c95ffdadcad81a884cbf..00ab7cbaa90b399ca08bdfba82991fbd5d2c9f7e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -56,22 +56,22 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { // These operators unconditionally produce float outputs SetDataTypeForAllOutputs(model, op, ArrayDataType::kFloat); break; - case OperatorType::kTensorFlowLess: - case OperatorType::kTensorFlowLessEqual: - case OperatorType::kTensorFlowGreater: - case OperatorType::kTensorFlowGreaterEqual: - case OperatorType::kTensorFlowEqual: - case OperatorType::kTensorFlowNotEqual: + case OperatorType::kLess: + case OperatorType::kLessEqual: + case OperatorType::kGreater: + case OperatorType::kGreaterEqual: + case OperatorType::kEqual: + case OperatorType::kNotEqual: // These operators unconditionally produce bool outputs SetDataTypeForAllOutputs(model, op, ArrayDataType::kBool); break; case OperatorType::kRank: - case OperatorType::kTensorFlowShape: + case OperatorType::kShape: // These operators only produce int32 outputs. SetDataTypeForAllOutputs(model, op, ArrayDataType::kInt32); break; - case OperatorType::kTensorFlowSplit: - case OperatorType::kTensorFlowConcat: + case OperatorType::kSplit: + case OperatorType::kConcat: case OperatorType::kFill: { // These operators produce an output with the same type as their 2nd input CHECK_GE(op->inputs.size(), 2); @@ -135,7 +135,7 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { model->GetArray(op->outputs[1]).data_type = ArrayDataType ::kInt32; break; } - case OperatorType::kTensorFlowUnsupported: { + case OperatorType::kUnsupported: { auto* unsupported_op = static_cast(op); // Some output tensors from the op could be eliminated by optimization. // This can make unsupported_op->output_data_types have more elements than @@ -175,6 +175,14 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); break; } + case OperatorType::kPow: { + CHECK_EQ(op->inputs.size(), 2); + CHECK(model->GetArray(op->inputs[0]).data_type == + model->GetArray(op->inputs[1]).data_type); + const ArrayDataType data_type = model->GetArray(op->inputs[0]).data_type; + SetDataTypeForAllOutputs(model, op, data_type); + break; + } default: { // These operators produce outputs with the same type as their 1st input CHECK_GT(op->inputs.size(), 0); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc index 6d51fc8c31e6c86701c3dc1fd07a9a5479114738..0f2592d05f6e01599735c5138c53ba7779ce805d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc @@ -27,11 +27,21 @@ namespace toco { namespace { -void ChangeArrayDataType(GraphTransformation* transformation, Array* array, +bool ChangeArrayDataType(GraphTransformation* transformation, Array* array, ArrayDataType new_data_type, const MinMax* new_minmax) { + // The code below assumes kInt16, see + // GetQuantizationParamsFromMinMax + if (new_data_type != ArrayDataType::kInt16) { + return false; + } + + bool changed = false; // Ensure the array ends up in the new type (if it hasn't yet been quantized). - array->final_data_type = new_data_type; + if ((array->final_data_type != new_data_type)) { + array->final_data_type = new_data_type; + changed = true; + } if (array->minmax && array->quantization_params) { // The array is already quantized and has min/max info. @@ -70,10 +80,10 @@ void ChangeArrayDataType(GraphTransformation* transformation, Array* array, // Directly change the type as the array was already quantized. array->data_type = new_data_type; - } else { + changed = true; + } else if (!array->quantization_params) { // Array has not yet been quantized so we can just set the final data type // and assign the new min/max value (if provided). - CHECK(!array->quantization_params); if (!array->minmax && new_minmax) { transformation->AddMessageF("Forcing new minmax to %g,%g (%s)", @@ -82,16 +92,18 @@ void ChangeArrayDataType(GraphTransformation* transformation, Array* array, auto& array_minmax = array->GetOrCreateMinMax(); array_minmax.min = new_minmax->min; array_minmax.max = new_minmax->max; + changed = true; } } + return changed; } // Returns true if the op blocks our backward recursive data type propagation. bool DoesOpBlockBackwardPropagation(const Operator& op) { switch (op.type) { case OperatorType::kConcatenation: - case OperatorType::kTensorFlowConcat: - case OperatorType::kTensorFlowConcatV2: + case OperatorType::kConcat: + case OperatorType::kConcatV2: // Concat shouldn't block propagation, but we do expect that all inputs // have the same range. return false; @@ -100,9 +112,10 @@ bool DoesOpBlockBackwardPropagation(const Operator& op) { // FakeQuant so make sure we move across them. case OperatorType::kGather: // Gathers need their parameters changed to the appropriate data type. - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: case OperatorType::kSelect: + case OperatorType::kTile: // Reshapes and transposes don't change values. return false; default: @@ -120,10 +133,13 @@ bool DoesOpInputBlockBackwardPropagation(const Operator& op, int input_index) { // Ignore gather indices. return input_index != 0; break; - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: // Ignore reshape/transpose shapes/dimensions. return input_index != 0; + case OperatorType::kTile: + // Ignore tile multiples. + return input_index != 0; default: return false; } @@ -155,9 +171,8 @@ bool RecursivelyBackwardPropagateDataType(GraphTransformation* transformation, "Adjusting input final data type of array %s from %s to %s", input, ArrayDataTypeName(input_array.final_data_type), ArrayDataTypeName(new_data_type)); - did_change = true; - ChangeArrayDataType(transformation, &input_array, new_data_type, - &new_minmax); + did_change |= ChangeArrayDataType(transformation, &input_array, + new_data_type, &new_minmax); // Walk up into all ops producing the inputs to this op. for (auto& producing_op : model->operators) { @@ -208,9 +223,8 @@ bool RecursivelyForwardPropagateDataType(GraphTransformation* transformation, "Adjusting output final data type of array %s from %s to %s", output, ArrayDataTypeName(output_array.final_data_type), ArrayDataTypeName(new_data_type)); - did_change = true; - ChangeArrayDataType(transformation, &output_array, new_data_type, - nullptr); + did_change |= ChangeArrayDataType(transformation, &output_array, + new_data_type, nullptr); // Walk down into all ops consuming the output of this op. for (auto& consuming_op : model->operators) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 9e4262223e416178e40f200e227ae6fa316a2728..8eb0423283a267652e3d51361b8a0440f46d0c8b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -120,49 +120,7 @@ void ComputeBinaryOperatorOutputSize(const Shape& input_shape_x, CHECK(output_array->has_shape()); } -int GetOutputDepthFromWeights(const Model& model, const Operator& op) { - const string& weights_name = op.inputs[1]; - const auto& weights_shape = model.GetArray(weights_name).shape(); - if (op.type == OperatorType::kConv || - op.type == OperatorType::kFullyConnected) { - return weights_shape.dims(0); - } else if (op.type == OperatorType::kDepthwiseConv) { - return weights_shape.dims(3); - } else { - LOG(FATAL) << "Unhandled operator type"; - } -} - -bool EnsureBiasVectorShape(Model* model, Operator* op) { - const string& weights_name = op->inputs[1]; - const auto& weights_array = model->GetArray(weights_name); - // Yield until weights shape has been resolved. - if (!weights_array.has_shape()) { - return false; - } - - if (op->inputs.size() < 3) { - return false; - } - auto& bias_array = model->GetArray(op->inputs[2]); - if (bias_array.has_shape()) { - return true; - } - - const int output_depth = GetOutputDepthFromWeights(*model, *op); - bias_array.copy_shape(Shape({output_depth})); - - auto& float_buffer = bias_array.GetMutableBuffer(); - float_buffer.data.resize(output_depth, 0); - - return true; -} - void ProcessConvOperator(Model* model, ConvOperator* op) { - if (!EnsureBiasVectorShape(model, op)) { - return; - } - const auto& input_array = model->GetArray(op->inputs[0]); // Yield until input dims have been resolved. if (!input_array.has_shape()) { @@ -211,12 +169,6 @@ void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { // might as well calculate the output shape and ensure it matches the // specified one - // Check if we have already run. - auto& output_array = model->GetArray(op->outputs[0]); - if (output_array.has_shape()) { - return; - } - // SPECIFIED OUTPUT SHAPE // The below is the specified, or prescribed output shape, _given_ to the // operator as an input. @@ -278,20 +230,26 @@ void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { << "TransposeConv input shape must have 4 dimensions. Input \"" << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " << toco::ShapeToString(weights_shape) << "."; - CHECK_EQ(input_shape.dims(3), weights_shape.dims(0)) + CHECK_EQ(input_shape.dims(3), weights_shape.dims(3)) << "Input shape depth and weight depth do not agree"; // Set the output shape according to the specified output shape. std::vector const& specified_output_shape = specified_output_shape_array.GetBuffer().data; + auto& output_array = model->GetArray(op->outputs[0]); *(output_array.mutable_shape()->mutable_dims()) = specified_output_shape; -} -void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { - if (!EnsureBiasVectorShape(model, op)) { - return; + // Set im2col array dimensions if there is one. + if (op->outputs.size() == 2) { + const int input_depth = weights_shape.dims(3); + auto& im2col_array = model->GetArray(op->outputs[1]); + im2col_array.copy_shape( + Shape{specified_output_shape[0], specified_output_shape[1], + specified_output_shape[2], input_depth * kheight * kwidth}); } +} +void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { const auto& input_array = model->GetArray(op->inputs[0]); // Yield until input dims have been resolved. if (!input_array.has_shape()) { @@ -321,7 +279,7 @@ void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { if (!op->depth_multiplier) { op->depth_multiplier = output_depth / input_depth; } - QCHECK_EQ(output_depth, input_depth * op->depth_multiplier) + CHECK_EQ(output_depth, input_depth * op->depth_multiplier) << "input/output depths and depth_multiplier don't match"; const int kheight = weights_shape.dims(1); @@ -406,10 +364,6 @@ void ProcessOpWithShapeInput(Model* model, Operator* op) { } void ProcessFullyConnectedOperator(Model* model, FullyConnectedOperator* op) { - if (!EnsureBiasVectorShape(model, op)) { - return; - } - const auto& input_array = model->GetArray(op->inputs[0]); // Yield until input dims have been resolved. if (!input_array.has_shape()) { @@ -568,11 +522,11 @@ void ProcessAddNOperator(Model* model, Operator* op) { bool KeepDims(const Operator& op) { switch (op.type) { - case OperatorType::kTensorFlowMin: + case OperatorType::kMin: // Reduction Min return static_cast(op).keep_dims; - case OperatorType::kTensorFlowMax: + case OperatorType::kMax: // Reduction Max return static_cast(op).keep_dims; - case OperatorType::kTensorFlowSum: + case OperatorType::kSum: return static_cast(op).keep_dims; case OperatorType::kMean: return static_cast(op).keep_dims; @@ -1085,9 +1039,6 @@ void ProcessGatherOperator(Model* model, GatherOperator* op) { QCHECK_GE(input_shape.dimensions_count(), 1); op->input_rank = input_shape.dimensions_count(); - // We only support 1-D indices. - QCHECK_EQ(indices_shape.dimensions_count(), 1); - // Copy the input dimensions to the output except for dimension 0, // where the dimension of indices_shape is used. // TODO(mgubin): if axis != 0 this is not true, change when it's supported. @@ -1337,8 +1288,8 @@ void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) { op->begin_mask, op->start_indices, op->strides, input_array.shape().dims().data(), axis); int stop_index = tflite::strided_slice::StopForAxis( - op->end_mask, op->stop_indices, op->strides, - input_array.shape().dims().data(), axis); + op->end_mask, op->shrink_axis_mask, op->stop_indices, op->strides, + input_array.shape().dims().data(), axis, start_index); int dim_size = ceil(static_cast(stop_index - start_index) / op->strides[axis]); @@ -1505,6 +1456,48 @@ void ProcessSparseToDenseOperator(Model* model, SparseToDenseOperator* op) { } } +void ProcessTileOperator(Model* model, TensorFlowTileOperator* op) { + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 1); + + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + // We have already run. + return; + } + + const auto& input_array = model->GetArray(op->inputs[0]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return; + } + const auto& input_shape = input_array.shape(); + + auto& multiples_array = model->GetArray(op->inputs[1]); + if (!multiples_array.has_shape()) { + // Yield until multiples shape been resolved. + return; + } + if (!multiples_array.buffer) { + // Yield until the multiples is constant. + return; + } + CHECK(multiples_array.data_type == ArrayDataType::kInt32) + << "Tile multiples input must be int32"; + + std::vector const& multiples = + multiples_array.GetBuffer().data; + CHECK_EQ(multiples.size(), input_shape.dimensions_count()) + << "Tile multiples input " << op->inputs[1] + << " must be same length as input dimensions"; + + auto* mutable_dims = output_array.mutable_shape()->mutable_dims(); + mutable_dims->resize(multiples.size()); + for (int i = 0; i < mutable_dims->size(); ++i) { + (*mutable_dims)[i] = input_shape.dims(i) * multiples[i]; + } +} + } // namespace bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { @@ -1531,14 +1524,14 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kLogistic: case OperatorType::kTanh: case OperatorType::kLocalResponseNormalization: - case OperatorType::kTensorFlowIdentity: + case OperatorType::kIdentity: case OperatorType::kFakeQuant: case OperatorType::kNeg: - case OperatorType::kTensorFlowRsqrt: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: - case OperatorType::kTensorFlowAll: - case OperatorType::kTensorFlowAssert: + case OperatorType::kRsqrt: + case OperatorType::kSqrt: + case OperatorType::kSquare: + case OperatorType::kAll: + case OperatorType::kAssert: case OperatorType::kCast: case OperatorType::kFloor: case OperatorType::kExp: @@ -1557,14 +1550,15 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kDiv: case OperatorType::kFloorDiv: case OperatorType::kFloorMod: - case OperatorType::kTensorFlowLess: - case OperatorType::kTensorFlowLessEqual: - case OperatorType::kTensorFlowGreater: - case OperatorType::kTensorFlowMaximum: - case OperatorType::kTensorFlowMinimum: - case OperatorType::kTensorFlowGreaterEqual: - case OperatorType::kTensorFlowEqual: - case OperatorType::kTensorFlowNotEqual: + case OperatorType::kLess: + case OperatorType::kLessEqual: + case OperatorType::kGreater: + case OperatorType::kMaximum: // Element-wise Maximum + case OperatorType::kMinimum: // Element-wise Minimum + case OperatorType::kGreaterEqual: + case OperatorType::kEqual: + case OperatorType::kNotEqual: + case OperatorType::kPow: ProcessSimpleBinaryOperator(model, op); break; case OperatorType::kAddN: @@ -1597,7 +1591,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessFullyConnectedOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: ProcessTensorFlowReshapeOperator( model, static_cast(op)); break; @@ -1610,9 +1604,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kL2Pool: ProcessL2PoolOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowMin: - case OperatorType::kTensorFlowMax: - case OperatorType::kTensorFlowSum: + case OperatorType::kMin: // Reduction Min + case OperatorType::kMax: // Reduction Max + case OperatorType::kSum: case OperatorType::kMean: ProcessTensorFlowReductionOperator(model, op); break; @@ -1623,34 +1617,26 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSliceOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowTile: - // We don't currently implement the propagation of fixed sizes through - // a TensorFlow Tile. - // - // Fortunately, we don't need to: so far, we have only dealt with Tile - // or Slice ops in subgraphs that are identified as L2Normalization. - // See IdentifyL2Normalization. - break; - case OperatorType::kTensorFlowSwitch: + case OperatorType::kSwitch: // We can't know the sizes of the outputs until we have resolved the // predicate, and once we have resolved the predicate, the whole // Switch node will get resolved away. // See ResolveTensorFlowSwitch. break; - case OperatorType::kTensorFlowMerge: + case OperatorType::kMerge: // No need to bother resolving TensorFlow Merge ops: other graph // transformations will remove them anyway. // See ResolveTensorFlowMerge. break; - case OperatorType::kTensorFlowSplit: + case OperatorType::kSplit: ProcessTensorFlowSplitOperator(model, static_cast(op)); break; case OperatorType::kSqueeze: ProcessSqueezeOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowConcat: - case OperatorType::kTensorFlowConcatV2: + case OperatorType::kConcat: + case OperatorType::kConcatV2: // Unimplemented, hopefully another graph transformation will // drop it or rewrite it. Concretely, either ResolveTensorFlowConcat // will resolve this node to a DepthConcatenation, or else we have @@ -1666,7 +1652,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kRank: ProcessRankOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowShape: + case OperatorType::kShape: ProcessShapeOperator(model, static_cast(op)); break; case OperatorType::kStack: @@ -1687,7 +1673,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessLstmCellOperator(model, static_cast(op)); break; case OperatorType::kBatchMatMul: - case OperatorType::kTensorFlowMatMul: + case OperatorType::kMatMul: // MatMul operators are converted to FullyConnected, after which their // shapes are propagated. break; @@ -1712,7 +1698,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kArgMax: ProcessArgMaxOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowUnsupported: + case OperatorType::kUnsupported: break; case OperatorType::kSvdf: ProcessSvdfOperator(model, static_cast(op)); @@ -1734,6 +1720,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSparseToDenseOperator(model, static_cast(op)); break; + case OperatorType::kTile: + ProcessTileOperator(model, static_cast(op)); + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index ab24c4f9966a37995f23f600263fe96aba6da2d6..58885b4950733bfc9d394127e597a08232cd5663 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -33,7 +33,7 @@ namespace { bool SupportsQuantization(const Operator& op) { auto type = op.type; - if (type == OperatorType::kTensorFlowUnsupported) { + if (type == OperatorType::kUnsupported) { auto* unsupported = static_cast(&op); return unsupported->quantized; } @@ -42,25 +42,25 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kConcatenation || type == OperatorType::kL2Normalization || type == OperatorType::kAdd || type == OperatorType::kAveragePool || type == OperatorType::kMaxPool || - type == OperatorType::kTensorFlowMinimum || - type == OperatorType::kTensorFlowMaximum || + type == OperatorType::kMinimum || type == OperatorType::kMaximum || type == OperatorType::kLogistic || type == OperatorType::kSoftmax || - type == OperatorType::kLogSoftmax || - type == OperatorType::kTensorFlowSplit || type == OperatorType::kSub || + type == OperatorType::kLogSoftmax || type == OperatorType::kSlice || + type == OperatorType::kResizeBilinear || + type == OperatorType::kSplit || type == OperatorType::kSub || type == OperatorType::kSqueeze || type == OperatorType::kPad || - type == OperatorType::kPadV2 || - type == OperatorType::kTensorFlowReshape || + type == OperatorType::kPadV2 || type == OperatorType::kReshape || type == OperatorType::kTanh || type == OperatorType::kMul || + type == OperatorType::kSpaceToBatchND || type == OperatorType::kSpaceToDepth || type == OperatorType::kStridedSlice || type == OperatorType::kDepthToSpace || type == OperatorType::kLstmCell || type == OperatorType::kGather || type == OperatorType::kTranspose || type == OperatorType::kMean || - type == OperatorType::kTensorFlowGreater || - type == OperatorType::kTensorFlowGreaterEqual || - type == OperatorType::kTensorFlowLess || - type == OperatorType::kTensorFlowLessEqual || - type == OperatorType::kSelect || type == OperatorType::kArgMax; + type == OperatorType::kGreater || + type == OperatorType::kGreaterEqual || type == OperatorType::kLess || + type == OperatorType::kLessEqual || type == OperatorType::kSelect || + type == OperatorType::kArgMax || type == OperatorType::kRelu || + type == OperatorType::kRelu1 || type == OperatorType::kRelu6; } const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) { @@ -326,14 +326,15 @@ bool ChooseQuantizationForOperatorOutput( output, OperatorTypeName(op.type)); return true; } - if ((op.type == OperatorType::kDepthToSpace) || - (op.type == OperatorType::kSpaceToDepth) || - (op.type == OperatorType::kTensorFlowReshape) || - (op.type == OperatorType::kTensorFlowSplit) || - (op.type == OperatorType::kConcatenation && - model->flags.change_concat_input_ranges())) { + if ((op.type == OperatorType::kConcatenation && + model->flags.change_concat_input_ranges()) || + op.type == OperatorType::kDepthToSpace || + op.type == OperatorType::kSpaceToDepth || + op.type == OperatorType::kReshape || op.type == OperatorType::kSplit || + op.type == OperatorType::kRelu || op.type == OperatorType::kRelu1 || + op.type == OperatorType::kRelu6) { int data_input_index = 0; - if (op.type == OperatorType::kTensorFlowSplit) { + if (op.type == OperatorType::kSplit) { data_input_index = 1; } // Copying and rearrangement ops should preserve the quantization parameters @@ -506,36 +507,47 @@ bool Quantize::Run(Model* model, std::size_t op_index) { // Check if the output of that Dequantize op was not used by any // other operator. We will then erase that Dequantize op. if (!CountOpsWithInput(*model, dequantize_op->outputs[0])) { - // If any of the model's output_arrays was pointing to the - // Dequantize op's output, let it point to the Dequantize op's - // input instead. - for (int i = 0; i < model->flags.output_arrays_size(); i++) { - if (model->flags.output_arrays(i) == dequantize_op->outputs[0]) { - // TODO(b/78013785): never rename output arrays. - if (IsInputArray(*model, dequantize_op->inputs[0])) { - // The op input is an input array and the output is an output - // array and we can't have an array be both. Insert a copy - // op to ensure the two arrays stay separate. - AddMessageF( - "Tried to rename output array %d while removing dequant " - "op %s but array is also an input; inserting copy %s " - "-> %s", - i, LogName(*dequantize_op), model->flags.output_arrays(i), - dequantize_op->inputs[0]); - InsertCopyOperator(model, dequantize_op->inputs[0], - dequantize_op->outputs[0]); - } else { - // Op output is strictly used as an output array, so we can - // just rename the array and directly bypass the op. - AddMessageF( - "Renaming output array %d after removing dequant op %s: " - "%s -> %s", - i, LogName(*dequantize_op), model->flags.output_arrays(i), - dequantize_op->inputs[0]); - model->flags.set_output_arrays(i, dequantize_op->inputs[0]); - model->EraseArray(dequantize_op->outputs[0]); + if (IsDiscardableArray(*model, dequantize_op->outputs[0])) { + // Usual case: we can just discard the dequantize output. + model->EraseArray(dequantize_op->outputs[0]); + } else { + // The dequantize output is not discardable. Special care needed. + // If any of the model's output_arrays was pointing to the + // Dequantize op's output, let it point to the Dequantize op's + // input instead. + for (int i = 0; i < model->flags.output_arrays_size(); i++) { + if (model->flags.output_arrays(i) == + dequantize_op->outputs[0]) { + // TODO(b/78013785): never rename output arrays. + if (IsInputArray(*model, dequantize_op->inputs[0])) { + // The op input is an input array and the output is an + // output array and we can't have an array be both. Insert a + // copy op to ensure the two arrays stay separate. + AddMessageF( + "Tried to rename output array %d while removing " + "dequant " + "op %s but array is also an input; inserting copy %s " + "-> %s", + i, LogName(*dequantize_op), + model->flags.output_arrays(i), + dequantize_op->inputs[0]); + InsertCopyOperator(model, dequantize_op->inputs[0], + dequantize_op->outputs[0]); + } else { + // Op output is strictly used as an output array, so we can + // just rename the array and directly bypass the op. + AddMessageF( + "Renaming output array %d after removing dequant op " + "%s: " + "%s -> %s", + i, LogName(*dequantize_op), + model->flags.output_arrays(i), + dequantize_op->inputs[0]); + model->flags.set_output_arrays(i, dequantize_op->inputs[0]); + model->EraseArray(dequantize_op->outputs[0]); + } + break; } - break; } } model->operators.erase(dequantize_it); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc index 35a0c465327f352863350e7a8af714d16b7be393..73ad326299bbd929afbb8dda2c41b97a126afbe1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc @@ -26,7 +26,7 @@ namespace toco { bool RemoveTensorFlowAssert::Run(Model* model, std::size_t op_index) { const auto assert_it = model->operators.begin() + op_index; const auto* assert_op = assert_it->get(); - if (assert_op->type != OperatorType::kTensorFlowAssert) { + if (assert_op->type != OperatorType::kAssert) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc index 404269bbfd9312bbbab32489783d9e4217ecbd89..7ec7752f25dad1c24b821733c0e6dafbd1cd8bf2 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc @@ -28,7 +28,7 @@ namespace toco { bool RemoveTensorFlowIdentity::Run(Model* model, std::size_t op_index) { const auto passthru_it = model->operators.begin() + op_index; const auto* passthru_op = passthru_it->get(); - if (passthru_op->type != OperatorType::kTensorFlowIdentity) { + if (passthru_op->type != OperatorType::kIdentity) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc index a950fe6442bc656b725a1f0687f4c024f4fb0f84..9f5d8b94507ec11957c3ae55ffca510eeb81ac89 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc @@ -97,7 +97,7 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation, "Cannot remove %s, neither its main input nor its output may be " "discarded", LogName(*passthru_op)); - if (passthru_op->type != OperatorType::kTensorFlowReshape && + if (passthru_op->type != OperatorType::kReshape && model->GetArray(main_input_name).has_shape()) { // We can't remove either array but we can remove the op. Converting it to // a reshape gives us some hope of later on fixing that (either in the diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc index eaee1c662b7cedb2baec7be47e12e348c3e7b25c..142c876b154755ac9c6b93e560f22ec8d6ec6563 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc @@ -47,11 +47,11 @@ bool IsTrivialMinMax(GraphTransformation* transformation, const Model& model, double clamp_min; double clamp_max; switch (op_type) { - case OperatorType::kTensorFlowMinimum: + case OperatorType::kMinimum: // Element-wise Minimum clamp_min = -std::numeric_limits::infinity(); clamp_max = clamp_value; break; - case OperatorType::kTensorFlowMaximum: + case OperatorType::kMaximum: // Element-wise Maximum clamp_min = clamp_value; clamp_max = std::numeric_limits::infinity(); break; @@ -72,8 +72,8 @@ bool IsTrivialMinMax(GraphTransformation* transformation, const Model& model, bool RemoveTrivialQuantizedMinMax::Run(Model* model, std::size_t op_index) { const auto it = model->operators.begin() + op_index; auto* op = it->get(); - if ((op->type != OperatorType::kTensorFlowMinimum && - op->type != OperatorType::kTensorFlowMaximum) || + if ((op->type != OperatorType::kMinimum && + op->type != OperatorType::kMaximum) || op->inputs.size() != 2) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc index e28d8cf01eafee64e08ac2cc4b43ea7c227456c2..404f27e067402474484d3ee8e23595fb9f93a6c9 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc @@ -30,7 +30,7 @@ namespace { bool IsReshapeTrivial(const Model& model, const Operator& op, RemoveTrivialReshape* transformation) { - CHECK(op.type == OperatorType::kTensorFlowReshape); + CHECK(op.type == OperatorType::kReshape); // One way in which a reshape can be trivial is if its // output shape is == its input shape @@ -58,7 +58,7 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, // is only consumed by another reshape. if (CountOpsWithInput(model, op.outputs[0]) == 1) { const auto* next_op = GetOpWithInput(model, op.outputs[0]); - if (next_op->type == OperatorType::kTensorFlowReshape) { + if (next_op->type == OperatorType::kReshape) { transformation->AddMessageF( "%s is trivial because its output is only consumed by another " "Reshape op %s", @@ -75,7 +75,7 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, bool RemoveTrivialReshape::Run(Model* model, std::size_t op_index) { const auto reshape_it = model->operators.begin() + op_index; auto* reshape_op = reshape_it->get(); - if (reshape_op->type != OperatorType::kTensorFlowReshape) { + if (reshape_op->type != OperatorType::kReshape) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc index 1956ab2d2021cda84a0d715534923d6174c30dd1..dde91234a8240f4518cd105c2cc4e79102735980 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc @@ -48,7 +48,7 @@ bool RemoveUnusedOp::Run(Model* model, std::size_t op_index) { for (const auto& rnn_state : model->flags.rnn_states()) { if (output == rnn_state.state_array()) { CHECK(op->type == OperatorType::kFill || - op->type == OperatorType::kTensorFlowIdentity); + op->type == OperatorType::kIdentity); found_output_as_rnn_state_array = true; break; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc index 9f5b7920cb937b021eb23fc1d5fdc3c1ff18a72d..550de83018f25a7aa4da82707fedb86434615fb0 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc @@ -37,8 +37,8 @@ bool IsElementwiseOperator(OperatorType optype) { case OperatorType::kRelu1: case OperatorType::kRelu6: case OperatorType::kTanh: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: + case OperatorType::kSqrt: + case OperatorType::kSquare: return true; default: return false; @@ -51,7 +51,7 @@ bool IsMoveOperator(OperatorType optype) { case OperatorType::kExpandDims: case OperatorType::kSpaceToDepth: case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: return true; default: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc index 9e7fe1b1ccd851dd998e59e75ff798f52f7c6e5a..c907a597cb719b68dbf36868a75e49a7c5181423 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc @@ -123,8 +123,8 @@ bool ReorderReshapeTranspose::Run(Model* model, std::size_t op_index) { } TensorFlowReshapeOperator* reshape_op = - ConvertOperator( - reshape_it->get(), OperatorType::kTensorFlowReshape); + ConvertOperator(reshape_it->get(), + OperatorType::kReshape); if (reshape_op == nullptr) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_to_space_nd_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_to_space_nd_attributes.cc index a06919e228dc2084f8943a714a0ca111d013c159..b8b35161d77e5b6dd8c30e03959dba3c60d1d56c 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_to_space_nd_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_to_space_nd_attributes.cc @@ -50,7 +50,7 @@ bool ResolveBatchToSpaceNDAttributes::Run(Model* model, std::size_t op_index) { // will delete this op. return false; } - std::vector crops_buffer = + const std::vector& crops_buffer = crops_array.GetBuffer().data; for (int i = 0; i < crops_dims[0]; ++i) { op->before_crops.push_back(crops_buffer[i * 2]); @@ -62,7 +62,7 @@ bool ResolveBatchToSpaceNDAttributes::Run(Model* model, std::size_t op_index) { if (!block_shape_array.has_shape()) return false; const std::vector& block_shape_dims = block_shape_array.shape().dims(); CHECK_EQ(block_shape_dims.size(), 1); - std::vector block_shape_buffer = + const std::vector& block_shape_buffer = block_shape_array.GetBuffer().data; for (int i = 0; i < block_shape_dims[0]; ++i) { op->block_shape.push_back(block_shape_buffer[i]); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc index 6e78653fad238085da5ba66166884093ea9b0214..f7e5aa6609bd4f7eb2a95750125e30a7803b36e1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc @@ -145,17 +145,17 @@ void EvaluateBinaryOperatorOnConstantInputs(Model* model, outval = floor(val0 / val1); } else if (binary_op->type == OperatorType::kFloorMod) { outval = val0 - (floor(val0 / val1) * val1); - } else if (binary_op->type == OperatorType::kTensorFlowMinimum) { + } else if (binary_op->type == OperatorType::kMinimum) { outval = std::min(val0, val1); - } else if (binary_op->type == OperatorType::kTensorFlowMaximum) { + } else if (binary_op->type == OperatorType::kMaximum) { outval = std::max(val0, val1); - } else if (binary_op->type == OperatorType::kTensorFlowLess) { + } else if (binary_op->type == OperatorType::kLess) { outval = val0 < val1; - } else if (binary_op->type == OperatorType::kTensorFlowLessEqual) { + } else if (binary_op->type == OperatorType::kLessEqual) { outval = val0 <= val1; - } else if (binary_op->type == OperatorType::kTensorFlowGreater) { + } else if (binary_op->type == OperatorType::kGreater) { outval = val0 > val1; - } else if (binary_op->type == OperatorType::kTensorFlowGreaterEqual) { + } else if (binary_op->type == OperatorType::kGreaterEqual) { outval = val0 >= val1; } else { LOG(FATAL) << "should not get here"; @@ -198,12 +198,12 @@ bool ResolveConstantBinaryOperator::Run(Model* model, std::size_t op_index) { binary_op->type != OperatorType::kDiv && binary_op->type != OperatorType::kFloorDiv && binary_op->type != OperatorType::kFloorMod && - binary_op->type != OperatorType::kTensorFlowMinimum && - binary_op->type != OperatorType::kTensorFlowMaximum && - binary_op->type != OperatorType::kTensorFlowLess && - binary_op->type != OperatorType::kTensorFlowLessEqual && - binary_op->type != OperatorType::kTensorFlowGreater && - binary_op->type != OperatorType::kTensorFlowGreaterEqual) { + binary_op->type != OperatorType::kMinimum && + binary_op->type != OperatorType::kMaximum && + binary_op->type != OperatorType::kLess && + binary_op->type != OperatorType::kLessEqual && + binary_op->type != OperatorType::kGreater && + binary_op->type != OperatorType::kGreaterEqual) { return false; } CHECK_EQ(binary_op->inputs.size(), 2); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc index 7e7ad383e7789891f5396845241e70143dc8b76f..41562ab393694d76c5cb6c5df5f7df2a71f893f5 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc @@ -25,7 +25,7 @@ namespace toco { bool ResolveConstantReshape::Run(Model* model, std::size_t op_index) { auto it = model->operators.begin() + op_index; const auto* base_op = it->get(); - if (base_op->type != OperatorType::kTensorFlowReshape) { + if (base_op->type != OperatorType::kReshape) { return false; } const auto* op = static_cast(base_op); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc index 9ea01acd05364224ce219bed533c999793a2a2f1..8a0e3e8995839a737b5671701a97b514b0fc7bf1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc @@ -22,8 +22,7 @@ namespace toco { bool ResolveConstantShapeOrRank::Run(Model* model, std::size_t op_index) { const auto it = model->operators.begin() + op_index; const auto* op = it->get(); - if (!(op->type == OperatorType::kTensorFlowShape || - op->type == OperatorType::kRank)) { + if (!(op->type == OperatorType::kShape || op->type == OperatorType::kRank)) { return false; } @@ -48,7 +47,7 @@ bool ResolveConstantShapeOrRank::Run(Model* model, std::size_t op_index) { // Compute the output CHECK(!output_array.buffer); auto& output_buffer = output_array.GetMutableBuffer(); - if (op->type == OperatorType::kTensorFlowShape) { + if (op->type == OperatorType::kShape) { // Copy the input shape into the output buffer. output_buffer.data = input_array.shape().dims(); } else if (op->type == OperatorType::kRank) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc index 69db1942cd52af810acf38a818997c71122d8500..a4d5f1923a1dffdff1ef51eb5317fa5794a8bc27 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc @@ -41,7 +41,7 @@ void Stack(Model* model, StackOperator const& op) { const auto& input_array = model->GetArray(op.inputs[i]); int input_size = RequiredBufferSizeForShape(input_array.shape()); memcpy(&output_data[dst_offset], &input_array.GetBuffer().data[0], - input_size * sizeof(Type)); + input_size * ElementSize(Type)); dst_offset += input_size; } CHECK_EQ(dst_offset, output_data.size()); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc index 1dd52e906900e997f282740404a81b9fcd21e867..9d8bd4fc39344a4ea1fa4942a2a99ec535b5bee8 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc @@ -38,6 +38,7 @@ void StridedSlice(StridedSliceOperator const& op, Array const& input_array, CHECK_EQ(op.new_axis_mask, 0); int num_input_axes = op.start_indices.size(); + CHECK_EQ(num_input_axes, op.start_indices.size()); CHECK_EQ(num_input_axes, op.stop_indices.size()); CHECK_EQ(num_input_axes, op.strides.size()); @@ -49,11 +50,16 @@ void StridedSlice(StridedSliceOperator const& op, Array const& input_array, // Initialize source coordinate Shape const& input_shape = input_array.shape(); Buffer const& input_buffer = input_array.GetBuffer(); - std::vector src_coord(op.start_indices.size()); + std::vector src_coord(num_input_axes); + std::vector stop_for_axis(num_input_axes); for (int axis = 0; axis < num_input_axes; axis++) { - src_coord[axis] = tflite::strided_slice::StartForAxis( + int start = tflite::strided_slice::StartForAxis( op.begin_mask, op.start_indices, op.strides, input_shape.dims().data(), axis); + src_coord[axis] = start; + stop_for_axis[axis] = tflite::strided_slice::StopForAxis( + op.end_mask, op.shrink_axis_mask, op.stop_indices, op.strides, + input_shape.dims().data(), axis, start); } // In order to handle any number (N) of dimensions, we copy elements one by @@ -76,9 +82,7 @@ void StridedSlice(StridedSliceOperator const& op, Array const& input_array, } // Check if we've overflowed. - int stop = tflite::strided_slice::StopForAxis( - op.end_mask, op.stop_indices, op.strides, input_shape.dims().data(), - axis); + int stop = stop_for_axis[axis]; if (tflite::strided_slice::LoopCondition(src_coord[axis], stop, stride)) { // Reset axis and set carry src_coord[axis] = tflite::strided_slice::StartForAxis( @@ -155,14 +159,7 @@ bool ResolveConstantStridedSlice::Run(Model* model, std::size_t op_index) { break; } - // Erase input array if no longer used - if (IsDiscardableArray(*model, op->inputs[0]) && - CountOpsWithInput(*model, op->inputs[0]) == 1) { - model->EraseArray(op->inputs[0]); - } - - // Erase the operator - model->operators.erase(it); + DeleteOpAndArraysIfUnused(model, it->get()); return true; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index f6c8f79d8d3311dc2294e3ec406a184b2a16a6b5..f89ef85fdb63ca4906c7f016e86bb1f9d8a7099a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -53,13 +53,13 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { case OperatorType::kCast: case OperatorType::kLog: case OperatorType::kNeg: - case OperatorType::kTensorFlowRsqrt: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: - case OperatorType::kTensorFlowSum: - case OperatorType::kTensorFlowMin: - case OperatorType::kTensorFlowMax: - case OperatorType::kTensorFlowReshape: + case OperatorType::kRsqrt: + case OperatorType::kSqrt: + case OperatorType::kSquare: + case OperatorType::kSum: + case OperatorType::kMin: // Reduction Min + case OperatorType::kMax: // Reduction Max + case OperatorType::kReshape: case OperatorType::kRelu6: case OperatorType::kRelu1: case OperatorType::kRelu: @@ -103,7 +103,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { // The min-max is only copied for ops that copy data without arithmetic. // In future trivial transpose, etc, can be handled here. - if (unary_op->type == OperatorType::kTensorFlowReshape) { + if (unary_op->type == OperatorType::kReshape) { CopyMinMaxFromFirstInput(*unary_op, model); } @@ -164,10 +164,10 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = outval; } - } else if (unary_op->type == OperatorType::kTensorFlowReshape) { + } else if (unary_op->type == OperatorType::kReshape) { CHECK(input_buffer_size == output_buffer_size); output_float_data = *input_float_data; - } else if (unary_op->type == OperatorType::kTensorFlowSum) { + } else if (unary_op->type == OperatorType::kSum) { CHECK_EQ(unary_op->inputs.size(), 2) << "Sum needs 2 inputs"; if (!IsConstantParameterArray(*model, unary_op->inputs[1])) { AddMessageF("Axis input is non-constant"); @@ -196,7 +196,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = sum; } - } else if (unary_op->type == OperatorType::kTensorFlowMin) { + } else if (unary_op->type == OperatorType::kMin) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { @@ -207,7 +207,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { min = std::min(min, (*input_float_data)[i]); } output_float_data[0] = min; - } else if (unary_op->type == OperatorType::kTensorFlowMax) { + } else if (unary_op->type == OperatorType::kMax) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { @@ -220,9 +220,9 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { output_float_data[0] = max; } else if (unary_op->type == OperatorType::kNeg || unary_op->type == OperatorType::kLog || - unary_op->type == OperatorType::kTensorFlowRsqrt || - unary_op->type == OperatorType::kTensorFlowSqrt || - unary_op->type == OperatorType::kTensorFlowSquare) { + unary_op->type == OperatorType::kRsqrt || + unary_op->type == OperatorType::kSqrt || + unary_op->type == OperatorType::kSquare) { // Element-wise ops. Should have perfectly matching sizes here. for (int i = 0; i < output_dims_count; i++) { CHECK_EQ(output_shape.dims(i), input_shape.dims(i)); @@ -235,11 +235,11 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { outval = -val; } else if (unary_op->type == OperatorType::kLog) { outval = std::log(val); - } else if (unary_op->type == OperatorType::kTensorFlowRsqrt) { + } else if (unary_op->type == OperatorType::kRsqrt) { outval = 1.0f / std::sqrt(val); - } else if (unary_op->type == OperatorType::kTensorFlowSqrt) { + } else if (unary_op->type == OperatorType::kSqrt) { outval = std::sqrt(val); - } else if (unary_op->type == OperatorType::kTensorFlowSquare) { + } else if (unary_op->type == OperatorType::kSquare) { outval = val * val; } else { LOG(FATAL) << "should not get here."; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc index bc70db0bd8c26319fa140616de96452260a01058..8266e2c205b65e9d8a969643f102bb852be9125b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reorder_axes.cc @@ -51,11 +51,12 @@ void ReorderAxes(AxesOrder input_axes_order, AxesOrder output_axes_order, } bool ResolveReorderAxes::Run(Model* model, std::size_t op_index) { - auto reorder_it = model->operators.begin() + op_index; - auto* reorder_op = static_cast(reorder_it->get()); - if (reorder_op->type != OperatorType::kReorderAxes) { + auto it = model->operators.begin() + op_index; + auto* op = it->get(); + if (op->type != OperatorType::kReorderAxes) { return false; } + auto* reorder_op = static_cast(op); const auto& input_array_name = reorder_op->inputs[0]; const auto& output_array_name = reorder_op->outputs[0]; auto& input_array = model->GetArray(input_array_name); @@ -95,7 +96,7 @@ bool ResolveReorderAxes::Run(Model* model, std::size_t op_index) { // Remove the op and output array. model->EraseArray(output_array_name); - model->operators.erase(reorder_it); + model->operators.erase(it); return true; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc index 2e063e35548aa5e51c3bcc94a2dfc7992180d014..b615c9a545695e5d14fa5809e0c38a770f23ea24 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc @@ -28,7 +28,7 @@ namespace toco { bool ResolveReshapeAttributes::Run(Model* model, std::size_t op_index) { const auto reshape_it = model->operators.begin() + op_index; auto* reshape_op = reshape_it->get(); - if (reshape_op->type != OperatorType::kTensorFlowReshape) { + if (reshape_op->type != OperatorType::kReshape) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_space_to_batch_nd_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_space_to_batch_nd_attributes.cc index dad6aceccfd201b3db07c29c99a8c6ef75bb89a1..fab50bec1fc5ec50cecba53845457931ed59c0b8 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_space_to_batch_nd_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_space_to_batch_nd_attributes.cc @@ -53,7 +53,7 @@ bool ResolveSpaceToBatchNDAttributes::Run(Model* model, std::size_t op_index) { // will delete this op. return false; } - std::vector paddings_buffer = + const std::vector& paddings_buffer = paddings_array.GetBuffer().data; for (int i = 0; i < paddings_dims[0]; ++i) { op->before_paddings.push_back(paddings_buffer[i * 2]); @@ -66,7 +66,7 @@ bool ResolveSpaceToBatchNDAttributes::Run(Model* model, std::size_t op_index) { if (!block_shape_array.has_shape()) return false; const std::vector& block_shape_dims = block_shape_array.shape().dims(); CHECK_EQ(block_shape_dims.size(), 1); - std::vector block_shape_buffer = + const std::vector& block_shape_buffer = block_shape_array.GetBuffer().data; for (int i = 0; i < block_shape_dims[0]; ++i) { op->block_shape.push_back(block_shape_buffer[i]); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc index dd3e73635ae0215510f0a8d1aee487da5af35700..e8bb85704e1c750300079681b5a12f6a488b6b48 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc @@ -36,7 +36,7 @@ bool ResolveSqueezeAttributes::Run(Model* model, std::size_t op_index) { // If the output is consumed by a reshape op, it's a trivial squeeze. if (CountOpsWithInput(*model, squeeze_op->outputs[0]) == 1) { const auto* next_op = GetOpWithInput(*model, squeeze_op->outputs[0]); - if (next_op->type == OperatorType::kTensorFlowReshape) { + if (next_op->type == OperatorType::kReshape) { AddMessageF( "%s is trivial because its output is only consumed by a " "Reshape op", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc index 5c0c1e3478fa0d94104d1b76bab176b98b314c50..fa5ee899334bdf2d39a6861b0e0c4548142e9d2a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc @@ -28,8 +28,8 @@ namespace toco { bool ResolveTensorFlowConcat::Run(Model* model, std::size_t op_index) { auto concat_it = model->operators.begin() + op_index; const auto* tf_concat_op = concat_it->get(); - if (tf_concat_op->type != OperatorType::kTensorFlowConcat && - tf_concat_op->type != OperatorType::kTensorFlowConcatV2) { + if (tf_concat_op->type != OperatorType::kConcat && + tf_concat_op->type != OperatorType::kConcatV2) { return false; } @@ -38,7 +38,7 @@ bool ResolveTensorFlowConcat::Run(Model* model, std::size_t op_index) { // of inputs: in Concat,the axis is the first input, while in // ConcatV2, it is the last input. std::size_t axis_pos = 0; - if (tf_concat_op->type == OperatorType::kTensorFlowConcatV2) { + if (tf_concat_op->type == OperatorType::kConcatV2) { axis_pos = tf_concat_op->inputs.size() - 1; } const string axis_name = tf_concat_op->inputs[axis_pos]; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc index 2a236d3f98784e8244942f94d5a250b5bc00a8ad..fcf30bd34725fc59bb819e75deda0dadf330f372 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc @@ -26,27 +26,40 @@ namespace toco { bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { auto matmul_it = model->operators.begin() + op_index; - if (matmul_it->get()->type != OperatorType::kTensorFlowMatMul) { + if (matmul_it->get()->type != OperatorType::kMatMul) { return false; } const auto* matmul_op = static_cast(matmul_it->get()); + // Handling transposition of the first input here isn't very simple because + // we need to know the actual shape in order to produce a proper + // TransposeOperator. However, the second input is supposed to be 2D, so we + // can actually handle transposition of that matrix, which happens to be more + // common anyway. + CHECK(!matmul_op->transpose_a); + // Reorder the axes on the second input. TensorFlow uses row-major ordering // on both inputs, however this is inefficient for the FullyConnected // operator. We'll transpose the second input to be in column-major order now // and let constant propagation optimize things (if possible). - auto* transpose_op = new TransposeOperator; - transpose_op->inputs = { - matmul_op->inputs[1], - CreateInt32Array( - model, - AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose/perm"), - {1, 0})}; - transpose_op->outputs = { - AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose")}; - model->GetOrCreateArray(transpose_op->outputs[0]); - model->operators.emplace(matmul_it, transpose_op); + string input_lhs = matmul_op->inputs[0]; + string input_rhs = matmul_op->inputs[1]; + if (!matmul_op->transpose_b) { + auto* transpose_op = new TransposeOperator; + transpose_op->inputs = { + matmul_op->inputs[1], + CreateInt32Array(model, + AvailableArrayName( + *model, matmul_op->inputs[1] + "/transpose/perm"), + {1, 0})}; + transpose_op->outputs = { + AvailableArrayName(*model, matmul_op->inputs[1] + "/transpose")}; + model->GetOrCreateArray(transpose_op->outputs[0]); + model->operators.emplace(matmul_it, transpose_op); + + input_rhs = transpose_op->outputs[0]; + } // Refresh iterator. matmul_it = model->operators.begin(); @@ -57,9 +70,6 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { } DCHECK_EQ(matmul_it->get(), matmul_op); - string input_lhs = matmul_op->inputs[0]; - string input_rhs = transpose_op->outputs[0]; - // Construct the new FullyConnectedOperator. auto* fc_op = new FullyConnectedOperator; fc_op->outputs = matmul_op->outputs; @@ -97,7 +107,7 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { // MatMul op as a FullyConnected. However, TensorFlow skips the Reshape ops if // the input doesn't need reshaping, so we can't just match (Reshape, MatMul) // pairs. - if (previous_op && previous_op->type == OperatorType::kTensorFlowReshape) { + if (previous_op && previous_op->type == OperatorType::kReshape) { AddMessageF("Combining %s and %s into %s", LogName(*previous_op), LogName(*matmul_op), LogName(*fc_op)); const auto& previous_op_output = previous_op->outputs[0]; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc index 38e0005890ac10410df4ddb5290be8fcc948c349..4edffe3d48fd880c0261b34fc407b8e2ac66ccb9 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc @@ -27,7 +27,7 @@ namespace toco { bool ResolveTensorFlowMerge::Run(Model* model, std::size_t op_index) { const auto merge_it = model->operators.begin() + op_index; const auto* merge_op = merge_it->get(); - if (merge_op->type != OperatorType::kTensorFlowMerge) { + if (merge_op->type != OperatorType::kMerge) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc index a418073441f1241a5acb1164b36f332828ea2e99..da8e7a2d1c06cf89b9708b404da7667565245f8f 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc @@ -27,7 +27,7 @@ namespace toco { bool ResolveTensorFlowSwitch::Run(Model* model, std::size_t op_index) { const auto switch_it = model->operators.begin() + op_index; const auto* switch_op = switch_it->get(); - if (switch_op->type != OperatorType::kTensorFlowSwitch) { + if (switch_op->type != OperatorType::kSwitch) { return false; } @@ -92,7 +92,7 @@ bool ResolveTensorFlowSwitch::Run(Model* model, std::size_t op_index) { if (*input_it == switch_op->outputs[nonselected_output_index]) { // Let us guard our assumption that only Merge nodes consume the outputs // of Switch nodes: - CHECK(other_op->type == OperatorType::kTensorFlowMerge); + CHECK(other_op->type == OperatorType::kMerge); input_it = other_op->inputs.erase(input_it); } else { ++input_it; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc deleted file mode 100644 index 1ddf54c778cd1fae7a8fce0ecb97209274e71ac0..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc +++ /dev/null @@ -1,97 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include -#include -#include -#include - -#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" -#include "tensorflow/contrib/lite/toco/model.h" -#include "tensorflow/contrib/lite/toco/tooling_util.h" -#include "tensorflow/core/platform/logging.h" - -namespace toco { - -namespace { - -void RemoveTileOperator(Model* model, Operator* tile_op, Operator* binary_op, - int operand_index) { - CHECK(tile_op->type == OperatorType::kTensorFlowTile); - CHECK_EQ(binary_op->inputs.size(), 2); - CHECK_EQ(tile_op->inputs.size(), 2); - const string tile_multiplier_array = tile_op->inputs[1]; - const string tile_output_array = tile_op->outputs[0]; - binary_op->inputs[operand_index] = tile_op->inputs[0]; - auto tile_it = model->operators.begin(); - for (; tile_it != model->operators.end(); ++tile_it) { - if (tile_it->get() == tile_op) { - break; - } - } - CHECK(tile_it != model->operators.end()); - CHECK(tile_it->get() == tile_op); - model->operators.erase(tile_it); - if (!CountOpsWithInput(*model, tile_multiplier_array) && - !GetOpWithOutput(*model, tile_multiplier_array)) { - model->EraseArray(tile_multiplier_array); - } - if (!CountOpsWithInput(*model, tile_output_array)) { - model->EraseArray(tile_output_array); - } -} -} // namespace - -bool ResolveTensorFlowTile::Run(Model* model, std::size_t op_index) { - const auto binary_it = model->operators.begin() + op_index; - auto* binary_op = binary_it->get(); - // Test for binary ops of types that we know how to resolve - if (binary_op->inputs.size() != 2) { - return false; - } - if (binary_op->type != OperatorType::kAdd && - binary_op->type != OperatorType::kMul && - binary_op->type != OperatorType::kSub && - binary_op->type != OperatorType::kDiv) { - return false; - } - - Operator* const op[2] = { - GetOpWithOutput(*model, binary_op->inputs[0]), - GetOpWithOutput(*model, binary_op->inputs[1]), - }; - - // In the unlikely case where both operands are Tile, we can't infer the - // output - // size without the Tile nodes, so we have to bail out. - if (op[0] && op[0]->type == OperatorType::kTensorFlowTile && op[1] && - op[1]->type == OperatorType::kTensorFlowTile) { - return false; - } - - for (int i = 0; i < 2; i++) { - if (op[i] && op[i]->type == OperatorType::kTensorFlowTile) { - // We can only remove a Tile operator is no other op than the present - // binary op was consuming its tiled output. - if (CountOpsWithInput(*model, binary_op->inputs[i]) == 1) { - AddMessageF("Removing %s", LogName(*op[i])); - RemoveTileOperator(model, op[i], binary_op, i); - return true; - } - } - } - return false; -} - -} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/experimental_shuffle_fc_weights.cc b/tensorflow/contrib/lite/toco/graph_transformations/shuffle_fc_weights.cc similarity index 96% rename from tensorflow/contrib/lite/toco/graph_transformations/experimental_shuffle_fc_weights.cc rename to tensorflow/contrib/lite/toco/graph_transformations/shuffle_fc_weights.cc index c00cdcb944b085dda41033b95c96537cc2e047c3..22c258cec5fde4144c4b048d5ec60a8604362cbb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/experimental_shuffle_fc_weights.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/shuffle_fc_weights.cc @@ -24,14 +24,14 @@ limitations under the License. namespace toco { -bool ExperimentalShuffleFCWeights::Run(Model* model, std::size_t op_index) { +bool ShuffleFCWeights::Run(Model* model, std::size_t op_index) { Operator* op = model->operators[op_index].get(); if (op->type != OperatorType::kFullyConnected) { return false; } FullyConnectedOperator* fc_op = static_cast(op); // Exit if this FC op already has shuffled weights - if (fc_op->experimental_shuffled_weights) { + if (fc_op->weights_format != FullyConnectedWeightsFormat::kDefault) { return false; } const Array& input_array = model->GetArray(fc_op->inputs[0]); @@ -135,7 +135,7 @@ bool ExperimentalShuffleFCWeights::Run(Model* model, std::size_t op_index) { CHECK_EQ(shuffled_data_ptr, shuffled_data.data() + rows * cols); // Switch this FC op to using the shuffled weights. weights_data = std::move(shuffled_data); - fc_op->experimental_shuffled_weights = true; + fc_op->weights_format = FullyConnectedWeightsFormat::kShuffled4x16Int8; AddMessageF("Applied experimental shuffling to the weights of %s", LogName(*op)); // Add a second output array to this FC op, serving as a workspace to perform diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index a18748ae96f0a26bafdf13b7f33699fdb3195bd0..5c32a39035f3c5396b09621bacaa58a7baa3ae9b 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -31,7 +31,6 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_cluster.h" #include "tensorflow/contrib/lite/toco/tensorflow_util.h" -#include "tensorflow/contrib/lite/toco/toco_port.h" #include "tensorflow/contrib/lite/toco/tooling_util.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/function.h" @@ -44,6 +43,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.pb.h" #include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/public/session_options.h" #include "tensorflow/core/public/version.h" @@ -63,8 +63,6 @@ using tensorflow::TensorShapeProto; namespace toco { -using port::Status; - namespace { bool HasAttr(const NodeDef& node, const string& attr_name) { return node.attr().count(attr_name) > 0; @@ -130,6 +128,42 @@ const AttrValue::ListValue& GetListAttr(const NodeDef& node, return attr.list(); } +tensorflow::Status CheckOptionalAttr(const NodeDef& node, + const string& attr_name, + const string& expected_value) { + if (HasAttr(node, attr_name)) { + const string& value = GetStringAttr(node, attr_name); + if (value != expected_value) { + return tensorflow::errors::InvalidArgument( + "Unexpected value for attribute '" + attr_name + "'. Expected '" + + expected_value + "'"); + } + } + return tensorflow::Status::OK(); +} + +tensorflow::Status CheckOptionalAttr( + const NodeDef& node, const string& attr_name, + const tensorflow::DataType& expected_value) { + if (HasAttr(node, attr_name)) { + const tensorflow::DataType& value = GetDataTypeAttr(node, attr_name); + if (value != expected_value) { + return tensorflow::errors::InvalidArgument( + "Unexpected value for attribute '" + attr_name + "'. Expected '" + + tensorflow::DataType_Name(expected_value) + "'"); + } + } + return tensorflow::Status::OK(); +} + +template +tensorflow::Status ExpectValue(const T1& v1, const T2& v2, + const string& description) { + if (v1 == v2) return tensorflow::Status::OK(); + return tensorflow::errors::InvalidArgument(absl::StrCat( + "Unexpected ", description, ": got ", v1, ", expected ", v2)); +} + ArrayDataType ConvertDataType(tensorflow::DataType dtype) { if (dtype == DT_UINT8) return ArrayDataType::kUint8; @@ -148,9 +182,10 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) { return ArrayDataType::kNone; } -Status ImportShape(const TFLITE_PROTO_NS::RepeatedPtrField< - tensorflow::TensorShapeProto_Dim>& input_dims, - int* input_flat_size, Shape* shape) { +tensorflow::Status ImportShape( + const TFLITE_PROTO_NS::RepeatedPtrField& + input_dims, + int* input_flat_size, Shape* shape) { std::vector input_dims_only_sizes; for (auto& d : input_dims) { if (d.size() == 0) { @@ -160,23 +195,24 @@ Status ImportShape(const TFLITE_PROTO_NS::RepeatedPtrField< // For now, tweaking this to record a 0-D shape instead. shape->mutable_dims()->clear(); if (input_flat_size != nullptr) *input_flat_size = 0; - return Status::OK(); + return tensorflow::Status::OK(); } // TensorFlow's shapes use int64s, while TOCO uses ints. if (d.size() > std::numeric_limits::max()) { - return Status(false, "Shape element overflows"); + return tensorflow::errors::InvalidArgument("Shape element overflows"); } input_dims_only_sizes.push_back(d.size()); } *shape->mutable_dims() = input_dims_only_sizes; - if (input_flat_size == nullptr) return Status::OK(); + if (input_flat_size == nullptr) return tensorflow::Status::OK(); return NumElements(input_dims_only_sizes, input_flat_size); } -Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportFloatArray(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_FLOAT); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -203,18 +239,18 @@ Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) { toco::port::CopyToBuffer(input_tensor.tensor_content(), reinterpret_cast(output_float_data.data())); } else { - return Status( - false, + return tensorflow::errors::InvalidArgument( absl::StrCat("Neither input_content (", input_tensor.tensor_content().size() / sizeof(float), ") nor float_val (", input_tensor.float_val_size(), ") have the right dimensions (", input_flat_size, ") for this float tensor")); } - return Status::OK(); + return tensorflow::Status::OK(); } -Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportQuint8Array(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_QUINT8); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -227,7 +263,11 @@ Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { output_array->GetMutableBuffer().data; output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); CHECK_GE(output_int_data.size(), input_flat_size); - if (input_tensor.int_val_size()) { + if (input_tensor.int_val_size() == 1) { + for (int i = 0; i < input_flat_size; i++) { + output_int_data[i] = input_tensor.int_val(0); + } + } else if (input_tensor.int_val_size() == input_flat_size) { for (int i = 0; i < input_tensor.int_val_size(); i++) { output_int_data[i] = input_tensor.int_val(i); } @@ -236,18 +276,18 @@ Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { toco::port::CopyToBuffer(input_tensor.tensor_content(), reinterpret_cast(output_int_data.data())); } else { - return Status( - false, + return tensorflow::errors::InvalidArgument( absl::StrCat("Neither input_content (", input_tensor.tensor_content().size() / sizeof(uint8_t), ") nor int_val (", input_tensor.int_val_size(), ") have the right dimensions (", input_flat_size, ") for this uint8 tensor")); } - return Status::OK(); + return tensorflow::Status::OK(); } -Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportInt32Array(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT32); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -260,7 +300,11 @@ Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { output_array->GetMutableBuffer().data; output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); CHECK_GE(output_int_data.size(), input_flat_size); - if (input_tensor.int_val_size()) { + if (input_tensor.int_val_size() == 1) { + for (int i = 0; i < input_flat_size; i++) { + output_int_data[i] = input_tensor.int_val(0); + } + } else if (input_tensor.int_val_size() == input_flat_size) { for (int i = 0; i < input_tensor.int_val_size(); i++) { output_int_data[i] = input_tensor.int_val(i); } @@ -269,18 +313,17 @@ Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { toco::port::CopyToBuffer(input_tensor.tensor_content(), reinterpret_cast(output_int_data.data())); } else { - return Status( - false, - absl::StrCat("Neither input_content (", - input_tensor.tensor_content().size() / sizeof(int32), - ") nor int_val (", input_tensor.int_val_size(), - ") have the right dimensions (", input_flat_size, - ") for this int32 tensor")); + return tensorflow::errors::InvalidArgument(absl::StrCat( + "Neither input_content (", + input_tensor.tensor_content().size() / sizeof(int32), ") nor int_val (", + input_tensor.int_val_size(), ") have the right dimensions (", + input_flat_size, ") for this int32 tensor")); } - return Status::OK(); + return tensorflow::Status::OK(); } -Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportInt64Array(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT64); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -293,8 +336,12 @@ Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { output_array->GetMutableBuffer().data; output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); CHECK_GE(output_int_data.size(), input_flat_size); - if (input_tensor.int64_val_size()) { - for (int i = 0; i < input_tensor.int64_val_size(); i++) { + if (input_tensor.int64_val_size() == 1) { + for (int i = 0; i < input_flat_size; i++) { + output_int_data[i] = input_tensor.int64_val(0); + } + } else if (input_tensor.int64_val_size() == input_flat_size) { + for (int i = 0; i < input_tensor.float_val_size(); i++) { output_int_data[i] = input_tensor.int64_val(i); } } else if (input_tensor.tensor_content().size() == @@ -302,18 +349,18 @@ Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { toco::port::CopyToBuffer(input_tensor.tensor_content(), reinterpret_cast(output_int_data.data())); } else { - return Status( - false, + return tensorflow::errors::InvalidArgument( absl::StrCat("Neither input_content (", input_tensor.tensor_content().size() / sizeof(int64), ") nor int64_val (", input_tensor.int64_val_size(), ") have the right dimensions (", input_flat_size, ") for this int64 tensor")); } - return Status::OK(); + return tensorflow::Status::OK(); } -Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportBoolArray(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_BOOL); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -327,7 +374,11 @@ Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()), false); CHECK_GE(output_bool_data.size(), input_flat_size); - if (input_tensor.bool_val_size()) { + if (input_tensor.bool_val_size() == 1) { + for (int i = 0; i < input_flat_size; i++) { + output_bool_data[i] = input_tensor.bool_val(0); + } + } else if (input_tensor.bool_val_size() == input_flat_size) { for (int i = 0; i < input_tensor.bool_val_size(); i++) { output_bool_data[i] = input_tensor.bool_val(i); } @@ -343,19 +394,19 @@ Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { // So far only encountered that in an array with 1 entry, let's // require that until we encounter a graph where that's not the case. if (output_bool_data.size() != 1) { - return Status( - false, absl::StrCat("Neither input_content (", - input_tensor.tensor_content().size(), - ") nor bool_val (", input_tensor.bool_val_size(), - ") have the right dimensions (", input_flat_size, - ") for this bool tensor")); + return tensorflow::errors::InvalidArgument(absl::StrCat( + "Neither input_content (", input_tensor.tensor_content().size(), + ") nor bool_val (", input_tensor.bool_val_size(), + ") have the right dimensions (", input_flat_size, + ") for this bool tensor")); } output_bool_data[0] = false; } - return Status::OK(); + return tensorflow::Status::OK(); } -Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) { +tensorflow::Status ImportStringArray(const TensorProto& input_tensor, + Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_STRING); const auto& input_shape = input_tensor.tensor_shape(); CHECK_LE(input_shape.dim_size(), 4); @@ -365,9 +416,9 @@ Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) { if (!status.ok()) return status; if (input_flat_size != input_tensor.string_val_size()) { - return Status(false, - "Input_content string_val doesn't have the right dimensions " - "for this string tensor"); + return tensorflow::errors::InvalidArgument( + "Input_content string_val doesn't have the right dimensions " + "for this string tensor"); } auto& output_string_data = @@ -377,7 +428,7 @@ Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) { for (int i = 0; i < input_flat_size; ++i) { output_string_data[i] = input_tensor.string_val(i); } - return Status::OK(); + return tensorflow::Status::OK(); } // Count the number of inputs of a given node. If @@ -391,18 +442,19 @@ int GetInputsCount(const NodeDef& node, return i; } } - return node.input_size(); - } else { - return node.input_size(); } + return node.input_size(); } -void CheckInputsCount(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - int expected_input_count) { - QCHECK_EQ(GetInputsCount(node, tf_import_flags), expected_input_count) - << node.op() << " node expects " << expected_input_count - << " input(s) other than control dependencies: " << node.DebugString(); +tensorflow::Status CheckInputsCount( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + int expected_input_count) { + if (GetInputsCount(node, tf_import_flags) != expected_input_count) { + return tensorflow::errors::FailedPrecondition( + node.op(), " node expects ", expected_input_count, + " input(s) other than control dependencies: ", node.DebugString()); + } + return tensorflow::Status::OK(); } template @@ -417,14 +469,14 @@ string CreateConstArray(Model* model, string const& name, return array_name; } -Status ConvertConstOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertConstOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Const"); const auto& tensor = GetTensorAttr(node, "value"); const auto dtype = GetDataTypeAttr(node, "dtype"); - Status status = Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); auto& array = model->GetOrCreateArray(node.name()); switch (dtype) { @@ -460,24 +512,21 @@ Status ConvertConstOperator(const NodeDef& node, array.GetMutableBuffer(); break; } - if (!status.ok()) { - status.AppendMessage(" (while processing node '" + node.name() + "')"); - } - return status; + TF_RETURN_WITH_CONTEXT_IF_ERROR( + status, " (while processing node '" + node.name() + "')"); + return tensorflow::Status::OK(); } -void ConvertConvOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertConvOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Conv2D"); - CheckInputsCount(node, tf_import_flags, 2); + TF_RETURN_IF_ERROR(CheckInputsCount(node, tf_import_flags, 2)); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. - if (HasAttr(node, "data_format")) { - CHECK_EQ(GetStringAttr(node, "data_format"), "NHWC"); - } - CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT); + TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "data_format", "NHWC")); + TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "T", DT_FLOAT)); const auto& input_name = node.input(0); const auto& weights_name = node.input(1); @@ -502,27 +551,26 @@ void ConvertConvOperator(const NodeDef& node, auto* conv = new ConvOperator; conv->inputs = {input_name, reordered_weights_name}; conv->outputs = {node.name()}; + if (!HasAttr(node, "strides")) { + return tensorflow::errors::InvalidArgument("Missing attribute 'strides'"); + } const auto& strides = GetListAttr(node, "strides"); - CHECK_EQ(strides.i_size(), 4); - CHECK_EQ(strides.i(0), 1); - CHECK_EQ(strides.i(3), 1); + TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides")); + TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)")); + TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)")); conv->stride_height = strides.i(1); conv->stride_width = strides.i(2); if (HasAttr(node, "dilations")) { const auto& dilations = GetListAttr(node, "dilations"); - CHECK_EQ(dilations.i_size(), 4); - CHECK_EQ(dilations.i(0), 1) - << "Can only import Conv ops with dilation along the height (1st) or " - "width (2nd) axis. TensorFlow op \"" - << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " - << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) - << "]."; - CHECK_EQ(dilations.i(3), 1) - << "Can only import Conv ops with dilation along the height (1st) or " - "width (2nd) axis. TensorFlow op \"" - << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " - << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) - << "]."; + TF_RETURN_IF_ERROR( + ExpectValue(dilations.i_size(), 4, "number of dilations")); + if (dilations.i(0) != 1 || dilations.i(3) != 1) { + return tensorflow::errors::InvalidArgument(absl::StrCat( + "Can only import Conv ops with dilation along the height " + "(1st) or width (2nd) axis. TensorFlow op \"", + node.name(), "\" had dilations:[ ", dilations.i(0), ", ", + dilations.i(1), ", ", dilations.i(2), ", ", dilations.i(3), "].")); + } conv->dilation_height_factor = dilations.i(1); conv->dilation_width_factor = dilations.i(2); } else { @@ -535,16 +583,19 @@ void ConvertConvOperator(const NodeDef& node, } else if (padding == "VALID") { conv->padding.type = PaddingType::kValid; } else { - LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; + return tensorflow::errors::InvalidArgument( + "Bad padding (only SAME and VALID are supported)"); } model->operators.emplace_back(conv); + + return tensorflow::Status::OK(); } -void ConvertDepthwiseConvOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDepthwiseConvOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "DepthwiseConv2dNative"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -591,13 +642,14 @@ void ConvertDepthwiseConvOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(conv); + return tensorflow::Status::OK(); } -void ConvertDepthToSpaceOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDepthToSpaceOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "DepthToSpace"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT); auto* op = new DepthToSpaceOperator; @@ -606,13 +658,14 @@ void ConvertDepthToSpaceOperator(const NodeDef& node, op->block_size = GetIntAttr(node, "block_size"); QCHECK_GE(op->block_size, 2); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSpaceToDepthOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSpaceToDepthOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SpaceToDepth"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); tensorflow::DataType dtype = GetDataTypeAttr(node, "T"); if (dtype != DT_FLOAT && dtype != DT_UINT8 && dtype != DT_INT32 && @@ -628,13 +681,14 @@ void ConvertSpaceToDepthOperator(const NodeDef& node, op->block_size = GetIntAttr(node, "block_size"); QCHECK_GE(op->block_size, 2); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBiasAddOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertBiasAddOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "BiasAdd"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto& input_name = node.input(0); const auto& bias_name = node.input(1); @@ -644,13 +698,14 @@ void ConvertBiasAddOperator(const NodeDef& node, biasadd->inputs.push_back(bias_name); biasadd->outputs.push_back(node.name()); model->operators.emplace_back(biasadd); + return tensorflow::Status::OK(); } -void ConvertRandomUniform(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertRandomUniform( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "RandomUniform"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); CHECK_EQ(GetDataTypeAttr(node, "T"), DT_INT32); auto op = absl::make_unique(); @@ -661,11 +716,12 @@ void ConvertRandomUniform(const NodeDef& node, op->seed2 = GetIntAttr(node, "seed2"); CHECK(model != nullptr); model->operators.emplace_back(std::move(op)); + return tensorflow::Status::OK(); } -void ConvertIdentityOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertIdentityOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Identity" || node.op() == "CheckNumerics" || node.op() == "PlaceholderWithDefault" || node.op() == "StopGradient"); auto* op = new TensorFlowIdentityOperator; @@ -682,13 +738,14 @@ void ConvertIdentityOperator(const NodeDef& node, op->inputs.push_back(input_name); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFakeQuantWithMinMaxArgs( +tensorflow::Status ConvertFakeQuantWithMinMaxArgs( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FakeQuantWithMinMaxArgs"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); auto* op = new FakeQuantOperator; op->inputs.push_back(node.input(0)); op->minmax.reset(new MinMax); @@ -699,9 +756,10 @@ void ConvertFakeQuantWithMinMaxArgs( // tf.fake_quant_with_min_max_args num_bits defaults to 8. op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFakeQuantWithMinMaxVars( +tensorflow::Status ConvertFakeQuantWithMinMaxVars( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FakeQuantWithMinMaxVars"); @@ -717,14 +775,14 @@ void ConvertFakeQuantWithMinMaxVars( op->outputs.push_back(node.name()); op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - -void ConvertSqueezeOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSqueezeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Squeeze"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); auto* op = new SqueezeOperator; op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); @@ -738,13 +796,14 @@ void ConvertSqueezeOperator(const NodeDef& node, } model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSumOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSumOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Sum"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSumOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -753,13 +812,14 @@ void ConvertSumOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertSplitOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSplitOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Split"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSplitOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -770,13 +830,14 @@ void ConvertSplitOperator(const NodeDef& node, } op->num_split = num_split; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSwitchOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSwitchOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Switch"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSwitchOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -784,13 +845,14 @@ void ConvertSwitchOperator(const NodeDef& node, // Switch operators have two outputs: "name" and "name:1". op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSoftmaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSoftmaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Softmax"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); auto* softmax = new SoftmaxOperator; softmax->inputs.push_back(input_name); @@ -799,13 +861,14 @@ void ConvertSoftmaxOperator(const NodeDef& node, CHECK(!node.attr().count("beta")); // Stab in the dark, just in case. softmax->beta = 1.f; model->operators.emplace_back(softmax); + return tensorflow::Status::OK(); } -void ConvertLRNOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertLRNOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "LRN"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); auto* lrn = new LocalResponseNormalizationOperator; lrn->inputs.push_back(input_name); @@ -815,13 +878,14 @@ void ConvertLRNOperator(const NodeDef& node, lrn->alpha = GetFloatAttr(node, "alpha"); lrn->beta = GetFloatAttr(node, "beta"); model->operators.emplace_back(lrn); + return tensorflow::Status::OK(); } -void ConvertMaxPoolOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMaxPoolOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "MaxPool"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -857,13 +921,14 @@ void ConvertMaxPoolOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(maxpool); + return tensorflow::Status::OK(); } -void ConvertAvgPoolOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertAvgPoolOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "AvgPool"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -895,13 +960,13 @@ void ConvertAvgPoolOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(avgpool); + return tensorflow::Status::OK(); } - -void ConvertBatchMatMulOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CheckInputsCount(node, tf_import_flags, 2); +tensorflow::Status ConvertBatchMatMulOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions CHECK(!HasAttr(node, "adj_a") || (GetBoolAttr(node, "adj_a") == false)); @@ -911,33 +976,36 @@ void ConvertBatchMatMulOperator(const NodeDef& node, batch_matmul->inputs = {node.input(0), node.input(1)}; batch_matmul->outputs = {node.name()}; model->operators.emplace_back(batch_matmul); + return tensorflow::Status::OK(); } -void ConvertMatMulOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CheckInputsCount(node, tf_import_flags, 2); +tensorflow::Status ConvertMatMulOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); - // Transpose flags should be easy to support, but we don't have a - // GraphDef with them to test on at the moment. - CHECK_EQ(HasAttr(node, "transpose_a") && GetBoolAttr(node, "transpose_a"), - false); - CHECK_EQ(HasAttr(node, "transpose_b") && GetBoolAttr(node, "transpose_b"), - false); CHECK(!HasAttr(node, "adjoint_a") || (GetBoolAttr(node, "adjoint_a") == false)); CHECK(!HasAttr(node, "adjoint_b") || (GetBoolAttr(node, "adjoint_b") == false)); auto* matmul = new TensorFlowMatMulOperator; + if (HasAttr(node, "transpose_a")) { + matmul->transpose_a = GetBoolAttr(node, "transpose_a"); + } + if (HasAttr(node, "transpose_b")) { + matmul->transpose_b = GetBoolAttr(node, "transpose_b"); + } + matmul->inputs = {node.input(0), node.input(1)}; matmul->outputs = {node.name()}; model->operators.emplace_back(matmul); + return tensorflow::Status::OK(); } -void ConvertConcatOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertConcatOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { Operator* op = nullptr; if (node.op() == "Concat") { op = new TensorFlowConcatOperator; @@ -957,13 +1025,14 @@ void ConvertConcatOperator(const NodeDef& node, } op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This method supports simple operators without additional attributes. template -void ConvertSimpleOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSimpleOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { auto* op = new Op; const int num_inputs = GetInputsCount(node, tf_import_flags); for (int i = 0; i < num_inputs; ++i) { @@ -971,22 +1040,23 @@ void ConvertSimpleOperator(const NodeDef& node, } op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This method supports simple operators without additional attributes. template -void ConvertSimpleOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CheckInputsCount(node, tf_import_flags, NumInputs); - ConvertSimpleOperator(node, tf_import_flags, model); +tensorflow::Status ConvertSimpleOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, NumInputs)); + return ConvertSimpleOperator(node, tf_import_flags, model); } -void ConvertMaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Max"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowMaxOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -995,13 +1065,14 @@ void ConvertMaxOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertMinOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMinOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Min"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowMinOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1010,12 +1081,12 @@ void ConvertMinOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } - -void ConvertUnsupportedOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertUnsupportedOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { LOG(INFO) << "Converting unsupported operation: " << node.op(); auto* op = new TensorFlowUnsupportedOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1038,15 +1109,16 @@ void ConvertUnsupportedOperator(const NodeDef& node, const auto& output_type = GetDataTypeAttr(node, "Tout"); op->output_data_types.push_back(ConvertDataType(output_type)); } + return tensorflow::Status::OK(); } -void ConvertStridedSliceOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertStridedSliceOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "StridedSlice"); // TODO(soroosh): The 4th input (strides) should be e optional, to be // consistent with TF. - CheckInputsCount(node, tf_import_flags, 4); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); auto* op = new StridedSliceOperator; for (const auto& input : node.input()) { @@ -1066,14 +1138,15 @@ void ConvertStridedSliceOperator(const NodeDef& node, : 0; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertPlaceholderOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertPlaceholderOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Placeholder" || node.op() == "LegacyFedInput"); if (node.op() == "Placeholder") { - CheckInputsCount(node, tf_import_flags, 0); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 0)); } auto& array = model->GetOrCreateArray(node.name()); if (node.attr().count("dtype")) { @@ -1098,17 +1171,20 @@ void ConvertPlaceholderOperator(const NodeDef& node, } } } + return tensorflow::Status::OK(); } -void ConvertNoOpOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) {} +tensorflow::Status ConvertNoOpOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + return tensorflow::Status::OK(); +} -void ConvertCastOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertCastOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Cast"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto tf_src_dtype = GetDataTypeAttr(node, "SrcT"); const auto tf_dst_dtype = GetDataTypeAttr(node, "DstT"); auto* op = new CastOperator; @@ -1117,27 +1193,31 @@ void ConvertCastOperator(const NodeDef& node, op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFloorOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertFloorOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Floor"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto data_type = GetDataTypeAttr(node, "T"); CHECK(data_type == DT_FLOAT); auto* op = new FloorOperator; op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertGatherOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertGatherOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Gather" || node.op() == "GatherV2"); - if (node.op() == "Gather") CheckInputsCount(node, tf_import_flags, 2); - if (node.op() == "GatherV2") CheckInputsCount(node, tf_import_flags, 3); + if (node.op() == "Gather") + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); + if (node.op() == "GatherV2") + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); const auto indices_data_type = GetDataTypeAttr(node, "Tindices"); CHECK(indices_data_type == DT_INT32 || indices_data_type == DT_INT64); auto* op = new GatherOperator; @@ -1147,13 +1227,14 @@ void ConvertGatherOperator(const NodeDef& node, // should read it an pass it on to the TF Lite Interpreter. op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertArgMaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertArgMaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "ArgMax"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto axis_data_type = HasAttr(node, "Tidx") ? GetDataTypeAttr(node, "Tidx") : DT_INT32; const auto output_type = HasAttr(node, "output_type") @@ -1167,13 +1248,14 @@ void ConvertArgMaxOperator(const NodeDef& node, op->inputs.push_back(node.input(1)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertResizeBilinearOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertResizeBilinearOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "ResizeBilinear"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new ResizeBilinearOperator; op->align_corners = false; @@ -1185,13 +1267,14 @@ void ConvertResizeBilinearOperator(const NodeDef& node, op->inputs.push_back(node.input(1)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBatchNormWithGlobalNormalizationOperator( +tensorflow::Status ConvertBatchNormWithGlobalNormalizationOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "BatchNormWithGlobalNormalization"); - CheckInputsCount(node, tf_import_flags, 5); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); // TODO(ahentz): to really match tensorflow we need to add variance_epsilon // to the input, before feeding it into TensorFlowRsqrtOperator. @@ -1234,13 +1317,14 @@ void ConvertBatchNormWithGlobalNormalizationOperator( op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFusedBatchNormOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertFusedBatchNormOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "FusedBatchNorm"); - CheckInputsCount(node, tf_import_flags, 5); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); // Declare shortcuts for the inputs. const string& gamma_input = node.input(1); @@ -1286,13 +1370,14 @@ void ConvertFusedBatchNormOperator(const NodeDef& node, op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSpaceToBatchNDOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSpaceToBatchNDOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SpaceToBatchND"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); CHECK_EQ(GetDataTypeAttr(node, "Tpaddings"), DT_INT32); auto* op = new SpaceToBatchNDOperator; @@ -1301,13 +1386,14 @@ void ConvertSpaceToBatchNDOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBatchToSpaceNDOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertBatchToSpaceNDOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "BatchToSpaceND"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); CHECK_EQ(GetDataTypeAttr(node, "Tcrops"), DT_INT32); auto* op = new BatchToSpaceNDOperator; @@ -1316,13 +1402,14 @@ void ConvertBatchToSpaceNDOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertMeanOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMeanOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Mean"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new MeanOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1333,11 +1420,12 @@ void ConvertMeanOperator(const NodeDef& node, } else if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertSvdfOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSvdfOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Svdf"); const int input_size = GetInputsCount(node, tf_import_flags); QCHECK(input_size == 3 || input_size == 4) @@ -1360,14 +1448,15 @@ void ConvertSvdfOperator(const NodeDef& node, } op->rank = node.attr().at("Rank").i(); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This is just bare bones support to get the shapes to propagate. -void ConvertTransposeConvOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertTransposeConvOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Conv2DBackpropInput"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); auto* op = new TransposeConvOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1408,11 +1497,13 @@ void ConvertTransposeConvOperator(const NodeDef& node, if (existing_transpose) { CHECK(existing_transpose->type == OperatorType::kTranspose); } else { - // Transpose weights from HWIO order to OHWI order, which is more efficient - // for computation + // Transpose weights from HWOI order to OHWI order, which is more efficient + // for computation. (Note that TensorFlow considers the order as HWIO + // because they consider this a backward conv, inverting the sense of + // input/output.) TransposeOperator* transpose = new TransposeOperator; string perm_array = CreateConstArray( - model, node.name() + "_transpose_perm", {3, 0, 1, 2}); + model, node.name() + "_transpose_perm", {2, 0, 1, 3}); transpose->inputs = {weights_name, perm_array}; transpose->outputs = {transposed_weights_name}; model->operators.emplace_back(transpose); @@ -1429,14 +1520,14 @@ void ConvertTransposeConvOperator(const NodeDef& node, "Conv2DBackpropInput nodes."; } model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - -void ConvertRangeOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertRangeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Range"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); auto* op = new RangeOperator; if (HasAttr(node, "Tidx")) { const auto dtype = toco::GetDataTypeAttr(node, "Tidx"); @@ -1449,11 +1540,12 @@ void ConvertRangeOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertStackOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertStackOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK((node.op() == "Stack") || (node.op() == "Pack")); auto* op = new StackOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1469,9 +1561,9 @@ void ConvertStackOperator(const NodeDef& node, op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : 0; op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - // Some TensorFlow ops only occur in graph cycles, representing // control flow. We do not currently support control flow, so we wouldn't // be able to fully support such graphs, including performing inference, @@ -1482,7 +1574,7 @@ void ConvertStackOperator(const NodeDef& node, // such ops as RNN back-edges, which is technically incorrect (does not // allow representing the op's semantics) but good enough to get a // graph visualization. -void ConvertOperatorSpecialCasedAsRNNBackEdge( +tensorflow::Status ConvertOperatorSpecialCasedAsRNNBackEdge( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { // At the moment, the only type of operator special-cased in this way is @@ -1495,6 +1587,23 @@ void ConvertOperatorSpecialCasedAsRNNBackEdge( rnn_state->set_discardable(true); rnn_state->set_state_array(node.name()); rnn_state->set_back_edge_source_array(node.input(0)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertShapeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "Shape"); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); + const auto out_type = + HasAttr(node, "out_type") ? GetDataTypeAttr(node, "out_type") : DT_INT32; + CHECK(out_type == DT_INT64 || out_type == DT_INT32); + auto op = absl::make_unique(); + op->output_data_type = ConvertDataType(out_type); + op->inputs.push_back(node.input(0)); + op->outputs.push_back(node.name()); + model->operators.push_back(std::move(op)); + return tensorflow::Status::OK(); } void StripCaretFromArrayNames(Model* model) { @@ -1637,9 +1746,9 @@ bool InlineAllFunctions(GraphDef* graphdef) { return graph_modified; } -void ConvertTopKV2Operator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertTopKV2Operator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK((node.op() == "TopK") || (node.op() == "TopKV2")); auto op = absl::make_unique(); op->inputs.push_back(node.input(0)); @@ -1649,22 +1758,23 @@ void ConvertTopKV2Operator(const NodeDef& node, model, node.name() + "k", {static_cast(GetIntAttr(node, "k"))}); op->inputs.push_back(k_array); } else { - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); op->inputs.push_back(node.input(1)); } // The op has two outputs. op->outputs.push_back(node.name()); op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertDynamicPartitionOperator( +tensorflow::Status ConvertDynamicPartitionOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { auto op = absl::make_unique(); CHECK(HasAttr(node, "num_partitions")); op->num_partitions = GetIntAttr(node, "num_partitions"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); CHECK_GT(op->num_partitions, 1); @@ -1673,11 +1783,12 @@ void ConvertDynamicPartitionOperator( op->outputs.push_back(node.name() + ":" + std::to_string(i)); } model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertDynamicStitchOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDynamicStitchOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { // The parallel and non-parallel variants are the same besides whether they // have a parallel loop; there are no behavioral differences. CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch"); @@ -1685,19 +1796,20 @@ void ConvertDynamicStitchOperator(const NodeDef& node, CHECK(HasAttr(node, "N")); op->num_partitions = GetIntAttr(node, "N"); // Expect all ID partitions + all value partitions. - CheckInputsCount(node, tf_import_flags, op->num_partitions * 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, op->num_partitions * 2)); for (int i = 0; i < op->num_partitions * 2; ++i) { op->inputs.push_back(node.input(i)); } op->outputs.push_back(node.name()); model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertSparseToDenseOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSparseToDenseOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SparseToDense"); - CheckInputsCount(node, tf_import_flags, 4); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); auto* op = new SparseToDenseOperator; for (const string& input : node.input()) { @@ -1709,215 +1821,133 @@ void ConvertSparseToDenseOperator(const NodeDef& node, ? GetBoolAttr(node, "validate_indices") : true; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } } // namespace namespace internal { -Status ImportTensorFlowNode(const tensorflow::NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { - // TODO(ahentz): Historically these functions all CHECK-fail on error. We've - // been slowly converting them to return Status. - if (node.op() == "Const") { - return ConvertConstOperator(node, tf_import_flags, model); - } else if (node.op() == "Conv2D") { - ConvertConvOperator(node, tf_import_flags, model); - } else if (node.op() == "Conv2DBackpropInput") { - ConvertTransposeConvOperator(node, tf_import_flags, model); - } else if (node.op() == "DepthwiseConv2dNative") { - ConvertDepthwiseConvOperator(node, tf_import_flags, model); - } else if (node.op() == "DepthToSpace") { - ConvertDepthToSpaceOperator(node, tf_import_flags, model); - } else if (node.op() == "SpaceToDepth") { - ConvertSpaceToDepthOperator(node, tf_import_flags, model); - } else if (node.op() == "BiasAdd") { - ConvertBiasAddOperator(node, tf_import_flags, model); - } else if (node.op() == "Relu") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Relu6") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sigmoid") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Tanh") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "MaxPool") { - ConvertMaxPoolOperator(node, tf_import_flags, model); - } else if (node.op() == "AvgPool") { - ConvertAvgPoolOperator(node, tf_import_flags, model); - } else if (node.op() == "Reshape") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "BatchMatMul") { - ConvertBatchMatMulOperator(node, tf_import_flags, model); - } else if (node.op() == "MatMul") { - ConvertMatMulOperator(node, tf_import_flags, model); - } else if (node.op() == "Div" || node.op() == "RealDiv") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Identity" || node.op() == "CheckNumerics" || - node.op() == "StopGradient") { - ConvertIdentityOperator(node, tf_import_flags, model); - } else if (node.op() == "FakeQuantWithMinMaxVars") { - ConvertFakeQuantWithMinMaxVars(node, tf_import_flags, model); - } else if (node.op() == "FakeQuantWithMinMaxArgs") { - ConvertFakeQuantWithMinMaxArgs(node, tf_import_flags, model); - } else if (node.op() == "Neg") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Rsqrt") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Squeeze") { - ConvertSqueezeOperator(node, tf_import_flags, model); - } else if (node.op() == "Sqrt") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Square") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Add") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "AddN") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Mul") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sub") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sum") { - ConvertSumOperator(node, tf_import_flags, model); - } else if (node.op() == "Tile") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Concat" || node.op() == "ConcatV2") { - ConvertConcatOperator(node, tf_import_flags, model); - } else if (node.op() == "LRN") { - ConvertLRNOperator(node, tf_import_flags, model); - } else if (node.op() == "Softmax") { - ConvertSoftmaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Log") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "LogSoftmax") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "All") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Assert") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Less") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "LessEqual") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Greater") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "GreaterEqual") { - ConvertSimpleOperator( - node, tf_import_flags, model); - } else if (node.op() == "Max") { - ConvertMaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Min") { - ConvertMinOperator(node, tf_import_flags, model); - } else if (node.op() == "Maximum") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Minimum") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Merge") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Pad") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "PadV2") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "StridedSlice") { - ConvertStridedSliceOperator(node, tf_import_flags, model); - } else if (node.op() == "Shape") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Slice") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Split") { - ConvertSplitOperator(node, tf_import_flags, model); - } else if (node.op() == "Switch") { - ConvertSwitchOperator(node, tf_import_flags, model); - } else if (node.op() == "Placeholder") { - ConvertPlaceholderOperator(node, tf_import_flags, model); - } else if (node.op() == "PlaceholderWithDefault") { - ConvertIdentityOperator(node, tf_import_flags, model); - } else if (node.op() == "LegacyFedInput") { - ConvertPlaceholderOperator(node, tf_import_flags, model); - } else if (node.op() == "NoOp") { - ConvertNoOpOperator(node, tf_import_flags, model); - } else if (node.op() == "Cast") { - ConvertCastOperator(node, tf_import_flags, model); - } else if (node.op() == "Floor") { - ConvertFloorOperator(node, tf_import_flags, model); - } else if (node.op() == "Gather" || node.op() == "GatherV2") { - ConvertGatherOperator(node, tf_import_flags, model); - } else if (node.op() == "ResizeBilinear") { - ConvertResizeBilinearOperator(node, tf_import_flags, model); - } else if (node.op() == "BatchNormWithGlobalNormalization") { - ConvertBatchNormWithGlobalNormalizationOperator(node, tf_import_flags, - model); - } else if (node.op() == "FusedBatchNorm") { - ConvertFusedBatchNormOperator(node, tf_import_flags, model); - } else if (node.op() == "SpaceToBatchND") { - ConvertSpaceToBatchNDOperator(node, tf_import_flags, model); - } else if (node.op() == "BatchToSpaceND") { - ConvertBatchToSpaceNDOperator(node, tf_import_flags, model); - } else if (node.op() == "Mean") { - ConvertMeanOperator(node, tf_import_flags, model); - } else if (node.op() == "Svdf") { - ConvertSvdfOperator(node, tf_import_flags, model); - } else if (node.op() == "NextIteration") { - ConvertOperatorSpecialCasedAsRNNBackEdge(node, tf_import_flags, model); - } else if (node.op() == "ExpandDims") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Fill") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "FloorDiv") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "FloorMod") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Range") { - ConvertRangeOperator(node, tf_import_flags, model); - } else if (node.op() == "Rank") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Stack" || node.op() == "Pack") { - ConvertStackOperator(node, tf_import_flags, model); - } else if (node.op() == "Transpose") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "ArgMax") { - ConvertArgMaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Exp") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "TopK" || node.op() == "TopKV2") { - ConvertTopKV2Operator(node, tf_import_flags, model); - } else if (node.op() == "DynamicPartition") { - ConvertDynamicPartitionOperator(node, tf_import_flags, model); - } else if (node.op() == "DynamicStitch" || - node.op() == "ParallelDynamicStitch") { - ConvertDynamicStitchOperator(node, tf_import_flags, model); - } else if (node.op() == "RandomUniform") { - ConvertRandomUniform(node, tf_import_flags, model); - } else if (node.op() == "Sin") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Select") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "SparseToDense") { - ConvertSparseToDenseOperator(node, tf_import_flags, model); - } else if (node.op() == "Equal") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "NotEqual") { - ConvertSimpleOperator(node, tf_import_flags, - model); + +using ConverterType = tensorflow::Status (*)( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model); +using ConverterMapType = std::unordered_map; + +ConverterMapType GetTensorFlowNodeConverterMap() { + return std::unordered_map({ + {"Add", ConvertSimpleOperator}, + {"AddN", ConvertSimpleOperator}, + {"All", ConvertSimpleOperator}, + {"ArgMax", ConvertArgMaxOperator}, + {"Assert", ConvertSimpleOperator}, + {"AvgPool", ConvertAvgPoolOperator}, + {"BatchMatMul", ConvertBatchMatMulOperator}, + {"BatchNormWithGlobalNormalization", + ConvertBatchNormWithGlobalNormalizationOperator}, + {"BatchToSpaceND", ConvertBatchToSpaceNDOperator}, + {"BiasAdd", ConvertBiasAddOperator}, + {"Cast", ConvertCastOperator}, + {"CheckNumerics", ConvertIdentityOperator}, + {"Concat", ConvertConcatOperator}, + {"ConcatV2", ConvertConcatOperator}, + {"Const", ConvertConstOperator}, + {"Conv2D", ConvertConvOperator}, + {"Conv2DBackpropInput", ConvertTransposeConvOperator}, + {"DepthToSpace", ConvertDepthToSpaceOperator}, + {"DepthwiseConv2dNative", ConvertDepthwiseConvOperator}, + {"Div", ConvertSimpleOperator}, + {"DynamicPartition", ConvertDynamicPartitionOperator}, + {"DynamicStitch", ConvertDynamicStitchOperator}, + {"Equal", ConvertSimpleOperator}, + {"Exp", ConvertSimpleOperator}, + {"ExpandDims", ConvertSimpleOperator}, + {"FakeQuantWithMinMaxArgs", ConvertFakeQuantWithMinMaxArgs}, + {"FakeQuantWithMinMaxVars", ConvertFakeQuantWithMinMaxVars}, + {"Fill", ConvertSimpleOperator}, + {"Floor", ConvertFloorOperator}, + {"FloorDiv", ConvertSimpleOperator}, + {"FloorMod", ConvertSimpleOperator}, + {"FusedBatchNorm", ConvertFusedBatchNormOperator}, + {"Gather", ConvertGatherOperator}, + {"GatherV2", ConvertGatherOperator}, + {"Greater", ConvertSimpleOperator}, + {"GreaterEqual", + ConvertSimpleOperator}, + {"Identity", ConvertIdentityOperator}, + {"LRN", ConvertLRNOperator}, + {"LegacyFedInput", ConvertPlaceholderOperator}, + {"Less", ConvertSimpleOperator}, + {"LessEqual", ConvertSimpleOperator}, + {"Log", ConvertSimpleOperator}, + {"Log", ConvertSimpleOperator}, + {"LogSoftmax", ConvertSimpleOperator}, + {"MatMul", ConvertMatMulOperator}, + {"Max", ConvertMaxOperator}, + {"MaxPool", ConvertMaxPoolOperator}, + {"Maximum", ConvertSimpleOperator}, + {"Mean", ConvertMeanOperator}, + {"Merge", ConvertSimpleOperator}, + {"Min", ConvertMinOperator}, + {"Minimum", ConvertSimpleOperator}, + {"Mul", ConvertSimpleOperator}, + {"Neg", ConvertSimpleOperator}, + {"NextIteration", ConvertOperatorSpecialCasedAsRNNBackEdge}, + {"NoOp", ConvertNoOpOperator}, + {"NotEqual", ConvertSimpleOperator}, + {"Pack", ConvertStackOperator}, + {"Pad", ConvertSimpleOperator}, + {"PadV2", ConvertSimpleOperator}, + {"ParallelDynamicStitch", ConvertDynamicStitchOperator}, + {"Placeholder", ConvertPlaceholderOperator}, + {"PlaceholderWithDefault", ConvertIdentityOperator}, + {"Pow", ConvertSimpleOperator}, + {"RandomUniform", ConvertRandomUniform}, + {"Range", ConvertRangeOperator}, + {"Rank", ConvertSimpleOperator}, + {"RealDiv", ConvertSimpleOperator}, + {"Relu", ConvertSimpleOperator}, + {"Relu6", ConvertSimpleOperator}, + {"Reshape", ConvertSimpleOperator}, + {"ResizeBilinear", ConvertResizeBilinearOperator}, + {"Rsqrt", ConvertSimpleOperator}, + {"Select", ConvertSimpleOperator}, + {"Shape", ConvertShapeOperator}, + {"Sigmoid", ConvertSimpleOperator}, + {"Sin", ConvertSimpleOperator}, + {"Slice", ConvertSimpleOperator}, + {"Softmax", ConvertSoftmaxOperator}, + {"SpaceToBatchND", ConvertSpaceToBatchNDOperator}, + {"SpaceToDepth", ConvertSpaceToDepthOperator}, + {"SparseToDense", ConvertSparseToDenseOperator}, + {"Split", ConvertSplitOperator}, + {"Sqrt", ConvertSimpleOperator}, + {"Square", ConvertSimpleOperator}, + {"Squeeze", ConvertSqueezeOperator}, + {"Stack", ConvertStackOperator}, + {"StopGradient", ConvertIdentityOperator}, + {"StridedSlice", ConvertStridedSliceOperator}, + {"Sub", ConvertSimpleOperator}, + {"Sum", ConvertSumOperator}, + {"Svdf", ConvertSvdfOperator}, + {"Switch", ConvertSwitchOperator}, + {"Tanh", ConvertSimpleOperator}, + {"Tile", ConvertSimpleOperator}, + {"TopK", ConvertTopKV2Operator}, + {"TopKV2", ConvertTopKV2Operator}, + {"Transpose", ConvertSimpleOperator}, + }); +} + +tensorflow::Status ImportTensorFlowNode( + const tensorflow::NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, Model* model, + const ConverterMapType& converter_map) { + auto converter = converter_map.find(node.op()); + if (converter == converter_map.end()) { + return ConvertUnsupportedOperator(node, tf_import_flags, model); } else { - ConvertUnsupportedOperator(node, tf_import_flags, model); + return converter->second(node, tf_import_flags, model); } - return Status::OK(); } } // namespace internal @@ -1943,10 +1973,13 @@ std::unique_ptr ImportTensorFlowGraphDef( } Model* model = new Model; + const internal::ConverterMapType& converter_map = + internal::GetTensorFlowNodeConverterMap(); for (auto node : inlined_graph.node()) { StripZeroOutputIndexFromInputs(&node); - auto status = internal::ImportTensorFlowNode(node, tf_import_flags, model); + auto status = internal::ImportTensorFlowNode(node, tf_import_flags, model, + converter_map); CHECK(status.ok()) << status.error_message(); } diff --git a/tensorflow/contrib/lite/toco/import_tensorflow_test.cc b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc index 835676662b9cb7ed20e578e2a35747a64ba443dc..90e6f698efee6a6a32da18a658e72c3e8b6550c0 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow_test.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc @@ -21,10 +21,10 @@ limitations under the License. #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/tensor_shape.pb.h" +#include "tensorflow/core/lib/core/status.h" namespace toco { -using port::Status; using tensorflow::AttrValue; using tensorflow::DT_BOOL; using tensorflow::DT_FLOAT; @@ -33,10 +33,17 @@ using tensorflow::DT_INT64; using tensorflow::DT_QUINT8; using tensorflow::DT_STRING; using tensorflow::NodeDef; +using tensorflow::Status; namespace internal { +using ConverterType = tensorflow::Status (*)( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model); +using ConverterMapType = std::unordered_map; + +ConverterMapType GetTensorFlowNodeConverterMap(); Status ImportTensorFlowNode(const NodeDef&, const TensorFlowImportFlags&, - Model*); + Model*, const ConverterMapType&); } // namespace internal namespace { @@ -104,8 +111,9 @@ class ShapeImportTest : public ::testing::TestWithParam { Status ImportNode(const NodeDef& node) { Model model; - return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), - &model); + const auto converter = internal::GetTensorFlowNodeConverterMap(); + return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), &model, + converter); } }; @@ -117,9 +125,10 @@ TEST_P(ShapeImportTest, ShapeElementIsNegative) { NodeDef node; BuildConstNode({1, -2, 10}, GetParam(), 0, &node); auto status = ImportNode(node); - EXPECT_EQ(status.error_message(), - "Tensor shape should not include negative values (while processing " - "node 'Node1')"); + EXPECT_EQ( + status.error_message(), + "Tensor shape should not include negative values\n\t (while processing " + "node 'Node1')"); } INSTANTIATE_TEST_CASE_P(ShapeElementIsNegative, ShapeImportTest, ::testing::ValuesIn(TestTypes())); @@ -129,7 +138,7 @@ TEST_P(ShapeImportTest, ShapeElementTooLarge) { BuildConstNode({3000000000}, GetParam(), 0, &node); auto status = ImportNode(node); EXPECT_EQ(status.error_message(), - "Shape element overflows (while processing node 'Node1')"); + "Shape element overflows\n\t (while processing node 'Node1')"); } INSTANTIATE_TEST_CASE_P(ShapeElementTooLarge, ShapeImportTest, ::testing::ValuesIn(TestTypes())); @@ -139,7 +148,7 @@ TEST_P(ShapeImportTest, ShapeTooLarge) { BuildConstNode({1000000, 2000000, 2000000, 2000000}, GetParam(), 0, &node); auto status = ImportNode(node); EXPECT_EQ(status.error_message(), - "Tensor shape is too large (while processing node 'Node1')"); + "Tensor shape is too large\n\t (while processing node 'Node1')"); } INSTANTIATE_TEST_CASE_P(ShapeTooLarge, ShapeImportTest, ::testing::ValuesIn(TestTypes())); @@ -148,11 +157,11 @@ TEST_P(ShapeImportTest, ValidShapeButZeroElements) { NodeDef node; BuildConstNode({1, 2, 2, 2}, GetParam(), 0, &node); auto status = ImportNode(node); - EXPECT_THAT( - status.error_message(), - ::testing::MatchesRegex( - "Neither input_content .0. nor .*_val .0. have the right " - "dimensions .8. for this .* tensor .while processing node 'Node1'.")); + EXPECT_THAT(status.error_message(), + ::testing::MatchesRegex( + "Neither input_content .0. nor .*_val .0. have the right " + "dimensions .8. for this .* tensor\n\t .while processing " + "node 'Node1'.")); } INSTANTIATE_TEST_CASE_P(ValidShapeButZeroElements, ShapeImportTest, ::testing::ValuesIn(TestTypes())); diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 81beb2937293efd0032fa736fa4b197df127d735..3a1d243f87b20651aafe3b31cb14804e94dee72b 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -15,6 +15,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ #define TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ +#include #include #include #include @@ -32,7 +33,7 @@ namespace toco { using tflite::QuantizationParams; -enum class OperatorType { +enum class OperatorType : uint8 { kNone, // General-purpose neural network operators. kAdd, @@ -96,38 +97,38 @@ enum class OperatorType { // Special operators used for importing TensorFlow nodes. // The general intent is to have some graph transformation either // drop them or rewrite them as general-purpose operators. - kTensorFlowAll, - kTensorFlowAssert, - kTensorFlowConcat, - kTensorFlowConcatV2, - kTensorFlowGreater, - kTensorFlowGreaterEqual, - kTensorFlowIdentity, - kTensorFlowLess, - kTensorFlowLessEqual, - kTensorFlowMax, - kTensorFlowMaximum, - kTensorFlowMin, - kTensorFlowMinimum, - kTensorFlowMatMul, - kTensorFlowMerge, + kAll, + kAssert, + kConcat, + kConcatV2, + kGreater, + kGreaterEqual, + kIdentity, + kLess, + kLessEqual, + kMax, // Reduction Max + kMaximum, // Element-wise Maximum + kMin, // Reduction Min + kMinimum, // Element-wise Minimum + kMatMul, + kMerge, kNeg, - kTensorFlowReshape, - kTensorFlowRsqrt, - kTensorFlowShape, - kTensorFlowSplit, - kTensorFlowSqrt, - kTensorFlowSquare, - kTensorFlowSum, - kTensorFlowSwitch, - kTensorFlowTile, + kReshape, + kRsqrt, + kShape, + kSplit, + kSqrt, + kSquare, + kSum, + kSwitch, + kTile, kTranspose, kTopK_V2, kDynamicPartition, kDynamicStitch, // An unsupported TF operation. It's only needed to be able to represent TF // graph internally and is expected to be dropped by graph transformations. - kTensorFlowUnsupported, + kUnsupported, // Finally, TensorFlow uses different conventions for axes ordering, // see AxesOrder, and this cannot always be resolved at the time of importing // nodes, as TensorFlow parameters may be constant-expression subgraphs @@ -136,8 +137,9 @@ enum class OperatorType { kReorderAxes, kSelect, kSparseToDense, - kTensorFlowEqual, - kTensorFlowNotEqual, + kEqual, + kNotEqual, + kPow, }; // Helper to deal with TensorFlow arrays using a different ordering of @@ -155,25 +157,27 @@ enum class AxesOrder { k1HWO, // Our standard for DepthwiseConv weights kHWIM, // TensorFlow DepthwiseConv weights kNHWC, // TensorFlow activations + kHWOI, // TensorFlow back-prop conv weights }; // The type of the scalars in an array. // Note that the type does not by itself tell whether the values in the array -// are real (are literally interpreted as real numbers) or quantized (only -// acquire a meaning as real numbers in conjunction with QuantizationParams). +// are non-quantized (can be accessed directly) or quantized (must be +// interpreted in conjunction with QuantizationParams). // // In practice though: -// float values are always real +// float values are never quantized // uint8 values are always quantized -// int32 values are either real or quantized (depending on whether +// int32 values are sometimes quantized (depending on whether // QuantizationParams are present). -// other types are unused at the moment. +// complex values are never quantized +// other types are never quantized at the moment. // // kNone means that we don't know the data type yet, or that we don't care // because we'll be dropping the array anyway (e.g. some exotic array types // may be involved only in debug-only subgraphs that we may not be interested // in actually supporting). -enum class ArrayDataType { +enum class ArrayDataType : uint8 { kNone, // 0 kBool, kFloat, @@ -185,7 +189,8 @@ enum class ArrayDataType { kUint32, kInt64, kUint64, // 10 - kString + kString, + kComplex64, }; // Compile-time logic to map ArrayDataType to the corresponding C++ scalar type @@ -239,6 +244,10 @@ template <> struct DataTypeImpl { typedef string Type; }; +template <> +struct DataTypeImpl { + typedef std::complex Type; +}; template using DataType = typename DataTypeImpl::Type; @@ -432,7 +441,8 @@ struct SpaceToDepthOperator : Operator { // input activations as a matrix, followed by a MatMul node. struct FullyConnectedOperator : Operator { FullyConnectedOperator() : Operator(OperatorType::kFullyConnected) {} - bool experimental_shuffled_weights = false; + FullyConnectedWeightsFormat weights_format = + FullyConnectedWeightsFormat::kDefault; }; // Dequantization operator, converting a quantized array of integers with @@ -800,7 +810,7 @@ struct DivOperator : Operator { // // TensorFlow equivalent: Identity struct TensorFlowIdentityOperator : Operator { - TensorFlowIdentityOperator() : Operator(OperatorType::kTensorFlowIdentity) {} + TensorFlowIdentityOperator() : Operator(OperatorType::kIdentity) {} }; // Batch matrix multiplication operator. This comes from the (deprecated) @@ -826,7 +836,9 @@ struct BatchMatMulOperator : Operator { // // TensorFlow equivalent: MatMul struct TensorFlowMatMulOperator : Operator { - TensorFlowMatMulOperator() : Operator(OperatorType::kTensorFlowMatMul) {} + TensorFlowMatMulOperator() : Operator(OperatorType::kMatMul) {} + bool transpose_a = false; + bool transpose_b = false; }; // Padding operator. Pads a tensor with zeros. @@ -960,7 +972,7 @@ struct StridedSliceOperator : Operator { // TensorFlow equivalent: Reshape --- except that we only support a special case // here, where the output shape is a matrix (2D) shape. struct TensorFlowReshapeOperator : Operator { - TensorFlowReshapeOperator() : Operator(OperatorType::kTensorFlowReshape) {} + TensorFlowReshapeOperator() : Operator(OperatorType::kReshape) {} std::vector shape; }; @@ -1130,7 +1142,7 @@ struct SelectOperator : Operator { // // TensorFlow equivalent: Rsqrt struct TensorFlowRsqrtOperator : Operator { - TensorFlowRsqrtOperator() : Operator(OperatorType::kTensorFlowRsqrt) {} + TensorFlowRsqrtOperator() : Operator(OperatorType::kRsqrt) {} }; // Stacks a list of rank-R tensors into one rank-(R+1) tensor. @@ -1156,10 +1168,10 @@ struct StackOperator : Operator { // This operation outputs a 1-D integer tensor representing the shape of // the input. // -// TensorFlow equivalent: Shape. We currently assume that the output is int32 -// and not int64. The output type could be stored herein. +// TensorFlow equivalent: Shape. struct TensorFlowShapeOperator : Operator { - TensorFlowShapeOperator() : Operator(OperatorType::kTensorFlowShape) {} + TensorFlowShapeOperator() : Operator(OperatorType::kShape) {} + ArrayDataType output_data_type = ArrayDataType::kInt32; }; // Element-wise square-root (x^0.5) operator. @@ -1169,7 +1181,7 @@ struct TensorFlowShapeOperator : Operator { // // TensorFlow equivalent: Sqrt struct TensorFlowSqrtOperator : Operator { - TensorFlowSqrtOperator() : Operator(OperatorType::kTensorFlowSqrt) {} + TensorFlowSqrtOperator() : Operator(OperatorType::kSqrt) {} }; // Element-wise square (x*x) operator. @@ -1179,7 +1191,7 @@ struct TensorFlowSqrtOperator : Operator { // // TensorFlow equivalent: Square struct TensorFlowSquareOperator : Operator { - TensorFlowSquareOperator() : Operator(OperatorType::kTensorFlowSquare) {} + TensorFlowSquareOperator() : Operator(OperatorType::kSquare) {} }; // Transposes a tensor. @@ -1207,24 +1219,24 @@ struct SubOperator : Operator { SubOperator() : Operator(OperatorType::kSub) {} }; -// Global sum reduction: computes the sum of all of entries in the input array. -// Thus the output is "0-dimensional": it consists of a single scalar value. +// Sum reduction: computes the sum of all of entries across the axes. // // Inputs: // inputs[0]: required: the input array // -// TensorFlow equivalent: Sum --- except that we only support the special case -// of global reduction across all dimensions. +// TensorFlow equivalent: Sum struct TensorFlowSumOperator : Operator { - TensorFlowSumOperator() : Operator(OperatorType::kTensorFlowSum) {} + TensorFlowSumOperator() : Operator(OperatorType::kSum) {} bool keep_dims = false; }; // TensorFlow Tile equivalent. Refer to TensorFlow documentation for details. -// Not fully supported, just a placeholder to handle TensorFlow graphs and -// support graph transformations to other operator types by matching sub-graphs. +// +// Inputs: +// inputs[0]: required: the input array +// inputs[1]: required: int array with length of rank(input[0]) struct TensorFlowTileOperator : Operator { - TensorFlowTileOperator() : Operator(OperatorType::kTensorFlowTile) {} + TensorFlowTileOperator() : Operator(OperatorType::kTile) {} }; // TensorFlow Slice equivalent. Refer to TensorFlow documentation for details. @@ -1239,7 +1251,7 @@ struct SliceOperator : Operator { // Not fully supported, just a placeholder to handle TensorFlow graphs and // support graph transformations to other operator types by matching sub-graphs. struct TensorFlowSplitOperator : Operator { - TensorFlowSplitOperator() : Operator(OperatorType::kTensorFlowSplit) {} + TensorFlowSplitOperator() : Operator(OperatorType::kSplit) {} int num_split = 0; }; @@ -1250,7 +1262,7 @@ struct TensorFlowSplitOperator : Operator { // dimension then we can change this op into a DepthConcatenation op. // Otherwise, we hope for some other graph transformation to drop this node. struct TensorFlowConcatOperator : Operator { - TensorFlowConcatOperator() : Operator(OperatorType::kTensorFlowConcat) {} + TensorFlowConcatOperator() : Operator(OperatorType::kConcat) {} }; // TensorFlow ConcatV2 equivalent. Refer to TensorFlow documentation for @@ -1261,7 +1273,7 @@ struct TensorFlowConcatOperator : Operator { // dimension then we can change this op into a DepthConcatenation op. // Otherwise, we hope for some other graph transformation to drop this node. struct TensorFlowConcatV2Operator : Operator { - TensorFlowConcatV2Operator() : Operator(OperatorType::kTensorFlowConcatV2) {} + TensorFlowConcatV2Operator() : Operator(OperatorType::kConcatV2) {} }; // TensorFlow Merge equivalent. Refer to TensorFlow documentation for details. @@ -1277,7 +1289,7 @@ struct TensorFlowConcatV2Operator : Operator { // control flow that can be resolved at tooling time (independently of input // activations). struct TensorFlowMergeOperator : Operator { - TensorFlowMergeOperator() : Operator(OperatorType::kTensorFlowMerge) {} + TensorFlowMergeOperator() : Operator(OperatorType::kMerge) {} }; // TensorFlow Switch equivalent. Refer to TensorFlow documentation for details. @@ -1300,7 +1312,7 @@ struct TensorFlowMergeOperator : Operator { // control flow that can be resolved at tooling time (independently of input // activations). struct TensorFlowSwitchOperator : Operator { - TensorFlowSwitchOperator() : Operator(OperatorType::kTensorFlowSwitch) {} + TensorFlowSwitchOperator() : Operator(OperatorType::kSwitch) {} }; // TensorFlow All equivalent. Refer to TensorFlow documentation for details. @@ -1309,7 +1321,7 @@ struct TensorFlowSwitchOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowAllOperator : Operator { - TensorFlowAllOperator() : Operator(OperatorType::kTensorFlowAll) {} + TensorFlowAllOperator() : Operator(OperatorType::kAll) {} }; // TensorFlow Assert equivalent. Refer to TensorFlow documentation for details. @@ -1317,7 +1329,7 @@ struct TensorFlowAllOperator : Operator { // support graph transformations to other operator types by matching sub-graphs. // Typically, we just drop Assert nodes. struct TensorFlowAssertOperator : Operator { - TensorFlowAssertOperator() : Operator(OperatorType::kTensorFlowAssert) {} + TensorFlowAssertOperator() : Operator(OperatorType::kAssert) {} }; // TensorFlow Less equivalent. Refer to TensorFlow documentation for details. @@ -1326,7 +1338,7 @@ struct TensorFlowAssertOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowLessOperator : Operator { - TensorFlowLessOperator() : Operator(OperatorType::kTensorFlowLess) {} + TensorFlowLessOperator() : Operator(OperatorType::kLess) {} }; // TensorFlow LessEqual equivalent. Refer to TensorFlow documentation for @@ -1336,8 +1348,7 @@ struct TensorFlowLessOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowLessEqualOperator : Operator { - TensorFlowLessEqualOperator() - : Operator(OperatorType::kTensorFlowLessEqual) {} + TensorFlowLessEqualOperator() : Operator(OperatorType::kLessEqual) {} }; // TensorFlow Less equivalent. Refer to TensorFlow documentation for details. @@ -1346,7 +1357,7 @@ struct TensorFlowLessEqualOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowGreaterOperator : Operator { - TensorFlowGreaterOperator() : Operator(OperatorType::kTensorFlowGreater) {} + TensorFlowGreaterOperator() : Operator(OperatorType::kGreater) {} }; // TensorFlow GreaterEqual equivalent. Refer to TensorFlow documentation for @@ -1356,8 +1367,7 @@ struct TensorFlowGreaterOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowGreaterEqualOperator : Operator { - TensorFlowGreaterEqualOperator() - : Operator(OperatorType::kTensorFlowGreaterEqual) {} + TensorFlowGreaterEqualOperator() : Operator(OperatorType::kGreaterEqual) {} }; // TensorFlow Equal equivalent. Refer to TensorFlow documentation for @@ -1367,13 +1377,13 @@ struct TensorFlowGreaterEqualOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowEqualOperator : Operator { - TensorFlowEqualOperator() : Operator(OperatorType::kTensorFlowEqual) {} + TensorFlowEqualOperator() : Operator(OperatorType::kEqual) {} }; // TensorFlow Not Equal equivalent. Refer to TensorFlow documentation for // details. struct TensorFlowNotEqualOperator : Operator { - TensorFlowNotEqualOperator() : Operator(OperatorType::kTensorFlowNotEqual) {} + TensorFlowNotEqualOperator() : Operator(OperatorType::kNotEqual) {} }; // Global max reduction: computes the max of all of entries in the input array. @@ -1385,7 +1395,7 @@ struct TensorFlowNotEqualOperator : Operator { // TensorFlow equivalent: Max --- except that we only support the special case // of global reduction across all dimensions. struct TensorFlowMaxOperator : Operator { - TensorFlowMaxOperator() : Operator(OperatorType::kTensorFlowMax) {} + TensorFlowMaxOperator() : Operator(OperatorType::kMax) {} bool keep_dims = false; }; @@ -1398,7 +1408,7 @@ struct TensorFlowMaxOperator : Operator { // TensorFlow equivalent: Min --- except that we only support the special case // of global reduction across all dimensions. struct TensorFlowMinOperator : Operator { - TensorFlowMinOperator() : Operator(OperatorType::kTensorFlowMin) {} + TensorFlowMinOperator() : Operator(OperatorType::kMin) {} bool keep_dims = false; }; @@ -1411,7 +1421,7 @@ struct TensorFlowMinOperator : Operator { // // TensorFlow equivalent: Maximum struct TensorFlowMaximumOperator : Operator { - TensorFlowMaximumOperator() : Operator(OperatorType::kTensorFlowMaximum) {} + TensorFlowMaximumOperator() : Operator(OperatorType::kMaximum) {} }; // Element-wise minimum operator. Currently it only supports scalar as @@ -1423,14 +1433,13 @@ struct TensorFlowMaximumOperator : Operator { // // TensorFlow equivalent: Minimum struct TensorFlowMinimumOperator : Operator { - TensorFlowMinimumOperator() : Operator(OperatorType::kTensorFlowMinimum) {} + TensorFlowMinimumOperator() : Operator(OperatorType::kMinimum) {} }; // General TF operation, unsupported by tf.mini. Expected to be dropped by // graph transformations. struct TensorFlowUnsupportedOperator : Operator { - TensorFlowUnsupportedOperator() - : Operator(OperatorType::kTensorFlowUnsupported) {} + TensorFlowUnsupportedOperator() : Operator(OperatorType::kUnsupported) {} // The original TF operation type. Used for diagnostic purposes. string tensorflow_op; @@ -1638,13 +1647,24 @@ struct SparseToDenseOperator : Operator { bool validate_indices; }; +// Pow operator: +// +// Inputs: +// Inputs[0]: required: A tensor. +// Inputs[1]: required: A tensor. +// +// TensorFlow equivalent: Pow. +struct PowOperator : Operator { + PowOperator() : Operator(OperatorType::kPow) {} +}; + // Alloc's are used for transient arrays only. An Alloc specifies which interval // of the "transient_data" workspace buffer passed to inference functions, is to // be used for the transient array at hand. The 'start' and 'end' values are // offsets from the start of the workspace buffer, expressed in bytes. struct Alloc { - int start = 0; - int end = 0; + int64 start = 0; + int64 end = 0; }; inline bool operator<(const Alloc& a, const Alloc& b) { diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 0f104d5e2d02dc852a2720c78995108a00924298..06072d1fcb0612ed8193b3a0be1317923fe95bcc 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -48,7 +48,7 @@ bool ParseModelFlagsFromCommandLineFlags( "that information from the input file."), Flag("input_arrays", parsed_flags.input_arrays.bind(), parsed_flags.input_arrays.default_value(), - "Names of the output arrays, comma-separated. If not specified, " + "Names of the input arrays, comma-separated. If not specified, " "will try to read that information from the input file."), Flag("output_array", parsed_flags.output_array.bind(), parsed_flags.output_array.default_value(), @@ -74,10 +74,10 @@ bool ParseModelFlagsFromCommandLineFlags( "height, input array width, input array depth."), Flag("batch_size", parsed_flags.batch_size.bind(), parsed_flags.batch_size.default_value(), - "Batch size for the model. Replaces the first dimension of an " - "input size array if undefined. Use only with SavedModels when " - "--input_shapes flag is not specified. Always use --input_shapes " - "flag with frozen graphs."), + "Deprecated. Batch size for the model. Replaces the first dimension " + "of an input size array if undefined. Use only with SavedModels " + "when --input_shapes flag is not specified. Always use " + "--input_shapes flag with frozen graphs."), Flag("input_data_type", parsed_flags.input_data_type.bind(), parsed_flags.input_data_type.default_value(), "Deprecated: use --input_data_types instead. Input array type, if " diff --git a/tensorflow/contrib/lite/toco/python/BUILD b/tensorflow/contrib/lite/toco/python/BUILD index a954f1d6ba65f21cb99df226790f4bf4951581b1..93fe756a55d378fa205ff88be5e18aff586e5dca 100644 --- a/tensorflow/contrib/lite/toco/python/BUILD +++ b/tensorflow/contrib/lite/toco/python/BUILD @@ -12,6 +12,7 @@ cc_library( deps = [ "//tensorflow/contrib/lite/toco:model_flags_proto_cc", "//tensorflow/contrib/lite/toco:toco_flags_proto_cc", + "//tensorflow/contrib/lite/toco:toco_graphviz_dump_options", "//tensorflow/contrib/lite/toco:toco_port", "//tensorflow/contrib/lite/toco:toco_tooling", "//tensorflow/core:lib", diff --git a/tensorflow/contrib/lite/toco/python/toco_python_api.cc b/tensorflow/contrib/lite/toco/python/toco_python_api.cc index 5b1db852b4f8e89c1a591cfe18a0ab0aa2db04c9..d93e104038741e6e59608f04115854d611f1f9ae 100644 --- a/tensorflow/contrib/lite/toco/python/toco_python_api.cc +++ b/tensorflow/contrib/lite/toco/python/toco_python_api.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/python/toco_python_api.h" #include "tensorflow/contrib/lite/toco/toco_flags.pb.h" +#include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h" #include "tensorflow/contrib/lite/toco/toco_port.h" #include "tensorflow/contrib/lite/toco/toco_tooling.h" #include "tensorflow/contrib/lite/toco/toco_types.h" @@ -62,7 +63,7 @@ PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw, std::string input_contents_txt = ConvertArg(input_contents_txt_raw, &error); if (error) return nullptr; - // Use toco to produce new outputs + // Use TOCO to produce new outputs. toco::ModelFlags model_flags; if (!model_flags.ParseFromString(model_flags_proto_txt)) { LOG(FATAL) << "Model proto failed to parse." << std::endl; @@ -71,6 +72,16 @@ PyObject* TocoConvert(PyObject* model_flags_proto_txt_raw, if (!toco_flags.ParseFromString(toco_flags_proto_txt)) { LOG(FATAL) << "Toco proto failed to parse." << std::endl; } + + auto& dump_options = *GraphVizDumpOptions::singleton(); + if (toco_flags.has_dump_graphviz_dir()) { + dump_options.dump_graphviz = toco_flags.dump_graphviz_dir(); + } + if (toco_flags.has_dump_graphviz_include_video()) { + dump_options.dump_graphviz_video = toco_flags.dump_graphviz_include_video(); + } + + // Convert model. std::unique_ptr model = toco::Import(toco_flags, model_flags, input_contents_txt); toco::Transform(toco_flags, model.get()); diff --git a/tensorflow/contrib/lite/toco/runtime/types.h b/tensorflow/contrib/lite/toco/runtime/types.h index f5de5a5781a5304634642680e6a3cef60e7b844b..207f2c1706ef4cc12572e381c38f61a504ece232 100644 --- a/tensorflow/contrib/lite/toco/runtime/types.h +++ b/tensorflow/contrib/lite/toco/runtime/types.h @@ -24,6 +24,7 @@ namespace toco { // TODO(ahentz): These are just stopgaps for now, untils we move all // the code over to tflite. using tflite::Dims; +using tflite::FullyConnectedWeightsFormat; using tflite::FusedActivationFunctionType; using tflite::RequiredBufferSizeForDims; diff --git a/tensorflow/contrib/lite/toco/tflite/BUILD b/tensorflow/contrib/lite/toco/tflite/BUILD index e1025c66642d2860c5916bf7625f1c0403c9901c..a02f90988b2863900b6a735fd69aa1975a762338 100644 --- a/tensorflow/contrib/lite/toco/tflite/BUILD +++ b/tensorflow/contrib/lite/toco/tflite/BUILD @@ -24,6 +24,7 @@ cc_library( deps = [ ":types", "//tensorflow/contrib/lite/schema:schema_fbs", + "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", "//tensorflow/core:protos_all_cc", "@com_google_absl//absl/memory", diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index a2d753657b0bf6c88f5c94a20a1240fb7c13a37c..19722468079a32b76f6952db6ca818da470a03ac 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -49,7 +49,7 @@ details::OperatorKey GetOperatorKey( const ::toco::Operator& op, const std::map>& ops_by_type) { string custom_code; - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { const TensorFlowUnsupportedOperator& unsupported_op = static_cast(op); custom_code = unsupported_op.tensorflow_op; @@ -99,7 +99,8 @@ void LoadOperatorsMap( Offset>> ExportTensors( const Model& model, const details::TensorsMap& tensors_map, - FlatBufferBuilder* builder, std::vector* buffers_to_write) { + FlatBufferBuilder* builder, std::vector* buffers_to_write, + const std::set& variable_tensor_indices) { // In the end we will need to produce a vector sorted by the indices of the // tensors in the tensors_map. std::map> ordered_tensors; @@ -139,9 +140,11 @@ Offset>> ExportTensors( scale, zero_point); int index = tensors_map.at(tensor_name); + bool is_variable = + variable_tensor_indices.find(index) != variable_tensor_indices.end(); ordered_tensors[index] = CreateTensor(*builder, builder->CreateVector(shape), type, buffer_index, - builder->CreateString(tensor_name), q_param); + builder->CreateString(tensor_name), q_param, is_variable); } std::vector> tensor_vector; @@ -208,7 +211,7 @@ Offset>> ExportOperatorCodes( ordered_opcodes[op_index] = CreateOperatorCode(*builder, builtin_ops[name], 0, op_version); } else { - // This could be a kTensorFlowUnsupported, in which case we should be + // This could be a kUnsupported, in which case we should be // able to retrieve the original Tensorflow name from the OperatorKey, or // this could be a proper TOCO operator that is completely unknown to TF // Lite. @@ -239,7 +242,10 @@ Offset>> ExportOperators( const Model& model, const std::map>& ops_by_type, const details::OperatorsMap& operators_map, - const details::TensorsMap& tensors_map, FlatBufferBuilder* builder) { + const details::TensorsMap& tensors_map, FlatBufferBuilder* builder, + std::set* variable_tensor_indices) { + variable_tensor_indices->clear(); + // The operators are in execution order, so we just follow tf.mini order. std::vector> op_vector; for (const auto& op : model.operators) { @@ -256,18 +262,36 @@ Offset>> ExportOperators( int op_index = operators_map.at(GetOperatorKey(*op, ops_by_type)); - // This is a custom op unless we can find it in ops_by_type, and even then - // it could be a custom op (such as kTensorFlowUnsupported). + auto tflite_op_it = ops_by_type.find(op->type); + BaseOperator* tflite_op = tflite_op_it == ops_by_type.end() + ? nullptr + : tflite_op_it->second.get(); + // This is a custom op unless we can find it in ops_by_type, and even then + // it could be a custom op (such as kUnsupported). auto options = Options::Custom(0); - if (ops_by_type.count(op->type) != 0) { - options = ops_by_type.at(op->type)->Serialize(*op, builder); + + std::vector mutating_input_variables; + if (tflite_op) { + options = tflite_op->Serialize(*op, builder); + mutating_input_variables = tflite_op->GetMutatingInputVariables(*op); + + if (!mutating_input_variables.empty()) { + for (int i = 0; i < op->inputs.size(); ++i) { + if (!mutating_input_variables[i]) { + continue; + } + int32_t variable_tensor_index = tensors_map.at(op->inputs[i]); + variable_tensor_indices->insert(variable_tensor_index); + } + } } // The only supported CustomOptionFormat is FLEXBUFFERS now. op_vector.push_back(CreateOperator( *builder, op_index, builder->CreateVector(inputs), builder->CreateVector(outputs), options.type, options.builtin, - options.custom, ::tflite::CustomOptionsFormat_FLEXBUFFERS)); + options.custom, ::tflite::CustomOptionsFormat_FLEXBUFFERS, + builder->CreateVector(mutating_input_variables))); } return builder->CreateVector(op_vector); @@ -308,13 +332,10 @@ void Export( Array empty_array; buffers_to_write.push_back(&empty_array); - auto tensors = ExportTensors(model, tensors_map, &builder, &buffers_to_write); - auto inputs = ExportInputTensors(model, tensors_map, &builder); - auto outputs = ExportOutputTensors(model, tensors_map, &builder); - std::set error_summary; auto op_codes = ExportOperatorCodes(model, ops_by_type, operators_map, &builder, &error_summary); + const string fake_quant_operation_name = "FAKE_QUANT"; if (error_summary.count(fake_quant_operation_name) != 0) { @@ -353,11 +374,18 @@ void Export( << absl::StrJoin(error_summary_final, ", ") << "."; } - auto ops = - ExportOperators(model, ops_by_type, operators_map, tensors_map, &builder); + std::set variable_tensor_indices; + auto ops = ExportOperators(model, ops_by_type, operators_map, tensors_map, + &builder, &variable_tensor_indices); + + auto tensors = ExportTensors(model, tensors_map, &builder, &buffers_to_write, + variable_tensor_indices); + auto inputs = ExportInputTensors(model, tensors_map, &builder); + auto outputs = ExportOutputTensors(model, tensors_map, &builder); // TODO(aselle): add support to toco for multiple subgraphs. - auto subgraph = CreateSubGraph(builder, tensors, inputs, outputs, ops); + auto subgraph = CreateSubGraph(builder, tensors, inputs, outputs, ops, + /* name */ 0); std::vector> subgraphs = {subgraph}; auto buffers = ExportBuffers(model, buffers_to_write, &builder); diff --git a/tensorflow/contrib/lite/toco/tflite/export.h b/tensorflow/contrib/lite/toco/tflite/export.h index 098d2163e6c2fe26f3cb9cdf9959df62a1a4baf0..58ea5c725c378827aac79f2a5a2cdca59ccc0162 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.h +++ b/tensorflow/contrib/lite/toco/tflite/export.h @@ -45,7 +45,7 @@ namespace details { using TensorsMap = std::unordered_map; // A key to identify an operator. -// Only when `type` is `kTensorFlowUnsupported`, `custom_code` is filled to +// Only when `type` is `kUnsupported`, `custom_code` is filled to // identify which operation is used. struct OperatorKey { OperatorKey(OperatorType type, const std::string& custom_code, int version) diff --git a/tensorflow/contrib/lite/toco/tflite/export_test.cc b/tensorflow/contrib/lite/toco/tflite/export_test.cc index 409e7d72a57076ec2832c5d12b52829477624f74..d1fdbcb8e9131e1d65fa32ca0395bbc17b2014e7 100644 --- a/tensorflow/contrib/lite/toco/tflite/export_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/export_test.cc @@ -73,8 +73,8 @@ TEST_F(ExportTest, LoadOperatorsMap) { EXPECT_EQ(0, operators[details::OperatorKey(OperatorType::kAdd, "", 1)]); EXPECT_EQ(1, operators[details::OperatorKey(OperatorType::kConv, "", 1)]); EXPECT_EQ(2, operators[details::OperatorKey(OperatorType::kSub, "", 1)]); - EXPECT_EQ(3, operators[details::OperatorKey( - OperatorType::kTensorFlowUnsupported, "MyCrazyOp", 1)]); + EXPECT_EQ(3, operators[details::OperatorKey(OperatorType::kUnsupported, + "MyCrazyOp", 1)]); } TEST_F(ExportTest, Export) { diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index c0e7ab2ef57ed8edf1b7cda08c64f6ae66172af3..1dd4915b31413e5afb04b45ee7c4893a2eded66d 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -113,15 +113,35 @@ void ImportOperators( << operators_table.size(); } string opname = operators_table.at(index); + + // Find and use the appropriate operator deserialization factory. + std::unique_ptr new_op = nullptr; if (ops_by_name.count(opname) == 0) { - LOG(FATAL) << "Op '" << opname << "' not supported"; + string effective_opname = "TENSORFLOW_UNSUPPORTED"; + if (ops_by_name.count(effective_opname) == 0) { + LOG(FATAL) << "Internal logic error: TENSORFLOW_UNSUPPORTED not found."; + } + new_op = ops_by_name.at(effective_opname) + ->Deserialize(input_op->builtin_options(), + input_op->custom_options()); + if (new_op->type == OperatorType::kUnsupported) { + auto* unsupported_op = + static_cast(new_op.get()); + unsupported_op->tensorflow_op = opname; + // TODO(b/109932940): Remove this when quantized is removed. + // For now, we assume all ops are quantized. + unsupported_op->quantized = true; + } else { + LOG(FATAL) << "Expected a TensorFlowUnsupportedOperator"; + } + } else { + new_op = ops_by_name.at(opname)->Deserialize(input_op->builtin_options(), + input_op->custom_options()); } - - auto new_op = ops_by_name.at(opname)->Deserialize( - input_op->builtin_options(), input_op->custom_options()); model->operators.emplace_back(new_op.release()); auto* op = model->operators.back().get(); + // Make sure all the inputs and outputs are hooked up. auto inputs = input_op->inputs(); for (int i = 0; i < inputs->Length(); i++) { auto input_index = inputs->Get(i); @@ -201,6 +221,8 @@ std::unique_ptr Import(const ModelFlags& model_flags, model.get()); ImportIOTensors(*input_model, tensors_table, model.get()); + UndoWeightsShuffling(model.get()); + return model; } diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 8bfd76db6ea070a7019489d20ab54a4e6eb20179..7e55ae92bd57447cc821b21b40ba289cb484a9ed 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -14,6 +14,9 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/toco/tflite/operator.h" +// TODO(ycling): Consider refactoring to extract the LSTM definition out of +// graph_transformation module. +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" #include "tensorflow/contrib/lite/toco/tflite/builtin_operator.h" #include "tensorflow/contrib/lite/toco/tflite/custom_operator.h" #include "tensorflow/contrib/lite/toco/tflite/simple_operator.h" @@ -311,16 +314,47 @@ class FullyConnected flatbuffers::FlatBufferBuilder* builder) const override { auto activation_function = ActivationFunction::Serialize(op.fused_activation_function); - return ::tflite::CreateFullyConnectedOptions(*builder, activation_function); + ::tflite::FullyConnectedOptionsWeightsFormat tflite_weights_format; + switch (op.weights_format) { + case FullyConnectedWeightsFormat::kDefault: + tflite_weights_format = + ::tflite::FullyConnectedOptionsWeightsFormat_DEFAULT; + break; + case FullyConnectedWeightsFormat::kShuffled4x16Int8: + tflite_weights_format = + ::tflite::FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8; + break; + default: + LOG(ERROR) << "Unhandled FC weights format"; + tflite_weights_format = + ::tflite::FullyConnectedOptionsWeightsFormat_DEFAULT; + } + return ::tflite::CreateFullyConnectedOptions(*builder, activation_function, + tflite_weights_format); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { op->fused_activation_function = ActivationFunction::Deserialize(options.fused_activation_function()); + switch (options.weights_format()) { + case ::tflite::FullyConnectedOptionsWeightsFormat_DEFAULT: + op->weights_format = FullyConnectedWeightsFormat::kDefault; + break; + case ::tflite::FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8: + op->weights_format = FullyConnectedWeightsFormat::kShuffled4x16Int8; + break; + default: + LOG(ERROR) << "Unhandled FC weights format"; + op->weights_format = FullyConnectedWeightsFormat::kDefault; + } } - int GetVersion(const Operator& op) const override { return 1; } + int GetVersion(const Operator& op) const override { + const auto& fc_op = static_cast(op); + return fc_op.weights_format == FullyConnectedWeightsFormat::kDefault ? 1 + : 2; + } }; class Gather : public BuiltinOperator GetMutatingInputVariables( + const Operator& op) const override { + const auto& lstm_op = static_cast(op); + + std::vector mutating_input_variables(op.inputs.size(), false); + switch (lstm_op.kernel_type) { + case LstmCellOperator::KERNEL_FULL: { + mutating_input_variables[kInputActivationStateTensor] = true; + mutating_input_variables[kInputCellStateTensor] = true; + break; + } + case LstmCellOperator::KERNEL_BASIC: { + mutating_input_variables[LstmCellOperator::PREV_ACTIV_INPUT] = true; + mutating_input_variables[LstmCellOperator::PREV_STATE_INPUT] = true; + break; + } + } + return mutating_input_variables; + } +}; + +class Mean : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->keep_dims = options.keep_dims(); + } + + int GetVersion(const Operator& op) const override { return 1; } }; -class Mean : public BuiltinOperator { +class Sum + : public BuiltinOperator { public: using BuiltinOperator::BuiltinOperator; flatbuffers::Offset WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - return ::tflite::CreateMeanOptions(*builder, op.keep_dims); + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); } void ReadOptions(const TfLiteOptions& options, @@ -876,6 +949,26 @@ class ExpandDims int GetVersion(const Operator& op) const override { return 1; } }; +class Shape + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateShapeOptions( + *builder, DataType::Serialize(op.output_data_type)); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->output_data_type = DataType::Deserialize(options.out_type()); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class TensorFlowUnsupported : public BaseOperator { public: using BaseOperator::BaseOperator; @@ -936,6 +1029,20 @@ class TensorFlowUnsupported : public BaseOperator { fbb->Bool(key, attr.b()); has_valid_attr = true; break; + case tensorflow::AttrValue::kList: + if (attr.list().i_size() > 0) { + auto start = fbb->StartVector(key); + for (const int64_t v : attr.list().i()) { + fbb->Add(v); + } + fbb->EndVector(start, /*typed=*/true, /*fixed=*/false); + has_valid_attr = true; + } else { + LOG(WARNING) + << "Ignoring unsupported type in list attribute with key '" + << key << "'"; + } + break; default: LOG(WARNING) << "Ignoring unsupported attribute type with key '" << key << "'"; @@ -972,6 +1079,14 @@ class TensorFlowUnsupported : public BaseOperator { case flexbuffers::TYPE_BOOL: (*attr)[key].set_b(value.AsBool()); break; + case flexbuffers::TYPE_VECTOR_INT: { + auto* list = (*attr)[key].mutable_list(); + const auto& vector = value.AsTypedVector(); + for (size_t i = 0; i < vector.size(); i++) { + list->add_i(vector[i].AsInt64()); + } + break; + } default: LOG(WARNING) << "Ignoring unsupported attribute type with key '" << key << "'"; @@ -1030,8 +1145,8 @@ std::vector> BuildOperatorList() { ops.emplace_back(new Pad(::tflite::BuiltinOperator_PAD, OperatorType::kPad)); ops.emplace_back( new PadV2(::tflite::BuiltinOperator_PADV2, OperatorType::kPadV2)); - ops.emplace_back(new Reshape(::tflite::BuiltinOperator_RESHAPE, - OperatorType::kTensorFlowReshape)); + ops.emplace_back( + new Reshape(::tflite::BuiltinOperator_RESHAPE, OperatorType::kReshape)); ops.emplace_back( new Softmax(::tflite::BuiltinOperator_SOFTMAX, OperatorType::kSoftmax)); ops.emplace_back(new SpaceToDepth(::tflite::BuiltinOperator_SPACE_TO_DEPTH, @@ -1042,12 +1157,13 @@ std::vector> BuildOperatorList() { OperatorType::kTranspose)); ops.emplace_back( new Mean(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); + ops.emplace_back(new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kSum)); ops.emplace_back(new ResizeBilinear(::tflite::BuiltinOperator_RESIZE_BILINEAR, OperatorType::kResizeBilinear)); ops.emplace_back( new Squeeze(::tflite::BuiltinOperator_SQUEEZE, OperatorType::kSqueeze)); - ops.emplace_back(new Split(::tflite::BuiltinOperator_SPLIT, - OperatorType::kTensorFlowSplit)); + ops.emplace_back( + new Split(::tflite::BuiltinOperator_SPLIT, OperatorType::kSplit)); ops.emplace_back(new StridedSlice(::tflite::BuiltinOperator_STRIDED_SLICE, OperatorType::kStridedSlice)); ops.emplace_back( @@ -1059,27 +1175,27 @@ std::vector> BuildOperatorList() { ops.emplace_back( new ArgMax(::tflite::BuiltinOperator_ARG_MAX, OperatorType::kArgMax)); ops.emplace_back( - new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTensorFlowTile)); + new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTile)); ops.emplace_back(new ExpandDims(::tflite::BuiltinOperator_EXPAND_DIMS, OperatorType::kExpandDims)); ops.emplace_back(new TransposeConv(::tflite::BuiltinOperator_TRANSPOSE_CONV, OperatorType::kTransposeConv)); ops.emplace_back(new SparseToDense(::tflite::BuiltinOperator_SPARSE_TO_DENSE, OperatorType::kSparseToDense)); + ops.emplace_back( + new Shape(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); // Custom Operators. ops.emplace_back( new DepthToSpace("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); ops.emplace_back(new FakeQuant("FAKE_QUANT", OperatorType::kFakeQuant)); - ops.emplace_back(new TensorFlowUnsupported( - "TENSORFLOW_UNSUPPORTED", OperatorType::kTensorFlowUnsupported)); + ops.emplace_back(new TensorFlowUnsupported("TENSORFLOW_UNSUPPORTED", + OperatorType::kUnsupported)); // There operators are supported by Toco, but not by TF Lite, and has no // attributes. ops.emplace_back( new SimpleOperator("ADDN", OperatorType::kAddN)); - ops.emplace_back(new SimpleOperator( - "RSQRT", OperatorType::kTensorFlowRsqrt)); // Simple Operators. ops.emplace_back(new SimpleOperator( "DEQUANTIZE", OperatorType::kDequantize)); @@ -1101,27 +1217,34 @@ std::vector> BuildOperatorList() { ops.emplace_back(new SimpleOperator( "LOG_SOFTMAX", OperatorType::kLogSoftmax)); ops.emplace_back(new SimpleOperator( - "MAXIMUM", OperatorType::kTensorFlowMaximum)); + "MAXIMUM", OperatorType::kMaximum)); // Element-wise Maximum ops.emplace_back(new SimpleOperator( - "MINIMUM", OperatorType::kTensorFlowMinimum)); + "MINIMUM", OperatorType::kMinimum)); // Element-wise Minimum ops.emplace_back(new SimpleOperator( - "GREATER", OperatorType::kTensorFlowGreater)); + "GREATER", OperatorType::kGreater)); ops.emplace_back(new SimpleOperator( - "GREATER_EQUAL", OperatorType::kTensorFlowGreaterEqual)); - ops.emplace_back(new SimpleOperator( - "LESS", OperatorType::kTensorFlowLess)); + "GREATER_EQUAL", OperatorType::kGreaterEqual)); + ops.emplace_back( + new SimpleOperator("LESS", OperatorType::kLess)); ops.emplace_back(new SimpleOperator( - "LESS_EQUAL", OperatorType::kTensorFlowLessEqual)); + "LESS_EQUAL", OperatorType::kLessEqual)); + ops.emplace_back(new SimpleOperator( + "EQUAL", OperatorType::kEqual)); + ops.emplace_back(new SimpleOperator( + "NOT_EQUAL", OperatorType::kNotEqual)); ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg)); ops.emplace_back( new SimpleOperator("SELECT", OperatorType::kSelect)); ops.emplace_back( new SimpleOperator("SLICE", OperatorType::kSlice)); + ops.emplace_back(new SimpleOperator("POW", OperatorType::kPow)); + // Element-wise operator ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin)); - ops.emplace_back(new SimpleOperator( - "EQUAL", OperatorType::kTensorFlowEqual)); - ops.emplace_back(new SimpleOperator( - "NOT_EQUAL", OperatorType::kTensorFlowNotEqual)); + ops.emplace_back(new SimpleOperator("LOG", OperatorType::kLog)); + ops.emplace_back( + new SimpleOperator("SQRT", OperatorType::kSqrt)); + ops.emplace_back(new SimpleOperator( + "RSQRT", OperatorType::kRsqrt)); return ops; } diff --git a/tensorflow/contrib/lite/toco/tflite/operator.h b/tensorflow/contrib/lite/toco/tflite/operator.h index 5e9c20e40dd6274e0839379883b6dbe53064a0fc..d9ea23edf2b08146773ca58762623397e0f6257c 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.h +++ b/tensorflow/contrib/lite/toco/tflite/operator.h @@ -87,6 +87,17 @@ class BaseOperator { // overridden. (See example in `operator_test.cc`) virtual int GetVersion(const Operator& op) const = 0; + // Given a Toco `Operator`, return a list of booleans indicating the op + // mutates which input variables. + // * If the op mutates any input variables, it should return a list of bool + // with the same length as inputs. + // * Otherwise, it will return an empty list. + virtual std::vector GetMutatingInputVariables( + const Operator& op) const { + // Most ops don't have variable tensors. This function can be overridden. + return std::vector(); + } + private: string name_; OperatorType type_; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 06bbe53516e296efdd0b12c0de06c30cf084b2c1..8b6808d3c78d8c51c1b33d09eb4082326100b028 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -74,8 +74,10 @@ class OperatorTest : public ::testing::Test { auto new_toco_op = op.Deserialize(output_options->builtin_options(), output_options->custom_options()); - CHECK(dynamic_cast(new_toco_op.get())) - << "Cannot cast " << HelpfulOperatorTypeName(*new_toco_op) << " to " + CHECK(new_toco_op->type == toco_op.type) + << "The type of the serialized and deserialized" + << HelpfulOperatorTypeName(*new_toco_op) + << " does not match the type of the original " << HelpfulOperatorTypeName(toco_op); return std::unique_ptr(dynamic_cast(new_toco_op.release())); @@ -110,19 +112,21 @@ TEST_F(OperatorTest, SimpleOperators) { CheckSimpleOperator("LOG_SOFTMAX", OperatorType::kLogSoftmax); CheckSimpleOperator( - "MAXIMUM", OperatorType::kTensorFlowMaximum); + "MAXIMUM", OperatorType::kMaximum); // Element-wise Maximum CheckSimpleOperator( - "MINIMUM", OperatorType::kTensorFlowMinimum); - CheckSimpleOperator("LESS", - OperatorType::kTensorFlowLess); + "MINIMUM", OperatorType::kMinimum); // Element-wise Minimum + CheckSimpleOperator("LESS", OperatorType::kLess); CheckSimpleOperator("NEG", OperatorType::kNeg); CheckSimpleOperator("SELECT", OperatorType::kSelect); CheckSimpleOperator("SLICE", OperatorType::kSlice); CheckSimpleOperator("SIN", OperatorType::kSin); - CheckSimpleOperator("EQUAL", - OperatorType::kTensorFlowEqual); - CheckSimpleOperator( - "NOT_EQUAL", OperatorType::kTensorFlowNotEqual); + CheckSimpleOperator("EQUAL", OperatorType::kEqual); + CheckSimpleOperator("NOT_EQUAL", + OperatorType::kNotEqual); + CheckSimpleOperator("LOG", OperatorType::kLog); + CheckSimpleOperator("SQRT", OperatorType::kSqrt); + CheckSimpleOperator("RSQRT", OperatorType::kRsqrt); + CheckSimpleOperator("POW", OperatorType::kPow); } TEST_F(OperatorTest, BuiltinAdd) { @@ -251,7 +255,7 @@ TEST_F(OperatorTest, BuiltinReshape) { TensorFlowReshapeOperator op; op.shape = {1, 2, 4, 5, 8}; auto output_toco_op = SerializeAndDeserialize( - GetOperator("RESHAPE", OperatorType::kTensorFlowReshape), op); + GetOperator("RESHAPE", OperatorType::kReshape), op); EXPECT_EQ(op.shape, output_toco_op->shape); } @@ -274,8 +278,8 @@ TEST_F(OperatorTest, BuiltinSpaceToDepth) { TEST_F(OperatorTest, CustomSplit) { TensorFlowSplitOperator op; op.num_split = 123; - auto output_toco_op = SerializeAndDeserialize( - GetOperator("SPLIT", OperatorType::kTensorFlowSplit), op); + auto output_toco_op = + SerializeAndDeserialize(GetOperator("SPLIT", OperatorType::kSplit), op); EXPECT_EQ(op.num_split, output_toco_op->num_split); } @@ -424,6 +428,14 @@ TEST_F(OperatorTest, BuiltinTransposeConv) { EXPECT_EQ(op.padding.type, output_toco_op->padding.type); } +TEST_F(OperatorTest, BuiltinShape) { + TensorFlowShapeOperator op; + op.output_data_type = ArrayDataType::kInt64; + auto output_toco_op = + SerializeAndDeserialize(GetOperator("SHAPE", OperatorType::kShape), op); + EXPECT_EQ(op.output_data_type, output_toco_op->output_data_type); +} + TEST_F(OperatorTest, BuiltinSparseToDense) { SparseToDenseOperator op; op.validate_indices = false; @@ -443,12 +455,17 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { (*attr)["str_attr"].set_s("Hello World"); (*attr)["int_attr"].set_i(17); (*attr)["bool_attr"].set_b(true); + { + auto* list = (*attr)["list_int_attr"].mutable_list(); + list->add_i(1); + list->add_i(20); + list->add_i(1LL << 40); + list->add_i(-(1LL << 40)); + } node_def.SerializeToString(&op.tensorflow_node_def); - auto output_toco_op = - SerializeAndDeserialize(GetOperator("TENSORFLOW_UNSUPPORTED", - OperatorType::kTensorFlowUnsupported), - op); + auto output_toco_op = SerializeAndDeserialize( + GetOperator("TENSORFLOW_UNSUPPORTED", OperatorType::kUnsupported), op); ::tensorflow::NodeDef output_node_def; output_node_def.ParseFromString(output_toco_op->tensorflow_node_def); @@ -457,15 +474,22 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { EXPECT_EQ("Hello World", output_attr.at("str_attr").s()); EXPECT_EQ(17, output_attr.at("int_attr").i()); EXPECT_EQ(true, output_attr.at("bool_attr").b()); + + { + const auto& list = output_attr.at("list_int_attr").list(); + ASSERT_EQ(4, list.i_size()); + EXPECT_EQ(1, list.i(0)); + EXPECT_EQ(20, list.i(1)); + EXPECT_EQ(1LL << 40, list.i(2)); + EXPECT_EQ(-(1LL << 40), list.i(3)); + } } TEST_F(OperatorTest, TensorFlowUnsupportedWithoutAttr) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; - auto output_toco_op = - SerializeAndDeserialize(GetOperator("TENSORFLOW_UNSUPPORTED", - OperatorType::kTensorFlowUnsupported), - op); + auto output_toco_op = SerializeAndDeserialize( + GetOperator("TENSORFLOW_UNSUPPORTED", OperatorType::kUnsupported), op); ::tensorflow::NodeDef output_node_def; output_node_def.ParseFromString(output_toco_op->tensorflow_node_def); diff --git a/tensorflow/contrib/lite/toco/tflite/types.cc b/tensorflow/contrib/lite/toco/tflite/types.cc index 4867c3a62e68406428644cd05bddf212008c2656..754f0b4b8c661355c99d9e5a86f2d7844414a303 100644 --- a/tensorflow/contrib/lite/toco/tflite/types.cc +++ b/tensorflow/contrib/lite/toco/tflite/types.cc @@ -88,6 +88,8 @@ void CopyBuffer(const ::tflite::Buffer& buffer, Array* array) { switch (array_data_type) { case ArrayDataType::kFloat: return ::tflite::TensorType_FLOAT32; + case ArrayDataType::kInt16: + return ::tflite::TensorType_INT16; case ArrayDataType::kInt32: return ::tflite::TensorType_INT32; case ArrayDataType::kInt64: @@ -98,6 +100,8 @@ void CopyBuffer(const ::tflite::Buffer& buffer, Array* array) { return ::tflite::TensorType_STRING; case ArrayDataType::kBool: return ::tflite::TensorType_BOOL; + case ArrayDataType::kComplex64: + return ::tflite::TensorType_COMPLEX64; default: // FLOAT32 is filled for unknown data types. // TODO(ycling): Implement type inference in TF Lite interpreter. @@ -109,6 +113,8 @@ ArrayDataType DataType::Deserialize(int tensor_type) { switch (::tflite::TensorType(tensor_type)) { case ::tflite::TensorType_FLOAT32: return ArrayDataType::kFloat; + case ::tflite::TensorType_INT16: + return ArrayDataType::kInt16; case ::tflite::TensorType_INT32: return ArrayDataType::kInt32; case ::tflite::TensorType_INT64: @@ -119,6 +125,8 @@ ArrayDataType DataType::Deserialize(int tensor_type) { return ArrayDataType::kUint8; case ::tflite::TensorType_BOOL: return ArrayDataType::kBool; + case ::tflite::TensorType_COMPLEX64: + return ArrayDataType::kComplex64; default: LOG(FATAL) << "Unhandled tensor type '" << tensor_type << "'."; } @@ -131,6 +139,8 @@ flatbuffers::Offset> DataBuffer::Serialize( switch (array.data_type) { case ArrayDataType::kFloat: return CopyBuffer(array, builder); + case ArrayDataType::kInt16: + return CopyBuffer(array, builder); case ArrayDataType::kInt32: return CopyBuffer(array, builder); case ArrayDataType::kInt64: @@ -141,6 +151,8 @@ flatbuffers::Offset> DataBuffer::Serialize( return CopyBuffer(array, builder); case ArrayDataType::kBool: return CopyBoolToBuffer(array, builder); + case ArrayDataType::kComplex64: + return CopyBuffer(array, builder); default: LOG(FATAL) << "Unhandled array data type."; } @@ -154,6 +166,8 @@ void DataBuffer::Deserialize(const ::tflite::Tensor& tensor, switch (tensor.type()) { case ::tflite::TensorType_FLOAT32: return CopyBuffer(buffer, array); + case ::tflite::TensorType_INT16: + return CopyBuffer(buffer, array); case ::tflite::TensorType_INT32: return CopyBuffer(buffer, array); case ::tflite::TensorType_INT64: @@ -164,6 +178,8 @@ void DataBuffer::Deserialize(const ::tflite::Tensor& tensor, return CopyBuffer(buffer, array); case ::tflite::TensorType_BOOL: return CopyBuffer(buffer, array); + case ::tflite::TensorType_COMPLEX64: + return CopyBuffer(buffer, array); default: LOG(FATAL) << "Unhandled tensor type."; } diff --git a/tensorflow/contrib/lite/toco/tflite/types_test.cc b/tensorflow/contrib/lite/toco/tflite/types_test.cc index 564f303b9bb41a777633ecabd666aa93ec3faefe..8e9f30ba3a6e6b98fa9c4237567b0797a5a797aa 100644 --- a/tensorflow/contrib/lite/toco/tflite/types_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/types_test.cc @@ -14,6 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/toco/tflite/types.h" +#include + #include #include @@ -71,7 +73,8 @@ TEST(DataType, SupportedTypes) { {ArrayDataType::kInt32, ::tflite::TensorType_INT32}, {ArrayDataType::kInt64, ::tflite::TensorType_INT64}, {ArrayDataType::kFloat, ::tflite::TensorType_FLOAT32}, - {ArrayDataType::kBool, ::tflite::TensorType_BOOL}}; + {ArrayDataType::kBool, ::tflite::TensorType_BOOL}, + {ArrayDataType::kComplex64, ::tflite::TensorType_COMPLEX64}}; for (auto x : testdata) { EXPECT_EQ(x.second, DataType::Serialize(x.first)); EXPECT_EQ(x.first, DataType::Deserialize(x.second)); @@ -151,6 +154,12 @@ TEST(DataBuffer, Int32) { ::testing::ElementsAre(1, 1 << 30)); } +TEST(DataBuffer, Int16) { + Array recovered = ToFlatBufferAndBack({1, 1 << 14}); + EXPECT_THAT(recovered.GetBuffer().data, + ::testing::ElementsAre(1, 1 << 14)); +} + TEST(DataBuffer, String) { Array recovered = ToFlatBufferAndBack( {"AA", "BBB", "Best. String. Ever."}); @@ -165,6 +174,14 @@ TEST(DataBuffer, Bool) { ::testing::ElementsAre(true, false, true)); } +TEST(DataBuffer, Complex64) { + Array recovered = ToFlatBufferAndBack( + {std::complex(1.0f, 2.0f), std::complex(3.0f, 4.0f)}); + EXPECT_THAT(recovered.GetBuffer().data, + ::testing::ElementsAre(std::complex(1.0f, 2.0f), + std::complex(3.0f, 4.0f))); +} + TEST(Padding, All) { EXPECT_EQ(::tflite::Padding_SAME, Padding::Serialize(PaddingType::kSame)); EXPECT_EQ(PaddingType::kSame, Padding::Deserialize(::tflite::Padding_SAME)); diff --git a/tensorflow/contrib/lite/toco/toco.cc b/tensorflow/contrib/lite/toco/toco.cc index 8041aa9e7fbfdaf44134395fee4b2bb01633893a..0b460bd178a49cafefd3438b7ae1c38a07b2ab7c 100644 --- a/tensorflow/contrib/lite/toco/toco.cc +++ b/tensorflow/contrib/lite/toco/toco.cc @@ -23,7 +23,6 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/toco_cmdline_flags.h" #include "tensorflow/contrib/lite/toco/toco_flags.pb.h" #include "tensorflow/contrib/lite/toco/toco_port.h" -#include "tensorflow/contrib/lite/toco/toco_saved_model.h" #include "tensorflow/contrib/lite/toco/toco_tooling.h" #include "tensorflow/contrib/lite/toco/toco_types.h" #include "tensorflow/core/platform/logging.h" @@ -49,17 +48,6 @@ void CheckFrozenModelPermissions(const Arg& input_file) { << input_file.value() << ".\n"; } -// Checks the permissions of the SavedModel directory. -void CheckSavedModelPermissions(const Arg& savedmodel_directory) { - QCHECK(savedmodel_directory.specified()) - << "Missing required flag --savedmodel_directory.\n"; - QCHECK( - port::file::Exists(savedmodel_directory.value(), port::file::Defaults()) - .ok()) - << "Specified savedmodel_directory does not exist: " - << savedmodel_directory.value() << ".\n"; -} - // Reads the contents of the GraphDef from either the frozen graph file or the // SavedModel directory. If it reads the SavedModel directory, it updates the // ModelFlags and TocoFlags accordingly. @@ -69,24 +57,16 @@ void ReadInputData(const ParsedTocoFlags& parsed_toco_flags, string* graph_def_contents) { port::CheckInitGoogleIsDone("InitGoogle is not done yet.\n"); - bool has_input_file = parsed_toco_flags.input_file.specified(); - bool has_savedmodel_dir = parsed_toco_flags.savedmodel_directory.specified(); - - // Ensure either input_file or savedmodel_directory flag has been set. - QCHECK_NE(has_input_file, has_savedmodel_dir) - << "Specify either input_file or savedmodel_directory flag.\n"; + // Ensure savedmodel_directory is not set. + QCHECK(!parsed_toco_flags.savedmodel_directory.specified()) + << "Use `tensorflow/contrib/lite/python/tflite_convert` script with " + << "SavedModel directories.\n"; // Checks the input file permissions and reads the contents. - if (has_input_file) { - CheckFrozenModelPermissions(parsed_toco_flags.input_file); - CHECK(port::file::GetContents(parsed_toco_flags.input_file.value(), - graph_def_contents, port::file::Defaults()) - .ok()); - } else { - CheckSavedModelPermissions(parsed_toco_flags.savedmodel_directory); - GetSavedModelContents(parsed_toco_flags, parsed_model_flags, toco_flags, - model_flags, graph_def_contents); - } + CheckFrozenModelPermissions(parsed_toco_flags.input_file); + CHECK(port::file::GetContents(parsed_toco_flags.input_file.value(), + graph_def_contents, port::file::Defaults()) + .ok()); } void ToolMain(const ParsedTocoFlags& parsed_toco_flags, diff --git a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc index 87a1e429b928bf59cb14597980602953732a7659..c6d0a03452f7477841d7e68665baf32dff45f41c 100644 --- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc @@ -41,7 +41,7 @@ bool ParseTocoFlagsFromCommandLineFlags( "extension."), Flag("savedmodel_directory", parsed_flags.savedmodel_directory.bind(), parsed_flags.savedmodel_directory.default_value(), - "Full path to the directory containing the SavedModel."), + "Deprecated. Full path to the directory containing the SavedModel."), Flag("output_file", parsed_flags.output_file.bind(), parsed_flags.output_file.default_value(), "Output file. " @@ -55,9 +55,9 @@ bool ParseTocoFlagsFromCommandLineFlags( "One of TENSORFLOW_GRAPHDEF, TFLITE, GRAPHVIZ_DOT."), Flag("savedmodel_tagset", parsed_flags.savedmodel_tagset.bind(), parsed_flags.savedmodel_tagset.default_value(), - "Comma-separated set of tags identifying the MetaGraphDef within " - "the SavedModel to analyze. All tags in the tag set must be " - "specified."), + "Deprecated. Comma-separated set of tags identifying the " + "MetaGraphDef within the SavedModel to analyze. All tags in the tag " + "set must be specified."), Flag("default_ranges_min", parsed_flags.default_ranges_min.bind(), parsed_flags.default_ranges_min.default_value(), "If defined, will be used as the default value for the min bound " diff --git a/tensorflow/contrib/lite/toco/toco_flags.proto b/tensorflow/contrib/lite/toco/toco_flags.proto index 4fe57879fb0f38a21aac01283bc68077aa4be771..b4a9870d5834d1d5689d15ebc131ac0ead3e9850 100644 --- a/tensorflow/contrib/lite/toco/toco_flags.proto +++ b/tensorflow/contrib/lite/toco/toco_flags.proto @@ -37,7 +37,7 @@ enum FileFormat { // of as properties of models, instead describing how models are to be // processed in the context of the present tooling job. // -// Next ID to use: 21. +// Next ID to use: 26. message TocoFlags { // Input file format optional FileFormat input_format = 1; @@ -174,4 +174,13 @@ message TocoFlags { // Computation is still done in float, but reduces model size (at the cost of // accuracy and latency). optional bool quantize_weights = 20 [default = false]; + + // Full filepath of folder to dump the graphs at various stages of processing + // GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order + // to keep the requirements of the output file. + optional string dump_graphviz_dir = 24; + + // Boolean indicating whether to dump the graph after every graph + // transformation. + optional bool dump_graphviz_include_video = 25; } diff --git a/tensorflow/contrib/lite/toco/toco_port.cc b/tensorflow/contrib/lite/toco/toco_port.cc index 3a5911c28dc5462b5d3747f6af6aa82026a23466..de76fd4032d24eff8a6c2fd0c16a911b9c00186b 100644 --- a/tensorflow/contrib/lite/toco/toco_port.cc +++ b/tensorflow/contrib/lite/toco/toco_port.cc @@ -16,6 +16,8 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/toco_port.h" #include "tensorflow/contrib/lite/toco/toco_types.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" #if defined(__ANDROID__) && defined(__ARM_ARCH_7A__) @@ -61,8 +63,12 @@ void CheckInitGoogleIsDone(const char* message) { namespace file { // Conversion to our wrapper Status. -Status ToStatus(const ::util::Status& uts) { - return Status(uts.ok(), uts.error_message()); +tensorflow::Status ToStatus(const ::util::Status& uts) { + if (!uts.ok()) { + return tensorflow::Status(tensorflow::errors::Code(uts.error_code()), + uts.error_message()); + } + return tensorflow::Status::OK(); } // Conversion to our wrapper Options. @@ -71,7 +77,7 @@ toco::port::file::Options ToOptions(const ::file::Options& options) { return Options(); } -Status Writable(const string& filename) { +tensorflow::Status Writable(const string& filename) { File* f = nullptr; const auto status = ::file::Open(filename, "w", &f, ::file::Defaults()); if (f) { @@ -80,22 +86,24 @@ Status Writable(const string& filename) { return ToStatus(status); } -Status Readable(const string& filename, const file::Options& options) { +tensorflow::Status Readable(const string& filename, + const file::Options& options) { return ToStatus(::file::Readable(filename, ::file::Defaults())); } -Status Exists(const string& filename, const file::Options& options) { +tensorflow::Status Exists(const string& filename, + const file::Options& options) { auto status = ::file::Exists(filename, ::file::Defaults()); return ToStatus(status); } -Status GetContents(const string& filename, string* contents, - const file::Options& options) { +tensorflow::Status GetContents(const string& filename, string* contents, + const file::Options& options) { return ToStatus(::file::GetContents(filename, contents, ::file::Defaults())); } -Status SetContents(const string& filename, const string& contents, - const file::Options& options) { +tensorflow::Status SetContents(const string& filename, const string& contents, + const file::Options& options) { return ToStatus(::file::SetContents(filename, contents, ::file::Defaults())); } @@ -139,37 +147,42 @@ void CheckInitGoogleIsDone(const char* message) { namespace file { -Status Writable(const string& filename) { +tensorflow::Status Writable(const string& filename) { FILE* f = fopen(filename.c_str(), "w"); if (f) { fclose(f); - return Status(true, ""); + return tensorflow::Status::OK(); } - return Status(false, "not writable"); + return tensorflow::errors::NotFound("not writable"); } -Status Readable(const string& filename, const file::Options& options) { +tensorflow::Status Readable(const string& filename, + const file::Options& options) { FILE* f = fopen(filename.c_str(), "r"); if (f) { fclose(f); - return Status(true, ""); + return tensorflow::Status::OK(); } - return Status(false, "not readable"); + return tensorflow::errors::NotFound("not readable"); } -Status Exists(const string& filename, const file::Options& options) { +tensorflow::Status Exists(const string& filename, + const file::Options& options) { struct stat statbuf; int ret = stat(filename.c_str(), &statbuf); - return Status(ret != -1, ""); + if (ret == -1) { + return tensorflow::errors::NotFound("file doesn't exist"); + } + return tensorflow::Status::OK(); } -Status GetContents(const string& path, string* output, - const file::Options& options) { +tensorflow::Status GetContents(const string& path, string* output, + const file::Options& options) { output->clear(); int fd = open(path.c_str(), O_RDONLY); if (fd == -1) { - return Status(false, "can't open() for read"); + return tensorflow::errors::NotFound("can't open() for read"); } // Direct read, for speed. @@ -180,25 +193,25 @@ Status GetContents(const string& path, string* output, if (size == 0) { // Done. close(fd); - return Status(true, ""); + return tensorflow::Status::OK(); } else if (size == -1) { // Error. close(fd); - return Status(false, "error during read()"); + return tensorflow::errors::Internal("error during read()"); } else { output->append(buffer, size); } } CHECK(0); - return Status(false, "internal error"); + return tensorflow::errors::Internal("internal error"); } -Status SetContents(const string& filename, const string& contents, - const file::Options& options) { +tensorflow::Status SetContents(const string& filename, const string& contents, + const file::Options& options) { int fd = open(filename.c_str(), O_WRONLY | O_CREAT, 0664); if (fd == -1) { - return Status(false, "can't open() for write"); + return tensorflow::errors::Internal("can't open() for write"); } size_t i = 0; @@ -207,13 +220,13 @@ Status SetContents(const string& filename, const string& contents, ssize_t written = write(fd, &contents[i], to_write); if (written == -1) { close(fd); - return Status(false, "write() error"); + return tensorflow::errors::Internal("write() error"); } i += written; } close(fd); - return Status(true, ""); + return tensorflow::Status::OK(); } string JoinPath(const string& base, const string& filename) { diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index b00b1e89e856190787d2d40096c9a5321bd80604..17f82b9dd7dcc633aa204038b6d965f4eb6967bb 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -21,6 +21,7 @@ limitations under the License. #include #include "google/protobuf/text_format.h" #include "tensorflow/contrib/lite/toco/format_port.h" +#include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/platform.h" #if defined(PLATFORM_GOOGLE) @@ -54,26 +55,6 @@ double round(double x); namespace toco { namespace port { -class Status { - public: - static Status OK() { return Status(true, ""); } - - // Create a failed status with no message. - Status() {} - - Status(bool ok, const string& message) : ok_(ok), message_(message) {} - - void AppendMessage(const string& message) { message_ += message; } - - bool ok() const { return ok_; } - - const string error_message() const { return message_; } - - private: - bool ok_ = false; - string message_; -}; - void InitGoogle(const char* usage, int* argc, char*** argv, bool remove_flags); void CheckInitGoogleIsDone(const char* message); @@ -83,14 +64,14 @@ inline Options Defaults() { Options o; return o; } -Status GetContents(const string& filename, string* contents, - const Options& options); -Status SetContents(const string& filename, const string& contents, - const Options& options); +tensorflow::Status GetContents(const string& filename, string* contents, + const Options& options); +tensorflow::Status SetContents(const string& filename, const string& contents, + const Options& options); string JoinPath(const string& base, const string& filename); -Status Writable(const string& filename); -Status Readable(const string& filename, const Options& options); -Status Exists(const string& filename, const Options& options); +tensorflow::Status Writable(const string& filename); +tensorflow::Status Readable(const string& filename, const Options& options); +tensorflow::Status Exists(const string& filename, const Options& options); } // namespace file // Copy `src` string to `dest`. User must ensure `dest` has enough space. diff --git a/tensorflow/contrib/lite/toco/toco_saved_model.cc b/tensorflow/contrib/lite/toco/toco_saved_model.cc deleted file mode 100644 index 26f55a66c729894a990258080e397bb42ea98a13..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/toco/toco_saved_model.cc +++ /dev/null @@ -1,189 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include -#include - -#include "absl/strings/numbers.h" -#include "tensorflow/contrib/lite/toco/model_cmdline_flags.h" -#include "tensorflow/contrib/lite/toco/toco_saved_model.h" -#include "tensorflow/core/framework/attr_value.pb.h" -#include "tensorflow/core/framework/node_def.pb.h" -#include "tensorflow/core/framework/tensor_shape.pb.h" - -namespace toco { -namespace { - -// Loads a SavedModel from the directory specified in parsed_toco_flags. -// Returns a SavedModelBundle with the requested MetaGraphDef. -const tensorflow::SavedModelBundle* LoadSavedModel( - const ParsedTocoFlags& parsed_toco_flags) { - const string model_path = parsed_toco_flags.savedmodel_directory.value(); - QCHECK(tensorflow::MaybeSavedModelDirectory(model_path)) - << "Model is not saved in the supported SavedModel format.\n"; - - // Gets the tags identifying the MetaGraphDef from the command line arguments. - string tags_str; - if (parsed_toco_flags.savedmodel_tagset.specified()) { - tags_str = parsed_toco_flags.savedmodel_tagset.value(); - } else { - tags_str = parsed_toco_flags.savedmodel_tagset.default_value(); - } - auto tags = absl::StrSplit(tags_str, ','); - - // Loads MetaGraphDef. - auto* bundle = new tensorflow::SavedModelBundle; - TF_CHECK_OK(tensorflow::LoadSavedModel(tensorflow::SessionOptions(), - tensorflow::RunOptions(), model_path, - tags, bundle)) - << "Failed to load exported model from " << model_path - << ". Ensure the model contains the required tags '" << tags_str - << "'.\n"; - return bundle; -} - -// Returns the array name without the postfix. -// -// e.g. reduces "input:0" to "input". -string GetArrayName(const string& name) { - const std::vector& names = absl::StrSplit(name, ':'); - return names[0]; -} - -// Returns the list of array names without the postfix sorted alphabetically. -std::set GetSortedNames(const std::unordered_set& names) { - std::vector final_names; - final_names.reserve(names.size()); - for (const auto& name : names) { - final_names.push_back(GetArrayName(name)); - } - return std::set(final_names.begin(), final_names.end()); -} - -// Gets the final shape after replacing the first dimension with batch size, if -// it is undefined (containing the value -1). Returns whether the shape is -// valid. -bool ReplaceShapeBatchSize(const tensorflow::TensorShapeProto& shape, - int batch_size, - tensorflow::TensorShapeProto* final_shape) { - for (int idx = 0; idx < shape.dim().size(); ++idx) { - int64 final_dim = shape.dim()[idx].size(); - if (final_dim == -1) { - if (idx > 0) return false; - final_dim = batch_size; - } - final_shape->add_dim()->set_size(final_dim); - } - return true; -} - -// Updates the input arrays in ModelFlags to contain the shape of the array. -void ProcessInputShapes(const tensorflow::GraphDef& graph_def, int batch_size, - ModelFlags* model_flags) { - // Build map of input array names to input arrays. - std::unordered_map input_data_map; - for (auto& input : *model_flags->mutable_input_arrays()) { - input_data_map[input.name()] = &input; - } - - // Adds shapes to the input arrays if the shape is valid. - for (const tensorflow::NodeDef& node_def : graph_def.node()) { - if (input_data_map.find(node_def.name()) != input_data_map.end()) { - const auto shape_it = node_def.attr().find("shape"); - if (shape_it != node_def.attr().end()) { - tensorflow::TensorShapeProto final_shape; - bool is_valid = ReplaceShapeBatchSize(shape_it->second.shape(), - batch_size, &final_shape); - - if (is_valid) { - auto* shape = input_data_map.at(node_def.name())->mutable_shape(); - QCHECK_EQ(shape->dims_size(), 0) - << "The shape for the input '" << node_def.name() - << "' was previously defined. For clarity please define inputs " - << "via --input_arrays and input_shapes flags.\n"; - for (const auto& dim : final_shape.dim()) { - shape->add_dims(dim.size()); - } - } - } - } - } - - // Checks all input arrays have a shape. - for (auto const& input : model_flags->input_arrays()) { - QCHECK(input.shape().dims_size() > 0) - << "A valid input shape was not found for input '" << input.name() - << "'. Please define via --input_arrays and --input_shapes flags.\n"; - } -} - -} // namespace - -void ParseMetaData(const tensorflow::GraphDef& graph_def, - const std::unordered_set& inputs, - const std::unordered_set& outputs, - const ParsedTocoFlags& parsed_toco_flags, - const ParsedModelFlags& parsed_model_flags, - TocoFlags* toco_flags, ModelFlags* model_flags) { - if (!parsed_model_flags.input_arrays.specified()) { - const std::set sorted_inputs = GetSortedNames(inputs); - for (const auto& input_name : sorted_inputs) { - model_flags->add_input_arrays()->set_name(input_name); - } - } - - if (!parsed_model_flags.output_arrays.specified()) { - const std::set sorted_outputs = GetSortedNames(outputs); - for (const auto& output_name : sorted_outputs) { - model_flags->add_output_arrays(GetArrayName(output_name)); - } - } - - if (!parsed_model_flags.input_shapes.specified()) { - int batch_size = parsed_model_flags.batch_size.value(); - ProcessInputShapes(graph_def, batch_size, model_flags); - } - - if (!parsed_toco_flags.inference_type.specified()) { - toco_flags->set_inference_type(IODataType::FLOAT); - } -} - -// TODO(nupurgarg): Add top level tests. -void GetSavedModelContents(const ParsedTocoFlags& parsed_toco_flags, - const ParsedModelFlags& parsed_model_flags, - TocoFlags* toco_flags, ModelFlags* model_flags, - string* graph_def_contents) { - // Loads the MetaGraphDef within a SavedModelBundle. - auto bundle = LoadSavedModel(parsed_toco_flags); - - // Converts the MetaGraphDef to frozen GraphDef. - tensorflow::GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_CHECK_OK(tensorflow::FreezeSavedModel(*bundle, &frozen_graph_def, &inputs, - &outputs)); - - // Reads the frozen GraphDef into a string. - QCHECK(frozen_graph_def.SerializeToString(graph_def_contents)) - << "Unable to generate serialized GraphDef.\n"; - - // Process inputs and outputs and metadata within GraphDef. - const tensorflow::GraphDef graph_def = bundle->meta_graph_def.graph_def(); - ParseMetaData(graph_def, inputs, outputs, parsed_toco_flags, - parsed_model_flags, toco_flags, model_flags); -} - -} // namespace toco diff --git a/tensorflow/contrib/lite/toco/toco_saved_model.h b/tensorflow/contrib/lite/toco/toco_saved_model.h deleted file mode 100644 index 7a0fabd82d90131a3b2d28c757c08dcb0f9e3988..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/toco/toco_saved_model.h +++ /dev/null @@ -1,53 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CONTRIB_LITE_TOCO_TOCO_SAVED_MODEL_H_ -#define TENSORFLOW_CONTRIB_LITE_TOCO_TOCO_SAVED_MODEL_H_ - -#include -#include - -#include "tensorflow/cc/tools/freeze_saved_model.h" -#include "tensorflow/contrib/lite/toco/args.h" -#include "tensorflow/contrib/lite/toco/model_flags.pb.h" -#include "tensorflow/contrib/lite/toco/toco_flags.pb.h" -#include "tensorflow/contrib/lite/toco/types.pb.h" - -namespace toco { - -// Parses metadata into `toco_flags` and `model_flags`. -// -// Stores `inputs` as input_arrays and `outputs` as output_arrays in -// `model_flags`. Infers input_shapes from the GraphDef and stores it in -// `model_flags` as part of the input_arrays. Assumes inference_type is FLOAT -// and stores it in `toco_flags`. -void ParseMetaData(const tensorflow::GraphDef& graph_def, - const std::unordered_set& inputs, - const std::unordered_set& outputs, - const ParsedTocoFlags& parsed_toco_flags, - const ParsedModelFlags& parsed_model_flags, - TocoFlags* toco_flags, ModelFlags* model_flags); - -// Generates a frozen graph from the SavedModel in the directory specified in -// `toco_flags`. Reads frozen graph contents into `graph_def_contents`. Parses -// metadata relating to the GraphDef into `toco_flags` and `model_flags`. -void GetSavedModelContents(const ParsedTocoFlags& parsed_toco_flags, - const ParsedModelFlags& parsed_model_flags, - TocoFlags* toco_flags, ModelFlags* model_flags, - string* graph_def_contents); - -} // namespace toco - -#endif // TENSORFLOW_CONTRIB_LITE_TOCO_TOCO_SAVED_MODEL_H_ diff --git a/tensorflow/contrib/lite/toco/toco_saved_model_test.cc b/tensorflow/contrib/lite/toco/toco_saved_model_test.cc deleted file mode 100644 index 5e122afe65dc29abc85f142f4019aae5058ace51..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/toco/toco_saved_model_test.cc +++ /dev/null @@ -1,274 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/contrib/lite/toco/toco_saved_model.h" -#include "absl/strings/str_join.h" -#include "tensorflow/cc/framework/scope.h" -#include "tensorflow/cc/ops/standard_ops.h" -#include "tensorflow/contrib/lite/toco/model_cmdline_flags.h" -#include "tensorflow/contrib/lite/toco/toco_cmdline_flags.h" -#include "tensorflow/core/lib/core/status_test_util.h" - -#include -#include - -namespace toco { -namespace { - -using tensorflow::ops::Add; -using tensorflow::ops::Const; -using tensorflow::ops::FakeQuantWithMinMaxArgs; -using tensorflow::ops::Placeholder; - -class TocoSavedModelTest : public ::testing::Test { - protected: - // Calls functions to process cmdline arguments and calls ParseMetaData. - // ParseMetaData parses input_arrays, output_arrays, and gets metadata from - // SavedModel it is not defined in the cmdline arguments. - void ProcessGraphDefMetadata(const std::unordered_set& inputs, - const std::unordered_set& outputs, - const tensorflow::GraphDef& graph_def) { - ReadTocoFlagsFromCommandLineFlags(parsed_toco_flags_, &toco_flags_); - ReadModelFlagsFromCommandLineFlags(parsed_model_flags_, &model_flags_); - ParseMetaData(graph_def, inputs, outputs, parsed_toco_flags_, - parsed_model_flags_, &toco_flags_, &model_flags_); - } - - // Gets the GraphDef from the SavedModelBundle and processes metadata. - void ProcessSavedModelMetadata(const std::unordered_set& inputs, - const std::unordered_set& outputs) { - const tensorflow::GraphDef graph_def = bundle_.meta_graph_def.graph_def(); - ProcessGraphDefMetadata(inputs, outputs, graph_def); - } - - // Returns a GraphDef representing a simple float model with a single input. - tensorflow::GraphDef GetFloatGraphDef(const std::vector& shape) { - tensorflow::GraphDef graph_def; - tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); - - tensorflow::Output input = - Placeholder(scope.WithOpName("input"), tensorflow::DT_FLOAT, - Placeholder::Shape(tensorflow::PartialTensorShape(shape))); - tensorflow::Output zero = Const(scope.WithOpName("zero"), 0.0f, {}); - tensorflow::Output add = Add(scope.WithOpName("add"), input, zero); - - TF_EXPECT_OK(scope.ToGraphDef(&graph_def)); - return graph_def; - } - - // Returns a GraphDef representing a simple float model with two inputs. - tensorflow::GraphDef GetComplexFloatGraphDef() { - tensorflow::GraphDef graph_def; - tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); - - tensorflow::Output inputA = - Placeholder(scope.WithOpName("inputA"), tensorflow::DT_FLOAT, - Placeholder::Shape(tensorflow::TensorShape({1, 3, 3, 1}))); - tensorflow::Output inputB = - Placeholder(scope.WithOpName("inputB"), tensorflow::DT_FLOAT, - Placeholder::Shape(tensorflow::TensorShape({1, 3, 3, 1}))); - tensorflow::Output add = Add(scope.WithOpName("add"), inputB, inputA); - - TF_EXPECT_OK(scope.ToGraphDef(&graph_def)); - return graph_def; - } - - // Returns a GraphDef representing a simple quantized model. - tensorflow::GraphDef GetQuantizedGraphDef() { - tensorflow::GraphDef graph_def; - tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); - - tensorflow::Output input = - Placeholder(scope.WithOpName("input"), tensorflow::DT_FLOAT, - Placeholder::Shape(tensorflow::TensorShape({1, 3, 3, 1}))); - tensorflow::Output zero = Const(scope.WithOpName("zero"), 0.0f, {}); - tensorflow::Output fake_quant = - FakeQuantWithMinMaxArgs(scope.WithOpName("quant"), zero); - tensorflow::Output add = Add(scope.WithOpName("add"), input, fake_quant); - - TF_EXPECT_OK(scope.ToGraphDef(&graph_def)); - return graph_def; - } - - // Gets the values in the input_arrays flag. - std::vector GetInputArrays() { - std::vector actual; - for (const auto& input : model_flags_.input_arrays()) { - actual.push_back(input.name()); - } - return actual; - } - - // Gets the values in the output_arrays flag. - std::vector GetOutputArrays() { - std::vector actual(model_flags_.output_arrays().begin(), - model_flags_.output_arrays().end()); - return actual; - } - - // Gets the shape of the given input array. - string GetInputShape(const string& input_array) { - for (const auto& input : model_flags_.input_arrays()) { - if (input.name() == input_array) { - std::vector dims; - for (int idx = 0; idx < input.shape().dims_size(); ++idx) { - dims.push_back(std::to_string(input.shape().dims(idx))); - } - return absl::StrJoin(dims, ","); - } - } - return ""; - } - - tensorflow::SavedModelBundle bundle_; - ParsedTocoFlags parsed_toco_flags_; - ParsedModelFlags parsed_model_flags_; - TocoFlags toco_flags_; - ModelFlags model_flags_; -}; - -// Tests if input_arrays, output_arrays, inference_type, and output_arrays are -// added to ModelFlags if they are not specified in cmdline arguments. -// Tests if the default batch size replaces a -1 in the first dimension. -TEST_F(TocoSavedModelTest, NoCmdLine) { - tensorflow::GraphDef graph_def = GetFloatGraphDef({-1, 3, 3, 1}); - - ProcessGraphDefMetadata({"input"}, {"add"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"input"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add"})); - EXPECT_EQ(GetInputShape("input"), "1,3,3,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Tests if the order of input_arrays and output_arrays is deterministic when -// they are taken from the SavedModel. -TEST_F(TocoSavedModelTest, NoCmdLineMultipleArrays) { - tensorflow::GraphDef graph_def = GetComplexFloatGraphDef(); - - // Note: The model does not have two outputs. However, the function does not - // need an accurate output_array list. This is only meant to test order. - ProcessGraphDefMetadata({"inputB", "inputA"}, {"add", "invalid"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"inputA", "inputB"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add", "invalid"})); - EXPECT_EQ(GetInputShape("inputA"), "1,3,3,1"); - EXPECT_EQ(GetInputShape("inputB"), "1,3,3,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Tests if input_shapes is inferred when input_arrays is passed in via cmdline -// arguments. -TEST_F(TocoSavedModelTest, InputNameWithoutInputShape) { - parsed_model_flags_.input_arrays.bind()("input"); - tensorflow::GraphDef graph_def = GetFloatGraphDef({2, 3, 3, 1}); - - ProcessGraphDefMetadata({"not_used_input"}, {"add"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"input"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add"})); - EXPECT_EQ(GetInputShape("input"), "2,3,3,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Ensures a failure occurs when input_shapes is defined without input_arrays. -TEST_F(TocoSavedModelTest, InputShapeWithoutInputName) { - parsed_model_flags_.input_shapes.bind()("1,224,224,1:9,12"); - tensorflow::GraphDef graph_def = GetFloatGraphDef({1, 3, 3, 1}); - - EXPECT_DEATH(ProcessGraphDefMetadata({"input"}, {"add"}, graph_def), - "failed: input_shapes.size\\(\\) == " - "model_flags->input_arrays_size\\(\\)"); -} - -// Tests if the cmdline values of input_arrays, input_shapes are used when -// specified with an empty GraphDef. -TEST_F(TocoSavedModelTest, InputArraysCmdLine) { - parsed_model_flags_.input_arrays.bind()("inputA,inputB"); - parsed_model_flags_.input_shapes.bind()("1,224,224,1:9,12"); - - ProcessSavedModelMetadata({"input0", "input1"}, {"output0", "output1"}); - EXPECT_EQ(GetInputArrays(), std::vector({"inputA", "inputB"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"output0", "output1"})); - EXPECT_EQ(GetInputShape("inputA"), "1,224,224,1"); - EXPECT_EQ(GetInputShape("inputB"), "9,12"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Tests if the cmdline values of input_arrays, input_shapes are used when -// specified even if values exist within the GraphDef. -TEST_F(TocoSavedModelTest, InputArraysCmdLineWithGraphDef) { - parsed_model_flags_.input_arrays.bind()("inputA"); - parsed_model_flags_.input_shapes.bind()("1,224,224,1"); - tensorflow::GraphDef graph_def = GetFloatGraphDef({1, 3, 3, 1}); - - ProcessGraphDefMetadata({"inputA"}, {"add"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"inputA"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add"})); - EXPECT_EQ(GetInputShape("inputA"), "1,224,224,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Tests if the cmdline values of input_arrays, input_shapes, inference_type, -// and output_arrays are used when specified with an empty GraphDef. -TEST_F(TocoSavedModelTest, AllParamsCmdLine) { - parsed_model_flags_.input_arrays.bind()("inputA,inputB"); - parsed_model_flags_.output_arrays.bind()("outputA,outputB"); - parsed_model_flags_.input_shapes.bind()("1,224,224,1:9,12"); - parsed_toco_flags_.inference_type.bind()("FLOAT"); - - ProcessSavedModelMetadata({"input0", "input1"}, {"output0", "output1"}); - EXPECT_EQ(GetInputArrays(), std::vector({"inputA", "inputB"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"outputA", "outputB"})); - EXPECT_EQ(GetInputShape("inputA"), "1,224,224,1"); - EXPECT_EQ(GetInputShape("inputB"), "9,12"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Tests if a quantized graph gives the correct values assuming type is passed -// in via command line. -TEST_F(TocoSavedModelTest, QuantizedNoCmdLine) { - parsed_toco_flags_.inference_type.bind()("QUANTIZED_UINT8"); - tensorflow::GraphDef graph_def = GetQuantizedGraphDef(); - - ProcessGraphDefMetadata({"input"}, {"add"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"input"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add"})); - EXPECT_EQ(GetInputShape("input"), "1,3,3,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::QUANTIZED_UINT8); -} - -// Tests if the provided batch size replaces a -1 in the first dimension of -// input shape. -TEST_F(TocoSavedModelTest, MissingShapeParameterValid) { - parsed_model_flags_.batch_size.bind()(3); - tensorflow::GraphDef graph_def = GetFloatGraphDef({-1, 3, 3, 1}); - - ProcessGraphDefMetadata({"input"}, {"add"}, graph_def); - EXPECT_EQ(GetInputArrays(), std::vector({"input"})); - EXPECT_EQ(GetOutputArrays(), std::vector({"add"})); - EXPECT_EQ(GetInputShape("input"), "3,3,3,1"); - EXPECT_EQ(toco_flags_.inference_type(), IODataType::FLOAT); -} - -// Ensures a failure occurs if there is a -1 in a dimension aside from the first -// position of input shape. -TEST_F(TocoSavedModelTest, MissingShapeParameterInvalid) { - parsed_model_flags_.batch_size.bind()(3); - tensorflow::GraphDef graph_def = GetFloatGraphDef({1, -1, 3, 1}); - - EXPECT_DEATH(ProcessGraphDefMetadata({"input"}, {"add"}, graph_def), - "A valid input shape was not found for input 'input'."); -} - -} // namespace -} // namespace toco diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index 1fe76f8163cdf23b27f8baaf2d9c6d99b1aa3747..fc1636831b266b6aa426c564a0c1c7ca99bc0ff1 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -34,11 +34,11 @@ limitations under the License. namespace toco { namespace { -// CHECK-fails if the model contains a kTensorFlowUnsupported operation. +// CHECK-fails if the model contains a kUnsupported operation. void CheckUnsupportedOperations(const Model& model) { std::set unsupported_ops; for (auto& op : model.operators) { - if (op->type == OperatorType::kTensorFlowUnsupported) { + if (op->type == OperatorType::kUnsupported) { unsupported_ops.insert( static_cast(op.get()) ->tensorflow_op); @@ -56,6 +56,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ConvertSqueezeToReshape); transformations->Add(new ConvertTrivialAddNToAdd); transformations->Add(new ConvertTrivialStackToReshape); + transformations->Add(new ConvertTrivialTileToConcat); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); transformations->Add(new ResolveReshapeAttributes); @@ -76,7 +77,9 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveTensorFlowMatMul); transformations->Add(new FuseBinaryIntoPrecedingAffine); transformations->Add(new FuseBinaryIntoFollowingAffine); + transformations->Add(new FuseBroadcastIntoFollowingBinary); transformations->Add(new MergeReshapeIntoPrecedingTranspose); + transformations->Add(new MoveBinaryOperatorBeforeReshape); transformations->Add(new ReorderElementwiseUnary); transformations->Add(new ReorderReshapeTranspose); transformations->Add(new ResolveBatchNormalization); @@ -94,7 +97,6 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveTensorFlowMerge); transformations->Add(new ResolveSqueezeAttributes); transformations->Add(new ResolveTensorFlowSwitch); - transformations->Add(new ResolveTensorFlowTile); transformations->Add(new ResolveTensorFlowConcat); transformations->Add(new ResolveMultiplyByZero); transformations->Add(new IdentifyDilatedConv); @@ -133,6 +135,8 @@ bool SupportsPreallocatedWorkspace(FileFormat format) { return (format == TFLITE); } +bool SupportsShuffledFCWeights(FileFormat format) { return format == TFLITE; } + bool IsRealValued(toco::ArrayDataType type) { // TODO(benoitjacob) - this is hardcoding that uint8 and int16 are only used // for quantized real-number values, and no other integer type is ever used @@ -334,6 +338,10 @@ void Transform(const TocoFlags& toco_flags, Model* model) { new RemoveFinalDequantizeOp, ensure_safe_for_int8_kernels, }); + if (SupportsShuffledFCWeights(output_format)) { + RunGraphTransformations(model, "shuffling of FC weights", + {new ShuffleFCWeights}); + } } else { GraphTransformationsSet dequantization_transformations{new Dequantize}; // Dequantize creates FakeQuant nodes. We may want to discard diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 5a82be39395c4505cf8ae893f531ab5f99fea417..01113506d0ebbf25c057ab0a50730a45eeef64a5 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -30,7 +30,7 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/dump_graphviz.h" #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/toco_graphviz_dump_options.h" -#include "tensorflow/contrib/lite/toco/toco_port.h" +#include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" namespace toco { @@ -338,23 +338,23 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Div) HANDLE_OPERATORTYPENAME_CASE(Tanh) HANDLE_OPERATORTYPENAME_CASE(Sin) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowAll) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowAssert) + HANDLE_OPERATORTYPENAME_CASE(All) + HANDLE_OPERATORTYPENAME_CASE(Assert) HANDLE_OPERATORTYPENAME_CASE(ExpandDims) HANDLE_OPERATORTYPENAME_CASE(Fill) HANDLE_OPERATORTYPENAME_CASE(FloorMod) HANDLE_OPERATORTYPENAME_CASE(FloorDiv) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowGreater) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowGreaterEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowIdentity) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowLess) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowLessEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMatMul) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMax) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMaximum) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMerge) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMin) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMinimum) + HANDLE_OPERATORTYPENAME_CASE(Greater) + HANDLE_OPERATORTYPENAME_CASE(GreaterEqual) + HANDLE_OPERATORTYPENAME_CASE(Identity) + HANDLE_OPERATORTYPENAME_CASE(Less) + HANDLE_OPERATORTYPENAME_CASE(LessEqual) + HANDLE_OPERATORTYPENAME_CASE(MatMul) + HANDLE_OPERATORTYPENAME_CASE(Max) // Reduction Max + HANDLE_OPERATORTYPENAME_CASE(Maximum) // Element-wise Maximum + HANDLE_OPERATORTYPENAME_CASE(Merge) + HANDLE_OPERATORTYPENAME_CASE(Min) // Reduction Min + HANDLE_OPERATORTYPENAME_CASE(Minimum) // Element-wise Minimum HANDLE_OPERATORTYPENAME_CASE(Neg) HANDLE_OPERATORTYPENAME_CASE(Pad) HANDLE_OPERATORTYPENAME_CASE(PadV2) @@ -362,22 +362,22 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Stack) HANDLE_OPERATORTYPENAME_CASE(Range) HANDLE_OPERATORTYPENAME_CASE(Rank) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowReshape) + HANDLE_OPERATORTYPENAME_CASE(Reshape) HANDLE_OPERATORTYPENAME_CASE(Squeeze) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowRsqrt) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowShape) + HANDLE_OPERATORTYPENAME_CASE(Rsqrt) + HANDLE_OPERATORTYPENAME_CASE(Shape) HANDLE_OPERATORTYPENAME_CASE(Slice) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSplit) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSqrt) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSquare) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSwitch) + HANDLE_OPERATORTYPENAME_CASE(Split) + HANDLE_OPERATORTYPENAME_CASE(Sqrt) + HANDLE_OPERATORTYPENAME_CASE(Square) + HANDLE_OPERATORTYPENAME_CASE(Switch) HANDLE_OPERATORTYPENAME_CASE(Sub) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSum) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowTile) + HANDLE_OPERATORTYPENAME_CASE(Sum) + HANDLE_OPERATORTYPENAME_CASE(Tile) HANDLE_OPERATORTYPENAME_CASE(Transpose) HANDLE_OPERATORTYPENAME_CASE(TransposeConv) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowConcat) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowConcatV2) + HANDLE_OPERATORTYPENAME_CASE(Concat) + HANDLE_OPERATORTYPENAME_CASE(ConcatV2) HANDLE_OPERATORTYPENAME_CASE(Cast) HANDLE_OPERATORTYPENAME_CASE(Floor) HANDLE_OPERATORTYPENAME_CASE(Gather) @@ -388,14 +388,15 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Svdf) HANDLE_OPERATORTYPENAME_CASE(ArgMax) HANDLE_OPERATORTYPENAME_CASE(TopK_V2) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowUnsupported) + HANDLE_OPERATORTYPENAME_CASE(Unsupported) HANDLE_OPERATORTYPENAME_CASE(Exp) HANDLE_OPERATORTYPENAME_CASE(DynamicPartition) HANDLE_OPERATORTYPENAME_CASE(DynamicStitch) HANDLE_OPERATORTYPENAME_CASE(Select) HANDLE_OPERATORTYPENAME_CASE(SparseToDense) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowNotEqual) + HANDLE_OPERATORTYPENAME_CASE(Equal) + HANDLE_OPERATORTYPENAME_CASE(NotEqual) + HANDLE_OPERATORTYPENAME_CASE(Pow) default: LOG(FATAL) << "Unhandled op type"; #undef HANDLE_OPERATORTYPENAME_CASE @@ -403,7 +404,7 @@ const char* OperatorTypeName(OperatorType type) { } string HelpfulOperatorTypeName(const Operator& op) { - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { return toco::port::StringF( "(Unsupported TensorFlow op: %s)", static_cast(op).tensorflow_op); @@ -413,16 +414,20 @@ string HelpfulOperatorTypeName(const Operator& op) { bool OperatorSupportsFusedActivation(OperatorType type) { switch (type) { - case OperatorType::kConcatenation: - case OperatorType::kFakeQuant: - case OperatorType::kGather: - case OperatorType::kSlice: - case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: - case OperatorType::kTensorFlowSplit: - return false; - default: + case OperatorType::kAdd: + case OperatorType::kAveragePool: + case OperatorType::kBatchNormalization: + case OperatorType::kConv: + case OperatorType::kDepthwiseConv: + case OperatorType::kDiv: + case OperatorType::kFullyConnected: + case OperatorType::kL2Pool: + case OperatorType::kMaxPool: + case OperatorType::kMul: + case OperatorType::kSub: return true; + default: + return false; } } @@ -442,8 +447,12 @@ void LogSummary(int log_level, const Model& model) { } void LogArray(int log_level, const Model& model, const string& name) { - const auto& array = model.GetArray(name); VLOG(log_level) << "Array: " << name; + if (!model.HasArray(name)) { + VLOG(log_level) << " DOES NOT EXIST"; + return; + } + const auto& array = model.GetArray(name); VLOG(log_level) << " Data type: " << ArrayDataTypeName(array.data_type); VLOG(log_level) << " Final type: " << ArrayDataTypeName(array.final_data_type); @@ -585,6 +594,13 @@ void UnextendShape(Shape* shape, int new_shape_size) { shape_dims.erase(shape_dims.begin(), shape_dims.begin() + size_reduction); } +bool IsValid(const Shape& shape) { + for (int i = 0; i < shape.dimensions_count(); ++i) { + if (shape.dims(i) < 1) return false; + } + return true; +} + void CheckShapeDimensions(const Shape& shape) { for (int i = 0; i < shape.dimensions_count(); ++i) { CHECK_GE(shape.dims()[i], 1) << "shape has dimension 0 at index << " << i @@ -1865,18 +1881,15 @@ void GetShuffleShape(AxesOrder input_axes_order, AxesOrder output_axes_order, output_axes_order == AxesOrder::kHWIO) { // 3210 <- 3210 // HWIO <- OHWI - (*shuffle)[0] = 1; - (*shuffle)[1] = 2; - (*shuffle)[2] = 3; - (*shuffle)[3] = 0; + *shuffle = {1, 2, 3, 0}; } else if (input_axes_order == AxesOrder::kHWIO && output_axes_order == AxesOrder::kOHWI) { // 3210 <- 3210 // OHWI <- HWIO - (*shuffle)[0] = 3; - (*shuffle)[1] = 0; - (*shuffle)[2] = 1; - (*shuffle)[3] = 2; + *shuffle = {3, 0, 1, 2}; + } else if (input_axes_order == AxesOrder::kOHWI && + output_axes_order == AxesOrder::kHWOI) { + *shuffle = {1, 2, 0, 3}; } else { LOG(FATAL) << "Bad shuffle"; } @@ -2022,6 +2035,8 @@ int AxesCount(AxesOrder axes_order) { return 4; case AxesOrder::kNHWC: return 4; + case AxesOrder::kHWOI: + return 4; default: LOG(FATAL) << "Bad AxesOrder"; return 0; @@ -2190,4 +2205,51 @@ void UseArraysExtraInfo(Model* model, bool quantize_output) { } } +void UndoWeightsShuffling(Model* model) { + for (const auto& op : model->operators) { + if (op->type != toco::OperatorType::kFullyConnected) { + continue; + } + const auto& fc_op = static_cast(*op); + if (fc_op.weights_format == FullyConnectedWeightsFormat::kDefault) { + continue; + } + const string& weights_name = fc_op.inputs[1]; + QCHECK_EQ(CountOpsWithInput(*model, weights_name), 1); + auto& weights_array = model->GetArray(weights_name); + QCHECK(weights_array.data_type == ArrayDataType::kUint8); + auto& weights_data = + weights_array.GetMutableBuffer().data; + const auto& weights_shape = weights_array.shape(); + QCHECK_EQ(weights_shape.dimensions_count(), 2); + const int rows = weights_shape.dims(0); + const int cols = weights_shape.dims(1); + QCHECK_EQ(rows % 4, 0); + QCHECK_EQ(cols % 16, 0); + CHECK_EQ(rows * cols, weights_data.size()); + // Compute the de-shuffled weights + std::vector deshuffled_data(weights_data.size()); + uint8* shuffled_data_ptr = weights_data.data(); + for (int r = 0; r < rows; r += 4) { + for (int c = 0; c < cols; c += 16) { + for (int i = 0; i < 4; i++) { + uint8* deshuffled_data_ptr = + deshuffled_data.data() + (r + i) * cols + c; + for (int j = 0; j < 16; j++) { + uint8 shuffled_val = *shuffled_data_ptr++; + // Deshuffling isn't only about deshuffling the storage layout, + // it's also about undoing the flipping of the sign bit, which is + // performed on the shuffled weights. + uint8 deshuffled_val = shuffled_val ^ 0x80; + *deshuffled_data_ptr++ = deshuffled_val; + } + } + } + } + CHECK_EQ(shuffled_data_ptr, weights_data.data() + rows * cols); + // Switch this FC op to using the deshuffled weights. + weights_data = std::move(deshuffled_data); + } +} + } // namespace toco diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 3b320e801349595396e573e225ffacf4c7607e52..5dbfa54fa0369676dce638aec171b409a468da9f 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -32,8 +32,9 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/runtime/types.h" #include "tensorflow/contrib/lite/toco/toco_flags.pb.h" -#include "tensorflow/contrib/lite/toco/toco_port.h" #include "tensorflow/contrib/lite/toco/types.pb.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" // TODO(aselle): Replace with using a container specific hash override instead. namespace std { @@ -100,6 +101,8 @@ std::vector>::iterator FindOp(Model& model, const char* OperatorTypeName(OperatorType type); string HelpfulOperatorTypeName(const Operator& op); +// Whether the operator can be fused with an activation function. Note that this +// will return false by default for new operators; fusing support is opt-in. bool OperatorSupportsFusedActivation(OperatorType type); void DumpGraphvizVideoFrame(const Model& model); @@ -112,7 +115,9 @@ void ExtendShape(Shape* shape, int new_shape_size); // TODO(b/36075966): Clean up when dims superseded by array shape. void UnextendShape(Shape* shape, int new_shape_size); -// Checks (using CHECK) that all dimensions of 'shape' are at least 1. +// Checks that all dimensions of 'shape' are at least 1. +bool IsValid(const Shape& shape); +// Same as above, but reports error using CHECK. void CheckShapeDimensions(const Shape& shape); // Given two shapes with potentially different dimensionality and dimension @@ -315,7 +320,7 @@ void UseArraysExtraInfo(Model* model, bool quantize_output); // doesn't have enough range to represent the sum of elements, an error is // returned. template -port::Status NumElements(const std::vector& shape, U* num_elements) { +tensorflow::Status NumElements(const std::vector& shape, U* num_elements) { static_assert( std::numeric_limits::max() <= std::numeric_limits::max(), "vector type exceed capabilities of NumElements"); @@ -326,19 +331,24 @@ port::Status NumElements(const std::vector& shape, U* num_elements) { // TensorFlow's shapes sometimes include -1 to represent an "unknown" // size but TOCO isn't able to create arrays of unknown sizes and will // crash in RequiredBufferSizeForShape(). - return port::Status(false, - "Tensor shape should not include negative values"); + return tensorflow::errors::InvalidArgument( + "Tensor shape should not include negative values"); } if (static_cast(dim) > std::numeric_limits::max() / *num_elements) { *num_elements = 0; - return port::Status(false, "Tensor shape is too large"); + return tensorflow::errors::InvalidArgument("Tensor shape is too large"); } *num_elements *= dim; } - return port::Status::OK(); + return tensorflow::Status::OK(); } +// A model file may have shuffled FC weights. +// When that happens, we want to de-shuffle them immediately on import, +// so that the rest of toco doesn't need to know about shuffled weights. +void UndoWeightsShuffling(Model* model); + } // namespace toco #endif // TENSORFLOW_CONTRIB_LITE_TOCO_TOOLING_UTIL_H_ diff --git a/tensorflow/contrib/lite/toco/tooling_util_test.cc b/tensorflow/contrib/lite/toco/tooling_util_test.cc index 87fd30db2cf54824a3c34ed875291d898f1a9e38..8609e5beddd200be4e5ebfe1fb2a79048e0e60ab 100644 --- a/tensorflow/contrib/lite/toco/tooling_util_test.cc +++ b/tensorflow/contrib/lite/toco/tooling_util_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/lib/core/status.h" namespace toco { @@ -99,7 +100,7 @@ static const char kLargeTensorMessage[] = "Tensor shape is too large"; TEST(NumElementsTest, Int) { int count; - port::Status status = port::Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); status = NumElements(std::vector{1024, 1024, 2047}, &count); EXPECT_TRUE(status.ok()); @@ -114,7 +115,7 @@ TEST(NumElementsTest, Int) { TEST(NumElementsTest, Int32) { int32_t count; - port::Status status = port::Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); status = NumElements(std::vector{1024, 1024, 2047}, &count); EXPECT_TRUE(status.ok()); @@ -129,7 +130,7 @@ TEST(NumElementsTest, Int32) { TEST(NumElementsTest, Int64) { int64_t count; - port::Status status = port::Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); status = NumElements(std::vector{16777216, 16777216, 32767}, &count); EXPECT_TRUE(status.ok()); @@ -144,7 +145,7 @@ TEST(NumElementsTest, Int64) { TEST(NumElementsTest, UnsignedInt32) { uint32_t count; - port::Status status = port::Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); status = NumElements(std::vector{1024, 2048, 2047}, &count); EXPECT_TRUE(status.ok()); @@ -159,7 +160,7 @@ TEST(NumElementsTest, UnsignedInt32) { TEST(NumElementsTest, UnsignedInt64) { uint64_t count; - port::Status status = port::Status::OK(); + tensorflow::Status status = tensorflow::Status::OK(); status = NumElements(std::vector{16777216, 16777216, 65535}, &count); @@ -174,4 +175,10 @@ TEST(NumElementsTest, UnsignedInt64) { EXPECT_EQ(status.error_message(), kLargeTensorMessage); } +TEST(FusedActivationTest, DefaultsToUnfused) { + EXPECT_TRUE(OperatorSupportsFusedActivation(OperatorType::kAdd)); + EXPECT_FALSE(OperatorSupportsFusedActivation(OperatorType::kNone)); + EXPECT_FALSE(OperatorSupportsFusedActivation(static_cast(255))); +} + } // namespace toco diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 5913847329eeae7373d0d21834dd37327e4068c4..a3df37358fac4d688ce7c513ed951cdd7e6bca1a 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -53,6 +53,7 @@ cc_test( ], tags = [ "tflite_not_portable_android", + "tflite_not_portable_ios", ], deps = [ ":gen_op_registration", diff --git a/tensorflow/contrib/lite/tools/benchmark/BUILD b/tensorflow/contrib/lite/tools/benchmark/BUILD index c5aa27d07c9a5cee0133b6ff99a8833a87d293d1..183a545295f690decec47f1c31aa473667408a3d 100644 --- a/tensorflow/contrib/lite/tools/benchmark/BUILD +++ b/tensorflow/contrib/lite/tools/benchmark/BUILD @@ -6,8 +6,9 @@ licenses(["notice"]) # Apache 2.0 load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") load("//tensorflow/contrib/lite:build_def.bzl", "tflite_linkopts") +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") -common_copts = ["-Wall"] +common_copts = ["-Wall"] + tflite_copts() cc_binary( name = "benchmark_model", @@ -16,13 +17,10 @@ cc_binary( "logging.h", ], copts = common_copts, - linkopts = select({ + linkopts = tflite_linkopts() + select({ "//tensorflow:android": [ - "-pie", - "-landroid", - "-lm", - "-z defs", - "-Wl,--exclude-libs,ALL", # Exclude syms in all libs from auto export + "-pie", # Android 5.0 and later supports only PIE + "-lm", # some builtin ops, e.g., tanh, need -lm ], "//conditions:default": [], }), @@ -36,7 +34,6 @@ cc_library( srcs = ["command_line_flags.cc"], hdrs = ["command_line_flags.h"], copts = common_copts, - visibility = ["//visibility:private"], ) cc_test( @@ -59,7 +56,6 @@ cc_library( ], hdrs = ["benchmark_tflite_model.h"], copts = common_copts, - linkopts = tflite_linkopts(), deps = [ ":benchmark_model_lib", "//tensorflow/contrib/lite:framework", @@ -70,6 +66,16 @@ cc_library( ], ) +cc_library( + name = "benchmark_params", + srcs = [ + "benchmark_params.cc", + "logging.h", + ], + hdrs = ["benchmark_params.h"], + copts = common_copts, +) + cc_library( name = "benchmark_model_lib", srcs = [ @@ -79,6 +85,7 @@ cc_library( hdrs = ["benchmark_model.h"], copts = common_copts, deps = [ + ":benchmark_params", ":command_line_flags", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", diff --git a/tensorflow/contrib/lite/tools/benchmark/README.md b/tensorflow/contrib/lite/tools/benchmark/README.md index e6f333aa5bb11449d5bf5d6c60cf77088649df8c..93769305bde210b58f3b2cb668a9d8c1ad0ce396 100644 --- a/tensorflow/contrib/lite/tools/benchmark/README.md +++ b/tensorflow/contrib/lite/tools/benchmark/README.md @@ -3,7 +3,38 @@ ## Description A simple C++ binary to benchmark a TFLite model and its individual operators, -both on desktop machines and on Android. +both on desktop machines and on Android. The binary takes a TFLite model, +generates random inputs and then repeatedly runs the model for specified number +of runs. Aggregrate latency statistics are reported after running the benchmark. + +The instructions below are for running the binary on Desktop and Android, +for iOS please use the +[iOS benchmark app] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios). + +## Parameters + +The binary takes the following required parameters: + +* `graph`: `string` \ + The path to the TFLite model file. +* `input_layer`: `string` \ + The name of the input layer, this is typically the first layer of the model. +* `input_layer_shape`: `string` \ + The shape of the input layer. This is a comma separated string of the shape + of tensor of input layer. + +and the following optional parameters: + +* `num_threads`: `int` (default=1) \ + The number of threads to use for running TFLite interpreter. +* `warmup_runs`: `int` (default=1) \ + The number of warmup runs to do before starting the benchmark. +* `run_delay`: `float` (default=-1.0) \ + The delay in seconds between subsequent benchmark runs. Non-positive values + mean use no delay. +* `use_nnapi`: `bool` (default=false) \ + Whether to use [Android NNAPI] (https://developer.android.com/ndk/guides/neuralnetworks/). + This API is available on recent Android devices. ## To build/install/run @@ -44,10 +75,8 @@ adb push mobilenet_quant_v1_224.tflite /data/local/tmp ``` adb shell /data/local/tmp/benchmark_model \ --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ - --input_layer="Placeholder" \ + --input_layer="input" \ --input_layer_shape="1,224,224,3" \ - --input_layer_type="uint8" \ - --output_layer="MobilenetV1/Predictions/Reshape_1" \ --num_threads=4 ``` @@ -66,14 +95,36 @@ bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model \ --graph=mobilenet_quant_v1_224.tflite \ --input_layer="Placeholder" \ --input_layer_shape="1,224,224,3" \ - --input_layer_type="uint8" \ - --output_layer="MobilenetV1/Predictions/Reshape_1" \ --num_threads=4 ``` The MobileNet graph used as an example here may be downloaded from https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip + +## Reducing variance between runs on Android. + +Most modern Android phones use [ARM big.LITTLE](https://en.wikipedia.org/wiki/ARM_big.LITTLE) +architecture where some cores are more power hungry but faster than other cores. +When running benchmarks on these phones there can be significant variance +between different runs of the benchmark. One way to reduce variance between runs +is to set the [CPU affinity](https://en.wikipedia.org/wiki/Processor_affinity) +before running the benchmark. On Android this can be done using the `taskset` +command. +E.g. for running the benchmark on big cores on Pixel 2 with a single thread one +can use the following command: + +``` +adb shell tasket f0 /data/local/tmp/benchmark_model \ + --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ + --input_layer="input" \ + --input_layer_shape="1,224,224,3" \ + --num_threads=1 +``` + +where `f0` is the affinity mask for big cores on Pixel 2. +Note: The affinity mask varies with the device. + ## Profiling model operators The benchmark model binary also allows you to profile operators and give execution times of each operator. To do this, compile the binary with a compiler flag that enables profiling to be compiled in. Pass **--copt=-DTFLITE_PROFILING_ENABLED** @@ -93,80 +144,66 @@ This compiles TFLite with profiling enabled, now you can run the benchmark binar ============================== Run Order ============================== [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 0.000 9.132 9.132 0.121% 0.121% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - DEPTHWISE_CONV_2D 9.135 3.280 3.280 0.043% 0.165% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6] - CONV_2D 12.419 6.877 6.877 0.091% 0.256% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - DEPTHWISE_CONV_2D 19.299 1.708 1.708 0.023% 0.278% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6] - CONV_2D 21.012 4.162 4.162 0.055% 0.334% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6] - DEPTHWISE_CONV_2D 25.177 3.520 3.520 0.047% 0.380% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6] - CONV_2D 28.701 10.218 10.218 0.136% 0.516% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - DEPTHWISE_CONV_2D 38.922 0.827 0.827 0.011% 0.527% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6] - CONV_2D 39.752 1.401 1.401 0.019% 0.545% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6] - DEPTHWISE_CONV_2D 41.156 1.290 1.290 0.017% 0.563% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6] - CONV_2D 42.448 5.995 5.995 0.080% 0.642% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - DEPTHWISE_CONV_2D 48.445 0.409 0.409 0.005% 0.647% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] - CONV_2D 48.856 6.167 6.167 0.082% 0.729% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] - DEPTHWISE_CONV_2D 55.026 0.629 0.629 0.008% 0.738% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] - CONV_2D 55.656 6.464 6.464 0.086% 0.823% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - DEPTHWISE_CONV_2D 62.124 0.647 0.647 0.009% 0.832% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] - CONV_2D 62.774 14.666 14.666 0.195% 1.026% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - DEPTHWISE_CONV_2D 77.444 0.635 0.635 0.008% 1.035% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] - CONV_2D 78.081 7.186 7.186 0.095% 1.130% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - DEPTHWISE_CONV_2D 85.270 0.646 0.646 0.009% 1.139% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6] - CONV_2D 85.918 9.529 9.529 0.126% 1.265% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - DEPTHWISE_CONV_2D 95.451 0.628 0.628 0.008% 1.273% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6] - CONV_2D 96.081 2.077 2.077 0.028% 1.301% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] - DEPTHWISE_CONV_2D 98.162 0.168 0.168 0.002% 1.303% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6] - CONV_2D 98.332 1.007 1.007 0.013% 1.317% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6] - DEPTHWISE_CONV_2D 99.342 0.288 0.288 0.004% 1.320% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6] - CONV_2D 99.632 8.197 8.197 0.109% 1.429% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - AVERAGE_POOL_2D 107.832 0.045 0.045 0.001% 1.430% 0.000 0 [MobilenetV1/Logits/AvgPool_1a/AvgPool] - CONV_2D 107.878 0.325 0.325 0.004% 1.434% 0.000 0 [MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd] - RESHAPE 108.206 0.003 0.003 0.000% 1.434% 0.000 0 [MobilenetV1/Predictions/Reshape] - SOFTMAX 108.211 0.038 0.038 0.001% 1.434% 0.000 0 [MobilenetV1/Predictions/Softmax] + CONV_2D 0.000 4.269 4.269 0.107% 0.107% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] + DEPTHWISE_CONV_2D 4.270 2.150 2.150 0.054% 0.161% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6] + CONV_2D 6.421 6.107 6.107 0.153% 0.314% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] + DEPTHWISE_CONV_2D 12.528 1.366 1.366 0.034% 0.348% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6] + CONV_2D 13.895 4.195 4.195 0.105% 0.454% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6] + DEPTHWISE_CONV_2D 18.091 1.260 1.260 0.032% 0.485% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6] + CONV_2D 19.352 6.652 6.652 0.167% 0.652% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] + DEPTHWISE_CONV_2D 26.005 0.698 0.698 0.018% 0.670% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6] + CONV_2D 26.703 3.344 3.344 0.084% 0.754% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6] + DEPTHWISE_CONV_2D 30.047 0.646 0.646 0.016% 0.770% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6] + CONV_2D 30.694 5.800 5.800 0.145% 0.915% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] + DEPTHWISE_CONV_2D 36.495 0.331 0.331 0.008% 0.924% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] + CONV_2D 36.826 2.838 2.838 0.071% 0.995% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] + DEPTHWISE_CONV_2D 39.665 0.439 0.439 0.011% 1.006% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] + CONV_2D 40.105 5.293 5.293 0.133% 1.139% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] + DEPTHWISE_CONV_2D 45.399 0.352 0.352 0.009% 1.147% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] + CONV_2D 45.752 5.322 5.322 0.133% 1.281% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] + DEPTHWISE_CONV_2D 51.075 0.357 0.357 0.009% 1.290% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] + CONV_2D 51.432 5.693 5.693 0.143% 1.433% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] + DEPTHWISE_CONV_2D 57.126 0.366 0.366 0.009% 1.442% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6] + CONV_2D 57.493 5.472 5.472 0.137% 1.579% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] + DEPTHWISE_CONV_2D 62.966 0.364 0.364 0.009% 1.588% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6] + CONV_2D 63.330 5.404 5.404 0.136% 1.724% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] + DEPTHWISE_CONV_2D 68.735 0.155 0.155 0.004% 1.728% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6] + CONV_2D 68.891 2.970 2.970 0.074% 1.802% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6] + DEPTHWISE_CONV_2D 71.862 0.206 0.206 0.005% 1.807% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6] + CONV_2D 72.069 5.888 5.888 0.148% 1.955% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] + AVERAGE_POOL_2D 77.958 0.036 0.036 0.001% 1.956% 0.000 0 [MobilenetV1/Logits/AvgPool_1a/AvgPool] + CONV_2D 77.994 1.445 1.445 0.036% 1.992% 0.000 0 [MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd] + RESHAPE 79.440 0.002 0.002 0.000% 1.992% 0.000 0 [MobilenetV1/Predictions/Reshape] + SOFTMAX 79.443 0.029 0.029 0.001% 1.993% 0.000 0 [MobilenetV1/Predictions/Softmax] ============================== Top by Computation Time ============================== [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 62.774 14.666 14.666 0.195% 0.195% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - CONV_2D 28.701 10.218 10.218 0.136% 0.330% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - CONV_2D 85.918 9.529 9.529 0.126% 0.456% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - CONV_2D 0.000 9.132 9.132 0.121% 0.578% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - CONV_2D 99.632 8.197 8.197 0.109% 0.686% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - CONV_2D 78.081 7.186 7.186 0.095% 0.782% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - CONV_2D 12.419 6.877 6.877 0.091% 0.873% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - CONV_2D 55.656 6.464 6.464 0.086% 0.958% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - CONV_2D 48.856 6.167 6.167 0.082% 1.040% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] - CONV_2D 42.448 5.995 5.995 0.080% 1.120% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - -============================== Top by Memory Use ============================== - [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - SOFTMAX 108.211 0.038 0.038 0.001% 0.001% 0.000 0 [MobilenetV1/Predictions/Softmax] - RESHAPE 108.206 0.003 0.003 0.000% 0.001% 0.000 0 [MobilenetV1/Predictions/Reshape] - CONV_2D 78.081 7.186 7.186 0.095% 0.096% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - DEPTHWISE_CONV_2D 77.444 0.635 0.635 0.008% 0.104% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] - CONV_2D 62.774 14.666 14.666 0.195% 0.299% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - DEPTHWISE_CONV_2D 62.124 0.647 0.647 0.009% 0.307% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] - CONV_2D 55.656 6.464 6.464 0.086% 0.393% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - DEPTHWISE_CONV_2D 55.026 0.629 0.629 0.008% 0.401% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] - CONV_2D 48.856 6.167 6.167 0.082% 0.483% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] - DEPTHWISE_CONV_2D 48.445 0.409 0.409 0.005% 0.489% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] + CONV_2D 19.352 6.652 6.652 0.167% 0.167% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] + CONV_2D 6.421 6.107 6.107 0.153% 0.320% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] + CONV_2D 72.069 5.888 5.888 0.148% 0.468% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] + CONV_2D 30.694 5.800 5.800 0.145% 0.613% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] + CONV_2D 51.432 5.693 5.693 0.143% 0.756% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] + CONV_2D 57.493 5.472 5.472 0.137% 0.893% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] + CONV_2D 63.330 5.404 5.404 0.136% 1.029% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] + CONV_2D 45.752 5.322 5.322 0.133% 1.162% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] + CONV_2D 40.105 5.293 5.293 0.133% 1.295% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] + CONV_2D 0.000 4.269 4.269 0.107% 1.402% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] Number of nodes executed: 31 ============================== Summary by node type ============================== [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called] - CONV_2D 15 1.861 86.679% 86.679% 0.000 0 - DEPTHWISE_CONV_2D 13 0.286 13.321% 100.000% 0.000 0 + CONV_2D 15 1.406 89.270% 89.270% 0.000 0 + DEPTHWISE_CONV_2D 13 0.169 10.730% 100.000% 0.000 0 SOFTMAX 1 0.000 0.000% 100.000% 0.000 0 RESHAPE 1 0.000 0.000% 100.000% 0.000 0 AVERAGE_POOL_2D 1 0.000 0.000% 100.000% 0.000 0 -Timings (microseconds): count=50 first=108164 curr=128308 min=102850 max=197072 avg=150805 std=24368 +Timings (microseconds): count=50 first=79449 curr=81350 min=77385 max=88213 avg=79732 std=1929 Memory (bytes): count=0 31 nodes observed -Average inference timings in us: Warmup: 135310, Init: 12123, no stats: 150988 - +Average inference timings in us: Warmup: 83235, Init: 38467, no stats: 79760.9 ``` diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc index a8a9a6112c1ec050be8d0bcfe9dc5f00df40d3ff..08648bcfe26365d180d984fde8f8e04b22eb45dd 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc @@ -48,6 +48,19 @@ namespace tflite { namespace benchmark { using tensorflow::Stat; +BenchmarkParams BenchmarkModel::DefaultParams() { + BenchmarkParams params; + params.AddParam("num_runs", BenchmarkParam::Create(50)); + params.AddParam("run_delay", BenchmarkParam::Create(-1.0f)); + params.AddParam("num_threads", BenchmarkParam::Create(1)); + params.AddParam("benchmark_name", BenchmarkParam::Create("")); + params.AddParam("output_prefix", BenchmarkParam::Create("")); + params.AddParam("warmup_runs", BenchmarkParam::Create(1)); + return params; +} + +BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {} + void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { auto inference_us = results.inference_time_us(); auto init_us = results.startup_latency_us(); @@ -60,24 +73,29 @@ void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { std::vector BenchmarkModel::GetFlags() { return { - Flag("num_runs", ¶ms_.num_runs, "number of runs"), - Flag("run_delay", ¶ms_.run_delay, "delay between runs in seconds"), - Flag("num_threads", ¶ms_.num_threads, "number of threads"), - Flag("benchmark_name", ¶ms_.benchmark_name, "benchmark name"), - Flag("output_prefix", ¶ms_.output_prefix, "benchmark output prefix"), - Flag("warmup_runs", ¶ms_.warmup_runs, - "how many runs to initialize model"), + CreateFlag("num_runs", ¶ms_, "number of runs"), + CreateFlag("run_delay", ¶ms_, "delay between runs in seconds"), + CreateFlag("num_threads", ¶ms_, "number of threads"), + CreateFlag("benchmark_name", ¶ms_, "benchmark name"), + CreateFlag("output_prefix", ¶ms_, + "benchmark output prefix"), + CreateFlag("warmup_runs", ¶ms_, + "how many runs to initialize model"), }; } void BenchmarkModel::LogFlags() { - TFLITE_LOG(INFO) << "Num runs: [" << params_.num_runs << "]"; - TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" << params_.run_delay + TFLITE_LOG(INFO) << "Num runs: [" << params_.Get("num_runs") << "]"; + TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" + << params_.Get("run_delay") << "]"; + TFLITE_LOG(INFO) << "Num threads: [" << params_.Get("num_threads") + << "]"; + TFLITE_LOG(INFO) << "Benchmark name: [" + << params_.Get("benchmark_name") << "]"; + TFLITE_LOG(INFO) << "Output prefix: [" + << params_.Get("output_prefix") << "]"; + TFLITE_LOG(INFO) << "Warmup runs: [" << params_.Get("warmup_runs") << "]"; - TFLITE_LOG(INFO) << "Num threads: [" << params_.num_threads << "]"; - TFLITE_LOG(INFO) << "Benchmark name: [" << params_.benchmark_name << "]"; - TFLITE_LOG(INFO) << "Output prefix: [" << params_.output_prefix << "]"; - TFLITE_LOG(INFO) << "Warmup runs: [" << params_.warmup_runs << "]"; } Stat BenchmarkModel::Run(int num_times, RunType run_type) { @@ -91,7 +109,7 @@ Stat BenchmarkModel::Run(int num_times, RunType run_type) { listeners_.OnSingleRunEnd(); run_stats.UpdateStat(end_us - start_us); - SleepForSeconds(params_.run_delay); + SleepForSeconds(params_.Get("run_delay")); } std::stringstream stream; @@ -117,8 +135,10 @@ void BenchmarkModel::Run(int argc, char **argv) { << "ms"; uint64_t input_bytes = ComputeInputBytes(); - Stat warmup_time_us = Run(params_.warmup_runs, WARMUP); - Stat inference_time_us = Run(params_.num_runs, REGULAR); + Stat warmup_time_us = + Run(params_.Get("warmup_runs"), WARMUP); + Stat inference_time_us = + Run(params_.Get("num_runs"), REGULAR); listeners_.OnBenchmarkEnd( {startup_latency_us, input_bytes, warmup_time_us, inference_time_us}); } diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h index d48f693693c2cee0cd2e2a6f2b4c590998feffb3..942e21f67a7f864f16b7b1b85b2599d5c872b5c7 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h @@ -23,6 +23,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_params.h" #include "tensorflow/contrib/lite/tools/benchmark/command_line_flags.h" #include "tensorflow/core/util/stats_calculator.h" @@ -63,17 +64,6 @@ class BenchmarkResults { tensorflow::Stat inference_time_us_; }; -struct BenchmarkParams { - BenchmarkParams() - : num_runs(50), warmup_runs(1), run_delay(-1.0), num_threads(1) {} - int num_runs; - int warmup_runs; - float run_delay; - int num_threads; - std::string benchmark_name; - std::string output_prefix; -}; - class BenchmarkListener { public: virtual void OnBenchmarkStart(const BenchmarkParams& params) {} @@ -130,12 +120,22 @@ class BenchmarkLoggingListener : public BenchmarkListener { void OnBenchmarkEnd(const BenchmarkResults& results) override; }; +template +Flag CreateFlag(const char* name, BenchmarkParams* params, + const std::string& usage) { + return Flag(name, [params, name](const T& val) { params->Set(name, val); }, + params->Get(name), usage); +} + // Benchmarks a model. // // Subclasses need to implement initialization and running of the model. // The results can be collected by adding BenchmarkListener(s). class BenchmarkModel { public: + static BenchmarkParams DefaultParams(); + BenchmarkModel(); + BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} virtual ~BenchmarkModel() {} bool ParseFlags(int argc, char** argv); virtual void Init() = 0; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc new file mode 100644 index 0000000000000000000000000000000000000000..1dcf580a9d4995e6cb3706d3562bc8a2f4670082 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_params.h" + +#include +#include +#include + +#include "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace tflite { +namespace benchmark { + +void BenchmarkParam::AssertHasSameType(BenchmarkParam::ParamType a, + BenchmarkParam::ParamType b) { + TFLITE_BENCHMARK_CHECK(a == b) << "Type mismatch while accessing parameter."; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_INT32; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_BOOL; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_FLOAT; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_STRING; +} + +void BenchmarkParams::AssertParamExists(const std::string& name) const { + TFLITE_BENCHMARK_CHECK(HasParam(name)) << name << " was not found."; +} + +} // namespace benchmark +} // namespace tflite diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h new file mode 100644 index 0000000000000000000000000000000000000000..33448dd1623577fdfda6316c588cc60ccbaa1994 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h @@ -0,0 +1,101 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ +#define TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace tflite { +namespace benchmark { + +template +class TypedBenchmarkParam; + +class BenchmarkParam { + protected: + enum class ParamType { TYPE_INT32, TYPE_FLOAT, TYPE_BOOL, TYPE_STRING }; + + public: + template + static std::unique_ptr Create(const T& default_value) { + return std::unique_ptr( + new TypedBenchmarkParam(default_value)); + } + + template + TypedBenchmarkParam* AsTyped() { + AssertHasSameType(GetValueType(), type_); + return static_cast*>(this); + } + virtual ~BenchmarkParam() {} + BenchmarkParam(ParamType type) : type_(type) {} + + private: + static void AssertHasSameType(ParamType a, ParamType b); + template + static ParamType GetValueType(); + + const ParamType type_; +}; + +template +class TypedBenchmarkParam : public BenchmarkParam { + public: + TypedBenchmarkParam(const T& value) + : BenchmarkParam(GetValueType()), value_(value) {} + void Set(const T& value) { value_ = value; } + + T Get() { return value_; } + + private: + T value_; +}; + +class BenchmarkParams { + public: + void AddParam(const std::string& name, + std::unique_ptr value) { + params_[name] = std::move(value); + } + + bool HasParam(const std::string& name) const { + return params_.find(name) != params_.end(); + } + + template + void Set(const std::string& name, const T& value) { + AssertParamExists(name); + params_.at(name)->AsTyped()->Set(value); + } + + template + T Get(const std::string& name) const { + AssertParamExists(name); + return params_.at(name)->AsTyped()->Get(); + } + + private: + void AssertParamExists(const std::string& name) const; + std::unordered_map> params_; +}; + +} // namespace benchmark +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc index 2e5b86627322c2c64b8ef665a91595174a5dd8dd..73affc26b034f415ae2a2101e0b558cdb94d8d5b 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc @@ -123,29 +123,11 @@ void FillRandomString(tflite::DynamicBuffer* buffer, } } -TfLiteType TfLiteTypeFromString(const string& input_layer_type) { - if (input_layer_type == "string") - return kTfLiteString; - else if (input_layer_type == "float") - return kTfLiteFloat32; - else if (input_layer_type == "uint8") - return kTfLiteUInt8; - else if (input_layer_type == "int32") - return kTfLiteInt32; - else if (input_layer_type == "int64") - return kTfLiteInt64; - else - return kTfLiteNoType; -} - bool PopulateInputLayerInfo( const string& names_string, const string& shapes_string, - const string& types_string, const string& values_string, std::vector* info) { std::vector names = Split(names_string, ','); std::vector shapes = Split(shapes_string, ':'); - std::vector types = Split(types_string, ','); - std::vector values = Split(values_string, ':'); if (names.size() != shapes.size()) { TFLITE_LOG(ERROR) << "The number of items in" @@ -158,17 +140,6 @@ bool PopulateInputLayerInfo( << " --input_layer_shape=1,224,224,4:1,20"; return false; } - if (names.size() != types.size()) { - TFLITE_LOG(ERROR) << "The number of items in" - << " --input_layer_type (" << types_string << ", with " - << types.size() << " items)" - << " must match the number of items in" - << " --input_layer (" << names_string << ", with " - << names.size() << " items)." - << " For example --input_layer=input1,input2" - << " --input_layer_type=float,int"; - return false; - } for (int i = 0; i < names.size(); ++i) { info->push_back(BenchmarkTfLiteModel::InputLayerInfo()); @@ -176,10 +147,6 @@ bool PopulateInputLayerInfo( input.name = names[i]; - input.data_type = TfLiteTypeFromString(types[i]); - TFLITE_BENCHMARK_CHECK(input.data_type != kTfLiteNoType) - << types[i] << " was an invalid type"; - TFLITE_BENCHMARK_CHECK(SplitAndParse(shapes[i], ',', &input.shape)) << "Incorrect size string specified: " << shapes[i]; for (int dim : input.shape) { @@ -190,30 +157,42 @@ bool PopulateInputLayerInfo( return false; } } - - if (i < values.size()) { - TFLITE_BENCHMARK_CHECK( - SplitAndParse(values[i], ',', &input.initialization_values)) - << "Incorrect initialization values string specified: " << values[i]; - } } return true; } +BenchmarkParams GetDefaultParams() { + BenchmarkParams default_params = BenchmarkModel::DefaultParams(); + default_params.AddParam("graph", BenchmarkParam::Create("")); + default_params.AddParam("input_layer", + BenchmarkParam::Create("")); + default_params.AddParam("input_layer_shape", + BenchmarkParam::Create("")); + default_params.AddParam("use_nnapi", BenchmarkParam::Create(false)); + return default_params; +} + } // namespace +BenchmarkTfLiteModel::BenchmarkTfLiteModel() + : BenchmarkModel(GetDefaultParams()) { + AddListener(&profiling_listener_); +} + +BenchmarkTfLiteModel::BenchmarkTfLiteModel(BenchmarkParams params) + : BenchmarkModel(std::move(params)) { + AddListener(&profiling_listener_); +} + std::vector BenchmarkTfLiteModel::GetFlags() { std::vector flags = BenchmarkTfLiteModel::BenchmarkModel::GetFlags(); std::vector specific_flags = { - Flag("graph", &graph, "graph file name"), - Flag("input_layer", &input_layer_string, "input layer names"), - Flag("input_layer_shape", &input_layer_shape_string, "input layer shape"), - Flag("input_layer_type", &input_layer_type_string, "input layer type"), - Flag("input_layer_values", &input_layer_values_string, - "values to initialize the inputs with"), - Flag("output_layer", &output_layer_string, "output layer name"), - Flag("use_nnapi", &use_nnapi, "use nnapi api")}; + CreateFlag("graph", ¶ms_, "graph file name"), + CreateFlag("input_layer", ¶ms_, "input layer names"), + CreateFlag("input_layer_shape", ¶ms_, + "input layer shape"), + CreateFlag("use_nnapi", ¶ms_, "use nnapi api")}; flags.insert(flags.end(), specific_flags.begin(), specific_flags.end()); return flags; @@ -221,23 +200,23 @@ std::vector BenchmarkTfLiteModel::GetFlags() { void BenchmarkTfLiteModel::LogFlags() { BenchmarkModel::LogFlags(); - TFLITE_LOG(INFO) << "Graph: [" << graph << "]"; - TFLITE_LOG(INFO) << "Input layers: [" << input_layer_string << "]"; - TFLITE_LOG(INFO) << "Input shapes: [" << input_layer_shape_string << "]"; - TFLITE_LOG(INFO) << "Input types: [" << input_layer_type_string << "]"; - TFLITE_LOG(INFO) << "Output layers: [" << output_layer_string << "]"; - TFLITE_LOG(INFO) << "Use nnapi : [" << use_nnapi << "]"; + TFLITE_LOG(INFO) << "Graph: [" << params_.Get("graph") << "]"; + TFLITE_LOG(INFO) << "Input layers: [" + << params_.Get("input_layer") << "]"; + TFLITE_LOG(INFO) << "Input shapes: [" + << params_.Get("input_layer_shape") << "]"; + TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get("use_nnapi") << "]"; } bool BenchmarkTfLiteModel::ValidateFlags() { - if (graph.empty()) { + if (params_.Get("graph").empty()) { TFLITE_LOG(ERROR) << "Please specify the name of your TF Lite input file with --graph"; return false; } - return PopulateInputLayerInfo(input_layer_string, input_layer_shape_string, - input_layer_type_string, - input_layer_values_string, &inputs); + return PopulateInputLayerInfo(params_.Get("input_layer"), + params_.Get("input_layer_shape"), + &inputs); } uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { @@ -251,6 +230,7 @@ uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { } void BenchmarkTfLiteModel::Init() { + std::string graph = params_.Get("graph"); model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); if (!model) { TFLITE_LOG(FATAL) << "Failed to mmap model " << graph; @@ -272,10 +252,14 @@ void BenchmarkTfLiteModel::Init() { } profiling_listener_.SetInterpreter(interpreter.get()); - if (params_.num_threads != -1) { - interpreter->SetNumThreads(params_.num_threads); + const int32_t num_threads = params_.Get("num_threads"); + + if (num_threads != -1) { + interpreter->SetNumThreads(num_threads); } + bool use_nnapi = params_.Get("use_nnapi"); + interpreter->UseNNAPI(use_nnapi); auto interpreter_inputs = interpreter->inputs(); @@ -293,8 +277,6 @@ void BenchmarkTfLiteModel::Init() { TFLITE_BENCHMARK_CHECK_EQ(t->name, input.name) << "Tensor # " << i << " is named " << t->name << " but flags call it " << input.name; - TFLITE_BENCHMARK_CHECK_EQ(t->type, input.data_type) - << "Could not match the type of input tensor " << t->name; } // Resize all non-string tensors. diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h index e70f6de1bf461f4e946ec83d8eea83ff4a15bfca..50cc3f24b3bd2f31555eac69ff208fa2480449b9 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h @@ -50,9 +50,8 @@ class ProfilingListener : public BenchmarkListener { // Benchmarks a TFLite model by running tflite interpreter. class BenchmarkTfLiteModel : public BenchmarkModel { public: - BenchmarkTfLiteModel() : use_nnapi(false) { - AddListener(&profiling_listener_); - } + BenchmarkTfLiteModel(); + BenchmarkTfLiteModel(BenchmarkParams params); std::vector GetFlags() override; void LogFlags() override; @@ -64,23 +63,13 @@ class BenchmarkTfLiteModel : public BenchmarkModel { struct InputLayerInfo { std::string name; - TfLiteType data_type; std::vector shape; - // Note that initialization_values is currently unused. - std::vector initialization_values; }; private: std::unique_ptr model; std::unique_ptr interpreter; - std::string graph; - std::string input_layer_string; - std::string input_layer_type_string; - std::string input_layer_shape_string; - std::string input_layer_values_string; - std::string output_layer_string; std::vector inputs; - bool use_nnapi; ProfilingListener profiling_listener_; }; diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc index 8195fc44beb288eec3c020791b47eefa01536fb7..ff818b9dcb5ee0b58b95c3dceae74083dbd4f0da 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include #include +#include #include namespace tflite { @@ -44,76 +45,79 @@ bool ParseFlag(const std::string& arg, const std::string& flag, } template -bool ParseFlag(const std::string& flag_value, T* value) { +bool ParseFlag(const std::string& flag_value, + const std::function& hook) { std::istringstream stream(flag_value); T read_value; stream >> read_value; if (!stream.eof() && !stream.good()) { return false; } - *value = read_value; + hook(read_value); return true; } -bool ParseBoolFlag(const std::string& flag_value, bool* value) { +bool ParseBoolFlag(const std::string& flag_value, + const std::function& hook) { if (flag_value != "true" && flag_value != "false") { return false; } - *value = (flag_value == "true"); + hook(flag_value == "true"); return true; } - -bool ParseStringFlag(const std::string& flag_value, std::string* value) { - *value = flag_value; - return true; -} - } // namespace -Flag::Flag(const char* name, int32_t* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + int32_t default_value, const std::string& usage_text) : name_(name), type_(TYPE_INT32), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, int64_t* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + int64_t default_value, const std::string& usage_text) : name_(name), type_(TYPE_INT64), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, float* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + float default_value, const std::string& usage_text) : name_(name), type_(TYPE_FLOAT), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, bool* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + bool default_value, const std::string& usage_text) : name_(name), type_(TYPE_BOOL), - value_hook_([dst](const std::string& flag_value) { - return ParseBoolFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseBoolFlag(flag_value, hook); }), - default_for_display_((*dst) ? "true" : "false"), + default_for_display_(default_value ? "true" : "false"), usage_text_(usage_text) {} -Flag::Flag(const char* name, std::string* dst, const std::string& usage_text) +Flag::Flag(const char* name, + const std::function& hook, + const std::string& default_value, const std::string& usage_text) : name_(name), type_(TYPE_STRING), - value_hook_([dst](const std::string& flag_value) { - return ParseStringFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + hook(flag_value); + return true; }), - default_for_display_(*dst), + default_for_display_(default_value), usage_text_(usage_text) {} bool Flag::Parse(const std::string& arg, bool* value_parsing_ok) const { diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h index 36f9e64767315a317338bc4d2db2ec2d43bee875..2e514ae3ead3b602b8217998ec09177b1e6a2376 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h @@ -33,10 +33,11 @@ namespace tflite { // int some_int = 10; // bool some_switch = false; // std::string some_name = "something"; +// // std::vector flag_list = { -// Flag("some_int", &some_int, "an integer that affects X"), -// Flag("some_switch", &some_switch, "a bool that affects Y"), -// Flag("some_name", &some_name, "a std::string that affects Z") +// Flag::CreateFlag("some_int", &some_int, "an integer that affects X"), +// Flag::CreateFlag("some_switch", &some_switch, "a bool that affects Y"), +// Flag::CreateFlag("some_name", &some_name, "a string that affects Z") // }; // // Get usage message before ParseFlags() to capture default values. // std::string usage = Flag::Usage(argv[0], flag_list); @@ -63,11 +64,21 @@ namespace tflite { // text, and a pointer to the corresponding variable. class Flag { public: - Flag(const char* name, int32_t* dst, const std::string& usage_text); - Flag(const char* name, int64_t* dst, const std::string& usage_text); - Flag(const char* name, bool* dst, const std::string& usage_text); - Flag(const char* name, std::string* dst, const std::string& usage_text); - Flag(const char* name, float* dst, const std::string& usage_text); + template + static Flag CreateFlag(const char* name, T* val, const char* usage) { + return Flag(name, [val](const T& v) { *val = v; }, *val, usage); + } + + Flag(const char* name, const std::function& hook, + int32_t default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + int64_t default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + float default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + bool default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + const std::string& default_value, const std::string& usage_text); private: friend class Flags; diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc index 620d61b027d30044ba9d449a8e308375f72ad76f..03da8051099899241fa5241374d754adb1aa93c6 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc @@ -34,15 +34,15 @@ TEST(CommandLineFlagsTest, BasicUsage) { "--some_name=somethingelse", "--some_float=42.0"}; int argc = 6; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - { - Flag("some_int32", &some_int32, "some int32"), - Flag("some_int64", &some_int64, "some int64"), - Flag("some_switch", &some_switch, "some switch"), - Flag("some_name", &some_name, "some name"), - Flag("some_float", &some_float, "some float"), - }); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + { + Flag::CreateFlag("some_int32", &some_int32, "some int32"), + Flag::CreateFlag("some_int64", &some_int64, "some int64"), + Flag::CreateFlag("some_switch", &some_switch, "some switch"), + Flag::CreateFlag("some_name", &some_name, "some name"), + Flag::CreateFlag("some_float", &some_float, "some float"), + }); EXPECT_EQ(true, parsed_ok); EXPECT_EQ(20, some_int32); @@ -57,9 +57,9 @@ TEST(CommandLineFlagsTest, EmptyStringFlag) { int argc = 2; std::string some_string = "invalid"; const char* argv_strings[] = {"program_name", "--some_string="}; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_string", &some_string, "some string")}); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + {Flag::CreateFlag("some_string", &some_string, "some string")}); EXPECT_EQ(true, parsed_ok); EXPECT_EQ(some_string, ""); @@ -72,7 +72,7 @@ TEST(CommandLineFlagsTest, BadIntValue) { const char* argv_strings[] = {"program_name", "--some_int=notanumber"}; bool parsed_ok = Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_int", &some_int, "some int")}); + {Flag::CreateFlag("some_int", &some_int, "some int")}); EXPECT_EQ(false, parsed_ok); EXPECT_EQ(10, some_int); @@ -83,9 +83,9 @@ TEST(CommandLineFlagsTest, BadBoolValue) { bool some_switch = false; int argc = 2; const char* argv_strings[] = {"program_name", "--some_switch=notabool"}; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_switch", &some_switch, "some switch")}); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + {Flag::CreateFlag("some_switch", &some_switch, "some switch")}); EXPECT_EQ(false, parsed_ok); EXPECT_EQ(false, some_switch); @@ -98,7 +98,7 @@ TEST(CommandLineFlagsTest, BadFloatValue) { const char* argv_strings[] = {"program_name", "--some_float=notanumber"}; bool parsed_ok = Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_float", &some_float, "some float")}); + {Flag::CreateFlag("some_float", &some_float, "some float")}); EXPECT_EQ(false, parsed_ok); EXPECT_NEAR(-23.23f, some_float, 1e-5f); @@ -136,10 +136,11 @@ TEST(CommandLineFlagsTest, UsageString) { // match against, and we don't want a flakey test. const std::string tool_name = "some_tool_name"; std::string usage = Flags::Usage( - tool_name + " ", {Flag("some_int", &some_int, "some int"), - Flag("some_int64", &some_int64, "some int64"), - Flag("some_switch", &some_switch, "some switch"), - Flag("some_name", &some_name, "some name")}); + tool_name + " ", + {Flag::CreateFlag("some_int", &some_int, "some int"), + Flag::CreateFlag("some_int64", &some_int64, "some int64"), + Flag::CreateFlag("some_switch", &some_switch, "some switch"), + Flag::CreateFlag("some_name", &some_name, "some name")}); // Match the usage message, being sloppy about whitespace. const char* expected_usage = " usage: some_tool_name \n" diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/README.md b/tensorflow/contrib/lite/tools/benchmark/ios/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c8d3307e29efaebdc5c309dc7e4262b54d64943f --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/README.md @@ -0,0 +1,43 @@ +# TFLite iOS benchmark app. + +## Description + +An iOS app to benchmark TFLite models. + +The app reads benchmark parameters from a JSON file named `benchmark_params.json` +in its `benchmark_data` directory. Any downloaded models for benchmarking should +also be placed in `benchmark_data` directory. + +The JSON file specifies the name of the model file and other benchmarking +parameters like inputs to the model, type of inputs, number of iterations, +number of threads. The default values in the JSON file are for the +Mobilenet_1.0_224 model +([paper](https://arxiv.org/pdf/1704.04861.pdf), +[tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)) + +## To build/install/run + +- Follow instructions at [iOS build for TFLite] +(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/ios.md) +to build TFLite. + +Running + +```bash +tensorflow/contrib/lite/build_ios_universal_lib.sh +``` +will also build `tensorflow/contrib/lite/gen/lib/benchmark-lib.a` . + +- Now copy the downloaded model file to `benchmark_data` directory. + +- Modify `benchmark_params.json` change the `input_layer`, `input_layer_shape` +and other benchmark parameters. + +- Change `Build Phases -> Copy Bundle Resources` and add the model file to the +resources that need to be copied. + +- Ensure that `Build Phases -> Link Binary With Library` contains the +`Accelerate framework` and `tensorflow/contrib/lite/gen/lib/benchmark-lib.a`. + +- Now try running the app. The app has a single button that runs the benchmark + on the model and displays results in a text view below. diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj new file mode 100644 index 0000000000000000000000000000000000000000..b908f733d49b56a6b41ebea4185f1fe8c11edc60 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj @@ -0,0 +1,381 @@ +// !$*UTF8*$! +{ + archiveVersion = 1; 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+ PRODUCT_BUNDLE_IDENTIFIER = example.TFLiteBenchmark; + PRODUCT_NAME = "$(TARGET_NAME)"; + TARGETED_DEVICE_FAMILY = "1,2"; + }; + name = Release; + }; +/* End XCBuildConfiguration section */ + +/* Begin XCConfigurationList section */ + 6FE93FF320D592D8008C9FE4 /* Build configuration list for PBXProject "TFLiteBenchmark" */ = { + isa = XCConfigurationList; + buildConfigurations = ( + 6FE9400C20D592DA008C9FE4 /* Debug */, + 6FE9400D20D592DA008C9FE4 /* Release */, + ); + defaultConfigurationIsVisible = 0; + defaultConfigurationName = Release; + }; + 6FE9400E20D592DA008C9FE4 /* Build configuration list for PBXNativeTarget "TFLiteBenchmark" */ = { + isa = XCConfigurationList; + buildConfigurations = ( + 6FE9400F20D592DA008C9FE4 /* Debug */, + 6FE9401020D592DA008C9FE4 /* Release */, + ); + defaultConfigurationIsVisible = 0; + defaultConfigurationName = Release; + }; +/* End XCConfigurationList section */ + }; + rootObject = 6FE93FF020D592D8008C9FE4 /* Project object */; +} diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.h b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.h new file mode 100644 index 0000000000000000000000000000000000000000..a55c03e00b5065e3b149c65f820f11d13c064d87 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.h @@ -0,0 +1,22 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import + +@interface AppDelegate : UIResponder + +@property(strong, nonatomic) UIWindow *window; + +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m new file mode 100644 index 0000000000000000000000000000000000000000..b1165940e9a29ac693d473a1c852b7b0681392fc --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m @@ -0,0 +1,27 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import "AppDelegate.h" + +@interface AppDelegate () + +@end + +@implementation AppDelegate +- (BOOL)application:(UIApplication *)application + didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { + return YES; +} +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json new file mode 100644 index 0000000000000000000000000000000000000000..d8db8d65fd79fd541b2b7eba75c7378af3448f9c --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json @@ -0,0 +1,98 @@ +{ + "images" : [ + { + "idiom" : "iphone", + "size" : "20x20", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "20x20", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "29x29", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "29x29", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "40x40", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "40x40", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "60x60", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "60x60", + "scale" : "3x" + }, + { + "idiom" : "ipad", + "size" : "20x20", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "20x20", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "29x29", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "29x29", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "40x40", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "40x40", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "76x76", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "76x76", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "83.5x83.5", + "scale" : "2x" + }, + { + "idiom" : "ios-marketing", + "size" : "1024x1024", + "scale" : "1x" + } + ], + "info" : { + "version" : 1, + "author" : "xcode" + } +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json new file mode 100644 index 0000000000000000000000000000000000000000..da4a164c918651cdd1e11dca5cc62c333f097601 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json @@ -0,0 +1,6 @@ +{ + "info" : { + "version" : 1, + "author" : "xcode" + } +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard new file mode 100644 index 0000000000000000000000000000000000000000..bfa36129419f8bd7ad73581cb9f07b8c6eec3fcf --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard new file mode 100644 index 0000000000000000000000000000000000000000..adcfe1ef4e708ea6f87c77f4a740b58e5027d3e5 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard @@ -0,0 +1,60 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h new file mode 100644 index 0000000000000000000000000000000000000000..ec6dea0546060881682c44ad451f4812a2f3d7ea --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h @@ -0,0 +1,21 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import + +@interface BenchmarkViewController : UIViewController +@property(weak, nonatomic) IBOutlet UITextView *resultsView; + +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm new file mode 100644 index 0000000000000000000000000000000000000000..356d5b0e17abc715de9b8f7a20ec7459f3468da1 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm @@ -0,0 +1,125 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import "BenchmarkViewController.h" +#import +#import +#import +#import +#import "tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h" +#import "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace { +NSString* FilePathForResourceName(NSString* filename) { + NSString* name = [filename stringByDeletingPathExtension]; + NSString* extension = [filename pathExtension]; + NSString* file_path = [[NSBundle mainBundle] pathForResource:name ofType:extension]; + if (file_path == NULL) { + TFLITE_LOG(FATAL) << "Couldn't find '" << [name UTF8String] << "." << [extension UTF8String] + << "' in bundle."; + } + return file_path; +} + +NSDictionary* ParseJson() { + NSString* params_json_path = FilePathForResourceName(@"benchmark_params.json"); + NSData* data = [NSData dataWithContentsOfFile:params_json_path]; + return [NSJSONSerialization JSONObjectWithData:data options:kNilOptions error:nil]; +} + +std::string FormatCommandLineParam(NSString* key, NSString* value) { + std::ostringstream stream; + stream << "--" << [key UTF8String] << "=" << [value UTF8String]; + return stream.str(); +} + +// Reads the |benchmark_params.json| to read command line parameters and returns them as a vector of +// strings. +void ReadCommandLineParameters(std::vector* params) { + NSDictionary* param_dict = ParseJson(); + for (NSString* key in param_dict) { + NSString* value = param_dict[key]; + if ([key isEqualToString:@"graph"]) { + value = FilePathForResourceName(value); + } + params->push_back(FormatCommandLineParam(key, value)); + } +} +std::vector StringVecToCharPtrVec(const std::vector& str_vec) { + std::vector charptr_vec; + std::transform(str_vec.begin(), str_vec.end(), std::back_inserter(charptr_vec), + [](const std::string& s) -> char* { return const_cast(s.c_str()); }); + return charptr_vec; +} + +class ResultsListener : public tflite::benchmark::BenchmarkListener { + public: + void OnBenchmarkEnd(const tflite::benchmark::BenchmarkResults& results) override; + std::string Results() { return results_; } + + private: + std::string results_; +}; + +void OutputMicrosecondsStatToStream(const tensorflow::Stat& time_us, + const std::string& prefix, std::ostringstream* stream) { + *stream << prefix << "Num runs: " << time_us.count() << "\n"; + + *stream << prefix << "Average: " << time_us.avg() / 1e3 << " ms\n"; + *stream << prefix << "Min: " << time_us.min() / 1e3 << " ms \n"; + *stream << prefix << "Max: " << time_us.max() / 1e3 << " ms \n"; + *stream << prefix << "Std deviation: " << time_us.std_deviation() / 1e3 << " ms\n"; +} + +void ResultsListener::OnBenchmarkEnd(const tflite::benchmark::BenchmarkResults& results) { + std::ostringstream stream; + const std::string prefix = " - "; + stream << "Startup latency: "; + stream << results.startup_latency_us() / 1e3 << " ms\n"; + stream << "\nInference:\n"; + OutputMicrosecondsStatToStream(results.inference_time_us(), prefix, &stream); + stream << "\nWarmup:\n"; + OutputMicrosecondsStatToStream(results.warmup_time_us(), prefix, &stream); + + results_ = stream.str(); +} + +std::string RunBenchmark() { + ResultsListener listener; + tflite::benchmark::BenchmarkTfLiteModel benchmark; + benchmark.AddListener(&listener); + // TODO(shashishekhar): Passing arguments like this is brittle, refactor the BenchmarkParams + // so that it contains arguments for BenchmarkTfLiteModel and set parameters using BenchmarkParams + std::vector command_line_params; + // Benchmark model expects first arg to be program name. + // push a string for name of program. + command_line_params.push_back("benchmark_tflite_model"); + ReadCommandLineParameters(&command_line_params); + std::vector argv = StringVecToCharPtrVec(command_line_params); + int argc = static_cast(argv.size()); + benchmark.Run(argc, argv.data()); + return listener.Results(); +} +} // namespace + +@interface BenchmarkViewController () +@end + +@implementation BenchmarkViewController +- (IBAction)onBenchmarkModel:(UIButton*)sender { + std::string results = RunBenchmark(); + [_resultsView setText:[NSString stringWithUTF8String:results.c_str()]]; +} +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist new file mode 100644 index 0000000000000000000000000000000000000000..96051cf08ff54b51f458eca6f0126dd99dfc51dc --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist @@ -0,0 +1,43 @@ + + + + + UILaunchStoryboardName + Main + CFBundleDevelopmentRegion + $(DEVELOPMENT_LANGUAGE) + CFBundleExecutable + $(EXECUTABLE_NAME) + CFBundleIdentifier + $(PRODUCT_BUNDLE_IDENTIFIER) + CFBundleInfoDictionaryVersion + 6.0 + CFBundleName + $(PRODUCT_NAME) + CFBundlePackageType + APPL + CFBundleShortVersionString + 1.0 + CFBundleVersion + 1 + LSRequiresIPhoneOS + + UIMainStoryboardFile + Main + UIRequiredDeviceCapabilities + + armv7 + + UISupportedInterfaceOrientations + + UIInterfaceOrientationPortrait + + UISupportedInterfaceOrientations~ipad + + UIInterfaceOrientationPortrait + UIInterfaceOrientationPortraitUpsideDown + UIInterfaceOrientationLandscapeLeft + UIInterfaceOrientationLandscapeRight + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json new file mode 100644 index 0000000000000000000000000000000000000000..d344a7a5efaef53500bc0f88d29ca7aecf59290a --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json @@ -0,0 +1,10 @@ +{ + "benchmark_name" : "mobile_net_benchmark", + "num_threads" : "4", + "num_runs" : "20", + "warmup_runs" : "1", + "graph" : "mobilenet_v1_1.0_224.tflite", + "input_layer" : "input", + "input_layer_shape" : "1,224,224,3", + "run_delay" : "-1" +} diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m new file mode 100644 index 0000000000000000000000000000000000000000..1e70b9cd1d82f320ec048642520dbc54dc0f7934 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m @@ -0,0 +1,23 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import +#import "AppDelegate.h" + +int main(int argc, char* argv[]) { + @autoreleasepool { + return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class])); + } +} diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py index 5a080cceabb55c307dcd1a457a9e30d24e0bd172..889accdd5aafae2931048ffdd26408cccb3c874e 100644 --- a/tensorflow/contrib/lookup/lookup_ops_test.py +++ b/tensorflow/contrib/lookup/lookup_ops_test.py @@ -1397,7 +1397,7 @@ class KeyValueTensorInitializerTest(test.TestCase): class IndexTableFromTensor(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_index_table_from_tensor_with_tensor_init(self): table = lookup.index_table_from_tensor( mapping=("brain", "salad", "surgery"), num_oov_buckets=1) @@ -1670,7 +1670,7 @@ class InitializeTableFromFileOpTest(test.TestCase): f.write("\n".join(values) + "\n") return vocabulary_file - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeStringTable(self): vocabulary_file = self._createVocabFile("one_column_1.txt") default_value = -1 diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index fc88f59e0948e1d3ed7cce9b809bf30ba280af12..fb9e77ae1bcfc3404f1fdf90ab2697a4e79a9836 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -30,6 +30,14 @@ arm64-v8a armeabi armeabi-v7a mips mips64 x86 x86_64 tegra)" exit 1 } +echo "********************************************************************" +echo "TensorFlow Lite is the recommended library for mobile and embedded machine learning inference." +echo "You are currently using an older version. Please switch over to TensorFlow Lite." +echo "" +echo "Link to the code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite" +echo "********************************************************************" +echo "" + if [[ -z "${NDK_ROOT}" ]]; then echo "NDK_ROOT should be set as an environment variable" 1>&2 exit 1 diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh index 0a458a27b3ac9b1a24b0f42de2f0166d515e8cd9..1d4677ef4bd1e8811998d1464e63902544153a49 100755 --- a/tensorflow/contrib/makefile/build_all_ios.sh +++ b/tensorflow/contrib/makefile/build_all_ios.sh @@ -31,6 +31,14 @@ usage() { exit 1 } +echo "********************************************************************" +echo "TensorFlow Lite is the recommended library for mobile and embedded machine learning inference." +echo "You are currently using an older version. Please switch over to TensorFlow Lite." +echo "" +echo "Link to the code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite" +echo "********************************************************************" +echo "" + DEFAULT_ARCH="i386 x86_64 armv7 armv7s arm64" while getopts "a:g:T" opt_name; do case "$opt_name" in diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt index 89db9ee2794ddf0a99951dca327e74c5d9694d23..6e7423f85e3b66e2f40b25c0b83d0fcaa54817a9 100644 --- a/tensorflow/contrib/makefile/tf_op_files.txt +++ b/tensorflow/contrib/makefile/tf_op_files.txt @@ -92,6 +92,7 @@ tensorflow/core/kernels/reduction_ops_common.cc tensorflow/core/kernels/reduction_ops_any.cc tensorflow/core/kernels/reduction_ops_all.cc tensorflow/core/kernels/roll_op.cc +tensorflow/core/kernels/queue_op.cc tensorflow/core/kernels/queue_ops.cc tensorflow/core/kernels/queue_base.cc tensorflow/core/kernels/pooling_ops_common.cc diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD index 4f2c82ca23011667662c74507fcbd99bcde4c7c0..66cb493e5c5bb9b8645e87dc7f5b274d916f64fc 100644 --- a/tensorflow/contrib/metrics/BUILD +++ b/tensorflow/contrib/metrics/BUILD @@ -77,7 +77,31 @@ py_test( py_test( name = "metric_ops_test", srcs = ["python/ops/metric_ops_test.py"], - shard_count = 16, + shard_count = 30, + srcs_version = "PY2AND3", + tags = ["noasan"], # times out b/63678675 + deps = [ + ":metrics_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:data_flow_ops", + "//tensorflow/python:errors", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variables", + "//third_party/py/numpy", + ], +) + +py_test( + name = "metric_ops_large_test", + size = "large", + srcs = ["python/ops/metric_ops_large_test.py"], srcs_version = "PY2AND3", tags = ["noasan"], # times out b/63678675 deps = [ diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index a6be2084aae6bb05f958929b45977ed21b570603..b14202ff9ec38016f926ee37c8acbd2bbb4c6ef5 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -1064,7 +1064,7 @@ def streaming_auc(predictions, name=name) -def _compute_dynamic_auc(labels, predictions, curve='ROC'): +def _compute_dynamic_auc(labels, predictions, curve='ROC', weights=None): """Computes the apporixmate AUC by a Riemann sum with data-derived thresholds. Computes the area under the ROC or PR curve using each prediction as a @@ -1077,13 +1077,22 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): predictions: A 1-D `Tensor` of predictions whose values are `float64`. curve: The name of the curve to be computed, 'ROC' for the Receiving Operating Characteristic or 'PR' for the Precision-Recall curve. + weights: A 1-D `Tensor` of weights whose values are `float64`. Returns: A scalar `Tensor` containing the area-under-curve value for the input. """ - # Count the total number of positive and negative labels in the input. + # Compute the total weight and the total positive weight. size = array_ops.size(predictions) - total_positive = math_ops.cast(math_ops.reduce_sum(labels), dtypes.int32) + if weights is None: + weights = array_ops.ones_like(labels, dtype=dtypes.float64) + labels, predictions, weights = metrics_impl._remove_squeezable_dimensions( + labels, predictions, weights) + total_weight = math_ops.reduce_sum(weights) + total_positive = math_ops.reduce_sum( + array_ops.where( + math_ops.greater(labels, 0), weights, + array_ops.zeros_like(labels, dtype=dtypes.float64))) def continue_computing_dynamic_auc(): """Continues dynamic auc computation, entered if labels are not all equal. @@ -1091,9 +1100,11 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): Returns: A scalar `Tensor` containing the area-under-curve value. """ - # Sort the predictions descending, and the corresponding labels as well. + # Sort the predictions descending, keeping the same order for the + # corresponding labels and weights. ordered_predictions, indices = nn.top_k(predictions, k=size) ordered_labels = array_ops.gather(labels, indices) + ordered_weights = array_ops.gather(weights, indices) # Get the counts of the unique ordered predictions. _, _, counts = array_ops.unique_with_counts(ordered_predictions) @@ -1103,23 +1114,39 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): array_ops.pad(math_ops.cumsum(counts), paddings=[[1, 0]]), dtypes.int32) # Count the positives to the left of the split indices. - positives = math_ops.cast( - array_ops.pad(math_ops.cumsum(ordered_labels), paddings=[[1, 0]]), - dtypes.int32) - true_positives = array_ops.gather(positives, splits) + true_positives = array_ops.gather( + array_ops.pad( + math_ops.cumsum( + array_ops.where( + math_ops.greater(ordered_labels, 0), ordered_weights, + array_ops.zeros_like(ordered_labels, + dtype=dtypes.float64))), + paddings=[[1, 0]]), splits) if curve == 'ROC': - # Count the negatives to the left of every split point and the total - # number of negatives for computing the FPR. - false_positives = math_ops.subtract(splits, true_positives) - total_negative = size - total_positive + # Compute the weight of the negatives to the left of every split point and + # the total weight of the negatives number of negatives for computing the + # FPR. + false_positives = array_ops.gather( + array_ops.pad( + math_ops.cumsum( + array_ops.where( + math_ops.less(ordered_labels, 1), ordered_weights, + array_ops.zeros_like( + ordered_labels, dtype=dtypes.float64))), + paddings=[[1, 0]]), splits) + total_negative = total_weight - total_positive x_axis_values = math_ops.truediv(false_positives, total_negative) y_axis_values = math_ops.truediv(true_positives, total_positive) elif curve == 'PR': x_axis_values = math_ops.truediv(true_positives, total_positive) # For conformance, set precision to 1 when the number of positive # classifications is 0. + positives = array_ops.gather( + array_ops.pad(math_ops.cumsum(ordered_weights), paddings=[[1, 0]]), + splits) y_axis_values = array_ops.where( - math_ops.greater(splits, 0), math_ops.truediv(true_positives, splits), + math_ops.greater(splits, 0), + math_ops.truediv(true_positives, positives), array_ops.ones_like(true_positives, dtype=dtypes.float64)) # Calculate trapezoid areas. @@ -1133,7 +1160,7 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): return control_flow_ops.cond( math_ops.logical_or( math_ops.equal(total_positive, 0), math_ops.equal( - total_positive, size)), + total_positive, total_weight)), true_fn=lambda: array_ops.constant(0, dtypes.float64), false_fn=continue_computing_dynamic_auc) @@ -1143,7 +1170,8 @@ def streaming_dynamic_auc(labels, curve='ROC', metrics_collections=(), updates_collections=(), - name=None): + name=None, + weights=None): """Computes the apporixmate AUC by a Riemann sum with data-derived thresholds. USAGE NOTE: this approach requires storing all of the predictions and labels @@ -1168,6 +1196,8 @@ def streaming_dynamic_auc(labels, should be added to. name: An optional name for the variable_scope that contains the metric variables. + weights: A 'Tensor' of non-negative weights whose values are castable to + `float64`. Will be flattened into a 1-D `Tensor`. Returns: auc: A scalar `Tensor` containing the current area-under-curve value. @@ -1195,14 +1225,24 @@ def streaming_dynamic_auc(labels, check_ops.assert_less_equal( labels, array_ops.ones_like(labels, dtypes.int64), - message='labels must be 0 or 1, at least one is >1') + message='labels must be 0 or 1, at least one is >1'), ]): preds_accum, update_preds = streaming_concat( predictions, name='concat_preds') labels_accum, update_labels = streaming_concat( labels, name='concat_labels') - update_op = control_flow_ops.group(update_labels, update_preds) - auc = _compute_dynamic_auc(labels_accum, preds_accum, curve=curve) + if weights is not None: + weights = array_ops.reshape( + math_ops.cast(weights, dtypes.float64), [-1]) + weights_accum, update_weights = streaming_concat( + weights, name='concat_weights') + update_op = control_flow_ops.group(update_labels, update_preds, + update_weights) + else: + weights_accum = None + update_op = control_flow_ops.group(update_labels, update_preds) + auc = _compute_dynamic_auc( + labels_accum, preds_accum, curve=curve, weights=weights_accum) if updates_collections: ops.add_to_collections(updates_collections, update_op) if metrics_collections: diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py new file mode 100644 index 0000000000000000000000000000000000000000..7acfc383eb9a659a600752cf57b4978daa8a07bc --- /dev/null +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py @@ -0,0 +1,66 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Large tests for metric_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin +from tensorflow.contrib.metrics.python.ops import metric_ops +from tensorflow.python.framework import dtypes as dtypes_lib +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +class StreamingPrecisionRecallAtEqualThresholdsLargeTest(test.TestCase): + + def setUp(self): + np.random.seed(1) + ops.reset_default_graph() + + def testLargeCase(self): + shape = [32, 512, 256, 1] + predictions = random_ops.random_uniform( + shape, 0.0, 1.0, dtype=dtypes_lib.float32) + labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5) + + result, update_op = metric_ops.precision_recall_at_equal_thresholds( + labels=labels, predictions=predictions, num_thresholds=201) + # Run many updates, enough to cause highly inaccurate values if the + # code used float32 for accumulation. + num_updates = 71 + + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in xrange(num_updates): + sess.run(update_op) + + prdata = sess.run(result) + + # Since we use random values, we won't know the tp/fp/tn/fn values, but + # tp and fp at threshold 0 should be the total number of positive and + # negative labels, hence their sum should be total number of pixels. + expected_value = 1.0 * np.product(shape) * num_updates + got_value = prdata.tp[0] + prdata.fp[0] + # They should be at least within 1. + self.assertNear(got_value, expected_value, 1.0) + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index b13f08a37d9e856d56903324fc6e7cf1457bb191..a09fc4abd461323d67e914c70932688816fed764 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -2127,6 +2127,44 @@ class StreamingDynamicAUCTest(test.TestCase): sess.run(update_op) self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-5) + def testWithWeights(self): + batch_size = 10 + num_batches = 100 + labels = np.array([]) + predictions = np.array([]) + weights = np.array([]) + tf_labels = variables.Variable( + array_ops.ones(batch_size, dtypes_lib.int32), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.int32) + tf_predictions = variables.Variable( + array_ops.ones(batch_size), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.float32) + tf_weights = variables.Variable( + array_ops.ones(batch_size), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.float32) + auc, update_op = metrics.streaming_dynamic_auc(tf_labels, + tf_predictions, + weights=tf_weights) + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in xrange(num_batches): + new_labels = np.random.randint(0, 2, size=batch_size) + noise = np.random.uniform(-0.2, 0.2, size=batch_size) + new_predictions = 0.4 + 0.2 * new_labels + noise + new_weights = np.random.uniform(0.0, 3.0, size=batch_size) + labels = np.concatenate([labels, new_labels]) + predictions = np.concatenate([predictions, new_predictions]) + weights = np.concatenate([weights, new_weights]) + sess.run([tf_labels.assign(new_labels), + tf_predictions.assign(new_predictions), + tf_weights.assign(new_weights)]) + sess.run(update_op) + expected_auc = _np_auc(predictions, labels, weights) + self.assertAlmostEqual(expected_auc, auc.eval()) + class AucWithConfidenceIntervalsTest(test.TestCase): @@ -2391,34 +2429,6 @@ class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): for _ in range(3): self._testResultsEqual(initial_result, result) - def testLargeCase(self): - self.skipTest("Test consistently timing out") - shape = [32, 512, 256, 1] - predictions = random_ops.random_uniform( - shape, 0.0, 1.0, dtype=dtypes_lib.float32) - labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5) - - result, update_op = metric_ops.precision_recall_at_equal_thresholds( - labels=labels, predictions=predictions, num_thresholds=201) - # Run many updates, enough to cause highly inaccurate values if the - # code used float32 for accumulation. - num_updates = 71 - - with self.test_session() as sess: - sess.run(variables.local_variables_initializer()) - for _ in xrange(num_updates): - sess.run(update_op) - - prdata = sess.run(result) - - # Since we use random values, we won't know the tp/fp/tn/fn values, but - # tp and fp at threshold 0 should be the total number of positive and - # negative labels, hence their sum should be total number of pixels. - expected_value = 1.0 * np.product(shape) * num_updates - got_value = prdata.tp[0] + prdata.fp[0] - # They should be at least within 1. - self.assertNear(got_value, expected_value, 1.0) - def _testCase(self, predictions, labels, @@ -4727,199 +4737,204 @@ class StreamingSparseRecallTest(test.TestCase): self._test_sparse_recall_at_top_k( labels, top_k_predictions, expected=1.0 / 2) - def test_one_label_at_k1_weighted(self): + def _test_one_label_at_k1_weighted(self, labels): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], - [0, 0, 1, 0]]) - dense_labels = np.array([[3], [2]], dtype=np.int64) - for labels in (sparse_labels, dense_labels): - # Class 3: 1 label, 2 predictions, 1 correct. - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=NAN, class_id=3, weights=(0.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0,)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(2.0,)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(2.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=NAN, - class_id=3, - weights=(0.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=NAN, - class_id=3, - weights=(0.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=NAN, - class_id=3, - weights=(0.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=NAN, - class_id=3, - weights=(0.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=2.0 / 2, - class_id=3, - weights=(2.0, 3.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=2.0 / 2, - class_id=3, - weights=(2.0, 3.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=3.0 / 3, - class_id=3, - weights=(3.0, 2.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=3.0 / 3, - class_id=3, - weights=(3.0, 2.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=0.3 / 0.3, - class_id=3, - weights=(0.3, 0.6)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=0.3 / 0.3, - class_id=3, - weights=(0.3, 0.6)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=0.6 / 0.6, - class_id=3, - weights=(0.6, 0.3)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=0.6 / 0.6, - class_id=3, - weights=(0.6, 0.3)) + # Class 3: 1 label, 2 predictions, 1 correct. + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=NAN, class_id=3, weights=(0.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0,)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(2.0,)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(2.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=NAN, + class_id=3, + weights=(0.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=NAN, + class_id=3, + weights=(0.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=NAN, + class_id=3, + weights=(0.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=NAN, + class_id=3, + weights=(0.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=2.0 / 2, + class_id=3, + weights=(2.0, 3.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=2.0 / 2, + class_id=3, + weights=(2.0, 3.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=3.0 / 3, + class_id=3, + weights=(3.0, 2.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=3.0 / 3, + class_id=3, + weights=(3.0, 2.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=0.3 / 0.3, + class_id=3, + weights=(0.3, 0.6)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=0.3 / 0.3, + class_id=3, + weights=(0.3, 0.6)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=0.6 / 0.6, + class_id=3, + weights=(0.6, 0.3)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=0.6 / 0.6, + class_id=3, + weights=(0.6, 0.3)) - # All classes: 2 labels, 2 predictions, 1 correct. - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=NAN, weights=(0.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=NAN, weights=(0.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(1.0,)) + # All classes: 2 labels, 2 predictions, 1 correct. + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=NAN, weights=(0.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=NAN, weights=(0.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(1.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(2.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(2.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 1, weights=(1.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 1, weights=(1.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.0 / 1, weights=(0.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.0 / 1, weights=(0.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(1.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(1.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(1.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(1.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=2.0 / 5, weights=(2.0, 3.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=2.0 / 5, weights=(2.0, 3.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=3.0 / 5, weights=(3.0, 2.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=3.0 / 5, weights=(3.0, 2.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=3.0 / 5, weights=(3.0, 2.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=3.0 / 5, weights=(3.0, 2.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.3 / 0.9, weights=(0.3, 0.6)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.3 / 0.9, weights=(0.3, 0.6)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.3 / 0.9, weights=(0.3, 0.6)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.3 / 0.9, weights=(0.3, 0.6)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.6 / 0.9, weights=(0.6, 0.3)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.6 / 0.9, weights=(0.6, 0.3)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.6 / 0.9, weights=(0.6, 0.3)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.6 / 0.9, weights=(0.6, 0.3)) + + def test_one_label_at_k1_weighted_sparse_labels(self): + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) + self._test_one_label_at_k1_weighted(sparse_labels) + + def test_one_label_at_k1_weighted_dense_labels(self): + dense_labels = np.array([[3], [2]], dtype=np.int64) + self._test_one_label_at_k1_weighted(dense_labels) def test_three_labels_at_k5_nan(self): predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9], diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py index 480f5f6eaf493c5c87c27cc9f8e510ea9c085a72..1b0383d24c0c472b4875d15c3650e37dfd2439e1 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py @@ -34,7 +34,7 @@ def _GetExampleIter(inputs): class FixedLossScaleManagerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_basic(self): itr = _GetExampleIter([True] * 10 + [False] * 10) @@ -84,13 +84,13 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): actual_outputs.append(self.evaluate(lsm.get_loss_scale())) self.assertEqual(actual_outputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increase_every_n_steps(self): inputs = [True] * 6 expected_outputs = [1, 2, 2, 4, 4, 8] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_keep_increasing_until_capped(self): init_loss_scale = np.finfo(np.float32).max / 4 + 10 max_float = np.finfo(np.float32).max @@ -104,7 +104,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decrease_every_n_steps(self): inputs = [False] * 6 init_loss_scale = 1024 @@ -112,7 +112,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_keep_decreasing_until_one(self): inputs = [False] * 10 init_loss_scale = 16 @@ -120,19 +120,19 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_incr_bad_step_clear_good_step(self): inputs = [True, True, True, False, True] expected_outputs = [1, 2, 2, 2, 2] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_incr_good_step_does_not_clear_bad_step(self): inputs = [True, True, True, False, True, False] expected_outputs = [1, 2, 2, 2, 2, 1] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trigger_loss_scale_update_each_step(self): """Test when incr_every_n_step and decr_every_n_nan_or_inf is 1.""" init_loss_scale = 1 @@ -145,7 +145,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_alternating_good_and_bad_gradients_trigger_each_step(self): init_loss_scale = 1 incr_every_n_step = 1 @@ -156,7 +156,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_alternating_good_and_bad_gradients_trigger_incr_every_2steps(self): init_loss_scale = 32 incr_every_n_step = 2 @@ -167,7 +167,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_random_mix_good_and_bad_gradients(self): init_loss_scale = 4 inputs = [ diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py index e4e5ccc33472ad5a12bd8111fb1ff6ebbd6f45f9..ef34f7bf7bf3eba047b50ce8abf883b0ed741a63 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py @@ -26,26 +26,32 @@ from tensorflow.python.training import optimizer class LossScaleOptimizer(optimizer.Optimizer): + # TODO(jamesqin): move mixed precision training explanation to __init__ + # docstring. """An optimizer that applies loss scaling in backprop. - This class is useful for mixed precision training on GPUs (or other potential - accelerators), which is an approach to improve compute throughput without loss - of model quality. - - The commmon configuration of mixed precision models is the following: - * variables are kept in high precision (e.g. float32). - * computations are done in lower precision (e.g. float16). variables are - casted to lower precision before they're used. - * (in training), final gradients are casted back to variable precision and get - applied. - - Because computations happen in lower precision, gradients in the backprop pass - might underflow in the smaller dynamic range, causing a model to converge at a - suboptimal level. This optimizer multiplies the loss by a factor before - backprop starts to prevent underflow. Before gradients are applied, they are - casted to higher precision and down-scaled by the same factor, so - mathematically the variable updates are no different from regular - same-precision training. + This class is useful for "mixed precision training" on GPUs (or other + potential accelerators), an approach to improve compute throughput without + compromising model quality. + + The canonical way to perform mixed precision training is the following: + * Model variables are kept in high precision (e.g. float32). + * Computations are done in lower precision (e.g. float16), which enjoys + performance speedup by virtue of hardware support. Variables are casted to + lower precision before they're used. + * Final gradients are casted back to high precision dtype, then used to update + variables. + + The side-effect of performing computation in lower precision, is that it comes + with smaller numerical range. During backproping, small gradients might + underflow in the reduced numerical range, causing a model to converge at + suboptimal level. + + To prevent underflow, this optimizer multiplies the loss by a factor before + backprop starts. Consequently, the gradients are linearly scaled up by the + same factor, thus not falling into the underflow zone. After that, to perserve + the correctness of backprop, the gradients are down-scaled by the same factor, + casted to the (higher) variable precision, then applied on the variables. See [Nvidia's manual on mixed precision training]( https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py index dded61ccd58eb79b338d7264e8a057c9456c8695..9009df0eefec13146090ba5fc2096e71ba6eb89d 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py @@ -54,7 +54,7 @@ class LossScaleOptimizerTest(test.TestCase): opt = loss_scale_opt_fn(opt) return x, loss, opt - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_float16_underflow_without_loss_scale(self): lr = 1 init_val = 1. @@ -73,7 +73,7 @@ class LossScaleOptimizerTest(test.TestCase): rtol=0, atol=min(symbolic_update, 1e-6)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_float16_with_loss_scale(self): lr = 1. init_val = 1. @@ -95,7 +95,7 @@ class LossScaleOptimizerTest(test.TestCase): rtol=0, atol=min(expected_update, 1e-6)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_compute_gradients_with_loss_scale(self): lr = 1 init_val = 1. @@ -115,7 +115,7 @@ class LossScaleOptimizerTest(test.TestCase): # Gradients aren't applied. self.assertAllClose(init_val, self.evaluate(x), rtol=0, atol=1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_compute_gradients_without_loss_scale(self): lr = 1 init_val = 1. @@ -127,7 +127,7 @@ class LossScaleOptimizerTest(test.TestCase): g_v = self.evaluate(grads_and_vars[0][0]) self.assertAllClose(g_v, 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_apply_gradients(self): x = variable_scope.get_variable("x", initializer=1., dtype=dtypes.float32) @@ -155,7 +155,7 @@ class LossScaleOptimizerTest(test.TestCase): actual_output.append(self.evaluate(x)) self.assertAllClose(expected_output, actual_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_apply_gradients_loss_scale_is_updated(self): class SimpleLossScaleManager(lsm_lib.LossScaleManager): diff --git a/tensorflow/contrib/mpi_collectives/BUILD b/tensorflow/contrib/mpi_collectives/BUILD index a7be92a35e0d62a61f7923ac61bb2c1267d039c6..ecac06354d2ce796f2a6021cdf2370d7c30ccab7 100644 --- a/tensorflow/contrib/mpi_collectives/BUILD +++ b/tensorflow/contrib/mpi_collectives/BUILD @@ -52,6 +52,7 @@ tf_custom_op_library( deps = [ ":mpi_defines", ":mpi_message_proto_cc", + "//tensorflow/stream_executor:stream_executor_headers_lib", "//third_party/mpi", ], ) diff --git a/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc b/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc index ed22ee667f1d73b3f86f77e09bad9bfec7e46391..e4b0c2c6541836243347d2950686c60ef06d2bfc 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc +++ b/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc @@ -73,7 +73,7 @@ limitations under the License. */ template -using StatusOr = se::port::StatusOr; +using StatusOr = stream_executor::port::StatusOr; using CPUDevice = Eigen::ThreadPoolDevice; using GPUDevice = Eigen::GpuDevice; diff --git a/tensorflow/contrib/nccl/BUILD b/tensorflow/contrib/nccl/BUILD index 334e70318dd88185cecd93ebeb2587861b7999b9..62996d1fd83f46145e9a1b773b1be57e27903127 100644 --- a/tensorflow/contrib/nccl/BUILD +++ b/tensorflow/contrib/nccl/BUILD @@ -19,17 +19,18 @@ load("//tensorflow:tensorflow.bzl", "cuda_py_test") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load("//tensorflow:tensorflow.bzl", "tf_kernel_library") load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") +load("//tensorflow:tensorflow.bzl", "if_not_windows_cuda") tf_custom_op_library( name = "python/ops/_nccl_ops.so", srcs = [ "ops/nccl_ops.cc", ], - gpu_srcs = [ + gpu_srcs = if_not_windows_cuda([ "kernels/nccl_manager.cc", "kernels/nccl_manager.h", "kernels/nccl_ops.cc", - ], + ]), deps = if_cuda([ "@local_config_nccl//:nccl", "//tensorflow/core:gpu_headers_lib", @@ -97,18 +98,19 @@ tf_gen_op_wrapper_py( deps = [":nccl_ops_op_lib"], ) +# Test only nccl ops lib without dso to test behavior when NCCL lib is not +# installed. See nccl_dependency_test for more details. +# +# Users should use the public nccl_py lib that also adds the dso. tf_custom_op_py_library( - name = "nccl_py", + name = "nccl_ops_lib_without_dso", srcs = [ "__init__.py", "python/ops/nccl_ops.py", ], - dso = [":python/ops/_nccl_ops.so"], kernels = if_cuda([":nccl_kernels"]) + [ ":nccl_ops_op_lib", ], - srcs_version = "PY2AND3", - visibility = ["//visibility:public"], deps = [ ":nccl_ops", "//tensorflow/contrib/util:util_py", @@ -120,6 +122,15 @@ tf_custom_op_py_library( ], ) +tf_custom_op_py_library( + name = "nccl_py", + dso = [":python/ops/_nccl_ops.so"], + visibility = ["//visibility:public"], + deps = [ + ":nccl_ops_lib_without_dso", + ], +) + cuda_py_test( name = "nccl_ops_test", size = "small", @@ -141,3 +152,25 @@ cuda_py_test( "notap", ], ) + +cuda_py_test( + name = "nccl_dependency_test", + size = "small", + srcs = ["python/ops/nccl_dependency_test.py"], + additional_deps = [ + ":nccl_ops_lib_without_dso", + "//tensorflow/python:constant_op", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:util", + "//tensorflow/python:client_testlib", + "//tensorflow/python:platform_test", + ], + # Disable this test internally as static linking is used internally and only + # run for OSS to verify that NCCL is an optional dynamic dependency. + tags = [ + "manual", + "noguitar", + "notap", + ], +) diff --git a/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py b/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c766080dbee7c9a6f4383ef6fa8cade7bba158af --- /dev/null +++ b/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py @@ -0,0 +1,59 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Dependency test for nccl to test behavior when NCCL is not installed.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib import nccl +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect + + +class NcclDependencyTest(test.TestCase): + """Verifies that importing nccl ops lib does not fail even if NCCL is not + installed but nccl ops throws an exception on use if NCCL is not installed. + """ + + def test_nccl_ops(self): + """Tests behavior of nccl ops when NCCL is not installed.""" + + public_methods = [ + m[0] + for m in tf_inspect.getmembers(nccl, tf_inspect.isfunction) + if not m[0].startswith('_') + ] + for method_name in public_methods: + with ops.device('/device:CPU:0'): + tensor = constant_op.constant(1) + + if method_name == 'broadcast': + arg = tensor + else: + arg = [tensor] + + nccl_op = getattr(nccl, method_name) + with ops.device('/device:CPU:0'): + with self.assertRaisesRegexp(errors_impl.NotFoundError, + r'cannot open shared object file'): + nccl_op(arg) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/nccl/python/ops/nccl_ops.py b/tensorflow/contrib/nccl/python/ops/nccl_ops.py index 794372a1f4b0dcc41bcf0da611f5bc2ec9301973..fa597cf3efcf915311047f3a483772c45cc314fd 100644 --- a/tensorflow/contrib/nccl/python/ops/nccl_ops.py +++ b/tensorflow/contrib/nccl/python/ops/nccl_ops.py @@ -26,8 +26,10 @@ from tensorflow.python.framework import device from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader -_nccl_ops_so = loader.load_op_library( - resource_loader.get_path_to_datafile('_nccl_ops.so')) + +_nccl_ops_so = None +_module_lock = threading.Lock() +_shared_name_counter = 0 def all_sum(tensors): @@ -61,12 +63,12 @@ def _all_sum_grad(op, grad): Raises: LookupError: If `reduction` is not `sum`. """ - if op.get_attr('reduction') != 'sum': + if op.get_attr('reduction') != b'sum': raise LookupError('No gradient defined for NcclAllReduce except sum.') _check_device(grad, expected=op.device) num_devices = op.get_attr('num_devices') - shared_name = op.get_attr('shared_name') + '_grad' + shared_name = op.get_attr('shared_name') + b'_grad' with ops.device(op.device): return gen_nccl_ops.nccl_all_reduce( @@ -160,7 +162,7 @@ def _reduce_sum_grad(op, grad): Raises: LookupError: If the reduction attribute of op is not `sum`. """ - if op.get_attr('reduction') != 'sum': + if op.get_attr('reduction') != b'sum': raise LookupError('No gradient defined for NcclReduce except sum.') _check_device(grad, expected=op.device) @@ -180,7 +182,7 @@ def broadcast(tensor): A tensor with the value of `src_tensor`, which can be used as input to ops on other GPU devices. """ - _check_graph_mode() + _validate_and_load_nccl_so() _check_device(tensor) with ops.device(tensor.device): @@ -212,7 +214,7 @@ def _apply_all_reduce(reduction, tensors): """Helper function for all_* functions.""" if not tensors: raise ValueError('Must pass >0 tensors to all reduce operations') - _check_graph_mode() + _validate_and_load_nccl_so() shared_name = _get_shared_name() res = [] @@ -234,7 +236,7 @@ def _apply_reduce(reduction, tensors): """Helper function for reduce_* functions.""" if not tensors: raise ValueError('Must pass >0 tensors to reduce operations') - _check_graph_mode() + _validate_and_load_nccl_so() for t in tensors: _check_device(t) @@ -246,14 +248,10 @@ def _apply_reduce(reduction, tensors): return result -_lock = threading.Lock() -_shared_name_counter = 0 - - def _get_shared_name(): global _shared_name_counter - with _lock: + with _module_lock: val = _shared_name_counter _shared_name_counter += 1 return 'c%s' % val @@ -266,6 +264,25 @@ def _check_device(tensor, expected=None): raise ValueError('Expected device %s, got %s' % (expected, tensor.device)) -def _check_graph_mode(): +def _maybe_load_nccl_ops_so(): + """Loads nccl ops so if it hasn't been loaded already.""" + + with _module_lock: + global _nccl_ops_so + if not _nccl_ops_so: + _nccl_ops_so = loader.load_op_library( + resource_loader.get_path_to_datafile('_nccl_ops.so')) + + +def _validate_and_load_nccl_so(): + """Validates calling context and loads nccl ops so file. + + Raises: + ValueError: Ops are not supported. + errors_impl.NotFoundError: nccl library is not installed. + """ + if context.executing_eagerly(): raise ValueError('Nccl ops are not supported in eager mode') + + _maybe_load_nccl_ops_so() diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index 13aa1d7e7a11877373a848c1ba865aa418790cd0..bbdf962d0480e52045d31f65b3d137ed3f11f2f1 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -19,6 +19,7 @@ py_library( "python/training/drop_stale_gradient_optimizer.py", "python/training/elastic_average_optimizer.py", "python/training/external_optimizer.py", + "python/training/ggt.py", "python/training/lazy_adam_optimizer.py", "python/training/model_average_optimizer.py", "python/training/moving_average_optimizer.py", @@ -28,15 +29,19 @@ py_library( "python/training/reg_adagrad_optimizer.py", "python/training/sign_decay.py", "python/training/variable_clipping_optimizer.py", + "python/training/weight_decay_optimizers.py", ], srcs_version = "PY2AND3", deps = [ + "//tensorflow/contrib/optimizer_v2:optimizer_v2_py", "//tensorflow/python:array_ops", "//tensorflow/python:clip_ops", "//tensorflow/python:control_flow_ops", "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_ops", "//tensorflow/python:gradients", "//tensorflow/python:init_ops", + "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", "//tensorflow/python:platform", "//tensorflow/python:state_ops", @@ -194,6 +199,25 @@ py_test( ], ) +py_test( + name = "weight_decay_optimizers_test", + srcs = ["python/training/weight_decay_optimizers_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:session", + "//tensorflow/python:variables", + "//third_party/py/numpy", + ], +) + tf_py_test( name = "drop_stale_gradient_optimizer_test", srcs = ["python/training/drop_stale_gradient_optimizer_test.py"], @@ -302,3 +326,21 @@ py_test( "//third_party/py/numpy", ], ) + +py_test( + name = "ggt_test", + srcs = ["python/training/ggt_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:platform_test", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:variables", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/opt/__init__.py b/tensorflow/contrib/opt/__init__.py index 4c13c8e247185213b798eb733ddcf65a07a8f64d..3e63e99030c46c254625ca8fdccce614cd60e8b0 100644 --- a/tensorflow/contrib/opt/__init__.py +++ b/tensorflow/contrib/opt/__init__.py @@ -22,15 +22,18 @@ from __future__ import print_function from tensorflow.contrib.opt.python.training.adamax import * from tensorflow.contrib.opt.python.training.addsign import * from tensorflow.contrib.opt.python.training.drop_stale_gradient_optimizer import * +from tensorflow.contrib.opt.python.training.elastic_average_optimizer import * from tensorflow.contrib.opt.python.training.external_optimizer import * +from tensorflow.contrib.opt.python.training.ggt import * from tensorflow.contrib.opt.python.training.lazy_adam_optimizer import * +from tensorflow.contrib.opt.python.training.model_average_optimizer import * from tensorflow.contrib.opt.python.training.moving_average_optimizer import * from tensorflow.contrib.opt.python.training.multitask_optimizer_wrapper import * from tensorflow.contrib.opt.python.training.nadam_optimizer import * +from tensorflow.contrib.opt.python.training.weight_decay_optimizers import * from tensorflow.contrib.opt.python.training.powersign import * from tensorflow.contrib.opt.python.training.variable_clipping_optimizer import * -from tensorflow.contrib.opt.python.training.elastic_average_optimizer import * -from tensorflow.contrib.opt.python.training.model_average_optimizer import * +from tensorflow.contrib.opt.python.training.weight_decay_optimizers import * # pylint: enable=wildcard-import from tensorflow.python.util.all_util import remove_undocumented @@ -46,6 +49,10 @@ _allowed_symbols = [ 'LazyAdamOptimizer', 'NadamOptimizer', 'MovingAverageOptimizer', + 'MomentumWOptimizer', + 'AdamWOptimizer', + 'DecoupledWeightDecayExtension', + 'extend_with_decoupled_weight_decay', 'ScipyOptimizerInterface', 'VariableClippingOptimizer', 'MultitaskOptimizerWrapper', @@ -53,7 +60,8 @@ _allowed_symbols = [ 'ElasticAverageOptimizer', 'ElasticAverageCustomGetter', 'ModelAverageOptimizer', - 'ModelAverageCustomGetter' + 'ModelAverageCustomGetter', + 'GGTOptimizer', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/opt/python/training/ggt.py b/tensorflow/contrib/opt/python/training/ggt.py new file mode 100644 index 0000000000000000000000000000000000000000..928c453517f825ed2d305ec498d07ac29c065f1a --- /dev/null +++ b/tensorflow/contrib/opt/python/training/ggt.py @@ -0,0 +1,312 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""GGT for Tensorflow.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import numpy as np +from tensorflow.contrib.optimizer_v2 import optimizer_v2 +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops + + +class GGTOptimizer(optimizer_v2.OptimizerV2): + """Optimizer that implements the GGT algorithm. + + GGT has an advantage over sgd and adam on large models with poor conditioning, + for example language models and CNNs, + see [ABCHSZZ 2018]([pdf](https://arxiv.org/pdf/1806.02958.pdf)). + """ + + def __init__(self, + learning_rate=0.001, + beta1=0.9, + use_locking=False, + name="GGT", + window=10, + eps=1e-4, + svd_eps=1e-6, + sigma_eps=1e-2): + """Construct a new GGT optimizer. + + Initialization: + + ``` + t <- 0 (Initialize timestep) + grad_buffer <- 0 (Initialize buffer for keeping past gradients) + flat_grad <- 0 (Initialize flattened gradient that contains gradients of all + variables) + m_0 <- 0 (Initialize 1st moment vector) + ``` + + Suppose all variables and their gradients are concatenated into vectors + `flat_vars` and `flat_grad`. The update rule for `flat_vars` + uses an optimization described at the beginning of section 2 of the paper: + + ``` + t <- t + 1 + + m_t <- beta1 * m_{t-1} + (1 - beta1) * flat_grad + grad_buffer[(t-1) % window, :] <- m_t + + M <- grad_buffer^T / sqrt(min(t, window)) + U, sigma, _ <- SVD(M^TM + I * svd_eps) + + sigma_sqrt_inv <- (sqrt(sigma) + sigma_eps)^(-3) + sigma_sqrt_min <- min(sqrt(sigma)) + + if sigma_sqrt_min > eps: + new_step <- M U diag(sigma_sqrt_inv) U^T M^T m_t + + (m_t - M U diag(1/sigma) U^T M^T m_t) / sigma_sqrt_min + else: + new_step <- M U diag(sigma_sqrt_inv) U^T M^T m_t + + flat_vars <- flat_vars - learning_rate * new_step + ``` + + GGT provides the power of full-matrix adaptive regularization at a cost not + much larger than SGD. As a result it is suited for large models where the + gradient covariance matrix has a poor condition number that slows down first + order methods. + GGT uses the preconditioner from full-matrix AdaGrad, with gradient history + attenuated exponentially as in Adam, and truncated to a window parameter. + It has provable guarantees even for non-convex optimization that is never + significantly worse than SGD and in some cases better. + + Args: + learning_rate: A float hyperparameter. The learning rate. + beta1: A float hyperparameter. The exponential decay rate for the 1st + moment estimates. + use_locking: If True use locks for update operations. + name: Optional name for the operations created when applying gradients. + Defaults to "GGT". + window: An integer hyperparameter. The number of first moments to keep in + computing the adaptive preconditioner. + eps: A float hyperparameter. Used to truncate small eigenvalues of the + gradient covariance matrix. + svd_eps: A float hyperparameter. Used to stabilize SVD. + sigma_eps: A float hyperparameter. Used to regularize matrix inversion. + """ + super(GGTOptimizer, self).__init__(use_locking, name) + self._set_hyper("lr", learning_rate) + self._set_hyper("beta1", beta1) + self._set_hyper("window", window) + self._set_hyper("eps", eps) + self._set_hyper("svd_eps", svd_eps) + self._set_hyper("sigma_eps", sigma_eps) + + self.index_dict = {} + self.shape_dict = {} + + def _create_vars(self, var_list, state): + # Construct ordered dictionary for variable dimensions, sorted by name. + shape_dict = {} + for v in var_list: + shape_dict[v.name] = np.prod(v.get_shape()).value + self.shape_dict = collections.OrderedDict( + sorted(shape_dict.items(), key=lambda t: t[0])) + + # Assign each variable its location in flat_grad. The locations are based on + # the order of sorted names. + idx = 0 + for v_name, v_dim in self.shape_dict.items(): + self.index_dict[v_name] = idx + idx += v_dim + + state.create_non_slot( + initial_value=math_ops.cast(0., dtype=var_list[0].dtype.base_dtype), + name="global_step") + + # Buffer for keeping past gradients. + window = state.get_hyper("window") + grad_buffer_init = array_ops.zeros( + [window, idx], dtype=var_list[0].dtype.base_dtype) + state.create_non_slot(initial_value=grad_buffer_init, name="grad_buffer") + + state.create_non_slot( + initial_value=array_ops.zeros( + (idx,), dtype=var_list[0].dtype.base_dtype), + name="moment1") + + # Flattened gradient that contains gradients for all variables in the model. + state.create_non_slot( + initial_value=array_ops.zeros( + (idx,), dtype=var_list[0].dtype.base_dtype), + name="flat_grad") + + def _get_global_step(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("global_step") + + def _get_moment1(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("moment1") + + def _get_grad_buffer(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("grad_buffer") + + def _get_flat_grad(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("flat_grad") + + def _apply_sparse(self, grad, var): + raise NotImplementedError("Sparse gradient updates are not supported.") + + def _prepare(self, state): + self._variables = [] + + def _apply_dense(self, grad, var, state): + self._variables.append(var) + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + + # Update flat_gradient at the index associated with the variable. + flat_grad = self._get_flat_grad(state) + new_flat_grad = array_ops.reshape(grad, [-1]) + flat_grad_updated = state_ops.scatter_update( + flat_grad, math_ops.range(start_index, end_index), new_flat_grad) + + return flat_grad_updated + + def _resource_apply_dense(self, grad, var, state): + self._variables.append(var) + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + + # Update flat_gradient at the index associated with the variable. + flat_grad = self._get_flat_grad(state) + new_flat_grad = array_ops.reshape(grad, [-1]) + flat_grad_updated = state_ops.scatter_update( + flat_grad, math_ops.range(start_index, end_index), new_flat_grad) + + return flat_grad_updated + + def _finish(self, state): + var_dtype = self._variables[0].dtype.base_dtype + # Update global step. + global_step = self._get_global_step(state) + update_global_step = state_ops.assign_add(global_step, 1.) + + # Update the first moment estimate. + beta1 = state.get_hyper("beta1", dtype=var_dtype) + moment1 = self._get_moment1(state) + flat_grad = self._get_flat_grad(state) + # moment1_t := beta1 * moment1_{t-1} + (1 - beta1) * flat_grad_t + update_moment1 = moment1.assign(beta1 * moment1 + (1. - beta1) * flat_grad) + + # Update the gradient buffer. + window = state.get_hyper("window") + grad_buffer = self._get_grad_buffer(state) + next_grad_index = math_ops.floormod( + math_ops.to_int32(update_global_step - 1.), window) + # grad_buffer[(t-1) % window] := moment1_t + update_grad_buffer = state_ops.scatter_update(grad_buffer, next_grad_index, + update_moment1) + + # Compute the update step. + eps = state.get_hyper("eps", dtype=var_dtype) + svd_eps = state.get_hyper("svd_eps", dtype=var_dtype) + sigma_eps = state.get_hyper("sigma_eps", dtype=var_dtype) + lr = state.get_hyper("lr", dtype=var_dtype) + denom = math_ops.sqrt( + math_ops.minimum( + ops.convert_to_tensor(update_global_step), + ops.convert_to_tensor(math_ops.cast(window, dtype=var_dtype)))) + moment1_2d = array_ops.expand_dims(update_moment1, -1) + + # m = grad_buffer^T / sqrt(min(t, window)) + # m has shape [model dimension, window], where model dimension is the sum + # of the dimensions of the flattened variables. + m = array_ops.transpose(math_ops.divide(update_grad_buffer, denom)) + + # sigma, u, _ = SVD(m^Tm + I * svd_eps) + mm = math_ops.matmul(m, m, transpose_a=True) + damping = math_ops.cast(linalg_ops.eye(window), dtype=var_dtype) * svd_eps + sigma, u, _ = linalg_ops.svd(mm + damping) + sigma_sqrt = math_ops.sqrt(sigma) + sigma_sqrt_min = math_ops.reduce_min(sigma_sqrt) + + # sigma_sqrt_inv = 1 / (\sqrt{sigma} + sigma_eps) ^ 3 + # We add sigma_eps to alleviate numerical instability. + # Note that (m^Tm)^(-3/2) = u diag(sigma_sqrt_inv) u^T. + sigma_sqrt_inv = math_ops.divide( + math_ops.cast(1.0, dtype=var_dtype), + math_ops.pow(sigma_sqrt + sigma_eps, 3)) + + # In full matrix AdaGrad, the update step computes (mm^T)^(-1/2)g, where the + # inversion of a model dimension by model dimension matrix is needed. To + # speed up this computation we calculate the following instead: + # m(m^Tm)^(-3/2)m^T moment1 = m u diag(sigma_sqrt_inv) u^T m^T moment1. + new_step = array_ops.expand_dims( + array_ops.zeros(flat_grad.get_shape(), dtype=var_dtype), -1) + head = math_ops.matmul( + m, + math_ops.matmul( + u, + math_ops.matmul( + array_ops.diag(sigma_sqrt_inv), + math_ops.matmul( + u, + math_ops.matmul(m, moment1_2d, transpose_a=True), + transpose_a=True)))) + + # When inverting (mm^t)^(1/2), we also add epsilon * I regularization for + # degenerate cases. We expand ((mm^t)^(1/2) + epsilon * I)^(-1) using + # Woodbury's identity. + # For full derivation please see paper at + # https://arxiv.org/pdf/1806.02958.pdf + tail = moment1_2d - math_ops.matmul( + m, + math_ops.matmul( + u, + math_ops.matmul( + array_ops.diag( + math_ops.divide(math_ops.cast(1.0, dtype=var_dtype), + sigma)), + math_ops.matmul( + u, + math_ops.matmul(m, moment1_2d, transpose_a=True), + transpose_a=True)))) + scaled_tail = math_ops.divide(tail, sigma_sqrt_min) + + update_new_step = control_flow_ops.cond( + sigma_sqrt_min > eps, lambda: math_ops.add(head, scaled_tail), + lambda: math_ops.add(new_step, head)) + + # Update each variable. + update_step = [] + for var in self._variables: + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + var_update_correct_shape = array_ops.reshape( + update_new_step[start_index:end_index], var.get_shape()) + var_updated = state_ops.assign_sub(var, lr * var_update_correct_shape) + update_step.append(var_updated) + + return control_flow_ops.group(update_step) diff --git a/tensorflow/contrib/opt/python/training/ggt_test.py b/tensorflow/contrib/opt/python/training/ggt_test.py new file mode 100644 index 0000000000000000000000000000000000000000..42162960b049cd90c663989fb4fc9d7f179a84ff --- /dev/null +++ b/tensorflow/contrib/opt/python/training/ggt_test.py @@ -0,0 +1,183 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for GGTOptimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from tensorflow.contrib.opt.python.training.ggt import GGTOptimizer +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def ggt_update_numpy(param, + g_t, + lr, + grad_buffer, + m, + window, + t, + beta1=0.9, + eps=1e-4, + svd_eps=1e-6, + sigma_eps=1e-2): + """Tests the correctness of one step of GGT.""" + m_t = m * beta1 + (1 - beta1) * g_t + grad_buffer[((t - 1) % window), :] = m_t + m_matrix = np.transpose(grad_buffer / np.sqrt(np.minimum(t, window))) + mm = np.dot(np.transpose(m_matrix), m_matrix) + damping = np.eye(window) * svd_eps + u, sigma, _ = np.linalg.svd(mm + damping) + + sigma_sqrt_inv = np.power(np.sqrt(sigma) + sigma_eps, -3) + new_step = np.linalg.multi_dot([ + m_matrix, u, + np.diag(sigma_sqrt_inv), + np.transpose(u), + np.transpose(m_matrix), m_t + ]) + + sigma_sqrt_min = np.sqrt(sigma).min() + + if sigma_sqrt_min > eps: + new_step += (m_t - np.linalg.multi_dot([ + m_matrix, u, + np.diag(1.0 / sigma), + np.transpose(u), + np.transpose(m_matrix), m_t + ])) * (1.0 / sigma_sqrt_min) + + param_t = param - lr * new_step + return param_t, m_t, grad_buffer + + +class GGTOptimizerTest(test.TestCase): + + def doTestBasic(self, use_resource=False): + # SVD does not support float16 + for i, dtype in enumerate([dtypes.float32, dtypes.float64]): + with self.test_session(graph=ops.Graph()): + # Initialize variables for numpy implementation. + m0 = 0.0 + window = 3 + grad_buffer = np.zeros((window, 4), dtype=dtype.as_numpy_dtype) + lr = 0.001 + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) + + if use_resource: + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + else: + var0 = variables.Variable(var0_np, name="var0") + var1 = variables.Variable(var1_np, name="var1") + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = GGTOptimizer(learning_rate=lr, window=window) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + opt_variables = opt.variables() + + m_t = opt._get_moment1() + grad_buffer_t = opt._get_grad_buffer() + g_t = opt._get_flat_grad() + self.assertTrue(m_t is not None) + self.assertTrue(grad_buffer_t is not None) + self.assertTrue(g_t is not None) + self.assertIn(m_t, opt_variables) + self.assertIn(grad_buffer_t, opt_variables) + self.assertIn(g_t, opt_variables) + + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + + if not context.executing_eagerly(): + self.evaluate(variables.global_variables_initializer()) + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + m_t = opt._get_moment1() + grad_buffer_t = opt._get_grad_buffer() + g_t = opt._get_flat_grad() + + # Run 3 steps of GGT + for t in range(1, 4): + if not context.executing_eagerly(): + self.evaluate(update) + elif t > 1: + opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + + if t == 1: + self.assertAllCloseAccordingToType( + np.array([0.01, 0.01, 0.001, 0.001]), self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, 0.001], [0., 0., 0., 0.], + [0., 0., 0., 0.]]), self.evaluate(grad_buffer_t)) + elif t == 2: + self.assertAllCloseAccordingToType( + np.array([0.019, 0.019, 0.0019, 0.0019]), self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, 0.001], + [0.019, 0.019, 0.0019, 0.0019], [0., 0., 0., 0.]]), + self.evaluate(grad_buffer_t)) + else: + self.assertAllCloseAccordingToType( + np.array([0.0271, 0.0271, 0.00271, 0.00271]), + self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, + 0.001], [0.019, 0.019, 0.0019, 0.0019], + [0.0271, 0.0271, 0.00271, 0.00271]]), + self.evaluate(grad_buffer_t)) + + self.assertAllCloseAccordingToType([0.1, 0.1, 0.01, 0.01], + self.evaluate(g_t)) + + var_np = np.append(var0_np, var1_np) + grads_np = np.append(grads0_np, grads1_np) + var_np, m0, grad_buffer = ggt_update_numpy(var_np, grads_np, lr, + grad_buffer, m0, window, t) + + var0_np = var_np[:2] + var1_np = var_np[2:] + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + + def testBasic(self): + with self.test_session(): + self.doTestBasic(use_resource=False) + + @test_util.run_in_graph_and_eager_modes(reset_test=True) + def testResourceBasic(self): + self.doTestBasic(use_resource=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py b/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..b9cf40eb7b2d11c98b93c51213145ca4e2670318 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py @@ -0,0 +1,362 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Base class to make optimizers weight decay ready.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.training import adam +from tensorflow.python.training import momentum as momentum_opt +from tensorflow.python.training import optimizer +from tensorflow.python.util.tf_export import tf_export + + +class DecoupledWeightDecayExtension(object): + """This class allows to extend optimizers with decoupled weight decay. + + It implements the decoupled weight decay described by Loshchilov & Hutter + (https://arxiv.org/pdf/1711.05101.pdf), in which the weight decay is + decoupled from the optimization steps w.r.t. to the loss function. + For SGD variants, this simplifies hyperparameter search since it decouples + the settings of weight decay and learning rate. + For adaptive gradient algorithms, it regularizes variables with large + gradients more than L2 regularization would, which was shown to yield better + training loss and generalization error in the paper above. + + This class alone is not an optimizer but rather extends existing + optimizers with decoupled weight decay. We explicitly define the two examples + used in the above paper (SGDW and AdamW), but in general this can extend + any OptimizerX by using + `extend_with_weight_decay(OptimizerX, weight_decay=weight_decay)`. + In order for it to work, it must be the first class the Optimizer with + weight decay inherits from, e.g. + + ```python + class AdamWOptimizer(DecoupledWeightDecayExtension, adam.AdamOptimizer): + def __init__(self, weight_decay, *args, **kwargs): + super(AdamWOptimizer, self).__init__(weight_decay, *args, **kwargs). + ``` + + Note that this extension decays weights BEFORE applying the update based + on the gradient, i.e. this extension only has the desired behaviour for + optimizers which do not depend on the value of'var' in the update step! + """ + + def __init__(self, weight_decay, **kwargs): + """Construct the extension class that adds weight decay to an optimizer. + + Args: + weight_decay: A `Tensor` or a floating point value, the factor by which + a variable is decayed in the update step. + **kwargs: Optional list or tuple or set of `Variable` objects to + decay. + """ + self._decay_var_list = None # is set in minimize or apply_gradients + self._weight_decay = weight_decay + # The tensors are initialized in call to _prepare + self._weight_decay_tensor = None + super(DecoupledWeightDecayExtension, self).__init__(**kwargs) + + def minimize(self, loss, global_step=None, var_list=None, + gate_gradients=optimizer.Optimizer.GATE_OP, + aggregation_method=None, colocate_gradients_with_ops=False, + name=None, grad_loss=None, decay_var_list=None): + """Add operations to minimize `loss` by updating `var_list` with decay. + + This function is the same as Optimizer.minimize except that it allows to + specify the variables that should be decayed using decay_var_list. + If decay_var_list is None, all variables in var_list are decayed. + + For more information see the documentation of Optimizer.minimize. + + Args: + loss: A `Tensor` containing the value to minimize. + global_step: Optional `Variable` to increment by one after the + variables have been updated. + var_list: Optional list or tuple of `Variable` objects to update to + minimize `loss`. Defaults to the list of variables collected in + the graph under the key `GraphKeys.TRAINABLE_VARIABLES`. + gate_gradients: How to gate the computation of gradients. Can be + `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`. + aggregation_method: Specifies the method used to combine gradient terms. + Valid values are defined in the class `AggregationMethod`. + colocate_gradients_with_ops: If True, try colocating gradients with + the corresponding op. + name: Optional name for the returned operation. + grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. + decay_var_list: Optional list of decay variables. + + Returns: + An Operation that updates the variables in `var_list`. If `global_step` + was not `None`, that operation also increments `global_step`. + + """ + self._decay_var_list = set(decay_var_list) if decay_var_list else False + return super(DecoupledWeightDecayExtension, self).minimize( + loss, global_step=global_step, var_list=var_list, + gate_gradients=gate_gradients, aggregation_method=aggregation_method, + colocate_gradients_with_ops=colocate_gradients_with_ops, name=name, + grad_loss=grad_loss) + + def apply_gradients(self, grads_and_vars, global_step=None, name=None, + decay_var_list=None): + """Apply gradients to variables and decay the variables. + + This function is the same as Optimizer.apply_gradients except that it + allows to specify the variables that should be decayed using + decay_var_list. If decay_var_list is None, all variables in var_list + are decayed. + + For more information see the documentation of Optimizer.apply_gradients. + + Args: + grads_and_vars: List of (gradient, variable) pairs as returned by + `compute_gradients()`. + global_step: Optional `Variable` to increment by one after the + variables have been updated. + name: Optional name for the returned operation. Default to the + name passed to the `Optimizer` constructor. + decay_var_list: Optional list of decay variables. + + Returns: + An `Operation` that applies the specified gradients. If `global_step` + was not None, that operation also increments `global_step`. + """ + self._decay_var_list = set(decay_var_list) if decay_var_list else False + return super(DecoupledWeightDecayExtension, self).apply_gradients( + grads_and_vars, global_step=global_step, name=name) + + def _prepare(self): + weight_decay = self._weight_decay + if callable(weight_decay): + weight_decay = weight_decay() + self._weight_decay_tensor = ops.convert_to_tensor( + weight_decay, name="weight_decay") + # Call the optimizers _prepare function. + super(DecoupledWeightDecayExtension, self)._prepare() + + def _decay_weights_op(self, var): + if not self._decay_var_list or var in self._decay_var_list: + return var.assign_sub(self._weight_decay * var, self._use_locking) + return control_flow_ops.no_op() + + def _decay_weights_sparse_op(self, var, indices, scatter_add): + if not self._decay_var_list or var in self._decay_var_list: + return scatter_add(var, indices, -self._weight_decay * var, + self._use_locking) + return control_flow_ops.no_op() + + # Here, we overwrite the apply functions that the base optimizer calls. + # super().apply_x resolves to the apply_x function of the BaseOptimizer. + def _apply_dense(self, grad, var): + with ops.control_dependencies([self._decay_weights_op(var)]): + return super(DecoupledWeightDecayExtension, self)._apply_dense(grad, var) + + def _resource_apply_dense(self, grad, var): + with ops.control_dependencies([self._decay_weights_op(var)]): + return super(DecoupledWeightDecayExtension, self)._resource_apply_dense( + grad, var) + + def _apply_sparse(self, grad, var): + scatter_add = state_ops.scatter_add + decay_op = self._decay_weights_sparse_op(var, grad.indices, scatter_add) + with ops.control_dependencies([decay_op]): + return super(DecoupledWeightDecayExtension, self)._apply_sparse( + grad, var) + + def _resource_scatter_add(self, x, i, v, _=None): + # last argument allows for one overflow argument, to have the same function + # signature as state_ops.scatter_add + with ops.control_dependencies( + [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): + return x.value() + + def _resource_apply_sparse(self, grad, var, indices): + scatter_add = self._resource_scatter_add + decay_op = self._decay_weights_sparse_op(var, indices, scatter_add) + with ops.control_dependencies([decay_op]): + return super(DecoupledWeightDecayExtension, self)._resource_apply_sparse( + grad, var, indices) + + +def extend_with_decoupled_weight_decay(base_optimizer): + """Factory function returning an optimizer class with decoupled weight decay. + + Returns an optimizer class. An instance of the returned class computes the + update step of `base_optimizer` and additionally decays the weights. + E.g., the class returned by + `extend_with_decoupled_weight_decay(tf.train.AdamOptimizer)` is equivalent to + `tf.contrib.opt.AdamWOptimizer`. + + The API of the new optimizer class slightly differs from the API of the + base optimizer: + - The first argument to the constructor is the weight decay rate. + - `minimize` and `apply_gradients` accept the optional keyword argument + `decay_var_list`, which specifies the variables that should be decayed. + If `None`, all variables that are optimized are decayed. + + Usage example: + ```python + # MyAdamW is a new class + MyAdamW = extend_with_decoupled_weight_decay(tf.train.AdamOptimizer) + # Create a MyAdamW object + optimizer = MyAdamW(weight_decay=0.001, learning_rate=0.001) + sess.run(optimizer.minimize(loss, decay_variables=[var1, var2])) + + Note that this extension decays weights BEFORE applying the update based + on the gradient, i.e. this extension only has the desired behaviour for + optimizers which do not depend on the value of'var' in the update step! + ``` + + Args: + base_optimizer: An optimizer class that inherits from tf.train.Optimizer. + + Returns: + A new optimizer class that inherits from DecoupledWeightDecayExtension + and base_optimizer. + """ + + class OptimizerWithDecoupledWeightDecay(DecoupledWeightDecayExtension, + base_optimizer): + """Base_optimizer with decoupled weight decay. + + This class computes the update step of `base_optimizer` and + additionally decays the variable with the weight decay being decoupled from + the optimization steps w.r.t. to the loss function, as described by + Loshchilov & Hutter (https://arxiv.org/pdf/1711.05101.pdf). + For SGD variants, this simplifies hyperparameter search since + it decouples the settings of weight decay and learning rate. + For adaptive gradient algorithms, it regularizes variables with large + gradients more than L2 regularization would, which was shown to yield + better training loss and generalization error in the paper above. + """ + + def __init__(self, weight_decay, *args, **kwargs): + # super delegation is necessary here + # pylint: disable=useless-super-delegation + super(OptimizerWithDecoupledWeightDecay, self).__init__( + weight_decay, *args, **kwargs) + # pylint: enable=useless-super-delegation + + return OptimizerWithDecoupledWeightDecay + + +@tf_export("contrib.opt.MomentumWOptimizer") +class MomentumWOptimizer(DecoupledWeightDecayExtension, + momentum_opt.MomentumOptimizer): + """Optimizer that implements the Momentum algorithm with weight_decay. + + This is an implementation of the SGDW optimizer described in "Fixing + Weight Decay Regularization in Adam" by Loshchilov & Hutter + (https://arxiv.org/abs/1711.05101) + ([pdf])(https://arxiv.org/pdf/1711.05101.pdf). + It computes the update step of `train.MomentumOptimizer` and additionally + decays the variable. Note that this is different from adding + L2 regularization on the variables to the loss. Decoupling the weight decay + from other hyperparameters (in particular the learning rate) simplifies + hyperparameter search. + + For further information see the documentation of the Momentum Optimizer. + + Note that this optimizer can also be instantiated as + ```python + extend_with_weight_decay(tf.train.MomentumOptimizer, + weight_decay=weight_decay) + ``` + """ + + def __init__(self, weight_decay, learning_rate, momentum, + use_locking=False, name="MomentumW", use_nesterov=False): + """Construct a new MomentumW optimizer. + + For further information see the documentation of the Momentum Optimizer. + + Args: + weight_decay: A `Tensor` or a floating point value. The weight decay. + learning_rate: A `Tensor` or a floating point value. The learning rate. + momentum: A `Tensor` or a floating point value. The momentum. + use_locking: If `True` use locks for update operations. + name: Optional name prefix for the operations created when applying + gradients. Defaults to "Momentum". + use_nesterov: If `True` use Nesterov Momentum. + See [Sutskever et al., 2013]( + http://jmlr.org/proceedings/papers/v28/sutskever13.pdf). + This implementation always computes gradients at the value of the + variable(s) passed to the optimizer. Using Nesterov Momentum makes the + variable(s) track the values called `theta_t + mu*v_t` in the paper. + + @compatibility(eager) + When eager execution is enabled, learning_rate, weight_decay and momentum + can each be a callable that takes no arguments and returns the actual value + to use. This can be useful for changing these values across different + invocations of optimizer functions. + @end_compatibility + """ + super(MomentumWOptimizer, self).__init__( + weight_decay, learning_rate=learning_rate, momentum=momentum, + use_locking=use_locking, name=name, use_nesterov=use_nesterov) + + +@tf_export("contrib.opt.AdamWOptimizer") +class AdamWOptimizer(DecoupledWeightDecayExtension, adam.AdamOptimizer): + """Optimizer that implements the Adam algorithm with weight decay. + + This is an implementation of the AdamW optimizer described in "Fixing + Weight Decay Regularization in Adam" by Loshchilov & Hutter + (https://arxiv.org/abs/1711.05101) + ([pdf])(https://arxiv.org/pdf/1711.05101.pdf). + + It computes the update step of `train.AdamOptimizer` and additionally decays + the variable. Note that this is different from adding L2 regularization on + the variables to the loss: it regularizes variables with large + gradients more than L2 regularization would, which was shown to yield better + training loss and generalization error in the paper above. + + For further information see the documentation of the Adam Optimizer. + + Note that this optimizer can also be instantiated as + ```python + extend_with_weight_decay(tf.train.AdamOptimizer, weight_decay=weight_decay) + ``` + """ + + def __init__(self, weight_decay, learning_rate=0.001, beta1=0.9, beta2=0.999, + epsilon=1e-8, use_locking=False, name="AdamW"): + """Construct a new AdamW optimizer. + + For further information see the documentation of the Adam Optimizer. + + Args: + weight_decay: A `Tensor` or a floating point value. The weight decay. + learning_rate: A Tensor or a floating point value. The learning rate. + beta1: A float value or a constant float tensor. + The exponential decay rate for the 1st moment estimates. + beta2: A float value or a constant float tensor. + The exponential decay rate for the 2nd moment estimates. + epsilon: A small constant for numerical stability. This epsilon is + "epsilon hat" in the Kingma and Ba paper (in the formula just before + Section 2.1), not the epsilon in Algorithm 1 of the paper. + use_locking: If True use locks for update operations. + name: Optional name for the operations created when applying gradients. + Defaults to "Adam". + """ + super(AdamWOptimizer, self).__init__( + weight_decay, learning_rate=learning_rate, beta1=beta1, beta2=beta2, + epsilon=epsilon, use_locking=use_locking, name=name) diff --git a/tensorflow/contrib/opt/python/training/weight_decay_optimizers_test.py b/tensorflow/contrib/opt/python/training/weight_decay_optimizers_test.py new file mode 100644 index 0000000000000000000000000000000000000000..76d8a5697acb79e7748175c4a81dfdd85807dd49 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/weight_decay_optimizers_test.py @@ -0,0 +1,188 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for optimizers with weight decay.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.opt.python.training import weight_decay_optimizers +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import adam + +WEIGHT_DECAY = 0.01 + + +def adamw_update_numpy(param, g_t, t, m, v, lr=0.001, beta1=0.9, + beta2=0.999, epsilon=1e-8): + lr_t = lr * np.sqrt(1 - beta2**t) / (1 - beta1**t) + + m_t = beta1 * m + (1 - beta1) * g_t + v_t = beta2 * v + (1 - beta2) * g_t * g_t + + param_t = (param - lr_t * m_t / (np.sqrt(v_t) + epsilon) - + (param * WEIGHT_DECAY)) + return param_t, m_t, v_t + + +def momentumw_update_numpy(param, g_t, m, lr=0.001, momentum=0.9, **_): + # v, t are not needed for momentum optimizer + m = momentum * m + g_t + param_t = param - lr * m - param * WEIGHT_DECAY + return param_t, m, None + + +class WeightDecayOptimizerTest(test.TestCase): + + def doTest(self, optimizer, update_fn, optimizer_name, slot_name, + use_resource=False, do_sparse=False): + for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): + with self.test_session(graph=ops.Graph()): + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) + + if use_resource: + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + else: + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + + if do_sparse: + grads0_np_indices = np.array([0, 1], dtype=np.int32) + grads0 = ops.IndexedSlices(constant_op.constant(grads0_np), + constant_op.constant(grads0_np_indices), + constant_op.constant([2])) + grads1_np_indices = np.array([0, 1], dtype=np.int32) + grads1 = ops.IndexedSlices(constant_op.constant(grads1_np), + constant_op.constant(grads1_np_indices), + constant_op.constant([2])) + else: + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = optimizer() + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + + if not context.executing_eagerly(): + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + self.evaluate(variables.global_variables_initializer()) + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + # Run 3 steps of the optimizer + for t in range(1, 4): + if not context.executing_eagerly(): + self.evaluate(update) + elif t > 1: + opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + + var0_np, m0, v0 = update_fn(var0_np, grads0_np, t=t, m=m0, v=v0) + var1_np, m1, v1 = update_fn(var1_np, grads1_np, t=t, m=m1, v=v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + if use_resource: + self.assertEqual("var0_%d/%s:0" % (i, optimizer_name), + opt.get_slot(var=var0, name=slot_name).name) + + +class AdamWOptimizerTest(WeightDecayOptimizerTest): + + @staticmethod + def get_optimizer(): + return weight_decay_optimizers.AdamWOptimizer(WEIGHT_DECAY) + + def testSparse(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "AdamW", "m", + use_resource=False, do_sparse=True) + + def testResourceSparse(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "AdamW", "m", + use_resource=True, do_sparse=True) + + def testBasic(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "AdamW", "m", + use_resource=False) + + @test_util.run_in_graph_and_eager_modes(reset_test=True) + def testResourceBasic(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "AdamW", "m", + use_resource=True) + + +class MomentumWOptimizerTest(WeightDecayOptimizerTest): + + @staticmethod + def get_optimizer(): + return weight_decay_optimizers.MomentumWOptimizer(WEIGHT_DECAY, 0.001, 0.9) + + def testSparse(self): + self.doTest(self.get_optimizer, momentumw_update_numpy, "MomentumW", + "momentum", use_resource=False, do_sparse=True) + + def testResourceSparse(self): + self.doTest(self.get_optimizer, momentumw_update_numpy, "MomentumW", + "momentum", use_resource=True, do_sparse=True) + + def testBasic(self): + self.doTest(self.get_optimizer, momentumw_update_numpy, "MomentumW", + "momentum", use_resource=False) + + @test_util.run_in_graph_and_eager_modes(reset_test=True) + def testResourceBasic(self): + self.doTest(self.get_optimizer, momentumw_update_numpy, "MomentumW", + "momentum", use_resource=True) + + +class ExtendWithWeightDecayTest(WeightDecayOptimizerTest): + + @staticmethod + def get_optimizer(): + adamw = weight_decay_optimizers.extend_with_decoupled_weight_decay( + adam.AdamOptimizer) + return adamw(WEIGHT_DECAY) + + def testBasic(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "Adam", "m", + use_resource=False) + + @test_util.run_in_graph_and_eager_modes(reset_test=True) + def testResourceBasic(self): + self.doTest(self.get_optimizer, adamw_update_numpy, "Adam", "m", + use_resource=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/optimizer_v2/adam.py b/tensorflow/contrib/optimizer_v2/adam.py index d538ad0fb02699ed8514f512208914f629a47436..631d4f44dfb646541244bfe1d15136dd29f02703 100644 --- a/tensorflow/contrib/optimizer_v2/adam.py +++ b/tensorflow/contrib/optimizer_v2/adam.py @@ -103,9 +103,9 @@ class AdamOptimizer(optimizer_v2.OptimizerV2): def _create_vars(self, var_list, state): # Non-slot variables end up on the same device(s). - state.create_non_slot(initial_value=state.get_hyper("beta1"), + state.create_non_slot(initial_value=lambda: state.get_hyper("beta1"), name="beta1_power") - state.create_non_slot(initial_value=state.get_hyper("beta2"), + state.create_non_slot(initial_value=lambda: state.get_hyper("beta2"), name="beta2_power") # Create slots for the first and second moments. diff --git a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py index 64b95786b5c7a71ee514201d8eb60c26975938b5..06ab58188a2fffa0e3a810d451875ca951a077b9 100644 --- a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py +++ b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py @@ -43,15 +43,15 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.training import saver as core_saver from tensorflow.python.training import training_util -from tensorflow.python.training.checkpointable import base as checkpointable -from tensorflow.python.training.checkpointable import util as checkpointable_utils +from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util -class NonLayerCheckpointable(checkpointable.Checkpointable): +class NonLayerCheckpointable(tracking.Checkpointable): def __init__(self): super(NonLayerCheckpointable, self).__init__() - self.a_variable = checkpointable_utils.add_variable( + self.a_variable = util.add_variable( self, name="a_variable", shape=[]) @@ -88,29 +88,6 @@ class _MirroringSaveable( self._mirrored_variable.assign(tensor)) -class _OwnsMirroredVariables(checkpointable.CheckpointableBase): - """A Checkpointable object which returns a more complex SaveableObject.""" - - def __init__(self): - self.non_dep_variable = variable_scope.get_variable( - name="non_dep_variable", initializer=6., use_resource=True) - self.mirrored = variable_scope.get_variable( - name="mirrored", initializer=15., use_resource=True) - - def _gather_saveables_for_checkpoint(self): - def _saveable_factory(name=self.non_dep_variable.name): - return _MirroringSaveable( - primary_variable=self.non_dep_variable, - mirrored_variable=self.mirrored, - name=name) - return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} - - # The Saver sorts by name before parsing, so we need a name property. - @property - def name(self): - return self.non_dep_variable.name - - class CheckpointingTests(test.TestCase): @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) @@ -122,7 +99,7 @@ class CheckpointingTests(test.TestCase): other_model = MyModel() optimizer = adam.AdamOptimizer(0.001) optimizer_step = training_util.get_or_create_global_step() - root_checkpointable = checkpointable_utils.Checkpoint( + root_checkpointable = util.Checkpoint( optimizer=optimizer, model=model, optimizer_step=optimizer_step) if context.executing_eagerly(): optimizer.minimize( @@ -137,11 +114,11 @@ class CheckpointingTests(test.TestCase): optimizer.minimize( other_model(input_value), global_step=optimizer_step) - self.evaluate(checkpointable_utils.gather_initializers( + self.evaluate(util.gather_initializers( root_checkpointable)) self.evaluate(train_op) named_variables, serialized_graph, _ = ( - checkpointable_utils._serialize_object_graph( + util._serialize_object_graph( root_checkpointable, saveables_cache=None)) expected_checkpoint_names = ( # Created in the root node, so no prefix. @@ -226,11 +203,11 @@ class CheckpointingTests(test.TestCase): optimizer_node.slot_variables[0] .slot_variable_node_id].attributes[0].checkpoint_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): model = MyModel() optimizer = adam.AdamOptimizer(0.001) - root_checkpointable = checkpointable_utils.Checkpoint( + root_checkpointable = util.Checkpoint( optimizer=optimizer, model=model) input_value = constant_op.constant([[3.]]) if context.executing_eagerly(): @@ -240,7 +217,7 @@ class CheckpointingTests(test.TestCase): train_op = optimizer.minimize(model(input_value)) # TODO(allenl): Make initialization more pleasant when graph building. root_checkpointable.save_counter # pylint: disable=pointless-statement - self.evaluate(checkpointable_utils.gather_initializers( + self.evaluate(util.gather_initializers( root_checkpointable)) self.evaluate(train_op) prefix = os.path.join(self.get_temp_dir(), "ckpt") @@ -266,7 +243,7 @@ class CheckpointingTests(test.TestCase): # Preserve beta1_power and beta2_power when appying gradients so we can # test that they've been restored correctly. beta1=1.0, beta2=1.0) - on_create_root = checkpointable_utils.Checkpoint( + on_create_root = util.Checkpoint( optimizer=on_create_optimizer, model=on_create_model) # Deferred restoration status = on_create_root.restore(save_path=save_path) @@ -298,7 +275,7 @@ class CheckpointingTests(test.TestCase): for training_continuation in range(3): model = MyModel() optimizer = adam.AdamOptimizer(0.001) - root = checkpointable_utils.Checkpoint( + root = util.Checkpoint( optimizer=optimizer, model=model, optimizer_step=training_util.get_or_create_global_step()) root.restore(core_saver.latest_checkpoint(checkpoint_directory)) @@ -322,7 +299,7 @@ class CheckpointingTests(test.TestCase): with ops.Graph().as_default(): model = MyModel() optimizer = adam.AdamOptimizer(0.001) - root = checkpointable_utils.Checkpoint( + root = util.Checkpoint( optimizer=optimizer, model=model, global_step=training_util.get_or_create_global_step()) input_value = constant_op.constant([[3.]]) @@ -347,7 +324,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(training_continuation + 1, session.run(root.save_counter)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAgnosticUsage(self): """Graph/eager agnostic usage.""" # Does create garbage when executing eagerly due to ops.Graph() creation. @@ -359,7 +336,7 @@ class CheckpointingTests(test.TestCase): graph=ops.get_default_graph()), test_util.device(use_gpu=True): model = MyModel() optimizer = adam.AdamOptimizer(0.001) - root = checkpointable_utils.Checkpoint( + root = util.Checkpoint( optimizer=optimizer, model=model, global_step=training_util.get_or_create_global_step()) checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory) @@ -381,7 +358,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(root.save_counter)) # pylint: disable=cell-var-from-loop - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWithDefun(self): num_training_steps = 2 checkpoint_directory = self.get_temp_dir() @@ -392,7 +369,7 @@ class CheckpointingTests(test.TestCase): model = MyModel() # Don't actually train so we can test variable values optimizer = adam.AdamOptimizer(0.) - root = checkpointable_utils.Checkpoint( + root = util.Checkpoint( optimizer=optimizer, model=model, global_step=training_util.get_or_create_global_step()) checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory) @@ -442,7 +419,7 @@ class CheckpointingTests(test.TestCase): optimizer = adam.AdamOptimizer(learning_rate=0.05) checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - checkpoint = checkpointable_utils.Checkpoint( + checkpoint = util.Checkpoint( model=model, optimizer=optimizer) for _ in range(2): checkpoint.save(checkpoint_prefix) @@ -453,12 +430,12 @@ class CheckpointingTests(test.TestCase): optimizer.apply_gradients( [(g, v) for g, v in zip(grad, model.vars)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeferredSlotRestoration(self): checkpoint_directory = self.get_temp_dir() - root = checkpointable.Checkpointable() - root.var = checkpointable_utils.add_variable( + root = tracking.Checkpointable() + root.var = util.add_variable( root, name="var", initializer=0.) optimizer = adam.AdamOptimizer(0.1) if context.executing_eagerly(): @@ -468,28 +445,28 @@ class CheckpointingTests(test.TestCase): # Note that `optimizer` has not been added as a dependency of # `root`. Create a one-off grouping so that slot variables for `root.var` # get initialized too. - self.evaluate(checkpointable_utils.gather_initializers( - checkpointable_utils.Checkpoint(root=root, optimizer=optimizer))) + self.evaluate(util.gather_initializers( + util.Checkpoint(root=root, optimizer=optimizer))) self.evaluate(train_op) self.evaluate(state_ops.assign(root.var, 12.)) - no_slots_path = checkpointable_utils.CheckpointableSaver(root).save( + no_slots_path = util.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "no_slots")) root.optimizer = optimizer self.evaluate(state_ops.assign(root.var, 13.)) self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var), 14.)) - slots_path = checkpointable_utils.CheckpointableSaver(root).save( + slots_path = util.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "with_slots")) - new_root = checkpointable.Checkpointable() + new_root = tracking.Checkpointable() # Load the slot-containing checkpoint (deferred), then immediately overwrite # the non-slot variable (also deferred). - slot_status = checkpointable_utils.CheckpointableSaver( + slot_status = util.CheckpointableSaver( new_root).restore(slots_path) - no_slot_status = checkpointable_utils.CheckpointableSaver( + no_slot_status = util.CheckpointableSaver( new_root).restore(no_slots_path) with self.assertRaises(AssertionError): no_slot_status.assert_consumed() - new_root.var = checkpointable_utils.add_variable( + new_root.var = util.add_variable( new_root, name="var", shape=[]) no_slot_status.assert_consumed() no_slot_status.run_restore_ops() @@ -525,12 +502,12 @@ class CheckpointingTests(test.TestCase): with graph.as_default(), self.test_session(graph): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) - self.evaluate(checkpointable_utils.gather_initializers(obj)) - saver = checkpointable_utils.CheckpointableSaver(obj) + self.evaluate(util.gather_initializers(obj)) + saver = util.CheckpointableSaver(obj) saver.save(checkpoint_prefix) before_ops = graph.get_operations() saver.save(checkpoint_prefix) @@ -543,12 +520,12 @@ class CheckpointingTests(test.TestCase): with graph.as_default(), self.test_session(graph): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) - self.evaluate(checkpointable_utils.gather_initializers(obj)) - saver = checkpointable_utils.CheckpointableSaver(obj) + self.evaluate(util.gather_initializers(obj)) + saver = util.CheckpointableSaver(obj) save_path = saver.save(checkpoint_prefix) saver.restore(save_path) before_ops = graph.get_operations() @@ -565,10 +542,10 @@ class CheckpointingTests(test.TestCase): first_session = session_lib.Session(graph=first_graph) with first_graph.as_default(), first_session.as_default(): first_variable = resource_variable_ops.ResourceVariable([1.]) - first_root_checkpointable = checkpointable_utils.Checkpoint( + first_root_checkpointable = util.Checkpoint( optimizer=optimizer, variable=first_variable) train_op = optimizer.minimize(first_variable.read_value) - self.evaluate(checkpointable_utils.gather_initializers( + self.evaluate(util.gather_initializers( first_root_checkpointable)) self.evaluate(train_op) self.evaluate(first_variable.assign([1.])) @@ -581,7 +558,7 @@ class CheckpointingTests(test.TestCase): second_graph = ops.Graph() with second_graph.as_default(), session_lib.Session(graph=second_graph): second_variable = resource_variable_ops.ResourceVariable([1.]) - second_root_checkpointable = checkpointable_utils.Checkpoint( + second_root_checkpointable = util.Checkpoint( optimizer=optimizer, variable=second_variable) train_op = optimizer.minimize(second_variable.read_value) second_root_checkpointable.restore(None).initialize_or_restore() @@ -616,7 +593,7 @@ class CheckpointingTests(test.TestCase): class TemplateTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore(self): def _templated(): @@ -631,7 +608,7 @@ class TemplateTests(test.TestCase): save_template = template.make_template("s1", _templated) v1_save, _, v2_save = save_template() optimizer = adam.AdamOptimizer(0.0) - save_root = checkpointable_utils.Checkpoint( + save_root = util.Checkpoint( my_template=save_template, optimizer=optimizer) optimizer.minimize(v1_save.read_value) self.evaluate([v.initializer for v in optimizer.variables()]) @@ -643,7 +620,7 @@ class TemplateTests(test.TestCase): load_template = template.make_template("s2", _templated) load_optimizer = adam.AdamOptimizer(0.0) - load_root = checkpointable_utils.Checkpoint( + load_root = util.Checkpoint( my_template=load_template, optimizer=load_optimizer) status = load_root.restore(save_path) var, var_plus_one, var2 = load_template() @@ -664,12 +641,12 @@ class CheckpointCompatibilityTests(test.TestCase): model = MyModel() optimizer = adam.AdamOptimizer(0.001) optimizer_step = training_util.get_or_create_global_step() - root_checkpointable = checkpointable_utils.Checkpoint( + root_checkpointable = util.Checkpoint( optimizer=optimizer, model=model, optimizer_step=optimizer_step) train_op = optimizer.minimize( functools.partial(model, input_value), global_step=optimizer_step) - self.evaluate(checkpointable_utils.gather_initializers( + self.evaluate(util.gather_initializers( root_checkpointable)) self.evaluate(train_op) # A regular variable, a slot variable, and a non-slot Optimizer variable @@ -712,7 +689,7 @@ class CheckpointCompatibilityTests(test.TestCase): sess=session, save_path=checkpoint_prefix, global_step=root.optimizer_step) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadFromNameBasedSaver(self): """Save a name-based checkpoint, load it using the object-based API.""" with test_util.device(use_gpu=True): @@ -721,7 +698,7 @@ class CheckpointCompatibilityTests(test.TestCase): self._set_sentinels(root) with self.assertRaises(AssertionError): self._check_sentinels(root) - object_saver = checkpointable_utils.CheckpointableSaver(root) + object_saver = util.CheckpointableSaver(root) self._set_sentinels(root) status = object_saver.restore(save_path) if context.executing_eagerly(): diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index f537318b32986c941b6c41eb363929e906027dd7..8c11d8bcfdf76bc12e13ffb58f917978e966476e 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -162,12 +162,12 @@ def _get_processor(v): def _var_key_v2(var): """Key for representing a primary variable, for looking up slots.""" # pylint: disable=protected-access - if hasattr(var, "_mirrored_container"): - mirrored_container = var._mirrored_container() - assert mirrored_container is not None + if hasattr(var, "_distributed_container"): + distributed_container = var._distributed_container() + assert distributed_container is not None if context.executing_eagerly(): - return mirrored_container._unique_id - return mirrored_container._shared_name + return distributed_container._unique_id + return distributed_container._shared_name if context.executing_eagerly(): return var._unique_id return var.op.name @@ -211,8 +211,9 @@ class _OptimizerV2State(object): # This dict starts with a single item with key "None" with the hyper # parameter value converted to a Tensor. Other items have dtype keys # with that Tensor cast to that dtype. - self._hyper = {name: {None: ops.convert_to_tensor(value, name=name)} - for name, (dynamic, value) in hyper.items() if not dynamic} + with ops.init_scope(): + self._hyper = {name: {None: ops.convert_to_tensor(value, name=name)} + for name, (dynamic, value) in hyper.items() if not dynamic} self._slots = {} self._non_slot_dict = {} # Extra state to help Optimizers implement Checkpointable. Holds information @@ -765,7 +766,8 @@ class OptimizerV2(optimizer_v1.Optimizer): # *after* loss() is evaluated, so we know what loss reduction it uses. if scale_loss_by_num_towers is None: scale_loss_by_num_towers = ( - distribute_lib.get_loss_reduction() == "mean") + distribute_lib.get_loss_reduction() == + variable_scope.VariableAggregation.MEAN) if scale_loss_by_num_towers: num_towers = distribute_lib.get_distribution_strategy().num_towers if num_towers > 1: @@ -783,7 +785,8 @@ class OptimizerV2(optimizer_v1.Optimizer): # Scale loss for number of towers (non-callable-loss case). if scale_loss_by_num_towers is None: scale_loss_by_num_towers = ( - distribute_lib.get_loss_reduction() == "mean") + distribute_lib.get_loss_reduction() == + variable_scope.VariableAggregation.MEAN) if scale_loss_by_num_towers: num_towers = distribute_lib.get_distribution_strategy().num_towers if num_towers > 1: @@ -895,7 +898,8 @@ class OptimizerV2(optimizer_v1.Optimizer): def _distributed_apply(self, distribution, grads_and_vars, global_step, name): """`apply_gradients` for use with a `DistributionStrategy`.""" - reduced_grads = distribution.batch_reduce("sum", grads_and_vars) + reduced_grads = distribution.batch_reduce( + variable_scope.VariableAggregation.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py index 8599af32f6f4cc5529cd812e83c02ef3812cb71e..ec033c4a0163ba9ed39e55fa9e92dfdadc9a1b2f 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py @@ -35,7 +35,7 @@ from tensorflow.python.platform import test class OptimizerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -113,7 +113,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoVariables(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: # pylint: disable=cell-var-from-loop @@ -128,7 +128,7 @@ class OptimizerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No.*variables'): sgd_op.minimize(loss) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -146,7 +146,7 @@ class OptimizerTest(test.TestCase): # var1 has no gradient sgd_op.minimize(loss, var_list=[var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -162,7 +162,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.minimize(loss, var_list=[var0, var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -176,7 +176,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.apply_gradients([(None, var0), (None, var1)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -216,7 +216,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([-14., -13.], self.evaluate(var0)) self.assertAllClose([-6., -5.], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeGradientsWithTensors(self): x = ops.convert_to_tensor(1.0) def f(): diff --git a/tensorflow/contrib/periodic_resample/BUILD b/tensorflow/contrib/periodic_resample/BUILD index 976b312e8345a801ad07f622b6117b88af2cf603..f2171efc959362c1e4392fefbd5842f0883571d7 100644 --- a/tensorflow/contrib/periodic_resample/BUILD +++ b/tensorflow/contrib/periodic_resample/BUILD @@ -97,6 +97,8 @@ tf_cc_test( ], deps = [ ":all_ops", + "//tensorflow/core:framework", + "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core:testlib", ], diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc index 55edf76fcd3eed461e1465b569e1c2e9e2facbc0..43b7c1799ffb2e27f9d15bc6011d49334867b6ec 100644 --- a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc +++ b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/shape_inference_testutil.h" +#include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/tensor_testutil.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py index 55479bf5f74299bf09f131a6127f9f11d6192d90..e3c48998305e9d9b6c185fd4c0f324fa0449c691 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py @@ -121,7 +121,8 @@ def _FoldFusedBatchNorms(graph, is_training, freeze_batch_norm_delay): scaled_weight_tensor = math_ops.multiply( weights, multiplier_tensor, name='mul_fold') new_layer_tensor = _CloneWithNewOperands( - match.layer_op, match.input_tensor, scaled_weight_tensor) + match.layer_op, match.input_tensor, scaled_weight_tensor, + match.batch_to_space_op) if correction_recip is not None: new_layer_tensor = math_ops.multiply( @@ -149,6 +150,8 @@ def _FindFusedBatchNorms(graph): _FusedBatchNormMatches. """ input_pattern = graph_matcher.OpTypePattern('*') + # In practice, the weight pattern can match a Variable or a SpaceToBatchND + # operation that follows a variable for atrous convolutions. weight_pattern = graph_matcher.OpTypePattern('*') gamma_pattern = graph_matcher.OpTypePattern('*') beta_pattern = graph_matcher.OpTypePattern('*') @@ -160,16 +163,27 @@ def _FindFusedBatchNorms(graph): layer_pattern = graph_matcher.OpTypePattern( 'Conv2D|DepthwiseConv2dNative|MatMul', inputs=[input_pattern, weight_pattern]) + batch_to_space_pattern = graph_matcher.OpTypePattern( + 'BatchToSpaceND', + inputs=[ + layer_pattern, + graph_matcher.OpTypePattern('*'), + graph_matcher.OpTypePattern('*') + ]) + layer_output_pattern = graph_matcher.OneofPattern( + [layer_pattern, batch_to_space_pattern]) # MatMul has a Reshape between it and FusedBatchNorm. matmul_reshape_pattern = graph_matcher.OpTypePattern( - 'Reshape', inputs=[layer_pattern, - graph_matcher.OpTypePattern('*')]) + 'Reshape', + inputs=[layer_output_pattern, + graph_matcher.OpTypePattern('*')]) batch_norm_pattern = graph_matcher.OpTypePattern( 'FusedBatchNorm', inputs=[ - graph_matcher.OneofPattern([matmul_reshape_pattern, layer_pattern]), - gamma_pattern, beta_pattern, mean_pattern, variance_pattern + graph_matcher.OneofPattern( + [matmul_reshape_pattern, layer_output_pattern]), gamma_pattern, + beta_pattern, mean_pattern, variance_pattern ]) matmul_bn_output_reshape_pattern = graph_matcher.OpTypePattern( 'Reshape', inputs=[batch_norm_pattern, @@ -192,6 +206,7 @@ def _FindFusedBatchNorms(graph): moving_variance_tensor = None bn_decay_mean_tensor = None bn_decay_var_tensor = None + batch_to_space_op = None layer_op = match_result.get_op(layer_pattern) layer_tensor = match_result.get_tensor(layer_pattern) bn_op = match_result.get_op(batch_norm_pattern) @@ -213,6 +228,7 @@ def _FindFusedBatchNorms(graph): if not output_tensor.consumers(): continue + batch_to_space_op = match_result.get_op(batch_to_space_pattern) input_tensor = match_result.get_tensor(input_pattern) weight_tensor = match_result.get_tensor(weight_pattern) gamma_tensor = match_result.get_tensor(gamma_pattern) @@ -276,7 +292,8 @@ def _FindFusedBatchNorms(graph): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon=batch_epsilon) + batch_epsilon=batch_epsilon, + batch_to_space_op=batch_to_space_op) def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, @@ -380,7 +397,8 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, return correction_scale, correction_recip, correction_offset -def _CloneWithNewOperands(layer_op, input_tensor, weight_tensor): +def _CloneWithNewOperands(layer_op, input_tensor, weight_tensor, + batch_to_space_op): """Clones layer_op with input_tensor and weight_tensor as new inputs.""" new_layer_name = layer_op.name.split('/')[-1] + '_Fold' if layer_op.type == 'Conv2D': @@ -400,12 +418,25 @@ def _CloneWithNewOperands(layer_op, input_tensor, weight_tensor): transpose_b=layer_op.get_attr('transpose_b'), name=new_layer_name) elif layer_op.type == 'DepthwiseConv2dNative': - return nn.depthwise_conv2d( + conv = nn.depthwise_conv2d( input_tensor, weight_tensor, + rate=layer_op.get_attr('dilations'), strides=layer_op.get_attr('strides'), padding=layer_op.get_attr('padding'), name=new_layer_name) + # Copy the batch to space operation if we have a atrous convolution. + if batch_to_space_op: + batch_to_space_op = layer_op.outputs[0].consumers()[0] + # TODO(suharshs): It's hard to make this name match with the unfused name. + # Restructure this code to not rely on scope at all. + new_batch_to_space_name = batch_to_space_op.name.split('/')[-1] + '_Fold' + conv = array_ops.batch_to_space_nd( + conv, + batch_to_space_op.inputs[1], + batch_to_space_op.inputs[2], + name=new_batch_to_space_name) + return conv else: raise ValueError('Cannot handle operation of type: %s' % layer_op.type) @@ -617,7 +648,8 @@ def _GetBatchNormParams(graph, context, has_scaling): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon=batch_epsilon) + batch_epsilon=batch_epsilon, + batch_to_space_op=None) def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, @@ -651,6 +683,11 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, '/BatchNorm/batchnorm_1/' + mul_scale_name) op_below = mul_scale.inputs[0].op + # Skip over the BatchToSpace operation in the case of atrous convolutions. + batch_to_space_op = None + if op_below.type == 'BatchToSpaceND': + batch_to_space_op = op_below + op_below = op_below.inputs[0].op weights = op_below.inputs[1] match = _GetBatchNormParams( graph=graph, context=context, has_scaling=has_scaling) @@ -691,7 +728,7 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, context + '/correction_mult') mul_fold = _CloneOp(mul_scale, context + '/mul_fold', [(0, weights)]) else: - raise ValueError('Cannot handle operation of type: %s' % op_below.op) + raise ValueError('Cannot handle operation of type: %s' % op_below.type) _AssertShapesMatch('mul_fold', mul_fold.inputs[0], mul_fold.outputs[0]) conv_or_fc_folded = _CloneOp(op_below, op_below.name + '_Fold', @@ -701,6 +738,13 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, context + '/BatchNorm/batchnorm_1/add_1') corrected_output = conv_or_fc_folded.outputs[0] + # Copy the batch to space operation if we have a atrous convolution. + if batch_to_space_op: + corrected_output = array_ops.batch_to_space_nd( + corrected_output, + batch_to_space_op.inputs[1], + batch_to_space_op.inputs[2], + name=batch_to_space_op.name + '_Fold') if correction_offset is not None: with ops.device(conv_or_fc_folded.device): corrected_output = math_ops.multiply(correction_recip, corrected_output, @@ -898,7 +942,8 @@ class _BatchNormMatch(object): def __init__(self, layer_op, bn_op, output_tensor, input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, variance_tensor, moving_mean_tensor, moving_variance_tensor, - bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon): + bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon, + batch_to_space_op): self._layer_op = layer_op self._bn_op = bn_op self._output_tensor = output_tensor @@ -913,6 +958,7 @@ class _BatchNormMatch(object): self._bn_decay_mean_tensor = bn_decay_mean_tensor self._bn_decay_var_tensor = bn_decay_var_tensor self._batch_epsilon = batch_epsilon + self._batch_to_space_op = batch_to_space_op @property def layer_op(self): @@ -969,3 +1015,7 @@ class _BatchNormMatch(object): @property def bn_decay_var_tensor(self): return self._bn_decay_var_tensor + + @property + def batch_to_space_op(self): + return self._batch_to_space_op diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py index bfa9d3bf705e327091098a8e416b7902f852605a..7c907ffd92c1ae0c762e41cc429b0e6ce053f6b9 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py @@ -438,6 +438,90 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): def testFoldDepthwiseConv2d(self): self._RunTestOverParameters(self._TestFoldDepthwiseConv2d) + def _TestFoldAtrousConv2d(self, relu, relu_op_name, with_bypass, has_scaling, + fused_batch_norm, freeze_batch_norm_delay): + """Tests folding: inputs -> AtrousConv2d with batch norm -> Relu*. + + Args: + relu: Callable that returns an Operation, a factory method for the Relu*. + relu_op_name: String, name of the Relu* operation. + with_bypass: Bool, when true there is an extra connection added from + inputs to just before Relu*. + has_scaling: Bool, when true the batch norm has scaling. + fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance + """ + g = ops.Graph() + with g.as_default(): + batch_size, height, width = 5, 128, 128 + inputs = array_ops.zeros((batch_size, height, width, 3)) + dilation_rate = 2 + activation_fn = None if with_bypass else relu + scope = 'test/test2' if with_bypass else 'test' + node = separable_conv2d( + inputs, + None, [3, 3], + rate=dilation_rate, + depth_multiplier=1.0, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation_fn, + normalizer_fn=batch_norm, + normalizer_params=self._BatchNormParams( + scale=has_scaling, fused=fused_batch_norm), + scope=scope) + if with_bypass: + node = math_ops.add(inputs, node, name='test/Add') + relu(node, name='test/' + relu_op_name) + + fold_batch_norms.FoldBatchNorms( + g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) + + folded_mul = g.get_operation_by_name(scope + '/mul_fold') + self.assertEqual(folded_mul.type, 'Mul') + if fused_batch_norm: + scale_reshape_op_name = scope + '/BatchNorm_Fold/scale_reshape' + else: + scale_reshape_op_name = scope + '/scale_reshape' + self._AssertInputOpsAre(folded_mul, + [scope + '/correction_mult', scale_reshape_op_name]) + self._AssertOutputGoesToOps(folded_mul, g, [scope + '/depthwise_Fold']) + + scale_reshape = g.get_operation_by_name(scale_reshape_op_name) + self.assertEqual(scale_reshape.type, 'Reshape') + self._AssertInputOpsAre(scale_reshape, [ + self._BatchNormMultiplierName(scope, has_scaling, fused_batch_norm), + scale_reshape_op_name + '/shape' + ]) + self._AssertOutputGoesToOps(scale_reshape, g, [scope + '/mul_fold']) + + folded_conv = g.get_operation_by_name(scope + '/depthwise_Fold') + self.assertEqual(folded_conv.type, 'DepthwiseConv2dNative') + self._AssertInputOpsAre( + folded_conv, [scope + '/mul_fold', scope + '/depthwise/SpaceToBatchND']) + if fused_batch_norm: + self._AssertOutputGoesToOps(folded_conv, g, + [scope + '/BatchToSpaceND_Fold']) + else: + self._AssertOutputGoesToOps(folded_conv, g, + [scope + '/depthwise/BatchToSpaceND_Fold']) + + folded_add = g.get_operation_by_name(scope + '/add_fold') + self.assertEqual(folded_add.type, 'Add') + self._AssertInputOpsAre(folded_add, [ + scope + '/correction_add', + self._BathNormBiasName(scope, fused_batch_norm) + ]) + output_op_names = ['test/Add' if with_bypass else 'test/' + relu_op_name] + self._AssertOutputGoesToOps(folded_add, g, output_op_names) + + for op in g.get_operations(): + self.assertFalse('//' in op.name, 'Double slash in op %s' % op.name) + + def testFoldAtrousConv2d(self): + self._RunTestOverParameters(self._TestFoldAtrousConv2d) + def _TestCompareFoldAndUnfolded(self, relu, relu_op_name, with_bypass, has_scaling, fused_batch_norm, freeze_batch_norm_delay): diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py index cbba72643f7f166c473b6181edc292f695c4cbc2..19e5bef1ea48ca4441cdef6b1a74e98e9cf6ddb9 100644 --- a/tensorflow/contrib/quantize/python/quantize.py +++ b/tensorflow/contrib/quantize/python/quantize.py @@ -194,6 +194,8 @@ def _FindLayersToQuantize(graph): / conv|fc | + [batch_to_space_nd] + | [post_conv_correction] | biasadd|folded_bias @@ -247,9 +249,21 @@ def _FindLayersToQuantize(graph): ], ordered_inputs=False) + # For atrous convolutions a BatchToSpaceND will occur after the depthwise + # convolution. + batch_to_space_pattern = graph_matcher.OpTypePattern( + 'BatchToSpaceND', + inputs=[ + layer_pattern, + graph_matcher.OpTypePattern('*'), + graph_matcher.OpTypePattern('*') + ]) + + layer_output_pattern = graph_matcher.OneofPattern( + [batch_to_space_pattern, layer_pattern]) folded_bias_mul_pattern = graph_matcher.OpTypePattern( 'Mul', - inputs=[graph_matcher.OpTypePattern('*'), layer_pattern], + inputs=[graph_matcher.OpTypePattern('*'), layer_output_pattern], ordered_inputs=False) post_layer_op_correction_pattern = graph_matcher.OpTypePattern( 'Add', @@ -265,7 +279,7 @@ def _FindLayersToQuantize(graph): ordered_inputs=False) bias_add_pattern = graph_matcher.OpTypePattern( - 'Add|BiasAdd', inputs=[layer_pattern, '*'], ordered_inputs=False) + 'Add|BiasAdd', inputs=[layer_output_pattern, '*'], ordered_inputs=False) # The bias can come from the bias add or the folded bias add. bypass_pattern = graph_matcher.OpTypePattern( @@ -373,14 +387,6 @@ def _FindLayersToQuantize(graph): return layer_matches -def _HasPostActivationBypass(activation_op): - for activation_tensor in activation_op.outputs: - for output_op in activation_tensor.consumers(): - if output_op.type == 'Add': - return True - return False - - class _LayerMatch(object): """Contains all information related to a matched Layer.""" diff --git a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py index db745aa56212af6a9c20e06ee9e4e5d6e27cf3c3..5e3af0a567536ef6fcfd86d82e94c0ba21077a85 100644 --- a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py +++ b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py @@ -276,6 +276,52 @@ class QuantizeTest(test_util.TensorFlowTestCase): graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, delay, use_resource) + def testQuantize_AtrousConvWithoutBatchNorm(self): + self._RunWithoutBatchNormTestOverParameters( + self._TestQuantize_AtrousConvWithoutBatchNorm) + + def _TestQuantize_AtrousConvWithoutBatchNorm( + self, activation, activation_op_name, with_bypass, delay, use_resource): + """Tests quantization: inputs -> atrous conv no batch norm -> Activation. + + Args: + activation: Callable that returns an Operation, a factory method for the + Activation. + activation_op_name: String, name of the Activation operation. + with_bypass: Bool, when true there is an extra connection added from + inputs to just before Activation. + delay: Int (optional), delay in number of steps until quantization starts. + use_resource: Bool, when true uses resource variables. + """ + graph = ops.Graph() + with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) + batch_size, height, width, depth = 5, 128, 128, 3 + inputs = array_ops.zeros((batch_size, height, width, depth)) + dilation_rate = 2 + activation_fn = None if with_bypass else activation + scope = 'test/test2' if with_bypass else 'test' + node = separable_conv2d( + inputs, + None, [3, 3], + rate=dilation_rate, + depth_multiplier=1.0, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation_fn, + scope=scope) + if with_bypass: + node = math_ops.add(inputs, node, name='test/Add') + node = activation(node, name='test/' + activation_op_name) + update_barrier = control_flow_ops.no_op(name='update_barrier') + with ops.control_dependencies([update_barrier]): + array_ops.identity(node, name='control_dependency') + quantize.Quantize(graph, True, quant_delay=delay) + + self._AssertCorrectQuantizedGraphWithoutBatchNorm( + graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, + delay, use_resource) + def _RunBatchNormTestOverParameters(self, test_fn): # TODO(suharshs): Use parameterized test once OSS TF supports it. parameters_list = [ @@ -543,6 +589,61 @@ class QuantizeTest(test_util.TensorFlowTestCase): graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, delay, use_resource) + def testQuantize_AtrousConvWithBatchNorm(self): + self._RunBatchNormTestOverParameters( + self._TestQuantize_AtrousConvWithBatchNorm) + + def _TestQuantize_AtrousConvWithBatchNorm( + self, activation, activation_op_name, with_bypass, delay, + fused_batch_norm, use_resource): + """Tests quantization: inputs -> atrous conv with batch norm -> Activation. + + Args: + activation: Callable that returns an Operation, a factory method for the + Activation. + activation_op_name: String, name of the Activation operation. + with_bypass: Bool, when true there is an extra connection added from + inputs to just before Activation. + delay: Int (optional), delay in number of steps until quantization starts. + fused_batch_norm: Bool, when true use FusedBatchNorm. + use_resource: Bool, when true uses resource variables. + """ + graph = ops.Graph() + with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) + batch_size, height, width, depth = 5, 128, 128, 3 + inputs = array_ops.zeros((batch_size, height, width, depth)) + dilation_rate = 2 + scope = 'test/test2' if with_bypass else 'test' + node = separable_conv2d( + inputs, + None, [3, 3], + rate=dilation_rate, + depth_multiplier=1.0, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=None, + normalizer_fn=batch_norm, + normalizer_params=self._BatchNormParams(fused_batch_norm), + scope=scope) + + # Manually add a bypass (optional) and an activation. + if with_bypass: + node = math_ops.add(inputs, node, name='test/Add') + + node = activation(node, name='test/' + activation_op_name) + + update_barrier = control_flow_ops.no_op(name='update_barrier') + with ops.control_dependencies([update_barrier]): + array_ops.identity(node, name='control_dependency') + + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, True, quant_delay=delay) + + self._AssertCorrectQuantizedGraphWithBatchNorm( + graph, scope, 'DepthwiseConv2dNative', activation_op_name, + with_bypass, delay, use_resource) + def _AssertIdempotent(self, graph): # Ensure that calling the rewrite again doesn't change the graph. graph_def_before = str(graph.as_graph_def()) diff --git a/tensorflow/contrib/recurrent/BUILD b/tensorflow/contrib/recurrent/BUILD index b3cb04ce26d96333f516f1298c8d5c331964f05b..f9827f766da022b184b3348fc24b1570bac8678f 100644 --- a/tensorflow/contrib/recurrent/BUILD +++ b/tensorflow/contrib/recurrent/BUILD @@ -102,5 +102,8 @@ cuda_py_tests( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", ], - tags = ["nopip"], + tags = [ + "nopip", + "optonly", + ], ) diff --git a/tensorflow/contrib/rnn/__init__.py b/tensorflow/contrib/rnn/__init__.py index 67f31785b57fddef67733c18c3b744322532c28c..07227bcb77d353200ee46763d51727ed9c0974a1 100644 --- a/tensorflow/contrib/rnn/__init__.py +++ b/tensorflow/contrib/rnn/__init__.py @@ -58,6 +58,7 @@ See @{$python/contrib.rnn} guide. @@Conv3DLSTMCell @@HighwayWrapper @@GLSTMCell +@@SRUCell @@AttentionCellWrapper diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index b8840a8f2420f1bc6c75f0a02e5465c595378dec..86f1e27abd53d011f37f06851dd6d0977853c8f4 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -443,7 +443,7 @@ class RNNCellTest(test.TestCase): self.assertTrue( float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) < 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWrapperCheckpointing(self): for wrapper_type in [ rnn_cell_impl.DropoutWrapper, diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index be99a5d67a3e49b1d522406601d050392f75e963..1c20d88fe4bcbe2c1f1e3413502dbf276f2d21b3 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -921,7 +921,7 @@ class LSTMTest(test.TestCase): # Smoke test, this should not raise an error rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicRNNWithTupleStates(self): num_units = 3 input_size = 5 @@ -997,7 +997,7 @@ class LSTMTest(test.TestCase): self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicRNNWithNestedTupleStates(self): num_units = 3 input_size = 5 @@ -1285,7 +1285,7 @@ class LSTMTest(test.TestCase): "Comparing individual variable gradients iteration %d" % i) self.assertAllEqual(a, b) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicEquivalentToStaticRNN(self): self._testDynamicEquivalentToStaticRNN(use_sequence_length=False) self._testDynamicEquivalentToStaticRNN(use_sequence_length=False) diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index 184144f64a56358206014a0f75473b4a9b16617a..c7fbeea3105ae4c9c9ec2fd131f3468018990028 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -250,7 +250,7 @@ class BeamSearchDecoder(decoder.Decoder): ``` tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch( encoder_outputs, multiplier=beam_width) - tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch( + tiled_encoder_final_state = tf.contrib.seq2seq.tile_batch( encoder_final_state, multiplier=beam_width) tiled_sequence_length = tf.contrib.seq2seq.tile_batch( sequence_length, multiplier=beam_width) diff --git a/tensorflow/contrib/seq2seq/python/ops/decoder.py b/tensorflow/contrib/seq2seq/python/ops/decoder.py index e69725ff8ab1ba4de880c914a6f5fdad5e54566d..f58268eff525a4b592c79acb32207e1a3f62bdc7 100644 --- a/tensorflow/contrib/seq2seq/python/ops/decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/decoder.py @@ -21,6 +21,7 @@ from __future__ import print_function import abc import six +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -182,19 +183,20 @@ def dynamic_decode(decoder, raise TypeError("Expected decoder to be type Decoder, but saw: %s" % type(decoder)) - def _is_xla_tensor(tensor): - try: - op = tensor.op - except AttributeError: - return False - if control_flow_util.IsInXLAContext(op): - return True - return False - with variable_scope.variable_scope(scope, "decoder") as varscope: - # Properly cache variable values inside the while_loop - if varscope.caching_device is None: - varscope.set_caching_device(lambda op: op.device) + # Determine context types. + ctxt = ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access + is_xla = control_flow_util.GetContainingXLAContext(ctxt) is not None + in_while_loop = ( + control_flow_util.GetContainingWhileContext(ctxt) is not None) + # Properly cache variable values inside the while_loop. + # Don't set a caching device when running in a loop, since it is possible + # that train steps could be wrapped in a tf.while_loop. In that scenario + # caching prevents forward computations in loop iterations from re-reading + # the updated weights. + if not context.executing_eagerly() and not in_while_loop: + if varscope.caching_device is None: + varscope.set_caching_device(lambda op: op.device) if maximum_iterations is not None: maximum_iterations = ops.convert_to_tensor( @@ -208,9 +210,6 @@ def dynamic_decode(decoder, decoder.output_dtype, decoder.batch_size) - is_xla = False - if any([_is_xla_tensor(i) for i in nest.flatten(initial_inputs)]): - is_xla = True if is_xla and maximum_iterations is None: raise ValueError("maximum_iterations is required for XLA compilation.") if maximum_iterations is not None: diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py index 03d6da7765ba5249a9fb22f56a469cf07c310479..f10d78259a3be3a3a6f7f78c196ab107f18a53aa 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py +++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py @@ -147,7 +147,7 @@ class SpectralOpsTest(test.TestCase): inverse_stft = spectral_ops.inverse_stft(stft, frame_length=8, fft_length=16, frame_step=8) expected_length = (stft.shape[0] - 1) * 8 + 8 - self.assertAllEqual([None], inverse_stft.shape.as_list()) + self.assertAllEqual([256], inverse_stft.shape.as_list()) self.assertAllEqual([expected_length], inverse_stft.eval().shape) def test_stft_and_inverse_stft(self): diff --git a/tensorflow/contrib/signal/python/kernel_tests/test_util.py b/tensorflow/contrib/signal/python/kernel_tests/test_util.py index 9a3603b6a97ef7c3a4b940b83281ebceda93c9db..7d6289532addfd4b4b867bf64d9113253bd1c76d 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/test_util.py +++ b/tensorflow/contrib/signal/python/kernel_tests/test_util.py @@ -39,6 +39,7 @@ def grappler_optimize(graph, fetches=None, rewriter_config=None): """ if rewriter_config is None: rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 if fetches is not None: for fetch in fetches: graph.add_to_collection('train_op', fetch) diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 3d0308aaf3da3b5b16fd22a2905db36917e8c97b..2c97834523424d0fab56330b4d9355a75427e0ef 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -33,7 +33,6 @@ from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.wrappers import hooks from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics @@ -242,7 +241,7 @@ class SingleEvaluationTest(test.TestCase): checkpoint_path = os.path.join(self.get_temp_dir(), 'this_file_doesnt_exist') log_dir = os.path.join(self.get_temp_dir(), 'error_raised') - with self.assertRaises(errors.NotFoundError): + with self.assertRaises(ValueError): evaluation.evaluate_once('', checkpoint_path, log_dir) def _prepareCheckpoint(self, checkpoint_path): diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py index 9305c6a11c4ec898c82553773e8e7277a54ab82e..85918bf8506623cf5e0c9106ae9ed80e233f5a7d 100644 --- a/tensorflow/contrib/solvers/python/ops/linear_equations.py +++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import linalg_ops def conjugate_gradient(operator, diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index f1ef218e74bbd225071324a8269fdfeb5de0e038..3e41e3d0b48ea06f9cb8c1862e27eacb5ebc4417 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -81,6 +81,19 @@ class EagerFileTest(test_util.TensorFlowTestCase): # test here that we're calling them correctly. self.assertTrue(gfile.Exists(logdir)) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerMemory(self): + training_util.get_or_create_global_step() + logdir = self.get_temp_dir() + with summary_ops.create_file_writer( + logdir, max_queue=0, + name='t0').as_default(), summary_ops.always_record_summaries(): + summary_ops.generic('tensor', 1, '') + summary_ops.scalar('scalar', 2.0) + summary_ops.histogram('histogram', [1.0]) + summary_ops.image('image', [[[[1.0]]]]) + summary_ops.audio('audio', [[1.0]], 1.0, 1) + def testDefunSummarys(self): training_util.get_or_create_global_step() logdir = tempfile.mkdtemp() diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index a5d8b061b6b26f9d05be40a1162481ae219b0e9c..adda0b758b172f5e80c165e4b28dbdbecef2ba16 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -49,7 +49,6 @@ tf_cuda_cc_test( tf_custom_op_library( name = "python/ops/_trt_engine_op.so", srcs = [ - "ops/trt_calib_op.cc", "ops/trt_engine_op.cc", ], deps = [ @@ -76,11 +75,9 @@ tf_cuda_library( cc_library( name = "trt_engine_op_kernel", srcs = [ - "kernels/trt_calib_op.cc", "kernels/trt_engine_op.cc", ], hdrs = [ - "kernels/trt_calib_op.h", "kernels/trt_engine_op.h", ], copts = tf_copts(), @@ -89,20 +86,22 @@ cc_library( ":trt_logging", ":trt_plugins", ":trt_resources", + ":trt_conversion", + ":utils", "//tensorflow/core:gpu_headers_lib", "//tensorflow/core:lib_proto_parsing", "//tensorflow/core:stream_executor_headers_lib", + "//tensorflow/core/grappler/costs:graph_properties", ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]) + tf_custom_op_library_additional_deps(), - # TODO(laigd) + # TODO(laigd): fix this by merging header file in cc file. alwayslink = 1, # buildozer: disable=alwayslink-with-hdrs ) tf_gen_op_libs( op_lib_names = [ "trt_engine_op", - "trt_calib_op", ], ) @@ -122,7 +121,6 @@ tf_gen_op_wrapper_py( name = "trt_engine_op", gen_locally = True, deps = [ - ":trt_calib_op_op_lib", ":trt_engine_op_op_lib", ":trt_logging", ":trt_shape_function", @@ -140,7 +138,6 @@ tf_custom_op_py_library( kernels = [ ":trt_engine_op_kernel", ":trt_engine_op_op_lib", - ":trt_calib_op_op_lib", ":trt_shape_function", ], srcs_version = "PY2AND3", @@ -191,7 +188,6 @@ tf_py_wrap_cc( deps = [ ":trt_conversion", ":trt_engine_op_kernel", - "//tensorflow/core:framework_lite", "//third_party/python_runtime:headers", ], ) @@ -211,6 +207,7 @@ tf_cuda_library( ], deps = [ ":trt_logging", + ":utils", "//tensorflow/core:framework_headers_lib", "//tensorflow/core:framework_lite", "//tensorflow/core:lib_proto_parsing", @@ -237,12 +234,12 @@ tf_cuda_library( ":trt_plugins", ":trt_logging", ":trt_resources", + ":utils", "//tensorflow/core/grappler/clusters:cluster", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", - "//tensorflow/core:framework", "//tensorflow/core:gpu_runtime", "//tensorflow/core:framework_lite", "//tensorflow/core:graph", @@ -343,3 +340,8 @@ py_test( "//tensorflow/python:framework_test_lib", ], ) + +cc_library( + name = "utils", + hdrs = ["convert/utils.h"], +) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index da4dd5a14cd74591fc9df63cd5868044e4e369ec..17b32c0e30bfce2bbf6ca86b5df590fab0e63d85 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_graph.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" +#include #include #include #include @@ -24,10 +24,17 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/contrib/tensorrt/segment/segment.h" #include "tensorflow/core/common_runtime/gpu/gpu_id.h" #include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" -#include "tensorflow/core/common_runtime/gpu/process_state.h" +#include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" +#include "tensorflow/core/framework/function.h" +#include "tensorflow/core/framework/graph_to_functiondef.h" +#include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/graph_constructor.h" @@ -39,17 +46,39 @@ limitations under the License. #include "tensorflow/core/grappler/utils.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/protobuf/config.pb.h" // NOLINT #include "tensorflow/core/protobuf/device_properties.pb.h" // NOLINT +#include "tensorflow/core/protobuf/rewriter_config.pb.h" // NOLINT +#include "tensorflow/core/util/device_name_utils.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" #include "tensorrt/include/NvInfer.h" - namespace tensorflow { namespace tensorrt { namespace convert { +using ::tensorflow::strings::StrAppend; +using ::tensorflow::strings::StrCat; + +// Returns compiled TRT version information {Maj, Min, Patch} +std::vector GetLinkedTensorRTVersion() { + return {NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR, NV_TENSORRT_PATCH}; +} + +// Returns loaded TRT library version {Maj, Min, Patch} +std::vector GetLoadedTensorRTVersion() { + int ver = getInferLibVersion(); + int ver_major = ver / 1000; + ver = ver - ver_major * 1000; + int ver_minor = ver / 100; + int ver_patch = ver - ver_minor * 100; + return {ver_major, ver_minor, ver_patch}; +} + namespace { bool IsTensorRTCandidate(const tensorflow::Node* node) { @@ -82,229 +111,6 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())); } -void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, - const std::set& subgraph_node_ids, - tensorflow::EdgeSet* incoming_edges) { - for (int node_id : subgraph_node_ids) { - const tensorflow::Node* node = graph.FindNodeId(node_id); - for (const tensorflow::Edge* edge : node->in_edges()) { - if (!subgraph_node_ids.count(edge->src()->id()) && - !edge->src()->IsSource() && !edge->IsControlEdge()) { - incoming_edges->insert(edge); - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " Y, "; - } else { - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " N, "; - } - } - } -} - -void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, - const std::set& subgraph_node_ids, - tensorflow::EdgeSet* outgoing_edges) { - for (int node_id : subgraph_node_ids) { - const tensorflow::Node* node = graph.FindNodeId(node_id); - for (const tensorflow::Edge* edge : node->out_edges()) { - if (!subgraph_node_ids.count(edge->dst()->id()) && - !edge->dst()->IsSink() && !edge->IsControlEdge()) { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " Y, "; - outgoing_edges->insert(edge); - } else { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " N, "; - } - } - } -} - -std::pair ParseTensorName(const string& name, - int default_idx = 0) { - string name_no_idx = name; - int idx = default_idx; - const size_t sep = name_no_idx.find_last_of(':'); - if (sep != string::npos) { - name_no_idx = name_no_idx.substr(0, sep); - idx = std::stoi(name.substr(sep + 1)); - } - return std::make_pair(name_no_idx, idx); -} - -std::unordered_map> BuildTensorNameMap( - const std::vector& tensor_names) { - std::unordered_map> result; - for (const string& tensor_name : tensor_names) { - string node_name; - int index; - std::tie(node_name, index) = ParseTensorName(tensor_name); - result[node_name].push_back(index); - } - return result; -} - -// TODO(sami): convert references to pointers -struct ConvertGraphParams { - ConvertGraphParams( - tensorflow::Graph& inp_graph, - const std::vector& output_node_names, - const std::set& subgraph_node_id_numbers, - size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, - const tensorflow::grappler::GraphProperties& current_graph_properties, - std::unordered_map>* output_edges, - int engine_precision_mode, const string& device_name, - std::shared_ptr allocator, int cuda_gpu_id) - : graph(inp_graph), - output_names(output_node_names), - subgraph_node_ids(subgraph_node_id_numbers), - max_batch_size(max_supported_batch_size), - max_workspace_size_bytes(max_consumed_workspace_size_bytes), - graph_properties(current_graph_properties), - output_edge_map(output_edges), - precision_mode(engine_precision_mode), - device_name_(device_name), - allocator_(allocator), - cuda_gpu_id_(cuda_gpu_id) {} - tensorflow::Graph& graph; - const std::vector& output_names; - const std::set& subgraph_node_ids; - size_t max_batch_size; - size_t max_workspace_size_bytes; - const tensorflow::grappler::GraphProperties& graph_properties; - std::unordered_map>* output_edge_map; - int precision_mode; - string device_name_; - std::shared_ptr allocator_; - int cuda_gpu_id_; - std::vector> subgraph_inputs; - std::vector> subgraph_outputs; - tensorflow::EdgeSet subgraph_incoming_edges; - tensorflow::EdgeSet subgraph_outgoing_edges; -}; - -static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { - GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, - &p->subgraph_incoming_edges); - - std::set> unique_tensors; - // Add only unique input source nodes. If output of an outside node is shared - // between multiple nodes inside the engine, only one edge should be created - for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); - } - p->subgraph_inputs.insert(p->subgraph_inputs.begin(), unique_tensors.begin(), - unique_tensors.end()); - GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, - &p->subgraph_outgoing_edges); - unique_tensors.clear(); - // Similar to above, if multiple ouside nodes are sharing the output of an - // internal node only one output port should be created and shared between - // outputs - for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); - } - p->subgraph_outputs.reserve(unique_tensors.size()); - p->subgraph_outputs.insert(p->subgraph_outputs.begin(), - unique_tensors.begin(), unique_tensors.end()); - return tensorflow::Status::OK(); -} - -tensorflow::Status GetCalibNode(ConvertGraphParams* params) { - TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); - tensorflow::NodeDef trt_node_def; - SubGraphParams s(params->graph, params->subgraph_node_ids, - params->subgraph_inputs, params->subgraph_outputs, - params->max_batch_size, params->max_workspace_size_bytes, - params->graph_properties, params->output_edge_map, - &trt_node_def, params->precision_mode, params->device_name_, - params->allocator_, params->cuda_gpu_id_); - TF_RETURN_IF_ERROR(InjectCalibrationNode(s)); - tensorflow::Status status; - tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); - - TF_RETURN_IF_ERROR(status); - - for (auto in_edge : - params->subgraph_incoming_edges) { // loop over incoming edges and - // attach them to calib node - auto src_output = in_edge->src_output(); - auto dst_node = in_edge->dst(); - auto dst_input = in_edge->dst_input(); - VLOG(1) << " update edge " << trt_node->name() << ":" << src_output - << " -> " << dst_node->name() << ":" << dst_input; - TF_RETURN_IF_ERROR( - params->graph.UpdateEdge(trt_node, src_output, dst_node, dst_input)); - } - return tensorflow::Status::OK(); -} - -tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { - TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); - tensorflow::NodeDef trt_node_def; - - SubGraphParams s(params->graph, params->subgraph_node_ids, - params->subgraph_inputs, params->subgraph_outputs, - params->max_batch_size, params->max_workspace_size_bytes, - params->graph_properties, params->output_edge_map, - &trt_node_def, params->precision_mode, params->device_name_, - params->allocator_, params->cuda_gpu_id_); - TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(s)); - tensorflow::Status status; - tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); - - // AddNode does not wire edges. - // Re-map incoming edges to use the new TRT node instead of the orig subgraph - std::map, int> subgraph_edge_to_input_map; - for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { - subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); - } - std::set> unique_tensors; - for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { - std::pair old_src = {edge->src()->id(), edge->src_output()}; - if (unique_tensors.count(old_src)) continue; - unique_tensors.insert(old_src); - int new_src_output = subgraph_edge_to_input_map.at(old_src); - params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, - new_src_output); - VLOG(1) << "Wire " << edge->src()->name() << ":" << edge->src_output() - << " -> " << trt_node->name() << ":" << new_src_output; - params->graph.RemoveEdge(edge); - } - if (VLOG_IS_ON(2)) { - VLOG(2) << "new edge count: " << trt_node->in_edges().size(); - for (const tensorflow::Edge* edge : trt_node->in_edges()) { - VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); - } - } - TF_RETURN_IF_ERROR(status); - - // Re-map outgoing edges to use the new TRT node instead of the orig subgraph - std::map, int> subgraph_edge_to_output_map; - for (size_t i = 0; i < params->subgraph_outputs.size(); ++i) { - subgraph_edge_to_output_map.insert({params->subgraph_outputs.at(i), i}); - } - TF_RETURN_IF_ERROR(status); - for (const tensorflow::Edge* edge : params->subgraph_outgoing_edges) { - std::pair old_src = {edge->src()->id(), edge->src_output()}; - int new_src_output = subgraph_edge_to_output_map.at(old_src); - TF_RETURN_IF_ERROR(params->graph.UpdateEdge( - trt_node, new_src_output, edge->dst(), edge->dst_input())); - VLOG(1) << "Wire " << trt_node->name() << ":" << new_src_output << " -> " - << edge->dst()->name() << ":" << edge->dst_input(); - } - // Remove the original subgraph - for (int node_id : params->subgraph_node_ids) { - tensorflow::Node* node = params->graph.FindNodeId(node_id); - // Don't remove the input placeholders - if (node->type_string() == "Placeholder") { - continue; - } - params->graph.RemoveNode(node); - } - return tensorflow::Status::OK(); -} - tensorflow::Status BuildNodeMap( const tensorflow::Graph& graph, std::unordered_map* node_map) { @@ -318,51 +124,77 @@ tensorflow::Status BuildNodeMap( } } // namespace + +// Function to get calibration from ResourceMgr and put them into nodedef. tensorflow::Status ConvertCalibGraphToInferGraph( - const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph) { + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph, + bool is_dyn_op) { VLOG(0) << "Starting Calib Conversion"; - tensorflow::Graph graph(tensorflow::OpRegistry::Global()); - TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( - tensorflow::GraphConstructorOptions(), graph_def, &graph)); - // get calib nodes - std::vector calib_nodes; - std::vector topo_order; - tensorflow::GetPostOrder(graph, &topo_order); - for (auto rit = topo_order.rbegin(); rit != topo_order.rend(); ++rit) { - auto node = *rit; - if (node->type_string() == "TRTCalibOp") { - VLOG(1) << "Found Calib Node " << node->name(); - calib_nodes.push_back(node); - } + infer_graph->CopyFrom(graph_def); + auto trt_rm = TRTResourceManager::instance(); + auto calib_rm = trt_rm->getManager("TRTCalibration"); + int num_nodes = infer_graph->node_size(); + if (!is_dyn_op) { + LOG(WARNING) << "Construction of static int8 engine is not implemented " + "yet!. Dynamic engine will be constructed"; } - VLOG(0) << "Num Calib nodes in graph= " << calib_nodes.size(); - if (calib_nodes.size() == 0) - return tensorflow::errors::FailedPrecondition( - "Graph doesn't contain any calibration nodes!." - " Please generate calibration graph and run calibration first"); - for (auto n : calib_nodes) { - TF_RETURN_IF_ERROR( - tensorrt::convert::ConvertCalibrationNodeToEngineNode(graph, n)); + for (int i = 0; i < num_nodes; ++i) { + auto n = infer_graph->mutable_node(i); + if (n->op() == "TRTEngineOp") { + VLOG(1) << "Processing " << n->name(); + string container_name = n->attr().at("segment_funcdef_name").s(); + TRTCalibrationResource* cres = nullptr; + auto status = calib_rm->Lookup(container_name, "Calibrator", &cres); + if (!status.ok()) { + LOG(ERROR) << "Could not get Calibration information. Did you run with " + "calibration data?"; + return tensorflow::errors::FailedPrecondition( + "Need to run graph with calibration data first!"); + } + if (cres->calibrator_) { + cres->calibrator_->waitAndSetDone(); + cres->thr_->join(); + const auto& calibration_table = + cres->calibrator_->getCalibrationTableAsString(); + if (!calibration_table.size()) { + LOG(ERROR) << "Calibration table is empty"; + return tensorflow::errors::Unknown( + "Calibration table is missing. This shouldn't have happened!"); + } + n->mutable_attr()->at("calibration_data").set_s(calibration_table); + } else { + LOG(ERROR) << "Can't get TRTCalibrator from resource manager!"; + return tensorflow::errors::Unknown( + "Can't get TRTCalibrator from resource manager!"); + } + cres->Unref(); + } } - graph.ToGraphDef(infer_graph); return tensorflow::Status::OK(); } +// Entry function from Python. tensorflow::Status ConvertGraphDefToTensorRT( const tensorflow::GraphDef& graph_def, const std::vector& output_names, size_t max_batch_size, size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, - int precision_mode = FP32MODE, int minimum_segment_size = 3) { + int precision_mode, int minimum_segment_size, bool is_dyn_op, + int max_cached_engines, std::vector cached_engine_batches) { // optimization pass tensorflow::grappler::GrapplerItem item; item.fetch = output_names; item.graph = graph_def; - + // grappler requires a virtual cluster with a proper GPU device + // in order to calculate flops>0 or fails with FATAL + // We add numbers from a Pascal card here to have flops>0 tensorflow::DeviceProperties device_properties; device_properties.set_type("GPU"); device_properties.mutable_environment()->insert({"architecture", "6"}); - tensorflow::grappler::Cluster* cluster = - new tensorflow::grappler::VirtualCluster({{"/GPU:0", device_properties}}); + device_properties.set_num_cores(3584); + device_properties.set_frequency(1531); + std::unique_ptr cluster( + new tensorflow::grappler::VirtualCluster( + {{"/GPU:0", device_properties}})); // single machine int num_cpu_cores = tensorflow::grappler::GetNumAvailableLogicalCPUCores(); @@ -370,134 +202,633 @@ tensorflow::Status ConvertGraphDefToTensorRT( VLOG(2) << "cpu_cores: " << num_cpu_cores; VLOG(2) << "gpus: " << num_gpus; tensorflow::RewriterConfig rw_cfg; + // use only const folding and layout for the time being since new optimizers + // break the graph for us + rw_cfg.add_optimizers("constfold"); + rw_cfg.add_optimizers("layout"); + rw_cfg.set_meta_optimizer_iterations(tensorflow::RewriterConfig::ONE); tensorflow::grappler::MetaOptimizer meta_opt(nullptr, rw_cfg); tensorflow::GraphDef gdef; - TF_RETURN_IF_ERROR(meta_opt.Optimize(cluster, item, &gdef)); + TF_RETURN_IF_ERROR(meta_opt.Optimize(cluster.get(), item, &gdef)); item.graph = gdef; // AJ refactoring shape inference through grappler/GraphProperties. tensorflow::grappler::GraphProperties static_graph_properties(item); TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); // Build full graph - - return ConvertAfterShapes(gdef, output_names, max_batch_size, - max_workspace_size_bytes, new_graph_def, - precision_mode, minimum_segment_size, - static_graph_properties, nullptr); + ConversionParams cp; + cp.input_graph_def = &gdef; + cp.output_names = &output_names; + cp.max_batch_size = max_batch_size; + cp.output_graph_def = new_graph_def; + cp.precision_mode = precision_mode; + cp.is_dyn_op = is_dyn_op; + cp.max_cached_engines = max_cached_engines; + cp.cached_engine_batches = cached_engine_batches; + cp.minimum_segment_size = minimum_segment_size; + cp.graph_properties = &static_graph_properties; + cp.max_workspace_size_bytes = max_workspace_size_bytes; + if (VLOG_IS_ON(5)) { + std::fstream f; + f.open("TRTConversionInput.pb", + std::fstream::out | std::fstream::binary | std::fstream::trunc); + f << gdef.SerializeAsString(); + f.close(); + } + return ConvertAfterShapes(cp); } -tensorflow::Status ConvertAfterShapes( - const tensorflow::GraphDef& gdef, const std::vector& output_names, - size_t max_batch_size, size_t max_workspace_size_bytes, - tensorflow::GraphDef* new_graph_def, int precision_mode, - int minimum_segment_size, +// Function to get subsegment information structure. +tensorflow::Status GetEngineInfo( + const tensorflow::Graph* g, const tensorflow::grappler::GraphProperties& graph_properties, - const tensorflow::grappler::Cluster* cluster) { - // Segment the graph into subgraphs that can be converted to TensorRT - tensorflow::tensorrt::segment::SegmentOptions segment_options; + const std::set& segment_nodes, + const std::unordered_map& node_map, + const std::vector& reverse_topo_order, + EngineInfo* info) { + std::vector subgraph_node_ids; + std::set segment_devices; + int input_port = 0; + int output_port = 0; + + // Map from src_node_name+port to the unique port numbers of the TRT op, where + // the src_node_name is the name of the source node of the input/output + // edge, thus there must not be any duplicates since source nodes of + // input/output edges must be in different split of the graph. + // TODO(aaroey): consider using node id and port instead. + std::unordered_map created_edges; + for (auto it = reverse_topo_order.rbegin(); it != reverse_topo_order.rend(); + ++it) { + const auto& node_name = (*it)->name(); + + if (segment_nodes.count(node_name) == 0) continue; + auto node = node_map.at(node_name); + auto node_device = node->requested_device(); + if (!node_device.empty()) { + segment_devices.insert(node_device); + } else { + if (node->has_assigned_device_name()) { + segment_devices.insert(node->assigned_device_name()); + } else { + VLOG(2) << "Node " << node->name() + << " neither have requested device nor assigned device"; + } + } + int node_id = node->id(); + subgraph_node_ids.push_back(node_id); + for (const auto edge : node->in_edges()) { + auto input_node = edge->src(); + if (segment_nodes.count(input_node->name()) == 0) { + // Add constant input node into the segment. We don't care if it has + // other output edges going into other engines or TF nodes. Since we add + // it only to the subsegment node list, not the subsegment itself, it + // won't be removed from the graph. If it doesn't have any edges, TF + // will prune it out. + if (input_node->type_string() == "Const") { + subgraph_node_ids.push_back(input_node->id()); + } else if (!edge->IsControlEdge() && !input_node->IsSource()) { + string s(input_node->name()); + StrAppend(&s, ":", edge->src_output()); + VLOG(1) << "Input edge = " << s; + int port = input_port; + if (created_edges.count(s)) { + port = created_edges.at(s); + } else { + created_edges.insert({s, port}); + input_port++; + } + info->connections.emplace_back(input_node->name(), input_node->id(), + edge->src_output(), node_name, node_id, + edge->dst_input(), true, port); + } + } + } + for (const auto edge : node->out_edges()) { + auto output_node = edge->dst(); + if (segment_nodes.count(output_node->name()) == 0 && + !edge->IsControlEdge() && !output_node->IsSink()) { + string s(node_name); + StrAppend(&s, ":", edge->src_output()); + VLOG(1) << "Output edge = " << s; + int port = output_port; + if (created_edges.count(s)) { + port = created_edges.at(s); + } else { + created_edges.insert({s, port}); + output_port++; + } + info->connections.emplace_back(output_node->name(), output_node->id(), + edge->dst_input(), node_name, node_id, + edge->src_output(), false, port); + } + } + } + + TF_RETURN_IF_ERROR(ConvertSegmentToGraphDef( + g, graph_properties, subgraph_node_ids, &info->connections, + &info->segment_graph_def, &info->engine_name)); + // TODO(sami): This should not happen once segmenter is updated. + if (segment_devices.size() == 1) { + info->device = *segment_devices.begin(); + } else if (segment_devices.size() > 1) { + LOG(WARNING) << "Detected multiple(" << segment_devices.size() + << ") devices for the segment. Picking first one to continue " + << "but this shouldn't have happened"; + info->device = *segment_devices.begin(); + } else { + VLOG(1) << "Segment devices size is 0"; + } + return Status::OK(); +} + +// Function to insert a TRT node into the graph. The graph is not modified if +// the returned status is not ok. +// 'alloc' is only used for creating static engine. +tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, + const std::vector& infos, int pos, + nvinfer1::IGpuAllocator* alloc, + int max_batch_size) { + const auto& info = infos.at(pos); + std::vector out_shapes; + std::vector input_shapes; + std::vector shapes; + std::vector inputs; + std::vector out_types; + VLOG(1) << "Processing " << info.engine_name; + + // Update the shape and data types of input/output nodes, and find all unique + // inputs. + for (const auto& conn : info.connections) { + if (!conn.is_input_edge) { + // Set the shapes and data types of output edge. + tensorflow::TensorShapeProto out_shape; + // shape of the output node inside segment + conn.inside_shape.AsProto(&out_shape); + if (out_shapes.size() <= conn.port_number) { + out_shapes.resize(conn.port_number + 1); + out_types.resize(conn.port_number + 1); + } + out_shapes.at(conn.port_number) = out_shape; + out_types.at(conn.port_number) = conn.connection_type; + continue; + } + + // Set the shapes and data types of input edge. + tensorflow::TensorShapeProto in_shape; + conn.outside_shape.AsProto(&in_shape); + if (input_shapes.size() <= conn.port_number) { + input_shapes.resize(conn.port_number + 1); + shapes.resize(conn.port_number + 1); + } + input_shapes.at(conn.port_number) = in_shape; + shapes.at(conn.port_number) = conn.outside_shape; + + string input_node = conn.outside_node_name; + int input_port = conn.outside_port; + bool found_engine = false; + // Rewire the inputs to other engines if they contain original input node. + // Note that we use the information of the engine here, not the information + // of the created TRT nodes, so we're able to find all the connections to + // any other engines beforehand. + for (size_t t = 0; t < infos.size(); ++t) { + if (t == pos) continue; + auto& engine_info = infos.at(t); + for (const auto& eng_conn : engine_info.connections) { + if (eng_conn.is_input_edge) continue; + if (eng_conn.inside_node_name == input_node) { + input_node = engine_info.engine_name; + if (eng_conn.inside_port == input_port) { + input_port = eng_conn.port_number; + found_engine = true; + break; + } + } + } + if (found_engine) break; + } + VLOG(1) << "Engine Input " << input_node << ":" << input_port << " -> " + << info.engine_name << ":" << inputs.size(); + // Skip duplicate inputs. + bool new_input = true; + for (const auto& inp : inputs) { + if (inp.node == input_node && inp.index == input_port) { + new_input = false; + break; + } + } + if (new_input) { + inputs.emplace_back(input_node, input_port, conn.connection_type); + } + } + + // Build the engine and get its serialized representation. + string segment_string; + if (info.engine_type == EngineInfo::EngineType::TRTStatic || + info.precision_mode == INT8MODE) { + // Create static engine for fp32/fp16 mode, and test validity of the engine + // for int8 mode. We don't want engine to fail at the calibration time. + // So we are constructing a FP32 engine here to check its validity, and if + // it is a valid engine then we put the serialized graphdef to the op. + // Otherwise we skip node creation for this engine. + Logger trt_logger; + TrtUniquePtrType engine; + // TODO(sami): What happens if 1st dim is not batch? + TF_RETURN_IF_ERROR(ConvertGraphDefToEngine( + info.segment_graph_def, + info.precision_mode == INT8MODE ? FP32MODE : info.precision_mode, + max_batch_size, info.max_workspace_size_bytes, shapes, &trt_logger, + alloc, /*calibrator=*/nullptr, &engine, + /*convert_successfully=*/nullptr)); + TrtUniquePtrType engine_data(engine->serialize()); + segment_string = + string((const char*)engine_data->data(), engine_data->size()); + if (info.precision_mode == INT8MODE) { + // See above comment about why not putting this inside the 'else' branch. + segment_string = info.segment_graph_def.SerializeAsString(); + } + } else { + segment_string = info.segment_graph_def.SerializeAsString(); + } + + // TODO(aaroey): use enum instead, and add a helper method to do the + // conversion. + string prec_string; + switch (info.precision_mode) { + case FP32MODE: + prec_string = "FP32"; + break; + case FP16MODE: + prec_string = "FP16"; + break; + case INT8MODE: + prec_string = "INT8"; + if (!TRTResourceManager::instance()->getManager("TRTCalibration")) { + LOG(ERROR) << "Failed to construct calibration storage"; + } + break; + default: + return tensorflow::errors::OutOfRange("Unknown precision mode"); + } + tensorflow::NodeDefBuilder node_builder(info.engine_name, "TRTEngineOp"); + if (!info.device.empty()) node_builder.Device(info.device); + if (VLOG_IS_ON(1)) { + string ins = StrCat(info.engine_name, " inputs= "); + for (const auto& ii : inputs) { + StrAppend(&ins, ii.node, ":", ii.index, " "); + } + VLOG(1) << ins; + } + node_builder.Input(inputs); + if (info.engine_type == EngineInfo::EngineType::TRTStatic && + info.cached_engine_batches.size()) { + LOG(WARNING) << "Cached engine batches are ignored for static engines"; + } + tensorflow::NodeDef trt_node; + tensorflow::Status status = + node_builder.Attr("input_shapes", input_shapes) + .Attr("output_shapes", out_shapes) + .Attr("static_engine", + info.engine_type == EngineInfo::EngineType::TRTStatic) + .Attr("segment_funcdef_name", + StrCat(info.engine_name, "_native_segment")) + .Attr("serialized_segment", segment_string) + .Attr("calibration_data", "") + .Attr("max_cached_engines_count", info.maximum_cached_engines) + .Attr("cached_engine_batches", {max_batch_size}) + .Attr("workspace_size_bytes", info.max_workspace_size_bytes) + .Attr("precision_mode", prec_string) + .Attr("OutT", out_types) + .Finalize(&trt_node); + if (!status.ok()) { + LOG(ERROR) << "Node construction failed with" << status; + return status; + } + VLOG(1) << "Adding TRTEngine " << info.engine_name << " to graph"; + + // Up until this point, graph is not modified. If we return !status.ok() from + // here, this segment will be skipped + tensorflow::Node* engine_node = graph->AddNode(trt_node, &status); + if (!status.ok()) { + LOG(ERROR) << "Adding node failed " << status; + return status; + } + // Updates the inputs of output edges destination nodes, and point them to the + // engine node. + for (auto& conn : info.connections) { + if (conn.is_input_edge) continue; + VLOG(1) << " Updating DBG " << engine_node->name() << " out_port " + << conn.port_number << " out_id " << conn.outside_id + << " name=" << conn.outside_node_name; + auto dst_node = graph->FindNodeId(conn.outside_id); + // dst_node can only be removed if it is an input node of another engine. + // In this case, other engines input edge is updated in nodedef to point to + // this engine. Even though edge doesn't exists in the graph, when it is + // deserialized again, correct edges will be constructed. This is a problem + // of graph->AddNode(). + if (!dst_node) continue; + VLOG(1) << "Updating " << engine_node->name() << ":" << conn.port_number + << " to " << dst_node->name() << ":" << conn.outside_port; + auto new_edge = graph->AddEdge(engine_node, conn.port_number, dst_node, + conn.outside_port); + CHECK(new_edge) << "Adding a new edge failed " << engine_node->name() << ":" + << conn.port_number << " -> " << dst_node->name() << ":" + << conn.outside_port; + } + return status; +} + +// Function to construct a funcdef from the segment and add it to the graph. +tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( + tensorflow::Graph* graph, const tensorflow::GraphDef& segment, + const string& name) { + tensorflow::Graph sgraph(graph->flib_def()); + tensorflow::GraphConstructorOptions gcopts; + TF_RETURN_IF_ERROR( + tensorflow::ConvertGraphDefToGraph(gcopts, segment, &sgraph)); + std::map io_nodes; + int num_inputs = 0; + for (auto n : sgraph.op_nodes()) { + if (tensorflow::str_util::StartsWith(n->name(), kInputPHName)) { + num_inputs++; + io_nodes.insert({n->name(), n}); + } else if (tensorflow::str_util::StartsWith(n->name(), kOutputPHName)) { + io_nodes.insert({n->name(), n}); + } + } + + for (int i = 0; i < num_inputs; ++i) { + auto name = StrCat(kInputPHName, i); + auto node = io_nodes[name]; + tensorflow::NodeDef nd; + tensorflow::NodeDefBuilder node_builder( + StrCat(name, "_Arg"), tensorflow::FunctionLibraryDefinition::kArgOp); + VLOG(1) << "Adding " << StrCat(name, "_Arg"); + TF_RETURN_IF_ERROR(node_builder.Attr("T", node->output_type(0)) + .Attr("index", i) + .Finalize(&nd)); + tensorflow::Status s; + auto node_arg = sgraph.AddNode(nd, &s); + if (!s.ok()) { + LOG(ERROR) << "Couldn't add _Arg node for " << name; + } + for (auto edge : node->out_edges()) { + sgraph.AddEdge(node_arg, 0, edge->dst(), edge->dst_input()); + VLOG(1) << "Updating funcdef input " << node_arg->name() << ":" << 0 + << " - > " << edge->dst()->name() << ":" << edge->dst_input(); + if (!s.ok()) { + LOG(ERROR) << "Failed to update edge from " << node_arg->name() + << " to " << edge->dst()->name() << ":" << edge->dst_input(); + } + } + sgraph.RemoveNode(node); + } + + for (int i = 0; i < io_nodes.size() - num_inputs; ++i) { + auto name = StrCat(kOutputPHName, i); + auto node = io_nodes[name]; + tensorflow::NodeDef nd; + tensorflow::NodeDefBuilder node_builder( + StrCat(name, "_Ret"), tensorflow::FunctionLibraryDefinition::kRetOp); + auto edge = *(node->in_edges().begin()); + tensorflow::NodeDefBuilder::NodeOut nout( + edge->src()->name(), edge->src_output(), + edge->src()->output_type(edge->src_output())); + VLOG(1) << " input " << nout.node << ":" << nout.index + << " dtype=" << tensorflow::DataTypeString(nout.data_type); + node_builder.Input({nout}); + TF_RETURN_IF_ERROR(node_builder.Attr("T", node->output_type(0)) + .Attr("index", i) + .Finalize(&nd)); + if (VLOG_IS_ON(3)) { + VLOG(3) << nd.DebugString(); + } + tensorflow::Status s; + auto node_ret = sgraph.AddNode(nd, &s); + if (!s.ok()) { + LOG(ERROR) << "Couldn't add _Ret node for " << name; + } + VLOG(1) << "Update edge from " << edge->src()->name() << ":" + << edge->src_output() << " - > " << node_ret->name() << ":" << 0; + sgraph.AddEdge(edge->src(), edge->src_output(), node_ret, 0); + s = sgraph.UpdateEdge(edge->src(), edge->src_output(), node_ret, 0); + if (!s.ok()) { + LOG(ERROR) << "Failed to update edge from " << edge->src()->name() << ":" + << edge->src_output() << " - > " << node_ret->name() << ":" + << 0; + } + sgraph.RemoveNode(node); + } + tensorflow::FunctionDefLibrary fdeflib; + auto native_segment = fdeflib.add_function(); + TF_RETURN_IF_ERROR(tensorflow::GraphToFunctionDef( + sgraph, StrCat(name, "_native_segment"), native_segment)); + if (VLOG_IS_ON(7)) { + VLOG(7) << name << " Function_Def "; + VLOG(7) << native_segment->DebugString(); + } + VLOG(1) << "Adding funcdef to graphlib"; + TF_RETURN_IF_ERROR(graph->AddFunctionLibrary(fdeflib)); + return tensorflow::Status::OK(); +} + +std::pair GetDeviceAndAllocator( + ConversionParams& params, EngineInfo& engine) { + int cuda_device_id = -1; + auto check_device_id = [](int tfid) -> int { + tensorflow::TfGpuId tf_gpu_id(tfid); + CudaGpuId cuda_gpu_id; + Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); + if (s.ok()) { + VLOG(1) << "Found TF GPU " << tf_gpu_id.value() << " at cuda device " + << cuda_gpu_id.value(); + return cuda_gpu_id.value(); + } + VLOG(2) << "TF GPU with id " << tfid << " do not exist " << s; + return -1; + }; + tensorflow::Allocator* dev_allocator = nullptr; + // we need to us PM here since in python path there is no way to get + // to allocators. + // TODO(sami): when grappler devices become available else path will not be + // necessary + auto pm = tensorflow::GPUProcessState::singleton(); + if (params.cluster) { // get allocator + tensorflow::Device* device = nullptr; + if (params.cluster->GetDeviceSet()) { + device = params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); + } + if (device) { + tensorflow::AllocatorAttributes alloc_attr; + dev_allocator = device->GetAllocator(alloc_attr); + VLOG(1) << "Using allocator " << dev_allocator->Name(); + } else { + LOG(WARNING) << "Cluster is set but device '" << engine.device + << "' is not found in the cluster"; + } + } else { // cluster not found, possibly a python call + VLOG(1) << "Cluster is not set, probably called from python"; + int found_device = 0; + bool try_gpu_ids = true; + // if device is set, try to find the device. Might be a problem for multi + // host case but TensorRT do not support multi host setups yet. + if (!engine.device.empty()) { + DeviceNameUtils::ParsedName parsed_name; + if (DeviceNameUtils::ParseFullName(engine.device, &parsed_name)) { + cuda_device_id = parsed_name.has_id ? parsed_name.id : -1; + } + try_gpu_ids = !parsed_name.has_id; + } + if (try_gpu_ids) { + while (found_device < 100) { + cuda_device_id = check_device_id(found_device); + if (cuda_device_id >= 0) break; + found_device++; + } + } + if (found_device == 100) { + LOG(ERROR) << " Can't find a GPU device to work with. Please " + "instantiate a session to initialize devices"; + return std::make_pair(cuda_device_id, dev_allocator); + } + LOG(WARNING) + << "Can't determine the device, constructing an allocator at device " + << found_device; + tensorflow::GPUOptions gpuoptions; + // this will be a noop if device is already initialized + gpuoptions.set_allow_growth(true); + tensorflow::TfGpuId tf_gpu_id(found_device); + dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + } + return std::make_pair(cuda_device_id, dev_allocator); +} + +// Entry function from optimization pass. +tensorflow::Status ConvertAfterShapes(ConversionParams& params) { + // Convert graphdef to graph. tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), - gdef.library()); + params.input_graph_def->library()); tensorflow::Graph graph(flib); TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( - tensorflow::GraphConstructorOptions(), gdef, &graph)); + tensorflow::GraphConstructorOptions(), *params.input_graph_def, &graph)); + // Segment the graph into subgraphs that can be converted to TensorRT + tensorflow::tensorrt::segment::SegmentOptions segment_options; // TODO(ben,jie,sami): exclude output nodes (DISCUSS IT) - for (auto node : output_names) { + for (auto node : *(params.output_names)) { segment_options.exclude_node_list.insert(node); } - - // TODO(sami): this should be passed as a knob!!!! - segment_options.minimum_segment_size = minimum_segment_size; - tensorflow::tensorrt::segment::SegmentNodesVector segments; + segment_options.minimum_segment_size = params.minimum_segment_size; + tensorflow::tensorrt::segment::SegmentNodesVector initial_segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - &graph, IsTensorRTCandidate, segment_options, &segments)); - if (segments.size() > 1) { - VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size(); + &graph, IsTensorRTCandidate, segment_options, &initial_segments)); + if (initial_segments.size() > 1) { + VLOG(0) << "MULTIPLE tensorrt candidate conversion: " + << initial_segments.size(); } + + // Get the EngineInfo for each segment. std::unordered_map node_map; TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map)); - std::unordered_map> output_edge_map; - int count = 0; float total_num_nodes_in_segments = 0.; - for (auto s : segments) { - total_num_nodes_in_segments += s.first.size(); - } - // We create the map here since cluster may not be available in all cases. - std::map name_to_device_map; - if (cluster) { - // TODO(aaroey): consider using DeviceSet::FindDeviceByName(), as in a - // distributed environment, devices from different workers can have same - // short name. - for (const auto dm : cluster->GetDeviceSet()->devices()) { - name_to_device_map[dm->name()] = dm; + std::vector engine_segments; + engine_segments.reserve(initial_segments.size()); + std::vector reverse_topo_order; + tensorflow::GetPostOrder(graph, &reverse_topo_order); + size_t total_engine_bytes_size = 0; + std::vector engine_bytes_size; + tensorflow::tensorrt::segment::SegmentNodesVector converted_segments; + converted_segments.reserve(initial_segments.size()); + for (size_t t = 0; t < initial_segments.size(); t++) { + auto& curr_segment = initial_segments.at(t); + EngineInfo curr_engine; + Status status = + GetEngineInfo(&graph, *params.graph_properties, curr_segment.first, + node_map, reverse_topo_order, &curr_engine); + if (!status.ok()) { + LOG(WARNING) << "Failed to get engine info for segment " << t << ": " + << status; + continue; } - } - for (const auto& segment_nodes_and_device : segments) { - const std::set& subgraph_node_names = - segment_nodes_and_device.first; - std::set subgraph_node_ids; - size_t max_mem_per_engine = - max_workspace_size_bytes * - ((float)subgraph_node_names.size() / total_num_nodes_in_segments); - std::stringstream oss; - for (const string& node_name : subgraph_node_names) { - oss << " " << node_name; - subgraph_node_ids.insert(node_map.at(node_name)->id()); + curr_engine.precision_mode = params.precision_mode; + curr_engine.engine_type = + (params.is_dyn_op || params.precision_mode == INT8MODE + ? EngineInfo::EngineType::TRTDynamic + : EngineInfo::EngineType::TRTStatic); + curr_engine.cached_engine_batches = params.cached_engine_batches; + curr_engine.maximum_cached_engines = params.max_cached_engines; + StrAppend(&curr_engine.engine_name, "my_trt_op_", t); + status = RegisterSegmentFunctionToFunctionLibrary( + &graph, curr_engine.segment_graph_def, curr_engine.engine_name); + if (!status.ok()) { + LOG(WARNING) << "Failed to register segment graphdef as a function " << t + << ": " << status; + continue; } - VLOG(1) << "Subgraph nodes at device " << segment_nodes_and_device.second - << " : " << oss.str(); - auto target_device = - name_to_device_map.find(segment_nodes_and_device.second); - std::shared_ptr allocator(0); + engine_bytes_size.push_back(curr_engine.segment_graph_def.ByteSizeLong()); + total_engine_bytes_size += engine_bytes_size.back(); + total_num_nodes_in_segments += curr_segment.first.size(); + engine_segments.push_back(std::move(curr_engine)); + converted_segments.push_back(std::move(curr_segment)); + + if (VLOG_IS_ON(8)) { + string fname = curr_engine.engine_name; + StrAppend(&fname, ".pb"); + std::fstream f; + f.open(fname.c_str(), std::fstream::out | std::fstream::binary); + f << engine_segments.at(t).segment_graph_def.SerializeAsString(); + f.close(); + } + } + + // Create a TRT node for each segment using its EngineInfo. + int old_cuda_device = 0; + auto err = cudaGetDevice(&old_cuda_device); + if (err != cudaSuccess) { + LOG(ERROR) << "Couldn't get current device: " << cudaGetErrorString(err); + } + VLOG(1) << "Current cuda device is " << old_cuda_device; + for (int i = 0; i < engine_segments.size(); ++i) { + auto& engine = engine_segments.at(i); + // Partition the workspace size by the average of node ratio and segment + // graphdef size + engine.max_workspace_size_bytes = + params.max_workspace_size_bytes * + (engine_bytes_size.at(i) / total_engine_bytes_size + + converted_segments.at(i).first.size() / total_num_nodes_in_segments) / + 2.0; + // The allocator is used to build the engine. The build and the built engine + // will be destroyed after we get the serialized engine string, so it's fine + // to use unique_ptr here. + std::unique_ptr alloc; + auto device_alloc = GetDeviceAndAllocator(params, engine); int cuda_device_id = 0; - if (target_device != name_to_device_map.end()) { - tensorflow::TfGpuId tf_gpu_id(target_device->second->parsed_name().id); - CudaGpuId cuda_gpu_id; - Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); - if (!s.ok()) { - LOG(ERROR) - << "Cuda device identification failed, using device 0. Error= " - << s; - } else { - cuda_device_id = cuda_gpu_id.value(); - } - tensorflow::GPUOptions gpuoptions; - // we need to us PM here since in python path there is no way to get to - // allocators - auto pm = tensorflow::ProcessState::singleton(); - // this should be instantiated by now - auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); - VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() - << " cuda device= " << cuda_device_id << " at " << dev_allocator; - allocator = std::make_shared(dev_allocator); - } else { // device unknown or not available - allocator = std::make_shared(); + if (device_alloc.first >= 0) { + cuda_device_id = device_alloc.first; + alloc.reset(new TRTDeviceAllocator(device_alloc.second)); + } else { + // Setting allocator as nullptr should get revert to the cudamalloc + LOG(WARNING) << "Can't identify the cuda device. Running on device 0 "; } - ConvertGraphParams p(graph, output_names, subgraph_node_ids, max_batch_size, - max_mem_per_engine, graph_properties, &output_edge_map, - precision_mode, segment_nodes_and_device.second, - allocator, cuda_device_id); - if (precision_mode == INT8MODE) { - tensorflow::Status status = GetCalibNode(&p); - if (status != tensorflow::Status::OK()) { - LOG(WARNING) << "subgraph conversion error for subgraph_index:" << count - << " due to: \"" << status.ToString() - << "\" SKIPPING......( " << subgraph_node_names.size() - << " nodes)"; + cudaSetDevice(cuda_device_id); + auto status = CreateTRTNode(&graph, engine_segments, i, alloc.get(), + params.max_batch_size); + // If status is ok, we successfully added the node to the graph and can + // remove segment ops. Otherwise graph is not modified. + if (status.ok()) { + for (auto node_name : converted_segments.at(i).first) { + graph.RemoveNode(node_map.at(node_name)); } } else { - tensorflow::Status status = ConvertSubGraphToTensorRT(&p); - if (status != tensorflow::Status::OK()) { - LOG(WARNING) << "subgraph conversion error for subgraph_index:" << count - << " due to: \"" << status.ToString() - << "\" SKIPPING......( " << subgraph_node_names.size() - << " nodes)"; - } + // Graph is not modified. + LOG(WARNING) << "Engine creation for segment " << i << ", composed of " + << converted_segments.at(i).first.size() + << " nodes failed: " << status << ". Skipping..."; } - count++; } - graph.ToGraphDef(new_graph_def); + cudaSetDevice(old_cuda_device); + graph.ToGraphDef(params.output_graph_def); + VLOG(1) << "Returning from conversion"; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index 65a67d7e73e32f904bd636a4f4aaefe32b0c092d..9d986e489043c0a0e16e379166aa2e8f7ac0b11f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -30,29 +30,60 @@ namespace tensorflow { namespace tensorrt { namespace convert { -// This method converts an already generated calibration graph which was used in -// calibration runs to an inference graph +struct ConversionParams { + ConversionParams() + : input_graph_def(nullptr), + max_batch_size(1), + max_workspace_size_bytes(1 << 30), + output_graph_def(nullptr), + precision_mode(1), + minimum_segment_size(3), + graph_properties(nullptr), + cluster(nullptr), + is_dyn_op(false), + fixed_input_size(true), + max_cached_engines(1) {} + const tensorflow::GraphDef* input_graph_def; + const std::vector* output_names; + size_t max_batch_size; + size_t max_workspace_size_bytes; + tensorflow::GraphDef* output_graph_def; + int precision_mode; + int minimum_segment_size; + const tensorflow::grappler::GraphProperties* graph_properties; + const tensorflow::grappler::Cluster* cluster; + bool is_dyn_op; // Whether to create engine on conversion or execution time + bool fixed_input_size; // Assume non-batch ranks of input tensors are fixed + int max_cached_engines; // maximum number of cached engines + std::vector cached_engine_batches; // list of cached engines +}; + +// This method extracts calibration information from the resource managers +// and puts them in to engine nodedefs. tensorflow::Status ConvertCalibGraphToInferGraph( - const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def); + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def, + bool is_dyn_op); -// max_batch_size: maximum batch size which can be used for inference for -// optimization targets inference run with max batch size. -// max_workspace_size_bytes: The upper bound of memory allowance for -// engine building. +// - max_batch_size: maximum batch size which can be used for inference for +// optimization targets inference run with max batch size. +// - max_workspace_size_bytes: The upper bound of memory allowance for engine +// building. tensorflow::Status ConvertGraphDefToTensorRT( const tensorflow::GraphDef& graph_def, const std::vector& output_names, size_t max_batch_size, size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, - int precision_mode, int minimum_segment_size); + int precision_mode = 1, int minimum_segment_size = 3, + bool is_dyn_op = false, int max_cached_engines = 1, + std::vector cached_engine_batches = {}); // Method to call from optimization pass -tensorflow::Status ConvertAfterShapes( - const tensorflow::GraphDef& graph, const std::vector& output_names, - size_t max_batch_size, size_t max_workspace_size_bytes, - tensorflow::GraphDef* new_graph_def, int precision_mode, - int minimum_segment_size, - const tensorflow::grappler::GraphProperties& graph_properties, - const tensorflow::grappler::Cluster* cluster); +tensorflow::Status ConvertAfterShapes(ConversionParams& params); + +// Return compile time TensorRT library version information. +std::vector GetLinkedTensorRTVersion(); + +// Return runtime time TensorRT library version information. +std::vector GetLoadedTensorRTVersion(); } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 4e4d295538edadd26a347a38ec141737f097f26f..146b9c7344b0a9c2b3ec87b395e9b1096dbef06c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -25,7 +24,9 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/core/framework/node_def.pb.h" // NOLINT @@ -37,6 +38,7 @@ limitations under the License. #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" @@ -54,8 +56,11 @@ limitations under the License. namespace tensorflow { namespace tensorrt { namespace convert { +using ::tensorflow::str_util::Split; + using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; + namespace { inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, @@ -121,12 +126,10 @@ static std::vector> CreateSamePadding( string GetCommonNameScope(const string& op_name_a, const string& op_name_b) { size_t last_scope_separator = 0; - for (size_t i = 0; i < std::min(op_name_a.size(), op_name_b.size()); ++i) { - if (op_name_a[i] != op_name_b[i]) { - break; - } else if (op_name_a[i] == '/') { - last_scope_separator = i + 1; - } + const size_t min_size = std::min(op_name_a.size(), op_name_b.size()); + for (size_t i = 0; i < min_size; ++i) { + if (op_name_a[i] != op_name_b[i]) break; + if (op_name_a[i] == '/') last_scope_separator = i + 1; } return op_name_a.substr(0, last_scope_separator); } @@ -417,20 +420,6 @@ void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights, } } -struct InferDeleter { - template - void operator()(T* obj) const { - if (obj) { - obj->destroy(); - } - } -}; - -template -inline std::shared_ptr infer_object(T* obj) { - return std::shared_ptr(obj, InferDeleter()); -} - class Converter; using OpConverter = @@ -444,7 +433,7 @@ class Converter { OpConverter plugin_converter_; nvinfer1::INetworkDefinition* trt_network_; std::list> temp_bufs_; - tensorflow::tensorrt::TRTWeightStore* weight_store_; + TRTWeightStore* weight_store_; bool fp16_; void register_op_converters(); tensorflow::Status get_inputs(const tensorflow::NodeDef& node_def, @@ -486,11 +475,11 @@ class Converter { public: explicit Converter(nvinfer1::INetworkDefinition* trt_network, - tensorflow::tensorrt::TRTWeightStore* ws, bool fp16) + TRTWeightStore* ws, bool fp16) : trt_network_(trt_network), weight_store_(ws), fp16_(fp16) { this->register_op_converters(); } - tensorflow::tensorrt::TRTWeightStore* weight_store() { return weight_store_; } + TRTWeightStore* weight_store() { return weight_store_; } TRT_ShapedWeights get_temp_weights(tensorflow::DataType type, nvinfer1::Dims shape) { TRT_ShapedWeights weights(type, nullptr, shape); @@ -2140,559 +2129,265 @@ void Converter::register_op_converters() { } // namespace -tensorflow::Status ConvertCalibrationNodeToEngineNode( - tensorflow::Graph& graph, tensorflow::Node* c_node) { - const auto ndef = c_node->def(); - - TFAttrs attrs(ndef); - std::vector segment_nodes( - attrs.get>("segment_nodes")); - std::vector output_nodes( - attrs.get>("segment_output_names")); - std::vector input_names( - attrs.get>("input_names")); - string res_name = attrs.get("resource_name"); - VLOG(1) << "Node name " << c_node->name() << " res_name " << res_name; - string engine_name = "my_trt_op"; - { - const auto node_id = tensorflow::str_util::Split(res_name, "_"); - engine_name += node_id.back(); - } - std::map node_maps; - - for (auto n : graph.op_nodes()) { - node_maps.insert({n->name(), n}); - } - std::set subgraph_ids; - for (const auto internal_node : segment_nodes) { - subgraph_ids.insert(node_maps.at(internal_node)->id()); - } - if (VLOG_IS_ON(2)) { - string node_names = StrCat(c_node->name(), " segment nodes= "); - - for (const auto& node_name : segment_nodes) { - StrAppend(&node_names, node_name, ", "); - } - VLOG(2) << node_names; +tensorflow::Status ConvertGraphDefToEngine( + const tensorflow::GraphDef& gdef, int precision_mode, int max_batch_size, + size_t max_workspace_size_bytes, + const std::vector& input_shapes, + Logger* logger, nvinfer1::IGpuAllocator* allocator, + TRTInt8Calibrator* calibrator, + TrtUniquePtrType* engine, + bool* convert_successfully) { + engine->reset(); + if (convert_successfully) *convert_successfully = false; + + // Create the builder. + TrtUniquePtrType builder( + nvinfer1::createInferBuilder(*logger)); + builder->setMaxBatchSize(max_batch_size); + // TODO(aaroey): use the allocator to allocate the TRT workspace. + builder->setMaxWorkspaceSize(max_workspace_size_bytes); +#if NV_TENSORRT_MAJOR > 3 + builder->setGpuAllocator(allocator); +#endif + if (precision_mode == FP16MODE) { + builder->setHalf2Mode(true); + } else if (precision_mode == INT8MODE) { + builder->setInt8Mode(true); + builder->setInt8Calibrator(calibrator); } - VLOG(1) << "Output Nodes:"; - std::vector out_types; - std::vector out_edges; + // Create the network. + auto trt_network = + TrtUniquePtrType(builder->createNetwork()); + if (!trt_network) { + return tensorflow::errors::Internal( + "Failed to create TensorRT network object"); + } + auto ws = std::unique_ptr(new TRTWeightStore()); - for (auto& i : output_nodes) { - auto node_port = tensorflow::str_util::Split(i, ":"); - VLOG(1) << " " << i << " in graph " << node_maps.count(i); - auto out_node_name = node_port.at(0); - if (node_port.size() > 1) { - VLOG(1) << "Multi port output" << node_port.at(0) << " " - << node_port.at(1) << " size=" << node_port.size(); - } - auto node_it = node_maps.find(out_node_name); - if (node_it != node_maps.end()) { - tensorflow::Node* out_node = node_it->second; - int port = 0; - if (node_port.size() == 2) { - port = std::strtoul(node_port.at(1).c_str(), nullptr, 10); - out_types.push_back(out_node->output_type(port)); - } else { - out_types.push_back(out_node->output_type(0)); + // Build the network + VLOG(1) << "Starting engine conversion "; + Converter converter(trt_network.get(), ws.get(), precision_mode == FP16MODE); + std::vector> output_tensors; + // Graph nodes are already topologically sorted during construction + for (const auto& node_def : gdef.node()) { + string node_name = node_def.name(); + VLOG(1) << "Converting op name=" << node_name << ", op=" << node_def.op(); + if (tensorflow::str_util::StartsWith(node_name, kInputPHName) && + (node_def.op() == "Placeholder")) { + nvinfer1::DimsCHW input_dim_pseudo_chw; + for (int i = 0; i < 8; i++) input_dim_pseudo_chw.d[i] = 0; + nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); + auto type_status = + ConvertDType(node_def.attr().at("dtype").type(), &dtype); + if (type_status != tensorflow::Status::OK()) { + LOG(WARNING) << "Type conversion failed for " << node_name; + return type_status; } - for (auto out_edge : out_node->out_edges()) { - if (subgraph_ids.count(out_edge->dst()->id())) - continue; // skip internal edges; - if (out_edge->src_output() == port) { - out_edges.push_back(out_edge); - VLOG(1) << "OUTPUT EDGE " << out_edge->src()->name() << ":" - << out_edge->src_output() << " -> " << out_edge->dst()->name() - << ":" << out_edge->dst_input(); + int32 slot_number = -1; + if (!tensorflow::strings::safe_strto32(node_name.c_str() + 8, + &slot_number)) { + LOG(ERROR) << "Failed to parse slot number from " << node_name + << " +8= " << node_name.c_str() + 8; + } + auto shape = input_shapes.at(slot_number); + if (shape.dims() > 8) { + LOG(ERROR) << "Tensor rank is greater than 8 for " << node_name + << " at input slot " << slot_number; + return tensorflow::errors::OutOfRange( + "Input tensor rank is greater than 8"); + } + if (VLOG_IS_ON(1)) { + string dim_str("dims="); + StrAppend(&dim_str, "[ ", shape.dim_size(0)); + for (int i = 1; i < shape.dims(); i++) { + StrAppend(&dim_str, ", ", shape.dim_size(i)); } + StrAppend(&dim_str, " ]"); + VLOG(1) << dim_str; + } + for (int i = 1; i < shape.dims(); i++) { + input_dim_pseudo_chw.d[i - 1] = shape.dim_size(i); } - } else { - LOG(WARNING) << " couldn't find output node " << out_node_name; - } - } - if (VLOG_IS_ON(1)) { - VLOG(1) << c_node->name() << " Input Nodes:"; - for (auto& i : input_names) { - VLOG(1) << " Input " << i << " in graph " << node_maps.count(i); - } - } - auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); - auto resmgr = trt_rm->getManager("TRTCalibOps"); - tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; - auto status = resmgr->Lookup(res_name, res_name, &calib_res); - if (!status.ok() || !calib_res->calibrator_) { - return tensorflow::errors::FailedPrecondition( - "You must run calibration" - " and inference conversion in the same process"); - } - - calib_res->calibrator_->setDone(); - calib_res->thr_->join(); - delete calib_res->thr_; - if (!calib_res->engine_) { - LOG(ERROR) << "Calibration failed!, engine does not exist. Did you run " - "calibration graph?"; - return tensorflow::errors::FailedPrecondition( - "Calibration graph needs to be executed on" - " calibration data before convertsion to inference graph"); - } - auto weight_rmgr = trt_rm->getManager("WeightStore"); - TF_CHECK_OK(weight_rmgr->Delete( - res_name, res_name)); - auto engine_plan = calib_res->engine_->serialize(); - calib_res->engine_->destroy(); - calib_res->network_->destroy(); - calib_res->builder_->destroy(); - calib_res->thr_ = nullptr; - calib_res->engine_ = nullptr; - calib_res->builder_ = nullptr; - tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); - std::vector income_edges; - income_edges.resize(c_node->num_inputs()); - for (const auto in_edge : c_node->in_edges()) { - auto src = in_edge->src(); - int dest_port = in_edge->dst_input(); - VLOG(1) << "Incoming connection " << src->name() << ":" - << in_edge->src_output() << " -> " << c_node->name() << ":" - << dest_port; - income_edges.at(dest_port) = {src->name(), in_edge->src_output(), - c_node->input_type(dest_port)}; - } - tensorflow::gtl::ArraySlice input_list( - income_edges); - if (VLOG_IS_ON(2)) { - for (const auto& inp : input_list) { - VLOG(2) << " Input from inputlist " << inp.node << ":" << inp.index << " " - << tensorflow::DataTypeString(inp.data_type); - } - } - op_builder.Input(input_list); - tensorflow::NodeDef engine_node; - const char* engine_plan_data = static_cast(engine_plan->data()); - string engine_plan_string(engine_plan_data, - engine_plan_data + engine_plan->size()); - status = op_builder.Attr("serialized_engine", engine_plan_string) - .Attr("input_nodes", input_names) - .Attr("output_nodes", output_nodes) - .Attr("OutT", out_types) - .Finalize(&engine_node); - if (!status.ok()) { - LOG(ERROR) << "Engine Node creation failed"; - return status; - } - auto trt_engine_node = graph.AddNode(engine_node, &status); - TF_RETURN_IF_ERROR(status); - std::map port_map; - for (size_t t = 0; t < output_nodes.size(); t++) { - port_map.insert({output_nodes.at(t), t}); - } - for (auto& i : out_edges) { - string s(i->src()->name()); - if (i->src_output()) StrAppend(&s, ":", i->src_output()); - int out_port = port_map.at(s); - VLOG(1) << "Connecting " << trt_engine_node->name() << ":" << out_port - << " -> " << i->dst()->name() << ":" << i->dst_input(); - TF_RETURN_IF_ERROR( - graph.UpdateEdge(trt_engine_node, out_port, i->dst(), i->dst_input())); - } - for (const auto ed : trt_engine_node->in_edges()) { - VLOG(1) << "In Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); - } - for (const auto ed : trt_engine_node->out_edges()) { - VLOG(1) << "Out Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); - } - VLOG(1) << "Segment nodes:"; - for (auto& i : segment_nodes) { - VLOG(1) << " " << i << " in graph " << node_maps.count(i); - auto it = node_maps.find(i); - if (it != node_maps.end()) { - graph.RemoveNode(it->second); - } - } - graph.RemoveNode(c_node); - return tensorflow::Status::OK(); -} -tensorflow::Status ReverseTopologicalSort( - const tensorrt::convert::SubGraphParams& s, - std::list* order) { - std::vector order_vec; - tensorflow::GetPostOrder(s.graph, &order_vec); - // Select just the subgraph - for (tensorflow::Node* node : order_vec) { - if (s.subgraph_node_ids.count(node->id())) { - // We want topological order to contstruct the - // network layer by layer - order->push_front(node); + input_dim_pseudo_chw.nbDims = shape.dims() - 1; + nvinfer1::ITensor* input_tensor = converter.network()->addInput( + node_name.c_str(), dtype, input_dim_pseudo_chw); + if (!input_tensor) { + return tensorflow::errors::InvalidArgument( + "Failed to create Input layer tensor ", node_name, + " rank=", shape.dims() - 1); + } + VLOG(1) << "Input tensor name :" << node_name; + if (!converter.insert_input_tensor(node_name, input_tensor)) { + return tensorflow::errors::AlreadyExists( + "Output tensor already exists for op: " + node_name); + } + } else if (tensorflow::str_util::StartsWith(node_name, kOutputPHName) && + (node_def.op() == "Identity")) { + int32 slot_number = -1; + if (!tensorflow::strings::safe_strto32(node_name.c_str() + 9, + &slot_number)) { + LOG(ERROR) << "Failed to parse slot number from " << node_name + << " +9=" << node_name.c_str() + 9; + } + if (output_tensors.size() <= slot_number) { + output_tensors.resize(slot_number + 1); + } + output_tensors.at(slot_number) = {node_def.input(0), node_name}; + } else { + VLOG(2) << "Converting node: " << node_def.name() << " , " + << node_def.op(); + TF_RETURN_IF_ERROR(converter.convert_node(node_def)); } } - return tensorflow::Status::OK(); -} - -tensorflow::Status SetInputList( - const tensorrt::convert::SubGraphParams& s, - tensorflow::NodeDefBuilder* op_builder, - const std::vector* input_names, - std::vector* input_dtypes) { - std::vector income_edges; - VLOG(2) << "input edge size: " << input_names->size(); - for (size_t i = 0; i < input_names->size(); ++i) { - VLOG(2) << "input edges: " << i << " " << input_names->at(i); - int output_idx = s.input_inds.at(i).second; - // we wired up the input here already, it is redundant to do it again in - // ConvertSubGraphToTensorRT(convert_graph.cc) - auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut( - input_names->at(i), output_idx, input_dtypes->at(i)); - income_edges.push_back(incoming_edge); - } - tensorflow::gtl::ArraySlice input_list( - income_edges); - op_builder->Input(input_list); - return tensorflow::Status::OK(); -} - -string SubgraphNameScopeGenerator(const std::list* order) { - string subgraph_name_scope; - if (!order->empty()) { - subgraph_name_scope = order->front()->name(); - } - for (const tensorflow::Node* node : *order) { - subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name()); - } - // TODO(sami,ben,jie): proper naming! - return subgraph_name_scope; -} - -tensorflow::Status ConvertSubgraph( - Converter& converter, tensorrt::convert::SubGraphParams& s, - std::list* order, std::vector* input_names, - std::vector* input_dtypes, - std::vector* output_names, - std::vector* output_dtypes, - const string& engine_name) { - std::set added_tensors; - for (const std::pair& input : s.input_inds) { - VLOG(2) << "parsing input. Node id= " << input.first; - int node_id = input.first; - int output_idx = input.second; - tensorflow::Node* node = s.graph.FindNodeId(node_id); - auto node_name = node->name(); - // input_names should use the node name in the graph - // here it should be the input tensor name -> matching the binding - // insert original node name without port - auto tensor_name = node_name; - if (output_idx != 0) { - tensor_name = StrCat(tensor_name, ":", output_idx); - } - - VLOG(2) << "input name: " << node_name << " tensor_name: " << tensor_name - << " idx: " << output_idx; - - auto shape_inference_node_name = node_name; - auto shape_inference_output_idx = output_idx; - // rewire the shape inference to original node in the graph - if (s.output_edge_map->count(tensor_name)) { - shape_inference_node_name = s.output_edge_map->at(tensor_name).second; - shape_inference_output_idx = s.output_edge_map->at(tensor_name).first; - } - if (shape_inference_output_idx < 0) continue; - VLOG(2) << "shapeinference name: " << shape_inference_node_name - << " idx: " << shape_inference_output_idx; - - if (!s.graph_properties.HasOutputProperties(shape_inference_node_name)) - return tensorflow::errors::Internal("failed to find input node: " + - shape_inference_node_name); - - auto op_info_vec = - s.graph_properties.GetOutputProperties(shape_inference_node_name); - if (static_cast(op_info_vec.size()) <= shape_inference_output_idx) - return tensorflow::errors::Internal( - "accessing output index of: ", shape_inference_output_idx, - ", at node: ", shape_inference_node_name, - " with output entry from shape_map: ", op_info_vec.size()); - - auto op_info = op_info_vec.at(shape_inference_output_idx); - tensorflow::DataType tf_dtype = op_info.dtype(); - - nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); - auto type_status = ConvertDType(tf_dtype, &dtype); - if (type_status != tensorflow::Status::OK()) { - LOG(WARNING) << "Type conversion failed for " << node_name; - return type_status; - } - - VLOG(2) << "Accessing output index of: " << output_idx - << ", at node: " << node_name - << " with output entry from shape_map: " << op_info_vec.size(); - // TODO(ben,jie): update TRT input format/dimension - nvinfer1::DimsCHW input_dim_pseudo_chw; - for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1; - - // TODO(jie): TRT 3.x only support 4 dimensional input tensor. - // update the code once TRT 4.0 comes out. - if (op_info.shape().dim_size() != 4) { - string err_str = "Require 4 dimensional input."; - StrAppend(&err_str, " Got ", op_info.shape().dim_size(), " ", - shape_inference_node_name); - return tensorflow::errors::Unimplemented(err_str); - } - - for (int i = 1; i < op_info.shape().dim_size(); i++) { - VLOG(2) << "dimension: " << i - << " , size: " << op_info.shape().dim(i).size(); - input_dim_pseudo_chw.d[i - 1] = op_info.shape().dim(i).size(); - } - - // TODO(ben,jie): proper way to restore input tensor name? - auto input_tensor_name = node_name; - if (output_idx != 0) { - input_tensor_name = StrCat(node_name, ":", output_idx); - } - if (added_tensors.count(input_tensor_name)) continue; - added_tensors.insert(input_tensor_name); - input_names->push_back(input_tensor_name); - input_dtypes->push_back(tf_dtype); - nvinfer1::ITensor* input_tensor = converter.network()->addInput( - input_tensor_name.c_str(), dtype, input_dim_pseudo_chw); - - if (!input_tensor) - return tensorflow::errors::InvalidArgument( - "Failed to create Input layer"); - VLOG(2) << "Input tensor name :" << input_tensor_name; - - if (!converter.insert_input_tensor(input_tensor_name, input_tensor)) - return tensorflow::errors::AlreadyExists( - "Output tensor already exists for op: " + input_tensor_name); - } - - for (const tensorflow::Node* node : *order) { - const tensorflow::NodeDef& node_def = node->def(); - VLOG(2) << "Converting node: " << node_def.name() << " , " << node_def.op(); - TF_RETURN_IF_ERROR(converter.convert_node(node_def)); - } - - VLOG(2) << "Finished conversion"; - - // Gather output metadata - int trt_engine_op_output_idx = 0; - added_tensors.clear(); - for (const std::pair& output : s.output_inds) { - int node_id = output.first; - int output_idx = output.second; - tensorflow::Node* node = s.graph.FindNodeId(node_id); - string op_name = node->name(); - string tensor_name = op_name; - - s.output_edge_map->insert( - {trt_engine_op_output_idx == 0 - ? engine_name - : StrCat(engine_name, ":", trt_engine_op_output_idx), - {output_idx, tensor_name}}); - trt_engine_op_output_idx++; - if (output_idx != 0) - tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); - VLOG(2) << "Output tensor name: " << tensor_name; - if (added_tensors.count(tensor_name)) continue; - added_tensors.insert(tensor_name); - output_names->push_back(tensor_name); - auto tensor_or_weights = converter.get_tensor(tensor_name); + for (const auto& output : output_tensors) { + auto tensor_or_weights = converter.get_tensor(output.first); if (!tensor_or_weights.is_tensor()) { - return tensorflow::errors::InvalidArgument("Output node '" + tensor_name + - "' is weights not tensor"); + return tensorflow::errors::InvalidArgument( + "Output node '" + output.first + "' is weights not tensor"); } nvinfer1::ITensor* tensor = tensor_or_weights.tensor(); + tensor->setName(output.second.c_str()); if (!tensor) { return tensorflow::errors::NotFound("Output tensor not found: " + - tensor_name); + output.first); } + VLOG(1) << "Marking output tensor " << output.first << ", as output tensor " + << output.second; + converter.network()->markOutput(*tensor); - tensorflow::DataType tf_dtype = node->output_type(output_idx); - output_dtypes->push_back(tf_dtype); - nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT; - TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype)); - tensor->setType(trt_dtype); } + if (convert_successfully) *convert_successfully = true; - return tensorflow::Status::OK(); -} - -tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { - // Visit nodes in reverse topological order and construct the TRT network. - // Toposort - std::list order; - TF_RETURN_IF_ERROR(ReverseTopologicalSort(s, &order)); - - static int static_id = 0; - string subgraph_name_scope = SubgraphNameScopeGenerator(&order); - // TODO(sami,ben,jie): proper naming! - string calib_op_name = - StrCat(subgraph_name_scope, "my_trt_calib_op_", static_id); - string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id); - static_id++; - - auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); - auto op_rmgr = trt_rmgr->getManager("TRTCalibOps"); - auto op_res = new tensorflow::tensorrt::TRTCalibrationResource(); - TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res)); - op_res->logger_ = new tensorflow::tensorrt::Logger(); - cudaSetDevice(s.cuda_gpu_id_); - op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); - op_res->allocator_ = s.allocator_; -#if NV_TENSORRT_MAJOR > 3 - op_res->builder_->setGpuAllocator(s.allocator_.get()); -#endif - if (!op_res->builder_) { - return tensorflow::errors::Internal( - "failed to create TensorRT builder object"); + // Build the engine. + VLOG(1) << "Starting engine creation"; + engine->reset(builder->buildCudaEngine(*converter.network())); + if (engine->get() == nullptr) { + return tensorflow::errors::Internal("Failed to build TensorRT engine"); } - - op_res->network_ = op_res->builder_->createNetwork(); - if (!op_res->network_) { - return tensorflow::errors::Internal( - "failed to create TensorRT network object"); - } - - // Build the network - auto weight_rmgr = trt_rmgr->getManager("WeightStore"); - auto ws = new tensorflow::tensorrt::TRTWeightStore(); - TF_CHECK_OK(weight_rmgr->Create(calib_op_name, calib_op_name, ws)); - Converter converter(op_res->network_, ws, s.precision_mode == FP16MODE); - - std::vector input_names; - std::vector input_dtypes; - std::vector output_names; - std::vector output_dtypes; - TF_RETURN_IF_ERROR(ConvertSubgraph(converter, s, &order, &input_names, - &input_dtypes, &output_names, - &output_dtypes, engine_name)); - - VLOG(2) << "Finished processing outputs"; - - // Build the engine - op_res->builder_->setMaxBatchSize(s.max_batch_size); - op_res->builder_->setMaxWorkspaceSize(s.max_workspace_size_bytes); - VLOG(0) << "Max batch size= " << s.max_batch_size - << " max workspace size= " << s.max_workspace_size_bytes; - - // Build the TRT op - // TODO(sami,ben,jie): proper naming! - tensorflow::NodeDefBuilder op_builder(calib_op_name, "TRTCalibOp"); - TF_RETURN_IF_ERROR(SetInputList(s, &op_builder, &input_names, &input_dtypes)); - - std::vector segment_names; - segment_names.reserve(s.subgraph_node_ids.size()); - for (int i : s.subgraph_node_ids) { - auto node = s.graph.FindNodeId(i); - segment_names.push_back(node->name()); - } - LOG(INFO) << "finished op preparation"; - - auto status = op_builder.Attr("segment_nodes", segment_names) - .Attr("input_names", input_names) - .Attr("segment_output_names", output_names) - .Attr("resource_name", calib_op_name) - .Finalize(s.trt_node); - - LOG(INFO) << status.ToString(); - LOG(INFO) << "finished op building"; - + VLOG(1) << "Finished conversion"; return tensorflow::Status::OK(); } -tensorflow::Status ConvertSubGraphToTensorRTNodeDef( - tensorrt::convert::SubGraphParams& s) { - // Visit nodes in reverse topological order and construct the TRT network. - std::list order; - TF_RETURN_IF_ERROR(ReverseTopologicalSort(s, &order)); - - static int static_id = 0; - string subgraph_name_scope = SubgraphNameScopeGenerator(&order); - string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id++); - - tensorflow::tensorrt::Logger trt_logger; - cudaSetDevice(s.cuda_gpu_id_); - auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger)); - if (!trt_builder) { - return tensorflow::errors::Internal( - "Failed to create TensorRT builder object"); - } -#if NV_TENSORRT_MAJOR > 3 - trt_builder->setGpuAllocator(s.allocator_.get()); -#endif - auto trt_network = infer_object(trt_builder->createNetwork()); - if (!trt_network) { - return tensorflow::errors::Internal( - "Failed to create TensorRT network object"); - } - - auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); - auto weight_rmgr = trt_rmgr->getManager("WeightStore"); - auto ws = new tensorflow::tensorrt::TRTWeightStore(); - TF_CHECK_OK(weight_rmgr->Create(engine_name, engine_name, ws)); - - // Build the network - Converter converter(trt_network.get(), ws, s.precision_mode == FP16MODE); - - std::vector input_names; - std::vector input_dtypes; - std::vector output_names; - std::vector output_dtypes; - TF_RETURN_IF_ERROR(ConvertSubgraph(converter, s, &order, &input_names, - &input_dtypes, &output_names, - &output_dtypes, engine_name)); - - VLOG(2) << "Finished output"; - - // Build the engine - trt_builder->setMaxBatchSize(s.max_batch_size); - trt_builder->setMaxWorkspaceSize(s.max_workspace_size_bytes); - VLOG(0) << "Max batch size= " << s.max_batch_size - << " max workspace size= " << s.max_workspace_size_bytes; - if (s.precision_mode == FP16MODE) { - trt_builder->setHalf2Mode(true); - VLOG(0) << "Using FP16 precision mode"; - } - LOG(INFO) << "starting build engine"; - string engine_plan_string; - { - auto trt_engine = - infer_object(trt_builder->buildCudaEngine(*converter.network())); - VLOG(0) << "Built network"; - if (trt_engine.get() == nullptr) { - return tensorflow::errors::Internal("Engine building failure"); +tensorflow::Status ConvertSegmentToGraphDef( + const tensorflow::Graph* graph, + const tensorflow::grappler::GraphProperties& graph_properties, + const std::vector& subgraph_node_ids, // In topological order + std::vector* connections, + tensorflow::GraphDef* segment_def, string* common_scope) { + std::set marker_nodes; + // Update connection shapes/data types and add corresponding input/output + // nodes in the segment graphdef. + for (size_t i = 0; i < connections->size(); ++i) { + auto& connection = connections->at(i); + auto outside_node = graph->FindNodeId(connection.outside_id); + if (!outside_node) { + // This should never happen, unless the original graph is problematic. + return tensorflow::errors::NotFound( + "Cannot find node with id ", connection.outside_id, " in the graph."); + } + // Updates the shape and data types of input/output connections. + tensorflow::DataType input_type = tensorflow::DT_FLOAT; + tensorflow::PartialTensorShape partial_shape; + if (connection.is_input_edge) { + if (graph_properties.HasOutputProperties(connection.outside_node_name)) { + auto output_params = + graph_properties.GetOutputProperties(connection.outside_node_name); + auto out_shape = output_params.at(connection.outside_port); + input_type = out_shape.dtype(); + std::vector dims; + partial_shape = out_shape.shape(); + connection.outside_shape = partial_shape; + } else { + VLOG(0) << "Unknown output shape" << outside_node->name(); + input_type = graph->FindNodeId(connection.outside_id) + ->output_type(connection.outside_port); + } + connection.connection_type = input_type; + + } else { // output edge + if (graph_properties.HasInputProperties(connection.outside_node_name)) { + auto input_params = + graph_properties.GetInputProperties(connection.outside_node_name); + auto in_shape = input_params.at(connection.outside_port); + input_type = in_shape.dtype(); + partial_shape = in_shape.shape(); + connection.inside_shape = partial_shape; + } else { + input_type = graph->FindNodeId(connection.inside_id) + ->output_type(connection.outside_port); + } + connection.connection_type = input_type; } - auto engine_plan = infer_object(trt_engine->serialize()); - VLOG(0) << "Serialized engine"; - const char* engine_plan_data = - static_cast(engine_plan->data()); - engine_plan_string = - string(engine_plan_data, engine_plan_data + engine_plan->size()); - } - TF_RETURN_IF_ERROR(weight_rmgr->Delete( - engine_name, engine_name)); - LOG(INFO) << "finished engine " << engine_name << " containing " - << s.subgraph_node_ids.size() << " nodes"; - - // Build the TRT op - tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); - TF_RETURN_IF_ERROR(SetInputList(s, &op_builder, &input_names, &input_dtypes)); - - VLOG(0) << "Finished op preparation"; - - auto status = op_builder.Attr("serialized_engine", engine_plan_string) - .Attr("input_nodes", input_names) - .Attr("output_nodes", output_names) - .Attr("OutT", output_dtypes) - .Device(s.device_name_) - .Finalize(s.trt_node); - - VLOG(0) << status.ToString() << " finished op building for " << engine_name - << " on device " << s.device_name_; + // Add dummy input/output nodes to the segment graphdef. + if (connection.is_input_edge) { + const string node_name = StrCat(kInputPHName, connection.port_number); + if (marker_nodes.count(node_name)) { + VLOG(1) << "Reusing input " << node_name << " for the edge " + << connection.outside_node_name << ":" + << connection.outside_port << " -> " + << connection.inside_node_name << ":" << connection.inside_port; + continue; + } + marker_nodes.insert(node_name); + auto seg_node = segment_def->add_node(); + tensorflow::NodeDefBuilder builder(node_name, "Placeholder"); + auto status = builder.Attr("shape", partial_shape) + .Attr("dtype", input_type) + .Finalize(seg_node); + VLOG(1) << "Constructing input " << node_name << " for the edge " + << connection.outside_node_name << ":" << connection.outside_port + << " -> " << connection.inside_node_name << ":" + << connection.inside_port; + } else { + const string node_name = StrCat(kOutputPHName, connection.port_number); + if (marker_nodes.count(node_name)) { + VLOG(1) << "Reusing output " << node_name << " for the edge " + << connection.inside_node_name << ":" << connection.inside_port + << " -> " << connection.outside_node_name << ":" + << connection.outside_port; + continue; + } + marker_nodes.insert(node_name); + auto seg_node = segment_def->add_node(); + tensorflow::NodeDefBuilder builder(node_name, "Identity"); + auto status = builder.Input(connection.inside_node_name, 0, input_type) + .Finalize(seg_node); + VLOG(1) << "Constructing output " << node_name << " for the edge " + << connection.inside_node_name << ":" << connection.inside_port + << " -> " << connection.outside_node_name << ":" + << connection.outside_port; + } + } // for each connection. + + std::unordered_map old_to_new_id_map; + // Copy internal nodes to new graphdef + string local_scope = graph->FindNodeId(*subgraph_node_ids.begin())->name(); + for (const auto node_id : subgraph_node_ids) { + const auto node = graph->FindNodeId(node_id); + local_scope = GetCommonNameScope(local_scope, node->name()); + old_to_new_id_map[node_id] = segment_def->node_size(); + auto snode = segment_def->add_node(); + snode->CopyFrom(node->def()); + VLOG(1) << "Copying " << snode->name() << " to subgraph"; + } + // Update the inputs of the new input nodes to point to placeholder nodes. + for (int i = 0; i < connections->size(); ++i) { + auto& connection = connections->at(i); + if (!connection.is_input_edge) continue; + auto snode = + segment_def->mutable_node(old_to_new_id_map[connection.inside_id]); + const string placeholder_name = + StrCat(kInputPHName, connection.port_number); + VLOG(1) << "Updating " << snode->name() << ":" << connection.inside_port + << " from " << snode->input(connection.inside_port) << " to " + << placeholder_name; + snode->set_input(connection.inside_port, placeholder_name); + } + *common_scope = local_scope; + VLOG(0) << "Segment @scope '" << local_scope << "', converted to graph"; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 3f6592cd25ff013cadc0621ba64f0553983dd10b..1a4c0e755d1cd1e88ac26c39996eb3a750421a0a 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -22,69 +22,112 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/lib/core/status.h" + #if GOOGLE_CUDA #if GOOGLE_TENSORRT namespace tensorflow { namespace tensorrt { +static const char* kInputPHName = "InputPH_"; +static const char* kOutputPHName = "OutputPH_"; namespace convert { +// TODO(aaroey): use an enum instead. const int FP32MODE = 0; const int FP16MODE = 1; const int INT8MODE = 2; -struct SubGraphParams { - SubGraphParams( - tensorflow::Graph& inp_graph, - const std::set& subgraph_node_id_numbers, - const std::vector>& input_indices, - const std::vector>& output_indices, - size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, - const tensorflow::grappler::GraphProperties& current_graph_properties, - std::unordered_map>* output_edges, - tensorflow::NodeDef* constructed_trt_node, - int engine_precision_mode = FP32MODE, const string& device_name = "", - std::shared_ptr allocator = nullptr, - int cuda_gpu_id = 0) - : graph(inp_graph), - subgraph_node_ids(subgraph_node_id_numbers), - input_inds(input_indices), - output_inds(output_indices), - max_batch_size(max_supported_batch_size), - max_workspace_size_bytes(max_consumed_workspace_size_bytes), - graph_properties(current_graph_properties), - output_edge_map(output_edges), - trt_node(constructed_trt_node), - precision_mode(engine_precision_mode), - device_name_(device_name), - allocator_(allocator), - cuda_gpu_id_(cuda_gpu_id) {} - - tensorflow::Graph& graph; - const std::set& subgraph_node_ids; - const std::vector>& input_inds; // {node_id, output_idx} - const std::vector>& output_inds; // {node_id, output_idx} - size_t max_batch_size; - size_t max_workspace_size_bytes; - const tensorflow::grappler::GraphProperties& graph_properties; - std::unordered_map>* output_edge_map; - tensorflow::NodeDef* trt_node; - const int precision_mode; - const string device_name_; - std::shared_ptr allocator_; - const int cuda_gpu_id_; +struct EngineConnection { + EngineConnection(const string& outside, int out_id, int out_port, + const string& inside, int in_id, int in_port, + bool input_edge, int port) + : outside_node_name(outside), + outside_id(out_id), + outside_port(out_port), + inside_node_name(inside), + inside_id(in_id), + inside_port(in_port), + is_input_edge(input_edge), + port_number(port) {} + + const string outside_node_name; + const int outside_id; + const int outside_port; + tensorflow::PartialTensorShape outside_shape; + + const string inside_node_name; + const int inside_id; + const int inside_port; + tensorflow::PartialTensorShape inside_shape; + + tensorflow::DataType connection_type; + bool is_input_edge; + + // The port number of the TRT node connecting to this edge. + int port_number; +}; + +struct EngineInfo { + EngineInfo() + : engine_type(EngineType::TRTStatic), + max_workspace_size_bytes(0), + precision_mode(FP32MODE) {} + + string engine_name; + string device; + tensorflow::GraphDef segment_graph_def; + + // The segment nodes that are on one side of the edges are topological sorted. + std::vector connections; + + enum class EngineType { TRTStatic = 0, TRTDynamic = 1 }; + EngineType engine_type; + int64 max_workspace_size_bytes; + int maximum_cached_engines; + std::vector cached_engine_batches; + int precision_mode; }; -// TODO(sami): Replace references with const reference or pointers -tensorflow::Status ConvertSubGraphToTensorRTNodeDef(SubGraphParams& params); -tensorflow::Status InjectCalibrationNode(SubGraphParams& params); -tensorflow::Status ConvertCalibrationNodeToEngineNode(tensorflow::Graph& graph, - tensorflow::Node* c_node); +// Constructs a graphdef from the segment in the given graph. Adds placeholder +// nodes for input edges (InputPH_*) and identity nodes for output edges +// (OutputPH_*). This function needs to be called before TensorRT nodes +// inserted in order to correctly get sizes from the original graph. +// +// - subgraph_node_ids: the node ids of the subgraph, must be sorted in +// topological order. +// - segment_def: the output GraphDef, whose non-input/output nodedefs will be +// sorted in topological order. +tensorflow::Status ConvertSegmentToGraphDef( + const tensorflow::Graph* graph, + const tensorflow::grappler::GraphProperties& graph_properties, + const std::vector& subgraph_node_ids, + std::vector* connections, + tensorflow::GraphDef* segment_def, string* common_scope); + +// Converts given subgraph to a TRT engine saved in 'engine'. Returns ok iff +// 'builder' successfully build the engine. If the result is not ok, 'engine' +// will be set to nullptr +// Once returned, 'builder' is not needed any more and can be safely detroyed. +// +// - convert_successfully: indicates whether the converson to TensorRT network +// is successful. This is different than successfully building the engine: +// building can still fail afterwards. +tensorflow::Status ConvertGraphDefToEngine( + const tensorflow::GraphDef& gdef, int precision_mode, int max_batch_size, + size_t max_workspace_size_bytes, + const std::vector& input_shapes, + Logger* logger, nvinfer1::IGpuAllocator* allocator, + TRTInt8Calibrator* calibrator, + TrtUniquePtrType* engine, + bool* convert_successfully); + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 8f634b1f74717310a69a6bab5d5224c9bdbf10cc..ec9dbfa13bfd0a158dcf41cf1fdb7128a2adf641 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -45,8 +45,24 @@ tensorflow::Status TRTOptimizationPass::Init( if (params.count("max_batch_size")) { maximum_batch_size_ = params.at("max_batch_size").i(); } - if (params.count("max_workspace_size_bytes")) + is_dynamic_op_ = false; + if (params.count("is_dynamic_op")) { + is_dynamic_op_ = params.at("is_dynamic_op").b(); + } + if (params.count("cached_engine_batches")) { + auto batch_vec = params.at("cached_engine_batches").list(); + batches_.reserve(batch_vec.i_size()); + for (const auto i : batch_vec.i()) { + batches_.push_back(i); + } + } + max_cached_batches_ = 1; + if (params.count("maximum_cached_engines")) { + max_cached_batches_ = params.at("maximum_cached_engines").i(); + } + if (params.count("max_workspace_size_bytes")) { maximum_workspace_size_ = params.at("max_workspace_size_bytes").i(); + } if (params.count("precision_mode")) { string pm = Uppercase(params.at("precision_mode").s()); if (pm == "FP32") { @@ -175,6 +191,17 @@ tensorflow::Status TRTOptimizationPass::Optimize( if (VLOG_IS_ON(1)) { PrintDebugInfo(cluster, item); } + // This is a hack to workaround optimizer issue. MetaOptimizer calls + // optimization passes on function objects as well, we should not modify + // generated funcdefs! This is fragile but we don't have any other option + // until framework fixes it. + if (item.id != "tf_graph") { + LOG(WARNING) << name_ + << " is probably called on funcdef! This optimizer must *NOT* " + "be called on function objects."; + *optimized_graph = item.graph; + return tensorflow::Status::OK(); + } int max_dim = -1; if (item.feed.size()) { for (const auto& f : item.feed) { @@ -204,11 +231,22 @@ tensorflow::Status TRTOptimizationPass::Optimize( } tensorflow::grappler::GraphProperties static_graph_properties(item); TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); - auto status = tensorflow::tensorrt::convert::ConvertAfterShapes( - item.graph, item.fetch, maximum_batch_size_, maximum_workspace_size_, - optimized_graph, precision_mode_, minimum_segment_size_, - static_graph_properties, cluster); + tensorflow::tensorrt::convert::ConversionParams cp; + cp.input_graph_def = &item.graph; + cp.output_names = &item.fetch; + cp.max_batch_size = maximum_batch_size_; + cp.max_workspace_size_bytes = maximum_workspace_size_; + cp.output_graph_def = optimized_graph; + cp.precision_mode = precision_mode_; + cp.minimum_segment_size = minimum_segment_size_; + cp.graph_properties = &static_graph_properties; + cp.cluster = cluster; + cp.is_dyn_op = is_dynamic_op_; + cp.cached_engine_batches = batches_; + cp.max_cached_engines = max_cached_batches_; + auto status = tensorflow::tensorrt::convert::ConvertAfterShapes(cp); VLOG(2) << optimized_graph->DebugString(); + VLOG(1) << "Returning from " << name_; return status; } diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h index d8ecead23efaa5c3bab95b8ba481e2307b0af772..463ed3883e4808408104c618a289989472c497ea 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h @@ -61,6 +61,9 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { int minimum_segment_size_; int precision_mode_; int maximum_batch_size_; + bool is_dynamic_op_; + std::vector batches_; + int max_cached_batches_; int64_t maximum_workspace_size_; }; diff --git a/tensorflow/contrib/tensorrt/convert/utils.h b/tensorflow/contrib/tensorrt/convert/utils.h new file mode 100644 index 0000000000000000000000000000000000000000..f601c06701fdbf983b708cf5f5c7d22634bb810b --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/utils.h @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ + +#include + +namespace tensorflow { +namespace tensorrt { + +template +struct TrtDestroyer { + void operator()(T* t) { + if (t) t->destroy(); + } +}; + +template +using TrtUniquePtrType = std::unique_ptr>; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc deleted file mode 100644 index aea44fd8a2fcc4c359a6cb0c98ae34711708326e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc +++ /dev/null @@ -1,136 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/contrib/tensorrt/kernels/trt_calib_op.h" -#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" -#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" -#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/tensor_shape.h" -#include "tensorflow/core/framework/tensor_types.h" -#include "tensorflow/core/framework/types.h" -#include "tensorflow/core/platform/stream_executor.h" - -#if GOOGLE_CUDA -#if GOOGLE_TENSORRT -#include "cuda/include/cuda_runtime_api.h" -#include "tensorrt/include/NvInfer.h" - -namespace tensorflow { -namespace tensorrt { - -TRTCalibOp::TRTCalibOp(OpKernelConstruction* context) : OpKernel(context) { - OP_REQUIRES_OK(context, context->GetAttr("segment_nodes", &segment_nodes_)); - OP_REQUIRES_OK(context, context->GetAttr("input_names", &input_names_)); - OP_REQUIRES_OK(context, context->GetAttr("resource_name", &resource_name_)); -}; - -#define TYPECASE(dt, X, Y) \ - case dt: { \ - return (void*)X->flat::Type>().data(); \ - } - -void* GetTensorAddress(const Tensor* tensor_ptr) { - auto tensor_type = tensor_ptr->dtype(); - switch (tensor_type) { - TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr); - TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr); - TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr); - default: { - LOG(FATAL) << "Unsupported Data type " - << tensorflow::DataTypeString(tensor_type); - return nullptr; - } - } -} - -void TRTCalibOp::Compute(tensorflow::OpKernelContext* ctx) { - // TODO(aaroey): make sure ctx->resource_mgr() is used in future PR. - auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); - auto res_mgr = trt_rm->getManager("TRTCalibOps"); - tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; - auto status = res_mgr->Lookup(resource_name_, resource_name_, &calib_res); - - if (!status.ok()) { - ctx->SetStatus(status); - return; - } - int num_inputs = ctx->num_inputs(); - // first run instantiate calibrator - if (calib_res->calibrator_ == nullptr) { - dev_tensors_.resize(num_inputs); - int batch_size = ctx->input(0).dim_size(0); - VLOG(1) << " Constructing calibrator"; - for (int i = 0; i < num_inputs; i++) { - // allocate workspace on device for inputs - const tensorflow::Tensor& t = ctx->input(i); - OP_REQUIRES_OK(ctx, - ctx->allocate_persistent(t.dtype(), t.shape(), - &dev_tensors_.at(i), nullptr)); - const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); - CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); - void* device_address = GetTensorAddress(device_tensor); - device_buffers_.emplace(input_names_.at(i), - std::pair( - device_address, device_tensor->TotalBytes())); - } - - calib_res->calibrator_ = - new TRTInt8Calibrator(device_buffers_, batch_size, resource_name_); - string label(resource_name_); - calib_res->thr_ = new std::thread([calib_res, label]() { - VLOG(1) << "Starting calibration thread, Calibration Resource @ " - << calib_res; - calib_res->builder_->setInt8Calibrator(calib_res->calibrator_); - calib_res->builder_->setInt8Mode(true); - calib_res->engine_ = calib_res->builder_->buildCudaEngine( - *calib_res->network_); // will loop until we terminate calibrator - VLOG(1) << "Calibration loop terminated " << label; - }); - VLOG(1) << "initialized calibrator resource"; - } // calibrator initialized - - // Pass input data to calibrator - std::unordered_map input_data; - for (int i = 0; i < num_inputs; i++) { - const Tensor& t = ctx->input(i); - void* data_address = GetTensorAddress(&t); - const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); - CHECK_EQ(t.TotalBytes(), - device_tensor->TotalBytes()); // use the tensor so FW keeps it - input_data.emplace(input_names_.at(i), data_address); - ctx->set_output(i, t); - } - VLOG(2) << "Filled map for sending"; - // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files - const cudaStream_t* stream = CHECK_NOTNULL( - reinterpret_cast(ctx->op_device_context() - ->stream() - ->implementation() - ->CudaStreamMemberHack())); - calib_res->calibrator_->setBatch(input_data, *stream); - VLOG(2) << "Passed calibration data"; - // TODO(aaroey): make sure we wait for the completion of calibration on the - // last batch in future PR. -}; - -#undef TYPECASE - -REGISTER_KERNEL_BUILDER(Name("TRTCalibOp").Device(DEVICE_GPU), TRTCalibOp); - -} // namespace tensorrt -} // namespace tensorflow -#endif -#endif diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h deleted file mode 100644 index 23df9db32f077a080eaff7479fcbe90d6a504c42..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h +++ /dev/null @@ -1,52 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H -#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H - -#include -#include -#include -#include -#include -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/tensor_shape.h" -#include "tensorflow/core/platform/types.h" - -#if GOOGLE_CUDA -#if GOOGLE_TENSORRT -namespace tensorflow { -namespace tensorrt { -// TODO(sami): Convert this to async kernel! -class TRTCalibOp : public OpKernel { - public: - explicit TRTCalibOp(OpKernelConstruction* context); - - void Compute(OpKernelContext* context) override; - - private: - string resource_name_; - std::vector segment_nodes_; - std::vector input_names_; - std::vector shapes_; - std::unordered_map> device_buffers_; - std::vector dev_tensors_; -}; -} // namespace tensorrt -} // namespace tensorflow -#endif -#endif -#endif // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 9ac8047944874181de228a6cc58e2dafe46abe50..8a17eb02f1af7c8f148c9cd4e14cc3876b6e13e3 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -14,8 +14,16 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" +#include +#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" +#include "tensorflow/core/framework/graph_to_functiondef.h" +#include "tensorflow/core/lib/core/refcount.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/types.h" @@ -25,144 +33,556 @@ limitations under the License. #include "cuda/include/cuda_runtime_api.h" namespace tensorflow { -static ::tensorflow::tensorrt::Logger logger; -using IRuntime = nvinfer1::IRuntime; -using Dims = nvinfer1::Dims; - namespace tensorrt { +static Logger logger; +using ::nvinfer1::IRuntime; +using ::tensorflow::strings::StrAppend; +using ::tensorflow::strings::StrCat; + +// A helper class to call done() when destructed for asynchronous execution. +// Helps simultaneous execution of native and TRT engines. +class AsyncHelper : public tensorflow::core::RefCounted { + public: + AsyncHelper(tensorflow::AsyncOpKernel::DoneCallback done) { done_ = done; } + ~AsyncHelper() override { done_(); } -TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { + private: + tensorflow::AsyncOpKernel::DoneCallback done_; +}; + +#define TYPECASE(dt, X, Y) \ + case dt: { \ + return (void*)X->flat::Type>().data(); \ + } + +void* GetTensorAddress(const Tensor* tensor_ptr) { + auto tensor_type = tensor_ptr->dtype(); + switch (tensor_type) { + TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr); + default: { + LOG(ERROR) << "Unsupported Data type " + << tensorflow::DataTypeString(tensor_type); + return nullptr; + } + } +} + +tensorflow::Status TRTEngineOp::ConstructFunctionHandle(OpKernelContext* ctx) { + VLOG(1) << "Constructing function handle"; + auto lib = ctx->function_library(); + if (lib == nullptr) { + return tensorflow::errors::Internal("Context function library is null"); + } + auto fdef = lib->GetFunctionLibraryDefinition()->Find(funcdef_name_); + if (fdef == nullptr) { + return tensorflow::errors::Internal("Native FunctionDef ", funcdef_name_, + " can't be found in function library"); + } + tensorflow::FunctionLibraryRuntime::InstantiateOptions inst_ops; + inst_ops.overlay_lib = nullptr; + inst_ops.state_handle = ""; + inst_ops.target = ctx->device()->name(); + native_func_ = 0; + auto status = lib->Instantiate(funcdef_name_, AttrSlice(&fdef->attr()), + inst_ops, &native_func_); + if (!status.ok()) { + LOG(ERROR) << " Instantiating native function " << funcdef_name_ + << " failed!"; + } + return status; +} + +TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) + : AsyncOpKernel(context) { // read serialized_engine OP_REQUIRES_OK(context, - context->GetAttr("serialized_engine", &serialized_engine_)); + context->GetAttr("serialized_segment", &serialized_segment_)); + OP_REQUIRES_OK(context, + context->GetAttr("workspace_size_bytes", &workspace_size_)); + OP_REQUIRES_OK(context, context->GetAttr("static_engine", &static_engine_)); + if (!static_engine_) { + if (!segment_graph_.ParseFromString(serialized_segment_)) { + LOG(ERROR) << "Parsing segment graph failed!"; + context->SetStatus(tensorflow::errors::InvalidArgument( + "Failed to parse segment graphdef!")); + return; + } + serialized_segment_.resize(0); + } + VLOG(1) << "Constructing " << name(); + string precision_string; + OP_REQUIRES_OK(context, + context->GetAttr("precision_mode", &precision_string)); + string calibration_data; + OP_REQUIRES_OK(context, + context->GetAttr("calibration_data", &calibration_data)); + OP_REQUIRES_OK(context, + context->GetAttr("segment_funcdef_name", &funcdef_name_)); + if (precision_string == "FP32") { + precision_mode_ = convert::FP32MODE; + } else if (precision_string == "FP16") { + precision_mode_ = convert::FP16MODE; + } else if (precision_string == "INT8") { + precision_mode_ = convert::INT8MODE; + } + calibration_mode_ = + (precision_mode_ == convert::INT8MODE && calibration_data.size() == 0); + if (calibration_data.size()) { + calibrator_.reset(new TRTInt8Calibrator(calibration_data)); + calibration_data.resize(0); + } + native_func_ = tensorflow::kInvalidHandle; + OP_REQUIRES_OK(context, context->GetAttr("max_cached_engines_count", + &max_cached_engines_)); + OP_REQUIRES_OK(context, + context->GetAttr("fixed_input_size", &fixed_input_size_)); + OP_REQUIRES_OK(context, context->GetAttr("cached_engine_batches", + &cached_engine_batches_)); + std::sort(cached_engine_batches_.begin(), cached_engine_batches_.end()); + if (VLOG_IS_ON(1)) { + string s("Engine Batches= "); + for (auto i : cached_engine_batches_) { + StrAppend(&s, i, " "); + } + VLOG(1) << s; + } +} - // register input output node name in trt_sub_graph - OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_)); - OP_REQUIRES_OK(context, context->GetAttr("output_nodes", &output_nodes_)); +void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, + AsyncHelper* helper) { + if (!calibration_mode_) { + VLOG(1) << "Executing native engine"; + } + std::vector inputs; + std::vector* outputs = new std::vector(); + if (native_func_ == tensorflow::kInvalidHandle) { + auto status = ConstructFunctionHandle(ctx); + if (!status.ok()) { + LOG(ERROR) << "Couldn't construct function handle " << funcdef_name_; + ctx->SetStatus(status); + return; + } + } + auto lib = ctx->function_library(); + tensorflow::FunctionLibraryRuntime::Options opts; + opts.step_id = ctx->step_id(); + opts.rendezvous = ctx->rendezvous(); + opts.cancellation_manager = ctx->cancellation_manager(); + opts.runner = ctx->runner(); + for (int i = 0; i < ctx->num_inputs(); i++) { + inputs.push_back(ctx->input(i)); + } + helper->Ref(); // Increment count for calculating native graph + VLOG(1) << "Executing native segment " << name(); + lib->Run(opts, native_func_, inputs, outputs, + [ctx, outputs, helper](const tensorflow::Status& s) { + tensorflow::core::ScopedUnref sc(helper); + VLOG(1) << "Native Segment completed"; + if (!s.ok()) { + ctx->SetStatus(s); + return; + } + for (size_t t = 0; t < outputs->size(); ++t) { + ctx->set_output(t, outputs->at(t)); + } + delete outputs; + }); } -void TRTEngineOp::Compute(OpKernelContext* context) { - // TODO(samikama) runtime should be taken from a resourcemanager as well. - // Only engine should be in the op and context and runtime should be taken - // from resourcemanager +void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, + AsyncHelper* helper) { + helper->Ref(); + tensorflow::core::ScopedUnref sc(helper); + // TODO(aaroey): remove the ResourceMgr singleton. + auto trt_rm = TRTResourceManager::instance(); + auto res_mgr = trt_rm->getManager("TRTCalibration"); + TRTCalibrationResource* calib_res = nullptr; + auto status = res_mgr->LookupOrCreate( + funcdef_name_, "Calibrator", &calib_res, + {[ctx, this](TRTCalibrationResource** cr) -> tensorflow::Status { + return this->AllocateCalibrationResources(ctx, cr); + }}); + if (!status.ok()) { + ctx->SetStatus(status); + return; + } + int num_inputs = ctx->num_inputs(); + // Pass input data to calibrator + std::unordered_map input_data; + for (int i = 0; i < num_inputs; i++) { + const Tensor& t = ctx->input(i); + void* data_address = GetTensorAddress(&t); + if (data_address == nullptr) { + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "Unsupported data type encountered in input ", i)); + return; + } + // Check the allocated buffer is sufficient for input + const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); + CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); + input_data.emplace(StrCat(kInputPHName, i), data_address); + } + VLOG(2) << "Filled map for sending"; + // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + const cudaStream_t* stream = CHECK_NOTNULL( + reinterpret_cast(ctx->op_device_context() + ->stream() + ->implementation() + ->CudaStreamMemberHack())); + calib_res->calibrator_->setBatch(input_data, *stream); + VLOG(2) << "Passed calibration data"; + ExecuteNativeSegment(ctx, helper); +} - if (!trt_execution_context_ptr_) { - IRuntime* infer = nvinfer1::createInferRuntime(logger); -#if NV_TENSORRT_MAJOR > 3 - auto device = context->device(); - auto dev_allocator = - device->GetAllocator(tensorflow::AllocatorAttributes()); - if (!dev_allocator) { - LOG(FATAL) << "Can't find device allocator for gpu device " - << device->name(); - } - allocator_ = std::make_shared(dev_allocator); - infer->setGpuAllocator(allocator_.get()); -#endif - trt_engine_ptr_.reset(infer->deserializeCudaEngine( - serialized_engine_.c_str(), serialized_engine_.size(), - PluginFactoryTensorRT::GetInstance())); - trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); - // Runtime is safe to delete after engine creation - infer->destroy(); - serialized_engine_.clear(); +int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { + int num_batch = ctx->input(0).shape().dim_size(0); + int smallest_engine = 0; + for (const auto i : cached_engine_batches_) { + if (i >= num_batch) { + smallest_engine = i; + break; + } } - int num_binding = context->num_inputs() + context->num_outputs(); - std::vector buffers(num_binding); + // TODO(sami): Need an LRU here + if (smallest_engine == 0) { + if (max_cached_engines_ > cached_engine_batches_.size()) { + smallest_engine = num_batch; + cached_engine_batches_.push_back(num_batch); + VLOG(1) << "Running with batch size " << num_batch; + } else { + string s("Engine buffer is full. buffer limit= "); + StrAppend(&s, max_cached_engines_, ", current entries= "); + for (auto i : cached_engine_batches_) StrAppend(&s, i, ", "); + StrAppend(&s, "Requested batch= ", num_batch); + LOG(ERROR) << s; + ctx->SetStatus(tensorflow::errors::ResourceExhausted( + "Requested batch size is not available and engine cache is full")); + return -1; + } + } + return smallest_engine; +} - size_t binding_index; - int num_batch = 0; - for (int i = 0; i < context->num_inputs(); i++) { - // Grab the input tensor - binding_index = trt_engine_ptr_->getBindingIndex(input_nodes_[i].c_str()); +void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, + tensorflow::AsyncOpKernel::DoneCallback done) { + auto helper = new AsyncHelper(done); + tensorflow::core::ScopedUnref sc(helper); + if (calibration_mode_) { + ExecuteCalibration(ctx, helper); + return; + } + const int smallest_engine = GetEngineBatch(ctx); + if (smallest_engine < 0) return; // GetEngineBatch already set the status. + + const int num_batch = ctx->input(0).shape().dim_size(0); + auto& engine_ctx_pair = GetEngine(smallest_engine, ctx); + auto& trt_engine_ptr = engine_ctx_pair.first; + if (!trt_engine_ptr) { + LOG(WARNING) << "Engine retrieval for batch size " << num_batch + << " failed Running native segment"; + ExecuteNativeSegment(ctx, helper); + return; + } - const Tensor& input_tensor = context->input(i); + const int num_binding = ctx->num_inputs() + ctx->num_outputs(); + std::vector buffers(num_binding); + for (int i = 0; i < ctx->num_inputs(); i++) { + const string inp_name = StrCat(kInputPHName, i); + const size_t binding_index = + trt_engine_ptr->getBindingIndex(inp_name.c_str()); + + const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); - if (i == 0) { - num_batch = input_shape.dim_size(0); - if (num_batch > trt_engine_ptr_->getMaxBatchSize()) { - LOG(FATAL) << "input tensor batch larger than max_batch_size: " - << trt_engine_ptr_->getMaxBatchSize(); - } - } else if (num_batch != input_shape.dim_size(0)) { - LOG(FATAL) << "input data inconsistent batch size"; - break; + if (num_batch != input_shape.dim_size(0)) { + LOG(ERROR) << "input data inconsistent batch size"; + ctx->SetStatus(tensorflow::errors::FailedPrecondition( + "Different batch sizes between input tensors")); + return; } - auto dtype = trt_engine_ptr_->getBindingDataType(binding_index); + auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { case nvinfer1::DataType::kFLOAT: buffers[binding_index] = (void*)(input_tensor.flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(FATAL) << "half size is not supported yet!"; - break; + LOG(ERROR) << "FP16 inputs are not supported yet!"; + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "FP16 inputs are not supported!")); + return; case nvinfer1::DataType::kINT8: - LOG(FATAL) << "int8 is not supported yet!"; - break; + LOG(ERROR) << "INT8 inputs are not supported yet!"; + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "INT8 inputs are not supported!")); + return; default: - LOG(FATAL) << "Unknown data type: " << int(dtype); - break; + LOG(ERROR) << "Unknown TRT data type: " << int(dtype); + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "Unknown output TRT data type! ", static_cast(dtype))); + return; } } - for (int i = 0; i < static_cast(output_nodes_.size()); i++) { - // This is bad that we have to reallocate output buffer every run. + for (int i = 0; i < ctx->num_outputs(); i++) { // Create an output tensor - binding_index = trt_engine_ptr_->getBindingIndex(output_nodes_[i].c_str()); + const string output_name = StrCat(kOutputPHName, i); + const size_t binding_index = + trt_engine_ptr->getBindingIndex(output_name.c_str()); Tensor* output_tensor = nullptr; TensorShape output_shape; if (binding_index != -1) { - auto dims = trt_engine_ptr_->getBindingDimensions(binding_index); + auto dims = trt_engine_ptr->getBindingDimensions(binding_index); std::vector trt_shape(dims.nbDims + 1); trt_shape[0] = num_batch; for (int j = 0; j < dims.nbDims; j++) trt_shape[j + 1] = dims.d[j]; - OP_REQUIRES_OK(context, - TensorShapeUtils::MakeShape( - trt_shape.data(), trt_shape.size(), &output_shape)); + OP_REQUIRES_OK( + ctx, TensorShapeUtils::MakeShape(trt_shape.data(), trt_shape.size(), + &output_shape)); } else { - LOG(FATAL) << "output node not found, at " << output_nodes_[i]; - break; + LOG(ERROR) << "output node not found, at " << output_name; + ctx->SetStatus(tensorflow::errors::Internal("output ", output_name, + " couldn't be found!")); + return; } - - OP_REQUIRES_OK(context, - context->allocate_output(i, output_shape, &output_tensor)); - auto dtype = trt_engine_ptr_->getBindingDataType(binding_index); + auto status = ctx->allocate_output(i, output_shape, &output_tensor); + if (!status.ok()) { + LOG(ERROR) << "Allocating output failed with " << status; + ctx->SetStatus(status); + return; + } + auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { case nvinfer1::DataType::kFLOAT: buffers[binding_index] = reinterpret_cast(output_tensor->flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(FATAL) << "half size is not supported yet!"; - break; + LOG(ERROR) << "half size is not supported yet!"; + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "Half outputs are not supported!")); + return; case nvinfer1::DataType::kINT8: - LOG(FATAL) << "int8 is not supported yet!"; - break; + LOG(ERROR) << "int8 is not supported yet!"; + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "INT8 outputs are not supported!")); + return; default: - LOG(FATAL) << "Unknown data type: " << int(dtype); - break; + LOG(ERROR) << "Unknown TRT data type: " << static_cast(dtype); + ctx->SetStatus(tensorflow::errors::InvalidArgument( + "Unsupported output data type! ", static_cast(dtype))); + return; } } // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files const cudaStream_t* stream = CHECK_NOTNULL( - reinterpret_cast(context->op_device_context() + reinterpret_cast(ctx->op_device_context() ->stream() ->implementation() ->CudaStreamMemberHack())); // TODO(jie): trt enqueue does not return error - auto ret = trt_execution_context_ptr_->enqueue(num_batch, &buffers[0], - *stream, nullptr); - VLOG(2) << "enqueue returns: " << ret; + auto& trt_execution_context_ptr = engine_ctx_pair.second; + auto ret = trt_execution_context_ptr->enqueue(num_batch, &buffers[0], *stream, + nullptr); + if (!ret) { + LOG(ERROR) << "Failed to enqueue batch for TRT engine: " << name(); + ctx->SetStatus(tensorflow::errors::Internal( + "Failed to enqueue batch for TRT engine: ", name())); + } // sync should be done by TF. } + TRTEngineOp::~TRTEngineOp() { - // Order matters! - trt_execution_context_ptr_.reset(); - trt_engine_ptr_.reset(); + // We need to manually destroy the engine and execution context before + // the allocator is destructed. + for (auto& eng : engine_map_) { + eng.second.first.reset(); + eng.second.second.reset(); + } allocator_.reset(); } + +nvinfer1::IGpuAllocator* TRTEngineOp::GetAllocator(OpKernelContext* ctx) { + if (allocator_) return allocator_.get(); + auto device = ctx->device(); + auto alloc = device->GetAllocator(tensorflow::AllocatorAttributes()); + if (!alloc) { + LOG(ERROR) << "Can't find device allocator for gpu device " + << device->name(); + ctx->SetStatus(tensorflow::errors::Internal( + "Can't get device allocator for device ", device->name())); + return nullptr; + } + allocator_.reset(new TRTDeviceAllocator(alloc)); + return allocator_.get(); +} + +TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, + OpKernelContext* ctx) { + static EngineCtxPair null_pair = { + TrtUniquePtrType(nullptr), + TrtUniquePtrType(nullptr)}; + // TODO(sami): This method needs to be re-written to use resource manager and + // with LRU mechanism option. + tensorflow::mutex_lock lock(engine_mutex_); + + if (static_engine_) { + if (engine_map_.size()) { + if (engine_map_.begin()->first >= batch_size) { + return engine_map_.begin()->second; + } + return null_pair; + } + TrtUniquePtrType infer(nvinfer1::createInferRuntime(logger)); +#if NV_TENSORRT_MAJOR > 3 + auto allocator = GetAllocator(ctx); + if (allocator == nullptr) { + // GetAllocator already set the Status. + return null_pair; + } + infer->setGpuAllocator(allocator); +#endif + TrtUniquePtrType static_engine( + infer->deserializeCudaEngine(serialized_segment_.c_str(), + serialized_segment_.size(), nullptr)); + auto raw_static_engine = static_engine.get(); + const auto max_batch_size = raw_static_engine->getMaxBatchSize(); + engine_map_[max_batch_size] = { + std::move(static_engine), + TrtUniquePtrType( + raw_static_engine->createExecutionContext())}; + // Runtime is safe to delete after engine creation + serialized_segment_.clear(); + if (max_batch_size < batch_size) return null_pair; + return engine_map_.at(max_batch_size); + } // static_engine_ + + // Handle the dynamic engine case. + auto engine_it = engine_map_.find(batch_size); + if (engine_it == engine_map_.end() && + engine_map_.size() < (size_t)max_cached_engines_) { + nvinfer1::IGpuAllocator* allocator = nullptr; +#if NV_TENSORRT_MAJOR > 3 + allocator = GetAllocator(ctx); + if (allocator == nullptr) { + // GetAllocator already set the Status. + return null_pair; + } +#endif + std::vector shapes; + for (int i = 0; i < ctx->num_inputs(); ++i) { + shapes.emplace_back(ctx->input(i).shape()); + } + TrtUniquePtrType engine; + bool convert_successfully = false; + VLOG(0) << name() << " Constructing a new engine with batch size " + << batch_size; + // Up to this point, calibrator_ can never be empty, since otherwise it + // means calibration_mode_ is true and this path won't get executed. + auto status = convert::ConvertGraphDefToEngine( + segment_graph_, precision_mode_, batch_size, workspace_size_, shapes, + &logger, allocator, calibrator_.get(), &engine, &convert_successfully); + if (!status.ok()) { + if (convert_successfully) { + // This means it fail to build the engine even when the network is built + // successfully, probably due to internal issues. In this case we don't + // retry in the future. + engine_map_[batch_size] = {nullptr, nullptr}; + } + LOG(ERROR) << "Engine creation for batch size " << batch_size + << " failed " << status; + ctx->SetStatus(tensorflow::errors::Internal("Engine creation failed!")); + return null_pair; + } + VLOG(1) << "Conversion is done"; + TrtUniquePtrType exec_context( + engine->createExecutionContext()); + engine_map_[batch_size] = {std::move(engine), std::move(exec_context)}; + } + return engine_map_.at(batch_size); +} + +tensorflow::Status TRTEngineOp::AllocateCalibrationResources( + tensorflow::OpKernelContext* ctx, TRTCalibrationResource** cr) { + auto cres = new TRTCalibrationResource(); + *cr = cres; + // Get the allocator. + auto alloc = ctx->device()->GetAllocator(tensorflow::AllocatorAttributes()); + if (!alloc) { + LOG(WARNING) << "Can't get device allocator will not be able to " + "allocate memory from TensorFlow memory pool"; + cres->allocator_.reset(new TRTCudaAllocator); + } else { + cres->allocator_.reset(new TRTDeviceAllocator(alloc)); + } + // Get the input shapes. + const int batch_size = ctx->input(0).dim_size(0); + const int num_inputs = ctx->num_inputs(); + std::vector shapes; + dev_tensors_.resize(num_inputs); + VLOG(1) << " Constructing calibrator"; + for (int i = 0; i < num_inputs; i++) { + // allocate workspace on device for inputs + const tensorflow::Tensor& t = ctx->input(i); + shapes.emplace_back(t.shape()); + Tensor* device_tensor; + TF_RETURN_IF_ERROR(ctx->allocate_persistent( + t.dtype(), t.shape(), &dev_tensors_.at(i), &device_tensor)); + CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); + void* device_address = GetTensorAddress(device_tensor); + if (device_address == nullptr) { + return tensorflow::errors::InvalidArgument( + "Unsupported data type encountered in input ", i); + } + device_buffers_.emplace( + StrCat(kInputPHName, i), + std::pair(device_address, device_tensor->TotalBytes())); + } + cres->calibrator_.reset( + new TRTInt8Calibrator(device_buffers_, batch_size, name())); + const string label(name()); + auto segment_graph = &segment_graph_; + const int cuda_gpu_id = ctx->device()->tensorflow_gpu_device_info()->gpu_id; + if (cuda_gpu_id < 0) { + LOG(ERROR) << "Can't get gpu_device_info from context->device()"; + return tensorflow::errors::InvalidArgument( + "Context->device doesn't contain device info!"); + } + const int64 workspace_size_bytes = workspace_size_; + cres->thr_.reset(new std::thread([cres, label, segment_graph, shapes, + cuda_gpu_id, workspace_size_bytes]() { + VLOG(0) << "Starting calibration thread on device " << cuda_gpu_id + << ", Calibration Resource @ " << cres; + auto err = cudaSetDevice(cuda_gpu_id); + if (err != cudaSuccess) { + // TODO(aaroey): should return error here. + LOG(ERROR) << "Couldn't set cuda device to " << cuda_gpu_id + << " in calibration thread"; + } + // ConvertGraphDefToEngine() will try to build the engine. This thread + // will loop inside buildCudaEngine() consuming the calibration data + // that is set by the TF op, and drive the builder until calibrator returns + // false. Engine is discarded after calibration table is generated + // + // TODO(aaroey): maybe setting the max batch size using the python + // calibration wrapper class. + auto s = convert::ConvertGraphDefToEngine( + *segment_graph, convert::INT8MODE, cres->calibrator_->getBatchSize(), + workspace_size_bytes, shapes, &cres->logger_, cres->allocator_.get(), + cres->calibrator_.get(), &cres->engine_, + /*convert_successfully=*/nullptr); + if (!s.ok()) { + LOG(ERROR) << "Calibration failed: " << s; + cres->calibrator_->setDone(); // Ignore further pushes + } + VLOG(1) << "Calibration loop terminated " << label; + })); + VLOG(1) << "initialized calibrator resource"; + return tensorflow::Status::OK(); +} + REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp); } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index e613a71422852e60565ba7554516d7eace6b9cc7..6fe318be6a6bc9f01ce3b52e0430f2090b53002b 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -19,9 +19,14 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" +#include "tensorflow/core/framework/function.h" +#include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/platform/mutex.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -30,32 +35,95 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -class Logger; - +class TRTInt8Calibrator; +class TRTCalibrationResource; +class AsyncHelper; // TODO(Sami): Remove this file? -class TRTEngineOp : public OpKernel { + +// This OP can construct TRTEngine on the fly and if construction of engine +// fails, executes equivalent subgraph as a TensorFlow function. +class TRTEngineOp : public AsyncOpKernel { public: explicit TRTEngineOp(OpKernelConstruction* context); - void Compute(OpKernelContext* context) override; + void ComputeAsync(OpKernelContext* context, + AsyncOpKernel::DoneCallback done) override; ~TRTEngineOp(); private: - template - struct Destroyer { - void operator()(T* d) { d->destroy(); } - }; - - template - using destroyed_ptr = std::unique_ptr>; - destroyed_ptr trt_engine_ptr_; + // Execute calibration + void ExecuteCalibration(OpKernelContext* ctx, AsyncHelper* helper); + + // Construct a function handle for executing native funcdef graph + Status ConstructFunctionHandle(OpKernelContext* ctx); + + // Execute replaced native segment as function Op. + void ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper); + + // Allocate necessary resources for calibration + Status AllocateCalibrationResources(OpKernelContext* ctx, + TRTCalibrationResource** cr); + // TODO(samikama): context should go to a resource manager! - destroyed_ptr trt_execution_context_ptr_; + typedef std::pair, + TrtUniquePtrType> + EngineCtxPair; + EngineCtxPair& GetEngine(int batch_size, OpKernelContext* ctx); + // Return engine batch closest to input batch. + int GetEngineBatch(OpKernelContext* ctx); + + nvinfer1::IGpuAllocator* GetAllocator(OpKernelContext* ctx); + + // map to keep engines and their execution context for given batch size. + std::unordered_map engine_map_; std::vector input_nodes_; std::vector output_nodes_; - std::shared_ptr allocator_; - string serialized_engine_; + + // keep device allocator for TRT. + std::unique_ptr allocator_; + + // serialized protobuf segment or trt engine depending on static_engine_ flag. + string serialized_segment_; + + // Name of the function for TF native execution of the segment. + string funcdef_name_; + + // GraphDef representation of the segment. + GraphDef segment_graph_; + + // Lookup table for temporary staging areas of input tensors for calibration. + std::unordered_map> device_buffers_; + + // Temporary staging areas for calibration inputs. + std::vector dev_tensors_; + + // Engine Precision mode. + int precision_mode_; + + // Whether engine is constructed during the conversion or needs to be + // constructed from protobuf segment. + bool static_engine_; + + // Whether to calibrate INT8 engine. + bool calibration_mode_; + + // Whether non-batch ranks of the inputs are assumed to be fixed or not for + // engine construction. + bool fixed_input_size_; + + // Batches of the cached engines + std::vector cached_engine_batches_; + + // Maximum number of cached engines + int max_cached_engines_; + + int64 workspace_size_; + mutex engine_mutex_; + FunctionLibraryRuntime::Handle native_func_; + + // The finalized calibrator for inference. + std::unique_ptr calibrator_; }; } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc deleted file mode 100644 index 4835e5065068ec7a59995eb7f6126b31aecf6704..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/shape_inference.h" -namespace tensorflow { - -REGISTER_OP("TRTCalibOp") - .Attr("segment_nodes: list(string)") // names of the ops in segment - .Attr("segment_output_names: list(string)") // names of the output ops in - // segment - .Attr("input_names: list(string)") // names of the inputs for - // passing into tensorrt - .Attr("resource_name: string") - .Attr("InT: list({int8, float16, float32})") - .Input("in_tensor: InT") - .Output("out_tensor: InT") - .SetShapeFn([](tensorflow::shape_inference::InferenceContext* c) { - for (int i = 0; i < c->num_inputs(); i++) { - c->set_output(i, c->input(i)); - } - return Status::OK(); - }); - -} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc index 079d73f7bec3f9a9740e455b31a259cec287f849..383635f428812984915a8c46ad3b92cc7b28a5f7 100644 --- a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc @@ -28,11 +28,19 @@ extern Status TRTEngineOpShapeInference(InferenceContext* c); } REGISTER_OP("TRTEngineOp") - .Attr("serialized_engine: string") - .Attr("input_nodes: list(string)") - .Attr("output_nodes: list(string)") - .Attr("InT: list({float32})") - .Attr("OutT: list({float32})") + .Attr("serialized_segment: string") + .Attr("input_shapes: list(shape)") + .Attr("output_shapes: list(shape)") + .Attr("segment_funcdef_name: string") + .Attr("InT: list({int8,float16,float32})") + .Attr("OutT: list({int8,float16,float32})") + .Attr("static_engine: bool = true") + .Attr("fixed_input_size: bool = true") + .Attr("cached_engine_batches: list(int) = []") + .Attr("max_cached_engines_count: int = 1") + .Attr("workspace_size_bytes: int") + .Attr("precision_mode: {'FP32', 'FP16', 'INT8', 'INT8CALIB'}") + .Attr("calibration_data: string = ''") .Input("in_tensor: InT") .Output("out_tensor: OutT") .SetShapeFn(shape_inference::TRTEngineOpShapeInference); diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index 338475d90ea55ab2c1bb8df77f27a71a4a36a5dd..79f512dbcf6bd4d84b98cf69630778734566391c 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -21,6 +21,8 @@ from __future__ import print_function # pylint: disable=unused-import,line-too-long import six as _six from tensorflow.contrib.tensorrt.wrap_conversion import calib_convert +from tensorflow.contrib.tensorrt.wrap_conversion import get_linked_tensorrt_version +from tensorflow.contrib.tensorrt.wrap_conversion import get_loaded_tensorrt_version from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert from tensorflow.core.framework import graph_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 @@ -29,7 +31,9 @@ from tensorflow.python.framework import errors_impl as _impl from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.grappler import tf_optimizer +from tensorflow.python.platform import tf_logging from tensorflow.python.util import compat + # pylint: enable=unused-import,line-too-long @@ -40,7 +44,10 @@ def create_inference_graph(input_graph_def, max_batch_size=1, max_workspace_size_bytes=2 << 20, precision_mode="FP32", - minimum_segment_size=3): + minimum_segment_size=3, + is_dynamic_op=False, + maximum_cached_engines=1, + cached_engine_batches=[]): """Python wrapper for the TRT transformation. Args: @@ -51,6 +58,10 @@ def create_inference_graph(input_graph_def, precision_mode: one of 'FP32', 'FP16' and 'INT8' minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. + is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT + network and engine at run time. + maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops. + cached_engine_batches: batch sizes used to pre-create cached engines. Returns: New GraphDef with TRTEngineOps placed in graph replacing subgraphs. @@ -65,6 +76,30 @@ def create_inference_graph(input_graph_def, "It should be one of {}").format( precision_mode, "{'FP32', 'FP16', 'INT8'}")) mode = supported_precision_modes[precision_mode.upper()] + compiled_version = get_linked_tensorrt_version() + loaded_version = get_loaded_tensorrt_version() + version_mismatch = False + if loaded_version[0] < compiled_version[0]: + tf_logging.error( + "TensorRT version mismatch. Tensorflow was compiled against " + + "TensorRT %s but library loaded from environment is TensorRT %s" % + (".".join([str(x) for x in compiled_version]), + ".".join([str(x) for x in loaded_version])) + + ". Please make sure that correct version of TensorRT " + + "is available in the system and added to ldconfig or LD_LIBRARY_PATH" + ) + raise RuntimeError("Incompatible TensorRT library version") + for i in zip(loaded_version, compiled_version): + if i[0] != i[1]: + tf_logging.warn("TensorRT mismatch. Compiled against version " + + "%s, but loaded %s. Things may not work" % + (".".join([str(x) for x in compiled_version]), + ".".join([str(x) for x in loaded_version]))) + version_mismatch = True + break + if not version_mismatch: + tf_logging.info("Running against TensorRT version %s" % ".".join( + [str(x) for x in loaded_version])) def py2bytes(inp): return inp @@ -100,7 +135,9 @@ def create_inference_graph(input_graph_def, # pair or strings where first one is encoded status and the second # one is the transformed graphs protobuf string. out = trt_convert(input_graph_def_str, out_names, max_batch_size, - max_workspace_size_bytes, mode, minimum_segment_size) + max_workspace_size_bytes, mode, minimum_segment_size, + is_dynamic_op, maximum_cached_engines, + cached_engine_batches) status = to_string(out[0]) output_graph_def_string = out[1] del input_graph_def_str # Save some memory @@ -120,11 +157,12 @@ def create_inference_graph(input_graph_def, return output_graph_def -def calib_graph_to_infer_graph(calibration_graph_def): +def calib_graph_to_infer_graph(calibration_graph_def, is_dynamic_op=False): """Convert an existing calibration graph to inference graph. Args: calibration_graph_def: the calibration GraphDef object with calibration data + is_dynamic_op: whether to create dynamic static engines from calibration Returns: New GraphDef with TRTEngineOps placed in graph replacing calibration nodes. Raises: @@ -141,9 +179,16 @@ def calib_graph_to_infer_graph(calibration_graph_def): to_string = py2string else: to_string = py3string - + is_calib_graph = False + for n in calibration_graph_def.node: + if n.op == "TRTEngineOp": + is_calib_graph = is_calib_graph or not n.attr["calibration_data"].s + if not is_calib_graph: + tf_logging.error( + "Not a calib graph. Doesn't seem to contain any calibration nodes.") + return None graph_str = calibration_graph_def.SerializeToString() - out = calib_convert(graph_str) + out = calib_convert(graph_str, is_dynamic_op) status = to_string(out[0]) output_graph_def_string = out[1] del graph_str # Save some memory diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 0f0508331c13055096714352e83fc360f0ef39b4..9f115990c3a3e6e92093e5f0d82b985af1b25482 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -50,7 +50,7 @@ TRTDeviceAllocator::TRTDeviceAllocator(tensorflow::Allocator* allocator) } void TRTDeviceAllocator::free(void* memory) { - VLOG(2) << "Deallocating " << memory; + VLOG(2) << "Deallocating @ " << memory; allocator_->DeallocateRaw(memory); } diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index a0c2540a7698bc46a65dbd967412351bac2a4dd2..c5d2cec730f4ae97e4c6bcc19897fd9f321122a7 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -16,7 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ #define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ - #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/core/framework/allocator.h" @@ -52,7 +51,9 @@ class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { // Allocator implementation wrapping TF device allocators. public: TRTDeviceAllocator(tensorflow::Allocator* allocator); - virtual ~TRTDeviceAllocator() {} + virtual ~TRTDeviceAllocator() { + VLOG(1) << "Destroying allocator attached to " << allocator_->Name(); + } void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc index dc7c93f869f5ef7c8eaa2a87eed26cfe69597fdb..dab1dd9343be7d5b033a3e04bf0b49fbbf37e9e5 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -16,7 +16,6 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include -#include #include #include "tensorflow/core/platform/logging.h" @@ -37,20 +36,29 @@ TRTInt8Calibrator::TRTInt8Calibrator( : batch_size_(batch_size), done_(false), dev_buffers_(dev_buffers), - calib_running_(false), + // Make sure setBatch() waits until getBatch() is called (the first time). + calib_running_(true), batch_is_set_(false), engine_name_(engine_name) {} +TRTInt8Calibrator::TRTInt8Calibrator(const string& calib_data) + : batch_size_(0), + done_(true), + calib_running_(false), + batch_is_set_(false), + calibration_table_(calib_data) {} + bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, const cudaStream_t stream) { tensorflow::mutex_lock lock(cond_mtx_); - while ((calib_running_ || batch_is_set_) && - !done_) { // wait while calibration is running - cond_.wait(lock); - } + + // Wait while the queue is full or calibration is running. + while ((calib_running_ || batch_is_set_) && !done_) cond_.wait(lock); if (done_) return false; CHECK(!calib_running_ && !batch_is_set_); VLOG(1) << "Set Batch Waiting finished"; + + // Sets the batch. for (const auto it : data) { auto devptr = dev_buffers_.find(it.first); if (devptr == dev_buffers_.end()) { @@ -59,8 +67,6 @@ bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, } const auto& d = devptr->second; - // TODO(aaroey): we should not use sync copy on default stream. Make sure - // stream->ThenMemcpy() is used in future PRs. // TODO(sami,aaroey): Need to figure out a way to ensure synchronization // between stream, perhaps using a tensor? auto status = cudaMemcpyAsync(d.first, it.second, d.second, @@ -72,8 +78,8 @@ bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, } // TODO(Sami, aaorey): Find an alternative way! - cudaStreamSynchronize( - stream); // we have to wait for the stream before returning! + // we have to wait for the stream before returning! + cudaStreamSynchronize(stream); batch_is_set_ = true; cond_.notify_all(); return true; @@ -82,23 +88,21 @@ bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, bool TRTInt8Calibrator::getBatch(void** bindings, const char** names, int num_bindings) { tensorflow::mutex_lock lock(cond_mtx_); + // Notify finish of last round of calibration. calib_running_ = false; cond_.notify_all(); - while ((!batch_is_set_ && !done_)) { // wait until new batch arrives - cond_.wait(lock); - } - if (done_) { - return false; - } + // Wait until new batch arrives + while ((!batch_is_set_ && !done_)) cond_.wait(lock); + if (done_) return false; + // Gets the batch for (int i = 0; i < num_bindings; i++) { auto it = dev_buffers_.find(names[i]); if (it == dev_buffers_.end()) { LOG(FATAL) << "Calibration engine asked for unknown tensor name '" << names[i] << "' at position " << i; } - bindings[i] = it->second.first; } batch_is_set_ = false; @@ -106,8 +110,21 @@ bool TRTInt8Calibrator::getBatch(void** bindings, const char** names, return true; } +void TRTInt8Calibrator::waitAndSetDone() { + tensorflow::mutex_lock lock(cond_mtx_); + // Wait while the queue is full or calibration is running, so we don't miss + // the last batch. + while ((calib_running_ || batch_is_set_) && !done_) cond_.wait(lock); + if (!done_) { + done_ = true; + cond_.notify_all(); + } +} + const void* TRTInt8Calibrator::readCalibrationCache(std::size_t& length) { - return nullptr; + if (calibration_table_.empty()) return nullptr; + length = calibration_table_.size(); + return calibration_table_.data(); } void TRTInt8Calibrator::setDone() { @@ -117,7 +134,11 @@ void TRTInt8Calibrator::setDone() { } void TRTInt8Calibrator::writeCalibrationCache(const void* ptr, - std::size_t length) {} + std::size_t length) { + calibration_table_ = string((const char*)ptr, length); + VLOG(1) << "Got calibration data for " << engine_name_ << " @" << ptr + << " length=" << length; +} TRTInt8Calibrator::~TRTInt8Calibrator() { VLOG(1) << "Destroying calibrator for " << engine_name_; } diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h index d77aa2c5ab184756adaee38f88180b3c128ebe03..65466c9741989fda5f82fc27d813d026f35fe386 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h @@ -36,32 +36,59 @@ namespace tensorrt { struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator { public: + // Construct a calibrator for future calibration. TRTInt8Calibrator( const std::unordered_map>& dev_buffers, int batch_size, string engine_name); + + // Construct a finalized calibrator where we don't need to run calibration any + // more, as the calibration data is provided. + TRTInt8Calibrator(const string& calibration_data); + + ~TRTInt8Calibrator(); + int getBatchSize() const override; + bool getBatch(void* bindings[], const char* names[], int num_bindings) override; + bool setBatch(const std::unordered_map& data, const cudaStream_t stream); + + // Wait until the last batch is consumed by the calibrator and set done. + void waitAndSetDone(); + + // Notify that calibration is done and future batches provided by setBatch() + // will be ignored. void setDone(); + + // If not null, calibration is skipped. const void* readCalibrationCache(std::size_t& length) override; + void writeCalibrationCache(const void* ptr, std::size_t length) override; - ~TRTInt8Calibrator(); + + const string& getCalibrationTableAsString() { return calibration_table_; } private: const int batch_size_; - tensorflow::mutex cond_mtx_; // mutex for condition_variable - tensorflow::condition_variable cond_; // condition variable to implement - // producer-consumer queue for - // calibration + + // mutex for condition_variable + tensorflow::mutex cond_mtx_; + + // condition variable to implement producer-consumer queue for calibration + tensorflow::condition_variable cond_; + + // Is calibration finished? bool done_; - const std::unordered_map> - dev_buffers_; // map to keep tensorrt input buffers and sizes keyed with - // buffer names + + // Map to keep tensorrt input buffers and sizes keyed with buffer names + const std::unordered_map> dev_buffers_; + bool calib_running_; bool batch_is_set_; + string engine_name_; + string calibration_table_; }; } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index e3469124acd4b9f6f4dd81b9998aa60bfe469b35..b7d5ffd6748ba34c6c4ddbfbfbb44edb6bf2aca8 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -22,6 +22,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" @@ -34,50 +35,48 @@ limitations under the License. namespace tensorflow { namespace tensorrt { + class TRTCalibrationResource : public tensorflow::ResourceBase { public: - TRTCalibrationResource() - : calibrator_(nullptr), - builder_(nullptr), - network_(nullptr), - engine_(nullptr), - logger_(nullptr), - thr_(nullptr) {} - ~TRTCalibrationResource() { VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString(); + builder_.reset(); + engine_.reset(); + // We need to manually destroy the builder and engine before the allocator + // is destroyed. + allocator_.reset(); } string DebugString() override { std::stringstream oss; - oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl - << " Builder = " << std::hex << builder_ << std::dec << std::endl - << " Network = " << std::hex << network_ << std::dec << std::endl - << " Engine = " << std::hex << engine_ << std::dec << std::endl - << " Logger = " << std::hex << logger_ << std::dec << std::endl - << " Allocator = " << std::hex << allocator_.get() << std::dec - << std::endl - << " Thread = " << std::hex << thr_ << std::dec << std::endl; + using std::dec; + using std::endl; + using std::hex; + oss << " Calibrator = " << hex << calibrator_.get() << dec << endl + << " Builder = " << hex << builder_.get() << dec << endl + << " Engine = " << hex << engine_.get() << dec << endl + << " Logger = " << hex << &logger_ << dec << endl + << " Allocator = " << hex << allocator_.get() << dec << endl + << " Thread = " << hex << thr_.get() << dec << endl; return oss.str(); } - TRTInt8Calibrator* calibrator_; - nvinfer1::IBuilder* builder_; - nvinfer1::INetworkDefinition* network_; - nvinfer1::ICudaEngine* engine_; - std::shared_ptr allocator_; - tensorflow::tensorrt::Logger* logger_; + std::unique_ptr calibrator_; + TrtUniquePtrType builder_; + TrtUniquePtrType engine_; + std::unique_ptr allocator_; + tensorflow::tensorrt::Logger logger_; // TODO(sami): Use threadpool threads! - std::thread* thr_; + std::unique_ptr thr_; }; -class TRTWeightStore : public tensorflow::ResourceBase { +class TRTWeightStore { public: TRTWeightStore() {} virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); } - string DebugString() override { + string DebugString() { std::stringstream oss; size_t len_bytes = 0; for (const auto& v : store_) { diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 1568dd915344e6ba982b5a5550cc5386e047ff9f..81b4bfe49fe375d19f4c7811459f38e25d2edea8 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -29,8 +29,9 @@ namespace tensorflow { namespace tensorrt { namespace segment { -// vector of segments, each entry contains a device name and a set of nodes in -// segment +// Vector of segments, each entry contains a set of node names and a device name +// in the segment. +// TODO(aaroey): use node pointer instead of node name. using SegmentNodesVector = std::vector, string>>; struct SegmentOptions { @@ -48,6 +49,8 @@ struct SegmentOptions { // in the vector describes a subgraph by giving a set of the names of // all the NodeDefs in that subgraph. // @return the status. +// +// TODO(aaroey): remove this method. tensorflow::Status SegmentGraph( const tensorflow::GraphDef& gdef, const std::function& candidate_fn, diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc index f36495f6b69ecb2f2a8d730b9ae4919fea3c04b8..227ac120dde8c986379c687987cd1bd822d559f7 100644 --- a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -29,61 +29,35 @@ namespace tensorflow { namespace shape_inference { tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { - tensorflow::tensorrt::Logger logger; - string serialized_engine; - TF_RETURN_IF_ERROR(context->GetAttr("serialized_engine", &serialized_engine)); - nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger); - nvinfer1::ICudaEngine* trt_engine = infer->deserializeCudaEngine( - serialized_engine.c_str(), serialized_engine.size(), - tensorrt::PluginFactoryTensorRT::GetInstance()); - - int num_batch = -1; - std::vector<::tensorflow::DataType> input_type; - TF_RETURN_IF_ERROR(context->GetAttr("InT", &input_type)); - for (size_t i = 0; i < context->num_inputs(); i++) { - // Check if input shape is legit - auto input_shape = context->input(i); - for (int j = 0; j < context->Rank(input_shape); j++) { - auto dim_handler = context->Dim(input_shape, j); - if (j == 0) { - if (i == 0) { - num_batch = context->Value(dim_handler); - } else if (num_batch != context->Value(dim_handler)) { - // TODO(jie): TensorRT engine requires consistent batch between inputs - // tensors. Segmenter should be aware of this. - LOG(FATAL) << "TensorRT engine requires consistent batch size"; - } - } - } + std::vector shapes; + for (int i = 0; i < context->num_outputs(); ++i) { + context->set_output(i, context->UnknownShape()); } - - // Arrange input here - std::vector input_nodes; - TF_RETURN_IF_ERROR(context->GetAttr("input_nodes", &input_nodes)); - - // Arrange output here - std::vector output_nodes; - TF_RETURN_IF_ERROR(context->GetAttr("output_nodes", &output_nodes)); - for (size_t i = 0; i < output_nodes.size(); i++) { - int binding_index = trt_engine->getBindingIndex(output_nodes[i].c_str()); - ShapeHandle output_shape; - std::vector dim_vec; - dim_vec.emplace_back(context->MakeDim(num_batch)); - if (binding_index != -1) { - auto dims = trt_engine->getBindingDimensions(binding_index); - for (int j = 0; j < dims.nbDims; j++) { - dim_vec.emplace_back(context->MakeDim(dims.d[j])); - } - } else { - LOG(FATAL) << "TensorRT engine cannot find binding: " << output_nodes[i]; - } - output_shape = context->MakeShape(dim_vec); - context->set_output(i, output_shape); + auto status = context->GetAttr("input_shapes", &shapes); + // it is ok to not to have shapes + if (!status.ok()) return Status::OK(); + if ((int)shapes.size() != context->num_inputs()) return Status::OK(); + bool different_input = false; + for (int i = 0; i < context->num_inputs(); ++i) { + if (shapes.at(i) != context->input_tensor(i)->shape()) + different_input = true; + } + if (different_input) return Status::OK(); + shapes.resize(0); + status = context->GetAttr("output_shapes", &shapes); + if (!status.ok()) return Status::OK(); + if ((int)shapes.size() != context->num_outputs()) return Status::OK(); + std::vector shape_handles(shapes.size()); + for (size_t i = 0; i < shapes.size(); ++i) { + status = + context->MakeShapeFromTensorShape(shapes.at(i), &shape_handles.at(i)); + if (!status.ok()) return Status::OK(); + } + for (int i = 0; i < context->num_outputs(); ++i) { + context->set_output(i, shape_handles.at(i)); } - return Status::OK(); } - } // namespace shape_inference } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 175ccd800686255092e241aa59568df407d6eebc..090aa8bdb0487973e186631af3b4edac48096a5f 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -20,6 +20,7 @@ from __future__ import print_function import argparse import numpy as np +import six as _six # normally we should do import tensorflow as tf and then # tf.placeholder, tf.constant, tf.nn.conv2d etc but @@ -35,10 +36,75 @@ from tensorflow.python.framework import dtypes as dtypes from tensorflow.python.framework import importer as importer from tensorflow.python.framework import ops as ops from tensorflow.python.ops import array_ops as aops +from tensorflow.python.ops import math_ops as mops from tensorflow.python.ops import nn as nn from tensorflow.python.ops import nn_ops as nn_ops +def py2bytes(inp): + return inp + + +def py3bytes(inp): + return inp.encode("utf-8", errors="surrogateescape") + + +def py2string(inp): + return inp + + +def py3string(inp): + return inp.decode("utf-8") + + +if _six.PY2: + to_bytes = py2bytes + to_string = py2string +else: + to_bytes = py3bytes + to_string = py3string + + +def get_multi_engine_graph_def(mode="FP32"): + """Create a simple graph and return its graph_def.""" + dtype = dtypes.float32 + if mode.upper() == "FP16": + dtype = dtypes.float16 + else: + pass + + g = ops.Graph() + with g.as_default(): + x = aops.placeholder(shape=[None, 3, 7, 5], name="input", dtype=dtype) + with g.name_scope("Global_scope"): + with g.name_scope("first_scope"): + e = cop.constant( + np.random.randn(3, 2, 3, 4), name="weights", dtype=dtype) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = cop.constant(np.random.randn(1, 4, 1, 1), name="bias1", dtype=dtype) + t = conv * b + + b = cop.constant(np.random.randn(1, 4, 1, 1), name="bias2", dtype=dtype) + q = conv / b + edge = mops.sin(q) + edge1 = mops.cos(conv) + with g.name_scope("test_scope"): + de = edge + edge1 + t -= edge1 + q *= edge + t += q + t -= de + k = aops.squeeze(t, name="output") + print(k.dtype) + return g.as_graph_def() + + def get_simple_graph_def(): """Create a simple graph and return its graph_def.""" g = ops.Graph() @@ -65,7 +131,9 @@ def get_simple_graph_def(): def execute_graph(gdef, dumm_inp): """Run given graphdef once.""" print("executing") - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) sessconfig = cpb2.ConfigProto(gpu_options=gpu_options) ops.reset_default_graph() g = ops.Graph() @@ -83,7 +151,9 @@ def execute_graph(gdef, dumm_inp): # for calibration. For this test script it is random data. def execute_calibration(gdef, dumm_inp): """Run given calibration graph multiple times.""" - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() g = ops.Graph() with g.as_default(): @@ -100,12 +170,17 @@ def execute_calibration(gdef, dumm_inp): return val -def user(run_graph=execute_graph, run_calibration=execute_calibration): +def user(multi_engine, + run_graph=execute_graph, + run_calibration=execute_calibration): """Example function that converts a graph to TFTRT graph.""" - - inp_dims = (100, 24, 24, 2) + if multi_engine: + inp_dims = (2, 3, 7, 5) + orig_graph = get_multi_engine_graph_def() + else: + inp_dims = (100, 24, 24, 2) + orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) - orig_graph = get_simple_graph_def() # use a frozen graph for inference # Get optimized graph trt_graph = trt.create_inference_graph( input_graph_def=orig_graph, @@ -113,8 +188,10 @@ def user(run_graph=execute_graph, run_calibration=execute_calibration): max_batch_size=inp_dims[0], max_workspace_size_bytes=1 << 25, precision_mode="FP32", # TRT Engine precision "FP32","FP16" or "INT8" - minimum_segment_size=2 # minimum number of nodes in an engine - ) + minimum_segment_size=2, # minimum number of nodes in an engine + is_dynamic_op=False, + maximum_cached_engines=1, + cached_engine_batches=[]) o1 = run_graph(orig_graph, dummy_input) o2 = run_graph(trt_graph, dummy_input) o3 = run_graph(trt_graph, dummy_input) @@ -126,40 +203,51 @@ def user(run_graph=execute_graph, run_calibration=execute_calibration): max_batch_size=inp_dims[0], max_workspace_size_bytes=1 << 25, precision_mode="FP16", # TRT Engine precision "FP32","FP16" or "INT8" - minimum_segment_size=2 # minimum number of nodes in an engine - ) + minimum_segment_size=2, # minimum number of nodes in an engine + is_dynamic_op=False, + maximum_cached_engines=1, + cached_engine_batches=[]) int8_calib_gdef = trt.create_inference_graph( input_graph_def=orig_graph, outputs=["output"], max_batch_size=inp_dims[0], max_workspace_size_bytes=1 << 25, precision_mode="INT8", # TRT Engine precision "FP32","FP16" or "INT8" - minimum_segment_size=2 # minimum number of nodes in an engine - ) + minimum_segment_size=2, # minimum number of nodes in an engine + is_dynamic_op=False, + maximum_cached_engines=1, + cached_engine_batches=[]) o4 = run_graph(fp16_graph, dummy_input) _ = run_calibration(int8_calib_gdef, dummy_input) int8_graph = trt.calib_graph_to_infer_graph(int8_calib_gdef) o5 = run_graph(int8_graph, dummy_input) - assert np.allclose(o1, o4) - assert np.allclose(o1, o5) + print("Is FP32 == FP16? %s (False is possible)" % np.allclose(o1, o4)) + print("Is FP32 == INT8? %s (False is possible)" % np.allclose(o1, o5)) print("Pass") -def auto(): +def auto(multi_engine): """Run the conversion as an optimization pass.""" - inp_dims = (100, 24, 24, 2) + if multi_engine: + inp_dims = (2, 3, 7, 5) + orig_graph = get_multi_engine_graph_def() + else: + inp_dims = (100, 24, 24, 2) + orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) - orig_graph = get_simple_graph_def() opt_config = rwpb2.RewriterConfig() + opt_config.meta_optimizer_iterations = opt_config.ONE opt_config.optimizers.extend(["constfold", "layout"]) custom_op = opt_config.custom_optimizers.add() custom_op.name = "TensorRTOptimizer" custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["precision_mode"].s = "FP32" + custom_op.parameter_map["precision_mode"].s = to_bytes("FP32") custom_op.parameter_map["max_batch_size"].i = inp_dims[0] custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 print(custom_op) - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) graph_options = cpb2.GraphOptions(rewrite_options=opt_config) sessconfig = cpb2.ConfigProto( gpu_options=gpu_options, graph_options=graph_options) @@ -168,7 +256,7 @@ def auto(): ops.reset_default_graph() with g.as_default(): inp, out = importer.import_graph_def( - graph_def=orig_graph, return_elements=["input", "output"]) + graph_def=orig_graph, return_elements=["input", "output"], name="") inp = inp.outputs[0] out = out.outputs[0] with csess.Session(config=sessconfig, graph=g) as sess: @@ -186,8 +274,14 @@ if "__main__" in __name__: action="store_true", help="Do TRT conversion automatically", default=False) + P.add_argument( + "--multi-engine", + "-m", + action="store_true", + help="Use a graph that will result in 2 engines", + default=False) flags, unparsed = P.parse_known_args() if flags.automatic: - auto() + auto(flags.multi_engine) else: - user() + user(flags.multi_engine) diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py index 0403b652d72877196c3537a3181529aeeb997395..d9c41f90d0ab111b48c37aeaae5f0ce3177646c2 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py @@ -18,131 +18,330 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from collections import namedtuple +import itertools import warnings import numpy as np +import six from tensorflow.contrib import tensorrt as trt -from tensorflow.core.protobuf import config_pb2 as cpb2 -from tensorflow.python.framework import constant_op as cop -from tensorflow.python.framework import dtypes as dtypes -from tensorflow.python.framework import importer as importer -from tensorflow.python.framework import ops as ops +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops as aops -from tensorflow.python.ops import nn as nn -from tensorflow.python.ops import nn_ops as nn_ops -from tensorflow.python.platform import googletest +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test +INPUT_NAME = "input" +OUTPUT_NAME = "output" +INPUT_DIMS = [100, 24, 24, 2] +MODE_FP32 = "FP32" +MODE_FP16 = "FP16" +MODE_INT8 = "INT8" -class IntegrationTest(test_util.TensorFlowTestCase): +if six.PY2: + to_bytes = lambda s: s + to_string = lambda s: s +else: + to_bytes = lambda s: s.encode("utf-8", errors="surrogateescape") + to_string = lambda s: s.decode("utf-8") + + +# TODO(aaroey): test graph with different dtypes. +def GetSingleEngineGraphDef(dtype=dtypes.float32): + """Create a graph containing single segment.""" + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + bias = constant_op.constant( + [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) + added = nn.bias_add(conv, bias, name="bias_add") + relu = nn.relu(added, "relu") + identity = array_ops.identity(relu, "identity") + pool = nn_ops.max_pool( + identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(pool, name=OUTPUT_NAME) + return g.as_graph_def() + + +# TODO(aaroey): test graph with different dtypes. +def GetMultiEngineGraphDef(dtype=dtypes.float32): + """Create a graph containing multiple segment.""" + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + c1 = constant_op.constant( + np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) + p = conv * c1 + c2 = constant_op.constant( + np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) + q = conv / c2 + + edge = math_ops.sin(q) + edge /= edge + r = edge + edge + + p -= edge + q *= edge + s = p + q + s -= r + array_ops.squeeze(s, name=OUTPUT_NAME) + return g.as_graph_def() + + +TestGraph = namedtuple("TestGraph", + ["gdef", "num_expected_engines", "expected_output_dims"]) + +TEST_GRAPHS = { + "SingleEngineGraph": + TestGraph( + gdef=GetSingleEngineGraphDef(), + num_expected_engines=1, + expected_output_dims=(100, 6, 6, 6)), + "MultiEngineGraph": + TestGraph( + gdef=GetMultiEngineGraphDef(), + num_expected_engines=2, + expected_output_dims=(100, 12, 12, 6)), + # TODO(aaroey): add a large complex graph to test. +} + + +class TfTrtIntegrationTest(test_util.TensorFlowTestCase): """Class to test Tensorflow-TensorRT integration.""" def setUp(self): """Setup method.""" - super(IntegrationTest, self).setUp() + super(TfTrtIntegrationTest, self).setUp() warnings.simplefilter("always") - inp_dims = (100, 24, 24, 2) - self._input = np.random.random_sample(inp_dims) - self._original_graph = self.get_simple_graph_def() - self._gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - self._config = cpb2.ConfigProto(gpu_options=self._gpu_options) - self._reference = self.run_graph(self._original_graph, self._input) - - def get_simple_graph_def(self): - """Create a simple graph and return its graph_def.""" - g = ops.Graph() - with g.as_default(): - a = aops.placeholder( - dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input") - e = cop.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtypes.float32) - conv = nn.conv2d( - input=a, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") - b = cop.constant( - [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtypes.float32) - t = nn.bias_add(conv, b, name="biasAdd") - relu = nn.relu(t, "relu") - idty = aops.identity(relu, "ID") - v = nn_ops.max_pool( - idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") - aops.squeeze(v, name="output") - return g.as_graph_def() - - def run_graph(self, gdef, dumm_inp): - """Run given graphdef once.""" - ops.reset_default_graph() + self._input = np.random.random_sample(INPUT_DIMS) + + def _GetConfigProto(self, + use_optimizer, + precision_mode=None, + is_dynamic_op=None): + if use_optimizer: + rewriter_cfg = rewriter_config_pb2.RewriterConfig() + rewriter_cfg.optimizers.extend(["constfold", "layout"]) + custom_op = rewriter_cfg.custom_optimizers.add() + custom_op.name = "TensorRTOptimizer" + custom_op.parameter_map["minimum_segment_size"].i = 3 + custom_op.parameter_map["max_batch_size"].i = self._input.shape[0] + custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op + custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 + custom_op.parameter_map["precision_mode"].s = to_bytes(precision_mode) + graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg) + else: + graph_options = config_pb2.GraphOptions() + + gpu_options = config_pb2.GPUOptions() + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options.per_process_gpu_memory_fraction = 0.50 + + config = config_pb2.ConfigProto( + gpu_options=gpu_options, graph_options=graph_options) + return config + + def _RunGraph(self, graph_key, gdef, input_data, config, num_runs=2): + """Run given graphdef multiple times.""" g = ops.Graph() with g.as_default(): inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=["input", "output"]) + graph_def=gdef, return_elements=[INPUT_NAME, OUTPUT_NAME], name="") inp = inp.outputs[0] out = out.outputs[0] with self.test_session( - graph=g, config=self._config, use_gpu=True, force_gpu=True) as sess: - val = sess.run(out, {inp: dumm_inp}) + graph=g, config=config, use_gpu=True, force_gpu=True) as sess: + val = None + # Defaults to 2 runs to verify result across multiple runs is same. + for _ in range(num_runs): + new_val = sess.run(out, {inp: input_data}) + self.assertEquals(TEST_GRAPHS[graph_key].expected_output_dims, + new_val.shape) + if val is not None: + self.assertAllEqual(new_val, val) + val = new_val return val # Use real data that is representative of the inference dataset # for calibration. For this test script it is random data. - def run_calibration(self, gdef, dumm_inp): - """Run given calibration graph multiple times.""" - ops.reset_default_graph() - g = ops.Graph() - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=["input", "output"]) - inp = inp.outputs[0] - out = out.outputs[0] - # run over real calibration data here, we are mimicking a calibration - # set of 30 different batches. Use as much calibration data as you want - with self.test_session( - graph=g, config=self._config, use_gpu=True, force_gpu=True) as sess: - for _ in range(30): - val = sess.run(out, {inp: dumm_inp}) - return val + def _RunCalibration(self, graph_key, gdef, input_data, config): + """Run calibration on given graph.""" + return self._RunGraph(graph_key, gdef, input_data, config, 30) - def get_trt_graph(self, mode): + def _GetTrtGraph(self, gdef, precision_mode, is_dynamic_op): """Return trt converted graph.""" - if mode in ["FP32", "FP16", "INT8"]: - return trt.create_inference_graph( - input_graph_def=self._original_graph, - outputs=["output"], - max_batch_size=self._input.shape[0], - max_workspace_size_bytes=1 << 25, - precision_mode=mode, # TRT Engine precision "FP32","FP16" or "INT8" - minimum_segment_size=2 # minimum number of nodes in an engine - ) - return None - - def testFP32(self): - """Test FP32 conversion. Results should be identical to native case.""" - trt_graph = self.get_trt_graph("FP32") - result = self.run_graph(trt_graph, self._input) - self.assertAllEqual(self._reference, result) - result1 = self.run_graph(trt_graph, self._input) - self.assertAllEqual(result1, result) - - def testFP16(self): - """Test FP16 conversion. Results may be different from native case.""" - trt_graph = self.get_trt_graph("FP16") - result = self.run_graph(trt_graph, self._input) - self.assertAllClose(self._reference, result, rtol=1.e-03) - result1 = self.run_graph(trt_graph, self._input) - self.assertAllEqual(result1, result) - - def testINT8(self): - """Test INT8 conversion. Results may be different from native case.""" - calib_graph = self.get_trt_graph("INT8") - result = self.run_calibration(calib_graph, self._input) - self.assertAllEqual(self._reference, result) - int8_graph = trt.calib_graph_to_infer_graph(calib_graph) - result = self.run_graph(int8_graph, self._input) - self.assertAllClose(self._reference, result, rtol=1.e-03) - result1 = self.run_graph(int8_graph, self._input) - self.assertAllEqual(result1, result) + return trt.create_inference_graph( + input_graph_def=gdef, + outputs=[OUTPUT_NAME], + max_batch_size=self._input.shape[0], + max_workspace_size_bytes=1 << 25, + precision_mode=precision_mode, + minimum_segment_size=2, + is_dynamic_op=is_dynamic_op) + + def _VerifyGraphDef(self, + graph_key, + gdef, + precision_mode=None, + is_calibrated=None, + dynamic_engine=None): + num_engines = 0 + for n in gdef.node: + if n.op == "TRTEngineOp": + num_engines += 1 + self.assertNotEqual("", n.attr["serialized_segment"].s) + self.assertNotEqual("", n.attr["segment_funcdef_name"].s) + self.assertEquals(n.attr["precision_mode"].s, precision_mode) + self.assertEquals(n.attr["static_engine"].b, not dynamic_engine) + if precision_mode == MODE_INT8 and is_calibrated: + self.assertNotEqual("", n.attr["calibration_data"].s) + else: + self.assertEquals("", n.attr["calibration_data"].s) + if precision_mode is None: + self.assertEquals(num_engines, 0) + else: + self.assertEquals(num_engines, + TEST_GRAPHS[graph_key].num_expected_engines) + + def _RunTest(self, graph_key, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine): + assert precision_mode in [MODE_FP32, MODE_FP16, MODE_INT8] + input_gdef = TEST_GRAPHS[graph_key].gdef + self._VerifyGraphDef(graph_key, input_gdef) + + # Get reference result without running trt. + config_no_trt = self._GetConfigProto(False) + print("Running original graph w/o trt, config:\n%s" % str(config_no_trt)) + ref_result = self._RunGraph(graph_key, input_gdef, self._input, + config_no_trt) + + # Run calibration if necessary. + if precision_mode == MODE_INT8: + + calib_config = self._GetConfigProto(use_optimizer, precision_mode, + dynamic_calib_engine) + print("Running calibration graph, config:\n%s" % str(calib_config)) + if use_optimizer: + self.assertTrue(False) + # TODO(aaroey): uncomment this and get infer_gdef when this mode is + # supported. + # result = self._RunCalibration(graph_key, input_gdef, self._input, + # calib_config) + else: + calib_gdef = self._GetTrtGraph(input_gdef, precision_mode, + dynamic_calib_engine) + self._VerifyGraphDef(graph_key, calib_gdef, precision_mode, False, + dynamic_calib_engine) + result = self._RunCalibration(graph_key, calib_gdef, self._input, + calib_config) + infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) + self._VerifyGraphDef(graph_key, infer_gdef, precision_mode, True, + dynamic_calib_engine) + self.assertAllClose(ref_result, result, rtol=1.e-03) + else: + infer_gdef = input_gdef + + # Run inference. + infer_config = self._GetConfigProto(use_optimizer, precision_mode, + dynamic_infer_engine) + print("Running final inference graph, config:\n%s" % str(infer_config)) + if use_optimizer: + result = self._RunGraph(graph_key, infer_gdef, self._input, infer_config) + else: + trt_infer_gdef = self._GetTrtGraph(infer_gdef, precision_mode, + dynamic_infer_engine) + self._VerifyGraphDef(graph_key, trt_infer_gdef, precision_mode, True, + dynamic_infer_engine) + result = self._RunGraph(graph_key, trt_infer_gdef, self._input, + infer_config) + self.assertAllClose(ref_result, result, rtol=1.e-03) + + def testIdempotence(self): + # Test that applying tensorrt optimizer or offline conversion tools multiple + # times to the same graph will result in same graph. + # TODO(aaroey): implement this. + pass + + +def GetTests(): + + def _GetTest(g, u, p, i, c): + + def _Test(self): + print("Running test with parameters: graph_key=%s, use_optimizer=%s, " + "precision_mode=%s, dynamic_infer_engine=%s, " + "dynamic_calib_engine=%s" % (g, u, p, i, c)) + self._RunTest(g, u, p, i, c) + + return _Test + + use_optimizer_options = [False, True] + precision_mode_options = [MODE_FP32, MODE_FP16, MODE_INT8] + dynamic_infer_engine_options = [False, True] + dynamic_calib_engine_options = [False, True] + for (graph_key, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine) in itertools.product( + TEST_GRAPHS, use_optimizer_options, precision_mode_options, + dynamic_infer_engine_options, dynamic_calib_engine_options): + if precision_mode == MODE_INT8: + if not dynamic_calib_engine and dynamic_infer_engine: + # TODO(aaroey): test this case, the conversion from static calibration + # engine to dynamic inference engine should be a noop. + continue + if use_optimizer: + # TODO(aaroey): if use_optimizer is True we need to get the inference + # graphdef using custom python wrapper class, which is not currently + # supported yet. + continue + if not dynamic_calib_engine: + # TODO(aaroey): construction of static calibration engine is not + # supported yet. + continue + if dynamic_calib_engine and not dynamic_infer_engine: + # TODO(aaroey): construction of static inference engine using dynamic + # calibration engine is not supported yet. + continue + else: # In non int8 mode. + if dynamic_calib_engine: + # dynamic_calib_engine doesn't affect non-int8 modes, so just let + # related tests run once on dynamic_calib_engine=False. + continue + yield _GetTest(graph_key, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine) if __name__ == "__main__": - googletest.main() + for index, t in enumerate(GetTests()): + setattr(TfTrtIntegrationTest, "testTfTRT_" + str(index), t) + test.main() diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i index 46480e99a113afb34702b0ecd71468d4bdc83f98..d6628cd1eb69e46b188de613dee803a2e0dd07d4 100644 --- a/tensorflow/contrib/tensorrt/trt_conversion.i +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -48,12 +48,53 @@ PyObject* pair_helper(std::pair* in) { } return tuple; } + +struct version_struct{ + int vmajor; + int vminor; + int vpatch; +}; + +PyObject* version_helper(version_struct* in) { + PyObject *tuple(nullptr); + tuple = Py_BuildValue("(iii)", in->vmajor, in->vminor, in->vpatch); + if (!tuple) { + if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "Tuple creation from version structure failed!"); + } + return NULL; + } + return tuple; +} +/* Define converters for vector */ +template<> +bool _PyObjAs(PyObject *pyobj, int* dest) { + *dest = PyLong_AsLong(pyobj); + return true; +} + +template<> +PyObject *_PyObjFrom(const int& src) { + return PyLong_FromLong(src); +} + %} + +_LIST_OUTPUT_TYPEMAP(int, PyLong_FromLong); + %typemap(out) std::pair { PyObject *tuple = pair_helper(&$1); if (!tuple) SWIG_fail; $result = tuple; } + +%typemap(out) version_struct { + PyObject *tuple = version_helper(&$1); + if (!tuple) SWIG_fail; + $result = tuple; +} + %{ #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" @@ -65,6 +106,8 @@ PyObject* pair_helper(std::pair* in) { %unignore tensorflow; %unignore trt_convert; %unignore calib_convert; +%unignore get_linked_tensorrt_version; +%unignore get_loaded_tensorrt_version; %{ @@ -74,7 +117,10 @@ std::pair trt_convert( size_t max_batch_size, size_t max_workspace_size_bytes, int precision_mode, - int minimum_segment_size + int minimum_segment_size, + bool is_dyn_op, + int max_cached_engines, + std::vector cached_engine_batches // Unfortunately we can't use TF_Status here since it // is in c/c_api and brings in a lot of other libraries // which in turn declare ops. These ops are included @@ -102,11 +148,12 @@ std::pair trt_convert( out_status = "InvalidArgument;Size of the output_names vector is 0"; return std::pair{out_status, ""}; } - tensorflow::GraphDef outGraph; + tensorflow::GraphDef out_graph; tensorflow::Status conversion_status = tensorflow::tensorrt::convert::ConvertGraphDefToTensorRT( graph_def, output_names, max_batch_size, max_workspace_size_bytes, - &outGraph, precision_mode, minimum_segment_size); + &out_graph, precision_mode, minimum_segment_size, + is_dyn_op, max_cached_engines, cached_engine_batches); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; @@ -116,7 +163,7 @@ std::pair trt_convert( return std::pair{out_status, ""}; } string result; - if (!outGraph.SerializeToString(&result)) { + if (!out_graph.SerializeToString(&result)) { out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; return std::pair{out_status, ""}; } @@ -128,7 +175,8 @@ std::pair trt_convert( #endif // GOOGLE_CUDA && GOOGLE_TENSORRT } -std::pair calib_convert(string graph_def_string // const tensorflow::GraphDef& +std::pair calib_convert( + string graph_def_string, bool is_dyn_op // unfortunately we can't use TF_Status here since it // is in c/c_api and brings in a lot of other libraries // which in turn declare ops. These ops are included @@ -147,11 +195,11 @@ std::pair calib_convert(string graph_def_string // const tenso out_status = "InvalidArgument;Couldn't interpret input as a GraphDef"; return std::pair{out_status, ""}; } - - tensorflow::GraphDef outGraph; + graph_def_string.resize(0); + tensorflow::GraphDef out_graph; tensorflow::Status conversion_status = - tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph(graph_def, - &outGraph); + tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph( + graph_def, &out_graph, is_dyn_op); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; @@ -161,7 +209,7 @@ std::pair calib_convert(string graph_def_string // const tenso return std::pair{out_status, ""}; } string result; - if (!outGraph.SerializeToString(&result)) { + if (!out_graph.SerializeToString(&result)) { out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; return std::pair{out_status, ""}; } @@ -172,15 +220,43 @@ std::pair calib_convert(string graph_def_string // const tenso return std::pair{"9;TensorRT is not enabled!", ""}; #endif // GOOGLE_CUDA && GOOGLE_TENSORRT } + +version_struct get_linked_tensorrt_version() { + // Return the version at the link time. + version_struct s; +#if GOOGLE_CUDA && GOOGLE_TENSORRT + const auto &lv = tensorflow::tensorrt::convert::GetLinkedTensorRTVersion(); + s.vmajor = lv[0]; + s.vminor = lv[1]; + s.vpatch = lv[2]; +#endif // GOOGLE_CUDA && GOOGLE_TENSORRT + return s; +} +version_struct get_loaded_tensorrt_version(){ + // Return the version from the loaded library. + version_struct s; +#if GOOGLE_CUDA && GOOGLE_TENSORRT + const auto &lv = tensorflow::tensorrt::convert::GetLoadedTensorRTVersion(); + s.vmajor = lv[0]; + s.vminor = lv[1]; + s.vpatch = lv[2]; +#endif // GOOGLE_CUDA && GOOGLE_TENSORRT + return s; +} + %} -std::pair calib_convert(string graph_def_string); +std::pair calib_convert(string graph_def_string, bool is_dyn_op); std::pair trt_convert(string graph_def_string, std::vector output_names, size_t max_batch_size, size_t max_workspace_size_bytes, - int precision_mode, int minimum_segment_size); - + int precision_mode, int minimum_segment_size, + bool is_dyn_op, + int max_cached_engines, + std::vector cached_engine_batches); +version_struct get_linked_tensorrt_version(); +version_struct get_loaded_tensorrt_version(); %unignoreall diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py index a28a5872b850b51630240bdeb3ff22f372613523..f236329fdb038ba5ab432c6b97f44bda7ccfe815 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head.py @@ -132,7 +132,8 @@ class TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acce loss=model_outputs.loss, mode=mode, eval_metric_ops=metrics, - predictions={}) + # needed for custom metrics. + predictions=model_outputs.predictions) def _predict_ops(self, features): """Add ops for prediction to the graph.""" @@ -210,12 +211,12 @@ class TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acce def create_estimator_spec(self, features, mode, labels=None): """Performs basic error checking and returns an EstimatorSpec.""" with ops.name_scope(self._name, "head"): - if labels: + if labels is not None and labels != {}: # for better error messages. raise ValueError( - "The model received a `labels` dictionary, which is " - "not supported. Pass '{}' and '{}' as " - "features.".format(feature_keys.TrainEvalFeatures.TIMES, - feature_keys.TrainEvalFeatures.VALUES)) + "The model received a `labels`, which is not supported. " + "Pass '{}' and '{}' as features.".format( + feature_keys.TrainEvalFeatures.TIMES, + feature_keys.TrainEvalFeatures.VALUES)) del labels features = { name: self._convert_feature_to_tensor(name=name, value=value) diff --git a/tensorflow/contrib/timeseries/python/timeseries/head_test.py b/tensorflow/contrib/timeseries/python/timeseries/head_test.py index c606db76a668235ab6a837159b9dec072b5fd801..ed8f29c321719e552c25f4d2183fdf4eb282e4b7 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy import six +from tensorflow.contrib.estimator.python.estimator import extenders from tensorflow.contrib.timeseries.examples import lstm as lstm_example from tensorflow.contrib.timeseries.python.timeseries import estimators as ts_estimators from tensorflow.contrib.timeseries.python.timeseries import feature_keys @@ -35,6 +36,7 @@ from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics from tensorflow.python.ops import variables @@ -53,9 +55,12 @@ class HeadTest(test.TestCase): model_fn = _stub_model_fn() for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL, estimator_lib.ModeKeys.PREDICT]: - with self.assertRaisesRegexp(ValueError, "labels"): + with self.assertRaisesRegexp(ValueError, "received a `labels`"): model_fn(features={}, labels={"a": "b"}, mode=mode) + with self.assertRaisesRegexp(ValueError, "received a `labels`"): + model_fn(features={}, labels=array_ops.zeros([]), mode=mode) + def test_unknown_mode(self): model_fn = _stub_model_fn() with self.assertRaisesRegexp(ValueError, "Unknown mode 'Not a mode'"): @@ -128,6 +133,44 @@ class EvaluationMetricsTests(test.TestCase): coordinator.request_stop() coordinator.join() + def test_custom_metrics(self): + """Tests that the custom metrics can be applied to the estimator.""" + model_dir = self.get_temp_dir() + estimator = ts_estimators.TimeSeriesRegressor( + model=lstm_example._LSTMModel(num_features=1, num_units=4), + optimizer=adam.AdamOptimizer(0.001), + config=estimator_lib.RunConfig(tf_random_seed=4), + model_dir=model_dir) + + def input_fn(): + return { + feature_keys.TrainEvalFeatures.TIMES: [[1, 2, 3], [7, 8, 9]], + feature_keys.TrainEvalFeatures.VALUES: + numpy.array([[[0.], [1.], [0.]], [[2.], [3.], [2.]]]) + } + + def metrics_fn(predictions, features): + # checking that the inputs are properly passed. + predict = predictions["mean"] + target = features[feature_keys.TrainEvalFeatures.VALUES][:, -1, 0] + return { + "plain_boring_metric386": + (math_ops.reduce_mean(math_ops.abs(predict - target)), + control_flow_ops.no_op()), + "fun_metric101": (math_ops.reduce_sum(predict + target), + control_flow_ops.no_op()), + } + + # Evaluation without training is enough for testing custom metrics. + estimator = extenders.add_metrics(estimator, metrics_fn) + evaluation = estimator.evaluate(input_fn, steps=1) + self.assertIn("plain_boring_metric386", evaluation) + self.assertIn("fun_metric101", evaluation) + # The values are deterministic because of fixed tf_random_seed. + # However if they become flaky, remove such exacts comparisons. + self.assertAllClose(evaluation["plain_boring_metric386"], 1.130380) + self.assertAllClose(evaluation["fun_metric101"], 10.435442) + class _StubModel(object): num_features = 3 diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index f84ff1bfe9b014733205a8e51b43f79c63b227cb..c08f088be78d1cb1caa18a805844541b3d573fad 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -181,6 +181,7 @@ py_library( ":datasets", ":profiler", ":tpu_py", + "//tensorflow/contrib/cluster_resolver:tpu_cluster_resolver_py", "//tensorflow/contrib/tpu/proto:compilation_result_proto_py", "//tensorflow/contrib/tpu/proto:topology_proto_py", "//tensorflow/core:protos_all_py", @@ -306,3 +307,13 @@ tf_py_test( "//tensorflow/python:framework_test_lib", ], ) + +tf_py_test( + name = "topology_test", + size = "small", + srcs = ["python/tpu/topology_test.py"], + additional_deps = [ + ":tpu", + "//tensorflow/python:framework_test_lib", + ], +) diff --git a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc index d389050e67f9a9e48b91583e5088058ec4e2832f..06553929dc44ca1f75ce64532a4dcdf1c8aae3eb 100644 --- a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc +++ b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc @@ -23,15 +23,23 @@ REGISTER_OP("CrossReplicaSum") .Input("input: T") .Output("output: T") .Attr("T: {bfloat16, float}") + .Attr("group_assignment: list(int) = []") .SetShapeFn(shape_inference::UnchangedShape) .Doc(R"doc( An Op to sum inputs across replicated TPU instances. Each -instance supplies its own input, and the output of each is the sum of -all the inputs. +instance supplies its own input. If group_assignment is empty, the output of +each is the sum of all the inputs, otherwise the output of each is the sum of +the inputs belonging to the same group. + +For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing +group_assignment=`[0,1,0,1]` sets `A, C` as group 0, and `B, D` as group 1. +Thus we get the outputs: `[A+C, B+D, A+C, B+D]`. input: The local input to the sum. output: The sum of all the distributed inputs. T: The type of elements to be summed. +group_assignment: The list of group ids. `group_assignment[i]` represents the + group id of replica i. )doc"); } // namespace tensorflow diff --git a/tensorflow/contrib/tpu/ops/replication_ops.cc b/tensorflow/contrib/tpu/ops/replication_ops.cc index ab2a7a0d4bec48d6b3b459bb3144e8ddae614ca0..15a2bb17a93212afe9ce5604a28d9dba5825f7d4 100644 --- a/tensorflow/contrib/tpu/ops/replication_ops.cc +++ b/tensorflow/contrib/tpu/ops/replication_ops.cc @@ -44,6 +44,27 @@ REGISTER_OP("TPUReplicatedInput") " with other shapes."); } c->set_output(0, cur); + + // If this is a resource, unify the resource shapes. + DataType dtype; + TF_RETURN_IF_ERROR(c->GetAttr("T", &dtype)); + if (dtype == DT_RESOURCE) { + const std::vector* shapes_and_types = + nullptr; + for (int i = c->num_inputs() - 1; i >= 0; --i) { + if (shapes_and_types) { + // The return value of MergeInputHandleShapesAndTypes indicates + // the shape was refined, not that there was an error. + // TODO(phawkins): there seems to be no way to discover errors. + (void)c->MergeInputHandleShapesAndTypes(i, *shapes_and_types); + } else { + shapes_and_types = c->input_handle_shapes_and_types(i); + } + } + if (shapes_and_types) { + c->set_output_handle_shapes_and_types(0, *shapes_and_types); + } + } return Status::OK(); }) .Doc( diff --git a/tensorflow/contrib/tpu/profiler/BUILD b/tensorflow/contrib/tpu/profiler/BUILD index dbf1ab6bbf0ddc7429d8e19279451eb862981e0c..38d1c3049ef7185f2f9f448361029d066678cdae 100644 --- a/tensorflow/contrib/tpu/profiler/BUILD +++ b/tensorflow/contrib/tpu/profiler/BUILD @@ -49,11 +49,11 @@ tf_cc_binary( ":tpu_profiler_analysis_proto_cc", ":tpu_profiler_proto_cc", ":version", + "//tensorflow:grpc++", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/platform/cloud:gcs_file_system", - "@grpc//:grpc++_unsecure", ], ) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index 99485322c6b9434f4c1700b9e2a6af00a65f794f..f80f5652af79d410946971573ae160fdd0b85f6d 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -18,7 +18,7 @@ limitations under the License. // Initiates a TPU profiling on the TPUProfiler service at service_addr, // receives and dumps the profile data to a tensorboard log directory. -#include "grpc++/grpc++.h" +#include "grpcpp/grpcpp.h" #include #include diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 508c7a842fb82ec080082d7e7f02f8d2f2a79447..7a5d01cca42351f6d4d8b41d43756560ce7874d3 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -17,12 +17,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from absl import flags - import os import subprocess import sys - +from absl import flags +from distutils.version import LooseVersion import tensorflow as tf # Cloud TPU Cluster Resolvers @@ -35,26 +34,26 @@ flags.DEFINE_string( None, help='GCE zone where the Cloud TPU is located in. If not specified, we ' 'will attempt to automatically detect the GCE project from metadata.') -flags.DEFINE_string('tpu_name', None, - 'Name of the Cloud TPU for Cluster Resolvers. You must ' - 'specify either this flag or --service_addr.') +flags.DEFINE_string( + 'tpu', None, 'Name of the Cloud TPU for Cluster Resolvers. You must ' + 'specify either this flag or --service_addr.') # Tool specific parameters flags.DEFINE_string( 'service_addr', None, 'Address of TPU profiler service e.g. ' - 'localhost:8466, you must specify either this flag or --tpu_name.') + 'localhost:8466, you must specify either this flag or --tpu.') flags.DEFINE_string( 'workers_list', None, 'The list of worker TPUs that we are about to profile' - ' e.g. 10.0.1.2, 10.0.1.3. You can specify this flag with --tpu_name or ' + ' e.g. 10.0.1.2, 10.0.1.3. You can specify this flag with --tpu or ' '--service_addr to profile a subset of tpu nodes. You can also use only' - '--tpu_name and leave this flag unspecified to profile all the tpus.') -flags.DEFINE_string('logdir', None, - 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' - 'gs://tb_bucket') + '--tpu and leave this flag unspecified to profile all the tpus.') +flags.DEFINE_string( + 'logdir', None, 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' + 'gs://tb_bucket') flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') -flags.DEFINE_integer('num_tracing_attempts', 3, - 'Automatically retry N times when no trace ' - 'event is collected.') +flags.DEFINE_integer( + 'num_tracing_attempts', 3, 'Automatically retry N times when no trace ' + 'event is collected.') flags.DEFINE_boolean('include_dataset_ops', True, 'Set to false to profile longer TPU ' 'device traces.') @@ -63,42 +62,50 @@ FLAGS = flags.FLAGS EXECUTABLE = 'data/capture_tpu_profile' JOB_NAME = 'worker' + def get_workers_list(cluster_resolver): cluster_spec = cluster_resolver.cluster_spec() task_indices = cluster_spec.task_indices(JOB_NAME) - workers_list = [cluster_spec.task_address(JOB_NAME, i).split(':')[0] - for i in task_indices] + workers_list = [ + cluster_spec.task_address(JOB_NAME, i).split(':')[0] for i in task_indices + ] return ','.join(workers_list) + def run_main(): tf.app.run(main) + def main(unused_argv=None): tf.logging.set_verbosity(tf.logging.INFO) + tf_version = tf.__version__ + print('TensorFlow version %s detected' % tf_version) - if FLAGS.service_addr is None and FLAGS.tpu_name is None: - sys.exit('You must specify either --service_addr or --tpu_name.') + if FLAGS.service_addr is None and FLAGS.tpu is None: + sys.exit('You must specify either --service_addr or --tpu.') tpu_cluster_resolver = None if FLAGS.service_addr is not None: - if FLAGS.tpu_name is not None: - tf.logging.warn('Both --service_addr and --tpu_name are set. Ignoring ' - '--tpu_name and using --service_addr.') + if FLAGS.tpu is not None: + tf.logging.warn('Both --service_addr and --tpu are set. Ignoring ' + '--tpu and using --service_addr.') service_addr = FLAGS.service_addr else: tpu_cluster_resolver = ( tf.contrib.cluster_resolver.TPUClusterResolver( - [FLAGS.tpu_name], - zone=FLAGS.tpu_zone, - project=FLAGS.gcp_project)) + [FLAGS.tpu], zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)) service_addr = tpu_cluster_resolver.get_master() service_addr = service_addr.replace('grpc://', '').replace(':8470', ':8466') - workers_list = "" - if FLAGS.workers_list is not None: - workers_list = FLAGS.workers_list - elif tpu_cluster_resolver is not None: - workers_list = get_workers_list(tpu_cluster_resolver) + workers_list = '' + if LooseVersion(tf_version) < LooseVersion('1.9'): + tf.logging.warn('Attempt to profile with legacy support under TensorFlow ' + 'version %s' % tf_version) + else: + if FLAGS.workers_list is not None: + workers_list = FLAGS.workers_list + elif tpu_cluster_resolver is not None: + workers_list = get_workers_list(tpu_cluster_resolver) if not FLAGS.logdir: sys.exit('logdir must be provided.') diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index ebd478fd02295108b9d2454963eb06165828b523..19f088f8b862ce7b114490151f2b6a8c260b8580 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,7 +20,7 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.6.0' +_VERSION = '1.9.0' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', @@ -46,7 +46,7 @@ setup( # 3 - Alpha # 4 - Beta # 5 - Production/Stable - 'Development Status :: 4 - Beta', + 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', diff --git a/tensorflow/contrib/tpu/profiler/version.h b/tensorflow/contrib/tpu/profiler/version.h index 618479e1a6ccf26a4103ea1f182b662d7d9998da..1bf49966d12db83f1e6904f8c00453bba278847c 100644 --- a/tensorflow/contrib/tpu/profiler/version.h +++ b/tensorflow/contrib/tpu/profiler/version.h @@ -16,6 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ #define TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ -#define TPU_PROFILER_VERSION "1.6.0" +#define TPU_PROFILER_VERSION "1.9.0" #endif // TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ diff --git a/tensorflow/contrib/tpu/proto/BUILD b/tensorflow/contrib/tpu/proto/BUILD index 7ecb36852c53bb74d70ed0f8c70ca1ce860a037a..26016f47dfb36990fd73267c70619878ac3450e5 100644 --- a/tensorflow/contrib/tpu/proto/BUILD +++ b/tensorflow/contrib/tpu/proto/BUILD @@ -2,7 +2,12 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library") +load( + "//tensorflow/core:platform/default/build_config.bzl", + "tf_additional_all_protos", + "tf_proto_library", + "tf_proto_library_py", +) tf_proto_library( name = "tpu_embedding_config_proto", @@ -22,12 +27,14 @@ tf_proto_library( visibility = ["//visibility:public"], ) -tf_proto_library( +tf_proto_library_py( name = "compilation_result_proto", srcs = [ "compilation_result.proto", ], - cc_api_version = 2, - protodeps = ["//tensorflow/core:protos_all"], + protodeps = tf_additional_all_protos() + [ + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_proto", + ], visibility = ["//visibility:public"], ) diff --git a/tensorflow/contrib/tpu/proto/compilation_result.proto b/tensorflow/contrib/tpu/proto/compilation_result.proto index cf52897de3d0fefa55e68a6b889ae9af7b45864a..88585a5bd10fc28aa34bb0de72de970e21b2adb2 100644 --- a/tensorflow/contrib/tpu/proto/compilation_result.proto +++ b/tensorflow/contrib/tpu/proto/compilation_result.proto @@ -3,6 +3,7 @@ syntax = "proto3"; option cc_enable_arenas = true; package tensorflow.tpu; +import "tensorflow/compiler/xla/service/hlo.proto"; import "tensorflow/core/lib/core/error_codes.proto"; // Describes the result of a TPU compilation. @@ -10,4 +11,7 @@ message CompilationResultProto { // The error message, if any, returned during compilation. error.Code status_code = 1; string status_error_message = 2; + + // HLO proto. + repeated xla.HloProto hlo_protos = 3; } diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py index 14c63a79763300dcfe8d6c8e09b90f8e9c772358..bf442d9116d2ceca499ffc66258c64b5b94dd881 100644 --- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py +++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py @@ -38,9 +38,8 @@ if platform.system() != "Windows": @ops.RegisterGradient("CrossReplicaSum") def _cross_replica_sum_grad(op, grad): - del op # Unused # The gradient of a cross replica sum is also a cross-replica sum. - return gen_tpu_ops.cross_replica_sum(grad) + return gen_tpu_ops.cross_replica_sum(grad, op.get_attr("group_assignment")) # This extra type checking exists to give a more helpful error message in # the common case that uint8 and int64 values are infed. Remove when both diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index f1a11fa6548b87d6222a97c72b8db5442c8ef774..754154438235f4c5e9e8db996acc8d843ab18431 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -19,15 +19,16 @@ To use, wrap your model with the `keras_support.tpu_model` function. Example usage: ``` -# Must activate before building TPU models -keras_support.setup_tpu_session(master_address) - image = tf.keras.layers.Input(shape=(28, 28, 3), name='image') c1 = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3))( image) flattened = tf.keras.layers.Flatten()(c1) logits = tf.keras.layers.Dense(10, activation='softmax')(flattened) model = tf.keras.Model(inputs=[image], outputs=[logits]) -model = keras_support.tpu_model(model) + +strategy = keras_support.TPUDistributionStrategy(num_cores_per_host=8) +model = keras_support.tpu_model(model, + strategy=strategy, + tpu_name_or_address=tpu_name) # Only TF optimizers are currently supported. model.compile(optimizer=tf.train.AdamOptimizer(), ...) @@ -35,9 +36,6 @@ model.compile(optimizer=tf.train.AdamOptimizer(), ...) # `images` and `labels` should be Numpy arrays. Support for tensor input # (e.g. datasets) is planned. model.fit(images, labels) - -# Invoke before shutting down -keras_support.shutdown_tpu_session() ``` """ @@ -48,9 +46,15 @@ from __future__ import division from __future__ import print_function import collections +import contextlib import re +import sys import time +import numpy as np + +from tensorflow.contrib.cluster_resolver.python.training import tpu_cluster_resolver +from tensorflow.contrib.distribute.python import tpu_strategy from tensorflow.contrib.framework.python.framework import experimental from tensorflow.contrib.tpu.proto import compilation_result_pb2 as tpu_compilation_result from tensorflow.contrib.tpu.python.ops import tpu_ops @@ -62,14 +66,17 @@ from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.keras import backend as K -from tensorflow.python.keras import layers from tensorflow.python.keras import models from tensorflow.python.keras import optimizers as keras_optimizers +from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.layers import embeddings from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging +TPUDistributionStrategy = tpu_strategy.TPUStrategy # pylint: disable=invalid-name + class TPUEmbedding(embeddings.Embedding): """TPU compatible embedding layer. @@ -93,10 +100,9 @@ class TPUEmbedding(embeddings.Embedding): class TPUModelOp( - collections.namedtuple( - 'TPUModelOp', - ['compile_op', 'execute_op', 'infeed_tensors', 'infeed_op', - 'outfeed_op'])): + collections.namedtuple('TPUModelOp', [ + 'compile_op', 'execute_op', 'infeed_tensors', 'infeed_op', 'outfeed_op' + ])): pass @@ -105,13 +111,69 @@ def _valid_name(tensor_name): return re.sub('[^a-zA-Z0-9_-]+', '', tensor_name) -def _replicated_optimizer(opt, num_replicas): +def _replicated_optimizer(opt): """Wrap the optimizer `opt` with CrossShardOptimizer if applicable.""" - if num_replicas == 1: - return opt return keras_optimizers.TFOptimizer( - optimizer=tpu_optimizer.CrossShardOptimizer(opt.optimizer) - ) + optimizer=tpu_optimizer.CrossShardOptimizer(opt.optimizer)) + + +class TPURewriteContext(object): + """Prepare the environment for a Keras model during `tpu.rewrite`. + + This overrides the default placeholder behaviour to instead refer to a preset + input mapping. Placeholders are unsupported in TPU compiled code, and must + be replaced with explicit inputs or values from the infeed queue. + + Instead of explicitly threading inputs all the way through the Keras codebase, + we override the behavior of the placeholder while compiling and inject the + Tensors from the infeed in place of the placeholder. + + Similarly, as we compile a new sub-graph for each unique shape and execution + mode, we need to override the behavior of an embedded `name_scope` call in + the base Keras layer code. This allows us to re-use the same weights across + many compiles and share a single session/graph. + """ + + def __init__(self, input_map): + self._input_map = input_map + self._default_placeholder = None + self._default_name_scope = None + + def __enter__(self): + + def _placeholder(dtype, shape=None, name=None): # pylint: disable=unused-argument + logging.info('Remapping placeholder for %s', name) + if name in self._input_map: + return self._input_map[name] + else: + logging.info('Default: %s', name) + return self._default_placeholder(dtype, shape, name) + + def _name_scope(name, default_name=None, values=None): + caller_frame = sys._getframe().f_back + caller_obj = caller_frame.f_locals.get('self') + if (caller_obj is not None and + isinstance(caller_obj, base_layer.Layer) and name is not None): + logging.info('Intercepted name_scope: %s', caller_obj) + return variable_scope.variable_scope( + name, default_name, values, reuse=variable_scope.AUTO_REUSE) + + return self._default_name_scope(name, default_name, values) + + self._default_placeholder = array_ops.placeholder + self._default_name_scope = ops.name_scope + self._default_make_variable = base_layer.make_variable + + array_ops.placeholder = _placeholder + ops.name_scope = _name_scope + base_layer.make_variable = variable_scope.get_variable + logging.info('Overriding default placeholder.') + return + + def __exit__(self, exc_type, exc_val, exc_tb): + array_ops.placeholder = self._default_placeholder + ops.name_scope = self._default_name_scope + base_layer.make_variable = self._default_make_variable class TPUFunction(object): @@ -126,19 +188,18 @@ class TPUFunction(object): instead of being injected as `feed_dict` items or fetches. """ - def __init__(self, model, execution_mode, num_replicas=1): + def __init__(self, model, execution_mode, strategy): self.model = model self.execution_mode = execution_mode + self._strategy = strategy self._compilation_cache = {} - self.num_replicas = num_replicas + self._cloned_model = None def _specialize_model(self, input_specs): """Specialize `self.model` (a Keras model) for the given input shapes.""" # Re-create our input and output layers inside our subgraph. They will be # attached to the true computation when we clone our model in `tpu_fn`. - K.set_learning_phase( - self.execution_mode == model_fn_lib.ModeKeys.TRAIN - ) + K.set_learning_phase(self.execution_mode == model_fn_lib.ModeKeys.TRAIN) # functools.partial and callable objects are not supported by tpu.rewrite def _model_fn(): @@ -164,23 +225,22 @@ class TPUFunction(object): infeed_tensors)) tpu_targets = [] - tpu_inputs = [] + tpu_input_map = {} # Sort infeed outputs into inputs and labels for calling our Keras model. for tensor, layer in zip(infeed_tensors, infeed_layers): if layer in self.model._input_layers: - tpu_inputs.append(layers.Input(name=layer.name, tensor=tensor)) + tpu_input_map[layer.name] = tensor if layer in self.model._output_layers: tpu_targets.append(tensor) - # Call our model with our infeed inputs (re-using the weights). - model_outputs = self.model(tpu_inputs) - child_model = models.Model(inputs=tpu_inputs, outputs=model_outputs) + # Clone our CPU model, running within the TPU device context. + with TPURewriteContext(tpu_input_map): + self._cloned_model = models.clone_model(self.model) if is_training or is_test: - child_model.compile( - optimizer=_replicated_optimizer(self.model.optimizer, - self.num_replicas), + self._cloned_model.compile( + optimizer=_replicated_optimizer(self.model.optimizer), loss=self.model.loss, loss_weights=self.model.loss_weights, metrics=self.model.metrics, @@ -190,37 +250,37 @@ class TPUFunction(object): # Compute our outfeed depending on the execution mode if is_training: - child_model._make_train_function() + self._cloned_model._make_train_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) - for tensor in child_model.train_function.outputs + for tensor in self._cloned_model.train_function.outputs ] return [ - child_model.train_function.updates_op, + self._cloned_model.train_function.updates_op, tpu_ops.outfeed_enqueue_tuple( - child_model.train_function.outputs, + self._cloned_model.train_function.outputs, name='outfeed-enqueue-train') ] elif is_test: - child_model._make_test_function() + self._cloned_model._make_test_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) - for tensor in child_model.test_function.outputs + for tensor in self._cloned_model.test_function.outputs ] return [ tpu_ops.outfeed_enqueue_tuple( - child_model.test_function.outputs, + self._cloned_model.test_function.outputs, name='outfeed-enqueue-test') ] elif is_predict: - child_model._make_predict_function() + self._cloned_model._make_predict_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) - for tensor in child_model.predict_function.outputs + for tensor in self._cloned_model.predict_function.outputs ] return [ tpu_ops.outfeed_enqueue_tuple( - child_model.predict_function.outputs, + self._cloned_model.predict_function.outputs, name='outfeed-enqueue-predict', ) ] @@ -235,7 +295,7 @@ class TPUFunction(object): # `execute op` replicates `_model_fn` `num_replicas` times, with each shard # running on a different logical core. compile_op, execute_op = tpu.split_compile_and_replicate( - _model_fn, inputs=[[]] * self.num_replicas) + _model_fn, inputs=[[]] * self._strategy.num_towers) # Generate CPU side operations to enqueue features/labels and dequeue # outputs from the model call. @@ -243,7 +303,7 @@ class TPUFunction(object): outfeed_op = [] shard_infeed_tensors = [] - for shard_id in range(self.num_replicas): + for shard_id in range(self._strategy.num_towers): with ops.device('/device:TPU:%d' % shard_id): infeed_tensors = [] for spec in input_specs: @@ -254,32 +314,35 @@ class TPUFunction(object): name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) shard_infeed_tensors.append(infeed_tensors) - infeed_op.append(tpu_ops.infeed_enqueue_tuple( - infeed_tensors, [spec.shape for spec in input_specs], - name='infeed-enqueue-%s-%d' % (self.execution_mode, shard_id))) + infeed_op.append( + tpu_ops.infeed_enqueue_tuple( + infeed_tensors, [spec.shape for spec in input_specs], + name='infeed-enqueue-%s-%d' % (self.execution_mode, shard_id))) - outfeed_op.extend(tpu_ops.outfeed_dequeue_tuple( - dtypes=[spec.dtype for spec in self._outfeed_spec], - shapes=[spec.shape for spec in self._outfeed_spec], - name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id))) + outfeed_op.extend( + tpu_ops.outfeed_dequeue_tuple( + dtypes=[spec.dtype for spec in self._outfeed_spec], + shapes=[spec.shape for spec in self._outfeed_spec], + name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id))) return TPUModelOp( - compile_op, execute_op, infeed_tensors=shard_infeed_tensors, - infeed_op=infeed_op, outfeed_op=outfeed_op) + compile_op, + execute_op, + infeed_tensors=shard_infeed_tensors, + infeed_op=infeed_op, + outfeed_op=outfeed_op) def _test_model_compiles(self, tpu_model_ops): """Verifies that the given TPUModelOp can be compiled via XLA.""" - session = K.get_session() - logging.info('Started compiling') start_time = time.clock() - result = session.run(tpu_model_ops.compile_op) + result = K.get_session().run(tpu_model_ops.compile_op) proto = tpu_compilation_result.CompilationResultProto() proto.ParseFromString(result) if proto.status_error_message: - raise RuntimeError( - 'Compilation failed: {}'.format(proto.status_error_message)) + raise RuntimeError('Compilation failed: {}'.format( + proto.status_error_message)) end_time = time.clock() logging.info('Finished compiling. Time elapsed: %s secs', @@ -296,17 +359,20 @@ class TPUFunction(object): Returns: List of lists containing the input to feed to each TPU shard. """ - if self.num_replicas == 1: + if self._strategy.num_towers == 1: return [inputs] batch_size = inputs[0].shape[0] - assert batch_size % self.num_replicas == 0, ( - 'batch_size must be divisible by num_replicas') - shard_size = batch_size // self.num_replicas + assert batch_size % self._strategy.num_towers == 0, ( + 'batch_size must be divisible by strategy.num_towers (%s vs %s)' % + (batch_size, self._strategy.num_towers) + ) + shard_size = batch_size // self._strategy.num_towers input_list = [] - for index in range(self.num_replicas): - shard_inputs = [x[index * shard_size:(index + 1) * shard_size] - for x in inputs] + for index in range(self._strategy.num_towers): + shard_inputs = [ + x[index * shard_size:(index + 1) * shard_size] for x in inputs + ] input_list.append(shard_inputs) return input_list @@ -343,12 +409,15 @@ class TPUFunction(object): shape_key = tuple([tuple(spec.shape.as_list()) for spec in input_specs]) if shape_key not in self._compilation_cache: - logging.info('New input shapes; (re-)compiling: mode=%s, %s', - self.execution_mode, input_specs) - new_tpu_model_ops = self._specialize_model(input_specs) - self._compilation_cache[shape_key] = new_tpu_model_ops - self._test_model_compiles(new_tpu_model_ops) - + with self.model.tpu_session(): + logging.info('New input shapes; (re-)compiling: mode=%s, %s', + self.execution_mode, input_specs) + new_tpu_model_ops = self._specialize_model(input_specs) + self._compilation_cache[shape_key] = new_tpu_model_ops + self._test_model_compiles(new_tpu_model_ops) + + # Initialize our TPU weights on the first compile. + self.model._initialize_weights(self._cloned_model) tpu_model_ops = self._compilation_cache[shape_key] infeed_dict = {} @@ -357,58 +426,83 @@ class TPUFunction(object): for tensor, value in zip(infeed_tensors, inputs): infeed_dict[tensor] = value - session = K.get_session() - _, _, outfeed_outputs = session.run([ - tpu_model_ops.infeed_op, tpu_model_ops.execute_op, - tpu_model_ops.outfeed_op - ], infeed_dict) + with self.model.tpu_session() as session: + _, _, outfeed_outputs = session.run([ + tpu_model_ops.infeed_op, tpu_model_ops.execute_op, + tpu_model_ops.outfeed_op + ], infeed_dict) # TODO(xiejw): Decide how to reduce outputs, or just discard all but first. - return outfeed_outputs[:len(outfeed_outputs) // self.num_replicas] - - -@experimental -def setup_tpu_session(master): - """Initializes and returns a Keras/TF session connected the TPU `master`.""" - session = tf_session.Session( - target=master, config=config_pb2.ConfigProto(isolate_session_state=True)) - K.set_session(session) - K.get_session().run(tpu.initialize_system()) - return session - - -@experimental -def shutdown_tpu_session(session=None): - """Shutdown the TPU attached to session. + if self.execution_mode == model_fn_lib.ModeKeys.PREDICT: + outputs = [[]] * len(self._outfeed_spec) + outputs_per_replica = len(self._outfeed_spec) - This should be called to cleanly shut down the TPU system before the client - exits. - - Args: - session: Session to shutdown, or None to use the default session. - - Returns: - - """ - if session is None: - session = K.get_session() + for i in range(self._strategy.num_towers): + output_group = outfeed_outputs[ + i * outputs_per_replica:(i+1) * outputs_per_replica + ] + for j in range(outputs_per_replica): + outputs[j].append(output_group[j]) - session.run(tpu.shutdown_system()) + return [np.concatenate(group) for group in outputs] + else: + return outfeed_outputs[:len(outfeed_outputs) // self._strategy.num_towers] class KerasTPUModel(models.Model): """TPU compatible Keras model wrapper.""" - def __init__(self, inputs, outputs, name, replicas=1): + def __init__(self, cpu_model, tpu_name_or_address, strategy): super(models.Model, self).__init__( # pylint: disable=bad-super-call - inputs=inputs, - outputs=outputs, - name=name, + inputs=cpu_model.inputs, + outputs=cpu_model.outputs, + name=cpu_model.name, ) + self.predict_function = None self.test_function = None self.train_function = None - self.replicas = replicas + self._strategy = strategy + + self._tpu_name_or_address = tpu_name_or_address + self._cpu_model = cpu_model + self._tpu_model = None + self._tpu_weights_initialized = False + self._graph = ops.Graph() + + cluster_resolver = tpu_cluster_resolver.TPUClusterResolver( + tpu_name_or_address) + cluster_spec = cluster_resolver.cluster_spec() + self._session = tf_session.Session( + graph=self._graph, + target=cluster_resolver.master(), + config=config_pb2.ConfigProto(isolate_session_state=True)) + + if cluster_spec: + self._session.cluster_def.CopyFrom(cluster_spec.as_cluster_def()) + + with self._graph.as_default(): + self._session.run(tpu.initialize_system()) + + # If the input CPU model has already been compiled, compile our TPU model + # immediately. + if self._cpu_model.optimizer: + self.compile( + self._cpu_model.optimizer, + self._cpu_model.loss, + self._cpu_model.metrics, + self._cpu_model.loss_weights, + self._cpu_model.sample_weight_mode, + self._cpu_model.weighted_metrics, + self._cpu_model.target_tensors, + ) + + def get_config(self): + return { + 'cpu_model': self._cpu_model, + 'tpu_name_or_address': self._tpu_name_or_address, + 'strategy': self._strategy, + } def compile(self, optimizer, @@ -430,6 +524,11 @@ class KerasTPUModel(models.Model): sample_weight_mode, weighted_metrics, target_tensors, **kwargs) + if not self._cpu_model.optimizer: + self._cpu_model.compile(optimizer, loss, metrics, loss_weights, + sample_weight_mode, weighted_metrics, + target_tensors, **kwargs) + # Keras optimizers are not compatible with TPU rewrite if not isinstance(self.optimizer, keras_optimizers.TFOptimizer): raise ValueError( @@ -437,37 +536,90 @@ class KerasTPUModel(models.Model): def _make_train_function(self): if not self.train_function: - self.train_function = TPUFunction(self, model_fn_lib.ModeKeys.TRAIN, - num_replicas=self.replicas) + self.train_function = TPUFunction( + self, model_fn_lib.ModeKeys.TRAIN, strategy=self._strategy) return self.train_function def _make_test_function(self): if not self.test_function: - self.test_function = TPUFunction(self, model_fn_lib.ModeKeys.EVAL) + self.test_function = TPUFunction( + self, model_fn_lib.ModeKeys.EVAL, strategy=self._strategy) return self.test_function def _make_predict_function(self): if not self.predict_function: - self.predict_function = TPUFunction(self, model_fn_lib.ModeKeys.PREDICT) + self.predict_function = TPUFunction( + self, model_fn_lib.ModeKeys.PREDICT, strategy=self._strategy) return self.predict_function - def cpu_model(self): - cpu_model = models.Model( - inputs=self.inputs, - outputs=self.outputs, - name=self.name, - ) + def _initialize_weights(self, cloned_model): + """Initialize TPU weights. - if self.optimizer: - cpu_model.compile( - optimizer=self.optimizer, - loss=self.loss, - metrics=self.metrics, - loss_weights=self.loss_weights, - ) + This is called on the first compile of the TPU model (first call to + fit/predict/evaluate). - return cpu_model + Args: + cloned_model: `keras.Model`, TPU model to initialize. + """ + if self._tpu_weights_initialized: + return + + self._tpu_model = cloned_model + self._tpu_weights_initialized = True + + weights = self._cpu_model.get_weights() + with self.tpu_session(): + logging.info('Setting weights on TPU model.') + cloned_model.set_weights(weights) + + def sync_to_cpu(self): + """Copy weights from the CPU, returning a synchronized CPU model.""" + if self._tpu_weights_initialized: + with self.tpu_session(): + logging.info('Copying TPU weights to the CPU') + tpu_weights = self._tpu_model.get_weights() + + self._cpu_model.set_weights(tpu_weights) + + return self._cpu_model + + def get_weights(self): + return self.sync_to_cpu().get_weights() + + def save_weights(self, *args, **kw): + return self.sync_to_cpu().save_weights(*args, **kw) + + def save(self, *args, **kw): + return self.sync_to_cpu().save(*args, **kw) + + def set_weights(self, weights): + # We may not have a TPU model available if we haven't run fit/predict, so + # we can't directly set the TPU weights here. + # Instead, reset CPU model weights and force TPU re-initialization at the + # next call. + self._cpu_model.set_weights(weights) + self._tpu_weights_initialized = False + + @contextlib.contextmanager + def tpu_session(self): + """Yields a TPU session and sets it as the default Keras session.""" + with self._graph.as_default(): + default_session = K.get_session() + # N.B. We have to call `K.set_session()` AND set our session as the + # TF default. `K.get_session()` surprisingly does not return the value + # supplied by K.set_session otherwise. + K.set_session(self._session) + with self._session.as_default(): + yield self._session + K.set_session(default_session) + + def shutdown(self): + logging.info('Shutting down TPU session.') + with self.tpu_session() as session: + session.run(tpu.shutdown_system()) + + self._session.close() def _validate_shapes(model): @@ -504,26 +656,8 @@ Output shape: %(output_shape)s @experimental -def tpu_model(model, replicas=None): - """Runs a model on TPU(s). - - Usage: - ``` - a = Input(shape=(32,)) - b = Dense(32)(a) - model = Model(inputs=a, outputs=b) - - model = keras_support.tpu_model(model) - model.compile( - optimizer=tf.train.GradientDescentOptimizer(learning_rate=1.0), - ...) - ``` - - If `replicas` is set, replicates the model computation on all TPU cores. The - model computation is replicated `num_replicas` times; each shard will run on a - different TPU core. - - Limitation: Currently, replication is only supported for training. +def tpu_model(model, tpu_name_or_address=None, strategy=None): + """Copy `model` along with weights to the TPU. Returns a TPU model. Usage: ``` @@ -531,17 +665,24 @@ def tpu_model(model, replicas=None): b = Dense(32)(a) model = Model(inputs=a, outputs=b) - model = keras_support.tpu_model(model, replicas=2) + # If `num_cores_per_host` is greater than one, batch parallelism will be used + # to run on multiple TPU cores. + strategy = keras_support.TPUDistributionStrategy(num_cores_per_host=8) + model = keras_support.tpu_model(model, strategy) model.compile( optimizer=tf.train.GradientDescentOptimizer(learning_rate=1.0), ...) + model.shutdown() ``` Args: model: A `KerasTPUModel`. - replicas: (Optional) Int, number of TPU cores which to create model - replicas. If `None`, the model runs on single core only, i.e., no - replication. + tpu_name_or_address: A string that is either the name of the Cloud TPU, + the grpc address of the Cloud TPU, or (Googlers only) the BNS name of the + Cloud TPU. If tpu_name_or_address is None, the TPUClusterResolver will + examine the environment to determine a potential Cloud TPU to use. + strategy: `TPUDistributionStrategy`. The strategy to use for replicating + model across multiple TPU cores. Returns: A new `KerasTPUModel` instance. @@ -550,7 +691,9 @@ def tpu_model(model, replicas=None): # TODO(xiejw): Validate TPU model. TPUModel only? # TODO(xiejw): Validate replicas. Full or 1. Shall we allow subset? # TODO(xiejw): Adds reduction option. - replicas = 1 if replicas is None else replicas + if strategy is None: + strategy = TPUDistributionStrategy(num_cores_per_host=1) return KerasTPUModel( - inputs=model.inputs, outputs=model.outputs, name=model.name, - replicas=replicas) + cpu_model=model, + tpu_name_or_address=tpu_name_or_address, + strategy=strategy) diff --git a/tensorflow/contrib/tpu/python/tpu/topology.py b/tensorflow/contrib/tpu/python/tpu/topology.py index cda9a63f204ed686b527c95dd5b4fd7786ac60cf..1fb26e701a392d5ef3bc40d5772d4541fa38f773 100644 --- a/tensorflow/contrib/tpu/python/tpu/topology.py +++ b/tensorflow/contrib/tpu/python/tpu/topology.py @@ -55,8 +55,9 @@ class Topology(object): rank 3 numpy int32 array that describes a valid coordinate mapping. """ + self._serialized = serialized + if serialized: - self._serialized = serialized self._parse_topology(serialized) else: self._mesh_shape = np.asarray(mesh_shape, dtype=np.int32) @@ -131,7 +132,7 @@ class Topology(object): proto.mesh_shape[:] = list(self._mesh_shape) proto.num_tasks = self._device_coordinates.shape[0] proto.num_tpu_devices_per_task = self._device_coordinates.shape[1] - proto.device_coordinates = list(self._device_coordinates.flatten()) + proto.device_coordinates.extend(list(self._device_coordinates.flatten())) self._serialized = proto.SerializeToString() return self._serialized diff --git a/tensorflow/contrib/tpu/python/tpu/topology_test.py b/tensorflow/contrib/tpu/python/tpu/topology_test.py new file mode 100644 index 0000000000000000000000000000000000000000..e67fdb263aa48a37f65c3623365ebcf8f98bebd4 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/topology_test.py @@ -0,0 +1,46 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +"""Tests for topology.py.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.tpu.python.tpu import topology + +from tensorflow.python.platform import test + + +class TopologyTest(test.TestCase): + + def testSerialization(self): + """Test if the class is able to generate serialzied string.""" + original_topology = topology.Topology( + mesh_shape=[1, 1, 2], + device_coordinates=[[[0, 0, 0], [0, 0, 1]]], + ) + serialized_str = original_topology.serialized() + new_topology = topology.Topology(serialized=serialized_str) + + # Make sure the topology recovered from serialized str is same as the + # original topology. + self.assertAllEqual( + original_topology.mesh_shape, new_topology.mesh_shape) + self.assertAllEqual( + original_topology.device_coordinates, new_topology.device_coordinates) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 1c482950e64a9537a2996df66ed9403e53cf8a71..6a64893d9abcd64360554ab00502cdf360b820b6 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -227,19 +227,26 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): class FakeOp(object): """A helper class to determine the current device. - Supports only the device set/get methods needed to run the + Supports only the type and device set/get methods needed to run the graph's _apply_device_function method. """ def __init__(self): self._device = "" + @property + def type(self): + return "FakeOp" + @property def device(self): return self._device def _set_device(self, device): - self._device = device.to_string() + if isinstance(device, pydev.DeviceSpec): + self._device = device.to_string() + else: + self._device = device if self._outside_compilation_cluster: raise NotImplementedError("Cannot nest outside_compilation clusters") @@ -591,16 +598,22 @@ def split_compile_and_replicate(computation, with tpu_function.tpu_shard_context( num_replicas), ops.control_dependencies([metadata]): - # The EncapsulateTPUComputations rewrite needs to identify the - # replicated arguments inside each computation. Adds identity operators - # tagged with an attribute _tpu_replicated_input to identify the - # replicated inputs. + # For backward compatibility reasons, we tag replicated inputs with the + # _tpu_replicated_input attribute. This does nothing and exists only for + # backward compatibility. + # TODO(phawkins): delete the attr_scope after 6/28/2018. # pylint: disable=protected-access - with graph._attr_scope({"_tpu_replicated_input": - attr_value_pb2.AttrValue(b=True)}): + with graph._attr_scope({ + "_tpu_replicated_input": attr_value_pb2.AttrValue(b=True) + }): + # Add identity ops so even unused inputs are "consumed" by the + # computation. This is to avoid orphaned TPUReplicatedInput nodes. + # TODO(phawkins): consider instead pruning unused TPUReplicatedInput + # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. computation_inputs = [ array_ops.identity(x, name="replicated_input_{}".format(i)) - for i, x in enumerate(computation_inputs)] + for i, x in enumerate(computation_inputs) + ] # pylint: enable=protected-access # If there is an infeed queue, adds the dequeued values to the @@ -623,15 +636,16 @@ def split_compile_and_replicate(computation, vscope.set_use_resource(saved_use_resource) - # If the computation returns `None`, add `no_op` here so that when user - # fetches `no_op` returned by this function, the TPUExecute node will be - # triggered. + # If the computation returns `None`, make it an empty tuple. if outputs is None: - outputs = (control_flow_ops.no_op(),) + outputs = tuple() # If the computation only returned one value, makes it a tuple. if not isinstance(outputs, (list, tuple)): outputs = (outputs,) + # Append `no_op` here so that fetching any return value of this function + # will trigger TPUExecute node. + outputs += (control_flow_ops.no_op(),) try: with ops.device(core(0)): outputs = [ diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 5b9aeaa8797b92b4cc596744812f440607054dce..aec59f3885ca7a2046c24ce5b94917ad6c3693e7 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -92,6 +92,19 @@ class TPUContext(object): """ return self._internal_ctx.num_replicas + @property + def num_hosts(self): + """The number of hosts for the TPU system.""" + return self._internal_ctx.num_hosts + + @property + def num_of_replicas_per_host(self): + """The number of replicas for each host.""" + if self._internal_ctx.model_parallelism_enabled: + raise ValueError( + 'num_of_replicas_per_host is not supported for model_parallelism') + return self._internal_ctx.num_of_replicas_per_host + def device_for_replica(self, replica_id): """Returns the tuple of (CPU device and device ordinal) for replica. @@ -384,9 +397,7 @@ class _InternalTPUContext(object): # On TPU if self.is_input_sharded_per_core() or ( self.is_input_per_host_with_iterators()): - # We prohibit per core input sharding for the model parallelism case, - # therefore it is safe to use num_cores here. - return global_batch_size // self.num_cores + return global_batch_size // self.num_replicas else: return global_batch_size // self.num_hosts @@ -484,25 +495,27 @@ class _InternalTPUContext(object): return _placement_function - @property - def tpu_ordinal_function(self): + def tpu_ordinal_function(self, host_id): """Returns the TPU ordinal fn.""" - def _tpu_ordinal_function(index): + def _tpu_ordinal_function(shard_index_in_host): """Return the TPU ordinal associated with a shard. Required because the enqueue ops are placed on CPU. Args: - index: the shard index + shard_index_in_host: the shard index Returns: The ordinal of the TPU device the shard's infeed should be placed on. """ if self.model_parallelism_enabled: - return self.device_assignment.tpu_ordinal(replica=index) + # We put both enqueue/dequeue ops at tpu.core(0) in each replica. + replica = self.device_assignment.lookup_replicas( + host_id, (0, 0, 0))[shard_index_in_host] + return self.device_assignment.tpu_ordinal(replica=replica) else: - return index % self.num_of_cores_per_host + return shard_index_in_host % self.num_of_cores_per_host return _tpu_ordinal_function diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 64ae35dfc5e6d385a23c2dba15562d71aae4d497..49cd318b8956369f49d77d3cb1b030e171fa07aa 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -81,12 +81,17 @@ _TPU_ESTIMATOR = 'tpu_estimator' _ITERATIONS_PER_LOOP_VAR = 'iterations_per_loop' _BATCH_SIZE_KEY = 'batch_size' _CTX_KEY = 'context' +_USE_TPU_KEY = 'use_tpu' _CROSS_REPLICA_SUM_OP = 'CrossReplicaSum' _ONE_GIGABYTE = 1024 * 1024 * 1024 _TPU_ENQUEUE_OPS = '_tpu_enqueue_ops' _TPU_TRAIN_OP = '_tpu_train_op' _REWRITE_FOR_INFERENCE_MODE = '_rewrite_for_inference' +# Ideally _USE_TPU_KEY should be reserved as well. However there are already +# models that make use of this key, thus it can not be reserved now to prevent +# breakage. In the long run, we would like to mitigate this by migrating models +# off of using _USE_TPU_KEY. _RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY, _CTX_KEY] @@ -211,8 +216,8 @@ class _SIGNAL(object): class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`. - See `EstimatorSpec` for `mode`, 'predictions, 'loss', 'train_op', and - 'export_outputs`. + See `EstimatorSpec` for `mode`, `predictions`, `loss`, `train_op`, and + `export_outputs`. For evaluation, `eval_metrics `is a tuple of `metric_fn` and `tensors`, where `metric_fn` runs on CPU to generate metrics and `tensors` represents the @@ -226,7 +231,7 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote size is the first dimension. Once all tensors are available at CPU host from all shards, they are concatenated (on CPU) and passed as positional arguments to the `metric_fn` if `tensors` is list or keyword arguments if `tensors` is - dict. `metric_fn` takes the `tensors` and returns a dict from metric string + a dict. `metric_fn` takes the `tensors` and returns a dict from metric string name to the result of calling a metric function, namely a `(metric_tensor, update_op)` tuple. See `TPUEstimator` for MNIST example how to specify the `eval_metrics`. @@ -664,6 +669,7 @@ def generate_per_core_enqueue_ops_fn_for_host( ctx, input_fn, inputs_structure_recorder, host_device, host_id): """Generates infeed enqueue ops for per-core input_fn on a single host.""" captured_infeed_queue = _CapturedObject() + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """A fn returns enqueue_ops.""" @@ -699,7 +705,7 @@ def generate_per_core_enqueue_ops_fn_for_host( per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( - per_host_sharded_inputs, tpu_ordinal_function=ctx.tpu_ordinal_function) + per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) return per_host_enqueue_ops return enqueue_ops_fn, captured_infeed_queue @@ -734,19 +740,7 @@ def generate_per_host_enqueue_ops_fn_for_host( if is_dataset: hooks.append(inputs.dataset_initializer_hook()) - # TODO(ylc): Refactoring the code to merge the tpu ordinal logic here and the - # _InternalTPUContext.tpu_ordinal_function. We should either introduce another - # abstraction or a different helper method. - def _tpu_ordinal_function_impl(shard_index_in_host): - # We put both enqueue/dequeue op at tpu.core(0) in each replica. - replica = ctx.device_assignment.lookup_replicas( - host_id, (0, 0, 0))[shard_index_in_host] - return ctx.device_assignment.tpu_ordinal(replica=replica) - - if ctx.model_parallelism_enabled: - tpu_ordinal_function = _tpu_ordinal_function_impl - else: - tpu_ordinal_function = None + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """A Fn returning the TPU infeed enqueue ops. @@ -782,7 +776,7 @@ def generate_per_host_enqueue_ops_fn_for_host( infeed_queue.split_inputs_and_generate_enqueue_ops( unsharded_tensor_list, placement_function=lambda x: device, - tpu_ordinal_function=tpu_ordinal_function)) + tpu_ordinal_function=tpu_ordinal_function_impl)) if signals is None: return per_host_enqueue_ops else: @@ -816,6 +810,7 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( raise TypeError('Most PREDICT not yet supported in PER_HOST_V2 mode.') hooks.append(inputs.dataset_initializer_hook()) + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """Generates the per_host enqueue ops.""" @@ -846,7 +841,7 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( - per_host_sharded_inputs, tpu_ordinal_function=ctx.tpu_ordinal_function) + per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) return per_host_enqueue_ops return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset @@ -1146,7 +1141,7 @@ class _InputPipeline(object): err_msg = ('Input pipeline contains one or more QueueRunners. ' 'It could be slow and not scalable. Please consider ' 'converting your input pipeline to use `tf.data` instead (see ' - 'https://www.tensorflow.org/programmers_guide/datasets for ' + 'https://www.tensorflow.org/guide/datasets for ' 'instructions.') if _WRAP_INPUT_FN_INTO_WHILE_LOOP: raise RuntimeError(err_msg) @@ -1343,8 +1338,55 @@ class _ModelFnWrapper(object): key, tensor)) return predictions + def _validate_model_features_and_labels(self, + features, + labels, + is_export_mode): + """Validates that the features and labels for the model function are valid. + + A valid features/labels object is the one with: + - Type: Tensor or a dictionary of Tensors + - Static shape if is_export_mode is False. + + Args: + features: the features that would be input to the model function. + labels: the labels that would be input to the model function. + is_export_mode: boolean value specifying if in export mode. + + Raises: + TypeError: If features/labels are not of the correct type. + ValueError: If features/labels have dynamic shape. + """ + + def validate(obj, obj_name): + """Helper validate function.""" + if not isinstance(obj, ops.Tensor) and not isinstance(obj, dict): + raise TypeError( + 'The {} to the model returned by input_fn must be either a Tensor ' + 'or a dictionary of Tensors. {}: {}'.format(obj_name, obj_name, + obj)) + if is_export_mode or self._ctx.is_running_on_cpu(is_export_mode): + return + if isinstance(obj, ops.Tensor): + if not obj.get_shape().is_fully_defined(): + raise ValueError( + 'The {} to the model returned by input_fn must have static shape.' + ' Tensor: {}'.format(obj_name, obj)) + else: + for (key, tensor) in obj.items(): + if not tensor.get_shape().is_fully_defined(): + raise ValueError( + 'The {} to the model returned by input_fn must have static ' + 'shape. Key: \'{}\', Tensor: {}'.format( + obj_name, key, tensor)) + + validate(features, 'features') + if labels is not None: + validate(labels, 'labels') + def _call_model_fn(self, features, labels, is_export_mode=False): """Calls the model_fn with required parameters.""" + self._validate_model_features_and_labels(features, labels, is_export_mode) model_fn_args = function_utils.fn_args(self._model_fn) kwargs = {} @@ -1377,8 +1419,11 @@ class _ModelFnWrapper(object): if batch_size_for_model_fn is not None: _add_item_to_params(params, _BATCH_SIZE_KEY, batch_size_for_model_fn) + running_on_cpu = self._ctx.is_running_on_cpu(is_export_mode) + _add_item_to_params(params, _USE_TPU_KEY, not running_on_cpu) + estimator_spec = self._model_fn(features=features, **kwargs) - if (self._ctx.is_running_on_cpu(is_export_mode) and + if (running_on_cpu and isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec)): # pylint: disable=protected-access # The estimator_spec will be passed to `Estimator` directly, which expects # type `EstimatorSpec`. @@ -1855,11 +1900,6 @@ class TPUEstimator(estimator_lib.Estimator): ... ``` - Current limitations: - -------------------- - - 1. Outside compilation does not work yet (b/79991729). - """ def __init__(self, @@ -2001,24 +2041,29 @@ class TPUEstimator(estimator_lib.Estimator): strip_default_attrs, save_variables=True, mode=model_fn_lib.ModeKeys.PREDICT, - export_tags=None): + export_tags=None, + check_variables=True): if mode != model_fn_lib.ModeKeys.PREDICT: raise NotImplementedError( 'TPUEstimator only handles mode PREDICT for export_savedmodel(); ' 'got {}.'.format(mode)) - super(TPUEstimator, self)._add_meta_graph_for_mode(builder, - input_receiver_fn_map, - checkpoint_path, - strip_default_attrs, - save_variables, - mode=mode) + (super(TPUEstimator, self). + _add_meta_graph_for_mode(builder, + input_receiver_fn_map, + checkpoint_path, + strip_default_attrs, + save_variables, + mode=mode, + export_tags=export_tags, + check_variables=check_variables)) if self._export_to_tpu: input_receiver_fn_map = {_REWRITE_FOR_INFERENCE_MODE: input_receiver_fn_map[mode]} export_tags = [tag_constants.SERVING, tag_constants.TPU] mode = _REWRITE_FOR_INFERENCE_MODE + # See b/110052256 for why `check_variables` is `False`. (super(TPUEstimator, self). _add_meta_graph_for_mode(builder, input_receiver_fn_map, @@ -2026,7 +2071,8 @@ class TPUEstimator(estimator_lib.Estimator): strip_default_attrs, save_variables=False, mode=mode, - export_tags=export_tags)) + export_tags=export_tags, + check_variables=False)) def _call_model_fn(self, features, labels, mode, config): if mode == _REWRITE_FOR_INFERENCE_MODE: @@ -2078,10 +2124,21 @@ class TPUEstimator(estimator_lib.Estimator): # Reconstruct `tensors`, but with `tpu_tensors` replaced with # `tpu_tensors_on_cpu`. - new_tensors = [ - tpu_tensors_on_cpu.pop(0) if _is_tpu_tensor(t) else t - for t in tensors - ] + new_tensors = [] + for t in tensors: + if _is_tpu_tensor(t): + new_tensors.append(tpu_tensors_on_cpu.pop(0)) + elif t is None: + new_tensors.append(None) + else: + # Only fetching `tpu_tensors_on_cpu` does not trigger + # TPU computation and blocks, so we add the control dependency here. + control_inputs = (tpu_tensors_on_cpu + if isinstance(tpu_tensors_on_cpu, (list, tuple)) + else (tpu_tensors_on_cpu,)) + with ops.control_dependencies(control_inputs): + new_tensors.append(array_ops.identity(t)) + # Reconstruct `tensors_dict`. new_tensors_dict = nest.pack_sequence_as(tensors_dict, new_tensors) # Reconstruct `export_outputs`. @@ -3062,7 +3119,7 @@ class _SignalsHelper(object): def __init__(self, signals): self._signal_keys = [] - for key in sorted(signals.iterkeys()): + for key in sorted(iter(signals.keys())): self._signal_keys.append(key) @property @@ -3074,7 +3131,7 @@ class _SignalsHelper(object): @staticmethod def as_tensor_list(signals): - return [signals[key] for key in sorted(signals.iterkeys())] + return [signals[key] for key in sorted(iter(signals.keys()))] def _verify_cross_hosts_transfer_size(tensor_dict, message): @@ -3100,7 +3157,7 @@ def _add_item_to_params(params, key, value): if isinstance(params, hparam.HParams): # For HParams, we need to use special API. if key in params: - params.key = value + params.set_hparam(key, value) else: params.add_hparam(key, value) else: diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py index e76cf83e4ddcd86ab3971bcecefe2e2dc979bf63..15f99d7eebddd46f9f6902b68f01e42359a72cbe 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py @@ -19,6 +19,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu_function from tensorflow.python.ops.losses import losses @@ -32,7 +34,8 @@ class CrossShardOptimizer(optimizer.Optimizer): def __init__(self, opt, reduction=losses.Reduction.MEAN, - name="CrossShardOptimizer"): + name="CrossShardOptimizer", + group_assignment=None): """Construct a new cross-shard optimizer. Args: @@ -40,6 +43,8 @@ class CrossShardOptimizer(optimizer.Optimizer): reduction: The reduction to apply to the shard losses. name: Optional name prefix for the operations created when applying gradients. Defaults to "CrossShardOptimizer". + group_assignment: Optional list of group ids for applying the optimizer + to subgroups. Raises: ValueError: If reduction is not a valid cross-shard reduction. @@ -50,6 +55,35 @@ class CrossShardOptimizer(optimizer.Optimizer): super(CrossShardOptimizer, self).__init__(False, name) self._opt = opt self._reduction = reduction + self._group_assignment = group_assignment + + def _verify_and_get_subgroup_size(self, group_assignment, num_shards): + """Verify group_assignment and get the subgroup size". + + Args: + group_assignment: list of group ids for applying the optimizer + to subgroups. + num_shards: The number of TPU shards. + + Returns: + The size of one subgroup in group_assignment. + + Raises: + ValueError: If group_assignment is invalid. + """ + if not group_assignment: + return None + if len(group_assignment) != num_shards: + raise ValueError("The size of group_assignment does not equal to " + "num_shard({0}). Got group_assignment={1}".format( + num_shards, self._group_assignment)) + subgroup_size_list = dict(collections.Counter(group_assignment)).values() + if all(subgroup_size_list[0] == size for size in subgroup_size_list): + return subgroup_size_list[0] + else: + raise ValueError("The size of each subgroup in group_assignment must " + "be equal. Got group_assignment={}".format( + self._group_assignment)) def compute_gradients(self, loss, var_list=None, **kwargs): """Compute gradients of "loss" for the variables in "var_list". @@ -71,7 +105,8 @@ class CrossShardOptimizer(optimizer.Optimizer): A list of (gradient, variable) pairs. Raises: - ValueError: If not within a tpu_shard_context. + ValueError: If not within a tpu_shard_context or group_assignment is + invalid. """ num_shards = tpu_function.get_tpu_context().number_of_shards if num_shards is None: @@ -79,9 +114,17 @@ class CrossShardOptimizer(optimizer.Optimizer): "CrossShardOptimizer should be used within a tpu_shard_context, but " "got unset number_of_shards. Assuming 1.") num_shards = 1 + + subgroup_size = self._verify_and_get_subgroup_size(self._group_assignment, + num_shards) + if num_shards > 1 and self._reduction == losses.Reduction.MEAN: - scale = 1.0 / num_shards + if self._group_assignment: + scale = 1.0 / subgroup_size + else: + scale = 1.0 / num_shards loss *= scale + return self._opt.compute_gradients(loss, var_list=var_list, **kwargs) def apply_gradients(self, grads_and_vars, global_step=None, name=None): @@ -110,7 +153,8 @@ class CrossShardOptimizer(optimizer.Optimizer): if grad is None: summed_grads_and_vars.append((grad, var)) else: - summed_grads_and_vars.append((tpu_ops.cross_replica_sum(grad), var)) + summed_grads_and_vars.append((tpu_ops.cross_replica_sum( + grad, self._group_assignment), var)) return self._opt.apply_gradients(summed_grads_and_vars, global_step, name) def get_slot(self, *args, **kwargs): diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD index 5de55b5f7f2a41ac6edd27e5a102e565f33df12c..76927e62e82d02de172a0851819716dc63180371 100644 --- a/tensorflow/contrib/training/BUILD +++ b/tensorflow/contrib/training/BUILD @@ -295,7 +295,7 @@ py_test( tags = ["notsan"], deps = [ ":training_py", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/kernel_tests/serialization:dataset_serialization_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:gradients", diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py index 409aba817c1ec37003eb98f000f6cf8918234c5d..a2444934bc21d58ed57d15494b3548a31ce3a2df 100644 --- a/tensorflow/contrib/training/python/training/tensor_queue_dataset.py +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import convert from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes @@ -45,14 +46,14 @@ class _PrependFromQueueAndPaddedBatchDataset(dataset_ops.Dataset): self._input_dataset = input_dataset self._batch_size = ops.convert_to_tensor( batch_size, dtype=dtypes.int64, name="batch_size") - # pylint: disable=protected-access if padded_shapes is None: self._padded_shapes = nest.map_structure( - dataset_ops._partial_shape_to_tensor, input_dataset.output_shapes) + convert.partial_shape_to_tensor, input_dataset.output_shapes) else: self._padded_shapes = nest.map_structure_up_to( - input_dataset.output_shapes, dataset_ops._partial_shape_to_tensor, + input_dataset.output_shapes, convert.partial_shape_to_tensor, padded_shapes) + # pylint: disable=protected-access padding_values = ( padding_values if padding_values is not None else dataset_ops._default_padding(input_dataset)) diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py index 0338f409a203c232e63e99534a8f6d6a43fa661e..df0a186f4f6963d7e874bb4ab74a8db7e10a52ee 100644 --- a/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py @@ -19,7 +19,7 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.contrib.training.python.training import tensor_queue_dataset as tqd from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes diff --git a/tensorflow/contrib/verbs/BUILD b/tensorflow/contrib/verbs/BUILD index 9720fd6e8657de18cf8d7565f834568ae52fdbda..19cb8983b6836266ebfac70c54657a96324e8435 100644 --- a/tensorflow/contrib/verbs/BUILD +++ b/tensorflow/contrib/verbs/BUILD @@ -53,12 +53,12 @@ cc_library( ":grpc_verbs_service_impl", ":rdma_mgr", ":verbs_service_proto_cc", + "//tensorflow:grpc++", "//tensorflow/core:lib_internal", "//tensorflow/core/distributed_runtime:session_mgr", "//tensorflow/core/distributed_runtime/rpc:async_service_interface", "//tensorflow/core/distributed_runtime/rpc:grpc_call", "//tensorflow/core/distributed_runtime/rpc:grpc_util", - "@grpc//:grpc++_unsecure", ], alwayslink = 1, ) @@ -69,7 +69,7 @@ cc_library( hdrs = ["grpc_verbs_service_impl.h"], deps = [ ":verbs_service_proto_cc", - "@grpc//:grpc++_unsecure", + "//tensorflow:grpc++", ], ) diff --git a/tensorflow/contrib/verbs/grpc_verbs_service.cc b/tensorflow/contrib/verbs/grpc_verbs_service.cc index 742f946c9536973eb8a6a11afda1b32ae4a7726b..af29abd91feda22824e57c19c13a3f48fb1d61b7 100644 --- a/tensorflow/contrib/verbs/grpc_verbs_service.cc +++ b/tensorflow/contrib/verbs/grpc_verbs_service.cc @@ -15,9 +15,9 @@ limitations under the License. #ifdef TENSORFLOW_USE_VERBS -#include "grpc++/alarm.h" -#include "grpc++/grpc++.h" -#include "grpc++/server_builder.h" +#include "grpcpp/alarm.h" +#include "grpcpp/grpcpp.h" +#include "grpcpp/server_builder.h" #include "tensorflow/contrib/verbs/grpc_verbs_service.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h" diff --git a/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc b/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc index 991f9a9d8bdf883b1b68bfa1fb6af7bf51b7e66a..4da7b59c69c88a4d04be37543aae7f03decd2c52 100644 --- a/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc +++ b/tensorflow/contrib/verbs/grpc_verbs_service_impl.cc @@ -15,14 +15,14 @@ limitations under the License. #include "tensorflow/contrib/verbs/grpc_verbs_service_impl.h" -#include "grpc++/impl/codegen/async_stream.h" -#include "grpc++/impl/codegen/async_unary_call.h" -#include "grpc++/impl/codegen/channel_interface.h" -#include "grpc++/impl/codegen/client_unary_call.h" -#include "grpc++/impl/codegen/method_handler_impl.h" -#include "grpc++/impl/codegen/rpc_service_method.h" -#include "grpc++/impl/codegen/service_type.h" -#include "grpc++/impl/codegen/sync_stream.h" +#include "grpcpp/impl/codegen/async_stream.h" +#include "grpcpp/impl/codegen/async_unary_call.h" +#include "grpcpp/impl/codegen/channel_interface.h" +#include "grpcpp/impl/codegen/client_unary_call.h" +#include "grpcpp/impl/codegen/method_handler_impl.h" +#include "grpcpp/impl/codegen/rpc_service_method.h" +#include "grpcpp/impl/codegen/service_type.h" +#include "grpcpp/impl/codegen/sync_stream.h" namespace tensorflow { diff --git a/tensorflow/contrib/verbs/grpc_verbs_service_impl.h b/tensorflow/contrib/verbs/grpc_verbs_service_impl.h index 1f0f10517e98a32ae882c027330091928f1a6ee2..abe5e08b07cd71b7ca28321e6eb2cf0eec5d1b0f 100644 --- a/tensorflow/contrib/verbs/grpc_verbs_service_impl.h +++ b/tensorflow/contrib/verbs/grpc_verbs_service_impl.h @@ -16,14 +16,14 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_GRPC_VERBS_SERVICE_IMPL_H_ #define TENSORFLOW_CONTRIB_GRPC_VERBS_SERVICE_IMPL_H_ -#include "grpc++/impl/codegen/async_stream.h" -#include "grpc++/impl/codegen/async_unary_call.h" -#include "grpc++/impl/codegen/proto_utils.h" -#include "grpc++/impl/codegen/rpc_method.h" -#include "grpc++/impl/codegen/service_type.h" -#include "grpc++/impl/codegen/status.h" -#include "grpc++/impl/codegen/stub_options.h" -#include "grpc++/impl/codegen/sync_stream.h" +#include "grpcpp/impl/codegen/async_stream.h" +#include "grpcpp/impl/codegen/async_unary_call.h" +#include "grpcpp/impl/codegen/proto_utils.h" +#include "grpcpp/impl/codegen/rpc_method.h" +#include "grpcpp/impl/codegen/service_type.h" +#include "grpcpp/impl/codegen/status.h" +#include "grpcpp/impl/codegen/stub_options.h" +#include "grpcpp/impl/codegen/sync_stream.h" #include "tensorflow/contrib/verbs/verbs_service.pb.h" diff --git a/tensorflow/contrib/verbs/rdma.cc b/tensorflow/contrib/verbs/rdma.cc index 86350a08e57e5050f18d019fe80d70f6381c1f7d..f7c979e86320d59ad033e2b8d7fcdff89ce0d133 100644 --- a/tensorflow/contrib/verbs/rdma.cc +++ b/tensorflow/contrib/verbs/rdma.cc @@ -24,8 +24,8 @@ limitations under the License. #include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/common_runtime/process_util.h" #if GOOGLE_CUDA +#include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#include "tensorflow/core/common_runtime/gpu/process_state.h" #endif #include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h" @@ -1084,7 +1084,7 @@ void RdmaTensorResponse::RecvHandler(Rendezvous::ParsedKey parsed, // The tensor must be copied from GPU to CPU, because either: // 1. The tensor is located on a non GDR compatible GPU. // 2. The tensor's meta-data has changed. - Allocator* alloc = ProcessState::singleton()->GetCUDAHostAllocator(0); + Allocator* alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0); copy = Tensor(alloc, in.dtype(), in.shape()); CountCopies(rm_.name_, (void*)DMAHelper::base(&in), (void*)DMAHelper::base(©), in.TotalBytes(), true); @@ -1541,7 +1541,7 @@ bool RdmaTensorRequest::AllocateTensors() { if (mr_ == nullptr) { // Can't RDMA directly to result. Use a proxy. proxy_tensor_ = - new Tensor(ProcessState::singleton()->GetCUDAHostAllocator(0), + new Tensor(GPUProcessState::singleton()->GetCUDAHostAllocator(0), result_tensor_->dtype(), result_tensor_->shape()); rdma_addr_ = DMAHelper::base(proxy_tensor_); mr_ = diff --git a/tensorflow/contrib/verbs/rdma_mgr.cc b/tensorflow/contrib/verbs/rdma_mgr.cc index 369bd986df5313955bc22d6e5c6d38815908ada3..9cb3d1fbbfdbc6d85a7a9799bd82438f0bf70c4f 100644 --- a/tensorflow/contrib/verbs/rdma_mgr.cc +++ b/tensorflow/contrib/verbs/rdma_mgr.cc @@ -21,8 +21,9 @@ limitations under the License. #include "tensorflow/contrib/verbs/grpc_verbs_client.h" #include "tensorflow/contrib/verbs/verbs_service.pb.h" #include "tensorflow/core/common_runtime/bfc_allocator.h" +#include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#include "tensorflow/core/common_runtime/gpu/process_state.h" +#include "tensorflow/core/common_runtime/process_state.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" #include "tensorflow/core/distributed_runtime/session_mgr.h" #include "tensorflow/core/framework/allocator_registry.h" @@ -282,7 +283,7 @@ void RdmaMgr::InitAllocators() { Allocator* allocators[] = { #if GOOGLE_CUDA - ProcessState::singleton()->GetCUDAHostAllocator(0), + GPUProcessState::singleton()->GetCUDAHostAllocator(0), ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA cpu_allocator(), @@ -323,7 +324,8 @@ void RdmaMgr::InitAllocators() { std::bind(&RdmaMemoryMgr::InsertMemoryRegion, &RdmaMemoryMgr::Singleton(), _1, _2, std::string(buf)); - ProcessState::singleton()->AddGPUAllocVisitor(bus_id, cuda_alloc_visitor); + GPUProcessState::singleton()->AddGPUAllocVisitor(bus_id, + cuda_alloc_visitor); LOG(INFO) << "Instrumenting GPU allocator with bus_id " << bus_id; } #endif // GOOGLE_CUDA diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 5de59eaef7cd9924696bab3586521e7ba04f972b..97880219b80d663e9ee4eb8f0373786b23284b54 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -89,6 +89,7 @@ load( "tf_generate_proto_text_sources", "tf_genrule_cmd_append_to_srcs", "tf_opts_nortti_if_android", + "tf_features_nomodules_if_android", ) load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl") load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu") @@ -232,9 +233,7 @@ tf_proto_library( name = "protos_all", srcs = [], cc_api_version = 2, - dart_api_version = 2, default_header = True, - j2objc_api_version = 1, java_api_version = 2, js_api_version = 2, protodeps = [ @@ -793,6 +792,7 @@ tf_cuda_library( "framework/graph_def_util.h", "framework/graph_to_functiondef.h", "framework/kernel_def_builder.h", + "framework/kernel_def_util.h", "framework/log_memory.h", "framework/lookup_interface.h", "framework/memory_types.h", @@ -879,6 +879,7 @@ cc_library( hdrs = [ "util/stats_calculator.h", ], + copts = tf_copts(), ) cc_library( @@ -901,6 +902,15 @@ cc_library( hdrs = ["util/ptr_util.h"], ) +cc_library( + name = "status_util", + hdrs = ["util/status_util.h"], + deps = [ + ":graph", + ":lib", + ], +) + cc_library( name = "reader_base", srcs = ["framework/reader_base.cc"], @@ -998,6 +1008,7 @@ tf_gen_op_libs( "nn_ops", "no_op", "parsing_ops", + "random_grad", "random_ops", "remote_fused_graph_ops", "resource_variable_ops", @@ -1196,6 +1207,7 @@ tf_cuda_library( hdrs = [ "common_runtime/device.h", "common_runtime/device_factory.h", + "common_runtime/function.h", "common_runtime/optimization_registry.h", "common_runtime/shape_refiner.h", "graph/algorithm.h", @@ -1250,6 +1262,7 @@ cc_library( "//tensorflow/core/kernels:fake_quant_ops", "//tensorflow/core/kernels:function_ops", "//tensorflow/core/kernels:functional_ops", + "//tensorflow/core/kernels:grappler", "//tensorflow/core/kernels:histogram_op", "//tensorflow/core/kernels:image", "//tensorflow/core/kernels:io", @@ -1910,6 +1923,7 @@ tf_proto_library_cc( srcs = ["protobuf/master_service.proto"], has_services = 1, cc_api_version = 2, + cc_grpc_version = 1, cc_stubby_versions = ["2"], protodeps = [":master_proto"], visibility = [ @@ -2237,9 +2251,7 @@ tf_proto_library( name = "error_codes_proto", srcs = ERROR_CODES_PROTO_SRCS, cc_api_version = 2, - dart_api_version = 2, default_header = True, - j2objc_api_version = 1, java_api_version = 2, js_api_version = 2, provide_cc_alias = True, @@ -2260,9 +2272,7 @@ tf_proto_library( name = "protos_all_proto", srcs = COMMON_PROTO_SRCS + ADDITIONAL_CORE_PROTO_SRCS, cc_api_version = 2, - dart_api_version = 2, default_header = True, - j2objc_api_version = 1, java_api_version = 2, js_api_version = 2, protodeps = [ @@ -2341,6 +2351,7 @@ FRAMEWORK_INTERNAL_PRIVATE_HEADERS = [ FRAMEWORK_INTERNAL_PUBLIC_HEADERS = [ "framework/op_segment.h", "framework/rendezvous.h", # only needed for tests + "framework/resource_var.h", "framework/tensor_reference.h", "framework/tracking_allocator.h", # only needed for tests "framework/unique_tensor_references.h", @@ -2636,6 +2647,7 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/dma_helper.h", "common_runtime/eigen_thread_pool.h", "common_runtime/executor.h", + "common_runtime/executor_factory.h", "common_runtime/graph_optimizer.h", "common_runtime/local_device.h", "common_runtime/lower_if_op.h", @@ -2658,6 +2670,8 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/step_stats_collector.h", "common_runtime/threadpool_device.h", "common_runtime/visitable_allocator.h", + "common_runtime/process_state.h", + "common_runtime/pool_allocator.h", "graph/gradients.h", "graph/quantize_training.h", ] + if_mkl(["graph/mkl_graph_util.h"]) @@ -2685,6 +2699,7 @@ tf_cuda_library( "common_runtime/device_resolver_local.cc", "common_runtime/device_set.cc", "common_runtime/executor.cc", + "common_runtime/executor_factory.cc", "common_runtime/function.cc", "common_runtime/graph_optimizer.cc", "common_runtime/graph_runner.cc", @@ -2695,7 +2710,9 @@ tf_cuda_library( "common_runtime/optimization_registry.cc", "common_runtime/parallel_concat_optimizer.cc", "common_runtime/placer.cc", + "common_runtime/pool_allocator.cc", "common_runtime/process_function_library_runtime.cc", + "common_runtime/process_state.cc", "common_runtime/process_util.cc", "common_runtime/renamed_device.cc", "common_runtime/rendezvous_mgr.cc", @@ -2882,6 +2899,7 @@ cc_library( ) GPU_RUNTIME_HEADERS = [ + "common_runtime/gpu/cuda_host_allocator.h", "common_runtime/gpu/gpu_bfc_allocator.h", "common_runtime/gpu/gpu_cudamalloc_allocator.h", "common_runtime/gpu/gpu_debug_allocator.h", @@ -2891,10 +2909,9 @@ GPU_RUNTIME_HEADERS = [ "common_runtime/gpu/gpu_id_utils.h", "common_runtime/gpu/gpu_init.h", "common_runtime/gpu/gpu_managed_allocator.h", + "common_runtime/gpu/gpu_process_state.h", "common_runtime/gpu/gpu_stream_util.h", "common_runtime/gpu/gpu_util.h", - "common_runtime/gpu/pool_allocator.h", - "common_runtime/gpu/process_state.h", "common_runtime/gpu_device_context.h", ] @@ -2907,11 +2924,10 @@ tf_cuda_library( "common_runtime/gpu/gpu_device.cc", "common_runtime/gpu/gpu_device_factory.cc", "common_runtime/gpu/gpu_managed_allocator.cc", + "common_runtime/gpu/gpu_process_state.cc", "common_runtime/gpu/gpu_stream_util.cc", "common_runtime/gpu/gpu_util.cc", "common_runtime/gpu/gpu_util_platform_specific.cc", - "common_runtime/gpu/pool_allocator.cc", - "common_runtime/gpu/process_state.cc", ], hdrs = GPU_RUNTIME_HEADERS, copts = tf_copts(), @@ -3369,10 +3385,12 @@ tf_cc_tests( "framework/bfloat16_test.cc", "framework/cancellation_test.cc", "framework/common_shape_fns_test.cc", + "framework/device_base_test.cc", "framework/function_test.cc", "framework/graph_def_util_test.cc", "framework/graph_to_functiondef_test.cc", "framework/kernel_def_builder_test.cc", + "framework/kernel_def_util_test.cc", "framework/memory_types_test.cc", "framework/node_def_builder_test.cc", "framework/node_def_util_test.cc", @@ -3397,6 +3415,7 @@ tf_cc_tests( "framework/variant_op_registry_test.cc", "framework/variant_test.cc", "graph/algorithm_test.cc", + "graph/control_flow_test.cc", "graph/edgeset_test.cc", "graph/graph_def_builder_test.cc", "graph/graph_partition_test.cc", @@ -3421,6 +3440,7 @@ tf_cc_tests( "util/semver_test.cc", "util/sparse/sparse_tensor_test.cc", "util/stat_summarizer_test.cc", + "util/status_util_test.cc", "util/tensor_format_test.cc", "util/tensor_slice_reader_test.cc", "util/tensor_slice_set_test.cc", @@ -3445,6 +3465,7 @@ tf_cc_tests( ":ops", ":protos_all_cc", ":protos_test_cc", + ":status_util", ":test", ":test_main", ":testlib", @@ -3903,13 +3924,13 @@ tf_cc_test( ], ) -tf_cc_test( +tf_cuda_cc_test( name = "common_runtime_direct_session_test", size = "small", srcs = ["common_runtime/direct_session_test.cc"], + args = [] + if_cuda(["--heap_check=local"]), # The GPU tracer leaks memory linkstatic = tf_kernel_tests_linkstatic(), deps = [ - ":core", ":core_cpu", ":core_cpu_internal", ":direct_session_internal", @@ -3922,6 +3943,7 @@ tf_cc_test( ":test", ":test_main", ":testlib", + "//third_party/eigen3", "//tensorflow/cc:cc_ops", "//tensorflow/core/kernels:control_flow_ops", "//tensorflow/core/kernels:cwise_op", @@ -3935,8 +3957,7 @@ tf_cc_test( "//tensorflow/core/kernels:queue_ops", "//tensorflow/core/kernels:session_ops", "//tensorflow/core/kernels:variable_ops", - "//third_party/eigen3", - ], + ] + if_cuda([":cuda"]), ) # This is identical to :common_runtime_direct_session_test with the addition of diff --git a/tensorflow/core/api_def/BUILD b/tensorflow/core/api_def/BUILD index 19d643880966f7607405539a5ad43d8e03dc13fb..06b797e32edc046bab498f8d775040d57ef62ce9 100644 --- a/tensorflow/core/api_def/BUILD +++ b/tensorflow/core/api_def/BUILD @@ -4,6 +4,7 @@ # The following targets can be used to access ApiDefs: # :base_api_def # :python_api_def +# :java_api_def package( default_visibility = ["//visibility:private"], @@ -29,6 +30,12 @@ filegroup( visibility = ["//tensorflow:internal"], ) +filegroup( + name = "java_api_def", + srcs = glob(["java_api/*"]), + visibility = ["//tensorflow:internal"], +) + cc_library( name = "excluded_ops_lib", srcs = ["excluded_ops.cc"], diff --git a/tensorflow/core/api_def/api_test.cc b/tensorflow/core/api_def/api_test.cc index 477a0b670e49f8aa4ee8c250d4957886eb865ed5..ae03a61ae66ec8d0119d91eefe8c64e61348e9b4 100644 --- a/tensorflow/core/api_def/api_test.cc +++ b/tensorflow/core/api_def/api_test.cc @@ -149,6 +149,33 @@ void TestAllApiDefAttributeNamesAreValid( } } } + +void TestDeprecatedAttributesSetCorrectly( + const std::unordered_map& api_defs_map) { + for (const auto& name_and_api_def : api_defs_map) { + int num_deprecated_endpoints = 0; + const auto& api_def = name_and_api_def.second; + for (const auto& endpoint : api_def.endpoint()) { + if (endpoint.deprecated()) { + ++num_deprecated_endpoints; + } + } + + const auto& name = name_and_api_def.first; + ASSERT_TRUE(api_def.deprecation_message().empty() || + num_deprecated_endpoints == 0) + << "Endpoints are set to 'deprecated' for deprecated op " << name + << ". If an op is deprecated (i.e. deprecation_message is set), " + << "all the endpoints are deprecated implicitly and 'deprecated' " + << "field should not be set."; + if (num_deprecated_endpoints > 0) { + ASSERT_NE(num_deprecated_endpoints, api_def.endpoint_size()) + << "All " << name << " endpoints are deprecated. Please, set " + << "deprecation_message in api_def_" << name << ".pbtxt instead. " + << "to indicate that the op is deprecated."; + } + } +} } // namespace class BaseApiTest : public ::testing::Test { @@ -171,7 +198,7 @@ TEST_F(BaseApiTest, AllOpsAreInApiDef) { if (excluded_ops->find(op.name()) != excluded_ops->end()) { continue; } - ASSERT_TRUE(api_defs_map_.find(op.name()) != api_defs_map_.end()) + EXPECT_TRUE(api_defs_map_.find(op.name()) != api_defs_map_.end()) << op.name() << " op does not have api_def_*.pbtxt file. " << "Please add api_def_" << op.name() << ".pbtxt file " << "under tensorflow/core/api_def/base_api/ directory."; @@ -236,6 +263,11 @@ TEST_F(BaseApiTest, AllApiDefAttributeNamesAreValid) { TestAllApiDefAttributeNamesAreValid(ops_, api_defs_map_); } +// Checks that deprecation is set correctly. +TEST_F(BaseApiTest, DeprecationSetCorrectly) { + TestDeprecatedAttributesSetCorrectly(api_defs_map_); +} + class PythonApiTest : public ::testing::Test { protected: PythonApiTest() { @@ -272,4 +304,9 @@ TEST_F(PythonApiTest, AllApiDefAttributeNamesAreValid) { TestAllApiDefAttributeNamesAreValid(ops_, api_defs_map_); } +// Checks that deprecation is set correctly. +TEST_F(PythonApiTest, DeprecationSetCorrectly) { + TestDeprecatedAttributesSetCorrectly(api_defs_map_); +} + } // namespace tensorflow diff --git a/tensorflow/core/api_def/base_api/api_def_BatchDatasetV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_BatchDatasetV2.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0c5b1eb45af6812bdd35e2fef43ac8c02a5b9388 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_BatchDatasetV2.pbtxt @@ -0,0 +1,18 @@ +op { + graph_op_name: "BatchDatasetV2" + visibility: HIDDEN + in_arg { + name: "batch_size" + description: < + - classname: tfo-landing-row-item-code-block + code_block: | + 
+        import tensorflow as tf
+        mnist = tf.keras.datasets.mnist
+
+        (x_train, y_train),(x_test, y_test) = mnist.load_data()
+        x_train, x_test = x_train / 255.0, x_test / 255.0
+
+        model = tf.keras.models.Sequential([
+          tf.keras.layers.Flatten(),
+          tf.keras.layers.Dense(512, activation=tf.nn.relu),
+          tf.keras.layers.Dropout(0.2),
+          tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+        ])
+        model.compile(optimizer='adam',
+                      loss='sparse_categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        model.fit(x_train, y_train, epochs=5)
+        model.evaluate(x_test, y_test)
+
+ {% dynamic if request.tld != 'cn' %} + Run in a Notebook + {% dynamic endif %} + + - items: + - custom_html: > +
+

Research and experimentation

+
+

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

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

ML at production scale

+
+

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

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

Google Colab: An easy way to learn and use TensorFlow

+

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

+ + - description: > +

Build your first ML app

+

Create and deploy TensorFlow models on web and mobile.

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

Web developers

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

Mobile developers

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

Videos and updates

+

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

+ items: + - description: > +

Get started with TensorFlow's High-Level APIs

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

Eager execution

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

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

+ youtube_id: uIcqeP7MFH0 + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=uIcqeP7MFH0 diff --git a/tensorflow/docs_src/get_started/basic_classification.md b/tensorflow/docs_src/get_started/basic_classification.md new file mode 100644 index 0000000000000000000000000000000000000000..91bbd85b2442522ef34eba236bf5bab2fc8654a7 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_classification.md @@ -0,0 +1,3 @@ +# Basic Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_regression.md b/tensorflow/docs_src/get_started/basic_regression.md new file mode 100644 index 0000000000000000000000000000000000000000..a535f22f5a41e7cb34cb8424b60d10d4ad43940e --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_regression.md @@ -0,0 +1,3 @@ +# Basic Regression + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_regression.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_text_classification.md b/tensorflow/docs_src/get_started/basic_text_classification.md new file mode 100644 index 0000000000000000000000000000000000000000..7c5d4f78968f94e4d5685a2dffe75ab649431e38 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_text_classification.md @@ -0,0 +1,3 @@ +# Basic Text Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_text_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index bbb25e20c62f6a2eec78668250a0e748494797c5..ddf239485a5546e0566d742f19c5d5b7025b157b 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ -# Get Started with Eager Execution +# Custom Training Walkthrough [Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 232d2f154703dc10320f9ee074c67d6e1a8ee850..bd2a80d9efee7f76111c6769b97d640af216c833 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -23,7 +23,7 @@ For more advanced users: * The @{$low_level_intro$Low Level Introduction} demonstrates how to use TensorFlow outside of the Estimator framework, for debugging and experimentation. - * The @{$programmers_guide$Programmer's Guide} details major + * The @{$guide$Programmer's Guide} details major TensorFlow components. * The @{$tutorials$Tutorials} provide walkthroughs of a variety of TensorFlow models. diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index e6cc8d565810683947e9cf9692e7cccb43916e66..99d2b2c3e1fa257fd03636eb44b2b65201ae311d 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -1,4 +1,10 @@ -index.md +### Learn and use ML +basic_classification.md: Basic classification +basic_text_classification.md: Text classification +basic_regression.md: Regression +overfit_and_underfit.md +save_and_restore_models.md +next_steps.md +### Research and experimentation eager.md -datasets_quickstart.md diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md new file mode 100644 index 0000000000000000000000000000000000000000..01c9f7204a7ddae16bcbd9eb5702516a39f8ce4c --- /dev/null +++ b/tensorflow/docs_src/get_started/next_steps.md @@ -0,0 +1,36 @@ +# Next steps + +## Learn more about TensorFlow + +* The [TensorFlow Guide](/guide) includes usage guides for the + high-level APIs, as well as advanced TensorFlow operations. +* [Premade Estimators](/guide/premade_estimators) are designed to + get results out of the box. Use TensorFlow without building your own models. +* [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and + deploy ML models in the browser and using Node.js. +* [TFLite](/mobile/tflite) allows mobile developers to do inference efficiently + on mobile devices. +* [TensorFlow Serving](/serving) is an open-source project that can put + TensorFlow models in production quickly. +* The [ecosystem](/ecosystem) contains more projects, including + [Magenta](https://magenta.tensorflow.org/), [TFX](/tfx), + [Swift for TensorFlow](https://github.com/tensorflow/swift), and more. + +## Learn more about machine learning + +Recommended resources include: + +* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/), + a course from Google that introduces machine learning concepts. +* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/), + notes from an intro course from Stanford. +* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/), + a course that teaches how convolutional networks work. +* [Machine Learning Recipes](https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal), + a video series that introduces basic machine learning concepts with few prerequisites. +* [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python), + a book by Francois Chollet about the Keras API, as well as an excellent hands on intro to Deep Learning. +* [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml), + a book by AurƩlien Geron's that is a clear getting-started guide to data science and deep learning. +* [Deep Learning](https://www.deeplearningbook.org/), a book by Ian Goodfellow et al. + that provides a technical dive into learning machine learning. diff --git a/tensorflow/docs_src/get_started/overfit_and_underfit.md b/tensorflow/docs_src/get_started/overfit_and_underfit.md new file mode 100644 index 0000000000000000000000000000000000000000..e5b5ae7b5a70f476c25cc7bb76572bf6433c289f --- /dev/null +++ b/tensorflow/docs_src/get_started/overfit_and_underfit.md @@ -0,0 +1,3 @@ +# Overfitting and Underfitting + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/overfit_and_underfit.ipynb) diff --git a/tensorflow/docs_src/get_started/save_and_restore_models.md b/tensorflow/docs_src/get_started/save_and_restore_models.md new file mode 100644 index 0000000000000000000000000000000000000000..44b377294562cf5a0c8139e88d0c7226506b32ba --- /dev/null +++ b/tensorflow/docs_src/get_started/save_and_restore_models.md @@ -0,0 +1,3 @@ +# Save and restore Models + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/save_and_restore_models.ipynb) diff --git a/tensorflow/docs_src/programmers_guide/checkpoints.md b/tensorflow/docs_src/guide/checkpoints.md similarity index 96% rename from tensorflow/docs_src/programmers_guide/checkpoints.md rename to tensorflow/docs_src/guide/checkpoints.md index 8dfd91e3c8368f4a649c5b5fa3947e97441ef390..dfb2626b8675ccc3db293498314fcc3e417bc1bd 100644 --- a/tensorflow/docs_src/programmers_guide/checkpoints.md +++ b/tensorflow/docs_src/guide/checkpoints.md @@ -8,9 +8,8 @@ Estimators. TensorFlow provides two model formats: * SavedModel, which is a format independent of the code that created the model. -This document focuses on checkpoints. For details on SavedModel, see the -@{$saved_model$Saving and Restoring} chapter of the -*TensorFlow Programmer's Guide*. +This document focuses on checkpoints. For details on `SavedModel`, see the +@{$saved_model$Saving and Restoring} guide. ## Sample code @@ -232,8 +231,7 @@ This separation will keep your checkpoints recoverable. Checkpoints provide an easy automatic mechanism for saving and restoring models created by Estimators. -See the @{$saved_model$Saving and Restoring} -chapter of the *TensorFlow Programmer's Guide* for details on: +See the @{$saved_model$Saving and Restoring} guide for details about: * Saving and restoring models using low-level TensorFlow APIs. * Exporting and importing models in the SavedModel format, which is a diff --git a/tensorflow/docs_src/programmers_guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/custom_estimators.md rename to tensorflow/docs_src/guide/custom_estimators.md index fb20b35c128b5bdafbb88ccb19df05f6a73c9977..a63e2bafb362c660d9203c609e46cdffb7955342 100644 --- a/tensorflow/docs_src/programmers_guide/custom_estimators.md +++ b/tensorflow/docs_src/guide/custom_estimators.md @@ -362,10 +362,10 @@ model's loss. This is the that will be optimized. We can calculate the loss by calling @{tf.losses.sparse_softmax_cross_entropy}. -The value returned by this function will be lowest, approximately 0, -probability of the correct class (at index `label`) is near 1.0. The loss value -returned is progressively larger as the probability of the correct class -decreases. +The value returned by this function will be approximately 0 at lowest, +when the probability of the correct class (at index `label`) is near 1.0. +The loss value returned is progressively larger as the probability of the +correct class decreases. This function returns the average over the whole batch. diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/guide/datasets.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/datasets.md rename to tensorflow/docs_src/guide/datasets.md diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/guide/datasets_for_estimators.md similarity index 97% rename from tensorflow/docs_src/get_started/datasets_quickstart.md rename to tensorflow/docs_src/guide/datasets_for_estimators.md index 020e40dd3b8f046f0144e3806468f58833f7b607..b04af78cd820f1b3506f62112f25dd8fdb73e76c 100644 --- a/tensorflow/docs_src/get_started/datasets_quickstart.md +++ b/tensorflow/docs_src/guide/datasets_for_estimators.md @@ -1,4 +1,4 @@ -# Datasets Quick Start +# Datasets for Estimators The @{tf.data} module contains a collection of classes that allows you to easily load data, manipulate it, and pipe it into your model. This document @@ -91,8 +91,8 @@ print(mnist_ds) ``` This will print the following line, showing the -@{$programmers_guide/tensors#shapes$shapes} and -@{$programmers_guide/tensors#data_types$types} of the items in +@{$guide/tensors#shapes$shapes} and +@{$guide/tensors#data_types$types} of the items in the dataset. Note that a `Dataset` does not know how many items it contains. ``` None @@ -128,7 +128,7 @@ print(dataset) Here we see that when a `Dataset` contains structured elements, the `shapes` and `types` of the `Dataset` take on the same structure. This dataset contains -dictionaries of @{$programmers_guide/tensors#rank$scalars}, all of type +dictionaries of @{$guide/tensors#rank$scalars}, all of type `tf.float64`. The first line of the iris `train_input_fn` uses the same functionality, but @@ -382,6 +382,6 @@ Estimator. Consider the following documents next: * The @{$low_level_intro#datasets$Low Level Introduction}, which demonstrates how to experiment directly with `tf.data.Datasets` using TensorFlow's low level APIs. -* @{$programmers_guide/datasets} which goes into great detail about additional +* @{$guide/datasets} which goes into great detail about additional functionality of `Datasets`. diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/guide/debugger.md similarity index 97% rename from tensorflow/docs_src/programmers_guide/debugger.md rename to tensorflow/docs_src/guide/debugger.md index 6bd941886d7fe883f2fc61a97dc1494e033ba8ac..dc4db58857c211f95bd7d5f2b3232e63f9877288 100644 --- a/tensorflow/docs_src/programmers_guide/debugger.md +++ b/tensorflow/docs_src/guide/debugger.md @@ -17,7 +17,7 @@ how to use the graphical user interface (GUI) of tfdbg, i.e., the Note: The TensorFlow debugger uses a [curses](https://en.wikipedia.org/wiki/Curses_\(programming_library\))-based text user interface. On Mac OS X, the `ncurses` library is required and can be -installed with `brew install homebrew/dupes/ncurses`. On Windows, curses isn't as +installed with `brew install ncurses`. On Windows, curses isn't as well supported, so a [readline](https://en.wikipedia.org/wiki/GNU_Readline)-based interface can be used with tfdbg by installing `pyreadline` with `pip`. If you use Anaconda3, you can install it with a command such as @@ -33,8 +33,9 @@ and [`inf`s](https://en.wikipedia.org/wiki/Infinity), a frequently-encountered type of bug in TensorFlow model development. The following example is for users who use the low-level [`Session`](https://www.tensorflow.org/api_docs/python/tf/Session) API of -TensorFlow. A later section of this document describes how to use **tfdbg** -with a higher-level API, namely `Estimator`s. +TensorFlow. Later sections of this document describe how to use **tfdbg** +with higher-level APIs of TensorFlow, including `tf.estimator`, +`tf.keras` / `keras` and `tf.contrib.slim`. To *observe* such an issue, run the following command without the debugger (the source code can be found [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/debug_mnist.py)): @@ -209,6 +210,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at | **`config`** | | **Set or show persistent TFDBG UI configuration.** | | | | `set` | Set the value of a config item: {`graph_recursion_depth`, `mouse_mode`}. | `config set graph_recursion_depth 3` | | | `show` | Show current persistent UI configuration. | `config show` | +| **`version`** | | **Print the version of TensorFlow and its key dependencies.** | `version` | | **`help`** | | **Print general help information** | `help` | | | `help ` | Print help for given command. | `help lt` | @@ -477,20 +479,31 @@ for more details. ## Debugging Keras Models with TFDBG -To use TFDBG with [Keras](https://keras.io/), let the Keras backend use -a TFDBG-wrapped Session object. For example, to use the CLI wrapper: +To use TFDBG with +[tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras), +let the Keras backend use a TFDBG-wrapped Session object. For example, to use +the CLI wrapper: ``` python import tensorflow as tf -from keras import backend as keras_backend from tensorflow.python import debug as tf_debug -keras_backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session())) +tf.keras.backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session())) # Define your keras model, called "model". -model.fit(...) # This will break into the TFDBG CLI. + +# Calls to `fit()`, 'evaluate()` and `predict()` methods will break into the +# TFDBG CLI. +model.fit(...) +model.evaluate(...) +model.predict(...) ``` +With minor modification, the preceding code example also works for the +[non-TensorFlow version of Keras](https://keras.io/) running against a +TensorFlow backend. You just need to replace `tf.keras.backend` with +`keras.backend`. + ## Debugging tf-slim with TFDBG TFDBG supports debugging of training and evaluation with diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/guide/eager.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/eager.md rename to tensorflow/docs_src/guide/eager.md diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/guide/embedding.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/embedding.md rename to tensorflow/docs_src/guide/embedding.md diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/guide/estimators.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/estimators.md rename to tensorflow/docs_src/guide/estimators.md index b13b47184d2b32fffb2390b0318fba8612d7826a..78b30c3040f646e4ae1bf97246666e8585e18057 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/guide/estimators.md @@ -81,7 +81,7 @@ of the following four steps: ... # manipulate dataset, extracting the feature dict and the label return feature_dict, label - (See @{$programmers_guide/datasets} for full details.) + (See @{$guide/datasets} for full details.) 2. **Define the feature columns.** Each @{tf.feature_column} identifies a feature name, its type, and any input pre-processing. diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/guide/faq.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/faq.md rename to tensorflow/docs_src/guide/faq.md diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/feature_columns.md rename to tensorflow/docs_src/guide/feature_columns.md index 90f5c53a17f23200f238f6b0d171e1e225330e27..1013ec910c1ebf9b781a9e84b6f5f33bcaa73690 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/guide/feature_columns.md @@ -534,7 +534,7 @@ embedding_column = tf.feature_column.embedding_column( dimension=embedding_dimensions) ``` -@{$programmers_guide/embedding$Embeddings} is a significant topic within machine +@{$guide/embedding$Embeddings} is a significant topic within machine learning. This information was just to get you started using them as feature columns. diff --git a/tensorflow/docs_src/programmers_guide/graph_viz.md b/tensorflow/docs_src/guide/graph_viz.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/graph_viz.md rename to tensorflow/docs_src/guide/graph_viz.md diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/guide/graphs.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/graphs.md rename to tensorflow/docs_src/guide/graphs.md index f0dd8def17fd6dfed241167a5ebb5be678152c16..e6246ef148d8a5ddea65be53f1bb32193d4845ad 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/guide/graphs.md @@ -93,7 +93,7 @@ to all API functions in the same context. For example: stored value. The @{tf.Variable} object also has methods such as @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that create @{tf.Operation} objects that, when executed, update the stored value. - (See @{$programmers_guide/variables} for more information about variables.) + (See @{$guide/variables} for more information about variables.) * Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the default graph that calculates gradients, and return a @{tf.Operation} that, diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/guide/index.md similarity index 71% rename from tensorflow/docs_src/programmers_guide/index.md rename to tensorflow/docs_src/guide/index.md index 0c2d4afb115c592c1925dde98b3a1a8c2a7ccad1..eefdb9ceae7a0d9ec45f476b0b3e82175830acc2 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/guide/index.md @@ -1,17 +1,17 @@ -# Programmer's Guide +# TensorFlow Guide The documents in this unit dive into the details of how TensorFlow works. The units are as follows: ## High Level APIs - * @{$programmers_guide/keras}, TensorFlow's high-level API for building and + * @{$guide/keras}, TensorFlow's high-level API for building and training deep learning models. - * @{$programmers_guide/eager}, an API for writing TensorFlow code + * @{$guide/eager}, an API for writing TensorFlow code imperatively, like you would use Numpy. - * @{$programmers_guide/estimators}, a high-level API that provides + * @{$guide/estimators}, a high-level API that provides fully-packaged models ready for large-scale training and production. - * @{$programmers_guide/datasets}, easy input pipelines to bring your data into + * @{$guide/datasets}, easy input pipelines to bring your data into your TensorFlow program. ## Estimators @@ -22,6 +22,7 @@ works. The units are as follows: design yourself. * @{$feature_columns}, which shows how an Estimator can handle a variety of input data types without changes to the model. +* @{$datasets_for_estimators} describes using tf.data with estimators. * @{$checkpoints}, which explains how to save training progress and resume where you left off. @@ -33,13 +34,13 @@ works. The units are as follows: ## Low Level APIs - * @{$programmers_guide/low_level_intro}, which introduces the + * @{$guide/low_level_intro}, which introduces the basics of how you can use TensorFlow outside of the high Level APIs. - * @{$programmers_guide/tensors}, which explains how to create, + * @{$guide/tensors}, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow. - * @{$programmers_guide/variables}, which details how + * @{$guide/variables}, which details how to represent shared, persistent state in your program. - * @{$programmers_guide/graphs}, which explains: + * @{$guide/graphs}, which explains: * dataflow graphs, which are TensorFlow's representation of computations as dependencies between operations. * sessions, which are TensorFlow's mechanism for running dataflow graphs @@ -49,19 +50,19 @@ works. The units are as follows: such as Estimators or Keras, the high-level API creates and manages graphs and sessions for you, but understanding graphs and sessions can still be helpful. - * @{$programmers_guide/saved_model}, which + * @{$guide/saved_model}, which explains how to save and restore variables and models. ## ML Concepts - * @{$programmers_guide/embedding}, which introduces the concept + * @{$guide/embedding}, which introduces the concept of embeddings, provides a simple example of training an embedding in TensorFlow, and explains how to view embeddings with the TensorBoard Embedding Projector. ## Debugging - * @{$programmers_guide/debugger}, which + * @{$guide/debugger}, which explains how to use the TensorFlow debugger (tfdbg). ## TensorBoard @@ -69,17 +70,17 @@ works. The units are as follows: TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard: - * @{$programmers_guide/summaries_and_tensorboard}, + * @{$guide/summaries_and_tensorboard}, which introduces TensorBoard. - * @{$programmers_guide/graph_viz}, which + * @{$guide/graph_viz}, which explains how to visualize the computational graph. - * @{$programmers_guide/tensorboard_histograms} which demonstrates the how to + * @{$guide/tensorboard_histograms} which demonstrates the how to use TensorBoard's histogram dashboard. ## Misc - * @{$programmers_guide/version_compat}, + * @{$guide/version_compat}, which explains backward compatibility guarantees and non-guarantees. - * @{$programmers_guide/faq}, which contains frequently asked + * @{$guide/faq}, which contains frequently asked questions about TensorFlow. diff --git a/tensorflow/docs_src/guide/keras.md b/tensorflow/docs_src/guide/keras.md new file mode 100644 index 0000000000000000000000000000000000000000..1d846df1044cd7100c083aa7d6b5be8f9cdd584e --- /dev/null +++ b/tensorflow/docs_src/guide/keras.md @@ -0,0 +1,623 @@ +# Keras + +Keras is a high-level API to build and train deep learning models. It's used for +fast prototyping, advanced research, and production, with three key advantages: + +- *User friendly*
+ Keras has a simple, consistent interface optimized for common use cases. It + provides clear and actionable feedback for user errors. +- *Modular and composable*
+ Keras models are made by connecting configurable building blocks together, + with few restrictions. +- *Easy to extend*
Write custom building blocks to express new ideas for + research. Create new layers, loss functions, and develop state-of-the-art + models. + +## Import tf.keras + +`tf.keras` is TensorFlow's implementation of the +[Keras API specification](https://keras.io){:.external}. This is a high-level +API to build and train models that includes first-class support for +TensorFlow-specific functionality, such as [eager execution](#eager_execution), +`tf.data` pipelines, and [Estimators](./estimators.md). +`tf.keras` makes TensorFlow easier to use without sacrificing flexibility and +performance. + +To get started, import `tf.keras` as part of your TensorFlow program setup: + +```python +import tensorflow as tf +from tensorflow import keras +``` + +`tf.keras` can run any Keras-compatible code, but keep in mind: + +* The `tf.keras` version in the latest TensorFlow release might not be the same + as the latest `keras` version from PyPI. Check `tf.keras.__version__`. +* When [saving a model's weights](#weights_only), `tf.keras` defaults to the + [checkpoint format](./checkpoints.md). Pass `save_format='h5'` to + use HDF5. + +## Build a simple model + +### Sequential model + +In Keras, you assemble *layers* to build *models*. A model is (usually) a graph +of layers. The most common type of model is a stack of layers: the +`tf.keras.Sequential` model. + +To build a simple, fully-connected network (i.e. multi-layer perceptron): + +```python +model = keras.Sequential() +# Adds a densely-connected layer with 64 units to the model: +model.add(keras.layers.Dense(64, activation='relu')) +# Add another: +model.add(keras.layers.Dense(64, activation='relu')) +# Add a softmax layer with 10 output units: +model.add(keras.layers.Dense(10, activation='softmax')) +``` + +### Configure the layers + +There are many `tf.keras.layers` available with some common constructor +parameters: + +* `activation`: Set the activation function for the layer. This parameter is + specified by the name of a built-in function or as a callable object. By + default, no activation is applied. +* `kernel_initializer` and `bias_initializer`: The initialization schemes + that create the layer's weights (kernel and bias). This parameter is a name or + a callable object. This defaults to the `"Glorot uniform"` initializer. +* `kernel_regularizer` and `bias_regularizer`: The regularization schemes + that apply the layer's weights (kernel and bias), such as L1 or L2 + regularization. By default, no regularization is applied. + +The following instantiates `tf.keras.layers.Dense` layers using constructor +arguments: + +```python +# Create a sigmoid layer: +layers.Dense(64, activation='sigmoid') +# Or: +layers.Dense(64, activation=tf.sigmoid) + +# A linear layer with L1 regularization of factor 0.01 applied to the kernel matrix: +layers.Dense(64, kernel_regularizer=keras.regularizers.l1(0.01)) +# A linear layer with L2 regularization of factor 0.01 applied to the bias vector: +layers.Dense(64, bias_regularizer=keras.regularizers.l2(0.01)) + +# A linear layer with a kernel initialized to a random orthogonal matrix: +layers.Dense(64, kernel_initializer='orthogonal') +# A linear layer with a bias vector initialized to 2.0s: +layers.Dense(64, bias_initializer=keras.initializers.constant(2.0)) +``` + +## Train and evaluate + +### Set up training + +After the model is constructed, configure its learning process by calling the +`compile` method: + +```python +model.compile(optimizer=tf.train.AdamOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) +``` + +`tf.keras.Model.compile` takes three important arguments: + +* `optimizer`: This object specifies the training procedure. Pass it optimizer + instances from the `tf.train` module, such as + [`AdamOptimizer`](/api_docs/python/tf/train/AdamOptimizer), + [`RMSPropOptimizer`](/api_docs/python/tf/train/RMSPropOptimizer), or + [`GradientDescentOptimizer`](/api_docs/python/tf/train/GradientDescentOptimizer). +* `loss`: The function to minimize during optimization. Common choices include + mean square error (`mse`), `categorical_crossentropy`, and + `binary_crossentropy`. Loss functions are specified by name or by + passing a callable object from the `tf.keras.losses` module. +* `metrics`: Used to monitor training. These are string names or callables from + the `tf.keras.metrics` module. + +The following shows a few examples of configuring a model for training: + +```python +# Configure a model for mean-squared error regression. +model.compile(optimizer=tf.train.AdamOptimizer(0.01), + loss='mse', # mean squared error + metrics=['mae']) # mean absolute error + +# Configure a model for categorical classification. +model.compile(optimizer=tf.train.RMSPropOptimizer(0.01), + loss=keras.losses.categorical_crossentropy, + metrics=[keras.metrics.categorical_accuracy]) +``` + +### Input NumPy data + +For small datasets, use in-memory [NumPy](https://www.numpy.org/){:.external} +arrays to train and evaluate a model. The model is "fit" to the training data +using the `fit` method: + +```python +import numpy as np + +data = np.random.random((1000, 32)) +labels = np.random.random((1000, 10)) + +model.fit(data, labels, epochs=10, batch_size=32) +``` + +`tf.keras.Model.fit` takes three important arguments: + +* `epochs`: Training is structured into *epochs*. An epoch is one iteration over + the entire input data (this is done in smaller batches). +* `batch_size`: When passed NumPy data, the model slices the data into smaller + batches and iterates over these batches during training. This integer + specifies the size of each batch. Be aware that the last batch may be smaller + if the total number of samples is not divisible by the batch size. +* `validation_data`: When prototyping a model, you want to easily monitor its + performance on some validation data. Passing this argumentā€”a tuple of inputs + and labelsā€”allows the model to display the loss and metrics in inference mode + for the passed data, at the end of each epoch. + +Here's an example using `validation_data`: + +```python +import numpy as np + +data = np.random.random((1000, 32)) +labels = np.random.random((1000, 10)) + +val_data = np.random.random((100, 32)) +val_labels = np.random.random((100, 10)) + +model.fit(data, labels, epochs=10, batch_size=32, + validation_data=(val_data, val_labels)) +``` + +### Input tf.data datasets + +Use the [Datasets API](./datasets.md) to scale to large datasets +or multi-device training. Pass a `tf.data.Dataset` instance to the `fit` +method: + +```python +# Instantiates a toy dataset instance: +dataset = tf.data.Dataset.from_tensor_slices((data, labels)) +dataset = dataset.batch(32) +dataset = dataset.repeat() + +# Don't forget to specify `steps_per_epoch` when calling `fit` on a dataset. +model.fit(dataset, epochs=10, steps_per_epoch=30) +``` + +Here, the `fit` method uses the `steps_per_epoch` argumentā€”this is the number of +training steps the model runs before it moves to the next epoch. Since the +`Dataset` yields batches of data, this snippet does not require a `batch_size`. + +Datasets can also be used for validation: + +```python +dataset = tf.data.Dataset.from_tensor_slices((data, labels)) +dataset = dataset.batch(32).repeat() + +val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_labels)) +val_dataset = val_dataset.batch(32).repeat() + +model.fit(dataset, epochs=10, steps_per_epoch=30, + validation_data=val_dataset, + validation_steps=3) +``` + +### Evaluate and predict + +The `tf.keras.Model.evaluate` and `tf.keras.Model.predict` methods can use NumPy +data and a `tf.data.Dataset`. + +To *evaluate* the inference-mode loss and metrics for the data provided: + +```python +model.evaluate(x, y, batch_size=32) + +model.evaluate(dataset, steps=30) +``` + +And to *predict* the output of the last layer in inference for the data provided, +as a NumPy array: + +``` +model.predict(x, batch_size=32) + +model.predict(dataset, steps=30) +``` + + +## Build advanced models + +### Functional API + +The `tf.keras.Sequential` model is a simple stack of layers that cannot +represent arbitrary models. Use the +[Keras functional API](https://keras.io/getting-started/functional-api-guide/){:.external} +to build complex model topologies such as: + +* Multi-input models, +* Multi-output models, +* Models with shared layers (the same layer called several times), +* Models with non-sequential data flows (e.g. residual connections). + +Building a model with the functional API works like this: + +1. A layer instance is callable and returns a tensor. +2. Input tensors and output tensors are used to define a `tf.keras.Model` + instance. +3. This model is trained just like the `Sequential` model. + +The following example uses the functional API to build a simple, fully-connected +network: + +```python +inputs = keras.Input(shape=(32,)) # Returns a placeholder tensor + +# A layer instance is callable on a tensor, and returns a tensor. +x = keras.layers.Dense(64, activation='relu')(inputs) +x = keras.layers.Dense(64, activation='relu')(x) +predictions = keras.layers.Dense(10, activation='softmax')(x) + +# Instantiate the model given inputs and outputs. +model = keras.Model(inputs=inputs, outputs=predictions) + +# The compile step specifies the training configuration. +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +# Trains for 5 epochs +model.fit(data, labels, batch_size=32, epochs=5) +``` + +### Model subclassing + +Build a fully-customizable model by subclassing `tf.keras.Model` and defining +your own forward pass. Create layers in the `__init__` method and set them as +attributes of the class instance. Define the forward pass in the `call` method. + +Model subclassing is particularly useful when +[eager execution](./eager.md) is enabled since the forward pass +can be written imperatively. + +Key Point: Use the right API for the job. While model subclassing offers +flexibility, it comes at a cost of greater complexity and more opportunities for +user errors. If possible, prefer the functional API. + +The following example shows a subclassed `tf.keras.Model` using a custom forward +pass: + +```python +class MyModel(keras.Model): + + def __init__(self, num_classes=10): + super(MyModel, self).__init__(name='my_model') + self.num_classes = num_classes + # Define your layers here. + self.dense_1 = keras.layers.Dense(32, activation='relu') + self.dense_2 = keras.layers.Dense(num_classes, activation='sigmoid') + + def call(self, inputs): + # Define your forward pass here, + # using layers you previously defined (in `__init__`). + x = self.dense_1(inputs) + return self.dense_2(x) + + def compute_output_shape(self, input_shape): + # You need to override this function if you want to use the subclassed model + # as part of a functional-style model. + # Otherwise, this method is optional. + shape = tf.TensorShape(input_shape).as_list() + shape[-1] = self.num_classes + return tf.TensorShape(shape) + + +# Instantiates the subclassed model. +model = MyModel(num_classes=10) + +# The compile step specifies the training configuration. +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +# Trains for 5 epochs. +model.fit(data, labels, batch_size=32, epochs=5) +``` + + +### Custom layers + +Create a custom layer by subclassing `tf.keras.layers.Layer` and implementing +the following methods: + +* `build`: Create the weights of the layer. Add weights with the `add_weight` + method. +* `call`: Define the forward pass. +* `compute_output_shape`: Specify how to compute the output shape of the layer + given the input shape. +* Optionally, a layer can be serialized by implementing the `get_config` method + and the `from_config` class method. + +Here's an example of a custom layer that implements a `matmul` of an input with +a kernel matrix: + +```python +class MyLayer(keras.layers.Layer): + + def __init__(self, output_dim, **kwargs): + self.output_dim = output_dim + super(MyLayer, self).__init__(**kwargs) + + def build(self, input_shape): + shape = tf.TensorShape((input_shape[1], self.output_dim)) + # Create a trainable weight variable for this layer. + self.kernel = self.add_weight(name='kernel', + shape=shape, + initializer='uniform', + trainable=True) + # Be sure to call this at the end + super(MyLayer, self).build(input_shape) + + def call(self, inputs): + return tf.matmul(inputs, self.kernel) + + def compute_output_shape(self, input_shape): + shape = tf.TensorShape(input_shape).as_list() + shape[-1] = self.output_dim + return tf.TensorShape(shape) + + def get_config(self): + base_config = super(MyLayer, self).get_config() + base_config['output_dim'] = self.output_dim + + @classmethod + def from_config(cls, config): + return cls(**config) + + +# Create a model using the custom layer +model = keras.Sequential([MyLayer(10), + keras.layers.Activation('softmax')]) + +# The compile step specifies the training configuration +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +# Trains for 5 epochs. +model.fit(data, targets, batch_size=32, epochs=5) +``` + + +## Callbacks + +A callback is an object passed to a model to customize and extend its behavior +during training. You can write your own custom callback, or use the built-in +`tf.keras.callbacks` that include: + +* `tf.keras.callbacks.ModelCheckpoint`: Save checkpoints of your model at + regular intervals. +* `tf.keras.callbacks.LearningRateScheduler`: Dynamically change the learning + rate. +* `tf.keras.callbacks.EarlyStopping`: Interrupt training when validation + performance has stopped improving. +* `tf.keras.callbacks.TensorBoard`: Monitor the model's behavior using + [TensorBoard](./summaries_and_tensorboard.md). + +To use a `tf.keras.callbacks.Callback`, pass it to the model's `fit` method: + +```python +callbacks = [ + # Interrupt training if `val_loss` stops improving for over 2 epochs + keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'), + # Write TensorBoard logs to `./logs` directory + keras.callbacks.TensorBoard(log_dir='./logs') +] +model.fit(data, labels, batch_size=32, epochs=5, callbacks=callbacks, + validation_data=(val_data, val_targets)) +``` + + +## Save and restore + +### Weights only + +Save and load the weights of a model using `tf.keras.Model.save_weights`: + +```python +# Save weights to a TensorFlow Checkpoint file +model.save_weights('./my_model') + +# Restore the model's state, +# this requires a model with the same architecture. +model.load_weights('my_model') +``` + +By default, this saves the model's weights in the +[TensorFlow checkpoint](./checkpoints.md) file format. Weights can +also be saved to the Keras HDF5 format (the default for the multi-backend +implementation of Keras): + +```python +# Save weights to a HDF5 file +model.save_weights('my_model.h5', save_format='h5') + +# Restore the model's state +model.load_weights('my_model.h5') +``` + + +### Configuration only + +A model's configuration can be savedā€”this serializes the model architecture +without any weights. A saved configuration can recreate and initialize the same +model, even without the code that defined the original model. Keras supports +JSON and YAML serialization formats: + +```python +# Serialize a model to JSON format +json_string = model.to_json() + +# Recreate the model (freshly initialized) +fresh_model = keras.models.from_json(json_string) + +# Serializes a model to YAML format +yaml_string = model.to_yaml() + +# Recreate the model +fresh_model = keras.models.from_yaml(yaml_string) +``` + +Caution: Subclassed models are not serializable because their architecture is +defined by the Python code in the body of the `call` method. + + +### Entire model + +The entire model can be saved to a file that contains the weight values, the +model's configuration, and even the optimizer's configuration. This allows you +to checkpoint a model and resume training laterā€”from the exact same +stateā€”without access to the original code. + +```python +# Create a trivial model +model = keras.Sequential([ + keras.layers.Dense(10, activation='softmax', input_shape=(32,)), + keras.layers.Dense(10, activation='softmax') +]) +model.compile(optimizer='rmsprop', + loss='categorical_crossentropy', + metrics=['accuracy']) +model.fit(data, targets, batch_size=32, epochs=5) + + +# Save entire model to a HDF5 file +model.save('my_model.h5') + +# Recreate the exact same model, including weights and optimizer. +model = keras.models.load_model('my_model.h5') +``` + + +## Eager execution + +[Eager execution](./eager.md) is an imperative programming +environment that evaluates operations immediately. This is not required for +Keras, but is supported by `tf.keras` and useful for inspecting your program and +debugging. + +All of the `tf.keras` model-building APIs are compatible with eager execution. +And while the `Sequential` and functional APIs can be used, eager execution +especially benefits *model subclassing* and building *custom layers*ā€”the APIs +that require you to write the forward pass as code (instead of the APIs that +create models by assembling existing layers). + +See the [eager execution guide](./eager.md#build_a_model) for +examples of using Keras models with custom training loops and `tf.GradientTape`. + + +## Distribution + +### Estimators + +The [Estimators](./estimators.md) API is used for training models +for distributed environments. This targets industry use cases such as +distributed training on large datasets that can export a model for production. + +A `tf.keras.Model` can be trained with the `tf.estimator` API by converting the +model to an `tf.estimator.Estimator` object with +`tf.keras.estimator.model_to_estimator`. See +[Creating Estimators from Keras models](./estimators.md#creating_estimators_from_keras_models). + +```python +model = keras.Sequential([layers.Dense(10,activation='softmax'), + layers.Dense(10,activation='softmax')]) + +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +estimator = keras.estimator.model_to_estimator(model) +``` + +Note: Enable [eager execution](./eager.md) for debugging +[Estimator input functions](./premade_estimators.md#create_input_functions) +and inspecting data. + +### Multiple GPUs + +`tf.keras` models can run on multiple GPUs using +`tf.contrib.distribute.DistributionStrategy`. This API provides distributed +training on multiple GPUs with almost no changes to existing code. + +Currently, `tf.contrib.distribute.MirroredStrategy` is the only supported +distribution strategy. `MirroredStrategy` does in-graph replication with +synchronous training using all-reduce on a single machine. To use +`DistributionStrategy` with Keras, convert the `tf.keras.Model` to a +`tf.estimator.Estimator` with `tf.keras.estimator.model_to_estimator`, then +train the estimator + +The following example distributes a `tf.keras.Model` across multiple GPUs on a +single machine. + +First, define a simple model: + +```python +model = keras.Sequential() +model.add(keras.layers.Dense(16, activation='relu', input_shape=(10,))) +model.add(keras.layers.Dense(1, activation='sigmoid')) + +optimizer = tf.train.GradientDescentOptimizer(0.2) + +model.compile(loss='binary_crossentropy', optimizer=optimizer) +model.summary() +``` + +Define an *input pipeline*. The `input_fn` returns a `tf.data.Dataset` object +used to distribute the data across multiple devicesā€”with each device processing +a slice of the input batch. + +```python +def input_fn(): + x = np.random.random((1024, 10)) + y = np.random.randint(2, size=(1024, 1)) + x = tf.cast(x, tf.float32) + dataset = tf.data.Dataset.from_tensor_slices((x, y)) + dataset = dataset.repeat(10) + dataset = dataset.batch(32) + return dataset +``` + +Next, create a `tf.estimator.RunConfig` and set the `train_distribute` argument +to the `tf.contrib.distribute.MirroredStrategy` instance. When creating +`MirroredStrategy`, you can specify a list of devices or set the `num_gpus` +argument. The default uses all available GPUs, like the following: + +```python +strategy = tf.contrib.distribute.MirroredStrategy() +config = tf.estimator.RunConfig(train_distribute=strategy) +``` + +Convert the Keras model to a `tf.estimator.Estimator` instance: + +```python +keras_estimator = keras.estimator.model_to_estimator( + keras_model=model, + config=config, + model_dir='/tmp/model_dir') +``` + +Finally, train the `Estimator` instance by providing the `input_fn` and `steps` +arguments: + +```python +keras_estimator.train(input_fn=input_fn, steps=10) +``` diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/guide/leftnav_files similarity index 95% rename from tensorflow/docs_src/programmers_guide/leftnav_files rename to tensorflow/docs_src/guide/leftnav_files index 3bcf864e13db0cef40cec74ab872c807c2ec2fb0..357a2a1cb929e05be03fe19bd9dded8050149998 100644 --- a/tensorflow/docs_src/programmers_guide/leftnav_files +++ b/tensorflow/docs_src/guide/leftnav_files @@ -10,6 +10,7 @@ estimators.md: Introduction to Estimators premade_estimators.md custom_estimators.md feature_columns.md +datasets_for_estimators.md checkpoints.md ### Accelerators diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/low_level_intro.md rename to tensorflow/docs_src/guide/low_level_intro.md index 478e2bb70bc7f58156398c9f9fef4e76ba581e1a..665a5568b49a4cf3ee47d60617116f73e0db364f 100644 --- a/tensorflow/docs_src/programmers_guide/low_level_intro.md +++ b/tensorflow/docs_src/guide/low_level_intro.md @@ -303,7 +303,7 @@ while True: break ``` -For more details on Datasets and Iterators see: @{$programmers_guide/datasets}. +For more details on Datasets and Iterators see: @{$guide/datasets}. ## Layers diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/premade_estimators.md rename to tensorflow/docs_src/guide/premade_estimators.md index f6dd75eacab1c99215ab918a0854b0a33d0d9cca..3e910c1fe2ebfdffc25044f15b3558407d407ef1 100644 --- a/tensorflow/docs_src/programmers_guide/premade_estimators.md +++ b/tensorflow/docs_src/guide/premade_estimators.md @@ -78,10 +78,10 @@ provides a programming stack consisting of multiple API layers: We strongly recommend writing TensorFlow programs with the following APIs: -* @{$programmers_guide/estimators$Estimators}, which represent a complete model. +* @{$guide/estimators$Estimators}, which represent a complete model. The Estimator API provides methods to train the model, to judge the model's accuracy, and to generate predictions. -* @{$get_started/datasets_quickstart$Datasets}, which build a data input +* @{$guide/datasets_for_estimators}, which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed it into your model. The Dataset API meshes well with the Estimators API. @@ -173,7 +173,7 @@ example is an Iris Versicolor. An Estimator is TensorFlow's high-level representation of a complete model. It handles the details of initialization, logging, saving and restoring, and many other features so you can concentrate on your model. For more details see -@{$programmers_guide/estimators}. +@{$guide/estimators}. An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow provides a collection of @@ -424,9 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the following material: * @{$checkpoints$Checkpoints} to learn how to save and restore models. -* @{$get_started/datasets_quickstart$Datasets} to learn more about importing - data into your - model. +* @{$guide/datasets_for_estimators} to learn more about importing + data into your model. * @{$custom_estimators$Creating Custom Estimators} to learn how to write your own Estimator, customized for a particular problem. - diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/saved_model.md rename to tensorflow/docs_src/guide/saved_model.md index c6ef87c54a3bc37dbfc0553232a8e3d30f8ee2f6..acc3d3ca0b74f4898523e4af0452f65463d8b94f 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -3,7 +3,7 @@ The @{tf.train.Saver} class provides methods to save and restore models. The @{tf.saved_model.simple_save} function is an easy way to build a @{tf.saved_model$saved model} suitable for serving. -[Estimators](@{$programmers_guide/estimators}) automatically save and restore +[Estimators](@{$guide/estimators}) automatically save and restore variables in the `model_dir`. ## Save and restore variables @@ -299,7 +299,7 @@ following: added attributes with defaults don't cause older model consumers to fail loading models regenerated with newer training binaries. -See [compatibility guidance](https://www.tensorflow.org/programmers_guide/version_compat) +See [compatibility guidance](./version_compat.md) for more information. ### Loading a SavedModel in Python @@ -794,11 +794,12 @@ Here's the syntax: ``` usage: saved_model_cli run [-h] --dir DIR --tag_set TAG_SET --signature_def SIGNATURE_DEF_KEY [--inputs INPUTS] - [--input_exprs INPUT_EXPRS] [--outdir OUTDIR] + [--input_exprs INPUT_EXPRS] + [--input_examples INPUT_EXAMPLES] [--outdir OUTDIR] [--overwrite] [--tf_debug] ``` -The `run` command provides the following two ways to pass inputs to the model: +The `run` command provides the following three ways to pass inputs to the model: * `--inputs` option enables you to pass numpy ndarray in files. * `--input_exprs` option enables you to pass Python expressions. @@ -847,7 +848,7 @@ dictionary is stored in the pickle file and the value corresponding to the *variable_name* will be used. -#### `--inputs_exprs` +#### `--input_exprs` To pass inputs through Python expressions, specify the `--input_exprs` option. This can be useful for when you don't have data @@ -869,7 +870,7 @@ example: (Note that the `numpy` module is already available to you as `np`.) -#### `--inputs_examples` +#### `--input_examples` To pass `tf.train.Example` as inputs, specify the `--input_examples` option. For each input key, it takes a list of dictionary, where each dictionary is an diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/guide/summaries_and_tensorboard.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md rename to tensorflow/docs_src/guide/summaries_and_tensorboard.md diff --git a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md b/tensorflow/docs_src/guide/tensorboard_histograms.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/tensorboard_histograms.md rename to tensorflow/docs_src/guide/tensorboard_histograms.md diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/guide/tensors.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/tensors.md rename to tensorflow/docs_src/guide/tensors.md index 1248c3cabe23c8d5f200fc1bf46e60851ba532a6..7227260f1a4ee08309f42d21bab8eaa3c77e3297 100644 --- a/tensorflow/docs_src/programmers_guide/tensors.md +++ b/tensorflow/docs_src/guide/tensors.md @@ -26,7 +26,7 @@ some cases it's only possible to find the shape of a tensor at graph execution time. Some types of tensors are special, and these will be covered in other -units of the Programmer's guide. The main ones are: +units of the TensorFlow guide. The main ones are: * `tf.Variable` * `tf.constant` @@ -230,7 +230,7 @@ yet_another = tf.reshape(matrixAlt, [13, 2, -1]) # ERROR! ## Data types In addition to dimensionality, Tensors have a data type. Refer to the -`tf.DataType` page in the programmer's guide for a full list of the data types. +`tf.DType` page for a complete list of the data types. It is not possible to have a `tf.Tensor` with more than one data type. It is possible, however, to serialize arbitrary data structures as `string`s and store diff --git a/tensorflow/docs_src/programmers_guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/using_gpu.md rename to tensorflow/docs_src/guide/using_gpu.md diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/using_tpu.md rename to tensorflow/docs_src/guide/using_tpu.md index 44aabf05571bb7f325a5d642f06362e0088607d2..41d80d9d60694c87675f07d8045713d9a117c7f1 100644 --- a/tensorflow/docs_src/programmers_guide/using_tpu.md +++ b/tensorflow/docs_src/guide/using_tpu.md @@ -171,7 +171,7 @@ This section details the changes you must make to the model function During regular usage TensorFlow attempts to determine the shapes of each `tf.Tensor` during graph construction. During execution any unknown shape dimensions are determined dynamically, -see @{$programmers_guide/tensors#shape$Tensor Shapes} for more details. +see @{$guide/tensors#shape$Tensor Shapes} for more details. To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}. XLA uses a similar system for determining shapes at compile time. XLA requires @@ -195,7 +195,7 @@ TPU. Build your evaluation metrics dictionary in a stand-alone `metric_fn`. - + Evaluation metrics are an essential part of training a model. These are fully supported on Cloud TPUs, but with a slightly different syntax. diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/guide/variables.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/variables.md rename to tensorflow/docs_src/guide/variables.md diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/guide/version_compat.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/version_compat.md rename to tensorflow/docs_src/guide/version_compat.md diff --git a/tensorflow/docs_src/install/index.md b/tensorflow/docs_src/install/index.md index 4f85383925bbb8a03372b020e448a0e604f3b999..c2e5a991d459f52be7c08a6da3bcad0c57e58934 100644 --- a/tensorflow/docs_src/install/index.md +++ b/tensorflow/docs_src/install/index.md @@ -6,6 +6,7 @@ operating systems: * macOS 10.12.6 (Sierra) or later. * Ubuntu 16.04 or later * Windows 7 or later. + * Raspbian 9.0 or later. Although you might be able to install TensorFlow on other laptop or desktop systems, we only support (and only fix issues in) the preceding configurations. @@ -16,6 +17,7 @@ that enables you to write applications in Python: * @{$install_linux$Installing TensorFlow on Ubuntu} * @{$install_mac$Installing TensorFlow on macOS} * @{$install_windows$Installing TensorFlow on Windows} + * @{$install_raspbian$Installing TensorFlow on a Raspberry Pi} * @{$install_sources$Installing TensorFlow from Sources} Many aspects of the Python TensorFlow API changed from version 0.n to 1.0. diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 55bc0f64e799ecb7115cc656c8a08ec1ce2a6108..2c126df5aa6263127fcdd7a9b01efcbaf3c15c46 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -6,7 +6,7 @@ a Go application. This guide explains how to install and set up the [TensorFlow Go package](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go). Warning: The TensorFlow Go API is *not* covered by the TensorFlow -[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +[API stability guarantees](../guide/version_semantics.md). ## Supported Platforms diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 637231da1252097a9a143edea23f3248c7cf2eb6..692dfc9cefe89b9d39a18a55284f8b521d620100 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -7,7 +7,7 @@ Java application. This guide explains how to install and use it in a Java application. Warning: The TensorFlow Java API is *not* covered by the TensorFlow -[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +[API stability guarantees](../guide/version_semantics.md). ## Supported Platforms diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index c8d706cf3c232d1ea91265bd7ad38d5227c440f0..c573acaf458a5c0bb52b7c3b314bd52ae60c4577 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -489,13 +489,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the following: - -* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) -* @{$get_started/eager} - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## TensorFlow GPU support diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 9d01271c5a0beebf75be9e32682583ddc4a666b1..584f1e2e35caff32a4f8aea5ab5fe94114470219 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -403,11 +403,7 @@ writing TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md new file mode 100644 index 0000000000000000000000000000000000000000..0caab6d335544bfc291894a79f9ed0441eb03561 --- /dev/null +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -0,0 +1,313 @@ +# Installing TensorFlow on Raspbian + +This guide explains how to install TensorFlow on a Raspberry Pi running +Raspbian. Although these instructions might also work on other Pi variants, we +have only tested (and we only support) these instructions on machines meeting +the following requirements: + +* Raspberry Pi devices running Raspbian 9.0 or higher + +## Determine how to install TensorFlow + +You must pick the mechanism by which you install TensorFlow. The supported +choices are as follows: + +* "Native" pip. +* Cross-compiling from sources. + +**We recommend pip installation.** + +## Installing with native pip + +We have uploaded the TensorFlow binaries to piwheels.org. Therefore, you can +install TensorFlow through pip. + +The [REQUIRED_PACKAGES section of +setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) +lists the packages that pip will install or upgrade. + +### Prerequisite: Python + +In order to install TensorFlow, your system must contain one of the following +Python versions: + +* Python 2.7 +* Python 3.4+ + +If your system does not already have one of the preceding Python versions, +[install](https://wiki.python.org/moin/BeginnersGuide/Download) it now. It +should already be included when Raspbian was installed though, so no extra steps +should be needed. + +### Prerequisite: pip + +[Pip](https://en.wikipedia.org/wiki/Pip_\(package_manager\)) installs and +manages software packages written in Python. If you intend to install with +native pip, then one of the following flavors of pip must be installed on your +system: + +* `pip3`, for Python 3.n (preferred). +* `pip`, for Python 2.7. + +`pip` or `pip3` was probably installed on your system when you installed Python. +To determine whether pip or pip3 is actually installed on your system, issue one +of the following commands: + +
$ pip3 -V # for Python 3.n
+$ pip -V  # for Python 2.7
+ +If it gives the error "Command not found", then the package has not been +installed yet. To install if for the first time, run: + +
$ sudo apt-get install python3-pip # for Python 3.n
+sudo apt-get install python-pip # for Python 2.7
+ +You can find more help on installing and upgrading pip in +[the Raspberry Pi documentation](https://www.raspberrypi.org/documentation/linux/software/python.md). + +### Prerequisite: Atlas + +[Atlas](http://math-atlas.sourceforge.net/) is a linear algebra library that +numpy depends on, and so needs to be installed before TensorFlow. To add it to +your system, run the following command: + +
$ sudo apt install libatlas-base-dev
+ +### Install TensorFlow + +Assuming the prerequisite software is installed on your Pi, install TensorFlow +by invoking **one** of the following commands: + + 
 $ pip3 install tensorflow     # Python 3.n
+     $ pip install tensorflow      # Python 2.7
+ +This can take some time on certain platforms like the Pi Zero, where some Python +packages like scipy that TensorFlow depends on need to be compiled before the +installation can complete. The Python 3 version will typically be faster to +install because piwheels.org has pre-built versions of the dependencies +available, so this is our recommended option. + +### Next Steps + +After installing TensorFlow, [validate your +installation](#ValidateYourInstallation) to confirm that the installation worked +properly. + +### Uninstalling TensorFlow + +To uninstall TensorFlow, issue one of following commands: + +
$ pip uninstall tensorflow
+$ pip3 uninstall tensorflow
+ +## Cross-compiling from sources + +Cross-compilation means building on a different machine than than you'll be +deploying on. Since Raspberry Pi's only have limited RAM and comparatively slow +processors, and TensorFlow has a large amount of source code to compile, it's +easier to use a MacOS or Linux desktop or laptop to handle the build process. +Because it can take over 24 hours to build on a Pi, and requires external swap +space to cope with the memory shortage, we recommend using cross-compilation if +you do need to compile TensorFlow from source. To make the dependency management +process easier, we also recommend using Docker to help simplify building. + +Note that we provide well-tested, pre-built TensorFlow binaries for Raspbian +systems. So, don't build a TensorFlow binary yourself unless you are very +comfortable building complex packages from source and dealing with the +inevitable aftermath should things not go exactly as documented + +### Prerequisite: Docker + +Install Docker on your machine as described in the [Docker +documentation](https://docs.docker.com/engine/installation/#/on-macos-and-windows). + +### Clone the TensorFlow repository + +Start the process of building TensorFlow by cloning a TensorFlow repository. + +To clone **the latest** TensorFlow repository, issue the following command: + +
$ git clone https://github.com/tensorflow/tensorflow
+ +The preceding git clone command creates a subdirectory named +`tensorflow`. After cloning, you may optionally build a **specific branch** +(such as a release branch) by invoking the following commands: + +
+$ cd tensorflow
+$ git checkout Branch # where Branch is the desired branch
+
+ +For example, to work with the `r1.0` release instead of the master release, +issue the following command: + +
$ git checkout r1.0
+ +### Build from source + +To compile TensorFlow and produce a binary pip can install, do the following: + +1. Start a terminal. +2. Navigate to the directory containing the tensorflow source code. +3. Run a command to cross-compile the library, for example: + +
$ CI_DOCKER_EXTRA_PARAMS="-e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.4" \
+tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 tensorflow/tools/ci_build/pi/build_raspberry_pi.sh
+
+ +This will build a pip .whl file for Python 3.4, with Arm v7 instructions that +will only work on the Pi models 2 or 3. These NEON instructions are required for +the fastest operation on those devices, but you can build a library that will +run across all Pi devices by passing `PI_ONE` at the end of the command line. +You can also target Python 2.7 by omitting the initial docker parameters. Here's +an example of building for Python 2.7 and Raspberry Pi model Zero or One +devices: + +
$ tensorflow/tools/ci_build/ci_build.sh PI tensorflow/tools/ci_build/pi/build_raspberry_pi.sh PI_ONE
+ +This will take some time to complete, typically twenty or thirty minutes, and +should produce a .whl file in an output-artifacts sub-folder inside your source +tree at the end. This wheel file can be installed through pip or pip3 (depending +on your Python version) by copying it to a Raspberry Pi and running a terminal +command like this (with the name of your actual file substituted): + +
$ pip3 install tensorflow-1.9.0-cp34-none-linux_armv7l.whl
+ +### Troubleshooting the build + +The build script uses Docker internally to create a Linux virtual machine to +handle the compilation. If you do have problems running the script, first check +that you're able to run Docker tests like `docker run hello-world` on your +system. + +If you're building from the latest development branch, try syncing to an older +version that's known to work, for example release 1.9, with a command like this: + +
$ git checkout r1.0
+ + + +## Validate your installation + +To validate your TensorFlow installation, do the following: + +1. Ensure that your environment is prepared to run TensorFlow programs. +2. Run a short TensorFlow program. + +### Prepare your environment + +If you installed on native pip, Virtualenv, or Anaconda, then do the following: + +1. Start a terminal. +2. If you installed TensorFlow source code, navigate to any directory *except* + one containing TensorFlow source code. + +### Run a short TensorFlow program + +Invoke python from your shell as follows: + +
$ python
+ +Enter the following short program inside the python interactive shell: + +```python +# Python +import tensorflow as tf +hello = tf.constant('Hello, TensorFlow!') +sess = tf.Session() +print(sess.run(hello)) +``` + +If the system outputs the following, then you are ready to begin writing +TensorFlow programs: + +
Hello, TensorFlow!
+ +If you're running with Python 3.5, you may see a warning when you first import +TensorFlow. This is not an error, and TensorFlow should continue to run with no +problems, despite the log message. + +If the system outputs an error message instead of a greeting, see [Common +installation problems](#common_installation_problems). + +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). + +## Common installation problems + +We are relying on Stack Overflow to document TensorFlow installation problems +and their remedies. The following table contains links to Stack Overflow answers +for some common installation problems. If you encounter an error message or +other installation problem not listed in the following table, search for it on +Stack Overflow. If Stack Overflow doesn't show the error message, ask a new +question about it on Stack Overflow and specify the `tensorflow` tag. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Stack Overflow Link Error Message
42006320
ImportError: Traceback (most recent call last):
+File ".../tensorflow/core/framework/graph_pb2.py", line 6, in 
+from google.protobuf import descriptor as _descriptor
+ImportError: cannot import name 'descriptor'
+
33623453
IOError: [Errno 2] No such file or directory:
+  '/tmp/pip-o6Tpui-build/setup.py'
+
35190574 
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
+  failed
42009190
+  Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
+  Found existing installation: setuptools 1.1.6
+  Uninstalling setuptools-1.1.6:
+  Exception:
+  ...
+  [Errno 1] Operation not permitted:
+  '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib'
33622019
ImportError: No module named copyreg
37810228During a pip install operation, the system returns: + 
OSError: [Errno 1] Operation not permitted
+
33622842An import tensorflow statement triggers an error such as the + following:
Traceback (most recent call last):
+  File "", line 1, in 
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
+    line 4, in 
+    from tensorflow.python import *
+    ...
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
+    line 22, in 
+    serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02
+      \x03(\x0b\x32
+      .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01
+      \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
+  TypeError: __init__() got an unexpected keyword argument 'syntax'
+
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index dc6c1e36fc237c2a160887e6417e7f373008309e..a641dc3a6f5436b3c321a0216fca7ac90d554b63 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -289,17 +289,27 @@ Note: If you're only interested in building the libraries for the TensorFlow C or Java APIs, see [Build the C or Java libraries](#BuildCorJava), you do not need to build the pip package in that case. -To build a pip package for TensorFlow with CPU-only support, -you would typically invoke the following command: +### CPU-only support + +To build a pip package for TensorFlow with CPU-only support: + +
+$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+
+ +To build a pip package for TensorFlow with CPU-only support for the IntelĀ® MKL-DNN: 
-$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+$ bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
-To build a pip package for TensorFlow with GPU support, -invoke the following command: +### GPU support + +To build a pip package for TensorFlow with GPU support: -
$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
+
+$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
+
**NOTE on gcc 5 or later:** the binary pip packages available on the TensorFlow website are built with gcc 4, which uses the older ABI. To @@ -362,7 +372,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 6c4f5b85ab2facdb274e9bdd36f6edb9ad79ba4b..7fe94f0bc3850b7210e83f746f8f8fd5b343cbd3 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -157,12 +157,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. - +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/leftnav_files b/tensorflow/docs_src/install/leftnav_files index e523e06f67aad508238ee0965f34ebe16c77bf90..ace275c0e82b794708bfc63c0e61d6bb3251a152 100644 --- a/tensorflow/docs_src/install/leftnav_files +++ b/tensorflow/docs_src/install/leftnav_files @@ -4,6 +4,7 @@ index.md install_linux.md: Ubuntu install_mac.md: MacOS install_windows.md: Windows +install_raspbian.md: Raspbian install_sources.md: From source >>> migration.md diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 7f2f8882a24702d167599452e66afbe720026808..fdf0bcf3c1135f0e702c7dda4d1d608a26169470 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -1,7 +1,7 @@ # Android Demo App An example Android application using TensorFLow Lite is available -[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app). +[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo). The demo is a sample camera app that classifies images continuously using either a quantized Mobilenet model or a floating point Inception-v3 model. To run the demo, a device running Android 5.0 ( API 21) or higher is required. @@ -44,20 +44,22 @@ app: Android Studio project. * Install all the Gradle extensions it requests. -To get a model, either: +Now you can build and run the demo app. -* Download the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip) - and unzip and copy `mobilenet_quant_v1_224.tflite` to the assets directory: - `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. -* Or, download the floating point [Inception-v3 model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip) - and unzip and copy `inceptionv3_non_slim_2015.tflite` to the assets - directory. Change the chosen classifier in - [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java)
+The build process downloads the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip), and unzips it into the assets directory: `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. + +Some additional details are available on the +[TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). + +### Using other models + +To use a different model: +* Download the floating point [Inception-v3 model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip). +* Unzip and copy `inceptionv3_non_slim_2015.tflite` to the assets directory. +* Change the chosen classifier in [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java)
from: `classifier = new ImageClassifierQuantizedMobileNet(getActivity());`
to: `classifier = new ImageClassifierFloatInception(getActivity());`. -Now you can build and run the demo app. - ## Build TensorFlow Lite and the demo app from source diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md index 562203482763991c412b523bd261b3163d361134..3d1733024e493042a2cc85aa9f2fec4b75eefa94 100644 --- a/tensorflow/docs_src/mobile/tflite/index.md +++ b/tensorflow/docs_src/mobile/tflite/index.md @@ -37,8 +37,9 @@ a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency. TensorFlow Lite provides an interface to leverage hardware acceleration, if -available on the device. It does so via the Android Neural Networks library, -released as part of Android O-MR1. +available on the device. It does so via the +[Android Neural Networks API](https://developer.android.com/ndk/guides/neuralnetworks/index.html), +available on Android 8.1 (API level 27) and higher. ## Why do we need a new mobile-specific library? @@ -116,6 +117,10 @@ following: Wear](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html) to all first-party and third-party apps. + Also see the complete list of + [TensorFlow Lite's supported models](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/models.md), + including the model sizes, performance numbers, and downloadable model files. + - Quantized versions of the MobileNet model, which runs faster than the non-quantized (float) version on CPU. @@ -131,10 +136,10 @@ compatibility with this release. ## Getting Started We recommend you try out TensorFlow Lite with the pre-tested models indicated -above. If you have an existing mode, you will need to test whether your model is -compatible with both the converter and the supported operator set. To test your -model, see the [documentation on -GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite). +above. If you have an existing model, you will need to test whether your model +is compatible with both the converter and the supported operator set. To test +your model, see the +[documentation on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite). ### Retrain Inception-V3 or MobileNet for a custom data set diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 5887c3d88bf8c7844349cc1cc0db224586e56719..4c4f3f39348f59aa018d19d4a7368f09bcef89ed 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -581,12 +581,21 @@ Computes a sum across replicas. Arguments | Type | Semantics --------- | ------- | ----------------------------- `operand` | `XlaOp` | Array to sum across replicas. +| `replica_group_ids` | `int64` vector | Group ID for each replica. | The output shape is the same as the input shape. For example, if there are two replicas and the operand has the value `(1.0, 2.5)` and `(3.0, 5.25)` respectively on the two replicas, then the output value from this op will be `(4.0, 7.75)` on both replicas. +`replica_group_ids` identifies the group ID of each replica. The group ID must +either be empty (all replicas belong to a single group), or contain the same +number of elements as the number of replicas. For example, if +`replica_group_ids` = {0, 1, 2, 3, 0, 1, 2, 3} has eight replicas, there are +four subgroups of replica IDs: {0, 4}, {1, 5}, {2, 6}, and {3, 7}. The size of +each subgroup *must* be identical, so, for example, using: +`replica_group_ids` = {0, 1, 2, 0} for four replicas is invalid. + Computing the result of CrossReplicaSum requires having one input from each replica, so if one replica executes a CrossReplicaSum node more times than another, then the former replica will wait forever. Since the replicas are all @@ -1299,12 +1308,10 @@ See also : : : parameters of type T and M of : : : : arbitrary type : | `dimensions` | `int64` array | array of map dimensions | -| `static_operands` | sequence of M `XlaOp`s | M arrays of arbitrary type | Applies a scalar function over the given `operands` arrays, producing an array of the same dimensions where each element is the result of the mapped function -applied to the corresponding elements in the input arrays with `static_operands` -given as additional input to `computation`. +applied to the corresponding elements in the input arrays. The mapped function is an arbitrary computation with the restriction that it has N inputs of scalar type `T` and a single output with type `S`. The output has @@ -2003,13 +2010,35 @@ Slice(b, {2, 1}, {4, 3}) produces: See also [`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). -Sorts the elements in the operand. +There are two versions of the Sort instruction: a single-operand and a +two-operand version. `Sort(operand)` +Arguments | Type | Semantics +--------- | ------- | -------------------- +`operand` | `XlaOp` | The operand to sort. + +Sorts the elements in the operand in ascending order. The operand must be rank-1. +If the operand's elements have floating point type, and the operand contains +NaN elements, the order of elements in the output is implementation-defined. + +`Sort(key, value)` + +Sorts both the key and the value operands. The keys are sorted as in the +single-operand version. The values are sorted according to the order of their +corresponding keys. For example, if the inputs are `keys = [3, 1]` and +`values = [42, 50]`, then the output of the sort is the tuple `{[1, 3], [50, 42]}`. +The sort is not guaranteed to be stable, that is, if the keys array contains +duplicates, the order of their corresponding values may not be preserved. + Arguments | Type | Semantics --------- | ------- | ------------------- -`operand` | `XlaOp` | The operand to sort +`keys` | `XlaOp` | The sort keys. +`values` | `XlaOp` | The values to sort. + +The `keys` and `values` operand must both be rank-1, and must have the same +dimensions, but may have different element types. ## Transpose diff --git a/tensorflow/docs_src/programmers_guide/keras.md b/tensorflow/docs_src/programmers_guide/keras.md deleted file mode 100644 index 6a9df12a25cf7aff1c9a4a2ec24d8568b26563ad..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/programmers_guide/keras.md +++ /dev/null @@ -1,715 +0,0 @@ -# Keras - -## What's Keras? - -Keras is a high-level API specification for building and training deep learning -models, suitable for fast prototyping, advanced research, and production. -It offers three key advantages: - -- **User friendliness.** Keras follows best practices for reducing - cognitive load: it offers consistent & simple interfaces, - it minimizes the number of user actions required for common use cases, - and it provides clear and actionable feedback upon user error. -- **Modularity and composability.** A Keras model is composed of - fully-configurable building blocks that can be plugged together - with as few restrictions as possible -- like Lego bricks. -- **Easy extensibility.** You can easily write your own building blocks - (such as new layers, new loss functions, new models where you write - the forward pass from scratch). This allows for total expressiveness, - making Keras suitable for advanced research. - - -## What's tf.keras? - -`tf.keras` is TensorFlow's implementation of the Keras API specification, that -serves as the TensorFlow high-level API: it's how you build models in TensorFlow. -`tf.keras` seamlessly integrates with the rest of the TensorFlow API -(such as `tf.data` input pipelines), bringing you the full power and flexibility -of TensorFlow through an easy-to-use interface. - -You can import `tf.keras` via: - -```python -from tensorflow import keras -``` - -What follows is a quick introduction to the basics of `tf.keras`. - - -## Table of contents - -- [Getting started: the Sequential model](#getting-started-the-sequential-model) -- [Configuring layers](#configuring-layers) -- [Configuring training](#configuring-training) -- [Training and evaluation](#training-and-evaluation) -- [Building advanced models: the functional API](#building-advanced-models-the-functional-api) -- [Building fully-customizable research models: the Model subclassing API](#building-fully-customizable-research-models-the-model-subclassing-api) -- [Callbacks](#callbacks) -- [Saving and serialization](#saving-and-serialization) -- [Developing custom layers](#developing-custom-layers) -- [Eager execution](#eager-execution) -- [Further reading](#further-reading) -- [FAQ](#faq) - - ---- - -## Getting started: the Sequential model - -In `tf.keras`, you're assembling together **layers** to build **models**. -A model is generally a graph of layers. -The most common type of model is just a stack of layers: the `Sequential` class. - -Here's how to build a simple fully-connected network (multi-layer perceptron): - -```python -from tensorflow import keras -from tensorflow.keras import layers - -model = keras.Sequential() -# This adds to the model a densely-connected layer with 64 units: -model.add(Dense(64, activation='relu')) -# Another one: -model.add(Dense(64, activation='relu')) -# This adds a softmax layer with 10 output units: -model.add(Dense(10, activation='softmax')) -``` - ---- - -## Configuring layers - -Each layer may have unique constructor arguments, but some common arguments include: - -- `activation`: the activation function to be used. - It could be specified by name, as a string (for built-in functions) - or as a callable object. By default, no activation is applied. -- `kernel_initializer` and `bias_initializer`: the initialization schemes to use - to create the layer's weights (kernel and bias). - Likewise, they may be passed either by name or by specifying a callable. - By default, the "Glorot uniform" initializer is used. -- `kernel_regularizer` and `bias_regularizer`: the regularization schemes to - apply to the layer's weights (kernel and bias), such as L1 - or L2 regularization. By default, no regularization is applied. - - -### Examples - -```python -import tensorflow as tf -from tensorflow.keras.layers import Dense -from tensorflow.keras import regularizers -from tensorflow.keras import initializers - -# A sigmoid layer: -Dense(64, activation='sigmoid') -# Another way to define the same sigmoid layer: -Dense(64, activation=tf.sigmoid) - -# A linear layer with L1 regularization of factor 0.01 -# applied to the kernel matrix: -Dense(64, kernel_regularizer=regularizers.l1(0.01)) -# A linear layer with L2 regularization of factor 0.01 -# applied to the bias vector: -Dense(64, bias_regularizer=regularizers.l2(0.01)) - -# A linear layer with a kernel initialized to a random orthogonal matrix: -Dense(64, kernel_initializer='orthogonal') -# A linear layer with a bias vector initialized to 2.0s: -Dense(64, bias_initializer=initializers.constant(2.0)) -``` - ---- - -## Configuring training - -Once your model looks good, configure its learning process by calling `compile`: - -```python -import tensorflow as tf - -model.compile(optimizer=tf.train.AdamOptimizer(0.001), - loss='categorical_crossentropy', - metrics=['accuracy']) -``` - -There are three key arguments that you need to specify: - -- An `optimizer`: this object specifies the training procedure. - We recommend that you pass instances of optimizers from the `tf.train` module - (such as [`AdamOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer), - [`RMSPropOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer), - or [`GradientDescentOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer)). -- A `loss` function to minimize: this specifies the optimization objective. - Common choices include mean square error (`mse`), `categorical_crossentropy` - and `binary_crossentropy`. Loss functions may be specified by name - or by passing a callable (e.g. from the `tf.keras.losses` module). -- Some `metrics` to monitor during training: again, you can pass these as either - string names or callables (e.g. from the `tf.keras.metrics` module). - - -### Examples - -```python -# Configures a model to do mean-squared error regression. -model.compile(optimizer=tf.train.AdamOptimizer(0.01), - loss='mse', # mean squared error - metrics=['mae']) # mean absolute error -``` -```python -# Configures a model to do categorical classification. -model.compile(optimizer=tf.train.RMSPropOptimizer(0.01), - loss=tf.keras.losses.categorical_crossentropy, - metrics=[tf.keras.metrics.categorical_accuracy]) -``` - ---- - -## Training and evaluation - -### From Numpy data - -When running locally on small datasets, the easiest way to do training and -evaluation is to pass data to your model as Numpy arrays of inputs and targets. -You can "fit" your model to some training data using the `model.fit()` method: - -```python -import numpy as np - -data = np.random.random(shape=(1000, 32)) -targets = np.random.random(shape=(1000, 10)) - -model.fit(data, targets, epochs=10, batch_size=32) -``` - -Here are some key arguments you can pass to the `fit` method: - -- `epochs`: Training is structured into **epochs**. An epoch is one iteration - over the entire input data (which is done in smaller batches). -- `batch_size`: when passing Numpy data, the model will slice the data into - smaller batches and iterate over these batches during training. - This integer specifies the size of each batch - (the last batch may be smaller if the total number of samples is not - divisible by the batch size). -- `validation_data`: when prototyping a model, you want to be able to quickly - monitor its performance on some validation data. - When you pass this argument (it expects a tuple of inputs and targets), - the model will display the loss and metrics in inference mode on the data - you passed, at the end of each epoch. - -Here's an example using `validation_data`: - -```python -import numpy as np - -data = np.random.random(shape=(1000, 32)) -targets = np.random.random(shape=(1000, 10)) - -val_data = np.random.random(shape=(100, 32)) -val_targets = np.random.random(shape=(100, 10)) - -model.fit(data, targets, epochs=10, batch_size=32, - validation_data=(val_data, val_targets)) -``` - -### From tf.data datasets - -When you need to scale to large datasets or multi-device training, -training from Numpy arrays in memory will not be ideal. -In such cases, you should use [the `tf.data` API](https://www.tensorflow.org/programmers_guide/datasets). -You can pass a `tf.data.Dataset` instance to the `fit` method: - -```python -import tensorflow as tf - -# Instantiates a toy dataset instance: -dataset = tf.data.Dataset.from_tensor_slices((data, targets)).batch(32) - -# Don't forget to specify `steps_per_epoch` when calling `fit` on a dataset. -model.fit(dataset, epochs=10, steps_per_epoch=30) -``` - -When doing so, the dataset itself will yield batches of data, -so the model does not need to be passed `batch_size` information. -Instead, the model needs to know for how many steps (or batches of data) -it should run at each epoch. -You specify this with the `steps_per_epoch` argument: it's the number of -training steps the model will run before moving on the next epoch. - -You can also pass datasets for validation: - -```python -dataset = tf.data.Dataset.from_tensor_slices((data, targets)).batch(32) -val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets)).batch(32) - -model.fit(dataset, epochs=10, steps_per_epoch=30, validation_data=val_dataset, validation_steps=3) -``` - -### Evaluate and predict - -In addition, you get access to the following methods -(both with Numpy data and dataset instances): - -- `model.evaluate(x, y, batch_size=32)` or `model.evaluate(dataset, steps=30)` - will return the inference-mode loss and metrics for the data provided. -- `model.predict(x, y, batch_size=32)` or `model.predict(dataset, steps=30)` - will return the output(s) of the last layer(s) in inference on the data - provided, as Numpy array(s). - ---- - -## Building advanced models: the functional API - -The `Sequential` model cannot represent arbitrary models -- only simple stacks -of layers. If you need to use more complex model topologies, -such as multi-input models, multi-output models, -models with a same layer called several times (shared layers), -or models with non-sequential data flows (e.g. residual connections), -you can use the 'functional API'. - -Here's how it works: - -- A layer instance is callable (on a tensor), and it returns a tensor. -- Input tensor(s) and output tensor(s) can then be used to define a `Model` instance. -- Such a model can be trained just like the `Sequential` model. - -Here's a basic example showing the same model we previously defined, -built using the functional API: - - -```python -from tensorflow import keras -from tensorflow.keras import layers - -# This returns a placeholder tensor: -inputs = keras.Input(shape=(784,)) - -# A layer instance is callable on a tensor, and returns a tensor. -x = layers.Dense(64, activation='relu')(inputs) -x = layers.Dense(64, activation='relu')(x) -predictions = layers.Dense(10, activation='softmax')(x) - -# Instantiates the model given inputs and outputs. -model = keras.Model(inputs=inputs, outputs=predictions) - -# The "compile" step specifies the training configuration. -model.compile(optimizer='rmsprop', - loss='categorical_crossentropy', - metrics=['accuracy']) - -# Trains for 5 epochs. -model.fit(data, labels, batch_size=32, epochs=5) -``` - -This API enables you to create models with multiple inputs and outputs, -and to "share" layers across different inputs -(i.e. to reuse a same instance multiple times). -For examples of these use cases, -please see [this guide to the functional API in Keras](https://keras.io/getting-started/functional-api-guide/). - ---- - -## Building fully-customizable research models: the Model subclassing API - -Besides `Sequential` and the functional API, one last, more flexible way to -define models is to directly subclass the `Model` class and define your own -forward pass manually. - -In this API, you instante layers in `__init__` and set them as attribute of the -class instance. Then you specify the forward pass in `call`. -This API is particularly valuable when using TensorFlow with [eager execution](https://www.tensorflow.org/programmers_guide/eager), -since eager execution allows you to write your forward pass in an -imperative fashion (as if you were writing Numpy code, for instance). - -```python -import tensorflow as tf -from tensorflow import keras - - -class MyModel(keras.Model): - - def __init__(self, num_classes=2): - super(MyModel, self).__init__(name='my_model') - self.num_classes = num_classes - # Define your layers here. - self.dense_1 = keras.layers.Dense(32, activation='relu') - self.dense_2 = keras.layers.Dense(num_classes, activation='sigmoid') - - def call(self, inputs): - # Define your forward pass here, - # using layers you previously defined (in `__init__`). - x = self.dense_1(inputs) - return self.dense_2(x) - - def compute_output_shape(self, input_shape): - # You need to override this function if you want to use the subclassed model - # as part of a functional-style model. - # Otherwise, this method is optional. - shape = tf.TensorShape(input_shape).as_list() - shape[-1] = self.num_classes - return tf.TensorShape(shape) - - -# Instantiates the subclassed model. -model = MyModel(num_classes=2) - -# The "compile" step specifies the training configuration. -model.compile(optimizer='rmsprop', - loss='categorical_crossentropy', - metrics=['accuracy']) - -# Trains for 5 epochs. -model.fit(data, labels, batch_size=32, epochs=5) -``` - -**Remember:** use the right API for the right job. -Using the `Model` subclassing API offers more flexibility, -but at the cost of greater complexity and a larger potential user error surface. -Prefer using the functional API when possible. - ---- - -## Callbacks - -Callbacks are objects that you can pass to your model that customize and extend -its behavior during training. -There are callbacks for saving checkpoints of your model at regular intervals -(`tf.keras.callbacks.ModelCheckpoint`), -to dynamically change the learning rate (`tf.keras.callbacks.LearningRateScheduler`) -or to interrupt training when validation performance has stopped improving -(`tf.keras.callbacks.EarlyStopping`). -You can also use a callback to monitor your model's behavior using -[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) -(`tf.keras.callbacks.TensorBoard`). -You can also write your own custom callbacks. - -Different built-in callback are found in `tf.keras.callbacks`. -You use them by passing a `Callback` instance to `fit`: - -```python -from tensorflow import keras - -callbacks = [ - # Interrupt training if `val_loss` stops improving for over 2 epochs - keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'), - # Write TensorBoard logs to `./logs` directory - keras.callbacks.TensorBoard(log_dir='./logs') -] -model.fit(data, labels, batch_size=32, epochs=5, callbacks=callbacks) -``` - ---- - -## Saving and serialization - -### Weights-only saving - -You can save the weight values of a model via `model.save_weights(filepath)`: - -```python -# Saves weights to a SavedModel file. -model.save_weights('my_model') - -# Restores the model's state -# (this requires a model that has the same architecture). -model.load_weights('my_model') -``` - -By default, this saves the weight in the TensorFlow -[`SavedModel`](https://www.tensorflow.org/programmers_guide/saved_model) format. -You could also save them in the Keras HDF5 format -(which is the default in the multi-backend implementation of Keras): - -```python -# Saves weights to a HDF5 file. -model.save_weights('my_model.h5', format='h5') - -# Restores the model's state. -model.load_weights('my_model.h5') -``` - -### Configuration-only saving (serialization) - -You can also save the model's configuration -(its architecture, without any weight values), -which allows you to recreate the same model later (freshly initialized) even if -you don't have the code that defined it anymore. -Two possible serialization formats are JSON and YAML: - -```python -from tensorflow.keras import models - -# Serializes a model to JSON. -json_string = model.to_json() -# Recreates the model (freshly initialized). -fresh_model = models.from_json(json_string) - -# Serializes a model to YAML. -yaml_string = model.to_yaml() -# Recreates the model. -fresh_model = models.from_yaml(yaml_string) -``` - -Note that this feature is not available with subclassed models, -because they are simply not serializable: -their architecture is defined as Python code -(the body of the `call` method of the model). - -### Whole-model saving - -Finally, you can also save a model wholesale, to a file that will contain both -the weight values, the model's configuration, -and even the optimizer's configuration. -The allows you to checkpoint a model and resume training later -- -from the exact same state -- even if you don't have access to the original code. - -```python -from tensorflow.keras import models - -model.save('my_model.h5') - -# Recreates the exact same model, complete with weights and optimizer. -model = models.load_model('my_model.h5') -``` - ---- - -## Developing custom layers - -You can write your own custom layers by subclassing the class -`tf.keras.layers.Layer`. You will need to implement the following three methods: - -- `build`: Creates the weights of the layer. - Weights should be added via the `add_weight` method. -- `call`: Specifies the forward pass. -- `compute_output_shape`: Specifies how to compute the output shape of the layer - given the input shape. - -Optionally, you may also implement the method `get_config()` and the -class method `from_config()` if you want your layer to be serializable. - -Here's a simple example of a custom layer that implements a `matmul` -of an input with a kernel matrix: - -```python -import tensorflow as tf -from tensorflow.keras import layers - -class MyLayer(layers.Layer): - - def __init__(self, output_dim, **kwargs): - self.output_dim = output_dim - super(MyLayer, self).__init__(**kwargs) - - def build(self, input_shape): - # Create a trainable weight variable for this layer. - self.kernel = self.add_weight(name='kernel', - shape=(input_shape[1], self.output_dim), - initializer='uniform', - trainable=True) - # Be sure to call this at the end - super(MyLayer, self).build(input_shape) - - def call(self, inputs): - return tf.matmul(inputs, self.kernel) - - def compute_output_shape(self, input_shape): - shape = tf.TensorShape(input_shape).as_list() - shape[-1] = self.output_dim - return tf.TensorShape(shape) - - def get_config(self): - base_config = super(MyLayer, self).get_config() - base_config['output_dim'] = self.output_dim - - @classmethod - def from_config(cls, config): - return cls(**config) -``` - ---- - -## Eager execution - -[Eager execution](https://www.tensorflow.org/programmers_guide/eager) -is a way to write TensorFlow code imperatively. - -All three `tf.keras` model-building APIs -(`Sequential`, the functional API `Model(inputs, outputs)`, -and the subclassing API `MyModel(Model)`) are compatible with eager execution. -When using `Sequential` or the functional API, it makes no difference to the -user experience whether the model is executing eagerly or not. -Eager execution is most beneficial when used with the `Model` subclassing API, -or when prototyping a custom layer -- that is to say, in APIs that require you -to *write a forward pass as code*, rather than in APIs that allow you to create -models by assembling together existing layers. - -While the same training and evaluating APIs presented in this guide work -as usual with eager execution, you can in addition -write custom training loops using the eager `GradientTape` -and define-by-run autodifferentiation: - -```python -import tensorflow as tf -from tensorflow.contrib import eager as tfe - -# This call begins the eager execution session. -tf.enable_eager_execution() - -model = ... # Defines a Keras model (we recommend Model subclassing in this case). -dataset = ... # Defines a `tf.data` dataset. - -optimizer = tf.train.AdamOptimizer(0.01) - -for data, labels in dataset: - # Runs the forward pass and loss computation under a `GradientTape` scope, - # which will record all operations in order to prepare for the backward pass. - with tfe.GradientTape() as tape: - predictions = model(data) - loss = loss_function(labels, predictions) - - # Runs the backward pass manually using the operations recorded - # by the gradient tape. - grads = tape.gradient(loss, model.trainable_weights) - optimizer.apply_gradients(zip(grads, model.trainable_weights), - global_step=tf.train.get_or_create_global_step()) -``` - ---- - -## Further reading - -### Documentation - -- [tf.keras documentation](https://www.tensorflow.org/api_docs/python/tf/keras) -- [keras.io](https://keras.io/) - -### tf.keras tutorials and examples - -- [Fashion-MNIST with tf.Keras](https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a) -- [Predicting the price of wine with the Keras Functional API and TensorFlow]( - https://medium.com/tensorflow/predicting-the-price-of-wine-with-the-keras-functional-api-and-tensorflow-a95d1c2c1b03) - - ---- - -## FAQ - -### What are the differences between tf.keras and the multi-backend Keras implementation? - -`tf.keras` includes first-class support for important TensorFlow-specific -functionality not found in other Keras implementations, in particular: - -- Support for eager execution. -- Support for the `tf.data` API. -- Integration with the - [`tf.estimator` API](https://www.tensorflow.org/programmers_guide/estimators), - via `tf.keras.estimator.model_to_estimator`. - -In terms of API differences: `tf.keras` is a full implementation of the -Keras API, so any code targeting the Keras API will run on `tf.keras`. -However, keep in mind that: - -- The `tf.keras` API version in the latest TensorFlow release might not be the - same as the latest `keras` version from PyPI. - Check out `tf.keras.__version__` if in doubt. -- In `tf.keras`, the default file format saved by `model.save_weights` is the - TensorFlow `SavedModel` format. - To use HDF5, you can pass the `format='h5'` argument. - - -### What is the relationship between tf.keras and tf.estimator? - -The [`tf.estimator` API](https://www.tensorflow.org/programmers_guide/estimators) -is a high-level TensorFlow API for training "estimator" models, -in particular in distributed settings. -This API targets industry use cases, such as distributed training -on large datasets with a focus on eventually exporting a production model. - -If you have a `tf.keras` model that would like to train with the `tf.estimator` -API, you can convert your model to an `Estimator` object via the -`model_to_estimator` utility](https://www.tensorflow.org/programmers_guide/estimators#creating_estimators_from_keras_models): - - -```python -estimator = tf.keras.estimator.model_to_estimator(model) -``` - -When using `model_to_estimator`, enabling eager execution is helpful for -developing and debugging your `input_fn` -(as it allows you to easily print your data). - - -### How can I run tf.keras models on multiple GPUs? - -You can run tf.keras models on multiple GPUs using the -[`DistributionStrategy API`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/DistributionStrategy). -The `DistributionStrategy` API allow you to distribute training on multiple GPUs -with almost no changes to your existing code. - -Currently [`MirroredStrategy`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/MirroredStrategy) -is the only supported strategy. -`MirroredStrategy` allows you to do in-graph replication with synchronous -training using all-reduce on a single machine. -To use `DistributionStrategy` with a `tf.keras` model, -you can use the `model_to_estimator` utility to convert a `tf.keras` model to -an `Estimator` and then train the estimator. - -Here is a simple example of distributing a `tf.keras` model across multiple GPUs -on a single machine. - -Let's first define a simple model: - -```python -model = tf.keras.Sequential() -model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,))) -model.add(tf.keras.layers.Dense(1, activation='sigmoid')) -optimizer = tf.train.GradientDescentOptimizer(0.2) -model.compile(loss='binary_crossentropy', optimizer=optimizer) -model.summary() -``` - -Let's use `model_to_estimator` to create an `Estimator` instance from the -`tf.keras` model defined above. - -```python -keras_estimator = tf.keras.estimator.model_to_estimator( - keras_model=model, - config=config, - model_dir='/tmp/model_dir') -``` - -We'll use `tf.data.Datasets` to define our input pipeline. -Our `input_fn` returns a `tf.data.Dataset` object that we then use to distribute -the data across multiple devices with each device processing -a slice of the input batch. - -```python -def input_fn(): - x = np.random.random((1024, 10)) - y = np.random.randint(2, size=(1024, 1)) - x = tf.cast(x, tf.float32) - dataset = tf.data.Dataset.from_tensor_slices((x, y)) - dataset = dataset.repeat(10) - dataset = dataset.batch(32) - return dataset -``` - -The next step is to create a `RunConfig` and set the train_distribute argument -to the new `MirroredStrategy` instance. -You can specify a list of devices or the `num_gpus` argument when creating -a `MirroredStrategy` instance. -Not specifying any arguments defaults to using all the available GPUs like we do -in this example. - -```python -strategy = tf.contrib.distribute.MirroredStrategy() -config = tf.estimator.RunConfig(train_distribute=strategy) -``` - -Call train on the `Estimator` instance providing the `input_fn` and `steps` -arguments as input: - -```python -keras_estimator.train(input_fn=input_fn, steps=10) -``` diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md index 6a4c9a9b0727208a158b1b57d13ca70290961ec2..44a32d9d1dcbd7d4be7a2063e9c5ae4affffe487 100644 --- a/tensorflow/docs_src/tutorials/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/deep_cnn.md @@ -268,7 +268,7 @@ in `cifar10_input.py`. `cifar10_train.py` periodically @{tf.train.Saver$saves} all model parameters in -@{$programmers_guide/saved_model$checkpoint files} +@{$guide/saved_model$checkpoint files} but it does *not* evaluate the model. The checkpoint file will be used by `cifar10_eval.py` to measure the predictive performance (see [Evaluating a Model](#evaluating-a-model) below). diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md index af01d3eaa12157f82c981de005708509f6652cca..6bd3a3a897d9cc11e9172e4ccde6fcad4f075ad1 100644 --- a/tensorflow/docs_src/tutorials/index.md +++ b/tensorflow/docs_src/tutorials/index.md @@ -2,9 +2,8 @@ This section contains tutorials demonstrating how to do specific tasks -in TensorFlow. If you are new to TensorFlow, we recommend reading the -documents in the "@{$get_started$Get Started}" section before reading -these tutorials. +in TensorFlow. If you are new to TensorFlow, we recommend reading +[Get Started with TensorFlow](/get_started/). ## Images diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md index 0f17899dae7ccd8686ac159548dec303401b8ad4..791909f5fd5be2913af1a093d967c9fbb6af89a3 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/layers.md @@ -470,51 +470,18 @@ as the loss metric. The following code calculates cross entropy when the model runs in either `TRAIN` or `EVAL` mode: ```python -onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10) -loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) +loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) ``` Let's take a closer look at what's happening above. -Our `labels` tensor contains a list of predictions for our examples, e.g. `[1, -9, ...]`. In order to calculate cross-entropy, first we need to convert `labels` -to the corresponding -[one-hot encoding](https://www.quora.com/What-is-one-hot-encoding-and-when-is-it-used-in-data-science): +Our `labels` tensor contains a list of prediction indices for our examples, e.g. `[1, +9, ...]`. `logits` contains the linear outputs of our last layer. -```none -[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 1], - ...] -``` - -We use the @{tf.one_hot} function -to perform this conversion. `tf.one_hot()` has two required arguments: - -* `indices`. The locations in the one-hot tensor that will have "on - values"ā€”i.e., the locations of `1` values in the tensor shown above. -* `depth`. The depth of the one-hot tensorā€”i.e., the number of target classes. - Here, the depth is `10`. +`tf.losses.sparse_softmax_cross_entropy`, calculates the softmax crossentropy +(aka: categorical crossentropy, negative log-likelihood) from these two inputs +in an efficient, numerically stable way. -The following code creates the one-hot tensor for our labels, `onehot_labels`: - -```python -onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10) -``` - -Because `labels` contains a series of values from 0ā€“9, `indices` is just our -`labels` tensor, with values cast to integers. The `depth` is `10` because we -have 10 possible target classes, one for each digit. - -Next, we compute cross-entropy of `onehot_labels` and the softmax of the -predictions from our logits layer. `tf.losses.softmax_cross_entropy()` takes -`onehot_labels` and `logits` as arguments, performs softmax activation on -`logits`, calculates cross-entropy, and returns our `loss` as a scalar `Tensor`: - -```python -loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) -``` ### Configure the Training Op @@ -627,7 +594,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`. > argument, TensorFlow will assign a default name. A couple easy ways to > discover the names applied to operations are to visualize your graph on > @{$graph_viz$TensorBoard}) or to enable the -> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}. +> @{$guide/debugger$TensorFlow Debugger (tfdbg)}. Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities diff --git a/tensorflow/docs_src/tutorials/leftnav_files b/tensorflow/docs_src/tutorials/leftnav_files index 888052428f951fa1a7cbd9c6d35497a056387097..eadd410d0812cfecbcb7cb01550e2f7e7f9da0db 100644 --- a/tensorflow/docs_src/tutorials/leftnav_files +++ b/tensorflow/docs_src/tutorials/leftnav_files @@ -3,10 +3,11 @@ index.md ### Images layers.md: MNIST image_recognition.md: Image Recognition -image_retraining.md: Image Retraining +/hub/tutorials/image_retraining.md: Image Retraining deep_cnn.md ### Sequences +/hub/tutorials/text_classification_with_tf_hub: Text Classification recurrent.md seq2seq.md: Neural Machine Translation recurrent_quickdraw.md: Drawing Classification diff --git a/tensorflow/examples/android/BUILD b/tensorflow/examples/android/BUILD index 07f096418f53219c9ec7000a4560d78a3ff609e1..f327b645f58f35cedd27baa8ab521e334c8e7b15 100644 --- a/tensorflow/examples/android/BUILD +++ b/tensorflow/examples/android/BUILD @@ -1,6 +1,8 @@ # Description: # TensorFlow camera demo app for Android. +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py index 307eede5c03780e9244b035f020fc7846290d4d9..740224744860fdd76bea9c4531242a4976b20784 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -17,7 +17,7 @@ This version is like fully_connected_feed.py but uses data converted to a TFRecords file containing tf.train.Example protocol buffers. See: -https://www.tensorflow.org/programmers_guide/reading_data#reading_from_files +https://www.tensorflow.org/guide/reading_data#reading_from_files for context. YOU MUST run convert_to_records before running this (but you only need to diff --git a/tensorflow/examples/tutorials/mnist/BUILD b/tensorflow/examples/tutorials/mnist/BUILD index d7bc6a5a7d1e4cd3927c7c5067ccc22993885994..d4070fdd1e015fb78dcf2ff72fe30b6f1746c8fb 100644 --- a/tensorflow/examples/tutorials/mnist/BUILD +++ b/tensorflow/examples/tutorials/mnist/BUILD @@ -97,7 +97,7 @@ py_binary( py_test( name = "fully_connected_feed_test", - size = "small", + size = "medium", srcs = [ "fully_connected_feed.py", ], diff --git a/tensorflow/go/attrs.go b/tensorflow/go/attrs.go new file mode 100644 index 0000000000000000000000000000000000000000..f86c5737bc79f1e349e442669615598949ecd333 --- /dev/null +++ b/tensorflow/go/attrs.go @@ -0,0 +1,245 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" +import ( + "fmt" + "unsafe" +) + +// makeCShape converts a shape specified in C.int64_t into a Shape. +func makeCShape(shape []C.int64_t) Shape { + s := Shape{dims: make([]int64, len(shape))} + for i, n := range shape { + s.dims[i] = int64(n) + } + return s +} + +// Attr returns the value of an attribute on op. It returns an error if the +// attribute does not exist. +func (op *Operation) Attr(name string) (interface{}, error) { + cname := C.CString(name) + defer C.free(unsafe.Pointer(cname)) + + status := newStatus() + meta := C.TF_OperationGetAttrMetadata(op.c, cname, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + if meta.is_list == 1 { + return listAttribute(op, cname, meta) + } + return scalarAttribute(op, cname, meta) +} + +func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + if meta.list_size == 0 { + return []string(nil), nil + } + values := make([]unsafe.Pointer, meta.list_size) + lengths := make([]C.size_t, meta.list_size) + // Add one element in case total_size is zero. + storage := make([]C.char, meta.total_size+1) + C.TF_OperationGetAttrStringList(op.c, cname, &values[0], &lengths[0], C.int(meta.list_size), unsafe.Pointer(&storage[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]string, meta.list_size) + for i, val := range values { + length := lengths[i] + list[i] = C.GoStringN((*C.char)(val), C.int(length)) + } + return list, nil + + case C.TF_ATTR_INT: + if meta.list_size == 0 { + return []int64(nil), nil + } + list := make([]C.int64_t, meta.list_size) + C.TF_OperationGetAttrIntList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]int64, meta.list_size) + for i, val := range list { + vals[i] = int64(val) + } + return vals, nil + + case C.TF_ATTR_FLOAT: + if meta.list_size == 0 { + return []float32(nil), nil + } + list := make([]C.float, meta.list_size) + C.TF_OperationGetAttrFloatList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]float32, meta.list_size) + for i, val := range list { + vals[i] = float32(val) + } + return vals, nil + + case C.TF_ATTR_BOOL: + if meta.list_size == 0 { + return []bool(nil), nil + } + list := make([]C.uchar, meta.list_size) + C.TF_OperationGetAttrBoolList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]bool, meta.list_size) + for i, val := range list { + vals[i] = val == 1 + } + return vals, nil + + case C.TF_ATTR_TYPE: + if meta.list_size == 0 { + return []DataType(nil), nil + } + list := make([]C.TF_DataType, meta.list_size) + C.TF_OperationGetAttrTypeList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]DataType, meta.list_size) + for i, val := range list { + vals[i] = DataType(val) + } + return vals, nil + + case C.TF_ATTR_TENSOR: + if meta.list_size == 0 { + return []*Tensor(nil), nil + } + list := make([]*C.TF_Tensor, meta.list_size) + C.TF_OperationGetAttrTensorList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]*Tensor, meta.list_size) + for i, t := range list { + vals[i] = newTensorFromC(t) + } + return vals, nil + + case C.TF_ATTR_SHAPE: + if meta.list_size == 0 { + return []Shape(nil), nil + } + dims := make([]*C.int64_t, meta.list_size) + numDims := make([]C.int, meta.list_size) + // Add one element in case total_size is zero. + storage := make([]C.int64_t, meta.total_size+1) + C.TF_OperationGetAttrShapeList(op.c, cname, &dims[0], &numDims[0], C.int(meta.list_size), &storage[0], C.int(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]Shape, meta.list_size) + for i, dim := range dims { + numDim := numDims[i] + // If the number of dimensions is unknown, default to empty shape. + if numDim < 0 { + continue + } + // A []C.int64_t slice backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + slice := (*[1 << 30]C.int64_t)(unsafe.Pointer(dim))[:numDim:numDim] + list[i] = makeCShape(slice) + } + return list, nil + + default: + return nil, fmt.Errorf("list type %v not supported", meta._type) + } +} + +func scalarAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + if meta.total_size == 0 { + return "", nil + } + v := make([]C.char, meta.total_size) + C.TF_OperationGetAttrString(op.c, cname, unsafe.Pointer(&v[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return C.GoStringN(&v[0], C.int(meta.total_size)), nil + + case C.TF_ATTR_INT: + var v C.int64_t + C.TF_OperationGetAttrInt(op.c, cname, &v, status.c) + return int64(v), status.Err() + + case C.TF_ATTR_FLOAT: + var v C.float + C.TF_OperationGetAttrFloat(op.c, cname, &v, status.c) + return float32(v), status.Err() + + case C.TF_ATTR_BOOL: + var v C.uchar + C.TF_OperationGetAttrBool(op.c, cname, &v, status.c) + return v == 1, status.Err() + + case C.TF_ATTR_TYPE: + var v C.TF_DataType + C.TF_OperationGetAttrType(op.c, cname, &v, status.c) + return DataType(v), status.Err() + + case C.TF_ATTR_TENSOR: + var v *C.TF_Tensor + C.TF_OperationGetAttrTensor(op.c, cname, &v, status.c) + if err := status.Err(); err != nil { + return nil, err + } + return newTensorFromC(v), nil + + case C.TF_ATTR_SHAPE: + numDims := meta.total_size + // If number of dims is unknown return empty shape to indicate that. + if numDims < 0 { + return Shape{}, nil + } + if numDims == 0 { + return ScalarShape(), nil + } + dims := make([]C.int64_t, numDims) + C.TF_OperationGetAttrShape(op.c, cname, (*C.int64_t)(unsafe.Pointer(&dims[0])), C.int(numDims), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return makeCShape(dims), nil + + default: + return nil, fmt.Errorf("type %v not supported", meta._type) + } +} diff --git a/tensorflow/go/attrs_test.go b/tensorflow/go/attrs_test.go new file mode 100644 index 0000000000000000000000000000000000000000..ea8af221aeef3bf1d2edeab4372ae00f0cc7e92d --- /dev/null +++ b/tensorflow/go/attrs_test.go @@ -0,0 +1,193 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "reflect" + "testing" +) + +func TestOperationAttrs(t *testing.T) { + g := NewGraph() + + i := 0 + makeConst := func(v interface{}) Output { + op, err := Const(g, fmt.Sprintf("const/%d/%+v", i, v), v) + i++ + if err != nil { + t.Fatal(err) + } + return op + } + + makeTensor := func(v interface{}) *Tensor { + tensor, err := NewTensor(v) + if err != nil { + t.Fatal(err) + } + return tensor + } + + cases := []OpSpec{ + { + Name: "type", + Type: "Placeholder", + Attrs: map[string]interface{}{ + "dtype": Float, + }, + }, + { + Name: "list(float)", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{1, 2, 3, 4}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32{0, 1, 2, 3, 4, 5}, + }, + }, + { + Name: "list(float) empty", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32(nil), + }, + }, + /* TODO(ashankar): debug this issue and add it back later. + { + Name: "list(type),list(shape)", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst(float32(1)), + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Float, Int32}, + "shapes": []Shape{ScalarShape(), MakeShape(1, 1)}, + }, + }, + { + Name: "list(type),list(shape) empty", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Int32}, + "shapes": []Shape(nil), + }, + }, + { + Name: "list(type) empty,string empty,int", + Type: "_XlaSendFromHost", + Input: []Input{ + OutputList([]Output{}), + makeConst(""), + }, + Attrs: map[string]interface{}{ + "Tinputs": []DataType(nil), + "key": "", + "device_ordinal": int64(0), + }, + }, + */ + { + Name: "list(int),int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": []int64{1, 2}, + }, + }, + { + Name: "list(int) empty,int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": ([]int64)(nil), + }, + }, + { + Name: "list(string),type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": []string{"foo", "bar"}, + }, + }, + { + Name: "list(string) empty,type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": ([]string)(nil), + }, + }, + { + Name: "tensor", + Type: "Const", + Attrs: map[string]interface{}{ + "dtype": String, + "value": makeTensor("foo"), + }, + }, + } + + for i, spec := range cases { + op, err := g.AddOperation(spec) + if err != nil { + t.Fatal(err) + } + for key, want := range spec.Attrs { + out, err := op.Attr(key) + if err != nil { + t.Fatal(err) + } + if !reflect.DeepEqual(out, want) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, out, want) + } + wantT, ok := want.(*Tensor) + if ok { + wantVal := wantT.Value() + outVal := out.(*Tensor).Value() + if !reflect.DeepEqual(outVal, wantVal) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, outVal, wantVal) + } + } + } + } +} diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index a02136369e03970d569211ad6c4fb26233019960..7f1f0970a6fd697419b4158f3a6517bca5bbe10e 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2914,34 +2914,131 @@ func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } -// Creates a sequence of numbers. +// Splits a tensor into `num_split` tensors along one dimension. // -// This operation creates a sequence of numbers that begins at `start` and -// extends by increments of `delta` up to but not including `limit`. +// Arguments: +// value: The tensor to split. +// size_splits: list containing the sizes of each output tensor along the split +// dimension. Must sum to the dimension of value along split_dim. +// Can contain one -1 indicating that dimension is to be inferred. +// axis: 0-D. The dimension along which to split. Must be in the range +// `[-rank(value), rank(value))`. // -// For example: // -// ``` -// # 'start' is 3 -// # 'limit' is 18 -// # 'delta' is 3 -// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] -// ``` +// Returns Tensors whose shape matches that of `value` +// except along `axis`, where their sizes are +// `size_splits[i]`. +func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_split": num_split} + opspec := tf.OpSpec{ + Type: "SplitV", + Input: []tf.Input{ + value, size_splits, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("SplitV", err) + return + } + return output +} + +// Splits a tensor into `num_split` tensors along one dimension. // // Arguments: -// start: 0-D (scalar). First entry in the sequence. -// limit: 0-D (scalar). Upper limit of sequence, exclusive. -// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. +// axis: 0-D. The dimension along which to split. Must be in the range +// `[-rank(value), rank(value))`. +// value: The tensor to split. +// num_split: The number of ways to split. Must evenly divide +// `value.shape[split_dim]`. // -// Returns 1-D. -func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { +// Returns They are identically shaped tensors, whose shape matches that of `value` +// except along `axis`, where their sizes are +// `values.shape[split_dim] / num_split`. +func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"num_split": num_split} opspec := tf.OpSpec{ - Type: "Range", + Type: "Split", Input: []tf.Input{ - start, limit, delta, + axis, value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("Split", err) + return + } + return output +} + +// Concatenates tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Concat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts a flat index or array of flat indices into a tuple of +// +// coordinate arrays. +// +// @compatibility(numpy) +// Equivalent to np.unravel_index +// @end_compatibility +// +// Arguments: +// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the +// flattened version of an array of dimensions dims. +// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling +// indices. +// +// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the +// same shape as the indices array. +func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnravelIndex", + Input: []tf.Input{ + indices, dims, }, } op := scope.AddOperation(opspec) @@ -3847,24 +3944,6 @@ func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Returns x + y element-wise. -// -// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Add", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // NthElementAttr is an optional argument to NthElement. type NthElementAttr func(optionalAttr) @@ -4134,69 +4213,6 @@ func Digamma(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Shuffle dimensions of x according to a permutation. -// -// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: -// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` -func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Transpose", - Input: []tf.Input{ - x, perm, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MinAttr is an optional argument to Min. -type MinAttr func(optionalAttr) - -// MinKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func MinKeepDims(value bool) MinAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the minimum of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Min", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter. type Conv2DBackpropFilterAttr func(optionalAttr) @@ -4671,6 +4687,24 @@ func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) return op.Output(0) } +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Add", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes square of x element-wise. // // I.e., \\(y = x * x = x^2\\). @@ -6105,32 +6139,103 @@ func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// DepthToSpaceAttr is an optional argument to DepthToSpace. -type DepthToSpaceAttr func(optionalAttr) - -// DepthToSpaceDataFormat sets the optional data_format attribute to value. -// If not specified, defaults to "NHWC" -func DepthToSpaceDataFormat(value string) DepthToSpaceAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// DepthToSpace for tensors of type T. +// Computes offsets of concat inputs within its output. // -// Rearranges data from depth into blocks of spatial data. -// This is the reverse transformation of SpaceToDepth. More specifically, -// this op outputs a copy of the input tensor where values from the `depth` -// dimension are moved in spatial blocks to the `height` and `width` dimensions. -// The attr `block_size` indicates the input block size and how the data is moved. +// For example: // -// * Chunks of data of size `block_size * block_size` from depth are rearranged -// into non-overlapping blocks of size `block_size x block_size` -// * The width the output tensor is `input_depth * block_size`, whereas the -// height is `input_height * block_size`. -// * The Y, X coordinates within each block of the output image are determined -// by the high order component of the input channel index. -// * The depth of the input tensor must be divisible by +// ``` +// # 'x' is [2, 2, 7] +// # 'y' is [2, 3, 7] +// # 'z' is [2, 5, 7] +// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] +// ``` +// +// This is typically used by gradient computations for a concat operation. +// +// Arguments: +// concat_dim: The dimension along which to concatenate. +// shape: The `N` int32 vectors representing shape of tensors being concatenated. +// +// Returns The `N` int32 vectors representing the starting offset +// of input tensors within the concatenated output. +func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatOffset", + Input: []tf.Input{ + concat_dim, tf.OutputList(shape), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil { + scope.UpdateErr("ConcatOffset", err) + return + } + return offset +} + +// Compute the lower regularized incomplete Gamma function `Q(a, x)`. +// +// The lower regularized incomplete Gamma function is defined as: +// +// +// \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\) +// +// where +// +// \\(gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt\\) +// +// is the lower incomplete Gamma function. +// +// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete +// Gamma function. +func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Igamma", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthToSpaceAttr is an optional argument to DepthToSpace. +type DepthToSpaceAttr func(optionalAttr) + +// DepthToSpaceDataFormat sets the optional data_format attribute to value. +// If not specified, defaults to "NHWC" +func DepthToSpaceDataFormat(value string) DepthToSpaceAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthToSpace for tensors of type T. +// +// Rearranges data from depth into blocks of spatial data. +// This is the reverse transformation of SpaceToDepth. More specifically, +// this op outputs a copy of the input tensor where values from the `depth` +// dimension are moved in spatial blocks to the `height` and `width` dimensions. +// The attr `block_size` indicates the input block size and how the data is moved. +// +// * Chunks of data of size `block_size * block_size` from depth are rearranged +// into non-overlapping blocks of size `block_size x block_size` +// * The width the output tensor is `input_depth * block_size`, whereas the +// height is `input_height * block_size`. +// * The Y, X coordinates within each block of the output image are determined +// by the high order component of the input channel index. +// * The depth of the input tensor must be divisible by // `block_size * block_size`. // // The `data_format` attr specifies the layout of the input and output tensors @@ -6924,6 +7029,69 @@ func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) return op.Output(0) } +// Shuffle dimensions of x according to a permutation. +// +// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: +// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` +func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Transpose", + Input: []tf.Input{ + x, perm, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MinAttr is an optional argument to Min. +type MinAttr func(optionalAttr) + +// MinKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MinKeepDims(value bool) MinAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the minimum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Min", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Transforms a Tensor into a serialized TensorProto proto. // // Arguments: @@ -7579,58 +7747,51 @@ func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_s return op.Output(0) } -// LRNGradAttr is an optional argument to LRNGrad. -type LRNGradAttr func(optionalAttr) - -// LRNGradDepthRadius sets the optional depth_radius attribute to value. +// Returns immutable tensor from memory region. // -// value: A depth radius. -// If not specified, defaults to 5 -func LRNGradDepthRadius(value int64) LRNGradAttr { - return func(m optionalAttr) { - m["depth_radius"] = value - } -} - -// LRNGradBias sets the optional bias attribute to value. +// The current implementation memmaps the tensor from a file. // -// value: An offset (usually > 0 to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNGradBias(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["bias"] = value +// Arguments: +// dtype: Type of the returned tensor. +// shape: Shape of the returned tensor. +// memory_region_name: Name of readonly memory region used by the tensor, see +// NewReadOnlyMemoryRegionFromFile in tensorflow::Env. +func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) { + if scope.Err() != nil { + return } -} + attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name} + opspec := tf.OpSpec{ + Type: "ImmutableConst", -// LRNGradAlpha sets the optional alpha attribute to value. -// -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNGradAlpha(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["alpha"] = value + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// LRNGradBeta sets the optional beta attribute to value. +// StringJoinAttr is an optional argument to StringJoin. +type StringJoinAttr func(optionalAttr) + +// StringJoinSeparator sets the optional separator attribute to value. // -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNGradBeta(value float32) LRNGradAttr { +// value: string, an optional join separator. +// If not specified, defaults to "" +func StringJoinSeparator(value string) StringJoinAttr { return func(m optionalAttr) { - m["beta"] = value + m["separator"] = value } } -// Gradients for Local Response Normalization. +// Joins the strings in the given list of string tensors into one tensor; // -// Arguments: -// input_grads: 4-D with shape `[batch, height, width, channels]`. -// input_image: 4-D with shape `[batch, height, width, channels]`. -// output_image: 4-D with shape `[batch, height, width, channels]`. +// with the given separator (default is an empty separator). // -// Returns The gradients for LRN. -func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { +// Arguments: +// inputs: A list of string tensors. The tensors must all have the same shape, +// or be scalars. Scalars may be mixed in; these will be broadcast to the shape +// of non-scalar inputs. +func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -7639,9 +7800,9 @@ func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_ a(attrs) } opspec := tf.OpSpec{ - Type: "LRNGrad", + Type: "StringJoin", Input: []tf.Input{ - input_grads, input_image, output_image, + tf.OutputList(inputs), }, Attrs: attrs, } @@ -7649,73 +7810,28 @@ func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_ return op.Output(0) } -// AnyAttr is an optional argument to Any. -type AnyAttr func(optionalAttr) +// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. +type ResourceApplyFtrlAttr func(optionalAttr) -// AnyKeepDims sets the optional keep_dims attribute to value. +// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value. // -// value: If true, retain reduced dimensions with length 1. +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. // If not specified, defaults to false -func AnyKeepDims(value bool) AnyAttr { +func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { return func(m optionalAttr) { - m["keep_dims"] = value + m["use_locking"] = value } } -// Computes the "logical or" of elements across dimensions of a tensor. +// Update '*var' according to the Ftrl-proximal scheme. // -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Any", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. -type ResourceApplyFtrlAttr func(optionalAttr) - -// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the Ftrl-proximal scheme. -// -// accum_new = accum + grad * grad -// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new +// accum_new = accum + grad * grad +// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new // // Arguments: // var_: Should be from a Variable(). @@ -8074,6 +8190,83 @@ func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPe return op.Output(0) } +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. +// +// Note that the hash function may change from time to time. +// This functionality will be deprecated and it's recommended to use +// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// +// Arguments: +// +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucket", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for the exponential linear (Elu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Elu operation. +// outputs: The outputs of the corresponding Elu operation. +// +// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, +// `gradients` otherwise. +func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains `count` elements from the `input_dataset`. +// +// Arguments: +// +// count: A scalar representing the number of elements from the `input_dataset` +// that should be taken. A value of `-1` indicates that all of `input_dataset` +// is taken. +// +// +func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TakeDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Reads the value of a variable. // // The tensor returned by this operation is immutable. @@ -8157,157 +8350,124 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe return scope.AddOperation(opspec) } -// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. -type ResourceSparseApplyFtrlAttr func(optionalAttr) +// EncodeJpegAttr is an optional argument to EncodeJpeg. +type EncodeJpegAttr func(optionalAttr) -// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// EncodeJpegFormat sets the optional format attribute to value. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { +// value: Per pixel image format. +// If not specified, defaults to "" +func EncodeJpegFormat(value string) EncodeJpegAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["format"] = value } } -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// accum_new = accum + grad * grad -// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. +// EncodeJpegQuality sets the optional quality attribute to value. // -// Returns the created operation. -func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrl", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, lr_power, - }, - Attrs: attrs, +// value: Quality of the compression from 0 to 100 (higher is better and slower). +// If not specified, defaults to 95 +func EncodeJpegQuality(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["quality"] = value } - return scope.AddOperation(opspec) } -// Returns which elements of x are Inf. +// EncodeJpegProgressive sets the optional progressive attribute to value. // -// @compatibility(numpy) -// Equivalent to np.isinf -// @end_compatibility -func IsInf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsInf", - Input: []tf.Input{ - x, - }, +// value: If True, create a JPEG that loads progressively (coarse to fine). +// If not specified, defaults to false +func EncodeJpegProgressive(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["progressive"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Computes the sum along sparse segments of a tensor divided by the sqrt of N. -// -// N is the size of the segment being reduced. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSqrtN", - Input: []tf.Input{ - data, indices, segment_ids, - }, +// value: If True, spend CPU/RAM to reduce size with no quality change. +// If not specified, defaults to false +func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["optimize_size"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. -// -// This Op does not require `a_indices` be sorted in standard lexicographic order. +// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. // -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. -// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. -// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. -// b: `ndims`-D Tensor. With shape `a_shape`. -func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, +// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// If not specified, defaults to true +func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["chroma_downsampling"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. -type StatelessTruncatedNormalAttr func(optionalAttr) +// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// +// value: Unit used to specify `x_density` and `y_density`: +// pixels per inch (`'in'`) or centimeter (`'cm'`). +// If not specified, defaults to "in" +func EncodeJpegDensityUnit(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["density_unit"] = value + } +} -// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// EncodeJpegXDensity sets the optional x_density attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { +// value: Horizontal pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegXDensity(value int64) EncodeJpegAttr { return func(m optionalAttr) { - m["dtype"] = value + m["x_density"] = value } } -// Outputs deterministic pseudorandom values from a truncated normal distribution. +// EncodeJpegYDensity sets the optional y_density attribute to value. // -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. +// value: Vertical pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegYDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["y_density"] = value + } +} + +// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. // -// The outputs are a deterministic function of `shape` and `seed`. +// value: If not empty, embed this XMP metadata in the image header. +// If not specified, defaults to "" +func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["xmp_metadata"] = value + } +} + +// JPEG-encode an image. +// +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// +// The attr `format` can be used to override the color format of the encoded +// output. Values can be: +// +// * `''`: Use a default format based on the number of channels in the image. +// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension +// of `image` must be 1. +// * `rgb`: Output an RGB JPEG image. The `channels` dimension +// of `image` must be 3. +// +// If `format` is not specified or is the empty string, a default format is picked +// in function of the number of channels in `image`: +// +// * 1: Output a grayscale image. +// * 3: Output an RGB image. // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// image: 3-D with shape `[height, width, channels]`. // -// Returns Random values with specified shape. -func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { +// Returns 0-D. JPEG-encoded image. +func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { if scope.Err() != nil { return } @@ -8316,9 +8476,9 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessTruncatedNormal", + Type: "EncodeJpeg", Input: []tf.Input{ - shape, seed, + image, }, Attrs: attrs, } @@ -8326,51 +8486,59 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt return op.Output(0) } -// RestoreSliceAttr is an optional argument to RestoreSlice. -type RestoreSliceAttr func(optionalAttr) +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) -// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// MultinomialSeed sets the optional seed attribute to value. // -// value: Index of file to open first if multiple files match -// `file_pattern`. See the documentation for `Restore`. -// If not specified, defaults to -1 -func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { return func(m optionalAttr) { - m["preferred_shard"] = value + m["seed"] = value } } -// Restores a tensor from checkpoint files. -// -// This is like `Restore` except that restored tensor can be listed as filling -// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the -// larger tensor and the slice that the restored tensor covers. +// MultinomialSeed2 sets the optional seed2 attribute to value. // -// The `shape_and_slice` input has the same format as the -// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. // // Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. -// tensor_name: Must have a single element. The name of the tensor to be -// restored. -// shape_and_slice: Scalar. The shapes and slice specifications to use when -// restoring a tensors. -// dt: The type of the tensor to be restored. +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. // -// Returns The restored tensor. -func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dt": dt} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "RestoreSlice", + Type: "Multinomial", Input: []tf.Input{ - file_pattern, tensor_name, shape_and_slice, + logits, num_samples, }, Attrs: attrs, } @@ -8378,89 +8546,89 @@ func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, s return op.Output(0) } -// Divides sparse updates into the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] /= updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] /= updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions multiply. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. // -//
-// -//
+// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. // // Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. // // Returns the created operation. -func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "ResourceScatterDiv", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// Mutually reduces multiple tensors of identical type and shape. -func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) { - if scope.Err() != nil { - return + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) } - attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} opspec := tf.OpSpec{ - Type: "CollectiveReduce", + Type: "ResourceSparseApplyAdagradDA", Input: []tf.Input{ - input, + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. -type StatelessRandomNormalAttr func(optionalAttr) +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) -// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { return func(m optionalAttr) { - m["dtype"] = value + m["use_locking"] = value } } -// Outputs deterministic pseudorandom values from a normal distribution. -// -// The generated values will have mean 0 and standard deviation 1. +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. // -// The outputs are a deterministic function of `shape` and `seed`. +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. // -// Returns Random values with specified shape. -func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -8469,186 +8637,112 @@ func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, option a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessRandomNormal", + Type: "ResourceSparseApplyFtrl", Input: []tf.Input{ - shape, seed, + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] = min(ref[indices, ...], updates[...]) -// -// # Vector indices (for each i) -// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions are combined. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// Returns which elements of x are Inf. // -// Returns the created operation. -func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +// @compatibility(numpy) +// Equivalent to np.isinf +// @end_compatibility +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ResourceScatterMin", + Type: "IsInf", Input: []tf.Input{ - resource, indices, updates, + x, }, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Reshapes a quantized tensor as per the Reshape op. +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. // -// ``` +// N is the size of the segment being reduced. // -// Arguments: +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. // -// shape: Defines the shape of the output tensor. -// input_min: The minimum value of the input. -// input_max: The maximum value of the input. +// Arguments: // -// Returns This value is copied from input_min.This value is copied from input_max. -func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "QuantizedReshape", - Input: []tf.Input{ - tensor, shape, input_min, input_max, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Returns the truth value of (x != y) element-wise. +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. // -// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "NotEqual", + Type: "SparseSegmentSqrtN", Input: []tf.Input{ - x, y, + data, indices, segment_ids, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Inverse 3D real-valued fast Fourier transform. -// -// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most 3 dimensions of `input`. -// -// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: -// The inner-most dimension contains the `fft_length / 2 + 1` unique components of -// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed -// from the size of the inner-most 3 dimensions of `input`. If the FFT length used -// to compute `input` is odd, it should be provided since it cannot be inferred -// properly. +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. // -// Along each axis `IRFFT3D` is computed on, if `fft_length` (or -// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. +// This Op does not require `a_indices` be sorted in standard lexicographic order. // // Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most 3 -// dimensions of `input` are replaced with the `fft_length` samples of their -// inverse 3D real Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.irfftn with 3 dimensions. -// @end_compatibility -func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "IRFFT3D", + Type: "SparseTensorDenseAdd", Input: []tf.Input{ - input, fft_length, + a_indices, a_values, a_shape, b, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// StringSplitAttr is an optional argument to StringSplit. -type StringSplitAttr func(optionalAttr) +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) -// StringSplitSkipEmpty sets the optional skip_empty attribute to value. +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. // -// value: A `bool`. If `True`, skip the empty strings from the result. -// If not specified, defaults to true -func StringSplitSkipEmpty(value bool) StringSplitAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { return func(m optionalAttr) { - m["skip_empty"] = value + m["dtype"] = value } } -// Split elements of `input` based on `delimiter` into a `SparseTensor`. -// -// Let N be the size of source (typically N will be the batch size). Split each -// element of `input` based on `delimiter` and return a `SparseTensor` -// containing the splitted tokens. Empty tokens are ignored. -// -// `delimiter` can be empty, or a string of split characters. If `delimiter` is an -// empty string, each element of `input` is split into individual single-byte -// character strings, including splitting of UTF-8 multibyte sequences. Otherwise -// every character of `delimiter` is a potential split point. +// Outputs deterministic pseudorandom values from a truncated normal distribution. // -// For example: -// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output -// will be +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. // -// indices = [0, 0; -// 0, 1; -// 1, 0; -// 1, 1; -// 1, 2] -// shape = [2, 3] -// values = ['hello', 'world', 'a', 'b', 'c'] +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// input: 1-D. Strings to split. -// delimiter: 0-D. Delimiter characters (bytes), or empty string. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse -// tensor, where the first value is N and the second value is the maximum number -// of tokens in a single input entry. -func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -8657,311 +8751,162 @@ func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional .. a(attrs) } opspec := tf.OpSpec{ - Type: "StringSplit", + Type: "StatelessTruncatedNormal", Input: []tf.Input{ - input, delimiter, + shape, seed, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. -type ResourceSparseApplyMomentumAttr func(optionalAttr) - -// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) -// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. // -// value: If `True`, the tensor passed to compute grad will be -// var - lr * momentum * accum, so in the end, the var you get is actually -// var - lr * momentum * accum. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { return func(m optionalAttr) { - m["use_nesterov"] = value + m["preferred_shard"] = value } } -// Update relevant entries in '*var' and '*accum' according to the momentum scheme. -// -// Set use_nesterov = True if you want to use Nesterov momentum. +// Restores a tensor from checkpoint files. // -// That is for rows we have grad for, we update var and accum as follows: +// This is like `Restore` except that restored tensor can be listed as filling +// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the +// larger tensor and the slice that the restored tensor covers. // -// accum = accum * momentum + grad -// var -= lr * accum +// The `shape_and_slice` input has the same format as the +// elements of the `shapes_and_slices` input of the `SaveSlices` op. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// momentum: Momentum. Must be a scalar. +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// shape_and_slice: Scalar. The shapes and slice specifications to use when +// restoring a tensors. +// dt: The type of the tensor to be restored. // -// Returns the created operation. -func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { +// Returns The restored tensor. +func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"dt": dt} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyMomentum", + Type: "RestoreSlice", Input: []tf.Input{ - var_, accum, lr, grad, indices, momentum, + file_pattern, tensor_name, shape_and_slice, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Returns the complex conjugate of a complex number. +// Divides sparse updates into the variable referenced by `resource`. // -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// complex numbers that are the complex conjugate of each element in `input`. The -// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the -// real part and *b* is the imaginary part. +// This operation computes // -// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// # Scalar indices +// ref[indices, ...] /= updates[...] // -// For example: +// # Vector indices (for each i) +// ref[indices[i], ...] /= updates[i, ...] // -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] -// ``` -func Conj(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Conj", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeBilinearAttr is an optional argument to ResizeBilinear. -type ResizeBilinearAttr func(optionalAttr) - -// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// Resize `images` to `size` using bilinear interpolation. +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. // -// Input images can be of different types but output images are always float. +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
// // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. // -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeBilinear", - Input: []tf.Input{ - images, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes softsign: `features / (abs(features) + 1)`. -func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { +// Returns the created operation. +func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Softsign", + Type: "ResourceScatterDiv", Input: []tf.Input{ - features, + resource, indices, updates, }, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Creates a TensorList which, when stacked, has the value of `tensor`. -// -// Each tensor in the result list corresponds to one row of the input tensor. -// -// tensor: The input tensor. -// output_handle: The list. -func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) { +// Mutually reduces multiple tensors of identical type and shape. +func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} opspec := tf.OpSpec{ - Type: "TensorListFromTensor", + Type: "CollectiveReduce", Input: []tf.Input{ - tensor, element_shape, + input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. -type GenerateVocabRemappingAttr func(optionalAttr) +// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. +type StatelessRandomNormalAttr func(optionalAttr) -// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. -// -// value: Number of entries in the old vocab file to consider. If -1, -// use the entire old vocabulary. -// If not specified, defaults to -1 +// StatelessRandomNormalDtype sets the optional dtype attribute to value. // -// REQUIRES: value >= -1 -func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { return func(m optionalAttr) { - m["old_vocab_size"] = value + m["dtype"] = value } } -// Given a path to new and old vocabulary files, returns a remapping Tensor of -// -// length `num_new_vocab`, where `remapping[i]` contains the row number in the old -// vocabulary that corresponds to row `i` in the new vocabulary (starting at line -// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` -// in the new vocabulary is not in the old vocabulary. The old vocabulary is -// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the -// default value of -1. -// -// `num_vocab_offset` enables -// use in the partitioned variable case, and should generally be set through -// examining partitioning info. The format of the files should be a text file, -// with each line containing a single entity within the vocabulary. -// -// For example, with `new_vocab_file` a text file containing each of the following -// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], -// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be -// `[0, -1, 2]`. +// Outputs deterministic pseudorandom values from a normal distribution. // -// The op also returns a count of how many entries in the new vocabulary -// were present in the old vocabulary, which is used to calculate the number of -// values to initialize in a weight matrix remapping +// The generated values will have mean 0 and standard deviation 1. // -// This functionality can be used to remap both row vocabularies (typically, -// features) and column vocabularies (typically, classes) from TensorFlow -// checkpoints. Note that the partitioning logic relies on contiguous vocabularies -// corresponding to div-partitioned variables. Moreover, the underlying remapping -// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should -// use the corresponding index_table_from_file() as the FeatureColumn framework -// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// new_vocab_file: Path to the new vocab file. -// old_vocab_file: Path to the old vocab file. -// new_vocab_offset: How many entries into the new vocab file to start reading. -// num_new_vocab: Number of entries in the new vocab file to remap. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns A Tensor of length num_new_vocab where the element at index i -// is equal to the old ID that maps to the new ID i. This element is -1 for any -// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. -func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { +// Returns Random values with specified shape. +func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "GenerateVocabRemapping", - Input: []tf.Input{ - new_vocab_file, old_vocab_file, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Assigns sparse updates to the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] = updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] = updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] -// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. -// -// Returns the created operation. -func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ResourceScatterUpdate", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// Creates and returns an empty tensor list. -// -// All list elements must be tensors of dtype element_dtype and shape compatible -// with element_shape. -// -// handle: an empty tensor list. -// element_dtype: the type of elements in the list. -// element_shape: a shape compatible with that of elements in the list. -func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.DataType) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"element_dtype": element_dtype} - opspec := tf.OpSpec{ - Type: "EmptyTensorList", + Type: "StatelessRandomNormal", Input: []tf.Input{ - element_shape, + shape, seed, }, Attrs: attrs, } @@ -8969,10 +8914,10 @@ func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.Dat return op.Output(0) } -// AvgPoolGradAttr is an optional argument to AvgPoolGrad. -type AvgPoolGradAttr func(optionalAttr) +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) -// AvgPoolGradDataFormat sets the optional data_format attribute to value. +// MaxPoolDataFormat sets the optional data_format attribute to value. // // value: Specify the data format of the input and output data. With the // default format "NHWC", the data is stored in the order of: @@ -8980,24 +8925,23 @@ type AvgPoolGradAttr func(optionalAttr) // Alternatively, the format could be "NCHW", the data storage order of: // [batch, in_channels, in_height, in_width]. // If not specified, defaults to "NHWC" -func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { +func MaxPoolDataFormat(value string) MaxPoolAttr { return func(m optionalAttr) { m["data_format"] = value } } -// Computes gradients of the average pooling function. +// Performs max pooling on the input. // // Arguments: -// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. -// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. -// the output of `avg_pool`. -// ksize: The size of the sliding window for each dimension of the input. -// strides: The stride of the sliding window for each dimension of the input. +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. // padding: The type of padding algorithm to use. // -// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. -func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -9006,9 +8950,9 @@ func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize a(attrs) } opspec := tf.OpSpec{ - Type: "AvgPoolGrad", + Type: "MaxPool", Input: []tf.Input{ - orig_input_shape, grad, + input, }, Attrs: attrs, } @@ -9016,321 +8960,457 @@ func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize return op.Output(0) } -// StageClearAttr is an optional argument to StageClear. -type StageClearAttr func(optionalAttr) +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) -// StageClearCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageClearCapacity(value int64) StageClearAttr { +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { return func(m optionalAttr) { - m["capacity"] = value + m["transpose_a"] = value } } -// StageClearMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageClearMemoryLimit(value int64) StageClearAttr { +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { return func(m optionalAttr) { - m["memory_limit"] = value + m["transpose_b"] = value } } -// StageClearContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StageClearContainer(value string) StageClearAttr { +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { return func(m optionalAttr) { - m["container"] = value + m["a_is_sparse"] = value } } -// StageClearSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StageClearSharedName(value string) StageClearAttr { +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["b_is_sparse"] = value } } -// Op removes all elements in the underlying container. +// Multiply matrix "a" by matrix "b". // -// Returns the created operation. -func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) { +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". This op is optimized for the case where at +// least one of "a" or "b" is sparse. The breakeven for using this versus a dense +// matrix multiply on one platform was 30% zero values in the sparse matrix. +// +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "StageClear", - + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. -type ComputeAccidentalHitsAttr func(optionalAttr) - -// ComputeAccidentalHitsSeed sets the optional seed attribute to value. +// Concatenates quantized tensors along one dimension. // -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// input_mins: The minimum scalar values for each of the input tensors. +// input_maxes: The maximum scalar values for each of the input tensors. // -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { - return func(m optionalAttr) { - m["seed2"] = value +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedConcat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + }, } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// Computes the ids of the positions in sampled_candidates that match true_labels. +// Slice a `SparseTensor` based on the `start` and `size`. // -// When doing log-odds NCE, the result of this op should be passed through a -// SparseToDense op, then added to the logits of the sampled candidates. This has -// the effect of 'removing' the sampled labels that match the true labels by -// making the classifier sure that they are sampled labels. +// For example, if the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] // // Arguments: -// true_classes: The true_classes output of UnpackSparseLabels. -// sampled_candidates: The sampled_candidates output of CandidateSampler. -// num_true: Number of true labels per context. +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. // -// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label -// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element -// is -FLOAT_MAX. -func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_true": num_true} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ComputeAccidentalHits", + Type: "SparseSlice", Input: []tf.Input{ - true_classes, sampled_candidates, + indices, values, shape, start, size, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) } -// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. -type QuantizedRelu6Attr func(optionalAttr) - -// QuantizedRelu6OutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QUINT8 -func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { - return func(m optionalAttr) { - m["out_type"] = value +// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = min(ref[indices, ...], updates[...]) +// +// # Vector indices (for each i) +// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions are combined. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return } + opspec := tf.OpSpec{ + Type: "ResourceScatterMin", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) } -// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` +// Reshapes a quantized tensor as per the Reshape op. +// +// ``` // // Arguments: // -// min_features: The float value that the lowest quantized value represents. -// max_features: The float value that the highest quantized value represents. +// shape: Defines the shape of the output tensor. +// input_min: The minimum value of the input. +// input_max: The maximum value of the input. // -// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. -func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { +// Returns This value is copied from input_min.This value is copied from input_max. +func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "QuantizedRelu6", + Type: "QuantizedReshape", Input: []tf.Input{ - features, min_features, max_features, + tensor, shape, input_min, input_max, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) } -// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. -type FixedLengthRecordReaderV2Attr func(optionalAttr) - -// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value. +// Returns the truth value of (x != y) element-wise. // -// value: Number of bytes in the header, defaults to 0. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["header_bytes"] = value +// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return } -} - -// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value. -// -// value: Number of bytes in the footer, defaults to 0. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["footer_bytes"] = value + opspec := tf.OpSpec{ + Type: "NotEqual", + Input: []tf.Input{ + x, y, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value. +// Inverse 3D real-valued fast Fourier transform. // -// value: Number of bytes to hop before each read. Default of 0 means using -// record_bytes. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["hop_bytes"] = value - } -} - -// FixedLengthRecordReaderV2Container sets the optional container attribute to value. +// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 3 dimensions of `input`. // -// value: If non-empty, this reader is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value. +// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 3 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. // -// value: If non-empty, this reader is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value +// Along each axis `IRFFT3D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 3D real Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.irfftn with 3 dimensions. +// @end_compatibility +func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IRFFT3D", + Input: []tf.Input{ + input, fft_length, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value. +// StringSplitAttr is an optional argument to StringSplit. +type StringSplitAttr func(optionalAttr) + +// StringSplitSkipEmpty sets the optional skip_empty attribute to value. // -// value: The type of encoding for the file. Currently ZLIB and GZIP -// are supported. Defaults to none. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr { +// value: A `bool`. If `True`, skip the empty strings from the result. +// If not specified, defaults to true +func StringSplitSkipEmpty(value bool) StringSplitAttr { return func(m optionalAttr) { - m["encoding"] = value + m["skip_empty"] = value } } -// A Reader that outputs fixed-length records from a file. +// Split elements of `input` based on `delimiter` into a `SparseTensor`. +// +// Let N be the size of source (typically N will be the batch size). Split each +// element of `input` based on `delimiter` and return a `SparseTensor` +// containing the splitted tokens. Empty tokens are ignored. +// +// `delimiter` can be empty, or a string of split characters. If `delimiter` is an +// empty string, each element of `input` is split into individual single-byte +// character strings, including splitting of UTF-8 multibyte sequences. Otherwise +// every character of `delimiter` is a potential split point. +// +// For example: +// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output +// will be +// +// indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// shape = [2, 3] +// values = ['hello', 'world', 'a', 'b', 'c'] // // Arguments: -// record_bytes: Number of bytes in the record. +// input: 1-D. Strings to split. +// delimiter: 0-D. Delimiter characters (bytes), or empty string. // -// Returns The handle to reference the Reader. -func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) { +// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse +// tensor, where the first value is N and the second value is the maximum number +// of tokens in a single input entry. +func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"record_bytes": record_bytes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "FixedLengthRecordReaderV2", - + Type: "StringSplit", + Input: []tf.Input{ + input, delimiter, + }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. +// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. +type ResourceSparseApplyMomentumAttr func(optionalAttr) + +// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. // -// The hash function is deterministic on the content of the string within the -// process. +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. // -// Note that the hash function may change from time to time. -// This functionality will be deprecated and it's recommended to use -// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. // -// Arguments: +// Set use_nesterov = True if you want to use Nesterov momentum. // -// num_buckets: The number of buckets. +// That is for rows we have grad for, we update var and accum as follows: // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { +// accum = accum * momentum + grad +// var -= lr * accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "StringToHashBucket", + Type: "ResourceSparseApplyMomentum", Input: []tf.Input{ - string_tensor, + var_, accum, lr, grad, indices, momentum, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Computes gradients for the exponential linear (Elu) operation. +// Returns the complex conjugate of a complex number. // -// Arguments: -// gradients: The backpropagated gradients to the corresponding Elu operation. -// outputs: The outputs of the corresponding Elu operation. +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// complex numbers that are the complex conjugate of each element in `input`. The +// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the +// real part and *b* is the imaginary part. // -// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, -// `gradients` otherwise. -func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { +// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] +// ``` +func Conj(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "EluGrad", + Type: "Conj", Input: []tf.Input{ - gradients, outputs, + input, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Creates a dataset that contains `count` elements from the `input_dataset`. +// ResizeBilinearAttr is an optional argument to ResizeBilinear. +type ResizeBilinearAttr func(optionalAttr) + +// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. // -// Arguments: +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// Resize `images` to `size` using bilinear interpolation. // -// count: A scalar representing the number of elements from the `input_dataset` -// that should be taken. A value of `-1` indicates that all of `input_dataset` -// is taken. +// Input images can be of different types but output images are always float. // +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. // -func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TakeDataset", + Type: "ResizeBilinear", Input: []tf.Input{ - input_dataset, count, + images, size, }, Attrs: attrs, } @@ -9338,251 +9418,201 @@ func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_ return op.Output(0) } -// The gradient operator for the SparseAdd op. -// -// The SparseAdd op calculates A + B, where A, B, and the sum are all represented -// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. -// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty -// values of A and B. -// -// Arguments: -// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to -// the non-empty values of the sum. -// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. -// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. -// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size -// `[nnz(sum), ndims]`. -// -// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the -// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the -// non-empty values of B. -func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { +// Computes softsign: `features / (abs(features) + 1)`. +func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseAddGrad", + Type: "Softsign", Input: []tf.Input{ - backprop_val_grad, a_indices, b_indices, sum_indices, + features, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// Computes atan of x element-wise. -func Atan(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Atan", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Encode audio data using the WAV file format. -// -// This operation will generate a string suitable to be saved out to create a .wav -// audio file. It will be encoded in the 16-bit PCM format. It takes in float -// values in the range -1.0f to 1.0f, and any outside that value will be clamped to -// that range. -// -// `audio` is a 2-D float Tensor of shape `[length, channels]`. -// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). -// -// Arguments: -// audio: 2-D with shape `[length, channels]`. -// sample_rate: Scalar containing the sample frequency. -// -// Returns 0-D. WAV-encoded file contents. -func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EncodeWav", - Input: []tf.Input{ - audio, sample_rate, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process. The hash function is a keyed hash function, where attribute `key` -// defines the key of the hash function. `key` is an array of 2 elements. -// -// A strong hash is important when inputs may be malicious, e.g. URLs with -// additional components. Adversaries could try to make their inputs hash to the -// same bucket for a denial-of-service attack or to skew the results. A strong -// hash prevents this by making it difficult, if not infeasible, to compute inputs -// that hash to the same bucket. This comes at a cost of roughly 4x higher compute -// time than `tf.string_to_hash_bucket_fast`. +// Creates a TensorList which, when stacked, has the value of `tensor`. // -// Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. -// key: The key for the keyed hash function passed as a list of two uint64 -// elements. +// Each tensor in the result list corresponds to one row of the input tensor. // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { +// tensor: The input tensor. +// output_handle: The list. +func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} opspec := tf.OpSpec{ - Type: "StringToHashBucketStrong", + Type: "TensorListFromTensor", Input: []tf.Input{ - input, + tensor, element_shape, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// RegexReplaceAttr is an optional argument to RegexReplace. -type RegexReplaceAttr func(optionalAttr) +// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. +type GenerateVocabRemappingAttr func(optionalAttr) -// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. // -// value: If True, the replacement is global, otherwise the replacement -// is done only on the first match. -// If not specified, defaults to true -func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { +// value: Number of entries in the old vocab file to consider. If -1, +// use the entire old vocabulary. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { return func(m optionalAttr) { - m["replace_global"] = value + m["old_vocab_size"] = value } } -// Replaces the match of pattern in input with rewrite. +// Given a path to new and old vocabulary files, returns a remapping Tensor of // -// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// length `num_new_vocab`, where `remapping[i]` contains the row number in the old +// vocabulary that corresponds to row `i` in the new vocabulary (starting at line +// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` +// in the new vocabulary is not in the old vocabulary. The old vocabulary is +// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the +// default value of -1. +// +// `num_vocab_offset` enables +// use in the partitioned variable case, and should generally be set through +// examining partitioning info. The format of the files should be a text file, +// with each line containing a single entity within the vocabulary. +// +// For example, with `new_vocab_file` a text file containing each of the following +// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], +// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be +// `[0, -1, 2]`. +// +// The op also returns a count of how many entries in the new vocabulary +// were present in the old vocabulary, which is used to calculate the number of +// values to initialize in a weight matrix remapping +// +// This functionality can be used to remap both row vocabularies (typically, +// features) and column vocabularies (typically, classes) from TensorFlow +// checkpoints. Note that the partitioning logic relies on contiguous vocabularies +// corresponding to div-partitioned variables. Moreover, the underlying remapping +// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should +// use the corresponding index_table_from_file() as the FeatureColumn framework +// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). // // Arguments: -// input: The text to be processed. -// pattern: The regular expression to match the input. -// rewrite: The rewrite to be applied to the matched expresion. +// new_vocab_file: Path to the new vocab file. +// old_vocab_file: Path to the old vocab file. +// new_vocab_offset: How many entries into the new vocab file to start reading. +// num_new_vocab: Number of entries in the new vocab file to remap. // -// Returns The text after applying pattern and rewrite. -func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { +// Returns A Tensor of length num_new_vocab where the element at index i +// is equal to the old ID that maps to the new ID i. This element is -1 for any +// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. +func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "RegexReplace", + Type: "GenerateVocabRemapping", Input: []tf.Input{ - input, pattern, rewrite, + new_vocab_file, old_vocab_file, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Computes numerical negative value element-wise. +// Assigns sparse updates to the variable referenced by `resource`. // -// I.e., \\(y = -x\\). -func Neg(scope *Scope, x tf.Output) (y tf.Output) { +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] = updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] +// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Neg", + Type: "ResourceScatterUpdate", Input: []tf.Input{ - x, + resource, indices, updates, }, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Execute a sub graph on a remote processor. -// -// The graph specifications(such as graph itself, input tensors and output names) -// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo -// as serialized_remote_fused_graph_execute_info. -// The specifications will be passed to a dedicated registered -// remote fused graph executor. The executor will send the graph specifications -// to a remote processor and execute that graph. The execution results -// will be passed to consumer nodes as outputs of this node. -// -// Arguments: -// inputs: Arbitrary number of tensors with arbitrary data types +// Creates and returns an empty tensor list. // -// serialized_remote_fused_graph_execute_info: Serialized protocol buffer -// of RemoteFusedGraphExecuteInfo which contains graph specifications. +// All list elements must be tensors of dtype element_dtype and shape compatible +// with element_shape. // -// Returns Arbitrary number of tensors with arbitrary data types -func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { +// handle: an empty tensor list. +// element_dtype: the type of elements in the list. +// element_shape: a shape compatible with that of elements in the list. +func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.DataType) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} + attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "RemoteFusedGraphExecute", + Type: "EmptyTensorList", Input: []tf.Input{ - tf.OutputList(inputs), + element_shape, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("RemoteFusedGraphExecute", err) - return - } - return outputs + return op.Output(0) } -// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. -type MaxPool3DGradGradAttr func(optionalAttr) +// AvgPoolGradAttr is an optional argument to AvgPoolGrad. +type AvgPoolGradAttr func(optionalAttr) -// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value. +// AvgPoolGradDataFormat sets the optional data_format attribute to value. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr { +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { return func(m optionalAttr) { m["data_format"] = value } } -// Computes second-order gradients of the maxpooling function. +// Computes gradients of the average pooling function. // // Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. +// the output of `avg_pool`. +// ksize: The size of the sliding window for each dimension of the input. +// strides: The stride of the sliding window for each dimension of the input. // padding: The type of padding algorithm to use. // -// Returns Gradients of gradients w.r.t. the input to `max_pool`. -func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) { +// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. +func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -9591,9 +9621,9 @@ func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPool3DGradGrad", + Type: "AvgPoolGrad", Input: []tf.Input{ - orig_input, orig_output, grad, + orig_input_shape, grad, }, Attrs: attrs, } @@ -9601,544 +9631,388 @@ func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output return op.Output(0) } -// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. -type Conv3DBackpropFilterV2Attr func(optionalAttr) +// StageClearAttr is an optional argument to StageClear. +type StageClearAttr func(optionalAttr) -// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { +// StageClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageClearCapacity(value int64) StageClearAttr { return func(m optionalAttr) { - m["data_format"] = value + m["capacity"] = value } } -// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. +// StageClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// value: 1-D tensor of length 5. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each -// filter element on that dimension. The dimension order is determined by the -// value of `data_format`, see above for details. Dilations in the batch and -// depth dimensions must be 1. -// If not specified, defaults to -func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { +// REQUIRES: value >= 0 +func StageClearMemoryLimit(value int64) StageClearAttr { return func(m optionalAttr) { - m["dilations"] = value + m["memory_limit"] = value } } -// Computes the gradients of 3-D convolution with respect to the filter. +// StageClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageClearContainer(value string) StageClearAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageClearSharedName(value string) StageClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes all elements in the underlying container. // -// Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter_sizes: An integer vector representing the tensor shape of `filter`, -// where `filter` is a 5-D -// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` -// tensor. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { +// Returns the created operation. +func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Conv3DBackpropFilterV2", - Input: []tf.Input{ - input, filter_sizes, out_backprop, - }, + Type: "StageClear", + Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. -type FakeQuantWithMinMaxVarsAttr func(optionalAttr) +// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. +type ComputeAccidentalHitsAttr func(optionalAttr) -// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { +// ComputeAccidentalHitsSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { return func(m optionalAttr) { - m["num_bits"] = value + m["seed"] = value } } -// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { +// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { return func(m optionalAttr) { - m["narrow_range"] = value + m["seed2"] = value } } -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// Computes the ids of the positions in sampled_candidates that match true_labels. // -// and `max` to 'outputs' tensor of same shape as `inputs`. +// When doing log-odds NCE, the result of this op should be passed through a +// SparseToDense op, then added to the logits of the sampled candidates. This has +// the effect of 'removing' the sampled labels that match the true labels by +// making the classifier sure that they are sampled labels. // -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// Arguments: +// true_classes: The true_classes output of UnpackSparseLabels. +// sampled_candidates: The sampled_candidates output of CandidateSampler. +// num_true: Number of true labels per context. // -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { +// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label +// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element +// is -FLOAT_MAX. +func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"num_true": num_true} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", + Type: "ComputeAccidentalHits", Input: []tf.Input{ - inputs, min, max, + true_classes, sampled_candidates, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Applies softmax to a batched N-D `SparseTensor`. -// -// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` -// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. -// -// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost -// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly -// zero elements do not participate*. Specifically, the algorithm is equivalent -// to the following: -// -// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix -// with shape `[B, C]`, along the size-C dimension; -// (2) Masks out the original implicitly-zero locations; -// (3) Renormalizes the remaining elements. -// -// Hence, the `SparseTensor` result has exactly the same non-zero indices and -// shape. -// -// Arguments: -// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a -// SparseTensor, in canonical ordering. -// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// -// Returns 1-D. The `NNZ` values for the result `SparseTensor`. -func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSoftmax", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, - }, +// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. +type QuantizedRelu6Attr func(optionalAttr) + +// QuantizedRelu6OutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { + return func(m optionalAttr) { + m["out_type"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Partitions `data` into `num_partitions` tensors using indices from `partitions`. -// -// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` -// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` -// are placed in `outputs[i]` in lexicographic order of `js`, and the first -// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. -// In detail, -// -// ```python -// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] -// -// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) -// ``` -// -// `data.shape` must start with `partitions.shape`. -// -// For example: -// -// ```python -// # Scalar partitions. -// partitions = 1 -// num_partitions = 2 -// data = [10, 20] -// outputs[0] = [] # Empty with shape [0, 2] -// outputs[1] = [[10, 20]] -// -// # Vector partitions. -// partitions = [0, 0, 1, 1, 0] -// num_partitions = 2 -// data = [10, 20, 30, 40, 50] -// outputs[0] = [10, 20, 50] -// outputs[1] = [30, 40] -// ``` -// -// See `dynamic_stitch` for an example on how to merge partitions back. -// -//
-// -//
+// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` // // Arguments: // -// partitions: Any shape. Indices in the range `[0, num_partitions)`. -// num_partitions: The number of partitions to output. -func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. +func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_partitions": num_partitions} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "DynamicPartition", + Type: "QuantizedRelu6", Input: []tf.Input{ - data, partitions, + features, min_features, max_features, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("DynamicPartition", err) - return - } - return outputs + return op.Output(0), op.Output(1), op.Output(2) } -// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. -type ResourceApplyAdagradAttr func(optionalAttr) +// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. +type FixedLengthRecordReaderV2Attr func(optionalAttr) -// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { +// value: Number of bytes in the header, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["use_locking"] = value + m["header_bytes"] = value } } -// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. -// If not specified, defaults to true -func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { +// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value. +// +// value: Number of bytes in the footer, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["update_slots"] = value + m["footer_bytes"] = value } } -// Update '*var' according to the adagrad scheme. -// -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. +// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value. // -// Returns the created operation. -func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdagrad", - Input: []tf.Input{ - var_, accum, lr, grad, - }, - Attrs: attrs, +// value: Number of bytes to hop before each read. Default of 0 means using +// record_bytes. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["hop_bytes"] = value } - return scope.AddOperation(opspec) } -// Return the shape of s0 op s1 with broadcast. +// FixedLengthRecordReaderV2Container sets the optional container attribute to value. // -// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the -// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. -func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BroadcastArgs", - Input: []tf.Input{ - s0, s1, - }, +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. -type DataFormatDimMapAttr func(optionalAttr) - -// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. +// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value. // -// value: source data format. -// If not specified, defaults to "NHWC" -func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["src_format"] = value + m["shared_name"] = value } } -// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. +// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value. // -// value: destination data format. -// If not specified, defaults to "NCHW" -func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { +// value: The type of encoding for the file. Currently ZLIB and GZIP +// are supported. Defaults to none. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["dst_format"] = value + m["encoding"] = value } } -// Returns the dimension index in the destination data format given the one in -// -// the source data format. +// A Reader that outputs fixed-length records from a file. // // Arguments: -// x: A Tensor with each element as a dimension index in source data format. -// Must be in the range [-4, 4). +// record_bytes: Number of bytes in the record. // -// Returns A Tensor with each element as a dimension index in destination data format. -func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { +// Returns The handle to reference the Reader. +func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"record_bytes": record_bytes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DataFormatDimMap", - Input: []tf.Input{ - x, - }, + Type: "FixedLengthRecordReaderV2", + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. -type ResourceApplyPowerSignAttr func(optionalAttr) - -// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and m tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the AddSign update. +// The gradient operator for the SparseAdd op. // -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g -// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g -// variable <- variable - lr_t * update +// The SparseAdd op calculates A + B, where A, B, and the sum are all represented +// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. +// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty +// values of A and B. // // Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// logbase: Must be a scalar. -// sign_decay: Must be a scalar. -// beta: Must be a scalar. -// grad: The gradient. +// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to +// the non-empty values of the sum. +// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. +// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. +// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size +// `[nnz(sum), ndims]`. // -// Returns the created operation. -func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { +// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the +// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the +// non-empty values of B. +func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceApplyPowerSign", + Type: "SparseAddGrad", Input: []tf.Input{ - var_, m, lr, logbase, sign_decay, beta, grad, + backprop_val_grad, a_indices, b_indices, sum_indices, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) } -// Locks a mutex resource. The output is the lock. So long as the lock tensor -// -// is alive, any other request to use `MutexLock` with this mutex will wait. -// -// This is particularly useful for creating a critical section when used in -// conjunction with `MutexLockIdentity`: -// -// ```python -// -// mutex = mutex_v2( -// shared_name=handle_name, container=container, name=name) -// -// def execute_in_critical_section(fn, *args, **kwargs): -// lock = gen_resource_variable_ops.mutex_lock(mutex) -// -// with ops.control_dependencies([lock]): -// r = fn(*args, **kwargs) -// -// with ops.control_dependencies(nest.flatten(r)): -// with ops.colocate_with(mutex): -// ensure_lock_exists = mutex_lock_identity(lock) -// -// # Make sure that if any element of r is accessed, all of -// # them are executed together. -// r = nest.map_structure(tf.identity, r) -// -// with ops.control_dependencies([ensure_lock_exists]): -// return nest.map_structure(tf.identity, r) -// ``` -// -// While `fn` is running in the critical section, no other functions which wish to -// use this critical section may run. -// -// Often the use case is that two executions of the same graph, in parallel, -// wish to run `fn`; and we wish to ensure that only one of them executes -// at a time. This is especially important if `fn` modifies one or more -// variables at a time. -// -// It is also useful if two separate functions must share a resource, but we -// wish to ensure the usage is exclusive. -// -// Arguments: -// mutex: The mutex resource to lock. -// -// Returns A tensor that keeps a shared pointer to a lock on the mutex; -// when the Tensor is destroyed, the use count on the shared pointer is decreased -// by 1. When it reaches 0, the lock is released. -func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { +// Computes atan of x element-wise. +func Atan(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "MutexLock", + Type: "Atan", Input: []tf.Input{ - mutex, + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the mean along segments of a tensor. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Computes a tensor such that -// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is -// over `j` such that `segment_ids[j] == i` and `N` is the total number of -// values summed. +// Encode audio data using the WAV file format. // -// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// This operation will generate a string suitable to be saved out to create a .wav +// audio file. It will be encoded in the 16-bit PCM format. It takes in float +// values in the range -1.0f to 1.0f, and any outside that value will be clamped to +// that range. // -//
-// -//
+// `audio` is a 2-D float Tensor of shape `[length, channels]`. +// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). // // Arguments: +// audio: 2-D with shape `[length, channels]`. +// sample_rate: Scalar containing the sample frequency. // -// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +// Returns 0-D. WAV-encoded file contents. +func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SegmentMean", + Type: "EncodeWav", Input: []tf.Input{ - data, segment_ids, + audio, sample_rate, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. -type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) - -// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// Converts each string in the input Tensor to its hash mod by a number of buckets. // -// value: If `True`, updating of the var, mg, ms, and mom tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value +// The hash function is deterministic on the content of the string within the +// process. The hash function is a keyed hash function, where attribute `key` +// defines the key of the hash function. `key` is an array of 2 elements. +// +// A strong hash is important when inputs may be malicious, e.g. URLs with +// additional components. Adversaries could try to make their inputs hash to the +// same bucket for a denial-of-service attack or to skew the results. A strong +// hash prevents this by making it difficult, if not infeasible, to compute inputs +// that hash to the same bucket. This comes at a cost of roughly 4x higher compute +// time than `tf.string_to_hash_bucket_fast`. +// +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// key: The key for the keyed hash function passed as a list of two uint64 +// elements. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} + opspec := tf.OpSpec{ + Type: "StringToHashBucketStrong", + Input: []tf.Input{ + input, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Update '*var' according to the centered RMSProp algorithm. -// -// The centered RMSProp algorithm uses an estimate of the centered second moment -// (i.e., the variance) for normalization, as opposed to regular RMSProp, which -// uses the (uncentered) second moment. This often helps with training, but is -// slightly more expensive in terms of computation and memory. -// -// Note that in dense implementation of this algorithm, mg, ms, and mom will -// update even if the grad is zero, but in this sparse implementation, mg, ms, -// and mom will not update in iterations during which the grad is zero. +// RegexReplaceAttr is an optional argument to RegexReplace. +type RegexReplaceAttr func(optionalAttr) + +// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. // -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// mean_grad = decay * mean_grad + (1-decay) * gradient -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) +// value: If True, the replacement is global, otherwise the replacement +// is done only on the first match. +// If not specified, defaults to true +func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { + return func(m optionalAttr) { + m["replace_global"] = value + } +} + +// Replaces the match of pattern in input with rewrite. // -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) // // Arguments: -// var_: Should be from a Variable(). -// mg: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. +// input: The text to be processed. +// pattern: The regular expression to match the input. +// rewrite: The rewrite to be applied to the matched expresion. // -// Returns the created operation. -func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { +// Returns The text after applying pattern and rewrite. +func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -10147,185 +10021,180 @@ func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Outp a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyCenteredRMSProp", + Type: "RegexReplace", Input: []tf.Input{ - var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, + input, pattern, rewrite, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Creates a dataset that batches `batch_size` elements from `input_dataset`. -// -// Arguments: -// -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. -// +// Computes numerical negative value element-wise. // -func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// I.e., \\(y = -x\\). +func Neg(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "BatchDataset", + Type: "Neg", Input: []tf.Input{ - input_dataset, batch_size, + x, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Says whether the targets are in the top `K` predictions. -// -// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the -// prediction for the target class is among the top `k` predictions among -// all predictions for example `i`. Note that the behavior of `InTopK` differs -// from the `TopK` op in its handling of ties; if multiple classes have the -// same prediction value and straddle the top-`k` boundary, all of those -// classes are considered to be in the top `k`. -// -// More formally, let -// -// \\(predictions_i\\) be the predictions for all classes for example `i`, -// \\(targets_i\\) be the target class for example `i`, -// \\(out_i\\) be the output for example `i`, +// Execute a sub graph on a remote processor. // -// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// The graph specifications(such as graph itself, input tensors and output names) +// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo +// as serialized_remote_fused_graph_execute_info. +// The specifications will be passed to a dedicated registered +// remote fused graph executor. The executor will send the graph specifications +// to a remote processor and execute that graph. The execution results +// will be passed to consumer nodes as outputs of this node. // // Arguments: -// predictions: A `batch_size` x `classes` tensor. -// targets: A `batch_size` vector of class ids. -// k: Number of top elements to look at for computing precision. +// inputs: Arbitrary number of tensors with arbitrary data types // -// Returns Computed precision at `k` as a `bool Tensor`. -func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { +// serialized_remote_fused_graph_execute_info: Serialized protocol buffer +// of RemoteFusedGraphExecuteInfo which contains graph specifications. +// +// Returns Arbitrary number of tensors with arbitrary data types +func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} opspec := tf.OpSpec{ - Type: "InTopKV2", + Type: "RemoteFusedGraphExecute", Input: []tf.Input{ - predictions, targets, k, + tf.OutputList(inputs), }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RemoteFusedGraphExecute", err) + return + } + return outputs } -// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. -type DecodeAndCropJpegAttr func(optionalAttr) +// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. +type MaxPool3DGradGradAttr func(optionalAttr) -// DecodeAndCropJpegChannels sets the optional channels attribute to value. +// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value. // -// value: Number of color channels for the decoded image. -// If not specified, defaults to 0 -func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr { return func(m optionalAttr) { - m["channels"] = value + m["data_format"] = value } } -// DecodeAndCropJpegRatio sets the optional ratio attribute to value. +// Computes second-order gradients of the maxpooling function. // -// value: Downscaling ratio. -// If not specified, defaults to 1 -func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["ratio"] = value - } -} - -// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. // -// value: If true use a slower but nicer upscaling of the -// chroma planes (yuv420/422 only). -// If not specified, defaults to true -func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["fancy_upscaling"] = value +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3DGradGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. +type Conv3DBackpropFilterV2Attr func(optionalAttr) + +// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. // -// value: If true try to recover an image from truncated input. -// If not specified, defaults to false -func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { return func(m optionalAttr) { - m["try_recover_truncated"] = value + m["data_format"] = value } } -// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. // -// value: The minimum required fraction of lines before a truncated -// input is accepted. -// If not specified, defaults to 1 -func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { +// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { return func(m optionalAttr) { - m["acceptable_fraction"] = value + m["dilations"] = value } } -// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. -// -// value: string specifying a hint about the algorithm used for -// decompression. Defaults to "" which maps to a system-specific -// default. Currently valid values are ["INTEGER_FAST", -// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal -// jpeg library changes to a version that does not have that specific -// option.) -// If not specified, defaults to "" -func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["dct_method"] = value - } -} - -// Decode and Crop a JPEG-encoded image to a uint8 tensor. -// -// The attr `channels` indicates the desired number of color channels for the -// decoded image. -// -// Accepted values are: -// -// * 0: Use the number of channels in the JPEG-encoded image. -// * 1: output a grayscale image. -// * 3: output an RGB image. -// -// If needed, the JPEG-encoded image is transformed to match the requested number -// of color channels. -// -// The attr `ratio` allows downscaling the image by an integer factor during -// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than -// downscaling the image later. -// -// -// It is equivalent to a combination of decode and crop, but much faster by only -// decoding partial jpeg image. +// Computes the gradients of 3-D convolution with respect to the filter. // // Arguments: -// contents: 0-D. The JPEG-encoded image. -// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. -// -// Returns 3-D with shape `[height, width, channels]`.. -func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 5-D +// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` +// tensor. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DecodeAndCropJpeg", + Type: "Conv3DBackpropFilterV2", Input: []tf.Input{ - contents, crop_window, + input, filter_sizes, out_backprop, }, Attrs: attrs, } @@ -10333,273 +10202,268 @@ func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, return op.Output(0) } -// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. -type AllCandidateSamplerAttr func(optionalAttr) +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) -// AllCandidateSamplerSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { return func(m optionalAttr) { - m["seed"] = value + m["num_bits"] = value } } -// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { return func(m optionalAttr) { - m["seed2"] = value + m["narrow_range"] = value } } -// Generates labels for candidate sampling with a learned unigram distribution. -// -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. -// -// For each batch, this op picks a single set of sampled candidate labels. +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` // -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. +// and `max` to 'outputs' tensor of same shape as `inputs`. // -// Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to produce. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. // -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "AllCandidateSampler", + Type: "FakeQuantWithMinMaxVars", Input: []tf.Input{ - true_classes, + inputs, min, max, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Adds two `SparseTensor` objects to produce another `SparseTensor`. +// Applies softmax to a batched N-D `SparseTensor`. // -// The input `SparseTensor` objects' indices are assumed ordered in standard -// lexicographic order. If this is not the case, before this step run -// `SparseReorder` to restore index ordering. +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. // -// By default, if two values sum to zero at some index, the output `SparseTensor` -// would still include that particular location in its index, storing a zero in the -// corresponding value slot. To override this, callers can specify `thresh`, -// indicating that if the sum has a magnitude strictly smaller than `thresh`, its -// corresponding value and index would then not be included. In particular, -// `thresh == 0` (default) means everything is kept and actual thresholding happens -// only for a positive value. +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: // -// In the following shapes, `nnz` is the count after taking `thresh` into account. +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. +// +// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. // // Arguments: -// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. -// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. -// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. -// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. -// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. -// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. -// thresh: 0-D. The magnitude threshold that determines if an output value/index -// pair takes space. -func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseAdd", + Type: "SparseSoftmax", Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, + sp_indices, sp_values, sp_shape, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// OrderedMapPeekAttr is an optional argument to OrderedMapPeek. -type OrderedMapPeekAttr func(optionalAttr) - -// OrderedMapPeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. // -// REQUIRES: value >= 0 -func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr { - return func(m optionalAttr) { - m["capacity"] = value +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, +// +// ```python +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] +// +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) +// ``` +// +// `data.shape` must start with `partitions.shape`. +// +// For example: +// +// ```python +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] +// ``` +// +// See `dynamic_stitch` for an example on how to merge partitions back. +// +//
+// +//
+// +// Arguments: +// +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_partitions": num_partitions} + opspec := tf.OpSpec{ + Type: "DynamicPartition", + Input: []tf.Input{ + data, partitions, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) + return } + return outputs } -// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. // -// REQUIRES: value >= 0 -func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// OrderedMapPeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapPeekContainer(value string) OrderedMapPeekAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { return func(m optionalAttr) { - m["container"] = value + m["use_locking"] = value } } -// OrderedMapPeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr { +// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["update_slots"] = value } } -// Op peeks at the values at the specified key. If the +// Update '*var' according to the adagrad scheme. // -// underlying container does not contain this key -// this op will block until it does. This Op is optimized for -// performance. -func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) { +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "OrderedMapPeek", + Type: "ResourceApplyAdagrad", Input: []tf.Input{ - key, indices, + var_, accum, lr, grad, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("OrderedMapPeek", err) - return - } - return values + return scope.AddOperation(opspec) } -// Inverse fast Fourier transform. -// -// Computes the inverse 1-dimensional discrete Fourier transform over the -// inner-most dimension of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its inverse 1D Fourier transform. +// Return the shape of s0 op s1 with broadcast. // -// @compatibility(numpy) -// Equivalent to np.fft.ifft -// @end_compatibility -func IFFT(scope *Scope, input tf.Output) (output tf.Output) { +// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the +// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. +func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "IFFT", + Type: "BroadcastArgs", Input: []tf.Input{ - input, + s0, s1, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Generates values in an interval. -// -// A sequence of `num` evenly-spaced values are generated beginning at `start`. -// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, -// so that the last one is exactly `stop`. -// -// For example: -// -// ``` -// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] -// ``` -// -// Arguments: -// start: First entry in the range. -// stop: Last entry in the range. -// num: Number of values to generate. +// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. +type DataFormatDimMapAttr func(optionalAttr) + +// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. // -// Returns 1-D. The generated values. -func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LinSpace", - Input: []tf.Input{ - start, stop, num, - }, +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["src_format"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. -type DestroyResourceOpAttr func(optionalAttr) - -// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. // -// value: whether to ignore the error when the resource -// doesn't exist. -// If not specified, defaults to true -func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { return func(m optionalAttr) { - m["ignore_lookup_error"] = value + m["dst_format"] = value } } -// Deletes the resource specified by the handle. +// Returns the dimension index in the destination data format given the one in // -// All subsequent operations using the resource will result in a NotFound -// error status. +// the source data format. // // Arguments: -// resource: handle to the resource to delete. +// x: A Tensor with each element as a dimension index in source data format. +// Must be in the range [-4, 4). // -// Returns the created operation. -func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { +// Returns A Tensor with each element as a dimension index in destination data format. +func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { if scope.Err() != nil { return } @@ -10608,56 +10472,48 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso a(attrs) } opspec := tf.OpSpec{ - Type: "DestroyResourceOp", + Type: "DataFormatDimMap", Input: []tf.Input{ - resource, + x, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. -type ResourceSparseApplyRMSPropAttr func(optionalAttr) +// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. +type ResourceApplyPowerSignAttr func(optionalAttr) -// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. // -// value: If `True`, updating of the var, ms, and mom tensors is protected -// by a lock; otherwise the behavior is undefined, but may exhibit less +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less // contention. // If not specified, defaults to false -func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { +func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// Update '*var' according to the RMSProp algorithm. -// -// Note that in dense implementation of this algorithm, ms and mom will -// update even if the grad is zero, but in this sparse implementation, ms -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// Update '*var' according to the AddSign update. // -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g +// variable <- variable - lr_t * update // // Arguments: // var_: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). +// m: Should be from a Variable(). // lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. +// logbase: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. // grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. // // Returns the created operation. -func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { +func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -10666,246 +10522,161 @@ func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyRMSProp", + Type: "ResourceApplyPowerSign", Input: []tf.Input{ - var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, + var_, m, lr, logbase, sign_decay, beta, grad, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// Returns the truth value of (x > y) element-wise. +// Locks a mutex resource. The output is the lock. So long as the lock tensor // -// *NOTE*: `Greater` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// is alive, any other request to use `MutexLock` with this mutex will wait. +// +// This is particularly useful for creating a critical section when used in +// conjunction with `MutexLockIdentity`: +// +// ```python +// +// mutex = mutex_v2( +// shared_name=handle_name, container=container, name=name) +// +// def execute_in_critical_section(fn, *args, **kwargs): +// lock = gen_resource_variable_ops.mutex_lock(mutex) +// +// with ops.control_dependencies([lock]): +// r = fn(*args, **kwargs) +// +// with ops.control_dependencies(nest.flatten(r)): +// with ops.colocate_with(mutex): +// ensure_lock_exists = mutex_lock_identity(lock) +// +// # Make sure that if any element of r is accessed, all of +// # them are executed together. +// r = nest.map_structure(tf.identity, r) +// +// with ops.control_dependencies([ensure_lock_exists]): +// return nest.map_structure(tf.identity, r) +// ``` +// +// While `fn` is running in the critical section, no other functions which wish to +// use this critical section may run. +// +// Often the use case is that two executions of the same graph, in parallel, +// wish to run `fn`; and we wish to ensure that only one of them executes +// at a time. This is especially important if `fn` modifies one or more +// variables at a time. +// +// It is also useful if two separate functions must share a resource, but we +// wish to ensure the usage is exclusive. +// +// Arguments: +// mutex: The mutex resource to lock. +// +// Returns A tensor that keeps a shared pointer to a lock on the mutex; +// when the Tensor is destroyed, the use count on the shared pointer is decreased +// by 1. When it reaches 0, the lock is released. +func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Greater", + Type: "MutexLock", Input: []tf.Input{ - x, y, + mutex, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. -type SampleDistortedBoundingBoxAttr func(optionalAttr) - -// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// Computes the mean along segments of a tensor. // -// value: If either `seed` or `seed2` are set to non-zero, the random number -// generator is seeded by the given `seed`. Otherwise, it is seeded by a random -// seed. -// If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. -// -// value: The cropped area of the image must contain at least this -// fraction of any bounding box supplied. The value of this parameter should be -// non-negative. In the case of 0, the cropped area does not need to overlap -// any of the bounding boxes supplied. -// If not specified, defaults to 0.1 -func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["min_object_covered"] = value - } -} - -// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. -// -// value: The cropped area of the image must have an aspect ratio = -// width / height within this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["aspect_ratio_range"] = value - } -} - -// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. -// -// value: The cropped area of the image must contain a fraction of the -// supplied image within this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["area_range"] = value - } -} - -// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. -// -// value: Number of attempts at generating a cropped region of the image -// of the specified constraints. After `max_attempts` failures, return the entire -// image. -// If not specified, defaults to 100 -func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["max_attempts"] = value - } -} - -// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. -// -// value: Controls behavior if no bounding boxes supplied. -// If true, assume an implicit bounding box covering the whole input. If false, -// raise an error. -// If not specified, defaults to false -func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["use_image_if_no_bounding_boxes"] = value - } -} - -// Generate a single randomly distorted bounding box for an image. -// -// Bounding box annotations are often supplied in addition to ground-truth labels -// in image recognition or object localization tasks. A common technique for -// training such a system is to randomly distort an image while preserving -// its content, i.e. *data augmentation*. This Op outputs a randomly distorted -// localization of an object, i.e. bounding box, given an `image_size`, -// `bounding_boxes` and a series of constraints. -// -// The output of this Op is a single bounding box that may be used to crop the -// original image. The output is returned as 3 tensors: `begin`, `size` and -// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the -// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize -// what the bounding box looks like. -// -// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The -// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and -// height of the underlying image. -// -// For example, -// -// ```python -// # Generate a single distorted bounding box. -// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( -// tf.shape(image), -// bounding_boxes=bounding_boxes) +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. // -// # Draw the bounding box in an image summary. -// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), -// bbox_for_draw) -// tf.summary.image('images_with_box', image_with_box) +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. // -// # Employ the bounding box to distort the image. -// distorted_image = tf.slice(image, begin, size) -// ``` +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. // -// Note that if no bounding box information is available, setting -// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit -// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is -// false and no bounding boxes are supplied, an error is raised. +//
+// +//
// // Arguments: -// image_size: 1-D, containing `[height, width, channels]`. -// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes -// associated with the image. // -// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to -// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to -// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. -// Provide as input to `tf.image.draw_bounding_boxes`. -func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { +// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SampleDistortedBoundingBox", + Type: "SegmentMean", Input: []tf.Input{ - image_size, bounding_boxes, + data, segment_ids, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// LRNAttr is an optional argument to LRN. -type LRNAttr func(optionalAttr) +// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. +type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) -// LRNDepthRadius sets the optional depth_radius attribute to value. +// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. // -// value: 0-D. Half-width of the 1-D normalization window. -// If not specified, defaults to 5 -func LRNDepthRadius(value int64) LRNAttr { +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { return func(m optionalAttr) { - m["depth_radius"] = value + m["use_locking"] = value } } -// LRNBias sets the optional bias attribute to value. +// Update '*var' according to the centered RMSProp algorithm. // -// value: An offset (usually positive to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNBias(value float32) LRNAttr { - return func(m optionalAttr) { - m["bias"] = value - } -} - -// LRNAlpha sets the optional alpha attribute to value. +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. // -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNAlpha(value float32) LRNAttr { - return func(m optionalAttr) { - m["alpha"] = value - } -} - -// LRNBeta sets the optional beta attribute to value. +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. // -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNBeta(value float32) LRNAttr { - return func(m optionalAttr) { - m["beta"] = value - } -} - -// Local Response Normalization. +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) // -// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last -// dimension), and each vector is normalized independently. Within a given vector, -// each component is divided by the weighted, squared sum of inputs within -// `depth_radius`. In detail, +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom // -// sqr_sum[a, b, c, d] = -// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) -// output = input / (bias + alpha * sqr_sum) ** beta +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. // -// For details, see [Krizhevsky et al., ImageNet classification with deep -// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. // -// Arguments: -// input: 4-D. -func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { +// Returns the created operation. +func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -10914,26 +10685,32 @@ func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) a(attrs) } opspec := tf.OpSpec{ - Type: "LRN", + Type: "ResourceSparseApplyCenteredRMSProp", Input: []tf.Input{ - input, + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Creates a dataset that zips together `input_datasets`. -func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// +// +func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ZipDataset", + Type: "BatchDataset", Input: []tf.Input{ - tf.OutputList(input_datasets), + input_dataset, batch_size, }, Attrs: attrs, } @@ -10941,44 +10718,141 @@ func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.Data return op.Output(0) } -// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. -type ResourceSparseApplyAdagradAttr func(optionalAttr) - -// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. +// Says whether the targets are in the top `K` predictions. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed precision at `k` as a `bool Tensor`. +func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InTopKV2", + Input: []tf.Input{ + predictions, targets, k, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. +type DecodeAndCropJpegAttr func(optionalAttr) + +// DecodeAndCropJpegChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["channels"] = value } } -// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// DecodeAndCropJpegRatio sets the optional ratio attribute to value. +// +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["ratio"] = value + } +} + +// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). // If not specified, defaults to true -func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { +func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["update_slots"] = value + m["fancy_upscaling"] = value } } -// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. +// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value + } +} + +// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. +// +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["dct_method"] = value + } +} + +// Decode and Crop a JPEG-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. +// +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. // -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) +// +// It is equivalent to a combination of decode and crop, but much faster by only +// decoding partial jpeg image. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. +// contents: 0-D. The JPEG-encoded image. +// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. // -// Returns the created operation. -func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { if scope.Err() != nil { return } @@ -10987,235 +10861,268 @@ func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, l a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagrad", + Type: "DecodeAndCropJpeg", Input: []tf.Input{ - var_, accum, lr, grad, indices, + contents, crop_window, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. -type StatelessRandomUniformAttr func(optionalAttr) +// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. +type AllCandidateSamplerAttr func(optionalAttr) -// StatelessRandomUniformDtype sets the optional dtype attribute to value. +// AllCandidateSamplerSeed sets the optional seed attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { return func(m optionalAttr) { - m["dtype"] = value + m["seed"] = value } } -// Outputs deterministic pseudorandom random values from a uniform distribution. +// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. // -// The generated values follow a uniform distribution in the range `[0, 1)`. The -// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. // -// The outputs are a deterministic function of `shape` and `seed`. +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to produce. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. // -// Returns Random values with specified shape. -func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessRandomUniform", + Type: "AllCandidateSampler", Input: []tf.Input{ - shape, seed, + true_classes, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Makes its input available to the next iteration. +// Adds two `SparseTensor` objects to produce another `SparseTensor`. // -// Arguments: -// data: The tensor to be made available to the next iteration. +// The input `SparseTensor` objects' indices are assumed ordered in standard +// lexicographic order. If this is not the case, before this step run +// `SparseReorder` to restore index ordering. // -// Returns The same tensor as `data`. -func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { +// By default, if two values sum to zero at some index, the output `SparseTensor` +// would still include that particular location in its index, storing a zero in the +// corresponding value slot. To override this, callers can specify `thresh`, +// indicating that if the sum has a magnitude strictly smaller than `thresh`, its +// corresponding value and index would then not be included. In particular, +// `thresh == 0` (default) means everything is kept and actual thresholding happens +// only for a positive value. +// +// In the following shapes, `nnz` is the count after taking `thresh` into account. +// +// Arguments: +// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. +// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. +// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. +// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. +// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. +// thresh: 0-D. The magnitude threshold that determines if an output value/index +// pair takes space. +func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "NextIteration", + Type: "SparseAdd", Input: []tf.Input{ - data, + a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, }, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Output a fact about factorials. -func Fact(scope *Scope) (fact tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Fact", +// OrderedMapPeekAttr is an optional argument to OrderedMapPeek. +type OrderedMapPeekAttr func(optionalAttr) + +// OrderedMapPeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["capacity"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Elementwise computes the bitwise XOR of `x` and `y`. +// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// The result will have those bits set, that are different in `x` and `y`. The -// computation is performed on the underlying representations of `x` and `y`. -func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// REQUIRES: value >= 0 +func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapPeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekContainer(value string) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapPeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified key. If the +// +// underlying container does not contain this key +// this op will block until it does. This Op is optimized for +// performance. +func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "BitwiseXor", + Type: "OrderedMapPeek", Input: []tf.Input{ - x, y, + key, indices, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapPeek", err) + return + } + return values } -// Deserialize `SparseTensor` objects. -// -// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where -// the last dimension stores serialized `SparseTensor` objects and the other N -// dimensions (N >= 0) correspond to a batch. The ranks of the original -// `SparseTensor` objects must all match. When the final `SparseTensor` is -// created, its rank is the rank of the incoming `SparseTensor` objects plus N; -// the sparse tensors have been concatenated along new dimensions, one for each -// batch. -// -// The output `SparseTensor` object's shape values for the original dimensions -// are the max across the input `SparseTensor` objects' shape values for the -// corresponding dimensions. The new dimensions match the size of the batch. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the serialized input is a `[2 x 3]` matrix representing two -// original `SparseTensor` objects: -// -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// -// and +// Inverse fast Fourier transform. // -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] +// Computes the inverse 1-dimensional discrete Fourier transform over the +// inner-most dimension of `input`. // -// then the final deserialized `SparseTensor` will be: +// Arguments: +// input: A complex64 tensor. // -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its inverse 1D Fourier transform. // -// Arguments: -// serialized_sparse: The serialized `SparseTensor` objects. The last dimension -// must have 3 columns. -// dtype: The `dtype` of the serialized `SparseTensor` objects. -func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { +// @compatibility(numpy) +// Equivalent to np.fft.ifft +// @end_compatibility +func IFFT(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} opspec := tf.OpSpec{ - Type: "DeserializeSparse", + Type: "IFFT", Input: []tf.Input{ - serialized_sparse, + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. -type ResourceScatterNdUpdateAttr func(optionalAttr) +// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. +type ResourceSparseApplyRMSPropAttr func(optionalAttr) -// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. +// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. // -// value: An optional bool. Defaults to True. If True, the assignment will -// be protected by a lock; otherwise the behavior is undefined, -// but may exhibit less contention. -// If not specified, defaults to true -func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { +// value: If `True`, updating of the var, ms, and mom tensors is protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// Applies sparse `updates` to individual values or slices within a given -// -// variable according to `indices`. -// -// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. -// -// `indices` must be integer tensor, containing indices into `ref`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. -// -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th -// dimension of `ref`. -// -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: -// -// ``` -// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. -// ``` -// -// For example, say we want to update 4 scattered elements to a rank-1 tensor to -// 8 elements. In Python, that update would look like this: -// -// ```python -// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) -// indices = tf.constant([[4], [3], [1] ,[7]]) -// updates = tf.constant([9, 10, 11, 12]) -// update = tf.scatter_nd_update(ref, indices, updates) -// with tf.Session() as sess: -// print sess.run(update) -// ``` +// Update '*var' according to the RMSProp algorithm. // -// The resulting update to ref would look like this: +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. // -// [1, 11, 3, 10, 9, 6, 7, 12] +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) // -// See @{tf.scatter_nd} for more details about how to make updates to -// slices. +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom // // Arguments: -// ref: A resource handle. Must be from a VarHandleOp. -// indices: A Tensor. Must be one of the following types: int32, int64. -// A tensor of indices into ref. -// updates: A Tensor. Must have the same type as ref. A tensor of updated -// values to add to ref. +// var_: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. // // Returns the created operation. -func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { +func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -11224,59 +11131,168 @@ func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, upd a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceScatterNdUpdate", + Type: "ResourceSparseApplyRMSProp", Input: []tf.Input{ - ref, indices, updates, + var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// SqueezeAttr is an optional argument to Squeeze. -type SqueezeAttr func(optionalAttr) - -// SqueezeAxis sets the optional axis attribute to value. +// Returns the truth value of (x > y) element-wise. // -// value: If specified, only squeezes the dimensions listed. The dimension -// index starts at 0. It is an error to squeeze a dimension that is not 1. Must -// be in the range `[-rank(input), rank(input))`. -// If not specified, defaults to <> +// *NOTE*: `Greater` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Greater", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. +type SampleDistortedBoundingBoxAttr func(optionalAttr) + +// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. // -// REQUIRES: len(value) >= 0 -func SqueezeAxis(value []int64) SqueezeAttr { +// value: If either `seed` or `seed2` are set to non-zero, the random number +// generator is seeded by the given `seed`. Otherwise, it is seeded by a random +// seed. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { - m["squeeze_dims"] = value + m["seed"] = value } } -// Removes dimensions of size 1 from the shape of a tensor. -// -// Given a tensor `input`, this operation returns a tensor of the same type with -// all dimensions of size 1 removed. If you don't want to remove all size 1 -// dimensions, you can remove specific size 1 dimensions by specifying -// `axis`. +// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. // -// For example: +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. // -// ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t)) ==> [2, 3] -// ``` +// value: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. +// If not specified, defaults to 0.1 +func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["min_object_covered"] = value + } +} + +// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. // -// Or, to remove specific size 1 dimensions: +// value: The cropped area of the image must have an aspect ratio = +// width / height within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value + } +} + +// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. // +// value: The cropped area of the image must contain a fraction of the +// supplied image within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["area_range"] = value + } +} + +// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// +// value: Number of attempts at generating a cropped region of the image +// of the specified constraints. After `max_attempts` failures, return the entire +// image. +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["max_attempts"] = value + } +} + +// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// +// value: Controls behavior if no bounding boxes supplied. +// If true, assume an implicit bounding box covering the whole input. If false, +// raise an error. +// If not specified, defaults to false +func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. +// +// Bounding box annotations are often supplied in addition to ground-truth labels +// in image recognition or object localization tasks. A common technique for +// training such a system is to randomly distort an image while preserving +// its content, i.e. *data augmentation*. This Op outputs a randomly distorted +// localization of an object, i.e. bounding box, given an `image_size`, +// `bounding_boxes` and a series of constraints. +// +// The output of this Op is a single bounding box that may be used to crop the +// original image. The output is returned as 3 tensors: `begin`, `size` and +// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the +// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize +// what the bounding box looks like. +// +// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, +// +// ```python +// # Generate a single distorted bounding box. +// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( +// tf.shape(image), +// bounding_boxes=bounding_boxes) +// +// # Draw the bounding box in an image summary. +// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), +// bbox_for_draw) +// tf.summary.image('images_with_box', image_with_box) +// +// # Employ the bounding box to distort the image. +// distorted_image = tf.slice(image, begin, size) // ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] -// ``` +// +// Note that if no bounding box information is available, setting +// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit +// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is +// false and no bounding boxes are supplied, an error is raised. // // Arguments: -// input: The `input` to squeeze. +// image_size: 1-D, containing `[height, width, channels]`. +// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes +// associated with the image. // -// Returns Contains the same data as `input`, but has one or more dimensions of -// size 1 removed. -func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { +// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to +// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to +// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. +// Provide as input to `tf.image.draw_bounding_boxes`. +func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { if scope.Err() != nil { return } @@ -11285,108 +11301,76 @@ func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf. a(attrs) } opspec := tf.OpSpec{ - Type: "Squeeze", + Type: "SampleDistortedBoundingBox", Input: []tf.Input{ - input, + image_size, bounding_boxes, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. -type ResourceApplyAdadeltaAttr func(optionalAttr) +// LRNAttr is an optional argument to LRN. +type LRNAttr func(optionalAttr) -// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// LRNDepthRadius sets the optional depth_radius attribute to value. // -// value: If True, updating of the var, accum and update_accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { +// value: 0-D. Half-width of the 1-D normalization window. +// If not specified, defaults to 5 +func LRNDepthRadius(value int64) LRNAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["depth_radius"] = value } } -// Update '*var' according to the adadelta scheme. -// -// accum = rho() * accum + (1 - rho()) * grad.square(); -// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; -// update_accum = rho() * update_accum + (1 - rho()) * update.square(); -// var -= update; -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// accum_update: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay factor. Must be a scalar. -// epsilon: Constant factor. Must be a scalar. -// grad: The gradient. +// LRNBias sets the optional bias attribute to value. // -// Returns the created operation. -func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdadelta", - Input: []tf.Input{ - var_, accum, accum_update, lr, rho, epsilon, grad, - }, - Attrs: attrs, +// value: An offset (usually positive to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNBias(value float32) LRNAttr { + return func(m optionalAttr) { + m["bias"] = value } - return scope.AddOperation(opspec) } -// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. -type NonMaxSuppressionAttr func(optionalAttr) +// LRNAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNAlpha(value float32) LRNAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} -// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. +// LRNBeta sets the optional beta attribute to value. // -// value: A float representing the threshold for deciding whether boxes -// overlap too much with respect to IOU. +// value: An exponent. // If not specified, defaults to 0.5 -func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { +func LRNBeta(value float32) LRNAttr { return func(m optionalAttr) { - m["iou_threshold"] = value + m["beta"] = value } } -// Greedily selects a subset of bounding boxes in descending order of score, +// Local Response Normalization. // -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system. Note that this -// algorithm is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression( -// boxes, scores, max_output_size, iou_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) +// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last +// dimension), and each vector is normalized independently. Within a given vector, +// each component is divided by the weighted, squared sum of inputs within +// `depth_radius`. In detail, // -// Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. +// sqr_sum[a, b, c, d] = +// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) +// output = input / (bias + alpha * sqr_sum) ** beta // -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { +// For details, see [Krizhevsky et al., ImageNet classification with deep +// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). +// +// Arguments: +// input: 4-D. +func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -11395,9 +11379,9 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp a(attrs) } opspec := tf.OpSpec{ - Type: "NonMaxSuppression", + Type: "LRN", Input: []tf.Input{ - boxes, scores, max_output_size, + input, }, Attrs: attrs, } @@ -11405,16 +11389,16 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp return op.Output(0) } -// Creates a dataset that emits `components` as a tuple of tensors once. -func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { +// Creates a dataset that zips together `input_datasets`. +func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_shapes": output_shapes} + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "TensorDataset", + Type: "ZipDataset", Input: []tf.Input{ - tf.OutputList(components), + tf.OutputList(input_datasets), }, Attrs: attrs, } @@ -11422,73 +11406,87 @@ func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shap return op.Output(0) } -// Component-wise multiplies a SparseTensor by a dense Tensor. +// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. +type ResourceSparseApplyAdagradAttr func(optionalAttr) + +// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. // -// The output locations corresponding to the implicitly zero elements in the sparse -// tensor will be zero (i.e., will not take up storage space), regardless of the -// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. // -// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not -// the other direction. +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) // // Arguments: -// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// dense: `R`-D. The dense Tensor operand. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. // -// Returns 1-D. The `N` values that are operated on. -func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { +// Returns the created operation. +func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "SparseDenseCwiseMul", + Type: "ResourceSparseApplyAdagrad", Input: []tf.Input{ - sp_indices, sp_values, sp_shape, dense, + var_, accum, lr, grad, indices, }, + Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// ResizeAreaAttr is an optional argument to ResizeArea. -type ResizeAreaAttr func(optionalAttr) +// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. +type StatelessRandomUniformAttr func(optionalAttr) -// ResizeAreaAlignCorners sets the optional align_corners attribute to value. +// StatelessRandomUniformDtype sets the optional dtype attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { return func(m optionalAttr) { - m["align_corners"] = value + m["dtype"] = value } } -// Resize `images` to `size` using area interpolation. -// -// Input images can be of different types but output images are always float. +// Outputs deterministic pseudorandom random values from a uniform distribution. // -// The range of pixel values for the output image might be slightly different -// from the range for the input image because of limited numerical precision. -// To guarantee an output range, for example `[0.0, 1.0]`, apply -// `tf.clip_by_value` to the output. +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. // -// Each output pixel is computed by first transforming the pixel's footprint into -// the input tensor and then averaging the pixels that intersect the footprint. An -// input pixel's contribution to the average is weighted by the fraction of its -// area that intersects the footprint. This is the same as OpenCV's INTER_AREA. +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) { +// Returns Random values with specified shape. +func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -11497,9 +11495,9 @@ func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...Resi a(attrs) } opspec := tf.OpSpec{ - Type: "ResizeArea", + Type: "StatelessRandomUniform", Input: []tf.Input{ - images, size, + shape, seed, }, Attrs: attrs, } @@ -11507,768 +11505,787 @@ func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...Resi return op.Output(0) } -// 2D real-valued fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform of a real-valued signal -// over the inner-most 2 dimensions of `input`. -// -// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the -// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension -// of `output`: the zero-frequency term, followed by the `fft_length / 2` -// positive-frequency terms. -// -// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. +// Makes its input available to the next iteration. // // Arguments: -// input: A float32 tensor. -// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. -// -// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. The -// inner-most dimension contains `fft_length / 2 + 1` unique frequency -// components. +// data: The tensor to be made available to the next iteration. // -// @compatibility(numpy) -// Equivalent to np.fft.rfft2 -// @end_compatibility -func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +// Returns The same tensor as `data`. +func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "RFFT2D", + Type: "NextIteration", Input: []tf.Input{ - input, fft_length, + data, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Pads a tensor with zeros. -// -// This operation pads a `input` with zeros according to the `paddings` you -// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the -// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates -// how many zeros to add before the contents of `input` in that dimension, and -// `paddings[D, 1]` indicates how many zeros to add after the contents of `input` -// in that dimension. -// -// The padded size of each dimension D of the output is: -// -// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` -// -// For example: -// -// ``` -// # 't' is [[1, 1], [2, 2]] -// # 'paddings' is [[1, 1], [2, 2]] -// # rank of 't' is 2 -// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] -// [0, 0, 1, 1, 0, 0] -// [0, 0, 2, 2, 0, 0] -// [0, 0, 0, 0, 0, 0]] -// ``` -func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { +// Output a fact about factorials. +func Fact(scope *Scope) (fact tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Pad", - Input: []tf.Input{ - input, paddings, - }, + Type: "Fact", } op := scope.AddOperation(opspec) return op.Output(0) } -// Checks whether a resource handle-based variable has been initialized. -// -// Arguments: -// resource: the input resource handle. +// Elementwise computes the bitwise XOR of `x` and `y`. // -// Returns a scalar boolean which is true if the variable has been -// initialized. -func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { +// The result will have those bits set, that are different in `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "VarIsInitializedOp", + Type: "BitwiseXor", Input: []tf.Input{ - resource, + x, y, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. +// Deserialize `SparseTensor` objects. // -// The hash function is deterministic on the content of the string within the -// process and will never change. However, it is not suitable for cryptography. -// This function may be used when CPU time is scarce and inputs are trusted or -// unimportant. There is a risk of adversaries constructing inputs that all hash -// to the same bucket. To prevent this problem, use a strong hash function with -// `tf.string_to_hash_bucket_strong`. +// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where +// the last dimension stores serialized `SparseTensor` objects and the other N +// dimensions (N >= 0) correspond to a batch. The ranks of the original +// `SparseTensor` objects must all match. When the final `SparseTensor` is +// created, its rank is the rank of the incoming `SparseTensor` objects plus N; +// the sparse tensors have been concatenated along new dimensions, one for each +// batch. // -// Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. +// The output `SparseTensor` object's shape values for the original dimensions +// are the max across the input `SparseTensor` objects' shape values for the +// corresponding dimensions. The new dimensions match the size of the batch. // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: The serialized `SparseTensor` objects. The last dimension +// must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets} + attrs := map[string]interface{}{"dtype": dtype} opspec := tf.OpSpec{ - Type: "StringToHashBucketFast", + Type: "DeserializeSparse", Input: []tf.Input{ - input, + serialized_sparse, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. -type TensorArrayGatherV3Attr func(optionalAttr) +// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. +type ResourceScatterNdUpdateAttr func(optionalAttr) -// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. +// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. // -// value: The expected shape of an element, if known. Used to -// validate the shapes of TensorArray elements. If this shape is not -// fully specified, gathering zero-size TensorArrays is an error. -// If not specified, defaults to -func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { return func(m optionalAttr) { - m["element_shape"] = value + m["use_locking"] = value } } -// Gather specific elements from the TensorArray into output `value`. +// Applies sparse `updates` to individual values or slices within a given // -// All elements selected by `indices` must have the same shape. +// variable according to `indices`. // -// Arguments: -// handle: The handle to a TensorArray. -// indices: The locations in the TensorArray from which to read tensor elements. -// flow_in: A float scalar that enforces proper chaining of operations. -// dtype: The type of the elem that is returned. +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. // -// Returns All of the elements in the TensorArray, concatenated along a new -// axis (the new dimension 0). -func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorArrayGatherV3", - Input: []tf.Input{ - handle, indices, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// This op consumes a lock created by `MutexLock`. +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. // -// This op exists to consume a tensor created by `MutexLock` (other than -// direct control dependencies). It should be the only that consumes the tensor, -// and will raise an error if it is not. Its only purpose is to keep the -// mutex lock tensor alive until it is consumed by this op. +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. // -// **NOTE**: This operation must run on the same device as its input. This may -// be enforced via the `colocate_with` mechanism. +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. +// ``` +// +// For example, say we want to update 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that update would look like this: +// +// ```python +// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1] ,[7]]) +// updates = tf.constant([9, 10, 11, 12]) +// update = tf.scatter_nd_update(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(update) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 11, 3, 10, 9, 6, 7, 12] +// +// See @{tf.scatter_nd} for more details about how to make updates to +// slices. // // Arguments: -// mutex_lock: A tensor returned by `MutexLock`. +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of updated +// values to add to ref. // // Returns the created operation. -func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { +func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ConsumeMutexLock", + Type: "ResourceScatterNdUpdate", Input: []tf.Input{ - mutex_lock, + ref, indices, updates, }, + Attrs: attrs, } return scope.AddOperation(opspec) } -// Returns x / y element-wise for integer types. +// SqueezeAttr is an optional argument to Squeeze. +type SqueezeAttr func(optionalAttr) + +// SqueezeAxis sets the optional axis attribute to value. // -// Truncation designates that negative numbers will round fractional quantities -// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different -// than Python semantics. See `FloorDiv` for a division function that matches -// Python Semantics. +// value: If specified, only squeezes the dimensions listed. The dimension +// index starts at 0. It is an error to squeeze a dimension that is not 1. Must +// be in the range `[-rank(input), rank(input))`. +// If not specified, defaults to <> // -// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TruncateDiv", - Input: []tf.Input{ - x, y, - }, +// REQUIRES: len(value) >= 0 +func SqueezeAxis(value []int64) SqueezeAttr { + return func(m optionalAttr) { + m["squeeze_dims"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Restores tensors from a V2 checkpoint. +// Removes dimensions of size 1 from the shape of a tensor. // -// For backward compatibility with the V1 format, this Op currently allows -// restoring from a V1 checkpoint as well: -// - This Op first attempts to find the V2 index file pointed to by "prefix", and -// if found proceed to read it as a V2 checkpoint; -// - Otherwise the V1 read path is invoked. -// Relying on this behavior is not recommended, as the ability to fall back to read -// V1 might be deprecated and eventually removed. +// Given a tensor `input`, this operation returns a tensor of the same type with +// all dimensions of size 1 removed. If you don't want to remove all size 1 +// dimensions, you can remove specific size 1 dimensions by specifying +// `axis`. // -// By default, restores the named tensors in full. If the caller wishes to restore -// specific slices of stored tensors, "shape_and_slices" should be non-empty -// strings and correspondingly well-formed. +// For example: // -// Callers must ensure all the named tensors are indeed stored in the checkpoint. +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t)) ==> [2, 3] +// ``` +// +// Or, to remove specific size 1 dimensions: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] +// ``` // // Arguments: -// prefix: Must have a single element. The prefix of a V2 checkpoint. -// tensor_names: shape {N}. The names of the tensors to be restored. -// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. -// Empty strings indicate that they are non-partitioned tensors. -// dtypes: shape {N}. The list of expected dtype for the tensors. Must match -// those stored in the checkpoint. +// input: The `input` to squeeze. // -// Returns shape {N}. The restored tensors, whose shapes are read from the -// checkpoint directly. -func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { +// Returns Contains the same data as `input`, but has one or more dimensions of +// size 1 removed. +func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "RestoreV2", + Type: "Squeeze", Input: []tf.Input{ - prefix, tensor_names, shape_and_slices, + input, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { - scope.UpdateErr("RestoreV2", err) - return - } - return tensors + return op.Output(0) } -// Receives a tensor value broadcast from another device. -func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} - opspec := tf.OpSpec{ - Type: "CollectiveBcastRecv", +// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. +type ResourceApplyAdadeltaAttr func(optionalAttr) - Attrs: attrs, +// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var, accum and update_accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Decode web-safe base64-encoded strings. +// Update '*var' according to the adadelta scheme. // -// Input may or may not have padding at the end. See EncodeBase64 for padding. -// Web-safe means that input must use - and _ instead of + and /. +// accum = rho() * accum + (1 - rho()) * grad.square(); +// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; +// update_accum = rho() * update_accum + (1 - rho()) * update.square(); +// var -= update; // // Arguments: -// input: Base64 strings to decode. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// accum_update: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. // -// Returns Decoded strings. -func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { +// Returns the created operation. +func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "DecodeBase64", + Type: "ResourceApplyAdadelta", Input: []tf.Input{ - input, + var_, accum, accum_update, lr, rho, epsilon, grad, }, + Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Store the input tensor in the state of the current session. +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) + +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. // -// Arguments: -// value: The tensor to be stored. +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { + return func(m optionalAttr) { + m["iou_threshold"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, // -// Returns The handle for the tensor stored in the session state, represented -// as a string. -func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "GetSessionHandle", + Type: "NonMaxSuppression", Input: []tf.Input{ - value, + boxes, scores, max_output_size, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. -type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) - -// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. -// -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// prox_v = var -// prox_v -= lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// -// Returns the created operation. -func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { +// Creates a dataset that emits `components` as a tuple of tensors once. +func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ResourceSparseApplyProximalAdagrad", + Type: "TensorDataset", Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, indices, + tf.OutputList(components), }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. -type MaxPool3DGradAttr func(optionalAttr) - -// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// Component-wise multiplies a SparseTensor by a dense Tensor. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of max pooling function. +// The output locations corresponding to the implicitly zero elements in the sparse +// tensor will be zero (i.e., will not take up storage space), regardless of the +// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. // // Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "MaxPool3DGrad", + Type: "SparseDenseCwiseMul", Input: []tf.Input{ - orig_input, orig_output, grad, + sp_indices, sp_values, sp_shape, dense, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// SparseReduceSumAttr is an optional argument to SparseReduceSum. -type SparseReduceSumAttr func(optionalAttr) - -// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the sum of elements across dimensions of a SparseTensor. +// 2D real-valued fast Fourier transform. // -// This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` -// instead of a sparse one. +// Computes the 2-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 2 dimensions of `input`. // -// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained -// with length 1. +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. // -// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor -// with a single element is returned. Additionally, the axes can be negative, -// which are interpreted according to the indexing rules in Python. +// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. // // Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. // -// Returns `R-K`-D. The reduced Tensor. -func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { +// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft2 +// @end_compatibility +func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SparseReduceSum", + Type: "RFFT2D", Input: []tf.Input{ - input_indices, input_values, input_shape, reduction_axes, + input, fft_length, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// VariableShapeAttr is an optional argument to VariableShape. -type VariableShapeAttr func(optionalAttr) - -// VariableShapeOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func VariableShapeOutType(value tf.DataType) VariableShapeAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Returns the shape of the variable pointed to by `resource`. +// Pads a tensor with zeros. // -// This operation returns a 1-D integer tensor representing the shape of `input`. +// This operation pads a `input` with zeros according to the `paddings` you +// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the +// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many zeros to add before the contents of `input` in that dimension, and +// `paddings[D, 1]` indicates how many zeros to add after the contents of `input` +// in that dimension. +// +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` // // For example: // // ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// shape(t) ==> [2, 2, 3] +// # 't' is [[1, 1], [2, 2]] +// # 'paddings' is [[1, 1], [2, 2]] +// # rank of 't' is 2 +// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] +// [0, 0, 1, 1, 0, 0] +// [0, 0, 2, 2, 0, 0] +// [0, 0, 0, 0, 0, 0]] // ``` -func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { +func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "VariableShape", + Type: "Pad", Input: []tf.Input{ - input, + input, paddings, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. -type SparseToSparseSetOperationAttr func(optionalAttr) - -// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Applies set operation along last dimension of 2 `SparseTensor` inputs. +// Checks whether a resource handle-based variable has been initialized. // -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// Arguments: +// resource: the input resource handle. // -// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the -// order and range of `set1` and `set2` indices. +// Returns a scalar boolean which is true if the variable has been +// initialized. +func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "VarIsInitializedOp", + Input: []tf.Input{ + resource, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. // -// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, -// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same -// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. +// The hash function is deterministic on the content of the string within the +// process and will never change. However, it is not suitable for cryptography. +// This function may be used when CPU time is scarce and inputs are trusted or +// unimportant. There is a risk of adversaries constructing inputs that all hash +// to the same bucket. To prevent this problem, use a strong hash function with +// `tf.string_to_hash_bucket_strong`. // -// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, -// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same -// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. // -// If `validate_indices` is `True`, this op validates the order and range of `set1` -// and `set2` indices. +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucketFast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. +type TensorArrayGatherV3Attr func(optionalAttr) + +// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. // -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Gather specific elements from the TensorArray into output `value`. // -// Arguments: -// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must -// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the -// max set size across `0...n-1` dimensions. -// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must -// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the -// max set size across `0...n-1` dimensions. +// All elements selected by `indices` must have the same shape. // +// Arguments: +// handle: The handle to a TensorArray. +// indices: The locations in the TensorArray from which to read tensor elements. +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. // -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { +// Returns All of the elements in the TensorArray, concatenated along a new +// axis (the new dimension 0). +func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"set_operation": set_operation} + attrs := map[string]interface{}{"dtype": dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "SparseToSparseSetOperation", + Type: "TensorArrayGatherV3", Input: []tf.Input{ - set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, + handle, indices, flow_in, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Computes softmax cross entropy cost and gradients to backpropagate. +// This op consumes a lock created by `MutexLock`. // -// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept -// a matrix of label probabilities, but rather a single label per row -// of features. This label is considered to have probability 1.0 for the -// given row. +// This op exists to consume a tensor created by `MutexLock` (other than +// direct control dependencies). It should be the only that consumes the tensor, +// and will raise an error if it is not. Its only purpose is to keep the +// mutex lock tensor alive until it is consumed by this op. // -// Inputs are the logits, not probabilities. +// **NOTE**: This operation must run on the same device as its input. This may +// be enforced via the `colocate_with` mechanism. // // Arguments: -// features: batch_size x num_classes matrix -// labels: batch_size vector with values in [0, num_classes). -// This is the label for the given minibatch entry. +// mutex_lock: A tensor returned by `MutexLock`. // -// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). -func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { +// Returns the created operation. +func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSoftmaxCrossEntropyWithLogits", + Type: "ConsumeMutexLock", Input: []tf.Input{ - features, labels, + mutex_lock, }, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return scope.AddOperation(opspec) } -// Fast Fourier transform. -// -// Computes the 1-dimensional discrete Fourier transform over the inner-most -// dimension of `input`. -// -// Arguments: -// input: A complex64 tensor. +// Returns x / y element-wise for integer types. // -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its 1D Fourier transform. +// Truncation designates that negative numbers will round fractional quantities +// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different +// than Python semantics. See `FloorDiv` for a division function that matches +// Python Semantics. // -// @compatibility(numpy) -// Equivalent to np.fft.fft -// @end_compatibility -func FFT(scope *Scope, input tf.Output) (output tf.Output) { +// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "FFT", + Type: "TruncateDiv", Input: []tf.Input{ - input, + x, y, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// Restores tensors from a V2 checkpoint. +// +// For backward compatibility with the V1 format, this Op currently allows +// restoring from a V1 checkpoint as well: +// - This Op first attempts to find the V2 index file pointed to by "prefix", and +// if found proceed to read it as a V2 checkpoint; +// - Otherwise the V1 read path is invoked. +// Relying on this behavior is not recommended, as the ability to fall back to read +// V1 might be deprecated and eventually removed. +// +// By default, restores the named tensors in full. If the caller wishes to restore +// specific slices of stored tensors, "shape_and_slices" should be non-empty +// strings and correspondingly well-formed. +// +// Callers must ensure all the named tensors are indeed stored in the checkpoint. // // Arguments: -// serialized: A scalar string containing a serialized TensorProto proto. -// out_type: The type of the serialized tensor. The provided type must match the -// type of the serialized tensor and no implicit conversion will take place. +// prefix: Must have a single element. The prefix of a V2 checkpoint. +// tensor_names: shape {N}. The names of the tensors to be restored. +// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. +// Empty strings indicate that they are non-partitioned tensors. +// dtypes: shape {N}. The list of expected dtype for the tensors. Must match +// those stored in the checkpoint. // -// Returns A Tensor of type `out_type`. -func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { +// Returns shape {N}. The restored tensors, whose shapes are read from the +// checkpoint directly. +func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + attrs := map[string]interface{}{"dtypes": dtypes} opspec := tf.OpSpec{ - Type: "ParseTensor", + Type: "RestoreV2", Input: []tf.Input{ - serialized, + prefix, tensor_names, shape_and_slices, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + if scope.Err() != nil { + return + } + var idx int + var err error + if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { + scope.UpdateErr("RestoreV2", err) + return + } + return tensors } -// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. -type MaxPoolWithArgmaxAttr func(optionalAttr) +// Receives a tensor value broadcast from another device. +func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + opspec := tf.OpSpec{ + Type: "CollectiveBcastRecv", -// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. -// If not specified, defaults to DT_INT64 -func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { - return func(m optionalAttr) { - m["Targmax"] = value + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Performs max pooling on the input and outputs both max values and indices. -// -// The indices in `argmax` are flattened, so that a maximum value at position -// `[b, y, x, c]` becomes flattened index -// `((b * height + y) * width + x) * channels + c`. +// Decode web-safe base64-encoded strings. // -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. +// Input may or may not have padding at the end. See EncodeBase64 for padding. +// Web-safe means that input must use - and _ instead of + and /. // // Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. +// input: Base64 strings to decode. // -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { +// Returns Decoded strings. +func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", + Type: "DecodeBase64", Input: []tf.Input{ input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. -type ResourceSparseApplyAdagradDAAttr func(optionalAttr) +// Store the input tensor in the state of the current session. +// +// Arguments: +// value: The tensor to be stored. +// +// Returns The handle for the tensor stored in the session state, represented +// as a string. +func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GetSessionHandle", + Input: []tf.Input{ + value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. +type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) + +// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. // // value: If True, updating of the var and accum tensors will be protected by // a lock; otherwise the behavior is undefined, but may exhibit less contention. // If not specified, defaults to false -func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { +func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// prox_v = var +// prox_v -= lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} // // Arguments: // var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. +// accum: Should be from a Variable(). // lr: Learning rate. Must be a scalar. // l1: L1 regularization. Must be a scalar. // l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. // // Returns the created operation. -func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { +func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -12277,209 +12294,361 @@ func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumul a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagradDA", + Type: "ResourceSparseApplyProximalAdagrad", Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + var_, accum, lr, l1, l2, grad, indices, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// EncodeJpegAttr is an optional argument to EncodeJpeg. -type EncodeJpegAttr func(optionalAttr) +// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. +type MaxPool3DGradAttr func(optionalAttr) -// EncodeJpegFormat sets the optional format attribute to value. +// MaxPool3DGradDataFormat sets the optional data_format attribute to value. // -// value: Per pixel image format. -// If not specified, defaults to "" -func EncodeJpegFormat(value string) EncodeJpegAttr { +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { return func(m optionalAttr) { - m["format"] = value + m["data_format"] = value } } -// EncodeJpegQuality sets the optional quality attribute to value. +// Computes gradients of max pooling function. // -// value: Quality of the compression from 0 to 100 (higher is better and slower). -// If not specified, defaults to 95 -func EncodeJpegQuality(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["quality"] = value +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { + if scope.Err() != nil { + return } -} - -// EncodeJpegProgressive sets the optional progressive attribute to value. -// -// value: If True, create a JPEG that loads progressively (coarse to fine). -// If not specified, defaults to false -func EncodeJpegProgressive(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["progressive"] = value + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3DGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. +// SparseReduceSumAttr is an optional argument to SparseReduceSum. +type SparseReduceSumAttr func(optionalAttr) + +// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. // -// value: If True, spend CPU/RAM to reduce size with no quality change. +// value: If true, retain reduced dimensions with length 1. // If not specified, defaults to false -func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { +func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { return func(m optionalAttr) { - m["optimize_size"] = value + m["keep_dims"] = value } } -// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. +// Computes the sum of elements across dimensions of a SparseTensor. // -// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. -// If not specified, defaults to true -func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["chroma_downsampling"] = value - } -} - -// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. // -// value: Unit used to specify `x_density` and `y_density`: -// pixels per inch (`'in'`) or centimeter (`'cm'`). -// If not specified, defaults to "in" -func EncodeJpegDensityUnit(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["density_unit"] = value +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { + if scope.Err() != nil { + return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceSum", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegXDensity sets the optional x_density attribute to value. -// -// value: Horizontal pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegXDensity(value int64) EncodeJpegAttr { +// VariableShapeAttr is an optional argument to VariableShape. +type VariableShapeAttr func(optionalAttr) + +// VariableShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func VariableShapeOutType(value tf.DataType) VariableShapeAttr { return func(m optionalAttr) { - m["x_density"] = value + m["out_type"] = value } } -// EncodeJpegYDensity sets the optional y_density attribute to value. +// Returns the shape of the variable pointed to by `resource`. // -// value: Vertical pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegYDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["y_density"] = value +// This operation returns a 1-D integer tensor representing the shape of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VariableShape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. -// -// value: If not empty, embed this XMP metadata in the image header. -// If not specified, defaults to "" -func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { +// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. +type SparseToSparseSetOperationAttr func(optionalAttr) + +// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { return func(m optionalAttr) { - m["xmp_metadata"] = value + m["validate_indices"] = value } } -// JPEG-encode an image. +// Applies set operation along last dimension of 2 `SparseTensor` inputs. // -// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. // -// The attr `format` can be used to override the color format of the encoded -// output. Values can be: +// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the +// order and range of `set1` and `set2` indices. // -// * `''`: Use a default format based on the number of channels in the image. -// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension -// of `image` must be 1. -// * `rgb`: Output an RGB JPEG image. The `channels` dimension -// of `image` must be 3. +// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, +// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same +// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. // -// If `format` is not specified or is the empty string, a default format is picked -// in function of the number of channels in `image`: +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. // -// * 1: Output a grayscale image. -// * 3: Output an RGB image. +// If `validate_indices` is `True`, this op validates the order and range of `set1` +// and `set2` indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. // // Arguments: -// image: 3-D with shape `[height, width, channels]`. +// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must +// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the +// max set size across `0...n-1` dimensions. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the +// max set size across `0...n-1` dimensions. // -// Returns 0-D. JPEG-encoded image. -func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"set_operation": set_operation} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "EncodeJpeg", + Type: "SparseToSparseSetOperation", Input: []tf.Input{ - image, + set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// MultinomialAttr is an optional argument to Multinomial. -type MultinomialAttr func(optionalAttr) +// Computes softmax cross entropy cost and gradients to backpropagate. +// +// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept +// a matrix of label probabilities, but rather a single label per row +// of features. This label is considered to have probability 1.0 for the +// given row. +// +// Inputs are the logits, not probabilities. +// +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size vector with values in [0, num_classes). +// This is the label for the given minibatch entry. +// +// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). +func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} -// MultinomialSeed sets the optional seed attribute to value. +// Fast Fourier transform. // -// value: If either seed or seed2 is set to be non-zero, the internal random number -// generator is seeded by the given seed. Otherwise, a random seed is used. -// If not specified, defaults to 0 -func MultinomialSeed(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed"] = value +// Computes the 1-dimensional discrete Fourier transform over the inner-most +// dimension of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft +// @end_compatibility +func FFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return } + opspec := tf.OpSpec{ + Type: "FFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// MultinomialSeed2 sets the optional seed2 attribute to value. +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. // -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func MultinomialSeed2(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed2"] = value +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. +// +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. // If not specified, defaults to DT_INT64 -func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { return func(m optionalAttr) { - m["output_dtype"] = value + m["Targmax"] = value } } -// Draws samples from a multinomial distribution. +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index +// `((b * height + y) * width + x) * channels + c`. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. // // Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. // -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Multinomial", + Type: "MaxPoolWithArgmax", Input: []tf.Input{ - logits, num_samples, + input, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } // Returns the truth value of NOT x element-wise. @@ -13001,62 +13170,6 @@ func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// Inverse 2D fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform over the -// inner-most 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft2 -// @end_compatibility -func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform over the inner-most -// 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft2 -// @end_compatibility -func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. type ResourceApplyProximalGradientDescentAttr func(optionalAttr) @@ -13400,173 +13513,28 @@ func FusedBatchNormGradIsTraining(value bool) FusedBatchNormGradAttr { } } -// Gradient for batch normalization. -// -// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". -// The size of 1D Tensors matches the dimension C of the 4D Tensors. -// -// Arguments: -// y_backprop: A 4D Tensor for the gradient with respect to y. -// x: A 4D Tensor for input data. -// scale: A 1D Tensor for scaling factor, to scale the normalized x. -// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch -// mean to be reused in gradient computation. When is_training is -// False, a 1D Tensor for the population mean to be reused in both -// 1st and 2nd order gradient computation. -// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch -// variance (inverted variance in the cuDNN case) to be reused in -// gradient computation. When is_training is False, a 1D Tensor -// for the population variance to be reused in both 1st and 2nd -// order gradient computation. -// -// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input -// in FusedBatchNorm. -func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FusedBatchNormGrad", - Input: []tf.Input{ - y_backprop, x, scale, reserve_space_1, reserve_space_2, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) -} - -// TopKAttr is an optional argument to TopK. -type TopKAttr func(optionalAttr) - -// TopKSorted sets the optional sorted attribute to value. -// -// value: If true the resulting `k` elements will be sorted by the values in -// descending order. -// If not specified, defaults to true -func TopKSorted(value bool) TopKAttr { - return func(m optionalAttr) { - m["sorted"] = value - } -} - -// Finds values and indices of the `k` largest elements for the last dimension. -// -// DEPRECATED at GraphDef version 7: Use TopKV2 instead -// -// If the input is a vector (rank-1), finds the `k` largest entries in the vector -// and outputs their values and indices as vectors. Thus `values[j]` is the -// `j`-th largest entry in `input`, and its index is `indices[j]`. -// -// For matrices (resp. higher rank input), computes the top `k` entries in each -// row (resp. vector along the last dimension). Thus, -// -// values.shape = indices.shape = input.shape[:-1] + [k] -// -// If two elements are equal, the lower-index element appears first. -// -// If `k` varies dynamically, use `TopKV2` below. -// -// Arguments: -// input: 1-D or higher with last dimension at least `k`. -// k: Number of top elements to look for along the last dimension (along each -// row for matrices). -// -// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. -func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"k": k} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TopK", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// ComplexAttr is an optional argument to Complex. -type ComplexAttr func(optionalAttr) - -// ComplexTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_COMPLEX64 -func ComplexTout(value tf.DataType) ComplexAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Converts two real numbers to a complex number. -// -// Given a tensor `real` representing the real part of a complex number, and a -// tensor `imag` representing the imaginary part of a complex number, this -// operation returns complex numbers elementwise of the form \\(a + bj\\), where -// *a* represents the `real` part and *b* represents the `imag` part. -// -// The input tensors `real` and `imag` must have the same shape. -// -// For example: -// -// ``` -// # tensor 'real' is [2.25, 3.25] -// # tensor `imag` is [4.75, 5.75] -// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] -// ``` -func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Complex", - Input: []tf.Input{ - real, imag, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ImagAttr is an optional argument to Imag. -type ImagAttr func(optionalAttr) - -// ImagTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func ImagTout(value tf.DataType) ImagAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Returns the imaginary part of a complex number. +// Gradient for batch normalization. // -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// type `float` that is the imaginary part of each element in `input`. All -// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* -// is the real part and *b* is the imaginary part returned by this operation. +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. // -// For example: +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. // -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.imag(input) ==> [4.75, 5.75] -// ``` -func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) { +// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { if scope.Err() != nil { return } @@ -13575,89 +13543,70 @@ func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output a(attrs) } opspec := tf.OpSpec{ - Type: "Imag", + Type: "FusedBatchNormGrad", Input: []tf.Input{ - input, + y_backprop, x, scale, reserve_space_1, reserve_space_2, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } -// Computes the maximum along segments of a tensor. +// TopKAttr is an optional argument to TopK. +type TopKAttr func(optionalAttr) + +// TopKSorted sets the optional sorted attribute to value. // -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. +// value: If true the resulting `k` elements will be sorted by the values in +// descending order. +// If not specified, defaults to true +func TopKSorted(value bool) TopKAttr { + return func(m optionalAttr) { + m["sorted"] = value + } +} + +// Finds values and indices of the `k` largest elements for the last dimension. // -// Computes a tensor such that -// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such -// that `segment_ids[j] == i`. +// DEPRECATED at GraphDef version 7: Use TopKV2 instead // -// If the max is empty for a given segment ID `i`, `output[i] = 0`. +// If the input is a vector (rank-1), finds the `k` largest entries in the vector +// and outputs their values and indices as vectors. Thus `values[j]` is the +// `j`-th largest entry in `input`, and its index is `indices[j]`. // -//
-// -//
+// For matrices (resp. higher rank input), computes the top `k` entries in each +// row (resp. vector along the last dimension). Thus, // -// Arguments: +// values.shape = indices.shape = input.shape[:-1] + [k] // -// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. Values should be sorted and can be repeated. +// If two elements are equal, the lower-index element appears first. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SegmentMax", - Input: []tf.Input{ - data, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes hyperbolic tangent of `x` element-wise. -func Tanh(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Tanh", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that skips `count` elements from the `input_dataset`. +// If `k` varies dynamically, use `TopKV2` below. // // Arguments: +// input: 1-D or higher with last dimension at least `k`. +// k: Number of top elements to look for along the last dimension (along each +// row for matrices). // -// count: A scalar representing the number of elements from the `input_dataset` -// that should be skipped. If count is -1, skips everything. -// -// -func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. +func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{"k": k} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "SkipDataset", + Type: "TopK", Input: []tf.Input{ - input_dataset, count, + input, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } // Compute the Hurwitz zeta function \\(\zeta(x, q)\\). @@ -13925,49 +13874,6 @@ func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// RealAttr is an optional argument to Real. -type RealAttr func(optionalAttr) - -// RealTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func RealTout(value tf.DataType) RealAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Returns the real part of a complex number. -// -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// type `float` that is the real part of each element in `input`. All elements in -// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real -// part returned by this operation and *b* is the imaginary part. -// -// For example: -// -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.real(input) ==> [-2.25, 3.25] -// ``` -func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Real", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // AudioSummaryAttr is an optional argument to AudioSummary. type AudioSummaryAttr func(optionalAttr) @@ -15375,31 +15281,6 @@ func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTrees return op.Output(0) } -// Concatenates tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Concat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. type ResourceApplyMomentumAttr func(optionalAttr) @@ -16271,48 +16152,104 @@ func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTable return func(m optionalAttr) { m["initial_num_buckets"] = value } -} - -// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value. -// -// value: The maximum ratio between number of entries and number of -// buckets before growing the table. Must be between 0 and 1. -// If not specified, defaults to 0.8 -func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["max_load_factor"] = value +} + +// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value. +// +// value: The maximum ratio between number of entries and number of +// buckets before growing the table. Must be between 0 and 1. +// If not specified, defaults to 0.8 +func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["max_load_factor"] = value + } +} + +// Creates an empty hash table that uses tensors as the backing store. +// +// It uses "open addressing" with quadratic reprobing to resolve +// collisions. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// empty_key: The key used to represent empty key buckets internally. Must not +// be used in insert or lookup operations. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableDenseHashTableV2", + Input: []tf.Input{ + empty_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Creates an empty hash table that uses tensors as the backing store. -// -// It uses "open addressing" with quadratic reprobing to resolve -// collisions. +// Inverse 2D fast Fourier transform. // -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a scalar. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. // // Arguments: -// empty_key: The key used to represent empty key buckets internally. Must not -// be used in insert or lookup operations. -// value_dtype: Type of the table values. +// input: A complex64 tensor. // -// Returns Handle to a table. -func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"value_dtype": value_dtype} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "MutableDenseHashTableV2", + Type: "IFFT2D", Input: []tf.Input{ - empty_key, + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -17348,250 +17285,548 @@ func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr { // tensor. // padding: The type of padding algorithm to use. // -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedConv2D", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatelessMultinomialAttr is an optional argument to StatelessMultinomial. +type StatelessMultinomialAttr func(optionalAttr) + +// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// seed: 2 seeds (shape [2]). +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessMultinomial", + Input: []tf.Input{ + logits, num_samples, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceGatherAttr is an optional argument to ResourceGather. +type ResourceGatherAttr func(optionalAttr) + +// ResourceGatherValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func ResourceGatherValidateIndices(value bool) ResourceGatherAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Gather slices from the variable pointed to by `resource` according to `indices`. +// +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: +// +// ```python +// # Scalar indices +// output[:, ..., :] = params[indices, :, ... :] +// +// # Vector indices +// output[i, :, ..., :] = params[indices[i], :, ... :] +// +// # Higher rank indices +// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// ``` +func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceGather", + Input: []tf.Input{ + resource, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Delete the TensorArray from its resource container. +// +// This enables the user to close and release the resource in the middle +// of a step/run. +// +// Arguments: +// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// +// Returns the created operation. +func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayCloseV3", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// Saves the input tensors to disk. +// +// The size of `tensor_names` must match the number of tensors in `data`. `data[i]` +// is written to `filename` with name `tensor_names[i]`. +// +// See also `SaveSlices`. +// +// Arguments: +// filename: Must have a single element. The name of the file to which we write +// the tensor. +// tensor_names: Shape `[N]`. The names of the tensors to be saved. +// data: `N` tensors to save. +// +// Returns the created operation. +func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Save", + Input: []tf.Input{ + filename, tensor_names, tf.OutputList(data), + }, + } + return scope.AddOperation(opspec) +} + +// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is +// +// true, this follows Python semantics in that the result here is consistent +// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. +// +// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FloorMod", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul. +type SparseTensorDenseMatMulAttr func(optionalAttr) + +// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value. +// +// value: Use the adjoint of A in the matrix multiply. If A is complex, this +// is transpose(conj(A)). Otherwise it's transpose(A). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_a"] = value + } +} + +// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value. +// +// value: Use the adjoint of B in the matrix multiply. If B is complex, this +// is transpose(conj(B)). Otherwise it's transpose(B). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_b"] = value + } +} + +// Multiply SparseTensor (of rank 2) "A" by dense matrix "B". +// +// No validity checking is performed on the indices of A. However, the following +// input format is recommended for optimal behavior: +// +// if adjoint_a == false: +// A should be sorted in lexicographically increasing order. Use SparseReorder +// if you're not sure. +// if adjoint_a == true: +// A should be sorted in order of increasing dimension 1 (i.e., "column major" +// order instead of "row major" order). +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. +// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. +// b: 2-D. A dense Matrix. +func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedConv2D", + Type: "SparseTensorDenseMatMul", Input: []tf.Input{ - input, filter, min_input, max_input, min_filter, max_filter, + a_indices, a_values, a_shape, b, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// StatelessMultinomialAttr is an optional argument to StatelessMultinomial. -type StatelessMultinomialAttr func(optionalAttr) - -// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value. -// If not specified, defaults to DT_INT64 -func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr { - return func(m optionalAttr) { - m["output_dtype"] = value - } + return op.Output(0) } -// Draws samples from a multinomial distribution. +// Deserialize and concatenate `SparseTensors` from a serialized minibatch. // -// Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. -// seed: 2 seeds (shape [2]). +// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where +// `N` is the minibatch size and the rows correspond to packed outputs of +// `SerializeSparse`. The ranks of the original `SparseTensor` objects +// must all match. When the final `SparseTensor` is created, it has rank one +// higher than the ranks of the incoming `SparseTensor` objects +// (they have been concatenated along a new row dimension). // -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) { +// The output `SparseTensor` object's shape values for all dimensions but the +// first are the max across the input `SparseTensor` objects' shape values +// for the corresponding dimensions. Its first shape value is `N`, the minibatch +// size. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. +// Must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"dtype": dtype} opspec := tf.OpSpec{ - Type: "StatelessMultinomial", + Type: "DeserializeManySparse", Input: []tf.Input{ - logits, num_samples, seed, + serialized_sparse, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceGatherAttr is an optional argument to ResourceGather. -type ResourceGatherAttr func(optionalAttr) - -// ResourceGatherValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func ResourceGatherValidateIndices(value bool) ResourceGatherAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } + return op.Output(0), op.Output(1), op.Output(2) } -// Gather slices from the variable pointed to by `resource` according to `indices`. +// Inverse real-valued fast Fourier transform. // -// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). -// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: +// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most dimension of `input`. // -// ```python -// # Scalar indices -// output[:, ..., :] = params[indices, :, ... :] +// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the +// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If +// `fft_length` is not provided, it is computed from the size of the inner-most +// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to +// compute `input` is odd, it should be provided since it cannot be inferred +// properly. // -// # Vector indices -// output[i, :, ..., :] = params[indices[i], :, ... :] +// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller +// than the corresponding dimension of `input`, the dimension is cropped. If it is +// larger, the dimension is padded with zeros. // -// # Higher rank indices -// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] -// ``` -func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) { +// Arguments: +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [1]. The FFT length. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most +// dimension of `input` is replaced with the `fft_length` samples of its inverse +// 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.irfft +// @end_compatibility +func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceGather", + Type: "IRFFT", Input: []tf.Input{ - resource, indices, + input, fft_length, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Delete the TensorArray from its resource container. +// Concatenates a list of `SparseTensor` along the specified dimension. // -// This enables the user to close and release the resource in the middle -// of a step/run. +// Concatenation is with respect to the dense versions of these sparse tensors. +// It is assumed that each input is a `SparseTensor` whose elements are ordered +// along increasing dimension number. +// +// All inputs' shapes must match, except for the concat dimension. The +// `indices`, `values`, and `shapes` lists must have the same length. +// +// The output shape is identical to the inputs', except along the concat +// dimension, where it is the sum of the inputs' sizes along that dimension. +// +// The output elements will be resorted to preserve the sort order along +// increasing dimension number. +// +// This op runs in `O(M log M)` time, where `M` is the total number of non-empty +// values across all inputs. This is due to the need for an internal sort in +// order to concatenate efficiently across an arbitrary dimension. +// +// For example, if `concat_dim = 1` and the inputs are +// +// sp_inputs[0]: shape = [2, 3] +// [0, 2]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// sp_inputs[1]: shape = [2, 4] +// [0, 1]: "d" +// [0, 2]: "e" +// +// then the output will be +// +// shape = [2, 7] +// [0, 2]: "a" +// [0, 4]: "d" +// [0, 5]: "e" +// [1, 0]: "b" +// [1, 1]: "c" +// +// Graphically this is equivalent to doing +// +// [ a] concat [ d e ] = [ a d e ] +// [b c ] [ ] [b c ] // // Arguments: -// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. Non-empty values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), +// where rank is the number of dimensions in each input `SparseTensor`. // -// Returns the created operation. -func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { +// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. +func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"concat_dim": concat_dim} opspec := tf.OpSpec{ - Type: "TensorArrayCloseV3", + Type: "SparseConcat", Input: []tf.Input{ - handle, + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), }, + Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// Saves the input tensors to disk. +// Generates sparse cross from a list of sparse and dense tensors. +// +// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each +// representing features of one feature column. It outputs a 2D `SparseTensor` with +// the batchwise crosses of these features. +// +// For example, if the inputs are +// +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" +// +// inputs[2]: Tensor [["f"], ["g"]] +// +// then the output will be +// +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) // -// The size of `tensor_names` must match the number of tensors in `data`. `data[i]` -// is written to `filename` with name `tensor_names[i]`. +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// hashed_output: If true, returns the hash of the cross instead of the string. +// This will allow us avoiding string manipulations. +// num_buckets: It is used if hashed_output is true. +// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. +// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` +// function to combine the crosses fingerprints. // -// See also `SaveSlices`. // -// Arguments: -// filename: Must have a single element. The name of the file to which we write -// the tensor. -// tensor_names: Shape `[N]`. The names of the tensors to be saved. -// data: `N` tensors to save. // -// Returns the created operation. -func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) { +// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. +func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} opspec := tf.OpSpec{ - Type: "Save", + Type: "SparseCross", Input: []tf.Input{ - filename, tensor_names, tf.OutputList(data), + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), }, + Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is +// Returns the element-wise min of two SparseTensors. // -// true, this follows Python semantics in that the result here is consistent -// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. +// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. // -// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// Arguments: +// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, in the canonical lexicographic ordering. +// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. +// a_shape: 1-D. Shape of the input SparseTensor. +// b_indices: counterpart to `a_indices` for the other operand. +// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. +// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// +// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. +func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "FloorMod", + Type: "SparseSparseMinimum", Input: []tf.Input{ - x, y, + a_indices, a_values, a_shape, b_indices, b_values, b_shape, }, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul. -type SparseTensorDenseMatMulAttr func(optionalAttr) +// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap. +type TakeManySparseFromTensorsMapAttr func(optionalAttr) -// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value. +// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value. // -// value: Use the adjoint of A in the matrix multiply. If A is complex, this -// is transpose(conj(A)). Otherwise it's transpose(A). -// If not specified, defaults to false -func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr { +// value: The container name for the `SparseTensorsMap` read by this op. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr { return func(m optionalAttr) { - m["adjoint_a"] = value + m["container"] = value } } -// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value. +// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value. // -// value: Use the adjoint of B in the matrix multiply. If B is complex, this -// is transpose(conj(B)). Otherwise it's transpose(B). -// If not specified, defaults to false -func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr { +// value: The shared name for the `SparseTensorsMap` read by this op. +// It should not be blank; rather the `shared_name` or unique Operation name +// of the Op that created the original `SparseTensorsMap` should be used. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr { return func(m optionalAttr) { - m["adjoint_b"] = value - } -} - -// Multiply SparseTensor (of rank 2) "A" by dense matrix "B". -// -// No validity checking is performed on the indices of A. However, the following -// input format is recommended for optimal behavior: -// -// if adjoint_a == false: -// A should be sorted in lexicographically increasing order. Use SparseReorder -// if you're not sure. -// if adjoint_a == true: -// A should be sorted in order of increasing dimension 1 (i.e., "column major" -// order instead of "row major" order). -// -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. -// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. -// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. -// b: 2-D. A dense Matrix. -func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseMatMul", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, - Attrs: attrs, + m["shared_name"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Deserialize and concatenate `SparseTensors` from a serialized minibatch. +// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. // -// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where -// `N` is the minibatch size and the rows correspond to packed outputs of -// `SerializeSparse`. The ranks of the original `SparseTensor` objects -// must all match. When the final `SparseTensor` is created, it has rank one +// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where +// `N` is the minibatch size and the rows correspond to the output handles of +// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the +// original `SparseTensor` objects that went into the given input ops must all +// match. When the final `SparseTensor` is created, it has rank one // higher than the ranks of the incoming `SparseTensor` objects -// (they have been concatenated along a new row dimension). +// (they have been concatenated along a new row dimension on the left). // // The output `SparseTensor` object's shape values for all dimensions but the // first are the max across the input `SparseTensor` objects' shape values @@ -17602,24 +17837,29 @@ func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Outp // standard lexicographic order. If this is not the case, after this // step run `SparseReorder` to restore index ordering. // -// For example, if the serialized input is a `[2 x 3]` matrix representing two -// original `SparseTensor` objects: +// For example, if the handles represent an input, which is a `[2, 3]` matrix +// representing two original `SparseTensor` objects: // +// ``` // index = [ 0] // [10] // [20] // values = [1, 2, 3] // shape = [50] +// ``` // // and // +// ``` // index = [ 2] // [10] // values = [4, 5] // shape = [30] +// ``` // -// then the final deserialized `SparseTensor` will be: +// then the final `SparseTensor` will be: // +// ``` // index = [0 0] // [0 10] // [0 20] @@ -17627,20 +17867,27 @@ func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Outp // [1 10] // values = [1, 2, 3, 4, 5] // shape = [2 50] +// ``` // // Arguments: -// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. -// Must have 3 columns. -// dtype: The `dtype` of the serialized `SparseTensor` objects. -func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { +// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. +// Shape: `[N]`. +// dtype: The `dtype` of the `SparseTensor` objects stored in the +// `SparseTensorsMap`. +// +// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`. +func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "DeserializeManySparse", + Type: "TakeManySparseFromTensorsMap", Input: []tf.Input{ - serialized_sparse, + sparse_handles, }, Attrs: attrs, } @@ -17648,443 +17895,321 @@ func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.D return op.Output(0), op.Output(1), op.Output(2) } -// StringJoinAttr is an optional argument to StringJoin. -type StringJoinAttr func(optionalAttr) - -// StringJoinSeparator sets the optional separator attribute to value. -// -// value: string, an optional join separator. -// If not specified, defaults to "" -func StringJoinSeparator(value string) StringJoinAttr { - return func(m optionalAttr) { - m["separator"] = value - } -} - -// Joins the strings in the given list of string tensors into one tensor; +// Assigns a new value to a variable. // -// with the given separator (default is an empty separator). +// Any ReadVariableOp with a control dependency on this op is guaranteed to return +// this value or a subsequent newer value of the variable. // // Arguments: -// inputs: A list of string tensors. The tensors must all have the same shape, -// or be scalars. Scalars may be mixed in; these will be broadcast to the shape -// of non-scalar inputs. -func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) { +// resource: handle to the resource in which to store the variable. +// value: the value to set the new tensor to use. +// +// Returns the created operation. +func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "StringJoin", + Type: "AssignVariableOp", Input: []tf.Input{ - tf.OutputList(inputs), + resource, value, }, - Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Returns immutable tensor from memory region. -// -// The current implementation memmaps the tensor from a file. +// Returns a tensor of ones with the same shape and type as x. // // Arguments: -// dtype: Type of the returned tensor. -// shape: Shape of the returned tensor. -// memory_region_name: Name of readonly memory region used by the tensor, see -// NewReadOnlyMemoryRegionFromFile in tensorflow::Env. -func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) { +// x: a tensor of type T. +// +// Returns a tensor of the same shape and type as x but filled with ones. +func OnesLike(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name} opspec := tf.OpSpec{ - Type: "ImmutableConst", - - Attrs: attrs, + Type: "OnesLike", + Input: []tf.Input{ + x, + }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Inverse real-valued fast Fourier transform. -// -// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most dimension of `input`. +// The gradient of SparseFillEmptyRows. // -// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the -// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If -// `fft_length` is not provided, it is computed from the size of the inner-most -// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to -// compute `input` is odd, it should be provided since it cannot be inferred -// properly. +// Takes vectors reverse_index_map, shaped `[N]`, and grad_values, +// shaped `[N_full]`, where `N_full >= N` and copies data into either +// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and +// `d_default_value` is a scalar. // -// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller -// than the corresponding dimension of `input`, the dimension is cropped. If it is -// larger, the dimension is padded with zeros. +// d_values[j] = grad_values[reverse_index_map[j]] +// d_default_value = sum_{k : 0 .. N_full - 1} ( +// grad_values[k] * 1{k not in reverse_index_map}) // // Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [1]. The FFT length. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most -// dimension of `input` is replaced with the `fft_length` samples of its inverse -// 1D Fourier transform. +// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows. +// grad_values: 1-D. The gradients from backprop. // -// @compatibility(numpy) -// Equivalent to np.fft.irfft -// @end_compatibility -func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +// Returns 1-D. The backprop into values.0-D. The backprop into default_value. +func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "IRFFT", + Type: "SparseFillEmptyRowsGrad", Input: []tf.Input{ - input, fft_length, + reverse_index_map, grad_values, }, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Concatenates a list of `SparseTensor` along the specified dimension. -// -// Concatenation is with respect to the dense versions of these sparse tensors. -// It is assumed that each input is a `SparseTensor` whose elements are ordered -// along increasing dimension number. -// -// All inputs' shapes must match, except for the concat dimension. The -// `indices`, `values`, and `shapes` lists must have the same length. -// -// The output shape is identical to the inputs', except along the concat -// dimension, where it is the sum of the inputs' sizes along that dimension. -// -// The output elements will be resorted to preserve the sort order along -// increasing dimension number. -// -// This op runs in `O(M log M)` time, where `M` is the total number of non-empty -// values across all inputs. This is due to the need for an internal sort in -// order to concatenate efficiently across an arbitrary dimension. -// -// For example, if `concat_dim = 1` and the inputs are -// -// sp_inputs[0]: shape = [2, 3] -// [0, 2]: "a" -// [1, 0]: "b" -// [1, 1]: "c" -// -// sp_inputs[1]: shape = [2, 4] -// [0, 1]: "d" -// [0, 2]: "e" +// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` +// if < 0, `scale * features` otherwise. // -// then the output will be +// Assumes weights to have zero mean and variance 1.0 / fan_in. // -// shape = [2, 7] -// [0, 2]: "a" -// [0, 4]: "d" -// [0, 5]: "e" -// [1, 0]: "b" -// [1, 1]: "c" +// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) +func Selu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Selu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SetSizeAttr is an optional argument to SetSize. +type SetSizeAttr func(optionalAttr) + +// SetSizeValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SetSizeValidateIndices(value bool) SetSizeAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Number of unique elements along last dimension of input `set`. // -// Graphically this is equivalent to doing +// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, +// and `set_shape`. The last dimension contains values in a set, duplicates are +// allowed but ignored. // -// [ a] concat [ d e ] = [ a d e ] -// [b c ] [ ] [b c ] +// If `validate_indices` is `True`, this op validates the order and range of `set` +// indices. // // Arguments: -// indices: 2-D. Indices of each input `SparseTensor`. -// values: 1-D. Non-empty values of each `SparseTensor`. -// shapes: 1-D. Shapes of each `SparseTensor`. -// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), -// where rank is the number of dimensions in each input `SparseTensor`. +// set_indices: 2D `Tensor`, indices of a `SparseTensor`. +// set_values: 1D `Tensor`, values of a `SparseTensor`. +// set_shape: 1D `Tensor`, shape of a `SparseTensor`. // -// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. -func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { +// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st +// `n-1` dimensions as `set`. Each value is the number of unique elements in +// the corresponding `[0...n-1]` dimension of `set`. +func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"concat_dim": concat_dim} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "SparseConcat", + Type: "SetSize", Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), + set_indices, set_values, set_shape, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Generates sparse cross from a list of sparse and dense tensors. -// -// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each -// representing features of one feature column. It outputs a 2D `SparseTensor` with -// the batchwise crosses of these features. -// -// For example, if the inputs are -// -// inputs[0]: SparseTensor with shape = [2, 2] -// [0, 0]: "a" -// [1, 0]: "b" -// [1, 1]: "c" -// -// inputs[1]: SparseTensor with shape = [2, 1] -// [0, 0]: "d" -// [1, 0]: "e" -// -// inputs[2]: Tensor [["f"], ["g"]] -// -// then the output will be -// -// shape = [2, 2] -// [0, 0]: "a_X_d_X_f" -// [1, 0]: "b_X_e_X_g" -// [1, 1]: "c_X_e_X_g" +// Computes the sign and the log of the absolute value of the determinant of // -// if hashed_output=true then the output will be +// one or more square matrices. // -// shape = [2, 2] -// [0, 0]: FingerprintCat64( -// Fingerprint64("f"), FingerprintCat64( -// Fingerprint64("d"), Fingerprint64("a"))) -// [1, 0]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("b"))) -// [1, 1]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("c"))) +// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions +// form square matrices. The outputs are two tensors containing the signs and +// absolute values of the log determinants for all N input submatrices +// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant). +// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU +// is the LU decomposition of the input and P is the corresponding +// permutation matrix. // // Arguments: -// indices: 2-D. Indices of each input `SparseTensor`. -// values: 1-D. values of each `SparseTensor`. -// shapes: 1-D. Shapes of each `SparseTensor`. -// dense_inputs: 2-D. Columns represented by dense `Tensor`. -// hashed_output: If true, returns the hash of the cross instead of the string. -// This will allow us avoiding string manipulations. -// num_buckets: It is used if hashed_output is true. -// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. -// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` -// function to combine the crosses fingerprints. -// -// +// input: Shape is `[N, M, M]`. // -// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed -// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. -func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { +// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants +// of the N input matrices. Shape is `[N]`. +func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} opspec := tf.OpSpec{ - Type: "SparseCross", + Type: "LogMatrixDeterminant", Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0), op.Output(1) } -// Concatenates quantized tensors along one dimension. +// SumAttr is an optional argument to Sum. +type SumAttr func(optionalAttr) + +// SumKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SumKeepDims(value bool) SumAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. // // Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// input_mins: The minimum scalar values for each of the input tensors. -// input_maxes: The maximum scalar values for each of the input tensors. +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. // -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { +// Returns The reduced tensor. +func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "QuantizedConcat", + Type: "Sum", Input: []tf.Input{ - concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + input, axis, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Slice a `SparseTensor` based on the `start` and `size`. -// -// For example, if the input is -// -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] -// -// Graphically the output tensors are: -// -// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] -// [ a ] -// [b c ] -// -// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] -// [ d e ] -// [ ] +// Delete the tensor specified by its handle in the session. // // Arguments: -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// start: 1-D. tensor represents the start of the slice. -// size: 1-D. tensor represents the size of the slice. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. +// handle: The handle for a tensor stored in the session state. // -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { +// Returns the created operation. +func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSlice", + Type: "DeleteSessionTensor", Input: []tf.Input{ - indices, values, shape, start, size, + handle, }, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return scope.AddOperation(opspec) } -// Returns the element-wise min of two SparseTensors. +// L2 Loss. // -// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// Computes half the L2 norm of a tensor without the `sqrt`: +// +// output = sum(t ** 2) / 2 // // Arguments: -// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, in the canonical lexicographic ordering. -// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. -// a_shape: 1-D. Shape of the input SparseTensor. -// b_indices: counterpart to `a_indices` for the other operand. -// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. -// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// t: Typically 2-D, but may have any dimensions. // -// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. -func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { +// Returns 0-D. +func L2Loss(scope *Scope, t tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSparseMinimum", + Type: "L2Loss", Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, + t, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap. -type TakeManySparseFromTensorsMapAttr func(optionalAttr) - -// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value. -// -// value: The container name for the `SparseTensorsMap` read by this op. -// If not specified, defaults to "" -func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr { - return func(m optionalAttr) { - m["container"] = value - } -} +// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation. +type DenseToSparseSetOperationAttr func(optionalAttr) -// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value. -// -// value: The shared name for the `SparseTensorsMap` read by this op. -// It should not be blank; rather the `shared_name` or unique Operation name -// of the Op that created the original `SparseTensorsMap` should be used. -// If not specified, defaults to "" -func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr { +// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["validate_indices"] = value } } -// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. -// -// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where -// `N` is the minibatch size and the rows correspond to the output handles of -// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the -// original `SparseTensor` objects that went into the given input ops must all -// match. When the final `SparseTensor` is created, it has rank one -// higher than the ranks of the incoming `SparseTensor` objects -// (they have been concatenated along a new row dimension on the left). -// -// The output `SparseTensor` object's shape values for all dimensions but the -// first are the max across the input `SparseTensor` objects' shape values -// for the corresponding dimensions. Its first shape value is `N`, the minibatch -// size. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the handles represent an input, which is a `[2, 3]` matrix -// representing two original `SparseTensor` objects: -// -// ``` -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// ``` +// Applies set operation along last dimension of `Tensor` and `SparseTensor`. // -// and +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. // -// ``` -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] -// ``` +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. // -// then the final `SparseTensor` will be: +// If `validate_indices` is `True`, this op validates the order and range of `set2` +// indices. // -// ``` -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] -// ``` +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. // // Arguments: -// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. -// Shape: `[N]`. -// dtype: The `dtype` of the `SparseTensor` objects stored in the -// `SparseTensorsMap`. +// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the +// max set size across `n-1` dimensions. // -// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`. -func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{"set_operation": set_operation} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TakeManySparseFromTensorsMap", + Type: "DenseToSparseSetOperation", Input: []tf.Input{ - sparse_handles, + set1, set2_indices, set2_values, set2_shape, }, Attrs: attrs, } @@ -18092,45 +18217,82 @@ func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype return op.Output(0), op.Output(1), op.Output(2) } -// MaxPoolAttr is an optional argument to MaxPool. -type MaxPoolAttr func(optionalAttr) +// Subtracts a value from the current value of a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to +// see the decremented value or a subsequent newer one. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value by which the variable will be incremented. +// +// Returns the created operation. +func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignSubVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} -// MaxPoolDataFormat sets the optional data_format attribute to value. +// RestoreAttr is an optional argument to Restore. +type RestoreAttr func(optionalAttr) + +// RestorePreferredShard sets the optional preferred_shard attribute to value. // -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolDataFormat(value string) MaxPoolAttr { +// value: Index of file to open first if multiple files match +// `file_pattern`. +// If not specified, defaults to -1 +func RestorePreferredShard(value int64) RestoreAttr { return func(m optionalAttr) { - m["data_format"] = value + m["preferred_shard"] = value } } -// Performs max pooling on the input. +// Restores a tensor from checkpoint files. +// +// Reads a tensor stored in one or several files. If there are several files (for +// instance because a tensor was saved as slices), `file_pattern` may contain +// wildcard symbols (`*` and `?`) in the filename portion only, not in the +// directory portion. +// +// If a `file_pattern` matches several files, `preferred_shard` can be used to hint +// in which file the requested tensor is likely to be found. This op will first +// open the file at index `preferred_shard` in the list of matching files and try +// to restore tensors from that file. Only if some tensors or tensor slices are +// not found in that first file, then the Op opens all the files. Setting +// `preferred_shard` to match the value passed as the `shard` input +// of a matching `Save` Op may speed up Restore. This attribute only affects +// performance, not correctness. The default value -1 means files are processed in +// order. +// +// See also `RestoreSlice`. // // Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// dt: The type of the tensor to be restored. // -// Returns The max pooled output tensor. -func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { +// Returns The restored tensor. +func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + attrs := map[string]interface{}{"dt": dt} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPool", + Type: "Restore", Input: []tf.Input{ - input, + file_pattern, tensor_name, }, Attrs: attrs, } @@ -18138,128 +18300,205 @@ func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padd return op.Output(0) } -// Assigns a new value to a variable. +// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear. +type QuantizedResizeBilinearAttr func(optionalAttr) + +// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. // -// Any ReadVariableOp with a control dependency on this op is guaranteed to return -// this value or a subsequent newer value of the variable. +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// Resize quantized `images` to `size` using quantized bilinear interpolation. +// +// Input images and output images must be quantized types. // // Arguments: -// resource: handle to the resource in which to store the variable. -// value: the value to set the new tensor to use. +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// // -// Returns the created operation. -func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "AssignVariableOp", + Type: "QuantizedResizeBilinear", Input: []tf.Input{ - resource, value, + images, size, min, max, }, + Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// Returns a tensor of ones with the same shape and type as x. +// Computes the minimum along segments of a tensor. +// +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. +// +// Computes a tensor such that +// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such +// that `segment_ids[j] == i`. +// +// If the min is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
// // Arguments: -// x: a tensor of type T. // -// Returns a tensor of the same shape and type as x but filled with ones. -func OnesLike(scope *Scope, x tf.Output) (y tf.Output) { +// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "OnesLike", + Type: "SegmentMin", Input: []tf.Input{ - x, + data, segment_ids, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// The gradient of SparseFillEmptyRows. +// SdcaOptimizerAttr is an optional argument to SdcaOptimizer. +type SdcaOptimizerAttr func(optionalAttr) + +// SdcaOptimizerAdaptative sets the optional adaptative attribute to value. // -// Takes vectors reverse_index_map, shaped `[N]`, and grad_values, -// shaped `[N_full]`, where `N_full >= N` and copies data into either -// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and -// `d_default_value` is a scalar. +// value: Whether to use Adaptive SDCA for the inner loop. +// If not specified, defaults to true +func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr { + return func(m optionalAttr) { + m["adaptative"] = value + } +} + +// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for // -// d_values[j] = grad_values[reverse_index_map[j]] -// d_default_value = sum_{k : 0 .. N_full - 1} ( -// grad_values[k] * 1{k not in reverse_index_map}) +// linear models with L1 + L2 regularization. As global optimization objective is +// strongly-convex, the optimizer optimizes the dual objective at each step. The +// optimizer applies each update one example at a time. Examples are sampled +// uniformly, and the optimizer is learning rate free and enjoys linear convergence +// rate. +// +// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
+// Shai Shalev-Shwartz, Tong Zhang. 2012 +// +// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ +// +// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, +// Peter Richtarik, Martin Takac. 2015 +// +// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 // // Arguments: -// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows. -// grad_values: 1-D. The gradients from backprop. +// sparse_example_indices: a list of vectors which contain example indices. +// sparse_feature_indices: a list of vectors which contain feature indices. +// sparse_feature_values: a list of vectors which contains feature value +// associated with each feature group. +// dense_features: a list of matrices which contains the dense feature values. +// example_weights: a vector which contains the weight associated with each +// example. +// example_labels: a vector which contains the label/target associated with each +// example. +// sparse_indices: a list of vectors where each value is the indices which has +// corresponding weights in sparse_weights. This field maybe omitted for the +// dense approach. +// sparse_weights: a list of vectors where each value is the weight associated with +// a sparse feature group. +// dense_weights: a list of vectors where the values are the weights associated +// with a dense feature group. +// example_state_data: a list of vectors containing the example state data. +// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, +// squared and hinge losses. +// l1: Symmetric l1 regularization strength. +// l2: Symmetric l2 regularization strength. +// num_loss_partitions: Number of partitions of the global loss function. +// num_inner_iterations: Number of iterations per mini-batch. // -// Returns 1-D. The backprop into values.0-D. The backprop into default_value. -func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) { +// Returns a list of vectors containing the updated example state +// data.a list of vectors where each value is the delta +// weights associated with a sparse feature group.a list of vectors where the values are the delta +// weights associated with a dense feature group. +func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "SparseFillEmptyRowsGrad", + Type: "SdcaOptimizer", Input: []tf.Input{ - reverse_index_map, grad_values, + tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` -// if < 0, `scale * features` otherwise. -// -// Assumes weights to have zero mean and variance 1.0 / fan_in. -// -// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) -func Selu(scope *Scope, features tf.Output) (activations tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "Selu", - Input: []tf.Input{ - features, - }, + var idx int + var err error + out_example_state_data = op.Output(idx) + if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return } - op := scope.AddOperation(opspec) - return op.Output(0) + if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return + } + return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights } -// SetSizeAttr is an optional argument to SetSize. -type SetSizeAttr func(optionalAttr) +// ShapeAttr is an optional argument to Shape. +type ShapeAttr func(optionalAttr) -// SetSizeValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func SetSizeValidateIndices(value bool) SetSizeAttr { +// ShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeOutType(value tf.DataType) ShapeAttr { return func(m optionalAttr) { - m["validate_indices"] = value + m["out_type"] = value } } -// Number of unique elements along last dimension of input `set`. -// -// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, -// and `set_shape`. The last dimension contains values in a set, duplicates are -// allowed but ignored. +// Returns the shape of a tensor. // -// If `validate_indices` is `True`, this op validates the order and range of `set` -// indices. +// This operation returns a 1-D integer tensor representing the shape of `input`. // -// Arguments: -// set_indices: 2D `Tensor`, indices of a `SparseTensor`. -// set_values: 1D `Tensor`, values of a `SparseTensor`. -// set_shape: 1D `Tensor`, shape of a `SparseTensor`. +// For example: // -// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st -// `n-1` dimensions as `set`. Each value is the number of unique elements in -// the corresponding `[0...n-1]` dimension of `set`. -func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) { +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -18268,9 +18507,9 @@ func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shap a(attrs) } opspec := tf.OpSpec{ - Type: "SetSize", + Type: "Shape", Input: []tf.Input{ - set_indices, set_values, set_shape, + input, }, Attrs: attrs, } @@ -18278,264 +18517,227 @@ func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shap return op.Output(0) } -// Computes the sign and the log of the absolute value of the determinant of -// -// one or more square matrices. -// -// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions -// form square matrices. The outputs are two tensors containing the signs and -// absolute values of the log determinants for all N input submatrices -// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant). -// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU -// is the LU decomposition of the input and P is the corresponding -// permutation matrix. +// Computes the power of one value to another. // -// Arguments: -// input: Shape is `[N, M, M]`. +// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for +// corresponding elements in `x` and `y`. For example: // -// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants -// of the N input matrices. Shape is `[N]`. -func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) { +// ``` +// # tensor 'x' is [[2, 2]], [3, 3]] +// # tensor 'y' is [[8, 16], [2, 3]] +// tf.pow(x, y) ==> [[256, 65536], [9, 27]] +// ``` +func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "LogMatrixDeterminant", + Type: "Pow", Input: []tf.Input{ - input, + x, y, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// SumAttr is an optional argument to Sum. -type SumAttr func(optionalAttr) - -// SumKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SumKeepDims(value bool) SumAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } + return op.Output(0) } -// Computes the sum of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. +// Computes fingerprints of the input strings. // // Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. +// input: vector of strings to compute fingerprints on. // -// Returns The reduced tensor. -func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) +// Returns a (N,2) shaped matrix where N is the number of elements in the input +// vector. Each row contains the low and high parts of the fingerprint. +func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return } opspec := tf.OpSpec{ - Type: "Sum", + Type: "SdcaFprint", Input: []tf.Input{ - input, axis, + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Delete the tensor specified by its handle in the session. +// RandomPoissonV2Attr is an optional argument to RandomPoissonV2. +type RandomPoissonV2Attr func(optionalAttr) + +// RandomPoissonV2Seed sets the optional seed attribute to value. // -// Arguments: -// handle: The handle for a tensor stored in the session state. +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomPoissonV2Seed2 sets the optional seed2 attribute to value. // -// Returns the created operation. -func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed2"] = value } - opspec := tf.OpSpec{ - Type: "DeleteSessionTensor", - Input: []tf.Input{ - handle, - }, +} + +// RandomPoissonV2Dtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_INT64 +func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["dtype"] = value } - return scope.AddOperation(opspec) } -// L2 Loss. +// Outputs random values from the Poisson distribution(s) described by rate. // -// Computes half the L2 norm of a tensor without the `sqrt`: +// This op uses two algorithms, depending on rate. If rate >= 10, then +// the algorithm by Hormann is used to acquire samples via +// transformation-rejection. +// See http://www.sciencedirect.com/science/article/pii/0167668793909974. // -// output = sum(t ** 2) / 2 +// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform +// random variables. +// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer +// Programming, Volume 2. Addison Wesley // // Arguments: -// t: Typically 2-D, but may have any dimensions. +// shape: 1-D integer tensor. Shape of independent samples to draw from each +// distribution described by the shape parameters given in rate. +// rate: A tensor in which each scalar is a "rate" parameter describing the +// associated poisson distribution. // -// Returns 0-D. -func L2Loss(scope *Scope, t tf.Output) (output tf.Output) { +// Returns A tensor with shape `shape + shape(rate)`. Each slice +// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for +// `rate[i0, i1, ...iN]`. +func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "L2Loss", + Type: "RandomPoissonV2", Input: []tf.Input{ - t, + shape, rate, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation. -type DenseToSparseSetOperationAttr func(optionalAttr) +// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve. +type MatrixTriangularSolveAttr func(optionalAttr) -// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// MatrixTriangularSolveLower sets the optional lower attribute to value. +// +// value: Boolean indicating whether the innermost matrices in `matrix` are +// lower or upper triangular. // If not specified, defaults to true -func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr { +func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { return func(m optionalAttr) { - m["validate_indices"] = value + m["lower"] = value } } -// Applies set operation along last dimension of `Tensor` and `SparseTensor`. +// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value. // -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// value: Boolean indicating whether to solve with `matrix` or its (block-wise) +// adjoint. // -// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, -// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same -// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. +// @compatibility(numpy) +// Equivalent to np.linalg.triangular_solve +// @end_compatibility +// If not specified, defaults to false +func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Solves systems of linear equations with upper or lower triangular matrices by // -// If `validate_indices` is `True`, this op validates the order and range of `set2` -// indices. +// backsubstitution. // -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. +// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form +// square matrices. If `lower` is `True` then the strictly upper triangular part +// of each inner-most matrix is assumed to be zero and not accessed. +// If `lower` is False then the strictly lower triangular part of each inner-most +// matrix is assumed to be zero and not accessed. +// `rhs` is a tensor of shape `[..., M, K]`. // -// Arguments: -// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. -// Dimension `n` contains values in a set, duplicates are allowed but ignored. -// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must -// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the -// max set size across `n-1` dimensions. +// The output is a tensor of shape `[..., M, K]`. If `adjoint` is +// `True` then the innermost matrices in `output` satisfy matrix equations +// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. +// If `adjoint` is `False` then the strictly then the innermost matrices in +// `output` satisfy matrix equations +// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. // +// Arguments: +// matrix: Shape is `[..., M, M]`. +// rhs: Shape is `[..., M, K]`. // -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { +// Returns Shape is `[..., M, K]`. +func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"set_operation": set_operation} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DenseToSparseSetOperation", + Type: "MatrixTriangularSolve", Input: []tf.Input{ - set1, set2_indices, set2_values, set2_shape, + matrix, rhs, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Subtracts a value from the current value of a variable. -// -// Any ReadVariableOp with a control dependency on this op is guaranteed to -// see the decremented value or a subsequent newer one. -// -// Arguments: -// resource: handle to the resource in which to store the variable. -// value: the value by which the variable will be incremented. -// -// Returns the created operation. -func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { +// Computes inverse hyperbolic sine of x element-wise. +func Asinh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "AssignSubVariableOp", + Type: "Asinh", Input: []tf.Input{ - resource, value, + x, }, } - return scope.AddOperation(opspec) -} - -// RestoreAttr is an optional argument to Restore. -type RestoreAttr func(optionalAttr) - -// RestorePreferredShard sets the optional preferred_shard attribute to value. -// -// value: Index of file to open first if multiple files match -// `file_pattern`. -// If not specified, defaults to -1 -func RestorePreferredShard(value int64) RestoreAttr { - return func(m optionalAttr) { - m["preferred_shard"] = value - } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Restores a tensor from checkpoint files. -// -// Reads a tensor stored in one or several files. If there are several files (for -// instance because a tensor was saved as slices), `file_pattern` may contain -// wildcard symbols (`*` and `?`) in the filename portion only, not in the -// directory portion. -// -// If a `file_pattern` matches several files, `preferred_shard` can be used to hint -// in which file the requested tensor is likely to be found. This op will first -// open the file at index `preferred_shard` in the list of matching files and try -// to restore tensors from that file. Only if some tensors or tensor slices are -// not found in that first file, then the Op opens all the files. Setting -// `preferred_shard` to match the value passed as the `shard` input -// of a matching `Save` Op may speed up Restore. This attribute only affects -// performance, not correctness. The default value -1 means files are processed in -// order. -// -// See also `RestoreSlice`. +// Creates a dataset with a range of values. Corresponds to python's xrange. // // Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. -// tensor_name: Must have a single element. The name of the tensor to be -// restored. -// dt: The type of the tensor to be restored. +// start: corresponds to start in python's xrange(). +// stop: corresponds to stop in python's xrange(). +// step: corresponds to step in python's xrange(). // -// Returns The restored tensor. -func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) { +// +func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dt": dt} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "Restore", + Type: "RangeDataset", Input: []tf.Input{ - file_pattern, tensor_name, + start, stop, step, }, Attrs: attrs, } @@ -18543,162 +18745,124 @@ func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf. return op.Output(0) } -// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear. -type QuantizedResizeBilinearAttr func(optionalAttr) +// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput. +type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr) -// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { return func(m optionalAttr) { - m["align_corners"] = value + m["dilations"] = value } } -// Resize quantized `images` to `size` using quantized bilinear interpolation. -// -// Input images and output images must be quantized types. +// Computes the gradients of depthwise convolution with respect to the input. // // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// -// +// input_sizes: An integer vector representing the shape of `input`, based +// on `data_format`. For example, if `data_format` is 'NHWC' then +// `input` is a 4-D `[batch, height, width, channels]` tensor. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, depthwise_multiplier]`. +// out_backprop: 4-D with shape based on `data_format`. +// For example, if `data_format` is 'NHWC' then +// out_backprop shape is `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. +// padding: The type of padding algorithm to use. // -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) { +// Returns 4-D with shape according to `data_format`. For example, if +// `data_format` is 'NHWC', output shape is `[batch, in_height, +// in_width, in_channels]`. Gradient w.r.t. the input of the +// convolution. +func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedResizeBilinear", + Type: "DepthwiseConv2dNativeBackpropInput", Input: []tf.Input{ - images, size, min, max, + input_sizes, filter, out_backprop, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Computes the minimum along segments of a tensor. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Computes a tensor such that -// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such -// that `segment_ids[j] == i`. -// -// If the min is empty for a given segment ID `i`, `output[i] = 0`. +// Stops gradient computation. // -//
-// -//
+// When executed in a graph, this op outputs its input tensor as-is. // -// Arguments: +// When building ops to compute gradients, this op prevents the contribution of +// its inputs to be taken into account. Normally, the gradient generator adds ops +// to a graph to compute the derivatives of a specified 'loss' by recursively +// finding out inputs that contributed to its computation. If you insert this op +// in the graph it inputs are masked from the gradient generator. They are not +// taken into account for computing gradients. // -// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. Values should be sorted and can be repeated. +// This is useful any time you want to compute a value with TensorFlow but need +// to pretend that the value was a constant. Some examples include: // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +// * The *EM* algorithm where the *M-step* should not involve backpropagation +// through the output of the *E-step*. +// * Contrastive divergence training of Boltzmann machines where, when +// differentiating the energy function, the training must not backpropagate +// through the graph that generated the samples from the model. +// * Adversarial training, where no backprop should happen through the adversarial +// example generation process. +func StopGradient(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SegmentMin", + Type: "StopGradient", Input: []tf.Input{ - data, segment_ids, + input, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// SdcaOptimizerAttr is an optional argument to SdcaOptimizer. -type SdcaOptimizerAttr func(optionalAttr) - -// SdcaOptimizerAdaptative sets the optional adaptative attribute to value. -// -// value: Whether to use Adaptive SDCA for the inner loop. -// If not specified, defaults to true -func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr { - return func(m optionalAttr) { - m["adaptative"] = value - } -} - -// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for -// -// linear models with L1 + L2 regularization. As global optimization objective is -// strongly-convex, the optimizer optimizes the dual objective at each step. The -// optimizer applies each update one example at a time. Examples are sampled -// uniformly, and the optimizer is learning rate free and enjoys linear convergence -// rate. -// -// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
-// Shai Shalev-Shwartz, Tong Zhang. 2012 -// -// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ -// -// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
-// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, -// Peter Richtarik, Martin Takac. 2015 -// -// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
-// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 -// -// Arguments: -// sparse_example_indices: a list of vectors which contain example indices. -// sparse_feature_indices: a list of vectors which contain feature indices. -// sparse_feature_values: a list of vectors which contains feature value -// associated with each feature group. -// dense_features: a list of matrices which contains the dense feature values. -// example_weights: a vector which contains the weight associated with each -// example. -// example_labels: a vector which contains the label/target associated with each -// example. -// sparse_indices: a list of vectors where each value is the indices which has -// corresponding weights in sparse_weights. This field maybe omitted for the -// dense approach. -// sparse_weights: a list of vectors where each value is the weight associated with -// a sparse feature group. -// dense_weights: a list of vectors where the values are the weights associated -// with a dense feature group. -// example_state_data: a list of vectors containing the example state data. -// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, -// squared and hinge losses. -// l1: Symmetric l1 regularization strength. -// l2: Symmetric l2 regularization strength. -// num_loss_partitions: Number of partitions of the global loss function. -// num_inner_iterations: Number of iterations per mini-batch. +// Eagerly executes a python function to compute func(input)->output. The // -// Returns a list of vectors containing the updated example state -// data.a list of vectors where each value is the delta -// weights associated with a sparse feature group.a list of vectors where the values are the delta -// weights associated with a dense feature group. -func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { +// semantics of the input, output, and attributes are the same as those for +// PyFunc. +func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"token": token, "Tout": Tout} opspec := tf.OpSpec{ - Type: "SdcaOptimizer", + Type: "EagerPyFunc", Input: []tf.Input{ - tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, + tf.OutputList(input), }, Attrs: attrs, } @@ -18708,103 +18872,152 @@ func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feat } var idx int var err error - out_example_state_data = op.Output(idx) - if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { - scope.UpdateErr("SdcaOptimizer", err) - return - } - if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { - scope.UpdateErr("SdcaOptimizer", err) + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("EagerPyFunc", err) return } - return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights + return output } -// SparseMatMulAttr is an optional argument to SparseMatMul. -type SparseMatMulAttr func(optionalAttr) - -// SparseMatMulTransposeA sets the optional transpose_a attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeA(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value +// Adds sparse updates to the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] += updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] += updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions add. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return } -} - -// SparseMatMulTransposeB sets the optional transpose_b attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeB(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value + opspec := tf.OpSpec{ + Type: "ResourceScatterAdd", + Input: []tf.Input{ + resource, indices, updates, + }, } + return scope.AddOperation(opspec) } -// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["a_is_sparse"] = value +// Says whether the targets are in the top `K` predictions. +// +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed Precision at `k` as a `bool Tensor`. +func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) { + if scope.Err() != nil { + return } -} - -// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["b_is_sparse"] = value + attrs := map[string]interface{}{"k": k} + opspec := tf.OpSpec{ + Type: "InTopK", + Input: []tf.Input{ + predictions, targets, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Multiply matrix "a" by matrix "b". -// -// The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". This op is optimized for the case where at -// least one of "a" or "b" is sparse. The breakeven for using this versus a dense -// matrix multiply on one platform was 30% zero values in the sparse matrix. +// Returns (x - y)(x - y) element-wise. // -// The gradient computation of this operation will only take advantage of sparsity -// in the input gradient when that gradient comes from a Relu. -func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { +// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SparseMatMul", + Type: "SquaredDifference", Input: []tf.Input{ - a, b, + x, y, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ShapeAttr is an optional argument to Shape. -type ShapeAttr func(optionalAttr) +// RandomGammaAttr is an optional argument to RandomGamma. +type RandomGammaAttr func(optionalAttr) -// ShapeOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func ShapeOutType(value tf.DataType) ShapeAttr { +// RandomGammaSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomGammaSeed(value int64) RandomGammaAttr { return func(m optionalAttr) { - m["out_type"] = value + m["seed"] = value } } -// Returns the shape of a tensor. +// RandomGammaSeed2 sets the optional seed2 attribute to value. // -// This operation returns a 1-D integer tensor representing the shape of `input`. +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomGammaSeed2(value int64) RandomGammaAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from the Gamma distribution(s) described by alpha. // -// For example: +// This op uses the algorithm by Marsaglia et al. to acquire samples via +// transformation-rejection from pairs of uniform and normal random variables. +// See http://dl.acm.org/citation.cfm?id=358414 // -// ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// shape(t) ==> [2, 2, 3] -// ``` -func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) { +// Arguments: +// shape: 1-D integer tensor. Shape of independent samples to draw from each +// distribution described by the shape parameters given in alpha. +// alpha: A tensor in which each scalar is a "shape" parameter describing the +// associated gamma distribution. +// +// Returns A tensor with shape `shape + shape(alpha)`. Each slice +// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for +// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha. +func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -18813,9 +19026,9 @@ func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Outp a(attrs) } opspec := tf.OpSpec{ - Type: "Shape", + Type: "RandomGamma", Input: []tf.Input{ - input, + shape, alpha, }, Attrs: attrs, } @@ -18823,43 +19036,69 @@ func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Outp return op.Output(0) } -// Computes the power of one value to another. +// Convert the quantized 'input' tensor into a lower-precision 'output', using the // -// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for -// corresponding elements in `x` and `y`. For example: +// actual distribution of the values to maximize the usage of the lower bit depth +// and adjusting the output min and max ranges accordingly. // -// ``` -// # tensor 'x' is [[2, 2]], [3, 3]] -// # tensor 'y' is [[8, 16], [2, 3]] -// tf.pow(x, y) ==> [[256, 65536], [9, 27]] -// ``` -func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// [input_min, input_max] are scalar floats that specify the range for the float +// interpretation of the 'input' data. For example, if input_min is -1.0f and +// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 +// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. +// +// This operator tries to squeeze as much precision as possible into an output with +// a lower bit depth by calculating the actual min and max values found in the +// data. For example, maybe that quint16 input has no values lower than 16,384 and +// none higher than 49,152. That means only half the range is actually needed, all +// the float interpretations are between -0.5f and 0.5f, so if we want to compress +// the data into a quint8 output, we can use that range rather than the theoretical +// -1.0f to 1.0f that is suggested by the input min and max. +// +// In practice, this is most useful for taking output from operations like +// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and +// may have large potential output ranges, but in practice have a distribution of +// input values that only uses a small fraction of the possible range. By feeding +// that output into this operator, we can reduce it from 32 bits down to 8 with +// minimal loss of accuracy. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// out_type: The type of the output. Should be a lower bit depth than Tinput. +// +// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"out_type": out_type} opspec := tf.OpSpec{ - Type: "Pow", + Type: "QuantizeDownAndShrinkRange", Input: []tf.Input{ - x, y, + input, input_min, input_max, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Computes fingerprints of the input strings. +// Forwards the input to the output. +// +// This operator represents the loop termination condition used by the +// "pivot" switches of a loop. // // Arguments: -// input: vector of strings to compute fingerprints on. +// input: A boolean scalar, representing the branch predicate of the Switch op. // -// Returns a (N,2) shaped matrix where N is the number of elements in the input -// vector. Each row contains the low and high parts of the fingerprint. -func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { +// Returns The same tensor as `input`. +func LoopCond(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SdcaFprint", + Type: "LoopCond", Input: []tf.Input{ input, }, @@ -18868,132 +19107,85 @@ func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } -// RandomPoissonV2Attr is an optional argument to RandomPoissonV2. -type RandomPoissonV2Attr func(optionalAttr) - -// RandomPoissonV2Seed sets the optional seed attribute to value. +// Computes the product along segments of a tensor. // -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomPoissonV2Seed2 sets the optional seed2 attribute to value. +// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of +// segments. // -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// RandomPoissonV2Dtype sets the optional dtype attribute to value. -// If not specified, defaults to DT_INT64 -func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random values from the Poisson distribution(s) described by rate. +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the product of all +// entries belonging to a segment such that: // -// This op uses two algorithms, depending on rate. If rate >= 10, then -// the algorithm by Hormann is used to acquire samples via -// transformation-rejection. -// See http://www.sciencedirect.com/science/article/pii/0167668793909974. +// \\(output_i = \prod_j data_j\\) where the product is over `j` such +// that `segment_ids[j] == i`. // -// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform -// random variables. -// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer -// Programming, Volume 2. Addison Wesley +// If there is no entry for a given segment ID `i`, it outputs 1. // // Arguments: -// shape: 1-D integer tensor. Shape of independent samples to draw from each -// distribution described by the shape parameters given in rate. -// rate: A tensor in which each scalar is a "rate" parameter describing the -// associated poisson distribution. // -// Returns A tensor with shape `shape + shape(rate)`. Each slice -// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for -// `rate[i0, i1, ...iN]`. -func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) { +// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s +// first dimension. +// +// +// Returns Has same shape as data, except for dimension 0 which +// has size `num_segments`. +func UnsortedSegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "RandomPoissonV2", + Type: "UnsortedSegmentProd", Input: []tf.Input{ - shape, rate, + data, segment_ids, num_segments, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve. -type MatrixTriangularSolveAttr func(optionalAttr) +// RandomUniformIntAttr is an optional argument to RandomUniformInt. +type RandomUniformIntAttr func(optionalAttr) -// MatrixTriangularSolveLower sets the optional lower attribute to value. +// RandomUniformIntSeed sets the optional seed attribute to value. // -// value: Boolean indicating whether the innermost matrices in `matrix` are -// lower or upper triangular. -// If not specified, defaults to true -func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomUniformIntSeed(value int64) RandomUniformIntAttr { return func(m optionalAttr) { - m["lower"] = value + m["seed"] = value } } -// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value. -// -// value: Boolean indicating whether to solve with `matrix` or its (block-wise) -// adjoint. +// RandomUniformIntSeed2 sets the optional seed2 attribute to value. // -// @compatibility(numpy) -// Equivalent to np.linalg.triangular_solve -// @end_compatibility -// If not specified, defaults to false -func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomUniformIntSeed2(value int64) RandomUniformIntAttr { return func(m optionalAttr) { - m["adjoint"] = value + m["seed2"] = value } } -// Solves systems of linear equations with upper or lower triangular matrices by -// -// backsubstitution. +// Outputs random integers from a uniform distribution. // -// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form -// square matrices. If `lower` is `True` then the strictly upper triangular part -// of each inner-most matrix is assumed to be zero and not accessed. -// If `lower` is False then the strictly lower triangular part of each inner-most -// matrix is assumed to be zero and not accessed. -// `rhs` is a tensor of shape `[..., M, K]`. +// The generated values are uniform integers in the range `[minval, maxval)`. +// The lower bound `minval` is included in the range, while the upper bound +// `maxval` is excluded. // -// The output is a tensor of shape `[..., M, K]`. If `adjoint` is -// `True` then the innermost matrices in `output` satisfy matrix equations -// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. -// If `adjoint` is `False` then the strictly then the innermost matrices in -// `output` satisfy matrix equations -// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. +// The random integers are slightly biased unless `maxval - minval` is an exact +// power of two. The bias is small for values of `maxval - minval` significantly +// smaller than the range of the output (either `2^32` or `2^64`). // // Arguments: -// matrix: Shape is `[..., M, M]`. -// rhs: Shape is `[..., M, K]`. +// shape: The shape of the output tensor. +// minval: 0-D. Inclusive lower bound on the generated integers. +// maxval: 0-D. Exclusive upper bound on the generated integers. // -// Returns Shape is `[..., M, K]`. -func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) { +// Returns A tensor of the specified shape filled with uniform random integers. +func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -19002,9 +19194,9 @@ func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, option a(attrs) } opspec := tf.OpSpec{ - Type: "MatrixTriangularSolve", + Type: "RandomUniformInt", Input: []tf.Input{ - matrix, rhs, + shape, minval, maxval, }, Attrs: attrs, } @@ -19012,38 +19204,60 @@ func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, option return op.Output(0) } -// Computes inverse hyperbolic sine of x element-wise. -func Asinh(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return +// RandomShuffleAttr is an optional argument to RandomShuffle. +type RandomShuffleAttr func(optionalAttr) + +// RandomShuffleSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomShuffleSeed(value int64) RandomShuffleAttr { + return func(m optionalAttr) { + m["seed"] = value } - opspec := tf.OpSpec{ - Type: "Asinh", - Input: []tf.Input{ - x, - }, +} + +// RandomShuffleSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomShuffleSeed2(value int64) RandomShuffleAttr { + return func(m optionalAttr) { + m["seed2"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Creates a dataset with a range of values. Corresponds to python's xrange. +// Randomly shuffles a tensor along its first dimension. // -// Arguments: -// start: corresponds to start in python's xrange(). -// stop: corresponds to stop in python's xrange(). -// step: corresponds to step in python's xrange(). +// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped +// to one and only one `output[i]`. For example, a mapping that might occur for a +// 3x2 tensor is: +// +// ``` +// [[1, 2], [[5, 6], +// [3, 4], ==> [1, 2], +// [5, 6]] [3, 4]] +// ``` // +// Arguments: +// value: The tensor to be shuffled. // -func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns A tensor of same shape and type as `value`, shuffled along its first +// dimension. +func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "RangeDataset", + Type: "RandomShuffle", Input: []tf.Input{ - start, stop, step, + value, }, Attrs: attrs, } @@ -19051,213 +19265,171 @@ func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, return op.Output(0) } -// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput. -type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr) +// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize. +type OrderedMapIncompleteSizeAttr func(optionalAttr) -// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value. +// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 // -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, height, width, channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, channels, height, width]. -// If not specified, defaults to "NHWC" -func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr { +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr { return func(m optionalAttr) { - m["data_format"] = value + m["capacity"] = value } } -// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value. +// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr { return func(m optionalAttr) { - m["dilations"] = value + m["memory_limit"] = value } } -// Computes the gradients of depthwise convolution with respect to the input. -// -// Arguments: -// input_sizes: An integer vector representing the shape of `input`, based -// on `data_format`. For example, if `data_format` is 'NHWC' then -// `input` is a 4-D `[batch, height, width, channels]` tensor. -// filter: 4-D with shape -// `[filter_height, filter_width, in_channels, depthwise_multiplier]`. -// out_backprop: 4-D with shape based on `data_format`. -// For example, if `data_format` is 'NHWC' then -// out_backprop shape is `[batch, out_height, out_width, out_channels]`. -// Gradients w.r.t. the output of the convolution. -// strides: The stride of the sliding window for each dimension of the input -// of the convolution. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape according to `data_format`. For example, if -// `data_format` is 'NHWC', output shape is `[batch, in_height, -// in_width, in_channels]`. Gradient w.r.t. the input of the -// convolution. -func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) { +// OrderedMapIncompleteSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of incomplete elements in the underlying container. +func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DepthwiseConv2dNativeBackpropInput", - Input: []tf.Input{ - input_sizes, filter, out_backprop, - }, + Type: "OrderedMapIncompleteSize", + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Stops gradient computation. -// -// When executed in a graph, this op outputs its input tensor as-is. -// -// When building ops to compute gradients, this op prevents the contribution of -// its inputs to be taken into account. Normally, the gradient generator adds ops -// to a graph to compute the derivatives of a specified 'loss' by recursively -// finding out inputs that contributed to its computation. If you insert this op -// in the graph it inputs are masked from the gradient generator. They are not -// taken into account for computing gradients. -// -// This is useful any time you want to compute a value with TensorFlow but need -// to pretend that the value was a constant. Some examples include: +// LRNGradAttr is an optional argument to LRNGrad. +type LRNGradAttr func(optionalAttr) + +// LRNGradDepthRadius sets the optional depth_radius attribute to value. // -// * The *EM* algorithm where the *M-step* should not involve backpropagation -// through the output of the *E-step*. -// * Contrastive divergence training of Boltzmann machines where, when -// differentiating the energy function, the training must not backpropagate -// through the graph that generated the samples from the model. -// * Adversarial training, where no backprop should happen through the adversarial -// example generation process. -func StopGradient(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "StopGradient", - Input: []tf.Input{ - input, - }, +// value: A depth radius. +// If not specified, defaults to 5 +func LRNGradDepthRadius(value int64) LRNGradAttr { + return func(m optionalAttr) { + m["depth_radius"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Eagerly executes a python function to compute func(input)->output. The +// LRNGradBias sets the optional bias attribute to value. // -// semantics of the input, output, and attributes are the same as those for -// PyFunc. -func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"token": token, "Tout": Tout} - opspec := tf.OpSpec{ - Type: "EagerPyFunc", - Input: []tf.Input{ - tf.OutputList(input), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return +// value: An offset (usually > 0 to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNGradBias(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["bias"] = value } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("EagerPyFunc", err) - return +} + +// LRNGradAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNGradAlpha(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["alpha"] = value } - return output } -// Adds sparse updates to the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] += updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] += updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions add. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// LRNGradBeta sets the optional beta attribute to value. // -//
-// -//
+// value: An exponent. +// If not specified, defaults to 0.5 +func LRNGradBeta(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Gradients for Local Response Normalization. // // Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// input_grads: 4-D with shape `[batch, height, width, channels]`. +// input_image: 4-D with shape `[batch, height, width, channels]`. +// output_image: 4-D with shape `[batch, height, width, channels]`. // -// Returns the created operation. -func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +// Returns The gradients for LRN. +func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ResourceScatterAdd", + Type: "LRNGrad", Input: []tf.Input{ - resource, indices, updates, + input_grads, input_image, output_image, }, + Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Says whether the targets are in the top `K` predictions. -// -// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the -// prediction for the target class is among the top `k` predictions among -// all predictions for example `i`. Note that the behavior of `InTopK` differs -// from the `TopK` op in its handling of ties; if multiple classes have the -// same prediction value and straddle the top-`k` boundary, all of those -// classes are considered to be in the top `k`. -// -// More formally, let +// AnyAttr is an optional argument to Any. +type AnyAttr func(optionalAttr) + +// AnyKeepDims sets the optional keep_dims attribute to value. // -// \\(predictions_i\\) be the predictions for all classes for example `i`, -// \\(targets_i\\) be the target class for example `i`, -// \\(out_i\\) be the output for example `i`, +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AnyKeepDims(value bool) AnyAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical or" of elements across dimensions of a tensor. // -// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. // // Arguments: -// predictions: A `batch_size` x `classes` tensor. -// targets: A `batch_size` vector of class ids. -// k: Number of top elements to look at for computing precision. +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. // -// Returns Computed Precision at `k` as a `bool Tensor`. -func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) { +// Returns The reduced tensor. +func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"k": k} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "InTopK", + Type: "Any", Input: []tf.Input{ - predictions, targets, + input, axis, }, Attrs: attrs, } @@ -19265,65 +19437,64 @@ func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (pr return op.Output(0) } -// Returns (x - y)(x - y) element-wise. +// Creates a sequence of numbers. // -// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// This operation creates a sequence of numbers that begins at `start` and +// extends by increments of `delta` up to but not including `limit`. +// +// For example: +// +// ``` +// # 'start' is 3 +// # 'limit' is 18 +// # 'delta' is 3 +// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] +// ``` +// +// Arguments: +// start: 0-D (scalar). First entry in the sequence. +// limit: 0-D (scalar). Upper limit of sequence, exclusive. +// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. +// +// Returns 1-D. +func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SquaredDifference", + Type: "Range", Input: []tf.Input{ - x, y, + start, limit, delta, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// RandomGammaAttr is an optional argument to RandomGamma. -type RandomGammaAttr func(optionalAttr) - -// RandomGammaSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomGammaSeed(value int64) RandomGammaAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) -// RandomGammaSeed2 sets the optional seed2 attribute to value. +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. // -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomGammaSeed2(value int64) RandomGammaAttr { +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { return func(m optionalAttr) { - m["seed2"] = value + m["ignore_lookup_error"] = value } } -// Outputs random values from the Gamma distribution(s) described by alpha. +// Deletes the resource specified by the handle. // -// This op uses the algorithm by Marsaglia et al. to acquire samples via -// transformation-rejection from pairs of uniform and normal random variables. -// See http://dl.acm.org/citation.cfm?id=358414 +// All subsequent operations using the resource will result in a NotFound +// error status. // // Arguments: -// shape: 1-D integer tensor. Shape of independent samples to draw from each -// distribution described by the shape parameters given in alpha. -// alpha: A tensor in which each scalar is a "shape" parameter describing the -// associated gamma distribution. +// resource: handle to the resource to delete. // -// Returns A tensor with shape `shape + shape(alpha)`. Each slice -// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for -// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha. -func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) { +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -19332,177 +19503,204 @@ func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...Ran a(attrs) } opspec := tf.OpSpec{ - Type: "RandomGamma", + Type: "DestroyResourceOp", Input: []tf.Input{ - shape, alpha, + resource, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Convert the quantized 'input' tensor into a lower-precision 'output', using the -// -// actual distribution of the values to maximize the usage of the lower bit depth -// and adjusting the output min and max ranges accordingly. +// Generates values in an interval. // -// [input_min, input_max] are scalar floats that specify the range for the float -// interpretation of the 'input' data. For example, if input_min is -1.0f and -// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 -// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. +// A sequence of `num` evenly-spaced values are generated beginning at `start`. +// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, +// so that the last one is exactly `stop`. // -// This operator tries to squeeze as much precision as possible into an output with -// a lower bit depth by calculating the actual min and max values found in the -// data. For example, maybe that quint16 input has no values lower than 16,384 and -// none higher than 49,152. That means only half the range is actually needed, all -// the float interpretations are between -0.5f and 0.5f, so if we want to compress -// the data into a quint8 output, we can use that range rather than the theoretical -// -1.0f to 1.0f that is suggested by the input min and max. +// For example: // -// In practice, this is most useful for taking output from operations like -// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and -// may have large potential output ranges, but in practice have a distribution of -// input values that only uses a small fraction of the possible range. By feeding -// that output into this operator, we can reduce it from 32 bits down to 8 with -// minimal loss of accuracy. +// ``` +// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] +// ``` // // Arguments: +// start: First entry in the range. +// stop: Last entry in the range. +// num: Number of values to generate. // -// input_min: The float value that the minimum quantized input value represents. -// input_max: The float value that the maximum quantized input value represents. -// out_type: The type of the output. Should be a lower bit depth than Tinput. +// Returns 1-D. The generated values. +func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LinSpace", + Input: []tf.Input{ + start, stop, num, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ComplexAttr is an optional argument to Complex. +type ComplexAttr func(optionalAttr) + +// ComplexTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func ComplexTout(value tf.DataType) ComplexAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Converts two real numbers to a complex number. // -// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { +// Given a tensor `real` representing the real part of a complex number, and a +// tensor `imag` representing the imaginary part of a complex number, this +// operation returns complex numbers elementwise of the form \\(a + bj\\), where +// *a* represents the `real` part and *b* represents the `imag` part. +// +// The input tensors `real` and `imag` must have the same shape. +// +// For example: +// +// ``` +// # tensor 'real' is [2.25, 3.25] +// # tensor `imag` is [4.75, 5.75] +// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] +// ``` +func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "QuantizeDownAndShrinkRange", + Type: "Complex", Input: []tf.Input{ - input, input_min, input_max, + real, imag, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Forwards the input to the output. +// ImagAttr is an optional argument to Imag. +type ImagAttr func(optionalAttr) + +// ImagTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ImagTout(value tf.DataType) ImagAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the imaginary part of a complex number. // -// This operator represents the loop termination condition used by the -// "pivot" switches of a loop. +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the imaginary part of each element in `input`. All +// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part returned by this operation. // -// Arguments: -// input: A boolean scalar, representing the branch predicate of the Switch op. +// For example: // -// Returns The same tensor as `input`. -func LoopCond(scope *Scope, input tf.Output) (output tf.Output) { +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.imag(input) ==> [4.75, 5.75] +// ``` +func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "LoopCond", + Type: "Imag", Input: []tf.Input{ input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the product along segments of a tensor. +// Computes the maximum along segments of a tensor. // -// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of // segments. // -// This operator is similar to the unsorted segment sum operator found -// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). -// Instead of computing the sum over segments, it computes the product of all -// entries belonging to a segment such that: -// -// \\(output_i = \prod_j data_j\\) where the product is over `j` such +// Computes a tensor such that +// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such // that `segment_ids[j] == i`. // -// If there is no entry for a given segment ID `i`, it outputs 1. +// If the max is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
// // Arguments: // // segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. -// +// first dimension. Values should be sorted and can be repeated. // // Returns Has same shape as data, except for dimension 0 which -// has size `num_segments`. -func UnsortedSegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { +// has size `k`, the number of segments. +func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "UnsortedSegmentProd", + Type: "SegmentMax", Input: []tf.Input{ - data, segment_ids, num_segments, + data, segment_ids, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// RandomUniformIntAttr is an optional argument to RandomUniformInt. -type RandomUniformIntAttr func(optionalAttr) - -// RandomUniformIntSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomUniformIntSeed(value int64) RandomUniformIntAttr { - return func(m optionalAttr) { - m["seed"] = value +// Computes hyperbolic tangent of `x` element-wise. +func Tanh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return } -} - -// RandomUniformIntSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomUniformIntSeed2(value int64) RandomUniformIntAttr { - return func(m optionalAttr) { - m["seed2"] = value + opspec := tf.OpSpec{ + Type: "Tanh", + Input: []tf.Input{ + x, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Outputs random integers from a uniform distribution. +// Creates a dataset that skips `count` elements from the `input_dataset`. // -// The generated values are uniform integers in the range `[minval, maxval)`. -// The lower bound `minval` is included in the range, while the upper bound -// `maxval` is excluded. +// Arguments: // -// The random integers are slightly biased unless `maxval - minval` is an exact -// power of two. The bias is small for values of `maxval - minval` significantly -// smaller than the range of the output (either `2^32` or `2^64`). +// count: A scalar representing the number of elements from the `input_dataset` +// that should be skipped. If count is -1, skips everything. // -// Arguments: -// shape: The shape of the output tensor. -// minval: 0-D. Inclusive lower bound on the generated integers. -// maxval: 0-D. Exclusive upper bound on the generated integers. // -// Returns A tensor of the specified shape filled with uniform random integers. -func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) { +func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "RandomUniformInt", + Type: "SkipDataset", Input: []tf.Input{ - shape, minval, maxval, + input_dataset, count, }, Attrs: attrs, } @@ -19510,49 +19708,31 @@ func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf return op.Output(0) } -// RandomShuffleAttr is an optional argument to RandomShuffle. -type RandomShuffleAttr func(optionalAttr) +// RealAttr is an optional argument to Real. +type RealAttr func(optionalAttr) -// RandomShuffleSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomShuffleSeed(value int64) RandomShuffleAttr { +// RealTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func RealTout(value tf.DataType) RealAttr { return func(m optionalAttr) { - m["seed"] = value + m["Tout"] = value } } -// RandomShuffleSeed2 sets the optional seed2 attribute to value. +// Returns the real part of a complex number. // -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomShuffleSeed2(value int64) RandomShuffleAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Randomly shuffles a tensor along its first dimension. +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the real part of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real +// part returned by this operation and *b* is the imaginary part. // -// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped -// to one and only one `output[i]`. For example, a mapping that might occur for a -// 3x2 tensor is: +// For example: // // ``` -// [[1, 2], [[5, 6], -// [3, 4], ==> [1, 2], -// [5, 6]] [3, 4]] +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.real(input) ==> [-2.25, 3.25] // ``` -// -// Arguments: -// value: The tensor to be shuffled. -// -// Returns A tensor of same shape and type as `value`, shuffled along its first -// dimension. -func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) { +func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -19561,9 +19741,9 @@ func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) a(attrs) } opspec := tf.OpSpec{ - Type: "RandomShuffle", + Type: "Real", Input: []tf.Input{ - value, + input, }, Attrs: attrs, } @@ -19571,57 +19751,54 @@ func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) return op.Output(0) } -// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize. -type OrderedMapIncompleteSizeAttr func(optionalAttr) +// ResizeAreaAttr is an optional argument to ResizeArea. +type ResizeAreaAttr func(optionalAttr) -// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// ResizeAreaAlignCorners sets the optional align_corners attribute to value. // -// REQUIRES: value >= 0 -func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr { +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { return func(m optionalAttr) { - m["capacity"] = value + m["align_corners"] = value } } -// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// Resize `images` to `size` using area interpolation. // -// REQUIRES: value >= 0 -func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// OrderedMapIncompleteSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of incomplete elements in the underlying container. -func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) { +// Input images can be of different types but output images are always float. +// +// The range of pixel values for the output image might be slightly different +// from the range for the input image because of limited numerical precision. +// To guarantee an output range, for example `[0.0, 1.0]`, apply +// `tf.clip_by_value` to the output. +// +// Each output pixel is computed by first transforming the pixel's footprint into +// the input tensor and then averaging the pixels that intersect the footprint. An +// input pixel's contribution to the average is weighted by the fraction of its +// area that intersects the footprint. This is the same as OpenCV's INTER_AREA. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "OrderedMapIncompleteSize", - + Type: "ResizeArea", + Input: []tf.Input{ + images, size, + }, Attrs: attrs, } op := scope.AddOperation(opspec) @@ -25053,6 +25230,41 @@ func LatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, o return op.Output(0) } +// Runs multiple additive regression ensemble predictors on input instances and +// +// computes the update to cached logits. It is designed to be used during training. +// It traverses the trees starting from cached tree id and cached node id and +// calculates the updates to be pushed to the cache. +// +// Arguments: +// +// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting +// tree of prediction. +// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting +// node of prediction. +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for partial logits +// shape. +// +// Returns Rank 2 Tensor containing logits update (with respect to cached +// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. +func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesTrainingPredict", + Input: []tf.Input{ + tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // MapSizeAttr is an optional argument to MapSize. type MapSizeAttr func(optionalAttr) @@ -29812,41 +30024,6 @@ func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Outpu return scope.AddOperation(opspec) } -// Runs multiple additive regression ensemble predictors on input instances and -// -// computes the update to cached logits. It is designed to be used during training. -// It traverses the trees starting from cached tree id and cached node id and -// calculates the updates to be pushed to the cache. -// -// Arguments: -// -// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting -// tree of prediction. -// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting -// node of prediction. -// bucketized_features: A list of rank 1 Tensors containing bucket id for each -// feature. -// logits_dimension: scalar, dimension of the logits, to be used for partial logits -// shape. -// -// Returns Rank 2 Tensor containing logits update (with respect to cached -// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. -func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"logits_dimension": logits_dimension} - opspec := tf.OpSpec{ - Type: "BoostedTreesTrainingPredict", - Input: []tf.Input{ - tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Elementwise computes the bitwise AND of `x` and `y`. // // The result will have those bits set, that are set in both `x` and `y`. The @@ -30533,180 +30710,3 @@ func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Outpu op := scope.AddOperation(opspec) return op.Output(0) } - -// Converts a flat index or array of flat indices into a tuple of -// -// coordinate arrays. -// -// @compatibility(numpy) -// Equivalent to np.unravel_index -// @end_compatibility -// -// Arguments: -// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the -// flattened version of an array of dimensions dims. -// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling -// indices. -// -// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the -// same shape as the indices array. -func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnravelIndex", - Input: []tf.Input{ - indices, dims, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Compute the lower regularized incomplete Gamma function `Q(a, x)`. -// -// The lower regularized incomplete Gamma function is defined as: -// -// -// \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\) -// -// where -// -// \\(gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt\\) -// -// is the lower incomplete Gamma function. -// -// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete -// Gamma function. -func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Igamma", - Input: []tf.Input{ - a, x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes offsets of concat inputs within its output. -// -// For example: -// -// ``` -// # 'x' is [2, 2, 7] -// # 'y' is [2, 3, 7] -// # 'z' is [2, 5, 7] -// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] -// ``` -// -// This is typically used by gradient computations for a concat operation. -// -// Arguments: -// concat_dim: The dimension along which to concatenate. -// shape: The `N` int32 vectors representing shape of tensors being concatenated. -// -// Returns The `N` int32 vectors representing the starting offset -// of input tensors within the concatenated output. -func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ConcatOffset", - Input: []tf.Input{ - concat_dim, tf.OutputList(shape), - }, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil { - scope.UpdateErr("ConcatOffset", err) - return - } - return offset -} - -// Splits a tensor into `num_split` tensors along one dimension. -// -// Arguments: -// axis: 0-D. The dimension along which to split. Must be in the range -// `[-rank(value), rank(value))`. -// value: The tensor to split. -// num_split: The number of ways to split. Must evenly divide -// `value.shape[split_dim]`. -// -// Returns They are identically shaped tensors, whose shape matches that of `value` -// except along `axis`, where their sizes are -// `values.shape[split_dim] / num_split`. -func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_split": num_split} - opspec := tf.OpSpec{ - Type: "Split", - Input: []tf.Input{ - axis, value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("Split", err) - return - } - return output -} - -// Splits a tensor into `num_split` tensors along one dimension. -// -// Arguments: -// value: The tensor to split. -// size_splits: list containing the sizes of each output tensor along the split -// dimension. Must sum to the dimension of value along split_dim. -// Can contain one -1 indicating that dimension is to be inferred. -// axis: 0-D. The dimension along which to split. Must be in the range -// `[-rank(value), rank(value))`. -// -// -// Returns Tensors whose shape matches that of `value` -// except along `axis`, where their sizes are -// `size_splits[i]`. -func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_split": num_split} - opspec := tf.OpSpec{ - Type: "SplitV", - Input: []tf.Input{ - value, size_splits, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("SplitV", err) - return - } - return output -} diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go index 8fcad61f4c6eec597d2b14fb8c9b4fa59987a829..25ec71870315917351d68db6a16d25fe037d543b 100644 --- a/tensorflow/go/operation.go +++ b/tensorflow/go/operation.go @@ -65,6 +65,11 @@ func (op *Operation) Output(i int) Output { return Output{op, i} } +// NumInputs returns the number of inputs of op. +func (op *Operation) NumInputs() int { + return int(C.TF_OperationNumInputs(op.c)) +} + // Output represents one of the outputs of an operation in the graph. Has a // DataType (and eventually a Shape). May be passed as an input argument to a // function for adding operations to a graph, or to a Session's Run() method to @@ -123,6 +128,67 @@ func (p Output) c() C.TF_Output { func (p Output) canBeAnInput() {} +// Consumers returns the inputs that consume this output. +func (p Output) Consumers() []Consumer { + max := int(C.TF_OperationOutputNumConsumers(p.c())) + if max == 0 { + return nil + } + inputs := make([]C.TF_Input, max) + n := C.TF_OperationOutputConsumers(p.c(), (*C.TF_Input)(unsafe.Pointer(&inputs[0])), C.int(max)) + inputs = inputs[:int(n)] + + var consumers []Consumer + for _, consumer := range inputs { + consumers = append(consumers, Consumer{ + Index: int(consumer.index), + Op: &Operation{ + c: consumer.oper, + g: p.Op.g, + }, + }) + } + + return consumers +} + +// Consumer identifies a specific input of an operation that consumes the output +// of another operation. +type Consumer struct { + // Op is the Operation that is consuming the output of another operation. + Op *Operation + + // Index is the index of the input within Op that the output of another + // operation is connected to. + Index int +} + +func (p Consumer) c() C.TF_Input { + if p.Op == nil { + // Attempt to provide a more useful panic message than "nil + // pointer dereference". + panic("nil-Operation. Consumer objects should only be created by a call to Output.Consumers") + } + return C.TF_Input{oper: p.Op.c, index: C.int(p.Index)} +} + +// DataType returns the type of the input. +func (p Consumer) DataType() DataType { + return DataType(C.TF_OperationInputType(p.c())) +} + +// Producer returns the Output that is connected to this Consumer. +func (p Consumer) Producer() Output { + output := C.TF_OperationInput(p.c()) + return Output{ + Op: &Operation{ + c: output.oper, + g: p.Op.g, + }, + Index: int(output.index), + } +} + // Input is the interface for specifying inputs to an operation being added to // a Graph. // diff --git a/tensorflow/go/operation_test.go b/tensorflow/go/operation_test.go index 40c951ab8c13f43e2063b9f9cfadcd44a6da72fe..06b65bdfb7eb814a2bead191374029cc0fdf025e 100644 --- a/tensorflow/go/operation_test.go +++ b/tensorflow/go/operation_test.go @@ -166,6 +166,68 @@ func TestOutputDataTypeAndShape(t *testing.T) { } } +func TestOperationInputs(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + y, err := Placeholder(g, "y", Float) + if err != nil { + t.Fatal(err) + } + add, err := Add(g, "add", x, y) + if err != nil { + t.Fatal(err) + } + addOp := add.Op + + if out := addOp.NumInputs(); out != 2 { + t.Fatalf("Got %d inputs, wanted 2", out) + } +} + +func TestOperationConsumers(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + a, err := Neg(g, "a", x) + if err != nil { + t.Fatal(err) + } + b, err := Neg(g, "b", x) + if err != nil { + t.Fatal(err) + } + + consumers := []*Operation{a.Op, b.Op} + + xConsumers := x.Consumers() + if out := len(xConsumers); out != 2 { + t.Fatalf("Got %d consumers, wanted 2", out) + } + + for i, consumer := range xConsumers { + got := consumer.Op.Name() + want := consumers[i].Name() + if got != want { + t.Fatalf("%d. Got op name %q, wanted %q", i, got, want) + } + + got = consumer.Producer().Op.Name() + want = x.Op.Name() + if got != want { + t.Fatalf("%d. Got op name %q, wanted %q", i, got, want) + } + } + + if len(b.Consumers()) != 0 { + t.Fatalf("expected %+v to have no consumers", b) + } +} + func forceGC() { var mem runtime.MemStats runtime.ReadMemStats(&mem) diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go index 2d25c04dc9b1d0bc2ae831f98c0879e73a6bfafa..f3338f6595793df82380f4ce63058ba4285c91dd 100644 --- a/tensorflow/go/tensor.go +++ b/tensorflow/go/tensor.go @@ -131,13 +131,9 @@ func ReadTensor(dataType DataType, shape []int64, r io.Reader) (*Tensor, error) } runtime.SetFinalizer(t, (*Tensor).finalize) raw := tensorData(t.c) - n, err := r.Read(raw) - if err != nil { + if _, err := io.ReadFull(r, raw); err != nil { return nil, err } - if uintptr(n) != nbytes { - return nil, fmt.Errorf("expected serialized tensor to be %v bytes, read %v", nbytes, n) - } return t, nil } diff --git a/tensorflow/go/tensor_test.go b/tensorflow/go/tensor_test.go index 793c36dd4db28fc5fdb713095c6d1d6713367a7a..dc533cd3e1c7198f902b2db850e8daff50f4cdeb 100644 --- a/tensorflow/go/tensor_test.go +++ b/tensorflow/go/tensor_test.go @@ -18,6 +18,7 @@ package tensorflow import ( "bytes" + "io" "reflect" "testing" ) @@ -226,6 +227,54 @@ func TestTensorSerializationErrors(t *testing.T) { } } +func TestReadTensorReadAll(t *testing.T) { + // Get the bytes of a tensor. + a := []float32{1.1, 1.2, 1.3} + ats, err := NewTensor(a) + if err != nil { + t.Fatal(err) + } + abuf := new(bytes.Buffer) + if _, err := ats.WriteContentsTo(abuf); err != nil { + t.Fatal(err) + } + + // Get the bytes of another tensor. + b := []float32{1.1, 1.2, 1.3} + bts, err := NewTensor(b) + if err != nil { + t.Fatal(err) + } + bbuf := new(bytes.Buffer) + if _, err := bts.WriteContentsTo(bbuf); err != nil { + t.Fatal(err) + } + + // Check that ReadTensor reads all bytes of both tensors, when the situation + // requires one than reads. + abbuf := io.MultiReader(abuf, bbuf) + abts, err := ReadTensor(Float, []int64{2, 3}, abbuf) + if err != nil { + t.Fatal(err) + } + abtsf32 := abts.Value().([][]float32) + expected := [][]float32{a, b} + + if len(abtsf32) != 2 { + t.Fatalf("first dimension %d is not 2", len(abtsf32)) + } + for i := 0; i < 2; i++ { + if len(abtsf32[i]) != 3 { + t.Fatalf("second dimension %d is not 3", len(abtsf32[i])) + } + for j := 0; j < 3; j++ { + if abtsf32[i][j] != expected[i][j] { + t.Errorf("value at %d %d not equal %f %f", i, j, abtsf32[i][j], expected[i][j]) + } + } + } +} + func benchmarkNewTensor(b *testing.B, v interface{}) { for i := 0; i < b.N; i++ { if t, err := NewTensor(v); err != nil || t == nil { diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD index 19d2133a55f347cfc3d4dc766e0593a0e188c967..73e210fae07d603feffefb6948b82910cf683043 100644 --- a/tensorflow/java/BUILD +++ b/tensorflow/java/BUILD @@ -56,6 +56,10 @@ java_library( srcs = glob(["src/gen/java/org/tensorflow/processor/**/*.java"]), javacopts = JAVACOPTS, resources = glob(["src/gen/resources/META-INF/services/javax.annotation.processing.Processor"]), + deps = [ + "@com_google_guava", + "@com_squareup_javapoet", + ], ) filegroup( @@ -70,6 +74,7 @@ tf_java_op_gen_srcjar( name = "java_op_gen_sources", api_def_srcs = [ "//tensorflow/core/api_def:base_api_def", + "//tensorflow/core/api_def:java_api_def", ], base_package = "org.tensorflow.op", gen_tool = ":java_op_gen_tool", diff --git a/tensorflow/java/README.md b/tensorflow/java/README.md index 2f1ce253b2facb6d86d5c44b60668823f660ae7e..c7382ff23138cd8121718d0b7552da0f0a2d78af 100644 --- a/tensorflow/java/README.md +++ b/tensorflow/java/README.md @@ -1,7 +1,7 @@ # TensorFlow for Java > *WARNING*: The TensorFlow Java API is not currently covered by the TensorFlow -> [API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +> [API stability guarantees](https://www.tensorflow.org/guide/version_semantics). > > For using TensorFlow on Android refer instead to > [contrib/android](https://www.tensorflow.org/code/tensorflow/contrib/android), @@ -23,8 +23,7 @@ native libraries will need to be built from source. 2. Setup the environment to build TensorFlow from source code ([Linux](https://www.tensorflow.org/install/install_sources#PrepareLinux) - or [Mac OS - X](https://www.tensorflow.org/install/install_sources#PrepareMac)). + or [macOS](https://www.tensorflow.org/install/install_sources#PrepareMac)). If you'd like to skip reading those details and do not care about GPU support, try the following: diff --git a/tensorflow/java/maven/.gitignore b/tensorflow/java/maven/.gitignore index ff080515d5e730b308bf78f7e28244c6c799cdc3..657e2a60bc57c0cf259c000476c75ae58d75fff2 100644 --- a/tensorflow/java/maven/.gitignore +++ b/tensorflow/java/maven/.gitignore @@ -11,4 +11,10 @@ tensorflow/src tensorflow/target proto/src proto/target +hadoop/src +hadoop/target +spark-connector/src +spark-connector/target +spark-connector/dependency-reduced-pom.xml +spark-connector/spark-warehouse pom.xml.versionsBackup diff --git a/tensorflow/java/maven/README.md b/tensorflow/java/maven/README.md index c7e8f0380629f492ade9ba47cdcb4bc286ac82bc..3e030dcd09c886983540b95640230eae3a6f2c0f 100644 --- a/tensorflow/java/maven/README.md +++ b/tensorflow/java/maven/README.md @@ -53,6 +53,12 @@ There are seven artifacts and thus `pom.xml`s involved in this release: 7. [`parentpom`](https://maven.apache.org/pom/index.html): Common settings shared by all of the above. +8. `hadoop`: The TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop. + The source code for this package is available in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/hadoop) + +9. `spark-connector`: A Scala library for loading and storing TensorFlow TFRecord + using Apache Spark DataFrames. The source code for this package is available + in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/spark/spark-tensorflow-connector) ## Updating the release diff --git a/tensorflow/java/maven/hadoop/pom.xml b/tensorflow/java/maven/hadoop/pom.xml new file mode 100644 index 0000000000000000000000000000000000000000..0642be06fa148933902ab450c5cf2f771e268828 --- /dev/null +++ b/tensorflow/java/maven/hadoop/pom.xml @@ -0,0 +1,24 @@ + + + 4.0.0 + TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop + hadoop + jar + + + https://github.com/tensorflow/ecosystem.git + git@github.com:tensorflow/ecosystem.git + scm:git:https://github.com/tensorflow/ecosystem.git + + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + 1.9.0-rc0 + ../ + + \ No newline at end of file diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index 08cc860f5795a4cf20f4ab2d09d2c2d37a52faf6..a7fa9ea5cc78f9d83cfb105f09837e958c60d5b4 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0 + 1.9.0-rc1 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index fcc7eacc33b7bab366159425405b4bf5b0216cf1..83aae29f1ea0f893c40597a1be6f77668d8206e9 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0 + 1.9.0-rc1 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 3d22d86a4970def52bf9a4a452a8131e1357341a..50bd8ee5f9e6d268976540ca8180380447bc8f18 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0 + 1.9.0-rc1 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 0a09a5ea7cb96776b8296f68f599c333559a0729..b4746794ea9e417bb0bb9253ca356976a48eb1e8 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.8.0 + 1.9.0-rc1 pom https://www.tensorflow.org @@ -32,6 +32,8 @@ libtensorflow_jni_gpu tensorflow proto + hadoop + spark-connector + 4.0.0 + TensorFlow TFRecord connector for Apache Spark DataFrames + spark-connector + jar + + + https://github.com/tensorflow/ecosystem.git + git@github.com:tensorflow/ecosystem.git + scm:git:https://github.com/tensorflow/ecosystem.git + + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + 1.9.0-rc0 + ../ + + \ No newline at end of file diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 0df1f2814906e548855522335f710e9702f8bb2a..157c4b8e82d6b8062ce8c9c98432cfe97a20d190 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.8.0 + 1.9.0-rc1 ../ tensorflow diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc index debd95fc621749fb9754015eacf3c9c7c7ec54a4..d5bd99bdd9d71f73288661380ec45e76c797fa75 100644 --- a/tensorflow/java/src/gen/cc/op_generator.cc +++ b/tensorflow/java/src/gen/cc/op_generator.cc @@ -35,7 +35,7 @@ namespace tensorflow { namespace java { namespace { -const char* kLicense = +constexpr const char kLicense[] = "/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n" "\n" "Licensed under the Apache License, Version 2.0 (the \"License\");\n" @@ -376,9 +376,6 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } } // op annotations - op_class.add_annotation( - Annotation::Create("Generated", "javax.annotation") - .attributes("value = \"TensorFlow Java Op Generator\"")); if (endpoint.deprecated()) { op_class.add_annotation(Annotation::Create("Deprecated")); string explanation; @@ -394,9 +391,12 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } if (!op.hidden()) { // expose the op in the Ops Graph API only if it is visible - op_class.add_annotation( - Annotation::Create("Operator", "org.tensorflow.op.annotation") - .attributes("group = \"" + endpoint.package() + "\"")); + Annotation oper_annot = + Annotation::Create("Operator", "org.tensorflow.op.annotation"); + if (endpoint.package() != kDefaultEndpointPackage) { + oper_annot.attributes("group = \"" + endpoint.package() + "\""); + } + op_class.add_annotation(oper_annot); } // create op class file const string op_dir_name = io::JoinPath( @@ -415,8 +415,12 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, SourceFileWriter writer(op_file.get()); std::list dependencies; CollectOpDependencies(op, mode, &dependencies); - writer.Write(kLicense).EndLine().BeginType(op_class, PUBLIC | FINAL, - &dependencies, &op_javadoc); + writer.Write(kLicense) + .EndLine() + .Write("// This class has been generated, DO NOT EDIT!") + .EndLine() + .EndLine() + .BeginType(op_class, PUBLIC | FINAL, &dependencies, &op_javadoc); if (!op.optional_attributes().empty()) { RenderOptionsClass(op, op_class, &writer); } diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 4bcfc7fe011423df71a899d18815d3558e01b35f..63e99fbb04fd6ba34f2bbd2bc3fe7644a31ddf7f 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -97,6 +97,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, *iterable_out = true; visited_attrs_.insert(std::make_pair(arg_def.number_attr(), Type::Int())); } + Type type = Type::Wildcard(); if (arg_def.type() != DataType::DT_INVALID) { // resolve type from DataType @@ -376,7 +377,7 @@ EndpointSpec CreateEndpoint(const OpDef& op_def, const ApiDef& api_def, package = name_tokens.at(0); name = name_tokens.at(1); } else { - package = "core"; // generate unclassified ops in the 'core' package + package = kDefaultEndpointPackage; name = name_tokens.at(0); } return EndpointSpec(package, diff --git a/tensorflow/java/src/gen/cc/op_specs.h b/tensorflow/java/src/gen/cc/op_specs.h index 034cf636ed071a9dccac643d0f89988b070a1efc..3b53c730df23c6f81f968f09b9d145a8efa1030a 100644 --- a/tensorflow/java/src/gen/cc/op_specs.h +++ b/tensorflow/java/src/gen/cc/op_specs.h @@ -27,6 +27,8 @@ limitations under the License. namespace tensorflow { namespace java { +constexpr const char kDefaultEndpointPackage[] = "core"; + class EndpointSpec { public: // A specification for an operation endpoint diff --git a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java index 11fda4fc22aeec9c2d94b5e884c11ceb2a66d29e..796d6a62dcf8551d8d68d9ff62077e7f09db4401 100644 --- a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java +++ b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java @@ -15,19 +15,44 @@ limitations under the License. package org.tensorflow.processor; +import com.google.common.base.CaseFormat; +import com.google.common.base.Strings; +import com.google.common.collect.HashMultimap; +import com.google.common.collect.Multimap; +import com.squareup.javapoet.ClassName; +import com.squareup.javapoet.FieldSpec; +import com.squareup.javapoet.JavaFile; +import com.squareup.javapoet.MethodSpec; +import com.squareup.javapoet.ParameterSpec; +import com.squareup.javapoet.TypeName; +import com.squareup.javapoet.TypeSpec; +import com.squareup.javapoet.TypeVariableName; import java.io.IOException; -import java.io.PrintWriter; +import java.util.Collection; import java.util.Collections; -import java.util.HashSet; +import java.util.HashMap; +import java.util.Map; import java.util.Set; +import java.util.regex.Matcher; +import java.util.regex.Pattern; import javax.annotation.processing.AbstractProcessor; import javax.annotation.processing.Filer; import javax.annotation.processing.Messager; import javax.annotation.processing.ProcessingEnvironment; import javax.annotation.processing.RoundEnvironment; import javax.lang.model.SourceVersion; +import javax.lang.model.element.AnnotationMirror; +import javax.lang.model.element.AnnotationValue; import javax.lang.model.element.Element; +import javax.lang.model.element.ExecutableElement; +import javax.lang.model.element.Modifier; import javax.lang.model.element.TypeElement; +import javax.lang.model.element.TypeParameterElement; +import javax.lang.model.element.VariableElement; +import javax.lang.model.type.TypeMirror; +import javax.lang.model.type.TypeVariable; +import javax.lang.model.util.ElementFilter; +import javax.lang.model.util.Elements; import javax.tools.Diagnostic.Kind; /** @@ -55,6 +80,7 @@ public final class OperatorProcessor extends AbstractProcessor { super.init(processingEnv); messager = processingEnv.getMessager(); filer = processingEnv.getFiler(); + elements = processingEnv.getElementUtils(); } @Override @@ -98,42 +124,77 @@ public final class OperatorProcessor extends AbstractProcessor { } // Collect all classes tagged with our annotation. - Set opClasses = new HashSet(); - if (!collectOpClasses(roundEnv, opClasses, annotation)) { + Multimap groupedMethods = HashMultimap.create(); + if (!collectOpsMethods(roundEnv, groupedMethods, annotation)) { return true; } // Nothing to do when there are no tagged classes. - if (opClasses.isEmpty()) { + if (groupedMethods.isEmpty()) { return true; } - // TODO:(kbsriram) validate operator classes and generate Op API. - writeApi(); + // Validate operator classes and generate Op API. + writeApi(groupedMethods); + hasRun = true; return true; } @Override public Set getSupportedAnnotationTypes() { - return Collections.singleton(String.format("%s.annotation.Operator", OP_PACKAGE)); + return Collections.singleton("org.tensorflow.op.annotation.Operator"); + } + + private static final Pattern JAVADOC_TAG_PATTERN = + Pattern.compile("@(?:param|return|throws|exception|see)\\s+.*"); + private static final TypeName T_OPS = ClassName.get("org.tensorflow.op", "Ops"); + private static final TypeName T_OPERATOR = + ClassName.get("org.tensorflow.op.annotation", "Operator"); + private static final TypeName T_SCOPE = ClassName.get("org.tensorflow.op", "Scope"); + private static final TypeName T_GRAPH = ClassName.get("org.tensorflow", "Graph"); + private static final TypeName T_STRING = ClassName.get(String.class); + + private Filer filer; + private Messager messager; + private Elements elements; + private boolean hasRun = false; + + private void error(Element e, String message, Object... args) { + if (args != null && args.length > 0) { + message = String.format(message, args); + } + messager.printMessage(Kind.ERROR, message, e); } - private void writeApi() { - // Generate an empty class for now and get the build working correctly. This will be changed to - // generate the actual API once we've done with build-related changes. - // TODO:(kbsriram) - try (PrintWriter writer = - new PrintWriter(filer.createSourceFile(String.format("%s.Ops", OP_PACKAGE)).openWriter())) { - writer.println(String.format("package %s;", OP_PACKAGE)); - writer.println("public class Ops{}"); + private void write(TypeSpec spec) { + try { + JavaFile.builder("org.tensorflow.op", spec).skipJavaLangImports(true).build().writeTo(filer); } catch (IOException e) { - error(null, "Unexpected failure generating API: %s", e.getMessage()); + throw new AssertionError(e); + } + } + + private void writeApi(Multimap groupedMethods) { + Map groups = new HashMap<>(); + + // Generate a API class for each group collected other than the default one (= empty string) + for (Map.Entry> entry : groupedMethods.asMap().entrySet()) { + if (!entry.getKey().isEmpty()) { + TypeSpec groupClass = buildGroupClass(entry.getKey(), entry.getValue()); + write(groupClass); + groups.put(entry.getKey(), ClassName.get("org.tensorflow.op", groupClass.name)); + } } + // Generate the top API class, adding any methods added to the default group + TypeSpec topClass = buildTopClass(groups, groupedMethods.get("")); + write(topClass); } - private boolean collectOpClasses( - RoundEnvironment roundEnv, Set opClasses, TypeElement annotation) { + private boolean collectOpsMethods( + RoundEnvironment roundEnv, + Multimap groupedMethods, + TypeElement annotation) { boolean result = true; for (Element e : roundEnv.getElementsAnnotatedWith(annotation)) { // @Operator can only apply to types, so e must be a TypeElement. @@ -145,20 +206,251 @@ public final class OperatorProcessor extends AbstractProcessor { result = false; continue; } - opClasses.add((TypeElement) e); + TypeElement opClass = (TypeElement) e; + // Skip deprecated operations for now, as we do not guarantee API stability yet + if (opClass.getAnnotation(Deprecated.class) == null) { + collectOpMethods(groupedMethods, opClass, annotation); + } } return result; } - private void error(Element e, String message, Object... args) { - if (args != null && args.length > 0) { - message = String.format(message, args); + private void collectOpMethods( + Multimap groupedMethods, TypeElement opClass, TypeElement annotation) { + AnnotationMirror am = getAnnotationMirror(opClass, annotation); + String groupName = getAnnotationElementValueAsString("group", am); + String methodName = getAnnotationElementValueAsString("name", am); + ClassName opClassName = ClassName.get(opClass); + if (Strings.isNullOrEmpty(methodName)) { + methodName = CaseFormat.UPPER_CAMEL.to(CaseFormat.LOWER_CAMEL, opClassName.simpleName()); + } + // Build a method for each @Operator found in the class path. There should be one method per + // operation factory called + // "create", which takes in parameter a scope and, optionally, a list of arguments + for (ExecutableElement opMethod : ElementFilter.methodsIn(opClass.getEnclosedElements())) { + if (opMethod.getModifiers().contains(Modifier.STATIC) + && opMethod.getSimpleName().contentEquals("create")) { + MethodSpec method = buildOpMethod(methodName, opClassName, opMethod); + groupedMethods.put(groupName, method); + } } - messager.printMessage(Kind.ERROR, message, e); } - private Filer filer; - private Messager messager; - private boolean hasRun = false; - private static final String OP_PACKAGE = "org.tensorflow.op"; + private MethodSpec buildOpMethod( + String methodName, ClassName opClassName, ExecutableElement factoryMethod) { + MethodSpec.Builder builder = + MethodSpec.methodBuilder(methodName) + .addModifiers(Modifier.PUBLIC) + .returns(TypeName.get(factoryMethod.getReturnType())) + .varargs(factoryMethod.isVarArgs()) + .addJavadoc("$L", buildOpMethodJavadoc(opClassName, factoryMethod)); + + for (TypeParameterElement tp : factoryMethod.getTypeParameters()) { + TypeVariableName tvn = TypeVariableName.get((TypeVariable) tp.asType()); + builder.addTypeVariable(tvn); + } + for (TypeMirror thrownType : factoryMethod.getThrownTypes()) { + builder.addException(TypeName.get(thrownType)); + } + StringBuilder call = new StringBuilder("return $T.create(scope"); + boolean first = true; + for (VariableElement param : factoryMethod.getParameters()) { + ParameterSpec p = ParameterSpec.get(param); + if (first) { + first = false; + continue; + } + call.append(", "); + call.append(p.name); + builder.addParameter(p); + } + call.append(")"); + builder.addStatement(call.toString(), opClassName); + return builder.build(); + } + + private String buildOpMethodJavadoc(ClassName opClassName, ExecutableElement factoryMethod) { + StringBuilder javadoc = new StringBuilder(); + javadoc + .append("Adds an {@link ") + .append(opClassName.simpleName()) + .append("} operation to the graph\n\n"); + + // Add all javadoc tags found in the operator factory method but the first one, which should be + // in all cases the + // 'scope' parameter that is implicitly passed by this API + Matcher tagMatcher = JAVADOC_TAG_PATTERN.matcher(elements.getDocComment(factoryMethod)); + boolean firstParam = true; + + while (tagMatcher.find()) { + String tag = tagMatcher.group(); + if (tag.startsWith("@param") && firstParam) { + firstParam = false; + } else { + javadoc.append(tag).append('\n'); + } + } + javadoc.append("@see {@link ").append(opClassName).append("}\n"); + + return javadoc.toString(); + } + + private static TypeSpec buildGroupClass(String group, Collection methods) { + MethodSpec.Builder ctorBuilder = + MethodSpec.constructorBuilder() + .addParameter(T_SCOPE, "scope") + .addStatement("this.scope = scope"); + + TypeSpec.Builder builder = + TypeSpec.classBuilder(CaseFormat.LOWER_CAMEL.to(CaseFormat.UPPER_CAMEL, group) + "Ops") + .addModifiers(Modifier.PUBLIC, Modifier.FINAL) + .addJavadoc( + "An API for adding {@code $L} operations to a {@link $T Graph}\n\n" + + "@see {@link $T}\n", + group, + T_GRAPH, + T_OPS) + .addMethods(methods) + .addMethod(ctorBuilder.build()); + + builder.addField( + FieldSpec.builder(T_SCOPE, "scope").addModifiers(Modifier.PRIVATE, Modifier.FINAL).build()); + + return builder.build(); + } + + private static TypeSpec buildTopClass( + Map groupToClass, Collection methods) { + MethodSpec.Builder ctorBuilder = + MethodSpec.constructorBuilder() + .addModifiers(Modifier.PRIVATE) + .addParameter(T_SCOPE, "scope") + .addStatement("this.scope = scope", T_SCOPE); + + for (Map.Entry entry : groupToClass.entrySet()) { + ctorBuilder.addStatement("$L = new $T(scope)", entry.getKey(), entry.getValue()); + } + + TypeSpec.Builder opsBuilder = + TypeSpec.classBuilder("Ops") + .addModifiers(Modifier.PUBLIC, Modifier.FINAL) + .addJavadoc( + "An API for building a {@link $T} with operation wrappers\n

\n" + + "Any operation wrapper found in the classpath properly annotated as an" + + "{@link $T @Operator} is exposed\n" + + "by this API or one of its subgroup.\n

Example usage:\n

{@code\n"
+                    + "try (Graph g = new Graph()) {\n"
+                    + "  Ops ops = new Ops(g);\n"
+                    + "  // Operations are typed classes with convenience\n"
+                    + "  // builders in Ops.\n"
+                    + "  Constant three = ops.constant(3);\n"
+                    + "  // Single-result operations implement the Operand\n"
+                    + "  // interface, so this works too.\n"
+                    + "  Operand four = ops.constant(4);\n"
+                    + "  // Most builders are found within a group, and accept\n"
+                    + "  // Operand types as operands\n"
+                    + "  Operand nine = ops.math().add(four, ops.constant(5));\n"
+                    + "  // Multi-result operations however offer methods to\n"
+                    + "  // select a particular result for use.\n"
+                    + "  Operand result = \n"
+                    + "      ops.math().add(ops.array().unique(s, a).y(), b);\n"
+                    + "  // Optional attributes\n"
+                    + "  ops.math().matMul(a, b, MatMul.transposeA(true));\n"
+                    + "  // Naming operators\n"
+                    + "  ops.withName(ā€œfooā€).constant(5); // name ā€œfooā€\n"
+                    + "  // Names can exist in a hierarchy\n"
+                    + "  Ops sub = ops.withSubScope(ā€œsubā€);\n"
+                    + "  sub.withName(ā€œbarā€).constant(4); // ā€œsub/barā€\n"
+                    + "}\n"
+                    + "}
\n", + T_GRAPH, + T_OPERATOR) + .addMethods(methods) + .addMethod(ctorBuilder.build()); + + opsBuilder.addMethod( + MethodSpec.methodBuilder("withSubScope") + .addModifiers(Modifier.PUBLIC) + .addParameter(T_STRING, "childScopeName") + .returns(T_OPS) + .addStatement("return new $T(scope.withSubScope(childScopeName))", T_OPS) + .addJavadoc( + "Returns an API that adds operations to the graph with the provided name prefix.\n" + + "\n@see {@link $T#withSubScope(String)}\n", + T_SCOPE) + .build()); + + opsBuilder.addMethod( + MethodSpec.methodBuilder("withName") + .addModifiers(Modifier.PUBLIC) + .addParameter(T_STRING, "opName") + .returns(T_OPS) + .addStatement("return new Ops(scope.withName(opName))") + .addJavadoc( + "Returns an API that uses the provided name for an op.\n\n" + + "@see {@link $T#withName(String)}\n", + T_SCOPE) + .build()); + + opsBuilder.addField( + FieldSpec.builder(T_SCOPE, "scope").addModifiers(Modifier.PRIVATE, Modifier.FINAL).build()); + + opsBuilder.addMethod( + MethodSpec.methodBuilder("scope") + .addModifiers(Modifier.PUBLIC, Modifier.FINAL) + .returns(T_SCOPE) + .addStatement("return scope") + .addJavadoc("Returns the current {@link $T scope} of this API\n", T_SCOPE) + .build()); + + for (Map.Entry entry : groupToClass.entrySet()) { + opsBuilder.addField( + FieldSpec.builder(entry.getValue(), entry.getKey()) + .addModifiers(Modifier.PUBLIC, Modifier.FINAL) + .build()); + + opsBuilder.addMethod( + MethodSpec.methodBuilder(entry.getKey()) + .addModifiers(Modifier.PUBLIC, Modifier.FINAL) + .returns(entry.getValue()) + .addStatement("return $L", entry.getKey()) + .addJavadoc( + "Returns an API for adding {@code $L} operations to the graph\n", entry.getKey()) + .build()); + } + + opsBuilder.addMethod( + MethodSpec.methodBuilder("create") + .addModifiers(Modifier.PUBLIC, Modifier.STATIC) + .addParameter(T_GRAPH, "graph") + .returns(T_OPS) + .addStatement("return new Ops(new $T(graph))", T_SCOPE) + .addJavadoc("Creates an API for adding operations to the provided {@code graph}\n") + .build()); + + return opsBuilder.build(); + } + + private static AnnotationMirror getAnnotationMirror(Element element, TypeElement annotation) { + for (AnnotationMirror am : element.getAnnotationMirrors()) { + if (am.getAnnotationType().asElement().equals(annotation)) { + return am; + } + } + throw new IllegalArgumentException( + "Annotation " + + annotation.getSimpleName() + + " not present on element " + + element.getSimpleName()); + } + + private static String getAnnotationElementValueAsString(String elementName, AnnotationMirror am) { + for (Map.Entry entry : + am.getElementValues().entrySet()) { + if (entry.getKey().getSimpleName().contentEquals(elementName)) { + return entry.getValue().getValue().toString(); + } + } + return ""; + } } diff --git a/tensorflow/java/src/main/java/org/tensorflow/Graph.java b/tensorflow/java/src/main/java/org/tensorflow/Graph.java index d4fd3db5f7325ae891832ff7b658f5d3ea0789a6..7d19696749bbbb944e591daf596562f13f6dc103 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/Graph.java +++ b/tensorflow/java/src/main/java/org/tensorflow/Graph.java @@ -143,6 +143,82 @@ public final class Graph implements AutoCloseable { } } + /** + * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s, + * i.e., {@code d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...} + *

+ * {@code dx} are used as initial gradients (which represent the symbolic partial derivatives of some loss function + * {@code L} w.r.t. {@code y}). {@code dx} must be null or have size of {@code y}. + *

+ * If {@code dx} is null, the implementation will use dx of {@link org.tensorflow.op.core.OnesLike OnesLike} for all + * shapes in {@code y}. + * + * @param y output of the function to derive + * @param x inputs of the function for which partial derivatives are computed + * @param dx if not null, the partial derivatives of some loss function {@code L} w.r.t. {@code y} + * @return the partial derivatives {@code dy} with the size of {@code x} + */ + public Output[] addGradients(Output[] y, Output[] x, Output[] dx) { + Output[] dy = new Output[x.length]; + final long[] yHandles = new long[y.length]; + final int[] yIndices = new int[y.length]; + final long[] xHandles = new long[x.length]; + final int[] xIndices = new int[x.length]; + long[] dxHandles = null; + int[] dxIndices = null; + + try (Reference ref = ref()) { + for (int i = 0; i < y.length; ++i) { + yHandles[i] = y[i].op().getUnsafeNativeHandle(); + yIndices[i] = y[i].index(); + } + for (int i = 0; i < x.length; ++i) { + xHandles[i] = x[i].op().getUnsafeNativeHandle(); + xIndices[i] = x[i].index(); + } + if (dx != null && dx.length > 0) { + dxHandles = new long[dx.length]; + dxIndices = new int[dx.length]; + + for (int i = 0; i < dx.length; ++i) { + dxHandles[i] = dx[i].op().getUnsafeNativeHandle(); + dxIndices[i] = dx[i].index(); + } + } + // Gradient outputs are returned in two continuous arrays concatenated into one. The first holds the native handles + // of the gradient operations while the second holds the index of their output + // e.g. given xHandles = [x0Handle, x1Handle, ...] and xIndices = [x0Index, x1Index, ..], we obtain + // dy = [dy0Handle, dy1Handle, ..., dy0Index, dy1Index, ...] + long[] dyHandlesAndIndices = + addGradients(ref.nativeHandle(), yHandles, yIndices, xHandles, xIndices, dxHandles, dxIndices); + int ndy = dyHandlesAndIndices.length >> 1; + if (ndy != dy.length) { + throw new IllegalStateException(String.valueOf(ndy) + " gradients were added to the graph when " + dy.length + + " were expected"); + } + for (int i = 0, j = ndy; i < ndy; ++i, ++j) { + Operation op = new Operation(this, dyHandlesAndIndices[i]); + dy[i] = new Output<>(op, (int) dyHandlesAndIndices[j]); + } + } + return dy; + } + + /** + * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s, + * i.e., {@code dy/dx_1, dy/dx_2...} + *

+ * This is a simplified version of {@link #addGradients(Output[], Output[], Output[]) where {@code y} is + * a single output and {@code dx} is null. + * + * @param y output of the function to derive + * @param x inputs of the function for which partial derivatives are computed + * @return the partial derivatives {@code dy} with the size of {@code x} + */ + public Output[] addGradients(Output y, Output[] x) { + return addGradients(new Output[]{y}, x, null); + } + private final Object nativeHandleLock = new Object(); private long nativeHandle; private int refcount = 0; @@ -254,6 +330,9 @@ public final class Graph implements AutoCloseable { private static native byte[] toGraphDef(long handle); + private static native long[] addGradients(long handle, long[] inputHandles, int[] inputIndices, + long[] outputHandles, int[] outputIndices, long[] gradInputHandles, int[] gradInputIndices); + static { TensorFlow.init(); } diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/core/Gradients.java b/tensorflow/java/src/main/java/org/tensorflow/op/core/Gradients.java new file mode 100644 index 0000000000000000000000000000000000000000..f4671c8af941dd732859080238fa48e0a22672b6 --- /dev/null +++ b/tensorflow/java/src/main/java/org/tensorflow/op/core/Gradients.java @@ -0,0 +1,153 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.op.core; + +import java.util.Arrays; +import java.util.Iterator; +import java.util.List; + +import org.tensorflow.Operand; +import org.tensorflow.Output; +import org.tensorflow.op.Op; +import org.tensorflow.op.Operands; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Operator; + +/** + * Adds operations to compute the partial derivatives of sum of {@code y}s w.r.t {@code x}s, + * i.e., {@code d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...} + *

+ * If {@code Options.dx()} values are set, they are as the initial symbolic partial derivatives of some loss + * function {@code L} w.r.t. {@code y}. {@code Options.dx()} must have the size of {@code y}. + *

+ * If {@code Options.dx()} is not set, the implementation will use dx of {@code OnesLike} for all + * shapes in {@code y}. + *

+ * The partial derivatives are returned in output {@code dy}, with the size of {@code x}. + *

+ * Example of usage: + * 

{@code
+ * Gradients gradients = Gradients.create(scope, Arrays.asList(loss), Arrays.asList(w, b));
+ * 
+ * Constant alpha = ops.constant(1.0f, Float.class);
+ * ApplyGradientDescent.create(scope, w, alpha, gradients.dy(0));
+ * ApplyGradientDescent.create(scope, b, alpha, gradients.dy(1));
+ * }
+ */ +@Operator +public class Gradients implements Op, Iterable> { + + /** + * Optional attributes for {@link Gradients} + */ + public static class Options { + + /** + * @param dx partial derivatives of some loss function {@code L} w.r.t. {@code y} + * @return this option builder + */ + public Options dx(Iterable> dx) { + this.dx = dx; + return this; + } + + private Iterable> dx; + + private Options() { + } + } + + /** + * Adds gradients computation ops to the graph according to scope. + * + * @param scope current graph scope + * @param y outputs of the function to derive + * @param x inputs of the function for which partial derivatives are computed + * @param options carries optional attributes values + * @return a new instance of {@code Gradients} + */ + public static Gradients create(Scope scope, Iterable> y, Iterable> x, Options... options) { + Output[] dx = null; + if (options != null) { + for (Options opts : options) { + if (opts.dx != null) { + dx = Operands.asOutputs(opts.dx); + } + } + } + Output[] gradOutputs = scope.graph().addGradients(Operands.asOutputs(y), Operands.asOutputs(x), dx); + return new Gradients(Arrays.asList(gradOutputs)); + } + + /** + * Adds gradients computation ops to the graph according to scope. + * + * This is a simplified version of {@link #create(Scope, Iterable, Iterable, Options...)} where {@code y} is + * a single output. + * + * @param scope current graph scope + * @param y output of the function to derive + * @param x inputs of the function for which partial derivatives are computed + * @param options carries optional attributes values + * @return a new instance of {@code Gradients} + */ + @SuppressWarnings({"unchecked", "rawtypes"}) + public static Gradients create(Scope scope, Operand y, Iterable> x, Options... options) { + return create(scope, (Iterable) Arrays.asList(y), x, options); + } + + /** + * @param dx partial derivatives of some loss function {@code L} w.r.t. {@code y} + * @return builder to add more options to this operation + */ + public Options dx(Iterable> dx) { + return new Options().dx(dx); + } + + @Override + @SuppressWarnings({"rawtypes", "unchecked"}) + public Iterator> iterator() { + return (Iterator) dy.iterator(); + } + + /** + * Partial derivatives of {@code y}s w.r.t. {@code x}s, with the size of {@code x} + */ + public List> dy() { + return dy; + } + + /** + * Returns a symbolic handle to one of the gradient operation output + *

+ * Warning: Does not check that the type of the tensor matches T. It is recommended to call + * this method with an explicit type parameter rather than letting it be inferred, e.g. {@code + * gradients.dy(0)} + * + * @param The expected element type of the tensors produced by this output. + * @param index The index of the output among the gradients added by this operation + */ + @SuppressWarnings("unchecked") + public Output dy(int index) { + return (Output) dy.get(index); + } + + private List> dy; + + private Gradients(List> dy) { + this.dy = dy; + } +} diff --git a/tensorflow/java/src/main/java/org/tensorflow/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/package-info.java index 521c5c610c1f775cf9174664f5b786786ce1181d..f353ee31459806eb2db98d23ac030c15258a77fb 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/package-info.java +++ b/tensorflow/java/src/main/java/org/tensorflow/package-info.java @@ -17,7 +17,7 @@ limitations under the License. * Defines classes to build, save, load and execute TensorFlow models. * *

WARNING: The API is currently experimental and is not covered by TensorFlow API stability + * href="https://www.tensorflow.org/guide/version_semantics">API stability * guarantees. See README.md for installation * instructions. diff --git a/tensorflow/java/src/main/native/graph_jni.cc b/tensorflow/java/src/main/native/graph_jni.cc index 0fef15527586555e7d3fc2c76403c6e5888fb236..dac6a345e917b618f7f1234c27959069650b51b7 100644 --- a/tensorflow/java/src/main/native/graph_jni.cc +++ b/tensorflow/java/src/main/native/graph_jni.cc @@ -16,7 +16,9 @@ limitations under the License. #include "tensorflow/java/src/main/native/graph_jni.h" #include +#include #include "tensorflow/c/c_api.h" +#include "tensorflow/java/src/main/native/utils_jni.h" #include "tensorflow/java/src/main/native/exception_jni.h" namespace { @@ -130,3 +132,55 @@ Java_org_tensorflow_Graph_toGraphDef(JNIEnv* env, jclass clazz, jlong handle) { TF_DeleteBuffer(buf); return ret; } + +JNIEXPORT jlongArray JNICALL +Java_org_tensorflow_Graph_addGradients(JNIEnv* env, jclass clazz, jlong handle, + jlongArray y_handles, jintArray y_indices, + jlongArray x_handles, jintArray x_indices, + jlongArray dx_handles, jintArray dx_indices) { + + TF_Graph* g = requireHandle(env, handle); + if (g == nullptr) return nullptr; + + const jint ny = env->GetArrayLength(y_handles); + const jint nx = env->GetArrayLength(x_handles); + + std::unique_ptr y(new TF_Output[ny]); + std::unique_ptr x(new TF_Output[nx]); + std::unique_ptr dx(nullptr); + std::unique_ptr dy(new TF_Output[nx]); + + resolveOutputs(env, "y", y_handles, y_indices, y.get(), ny); + resolveOutputs(env, "x", x_handles, x_indices, x.get(), nx); + if (dx_handles != nullptr) { + if (env->GetArrayLength(dx_handles) != ny) { + throwException(env, kIllegalArgumentException, + "expected %d, got %d dx handles", ny, + env->GetArrayLength(dx_handles)); + } + dx.reset(new TF_Output[ny]); + resolveOutputs(env, "dx", dx_handles, dx_indices, dx.get(), ny); + } + if (env->ExceptionCheck()) return nullptr; + + TF_Status* status = TF_NewStatus(); + TF_AddGradients(g, y.get(), ny, x.get(), nx, dx.get(), status, dy.get()); + + if (!throwExceptionIfNotOK(env, status)) { + TF_DeleteStatus(status); + return nullptr; + } + TF_DeleteStatus(status); + + // returned array contains both op handles and output indices, in pair + jlongArray dy_handles_and_indices = env->NewLongArray(nx << 1); + jlong* dy_elems = env->GetLongArrayElements(dy_handles_and_indices, nullptr); + for (int i = 0, j = nx; i < nx; ++i, ++j) { + TF_Output dy_output = dy.get()[i]; + dy_elems[i] = reinterpret_cast(dy_output.oper); + dy_elems[j] = static_cast(dy_output.index); + } + env->ReleaseLongArrayElements(dy_handles_and_indices, dy_elems, 0); + + return dy_handles_and_indices; +} diff --git a/tensorflow/java/src/main/native/graph_jni.h b/tensorflow/java/src/main/native/graph_jni.h index dd2e038332f7d39e6460d6cfef40a9df7e348758..4f87e8d5a79d3ac46f7813ba4344bbfda069b557 100644 --- a/tensorflow/java/src/main/native/graph_jni.h +++ b/tensorflow/java/src/main/native/graph_jni.h @@ -73,6 +73,15 @@ JNIEXPORT jbyteArray JNICALL Java_org_tensorflow_Graph_toGraphDef(JNIEnv *, jclass, jlong); +/* + * Class: org_tensorflow_Graph + * Method: name + * Signature: (J[J[I[J[I[J[I)[J + */ +JNIEXPORT jlongArray JNICALL Java_org_tensorflow_Graph_addGradients(JNIEnv *, + jclass, jlong, jlongArray, jintArray, jlongArray, jintArray, jlongArray, + jintArray); + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/java/src/main/native/session_jni.cc b/tensorflow/java/src/main/native/session_jni.cc index 2cd542d3c9be536a42037e9ef533ed629dd3ac9f..cb54daf13795c24e11566845892da6b5c4896cf5 100644 --- a/tensorflow/java/src/main/native/session_jni.cc +++ b/tensorflow/java/src/main/native/session_jni.cc @@ -17,6 +17,7 @@ limitations under the License. #include #include "tensorflow/c/c_api.h" +#include "tensorflow/java/src/main/native/utils_jni.h" #include "tensorflow/java/src/main/native/exception_jni.h" #include "tensorflow/java/src/main/native/session_jni.h" @@ -55,37 +56,6 @@ void resolveHandles(JNIEnv* env, const char* type, jlongArray src_array, env->ReleaseLongArrayElements(src_array, src_start, JNI_ABORT); } -void resolveOutputs(JNIEnv* env, const char* type, jlongArray src_op, - jintArray src_index, TF_Output* dst, jint n) { - if (env->ExceptionCheck()) return; - jint len = env->GetArrayLength(src_op); - if (len != n) { - throwException(env, kIllegalArgumentException, - "expected %d, got %d %s Operations", n, len, type); - return; - } - len = env->GetArrayLength(src_index); - if (len != n) { - throwException(env, kIllegalArgumentException, - "expected %d, got %d %s Operation output indices", n, len, - type); - return; - } - jlong* op_handles = env->GetLongArrayElements(src_op, nullptr); - jint* indices = env->GetIntArrayElements(src_index, nullptr); - for (int i = 0; i < n; ++i) { - if (op_handles[i] == 0) { - throwException(env, kNullPointerException, "invalid %s (#%d of %d)", type, - i, n); - break; - } - dst[i] = TF_Output{reinterpret_cast(op_handles[i]), - static_cast(indices[i])}; - } - env->ReleaseIntArrayElements(src_index, indices, JNI_ABORT); - env->ReleaseLongArrayElements(src_op, op_handles, JNI_ABORT); -} - void TF_MaybeDeleteBuffer(TF_Buffer* buf) { if (buf == nullptr) return; TF_DeleteBuffer(buf); diff --git a/tensorflow/java/src/main/native/utils_jni.cc b/tensorflow/java/src/main/native/utils_jni.cc new file mode 100644 index 0000000000000000000000000000000000000000..069ac05a1c39408dc02f5bbf9a7fc50fd095cc96 --- /dev/null +++ b/tensorflow/java/src/main/native/utils_jni.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/java/src/main/native/utils_jni.h" + +#include "tensorflow/java/src/main/native/exception_jni.h" + +void resolveOutputs(JNIEnv* env, const char* type, jlongArray src_op, + jintArray src_index, TF_Output* dst, jint n) { + if (env->ExceptionCheck()) return; + jint len = env->GetArrayLength(src_op); + if (len != n) { + throwException(env, kIllegalArgumentException, + "expected %d, got %d %s Operations", n, len, type); + return; + } + len = env->GetArrayLength(src_index); + if (len != n) { + throwException(env, kIllegalArgumentException, + "expected %d, got %d %s Operation output indices", n, len, + type); + return; + } + jlong* op_handles = env->GetLongArrayElements(src_op, nullptr); + jint* indices = env->GetIntArrayElements(src_index, nullptr); + for (int i = 0; i < n; ++i) { + if (op_handles[i] == 0) { + throwException(env, kNullPointerException, "invalid %s (#%d of %d)", type, + i, n); + break; + } + dst[i] = TF_Output{reinterpret_cast(op_handles[i]), + static_cast(indices[i])}; + } + env->ReleaseIntArrayElements(src_index, indices, JNI_ABORT); + env->ReleaseLongArrayElements(src_op, op_handles, JNI_ABORT); +} + + + + diff --git a/tensorflow/java/src/main/native/utils_jni.h b/tensorflow/java/src/main/native/utils_jni.h new file mode 100644 index 0000000000000000000000000000000000000000..352298e7de1d07cebc1a287774c9bef85c9a6ae4 --- /dev/null +++ b/tensorflow/java/src/main/native/utils_jni.h @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_JAVA_UTILS_JNI_H_ +#define TENSORFLOW_JAVA_UTILS_JNI_H_ + +#include + +#include "tensorflow/c/c_api.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +void resolveOutputs(JNIEnv* env, const char* type, jlongArray src_op, + jintArray src_index, TF_Output* dst, jint n); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus +#endif /* TENSORFLOW_JAVA_UTILS_JNI_H_ */ diff --git a/tensorflow/java/src/test/java/org/tensorflow/GraphTest.java b/tensorflow/java/src/test/java/org/tensorflow/GraphTest.java index c540299bdcfcd7bc5969caf82b29144bad24201f..c2e52c22c6dc58a3002b536e64c4607b675804f7 100644 --- a/tensorflow/java/src/test/java/org/tensorflow/GraphTest.java +++ b/tensorflow/java/src/test/java/org/tensorflow/GraphTest.java @@ -22,6 +22,7 @@ import static org.junit.Assert.assertTrue; import java.util.HashSet; import java.util.Iterator; + import org.junit.Test; import org.junit.runner.RunWith; import org.junit.runners.JUnit4; @@ -129,4 +130,106 @@ public class GraphTest { // expected exception. } } + + @Test + public void addGradientsToGraph() { + try (Graph g = new Graph(); + Session s = new Session(g)) { + + Output x1 = TestUtil.placeholder(g, "x1", Float.class); + Output x2 = TestUtil.placeholder(g, "x2", Float.class); + Output y0 = TestUtil.square(g, "y0", x1); + Output y1 = TestUtil.square(g, "y1", y0); + Output y2 = TestUtil.addN(g, y0, x2); + + Output[] grads0 = g.addGradients(y1, toArray(x1)); + assertNotNull(grads0); + assertEquals(1, grads0.length); + assertEquals(DataType.FLOAT, grads0[0].dataType()); + + Output[] grads1 = g.addGradients(y2, toArray(x1, x2)); + assertNotNull(grads1); + assertEquals(2, grads1.length); + assertEquals(DataType.FLOAT, grads1[0].dataType()); + assertEquals(DataType.FLOAT, grads1[1].dataType()); + + try (Tensor c1 = Tensors.create(3.0f); + Tensor c2 = Tensors.create(2.0f); + TestUtil.AutoCloseableList> outputs = new TestUtil.AutoCloseableList<>( + s.runner() + .feed(x1, c1) + .feed(x2, c2) + .fetch(grads0[0]) + .fetch(grads1[0]) + .fetch(grads1[1]) + .run())) { + + assertEquals(3, outputs.size()); + assertEquals(108.0f, outputs.get(0).floatValue(), 0.0f); + assertEquals(6.0f, outputs.get(1).floatValue(), 0.0f); + assertEquals(1.0f, outputs.get(2).floatValue(), 0.0f); + } + } + } + + @Test + public void addGradientSumsToGraph() { + try (Graph g = new Graph(); + Session s = new Session(g)) { + + Output x = TestUtil.placeholder(g, "x", Float.class); + Output y0 = TestUtil.square(g, "y0", x); + Output y1 = TestUtil.square(g, "y1", y0); + + Output[] grad = g.addGradients(toArray(y0, y1), toArray(x), null); + assertNotNull(grad); + assertEquals(1, grad.length); + assertEquals(DataType.FLOAT, grad[0].dataType()); + + try (Tensor c = Tensors.create(3.0f); + Tensor output = s.runner() + .feed(x, c) + .fetch(grad[0]) + .run() + .get(0)) { + + assertEquals(114.0f, output.floatValue(), 0.0f); + } + } + } + + @Test + public void addGradientsWithInitialValuesToGraph() { + try (Graph g = new Graph(); + Session s = new Session(g)) { + + Output x = TestUtil.placeholder(g, "x", Float.class); + Output y0 = TestUtil.square(g, "y0", x); + Output y1 = TestUtil.square(g, "y1", y0); + + Output[] grad0 = g.addGradients(y1, toArray(y0)); + assertNotNull(grad0); + assertEquals(1, grad0.length); + assertEquals(DataType.FLOAT, grad0[0].dataType()); + + Output[] grad1 = g.addGradients(toArray(y0), toArray(x), toArray(grad0[0])); + assertNotNull(grad1); + assertEquals(1, grad1.length); + assertEquals(DataType.FLOAT, grad1[0].dataType()); + + try (Tensor c = Tensors.create(3.0f); + Tensor output = s.runner() + .feed(x, c) + .fetch(grad1[0]) + .run() + .get(0)) { + + assertEquals(108.0f, output.floatValue(), 0.0f); + } + } + } + + private static Output[] toArray(Output... outputs) { + return outputs; + } } diff --git a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java index e8cc76c2a6458193161a98e17483fe73de107b77..7d5980bcdedebedcd2fa4722e85abc1d598fb4fd 100644 --- a/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java +++ b/tensorflow/java/src/test/java/org/tensorflow/SessionTest.java @@ -20,8 +20,6 @@ import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; import static org.junit.Assert.fail; -import java.util.ArrayList; -import java.util.Collection; import org.junit.Test; import org.junit.runner.RunWith; import org.junit.runners.JUnit4; @@ -36,8 +34,8 @@ public class SessionTest { Session s = new Session(g)) { TestUtil.transpose_A_times_X(g, new int[][] {{2}, {3}}); try (Tensor x = Tensors.create(new int[][] {{5}, {7}}); - AutoCloseableList> outputs = - new AutoCloseableList>(s.runner().feed("X", x).fetch("Y").run())) { + TestUtil.AutoCloseableList> outputs = + new TestUtil.AutoCloseableList>(s.runner().feed("X", x).fetch("Y").run())) { assertEquals(1, outputs.size()); final int[][] expected = {{31}}; assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1])); @@ -53,8 +51,8 @@ public class SessionTest { Output feed = g.operation("X").output(0); Output fetch = g.operation("Y").output(0); try (Tensor x = Tensors.create(new int[][] {{5}, {7}}); - AutoCloseableList> outputs = - new AutoCloseableList>(s.runner().feed(feed, x).fetch(fetch).run())) { + TestUtil.AutoCloseableList> outputs = + new TestUtil.AutoCloseableList>(s.runner().feed(feed, x).fetch(fetch).run())) { assertEquals(1, outputs.size()); final int[][] expected = {{31}}; assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1])); @@ -112,7 +110,7 @@ public class SessionTest { .setOptions(fullTraceRunOptions()) .runAndFetchMetadata(); // Sanity check on outputs. - AutoCloseableList> outputs = new AutoCloseableList>(result.outputs); + TestUtil.AutoCloseableList> outputs = new TestUtil.AutoCloseableList>(result.outputs); assertEquals(1, outputs.size()); final int[][] expected = {{31}}; assertArrayEquals(expected, outputs.get(0).copyTo(new int[1][1])); @@ -135,8 +133,8 @@ public class SessionTest { Session s = new Session(g)) { TestUtil.constant(g, "c1", 2718); TestUtil.constant(g, "c2", 31415); - AutoCloseableList> outputs = - new AutoCloseableList>(s.runner().fetch("c2").fetch("c1").run()); + TestUtil.AutoCloseableList> outputs = + new TestUtil.AutoCloseableList>(s.runner().fetch("c2").fetch("c1").run()); assertEquals(2, outputs.size()); assertEquals(31415, outputs.get(0).intValue()); assertEquals(2718, outputs.get(1).intValue()); @@ -164,28 +162,6 @@ public class SessionTest { Session s = new Session(g, singleThreadConfigProto())) {} } - private static final class AutoCloseableList extends ArrayList - implements AutoCloseable { - AutoCloseableList(Collection c) { - super(c); - } - - @Override - public void close() { - Exception toThrow = null; - for (AutoCloseable c : this) { - try { - c.close(); - } catch (Exception e) { - toThrow = e; - } - } - if (toThrow != null) { - throw new RuntimeException(toThrow); - } - } - } - private static byte[] fullTraceRunOptions() { // Ideally this would use the generated Java sources for protocol buffers // and end up with something like the snippet below. However, generating diff --git a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java index c973b5a3d8b2be8ee21710d65732bc1e5c3b520a..4e848864167982c750b390a77a1ab7f5d0d40fe9 100644 --- a/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java +++ b/tensorflow/java/src/test/java/org/tensorflow/TestUtil.java @@ -16,9 +16,34 @@ limitations under the License. package org.tensorflow; import java.lang.reflect.Array; +import java.util.ArrayList; +import java.util.Collection; /** Static utility functions. */ public class TestUtil { + + public static final class AutoCloseableList extends ArrayList + implements AutoCloseable { + AutoCloseableList(Collection c) { + super(c); + } + + @Override + public void close() { + Exception toThrow = null; + for (AutoCloseable c : this) { + try { + c.close(); + } catch (Exception e) { + toThrow = e; + } + } + if (toThrow != null) { + throw new RuntimeException(toThrow); + } + } + } + public static Output constant(Graph g, String name, Object value) { try (Tensor t = Tensor.create(value)) { return g.opBuilder("Const", name) @@ -36,7 +61,7 @@ public class TestUtil { .output(0); } - public static Output addN(Graph g, Output... inputs) { + public static Output addN(Graph g, Output... inputs) { return g.opBuilder("AddN", "AddN").addInputList(inputs).build().output(0); } @@ -58,6 +83,13 @@ public class TestUtil { .setAttr("num_split", numSplit) .build(); } + + public static Output square(Graph g, String name, Output value) { + return g.opBuilder("Square", name) + .addInput(value) + .build() + .output(0); + } public static void transpose_A_times_X(Graph g, int[][] a) { Output aa = constant(g, "A", a); diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 86721cb856f0ff89a84ea5bd5efcd617245d449d..ebfcfff4a5263ec8af31b461d274a8a6f9b6ec34 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -4,14 +4,16 @@ # Public targets: # ":platform" - Low-level and platform-specific Python code. -package(default_visibility = [ +visibility = [ "//engedu/ml/tf_from_scratch:__pkg__", "//tensorflow:internal", "//tensorflow/contrib/lite/toco/python:__pkg__", "//tensorflow_models:__subpackages__", # TODO(aselle): to pass open source test. "//bazel_pip/tensorflow/contrib/lite/toco/python:__pkg__", -]) +] + +package(default_visibility = visibility) licenses(["notice"]) # Apache 2.0 @@ -55,12 +57,12 @@ py_library( "//tensorflow/contrib/lite/toco/python:__pkg__", # TODO(b/34059704): remove when fixed "//tensorflow/python/debug:__pkg__", # TODO(b/34059704): remove when fixed "//tensorflow/python/tools:__pkg__", # TODO(b/34059704): remove when fixed - "//tensorflow/tools/api/generator:__pkg__", "//tensorflow/tools/quantization:__pkg__", # TODO(b/34059704): remove when fixed ], deps = [ ":no_contrib", "//tensorflow/contrib:contrib_py", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -125,13 +127,14 @@ py_library( ":util", ":weights_broadcast_ops", "//tensorflow/core:protos_all_py", + "//tensorflow/python/compat", "//tensorflow/python/data", - "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//tensorflow/python/keras", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/linalg", "//tensorflow/python/ops/losses", + "//tensorflow/python/ops/parallel_for", "//tensorflow/python/profiler", "//tensorflow/python/saved_model", "//third_party/py/numpy", @@ -278,6 +281,9 @@ cc_library( name = "ndarray_tensor_bridge", srcs = ["lib/core/ndarray_tensor_bridge.cc"], hdrs = ["lib/core/ndarray_tensor_bridge.h"], + visibility = visibility + [ + "//learning/deepmind/courier:__subpackages__", + ], deps = [ ":bfloat16_lib", ":numpy_lib", @@ -358,6 +364,9 @@ cc_library( name = "ndarray_tensor", srcs = ["lib/core/ndarray_tensor.cc"], hdrs = ["lib/core/ndarray_tensor.h"], + visibility = visibility + [ + "//learning/deepmind/courier:__subpackages__", + ], deps = [ ":bfloat16_lib", ":ndarray_tensor_bridge", @@ -690,12 +699,22 @@ py_library( ], ) +py_library( + name = "error_interpolation", + srcs = [ + "framework/error_interpolation.py", + ], + srcs_version = "PY2AND3", + deps = [], +) + py_library( name = "function", srcs = ["framework/function.py"], srcs_version = "PY2AND3", deps = [ ":array_ops", + ":cond_v2_impl", ":dtypes", ":framework_ops", ":graph_to_function_def", @@ -712,6 +731,7 @@ py_library( srcs = ["framework/graph_to_function_def.py"], srcs_version = "PY2AND3", deps = [ + ":cond_v2_impl", ":op_def_registry", "//tensorflow/core:protos_all_py", ], @@ -990,6 +1010,18 @@ py_test( ], ) +py_test( + name = "framework_error_interpolation_test", + size = "small", + srcs = ["framework/error_interpolation_test.py"], + main = "framework/error_interpolation_test.py", + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":error_interpolation", + ], +) + py_test( name = "framework_subscribe_test", size = "small", @@ -1052,7 +1084,6 @@ tf_gen_op_wrapper_private_py( name = "functional_ops_gen", visibility = [ "//learning/brain/python/ops:__pkg__", - "//tensorflow/contrib/control_flow:__pkg__", ], ) @@ -1600,6 +1631,9 @@ tf_gen_op_wrapper_private_py( tf_gen_op_wrapper_private_py( name = "resource_variable_ops_gen", + visibility = [ + "//tensorflow/compiler/tf2xla:internal", + ], ) tf_gen_op_wrapper_private_py( @@ -1827,6 +1861,7 @@ py_library( "tensor_shape", ":array_ops", ":array_ops_gen", + ":cond_v2_impl", ":constant_op", ":control_flow_ops_gen", ":control_flow_util", @@ -1855,6 +1890,37 @@ py_library( ], ) +py_library( + name = "cond_v2", + srcs = [ + "ops/cond_v2.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":cond_v2_impl", + ":function", + ":function_def_to_graph", + ":gradients", + ], +) + +py_library( + name = "cond_v2_impl", + srcs = [ + "ops/cond_v2_impl.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":c_api_util", + ":framework_ops", + ":functional_ops_gen", + ":pywrap_tensorflow", + ":util", + "//tensorflow/core:protos_all_py", + ], +) + py_library( name = "ctc_ops", srcs = ["ops/ctc_ops.py"], @@ -1921,6 +1987,8 @@ py_library( ":math_ops", ":platform", ":resource_variable_ops", + ":sparse_ops", + ":tensor_shape", ":variables", ], ) @@ -1937,6 +2005,7 @@ py_library( ":array_grad", ":array_ops", ":bitwise_ops", + ":cond_v2_impl", ":control_flow_grad", ":control_flow_ops", ":control_flow_util", @@ -1953,6 +2022,7 @@ py_library( ":math_grad", ":math_ops", ":platform", + ":random_grad", ":resource_variable_ops", ":spectral_grad", ":util", @@ -2331,6 +2401,19 @@ py_library( ], ) +py_library( + name = "random_grad", + srcs = ["ops/random_grad.py"], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":dtypes", + ":framework_ops", + ":math_ops", + ":random_ops_gen", + ], +) + py_library( name = "random_ops", srcs = ["ops/random_ops.py"], @@ -2391,6 +2474,7 @@ py_library( srcs = ["ops/script_ops.py"], srcs_version = "PY2AND3", deps = [ + ":array_ops", ":framework_for_generated_wrappers", ":script_ops_gen", "//third_party/py/numpy", @@ -2530,6 +2614,7 @@ py_library( ":check_ops", ":confusion_matrix", ":control_flow_ops", + ":distribute", ":framework", ":framework_for_generated_wrappers", ":math_ops", @@ -3337,6 +3422,19 @@ py_library( ], ) +py_test( + name = "lock_util_test", + size = "small", + srcs = ["util/lock_util_test.py"], + main = "util/lock_util_test.py", + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":util", + "@absl_py//absl/testing:parameterized", + ], +) + tf_proto_library( name = "protos_all", srcs = glob( @@ -3655,6 +3753,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":c_api_util", + ":error_interpolation", ":errors", ":framework", ":framework_for_generated_wrappers", @@ -3855,7 +3954,7 @@ tf_cuda_library( tf_py_test( name = "session_test", - size = "small", + size = "medium", srcs = ["client/session_test.py"], additional_deps = [ ":array_ops", @@ -4037,6 +4136,19 @@ py_test( ], ) +py_test( + name = "tf_record_test", + size = "small", + srcs = ["lib/io/tf_record_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":errors", + ":lib", + ":util", + ], +) + cuda_py_test( name = "adam_test", size = "small", diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index cf707fb2c731c0db57c2335d3ffd49b292c811cc..a2ab63bb48799d5b93882bb87ab40b02dbb96621 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -79,7 +79,6 @@ from tensorflow.python.ops import initializers_ns as initializers # Bring in subpackages. from tensorflow.python import data from tensorflow.python import keras -from tensorflow.python.estimator import estimator_lib as estimator from tensorflow.python.feature_column import feature_column_lib as feature_column from tensorflow.python.layers import layers from tensorflow.python.ops import bitwise_ops as bitwise diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 648e35cdf25ae10ea8bb346cfb1cd12e24215c13..e037925961f2bfc8b8906fa81c2d7908ea590a62 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -361,7 +361,7 @@ class _ListFetchMapper(_FetchMapper): for m, vi in zip(self._mappers, self._value_indices): results.append(m.build_results([values[j] for j in vi])) # Return a value of the original type of the fetches. - if self._fetch_type == list: + if issubclass(self._fetch_type, list): return results elif self._fetch_type == tuple: return tuple(results) @@ -1291,7 +1291,7 @@ class BaseSession(SessionInterface): raise type(e)(node_def, op, message) def _extend_graph(self): - with self._graph._lock: # pylint: disable=protected-access + with self._graph._session_run_lock(): # pylint: disable=protected-access tf_session.ExtendSession(self._session) # The threshold to run garbage collection to delete dead tensors. @@ -1369,12 +1369,24 @@ class BaseSession(SessionInterface): finally: tf_session.TF_DeleteBuffer(options_ptr) - def __call__(self, *args): + def __call__(self, *args, **kwargs): # TODO(b/74355905): Support argument and return value nested structures, # and tensor-like objects such as SparseTensors. - with errors.raise_exception_on_not_ok_status() as status: - return tf_session.TF_SessionRunCallable( - self._session._session, self._handle, args, status, None) + run_metadata = kwargs.get('run_metadata', None) + try: + run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None + # TODO(mrry): Switch to raising an exception from the SWIG wrapper. + with errors.raise_exception_on_not_ok_status() as status: + ret = tf_session.TF_SessionRunCallable( + self._session._session, self._handle, args, status, + run_metadata_ptr) + if run_metadata: + proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) + run_metadata.ParseFromString(compat.as_bytes(proto_data)) + finally: + if run_metadata_ptr: + tf_session.TF_DeleteBuffer(run_metadata_ptr) + return ret def __del__(self): # NOTE(mrry): It is possible that `self._session.__del__()` could be diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index 482497078cd3e0544b7465fc7c0be0dc81b5ff6a..b72e029d1ccb688f5992f6cc8695969be5e5e2e3 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function import collections +import random import os import sys import threading @@ -1040,40 +1041,72 @@ class SessionTest(test_util.TensorFlowTestCase): for t in threads: t.join() - def testParallelRunAndBuild(self): + @staticmethod + def _build_graph(): + time.sleep(random.random() * 0.1) + # Do some graph construction. Try to exercise non-trivial paths. + graph = ops.get_default_graph() + gdef = None + for _ in range(10): + x = array_ops.placeholder(dtype=dtypes.float32) + with ops.colocate_with(x): + y = array_ops.placeholder(dtype=dtypes.float32) + with ops.device('/cpu:0'): + z = control_flow_ops.while_loop( + lambda x, y: x < 10, lambda x, y: (x + 1, x * y), [x, y]) + with graph._attr_scope({'_a': attr_value_pb2.AttrValue(b=False)}): + gradients_impl.gradients(z, [x, y]) + if gdef is None: + gdef = graph.as_graph_def() + else: + importer.import_graph_def(gdef, name='import') + + def testParallelRunAndSingleBuild(self): with session.Session() as sess: c = constant_op.constant(5.0) stop = threading.Event() def run_loop(): while not stop.is_set(): + time.sleep(random.random() * 0.1) self.assertEqual(sess.run(c), 5.0) - threads = [self.checkedThread(target=run_loop) for _ in range(100)] + threads = [self.checkedThread(target=run_loop) for _ in range(10)] for t in threads: t.start() - # Do some graph construction. Try to exercise non-trivial paths. - graph = ops.get_default_graph() - gdef = None - for _ in range(10): - x = array_ops.placeholder(dtype=dtypes.float32) - with ops.colocate_with(x): - y = array_ops.placeholder(dtype=dtypes.float32) - with ops.device('/cpu:0'): - z = control_flow_ops.while_loop( - lambda x, y: x < 10, lambda x, y: (x + 1, x * y), [x, y]) - with graph._attr_scope({'_a': attr_value_pb2.AttrValue(b=False)}): - gradients_impl.gradients(z, [x, y]) - if gdef is None: - gdef = graph.as_graph_def() - else: - importer.import_graph_def(gdef, name='import') + SessionTest._build_graph() stop.set() for t in threads: t.join() + def testParallelRunAndParallelBuild(self): + with session.Session() as sess: + c = constant_op.constant(5.0) + stop = threading.Event() + + def run_loop(): + while not stop.is_set(): + time.sleep(random.random() * 0.1) + self.assertEqual(sess.run(c), 5.0) + + run_threads = [self.checkedThread(target=run_loop) for _ in range(10)] + for t in run_threads: + t.start() + + build_threads = [self.checkedThread(target=SessionTest._build_graph) + for _ in range(10)] + for t in build_threads: + t.start() + for t in build_threads: + t.join() + + # Let the run_threads run until the build threads are finished. + stop.set() + for t in run_threads: + t.join() + def testRunFeedDict(self): with session.Session() as s: x = array_ops.zeros([2]) @@ -1364,6 +1397,20 @@ class SessionTest(test_util.TensorFlowTestCase): for _ in range(5): self.assertEqual([2.0], callable_fn(np.array(1.0, dtype=np.float32))) + def testOptimizedMakeCallableWithRunMetadata(self): + with session.Session() as sess: + ph = array_ops.placeholder(dtypes.float32) + a = math_ops.add(ph, 1.0) + callable_opts = config_pb2.CallableOptions() + callable_opts.feed.append(ph.name) + callable_opts.fetch.append(a.name) + callable_opts.run_options.trace_level = config_pb2.RunOptions.FULL_TRACE + callable_fn = sess._make_callable_from_options(callable_opts) + run_metadata = config_pb2.RunMetadata() + self.assertEqual([2.0], callable_fn(np.array(1.0, dtype=np.float32), + run_metadata=run_metadata)) + self.assertGreater(len(run_metadata.step_stats.dev_stats), 0) + def testFeedError(self): with session.Session() as sess: feed_t = array_ops.placeholder(dtype=dtypes.float32) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 1db1432d6521bb5f48558081916158792010b1c5..985cb904360ac293461936bf67fb1b1de2c77b4a 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -135,7 +135,7 @@ tensorflow::ImportNumpy(); // Convert TF_DeviceListMemoryBytes and TF_Dim int64_t output to Python integers %typemap(out) int64_t { - $result = PyInt_FromLong($1); + $result = PyLong_FromLongLong($1); } // We use TF_OperationGetControlInputs_wrapper instead of @@ -610,7 +610,7 @@ def TF_Reset(target, containers=None, config=None): } for (size_t i = 0; i < $1.size(); ++i) { - PyList_SET_ITEM($result, i, PyInt_FromLong($1[i])); + PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i])); } } @@ -673,7 +673,7 @@ def TF_Reset(target, containers=None, config=None): } for (size_t i = 0; i < $1.size(); ++i) { - PyList_SET_ITEM($result, i, PyInt_FromLong($1[i])); + PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i])); } } diff --git a/tensorflow/python/compat/BUILD b/tensorflow/python/compat/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..58ceafca0638a90c2e66ddea0e4bbb1547455f48 --- /dev/null +++ b/tensorflow/python/compat/BUILD @@ -0,0 +1,22 @@ +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "tf_py_test") + +py_library( + name = "compat", + srcs = ["compat.py"], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:internal"], +) + +tf_py_test( + name = "compat_test", + size = "small", + srcs = ["compat_test.py"], + additional_deps = [ + ":compat", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py new file mode 100644 index 0000000000000000000000000000000000000000..68a6421c2c56c9f007cbd8aee3111c4abfde691c --- /dev/null +++ b/tensorflow/python/compat/compat.py @@ -0,0 +1,125 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for API compatibility between TensorFlow release versions. + +See +@{$guide/version_compat#backward_and_partial_forward_compatibility} +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import datetime +from tensorflow.python.util import tf_contextlib + +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 8, 1) + + +def forward_compatible(year, month, day): + """Return true if the forward compatibility window has expired. + + Forward-compatibility refers to scenarios where the producer of a TensorFlow + model (a GraphDef or SavedModel) is compiled against a version of the + TensorFlow library newer than what the consumer was compiled against. The + "producer" is typically a Python program that constructs and trains a model + while the "consumer" is typically another program that loads and serves the + model. + + TensorFlow has been supporting a 3 week forward-compatibility window for + programs compiled from source at HEAD. + + For example, consider the case where a new operation `MyNewAwesomeAdd` is + created with the intent of replacing the implementation of an existing Python + wrapper - `tf.add`. The Python wrapper implementation should change from + something like: + + ```python + def add(inputs, name=None): + return gen_math_ops.add(inputs, name) + ``` + + to: + + ```python + from tensorflow.python.compat import compat + + def add(inputs, name=None): + if compat.forward_compatible(year, month, day): + # Can use the awesome new implementation. + return gen_math_ops.my_new_awesome_add(inputs, name) + # To maintain forward compatibiltiy, use the old implementation. + return gen_math_ops.add(inputs, name) + ``` + + Where `year`, `month`, and `day` specify the date beyond which binaries + that consume a model are expected to have been updated to include the + new operations. This date is typically at least 3 weeks beyond the date + the code that adds the new operation is committed. + + Args: + year: A year (e.g., 2018). + month: A month (1 <= month <= 12) in year. + day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. + + Returns: + True if the caller can expect that serialized TensorFlow graphs produced + can be consumed by programs that are compiled with the TensorFlow library + source code after (year, month, day). + """ + return _FORWARD_COMPATIBILITY_HORIZON > datetime.date(year, month, day) + + +@tf_contextlib.contextmanager +def forward_compatibility_horizon(year, month, day): + """Context manager for testing forward compatibility of generated graphs. + + To ensure forward compatibility of generated graphs (see `forward_compatible`) + with older binaries, new features can be gated with: + + ```python + if compat.forward_compatible(year=2018, month=08, date=01): + generate_graph_with_new_features() + else: + generate_graph_so_older_binaries_can_consume_it() + ``` + + However, when adding new features, one may want to unittest it before + the forward compatibility window expires. This context manager enables + such tests. For example: + + ```python + from tensorflow.python.compat import compat + + def testMyNewFeature(self): + with compat.forward_compatibility_horizon(2018, 08, 02): + # Test that generate_graph_with_new_features() has an effect + ``` + + Args : + year: A year (e.g. 2018). + month: A month (1 <= month <= 12) in year. + day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. + + Yields: + Nothing. + """ + global _FORWARD_COMPATIBILITY_HORIZON + try: + old_compat_date = _FORWARD_COMPATIBILITY_HORIZON + _FORWARD_COMPATIBILITY_HORIZON = datetime.date(year, month, day) + yield + finally: + _FORWARD_COMPATIBILITY_HORIZON = old_compat_date diff --git a/tensorflow/python/compat/compat_test.py b/tensorflow/python/compat/compat_test.py new file mode 100644 index 0000000000000000000000000000000000000000..946abbb300d66e7be5ea317e365bc75cbcf6941c --- /dev/null +++ b/tensorflow/python/compat/compat_test.py @@ -0,0 +1,70 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for forward and backwards compatibility utilties.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import datetime +from tensorflow.python.compat import compat +from tensorflow.python.platform import test + + +class CompatTest(test.TestCase): + + def _compatibility_date(self): + date = compat._FORWARD_COMPATIBILITY_HORIZON # pylint: disable=protected-access + return (date.year, date.month, date.day) + + def _n_days_after(self, n): + date = compat._FORWARD_COMPATIBILITY_HORIZON + datetime.timedelta(days=n) # pylint: disable=protected-access + return (date.year, date.month, date.day) + + def test_basic(self): + compatibility_date = self._compatibility_date() + one_day_before = self._n_days_after(-1) + self.assertTrue(compat.forward_compatible(*one_day_before)) + self.assertFalse(compat.forward_compatible(*compatibility_date)) + + def test_decorator(self): + compatibility_date = self._compatibility_date() + one_day_after = self._n_days_after(1) + with compat.forward_compatibility_horizon(*one_day_after): + self.assertTrue(compat.forward_compatible(*compatibility_date)) + self.assertFalse(compat.forward_compatible(*one_day_after)) + + # After exiting context manager, value should be reset. + self.assertFalse(compat.forward_compatible(*compatibility_date)) + + def test_decorator_with_failure(self): + compatibility_date = self._compatibility_date() + one_day_after = self._n_days_after(1) + + class DummyError(Exception): + pass + + try: + with compat.forward_compatibility_horizon(*one_day_after): + raise DummyError() + except DummyError: + pass # silence DummyError + + # After exiting context manager, value should be reset. + self.assertFalse(compat.forward_compatible(*compatibility_date)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/data/__init__.py b/tensorflow/python/data/__init__.py index 7efe0948e7729c398f972977b51426d80b8cd83e..3b9bf2469e6d41fd0e8c5199af677e60bedf93f9 100644 --- a/tensorflow/python/data/__init__.py +++ b/tensorflow/python/data/__init__.py @@ -14,7 +14,7 @@ # ============================================================================== """`tf.data.Dataset` API for input pipelines. -See the @{$datasets$Importing Data} Programmer's Guide for an overview. +See @{$guide/datasets$Importing Data} for an overview. """ from __future__ import absolute_import diff --git a/tensorflow/python/data/kernel_tests/BUILD b/tensorflow/python/data/kernel_tests/BUILD index c8fabc4363bb61604c2259185a684c7cb07f4594..3bde62fa1d8a71c0d6f2bbfbff29bb842a9248f0 100644 --- a/tensorflow/python/data/kernel_tests/BUILD +++ b/tensorflow/python/data/kernel_tests/BUILD @@ -15,6 +15,7 @@ tf_py_test( size = "small", srcs = ["batch_dataset_op_test.py"], additional_deps = [ + "@absl_py//absl/testing:parameterized", "//third_party/py/numpy", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", @@ -178,6 +179,7 @@ tf_py_test( size = "small", srcs = ["prefetch_dataset_op_test.py"], additional_deps = [ + "@absl_py//absl/testing:parameterized", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dataset_ops_gen", diff --git a/tensorflow/python/data/kernel_tests/batch_dataset_op_test.py b/tensorflow/python/data/kernel_tests/batch_dataset_op_test.py index bd80b9dbf561de16168b05facf0086dadcda6444..89de55dd4f9fdc612663c839b926684d27d48c54 100644 --- a/tensorflow/python/data/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/batch_dataset_op_test.py @@ -18,10 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import math +import time +from absl.testing import parameterized import numpy as np +from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -35,73 +37,83 @@ from tensorflow.python.platform import test from tensorflow.python.util import compat -class BatchDatasetTest(test.TestCase): +class BatchDatasetTest(test.TestCase, parameterized.TestCase): + + @parameterized.named_parameters( + ('even', 28, 14, False), + ('uneven_with_remainder', 28, 15, False), + ('uneven_without_remainder', 28, 15, True), + ('empty', 0, 14, False), + ) + def testBatchDataset(self, count, batch_size, drop_remainder): + """Tests the batch dataset logic for various input configurations. + + Args: + count: the number of input elements + batch_size: the batch size + drop_remainder: whether a smaller batch size should be produced if batch + size does not divide number of inputs evenly + """ - def testBatchDataset(self): - """Test an dataset that maps a TF function across its input elements.""" # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> # RepeatDataset(count) -> BatchDataset(batch_size). components = (np.arange(7), np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + count_t = array_ops.placeholder(dtypes.int64, shape=[]) + batch_size_t = array_ops.placeholder(dtypes.int64, shape=[]) + drop_remainder_t = array_ops.placeholder(dtypes.bool, shape=[]) def _map_fn(x, y, z): return math_ops.square(x), math_ops.square(y), math_ops.square(z) iterator = ( dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(count).batch(batch_size).make_initializable_iterator()) + .repeat(count).batch(batch_size, + drop_remainder).make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() - self.assertEqual([[None] + list(c.shape[1:]) for c in components], + if drop_remainder: + dim0 = batch_size + else: + dim0 = None + self.assertEqual([[dim0] + list(c.shape[1:]) for c in components], [t.shape.as_list() for t in get_next]) with self.test_session() as sess: - # Batch of a finite input, where the batch_size divides the - # total number of elements. - sess.run(init_op, feed_dict={count: 28, batch_size: 14}) - num_batches = (28 * 7) // 14 - for i in range(num_batches): + sess.run( + init_op, + feed_dict={ + count_t: count, + batch_size_t: batch_size, + drop_remainder_t: drop_remainder + }) + num_full_batches = (count * 7) // batch_size + for i in range(num_full_batches): result = sess.run(get_next) for component, result_component in zip(components, result): - for j in range(14): - self.assertAllEqual(component[(i * 14 + j) % 7]**2, + for j in range(batch_size): + self.assertAllEqual(component[(i * batch_size + j) % 7]**2, result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Batch of a finite input, where the batch_size does not - # divide the total number of elements. - sess.run(init_op, feed_dict={count: 14, batch_size: 8}) - - # We expect (num_batches - 1) full-sized batches. - num_batches = int(math.ceil((14 * 7) / 8)) - for i in range(num_batches - 1): + if not drop_remainder and (count * 7) % batch_size > 0: result = sess.run(get_next) for component, result_component in zip(components, result): - for j in range(8): - self.assertAllEqual(component[(i * 8 + j) % 7]**2, - result_component[j]) - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range((14 * 7) % 8): - self.assertAllEqual(component[((num_batches - 1) * 8 + j) % 7]**2, - result_component[j]) + for j in range((count * 7) % batch_size): + self.assertAllEqual( + component[(num_full_batches * batch_size + j) % 7]**2, + result_component[j]) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # Batch of an empty input should fail straight away. - sess.run(init_op, feed_dict={count: 0, batch_size: 8}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + def testBatchDatasetInvalidBatchSize(self): + iterator = (dataset_ops.Dataset.range(10).batch(0).make_one_shot_iterator()) + get_next = iterator.get_next() - # Empty batch should be an initialization time error. + with self.test_session() as sess: with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, batch_size: 0}) + sess.run(get_next) def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) @@ -210,66 +222,108 @@ class BatchDatasetTest(test.TestCase): r'First element had shape \[3\] and element 2 had shape \[4\].'): sess.run(next_element) - def testPaddedBatchDataset(self): - seq_lens = array_ops.placeholder(dtypes.int32, shape=[None]) - padded_shape = array_ops.placeholder(dtypes.int64, shape=[1]) + +def _random_seq_lens(count): + return np.random.randint(20, size=(count,)).astype(np.int32) + + +class PaddedBatchDatasetTest(test.TestCase, parameterized.TestCase): + + @parameterized.named_parameters( + ('default_padding', _random_seq_lens(32), 4, [-1], False), + ('constant_padding', _random_seq_lens(32), 4, [25], False), + ('uneven_with_remainder', _random_seq_lens(34), 4, [-1], False), + ('uneven_without_remainder', _random_seq_lens(34), 4, [-1], True), + ) + def testPaddedBatchDataset(self, seq_lens, batch_size, padded_shapes, + drop_remainder): + """Tests the padded batch dataset logic for various input configurations. + + Args: + seq_lens: the input sequence lengths + batch_size: the batch size + padded_shapes: the padded shapes to use + drop_remainder: whether a smaller batch size should be produced if batch + size does not divide number of inputs evenly + """ + + seq_lens_t = array_ops.placeholder(dtypes.int32, shape=[None]) + batch_size_t = array_ops.placeholder(dtypes.int64, shape=[]) + padded_shapes_t = array_ops.placeholder(dtypes.int64, shape=[1]) + drop_remainder_t = array_ops.placeholder(dtypes.bool, shape=[]) iterator = ( - dataset_ops.Dataset.from_tensor_slices(seq_lens) + dataset_ops.Dataset.from_tensor_slices(seq_lens_t) .map(lambda x: array_ops.fill([x], x)).padded_batch( - 4, padded_shapes=padded_shape).make_initializable_iterator()) + batch_size=batch_size_t, + drop_remainder=drop_remainder_t, + padded_shapes=padded_shapes_t).make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() with self.test_session() as sess: - # Test with random sequence lengths, and max padding. - random_seq_lens = np.random.randint(20, size=(32,)).astype(np.int32) sess.run( - init_op, feed_dict={ - padded_shape: [-1], - seq_lens: random_seq_lens + init_op, + feed_dict={ + seq_lens_t: seq_lens, + batch_size_t: batch_size, + padded_shapes_t: padded_shapes, + drop_remainder_t: drop_remainder, }) - for i in range(8): + + num_full_batches = len(seq_lens) // batch_size + + for i in range(num_full_batches): result = sess.run(get_next) - padded_len = np.max(result) - self.assertEqual((4, padded_len), result.shape) - for j in range(4): - seq_len = random_seq_lens[(i * 4) + j] + padded_len = padded_shapes[0] + if padded_len is None or padded_len == -1: + padded_len = np.max(result) if result.size > 0 else 0 + self.assertEqual((batch_size, padded_len), result.shape) + for j in range(batch_size): + seq_len = seq_lens[(i * batch_size) + j] self.assertAllEqual(result[j, :seq_len], [seq_len] * seq_len) - self.assertAllEqual(result[j, seq_len:], [0] * (padded_len - seq_len)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + self.assertAllEqual(result[j, seq_len:], + [0] * (padded_len - seq_len)) - # Test with random sequence lengths, and constant padding. - sess.run( - init_op, feed_dict={ - padded_shape: [25], - seq_lens: random_seq_lens - }) - for i in range(8): + if not drop_remainder and len(seq_lens) % batch_size > 0: result = sess.run(get_next) - self.assertEqual((4, 25), result.shape) - for j in range(4): - seq_len = random_seq_lens[(i * 4) + j] + padded_len = np.max(result) if result.size > 0 else 0 + self.assertEqual((len(seq_lens) % batch_size, padded_len), + result.shape) + for j in range(len(seq_lens) % batch_size): + seq_len = seq_lens[num_full_batches * batch_size + j] self.assertAllEqual(result[j, :seq_len], [seq_len] * seq_len) - self.assertAllEqual(result[j, seq_len:], [0] * (25 - seq_len)) + self.assertAllEqual(result[j, seq_len:], + [0] * (padded_len - seq_len)) + with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # Test correct handling of empty tensors. - sess.run(init_op, feed_dict={padded_shape: [-1], seq_lens: [0, 0, 0, 0]}) + def testPaddedBatchShortPadding(self): + iterator = ( + dataset_ops.Dataset.from_tensor_slices([6, 5, 5, 5, 5]) + .map(lambda x: array_ops.fill([x], x)).padded_batch( + batch_size=4, padded_shapes=[5]).make_one_shot_iterator()) + get_next = iterator.get_next() + + with self.test_session() as sess: + with self.assertRaises(errors.DataLossError): + sess.run(get_next) + + def testPaddedBatchEmptyTensors(self): + iterator = ( + dataset_ops.Dataset.from_tensor_slices([0, 0, 0, 0]) + .map(lambda x: array_ops.fill([x], x)).padded_batch( + batch_size=4, padded_shapes=[-1]).make_one_shot_iterator()) + get_next = iterator.get_next() + + with self.test_session() as sess: result = sess.run(get_next) self.assertAllEqual([[], [], [], []], result) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # Test error handling with constant sequence lengths, and - # too-short padding. - sess.run(init_op, feed_dict={padded_shape: [5], seq_lens: [6, 5, 5, 5]}) - with self.assertRaises(errors.DataLossError): - result = sess.run(get_next) - def testPaddedBatchDatasetNonDefaultPadding(self): seq_lens = array_ops.placeholder(dtypes.int32, shape=[None]) padded_shape = array_ops.placeholder(dtypes.int64, shape=[1]) @@ -371,6 +425,94 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(TypeError): _ = dataset_ops.Dataset.range(10).map(_map_fn).padded_batch(10) + def testPaddedBatchShapeError(self): + with self.assertRaisesRegexp( + ValueError, r'The padded shape \(1,\) is not compatible with the ' + r'corresponding input component shape \(\).'): + _ = dataset_ops.Dataset.range(10).padded_batch(5, padded_shapes=[1]) + + with self.assertRaisesRegexp( + ValueError, r'The padded shape \(1,\) is not compatible with the ' + r'corresponding input component shape \(3,\).'): + _ = dataset_ops.Dataset.from_tensors([1, 2, 3]).padded_batch( + 5, padded_shapes=[1]) + + with self.assertRaisesRegexp( + ValueError, r'Padded shape .* must be a 1-D tensor ' + r'of tf.int64 values, but its shape was \(2, 2\).'): + _ = dataset_ops.Dataset.from_tensors([1, 2, 3]).padded_batch( + 5, padded_shapes=[[1, 1], [1, 1]]) + + with self.assertRaisesRegexp( + TypeError, r'Padded shape .* must be a 1-D tensor ' + r'of tf.int64 values, but its element type was float32.'): + _ = dataset_ops.Dataset.from_tensors([1, 2, 3]).padded_batch( + 5, padded_shapes=constant_op.constant([1., 2., 3.])) + + with self.assertRaisesRegexp( + ValueError, r'The padded shape \(1,\) is not compatible with the ' + r'corresponding input component shape \(\).'): + shape_as_tensor = constant_op.constant([1], dtype=dtypes.int64) + _ = dataset_ops.Dataset.range(10).padded_batch( + 5, padded_shapes=shape_as_tensor) + + with self.assertRaisesRegexp( + ValueError, r'The padded shape \(\?, \?\) is not compatible with the ' + r'corresponding input component shape \(\).'): + shape_as_tensor = array_ops.placeholder(dtypes.int64, shape=[2]) + _ = dataset_ops.Dataset.range(10).padded_batch( + 5, padded_shapes=shape_as_tensor) + + +class BatchDatasetBenchmark(test.Benchmark): + + def benchmarkBatchSparse(self): + non_zeros_per_row_values = [0, 1, 5, 10, 100] + batch_size_values = [1, 32, 64, 128, 1024] + + sparse_placeholder = array_ops.sparse_placeholder(dtype=dtypes.int64) + batch_size_placeholder = array_ops.placeholder(dtype=dtypes.int64, shape=[]) + + dataset = dataset_ops.Dataset.from_tensors(sparse_placeholder).repeat( + ).batch(batch_size_placeholder) + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() + + for non_zeros_per_row in non_zeros_per_row_values: + + sparse_value = sparse_tensor.SparseTensorValue( + indices=np.arange(non_zeros_per_row, dtype=np.int64)[:, np.newaxis], + values=np.arange(non_zeros_per_row, dtype=np.int64), + dense_shape=[1000]) + + for batch_size in batch_size_values: + + with session.Session() as sess: + sess.run(iterator.initializer, feed_dict={ + sparse_placeholder: sparse_value, + batch_size_placeholder: batch_size}) + # Run five steps to warm up the session caches before taking the + # first measurement. + for _ in range(5): + sess.run(next_element.indices.op) + deltas = [] + for _ in range(100): + start = time.time() + for _ in range(100): + sess.run(next_element.indices.op) + end = time.time() + deltas.append(end - start) + + median_wall_time = np.median(deltas) / 100.0 + + print('Batch sparse dataset non-zeros per row: %d batch_size: %d ' + 'wall time: %f' + % (non_zeros_per_row, batch_size, median_wall_time)) + self.report_benchmark( + iters=10000, wall_time=median_wall_time, + name='benchmark_batch_sparse_dataset_nnz_%d_batch_size_%d' % ( + non_zeros_per_row, batch_size)) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py index 296a76ec887ae7c31cb9d0bd2afd6d1fe827d95c..fb55ae140058349753731b0c257acb3cf3def0a3 100644 --- a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py +++ b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py @@ -259,9 +259,7 @@ class DatasetConstructorTest(test.TestCase): sess.run(init_op) self.assertAllEqual([1, 2, 3], sess.run(get_next)) self.assertAllEqual([4, 5, 6], sess.run(get_next)) - # NOTE(mrry): Type name in message differs between Python 2 (`long`) and - # 3 (`int`). - with self.assertRaisesOpError(r"invalid literal for"): + with self.assertRaisesOpError("The expected type was int64"): sess.run(get_next) self.assertAllEqual([7, 8, 9], sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): @@ -290,6 +288,34 @@ class DatasetConstructorTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testFromGeneratorStructureError(self): + def generator(): + yield 1, 2 + yield 3, 4 + yield 5 + yield 6, 7, 8 + yield 9, 10 + + iterator = (dataset_ops.Dataset.from_generator( + generator, output_types=(dtypes.int64, dtypes.int64)) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + self.assertEqual((1, 2), sess.run(get_next)) + self.assertEqual((3, 4), sess.run(get_next)) + with self.assertRaisesOpError( + r"The expected structure was \(tf\.int64, tf\.int64\)"): + sess.run(get_next) + with self.assertRaisesOpError( + r"The expected structure was \(tf\.int64, tf\.int64\)"): + sess.run(get_next) + self.assertEqual((9, 10), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + def testFromGeneratorHeterogeneous(self): def generator(): yield 1 diff --git a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py index 1ad0b9de5e76e3edd66303ab4666108f43a27428..0ecd821e9e473522b0cf4bd7bbceb071ecf5bb9e 100644 --- a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function from collections import namedtuple import threading import time +import warnings import numpy as np @@ -638,6 +639,33 @@ class MapDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testWarnOnLookupTable(self): + def collecting_function(x): + _ = lookup_ops.HashTable( + lookup_ops.KeyValueTensorInitializer([], []), 0.0, name="t1") + return x + + warnings.simplefilter("always") + with warnings.catch_warnings(record=True) as w: + _ = dataset_ops.Dataset.range(10).map(collecting_function) + # NOTE(mrry): Python 3 prints other warnings in addition to the one we are + # testing, so we search for the expected warning. + self.assertGreaterEqual(len(w), 1) + found_warning = False + for warning in w: + if ("Creating lookup tables inside a function passed to Dataset.map() is " + "not supported." in str(warning)): + found_warning = True + break + self.assertTrue(found_warning) + + def testNestedDatasetError(self): + dataset = dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0]) + with self.assertRaisesRegexp( + NotImplementedError, r"The Dataset.map\(\) transformation does not " + "currently support nested datasets as outputs."): + _ = dataset.map(dataset_ops.Dataset.from_tensor_slices) + class MapDatasetBenchmark(test.Benchmark): diff --git a/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py b/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py index 646324cb95df6fc1fa0a901ebdccc8d4ef74a66c..63a0830272dca254866c1609fec3677ab28749d5 100644 --- a/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py @@ -17,6 +17,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized + from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -24,35 +26,33 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class PrefetchDatasetTest(test.TestCase): +class PrefetchDatasetTest(test.TestCase, parameterized.TestCase): - def testBufferSize(self): - buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) + @parameterized.parameters((-1), (0), (5)) + def testBufferSize(self, buffer_size): + buffer_size_t = array_ops.placeholder(dtypes.int64, shape=[]) iterator = dataset_ops.Dataset.range(10).prefetch( - buffer_size=buffer_size).make_initializable_iterator() + buffer_size=buffer_size_t).make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: 5}) + sess.run(init_op, feed_dict={buffer_size_t: buffer_size}) for m in range(10): self.assertEqual(m, sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - def testInvalidBufferSize(self): - buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) + @parameterized.parameters((-2), (-42)) + def testInvalidBufferSize(self, buffer_size): + buffer_size_t = array_ops.placeholder(dtypes.int64, shape=[]) iterator = dataset_ops.Dataset.range(10).prefetch( - buffer_size=buffer_size).make_initializable_iterator() + buffer_size=buffer_size_t).make_initializable_iterator() init_op = iterator.initializer with self.assertRaisesRegexp(errors.InvalidArgumentError, "buffer_size"): with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: 0}) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, "buffer_size"): - with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: -5}) + sess.run(init_op, feed_dict={buffer_size_t: buffer_size}) if __name__ == "__main__": diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 5f17444797d00b86ecc60b42f9bb70306f2c1302..89265d95752629e25f37e86efeb5aa699f951c7a 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -19,6 +19,7 @@ from __future__ import print_function import abc import threading +import warnings import numpy as np import six @@ -32,6 +33,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util @@ -106,12 +108,7 @@ class Dataset(object): if shared_name is None: shared_name = "" iterator_resource = gen_dataset_ops.iterator( - container="", - shared_name=shared_name, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + container="", shared_name=shared_name, **flat_structure(self)) with ops.colocate_with(iterator_resource): initializer = gen_dataset_ops.make_iterator(self._as_variant_tensor(), iterator_resource) @@ -169,13 +166,8 @@ class Dataset(object): return iterator_ops.Iterator( gen_dataset_ops.one_shot_iterator( - dataset_factory=_make_dataset, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, - self.output_classes))), None, - self.output_types, self.output_shapes, self.output_classes) + dataset_factory=_make_dataset, **flat_structure(self)), + None, self.output_types, self.output_shapes, self.output_classes) @abc.abstractproperty def output_classes(self): @@ -221,6 +213,13 @@ class Dataset(object): def from_tensors(tensors): """Creates a `Dataset` with a single element, comprising the given tensors. + Note that if `tensors` contains a NumPy array, and eager execution is not + enabled, the values will be embedded in the graph as one or more + @{tf.constant} operations. For large datasets (> 1 GB), this can waste + memory and run into byte limits of graph serialization. If tensors contains + one or more large NumPy arrays, consider the alternative described in + @{$guide/datasets#consuming_numpy_arrays$this guide}. + Args: tensors: A nested structure of tensors. @@ -233,6 +232,13 @@ class Dataset(object): def from_tensor_slices(tensors): """Creates a `Dataset` whose elements are slices of the given tensors. + Note that if `tensors` contains a NumPy array, and eager execution is not + enabled, the values will be embedded in the graph as one or more + @{tf.constant} operations. For large datasets (> 1 GB), this can waste + memory and run into byte limits of graph serialization. If tensors contains + one or more large NumPy arrays, consider the alternative described in + @{$guide/datasets#consuming_numpy_arrays$this guide}. + Args: tensors: A nested structure of tensors, each having the same size in the 0th dimension. @@ -407,13 +413,23 @@ class Dataset(object): # Use the same _convert function from the py_func() implementation to # convert the returned values to arrays early, so that we can inspect # their values. - # pylint: disable=protected-access - ret_arrays = [ - script_ops.FuncRegistry._convert(ret, dtype=dtype.as_numpy_dtype) - for ret, dtype in zip( - nest.flatten_up_to(output_types, values), flattened_types) - ] - # pylint: enable=protected-access + try: + flattened_values = nest.flatten_up_to(output_types, values) + except (TypeError, ValueError): + raise TypeError( + "`generator` yielded an element that did not match the expected " + "structure. The expected structure was %s, but the yielded " + "element was %s." % (output_types, values)) + ret_arrays = [] + for ret, dtype in zip(flattened_values, flattened_types): + try: + ret_arrays.append(script_ops.FuncRegistry._convert( # pylint: disable=protected-access + ret, dtype=dtype.as_numpy_dtype)) + except (TypeError, ValueError): + raise TypeError( + "`generator` yielded an element that could not be converted to " + "the expected type. The expected type was %s, but the yielded " + "element was %s." % (dtype.name, ret)) # Additional type and shape checking to ensure that the components # of the generated element match the `output_types` and `output_shapes` @@ -790,35 +806,50 @@ class Dataset(object): return self._enumerate().filter(filter_fn).map(lambda _, elem: elem) - def batch(self, batch_size): + def batch(self, batch_size, drop_remainder=False): """Combines consecutive elements of this dataset into batches. - NOTE: If the number of elements (`N`) in this dataset is not an exact - multiple of `batch_size`, the final batch contain smaller tensors with - shape `N % batch_size` in the batch dimension. If your program depends on - the batches having the same shape, consider using the - @{tf.contrib.data.batch_and_drop_remainder} transformation instead. + The tensors in the resulting element will have an additional outer + dimension, which will be `batch_size` (or `N % batch_size` for the last + element if `batch_size` does not divide the number of input elements `N` + evenly and `drop_remainder` is `False`). If your program depends on the + batches having the same outer dimension, you should set the `drop_remainder` + argument to `True` to prevent the smaller batch from being produced. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. + drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing + whether the last batch should be dropped in the case its has fewer than + `batch_size` elements; the default behavior is not to drop the smaller + batch. Returns: Dataset: A `Dataset`. """ - return BatchDataset(self, batch_size) + return BatchDataset(self, batch_size, drop_remainder) - def padded_batch(self, batch_size, padded_shapes, padding_values=None): + def padded_batch(self, + batch_size, + padded_shapes, + padding_values=None, + drop_remainder=False): """Combines consecutive elements of this dataset into padded batches. This transformation combines multiple consecutive elements of the input - dataset into a single element. Like @{tf.data.Dataset.batch}, the tensors - in the resulting element have an additional outer dimension, which will be - `batch_size` for all but the last element, and `N % batch_size` for the - last element (where `N` is the number of elements in this dataset). Unlike - @{tf.data.Dataset.batch}, the elements may have different shapes for some - of their components, and this transformation will pad each component to - the respective shape in `padding_shapes`. The `padding_shapes` argument + dataset into a single element. + + Like @{tf.data.Dataset.batch}, the tensors in the resulting element will + have an additional outer dimension, which will be `batch_size` (or + `N % batch_size` for the last element if `batch_size` does not divide the + number of input elements `N` evenly and `drop_remainder` is `False`). If + your program depends on the batches having the same outer dimension, you + should set the `drop_remainder` argument to `True` to prevent the smaller + batch from being produced. + + Unlike @{tf.data.Dataset.batch}, the input elements to be batched may have + different shapes, and this transformation will pad each component to the + respective shape in `padding_shapes`. The `padding_shapes` argument determines the resulting shape for each dimension of each component in an output element: @@ -828,12 +859,6 @@ class Dataset(object): will be padded out to the maximum length of all elements in that dimension. - NOTE: If the number of elements (`N`) in this dataset is not an exact - multiple of `batch_size`, the final batch contain smaller tensors with - shape `N % batch_size` in the batch dimension. If your program depends on - the batches having the same shape, consider using the - @{tf.contrib.data.padded_batch_and_drop_remainder} transformation instead. - See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements that may have different shapes into a @{tf.SparseTensor}. @@ -851,14 +876,95 @@ class Dataset(object): `tf.Tensor`, representing the padding values to use for the respective components. Defaults are `0` for numeric types and the empty string for string types. + drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing + whether the last batch should be dropped in the case its has fewer than + `batch_size` elements; the default behavior is not to drop the smaller + batch. Returns: Dataset: A `Dataset`. """ - return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values) + return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values, + drop_remainder) def map(self, map_func, num_parallel_calls=None): - """Maps `map_func` across this dataset. + """Maps `map_func` across the elements of this dataset. + + This transformation applies `map_func` to each element of this dataset, and + returns a new dataset containing the transformed elements, in the same + order as they appeared in the input. + + For example: + + ```python + # NOTE: The following examples use `{ ... }` to represent the + # contents of a dataset. + a = { 1, 2, 3, 4, 5 } + + a.map(lambda x: x + 1) = { 2, 3, 4, 5, 6 } + ``` + + The input signature of `map_func` is determined by the structure of each + element in this dataset. For example: + + ```python + # Each element is a `tf.Tensor` object. + a = { 1, 2, 3, 4, 5 } + # `map_func` takes a single argument of type `tf.Tensor` with the same + # shape and dtype. + result = a.map(lambda x: ...) + + # Each element is a tuple containing two `tf.Tensor` objects. + b = { (1, "foo"), (2, "bar"), (3, "baz") } + # `map_func` takes two arguments of type `tf.Tensor`. + result = b.map(lambda x_int, y_str: ...) + + # Each element is a dictionary mapping strings to `tf.Tensor` objects. + c = { {"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}, {"a": 3, "b": "baz"} } + # `map_func` takes a single argument of type `dict` with the same keys as + # the elements. + result = c.map(lambda d: ...) + ``` + + The value or values returned by `map_func` determine the structure of each + element in the returned dataset. + + ```python + # `map_func` returns a scalar `tf.Tensor` of type `tf.float32`. + def f(...): + return tf.constant(37.0) + result = dataset.map(f) + result.output_classes == tf.Tensor + result.output_types == tf.float32 + result.output_shapes == [] # scalar + + # `map_func` returns two `tf.Tensor` objects. + def g(...): + return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"]) + result = dataset.map(g) + result.output_classes == (tf.Tensor, tf.Tensor) + result.output_types == (tf.float32, tf.string) + result.output_shapes == ([], [3]) + + # Python primitives, lists, and NumPy arrays are implicitly converted to + # `tf.Tensor`. + def h(...): + return 37.0, ["Foo", "Bar", "Baz"], np.array([1.0, 2.0] dtype=np.float64) + result = dataset.map(h) + result.output_classes == (tf.Tensor, tf.Tensor, tf.Tensor) + result.output_types == (tf.float32, tf.string, tf.float64) + result.output_shapes == ([], [3], [2]) + + # `map_func` can return nested structures. + def i(...): + return {"a": 37.0, "b": [42, 16]}, "foo" + result.output_classes == ({"a": tf.Tensor, "b": tf.Tensor}, tf.Tensor) + result.output_types == ({"a": tf.float32, "b": tf.int32}, tf.string) + result.output_shapes == ({"a": [], "b": [2]}, []) + ``` + + In addition to `tf.Tensor` objects, `map_func` can accept as arguments and + return `tf.SparseTensor` objects. Args: map_func: A function mapping a nested structure of tensors (having @@ -1119,6 +1225,271 @@ class SparseTensorSliceDataset(Dataset): return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64) +class _NestedDatasetComponent(object): + """The structure of a `Dataset` nested in a component of another `Dataset`. + + A `StructuredFunctionWrapper` around a function that returns a `Dataset` as + one of its components will have a `NestedDatasetComponent` in the + corresponding position in the `output_classes`, `output_shapes`, and + `output_types` properties. + + NOTE(mrry): This class is not currently exposed via the public API. Support + for nested datasets can be enabled on a function-by-function basis by setting + `experimental_nested_dataset_support=True` in the `StructuredFunctionWrapper` + initializer. + + TODO(b/110122868): Add this class, or something equivalent, to the public API. + We are considering revising the public API for accessing Dataset structure + (`output_classes` etc.) based on experience with nested datasets and other + custom component types. + """ + + def __init__(self, dataset): + self._output_classes = dataset.output_classes + self._output_shapes = dataset.output_shapes + self._output_types = dataset.output_types + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + +class _VariantDataset(Dataset): + """A Dataset wrapper around a @{tf.variant}-typed function argument.""" + + def __init__(self, dataset_variant, structure): + super(_VariantDataset, self).__init__() + self._dataset_variant = dataset_variant + self._structure = structure + + def _as_variant_tensor(self): + return self._dataset_variant + + @property + def output_classes(self): + return self._structure.output_classes + + @property + def output_shapes(self): + return self._structure.output_shapes + + @property + def output_types(self): + return self._structure.output_types + + +class StructuredFunctionWrapper(object): + """A wrapper for `Defun` that supports structured arguments and return values. + """ + + def __init__(self, func, transformation_name, dataset=None, + input_classes=None, input_shapes=None, input_types=None, + add_to_graph=True, experimental_nested_dataset_support=False): + """Creates a new `StructuredFunctionWrapper` for the given function. + + Args: + func: A function from a nested structure to another nested structure. + transformation_name: Human-readable name of the transformation in which + this function is being instantiated, for error messages. + dataset: (Optional.) A @{tf.data.Dataset}. If given, the structure of this + dataset will be assumed as the structure for `func` arguments; otherwise + `input_classes`, `input_shapes`, and `input_types` must be defined. + input_classes: (Optional.) A nested structure of `type`. If given, this + argument defines the Python types for `func` arguments. + input_shapes: (Optional.) A nested structure of @{tf.TensorShape}. If + given, this argument defines the shapes and structure for `func` + arguments. + input_types: (Optional.) A nested structure of @{tf.DType}. If given, this + argument defines the element types and structure for `func` arguments. + add_to_graph: (Optional.) If `True`, the function will be added to the + default graph. + experimental_nested_dataset_support: (Optional.) If `True`, the function + will support @{tf.data.Dataset} objects as arguments and return values. + + Raises: + ValueError: If an invalid combination of `dataset`, `input_classes`, + `input_shapes`, and `input_types` is passed. + """ + if dataset is None: + if input_classes is None or input_shapes is None or input_types is None: + raise ValueError("Either `dataset`, or all of `input_classes`, " + "`input_shapes`, and `input_types` must be specified.") + self._input_shapes = input_shapes + self._input_types = input_types + self._input_classes = input_classes + else: + if not (input_classes is None and input_shapes is None and + input_types is None): + raise ValueError("Either `dataset`, or all of `input_classes`, " + "`input_shapes`, and `input_types` must be specified.") + self._input_shapes = dataset.output_shapes + self._input_types = dataset.output_types + self._input_classes = dataset.output_classes + + self._transformation_name = transformation_name + + # TODO(b/110122868): Enable this support for all `tf.data` functions. + self._nested_dataset_support = experimental_nested_dataset_support + + @function.Defun(*self._defun_args()) + def tf_data_structured_function_wrapper(*args): + """Wrapper for passing nested structures to and from tf.data functions.""" + flat_args = [] + for arg, arg_class, arg_shape, arg_type in zip( + args, + nest.flatten(self._input_classes), + nest.flatten(self._input_shapes), + nest.flatten(self._input_types)): + # TODO(b/110122868): Add a registration mechanism for new component + # types. + if arg_class is sparse_tensor_lib.SparseTensor: + arg = sparse.deserialize_sparse_tensors( + arg, arg_type, arg_shape, arg_class) + arg.indices.set_shape([None, arg_shape.ndims]) + arg.dense_shape.set_shape([arg_shape.ndims]) + elif isinstance(arg_class, _NestedDatasetComponent): + assert self._nested_dataset_support + arg = _VariantDataset(arg, arg_class) + else: + arg.set_shape(arg_shape) + flat_args.append(arg) + nested_args = nest.pack_sequence_as(self._input_classes, flat_args) + if not _should_unpack_args(nested_args): + nested_args = (nested_args,) + + ret = func(*nested_args) + # If `func` returns a list of tensors, `nest.flatten()` and + # `ops.convert_to_tensor()` would conspire to attempt to stack + # those tensors into a single tensor, because the customized + # version of `nest.flatten()` does not recurse into lists. Since + # it is more likely that the list arose from returning the + # result of an operation (such as `tf.py_func()`) that returns a + # list of not-necessarily-stackable tensors, we treat the + # returned value is a `tuple` instead. A user wishing to pack + # the return value into a single tensor can use an explicit + # `tf.stack()` before returning. + if isinstance(ret, list): + ret = tuple(ret) + + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + flat_ret = [] + flat_classes = [] + flat_shapes = [] + flat_types = [] + for t in nest.flatten(ret): + # TODO(b/110122868): Add a registration mechanism for new component + # types. + if sparse_tensor_lib.is_sparse(t): + t = sparse_tensor_lib.SparseTensor.from_value(t) + flat_ret.append(sparse.serialize_sparse_tensors(t)) + flat_classes.append(sparse_tensor_lib.SparseTensor) + flat_shapes.append(t.get_shape()) + flat_types.append(t.dtype) + elif isinstance(t, Dataset): + if not self._nested_dataset_support: + raise NotImplementedError( + "The %s transformation does not currently support nested " + "datasets as outputs." % self._transformation_name) + + flat_ret.append(t._as_variant_tensor()) # pylint: disable=protected-access + component = _NestedDatasetComponent(t) + flat_classes.append(component) + flat_shapes.append(component) + flat_types.append(component) + else: + t = ops.convert_to_tensor(t) + flat_ret.append(t) + flat_classes.append(ops.Tensor) + flat_shapes.append(t.get_shape()) + flat_types.append(t.dtype) + + ret = nest.pack_sequence_as(ret, flat_ret) + self._output_classes = nest.pack_sequence_as(ret, flat_classes) + self._output_shapes = nest.pack_sequence_as(ret, flat_shapes) + self._output_types = nest.pack_sequence_as(ret, flat_types) + + _warn_if_collections(transformation_name) + + return flat_ret + + self._function = tf_data_structured_function_wrapper + if add_to_graph: + self._function.add_to_graph(ops.get_default_graph()) + else: + # Use the private method that will execute + # `tf_data_structured_function_wrapper` but delay adding it to the graph + # in case (e.g.) we need to rerun the function. + self._function._create_definition_if_needed() # pylint: disable=protected-access + + def _defun_args(self): + """Returns a flat list of @{tf.DType} for the input element structure.""" + ret = [] + for input_type, input_class in zip(nest.flatten(self._input_types), + nest.flatten(self._input_classes)): + # TODO(b/110122868): Add a registration mechanism for new component types. + if input_class is sparse_tensor_lib.SparseTensor: + ret.append(dtypes.variant) + elif isinstance(input_class, _NestedDatasetComponent): + if not self._nested_dataset_support: + raise NotImplementedError( + "The %s transformation does not currently support nested " + "datasets as inputs." % self._transformation_name) + ret.append(dtypes.variant) + else: + assert isinstance(input_type, dtypes.DType) + ret.append(input_type) + return ret + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + @property + def function(self): + return self._function + + +def flat_structure(dataset): + """Helper for setting `output_shapes` and `output_types` attrs of Dataset ops. + + Most Dataset op constructors expect `output_shapes` and `output_types` + arguments that represent the flattened structure of an element. This helper + function generates these attrs as a keyword argument dictionary, allowing + `Dataset._as_variant_tensor()` implementations to pass + `**flat_structure(self)` to the op constructor. + + Args: + dataset: A @{tf.data.Dataset}. + + Returns: + A dictionary of keyword arguments that can be passed to many Dataset op + constructors. + """ + return { + "output_shapes": nest.flatten(sparse.as_dense_shapes( + dataset.output_shapes, dataset.output_classes)), + "output_types": nest.flatten(sparse.as_dense_types( + dataset.output_types, dataset.output_classes)), + } + + class _GeneratorDataset(Dataset): """A `Dataset` that generates elements by invoking a function.""" @@ -1151,137 +1522,26 @@ class _GeneratorDataset(Dataset): init_args_types = nest.pack_sequence_as( init_args, [t.dtype for t in nest.flatten(init_args)]) - @function.Defun(*nest.flatten( - sparse.as_dense_types(init_args_types, init_args_classes))) - def tf_init_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - dense_shapes = sparse.as_dense_shapes(init_args_shapes, init_args_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(init_args_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, init_args_types, init_args_shapes, init_args_classes) - if _should_unpack_args(nested_args): - ret = init_func(*nested_args) - else: - ret = init_func(nested_args) - - # If `init_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._state_classes = sparse.get_classes(ret) - self._state_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._state_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._init_func = tf_init_func - self._init_func.add_to_graph(ops.get_default_graph()) - - # These members will be initialized by `tf_next_func`. - self._output_classes = None - self._output_shapes = None - self._output_types = None - - @function.Defun(*nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes))) - def tf_next_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(self._state_shapes, - self._state_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - if _should_unpack_args(nested_args): - ret = next_func(*nested_args) - else: - ret = next_func(nested_args) - - # If `next_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._next_func = tf_next_func - self._next_func.add_to_graph(ops.get_default_graph()) - - @function.Defun(*nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes))) - def tf_finalize_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the state. - dense_shapes = sparse.as_dense_shapes(self._state_shapes, - self._state_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - if _should_unpack_args(nested_args): - return finalize_func(*nested_args) - else: - return finalize_func(nested_args) - - self._finalize_func = tf_finalize_func - self._finalize_func.add_to_graph(ops.get_default_graph()) + wrapped_init_func = StructuredFunctionWrapper( + init_func, "GeneratorDataset", input_classes=init_args_classes, + input_shapes=init_args_shapes, input_types=init_args_types) + self._state_classes = wrapped_init_func.output_classes + self._state_shapes = wrapped_init_func.output_shapes + self._state_types = wrapped_init_func.output_types + self._init_func = wrapped_init_func.function + + wrapped_next_func = StructuredFunctionWrapper( + next_func, "GeneratorDataset", input_classes=self._state_classes, + input_shapes=self._state_shapes, input_types=self._state_types) + self._output_classes = wrapped_next_func.output_classes + self._output_shapes = wrapped_next_func.output_shapes + self._output_types = wrapped_next_func.output_types + self._next_func = wrapped_next_func.function + + wrapped_finalize_func = StructuredFunctionWrapper( + finalize_func, "GeneratorDataset", input_classes=self._state_classes, + input_shapes=self._state_shapes, input_types=self._state_types) + self._finalize_func = wrapped_finalize_func.function def _as_variant_tensor(self): return gen_dataset_ops.generator_dataset( @@ -1291,10 +1551,7 @@ class _GeneratorDataset(Dataset): init_func=self._init_func, next_func=self._next_func, finalize_func=self._finalize_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1331,16 +1588,7 @@ class ZipDataset(Dataset): # pylint: disable=protected-access return gen_dataset_ops.zip_dataset( [ds._as_variant_tensor() for ds in nest.flatten(self._datasets)], - output_shapes=[ - s - for ds in nest.flatten(self._datasets) - for s in nest.flatten(ds.output_shapes) - ], - output_types=[ - t - for ds in nest.flatten(self._datasets) - for t in nest.flatten(ds.output_types) - ]) + **flat_structure(self)) # pylint: enable=protected-access @property @@ -1385,10 +1633,7 @@ class ConcatenateDataset(Dataset): return gen_dataset_ops.concatenate_dataset( self._input_dataset._as_variant_tensor(), self._dataset_to_concatenate._as_variant_tensor(), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) # pylint: enable=protected-access @property @@ -1426,10 +1671,7 @@ class RepeatDataset(Dataset): return gen_dataset_ops.repeat_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1453,6 +1695,7 @@ class RangeDataset(Dataset): self._parse_args(*args) def _parse_args(self, *args): + """Parse arguments according to the same rules as the `range()` builtin.""" if len(args) == 1: self._start = self._build_tensor(0, "start") self._stop = self._build_tensor(args[0], "stop") @@ -1476,10 +1719,7 @@ class RangeDataset(Dataset): start=self._start, stop=self._stop, step=self._step, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1508,10 +1748,7 @@ class CacheDataset(Dataset): return gen_dataset_ops.cache_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access filename=self._filename, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1571,10 +1808,7 @@ class ShuffleDataset(Dataset): seed=self._seed, seed2=self._seed2, reshuffle_each_iteration=self._reshuffle_each_iteration, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1602,10 +1836,7 @@ class TakeDataset(Dataset): return gen_dataset_ops.take_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1633,10 +1864,7 @@ class SkipDataset(Dataset): return gen_dataset_ops.skip_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1654,21 +1882,28 @@ class SkipDataset(Dataset): class BatchDataset(Dataset): """A `Dataset` that batches contiguous elements from its input.""" - def __init__(self, input_dataset, batch_size): + def __init__(self, input_dataset, batch_size, drop_remainder): """See `Dataset.batch()` for details.""" super(BatchDataset, self).__init__() self._input_dataset = input_dataset self._batch_size = ops.convert_to_tensor( batch_size, dtype=dtypes.int64, name="batch_size") + self._drop_remainder = ops.convert_to_tensor( + drop_remainder, dtype=dtypes.bool, name="drop_remainder") def _as_variant_tensor(self): - return gen_dataset_ops.batch_dataset( - self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - batch_size=self._batch_size, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + # TODO(jsimsa): Switch to using v2 only any time after 6/30/2018. + if smart_cond.smart_constant_value(self._drop_remainder) is False: + return gen_dataset_ops.batch_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + batch_size=self._batch_size, + **flat_structure(self)) + else: + return gen_dataset_ops.batch_dataset_v2( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + batch_size=self._batch_size, + drop_remainder=self._drop_remainder, + **flat_structure(self)) @property def output_classes(self): @@ -1678,7 +1913,9 @@ class BatchDataset(Dataset): def output_shapes(self): input_shapes = self._input_dataset.output_shapes return nest.pack_sequence_as(input_shapes, [ - tensor_shape.vector(None).concatenate(s) + tensor_shape.vector( + tensor_util.constant_value(self._batch_size) if smart_cond. + smart_constant_value(self._drop_remainder) else None).concatenate(s) for s in nest.flatten(self._input_dataset.output_shapes) ]) @@ -1687,20 +1924,77 @@ class BatchDataset(Dataset): return self._input_dataset.output_types -def _partial_shape_to_tensor(shape_like): +def _is_padded_shape_compatible_with(padded_shape, input_component_shape): + """Returns `True` if `input_component_shape` can be padded to `padded_shape`. + + Args: + padded_shape: A `tf.TensorShape`. + input_component_shape: A `tf.TensorShape`. + + Returns: + `True` if `input_component_shape` can be padded to `padded_shape`, otherwise + `False`. + """ + + if padded_shape.dims is None or input_component_shape.dims is None: + return True + if len(padded_shape.dims) != len(input_component_shape.dims): + return False + for padded_dim, input_dim in zip( + padded_shape.dims, input_component_shape.dims): + if (padded_dim.value is not None and input_dim.value is not None + and padded_dim.value < input_dim.value): + return False + return True + + +def _padded_shape_to_tensor(padded_shape, input_component_shape): + """Converts `padded_shape` to a `tf.Tensor` representing that shape. + + Args: + padded_shape: A shape-like object, which may be a `tf.TensorShape`, a Python + sequence, or a 1-D `tf.Tensor` of `tf.int64` elements. + input_component_shape: A `tf.TensorShape`, with which `padded_shape` must + be compatible. + + Returns: + A 1-D `tf.Tensor` of `tf.int64` elements, representing `padded_shape`. + + Raises: + ValueError: If `padded_shape` is not a shape or not compatible with + `input_component_shape`. + TypeError: If `padded_shape` is not convertible to a `tf.int64` tensor. + """ try: - # First attempt to convert the input to a shape, and return the - # "canonical" tensor representation, which uses `-1` in place of - # `None`. - shape_like = tensor_shape.as_shape(shape_like) - return ops.convert_to_tensor( - [dim if dim is not None else -1 for dim in shape_like.as_list()], - dtype=dtypes.int64) + # Try to convert the `padded_shape` to a `tf.TensorShape` + padded_shape_as_shape = tensor_shape.as_shape(padded_shape) + # We will return the "canonical" tensor representation, which uses + # `-1` in place of `None`. + ret = ops.convert_to_tensor( + [dim if dim is not None else -1 + for dim in padded_shape_as_shape.as_list()], dtype=dtypes.int64) except (TypeError, ValueError): # The argument was not trivially convertible to a # `tf.TensorShape`, so fall back on the conversion to tensor # machinery. - return ops.convert_to_tensor(shape_like, dtype=dtypes.int64) + ret = ops.convert_to_tensor(padded_shape, preferred_dtype=dtypes.int64) + if ret.shape.dims is not None and len(ret.shape.dims) != 1: + raise ValueError( + "Padded shape %s must be a 1-D tensor of tf.int64 values, but its " + "shape was %s." % (padded_shape, ret.shape)) + if ret.dtype != dtypes.int64: + raise TypeError( + "Padded shape %s must be a 1-D tensor of tf.int64 values, but its " + "element type was %s." % (padded_shape, ret.dtype.name)) + padded_shape_as_shape = tensor_util.constant_value_as_shape(ret) + + if not _is_padded_shape_compatible_with(padded_shape_as_shape, + input_component_shape): + raise ValueError("The padded shape %s is not compatible with the " + "corresponding input component shape %s." + % (padded_shape_as_shape, input_component_shape)) + + return ret def _padding_value_to_tensor(value, output_type): @@ -1727,7 +2021,7 @@ def _padding_value_to_tensor(value, output_type): def _default_padding(input_dataset): - + """Returns default padding tensors in a structure matching `input_dataset`.""" def make_zero(t): if t.base_dtype == dtypes.string: return "" @@ -1742,7 +2036,8 @@ def _default_padding(input_dataset): class PaddedBatchDataset(Dataset): """A `Dataset` that batches and pads contiguous elements from its input.""" - def __init__(self, input_dataset, batch_size, padded_shapes, padding_values): + def __init__(self, input_dataset, batch_size, padded_shapes, padding_values, + drop_remainder): """See `Dataset.batch()` for details.""" super(PaddedBatchDataset, self).__init__() if sparse.any_sparse(input_dataset.output_classes): @@ -1755,23 +2050,51 @@ class PaddedBatchDataset(Dataset): padding_values = ( padding_values if padding_values is not None else _default_padding(input_dataset)) - self._padded_shapes = nest.map_structure_up_to( - input_dataset.output_shapes, _partial_shape_to_tensor, padded_shapes) + + flat_padded_shapes = nest.flatten_up_to(input_dataset.output_shapes, + padded_shapes) + + flat_padded_shapes_as_tensors = [] + + for input_component_shape, padded_shape in zip( + nest.flatten(input_dataset.output_shapes), flat_padded_shapes): + flat_padded_shapes_as_tensors.append( + _padded_shape_to_tensor(padded_shape, input_component_shape)) + + self._padded_shapes = nest.pack_sequence_as(input_dataset.output_shapes, + flat_padded_shapes_as_tensors) + self._padding_values = nest.map_structure_up_to( input_dataset.output_shapes, _padding_value_to_tensor, padding_values, input_dataset.output_types) + self._drop_remainder = ops.convert_to_tensor( + drop_remainder, dtype=dtypes.bool, name="drop_remainder") def _as_variant_tensor(self): - return gen_dataset_ops.padded_batch_dataset( - self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - batch_size=self._batch_size, - padded_shapes=[ - ops.convert_to_tensor(s, dtype=dtypes.int64) - for s in nest.flatten(self._padded_shapes) - ], - padding_values=nest.flatten(self._padding_values), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + # TODO(jsimsa): Switch to using v2 only any time after 6/30/2018. + if smart_cond.smart_constant_value(self._drop_remainder) is False: + return gen_dataset_ops.padded_batch_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + batch_size=self._batch_size, + padded_shapes=[ + ops.convert_to_tensor(s, dtype=dtypes.int64) + for s in nest.flatten(self._padded_shapes) + ], + padding_values=nest.flatten(self._padding_values), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + else: + return gen_dataset_ops.padded_batch_dataset_v2( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + batch_size=self._batch_size, + padded_shapes=[ + ops.convert_to_tensor(s, dtype=dtypes.int64) + for s in nest.flatten(self._padded_shapes) + ], + padding_values=nest.flatten(self._padding_values), + drop_remainder=self._drop_remainder, + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) @property def output_classes(self): @@ -1781,8 +2104,10 @@ class PaddedBatchDataset(Dataset): def output_shapes(self): def _padded_shape_to_batch_shape(s): - return tensor_shape.vector(None).concatenate( - tensor_util.constant_value_as_shape(s)) + return tensor_shape.vector( + tensor_util.constant_value(self._batch_size) if smart_cond. + smart_constant_value(self._drop_remainder) else None).concatenate( + tensor_util.constant_value_as_shape(s)) return nest.map_structure(_padded_shape_to_batch_shape, self._padded_shapes) @@ -1796,6 +2121,24 @@ def _should_unpack_args(args): return type(args) is tuple # pylint: disable=unidiomatic-typecheck +def _warn_if_collections(transformation_name): + """Prints warning message if the current graph uses common graph collections. + + NOTE(mrry): Currently a warning is only generated for lookup tables. Any + variables created will be automatically hoisted out to the outermost scope + using `init_scope()`. Some collections (such as for control-flow contexts) + are benign and should not generate a warning. + + Args: + transformation_name: A human-readable name for the transformation. + """ + if ops.get_default_graph().get_collection(ops.GraphKeys.TABLE_INITIALIZERS): + warnings.warn("Creating lookup tables inside a function passed to %s is not" + " supported. Create each table outside the function, and " + "capture it inside the function to use it." + % transformation_name) + + class MapDataset(Dataset): """A `Dataset` that maps a function over elements in its input.""" @@ -1804,64 +2147,12 @@ class MapDataset(Dataset): super(MapDataset, self).__init__() self._input_dataset = input_dataset - self._output_classes = None - self._output_shapes = None - self._output_types = None - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - ret = map_func(*nested_args) - else: - ret = map_func(nested_args) - - # If `map_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + map_func, "Dataset.map()", input_dataset) + self._output_classes = wrapped_func.output_classes + self._output_shapes = wrapped_func.output_shapes + self._output_types = wrapped_func.output_types + self._map_func = wrapped_func.function def _as_variant_tensor(self): input_t = self._input_dataset._as_variant_tensor() # pylint: disable=protected-access @@ -1869,10 +2160,7 @@ class MapDataset(Dataset): input_t, self._map_func.captured_inputs, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1905,10 +2193,7 @@ class ParallelMapDataset(MapDataset): self._map_func.captured_inputs, f=self._map_func, num_parallel_calls=self._num_parallel_calls, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) # pylint: enable=protected-access @@ -1920,47 +2205,22 @@ class FlatMapDataset(Dataset): super(FlatMapDataset, self).__init__() self._input_dataset = input_dataset - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - dataset = map_func(*nested_args) - else: - dataset = map_func(nested_args) - - if not isinstance(dataset, Dataset): - raise TypeError("`map_func` must return a `Dataset` object.") - - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - - return dataset._as_variant_tensor() # pylint: disable=protected-access - - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + map_func, self._transformation_name(), input_dataset, + experimental_nested_dataset_support=True) + if not isinstance(wrapped_func.output_classes, _NestedDatasetComponent): + raise TypeError("`map_func` must return a `Dataset` object.") + self._output_classes = wrapped_func.output_classes.output_classes + self._output_types = wrapped_func.output_types.output_types + self._output_shapes = wrapped_func.output_shapes.output_shapes + self._map_func = wrapped_func.function def _as_variant_tensor(self): return gen_dataset_ops.flat_map_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._map_func.captured_inputs, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1974,6 +2234,9 @@ class FlatMapDataset(Dataset): def output_types(self): return self._output_types + def _transformation_name(self): + return "Dataset.flat_map()" + class InterleaveDataset(FlatMapDataset): """A `Dataset` that maps a function over its input and interleaves the result. @@ -1994,10 +2257,10 @@ class InterleaveDataset(FlatMapDataset): self._cycle_length, self._block_length, f=self._map_func, # pylint: disable=protected-access - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) + + def _transformation_name(self): + return "Dataset.interleave()" class FilterDataset(Dataset): @@ -2007,46 +2270,20 @@ class FilterDataset(Dataset): """See `Dataset.filter()` for details.""" super(FilterDataset, self).__init__() self._input_dataset = input_dataset - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_predicate(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - ret = predicate(*nested_args) - else: - ret = predicate(nested_args) - - ret = ops.convert_to_tensor(ret, dtype=dtypes.bool) - if not (ret.dtype == dtypes.bool and - ret.shape.is_compatible_with(tensor_shape.scalar())): - raise ValueError("`predicate` must return a scalar boolean tensor.") - - return ret - - self._predicate = tf_predicate - self._predicate.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + predicate, "Dataset.filter()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.bool and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError("`predicate` must return a scalar boolean tensor.") + self._predicate = wrapped_func.function def _as_variant_tensor(self): return gen_dataset_ops.filter_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access other_arguments=self._predicate.captured_inputs, predicate=self._predicate, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -2077,10 +2314,7 @@ class PrefetchDataset(Dataset): return gen_dataset_ops.prefetch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access buffer_size=self._buffer_size, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index a73a8b5cdc494d7a14c1a2bcb6aa766dbf819403..066e09969c0ba8f054ada42a40960c7513945963 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -19,8 +19,6 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import convert -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -150,12 +148,12 @@ class ParallelInterleaveDataset(dataset_ops.InterleaveDataset): self._buffer_output_elements, self._prefetch_input_elements, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access + def _transformation_name(self): + return "tf.contrib.data.parallel_interleave()" + @tf_export("data.TFRecordDataset") class TFRecordDataset(dataset_ops.Dataset): diff --git a/tensorflow/python/data/util/BUILD b/tensorflow/python/data/util/BUILD index 0fc32d51b9fe581a54519139f3bf12118f8f4028..5fcc62b60b696e05d7674c0bf46f57e71d6cc007 100644 --- a/tensorflow/python/data/util/BUILD +++ b/tensorflow/python/data/util/BUILD @@ -70,6 +70,7 @@ py_library( "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:tensor_shape", ], ) diff --git a/tensorflow/python/data/util/convert.py b/tensorflow/python/data/util/convert.py index eeb1d700f3c67a1a2ab627aa8a291755bc2127e4..746b3d66de082d59e8c1e316c51e2a9ab7670e6d 100644 --- a/tensorflow/python/data/util/convert.py +++ b/tensorflow/python/data/util/convert.py @@ -20,6 +20,7 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape def optional_param_to_tensor(argument_name, @@ -32,3 +33,40 @@ def optional_param_to_tensor(argument_name, else: return constant_op.constant( argument_default, dtype=argument_dtype, name=argument_name) + + +def partial_shape_to_tensor(shape_like): + """Returns a @{tf.Tensor} that represents the given shape. + + Args: + shape_like: A value that can be converted to a @{tf.TensorShape} or a + @{tf.Tensor}. + + Returns: + A 1-D `tf.Tensor` of `tf.int64` elements representing the given shape, where + `-1` is substituted for any unknown dimensions. + """ + try: + # First attempt to convert the input to a shape, and return the + # "canonical" tensor representation, which uses `-1` in place of + # `None`. + shape_like = tensor_shape.as_shape(shape_like) + return ops.convert_to_tensor( + [dim if dim is not None else -1 for dim in shape_like.as_list()], + dtype=dtypes.int64) + except (TypeError, ValueError): + # The argument was not trivially convertible to a + # `tf.TensorShape`, so fall back on the conversion to tensor + # machinery. + ret = ops.convert_to_tensor(shape_like, preferred_dtype=dtypes.int64) + if ret.shape.dims is not None and len(ret.shape.dims) != 1: + raise ValueError("The given shape %s must be a 1-D tensor of tf.int64 " + "values, but the shape was %s." + % (shape_like, ret.shape)) + if ret.dtype != dtypes.int64: + raise TypeError("The given shape %s must be a 1-D tensor of tf.int64 " + "values, but the element type was %s." + % (shape_like, ret.dtype.name)) + + return ret + diff --git a/tensorflow/python/data/util/convert_test.py b/tensorflow/python/data/util/convert_test.py index 2cb6488070eb422f6c8d56ca5d712cbdf09fa883..6a67093e48c988b01b8137a544078d570aabf74f 100644 --- a/tensorflow/python/data/util/convert_test.py +++ b/tensorflow/python/data/util/convert_test.py @@ -19,7 +19,9 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.util import convert +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -48,6 +50,77 @@ class ConvertTest(test.TestCase): with self.test_session() as sess: self.assertEqual(compat.as_bytes("value"), sess.run(resp)) + def testPartialShapeToTensorKnownDimension(self): + with self.test_session() as sess: + self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([1])))) + self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor((1,)))) + self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor([1]))) + self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([1], dtype=dtypes.int64)))) + + def testPartialShapeToTensorUnknownDimension(self): + with self.test_session() as sess: + self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([None])))) + self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( + (None,)))) + self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( + [None]))) + self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( + [-1]))) + self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([-1], dtype=dtypes.int64)))) + + with self.assertRaisesRegexp( + ValueError, r"The given shape .* must be a 1-D tensor of tf.int64 " + r"values, but the shape was \(2, 2\)."): + convert.partial_shape_to_tensor(constant_op.constant( + [[1, 1], [1, 1]], dtype=dtypes.int64)) + + with self.assertRaisesRegexp( + TypeError, r"The given shape .* must be a 1-D tensor of tf.int64 " + r"values, but the element type was float32."): + convert.partial_shape_to_tensor(constant_op.constant([1., 1.])) + + def testPartialShapeToTensorMultipleDimensions(self): + with self.test_session() as sess: + self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([3, 6])))) + self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( + (3, 6)))) + self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( + [3, 6]))) + self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([3, 6], dtype=dtypes.int64)))) + + self.assertAllEqual([3, -1], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([3, None])))) + self.assertAllEqual([3, -1], sess.run(convert.partial_shape_to_tensor( + (3, None)))) + self.assertAllEqual([3, -1], sess.run(convert.partial_shape_to_tensor( + [3, None]))) + self.assertAllEqual([3, -1], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([3, -1], dtype=dtypes.int64)))) + + self.assertAllEqual([-1, -1], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([None, None])))) + self.assertAllEqual([-1, -1], sess.run(convert.partial_shape_to_tensor( + (None, None)))) + self.assertAllEqual([-1, -1], sess.run(convert.partial_shape_to_tensor( + [None, None]))) + self.assertAllEqual([-1, -1], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([-1, -1], dtype=dtypes.int64)))) + + def testPartialShapeToTensorScalar(self): + with self.test_session() as sess: + self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor( + tensor_shape.TensorShape([])))) + self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor(()))) + self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor([]))) + self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor( + constant_op.constant([], dtype=dtypes.int64)))) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/util/random_seed_test.py b/tensorflow/python/data/util/random_seed_test.py index 33227e82afe6fe1c748693d107d4e9844abb8e09..a809151e6ef57de8a39806b8164f818d94b8a783 100644 --- a/tensorflow/python/data/util/random_seed_test.py +++ b/tensorflow/python/data/util/random_seed_test.py @@ -30,7 +30,7 @@ from tensorflow.python.platform import test class RandomSeedTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandomSeed(self): zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero') one_t = constant_op.constant(1, dtype=dtypes.int64, name='one') diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index 09062abd7446628ede12e782e202ee0e55905879..c025dc8aa58a500ace3e28ba4528abd4f4c38ba7 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -5,7 +5,7 @@ # # ":debug_py": Public Python methods and classes of tfdbg. # For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg -# For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger +# For a user interface walkthrough, see https://www.tensorflow.org/guide/debugger # ":grpc_debug_server": Server interface for grpc:// debug URLs. package( @@ -167,6 +167,7 @@ py_library( srcs_version = "PY2AND3", deps = [ "//tensorflow/python:platform", + "//third_party/py/numpy", "@six_archive//:six", ], ) @@ -453,6 +454,17 @@ py_binary( ], ) +py_binary( + name = "debug_keras", + srcs = ["examples/debug_keras.py"], + srcs_version = "PY2AND3", + deps = [ + ":debug_py", + "//tensorflow:tensorflow_py", + "//third_party/py/numpy", + ], +) + py_test( name = "common_test", size = "small", @@ -802,6 +814,7 @@ py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform", "//tensorflow/python:platform_test", + "//third_party/py/numpy", ], ) @@ -1084,6 +1097,7 @@ py_test( "//tensorflow/python:state_ops", "//tensorflow/python:training", "//tensorflow/python:variables", + "//third_party/py/numpy", ], ) @@ -1094,6 +1108,7 @@ sh_test( data = [ ":debug_errors", ":debug_fibonacci", + ":debug_keras", ":debug_mnist", ":debug_tflearn_iris", ":offline_analyzer", diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md index 269bbb19bdb898d1d81d0b9c618a284a437e68b9..9c16af4d79754cee5d77158d5c2466412c6b9e68 100644 --- a/tensorflow/python/debug/README.md +++ b/tensorflow/python/debug/README.md @@ -28,7 +28,7 @@ models: * Easy access through session wrappers * Easy integration with common high-level APIs, such as - [TensorFlow Estimators](https://www.tensorflow.org/programmers_guide/estimators) and + [TensorFlow Estimators](https://www.tensorflow.org/guide/estimators) and [Keras](https://keras.io/) * Inspection of runtime tensor values and node connections * Conditional breaking after runs that generate tensors satisfying given @@ -43,7 +43,7 @@ models: ## How to use TFDBG? -* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger. +* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/guide/debugger. * For information on the web GUI of TFDBG (TensorBoard Debugger Plugin), see [this README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). * For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg. diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py index dea019fef58015fbd7982a81319dcabe4e5f4930..6a368682de5db12e128f010bfe0c9bbf9cf3b997 100644 --- a/tensorflow/python/debug/cli/cli_shared.py +++ b/tensorflow/python/debug/cli/cli_shared.py @@ -451,42 +451,48 @@ def get_error_intro(tf_error): sample commands for debugging. """ - op_name = tf_error.op.name + if hasattr(tf_error, "op") and hasattr(tf_error.op, "name"): + op_name = tf_error.op.name + else: + op_name = None intro_lines = [ "--------------------------------------", RL("!!! An error occurred during the run !!!", "blink"), "", - "You may use the following commands to debug:", ] out = debugger_cli_common.rich_text_lines_from_rich_line_list(intro_lines) - out.extend( - _recommend_command("ni -a -d -t %s" % op_name, - "Inspect information about the failing op.", - create_link=True)) - out.extend( - _recommend_command("li -r %s" % op_name, - "List inputs to the failing op, recursively.", - create_link=True)) - - out.extend( - _recommend_command( - "lt", - "List all tensors dumped during the failing run() call.", - create_link=True)) + if op_name is not None: + out.extend(debugger_cli_common.RichTextLines( + ["You may use the following commands to debug:"])) + out.extend( + _recommend_command("ni -a -d -t %s" % op_name, + "Inspect information about the failing op.", + create_link=True)) + out.extend( + _recommend_command("li -r %s" % op_name, + "List inputs to the failing op, recursively.", + create_link=True)) + + out.extend( + _recommend_command( + "lt", + "List all tensors dumped during the failing run() call.", + create_link=True)) + else: + out.extend(debugger_cli_common.RichTextLines([ + "WARNING: Cannot determine the name of the op that caused the error."])) more_lines = [ "", - "Op name: " + op_name, + "Op name: %s" % op_name, "Error type: " + str(type(tf_error)), "", "Details:", str(tf_error), "", - "WARNING: Using client GraphDef due to the error, instead of " - "executor GraphDefs.", "--------------------------------------", "", ] diff --git a/tensorflow/python/debug/cli/cli_shared_test.py b/tensorflow/python/debug/cli/cli_shared_test.py index 3d7939490dfe08118ee4972541c4166b2a536608..07b364db9f2aab9c11ecb769a94f36e0809d70a0 100644 --- a/tensorflow/python/debug/cli/cli_shared_test.py +++ b/tensorflow/python/debug/cli/cli_shared_test.py @@ -372,6 +372,11 @@ class GetErrorIntroTest(test_util.TensorFlowTestCase): self.assertEqual("Details:", error_intro.lines[14]) self.assertStartsWith(error_intro.lines[15], "foo description") + def testGetErrorIntroForNoOpName(self): + tf_error = errors.OpError(None, None, "Fake OpError", -1) + error_intro = cli_shared.get_error_intro(tf_error) + self.assertIn("Cannot determine the name of the op", error_intro.lines[3]) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/debug/cli/debugger_cli_common.py b/tensorflow/python/debug/cli/debugger_cli_common.py index 12e79ab07a4655c7d41f41d2e71906273e154a08..02563fde845e7951046a8bcd65899ef5e1fcc35f 100644 --- a/tensorflow/python/debug/cli/debugger_cli_common.py +++ b/tensorflow/python/debug/cli/debugger_cli_common.py @@ -23,9 +23,11 @@ import re import sre_constants import traceback +import numpy as np import six from six.moves import xrange # pylint: disable=redefined-builtin +from tensorflow.python import pywrap_tensorflow_internal from tensorflow.python.platform import gfile HELP_INDENT = " " @@ -131,6 +133,25 @@ def rich_text_lines_from_rich_line_list(rich_text_list, annotations=None): return RichTextLines(lines, font_attr_segs, annotations=annotations) +def get_tensorflow_version_lines(include_dependency_versions=False): + """Generate RichTextLines with TensorFlow version info. + + Args: + include_dependency_versions: Include the version of TensorFlow's key + dependencies, such as numpy. + + Returns: + A formatted, multi-line `RichTextLines` object. + """ + lines = ["TensorFlow version: %s" % pywrap_tensorflow_internal.__version__] + lines.append("") + if include_dependency_versions: + lines.append("Dependency version(s):") + lines.append(" numpy: %s" % np.__version__) + lines.append("") + return RichTextLines(lines) + + class RichTextLines(object): """Rich multi-line text. @@ -538,6 +559,8 @@ class CommandHandlerRegistry(object): HELP_COMMAND = "help" HELP_COMMAND_ALIASES = ["h"] + VERSION_COMMAND = "version" + VERSION_COMMAND_ALIASES = ["ver"] def __init__(self): # A dictionary from command prefix to handler. @@ -562,6 +585,13 @@ class CommandHandlerRegistry(object): "Print this help message.", prefix_aliases=self.HELP_COMMAND_ALIASES) + # Register a default handler for the command "version". + self.register_command_handler( + self.VERSION_COMMAND, + self._version_handler, + "Print the versions of TensorFlow and its key dependencies.", + prefix_aliases=self.VERSION_COMMAND_ALIASES) + def register_command_handler(self, prefix, handler, @@ -763,6 +793,11 @@ class CommandHandlerRegistry(object): else: return RichTextLines(["ERROR: help takes only 0 or 1 input argument."]) + def _version_handler(self, args, screen_info=None): + del args # Unused currently. + del screen_info # Unused currently. + return get_tensorflow_version_lines(include_dependency_versions=True) + def _resolve_prefix(self, token): """Resolve command prefix from the prefix itself or its alias. diff --git a/tensorflow/python/debug/cli/debugger_cli_common_test.py b/tensorflow/python/debug/cli/debugger_cli_common_test.py index 1b7a5962fe7dc4e19446c3e3b0aeab672eb30f1f..aba95e5820b1d8c6b3811fc69328317ce2c3ac64 100644 --- a/tensorflow/python/debug/cli/debugger_cli_common_test.py +++ b/tensorflow/python/debug/cli/debugger_cli_common_test.py @@ -21,6 +21,9 @@ import os import stat import tempfile +import numpy as np + +from tensorflow.python import pywrap_tensorflow_internal from tensorflow.python.debug.cli import debugger_cli_common from tensorflow.python.framework import test_util from tensorflow.python.platform import gfile @@ -547,7 +550,10 @@ class CommandHandlerRegistryTest(test_util.TensorFlowTestCase): " Show screen width in number of columns.", "", "", "help", " Aliases: h", "", " Print this help message.", "", "", "noop", " Aliases: n, NOOP", "", - " No operation.", " I.e., do nothing.", "", ""], + " No operation.", " I.e., do nothing.", "", "", + "version", " Aliases: ver", "", + " Print the versions of TensorFlow and its key " + "dependencies.", "", ""], output.lines) # Get help for one specific command prefix. @@ -575,7 +581,9 @@ class CommandHandlerRegistryTest(test_util.TensorFlowTestCase): self.assertEqual(help_intro.lines + [ "help", " Aliases: h", "", " Print this help message.", "", "", "noop", " Aliases: n, NOOP", "", " No operation.", - " I.e., do nothing.", "", "" + " I.e., do nothing.", "", "", + "version", " Aliases: ver", "", + " Print the versions of TensorFlow and its key dependencies.", "", "" ], output.lines) @@ -1147,5 +1155,22 @@ class MenuTest(test_util.TensorFlowTestCase): self.assertEqual((40, 50, ["bold"]), output.font_attr_segs[0][2]) +class GetTensorFlowVersionLinesTest(test_util.TensorFlowTestCase): + + def testGetVersionWithoutDependencies(self): + out = debugger_cli_common.get_tensorflow_version_lines() + self.assertEqual(2, len(out.lines)) + self.assertEqual( + "TensorFlow version: %s" % pywrap_tensorflow_internal.__version__, + out.lines[0]) + + def testGetVersionWithDependencies(self): + out = debugger_cli_common.get_tensorflow_version_lines(True) + self.assertIn( + "TensorFlow version: %s" % pywrap_tensorflow_internal.__version__, + out.lines) + self.assertIn(" numpy: %s" % np.__version__, out.lines) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/debug/examples/README.md b/tensorflow/python/debug/examples/README.md index cb4d484092fe39698de1ff11e4d50d4879960e0c..3b431e04dc3565037dc018991bea68ab019e8af0 100644 --- a/tensorflow/python/debug/examples/README.md +++ b/tensorflow/python/debug/examples/README.md @@ -3,7 +3,7 @@ Hi, there! The documentation of **TensorFlow Debugger (tfdbg)** has moved. See the source version at -[this new location](../../../docs_src/programmers_guide/debugger.md). +[this new location](../../../docs_src/guide/debugger.md). See the public website version at -[https://www.tensorflow.org/programmers_guide/debugger](https://www.tensorflow.org/programmers_guide/debugger). +[https://www.tensorflow.org/guide/debugger](https://www.tensorflow.org/guide/debugger). diff --git a/tensorflow/python/debug/examples/debug_keras.py b/tensorflow/python/debug/examples/debug_keras.py new file mode 100644 index 0000000000000000000000000000000000000000..3272d85ade957b254b2c1a0977156179cd71bb9d --- /dev/null +++ b/tensorflow/python/debug/examples/debug_keras.py @@ -0,0 +1,89 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""tfdbg example: debugging tf.keras models training on tf.data.Dataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +import numpy as np +import tensorflow as tf + +from tensorflow.python import debug as tf_debug + + +def main(_): + # Create a dummy dataset. + num_examples = 8 + steps_per_epoch = 2 + input_dims = 3 + output_dims = 1 + xs = np.zeros([num_examples, input_dims]) + ys = np.zeros([num_examples, output_dims]) + dataset = tf.data.Dataset.from_tensor_slices( + (xs, ys)).repeat(num_examples).batch(int(num_examples / steps_per_epoch)) + + sess = tf.Session() + if FLAGS.debug: + # Use the command-line interface (CLI) of tfdbg. + sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type=FLAGS.ui_type) + elif FLAGS.tensorboard_debug_address: + # Use the TensorBoard Debugger Plugin (GUI of tfdbg). + sess = tf_debug.TensorBoardDebugWrapperSession( + sess, FLAGS.tensorboard_debug_address) + tf.keras.backend.set_session(sess) + + # Create a dummy model. + model = tf.keras.Sequential([ + tf.keras.layers.Dense(1, input_shape=[input_dims])]) + model.compile(loss="mse", optimizer="sgd") + + # Train the model using the dummy dataset created above. + model.fit(dataset, epochs=FLAGS.epochs, steps_per_epoch=steps_per_epoch) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.register("type", "bool", lambda v: v.lower() == "true") + parser.add_argument( + "--debug", + type="bool", + nargs="?", + const=True, + default=False, + help="Use debugger to track down bad values during training. " + "Mutually exclusive with the --tensorboard_debug_address flag.") + parser.add_argument( + "--ui_type", + type=str, + default="curses", + help="Command-line user interface type (curses | readline).") + parser.add_argument( + "--tensorboard_debug_address", + type=str, + default=None, + help="Connect to the TensorBoard Debugger Plugin backend specified by " + "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " + "--debug flag.") + parser.add_argument( + "--epochs", + type=int, + default=2, + help="Number of epochs to train the model for.") + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/examples_test.sh b/tensorflow/python/debug/examples/examples_test.sh index 2df6c0b6a2701022e3fed6648208b9708197bebc..2d35b2d8bb10d17decfa404afd5004d3409c06e5 100755 --- a/tensorflow/python/debug/examples/examples_test.sh +++ b/tensorflow/python/debug/examples/examples_test.sh @@ -48,12 +48,14 @@ if [[ -z "${PYTHON_BIN_PATH}" ]]; then DEBUG_ERRORS_BIN="$TEST_SRCDIR/org_tensorflow/tensorflow/python/debug/debug_errors" DEBUG_MNIST_BIN="$TEST_SRCDIR/org_tensorflow/tensorflow/python/debug/debug_mnist" DEBUG_TFLEARN_IRIS_BIN="$TEST_SRCDIR/org_tensorflow/tensorflow/python/debug/debug_tflearn_iris" + DEBUG_KERAS_BIN="$TEST_SRCDIR/org_tensorflow/tensorflow/python/debug/debug_keras" OFFLINE_ANALYZER_BIN="$TEST_SRCDIR/org_tensorflow/tensorflow/python/debug/offline_analyzer" else DEBUG_FIBONACCI_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.examples.debug_fibonacci" DEBUG_ERRORS_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.examples.debug_errors" DEBUG_MNIST_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.examples.debug_mnist" DEBUG_TFLEARN_IRIS_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.examples.debug_tflearn_iris" + DEBUG_KERAS_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.examples.debug_keras" OFFLINE_ANALYZER_BIN="${PYTHON_BIN_PATH} -m tensorflow.python.debug.cli.offline_analyzer" fi @@ -69,6 +71,12 @@ run exit EOF +cat << EOF | ${DEBUG_ERRORS_BIN} --error=uninitialized_variable --debug --ui_type=readline +run +ni -a -d -t v/read +exit +EOF + cat << EOF | ${DEBUG_MNIST_BIN} --debug --max_steps=1 --fake_data --ui_type=readline run -t 1 run --node_name_filter hidden --op_type_filter MatMul @@ -90,6 +98,11 @@ if [[ -d "${CUSTOM_DUMP_ROOT}" ]]; then exit 1 fi +# Test debugging of tf.keras. +cat << EOF | "${DEBUG_KERAS_BIN}" --debug --ui_type=readline +run -f has_inf_or_nan +EOF + # Test offline_analyzer. echo echo "Testing offline_analyzer" diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py index 8a65ad087b3002d8ad93f3a64f48715d26ff62d8..7c96c2878c78d5650f3d1907065cc17c4eb71f5c 100644 --- a/tensorflow/python/debug/lib/debug_data.py +++ b/tensorflow/python/debug/lib/debug_data.py @@ -748,7 +748,7 @@ class DebugDumpDir(object): return sum(len(self._dump_tensor_data[device_name]) for device_name in self._dump_tensor_data) - def _load_partition_graphs(self, partition_graphs, validate): + def _load_partition_graphs(self, client_partition_graphs, validate): """Load and process partition graphs. Load the graphs; parse the input and control input structure; obtain the @@ -757,8 +757,10 @@ class DebugDumpDir(object): tensor dumps. Args: - partition_graphs: A repeated field of GraphDefs representing the - partition graphs executed by the TensorFlow runtime. + client_partition_graphs: A repeated field of GraphDefs representing the + partition graphs executed by the TensorFlow runtime, from the Python + client. These partition graphs are used only if partition graphs + cannot be loaded from the dump directory on the file system. validate: (`bool`) Whether the dump files are to be validated against the partition graphs. @@ -769,24 +771,23 @@ class DebugDumpDir(object): self._debug_graphs = {} self._node_devices = {} - if partition_graphs: - partition_graphs_and_device_names = [ - (partition_graph, None) for partition_graph in partition_graphs] - else: - partition_graphs_and_device_names = [] - for device_name in self._device_names: - partition_graph = None - if device_name in self._dump_graph_file_paths: - partition_graph = _load_graph_def_from_event_file( - self._dump_graph_file_paths[device_name]) - else: - partition_graph = self._find_partition_graph(partition_graphs, - device_name) - if partition_graph: - partition_graphs_and_device_names.append((partition_graph, - device_name)) - else: - logging.warn("Failed to load partition graphs from disk.") + partition_graphs_and_device_names = [] + for device_name in self._device_names: + partition_graph = None + if device_name in self._dump_graph_file_paths: + partition_graph = _load_graph_def_from_event_file( + self._dump_graph_file_paths[device_name]) + else: + logging.warn( + "Failed to load partition graphs for device %s from disk. " + "As a fallback, the client graphs will be used. This " + "may cause mismatches in device names." % device_name) + partition_graph = self._find_partition_graph(client_partition_graphs, + device_name) + + if partition_graph: + partition_graphs_and_device_names.append((partition_graph, + device_name)) for partition_graph, maybe_device_name in partition_graphs_and_device_names: debug_graph = debug_graphs.DebugGraph(partition_graph, diff --git a/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py b/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py index bd00f738610627a4b3bc7c61476164188a7b460c..676097fde95e2e5a685e8e43f8f38d3e62e7084a 100644 --- a/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py +++ b/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py @@ -44,7 +44,8 @@ class ReconstructNonDebugGraphTest(test_util.TensorFlowTestCase): def _no_rewrite_session_config(self): rewriter_config = rewriter_config_pb2.RewriterConfig( - dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1) graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) return config_pb2.ConfigProto(graph_options=graph_options) diff --git a/tensorflow/python/debug/wrappers/framework.py b/tensorflow/python/debug/wrappers/framework.py index c530204bbf6959f56a72c6e67add91f1e575f067..b9524ce649c7d6d888affacc22cfadd41dbe2e40 100644 --- a/tensorflow/python/debug/wrappers/framework.py +++ b/tensorflow/python/debug/wrappers/framework.py @@ -392,6 +392,9 @@ class BaseDebugWrapperSession(session.SessionInterface): self._default_session_context_manager = None + # A cache for callables created from CallableOptions. + self._cached_callables_from_options = dict() + @property def graph(self): return self._sess.graph @@ -414,7 +417,8 @@ class BaseDebugWrapperSession(session.SessionInterface): options=None, run_metadata=None, callable_runner=None, - callable_runner_args=None): + callable_runner_args=None, + callable_options=None): """Wrapper around Session.run() that inserts tensor watch options. Args: @@ -424,7 +428,12 @@ class BaseDebugWrapperSession(session.SessionInterface): run_metadata: Same as the `run_metadata` arg to regular `Session.run()`. callable_runner: A `callable` returned by `Session.make_callable()`. If not `None`, `fetches` and `feed_dict` must both be `None`. - callable_runner_args: An optional list of arguments to `callable_runner`. + Mutually exclusive with `callable_options`. + callable_runner_args: An optional list of arguments to `callable_runner` + or for `callable_options`. + callable_options: An instance of `config_pb2.CallableOptions`, to be + used with `Session._make_callable_from_options()`. Mutually exclusive + with `callable_runner`. Returns: Simply forwards the output of the wrapped `Session.run()` call. @@ -433,13 +442,17 @@ class BaseDebugWrapperSession(session.SessionInterface): ValueError: On invalid `OnRunStartAction` value. Or if `callable_runner` is not `None` and either or both of `fetches` and `feed_dict` is `None`. """ - if not callable_runner: + if callable_runner and callable_options: + raise ValueError( + "callable_runner and callable_options are mutually exclusive, but " + "are both specified in this call to BaseDebugWrapperSession.run().") + + if not (callable_runner or callable_options): self.increment_run_call_count() - else: - if fetches or feed_dict: - raise ValueError( - "callable_runner and fetches/feed_dict are mutually exclusive, but " - "are used simultaneously.") + elif callable_runner and (fetches or feed_dict): + raise ValueError( + "callable_runner and fetches/feed_dict are mutually exclusive, " + "but are used simultaneously.") empty_fetches = not nest.flatten(fetches) if empty_fetches: @@ -449,6 +462,11 @@ class BaseDebugWrapperSession(session.SessionInterface): if self._is_disabled_thread() or empty_fetches: if callable_runner: return callable_runner(*callable_runner_args) + elif callable_options: + # pylint:disable=protected-access + return self._sess._make_callable_from_options( + callable_options)(*callable_runner_args) + # pylint:enable=protected-access else: return self._sess.run(fetches, feed_dict=feed_dict, @@ -464,19 +482,30 @@ class BaseDebugWrapperSession(session.SessionInterface): if run_start_resp.action == OnRunStartAction.DEBUG_RUN: # Decorate RunOption to fill in debugger tensor watch specifications. - decorated_run_options = options or config_pb2.RunOptions() + decorated_run_options = None + if callable_options: + callable_options_id = id(callable_options) + if callable_options_id not in self._cached_callables_from_options: + # Make a copy of callable_options to avoid mutating it. + new_callable_options = config_pb2.CallableOptions() + new_callable_options.CopyFrom(callable_options) + decorated_run_options = new_callable_options.run_options + else: + decorated_run_options = options or config_pb2.RunOptions() + run_metadata = run_metadata or config_pb2.RunMetadata() - self._decorate_run_options_for_debug( - decorated_run_options, - run_start_resp.debug_urls, - debug_ops=run_start_resp.debug_ops, - node_name_regex_whitelist=run_start_resp.node_name_regex_whitelist, - op_type_regex_whitelist=run_start_resp.op_type_regex_whitelist, - tensor_dtype_regex_whitelist=( - run_start_resp.tensor_dtype_regex_whitelist), - tolerate_debug_op_creation_failures=( - run_start_resp.tolerate_debug_op_creation_failures)) + if decorated_run_options: + self._decorate_run_options_for_debug( + decorated_run_options, + run_start_resp.debug_urls, + debug_ops=run_start_resp.debug_ops, + node_name_regex_whitelist=run_start_resp.node_name_regex_whitelist, + op_type_regex_whitelist=run_start_resp.op_type_regex_whitelist, + tensor_dtype_regex_whitelist=( + run_start_resp.tensor_dtype_regex_whitelist), + tolerate_debug_op_creation_failures=( + run_start_resp.tolerate_debug_op_creation_failures)) # Invoke the run() method of the wrapped Session. Catch any TensorFlow # runtime errors. @@ -486,6 +515,19 @@ class BaseDebugWrapperSession(session.SessionInterface): retvals = callable_runner(*callable_runner_args, options=decorated_run_options, run_metadata=run_metadata) + elif callable_options: + # pylint:disable=protected-access + if callable_options_id in self._cached_callables_from_options: + callable_object = self._cached_callables_from_options[ + callable_options_id] + else: + callable_object = self._sess._make_callable_from_options( + new_callable_options) + self._cached_callables_from_options[ + callable_options_id] = callable_object + # pylint:enable=protected-access + retvals = callable_object( + *callable_runner_args, run_metadata=run_metadata) else: retvals = self._sess.run(fetches, feed_dict=feed_dict, @@ -590,7 +632,14 @@ class BaseDebugWrapperSession(session.SessionInterface): run_metadata=kwargs.get("run_metadata", None), callable_runner=runner, callable_runner_args=runner_args) + return wrapped_runner + def _make_callable_from_options(self, callable_options): + def wrapped_runner(*feed_values, **kwargs): + return self.run(None, + run_metadata=kwargs.get("run_metadata", None), + callable_options=callable_options, + callable_runner_args=feed_values) return wrapped_runner @property diff --git a/tensorflow/python/debug/wrappers/grpc_wrapper.py b/tensorflow/python/debug/wrappers/grpc_wrapper.py index 1f9c8fa5a96b4d6826fae0870608e0e737c7cd88..85944fa61118114cc73f9288f3f974f0a5a8a839 100644 --- a/tensorflow/python/debug/wrappers/grpc_wrapper.py +++ b/tensorflow/python/debug/wrappers/grpc_wrapper.py @@ -215,7 +215,8 @@ class TensorBoardDebugWrapperSession(GrpcDebugWrapperSession): options=None, run_metadata=None, callable_runner=None, - callable_runner_args=None): + callable_runner_args=None, + callable_options=None): if self._send_traceback_and_source_code: self._sent_graph_version = publish_traceback( self._grpc_debug_server_urls, self.graph, feed_dict, fetches, @@ -226,4 +227,5 @@ class TensorBoardDebugWrapperSession(GrpcDebugWrapperSession): options=options, run_metadata=run_metadata, callable_runner=callable_runner, - callable_runner_args=callable_runner_args) + callable_runner_args=callable_runner_args, + callable_options=callable_options) diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper.py b/tensorflow/python/debug/wrappers/local_cli_wrapper.py index c8625655e51a43a222addedd4beecdd3515d7fb6..668ffb57f10a69ce7e11e889fe613afbd618e823 100644 --- a/tensorflow/python/debug/wrappers/local_cli_wrapper.py +++ b/tensorflow/python/debug/wrappers/local_cli_wrapper.py @@ -290,6 +290,7 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession): if self._run_call_count == 1: # Show logo at the onset of the first run. help_intro.extend(cli_shared.get_tfdbg_logo()) + help_intro.extend(debugger_cli_common.get_tensorflow_version_lines()) help_intro.extend(debugger_cli_common.RichTextLines("Upcoming run:")) help_intro.extend(self._run_info) @@ -466,6 +467,7 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession): if self._run_call_count == 1: output.extend(cli_shared.get_tfdbg_logo()) + output.extend(debugger_cli_common.get_tensorflow_version_lines()) output.extend(self._run_info) if (not self._is_run_start and @@ -594,7 +596,7 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession): # Register tab completion for the filter names. curses_cli.register_tab_comp_context(["run", "r"], list(self._tensor_filters.keys())) - if self._feed_dict: + if self._feed_dict and hasattr(self._feed_dict, "keys"): # Register tab completion for feed_dict keys. feed_keys = [common.get_graph_element_name(key) for key in self._feed_dict.keys()] diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py index b06fa26a935b42709575f8e400e0bda951ffbbc7..05c9eaa4d27319ecf5e12fdeb0a973246c61704a 100644 --- a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py +++ b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py @@ -21,7 +21,10 @@ import os import shutil import tempfile +import numpy as np + from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.debug.cli import cli_shared from tensorflow.python.debug.cli import debugger_cli_common @@ -149,7 +152,13 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase): dtypes.float32, shape=([5, 5]), name="sparse_placeholder") self.sparse_add = sparse_ops.sparse_add(self.sparse_ph, self.sparse_ph) - self.sess = session.Session() + rewriter_config = rewriter_config_pb2.RewriterConfig( + disable_model_pruning=True, + arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) + graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) + config_proto = config_pb2.ConfigProto(graph_options=graph_options) + self.sess = session.Session(config=config_proto) # Initialize variable. self.sess.run(variables.global_variables_initializer()) @@ -393,6 +402,113 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase): self.assertAllClose(42.0, tensor_runner(41.0, 1.0)) self.assertEqual(1, len(wrapped_sess.observers["debug_dumps"])) + def testDebuggingMakeCallableFromOptionsWithZeroFeedWorks(self): + variable_1 = variables.Variable( + 10.5, dtype=dtypes.float32, name="variable_1") + a = math_ops.add(variable_1, variable_1, "callable_a") + math_ops.add(a, a, "callable_b") + self.sess.run(variable_1.initializer) + + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"]] * 3, self.sess, dump_root=self._tmp_dir) + callable_options = config_pb2.CallableOptions() + callable_options.fetch.append("callable_b") + sess_callable = wrapped_sess._make_callable_from_options(callable_options) + + for _ in range(2): + callable_output = sess_callable() + self.assertAllClose(np.array(42.0, dtype=np.float32), callable_output[0]) + + debug_dumps = wrapped_sess.observers["debug_dumps"] + self.assertEqual(2, len(debug_dumps)) + for debug_dump in debug_dumps: + node_names = [datum.node_name for datum in debug_dump.dumped_tensor_data] + self.assertItemsEqual( + ["callable_a", "callable_b", "variable_1", "variable_1/read"], + node_names) + + def testDebuggingMakeCallableFromOptionsWithOneFeedWorks(self): + ph1 = array_ops.placeholder(dtypes.float32, name="callable_ph1") + a = math_ops.add(ph1, ph1, "callable_a") + math_ops.add(a, a, "callable_b") + + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"]] * 3, self.sess, dump_root=self._tmp_dir) + callable_options = config_pb2.CallableOptions() + callable_options.feed.append("callable_ph1") + callable_options.fetch.append("callable_b") + sess_callable = wrapped_sess._make_callable_from_options(callable_options) + + ph1_value = np.array([10.5, -10.5], dtype=np.float32) + + for _ in range(2): + callable_output = sess_callable(ph1_value) + self.assertAllClose( + np.array([42.0, -42.0], dtype=np.float32), callable_output[0]) + + debug_dumps = wrapped_sess.observers["debug_dumps"] + self.assertEqual(2, len(debug_dumps)) + for debug_dump in debug_dumps: + node_names = [datum.node_name for datum in debug_dump.dumped_tensor_data] + self.assertItemsEqual(["callable_a", "callable_b"], node_names) + + def testDebuggingMakeCallableFromOptionsWithTwoFeedsWorks(self): + ph1 = array_ops.placeholder(dtypes.float32, name="callable_ph1") + ph2 = array_ops.placeholder(dtypes.float32, name="callable_ph2") + a = math_ops.add(ph1, ph2, "callable_a") + math_ops.add(a, a, "callable_b") + + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"]] * 3, self.sess, dump_root=self._tmp_dir) + callable_options = config_pb2.CallableOptions() + callable_options.feed.append("callable_ph1") + callable_options.feed.append("callable_ph2") + callable_options.fetch.append("callable_b") + sess_callable = wrapped_sess._make_callable_from_options(callable_options) + + ph1_value = np.array(5.0, dtype=np.float32) + ph2_value = np.array(16.0, dtype=np.float32) + + for _ in range(2): + callable_output = sess_callable(ph1_value, ph2_value) + self.assertAllClose(np.array(42.0, dtype=np.float32), callable_output[0]) + + debug_dumps = wrapped_sess.observers["debug_dumps"] + self.assertEqual(2, len(debug_dumps)) + for debug_dump in debug_dumps: + node_names = [datum.node_name for datum in debug_dump.dumped_tensor_data] + self.assertItemsEqual(["callable_a", "callable_b"], node_names) + + def testDebugMakeCallableFromOptionsWithCustomOptionsAndMetadataWorks(self): + variable_1 = variables.Variable( + 10.5, dtype=dtypes.float32, name="variable_1") + a = math_ops.add(variable_1, variable_1, "callable_a") + math_ops.add(a, a, "callable_b") + self.sess.run(variable_1.initializer) + + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"], ["run"]], self.sess, dump_root=self._tmp_dir) + callable_options = config_pb2.CallableOptions() + callable_options.fetch.append("callable_b") + callable_options.run_options.trace_level = config_pb2.RunOptions.FULL_TRACE + + sess_callable = wrapped_sess._make_callable_from_options(callable_options) + + run_metadata = config_pb2.RunMetadata() + # Call the callable with a custom run_metadata. + callable_output = sess_callable(run_metadata=run_metadata) + # Verify that step_stats is populated in the custom run_metadata. + self.assertTrue(run_metadata.step_stats) + self.assertAllClose(np.array(42.0, dtype=np.float32), callable_output[0]) + + debug_dumps = wrapped_sess.observers["debug_dumps"] + self.assertEqual(1, len(debug_dumps)) + debug_dump = debug_dumps[0] + node_names = [datum.node_name for datum in debug_dump.dumped_tensor_data] + self.assertItemsEqual( + ["callable_a", "callable_b", "variable_1", "variable_1/read"], + node_names) + def testRuntimeErrorShouldBeCaught(self): wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( [["run"], ["run"]], self.sess, dump_root=self._tmp_dir) diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index dee86966f1bb08540c69f158e13ce6a288bd9821..6ede8e4f4d9c549faae3223d400d25b7712bbc74 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -32,6 +32,7 @@ cc_library( "//tensorflow/python:numpy_lib", "//tensorflow/python:py_seq_tensor", "//tensorflow/python:safe_ptr", + "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", ], ) @@ -391,3 +392,20 @@ py_library( srcs = ["imperative_grad.py"], srcs_version = "PY2AND3", ) + +cuda_py_test( + name = "memory_test", + size = "medium", + srcs = ["memory_test.py"], + additional_deps = [ + "//tensorflow/python/eager:backprop", + "//tensorflow/python/keras", + "//tensorflow/python/eager:test", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + ], + tags = [ + "optonly", # The test is too slow in non-opt mode + ], +) diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index bd97b181ff7fa5a38ea8ab16e55b3ade7b599261..3e3c82e56a8c957839e420550bfb073d400b4a77 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -605,7 +605,9 @@ def _zeros(shape, dtype): # TODO(apassos): need to save enough information about variant tensors to do # a zeros return None - cache_key = shape, dtype, device + # pylint: disable=protected-access + cache_key = shape, dtype, device, context.context()._eager_context.mode + # pylint: enable=protected-access cached = _zeros_cache.get(cache_key) if cached is None: cached = _fast_fill(0, shape, dtype) diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 826c6683b9668ab892883119a533ee8d497d7b58..ebbd3cd98e892fddb556fc95a4292e05d16fc167 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -46,7 +46,7 @@ from tensorflow.python.training import training class BackpropTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateGradients(self): def fn(x): @@ -251,7 +251,7 @@ class BackpropTest(test.TestCase): g, = backprop.gradients_function(loss, [0])(logits, labels) self.assertAllEqual(g.numpy(), [[-0.5, 0.5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientWithinTapeBlock(self): v1 = resource_variable_ops.ResourceVariable(1.) self.evaluate(v1.initializer) @@ -265,7 +265,7 @@ class BackpropTest(test.TestCase): grad = t.gradient(loss, v1) self.assertAllEqual(self.evaluate(grad), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestedSelfContexts(self): v1 = resource_variable_ops.ResourceVariable(1.) self.evaluate(v1.initializer) @@ -435,7 +435,7 @@ class BackpropTest(test.TestCase): self.assertEqual(backprop.implicit_grad(f)()[0][0], None) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeRepeatedSource(self): with backprop.GradientTape(persistent=False) as g: x = constant_op.constant(3.0) @@ -445,7 +445,7 @@ class BackpropTest(test.TestCase): self.assertEqual(self.evaluate(grad), [2.0, 2.0]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentGradientTapeRepeatedSource(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -459,7 +459,7 @@ class BackpropTest(test.TestCase): self.assertEqual(self.evaluate(grad), [3.0, 11.0]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeStructure(self): with backprop.GradientTape(persistent=True) as g: # Using different constant values because constant tensors are @@ -482,7 +482,7 @@ class BackpropTest(test.TestCase): [1.0, {'x2': 2.0, 'x3': 3.0}]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTape(self): with backprop.GradientTape() as g: x = constant_op.constant(3.0) @@ -497,7 +497,7 @@ class BackpropTest(test.TestCase): grad = g.gradient(y, [x])[0] self.assertEqual(self.evaluate(grad), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeWithCond(self): x = constant_op.constant(3.0) @@ -518,7 +518,7 @@ class BackpropTest(test.TestCase): dy = g.gradient(y, [x])[0] self.assertEqual(self.evaluate(dy), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeWithWhileLoop(self): i = constant_op.constant(1) x = constant_op.constant(2.) @@ -553,7 +553,7 @@ class BackpropTest(test.TestCase): g.gradient(y, [x]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentTape(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -567,7 +567,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHigherOrderGradient(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -584,7 +584,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentNestedTape(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -605,7 +605,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeVariable(self): v = resource_variable_ops.ResourceVariable(1.0, name='v') self.evaluate(v.initializer) @@ -615,7 +615,7 @@ class BackpropTest(test.TestCase): self.assertAllEqual(self.evaluate(grad), 2.0) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestedGradients(self): x = constant_op.constant(3.0) with backprop.GradientTape() as g: @@ -900,6 +900,33 @@ class BackpropTest(test.TestCase): 'did you forget to return a value from fn?'): val_and_grads_fn(x, y) + def testZerosCacheDoesntLeakAcrossModes(self): + with ops.Graph().as_default(): + t = random_ops.random_normal(shape=[100, 2]) + x = random_ops.random_normal(shape=[100, 4]) + dy = random_ops.random_normal(shape=[100, 4]) + with backprop.GradientTape() as gradient_tape: + gradient_tape.watch(x) + x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1) + y1 = x1 ** 2. + y = array_ops.concat([y1, t], axis=1) + + dx = gradient_tape.gradient(y, x, output_gradients=dy) + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + sess.run(dx) + + t = random_ops.random_normal(shape=[100, 2]) + x = random_ops.random_normal(shape=[100, 4]) + dy = random_ops.random_normal(shape=[100, 4]) + with backprop.GradientTape() as gradient_tape: + gradient_tape.watch(x) + x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1) + y1 = x1 ** 2. + y = array_ops.concat([y1, t], axis=1) + + dx = gradient_tape.gradient(y, x, output_gradients=dy) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index 9e146f021e813886b42ca72b07122b485901a24b..85b9491903de2ea6ffe1c5ac7ef76efdfda2818b 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -143,7 +143,11 @@ class Context(object): # TODO(agarwal): create and link in some documentation for `execution_mode`. # pylint: disable=redefined-outer-name - def __init__(self, config=None, device_policy=None, execution_mode=None): + def __init__(self, + config=None, + device_policy=None, + execution_mode=None, + server_def=None): """Creates a new Context. Args: @@ -192,6 +196,7 @@ class Context(object): if execution_mode is None: execution_mode = SYNC self._execution_mode = execution_mode + self._server_def = server_def # pylint: enable=redefined-outer-name @@ -231,6 +236,9 @@ class Context(object): opts, self._device_policy) if self._execution_mode == ASYNC: pywrap_tensorflow.TFE_ContextOptionsSetAsync(opts, True) + if self._server_def is not None: + server_def_str = self._server_def.SerializeToString() + pywrap_tensorflow.TFE_ContextOptionsSetServerDef(opts, server_def_str) self._context_handle = pywrap_tensorflow.TFE_NewContext(opts) finally: pywrap_tensorflow.TFE_DeleteContextOptions(opts) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 03393bcd46c4670fcac8f01f274137ee60df0bea..08470f65b04a3f2a9baef88464c8574f507df6c3 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import collections +import functools import numpy as np @@ -46,8 +47,11 @@ def capture_value(tensor_map, value, dtype, name): """Capture a value from outside the function, to pass in as an extra arg.""" captured_value = tensor_map.get(ops.tensor_id(value), None) if captured_value is None: - captured_value = graph_placeholder( - dtype=dtype or value.dtype, shape=value.shape, name=name) + # Note: setting ops.control_dependencies(None) ensures we always put + # capturing placeholders outside of any control flow context. + with ops.control_dependencies(None): + captured_value = graph_placeholder( + dtype=dtype or value.dtype, shape=value.shape, name=name) if captured_value.dtype == dtypes_module.resource: if ops._USE_C_SHAPES: # pylint: disable=protected-access if isinstance(value, ops.EagerTensor): @@ -222,6 +226,11 @@ def _inference_name(n): return "__inference_%s_%s" % (n, ops.uid()) +def _register(fn): + """Registers the function `fn`.""" + context.context().add_function(fn) + + # TODO(apassos) get rid of this by splitting framework.function._DefinedFunction # so it doesn't have the definition-generating logic and is just a container for # an already-defined function. @@ -308,7 +317,7 @@ class GraphModeFunction(object): graph, operations, outputs, - func_outputs, + python_func_outputs, output_shapes, variables=None, attrs=None): @@ -327,9 +336,10 @@ class GraphModeFunction(object): definition. outputs: a flat list of the Tensors in the graph used as outputs to the function - func_outputs: a possibly nested python object which will be returned by - this function. The Tensors in this structure will be replaced by their - corresponding values in outputs. + python_func_outputs: a possibly nested python object which will be + returned by this function. The Tensors in this structure will be + replaced by their corresponding values in outputs. Note that this + structure might contain Python `None`s. output_shapes: List of shapes of all tensors in outputs variables: (optional) List of variables to watch during function execution. @@ -351,9 +361,10 @@ class GraphModeFunction(object): self._function_def = defined_function self._num_outputs = len(defined_function.signature.output_arg) self._ops = operations - self._func_outputs = func_outputs - self._returns = [func_outputs] if isinstance( - func_outputs, (ops.Tensor, type(None))) else _flatten(func_outputs) + self._python_func_outputs = python_func_outputs + self._python_returns = [python_func_outputs] if isinstance( + python_func_outputs, + (ops.Tensor, type(None))) else _flatten(python_func_outputs) self._output_shapes = output_shapes self._variables = variables if variables is not None else [] @@ -368,7 +379,7 @@ class GraphModeFunction(object): c_captured_tensors = set() existing_op_len = len(self._graph.get_operations()) - filtered_outputs = [x for x in self._returns if x is not None] + filtered_outputs = [x for x in self._python_returns if x is not None] self._out_grad_placeholders = [ graph_placeholder(x.dtype, x.shape) for x in filtered_outputs] in_gradients = gradients_impl.gradients( @@ -444,10 +455,16 @@ class GraphModeFunction(object): if not outputs: return op outputs = [outputs] if isinstance(outputs, ops.Tensor) else list(outputs) - for i, s in enumerate(self._output_shapes): - outputs[i].set_shape(s) - real_outputs = outputs[:len(self._returns)] - side_outputs = outputs[len(self._returns):] + + shapes = [shape for shape in self._output_shapes if shape is not None] + for i, shape in enumerate(shapes): + outputs[i].set_shape(shape) + + # `real_outputs` are the actual outputs of the inference graph function; + # `side_outputs` are the intermediate Tensors that were added as outputs to + # the forward graph function so that we can compute its gradient. + real_outputs = outputs[:self._num_outputs] + side_outputs = outputs[self._num_outputs:] def backward_function(*args): return self._backward_function(*(list(args) + side_outputs)) # pylint: disable=not-callable @@ -464,8 +481,8 @@ class GraphModeFunction(object): def output_shapes(self): """The function's output shapes.""" # TODO(ebrevdo): Should we only keep the output shapes associated - # with len(self._returns) outputs? - outputs_list = nest.flatten(self._func_outputs) + # with len(self._python_returns) outputs? + outputs_list = nest.flatten(self._python_func_outputs) j = 0 for i, o in enumerate(outputs_list): if o is not None: @@ -479,12 +496,12 @@ class GraphModeFunction(object): else: outputs_list[i] = self._output_shapes[j] j += 1 - return nest.pack_sequence_as(self._func_outputs, outputs_list) + return nest.pack_sequence_as(self._python_func_outputs, outputs_list) @property def output_dtypes(self): return nest.map_structure( - lambda x: x.dtype if x is not None else None, self._func_outputs) + lambda x: x.dtype if x is not None else None, self._python_func_outputs) @property def captured_inputs(self): @@ -538,8 +555,10 @@ class GraphModeFunction(object): result = op.outputs if not result: return op - for i, s in enumerate(self._output_shapes): - result[i].set_shape(s) + + shapes = [shape for shape in self._output_shapes if shape is not None] + for i, shape in enumerate(shapes): + result[i].set_shape(shape) return self._build_call_outputs(result) @@ -551,11 +570,11 @@ class GraphModeFunction(object): Returns: The actual call output. """ - if self._func_outputs is None: + if self._python_func_outputs is None: return None # Use `nest.flatten` instead of `_flatten` in order to preserve any - # IndexedSlices in `self._func_outputs`. - outputs_list = nest.flatten(self._func_outputs) + # IndexedSlices in `self._python_func_outputs`. + outputs_list = nest.flatten(self._python_func_outputs) j = 0 for i, o in enumerate(outputs_list): if o is not None: @@ -575,7 +594,7 @@ class GraphModeFunction(object): else: outputs_list[i] = result[j] j += 1 - ret = nest.pack_sequence_as(self._func_outputs, outputs_list) + ret = nest.pack_sequence_as(self._python_func_outputs, outputs_list) return ret @@ -591,7 +610,11 @@ def _get_defun_inputs(args): return nest.pack_sequence_as(args, ret) -def _defun_internal(name, func, compiled, args, kwds): +def _deterministic_dict_values(kwds): + return tuple(kwds[key] for key in sorted(kwds)) + + +def _trace_and_define_function(name, func, compiled, args, kwds): """Defines and returns graph-mode version of func.""" graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access with context.graph_mode(): @@ -608,7 +631,8 @@ def _defun_internal(name, func, compiled, args, kwds): tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection( collection) with tmp_graph.as_default(), AutomaticControlDependencies() as a: - func_inputs = _get_defun_inputs(args) + func_args = _get_defun_inputs(args) + func_kwds = _get_defun_inputs(kwds) def convert(x): if x is None: @@ -619,7 +643,7 @@ def _defun_internal(name, func, compiled, args, kwds): this_tape = tape.push_new_tape() try: - func_outputs = func(*func_inputs, **kwds) + func_outputs = func(*func_args, **func_kwds) func_outputs = nest.map_structure(convert, func_outputs) finally: tape.pop_tape(this_tape) @@ -643,8 +667,11 @@ def _defun_internal(name, func, compiled, args, kwds): x.shape if isinstance(x, ops.Tensor) else None for x in outputs_list) - flat_inputs = [x for x in nest.flatten(func_inputs) - if isinstance(x, ops.Tensor)] + func_kwds_values = _deterministic_dict_values(func_kwds) + flat_inputs = [ + x for x in nest.flatten(func_args) + nest.flatten(func_kwds_values) + if isinstance(x, ops.Tensor) + ] all_inputs = flat_inputs + list(extra_placeholders) all_ignored_ops = frozenset(x.op for x in all_inputs) fname = _inference_name(name) @@ -699,42 +726,89 @@ def _cache_key(x): return x -def _register(fn): - """Registers the function `fn`.""" - context.context().add_function(fn) +class _PolymorphicFunction(object): + """Wrapper class for the graph functions defined for a Python function. + See the documentation for `defun` for more information on the semantics of + defined functions. + """ -# TODO(apassos): better error messages for non-hashable arguments. -def named_defun(func, name, compiled=False): - """Defines a function with a given name. + def __init__(self, python_function, name, compiled=False): + """Initializes a polymorphic function. - See the documentation for `defun` for more information on the semantics of - this function. + Args: + python_function: the function to be wrapped. + name: the name given to it. + compiled: if True, the framework will attempt to compile func with XLA. + """ - Args: - func: the function to be wrapped. - name: the name given to it. - compiled: if true, the framework will attempt to compile func with XLA. + self._python_function = python_function + self._name = name + self._compiled = compiled + self._arguments_to_functions = {} + self._variables = [] + + def __get__(self, instance, owner): + """Makes it possible to defun instance methods.""" + del owner + # `instance` here is the instance that this `_PolymorphicFunction` was + # accessed through; e.g., for + # + # class Foo(object): + # + # @function.defun + # def bar(self): + # ... + # + # foo = Foo() + # foo.bar() # `foo.bar` is a `_PolymorphicFunction` instance + # + # then `instance` will be `foo` (and `owner` will be `Foo`). + return functools.partial(self.__call__, instance) - Returns: - the wrapped function. - """ - arguments_to_functions = {} + def _maybe_define_function(self, *args, **kwds): + """Gets a function for these inputs, defining it if necessary. - def decorated(*args, **kwds): - """Decorated version of func.""" - # Macroexpand on non-Tensor arguments - cache_key = tuple(_cache_key(x) for x in args) - if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): - raise ValueError("Tensor keyword arguments are not supported.") - cache_key = (cache_key, tuple(kwds.items())) + Args: + *args: args for the Python function; used to compute the signature + **kwds: kwds for the Python function; used to compute the signature - if cache_key not in arguments_to_functions: - arguments_to_functions[cache_key] = _defun_internal( - name, func, compiled, args, kwds) - return arguments_to_functions[cache_key](*args) + Returns: + A graph function corresponding to the input signature implied by args and + kwds, as well as the inputs that the object should be called with. + """ - return decorated + # TODO(apassos): Better error messages for non-hashable arguments. + kwd_values = _deterministic_dict_values(kwds) + inputs = args + kwd_values + signature = tuple(_cache_key(x) for x in inputs) + # The graph, or whether we're executing eagerly, should be a part of the + # signature so we don't improperly capture tensors such as variables. + signature += tuple([context.executing_eagerly() or ops.get_default_graph()]) + + if signature not in self._arguments_to_functions: + graph_function = _trace_and_define_function( + self._name, self._python_function, self._compiled, args, kwds) + self._arguments_to_functions[signature] = graph_function + self._variables.extend( + [v for v in graph_function.variables if v not in self._variables]) + return graph_function, inputs + else: + return self._arguments_to_functions[signature], inputs + + def __call__(self, *args, **kwds): + """Calls a graph function specialized for this input signature.""" + graph_function, inputs = self._maybe_define_function(*args, **kwds) + return graph_function(*inputs) + + def call_python_function(self, *args, **kwargs): + """Directly calls the wrapped python function.""" + return self._python_function(*args, **kwargs) + + @property + def variables(self): + """Returns a list of variables used in any of the defined functions.""" + return self._variables # TODO(akshayka): Remove the `compiled` flag and create a separate @@ -745,22 +819,28 @@ def defun(func=None, compiled=False): `defun` (short for "define function") trace-compiles a Python function composed of TensorFlow operations into a callable that executes a @{tf.Graph} - containing those operations. When eager execution is enabled, the ability to - create graphs from Python functions makes it possible to incrementally trade - off debugability and interactivity for performance. Functions compiled with - `defun` cannot be inspected with `pdb` and `print` statements; however, - executing a graph generated by `defun` sometimes takes less time and memory - than eagerly executing the corresponding Python function, since specifying - computations as graphs allows for optimizations like automatic buffer reuse - and parallelization among ops. Note that executing a `defun`-compiled function + containing those operations. The callable produced by `defun` contains only + the subgraph of TensorFlow operations that were executed when the Python + function was called with a particular input signature, defined as a list + of the shapes and dtypes of the Python function's Tensor-valued arguments and + the values of its non-Tensor Python objects. In particular, `defun` is _not_ a + compiler for arbitrary Python code. + + When eager execution is enabled, the ability to create graphs from Python + functions makes it possible to incrementally trade off debugability and + interactivity for performance. Functions compiled with `defun` cannot be + inspected with `pdb` and `print` statements; however, executing a graph + generated by `defun` sometimes takes less time and memory than eagerly + executing the corresponding Python function, since specifying computations as + graphs allows for optimizations like automatic buffer reuse and + parallelization among ops. Note that executing a `defun`-compiled function incurs a small constant overhead, so eagerly executing sufficiently small Python functions might take less time than executing their corresponding `defun`-generated graphs. - For a Python function to be compatible with `defun`, the values of its keyword - arguments cannot be Tensors and all of its arguments, including its keyword - arguments, must be hashable Python objects or lists thereof. Additionally, it - must return zero or more @{tf.Tensor} objects. + For a Python function to be compatible with `defun`, all of its arguments must + be hashable Python objects or lists thereof. Additionally, it must return zero + or more @{tf.Tensor} objects. _Example Usage_ @@ -833,20 +913,23 @@ def defun(func=None, compiled=False): _Tracing and Input Signatures_. The signature of inputs supplied to `F` is defined to be a tuple of the shapes - and dtypes of Tensor-typed arguments and the values of non-Tensor arguments - and keyword arguments. Every time `F` is invoked, the signature of its inputs - are inferred. The first time `F(*args, **kwargs)` is invoked with a particular - signature, `f(*args, **kwargs)` is executed and all the TensorFlow operations - that `f` executes, along with the Tensors that flow between them, are recorded - in a TensorFlow graph. `F` caches this graph and binds it to the inputs' - signature; every subsequent invocation of `F` with inputs conforming to this - signature will immediately retrieve the cached graph and pass it to the - TensorFlow runtime for execution. - - Be aware that because `F` only logs TensorFlow operations, all non-TensorFlow - operations that `f` executes will only shape the _construction_ of the graphs - that `F` executes: They won't be executed when the graphs themselves are - executed. For example, whereas the Python function + and dtypes of Tensor-typed arguments and the values of non-Tensor arguments, + where "arguments" includes both args and kwargs. Every time `F` is invoked, + the signature of its inputs are inferred. The first time `F(*args, **kwargs)` + is invoked with a particular signature, `f(*args, **kwargs)` is executed and + all the TensorFlow operations that `f` executes, along with the Tensors that + flow between them, are recorded in a TensorFlow graph. `F` caches this graph + and binds it to the inputs' signature; every subsequent invocation of `F` with + inputs conforming to this signature will immediately retrieve the cached graph + and pass it to the TensorFlow runtime for execution. + + Be aware that because `F` only logs TensorFlow operations, all the other + Python code that `f` executes will only shape the _construction_ of the graphs + that `F` executes: the Python code won't be executed when the graphs + themselves are executed, though it will be executed every time the Python + function is traced (and a given Python function might be traced multiple + times, once for each input signature it is invoked with). For example, whereas + the Python function ```python import tensorflow as tf @@ -854,17 +937,23 @@ def defun(func=None, compiled=False): tf.enable_eager_execution() - matrix = tf.eye(5) - # `matrix` is assumed to be a Tensor def add_noise(): - return matrix + np.random.randn(matrix.shape[0], matrix.shape[1]) + return tf.eye(5) + np.random.randn(5, 5) ``` will return a different output everytime it is invoked, the compiled function `compiled = tf.contrib.eager.defun(add_noise)` will return the same value every time it is called, since a particular random offset generated by NumPy will be inserted into the graph as a TensorFlow constant. The solution is to - replace the call to `np.random.randn` with `tf.random_normal(matrix.shape)`. + replace the call to `np.random.randn` with `tf.random_normal((5, 5))`. + + _Python Side-Effects_ + A corollary of the previous discussion on tracing is the following: If a + Python function `f` has Python side-effects, then executing `f` multiple times + will not necessarily be semantically equivalent to executing `F = + tf.contrib.eager.defun(f)` multiple times; this difference is due to the fact + that `defun` only captures the subgraph of TensorFlow operations that is + constructed when `f` is called in a graph-building context. _Python Control Flow_. The structure of many machine learning computations depend upon whether one is @@ -991,7 +1080,7 @@ def defun(func=None, compiled=False): except AttributeError: name = "function" return tf_decorator.make_decorator( - function, named_defun(function, name, compiled=compiled)) + function, _PolymorphicFunction(function, name, compiled=compiled)) # This code path is for the `foo = tfe.defun(foo, ...)` use case if func is not None: @@ -1048,15 +1137,8 @@ def make_defun_op(func, *args, **kwds): A wrapper object which can be queried for its output properties, and which can be called directly the way a `@defun` wrapped function can. - - Raises: - ValueError: if any of the keyword arguments to `func` are `EagerTensor` - objects (not yet supported). """ - name = func.__name__ - if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): - raise ValueError("Tensor keyword arguments are not supported.") - return _defun_internal(name, func, False, args, kwds) + return _trace_and_define_function(func.__name__, func, False, args, kwds) class AutomaticControlDependencies(object): diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index cfdbe5f079058906d18268c924819e0cc428bca7..e1801b7ec655f38b1c9988cc30bc07530268400e 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -34,6 +34,7 @@ from tensorflow.python.layers import convolutional from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -90,6 +91,32 @@ class FunctionTest(test.TestCase): self.assertAllEqual(step(), 2.0) + def testGraphGradientVariable(self): + with ops.Graph().as_default(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + + @function.defun + def f(): + return 2.0 * v + + node = f() + grads, = gradients_impl.gradients(node, v) + v.initializer.run() + self.assertAllEqual(grads.eval(), 2.0) + self.assertEqual(grads.shape, v.shape) + + def testGraphEagerIsolation(self): + + @function.defun + def f(): + v = resource_variable_ops.ResourceVariable(1.0) + return v.read_value() + + self.assertAllEqual(f(), 1.0) + + with ops.Graph().as_default(): + self.assertEqual(f().shape, ()) + def testBasicDefunOpGraphMode(self): matmul = function.defun(math_ops.matmul) @@ -196,6 +223,21 @@ class FunctionTest(test.TestCase): compiled = function.defun(f) compiled() + def testVariableInLoopInFunction(self): + + @function.defun + def test_function(): + + def loop_test(_): + return False + + def loop_body(_): + return variable_scope.get_variable('a', shape=()) + + return control_flow_ops.while_loop(loop_test, loop_body, [0.0]) + + self.assertEqual(test_function().shape, []) + def testDefunShapeInferenceWithCapturedResourceVariableInGraphMode(self): with context.graph_mode(): v = resource_variable_ops.ResourceVariable([[1, 2], [3, 4]]) @@ -512,6 +554,60 @@ class FunctionTest(test.TestCase): g = backprop.gradients_function(wrapper, [0])(constant_op.constant(0.0)) self.assertAllEqual(g[0], 1.) + @function.defun + def foo(a): + return None, a * a + + x = constant_op.constant(5.0) + with backprop.GradientTape() as tp: + tp.watch(x) + none, r = foo(x) + g = tp.gradient(r, x) + + self.assertIs(none, None) + self.assertAllEqual(r, 25.0) + self.assertAllEqual(g, 2 * 5.0) + + def testNestedDifferentiableFunction(self): + @function.defun + def foo(a, b): + return a * math_ops.add(a, b) + + @function.defun + def bar(x): + return foo(x, 1.0) + + x = constant_op.constant(5.0) + with backprop.GradientTape() as tp: + tp.watch(x) + result = bar(x) + grad = tp.gradient(result, x) + + self.assertAllEqual(grad, 2 * 5.0 + 1.0) + + def testNestedDifferentiableFunctionNoneOutputs(self): + @function.defun + def foo(a, b): + return None, a * math_ops.add(a, b), None, 2*a + + @function.defun + def bar(x): + return foo(x, 1.0) + + x = constant_op.constant(5.0) + with backprop.GradientTape(persistent=True) as tp: + tp.watch(x) + none1, r1, none2, r2 = bar(x) + g1 = tp.gradient(r1, x) + g2 = tp.gradient(r2, x) + + self.assertAllEqual(r1, 30.0) + self.assertAllEqual(r2, 10.0) + self.assertIs(none1, None) + self.assertIs(none2, None) + self.assertAllEqual(g1, 2 * 5.0 + 1.0) + self.assertAllEqual(g2, 2.0) + def testNoneOutput(self): @function.defun @@ -633,6 +729,125 @@ class FunctionTest(test.TestCase): y = model(x) self.assertAllEqual([[[[4.0]]]], y.numpy()) + def testVariablesAreTracked(self): + v = resource_variable_ops.ResourceVariable(1.0) + + def foo(x): + return v * x + + defined = function.defun(foo) + + x = constant_op.constant([1.0]) + self.assertAllEqual(defined.variables, []) + _ = defined(x) + self.assertAllEqual(defined.variables, [v]) + + x = constant_op.constant([1.0, 2.0]) + _ = defined(x) # ensure the variables list remains the same + self.assertAllEqual(defined.variables, [v]) + + def testTensorKeywordArguments(self): + + def foo(a, b): + del a + return b + + defined = function.defun(foo) + a = constant_op.constant(2.0) + b = constant_op.constant([1.0, 2.0]) + one = defined(a, b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + two = defined(a=a, b=b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + three = defined(b=b, a=a) + self.assertEqual(len(defined._arguments_to_functions), 1) + + four = defined(a, b=b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + # The next call corresponds to a new input signature, hence + # we expect another function to be defined. + five = defined(b, a) + self.assertEqual(len(defined._arguments_to_functions), 2) + + six = defined(a=b, b=a) + self.assertEqual(len(defined._arguments_to_functions), 2) + + seven = defined(b=a, a=b) + self.assertEqual(len(defined._arguments_to_functions), 2) + + self.assertAllEqual(one, [1.0, 2.0]) + self.assertAllEqual(two, [1.0, 2.0]) + self.assertAllEqual(three, [1.0, 2.0]) + self.assertAllEqual(four, [1.0, 2.0]) + self.assertAllEqual(five, 2.0) + self.assertAllEqual(six, 2.0) + self.assertAllEqual(seven, 2.0) + + def testGradientWithKeywordArguments(self): + matmul = function.defun(math_ops.matmul) + + def sq(x): + return matmul(a=x, b=x, transpose_a=True) + + t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) + grad_t, = backprop.gradients_function(sq, [0])(t) + self.assertAllEqual(grad_t, [[6, 6], [14, 14]]) + + with backprop.GradientTape(persistent=True) as gtape: + gtape.watch(t) + one = matmul(t, b=t, transpose_a=True) + two = matmul(b=t, a=t, transpose_a=True) + three = matmul(a=t, b=t, transpose_a=True) + + for output in [one, two, three]: + self.assertAllEqual(gtape.gradient(output, t), [[6, 6], [14, 14]]) + + def testGradientInFunctionWithKeywordArguments(self): + + @function.defun + def f(x): + return backprop.gradients_function(lambda y: y * y, [0])(x)[0] + + self.assertAllEqual(f(x=constant_op.constant(1.0)), 2.0) + + def testDecoratingInstanceMethod(self): + + class Foo(object): + + def one(self, tensor): + return tensor + + @function.defun + def two(self, tensor): + return self.one(tensor) + + foo = Foo() + t = constant_op.constant(1.0) + out = foo.two(t) + self.assertEqual(float(out), 1.0) + + def testPythonCallWithSideEffects(self): + state = [] + + @function.defun + def side_effecting_function(): + state.append(0) + + side_effecting_function() + self.assertAllEqual(state, [0]) + + # The second invocation should call the graph function, which shouldn't + # trigger the list append. + side_effecting_function() + self.assertAllEqual(state, [0]) + + # Whereas calling the python function directly should create a side-effect. + side_effecting_function.call_python_function() + self.assertAllEqual(state, [0, 0]) + @test_util.with_c_shapes class AutomaticControlDependenciesTest(test.TestCase): diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py index 760a1485523798c6587e95804488a14b42a69bc0..848adf4fd3b2c93e7b5afb3ec2911857663c29bb 100644 --- a/tensorflow/python/eager/graph_callable.py +++ b/tensorflow/python/eager/graph_callable.py @@ -110,13 +110,25 @@ class _VariableCapturingScope(object): """ # TODO(apassos) ignoring the regularizer and partitioner here; figure out # how to deal with these. - def _custom_getter(getter=None, name=None, shape=None, dtype=dtypes.float32, # pylint: disable=missing-docstring - initializer=None, regularizer=None, reuse=None, - trainable=True, collections=None, caching_device=None, # pylint: disable=redefined-outer-name - partitioner=None, validate_shape=True, - use_resource=None): + def _custom_getter( # pylint: disable=missing-docstring + getter=None, + name=None, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + reuse=None, + trainable=True, + collections=None, + caching_device=None, # pylint: disable=redefined-outer-name + partitioner=None, + validate_shape=True, + use_resource=None, + aggregation=variable_scope.VariableAggregation.NONE, + synchronization=variable_scope.VariableSynchronization.AUTO): del getter, regularizer, partitioner, validate_shape, use_resource, dtype - del collections, initializer, trainable, reuse, caching_device, shape, + del collections, initializer, trainable, reuse, caching_device, shape + del aggregation, synchronization assert name in self.variables v = self.variables[name] return v.variable @@ -136,13 +148,24 @@ class _VariableCapturingScope(object): """ # TODO(apassos) ignoring the regularizer and partitioner here; figure out # how to deal with these. - def _custom_getter(getter=None, name=None, shape=None, dtype=dtypes.float32, # pylint: disable=missing-docstring - initializer=None, regularizer=None, reuse=None, - trainable=True, collections=None, caching_device=None, # pylint: disable=redefined-outer-name - partitioner=None, validate_shape=True, - use_resource=None): + def _custom_getter( # pylint: disable=missing-docstring + getter=None, + name=None, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + reuse=None, + trainable=True, + collections=None, + caching_device=None, # pylint: disable=redefined-outer-name + partitioner=None, + validate_shape=True, + use_resource=None, + aggregation=variable_scope.VariableAggregation.NONE, + synchronization=variable_scope.VariableSynchronization.AUTO): del getter, regularizer, collections, caching_device, partitioner - del use_resource, validate_shape + del use_resource, validate_shape, aggregation, synchronization if name in self.tf_variables: if reuse: return self.tf_variables[name].initialized_value() diff --git a/tensorflow/python/eager/memory_test.py b/tensorflow/python/eager/memory_test.py new file mode 100644 index 0000000000000000000000000000000000000000..74c6cbdd319a3a0476adbff08fc6e70fee65df5c --- /dev/null +++ b/tensorflow/python/eager/memory_test.py @@ -0,0 +1,108 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for memory leaks in eager execution. + +It is possible that this test suite will eventually become flaky due to taking +too long to run (since the tests iterate many times), but for now they are +helpful for finding memory leaks since not all PyObject leaks are found by +introspection (test_util decorators). Please be careful adding new tests here. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python import keras +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context +from tensorflow.python.eager import test +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops + +# memory_profiler might not be available in the OSS version of TensorFlow. +try: + import memory_profiler # pylint:disable=g-import-not-at-top +except ImportError: + memory_profiler = None + + +class SingleLayerNet(keras.Model): + """Simple keras model used to ensure that there are no leaks.""" + + def __init__(self): + super(SingleLayerNet, self).__init__() + self.fc1 = keras.layers.Dense(5) + + def call(self, x): + return self.fc1(x) + + +class MemoryTest(test.TestCase): + + def assertNotIncreasingMemory(self, + f, + num_iters=100000, + increase_threshold_absolute_mb=10): + """Assert memory usage doesn't increase beyond given threshold for f.""" + + with context.eager_mode(): + # Warm up. + f() + + initial = memory_profiler.memory_usage(-1)[0] + + for _ in xrange(num_iters): + f() + + increase = memory_profiler.memory_usage(-1)[0] - initial + + assert increase < increase_threshold_absolute_mb, ( + "Increase is too high. Initial memory usage: %f MB. Increase: %f MB. " + "Maximum allowed increase: %f") % (initial, increase, + increase_threshold_absolute_mb) + + def testMemoryLeakInSimpleModelForwardOnly(self): + if memory_profiler is None: + self.skipTest("memory_profiler required to run this test") + + inputs = array_ops.zeros([32, 100], dtypes.float32) + net = SingleLayerNet() + + def f(): + with backprop.GradientTape(): + net(inputs) + + self.assertNotIncreasingMemory(f) + + def testMemoryLeakInSimpleModelForwardAndBackward(self): + if memory_profiler is None: + self.skipTest("memory_profiler required to run this test") + + inputs = array_ops.zeros([32, 100], dtypes.float32) + net = SingleLayerNet() + + def f(): + with backprop.GradientTape() as tape: + result = net(inputs) + + tape.gradient(result, net.variables) + + del tape + + self.assertNotIncreasingMemory(f) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index e3ce0ef9d02be1576bd76b8a17fa2f220d33e486..57b4dab51cc766042dfa895b197b3e3de037269d 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -205,14 +205,20 @@ bool ParseDimensionValue(const string& key, PyObject* py_value, } bool ParseStringValue(const string& key, PyObject* py_value, TF_Status* status, - const char** value) { + tensorflow::StringPiece* value) { if (PyBytes_Check(py_value)) { - *value = PyBytes_AsString(py_value); + Py_ssize_t size = 0; + char* buf = nullptr; + if (PyBytes_AsStringAndSize(py_value, &buf, &size) < 0) return false; + *value = tensorflow::StringPiece(buf, size); return true; } #if PY_MAJOR_VERSION >= 3 if (PyUnicode_Check(py_value)) { - *value = PyUnicode_AsUTF8(py_value); + Py_ssize_t size = 0; + char* buf = PyUnicode_AsUTF8AndSize(py_value, &size); + if (buf == nullptr) return false; + *value = tensorflow::StringPiece(buf, size); return true; } #endif @@ -275,8 +281,16 @@ bool SetOpAttrList( } if (type == TF_ATTR_STRING) { - PARSE_LIST(const char*, ParseStringValue); - TFE_OpSetAttrStringList(op, key, values.get(), num_values); + std::unique_ptr values(new const void*[num_values]); + std::unique_ptr lengths(new size_t[num_values]); + for (int i = 0; i < num_values; ++i) { + tensorflow::StringPiece value; + tensorflow::Safe_PyObjectPtr py_value(PySequence_ITEM(py_list, i)); + if (!ParseStringValue(key, py_value.get(), status, &value)) return false; + values[i] = value.data(); + lengths[i] = value.size(); + } + TFE_OpSetAttrStringList(op, key, values.get(), lengths.get(), num_values); } else if (type == TF_ATTR_INT) { PARSE_LIST(int64_t, ParseInt64Value); TFE_OpSetAttrIntList(op, key, values.get(), num_values); @@ -379,12 +393,15 @@ void SetOpAttrListDefault( TF_Status* status) { if (type == TF_ATTR_STRING) { int num_values = attr.default_value().list().s_size(); - std::unique_ptr values(new const char*[num_values]); + std::unique_ptr values(new const void*[num_values]); + std::unique_ptr lengths(new size_t[num_values]); (*attr_list_sizes)[key] = num_values; for (int i = 0; i < num_values; i++) { - values[i] = attr.default_value().list().s(i).data(); + const string& v = attr.default_value().list().s(i); + values[i] = v.data(); + lengths[i] = v.size(); } - TFE_OpSetAttrStringList(op, key, values.get(), num_values); + TFE_OpSetAttrStringList(op, key, values.get(), lengths.get(), num_values); } else if (type == TF_ATTR_INT) { int num_values = attr.default_value().list().i_size(); std::unique_ptr values(new int64_t[num_values]); @@ -470,9 +487,9 @@ bool SetOpAttrScalar( tensorflow::gtl::FlatMap* attr_list_sizes, TF_Status* status) { if (type == TF_ATTR_STRING) { - const char* value; + tensorflow::StringPiece value; if (!ParseStringValue(key, py_value, status, &value)) return false; - TFE_OpSetAttrString(op, key, value); + TFE_OpSetAttrString(op, key, value.data(), value.size()); } else if (type == TF_ATTR_INT) { int64_t value; if (!ParseInt64Value(key, py_value, status, &value)) return false; @@ -533,7 +550,7 @@ bool SetOpAttrScalar( // (which is what the various "defun" or "Defun" decorators do). // And in the future also allow an object that can encapsulate // the function name and its attribute values. - const char* func_name = nullptr; + tensorflow::StringPiece func_name; if (!ParseStringValue(key, py_value, status, &func_name)) { PyObject* name_attr = PyObject_GetAttrString(py_value, "name"); if (name_attr == nullptr || @@ -549,7 +566,8 @@ bool SetOpAttrScalar( return false; } } - TFE_Op* func = TFE_NewOp(ctx, func_name, status); + TFE_Op* func = TFE_NewOp( + ctx, string(func_name.data(), func_name.size()).c_str(), status); if (TF_GetCode(status) != TF_OK) return false; TFE_OpSetAttrFunction(op, key, func); TFE_DeleteOp(func); @@ -873,22 +891,6 @@ static tensorflow::DataType FastTensorDtype(PyObject* tensor) { return static_cast(id); } -static tensorflow::int64 FastHandleId(PyObject* variable) { - PyObject* handle = PyObject_GetAttrString(variable, "handle"); - if (handle == nullptr) { - return -1; - } - tensorflow::int64 id = FastTensorId(handle); - Py_DECREF(handle); - return id; -} - -struct CompareByHandleId { - bool operator()(PyObject* lhs, PyObject* rhs) { - return FastHandleId(lhs) < FastHandleId(rhs); - } -}; - class GradientTape : public tensorflow::eager::GradientTape { public: @@ -897,35 +899,63 @@ class GradientTape persistent) {} virtual ~GradientTape() { - for (PyObject* v : watched_variables_) { - Py_DECREF(v); + for (const IdAndVariable& v : watched_variables_) { + Py_DECREF(v.variable); } } void WatchVariable(PyObject* v) { - auto insert_result = watched_variables_.insert(v); - if (insert_result.second) { - // Only increment the reference count if we aren't already watching this - // variable. - Py_INCREF(v); - } - PyObject* handle = PyObject_GetAttrString(v, "handle"); + tensorflow::Safe_PyObjectPtr handle(PyObject_GetAttrString(v, "handle")); if (handle == nullptr) { return; } - tensorflow::int64 id = FastTensorId(handle); - Py_DECREF(handle); + tensorflow::int64 id = FastTensorId(handle.get()); + if (!PyErr_Occurred()) { this->Watch(id); } + + tensorflow::mutex_lock l(watched_variables_mu_); + auto insert_result = watched_variables_.emplace(id, v); + + if (insert_result.second) { + // Only increment the reference count if we aren't already watching this + // variable. + Py_INCREF(v); + } } - const std::set WatchedVariables() { - return watched_variables_; + PyObject* GetVariablesAsPyTuple() { + tensorflow::mutex_lock l(watched_variables_mu_); + PyObject* result = PyTuple_New(watched_variables_.size()); + Py_ssize_t pos = 0; + for (const IdAndVariable& id_and_variable : watched_variables_) { + PyTuple_SET_ITEM(result, pos++, id_and_variable.variable); + Py_INCREF(id_and_variable.variable); + } + return result; } private: - std::set watched_variables_; + // We store an IdAndVariable in the map since the map needs to be locked + // during insert, but should not call back into python during insert to avoid + // deadlocking with the GIL. + struct IdAndVariable { + tensorflow::int64 id; + PyObject* variable; + + IdAndVariable(tensorflow::int64 id, PyObject* variable) + : id(id), variable(variable) {} + }; + struct CompareById { + bool operator()(const IdAndVariable& lhs, const IdAndVariable& rhs) const { + return lhs.id < rhs.id; + } + }; + + tensorflow::mutex watched_variables_mu_; + std::set watched_variables_ + GUARDED_BY(watched_variables_mu_); }; typedef struct { @@ -1217,15 +1247,7 @@ void TFE_Py_TapeSetWatchVariable(PyObject* variable) { } PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) { - const auto& watched_variables = - reinterpret_cast(tape)->tape->WatchedVariables(); - PyObject* result = PyTuple_New(watched_variables.size()); - Py_ssize_t pos = 0; - for (PyObject* variable : watched_variables) { - PyTuple_SET_ITEM(result, pos++, variable); - Py_INCREF(variable); - } - return result; + return reinterpret_cast(tape)->tape->GetVariablesAsPyTuple(); } namespace { @@ -1869,6 +1891,8 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs, delete backward_function; }); + Py_DECREF(num_inputs); + Py_RETURN_NONE; } @@ -1927,8 +1951,10 @@ bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info, Py_INCREF(output->get()); // stay alive after since tuple steals. PyTuple_SET_ITEM(outputs.get(), 0, output->get()); - if (!RecordGradient(GetPythonObjectFromString("ReadVariableOp"), - inputs.get(), Py_None, outputs.get(), Py_None)) { + tensorflow::Safe_PyObjectPtr op_string( + GetPythonObjectFromString("ReadVariableOp")); + if (!RecordGradient(op_string.get(), inputs.get(), Py_None, outputs.get(), + Py_None)) { return false; } } diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index d538c6c415b1fb756f7cea4ca73af7448cd231f9..8ee38d35cc152e6c281e83d7fd49540ddaee2a7e 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -1,8 +1,4 @@ -package( - default_visibility = [ - "//tensorflow:internal", - ], -) +package(default_visibility = ["//tensorflow:internal"]) licenses(["notice"]) # Apache 2.0 @@ -10,8 +6,15 @@ load("//tensorflow:tensorflow.bzl", "py_test") py_library( name = "estimator_py", - srcs = ["estimator_lib.py"], + srcs = [ + "__init__.py", + "estimator_lib.py", + ], srcs_version = "PY2AND3", + visibility = [ + "//tensorflow:__pkg__", + "//tensorflow:internal", + ], deps = [ ":baseline", ":boosted_trees", @@ -27,7 +30,7 @@ py_library( ":parsing_utils", ":run_config", ":training", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -37,10 +40,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":gc", - "//tensorflow/python:errors", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:util", ], @@ -54,10 +54,7 @@ py_test( deps = [ ":estimator", ":exporter", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -66,8 +63,7 @@ py_library( srcs = ["gc.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -78,10 +74,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":gc", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -91,12 +84,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":export_output", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -109,12 +97,7 @@ py_test( deps = [ ":export_output", ":model_fn", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -126,11 +109,7 @@ py_library( ":estimator", ":exporter", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -149,13 +128,7 @@ py_test( ":inputs", ":run_config", ":training", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -164,7 +137,7 @@ py_library( srcs = ["run_config.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:protos_all_py", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -176,8 +149,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -190,14 +162,7 @@ py_library( ":head", ":model_fn", ":optimizers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -221,26 +186,7 @@ py_test( ":numpy_io", ":pandas_io", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -253,20 +199,7 @@ py_library( ":estimator", ":head", ":model_fn", - "//tensorflow/python:array_ops", - "//tensorflow/python:boosted_trees_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -275,21 +208,13 @@ py_test( size = "medium", srcs = ["canned/boosted_trees_test.py"], srcs_version = "PY2AND3", + tags = [ + "optonly", + ], deps = [ ":boosted_trees", - "//tensorflow/core/kernels/boosted_trees:boosted_trees_proto_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:platform_test", - "//tensorflow/python:resources", - "//tensorflow/python:training", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", + ":inputs", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -302,14 +227,7 @@ py_library( ":head", ":model_fn", ":optimizers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:summary", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -326,22 +244,7 @@ py_library( ":model_fn", ":numpy_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -364,16 +267,7 @@ py_test( ":numpy_io", ":pandas_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -389,19 +283,7 @@ py_library( ":linear", ":model_fn", ":optimizers", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -424,17 +306,7 @@ py_test( ":numpy_io", ":pandas_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:nn", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -446,10 +318,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/data", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -460,10 +329,7 @@ py_test( tags = ["notsan"], # b/67510291 deps = [ ":util", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:training", - "//tensorflow/python/data", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -480,21 +346,7 @@ py_library( ":model_fn", ":run_config", ":util", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:metrics", - "//tensorflow/python:platform", - "//tensorflow/python:random_seed", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/data", - "//tensorflow/python/saved_model:builder", - "//tensorflow/python/saved_model:constants", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -513,29 +365,7 @@ py_test( ":model_fn", ":numpy_io", ":run_config", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:lib", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:saver_test_utils", - "//tensorflow/python:session", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", - "//tensorflow/python/data", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:loader", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -548,9 +378,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:dtypes", - "//tensorflow/python:parsing_ops", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -561,10 +389,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":parsing_utils", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:parsing_ops", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -573,9 +398,7 @@ py_library( srcs = ["export/export_output.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python/saved_model:signature_def_utils", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -587,13 +410,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":export_output", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -606,7 +423,7 @@ py_library( deps = [ ":export_export", ":export_output", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -618,13 +435,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":util", - "//tensorflow/python:array_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:tensor_shape", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -637,17 +448,8 @@ py_test( deps = [ ":export_export", ":export_output", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python/saved_model:signature_def_utils", + ":util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -660,24 +462,7 @@ py_library( ":metric_keys", ":model_fn", ":prediction_keys", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:nn", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:weights_broadcast_ops", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -696,23 +481,7 @@ py_test( ":model_fn", ":numpy_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -725,7 +494,7 @@ py_library( deps = [ ":numpy_io", ":pandas_io", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -737,11 +506,7 @@ py_library( ":estimator", ":head", ":optimizers", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -759,25 +524,7 @@ py_library( ":numpy_io", ":pandas_io", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -795,7 +542,7 @@ py_test( deps = [ ":linear", ":linear_testing_utils", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -824,9 +571,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":numpy_io", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -835,7 +580,7 @@ py_library( srcs = ["canned/optimizers.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -847,8 +592,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":optimizers", - "//tensorflow/python:client_testlib", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -866,9 +610,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":pandas_io", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -888,15 +630,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -910,7 +644,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":inputs_queues", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -921,10 +655,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":inputs_queues", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:session", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -937,32 +668,7 @@ py_library( ":export_export", ":model_fn", ":run_config", - "//tensorflow/python:check_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:platform", - "//tensorflow/python:random_seed", - "//tensorflow/python:session", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:summary", - "//tensorflow/python:tensor_util", - "//tensorflow/python:training", - "//tensorflow/python:training_util", - "//tensorflow/python:util", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", - "//tensorflow/python/keras:backend", - "//tensorflow/python/keras:engine", - "//tensorflow/python/keras:layers", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -971,21 +677,47 @@ py_test( size = "large", srcs = ["keras_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "no_windows", + "notsan", + ], deps = [ ":keras", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:run_config", - "//tensorflow/python/keras", - "//tensorflow/python/keras:backend", - "//tensorflow/python/keras:engine", "//third_party/py/numpy", ], ) + +py_library( + name = "expect_numpy_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect numpy to already be installed on the system, e.g. via + # `pip install numpy` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_pandas_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect pandas to already be installed on the system, e.g. via + # `pip install pandas` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_six_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect six to already be installed on the system, e.g. via + # `pip install six` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_tensorflow_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect tensorflow to already be installed on the system, e.g. via + # `pip install tensorflow` or `pip install tensorflow_gpu` + visibility = ["//visibility:public"], +) diff --git a/tensorflow/python/estimator/__init__.py b/tensorflow/python/estimator/__init__.py index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..8cf8df567f0e36604b5c3f6fe992b572d6632954 100644 --- a/tensorflow/python/estimator/__init__.py +++ b/tensorflow/python/estimator/__init__.py @@ -0,0 +1,25 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Import Estimator APIs. + +Note: This file is imported by the create_estimator_api genrule. It must +transitively import all Estimator modules/packages for their @estimator_export +annotations to generate the public Estimator python API. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow.python.estimator.estimator_lib diff --git a/tensorflow/python/estimator/api/BUILD b/tensorflow/python/estimator/api/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..aa5a29e6dd148c39ebb098cb99cb1907d9c5a9d9 --- /dev/null +++ b/tensorflow/python/estimator/api/BUILD @@ -0,0 +1,18 @@ +package( + default_visibility = [ + "//tensorflow:internal", + ], +) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow/tools/api/generator:api_gen.bzl", "gen_api_init_files") +load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES") + +gen_api_init_files( + name = "estimator_python_api_gen", + api_name = "estimator", + output_files = ESTIMATOR_API_INIT_FILES, + package = "tensorflow.python.estimator", + package_dep = "//tensorflow/python/estimator:estimator_py", +) diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py index 980c0573726945bcc80863319da98a220c86bd91..20c7a69b7cb071365e5442b512c1a858a7e0b246 100644 --- a/tensorflow/python/estimator/canned/baseline.py +++ b/tensorflow/python/estimator/canned/baseline.py @@ -24,10 +24,10 @@ Example: classifier = BaselineClassifier(n_classes=3) # Input builders -def input_fn_train: # returns x, y (where y represents label's class index). +def input_fn_train(): # returns x, y (where y represents label's class index). pass -def input_fn_eval: # returns x, y (where y represents label's class index). +def input_fn_eval(): # returns x, y (where y represents label's class index). pass # Fit model. @@ -59,7 +59,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops.losses import losses from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # The default learning rate of 0.3 is a historical artifact of the initial # implementation, but seems a reasonable choice. @@ -174,7 +174,7 @@ def _baseline_model_fn(features, labels, mode, head, optimizer, train_op_fn=train_op_fn) -@tf_export('estimator.BaselineClassifier') +@estimator_export('estimator.BaselineClassifier') class BaselineClassifier(estimator.Estimator): """A classifier that can establish a simple baseline. @@ -215,6 +215,13 @@ class BaselineClassifier(estimator.Estimator): * if `weight_column` is not `None`, a feature with `key=weight_column` whose value is a `Tensor`. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility """ def __init__(self, @@ -277,7 +284,7 @@ class BaselineClassifier(estimator.Estimator): config=config) -@tf_export('estimator.BaselineRegressor') +@estimator_export('estimator.BaselineRegressor') class BaselineRegressor(estimator.Estimator): """A regressor that can establish a simple baseline. @@ -313,6 +320,13 @@ class BaselineRegressor(estimator.Estimator): * if `weight_column` is not `None`, a feature with `key=weight_column` whose value is a `Tensor`. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility """ def __init__(self, diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py index 4e6010a162be6e6b7288900b428d7841db6e453c..8afef1b65a8d57e2b7ce3e4e512c622ca107ab83 100644 --- a/tensorflow/python/estimator/canned/boosted_trees.py +++ b/tensorflow/python/estimator/canned/boosted_trees.py @@ -39,7 +39,7 @@ from tensorflow.python.summary import summary from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # TODO(nponomareva): Reveal pruning params here. _TreeHParams = collections.namedtuple('TreeHParams', [ @@ -168,9 +168,10 @@ def _group_features_by_num_buckets(sorted_feature_columns): # pylint:enable=protected-access # Replace the dummy key with the real max num of buckets for all bucketized # columns. - bucket_size_to_feature_ids_dict[ - max_buckets_for_bucketized] = bucket_size_to_feature_ids_dict[ - _DUMMY_NUM_BUCKETS] + if max_buckets_for_bucketized not in bucket_size_to_feature_ids_dict: + bucket_size_to_feature_ids_dict[max_buckets_for_bucketized] = [] + bucket_size_to_feature_ids_dict[max_buckets_for_bucketized].extend( + bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS]) del bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS] feature_ids_list = list(bucket_size_to_feature_ids_dict.values()) @@ -712,9 +713,17 @@ def _create_regression_head(label_dimension, weight_column=None): # pylint: enable=protected-access -@tf_export('estimator.BoostedTreesClassifier') +@estimator_export('estimator.BoostedTreesClassifier') class BoostedTreesClassifier(estimator.Estimator): - """A Classifier for Tensorflow Boosted Trees models.""" + """A Classifier for Tensorflow Boosted Trees models. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ def __init__(self, feature_columns, @@ -830,9 +839,17 @@ class BoostedTreesClassifier(estimator.Estimator): model_fn=_model_fn, model_dir=model_dir, config=config) -@tf_export('estimator.BoostedTreesRegressor') +@estimator_export('estimator.BoostedTreesRegressor') class BoostedTreesRegressor(estimator.Estimator): - """A Regressor for Tensorflow Boosted Trees models.""" + """A Regressor for Tensorflow Boosted Trees models. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ def __init__(self, feature_columns, diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py index 9ea4f484744762a98c67207d582bcc5b7be8d850..33e9e69b041a7d250c9d86bdf8912bf0585f7d81 100644 --- a/tensorflow/python/estimator/canned/boosted_trees_test.py +++ b/tensorflow/python/estimator/canned/boosted_trees_test.py @@ -500,6 +500,50 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): self.assertEqual(2, ensemble.trees[0].nodes[0].bucketized_split.feature_id) self.assertEqual(0, ensemble.trees[0].nodes[0].bucketized_split.threshold) + def testTrainEvaluateAndPredictWithOnlyIndicatorColumn(self): + categorical = feature_column.categorical_column_with_vocabulary_list( + key='categorical', vocabulary_list=('bad', 'good', 'ok')) + feature_indicator = feature_column.indicator_column(categorical) + + labels = np.array([[0.], [5.7], [5.7], [0.], [0.]], dtype=np.float32) + # Our categorical feature defines the labels perfectly + input_fn = numpy_io.numpy_input_fn( + x={ + 'categorical': np.array(['bad', 'good', 'good', 'ok', 'bad']), + }, + y=labels, + batch_size=5, + shuffle=False) + + # Train depth 1 tree. + est = boosted_trees.BoostedTreesRegressor( + feature_columns=[feature_indicator], + n_batches_per_layer=1, + n_trees=1, + learning_rate=1.0, + max_depth=1) + + num_steps = 1 + est.train(input_fn, steps=num_steps) + ensemble = self._assert_checkpoint_and_return_model( + est.model_dir, global_step=1, finalized_trees=1, attempted_layers=1) + + # We learnt perfectly. + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['loss'], 0) + + predictions = list(est.predict(input_fn)) + self.assertAllClose( + labels, + [pred['predictions'] for pred in predictions]) + + self.assertEqual(3, len(ensemble.trees[0].nodes)) + + # Check that the split happened on 'good' value, which will be encoded as + # feature with index 1 (0 - 'bad', 2 - 'ok') + self.assertEqual(1, ensemble.trees[0].nodes[0].bucketized_split.feature_id) + self.assertEqual(0, ensemble.trees[0].nodes[0].bucketized_split.threshold) + class ModelFnTests(test_util.TensorFlowTestCase): """Tests bt_model_fn including unexposed internal functionalities.""" diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py index 1feac36f356cc5b2615217b7ca69a79d2a781ca6..c08cf61220716730fa495c6e327b91e8f3c69cd5 100644 --- a/tensorflow/python/estimator/canned/dnn.py +++ b/tensorflow/python/estimator/canned/dnn.py @@ -26,13 +26,14 @@ from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import optimizers from tensorflow.python.feature_column import feature_column as feature_column_lib from tensorflow.python.layers import core as core_layers +from tensorflow.python.layers import normalization from tensorflow.python.ops import init_ops from tensorflow.python.ops import nn from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import variable_scope from tensorflow.python.ops.losses import losses from tensorflow.python.summary import summary -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # The default learning rate of 0.05 is a historical artifact of the initial # implementation, but seems a reasonable choice. @@ -45,7 +46,7 @@ def _add_hidden_layer_summary(value, tag): def _dnn_logit_fn_builder(units, hidden_units, feature_columns, activation_fn, - dropout, input_layer_partitioner): + dropout, input_layer_partitioner, batch_norm): """Function builder for a dnn logit_fn. Args: @@ -58,6 +59,7 @@ def _dnn_logit_fn_builder(units, hidden_units, feature_columns, activation_fn, dropout: When not `None`, the probability we will drop out a given coordinate. input_layer_partitioner: Partitioner for input layer. + batch_norm: Whether to use batch normalization after each hidden layer. Returns: A logit_fn (see below). @@ -83,6 +85,7 @@ def _dnn_logit_fn_builder(units, hidden_units, feature_columns, activation_fn, A `Tensor` representing the logits, or a list of `Tensor`'s representing multiple logits in the MultiHead case. """ + is_training = mode == model_fn.ModeKeys.TRAIN with variable_scope.variable_scope( 'input_from_feature_columns', values=tuple(six.itervalues(features)), @@ -98,8 +101,20 @@ def _dnn_logit_fn_builder(units, hidden_units, feature_columns, activation_fn, activation=activation_fn, kernel_initializer=init_ops.glorot_uniform_initializer(), name=hidden_layer_scope) - if dropout is not None and mode == model_fn.ModeKeys.TRAIN: + if dropout is not None and is_training: net = core_layers.dropout(net, rate=dropout, training=True) + if batch_norm: + # TODO(hjm): In future, if this becomes popular, we can enable + # customization of the batch normalization params by accepting a + # list of `BatchNormalization` instances as `batch_norm`. + net = normalization.batch_normalization( + net, + # The default momentum 0.99 actually crashes on certain + # problem, so here we use 0.999, which is the default of + # tf.contrib.layers.batch_norm. + momentum=0.999, + training=is_training, + name='batchnorm_%d' % layer_id) _add_hidden_layer_summary(net, hidden_layer_scope.name) with variable_scope.variable_scope('logits', values=(net,)) as logits_scope: @@ -127,7 +142,8 @@ def _dnn_model_fn(features, dropout=None, input_layer_partitioner=None, config=None, - tpu_estimator_spec=False): + tpu_estimator_spec=False, + batch_norm=False): """Deep Neural Net model_fn. Args: @@ -150,6 +166,7 @@ def _dnn_model_fn(features, config: `RunConfig` object to configure the runtime settings. tpu_estimator_spec: Whether to return a `_TPUEstimatorSpec` or or `model_fn.EstimatorSpec` instance. + batch_norm: Whether to use batch normalization after each hidden layer. Returns: An `EstimatorSpec` instance. @@ -182,7 +199,8 @@ def _dnn_model_fn(features, feature_columns=feature_columns, activation_fn=activation_fn, dropout=dropout, - input_layer_partitioner=input_layer_partitioner) + input_layer_partitioner=input_layer_partitioner, + batch_norm=batch_norm) logits = logit_fn(features=features, mode=mode) if tpu_estimator_spec: @@ -201,7 +219,7 @@ def _dnn_model_fn(features, logits=logits) -@tf_export('estimator.DNNClassifier') +@estimator_export('estimator.DNNClassifier') class DNNClassifier(estimator.Estimator): """A classifier for TensorFlow DNN models. @@ -230,6 +248,17 @@ class DNNClassifier(estimator.Estimator): l1_regularization_strength=0.001 )) + # Or estimator using an optimizer with a learning rate decay. + estimator = DNNClassifier( + feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], + hidden_units=[1024, 512, 256], + optimizer=lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + # Or estimator with warm-starting from a previous checkpoint. estimator = DNNClassifier( feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], @@ -266,7 +295,10 @@ class DNNClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -285,6 +317,7 @@ class DNNClassifier(estimator.Estimator): config=None, warm_start_from=None, loss_reduction=losses.Reduction.SUM, + batch_norm=False, ): """Initializes a `DNNClassifier` instance. @@ -314,8 +347,9 @@ class DNNClassifier(estimator.Estimator): encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 . Also there will be errors if vocabulary is not provided and labels are string. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to Adagrad optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to Adagrad optimizer. activation_fn: Activation function applied to each layer. If `None`, will use `tf.nn.relu`. dropout: When not `None`, the probability we will drop out a given @@ -330,6 +364,7 @@ class DNNClassifier(estimator.Estimator): names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + batch_norm: Whether to use batch normalization after each hidden layer. """ head = head_lib._binary_logistic_or_multi_class_head( # pylint: disable=protected-access n_classes, weight_column, label_vocabulary, loss_reduction) @@ -346,14 +381,15 @@ class DNNClassifier(estimator.Estimator): activation_fn=activation_fn, dropout=dropout, input_layer_partitioner=input_layer_partitioner, - config=config) + config=config, + batch_norm=batch_norm) super(DNNClassifier, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config, warm_start_from=warm_start_from) -@tf_export('estimator.DNNRegressor') +@estimator_export('estimator.DNNRegressor') class DNNRegressor(estimator.Estimator): """A regressor for TensorFlow DNN models. @@ -382,6 +418,17 @@ class DNNRegressor(estimator.Estimator): l1_regularization_strength=0.001 )) + # Or estimator using an optimizer with a learning rate decay. + estimator = DNNRegressor( + feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], + hidden_units=[1024, 512, 256], + optimizer=lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + # Or estimator with warm-starting from a previous checkpoint. estimator = DNNRegressor( feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], @@ -418,7 +465,10 @@ class DNNRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -436,6 +486,7 @@ class DNNRegressor(estimator.Estimator): config=None, warm_start_from=None, loss_reduction=losses.Reduction.SUM, + batch_norm=False, ): """Initializes a `DNNRegressor` instance. @@ -459,8 +510,9 @@ class DNNRegressor(estimator.Estimator): used as a key to fetch weight tensor from the `features`. If it is a `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, then weight_column.normalizer_fn is applied on it to get weight tensor. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to Adagrad optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to Adagrad optimizer. activation_fn: Activation function applied to each layer. If `None`, will use `tf.nn.relu`. dropout: When not `None`, the probability we will drop out a given @@ -475,6 +527,7 @@ class DNNRegressor(estimator.Estimator): names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + batch_norm: Whether to use batch normalization after each hidden layer. """ def _model_fn(features, labels, mode, config): @@ -492,7 +545,8 @@ class DNNRegressor(estimator.Estimator): activation_fn=activation_fn, dropout=dropout, input_layer_partitioner=input_layer_partitioner, - config=config) + config=config, + batch_norm=batch_norm) super(DNNRegressor, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config, diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index 95efc0a028bc90911106a8947dcfc199ddd29444..5f453d6fe8561957f259a76b66b8d03ee012ab13 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -37,7 +37,7 @@ from tensorflow.python.summary import summary from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import sync_replicas_optimizer from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # The default learning rates are a historical artifact of the initial # implementation. @@ -88,7 +88,8 @@ def _dnn_linear_combined_model_fn(features, dnn_activation_fn=nn.relu, dnn_dropout=None, input_layer_partitioner=None, - config=None): + config=None, + batch_norm=False): """Deep Neural Net and Linear combined model_fn. Args: @@ -115,6 +116,7 @@ def _dnn_linear_combined_model_fn(features, coordinate. input_layer_partitioner: Partitioner for input layer. config: `RunConfig` object to configure the runtime settings. + batch_norm: Whether to use batch normalization after each hidden layer. Returns: An `EstimatorSpec` instance. @@ -164,7 +166,8 @@ def _dnn_linear_combined_model_fn(features, feature_columns=dnn_feature_columns, activation_fn=dnn_activation_fn, dropout=dnn_dropout, - input_layer_partitioner=input_layer_partitioner) + input_layer_partitioner=input_layer_partitioner, + batch_norm=batch_norm) dnn_logits = dnn_logit_fn(features=features, mode=mode) linear_parent_scope = 'linear' @@ -225,7 +228,7 @@ def _dnn_linear_combined_model_fn(features, logits=logits) -@tf_export('estimator.DNNLinearCombinedClassifier') +@estimator_export('estimator.DNNLinearCombinedClassifier') class DNNLinearCombinedClassifier(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined classification models. @@ -257,12 +260,19 @@ class DNNLinearCombinedClassifier(estimator.Estimator): # warm-start settings warm_start_from="/path/to/checkpoint/dir") - # To apply L1 and L2 regularization, you can set optimizers as follows: + # To apply L1 and L2 regularization, you can set dnn_optimizer to: tf.train.ProximalAdagradOptimizer( learning_rate=0.1, l1_regularization_strength=0.001, l2_regularization_strength=0.001) - # It is same for FtrlOptimizer. + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. # Input builders def input_fn_train: # returns x, y @@ -292,7 +302,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -311,7 +324,8 @@ class DNNLinearCombinedClassifier(estimator.Estimator): input_layer_partitioner=None, config=None, warm_start_from=None, - loss_reduction=losses.Reduction.SUM): + loss_reduction=losses.Reduction.SUM, + batch_norm=False): """Initializes a DNNLinearCombinedClassifier instance. Args: @@ -322,12 +336,16 @@ class DNNLinearCombinedClassifier(estimator.Estimator): used by linear part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the linear part of the model. Defaults to FTRL optimizer. + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. dnn_feature_columns: An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the deep part of the model. Defaults to Adagrad optimizer. + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. dnn_hidden_units: List of hidden units per layer. All layers are fully connected. dnn_activation_fn: Activation function applied to each layer. If None, @@ -360,6 +378,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator): names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + batch_norm: Whether to use batch normalization after each hidden layer. Raises: ValueError: If both linear_feature_columns and dnn_features_columns are @@ -399,14 +418,15 @@ class DNNLinearCombinedClassifier(estimator.Estimator): dnn_activation_fn=dnn_activation_fn, dnn_dropout=dnn_dropout, input_layer_partitioner=input_layer_partitioner, - config=config) + config=config, + batch_norm=batch_norm) super(DNNLinearCombinedClassifier, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config, warm_start_from=warm_start_from) -@tf_export('estimator.DNNLinearCombinedRegressor') +@estimator_export('estimator.DNNLinearCombinedRegressor') class DNNLinearCombinedRegressor(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined models for regression. @@ -438,12 +458,19 @@ class DNNLinearCombinedRegressor(estimator.Estimator): # warm-start settings warm_start_from="/path/to/checkpoint/dir") - # To apply L1 and L2 regularization, you can set optimizers as follows: + # To apply L1 and L2 regularization, you can set dnn_optimizer to: tf.train.ProximalAdagradOptimizer( learning_rate=0.1, l1_regularization_strength=0.001, l2_regularization_strength=0.001) - # It is same for FtrlOptimizer. + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. # Input builders def input_fn_train: # returns x, y @@ -473,7 +500,10 @@ class DNNLinearCombinedRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -491,7 +521,8 @@ class DNNLinearCombinedRegressor(estimator.Estimator): input_layer_partitioner=None, config=None, warm_start_from=None, - loss_reduction=losses.Reduction.SUM): + loss_reduction=losses.Reduction.SUM, + batch_norm=False): """Initializes a DNNLinearCombinedRegressor instance. Args: @@ -502,12 +533,16 @@ class DNNLinearCombinedRegressor(estimator.Estimator): used by linear part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the linear part of the model. Defaults to FTRL optimizer. + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. dnn_feature_columns: An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from `FeatureColumn`. dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to - the deep part of the model. Defaults to Adagrad optimizer. + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. dnn_hidden_units: List of hidden units per layer. All layers are fully connected. dnn_activation_fn: Activation function applied to each layer. If None, @@ -534,6 +569,7 @@ class DNNLinearCombinedRegressor(estimator.Estimator): names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + batch_norm: Whether to use batch normalization after each hidden layer. Raises: ValueError: If both linear_feature_columns and dnn_features_columns are @@ -564,7 +600,8 @@ class DNNLinearCombinedRegressor(estimator.Estimator): dnn_activation_fn=dnn_activation_fn, dnn_dropout=dnn_dropout, input_layer_partitioner=input_layer_partitioner, - config=config) + config=config, + batch_norm=batch_norm) super(DNNLinearCombinedRegressor, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config, diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py index 06a648777f8f730b4c739a69528090c5821f2681..ba1782125905fd14ec9b89a29c891062824028f3 100644 --- a/tensorflow/python/estimator/canned/dnn_testing_utils.py +++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py @@ -65,6 +65,11 @@ from tensorflow.python.training import training_util LEARNING_RATE_NAME = 'dnn/regression_head/dnn/learning_rate' HIDDEN_WEIGHTS_NAME_PATTERN = 'dnn/hiddenlayer_%d/kernel' HIDDEN_BIASES_NAME_PATTERN = 'dnn/hiddenlayer_%d/bias' +BATCH_NORM_BETA_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/beta' +BATCH_NORM_GAMMA_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/gamma' +BATCH_NORM_MEAN_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/moving_mean' +BATCH_NORM_VARIANCE_NAME_PATTERN = ( + 'dnn/hiddenlayer_%d/batchnorm_%d/moving_variance') LOGITS_WEIGHTS_NAME = 'dnn/logits/kernel' LOGITS_BIASES_NAME = 'dnn/logits/bias' OCCUPATION_EMBEDDING_NAME = ('dnn/input_from_feature_columns/input_layer/' @@ -89,7 +94,10 @@ def assert_close(expected, actual, rtol=1e-04, message='', name='assert_close'): name=scope) -def create_checkpoint(weights_and_biases, global_step, model_dir): +def create_checkpoint(weights_and_biases, + global_step, + model_dir, + batch_norm_vars=None): """Create checkpoint file with provided model weights. Args: @@ -98,12 +106,20 @@ def create_checkpoint(weights_and_biases, global_step, model_dir): model_dir: Directory into which checkpoint is saved. """ weights, biases = zip(*weights_and_biases) + if batch_norm_vars: + assert len(batch_norm_vars) == len(weights_and_biases) - 1 + (bn_betas, bn_gammas, bn_means, bn_variances) = zip(*batch_norm_vars) model_weights = {} # Hidden layer weights. for i in range(0, len(weights) - 1): model_weights[HIDDEN_WEIGHTS_NAME_PATTERN % i] = weights[i] model_weights[HIDDEN_BIASES_NAME_PATTERN % i] = biases[i] + if batch_norm_vars: + model_weights[BATCH_NORM_BETA_NAME_PATTERN % (i, i)] = bn_betas[i] + model_weights[BATCH_NORM_GAMMA_NAME_PATTERN % (i, i)] = bn_gammas[i] + model_weights[BATCH_NORM_MEAN_NAME_PATTERN % (i, i)] = bn_means[i] + model_weights[BATCH_NORM_VARIANCE_NAME_PATTERN % (i, i)] = bn_variances[i] # Output layer weights. model_weights[LOGITS_WEIGHTS_NAME] = weights[-1] @@ -503,8 +519,13 @@ class BaseDNNLogitFnTest(object): writer_cache.FileWriterCache.clear() shutil.rmtree(self._model_dir) - def _test_logits(self, mode, hidden_units, logits_dimension, inputs, - expected_logits): + def _test_logits(self, + mode, + hidden_units, + logits_dimension, + inputs, + expected_logits, + batch_norm=False): """Tests that the expected logits are calculated.""" with ops.Graph().as_default(): # Global step needed for MonitoredSession, which is in turn used to @@ -525,7 +546,8 @@ class BaseDNNLogitFnTest(object): ], activation_fn=nn.relu, dropout=None, - input_layer_partitioner=input_layer_partitioner) + input_layer_partitioner=input_layer_partitioner, + batch_norm=batch_norm) logits = logit_fn( features={'age': constant_op.constant(inputs)}, mode=mode) with monitored_session.MonitoredTrainingSession( @@ -556,6 +578,69 @@ class BaseDNNLogitFnTest(object): inputs=[[10.]], expected_logits=[[-2.08]]) + def test_one_dim_logits_with_batch_norm(self): + """Tests one-dimensional logits. + + input_layer = [[10]] + hidden_layer_0 = [[relu(0.6*10 +1), relu(0.5*10 -1)]] = [[7, 4]] + hidden_layer_0 = [[relu(0.6*20 +1), relu(0.5*20 -1)]] = [[13, 9]] + + batch_norm_0, training (epsilon = 0.001): + mean1 = 1/2*(7+13) = 10, + variance1 = 1/2*(3^2+3^2) = 9 + x11 = (7-10)/sqrt(9+0.001) = -0.999944449, + x21 = (13-10)/sqrt(9+0.001) = 0.999944449, + + mean2 = 1/2*(4+9) = 6.5, + variance2 = 1/2*(2.5^2+.2.5^2) = 6.25 + x12 = (4-6.5)/sqrt(6.25+0.001) = -0.99992001, + x22 = (9-6.5)/sqrt(6.25+0.001) = 0.99992001, + + logits = [[-1*(-0.999944449) + 2*(-0.99992001) + 0.3], + [-1*0.999944449 + 2*0.99992001 + 0.3]] + = [[-0.699895571],[1.299895571]] + + batch_norm_0, not training (epsilon = 0.001): + moving_mean1 = 0, moving_variance1 = 1 + x11 = (7-0)/sqrt(1+0.001) = 6.996502623, + x21 = (13-0)/sqrt(1+0.001) = 12.993504871, + moving_mean2 = 0, moving_variance2 = 1 + x12 = (4-0)/sqrt(1+0.001) = 3.998001499, + x22 = (9-0)/sqrt(1+0.001) = 8.995503372, + + logits = [[-1*6.996502623 + 2*3.998001499 + 0.3], + [-1*12.993504871 + 2*8.995503372 + 0.3]] + = [[1.299500375],[5.297501873]] + """ + base_global_step = 100 + create_checkpoint( + ( + ([[.6, .5]], [1., -1.]), + ([[-1.], [2.]], [.3]), + ), + base_global_step, + self._model_dir, + batch_norm_vars=([[0, 0], # beta. + [1, 1], # gamma. + [0, 0], # moving mean. + [1, 1], # moving variance. + ],)) + self._test_logits( + model_fn.ModeKeys.TRAIN, + hidden_units=[2], + logits_dimension=1, + inputs=[[10.], [20.]], + expected_logits=[[-0.699895571], [1.299895571]], + batch_norm=True) + for mode in [model_fn.ModeKeys.EVAL, model_fn.ModeKeys.PREDICT]: + self._test_logits( + mode, + hidden_units=[2], + logits_dimension=1, + inputs=[[10.], [20.]], + expected_logits=[[1.299500375], [5.297501873]], + batch_norm=True) + def test_multi_dim_logits(self): """Tests multi-dimensional logits. @@ -706,7 +791,8 @@ class BaseDNNLogitFnTest(object): ], activation_fn=nn.relu, dropout=None, - input_layer_partitioner=input_layer_partitioner) + input_layer_partitioner=input_layer_partitioner, + batch_norm=False) logits = logit_fn( features={ 'age': constant_op.constant(inputs[0]), diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index 81657f0c01644524f1f706a0d42dd67e1345273e..e22df849e52000e125c6bf2015485e3496f8bb8d 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -33,7 +33,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops.losses import losses from tensorflow.python.summary import summary from tensorflow.python.training import ftrl -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # The default learning rate of 0.2 is a historical artifact of the initial @@ -164,7 +164,7 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, logits=logits) -@tf_export('estimator.LinearClassifier') +@estimator_export('estimator.LinearClassifier') class LinearClassifier(estimator.Estimator): """Linear classifier model. @@ -193,6 +193,17 @@ class LinearClassifier(estimator.Estimator): l1_regularization_strength=0.001 )) + # Or estimator using an optimizer with a learning rate decay. + estimator = LinearClassifier( + feature_columns=[categorical_column_a, + categorical_feature_a_x_categorical_feature_b], + optimizer=lambda: tf.train.FtrlOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + # Or estimator with warm-starting from a previous checkpoint. estimator = LinearClassifier( feature_columns=[categorical_column_a, @@ -227,7 +238,10 @@ class LinearClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -269,8 +283,9 @@ class LinearClassifier(estimator.Estimator): encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 . Also there will be errors if vocabulary is not provided and labels are string. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to FTRL optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to FTRL optimizer. config: `RunConfig` object to configure the runtime settings. partitioner: Optional. Partitioner for input layer. warm_start_from: A string filepath to a checkpoint to warm-start from, or @@ -317,7 +332,7 @@ class LinearClassifier(estimator.Estimator): warm_start_from=warm_start_from) -@tf_export('estimator.LinearRegressor') +@estimator_export('estimator.LinearRegressor') class LinearRegressor(estimator.Estimator): """An estimator for TensorFlow Linear regression problems. @@ -332,10 +347,31 @@ class LinearRegressor(estimator.Estimator): categorical_feature_a_x_categorical_feature_b = crossed_column(...) + # Estimator using the default optimizer. estimator = LinearRegressor( feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b]) + # Or estimator using the FTRL optimizer with regularization. + estimator = LinearRegressor( + feature_columns=[categorical_column_a, + categorical_feature_a_x_categorical_feature_b], + optimizer=tf.train.FtrlOptimizer( + learning_rate=0.1, + l1_regularization_strength=0.001 + )) + + # Or estimator using an optimizer with a learning rate decay. + estimator = LinearRegressor( + feature_columns=[categorical_column_a, + categorical_feature_a_x_categorical_feature_b], + optimizer=lambda: tf.train.FtrlOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96)) + # Or estimator with warm-starting from a previous checkpoint. estimator = LinearRegressor( feature_columns=[categorical_column_a, @@ -370,7 +406,10 @@ class LinearRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -403,8 +442,9 @@ class LinearRegressor(estimator.Estimator): used as a key to fetch weight tensor from the `features`. If it is a `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, then weight_column.normalizer_fn is applied on it to get weight tensor. - optimizer: An instance of `tf.Optimizer` used to train the model. Defaults - to FTRL optimizer. + optimizer: An instance of `tf.Optimizer` used to train the model. Can also + be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or + callable. Defaults to FTRL optimizer. config: `RunConfig` object to configure the runtime settings. partitioner: Optional. Partitioner for input layer. warm_start_from: A string filepath to a checkpoint to warm-start from, or diff --git a/tensorflow/python/estimator/canned/optimizers.py b/tensorflow/python/estimator/canned/optimizers.py index f72c5ca5cbb2721d967ad9ef9dfa896f7ccce240..8f51cc3a80dd9b91eb24a83577b7d0614615e008 100644 --- a/tensorflow/python/estimator/canned/optimizers.py +++ b/tensorflow/python/estimator/canned/optimizers.py @@ -72,6 +72,8 @@ def get_optimizer_instance(opt, learning_rate=None): raise ValueError( 'Unsupported optimizer name: {}. Supported names are: {}'.format( opt, tuple(sorted(six.iterkeys(_OPTIMIZER_CLS_NAMES))))) + if callable(opt): + opt = opt() if not isinstance(opt, optimizer_lib.Optimizer): raise ValueError( 'The given object is not an Optimizer instance. Given: {}'.format(opt)) diff --git a/tensorflow/python/estimator/canned/optimizers_test.py b/tensorflow/python/estimator/canned/optimizers_test.py index ee28756155afd5ae3421475c3d41542db9411345..eadabdbc496334270cd792f5b8d5ff39a446bcf7 100644 --- a/tensorflow/python/estimator/canned/optimizers_test.py +++ b/tensorflow/python/estimator/canned/optimizers_test.py @@ -28,6 +28,13 @@ from tensorflow.python.training import optimizer as optimizer_lib from tensorflow.python.training import rmsprop +class _TestOptimizer(optimizer_lib.Optimizer): + + def __init__(self): + super(_TestOptimizer, self).__init__( + use_locking=False, name='TestOptimizer') + + class GetOptimizerInstance(test.TestCase): def test_unsupported_name(self): @@ -66,12 +73,6 @@ class GetOptimizerInstance(test.TestCase): self.assertAlmostEqual(0.1, opt._learning_rate) def test_object(self): - class _TestOptimizer(optimizer_lib.Optimizer): - - def __init__(self): - super(_TestOptimizer, self).__init__( - use_locking=False, name='TestOptimizer') - opt = optimizers.get_optimizer_instance(_TestOptimizer()) self.assertIsInstance(opt, _TestOptimizer) @@ -80,6 +81,23 @@ class GetOptimizerInstance(test.TestCase): ValueError, 'The given object is not an Optimizer instance'): optimizers.get_optimizer_instance((1, 2, 3)) + def test_callable(self): + def _optimizer_fn(): + return _TestOptimizer() + opt = optimizers.get_optimizer_instance(_optimizer_fn) + self.assertIsInstance(opt, _TestOptimizer) + + def test_lambda(self): + opt = optimizers.get_optimizer_instance(lambda: _TestOptimizer()) # pylint: disable=unnecessary-lambda + self.assertIsInstance(opt, _TestOptimizer) + + def test_callable_returns_invalid(self): + def _optimizer_fn(): + return (1, 2, 3) + with self.assertRaisesRegexp( + ValueError, 'The given object is not an Optimizer instance'): + optimizers.get_optimizer_instance(_optimizer_fn) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/canned/parsing_utils.py b/tensorflow/python/estimator/canned/parsing_utils.py index 74e5e5a1bed80229c68daa3ff33ee7af4004bf47..1ae0f1e9f7781be84e71790146a90cf99a5e9831 100644 --- a/tensorflow/python/estimator/canned/parsing_utils.py +++ b/tensorflow/python/estimator/canned/parsing_utils.py @@ -23,10 +23,10 @@ import six from tensorflow.python.feature_column import feature_column as fc from tensorflow.python.framework import dtypes from tensorflow.python.ops import parsing_ops -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export -@tf_export('estimator.classifier_parse_example_spec') +@estimator_export('estimator.classifier_parse_example_spec') def classifier_parse_example_spec(feature_columns, label_key, label_dtype=dtypes.int64, @@ -166,7 +166,7 @@ def classifier_parse_example_spec(feature_columns, return parsing_spec -@tf_export('estimator.regressor_parse_example_spec') +@estimator_export('estimator.regressor_parse_example_spec') def regressor_parse_example_spec(feature_columns, label_key, label_dtype=dtypes.float32, diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 4be1af1e6699ae632f919499261f627562fc67e9..350a95eea1f1112ea270156855409d7a1b264bfb 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -38,6 +38,7 @@ from tensorflow.python.estimator import run_config from tensorflow.python.estimator import util as estimator_util from tensorflow.python.estimator.export import export as export_helpers from tensorflow.python.estimator.export import export_output +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops @@ -66,14 +67,14 @@ from tensorflow.python.util import compat from tensorflow.python.util import compat_internal from tensorflow.python.util import function_utils from tensorflow.python.util import nest -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export _VALID_MODEL_FN_ARGS = set( ['features', 'labels', 'mode', 'params', 'self', 'config']) -@tf_export('estimator.Estimator') +@estimator_export('estimator.Estimator') class Estimator(object): """Estimator class to train and evaluate TensorFlow models. @@ -103,6 +104,15 @@ class Estimator(object): None of `Estimator`'s methods can be overridden in subclasses (its constructor enforces this). Subclasses should use `model_fn` to configure the base class, and may add methods implementing specialized functionality. + + @compatbility(eager) + Calling methods of `Estimator` will work while eager execution is enabled. + However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator` + will switch to graph model before calling all user-provided functions (incl. + hooks), so their code has to be compatible with graph mode execution. Note + that `input_fn` code using `tf.data` generally works in both graph and eager + modes. + @end_compatibility """ def __init__(self, model_fn, model_dir=None, config=None, params=None, @@ -566,7 +576,8 @@ class Estimator(object): allowed_overrides = set([ '_call_input_fn', '_create_global_step', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', - '_tf_api_names', '_validate_features_in_predict_input', + '_tf_api_names', '_estimator_api_names', '_estimator_api_constants', + '_validate_features_in_predict_input', '_call_model_fn', '_add_meta_graph_for_mode' ]) estimator_members = set([m for m in Estimator.__dict__.keys() @@ -838,7 +849,8 @@ class Estimator(object): strip_default_attrs, save_variables=True, mode=model_fn_lib.ModeKeys.PREDICT, - export_tags=None): + export_tags=None, + check_variables=True): # pylint: disable=line-too-long """Loads variables and adds them along with a MetaGraphDef for saving. @@ -859,6 +871,10 @@ class Estimator(object): mode: tf.estimator.ModeKeys value indicating which mode will be exported. export_tags: The set of tags with which to save `MetaGraphDef`. If None, a default set will be selected to matched the passed mode. + check_variables: bool, whether to check the checkpoint has all variables. + + Raises: + ValueError: if `save_variables` is `True` and `check_variable` is `False`. """ # pylint: enable=line-too-long if export_tags is None: @@ -899,16 +915,20 @@ class Estimator(object): # SavedModel for restore later. graph_saver = estimator_spec.scaffold.saver or saver.Saver(sharded=True) - try: - graph_saver.restore(session, checkpoint_path) - except errors.NotFoundError as e: - msg = ('Could not load all requested variables from the checkpoint. ' - 'Please make sure your model_fn does not expect variables ' - 'that were not saved in the checkpoint.\n\n' - 'Encountered error with mode `{}` while restoring checkpoint ' - 'from: `{}`. Full Traceback:\n\n{}').format( - mode, checkpoint_path, e) - raise ValueError(msg) + if save_variables and not check_variables: + raise ValueError('If `save_variables` is `True, `check_variables`' + 'must not be `False`.') + if check_variables: + try: + graph_saver.restore(session, checkpoint_path) + except errors.NotFoundError as e: + msg = ('Could not load all requested variables from checkpoint. ' + 'Please make sure your model_fn does not expect variables ' + 'that were not saved in the checkpoint.\n\n' + 'Encountered error with mode `{}` while restoring ' + 'checkpoint from: `{}`. Full Traceback:\n\n{}').format( + mode, checkpoint_path, e) + raise ValueError(msg) # We add the train op explicitly for now, so that we don't have to # change the Builder public interface. Note that this is a no-op @@ -1123,6 +1143,18 @@ class Estimator(object): return self._train_model_default(input_fn, hooks, saving_listeners) def _train_model_default(self, input_fn, hooks, saving_listeners): + """Initiate training with input_fn, without DistributionStrategies. + + Args: + input_fn: A function that provides input data for training as minibatches. + hooks: List of `SessionRunHook` subclass instances. Used for callbacks + inside the training loop. + saving_listeners: list of `CheckpointSaverListener` objects. Used for + callbacks that run immediately before or after checkpoint savings. + + Returns: + Loss from training + """ worker_hooks = [] with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) @@ -1139,29 +1171,86 @@ class Estimator(object): saving_listeners) def _train_model_distributed(self, input_fn, hooks, saving_listeners): + """Initiate training with input_fn, using DistributionStrategies. + + Args: + input_fn: A function that provides input data for training as minibatches. + hooks: List of `SessionRunHook` subclass instances. Used for callbacks + inside the training loop. + saving_listeners: list of `CheckpointSaverListener` objects. Used for + callbacks that run immediately before or after checkpoint savings. + + Returns: + Loss from training + """ self._distribution.configure(self._session_config) + + # TODO(sourabhbajaj): Remove this hack once we migrate the other strategies + # to use the new API + is_tpu_strategy = self._distribution.__class__.__name__ == 'TPUStrategy' + worker_hooks = [] with ops.Graph().as_default() as g: with self._distribution.scope(): random_seed.set_random_seed(self._config.tf_random_seed) - features, labels, input_hooks = ( - self._get_features_and_labels_from_input_fn( - input_fn, model_fn_lib.ModeKeys.TRAIN)) - worker_hooks.extend(input_hooks) - global_step_tensor = self._create_and_assert_global_step(g) - # The default destination for the global_step_tensor fetch call is the - # CPU. - global_step_read_tensor = self._distribution.fetch(global_step_tensor) - # we want to add to the global collection in the main thread not the - # tower threads. - ops.add_to_collection(training_util.GLOBAL_STEP_READ_KEY, - global_step_read_tensor) - grouped_estimator_spec = self._distribution.call_for_each_tower( - self._call_model_fn, - features, - labels, # although this will be None it seems - model_fn_lib.ModeKeys.TRAIN, - self.config) + + if is_tpu_strategy: + # Create the iterator for run_on_dataset function + # TODO(sourabhbajaj): refactor this out to call a function on the + # strategy + dataset = self._distribution.distribute_dataset( + lambda: self._call_input_fn(input_fn, # pylint: disable=g-long-lambda + model_fn_lib.ModeKeys.TRAIN)) + iterator = dataset.make_initializable_iterator() + worker_hooks.append( + estimator_util._DatasetInitializerHook(iterator)) # pylint: disable=protected-access + + global_step_tensor = self._create_and_assert_global_step(g) + # we want to add to the global collection in the main thread not the + # tower threads. + ops.add_to_collection(training_util.GLOBAL_STEP_READ_KEY, + self._distribution.read_var(global_step_tensor)) + + # Create a step_fn from the train_op of grouped_estimator_spec + def step_fn(ctx, inputs): + """A single step that is passed to run_on_dataset.""" + features, labels = inputs + estimator_spec = self._distribution.call_for_each_tower( + self._call_model_fn, + features, + labels, + model_fn_lib.ModeKeys.TRAIN, + self.config) + ctx.last_step_outputs = estimator_spec.loss + ctx.non_tensor_outputs = {'estimator_spec': estimator_spec} + with ops.control_dependencies([estimator_spec.train_op]): + return array_ops.identity(estimator_spec.loss) + + # Create new train_op post graph rewrites + # TODO(sourabhbajaj): Make sure train_steps and tpu_iterations + # work correctly. Currently hardcoded at 2 + initial_training_loss = constant_op.constant(1e7) + distributed_train_op, tpu_result, ctx = \ + self._distribution._run_steps_on_dataset( # pylint: disable=protected-access + step_fn, iterator, iterations=2, + initial_loop_values=initial_training_loss) + grouped_estimator_spec = ctx.non_tensor_outputs['estimator_spec'] + else: + features, labels, input_hooks = ( + self._get_features_and_labels_from_input_fn( + input_fn, model_fn_lib.ModeKeys.TRAIN)) + worker_hooks.extend(input_hooks) + global_step_tensor = self._create_and_assert_global_step(g) + # we want to add to the global collection in the main thread not the + # tower threads. + ops.add_to_collection(training_util.GLOBAL_STEP_READ_KEY, + self._distribution.read_var(global_step_tensor)) + grouped_estimator_spec = self._distribution.call_for_each_tower( + self._call_model_fn, + features, + labels, # although this will be None it seems + model_fn_lib.ModeKeys.TRAIN, + self.config) # TODO(anjalisridhar): Figure out how to resolve the following scaffold # parameters: init_feed_dict, init_fn. @@ -1189,10 +1278,16 @@ class Estimator(object): else: init_op = None + def _unwrap_and_concat(value): + value = nest.flatten(self._distribution.unwrap(value)) + if len(value) != 1: + return array_ops.concat(value) + return value[0] + ready_op = self._distribution.call_for_each_tower( create_per_tower_ready_op, grouped_estimator_spec.scaffold) if ready_op is not None: - ready_op = self._distribution.group(ready_op) + ready_op = _unwrap_and_concat(ready_op) else: ready_op = None @@ -1200,8 +1295,7 @@ class Estimator(object): create_per_tower_ready_for_local_init_op, grouped_estimator_spec.scaffold) if ready_for_local_init_op is not None: - ready_for_local_init_op = self._distribution.group( - ready_for_local_init_op) + ready_for_local_init_op = _unwrap_and_concat(ready_for_local_init_op) else: ready_for_local_init_op = None @@ -1242,18 +1336,33 @@ class Estimator(object): training_chief_hooks = get_hooks_from_the_first_device( grouped_estimator_spec.training_chief_hooks) + # TODO(sourabhbajaj): Merge the two code paths once we can + # handle per device variables correctly in reduce and can output + # the loss scaler. + if is_tpu_strategy: + loss = self._distribution.unwrap( + self._distribution.reduce(distribute_lib.get_loss_reduction(), + tpu_result)[0])[0] + worker_hooks.append( + estimator_util.StrategyInitFinalizeHook( + self._distribution.get_initialization_ops, + self._distribution.get_finalize_ops)) + else: + loss = self._distribution.unwrap( + self._distribution.reduce(distribute_lib.get_loss_reduction(), + grouped_estimator_spec.loss, + destinations='/device:CPU:0'))[0] + distributed_train_op = grouped_estimator_spec.train_op + estimator_spec = model_fn_lib.EstimatorSpec( mode=grouped_estimator_spec.mode, - loss=self._distribution.unwrap( - self._distribution.reduce(distribute_lib.get_loss_reduction(), - grouped_estimator_spec.loss, - destinations='/device:CPU:0'))[0], - train_op=self._distribution.group(grouped_estimator_spec.train_op), + loss=loss, + train_op=self._distribution.group(distributed_train_op), training_hooks=training_hooks, training_chief_hooks=training_chief_hooks, scaffold=scaffold) return self._train_with_estimator_spec(estimator_spec, worker_hooks, - hooks, global_step_read_tensor, + hooks, global_step_tensor, saving_listeners) def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, @@ -1634,11 +1743,12 @@ def _has_dataset_or_queue_runner(maybe_tensor): # Now, check queue. return ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS) + VocabInfo = warm_starting_util.VocabInfo # pylint: disable=invalid-name -tf_export('estimator.VocabInfo', allow_multiple_exports=True)(VocabInfo) +estimator_export('estimator.VocabInfo')(VocabInfo) -@tf_export('estimator.WarmStartSettings') +@estimator_export('estimator.WarmStartSettings') class WarmStartSettings( collections.namedtuple('WarmStartSettings', [ 'ckpt_to_initialize_from', diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index a43b820f322d70093a5015457fea294e436daeea..2a0e4e761755e272a316ce2d326b0c0a51ecbaba 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -38,6 +38,7 @@ from tensorflow.python.estimator.export import export_output from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util @@ -61,6 +62,7 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import loader from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants from tensorflow.python.summary import summary from tensorflow.python.summary import summary_iterator @@ -1295,6 +1297,31 @@ class EstimatorEvaluateTest(test.TestCase): dummy_input_fn, steps=1, checkpoint_path=est1.latest_checkpoint()) self.assertEqual(5, scores['global_step']) + def test_wrong_shape_throws_reasonable_error(self): + """Make sure we are helpful when model_fns change. See b/110263146.""" + def _get_model_fn(val=1): + def _model_fn(features, labels, mode): + del features, labels # unused + variables.Variable(val, name='weight') + return model_fn_lib.EstimatorSpec( + mode=mode, + predictions=constant_op.constant([[1.]]), + loss=constant_op.constant(0.), + train_op=state_ops.assign_add(training.get_global_step(), 1)) + return _model_fn + + model_fn_1 = _get_model_fn() + model_fn_2 = _get_model_fn(val=[1]) + + est1 = estimator.Estimator(model_fn=model_fn_1) + est1.train(dummy_input_fn, steps=5) + est2 = estimator.Estimator( + model_fn=model_fn_2, model_dir=est1.model_dir) + + expected_msg = 'Restoring from checkpoint failed.*a mismatch between' + with self.assertRaisesRegexp(errors.InvalidArgumentError, expected_msg): + est2.train(dummy_input_fn, steps=1,) + def test_scaffold_is_used(self): def _model_fn_scaffold(features, labels, mode): @@ -2829,6 +2856,45 @@ class EstimatorExportTest(test.TestCase): # Clean up. gfile.DeleteRecursively(tmpdir) + def test_export_savedmodel_no_export_outputs(self): + """Ensure that an EstimatorSpec without outputs defined can be exported.""" + + def _model_fn(features, labels, mode): + _, _ = features, labels + variables.Variable(1., name='weight') + return model_fn_lib.EstimatorSpec( + mode, + predictions=constant_op.constant(10.), + loss=constant_op.constant(1.), + train_op=state_ops.assign_add(training.get_global_step(), 1)) + + tmpdir = tempfile.mkdtemp() + est = estimator.Estimator(model_fn=_model_fn) + est.train(input_fn=dummy_input_fn, steps=1) + + # Perform the export. + export_dir_base = os.path.join( + compat.as_bytes(tmpdir), compat.as_bytes('no_export_outputs')) + export_dir = est.export_savedmodel( + export_dir_base, _get_serving_input_receiver_fn()) + + # Check that all the files are in the right places. + self.assertTrue(gfile.Exists(export_dir_base)) + self._validate_exported_files(export_dir) + + # Restore, to validate that the export was well-formed. + with ops.Graph().as_default() as graph: + with session.Session(graph=graph) as sess: + meta_graph = loader.load(sess, [tag_constants.SERVING], export_dir) + graph_ops = [x.name for x in graph.get_operations()] + self.assertTrue('weight' in graph_ops) + + sig_def = meta_graph.signature_def + self.assertEqual(len(sig_def), 1) + sig_outputs = sig_def[ + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].outputs + self.assertEqual(sig_outputs['output'].name, 'Const:0') + class EstimatorHookOrderingTest(test.TestCase): @@ -2873,7 +2939,7 @@ class EstimatorHookOrderingTest(test.TestCase): class EstimatorIntegrationTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_complete_flow_with_a_simple_linear_model(self): def _model_fn(features, labels, mode): diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index ff19a0a7f435594bd1d8dd41d3c003131434a8b5..ca26341445e86ad554ac2e7cbf643c7775dd9825 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.util import compat -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export _SINGLE_FEATURE_DEFAULT_NAME = 'feature' _SINGLE_RECEIVER_DEFAULT_NAME = 'input' @@ -93,7 +93,7 @@ def _check_tensor_key(name, error_label='feature'): raise ValueError('{} keys must be strings: {}.'.format(error_label, name)) -@tf_export('estimator.export.ServingInputReceiver') +@estimator_export('estimator.export.ServingInputReceiver') class ServingInputReceiver( collections.namedtuple( 'ServingInputReceiver', @@ -161,7 +161,7 @@ class ServingInputReceiver( receiver_tensors_alternatives=receiver_tensors_alternatives) -@tf_export('estimator.export.TensorServingInputReceiver') +@estimator_export('estimator.export.TensorServingInputReceiver') class TensorServingInputReceiver( collections.namedtuple( 'TensorServingInputReceiver', @@ -263,7 +263,7 @@ class SupervisedInputReceiver( receiver_tensors=receiver_tensors) -@tf_export('estimator.export.build_parsing_serving_input_receiver_fn') +@estimator_export('estimator.export.build_parsing_serving_input_receiver_fn') def build_parsing_serving_input_receiver_fn(feature_spec, default_batch_size=None): """Build a serving_input_receiver_fn expecting fed tf.Examples. @@ -313,7 +313,7 @@ def _placeholders_from_receiver_tensors_dict(input_vals, } -@tf_export('estimator.export.build_raw_serving_input_receiver_fn') +@estimator_export('estimator.export.build_raw_serving_input_receiver_fn') def build_raw_serving_input_receiver_fn(features, default_batch_size=None): """Build a serving_input_receiver_fn expecting feature Tensors. @@ -333,11 +333,7 @@ def build_raw_serving_input_receiver_fn(features, default_batch_size=None): """A serving_input_receiver_fn that expects features to be fed directly.""" receiver_tensors = _placeholders_from_receiver_tensors_dict( features, default_batch_size) - - # TODO(b/34885899): remove the unnecessary copy - # The features provided are simply the placeholders, but we defensively copy - # the dict because it may be mutated. - return ServingInputReceiver(receiver_tensors, receiver_tensors.copy()) + return ServingInputReceiver(receiver_tensors, receiver_tensors) return serving_input_receiver_fn diff --git a/tensorflow/python/estimator/export/export_output.py b/tensorflow/python/estimator/export/export_output.py index d387ea2940e7a450afe28b884c52113355c70fe6..6c26d299851eaea74f1e564d0fac217f238d76a2 100644 --- a/tensorflow/python/estimator/export/export_output.py +++ b/tensorflow/python/estimator/export/export_output.py @@ -26,10 +26,10 @@ import six from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.saved_model import signature_def_utils -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export -@tf_export('estimator.export.ExportOutput') +@estimator_export('estimator.export.ExportOutput') class ExportOutput(object): """Represents an output of a model that can be served. @@ -100,7 +100,7 @@ class ExportOutput(object): return output_dict -@tf_export('estimator.export.ClassificationOutput') +@estimator_export('estimator.export.ClassificationOutput') class ClassificationOutput(ExportOutput): """Represents the output of a classification head. @@ -169,7 +169,7 @@ class ClassificationOutput(ExportOutput): examples, self.classes, self.scores) -@tf_export('estimator.export.RegressionOutput') +@estimator_export('estimator.export.RegressionOutput') class RegressionOutput(ExportOutput): """Represents the output of a regression head.""" @@ -202,7 +202,7 @@ class RegressionOutput(ExportOutput): return signature_def_utils.regression_signature_def(examples, self.value) -@tf_export('estimator.export.PredictOutput') +@estimator_export('estimator.export.PredictOutput') class PredictOutput(ExportOutput): """Represents the output of a generic prediction head. diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index 766ea23f2a3d145573cdd7f30f4f8427a2ac39c7..b18212cfcda8f817f909672007c5b000db718232 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -28,10 +28,10 @@ from tensorflow.python.framework import errors_impl from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging from tensorflow.python.summary import summary_iterator -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export -@tf_export('estimator.Exporter') +@estimator_export('estimator.Exporter') class Exporter(object): """A class representing a type of model export.""" @@ -172,7 +172,7 @@ def _verify_compare_fn_args(compare_fn): (compare_fn, non_valid_args)) -@tf_export('estimator.BestExporter') +@estimator_export('estimator.BestExporter') class BestExporter(Exporter): """This class exports the serving graph and checkpoints of the best models. @@ -367,7 +367,7 @@ class BestExporter(Exporter): return best_eval_result -@tf_export('estimator.FinalExporter') +@estimator_export('estimator.FinalExporter') class FinalExporter(Exporter): """This class exports the serving graph and checkpoints in the end. @@ -418,7 +418,7 @@ class FinalExporter(Exporter): is_the_final_export) -@tf_export('estimator.LatestExporter') +@estimator_export('estimator.LatestExporter') class LatestExporter(Exporter): """This class regularly exports the serving graph and checkpoints. diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index eefc7c712d79d8d02632ccb928f7ab4af02b2596..a6cefdece21fa8ce944095cb5d3395f2b67142bd 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -24,7 +24,7 @@ import numpy as np from six import string_types from tensorflow.python.estimator.inputs.queues import feeding_functions -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export # Key name to pack the target into dict of `features`. See # `_get_unique_target_key` for details. @@ -87,7 +87,7 @@ def _validate_and_convert_features(x): return ordered_dict_data -@tf_export('estimator.inputs.numpy_input_fn') +@estimator_export('estimator.inputs.numpy_input_fn') def numpy_input_fn(x, y=None, batch_size=128, diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 1ed6ed4d846a47d70a72c1363567ce918bb007a6..616bcb410f8119e170e991f8320c5b6448ee85c9 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -18,10 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import six +import uuid import numpy as np from tensorflow.python.estimator.inputs.queues import feeding_functions -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export try: # pylint: disable=g-import-not-at-top @@ -35,7 +37,23 @@ except ImportError: HAS_PANDAS = False -@tf_export('estimator.inputs.pandas_input_fn') +def _get_unique_target_key(features, target_column_name): + """Returns a key that does not exist in the input DataFrame `features`. + + Args: + features: DataFrame + target_column_name: Name of the target column as a `str` + + Returns: + A unique key that can be used to insert the target into + features. + """ + if target_column_name in features: + target_column_name += '_' + str(uuid.uuid4()) + return target_column_name + + +@estimator_export('estimator.inputs.pandas_input_fn') def pandas_input_fn(x, y=None, batch_size=128, @@ -50,7 +68,7 @@ def pandas_input_fn(x, Args: x: pandas `DataFrame` object. - y: pandas `Series` object. `None` if absent. + y: pandas `Series` object or `DataFrame`. `None` if absent. batch_size: int, size of batches to return. num_epochs: int, number of epochs to iterate over data. If not `None`, read attempts that would exceed this value will raise `OutOfRangeError`. @@ -60,7 +78,8 @@ def pandas_input_fn(x, num_threads: Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1. - target_column: str, name to give the target column `y`. + target_column: str, name to give the target column `y`. This parameter + is not used when `y` is a `DataFrame`. Returns: Function, that has signature of ()->(dict of `features`, `target`) @@ -79,6 +98,9 @@ def pandas_input_fn(x, '(it is recommended to set it as True for training); ' 'got {}'.format(shuffle)) + if not isinstance(target_column, six.string_types): + raise TypeError('target_column must be a string type') + x = x.copy() if y is not None: if target_column in x: @@ -88,7 +110,13 @@ def pandas_input_fn(x, if not np.array_equal(x.index, y.index): raise ValueError('Index for x and y are mismatched.\nIndex for x: %s\n' 'Index for y: %s\n' % (x.index, y.index)) - x[target_column] = y + if isinstance(y, pd.DataFrame): + y_columns = [(column, _get_unique_target_key(x, column)) + for column in list(y)] + target_column = [v for _, v in y_columns] + x[target_column] = y + else: + x[target_column] = y # TODO(mdan): These are memory copies. We probably don't need 4x slack space. # The sizes below are consistent with what I've seen elsewhere. @@ -118,7 +146,12 @@ def pandas_input_fn(x, features = features[1:] features = dict(zip(list(x.columns), features)) if y is not None: - target = features.pop(target_column) + if isinstance(target_column, list): + keys = [k for k, _ in y_columns] + values = [features.pop(column) for column in target_column] + target = {k: v for k, v in zip(keys, values)} + else: + target = features.pop(target_column) return features, target return features return input_fn diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index dcecf6dd61c4d24a36b2be8f054c066050d088fc..6f13bc95d2d315ad1aabfd89d5d479d65fe08502 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -47,6 +47,16 @@ class PandasIoTest(test.TestCase): y = pd.Series(np.arange(-32, -28), index=index) return x, y + def makeTestDataFrameWithYAsDataFrame(self): + index = np.arange(100, 104) + a = np.arange(4) + b = np.arange(32, 36) + a_label = np.arange(10, 14) + b_label = np.arange(50, 54) + x = pd.DataFrame({'a': a, 'b': b}, index=index) + y = pd.DataFrame({'a_target': a_label, 'b_target': b_label}, index=index) + return x, y + def callInputFnOnce(self, input_fn, session): results = input_fn() coord = coordinator.Coordinator() @@ -65,6 +75,19 @@ class PandasIoTest(test.TestCase): pandas_io.pandas_input_fn( x, y_noindex, batch_size=2, shuffle=False, num_epochs=1) + def testPandasInputFn_RaisesWhenTargetColumnIsAList(self): + if not HAS_PANDAS: + return + + x, y = self.makeTestDataFrame() + + with self.assertRaisesRegexp(TypeError, + 'target_column must be a string type'): + pandas_io.pandas_input_fn(x, y, batch_size=2, + shuffle=False, + num_epochs=1, + target_column=['one', 'two']) + def testPandasInputFn_NonBoolShuffle(self): if not HAS_PANDAS: return @@ -90,6 +113,53 @@ class PandasIoTest(test.TestCase): self.assertAllEqual(features['b'], [32, 33]) self.assertAllEqual(target, [-32, -31]) + def testPandasInputFnWhenYIsDataFrame_ProducesExpectedOutput(self): + if not HAS_PANDAS: + return + with self.test_session() as session: + x, y = self.makeTestDataFrameWithYAsDataFrame() + input_fn = pandas_io.pandas_input_fn( + x, y, batch_size=2, shuffle=False, num_epochs=1) + + features, targets = self.callInputFnOnce(input_fn, session) + + self.assertAllEqual(features['a'], [0, 1]) + self.assertAllEqual(features['b'], [32, 33]) + self.assertAllEqual(targets['a_target'], [10, 11]) + self.assertAllEqual(targets['b_target'], [50, 51]) + + def testPandasInputFnYIsDataFrame_HandlesOverlappingColumns(self): + if not HAS_PANDAS: + return + with self.test_session() as session: + x, y = self.makeTestDataFrameWithYAsDataFrame() + y = y.rename(columns={'a_target': 'a', 'b_target': 'b'}) + input_fn = pandas_io.pandas_input_fn( + x, y, batch_size=2, shuffle=False, num_epochs=1) + + features, targets = self.callInputFnOnce(input_fn, session) + + self.assertAllEqual(features['a'], [0, 1]) + self.assertAllEqual(features['b'], [32, 33]) + self.assertAllEqual(targets['a'], [10, 11]) + self.assertAllEqual(targets['b'], [50, 51]) + + def testPandasInputFnYIsDataFrame_HandlesOverlappingColumnsInTargets(self): + if not HAS_PANDAS: + return + with self.test_session() as session: + x, y = self.makeTestDataFrameWithYAsDataFrame() + y = y.rename(columns={'a_target': 'a', 'b_target': 'a_n'}) + input_fn = pandas_io.pandas_input_fn( + x, y, batch_size=2, shuffle=False, num_epochs=1) + + features, targets = self.callInputFnOnce(input_fn, session) + + self.assertAllEqual(features['a'], [0, 1]) + self.assertAllEqual(features['b'], [32, 33]) + self.assertAllEqual(targets['a'], [10, 11]) + self.assertAllEqual(targets['a_n'], [50, 51]) + def testPandasInputFn_ProducesOutputsForLargeBatchAndMultipleEpochs(self): if not HAS_PANDAS: return diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 2f439f765e6811335667b62437f7aafc934904dc..cb37f99704a8d01af6149bd3c8030b653981d0e2 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -45,7 +45,8 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import data_structures _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY @@ -123,8 +124,8 @@ def _create_ordered_io(keras_model, estimator_io, is_input=True): 'It needs to match one ' 'of the following: %s' % ('input' if is_input else 'output', key, ', '.join(keras_io_names))) - tensors = [_convert_tensor(estimator_io[io_name]) - for io_name in keras_io_names] + tensors = [_convert_tensor(estimator_io[io_name]) + for io_name in keras_io_names] return tensors else: # Plain array. @@ -242,8 +243,17 @@ def _in_place_subclassed_model_state_restoration(model): # Restore layers and build attributes if (hasattr(model, '_original_attributes_cache') and model._original_attributes_cache is not None): - model._layers = [] + # Models have sticky attribute assignment, so we want to be careful to add + # back the previous attributes and track Layers by their original names + # without adding dependencies on "utility" attributes which Models exempt + # when they're constructed. + model._layers = data_structures.NoDependency([]) for name, value in model._original_attributes_cache.items(): + if not isinstance(value, checkpointable.CheckpointableBase): + # If this value is not already checkpointable, it's probably that way + # for a reason; we don't want to start tracking data structures that the + # original Model didn't. + value = data_structures.NoDependency(value) setattr(model, name, value) model._original_attributes_cache = None else: @@ -446,7 +456,6 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects, saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt')) -@tf_export('keras.estimator.model_to_estimator') def model_to_estimator(keras_model=None, keras_model_path=None, custom_objects=None, @@ -455,7 +464,7 @@ def model_to_estimator(keras_model=None, """Constructs an `Estimator` instance from given keras model. For usage example, please see - @{$programmers_guide/estimators$creating_estimators_from_keras_models}. + @{$guide/estimators$creating_estimators_from_keras_models}. Args: keras_model: A compiled Keras model object. This argument is mutually diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index 3edf9fe940b19c7a0b1a7c21a9674189faba5acb..a9fd8f8e1a4259fece1a5996343970900c853ce0 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -23,7 +23,7 @@ import collections import six -from tensorflow.python.estimator.export.export_output import ExportOutput +from tensorflow.python.estimator.export import export_output as export_output_lib from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops @@ -32,10 +32,10 @@ from tensorflow.python.saved_model import tag_constants from tensorflow.python.training import monitored_session from tensorflow.python.training import session_run_hook from tensorflow.python.util import nest -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export -@tf_export('estimator.ModeKeys') +@estimator_export('estimator.ModeKeys') class ModeKeys(object): """Standard names for model modes. @@ -62,7 +62,7 @@ EXPORT_TAG_MAP = { } -@tf_export('estimator.EstimatorSpec') +@estimator_export('estimator.EstimatorSpec') class EstimatorSpec( collections.namedtuple('EstimatorSpec', [ 'mode', 'predictions', 'loss', 'train_op', 'eval_metric_ops', @@ -99,7 +99,7 @@ class EstimatorSpec( ignored in eval and infer modes. Example: ```python - def my_model_fn(mode, features, labels): + def my_model_fn(features, labels, mode): predictions = ... loss = ... train_op = ... @@ -114,7 +114,7 @@ class EstimatorSpec( given mode. Example: ```python - def my_model_fn(mode, features, labels): + def my_model_fn(features, labels, mode): if (mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL): loss = ... @@ -158,6 +158,8 @@ class EstimatorSpec( Multi-headed models should specify one entry for each head, one of which must be named using signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY. + If no entry is provided, a default `PredictOutput` mapping to + `predictions` will be created. training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to run on the chief worker during training. training_hooks: Iterable of `tf.train.SessionRunHook` objects to run @@ -232,29 +234,9 @@ class EstimatorSpec( _check_is_tensor_or_operation(metric_update, 'eval_metric_ops[{}]'.format(key)) - # Validate export_outputs. - if export_outputs is not None: - if not isinstance(export_outputs, dict): - raise TypeError('export_outputs must be dict, given: {}'.format( - export_outputs)) - for v in six.itervalues(export_outputs): - if not isinstance(v, ExportOutput): - raise TypeError( - 'Values in export_outputs must be ExportOutput objects. ' - 'Given: {}'.format(export_outputs)) - # Note export_outputs is allowed to be empty. - if len(export_outputs) == 1: - (key, value), = export_outputs.items() - if key != signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: - export_outputs[ - signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = value - if len(export_outputs) > 1: - if (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY - not in export_outputs): - raise ValueError( - 'Multiple export_outputs were provided, but none of them is ' - 'specified as the default. Do this by naming one of them with ' - 'signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.') + # Validate the passed export outputs, or generate defaults. + if mode == ModeKeys.PREDICT: + export_outputs = _get_export_outputs(export_outputs, predictions) # Validate that all tensors and ops are from the default graph. default_graph = ops.get_default_graph() @@ -286,11 +268,11 @@ class EstimatorSpec( raise ValueError(error_message_template.format('train_op', train_op.name)) for key, value in list(six.iteritems(eval_metric_ops)): values = nest.flatten(value) - for value in values: - if value.graph is not default_graph: + for val in values: + if val.graph is not default_graph: raise ValueError(error_message_template.format( 'eval_metric_ops', - '{0}: {1}'.format(key, value.name))) + '{0}: {1}'.format(key, val.name))) # Validate hooks. training_chief_hooks = tuple(training_chief_hooks or []) @@ -334,6 +316,70 @@ class EstimatorSpec( return EstimatorSpec(*new_fields) +def _get_export_outputs(export_outputs, predictions): + """Validate export_outputs or create default export_outputs. + + Args: + export_outputs: Describes the output signatures to be exported to + `SavedModel` and used during serving. Should be a dict or None. + predictions: Predictions `Tensor` or dict of `Tensor`. + + Returns: + Valid export_outputs dict + + Raises: + TypeError: if export_outputs is not a dict or its values are not + ExportOutput instances. + """ + if export_outputs is None: + default_output = export_output_lib.PredictOutput(predictions) + export_outputs = { + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: default_output} + + if not isinstance(export_outputs, dict): + raise TypeError('export_outputs must be dict, given: {}'.format( + export_outputs)) + for v in six.itervalues(export_outputs): + if not isinstance(v, export_output_lib.ExportOutput): + raise TypeError( + 'Values in export_outputs must be ExportOutput objects. ' + 'Given: {}'.format(export_outputs)) + + _maybe_add_default_serving_output(export_outputs) + + return export_outputs + + +def _maybe_add_default_serving_output(export_outputs): + """Add a default serving output to the export_outputs if not present. + + Args: + export_outputs: Describes the output signatures to be exported to + `SavedModel` and used during serving. Should be a dict. + + Returns: + export_outputs dict with default serving signature added if necessary + + Raises: + ValueError: if multiple export_outputs were provided without a default + serving key. + """ + if len(export_outputs) == 1: + (key, value), = export_outputs.items() + if key != signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: + export_outputs[ + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = value + if len(export_outputs) > 1: + if (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + not in export_outputs): + raise ValueError( + 'Multiple export_outputs were provided, but none of them is ' + 'specified as the default. Do this by naming one of them with ' + 'signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.') + + return export_outputs + + class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ 'mode', 'predictions', diff --git a/tensorflow/python/estimator/model_fn_test.py b/tensorflow/python/estimator/model_fn_test.py index b7eeeb437cb4a624cdee552be3032364b18a8290..08e41fd4146e9254fc8cc7da6bc809e80d053a5b 100644 --- a/tensorflow/python/estimator/model_fn_test.py +++ b/tensorflow/python/estimator/model_fn_test.py @@ -592,6 +592,27 @@ class EstimatorSpecInferTest(test.TestCase): predictions=predictions, export_outputs=export_outputs) + def testDefaultExportOutputCreated(self): + """Ensure that a default PredictOutput is created for export.""" + with ops.Graph().as_default(), self.test_session(): + predictions = constant_op.constant(1.) + self._assertDefaultExportOutputForPredictions(predictions) + + def testDefaultExportOutputCreatedDict(self): + """Ensure that a default PredictOutput is created for export for dicts.""" + with ops.Graph().as_default(), self.test_session(): + predictions = {'loss': constant_op.constant(1.), + 'score': constant_op.constant(10.)} + self._assertDefaultExportOutputForPredictions(predictions) + + def _assertDefaultExportOutputForPredictions(self, predictions): + spec = model_fn.EstimatorSpec( + mode=model_fn.ModeKeys.PREDICT, predictions=predictions) + + expected = export_output.PredictOutput(predictions).outputs + serving_output = spec.export_outputs[ + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] + self.assertEqual(serving_output.outputs, expected) if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py index c7707be8397d950f4e5993b678c215128d3d8b9f..3d60c63b68968c98a00364948bd3de0581daadd4 100644 --- a/tensorflow/python/estimator/run_config.py +++ b/tensorflow/python/estimator/run_config.py @@ -25,11 +25,12 @@ import os import six from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util import compat_internal from tensorflow.python.util import function_utils -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export _USE_DEFAULT = object() @@ -296,7 +297,7 @@ class TaskType(object): EVALUATOR = 'evaluator' -@tf_export('estimator.RunConfig') +@estimator_export('estimator.RunConfig') class RunConfig(object): """This class specifies the configurations for an `Estimator` run.""" @@ -484,6 +485,43 @@ class RunConfig(object): self._init_distributed_setting_from_environment_var(tf_config) + # Get session_config only for distributed mode (cluster_spec is present). + if not self._session_config and self._cluster_spec: + RunConfig._replace( + self, + allowed_properties_list=_DEFAULT_REPLACEABLE_LIST, + session_config=self._get_default_session_config()) + + def _get_default_session_config(self): + """Returns None or tf.ConfigProto instance with default device_filters set. + + Device filters are set such that chief/master and worker communicates with + only ps. session_config=None for evaluators or any other TaskType. + """ + + rewrite_opts = rewriter_config_pb2.RewriterConfig( + meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE) + graph_opts = config_pb2.GraphOptions(rewrite_options=rewrite_opts) + + device_filters = None + if self._task_type == TaskType.MASTER: + device_filters = ['/job:ps', '/job:master'] + elif self._task_type == TaskType.CHIEF: + device_filters = ['/job:ps', '/job:chief'] + elif self._task_type == TaskType.WORKER: + device_filters = ['/job:ps', '/job:worker/task:%d' % self._task_id] + elif self._task_type == TaskType.PS: + device_filters = ['/job:ps', '/job:worker', '/job:master'] + else: + # If the task_type is `EVALUATOR` or something other than the ones in + # TaskType then don't set any device filters. + return None + + return config_pb2.ConfigProto( + allow_soft_placement=True, + graph_options=graph_opts, + device_filters=device_filters) + def _init_distributed_setting_from_environment_var(self, tf_config): """Initialize distributed properties based on `tf_config`.""" diff --git a/tensorflow/python/estimator/run_config_test.py b/tensorflow/python/estimator/run_config_test.py index c8b12605e1aaad11e114e4ace63697b93f3b2b92..06df7cb9dd4ae3d167d622601e551079b64e80a2 100644 --- a/tensorflow/python/estimator/run_config_test.py +++ b/tensorflow/python/estimator/run_config_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import json from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.platform import test @@ -290,6 +291,7 @@ class RunConfigDistributedSettingTest(test.TestCase): expected_num_worker_replicas=1, expected_num_ps_replicas=0) self.assertEqual(0, run_config.global_id_in_cluster) + self.assertIsNone(run_config.session_config, None) def test_session_master_for_local(self): tf_config = {'session_master': '_my_master'} @@ -1119,5 +1121,115 @@ class RunConfigModelDirTest(test.TestCase): _create_run_config_with_cluster_spec(tf_config) +class RunConfigSessionConfigTest(test.TestCase): + + def _assert_equal_session_config(self, session_config, + expected_device_filters): + + rewrite_opts = rewriter_config_pb2.RewriterConfig( + meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE) + graph_opts = config_pb2.GraphOptions(rewrite_options=rewrite_opts) + expected_session_config = config_pb2.ConfigProto( + allow_soft_placement=True, + graph_options=graph_opts, + device_filters=expected_device_filters) + self.assertEqual(session_config, expected_session_config) + + def test_master_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.MASTER: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': run_config_lib.TaskType.MASTER, + 'index': 0 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self._assert_equal_session_config(run_config.session_config, + ['/job:ps', '/job:master']) + + def test_chief_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.CHIEF: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': run_config_lib.TaskType.CHIEF, + 'index': 0 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self._assert_equal_session_config(run_config.session_config, + ['/job:ps', '/job:chief']) + + def test_worker_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.MASTER: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': run_config_lib.TaskType.WORKER, + 'index': 1 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self._assert_equal_session_config(run_config.session_config, + ['/job:ps', '/job:worker/task:1']) + + def test_ps_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.MASTER: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': run_config_lib.TaskType.PS, + 'index': 1 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self._assert_equal_session_config(run_config.session_config, + ['/job:ps', '/job:worker', '/job:master']) + + def test_evaluator_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.CHIEF: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': run_config_lib.TaskType.EVALUATOR, + 'index': 0 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self.assertIsNone(run_config.session_config) + + def test_other_type_session_config(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.MASTER: ['host0:0'], + run_config_lib.TaskType.PS: ['host1:1', 'host2:2'], + 'other_type': ['host3:1', 'host4:2'], + run_config_lib.TaskType.WORKER: ['host3:3', 'host4:4', 'host5:5'] + }, + 'task': { + 'type': 'other_type', + 'index': 0 + } + } + run_config = _create_run_config_with_cluster_spec(tf_config) + self.assertIsNone(run_config.session_config) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index fb6a68b4f7c6d56d1ef98e1cf38a21f57004fff5..57301010920be90c63e00594d686df3a09466c91 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -35,7 +35,7 @@ from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import server_lib from tensorflow.python.training import session_run_hook from tensorflow.python.util import compat -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.util.tf_export import estimator_export _MAX_DELAY_SECS = 60 _DELAY_SECS_PER_WORKER = 5 @@ -115,7 +115,7 @@ def _is_google_env(): return tf_config.get(_ENVIRONMENT_KEY) == _ENVIRONMENT_GOOGLE_VALUE -@tf_export('estimator.TrainSpec') +@estimator_export('estimator.TrainSpec') class TrainSpec( collections.namedtuple('TrainSpec', ['input_fn', 'max_steps', 'hooks'])): """Configuration for the "train" part for the `train_and_evaluate` call. @@ -167,7 +167,7 @@ class TrainSpec( cls, input_fn=input_fn, max_steps=max_steps, hooks=hooks) -@tf_export('estimator.EvalSpec') +@estimator_export('estimator.EvalSpec') class EvalSpec( collections.namedtuple('EvalSpec', [ 'input_fn', 'steps', 'name', 'hooks', 'exporters', 'start_delay_secs', @@ -263,7 +263,7 @@ class EvalSpec( throttle_secs=throttle_secs) -@tf_export('estimator.train_and_evaluate') +@estimator_export('estimator.train_and_evaluate') def train_and_evaluate(estimator, train_spec, eval_spec): """Train and evaluate the `estimator`. @@ -278,10 +278,7 @@ def train_and_evaluate(estimator, train_spec, eval_spec): supported distributed training configuration is between-graph replication. Overfitting: In order to avoid overfitting, it is recommended to set up the - training `input_fn` to shuffle the training data properly. It is also - recommended to train the model a little longer, say multiple epochs, before - performing evaluation, as the input pipeline starts from scratch for each - training. It is particularly important for local training and evaluation. + training `input_fn` to shuffle the training data properly. Stop condition: In order to support both distributed and non-distributed configuration reliably, the only supported stop condition for model @@ -470,6 +467,61 @@ class _StopAtSecsHook(session_run_hook.SessionRunHook): run_context.request_stop() +class _NewCheckpointListenerForEvaluate( + basic_session_run_hooks.CheckpointSaverListener): + """A saver listener to run evaluate with every checkpoint.""" + + def __init__(self, evaluator, eval_throttle_secs, continuous_eval_listener): + self._evaluator = evaluator + self._eval_throttle_secs = eval_throttle_secs + self._continuous_eval_listener = continuous_eval_listener + self.eval_result, self.export_results = None, None + + def begin(self): + self._timer = basic_session_run_hooks.SecondOrStepTimer( + every_secs=self._eval_throttle_secs) + self._is_first_run = True + + def after_save(self, session, global_step_value): + del session # unused; required by signature. + # skip first run model is not trained yet. + if self._is_first_run: + self._is_first_run = False + return + + if not self._continuous_eval_listener.before_eval(): + logging.info('Exiting training and evaluation loop, as requested by ' + '_ContinuousEvalListener.before_eval.') + return True + if self._timer.should_trigger_for_step(global_step_value): + self._evaluate(global_step_value) # updates self.eval_result + if not self._continuous_eval_listener.after_eval(self.eval_result): + logging.info('Exiting evaluation, as requested by ' + '_ContinuousEvalListener.after_eval.') + return True + else: + # TODO(ispir): add remaining time in the log. + logging.info('Skip the current checkpoint eval due to throttle secs ' + '({} secs).'.format(self._eval_throttle_secs)) + + def end(self, session, global_step_value): + # Evaluate if the last step has not been evaluated, yet. + if global_step_value != self._timer.last_triggered_step(): + if self._continuous_eval_listener.before_eval(): + self._evaluate(global_step_value) + self._continuous_eval_listener.after_eval(self.eval_result) + + def _evaluate(self, global_step_value): + self._timer.update_last_triggered_step(global_step_value) + self.eval_result, self.export_results = ( + self._evaluator.evaluate_and_export()) + if self.eval_result.status != _EvalStatus.EVALUATED: + # This is unexpected; should never happen. + # Training should always end with a new checkpoint. + raise RuntimeError('There was no new checkpoint after the training. ' + 'Eval status: {}'.format(self.eval_result.status)) + + class _TrainingExecutor(object): """The executor to run `Estimator` training and evaluation. @@ -576,28 +628,6 @@ class _TrainingExecutor(object): def run_master(self): """Runs task master.""" - - class NewCheckpointListener( - basic_session_run_hooks.CheckpointSaverListener): - - def __init__(self, evaluator, eval_throttle_secs): - self._evaluator = evaluator - self._eval_throttle_secs = eval_throttle_secs - - def begin(self): - self._timer = basic_session_run_hooks.SecondOrStepTimer( - every_secs=self._eval_throttle_secs) - - def after_save(self, session, global_step_value): - del session # unused; required by signature. - - if self._timer.should_trigger_for_step(global_step_value): - self._timer.update_last_triggered_step(global_step_value) - self._evaluator.evaluate_and_export() - else: - logging.info('Skip the current checkpoint eval due to throttle secs ' - '({} secs).'.format(self._eval_throttle_secs)) - _assert_eval_spec(self._eval_spec) # Final export signal: For any eval result with global_step >= train @@ -617,16 +647,12 @@ class _TrainingExecutor(object): # When the underlying `Estimator` object saves a new checkpoint, we would # like this callback to be called so that evaluation and export can trigger. saving_listeners = [ - NewCheckpointListener(evaluator, self._eval_spec.throttle_secs) + _NewCheckpointListenerForEvaluate(evaluator, + self._eval_spec.throttle_secs, + _ContinuousEvalListener()) ] self._start_distributed_training(saving_listeners=saving_listeners) - if not evaluator.is_final_export_triggered: - logging.info('Training has already ended. But the last eval is skipped ' - 'due to eval throttle_secs. Now evaluating the final ' - 'checkpoint.') - evaluator.evaluate_and_export() - def run_evaluator(self): """Runs task evaluator.""" # TODO(xiejw): To allow execution framework to add continuous eval listener. @@ -640,68 +666,33 @@ class _TrainingExecutor(object): def run_local(self): """Runs training and evaluation locally (non-distributed).""" - - def _should_stop_local_train(global_step): - if self._train_spec.max_steps is None: - return False - if global_step >= self._train_spec.max_steps: - return True - return False - _assert_eval_spec(self._eval_spec) - if self._eval_spec.throttle_secs <= 0: - raise ValueError('eval_spec.throttle_secs should be positive, given: {}.' - 'It is used do determine how long each training ' - 'iteration should go when train and evaluate ' - 'locally.'.format(self._eval_spec.throttle_secs)) - - stop_hook = _StopAtSecsHook(self._eval_spec.throttle_secs) - train_hooks = ( - list(self._train_spec.hooks) + [stop_hook] + list(self._train_hooks)) + train_hooks = list(self._train_spec.hooks) + list(self._train_hooks) logging.info('Start train and evaluate loop. The evaluate will happen ' - 'after {} secs (eval_spec.throttle_secs) or training is ' - 'finished.'.format(self._eval_spec.throttle_secs)) + 'after every checkpoint. Checkpoint frequency is determined ' + 'based on RunConfig arguments: save_checkpoints_steps {} or ' + 'save_checkpoints_secs {}.'.format( + self._estimator.config.save_checkpoints_steps, + self._estimator.config.save_checkpoints_secs)) evaluator = _TrainingExecutor._Evaluator(self._estimator, self._eval_spec, self._train_spec.max_steps) - eval_result = _EvalResult(status=_EvalStatus.MISSING_CHECKPOINT) - export_results = [] - - while True: - self._estimator.train( - input_fn=self._train_spec.input_fn, - max_steps=self._train_spec.max_steps, - hooks=train_hooks) - - if not self._continuous_eval_listener.before_eval(): - logging.info('Exiting training and evaluation loop, as requested by ' - '_ContinuousEvalListener.before_eval.') - break - - # Final export signal: For any eval result with global_step >= train - # max_steps, the evaluator will send the final export signal. The - # _should_stop_local_train will then end the while True as the stopping - # condition is satisfied (both checks use the same global_step value, - # i.e., no race condition) - eval_result, export_results = evaluator.evaluate_and_export() - - if eval_result.status != _EvalStatus.EVALUATED: - # This is unexpected; should never happen. - # Training should always end with a new checkpoint. - raise RuntimeError('There was no new checkpoint after the training. ' - 'Eval status: {}'.format(eval_result.status)) - - if not self._continuous_eval_listener.after_eval(eval_result): - logging.info('Exiting evaluation, as requested by ' - '_ContinuousEvalListener.after_eval.') - break + listener_for_eval = _NewCheckpointListenerForEvaluate( + evaluator, self._eval_spec.throttle_secs, + self._continuous_eval_listener) + saving_listeners = [listener_for_eval] + + self._estimator.train( + input_fn=self._train_spec.input_fn, + max_steps=self._train_spec.max_steps, + hooks=train_hooks, + saving_listeners=saving_listeners) - if _should_stop_local_train( - eval_result.metrics[ops.GraphKeys.GLOBAL_STEP]): - break - return eval_result.metrics, export_results + eval_result = listener_for_eval.eval_result or _EvalResult( + status=_EvalStatus.MISSING_CHECKPOINT) + return eval_result.metrics, listener_for_eval.export_results def _start_std_server(self, config): """Creates, starts, and returns a server_lib.Server.""" diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py index 2c838db7a4de98d941752ce9d5ddf8f2b47a46f1..6bee7cbe83a5e9b623ea16ebe48cce93e27534e2 100644 --- a/tensorflow/python/estimator/training_test.py +++ b/tensorflow/python/estimator/training_test.py @@ -29,17 +29,21 @@ import time import numpy as np +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import exporter as exporter_lib +from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator import training from tensorflow.python.estimator.canned import dnn from tensorflow.python.estimator.canned import prediction_keys from tensorflow.python.estimator.export import export as export_lib -from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.feature_column import feature_column +from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import state_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging @@ -49,6 +53,7 @@ from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import monitored_session from tensorflow.python.training import server_lib from tensorflow.python.training import session_run_hook +from tensorflow.python.training import training_util from tensorflow.python.util import compat _DEFAULT_EVAL_STEPS = 100 @@ -885,7 +890,8 @@ class TrainingExecutorRunMasterTest(test.TestCase): # `after_save`. del args, kwargs saving_listeners[0].begin() - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=0) + saving_listeners[0].after_save(session=None, global_step_value=10) mock_est = test.mock.Mock( spec=estimator_lib.Estimator, model_dir='path/', train=estimator_train) @@ -930,7 +936,10 @@ class TrainingExecutorRunMasterTest(test.TestCase): del args, kwargs saving_listeners[0].begin() - # Call three times. + # Call four times. + mock_timer.should_trigger_for_step.return_value = True + saving_listeners[0].after_save(session=None, global_step_value=None) + mock_timer.should_trigger_for_step.return_value = True saving_listeners[0].after_save(session=None, global_step_value=None) @@ -979,14 +988,19 @@ class TrainingExecutorRunMasterTest(test.TestCase): del args, kwargs saving_listeners[0].begin() - # Call two times. + # Call tree times (one for first saving). mock_timer.should_trigger_for_step.return_value = True - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=0) + + mock_timer.should_trigger_for_step.return_value = True + saving_listeners[0].after_save(session=None, global_step_value=125) - # The final ckpt is skipped by the timer. It will be picked up the final - # export check in the code. mock_timer.should_trigger_for_step.return_value = False - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=250) + + # At the end evaluate should be called even if throttle secs prevents it. + mock_timer.should_trigger_for_step.return_value = False + saving_listeners[0].end(session=None, global_step_value=300) mock_est.train = estimator_train mock_est.latest_checkpoint.side_effect = ['ckpt1', 'ckpt2'] @@ -1566,28 +1580,31 @@ class StopAtSecsHookTest(test.TestCase): class TrainingExecutorRunLocalTest(test.TestCase): """Tests run_local of _TrainingExecutor.""" + def _model_fn(self, features, labels, mode): + del labels + with ops.control_dependencies([features]): + train_op = state_ops.assign_add(training_util.get_global_step(), 1) + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(0.), + train_op=train_op, + predictions=constant_op.constant([[10.]]), + eval_metric_ops={'mean_of_features': metrics_lib.mean(features)}) + + def _input_fn(self, repeat=True): + ds = dataset_ops.Dataset.from_tensors([1]) + if repeat: + return ds.repeat() + return ds + def unique_checkpoint_every_time_fn(self): return 'checkpoint_path_%s/' % random.random() - def test_send_stop_at_secs_to_train(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn - train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=2, hooks=[_FakeHook()]) - eval_spec = training.EvalSpec( - input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100) - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} - - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - executor.run_local() - - stop_hook = mock_est.train.call_args[1]['hooks'][-1] - self.assertIsInstance(stop_hook, training._StopAtSecsHook) - self.assertEqual(eval_spec.throttle_secs, stop_hook._stop_after_secs) - - def test_runs_in_a_loop_until_max_steps(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + def test_runs_evaluate_with_every_new_checkpoint(self): + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.times_export_was_called = 0 mock_est.times_final_export_was_called = 0 @@ -1604,42 +1621,30 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.name = 'see_how_many_times_export_is_called' exporter.export = export - train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=22) eval_spec = training.EvalSpec( - input_fn=lambda: 1, - hooks=[_FakeHook()], - throttle_secs=100, + input_fn=lambda: self._input_fn(repeat=False), + throttle_secs=0, exporters=exporter) - # should be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() - self.assertEqual(3, mock_est.train.call_count) + self.assertEqual(1, mock_est.train.call_count) self.assertEqual(3, mock_est.evaluate.call_count) self.assertEqual(3, mock_est.times_export_was_called) self.assertEqual(1, mock_est.times_final_export_was_called) def test_runs_with_eval_listener_before_eval(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn - train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100) - # should be called 2 times without the evallistener - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=12) + eval_spec = training.EvalSpec(input_fn=lambda: self._input_fn(repeat=False)) + mock_est.evaluate.side_effect = [{_GLOBAL_STEP_KEY: train_spec.max_steps}] class _Listener(training._ContinuousEvalListener): @@ -1658,67 +1663,61 @@ class TrainingExecutorRunLocalTest(test.TestCase): self.assertEqual(1, mock_est.train.call_count) self.assertEqual(0, mock_est.evaluate.call_count) - self.assertEqual(1, listener.call_count) def test_runs_with_eval_listener_after_eval(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) - train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100) - # should be called 2 times without the evallistener - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=3000) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) class _Listener(training._ContinuousEvalListener): - def __init__(self, test_case): + def __init__(self): self.call_count = 0 - self._test_case = test_case def after_eval(self, eval_result): self.call_count += 1 - self._test_case.assertEqual( - train_spec.max_steps - 50, eval_result.metrics[_GLOBAL_STEP_KEY]) return False # Will stop the run_local after first eval. - listener = _Listener(test_case=self) + listener = _Listener() executor = training._TrainingExecutor( mock_est, train_spec, eval_spec, continuous_eval_listener=listener) - executor.run_local() + metrics, _ = executor.run_local() # pylint: disable=assignment-from-no-return self.assertEqual(1, mock_est.train.call_count) self.assertEqual(1, mock_est.evaluate.call_count) self.assertEqual(1, listener.call_count) + # Should be less than max_steps since listener did early stopping. + self.assertLess(metrics[_GLOBAL_STEP_KEY], train_spec.max_steps) def test_handles_no_new_checkpoint_found(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = ( - 'no_new_checkpoints_after_the_first_train_step') + est = estimator_lib.Estimator( + model_fn=self._model_fn, + # disable saving checkpoint + config=run_config_lib.RunConfig( + save_checkpoints_steps=None, save_checkpoints_secs=None)) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=300, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100) - # It was going to be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + input_fn=lambda: self._input_fn(repeat=False), + hooks=[_FakeHook()], + throttle_secs=100) - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - with self.assertRaisesRegexp(RuntimeError, _STALE_CHECKPOINT_MSG): + executor = training._TrainingExecutor(est, train_spec, eval_spec) + with self.assertRaisesRegexp(ValueError, + 'There should be a CheckpointSaverHook'): executor.run_local() def test_final_export_is_true_in_the_end(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.times_export_fn_was_called = 0 mock_est.times_the_final_export_was_true = 0 @@ -1734,37 +1733,29 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.export = export train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=12, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, - hooks=[_FakeHook()], - throttle_secs=100, + input_fn=lambda: self._input_fn(repeat=False), + throttle_secs=0, exporters=exporter) - # should be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() - self.assertEqual(3, mock_est.train.call_count) - self.assertEqual(3, mock_est.evaluate.call_count) - self.assertEqual(3, mock_est.times_export_fn_was_called) + self.assertEqual(1, mock_est.train.call_count) + self.assertEqual(2, mock_est.evaluate.call_count) + self.assertEqual(2, mock_est.times_export_fn_was_called) self.assertEqual(1, mock_est.times_the_final_export_was_true) def test_train_and_evaluate_args(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = 'checkpoint_path/' + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=300, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, steps=2, hooks=[_FakeHook()], name='local_eval') - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} + input_fn=lambda: self._input_fn(repeat=False), + steps=2, + hooks=[_FakeHook()], + name='local_eval') executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() @@ -1773,11 +1764,11 @@ class TrainingExecutorRunLocalTest(test.TestCase): name=eval_spec.name, input_fn=eval_spec.input_fn, steps=eval_spec.steps, - checkpoint_path='checkpoint_path/', + checkpoint_path=est.latest_checkpoint(), hooks=eval_spec.hooks) train_args = mock_est.train.call_args[1] - self.assertEqual(list(train_spec.hooks), list(train_args['hooks'][:-1])) + self.assertEqual(list(train_spec.hooks), list(train_args['hooks'])) self.assertEqual(train_spec.input_fn, train_args['input_fn']) self.assertEqual(train_spec.max_steps, train_args['max_steps']) @@ -1812,25 +1803,11 @@ class TrainingExecutorRunLocalTest(test.TestCase): if not isinstance(h, training._StopAtSecsHook) ]) - def test_errors_out_if_throttle_secs_is_zero(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=0) - - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - with self.assertRaisesRegexp(ValueError, 'throttle_secs'): - executor.run_local() - def test_that_export_is_called_with_run_local(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - mock_train_spec = test.mock.Mock(spec=training.TrainSpec) - mock_train_spec.max_steps = 200 - mock_est.evaluate.return_value = { - _GLOBAL_STEP_KEY: mock_train_spec.max_steps - } - # _validate_hooks would have made sure that train_spec.hooks is [], when - # None were passed. - mock_train_spec.hooks = [] + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=12) + mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} def export(estimator, *args, **kwargs): del args, kwargs @@ -1842,13 +1819,13 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.export = export eval_spec = training.EvalSpec( - input_fn=lambda: 1, + input_fn=lambda: self._input_fn(repeat=False), steps=2, start_delay_secs=0, throttle_secs=213, exporters=exporter) - executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec) + executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) # pylint: disable=assignment-from-no-return _, export_results = executor.run_local() # pylint: enable=assignment-from-no-return @@ -1857,9 +1834,13 @@ class TrainingExecutorRunLocalTest(test.TestCase): self.assertEqual(export_results, ['path_to_export']) def test_errors_out_if_evaluate_returns_empty_dict(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = {} executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) @@ -1867,18 +1848,26 @@ class TrainingExecutorRunLocalTest(test.TestCase): executor.run_local() def test_errors_out_if_evaluate_returns_non_dict(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = 123 executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_RESULT_TYPE_ERR): executor.run_local() def test_errors_out_if_evaluate_returns_dict_without_global_step(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = {'loss': 123} executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) @@ -1887,19 +1876,21 @@ class TrainingExecutorRunLocalTest(test.TestCase): executor.run_local() def test_train_and_evaluate_return_metrics(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = 'checkpoint_path/' + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=12, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, steps=2, hooks=[_FakeHook()], name='local_eval') - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} + input_fn=lambda: self._input_fn(repeat=False), + steps=2, + hooks=[_FakeHook()], + name='local_eval') executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) # pylint: disable=assignment-from-no-return metrics, _ = executor.run_local() # pylint: enable=assignment-from-no-return - self.assertEqual(metrics['global_step'], 300) + self.assertEqual(metrics['global_step'], 12) class TrainAndEvaluateRunTest(test.TestCase): @@ -2096,7 +2087,7 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # max_steps should be larger than save_summary_steps max_steps = 10 - save_summary_steps = 2 + save_summary_steps = 9 data = np.linspace( 0., n_classes - 1., batch_size * input_dimension, dtype=np.float32) @@ -2104,24 +2095,20 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1)) # learn y = x - train_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - y=y_data, - batch_size=batch_size, - num_epochs=None, - shuffle=True) - - eval_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - y=y_data, - batch_size=batch_size, - num_epochs=1, - shuffle=False) - - predict_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - batch_size=batch_size, - shuffle=False) + def train_input_fn(): + return dataset_ops.Dataset.from_tensor_slices(({ + 'x': x_data + }, y_data)).batch(batch_size).repeat().shuffle(1000) + + def eval_input_fn(): + return dataset_ops.Dataset.from_tensor_slices(({ + 'x': x_data + }, y_data)).batch(batch_size) + + def predict_input_fn(): + return dataset_ops.Dataset.from_tensor_slices({ + 'x': x_data + }).batch(batch_size) feature_columns = [ feature_column.numeric_column('x', shape=(input_dimension,))] @@ -2137,9 +2124,11 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): max_steps=max_steps) eval_spec = training.EvalSpec( - name=eval_name, input_fn=eval_input_fn, steps=None, + name=eval_name, + input_fn=eval_input_fn, + steps=None, exporters=self._get_exporter(exporter_name, feature_columns), - throttle_secs=2) + throttle_secs=0) training.train_and_evaluate(est, train_spec, eval_spec) @@ -2148,15 +2137,12 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # Examine the training events. Use a range to check global step to avoid # flakyness due to global step race condition. - training_loss, training_global_step = self._extract_loss_and_global_step( - est.model_dir) + training_loss, _ = self._extract_loss_and_global_step(est.model_dir) self.assertIsNotNone(training_loss) - self.assertTrue( - max_steps - save_summary_steps < training_global_step <= max_steps) # Examine the eval events. The global step should be accurate. eval_loss, eval_global_step = self._extract_loss_and_global_step( - event_folder=os.path.join(est.model_dir, 'eval_' + eval_name)) + event_folder=est.eval_dir(eval_name)) self.assertIsNotNone(eval_loss) self.assertEqual(max_steps, eval_global_step) diff --git a/tensorflow/python/estimator/util.py b/tensorflow/python/estimator/util.py index 924ca309ff0455d3bb06be61ce65bb0a61e84fb0..d4a75478d53f5b3dc8e66df98a78b51a6d25aab8 100644 --- a/tensorflow/python/estimator/util.py +++ b/tensorflow/python/estimator/util.py @@ -22,6 +22,7 @@ from __future__ import print_function import os import time +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training @@ -129,3 +130,24 @@ class _DatasetInitializerHook(training.SessionRunHook): def after_create_session(self, session, coord): del coord session.run(self._initializer) + + +class StrategyInitFinalizeHook(training.SessionRunHook): + """Creates a SessionRunHook that initializes and shutsdown devices.""" + + def __init__(self, initialization_fn, finalize_fn): + self._initialization_fn = initialization_fn + self._finalize_fn = finalize_fn + + def begin(self): + self._init_ops = self._initialization_fn() + self._finalize_ops = self._finalize_fn() + + def after_create_session(self, session, coord): + logging.info('Initialize system') + session.run(self._init_ops, + options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000)) + + def end(self, session): + logging.info('Finalize system.') + session.run(self._finalize_ops) diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index af2ead9b8405696977cc9a40be750b939541006a..d091d2fe0ac688773b27d80f37fbf3083b8ffa1f 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -172,7 +172,7 @@ def _internal_input_layer(features, scope=None): """See input_layer. `scope` is a name or variable scope to use.""" - feature_columns = _clean_feature_columns(feature_columns) + feature_columns = _normalize_feature_columns(feature_columns) for column in feature_columns: if not isinstance(column, _DenseColumn): raise ValueError( @@ -350,10 +350,23 @@ def linear_model(features, prediction itself for linear regression problems. Note on supported columns: `linear_model` treats categorical columns as - `indicator_column`s while `input_layer` explicitly requires wrapping each - of them with an `embedding_column` or an `indicator_column`. + `indicator_column`s. To be specific, assume the input as `SparseTensor` looks + like: - Example: + ```python + shape = [2, 2] + { + [0, 0]: "a" + [1, 0]: "b" + [1, 1]: "c" + } + ``` + `linear_model` assigns weights for the presence of "a", "b", "c' implicitly, + just like `indicator_column`, while `input_layer` explicitly requires wrapping + each of categorical columns with an `embedding_column` or an + `indicator_column`. + + Example of usage: ```python price = numeric_column('price') @@ -374,13 +387,44 @@ def linear_model(features, to your model. All items should be instances of classes derived from `_FeatureColumn`s. units: An integer, dimensionality of the output space. Default value is 1. - sparse_combiner: A string specifying how to reduce if a sparse column is - multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" - the default. "sqrtn" often achieves good accuracy, in particular with - bag-of-words columns. It combines each sparse columns independently. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. Except `numeric_column`, almost all columns passed to + `linear_model` are considered as categorical columns. It combines each + categorical column independently. Currently "mean", "sqrtn" and "sum" are + supported, with "sum" the default for linear model. "sqrtn" often achieves + good accuracy, in particular with bag-of-words columns. * "sum": do not normalize features in the column * "mean": do l1 normalization on features in the column * "sqrtn": do l2 normalization on features in the column + For example, for two features represented as the categorical columns: + + ```python + # Feature 1 + + shape = [2, 2] + { + [0, 0]: "a" + [0, 1]: "b" + [1, 0]: "c" + } + + # Feature 2 + + shape = [2, 3] + { + [0, 0]: "d" + [1, 0]: "e" + [1, 1]: "f" + [1, 2]: "g" + } + ``` + with `sparse_combiner` as "mean", the linear model outputs conceptly are: + ``` + y_0 = 1.0 / 2.0 * ( w_a + w_ b) + w_c + b_0 + y_1 = w_d + 1.0 / 3.0 * ( w_e + w_ f + w_g) + b_1 + ``` + where `y_i` is the output, `b_i` is the bias, and `w_x` is the weight + assigned to the presence of `x` in the input features. weight_collections: A list of collection names to which the Variable will be added. Note that, variables will also be added to collections `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`. @@ -408,13 +452,15 @@ def linear_model(features, ValueError: if an item in `feature_columns` is neither a `_DenseColumn` nor `_CategoricalColumn`. """ + with variable_scope.variable_scope(None, 'linear_model') as vs: + model_name = _strip_leading_slashes(vs.name) linear_model_layer = _LinearModel( feature_columns=feature_columns, units=units, sparse_combiner=sparse_combiner, weight_collections=weight_collections, trainable=trainable, - name='linear_model') + name=model_name) retval = linear_model_layer(features) # pylint: disable=not-callable if cols_to_vars is not None: cols_to_vars.update(linear_model_layer.cols_to_vars()) @@ -422,13 +468,25 @@ def linear_model(features, def _add_to_collections(var, weight_collections): - # TODO(rohanj): Explore adding a _get_variable_list method on `Variable` - # so that we don't have to do this check. - if isinstance(var, variables.PartitionedVariable): - for constituent_var in list(var): - ops.add_to_collections(weight_collections, constituent_var) - else: - ops.add_to_collections(weight_collections, var) + """Adds a var to the list of weight_collections provided. + + Handles the case for partitioned and non-partitioned variables. + + Args: + var: A variable or Partitioned Variable. + weight_collections: List of collections to add variable to. + """ + for weight_collection in weight_collections: + # The layer self.add_variable call already adds it to GLOBAL_VARIABLES. + if weight_collection == ops.GraphKeys.GLOBAL_VARIABLES: + continue + # TODO(rohanj): Explore adding a _get_variable_list method on `Variable` + # so that we don't have to do this check. + if isinstance(var, variables.PartitionedVariable): + for constituent_var in list(var): + ops.add_to_collection(weight_collection, constituent_var) + else: + ops.add_to_collection(weight_collection, var) class _FCLinearWrapper(base.Layer): @@ -536,8 +594,11 @@ class _LinearModel(training.Model): name=None, **kwargs): super(_LinearModel, self).__init__(name=name, **kwargs) - self._feature_columns = _clean_feature_columns(feature_columns) + self._feature_columns = _normalize_feature_columns( + feature_columns) self._weight_collections = list(weight_collections or []) + if ops.GraphKeys.GLOBAL_VARIABLES not in self._weight_collections: + self._weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES) if ops.GraphKeys.MODEL_VARIABLES not in self._weight_collections: self._weight_collections.append(ops.GraphKeys.MODEL_VARIABLES) @@ -643,7 +704,7 @@ def _transform_features(features, feature_columns): Returns: A `dict` mapping `_FeatureColumn` to `Tensor` and `SparseTensor` values. """ - feature_columns = _clean_feature_columns(feature_columns) + feature_columns = _normalize_feature_columns(feature_columns) outputs = {} with ops.name_scope( None, default_name='transform_features', values=features.values()): @@ -911,7 +972,8 @@ def shared_embedding_columns( tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `None`. - max_norm: If not `None`, embedding values are l2-normalized to this value. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value, before combining. trainable: Whether or not the embedding is trainable. Default is True. Returns: @@ -925,7 +987,12 @@ def shared_embedding_columns( ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt` is specified. ValueError: if `initializer` is specified and is not callable. + RuntimeError: if eager execution is enabled. """ + if context.executing_eagerly(): + raise RuntimeError('shared_embedding_columns are not supported when eager ' + 'execution is enabled.') + if (dimension is None) or (dimension < 1): raise ValueError('Invalid dimension {}.'.format(dimension)) if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None): @@ -970,16 +1037,6 @@ def shared_embedding_columns( shared_embedding_collection_name = '_'.join(c.name for c in sorted_columns) shared_embedding_collection_name += '_shared_embedding' - # Create the state (_SharedEmbeddingColumnLayer) here. - embedding_shape = num_buckets, dimension - - shared_embedding_column_layer = _EmbeddingColumnLayer( - embedding_shape=embedding_shape, - initializer=initializer, - weight_collections=[], - trainable=trainable, - name=shared_embedding_collection_name) - result = [] for column in categorical_columns: result.append( @@ -988,16 +1045,12 @@ def shared_embedding_columns( initializer=initializer, dimension=dimension, combiner=combiner, - var_scope_name=shared_embedding_collection_name, + shared_embedding_collection_name=shared_embedding_collection_name, ckpt_to_load_from=ckpt_to_load_from, tensor_name_in_ckpt=tensor_name_in_ckpt, max_norm=max_norm, trainable=trainable)) - for single_result in result: - single_result._set_layer(shared_embedding_column_layer) # pylint: disable=protected-access - single_result._set_all_columns(result) # pylint: disable=protected-access - return result @@ -1182,12 +1235,13 @@ def categorical_column_with_hash_bucket(key, Use this when your sparse features are in string or integer format, and you want to distribute your inputs into a finite number of buckets by hashing. - output_id = Hash(input_feature_string) % bucket_size + output_id = Hash(input_feature_string) % bucket_size for string type input. + For int type input, the value is converted to its string representation first + and then hashed by the same formula. For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example: @@ -1249,8 +1303,7 @@ def categorical_column_with_vocabulary_file(key, For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example with `num_oov_buckets`: File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state @@ -1366,8 +1419,7 @@ def categorical_column_with_vocabulary_list( For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example with `num_oov_buckets`: In the following example, each input in `vocabulary_list` is assigned an ID @@ -1480,8 +1532,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None): For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. In the following examples, each input in the range `[0, 1000000)` is assigned the same value. All other inputs are assigned `default_value` 0. Note that a @@ -1538,8 +1589,14 @@ def categorical_column_with_identity(key, num_buckets, default_value=None): def indicator_column(categorical_column): """Represents multi-hot representation of given categorical column. - Used to wrap any `categorical_column_*` (e.g., to feed to DNN). Use - `embedding_column` if the inputs are sparse. + - For DNN model, `indicator_column` can be used to wrap any + `categorical_column_*` (e.g., to feed to DNN). Consider to Use + `embedding_column` if the number of buckets/unique(values) are large. + + - For Wide (aka linear) model, `indicator_column` is the internal + representation for categorical column when passing categorical column + directly (as any element in feature_columns) to `linear_model`. See + `linear_model` for details. ```python name = indicator_column(categorical_column_with_vocabulary_list( @@ -1813,11 +1870,8 @@ class _EmbeddingColumnLayer(base.Layer): dtype=dtypes.float32, initializer=self._initializer, trainable=self.trainable) - # self.add_variable already appends to GLOBAL_VARIABLES collection. if self._weight_collections and not context.executing_eagerly(): - for weight_collection in self._weight_collections: - if weight_collection != ops.GraphKeys.GLOBAL_VARIABLES: - _add_to_collections(self._embedding_weight_var, [weight_collection]) + _add_to_collections(self._embedding_weight_var, self._weight_collections) self.built = True def call(self, _): @@ -1956,7 +2010,7 @@ def _create_weighted_sum(column, weight_collections, trainable, weight_var=None): - """Creates a weighted sum for a dense or sparse column for linear_model.""" + """Creates a weighted sum for a dense/categorical column for linear_model.""" if isinstance(column, _CategoricalColumn): return _create_categorical_column_weighted_sum( column=column, @@ -2055,7 +2109,34 @@ def _create_categorical_column_weighted_sum(column, weight_collections, trainable, weight_var=None): - """Create a weighted sum of a categorical column for linear_model.""" + # pylint: disable=g-doc-return-or-yield,g-doc-args + """Create a weighted sum of a categorical column for linear_model. + + Note to maintainer: As implementation details, the weighted sum is + implemented via embedding_lookup_sparse toward efficiency. Mathematically, + they are the same. + + To be specific, conceptually, categorical column can be treated as multi-hot + vector. Say: + + ```python + x = [0 0 1] # categorical column input + w = [a b c] # weights + ``` + The weighted sum is `c` in this case, which is same as `w[2]`. + + Another example is + + ```python + x = [0 1 1] # categorical column input + w = [a b c] # weights + ``` + The weighted sum is `b + c` in this case, which is same as `w[2] + w[3]`. + + For both cases, we can implement weighted sum via embedding_lookup with + sparse_combiner = "sum". + """ + sparse_tensors = column._get_sparse_tensors( # pylint: disable=protected-access builder, weight_collections=weight_collections, @@ -2077,7 +2158,7 @@ def _create_categorical_column_weighted_sum(column, initializer=init_ops.zeros_initializer(), trainable=trainable, collections=weight_collections) - return _safe_embedding_lookup_sparse( + return embedding_ops.safe_embedding_lookup_sparse( weight, id_tensor, sparse_weights=weight_tensor, @@ -2249,7 +2330,7 @@ def _shape_offsets(shape): # TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py -def _to_sparse_input(input_tensor, ignore_value=None): +def _to_sparse_input_and_drop_ignore_values(input_tensor, ignore_value=None): """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells. If `input_tensor` is already a `SparseTensor`, just return it. @@ -2293,8 +2374,22 @@ def _to_sparse_input(input_tensor, ignore_value=None): input_tensor, out_type=dtypes.int64, name='dense_shape')) -def _clean_feature_columns(feature_columns): - """Verifies and normalizes `feature_columns` input.""" +def _normalize_feature_columns(feature_columns): + """Normalizes the `feature_columns` input. + + This method converts the `feature_columns` to list type as best as it can. In + addition, verifies the type and other parts of feature_columns, required by + downstream library. + + Args: + feature_columns: The raw feature columns, usually passed by users. + + Returns: + The normalized feature column list. + + Raises: + ValueError: for any invalid inputs, such as empty, duplicated names, etc. + """ if isinstance(feature_columns, _FeatureColumn): feature_columns = [feature_columns] @@ -2420,6 +2515,7 @@ class _BucketizedColumn(_DenseColumn, _CategoricalColumn, def _get_sparse_tensors(self, inputs, weight_collections=None, trainable=None): + """Converts dense inputs to SparseTensor so downstream code can use it.""" input_tensor = inputs.get(self) batch_size = array_ops.shape(input_tensor)[0] # By construction, source_column is always one-dimensional. @@ -2498,7 +2594,7 @@ class _EmbeddingColumn( }) # Return embedding lookup result. - return _safe_embedding_lookup_sparse( + return embedding_ops.safe_embedding_lookup_sparse( embedding_weights=embedding_weights, sparse_ids=sparse_ids, sparse_weights=sparse_weights, @@ -2553,12 +2649,12 @@ def _get_graph_for_variable(var): class _SharedEmbeddingColumn( - _DenseColumn, + _DenseColumn, _SequenceDenseColumn, collections.namedtuple( '_SharedEmbeddingColumn', ('categorical_column', 'dimension', 'combiner', 'initializer', - 'var_scope_name', 'ckpt_to_load_from', 'tensor_name_in_ckpt', - 'max_norm', 'trainable'))): + 'shared_embedding_collection_name', 'ckpt_to_load_from', + 'tensor_name_in_ckpt', 'max_norm', 'trainable'))): """See `embedding_column`.""" @property @@ -2569,7 +2665,7 @@ class _SharedEmbeddingColumn( @property def _var_scope_name(self): - return self.var_scope_name + return self.shared_embedding_collection_name @property def _parse_example_spec(self): @@ -2578,29 +2674,17 @@ class _SharedEmbeddingColumn( def _transform_feature(self, inputs): return inputs.get(self.categorical_column) - def _set_layer(self, layer): - self._layer = layer - - def _set_all_columns(self, all_columns): - self._all_columns = all_columns - - def _reset_config(self): - config = self._layer.get_config() - config['embedding_shape'] = ( - self.categorical_column._num_buckets, # pylint: disable=protected-access - self.dimension) - config['initializer'] = self.initializer - self._layer = self._layer.__class__.from_config(config) - for column in self._all_columns: - column._set_layer(self._layer) # pylint: disable=protected-access - @property def _variable_shape(self): if not hasattr(self, '_shape'): self._shape = tensor_shape.vector(self.dimension) return self._shape - def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + def _get_dense_tensor_internal(self, + inputs, + weight_collections=None, + trainable=None): + """Private method that follows the signature of _get_dense_tensor.""" # This method is called from a variable_scope with name _var_scope_name, # which is shared among all shared embeddings. Open a name_scope here, so # that the ops for different columns have distinct names. @@ -2611,19 +2695,38 @@ class _SharedEmbeddingColumn( sparse_ids = sparse_tensors.id_tensor sparse_weights = sparse_tensors.weight_tensor - self._layer.set_weight_collections(weight_collections) - embedding_weights = self._layer( - None, scope=variable_scope.get_variable_scope()) - # If we're in graph mode and this is called with a different graph, - # then we should reset. - if not context.executing_eagerly() and ( - ops.get_default_graph() != - _get_graph_for_variable(embedding_weights)): - self._reset_config() - self._layer.set_weight_collections(weight_collections) - embedding_weights = self._layer( - None, scope=variable_scope.get_variable_scope()) - + embedding_shape = (self.categorical_column._num_buckets, self.dimension) # pylint: disable=protected-access + shared_embedding_collection = ops.get_collection( + self.shared_embedding_collection_name) + if shared_embedding_collection: + if len(shared_embedding_collection) > 1: + raise ValueError( + 'Collection {} can only contain one variable. ' + 'Suggested fix A: Choose a unique name for this collection. ' + 'Suggested fix B: Do not add any variables to this collection. ' + 'The feature_column library already adds a variable under the ' + 'hood.'.format(shared_embedding_collection)) + embedding_weights = shared_embedding_collection[0] + if embedding_weights.get_shape() != embedding_shape: + raise ValueError( + 'Shared embedding collection {} contains variable {} of ' + 'unexpected shape {}. Expected shape is {}. ' + 'Suggested fix A: Choose a unique name for this collection. ' + 'Suggested fix B: Do not add any variables to this collection. ' + 'The feature_column library already adds a variable under the ' + 'hood.'.format(self.shared_embedding_collection_name, + embedding_weights.name, + embedding_weights.get_shape(), embedding_shape)) + else: + embedding_weights = variable_scope.get_variable( + name='embedding_weights', + shape=embedding_shape, + dtype=dtypes.float32, + initializer=self.initializer, + trainable=self.trainable and trainable, + collections=weight_collections) + ops.add_to_collection(self.shared_embedding_collection_name, + embedding_weights) if self.ckpt_to_load_from is not None: to_restore = embedding_weights if isinstance(to_restore, variables.PartitionedVariable): @@ -2633,7 +2736,7 @@ class _SharedEmbeddingColumn( }) # Return embedding lookup result. - return _safe_embedding_lookup_sparse( + return embedding_ops.safe_embedding_lookup_sparse( embedding_weights=embedding_weights, sparse_ids=sparse_ids, sparse_weights=sparse_weights, @@ -2641,6 +2744,44 @@ class _SharedEmbeddingColumn( name='%s_weights' % self.name, max_norm=self.max_norm) + def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + if isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must not be of type _SequenceCategoricalColumn. ' + 'Suggested fix A: If you wish to use input_layer, use a ' + 'non-sequence categorical_column_with_*. ' + 'Suggested fix B: If you wish to create sequence input, use ' + 'sequence_input_layer instead of input_layer. ' + 'Given (type {}): {}'.format(self.name, type(self.categorical_column), + self.categorical_column)) + return self._get_dense_tensor_internal( + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + + def _get_sequence_dense_tensor(self, + inputs, + weight_collections=None, + trainable=None): + if not isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must be of type _SequenceCategoricalColumn ' + 'to use sequence_input_layer. ' + 'Suggested fix: Use one of sequence_categorical_column_with_*. ' + 'Given (type {}): {}'.format(self.name, type(self.categorical_column), + self.categorical_column)) + dense_tensor = self._get_dense_tensor_internal( # pylint: disable=protected-access + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) # pylint: disable=protected-access + sequence_length = _sequence_length_from_sparse_tensor( + sparse_tensors.id_tensor) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + def _create_tuple(shape, value): """Returns a tuple with given shape and filled with value.""" @@ -2762,7 +2903,7 @@ class _HashedCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor): raise ValueError('SparseColumn input must be a SparseTensor.') @@ -2813,7 +2954,7 @@ class _VocabularyFileCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if self.dtype.is_integer != input_tensor.dtype.is_integer: raise ValueError( @@ -2865,7 +3006,7 @@ class _VocabularyListCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if self.dtype.is_integer != input_tensor.dtype.is_integer: raise ValueError( @@ -2917,7 +3058,7 @@ class _IdentityCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(dtypes.int64)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if not input_tensor.dtype.is_integer: raise ValueError( @@ -2999,7 +3140,8 @@ class _WeightedCategoricalColumn( self.dtype, weight_tensor.dtype)) if not isinstance(weight_tensor, sparse_tensor_lib.SparseTensor): # The weight tensor can be a regular Tensor. In this case, sparsify it. - weight_tensor = _to_sparse_input(weight_tensor, ignore_value=0.0) + weight_tensor = _to_sparse_input_and_drop_ignore_values( + weight_tensor, ignore_value=0.0) if not weight_tensor.dtype.is_floating: weight_tensor = math_ops.to_float(weight_tensor) return (inputs.get(self.categorical_column), weight_tensor) @@ -3086,161 +3228,6 @@ def _collect_leaf_level_keys(cross): return leaf_level_keys -# TODO(zakaria): Move this to embedding_ops and make it public. -def _safe_embedding_lookup_sparse(embedding_weights, - sparse_ids, - sparse_weights=None, - combiner='mean', - default_id=None, - name=None, - partition_strategy='div', - max_norm=None): - """Lookup embedding results, accounting for invalid IDs and empty features. - - The partitioned embedding in `embedding_weights` must all be the same shape - except for the first dimension. The first dimension is allowed to vary as the - vocabulary size is not necessarily a multiple of `P`. `embedding_weights` - may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a - partitioner. - - Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs - with non-positive weight. For an entry with no features, the embedding vector - for `default_id` is returned, or the 0-vector if `default_id` is not supplied. - - The ids and weights may be multi-dimensional. Embeddings are always aggregated - along the last dimension. - - Args: - embedding_weights: A list of `P` float `Tensor`s or values representing - partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` - created by partitioning along dimension 0. The total unpartitioned - shape should be `[e_0, e_1, ..., e_m]`, where `e_0` represents the - vocab size and `e_1, ..., e_m` are the embedding dimensions. - sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the - ids. `d_0` is typically batch size. - sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing - float weights corresponding to `sparse_ids`, or `None` if all weights - are be assumed to be 1.0. - combiner: A string specifying how to combine embedding results for each - entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" - the default. - default_id: The id to use for an entry with no features. - name: A name for this operation (optional). - partition_strategy: A string specifying the partitioning strategy. - Currently `"div"` and `"mod"` are supported. Default is `"div"`. - max_norm: If not `None`, all embeddings are l2-normalized to max_norm before - combining. - - - Returns: - Dense `Tensor` of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`. - - Raises: - ValueError: if `embedding_weights` is empty. - """ - if embedding_weights is None: - raise ValueError('Missing embedding_weights %s.' % embedding_weights) - if isinstance(embedding_weights, variables.PartitionedVariable): - embedding_weights = list(embedding_weights) # get underlying Variables. - if not isinstance(embedding_weights, list): - embedding_weights = [embedding_weights] - if len(embedding_weights) < 1: - raise ValueError('Missing embedding_weights %s.' % embedding_weights) - - dtype = sparse_weights.dtype if sparse_weights is not None else None - embedding_weights = [ - ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights - ] - - with ops.name_scope(name, 'embedding_lookup', - embedding_weights + [sparse_ids, - sparse_weights]) as scope: - # Reshape higher-rank sparse ids and weights to linear segment ids. - original_shape = sparse_ids.dense_shape - original_rank_dim = sparse_ids.dense_shape.get_shape()[0] - original_rank = ( - array_ops.size(original_shape) - if original_rank_dim.value is None - else original_rank_dim.value) - sparse_ids = sparse_ops.sparse_reshape(sparse_ids, [ - math_ops.reduce_prod( - array_ops.slice(original_shape, [0], [original_rank - 1])), - array_ops.gather(original_shape, original_rank - 1)]) - if sparse_weights is not None: - sparse_weights = sparse_tensor_lib.SparseTensor( - sparse_ids.indices, - sparse_weights.values, sparse_ids.dense_shape) - - # Prune invalid ids and weights. - sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights) - if combiner != 'sum': - sparse_ids, sparse_weights = _prune_invalid_weights( - sparse_ids, sparse_weights) - - # Fill in dummy values for empty features, if necessary. - sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(sparse_ids, - default_id or - 0) - if sparse_weights is not None: - sparse_weights, _ = sparse_ops.sparse_fill_empty_rows(sparse_weights, 1.0) - - result = embedding_ops.embedding_lookup_sparse( - embedding_weights, - sparse_ids, - sparse_weights, - combiner=combiner, - partition_strategy=partition_strategy, - name=None if default_id is None else scope, - max_norm=max_norm) - - if default_id is None: - # Broadcast is_row_empty to the same shape as embedding_lookup_result, - # for use in Select. - is_row_empty = array_ops.tile( - array_ops.reshape(is_row_empty, [-1, 1]), - array_ops.stack([1, array_ops.shape(result)[1]])) - - result = array_ops.where(is_row_empty, - array_ops.zeros_like(result), - result, - name=scope) - - # Reshape back from linear ids back into higher-dimensional dense result. - final_result = array_ops.reshape( - result, - array_ops.concat([ - array_ops.slice( - math_ops.cast(original_shape, dtypes.int32), [0], - [original_rank - 1]), - array_ops.slice(array_ops.shape(result), [1], [-1]) - ], 0)) - final_result.set_shape(tensor_shape.unknown_shape( - (original_rank_dim - 1).value).concatenate(result.get_shape()[1:])) - return final_result - - -def _prune_invalid_ids(sparse_ids, sparse_weights): - """Prune invalid IDs (< 0) from the input ids and weights.""" - is_id_valid = math_ops.greater_equal(sparse_ids.values, 0) - if sparse_weights is not None: - is_id_valid = math_ops.logical_and( - is_id_valid, - array_ops.ones_like(sparse_weights.values, dtype=dtypes.bool)) - sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid) - if sparse_weights is not None: - sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid) - return sparse_ids, sparse_weights - - -def _prune_invalid_weights(sparse_ids, sparse_weights): - """Prune invalid weights (< 0) from the input ids and weights.""" - if sparse_weights is not None: - is_weights_valid = math_ops.greater(sparse_weights.values, 0) - sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_weights_valid) - sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_weights_valid) - return sparse_ids, sparse_weights - - class _IndicatorColumn(_DenseColumn, _SequenceDenseColumn, collections.namedtuple('_IndicatorColumn', ['categorical_column'])): @@ -3277,10 +3264,14 @@ class _IndicatorColumn(_DenseColumn, _SequenceDenseColumn, sp_ids=id_tensor, sp_values=weight_tensor, vocab_size=int(self._variable_shape[-1])) - # Remove (?, -1) index + # Remove (?, -1) index. weighted_column = sparse_ops.sparse_slice(weighted_column, [0, 0], weighted_column.dense_shape) - return sparse_ops.sparse_tensor_to_dense(weighted_column) + # Use scatter_nd to merge duplicated indices if existed, + # instead of sparse_tensor_to_dense. + return array_ops.scatter_nd(weighted_column.indices, + weighted_column.values, + weighted_column.dense_shape) dense_id_tensor = sparse_ops.sparse_tensor_to_dense( id_tensor, default_value=-1) diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index 627430d6bc5995cf054482ac3004098b8a2472ab..5bb47bfa47cf8fe0311d63f325198bcb7ecd5f9c 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -1257,14 +1257,14 @@ class CrossedColumnTest(test.TestCase): }, (crossed,)) -def get_linear_model_bias(): - with variable_scope.variable_scope('linear_model', reuse=True): +def get_linear_model_bias(name='linear_model'): + with variable_scope.variable_scope(name, reuse=True): return variable_scope.get_variable('bias_weights') -def get_linear_model_column_var(column): +def get_linear_model_column_var(column, name='linear_model'): return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, - 'linear_model/' + column.name)[0] + name + '/' + column.name)[0] def get_keras_linear_model_predictions(features, @@ -1928,6 +1928,27 @@ class LinearModelTest(test.TestCase): with self.assertRaisesOpError('Feature .* cannot have rank 0'): sess.run(net, feed_dict={features['price']: np.array(1)}) + def test_multiple_linear_models(self): + price = fc.numeric_column('price') + with ops.Graph().as_default(): + features1 = {'price': [[1.], [5.]]} + features2 = {'price': [[2.], [10.]]} + predictions1 = fc.linear_model(features1, [price]) + predictions2 = fc.linear_model(features2, [price]) + bias1 = get_linear_model_bias(name='linear_model') + bias2 = get_linear_model_bias(name='linear_model_1') + price_var1 = get_linear_model_column_var(price, name='linear_model') + price_var2 = get_linear_model_column_var(price, name='linear_model_1') + with _initialized_session() as sess: + self.assertAllClose([0.], bias1.eval()) + sess.run(price_var1.assign([[10.]])) + sess.run(bias1.assign([5.])) + self.assertAllClose([[15.], [55.]], predictions1.eval()) + self.assertAllClose([0.], bias2.eval()) + sess.run(price_var2.assign([[10.]])) + sess.run(bias2.assign([5.])) + self.assertAllClose([[25.], [105.]], predictions2.eval()) + class _LinearModelTest(test.TestCase): @@ -2586,7 +2607,7 @@ class _LinearModelTest(test.TestCase): class InputLayerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_retrieving_input(self): features = {'a': [0.]} input_layer = InputLayer(fc.numeric_column('a')) @@ -4559,12 +4580,12 @@ class IndicatorColumnTest(test.TestCase): weights = fc.weighted_categorical_column(ids, 'weights') indicator = fc.indicator_column(weights) features = { - 'ids': constant_op.constant([['c', 'b', 'a']]), - 'weights': constant_op.constant([[2., 4., 6.]]) + 'ids': constant_op.constant([['c', 'b', 'a', 'c']]), + 'weights': constant_op.constant([[2., 4., 6., 1.]]) } indicator_tensor = _transform_features(features, [indicator])[indicator] with _initialized_session(): - self.assertAllEqual([[6., 4., 2.]], indicator_tensor.eval()) + self.assertAllEqual([[6., 4., 3.]], indicator_tensor.eval()) def test_transform_with_missing_value_in_weighted_column(self): # Github issue 12583 @@ -5329,9 +5350,9 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertIsNone(embedding_column_a.ckpt_to_load_from) self.assertIsNone(embedding_column_b.ckpt_to_load_from) self.assertEqual('aaa_bbb_shared_embedding', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('aaa_bbb_shared_embedding', - embedding_column_b.var_scope_name) + embedding_column_b.shared_embedding_collection_name) self.assertIsNone(embedding_column_a.tensor_name_in_ckpt) self.assertIsNone(embedding_column_b.tensor_name_in_ckpt) self.assertIsNone(embedding_column_a.max_norm) @@ -5378,9 +5399,9 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertEqual('my_combiner', embedding_column_a.combiner) self.assertEqual('my_combiner', embedding_column_b.combiner) self.assertEqual('shared_embedding_collection_name', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('shared_embedding_collection_name', - embedding_column_b.var_scope_name) + embedding_column_b.shared_embedding_collection_name) self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from) self.assertEqual('my_ckpt', embedding_column_b.ckpt_to_load_from) self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt) @@ -5431,7 +5452,7 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertEqual(embedding_dimension, embedding_column_a.dimension) self.assertEqual('my_combiner', embedding_column_a.combiner) self.assertEqual('shared_embedding_collection_name', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from) self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt) self.assertEqual(42., embedding_column_a.max_norm) diff --git a/tensorflow/python/framework/error_interpolation.py b/tensorflow/python/framework/error_interpolation.py new file mode 100644 index 0000000000000000000000000000000000000000..9ccae761471e24ddb1d4d6acd89ebcc9650d1320 --- /dev/null +++ b/tensorflow/python/framework/error_interpolation.py @@ -0,0 +1,92 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Function for interpolating formatted errors from the TensorFlow runtime. + +Exposes the function `interpolate` to interpolate messages with tags of the form +^^type:name:format^^. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import itertools +import re +import string + +import six + +_NAME_REGEX = r"[A-Za-z0-9.][A-Za-z0-9_.\-/]*?" +_FORMAT_REGEX = r"[A-Za-z0-9_.\-/${}:]+" +_TAG_REGEX = r"\^\^({name}):({name}):({fmt})\^\^".format( + name=_NAME_REGEX, fmt=_FORMAT_REGEX) +_INTERPOLATION_REGEX = r"^(.*?)({tag})".format(tag=_TAG_REGEX) +_INTERPOLATION_PATTERN = re.compile(_INTERPOLATION_REGEX) + +_ParseTag = collections.namedtuple("_ParseTag", ["type", "name", "format"]) + + +def _parse_message(message): + """Parses the message. + + Splits the message into separators and tags. Tags are named tuples + representing the string ^^type:name:format^^ and they are separated by + separators. For example, in + "123^^node:Foo:${file}^^456^^node:Bar:${line}^^789", there are two tags and + three separators. The separators are the numeric characters. + + Args: + message: String to parse + + Returns: + (list of separator strings, list of _ParseTags). + + For example, if message is "123^^node:Foo:${file}^^456" then this function + returns (["123", "456"], [_ParseTag("node", "Foo", "${file}")]) + """ + seps = [] + tags = [] + pos = 0 + while pos < len(message): + match = re.match(_INTERPOLATION_PATTERN, message[pos:]) + if match: + seps.append(match.group(1)) + tags.append(_ParseTag(match.group(3), match.group(4), match.group(5))) + pos += match.end() + else: + break + seps.append(message[pos:]) + return seps, tags + + +# TODO(jtkeeling): Modify to actually interpolate format strings rather than +# echoing them. +def interpolate(error_message): + """Interpolates an error message. + + The error message can contain tags of the form ^^type:name:format^^ which will + be replaced. + + Args: + error_message: A string to interpolate. + + Returns: + The string with tags of the form ^^type:name:format^^ interpolated. + """ + seps, tags = _parse_message(error_message) + subs = [string.Template(tag.format).safe_substitute({}) for tag in tags] + return "".join( + itertools.chain(*six.moves.zip_longest(seps, subs, fillvalue=""))) diff --git a/tensorflow/python/framework/error_interpolation_test.py b/tensorflow/python/framework/error_interpolation_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ad448deb622cb6a3d24e502d7238d3f614d5af4d --- /dev/null +++ b/tensorflow/python/framework/error_interpolation_test.py @@ -0,0 +1,49 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.python.framework.errors.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import error_interpolation +from tensorflow.python.platform import test + + +class InterpolateTest(test.TestCase): + + def testNothingToDo(self): + normal_string = "This is just a normal string" + interpolated_string = error_interpolation.interpolate(normal_string) + self.assertEqual(interpolated_string, normal_string) + + def testOneTag(self): + one_tag_string = "^^node:Foo:${file}^^" + interpolated_string = error_interpolation.interpolate(one_tag_string) + self.assertEqual(interpolated_string, "${file}") + + def testTwoTagsNoSeps(self): + two_tags_no_seps = "^^node:Foo:${file}^^^^node:Bar:${line}^^" + interpolated_string = error_interpolation.interpolate(two_tags_no_seps) + self.assertEqual(interpolated_string, "${file}${line}") + + def testTwoTagsWithSeps(self): + two_tags_with_seps = "123^^node:Foo:${file}^^456^^node:Bar:${line}^^789" + interpolated_string = error_interpolation.interpolate(two_tags_with_seps) + self.assertEqual(interpolated_string, "123${file}456${line}789") + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 82ecba310ba4cbad62b8fda073a006b28be2c3ad..6525607faea62a461ee38fa0393ac29b809bb9b6 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -23,6 +23,7 @@ from __future__ import print_function import collections import hashlib +import sys from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import function_pb2 @@ -33,12 +34,17 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import graph_to_function_def from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._function = sys.modules[__name__] # pylint: disable=protected-access + class Defun(object): """Decorator used to define TensorFlow functions. @@ -650,6 +656,41 @@ class _FuncGraph(ops.Graph): # TODO(skyewm): is this needed? self.extra_vars = [] + # pylint: disable=g-doc-return-or-yield + + @tf_contextlib.contextmanager + def container(self, container_name): + """Returns a context manager that specifies the resource container to use. + + Overridden from @{tf.Graph} to update both the init_scope container + and the present inner container. This is necessary to make sure setting + containers applies correctly both to created variables and to stateful + ops. + + Args: + container_name: container name string. + + Returns: + A context manager for defining resource containers for stateful ops, + yields the container name. + """ + original_container = self._container + # pylint: disable=protected-access + with ops.init_scope(): + original_init_container = ops.get_default_graph()._container + try: + self._container = container_name + with ops.init_scope(): + ops.get_default_graph()._container = container_name + yield self._container + finally: + self._container = original_container + with ops.init_scope(): + ops.get_default_graph()._container = original_init_container + # pylint: enable=protected-access + + # pylint: enable=g-doc-return-or-yield + def getvar( self, getter, @@ -773,7 +814,9 @@ class _FuncGraph(ops.Graph): def func_graph_from_py_func(func, arg_names, arg_types, name=None, - capture_by_value=False, device=None): + capture_by_value=False, device=None, + colocation_stack=None, container=None, + collections_ref=None): """Returns a _FuncGraph generated from `func`. Args: @@ -786,6 +829,10 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, capture_by_value: boolean. If True, captured values will be copied into the function body. device: device name or function. + colocation_stack: A colocation stack (list) the _FuncGraph should use. + container: A container name the _FuncGraph should start with. + collections_ref: A reference to a collections dict the _FuncGraph should + use internally. Returns: A _FuncGraph. @@ -796,7 +843,17 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, if not name: name = _get_func_name(func) func_graph = _FuncGraph(name, capture_by_value) + with func_graph.as_default(), ops.device(device): + # pylint: disable=protected-access + if collections_ref is not None: + func_graph._collections = collections_ref + if container is not None: + func_graph._container = container + if colocation_stack is not None: + func_graph._colocation_stack = colocation_stack + # pylint: enable=protected-access + # Create placeholders for the function arguments. for (argname, argtype) in zip(arg_names, arg_types): argholder = array_ops.placeholder(argtype, name=argname) diff --git a/tensorflow/python/framework/function_def_to_graph.py b/tensorflow/python/framework/function_def_to_graph.py index 4fecc41343f26291dac8455f6c972a755b65ecfc..46c9c4c14adc7d4adeb11b45210cb296acb55086 100644 --- a/tensorflow/python/framework/function_def_to_graph.py +++ b/tensorflow/python/framework/function_def_to_graph.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys + from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.core.framework import versions_pb2 @@ -25,6 +27,10 @@ from tensorflow.python.framework import function from tensorflow.python.framework import importer from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import versions +from tensorflow.python.ops import cond_v2_impl + +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._function_def_to_graph = sys.modules[__name__] # pylint: disable=protected-access def function_def_to_graph(fdef, input_shapes=None): diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 72eb7e0eeb73fb1f8725ab2cbd4182e543c79b9f..699d2b70d176db7718a6e480f9f7b08a65ae6a8e 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -407,11 +407,11 @@ def import_graph_def(graph_def, _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) - # _ProcessNewOps mutates the new operations. _lock ensures a Session.run - # call cannot occur between creating the TF_Operations in the + # _ProcessNewOps mutates the new operations. _mutation_lock ensures a + # Session.run call cannot occur between creating the TF_Operations in the # TF_GraphImportGraphDefWithResults call and mutating the them in # _ProcessNewOps. - with graph._lock: # pylint: disable=protected-access + with graph._mutation_lock(): # pylint: disable=protected-access with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: try: results = c_api.TF_GraphImportGraphDefWithResults( diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index b440cde3ad4268b9f53da77af0c570f7c0f51d65..cf0b1e36fb3f02c85873a0da81dc056d2fbd5f6a 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -55,6 +55,7 @@ from tensorflow.python.platform import app from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import decorator_utils +from tensorflow.python.util import lock_util from tensorflow.python.util import tf_contextlib from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.tf_export import tf_export @@ -63,7 +64,7 @@ from tensorflow.python.util.tf_export import tf_export # Temporary global switches determining if we should enable the work-in-progress # calls to the C API. These will be removed once all functionality is supported. _USE_C_API = True -_USE_C_SHAPES = os.getenv("TF_C_API_GRAPH_CONSTRUCTION_SHAPES", "0") is not "0" +_USE_C_SHAPES = os.getenv("TF_C_API_GRAPH_CONSTRUCTION_SHAPES", "1") != "0" def tensor_id(tensor): @@ -2599,6 +2600,10 @@ def _name_from_scope_name(name): return name[:-1] if (name and name[-1] == "/") else name +_MUTATION_LOCK_GROUP = 0 +_SESSION_RUN_LOCK_GROUP = 1 + + @tf_export("Graph") class Graph(object): """A TensorFlow computation, represented as a dataflow graph. @@ -2648,20 +2653,21 @@ class Graph(object): def __init__(self): """Creates a new, empty Graph.""" - # Protects core state that can be returned via public accessors, as well as - # synchronizes Session.run calls with methods that create and mutate ops - # (e.g. Graph.create_op()). This synchronization is necessary because it's - # illegal to modify an operation after it's been run. Thread-safety is - # provided on a best-effort basis to support buggy programs, and is not - # guaranteed by the public `tf.Graph` API. - # - # The lock must be reentrant because create_op can be called recursively due - # to control flow. Without a reentrant lock, many methods would also need a - # "locked" version or parameter (including generated code). + # Protects core state that can be returned via public accessors. + # Thread-safety is provided on a best-effort basis to support buggy + # programs, and is not guaranteed by the public `tf.Graph` API. # # NOTE(mrry): This does not protect the various stacks. A warning will # be reported if these are used from multiple threads self._lock = threading.RLock() + # The group lock synchronizes Session.run calls with methods that create + # and mutate ops (e.g. Graph.create_op()). This synchronization is + # necessary because it's illegal to modify an operation after it's been run. + # The group lock allows any number of threads to mutate ops at the same time + # but if any modification is going on, all Session.run calls have to wait. + # Similarly, if one or more Session.run calls are going on, all mutate ops + # have to wait until all Session.run calls have finished. + self._group_lock = lock_util.GroupLock(num_groups=2) self._nodes_by_id = dict() # GUARDED_BY(self._lock) self._next_id_counter = 0 # GUARDED_BY(self._lock) self._nodes_by_name = dict() # GUARDED_BY(self._lock) @@ -3192,9 +3198,9 @@ class Graph(object): input_ops = set([t.op for t in inputs]) control_inputs = self._control_dependencies_for_inputs(input_ops) - # _create_op_helper mutates the new Operation. _lock ensures a Session.run - # call cannot occur between creating and mutating the op. - with self._lock: + # _create_op_helper mutates the new Operation. `_mutation_lock` ensures a + # Session.run call cannot occur between creating and mutating the op. + with self._mutation_lock(): ret = Operation( node_def, self, @@ -4727,6 +4733,20 @@ class Graph(object): else: self._graph_control_dependencies_stack = control_dependencies + def _mutation_lock(self): + """Returns a lock to guard code that creates & mutates ops. + + See the comment for self._group_lock for more info. + """ + return self._group_lock.group(_MUTATION_LOCK_GROUP) + + def _session_run_lock(self): + """Returns a lock to guard code for Session.run. + + See the comment for self._group_lock for more info. + """ + return self._group_lock.group(_SESSION_RUN_LOCK_GROUP) + # TODO(agarwal): currently device directives in an outer eager scope will not # apply to inner graph mode code. Fix that. @@ -5155,7 +5175,8 @@ def init_scope(): @tf_export("enable_eager_execution") -def enable_eager_execution(config=None, device_policy=None, +def enable_eager_execution(config=None, + device_policy=None, execution_mode=None): """Enables eager execution for the lifetime of this program. @@ -5215,6 +5236,31 @@ def enable_eager_execution(config=None, device_policy=None, TensorFlow graph, or if options provided conflict with a previous call to this function. """ + return enable_eager_execution_internal( + config, device_policy, execution_mode, None) + + +def enable_eager_execution_internal(config=None, + device_policy=None, + execution_mode=None, + server_def=None): + """Enables eager execution for the lifetime of this program. + + Most of the doc string for enable_eager_execution is relevant here as well. + Args: + config: See enable_eager_execution doc string + device_policy: See enable_eager_execution doc string + execution_mode: See enable_eager_execution doc string + server_def: (Optional.) A tensorflow::ServerDef proto. + Enables execution on remote devices. GrpcServers need to be started by + creating an identical server_def to this, and setting the appropriate + task_indexes, so that the servers can communicate. It will then be + possible to execute operations on remote devices. + + Raises: + ValueError + + """ if config is not None and not isinstance(config, config_pb2.ConfigProto): raise TypeError( "config must be a tf.ConfigProto, but got %s" % type(config)) @@ -5242,7 +5288,8 @@ def enable_eager_execution(config=None, device_policy=None, context._context = context.Context( config=config, device_policy=device_policy, - execution_mode=execution_mode) + execution_mode=execution_mode, + server_def=server_def) elif ((config is not None and config is not context._context._config) or (device_policy is not None and device_policy is not context._context._device_policy) or diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index 750df4d8e3926fbaee7a38978457e448c21d64c7..150100d771bb41d3693d39dc6fa19baa40da4c04 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -1690,7 +1690,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): # e should be dominated by c. self.assertEqual(e.op.control_inputs, []) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEager(self): def future(): future.calls += 1 @@ -1875,7 +1875,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): class OpScopeTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNames(self): with ops.name_scope("foo") as foo: self.assertEqual("foo/", foo) @@ -1906,7 +1906,7 @@ class OpScopeTest(test_util.TensorFlowTestCase): with ops.name_scope("a//b/c") as foo10: self.assertEqual("a//b/c/", foo10) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerDefaultScopeName(self): with ops.name_scope(None, "default") as scope: self.assertEqual(scope, "default/") diff --git a/tensorflow/python/framework/random_seed_test.py b/tensorflow/python/framework/random_seed_test.py index 194492268631abfa911bd45f13a302c09a2c8bda..6696bffc6c553f3fcf458f52cb9cd386e2711ff4 100644 --- a/tensorflow/python/framework/random_seed_test.py +++ b/tensorflow/python/framework/random_seed_test.py @@ -26,7 +26,7 @@ from tensorflow.python.platform import test class RandomSeedTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandomSeed(self): test_cases = [ # Each test case is a tuple with input to get_seed: diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py index 35fff80c61b98e7603d3b7b5df3cabdb59059a72..d6edc1364369e1b4d06093879571cdb4e9ffe409 100644 --- a/tensorflow/python/framework/tensor_util_test.py +++ b/tensorflow/python/framework/tensor_util_test.py @@ -941,7 +941,7 @@ class ConstantValueTest(test.TestCase): class ConstantValueAsShapeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstant(self): np_val = np.random.rand(3).astype(np.int32) tf_val = constant_op.constant(np_val) @@ -954,13 +954,13 @@ class ConstantValueAsShapeTest(test.TestCase): tensor_shape.TensorShape([]), tensor_util.constant_value_as_shape(tf_val)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShape(self): tf_val = array_ops.shape(constant_op.constant(0.0, shape=[1, 2, 3])) c_val = tensor_util.constant_value_as_shape(tf_val) self.assertEqual(tensor_shape.TensorShape([1, 2, 3]), c_val) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMinusOneBecomesNone(self): tf_val = constant_op.constant([-1, 1, -1], shape=[3]) c_val = tensor_util.constant_value_as_shape(tf_val) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 4a6146e0a6b09c1f0d595fcac8f3341f109b6f12..2bc2a189fa8e825613ca834e2c06ea916074d455 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -27,6 +27,7 @@ import random import re import tempfile import threading +import unittest import numpy as np import six @@ -61,13 +62,13 @@ from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import versions from tensorflow.python.ops import array_ops -from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util import compat from tensorflow.python.util import nest +from tensorflow.python.util import tf_inspect from tensorflow.python.util.protobuf import compare from tensorflow.python.util.tf_export import tf_export @@ -414,8 +415,28 @@ def assert_no_new_pyobjects_executing_eagerly(f): f(self, **kwargs) gc.collect() previous_count = len(gc.get_objects()) + collection_sizes_before = { + collection: len(ops.get_collection(collection)) + for collection in ops.get_default_graph().collections} for _ in range(3): f(self, **kwargs) + # Note that gc.get_objects misses anything that isn't subject to garbage + # collection (C types). Collections are a common source of leaks, so we + # test for collection sizes explicitly. + for collection_key in ops.get_default_graph().collections: + collection = ops.get_collection(collection_key) + size_before = collection_sizes_before.get(collection_key, 0) + if len(collection) > size_before: + raise AssertionError( + ("Collection %s increased in size from " + "%d to %d (current items %s).") + % (collection_key, size_before, len(collection), collection)) + # Make sure our collection checks don't show up as leaked memory by + # removing references to temporary variables. + del collection + del collection_key + del size_before + del collection_sizes_before gc.collect() # There should be no new Python objects hanging around. new_count = len(gc.get_objects()) @@ -552,14 +573,14 @@ def assert_no_garbage_created(f): def run_all_in_graph_and_eager_modes(cls): """Execute all test methods in the given class with and without eager.""" - base_decorator = run_in_graph_and_eager_modes() + base_decorator = run_in_graph_and_eager_modes for name, value in cls.__dict__.copy().items(): if callable(value) and name.startswith("test"): setattr(cls, name, base_decorator(value)) return cls -def run_in_graph_and_eager_modes(__unused__=None, +def run_in_graph_and_eager_modes(func=None, config=None, use_gpu=True, reset_test=True, @@ -577,7 +598,7 @@ def run_in_graph_and_eager_modes(__unused__=None, ```python class MyTests(tf.test.TestCase): - @run_in_graph_and_eager_modes() + @run_in_graph_and_eager_modes def test_foo(self): x = tf.constant([1, 2]) y = tf.constant([3, 4]) @@ -594,7 +615,9 @@ def run_in_graph_and_eager_modes(__unused__=None, Args: - __unused__: Prevents silently skipping tests. + func: function to be annotated. If `func` is None, this method returns a + decorator the can be applied to a function. If `func` is not None this + returns the decorator applied to `func`. config: An optional config_pb2.ConfigProto to use to configure the session when executing graphs. use_gpu: If True, attempt to run as many operations as possible on GPU. @@ -616,20 +639,19 @@ def run_in_graph_and_eager_modes(__unused__=None, eager execution enabled. """ - assert not __unused__, "Add () after run_in_graph_and_eager_modes." - def decorator(f): - def decorated(self, **kwargs): - with context.graph_mode(): - with self.test_session(use_gpu=use_gpu): - f(self, **kwargs) + if tf_inspect.isclass(f): + raise ValueError( + "`run_test_in_graph_and_eager_modes` only supports test methods. " + "Did you mean to use `run_all_tests_in_graph_and_eager_modes`?") - if reset_test: - # This decorator runs the wrapped test twice. - # Reset the test environment between runs. - self.tearDown() - self._tempdir = None - self.setUp() + def decorated(self, **kwargs): + try: + with context.graph_mode(): + with self.test_session(use_gpu=use_gpu, config=config): + f(self, **kwargs) + except unittest.case.SkipTest: + pass def run_eagerly(self, **kwargs): if not use_gpu: @@ -644,10 +666,20 @@ def run_in_graph_and_eager_modes(__unused__=None, assert_no_garbage_created(run_eagerly)) with context.eager_mode(): + if reset_test: + # This decorator runs the wrapped test twice. + # Reset the test environment between runs. + self.tearDown() + self._tempdir = None + self.setUp() + run_eagerly(self, **kwargs) return decorated + if func is not None: + return decorator(func) + return decorator @@ -830,14 +862,13 @@ class TensorFlowTestCase(googletest.TestCase): def _eval_tensor(self, tensor): if tensor is None: return None - elif isinstance(tensor, ops.EagerTensor): - return tensor.numpy() - elif isinstance(tensor, resource_variable_ops.ResourceVariable): - return tensor.read_value().numpy() elif callable(tensor): return self._eval_helper(tensor()) else: - raise ValueError("Unsupported type %s." % type(tensor)) + try: + return tensor.numpy() + except AttributeError as e: + six.raise_from(ValueError("Unsupported type %s." % type(tensor)), e) def _eval_helper(self, tensors): if tensors is None: @@ -1242,11 +1273,11 @@ class TensorFlowTestCase(googletest.TestCase): b, rtol=rtol, atol=atol, - msg="Mismatched value: a%s is different from b%s." % (path_str, - path_str)) + msg=("Mismatched value: a%s is different from b%s. %s" % + (path_str, path_str, msg))) except TypeError as e: - msg = "Error: a%s has %s, but b%s has %s" % (path_str, type(a), - path_str, type(b)) + msg = ("Error: a%s has %s, but b%s has %s. %s" % + (path_str, type(a), path_str, type(b), msg)) e.args = ((e.args[0] + " : " + msg,) + e.args[1:]) raise diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index 0178908bcc9c0613353e3beea8e1eb11638f9531..122c14c8473f133f6a3bed1e6297394eaa1b845c 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -569,7 +569,7 @@ class TestUtilTest(test_util.TensorFlowTestCase): self.assertEqual(a_np_rand, b_np_rand) self.assertEqual(a_rand, b_rand) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_callable_evaluate(self): def model(): return resource_variable_ops.ResourceVariable( @@ -578,7 +578,7 @@ class TestUtilTest(test_util.TensorFlowTestCase): with context.eager_mode(): self.assertEqual(2, self.evaluate(model)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_nested_tensors_evaluate(self): expected = {"a": 1, "b": 2, "nested": {"d": 3, "e": 4}} nested = {"a": constant_op.constant(1), @@ -588,6 +588,27 @@ class TestUtilTest(test_util.TensorFlowTestCase): self.assertEqual(expected, self.evaluate(nested)) + def test_run_in_graph_and_eager_modes(self): + l = [] + def inc(self, with_brackets): + del self # self argument is required by run_in_graph_and_eager_modes. + mode = "eager" if context.executing_eagerly() else "graph" + with_brackets = "with_brackets" if with_brackets else "without_brackets" + l.append((with_brackets, mode)) + + f = test_util.run_in_graph_and_eager_modes(inc) + f(self, with_brackets=False) + f = test_util.run_in_graph_and_eager_modes()(inc) + f(self, with_brackets=True) + + self.assertEqual(len(l), 4) + self.assertEqual(set(l), { + ("with_brackets", "graph"), + ("with_brackets", "eager"), + ("without_brackets", "graph"), + ("without_brackets", "eager"), + }) + def test_get_node_def_from_graph(self): graph_def = graph_pb2.GraphDef() node_foo = graph_def.node.add() @@ -595,6 +616,55 @@ class TestUtilTest(test_util.TensorFlowTestCase): self.assertIs(test_util.get_node_def_from_graph("foo", graph_def), node_foo) self.assertIsNone(test_util.get_node_def_from_graph("bar", graph_def)) + def test_run_in_eager_and_graph_modes_test_class(self): + msg = "`run_test_in_graph_and_eager_modes` only supports test methods.*" + with self.assertRaisesRegexp(ValueError, msg): + @test_util.run_in_graph_and_eager_modes() + class Foo(object): + pass + del Foo # Make pylint unused happy. + + def test_run_in_eager_and_graph_modes_skip_graph_runs_eager(self): + modes = [] + def _test(self): + if not context.executing_eagerly(): + self.skipTest("Skipping in graph mode") + modes.append("eager" if context.executing_eagerly() else "graph") + test_util.run_in_graph_and_eager_modes(_test)(self) + self.assertEqual(modes, ["eager"]) + + def test_run_in_eager_and_graph_modes_skip_eager_runs_graph(self): + modes = [] + def _test(self): + if context.executing_eagerly(): + self.skipTest("Skipping in eager mode") + modes.append("eager" if context.executing_eagerly() else "graph") + test_util.run_in_graph_and_eager_modes(_test)(self) + self.assertEqual(modes, ["graph"]) + + def test_run_in_graph_and_eager_modes_setup_in_same_mode(self): + modes = [] + mode_name = lambda: "eager" if context.executing_eagerly() else "graph" + + class ExampleTest(test_util.TensorFlowTestCase): + + def runTest(self): + pass + + def setUp(self): + modes.append("setup_" + mode_name()) + + @test_util.run_in_graph_and_eager_modes + def testBody(self): + modes.append("run_" + mode_name()) + + e = ExampleTest() + e.setUp() + e.testBody() + + self.assertEqual(modes[0:2], ["setup_graph", "run_graph"]) + self.assertEqual(modes[2:], ["setup_eager", "run_eager"]) + class GarbageCollectionTest(test_util.TensorFlowTestCase): @@ -619,7 +689,7 @@ class GarbageCollectionTest(test_util.TensorFlowTestCase): ReferenceCycleTest().test_has_no_cycle() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_no_leaked_tensor_decorator(self): class LeakedTensorTest(object): diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index af5d709f7e936e0438d5e03f60b44bc0017cb4b6..7d07c77c797668c858014cc31cf713050627d72f 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -158,6 +158,7 @@ def _get_config(layout_optimizer=True): layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, # do not remove duplicated nodes arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF) + rewrite_options.min_graph_nodes = -1 graph_options = config_pb2.GraphOptions( rewrite_options=rewrite_options, build_cost_model=1) config = config_pb2.ConfigProto(graph_options=graph_options) @@ -1443,7 +1444,8 @@ class LayoutOptimizerTest(test.TestCase): def testGradient(self): meta_graph = _simple_metagraph() rewrite_options = rewriter_config_pb2.RewriterConfig( - layout_optimizer=rewriter_config_pb2.RewriterConfig.ON) + layout_optimizer=rewriter_config_pb2.RewriterConfig.ON, + min_graph_nodes=-1) optimized_graph = tf_optimizer.OptimizeGraph( rewrite_options, meta_graph, cluster=_get_cluster()) @@ -1457,7 +1459,8 @@ class LayoutOptimizerTest(test.TestCase): def testDepthwise(self): meta_graph = _simple_metagraph(depthwise=True) rewrite_options = rewriter_config_pb2.RewriterConfig( - layout_optimizer=rewriter_config_pb2.RewriterConfig.ON) + layout_optimizer=rewriter_config_pb2.RewriterConfig.ON, + min_graph_nodes=-1) optimized_graph = tf_optimizer.OptimizeGraph( rewrite_options, meta_graph, cluster=_get_cluster()) diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 7ed4b128e495c484d294ece40541427f21856cf1..b658edff2dffac9856432c575b9af0d2f0b1986b 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -76,7 +76,8 @@ class MemoryOptimizerSwapTest(test.TestCase): disable_model_pruning=True, meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, - memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL) + memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL, + min_graph_nodes=-1) graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) self.assertEqual(len(graph.node), graph_size + 2) @@ -133,6 +134,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS), original_metagraph) self.assertGreater( @@ -158,6 +160,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, # Checks that name scope "gradients/" also match sub-scope. @@ -297,6 +300,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): if 'Recomputed/' in node.name])) rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL), metagraph) self.assertEqual( diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py index 1c0f072dd32d38f048cfa48d38b45264951d095e..5a9afe725753749ea42d53382731ab14a3cf24f5 100644 --- a/tensorflow/python/grappler/tf_optimizer_test.py +++ b/tensorflow/python/grappler/tf_optimizer_test.py @@ -47,6 +47,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): rewriter_config = rewriter_config_pb2.RewriterConfig() rewriter_config.optimizers.append('constfold') + rewriter_config.min_graph_nodes = -1 graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) @@ -68,6 +69,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): # Optimize the graph. mg = meta_graph.create_meta_graph_def(graph=g) rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) # Check that the nodes referenced in various collections have been preserved @@ -109,6 +111,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): # Optimize the graph. mg = meta_graph.create_meta_graph_def(graph=g) rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) mg.graph_def.CopyFrom(optimized_graph) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index fe40c9fbed7c041ad6b6dc8cdb1c50b80f57a48f..8b6b28bc776fa500a93d0a3fb3bf91081ba86967 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -39,6 +39,7 @@ py_library( "datasets/imdb.py", "datasets/mnist.py", "datasets/reuters.py", + "estimator/__init__.py", "preprocessing/__init__.py", "preprocessing/image.py", "preprocessing/sequence.py", @@ -135,7 +136,7 @@ py_library( deps = [ ":backend", "//tensorflow/python/data", - "//tensorflow/python/training/checkpointable:data_structures_base", + "//tensorflow/python/training/checkpointable:data_structures", "@six_archive//:six", ], ) @@ -549,7 +550,7 @@ py_test( py_test( name = "gru_test", - size = "medium", + size = "large", srcs = ["layers/gru_test.py"], srcs_version = "PY2AND3", tags = ["notsan"], # http://b/62136390 @@ -858,7 +859,7 @@ py_test( py_test( name = "backend_test", - size = "small", + size = "medium", srcs = ["backend_test.py"], srcs_version = "PY2AND3", deps = [ @@ -866,6 +867,7 @@ py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:util", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/keras/__init__.py b/tensorflow/python/keras/__init__.py index 3493069a5bf53ffbfe6447f2c1b3df7ac64cbf3a..198c66d9e184c82423e529540b92ad447b947cf8 100644 --- a/tensorflow/python/keras/__init__.py +++ b/tensorflow/python/keras/__init__.py @@ -27,6 +27,7 @@ from tensorflow.python.keras import backend from tensorflow.python.keras import callbacks from tensorflow.python.keras import constraints from tensorflow.python.keras import datasets +from tensorflow.python.keras import estimator from tensorflow.python.keras import initializers from tensorflow.python.keras import layers from tensorflow.python.keras import losses diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index 92ad7c7e36d6b00761a7d00b059e272b53b2eed5..f608dea430f0573503713f0cbc60f8921e6df51e 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -32,7 +32,7 @@ def softmax(x, axis=-1): """Softmax activation function. Arguments: - x : Tensor. + x : Input tensor. axis: Integer, axis along which the softmax normalization is applied. Returns: @@ -49,23 +49,45 @@ def softmax(x, axis=-1): s = math_ops.reduce_sum(e, axis=axis, keepdims=True) return e / s else: - raise ValueError('Cannot apply softmax to a tensor that is 1D') + raise ValueError('Cannot apply softmax to a tensor that is 1D. ' + 'Received input: %s' % (x,)) @tf_export('keras.activations.elu') def elu(x, alpha=1.0): + """Exponential linear unit. + + Arguments: + x: Input tensor. + alpha: A scalar, slope of negative section. + + Returns: + The exponential linear activation: `x` if `x > 0` and + `alpha * (exp(x)-1)` if `x < 0`. + + Reference: + - [Fast and Accurate Deep Network Learning by Exponential + Linear Units (ELUs)](https://arxiv.org/abs/1511.07289) + """ return K.elu(x, alpha) @tf_export('keras.activations.selu') def selu(x): - """Scaled Exponential Linear Unit. (Klambauer et al., 2017). + """Scaled Exponential Linear Unit (SELU). + + SELU is equal to: `scale * elu(x, alpha)`, where alpha and scale + are pre-defined constants. The values of `alpha` and `scale` are + chosen so that the mean and variance of the inputs are preserved + between two consecutive layers as long as the weights are initialized + correctly (see `lecun_normal` initialization) and the number of inputs + is "large enough" (see references for more information). Arguments: x: A tensor or variable to compute the activation function for. Returns: - Tensor with the same shape and dtype as `x`. + The scaled exponential unit activation: `scale * elu(x, alpha)`. # Note - To be used together with the initialization "lecun_normal". @@ -81,16 +103,44 @@ def selu(x): @tf_export('keras.activations.softplus') def softplus(x): + """Softplus activation function. + + Arguments: + x: Input tensor. + + Returns: + The softplus activation: `log(exp(x) + 1)`. + """ return nn.softplus(x) @tf_export('keras.activations.softsign') def softsign(x): + """Softsign activation function. + + Arguments: + x: Input tensor. + + Returns: + The softplus activation: `x / (abs(x) + 1)`. + """ return nn.softsign(x) @tf_export('keras.activations.relu') def relu(x, alpha=0., max_value=None): + """Rectified Linear Unit. + + Arguments: + x: Input tensor. + alpha: Slope of the negative part. Defaults to zero. + max_value: Maximum value for the output. + + Returns: + The (leaky) rectified linear unit activation: `x` if `x > 0`, + `alpha * x` if `x < 0`. If `max_value` is defined, the result + is truncated to this value. + """ return K.relu(x, alpha=alpha, max_value=max_value) @@ -106,6 +156,19 @@ def sigmoid(x): @tf_export('keras.activations.hard_sigmoid') def hard_sigmoid(x): + """Hard sigmoid activation function. + + Faster to compute than sigmoid activation. + + Arguments: + x: Input tensor. + + Returns: + Hard sigmoid activation: + - `0` if `x < -2.5` + - `1` if `x > 2.5` + - `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`. + """ return K.hard_sigmoid(x) diff --git a/tensorflow/python/keras/applications/densenet.py b/tensorflow/python/keras/applications/densenet.py index f81f10719a31e2e79589d3b389049353c992091c..8df6d086111c4b179d2f0c7b5c1130a6cd95aaab 100644 --- a/tensorflow/python/keras/applications/densenet.py +++ b/tensorflow/python/keras/applications/densenet.py @@ -31,7 +31,6 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import AveragePooling2D from tensorflow.python.keras.layers import BatchNormalization @@ -44,6 +43,7 @@ from tensorflow.python.keras.layers import Input from tensorflow.python.keras.layers import MaxPooling2D from tensorflow.python.keras.layers import ZeroPadding2D from tensorflow.python.keras.models import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.util.tf_export import tf_export @@ -238,7 +238,7 @@ def DenseNet(blocks, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input diff --git a/tensorflow/python/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/applications/inception_resnet_v2.py index fe1d0f2d4fb47f7ebab38f94afc8ace2f7b73cbc..14e3b6aa60dbfa7e62e04849d35633eed162a416 100644 --- a/tensorflow/python/keras/applications/inception_resnet_v2.py +++ b/tensorflow/python/keras/applications/inception_resnet_v2.py @@ -31,7 +31,6 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import AveragePooling2D from tensorflow.python.keras.layers import BatchNormalization @@ -44,6 +43,7 @@ from tensorflow.python.keras.layers import Input from tensorflow.python.keras.layers import Lambda from tensorflow.python.keras.layers import MaxPooling2D from tensorflow.python.keras.models import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -354,7 +354,7 @@ def InceptionResNetV2(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor` if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input diff --git a/tensorflow/python/keras/applications/inception_v3.py b/tensorflow/python/keras/applications/inception_v3.py index 857ad49dae9ef234fe7d8251601ee122de39c947..b5e28c781f71e67b8d835b50070b49add2d7930a 100644 --- a/tensorflow/python/keras/applications/inception_v3.py +++ b/tensorflow/python/keras/applications/inception_v3.py @@ -37,7 +37,6 @@ from tensorflow.python.keras import layers from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import AveragePooling2D from tensorflow.python.keras.layers import BatchNormalization @@ -48,6 +47,7 @@ from tensorflow.python.keras.layers import GlobalMaxPooling2D from tensorflow.python.keras.layers import Input from tensorflow.python.keras.layers import MaxPooling2D from tensorflow.python.keras.models import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -375,7 +375,7 @@ def InceptionV3(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py index 9d845be0d5b1ab06dd8a41bc04f75ae7b5f00789..e56c695a288026d12de6bc0bdb65706c71eefe14 100644 --- a/tensorflow/python/keras/applications/mobilenet.py +++ b/tensorflow/python/keras/applications/mobilenet.py @@ -78,8 +78,7 @@ from tensorflow.python.keras import regularizers from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine.network import get_source_inputs +from tensorflow.python.keras.engine.base_layer import InputSpec from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import BatchNormalization from tensorflow.python.keras.layers import Conv2D @@ -92,6 +91,7 @@ from tensorflow.python.keras.layers import Reshape from tensorflow.python.keras.layers import ZeroPadding2D from tensorflow.python.keras.models import Model from tensorflow.python.keras.utils import conv_utils +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -317,7 +317,7 @@ def MobileNet(input_shape=None, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input diff --git a/tensorflow/python/keras/applications/nasnet.py b/tensorflow/python/keras/applications/nasnet.py index b521bc673139403dcdecbba8e35b5bafec2d42bf..ff79b3a057b8fd6ab3b0edf652a5bede0e2d7b87 100644 --- a/tensorflow/python/keras/applications/nasnet.py +++ b/tensorflow/python/keras/applications/nasnet.py @@ -49,7 +49,6 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras.applications.inception_v3 import preprocess_input -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import add from tensorflow.python.keras.layers import AveragePooling2D @@ -65,6 +64,7 @@ from tensorflow.python.keras.layers import MaxPooling2D from tensorflow.python.keras.layers import SeparableConv2D from tensorflow.python.keras.layers import ZeroPadding2D from tensorflow.python.keras.models import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -290,7 +290,7 @@ def NASNet(input_shape=None, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input diff --git a/tensorflow/python/keras/applications/resnet50.py b/tensorflow/python/keras/applications/resnet50.py index 508550f445e39dcf2a249bc91aaee289abfe3d1f..6afc08681214c5dbb0577623d30e27e9988c6a57 100644 --- a/tensorflow/python/keras/applications/resnet50.py +++ b/tensorflow/python/keras/applications/resnet50.py @@ -34,7 +34,6 @@ from tensorflow.python.keras import layers from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import AveragePooling2D from tensorflow.python.keras.layers import BatchNormalization @@ -277,7 +276,7 @@ def ResNet50(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. diff --git a/tensorflow/python/keras/applications/vgg16.py b/tensorflow/python/keras/applications/vgg16.py index 659a6533e6772402663aee891ed90df792b12f09..cef0230da96ed4b9c992e57839ebb2071383e3b1 100644 --- a/tensorflow/python/keras/applications/vgg16.py +++ b/tensorflow/python/keras/applications/vgg16.py @@ -32,7 +32,6 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Conv2D from tensorflow.python.keras.layers import Dense from tensorflow.python.keras.layers import Flatten @@ -202,7 +201,7 @@ def VGG16(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. diff --git a/tensorflow/python/keras/applications/vgg19.py b/tensorflow/python/keras/applications/vgg19.py index 5e27ab8fb1fb99c65566cc4519798e3b8e0e1b0b..c4031f551003eda076380d1ae5208ee0876f5750 100644 --- a/tensorflow/python/keras/applications/vgg19.py +++ b/tensorflow/python/keras/applications/vgg19.py @@ -32,7 +32,6 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Conv2D from tensorflow.python.keras.layers import Dense from tensorflow.python.keras.layers import Flatten @@ -211,7 +210,7 @@ def VGG19(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. diff --git a/tensorflow/python/keras/applications/xception.py b/tensorflow/python/keras/applications/xception.py index e1be8a3c46e6eafa43405f1472a2f0292b73aa0c..01397cfac2563273ba1215003df1afab293b6b20 100644 --- a/tensorflow/python/keras/applications/xception.py +++ b/tensorflow/python/keras/applications/xception.py @@ -44,7 +44,6 @@ from tensorflow.python.keras import layers from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.engine.network import get_source_inputs from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import BatchNormalization from tensorflow.python.keras.layers import Conv2D @@ -55,6 +54,7 @@ from tensorflow.python.keras.layers import Input from tensorflow.python.keras.layers import MaxPooling2D from tensorflow.python.keras.layers import SeparableConv2D from tensorflow.python.keras.models import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -302,7 +302,7 @@ def Xception(include_top=True, # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: - inputs = get_source_inputs(input_tensor) + inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index af3d1fa33d3431e7b13d1910a8581393e7b912c6..824513dce07fc31edc6f8eca512efd99a1a258cc 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -22,6 +22,7 @@ from __future__ import division from __future__ import print_function import collections +import itertools import json import os import weakref @@ -2794,10 +2795,15 @@ class Function(object): if not isinstance(self.fetches, list): self.fetches = [self.fetches] # The main use case of `fetches` being passed to a model is the ability - # to run custom updates (since the outputs of fetches are never returned). + # to run custom updates # This requires us to wrap fetches in `identity` ops. self.fetches = [array_ops.identity(x) for x in self.fetches] self.session_kwargs = session_kwargs + # This mapping keeps track of the function that should receive the + # output from a fetch in `fetches`: { fetch: function(fetch_output) } + # A Callback can use this to register a function with access to the + # output values for a fetch it added. + self.fetch_callbacks = dict() if session_kwargs: raise ValueError('Some keys in session_kwargs are not supported at this ' @@ -2807,6 +2813,7 @@ class Function(object): self._feed_arrays = None self._feed_symbols = None self._symbol_vals = None + self._fetches = None self._session = None def _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session): @@ -2852,8 +2859,14 @@ class Function(object): self._feed_arrays = feed_arrays self._feed_symbols = feed_symbols self._symbol_vals = symbol_vals + self._fetches = list(self.fetches) self._session = session + def _call_fetch_callbacks(self, fetches_output): + for fetch, output in zip(self._fetches, fetches_output): + if fetch in self.fetch_callbacks: + self.fetch_callbacks[fetch](output) + def __call__(self, inputs): if not isinstance(inputs, (list, tuple)): raise TypeError('`inputs` should be a list or tuple.') @@ -2880,21 +2893,24 @@ class Function(object): feed_arrays.append(tensor) # We need to do array conversion and type casting at this level, since # `callable_fn` only supports exact matches. - array_vals.append(np.asarray(value, dtype=tensor.dtype.base_dtype.name)) + tensor_type = dtypes_module.as_dtype(tensor.dtype) + array_vals.append(np.asarray(value, + dtype=tensor_type.as_numpy_dtype)) + if self.feed_dict: for key in sorted(self.feed_dict.keys()): array_vals.append( np.asarray(self.feed_dict[key], dtype=key.dtype.base_dtype.name)) # Refresh callable if anything has changed. - if (self._callable_fn is None or - feed_arrays != self._feed_arrays or + if (self._callable_fn is None or feed_arrays != self._feed_arrays or symbol_vals != self._symbol_vals or - feed_symbols != self._feed_symbols or + feed_symbols != self._feed_symbols or self.fetches != self._fetches or session != self._session): self._make_callable(feed_arrays, feed_symbols, symbol_vals, session) fetched = self._callable_fn(*array_vals) + self._call_fetch_callbacks(fetched[-len(self._fetches):]) return fetched[:len(self.outputs)] @@ -2973,30 +2989,29 @@ def rnn(step_function, Arguments: step_function: RNN step function. - Parameters; - input; tensor with shape `(samples, ...)` (no time dimension), + Args; + input; Tensor with shape `(samples, ...)` (no time dimension), representing input for the batch of samples at a certain time step. - states; list of tensors. + states; List of tensors. Returns; - output; tensor with shape `(samples, output_dim)` + output; Tensor with shape `(samples, output_dim)` (no time dimension). - new_states; list of tensors, same length and shapes + new_states; List of tensors, same length and shapes as 'states'. The first state in the list must be the output tensor at the previous timestep. - inputs: tensor of temporal data of shape `(samples, time, ...)` + inputs: Tensor of temporal data of shape `(samples, time, ...)` (at least 3D). - initial_states: tensor with shape (samples, output_dim) + initial_states: Tensor with shape `(samples, output_dim)` (no time dimension), containing the initial values for the states used in the step function. - go_backwards: boolean. If True, do the iteration over the time + go_backwards: Boolean. If True, do the iteration over the time dimension in reverse order and return the reversed sequence. - mask: binary tensor with shape `(samples, time, 1)`, + mask: Binary tensor with shape `(samples, time, 1)`, with a zero for every element that is masked. - constants: a list of constant values passed at each step. - unroll: whether to unroll the RNN or to use a symbolic loop - (`while_loop` or `scan` depending on backend). + constants: List of constant values passed at each step. + unroll: Whether to unroll the RNN or to use a symbolic `while_loop`. input_length: If specified, assume time dimension is of this length. Returns: @@ -3158,10 +3173,16 @@ def rnn(step_function, array_ops.stack( [1, array_ops.shape(output)[1]])) output = array_ops.where(tiled_mask_t, output, states[0]) - new_states = [ - array_ops.where(tiled_mask_t, new_states[i], states[i]) - for i in range(len(states)) - ] + + masked_states = [] + for i in range(len(states)): + states_dim = array_ops.shape(new_states[i])[1] + stacked_states_dim = array_ops.stack([1, states_dim]) + tiled_mask = array_ops.tile(mask_t, stacked_states_dim) + masked_state = array_ops.where(tiled_mask, new_states[i], states[i]) + masked_states.append(masked_state) + new_states = masked_states + output_ta_t = output_ta_t.write(time, output) return (time + 1, output_ta_t) + tuple(new_states) else: @@ -3637,12 +3658,12 @@ def _preprocess_conv1d_input(x, data_format): Returns: A tensor. """ - tf_data_format = 'NHWC' # to pass TF Conv2dNative operations + tf_data_format = 'NWC' # to pass TF Conv2dNative operations if data_format == 'channels_first': if not _has_nchw_support(): x = array_ops.transpose(x, (0, 2, 1)) # NCW -> NWC else: - tf_data_format = 'NCHW' + tf_data_format = 'NCW' return x, tf_data_format @@ -3741,10 +3762,8 @@ def conv1d(x, x = temporal_padding(x, (left_pad, 0)) padding = 'valid' padding = _preprocess_padding(padding) - if data_format == 'channels_last': - tf_data_format = 'NWC' - else: - tf_data_format = 'NCW' + + x, tf_data_format = _preprocess_conv1d_input(x, data_format) x = nn.convolution( input=x, filter=kernel, @@ -3752,6 +3771,8 @@ def conv1d(x, strides=(strides,), padding=padding, data_format=tf_data_format) + if data_format == 'channels_first' and tf_data_format == 'NWC': + x = array_ops.transpose(x, (0, 2, 1)) # NWC -> NCW return x @@ -3892,11 +3913,16 @@ def separable_conv1d(x, if data_format not in {'channels_first', 'channels_last'}: raise ValueError('Unknown data_format: ' + str(data_format)) + if isinstance(strides, int): + strides = (strides,) + if isinstance(dilation_rate, int): + dilation_rate = (dilation_rate,) + x, tf_data_format = _preprocess_conv1d_input(x, data_format) padding = _preprocess_padding(padding) if not isinstance(strides, tuple): strides = tuple(strides) - if tf_data_format == 'NHWC': + if tf_data_format == 'NWC': spatial_start_dim = 1 strides = (1,) + strides * 2 + (1,) else: @@ -3918,7 +3944,7 @@ def separable_conv1d(x, x = array_ops.squeeze(x, [spatial_start_dim]) - if data_format == 'channels_first' and tf_data_format == 'NHWC': + if data_format == 'channels_first' and tf_data_format == 'NWC': x = array_ops.transpose(x, (0, 2, 1)) # NWC -> NCW return x @@ -4238,45 +4264,115 @@ def pool3d(x, return x -def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None): - """Apply 1D conv with un-shared weights. - - Arguments: - inputs: 3D tensor with shape: (batch_size, steps, input_dim) - kernel: the unshared weight for convolution, - with shape (output_length, feature_dim, filters) - kernel_size: a tuple of a single integer, - specifying the length of the 1D convolution window - strides: a tuple of a single integer, - specifying the stride length of the convolution - data_format: the data format, channels_first or channels_last - - Returns: - the tensor after 1d conv with un-shared weights, with shape (batch_size, - output_length, filters) +def local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format=None): + """Apply N-D convolution with un-shared weights. + + Arguments: + inputs: (N+2)-D tensor with shape + (batch_size, channels_in, d_in1, ..., d_inN) + if data_format='channels_first', or + (batch_size, d_in1, ..., d_inN, channels_in) + if data_format='channels_last'. + kernel: the unshared weight for N-D convolution, + with shape (output_items, feature_dim, channels_out), where + feature_dim = np.prod(kernel_size) * channels_in, + output_items = np.prod(output_shape). + kernel_size: a tuple of N integers, specifying the + spatial dimensions of the N-D convolution window. + strides: a tuple of N integers, specifying the strides + of the convolution along the spatial dimensions. + output_shape: a tuple of (d_out1, ..., d_outN) specifying the spatial + dimensionality of the output. + data_format: string, "channels_first" or "channels_last". + + Returns: + An (N+2)-D tensor with shape: + (batch_size, channels_out) + output_shape + if data_format='channels_first', or: + (batch_size,) + output_shape + (channels_out,) + if data_format='channels_last'. Raises: - ValueError: if `data_format` is neither `channels_last` or - `channels_first`. + ValueError: if `data_format` is neither + `channels_last` nor `channels_first`. """ if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: raise ValueError('Unknown data_format: ' + str(data_format)) - stride = strides[0] kernel_shape = int_shape(kernel) - output_length = kernel_shape[0] feature_dim = kernel_shape[1] + channels_out = kernel_shape[-1] + ndims = len(output_shape) + spatial_dimensions = list(range(ndims)) xs = [] - for i in range(output_length): - slice_length = slice(i * stride, i * stride + kernel_size[0]) - xs.append(reshape(inputs[:, slice_length, :], (1, -1, feature_dim))) + output_axes_ticks = [range(axis_max) for axis_max in output_shape] + for position in itertools.product(*output_axes_ticks): + slices = [slice(None)] + + if data_format == 'channels_first': + slices.append(slice(None)) + + slices.extend([slice(position[d] * strides[d], + position[d] * strides[d] + kernel_size[d]) + for d in spatial_dimensions]) + + if data_format == 'channels_last': + slices.append(slice(None)) + + xs.append(reshape(inputs[slices], (1, -1, feature_dim))) + x_aggregate = concatenate(xs, axis=0) - # Shape: `(output_length, batch_size, filters)`. output = batch_dot(x_aggregate, kernel) - return permute_dimensions(output, (1, 0, 2)) + output = reshape(output, output_shape + (-1, channels_out)) + + if data_format == 'channels_first': + permutation = [ndims, ndims + 1] + spatial_dimensions + else: + permutation = [ndims] + spatial_dimensions + [ndims + 1] + + return permute_dimensions(output, permutation) + + +def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None): + """Apply 1D conv with un-shared weights. + + Arguments: + inputs: 3D tensor with shape: + (batch_size, steps, input_dim) + if data_format is "channels_last" or + (batch_size, input_dim, steps) + if data_format is "channels_first". + kernel: the unshared weight for convolution, + with shape (output_length, feature_dim, filters). + kernel_size: a tuple of a single integer, + specifying the length of the 1D convolution window. + strides: a tuple of a single integer, + specifying the stride length of the convolution. + data_format: the data format, channels_first or channels_last. + + Returns: + A 3d tensor with shape: + (batch_size, output_length, filters) + if data_format='channels_first' + or 3D tensor with shape: + (batch_size, filters, output_length) + if data_format='channels_last'. + """ + output_shape = (kernel.shape[0],) + return local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format) def local_conv2d(inputs, @@ -4289,64 +4385,34 @@ def local_conv2d(inputs, Arguments: inputs: 4D tensor with shape: - (batch_size, filters, new_rows, new_cols) - if data_format='channels_first' - or 4D tensor with shape: - (batch_size, new_rows, new_cols, filters) - if data_format='channels_last'. + (batch_size, filters, new_rows, new_cols) + if data_format='channels_first' + or 4D tensor with shape: + (batch_size, new_rows, new_cols, filters) + if data_format='channels_last'. kernel: the unshared weight for convolution, - with shape (output_items, feature_dim, filters) + with shape (output_items, feature_dim, filters). kernel_size: a tuple of 2 integers, specifying the - width and height of the 2D convolution window. + width and height of the 2D convolution window. strides: a tuple of 2 integers, specifying the strides - of the convolution along the width and height. - output_shape: a tuple with (output_row, output_col) - data_format: the data format, channels_first or channels_last + of the convolution along the width and height. + output_shape: a tuple with (output_row, output_col). + data_format: the data format, channels_first or channels_last. Returns: - A 4d tensor with shape: + A 4D tensor with shape: (batch_size, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, new_rows, new_cols, filters) if data_format='channels_last'. - - Raises: - ValueError: if `data_format` is neither - `channels_last` or `channels_first`. """ - if data_format is None: - data_format = image_data_format() - if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format: ' + str(data_format)) - - stride_row, stride_col = strides - output_row, output_col = output_shape - kernel_shape = int_shape(kernel) - feature_dim = kernel_shape[1] - filters = kernel_shape[2] - - xs = [] - for i in range(output_row): - for j in range(output_col): - slice_row = slice(i * stride_row, i * stride_row + kernel_size[0]) - slice_col = slice(j * stride_col, j * stride_col + kernel_size[1]) - if data_format == 'channels_first': - xs.append( - reshape(inputs[:, :, slice_row, slice_col], (1, -1, feature_dim))) - else: - xs.append( - reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim))) - - x_aggregate = concatenate(xs, axis=0) - output = batch_dot(x_aggregate, kernel) - output = reshape(output, (output_row, output_col, -1, filters)) - - if data_format == 'channels_first': - output = permute_dimensions(output, (2, 3, 0, 1)) - else: - output = permute_dimensions(output, (2, 0, 1, 3)) - return output + return local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format) @tf_export('keras.backend.bias_add') @@ -4704,8 +4770,13 @@ def foldr(fn, elems, initializer=None, name=None): # Load Keras default configuration from config file if present. -_keras_base_dir = os.path.expanduser('~') -_keras_dir = os.path.join(_keras_base_dir, '.keras') +# Set Keras base dir path given KERAS_HOME env variable, if applicable. +# Otherwise either ~/.keras or /tmp. +if 'KERAS_HOME' in os.environ: + _keras_dir = os.environ.get('KERAS_HOME') +else: + _keras_base_dir = os.path.expanduser('~') + _keras_dir = os.path.join(_keras_base_dir, '.keras') _config_path = os.path.expanduser(os.path.join(_keras_dir, 'keras.json')) if os.path.exists(_config_path): try: diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 58df263a4f24278f8b61bd9e89f5d8af5e589c6d..36478ea089a871667908d70e33422aef8444a3e4 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -17,10 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np import scipy.sparse from tensorflow.python import keras +from tensorflow.python.framework import dtypes from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -274,6 +276,36 @@ class BackendUtilsTest(test.TestCase): self.assertEqual( keras.backend.get_session().run(fetches=[x, y]), [30., 40.]) + def test_function_fetch_callbacks(self): + + class CallbackStub(object): + + def __init__(self): + self.times_called = 0 + self.callback_result = 0 + + def _fetch_callback(self, result): + self.times_called += 1 + self.callback_result = result + + with self.test_session(): + callback = CallbackStub() + x_placeholder = keras.backend.placeholder(shape=()) + y_placeholder = keras.backend.placeholder(shape=()) + + callback_op = x_placeholder * y_placeholder + + f = keras.backend.function( + inputs=[x_placeholder, y_placeholder], + outputs=[x_placeholder + y_placeholder]) + f.fetches.append(callback_op) + f.fetch_callbacks[callback_op] = callback._fetch_callback + + _ = f([10., 20.]) + + self.assertEqual(callback.times_called, 1) + self.assertEqual(callback.callback_result, 200) + class BackendVariableTest(test.TestCase): @@ -661,7 +693,7 @@ class BackendShapeOpsTest(test.TestCase): np_kwargs={'data_format': 'channels_first'}) -class BackendNNOpsTest(test.TestCase): +class BackendNNOpsTest(test.TestCase, parameterized.TestCase): def test_bias_add(self): with self.test_session(): @@ -810,6 +842,118 @@ class BackendNNOpsTest(test.TestCase): padding='same', data_format='channels_last') self.assertEqual(y.get_shape().as_list(), [10, 5, 5]) + def test_local_conv_channels_dim(self): + filters = 3 + batch_size = 2 + + for input_shape in [(3, 5), (2, 3, 5), (2, 5, 3, 4)]: + channels_in = input_shape[0] + input_spatial_shape = input_shape[1:] + dim = len(input_spatial_shape) + + inputs = np.random.normal(0, 1, (batch_size,) + input_shape) + inputs_cf = keras.backend.variable(inputs) + + for kernel_size in [1, 2]: + for stride in [1, 2]: + kernel_sizes = (kernel_size,) * dim + strides = (stride,) * dim + + output_shape = tuple([(i - kernel_size + stride) // stride + for i in input_spatial_shape]) + + kernel_shape = (np.prod(output_shape), + np.prod(kernel_sizes) * channels_in, + filters) + + kernel = np.random.normal( + 0, + 1, + output_shape + (channels_in, np.prod(kernel_sizes), filters) + ) + + kernel_cf = np.reshape(kernel, kernel_shape) + kernel_cf = keras.backend.variable(kernel_cf) + + conv_cf = keras.backend.local_conv(inputs_cf, + kernel_cf, + kernel_sizes, + strides, + output_shape, + 'channels_first') + + inputs_cl = np.transpose(inputs, [0, 2] + list(range(3, dim + 2)) + + [1]) + inputs_cl = keras.backend.variable(inputs_cl) + + kernel_cl = np.reshape( + np.transpose(kernel, list(range(dim)) + [dim + 1, dim, dim + 2]), + kernel_shape + ) + kernel_cl = keras.backend.variable(kernel_cl) + + conv_cl = keras.backend.local_conv(inputs_cl, + kernel_cl, + kernel_sizes, + strides, + output_shape, + 'channels_last') + with self.test_session(): + conv_cf = keras.backend.eval(conv_cf) + conv_cl = keras.backend.eval(conv_cl) + + self.assertAllCloseAccordingToType( + conv_cf, + np.transpose(conv_cl, + [0, dim + 1] + list(range(1, dim + 1))), + atol=1e-5 + ) + + @parameterized.named_parameters( + ('local_conv1d', (5, 6), (3,), (1,), (3,)), + ('local_conv2d', (4, 5, 6), (3, 3), (1, 1), (2, 3))) + def test_local_conv_1d_and_2d(self, + input_shape, + kernel_sizes, + strides, + output_shape): + filters = 3 + batch_size = 2 + + inputs = np.random.normal(0, 1, (batch_size,) + input_shape) + inputs = keras.backend.variable(inputs) + + kernel = np.random.normal(0, 1, (np.prod(output_shape), + np.prod(kernel_sizes) * input_shape[-1], + filters)) + kernel = keras.backend.variable(kernel) + + local_conv = keras.backend.local_conv(inputs, + kernel, + kernel_sizes, + strides, + output_shape, + 'channels_last') + if len(output_shape) == 1: + local_conv_dim = keras.backend.local_conv1d(inputs, + kernel, + kernel_sizes, + strides, + 'channels_last') + else: + local_conv_dim = keras.backend.local_conv2d(inputs, + kernel, + kernel_sizes, + strides, + output_shape, + 'channels_last') + + with self.test_session(): + local_conv = keras.backend.eval(local_conv) + local_conv_dim = keras.backend.eval(local_conv_dim) + + self.assertAllCloseAccordingToType(local_conv, local_conv_dim) + def test_conv2d(self): val = np.random.random((10, 4, 10, 10)) x = keras.backend.variable(val) @@ -963,7 +1107,7 @@ class BackendNNOpsTest(test.TestCase): {'go_backwards': False, 'mask': mask, 'unroll': True}, ] with self.test_session(): - for (i, kwargs) in enumerate(kwargs_list): + for i, kwargs in enumerate(kwargs_list): last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs, initial_states, **kwargs) @@ -1010,6 +1154,115 @@ class BackendNNOpsTest(test.TestCase): for b_s, b_u_s in zip(state_list[2], state_list[3]): self.assertAllClose(b_s, b_u_s, atol=1e-04) + def test_rnn_additional_states(self): + # implement a simple RNN + num_samples = 4 + input_dim = 5 + output_dim = 3 + timesteps = 6 + + input_val = np.random.random( + (num_samples, timesteps, input_dim)).astype(np.float32) + init_state_val = np.random.random( + (num_samples, output_dim)).astype(np.float32) + w_i_val = np.random.random((input_dim, output_dim)).astype(np.float32) + w_o_val = np.random.random((output_dim, output_dim)).astype(np.float32) + np_mask = np.random.randint(2, size=(num_samples, timesteps)) + + def rnn_step_fn(): + w_i = keras.backend.variable(w_i_val) + w_o = keras.backend.variable(w_o_val) + + def step_function(x, states): + assert len(states) == 2 + prev_output = states[0] + output = keras.backend.dot(x, w_i) + keras.backend.dot(prev_output, w_o) + return output, [output, + keras.backend.concatenate([output, output], axis=-1)] + + return step_function + + # test default setup + last_output_list = [[], [], [], [], [], []] + outputs_list = [[], [], [], [], [], []] + state_list = [[], [], [], [], [], []] + additional_state_list = [[], [], [], [], [], []] + + rnn_fn = rnn_step_fn() + inputs = keras.backend.variable(input_val) + initial_states = [keras.backend.variable(init_state_val), + np.concatenate([init_state_val, init_state_val], axis=-1)] + mask = keras.backend.variable(np_mask) + + kwargs_list = [ + {'go_backwards': False, 'mask': None}, + {'go_backwards': False, 'mask': None, 'unroll': True}, + {'go_backwards': True, 'mask': None}, + {'go_backwards': True, 'mask': None, 'unroll': True}, + {'go_backwards': False, 'mask': mask}, + {'go_backwards': False, 'mask': mask, 'unroll': True}, + ] + with self.test_session(): + for i, kwargs in enumerate(kwargs_list): + last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs, + initial_states, + **kwargs) + # check static shape inference + self.assertEqual(last_output.get_shape().as_list(), + [num_samples, output_dim]) + self.assertEqual(outputs.get_shape().as_list(), + [num_samples, timesteps, output_dim]) + # for state in new_states: + # self.assertEquals(state.get_shape().as_list(), + # [num_samples, output_dim]) + self.assertEqual(new_states[0].get_shape().as_list(), + [num_samples, output_dim]) + self.assertEqual(new_states[1].get_shape().as_list(), + [num_samples, 2 * output_dim]) + + last_output_list[i].append(keras.backend.eval(last_output)) + outputs_list[i].append(keras.backend.eval(outputs)) + self.assertEqual(len(new_states), 2) + state_list[i].append(keras.backend.eval(new_states[0])) + additional_state_list[i].append(keras.backend.eval(new_states[1])) + + def assert_list_pairwise(z_list, atol=1e-05): + for (z1, z2) in zip(z_list[1:], z_list[:-1]): + self.assertAllClose(z1, z2, atol=atol) + + assert_list_pairwise(last_output_list[0], atol=1e-04) + assert_list_pairwise(outputs_list[0], atol=1e-04) + assert_list_pairwise(state_list[0], atol=1e-04) + assert_list_pairwise(additional_state_list[0], atol=1e-04) + assert_list_pairwise(last_output_list[2], atol=1e-04) + assert_list_pairwise(outputs_list[2], atol=1e-04) + assert_list_pairwise(state_list[2], atol=1e-04) + assert_list_pairwise(additional_state_list[2], atol=1e-04) + + for l, u_l in zip(last_output_list[0], last_output_list[1]): + self.assertAllClose(l, u_l, atol=1e-04) + + for o, u_o in zip(outputs_list[0], outputs_list[1]): + self.assertAllClose(o, u_o, atol=1e-04) + + for s, u_s in zip(state_list[0], state_list[1]): + self.assertAllClose(s, u_s, atol=1e-04) + + for s, u_s in zip(additional_state_list[0], additional_state_list[1]): + self.assertAllClose(s, u_s, atol=1e-04) + + for b_l, b_u_l in zip(last_output_list[2], last_output_list[3]): + self.assertAllClose(b_l, b_u_l, atol=1e-04) + + for b_o, b_u_o in zip(outputs_list[2], outputs_list[3]): + self.assertAllClose(b_o, b_u_o, atol=1e-04) + + for b_s, b_u_s in zip(state_list[2], state_list[3]): + self.assertAllClose(b_s, b_u_s, atol=1e-04) + + for s, u_s in zip(additional_state_list[2], additional_state_list[3]): + self.assertAllClose(s, u_s, atol=1e-04) + def test_normalize_batch_in_training(self): val = np.random.random((10, 3, 10, 10)) x = keras.backend.variable(val) @@ -1165,6 +1418,13 @@ class TestRandomOps(test.TestCase): self.assertAllClose(np.max(y), 2., atol=0.1) self.assertAllClose(np.min(y), -2., atol=0.1) + def test_string_input(self): + seq = keras.Sequential([ + keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string), + keras.layers.Lambda(lambda x: x[0]) + ]) + preds = seq.predict([['tensorflow eager']]) + self.assertEqual(preds.shape, (1,)) if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 8d0386e8ddc9a03d231ce81636c3159a95c26b1f..3ae06d7ab870f7125a123de51fab95d543efe56c 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -24,6 +24,7 @@ from collections import Iterable from collections import OrderedDict import csv import json +import math import os import time @@ -496,6 +497,9 @@ class EarlyStopping(Callback): monitored has stopped increasing; in `auto` mode, the direction is automatically inferred from the name of the monitored quantity. + baseline: baseline value for the monitored quantity. + Training will stop if the model doesn't show improvement over the + baseline. """ def __init__(self, @@ -503,13 +507,15 @@ class EarlyStopping(Callback): min_delta=0, patience=0, verbose=0, - mode='auto'): + mode='auto', + baseline=None): super(EarlyStopping, self).__init__() self.monitor = monitor self.patience = patience self.verbose = verbose - self.min_delta = min_delta + self.baseline = baseline + self.min_delta = abs(min_delta) self.wait = 0 self.stopped_epoch = 0 @@ -537,7 +543,10 @@ class EarlyStopping(Callback): # Allow instances to be re-used self.wait = 0 self.stopped_epoch = 0 - self.best = np.Inf if self.monitor_op == np.less else -np.Inf + if self.baseline is not None: + self.best = self.baseline + else: + self.best = np.Inf if self.monitor_op == np.less else -np.Inf def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) @@ -635,7 +644,11 @@ class LearningRateScheduler(Callback): def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, 'lr'): raise ValueError('Optimizer must have a "lr" attribute.') - lr = self.schedule(epoch) + try: # new API + lr = float(K.get_value(self.model.optimizer.lr)) + lr = self.schedule(epoch, lr) + except TypeError: # Support for old API for backward compatibility + lr = self.schedule(epoch) if not isinstance(lr, (float, np.float32, np.float64)): raise ValueError('The output of the "schedule" function ' 'should be float.') @@ -711,8 +724,13 @@ class TensorBoard(Callback): self.write_grads = write_grads self.write_images = write_images self.batch_size = batch_size + self._current_batch = 0 + # abstracted writer class to be able to stub for testing + self._writer_class = tf_summary.FileWriter def set_model(self, model): + """Sets Keras model and creates summary ops.""" + self.model = model self.sess = K.get_session() if self.histogram_freq and self.merged is None: @@ -763,54 +781,41 @@ class TensorBoard(Callback): self.merged = tf_summary.merge_all() if self.write_graph: - self.writer = tf_summary.FileWriter(self.log_dir, self.sess.graph) + self.writer = self._writer_class(self.log_dir, self.sess.graph) else: - self.writer = tf_summary.FileWriter(self.log_dir) + self.writer = self._writer_class(self.log_dir) - def on_epoch_end(self, epoch, logs=None): - logs = logs or {} + def _fetch_callback(self, summary): + self.writer.add_summary( + summary, self._epoch + self._current_batch / self._batches_per_epoch) + self._current_batch += 1 + + def on_epoch_begin(self, epoch, logs=None): + """Add histogram op to Model test_function callbacks, reset batch count.""" if not self.validation_data and self.histogram_freq: raise ValueError('If printing histograms, validation_data must be ' 'provided, and cannot be a generator.') - if self.validation_data and self.histogram_freq: - if epoch % self.histogram_freq == 0: - - val_data = self.validation_data - tensors = ( - self.model.inputs + self.model.targets + self.model.sample_weights) - - if self.model.uses_learning_phase: - tensors += [K.learning_phase()] - - assert len(val_data) == len(tensors) - val_size = val_data[0].shape[0] - i = 0 - while i < val_size: - step = min(self.batch_size, val_size - i) - batch_val = [] - batch_val.append(val_data[0][i:i + step] - if val_data[0] is not None else None) - batch_val.append(val_data[1][i:i + step] - if val_data[1] is not None else None) - batch_val.append(val_data[2][i:i + step] - if val_data[2] is not None else None) - if self.model.uses_learning_phase: - # do not slice the learning phase - batch_val = [x[i:i + step] if x is not None else None - for x in val_data[:-1]] - batch_val.append(val_data[-1]) - else: - batch_val = [x[i:i + step] if x is not None else None - for x in val_data] - feed_dict = {} - for key, val in zip(tensors, batch_val): - if val is not None: - feed_dict[key] = val - result = self.sess.run([self.merged], feed_dict=feed_dict) - summary_str = result[0] - self.writer.add_summary(summary_str, epoch) - i += self.batch_size + if self.histogram_freq and epoch % self.histogram_freq == 0: + self._epoch = epoch + self._current_batch = 0 + self._batches_per_epoch = math.ceil( + self.validation_data[0].shape[0] / self.batch_size) + if self.merged not in self.model.test_function.fetches: + self.model.test_function.fetches.append(self.merged) + self.model.test_function.fetch_callbacks[ + self.merged] = self._fetch_callback + + def on_epoch_end(self, epoch, logs=None): + """Checks if summary ops should run next epoch, logs scalar summaries.""" + + logs = logs or {} + + if self.histogram_freq and self.histogram_freq > 1: + if self.merged in self.model.test_function.fetches: + self.model.test_function.fetches.remove(self.merged) + if self.merged in self.model.test_function.fetch_callbacks: + self.model.test_function.fetch_callbacks.pop(self.merged) for name, value in logs.items(): if name in ['batch', 'size']: diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index eb40fb4acc11d278fd456b95af0f24058b0df7c1..d56f2f5bfc7d7045a4c1d2bde764fe1143764922 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -27,6 +27,7 @@ import unittest import numpy as np +from tensorflow.core.framework import summary_pb2 from tensorflow.python import keras from tensorflow.python.keras import testing_utils from tensorflow.python.platform import test @@ -273,16 +274,43 @@ class KerasCallbacksTest(test.TestCase): 1, activation='sigmoid'),)) model.compile( optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy']) - stopper = keras.callbacks.EarlyStopping(monitor='acc', patience=patience) weights = model.get_weights() + stopper = keras.callbacks.EarlyStopping(monitor='acc', patience=patience) hist = model.fit(data, labels, callbacks=[stopper], verbose=0, epochs=20) assert len(hist.epoch) >= patience # This should allow training to go for at least `patience` epochs model.set_weights(weights) hist = model.fit(data, labels, callbacks=[stopper], verbose=0, epochs=20) - assert len(hist.epoch) >= patience + assert len(hist.epoch) >= patience + + def test_EarlyStopping_with_baseline(self): + with self.test_session(): + np.random.seed(1337) + baseline = 0.5 + (data, labels), _ = testing_utils.get_test_data( + train_samples=100, + test_samples=50, + input_shape=(1,), + num_classes=NUM_CLASSES) + model = keras.models.Sequential((keras.layers.Dense( + 1, input_dim=1, activation='relu'), keras.layers.Dense( + 1, activation='sigmoid'),)) + model.compile( + optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy']) + + stopper = keras.callbacks.EarlyStopping(monitor='acc', + baseline=baseline) + hist = model.fit(data, labels, callbacks=[stopper], verbose=0, epochs=20) + assert len(hist.epoch) == 1 + + patience = 3 + stopper = keras.callbacks.EarlyStopping(monitor='acc', + patience=patience, + baseline=baseline) + hist = model.fit(data, labels, callbacks=[stopper], verbose=0, epochs=20) + assert len(hist.epoch) >= patience def test_RemoteMonitor(self): if requests is None: @@ -321,8 +349,26 @@ class KerasCallbacksTest(test.TestCase): callbacks=cbks, epochs=5, verbose=0) - assert (float(keras.backend.get_value(model.optimizer.lr)) - 0.2 - ) < keras.backend.epsilon() + assert ( + float(keras.backend.get_value( + model.optimizer.lr)) - 0.2) < keras.backend.epsilon() + + cbks = [keras.callbacks.LearningRateScheduler(lambda x, lr: lr / 2)] + model.compile( + loss='categorical_crossentropy', + optimizer='sgd', + metrics=['accuracy']) + model.fit( + x_train, + y_train, + batch_size=BATCH_SIZE, + validation_data=(x_test, y_test), + callbacks=cbks, + epochs=2, + verbose=0) + assert ( + float(keras.backend.get_value( + model.optimizer.lr)) - 0.01 / 4) < keras.backend.epsilon() def test_ReduceLROnPlateau(self): with self.test_session(): @@ -856,6 +902,80 @@ class KerasCallbacksTest(test.TestCase): callbacks=callbacks_factory(histogram_freq=1)) assert os.path.isdir(filepath) + def test_Tensorboard_histogram_summaries_in_test_function(self): + + class FileWriterStub(object): + + def __init__(self, logdir, graph=None): + self.logdir = logdir + self.graph = graph + self.steps_seen = [] + + def add_summary(self, summary, global_step): + summary_obj = summary_pb2.Summary() + + # ensure a valid Summary proto is being sent + if isinstance(summary, bytes): + summary_obj.ParseFromString(summary) + else: + assert isinstance(summary, summary_pb2.Summary) + summary_obj = summary + + # keep track of steps seen for the merged_summary op, + # which contains the histogram summaries + if len(summary_obj.value) > 1: + self.steps_seen.append(global_step) + + def flush(self): + pass + + def close(self): + pass + + np.random.seed(1337) + tmpdir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, tmpdir) + (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( + train_samples=TRAIN_SAMPLES, + test_samples=TEST_SAMPLES, + input_shape=(INPUT_DIM,), + num_classes=NUM_CLASSES) + y_test = keras.utils.to_categorical(y_test) + y_train = keras.utils.to_categorical(y_train) + + with self.test_session(): + model = keras.models.Sequential() + model.add( + keras.layers.Dense( + NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) + # non_trainable_weights: moving_variance, moving_mean + model.add(keras.layers.BatchNormalization()) + model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) + model.compile( + loss='categorical_crossentropy', + optimizer='sgd', + metrics=['accuracy']) + tsb = keras.callbacks.TensorBoard( + log_dir=tmpdir, + histogram_freq=1, + write_images=True, + write_grads=True, + batch_size=5) + tsb._writer_class = FileWriterStub + cbks = [tsb] + + # fit with validation data + model.fit( + x_train, + y_train, + batch_size=BATCH_SIZE, + validation_data=(x_test, y_test), + callbacks=cbks, + epochs=3, + verbose=0) + + self.assertAllEqual(tsb.writer.steps_seen, [0, 0.5, 1, 1.5, 2, 2.5]) + @unittest.skipIf( os.name == 'nt', 'use_multiprocessing=True does not work on windows properly.') diff --git a/tensorflow/python/keras/datasets/boston_housing.py b/tensorflow/python/keras/datasets/boston_housing.py index 4c4cab8c0865098ebed1a7fbe29246ef51bb9833..eeb7cbc44a72a5c624f8d1d1d9dbfab1fcd1b225 100644 --- a/tensorflow/python/keras/datasets/boston_housing.py +++ b/tensorflow/python/keras/datasets/boston_housing.py @@ -45,10 +45,9 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113): origin=origin_folder + 'boston_housing.npz', file_hash= 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5') - f = np.load(path) - x = f['x'] - y = f['y'] - f.close() + with np.load(path) as f: + x = f['x'] + y = f['y'] np.random.seed(seed) indices = np.arange(len(x)) diff --git a/tensorflow/python/keras/datasets/mnist.py b/tensorflow/python/keras/datasets/mnist.py index 03564accc74507713d198b8ba1ed8c08bd597e8d..a96b581960f3d5f60994fe92a1424e793d7e39c7 100644 --- a/tensorflow/python/keras/datasets/mnist.py +++ b/tensorflow/python/keras/datasets/mnist.py @@ -47,8 +47,8 @@ def load_data(path='mnist.npz'): path, origin=origin_folder + 'mnist.npz', file_hash='8a61469f7ea1b51cbae51d4f78837e45') - f = np.load(path) - x_train, y_train = f['x_train'], f['y_train'] - x_test, y_test = f['x_test'], f['y_test'] - f.close() - return (x_train, y_train), (x_test, y_test) + with np.load(path) as f: + x_train, y_train = f['x_train'], f['y_train'] + x_test, y_test = f['x_test'], f['y_test'] + + return (x_train, y_train), (x_test, y_test) diff --git a/tensorflow/python/keras/datasets/reuters.py b/tensorflow/python/keras/datasets/reuters.py index 2120b4b2421c652c9587a2e644bf008c3ece3980..cb796bb06cf09157cc510b55e3981d518fd8b433 100644 --- a/tensorflow/python/keras/datasets/reuters.py +++ b/tensorflow/python/keras/datasets/reuters.py @@ -130,7 +130,5 @@ def get_word_index(path='reuters_word_index.json'): path, origin=origin_folder + 'reuters_word_index.json', file_hash='4d44cc38712099c9e383dc6e5f11a921') - f = open(path) - data = json.load(f) - f.close() - return data + with open(path) as f: + return json.load(f) diff --git a/tensorflow/python/keras/engine/__init__.py b/tensorflow/python/keras/engine/__init__.py index ec7c0831992b2691c442bbd30445dbff8dba662f..26aed34766f9e1e2094db7a4c8b66ff057dacc4b 100644 --- a/tensorflow/python/keras/engine/__init__.py +++ b/tensorflow/python/keras/engine/__init__.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +# TODO(fchollet): Remove hourglass imports once external code is done importing +# non-public APIs. from tensorflow.python.keras.engine.base_layer import InputSpec from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.engine.input_layer import Input from tensorflow.python.keras.engine.input_layer import InputLayer -from tensorflow.python.keras.engine.network import get_source_inputs -from tensorflow.python.keras.engine.network import Network -from tensorflow.python.keras.engine.training import Model +from tensorflow.python.keras.utils.layer_utils import get_source_inputs del absolute_import del division diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 4814275fd5ba53e7845f383b3447a6ef9f47f6c2..361778570bc7e87bc0642a2c52d43762c6828eb4 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -116,6 +116,7 @@ class Layer(checkpointable.CheckpointableBase): constraints on inputs that can be accepted by the layer. """ + @checkpointable.no_automatic_dependency_tracking def __init__(self, trainable=True, name=None, dtype=None, **kwargs): # These properties should be set by the user via keyword arguments. # note that 'dtype', 'input_shape' and 'batch_input_shape' @@ -217,7 +218,7 @@ class Layer(checkpointable.CheckpointableBase): @activity_regularizer.setter def activity_regularizer(self, regularizer): """Optional regularizer function for the output of this layer.""" - self._activity_regularizer = regularizer + self._activity_regularizer = self._no_dependency(regularizer) @property def trainable_weights(self): @@ -658,7 +659,8 @@ class Layer(checkpointable.CheckpointableBase): self._compute_previous_mask): previous_mask = collect_previous_mask(inputs) if not hasattr(self, '_call_fn_args'): - self._call_fn_args = function_utils.fn_args(self.call) + self._call_fn_args = self._no_dependency( + function_utils.fn_args(self.call)) if ('mask' in self._call_fn_args and 'mask' not in kwargs and not generic_utils.is_all_none(previous_mask)): # The previous layer generated a mask, and mask was not explicitly pass diff --git a/tensorflow/python/keras/engine/input_layer.py b/tensorflow/python/keras/engine/input_layer.py index 7996110829b56b6f7f0b3e4c5b6c5b9f35affb64..8a4018a0df50b8d4c9df5900ffddfcdc093f161f 100644 --- a/tensorflow/python/keras/engine/input_layer.py +++ b/tensorflow/python/keras/engine/input_layer.py @@ -215,7 +215,7 @@ def Input( # pylint: disable=invalid-name if dtype is None: dtype = K.floatx() - if not shape and tensor is None: + if shape is None and tensor is None: raise ValueError('Please provide to Input either a `shape`' ' or a `tensor` argument. Note that ' '`shape` does not include the batch ' diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index f53898d8e3596aaef6685c18df101e5b25ba029e..a4d96de74fc90e31d52f9a67e845a84f9ceb5034 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import copy +import functools import json import os import weakref @@ -42,7 +43,8 @@ from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable -from tensorflow.python.training.checkpointable import data_structures_base +from tensorflow.python.training.checkpointable import data_structures +from tensorflow.python.training.checkpointable import layer_utils as checkpointable_layer_utils from tensorflow.python.training.checkpointable import util as checkpointable_utils from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect @@ -79,6 +81,20 @@ class Network(base_layer.Layer): # Subclassed network self._init_subclassed_network(**kwargs) + # Several Network methods have "no_automatic_dependency_tracking" + # annotations. Since Network does automatic dependency tracking on attribute + # assignment, including for common data structures such as lists, by default + # we'd have quite a few empty dependencies which users don't care about (or + # would need some way to ignore dependencies automatically, which is confusing + # when applied to user code). Some attributes, such as _layers, would cause + # structural issues (_layers being the place where Layers assigned to tracked + # attributes are stored). + # + # Aside from these aesthetic and structural issues, useless dependencies on + # empty lists shouldn't cause issues; adding or removing them will not break + # checkpoints, but may cause "all Python objects matched" assertions to fail + # (in which case less strict assertions may be substituted if necessary). + @checkpointable.no_automatic_dependency_tracking def _base_init(self, name=None): # The following are implemented as property functions: # self.trainable_weights @@ -133,6 +149,7 @@ class Network(base_layer.Layer): # restore operations when graph building. self._in_progress_restore_finalizer = None + @checkpointable.no_automatic_dependency_tracking def _init_graph_network(self, inputs, outputs, name=None): self._call_convention = base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT # Normalize and set self.inputs, self.outputs. @@ -291,6 +308,7 @@ class Network(base_layer.Layer): for layer in self._output_layers: self.output_names.append(layer.name) + @checkpointable.no_automatic_dependency_tracking def _init_subclassed_network(self, name=None): self._base_init(name=name) self._is_graph_network = False @@ -360,14 +378,35 @@ class Network(base_layer.Layer): self._track_checkpointable( layer, name='layer-%d' % layer_index, overwrite=True) + def _no_dependency(self, value): + """Override to allow `Layer` to disable dependency tracking. + + `CheckpointableBase` defines this method, whose semantics are "if a subclass + does dependency tracking, this method exempts `value`." Layer uses + `_no_dependency` to exempt some of its attribute assignments (conditional on + attribute assignment causing tracking in the subclass). + + Args: + value: An object which will be assigned to an object attribute, whose + value should not be tracked. + + Returns: + A wrapped object which, when assigned to an attribute, will not be + tracked (`value` will be stored in the attribute). + """ + return data_structures.NoDependency(value) + def __setattr__(self, name, value): - no_dependency = isinstance(value, checkpointable.NoDependency) - if no_dependency: - value = value.value + if not getattr(self, '_setattr_tracking', True): + super(Network, self).__setattr__(name, value) + return + no_dependency = isinstance(value, data_structures.NoDependency) + value = data_structures.sticky_attribute_assignment( + checkpointable=self, value=value, name=name) if isinstance(value, ( base_layer.Layer, Network, - data_structures_base.CheckpointableDataStructureBase)): + data_structures.CheckpointableDataStructure)): try: is_graph_network = self._is_graph_network except AttributeError: @@ -375,7 +414,9 @@ class Network(base_layer.Layer): 'forgot to call `super(YourClass, self).__init__()`.' ' Always start with this line.') if not is_graph_network: - if value not in self._layers: + # We need to check object identity to avoid de-duplicating empty + # container types which compare equal. + if not any((layer is value for layer in self._layers)): self._layers.append(value) if hasattr(value, '_use_resource_variables'): # In subclassed models, legacy layers (tf.layers) must always use @@ -383,12 +424,6 @@ class Network(base_layer.Layer): value._use_resource_variables = True if (not no_dependency and isinstance(value, checkpointable.CheckpointableBase)): - # Layer (and therefore Network/Model) inherit from CheckpointableBase - # rather than Checkpointable, which means there is no Checkpointable - # __setattr__ override (it would be a performance issue for functional - # layers). Therefore Model tracks Checkpointable objects itself. - self._track_checkpointable( - checkpointable=value, name=name, overwrite=True) if ( # For subclassed models only, users may add extra weights/variables # simply by assigning them to attributes. not self._is_graph_network @@ -491,7 +526,8 @@ class Network(base_layer.Layer): @property def layers(self): - return self._layers + return checkpointable_layer_utils.filter_empty_layer_containers( + self._layers) def get_layer(self, name=None, index=None): """Retrieves a layer based on either its name (unique) or index. @@ -526,6 +562,28 @@ class Network(base_layer.Layer): return layer raise ValueError('No such layer: ' + name) + @property + def _unfiltered_updates(self): + if context.executing_eagerly(): + return [] + updates = [] + for layer in self.layers: + if isinstance(layer, Network): + updates += layer._unfiltered_updates + else: + updates += layer.updates + return updates + + @property + def _unfiltered_losses(self): + losses = [] + for layer in self.layers: + if isinstance(layer, Network): + losses += layer._unfiltered_losses + else: + losses += layer.losses + return losses + @property def updates(self): """Retrieves the network's updates. @@ -535,6 +593,8 @@ class Network(base_layer.Layer): (e.g. will not include updates that were created by layers of this model outside of the model). + When the network has no registered inputs, all updates are returned. + Effectively, `network.updates` behaves like `layer.updates`. Concrete example: @@ -580,22 +640,20 @@ class Network(base_layer.Layer): if not self.trainable and not self.stateful: return [] - updates = [] - for layer in self.layers: - updates += layer.updates + updates = self._unfiltered_updates # `updates` might contain irrelevant updates, so it needs to be filtered # with respect to inputs the model has been called on. - if self.inputs: - relevant_inputs = self.inputs[:] - else: - relevant_inputs = [] - for i in range(1, len(self._inbound_nodes)): + relevant_inputs = [] + for i in range(0, len(self._inbound_nodes)): inputs = self.get_input_at(i) if isinstance(inputs, list): relevant_inputs += inputs else: relevant_inputs.append(inputs) + if not relevant_inputs: + return updates + reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, updates) relevant_conditional_updates = [x for x in updates if x in reachable] unconditional_updates = [ @@ -614,25 +672,25 @@ class Network(base_layer.Layer): (e.g. will not include losses that depend on tensors that aren't inputs to this model). + When the network has no registered inputs, all losses are returned. + Returns: A list of loss tensors. """ - losses = [] - for layer in self.layers: - losses += layer.losses + losses = self._unfiltered_losses if context.executing_eagerly(): return losses - if self.inputs: - relevant_inputs = self.inputs[:] - else: - relevant_inputs = [] - for i in range(1, len(self._inbound_nodes)): + relevant_inputs = [] + for i in range(0, len(self._inbound_nodes)): inputs = self.get_input_at(i) if isinstance(inputs, list): relevant_inputs += inputs else: relevant_inputs.append(inputs) + if not relevant_inputs: + return losses + reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, losses) relevant_conditional_losses = [x for x in losses if x in reachable] unconditional_losses = [ @@ -642,14 +700,14 @@ class Network(base_layer.Layer): @property def trainable_weights(self): - return layer_utils.gather_trainable_weights( + return checkpointable_layer_utils.gather_trainable_weights( trainable=self.trainable, sub_layers=self.layers, extra_variables=self._extra_variables) @property def non_trainable_weights(self): - return layer_utils.gather_non_trainable_weights( + return checkpointable_layer_utils.gather_non_trainable_weights( trainable=self.trainable, sub_layers=self.layers, extra_variables=self._extra_variables) @@ -1300,7 +1358,11 @@ class Network(base_layer.Layer): with h5py.File(filepath, 'w') as f: saving.save_weights_to_hdf5_group(f, self.layers) else: - self._checkpointable_saver.save(filepath) + if context.executing_eagerly(): + session = None + else: + session = backend.get_session() + self._checkpointable_saver.save(filepath, session=session) def load_weights(self, filepath, by_name=False): """Loads all layer weights, either from a TensorFlow or an HDF5 weight file. @@ -1360,7 +1422,8 @@ class Network(base_layer.Layer): 'loading TensorFlow-formatted weights (got by_name=True to ' 'load_weights).') if not context.executing_eagerly(): - finalizer = status.run_restore_ops + session = backend.get_session() + finalizer = functools.partial(status.run_restore_ops, session=session) if self.built: finalizer() else: @@ -1490,47 +1553,6 @@ class Network(base_layer.Layer): print_fn=print_fn) -def get_source_inputs(tensor, layer=None, node_index=None): - """Returns the list of input tensors necessary to compute `tensor`. - - Output will always be a list of tensors - (potentially with 1 element). - - Arguments: - tensor: The tensor to start from. - layer: Origin layer of the tensor. Will be - determined via tensor._keras_history if not provided. - node_index: Origin node index of the tensor. - - Returns: - List of input tensors. - """ - if not hasattr(tensor, '_keras_history'): - return tensor - - if layer is None or node_index: - layer, node_index, _ = tensor._keras_history - if not layer._inbound_nodes: - return [tensor] - else: - node = layer._inbound_nodes[node_index] - if not node.inbound_layers: - # Reached an Input layer, stop recursion. - return node.input_tensors - else: - source_tensors = [] - for i in range(len(node.inbound_layers)): - x = node.input_tensors[i] - layer = node.inbound_layers[i] - node_index = node.node_indices[i] - previous_sources = get_source_inputs(x, layer, node_index) - # Avoid input redundancy. - for x in previous_sources: - if x not in source_tensors: - source_tensors.append(x) - return source_tensors - - def _is_hdf5_filepath(filepath): return filepath.endswith('.h5') or filepath.endswith('.keras') diff --git a/tensorflow/python/keras/engine/saving.py b/tensorflow/python/keras/engine/saving.py index b9a2e1f25f637dc8017f751bbdd400c1e5c9dd44..d5ccd44604b6b84ea0ceb4fa1c270b2c7dddc147 100644 --- a/tensorflow/python/keras/engine/saving.py +++ b/tensorflow/python/keras/engine/saving.py @@ -351,7 +351,10 @@ def preprocess_weights_for_loading(layer, weights, original_keras_version=None, original_backend=None): - """Converts layers weights from Keras 1 format to Keras 2. + """Preprocess layer weights between different Keras formats. + + Converts layers weights from Keras 1 format to Keras 2 and also weights of + CuDNN layers in Keras 2. Arguments: layer: Layer instance. @@ -363,7 +366,18 @@ def preprocess_weights_for_loading(layer, Returns: A list of weights values (Numpy arrays). """ - if layer.__class__.__name__ == 'Bidirectional': + def convert_nested_bidirectional(weights): + """Converts layers nested in `Bidirectional` wrapper. + + This function uses `preprocess_weights_for_loading()` for converting + layers. + + Arguments: + weights: List of weights values (Numpy arrays). + + Returns: + A list of weights values (Numpy arrays). + """ num_weights_per_layer = len(weights) // 2 forward_weights = preprocess_weights_for_loading( layer.forward_layer, weights[:num_weights_per_layer], @@ -371,7 +385,69 @@ def preprocess_weights_for_loading(layer, backward_weights = preprocess_weights_for_loading( layer.backward_layer, weights[num_weights_per_layer:], original_keras_version, original_backend) - weights = forward_weights + backward_weights + return forward_weights + backward_weights + + def convert_nested_time_distributed(weights): + """Converts layers nested in `TimeDistributed` wrapper. + + This function uses `preprocess_weights_for_loading()` for converting nested + layers. + + Arguments: + weights: List of weights values (Numpy arrays). + + Returns: + A list of weights values (Numpy arrays). + """ + return preprocess_weights_for_loading( + layer.layer, weights, original_keras_version, original_backend) + + def convert_nested_model(weights): + """Converts layers nested in `Model` or `Sequential`. + + This function uses `preprocess_weights_for_loading()` for converting nested + layers. + + Arguments: + weights: List of weights values (Numpy arrays). + + Returns: + A list of weights values (Numpy arrays). + """ + new_weights = [] + # trainable weights + for sublayer in layer.layers: + num_weights = len(sublayer.trainable_weights) + if num_weights > 0: + new_weights.extend(preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + + # non-trainable weights + for sublayer in layer.layers: + num_weights = len([l for l in sublayer.weights + if l not in sublayer.trainable_weights]) + if num_weights > 0: + new_weights.extend(preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + return new_weights + + # Convert layers nested in Bidirectional/Model/Sequential. + # Both transformation should be ran for both Keras 1->2 conversion + # and for conversion of CuDNN layers. + if layer.__class__.__name__ == 'Bidirectional': + weights = convert_nested_bidirectional(weights) + if layer.__class__.__name__ == 'TimeDistributed': + weights = convert_nested_time_distributed(weights) + elif layer.__class__.__name__ in ['Model', 'Sequential']: + weights = convert_nested_model(weights) if original_keras_version == '1': if layer.__class__.__name__ == 'TimeDistributed': @@ -446,35 +522,6 @@ def preprocess_weights_for_loading(layer, recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0)) weights = [kernel, recurrent_kernel, bias] - if layer.__class__.__name__ in ['Model', 'Sequential']: - new_weights = [] - # trainable weights - for sublayer in layer.layers: - num_weights = len(sublayer.trainable_weights) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - - # non-trainable weights - for sublayer in layer.layers: - num_weights = len([ - l for l in sublayer.weights if l not in sublayer.trainable_weights - ]) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - weights = new_weights - conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] if layer.__class__.__name__ in conv_layers: if original_backend == 'theano': @@ -486,6 +533,7 @@ def preprocess_weights_for_loading(layer, if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) + # convert CuDNN layers return _convert_rnn_weights(layer, weights) @@ -624,7 +672,7 @@ def _convert_rnn_weights(layer, weights): kernels = transform_kernels(weights[0], transpose_input(from_cudnn), n_gates) recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates) - biases = weights[2].reshape((2, -1) if from_cudnn else -1) + biases = np.array(weights[2]).reshape((2, -1) if from_cudnn else -1) return [kernels, recurrent_kernels, biases] if bias_shape == (2 * units * n_gates,): @@ -806,7 +854,16 @@ def load_weights_from_hdf5_group_by_name(f, layers): str(len(weight_values)) + ' element(s).') # Set values. for i in range(len(weight_values)): - weight_value_tuples.append((symbolic_weights[i], weight_values[i])) + if K.int_shape(symbolic_weights[i]) != weight_values[i].shape: + raise ValueError('Layer #' + str(k) +' (named "' + layer.name + + '"), weight ' + str(symbolic_weights[i]) + + ' has shape {}'.format(K.int_shape( + symbolic_weights[i])) + + ', but the saved weight has shape ' + + str(weight_values[i].shape) + '.') + + else: + weight_value_tuples.append((symbolic_weights[i], weight_values[i])) K.batch_set_value(weight_value_tuples) diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 4352c5cb189d41ae70a2b4dbd6ed154b3ad4f7bb..030328f2a66f0ec406ac271aecfbf2dbebf22f5f 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -21,7 +21,6 @@ from __future__ import print_function import os import shutil import tempfile - from absl.testing import parameterized import numpy as np @@ -31,6 +30,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.keras.engine import saving from tensorflow.python.keras.engine import training from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops @@ -248,6 +248,82 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): self.assertAllClose(y, ref_y) + def test_sequential_weight_loading_group_name_with_incorrect_length(self): + if h5py is None: + return + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') + + num_hidden = 5 + input_dim = 3 + num_classes = 2 + with self.test_session(): + ref_model = keras.models.Sequential() + ref_model.add(keras.layers.Dense(num_hidden, input_dim=input_dim, + name='d1')) + ref_model.add(keras.layers.Dense(num_classes, name='d2')) + ref_model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + + f_ref_model = h5py.File(h5_path, 'w') + saving.save_weights_to_hdf5_group(f_ref_model, ref_model.layers) + + f_model = h5py.File(h5_path, 'r') + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, use_bias=False, + input_dim=input_dim, name='d1')) + model.add(keras.layers.Dense(num_classes, name='d2')) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + with self.assertRaisesRegexp(ValueError, + r'Layer #0 \(named \"d1\"\) expects 1 ' + r'weight\(s\), but the saved weights have 2 ' + r'element\(s\)\.'): + saving.load_weights_from_hdf5_group_by_name(f_model, model.layers) + + def test_sequential_weight_loading_group_name_with_incorrect_shape(self): + if h5py is None: + return + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') + + num_hidden = 5 + input_dim = 3 + num_classes = 2 + with self.test_session(): + ref_model = keras.models.Sequential() + ref_model.add(keras.layers.Dense(num_hidden, input_dim=input_dim, + name='d1')) + ref_model.add(keras.layers.Dense(num_classes, name='d2')) + ref_model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + + f_ref_model = h5py.File(h5_path, 'w') + saving.save_weights_to_hdf5_group(f_ref_model, ref_model.layers) + + f_model = h5py.File(h5_path, 'r') + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden + 5, input_dim=input_dim, + name='d1')) + model.add(keras.layers.Dense(num_classes, name='d2')) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + with self.assertRaisesRegexp(ValueError, + r'Layer #0 \(named "d1"\), weight ' + r' has ' + r'shape \(3, 10\), but the saved weight has ' + r'shape \(3, 5\)\.'): + saving.load_weights_from_hdf5_group_by_name(f_model, model.layers) + class TestWholeModelSaving(test.TestCase): @@ -428,26 +504,27 @@ class TestWholeModelSaving(test.TestCase): os.remove(fname) def test_saving_lambda_numpy_array_arguments(self): - if h5py is None: - self.skipTest('h5py required to run this test') + with self.test_session(): + if h5py is None: + self.skipTest('h5py required to run this test') - mean = np.random.random((4, 2, 3)) - std = np.abs(np.random.random((4, 2, 3))) + 1e-5 - inputs = keras.layers.Input(shape=(4, 2, 3)) - output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, - arguments={'mu': mean, 'std': std})(inputs) - model = keras.models.Model(inputs, output) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + mean = np.random.random((4, 2, 3)) + std = np.abs(np.random.random((4, 2, 3))) + 1e-5 + inputs = keras.layers.Input(shape=(4, 2, 3)) + output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, + arguments={'mu': mean, 'std': std})(inputs) + model = keras.models.Model(inputs, output) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) - self.assertAllClose(mean, model.layers[1].arguments['mu']) - self.assertAllClose(std, model.layers[1].arguments['std']) + self.assertAllClose(mean, model.layers[1].arguments['mu']) + self.assertAllClose(std, model.layers[1].arguments['std']) def test_saving_model_with_long_layer_names(self): if h5py is None: @@ -586,7 +663,7 @@ class SubclassedModel(training.Model): class TestWeightSavingAndLoadingTFFormat(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensorflow_format_overwrite(self): with self.test_session() as session: model = SubclassedModel() @@ -604,6 +681,25 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): # Indirectly tests that the user is prompted model.save_weights(prefix, save_format='tensorflow', overwrite=False) + def test_no_default_session(self): + with ops.Graph().as_default(): + self.assertFalse(ops.get_default_session()) + data = np.random.random((1000, 32)).astype(np.float32) + labels = np.random.random((1000, 10)).astype(np.float32) + + model = keras.models.Sequential([ + keras.layers.Dense(10, activation='softmax'), + keras.layers.Dense(10, activation='softmax')]) + + model.compile(optimizer=training_module.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + + model.fit(data, labels) + fname = os.path.join(self.get_temp_dir(), 'weights', 'ckpt') + model.save_weights(fname) + model.load_weights(fname) + def test_no_graph_pollution(self): with context.graph_mode(): graph = ops.Graph() @@ -656,7 +752,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): restore_on_create_y = self.evaluate(restore_on_create_y_tensor) self.assertAllClose(ref_y, restore_on_create_y) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model(self): def _make_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -666,7 +762,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): self._weight_loading_test_template(_make_graph_model) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_subclassed_model(self): self._weight_loading_test_template(SubclassedModel) @@ -700,7 +796,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): y = self.evaluate(model(x)) self.assertAllClose(ref_y, y) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model_added_layer(self): def _save_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -720,7 +816,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): _save_graph_model, _restore_graph_model, _restore_init_fn) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model_added_no_weight_layer(self): def _save_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -741,7 +837,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): _save_graph_model, _restore_graph_model, _restore_init_fn) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_subclassed_model_added_layer(self): class SubclassedModelRestore(training.Model): diff --git a/tensorflow/python/keras/engine/sequential.py b/tensorflow/python/keras/engine/sequential.py index 52e29b0ffad7d26d2a2fbe8f287146daaffa3059..371504a503168e7443895bb22a57126b274da226 100644 --- a/tensorflow/python/keras/engine/sequential.py +++ b/tensorflow/python/keras/engine/sequential.py @@ -24,11 +24,12 @@ import copy from tensorflow.python.keras import backend as K from tensorflow.python.keras import layers as layer_module from tensorflow.python.keras.engine import base_layer -from tensorflow.python.keras.engine import network from tensorflow.python.keras.engine.input_layer import Input from tensorflow.python.keras.engine.input_layer import InputLayer from tensorflow.python.keras.engine.training import Model +from tensorflow.python.keras.utils import layer_utils from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util.tf_export import tf_export @@ -108,6 +109,7 @@ class Sequential(Model): return self._layers[1:] return self._layers + @checkpointable.no_automatic_dependency_tracking def add(self, layer): """Adds a layer instance on top of the layer stack. @@ -146,8 +148,6 @@ class Sequential(Model): first_layer = layer.layers[0] while isinstance(first_layer, (Model, Sequential)): first_layer = first_layer.layers[0] - batch_shape = first_layer._batch_input_shape - dtype = first_layer.dtype if hasattr(first_layer, '_batch_input_shape'): batch_shape = first_layer._batch_input_shape @@ -179,7 +179,7 @@ class Sequential(Model): 'use the functional API.') self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] - self.inputs = network.get_source_inputs(self.outputs[0]) + self.inputs = layer_utils.get_source_inputs(self.outputs[0]) elif self.outputs: output_tensor = layer(self.outputs[0]) if isinstance(output_tensor, list): @@ -193,6 +193,7 @@ class Sequential(Model): else: self._layers.append(layer) + @checkpointable.no_automatic_dependency_tracking def pop(self): """Removes the last layer in the model. @@ -212,6 +213,7 @@ class Sequential(Model): self.outputs = [self.layers[-1].output] self.build() + @checkpointable.no_automatic_dependency_tracking def build(self, input_shape=None): if input_shape and not self.inputs: batch_shape = tuple(input_shape) @@ -222,11 +224,16 @@ class Sequential(Model): for layer in self._layers: x = layer(x) self.outputs = [x] + # Make sure that the model's input shape will be preserved during + # serialization. + if self._layers: + self._layers[0]._batch_input_shape = batch_shape if self.inputs: self._init_graph_network(self.inputs, self.outputs, name=self.name) self.built = True - self._track_layers(self._layers) + if self._layers: + self._track_layers(self._layers) def predict_proba(self, x, batch_size=32, verbose=0): """Generates class probability predictions for the input samples. diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py index 69a288e69b60b03383b2cb54f8a2fde641516628..0f54e29cee38bd12d691b03ae98d3e578b7ff907 100644 --- a/tensorflow/python/keras/engine/sequential_test.py +++ b/tensorflow/python/keras/engine/sequential_test.py @@ -33,7 +33,7 @@ class TestSequential(test.TestCase): """Most Sequential model API tests are covered in `training_test.py`. """ - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_basic_methods(self): model = keras.models.Sequential() model.add(keras.layers.Dense(1, input_dim=2)) @@ -44,7 +44,7 @@ class TestSequential(test.TestCase): self.assertEqual(len(model.weights), 2 * 2) self.assertEqual(model.get_layer(name='dp').name, 'dp') - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_pop(self): num_hidden = 5 input_dim = 3 @@ -77,7 +77,7 @@ class TestSequential(test.TestCase): with self.assertRaises(TypeError): model.pop() - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_with_np_arrays(self): num_hidden = 5 input_dim = 3 @@ -102,7 +102,7 @@ class TestSequential(test.TestCase): [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_with_dataset_iterators(self): if not context.executing_eagerly(): # TODO(psv/fchollet): Add support for this use case in graph mode. @@ -136,7 +136,7 @@ class TestSequential(test.TestCase): [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_invalid_use_cases(self): # Added objects must be layer instances with self.assertRaises(TypeError): @@ -160,7 +160,7 @@ class TestSequential(test.TestCase): model.add(keras.layers.Dense(1, input_dim=1)) model.add(MyLayer()) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_nested_sequential_trainability(self): input_dim = 20 num_units = 10 @@ -209,6 +209,30 @@ class TestSequential(test.TestCase): x2 = model.predict(val_a) assert np.abs(np.sum(x1 - x2)) > 1e-5 + def test_sequential_deferred_build_serialization(self): + num_hidden = 5 + input_dim = 3 + batch_size = 5 + num_classes = 2 + + model = keras.models.Sequential() + # We don't specify the input shape. + model.add(keras.layers.Dense(num_hidden)) + model.add(keras.layers.Dense(num_classes)) + model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + self.assertFalse(model.built) + + x = np.random.random((batch_size, input_dim)) + y = np.random.random((batch_size, num_classes)) + model.train_on_batch(x, y) + self.assertTrue(model.built) + + config = model.get_config() + new_model = keras.models.Sequential.from_config(config) + self.assertTrue(new_model.built) + self.assertEqual(len(model.layers), 2) + self.assertEqual(len(model.weights), 4) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 183e26e8bf813ec0a8c84920a93dcb79a291ca9d..3eb69bd7f3d42f5cd8d6cc6d2d32cc9eb808d9a4 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -26,6 +26,8 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.keras.engine import base_layer +from tensorflow.python.keras.engine import input_layer as input_layer_lib +from tensorflow.python.keras.engine import network as network_lib from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops @@ -62,7 +64,7 @@ class TopologyConstructionTest(test.TestCase): inputs=True) return inputs + 1 - x1 = keras.Input(shape=(1,)) + x1 = input_layer_lib.Input(shape=(1,)) layer = MyLayer() _ = layer.apply(x1) @@ -70,7 +72,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_updates_for(x1)), 1) self.assertEqual(len(layer.get_updates_for(None)), 1) - x2 = keras.Input(shape=(1,)) + x2 = input_layer_lib.Input(shape=(1,)) y2 = layer.apply(x2) self.assertEqual(len(layer.updates), 3) @@ -78,17 +80,17 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_updates_for(x2)), 1) self.assertEqual(len(layer.get_updates_for(None)), 1) - network = keras.engine.Network(x2, y2) + network = network_lib.Network(x2, y2) self.assertEqual(len(network.updates), 2) self.assertEqual(len(network.get_updates_for(x1)), 0) self.assertEqual(len(network.get_updates_for(x2)), 1) self.assertEqual(len(network.get_updates_for(None)), 1) - x3 = keras.Input(shape=(1,)) + x3 = input_layer_lib.Input(shape=(1,)) _ = layer.apply(x3) self.assertEqual(len(network.updates), 2) - x4 = keras.Input(shape=(1,)) + x4 = input_layer_lib.Input(shape=(1,)) _ = network(x4) self.assertEqual(len(network.updates), 3) self.assertEqual(len(network.get_updates_for(x2)), 1) @@ -104,7 +106,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(network.get_updates_for(x4)), 2) def test_get_updates_bn(self): - x1 = keras.Input(shape=(1,)) + x1 = input_layer_lib.Input(shape=(1,)) layer = keras.layers.BatchNormalization() _ = layer.apply(x1) @@ -134,7 +136,7 @@ class TopologyConstructionTest(test.TestCase): inputs=True) return inputs + 1 - x1 = keras.Input(shape=(1,)) + x1 = input_layer_lib.Input(shape=(1,)) layer = MyLayer() _ = layer.apply(x1) @@ -142,7 +144,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_losses_for(x1)), 1) self.assertEqual(len(layer.get_losses_for(None)), 1) - x2 = keras.Input(shape=(1,)) + x2 = input_layer_lib.Input(shape=(1,)) y2 = layer.apply(x2) self.assertEqual(len(layer.losses), 3) @@ -150,17 +152,17 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_losses_for(x2)), 1) self.assertEqual(len(layer.get_losses_for(None)), 1) - network = keras.engine.Network(x2, y2) + network = network_lib.Network(x2, y2) self.assertEqual(len(network.losses), 2) self.assertEqual(len(network.get_losses_for(x1)), 0) self.assertEqual(len(network.get_losses_for(x2)), 1) self.assertEqual(len(network.get_losses_for(None)), 1) - x3 = keras.Input(shape=(1,)) + x3 = input_layer_lib.Input(shape=(1,)) _ = layer.apply(x3) self.assertEqual(len(network.losses), 2) - x4 = keras.Input(shape=(1,)) + x4 = input_layer_lib.Input(shape=(1,)) _ = network(x4) self.assertEqual(len(network.losses), 3) self.assertEqual(len(network.get_losses_for(x2)), 1) @@ -177,8 +179,8 @@ class TopologyConstructionTest(test.TestCase): def testTopologicalAttributes(self): # test layer attributes / methods related to cross-layer connectivity. - a = keras.Input(shape=(32,), name='input_a') - b = keras.Input(shape=(32,), name='input_b') + a = input_layer_lib.Input(shape=(32,), name='input_a') + b = input_layer_lib.Input(shape=(32,), name='input_b') # test input, output, input_shape, output_shape test_layer = keras.layers.Dense(16, name='test_layer') @@ -219,15 +221,15 @@ class TopologyConstructionTest(test.TestCase): _ = new_dense.input_shape with self.assertRaises(AttributeError): new_dense = keras.layers.Dense(16) - a = keras.Input(shape=(3, 32)) - a = keras.Input(shape=(5, 32)) + a = input_layer_lib.Input(shape=(3, 32)) + a = input_layer_lib.Input(shape=(5, 32)) a_2 = dense(a) b_2 = dense(b) _ = new_dense.input_shape with self.assertRaises(AttributeError): new_dense = keras.layers.Dense(16) - a = keras.Input(shape=(3, 32)) - a = keras.Input(shape=(5, 32)) + a = input_layer_lib.Input(shape=(3, 32)) + a = input_layer_lib.Input(shape=(5, 32)) a_2 = dense(a) b_2 = dense(b) _ = new_dense.output_shape @@ -239,7 +241,7 @@ class TopologyConstructionTest(test.TestCase): def call(self, inputs): return [inputs**2, inputs**3] - x = keras.Input(shape=(32,)) + x = input_layer_lib.Input(shape=(32,)) test_layer = PowersLayer() p1, p2 = test_layer(x) # pylint: disable=not-callable @@ -256,8 +258,8 @@ class TopologyConstructionTest(test.TestCase): assert len(inputs) == 2 return inputs[0] + inputs[1] - a = keras.Input(shape=(32,)) - b = keras.Input(shape=(32,)) + a = input_layer_lib.Input(shape=(32,)) + b = input_layer_lib.Input(shape=(32,)) test_layer = AddLayer() y = test_layer([a, b]) # pylint: disable=not-callable @@ -268,10 +270,10 @@ class TopologyConstructionTest(test.TestCase): def testBasicNetwork(self): # minimum viable network - x = keras.Input(shape=(32,)) + x = input_layer_lib.Input(shape=(32,)) dense = keras.layers.Dense(2) y = dense(x) - network = keras.engine.Network(x, y, name='dense_network') + network = network_lib.Network(x, y, name='dense_network') # test basic attributes self.assertEqual(network.name, 'dense_network') @@ -282,7 +284,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights) # test callability on Input - x_2 = keras.Input(shape=(32,)) + x_2 = input_layer_lib.Input(shape=(32,)) y_2 = network(x_2) self.assertEqual(y_2.get_shape().as_list(), [None, 2]) @@ -506,7 +508,7 @@ class TopologyConstructionTest(test.TestCase): self.assertListEqual([x.shape for x in fn_outputs], [(10, 64), (10, 5)]) # test get_source_inputs - self.assertListEqual(keras.engine.network.get_source_inputs(c), [a, b]) + self.assertListEqual(keras.engine.get_source_inputs(c), [a, b]) # serialization / deserialization json_config = model.to_json() @@ -778,12 +780,12 @@ class TopologyConstructionTest(test.TestCase): self.evaluate(getattr(b, '_keras_mask'))) self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b)) else: - x = keras.Input(shape=(32,)) + x = input_layer_lib.Input(shape=(32,)) y = MaskedLayer()(x) # pylint: disable=not-callable - network = keras.engine.Network(x, y) + network = network_lib.Network(x, y) # test callability on Input - x_2 = keras.Input(shape=(32,)) + x_2 = input_layer_lib.Input(shape=(32,)) y_2 = network(x_2) self.assertEqual(y_2.get_shape().as_list(), [None, 32]) @@ -797,14 +799,14 @@ class TopologyConstructionTest(test.TestCase): def reg(x): return math_ops.reduce_sum(x) - net_a_input = keras.Input((2,)) + net_a_input = input_layer_lib.Input((2,)) net_a = net_a_input net_a = keras.layers.Dense(2, kernel_initializer='ones', use_bias=False, activity_regularizer=reg)(net_a) model_a = keras.Model([net_a_input], [net_a]) - net_b_input = keras.Input((2,)) + net_b_input = input_layer_lib.Input((2,)) net_b = model_a(net_b_input) model_b = keras.Model([net_b_input], [net_b]) @@ -817,7 +819,7 @@ class TopologyConstructionTest(test.TestCase): with self.test_session(): x_val = np.random.random((10, 5)) - x = keras.Input(shape=(5,)) + x = input_layer_lib.Input(shape=(5,)) a = keras.layers.Dense(5, name='A') b = keras.layers.Dense(5, name='B') output = a(b(a(b(x)))) @@ -837,7 +839,7 @@ class TopologyConstructionTest(test.TestCase): def test_layer_sharing_at_heterogenous_depth_with_concat(self): with self.test_session(): input_shape = (16, 9, 3) - input_layer = keras.Input(shape=input_shape) + input_layer = input_layer_lib.Input(shape=input_shape) a = keras.layers.Dense(3, name='dense_A') b = keras.layers.Dense(3, name='dense_B') @@ -924,7 +926,7 @@ class DeferredModeTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testSimpleNetworkBuilding(self): - inputs = keras.engine.Input(shape=(32,)) + inputs = input_layer_lib.Input(shape=(32,)) if context.executing_eagerly(): self.assertIsInstance(inputs, base_layer.DeferredTensor) self.assertEqual(inputs.dtype.name, 'float32') @@ -937,8 +939,8 @@ class DeferredModeTest(test.TestCase): self.assertEqual(x.shape.as_list(), [None, 2]) outputs = keras.layers.Dense(4)(x) - network = keras.engine.Network(inputs, outputs) - self.assertIsInstance(network, keras.engine.Network) + network = network_lib.Network(inputs, outputs) + self.assertIsInstance(network, network_lib.Network) if context.executing_eagerly(): # It should be possible to call such a network on EagerTensors. @@ -949,8 +951,8 @@ class DeferredModeTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testMultiIONetworkbuilding(self): - input_a = keras.engine.Input(shape=(32,)) - input_b = keras.engine.Input(shape=(16,)) + input_a = input_layer_lib.Input(shape=(32,)) + input_b = input_layer_lib.Input(shape=(16,)) a = keras.layers.Dense(16)(input_a) class AddLayer(keras.layers.Layer): @@ -964,7 +966,7 @@ class DeferredModeTest(test.TestCase): c = AddLayer()([a, input_b]) # pylint: disable=not-callable c = keras.layers.Dense(2)(c) - network = keras.engine.Network([input_a, input_b], [a, c]) + network = network_lib.Network([input_a, input_b], [a, c]) if context.executing_eagerly(): a_val = constant_op.constant( np.random.random((10, 32)).astype('float32')) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index aca63f822bb1c5738e60a2a11cc698c3a6b4a315..8e632651fa7553fbc7ce31aa42e9963b606d20f9 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -42,6 +42,7 @@ from tensorflow.python.keras.utils.generic_utils import slice_arrays from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer as tf_optimizer_module +from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util.tf_export import tf_export @@ -115,6 +116,7 @@ class Model(Network): # Create a cache for dataset - uninitialized iterators self._dataset_iterator_cache = weakref.WeakKeyDictionary() + @checkpointable.no_automatic_dependency_tracking def compile(self, optimizer, loss=None, @@ -178,6 +180,11 @@ class Model(Network): raise ValueError('Only TF native optimizers are supported in Eager mode.') self.optimizer = optimizers.get(optimizer) + # We've disabled automatic dependency tracking for this method, but do want + # to add a checkpoint dependency on the optimizer if it's checkpointable. + if isinstance(self.optimizer, checkpointable.CheckpointableBase): + self._track_checkpointable( + self.optimizer, name='optimizer', overwrite=True) self.loss = loss self.metrics = metrics or [] self.loss_weights = loss_weights @@ -941,6 +948,7 @@ class Model(Network): str(x[0].shape[0]) + ' samples') return x, y, sample_weights + @checkpointable.no_automatic_dependency_tracking def _set_inputs(self, inputs, training=None): """Set model's input and output specs based on the input data received. @@ -989,6 +997,7 @@ class Model(Network): else: self._symbolic_set_inputs(inputs, training=training) + @checkpointable.no_automatic_dependency_tracking def _eager_set_inputs(self, inputs): """Set model's input and output specs based on the input data received. @@ -1011,14 +1020,16 @@ class Model(Network): # to keep track of number of inputs and outputs and their ndim. if isinstance(inputs, (list, tuple)): if tensor_util.is_tensor(inputs[0]): - dummy_output_values = self.call(inputs) + dummy_output_values = self.call( + training_utils.cast_if_floating_dtype(inputs)) else: dummy_output_values = self.call( [ops.convert_to_tensor(v, dtype=K.floatx()) for v in inputs]) dummy_input_values = list(inputs) else: if tensor_util.is_tensor(inputs): - dummy_output_values = self.call(inputs) + dummy_output_values = self.call( + training_utils.cast_if_floating_dtype(inputs)) else: dummy_output_values = self.call( ops.convert_to_tensor(inputs, dtype=K.floatx())) @@ -1039,6 +1050,7 @@ class Model(Network): 'output_%d' % (i + 1) for i in range(len(dummy_output_values))] self.built = True + @checkpointable.no_automatic_dependency_tracking def _symbolic_set_inputs(self, inputs, outputs=None, training=None): """Set model's inputs and output specs based. @@ -1619,7 +1631,10 @@ class Model(Network): # Validate and standardize user data. inputs, _, _ = self._standardize_user_data(x) if context.executing_eagerly(): - if not isinstance(inputs, iterator_ops.EagerIterator): + if (isinstance(x, iterator_ops.EagerIterator) or + (isinstance(x, dataset_ops.Dataset) and context.executing_eagerly())): + inputs = training_utils.cast_if_floating_dtype(inputs) + else: inputs = [ ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs ] diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py index 93f4f1bd1dde848d9d3afbfd1dcbd26741b9c745..e82f5c03320094348213ac3d22cc13709c6af08c 100644 --- a/tensorflow/python/keras/engine/training_arrays.py +++ b/tensorflow/python/keras/engine/training_arrays.py @@ -124,6 +124,12 @@ def fit_loop(model, callback_metrics = copy.copy(out_labels) + [ 'val_' + n for n in out_labels ] + if callbacks is not None and any( + [isinstance(callback, cbks.TensorBoard) for callback in callbacks]): + # need to create the test_function before start of the first epoch + # because TensorBoard callback on_epoch_begin adds summary to the + # list of fetches of the test_function + model._make_test_function() else: callback_metrics = copy.copy(out_labels) @@ -185,6 +191,7 @@ def fit_loop(model, callbacks.on_epoch_begin(epoch) epoch_logs = {} if steps_per_epoch is not None: + # Step-wise fit loop. for step_index in range(steps_per_epoch): batch_logs = {} batch_logs['batch'] = step_index @@ -215,7 +222,6 @@ def fit_loop(model, val_inputs, val_targets, sample_weights=val_sample_weights, - batch_size=batch_size, steps=validation_steps, verbose=0) if not isinstance(val_outs, list): @@ -224,6 +230,7 @@ def fit_loop(model, for l, o in zip(out_labels, val_outs): epoch_logs['val_' + l] = o else: + # Sample-wise fit loop. if shuffle == 'batch': index_array = training_utils.batch_shuffle(index_array, batch_size) elif shuffle: diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index a70b488f255dd43fd8272108f8933cac0d72ee91..e8838cd3bca7b3afba80504f9e705943474423c5 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -255,6 +255,8 @@ def iterator_fit_loop(model, # Validate and standardize data. x, y, sample_weights = model._standardize_user_data( x, y, class_weight=class_weight) + x = training_utils.cast_if_floating_dtype(x) + y = training_utils.cast_if_floating_dtype(y) if sample_weights: sample_weights = [ ops.convert_to_tensor(val, dtype=backend.floatx()) @@ -471,6 +473,8 @@ def iterator_test_loop(model, inputs, steps, verbose=0): # Validate and standardize data. x, y, sample_weights = model._standardize_user_data(x, y) + x = training_utils.cast_if_floating_dtype(x) + y = training_utils.cast_if_floating_dtype(y) # Calculate model output, loss values. loss_outs, loss, loss_metrics = _model_loss( @@ -639,6 +643,7 @@ def iterator_predict_loop(model, inputs, steps, verbose=0): # Validate and standardize data. x, _, _ = model._standardize_user_data(x) + x = training_utils.cast_if_floating_dtype(x) if model._expects_training_arg: batch_outs = model.call(x[0] if len(x) == 1 else x, training=False) @@ -814,7 +819,10 @@ def train_on_batch(model, inputs, targets, sample_weights=None): Returns: total loss and the loss associated with each output. """ - if len(inputs) and not tensor_util.is_tensor(inputs[0]): + if len(inputs) and tensor_util.is_tensor(inputs[0]): + inputs = training_utils.cast_if_floating_dtype(inputs) + targets = training_utils.cast_if_floating_dtype(targets) + else: inputs = [ ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs ] @@ -849,7 +857,10 @@ def test_on_batch(model, inputs, targets, sample_weights=None): Returns: total loss, loss and metrics associated with each output. """ - if len(inputs) and not tensor_util.is_tensor(inputs[0]): + if len(inputs) and tensor_util.is_tensor(inputs[0]): + inputs = training_utils.cast_if_floating_dtype(inputs) + targets = training_utils.cast_if_floating_dtype(targets) + else: inputs = [ ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs ] diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py index 7906d208ebb0c77cfc9666976e8e1b2c9d6a55d1..bdb30351290644e2f7e8135c047ef6732054a08a 100644 --- a/tensorflow/python/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/engine/training_eager_test.py @@ -403,6 +403,24 @@ class TrainingTest(test.TestCase): model.train_on_batch(inputs, targets) model.test_on_batch(inputs, targets) + def test_generator_methods(self): + model = keras.Sequential() + model.add(keras.layers.Dense(4, input_shape=(3,))) + optimizer = RMSPropOptimizer(learning_rate=0.001) + model.compile(optimizer, 'mse', metrics=['mae']) + + x = np.random.random((10, 3)) + y = np.random.random((10, 4)) + + def iterator(): + while True: + yield x, y + + model.fit_generator(iterator(), steps_per_epoch=3, epochs=1) + model.evaluate_generator(iterator(), steps=3) + out = model.predict_generator(iterator(), steps=3) + self.assertEqual(out.shape, (30, 4)) + class LossWeightingTest(test.TestCase): @@ -629,7 +647,7 @@ class LossWeightingTest(test.TestCase): class CorrectnessTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_loss_correctness(self): # Test that training loss is the same in eager and graph # (by comparing it to a reference value in a deterministic case) @@ -650,7 +668,7 @@ class CorrectnessTest(test.TestCase): self.assertEqual( np.around(history.history['loss'][-1], decimals=4), 0.6173) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness(self): model = keras.Sequential() model.add(keras.layers.Dense(3, @@ -671,7 +689,7 @@ class CorrectnessTest(test.TestCase): outs = model.evaluate(x, y) self.assertEqual(outs[1], 0.) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_loss_correctness_with_iterator(self): # Test that training loss is the same in eager and graph # (by comparing it to a reference value in a deterministic case) @@ -694,7 +712,7 @@ class CorrectnessTest(test.TestCase): history = model.fit(iterator, epochs=1, steps_per_epoch=10) self.assertEqual(np.around(history.history['loss'][-1], decimals=4), 0.6173) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness_with_iterator(self): model = keras.Sequential() model.add( diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index 5c02d363824a58c9502aa37e389dd062f33c153a..d9e548f01f86fd96c3abd7b3cdaf5106653393fd 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -129,8 +129,10 @@ class TrainingTest(test.TestCase): { 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}, + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }, epochs=1, batch_size=5, verbose=0) @@ -138,8 +140,10 @@ class TrainingTest(test.TestCase): { 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}, + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }, epochs=1, batch_size=5, verbose=1) @@ -147,8 +151,10 @@ class TrainingTest(test.TestCase): { 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}, + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }, validation_data=({ 'input_a': input_a_np, 'input_b': input_b_np @@ -162,8 +168,10 @@ class TrainingTest(test.TestCase): model.train_on_batch({ 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}) + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }) # Test with lists for loss, metrics loss = ['mae', 'mse'] @@ -285,16 +293,20 @@ class TrainingTest(test.TestCase): { 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}, + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }, batch_size=5, verbose=0) model.evaluate( { 'input_a': input_a_np, 'input_b': input_b_np - }, {'dense': output_d_np, - 'dropout': output_e_np}, + }, { + 'dense': output_d_np, + 'dropout': output_e_np + }, batch_size=5, verbose=1) @@ -349,9 +361,11 @@ class TrainingTest(test.TestCase): with self.test_session(): test_inputs = [ - scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)] + scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2) + ] test_outputs = [ - scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)] + scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5) + ] in1 = keras.layers.Input(shape=(3,)) in2 = keras.layers.Input(shape=(3,)) out1 = keras.layers.Dropout(0.5, name='dropout')(in1) @@ -1682,7 +1696,7 @@ class TestTrainingWithDataTensors(test.TestCase): model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metric_names_are_identical_in_graph_and_eager(self): a = keras.layers.Input(shape=(3,), name='input_a') b = keras.layers.Input(shape=(3,), name='input_b') @@ -1709,7 +1723,7 @@ class TestTrainingWithDataTensors(test.TestCase): class TestTrainingWithDatasetIterators(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_training_and_eval_methods_on_iterators_single_io(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1721,8 +1735,8 @@ class TestTrainingWithDatasetIterators(test.TestCase): metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) @@ -1786,8 +1800,8 @@ class TestTrainingWithDatasetIterators(test.TestCase): metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) @@ -1799,7 +1813,7 @@ class TestTrainingWithDatasetIterators(test.TestCase): ops.get_default_graph().finalize() model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_iterators_running_out_of_data(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1811,8 +1825,8 @@ class TestTrainingWithDatasetIterators(test.TestCase): metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(2) dataset = dataset.batch(10) @@ -1838,8 +1852,8 @@ class TestTrainingWithDataset(test.TestCase): metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) @@ -1853,7 +1867,7 @@ class TestTrainingWithDataset(test.TestCase): model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0, validation_data=dataset, validation_steps=2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_training_and_eval_methods_on_dataset(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1865,8 +1879,8 @@ class TestTrainingWithDataset(test.TestCase): metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) @@ -1928,8 +1942,8 @@ class TestTrainingWithDataset(test.TestCase): model.compile(optimizer, loss) # User forgets to batch the dataset - inputs = np.zeros((10, 3), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 3)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) @@ -1938,8 +1952,8 @@ class TestTrainingWithDataset(test.TestCase): model.train_on_batch(dataset) # Wrong input shape - inputs = np.zeros((10, 5), dtype=np.float32) - targets = np.zeros((10, 4), dtype=np.float32) + inputs = np.zeros((10, 5)) + targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index b93f999444c64531890ee003f8de048058687fa3..728a2b493b9f076cc2942766d2677c1f24fb3c15 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -553,6 +553,10 @@ def standardize_weights(y, def has_symbolic_tensors(ls): if context.executing_eagerly(): return False + return has_tensors(ls) + + +def has_tensors(ls): if isinstance(ls, (list, tuple)): return any(tensor_util.is_tensor(v) for v in ls) return tensor_util.is_tensor(ls) @@ -692,3 +696,29 @@ def check_steps_argument(input_data, steps, steps_name): input_type=input_type_str, steps_name=steps_name)) return True return False + + +def cast_if_floating_dtype(x): + """Casts the given data tensors to the default floating point type. + + Casts only if the input is already a floating point type. + Args: + x: tensor or list/tuple of tensors. + + Returns: + Converted input. + + Raises: + RuntimeError: if data isn't tensors. + """ + if not has_tensors(x): + raise RuntimeError( + 'Please provide tensors for casting, got: {x}'.format(x=x)) + + if isinstance(x, (list, tuple)): + return [ + math_ops.cast(val, dtype=K.floatx()) + if tensor_util.is_tensor(val) and val.dtype.is_floating else val + for val in x + ] + return math_ops.cast(x, dtype=K.floatx()) if x.dtype.is_floating else x diff --git a/tensorflow/python/keras/estimator/__init__.py b/tensorflow/python/keras/estimator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b244beb5b58cf339a4687216b87418c88b953c17 --- /dev/null +++ b/tensorflow/python/keras/estimator/__init__.py @@ -0,0 +1,46 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Keras estimator API.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.util.tf_export import tf_export + +# Keras has undeclared dependency on tensorflow/estimator:estimator_py. +# As long as you depend //third_party/py/tensorflow:tensorflow target +# everything will work as normal. + +try: + from tensorflow.python.estimator import keras as keras_lib # pylint: disable=g-import-not-at-top + model_to_estimator = tf_export('keras.estimator.model_to_estimator')( + keras_lib.model_to_estimator) +except Exception: # pylint: disable=broad-except + + # pylint: disable=unused-argument + def stub_model_to_estimator(keras_model=None, + keras_model_path=None, + custom_objects=None, + model_dir=None, + config=None): + raise NotImplementedError( + 'tf.keras.estimator.model_to_estimator function not available in your ' + 'installation.') + # pylint: enable=unused-argument + + model_to_estimator = tf_export('keras.estimator.model_to_estimator')( + stub_model_to_estimator) + diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index 8fb663a17e16f9a16c67393327347f6cc463a5b6..e3a686f45d92dde8ea90d496b3cb5099f6b84b58 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -20,15 +20,16 @@ from __future__ import print_function # Generic layers. # pylint: disable=g-bad-import-order -from tensorflow.python.keras.engine import Input -from tensorflow.python.keras.engine import InputLayer -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.input_layer import Input +from tensorflow.python.keras.engine.input_layer import InputLayer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer # Advanced activations. from tensorflow.python.keras.layers.advanced_activations import LeakyReLU from tensorflow.python.keras.layers.advanced_activations import PReLU from tensorflow.python.keras.layers.advanced_activations import ELU +from tensorflow.python.keras.layers.advanced_activations import ReLU from tensorflow.python.keras.layers.advanced_activations import ThresholdedReLU from tensorflow.python.keras.layers.advanced_activations import Softmax @@ -86,9 +87,11 @@ from tensorflow.python.keras.layers.local import LocallyConnected2D # Merge layers. from tensorflow.python.keras.layers.merge import Add +from tensorflow.python.keras.layers.merge import Subtract from tensorflow.python.keras.layers.merge import Multiply from tensorflow.python.keras.layers.merge import Average from tensorflow.python.keras.layers.merge import Maximum +from tensorflow.python.keras.layers.merge import Minimum from tensorflow.python.keras.layers.merge import Concatenate from tensorflow.python.keras.layers.merge import Dot from tensorflow.python.keras.layers.merge import add diff --git a/tensorflow/python/keras/layers/advanced_activations.py b/tensorflow/python/keras/layers/advanced_activations.py index 8ade3c317456a88181f6005c620953817463595b..eba10da6f3ce1367f4cb0180d16efdc5913fcddc 100644 --- a/tensorflow/python/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/layers/advanced_activations.py @@ -23,8 +23,8 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import math_ops from tensorflow.python.util.tf_export import tf_export @@ -278,3 +278,40 @@ class Softmax(Layer): @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): return input_shape + + +@tf_export('keras.layers.ReLU') +class ReLU(Layer): + """Rectified Linear Unit activation function. + + Input shape: + Arbitrary. Use the keyword argument `input_shape` + (tuple of integers, does not include the samples axis) + when using this layer as the first layer in a model. + + Output shape: + Same shape as the input. + + Arguments: + max_value: float >= 0. Maximum activation value. + """ + + def __init__(self, max_value=None, **kwargs): + super(ReLU, self).__init__(**kwargs) + self.support_masking = True + self.max_value = K.cast_to_floatx(max_value) + if self.max_value < 0.: + raise ValueError('max_value of Relu layer ' + 'cannot be negative value: ' + str(max_value)) + + def call(self, inputs): + return activations.relu(inputs, max_value=self.max_value) + + def get_config(self): + config = {'max_value': self.max_value} + base_config = super(ReLU, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + @tf_utils.shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape diff --git a/tensorflow/python/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/layers/advanced_activations_test.py index 81c76db14cd3741687bf5e2bec66e5354e9f6312..9e1f15b1bc508d8be0a2c0190d07eb1c2bed95c4 100644 --- a/tensorflow/python/keras/layers/advanced_activations_test.py +++ b/tensorflow/python/keras/layers/advanced_activations_test.py @@ -62,6 +62,20 @@ class AdvancedActivationsTest(test.TestCase): kwargs={'axis': 1}, input_shape=(2, 3, 4)) + def test_relu(self): + with self.test_session(): + testing_utils.layer_test(keras.layers.ReLU, + kwargs={'max_value': 10}, + input_shape=(2, 3, 4)) + + def test_relu_with_invalid_arg(self): + with self.assertRaisesRegexp( + ValueError, 'max_value of Relu layer cannot be negative value: -10'): + with self.test_session(): + testing_utils.layer_test(keras.layers.ReLU, + kwargs={'max_value': -10}, + input_shape=(2, 3, 4)) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py index ce1c84e98d04c84aad7aa381b2536facfae2322d..a57ac121ed7486a9beb64e6dd7ed3b132ca258df 100644 --- a/tensorflow/python/keras/layers/convolutional.py +++ b/tensorflow/python/keras/layers/convolutional.py @@ -26,8 +26,8 @@ from tensorflow.python.keras import backend from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer # imports for backwards namespace compatibility # pylint: disable=unused-import from tensorflow.python.keras.layers.pooling import AveragePooling1D @@ -151,21 +151,23 @@ class Conv(Layer): input_dim = int(input_shape[channel_axis]) kernel_shape = self.kernel_size + (input_dim, self.filters) - self.kernel = self.add_variable(name='kernel', - shape=kernel_shape, - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint, - trainable=True, - dtype=self.dtype) + self.kernel = self.add_weight( + name='kernel', + shape=kernel_shape, + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint, + trainable=True, + dtype=self.dtype) if self.use_bias: - self.bias = self.add_variable(name='bias', - shape=(self.filters,), - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - trainable=True, - dtype=self.dtype) + self.bias = self.add_weight( + name='bias', + shape=(self.filters,), + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint, + trainable=True, + dtype=self.dtype) else: self.bias = None self.input_spec = InputSpec(ndim=self.rank + 2, @@ -380,11 +382,11 @@ class Conv2D(Conv): filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the - width and height of the 2D convolution window. + height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. + specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying @@ -611,11 +613,11 @@ class Conv2DTranspose(Conv2D): filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the - width and height of the 2D convolution window. + height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. + specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying @@ -720,21 +722,23 @@ class Conv2DTranspose(Conv2D): self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim}) kernel_shape = self.kernel_size + (self.filters, input_dim) - self.kernel = self.add_variable(name='kernel', - shape=kernel_shape, - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint, - trainable=True, - dtype=self.dtype) + self.kernel = self.add_weight( + name='kernel', + shape=kernel_shape, + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint, + trainable=True, + dtype=self.dtype) if self.use_bias: - self.bias = self.add_variable(name='bias', - shape=(self.filters,), - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - trainable=True, - dtype=self.dtype) + self.bias = self.add_weight( + name='bias', + shape=(self.filters,), + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint, + trainable=True, + dtype=self.dtype) else: self.bias = None self.built = True @@ -961,7 +965,7 @@ class Conv3DTranspose(Conv3D): kernel_shape = self.kernel_size + (self.filters, input_dim) self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim}) - self.kernel = self.add_variable( + self.kernel = self.add_weight( 'kernel', shape=kernel_shape, initializer=self.kernel_initializer, @@ -970,7 +974,7 @@ class Conv3DTranspose(Conv3D): trainable=True, dtype=self.dtype) if self.use_bias: - self.bias = self.add_variable( + self.bias = self.add_weight( 'bias', shape=(self.filters,), initializer=self.bias_initializer, @@ -1191,6 +1195,7 @@ class SeparableConv(Conv): dilation_rate=dilation_rate, activation=activations.get(activation), use_bias=use_bias, + bias_initializer=initializers.get(bias_initializer), bias_regularizer=regularizers.get(bias_regularizer), activity_regularizer=regularizers.get(activity_regularizer), bias_constraint=bias_constraint, @@ -1222,7 +1227,7 @@ class SeparableConv(Conv): pointwise_kernel_shape = ( 1,) * self.rank + (self.depth_multiplier * input_dim, self.filters) - self.depthwise_kernel = self.add_variable( + self.depthwise_kernel = self.add_weight( name='depthwise_kernel', shape=depthwise_kernel_shape, initializer=self.depthwise_initializer, @@ -1230,7 +1235,7 @@ class SeparableConv(Conv): constraint=self.depthwise_constraint, trainable=True, dtype=self.dtype) - self.pointwise_kernel = self.add_variable( + self.pointwise_kernel = self.add_weight( name='pointwise_kernel', shape=pointwise_kernel_shape, initializer=self.pointwise_initializer, @@ -1239,13 +1244,14 @@ class SeparableConv(Conv): trainable=True, dtype=self.dtype) if self.use_bias: - self.bias = self.add_variable(name='bias', - shape=(self.filters,), - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - trainable=True, - dtype=self.dtype) + self.bias = self.add_weight( + name='bias', + shape=(self.filters,), + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint, + trainable=True, + dtype=self.dtype) else: self.bias = None self.built = True @@ -1447,11 +1453,11 @@ class SeparableConv2D(SeparableConv): filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the - width and height of the 2D convolution window. + height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. + specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying @@ -1591,11 +1597,11 @@ class DepthwiseConv2D(Conv2D): Arguments: kernel_size: An integer or tuple/list of 2 integers, specifying the - width and height of the 2D convolution window. + height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. + specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying @@ -1724,7 +1730,7 @@ class DepthwiseConv2D(Conv2D): dilation_rate=self.dilation_rate, data_format=self.data_format) - if self.bias: + if self.use_bias: outputs = backend.bias_add( outputs, self.bias, @@ -2002,7 +2008,7 @@ class ZeroPadding2D(Layer): Arguments: padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. - If int: the same symmetric padding - is applied to width and height. + is applied to height and width. - If tuple of 2 ints: interpreted as two different symmetric padding values for height and width: @@ -2101,7 +2107,7 @@ class ZeroPadding3D(Layer): Arguments: padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints. - If int: the same symmetric padding - is applied to width and height. + is applied to height and width. - If tuple of 3 ints: interpreted as two different symmetric padding values for height and width: @@ -2261,12 +2267,12 @@ class Cropping1D(Layer): class Cropping2D(Layer): """Cropping layer for 2D input (e.g. picture). - It crops along spatial dimensions, i.e. width and height. + It crops along spatial dimensions, i.e. height and width. Arguments: cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. - If int: the same symmetric cropping - is applied to width and height. + is applied to height and width. - If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: diff --git a/tensorflow/python/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/layers/convolutional_recurrent.py index c731508b3c32d93895432fd5174c1f57557b10dc..84d794cada86b15755c28592d4c8093a4d3ef87e 100644 --- a/tensorflow/python/keras/layers/convolutional_recurrent.py +++ b/tensorflow/python/keras/layers/convolutional_recurrent.py @@ -26,8 +26,8 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.layers.recurrent import _generate_dropout_mask from tensorflow.python.keras.layers.recurrent import _standardize_args from tensorflow.python.keras.layers.recurrent import RNN diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index 167cabaeecb0c4ce9a785e7a990aa715f2d1a5b3..f904744422a4b1296e8f5e8a34373fd0344dc643 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -45,7 +45,7 @@ class Convolution1DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, length, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv1d(self): kwargs = { 'filters': 2, @@ -117,7 +117,7 @@ class Conv2DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv2d(self): kwargs = { 'filters': 2, @@ -192,7 +192,7 @@ class Conv2DTransposeTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv2dtranspose(self): kwargs = { 'filters': 2, @@ -258,7 +258,7 @@ class Conv3DTransposeTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, depth, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv3dtranspose(self): kwargs = { 'filters': 2, @@ -322,7 +322,7 @@ class SeparableConv1DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, length, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_separable_conv1d(self): kwargs = { 'filters': 2, @@ -398,7 +398,7 @@ class SeparableConv2DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_separable_conv2d(self): kwargs = { 'filters': 2, @@ -477,7 +477,7 @@ class Conv3DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, depth, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv3d(self): kwargs = { 'filters': 2, @@ -529,7 +529,7 @@ class Conv3DTest(test.TestCase): class ZeroPaddingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_1d(self): num_samples = 2 input_dim = 2 @@ -581,7 +581,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding1D(padding=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_2d(self): num_samples = 2 stack_size = 2 @@ -660,7 +660,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding2D(padding=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_3d(self): num_samples = 2 stack_size = 2 @@ -702,13 +702,13 @@ class ZeroPaddingTest(test.TestCase): class UpSamplingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_1d(self): with self.test_session(use_gpu=True): testing_utils.layer_test( keras.layers.UpSampling1D, kwargs={'size': 2}, input_shape=(3, 5, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_2d(self): num_samples = 2 stack_size = 2 @@ -758,7 +758,7 @@ class UpSamplingTest(test.TestCase): np.testing.assert_allclose(np_output, expected_out) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_3d(self): num_samples = 2 stack_size = 2 @@ -818,7 +818,7 @@ class UpSamplingTest(test.TestCase): class CroppingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_1d(self): num_samples = 2 time_length = 4 @@ -837,7 +837,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping1D(cropping=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_2d(self): num_samples = 2 stack_size = 2 @@ -905,7 +905,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping2D(cropping=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_3d(self): num_samples = 2 stack_size = 2 @@ -995,6 +995,7 @@ class DepthwiseConv2DTest(test.TestCase): 'bias_regularizer': 'l2', 'activity_regularizer': 'l2', 'depthwise_constraint': 'unit_norm', + 'use_bias': True, 'strides': (2, 2), } self._run_test(kwargs, 'depth_multiplier', [1]) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index db0c22038018ca5beabb36f992574306b4eca23d..2bf6229ccba808360e73a333bdec3dac624d81ce 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -33,8 +33,8 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import conv_utils from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils @@ -906,21 +906,23 @@ class Dense(Layer): 'should be defined. Found `None`.') self.input_spec = InputSpec(min_ndim=2, axes={-1: input_shape[-1].value}) - self.kernel = self.add_variable('kernel', - shape=[input_shape[-1].value, self.units], - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint, - dtype=self.dtype, - trainable=True) + self.kernel = self.add_weight( + 'kernel', + shape=[input_shape[-1].value, self.units], + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint, + dtype=self.dtype, + trainable=True) if self.use_bias: - self.bias = self.add_variable('bias', - shape=[self.units,], - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - dtype=self.dtype, - trainable=True) + self.bias = self.add_weight( + 'bias', + shape=[self.units,], + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint, + dtype=self.dtype, + trainable=True) else: self.bias = None self.built = True diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index ff8af976b99376b037af81ed81707332ccf9937e..226403c5927ed22394b708178679d1efa11dd790 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -51,7 +51,7 @@ class CoreLayersTest(test.TestCase): dropout = keras.layers.Dropout(0.5) self.assertEqual(True, dropout.supports_masking) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_spatial_dropout(self): testing_utils.layer_test( keras.layers.SpatialDropout1D, @@ -78,7 +78,7 @@ class CoreLayersTest(test.TestCase): kwargs={'rate': 0.5, 'data_format': 'channels_first'}, input_shape=(2, 3, 4, 4, 5)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_activation(self): # with string argument testing_utils.layer_test( @@ -92,7 +92,7 @@ class CoreLayersTest(test.TestCase): kwargs={'activation': keras.backend.relu}, input_shape=(3, 2)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_reshape(self): testing_utils.layer_test( keras.layers.Reshape, @@ -114,12 +114,12 @@ class CoreLayersTest(test.TestCase): kwargs={'target_shape': (-1, 1)}, input_shape=(None, None, 2)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_permute(self): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_flatten(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) @@ -134,7 +134,7 @@ class CoreLayersTest(test.TestCase): np.transpose(inputs, (0, 2, 3, 1)), (-1, 5 * 5 * 3)) self.assertAllClose(outputs, target_outputs) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_repeat_vector(self): testing_utils.layer_test( keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2)) @@ -173,7 +173,7 @@ class CoreLayersTest(test.TestCase): config = ld.get_config() ld = keras.layers.Lambda.from_config(config) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dense(self): testing_utils.layer_test( keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2)) diff --git a/tensorflow/python/keras/layers/cudnn_recurrent.py b/tensorflow/python/keras/layers/cudnn_recurrent.py index ad6594279d037c8dc0e1408955d2a2eebd51ce1d..cf2b0c476c7229a288f4b4f7b31de09388ade40f 100644 --- a/tensorflow/python/keras/layers/cudnn_recurrent.py +++ b/tensorflow/python/keras/layers/cudnn_recurrent.py @@ -25,7 +25,7 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec +from tensorflow.python.keras.engine.base_layer import InputSpec from tensorflow.python.keras.layers.recurrent import RNN from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_cudnn_rnn_ops diff --git a/tensorflow/python/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/layers/cudnn_recurrent_test.py index 9d186f8c586bd9f626e142a855be6d2cf00d7121..8fd970239f205031954c728474abdf10ea80e99e 100644 --- a/tensorflow/python/keras/layers/cudnn_recurrent_test.py +++ b/tensorflow/python/keras/layers/cudnn_recurrent_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os +import tempfile from absl.testing import parameterized import numpy as np @@ -30,7 +32,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class CuDNNTest(test.TestCase, parameterized.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_cudnn_rnn_basics(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): @@ -58,7 +60,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): 'go_backwards': go_backwards}, input_shape=(num_samples, timesteps, input_size)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainability(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): @@ -217,27 +219,14 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): out5 = model.predict(np.ones((num_samples, timesteps))) self.assertNotEqual(out4.max(), out5.max()) - # TODO(psv): Add generic cross product helper function for parametrized tests. @parameterized.named_parameters( - ('cudnnlstm_to_lstm_unidirectional_impl_1', 'LSTM', False, False, 1), - ('cudnnlstm_to_lstm_bidirectional_impl_1', 'LSTM', False, True, 1), - ('lstm_to_cudnnlstm_unidirectional_impl_1', 'LSTM', True, False, 1), - ('lstm_to_cudnnlstm_bidirectional_impl_1', 'LSTM', True, True, 1), - ('cudnngru_to_gru_unidirectional_impl_1', 'GRU', False, False, 1), - ('cudnngru_to_gru_bidirectional_impl_1', 'GRU', False, True, 1), - ('gru_to_cudnngru_unidirectional_impl_1', 'GRU', True, False, 1), - ('gru_to_cudnngru_bidirectional_impl_1', 'GRU', True, True, 1), - ('cudnnlstm_to_lstm_unidirectional_impl_2', 'LSTM', False, False, 2), - ('cudnnlstm_to_lstm_bidirectional_impl_2', 'LSTM', False, True, 2), - ('lstm_to_cudnnlstm_unidirectional_impl_2', 'LSTM', True, False, 2), - ('lstm_to_cudnnlstm_bidirectional_impl_2', 'LSTM', True, True, 2), - ('cudnngru_to_gru_unidirectional_impl_2', 'GRU', False, False, 2), - ('cudnngru_to_gru_bidirectional_impl_2', 'GRU', False, True, 2), - ('gru_to_cudnngru_unidirectional_impl_2', 'GRU', True, False, 2), - ('gru_to_cudnngru_bidirectional_impl_2', 'GRU', True, True, 2), - ) + *testing_utils.generate_combinations_with_testcase_name( + rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False], + bidirectional=[True, False], implementation=[1, 2], + model_nest_level=[1, 2], model_type=['seq', 'func'])) def test_load_weights_between_noncudnn_rnn(self, rnn_type, to_cudnn, - bidirectional, implementation): + bidirectional, implementation, + model_nest_level, model_type): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): input_size = 10 @@ -261,14 +250,6 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): cudnn_rnn_layer_class = keras.layers.CuDNNGRU rnn_layer_kwargs['reset_after'] = True - def convert_weights(source_layer, target_layer): - weights = source_layer.get_weights() - weights = keras.engine.saving.preprocess_weights_for_loading( - target_layer, weights) - target_layer.set_weights(weights) - - input_layer = keras.layers.InputLayer(input_shape) - layer = rnn_layer_class(units, **rnn_layer_kwargs) if bidirectional: layer = keras.layers.Bidirectional(layer) @@ -277,18 +258,96 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): if bidirectional: cudnn_layer = keras.layers.Bidirectional(cudnn_layer) - model = keras.models.Sequential([input_layer, layer]) - cudnn_model = keras.models.Sequential([input_layer, cudnn_layer]) + model = self._make_nested_model(input_shape, layer, model_nest_level, + model_type) + cudnn_model = self._make_nested_model(input_shape, cudnn_layer, + model_nest_level, model_type) + + if to_cudnn: + self._convert_model_weights(model, cudnn_model) + else: + self._convert_model_weights(cudnn_model, model) + + self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs), + atol=1e-4) + + def _make_nested_model(self, input_shape, layer, level=1, model_type='func'): + # example: make_nested_seq_model((1,), Dense(10), level=2).summary() + def make_nested_seq_model(input_shape, layer, level=1): + model = layer + for i in range(1, level + 1): + layers = [keras.layers.InputLayer(input_shape), + model] if (i == 1) else [model] + model = keras.models.Sequential(layers) + return model + + # example: make_nested_func_model((1,), Dense(10), level=2).summary() + def make_nested_func_model(input_shape, layer, level=1): + model_input = keras.layers.Input(input_shape) + model = layer + for _ in range(level): + model = keras.models.Model(model_input, model(model_input)) + return model + + if model_type == 'func': + return make_nested_func_model(input_shape, layer, level) + elif model_type == 'seq': + return make_nested_seq_model(input_shape, layer, level) + + def _convert_model_weights(self, source_model, target_model): + _, fname = tempfile.mkstemp('.h5') + source_model.save_weights(fname) + target_model.load_weights(fname) + os.remove(fname) + + @parameterized.named_parameters( + *testing_utils.generate_combinations_with_testcase_name( + rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False])) + def test_load_weights_between_noncudnn_rnn_time_distributed(self, rnn_type, + to_cudnn): + # Similar test as test_load_weights_between_noncudnn_rnn() but has different + # rank of input due to usage of TimeDistributed. Issue: #10356. + if test.is_gpu_available(cuda_only=True): + with self.test_session(use_gpu=True): + input_size = 10 + steps = 6 + timesteps = 6 + input_shape = (timesteps, steps, input_size) + units = 2 + num_samples = 32 + inputs = np.random.random((num_samples, timesteps, steps, input_size)) + + rnn_layer_kwargs = { + 'recurrent_activation': 'sigmoid', + # ensure biases are non-zero and properly converted + 'bias_initializer': 'random_uniform', + } + if rnn_type == 'LSTM': + rnn_layer_class = keras.layers.LSTM + cudnn_rnn_layer_class = keras.layers.CuDNNLSTM + else: + rnn_layer_class = keras.layers.GRU + cudnn_rnn_layer_class = keras.layers.CuDNNGRU + rnn_layer_kwargs['reset_after'] = True + + layer = rnn_layer_class(units, **rnn_layer_kwargs) + layer = keras.layers.TimeDistributed(layer) + + cudnn_layer = cudnn_rnn_layer_class(units) + cudnn_layer = keras.layers.TimeDistributed(cudnn_layer) + + model = self._make_nested_model(input_shape, layer) + cudnn_model = self._make_nested_model(input_shape, cudnn_layer) if to_cudnn: - convert_weights(layer, cudnn_layer) + self._convert_model_weights(model, cudnn_model) else: - convert_weights(cudnn_layer, layer) + self._convert_model_weights(cudnn_model, model) - self.assertAllClose( - model.predict(inputs), cudnn_model.predict(inputs), atol=1e-4) + self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs), + atol=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_cudnnrnn_bidirectional(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): diff --git a/tensorflow/python/keras/layers/embeddings.py b/tensorflow/python/keras/layers/embeddings.py index 25eeeee9529bcb52e608eeb9468c210eea8bd8be..910fff720f6312041a25922cf5c63dfa8f83ec76 100644 --- a/tensorflow/python/keras/layers/embeddings.py +++ b/tensorflow/python/keras/layers/embeddings.py @@ -22,7 +22,7 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops diff --git a/tensorflow/python/keras/layers/gru_test.py b/tensorflow/python/keras/layers/gru_test.py index 234434f7a0205c7dda80d308e4780cd761352d77..57f660b6d5a70b950918a3f6d75c87ecccf76f82 100644 --- a/tensorflow/python/keras/layers/gru_test.py +++ b/tensorflow/python/keras/layers/gru_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class GRULayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_GRU(self): num_samples = 2 timesteps = 3 @@ -41,7 +41,7 @@ class GRULayerTest(test.TestCase): 'return_sequences': True}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_GRU(self): num_samples = 2 timesteps = 3 @@ -55,7 +55,7 @@ class GRULayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_GRU(self): num_samples = 2 timesteps = 3 @@ -68,7 +68,7 @@ class GRULayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_GRU(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/local.py b/tensorflow/python/keras/layers/local.py index 46c18b763e80b58da1ec0c2655978753af75b4f8..0ebafe07cc45698200d0b1fa858a436c7a08820e 100644 --- a/tensorflow/python/keras/layers/local.py +++ b/tensorflow/python/keras/layers/local.py @@ -23,8 +23,8 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import conv_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.util.tf_export import tf_export @@ -62,6 +62,16 @@ class LocallyConnected1D(Layer): any `dilation_rate` value != 1. padding: Currently only supports `"valid"` (case-insensitive). `"same"` may be supported in the future. + data_format: A string, + one of `channels_last` (default) or `channels_first`. + The ordering of the dimensions in the inputs. + `channels_last` corresponds to inputs with shape + `(batch, length, channels)` while `channels_first` + corresponds to inputs with shape + `(batch, channels, length)`. + It defaults to the `image_data_format` value found in your + Keras config file at `~/.keras/keras.json`. + If you never set it, then it will be "channels_last". activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). @@ -122,13 +132,17 @@ class LocallyConnected1D(Layer): @tf_utils.shape_type_conversion def build(self, input_shape): - input_dim = input_shape[2] + if self.data_format == 'channels_first': + input_dim, input_length = input_shape[1], input_shape[2] + else: + input_dim, input_length = input_shape[2], input_shape[1] + if input_dim is None: raise ValueError('Axis 2 of input should be fully-defined. ' 'Found shape:', input_shape) - output_length = conv_utils.conv_output_length( - input_shape[1], self.kernel_size[0], self.padding, self.strides[0]) - self.kernel_shape = (output_length, self.kernel_size[0] * input_dim, + self.output_length = conv_utils.conv_output_length( + input_length, self.kernel_size[0], self.padding, self.strides[0]) + self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, @@ -138,28 +152,43 @@ class LocallyConnected1D(Layer): constraint=self.kernel_constraint) if self.use_bias: self.bias = self.add_weight( - shape=(output_length, self.filters), + shape=(self.output_length, self.filters), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None - self.input_spec = InputSpec(ndim=3, axes={2: input_dim}) + + if self.data_format == 'channels_first': + self.input_spec = InputSpec(ndim=3, axes={1: input_dim}) + else: + self.input_spec = InputSpec(ndim=3, axes={-1: input_dim}) self.built = True @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): - length = conv_utils.conv_output_length(input_shape[1], self.kernel_size[0], + if self.data_format == 'channels_first': + input_length = input_shape[2] + else: + input_length = input_shape[1] + + length = conv_utils.conv_output_length(input_length, self.kernel_size[0], self.padding, self.strides[0]) - return (input_shape[0], length, self.filters) + + if self.data_format == 'channels_first': + return (input_shape[0], self.filters, length) + elif self.data_format == 'channels_last': + return (input_shape[0], length, self.filters) def call(self, inputs): - output = K.local_conv1d(inputs, self.kernel, self.kernel_size, self.strides) + output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides, + (self.output_length,), self.data_format) + if self.use_bias: - output = K.bias_add(output, self.bias) - if self.activation is not None: - output = self.activation(output) + output = K.bias_add(output, self.bias, data_format=self.data_format) + + output = self.activation(output) return output def get_config(self): @@ -172,6 +201,8 @@ class LocallyConnected1D(Layer): self.strides, 'padding': self.padding, + 'data_format': + self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': @@ -370,9 +401,8 @@ class LocallyConnected2D(Layer): return (input_shape[0], rows, cols, self.filters) def call(self, inputs): - output = K.local_conv2d(inputs, self.kernel, self.kernel_size, self.strides, - (self.output_row, self.output_col), - self.data_format) + output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides, + (self.output_row, self.output_col), self.data_format) if self.use_bias: output = K.bias_add(output, self.bias, data_format=self.data_format) diff --git a/tensorflow/python/keras/layers/local_test.py b/tensorflow/python/keras/layers/local_test.py index 90ae1719e171b19e1c3b95fef434bd53285c858c..9639e0251f5a56e4130b13c0185792fe11da2532 100644 --- a/tensorflow/python/keras/layers/local_test.py +++ b/tensorflow/python/keras/layers/local_test.py @@ -28,7 +28,7 @@ from tensorflow.python.platform import test class LocallyConnectedLayersTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_1d(self): num_samples = 2 num_steps = 8 @@ -40,16 +40,17 @@ class LocallyConnectedLayersTest(test.TestCase): for strides in [1]: if padding == 'same' and strides != 1: continue - - testing_utils.layer_test( - keras.layers.LocallyConnected1D, - kwargs={ - 'filters': filters, - 'kernel_size': filter_length, - 'padding': padding, - 'strides': strides - }, - input_shape=(num_samples, num_steps, input_dim)) + for data_format in ['channels_first', 'channels_last']: + testing_utils.layer_test( + keras.layers.LocallyConnected1D, + kwargs={ + 'filters': filters, + 'kernel_size': filter_length, + 'padding': padding, + 'strides': strides, + 'data_format': data_format + }, + input_shape=(num_samples, num_steps, input_dim)) def test_locallyconnected_1d_regularization(self): num_samples = 2 @@ -57,37 +58,41 @@ class LocallyConnectedLayersTest(test.TestCase): input_dim = 5 filter_length = 3 filters = 4 - kwargs = { - 'filters': filters, - 'kernel_size': filter_length, - 'kernel_regularizer': 'l2', - 'bias_regularizer': 'l2', - 'activity_regularizer': 'l2', - } - - with self.test_session(): - layer = keras.layers.LocallyConnected1D(**kwargs) - layer.build((num_samples, num_steps, input_dim)) - self.assertEqual(len(layer.losses), 2) - layer( - keras.backend.variable(np.ones((num_samples, num_steps, input_dim)))) - self.assertEqual(len(layer.losses), 3) - - k_constraint = keras.constraints.max_norm(0.01) - b_constraint = keras.constraints.max_norm(0.01) - kwargs = { - 'filters': filters, - 'kernel_size': filter_length, - 'kernel_constraint': k_constraint, - 'bias_constraint': b_constraint, - } - with self.test_session(): - layer = keras.layers.LocallyConnected1D(**kwargs) - layer.build((num_samples, num_steps, input_dim)) - self.assertEqual(layer.kernel.constraint, k_constraint) - self.assertEqual(layer.bias.constraint, b_constraint) - - @tf_test_util.run_in_graph_and_eager_modes() + for data_format in ['channels_first', 'channels_last']: + kwargs = { + 'filters': filters, + 'kernel_size': filter_length, + 'kernel_regularizer': 'l2', + 'bias_regularizer': 'l2', + 'activity_regularizer': 'l2', + 'data_format': data_format + } + + with self.test_session(): + layer = keras.layers.LocallyConnected1D(**kwargs) + layer.build((num_samples, num_steps, input_dim)) + self.assertEqual(len(layer.losses), 2) + layer( + keras.backend.variable(np.ones((num_samples, + num_steps, + input_dim)))) + self.assertEqual(len(layer.losses), 3) + + k_constraint = keras.constraints.max_norm(0.01) + b_constraint = keras.constraints.max_norm(0.01) + kwargs = { + 'filters': filters, + 'kernel_size': filter_length, + 'kernel_constraint': k_constraint, + 'bias_constraint': b_constraint, + } + with self.test_session(): + layer = keras.layers.LocallyConnected1D(**kwargs) + layer.build((num_samples, num_steps, input_dim)) + self.assertEqual(layer.kernel.constraint, k_constraint) + self.assertEqual(layer.bias.constraint, b_constraint) + + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_2d(self): num_samples = 8 filters = 3 @@ -113,6 +118,7 @@ class LocallyConnectedLayersTest(test.TestCase): }, input_shape=(num_samples, num_row, num_col, stack_size)) + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_2d_channels_first(self): num_samples = 8 filters = 3 @@ -120,15 +126,14 @@ class LocallyConnectedLayersTest(test.TestCase): num_row = 6 num_col = 10 - with self.test_session(): - testing_utils.layer_test( - keras.layers.LocallyConnected2D, - kwargs={ - 'filters': filters, - 'kernel_size': 3, - 'data_format': 'channels_first' - }, - input_shape=(num_samples, num_row, num_col, stack_size)) + testing_utils.layer_test( + keras.layers.LocallyConnected2D, + kwargs={ + 'filters': filters, + 'kernel_size': 3, + 'data_format': 'channels_first' + }, + input_shape=(num_samples, num_row, num_col, stack_size)) def test_locallyconnected_2d_regularization(self): num_samples = 8 diff --git a/tensorflow/python/keras/layers/lstm_test.py b/tensorflow/python/keras/layers/lstm_test.py index 87cb344bf82b73b6af9830a4428a5ba099135324..ae381f595565cf0d060320354cb32585c1067f72 100644 --- a/tensorflow/python/keras/layers/lstm_test.py +++ b/tensorflow/python/keras/layers/lstm_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class LSTMLayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_LSTM(self): num_samples = 2 timesteps = 3 @@ -56,7 +56,7 @@ class LSTMLayerTest(test.TestCase): outputs = model.layers[-1].output self.assertEquals(outputs.get_shape().as_list(), [None, timesteps, units]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_LSTM(self): num_samples = 2 timesteps = 3 @@ -70,7 +70,7 @@ class LSTMLayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_LSTM(self): num_samples = 2 timesteps = 3 @@ -83,7 +83,7 @@ class LSTMLayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_LSTM(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/merge.py b/tensorflow/python/keras/layers/merge.py index 683e3e0ed1ce9a1fc56dcae0c0c8841148f008d5..f295af3fe04d87d260e4f6a98762dcfb90883531 100644 --- a/tensorflow/python/keras/layers/merge.py +++ b/tensorflow/python/keras/layers/merge.py @@ -250,6 +250,7 @@ class Add(_Merge): return output +@tf_export('keras.layers.Subtract') class Subtract(_Merge): """Layer that subtracts two inputs. @@ -336,6 +337,7 @@ class Maximum(_Merge): return output +@tf_export('keras.layers.Minimum') class Minimum(_Merge): """Layer that computes the minimum (element-wise) a list of inputs. @@ -446,8 +448,8 @@ class Concatenate(_Merge): class Dot(_Merge): """Layer that computes a dot product between samples in two tensors. - E.g. if applied to two tensors `a` and `b` of shape `(batch_size, n)`, - the output will be a tensor of shape `(batch_size, 1)` + E.g. if applied to a list of two tensors `a` and `b` of shape + `(batch_size, n)`, the output will be a tensor of shape `(batch_size, 1)` where each entry `i` will be the dot product between `a[i]` and `b[i]`. @@ -586,6 +588,7 @@ def add(inputs, **kwargs): return Add(**kwargs)(inputs) +@tf_export('keras.layers.subtract') def subtract(inputs, **kwargs): """Functional interface to the `Subtract` layer. @@ -656,6 +659,7 @@ def maximum(inputs, **kwargs): return Maximum(**kwargs)(inputs) +@tf_export('keras.layers.minimum') def minimum(inputs, **kwargs): """Functional interface to the `Minimum` layer. diff --git a/tensorflow/python/keras/layers/merge_test.py b/tensorflow/python/keras/layers/merge_test.py index 8a097cf7f57d06155f26e3099554e34a54186189..39bc98d039624d50788e1b7995dc5fba300a5276 100644 --- a/tensorflow/python/keras/layers/merge_test.py +++ b/tensorflow/python/keras/layers/merge_test.py @@ -28,7 +28,7 @@ from tensorflow.python.platform import test class MergeLayersTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_add(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -76,7 +76,7 @@ class MergeLayersTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.add([i1]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_multiply(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -92,7 +92,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, x1 * x2 * x3, atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_average(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -106,7 +106,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_maximum(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -120,7 +120,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_minimum(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -134,7 +134,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_concatenate(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -169,7 +169,7 @@ class MergeLayersTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'called on a list'): keras.layers.concatenate([i1], axis=-1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_dot(self): i1 = keras.layers.Input(shape=(4,)) i2 = keras.layers.Input(shape=(4,)) @@ -215,7 +215,7 @@ class MergeLayersTest(test.TestCase): dot = keras.layers.Dot(1) dot.compute_output_shape(1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_subtract(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) diff --git a/tensorflow/python/keras/layers/noise.py b/tensorflow/python/keras/layers/noise.py index a895caa25b91702d92002f84fe44b5b5c3a8ca0c..cb7cee3ebc3ebd2413836b876f2aaf21985f1d9c 100644 --- a/tensorflow/python/keras/layers/noise.py +++ b/tensorflow/python/keras/layers/noise.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python.keras import backend as K -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops diff --git a/tensorflow/python/keras/layers/noise_test.py b/tensorflow/python/keras/layers/noise_test.py index bde2185f03bd45c1c9fecbd6fe5544a17e9c04ef..aa2be62390b0dcf0656a533cba9bdbe9ceee09dd 100644 --- a/tensorflow/python/keras/layers/noise_test.py +++ b/tensorflow/python/keras/layers/noise_test.py @@ -40,7 +40,7 @@ class NoiseLayersTest(test.TestCase): kwargs={'rate': 0.5}, input_shape=(3, 2, 3)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_AlphaDropout(self): testing_utils.layer_test( keras.layers.AlphaDropout, diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py index 7743d00c0f000c4087ee85ccb08c5d0c1b20d807..8b894ca6b1c256210bb9ded33ae36da2fc4c001a 100644 --- a/tensorflow/python/keras/layers/normalization.py +++ b/tensorflow/python/keras/layers/normalization.py @@ -26,14 +26,15 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.util.tf_export import tf_export @@ -182,8 +183,9 @@ class BatchNormalization(Layer): def _add_tower_local_variable(self, *args, **kwargs): tower_context = distribute_lib.get_tower_context() - with tower_context.tower_local_var_scope('mean'): - return self.add_variable(*args, **kwargs) + with tower_context.tower_local_var_scope( + variable_scope.VariableAggregation.MEAN): + return self.add_weight(*args, **kwargs) def build(self, input_shape): input_shape = tensor_shape.TensorShape(input_shape) @@ -276,7 +278,7 @@ class BatchNormalization(Layer): self.axis[idx] = x + 1 # Account for added dimension if self.scale: - self.gamma = self.add_variable( + self.gamma = self.add_weight( name='gamma', shape=param_shape, dtype=param_dtype, @@ -291,7 +293,7 @@ class BatchNormalization(Layer): 1.0, dtype=param_dtype, shape=param_shape) if self.center: - self.beta = self.add_variable( + self.beta = self.add_weight( name='beta', shape=param_shape, dtype=param_dtype, @@ -364,11 +366,12 @@ class BatchNormalization(Layer): def _assign_moving_average(self, variable, value, momentum): with ops.name_scope(None, 'AssignMovingAvg', [variable, value, momentum]) as scope: - decay = ops.convert_to_tensor(1.0 - momentum, name='decay') - if decay.dtype != variable.dtype.base_dtype: - decay = math_ops.cast(decay, variable.dtype.base_dtype) - update_delta = (variable - value) * decay - return state_ops.assign_sub(variable, update_delta, name=scope) + with ops.colocate_with(variable): + decay = ops.convert_to_tensor(1.0 - momentum, name='decay') + if decay.dtype != variable.dtype.base_dtype: + decay = math_ops.cast(decay, variable.dtype.base_dtype) + update_delta = (variable - value) * decay + return state_ops.assign_sub(variable, update_delta, name=scope) def _fused_batch_norm(self, inputs, training): """Returns the output of fused batch norm.""" diff --git a/tensorflow/python/keras/layers/pooling.py b/tensorflow/python/keras/layers/pooling.py index 10a82b285eff6f6b414e67441ceb88976ca2368f..912e8bd619db8b35a54853c0752382479567fd04 100644 --- a/tensorflow/python/keras/layers/pooling.py +++ b/tensorflow/python/keras/layers/pooling.py @@ -20,8 +20,8 @@ from __future__ import print_function from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import backend -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import conv_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn diff --git a/tensorflow/python/keras/layers/pooling_test.py b/tensorflow/python/keras/layers/pooling_test.py index cbd58a22879975b7dbaab8290f59cee573b272cd..2cd9939e66ff869dac5058d2dd00d8d495e40f55 100644 --- a/tensorflow/python/keras/layers/pooling_test.py +++ b/tensorflow/python/keras/layers/pooling_test.py @@ -27,14 +27,14 @@ from tensorflow.python.platform import test class GlobalPoolingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_1d(self): testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D, input_shape=(3, 4, 5)) testing_utils.layer_test( keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_2d(self): testing_utils.layer_test( keras.layers.pooling.GlobalMaxPooling2D, @@ -53,7 +53,7 @@ class GlobalPoolingTest(test.TestCase): kwargs={'data_format': 'channels_last'}, input_shape=(3, 5, 6, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_3d(self): testing_utils.layer_test( keras.layers.pooling.GlobalMaxPooling3D, @@ -75,7 +75,7 @@ class GlobalPoolingTest(test.TestCase): class Pooling2DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_2d(self): pool_size = (3, 3) for strides in [(1, 1), (2, 2)]: @@ -88,7 +88,7 @@ class Pooling2DTest(test.TestCase): }, input_shape=(3, 5, 6, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_2d(self): testing_utils.layer_test( keras.layers.AveragePooling2D, @@ -122,7 +122,7 @@ class Pooling2DTest(test.TestCase): class Pooling3DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_3d(self): pool_size = (3, 3, 3) testing_utils.layer_test( @@ -141,7 +141,7 @@ class Pooling3DTest(test.TestCase): }, input_shape=(3, 4, 11, 12, 10)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_3d(self): pool_size = (3, 3, 3) testing_utils.layer_test( @@ -163,7 +163,7 @@ class Pooling3DTest(test.TestCase): class Pooling1DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_1d(self): for padding in ['valid', 'same']: for stride in [1, 2]: @@ -173,7 +173,7 @@ class Pooling1DTest(test.TestCase): 'padding': padding}, input_shape=(3, 5, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_1d(self): for padding in ['valid', 'same']: for stride in [1, 2]: diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 7e509fb45182653d938adfd679e204cc7ea1e900..32d25c5a650d3b66d944eee945cafa2d6f54d405 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -29,8 +29,8 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops diff --git a/tensorflow/python/keras/layers/serialization.py b/tensorflow/python/keras/layers/serialization.py index be306c0af765dd79bcc2b7651d97957c1cf80519..7c45e08b5c48084cc57569a4d1102a0a7c5b29e1 100644 --- a/tensorflow/python/keras/layers/serialization.py +++ b/tensorflow/python/keras/layers/serialization.py @@ -20,8 +20,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.keras.engine import Input -from tensorflow.python.keras.engine import InputLayer +from tensorflow.python.keras.engine.input_layer import Input +from tensorflow.python.keras.engine.input_layer import InputLayer from tensorflow.python.keras.layers.advanced_activations import * from tensorflow.python.keras.layers.convolutional import * from tensorflow.python.keras.layers.convolutional_recurrent import * diff --git a/tensorflow/python/keras/layers/simplernn_test.py b/tensorflow/python/keras/layers/simplernn_test.py index 3d24b0d5045d9c264f32adedaa0e91cdc5cbb0cf..18fefbe84f6f46f2043c6586ecbc85ea76c55ea0 100644 --- a/tensorflow/python/keras/layers/simplernn_test.py +++ b/tensorflow/python/keras/layers/simplernn_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class SimpleRNNLayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -41,7 +41,7 @@ class SimpleRNNLayerTest(test.TestCase): 'return_sequences': True}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -55,7 +55,7 @@ class SimpleRNNLayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -68,7 +68,7 @@ class SimpleRNNLayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_SimpleRNN(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py index 7759561ef94c4a81552ef7b40ea71e49bbb743ae..e61acf8e771eb8de1c466ffa5e1c4c7f543f77ef 100644 --- a/tensorflow/python/keras/layers/wrappers.py +++ b/tensorflow/python/keras/layers/wrappers.py @@ -23,8 +23,8 @@ import copy from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import backend as K -from tensorflow.python.keras.engine import InputSpec -from tensorflow.python.keras.engine import Layer +from tensorflow.python.keras.engine.base_layer import InputSpec +from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.layers.recurrent import _standardize_args from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils @@ -45,7 +45,9 @@ class Wrapper(Layer): """ def __init__(self, layer, **kwargs): + assert isinstance(layer, Layer) self.layer = layer + self._track_checkpointable(layer, name='layer') # Tracks mapping of Wrapper inputs to inner layer inputs. Useful when # the inner layer has update ops that depend on its inputs (as opposed # to the inputs to the Wrapper layer). @@ -154,9 +156,16 @@ class TimeDistributed(Wrapper): Arguments: layer: a layer instance. + + Raises: + ValueError: If not initialized with a `Layer` instance. """ def __init__(self, layer, **kwargs): + if not isinstance(layer, Layer): + raise ValueError( + 'Please initialize `TimeDistributed` layer with a ' + '`Layer` instance. You passed: {input}'.format(input=layer)) super(TimeDistributed, self).__init__(layer, **kwargs) self.supports_masking = True @@ -166,7 +175,10 @@ class TimeDistributed(Wrapper): self.input_spec = InputSpec(shape=input_shape) child_input_shape = [input_shape[0]] + input_shape[2:] if not self.layer.built: - self.layer.build(child_input_shape) + # The base layer class calls a conversion function on the input shape to + # convert it to a TensorShape. The conversion function requires a + # tuple which is why we cast the shape. + self.layer.build(tuple(child_input_shape)) self.layer.built = True super(TimeDistributed, self).build() self.built = True @@ -249,7 +261,8 @@ class Bidirectional(Wrapper): they will be returned as a list. Raises: - ValueError: In case of invalid `merge_mode` argument. + ValueError: If not initialized with a `Layer` instance or + In case of invalid `merge_mode` argument. Examples: @@ -265,6 +278,10 @@ class Bidirectional(Wrapper): """ def __init__(self, layer, merge_mode='concat', weights=None, **kwargs): + if not isinstance(layer, Layer): + raise ValueError( + 'Please initialize `Bidirectional` layer with a ' + '`Layer` instance. You passed: {input}'.format(input=layer)) if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]: raise ValueError('Invalid merge mode. ' 'Merge mode should be one of ' diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index 5eab6aba8a5f9a7e70f55685a9cd9ae6e0cf024d..c8f0d216e6f7a3bb715286bd6e7975a5dc1ac1cc 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -23,8 +23,10 @@ import copy import numpy as np from tensorflow.python import keras +from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.platform import test +from tensorflow.python.training.checkpointable import util as checkpointable_util from tensorflow.python.training.rmsprop import RMSPropOptimizer @@ -69,7 +71,7 @@ class _RNNCellWithConstants(keras.layers.Layer): class TimeDistributedTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_timedistributed_dense(self): model = keras.models.Sequential() model.add( @@ -85,6 +87,10 @@ class TimeDistributedTest(test.TestCase): # test config model.get_config() + checkpointed_objects = set(checkpointable_util.list_objects(model)) + for v in model.variables: + self.assertIn(v, checkpointed_objects) + def test_timedistributed_static_batch_size(self): model = keras.models.Sequential() model.add( @@ -97,6 +103,13 @@ class TimeDistributedTest(test.TestCase): epochs=1, batch_size=10) + def test_timedistributed_invalid_init(self): + x = constant_op.constant(np.zeros((1, 1)).astype('float32')) + with self.assertRaisesRegexp( + ValueError, + 'Please initialize `TimeDistributed` layer with a `Layer` instance.'): + keras.layers.TimeDistributed(x) + def test_timedistributed_conv2d(self): with self.test_session(): model = keras.models.Sequential() @@ -220,6 +233,13 @@ class BidirectionalTest(test.TestCase): model = keras.models.model_from_json(model.to_json()) model.summary() + def test_bidirectional_invalid_init(self): + x = constant_op.constant(np.zeros((1, 1)).astype('float32')) + with self.assertRaisesRegexp( + ValueError, + 'Please initialize `Bidirectional` layer with a `Layer` instance.'): + keras.layers.Bidirectional(x) + def test_bidirectional_weight_loading(self): rnn = keras.layers.SimpleRNN samples = 2 @@ -424,6 +444,42 @@ class BidirectionalTest(test.TestCase): layer.trainable = True assert len(layer.trainable_weights) == 6 + def test_Bidirectional_updates(self): + with self.test_session(): + x = keras.layers.Input(shape=(3, 2)) + x_reachable_update = x * x + layer = keras.layers.Bidirectional(keras.layers.SimpleRNN(3)) + _ = layer(x) + assert not layer.updates + assert not layer.get_updates_for(None) + assert not layer.get_updates_for(x) + layer.forward_layer.add_update(x_reachable_update, inputs=x) + layer.forward_layer.add_update(1, inputs=None) + layer.backward_layer.add_update(x_reachable_update, inputs=x) + layer.backward_layer.add_update(1, inputs=None) + assert len(layer.updates) == 4 + assert len(layer.get_updates_for(None)) == 2 + assert len(layer.get_updates_for(x)) == 2 + + def test_Bidirectional_losses(self): + with self.test_session(): + x = keras.layers.Input(shape=(3, 2)) + x_reachable_loss = x * x + layer = keras.layers.Bidirectional( + keras.layers.SimpleRNN( + 3, kernel_regularizer='l1', bias_regularizer='l1')) + _ = layer(x) + assert len(layer.losses) == 4 + assert len(layer.get_losses_for(None)) == 4 + assert not layer.get_losses_for(x) + layer.forward_layer.add_loss(x_reachable_loss, inputs=x) + layer.forward_layer.add_loss(1, inputs=None) + layer.backward_layer.add_loss(x_reachable_loss, inputs=x) + layer.backward_layer.add_loss(1, inputs=None) + assert len(layer.losses) == 8 + assert len(layer.get_losses_for(None)) == 6 + assert len(layer.get_losses_for(x)) == 2 + def test_Bidirectional_with_constants(self): with self.test_session(): # Test basic case. diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 8fb957da439dd490bc3378df96f611733335c809..3ac4852eff6910a9861ae959f990978cea33d595 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -31,7 +31,7 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test -from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import data_structures from tensorflow.python.training.rmsprop import RMSPropOptimizer try: @@ -173,7 +173,7 @@ def get_nested_model_3(input_dim, num_classes): class ModelSubclassingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_io_workflow_with_np_arrays(self): num_classes = 2 num_samples = 100 @@ -192,7 +192,7 @@ class ModelSubclassingTest(test.TestCase): model.fit(x, y, epochs=2, batch_size=32, verbose=0) _ = model.evaluate(x, y, verbose=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_multi_io_workflow_with_np_arrays(self): num_classes = (2, 3) num_samples = 1000 @@ -251,7 +251,7 @@ class ModelSubclassingTest(test.TestCase): model.fit([x1, x2], [y1, y2], epochs=2, steps_per_epoch=10, verbose=0) _ = model.evaluate(steps=10, verbose=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_io_workflow_with_dataset_iterators(self): num_classes = 2 num_samples = 10 @@ -325,7 +325,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.inputs), 2) self.assertEqual(len(model.outputs), 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_updates(self): # test that updates get run during training num_samples = 100 @@ -352,7 +352,74 @@ class ModelSubclassingTest(test.TestCase): y_new = model.predict(x) self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1) - @test_util.run_in_graph_and_eager_modes() + def test_updates_and_losses_for_nested_models_in_subclassed_model(self): + + # Case 1: deferred-build sequential nested in subclass. + class TestModel1(keras.Model): + + def __init__(self): + super(TestModel1, self).__init__() + self.fc = keras.layers.Dense(10, input_shape=(784,), + activity_regularizer='l1') + self.bn = keras.Sequential([keras.layers.BatchNormalization(axis=1)]) + + def call(self, x): + return self.bn(self.fc(x)) + + with self.test_session(): + model = TestModel1() + + x = array_ops.ones(shape=[100, 784], dtype='float32') + model(x) + self.assertEqual(len(model.get_updates_for(x)), 2) + self.assertEqual(len(model.get_losses_for(x)), 1) + + # Case 2: placeholder-sequential nested in subclass. + class TestModel2(keras.Model): + + def __init__(self): + super(TestModel2, self).__init__() + self.fc = keras.layers.Dense(10, input_shape=(784,), + activity_regularizer='l1') + self.bn = keras.Sequential( + [keras.layers.BatchNormalization(axis=1, input_shape=(10,))]) + + def call(self, x): + return self.bn(self.fc(x)) + + with self.test_session(): + model = TestModel2() + + x = array_ops.ones(shape=[100, 784], dtype='float32') + model(x) + self.assertEqual(len(model.get_updates_for(x)), 2) + self.assertEqual(len(model.get_losses_for(x)), 1) + + # Case 3: functional-API model nested in subclass. + inputs = keras.Input((10,)) + outputs = keras.layers.BatchNormalization(axis=1)(inputs) + bn = keras.Model(inputs, outputs) + + class TestModel3(keras.Model): + + def __init__(self): + super(TestModel3, self).__init__() + self.fc = keras.layers.Dense(10, input_shape=(784,), + activity_regularizer='l1') + self.bn = bn + + def call(self, x): + return self.bn(self.fc(x)) + + with self.test_session(): + model = TestModel3() + + x = array_ops.ones(shape=[100, 784], dtype='float32') + model(x) + self.assertEqual(len(model.get_updates_for(x)), 2) + self.assertEqual(len(model.get_losses_for(x)), 1) + + @test_util.run_in_graph_and_eager_modes def test_training_and_inference_behavior(self): # test that dropout is applied in training and not inference @@ -380,7 +447,7 @@ class ModelSubclassingTest(test.TestCase): loss = model.train_on_batch(x, y) self.assertGreater(loss, 0.1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_training_methods(self): # test fit, train_on_batch # on different input types: list, dict @@ -433,14 +500,14 @@ class ModelSubclassingTest(test.TestCase): model = MultiIOTestModel(num_classes=num_classes, use_bn=True) model.predict_on_batch([x1, x2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainable_mutation(self): # test that you can change `trainable` on a model or layer, and that # it freezes the model state during training # TODO(fchollet): add test after we unify BN behavior in eager and symbolic. pass - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_saving(self): num_classes = (2, 3) @@ -482,7 +549,7 @@ class ModelSubclassingTest(test.TestCase): self.assertAllClose(y_ref_1, y1, atol=1e-5) self.assertAllClose(y_ref_2, y2, atol=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_summary(self): class ToString(object): @@ -508,7 +575,7 @@ class ModelSubclassingTest(test.TestCase): model.summary(print_fn=print_fn) self.assertTrue('Trainable params: 587' in print_fn.contents) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_subclass_nested_in_subclass(self): num_classes = 2 num_samples = 100 @@ -531,7 +598,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.trainable_weights), 6 + len(model.test_net.trainable_weights)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_graph_nested_in_subclass(self): num_classes = 2 num_samples = 100 @@ -554,7 +621,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.trainable_weights), 6 + len(model.test_net.trainable_weights)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_subclass_nested_in_graph(self): num_classes = 2 num_samples = 100 @@ -576,7 +643,7 @@ class ModelSubclassingTest(test.TestCase): len(model.non_trainable_weights), 4) self.assertEqual(len(model.trainable_weights), 12) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_support_for_manual_training_arg(self): # In most cases, the `training` argument is left unspecified, in which # case it defaults to value corresponding to the Model method being used @@ -612,8 +679,8 @@ class ModelSubclassingTest(test.TestCase): def __init__(self): super(Foo, self).__init__() self.isdep = keras.layers.Dense(1) - self.notdep = checkpointable.NoDependency(keras.layers.Dense(2)) - self.notdep_var = checkpointable.NoDependency( + self.notdep = data_structures.NoDependency(keras.layers.Dense(2)) + self.notdep_var = data_structures.NoDependency( resource_variable_ops.ResourceVariable(1., name='notdep_var')) m = Foo() @@ -685,7 +752,7 @@ class CustomCallModel(keras.Model): class CustomCallSignatureTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_no_inputs_in_signature(self): model = CustomCallModel() first = array_ops.ones([2, 3]) @@ -699,7 +766,7 @@ class CustomCallSignatureTests(test.TestCase): output = model(first, second=second, training=False) self.assertAllClose(expected_output, self.evaluate(output)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_inputs_in_signature(self): class HasInputsAndOtherPositional(keras.Model): @@ -716,7 +783,7 @@ class CustomCallSignatureTests(test.TestCase): x1, x2 = keras.Input((1, 1)), keras.Input((1, 1)) model(x1, x2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_kwargs_in_signature(self): class HasKwargs(keras.Model): @@ -730,7 +797,7 @@ class CustomCallSignatureTests(test.TestCase): if not context.executing_eagerly(): six.assertCountEqual(self, [arg], model.inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_args_in_signature(self): class HasArgs(keras.Model): diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index e6e45902a8f117e5765249da18afa7cc35aa6b16..ad3819e6e730b48e294b340d39fddeb6d7f2d6bf 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -129,7 +129,7 @@ class TestModelCloning(test.TestCase): class CheckpointingTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_optimizer_dependency(self): model = keras.models.Sequential() model.add(keras.layers.Dense(1, input_shape=(4,))) diff --git a/tensorflow/python/keras/optimizers.py b/tensorflow/python/keras/optimizers.py index f58aeaea1acae2717f00a0323b5ff297a8cc8b46..0b440185ca7ccfc4fadf5419e6ceb4c64a554e1d 100644 --- a/tensorflow/python/keras/optimizers.py +++ b/tensorflow/python/keras/optimizers.py @@ -19,17 +19,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy - import six from six.moves import zip # pylint: disable=redefined-builtin -from tensorflow.python.framework import dtypes as dtypes_module -from tensorflow.python.framework import ops from tensorflow.python.keras import backend as K from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object from tensorflow.python.keras.utils.generic_utils import serialize_keras_object -from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import clip_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import distribute as distribute_lib @@ -39,37 +35,6 @@ from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util.tf_export import tf_export -def clip_norm(g, c, n): - """Clip a tensor by norm. - - Arguments: - g: gradient tensor to clip. - c: clipping threshold. - n: norm of gradient tensor. - - Returns: - Clipped gradient tensor. - """ - if c > 0: - condition = n >= c - then_expression = lambda: math_ops.scalar_mul(c / n, g) - else_expression = lambda: g - - # saving the shape to avoid converting sparse tensor to dense - if isinstance(g, ops.Tensor): - g_shape = copy.copy(g.get_shape()) - elif isinstance(g, ops.IndexedSlices): - g_shape = copy.copy(g.dense_shape) - if condition.dtype != dtypes_module.bool: - condition = math_ops.cast(condition, 'bool') - g = control_flow_ops.cond(condition, then_expression, else_expression) - if isinstance(g, ops.Tensor): - g.set_shape(g_shape) - elif isinstance(g, ops.IndexedSlices): - g._dense_shape = g_shape # pylint: disable=protected-access - return g - - @tf_export('keras.optimizers.Optimizer') class Optimizer(object): """Abstract optimizer base class. @@ -91,6 +56,9 @@ class Optimizer(object): if k not in allowed_kwargs: raise TypeError('Unexpected keyword argument ' 'passed to optimizer: ' + str(k)) + # checks that clipnorm >= 0 and clipvalue >= 0 + if kwargs[k] < 0: + raise ValueError('Expected {} >= 0, received: {}'.format(k, kwargs[k])) self.__dict__.update(kwargs) self.updates = [] self.weights = [] @@ -119,12 +87,13 @@ class Optimizer(object): 'gradient defined (i.e. are differentiable). ' 'Common ops without gradient: ' 'K.argmax, K.round, K.eval.') - if hasattr(self, 'clipnorm') and self.clipnorm > 0: - norm = K.sqrt( - sum([math_ops.reduce_sum(math_ops.square(g)) for g in grads])) - grads = [clip_norm(g, self.clipnorm, norm) for g in grads] - if hasattr(self, 'clipvalue') and self.clipvalue > 0: - grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads] + if hasattr(self, 'clipnorm'): + grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] + if hasattr(self, 'clipvalue'): + grads = [ + clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) + for g in grads + ] return grads def set_weights(self, weights): @@ -719,12 +688,13 @@ class Nadam(Optimizer): return dict(list(base_config.items()) + list(config.items())) -class TFOptimizer(Optimizer, checkpointable.Checkpointable): +class TFOptimizer(Optimizer, checkpointable.CheckpointableBase): """Wrapper class for native TensorFlow optimizers. """ def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer + self._track_checkpointable(optimizer, name='optimizer') with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') diff --git a/tensorflow/python/keras/optimizers_test.py b/tensorflow/python/keras/optimizers_test.py index 92b0cf326158adb1c6124384571a075196dbd3cc..55fc3fdcf47b4e5589e2253fffdc97d33f5b481b 100644 --- a/tensorflow/python/keras/optimizers_test.py +++ b/tensorflow/python/keras/optimizers_test.py @@ -145,6 +145,12 @@ class KerasOptimizersTest(test.TestCase): with self.assertRaises(NotImplementedError): optimizer.from_config(None) + def test_negative_clipvalue_or_clipnorm(self): + with self.assertRaises(ValueError): + _ = keras.optimizers.SGD(lr=0.01, clipvalue=-0.5) + with self.assertRaises(ValueError): + _ = keras.optimizers.Adam(clipnorm=-2.0) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/testing_utils.py b/tensorflow/python/keras/testing_utils.py index e7cb45d5e110dcb749ae2b1b86dd8dd5b8ded4ef..17aba7d86c236d9bb30d3a3376b3aac40b69e77d 100644 --- a/tensorflow/python/keras/testing_utils.py +++ b/tensorflow/python/keras/testing_utils.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from collections import OrderedDict import numpy as np from tensorflow.python import keras @@ -183,3 +184,76 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, # for further checks in the caller function return actual_output + + +def _combine_named_parameters(**kwargs): + """Generate combinations based on its keyword arguments. + + Two sets of returned combinations can be concatenated using +. Their product + can be computed using `times()`. + + Args: + **kwargs: keyword arguments of form `option=[possibilities, ...]` + or `option=the_only_possibility`. + + Returns: + a list of dictionaries for each combination. Keys in the dictionaries are + the keyword argument names. Each key has one value - one of the + corresponding keyword argument values. + """ + if not kwargs: + return [OrderedDict()] + + sort_by_key = lambda k: k[0][0] + kwargs = OrderedDict(sorted(kwargs.items(), key=sort_by_key)) + first = list(kwargs.items())[0] + + rest = dict(list(kwargs.items())[1:]) + rest_combined = _combine_named_parameters(**rest) + + key = first[0] + values = first[1] + if not isinstance(values, list): + values = [values] + + combinations = [ + OrderedDict(sorted(list(combined.items()) + [(key, v)], key=sort_by_key)) + for v in values + for combined in rest_combined + ] + return combinations + + +def generate_combinations_with_testcase_name(**kwargs): + """Generate combinations based on its keyword arguments using combine(). + + This function calls combine() and appends a testcase name to the list of + dictionaries returned. The 'testcase_name' key is a required for named + parameterized tests. + + Args: + **kwargs: keyword arguments of form `option=[possibilities, ...]` + or `option=the_only_possibility`. + + Returns: + a list of dictionaries for each combination. Keys in the dictionaries are + the keyword argument names. Each key has one value - one of the + corresponding keyword argument values. + """ + combinations = _combine_named_parameters(**kwargs) + named_combinations = [] + for combination in combinations: + assert isinstance(combination, OrderedDict) + name = ''.join([ + '_{}_{}'.format( + ''.join(filter(str.isalnum, key)), + ''.join(filter(str.isalnum, str(value)))) + for key, value in combination.items() + ]) + named_combinations.append( + OrderedDict( + list(combination.items()) + [('testcase_name', + '_test{}'.format(name))])) + + return named_combinations + diff --git a/tensorflow/python/keras/utils/data_utils.py b/tensorflow/python/keras/utils/data_utils.py index a1f89d9d43400983baaf81d47aeb480d4d8f30c4..c1ee34ae467b7037bafa53ea1a9b4b8596917df4 100644 --- a/tensorflow/python/keras/utils/data_utils.py +++ b/tensorflow/python/keras/utils/data_utils.py @@ -324,12 +324,12 @@ def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535): class Sequence(object): """Base object for fitting to a sequence of data, such as a dataset. - Every `Sequence` must implements the `__getitem__` and the `__len__` methods. + Every `Sequence` must implement the `__getitem__` and the `__len__` methods. If you want to modify your dataset between epochs you may implement `on_epoch_end`. The method `__getitem__` should return a complete batch. - # Notes + Notes: `Sequence` are a safer way to do multiprocessing. This structure guarantees that the network will only train once diff --git a/tensorflow/python/keras/utils/io_utils.py b/tensorflow/python/keras/utils/io_utils.py index f82e3277de70a631c93f0ef3c240f41ddb3390a7..62674a9c77fc410a551d2ac79c22ecf959b16fc3 100644 --- a/tensorflow/python/keras/utils/io_utils.py +++ b/tensorflow/python/keras/utils/io_utils.py @@ -102,13 +102,12 @@ class HDF5Matrix(object): idx = (self.start + key).tolist() else: raise IndexError - elif isinstance(key, list): + else: + # Assume list/iterable if max(key) + self.start < self.end: idx = [x + self.start for x in key] else: raise IndexError - else: - raise IndexError if self.normalizer is not None: return self.normalizer(self.data[idx]) else: diff --git a/tensorflow/python/keras/utils/io_utils_test.py b/tensorflow/python/keras/utils/io_utils_test.py index 3895dca68e37e1597b93d8eeded7e5cfb0d3e338..81bb661edd8d815f8565285ad5dc8126f4f52e98 100644 --- a/tensorflow/python/keras/utils/io_utils_test.py +++ b/tensorflow/python/keras/utils/io_utils_test.py @@ -22,6 +22,7 @@ import os import shutil import numpy as np +import six from tensorflow.python import keras from tensorflow.python.platform import test @@ -95,6 +96,29 @@ class TestIOUtils(test.TestCase): self.assertEqual(out_eval.shape, ()) self.assertGreater(out_eval, 0) + # test slicing for shortened array + self.assertEqual(len(x_train[0:]), len(x_train)) + + # test __getitem__ invalid use cases + with self.assertRaises(IndexError): + _ = x_train[1000] + with self.assertRaises(IndexError): + _ = x_train[1000: 1001] + with self.assertRaises(IndexError): + _ = x_train[[1000, 1001]] + with self.assertRaises(IndexError): + _ = x_train[six.moves.range(1000, 1001)] + with self.assertRaises(IndexError): + _ = x_train[np.array([1000])] + with self.assertRaises(TypeError): + _ = x_train[None] + + # test normalizer + normalizer = lambda x: x + 1 + normalized_x_train = keras.utils.io_utils.HDF5Matrix( + h5_path, 'my_data', start=0, end=150, normalizer=normalizer) + self.assertAllClose(normalized_x_train[0][0], x_train[0][0] + 1) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/utils/layer_utils.py b/tensorflow/python/keras/utils/layer_utils.py index 88daff0461593f6270f3be8c06a277c7e6751286..1f28c59ea41a96461a7faba2c41f5e65e6af0180 100644 --- a/tensorflow/python/keras/utils/layer_utils.py +++ b/tensorflow/python/keras/utils/layer_utils.py @@ -26,6 +26,47 @@ from tensorflow.python.keras.utils.conv_utils import convert_kernel from tensorflow.python.util.tf_export import tf_export +def get_source_inputs(tensor, layer=None, node_index=None): + """Returns the list of input tensors necessary to compute `tensor`. + + Output will always be a list of tensors + (potentially with 1 element). + + Arguments: + tensor: The tensor to start from. + layer: Origin layer of the tensor. Will be + determined via tensor._keras_history if not provided. + node_index: Origin node index of the tensor. + + Returns: + List of input tensors. + """ + if not hasattr(tensor, '_keras_history'): + return tensor + + if layer is None or node_index: + layer, node_index, _ = tensor._keras_history + if not layer._inbound_nodes: + return [tensor] + else: + node = layer._inbound_nodes[node_index] + if not node.inbound_layers: + # Reached an Input layer, stop recursion. + return node.input_tensors + else: + source_tensors = [] + for i in range(len(node.inbound_layers)): + x = node.input_tensors[i] + layer = node.inbound_layers[i] + node_index = node.node_indices[i] + previous_sources = get_source_inputs(x, layer, node_index) + # Avoid input redundancy. + for x in previous_sources: + if x not in source_tensors: + source_tensors.append(x) + return source_tensors + + def count_params(weights): """Count the total number of scalars composing the weights. diff --git a/tensorflow/python/keras/utils/multi_gpu_utils.py b/tensorflow/python/keras/utils/multi_gpu_utils.py index e5442f04e316c6c2ec6f814cf8ae2aad546dc7d7..e1c49bc85221aa94241ed746c2063aadf881f3cd 100644 --- a/tensorflow/python/keras/utils/multi_gpu_utils.py +++ b/tensorflow/python/keras/utils/multi_gpu_utils.py @@ -196,7 +196,7 @@ def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False): batch_size = shape[:1] input_shape = shape[1:] step = batch_size // parts - if i == num_gpus - 1: + if i == parts - 1: size = batch_size - step * i else: size = step diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index 5d29c2e5f86bd3c4997cc3f18f4cb760dc87d63b..6bfd1936e38da0b03bb6a9baba7d899957283349 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -893,6 +893,7 @@ tf_py_test( "//third_party/py/numpy", "//tensorflow/python:client_testlib", "//tensorflow/python:framework", + "//tensorflow/python:sparse_grad", "//tensorflow/python:sparse_ops", ], ) @@ -2754,6 +2755,7 @@ cuda_py_test( "//tensorflow/python:embedding_ops", "//tensorflow/python:framework", "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:init_ops", "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", "//tensorflow/python:partitioned_variables", @@ -3087,3 +3089,22 @@ tf_py_test( data = [":invalid_op.so"], tags = ["no_pip"], ) + +tf_py_test( + name = "cond_v2_test", + size = "small", + srcs = ["cond_v2_test.py"], + additional_deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:cond_v2", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework", + "//tensorflow/python:framework_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:training", + ], + grpc_enabled = True, +) diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index 08bf2d9c644bcde2a80e6138557dae6e19383dfd..40567571e6d259eff3f013c67d1d1f9504fcb9e4 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1006,7 +1006,7 @@ class SliceAssignTest(test_util.TensorFlowTestCase): class ShapeSizeRankTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDenseShape(self): t_value = [[0, 42], [24, 0]] self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(t_value))) @@ -1018,7 +1018,7 @@ class ShapeSizeRankTest(test_util.TensorFlowTestCase): self.assertEqual(4, self.evaluate(array_ops.size(t))) self.assertEqual(2, self.evaluate(array_ops.rank(t))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSparseShape(self): sp_value = sparse_tensor.SparseTensorValue( indices=((0, 1), (1, 0)), values=(42, 24), dense_shape=(2, 2)) @@ -1031,7 +1031,7 @@ class ShapeSizeRankTest(test_util.TensorFlowTestCase): self.assertEqual(4, self.evaluate(array_ops.size(sp))) self.assertEqual(2, self.evaluate(array_ops.rank(sp))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSizeDtype(self): tensor = [1] self.assertEqual(dtypes.int32, self.evaluate(array_ops.size(tensor)).dtype) @@ -1123,7 +1123,7 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): class ConcatSliceResourceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConcatSlice(self): r1 = test_ops.stub_resource_handle_op(container="a", shared_name="b") r2 = test_ops.stub_resource_handle_op(container="a", shared_name="c") diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 0ef08581c9f931b991ef0c1218dc503345e248c2..b98e5fd3866cde007c6c00ae0cf04b1f1c46c6f2 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -124,7 +124,7 @@ class AtrousConvolutionTest(test.TestCase): x, w, "VALID", dilation_rate=[2, 2], data_format="NCHW") self.assertEqual(y.shape.as_list(), [1, 20, None, None]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution2D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -139,7 +139,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=dilation_rate, ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution3D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -158,7 +158,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=dilation_rate, ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution1D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -173,7 +173,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=[rate], ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolutionNC(self): if test.is_gpu_available(cuda_only=True): # "NCW" and "NCHW" formats are currently supported only on CUDA. @@ -197,7 +197,7 @@ class AtrousConvolutionTest(test.TestCase): data_format="NCHW", ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousSequence(self): """Tests optimization of sequence of atrous convolutions. diff --git a/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py index 92cd53a031e73d4ff4ac50c2465f32a2c20545a7..4e31b1ea2a796a2e83696d278cf1b4784d177150 100644 --- a/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py +++ b/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py @@ -910,7 +910,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): feature_1_values = [11, 27] # Example 1: tree 0: 1.14, tree 1: 5.0, tree 2: 5.0 = > - # logit = 0.1*5.0+0.2*5.0+1*5 + # logit = 0.1*1.14+0.2*5.0+1*5 # Example 2: tree 0: 1.14, tree 1: 7.0, tree 2: -7 = > # logit= 0.1*1.14+0.2*7.0-1*7.0 expected_logits = [[6.114], [-5.486]] @@ -925,5 +925,147 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(expected_logits, logits) +class FeatureContribsOpsTest(test_util.TensorFlowTestCase): + """Tests feature contribs ops for model understanding.""" + + def testContribsMultipleTree(self): + """Tests that the contribs work when we have multiple trees.""" + with self.test_session() as session: + tree_ensemble_config = boosted_trees_pb2.TreeEnsemble() + text_format.Merge( + """ + trees { + nodes { + bucketized_split { + feature_id: 2 + threshold: 28 + left_id: 1 + right_id: 2 + } + metadata { + gain: 7.62 + original_leaf: {scalar: 2.1} + } + } + nodes { + leaf { + scalar: 1.14 + } + } + nodes { + leaf { + scalar: 8.79 + } + } + } + trees { + nodes { + bucketized_split { + feature_id: 2 + threshold: 26 + left_id: 1 + right_id: 2 + } + } + nodes { + bucketized_split { + feature_id: 0 + threshold: 50 + left_id: 3 + right_id: 4 + } + metadata { + original_leaf: {scalar: 5.5} + } + } + nodes { + leaf { + scalar: 7.0 + } + } + nodes { + leaf { + scalar: 5.0 + } + } + nodes { + leaf { + scalar: 6.0 + } + } + } + trees { + nodes { + bucketized_split { + feature_id: 0 + threshold: 34 + left_id: 1 + right_id: 2 + } + } + nodes { + leaf { + scalar: -7.0 + } + } + nodes { + leaf { + scalar: 5.0 + } + } + } + tree_weights: 0.1 + tree_weights: 0.2 + tree_weights: 1.0 + tree_metadata: { + num_layers_grown: 1} + tree_metadata: { + num_layers_grown: 2} + tree_metadata: { + num_layers_grown: 1} + """, tree_ensemble_config) + + tree_ensemble = boosted_trees_ops.TreeEnsemble( + 'ensemble', serialized_proto=tree_ensemble_config.SerializeToString()) + tree_ensemble_handle = tree_ensemble.resource_handle + resources.initialize_resources(resources.shared_resources()).run() + + feature_0_values = [36, 32] + feature_1_values = [13, -29] # Unused. Feature is not in above ensemble. + feature_2_values = [11, 27] + + # Expected logits are computed by traversing the logit path and + # subtracting child logits from parent logits. + bias = 2.1 * 0.1 # Root node of tree_0. + expected_feature_ids = ((2, 2, 0, 0), (2, 2, 0)) + # example_0 : (bias, 0.1 * 1.14, 0.2 * 5.5 + .114, 0.2 * 5. + .114, + # 1.0 * 5.0 + 0.2 * 5. + .114) + # example_1 : (bias, 0.1 * 1.14, 0.2 * 7 + .114, + # 1.0 * -7. + 0.2 * 7 + .114) + expected_logits_paths = ((bias, 0.114, 1.214, 1.114, 6.114), + (bias, 0.114, 1.514, -5.486)) + + bucketized_features = [ + feature_0_values, feature_1_values, feature_2_values + ] + + debug_op = boosted_trees_ops.example_debug_outputs( + tree_ensemble_handle, + bucketized_features=bucketized_features, + logits_dimension=1) + + serialized_examples_debug_outputs = session.run(debug_op) + feature_ids = [] + logits_paths = [] + for example in serialized_examples_debug_outputs: + example_debug_outputs = boosted_trees_pb2.DebugOutput() + example_debug_outputs.ParseFromString(example) + feature_ids.append(example_debug_outputs.feature_ids) + logits_paths.append(example_debug_outputs.logits_path) + + self.assertAllClose(feature_ids, expected_feature_ids) + self.assertAllClose(logits_paths, expected_logits_paths) + + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py index 7ef841c96b5cec9c7ae56c631896231ed663b8be..bda6ca5ca91ab1f55c4586f604a116a9b3fed874 100644 --- a/tensorflow/python/kernel_tests/check_ops_test.py +++ b/tensorflow/python/kernel_tests/check_ops_test.py @@ -34,45 +34,45 @@ from tensorflow.python.platform import test class AssertProperIterableTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_tensor_raises(self): tensor = constant_op.constant(1) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(tensor) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_sparse_tensor_raises(self): ten = sparse_tensor.SparseTensor( indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4]) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(ten) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_ndarray_raises(self): array = np.array([1, 2, 3]) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(array) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_string_raises(self): mystr = "hello" with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(mystr) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_non_iterable_object_raises(self): non_iterable = 1234 with self.assertRaisesRegexp(TypeError, "to be iterable"): check_ops.assert_proper_iterable(non_iterable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_list_does_not_raise(self): list_of_stuff = [ constant_op.constant([11, 22]), constant_op.constant([1, 2]) ] check_ops.assert_proper_iterable(list_of_stuff) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_generator_does_not_raise(self): generator_of_stuff = (constant_op.constant([11, 22]), constant_op.constant( [1, 2])) @@ -81,14 +81,14 @@ class AssertProperIterableTest(test.TestCase): class AssertEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies([check_ops.assert_equal(small, small)]): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_scalar_comparison(self): const_true = constant_op.constant(True, name="true") const_false = constant_op.constant(False, name="false") @@ -101,7 +101,7 @@ class AssertEqualTest(test.TestCase): x = check_ops.assert_equal(small, small) assert x is None - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): # Static check static_small = constant_op.constant([1, 2], name="small") @@ -179,7 +179,7 @@ First 2 elements of y: check_ops.assert_equal(big, small, message="big does not equal small", summarize=2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): # Static check static_small = constant_op.constant([3, 1], name="small") @@ -196,7 +196,7 @@ First 2 elements of y: with self.assertRaisesOpError("small.*big"): out.eval(feed_dict={small: [3, 1], big: [4, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal_and_broadcastable_shapes(self): small = constant_op.constant([[1, 2], [1, 2]], name="small") small_2 = constant_op.constant([1, 2], name="small_2") @@ -204,7 +204,7 @@ First 2 elements of y: out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") small_2 = constant_op.constant([1, 1], name="small_2") @@ -219,13 +219,13 @@ First 2 elements of y: out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_not_equal_and_broadcastable_shapes(self): cond = constant_op.constant([True, False], name="small") with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(cond, False, message="fail") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -236,7 +236,7 @@ First 2 elements of y: class AssertNoneEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_not_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([10, 20], name="small") @@ -245,7 +245,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([3, 1], name="small") with self.assertRaisesOpError("x != y did not hold"): @@ -254,7 +254,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_not_equal_and_broadcastable_shapes(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3], name="big") @@ -263,7 +263,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_not_equal_but_non_broadcastable_shapes(self): with self.test_session(): small = constant_op.constant([1, 1, 1], name="small") @@ -280,7 +280,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): with self.test_session(): larry = constant_op.constant([]) @@ -300,7 +300,7 @@ class AssertNoneEqualTest(test.TestCase): class AssertAllCloseTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): x = constant_op.constant(1., name="x") y = constant_op.constant(1., name="y") @@ -309,7 +309,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_32_bit_due_to_default_rtol(self): eps = np.finfo(np.float32).eps # Default rtol/atol is 10*eps @@ -320,7 +320,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_32_bit_due_to_default_atol(self): eps = np.finfo(np.float32).eps # Default rtol/atol is 10*eps @@ -331,7 +331,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_64_bit_due_to_default_rtol(self): eps = np.finfo(np.float64).eps # Default rtol/atol is 10*eps @@ -342,7 +342,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_64_bit_due_to_default_atol(self): eps = np.finfo(np.float64).eps # Default rtol/atol is 10*eps @@ -353,7 +353,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_due_to_custom_rtol(self): x = constant_op.constant(1., name="x") y = constant_op.constant(1.1, name="y") @@ -363,7 +363,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_due_to_custom_atol(self): x = constant_op.constant(0., name="x") y = constant_op.constant(0.1, name="y", dtype=np.float32) @@ -373,7 +373,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -381,7 +381,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(larry) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_atol_violated(self): x = constant_op.constant(10., name="x") y = constant_op.constant(10.2, name="y") @@ -392,7 +392,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_default_rtol_violated(self): x = constant_op.constant(0.1, name="x") y = constant_op.constant(0.0, name="y") @@ -412,7 +412,7 @@ class AssertAllCloseTest(test.TestCase): class AssertLessTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([1, 2], name="small") with self.assertRaisesOpError("failure message.*\n*.* x < y did not hold"): @@ -422,7 +422,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -431,7 +431,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([4, 2], name="big") @@ -439,7 +439,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 2], name="big") @@ -447,7 +447,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") big = constant_op.constant([3, 2], name="big") @@ -462,7 +462,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -480,7 +480,7 @@ class AssertLessTest(test.TestCase): class AssertLessEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies( @@ -488,7 +488,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -499,7 +499,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 2], name="big") @@ -507,7 +507,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 1], name="big") @@ -515,7 +515,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([1, 1, 1], name="big") @@ -531,7 +531,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -543,7 +543,7 @@ class AssertLessEqualTest(test.TestCase): class AssertGreaterTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([1, 2], name="small") with self.assertRaisesOpError("fail"): @@ -553,7 +553,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -562,7 +562,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(big) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([4, 2], name="big") @@ -570,7 +570,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 2], name="big") @@ -578,7 +578,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") big = constant_op.constant([3, 2], name="big") @@ -593,7 +593,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -604,7 +604,7 @@ class AssertGreaterTest(test.TestCase): class AssertGreaterEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies( @@ -612,7 +612,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -623,7 +623,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 2], name="big") @@ -632,7 +632,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_equal_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 1], name="big") @@ -641,7 +641,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="big") big = constant_op.constant([3, 1], name="small") @@ -657,7 +657,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -669,14 +669,14 @@ class AssertGreaterEqualTest(test.TestCase): class AssertNegativeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_negative(self): frank = constant_op.constant([-1, -2], name="frank") with ops.control_dependencies([check_ops.assert_negative(frank)]): out = array_ops.identity(frank) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_positive(self): doug = constant_op.constant([1, 2], name="doug") with self.assertRaisesOpError("fail"): @@ -686,7 +686,7 @@ class AssertNegativeTest(test.TestCase): out = array_ops.identity(doug) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_zero(self): claire = constant_op.constant([0], name="claire") with self.assertRaisesOpError("x < 0 did not hold"): @@ -694,7 +694,7 @@ class AssertNegativeTest(test.TestCase): out = array_ops.identity(claire) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is negative when it satisfies: # For every element x_i in x, x_i < 0 @@ -708,7 +708,7 @@ class AssertNegativeTest(test.TestCase): class AssertPositiveTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_negative(self): freddie = constant_op.constant([-1, -2], name="freddie") with self.assertRaisesOpError("fail"): @@ -718,14 +718,14 @@ class AssertPositiveTest(test.TestCase): out = array_ops.identity(freddie) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_positive(self): remmy = constant_op.constant([1, 2], name="remmy") with ops.control_dependencies([check_ops.assert_positive(remmy)]): out = array_ops.identity(remmy) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_zero(self): meechum = constant_op.constant([0], name="meechum") with self.assertRaisesOpError("x > 0 did not hold"): @@ -733,7 +733,7 @@ class AssertPositiveTest(test.TestCase): out = array_ops.identity(meechum) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is positive when it satisfies: # For every element x_i in x, x_i > 0 @@ -747,7 +747,7 @@ class AssertPositiveTest(test.TestCase): class AssertRankTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 1 @@ -768,7 +768,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("fail.*my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 0 @@ -784,7 +784,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_large_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 0 @@ -802,7 +802,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 1 @@ -818,7 +818,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 2 @@ -836,7 +836,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_scalar_static(self): tensor = constant_op.constant([1, 2], name="my_tensor") with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"): @@ -852,7 +852,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, rank_tensor)]): array_ops.identity(tensor).eval(feed_dict={rank_tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_integer_static(self): tensor = constant_op.constant([1, 2], name="my_tensor") with self.assertRaisesRegexp(TypeError, @@ -873,7 +873,7 @@ class AssertRankTest(test.TestCase): class AssertRankInTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") with self.assertRaisesRegexp( @@ -890,7 +890,7 @@ class AssertRankInTest(test.TestCase): with self.assertRaisesOpError("fail.*my_tensor.*rank"): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_matches_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): @@ -906,7 +906,7 @@ class AssertRankInTest(test.TestCase): check_ops.assert_rank_in(tensor_rank0, desired_ranks)]): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_matches_static_rank(self): tensor_rank1 = constant_op.constant([42, 43], name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): @@ -924,7 +924,7 @@ class AssertRankInTest(test.TestCase): tensor_rank1: (42.0, 43.0) }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_mismatches_static_rank(self): tensor_rank1 = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(ValueError, "rank"): @@ -942,7 +942,7 @@ class AssertRankInTest(test.TestCase): tensor_rank1: (42.0, 43.0) }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_scalar_static(self): tensor = constant_op.constant((42, 43), name="my_tensor") desired_ranks = ( @@ -966,7 +966,7 @@ class AssertRankInTest(test.TestCase): desired_ranks[1]: [2, 1], }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_integer_static(self): tensor = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(TypeError, @@ -987,7 +987,7 @@ class AssertRankInTest(test.TestCase): class AssertRankAtLeastTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 1 @@ -1005,7 +1005,7 @@ class AssertRankAtLeastTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 0 @@ -1021,7 +1021,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_ten_doesnt_raise_raise_if_rank_too_large_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 0 @@ -1037,7 +1037,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 1 @@ -1053,7 +1053,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 2 @@ -1074,7 +1074,7 @@ class AssertRankAtLeastTest(test.TestCase): class AssertNonNegativeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_negative(self): zoe = constant_op.constant([-1, -2], name="zoe") with self.assertRaisesOpError("x >= 0 did not hold"): @@ -1082,14 +1082,14 @@ class AssertNonNegativeTest(test.TestCase): out = array_ops.identity(zoe) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_zero_and_positive(self): lucas = constant_op.constant([0, 2], name="lucas") with ops.control_dependencies([check_ops.assert_non_negative(lucas)]): out = array_ops.identity(lucas) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is non-negative when it satisfies: # For every element x_i in x, x_i >= 0 @@ -1103,14 +1103,14 @@ class AssertNonNegativeTest(test.TestCase): class AssertNonPositiveTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_zero_and_negative(self): tom = constant_op.constant([0, -2], name="tom") with ops.control_dependencies([check_ops.assert_non_positive(tom)]): out = array_ops.identity(tom) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_positive(self): rachel = constant_op.constant([0, 2], name="rachel") with self.assertRaisesOpError("x <= 0 did not hold"): @@ -1118,7 +1118,7 @@ class AssertNonPositiveTest(test.TestCase): out = array_ops.identity(rachel) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is non-positive when it satisfies: # For every element x_i in x, x_i <= 0 @@ -1132,14 +1132,14 @@ class AssertNonPositiveTest(test.TestCase): class AssertIntegerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_integer(self): integers = constant_op.constant([1, 2], name="integers") with ops.control_dependencies([check_ops.assert_integer(integers)]): out = array_ops.identity(integers) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_float(self): floats = constant_op.constant([1.0, 2.0], name="floats") with self.assertRaisesRegexp(TypeError, "Expected.*integer"): @@ -1148,7 +1148,7 @@ class AssertIntegerTest(test.TestCase): class AssertTypeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_correct_type(self): integers = constant_op.constant([1, 2], dtype=dtypes.int64) with ops.control_dependencies([ @@ -1156,7 +1156,7 @@ class AssertTypeTest(test.TestCase): out = array_ops.identity(integers) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_wrong_type(self): floats = constant_op.constant([1.0, 2.0], dtype=dtypes.float16) with self.assertRaisesRegexp(TypeError, "must be of type.*float32"): @@ -1165,74 +1165,74 @@ class AssertTypeTest(test.TestCase): class IsStrictlyIncreasingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_constant_tensor_is_not_strictly_increasing(self): self.assertFalse(self.evaluate(check_ops.is_strictly_increasing([1, 1, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decreasing_tensor_is_not_strictly_increasing(self): self.assertFalse(self.evaluate( check_ops.is_strictly_increasing([1, 0, -1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_2d_decreasing_tensor_is_not_strictly_increasing(self): self.assertFalse( self.evaluate(check_ops.is_strictly_increasing([[1, 3], [2, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_tensor_is_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1, 2, 3]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_two_tensor(self): self.assertTrue( self.evaluate(check_ops.is_strictly_increasing([[-1, 2], [3, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensor_with_one_element_is_strictly_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_is_strictly_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([]))) class IsNonDecreasingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_constant_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 1, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decreasing_tensor_is_not_non_decreasing(self): self.assertFalse(self.evaluate(check_ops.is_non_decreasing([3, 2, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_2d_decreasing_tensor_is_not_non_decreasing(self): self.assertFalse(self.evaluate( check_ops.is_non_decreasing([[1, 3], [2, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_one_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 2, 3]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_two_tensor(self): self.assertTrue(self.evaluate( check_ops.is_non_decreasing([[-1, 2], [3, 3]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensor_with_one_element_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([]))) class FloatDTypeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_assert_same_float_dtype(self): self.assertIs(dtypes.float32, check_ops.assert_same_float_dtype(None, None)) @@ -1286,7 +1286,7 @@ class FloatDTypeTest(test.TestCase): class AssertScalarTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_assert_scalar(self): check_ops.assert_scalar(constant_op.constant(3)) check_ops.assert_scalar(constant_op.constant("foo")) diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py new file mode 100644 index 0000000000000000000000000000000000000000..759db5d5f43a144150918446e6ce206b3095904f --- /dev/null +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -0,0 +1,536 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Tests for cond_v2.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2 +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import saver +from tensorflow.python.util import compat + + +class NewCondTest(test.TestCase): + + def _testCond(self, true_fn, false_fn, train_vals): + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") + + expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") + actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual") + + expected_grad = gradients_impl.gradients(expected, train_vals) + actual_grad = gradients_impl.gradients(actual, train_vals) + + expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( + (expected, actual, expected_grad, actual_grad), {pred: True}) + self.assertEqual(expected_val, actual_val) + self.assertEqual(expected_grad_val, actual_grad_val) + + expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( + (expected, actual, expected_grad, actual_grad), {pred: False}) + self.assertEqual(expected_val, actual_val) + self.assertEqual(expected_grad_val, actual_grad_val) + + def testBasic(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return x * 2.0 + + def false_fn(): + return y * 3.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testBasic2(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return x * y * 2.0 + + def false_fn(): + return 2.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testNoInputs(self): + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") + + def true_fn(): + return constant_op.constant(1.0) + + def false_fn(): + return constant_op.constant(2.0) + + out = cond_v2.cond_v2(pred, true_fn, false_fn) + + self.assertEqual(sess.run(out, {pred: True}), [1.0]) + self.assertEqual(sess.run(out, {pred: False}), [2.0]) + + def _createCond(self, name): + pred = constant_op.constant(True, name="pred") + x = constant_op.constant(1.0, name="x") + + def true_fn(): + return x + + def false_fn(): + return x + 1 + + return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)[0].op + + def testDefaultName(self): + with ops.Graph().as_default(): + cond = self._createCond(None) + self.assertEqual(cond.name, "cond") + self.assertIn("cond_true", ops.get_default_graph()._functions) + self.assertIn("cond_false", ops.get_default_graph()._functions) + + with ops.Graph().as_default(): + with ops.name_scope("foo"): + cond = self._createCond("") + self.assertEqual(cond.name, "foo/cond") + self.assertIn("foo_cond_true", ops.get_default_graph()._functions) + self.assertIn("foo_cond_false", ops.get_default_graph()._functions) + + cond2 = self._createCond(None) + self.assertEqual(cond2.name, "foo/cond_1") + self.assertIn("foo_cond_1_true", ops.get_default_graph()._functions) + self.assertIn("foo_cond_1_false", ops.get_default_graph()._functions) + + def testSecondDerivative(self): + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(3.0, name="x") + + def true_fn(): + return math_ops.pow(x, 3) + + def false_fn(): + return x + + cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") + cond_grad = gradients_impl.gradients(cond, [x]) + cond_grad_grad = gradients_impl.gradients(cond_grad, [x]) + + # d[x^3]/dx = 3x^2 + true_val = sess.run(cond_grad, {pred: True}) + self.assertEqual(true_val, [27.0]) + # d[x]/dx = 1 + false_val = sess.run(cond_grad, {pred: False}) + self.assertEqual(false_val, [1.0]) + + true_val = sess.run(cond_grad_grad, {pred: True}) + # d2[x^3]/dx2 = 6x + self.assertEqual(true_val, [18.0]) + false_val = sess.run(cond_grad_grad, {pred: False}) + # d2[x]/dx2 = 0 + self.assertEqual(false_val, [0.0]) + + def testGradientOfDeserializedCond(self): + with ops.Graph().as_default(): + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(3.0, name="x") + ops.add_to_collection("x", x) + + def true_fn(): + return math_ops.pow(x, 3) + + def false_fn(): + return x + + ops.add_to_collection("pred", pred) + cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") + for c in cond: + ops.add_to_collection("cond", c) + meta_graph = saver.export_meta_graph() + + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + saver.import_meta_graph(meta_graph) + x = ops.get_collection("x")[0] + pred = ops.get_collection("pred")[0] + cond = ops.get_collection("cond") + cond_grad = gradients_impl.gradients(cond, [x], name="cond_grad") + cond_grad_grad = gradients_impl.gradients( + cond_grad, [x], name="cond_grad_grad") + # d[x^3]/dx = 3x^2 + true_val = sess.run(cond_grad, {pred: True}) + self.assertEqual(true_val, [27.0]) + # d[x]/dx = 1 + false_val = sess.run(cond_grad, {pred: False}) + self.assertEqual(false_val, [1.0]) + + true_val = sess.run(cond_grad_grad, {pred: True}) + # d2[x^3]/dx2 = 6x + self.assertEqual(true_val, [18.0]) + false_val = sess.run(cond_grad_grad, {pred: False}) + # d2[x]/dx2 = 0 + self.assertEqual(false_val, [0.0]) + + def testLowering(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + out_cond = self._createCond("cond") + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond, options=run_options, run_metadata=run_metadata) + + # If lowering was enabled, there should be a `Switch` node + switch_found = any( + any(node.op == "Switch" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertTrue(switch_found, + "A `Switch` op should exist if the graph was lowered.") + + # If lowering was enabled, there should be no `If` node + if_found = any( + any(node.op == "If" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertFalse(if_found, + "An `If` op was found, but it should be lowered.") + + def testLoweringDisabledInXLA(self): + with self.test_session(graph=ops.Graph()) as sess: + # Build the cond_v2 in an XLA context + xla_context = control_flow_ops.XLAControlFlowContext() + xla_context.Enter() + out_cond = self._createCond("cond") + xla_context.Exit() + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond, options=run_options, run_metadata=run_metadata) + + # Lowering disabled in XLA, there should be no `Switch` node + switch_found = any( + any(node.op == "Switch" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertFalse( + switch_found, + "A `Switch` op exists, but the graph should not be lowered.") + + # Lowering disabled in XLA, there should still be an `If` node + if_found = any( + any(node.op == "If" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertTrue( + if_found, + "An `If` op was not found, but the graph should not be lowered.") + + +class CondV2CollectionTest(test.TestCase): + + def testCollectionIntValueAccessInCond(self): + """Read values from graph collections inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = 2 + y = 5 + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + def fn(): + x_const = constant_op.constant(ops.get_collection("x")[0]) + y_const = constant_op.constant(ops.get_collection("y")[0]) + return math_ops.add(x_const, y_const) + + cnd = cond_v2.cond_v2(True, fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionTensorValueAccessInCond(self): + """Read tensors from collections inside of cond_v2 & use them.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + + def fn(): + x_read = ops.get_collection("x")[0] + y_read = ops.get_collection("y")[0] + return math_ops.add(x_read, y_read) + + cnd = cond_v2.cond_v2(math_ops.less(x, y), fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionIntValueWriteInCond(self): + """Make sure Int writes to collections work inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + def true_fn(): + z = math_ops.add(x, y) + ops.add_to_collection("z", 7) + return math_ops.mul(x, z) + + def false_fn(): + z = math_ops.add(x, y) + return math_ops.mul(x, z) + + cnd = cond_v2.cond_v2( + True, true_fn, + false_fn) + self.assertEquals(cnd[0].eval(), 14) + + read_z_collection = ops.get_collection("z") + self.assertEquals(read_z_collection, [7]) + + +class CondV2ContainerTest(test.TestCase): + + def testContainer(self): + """Set containers outside & inside of cond_v2. + + Make sure the containers are set correctly for both variable creation + (tested by variables.Variable) and for stateful ops (tested by FIFOQueue) + """ + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + v0 = variables.Variable([0]) + q0 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + def container(node): + return node.op.get_attr("container") + + self.assertEqual(compat.as_bytes(""), container(v0)) + self.assertEqual(compat.as_bytes(""), container(q0.queue_ref)) + + def true_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2t"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2t"), container(v2)) + self.assertEqual(compat.as_bytes("l2t"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(2.0) + + def false_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2f"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2f"), container(v2)) + self.assertEqual(compat.as_bytes("l2f"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(6.0) + + with ops.container("l1"): + cnd_true = cond_v2.cond_v2(True, true_fn, false_fn) + self.assertEquals(cnd_true[0].eval(), 2) + + cnd_false = cond_v2.cond_v2(False, true_fn, false_fn) + self.assertEquals(cnd_false[0].eval(), 6) + + v4 = variables.Variable([3]) + q4 = data_flow_ops.FIFOQueue(1, dtypes.float32) + v5 = variables.Variable([4]) + q5 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v4)) + self.assertEqual(compat.as_bytes("l1"), container(q4.queue_ref)) + self.assertEqual(compat.as_bytes(""), container(v5)) + self.assertEqual(compat.as_bytes(""), container(q5.queue_ref)) + + +class CondV2ColocationGroupAndDeviceTest(test.TestCase): + + def testColocateWithBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + with ops.colocate_with(b.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testColocateWithInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn2(): + with ops.colocate_with(b.op): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant([2.0], name="d") + self.assertEqual([b"loc:@a"], d.op.colocation_groups()) + + def testColocateWithInCondGraphPartitioning(self): + with ops.Graph().as_default() as g: + with self.test_session( + graph=g, + config=config_pb2.ConfigProto(device_count={"CPU": 2}) + ) as sess: + + with ops.device("/device:CPU:0"): + a = constant_op.constant([2.0], name="a") + with ops.device("/device:CPU:1"): + b = constant_op.constant([2.0], name="b") + + def fn(): + with ops.colocate_with(b.op): + c = math_ops.add(a, a, name="c") + return c + out_cond_2 = cond_v2.cond_v2(True, fn, fn)[0] + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond_2, options=run_options, run_metadata=run_metadata) + + # We expect there to be two partitions because of the + # colocate_with. We are only running the cond, which has a data + # dependency on `a` but not on `b`. So, without the colocate_with + # we would expect execution on just one device. + self.assertTrue(len(run_metadata.partition_graphs) >= 2) + + def testDeviceBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn(): + c = constant_op.constant(3.0) + self.assertEqual("/device:CPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:GPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testDeviceInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn2(): + with ops.device("/device:GPU:0"): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant(4.0) + self.assertEqual("/device:CPU:0", d.op.device) + + def testDeviceInCondGraphPartitioning(self): + with ops.Graph().as_default() as g: + with self.test_session( + graph=g, + config=config_pb2.ConfigProto(device_count={"CPU": 2}) + ) as sess: + + def fn(): + with ops.device("/device:CPU:1"): + c = math_ops.add(a, a, name="c") + return c + + with ops.device("/device:CPU:0"): + a = constant_op.constant([2.0], name="a") + out_cond_2 = cond_v2.cond_v2(True, fn, fn)[0] + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond_2, options=run_options, run_metadata=run_metadata) + + self.assertTrue(len(run_metadata.partition_graphs) >= 2) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/kernel_tests/confusion_matrix_test.py b/tensorflow/python/kernel_tests/confusion_matrix_test.py index 79e419867d70071280b7c88b6bfa820b935b24cd..ae6875340e776fc6808be3f4afeb59644245c886 100644 --- a/tensorflow/python/kernel_tests/confusion_matrix_test.py +++ b/tensorflow/python/kernel_tests/confusion_matrix_test.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import test class ConfusionMatrixTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExample(self): """This is a test of the example provided in pydoc.""" with self.test_session(): diff --git a/tensorflow/python/kernel_tests/constant_op_eager_test.py b/tensorflow/python/kernel_tests/constant_op_eager_test.py index 8e9d75667d49bf9e377ccb9290a3a91786b5a1cb..a0d5557b925162b254e34e9fc0971393ec119059 100644 --- a/tensorflow/python/kernel_tests/constant_op_eager_test.py +++ b/tensorflow/python/kernel_tests/constant_op_eager_test.py @@ -32,6 +32,9 @@ from tensorflow.python.util import compat # TODO(josh11b): add tests with lists/tuples, Shape. +# TODO(ashankar): Collapse with tests in constant_op_test.py and use something +# like the test_util.run_in_graph_and_eager_modes decorator to confirm +# equivalence between graph and eager execution. class ConstantTest(test.TestCase): def _testCpu(self, x): @@ -280,6 +283,34 @@ class ConstantTest(test.TestCase): with self.assertRaisesRegexp(ValueError, None): constant_op.constant([[1, 2], [3], [4, 5]]) + # TODO(ashankar): This test fails with graph construction since + # tensor_util.make_tensor_proto (invoked from constant_op.constant) + # does not handle iterables (it relies on numpy conversion). + # For consistency, should graph construction handle Python objects + # that implement the sequence protocol (but not numpy conversion), + # or should eager execution fail on such sequences? + def testCustomSequence(self): + + # This is inspired by how many objects in pandas are implemented: + # - They implement the Python sequence protocol + # - But may raise a KeyError on __getitem__(self, 0) + # See https://github.com/tensorflow/tensorflow/issues/20347 + class MySeq(object): + + def __getitem__(self, key): + if key != 1 and key != 3: + raise KeyError(key) + return key + + def __len__(self): + return 2 + + def __iter__(self): + l = list([1, 3]) + return l.__iter__() + + self.assertAllEqual([1, 3], self.evaluate(constant_op.constant(MySeq()))) + class AsTensorTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 450428707dab0d5fb88d180b8e54bb2c56958d8d..474d06b8f3a4276c65711d74ba0d1db6fb06cbf9 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -345,7 +345,7 @@ class Conv2DTest(test.TestCase): self.assertAllClose(expected, np.ravel(value), atol=tol, rtol=tol) self.assertShapeEqual(value, conv) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x1Filter(self): expected_output = [ 30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, @@ -358,7 +358,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Filter2x1Dilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 4, 4, 1], @@ -367,7 +367,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmpty(self): expected_output = [] self._VerifyValues( @@ -377,7 +377,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[0, 2, 3, 3], @@ -386,7 +386,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Filter(self): # The outputs are computed using third_party/py/IPython/notebook. expected_output = [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0] @@ -397,7 +397,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 2, 3, 3], @@ -406,7 +406,7 @@ class Conv2DTest(test.TestCase): dilations=[1, 2], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x2Filter(self): # The outputs are computed using third_party/py/IPython/notebook. expected_output = [ @@ -420,7 +420,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x2FilterDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 2, 3, 3], @@ -429,7 +429,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride2(self): expected_output = [2271.0, 2367.0, 2463.0] self._VerifyValues( @@ -439,7 +439,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride2Same(self): expected_output = [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0] self._VerifyValues( @@ -449,7 +449,7 @@ class Conv2DTest(test.TestCase): padding="SAME", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride1x2(self): expected_output = [58.0, 78.0, 98.0, 118.0, 138.0, 158.0] self._VerifyValues( @@ -459,7 +459,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSmallerThanStrideValid(self): expected_output = [65, 95, 275, 305] self._VerifyValues( @@ -469,7 +469,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSmallerThanStrideSame(self): self._VerifyValues( tensor_in_sizes=[1, 3, 3, 1], @@ -492,7 +492,7 @@ class Conv2DTest(test.TestCase): padding="SAME", expected=[44, 28, 41, 16]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSize(self): self._VerifyValues( tensor_in_sizes=[1, 2, 2, 1], @@ -501,7 +501,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=[50, 60]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 3, 3, 1], @@ -587,9 +587,9 @@ class Conv2DTest(test.TestCase): values.append(_GetVal(data_format, use_gpu)) for i in range(1, len(values)): - self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4) + self.assertAllClose(values[0], values[i], rtol=1e-2, atol=1e-2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth1ValidBackpropInput(self): expected_output = [1.0, 4.0, 4.0, 3.0, 10.0, 8.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -604,7 +604,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyBackpropInput(self): expected_output = [] for (data_format, use_gpu) in GetTestConfigs(): @@ -619,7 +619,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropInput(self): expected_output = [ 14.0, 32.0, 50.0, 100.0, 163.0, 226.0, 167.0, 212.0, 257.0, 122.0, @@ -639,7 +639,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropInputStride1x2(self): expected_output = [ 1.0, 2.0, 2.0, 4.0, 3.0, 6.0, 7.0, 12.0, 11.0, 18.0, 15.0, 24.0, 12.0, @@ -657,7 +657,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DStrideTwoFilterOneSameBackpropInput(self): expected_output = [ 1.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 4.0, 0.0, 0.0, 0.0, @@ -675,7 +675,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeBackpropInput(self): expected_output = [5.0, 11.0, 17.0, 23.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -759,7 +759,7 @@ class Conv2DTest(test.TestCase): for i in range(1, len(values)): self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth1ValidBackpropFilter(self): expected = [5.0, 8.0, 14.0, 17.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -773,7 +773,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyBackpropFilter(self): expected = [] for (data_format, use_gpu) in GetTestConfigs(): @@ -787,7 +787,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DBackpropFilterWithEmptyInput(self): expected = [0, 0, 0, 0] for (data_format, use_gpu) in GetTestConfigs(): @@ -801,7 +801,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropFilter(self): expected = [ 17.0, 22.0, 27.0, 22.0, 29.0, 36.0, 27.0, 36.0, 45.0, 32.0, 43.0, 54.0, @@ -820,7 +820,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropFilterStride1x2(self): expected = [161.0, 182.0, 287.0, 308.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -834,7 +834,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DStrideTwoFilterOneSameBackpropFilter(self): expected_output = [78.] for (data_format, use_gpu) in GetTestConfigs(): @@ -848,7 +848,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeBackpropFilter(self): expected_output = [1.0, 2.0, 2.0, 4.0, 3.0, 6.0, 4.0, 8.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -1897,19 +1897,19 @@ if __name__ == "__main__": for index, (input_size_, filter_size_, output_size_, stride_, padding_) in enumerate(GetShrunkInceptionShapes()): setattr(Conv2DTest, "testInceptionFwd_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdTest(input_size_, filter_size_, stride_, padding_))) setattr( Conv2DTest, "testInceptionFwdDilatedConv_" + str(index), - test_util.run_in_graph_and_eager_modes()(GetInceptionFwdDilatedConvTest( + test_util.run_in_graph_and_eager_modes(GetInceptionFwdDilatedConvTest( input_size_, filter_size_, stride_, padding_))) setattr(Conv2DTest, "testInceptionBackInput_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackInputTest(input_size_, filter_size_, output_size_, stride_, padding_))) setattr(Conv2DTest, "testInceptionBackFilter_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackFilterTest(input_size_, filter_size_, output_size_, [stride_, stride_], padding_))) @@ -1924,17 +1924,17 @@ if __name__ == "__main__": fshape = [1, 1, 1, 256] oshape = [1, 400, 400, 256] setattr(Conv2DTest, "testInceptionFwd_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdTest(ishape, fshape, 1, "SAME", gpu_only=True))) setattr(Conv2DTest, "testInceptionFwdDilatedConv_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdDilatedConvTest(ishape, fshape, 1, "SAME"))) setattr(Conv2DTest, "testInceptionBackInput_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackInputTest(ishape, fshape, oshape, 1, "SAME", gpu_only=True))) setattr(Conv2DTest, "testInceptionBackFilter_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackFilterTest(ishape, fshape, oshape, [1, 1], "SAME", gpu_only=True))) test.main() diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index 1128cd7a633d2b19f92aa430006ee7ec5b2a40f5..b61232cdedecacf0cc0f9b1661486a52afc86c2e 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -96,7 +96,8 @@ class UnaryOpTest(test.TestCase): np_ans = np_func(x) with self.test_session(use_gpu=False): inx = ops.convert_to_tensor(x) - if x.dtype in (np.float32, np.float64): + if x.dtype in (np.float32, np.float64, + dtypes_lib.bfloat16.as_numpy_dtype): y = 1.1 * tf_func(inx) np_ans *= 1.1 else: @@ -105,6 +106,8 @@ class UnaryOpTest(test.TestCase): self.assertShapeEqual(np_ans, y) if x.dtype == np.float16: self.assertAllClose(np_ans, tf_cpu, rtol=1e-3, atol=1e-3) + elif x.dtype == dtypes_lib.bfloat16.as_numpy_dtype: + self.assertAllClose(np_ans, tf_cpu, rtol=1e-2, atol=1e-2) else: self.assertAllClose(np_ans, tf_cpu) @@ -241,6 +244,12 @@ class UnaryOpTest(test.TestCase): math_ops.lgamma) self._compareBoth(x, np.vectorize(math.erf), math_ops.erf) self._compareBoth(x, np.vectorize(math.erfc), math_ops.erfc) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self._compareBoth(x, special.i0e, math_ops.bessel_i0e) + self._compareBoth(x, special.i1e, math_ops.bessel_i1e) + except ImportError as e: + tf_logging.warn("Cannot test special functions: %s" % str(e)) self._compareBothSparse(x, np.abs, math_ops.abs) self._compareBothSparse(x, np.negative, math_ops.negative) @@ -286,6 +295,12 @@ class UnaryOpTest(test.TestCase): self._compareBoth(x, np.arcsin, math_ops.asin) self._compareBoth(x, np.arccos, math_ops.acos) self._compareBoth(x, np.arctan, math_ops.atan) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self._compareBoth(x, special.i0e, math_ops.bessel_i0e) + self._compareBoth(x, special.i1e, math_ops.bessel_i1e) + except ImportError as e: + tf_logging.warn("Cannot test special functions: %s" % str(e)) self._compareBothSparse(x, np.abs, math_ops.abs) self._compareBothSparse(x, np.negative, math_ops.negative) @@ -334,6 +349,12 @@ class UnaryOpTest(test.TestCase): self._compareBoth(k, np.arcsin, math_ops.asin) self._compareBoth(k, np.arccos, math_ops.acos) self._compareBoth(k, np.tan, math_ops.tan) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self._compareBoth(x, special.i0e, math_ops.bessel_i0e) + self._compareBoth(x, special.i1e, math_ops.bessel_i1e) + except ImportError as e: + tf_logging.warn("Cannot test special functions: %s" % str(e)) self._compareBothSparse(x, np.abs, math_ops.abs) self._compareBothSparse(x, np.negative, math_ops.negative) @@ -370,6 +391,12 @@ class UnaryOpTest(test.TestCase): math_ops.lgamma) self._compareBoth(x, np.vectorize(math.erf), math_ops.erf) self._compareBoth(x, np.vectorize(math.erfc), math_ops.erfc) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self._compareBoth(x, special.i0e, math_ops.bessel_i0e) + self._compareBoth(x, special.i1e, math_ops.bessel_i1e) + except ImportError as e: + tf_logging.warn("Cannot test special functions: %s" % str(e)) self._compareBothSparse(x, np.abs, math_ops.abs) self._compareBothSparse(x, np.negative, math_ops.negative) @@ -644,12 +671,11 @@ class BinaryOpTest(test.TestCase): self._compareCpu(x, y, np_func, tf_func, also_compare_variables) if x.dtype in (np.float16, np.float32, np.float64, np.complex64, np.complex128): - if tf_func not in (_FLOORDIV, math_ops.floordiv, math_ops.igamma, - math_ops.igammac, math_ops.zeta, math_ops.polygamma): + if tf_func not in (_FLOORDIV, math_ops.floordiv, math_ops.zeta, + math_ops.polygamma): self._compareGradientX(x, y, np_func, tf_func) self._compareGradientY(x, y, np_func, tf_func) - if tf_func in (math_ops.igamma, math_ops.igammac, math_ops.zeta, - math_ops.polygamma): + if tf_func in (math_ops.zeta, math_ops.polygamma): # These methods only support gradients in the second parameter self._compareGradientY(x, y, np_func, tf_func) self._compareGpu(x, y, np_func, tf_func) diff --git a/tensorflow/python/kernel_tests/dct_ops_test.py b/tensorflow/python/kernel_tests/dct_ops_test.py index 93b2ff4561bcc8fd13855cde444c4b6237d7949b..97d7e2d8f90a620b693e2c81adc616d399e13bd6 100644 --- a/tensorflow/python/kernel_tests/dct_ops_test.py +++ b/tensorflow/python/kernel_tests/dct_ops_test.py @@ -40,50 +40,92 @@ def try_import(name): # pylint: disable=invalid-name fftpack = try_import("scipy.fftpack") +def _np_dct2(signals, norm=None): + """Computes the DCT-II manually with NumPy.""" + # X_k = sum_{n=0}^{N-1} x_n * cos(\frac{pi}{N} * (n + 0.5) * k) k=0,...,N-1 + dct_size = signals.shape[-1] + dct = np.zeros_like(signals) + for k in range(dct_size): + phi = np.cos(np.pi * (np.arange(dct_size) + 0.5) * k / dct_size) + dct[..., k] = np.sum(signals * phi, axis=-1) + # SciPy's `dct` has a scaling factor of 2.0 which we follow. + # https://github.com/scipy/scipy/blob/v0.15.1/scipy/fftpack/src/dct.c.src + if norm == "ortho": + # The orthonormal scaling includes a factor of 0.5 which we combine with + # the overall scaling of 2.0 to cancel. + dct[..., 0] *= np.sqrt(1.0 / dct_size) + dct[..., 1:] *= np.sqrt(2.0 / dct_size) + else: + dct *= 2.0 + return dct + + +def _np_dct3(signals, norm=None): + """Computes the DCT-III manually with NumPy.""" + # SciPy's `dct` has a scaling factor of 2.0 which we follow. + # https://github.com/scipy/scipy/blob/v0.15.1/scipy/fftpack/src/dct.c.src + dct_size = signals.shape[-1] + signals = np.array(signals) # make a copy so we can modify + if norm == "ortho": + signals[..., 0] *= np.sqrt(4.0 / dct_size) + signals[..., 1:] *= np.sqrt(2.0 / dct_size) + else: + signals *= 2.0 + dct = np.zeros_like(signals) + # X_k = 0.5 * x_0 + + # sum_{n=1}^{N-1} x_n * cos(\frac{pi}{N} * n * (k + 0.5)) k=0,...,N-1 + half_x0 = 0.5 * signals[..., 0] + for k in range(dct_size): + phi = np.cos(np.pi * np.arange(1, dct_size) * (k + 0.5) / dct_size) + dct[..., k] = half_x0 + np.sum(signals[..., 1:] * phi, axis=-1) + return dct + + +NP_DCT = {2: _np_dct2, 3: _np_dct3} +NP_IDCT = {2: _np_dct3, 3: _np_dct2} + + class DCTOpsTest(test.TestCase): - def _np_dct2(self, signals, norm=None): - """Computes the DCT-II manually with NumPy.""" - # X_k = sum_{n=0}^{N-1} x_n * cos(\frac{pi}{N} * (n + 0.5) * k) k=0,...,N-1 - dct_size = signals.shape[-1] - dct = np.zeros_like(signals) - for k in range(dct_size): - phi = np.cos(np.pi * (np.arange(dct_size) + 0.5) * k / dct_size) - dct[..., k] = np.sum(signals * phi, axis=-1) - # SciPy's `dct` has a scaling factor of 2.0 which we follow. - # https://github.com/scipy/scipy/blob/v0.15.1/scipy/fftpack/src/dct.c.src - if norm == "ortho": - # The orthonormal scaling includes a factor of 0.5 which we combine with - # the overall scaling of 2.0 to cancel. - dct[..., 0] *= np.sqrt(1.0 / dct_size) - dct[..., 1:] *= np.sqrt(2.0 / dct_size) - else: - dct *= 2.0 - return dct - - def _compare(self, signals, norm, atol=5e-4, rtol=5e-4): - """Compares the DCT to SciPy (if available) and a NumPy implementation.""" - np_dct = self._np_dct2(signals, norm) - tf_dct = spectral_ops.dct(signals, type=2, norm=norm).eval() + def _compare(self, signals, norm, dct_type, atol=5e-4, rtol=5e-4): + """Compares (I)DCT to SciPy (if available) and a NumPy implementation.""" + np_dct = NP_DCT[dct_type](signals, norm) + tf_dct = spectral_ops.dct(signals, type=dct_type, norm=norm).eval() self.assertAllClose(np_dct, tf_dct, atol=atol, rtol=rtol) + np_idct = NP_IDCT[dct_type](signals, norm) + tf_idct = spectral_ops.idct(signals, type=dct_type, norm=norm).eval() + self.assertAllClose(np_idct, tf_idct, atol=atol, rtol=rtol) if fftpack: - scipy_dct = fftpack.dct(signals, type=2, norm=norm) + scipy_dct = fftpack.dct(signals, type=dct_type, norm=norm) self.assertAllClose(scipy_dct, tf_dct, atol=atol, rtol=rtol) + scipy_idct = fftpack.idct(signals, type=dct_type, norm=norm) + self.assertAllClose(scipy_idct, tf_idct, atol=atol, rtol=rtol) + # Verify inverse(forward(s)) == s, up to a normalization factor. + tf_idct_dct = spectral_ops.idct( + tf_dct, type=dct_type, norm=norm).eval() + tf_dct_idct = spectral_ops.dct( + tf_idct, type=dct_type, norm=norm).eval() + if norm is None: + tf_idct_dct *= 0.5 / signals.shape[-1] + tf_dct_idct *= 0.5 / signals.shape[-1] + self.assertAllClose(signals, tf_idct_dct, atol=atol, rtol=rtol) + self.assertAllClose(signals, tf_dct_idct, atol=atol, rtol=rtol) def test_random(self): """Test randomly generated batches of data.""" with spectral_ops_test_util.fft_kernel_label_map(): with self.test_session(use_gpu=True): - for shape in ([2, 20], [1], [2], [3], [10], [2, 20], [2, 3, 25]): + for shape in ([1], [2], [3], [10], [2, 20], [2, 3, 25]): signals = np.random.rand(*shape).astype(np.float32) for norm in (None, "ortho"): - self._compare(signals, norm) + self._compare(signals, norm, 2) + self._compare(signals, norm, 3) def test_error(self): signals = np.random.rand(10) # Unsupported type. with self.assertRaises(ValueError): - spectral_ops.dct(signals, type=3) + spectral_ops.dct(signals, type=1) # Unknown normalization. with self.assertRaises(ValueError): spectral_ops.dct(signals, norm="bad") diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py index 5e223b18281ed9c06a3f72a16b6d22290851f37b..7134e02c348b47048cff5b0c205d1dd613c31a81 100644 --- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py @@ -356,7 +356,7 @@ class DepthwiseConv2DTest(test.TestCase): with self.test_session(graph=graph, use_gpu=use_gpu) as sess: tolerance = { dtypes.float16: 4e-0, - dtypes.float32: 5e-4, + dtypes.float32: 8e-4, dtypes.float64: 1e-12, }[data_type] diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD index cf2e8832fd5225e4d4be617a97b355bb410084c2..14532965d8c2c62139b3cd922acb9f90c0691d53 100644 --- a/tensorflow/python/kernel_tests/distributions/BUILD +++ b/tensorflow/python/kernel_tests/distributions/BUILD @@ -93,6 +93,7 @@ cuda_py_test( size = "small", srcs = ["categorical_test.py"], additional_deps = [ + "@absl_py//absl/testing:parameterized", "//tensorflow/python/ops/distributions", "//third_party/py/numpy", "//tensorflow/python:array_ops", @@ -134,6 +135,10 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:platform_test", ], + tags = [ + "noguitar", # b/110489471 + "notap", # b/110489471 + ], ) cuda_py_test( diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py index 095d1cde1530f15fd2a7ff4cb7f56424f276be5a..9ad77a54cbc730296508e4fe74248d2413029151 100644 --- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py +++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util @@ -57,14 +58,14 @@ def entropy(p): class BernoulliTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testP(self): p = [0.2, 0.4] dist = bernoulli.Bernoulli(probs=p) with self.test_session(): self.assertAllClose(p, self.evaluate(dist.probs)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogits(self): logits = [-42., 42.] dist = bernoulli.Bernoulli(logits=logits) @@ -82,7 +83,7 @@ class BernoulliTest(test.TestCase): with self.test_session(): self.assertAllClose(special.logit(p), self.evaluate(dist.logits)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidP(self): invalid_ps = [1.01, 2.] for p in invalid_ps: @@ -104,7 +105,7 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(probs=p) self.assertEqual(p, self.evaluate(dist.probs)) # Should not fail - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShapes(self): with self.test_session(): for batch_shape in ([], [1], [2, 3, 4]): @@ -115,7 +116,7 @@ class BernoulliTest(test.TestCase): self.assertAllEqual([], dist.event_shape.as_list()) self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDtype(self): dist = make_bernoulli([]) self.assertEqual(dist.dtype, dtypes.int32) @@ -133,7 +134,7 @@ class BernoulliTest(test.TestCase): self.assertEqual(dist64.dtype, dist64.sample(5).dtype) self.assertEqual(dist64.dtype, dist64.mode().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testPmf(self, **kwargs): dist = bernoulli.Bernoulli(**kwargs) with self.test_session(): @@ -174,7 +175,7 @@ class BernoulliTest(test.TestCase): p: [0.2, 0.3, 0.4] }), [[0.2, 0.7, 0.4]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPmfInvalid(self): p = [0.1, 0.2, 0.7] with self.test_session(): @@ -184,7 +185,7 @@ class BernoulliTest(test.TestCase): with self.assertRaisesOpError("Elements cannot exceed 1."): self.evaluate(dist.prob([2, 0, 1])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPmfWithP(self): p = [[0.2, 0.4], [0.3, 0.6]] self._testPmf(probs=p) @@ -226,21 +227,21 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(probs=[[0.5], [0.5]]) self.assertEqual((2, 1), dist.log_prob(1).get_shape()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBoundaryConditions(self): with self.test_session(): dist = bernoulli.Bernoulli(probs=1.0) self.assertAllClose(np.nan, self.evaluate(dist.log_prob(0))) self.assertAllClose([np.nan], [self.evaluate(dist.log_prob(1))]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEntropyNoBatch(self): p = 0.2 dist = bernoulli.Bernoulli(probs=p) with self.test_session(): self.assertAllClose(self.evaluate(dist.entropy()), entropy(p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEntropyWithBatch(self): p = [[0.1, 0.7], [0.2, 0.6]] dist = bernoulli.Bernoulli(probs=p, validate_args=False) @@ -250,7 +251,7 @@ class BernoulliTest(test.TestCase): [[entropy(0.1), entropy(0.7)], [entropy(0.2), entropy(0.6)]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSampleN(self): with self.test_session(): p = [0.2, 0.6] @@ -272,6 +273,16 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(np.log([.2, .4])) self.assertAllEqual((1, 2), dist.sample(1, seed=42).get_shape().as_list()) + @test_util.run_in_graph_and_eager_modes + def testNotReparameterized(self): + p = constant_op.constant([0.2, 0.6]) + with backprop.GradientTape() as tape: + tape.watch(p) + dist = bernoulli.Bernoulli(probs=p) + samples = dist.sample(100) + grad_p = tape.gradient(samples, p) + self.assertIsNone(grad_p) + def testSampleActsLikeSampleN(self): with self.test_session() as sess: p = [0.2, 0.6] @@ -282,18 +293,18 @@ class BernoulliTest(test.TestCase): self.evaluate(dist.sample(n, seed)), self.evaluate(dist.sample(n, seed))) n = array_ops.placeholder(dtypes.int32) - sample, sample = sess.run([dist.sample(n, seed), dist.sample(n, seed)], - feed_dict={n: 1000}) - self.assertAllEqual(sample, sample) + sample1, sample2 = sess.run([dist.sample(n, seed), dist.sample(n, seed)], + feed_dict={n: 1000}) + self.assertAllEqual(sample1, sample2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMean(self): with self.test_session(): p = np.array([[0.2, 0.7], [0.5, 0.4]], dtype=np.float32) dist = bernoulli.Bernoulli(probs=p) self.assertAllEqual(self.evaluate(dist.mean()), p) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarianceAndStd(self): var = lambda p: p * (1. - p) with self.test_session(): @@ -310,7 +321,7 @@ class BernoulliTest(test.TestCase): [np.sqrt(var(0.5)), np.sqrt(var(0.4))]], dtype=np.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBernoulliBernoulliKL(self): batch_size = 6 a_p = np.array([0.5] * batch_size, dtype=np.float32) diff --git a/tensorflow/python/kernel_tests/distributions/beta_test.py b/tensorflow/python/kernel_tests/distributions/beta_test.py index 4bc8303ebb6939f3f8e2637120b6510c225c2f12..36f3ffc333f74e3f6e672b6ba1591bf8de08a010 100644 --- a/tensorflow/python/kernel_tests/distributions/beta_test.py +++ b/tensorflow/python/kernel_tests/distributions/beta_test.py @@ -21,6 +21,7 @@ import importlib import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape @@ -282,6 +283,18 @@ class BetaTest(test.TestCase): self.assertAllClose( np.cov(sample_values, rowvar=0), stats.beta.var(a, b), atol=1e-1) + def testBetaFullyReparameterized(self): + a = constant_op.constant(1.0) + b = constant_op.constant(2.0) + with backprop.GradientTape() as tape: + tape.watch(a) + tape.watch(b) + beta = beta_lib.Beta(a, b) + samples = beta.sample(100) + grad_a, grad_b = tape.gradient(samples, [a, b]) + self.assertIsNotNone(grad_a) + self.assertIsNotNone(grad_b) + # Test that sampling with the same seed twice gives the same results. def testBetaSampleMultipleTimes(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/distributions/categorical_test.py b/tensorflow/python/kernel_tests/distributions/categorical_test.py index ca2358fe99934e110ba743c6085d1f25ff0f5e5e..d8939433ce68ffa561e8e2200826f88dbe283ac2 100644 --- a/tensorflow/python/kernel_tests/distributions/categorical_test.py +++ b/tensorflow/python/kernel_tests/distributions/categorical_test.py @@ -18,8 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util @@ -40,7 +42,7 @@ def make_categorical(batch_shape, num_classes, dtype=dtypes.int32): return categorical.Categorical(logits, dtype=dtype) -class CategoricalTest(test.TestCase): +class CategoricalTest(test.TestCase, parameterized.TestCase): def testP(self): p = [0.2, 0.8] @@ -131,7 +133,7 @@ class CategoricalTest(test.TestCase): with self.test_session(): self.assertAllClose(dist.prob(0).eval(), 0.2) - def testCDFWithDynamicEventShape(self): + def testCDFWithDynamicEventShapeKnownNdims(self): """Test that dynamically-sized events with unknown shape work.""" batch_size = 2 histograms = array_ops.placeholder(dtype=dtypes.float32, @@ -167,6 +169,21 @@ class CategoricalTest(test.TestCase): self.assertAllClose(actual_cdf_one, expected_cdf_one) self.assertAllClose(actual_cdf_two, expected_cdf_two) + @parameterized.named_parameters( + ("test1", [0, 1], [[0.5, 0.3, 0.2], [1.0, 0.0, 0.0]], [0.0, 1.0]), + ("test2", [2, 5], [[0.9, 0.0, 0.0, 0.0, 0.0, 0.1], + [0.15, 0.2, 0.05, 0.35, 0.13, 0.12]], [0.9, 0.88])) + def testCDFWithDynamicEventShapeUnknownNdims( + self, events, histograms, expected_cdf): + """Test that dynamically-sized events with unknown shape work.""" + event_ph = array_ops.placeholder_with_default(events, shape=None) + histograms_ph = array_ops.placeholder_with_default(histograms, shape=None) + dist = categorical.Categorical(probs=histograms_ph) + cdf_op = dist.cdf(event_ph) + + actual_cdf = self.evaluate(cdf_op) + self.assertAllClose(actual_cdf, expected_cdf) + def testCDFWithBatch(self): histograms = [[0.1, 0.2, 0.3, 0.25, 0.15], [0.0, 0.75, 0.2, 0.05, 0.0]] @@ -360,6 +377,15 @@ class CategoricalTest(test.TestCase): self.assertAllClose( [0.4**2 + 0.6**2], [prob_val[:, :, :, 1].mean()], atol=1e-2) + def testNotReparameterized(self): + p = constant_op.constant([0.3, 0.3, 0.4]) + with backprop.GradientTape() as tape: + tape.watch(p) + dist = categorical.Categorical(p) + samples = dist.sample(100) + grad_p = tape.gradient(samples, p) + self.assertIsNone(grad_p) + def testLogPMFBroadcasting(self): with self.test_session(): # 1 x 2 x 2 diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py index 7922fb0606c6f4b475b25da716d5f9a169e213b5..1b9edcc85a7581de1cb1bd93fdbb9d47b8d1b84a 100644 --- a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py @@ -17,6 +17,9 @@ from __future__ import division from __future__ import print_function import numpy as np + +from tensorflow.python.eager import backprop +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops @@ -250,10 +253,10 @@ class DirichletMultinomialTest(test.TestCase): dist.variance(), dist.stddev(), ]) - self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.04) - self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.05) - self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.05) - self.assertAllClose(sample_stddev_, analytic_stddev, atol=0., rtol=0.02) + self.assertAllClose(sample_mean_, analytic_mean, atol=0.04, rtol=0.) + self.assertAllClose(sample_cov_, analytic_cov, atol=0.05, rtol=0.) + self.assertAllClose(sample_var_, analytic_var, atol=0.05, rtol=0.) + self.assertAllClose(sample_stddev_, analytic_stddev, atol=0.02, rtol=0.) def testCovariance(self): # Shape [2] @@ -442,7 +445,7 @@ class DirichletMultinomialTest(test.TestCase): dist.covariance(), ]) self.assertAllEqual([4, 3, 2], sample_mean.get_shape()) - self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.15) + self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.20) self.assertAllEqual([4, 3, 2, 2], sample_covariance.get_shape()) self.assertAllClose( actual_covariance_, sample_covariance_, atol=0., rtol=0.20) @@ -470,10 +473,25 @@ class DirichletMultinomialTest(test.TestCase): dist.covariance(), ]) self.assertAllEqual([4], sample_mean.get_shape()) - self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.05) + self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.20) self.assertAllEqual([4, 4], sample_covariance.get_shape()) self.assertAllClose( - actual_covariance_, sample_covariance_, atol=0., rtol=0.15) + actual_covariance_, sample_covariance_, atol=0., rtol=0.20) + + def testNotReparameterized(self): + total_count = constant_op.constant(5.0) + concentration = constant_op.constant([0.1, 0.1, 0.1]) + with backprop.GradientTape() as tape: + tape.watch(total_count) + tape.watch(concentration) + dist = ds.DirichletMultinomial( + total_count=total_count, + concentration=concentration) + samples = dist.sample(100) + grad_total_count, grad_concentration = tape.gradient( + samples, [total_count, concentration]) + self.assertIsNone(grad_total_count) + self.assertIsNone(grad_concentration) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_test.py index bcec6ef610d0389f4b0f164ff4ab1a1cd1f6d1e5..67ed0447ede39d7f0738c8caf3cc665bcfe5fd0b 100644 --- a/tensorflow/python/kernel_tests/distributions/dirichlet_test.py +++ b/tensorflow/python/kernel_tests/distributions/dirichlet_test.py @@ -20,6 +20,7 @@ import importlib import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util @@ -190,10 +191,10 @@ class DirichletTest(test.TestCase): dist.stddev(), ]) - self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.04) - self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.06) - self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.03) - self.assertAllClose(sample_stddev_, analytic_stddev, atol=0., rtol=0.02) + self.assertAllClose(sample_mean_, analytic_mean, atol=0.04, rtol=0.) + self.assertAllClose(sample_cov_, analytic_cov, atol=0.06, rtol=0.) + self.assertAllClose(sample_var_, analytic_var, atol=0.03, rtol=0.) + self.assertAllClose(sample_stddev_, analytic_stddev, atol=0.02, rtol=0.) def testVariance(self): with self.test_session(): @@ -264,6 +265,15 @@ class DirichletTest(test.TestCase): a=1., b=2.).cdf)[0], 0.01) + def testDirichletFullyReparameterized(self): + alpha = constant_op.constant([1.0, 2.0, 3.0]) + with backprop.GradientTape() as tape: + tape.watch(alpha) + dirichlet = dirichlet_lib.Dirichlet(alpha) + samples = dirichlet.sample(100) + grad_alpha = tape.gradient(samples, alpha) + self.assertIsNotNone(grad_alpha) + def testDirichletDirichletKL(self): conc1 = np.array([[1., 2., 3., 1.5, 2.5, 3.5], [1.5, 2.5, 3.5, 4.5, 5.5, 6.5]]) diff --git a/tensorflow/python/kernel_tests/distributions/exponential_test.py b/tensorflow/python/kernel_tests/distributions/exponential_test.py index ebcd41b0e24ae8093752c84cf5077029f2ac9330..850da3e9697ab5f087761e9988094a3015636c36 100644 --- a/tensorflow/python/kernel_tests/distributions/exponential_test.py +++ b/tensorflow/python/kernel_tests/distributions/exponential_test.py @@ -23,6 +23,7 @@ import importlib import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import nn_ops @@ -163,6 +164,15 @@ class ExponentialTest(test.TestCase): stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01) + def testFullyReparameterized(self): + lam = constant_op.constant([0.1, 1.0]) + with backprop.GradientTape() as tape: + tape.watch(lam) + exponential = exponential_lib.Exponential(rate=lam) + samples = exponential.sample(100) + grad_lam = tape.gradient(samples, lam) + self.assertIsNotNone(grad_lam) + def testExponentialWithSoftplusRate(self): with self.test_session(): lam = [-2.2, -3.4] diff --git a/tensorflow/python/kernel_tests/distributions/gamma_test.py b/tensorflow/python/kernel_tests/distributions/gamma_test.py index 5e4813ac0762d2855d7fbe6754fe1466c29c06c9..297e20264c6d36f5b9098005393302337e3d1315 100644 --- a/tensorflow/python/kernel_tests/distributions/gamma_test.py +++ b/tensorflow/python/kernel_tests/distributions/gamma_test.py @@ -21,9 +21,10 @@ import importlib import numpy as np -from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import gamma as gamma_lib @@ -45,6 +46,7 @@ special = try_import("scipy.special") stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class GammaTest(test.TestCase): def testGammaShape(self): @@ -53,9 +55,9 @@ class GammaTest(test.TestCase): beta = constant_op.constant(11.0) gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) - self.assertEqual(gamma.batch_shape_tensor().eval(), (5,)) + self.assertEqual(self.evaluate(gamma.batch_shape_tensor()), (5,)) self.assertEqual(gamma.batch_shape, tensor_shape.TensorShape([5])) - self.assertAllEqual(gamma.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(gamma.event_shape_tensor()), []) self.assertEqual(gamma.event_shape, tensor_shape.TensorShape([])) def testGammaLogPDF(self): @@ -74,8 +76,8 @@ class GammaTest(test.TestCase): if not stats: return expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v) - self.assertAllClose(log_pdf.eval(), expected_log_pdf) - self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) + self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) + self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testGammaLogPDFMultidimensional(self): with self.test_session(): @@ -87,10 +89,10 @@ class GammaTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) log_pdf = gamma.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = gamma.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: return @@ -108,10 +110,10 @@ class GammaTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) log_pdf = gamma.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = gamma.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: @@ -135,7 +137,7 @@ class GammaTest(test.TestCase): if not stats: return expected_cdf = stats.gamma.cdf(x, alpha_v, scale=1 / beta_v) - self.assertAllClose(cdf.eval(), expected_cdf) + self.assertAllClose(self.evaluate(cdf), expected_cdf) def testGammaMean(self): with self.test_session(): @@ -146,7 +148,7 @@ class GammaTest(test.TestCase): if not stats: return expected_means = stats.gamma.mean(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.mean().eval(), expected_means) + self.assertAllClose(self.evaluate(gamma.mean()), expected_means) def testGammaModeAllowNanStatsIsFalseWorksWhenAllBatchMembersAreDefined(self): with self.test_session(): @@ -155,7 +157,7 @@ class GammaTest(test.TestCase): gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v) expected_modes = (alpha_v - 1) / beta_v self.assertEqual(gamma.mode().get_shape(), (3,)) - self.assertAllClose(gamma.mode().eval(), expected_modes) + self.assertAllClose(self.evaluate(gamma.mode()), expected_modes) def testGammaModeAllowNanStatsFalseRaisesForUndefinedBatchMembers(self): with self.test_session(): @@ -166,7 +168,7 @@ class GammaTest(test.TestCase): rate=beta_v, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): - gamma.mode().eval() + self.evaluate(gamma.mode()) def testGammaModeAllowNanStatsIsTrueReturnsNaNforUndefinedBatchMembers(self): with self.test_session(): @@ -179,7 +181,7 @@ class GammaTest(test.TestCase): expected_modes = (alpha_v - 1) / beta_v expected_modes[0] = np.nan self.assertEqual(gamma.mode().get_shape(), (3,)) - self.assertAllClose(gamma.mode().eval(), expected_modes) + self.assertAllClose(self.evaluate(gamma.mode()), expected_modes) def testGammaVariance(self): with self.test_session(): @@ -190,7 +192,7 @@ class GammaTest(test.TestCase): if not stats: return expected_variances = stats.gamma.var(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.variance().eval(), expected_variances) + self.assertAllClose(self.evaluate(gamma.variance()), expected_variances) def testGammaStd(self): with self.test_session(): @@ -201,7 +203,7 @@ class GammaTest(test.TestCase): if not stats: return expected_stddev = stats.gamma.std(alpha_v, scale=1. / beta_v) - self.assertAllClose(gamma.stddev().eval(), expected_stddev) + self.assertAllClose(self.evaluate(gamma.stddev()), expected_stddev) def testGammaEntropy(self): with self.test_session(): @@ -212,10 +214,10 @@ class GammaTest(test.TestCase): if not stats: return expected_entropy = stats.gamma.entropy(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.entropy().eval(), expected_entropy) + self.assertAllClose(self.evaluate(gamma.entropy()), expected_entropy) def testGammaSampleSmallAlpha(self): - with session.Session(): + with self.test_session(): alpha_v = 0.05 beta_v = 1.0 alpha = constant_op.constant(alpha_v) @@ -223,7 +225,7 @@ class GammaTest(test.TestCase): n = 100000 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertTrue(self._kstest(alpha_v, beta_v, sample_values)) @@ -240,7 +242,7 @@ class GammaTest(test.TestCase): atol=.15) def testGammaSample(self): - with session.Session(): + with self.test_session(): alpha_v = 4.0 beta_v = 3.0 alpha = constant_op.constant(alpha_v) @@ -248,7 +250,7 @@ class GammaTest(test.TestCase): n = 100000 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertTrue(self._kstest(alpha_v, beta_v, sample_values)) @@ -264,14 +266,26 @@ class GammaTest(test.TestCase): stats.gamma.var(alpha_v, scale=1 / beta_v), atol=.15) + def testGammaFullyReparameterized(self): + alpha = constant_op.constant(4.0) + beta = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(alpha) + tape.watch(beta) + gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) + samples = gamma.sample(100) + grad_alpha, grad_beta = tape.gradient(samples, [alpha, beta]) + self.assertIsNotNone(grad_alpha) + self.assertIsNotNone(grad_beta) + def testGammaSampleMultiDimensional(self): - with session.Session(): + with self.test_session(): alpha_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 beta_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v) n = 10000 samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(alpha_v + beta_v) # 10 x 100 @@ -283,11 +297,11 @@ class GammaTest(test.TestCase): sample_values.mean(axis=0), stats.gamma.mean( alpha_bc, scale=1 / beta_bc), - rtol=.035) + atol=0., rtol=.05) self.assertAllClose( sample_values.var(axis=0), stats.gamma.var(alpha_bc, scale=1 / beta_bc), - atol=4.5) + atol=10.0, rtol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(alpha_v, [-1])): @@ -306,12 +320,12 @@ class GammaTest(test.TestCase): return ks < 0.02 def testGammaPdfOfSampleMultiDims(self): - with session.Session() as sess: + with self.test_session(): gamma = gamma_lib.Gamma(concentration=[7., 11.], rate=[[5.], [6.]]) num = 50000 samples = gamma.sample(num, seed=137) pdfs = gamma.prob(samples) - sample_vals, pdf_vals = sess.run([samples, pdfs]) + sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.get_shape(), (num, 2, 2)) self.assertEqual(pdfs.get_shape(), (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) @@ -345,18 +359,18 @@ class GammaTest(test.TestCase): with self.test_session(): alpha_v = constant_op.constant(0.0, name="alpha") beta_v = constant_op.constant(1.0, name="beta") - gamma = gamma_lib.Gamma(concentration=alpha_v, - rate=beta_v, - validate_args=True) - with self.assertRaisesOpError("alpha"): - gamma.mean().eval() + with self.assertRaisesOpError("x > 0"): + gamma = gamma_lib.Gamma(concentration=alpha_v, + rate=beta_v, + validate_args=True) + self.evaluate(gamma.mean()) alpha_v = constant_op.constant(1.0, name="alpha") beta_v = constant_op.constant(0.0, name="beta") - gamma = gamma_lib.Gamma(concentration=alpha_v, - rate=beta_v, - validate_args=True) - with self.assertRaisesOpError("beta"): - gamma.mean().eval() + with self.assertRaisesOpError("x > 0"): + gamma = gamma_lib.Gamma(concentration=alpha_v, + rate=beta_v, + validate_args=True) + self.evaluate(gamma.mean()) def testGammaWithSoftplusConcentrationRate(self): with self.test_session(): @@ -364,10 +378,10 @@ class GammaTest(test.TestCase): beta_v = constant_op.constant([1.0, -3.6], name="beta") gamma = gamma_lib.GammaWithSoftplusConcentrationRate( concentration=alpha_v, rate=beta_v) - self.assertAllEqual(nn_ops.softplus(alpha_v).eval(), - gamma.concentration.eval()) - self.assertAllEqual(nn_ops.softplus(beta_v).eval(), - gamma.rate.eval()) + self.assertAllEqual(self.evaluate(nn_ops.softplus(alpha_v)), + self.evaluate(gamma.concentration)) + self.assertAllEqual(self.evaluate(nn_ops.softplus(beta_v)), + self.evaluate(gamma.rate)) def testGammaGammaKL(self): alpha0 = np.array([3.]) @@ -377,15 +391,15 @@ class GammaTest(test.TestCase): beta1 = np.array([0.5, 1., 1.5, 2., 2.5, 3.]) # Build graph. - with self.test_session() as sess: + with self.test_session(): g0 = gamma_lib.Gamma(concentration=alpha0, rate=beta0) g1 = gamma_lib.Gamma(concentration=alpha1, rate=beta1) x = g0.sample(int(1e4), seed=0) kl_sample = math_ops.reduce_mean(g0.log_prob(x) - g1.log_prob(x), 0) kl_actual = kullback_leibler.kl_divergence(g0, g1) - # Execute graph. - [kl_sample_, kl_actual_] = sess.run([kl_sample, kl_actual]) + # Execute graph. + [kl_sample_, kl_actual_] = self.evaluate([kl_sample, kl_actual]) self.assertEqual(beta0.shape, kl_actual.get_shape()) @@ -399,7 +413,7 @@ class GammaTest(test.TestCase): + alpha0 * (beta1 / beta0 - 1.)) self.assertAllClose(kl_expected, kl_actual_, atol=0., rtol=1e-6) - self.assertAllClose(kl_sample_, kl_actual_, atol=0., rtol=1e-2) + self.assertAllClose(kl_sample_, kl_actual_, atol=0., rtol=1e-1) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/laplace_test.py b/tensorflow/python/kernel_tests/distributions/laplace_test.py index 918c7f63f2065525338632ba68cb180c7c50dea6..24b243f647e495c47d57f914951263e3ee4ca7a5 100644 --- a/tensorflow/python/kernel_tests/distributions/laplace_test.py +++ b/tensorflow/python/kernel_tests/distributions/laplace_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util @@ -255,6 +256,18 @@ class LaplaceTest(test.TestCase): atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) + def testLaplaceFullyReparameterized(self): + loc = constant_op.constant(4.0) + scale = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(loc) + tape.watch(scale) + laplace = laplace_lib.Laplace(loc=loc, scale=scale) + samples = laplace.sample(100) + grad_loc, grad_scale = tape.gradient(samples, [loc, scale]) + self.assertIsNotNone(grad_loc) + self.assertIsNotNone(grad_scale) + def testLaplaceSampleMultiDimensional(self): with session.Session(): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 diff --git a/tensorflow/python/kernel_tests/distributions/multinomial_test.py b/tensorflow/python/kernel_tests/distributions/multinomial_test.py index e24e8ade73a7ad762c877214f5ec3ee0848863fe..bfd40ba2b7a5d32e957507b36d44e1198bd3867f 100644 --- a/tensorflow/python/kernel_tests/distributions/multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/multinomial_test.py @@ -18,6 +18,8 @@ from __future__ import print_function import numpy as np +from tensorflow.python.eager import backprop +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops @@ -310,10 +312,10 @@ class MultinomialTest(test.TestCase): dist.covariance(), ]) self.assertAllEqual([4, 3, 2], sample_mean.get_shape()) - self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.07) + self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.10) self.assertAllEqual([4, 3, 2, 2], sample_covariance.get_shape()) self.assertAllClose( - actual_covariance_, sample_covariance_, atol=0., rtol=0.10) + actual_covariance_, sample_covariance_, atol=0., rtol=0.20) def testSampleUnbiasedScalarBatch(self): with self.test_session() as sess: @@ -338,10 +340,24 @@ class MultinomialTest(test.TestCase): dist.covariance(), ]) self.assertAllEqual([4], sample_mean.get_shape()) - self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.07) + self.assertAllClose(actual_mean_, sample_mean_, atol=0., rtol=0.10) self.assertAllEqual([4, 4], sample_covariance.get_shape()) self.assertAllClose( - actual_covariance_, sample_covariance_, atol=0., rtol=0.10) + actual_covariance_, sample_covariance_, atol=0., rtol=0.20) + + def testNotReparameterized(self): + total_count = constant_op.constant(5.0) + p = constant_op.constant([0.2, 0.6]) + with backprop.GradientTape() as tape: + tape.watch(total_count) + tape.watch(p) + dist = multinomial.Multinomial( + total_count=total_count, + probs=p) + samples = dist.sample(100) + grad_total_count, grad_p = tape.gradient(samples, [total_count, p]) + self.assertIsNone(grad_total_count) + self.assertIsNone(grad_p) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/normal_test.py b/tensorflow/python/kernel_tests/distributions/normal_test.py index d793e03272909cc97543e313041b6ae7f487ae3f..7ff48c0c10f4d2cd18072a22cdcef0fefc530eae 100644 --- a/tensorflow/python/kernel_tests/distributions/normal_test.py +++ b/tensorflow/python/kernel_tests/distributions/normal_test.py @@ -23,6 +23,7 @@ import math import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -77,20 +78,20 @@ class NormalTest(test.TestCase): self.assertEqual(expected, mu_shape) self.assertEqual(expected, sigma_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testParamShapes(self): sample_shape = [10, 3, 4] self._testParamShapes(sample_shape, sample_shape) self._testParamShapes(constant_op.constant(sample_shape), sample_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testParamStaticShapes(self): sample_shape = [10, 3, 4] self._testParamStaticShapes(sample_shape, sample_shape) self._testParamStaticShapes( tensor_shape.TensorShape(sample_shape), sample_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalWithSoftplusScale(self): with self.test_session(): mu = array_ops.zeros((10, 3)) @@ -100,7 +101,7 @@ class NormalTest(test.TestCase): self.assertAllEqual( self.evaluate(nn_ops.softplus(rho)), self.evaluate(normal.scale)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogPDF(self): with self.test_session(): batch_size = 6 @@ -134,7 +135,7 @@ class NormalTest(test.TestCase): self.assertAllClose(expected_log_pdf, self.evaluate(log_pdf)) self.assertAllClose(np.exp(expected_log_pdf), self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogPDFMultidimensional(self): with self.test_session(): batch_size = 6 @@ -172,7 +173,7 @@ class NormalTest(test.TestCase): self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalCDF(self): with self.test_session(): batch_size = 50 @@ -194,7 +195,7 @@ class NormalTest(test.TestCase): expected_cdf = stats.norm(mu, sigma).cdf(x) self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -217,7 +218,7 @@ class NormalTest(test.TestCase): expected_sf = stats.norm(mu, sigma).sf(x) self.assertAllClose(expected_sf, self.evaluate(sf), atol=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogCDF(self): with self.test_session(): batch_size = 50 @@ -239,7 +240,7 @@ class NormalTest(test.TestCase): if not stats: return expected_cdf = stats.norm(mu, sigma).logcdf(x) - self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0, rtol=1e-5) + self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0, rtol=1e-3) def testFiniteGradientAtDifficultPoints(self): for dtype in [np.float32, np.float64]: @@ -261,7 +262,7 @@ class NormalTest(test.TestCase): self.assertAllFinite(grads[0]) self.assertAllFinite(grads[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -285,7 +286,7 @@ class NormalTest(test.TestCase): expected_sf = stats.norm(mu, sigma).logsf(x) self.assertAllClose(expected_sf, self.evaluate(sf), atol=0, rtol=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalEntropyWithScalarInputs(self): # Scipy.stats.norm cannot deal with the shapes in the other test. with self.test_session(): @@ -307,7 +308,7 @@ class NormalTest(test.TestCase): expected_entropy = stats.norm(mu_v, sigma_v).entropy() self.assertAllClose(expected_entropy, self.evaluate(entropy)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalEntropy(self): with self.test_session(): mu_v = np.array([1.0, 1.0, 1.0]) @@ -328,7 +329,7 @@ class NormalTest(test.TestCase): self.assertAllEqual(normal.batch_shape, entropy.get_shape()) self.assertAllEqual(normal.batch_shape, self.evaluate(entropy).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalMeanAndMode(self): with self.test_session(): # Mu will be broadcast to [7, 7, 7]. @@ -343,7 +344,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.mode().get_shape()) self.assertAllEqual([7., 7, 7], self.evaluate(normal.mode())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalQuantile(self): with self.test_session(): batch_size = 52 @@ -395,7 +396,7 @@ class NormalTest(test.TestCase): def testQuantileFiniteGradientAtDifficultPointsFloat64(self): self._baseQuantileFiniteGradientAtDifficultPoints(np.float64) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalVariance(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -407,7 +408,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.variance().get_shape()) self.assertAllEqual([49., 49, 49], self.evaluate(normal.variance())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalStandardDeviation(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -419,7 +420,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.stddev().get_shape()) self.assertAllEqual([7., 7, 7], self.evaluate(normal.stddev())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalSample(self): with self.test_session(): mu = constant_op.constant(3.0) @@ -453,7 +454,19 @@ class NormalTest(test.TestCase): self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) - @test_util.run_in_graph_and_eager_modes() + def testNormalFullyReparameterized(self): + mu = constant_op.constant(4.0) + sigma = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(mu) + tape.watch(sigma) + normal = normal_lib.Normal(loc=mu, scale=sigma) + samples = normal.sample(100) + grad_mu, grad_sigma = tape.gradient(samples, [mu, sigma]) + self.assertIsNotNone(grad_mu) + self.assertIsNotNone(grad_sigma) + + @test_util.run_in_graph_and_eager_modes def testNormalSampleMultiDimensional(self): with self.test_session(): batch_size = 2 @@ -489,7 +502,7 @@ class NormalTest(test.TestCase): self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNegativeSigmaFails(self): with self.test_session(): with self.assertRaisesOpError("Condition x > 0 did not hold"): @@ -497,7 +510,7 @@ class NormalTest(test.TestCase): loc=[1.], scale=[-5.], validate_args=True, name="G") self.evaluate(normal.mean()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalShape(self): with self.test_session(): mu = constant_op.constant([-3.0] * 5) @@ -524,7 +537,7 @@ class NormalTest(test.TestCase): feed_dict={mu: 5.0, sigma: [1.0, 2.0]}), [2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalNormalKL(self): batch_size = 6 mu_a = np.array([3.0] * batch_size) diff --git a/tensorflow/python/kernel_tests/distributions/special_math_test.py b/tensorflow/python/kernel_tests/distributions/special_math_test.py index 4565bf5c4669b4d416049816046f6f8ed187270d..a634194ce5293f4d7e7a68aa661080ed06493297 100644 --- a/tensorflow/python/kernel_tests/distributions/special_math_test.py +++ b/tensorflow/python/kernel_tests/distributions/special_math_test.py @@ -89,7 +89,7 @@ class NdtriTest(test.TestCase): all_true = np.ones_like(is_finite, dtype=np.bool) self.assertAllEqual(all_true, is_finite) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtri(self): """Verifies that ndtri computation is correct.""" with self.test_session(): @@ -138,11 +138,11 @@ class NdtriTest(test.TestCase): lambda x: special_math.ndtri(x), p) # pylint: disable=unnecessary-lambda self.assertAllFinite(self.evaluate(grads[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtriFiniteGradientFloat32(self): self._baseNdtriFiniteGradientTest(np.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtriFiniteGradientFloat64(self): self._baseNdtriFiniteGradientTest(np.float64) diff --git a/tensorflow/python/kernel_tests/distributions/student_t_test.py b/tensorflow/python/kernel_tests/distributions/student_t_test.py index a4fdb658e857d832d5bf69485bbfb2517646a7b7..05590542efe2623e608f783233db68240331ba20 100644 --- a/tensorflow/python/kernel_tests/distributions/student_t_test.py +++ b/tensorflow/python/kernel_tests/distributions/student_t_test.py @@ -23,6 +23,7 @@ import math import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import random_seed from tensorflow.python.framework import test_util @@ -172,11 +173,11 @@ class StudentTTest(test.TestCase): sample_values = self.evaluate(samples) n_val = 200000 self.assertEqual(sample_values.shape, (n_val,)) - self.assertAllClose(sample_values.mean(), mu_v, rtol=1e-2, atol=0) + self.assertAllClose(sample_values.mean(), mu_v, rtol=0.1, atol=0) self.assertAllClose( sample_values.var(), sigma_v**2 * df_v / (df_v - 2), - rtol=1e-2, + rtol=0.1, atol=0) self._checkKLApprox(df_v, mu_v, sigma_v, sample_values) @@ -215,11 +216,11 @@ class StudentTTest(test.TestCase): def testStudentSampleMultiDimensional(self): with self.test_session(): batch_size = 7 - df = constant_op.constant([[3., 7.]] * batch_size) + df = constant_op.constant([[5., 7.]] * batch_size) mu = constant_op.constant([[3., -3.]] * batch_size) sigma = constant_op.constant([[math.sqrt(10.), math.sqrt(15.)]] * batch_size) - df_v = [3., 7.] + df_v = [5., 7.] mu_v = [3., -3.] sigma_v = [np.sqrt(10.), np.sqrt(15.)] n = constant_op.constant(200000) @@ -228,21 +229,21 @@ class StudentTTest(test.TestCase): sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (200000, batch_size, 2)) self.assertAllClose( - sample_values[:, 0, 0].mean(), mu_v[0], rtol=1e-2, atol=0) + sample_values[:, 0, 0].mean(), mu_v[0], rtol=0.1, atol=0) self.assertAllClose( sample_values[:, 0, 0].var(), sigma_v[0]**2 * df_v[0] / (df_v[0] - 2), - rtol=1e-1, + rtol=0.2, atol=0) self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 0]) self.assertAllClose( - sample_values[:, 0, 1].mean(), mu_v[1], rtol=1e-2, atol=0) + sample_values[:, 0, 1].mean(), mu_v[1], rtol=0.1, atol=0) self.assertAllClose( sample_values[:, 0, 1].var(), sigma_v[1]**2 * df_v[1] / (df_v[1] - 2), - rtol=1e-1, + rtol=0.2, atol=0) - self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 1]) + self._checkKLApprox(df_v[1], mu_v[1], sigma_v[1], sample_values[:, 0, 1]) def _checkKLApprox(self, df, mu, sigma, samples): n = samples.size @@ -272,7 +273,7 @@ class StudentTTest(test.TestCase): self.assertEqual(student.entropy().get_shape(), (3,)) self.assertEqual(student.log_prob(2.).get_shape(), (3,)) self.assertEqual(student.prob(2.).get_shape(), (3,)) - self.assertEqual(student.sample(37, seed=123456).get_shape(), (37, 3,)) + self.assertEqual(student.sample(37).get_shape(), (37, 3,)) _check(student_t.StudentT(df=[2., 3., 4.,], loc=2., scale=1.)) _check(student_t.StudentT(df=7., loc=[2., 3., 4.,], scale=1.)) @@ -445,15 +446,30 @@ class StudentTTest(test.TestCase): self.assertEqual(samples.get_shape(), (num,)) self.assertEqual(pdfs.get_shape(), (num,)) self.assertEqual(mean.get_shape(), ()) - self.assertNear(np.pi, np.mean(sample_vals), err=0.02) + self.assertNear(np.pi, np.mean(sample_vals), err=0.1) self.assertNear(np.pi, mean_val, err=1e-6) # Verify integral over sample*pdf ~= 1. # Tolerance increased since eager was getting a value of 1.002041. - self._assertIntegral(sample_vals, pdf_vals, err=3e-3) + self._assertIntegral(sample_vals, pdf_vals, err=5e-2) if not stats: return self.assertNear(stats.t.pdf(np.pi, 3., loc=np.pi), mean_pdf_val, err=1e-6) + def testFullyReparameterized(self): + df = constant_op.constant(2.0) + mu = constant_op.constant(1.0) + sigma = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(df) + tape.watch(mu) + tape.watch(sigma) + student = student_t.StudentT(df=df, loc=mu, scale=sigma) + samples = student.sample(100) + grad_df, grad_mu, grad_sigma = tape.gradient(samples, [df, mu, sigma]) + self.assertIsNotNone(grad_df) + self.assertIsNotNone(grad_mu) + self.assertIsNotNone(grad_sigma) + def testPdfOfSampleMultiDims(self): student = student_t.StudentT(df=[7., 11.], loc=[[5.], [6.]], scale=3.) self.assertAllEqual([], student.event_shape) @@ -466,22 +482,22 @@ class StudentTTest(test.TestCase): sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.get_shape(), (num, 2, 2)) self.assertEqual(pdfs.get_shape(), (num, 2, 2)) - self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03) - self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03) - self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) - self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) - self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) - self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) + self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=0.1) + self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=0.1) + self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.05) + self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.05) + self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.05) + self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.05) if not stats: return self.assertNear( stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 0]), - err=.4) + err=1.0) self.assertNear( stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 1]), - err=.4) + err=1.0) def _assertIntegral(self, sample_vals, pdf_vals, err=1.5e-3): s_p = zip(sample_vals, pdf_vals) diff --git a/tensorflow/python/kernel_tests/distributions/uniform_test.py b/tensorflow/python/kernel_tests/distributions/uniform_test.py index e74051c9013b7d51914868e66022546ae8862b60..bc9c267b9a5eac6fd8c9c4290dcc4b56865ddb50 100644 --- a/tensorflow/python/kernel_tests/distributions/uniform_test.py +++ b/tensorflow/python/kernel_tests/distributions/uniform_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import tensor_shape @@ -47,7 +48,7 @@ stats = try_import("scipy.stats") class UniformTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformRange(self): with self.test_session(): a = 3.0 @@ -57,7 +58,7 @@ class UniformTest(test.TestCase): self.assertAllClose(b, self.evaluate(uniform.high)) self.assertAllClose(b - a, self.evaluate(uniform.range())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformPDF(self): with self.test_session(): a = constant_op.constant([-3.0] * 5 + [15.0]) @@ -83,7 +84,7 @@ class UniformTest(test.TestCase): log_pdf = uniform.log_prob(x) self.assertAllClose(np.log(expected_pdf), self.evaluate(log_pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformShape(self): with self.test_session(): a = constant_op.constant([-3.0] * 5) @@ -95,7 +96,7 @@ class UniformTest(test.TestCase): self.assertAllEqual(self.evaluate(uniform.event_shape_tensor()), []) self.assertEqual(uniform.event_shape, tensor_shape.TensorShape([])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformPDFWithScalarEndpoint(self): with self.test_session(): a = constant_op.constant([0.0, 5.0]) @@ -108,7 +109,7 @@ class UniformTest(test.TestCase): pdf = uniform.prob(x) self.assertAllClose(expected_pdf, self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformCDF(self): with self.test_session(): batch_size = 6 @@ -132,7 +133,7 @@ class UniformTest(test.TestCase): log_cdf = uniform.log_cdf(x) self.assertAllClose(np.log(_expected_cdf()), self.evaluate(log_cdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformEntropy(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0]) @@ -142,7 +143,7 @@ class UniformTest(test.TestCase): expected_entropy = np.log(b_v - a_v) self.assertAllClose(expected_entropy, self.evaluate(uniform.entropy())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformAssertMaxGtMin(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0], dtype=np.float32) @@ -153,7 +154,7 @@ class UniformTest(test.TestCase): uniform = uniform_lib.Uniform(low=a_v, high=b_v, validate_args=True) self.evaluate(uniform.low) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSample(self): with self.test_session(): a = constant_op.constant([3.0, 4.0]) @@ -168,15 +169,15 @@ class UniformTest(test.TestCase): sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000, 2)) self.assertAllClose( - sample_values[::, 0].mean(), (b_v + a1_v) / 2, atol=1e-2) + sample_values[::, 0].mean(), (b_v + a1_v) / 2, atol=1e-1, rtol=0.) self.assertAllClose( - sample_values[::, 1].mean(), (b_v + a2_v) / 2, atol=1e-2) + sample_values[::, 1].mean(), (b_v + a2_v) / 2, atol=1e-1, rtol=0.) self.assertFalse( np.any(sample_values[::, 0] < a1_v) or np.any(sample_values >= b_v)) self.assertFalse( np.any(sample_values[::, 1] < a2_v) or np.any(sample_values >= b_v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testUniformSampleMultiDimensional(self): # DISABLED: Please enable this test once b/issues/30149644 is resolved. with self.test_session(): @@ -207,7 +208,7 @@ class UniformTest(test.TestCase): self.assertAllClose( sample_values[:, 0, 1].mean(), (a_v[1] + b_v[1]) / 2, atol=1e-2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformMean(self): with self.test_session(): a = 10.0 @@ -218,7 +219,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.mean()), s_uniform.mean()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformVariance(self): with self.test_session(): a = 10.0 @@ -229,7 +230,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.variance()), s_uniform.var()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformStd(self): with self.test_session(): a = 10.0 @@ -240,7 +241,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.stddev()), s_uniform.std()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformNans(self): with self.test_session(): a = 10.0 @@ -258,7 +259,7 @@ class UniformTest(test.TestCase): self.assertFalse(is_nan[0]) self.assertTrue(is_nan[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSamplePdf(self): with self.test_session(): a = 10.0 @@ -268,7 +269,7 @@ class UniformTest(test.TestCase): self.evaluate( math_ops.reduce_all(uniform.prob(uniform.sample(10)) > 0))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformBroadcasting(self): with self.test_session(): a = 10.0 @@ -279,7 +280,7 @@ class UniformTest(test.TestCase): expected_pdf = np.array([[1.0, 0.1], [0.0, 0.1], [1.0, 0.0]]) self.assertAllClose(expected_pdf, self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSampleWithShape(self): with self.test_session(): a = 10.0 @@ -299,6 +300,18 @@ class UniformTest(test.TestCase): expected_pdf = [1.0, 0.1] self.assertAllClose(expected_pdf, self.evaluate(pdf)) + def testFullyReparameterized(self): + a = constant_op.constant(0.1) + b = constant_op.constant(0.8) + with backprop.GradientTape() as tape: + tape.watch(a) + tape.watch(b) + uniform = uniform_lib.Uniform(a, b) + samples = uniform.sample(100) + grad_a, grad_b = tape.gradient(samples, [a, b]) + self.assertIsNotNone(grad_a) + self.assertIsNotNone(grad_b) + # Eager doesn't pass due to a type mismatch in one of the ops. def testUniformFloat64(self): uniform = uniform_lib.Uniform( diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py index 2f256d3e8beac145a14ca1dd63f267fb5f4ef3a5..9d38ffcb4a963efb71153f59d6269ba84a5d1379 100644 --- a/tensorflow/python/kernel_tests/distributions/util_test.py +++ b/tensorflow/python/kernel_tests/distributions/util_test.py @@ -59,65 +59,6 @@ def _logit(x): class AssertCloseTest(test.TestCase): - def testAssertCloseIntegerDtype(self): - x = array_ops.placeholder(dtypes.int32) - y = x - z = array_ops.placeholder(dtypes.int32) - feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]} - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, y)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with ops.control_dependencies([du.assert_close(y, x)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, z)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - array_ops.identity(y).eval(feed_dict=feed_dict) - - def testAssertCloseNonIntegerDtype(self): - x = array_ops.placeholder(dtypes.float32) - y = x + 1e-8 - z = array_ops.placeholder(dtypes.float32) - feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]} - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, y)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with ops.control_dependencies([du.assert_close(y, x)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, z)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - array_ops.identity(y).eval(feed_dict=feed_dict) - - @test_util.run_in_graph_and_eager_modes() - def testAssertCloseEpsilon(self): - x = [0., 5, 10, 15, 20] - # x != y - y = [0.1, 5, 10, 15, 20] - # x = z - z = [1e-8, 5, 10, 15, 20] - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, z)]): - self.evaluate(array_ops.identity(x)) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, y)]): - self.evaluate(array_ops.identity(x)) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - self.evaluate(array_ops.identity(y)) - def testAssertIntegerForm(self): # This should only be detected as an integer. x = array_ops.placeholder(dtypes.float32) @@ -150,21 +91,21 @@ class AssertCloseTest(test.TestCase): class MaybeGetStaticTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticInt(self): x = 2 self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticNumpyArray(self): x = np.array(2, dtype=np.int32) self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticConstant(self): x = constant_op.constant(2, dtype=dtypes.int32) self.assertEqual(np.array(2, dtype=np.int32), du.maybe_get_static_value(x)) @@ -179,7 +120,7 @@ class MaybeGetStaticTest(test.TestCase): class GetLogitsAndProbsTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testImproperArguments(self): with self.test_session(): with self.assertRaises(ValueError): @@ -188,7 +129,7 @@ class GetLogitsAndProbsTest(test.TestCase): with self.assertRaises(ValueError): du.get_logits_and_probs(logits=[0.1], probs=[0.1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogits(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) logits = _logit(p) @@ -200,7 +141,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(p, self.evaluate(new_p), rtol=1e-5, atol=0.) self.assertAllClose(logits, self.evaluate(new_logits), rtol=1e-5, atol=0.) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogitsMultidimensional(self): p = np.array([0.2, 0.3, 0.5], dtype=np.float32) logits = np.log(p) @@ -212,7 +153,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(self.evaluate(new_p), p) self.assertAllClose(self.evaluate(new_logits), logits) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbability(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) @@ -223,7 +164,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(_logit(p), self.evaluate(new_logits)) self.assertAllClose(p, self.evaluate(new_p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) @@ -234,7 +175,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(np.log(p), self.evaluate(new_logits)) self.assertAllClose(p, self.evaluate(new_p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityValidateArgs(self): p = [0.01, 0.2, 0.5, 0.7, .99] # Component less than 0. @@ -265,7 +206,7 @@ class GetLogitsAndProbsTest(test.TestCase): probs=p3, validate_args=False) self.evaluate(prob) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityValidateArgsMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) # Component less than 0. Still sums to 1. @@ -367,7 +308,7 @@ class EmbedCheckCategoricalEventShapeTest(test.TestCase): param) checked_param.eval(feed_dict={param: np.ones([int(2**11+1)])}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUnsupportedDtype(self): with self.test_session(): with self.assertRaises(TypeError): @@ -552,7 +493,7 @@ class RotateTransposeTest(test.TestCase): x = np.array(x) return np.transpose(x, np.roll(np.arange(len(x.shape)), shift)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRollStatic(self): with self.test_session(): if context.executing_eagerly(): diff --git a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py index 159cba5fa3d69be5e3e3b22a85138c29d03981cc..c4d4ce780be2fa5a2617874ddb608e41edf70c36 100644 --- a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py @@ -27,7 +27,6 @@ from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl import tensorflow.python.ops.data_flow_grad # pylint: disable=unused-import from tensorflow.python.platform import test -from tensorflow.python.framework import dtypes class DynamicStitchTestBase(object): diff --git a/tensorflow/python/kernel_tests/embedding_ops_test.py b/tensorflow/python/kernel_tests/embedding_ops_test.py index e53ca1dcaa520b6937aefa45e2740f1c94188b09..55d75cb4749d6f1a33d6cf7a993a336d1afcf992 100644 --- a/tensorflow/python/kernel_tests/embedding_ops_test.py +++ b/tensorflow/python/kernel_tests/embedding_ops_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import itertools +import math import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin @@ -31,6 +32,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import gradient_checker +from tensorflow.python.ops import init_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import partitioned_variables @@ -736,6 +738,222 @@ class EmbeddingLookupSparseTest(test.TestCase): x, sp_ids, sp_weights, combiner="mean") +class SafeEmbeddingLookupSparseTest(test.TestCase): + + def _random_weights(self, vocab_size=4, embed_dim=4, num_shards=1): + assert vocab_size > 0 + assert embed_dim > 0 + assert num_shards > 0 + assert num_shards <= vocab_size + + embedding_weights = partitioned_variables.create_partitioned_variables( + shape=[vocab_size, embed_dim], + slicing=[num_shards, 1], + initializer=init_ops.truncated_normal_initializer( + mean=0.0, stddev=1.0 / math.sqrt(vocab_size), dtype=dtypes.float32)) + for w in embedding_weights: + w.initializer.run() + embedding_weights = [w.eval() for w in embedding_weights] + return embedding_weights + + def _ids_and_weights_2d(self): + # Each row demonstrates a test case: + # Row 0: multiple valid ids, 1 invalid id, weighted mean + # Row 1: all ids are invalid (leaving no valid ids after pruning) + # Row 2: no ids to begin with + # Row 3: single id + # Row 4: all ids have <=0 weight + indices = [[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [4, 0], [4, 1]] + ids = [0, 1, -1, -1, 2, 0, 1] + weights = [1.0, 2.0, 1.0, 1.0, 3.0, 0.0, -0.5] + shape = [5, 4] + + sparse_ids = sparse_tensor.SparseTensor( + constant_op.constant(indices, dtypes.int64), + constant_op.constant(ids, dtypes.int64), + constant_op.constant(shape, dtypes.int64)) + + sparse_weights = sparse_tensor.SparseTensor( + constant_op.constant(indices, dtypes.int64), + constant_op.constant(weights, dtypes.float32), + constant_op.constant(shape, dtypes.int64)) + + return sparse_ids, sparse_weights + + def _ids_and_weights_3d(self): + # Each (2-D) index demonstrates a test case: + # Index 0, 0: multiple valid ids, 1 invalid id, weighted mean + # Index 0, 1: all ids are invalid (leaving no valid ids after pruning) + # Index 0, 2: no ids to begin with + # Index 1, 0: single id + # Index 1, 1: all ids have <=0 weight + # Index 1, 2: no ids to begin with + indices = [[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 1, 0], [1, 0, 0], [1, 1, 0], + [1, 1, 1]] + ids = [0, 1, -1, -1, 2, 0, 1] + weights = [1.0, 2.0, 1.0, 1.0, 3.0, 0.0, -0.5] + shape = [2, 3, 4] + + sparse_ids = sparse_tensor.SparseTensor( + constant_op.constant(indices, dtypes.int64), + constant_op.constant(ids, dtypes.int64), + constant_op.constant(shape, dtypes.int64)) + + sparse_weights = sparse_tensor.SparseTensor( + constant_op.constant(indices, dtypes.int64), + constant_op.constant(weights, dtypes.float32), + constant_op.constant(shape, dtypes.int64)) + + return sparse_ids, sparse_weights + + def test_safe_embedding_lookup_sparse_return_zero_vector(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, sparse_weights = self._ids_and_weights_2d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, sparse_weights).eval()) + + self.assertAllClose( + embedding_lookup_result, + [(1.0 * embedding_weights[0][0] + 2.0 * embedding_weights[0][1]) / + 3.0, [0] * 4, [0] * 4, embedding_weights[0][2], [0] * 4]) + + def test_safe_embedding_lookup_sparse_return_special_vector(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, sparse_weights = self._ids_and_weights_2d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, sparse_weights, default_id=3).eval()) + + self.assertAllClose( + embedding_lookup_result, + [(1.0 * embedding_weights[0][0] + 2.0 * embedding_weights[0][1]) / + 3.0, embedding_weights[0][3], embedding_weights[0][3], + embedding_weights[0][2], embedding_weights[0][3]]) + + def test_safe_embedding_lookup_sparse_no_weights(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, _ = self._ids_and_weights_2d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, None).eval()) + + self.assertAllClose( + embedding_lookup_result, + [(embedding_weights[0][0] + embedding_weights[0][1]) / 2.0, [0] * 4, + [0] * 4, embedding_weights[0][2], ( + embedding_weights[0][0] + embedding_weights[0][1]) / 2.0]) + + def test_safe_embedding_lookup_sparse_partitioned(self): + with self.test_session(): + embedding_weights = self._random_weights(num_shards=3) + sparse_ids, _ = self._ids_and_weights_2d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, None).eval()) + + embedding_weights = list(itertools.chain(*embedding_weights)) + self.assertAllClose(embedding_lookup_result, + [(embedding_weights[0] + embedding_weights[1]) / 2.0, + [0] * 4, [0] * 4, embedding_weights[2], + (embedding_weights[0] + embedding_weights[1]) / 2.0]) + + def test_safe_embedding_lookup_sparse_partitioned_inconsistent_weights(self): + with self.test_session(): + embedding_weights = self._random_weights(num_shards=3) + sparse_ids, sparse_weights = self._ids_and_weights_2d() + + embedding_weights[1] = embedding_weights[1].astype(np.float64) + self.assertRaises(TypeError, embedding_ops.safe_embedding_lookup_sparse, + embedding_weights, sparse_ids) + embedding_weights = [ + constant_op.constant(w, dtype=dtypes.float64) + for w in embedding_weights + ] + self.assertRaises(ValueError, embedding_ops.safe_embedding_lookup_sparse, + embedding_weights, sparse_ids, sparse_weights) + + def test_safe_embedding_lookup_sparse_3d_return_zero_vector(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, sparse_weights = self._ids_and_weights_3d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, sparse_weights).eval()) + + self.assertAllClose(embedding_lookup_result, [[ + (1.0 * embedding_weights[0][0] + 2.0 * embedding_weights[0][1]) / 3.0, + [0] * 4, [0] * 4 + ], [embedding_weights[0][2], [0] * 4, [0] * 4]]) + + def test_safe_embedding_lookup_sparse_3d_return_special_vector(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, sparse_weights = self._ids_and_weights_3d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, sparse_weights, default_id=3).eval()) + + self.assertAllClose( + embedding_lookup_result, + [[(1.0 * embedding_weights[0][0] + 2.0 * embedding_weights[0][1]) / + 3.0, embedding_weights[0][3], embedding_weights[0][3]], [ + embedding_weights[0][2], embedding_weights[0][3], + embedding_weights[0][3] + ]]) + + def test_safe_embedding_lookup_sparse_3d_no_weights(self): + with self.test_session(): + embedding_weights = self._random_weights() + sparse_ids, _ = self._ids_and_weights_3d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, None).eval()) + + self.assertAllClose(embedding_lookup_result, [[( + embedding_weights[0][0] + embedding_weights[0][1]) / 2.0, [0] * 4, [ + 0 + ] * 4], [ + embedding_weights[0][2], + (embedding_weights[0][0] + embedding_weights[0][1]) / 2.0, [0] * 4 + ]]) + + def test_safe_embedding_lookup_sparse_3d_partitioned(self): + with self.test_session(): + embedding_weights = self._random_weights(num_shards=3) + sparse_ids, _ = self._ids_and_weights_3d() + + embedding_lookup_result = (embedding_ops.safe_embedding_lookup_sparse( + embedding_weights, sparse_ids, None).eval()) + + embedding_weights = list(itertools.chain(*embedding_weights)) + self.assertAllClose(embedding_lookup_result, [[ + (embedding_weights[0] + embedding_weights[1]) / 2.0, [0] * 4, [0] * 4 + ], [ + embedding_weights[2], + (embedding_weights[0] + embedding_weights[1]) / 2.0, [0] * 4 + ]]) + + def test_safe_embedding_lookup_sparse_3d_partitioned_inconsistent_weights( + self): + with self.test_session(): + embedding_weights = self._random_weights(num_shards=3) + sparse_ids, sparse_weights = self._ids_and_weights_3d() + + embedding_weights[1] = embedding_weights[1].astype(np.float64) + self.assertRaises(TypeError, embedding_ops.safe_embedding_lookup_sparse, + embedding_weights, sparse_ids) + embedding_weights = [ + constant_op.constant(w, dtype=dtypes.float64) + for w in embedding_weights + ] + self.assertRaises(ValueError, embedding_ops.safe_embedding_lookup_sparse, + embedding_weights, sparse_ids, sparse_weights) + + class DynamicStitchOpTest(test.TestCase): def testCint32Cpu(self): diff --git a/tensorflow/python/kernel_tests/fifo_queue_test.py b/tensorflow/python/kernel_tests/fifo_queue_test.py index ce73e7ad3e5f822363c697609dfa163b6f13751a..9e7b5283381dd7bc0725e1ab6fb9d7d13153f02d 100644 --- a/tensorflow/python/kernel_tests/fifo_queue_test.py +++ b/tensorflow/python/kernel_tests/fifo_queue_test.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops @@ -125,12 +126,21 @@ class FIFOQueueTest(test.TestCase): q.enqueue_many([[1, 2, 3, 4], [[1, 1], [2, 2], [3, 3], [4, 4]]]).run() self.assertEqual(4, q.size().eval()) + @test_util.run_in_graph_and_eager_modes def testMultipleDequeues(self): - with self.test_session() as session: - q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) - q.enqueue_many([[1, 2, 3]]).run() - a, b, c = session.run([q.dequeue(), q.dequeue(), q.dequeue()]) - self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) + q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q.enqueue_many([[1, 2, 3]])) + a, b, c = self.evaluate([q.dequeue(), q.dequeue(), q.dequeue()]) + self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) + + @test_util.run_in_graph_and_eager_modes + def testQueuesDontShare(self): + q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q.enqueue(1)) + q2 = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) + self.evaluate(q2.enqueue(2)) + self.assertAllEqual(self.evaluate(q2.dequeue()), 2) + self.assertAllEqual(self.evaluate(q.dequeue()), 1) def testEnqueueDictWithoutNames(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py index facadc971ff516e4f9edea0c4f52ab0953ec5fce..bfd4a8fd49c22950cc2d0f0117ca635fbdcb6caa 100644 --- a/tensorflow/python/kernel_tests/functional_ops_test.py +++ b/tensorflow/python/kernel_tests/functional_ops_test.py @@ -56,7 +56,7 @@ def simple_scoped_fn(a, x): class FunctionalOpsTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_Simple(self): with self.test_session(): elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -72,7 +72,7 @@ class FunctionalOpsTest(test.TestCase): initializer=10) self.assertAllEqual(880, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -83,7 +83,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(22, r_value[0]) self.assertAllEqual(20, r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -111,7 +111,7 @@ class FunctionalOpsTest(test.TestCase): self.assertEqual(len(variables.trainable_variables()), 1) self.assertAllEqual(880, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_Simple(self): with self.test_session(): elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -127,7 +127,7 @@ class FunctionalOpsTest(test.TestCase): initializer=10) self.assertAllEqual(1282, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -138,7 +138,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(22, r_value[0]) self.assertAllEqual(20, r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -182,7 +182,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(720.0, self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_Simple(self): with self.test_session(): nums = [1, 2, 3, 4, 5, 6] @@ -202,7 +202,7 @@ class FunctionalOpsTest(test.TestCase): values=constant_op.constant([0, 1, 2]), dense_shape=[2, 2])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapOverScalarErrors(self): with self.assertRaisesRegexp(ValueError, "not scalars"): functional_ops.map_fn(lambda x: x, [1, 2]) @@ -251,7 +251,7 @@ class FunctionalOpsTest(test.TestCase): r = gradients_impl.gradients(y, elems)[0] self.assertAllEqual([4.0, 8.0, 12.0, 16.0, 20.0, 24.0], self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_SimpleNotTensor(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -260,7 +260,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual( np.array([(x + 3) * 2 for x in nums]), self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_SingleInputMultiOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -275,7 +275,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual((nums + 3) * 2, received[0]) self.assertAllEqual(-(nums + 3) * 2, received[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiOutputMismatchedDtype(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -287,7 +287,7 @@ class FunctionalOpsTest(test.TestCase): nums, dtype=[dtypes.int64, dtypes.int64]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiInputSingleOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -298,7 +298,7 @@ class FunctionalOpsTest(test.TestCase): received = self.evaluate(r) self.assertAllEqual(nums * nums + (-nums), received) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiInputSameStructureOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -313,7 +313,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(-nums, received[1]) self.assertAllEqual(nums, received[2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_Simple(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data") @@ -328,7 +328,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([2., 4., 12., 48., 240., 1440.], self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_Reverse(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data") @@ -345,7 +345,7 @@ class FunctionalOpsTest(test.TestCase): self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -357,7 +357,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([1.0, 2.0, 6.0, 24.0, 120.0, 720.0], r_value[0]) self.assertAllEqual([1.0, -2.0, 6.0, -24.0, 120.0, -720.0], r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -367,7 +367,7 @@ class FunctionalOpsTest(test.TestCase): (elems + 1, -elems), initializer) self.assertAllEqual([1.0, 1.0, 1.0, 1.0, 1.0, 1.0], self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiInputSameTypeOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -377,7 +377,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(np.cumsum(elems), r_value[0]) self.assertAllEqual(np.cumsum(-elems), r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiOutputMismatchedInitializer(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -408,7 +408,7 @@ class FunctionalOpsTest(test.TestCase): results = np.array([6, 16, 38, 84, 178, 368]) self.assertAllEqual(results, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScanFoldl_Nested(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0], name="data") @@ -467,7 +467,7 @@ class FunctionalOpsTest(test.TestCase): variables.global_variables_initializer().run() sess.run(grad) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) @@ -479,7 +479,7 @@ class FunctionalOpsTest(test.TestCase): y = functional_ops.foldl(fn, x, initializer=initializer) self.assertAllEqual(y.get_shape(), self.evaluate(y).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) @@ -491,7 +491,7 @@ class FunctionalOpsTest(test.TestCase): y = functional_ops.map_fn(lambda e: e, x) self.assertIs(None, y.get_shape().dims) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapEmptyScalar(self): with self.test_session(): map_return = functional_ops.map_fn(lambda x: 1, constant_op.constant([])) @@ -507,7 +507,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([0, 3, 2], map_return.get_shape().dims) self.assertAllEqual([0, 3, 2], self.evaluate(map_return).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScanShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) @@ -604,6 +604,25 @@ class FunctionalOpsTest(test.TestCase): mul = sess.run(remote_op) self.assertEqual(mul, [6]) + def testRemoteFunctionSameDeviceDirectSession(self): + + @function.Defun(dtypes.int32, dtypes.int32) + def _remote_fn(a, b): + return math_ops.multiply(a, b) + + with ops.device("/cpu:0"): + a = variables.Variable(2, dtype=dtypes.int32) + b = variables.Variable(3, dtype=dtypes.int32) + + with ops.device("/cpu:0"): + remote_op = functional_ops.remote_call( + args=[a, b], Tout=[dtypes.int32], f=_remote_fn, target="/cpu:0") + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + mul = sess.run(remote_op) + self.assertEqual(mul, [6]) + def testRemoteFunctionCPUGPU(self): if not test_util.is_gpu_available(): self.skipTest("No GPU available") @@ -652,6 +671,24 @@ class FunctionalOpsTest(test.TestCase): mul = sess.run(remote_op) self.assertEqual(mul, 9.0) + def testRemoteFunctionGPUCPUStrings(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + @function.Defun(dtypes.string) + def _remote_fn(inp): + return array_ops.identity(inp) + + a = array_ops.constant("a") + + with ops.device("/gpu:0"): + remote_op = functional_ops.remote_call( + args=[a], Tout=[dtypes.string], f=_remote_fn, target="/cpu:0") + + with self.test_session() as sess: + ret = sess.run(remote_op) + self.assertAllEqual(ret, [b"a"]) + def testRemoteFunctionCrossProcess(self): workers, _ = test_util.create_local_cluster(2, 1) diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index 795aa67248f66e72f8f772845c4ca5b2b1b06d3d..927ca012ae6fc876364734c6f9bafd62ccc87467 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -364,14 +364,52 @@ class UniformUnitScalingInitializationTest(test.TestCase): class VarianceScalingInitializationTest(test.TestCase): + def testTruncatedNormalDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer( + distribution='truncated_normal') + + with self.test_session(use_gpu=True), \ + test.mock.patch.object( + random_ops, 'truncated_normal', wraps=random_ops.truncated_normal) \ + as mock_truncated_normal: + x = init(shape).eval() + self.assertTrue(mock_truncated_normal.called) + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + def testNormalDistribution(self): shape = [100, 100] expect_mean = 0. expect_var = 1. / shape[0] init = init_ops.variance_scaling_initializer(distribution='normal') - with self.test_session(use_gpu=True): + with self.test_session(use_gpu=True), \ + test.mock.patch.object( + random_ops, 'truncated_normal', wraps=random_ops.truncated_normal) \ + as mock_truncated_normal: + x = init(shape).eval() + self.assertTrue(mock_truncated_normal.called) + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + + def testUntruncatedNormalDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer( + distribution='untruncated_normal') + + with self.test_session(use_gpu=True), \ + test.mock.patch.object( + random_ops, 'random_normal', wraps=random_ops.random_normal) \ + as mock_random_normal: x = init(shape).eval() + self.assertTrue(mock_random_normal.called) self.assertNear(np.mean(x), expect_mean, err=1e-2) self.assertNear(np.var(x), expect_var, err=1e-2) diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD index 0123adc2c3e5c32fd86ef11e7b1f552964232abd..69d3aa401751f56ea338a5ac4b24d65e68dbddeb 100644 --- a/tensorflow/python/kernel_tests/linalg/BUILD +++ b/tensorflow/python/kernel_tests/linalg/BUILD @@ -107,6 +107,10 @@ cuda_py_test( "//tensorflow/python:random_ops", ], shard_count = 5, + tags = [ + "noasan", + "optonly", + ], ) cuda_py_test( @@ -124,7 +128,10 @@ cuda_py_test( "//tensorflow/python:random_ops", ], shard_count = 5, - tags = ["optonly"], # Test is flaky without optimization. + tags = [ + "noasan", + "optonly", + ], ) cuda_py_test( @@ -141,6 +148,10 @@ cuda_py_test( "//tensorflow/python:platform_test", ], shard_count = 5, + tags = [ + "noasan", + "optonly", + ], ) cuda_py_test( @@ -178,6 +189,10 @@ cuda_py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", ], + tags = [ + "noasan", + "optonly", + ], ) cuda_py_test( @@ -214,4 +229,8 @@ cuda_py_test( "//tensorflow/python:platform_test", ], shard_count = 5, + tags = [ + "noasan", + "optonly", + ], ) diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py index 2b80f01b73441185281a3e2ef4db003b150c1e12..3ede2aceaa51c2795029ba13b763fed3e2ddc441 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_block_diag_test.py @@ -80,7 +80,7 @@ class SquareLinearOperatorBlockDiagTest( build_info((2, 1, 5, 5), blocks=[(2, 1, 2, 2), (1, 3, 3)]), ] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) expected_blocks = ( build_info.__dict__["blocks"] if "blocks" in build_info.__dict__ @@ -91,26 +91,19 @@ class SquareLinearOperatorBlockDiagTest( for block_shape in expected_blocks ] + lin_op_matrices = matrices + if use_placeholder: - matrices_ph = [ - array_ops.placeholder(dtype=dtype) for _ in expected_blocks - ] - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrices = self.evaluate(matrices) - operator = block_diag.LinearOperatorBlockDiag( - [linalg.LinearOperatorFullMatrix( - m_ph, is_square=True) for m_ph in matrices_ph], - is_square=True) - feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)} - else: - operator = block_diag.LinearOperatorBlockDiag( - [linalg.LinearOperatorFullMatrix( - m, is_square=True) for m in matrices]) - feed_dict = None - # Should be auto-set. - self.assertTrue(operator.is_square) + lin_op_matrices = [ + array_ops.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorFullMatrix( + l, is_square=True) for l in lin_op_matrices]) + + # Should be auto-set. + self.assertTrue(operator.is_square) # Broadcast the shapes. expected_shape = list(build_info.shape) @@ -123,7 +116,7 @@ class SquareLinearOperatorBlockDiagTest( block_diag_dense.set_shape( expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]]) - return operator, block_diag_dense, feed_dict + return operator, block_diag_dense def test_is_x_flags(self): # Matrix with two positive eigenvalues, 1, and 1. diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py index 5713d169696c78e996332b7a515a3ee2eedca839..7261d4bb3bc4aa24f51be21f9ac261549dca58d5 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py @@ -95,7 +95,7 @@ class LinearOperatorCirculantTestSelfAdjointOperator( # real, the matrix will not be real. return [dtypes.complex64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = build_info.shape # For this test class, we are creating real spectrums. # We also want the spectrum to have eigenvalues bounded away from zero. @@ -107,22 +107,18 @@ class LinearOperatorCirculantTestSelfAdjointOperator( # zero, so the operator will still be self-adjoint. spectrum = math_ops.cast(spectrum, dtype) + lin_op_spectrum = spectrum + if use_placeholder: - spectrum_ph = array_ops.placeholder(dtypes.complex64) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # it is random and we want the same value used for both mat and feed_dict. - spectrum = spectrum.eval() - operator = linalg.LinearOperatorCirculant( - spectrum_ph, is_self_adjoint=True, input_output_dtype=dtype) - feed_dict = {spectrum_ph: spectrum} - else: - operator = linalg.LinearOperatorCirculant( - spectrum, is_self_adjoint=True, input_output_dtype=dtype) - feed_dict = None + lin_op_spectrum = array_ops.placeholder_with_default( + spectrum, shape=None) + + operator = linalg.LinearOperatorCirculant( + lin_op_spectrum, is_self_adjoint=True, input_output_dtype=dtype) mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype) - return operator, mat, feed_dict + return operator, mat def test_simple_hermitian_spectrum_gives_operator_with_zero_imag_part(self): with self.test_session(): @@ -149,7 +145,7 @@ class LinearOperatorCirculantTestHermitianSpectrum( def _dtypes_to_test(self): return [dtypes.float32, dtypes.complex64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = build_info.shape # For this test class, we are creating Hermitian spectrums. # We also want the spectrum to have eigenvalues bounded away from zero. @@ -172,22 +168,18 @@ class LinearOperatorCirculantTestHermitianSpectrum( spectrum = math_ops.fft(h_c) + lin_op_spectrum = spectrum + if use_placeholder: - spectrum_ph = array_ops.placeholder(dtypes.complex64) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # it is random and we want the same value used for both mat and feed_dict. - spectrum = spectrum.eval() - operator = linalg.LinearOperatorCirculant( - spectrum_ph, input_output_dtype=dtype) - feed_dict = {spectrum_ph: spectrum} - else: - operator = linalg.LinearOperatorCirculant( - spectrum, input_output_dtype=dtype) - feed_dict = None + lin_op_spectrum = array_ops.placeholder_with_default( + spectrum, shape=None) + + operator = linalg.LinearOperatorCirculant( + lin_op_spectrum, input_output_dtype=dtype) mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype) - return operator, mat, feed_dict + return operator, mat def test_simple_hermitian_spectrum_gives_operator_with_zero_imag_part(self): with self.test_session(): @@ -213,7 +205,7 @@ class LinearOperatorCirculantTestNonHermitianSpectrum( def _dtypes_to_test(self): return [dtypes.complex64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = build_info.shape # Will be well conditioned enough to get accurate solves. spectrum = linear_operator_test_util.random_sign_uniform( @@ -222,22 +214,18 @@ class LinearOperatorCirculantTestNonHermitianSpectrum( minval=1., maxval=2.) + lin_op_spectrum = spectrum + if use_placeholder: - spectrum_ph = array_ops.placeholder(dtypes.complex64) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # it is random and we want the same value used for both mat and feed_dict. - spectrum = spectrum.eval() - operator = linalg.LinearOperatorCirculant( - spectrum_ph, input_output_dtype=dtype) - feed_dict = {spectrum_ph: spectrum} - else: - operator = linalg.LinearOperatorCirculant( - spectrum, input_output_dtype=dtype) - feed_dict = None + lin_op_spectrum = array_ops.placeholder_with_default( + spectrum, shape=None) + + operator = linalg.LinearOperatorCirculant( + lin_op_spectrum, input_output_dtype=dtype) mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype) - return operator, mat, feed_dict + return operator, mat def test_simple_hermitian_spectrum_gives_operator_with_zero_imag_part(self): with self.test_session(): @@ -432,7 +420,7 @@ class LinearOperatorCirculant2DTestHermitianSpectrum( def _dtypes_to_test(self): return [dtypes.float32, dtypes.complex64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = build_info.shape # For this test class, we are creating Hermitian spectrums. # We also want the spectrum to have eigenvalues bounded away from zero. @@ -455,22 +443,18 @@ class LinearOperatorCirculant2DTestHermitianSpectrum( spectrum = math_ops.fft2d(h_c) + lin_op_spectrum = spectrum + if use_placeholder: - spectrum_ph = array_ops.placeholder(dtypes.complex64) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # it is random and we want the same value used for both mat and feed_dict. - spectrum = spectrum.eval() - operator = linalg.LinearOperatorCirculant2D( - spectrum_ph, input_output_dtype=dtype) - feed_dict = {spectrum_ph: spectrum} - else: - operator = linalg.LinearOperatorCirculant2D( - spectrum, input_output_dtype=dtype) - feed_dict = None + lin_op_spectrum = array_ops.placeholder_with_default( + spectrum, shape=None) + + operator = linalg.LinearOperatorCirculant2D( + lin_op_spectrum, input_output_dtype=dtype) mat = self._spectrum_to_circulant_2d(spectrum, shape, dtype=dtype) - return operator, mat, feed_dict + return operator, mat class LinearOperatorCirculant2DTestNonHermitianSpectrum( @@ -486,7 +470,7 @@ class LinearOperatorCirculant2DTestNonHermitianSpectrum( def _dtypes_to_test(self): return [dtypes.complex64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = build_info.shape # Will be well conditioned enough to get accurate solves. spectrum = linear_operator_test_util.random_sign_uniform( @@ -495,22 +479,18 @@ class LinearOperatorCirculant2DTestNonHermitianSpectrum( minval=1., maxval=2.) + lin_op_spectrum = spectrum + if use_placeholder: - spectrum_ph = array_ops.placeholder(dtypes.complex64) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # it is random and we want the same value used for both mat and feed_dict. - spectrum = spectrum.eval() - operator = linalg.LinearOperatorCirculant2D( - spectrum_ph, input_output_dtype=dtype) - feed_dict = {spectrum_ph: spectrum} - else: - operator = linalg.LinearOperatorCirculant2D( - spectrum, input_output_dtype=dtype) - feed_dict = None + lin_op_spectrum = array_ops.placeholder_with_default( + spectrum, shape=None) + + operator = linalg.LinearOperatorCirculant2D( + lin_op_spectrum, input_output_dtype=dtype) mat = self._spectrum_to_circulant_2d(spectrum, shape, dtype=dtype) - return operator, mat, feed_dict + return operator, mat def test_real_hermitian_spectrum_gives_real_symmetric_operator(self): with self.test_session() as sess: diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py index f96b9ccdaacae7d8e0552ed3d74ce53808fed963..612a50bcec771f8511d20d19b312a797d531f109 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py @@ -44,7 +44,7 @@ class SquareLinearOperatorCompositionTest( self._rtol[dtypes.float32] = 1e-4 self._rtol[dtypes.complex64] = 1e-4 - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): sess = ops.get_default_session() shape = list(build_info.shape) @@ -56,33 +56,23 @@ class SquareLinearOperatorCompositionTest( for _ in range(num_operators) ] + lin_op_matrices = matrices + if use_placeholder: - matrices_ph = [ - array_ops.placeholder(dtype=dtype) for _ in range(num_operators) - ] - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrices = sess.run(matrices) - operator = linalg.LinearOperatorComposition( - [linalg.LinearOperatorFullMatrix(m_ph) for m_ph in matrices_ph], - is_square=True) - feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)} - else: - operator = linalg.LinearOperatorComposition( - [linalg.LinearOperatorFullMatrix(m) for m in matrices]) - feed_dict = None - # Should be auto-set. - self.assertTrue(operator.is_square) - - # Convert back to Tensor. Needed if use_placeholder, since then we have - # already evaluated each matrix to a numpy array. + lin_op_matrices = [ + array_ops.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = linalg.LinearOperatorComposition( + [linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices], + is_square=True) + matmul_order_list = list(reversed(matrices)) - mat = ops.convert_to_tensor(matmul_order_list[0]) + mat = matmul_order_list[0] for other_mat in matmul_order_list[1:]: mat = math_ops.matmul(other_mat, mat) - return operator, mat, feed_dict + return operator, mat def test_is_x_flags(self): # Matrix with two positive eigenvalues, 1, and 1. @@ -148,7 +138,7 @@ class NonSquareLinearOperatorCompositionTest( self._rtol[dtypes.float32] = 1e-4 self._rtol[dtypes.complex64] = 1e-4 - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): sess = ops.get_default_session() shape = list(build_info.shape) @@ -170,30 +160,22 @@ class NonSquareLinearOperatorCompositionTest( shape_2, dtype=dtype) ] + lin_op_matrices = matrices + if use_placeholder: - matrices_ph = [ - array_ops.placeholder(dtype=dtype) for _ in range(num_operators) - ] - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrices = sess.run(matrices) - operator = linalg.LinearOperatorComposition( - [linalg.LinearOperatorFullMatrix(m_ph) for m_ph in matrices_ph]) - feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)} - else: - operator = linalg.LinearOperatorComposition( - [linalg.LinearOperatorFullMatrix(m) for m in matrices]) - feed_dict = None - - # Convert back to Tensor. Needed if use_placeholder, since then we have - # already evaluated each matrix to a numpy array. + lin_op_matrices = [ + array_ops.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = linalg.LinearOperatorComposition( + [linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices]) + matmul_order_list = list(reversed(matrices)) - mat = ops.convert_to_tensor(matmul_order_list[0]) + mat = matmul_order_list[0] for other_mat in matmul_order_list[1:]: mat = math_ops.matmul(other_mat, mat) - return operator, mat, feed_dict + return operator, mat def test_static_shapes(self): operators = [ diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py index 0a0e31c716ecfa10ed93cff92fa908a240f8495e..83cc8c483f9aec6dd0ddf3f961a8180af7515e40 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py @@ -34,25 +34,21 @@ class LinearOperatorDiagTest( linear_operator_test_util.SquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) diag = linear_operator_test_util.random_sign_uniform( shape[:-1], minval=1., maxval=2., dtype=dtype) + + lin_op_diag = diag + if use_placeholder: - diag_ph = array_ops.placeholder(dtype=dtype) - # Evaluate the diag here because (i) you cannot feed a tensor, and (ii) - # diag is random and we want the same value used for both mat and - # feed_dict. - diag = diag.eval() - operator = linalg.LinearOperatorDiag(diag_ph) - feed_dict = {diag_ph: diag} - else: - operator = linalg.LinearOperatorDiag(diag) - feed_dict = None + lin_op_diag = array_ops.placeholder_with_default(diag, shape=None) + + operator = linalg.LinearOperatorDiag(lin_op_diag) - mat = array_ops.matrix_diag(diag) + matrix = array_ops.matrix_diag(diag) - return operator, mat, feed_dict + return operator, matrix def test_assert_positive_definite_raises_for_zero_eigenvalue(self): # Matrix with one positive eigenvalue and one zero eigenvalue. diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py index b3da623b5e8d8c99c6777e75e2d49f24dab1c96b..1a40a29ec6a040ca3d98e0b27492b1379d30cb4b 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops @@ -36,30 +35,20 @@ class SquareLinearOperatorFullMatrixTest( linear_operator_test_util.SquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) matrix = linear_operator_test_util.random_positive_definite_matrix( shape, dtype) + lin_op_matrix = matrix + if use_placeholder: - matrix_ph = array_ops.placeholder(dtype=dtype) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrix = matrix.eval() - operator = linalg.LinearOperatorFullMatrix(matrix_ph, is_square=True) - feed_dict = {matrix_ph: matrix} - else: - # is_square should be auto-detected here. - operator = linalg.LinearOperatorFullMatrix(matrix) - feed_dict = None + lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None) - # Convert back to Tensor. Needed if use_placeholder, since then we have - # already evaluated matrix to a numpy array. - mat = ops.convert_to_tensor(matrix) + operator = linalg.LinearOperatorFullMatrix(lin_op_matrix, is_square=True) - return operator, mat, feed_dict + return operator, matrix def test_is_x_flags(self): # Matrix with two positive eigenvalues. @@ -136,32 +125,20 @@ class SquareLinearOperatorFullMatrixSymmetricPositiveDefiniteTest( def _dtypes_to_test(self): return [dtypes.float32, dtypes.float64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) matrix = linear_operator_test_util.random_positive_definite_matrix( shape, dtype, force_well_conditioned=True) + lin_op_matrix = matrix + if use_placeholder: - matrix_ph = array_ops.placeholder(dtype=dtype) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrix = matrix.eval() - # is_square is auto-set because of self_adjoint/pd. - operator = linalg.LinearOperatorFullMatrix( - matrix_ph, is_self_adjoint=True, is_positive_definite=True) - feed_dict = {matrix_ph: matrix} - else: - operator = linalg.LinearOperatorFullMatrix( - matrix, is_self_adjoint=True, is_positive_definite=True) - feed_dict = None - - # Convert back to Tensor. Needed if use_placeholder, since then we have - # already evaluated matrix to a numpy array. - mat = ops.convert_to_tensor(matrix) - - return operator, mat, feed_dict + lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None) + + operator = linalg.LinearOperatorFullMatrix(lin_op_matrix, is_square=True) + + return operator, matrix def test_is_x_flags(self): # Matrix with two positive eigenvalues. @@ -210,26 +187,18 @@ class NonSquareLinearOperatorFullMatrixTest( linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) matrix = linear_operator_test_util.random_normal(shape, dtype=dtype) + + lin_op_matrix = matrix + if use_placeholder: - matrix_ph = array_ops.placeholder(dtype=dtype) - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrix = matrix.eval() - operator = linalg.LinearOperatorFullMatrix(matrix_ph) - feed_dict = {matrix_ph: matrix} - else: - operator = linalg.LinearOperatorFullMatrix(matrix) - feed_dict = None + lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None) - # Convert back to Tensor. Needed if use_placeholder, since then we have - # already evaluated matrix to a numpy array. - mat = ops.convert_to_tensor(matrix) + operator = linalg.LinearOperatorFullMatrix(lin_op_matrix, is_square=True) - return operator, mat, feed_dict + return operator, matrix def test_is_x_flags(self): matrix = [[3., 2., 1.], [1., 1., 1.]] diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py index 59f63f949e96991193412d3574603e58a75cb6e5..35dcf4417c313f5cbc00c8b66b4c5d1f2e157212 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py @@ -43,7 +43,7 @@ class LinearOperatorIdentityTest( # 16bit. return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) assert shape[-1] == shape[-2] @@ -54,13 +54,7 @@ class LinearOperatorIdentityTest( num_rows, batch_shape=batch_shape, dtype=dtype) mat = linalg_ops.eye(num_rows, batch_shape=batch_shape, dtype=dtype) - # Nothing to feed since LinearOperatorIdentity takes no Tensor args. - if use_placeholder: - feed_dict = {} - else: - feed_dict = None - - return operator, mat, feed_dict + return operator, mat def test_assert_positive_definite(self): with self.test_session(): @@ -261,7 +255,7 @@ class LinearOperatorScaledIdentityTest( # 16bit. return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) assert shape[-1] == shape[-2] @@ -274,24 +268,23 @@ class LinearOperatorScaledIdentityTest( multiplier = linear_operator_test_util.random_sign_uniform( shape=batch_shape, minval=1., maxval=2., dtype=dtype) - operator = linalg_lib.LinearOperatorScaledIdentity(num_rows, multiplier) # Nothing to feed since LinearOperatorScaledIdentity takes no Tensor args. + lin_op_multiplier = multiplier + if use_placeholder: - multiplier_ph = array_ops.placeholder(dtype=dtype) - multiplier = multiplier.eval() - operator = linalg_lib.LinearOperatorScaledIdentity( - num_rows, multiplier_ph) - feed_dict = {multiplier_ph: multiplier} - else: - feed_dict = None + lin_op_multiplier = array_ops.placeholder_with_default( + multiplier, shape=None) + + operator = linalg_lib.LinearOperatorScaledIdentity( + num_rows, lin_op_multiplier) multiplier_matrix = array_ops.expand_dims( array_ops.expand_dims(multiplier, -1), -1) - mat = multiplier_matrix * linalg_ops.eye( + matrix = multiplier_matrix * linalg_ops.eye( num_rows, batch_shape=batch_shape, dtype=dtype) - return operator, mat, feed_dict + return operator, matrix def test_assert_positive_definite_does_not_raise_when_positive(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py index 784c730bbc8179dd1302294b2d558e8a0c532c0c..e26b946151dd8ddb923e34352feb6b483f9752fc 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_kronecker_test.py @@ -101,7 +101,7 @@ class SquareLinearOperatorKroneckerTest( def _tests_to_skip(self): return ["det", "solve", "solve_with_broadcast"] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) expected_factors = build_info.__dict__["factors"] matrices = [ @@ -110,26 +110,15 @@ class SquareLinearOperatorKroneckerTest( for block_shape in expected_factors ] + lin_op_matrices = matrices + if use_placeholder: - matrices_ph = [ - array_ops.placeholder(dtype=dtype) for _ in expected_factors - ] - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - matrices = self.evaluate(matrices) - operator = kronecker.LinearOperatorKronecker( - [linalg.LinearOperatorFullMatrix( - m_ph, is_square=True) for m_ph in matrices_ph], - is_square=True) - feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)} - else: - operator = kronecker.LinearOperatorKronecker( - [linalg.LinearOperatorFullMatrix( - m, is_square=True) for m in matrices]) - feed_dict = None - # Should be auto-set. - self.assertTrue(operator.is_square) + lin_op_matrices = [ + array_ops.placeholder_with_default(m, shape=None) for m in matrices] + + operator = kronecker.LinearOperatorKronecker( + [linalg.LinearOperatorFullMatrix( + l, is_square=True) for l in lin_op_matrices]) matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices) @@ -138,7 +127,7 @@ class SquareLinearOperatorKroneckerTest( if not use_placeholder: kronecker_dense.set_shape(shape) - return operator, kronecker_dense, feed_dict + return operator, kronecker_dense def test_is_x_flags(self): # Matrix with two positive eigenvalues, 1, and 1. diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py index 8095f6419ef0d9543339cf1f4ee9cd4783f852b9..34b35a4ffb878c63f851f2b31491e7bfa4057417 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py @@ -68,7 +68,7 @@ class BaseLinearOperatorLowRankUpdatetest(object): build_info((3, 4, 4)), build_info((2, 1, 4, 4))] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): # Recall A = L + UDV^H shape = list(build_info.shape) diag_shape = shape[:-1] @@ -80,17 +80,17 @@ class BaseLinearOperatorLowRankUpdatetest(object): # operator, with condition number as high as 1e4. base_diag = linear_operator_test_util.random_uniform( diag_shape, minval=1e-4, maxval=1., dtype=dtype) - base_diag_ph = array_ops.placeholder(dtype=dtype) + lin_op_base_diag = base_diag # U u = linear_operator_test_util.random_normal_correlated_columns( u_perturbation_shape, dtype=dtype) - u_ph = array_ops.placeholder(dtype=dtype) + lin_op_u = u # V v = linear_operator_test_util.random_normal_correlated_columns( u_perturbation_shape, dtype=dtype) - v_ph = array_ops.placeholder(dtype=dtype) + lin_op_v = v # D if self._is_diag_update_positive: @@ -99,42 +99,25 @@ class BaseLinearOperatorLowRankUpdatetest(object): else: diag_update = linear_operator_test_util.random_normal( diag_update_shape, stddev=1e-4, dtype=dtype) - diag_update_ph = array_ops.placeholder(dtype=dtype) + lin_op_diag_update = diag_update if use_placeholder: - # Evaluate here because (i) you cannot feed a tensor, and (ii) - # values are random and we want the same value used for both mat and - # feed_dict. - base_diag = base_diag.eval() - u = u.eval() - v = v.eval() - diag_update = diag_update.eval() - - # In all cases, set base_operator to be positive definite. - base_operator = linalg.LinearOperatorDiag( - base_diag_ph, is_positive_definite=True) - - operator = linalg.LinearOperatorLowRankUpdate( - base_operator, - u=u_ph, - v=v_ph if self._use_v else None, - diag_update=diag_update_ph if self._use_diag_update else None, - is_diag_update_positive=self._is_diag_update_positive) - feed_dict = { - base_diag_ph: base_diag, - u_ph: u, - v_ph: v, - diag_update_ph: diag_update} - else: - base_operator = linalg.LinearOperatorDiag( - base_diag, is_positive_definite=True) - operator = linalg.LinearOperatorLowRankUpdate( - base_operator, - u, - v=v if self._use_v else None, - diag_update=diag_update if self._use_diag_update else None, - is_diag_update_positive=self._is_diag_update_positive) - feed_dict = None + lin_op_base_diag = array_ops.placeholder_with_default( + base_diag, shape=None) + lin_op_u = array_ops.placeholder_with_default(u, shape=None) + lin_op_v = array_ops.placeholder_with_default(v, shape=None) + lin_op_diag_update = array_ops.placeholder_with_default( + diag_update, shape=None) + + base_operator = linalg.LinearOperatorDiag( + lin_op_base_diag, is_positive_definite=True) + + operator = linalg.LinearOperatorLowRankUpdate( + base_operator, + lin_op_u, + v=lin_op_v if self._use_v else None, + diag_update=lin_op_diag_update if self._use_diag_update else None, + is_diag_update_positive=self._is_diag_update_positive) # The matrix representing L base_diag_mat = array_ops.matrix_diag(base_diag) @@ -146,28 +129,28 @@ class BaseLinearOperatorLowRankUpdatetest(object): if self._use_v and self._use_diag_update: # In this case, we have L + UDV^H and it isn't symmetric. expect_use_cholesky = False - mat = base_diag_mat + math_ops.matmul( + matrix = base_diag_mat + math_ops.matmul( u, math_ops.matmul(diag_update_mat, v, adjoint_b=True)) elif self._use_v: # In this case, we have L + UDV^H and it isn't symmetric. expect_use_cholesky = False - mat = base_diag_mat + math_ops.matmul(u, v, adjoint_b=True) + matrix = base_diag_mat + math_ops.matmul(u, v, adjoint_b=True) elif self._use_diag_update: # In this case, we have L + UDU^H, which is PD if D > 0, since L > 0. expect_use_cholesky = self._is_diag_update_positive - mat = base_diag_mat + math_ops.matmul( + matrix = base_diag_mat + math_ops.matmul( u, math_ops.matmul(diag_update_mat, u, adjoint_b=True)) else: # In this case, we have L + UU^H, which is PD since L > 0. expect_use_cholesky = True - mat = base_diag_mat + math_ops.matmul(u, u, adjoint_b=True) + matrix = base_diag_mat + math_ops.matmul(u, u, adjoint_b=True) if expect_use_cholesky: self.assertTrue(operator._use_cholesky) else: self.assertFalse(operator._use_cholesky) - return operator, mat, feed_dict + return operator, matrix class LinearOperatorLowRankUpdatetestWithDiagUseCholesky( diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py index a57d2f085e089fb913f09fdd9b07cf13aa7f3c35..167c6cacd1a5bbbaa70a7fdd236ddd70ea8cd4e8 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py @@ -38,28 +38,23 @@ class LinearOperatorLowerTriangularTest( # matrix_triangular_solve. return [dtypes.float32, dtypes.float64] - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): shape = list(build_info.shape) # Upper triangle will be nonzero, but ignored. # Use a diagonal that ensures this matrix is well conditioned. tril = linear_operator_test_util.random_tril_matrix( shape, dtype=dtype, force_well_conditioned=True, remove_upper=False) + lin_op_tril = tril + if use_placeholder: - tril_ph = array_ops.placeholder(dtype=dtype) - # Evaluate the tril here because (i) you cannot feed a tensor, and (ii) - # tril is random and we want the same value used for both mat and - # feed_dict. - tril = tril.eval() - operator = linalg.LinearOperatorLowerTriangular(tril_ph) - feed_dict = {tril_ph: tril} - else: - operator = linalg.LinearOperatorLowerTriangular(tril) - feed_dict = None + lin_op_tril = array_ops.placeholder_with_default(lin_op_tril, shape=None) + + operator = linalg.LinearOperatorLowerTriangular(lin_op_tril) - mat = array_ops.matrix_band_part(tril, -1, 0) + matrix = array_ops.matrix_band_part(tril, -1, 0) - return operator, mat, feed_dict + return operator, matrix def test_assert_non_singular(self): # Singlular matrix with one positive eigenvalue and one zero eigenvalue. diff --git a/tensorflow/python/kernel_tests/list_ops_test.py b/tensorflow/python/kernel_tests/list_ops_test.py index 49855200c2427a88a4bd582c2ef786c38a6fa76a..bf82e08551e6a276b95bf77f7932c31d7a844a78 100644 --- a/tensorflow/python/kernel_tests/list_ops_test.py +++ b/tensorflow/python/kernel_tests/list_ops_test.py @@ -46,7 +46,7 @@ def scalar_shape(): @test_util.with_c_shapes class ListOpsTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPop(self): l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, element_shape=scalar_shape()) @@ -54,14 +54,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPopGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testPushPop() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStack(self): l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, element_shape=scalar_shape()) @@ -70,14 +70,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [1.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStackGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testStack() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorListFromTensor(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape()) @@ -87,14 +87,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(self.evaluate(e), 1.0) self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFromTensorGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testTensorListFromTensor() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetItem(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape()) @@ -104,14 +104,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [3.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testGetSetItem() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUnknownShape(self): l = list_ops.empty_tensor_list( element_dtype=dtypes.float32, element_shape=-1) @@ -122,7 +122,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCPUGPUCopy(self): if not context.num_gpus(): return @@ -140,7 +140,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_pop_back( l_cpu, element_dtype=dtypes.float32)[1]), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStack(self): with context.graph_mode(), self.test_session(): tl = list_ops.empty_tensor_list( @@ -152,7 +152,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32)), [[1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackInLoop(self): with context.graph_mode(), self.test_session(): t1 = list_ops.empty_tensor_list( @@ -170,7 +170,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): s1 = list_ops.tensor_list_stack(t1, element_dtype=dtypes.int32) self.assertAllEqual(self.evaluate(s1), [0, 1, 2, 3]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackSwitchDtype(self): with context.graph_mode(), self.test_session(): list_ = list_ops.empty_tensor_list( @@ -192,7 +192,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): np_s1 = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.float32) self.assertAllEqual(self.evaluate(s1), np_s1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackInLoopSwitchDtype(self): with context.graph_mode(), self.test_session(): t1 = list_ops.empty_tensor_list( @@ -216,7 +216,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): np_s1 = np.vstack([np.arange(1, 4) * i for i in range(4)]) self.assertAllEqual(self.evaluate(s1), np_s1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSerialize(self): # pylint: disable=g-import-not-at-top try: @@ -248,7 +248,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): worker_e = array_ops.identity(e) self.assertAllEqual(self.evaluate(worker_e), [2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPopGradients(self): with backprop.GradientTape() as tape: l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, @@ -260,7 +260,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): e = 2 * e self.assertAllEqual(self.evaluate(tape.gradient(e, [c])[0]), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStackFromTensorGradients(self): with backprop.GradientTape() as tape: c = constant_op.constant([1.0, 2.0]) @@ -272,7 +272,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): grad = tape.gradient(result, [c])[0] self.assertAllEqual(self.evaluate(grad), [2.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetGradients(self): with backprop.GradientTape() as tape: c = constant_op.constant([1.0, 2.0]) @@ -288,14 +288,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0]) self.assertAllEqual(self.evaluate(grad_c2), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSetOutOfBounds(self): c = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) with self.assertRaises(errors.InvalidArgumentError): self.evaluate(list_ops.tensor_list_set_item(l, 20, 3.0)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceVariableScatterGather(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) @@ -319,7 +319,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): [[1.0, 2.0]] * 4) self.assertAllEqual(self.evaluate(updated_v_stacked), expected) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConcat(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) @@ -379,7 +379,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_int_tls, element_dtype=dtypes.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushBackBatch(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) diff --git a/tensorflow/python/kernel_tests/logging_ops_test.py b/tensorflow/python/kernel_tests/logging_ops_test.py index 28c85fa13ad100c38382d2b787ff965f9e3ca44e..e635a71c78484278b54bfc4de70e232834c37a0a 100644 --- a/tensorflow/python/kernel_tests/logging_ops_test.py +++ b/tensorflow/python/kernel_tests/logging_ops_test.py @@ -59,7 +59,7 @@ class LoggingOpsTest(test.TestCase): class PrintGradientTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPrintShape(self): inp = constant_op.constant(2.0, shape=[100, 32]) inp_printed = logging_ops.Print(inp, [inp]) diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index 1123c20a165ba93bd380fa471a8be91f7005d7bb..87fc715783b972a20465827d697cf06637588154 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -118,6 +119,14 @@ class AbsoluteDifferenceLossTest(test.TestCase): with self.test_session(): self.assertAlmostEqual(0.0, loss.eval(), 3) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerNoMemoryLeaked(self): + # This is a somewhat convoluted way of testing that nothing gets added to + # a global collection. + predictions = constant_op.constant([4, 8, 12, 8, 1, 3], shape=(2, 3)) + labels = constant_op.constant([1, 9, 2, -5, -2, 6], shape=(2, 3)) + losses.absolute_difference(labels, predictions) + class SoftmaxCrossEntropyLossTest(test.TestCase): @@ -246,6 +255,13 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): self.assertEquals(loss.op.name, 'sparse_softmax_cross_entropy_loss/value') self.assertAlmostEqual(loss.eval(), 0.0, 3) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerNoMemoryLeaked(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], + [0.0, 0.0, 10.0]]) + labels = constant_op.constant([[0], [1], [2]], dtype=dtypes.int32) + losses.sparse_softmax_cross_entropy(labels, logits) + def testAllCorrectInt64Labels(self): with self.test_session(): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index b59e3dd7e724de68ac9d6327bedbb7e2feaf399a..50154a45a8b58f270509e404737c8650cbd2c5ff 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -27,6 +27,7 @@ from six.moves import queue from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.client import session as session_lib +from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.eager import function from tensorflow.python.framework import constant_op @@ -35,6 +36,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import script_ops @@ -458,7 +460,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(initial_size, script_ops._py_funcs.size()) # ----- Tests for eager_py_func ----- - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerSingleOutputInt32(self): a = array_ops.ones((3, 3), dtype=dtypes.int32) x = array_ops.ones((3, 1), dtype=dtypes.int32) @@ -466,7 +468,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllEqual(ret, [[3], [3], [3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerSingleOutputFloat32(self): with test_util.device(use_gpu=True): a = array_ops.ones((3, 3), dtype=dtypes.float32) @@ -475,7 +477,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllClose(ret, [[3.0], [3.0], [3.0]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerArrayOutput(self): with test_util.device(use_gpu=True): a = array_ops.ones((3, 3), dtype=dtypes.float32) @@ -485,7 +487,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllEqual(ret, [[[3.0], [3.0], [3.0]]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerReturnNone(self): with test_util.device(use_gpu=True): def no_return_value(): @@ -498,7 +500,7 @@ class PyFuncTest(test.TestCase): else: self.assertIsNone(ret) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerPyFuncInDefun(self): with test_util.device(use_gpu=True): def wrapper(): @@ -510,7 +512,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(wrapped()) self.assertAllEqual(ret, [[3.0], [3.0], [3.0]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerExceptionHandling(self): with test_util.device(use_gpu=True): self._testExceptionHandling( @@ -529,11 +531,10 @@ class PyFuncTest(test.TestCase): self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerReturningVariableRaisesError(self): def return_variable(): - variable = resource_variable_ops.ResourceVariable(0.0) - return variable + return resource_variable_ops.ResourceVariable(0.0) with self.assertRaisesRegexp(errors.UnknownError, "Attempting to return a variable"): @@ -541,6 +542,99 @@ class PyFuncTest(test.TestCase): return_variable, inp=[], Tout=dtypes.float32) self.evaluate(output) + @test_util.run_in_graph_and_eager_modes + def testEagerGradientTape(self): + + def f(x): + return x**2 + + x = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(x) + y = script_ops.eager_py_func(f, inp=[x], Tout=dtypes.float32) + dy_dx = tape.gradient(y, x) + self.assertEqual(self.evaluate(dy_dx), 6.0) + + def testEagerGradientGraph(self): + + def f(x): + return x**2 + + x = constant_op.constant(3.0) + y = script_ops.eager_py_func(f, inp=[x], Tout=dtypes.float32) + dy_dx = gradients_impl.gradients(y, x)[0] + self.assertEqual(self.evaluate(dy_dx), 6.0) + + @test_util.run_in_graph_and_eager_modes + def testEagerGradientTapeMultipleArgs(self): + + def f(x, y): + return x**2 + y**2 + + x = constant_op.constant(3.0) + y = constant_op.constant(4.0) + with backprop.GradientTape() as tape: + tape.watch(x) + tape.watch(y) + z = script_ops.eager_py_func(f, inp=[x, y], Tout=dtypes.float32) + + dz_dx, dz_dy = tape.gradient(z, [x, y]) + self.assertEqual(self.evaluate(dz_dx), 6.0) + self.assertEqual(self.evaluate(dz_dy), 8.0) + + def testEagerGradientGraphMultipleArgs(self): + + def f(x, y): + return x**2 + y**2 + + x = constant_op.constant(3.0) + y = constant_op.constant(4.0) + z = script_ops.eager_py_func(f, inp=[x, y], Tout=dtypes.float32) + + dz_dx, dz_dy = gradients_impl.gradients(z, [x, y]) + self.assertEqual(self.evaluate(dz_dx), 6.0) + self.assertEqual(self.evaluate(dz_dy), 8.0) + + def testEagerGradientGraphLogHuber(self): + + def log_huber(x, m): + if math_ops.abs(x) <= m: + return x**2 + else: + return m**2 * (1 - 2 * math_ops.log(m) + math_ops.log(x**2)) + + x = array_ops.placeholder(dtypes.float32) + m = array_ops.placeholder(dtypes.float32) + + y = script_ops.eager_py_func( + func=log_huber, inp=[x, m], Tout=dtypes.float32) + dy_dx = gradients_impl.gradients(y, x)[0] + + with self.test_session() as sess: + # Takes the first branch of log_huber. + y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0}) + self.assertEqual(y, 1.0) + self.assertEqual(dy_dx, 2.0) + + def testEagerRespectsDevicePlacmentOfOp(self): + + def f(x): + return math_ops.square(x) + + def g(x): + return math_ops.add(x, x) + + with ops.device("/CPU:0"): + # Explicitly ask for the py_funcs to execute on CPU, even if + # a GPU is available. + x = array_ops.placeholder(dtypes.float32) + y = script_ops.eager_py_func(func=f, inp=[x], Tout=dtypes.float32) + z = script_ops.eager_py_func(func=g, inp=[y], Tout=dtypes.float32) + + with self.test_session(use_gpu=True) as sess: + output = sess.run(z, feed_dict={x: 3.0}) + self.assertEqual(output, 18.0) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/random/BUILD b/tensorflow/python/kernel_tests/random/BUILD index acd7566eec8e3fffd74db33234b03a0c87427a3e..3b3a28fc9a24104cc9032ab23dfc51e690d3ec94 100644 --- a/tensorflow/python/kernel_tests/random/BUILD +++ b/tensorflow/python/kernel_tests/random/BUILD @@ -107,6 +107,23 @@ cuda_py_test( tags = ["nozapfhahn"], ) +cuda_py_test( + name = "random_grad_test", + size = "small", + srcs = ["random_grad_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:random_grad", + "//tensorflow/python:random_ops", + ], +) + cuda_py_test( name = "random_poisson_test", size = "medium", diff --git a/tensorflow/python/kernel_tests/random/multinomial_op_test.py b/tensorflow/python/kernel_tests/random/multinomial_op_test.py index 051c7d86bf2342f15b587fc350bfbede7fae2285..bd64d61af8e793e71a319b6ac1af95bd7dd16a3d 100644 --- a/tensorflow/python/kernel_tests/random/multinomial_op_test.py +++ b/tensorflow/python/kernel_tests/random/multinomial_op_test.py @@ -54,7 +54,7 @@ native_sampler = random_ops.multinomial class MultinomialTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSmallEntropy(self): random_seed.set_random_seed(1618) for output_dtype in [np.int32, np.int64]: diff --git a/tensorflow/python/kernel_tests/random/random_grad_test.py b/tensorflow/python/kernel_tests/random/random_grad_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c1d455b785bbf562fb41f30cab7e0bb723a7b894 --- /dev/null +++ b/tensorflow/python/kernel_tests/random/random_grad_test.py @@ -0,0 +1,240 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.ops.random_grad.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_grad +from tensorflow.python.ops import random_ops +from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging + + +class AddLeadingUnitDimensionsTest(test.TestCase): + + def testBasic(self): + ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 3) + self.assertAllEqual(ret.shape, [1, 1, 1, 3, 2, 1]) + + def testZeroExtraDimensions(self): + ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 0) + self.assertAllEqual(ret.shape, [3, 2, 1]) + + def testScalarInput(self): + ret = random_grad.add_leading_unit_dimensions(1.0, 2) + self.assertAllEqual(ret.shape, [1, 1]) + + def testUnknownShape(self): + x = array_ops.placeholder(dtypes.float32) + num_dimensions = array_ops.placeholder(dtypes.int32) + ret = random_grad.add_leading_unit_dimensions(x, num_dimensions) + with self.test_session() as sess: + ret_val = sess.run(ret, {x: np.ones([2, 2]), num_dimensions: 2}) + self.assertAllEqual(ret_val.shape, [1, 1, 2, 2]) + + +class RandomGammaGradTest(test.TestCase): + """Tests for derivative of a sample ~ Gamma(alpha, beta) wrt alpha and beta. + + The sample is an "implicit" function of alpha, beta and the independent random + noise u. The derivatives we are looking for are + d sample(alpha, beta, u) / dalpha (and dbeta). + + The derivative w.r.t. beta is computed by the standard automatic + differentiation, so we trust that it is computed correctly. + + The derivative w.r.t. alpha is computed by Eigen function, so we test it in + several ways. Unfortunately, the standard derivative checking by perturbing + the parameter is impossible here, because we cannot fix the value of u + in the random sampler. Instead, we compare the derivative for the given pair + of (sample, alpha) to the values computed in various ways, and also check + some statistical properties of the derivative. + """ + + def testGradientsShape(self): + shape = [2, 3] + alpha = array_ops.ones([2, 2]) + beta = array_ops.ones([1, 2]) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + self.assertAllEqual(grads_alpha.shape, alpha.shape) + self.assertAllEqual(grads_beta.shape, beta.shape) + + def testGradientsShapeWithOneSamplePerParameter(self): + shape = [] + alpha = array_ops.ones([2, 2]) + beta = array_ops.ones([1, 2]) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + self.assertAllEqual(grads_alpha.shape, alpha.shape) + self.assertAllEqual(grads_beta.shape, beta.shape) + + def testGradientsUnknownShape(self): + shape = array_ops.placeholder(dtypes.int32) + alpha = array_ops.placeholder(dtypes.float32) + beta = array_ops.placeholder(dtypes.float32) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + + alpha_val = np.ones([1, 2]) + beta_val = np.ones([2, 1]) + with self.test_session() as sess: + grads_alpha_val, grads_beta_val = sess.run( + [grads_alpha, grads_beta], + {alpha: alpha_val, beta: beta_val, shape: [2, 1]}) + self.assertAllEqual(grads_alpha_val.shape, alpha_val.shape) + self.assertAllEqual(grads_beta_val.shape, beta_val.shape) + + def _testCompareToExplicitDerivative(self, dtype): + """Compare to the explicit reparameterization derivative. + + Verifies that the computed derivative satisfies + dsample / dalpha = d igammainv(alpha, u) / dalpha, + where u = igamma(alpha, sample). + + Args: + dtype: TensorFlow dtype to perform the computations in. + """ + delta = 1e-3 + np_dtype = dtype.as_numpy_dtype + try: + from scipy import misc # pylint: disable=g-import-not-at-top + from scipy import special # pylint: disable=g-import-not-at-top + + alpha_val = np.logspace(-2, 3, dtype=np_dtype) + alpha = constant_op.constant(alpha_val) + sample = random_ops.random_gamma([], alpha, np_dtype(1.0), dtype=dtype) + actual = gradients_impl.gradients(sample, alpha)[0] + + (sample_val, actual_val) = self.evaluate((sample, actual)) + + u = special.gammainc(alpha_val, sample_val) + expected_val = misc.derivative( + lambda alpha_prime: special.gammaincinv(alpha_prime, u), + alpha_val, dx=delta * alpha_val) + + self.assertAllClose(actual_val, expected_val, rtol=1e-3, atol=1e-3) + except ImportError as e: + tf_logging.warn("Cannot use special functions in a test: %s" % str(e)) + + def testCompareToExplicitDerivativeFloat(self): + self._testCompareToExplicitDerivative(dtypes.float32) + + def testCompareToExplicitDerivativeDouble(self): + self._testCompareToExplicitDerivative(dtypes.float64) + + def _testCompareToImplicitDerivative(self, dtype): + """Compare to the implicit reparameterization derivative. + + Let's derive the formula we compare to. + + Start from the fact that CDF maps a random variable to the Uniform + random variable: + igamma(alpha, sample) = u, where u ~ Uniform(0, 1). + + Apply d / dalpha to both sides: + d igamma(alpha, sample) / dalpha + + d igamma(alpha, sample) / dsample * dsample/dalpha = 0 + d igamma(alpha, sample) / dalpha + + d igamma(alpha, sample) / dsample * dsample / dalpha = 0 + dsample/dalpha = - (d igamma(alpha, sample) / dalpha) + / d igamma(alpha, sample) / dsample + + This is the equation (8) of https://arxiv.org/abs/1805.08498 + + Args: + dtype: TensorFlow dtype to perform the computations in. + """ + np_dtype = dtype.as_numpy_dtype + alpha = constant_op.constant(np.logspace(-2, 3, dtype=np_dtype)) + sample = random_ops.random_gamma([], alpha, np_dtype(1.0), dtype=dtype) + actual = gradients_impl.gradients(sample, alpha)[0] + + sample_sg = array_ops.stop_gradient(sample) + cdf = math_ops.igamma(alpha, sample_sg) + dcdf_dalpha, dcdf_dsample = gradients_impl.gradients( + cdf, [alpha, sample_sg]) + # Numerically unstable due to division, do not try at home. + expected = -dcdf_dalpha / dcdf_dsample + + (actual_val, expected_val) = self.evaluate((actual, expected)) + + self.assertAllClose(actual_val, expected_val, rtol=1e-3, atol=1e-3) + + def testCompareToImplicitDerivativeFloat(self): + self._testCompareToImplicitDerivative(dtypes.float32) + + def testCompareToImplicitDerivativeDouble(self): + self._testCompareToImplicitDerivative(dtypes.float64) + + def testAverageAlphaGradient(self): + """Statistical test for the gradient. + + Using the equation (5) of https://arxiv.org/abs/1805.08498, we have + 1 = d/dalpha E_{sample ~ Gamma(alpha, 1)} sample + = E_{sample ~ Gamma(alpha, 1)} dsample/dalpha. + Here we verify that the rhs is fairly close to one. + The convergence speed is not great, so we use many samples and loose bounds. + """ + num_samples = 1000 + alpha = constant_op.constant([0.8, 1e1, 1e3], dtype=dtypes.float32) + sample = random_ops.random_gamma([num_samples], alpha) + # We need to average the gradients, which is equivalent to averaging the + # samples and then doing backprop. + mean_sample = math_ops.reduce_mean(sample, axis=0) + dsample_dalpha = gradients_impl.gradients(mean_sample, alpha)[0] + dsample_dalpha_val = self.evaluate(dsample_dalpha) + self.assertAllClose(dsample_dalpha_val, [1.0] * 3, atol=1e-1, rtol=1e-1) + + def testQuadraticLoss(self): + """Statistical test for the gradient. + + The equation (5) of https://arxiv.org/abs/1805.08498 says + d/dalpha E_{sample ~ Gamma(alpha, 1)} f(sample) + = E_{sample ~ Gamma(alpha, 1)} df(sample)/dalpha. + + Choose a quadratic loss function f(sample) = (sample - t)^2. + Then, the lhs can be computed analytically: + d/dalpha E_{sample ~ Gamma(alpha, 1)} f(sample) + = d/dalpha [ (alpha + alpha^2) - 2 * t * alpha + t^2 ] + = 1 + 2 * alpha - 2 * t. + + We compare the Monte-Carlo estimate of the expectation with the + true gradient. + """ + num_samples = 1000 + t = 0.3 + alpha = 0.5 + expected = 1 + 2 * alpha - 2 * t + + alpha = constant_op.constant(alpha) + sample = random_ops.random_gamma([num_samples], alpha, 1.0) + loss = math_ops.reduce_mean(math_ops.square(sample - t)) + dloss_dalpha = gradients_impl.gradients(loss, alpha)[0] + dloss_dalpha_val = self.evaluate(dloss_dalpha) + self.assertAllClose(expected, dloss_dalpha_val, atol=1e-1, rtol=1e-1) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py index 7be473a5e750d9d6880f112cb0ca89b3ae61a7fd..8e06e1abfb52244e8c1a9b4ed15a270f6048e028 100644 --- a/tensorflow/python/kernel_tests/reader_ops_test.py +++ b/tensorflow/python/kernel_tests/reader_ops_test.py @@ -25,8 +25,6 @@ import shutil import threading import zlib -import six - from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -703,228 +701,6 @@ class TFRecordReaderTest(TFCompressionTestCase): self.assertAllEqual(self._Record(i, j), v) -class TFRecordWriterTest(TFCompressionTestCase): - - def setUp(self): - super(TFRecordWriterTest, self).setUp() - - def _AssertFilesEqual(self, a, b, equal): - for an, bn in zip(a, b): - with open(an, "rb") as af, open(bn, "rb") as bf: - if equal: - self.assertEqual(af.read(), bf.read()) - else: - self.assertNotEqual(af.read(), bf.read()) - - def testWriteReadZLibFiles(self): - # Write uncompressed then compress manually. - options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) - files = self._CreateFiles(options, prefix="uncompressed") - zlib_files = [ - self._ZlibCompressFile(fn, "tfrecord_%s.z" % i) - for i, fn in enumerate(files) - ] - self._AssertFilesEqual(files, zlib_files, False) - - # Now write compressd and verify same. - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - compressed_files = self._CreateFiles(options, prefix="compressed") - self._AssertFilesEqual(compressed_files, zlib_files, True) - - # Decompress compress and verify same. - uncompressed_files = [ - self._ZlibDecompressFile(fn, "tfrecord_%s.z" % i) - for i, fn in enumerate(compressed_files) - ] - self._AssertFilesEqual(uncompressed_files, files, True) - - def testWriteReadGzipFiles(self): - # Write uncompressed then compress manually. - options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) - files = self._CreateFiles(options, prefix="uncompressed") - gzip_files = [ - self._GzipCompressFile(fn, "tfrecord_%s.gz" % i) - for i, fn in enumerate(files) - ] - self._AssertFilesEqual(files, gzip_files, False) - - # Now write compressd and verify same. - options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) - compressed_files = self._CreateFiles(options, prefix="compressed") - - # Note: Gzips written by TFRecordWriter add 'tfrecord_0' so - # compressed_files can't be compared with gzip_files - - # Decompress compress and verify same. - uncompressed_files = [ - self._GzipDecompressFile(fn, "tfrecord_%s.gz" % i) - for i, fn in enumerate(compressed_files) - ] - self._AssertFilesEqual(uncompressed_files, files, True) - - -class TFRecordWriterZlibTest(TFCompressionTestCase): - - def testOneEpoch(self): - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - files = self._CreateFiles(options) - with self.test_session() as sess: - reader = io_ops.TFRecordReader(name="test_reader", options=options) - queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=()) - key, value = reader.read(queue) - - queue.enqueue_many([files]).run() - queue.close().run() - for i in range(self._num_files): - for j in range(self._num_records): - k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % files[i])) - self.assertAllEqual(self._Record(i, j), v) - - with self.assertRaisesOpError("is closed and has insufficient elements " - "\\(requested 1, current size 0\\)"): - k, v = sess.run([key, value]) - - def testZLibFlushRecord(self): - fn = self._WriteRecordsToFile([b"small record"], "small_record") - with open(fn, "rb") as h: - buff = h.read() - - # creating more blocks and trailing blocks shouldn't break reads - compressor = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS) - - output = b"" - for c in buff: - if isinstance(c, int): - c = six.int2byte(c) - output += compressor.compress(c) - output += compressor.flush(zlib.Z_FULL_FLUSH) - - output += compressor.flush(zlib.Z_FULL_FLUSH) - output += compressor.flush(zlib.Z_FULL_FLUSH) - output += compressor.flush(zlib.Z_FINISH) - - # overwrite the original file with the compressed data - with open(fn, "wb") as h: - h.write(output) - - with self.test_session() as sess: - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - reader = io_ops.TFRecordReader(name="test_reader", options=options) - queue = data_flow_ops.FIFOQueue(1, [dtypes.string], shapes=()) - key, value = reader.read(queue) - queue.enqueue(fn).run() - queue.close().run() - k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % fn)) - self.assertAllEqual(b"small record", v) - - def testZlibReadWrite(self): - """Verify that files produced are zlib compatible.""" - original = [b"foo", b"bar"] - fn = self._WriteRecordsToFile(original, "zlib_read_write.tfrecord") - zfn = self._ZlibCompressFile(fn, "zlib_read_write.tfrecord.z") - - # read the compressed contents and verify. - actual = [] - for r in tf_record.tf_record_iterator( - zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): - actual.append(r) - self.assertEqual(actual, original) - - def testZlibReadWriteLarge(self): - """Verify that writing large contents also works.""" - - # Make it large (about 5MB) - original = [_TEXT * 10240] - fn = self._WriteRecordsToFile(original, "zlib_read_write_large.tfrecord") - zfn = self._ZlibCompressFile(fn, "zlib_read_write_large.tfrecord.z") - - actual = [] - for r in tf_record.tf_record_iterator( - zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): - actual.append(r) - self.assertEqual(actual, original) - - def testGzipReadWrite(self): - """Verify that files produced are gzip compatible.""" - original = [b"foo", b"bar"] - fn = self._WriteRecordsToFile(original, "gzip_read_write.tfrecord") - gzfn = self._GzipCompressFile(fn, "tfrecord.gz") - - actual = [] - for r in tf_record.tf_record_iterator( - gzfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.GZIP)): - actual.append(r) - self.assertEqual(actual, original) - - -class TFRecordIteratorTest(TFCompressionTestCase): - - def setUp(self): - super(TFRecordIteratorTest, self).setUp() - self._num_records = 7 - - def testIterator(self): - records = [self._Record(0, i) for i in range(self._num_records)] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(records, "compressed_records", options) - - reader = tf_record.tf_record_iterator(fn, options) - for expected in records: - record = next(reader) - self.assertAllEqual(expected, record) - with self.assertRaises(StopIteration): - record = next(reader) - - def testWriteZlibRead(self): - """Verify compression with TFRecordWriter is zlib library compatible.""" - original = [b"foo", b"bar"] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(original, "write_zlib_read.tfrecord.z", - options) - - zfn = self._ZlibDecompressFile(fn, "write_zlib_read.tfrecord") - actual = list(tf_record.tf_record_iterator(zfn)) - self.assertEqual(actual, original) - - def testWriteZlibReadLarge(self): - """Verify compression for large records is zlib library compatible.""" - # Make it large (about 5MB) - original = [_TEXT * 10240] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(original, "write_zlib_read_large.tfrecord.z", - options) - zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tfrecord") - actual = list(tf_record.tf_record_iterator(zfn)) - self.assertEqual(actual, original) - - def testWriteGzipRead(self): - original = [b"foo", b"bar"] - options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) - fn = self._WriteRecordsToFile(original, "write_gzip_read.tfrecord.gz", - options) - - gzfn = self._GzipDecompressFile(fn, "write_gzip_read.tfrecord") - actual = list(tf_record.tf_record_iterator(gzfn)) - self.assertEqual(actual, original) - - def testBadFile(self): - """Verify that tf_record_iterator throws an exception on bad TFRecords.""" - fn = os.path.join(self.get_temp_dir(), "bad_file") - with tf_record.TFRecordWriter(fn) as writer: - writer.write(b"123") - fn_truncated = os.path.join(self.get_temp_dir(), "bad_file_truncated") - with open(fn, "rb") as f: - with open(fn_truncated, "wb") as f2: - # DataLossError requires that we've written the header, so this must - # be at least 12 bytes. - f2.write(f.read(14)) - with self.assertRaises(errors_impl.DataLossError): - for _ in tf_record.tf_record_iterator(fn_truncated): - pass - - class AsyncReaderTest(test.TestCase): def testNoDeadlockFromQueue(self): diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 82e0d153c217126dedc0fc32c013a97a7935873d..0fb0b8895cbc847639999ad1bd23e7fb04c86034 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -145,14 +145,18 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertIn("", str(handle)) self.assertIn("", repr(handle)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDtypeSurvivesIdentity(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) id_handle = array_ops.identity(handle) self.evaluate(resource_variable_ops.assign_variable_op( id_handle, constant_op.constant(0, dtype=dtypes.int32))) - @test_util.run_in_graph_and_eager_modes() + def testUnreadOpName(self): + v = resource_variable_ops.ResourceVariable(1.0) + self.assertNotEqual(v.name, v.assign_add(1.0).name) + + @test_util.run_in_graph_and_eager_modes def testCreateRead(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) self.evaluate(resource_variable_ops.assign_variable_op( @@ -161,7 +165,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)) self.assertAllEqual(1, value) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testManyAssigns(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) create = resource_variable_ops.assign_variable_op( @@ -179,7 +183,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual(f, 1) self.assertEqual(s, 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignAdd(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) self.evaluate(resource_variable_ops.assign_variable_op( @@ -190,7 +194,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)) self.assertEqual(read, 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterAdd(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -203,7 +207,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterSub(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -216,7 +220,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[-1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMul(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -229,7 +233,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterDiv(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -242,7 +246,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[2]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMin(self): with ops.device("cpu:0"): handle = resource_variable_ops.var_handle_op( @@ -279,7 +283,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): meta_graph_two = saver.export_meta_graph(graph=graph) self.assertEqual(meta_graph_def, meta_graph_two) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMax(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -292,7 +296,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[6]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterAddScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -305,7 +309,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterSubScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -318,7 +322,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[-1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMulScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -331,7 +335,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterDivScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -344,7 +348,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[2]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMinScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -357,7 +361,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMaxScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -422,7 +426,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_update(ref, indices, updates) self.assertAllEqual(ref.read_value(), [True, True, True]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstraintArg(self): constraint = lambda x: x v = resource_variable_ops.ResourceVariable( @@ -462,32 +466,32 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): with self.assertRaises(errors.OutOfRangeError): state_ops.count_up_to(v, 1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFnDtype(self): v = resource_variable_ops.ResourceVariable( initial_value=lambda: 1, dtype=dtypes.float32, name="var0") self.assertEqual(dtypes.float32, v.value().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFnNoDtype(self): v = resource_variable_ops.ResourceVariable(initial_value=lambda: 1, name="var2") self.assertEqual(dtypes.int32, v.value().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeAllVariables(self): v = resource_variable_ops.ResourceVariable(1, dtype=dtypes.float32, name="var0") self.evaluate(variables.global_variables_initializer()) self.assertEqual(1.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOperatorOverload(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) self.assertEqual(2.0, self.evaluate(v + v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -505,7 +509,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(4.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoad(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -557,7 +561,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): variable_def=trainable_variable.to_proto()) .trainable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSparseRead(self): with self.test_session(): init_value = np.reshape(np.arange(np.power(4, 3)), (4, 4, 4)) @@ -579,7 +583,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEquals(v._handle, w._handle) self.assertEquals(v._graph_element, w._graph_element) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignAddMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -597,7 +601,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(4.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignSubMethod(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -615,7 +619,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(0.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDestroyResource(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -704,7 +708,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): w_read = resource_variable_ops.read_variable_op(w, v.dtype.base_dtype) self.assertEqual(300.0, self.evaluate(w_read)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShape(self): v = resource_variable_ops.ResourceVariable( name="var4", initial_value=array_ops.ones(shape=[10, 20, 35])) @@ -838,7 +842,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_update(v, [1], [3]) self.assertAllEqual([1.0, 3.0], v.numpy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterUpdateInvalidArgs(self): v = resource_variable_ops.ResourceVariable([0, 1, 2, 3], name="update") # The exact error and message differ between graph construction (where the diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index fe5ad84c104502f0e09d3a963b406f49d6b97b71..957baf8c6089a6a033f54762fef290399d80cd09 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -81,6 +81,25 @@ class ScalarStateRNNCell(rnn_cell_impl.RNNCell): return (input_, state + 1) +class UnbalancedOutputRNNCell(rnn_cell_impl.RNNCell): + """RNN Cell generating (output, new_state) = (input + 1, state + 1).""" + + @property + def output_size(self): + return tensor_shape.TensorShape(1), tensor_shape.TensorShape((2)) + + @property + def state_size(self): + return tensor_shape.TensorShape([]) + + def zero_state(self, batch_size, dtype): + return array_ops.zeros([], dtype=dtypes.int32) + + def call(self, input_, state, scope=None): + concatenated = array_ops.concat((input_, input_), axis=-1) + return (input_, concatenated), state + 1 + + class TensorArrayStateRNNCell(rnn_cell_impl.RNNCell): """RNN Cell its state as a TensorArray.""" @@ -108,7 +127,7 @@ class RNNTest(test.TestCase): self._seed = 23489 np.random.seed(self._seed) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidSequenceLengthShape(self): cell = Plus1RNNCell() if context.executing_eagerly(): @@ -122,7 +141,7 @@ class RNNTest(test.TestCase): dtype=dtypes.float32, sequence_length=[[4]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBatchSizeFromInput(self): cell = Plus1RNNCell() in_eager_mode = context.executing_eagerly() @@ -162,7 +181,7 @@ class RNNTest(test.TestCase): self.assertEqual(None, outputs.shape[0].value) self.assertEqual(None, state.shape[0].value) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScalarStateIsAccepted(self): cell = ScalarStateRNNCell() in_eager_mode = context.executing_eagerly() @@ -182,7 +201,29 @@ class RNNTest(test.TestCase): self.assertAllEqual([[[1], [2], [3], [4]]], outputs) self.assertAllEqual(4, state) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes + def testUnbalancedOutputIsAccepted(self): + cell = UnbalancedOutputRNNCell() + in_eager_mode = context.executing_eagerly() + + if in_eager_mode: + inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) + + with self.test_session() as sess: + outputs, state = rnn.dynamic_rnn( + cell, inputs, dtype=dtypes.float32, sequence_length=[4]) + if not in_eager_mode: + outputs, state = sess.run( + [outputs, state], feed_dict={inputs: [[[1], [2], [3], [4]]]}) + + self.assertIsInstance(outputs, tuple) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs[0]) + self.assertAllEqual([[[1, 1], [2, 2], [3, 3], [4, 4]]], outputs[1]) + self.assertAllEqual(4, state) + + @test_util.run_in_graph_and_eager_modes def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() in_eager_mode = context.executing_eagerly() @@ -215,7 +256,7 @@ class RNNTest(test.TestCase): cell_output, _ = cell(array_ops.zeros(in_shape, dtype), state_output) self.assertAllEqual([batch_size, out_size], cell_output.shape.as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCellsBuild(self): f32 = dtypes.float32 f64 = dtypes.float64 diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index faa4b49a8d7d8b0169f10592845d3d30a3996c41..f9b9c77bbf7e2a8afdbfbd0929a68856b8aae51c 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -369,7 +369,7 @@ class ScatterNdTest(test.TestCase): del input_ # input_ is not used in scatter_nd return array_ops.scatter_nd(indices, updates, shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidShape(self): # TODO(apassos) figure out how to unify these errors with self.assertRaises(errors.InvalidArgumentError diff --git a/tensorflow/python/kernel_tests/shape_ops_test.py b/tensorflow/python/kernel_tests/shape_ops_test.py index 7368251ab69574cc6cba703e605f108c6ab45649..34e34d9d1b2034d8679844f051358f020a44587a 100644 --- a/tensorflow/python/kernel_tests/shape_ops_test.py +++ b/tensorflow/python/kernel_tests/shape_ops_test.py @@ -642,6 +642,29 @@ class TileTest(test.TestCase): err = gradient_checker.compute_gradient_error(a, [4, 2], tiled, [4, 4]) self.assertLess(err, 1e-3) + def testGradientWithSparseGradWithRank1(self): + inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], + dtype=dtypes.float32) + outputs = array_ops.gather(array_ops.tile(inputs, [3]), + [1, 5, 9, 3, 7, 2, 2, 2]) + with self.test_session(): + error = gradient_checker.compute_gradient_error( + inputs, inputs.get_shape().as_list(), + outputs, outputs.get_shape().as_list()) + self.assertLess(error, 1e-4) + + def testGradientWithSparseGradWithRank3(self): + inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], + dtype=dtypes.float32) + inputs = array_ops.reshape(inputs, [-1, 1, 1]) + outputs = array_ops.gather(array_ops.tile(inputs, [3, 4, 2]), + [1, 5, 9, 3, 7, 2, 2, 2]) + with self.test_session(): + error = gradient_checker.compute_gradient_error( + inputs, inputs.get_shape().as_list(), + outputs, outputs.get_shape().as_list()) + self.assertLess(error, 1e-4) + def testShapeFunctionEdgeCases(self): # Unknown multiples shape. inp = constant_op.constant(0.0, shape=[4, 4, 4, 4]) diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py index 5fc9bef21816e3a12f0d274bab1fc82a83546422..402f67619b41a5f13c6603eb6665974a09a8f4fb 100644 --- a/tensorflow/python/kernel_tests/slice_op_test.py +++ b/tensorflow/python/kernel_tests/slice_op_test.py @@ -225,7 +225,7 @@ class SliceTest(test.TestCase): self.assertAllEqual(m1.get_shape().as_list(), [1, 2, 3]) m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1]) - self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None]) + self.assertAllEqual(m2.get_shape().as_list(), [1, 2, 3]) def _testGradientSlice(self, input_shape, slice_begin, slice_size): diff --git a/tensorflow/python/kernel_tests/sparse_serialization_ops_test.py b/tensorflow/python/kernel_tests/sparse_serialization_ops_test.py index 27b39a626fcc6b2705bf9e797b5293ed3f1c7820..3847cebc7dcabd66c26a4e4551e5856c6a927a33 100644 --- a/tensorflow/python/kernel_tests/sparse_serialization_ops_test.py +++ b/tensorflow/python/kernel_tests/sparse_serialization_ops_test.py @@ -300,6 +300,51 @@ class SerializeSparseTest(test.TestCase): sparse_ops.serialize_many_sparse, sparse_ops.deserialize_sparse, dtypes.variant) + def testVariantSerializeDeserializeScalar(self): + with self.test_session(use_gpu=False) as sess: + indices_value = np.array([[]], dtype=np.int64) + values_value = np.array([37], dtype=np.int32) + shape_value = np.array([], dtype=np.int64) + sparse_tensor = self._SparseTensorPlaceholder() + serialized = sparse_ops.serialize_sparse( + sparse_tensor, out_type=dtypes.variant) + deserialized = sparse_ops.deserialize_sparse( + serialized, dtype=dtypes.int32) + deserialized_value = sess.run( + deserialized, + feed_dict={ + sparse_tensor.indices: indices_value, + sparse_tensor.values: values_value, + sparse_tensor.dense_shape: shape_value + }) + self.assertAllEqual(deserialized_value.indices, indices_value) + self.assertAllEqual(deserialized_value.values, values_value) + self.assertAllEqual(deserialized_value.dense_shape, shape_value) + + def testVariantSerializeDeserializeScalarBatch(self): + with self.test_session(use_gpu=False) as sess: + indices_value = np.array([[]], dtype=np.int64) + values_value = np.array([37], dtype=np.int32) + shape_value = np.array([], dtype=np.int64) + sparse_tensor = self._SparseTensorPlaceholder() + serialized = sparse_ops.serialize_sparse( + sparse_tensor, out_type=dtypes.variant) + stacked = array_ops.stack([serialized, serialized]) + deserialized = sparse_ops.deserialize_sparse(stacked, dtype=dtypes.int32) + deserialized_value = sess.run( + deserialized, + feed_dict={ + sparse_tensor.indices: indices_value, + sparse_tensor.values: values_value, + sparse_tensor.dense_shape: shape_value + }) + self.assertAllEqual(deserialized_value.indices, + np.array([[0], [1]], dtype=np.int64)) + self.assertAllEqual(deserialized_value.values, + np.array([37, 37], dtype=np.int32)) + self.assertAllEqual(deserialized_value.dense_shape, + np.array([2], dtype=np.int64)) + def _testDeserializeFailsWrongTypeHelper(self, serialize_fn, deserialize_fn, diff --git a/tensorflow/python/kernel_tests/sparse_slice_op_test.py b/tensorflow/python/kernel_tests/sparse_slice_op_test.py index da116601f833cc6b471e383e030c5fbe93b52ac5..97f30daf4a9c9615e1b42a1ba94e693e166bbc1c 100644 --- a/tensorflow/python/kernel_tests/sparse_slice_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_slice_op_test.py @@ -21,13 +21,15 @@ from __future__ import print_function import numpy as np from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import sparse_ops +import tensorflow.python.ops.sparse_grad # pylint: disable=unused-import from tensorflow.python.platform import test class SparseSliceOpTest(test.TestCase): - def _SparseTensor_4x6(self): + def _SparseTensor_4x6(self, val_dtype=np.int64): # [0 | |2 | |4 |5 ] # [ |11| |13|14| ] # [20| | |23| |25] @@ -37,7 +39,7 @@ class SparseSliceOpTest(test.TestCase): [2, 3], [2, 5], [3, 0], [3, 2], [3, 3], [3, 5]]).astype( np.int64) val = np.array([0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype( - np.int64) + val_dtype) shape = np.array([4, 6]).astype(np.int64) return sparse_tensor.SparseTensor(ind, val, shape) @@ -244,6 +246,22 @@ class SparseSliceOpTest(test.TestCase): self.assertAllEqual(sparse_tensor5.values.eval(), [5, 25, 35]) self.assertAllEqual(sparse_tensor5.dense_shape.eval(), [4, 1]) + def testGradients(self): + sp_input = self._SparseTensor_4x6(val_dtype=np.float32) + start_and_size = [([0, 0], [4, 2]), + ([0, 2], [5, 2]), + ([0, 4], [5, 3])] + + with self.test_session(use_gpu=False): + for start, size in start_and_size: + sp_output = sparse_ops.sparse_slice(sp_input, start, size) + nnz_in = len(sp_input.values.eval()) + nnz_out = len(sp_output.values.eval()) + + err = gradient_checker.compute_gradient_error( + [sp_input.values], [(nnz_in,)], sp_output.values, (nnz_out,)) + self.assertLess(err, 1e-3) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py index 8cfee3eb933afcea7a58d5632948b87b0c4c10df..419cd5ecdafab92910cd06fb18148796f70afb44 100644 --- a/tensorflow/python/kernel_tests/split_op_test.py +++ b/tensorflow/python/kernel_tests/split_op_test.py @@ -95,7 +95,7 @@ class SplitOpTest(test.TestCase): sess.run(array_ops.split(value, size_splits), {size_splits: [2, 2, 6]}) self.assertTrue("Cannot infer num from shape" in str(context.exception)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExplicitNum(self): size_splits = array_ops.constant([2, 2, 6], dtype=dtypes.int32) value = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] @@ -109,7 +109,7 @@ class SplitOpTest(test.TestCase): self.assertAllEqual(r[1], value[2:4]) self.assertAllEqual(r[2], value[4:]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testListOfScalarTensors(self): a = math_ops.to_int32(5) b = math_ops.to_int32(6) @@ -168,7 +168,7 @@ class SplitOpTest(test.TestCase): offset += size_splits[i] self.assertAllEqual(result[i], inp[slices]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSpecialCasesVariable(self): self._testSpecialCasesVariable() for dtype in _TEST_DTYPES: @@ -210,13 +210,13 @@ class SplitOpTest(test.TestCase): self.assertAllEqual(np_ans[i], out[i]) self.assertShapeEqual(np_ans[i], tf_ans[i]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitRows(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) self._compare(inp, 0, 4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitCols(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) @@ -232,7 +232,7 @@ class SplitOpTest(test.TestCase): self.assertEqual(out[i].shape, expected_shape) self.assertEqual(expected_shape, tf_ans[i].get_shape()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEmpty(self): # Note: np.split returns a rank-0 empty ndarray # if the input ndarray is empty. @@ -244,7 +244,7 @@ class SplitOpTest(test.TestCase): self._testEmpty(inp, 2, 3, (8, 0, 7)) self._testEmpty(inp, 2, 7, (8, 0, 3)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testIdentity(self): for dtype in _TEST_DTYPES: inp = self._makeData((2, 2, 2), dtype) @@ -252,7 +252,7 @@ class SplitOpTest(test.TestCase): self._compare(inp, 1, 1) self._compare(inp, 2, 1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitDim0(self): for dtype in _TEST_DTYPES: self._compare(self._makeData((6, 10, 18), dtype), 0, 3) @@ -281,7 +281,7 @@ class SplitOpTest(test.TestCase): offset += length self.assertAllEqual(result[i], inp[slices]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandom(self): for dtype in _TEST_DTYPES: for _ in range(5): diff --git a/tensorflow/python/kernel_tests/template_test.py b/tensorflow/python/kernel_tests/template_test.py index 1b935d5286729e9e802c56e90e2ae7ab72a6e080..0b3a396d6bf46fb46416662a9443ed7b5811e15c 100644 --- a/tensorflow/python/kernel_tests/template_test.py +++ b/tensorflow/python/kernel_tests/template_test.py @@ -150,7 +150,7 @@ class TemplateTest(test.TestCase): # Parameters are tied, so the loss should have gone down after training. self.assertLess(final_test_loss.numpy(), initial_test_loss.numpy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_skip_stack_frames(self): first = traceback.format_stack() second = traceback.format_stack() @@ -158,7 +158,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(result)) self.assertNotEqual(len(first), len(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_with_name(self): tmpl1 = template.make_template("s1", variable_scoped_function) tmpl2 = template.make_template("s1", variable_scoped_function) @@ -204,7 +204,7 @@ class TemplateTest(test.TestCase): self.assertEqual(v1, v3) self.assertEqual("s1/dummy:0", v1.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_in_scope(self): tmpl1 = template.make_template("s1", variable_scoped_function) tmpl2 = template.make_template("s1", variable_scoped_function) @@ -221,7 +221,7 @@ class TemplateTest(test.TestCase): self.assertEqual("scope/s1/dummy:0", v1.name) self.assertEqual("scope/s1_1/dummy:0", v3.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_with_internal_reuse(self): tmpl1 = template.make_template("s1", internally_variable_scoped_function) tmpl2 = template.make_template("s1", internally_variable_scoped_function) @@ -237,13 +237,13 @@ class TemplateTest(test.TestCase): with self.assertRaises(ValueError): tmpl1("not_test") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_without_name(self): with self.assertRaisesRegexp( ValueError, "name cannot be None."): template.make_template(None, variable_scoped_function) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_make_template(self): # Test both that we can call it with positional and keywords. tmpl1 = template.make_template( @@ -266,7 +266,7 @@ class TemplateTest(test.TestCase): with self.assertRaises(ValueError): tmpl() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_enforces_no_extra_trainable_variables_eager(self): tmpl = template.make_template("s", function_with_side_create, @@ -287,7 +287,7 @@ class TemplateTest(test.TestCase): trainable=False) self.assertEqual(tmpl(name="1"), tmpl(name="2")) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_internal_variable_reuse(self): def nested(): @@ -310,7 +310,7 @@ class TemplateTest(test.TestCase): self.assertEqual("s1/nested/x:0", v1.name) self.assertEqual("s1_1/nested/x:0", v3.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_nested_templates(self): def nested_template(): @@ -360,7 +360,7 @@ class TemplateTest(test.TestCase): self.assertEqual("nested", tmpl1._checkpoint_dependencies[0].name) self.assertEqual("nested_1", tmpl1._checkpoint_dependencies[1].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_nested_templates_with_defun(self): def variable_scoped_function_no_return_value(trainable=True): @@ -429,7 +429,7 @@ class TemplateTest(test.TestCase): "a", partial, create_graph_function_=True) self.assertAllEqual(tmpl(ops.convert_to_tensor(1.0)), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_immediate_scope_creation(self): # Create templates in scope a then call in scope b. make_template should # capture the scope the first time it is called, and make_immediate_template @@ -454,7 +454,7 @@ class TemplateTest(test.TestCase): self.assertEqual("ctor_scope/a/dummy:0", inner_imm_var.name) self.assertEqual("call_scope/b/dummy:0", inner_defer_var.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_scope_access(self): # Ensure that we can access the scope inside the template, because the name # of that scope may be different from the name we pass to make_template, due @@ -479,7 +479,7 @@ class TemplateTest(test.TestCase): # Template is called at the top level, so there is no preceding "foo_2". self.assertEqual(tc.variable_scope.name, "blah") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_custom_getter(self): # Custom getter that maintains call count and forwards to true getter custom_getter_count = [0] @@ -512,7 +512,7 @@ class TemplateTest(test.TestCase): tmpl2() self.assertEqual(custom_getter_count[0], 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_fails_gracefully(self): for create_scope_now in [True, False]: def module_function_with_one_arg(inputs): @@ -535,7 +535,7 @@ class TemplateTest(test.TestCase): templatized_function(data) self.assertTrue(templatized_function._variables_created) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_name_scopes_for_variable_scopes(self): # Test that name scopes are not unnecessarily uniquified (but are # still uniquified when necessary). @@ -586,7 +586,7 @@ class TemplateTest(test.TestCase): "Second application of template should also get " "a freshly uniquified name scope.") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_global_variables(self): # Make sure global_variables are created. with variable_scope.variable_scope("foo"): @@ -608,7 +608,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(ta.global_variables)) self.assertEqual(2, len(tb.global_variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainable_variables(self): # Make sure trainable_variables are created. with variable_scope.variable_scope("foo2"): @@ -632,7 +632,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(ta.variables)) self.assertEqual(1, len(tb.variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_non_trainable_variables(self): # Make sure non_trainable_variables are created. with variable_scope.variable_scope("foo2"): @@ -675,7 +675,7 @@ class TemplateTest(test.TestCase): self.assertEqual(0, len(ta.local_variables)) self.assertEqual(1, len(tb.local_variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_make_template_with_defun(self): def variable_scoped_function_no_return_value(scope_name): diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index c0b36f143d109eb28e2784b49e8fd4099b5799a6..6de6fbe7679fa8e95d3032b04fb81b43ac3a60d9 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -26,11 +26,13 @@ from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops @@ -73,7 +75,7 @@ class TensorArrayTest(test.TestCase): super(TensorArrayTest, cls).tearDownClass() session_lib.Session.reset(cls._workers[0].target) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteRead(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -121,11 +123,11 @@ class TensorArrayTest(test.TestCase): self._testTensorArrayWritePack(dtypes.complex128) self._testTensorArrayWritePack(dtypes.string) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWritePack(self): self._testTensorArrayWritePackMaybeLegacy() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEmptyTensorArrayPack(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -159,7 +161,7 @@ class TensorArrayTest(test.TestCase): convert([[4.0, 5.0], [104.0, 105.0], [204.0, 205.0], [6.0, 7.0], [106.0, 107.0], [8.0, 9.0]]), c0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteConcat(self): self._testTensorArrayWriteConcat(dtypes.float32) self._testTensorArrayWriteConcat(dtypes.float64) @@ -182,7 +184,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([[0.0, 0.0], [4.0, 5.0], [0.0, 0.0]], self.evaluate(ta.write(1, [[4.0, 5.0]]).concat())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadOrPackNotAllValuesAvailableFillsZeros(self): self._testTensorArrayReadOrPackNotAllValuesAvailableFillsZeros() @@ -198,7 +200,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([[0.0, 0.0], [4.0, 5.0], [0.0, 0.0]], self.evaluate(ta.write(1, [[4.0, 5.0]]).concat())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadOrPackNotAllValuesAvailableInferShapeFillsZeros(self): self._testTensorArrayReadOrPackNotAllValuesAvailableInferShapeFillsZeros() @@ -249,7 +251,7 @@ class TensorArrayTest(test.TestCase): self._testTensorArrayUnpackRead(dtypes.complex128) self._testTensorArrayUnpackRead(dtypes.string) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayUnpackRead(self): self._testTensorArrayUnpackReadMaybeLegacy() @@ -295,7 +297,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(convert([]).reshape(0, 2), d1) self.assertAllEqual(convert([[3.0, 301.0]]), d2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArraySplitRead(self): self._testTensorArraySplitRead(dtypes.float32) self._testTensorArraySplitRead(dtypes.float64) @@ -395,7 +397,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(t_g_ta_0, t_g_ta_1) self.assertAllEqual([[4.0, 5.0]], d_r1_0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -414,7 +416,7 @@ class TensorArrayTest(test.TestCase): "resizeable and size is: 3"): self.evaluate(ta.write(3, 3.0).flow) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -448,7 +450,7 @@ class TensorArrayTest(test.TestCase): "it has already been written to."): self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayConcatIncompatibleShapesFails(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -480,7 +482,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError("shape"): self.evaluate(w3.concat()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArraySplitIncompatibleShapesFails(self): with self.test_session(use_gpu=True): in_eager_mode = context.executing_eagerly() @@ -549,7 +551,59 @@ class TensorArrayTest(test.TestCase): dtypes.complex64, dtypes.complex128): self._testTensorArrayWriteGradientAddMultipleAdds(dtype) - @test_util.run_in_graph_and_eager_modes() + def testTensorArrayGradWithShapeKnownElementShape(self): + with self.test_session(use_gpu=True) as sess: + ta = tensor_array_ops.TensorArray( + size=3, + dtype=dtypes.float32, + element_shape=tensor_shape.TensorShape([2, 3])) + handle, flow = data_flow_ops.tensor_array_grad_with_shape( + handle=ta.handle, + flow_in=ta.flow, + shape_to_prepend=tensor_shape.TensorShape([4, 5]), + source="source") + ta_grad = tensor_array_ops.TensorArray( + dtypes.float32, handle=handle, flow=flow) + value = array_ops.placeholder(dtypes.float32) + ta_grad = ta_grad.write(0, value) + read_value = ta_grad.read(0) + + # Make sure shape inference worked. + self.assertAllEqual([None, None, 2, 3], read_value.shape.as_list()) + # Writing with wrong shape should not work. + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "Could not write to TensorArray"): + fed_value = np.random.random([2, 3]) + sess.run(read_value, feed_dict={value: fed_value}) + # Writing with correct shape should work. + fed_value = np.random.random([4, 5, 2, 3]) + self.assertAllClose(fed_value, + sess.run(read_value, feed_dict={value: fed_value})) + + def testTensorArrayGradWithShapeUnknownElementShape(self): + with self.test_session(use_gpu=True) as sess: + ta = tensor_array_ops.TensorArray( + size=3, dtype=dtypes.float32, + element_shape=None) # Note that element_shape is unknown + handle, flow = data_flow_ops.tensor_array_grad_with_shape( + handle=ta.handle, + flow_in=ta.flow, + shape_to_prepend=tensor_shape.TensorShape([4, 5]), + source="source") + ta_grad = tensor_array_ops.TensorArray( + dtypes.float32, handle=handle, flow=flow) + value = array_ops.placeholder(dtypes.float32) + ta_grad = ta_grad.write(0, value) + read_value = ta_grad.read(0) + + # Make sure shape inference worked. + self.assertIsNone(read_value.shape.ndims) + # Write with some shape and check read value. + fed_value = np.random.random([4, 5, 7]) + self.assertAllClose(fed_value, + sess.run(read_value, feed_dict={value: fed_value})) + + @test_util.run_in_graph_and_eager_modes def testMultiTensorArray(self): with self.test_session(use_gpu=True): h1 = tensor_array_ops.TensorArray( @@ -652,7 +706,7 @@ class TensorArrayTest(test.TestCase): def testTensorArrayGradientWritePackConcatAndRead(self): self._testTensorArrayGradientWritePackConcatAndRead() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadTwice(self): with self.test_session(use_gpu=True): value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) @@ -757,14 +811,14 @@ class TensorArrayTest(test.TestCase): def testTensorArrayGradientDynamicUnpackRead(self): self._testTensorArrayGradientDynamicUnpackRead() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCloseTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) self.evaluate(ta.close()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSizeTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -772,7 +826,7 @@ class TensorArrayTest(test.TestCase): s = ta.size() self.assertAllEqual(3, self.evaluate(s)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWriteCloseTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -870,7 +924,7 @@ class TensorArrayTest(test.TestCase): self.assertAllClose(grad_val.sum(axis=0), var_grad_t) self.assertAllClose(grad_val.sum(axis=0), state0_grad_t) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWhileLoopWritePackGradients(self): self._testWhileLoopWritePackGradients( dynamic_size=False, dtype=dtypes.float32) @@ -882,7 +936,7 @@ class TensorArrayTest(test.TestCase): self._testWhileLoopWritePackGradients( dynamic_size=True, dtype=dtypes.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradSerialTwoLoops(self): with self.test_session(use_gpu=True): def loop(x): @@ -1059,7 +1113,7 @@ class TensorArrayTest(test.TestCase): r5 = w5.read(0) self.assertAllEqual([5, 4, 2, 3], r5.get_shape().as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testUnpackShape(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -1093,7 +1147,7 @@ class TensorArrayTest(test.TestCase): def testUnpackShape(self): self._testUnpackShape() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitShape(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -1235,7 +1289,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([10.0, -10.0], read_vals[1]) self.assertAllEqual([[2.0, 3.0], [4.0, 5.0]], grad_vals[0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteGatherAndGradients(self): with self.test_session(use_gpu=True) as session: ta = tensor_array_ops.TensorArray( @@ -1379,7 +1433,7 @@ class TensorArrayTest(test.TestCase): self.assertFalse( [s for s in dev_stats[d] if "/TensorArray" in s.node_name]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayIdentity(self): with self.test_session(use_gpu=True): ta0 = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2, diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 2ee53df9317331dafd96f7884e9a8728cf443923..054c6f9dd79156bc4b4f3179528fe56235fdf369 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -57,7 +57,7 @@ class VariableScopeTest(test.TestCase): v1 = vs.get_variable("v", [1]) self.assertEqual(v, v1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResource(self): vs = variable_scope._get_default_variable_store() v1 = vs.get_variable("v", [1], use_resource=True) @@ -87,7 +87,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( set(expected_names), set([v.name for v in vs._vars.values()])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeInitializer(self): init = init_ops.constant_initializer(0.3) with variable_scope.variable_scope("tower0") as tower: @@ -100,7 +100,7 @@ class VariableScopeTest(test.TestCase): self.evaluate(variables_lib.variables_initializer([w])) self.assertAllClose(self.evaluate(w.value()), 0.3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeConstraint(self): constraint = lambda x: 0. * x with variable_scope.variable_scope("tower1") as tower: @@ -117,7 +117,7 @@ class VariableScopeTest(test.TestCase): variables_lib.global_variables_initializer().run() self.assertAllEqual(compat.as_bytes(v.eval()), b"") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeDType(self): with variable_scope.variable_scope("tower2") as tower: with variable_scope.variable_scope("foo", dtype=dtypes.float16): @@ -197,7 +197,7 @@ class VariableScopeTest(test.TestCase): self.assertAllEqual([v1, v2], [v3, v4]) f() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerVariablesStoreAddsToCollections(self): store = variable_scope.EagerVariableStore() with store.as_default(): @@ -214,7 +214,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( ops.get_collection(ops.GraphKeys.CONCATENATED_VARIABLES), [concat]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerVariablesOutsideStoreNotAddedToCollections(self): if not context.executing_eagerly(): return @@ -223,7 +223,7 @@ class VariableScopeTest(test.TestCase): self.assertFalse(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) self.assertFalse(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFromNonTensorValue(self): v = variable_scope.get_variable("v4", initializer=4, dtype=dtypes.int32) self.evaluate(variables_lib.variables_initializer([v])) @@ -239,7 +239,7 @@ class VariableScopeTest(test.TestCase): with self.assertRaises(error): variable_scope.get_variable("x4", initializer={}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFromNonInitializer(self): # Test various dtypes with zeros initializer as following: types = [ @@ -294,7 +294,7 @@ class VariableScopeTest(test.TestCase): v_tower = variable_scope.get_variable("v", []) self.assertFalse(v_tower.value().device.startswith(caching_device)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeRegularizer(self): init = init_ops.constant_initializer(0.3) @@ -339,7 +339,7 @@ class VariableScopeTest(test.TestCase): losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) self.assertEqual(3, len(losses)) # No new loss added. - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeFromValue(self): init = constant_op.constant(0.1) w = variable_scope.get_variable("v", initializer=init) @@ -428,7 +428,7 @@ class VariableScopeTest(test.TestCase): sess.run(v0.initializer) sess.run(add) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetVariableScope(self): # Test the get_variable_scope() function and setting properties of result. init = init_ops.constant_initializer(0.3) @@ -449,7 +449,7 @@ class VariableScopeTest(test.TestCase): new_init = variable_scope.get_variable_scope().initializer self.assertEqual(new_init, None) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScope(self): with variable_scope.variable_scope("tower4") as tower: self.assertEqual(tower.name, "tower4") @@ -468,7 +468,7 @@ class VariableScopeTest(test.TestCase): with ops.name_scope("scope") as sc: self.assertEqual(sc, "tower6/tower4/scope/") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeNameScope(self): with ops.name_scope("testVarScopeNameScope1"): with variable_scope.variable_scope("tower") as tower: @@ -961,7 +961,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( constant_op.constant([], name="c").name, "another/inner/c:0") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetLocalVar(self): # Check that local variable respects naming. with variable_scope.variable_scope("outer") as outer: @@ -1253,6 +1253,31 @@ class VariableScopeWithCustomGetterTest(test.TestCase): self.assertEqual(v3, v4) self.assertEqual(3, called[0]) # skipped one in the first new_scope + def testSynchronizationAndAggregationWithCustomGetter(self): + called = [0] + synchronization = variable_scope.VariableSynchronization.AUTO + aggregation = variable_scope.VariableAggregation.NONE + + def custom_getter(getter, *args, **kwargs): + called[0] += 1 + + # Verify synchronization and aggregation kwargs are as expected. + self.assertEqual(kwargs["synchronization"], synchronization) + self.assertEqual(kwargs["aggregation"], aggregation) + return getter(*args, **kwargs) + + with variable_scope.variable_scope("scope", custom_getter=custom_getter): + variable_scope.get_variable("v", [1]) + self.assertEqual(1, called[0]) + + with variable_scope.variable_scope("scope", custom_getter=custom_getter): + synchronization = variable_scope.VariableSynchronization.ON_READ + aggregation = variable_scope.VariableAggregation.MEAN + variable_scope.get_variable( + "v1", [1], synchronization=synchronization, aggregation=aggregation) + + self.assertEqual(2, called[0]) + def testCustomGetterWithReuse(self): # Custom getter can choose to behave differently on reused variables. def custom_getter(getter, *args, **kwargs): @@ -1355,6 +1380,23 @@ class VariableScopeWithCustomGetterTest(test.TestCase): self.assertAllEqual(variable_names, ["forced_name"]) + called = [False] + + def creater_c(next_creator, **kwargs): + called[0] = True + self.assertEqual(kwargs["synchronization"], + variable_scope.VariableSynchronization.ON_WRITE) + self.assertEqual(kwargs["aggregation"], + variable_scope.VariableAggregation.MEAN) + return next_creator(**kwargs) + + with variable_scope.variable_creator_scope(creater_c): + variable_scope.get_variable( + "v", [], + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation=variable_scope.VariableAggregation.MEAN) + self.assertTrue(called[0]) + class PartitionInfoTest(test.TestCase): diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index eda036ece4a7d74e5752e80a4a2f4e4ada1b0a38..b8969a41aba1f8ee84233ce7ac398193183d292f 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -191,7 +191,7 @@ class Layer(base_layer.Layer): RuntimeError: If called with partioned variable regularization and eager execution is enabled. """ - + def _should_add_regularizer(variable, existing_variable_set): if isinstance(variable, tf_variables.PartitionedVariable): for var in variable: diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index ab49e37b90e183034ae7ab720fa92b06f39b2aed..298e96e711cbf8a0f625f95d737d1e7a83f4431d 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -39,7 +39,7 @@ from tensorflow.python.platform import test class BaseLayerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerProperties(self): layer = base_layers.Layer(name='my_layer') self.assertEqual(layer.variables, []) @@ -53,13 +53,13 @@ class BaseLayerTest(test.TestCase): layer = base_layers.Layer(name='my_layer', trainable=False) self.assertEqual(layer.trainable, False) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInt64Layer(self): layer = base_layers.Layer(name='my_layer', dtype='int64') layer.add_variable('my_var', [2, 2]) self.assertEqual(layer.name, 'my_layer') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAddWeight(self): layer = base_layers.Layer(name='my_layer') @@ -116,7 +116,7 @@ class BaseLayerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'activity_regularizer'): core_layers.Dense(1, activity_regularizer=lambda *args, **kwargs: 0.) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCall(self): class MyLayer(base_layers.Layer): @@ -132,7 +132,7 @@ class BaseLayerTest(test.TestCase): # op is only supported in GRAPH mode self.assertEqual(outputs.op.name, 'my_layer/Square') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeepCopy(self): class MyLayer(base_layers.Layer): @@ -155,7 +155,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer_copy._graph, layer._graph) self.assertEqual(layer_copy._private_tensor, layer._private_tensor) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScopeNaming(self): class PrivateLayer(base_layers.Layer): @@ -203,7 +203,7 @@ class BaseLayerTest(test.TestCase): my_layer_scoped1.apply(inputs) self.assertEqual(my_layer_scoped1._scope.name, 'var_scope/my_layer_1') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -230,7 +230,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1], [2]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecMinNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -258,7 +258,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[[1], [2]]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecMaxNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -286,7 +286,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1], [2]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecDtypeCheck(self): class CustomerLayer(base_layers.Layer): @@ -306,7 +306,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant(1.0, dtype=dtypes.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecAxesCheck(self): class CustomerLayer(base_layers.Layer): @@ -328,7 +328,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1, 2], [3, 4], [5, 6]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecShapeCheck(self): class CustomerLayer(base_layers.Layer): @@ -348,7 +348,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1, 2, 3], [4, 5, 6]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoInputSpec(self): class CustomerLayer(base_layers.Layer): @@ -369,7 +369,7 @@ class BaseLayerTest(test.TestCase): layer.apply(array_ops.placeholder('int32')) layer.apply(array_ops.placeholder('int32', shape=(2, 3))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_count_params(self): dense = core_layers.Dense(16) dense.build((None, 4)) @@ -379,7 +379,7 @@ class BaseLayerTest(test.TestCase): with self.assertRaises(ValueError): dense.count_params() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDictInputOutput(self): class DictLayer(base_layers.Layer): @@ -589,6 +589,5 @@ class BaseLayerTest(test.TestCase): ValueError, 'Input graph and Layer graph are not the same'): layer.apply(constant_op.constant([[1.]])) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index 267d78dbcb27392a528bf09414b857d9b1a7c2f9..36cef3855e5233bf878a7dab178cb2a5f4a779c2 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -217,7 +217,6 @@ def conv1d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -421,7 +420,6 @@ def conv2d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -627,7 +625,6 @@ def conv3d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -1266,7 +1263,6 @@ def conv2d_transpose(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -1438,7 +1434,6 @@ def conv3d_transpose(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index abbacac442c5bb20feeb255d4ad3f90626c75327..aadff231dabb06a7c05446fb92f758de57a744da 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -184,7 +184,6 @@ def dense( bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index cf45b07637108422f1c612390bb01efdad6d5bcf..040c1cddc0f2540eec5fcf3442bed3f4800bec7c 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -41,7 +41,7 @@ from tensorflow.python.platform import test class DenseTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDenseProperties(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') self.assertEqual(dense.units, 2) @@ -91,14 +91,14 @@ class DenseTest(test.TestCase): core_layers.Dense(5)(inputs) core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')(inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallTensorDot(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') inputs = random_ops.random_uniform((5, 4, 3), seed=1) outputs = dense(inputs) self.assertListEqual([5, 4, 2], outputs.get_shape().as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoBias(self): dense = core_layers.Dense(2, use_bias=False, name='my_dense') inputs = random_ops.random_uniform((5, 2), seed=1) @@ -112,7 +112,7 @@ class DenseTest(test.TestCase): self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') self.assertEqual(dense.bias, None) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonTrainable(self): dense = core_layers.Dense(2, trainable=False, name='my_dense') inputs = random_ops.random_uniform((5, 2), seed=1) @@ -125,7 +125,7 @@ class DenseTest(test.TestCase): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOutputShape(self): dense = core_layers.Dense(7, activation=nn_ops.relu, name='my_dense') inputs = random_ops.random_uniform((5, 3), seed=1) @@ -165,7 +165,7 @@ class DenseTest(test.TestCase): dense = core_layers.Dense(4, name='my_dense') dense(inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testActivation(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') inputs = random_ops.random_uniform((5, 3), seed=1) @@ -325,7 +325,7 @@ class DenseTest(test.TestCase): var_key = 'test2/dense/kernel' self.assertEqual(var_dict[var_key].name, '%s:0' % var_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeOutputShape(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') ts = tensor_shape.TensorShape @@ -347,7 +347,7 @@ class DenseTest(test.TestCase): dense.compute_output_shape(ts([None, 4, 3])).as_list()) # pylint: enable=protected-access - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstraints(self): k_constraint = lambda x: x / math_ops.reduce_sum(x) b_constraint = lambda x: x / math_ops.reduce_max(x) @@ -369,7 +369,7 @@ def _get_variable_dict_from_varstore(): class DropoutTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDropoutProperties(self): dp = core_layers.Dropout(0.5, name='dropout') self.assertEqual(dp.rate, 0.5) @@ -377,7 +377,7 @@ class DropoutTest(test.TestCase): dp.apply(array_ops.ones(())) self.assertEqual(dp.name, 'dropout') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBooleanLearningPhase(self): dp = core_layers.Dropout(0.5) inputs = array_ops.ones((5, 3)) @@ -402,7 +402,7 @@ class DropoutTest(test.TestCase): np_output = sess.run(dropped, feed_dict={training: False}) self.assertAllClose(np.ones((5, 5)), np_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicNoiseShape(self): inputs = array_ops.ones((5, 3, 2)) noise_shape = [None, 1, None] diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py index d082e312e9a3750726235a0360ef466fb8915208..f7bc10a6a634d4f821894f1f07106ba340d421af 100644 --- a/tensorflow/python/layers/normalization.py +++ b/tensorflow/python/layers/normalization.py @@ -44,7 +44,7 @@ class BatchNormalization(keras_layers.BatchNormalization, base.Layer): normalized, typically the features axis/axes. For instance, after a `Conv2D` layer with `data_format="channels_first"`, set `axis=1`. If a list of axes is provided, each axis in `axis` will be normalized - simultaneously. Default is `-1` which takes uses last axis. Note: when + simultaneously. Default is `-1` which uses the last axis. Note: when using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and `moving_variance` variables are the same rank as the input Tensor, with dimension size 1 in all reduced (non-axis) dimensions). @@ -308,7 +308,6 @@ def batch_normalization(inputs, virtual_batch_size=virtual_batch_size, adjustment=adjustment, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs, training=training) diff --git a/tensorflow/python/lib/core/bfloat16.cc b/tensorflow/python/lib/core/bfloat16.cc index 77fa2c1f66d2214dbb08e4d0ad3437fa4fe02822..fde3a83770280038b777a141693d117dace4b41f 100644 --- a/tensorflow/python/lib/core/bfloat16.cc +++ b/tensorflow/python/lib/core/bfloat16.cc @@ -446,6 +446,16 @@ npy_bool NPyBfloat16_NonZero(void* data, void* arr) { return x != static_cast(0); } +int NPyBfloat16_Fill(void* buffer_raw, npy_intp length, void* ignored) { + bfloat16* const buffer = reinterpret_cast(buffer_raw); + const float start(buffer[0]); + const float delta = static_cast(buffer[1]) - start; + for (npy_intp i = 2; i < length; ++i) { + buffer[i] = static_cast(start + i * delta); + } + return 0; +} + // NumPy casts // Performs a NumPy array cast from type 'From' to 'To'. @@ -548,6 +558,7 @@ bool Initialize() { NPyBfloat16_ArrFuncs.copyswapn = NPyBfloat16_CopySwapN; NPyBfloat16_ArrFuncs.copyswap = NPyBfloat16_CopySwap; NPyBfloat16_ArrFuncs.nonzero = NPyBfloat16_NonZero; + NPyBfloat16_ArrFuncs.fill = NPyBfloat16_Fill; Py_TYPE(&NPyBfloat16_Descr) = &PyArrayDescr_Type; npy_bfloat16_ = PyArray_RegisterDataType(&NPyBfloat16_Descr); diff --git a/tensorflow/python/lib/core/bfloat16_test.py b/tensorflow/python/lib/core/bfloat16_test.py index 09d4b01fa43babdc09f8f255e79bbed539ddc04c..bc928cd9e5ef4d5a0ec0ce73e853e3e022a1f6fa 100644 --- a/tensorflow/python/lib/core/bfloat16_test.py +++ b/tensorflow/python/lib/core/bfloat16_test.py @@ -245,6 +245,20 @@ class Bfloat16NumPyTest(test.TestCase): np.logaddexp(x.astype(bfloat16), y.astype(bfloat16)), atol=2e-2) + def testArange(self): + self.assertAllEqual( + np.arange(100, dtype=np.float32).astype(bfloat16), + np.arange(100, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-10.5, 7.8, 0.5, dtype=np.float32).astype(bfloat16), + np.arange(-10.5, 7.8, 0.5, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-0., -7., -0.25, dtype=np.float32).astype(bfloat16), + np.arange(-0., -7., -0.25, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-16384., 16384., 64., dtype=np.float32).astype(bfloat16), + np.arange(-16384., 16384., 64., dtype=bfloat16)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/lib/core/ndarray_tensor.cc b/tensorflow/python/lib/core/ndarray_tensor.cc index 2acab927643fb517b05111f3c3c6a25d12161121..ec1ba7b8f7d611ad659ac483505a7d86bf4b31e5 100644 --- a/tensorflow/python/lib/core/ndarray_tensor.cc +++ b/tensorflow/python/lib/core/ndarray_tensor.cc @@ -411,7 +411,7 @@ Status PyArrayToTF_Tensor(PyObject* ndarray, Safe_TF_TensorPtr* out_tensor) { // Make sure we dereference this array object in case of error, etc. Safe_PyObjectPtr array_safe(make_safe( - PyArray_FromAny(ndarray, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr))); + PyArray_FromAny(ndarray, nullptr, 0, 0, NPY_ARRAY_CARRAY_RO, nullptr))); if (!array_safe) return errors::InvalidArgument("Not a ndarray."); PyArrayObject* array = reinterpret_cast(array_safe.get()); diff --git a/tensorflow/python/lib/core/numpy.h b/tensorflow/python/lib/core/numpy.h index 25322b458b8475882830599dd4ae02f10d97094b..d4621d61ee98b9eb4b19213145059d242c88f40c 100644 --- a/tensorflow/python/lib/core/numpy.h +++ b/tensorflow/python/lib/core/numpy.h @@ -29,7 +29,9 @@ limitations under the License. #define NO_IMPORT_ARRAY #endif +// Place `` before to avoid build failure in macOS. #include +#include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index 30c1a9c75986f242c6cf5a8aa2ed1b64938d2bda..57139986af7d2adc3670529d1bb22233f167ced0 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -55,37 +55,35 @@ struct PyCall { string token; // The device on which Tensors are stored; only used for EagerPyFunc. - Device* device; - - // True if and only if the op has been placed on a GPU. - bool gpu; + Device* device = nullptr; // True if the call is associated with an EagerPyFunc. - bool eager; + bool eager = false; // Inputs and outputs of this function invocation. std::vector ins; std::vector out; }; +bool IsCPUDevice(const Device* d) { + return d == nullptr || d->tensorflow_gpu_device_info() == nullptr; +} + // Givens the 'call', prepares the token and inputs as a python tuple // that is appropriate for calling the trampoline. Status MakeArgTuple(const PyCall* call, PyObject** tuple) { int64 n = call->ins.size(); PyObject* lst = PyList_New(n); CHECK(lst); + // TFE_TensorHandle assumes that CPU is identified by nullptr. + Device* device = IsCPUDevice(call->device) ? nullptr : call->device; for (int64 i = 0; i < n; ++i) { PyObject* arg = nullptr; const Tensor& t = call->ins[i]; if (call->eager) { - if (call->gpu) { - arg = EagerTensorFromHandle( - new TFE_TensorHandle(t, call->device, call->device)); - } else { - // TFE_TensorHandle assumes that CPU is identified by `nullptr`. - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr, nullptr)); - } + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, device, device)); if (arg == nullptr) { + Py_DECREF(lst); return errors::Internal("Unable to procure EagerTensor from Tensor."); } } else { @@ -97,8 +95,9 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { } PyList_SetItem(lst, i, arg); } - *tuple = Py_BuildValue("(sON)", call->token.c_str(), - call->gpu ? Py_True : Py_False, lst); + const char* device_name = + device == nullptr ? nullptr : device->attributes().name().c_str(); + *tuple = Py_BuildValue("(ssN)", call->token.c_str(), device_name, lst); CHECK(*tuple); return Status::OK(); } @@ -167,9 +166,40 @@ bool IsSingleNone(PyObject* obj) { } // Retrieves a Tensor from `eager_tensor` and stores it in `output_tensor`. +// Validates that `output_tensor` is backed by memory in `expected_device` +// (which is assumed to be a local device, one on which the kernel was +// executed.) +// +// It may be nice to copy the tensor to the right device instead of failing if +// it isn't already there. This is left as a future exercise. The required +// device-copying logic is implemented in Python at the moment. tensorflow::Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor, + const Device* expected_device, const Tensor** output_tensor) { - return EagerTensor_Handle(eager_tensor)->handle->Tensor(output_tensor); + auto handle = EagerTensor_Handle(eager_tensor)->handle; + Device* actual_device = nullptr; + TF_RETURN_IF_ERROR(handle->Device(&actual_device)); + TF_RETURN_IF_ERROR(handle->Tensor(output_tensor)); + // actual_device may be nullptr, which implies local CPU. + if (expected_device == actual_device) return Status::OK(); + const string& expected_device_name = expected_device->attributes().name(); + if (actual_device == nullptr) { + if (!IsCPUDevice(expected_device)) { + return errors::Internal( + "expected the py_func to return a Tensor backed by memory in ", + expected_device_name, + ", but is actually backed by local host memory. This is a bug."); + } + return Status::OK(); + } + const string& actual_device_name = actual_device->attributes().name(); + if (actual_device_name != expected_device_name) { + return errors::Internal( + "expected the py_func to return a Tensor backed by memory in ", + expected_device_name, ", but is actually in ", actual_device_name, + ". This is a bug."); + } + return Status::OK(); } // Calls the registered py function through the trampoline. @@ -224,7 +254,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { const PyObject* item = PyList_GetItem(result, i); if (EagerTensor_CheckExact(item)) { const Tensor* tensor = nullptr; - s = ExtractTensorFromEagerTensor(item, &tensor); + s = ExtractTensorFromEagerTensor(item, call->device, &tensor); if (s.ok()) t = *tensor; } else { s = errors::FailedPrecondition( @@ -245,7 +275,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { DCHECK(call->eager); if (result != Py_None) { const Tensor* t = nullptr; - s = ExtractTensorFromEagerTensor(result, &t); + s = ExtractTensorFromEagerTensor(result, call->device, &t); if (s.ok()) call->out.push_back(*t); } } else if (PyArray_Check(result)) { @@ -449,13 +479,11 @@ class PyFuncOp : public OpKernel { explicit PyFuncOp(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("token", &token_)); eager_ = type_string() == "EagerPyFunc"; - gpu_ = ctx->device_type().type_string() == DEVICE_GPU; } void Compute(OpKernelContext* ctx) override { PyCall call; call.token = token_; - call.gpu = gpu_; call.eager = eager_; if (call.eager) { // Eager's C API uses `Device`, whereas `OpKernelContext` stores a @@ -464,6 +492,7 @@ class PyFuncOp : public OpKernel { if (call.device == nullptr) { ctx->CtxFailureWithWarning( errors::Internal("Unrecognized device class")); + return; } } @@ -508,9 +537,6 @@ class PyFuncOp : public OpKernel { private: string token_; - // True if and only if this op has been placed on a GPU. - bool gpu_; - // True if and only if this op should execute the python function eagerly, // i.e., if and only if the eager attribute is set. bool eager_; diff --git a/tensorflow/python/lib/core/py_seq_tensor.cc b/tensorflow/python/lib/core/py_seq_tensor.cc index 386be35ba2ff1fed07d6b6f5ee5d60a0f2039441..3b4f12ae31b9e905ed15e86533e648b4c95736e1 100644 --- a/tensorflow/python/lib/core/py_seq_tensor.cc +++ b/tensorflow/python/lib/core/py_seq_tensor.cc @@ -88,6 +88,41 @@ bool IsPyDimension(PyObject* obj) { return ret; } +// Sets *elem to a NEW reference to an element in seq on success. +// REQUIRES: PySequence_Check(seq) && PySequence_Length(seq) > 0. +Status SampleElementFromSequence(PyObject* seq, PyObject** elem) { + *elem = PySequence_GetItem(seq, 0); + if (*elem != nullptr) return Status::OK(); + // seq may implement the sequence protocol (i.e., implement __getitem__) + // but may legitimately not have a 0-th element (__getitem__(self, 0) + // raises a KeyError). For example: + // seq = pandas.Series([0, 1, 2], index=[2, 4, 6]) + // + // We don't actually care for the element at key 0, any element will do + // for inferring the element types. All elements are expected to + // have the same type, and this will be validated when converting + // to an EagerTensor. + PyErr_Clear(); + Safe_PyObjectPtr iter(PyObject_GetIter(seq)); + if (PyErr_Occurred()) { + return errors::InvalidArgument("Cannot infer dtype of a ", + Py_TYPE(seq)->tp_name, + " object: ", PyExceptionFetch()); + } + *elem = PyIter_Next(iter.get()); + if (PyErr_Occurred()) { + return errors::InvalidArgument( + "Cannot infer dtype of a ", Py_TYPE(seq)->tp_name, + " object, as iter().next() failed: ", PyExceptionFetch()); + } + if (*elem == nullptr) { + return errors::InvalidArgument("Cannot infer dtype of a ", + Py_TYPE(seq)->tp_name, + " object since it is an empty sequence"); + } + return Status::OK(); +} + Status InferShapeAndType(PyObject* obj, TensorShape* shape, DataType* dtype) { std::vector refs_to_clean; while (true) { @@ -98,7 +133,9 @@ Status InferShapeAndType(PyObject* obj, TensorShape* shape, DataType* dtype) { auto length = PySequence_Length(obj); if (length > 0) { shape->AddDim(length); - obj = PySequence_GetItem(obj, 0); + PyObject* elem = nullptr; + TF_RETURN_IF_ERROR(SampleElementFromSequence(obj, &elem)); + obj = elem; refs_to_clean.push_back(make_safe(obj)); continue; } else if (length == 0) { diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc index dcda1f4a446dd77af84ea1d434370d2de47fdc2e..6b6c82015fd2b73e410d64306ecbd613ccf1967c 100644 --- a/tensorflow/python/lib/core/py_util.cc +++ b/tensorflow/python/lib/core/py_util.cc @@ -15,7 +15,9 @@ limitations under the License. #include "tensorflow/python/lib/core/py_util.h" +// Place `` before to avoid build failure in macOS. #include +#include #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/strings/strcat.h" diff --git a/tensorflow/python/lib/io/tf_record_test.py b/tensorflow/python/lib/io/tf_record_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dcc1a25f420b434e6aa7d37cdf65f693e4d8c01a --- /dev/null +++ b/tensorflow/python/lib/io/tf_record_test.py @@ -0,0 +1,322 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tf_record.TFRecordWriter and tf_record.tf_record_iterator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os +import zlib + +import six + +from tensorflow.python.framework import errors_impl +from tensorflow.python.lib.io import tf_record +from tensorflow.python.platform import test +from tensorflow.python.util import compat + +prefix_path = "third_party/tensorflow/core/lib" + +# pylint: disable=invalid-name +TFRecordCompressionType = tf_record.TFRecordCompressionType +# pylint: enable=invalid-name + +# Edgar Allan Poe's 'Eldorado' +_TEXT = b"""Gaily bedight, + A gallant knight, + In sunshine and in shadow, + Had journeyed long, + Singing a song, + In search of Eldorado. + + But he grew old + This knight so bold + And o'er his heart a shadow + Fell as he found + No spot of ground + That looked like Eldorado. + + And, as his strength + Failed him at length, + He met a pilgrim shadow + 'Shadow,' said he, + 'Where can it be + This land of Eldorado?' + + 'Over the Mountains + Of the Moon' + Down the Valley of the Shadow, + Ride, boldly ride,' + The shade replied, + 'If you seek for Eldorado!' + """ + + +class TFCompressionTestCase(test.TestCase): + + def setUp(self): + super(TFCompressionTestCase, self).setUp() + self._num_files = 2 + self._num_records = 7 + + def _Record(self, f, r): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _CreateFiles(self, options=None, prefix=""): + filenames = [] + for i in range(self._num_files): + name = prefix + "tfrecord.%d.txt" % i + records = [self._Record(i, j) for j in range(self._num_records)] + fn = self._WriteRecordsToFile(records, name, options) + filenames.append(fn) + return filenames + + def _WriteRecordsToFile(self, records, name="tfrecord", options=None): + fn = os.path.join(self.get_temp_dir(), name) + with tf_record.TFRecordWriter(fn, options=options) as writer: + for r in records: + writer.write(r) + return fn + + def _ZlibCompressFile(self, infile, name="tfrecord.z"): + # zlib compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = zlib.compress(f.read()) + + zfn = os.path.join(self.get_temp_dir(), name) + with open(zfn, "wb") as f: + f.write(cdata) + return zfn + + def _GzipCompressFile(self, infile, name="tfrecord.gz"): + # gzip compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = f.read() + + gzfn = os.path.join(self.get_temp_dir(), name) + with gzip.GzipFile(gzfn, "wb") as f: + f.write(cdata) + return gzfn + + def _ZlibDecompressFile(self, infile, name="tfrecord"): + with open(infile, "rb") as f: + cdata = zlib.decompress(f.read()) + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + def _GzipDecompressFile(self, infile, name="tfrecord"): + with gzip.GzipFile(infile, "rb") as f: + cdata = f.read() + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + +class TFRecordWriterTest(TFCompressionTestCase): + + def setUp(self): + super(TFRecordWriterTest, self).setUp() + + def _AssertFilesEqual(self, a, b, equal): + for an, bn in zip(a, b): + with open(an, "rb") as af, open(bn, "rb") as bf: + if equal: + self.assertEqual(af.read(), bf.read()) + else: + self.assertNotEqual(af.read(), bf.read()) + + def testWriteReadZLibFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + zlib_files = [ + self._ZlibCompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, zlib_files, False) + + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + compressed_files = self._CreateFiles(options, prefix="compressed") + self._AssertFilesEqual(compressed_files, zlib_files, True) + + # Decompress compress and verify same. + uncompressed_files = [ + self._ZlibDecompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) + + def testWriteReadGzipFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + gzip_files = [ + self._GzipCompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, gzip_files, False) + + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + compressed_files = self._CreateFiles(options, prefix="compressed") + + # Note: Gzips written by TFRecordWriter add 'tfrecord_0' so + # compressed_files can't be compared with gzip_files + + # Decompress compress and verify same. + uncompressed_files = [ + self._GzipDecompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) + + +class TFRecordWriterZlibTest(TFCompressionTestCase): + + def testZLibFlushRecord(self): + original = [b"small record"] + fn = self._WriteRecordsToFile(original, "small_record") + with open(fn, "rb") as h: + buff = h.read() + + # creating more blocks and trailing blocks shouldn't break reads + compressor = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS) + + output = b"" + for c in buff: + if isinstance(c, int): + c = six.int2byte(c) + output += compressor.compress(c) + output += compressor.flush(zlib.Z_FULL_FLUSH) + + output += compressor.flush(zlib.Z_FULL_FLUSH) + output += compressor.flush(zlib.Z_FULL_FLUSH) + output += compressor.flush(zlib.Z_FINISH) + + # overwrite the original file with the compressed data + with open(fn, "wb") as h: + h.write(output) + + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(fn, options=options)) + self.assertEqual(actual, original) + + def testZlibReadWrite(self): + """Verify that files produced are zlib compatible.""" + original = [b"foo", b"bar"] + fn = self._WriteRecordsToFile(original, "zlib_read_write.tfrecord") + zfn = self._ZlibCompressFile(fn, "zlib_read_write.tfrecord.z") + + # read the compressed contents and verify. + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(zfn, options=options)) + self.assertEqual(actual, original) + + def testZlibReadWriteLarge(self): + """Verify that writing large contents also works.""" + + # Make it large (about 5MB) + original = [_TEXT * 10240] + fn = self._WriteRecordsToFile(original, "zlib_read_write_large.tfrecord") + zfn = self._ZlibCompressFile(fn, "zlib_read_write_large.tfrecord.z") + + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(zfn, options=options)) + self.assertEqual(actual, original) + + def testGzipReadWrite(self): + """Verify that files produced are gzip compatible.""" + original = [b"foo", b"bar"] + fn = self._WriteRecordsToFile(original, "gzip_read_write.tfrecord") + gzfn = self._GzipCompressFile(fn, "tfrecord.gz") + + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + actual = list(tf_record.tf_record_iterator(gzfn, options=options)) + self.assertEqual(actual, original) + + +class TFRecordIteratorTest(TFCompressionTestCase): + + def setUp(self): + super(TFRecordIteratorTest, self).setUp() + self._num_records = 7 + + def testIterator(self): + records = [self._Record(0, i) for i in range(self._num_records)] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(records, "compressed_records", options) + + reader = tf_record.tf_record_iterator(fn, options) + for expected in records: + record = next(reader) + self.assertAllEqual(expected, record) + with self.assertRaises(StopIteration): + record = next(reader) + + def testWriteZlibRead(self): + """Verify compression with TFRecordWriter is zlib library compatible.""" + original = [b"foo", b"bar"] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read.tfrecord.z", + options) + + zfn = self._ZlibDecompressFile(fn, "write_zlib_read.tfrecord") + actual = list(tf_record.tf_record_iterator(zfn)) + self.assertEqual(actual, original) + + def testWriteZlibReadLarge(self): + """Verify compression for large records is zlib library compatible.""" + # Make it large (about 5MB) + original = [_TEXT * 10240] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read_large.tfrecord.z", + options) + zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tfrecord") + actual = list(tf_record.tf_record_iterator(zfn)) + self.assertEqual(actual, original) + + def testWriteGzipRead(self): + original = [b"foo", b"bar"] + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + fn = self._WriteRecordsToFile(original, "write_gzip_read.tfrecord.gz", + options) + + gzfn = self._GzipDecompressFile(fn, "write_gzip_read.tfrecord") + actual = list(tf_record.tf_record_iterator(gzfn)) + self.assertEqual(actual, original) + + def testBadFile(self): + """Verify that tf_record_iterator throws an exception on bad TFRecords.""" + fn = os.path.join(self.get_temp_dir(), "bad_file") + with tf_record.TFRecordWriter(fn) as writer: + writer.write(b"123") + fn_truncated = os.path.join(self.get_temp_dir(), "bad_file_truncated") + with open(fn, "rb") as f: + with open(fn_truncated, "wb") as f2: + # DataLossError requires that we've written the header, so this must + # be at least 12 bytes. + f2.write(f.read(14)) + with self.assertRaises(errors_impl.DataLossError): + for _ in tf_record.tf_record_iterator(fn_truncated): + pass + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py index 3678bd4c1f6a4500622b6d9e8334cb1ebae46578..fe459a96b98733f8a706b0c3b84000c5a74894ad 100644 --- a/tensorflow/python/ops/array_grad.py +++ b/tensorflow/python/ops/array_grad.py @@ -568,7 +568,6 @@ ops.NotDifferentiable("Size") @ops.RegisterGradient("Tile") def _TileGrad(op, grad): """Sum reduces grad along the tiled dimensions.""" - assert isinstance(grad, ops.Tensor) input_shape = array_ops.shape(op.inputs[0]) # We interleave multiples and input_shape to get split_shape, # reshape grad to split_shape, and reduce along all even @@ -581,6 +580,13 @@ def _TileGrad(op, grad): split_shape = array_ops.reshape( array_ops.transpose(array_ops.stack([op.inputs[1], input_shape])), [-1]) axes = math_ops.range(0, array_ops.size(split_shape), 2) + # Sum reduces grad along the first dimension for IndexedSlices + if isinstance(grad, ops.IndexedSlices): + grad = math_ops.unsorted_segment_sum( + grad.values, + math_ops.mod(grad.indices, input_shape[0]), + input_shape[0]) + split_shape = array_ops.concat([[1], split_shape[1:]], axis=0) input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes) # Fix shape inference if not context.executing_eagerly(): diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index fae63b1132cca527c6bc5d5f9f5c8be2952d8f3c..361667ec49aba9705787c3c7ac096add36afb40b 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -41,6 +41,7 @@ from tensorflow.python.ops import gen_math_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_array_ops import * +from tensorflow.python.ops.gen_array_ops import reverse_v2 as reverse # pylint: disable=unused-import from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export # pylint: enable=wildcard-import @@ -2609,14 +2610,6 @@ def where(condition, x=None, y=None, name=None): raise ValueError("x and y must both be non-None or both be None.") -@tf_export("reverse") -def reverse(tensor, axis, name=None): - return gen_array_ops.reverse_v2(tensor, axis, name) - - -reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ - - # pylint: disable=redefined-builtin @tf_export("reverse_sequence") @deprecation.deprecated_args( diff --git a/tensorflow/python/ops/boosted_trees_ops.py b/tensorflow/python/ops/boosted_trees_ops.py index 2a2bcdd9d69b7a0aed1e7f3d3197cf6d7dd98451..9ebb607c475d444bfc78369b8f5415ac93b0dee2 100644 --- a/tensorflow/python/ops/boosted_trees_ops.py +++ b/tensorflow/python/ops/boosted_trees_ops.py @@ -25,6 +25,7 @@ from tensorflow.python.ops import resources # Re-exporting ops used by other modules. # pylint: disable=unused-import from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_calculate_best_gains_per_feature as calculate_best_gains_per_feature +from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_example_debug_outputs as example_debug_outputs from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_make_stats_summary as make_stats_summary from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_predict as predict from tensorflow.python.ops.gen_boosted_trees_ops import boosted_trees_training_predict as training_predict diff --git a/tensorflow/python/ops/collective_ops.py b/tensorflow/python/ops/collective_ops.py index a05fd15eca12a423bf02dfb13044dd1f7630b99c..98668facd5bc56892fa00f258dfebcbe93c063da 100644 --- a/tensorflow/python/ops/collective_ops.py +++ b/tensorflow/python/ops/collective_ops.py @@ -22,7 +22,7 @@ from tensorflow.python.ops import gen_collective_ops def all_reduce(t, group_size, group_key, instance_key, merge_op, final_op, - subdiv_offsets=(0)): + subdiv_offsets=(0,)): """Reduces tensors collectively, across devices. Args: diff --git a/tensorflow/python/ops/collective_ops_test.py b/tensorflow/python/ops/collective_ops_test.py index 8e16cffdf4917ba361a3c313047e39af514273bc..9cc64ef9f631faf2f76c3dbb3e70e1f37bbe4b1a 100644 --- a/tensorflow/python/ops/collective_ops_test.py +++ b/tensorflow/python/ops/collective_ops_test.py @@ -37,11 +37,11 @@ class CollectiveOpTest(test.TestCase): with ops.device('/CPU:0'): in0 = constant_op.constant(t0) colred0 = collective_ops.all_reduce(in0, 2, group_key, instance_key, - 'Add', 'Div', [0]) + 'Add', 'Div') with ops.device('/CPU:1'): in1 = constant_op.constant(t1) colred1 = collective_ops.all_reduce(in1, 2, group_key, instance_key, - 'Add', 'Div', [0]) + 'Add', 'Div') run_options = config_pb2.RunOptions() run_options.experimental.collective_graph_key = 1 results = sess.run([colred0, colred1], options=run_options) diff --git a/tensorflow/python/ops/cond_v2.py b/tensorflow/python/ops/cond_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..76173e0f309b80402a15acdab5d2af49f35de741 --- /dev/null +++ b/tensorflow/python/ops/cond_v2.py @@ -0,0 +1,32 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""cond_v2 wrapper module. + +This imports the cond_v2 method and all necessary dependencies (this is to avoid +circular dependencies in the cond_v2 implementation). See cond_v2_impl for more +information. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import +from tensorflow.python.framework import function +from tensorflow.python.framework import function_def_to_graph +from tensorflow.python.ops import gradients_impl + +from tensorflow.python.ops.cond_v2_impl import cond_v2 +# pylint: enable=unused-import diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/python/ops/cond_v2_impl.py similarity index 84% rename from tensorflow/contrib/control_flow/python/cond_v2.py rename to tensorflow/python/ops/cond_v2_impl.py index 9ffad9caa92d2d3be8f598758a443b0eceb8d4d8..d310f83dca97889157eb078b11a3ca51caae2fc2 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -17,23 +17,32 @@ This is a version of cond that emits a single If op, as well as the gradient function for If ops produced by cond_v2. This will eventually replace the current tf.cond implementation once it reaches feature and performance parity. + +NOTE: most users of cond_v2 should import cond_v2, not this module! This module +does not contain all the necessary imports to prevent circular dependencies, +while cond_v2 does. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.core.framework import attr_value_pb2 from tensorflow.python import pywrap_tensorflow as c_api from tensorflow.python.framework import c_api_util -from tensorflow.python.framework import function -from tensorflow.python.framework import function_def_to_graph from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import gen_functional_ops -from tensorflow.python.ops import gradients_impl from tensorflow.python.util import compat +# The following modules cannot be imported directly because they cause circular +# dependencies. These are set in each corresponding module. +_function = None +_function_def_to_graph = None +_gradients_impl = None + # NOTE(skyewm): TensorFlow uses protected class methods and fields to signify # that they aren't part of the official public API. These protected members # often need to be used by implementation code however. Rather than litter the @@ -44,11 +53,34 @@ from tensorflow.python.util import compat def cond_v2(pred, true_fn, false_fn, name="cond"): """Like tf.cond, except emits a single If op.""" + if not name: + name = "cond" + with ops.name_scope(name) as scope: - true_graph = function.func_graph_from_py_func(true_fn, [], [], - name="%s_true" % scope) - false_graph = function.func_graph_from_py_func(false_fn, [], [], - name="%s_false" % scope) + # Identify if there is a caller device, & get the innermost if possible. + device_stack = ops.get_default_graph()._device_function_stack + caller_device = device_stack[-1] if device_stack else None + + caller_colocation_stack = ops.get_default_graph()._colocation_stack + caller_container = ops.get_default_graph()._container + caller_collection_ref = ops.get_default_graph()._collections + + func_name_prefix = scope.replace("/", "_") + + true_graph = _function.func_graph_from_py_func( + true_fn, [], [], + name="%strue" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) + false_graph = _function.func_graph_from_py_func( + false_fn, [], [], + name="%sfalse" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) _check_same_outputs(true_graph, false_graph) # Add inputs to true_graph and false_graph to make them match. Note that @@ -80,6 +112,22 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): _create_new_tf_function(false_graph), name=scope) + # Set the flag to enable lowering on the `if` op if necessary + # Lowering allows cond_v2 to avoid some of the limitations of Functions, + # allowing users to specify devices & colocation inside of cond_v2 branches, + # and enabling non-strict evaluation & partial pruning of cond_v2 branches. + # This brings cond_v2 closer to feature parity with tf.cond. + # + # However, we do not lower `If` in the XLA context because it is easier for + # XLA to apply its own optimizations when dealing with un-lowered `If` + # operators than with lowered switch/merge control flow. + # + # TODO(b/110167197) this approach requires cond_v2 to have at least 1 output + if_op = tensors[0].op + if not control_flow_util.IsInXLAContext(if_op): + if_op._set_attr("_lower_using_switch_merge", + attr_value_pb2.AttrValue(b=True)) + return tensors[:num_cond_outputs] @@ -146,11 +194,13 @@ def _get_func_graphs(if_op): A 2-tuple of the `_FuncGraph`s of the then_branch and else_branch. """ def _get_func_graph_for_branch(branch_name): + """Generates and returns a _FuncGraph for the given branch.""" extra_inputs = if_op.inputs[1:] # First input is pred. input_shapes = [t.shape for t in extra_inputs] func_name = if_op.get_attr(branch_name).name fdef = if_op.graph._get_function(func_name).definition - func_graph = function_def_to_graph.function_def_to_graph(fdef, input_shapes) + func_graph = _function_def_to_graph.function_def_to_graph( + fdef, input_shapes) func_graph.extra_inputs = extra_inputs func_graph.extra_args = func_graph.inputs func_graph._captured = dict(zip(extra_inputs, func_graph.inputs)) @@ -182,7 +232,7 @@ def _grad_fn(func_graph, grads): ys = [] grad_ys = [] for y, grad_y in zip(func_graph.outputs, grads): - if not gradients_impl._IsTrainable(y): + if not _gradients_impl._IsTrainable(y): continue ys.append(y) grad_ys.append(grad_y) @@ -191,7 +241,7 @@ def _grad_fn(func_graph, grads): # func_graph in the current graph, which requires capturing tensors from # func_graph. The captured func_graph tensors are resolved to external tensors # in _get_grad_inputs. - result = gradients_impl._GradientsHelper( + result = _gradients_impl._GradientsHelper( ys, func_graph.inputs, grad_ys=grad_ys, src_graph=func_graph) @@ -207,8 +257,8 @@ def _grad_fn(func_graph, grads): def _create_grad_func(func_graph, grads, name): """Returns the _FuncGraph representation of _grad_fn.""" - return function.func_graph_from_py_func(lambda: _grad_fn(func_graph, grads), - [], [], name) + return _function.func_graph_from_py_func(lambda: _grad_fn(func_graph, grads), + [], [], name) def _get_grad_inputs(if_op, cond_graph, grad_graph): @@ -274,8 +324,8 @@ def _create_new_tf_function(func_graph): # TODO(b/109833212): this sucks, we're serializing the TF_Function*, # deserializing it into a Python FunctionDef, then reserializing it to create # a new TF_Function that we add to the graph. - fdef = function.function_def_from_tf_function(c_func) - defined_func = function._from_definition(fdef) + fdef = _function.function_def_from_tf_function(c_func) + defined_func = _function._from_definition(fdef) defined_func.add_to_graph(ops.get_default_graph()) return func_graph.name diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 2e5a801f8e96aa1266695a1440d98e6bff53607c..fc37805c79916ca9108481f7b6e69c381c2ff9d2 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -24,6 +24,7 @@ from __future__ import print_function import abc import collections import functools +import os import six @@ -38,6 +39,7 @@ from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import control_flow_util as util from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_control_flow_ops @@ -57,6 +59,10 @@ from tensorflow.python.util import nest from tensorflow.python.util import tf_should_use from tensorflow.python.util.tf_export import tf_export + +_ENABLE_COND_V2 = os.getenv("TF_ENABLE_COND_V2", "0") != "0" + + # We override the 'tuple' for a control flow op, so we keep python's # existing 'tuple' for later use in this module. _basetuple = tuple @@ -596,7 +602,6 @@ def _EnforceShapeInvariant(merge_var, next_var): enter = merge_var.op.inputs[0].op assert util.IsLoopEnter(enter) input_t = enter.inputs[0] - assert input_t.shape == m_shape raise ValueError( "Input tensor '%s' enters the loop with shape %s, but has shape %s " "after one iteration. To allow the shape to vary across iterations, " @@ -1994,6 +1999,9 @@ def cond(pred, ``` """ + if _ENABLE_COND_V2: + return cond_v2_impl.cond_v2(pred, true_fn, false_fn, name) + # We needed to make true_fn/false_fn keyword arguments for # backwards-compatibility. This check exists so that we can convert back to # having them be positional arguments. @@ -2935,9 +2943,10 @@ class WhileContext(ControlFlowContext): loop_vars = ops.convert_n_to_tensor_or_indexed_slices(loop_vars) try: self.Enter() - # _BuildLoop calls _update_input in several places. _lock ensures a - # Session.run call cannot occur between creating and mutating new ops. - with ops.get_default_graph()._lock: # pylint: disable=protected-access + # _BuildLoop calls _update_input in several places. _mutation_lock() + # ensures a Session.run call cannot occur between creating and mutating + # new ops. + with ops.get_default_graph()._mutation_lock(): # pylint: disable=protected-access original_body_result, exit_vars = self._BuildLoop( pred, body, original_loop_vars, loop_vars, shape_invariants) finally: @@ -3126,6 +3135,7 @@ def while_loop(cond, happen is that the thread updating `x` can never get ahead of the counter thread because the thread incrementing `x` depends on the value of the counter. + ```python import tensorflow as tf @@ -3340,12 +3350,6 @@ def group(*inputs, **kwargs): if not hasattr(inp, "device"): raise TypeError("Expected tf.group() expected Tensor arguments not " "'%s' with type '%s'" % (inp, type(inp))) - if not hasattr(inp, "device"): - if isinstance(inp, list): - raise TypeError("To call tf.group() with a list, use " - "tf.group(*[...]) not tf.group([...]).") - raise TypeError("Expected tf.group() expected Tensor arguments not " - "'%s' with type '%s'" % (inp, type(inp))) dev = inp.device if dev in ops_on_device: ops_on_device[dev].append(inp) diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index 59bb925df0f25b3bf88112bc3eb1b13b21ace414..43fe045bcb10d2fc383381f92f2bc44c5362ac7d 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -939,7 +939,7 @@ class CaseTest(test_util.TensorFlowTestCase): class WhileLoopTestCase(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWhileLoopWithSingleVariable(self): i = constant_op.constant(0) c = lambda i: math_ops.less(i, 10) @@ -948,7 +948,7 @@ class WhileLoopTestCase(test_util.TensorFlowTestCase): self.assertEqual(self.evaluate(r), 10) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerWhileLoopWithSingleVariable_bodyReturnsTuple(self): i = constant_op.constant(0) c = lambda i: math_ops.less(i, 10) diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py index 907df85cd954d2a897ba9a0c4b21be8586859380..aacdaa7ad019d8aae2d0b533cde8412ab0f0fa22 100644 --- a/tensorflow/python/ops/conv2d_benchmark.py +++ b/tensorflow/python/ops/conv2d_benchmark.py @@ -21,6 +21,8 @@ from __future__ import print_function import itertools import time +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session as session_lib from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -28,22 +30,32 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables +from tensorflow.python.platform import flags from tensorflow.python.platform import test +FLAGS = flags.FLAGS -def build_graph(device, input_shape, filter_shape, strides, padding, dtype, - num_iters, warmup_iters): +flags.DEFINE_boolean( + "enable_layout_optimizer", False, + "If true, enables layout optimizer to update input data format for faster " + "execution of convolution ops.") + + +def build_graph(device, dtype, data_format, input_shape, filter_shape, strides, + padding, num_iters, warmup_iters): """builds a graph containing a sequence of conv2d operations. Args: device: String, the device to run on. + dtype: Data type for the convolution. + data_format: A string from: "NHWC" or "NCHW". Data format for input and + output data. input_shape: Shape of the input tensor. filter_shape: Shape of the filter tensor. strides: A list of ints. 1-D of length 4. The stride of sliding window for each dimension of input. padding: A string from: "SAME", "VALID". The type of padding algorithm to use. - dtype: Data type for the convolution. num_iters: number of iterations to run conv2d. warmup_iters: number of iterations for warmup runs. @@ -57,22 +69,23 @@ def build_graph(device, input_shape, filter_shape, strides, padding, dtype, random_ops.truncated_normal(filter_shape, dtype=dtype)) outputs = [] - conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC") + conv2d_op = nn_ops.conv2d( + inp, filt, strides, padding, data_format=data_format) outputs.append(conv2d_op) for _ in range(1, num_iters): with ops.control_dependencies([conv2d_op]): conv2d_op = nn_ops.conv2d( - inp, filt, strides, padding, data_format="NHWC") + inp, filt, strides, padding, data_format=data_format) outputs.append(conv2d_op) warmup_groups = [] warmup_conv2d_op = nn_ops.conv2d( - inp, filt, strides, padding, data_format="NHWC") + inp, filt, strides, padding, data_format=data_format) warmup_groups.append(warmup_conv2d_op) for _ in range(1, warmup_iters): with ops.control_dependencies([warmup_conv2d_op]): warmup_conv2d_op = nn_ops.conv2d( - inp, filt, strides, padding, data_format="NHWC") + inp, filt, strides, padding, data_format=data_format) warmup_groups.append(warmup_conv2d_op) return control_flow_ops.group(*warmup_groups), control_flow_ops.group( *outputs) @@ -81,12 +94,15 @@ def build_graph(device, input_shape, filter_shape, strides, padding, dtype, class Conv2DBenchmark(test.Benchmark): """Benchmark conv2d!""" - def _run_graph(self, device, input_shape, filter_shape, strides, padding, - dtype, num_iters, warmup_iters): + def _run_graph(self, device, dtype, data_format, input_shape, filter_shape, + strides, padding, num_iters, warmup_iters): """runs the graph and print its execution time. Args: device: String, the device to run on. + dtype: Data type for the convolution. + data_format: A string from: "NHWC" or "NCHW". Data format for input and + output data. input_shape: Shape of the input tensor. filter_shape: Shape of the filter tensor. strides: A list of ints. 1-D of length 4. The stride of sliding @@ -94,7 +110,6 @@ class Conv2DBenchmark(test.Benchmark): padding: A string from: "SAME", "VALID". The type of padding algorithm to use. num_iters: Number of iterations to run the benchmark. - dtype: Data type for the convolution. num_iters: number of iterations to run conv2d. warmup_iters: number of iterations for warmup runs. @@ -103,10 +118,27 @@ class Conv2DBenchmark(test.Benchmark): """ graph = ops.Graph() with graph.as_default(): - warmup_outputs, outputs = build_graph(device, input_shape, filter_shape, - strides, padding, dtype, num_iters, - warmup_iters) - with session_lib.Session(graph=graph) as session: + warmup_outputs, outputs = build_graph(device, dtype, data_format, + input_shape, filter_shape, strides, + padding, num_iters, warmup_iters) + + config = config_pb2.ConfigProto() + config.graph_options.optimizer_options.opt_level = -1 + rewrite_options = config.graph_options.rewrite_options + + # Disable layout optimizer to not change input data_format. + rewrite_options.layout_optimizer = ( + rewriter_config_pb2.RewriterConfig.ON if FLAGS.enable_layout_optimizer + else rewriter_config_pb2.RewriterConfig.OFF) + # Convolution ops are effectively noop in the test graph as we are not + # fetching the convolution outputs. Disable dependency optimizer to not + # remove the conv ops. + rewrite_options.dependency_optimization = ( + rewriter_config_pb2.RewriterConfig.OFF) + + with session_lib.Session(graph=graph, config=config) as session: + # TODO(hinsu): Use run_op_benchmark method from test.Benchmark to run + # benchmark along with warmup. variables.global_variables_initializer().run() # warmup runs session.run(warmup_outputs) @@ -114,20 +146,21 @@ class Conv2DBenchmark(test.Benchmark): start_time = time.time() session.run(outputs) duration = (time.time() - start_time) / num_iters - print("%s %s inputshape:%s filtershape:%s strides:%s padding:%s " + print("%s %s %s inputshape:%s filtershape:%s strides:%s padding:%s " "%d iters: %.8f sec" % - (device, str(dtype), str(input_shape).replace(" ", ""), - str(filter_shape).replace(" ", ""), + (device, str(dtype), data_format, str(input_shape).replace( + " ", ""), str(filter_shape).replace(" ", ""), str(strides).replace(" ", ""), padding, num_iters, duration)) name_template = ( - "conv2d_{device}_{datatype}_input_shape_{inputshape}_" + "conv2d_{device}_{datatype}_{data_format}_input_shape_{inputshape}_" "filter_shape_{filtershape}_strides_{strides}_padding_{padding}") self.report_benchmark( name=name_template.format( device=device, datatype=str(dtype), + data_format=str(data_format), inputshape=str(input_shape).replace(" ", ""), filtershape=str(filter_shape).replace(" ", ""), strides=str(strides).replace(" ", ""), @@ -140,24 +173,37 @@ class Conv2DBenchmark(test.Benchmark): def benchmark_conv2d(self): print("conv2d benchmark:") - h = 500 - w = 500 - fh = 3 - fw = 3 - input_shapes = [] - filter_shapes = [] data_types = [dtypes.float32, dtypes.float16] - for b, c in itertools.product([4, 16, 32], [i for i in range(3, 16)]): - input_shapes += [[b, h, w, c]] - filter_shapes += [[fh, fw, c, b]] - strides = [[1, 2, 2, 1]] + data_formats = ["NHWC", "NCHW"] + in_channels = list(range(3, 16)) + out_channels = [4, 16, 32] + hw_strides = [[2, 2]] paddings = ["VALID", "SAME"] - for ishape, fshape in zip(input_shapes, filter_shapes): - for dtype in data_types: - for stride in strides: - for padding in paddings: - self._run_graph("gpu", ishape, fshape, stride, padding, dtype, 80, - 2) + + args_lists = [ + data_types, data_formats, in_channels, out_channels, hw_strides, + paddings + ] + for args in itertools.product(*args_lists): + dtype, data_format, in_channel, out_channel, hw_stride, padding = args + + # Keep batch size same as out channels just to reduce the number of + # different configurations to benchmark. + batch_size = out_channel + h, w, fh, fw = 500, 500, 3, 3 + if data_format == "NHWC": + ishape = [batch_size, h, w, in_channel] + stride = [1] + hw_stride + [1] + elif data_format == "NCHW": + ishape = [batch_size, in_channel, h, w] + stride = [1, 1] + hw_stride + else: + raise ValueError("Unknown data_format: " + str(data_format)) + fshape = [fh, fw, in_channel, out_channel] + num_iters = 80 + warmup_iters = 2 + self._run_graph("gpu", dtype, data_format, ishape, fshape, stride, + padding, num_iters, warmup_iters) if __name__ == "__main__": diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index d934f27cb96f4a65e2adf860e0c5e08b7bd0b7d4..ca24f11054039472baaefd301e45f57c9444f60d 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -89,7 +89,7 @@ def custom_gradient(f): operations in `f` to `x`. - `grad_fn` is a function with the signature `g(*grad_ys)` which returns a list of `Tensor`s - the derivatives of `Tensor`s in `y` with respect - to the `Tensor`s in `x. `grad_ys` is a `Tensor` or sequence of + to the `Tensor`s in `x`. `grad_ys` is a `Tensor` or sequence of `Tensor`s the same size as `y` holding the initial value gradients for each `Tensor` in `y`. If `f` uses `Variable`s (that are not part of the inputs), i.e. through `get_variable`, then `grad_fn` should have diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 62c5adc385a2e87d27298c72f8dd2f67303119df..abf597ca55c647cca3f6012ed602a815298e1ed3 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -35,6 +35,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_data_flow_ops import * @@ -129,11 +130,6 @@ class QueueBase(object): @{tf.RandomShuffleQueue} for concrete implementations of this class, and instructions on how to create them. - - @compatibility(eager) - Queues are not compatible with eager execution. Instead, please - use `tf.data` to get data into your model. - @end_compatibility """ def __init__(self, dtypes, shapes, names, queue_ref): @@ -157,12 +153,7 @@ class QueueBase(object): Raises: ValueError: If one of the arguments is invalid. - RuntimeError: If eager execution is enabled. """ - if context.executing_eagerly(): - raise RuntimeError( - "Queues are not supported when eager execution is enabled. " - "Instead, please use tf.data to get data into your model.") self._dtypes = dtypes if shapes is not None: if len(shapes) != len(dtypes): @@ -179,6 +170,8 @@ class QueueBase(object): self._queue_ref = queue_ref if context.executing_eagerly(): self._name = context.context().scope_name + self._resource_deleter = resource_variable_ops.EagerResourceDeleter( + queue_ref, None) else: self._name = self._queue_ref.op.name.split("/")[-1] @@ -605,6 +598,11 @@ class QueueBase(object): else: return gen_data_flow_ops.queue_size(self._queue_ref, name=name) +def _shared_name(shared_name): + if context.executing_eagerly(): + return str(ops.uid()) + return shared_name + @tf_export("RandomShuffleQueue") class RandomShuffleQueue(QueueBase): @@ -612,11 +610,6 @@ class RandomShuffleQueue(QueueBase): See @{tf.QueueBase} for a description of the methods on this class. - - @compatibility(eager) - Queues are not compatible with eager execution. Instead, please - use `tf.data` to get data into your model. - @end_compatibility """ def __init__(self, @@ -690,7 +683,7 @@ class RandomShuffleQueue(QueueBase): min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2, - shared_name=shared_name, + shared_name=_shared_name(shared_name), name=name) super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -702,11 +695,6 @@ class FIFOQueue(QueueBase): See @{tf.QueueBase} for a description of the methods on this class. - - @compatibility(eager) - Queues are not compatible with eager execution. Instead, please - use `tf.data` to get data into your model. - @end_compatibility """ def __init__(self, @@ -752,7 +740,7 @@ class FIFOQueue(QueueBase): component_types=dtypes, shapes=shapes, capacity=capacity, - shared_name=shared_name, + shared_name=_shared_name(shared_name), name=name) super(FIFOQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -767,11 +755,6 @@ class PaddingFIFOQueue(QueueBase): See @{tf.QueueBase} for a description of the methods on this class. - - @compatibility(eager) - Queues are not compatible with eager execution. Instead, please - use `tf.data` to get data into your model. - @end_compatibility """ def __init__(self, @@ -831,7 +814,7 @@ class PaddingFIFOQueue(QueueBase): component_types=dtypes, shapes=shapes, capacity=capacity, - shared_name=shared_name, + shared_name=_shared_name(shared_name), name=name) super(PaddingFIFOQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -843,11 +826,6 @@ class PriorityQueue(QueueBase): See @{tf.QueueBase} for a description of the methods on this class. - - @compatibility(eager) - Queues are not compatible with eager execution. Instead, please - use `tf.data` to get data into your model. - @end_compatibility """ def __init__(self, @@ -899,7 +877,7 @@ class PriorityQueue(QueueBase): component_types=types, shapes=shapes, capacity=capacity, - shared_name=shared_name, + shared_name=_shared_name(shared_name), name=name) priority_dtypes = [_dtypes.int64] + types diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py index f28f76b6c42a861c51c1fc06f99fa73b71b625a9..99d30b0bd112b62c625a94b43da589f9717d0774 100644 --- a/tensorflow/python/ops/distributions/beta.py +++ b/tensorflow/python/ops/distributions/beta.py @@ -84,13 +84,24 @@ class Beta(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. + Warning: The samples can be zero due to finite precision. + This happens more often when some of the concentrations are very small. + Make sure to round the samples to `np.finfo(dtype).tiny` before computing the + density. + + Samples of this distribution are reparameterized (pathwise differentiable). + The derivatives are computed using the approach described in the paper + + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) + #### Examples ```python # Create a batch of three Beta distributions. alpha = [1, 2, 3] beta = [1, 2, 3] - dist = Beta(alpha, beta) + dist = tf.distributions.Beta(alpha, beta) dist.sample([4, 5]) # Shape [4, 5, 3] @@ -106,7 +117,7 @@ class Beta(distribution.Distribution): # Create batch_shape=[2, 3] via parameter broadcast: alpha = [[1.], [2]] # Shape [2, 1] beta = [3., 4, 5] # Shape [3] - dist = Beta(alpha, beta) + dist = tf.distributions.Beta(alpha, beta) # alpha broadcast as: [[1., 1, 1,], # [2, 2, 2]] @@ -122,6 +133,18 @@ class Beta(distribution.Distribution): dist.prob(x) # Shape [2, 3] ``` + Compute the gradients of samples w.r.t. the parameters: + + ```python + alpha = tf.constant(1.0) + beta = tf.constant(2.0) + dist = tf.distributions.Beta(alpha, beta) + samples = dist.sample(5) # Shape [5] + loss = tf.reduce_mean(tf.square(samples)) # Arbitrary loss function + # Unbiased stochastic gradients of the loss function + grads = tf.gradients(loss, [alpha, beta]) + ``` + """ def __init__(self, @@ -165,7 +188,7 @@ class Beta(distribution.Distribution): dtype=self._total_concentration.dtype, validate_args=validate_args, allow_nan_stats=allow_nan_stats, - reparameterization_type=distribution.NOT_REPARAMETERIZED, + reparameterization_type=distribution.FULLY_REPARAMETERIZED, parameters=parameters, graph_parents=[self._concentration1, self._concentration0, diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py index b88a0518b6db15021b9917d4c2b5ffb7bcf9484f..dd25fce2ec860456fdbbad903032cf4bcda9daba 100644 --- a/tensorflow/python/ops/distributions/categorical.py +++ b/tensorflow/python/ops/distributions/categorical.py @@ -32,12 +32,8 @@ from tensorflow.python.ops.distributions import util as distribution_util from tensorflow.python.util.tf_export import tf_export -def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32): +def _broadcast_cat_event_and_params(event, params, base_dtype): """Broadcasts the event or distribution parameters.""" - if event.shape.ndims is None: - raise NotImplementedError( - "Cannot broadcast with an event tensor of unknown rank.") - if event.dtype.is_integer: pass elif event.dtype.is_floating: @@ -47,15 +43,18 @@ def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32): else: raise TypeError("`value` should have integer `dtype` or " "`self.dtype` ({})".format(base_dtype)) - - if params.get_shape()[:-1] == event.get_shape(): - params = params - else: - params *= array_ops.ones_like( - array_ops.expand_dims(event, -1), dtype=params.dtype) + shape_known_statically = ( + params.shape.ndims is not None and + params.shape[:-1].is_fully_defined() and + event.shape.is_fully_defined()) + if not shape_known_statically or params.shape[:-1] != event.shape: + params *= array_ops.ones_like(event[..., array_ops.newaxis], + dtype=params.dtype) params_shape = array_ops.shape(params)[:-1] event *= array_ops.ones(params_shape, dtype=event.dtype) - event.set_shape(tensor_shape.TensorShape(params.get_shape()[:-1])) + if params.shape.ndims is not None: + event.set_shape(tensor_shape.TensorShape(params.shape[:-1])) + return event, params diff --git a/tensorflow/python/ops/distributions/dirichlet.py b/tensorflow/python/ops/distributions/dirichlet.py index 72567e62f78665947c001282c9c4f4929e9ea0ef..9104a1d071af3d7b7d40838148f2e49301fa39ba 100644 --- a/tensorflow/python/ops/distributions/dirichlet.py +++ b/tensorflow/python/ops/distributions/dirichlet.py @@ -90,13 +90,24 @@ class Dirichlet(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. + Warning: Some components of the samples can be zero due to finite precision. + This happens more often when some of the concentrations are very small. + Make sure to round the samples to `np.finfo(dtype).tiny` before computing the + density. + + Samples of this distribution are reparameterized (pathwise differentiable). + The derivatives are computed using the approach described in the paper + + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) + #### Examples ```python # Create a single trivariate Dirichlet, with the 3rd class being three times # more frequent than the first. I.e., batch_shape=[], event_shape=[3]. alpha = [1., 2, 3] - dist = Dirichlet(alpha) + dist = tf.distributions.Dirichlet(alpha) dist.sample([4, 5]) # shape: [4, 5, 3] @@ -118,7 +129,7 @@ class Dirichlet(distribution.Distribution): # Create batch_shape=[2], event_shape=[3]: alpha = [[1., 2, 3], [4, 5, 6]] # shape: [2, 3] - dist = Dirichlet(alpha) + dist = tf.distributions.Dirichlet(alpha) dist.sample([4, 5]) # shape: [4, 5, 2, 3] @@ -129,6 +140,17 @@ class Dirichlet(distribution.Distribution): dist.prob(x) # shape: [2] ``` + Compute the gradients of samples w.r.t. the parameters: + + ```python + alpha = tf.constant([1.0, 2.0, 3.0]) + dist = tf.distributions.Dirichlet(alpha) + samples = dist.sample(5) # Shape [5, 3] + loss = tf.reduce_mean(tf.square(samples)) # Arbitrary loss function + # Unbiased stochastic gradients of the loss function + grads = tf.gradients(loss, alpha) + ``` + """ def __init__(self, @@ -165,7 +187,7 @@ class Dirichlet(distribution.Distribution): dtype=self._concentration.dtype, validate_args=validate_args, allow_nan_stats=allow_nan_stats, - reparameterization_type=distribution.NOT_REPARAMETERIZED, + reparameterization_type=distribution.FULLY_REPARAMETERIZED, parameters=parameters, graph_parents=[self._concentration, self._total_concentration], @@ -290,10 +312,8 @@ class Dirichlet(distribution.Distribution): if not self.validate_args: return x return control_flow_ops.with_dependencies([ - check_ops.assert_positive( - x, - message="samples must be positive"), - distribution_util.assert_close( + check_ops.assert_positive(x, message="samples must be positive"), + check_ops.assert_near( array_ops.ones([], dtype=self.dtype), math_ops.reduce_sum(x, -1), message="sample last-dimension must sum to `1`"), diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py index 41dcd401887a124780a35c3dbd84140553860485..c03ef967e68474b0313de01d48252c8274e37a21 100644 --- a/tensorflow/python/ops/distributions/distribution.py +++ b/tensorflow/python/ops/distributions/distribution.py @@ -212,7 +212,7 @@ class ReparameterizationType(object): reparameterized, and straight-through gradients are either partially unsupported or are not supported at all. In this case, for purposes of e.g. RL or variational inference, it is generally safest to wrap the - sample results in a `stop_gradients` call and instead use policy + sample results in a `stop_gradients` call and use policy gradients / surrogate loss instead. """ diff --git a/tensorflow/python/ops/distributions/exponential.py b/tensorflow/python/ops/distributions/exponential.py index 24bc3f3d3eb06a01d5173cb6c7fb0f09172a0587..4325a14449dd9a13dabb65a240ede452544c761a 100644 --- a/tensorflow/python/ops/distributions/exponential.py +++ b/tensorflow/python/ops/distributions/exponential.py @@ -103,9 +103,6 @@ class Exponential(gamma.Gamma): allow_nan_stats=allow_nan_stats, validate_args=validate_args, name=name) - # While the Gamma distribution is not reparameterizable, the exponential - # distribution is. - self._reparameterization_type = True self._parameters = parameters self._graph_parents += [self._rate] diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py index 163a27f7585518c321dd1ea59b71029e2ae6a1e7..b631f0247c59e518fbd4925065d33345d4ea8e47 100644 --- a/tensorflow/python/ops/distributions/gamma.py +++ b/tensorflow/python/ops/distributions/gamma.py @@ -55,7 +55,7 @@ class Gamma(distribution.Distribution): ```none pdf(x; alpha, beta, x > 0) = x**(alpha - 1) exp(-x beta) / Z - Z = Gamma(alpha) beta**alpha + Z = Gamma(alpha) beta**(-alpha) ``` where: @@ -85,14 +85,35 @@ class Gamma(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. - WARNING: This distribution may draw 0-valued samples for small `concentration` - values. See note in `tf.random_gamma` docstring. + Warning: The samples of this distribution are always non-negative. However, + the samples that are smaller than `np.finfo(dtype).tiny` are rounded + to this value, so it appears more often than it should. + This should only be noticeable when the `concentration` is very small, or the + `rate` is very large. See note in `tf.random_gamma` docstring. + + Samples of this distribution are reparameterized (pathwise differentiable). + The derivatives are computed using the approach described in the paper + + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) #### Examples ```python - dist = Gamma(concentration=3.0, rate=2.0) - dist2 = Gamma(concentration=[3.0, 4.0], rate=[2.0, 3.0]) + dist = tf.distributions.Gamma(concentration=3.0, rate=2.0) + dist2 = tf.distributions.Gamma(concentration=[3.0, 4.0], rate=[2.0, 3.0]) + ``` + + Compute the gradients of samples w.r.t. the parameters: + + ```python + concentration = tf.constant(3.0) + rate = tf.constant(2.0) + dist = tf.distributions.Gamma(concentration, rate) + samples = dist.sample(5) # Shape [5] + loss = tf.reduce_mean(tf.square(samples)) # Arbitrary loss function + # Unbiased stochastic gradients of the loss function + grads = tf.gradients(loss, [concentration, rate]) ``` """ @@ -141,7 +162,7 @@ class Gamma(distribution.Distribution): dtype=self._concentration.dtype, validate_args=validate_args, allow_nan_stats=allow_nan_stats, - reparameterization_type=distribution.NOT_REPARAMETERIZED, + reparameterization_type=distribution.FULLY_REPARAMETERIZED, parameters=parameters, graph_parents=[self._concentration, self._rate], diff --git a/tensorflow/python/ops/distributions/student_t.py b/tensorflow/python/ops/distributions/student_t.py index 20a2d16181442bede797ded5e4d3ebbd3d55ca2b..e0cf6f86f10eec76bf94cd74f64202c452425886 100644 --- a/tensorflow/python/ops/distributions/student_t.py +++ b/tensorflow/python/ops/distributions/student_t.py @@ -80,6 +80,12 @@ class StudentT(distribution.Distribution): variance. However it is not actually the std. deviation; the Student's t-distribution std. dev. is `scale sqrt(df / (df - 2))` when `df > 2`. + Samples of this distribution are reparameterized (pathwise differentiable). + The derivatives are computed using the approach described in the paper + + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) + #### Examples Examples of initialization of one or a batch of distributions. @@ -118,6 +124,19 @@ class StudentT(distribution.Distribution): dist.prob(3.0) ``` + Compute the gradients of samples w.r.t. the parameters: + + ```python + df = tf.constant(2.0) + loc = tf.constant(2.0) + scale = tf.constant(11.0) + dist = tf.distributions.StudentT(df=df, loc=loc, scale=scale) + samples = dist.sample(5) # Shape [5] + loss = tf.reduce_mean(tf.square(samples)) # Arbitrary loss function + # Unbiased stochastic gradients of the loss function + grads = tf.gradients(loss, [df, loc, scale]) + ``` + """ # pylint: enable=line-too-long @@ -168,7 +187,7 @@ class StudentT(distribution.Distribution): (self._df, self._loc, self._scale)) super(StudentT, self).__init__( dtype=self._scale.dtype, - reparameterization_type=distribution.NOT_REPARAMETERIZED, + reparameterization_type=distribution.FULLY_REPARAMETERIZED, validate_args=validate_args, allow_nan_stats=allow_nan_stats, parameters=parameters, diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py index 401676bf842b4dd76fc64b5f4599804a0f3a46f8..3e480a79f52b178789a2d34e98c6af31048c07b1 100644 --- a/tensorflow/python/ops/distributions/util.py +++ b/tensorflow/python/ops/distributions/util.py @@ -36,43 +36,6 @@ from tensorflow.python.ops import nn from tensorflow.python.util import tf_inspect -def assert_close( - x, y, data=None, summarize=None, message=None, name="assert_close"): - """Assert that x and y are within machine epsilon of each other. - - Args: - x: Floating-point `Tensor` - y: Floating-point `Tensor` - data: The tensors to print out if the condition is `False`. Defaults to - error message and first few entries of `x` and `y`. - summarize: Print this many entries of each tensor. - message: A string to prefix to the default message. - name: A name for this operation (optional). - - Returns: - Op raising `InvalidArgumentError` if |x - y| > machine epsilon. - """ - message = message or "" - x = ops.convert_to_tensor(x, name="x") - y = ops.convert_to_tensor(y, name="y") - - if data is None: - data = [ - message, - "Condition x ~= y did not hold element-wise: x = ", x, "y = ", y - ] - - if x.dtype.is_integer: - return check_ops.assert_equal( - x, y, data=data, summarize=summarize, message=message, name=name) - - with ops.name_scope(name, "assert_close", [x, y, data]): - tol = np.finfo(x.dtype.as_numpy_dtype).eps - condition = math_ops.reduce_all(math_ops.less_equal(math_ops.abs(x-y), tol)) - return control_flow_ops.Assert( - condition, data, summarize=summarize) - - def assert_integer_form( x, data=None, summarize=None, message=None, int_dtype=None, name="assert_integer_form"): @@ -241,8 +204,12 @@ def get_logits_and_probs(logits=None, dependencies = [check_ops.assert_non_negative(probs)] if multidimensional: probs = embed_check_categorical_event_shape(probs) - dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one, - message="probs does not sum to 1.")] + dependencies += [ + check_ops.assert_near( + math_ops.reduce_sum(probs, -1), + one, + message="probs does not sum to 1.") + ] else: dependencies += [check_ops.assert_less_equal( probs, one, message="probs has components greater than 1.")] diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index bcc717b043f226a18344de31b36f09d5064f25a3..27c2fa701760f000db2463aaba0b496b3550ddff 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -23,6 +23,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops # Imports gradient definitions. @@ -30,6 +31,7 @@ from tensorflow.python.ops import data_flow_grad # pylint: disable=unused-impor from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -43,8 +45,8 @@ def _clip(params, ids, max_norm): Args: params: A `Tensor` of embeddings retrieved by `gather`. ids: The `ids` argument that was passed to `gather`. - max_norm: If provided, the embeddings are l2-normalized to the value of - max_norm. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value. Returns: A `Tensor` with the same type as `params`. @@ -290,8 +292,8 @@ def embedding_lookup( in `indices` are always validated to be within range. If assigned to GPU, out-of-bound indices result in safe but unspecified behavior, which may include raising an error. - max_norm: If provided, embedding values are l2-normalized to the value of - max_norm. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value. Returns: A `Tensor` with the same type as the tensors in `params`. @@ -346,8 +348,8 @@ def embedding_lookup_sparse(params, "mean" is the weighted sum divided by the total weight. "sqrtn" is the weighted sum divided by the square root of the sum of the squares of the weights. - max_norm: If provided, each embedding is normalized to have l2 norm equal - to max_norm before combining. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value, before combining. Returns: A dense tensor representing the combined embeddings for the @@ -479,3 +481,158 @@ def embedding_lookup_sparse(params, assert False, "Unrecognized combiner" return embeddings + + +@tf_export("nn.safe_embedding_lookup_sparse") +def safe_embedding_lookup_sparse(embedding_weights, + sparse_ids, + sparse_weights=None, + combiner='mean', + default_id=None, + name=None, + partition_strategy='div', + max_norm=None): + """Lookup embedding results, accounting for invalid IDs and empty features. + + The partitioned embedding in `embedding_weights` must all be the same shape + except for the first dimension. The first dimension is allowed to vary as the + vocabulary size is not necessarily a multiple of `P`. `embedding_weights` + may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a + partitioner. + + Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs + with non-positive weight. For an entry with no features, the embedding vector + for `default_id` is returned, or the 0-vector if `default_id` is not supplied. + + The ids and weights may be multi-dimensional. Embeddings are always aggregated + along the last dimension. + + Args: + embedding_weights: A list of `P` float `Tensor`s or values representing + partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` + created by partitioning along dimension 0. The total unpartitioned + shape should be `[e_0, e_1, ..., e_m]`, where `e_0` represents the + vocab size and `e_1, ..., e_m` are the embedding dimensions. + sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the + ids. `d_0` is typically batch size. + sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing + float weights corresponding to `sparse_ids`, or `None` if all weights + are be assumed to be 1.0. + combiner: A string specifying how to combine embedding results for each + entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" + the default. + default_id: The id to use for an entry with no features. + name: A name for this operation (optional). + partition_strategy: A string specifying the partitioning strategy. + Currently `"div"` and `"mod"` are supported. Default is `"div"`. + max_norm: If not `None`, all embeddings are l2-normalized to max_norm before + combining. + + + Returns: + Dense `Tensor` of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`. + + Raises: + ValueError: if `embedding_weights` is empty. + """ + if embedding_weights is None: + raise ValueError('Missing embedding_weights %s.' % embedding_weights) + if isinstance(embedding_weights, variables.PartitionedVariable): + embedding_weights = list(embedding_weights) # get underlying Variables. + if not isinstance(embedding_weights, list): + embedding_weights = [embedding_weights] + if len(embedding_weights) < 1: + raise ValueError('Missing embedding_weights %s.' % embedding_weights) + + dtype = sparse_weights.dtype if sparse_weights is not None else None + embedding_weights = [ + ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights + ] + + with ops.name_scope(name, 'embedding_lookup', + embedding_weights + [sparse_ids, + sparse_weights]) as scope: + # Reshape higher-rank sparse ids and weights to linear segment ids. + original_shape = sparse_ids.dense_shape + original_rank_dim = sparse_ids.dense_shape.get_shape()[0] + original_rank = ( + array_ops.size(original_shape) + if original_rank_dim.value is None + else original_rank_dim.value) + sparse_ids = sparse_ops.sparse_reshape(sparse_ids, [ + math_ops.reduce_prod( + array_ops.slice(original_shape, [0], [original_rank - 1])), + array_ops.gather(original_shape, original_rank - 1)]) + if sparse_weights is not None: + sparse_weights = sparse_tensor.SparseTensor( + sparse_ids.indices, + sparse_weights.values, sparse_ids.dense_shape) + + # Prune invalid ids and weights. + sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights) + if combiner != 'sum': + sparse_ids, sparse_weights = _prune_invalid_weights( + sparse_ids, sparse_weights) + + # Fill in dummy values for empty features, if necessary. + sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(sparse_ids, + default_id or + 0) + if sparse_weights is not None: + sparse_weights, _ = sparse_ops.sparse_fill_empty_rows(sparse_weights, 1.0) + + result = embedding_lookup_sparse( + embedding_weights, + sparse_ids, + sparse_weights, + combiner=combiner, + partition_strategy=partition_strategy, + name=None if default_id is None else scope, + max_norm=max_norm) + + if default_id is None: + # Broadcast is_row_empty to the same shape as embedding_lookup_result, + # for use in Select. + is_row_empty = array_ops.tile( + array_ops.reshape(is_row_empty, [-1, 1]), + array_ops.stack([1, array_ops.shape(result)[1]])) + + result = array_ops.where(is_row_empty, + array_ops.zeros_like(result), + result, + name=scope) + + # Reshape back from linear ids back into higher-dimensional dense result. + final_result = array_ops.reshape( + result, + array_ops.concat([ + array_ops.slice( + math_ops.cast(original_shape, dtypes.int32), [0], + [original_rank - 1]), + array_ops.slice(array_ops.shape(result), [1], [-1]) + ], 0)) + final_result.set_shape(tensor_shape.unknown_shape( + (original_rank_dim - 1).value).concatenate(result.get_shape()[1:])) + return final_result + + +def _prune_invalid_ids(sparse_ids, sparse_weights): + """Prune invalid IDs (< 0) from the input ids and weights.""" + is_id_valid = math_ops.greater_equal(sparse_ids.values, 0) + if sparse_weights is not None: + is_id_valid = math_ops.logical_and( + is_id_valid, + array_ops.ones_like(sparse_weights.values, dtype=dtypes.bool)) + sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid) + if sparse_weights is not None: + sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid) + return sparse_ids, sparse_weights + + +def _prune_invalid_weights(sparse_ids, sparse_weights): + """Prune invalid weights (< 0) from the input ids and weights.""" + if sparse_weights is not None: + is_weights_valid = math_ops.greater(sparse_weights.values, 0) + sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_weights_valid) + sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_weights_valid) + return sparse_ids, sparse_weights diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 7385cb758514e160efec61d731e734d1af126742..713a8ab2cc1d614bb8c0489c03bd7b0d3424af64 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -20,6 +20,7 @@ from __future__ import print_function import collections import contextlib +import sys import warnings import numpy as np @@ -30,12 +31,14 @@ from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops # pylint: disable=unused-import +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util @@ -47,12 +50,16 @@ from tensorflow.python.ops import logging_ops # pylint: disable=unused-import from tensorflow.python.ops import manip_grad # pylint: disable=unused-import from tensorflow.python.ops import math_grad # pylint: disable=unused-import from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_grad # pylint: disable=unused-import from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import spectral_grad # pylint: disable=unused-import from tensorflow.python.ops import tensor_array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._gradients_impl = sys.modules[__name__] # pylint: disable=protected-access + # Warn the user if we convert a sparse representation to dense with at # least this number of elements. _LARGE_SPARSE_NUM_ELEMENTS = 100000000 @@ -107,12 +114,14 @@ ops.register_tensor_conversion_function(ops.IndexedSlices, _IndexedSlicesToTensor) -def _MarkReachedOps(from_ops, reached_ops): +def _MarkReachedOps(from_ops, reached_ops, func_graphs): """Mark all ops reached from "from_ops". Args: from_ops: list of Operations. reached_ops: set of Operations. + func_graphs: list of function._FuncGraphs. This method will traverse through + these functions if they capture from_ops or any reachable ops. """ queue = collections.deque() queue.extend(from_ops) @@ -122,36 +131,11 @@ def _MarkReachedOps(from_ops, reached_ops): reached_ops.add(op) for output in op.outputs: if _IsBackpropagatable(output): - queue.extend(output.consumers()) - - -def _GatherInputs(to_ops, reached_ops): - """List all inputs of to_ops that are in reached_ops. + queue.extend(_Consumers(output, func_graphs)) - Args: - to_ops: list of Operations. - reached_ops: set of Operations. - Returns: - The list of all inputs of to_ops that are in reached_ops. - That list includes all elements of to_ops. - """ - inputs = [] - queue = collections.deque() - queue.extend(to_ops) - while queue: - op = queue.popleft() - # We are interested in this op. - if op in reached_ops: - inputs.append(op) - # Clear the boolean so we won't add the inputs again. - reached_ops.remove(op) - for inp in op.inputs: - queue.append(inp.op) - return inputs - - -def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops): +def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops, func_graphs, + xs): """Initialize the pending count for ops between two lists of Operations. 'pending_count[op]' indicates the number of backprop inputs @@ -161,6 +145,11 @@ def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops): to_ops: list of Operations. from_ops: list of Operations. colocate_gradients_with_ops: Python bool. See docstring of gradients(). + func_graphs: list of function._FuncGraphs. This method will traverse through + these functions if they capture from_ops or any reachable ops. This is + useful if to_ops occur in a function and from_ops are in an outer function + or graph. + xs: list of Tensors. Returns: A tuple containing: (1) the subset of to_ops reachable from from_ops by a @@ -171,7 +160,7 @@ def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops): """ # Mark reachable ops from from_ops. reached_ops = set() - _MarkReachedOps(from_ops, reached_ops) + _MarkReachedOps(from_ops, reached_ops, func_graphs) # X in reached_ops iff X is reachable from from_ops by a path of zero or more # backpropagatable tensors. @@ -190,7 +179,7 @@ def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops): between_op_list.append(op) # Clear the boolean so we won't add the inputs again. reached_ops.remove(op) - for inp in op.inputs: + for inp in _Inputs(op, xs): queue.append(inp.op) # X in between_ops iff X is on a path of zero or more backpropagatable tensors # between from_ops and to_ops @@ -202,7 +191,7 @@ def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops): # Initialize pending count for between ops. pending_count = collections.defaultdict(int) for op in between_op_list: - for x in op.inputs: + for x in _Inputs(op, xs): if x.op in between_ops: pending_count[x.op] += 1 @@ -323,7 +312,7 @@ def _VerifyGeneratedGradients(grads, op): "inputs %d" % (len(grads), op.node_def, len(op.inputs))) -def _StopOps(from_ops, stop_gradient_ops, pending_count): +def _StopOps(from_ops, stop_gradient_ops, pending_count, xs): """The set of ops that terminate the gradient computation. This computes the frontier of the forward graph *before* which backprop @@ -339,6 +328,7 @@ def _StopOps(from_ops, stop_gradient_ops, pending_count): from_ops: list of Operations. stop_gradient_ops: list of Operations never to backprop through. pending_count: mapping from operation to number of backprop inputs. + xs: list of Tensors. Returns: The set of operations. @@ -346,7 +336,7 @@ def _StopOps(from_ops, stop_gradient_ops, pending_count): stop_ops = set() for op in from_ops: is_stop_op = True - for inp in op.inputs: + for inp in _Inputs(op, xs): if pending_count[inp.op] > 0: is_stop_op = False break @@ -366,15 +356,19 @@ def _maybe_colocate_with(op, gradient_uid, colocate_gradients_with_ops): # pyli yield -def _SymGrad(op, out_grads): +def _SymGrad(op, out_grads, xs): """Backprop through a function call node op given its outputs' gradients.""" - f_in = [x for x in op.inputs] + out_grads - f_types = [x.dtype for x in op.inputs] + f_in = [x for x in _Inputs(op, xs)] + out_grads + f_types = [x.dtype for x in _Inputs(op, xs)] f = attr_value_pb2.NameAttrList() f.name = op.type for k in op.node_def.attr: f.attr[k].CopyFrom(op.node_def.attr[k]) - in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f) + # TODO(apassos) use a better dtype here + in_grads = functional_ops.symbolic_gradient( + input=f_in, + Tout=[x if x != dtypes.resource else dtypes.float32 for x in f_types], + f=f) return in_grads @@ -415,7 +409,7 @@ def _MaybeCompile(scope, op, func, grad_fn): return grad_fn() -def _RaiseNoGradWrtInitialLoopValError(op, from_ops): +def _RaiseNoGradWrtInitialLoopValError(op, from_ops, xs): """Raises an error if we backprop through a loop var.""" # Find the nearest 'to_op' reachable from 'op' to provide a more helpful error # message. @@ -429,7 +423,7 @@ def _RaiseNoGradWrtInitialLoopValError(op, from_ops): if curr_op in from_ops: target_op = curr_op break - queue.extend(t.op for t in curr_op.inputs) + queue.extend(t.op for t in _Inputs(curr_op, xs)) assert target_op raise ValueError( "Cannot compute gradient inside while loop with respect to op '%s'. " @@ -439,6 +433,68 @@ def _RaiseNoGradWrtInitialLoopValError(op, from_ops): % target_op.name) +def _MaybeCaptured(t): + """If t is a captured value placeholder, returns the original captured value. + + Args: + t: Tensor + + Returns: + A tensor, potentially from a different Graph/function._FuncGraph. + """ + # pylint: disable=protected-access + if isinstance(t.op.graph, function._FuncGraph) and t.op.type == "Placeholder": + for input_t, placeholder_t in t.op.graph._captured.items(): + if t == placeholder_t: + return _MaybeCaptured(input_t) + # pylint: enable=protected-access + return t + + +# TODO(skyewm): plumbing xs through everywhere is ugly, consider making +# _GradientsHelper a class with xs as a member variable. +def _Inputs(op, xs): + """Returns the inputs of op, crossing closure boundaries where necessary. + + Args: + op: Operation + xs: list of Tensors we are differentiating w.r.t. + + Returns: + A list of tensors. The tensors may be from multiple + Graph/function._FuncGraphs if op is in a function._FuncGraph and has + captured inputs. + """ + if isinstance(op.graph, function._FuncGraph): # pylint: disable=protected-access + # If we're differentiating w.r.t. `t`, do not attempt to traverse through it + # to a captured value. The algorithm needs to "see" `t` in this case, even + # if it's a function input for a captured value, whereas usually we'd like + # to traverse through these closures as if the captured value was the direct + # input to op. + return [t if (t in xs) else _MaybeCaptured(t) for t in op.inputs] + else: + return op.inputs + + +def _Consumers(t, func_graphs): + """Returns the consumers of t, crossing closure boundaries where necessary. + + Args: + t: Tensor + func_graphs: a list of function._FuncGraphs that may have captured t. + + Returns: + A list of tensors. The tensors will be from the current graph and/or + func_graphs. + """ + consumers = t.consumers() + for func in func_graphs: + for input_t, placeholder in func._captured.items(): # pylint: disable=protected-access + if input_t == t: + consumers.extend(_Consumers(placeholder, func_graphs)) + return consumers + + @tf_export("gradients") def gradients(ys, xs, @@ -524,10 +580,10 @@ def gradients(ys, RuntimeError: if called in Eager mode. """ - # Creating the gradient graph for control flow mutates Operations. _lock - # ensures a Session.run call cannot occur between creating and mutating new - # ops. - with ops.get_default_graph()._lock: # pylint: disable=protected-access + # Creating the gradient graph for control flow mutates Operations. + # _mutation_lock ensures a Session.run call cannot occur between creating and + # mutating new ops. + with ops.get_default_graph()._mutation_lock(): # pylint: disable=protected-access return _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients) @@ -543,12 +599,19 @@ def _GradientsHelper(ys, src_graph=None): """Implementation of gradients().""" if context.executing_eagerly(): - raise RuntimeError("tf.gradients not supported when eager execution " - "is enabled. Use tf.contrib.eager.GradientTape " - "instead.") + raise RuntimeError("tf.gradients is not supported when eager execution " + "is enabled. Use tf.GradientTape instead.") if src_graph is None: src_graph = ops.get_default_graph() + # If src_graph is a _FuncGraph (i.e. a function body), gather it and all + # ancestor graphs. This is necessary for correctly handling captured values. + func_graphs = [] + curr_graph = src_graph + while isinstance(curr_graph, function._FuncGraph): # pylint: disable=protected-access + func_graphs.append(curr_graph) + curr_graph = curr_graph._outer_graph # pylint: disable=protected-access + ys = _AsList(ys) xs = _AsList(xs) stop_gradients = [] if stop_gradients is None else _AsList(stop_gradients) @@ -583,12 +646,13 @@ def _GradientsHelper(ys, # Initialize the pending count for ops in the connected subgraph from ys # to the xs. if len(ys) > 1: - ys = [array_ops.identity(y) if y.consumers() else y for y in ys] + ys = [array_ops.identity(y) if _Consumers(y, func_graphs) else y + for y in ys] to_ops = [t.op for t in ys] from_ops = [t.op for t in xs] stop_gradient_ops = [t.op for t in stop_gradients] reachable_to_ops, pending_count, loop_state = _PendingCount( - to_ops, from_ops, colocate_gradients_with_ops) + to_ops, from_ops, colocate_gradients_with_ops, func_graphs, xs) # Iterate over the collected ops. # @@ -622,7 +686,7 @@ def _GradientsHelper(ys, _SetGrad(grads, y, loop_state.ZerosLikeForExit(y)) queue.append(y.op) - stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count) + stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count, xs) while queue: # generate gradient subgraph for op. op = queue.popleft() @@ -671,7 +735,7 @@ def _GradientsHelper(ys, op._control_flow_context.IsWhileContext() and op._control_flow_context == ops.get_default_graph()._get_control_flow_context()): - _RaiseNoGradWrtInitialLoopValError(op, from_ops) + _RaiseNoGradWrtInitialLoopValError(op, from_ops, xs) # pylint: enable=protected-access if (grad_fn or is_func_call) and has_out_grads: @@ -703,7 +767,7 @@ def _GradientsHelper(ys, # For function call ops, we add a 'SymbolicGradient' # node to the graph to compute gradients. in_grads = _MaybeCompile(grad_scope, op, func_call, - lambda: _SymGrad(op, out_grads)) + lambda: _SymGrad(op, out_grads, xs)) in_grads = _AsList(in_grads) _VerifyGeneratedGradients(in_grads, op) if gate_gradients and len([x for x in in_grads @@ -718,8 +782,8 @@ def _GradientsHelper(ys, else: # If no grad_fn is defined or none of out_grads is available, # just propagate a list of None backwards. - in_grads = [None] * len(op.inputs) - for i, (t_in, in_grad) in enumerate(zip(op.inputs, in_grads)): + in_grads = [None] * len(_Inputs(op, xs)) + for i, (t_in, in_grad) in enumerate(zip(_Inputs(op, xs), in_grads)): if in_grad is not None: if (isinstance(in_grad, ops.Tensor) and t_in.dtype != dtypes.resource): @@ -737,7 +801,8 @@ def _GradientsHelper(ys, loop_state.ExitGradWhileContext(op, before=False) # Update pending count for the inputs of op and enqueue ready ops. - _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state) + _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state, + xs) if loop_state: loop_state.PostProcessing() @@ -756,9 +821,10 @@ def _HasAnyNotNoneGrads(grads, op): return False -def _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state): +def _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state, + xs): """Update pending count for the inputs of op and enqueue ready ops.""" - for x in op.inputs: + for x in _Inputs(op, xs): pending_count[x.op] -= 1 ready = (pending_count[x.op] == 0) if loop_state and not ready: diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index d81c756f1cbc0a46d094066cda369067f7d3d1f6..d02fcf4ee27c180003e5b026e486a4ec0ad11e7d 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -57,90 +57,8 @@ from tensorflow.python.ops.nn_ops import bias_add from tensorflow.python.platform import googletest -def _OpsBetween(to_ops, from_ops): - """Build the list of operations between two lists of Operations. - - Args: - to_ops: list of Operations. - from_ops: list of Operations. - - Returns: - The list of operations between "from_ops" and "to_ops", sorted by - decreasing operation id. This list contains all elements of to_ops. - - TODO(touts): Think about returning an empty list if from_ops are not - reachable from to_ops. Presently it returns to_ops in that case. - """ - # Ops that are reachable from the output of "input_ops". - reached_ops = set() - # We only care to reach up to "output_ops" so we mark the - # output ops as reached to avoid recursing past them. - for op in to_ops: - reached_ops.add(op) - gradients_impl._MarkReachedOps(from_ops, reached_ops) - between_ops = gradients_impl._GatherInputs(to_ops, reached_ops) - between_ops.sort(key=lambda x: -x._id) - return between_ops - - class GradientsTest(test_util.TensorFlowTestCase): - def _OpNames(self, op_list): - return ["%s/%d" % (str(op.name), op._id) for op in op_list] - - def _assertOpListEqual(self, ops1, ops2): - self.assertEquals(self._OpNames(ops1), self._OpNames(ops2)) - - def testOpsBetweenSimple(self): - with ops.Graph().as_default(): - t1 = constant(1.0) - t2 = constant(2.0) - t3 = array_ops.stack([t1, t2]) - # Full graph - self._assertOpListEqual([t3.op, t2.op, t1.op], - _OpsBetween([t3.op], [t1.op, t2.op])) - # Only t1, t3. - self._assertOpListEqual([t3.op, t1.op], _OpsBetween([t3.op], [t1.op])) - - def testOpsBetweenUnreachable(self): - with ops.Graph().as_default(): - t1 = constant(1.0) - t2 = constant(2.0) - _ = array_ops.stack([t1, t2]) - t4 = constant(1.0) - t5 = constant(2.0) - t6 = array_ops.stack([t4, t5]) - # Elements of to_ops are always listed. - self._assertOpListEqual([t6.op], _OpsBetween([t6.op], [t1.op])) - - def testOpsBetweenCut(self): - with ops.Graph().as_default(): - t1 = constant(1.0) - t2 = constant(2.0) - t3 = array_ops.stack([t1, t2]) - t4 = constant([1.0]) - t5 = array_ops.concat([t4, t3], 0) - t6 = constant([2.0]) - t7 = array_ops.concat([t5, t6], 0) - self._assertOpListEqual([t7.op, t5.op, t4.op], - _OpsBetween([t7.op], [t4.op])) - - def testOpsBetweenCycle(self): - with ops.Graph().as_default(): - t1 = constant(1.0) - t2 = constant(2.0) - t3 = array_ops.stack([t1, t2]) - t4 = array_ops.concat([t3, t3, t3], 0) - t5 = constant([1.0]) - t6 = array_ops.concat([t4, t5], 0) - t7 = array_ops.concat([t6, t3], 0) - self._assertOpListEqual([t6.op, t4.op, t3.op], - _OpsBetween([t6.op], [t3.op])) - self._assertOpListEqual([t7.op, t6.op, t5.op, t4.op, t3.op, t1.op], - _OpsBetween([t7.op], [t1.op, t5.op])) - self._assertOpListEqual([t6.op, t5.op, t4.op, t3.op, t2.op], - _OpsBetween([t6.op], [t2.op, t5.op])) - def testGradients(self): with ops.Graph().as_default(): inp = constant(1.0, shape=[32, 100], name="in") @@ -519,6 +437,96 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): grad_func=grad_func, python_grad_func=self._PythonGradient) f.add_to_graph(ops.Graph()) + def testGradientWrtCaptured(self): + with ops.Graph().as_default(): + x = constant_op.constant(1.0, name="x") + + @function.Defun() + def Foo(): + y = math_ops.multiply(x, 2.0, name="y") + g = gradients_impl.gradients(y, x) + return g[0] + + f = Foo() + with self.test_session() as sess: + self.assertEqual(sess.run(f), 2.0) + + def testGradientOfCaptured(self): + with ops.Graph().as_default(): + x = constant_op.constant(1.0, name="x") + y = math_ops.multiply(x, 2.0, name="y") + + @function.Defun() + def Foo(): + g = gradients_impl.gradients(y, x) + return g[0] + + f = Foo() + with self.test_session() as sess: + self.assertEqual(sess.run(f), 2.0) + + def testCapturedResourceVariable(self): + with ops.Graph().as_default(): + var = resource_variable_ops.ResourceVariable(1.0, name="var") + + @function.Defun() + def Foo(): + y = math_ops.multiply(var, 2.0, name="y") + g = gradients_impl.gradients(y, var) + return g[0] + + f = Foo() + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + self.assertEqual(sess.run(f), 2.0) + + def testCapturedNested(self): + with ops.Graph().as_default(): + x1 = constant_op.constant(1.0, name="x1") + x2 = constant_op.constant(2.0, name="x2") + x3 = math_ops.multiply(x1, x2, name="x3") + + @function.Defun() + def Outer(): + outer1 = array_ops.identity(x1, name="outer1") + + @function.Defun() + def Inner(): + inner1 = array_ops.identity(outer1, name="inner1") + inner2 = array_ops.identity(x2, name="inner2") + inner3 = array_ops.identity(x3, name="inner3") + return gradients_impl.gradients([inner1, inner2, inner3, x1], + [x1, x2]) + + return Inner() + + x1_grad, x2_grad = Outer() + with self.test_session() as sess: + # 1.0 + None + 2.0 + 1.0 = 4.0 + self.assertEqual(sess.run(x1_grad), 4.0) + # None + 1.0 + 1.0 + None = 2.0 + self.assertEqual(sess.run(x2_grad), 2.0) + + def testCapturedFromFunction(self): + with ops.Graph().as_default(): + x = constant_op.constant(1.0, name="x") + + @function.Defun() + def Outer(): + y = math_ops.multiply(x, 2.0, name="y") + + @function.Defun() + def Inner(): + z = math_ops.multiply(y, 3.0, name="z") + g = gradients_impl.gradients(z, y) + return g[0] + + return Inner() + + z_grad = Outer() + with self.test_session() as sess: + self.assertEqual(sess.run(z_grad), 3.0) + class StopGradientTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 95d05cd4d10745999f2236168ad00d56f6f047f2..a2eae452ae551eb1792e5b21477d31c55d64fd79 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -57,6 +57,7 @@ ops.NotDifferentiable('NonMaxSuppression') ops.NotDifferentiable('NonMaxSuppressionV2') +# pylint: disable=invalid-name def _assert(cond, ex_type, msg): """A polymorphic assert, works with tensors and boolean expressions. @@ -939,12 +940,13 @@ class ResizeMethod(object): def resize_images(images, size, method=ResizeMethod.BILINEAR, - align_corners=False): + align_corners=False, + preserve_aspect_ratio=False): """Resize `images` to `size` using the specified `method`. Resized images will be distorted if their original aspect ratio is not the same as `size`. To avoid distortions see - @{tf.image.resize_image_with_crop_or_pad}. + @{tf.image.resize_image_with_pad}. `method` can be one of: @@ -971,6 +973,10 @@ def resize_images(images, align_corners: bool. If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to `False`. + preserve_aspect_ratio: Whether to preserve the aspect ratio. If this is set, + then `images` will be resized to a size that fits in `size` while + preserving the aspect ratio of the original image. Scales up the image if + `size` is bigger than the current size of the `image`. Defaults to False. Raises: ValueError: if the shape of `images` is incompatible with the @@ -1009,6 +1015,28 @@ def resize_images(images, new_height_const = size_const_as_shape[0].value new_width_const = size_const_as_shape[1].value + if preserve_aspect_ratio: + # Get the current shapes of the image, even if dynamic. + _, current_height, current_width, _ = _ImageDimensions(images, rank=4) + + # do the computation to find the right scale and height/width. + scale_factor_height = (math_ops.to_float(new_height_const) / + math_ops.to_float(current_height)) + scale_factor_width = (math_ops.to_float(new_width_const) / + math_ops.to_float(current_width)) + scale_factor = math_ops.minimum(scale_factor_height, scale_factor_width) + scaled_height_const = math_ops.to_int32(scale_factor * + math_ops.to_float(current_height)) + scaled_width_const = math_ops.to_int32(scale_factor * + math_ops.to_float(current_width)) + + # NOTE: Reset the size and other constants used later. + size = ops.convert_to_tensor([scaled_height_const, scaled_width_const], + dtypes.int32, name='size') + size_const_as_shape = tensor_util.constant_value_as_shape(size) + new_height_const = size_const_as_shape[0].value + new_width_const = size_const_as_shape[1].value + # If we can determine that the height and width will be unmodified by this # transformation, we avoid performing the resize. if all(x is not None @@ -1042,6 +1070,106 @@ def resize_images(images, return images +@tf_export('image.resize_image_with_pad') +def resize_image_with_pad(image, + target_height, + target_width, + method=ResizeMethod.BILINEAR): + """Resizes and pads an image to a target width and height. + + Resizes an image to a target width and height by keeping + the aspect ratio the same without distortion. If the target + dimensions don't match the image dimensions, the image + is resized and then padded with zeroes to match requested + dimensions. + + Args: + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. + target_height: Target height. + target_width: Target width. + method: Method to use for resizing image. See `resize_images()` + + Raises: + ValueError: if `target_height` or `target_width` are zero or negative. + + Returns: + Resized and padded image. + If `images` was 4-D, a 4-D float Tensor of shape + `[batch, new_height, new_width, channels]`. + If `images` was 3-D, a 3-D float Tensor of shape + `[new_height, new_width, channels]`. + """ + with ops.name_scope(None, 'resize_image_with_pad', [image]): + image = ops.convert_to_tensor(image, name='image') + image_shape = image.get_shape() + is_batch = True + if image_shape.ndims == 3: + is_batch = False + image = array_ops.expand_dims(image, 0) + elif image_shape.ndims is None: + is_batch = False + image = array_ops.expand_dims(image, 0) + image.set_shape([None] * 4) + elif image_shape.ndims != 4: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') + + assert_ops = _CheckAtLeast3DImage(image, require_static=False) + assert_ops += _assert(target_width > 0, ValueError, + 'target_width must be > 0.') + assert_ops += _assert(target_height > 0, ValueError, + 'target_height must be > 0.') + + image = control_flow_ops.with_dependencies(assert_ops, image) + + def max_(x, y): + if _is_tensor(x) or _is_tensor(y): + return math_ops.maximum(x, y) + else: + return max(x, y) + + _, height, width, _ = _ImageDimensions(image, rank=4) + + # convert values to float, to ease divisions + f_height = math_ops.cast(height, dtype=dtypes.float64) + f_width = math_ops.cast(width, dtype=dtypes.float64) + f_target_height = math_ops.cast(target_height, dtype=dtypes.float64) + f_target_width = math_ops.cast(target_width, dtype=dtypes.float64) + + # Find the ratio by which the image must be adjusted + # to fit within the target + ratio = max_(f_width / f_target_width, f_height / f_target_height) + resized_height_float = f_height / ratio + resized_width_float = f_width / ratio + resized_height = math_ops.cast( + math_ops.floor(resized_height_float), dtype=dtypes.int32) + resized_width = math_ops.cast( + math_ops.floor(resized_width_float), dtype=dtypes.int32) + + padding_height = (f_target_height - resized_height_float) / 2 + padding_width = (f_target_width - resized_width_float) / 2 + f_padding_height = math_ops.floor(padding_height) + f_padding_width = math_ops.floor(padding_width) + p_height = max_(0, math_ops.cast(f_padding_height, dtype=dtypes.int32)) + p_width = max_(0, math_ops.cast(f_padding_width, dtype=dtypes.int32)) + + # Resize first, then pad to meet requested dimensions + resized = resize_images(image, [resized_height, resized_width], method) + + padded = pad_to_bounding_box(resized, p_height, p_width, target_height, + target_width) + + if padded.get_shape().ndims is None: + raise ValueError('padded contains no shape.') + + _ImageDimensions(padded, rank=4) + + if not is_batch: + padded = array_ops.squeeze(padded, squeeze_dims=[0]) + + return padded + + @tf_export('image.per_image_standardization') def per_image_standardization(image): """Linearly scales `image` to have zero mean and unit norm. @@ -1469,6 +1597,75 @@ def adjust_hue(image, delta, name=None): return convert_image_dtype(rgb_altered, orig_dtype) +# pylint: disable=invalid-name +@tf_export('image.random_jpeg_quality') +def random_jpeg_quality(image, min_jpeg_quality, max_jpeg_quality, seed=None): + """Randomly changes jpeg encoding quality for inducing jpeg noise. + + `min_jpeg_quality` must be in the interval `[0, 100]` and less than + `max_jpeg_quality`. + `max_jpeg_quality` must be in the interval `[0, 100]`. + + Args: + image: RGB image or images. Size of the last dimension must be 3. + min_jpeg_quality: Minimum jpeg encoding quality to use. + max_jpeg_quality: Maximum jpeg encoding quality to use. + seed: An operation-specific seed. It will be used in conjunction + with the graph-level seed to determine the real seeds that will be + used in this operation. Please see the documentation of + set_random_seed for its interaction with the graph-level random seed. + + Returns: + Adjusted image(s), same shape and DType as `image`. + + Raises: + ValueError: if `min_jpeg_quality` or `max_jpeg_quality` is invalid. + """ + if (min_jpeg_quality < 0 or max_jpeg_quality < 0 or + min_jpeg_quality > 100 or max_jpeg_quality > 100): + raise ValueError('jpeg encoding range must be between 0 and 100.') + + if min_jpeg_quality >= max_jpeg_quality: + raise ValueError('`min_jpeg_quality` must be less than `max_jpeg_quality`.') + + np.random.seed(seed) + jpeg_quality = np.random.randint(min_jpeg_quality, max_jpeg_quality) + return adjust_jpeg_quality(image, jpeg_quality) + + +@tf_export('image.adjust_jpeg_quality') +def adjust_jpeg_quality(image, jpeg_quality, name=None): + """Adjust jpeg encoding quality of an RGB image. + + This is a convenience method that adjusts jpeg encoding quality of an + RGB image. + + `image` is an RGB image. The image's encoding quality is adjusted + to `jpeg_quality`. + `jpeg_quality` must be in the interval `[0, 100]`. + + Args: + image: RGB image or images. Size of the last dimension must be 3. + jpeg_quality: int. jpeg encoding quality. + name: A name for this operation (optional). + + Returns: + Adjusted image(s), same shape and DType as `image`. + """ + with ops.name_scope(name, 'adjust_jpeg_quality', [image]) as name: + image = ops.convert_to_tensor(image, name='image') + # Remember original dtype to so we can convert back if needed + orig_dtype = image.dtype + # Convert to uint8 + image = convert_image_dtype(image, dtypes.uint8) + # Encode image to jpeg with given jpeg quality + image = gen_image_ops.encode_jpeg(image, quality=jpeg_quality) + # Decode jpeg image + image = gen_image_ops.decode_jpeg(image) + # Convert back to original dtype and return + return convert_image_dtype(image, orig_dtype) + + @tf_export('image.random_saturation') def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index ae45037c1772ce86e0cfeea75bb49c6cc28d0db4..cf9761803bf9654e21ec12e1f1c7193b3e88c020 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -2599,6 +2599,182 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): y = image_ops.resize_images(single_image, [55, 66]) self.assertTrue(y.op.name.startswith("resize_images")) + def _ResizeImageCall(self, x, max_h, max_w, preserve_aspect_ratio, + use_tensor_inputs): + if use_tensor_inputs: + target_max = ops.convert_to_tensor([max_h, max_w]) + x_tensor = array_ops.placeholder(x.dtype, shape=[None] * x.ndim) + feed_dict = {x_tensor: x} + else: + target_max = [max_h, max_w] + x_tensor = x + feed_dict = {} + + y = image_ops.resize_images(x_tensor, target_max, + preserve_aspect_ratio=preserve_aspect_ratio) + + with self.test_session(use_gpu=True): + return y.eval(feed_dict=feed_dict) + + def _assertResizeEqual(self, x, x_shape, y, y_shape, + preserve_aspect_ratio=True, + use_tensor_inputs_options=None): + use_tensor_inputs_options = use_tensor_inputs_options or [False, True] + target_height, target_width, _ = y_shape + x = np.array(x).reshape(x_shape) + y = np.array(y).reshape(y_shape) + + for use_tensor_inputs in use_tensor_inputs_options: + y_tf = self._ResizeImageCall(x, target_height, target_width, + preserve_aspect_ratio, use_tensor_inputs) + self.assertAllClose(y, y_tf) + + def _assertResizeCheckShape(self, x, x_shape, target_shape, + y_shape, preserve_aspect_ratio=True, + use_tensor_inputs_options=None): + use_tensor_inputs_options = use_tensor_inputs_options or [False, True] + target_height, target_width = target_shape + x = np.array(x).reshape(x_shape) + y = np.zeros(y_shape) + + for use_tensor_inputs in use_tensor_inputs_options: + y_tf = self._ResizeImageCall(x, target_height, target_width, + preserve_aspect_ratio, use_tensor_inputs) + self.assertShapeEqual(y, ops.convert_to_tensor(y_tf)) + + def testPreserveAspectRatioMultipleImages(self): + x_shape = [10, 100, 100, 10] + x = np.random.uniform(size=x_shape) + + self._assertResizeCheckShape(x, x_shape, [250, 250], [10, 250, 250, 10], + preserve_aspect_ratio=False) + + def testPreserveAspectRatioNoOp(self): + x_shape = [10, 10, 10] + x = np.random.uniform(size=x_shape) + + self._assertResizeEqual(x, x_shape, x, x_shape) + + def testPreserveAspectRatioSmaller(self): + x_shape = [100, 100, 10] + x = np.random.uniform(size=x_shape) + + self._assertResizeCheckShape(x, x_shape, [75, 50], [50, 50, 10]) + + def testPreserveAspectRatioSmallerMultipleImages(self): + x_shape = [10, 100, 100, 10] + x = np.random.uniform(size=x_shape) + + self._assertResizeCheckShape(x, x_shape, [75, 50], [10, 50, 50, 10]) + + def testPreserveAspectRatioLarger(self): + x_shape = [100, 100, 10] + x = np.random.uniform(size=x_shape) + + self._assertResizeCheckShape(x, x_shape, [150, 200], [150, 150, 10]) + + def testPreserveAspectRatioSameRatio(self): + x_shape = [1920, 1080, 3] + x = np.random.uniform(size=x_shape) + + self._assertResizeCheckShape(x, x_shape, [3840, 2160], [3840, 2160, 3]) + + +class ResizeImageWithPadTest(test_util.TensorFlowTestCase): + + def _ResizeImageWithPad(self, x, target_height, target_width, + use_tensor_inputs): + if use_tensor_inputs: + target_height = ops.convert_to_tensor(target_height) + target_width = ops.convert_to_tensor(target_width) + x_tensor = array_ops.placeholder(x.dtype, shape=[None] * x.ndim) + feed_dict = {x_tensor: x} + else: + x_tensor = x + feed_dict = {} + + y = image_ops.resize_image_with_pad(x_tensor, target_height, + target_width) + if not use_tensor_inputs: + self.assertTrue(y.get_shape().is_fully_defined()) + + with self.test_session(use_gpu=True): + return y.eval(feed_dict=feed_dict) + + def _assertReturns(self, + x, + x_shape, + y, + y_shape, + use_tensor_inputs_options=None): + use_tensor_inputs_options = use_tensor_inputs_options or [False, True] + target_height, target_width, _ = y_shape + x = np.array(x).reshape(x_shape) + y = np.array(y).reshape(y_shape) + + for use_tensor_inputs in use_tensor_inputs_options: + y_tf = self._ResizeImageWithPad(x, target_height, target_width, + use_tensor_inputs) + self.assertAllClose(y, y_tf) + + def _assertRaises(self, + x, + x_shape, + target_height, + target_width, + err_msg, + use_tensor_inputs_options=None): + use_tensor_inputs_options = use_tensor_inputs_options or [False, True] + x = np.array(x).reshape(x_shape) + + for use_tensor_inputs in use_tensor_inputs_options: + try: + self._ResizeImageWithPad(x, target_height, target_width, + use_tensor_inputs) + except Exception as e: # pylint: disable=broad-except + if err_msg not in str(e): + raise + else: + raise AssertionError("Exception not raised: %s" % err_msg) + + def _assertShapeInference(self, pre_shape, height, width, post_shape): + image = array_ops.placeholder(dtypes.float32, shape=pre_shape) + y = image_ops.resize_image_with_pad(image, height, width) + self.assertEqual(y.get_shape().as_list(), post_shape) + + def testNoOp(self): + x_shape = [10, 10, 10] + x = np.random.uniform(size=x_shape) + + self._assertReturns(x, x_shape, x, x_shape) + + def testPad(self): + # Reduce vertical dimension + x = [1, 2, 3, 4, 5, 6, 7, 8] + x_shape = [2, 4, 1] + + y = [0, 1, 3, 0] + y_shape = [1, 4, 1] + + self._assertReturns(x, x_shape, y, y_shape) + + # Reduce horizontal dimension + x = [1, 2, 3, 4, 5, 6, 7, 8] + x_shape = [2, 4, 1] + + y = [1, 3, 0, 0] + y_shape = [2, 2, 1] + + self._assertReturns(x, x_shape, y, y_shape) + + x = [1, 2, 3, 4, 5, 6, 7, 8] + x_shape = [2, 4, 1] + + y = [1, 3] + y_shape = [1, 2, 1] + + self._assertReturns(x, x_shape, y, y_shape) + class ResizeImageWithCropOrPadTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 724fcc39cddbdd2a8acd9c0bbaa7b968c6d1510d..5bfc5ce2a7a1913b097ee67d1b18d684b5ebcaa5 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -43,7 +43,8 @@ from tensorflow.python.ops import linalg_ops_impl from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from tensorflow.python.util.deprecation import deprecated +from tensorflow.python.util.deprecation import ( + deprecated, deprecated_arg_values) from tensorflow.python.util.tf_export import tf_export @@ -409,8 +410,10 @@ class UniformUnitScaling(Initializer): class VarianceScaling(Initializer): """Initializer capable of adapting its scale to the shape of weights tensors. - With `distribution="normal"`, samples are drawn from a truncated normal - distribution centered on zero, with `stddev = sqrt(scale / n)` + With `distribution="truncated_normal" or "untruncated_normal"`, + samples are drawn from a truncated/untruncated normal + distribution with a mean of zero and a standard deviation (after truncation, + if used) `stddev = sqrt(scale / n)` where n is: - number of input units in the weight tensor, if mode = "fan_in" - number of output units, if mode = "fan_out" @@ -433,10 +436,14 @@ class VarianceScaling(Initializer): "distribution" arguments. """ + @deprecated_arg_values( + None, + "`normal` is a deprecated alias for `truncated_normal`", + distribution="normal") def __init__(self, scale=1.0, mode="fan_in", - distribution="normal", + distribution="truncated_normal", seed=None, dtype=dtypes.float32): if scale <= 0.: @@ -444,7 +451,8 @@ class VarianceScaling(Initializer): if mode not in {"fan_in", "fan_out", "fan_avg"}: raise ValueError("Invalid `mode` argument:", mode) distribution = distribution.lower() - if distribution not in {"normal", "uniform"}: + if distribution not in {"normal", "uniform", + "truncated_normal", "untruncated_normal"}: raise ValueError("Invalid `distribution` argument:", distribution) self.scale = scale self.mode = mode @@ -466,11 +474,15 @@ class VarianceScaling(Initializer): scale /= max(1., fan_out) else: scale /= max(1., (fan_in + fan_out) / 2.) - if self.distribution == "normal": + if self.distribution == "normal" or self.distribution == "truncated_normal": # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) stddev = math.sqrt(scale) / .87962566103423978 return random_ops.truncated_normal( shape, 0.0, stddev, dtype, seed=self.seed) + elif self.distribution == "untruncated_normal": + stddev = math.sqrt(scale) + return random_ops.random_normal( + shape, 0.0, stddev, dtype, seed=self.seed) else: limit = math.sqrt(3.0 * scale) return random_ops.random_uniform( @@ -551,7 +563,9 @@ class ConvolutionDeltaOrthogonal(Initializer): The shape of the tensor must have length 3, 4 or 5. The number of input filters must not exceed the number of output filters. The center pixels of the - tensor form an orthogonal matrix. Other pixels are set to be zero. + tensor form an orthogonal matrix. Other pixels are set to be zero. See + algorithm 2 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 + Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -672,6 +686,7 @@ class ConvolutionOrthogonal2D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -807,6 +822,7 @@ class ConvolutionOrthogonal1D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -923,6 +939,7 @@ class ConvolutionOrthogonal3D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 [Xiao et al., 2018] in: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. diff --git a/tensorflow/python/ops/linalg/linear_operator_test_util.py b/tensorflow/python/ops/linalg/linear_operator_test_util.py index 1b5bb9470c4406ad075f2f6d5c38661311472727..78c85db557047ebcc3dd655deae62acbcef929c7 100644 --- a/tensorflow/python/ops/linalg/linear_operator_test_util.py +++ b/tensorflow/python/ops/linalg/linear_operator_test_util.py @@ -102,7 +102,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): raise NotImplementedError("operator_build_infos has not been implemented.") @abc.abstractmethod - def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + def _operator_and_matrix(self, build_info, dtype, use_placeholder): """Build a batch matrix and an Operator that should have similar behavior. Every operator acts like a (batch) matrix. This method returns both @@ -118,9 +118,6 @@ class LinearOperatorDerivedClassTest(test.TestCase): Returns: operator: `LinearOperator` subclass instance. mat: `Tensor` representing operator. - feed_dict: Dictionary. - If placholder is True, this must contains everything needed to be fed - to sess.run calls at runtime to make the operator work. """ # Create a matrix as a numpy array with desired shape/dtype. # Create a LinearOperator that should have the same behavior as the matrix. @@ -189,12 +186,12 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_dense = operator.to_dense() if not use_placeholder: self.assertAllEqual(build_info.shape, op_dense.get_shape()) - op_dense_v, mat_v = sess.run([op_dense, mat], feed_dict=feed_dict) + op_dense_v, mat_v = sess.run([op_dense, mat]) self.assertAC(op_dense_v, mat_v) def test_det(self): @@ -204,14 +201,13 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_det = operator.determinant() if not use_placeholder: self.assertAllEqual(build_info.shape[:-2], op_det.get_shape()) op_det_v, mat_det_v = sess.run( - [op_det, linalg_ops.matrix_determinant(mat)], - feed_dict=feed_dict) + [op_det, linalg_ops.matrix_determinant(mat)]) self.assertAC(op_det_v, mat_det_v) def test_log_abs_det(self): @@ -221,7 +217,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_log_abs_det = operator.log_abs_determinant() _, mat_log_abs_det = linalg.slogdet(mat) @@ -229,7 +225,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): self.assertAllEqual( build_info.shape[:-2], op_log_abs_det.get_shape()) op_log_abs_det_v, mat_log_abs_det_v = sess.run( - [op_log_abs_det, mat_log_abs_det], feed_dict=feed_dict) + [op_log_abs_det, mat_log_abs_det]) self.assertAC(op_log_abs_det_v, mat_log_abs_det_v) def _test_matmul(self, with_batch): @@ -246,7 +242,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): for adjoint_arg in self._adjoint_arg_options: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) x = self._make_x( operator, adjoint=adjoint, with_batch=with_batch) @@ -264,7 +260,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): self.assertAllEqual(op_matmul.get_shape(), mat_matmul.get_shape()) op_matmul_v, mat_matmul_v = sess.run( - [op_matmul, mat_matmul], feed_dict=feed_dict) + [op_matmul, mat_matmul]) self.assertAC(op_matmul_v, mat_matmul_v) def test_matmul(self): @@ -289,7 +285,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): for adjoint_arg in self._adjoint_arg_options: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) rhs = self._make_rhs( operator, adjoint=adjoint, with_batch=with_batch) @@ -307,8 +303,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): if not use_placeholder: self.assertAllEqual(op_solve.get_shape(), mat_solve.get_shape()) - op_solve_v, mat_solve_v = sess.run( - [op_solve, mat_solve], feed_dict=feed_dict) + op_solve_v, mat_solve_v = sess.run([op_solve, mat_solve]) self.assertAC(op_solve_v, mat_solve_v) def test_solve(self): @@ -326,14 +321,13 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_trace = operator.trace() mat_trace = math_ops.trace(mat) if not use_placeholder: self.assertAllEqual(op_trace.get_shape(), mat_trace.get_shape()) - op_trace_v, mat_trace_v = sess.run( - [op_trace, mat_trace], feed_dict=feed_dict) + op_trace_v, mat_trace_v = sess.run([op_trace, mat_trace]) self.assertAC(op_trace_v, mat_trace_v) def test_add_to_tensor(self): @@ -343,15 +337,14 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_plus_2mat = operator.add_to_tensor(2 * mat) if not use_placeholder: self.assertAllEqual(build_info.shape, op_plus_2mat.get_shape()) - op_plus_2mat_v, mat_v = sess.run( - [op_plus_2mat, mat], feed_dict=feed_dict) + op_plus_2mat_v, mat_v = sess.run([op_plus_2mat, mat]) self.assertAC(op_plus_2mat_v, 3 * mat_v) @@ -362,7 +355,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED - operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( + operator, mat = self._operator_and_matrix( build_info, dtype, use_placeholder=use_placeholder) op_diag_part = operator.diag_part() mat_diag_part = array_ops.matrix_diag_part(mat) @@ -372,7 +365,7 @@ class LinearOperatorDerivedClassTest(test.TestCase): op_diag_part.get_shape()) op_diag_part_, mat_diag_part_ = sess.run( - [op_diag_part, mat_diag_part], feed_dict=feed_dict) + [op_diag_part, mat_diag_part]) self.assertAC(op_diag_part_, mat_diag_part_) diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index de9b3c6909ddd9c22ac4bced5ec48e4de354bd19..66633c8b12f60c86760f906aa8e4312c7394e796 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -192,6 +192,11 @@ def compute_weighted_loss( on some model parameters but you do not want this to affect the loss gradient, you need to apply @{tf.stop_gradient} to `weights` before passing them to `compute_weighted_loss`. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ Reduction.validate(reduction) with ops.name_scope(scope, "weighted_loss", (losses, weights)): @@ -260,6 +265,11 @@ def absolute_difference( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid or if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -306,6 +316,11 @@ def cosine_distance( Raises: ValueError: If `predictions` shape doesn't match `labels` shape, or `axis`, `labels`, `predictions` or `weights` is `None`. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ axis = deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: @@ -353,6 +368,11 @@ def hinge_loss(labels, logits, weights=1.0, scope=None, Raises: ValueError: If the shapes of `logits` and `labels` don't match or if `labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -416,6 +436,11 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -477,6 +502,11 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -540,6 +570,11 @@ def mean_pairwise_squared_error( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -618,6 +653,11 @@ def mean_squared_error( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -670,6 +710,11 @@ def sigmoid_cross_entropy( ValueError: If the shape of `logits` doesn't match that of `multi_class_labels` or if the shape of `weights` is invalid, or if `weights` is None. Also if `multi_class_labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if multi_class_labels is None: raise ValueError("multi_class_labels must not be None.") @@ -731,6 +776,11 @@ def softmax_cross_entropy( ValueError: If the shape of `logits` doesn't match that of `onehot_labels` or if the shape of `weights` is invalid or if `weights` is None. Also if `onehot_labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if onehot_labels is None: raise ValueError("onehot_labels must not be None.") @@ -828,7 +878,8 @@ def sparse_softmax_cross_entropy( exception when this op is run on CPU, and return `NaN` for corresponding loss and gradient rows on GPU. logits: Unscaled log probabilities of shape - `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float32` or `float64`. + `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32` or + `float64`. weights: Coefficients for the loss. This must be scalar or broadcastable to `labels` (i.e. same rank and each dimension is either 1 or the same). scope: the scope for the operations performed in computing the loss. @@ -842,6 +893,11 @@ def sparse_softmax_cross_entropy( Raises: ValueError: If the shapes of `logits`, `labels`, and `weights` are incompatible, or if any of them are None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") diff --git a/tensorflow/python/ops/losses/util.py b/tensorflow/python/ops/losses/util.py index 10646af8a983f149cf0620bf355cf0bc1fa697fb..97bba46661d056fd336c68988e3bc17ef4232487 100644 --- a/tensorflow/python/ops/losses/util.py +++ b/tensorflow/python/ops/losses/util.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops @@ -32,7 +33,10 @@ def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES): loss: A loss `Tensor`. loss_collection: Optional collection to add the loss to. """ - if loss_collection: + # Since we have no way of figuring out when a training iteration starts or + # ends, holding on to a loss when executing eagerly is indistingishable from + # leaking memory. We instead leave the collection empty. + if loss_collection and not context.executing_eagerly(): ops.add_to_collection(loss_collection, loss) diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 563c0b3ab3f6316b89f5ea76f5d075d9f4b77eea..f0c6bd532fcdb76922ce4d5aa7fa13936db81b2f 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -620,29 +620,59 @@ def _DigammaGrad(op, grad): return grad * math_ops.polygamma(array_ops.constant(1, dtype=x.dtype), x) +@ops.RegisterGradient("BesselI0e") +def _BesselI0eGrad(op, grad): + """Compute gradient of bessel_i0e(x) with respect to its argument.""" + x = op.inputs[0] + y = op.outputs[0] + with ops.control_dependencies([grad]): + return grad * (math_ops.bessel_i1e(x) - math_ops.sign(x) * y) + + +@ops.RegisterGradient("BesselI1e") +def _BesselI1eGrad(op, grad): + """Compute gradient of bessel_i1e(x) with respect to its argument.""" + x = op.inputs[0] + y = op.outputs[0] + with ops.control_dependencies([grad]): + # For x = 0, the correct gradient is 0.5. + # However, the main branch gives NaN because of the division by x, so + # we impute the gradient manually. + # An alternative solution is to express the gradient via bessel_i0e and + # bessel_i2e, but the latter is not yet implemented in Eigen. + eps = np.finfo(x.dtype.as_numpy_dtype).eps + zeros = array_ops.zeros_like(x) + x_is_not_tiny = math_ops.abs(x) > eps + safe_x = array_ops.where(x_is_not_tiny, x, eps + zeros) + dy_dx = math_ops.bessel_i0e(safe_x) - y * ( + math_ops.sign(safe_x) + math_ops.reciprocal(safe_x)) + return grad * array_ops.where(x_is_not_tiny, dy_dx, 0.5 + zeros) + + @ops.RegisterGradient("Igamma") def _IgammaGrad(op, grad): - """Returns gradient of igamma(a, x) with respect to x.""" - # TODO(ebrevdo): Perhaps add the derivative w.r.t. a + """Returns gradient of igamma(a, x) with respect to a and x.""" a = op.inputs[0] x = op.inputs[1] sa = array_ops.shape(a) sx = array_ops.shape(x) - unused_ra, rx = gen_array_ops.broadcast_gradient_args(sa, sx) + ra, rx = gen_array_ops.broadcast_gradient_args(sa, sx) - # Perform operations in log space before summing, because Gamma(a) - # and Gamma'(a) can grow large. - partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) - math_ops.lgamma(a)) - # TODO(b/36815900): Mark None return values as NotImplemented - return (None, array_ops.reshape( - math_ops.reduce_sum(partial_x * grad, rx), sx)) + with ops.control_dependencies([grad]): + partial_a = gen_math_ops.igamma_grad_a(a, x) + # Perform operations in log space before summing, because Gamma(a) + # and Gamma'(a) can grow large. + partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) + - math_ops.lgamma(a)) + return (array_ops.reshape(math_ops.reduce_sum(partial_a * grad, ra), sa), + array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)) @ops.RegisterGradient("Igammac") def _IgammacGrad(op, grad): - """Returns gradient of igammac(a, x) = 1 - igamma(a, x) w.r.t. x.""" - _, igamma_grad_x = _IgammaGrad(op, grad) - return None, -igamma_grad_x + """Returns gradient of igammac(a, x) = 1 - igamma(a, x) w.r.t. a and x.""" + igamma_grad_a, igamma_grad_x = _IgammaGrad(op, grad) + return (-igamma_grad_a, -igamma_grad_x) @ops.RegisterGradient("Betainc") diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index a141f1e2e0cbdd8a8a210937e21325f45808d8ef..cdb6dc8f22919420ff44e217578315d17cb93d8c 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1990,7 +1990,7 @@ def matmul(a, sparse_matmul_types = [dtypes.bfloat16, dtypes.float32] use_sparse_matmul = ( a.dtype in sparse_matmul_types and b.dtype in sparse_matmul_types) - if (a.dtype == dtypes.bfloat16 or b.dtype == dtypes.bfloat16 and + if ((a.dtype == dtypes.bfloat16 or b.dtype == dtypes.bfloat16) and a.dtype != b.dtype): # matmul currently doesn't handle mixed-precision inputs. use_sparse_matmul = True @@ -2954,6 +2954,67 @@ def polyval(coeffs, x, name=None): p = c + p * x return p + +@tf_export("math.bessel_i0e") +def bessel_i0e(x, name=None): + """Computes the Bessel i0e function of `x` element-wise. + + Exponentially scaled modified Bessel function of order 0 defined as + `bessel_i0e(x) = exp(-abs(x)) bessel_i0(x)`. + + This function is faster and numerically stabler than `bessel_i0(x)`. + + Args: + x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`, + `float32`, `float64`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`. + + @compatibility(scipy) + Equivalent to scipy.special.i0e + @end_compatibility + """ + with ops.name_scope(name, "bessel_i0e", [x]) as name: + if isinstance(x, sparse_tensor.SparseTensor): + x_i0e = gen_math_ops.bessel_i0e(x.values, name=name) + return sparse_tensor.SparseTensor( + indices=x.indices, values=x_i0e, dense_shape=x.dense_shape) + else: + return gen_math_ops.bessel_i0e(x, name=name) + + +@tf_export("math.bessel_i1e") +def bessel_i1e(x, name=None): + """Computes the Bessel i1e function of `x` element-wise. + + Exponentially scaled modified Bessel function of order 1 defined as + `bessel_i1e(x) = exp(-abs(x)) bessel_i1(x)`. + + This function is faster and numerically stabler than `bessel_i1(x)`. + + Args: + x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`, + `float32`, `float64`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`. + + @compatibility(scipy) + Equivalent to scipy.special.i1e + @end_compatibility + """ + with ops.name_scope(name, "bessel_i1e", [x]) as name: + if isinstance(x, sparse_tensor.SparseTensor): + x_i1e = gen_math_ops.bessel_i1e(x.values, name=name) + return sparse_tensor.SparseTensor( + indices=x.indices, values=x_i1e, dense_shape=x.dense_shape) + else: + return gen_math_ops.bessel_i1e(x, name=name) + + # FFT ops were moved to tf.spectral. tf.fft symbols were part of the TensorFlow # 1.0 API so we leave these here for backwards compatibility. fft = gen_spectral_ops.fft diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index 980c92b0d592bccc34e1fbee636ebdd39056f2fc..6b709e5e7faf0a74f966f446ba9d33ee1087908a 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -37,14 +37,14 @@ log = np.log class ReduceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceAllDims(self): x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) with test_util.device(use_gpu=True): y_tf = self.evaluate(math_ops.reduce_sum(x)) self.assertEqual(y_tf, 21) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceExplicitAxes(self): x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) with test_util.device(use_gpu=True): @@ -57,7 +57,7 @@ class ReduceTest(test_util.TensorFlowTestCase): for axis in (None, (0, 1), (-1, -2), (-2, -1, 0, 1)): self.assertEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)), 21) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceInvalidAxis(self): if context.executing_eagerly(): # The shape check is in run a graph construction time. In eager mode, @@ -150,7 +150,7 @@ class LogSumExpTest(test_util.TensorFlowTestCase): class RoundTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRounding(self): x = np.arange(-5.0, 5.0, .25) for dtype in [np.float32, np.double, np.int32]: @@ -194,7 +194,7 @@ class ModTest(test_util.TensorFlowTestCase): class SquaredDifferenceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSquaredDifference(self): for dtype in [np.int32, np.float16]: x = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) @@ -207,7 +207,7 @@ class SquaredDifferenceTest(test_util.TensorFlowTestCase): class ApproximateEqualTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testApproximateEqual(self): for dtype in [np.float32, np.double]: x = dtype(1) @@ -235,10 +235,19 @@ class ApproximateEqualTest(test_util.TensorFlowTestCase): z_tf = self.evaluate(math_ops.approximate_equal(x, y, tolerance=0.0001)) self.assertAllEqual(z, z_tf) + def testApproximateEqualShape(self): + for dtype in [np.float32, np.double]: + x = np.array([1, 2], dtype=dtype) + y = np.array([[1, 2]], dtype=dtype) + # The inputs 'x' and 'y' must have the same shape. + with self.assertRaisesRegexp( + ValueError, "Shapes must be equal rank, but are 1 and 2"): + math_ops.approximate_equal(x, y) + class ScalarMulTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsRefs(self): if context.executing_eagerly(): var = resource_variable_ops.ResourceVariable(10, name="var") @@ -250,14 +259,14 @@ class ScalarMulTest(test_util.TensorFlowTestCase): self.evaluate(init) self.assertEqual(30, self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsConstant(self): const = constant_op.constant(10) result = math_ops.scalar_mul(3, const) with test_util.device(use_gpu=True): self.assertEqual(30, self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsTensor(self): tensor = array_ops.ones([10, 10]) result = math_ops.scalar_mul(3, tensor) @@ -266,7 +275,7 @@ class ScalarMulTest(test_util.TensorFlowTestCase): with test_util.device(use_gpu=True): self.assertAllEqual(self.evaluate(expected), self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsIndexedSlices(self): values = constant_op.constant([2, 3, 5, 7, 0, -1], shape=[3, 2]) indices = constant_op.constant([0, 2, 5]) diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py index 47eea6ef6b58abd4819544e29783048964104922..bfd225b0d837783fc854835f862fb4a12550fffc 100644 --- a/tensorflow/python/ops/metrics_impl.py +++ b/tensorflow/python/ops/metrics_impl.py @@ -34,21 +34,55 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import weights_broadcast_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export def metric_variable(shape, dtype, validate_shape=True, name=None): - """Create variable in `GraphKeys.(LOCAL|METRIC_VARIABLES`) collections.""" - - return variable_scope.variable( - lambda: array_ops.zeros(shape, dtype), - trainable=False, - collections=[ - ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES - ], - validate_shape=validate_shape, - name=name) + """Create variable in `GraphKeys.(LOCAL|METRIC_VARIABLES)` collections. + + If running in a `DistributionStrategy` context, the variable will be + "tower local". This means: + + * The returned object will be a container with separate variables + per replica/tower of the model. + + * When writing to the variable, e.g. using `assign_add` in a metric + update, the update will be applied to the variable local to the + replica/tower. + + * To get a metric's result value, we need to sum the variable values + across the replicas/towers before computing the final answer. + Furthermore, the final answer should be computed once instead of + in every replica/tower. Both of these are accomplished by + running the computation of the final result value inside + `tf.contrib.distribute.get_tower_context().merge_call(fn)`. + Inside the `merge_call()`, ops are only added to the graph once + and access to a tower-local variable in a computation returns + the sum across all replicas/towers. + + Args: + shape: Shape of the created variable. + dtype: Type of the created variable. + validate_shape: (Optional) Whether shape validation is enabled for + the created variable. + name: (Optional) String name of the created variable. + + Returns: + A (non-trainable) variable initialized to zero, or if inside a + `DistributionStrategy` scope a tower-local variable container. + """ + with distribute_lib.get_tower_context().tower_local_var_scope( + variable_scope.VariableAggregation.SUM): + # Note that "tower local" implies trainable=False. + return variable_scope.variable( + lambda: array_ops.zeros(shape, dtype), + collections=[ + ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES + ], + validate_shape=validate_shape, + name=name) def _remove_squeezable_dimensions(predictions, labels, weights): @@ -333,11 +367,15 @@ def mean(values, with ops.control_dependencies([values]): update_count_op = state_ops.assign_add(count, num_values) - mean_t = _safe_div(total, count, 'value') - update_op = _safe_div(update_total_op, update_count_op, 'update_op') + def aggregate_across_towers(_, t, c): + mean_t = _safe_div(t, c, 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, mean_t) + return mean_t - if metrics_collections: - ops.add_to_collections(metrics_collections, mean_t) + mean_t = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, total, count) + update_op = _safe_div(update_total_op, update_count_op, 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -572,6 +610,17 @@ def _confusion_matrix_at_thresholds(labels, return values, update_ops +def _aggregate_variable(v, collections): + + def f(distribution, value): + value = distribution.read_var(value) + if collections: + ops.add_to_collections(collections, value) + return value + + return distribute_lib.get_tower_context().merge_call(f, v) + + @tf_export('metrics.auc') def auc(labels, predictions, @@ -757,14 +806,18 @@ def auc(labels, raise ValueError('Invalid summation_method: %s' % summation_method) # sum up the areas of all the trapeziums - auc_value = compute_auc(values['tp'], values['fn'], values['tn'], - values['fp'], 'value') + def aggregate_auc(_, values): + auc_value = compute_auc(values['tp'], values['fn'], values['tn'], + values['fp'], 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, auc_value) + return auc_value + + auc_value = distribute_lib.get_tower_context().merge_call( + aggregate_auc, values) update_op = compute_auc(update_ops['tp'], update_ops['fn'], update_ops['tn'], update_ops['fp'], 'update_op') - if metrics_collections: - ops.add_to_collections(metrics_collections, auc_value) - if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -992,15 +1045,18 @@ def mean_per_class_accuracy(labels, update_total_op = state_ops.scatter_add(total, labels, ones) update_count_op = state_ops.scatter_add(count, labels, is_correct) - per_class_accuracy = _safe_div(count, total, None) + def aggregate_mean_accuracy(_, count, total): + per_class_accuracy = _safe_div(count, total, None) + mean_accuracy_v = math_ops.reduce_mean( + per_class_accuracy, name='mean_accuracy') + if metrics_collections: + ops.add_to_collections(metrics_collections, mean_accuracy_v) + return mean_accuracy_v - mean_accuracy_v = math_ops.reduce_mean( - per_class_accuracy, name='mean_accuracy') - update_op = _safe_div(update_count_op, update_total_op, name='update_op') - - if metrics_collections: - ops.add_to_collections(metrics_collections, mean_accuracy_v) + mean_accuracy_v = distribute_lib.get_tower_context().merge_call( + aggregate_mean_accuracy, count, total) + update_op = _safe_div(update_count_op, update_total_op, name='update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -1071,7 +1127,7 @@ def mean_iou(labels, total_cm, update_op = _streaming_confusion_matrix(labels, predictions, num_classes, weights) - def compute_mean_iou(name): + def compute_mean_iou(total_cm, name): """Compute the mean intersection-over-union via the confusion matrix.""" sum_over_row = math_ops.to_float(math_ops.reduce_sum(total_cm, 0)) sum_over_col = math_ops.to_float(math_ops.reduce_sum(total_cm, 1)) @@ -1098,10 +1154,14 @@ def mean_iou(labels, math_ops.reduce_sum(iou, name=name) / num_valid_entries, 0) return result - mean_iou_v = compute_mean_iou('mean_iou') + def mean_iou_across_towers(_, v): + mean_iou_v = compute_mean_iou(v, 'mean_iou') + if metrics_collections: + ops.add_to_collections(metrics_collections, mean_iou_v) + return mean_iou_v - if metrics_collections: - ops.add_to_collections(metrics_collections, mean_iou_v) + mean_iou_v = distribute_lib.get_tower_context().merge_call( + mean_iou_across_towers, total_cm) if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -1310,12 +1370,16 @@ def mean_tensor(values, with ops.control_dependencies([values]): update_count_op = state_ops.assign_add(count, num_values) - mean_t = _safe_div(total, count, 'value') - update_op = _safe_div(update_total_op, update_count_op, 'update_op') + def aggregate_across_towers(_, t, c): + mean_t = _safe_div(t, c, 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, mean_t) + return mean_t - if metrics_collections: - ops.add_to_collections(metrics_collections, mean_t) + mean_t = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, total, count) + update_op = _safe_div(update_total_op, update_count_op, 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -1413,12 +1477,9 @@ def _count_condition(values, weights = math_ops.to_float(weights) values = math_ops.multiply(values, weights) - value_tensor = array_ops.identity(count) - update_op = state_ops.assign_add(count, math_ops.reduce_sum(values)) - - if metrics_collections: - ops.add_to_collections(metrics_collections, value_tensor) + value_tensor = _aggregate_variable(count, metrics_collections) + update_op = state_ops.assign_add(count, math_ops.reduce_sum(values)) if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -1525,13 +1586,12 @@ def false_negatives_at_thresholds(labels, values, update_ops = _confusion_matrix_at_thresholds( labels, predictions, thresholds, weights=weights, includes=('fn',)) - if metrics_collections: - ops.add_to_collections(metrics_collections, values['fn']) + fn_value = _aggregate_variable(values['fn'], metrics_collections) if updates_collections: ops.add_to_collections(updates_collections, update_ops['fn']) - return values['fn'], update_ops['fn'] + return fn_value, update_ops['fn'] @tf_export('metrics.false_positives') @@ -1635,13 +1695,12 @@ def false_positives_at_thresholds(labels, values, update_ops = _confusion_matrix_at_thresholds( labels, predictions, thresholds, weights=weights, includes=('fp',)) - if metrics_collections: - ops.add_to_collections(metrics_collections, values['fp']) + fp_value = _aggregate_variable(values['fp'], metrics_collections) if updates_collections: ops.add_to_collections(updates_collections, update_ops['fp']) - return values['fp'], update_ops['fp'] + return fp_value, update_ops['fp'] @tf_export('metrics.true_negatives') @@ -1745,13 +1804,12 @@ def true_negatives_at_thresholds(labels, values, update_ops = _confusion_matrix_at_thresholds( labels, predictions, thresholds, weights=weights, includes=('tn',)) - if metrics_collections: - ops.add_to_collections(metrics_collections, values['tn']) + tn_value = _aggregate_variable(values['tn'], metrics_collections) if updates_collections: ops.add_to_collections(updates_collections, update_ops['tn']) - return values['tn'], update_ops['tn'] + return tn_value, update_ops['tn'] @tf_export('metrics.true_positives') @@ -1855,13 +1913,12 @@ def true_positives_at_thresholds(labels, values, update_ops = _confusion_matrix_at_thresholds( labels, predictions, thresholds, weights=weights, includes=('tp',)) - if metrics_collections: - ops.add_to_collections(metrics_collections, values['tp']) + tp_value = _aggregate_variable(values['tp'], metrics_collections) if updates_collections: ops.add_to_collections(updates_collections, update_ops['tp']) - return values['tp'], update_ops['tp'] + return tp_value, update_ops['tp'] @tf_export('metrics.precision') @@ -1945,13 +2002,17 @@ def precision(labels, return array_ops.where( math_ops.greater(tp + fp, 0), math_ops.div(tp, tp + fp), 0, name) - p = compute_precision(true_p, false_p, 'value') - update_op = compute_precision(true_positives_update_op, - false_positives_update_op, 'update_op') + def once_across_towers(_, true_p, false_p): + p = compute_precision(true_p, false_p, 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, p) + return p - if metrics_collections: - ops.add_to_collections(metrics_collections, p) + p = distribute_lib.get_tower_context().merge_call( + once_across_towers, true_p, false_p) + update_op = compute_precision(true_positives_update_op, + false_positives_update_op, 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -2025,13 +2086,17 @@ def precision_at_thresholds(labels, def compute_precision(tp, fp, name): return math_ops.div(tp, epsilon + tp + fp, name='precision_' + name) - prec = compute_precision(values['tp'], values['fp'], 'value') - update_op = compute_precision(update_ops['tp'], update_ops['fp'], - 'update_op') + def precision_across_towers(_, values): + prec = compute_precision(values['tp'], values['fp'], 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, prec) + return prec - if metrics_collections: - ops.add_to_collections(metrics_collections, prec) + prec = distribute_lib.get_tower_context().merge_call( + precision_across_towers, values) + update_op = compute_precision(update_ops['tp'], update_ops['fp'], + 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -2050,7 +2115,7 @@ def recall(labels, The `recall` function creates two local variables, `true_positives` and `false_negatives`, that are used to compute the recall. This value is ultimately returned as `recall`, an idempotent operation that simply divides - `true_positives` by the sum of `true_positives` and `false_negatives`. + `true_positives` by the sum of `true_positives` and `false_negatives`. For estimation of the metric over a stream of data, the function creates an `update_op` that updates these variables and returns the `recall`. `update_op` @@ -2117,13 +2182,17 @@ def recall(labels, math_ops.greater(true_p + false_n, 0), math_ops.div(true_p, true_p + false_n), 0, name) - rec = compute_recall(true_p, false_n, 'value') - update_op = compute_recall(true_positives_update_op, - false_negatives_update_op, 'update_op') + def once_across_towers(_, true_p, false_n): + rec = compute_recall(true_p, false_n, 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, rec) + return rec - if metrics_collections: - ops.add_to_collections(metrics_collections, rec) + rec = distribute_lib.get_tower_context().merge_call( + once_across_towers, true_p, false_n) + update_op = compute_recall(true_positives_update_op, + false_negatives_update_op, 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -2552,11 +2621,17 @@ def recall_at_top_k(labels, class_id=class_id, weights=weights) - metric = math_ops.div(tp, math_ops.add(tp, fn), name=scope) + def aggregate_across_towers(_, tp, fn): + metric = math_ops.div(tp, math_ops.add(tp, fn), name=scope) + if metrics_collections: + ops.add_to_collections(metrics_collections, metric) + return metric + + metric = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, tp, fn) + update = math_ops.div( tp_update, math_ops.add(tp_update, fn_update), name='update') - if metrics_collections: - ops.add_to_collections(metrics_collections, metric) if updates_collections: ops.add_to_collections(updates_collections, update) return metric, update @@ -2627,12 +2702,16 @@ def recall_at_thresholds(labels, def compute_recall(tp, fn, name): return math_ops.div(tp, epsilon + tp + fn, name='recall_' + name) - rec = compute_recall(values['tp'], values['fn'], 'value') - update_op = compute_recall(update_ops['tp'], update_ops['fn'], 'update_op') + def recall_across_towers(_, values): + rec = compute_recall(values['tp'], values['fn'], 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, rec) + return rec - if metrics_collections: - ops.add_to_collections(metrics_collections, rec) + rec = distribute_lib.get_tower_context().merge_call( + recall_across_towers, values) + update_op = compute_recall(update_ops['tp'], update_ops['fn'], 'update_op') if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -2698,13 +2777,16 @@ def root_mean_squared_error(labels, mse, update_mse_op = mean_squared_error(labels, predictions, weights, None, None, name or 'root_mean_squared_error') + def once_across_towers(_, mse): + rmse = math_ops.sqrt(mse) + if metrics_collections: + ops.add_to_collections(metrics_collections, rmse) + return rmse - rmse = math_ops.sqrt(mse) - update_rmse_op = math_ops.sqrt(update_mse_op) - - if metrics_collections: - ops.add_to_collections(metrics_collections, rmse) + rmse = distribute_lib.get_tower_context().merge_call( + once_across_towers, mse) + update_rmse_op = math_ops.sqrt(update_mse_op) if updates_collections: ops.add_to_collections(updates_collections, update_rmse_op) @@ -2797,15 +2879,19 @@ def sensitivity_at_specificity(labels, return math_ops.div(tp[tf_index], tp[tf_index] + fn[tf_index] + kepsilon, name) - sensitivity = compute_sensitivity_at_specificity( - values['tp'], values['tn'], values['fp'], values['fn'], 'value') + def aggregate_across_towers(_, values): + sensitivity = compute_sensitivity_at_specificity( + values['tp'], values['tn'], values['fp'], values['fn'], 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, sensitivity) + return sensitivity + + sensitivity = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, values) + update_op = compute_sensitivity_at_specificity( update_ops['tp'], update_ops['tn'], update_ops['fp'], update_ops['fn'], 'update_op') - - if metrics_collections: - ops.add_to_collections(metrics_collections, sensitivity) - if updates_collections: ops.add_to_collections(updates_collections, update_op) @@ -3070,11 +3156,16 @@ def _streaming_sparse_average_precision_at_top_k(labels, total_update = state_ops.assign_add(total_var, batch_total, name='update') # Divide total by max to get mean, for both vars and the update ops. - mean_average_precision = _safe_scalar_div(total_var, max_var, name='mean') - update = _safe_scalar_div(total_update, max_update, name=scope) + def aggregate_across_towers(_, total_var, max_var): + mean_average_precision = _safe_scalar_div(total_var, max_var, name='mean') + if metrics_collections: + ops.add_to_collections(metrics_collections, mean_average_precision) + return mean_average_precision - if metrics_collections: - ops.add_to_collections(metrics_collections, mean_average_precision) + mean_average_precision = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, total_var, max_var) + + update = _safe_scalar_div(total_update, max_update, name=scope) if updates_collections: ops.add_to_collections(updates_collections, update) @@ -3351,11 +3442,17 @@ def precision_at_top_k(labels, class_id=class_id, weights=weights) - metric = math_ops.div(tp, math_ops.add(tp, fp), name=scope) + def aggregate_across_towers(_, tp, fp): + metric = math_ops.div(tp, math_ops.add(tp, fp), name=scope) + if metrics_collections: + ops.add_to_collections(metrics_collections, metric) + return metric + + metric = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, tp, fp) + update = math_ops.div( tp_update, math_ops.add(tp_update, fp_update), name='update') - if metrics_collections: - ops.add_to_collections(metrics_collections, metric) if updates_collections: ops.add_to_collections(updates_collections, update) return metric, update @@ -3583,15 +3680,19 @@ def specificity_at_sensitivity(labels, return math_ops.div(tn[tf_index], tn[tf_index] + fp[tf_index] + kepsilon, name) - specificity = compute_specificity_at_sensitivity( - values['tp'], values['tn'], values['fp'], values['fn'], 'value') + def aggregate_across_towers(_, values): + specificity = compute_specificity_at_sensitivity( + values['tp'], values['tn'], values['fp'], values['fn'], 'value') + if metrics_collections: + ops.add_to_collections(metrics_collections, specificity) + return specificity + + specificity = distribute_lib.get_tower_context().merge_call( + aggregate_across_towers, values) + update_op = compute_specificity_at_sensitivity( update_ops['tp'], update_ops['tn'], update_ops['fp'], update_ops['fn'], 'update_op') - - if metrics_collections: - ops.add_to_collections(metrics_collections, specificity) - if updates_collections: ops.add_to_collections(updates_collections, update_op) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 0c2f5b06c497e8ca7db20ac09938c86b425d66a0..41d54a6c2f9d8cd961cea398da679fd81361b848 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2009,7 +2009,8 @@ def sparse_softmax_cross_entropy_with_logits( exception when this op is run on CPU, and return `NaN` for corresponding loss and gradient rows on GPU. logits: Unscaled log probabilities of shape - `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float32` or `float64`. + `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32`, or + `float64`. name: A name for the operation (optional). Returns: @@ -2166,7 +2167,7 @@ def _calc_conv_flops(graph, node): filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) filter_in_depth = int(filter_shape[2]) - output_count = np.prod(output_shape.as_list()) + output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats( "flops", (output_count * filter_in_depth * filter_height * filter_width * 2)) @@ -2184,7 +2185,7 @@ def _calc_depthwise_conv_flops(graph, node): output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) - output_count = np.prod(output_shape.as_list()) + output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) @@ -2594,7 +2595,7 @@ def _calc_dilation2d_flops(graph, node): output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) - output_count = np.prod(output_shape.as_list()) + output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 035b4735affbd37f9de94057eed6f7b5d9aadd6e..ae24ca0552e7ba2823ec9404ecc848f510cce464 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -76,7 +76,7 @@ class SoftmaxTest(test_lib.TestCase): z = u.sum(1)[:, np.newaxis] return u / z - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSoftmax(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -123,7 +123,7 @@ class LogPoissonLossTest(test_lib.TestCase): lpl += np.ma.masked_array(stirling_approx, mask=(z <= 1)).filled(0.) return lpl - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogPoissonLoss(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -164,7 +164,7 @@ class LogSoftmaxTest(test_lib.TestCase): u = x - m return u - np.log(np.sum(np.exp(u), 1, keepdims=True)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogSoftmax(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -201,7 +201,7 @@ class LogSoftmaxTest(test_lib.TestCase): class L2LossTest(test_lib.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2Loss(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant( @@ -235,7 +235,7 @@ class L2NormalizeTest(test_lib.TestCase): norm = np.apply_along_axis(np.linalg.norm, dim, x) return x / np.expand_dims(norm, dim) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2Normalize(self): x_shape = [20, 7, 3] np.random.seed(1) @@ -246,7 +246,7 @@ class L2NormalizeTest(test_lib.TestCase): y_tf = nn_impl.l2_normalize(x_tf, dim) self.assertAllClose(y_np, self.evaluate(y_tf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2NormalizeDimArray(self): x_shape = [20, 7, 3] np.random.seed(1) diff --git a/tensorflow/python/ops/parallel_for/BUILD b/tensorflow/python/ops/parallel_for/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..065c2caedc9d334543512941f3513e45360b460f --- /dev/null +++ b/tensorflow/python/ops/parallel_for/BUILD @@ -0,0 +1,129 @@ +package( + default_visibility = [ + "//tensorflow:internal", + ], +) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +licenses(["notice"]) # Apache 2.0 + +py_library( + name = "parallel_for", + srcs = [ + "__init__.py", + "control_flow_ops.py", + "gradients.py", + "pfor.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + ":gradients", + "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:data_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:functional_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:nn_ops", + "//tensorflow/python:platform", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:tensor_shape", + "//tensorflow/python:tensor_util", + "//tensorflow/python:util", + "@absl_py//absl/flags", + ], +) + +py_library( + name = "pfor_lib", + srcs = ["pfor.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:data_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:functional_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:nn_ops", + "//tensorflow/python:platform", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:tensor_shape", + "//tensorflow/python:tensor_util", + "@absl_py//absl/flags", + ], +) + +py_library( + name = "control_flow_ops", + srcs = ["control_flow_ops.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":pfor_lib", + "//tensorflow/python:array_ops", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:util", + ], +) + +cuda_py_test( + name = "control_flow_ops_test", + srcs = ["control_flow_ops_test.py"], + additional_deps = [ + ":control_flow_ops", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:gradients", + "//tensorflow/python:logging_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:session", + "//tensorflow/python:tensor_array_grad", + "//tensorflow/python:random_ops", + "//tensorflow/python:util", + ], +) + +py_library( + name = "gradients", + srcs = ["gradients.py"], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:util", + ], +) + +cuda_py_test( + name = "gradients_test", + size = "large", + srcs = ["gradients_test.py"], + additional_deps = [ + ":control_flow_ops", + ":gradients", + "//third_party/py/numpy", + "//tensorflow/python:layers", + "//tensorflow/python:client_testlib", + "//tensorflow/python:random_ops", + "//tensorflow/python/ops/losses", + ], + tags = ["no_gpu"], # TODO(b/80127739): test is flaky +) diff --git a/tensorflow/python/ops/parallel_for/__init__.py b/tensorflow/python/ops/parallel_for/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b49d865968b0bab02380cb934431f4933590570e --- /dev/null +++ b/tensorflow/python/ops/parallel_for/__init__.py @@ -0,0 +1,35 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Ops for pfor, for_loop, jacobian.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.ops.parallel_for import * # pylint: disable=wildcard-import +from tensorflow.python.ops.parallel_for.control_flow_ops import for_loop +from tensorflow.python.ops.parallel_for.control_flow_ops import pfor +from tensorflow.python.ops.parallel_for.gradients import batch_jacobian +from tensorflow.python.ops.parallel_for.gradients import jacobian +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + 'pfor', + 'for_loop', + 'jacobian', + 'batch_jacobian', +] + +remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/python/ops/parallel_for/control_flow_ops.py b/tensorflow/python/ops/parallel_for/control_flow_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..ccf2eb82146969532c84b7d56d40974e94337507 --- /dev/null +++ b/tensorflow/python/ops/parallel_for/control_flow_ops.py @@ -0,0 +1,123 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""for_loop and pfor ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.ops.parallel_for.pfor import PFor +from tensorflow.python.util import nest + + +def for_loop(loop_fn, loop_fn_dtypes, iters): + """Runs `loop_fn` `iters` times and stacks the outputs. + + + Runs `loop_fn` `iters` times, with input values from 0 to `iters - 1`, and + stacks corresponding outputs of the different runs. + + Args: + loop_fn: A function that takes an int32 scalar tf.Tensor object representing + the iteration number, and returns a possibly nested structure of tensor + objects. The shape of these outputs should not depend on the input. + loop_fn_dtypes: dtypes for the outputs of loop_fn. + iters: Number of iterations for which to run loop_fn. + + Returns: + Returns a nested structure of stacked output tensor objects with the same + nested structure as the output of `loop_fn`. + """ + + flat_loop_fn_dtypes = nest.flatten(loop_fn_dtypes) + + def while_body(i, *ta_list): + """Body of while loop.""" + fn_output = nest.flatten(loop_fn(i)) + if len(fn_output) != len(flat_loop_fn_dtypes): + raise ValueError( + "Number of expected outputs, %d, does not match the number of " + "actual outputs, %d, from loop_fn" % (len(flat_loop_fn_dtypes), + len(fn_output))) + outputs = [] + for out, ta in zip(fn_output, ta_list): + # TODO(agarwal): support returning Operation objects from loop_fn. + assert isinstance(out, ops.Tensor) + outputs.append(ta.write(i, array_ops.expand_dims(out, 0))) + return tuple([i + 1] + outputs) + + ta_list = control_flow_ops.while_loop( + lambda i, *ta: i < iters, while_body, [0] + [ + tensor_array_ops.TensorArray(dtype, iters) + for dtype in flat_loop_fn_dtypes + ])[1:] + + # TODO(rachelim): enable this for sparse tensors + return nest.pack_sequence_as(loop_fn_dtypes, [ta.concat() for ta in ta_list]) + + +def pfor(loop_fn, iters): + """Equivalent to running `loop_fn` `iters` times and stacking the outputs. + + `pfor` has functionality similar to `for_loop`, i.e. running `loop_fn` `iters` + times, with input from 0 to `iters - 1`, and stacking corresponding output of + each iteration. However the implementation does not use a tf.while_loop. + Instead it adds new operations to the graph that collectively compute the same + value as what running `loop_fn` in a loop would compute. + + + This is an experimental feature and currently has a lot of limitations: + - There should be no data depenendency between the different iterations. For + example, a future iteration should not depend on a value or side-effect of + a previous iteration. + - Stateful kernels may mostly not be supported since these often imply a + data dependency or ordering of the iterations. We do support a limited set + of such stateful kernels though (like RandomFoo, Variable operations like + reads, etc). + - Conversion works only on a limited set of kernels for which a converter + has been registered. + - loop_fn cannot currently contain control flow operations like + tf.while_loop or tf.cond. + - `loop_fn` should return nested structure of Tensors or Operations. However + if an Operation is returned, it should have zero outputs. + - The shape and dtype of `loop_fn` outputs should not depend on the input + to loop_fn. + + Args: + loop_fn: A function that takes an int32 scalar tf.Tensor object representing + the iteration number, and returns a possibly nested structure of Tensor or + Operation objects. + iters: Number of iterations for which to run loop_fn. + + Returns: + Returns a nested structure of stacked tensor objects with the same nested + structure as the output of `loop_fn`. + """ + existing_ops = set(ops.get_default_graph().get_operations()) + with ops.name_scope("loop_body"): + loop_var = array_ops.placeholder(dtypes.int32, shape=[]) + loop_fn_outputs = loop_fn(loop_var) + new_ops = set(ops.get_default_graph().get_operations()) - existing_ops + iters = ops.convert_to_tensor(iters) + with ops.name_scope("pfor"): + converter = PFor(loop_var, iters, new_ops) + outputs = [] + for loop_fn_output in nest.flatten(loop_fn_outputs): + outputs.append(converter.convert(loop_fn_output)) + return nest.pack_sequence_as(loop_fn_outputs, outputs) diff --git a/tensorflow/python/ops/parallel_for/control_flow_ops_test.py b/tensorflow/python/ops/parallel_for/control_flow_ops_test.py new file mode 100644 index 0000000000000000000000000000000000000000..1134a6c4a65c50272bde429866f7ddaa22780408 --- /dev/null +++ b/tensorflow/python/ops/parallel_for/control_flow_ops_test.py @@ -0,0 +1,1351 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for pfor and for_loop.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time + +from absl import flags +import numpy as np + +from tensorflow.python.client import session +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops import gradients as gradient_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import rnn +from tensorflow.python.ops import rnn_cell +from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.ops import variables +from tensorflow.python.ops.parallel_for import control_flow_ops as pfor_control_flow_ops +from tensorflow.python.platform import test +from tensorflow.python.util import nest + + +class PForTest(test.TestCase): + + def _run_targets(self, targets1, targets2=None, run_init=True): + targets1 = nest.flatten(targets1) + targets2 = ([] if targets2 is None else nest.flatten(targets2)) + assert len(targets1) == len(targets2) or not targets2 + if run_init: + init = variables.global_variables_initializer() + self.evaluate(init) + return self.evaluate(targets1 + targets2) + + def run_and_assert_equal(self, targets1, targets2): + outputs = self._run_targets(targets1, targets2) + outputs = nest.flatten(outputs) # flatten SparseTensorValues + n = len(outputs) // 2 + for i in range(n): + if outputs[i + n].dtype != np.object: + self.assertAllClose(outputs[i + n], outputs[i], rtol=1e-4, atol=1e-5) + else: + self.assertAllEqual(outputs[i + n], outputs[i]) + + def _test_loop_fn(self, loop_fn, iters, loop_fn_dtypes=dtypes.float32): + t1 = pfor_control_flow_ops.pfor(loop_fn, iters=iters) + t2 = pfor_control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, iters=iters) + self.run_and_assert_equal(t1, t2) + + def test_op_conversion_fallback_to_while_loop(self): + # Note that we used top_k op for this test. If a converter gets defined for + # it, we will need to find another op for which a converter has not been + # defined. + x = random_ops.random_uniform([3, 2, 4]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + return nn.top_k(x_i) + + with self.assertRaisesRegexp(ValueError, "No converter defined"): + self._test_loop_fn( + loop_fn, 3, loop_fn_dtypes=[dtypes.float32, dtypes.int32]) + flags.FLAGS.op_conversion_fallback_to_while_loop = True + self._test_loop_fn( + loop_fn, 3, loop_fn_dtypes=[dtypes.float32, dtypes.int32]) + flags.FLAGS.op_conversion_fallback_to_while_loop = False + + +class ArrayTest(PForTest): + + def test_gather(self): + x = random_ops.random_uniform([3, 3, 3]) + + def loop_fn(i): + outputs = [] + x_i = array_ops.gather(x, i) + for y in [x, x_i]: + axes = [0, 2, -1] if y == x else [0] + for axis in axes: + outputs.append(array_ops.gather(y, 2, axis=axis)) + outputs.append(array_ops.gather(y, i, axis=axis)) + outputs.append(array_ops.gather(y, [i], axis=axis)) + outputs.append(array_ops.gather(y, [i, 2], axis=axis)) + outputs.append(array_ops.gather(y, [[2, i], [i, 1]], axis=axis)) + return outputs + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 20) + + def test_shape(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + return array_ops.shape(x_i), array_ops.shape(x_i, out_type=dtypes.int64) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32, dtypes.int64]) + + def test_size(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + return array_ops.size(x_i), array_ops.size(x_i, out_type=dtypes.int64) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32, dtypes.int64]) + + def test_rank(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + return array_ops.rank(x_i) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_shape_n(self): + x = random_ops.random_uniform([3, 2, 3]) + y = random_ops.random_uniform([3]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + y_i = array_ops.gather(y, i) + return array_ops.shape_n([x_i, x, y, y_i]), array_ops.shape_n( + [x_i, x, y, y_i], out_type=dtypes.int64) + + self._test_loop_fn( + loop_fn, 3, loop_fn_dtypes=[dtypes.int32] * 4 + [dtypes.int64] * 4) + + def test_reshape(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.reshape(x1, [-1]), array_ops.reshape(x1, [1, 3, 1, -1]) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_expand_dims(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.expand_dims( + x1, axis=-1), array_ops.expand_dims( + x1, axis=1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_slice(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.slice(x1, begin=(0, 1), size=(2, 1)) + + self._test_loop_fn(loop_fn, 3) + + def test_tile(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.tile(x1, [2, 1]) + + self._test_loop_fn(loop_fn, 3) + + def test_tile_loop_dependent(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.tile(x1, [i, 1]) + + with self.assertRaisesRegexp(ValueError, "expected to be loop invariant"): + pfor_control_flow_ops.pfor(loop_fn, 2) + + def test_pack(self): + x = random_ops.random_uniform([3, 2, 3]) + y = random_ops.random_uniform([2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.stack([x1, y], axis=-1) + + self._test_loop_fn(loop_fn, 1) + + def test_unpack(self): + x = random_ops.random_uniform([3, 2, 3, 4]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + return array_ops.unstack( + x_i, 4, axis=-1), array_ops.unstack( + x_i, 3, axis=1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 7) + + def test_pad(self): + x = random_ops.random_uniform([3, 2, 3]) + padding = constant_op.constant([[1, 2], [3, 4]]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.pad(x1, padding, mode="CONSTANT") + + self._test_loop_fn(loop_fn, 3) + + def test_split(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.split(x1, 2, axis=0), array_ops.split(x1, 3, axis=-1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 5) + + def test_transpose(self): + x = random_ops.random_uniform([3, 2, 3, 4]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.transpose(x1, [2, 1, 0]) + + self._test_loop_fn(loop_fn, 3) + + def test_zeros_like(self): + x = random_ops.random_uniform([3, 2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + z = array_ops.zeros_like(x1), + return z, z + x1 + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_concat_v2(self): + x = random_ops.random_uniform([3, 2, 3]) + y = random_ops.random_uniform([2, 3]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return array_ops.concat( + [x1, x1, y], axis=0), array_ops.concat( + [x1, x1, y], axis=-1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_unary_cwise_ops(self): + for op in [array_ops.identity, array_ops.stop_gradient]: + x = random_ops.random_uniform([3, 5]) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + x1 = array_ops.gather(x, i) + y = op(x1) + x1 + loss = nn.l2_loss(y) + return op(x), y, gradient_ops.gradients(loss, x1) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 3) + + def test_strided_slice(self): + x = random_ops.random_uniform([3, 3, 4, 4, 2, 2, 2]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + y = x_i[:2, ::2, 1::3, ..., array_ops.newaxis, 1] + loss = nn.l2_loss(y) + return y, gradient_ops.gradients(loss, x_i) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + +class MathTest(PForTest): + + def test_unary_cwise_ops(self): + for op in [ + math_ops.tanh, nn.relu, math_ops.sigmoid, math_ops.negative, + math_ops.square + ]: + x = random_ops.random_uniform([3, 5]) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + x1 = array_ops.gather(x, i) + y = op(x1) + loss = math_ops.reduce_sum(y * y) + return op(x), y, gradient_ops.gradients(loss, x1) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 3) + + def test_unary_cwise_no_grad(self): + for op in [math_ops.ceil, math_ops.floor, math_ops.logical_not]: + x = random_ops.random_uniform([3, 5]) + if op == math_ops.logical_not: + x = x > 0 + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + return op(array_ops.gather(x, i)) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=x.dtype) + + def test_binary_cwise_ops(self): + logical_ops = [ + math_ops.logical_and, math_ops.logical_or, math_ops.logical_xor + ] + bool_ops = [ + math_ops.less, math_ops.less_equal, math_ops.greater, + math_ops.greater_equal, math_ops.equal, math_ops.not_equal + ] + float_ops = [ + math_ops.add, math_ops.subtract, math_ops.multiply, math_ops.divide, + math_ops.maximum, math_ops.minimum + ] + for op in logical_ops + bool_ops + float_ops: + x = random_ops.random_uniform([7, 3, 5]) + y = random_ops.random_uniform([3, 5]) + if op in logical_ops: + x = x > 0 + y = y > 0 + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + x1 = array_ops.gather(x, i) + y1 = array_ops.gather(y, i) + return op(x, y), op(x1, y), op(x, y1), op(x1, y1), op(x1, x1) + + # pylint: enable=cell-var-from-loop + + dtype = dtypes.float32 if op in float_ops else dtypes.bool + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtype] * 5) + + def test_addn(self): + x = random_ops.random_uniform([2, 3, 5]) + y = random_ops.random_uniform([3, 5]) + z = random_ops.random_uniform([3, 5]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return math_ops.add_n([x1, y, z]) + + self._test_loop_fn(loop_fn, 2) + + def test_matmul(self): + for tr_a in (True, False): + for tr_b in (True, False): + for stack_a in (True, False): + for stack_b in (True, False): + shape_a = (5, 3) if tr_a else (3, 5) + if stack_a: + shape_a = (2,) + shape_a + shape_b = (7, 5) if tr_b else (5, 7) + if stack_b: + shape_b = (2,) + shape_b + + x = random_ops.random_uniform(shape_a) + y = random_ops.random_uniform(shape_b) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) if stack_a else x + b = array_ops.gather(y, i) if stack_b else y + return math_ops.matmul(a, b, transpose_a=tr_a, transpose_b=tr_b) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + def test_batch_matmul(self): + for tr_a in (True, False): + for tr_b in (True, False): + for stack_a in (True, False): + for stack_b in (True, False): + shape_a = (4, 5, 3) if tr_a else (4, 3, 5) + if stack_a: + shape_a = (2,) + shape_a + shape_b = (4, 7, 5) if tr_b else (4, 5, 7) + if stack_b: + shape_b = (2,) + shape_b + + x = random_ops.random_uniform(shape_a) + y = random_ops.random_uniform(shape_b) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) if stack_a else x + b = array_ops.gather(y, i) if stack_b else y + return math_ops.matmul(a, b, transpose_a=tr_a, transpose_b=tr_b) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + def test_reduction(self): + x = random_ops.random_uniform([2, 3, 4, 5]) + for op in [ + math_ops.reduce_sum, math_ops.reduce_prod, math_ops.reduce_max, + math_ops.reduce_min + ]: + for axis in ([1], None, [0, 2]): + for keepdims in (True, False): + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) + return op(a, axis=axis, keepdims=keepdims) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + def test_cum_sum(self): + x = random_ops.random_uniform([2, 3, 4, 5]) + for axis in (1, -2): + for exclusive in (True, False): + for reverse in (True, False): + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) + return math_ops.cumsum( + a, axis=axis, exclusive=exclusive, reverse=reverse) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + def test_cum_prod(self): + x = random_ops.random_uniform([2, 3, 4, 5]) + for axis in (1, -2): + for exclusive in (True, False): + for reverse in (True, False): + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) + return math_ops.cumprod( + a, axis=axis, exclusive=exclusive, reverse=reverse) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + def test_bias_add(self): + x_shape = [2, 3, 4, 5, 6] + x = random_ops.random_uniform(x_shape) + for data_format in ("NCHW", "NHWC"): + bias_dim = 2 if data_format == "NCHW" else -1 + bias_shape = x_shape[bias_dim] + bias = random_ops.random_uniform([bias_shape]) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a = array_ops.gather(x, i) + y = nn.bias_add(a, bias, data_format=data_format) + loss = math_ops.reduce_sum(y * y) + return y, gradient_ops.gradients(loss, bias) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn( + loop_fn, 2, loop_fn_dtypes=[dtypes.float32, dtypes.float32]) + + def test_unsorted_segment_sum(self): + t = random_ops.random_uniform([3, 3, 2]) + segment_ids = constant_op.constant([[0, 0, 2], [0, 1, 2], [2, 2, 2]]) + num_segments = 3 + + def loop_fn(i): + data = array_ops.gather(t, i) + data_0 = array_ops.gather(t, 0) + seg_ids = array_ops.gather(segment_ids, i) + return (math_ops.unsorted_segment_sum(data, seg_ids, num_segments), + math_ops.unsorted_segment_sum(data_0, seg_ids, num_segments)) + + self._test_loop_fn(loop_fn, 3, [dtypes.float32] * 2) + + def test_cast(self): + x = constant_op.constant([[1], [2]]) + y = constant_op.constant([[1.0], [2.0]]) + + def loop_fn(i): + return (math_ops.cast(array_ops.gather(x, i), dtypes.float32), + math_ops.cast(array_ops.gather(y, i), dtypes.int32)) + + self._test_loop_fn( + loop_fn, 2, loop_fn_dtypes=[dtypes.float32, dtypes.int32]) + + def test_tanh_axpy(self): + a = constant_op.constant(3.) + x = random_ops.random_uniform([4, 5]) + y = random_ops.random_uniform([6, 5]) + n = x.shape[0] + + def loop_fn(i): + return math_ops.tanh(a * array_ops.gather(x, i) + array_ops.gather(y, i)) + + self._test_loop_fn(loop_fn, n) + + def test_select(self): + cond = constant_op.constant([True, False]) + a = random_ops.random_uniform([2, 3, 5]) + b = random_ops.random_uniform([2, 3, 5]) + for cond_shape in [2], [2, 3], [2, 3, 5]: + cond = random_ops.random_uniform(cond_shape) > 0.5 + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + a_i = array_ops.gather(a, i) + b_i = array_ops.gather(b, i) + cond_i = array_ops.gather(cond, i) + return array_ops.where(cond_i, a_i, b_i) + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 2) + + +class NNTest(PForTest): + + def test_conv2d(self): + x = random_ops.random_uniform([3, 2, 12, 12, 3]) + filt = random_ops.random_uniform([3, 3, 3, 7]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return nn.conv2d( + x1, filt, strides=[1, 2, 2, 1], padding="VALID", data_format="NHWC") + + self._test_loop_fn(loop_fn, 3) + + def test_conv2d_backprop_input(self): + x_shape = [2, 12, 12, 3] + filt = random_ops.random_uniform([3, 3, 3, 7]) + grad = random_ops.random_uniform([3, 2, 5, 5, 7]) + + def loop_fn(i): + grad1 = array_ops.gather(grad, i) + return nn.conv2d_backprop_input( + x_shape, + filt, + grad1, + strides=[1, 2, 2, 1], + padding="VALID", + data_format="NHWC") + + self._test_loop_fn(loop_fn, 3) + + def test_conv2d_backprop_filter(self): + x = random_ops.random_uniform([3, 2, 12, 12, 3]) + x_0 = array_ops.gather(x, 0) + filter_sizes = [3, 3, 3, 7] + grad = random_ops.random_uniform([3, 2, 5, 5, 7]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + grad_i = array_ops.gather(grad, i) + return [ + nn.conv2d_backprop_filter( + inp, + filter_sizes, + grad_i, + strides=[1, 2, 2, 1], + padding="VALID", + data_format="NHWC") for inp in [x_i, x_0] + ] + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_avg_pool(self): + x = random_ops.random_uniform([3, 2, 12, 12, 3]) + ksize = [1, 3, 3, 1] + + def loop_fn(i): + x1 = array_ops.gather(x, i) + output = nn.avg_pool( + x1, ksize, strides=[1, 2, 2, 1], padding="VALID", data_format="NHWC") + loss = nn.l2_loss(output) + return output, gradient_ops.gradients(loss, x1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_max_pool(self): + x = random_ops.random_uniform([3, 2, 12, 12, 3]) + ksize = [1, 3, 3, 1] + + def loop_fn(i): + x1 = array_ops.gather(x, i) + output = nn.max_pool( + x1, ksize, strides=[1, 2, 2, 1], padding="VALID", data_format="NHWC") + loss = nn.l2_loss(output) + return output, gradient_ops.gradients(loss, x1) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + def test_fused_batch_norm(self): + data_formats = ["NHWC"] + if test.is_gpu_available(): + data_formats.append("NCHW") + for is_training in (True, False): + for data_format in data_formats: + if data_format == "NCHW": + x = random_ops.random_uniform([3, 1, 2, 5, 5]) + else: + x = random_ops.random_uniform([3, 1, 5, 5, 2]) + scale = random_ops.random_uniform([2]) + offset = random_ops.random_uniform([2]) + mean = None if is_training else random_ops.random_uniform([2]) + variance = None if is_training else random_ops.random_uniform([2]) + + # pylint: disable=cell-var-from-loop + def loop_fn(i): + x1 = array_ops.gather(x, i) + outputs = nn.fused_batch_norm( + x1, + scale, + offset, + mean=mean, + variance=variance, + epsilon=0.01, + data_format=data_format, + is_training=is_training) + outputs = list(outputs) + # We only test the first value of outputs when is_training is False. + # It looks like CPU and GPU have different outputs for batch_mean and + # batch_variance for this case. + if not is_training: + outputs[1] = constant_op.constant(0.) + outputs[2] = constant_op.constant(0.) + loss = nn.l2_loss(outputs[0]) + gradients = gradient_ops.gradients(loss, [x1, scale, offset]) + return outputs + gradients + + # pylint: enable=cell-var-from-loop + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 6) + + def test_softmax_cross_entropy_with_logits(self): + logits = random_ops.random_uniform([3, 2, 4]) + labels = random_ops.random_uniform([3, 2, 4]) + labels /= math_ops.reduce_sum(labels, axis=[2], keepdims=True) + + def loop_fn(i): + logits_i = array_ops.gather(logits, i) + labels_i = array_ops.gather(labels, i) + loss = nn.softmax_cross_entropy_with_logits( + labels=labels_i, logits=logits_i) + return loss, gradient_ops.gradients(math_ops.reduce_sum(loss), logits_i) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 2) + + +class RandomTest(PForTest): + + # The random values generated in the two implementations are not guaranteed to + # match. So we only check the returned shapes. + def run_and_assert_equal(self, targets1, targets2): + outputs = self._run_targets(targets1, targets2) + n = len(outputs) // 2 + for i in range(n): + self.assertAllEqual(outputs[i].shape, outputs[i + n].shape) + + def test_random_uniform(self): + + def loop_fn(_): + return random_ops.random_uniform([3]) + + self._test_loop_fn(loop_fn, 5) + + def test_random_uniform_int(self): + + def loop_fn(_): + return random_ops.random_uniform([3], maxval=1, dtype=dtypes.int32) + + self._test_loop_fn(loop_fn, 5, loop_fn_dtypes=dtypes.int32) + + def test_random_standard_normal(self): + + def loop_fn(_): + return random_ops.random_normal([3]) + + self._test_loop_fn(loop_fn, 5) + + def test_truncated_normal(self): + + def loop_fn(_): + return random_ops.truncated_normal([3]) + + self._test_loop_fn(loop_fn, 5) + + def test_random_gamma(self): + + def loop_fn(_): + return random_ops.random_gamma([3], alpha=[0.5]) + + self._test_loop_fn(loop_fn, 5) + + def test_random_poisson_v2(self): + + def loop_fn(_): + return random_ops.random_poisson(lam=[1.3], shape=[3]) + + self._test_loop_fn(loop_fn, 5) + + +class LoggingTest(PForTest): + + def test_print(self): + x = random_ops.random_uniform([3, 5]) + + def loop_fn(i): + x1 = array_ops.gather(x, i) + return logging_ops.Print( + x1, [x1, "x1", array_ops.shape(x1)], summarize=10) + + self._test_loop_fn(loop_fn, 3) + + def test_assert(self): + + def loop_fn(i): + return control_flow_ops.Assert(i < 10, [i, [10], [i + 1]]) + + # TODO(agarwal): make this work with for_loop. + with session.Session() as sess: + sess.run(pfor_control_flow_ops.pfor(loop_fn, 3)) + + +class TensorArrayTest(PForTest): + + def test_create_outside_and_read(self): + + ta = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, 0).write(1, 1) + + def loop_fn(i): + return ta.read(i), ta.read(0) + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 2) + + def test_create_outside_and_gather(self): + + ta = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, 0).write(1, 1) + + def loop_fn(i): + return ta.gather([i]), ta.gather([0, 1]) + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 2) + + def test_create_outside_and_write_and_scatter(self): + + t = tensor_array_ops.TensorArray(dtypes.int32, 10, clear_after_read=False) + handle = t.handle + + def loop_fn(i): + ta = t.write(i + 2, 2 * i).write(i, 5) + ta = ta.scatter([4 + i], [4]).scatter([6 + i, 8 + i], [6 + i, 8 + i]) + return ta.flow + + t1 = pfor_control_flow_ops.pfor(loop_fn, iters=2) + out1 = tensor_array_ops.TensorArray( + dtypes.int32, handle=handle, flow=t1[-1]).stack() + output1 = self._run_targets(out1) + + t2 = pfor_control_flow_ops.for_loop(loop_fn, dtypes.float32, iters=2) + out2 = tensor_array_ops.TensorArray( + dtypes.int32, handle=handle, flow=t2[-1]).stack() + output2 = self._run_targets(out2) + self.assertAllClose(output2, output1) + + def test_create_inside_and_write(self): + + def loop_fn(i): + # TODO(agarwal): switching the order of writes to ta1 does not work. + ta1 = tensor_array_ops.TensorArray(dtypes.int32, 2).write(0, i).write( + 1, 1) + ta2 = tensor_array_ops.TensorArray(dtypes.int32, 1).write(0, 1) + return ta1.stack(), ta2.stack() + + self._test_loop_fn(loop_fn, 3, [dtypes.int32] * 2) + + def test_create_inside_and_scatter(self): + + def loop_fn(i): + # TODO(agarwal): switching the order of scatter to ta1 does not work. + ta1 = tensor_array_ops.TensorArray(dtypes.int32, 2).scatter( + [0], [[i, 2]]).scatter([1], [[1, 2]]) + ta2 = tensor_array_ops.TensorArray(dtypes.int32, + 2).scatter([0], [3]).scatter([1], [4]) + return ta1.stack(), ta2.stack() + + self._test_loop_fn(loop_fn, 3, [dtypes.int32] * 2) + + def test_create_inside_and_read(self): + + def loop_fn(i): + ta1 = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, i).write(1, 1) + ta2 = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, 1).write(1, 2) + # TODO(agarwal): ta1.read(i) currently is not supported. + return ta1.read(0), ta2.read(0), ta2.read(i) + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 3) + + def test_create_inside_and_gather(self): + + def loop_fn(i): + ta1 = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, i).write(1, 1) + ta2 = tensor_array_ops.TensorArray( + dtypes.int32, 2, clear_after_read=False).write(0, 1).write(1, 2) + # TODO(agarwal): ta1.read(i) currently is not supported. + return ta1.gather([0, 1]), ta2.gather([0, 1]), ta2.gather([i]) + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 3) + + def test_grad(self): + x = random_ops.random_uniform([3, 2]) + ta = tensor_array_ops.TensorArray( + dtypes.float32, 3, clear_after_read=False).unstack(x) + y = math_ops.square(ta.stack()) + + def loop_fn(i): + y_i = array_ops.gather(y, i) + grad = gradient_ops.gradients(y_i, x)[0] + return array_ops.gather(grad, i) + + t1 = pfor_control_flow_ops.pfor(loop_fn, iters=3) + # y = x * x. Hence dy/dx = 2 * x. + actual_grad = 2.0 * x + with session.Session() as sess: + actual_grad, computed_grad = sess.run([t1, actual_grad]) + self.assertAllClose(actual_grad, computed_grad) + + +class StackTest(PForTest): + + def test_stack_inside_loop_invariant(self): + + def loop_fn(_): + s = data_flow_ops.stack_v2(max_size=4, elem_type=dtypes.int32) + op1 = data_flow_ops.stack_push_v2(s, 1) + with ops.control_dependencies([op1]): + op2 = data_flow_ops.stack_push_v2(s, 2) + with ops.control_dependencies([op2]): + e2 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + with ops.control_dependencies([e2]): + e1 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + return e1, e2 + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 2) + + def test_stack_inside_push_loop_dependent(self): + + def loop_fn(i): + s = data_flow_ops.stack_v2(max_size=4, elem_type=dtypes.int32) + op1 = data_flow_ops.stack_push_v2(s, i) + with ops.control_dependencies([op1]): + op2 = data_flow_ops.stack_push_v2(s, 2) + with ops.control_dependencies([op2]): + e2 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + with ops.control_dependencies([e2]): + e1 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + return e1, e2 + + self._test_loop_fn(loop_fn, 2, [dtypes.int32] * 2) + + def test_stack_outside_pop(self): + s = data_flow_ops.stack_v2(max_size=4, elem_type=dtypes.int32) + op = data_flow_ops.stack_push_v2(s, 5) + with ops.control_dependencies([op]): + op = data_flow_ops.stack_push_v2(s, 6) + with ops.control_dependencies([op]): + op = data_flow_ops.stack_push_v2(s, 7) + + def loop_fn(_): + e1 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + with ops.control_dependencies([e1]): + e2 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + return e1, e2 + + with ops.control_dependencies([op]): + e1, e2 = pfor_control_flow_ops.pfor(loop_fn, iters=2) + with ops.control_dependencies([e1, e2]): + e3 = data_flow_ops.stack_pop_v2(s, elem_type=dtypes.int32) + v1, v2, v3 = self._run_targets([e1, e2, e3], run_init=False) + self.assertAllEqual([7, 7], v1) + self.assertAllEqual([6, 6], v2) + self.assertAllEqual(5, v3) + + def test_stack_outside_push(self): + s = data_flow_ops.stack_v2(max_size=4, elem_type=dtypes.int32) + + def loop_fn(_): + return data_flow_ops.stack_push_v2(s, 7) + + with self.assertRaisesRegexp(ValueError, "StackPushV2 not allowed.*"): + pfor_control_flow_ops.pfor(loop_fn, iters=2) + + +# TODO(agarwal): test nested while_loops. This currently requires converting a +# tf.cond. +class ControlFlowTest(PForTest): + + def test_while_outside_loop(self): + + x = control_flow_ops.while_loop(lambda j: j < 4, lambda j: j + 1, [0]) + + def loop_fn(i): + return x + i + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_invariant_while(self): + + def loop_fn(_): + return control_flow_ops.while_loop(lambda j: j < 4, lambda j: j + 1, [0]) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_invariant_while_with_control_dependency(self): + + def loop_fn(i): + with ops.control_dependencies([i]): + return control_flow_ops.while_loop(lambda j: j < 4, lambda j: j + 1, + [0]) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_while_with_stateful_ops(self): + + def loop_fn(_): + return control_flow_ops.while_loop( + lambda j, x: j < 4, + lambda j, x: (j + 1, x + random_ops.random_uniform([])), [0, 0.])[0] + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_while_unstacked_condition(self): + + def loop_fn(i): + return control_flow_ops.while_loop(lambda j, x: j < 4, + lambda j, x: (j + 1, x + i), [0, 0]) + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32, dtypes.int32]) + + def test_while(self): + x = random_ops.random_uniform([3, 5]) + lengths = constant_op.constant([4, 0, 2]) + + def loop_fn(i): + x_i = array_ops.gather(x, i) + lengths_i = array_ops.gather(lengths, i) + + _, total = control_flow_ops.while_loop( + lambda j, _: j < lengths_i, + lambda j, t: (j + 1, t + array_ops.gather(x_i, j)), [0, 0.]) + return total + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32]) + + def test_while_jacobian(self): + x = random_ops.random_uniform([1, 3]) + y = random_ops.random_uniform([3, 3]) + + # out = x @ y @ y @ y @ y, where @ is matmul operator. + _, out = control_flow_ops.while_loop( + lambda i, _: i < 4, lambda i, out: (i + 1, math_ops.matmul(out, y)), + [0, x]) + + def loop_fn(i): + out_i = array_ops.gather(out, i, axis=1) + return array_ops.reshape(gradient_ops.gradients(out_i, x)[0], [-1]) + + out = pfor_control_flow_ops.pfor(loop_fn, iters=3) + + # The above code does not work with tf.while_loop instead of pfor. So we + # manually compute the expected output here. + # Note that gradient of output w.r.t is (y @ y @ y @ y)^T. + expected_output = y + for _ in range(3): + expected_output = math_ops.matmul(expected_output, y) + expected_output = array_ops.transpose(expected_output, [1, 0]) + + with session.Session() as sess: + out, expected = sess.run([out, expected_output]) + self.assertAllClose(expected, out) + + def test_tensor_array_as_loop_variable(self): + + def loop_fn(i): + + def body(j, ta): + ta = ta.write(j, i + j * j) + return j + 1, ta + + _, ta = control_flow_ops.while_loop( + lambda j, _: j < 4, body, + (0, tensor_array_ops.TensorArray(dtypes.int32, size=4))) + return ta.stack() + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_read_tensor_array_partitioned_indices(self): + # Note that tensor array values are pfor loop dependent, and the while loop + # termination condition is also dependent on pfor iteration. + def loop_fn(i): + ta = tensor_array_ops.TensorArray(dtypes.int32, size=6) + ta = ta.unstack(i + list(range(5))) + + def body(j, s): + return j + 1, s + ta.read(j) + + _, s = control_flow_ops.while_loop(lambda j, _: j < i, + body, + (0, 0)) + return s + + self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.int32]) + + def test_external_while_loop_grad(self): + # Here we test that external while_loops that are extended from inside pfor + # (due to gradient calls) are not actually converted. If the below was + # converted all pfor iterations would write to the same tensor array + # indices. + x = constant_op.constant(1.) + + def body(j, ta): + ta = ta.write(j, x) + return j + 1, ta + + _, ta = control_flow_ops.while_loop( + lambda j, _: j < 4, body, + (0, tensor_array_ops.TensorArray(dtypes.float32, size=4))) + out = ta.stack() + + def loop_fn(i): + out_i = array_ops.gather(out, i) + return gradient_ops.gradients(out_i, x)[0] + + with session.Session() as sess: + # out is [x, x, x]. Hence the gradients should be [1, 1, 1]. + self.assertAllEqual([1, 1, 1], + sess.run(pfor_control_flow_ops.pfor(loop_fn, 3))) + + def test_tensor_array_grad(self): + inp = constant_op.constant(np.random.rand(3, 4, 2), dtype=dtypes.float32) + ta = tensor_array_ops.TensorArray(dtypes.float32, size=3) + ta = ta.unstack(inp) + + def loop_fn(i): + + def body(j, x): + value = ta.gather([j]) + value = array_ops.gather(array_ops.reshape(value, [4, 2]), i) + return j + 1, x + value + + _, out = control_flow_ops.while_loop(lambda j, _: j < 3, body, + (0, array_ops.zeros([2]))) + out = math_ops.reduce_prod(out) + return out, gradient_ops.gradients(out, inp)[0] + + pfor_out, pfor_out_grad = pfor_control_flow_ops.pfor(loop_fn, 4) + # Note that tf.while_loop does not work in the setup above. So we manually + # construct the equivalent computation of the above loops here. + real_out = math_ops.reduce_sum(inp, reduction_indices=[0]) + real_out = math_ops.reduce_prod(real_out, reduction_indices=[1]) + # Note that gradients of real_out will accumulate the gradients across the + # output value. Hence we do the same aggregation on pfor_out_grad. + real_out_grad = gradient_ops.gradients(real_out, inp)[0] + sum_pfor_out_grad = math_ops.reduce_sum( + pfor_out_grad, reduction_indices=[0]) + + with session.Session() as sess: + v1, v2, v1_grad, v2_grad = sess.run( + [pfor_out, real_out, sum_pfor_out_grad, real_out_grad]) + self.assertAllClose(v1, v2) + self.assertAllClose(v1_grad, v2_grad) + + +def dynamic_lstm_input_fn(batch_size, state_size, max_steps): + # We make inputs and sequence_length constant so that multiple session.run + # calls produce the same result. + inputs = constant_op.constant( + np.random.rand(batch_size, max_steps, state_size), dtype=dtypes.float32) + sequence_length = np.random.randint(0, size=[batch_size], high=max_steps + 1) + sequence_length = constant_op.constant(sequence_length, dtype=dtypes.int32) + return inputs, sequence_length + + +def create_dynamic_lstm(cell_fn, batch_size, state_size, max_steps): + cell = cell_fn(state_size) + inputs, sequence_length = dynamic_lstm_input_fn(batch_size, + state_size, + max_steps) + inputs_ta = tensor_array_ops.TensorArray( + dtypes.float32, size=max_steps, element_shape=[batch_size, state_size]) + inputs_time_major = array_ops.transpose(inputs, [1, 0, 2]) + inputs_ta = inputs_ta.unstack(inputs_time_major) + zeros = array_ops.zeros([state_size]) + + def loop_fn(i): + sequence_length_i = array_ops.gather(sequence_length, i) + + def body_fn(t, state, ta): + inputs_t = array_ops.expand_dims( + array_ops.gather(inputs_ta.read(t), i), 0) + output, new_state = cell(inputs_t, state) + output = array_ops.reshape(output, [-1]) + # TODO(agarwal): one optimization that dynamic_rnn uses is to avoid the + # array_ops.where when t < min(sequence_length). Doing that requires + # supporting tf.cond pfor conversion. + done = t >= sequence_length_i + output = array_ops.where(done, zeros, output) + ta = ta.write(t, output) + new_state = [array_ops.where(done, s, ns) for s, ns in + zip(nest.flatten(state), nest.flatten(new_state))] + new_state = nest.pack_sequence_as(state, new_state) + return t + 1, new_state, ta + + def condition_fn(t, _, unused): + del unused + return t < max_steps + + initial_state = cell.zero_state(1, dtypes.float32) + _, state, ta = control_flow_ops.while_loop(condition_fn, body_fn, [ + 0, initial_state, + tensor_array_ops.TensorArray(dtypes.float32, max_steps) + ]) + + new_state = [array_ops.reshape(x, [-1]) for x in nest.flatten(state)] + new_state = nest.pack_sequence_as(initial_state, new_state) + return ta.stack(), new_state + + pfor_output = pfor_control_flow_ops.pfor(loop_fn, batch_size) + tf_output = rnn.dynamic_rnn( + cell, + inputs, + sequence_length=sequence_length, + initial_state=cell.zero_state(batch_size, dtypes.float32)) + return pfor_output, tf_output + + +class RNNTest(PForTest): + + def test_dynamic_rnn(self): + pfor_outputs, tf_outputs = create_dynamic_lstm(rnn_cell.BasicRNNCell, + 3, 5, 7) + self.run_and_assert_equal(pfor_outputs, tf_outputs) + + def test_dynamic_lstm(self): + pfor_outputs, tf_outputs = create_dynamic_lstm(rnn_cell.BasicLSTMCell, + 3, 5, 7) + self.run_and_assert_equal(pfor_outputs, tf_outputs) + + +# TODO(agarwal): benchmark numbers on GPU for graphs based on while_loop +# conversion don't look good. Some of it seems like lot of copies between host +# and device. Optimize that. +class Benchmarks(test.Benchmark): + + def _run(self, targets, iters, name=None): + + def _done(t): + # Note that we don't use tf.control_dependencies since that will not make + # sure that the computation on GPU has actually finished. So we fetch the + # first element of the output, and assume that this will not be called on + # empty tensors. + return array_ops.gather(array_ops.reshape(t, [-1]), 0) + + targets = [_done(x) for x in nest.flatten(targets)] + sess = session.Session() + with sess: + init = variables.global_variables_initializer() + sess.run(init) + sess.run(targets) + begin = time.time() + for _ in range(iters): + sess.run(targets) + end = time.time() + avg_time_ms = 1000 * (end - begin) / iters + self.report_benchmark(iters=iters, wall_time=avg_time_ms, name=name) + return avg_time_ms + + def benchmark_basic_while(self): + with ops.Graph().as_default(): + + def loop_fn(i): + _, s = control_flow_ops.while_loop( + lambda t, x: t < i, + lambda t, x: (t + 1, x + i), + [0, 0]) + return s + + iters = 50 + pfor_output = pfor_control_flow_ops.pfor(loop_fn, iters) + for_loop_output = pfor_control_flow_ops.for_loop(loop_fn, dtypes.int32, + iters) + self._run(pfor_output, 100, name="pfor_basic") + self._run(for_loop_output, 100, name="for_loop_basic") + + def benchmark_dynamic_rnn(self): + with ops.Graph().as_default(): + pfor_outputs, tf_outputs = create_dynamic_lstm(rnn_cell.BasicRNNCell, + 128, 512, 16) + self._run(pfor_outputs, 100, name="pfor_rnn") + self._run(tf_outputs, 100, name="tf_rnn") + + def benchmark_dynamic_lstm(self): + with ops.Graph().as_default(): + pfor_outputs, tf_outputs = create_dynamic_lstm(rnn_cell.BasicLSTMCell, + 128, 512, 16) + self._run(pfor_outputs, 100, name="pfor_lstm") + self._run(tf_outputs, 100, name="tf_lstm") + + +class SparseTest(PForTest): + + def test_var_loop_len(self): + num_iters = array_ops.placeholder(dtypes.int32) + + def loop_fn(_): + return sparse_tensor.SparseTensor([[0], [1], [2]], [4, 5, 6], + [3]) # [0, 2, 0] + + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + with self.test_session() as sess: + sess.run(pfor, feed_dict={num_iters: 3}) + + def test_sparse_result_none_stacked(self): + num_iters = 10 + + def loop_fn(_): + return sparse_tensor.SparseTensor([[0], [1], [2]], [4, 5, 6], + [3]) # [0, 2, 0] + + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + + indices = [[i, j] for i in range(num_iters) for j in range(3)] + values = [4, 5, 6] * num_iters + dense_shapes = [num_iters, 3] + # Expected result: [[4, 5, 6], [4, 5, 6], [4, 5, 6], ...] + manual = sparse_tensor.SparseTensor(indices, values, dense_shapes) + self.run_and_assert_equal(pfor, manual) + + def test_sparse_result_all_stacked(self): + num_iters = 10 + + def loop_fn(i): + i = array_ops.expand_dims(math_ops.cast(i, dtypes.int64), 0) + indices = array_ops.expand_dims(i, 0) + return sparse_tensor.SparseTensor(indices, i, i + 1) # [0, ..., 0, i] + + # Expected result: [[0], [0, 1], [0, 0, 2], [0, 0, 0, 3], ...] + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + manual = sparse_tensor.SparseTensor([[i, i] for i in range(num_iters)], + list(range(num_iters)), + (num_iters, num_iters)) + self.run_and_assert_equal(pfor, manual) + + def test_sparse_result_indices_stacked(self): + num_iters = 10 + + def loop_fn(i): + i = array_ops.expand_dims(math_ops.cast(i, dtypes.int64), 0) + indices = array_ops.expand_dims(i, 0) + return sparse_tensor.SparseTensor(indices, [1], [num_iters]) + + # Expected result: identity matrix size num_iters * num_iters + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + manual = sparse_tensor.SparseTensor([[i, i] for i in range(num_iters)], + [1] * num_iters, (num_iters, num_iters)) + self.run_and_assert_equal(pfor, manual) + + def test_sparse_result_values_stacked(self): + num_iters = 10 + + def loop_fn(i): + i = array_ops.expand_dims(math_ops.cast(i, dtypes.int64), 0) + return sparse_tensor.SparseTensor([[0]], i, [num_iters]) # [i, 0, ..., 0] + + # Expected result: [[1, 0, ...], [2, 0, ...], [3, 0, ...], ...] + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + manual = sparse_tensor.SparseTensor([[i, 0] for i in range(num_iters)], + list(range(num_iters)), + (num_iters, num_iters)) + self.run_and_assert_equal(pfor, manual) + + def test_sparse_result_shapes_stacked(self): + num_iters = 10 + + def loop_fn(i): + i = array_ops.expand_dims(math_ops.cast(i, dtypes.int64), 0) + return sparse_tensor.SparseTensor([[0]], [1], i + 1) # [1, 0, ..., 0] + + # Expected result: [[1, 0, 0, ...], [1, 0, 0, ...], ...] + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + manual = sparse_tensor.SparseTensor([[i, 0] for i in range(num_iters)], + [1] * num_iters, (num_iters, num_iters)) + self.run_and_assert_equal(pfor, manual) + + def test_sparse_result_shapes_stacked_2D(self): + num_iters = 10 + + def loop_fn(i): + i = array_ops.expand_dims(math_ops.cast(i + 1, dtypes.int64), 0) + shape = array_ops.concat([i, i], 0) + return sparse_tensor.SparseTensor([[0, 0]], [1], shape) # [1, 0, ..., 0] + + # Expected result: [[[1, 0, ...], [0, ..., 0], [0, ..., 0], ...], ...] + pfor = pfor_control_flow_ops.pfor(loop_fn, num_iters) + manual = sparse_tensor.SparseTensor([[i, 0, 0] for i in range(num_iters)], + [1] * num_iters, + (num_iters, num_iters, num_iters)) + self.run_and_assert_equal(pfor, manual) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/parallel_for/gradients.py b/tensorflow/python/ops/parallel_for/gradients.py new file mode 100644 index 0000000000000000000000000000000000000000..ee3d5c9b86ed186f76e113351646b3dda153e72b --- /dev/null +++ b/tensorflow/python/ops/parallel_for/gradients.py @@ -0,0 +1,126 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Jacobian ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import gradients as gradient_ops +from tensorflow.python.ops.parallel_for import control_flow_ops +from tensorflow.python.util import nest + + +def jacobian(output, inputs, use_pfor=True): + """Computes jacobian of `output` w.r.t. `inputs`. + + Args: + output: A tensor. + inputs: A tensor or a nested structure of tensor objects. + use_pfor: If true, uses pfor for computing the jacobian. Else uses + tf.while_loop. + + Returns: + A tensor or a nested strucutre of tensors with the same structure as + `inputs`. Each entry is the jacobian of `output` w.rt. to the corresponding + value in `inputs`. If output has shape [y_1, ..., y_n] and inputs_i has + shape [x_1, ..., x_m], the corresponding jacobian has shape + [y_1, ..., y_n, x_1, ..., x_m]. + """ + flat_inputs = nest.flatten(inputs) + output_shape = array_ops.shape(output) + output = array_ops.reshape(output, [-1]) + + def loop_fn(i): + y = array_ops.gather(output, i) + return gradient_ops.gradients(y, flat_inputs) + + try: + output_size = int(output.shape[0]) + except TypeError: + output_size = array_ops.shape(output)[0] + + if use_pfor: + pfor_outputs = control_flow_ops.pfor(loop_fn, output_size) + else: + pfor_outputs = control_flow_ops.for_loop( + loop_fn, [output.dtype] * len(flat_inputs), output_size) + + for i, out in enumerate(pfor_outputs): + new_shape = array_ops.concat( + [output_shape, array_ops.shape(out)[1:]], axis=0) + out = array_ops.reshape(out, new_shape) + pfor_outputs[i] = out + + return nest.pack_sequence_as(inputs, pfor_outputs) + + +def batch_jacobian(output, inp, use_pfor=True): + """Computes and stacks jacobians of `output[i,...]` w.r.t. `input[i,...]`. + + e.g. + x = tf.constant([[1, 2], [3, 4]], dtype=tf.float32) + y = x * x + jacobian = batch_jacobian(y, x) + # => [[[2, 0], [0, 4]], [[6, 0], [0, 8]]] + + Args: + output: A tensor with shape [b, y1, ..., y_n]. `output[i,...]` should + only depend on `inp[i,...]`. + inp: A tensor with shape [b, x1, ..., x_m] + use_pfor: If true, uses pfor for computing the Jacobian. Else uses a + tf.while_loop. + + Returns: + A tensor `t` with shape [b, y_1, ..., y_n, x1, ..., x_m] where `t[i, ...]` + is the jacobian of `output[i, ...]` w.r.t. `inp[i, ...]`, i.e. stacked + per-example jacobians. + + Raises: + ValueError: if first dimension of `output` and `inp` do not match. + """ + output_shape = output.shape + if not output_shape[0].is_compatible_with(inp.shape[0]): + raise ValueError("Need first dimension of output shape (%s) and inp shape " + "(%s) to match." % (output.shape, inp.shape)) + if output_shape.is_fully_defined(): + batch_size = int(output_shape[0]) + output_row_size = output_shape.num_elements() // batch_size + else: + output_shape = array_ops.shape(output) + batch_size = output_shape[0] + output_row_size = array_ops.size(output) // batch_size + inp_shape = array_ops.shape(inp) + # Flatten output to 2-D. + with ops.control_dependencies( + [check_ops.assert_equal(batch_size, inp_shape[0])]): + output = array_ops.reshape(output, [batch_size, output_row_size]) + + def loop_fn(i): + y = array_ops.gather(output, i, axis=1) + return gradient_ops.gradients(y, inp)[0] + + if use_pfor: + pfor_output = control_flow_ops.pfor(loop_fn, output_row_size) + else: + pfor_output = control_flow_ops.for_loop(loop_fn, output.dtype, + output_row_size) + pfor_output = array_ops.reshape(pfor_output, + [output_row_size, batch_size, -1]) + output = array_ops.transpose(pfor_output, [1, 0, 2]) + new_shape = array_ops.concat([output_shape, inp_shape[1:]], axis=0) + return array_ops.reshape(output, new_shape) diff --git a/tensorflow/python/ops/parallel_for/gradients_test.py b/tensorflow/python/ops/parallel_for/gradients_test.py new file mode 100644 index 0000000000000000000000000000000000000000..310a2154f71c29702de1d43d8fc4af931b3217eb --- /dev/null +++ b/tensorflow/python/ops/parallel_for/gradients_test.py @@ -0,0 +1,568 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for jacobian and batch_jacobian ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import time + +import numpy as np + +from tensorflow.python.client import session +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.keras.engine import training as keras_training +from tensorflow.python.layers import layers as tf_layers +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients as gradient_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import rnn +from tensorflow.python.ops import rnn_cell +from tensorflow.python.ops import variables +from tensorflow.python.ops.losses import losses +from tensorflow.python.ops.parallel_for import control_flow_ops +from tensorflow.python.ops.parallel_for import gradients +from tensorflow.python.platform import test +from tensorflow.python.util import nest + + +class FullyConnectedModel(object): + + def __init__(self, activation_size, num_layers): + self._layers = [ + tf_layers.Dense(activation_size, activation=nn.relu) + for _ in range(num_layers) + ] + + def __call__(self, inp): + activation = inp + for layer in self._layers: + activation = layer(activation) + return activation + + +def fully_connected_model_fn(batch_size, activation_size, num_layers): + model = FullyConnectedModel(activation_size, num_layers) + inp = random_ops.random_normal([batch_size, activation_size]) + return inp, model(inp) + + +def lstm_model_fn(batch_size, state_size, steps): + inputs = [ + random_ops.random_normal([batch_size, state_size]) for _ in range(steps) + ] + cell = rnn_cell.BasicLSTMCell(state_size) + init_state = cell.zero_state(batch_size, dtypes.float32) + state = init_state + for inp in inputs: + _, state = cell(inp, state) + return init_state.c, state.c + + +def dynamic_lstm_model_fn(batch_size, state_size, max_steps): + # We make inputs and sequence_length constant so that multiple session.run + # calls produce the same result. + inputs = constant_op.constant( + np.random.rand(batch_size, max_steps, state_size), dtype=dtypes.float32) + sequence_length = constant_op.constant( + np.random.randint(0, size=[batch_size], high=max_steps + 1), + dtype=dtypes.int32) + + cell = rnn_cell.BasicLSTMCell(state_size) + initial_state = cell.zero_state(batch_size, dtypes.float32) + return inputs, rnn.dynamic_rnn( + cell, + inputs, + sequence_length=sequence_length, + initial_state=initial_state) + + +def create_fc_batch_jacobian(batch_size, activation_size, num_layers): + inp, output = fully_connected_model_fn(batch_size, activation_size, + num_layers) + pfor_jacobian = gradients.batch_jacobian(output, inp, use_pfor=True) + while_jacobian = gradients.batch_jacobian(output, inp, use_pfor=False) + return pfor_jacobian, while_jacobian + + +def create_lstm_batch_jacobian(batch_size, state_size, steps): + inp, output = lstm_model_fn(batch_size, state_size, steps) + pfor_jacobian = gradients.batch_jacobian(output, inp, use_pfor=True) + while_jacobian = gradients.batch_jacobian(output, inp, use_pfor=False) + return pfor_jacobian, while_jacobian + + +def create_dynamic_lstm_batch_jacobian(batch_size, state_size, max_steps): + inp, (_, final_state) = dynamic_lstm_model_fn(batch_size, state_size, + max_steps) + pfor_jacobian = gradients.batch_jacobian(final_state.c, inp, use_pfor=True) + # Note that use_pfor=False does not work above given the current limitations + # on implementation of while_loop. So we statically unroll the looping in the + # jacobian computation. + while_gradients = [ + gradient_ops.gradients(array_ops.gather(final_state.c, i, axis=1), inp)[0] + for i in range(state_size) + ] + return pfor_jacobian, while_gradients + + +def create_lstm_batch_hessian(batch_size, state_size, steps): + inp, output = lstm_model_fn(batch_size, state_size, steps) + pfor_jacobian = gradients.batch_jacobian(output, inp, use_pfor=True) + pfor_jacobian = array_ops.reshape(pfor_jacobian, [batch_size, -1]) + pfor_hessian = gradients.batch_jacobian(pfor_jacobian, inp, use_pfor=True) + # TODO(agarwal): using two nested while_loop doesn't seem to work here. + # Hence we use pfor_jacobian for computing while_hessian. + while_jacobian = pfor_jacobian + while_hessian = gradients.batch_jacobian(while_jacobian, inp, use_pfor=False) + return pfor_hessian, while_hessian + + +def create_lstm_hessian(batch_size, state_size, steps): + _, output = lstm_model_fn(batch_size, state_size, steps) + weights = variables.trainable_variables() + pfor_jacobians = gradients.jacobian(output, weights, use_pfor=True) + pfor_hessians = [ + gradients.jacobian(x, weights, use_pfor=True) for x in pfor_jacobians + ] + # TODO(agarwal): using two nested while_loop doesn't seem to work here. + # Hence we use pfor_jacobians for computing while_hessians. + while_jacobians = pfor_jacobians + while_hessians = [ + gradients.jacobian(x, weights, use_pfor=False) for x in while_jacobians + ] + return pfor_hessians, while_hessians + + +def create_fc_per_eg_grad(batch_size, activation_size, num_layers): + inp = random_ops.random_normal([batch_size, activation_size]) + layers = [ + tf_layers.Dense(activation_size, activation=nn.relu) + for _ in range(num_layers) + ] + projection = tf_layers.Dense(1) + + def model_fn(activation): + for layer in layers: + activation = layer(activation) + activation = projection(activation) + activation = nn.l2_loss(activation) + return gradient_ops.gradients(activation, variables.trainable_variables()) + + def loop_fn(i): + return model_fn(array_ops.expand_dims(array_ops.gather(inp, i), 0)) + + pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size) + loop_fn_dtypes = [x.dtype for x in variables.trainable_variables()] + while_outputs = control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, batch_size) + return pfor_outputs, while_outputs + + +def create_lstm_per_eg_grad(batch_size, state_size, steps): + inputs = [ + random_ops.random_normal([batch_size, state_size]) for _ in range(steps) + ] + cell = rnn_cell.BasicLSTMCell(state_size) + init_state = cell.zero_state(batch_size, dtypes.float32) + + def model_fn(inps, init_state): + state = init_state + for inp in inps: + _, state = cell(inp, state) + output = nn.l2_loss(state.c) + return gradient_ops.gradients(output, variables.trainable_variables()) + + def loop_fn(i): + loop_inputs = [ + array_ops.expand_dims(array_ops.gather(x, i), 0) for x in inputs + ] + loop_init_state = rnn_cell.LSTMStateTuple( + *[array_ops.expand_dims(array_ops.gather(x, i), 0) for x in init_state]) + return model_fn(loop_inputs, loop_init_state) + + pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size) + loop_fn_dtypes = [x.dtype for x in variables.trainable_variables()] + while_outputs = control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, batch_size) + return pfor_outputs, while_outputs + + +# Importing the code from tensorflow_models seems to cause errors. Hence we +# duplicate the model definition here. +# TODO(agarwal): Use the version in tensorflow_models/official instead. +class Mnist(keras_training.Model): + + def __init__(self, data_format): + """Creates a model for classifying a hand-written digit. + + Args: + data_format: Either 'channels_first' or 'channels_last'. + """ + super(Mnist, self).__init__() + if data_format == "channels_first": + self._input_shape = [-1, 1, 28, 28] + else: + assert data_format == "channels_last" + self._input_shape = [-1, 28, 28, 1] + + self.conv1 = tf_layers.Conv2D( + 32, 5, padding="same", data_format=data_format, activation=nn.relu) + self.conv2 = tf_layers.Conv2D( + 64, 5, padding="same", data_format=data_format, activation=nn.relu) + self.fc1 = tf_layers.Dense(1024, activation=nn.relu) + self.fc2 = tf_layers.Dense(10) + self.dropout = tf_layers.Dropout(0.4) + self.max_pool2d = tf_layers.MaxPooling2D( + (2, 2), (2, 2), padding="same", data_format=data_format) + + def __call__(self, inputs, training): + """Add operations to classify a batch of input images. + + Args: + inputs: A Tensor representing a batch of input images. + training: A boolean. Set to True to add operations required only when + training the classifier. + + Returns: + A logits Tensor with shape [, 10]. + """ + y = array_ops.reshape(inputs, self._input_shape) + y = self.conv1(y) + y = self.max_pool2d(y) + y = self.conv2(y) + y = self.max_pool2d(y) + y = tf_layers.flatten(y) + y = self.fc1(y) + y = self.dropout(y, training=training) + return self.fc2(y) + + +def create_mnist_per_eg_grad(batch_size, data_format, training): + images = random_ops.random_uniform([batch_size, 28, 28]) + sparse_labels = np.random.randint( + low=0, high=10, size=[batch_size]).astype(np.int32) + labels = np.zeros((batch_size, 10)).astype(np.float32) + labels[np.arange(batch_size), sparse_labels] = 1. + model = Mnist(data_format) + + def loop_fn(i): + image = array_ops.gather(images, i) + label = array_ops.gather(labels, i) + logits = array_ops.reshape(model(image, training=training), [-1]) + loss = losses.softmax_cross_entropy( + logits=logits, onehot_labels=label, reduction=losses.Reduction.NONE) + return gradient_ops.gradients(loss, variables.trainable_variables()) + + pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size) + while_outputs = control_flow_ops.for_loop( + loop_fn, [dtypes.float32] * len(variables.trainable_variables()), + batch_size) + return pfor_outputs, while_outputs + + +def create_mnist_per_eg_jacobian(batch_size, data_format, training): + images = random_ops.random_uniform([batch_size, 28, 28]) + model = Mnist(data_format) + + def loop_fn(i, use_pfor): + image = array_ops.gather(images, i) + logits = array_ops.reshape(model(image, training=training), [-1]) + return gradients.jacobian( + logits, variables.trainable_variables(), use_pfor=use_pfor) + + pfor_outputs = control_flow_ops.pfor( + functools.partial(loop_fn, use_pfor=True), + batch_size) + while_outputs = control_flow_ops.for_loop( + functools.partial(loop_fn, use_pfor=False), + [dtypes.float32] * len(variables.trainable_variables()), batch_size) + return pfor_outputs, while_outputs + + +def create_fc_per_eg_jacobians(batch_size, activation_size, num_layers): + model = FullyConnectedModel(activation_size=activation_size, + num_layers=num_layers) + inp = random_ops.random_normal([batch_size, activation_size]) + output = model(inp) + jacobians = gradients.jacobian(output, variables.trainable_variables()) + + def loop_fn(i, use_pfor): + inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0) + output = array_ops.reshape(model(inp_i), [-1]) + return gradients.jacobian( + output, variables.trainable_variables(), use_pfor=use_pfor) + + per_eg_jacobians_pfor = control_flow_ops.pfor( + functools.partial(loop_fn, use_pfor=True), + batch_size) + per_eg_jacobians_while = control_flow_ops.for_loop( + functools.partial(loop_fn, use_pfor=False), + [dtypes.float32] * len(variables.trainable_variables()), batch_size) + return jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while + + +class GradientsTest(test.TestCase): + + def run_and_assert_equal(self, targets1, targets2, atol=1e-4, rtol=1e-4): + targets1 = nest.flatten(targets1) + targets2 = nest.flatten(targets2) + assert len(targets1) == len(targets2) + init = variables.global_variables_initializer() + self.evaluate(init) + outputs = self.evaluate(targets1 + targets2) + n = len(outputs) // 2 + for i in range(n): + self.assertAllClose(outputs[i], outputs[i + n], rtol=rtol, atol=atol) + + def test_jacobian_fixed_shape(self): + x = random_ops.random_uniform([2, 2]) + y = math_ops.matmul(x, x, transpose_a=True) + jacobian_pfor = gradients.jacobian(y, x, use_pfor=True) + jacobian_while = gradients.jacobian(y, x, use_pfor=False) + answer = ops.convert_to_tensor([[ + gradient_ops.gradients(y[0][0], x)[0], + gradient_ops.gradients(y[0][1], x)[0] + ], [ + gradient_ops.gradients(y[1][0], x)[0], + gradient_ops.gradients(y[1][1], x)[0] + ]]) + self.run_and_assert_equal(answer, jacobian_pfor) + self.run_and_assert_equal(answer, jacobian_while) + + def test_jacobian_unknown_shape(self): + with self.test_session() as sess: + x = array_ops.placeholder(dtypes.float32, shape=[None, None]) + y = math_ops.matmul(x, x, transpose_a=True) + jacobian_pfor = gradients.jacobian(y, x, use_pfor=True) + jacobian_while = gradients.jacobian(y, x, use_pfor=False) + answer = ops.convert_to_tensor([[ + gradient_ops.gradients(y[0][0], x)[0], + gradient_ops.gradients(y[0][1], x)[0] + ], [ + gradient_ops.gradients(y[1][0], x)[0], + gradient_ops.gradients(y[1][1], x)[0] + ]]) + ans, pfor_value, while_value = sess.run( + [answer, jacobian_pfor, jacobian_while], + feed_dict={x: [[1, 2], [3, 4]]}) + self.assertAllClose(ans, pfor_value) + self.assertAllClose(ans, while_value) + + def test_batch_jacobian_bad_shapes(self): + x = random_ops.random_uniform([2, 2]) + y = random_ops.random_uniform([3, 2]) + with self.assertRaisesRegexp(ValueError, "Need first dimension of output"): + gradients.batch_jacobian(y, x, use_pfor=True) + + def test_batch_jacobian_bad_unknown_shapes(self): + with self.test_session() as sess: + x = array_ops.placeholder(dtypes.float32) + y = array_ops.concat([x, x], axis=0) + jacobian = gradients.batch_jacobian(y, x) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "assertion failed"): + sess.run(jacobian, feed_dict={x: [[1, 2], [3, 4]]}) + + def test_batch_jacobian_fixed_shape(self): + x = random_ops.random_uniform([2, 3, 5]) + y = x * x + batch_jacobian_pfor = gradients.batch_jacobian(y, x, use_pfor=True) + batch_jacobian_while = gradients.batch_jacobian(y, x, use_pfor=False) + two_x = 2 * x + answer = array_ops.stack( + [array_ops.diag(two_x[0]), + array_ops.diag(two_x[1])]) + self.run_and_assert_equal(answer, batch_jacobian_pfor) + self.run_and_assert_equal(answer, batch_jacobian_while) + + def test_batch_jacobian_unknown_shape(self): + with self.test_session() as sess: + x = array_ops.placeholder(dtypes.float32) + y = x * x + batch_jacobian_pfor = gradients.batch_jacobian(y, x, use_pfor=True) + batch_jacobian_while = gradients.batch_jacobian(y, x, use_pfor=False) + two_x = 2 * x + answer = array_ops.stack( + [array_ops.diag(two_x[0]), + array_ops.diag(two_x[1])]) + ans, pfor_value, while_value = sess.run( + [answer, batch_jacobian_pfor, batch_jacobian_while], + feed_dict={x: [[1, 2], [3, 4]]}) + self.assertAllClose(ans, pfor_value) + self.assertAllClose(ans, while_value) + + def test_fc_batch_jacobian(self): + pfor_jacobian, while_jacobian = create_fc_batch_jacobian(8, 4, 2) + self.run_and_assert_equal(pfor_jacobian, while_jacobian) + + def test_lstm_batch_jacobian(self): + pfor_jacobian, while_jacobian = create_lstm_batch_jacobian(8, 4, 2) + self.run_and_assert_equal(pfor_jacobian, while_jacobian) + + def test_dynamic_lstm_batch_jacobian(self): + pfor_jacobian, while_gradients = create_dynamic_lstm_batch_jacobian(8, 4, 3) + with session.Session() as sess: + init = variables.global_variables_initializer() + sess.run(init) + pfor = sess.run(pfor_jacobian) + for i in range(4): + while_i = sess.run(while_gradients[i]) + self.assertAllClose(while_i, pfor[:, i, ...]) + + def test_lstm_hessian(self): + pfor_hessian, while_hessian = create_lstm_hessian(2, 2, 2) + self.run_and_assert_equal(pfor_hessian, while_hessian) + + def test_lstm_batch_hessian(self): + pfor_hessian, while_hessian = create_lstm_batch_hessian(2, 2, 2) + self.run_and_assert_equal(pfor_hessian, while_hessian) + + def test_fc_per_eg_grad(self): + pfor_outputs, while_outputs = create_fc_per_eg_grad(8, 4, 2) + self.run_and_assert_equal(pfor_outputs, while_outputs) + + def test_lstm_per_eg_grad(self): + pfor_outputs, while_outputs = create_lstm_per_eg_grad(8, 4, 2) + self.run_and_assert_equal(pfor_outputs, while_outputs) + + def test_mnist_per_eg_grad(self): + data_format = ("channels_first" + if test.is_gpu_available() else "channels_last") + # Note that we we are setting training=False here so that dropout produces + # the same result with pfor and with while_loop. + pfor_outputs, while_outputs = create_mnist_per_eg_grad( + 4, data_format, training=False) + self.run_and_assert_equal(pfor_outputs, while_outputs, rtol=1e-3) + + def test_mnist_per_eg_jacobian(self): + data_format = ("channels_first" + if test.is_gpu_available() else "channels_last") + # Note that we we are setting training=False here so that dropout produces + # the same result with pfor and with while_loop. + pfor_outputs, while_outputs = create_mnist_per_eg_jacobian( + 2, data_format, training=False) + self.run_and_assert_equal(pfor_outputs, while_outputs, rtol=1e-3) + + def test_fc_jacobian(self): + jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while = ( + create_fc_per_eg_jacobians(batch_size=8, + activation_size=4, + num_layers=2)) + self.run_and_assert_equal(jacobians, per_eg_jacobians_pfor, + rtol=2e-3, atol=1e-3) + self.run_and_assert_equal(jacobians, per_eg_jacobians_while, + rtol=2e-3, atol=1e-3) + + +class GradientsBenchmarks(test.Benchmark): + + def _run(self, targets, iters, name=None): + + def _done(t): + # Note that we don't use tf.control_dependencies since that will not make + # sure that the computation on GPU has actually finished. So we fetch the + # first element of the output, and assume that this will not be called on + # empty tensors. + return array_ops.gather(array_ops.reshape(t, [-1]), 0) + + targets = [_done(x) for x in nest.flatten(targets)] + sess = session.Session() + with sess: + init = variables.global_variables_initializer() + sess.run(init) + sess.run(targets) + begin = time.time() + for _ in range(iters): + sess.run(targets) + end = time.time() + avg_time_ms = 1000 * (end - begin) / iters + self.report_benchmark(iters=iters, wall_time=avg_time_ms, name=name) + return avg_time_ms + + def benchmark_fc_batch_jacobian(self): + with ops.Graph().as_default(): + pfor_jacobian, while_jacobian = create_fc_batch_jacobian(100, 32, 20) + self._run(pfor_jacobian, 100, name="fc_batch_jacobian_pfor") + self._run(while_jacobian, 20, name="fc_batch_jacobian_while") + + def benchmark_lstm_batch_jacobian(self): + with ops.Graph().as_default(): + pfor_jacobian, while_jacobian = create_lstm_batch_jacobian(100, 32, 8) + self._run(pfor_jacobian, 100, name="lstm_batch_jacobian_pfor") + self._run(while_jacobian, 20, name="lstm_batch_jacobian_while") + + def benchmark_lstm_hessian(self): + with ops.Graph().as_default(): + pfor_hessian, while_hessian = create_lstm_hessian(2, 2, 10) + self._run(pfor_hessian, 20, name="lstm_hessian_pfor") + self._run(while_hessian, 3, name="lstm_hessian_while_pfor") + + def benchmark_lstm_batch_hessian(self): + with ops.Graph().as_default(): + pfor_hessian, while_hessian = create_lstm_batch_hessian(4, 4, 10) + self._run(pfor_hessian, 100, name="lstm_batch_hessian_pfor") + self._run(while_hessian, 20, name="lstm_batch_hessian_while_pfor") + + def benchmark_fc_per_eg_grad(self): + with ops.Graph().as_default(): + pfor_outputs, while_outputs = create_fc_per_eg_grad(100, 32, 3) + self._run(pfor_outputs, 100, name="fc_per_eg_grad_pfor") + self._run(while_outputs, 20, name="fc_per_eg_grad_while") + + def benchmark_lstm_per_eg_grad(self): + with ops.Graph().as_default(): + pfor_outputs, while_outputs = create_lstm_per_eg_grad(100, 32, 8) + self._run(pfor_outputs, 100, name="lstm_per_eg_grad_pfor") + self._run(while_outputs, 20, name="lstm_per_eg_grad_while") + + def benchmark_mnist_per_eg_grad(self): + with ops.Graph().as_default(): + data_format = ("channels_first" + if test.is_gpu_available() else "channels_last") + pfor_outputs, while_outputs = create_mnist_per_eg_grad( + 128, data_format, training=True) + self._run(pfor_outputs, 20, name="mnist_per_eg_grad_pfor") + self._run(while_outputs, 20, name="mnist_per_eg_grad_while") + + def benchmark_mnist_per_eg_jacobian(self): + with ops.Graph().as_default(): + data_format = ("channels_first" + if test.is_gpu_available() else "channels_last") + pfor_outputs, while_outputs = create_mnist_per_eg_jacobian( + 16, data_format, training=True) + self._run(pfor_outputs, 20, name="mnist_per_eg_jacobian_pfor") + self._run(while_outputs, 20, name="mnist_per_eg_jacobian_while") + + def benchmark_fc_per_eg_jacobian(self): + with ops.Graph().as_default(): + jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while = ( + create_fc_per_eg_jacobians(batch_size=128, + activation_size=32, + num_layers=3)) + self._run(jacobians, 30, name="fc_jacobians_pfor") + self._run(per_eg_jacobians_pfor, 100, + name="fc_per_eg_jacobians_pfor") + self._run(per_eg_jacobians_while, 10, + name="fc_per_eg_jacobians_while") + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py new file mode 100644 index 0000000000000000000000000000000000000000..1c0ef24040a3501cb89d2fd4b53778f53589a0d3 --- /dev/null +++ b/tensorflow/python/ops/parallel_for/pfor.py @@ -0,0 +1,2484 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Compiled parallel-for loop.""" +# pylint: disable=missing-docstring + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from absl import flags + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops import functional_ops +from tensorflow.python.ops import gen_sparse_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.platform import tf_logging as logging + +flags.DEFINE_bool( + "op_conversion_fallback_to_while_loop", False, + "If true, falls back to using a while loop for ops for " + "which a converter is not defined.") + + +def _stack(t, length): + """stacks `t` `length` times.""" + ones = array_ops.ones_like(array_ops.shape(t)) + multiples = array_ops.concat([length, ones], 0) + t = array_ops.tile(array_ops.expand_dims(t, 0), multiples) + return wrap(t, True) + + +# The following stateful ops can be safely called once, and with the same +# signature as the unconverted version, if their inputs are loop invariant. +# TODO(agarwal): implement a strategy for converting Variable reads/writes. The +# plan is to map each read/write in the loop_fn to a corresponding merged +# read/write in the converted graph. Writes need to be mergeable (e.g. +# AssignAdd) to be used in `pfor`. Given a certain read/write order in the +# loop_fn, doing a one-to-one conversion will simulate executing such +# instructions in lock-step across all iterations. +passthrough_stateful_ops = set([ + "VariableV2", + "VarHandleOp", + "ReadVariableOp", + "StackV2", + "TensorArrayWriteV3", + "TensorArrayReadV3", + "TensorArraySizeV3", +]) + + +def _is_stateful_pfor_op(op): + if isinstance(op, WhileOp): + return op.is_stateful + if op.type == "Const": + # Const didn't have an op_def. + return False + if op.type in passthrough_stateful_ops: + return False + assert hasattr(op, "op_def") and op.op_def is not None, op + return op.op_def.is_stateful + + +# pylint: disable=protected-access +class WhileOp(object): + """Object for storing state for converting the outputs of a while_loop.""" + + def __init__(self, exit_node, pfor_ops): + """Initializer. + + Args: + exit_node: A tensor output from the while_loop. + pfor_ops: list of ops inside the current pfor loop. + """ + self._pfor_ops = set(pfor_ops) + self._pfor_op_ids = set([x._id for x in pfor_ops]) + assert isinstance(exit_node, ops.Tensor) + self._while_context = exit_node.op._get_control_flow_context() + assert isinstance(self._while_context, control_flow_ops.WhileContext) + self._context_name = self._while_context.name + self._condition = self._while_context.pivot.op.inputs[0] + # Parts of an external while_loop could be created inside a pfor loop. + # However for the purpose here, we declare such loops to be external. Also + # note that we check if the condition was created inside or outside to + # determine if the while_loop was first created inside or outside. + # TODO(agarwal): check that the Enter and Exit of this loop are unstacked. + self._is_inside_loop = self.op_is_inside_loop(self._condition.op) + if self._is_inside_loop: + for e in self._while_context.loop_exits: + assert self.op_is_inside_loop(e.op) + + # Note the code below tries to reverse engineer an existing while_loop graph + # by assuming the following pattern of nodes. + # + # NextIteration <---- Body <--- Enter + # | ^ + # V ___| Y + # Enter -> Merge -> Switch___ + # ^ | N + # | V + # LoopCond Exit + + # Node that elements in the list below correspond one-to-one with each + # other. i.e. these lists are the same size, and the i_th entry corresponds + # to different Operations/Tensors of a single cycle as illustrated above. + # List of Switch ops (ops.Operation) that feed into an Exit Node. + self._exit_switches = [] + # List of inputs (ops.Tensor) to NextIteration. + self._body_outputs = [] + # List of list of control inputs of the NextIteration nodes. + self._next_iter_control_inputs = [] + # List of Merge ops (ops.Operation). + self._enter_merges = [] + # List of output (ops.Tensor) of Exit nodes. + self._outputs = [] + + # List of Enter Tensors. + # There are two types of Enter nodes: + # - The Enter nodes that are used in the `loop_vars` argument to + # `while_loop` (see + # https://www.tensorflow.org/api_docs/python/tf/while_loop). We collect + # these Enter nodes immediately below by tracing backwards from the Exit + # nodes via Exit <- Switch <- Merge <- Enter. You can see this chain in the + # diagram above. This allows us to have a 1:1 correspondence between the + # self._outputs and the first elements in self._enters. + # - The Enter nodes that are used only by the body. They don't appear in the + # `loop_vars` and are not returned from the `while_loop`. In Python code, + # they are usually captured by the body lambda. We collect them below by + # iterating over all the ops in the graph. They are appended to the end of + # self._enters and don't correspond to any outputs in self._outputs. + self._enters = [] + + for e in self._while_context.loop_exits: + self._outputs.append(e.op.outputs[0]) + switch = e.op.inputs[0].op + assert switch.type == "Switch", switch + self._exit_switches.append(switch) + merge = switch.inputs[0].op + assert merge.type == "Merge", merge + self._enter_merges.append(merge) + enter = merge.inputs[0].op + assert enter.type == "Enter", enter + self._enters.append(enter.outputs[0]) + next_iter = merge.inputs[1].op + assert next_iter.type == "NextIteration", next_iter + self._body_outputs.append(next_iter.inputs[0]) + self._next_iter_control_inputs.append(next_iter.control_inputs) + + # Collect all the Enter nodes that are not part of `loop_vars`, the second + # category described above. + # Also track whether the loop body has any stateful ops. + self._is_stateful = False + for op in ops.get_default_graph().get_operations(): + # TODO(agarwal): make sure this works with nested case. + control_flow_context = op._get_control_flow_context() + if control_flow_context is None: + continue + if control_flow_context.name == self._context_name: + self._is_stateful |= _is_stateful_pfor_op(op) + if op.type == "Enter": + output = op.outputs[0] + if output not in self._enters: + self._enters.append(output) + + def __str__(self): + """String representation.""" + return "while_loop(%s)" % self.name + + @property + def inputs(self): + """Input to all the Enter nodes.""" + return [x.op.inputs[0] for x in self._enters] + + @property + def control_inputs(self): + """Control input to all the Enter nodes.""" + control_inputs = [] + for x in self._enters: + control_inputs.extend(x.op.control_inputs) + return control_inputs + + @property + def outputs(self): + """Outputs of all the Exit nodes.""" + return self._outputs + + @property + def name(self): + """Context name for the while loop.""" + return self._context_name + + @property + def is_inside_loop(self): + """Returns true if the while_loop was created inside the pfor.""" + return self._is_inside_loop + + def op_is_inside_loop(self, op): + """True if op was created inside the pfor loop body.""" + assert isinstance(op, ops.Operation) + # Note that we use self._pfor_op_ids for the check and not self._pfor_ops + # since it appears there tensorflow API could return different python + # objects representing the same Operation node. + return op._id in self._pfor_op_ids + + @property + def is_stateful(self): + return self._is_stateful + + @property + def pfor_converter(self): + """Return a converter for the while loop.""" + return self + + def _init_pfor(self, parent_pfor, indices, cond_stacked, inputs, + inputs_stacked): + """Create a PFor object for converting parts of the while_loop. + + Args: + parent_pfor: PFor object being used for converting the while_loop. + indices: int32 Tensor of ids for the iterations that are still active + (i.e. did not exit the while_loop). + cond_stacked: True if the while_loop condition is stacked. + inputs: list of input Tensors corresponding 1-to-1 with self._enters. Note + that these Tensors are a subset of the loop variables for the generated + while_loop. + inputs_stacked: List of booleans corresponding 1-to-1 with `inputs`, + indicating if the value is stacked or not. + + Returns: + A PFor instance. The instance is initialized by adding conversion mappings + of nodes that will be external to the conversion that the returned + instance will be used for. e.g. Enter nodes as well as Merge and Switch + outputs are mapped to converted values. + """ + num_outputs = len(self._outputs) + assert len(inputs) == len(self._enters) + assert len(inputs_stacked) == len(self._enters) + loop_var = parent_pfor.loop_var + loop_len = array_ops.size(indices) + pfor = PFor( + loop_var, + loop_len, + pfor_ops=self._pfor_ops, + all_indices=indices, + all_indices_partitioned=cond_stacked) + # Map all Enter nodes to the inputs. + for enter, inp, stacked in zip(self._enters, inputs, inputs_stacked): + pfor._add_conversion(enter, wrap(inp, stacked)) + # Map outputs of Switch and Merge. + for i in range(num_outputs): + wrapped_inp = wrap(inputs[i], inputs_stacked[i]) + merge = self._enter_merges[i] + pfor._add_conversion(merge.outputs[0], wrapped_inp) + # Note that second output of Merge is typically not used, except possibly + # as a control dependency. To avoid trying to output the correct value, we + # employ a hack here. We output a dummy invalid value with an incorrect + # dtype. This will allow control dependency to work but if using it as an + # input, it should typically lead to errors during graph construction due + # to dtype mismatch. + # TODO(agarwal): Check in the original graph to see if there are any + # consumers of this Tensor that use it as an input. + pfor._add_conversion(merge.outputs[1], + wrap(constant_op.constant(-1.0), False)) + switch = self._exit_switches[i] + # Don't need to worry about switch.output[0] which will feed to Exit node. + pfor._add_conversion(switch.outputs[1], wrapped_inp) + return pfor + + def _convert_enter(self, parent_pfor, enter): + """Converts an Enter node.""" + inp, stacked, _ = parent_pfor._convert_helper(enter.op.inputs[0]) + control_inputs = [ + parent_pfor._convert_helper(x).t for x in enter.op.control_inputs + ] + if control_inputs: + with ops.control_dependencies(control_inputs): + inp = array_ops.identity(inp) + return inp, stacked + + def _maybe_stacked(self, cache, inp): + """Heuristic to figue out if the coverting inp leads to a stacked value. + + + Args: + cache: map from Tensor to boolean indicating stacked/unstacked. + inp: input Tensor. + + Returns: + True if `inp` could get stacked. If the function returns False, the + converted value should be guaranteed to be unstacked. If returning True, + it may or may not be stacked. + """ + if inp in cache: + return cache[inp] + if not self.op_is_inside_loop(inp.op): + return False + op = inp.op + output = False + if op.type in [ + "Shape", + "Rank" + "ShapeN", + "ZerosLike", + "TensorArrayV3", + "TensorArraySizeV3", + ]: + output = False + elif _is_stateful_pfor_op(op): + # This may be fairly aggressive. + output = True + elif op.type == "Exit": + # This may be fairly aggressive. + output = True + else: + for t in op.inputs: + if self._maybe_stacked(cache, t): + output = True + break + cache[inp] = output + return output + + def _create_init_values(self, pfor_input): + """Create arguments passed to converted while_loop.""" + with ops.name_scope("while_init"): + loop_len_vector = pfor_input.pfor.loop_len_vector + loop_len = loop_len_vector[0] + num_outputs = len(self._outputs) + + inputs = [] + maybe_stacked_cache = {} + # Convert all the Enters. Need to do this before checking for stacking + # below. + for i, enter in enumerate(self._enters): + inp, stacked = self._convert_enter(pfor_input.pfor, enter) + inputs.append(inp) + maybe_stacked_cache[enter] = stacked + # Since this enter node is part of the `loop_vars`, it corresponds to an + # output and its preceding switch. We mark this switch's output the same + # stackness, to act at the base case for the logic below. Below, we will + # be going through the body figuring out which inputs might need to be + # stacked and which inputs can safely remain unstacked. + if i < num_outputs: + maybe_stacked_cache[self._exit_switches[i].outputs[1]] = stacked + + # Shape invariants for init_values corresponding to self._enters. + input_shape_invariants = [] + # TensorArrays for outputs of converted while loop + output_tas = [] + # Shape invariants for output TensorArrays. + ta_shape_invariants = [] + # List of booleans indicating stackness of inputs, i.e. tensors + # corresponding to self._enters. + inputs_stacked = [] + for i, inp in enumerate(inputs): + enter = self._enters[i] + inp_stacked = self._maybe_stacked(maybe_stacked_cache, enter) + # Note that even when an input is unstacked, the body could make it + # stacked. we use a heuristic below to figure out if body may be making + # it stacked. + if i < num_outputs: + body_output = self._body_outputs[i] + if enter.op in self._pfor_ops: + body_output_stacked = self._maybe_stacked(maybe_stacked_cache, + body_output) + else: + # If constructed outside of pfor loop, then the output would not be + # stacked. + body_output_stacked = False + if body_output_stacked and not inp_stacked: + inp = _stack(inp, loop_len_vector).t + inputs[i] = inp + inp_stacked = True + # TODO(agarwal): other attributes for the TensorArray ? + output_tas.append(tensor_array_ops.TensorArray(inp.dtype, loop_len)) + ta_shape_invariants.append(tensor_shape.TensorShape(None)) + + inputs_stacked.append(inp_stacked) + input_shape_invariants.append(tensor_shape.TensorShape(None)) + + # See documentation for __call__ for the structure of init_values. + init_values = [True, pfor_input.pfor.all_indices] + inputs + output_tas + # TODO(agarwal): try stricter shape invariants + shape_invariants = ( + [tensor_shape.TensorShape(None), + tensor_shape.TensorShape(None) + ] + input_shape_invariants + ta_shape_invariants) + + return init_values, inputs_stacked, shape_invariants + + def _process_cond_unstacked(self, conditions, indices, inputs, output_tas): + """Handles case when condition is unstacked. + + Note that all iterations end together. So we don't need to partition the + inputs. When all iterations are done, we write the inputs to the + TensorArrays. Note that we only write to index 0 of output_tas. Since all + iterations end together, they can all be output together. + """ + not_all_done = array_ops.reshape(conditions, []) + new_output_tas = [] + # pylint: disable=cell-var-from-loop + for i, out_ta in enumerate(output_tas): + inp = inputs[i] + new_output_tas.append( + control_flow_ops.cond(not_all_done, + lambda: out_ta, + lambda: out_ta.write(0, inp))) + # pylint: enable=cell-var-from-loop + return not_all_done, indices, inputs, new_output_tas + + def _process_cond_stacked(self, conditions, indices, inputs, inputs_stacked, + output_tas): + num_outputs = len(self._outputs) + # Compute if all iterations are done. + not_all_done = math_ops.reduce_any(conditions) + conditions_int = math_ops.cast(conditions, dtypes.int32) + # Partition the indices. + done_indices, new_indices = data_flow_ops.dynamic_partition( + indices, conditions_int, 2) + + new_inputs = [] + new_output_tas = [] + for i, (inp, stacked) in enumerate(zip(inputs, inputs_stacked)): + # Partition the inputs. + if stacked: + done_inp, new_inp = data_flow_ops.dynamic_partition( + inp, conditions_int, 2) + else: + # TODO(agarwal): avoid this stacking. See TODO earlier in + # _process_cond_unstacked. + done_inp = _stack(inp, [array_ops.size(done_indices)]).t + new_inp = inp + new_inputs.append(new_inp) + # For iterations that are done, write them to TensorArrays. + if i < num_outputs: + out_ta = output_tas[i] + # Note that done_indices can be empty. done_inp should also be empty in + # that case. + new_output_tas.append(out_ta.scatter(done_indices, done_inp)) + return not_all_done, new_indices, new_inputs, new_output_tas + + def _process_body(self, pfor_input, inputs_stacked, + new_indices, cond_stacked, new_inputs, + not_all_done): + """Convert the body function.""" + + def true_fn(control_inputs, body_pfor, body_output, stacked): + """Converts the body function for all but last iteration. + + This essentially converts body_output. Additionally, it needs to handle + any control dependencies on the NextIteration node. So it creates another + Identity node with the converted dependencies. + """ + converted_control_inp = [] + for x in control_inputs: + for t in x.outputs: + converted_control_inp.append(body_pfor._convert_helper(t).t) + if stacked: + # Note convert always does the stacking. + output = body_pfor.convert(body_output) + else: + output, convert_stacked, _ = body_pfor._convert_helper(body_output) + assert convert_stacked == stacked, body_output + with ops.control_dependencies(converted_control_inp): + return array_ops.identity(output) + + body_pfor = self._init_pfor(pfor_input.pfor, new_indices, + cond_stacked, new_inputs, + inputs_stacked) + new_outputs = [] + + for i, (body_output, stacked) in enumerate( + zip(self._body_outputs, inputs_stacked)): + control_inp = self._next_iter_control_inputs[i] + out_dtype = body_output.dtype + # Note that we want to run the body only if not all pfor iterations are + # done. If all are done, we return empty tensors since these values will + # not be used. Notice that the value returned by the loop is based on + # TensorArrays and not directly on these returned values. + # pylint: disable=cell-var-from-loop + new_output = control_flow_ops.cond( + not_all_done, + lambda: true_fn(control_inp, body_pfor, body_output, stacked), + lambda: constant_op.constant([], dtype=out_dtype)) + # pylint: enable=cell-var-from-loop + new_outputs.append(new_output) + return new_outputs + + def __call__(self, pfor_input): + """Converter for the while_loop. + + The conversion of a while_loop is another while_loop. + + The arguments to this converted while_loop are as follows: + not_all_done: Boolean scalar Tensor indicating if all the pfor iterations + are done. + indices: int32 1-D Tensor storing the id of the iterations that are not + done. + args: Remaining arguments. These can be divided into 3 categories: + - First set of arguments are the tensors that correspond to the initial + elements of self._enters. The elements that appear in original while + loop's `loop_vars`. + - The second set of arguments are the tensors that correspond to the + remaining elements of self._enters. These are the tensors that directly + enter the original while loop body. + - Finally, the last set of arguments are TensorArrays. These TensorArrays + correspond to the outputs of the original while_loop, i.e. to the + elements in self._outputs. Each TensorArray has `PFor.loop_len` + elements, i.e. the number of pfor iterations. At the end, the i'th + element of each TensorArray will contain the output computed by the + i'th iteration of pfor. Note that elements can be written into these + tensors arrays in any order, depending on when the corresponding pfor + iteration is done. + If the original while_loop had `k` tensors in its `loop_vars` and its body + directly captured `m` tensors, the `args` will contain `2 * k + m` values. + + In each iteration, the while_loop body recomputes the condition for all + active pfor iterations to see which of them are now done. It then partitions + all the inputs and passes them along to the converted body. Values for all + the iterations that are done are written to TensorArrays indexed by the pfor + iteration number. When all iterations are done, the TensorArrays are stacked + to get the final value. + + Args: + pfor_input: A PForInput object corresponding to the output of any Exit + node from this while loop. + + Returns: + List of converted outputs. + """ + # Create init_values that will be passed to the while_loop. + init_values, inputs_stacked, shape_invariants = self._create_init_values( + pfor_input) + # Note that we use a list as a hack since we need the nested function body + # to set the value of cond_is_stacked. python2.x doesn't support nonlocal + # variables. + cond_is_stacked = [None] + + def cond(not_all_done, *_): + return not_all_done + + def body(not_all_done, indices, *args): + # See documentatin for __call__ for the structure of *args. + num_enters = len(self._enters) + inputs = args[:num_enters] + output_tas = args[num_enters:] + # TODO(agarwal): see which outputs have consumers and only populate the + # TensorArrays corresonding to those. Or do those paths get trimmed out + # from inside the while_loop body? + assert len(inputs) >= len(output_tas) + assert len(inputs) == len(inputs_stacked) + + # Convert condition + with ops.name_scope("while_cond"): + # Note that we set cond_stacked to True here. At this point we don't + # know if it could be loop invariant, hence the conservative value is + # to assume stacked. + cond_pfor = self._init_pfor(pfor_input.pfor, indices, + cond_stacked=True, + inputs=inputs, + inputs_stacked=inputs_stacked) + conditions, cond_stacked, _ = cond_pfor._convert_helper(self._condition) + cond_is_stacked[0] = cond_stacked + + # Recompute the new condition, write outputs of done iterations, and + # partition the inputs if needed. + if not cond_stacked: + (not_all_done, new_indices, + new_inputs, new_output_tas) = self._process_cond_unstacked( + conditions, indices, inputs, output_tas) + else: + (not_all_done, new_indices, + new_inputs, new_output_tas) = self._process_cond_stacked( + conditions, indices, inputs, inputs_stacked, output_tas) + + # Convert body + with ops.name_scope("while_body"): + # Compute the outputs from the body. + new_outputs = self._process_body(pfor_input, inputs_stacked, + new_indices, cond_stacked, new_inputs, + not_all_done) + + # Note that the first num_outputs new values of inputs are computed using + # the body. Rest of them were direct Enters into the condition/body and + # the partitioning done earlier is sufficient to give the new value. + num_outputs = len(self._outputs) + new_args = ([not_all_done, new_indices] + new_outputs + list( + new_inputs[num_outputs:]) + new_output_tas) + return tuple(new_args) + + while_outputs = control_flow_ops.while_loop( + cond, body, init_values, shape_invariants=shape_invariants) + output_tas = while_outputs[-len(self._outputs):] + outputs = [] + assert cond_is_stacked[0] is not None + for inp_stacked, ta in zip(inputs_stacked, output_tas): + if cond_is_stacked[0]: + outputs.append(wrap(ta.stack(), True)) + else: + # Note that if while_loop condition is unstacked, all iterations exit at + # the same time and we wrote those outputs in index 0 of the tensor + # array. + outputs.append(wrap(ta.read(0), inp_stacked)) + return outputs + + +class _PforInput(object): + """Input object passed to registered pfor converters.""" + + def __init__(self, pfor, op, inputs): + """Creates a _PforInput object. + + Args: + pfor: PFor converter object. + op: the Operation object that is being converted. + inputs: list of WrappedTensor objects representing converted values of the + inputs of `op`. + """ + self.pfor = pfor + self._op = op + self._inputs = inputs + + def stack_inputs(self, stack_indices=None): + """Stacks unstacked inputs at `stack_indices`. + + Args: + stack_indices: indices of inputs at which stacking is done. If None, + stacking is done at all indices. + """ + if stack_indices is None: + stack_indices = range(len(self._inputs)) + length = self.pfor.loop_len_vector + for i in stack_indices: + inp = self._inputs[i] + if not inp.is_stacked: + self._inputs[i] = _stack(inp.t, length) + + def expanddim_inputs_for_broadcast(self): + """Reshapes stacked inputs to prepare them for broadcast. + + Since stacked inputs have an extra leading dimension, automatic broadcasting + rules could incorrectly try to expand dimensions before that leading + dimension. To avoid that, we reshape these stacked inputs to the maximum + rank they will need to be broadcasted to. + """ + if not self._inputs: + return + + # Find max rank + def _get_rank(x): + rank = array_ops.rank(x.t) + if not x.is_stacked: + rank += 1 + return rank + + ranks = [_get_rank(x) for x in self._inputs] + max_rank = ranks[0] + for rank in ranks[1:]: + max_rank = math_ops.maximum(rank, max_rank) + + for i, inp in enumerate(self._inputs): + if inp.is_stacked: + shape = array_ops.shape(inp.t) + rank_diff = array_ops.reshape(max_rank - ranks[i], [1]) + ones = array_ops.tile([1], rank_diff) + new_shape = array_ops.concat([shape[:1], ones, shape[1:]], axis=0) + self._inputs[i] = wrap(array_ops.reshape(inp.t, new_shape), True) + + @property + def inputs(self): + return self._inputs + + @property + def num_inputs(self): + return len(self._inputs) + + def input(self, index): + assert len(self._inputs) > index, (index, self._inputs) + return self._inputs[index] + + def stacked_input(self, index): + t, is_stacked, _ = self.input(index) + if not is_stacked: + op_type = self.op_type + op_def = getattr(self._op, "op_def", None) + if op_def is None: + input_name = "at index %d" % index + else: + input_name = "\"%s\"" % op_def.input_arg[index].name + raise ValueError("Input %s of op \"%s\" expected to be not loop invariant" + ".\nError while converting op %s" + "with converted inputs\n%s" % (input_name, op_type, + self._op, self.inputs)) + return t + + def unstacked_input(self, index): + t, is_stacked, _ = self.input(index) + if is_stacked: + op_type = self.op_type + op_def = getattr(self._op, "op_def", None) + if op_def is None: + input_name = "at index %d" % index + else: + input_name = "\"%s\"" % op_def.input_arg[index].name + raise ValueError("Input %s of op \"%s\" expected to be loop invariant" + ".\nError while converting op %s" + "with converted inputs\n%s" % (input_name, op_type, + self._op, self.inputs)) + return t + + @property + def op(self): + return self._op + + @property + def op_type(self): + return self._op.type + + def get_attr(self, attr): + return self._op.get_attr(attr) + + @property + def outputs(self): + return self._op.outputs + + def output(self, index): + assert index < len(self._op.outputs) + return self._op.outputs[index] + + +_pfor_converter_registry = {} + + +class RegisterPFor(object): + """Utility to register converters for pfor. + + Usage: + @RegisterPFor(foo_op_type) + def _foo_converter(pfor_input): + ... + + The above will register conversion function `_foo_converter` for handling + conversion of `foo_op_type`. During conversion, the registered functin will be + called with a single argument of type `PForInput` which will contain state + needed for the conversion. This registered function should output a list of + WrappedTensor object with the same length as the number of outputs of op being + converted. If the op had zero outputs, then it should return a ops.Operation + object. + """ + + def __init__(self, op_type): + """Creates an object to register a converter for op with type `op_type`.""" + self.op_type = op_type + + def __call__(self, converter): + name = self.op_type + assert name not in _pfor_converter_registry, "Re-registering %s " % name + _pfor_converter_registry[name] = converter + return converter + + +class RegisterPForWithArgs(RegisterPFor): + """Utility to register converters for pfor. + + Usage: + @RegisteRPFor(foo_op_type, foo=value, ....) + def _foo_converter(pfor_input, foo=None, ....): + ... + + See RegisterPFor for details on the conversion function. + `RegisterPForWithArgs` allows binding extra arguments to the + conversion function at registration time. + """ + + def __init__(self, op_type, *args, **kw_args): + super(RegisterPForWithArgs, self).__init__(op_type) + self._args = args + self._kw_args = kw_args + + def __call__(self, converter): + + def _f(pfor_input): + return converter(pfor_input, self.op_type, *self._args, **self._kw_args) + + super(RegisterPForWithArgs, self).__call__(_f) + return converter + + +def _create_op(op_type, inputs, op_dtypes, attrs=None): + """Utility to create an op.""" + return ops.get_default_graph().create_op( + op_type, inputs, op_dtypes, attrs=attrs, compute_device=True) + + +WrappedTensor = collections.namedtuple("WrappedTensor", + ["t", "is_stacked", "is_sparse_stacked"]) +"""Wrapper around the result of a Tensor conversion. + +The additional fields are useful for keeping track of the conversion state as +data flows through the ops in the loop body. For every op whose output is a +Tensor, its converter should return either a WrappedTensor or a list of +WrappedTensors. + +Args: + t: The converted tensor + is_stacked: True if the tensor is stacked, i.e. represents the results of all + the iterations of the loop, where each row i of the tensor corresponds to + that op's output on iteration i of the loop. False if the tensor is not + stacked, i.e. represents the result of the op on of a single iteration of + the loop, where the result does not vary between iterations. + is_sparse_stacked: True if the tensor corresponds to a component tensor + (indices, values, or dense_shape) of a sparse tensor, and has been logically + stacked via a sparse conversion. +""" + + +def wrap(tensor, is_stacked=True, is_sparse_stacked=False): + """Helper to create a WrappedTensor object.""" + assert isinstance(is_stacked, bool) + assert isinstance(is_sparse_stacked, bool) + assert isinstance(tensor, ops.Tensor) + assert not is_sparse_stacked or is_stacked, ("If the wrapped tensor is " + "stacked via a sparse " + "conversion, it must also be " + "stacked.") + return WrappedTensor(tensor, is_stacked, is_sparse_stacked) + + +def _fallback_converter(pfor_input): + logging.warn("Using a while_loop for converting %s", pfor_input.op_type) + output_dtypes = [x.dtype for x in pfor_input.outputs] + iters = pfor_input.pfor.loop_len_vector[0] + + def while_body(i, *ta_list): + """Body of while loop.""" + inputs = [ + x[i, ...] if stacked else x for x, stacked, _ in pfor_input.inputs + ] + op_outputs = _create_op( + pfor_input.op_type, + inputs, + output_dtypes, + attrs=pfor_input.op.node_def.attr).outputs + + outputs = [] + for out, ta in zip(op_outputs, ta_list): + assert isinstance(out, ops.Tensor) + outputs.append(ta.write(i, array_ops.expand_dims(out, 0))) + return tuple([i + 1] + outputs) + + ta_list = control_flow_ops.while_loop( + lambda i, *ta: i < iters, while_body, [0] + [ + tensor_array_ops.TensorArray(dtype, iters) for dtype in output_dtypes + ])[1:] + return tuple([wrap(ta.concat(), True) for ta in ta_list]) + + +class PFor(object): + """Implementation of rewrite of parallel-for loops. + + This class takes a DAG or a set of DAGs representing the body of a + parallel-for loop, and adds new operations to the graph that implements + functionality equivalent to running that loop body for a specified number of + iterations. This new set of nodes may or may not use a tensorflow loop + construct. + + The process of conversion does not delete or change any existing operations. + It only adds operations that efficiently implement the equivalent + functionality. We refer to the added ops as "converted ops". + + The conversion process uses a simple greedy heuristic. It walks the loop body + and tries to express the functionality of running each node in a loop with a + new set of nodes. When converting an op several cases are possible: + - The op is not inside the loop body. Hence it can be used as is. + - The op does not depend on the iteration number and is stateless. In this + case, it can be used as is. + - The op is not stateful, and depends on iteration number only through control + dependencies. In this case, we can create a single op with same inputs and + attributes, but with "converted" control dependencies. + - The op is not stateful, and all its inputs are loop invariant. In this + case, similar to above, we can create a single op with same inputs and + attributes, but with "converted" control dependencies. + - The op is stateful or at least one of the inputs is not loop invariant. In + this case, we run the registered converter for that op to create a set of + converted ops. All nodes in the set will have converted control dependencies + corresponding to control dependencies of the original op. If the op returned + multiple outputs, "converted outputs" could be produced by different ops in + this set. + """ + + def __init__(self, + loop_var, + loop_len, + pfor_ops, + all_indices=None, + all_indices_partitioned=False): + """Creates an object to rewrite a parallel-for loop. + + Args: + loop_var: ops.Tensor output of a Placeholder operation. The value should + be an int32 scalar representing the loop iteration number. + loop_len: A scalar or scalar Tensor representing the number of iterations + the loop is run for. + pfor_ops: List of all ops inside the loop body. + all_indices: If not None, an int32 vector with size `loop_len` + representing the iteration ids that are still active. These values + should be unique and sorted. However they may not be contiguous. This is + typically the case when inside a control flow construct which has + partitioned the indices of the iterations that are being converted. + all_indices_partitioned: If True, this object is being constructed from a + control flow construct where not all the pfor iterations are guaranteed + to be active. + """ + assert isinstance(loop_var, ops.Tensor) + assert loop_var.op.type == "Placeholder" + self._loop_var = loop_var + loop_len_value = tensor_util.constant_value(loop_len) + if loop_len_value is not None: + loop_len = loop_len_value + self._loop_len_vector = array_ops.reshape(loop_len, [1]) + self._all_indices_partitioned = all_indices_partitioned + if all_indices_partitioned: + assert all_indices is not None + self.all_indices = ( + math_ops.range(loop_len) if all_indices is None else all_indices) + + self._conversion_map = {} + self._conversion_map[loop_var] = wrap(self.all_indices, True) + self._pfor_ops = set(pfor_ops) + self._pfor_op_ids = set([x._id for x in pfor_ops]) + + def op_is_inside_loop(self, op): + """True if op was created inside the pfor loop body.""" + assert isinstance(op, ops.Operation) + # Note that we use self._pfor_op_ids for the check and not self._pfor_ops + # since it appears there tensorflow API could return different python + # objects representing the same Operation node. + return op._id in self._pfor_op_ids + + def _convert_sparse(self, y): + """Returns the converted value corresponding to SparseTensor y. + + For SparseTensors, instead of stacking the component tensors separately, + resulting in component tensors with shapes (N, m, rank), (N, m), and (N, + rank) respectively for indices, values, and dense_shape (where N is the loop + length and m is the number of sparse tensor values per loop iter), we want + to logically stack the SparseTensors, to create a SparseTensor whose + components are size (N * m, rank + 1), (N * m, ), and (rank + 1,) + respectively. + + Here, we try to get the conversion of each component tensor. + If the tensors are stacked via a sparse conversion, return the resulting + SparseTensor composed of the converted components. Otherwise, the component + tensors are either unstacked or stacked naively. In the latter case, we + unstack the component tensors to reform loop_len SparseTensor elements, + then correctly batch them. + + The unstacked tensors must have the same rank. Each dimension of each + SparseTensor will expand to be the largest among all SparseTensor elements + for that dimension. For example, if there are N SparseTensors of rank 3 + being stacked, with N dense shapes, where the i_th shape is (x_i, y_i, z_i), + the new dense shape will be (N, max_i(x_i), max_i(y_i), max_i(z_i)). + + Args: + y: A tf.SparseTensor. + + Returns: + A tf.SparseTensor that is the converted value corresponding to y. + """ + outputs = [ + self._convert_helper(t) for t in (y.indices, y.values, y.dense_shape) + ] + assert all(isinstance(o, WrappedTensor) for o in outputs) + + if all(w.is_sparse_stacked for w in outputs): + return sparse_tensor.SparseTensor(*[w.t for w in outputs]) + + assert not any(w.is_sparse_stacked for w in outputs), ( + "Error converting SparseTensor. All components should be logically " + "stacked, or none.") + + # If component tensors were not sparsely stacked, they are either unstacked + # or stacked without knowledge that they are components of sparse tensors. + # In this case, we have to restack them. + return self._restack_sparse_tensor_logically( + *[self._unwrap_or_tile(w) for w in outputs]) + + def _restack_sparse_tensor_logically(self, indices, values, shape): + sparse_tensor_rank = indices.get_shape()[-1].value + if sparse_tensor_rank is not None: + sparse_tensor_rank += 1 + + def map_fn(args): + res = gen_sparse_ops.serialize_sparse( + args[0], args[1], args[2], out_type=dtypes.variant) + return res + + # Applies a map function to the component tensors to serialize each + # sparse tensor element and batch them all, then deserializes the batch. + # TODO(rachelim): Try to do this without map_fn -- add the right offsets + # to shape and indices tensors instead. + result = functional_ops.map_fn( + map_fn, [indices, values, shape], dtype=dtypes.variant) + return sparse_ops.deserialize_sparse( + result, dtype=values.dtype, rank=sparse_tensor_rank) + + def _unwrap_or_tile(self, wrapped_tensor): + """Given a wrapped tensor, unwrap if stacked. Otherwise, tiles it.""" + output, is_stacked = wrapped_tensor.t, wrapped_tensor.is_stacked + if is_stacked: + return output + else: + return _stack(output, self._loop_len_vector).t + + def convert(self, y): + """Returns the converted value corresponding to y. + + Args: + y: A ops.Tensor or a ops.Operation object. If latter, y should not have + any outputs. + + Returns: + If y does not need to be converted, it returns y as is. Else it returns + the "converted value" corresponding to y. + """ + if isinstance(y, sparse_tensor.SparseTensor): + return self._convert_sparse(y) + output = self._convert_helper(y) + if isinstance(output, WrappedTensor): + assert isinstance(y, ops.Tensor) + return self._unwrap_or_tile(output) + else: + assert isinstance(y, ops.Operation) + assert not y.outputs + assert isinstance(output, ops.Operation) + return output + + def _was_converted(self, t): + """True if t is not a conversion of itself.""" + converted_t = self._conversion_map[t] + return converted_t.t is not t + + def _add_conversion(self, old_output, new_output): + self._conversion_map[old_output] = new_output + + def _convert_helper(self, op_or_tensor): + stack = [op_or_tensor] + while stack: + y = stack[0] + if y in self._conversion_map: + assert isinstance(self._conversion_map[y], + (WrappedTensor, ops.Operation)) + stack.pop(0) + continue + if isinstance(y, ops.Operation): + assert not y.outputs, ( + "We only support converting Operation objects with no outputs. " + "Got %s", y) + y_op = y + else: + assert isinstance(y, ops.Tensor), y + y_op = y.op + + is_while_loop = y_op.type == "Exit" + if is_while_loop: + while_op = WhileOp(y, pfor_ops=self._pfor_ops) + is_inside_loop = while_op.is_inside_loop + # If all nodes in the while_loop graph were created inside the pfor, we + # treat the whole loop subgraph as a single op (y_op) and try to convert + # it. For while_loops that are created completely or partially outside, + # we treat them as external and should be able to simply return the Exit + # node output as is without needing any conversion. Note that for + # while_loops that are partially constructed inside, we assume they will + # be loop invariant. If that is not the case, it will create runtime + # errors since the converted graph would depend on the self._loop_var + # placeholder. + if is_inside_loop: + y_op = while_op + else: + is_inside_loop = self.op_is_inside_loop(y_op) + + # If this op was not created inside the loop body, we will return as is. + # 1. Convert inputs and control inputs. + + def _add_to_stack(x): + if x not in self._conversion_map: + stack.insert(0, x) + return True + else: + return False + + if is_inside_loop: + added_to_stack = False + for inp in y_op.inputs: + added_to_stack |= _add_to_stack(inp) + for cinp in y_op.control_inputs: + if cinp.outputs: + for t in cinp.outputs: + added_to_stack |= _add_to_stack(t) + else: + added_to_stack |= _add_to_stack(cinp) + if added_to_stack: + continue + + converted_inputs = [self._conversion_map[inp] for inp in y_op.inputs] + some_input_converted = any( + [self._was_converted(x) for x in y_op.inputs]) + some_input_stacked = any([x.is_stacked for x in converted_inputs]) + + converted_control_ops = set() + some_control_input_converted = False + for cinp in y_op.control_inputs: + if cinp.outputs: + for t in cinp.outputs: + converted_t = self._conversion_map[t] + if self._was_converted(t): + some_control_input_converted = True + converted_control_ops.add(converted_t.t.op) + else: + converted_cinp = self._conversion_map[cinp] + assert isinstance(converted_cinp, ops.Operation) + if converted_cinp != cinp: + some_control_input_converted = True + converted_control_ops.add(converted_cinp) + converted_control_ops = list(converted_control_ops) + is_stateful = _is_stateful_pfor_op(y_op) + else: + converted_inputs = [] + converted_control_ops = [] + logging.vlog(3, "converting op:%s\ninputs:%s\ncontrol_inputs:%s", y_op, + converted_inputs, converted_control_ops) + + # 2. Convert y_op + # If converting a while_loop, we let the while_loop convertor deal with + # putting the control dependencies appropriately. + control_dependencies = [] if is_while_loop else converted_control_ops + with ops.control_dependencies(control_dependencies), ops.name_scope( + y_op.name + "/pfor/"): + # None of the inputs and control inputs were converted. + if (not is_inside_loop or + (not is_stateful and not some_input_converted and + not some_control_input_converted)): + if y == y_op: + assert not isinstance(y_op, WhileOp) + new_outputs = y_op + else: + new_outputs = [wrap(x, False) for x in y_op.outputs] + elif not (is_stateful or is_while_loop or some_input_stacked): + # All inputs are unstacked or uncoverted but some control inputs are + # converted. + # TODO(rachelim): Handle the case where some inputs are sparsely + # stacked (i.e. any([x.is_sparse_stacked for x in converted_inputs])) + new_op = _create_op(y_op.type, [x.t for x in converted_inputs], + [x.dtype for x in y_op.outputs], + y_op.node_def.attr) + if y == y_op: + new_outputs = new_op + else: + new_outputs = [wrap(x, False) for x in new_op.outputs] + else: + # Either some inputs are not loop invariant or op is stateful. + if hasattr(y_op, "pfor_converter"): + converter = y_op.pfor_converter + else: + converter = _pfor_converter_registry.get(y_op.type, None) + if converter is None: + if flags.FLAGS.op_conversion_fallback_to_while_loop: + converter = _fallback_converter + else: + raise ValueError( + "No converter defined for %s\n%s\ninputs: %s. " + "\nEither add a converter or set " + "--op_conversion_fallback_to_while_loop=True, " + "which may run slower" % (y_op.type, y_op, converted_inputs)) + # TODO(rachelim): Handle the case where some inputs are sparsely + # stacked. We should only call the converter if it supports handling + # those inputs. + new_outputs = converter(_PforInput(self, y_op, converted_inputs)) + if isinstance(new_outputs, WrappedTensor): + new_outputs = [new_outputs] + assert isinstance(new_outputs, + (list, tuple, ops.Operation)), new_outputs + logging.vlog(2, "converted %s %s", y_op, new_outputs) + + # Insert into self._conversion_map + if y == y_op: + assert isinstance(new_outputs, ops.Operation) + self._add_conversion(y_op, new_outputs) + else: + for old_output, new_output in zip(y_op.outputs, new_outputs): + assert isinstance(new_output, WrappedTensor), (new_output, y, y_op) + self._add_conversion(old_output, new_output) + stack.pop(0) + + return self._conversion_map[op_or_tensor] + + @property + def loop_len_vector(self): + """Returns a single element vector whose value is number of iterations.""" + return self._loop_len_vector + + @property + def loop_var(self): + """Returns placeholder loop variable.""" + return self._loop_var + + @property + def pfor_ops(self): + return self._pfor_ops + + @property + def all_indices_partitioned(self): + """all_indices_partitioned property. + + Returns: + True if we are inside a control flow construct and not all pfor iterations + may be active. + """ + return self._all_indices_partitioned + +# nn_ops + + +def _flatten_first_two_dims(x): + """Merges first two dimensions.""" + old_shape = array_ops.shape(x) + new_shape = array_ops.concat([[-1], old_shape[2:]], axis=0) + return array_ops.reshape(x, new_shape) + + +def _unflatten_first_dim(x, first_dim): + """Splits first dimension into [first_dim, -1].""" + old_shape = array_ops.shape(x) + new_shape = array_ops.concat([first_dim, [-1], old_shape[1:]], axis=0) + return array_ops.reshape(x, new_shape) + + +def _inputs_with_flattening(pfor_input, input_indices): + """Stacks and flattens first dim of inputs at indices `input_indices`.""" + if input_indices is None: + input_indices = [] + pfor_input.stack_inputs(stack_indices=input_indices) + inputs = [] + for i in range(pfor_input.num_inputs): + if i in input_indices: + inp = pfor_input.stacked_input(i) + inp = _flatten_first_two_dims(inp) + else: + inp = pfor_input.unstacked_input(i) + inputs.append(inp) + return inputs + + +@RegisterPForWithArgs("Conv2D", dims=[0]) +@RegisterPForWithArgs("AvgPool", dims=[0]) +@RegisterPForWithArgs("MaxPool", dims=[0]) +@RegisterPForWithArgs("MaxPoolGrad", dims=[0, 1, 2]) +@RegisterPForWithArgs("SoftmaxCrossEntropyWithLogits", dims=[0, 1]) +def _convert_flatten_batch(pfor_input, op_type, dims): + del op_type + inputs = _inputs_with_flattening(pfor_input, dims) + outputs = _create_op( + pfor_input.op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + n = pfor_input.pfor.loop_len_vector + outputs = [_unflatten_first_dim(x, n) for x in outputs] + return [wrap(x, True) for x in outputs] + + +_channel_flatten_input_cache = {} + + +def _channel_flatten_input(x, data_format): + """Merge the stack dimension with the channel dimension. + + If S is pfor's stacking dimension, then, + - for SNCHW, we transpose to NSCHW. If N dimension has size 1, the transpose + should be cheap. + - for SNHWC, we transpose to NHWCS. + We then merge the S and C dimension. + + Args: + x: ops.Tensor to transform. + data_format: "NCHW" or "NHWC". + + Returns: + A 3-element tuple with the transformed value, along with the shape for + reshape and order for transpose required to transform back. + """ + + graph = ops.get_default_graph() + cache_key = (graph, x, data_format) + if cache_key not in _channel_flatten_input_cache: + x_shape = array_ops.shape(x) + if data_format == b"NCHW": + order = [1, 0, 2, 3, 4] + shape = array_ops.concat([x_shape[1:2], [-1], x_shape[3:]], axis=0) + reverse_order = order + else: + order = [1, 2, 3, 0, 4] + shape = array_ops.concat([x_shape[1:4], [-1]], axis=0) + reverse_order = [3, 0, 1, 2, 4] + # Move S dimension next to C dimension. + x = array_ops.transpose(x, order) + reverse_shape = array_ops.shape(x) + # Reshape to merge the S and C dimension. + x = array_ops.reshape(x, shape) + outputs = x, reverse_order, reverse_shape + _channel_flatten_input_cache[cache_key] = outputs + else: + outputs = _channel_flatten_input_cache[cache_key] + return outputs + + +# Note that with training=True, running FusedBatchNorm on individual examples +# is very different from running FusedBatchNorm on a batch of those examples. +# This is because, for the latter case, the operation can be considered as first +# computing the mean and variance over all the examples and then using these +# to scale all those examples. This creates a data dependency between these +# different "iterations" since the inputs to the scaling step depends on the +# statistics coming from all these inputs. +# As with other kernels, the conversion here effectively runs the kernel +# independently for each iteration, and returns outputs by stacking outputs from +# each of those iterations. +@RegisterPFor("FusedBatchNorm") +def _convert_fused_batch_norm(pfor_input): + is_training = pfor_input.get_attr("is_training") + # When BatchNorm is used with training=False, mean and variance are provided + # externally and used as is by the op. Thus, we can merge the S and N + # dimensions as we do for regular operations. + # When BatchNorm is used with training=True, mean and variance are computed + # for each channel across the batch dimension (first one). If we merge S and N + # dimensions, mean and variances will be computed over a larger set. So, we + # merge the S and C dimensions instead. + if not is_training: + # We return zeros for batch_mean and batch_variance output. Note that CPU + # and GPU seem to have different behavior for those two outputs. CPU outputs + # zero because these values are not used during inference. GPU outputs + # something, probably real means and variances. + inputs = _inputs_with_flattening(pfor_input, [0]) + outputs = _create_op( + pfor_input.op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + y = outputs[0] + n = pfor_input.pfor.loop_len_vector + y = _unflatten_first_dim(y, n) + mean = pfor_input.unstacked_input(3) + zeros = array_ops.zeros_like(mean) + return [wrap(y, True), wrap(zeros, False), wrap(zeros, False)] + + pfor_input.stack_inputs() + data_format = pfor_input.get_attr("data_format") + # We merge the first dimension with the "C" dimension, run FusedBatchNorm, and + # then transpose back. + x = pfor_input.stacked_input(0) + x, reverse_order, reverse_shape = _channel_flatten_input(x, data_format) + # Note that we stack all the other inputs as well so that they are the same + # size as the new size of the channel dimension. + inputs = [x] + [ + array_ops.reshape(pfor_input.stacked_input(i), [-1]) + for i in range(1, pfor_input.num_inputs) + ] + outputs = _create_op( + pfor_input.op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + y = outputs[0] + y = array_ops.reshape(y, reverse_shape) + y = array_ops.transpose(y, reverse_order) + n = pfor_input.pfor.loop_len_vector + outputs = [_unflatten_first_dim(x, n) for x in outputs[1:]] + outputs = [y] + outputs + return [wrap(x, True) for x in outputs] + + +@RegisterPFor("FusedBatchNormGrad") +def _convert_fused_batch_norm_grad(pfor_input): + pfor_input.stack_inputs() + data_format = pfor_input.get_attr("data_format") + y_backprop = pfor_input.stacked_input(0) + y_backprop, _, _ = _channel_flatten_input(y_backprop, data_format) + x = pfor_input.stacked_input(1) + x, x_reverse_order, x_reverse_shape = _channel_flatten_input(x, data_format) + inputs = [y_backprop, x] + [ + array_ops.reshape(pfor_input.stacked_input(i), [-1]) + for i in range(2, pfor_input.num_inputs) + ] + outputs = _create_op( + pfor_input.op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + x_backprop = outputs[0] + x_backprop = array_ops.reshape(x_backprop, x_reverse_shape) + x_backprop = array_ops.transpose(x_backprop, x_reverse_order) + n = pfor_input.pfor.loop_len_vector + outputs = [_unflatten_first_dim(x, n) for x in outputs[1:]] + outputs = [x_backprop] + outputs + return [wrap(output, True) for output in outputs] + + +@RegisterPForWithArgs("Conv2DBackpropInput", flatten_dims=[2], shape_dim=0) +@RegisterPForWithArgs("AvgPoolGrad", flatten_dims=[1], shape_dim=0) +def _convert_flatten_batch_shape_input(pfor_input, op_type, flatten_dims, + shape_dim): + del op_type + inputs = _inputs_with_flattening(pfor_input, flatten_dims) + n = pfor_input.pfor.loop_len_vector + # Adjust the `input_sizes` input. + ones = array_ops.ones( + [array_ops.shape(inputs[shape_dim])[0] - 1], dtype=n.dtype) + inputs[shape_dim] *= array_ops.concat([n, ones], axis=0) + outputs = _create_op( + pfor_input.op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + outputs = [_unflatten_first_dim(x, n) for x in outputs] + return [wrap(x, True) for x in outputs] + + +@RegisterPFor("Conv2DBackpropFilter") +def _convert_conv2d_backprop_filter(pfor_input): + pfor_input.stack_inputs(stack_indices=[2]) + inputs, inputs_stacked, _ = pfor_input.input(0) + filter_sizes = pfor_input.unstacked_input(1) + grads = pfor_input.stacked_input(2) + strides = pfor_input.get_attr("strides") + padding = pfor_input.get_attr("padding") + use_cudnn_on_gpu = pfor_input.get_attr("use_cudnn_on_gpu") + data_format = pfor_input.get_attr("data_format") + dilations = pfor_input.get_attr("dilations") + if inputs_stacked: + # TODO(agarwal): Implement this efficiently. + logging.warn("Conv2DBackpropFilter uses a while_loop. Fix that!") + + def while_body(i, ta): + inp_i = inputs[i, ...] + grad_i = grads[i, ...] + output = nn_ops.conv2d_backprop_filter( + inp_i, + filter_sizes, + grad_i, + strides=strides, + padding=padding, + use_cudnn_on_gpu=use_cudnn_on_gpu, + data_format=data_format, + dilations=dilations) + return i + 1, ta.write(i, array_ops.expand_dims(output, 0)) + + n = array_ops.reshape(pfor_input.pfor.loop_len_vector, []) + _, ta = control_flow_ops.while_loop( + lambda i, ta: i < n, while_body, + (0, tensor_array_ops.TensorArray(inputs.dtype, n))) + output = ta.concat() + return wrap(output, True) + else: + # We merge the stack dimension with the channel dimension of the gradients + # and pretend we had a larger filter (see change to filter_sizes below). + # Once the filter backprop is computed, we reshape and transpose back + # appropriately. + grads, _, _ = _channel_flatten_input(grads, data_format) + n = pfor_input.pfor.loop_len_vector + old_filter_sizes = filter_sizes + filter_sizes *= array_ops.concat([[1, 1, 1], n], axis=0) + output = nn_ops.conv2d_backprop_filter( + inputs, + filter_sizes, + grads, + strides=strides, + padding=padding, + use_cudnn_on_gpu=use_cudnn_on_gpu, + data_format=data_format, + dilations=dilations) + new_filter_shape = array_ops.concat([old_filter_sizes[:3], n, [-1]], axis=0) + output = array_ops.reshape(output, new_filter_shape) + output = array_ops.transpose(output, [3, 0, 1, 2, 4]) + return wrap(output, True) + + +# array_ops + + +@RegisterPForWithArgs("Identity", array_ops.identity) +@RegisterPForWithArgs("StopGradient", array_ops.stop_gradient) +def _convert_identity(pfor_input, op_type, op_func): + del op_type + return wrap(op_func(*[x.t for x in pfor_input.inputs]), True) + + +@RegisterPFor("Reshape") +def _convert_reshape(pfor_input): + t = pfor_input.stacked_input(0) + shape = pfor_input.unstacked_input(1) + new_dim = array_ops.shape(t)[:1] + new_shape = array_ops.concat([new_dim, shape], axis=0) + return wrap(array_ops.reshape(t, new_shape), True) + + +@RegisterPFor("ExpandDims") +def _convert_expanddims(pfor_input): + t = pfor_input.stacked_input(0) + dim = pfor_input.unstacked_input(1) + dim += math_ops.cast(dim >= 0, dtypes.int32) + return wrap(array_ops.expand_dims(t, axis=dim), True) + + +@RegisterPFor("Slice") +def _convert_slice(pfor_input): + t = pfor_input.stacked_input(0) + begin = pfor_input.unstacked_input(1) + size = pfor_input.unstacked_input(2) + begin = array_ops.concat([[0], begin], axis=0) + size = array_ops.concat([[-1], size], axis=0) + return wrap(array_ops.slice(t, begin, size), True) + + +@RegisterPFor("Tile") +def _convert_tile(pfor_input): + t = pfor_input.stacked_input(0) + multiples = pfor_input.unstacked_input(1) + multiples = array_ops.concat([[1], multiples], 0) + return wrap(array_ops.tile(t, multiples), True) + + +@RegisterPFor("Pack") +def _convert_pack(pfor_input): + pfor_input.stack_inputs() + axis = pfor_input.get_attr("axis") + if axis >= 0: + axis += 1 + return wrap( + array_ops.stack([x.t for x in pfor_input.inputs], axis=axis), True) + + +@RegisterPFor("Unpack") +def _convert_unpack(pfor_input): + value = pfor_input.stacked_input(0) + axis = pfor_input.get_attr("axis") + if axis >= 0: + axis += 1 + num = pfor_input.get_attr("num") + return [wrap(x, True) for x in array_ops.unstack(value, axis=axis, num=num)] + + +@RegisterPFor("Pad") +def _convert_pad(pfor_input): + t = pfor_input.stacked_input(0) + paddings = pfor_input.unstacked_input(1) + paddings = array_ops.concat([[[0, 0]], paddings], 0) + return wrap(array_ops.pad(t, paddings, mode="CONSTANT"), True) + + +@RegisterPFor("Split") +def _convert_split(pfor_input): + split_dim = pfor_input.unstacked_input(0) + t = pfor_input.stacked_input(1) + num_split = pfor_input.get_attr("num_split") + split_dim += math_ops.cast(split_dim >= 0, dtypes.int32) + return [wrap(x, True) for x in array_ops.split(t, num_split, axis=split_dim)] + + +@RegisterPFor("Transpose") +def _convert_transpose(pfor_input): + t = pfor_input.stacked_input(0) + perm = pfor_input.unstacked_input(1) + new_perm = array_ops.concat([[0], perm + 1], axis=0) + return wrap(array_ops.transpose(t, new_perm), True) + + +@RegisterPFor("ZerosLike") +def _convert_zeroslike(pfor_input): + t = pfor_input.stacked_input(0) + shape = array_ops.shape(t)[1:] + return wrap(array_ops.zeros(shape, dtype=t.dtype), False) + + +@RegisterPFor("Gather") +@RegisterPFor("GatherV2") +def _convert_gather(pfor_input): + param, param_stacked, _ = pfor_input.input(0) + indices, indices_stacked, _ = pfor_input.input(1) + op_type = pfor_input.op_type + if op_type == "Gather": + validate_indices = pfor_input.get_attr("validate_indices") + axis = 0 + else: + validate_indices = None + axis = pfor_input.unstacked_input(2) + axis_value = tensor_util.constant_value(axis) + if axis_value is not None: + axis = axis_value + if indices_stacked and not param_stacked: + if indices == pfor_input.pfor.all_indices and axis == 0: + param_shape0 = param.shape[0].value + indices_shape0 = indices.shape[0].value + if param_shape0 is not None and indices_shape0 == param_shape0: + # Note that with loops and conditionals, indices may not be contiguous. + # However they will be sorted and unique. So if the shape matches, then + # it must be picking up all the rows of param. + return wrap(param, True) + # TODO(agarwal): use array_ops.slice here. + output = array_ops.gather( + param, indices, validate_indices=validate_indices, axis=axis) + if axis != 0: + axis = control_flow_ops.cond( + axis < 0, lambda: axis + array_ops.rank(param), lambda: axis) + order = array_ops.concat( + [[axis], + math_ops.range(axis), + math_ops.range(axis + 1, array_ops.rank(output))], + axis=0) + output = control_flow_ops.cond( + math_ops.equal(axis, 0), lambda: output, + lambda: array_ops.transpose(output, order)) + return wrap(output, True) + if param_stacked: + loop_len_vector = pfor_input.pfor.loop_len_vector + pfor_input.stack_inputs(stack_indices=[1]) + indices = pfor_input.stacked_input(1) + param_flat = _flatten_first_two_dims(param) + + # Recompute indices to handle stacked param. + indices_offset = math_ops.range( + loop_len_vector[0]) * array_ops.shape(param)[1] + # Reshape indices_offset to allow broadcast addition + ones = array_ops.ones([array_ops.rank(indices) - 1], dtype=dtypes.int32) + new_shape = array_ops.concat([loop_len_vector, ones], axis=0) + indices_offset = array_ops.reshape(indices_offset, new_shape) + indices += indices_offset + + # TODO(agarwal): handle axis != 0. May need to transpose param or + # array_ops.gather_nd. + if isinstance(axis, ops.Tensor): + axis_value = tensor_util.constant_value(axis) + else: + try: + axis_value = int(axis) + except TypeError: + axis_value = None + msg = ("Gather, where indices and param are both loop dependent, currently " + "requires axis=0") + if axis_value is not None and axis_value != 0: + raise ValueError("Error while converting %s. %s. Got axis=%d" % + (pfor_input.op, msg, axis)) + with ops.control_dependencies( + [check_ops.assert_equal(axis, 0, message=msg)]): + output = array_ops.gather(param_flat, indices) + return wrap(output, True) + + +@RegisterPFor("ConcatV2") +def _convert_concatv2(pfor_input): + n = pfor_input.num_inputs + pfor_input.stack_inputs(stack_indices=range(n - 1)) + axis = pfor_input.unstacked_input(n - 1) + axis += math_ops.cast(axis >= 0, axis.dtype) + return wrap( + array_ops.concat([x.t for x in pfor_input.inputs[:n - 1]], axis=axis), + True) + + +@RegisterPFor("StridedSlice") +def _convert_strided_slice(pfor_input): + inp = pfor_input.stacked_input(0) + begin = pfor_input.unstacked_input(1) + end = pfor_input.unstacked_input(2) + strides = pfor_input.unstacked_input(3) + begin_mask = pfor_input.get_attr("begin_mask") + end_mask = pfor_input.get_attr("end_mask") + ellipsis_mask = pfor_input.get_attr("ellipsis_mask") + new_axis_mask = pfor_input.get_attr("new_axis_mask") + shrink_axis_mask = pfor_input.get_attr("shrink_axis_mask") + + begin = array_ops.concat([[0], begin], axis=0) + end = array_ops.concat([[0], end], axis=0) + strides = array_ops.concat([[1], strides], axis=0) + begin_mask = begin_mask << 1 | 1 + end_mask = end_mask << 1 | 1 + ellipsis_mask <<= 1 + new_axis_mask <<= 1 + shrink_axis_mask <<= 1 + return wrap( + array_ops.strided_slice( + inp, + begin, + end, + strides, + begin_mask=begin_mask, + end_mask=end_mask, + ellipsis_mask=ellipsis_mask, + new_axis_mask=new_axis_mask, + shrink_axis_mask=shrink_axis_mask), True) + + +@RegisterPFor("StridedSliceGrad") +def _convert_strided_slice_grad(pfor_input): + shape = pfor_input.unstacked_input(0) + begin = pfor_input.unstacked_input(1) + end = pfor_input.unstacked_input(2) + strides = pfor_input.unstacked_input(3) + dy = pfor_input.stacked_input(4) + begin_mask = pfor_input.get_attr("begin_mask") + end_mask = pfor_input.get_attr("end_mask") + ellipsis_mask = pfor_input.get_attr("ellipsis_mask") + new_axis_mask = pfor_input.get_attr("new_axis_mask") + shrink_axis_mask = pfor_input.get_attr("shrink_axis_mask") + + shape = array_ops.concat([pfor_input.pfor.loop_len_vector, shape], axis=0) + begin = array_ops.concat([[0], begin], axis=0) + end = array_ops.concat([[0], end], axis=0) + strides = array_ops.concat([[1], strides], axis=0) + begin_mask = begin_mask << 1 | 1 + end_mask = end_mask << 1 | 1 + ellipsis_mask <<= 1 + new_axis_mask <<= 1 + shrink_axis_mask <<= 1 + return wrap( + array_ops.strided_slice_grad( + shape, + begin, + end, + strides, + dy, + begin_mask=begin_mask, + end_mask=end_mask, + ellipsis_mask=ellipsis_mask, + new_axis_mask=new_axis_mask, + shrink_axis_mask=shrink_axis_mask), True) + + +# math_ops + + +@RegisterPFor("MatMul") +def _convert_matmul(pfor_input): + # TODO(agarwal): Check if tiling is faster than two transposes. + a, a_stacked, _ = pfor_input.input(0) + b, b_stacked, _ = pfor_input.input(1) + tr_a = pfor_input.get_attr("transpose_a") + tr_b = pfor_input.get_attr("transpose_b") + if a_stacked and b_stacked: + output = wrap(math_ops.matmul(a, b, adjoint_a=tr_a, adjoint_b=tr_b), True) + return output + elif a_stacked: + if tr_a: + a = array_ops.transpose(a, [0, 2, 1]) + if a.shape.is_fully_defined(): + x, y, z = a.shape + else: + x, y, z = [ + array_ops.reshape(i, []) + for i in array_ops.split(array_ops.shape(a), 3) + ] + a = array_ops.reshape(a, [x * y, z]) + prod = math_ops.matmul(a, b, transpose_b=tr_b) + return wrap(array_ops.reshape(prod, [x, y, -1]), True) + else: + assert b_stacked + if tr_b: + perm = [2, 0, 1] + b = array_ops.transpose(b, perm) + else: + # As an optimization, if one of the first two dimensions is 1, then we can + # reshape instead of transpose. + # TODO(agarwal): This check can be done inside Transpose kernel. + b_shape = array_ops.shape(b) + min_dim = math_ops.minimum(b_shape[0], b_shape[1]) + perm = control_flow_ops.cond( + math_ops.equal(min_dim, 1), lambda: [0, 1, 2], lambda: [1, 0, 2]) + new_shape = array_ops.stack([b_shape[1], b_shape[0], b_shape[2]]) + b = array_ops.transpose(b, perm) + b = array_ops.reshape(b, new_shape) + + if b.shape.is_fully_defined(): + x, y, z = b.shape + else: + x, y, z = [ + array_ops.reshape(i, []) + for i in array_ops.split(array_ops.shape(b), 3) + ] + b = array_ops.reshape(b, [x, y * z]) + prod = math_ops.matmul(a, b, transpose_a=tr_a) + prod = array_ops.reshape(prod, [-1, y, z]) + prod = array_ops.transpose(prod, [1, 0, 2]) + return wrap(prod, True) + + +@RegisterPFor("BatchMatMul") +def _convert_batch_mat_mul(pfor_input): + # TODO(agarwal): There may be a more efficient way to do this instead of + # stacking the inputs. + pfor_input.stack_inputs() + x = pfor_input.stacked_input(0) + y = pfor_input.stacked_input(1) + adj_x = pfor_input.get_attr("adj_x") + adj_y = pfor_input.get_attr("adj_y") + + x = _flatten_first_two_dims(x) + y = _flatten_first_two_dims(y) + output = math_ops.matmul(x, y, adjoint_a=adj_x, adjoint_b=adj_y) + output = _unflatten_first_dim(output, pfor_input.pfor.loop_len_vector) + return wrap(output, True) + + +@RegisterPForWithArgs("Sum", math_ops.reduce_sum) +@RegisterPForWithArgs("Prod", math_ops.reduce_prod) +@RegisterPForWithArgs("Max", math_ops.reduce_max) +@RegisterPForWithArgs("Min", math_ops.reduce_min) +def _convert_reduction(pfor_input, _, op_func): + t = pfor_input.stacked_input(0) + indices = pfor_input.unstacked_input(1) + # Shift positive indices by one to account for the extra dimension. + indices += math_ops.cast(indices >= 0, dtypes.int32) + keep_dims = pfor_input.get_attr("keep_dims") + return wrap(op_func(t, indices, keepdims=keep_dims), True) + + +@RegisterPForWithArgs("Cumsum", math_ops.cumsum) +@RegisterPForWithArgs("Cumprod", math_ops.cumprod) +def _convert_cumfoo(pfor_input, _, op_func): + t = pfor_input.stacked_input(0) + axis = pfor_input.unstacked_input(1) + # Shift positive indices by one to account for the extra dimension. + axis += math_ops.cast(axis >= 0, dtypes.int32) + exclusive = pfor_input.get_attr("exclusive") + reverse = pfor_input.get_attr("reverse") + return wrap(op_func(t, axis, exclusive=exclusive, reverse=reverse), True) + + +@RegisterPFor("BiasAdd") +def _convert_biasadd(pfor_input): + t = pfor_input.stacked_input(0) + bias = pfor_input.unstacked_input(1) + data_format = pfor_input.get_attr("data_format") + if data_format != b"NCHW": + return wrap(nn_ops.bias_add(t, bias, data_format=data_format), True) + shape = array_ops.shape(t) + flattened_shape = array_ops.concat([[-1], shape[2:]], axis=0) + t = array_ops.reshape(t, flattened_shape) + t = nn_ops.bias_add(t, bias, data_format=b"NCHW") + t = array_ops.reshape(t, shape) + return wrap(t, True) + + +@RegisterPFor("UnsortedSegmentSum") +def _convert_unsortedsegmentsum(pfor_input): + data, data_stacked, _ = pfor_input.input(0) + # TODO(agarwal): handle unstacked? + segment_ids = pfor_input.stacked_input(1) + # TODO(agarwal): handle stacked? + num_segments = pfor_input.unstacked_input(2) + if not data_stacked: + data = _stack(data, pfor_input.pfor.loop_len_vector).t + segment_shape = array_ops.shape(segment_ids) + n = segment_shape[0] + ones = array_ops.ones_like(segment_shape)[1:] + segment_offset = num_segments * math_ops.range(n) + segment_offset = array_ops.reshape(segment_offset, + array_ops.concat([[n], ones], axis=0)) + segment_ids += segment_offset + num_segments *= n + output = math_ops.unsorted_segment_sum(data, segment_ids, num_segments) + new_output_shape = array_ops.concat( + [[n, -1], array_ops.shape(output)[1:]], axis=0) + output = array_ops.reshape(output, new_output_shape) + return wrap(output, True) + + +@RegisterPFor("Cast") +def _convert_cast(pfor_input): + inp = pfor_input.stacked_input(0) + dtype = pfor_input.get_attr("DstT") + return wrap(math_ops.cast(inp, dtype), True) + + +# Note that ops handled here do not have attributes except "T", and hence don't +# need extra arguments passed to the cwise_op call below. +@RegisterPForWithArgs("Add", math_ops.add) +@RegisterPForWithArgs("Ceil", math_ops.ceil) +@RegisterPForWithArgs("Equal", math_ops.equal) +@RegisterPForWithArgs("NotEqual", math_ops.not_equal) +@RegisterPForWithArgs("Floor", math_ops.floor) +@RegisterPForWithArgs("Greater", math_ops.greater) +@RegisterPForWithArgs("GreaterEqual", math_ops.greater_equal) +@RegisterPForWithArgs("Less", math_ops.less) +@RegisterPForWithArgs("LessEqual", math_ops.less_equal) +@RegisterPForWithArgs("LogicalOr", math_ops.logical_or) +@RegisterPForWithArgs("LogicalAnd", math_ops.logical_and) +@RegisterPForWithArgs("LogicalNot", math_ops.logical_not) +@RegisterPForWithArgs("LogicalXor", math_ops.logical_xor) +@RegisterPForWithArgs("Maximum", math_ops.maximum) +@RegisterPForWithArgs("Minimum", math_ops.minimum) +@RegisterPForWithArgs("Mul", math_ops.multiply) +@RegisterPForWithArgs("Neg", math_ops.negative) +@RegisterPForWithArgs("RealDiv", math_ops.divide) +@RegisterPForWithArgs("Relu", nn_ops.relu) +@RegisterPForWithArgs("Sigmoid", math_ops.sigmoid) +@RegisterPForWithArgs("Square", math_ops.square) +@RegisterPForWithArgs("Sub", math_ops.subtract) +@RegisterPForWithArgs("Tanh", math_ops.tanh) +def _convert_cwise(pfor_input, op_type, op_func): + del op_type + pfor_input.expanddim_inputs_for_broadcast() + return wrap(op_func(*[x.t for x in pfor_input.inputs]), True) + + +@RegisterPFor("Shape") +def _convert_shape(pfor_input): + out_type = pfor_input.get_attr("out_type") + return wrap( + array_ops.shape(pfor_input.stacked_input(0), out_type=out_type)[1:], + False) + + +@RegisterPFor("ShapeN") +def _convert_shape_n(pfor_input): + out_type = pfor_input.get_attr("out_type") + shapes = [ + array_ops.shape(x, out_type=out_type)[1:] + if stacked else array_ops.shape(x) for x, stacked, _ in pfor_input.inputs + ] + return [wrap(x, False) for x in shapes] + + +@RegisterPFor("Size") +def _convert_size(pfor_input): + out_type = pfor_input.get_attr("out_type") + n = math_ops.cast(pfor_input.pfor.loop_len_vector[0], out_type) + return wrap( + array_ops.size(pfor_input.stacked_input(0), out_type=out_type) // n, + False) + + +@RegisterPFor("Rank") +def _convert_rank(pfor_input): + return wrap(array_ops.rank(pfor_input.stacked_input(0)) - 1, False) + + +@RegisterPFor("AddN") +def _convert_addn(pfor_input): + # AddN does not support broadcasting. + pfor_input.stack_inputs() + return wrap(math_ops.add_n([x.t for x in pfor_input.inputs]), True) + + +@RegisterPFor("BiasAddGrad") +def _convert_biasaddgrad(pfor_input): + grad = pfor_input.stacked_input(0) + fmt = pfor_input.get_attr("data_format") + if fmt == b"NCHW": + output = math_ops.reduce_sum(grad, axis=[1, 3, 4], keepdims=False) + else: + grad_shape = array_ops.shape(grad) + last_dim_shape = grad_shape[-1] + first_dim_shape = grad_shape[0] + output = array_ops.reshape(grad, [first_dim_shape, -1, last_dim_shape]) + output = math_ops.reduce_sum(output, axis=[1], keepdims=False) + return wrap(output, True) + + +# Some required ops are not exposed under the tf namespace. Hence relying on +# _create_op to create them. +@RegisterPForWithArgs("ReluGrad") +@RegisterPForWithArgs("TanhGrad") +@RegisterPForWithArgs("SigmoidGrad") +def _convert_grads(pfor_input, op_type, *args, **kw_args): + del args + del kw_args + # TODO(agarwal): Looks like these ops don't support broadcasting. Hence we + # have to use tiling here. + pfor_input.stack_inputs() + outputs = _create_op( + op_type, [x.t for x in pfor_input.inputs], + [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + return [wrap(x, True) for x in outputs] + + +@RegisterPFor("Select") +def _convert_select(pfor_input): + pfor_input.stack_inputs() + cond = pfor_input.stacked_input(0) + t = pfor_input.stacked_input(1) + e = pfor_input.stacked_input(2) + cond_rank = array_ops.rank(cond) + cond, t, e = control_flow_ops.cond( + cond_rank > 1, lambda: _inputs_with_flattening(pfor_input, [0, 1, 2]), + lambda: [cond, t, e]) + outputs = _create_op( + pfor_input.op_type, [cond, t, e], [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + n = pfor_input.pfor.loop_len_vector + out = control_flow_ops.cond(cond_rank > 1, + lambda: _unflatten_first_dim(outputs[0], n), + lambda: outputs[0]) + return [wrap(out, True) for x in outputs] + + +# random_ops + + +@RegisterPForWithArgs("RandomUniform") +@RegisterPForWithArgs("RandomUniformInt") +@RegisterPForWithArgs("RandomStandardNormal") +@RegisterPForWithArgs("TruncatedNormal") +@RegisterPForWithArgs("RandomGamma") +@RegisterPForWithArgs("RandomPoissonV2") +def _convert_random(pfor_input, op_type, *args, **kw_args): + del args + del kw_args + inputs = [pfor_input.unstacked_input(i) for i in range(pfor_input.num_inputs)] + # inputs[0] is "shape" + inputs[0] = array_ops.concat( + [pfor_input.pfor.loop_len_vector, inputs[0]], axis=0) + logging.warning( + "Note that %s inside pfor op may not give same output as " + "inside a sequential loop.", op_type) + outputs = _create_op( + op_type, + inputs, [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + return [wrap(x, True) for x in outputs] + + +# logging_ops + + +@RegisterPFor("Assert") +def _convert_assert(pfor_input): + cond, cond_stacked, _ = pfor_input.input(0) + if cond_stacked: + cond = math_ops.reduce_all(cond) + + data_list = [x.t for x in pfor_input.inputs][1:] + return _create_op("Assert", [cond] + data_list, [], + attrs=pfor_input.op.node_def.attr) + + +@RegisterPFor("Print") +def _convert_print(pfor_input): + # Note that we don't stack all the inputs. Hence unstacked values are printed + # once here vs multiple times in a while_loop. + pfor_input.stack_inputs([0]) + outputs = _create_op( + "Print", [x.t for x in pfor_input.inputs], + [x.dtype for x in pfor_input.outputs], + attrs=pfor_input.op.node_def.attr).outputs + return [wrap(x, True) for x in outputs] + + +# data_flow_ops + +# TensorArray conversion is tricky since we don't support arrays of +# TensorArrays. For converting them, we consider two distinct cases: +# +# 1. The array is constructed outside the pfor call, and read/written inside the +# loop. +# This is an easier case since we don't need to make an array of TensorArrays. +# A correctness requirement is that these parallel iterations shouldn't attempt +# to write to the same location. Hence at conversion time we disallow indices to +# be loop-invariant as that would guarantee a collision. Even if the indices are +# not loop-invariant, they could conflict and that shall trigger runtime errors. +# +# 2. The array is constructed and used entirely inside each pfor iteration. +# For simplicity, here we require that the indices used for write/scatter are +# "unstacked". Otherwise it becomes hard to merge the TensorArrays created in +# different pfor iterations. We consider two sub_cases: +# +# 2a Elements written to the array are "stacked" +# To simulate multiple TensorArrays, we may increase the dimension of each +# element of the array. i.e. the i_th row of the j_th entry of the converted +# TensorArray corresponds to to the j_th entry of the TensorArray in the i_th +# pfor iteration. +# +# 2b Elements written to the array are "unstacked" +# In this case we don't increase the dimensions to avoid redundant tiling. Each +# iteration is trying to write the same value. So we convert that to a single +# write. +# +# Here are some tricks used to implement the above: +# - TensorArrayV3 constructor encodes the element shape as an attr. Instead of +# trying to trace whether future writes are stacked or unstacked in order to set +# this attr, we set it to correspond to unknown shape. +# - We use the "flow" output of the different ops to track whether the array +# elements are stacked or unstacked. If a stacked write/scatter is done, we make +# the flow stacked as well. +# - We use some heuristic traversal of the graph to track whether the +# TensorArray handle was created inside or outside the pfor loop. + + +@RegisterPFor("TensorArrayV3") +def _convert_tensor_array_v3(pfor_input): + size = pfor_input.unstacked_input(0) + dtype = pfor_input.get_attr("dtype") + dynamic_size = pfor_input.get_attr("dynamic_size") + clear_after_read = pfor_input.get_attr("clear_after_read") + identical_element_shapes = pfor_input.get_attr("identical_element_shapes") + tensor_array_name = pfor_input.get_attr("tensor_array_name") + handle, flow = data_flow_ops.tensor_array_v3( + size, + dtype=dtype, + # We don't set element shape since we don't know if writes are stacked or + # not yet. + element_shape=None, + dynamic_size=dynamic_size, + clear_after_read=clear_after_read, + identical_element_shapes=identical_element_shapes, + tensor_array_name=tensor_array_name) + # Note we keep flow unstacked for now since we don't know if writes will be + # stacked or not. + return wrap(handle, False), wrap(flow, False) + + +@RegisterPFor("TensorArraySizeV3") +def _convert_tensor_array_size_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + flow, flow_stacked, _ = pfor_input.input(1) + if flow_stacked: + flow = _unstack_flow(flow) + size = data_flow_ops.tensor_array_size_v3(handle, flow) + return wrap(size, False) + + +def _handle_inside_pfor(pfor_input, handle): + """Returns True if handle was created inside the pfor loop.""" + # We use some heuristic to find the original TensorArray creation op. + # The logic should handle the common cases (except cond based subgraphs). + # In theory the user could perform different operations on the handle (like + # Reshape, stack multiple handles, etc) which could break this logic. + # TODO(agarwal): handle Switch/Merge. + while handle.op.type in ("Enter", "Identity"): + handle = handle.op.inputs[0] + if handle.op.type not in [ + "TensorArrayV3", "TensorArrayGradV3", "TensorArrayGradWithShape"]: + raise ValueError("Unable to find source for handle %s" % handle) + else: + return pfor_input.pfor.op_is_inside_loop(handle.op) + + +def _unstack_flow(value): + # TODO(agarwal): consider looking if this is a Tile op then get its input. + # This may avoid running the Tile operations. + return array_ops.gather(value, 0) + + +@RegisterPFor("TensorArrayReadV3") +def _convert_tensor_array_read_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + index, index_stacked, _ = pfor_input.input(1) + dtype = pfor_input.get_attr("dtype") + flow, flow_stacked, _ = pfor_input.input(2) + if flow_stacked: + flow = _unstack_flow(flow) + + is_inside_pfor = _handle_inside_pfor(pfor_input, pfor_input.op.inputs[0]) + if is_inside_pfor: + # Note that if we are inside a control flow construct inside the pfor, and + # only some of the iterations are doing the read (i.e. + # `all_indices_partitioned` is True), then the read operation should only + # return values for the currently active pfor iterations (`all_indices` + # below). Hence, whenever the returned value is stacked (i.e. `flow` is + # stacked), we may need to do an extra gather after reading the values. Also + # note that if `is_inside` is false, then values in the tensor array are + # unstacked. So the check is only needed in this branch. + all_indices = pfor_input.pfor.all_indices + all_indices_partitioned = pfor_input.pfor.all_indices_partitioned + # Note: flow_stacked indicates if values in the TensorArray are stacked or + # not. + if index_stacked: + if flow_stacked: + raise ValueError( + "It looks like TensorArrayReadV3 was called on a TensorArray whose" + " values are not loop-invariant, and the read indices were also" + " not loop invariant. This is currently unsupported.") + value = data_flow_ops.tensor_array_gather_v3( + handle, index, flow, dtype=dtype) + return wrap(value, True) + value = data_flow_ops.tensor_array_read_v3( + handle, index, flow, dtype=dtype) + if flow_stacked and all_indices_partitioned: + value = array_ops.gather(value, all_indices) + return wrap(value, flow_stacked) + # Values in the TensorArray should be unstacked (since different iterations + # couldn't write to the same location). So whether output is stacked or not + # depends on index_stacked. + if index_stacked: + value = data_flow_ops.tensor_array_gather_v3( + handle, index, flow, dtype=dtype) + else: + value = data_flow_ops.tensor_array_read_v3( + handle, index, flow, dtype=dtype) + return wrap(value, index_stacked) + + +@RegisterPFor("TensorArrayWriteV3") +def _convert_tensor_array_write_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + index, index_stacked, _ = pfor_input.input(1) + value, value_stacked, _ = pfor_input.input(2) + flow, flow_stacked, _ = pfor_input.input(3) + if value_stacked and pfor_input.pfor.all_indices_partitioned: + # Looks like we are in a control flow in a pfor where not all iterations are + # active now. We don't allow that since that could lead to different indices + # having different shapes which will be hard to merge later. + raise ValueError("Writing non loop invariant values to TensorArray from " + "inside a while_loop/cond not supported.") + if flow_stacked: + flow = _unstack_flow(flow) + is_inside = _handle_inside_pfor(pfor_input, pfor_input.op.inputs[0]) + if is_inside: + if index_stacked: + raise ValueError("Need indices for %s to be loop invariant" % handle) + if not flow_stacked and not value_stacked: + flow_out = data_flow_ops.tensor_array_write_v3(handle, index, value, flow) + return wrap(flow_out, False) + else: + if not value_stacked: + value = _stack(value, pfor_input.pfor.loop_len_vector).t + # TODO(agarwal): Note that if flow is unstacked and value is stacked, then + # this may or may not be a safe situation. flow is unstacked both for a + # freshly created TensorArray, as well as after unstacked values are + # written to it. If it is the latter, then we cannot write a stacked value + # now since that may cause runtime errors due to different shapes in the + # array. At the moment we are not able to handle this gracefully and + # distinguish between the two cases. That would require some heuristic + # traversal of the graph to figure out whether all the writes are + # unstacked or not. + flow_out = data_flow_ops.tensor_array_write_v3(handle, index, value, flow) + return _stack(flow_out, pfor_input.pfor.loop_len_vector) + else: + if not index_stacked: + raise ValueError("Need indices for %s to be not loop invariant" % handle) + # Note that even when index_stacked is true, actual values in index may + # still not be unique. However that will cause runtime error when executing + # the scatter operation below. + if not value_stacked: + value = _stack(value, pfor_input.pfor.loop_len_vector).t + flow_out = data_flow_ops.tensor_array_scatter_v3(handle, index, value, flow) + return _stack(flow_out, pfor_input.pfor.loop_len_vector) + + +def _transpose_first_two_dims(value): + # TODO(agarwal): optimize if one of the dims == 1. + value_shape = array_ops.shape(value) + v0 = value_shape[0] + v1 = value_shape[1] + value = array_ops.reshape(value, [v0, v1, -1]) + value = array_ops.transpose(value, [1, 0, 2]) + new_shape = array_ops.concat([[v1, v0], value_shape[2:]], axis=0) + return array_ops.reshape(value, new_shape) + + +@RegisterPFor("TensorArrayGatherV3") +def _convert_tensor_array_gather_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + indices, indices_stacked, _ = pfor_input.input(1) + indices = array_ops.reshape(indices, [-1]) + flow, flow_stacked, _ = pfor_input.input(2) + if flow_stacked: + flow = _unstack_flow(flow) + dtype = pfor_input.get_attr("dtype") + # TODO(agarwal): support element_shape attr? + + n = pfor_input.pfor.loop_len_vector + value = data_flow_ops.tensor_array_gather_v3( + handle, indices, flow, dtype=dtype) + is_inside = _handle_inside_pfor(pfor_input, pfor_input.op.inputs[0]) + if is_inside: + # flow_stacked indicates if values in the TensorArray are stacked or not. + if indices_stacked: + if flow_stacked: + raise ValueError( + "It looks like TensorArrayGatherV3 was called on a TensorArray " + "whose values are not loop-invariant, and the indices were also " + "not loop invariant. This is currently unsupported.") + else: + value = _unflatten_first_dim(value, n) + return wrap(value, True) + else: + if flow_stacked: + # Since elements in this array are stacked and `value` was produced by + # gather, its first two dims are "gathered elements" and "stack + # dimension". Our semantics require these two to be flipped. + value = _transpose_first_two_dims(value) + return wrap(value, flow_stacked) + else: + # Values in the TensorArray should be unstacked (since different iterations + # couldn't write to the same location). So whether output is stacked or not + # depends on indices_stacked. + if indices_stacked: + value = _unflatten_first_dim(value, n) + return wrap(value, indices_stacked) + + +@RegisterPFor("TensorArrayScatterV3") +def _convert_tensor_array_scatter_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + indices, indices_stacked, _ = pfor_input.input(1) + indices = array_ops.reshape(indices, [-1]) + value, value_stacked, _ = pfor_input.input(2) + flow, flow_stacked, _ = pfor_input.input(3) + + if flow_stacked: + flow = _unstack_flow(flow) + + is_inside = _handle_inside_pfor(pfor_input, pfor_input.op.inputs[0]) + if is_inside: + if indices_stacked: + raise ValueError("Need indices for %s to be loop invariant" % handle) + # Note that flow_stacked indicates if existing values in the array are + # stacked or not. + if not flow_stacked and not value_stacked: + flow_out = data_flow_ops.tensor_array_scatter_v3(handle, indices, value, + flow) + return wrap(flow_out, False) + if not value_stacked: + # TODO(agarwal): tile in the second dimension directly instead of + # transposing below. + value = _stack(value, pfor_input.pfor.loop_len_vector).t + + value = _transpose_first_two_dims(value) + # TODO(agarwal): Note that if a previous write was unstacked, flow will be + # unstacked, and a stacked value may be written here which may cause + # runtime error due to different elements having different shape. We do + # not try to prevent that. + flow_out = data_flow_ops.tensor_array_scatter_v3(handle, indices, value, + flow) + return _stack(flow_out, pfor_input.pfor.loop_len_vector) + if not indices_stacked: + raise ValueError("Need indices for %s to be not loop invariant" % handle) + if not value_stacked: + value = _stack(value, pfor_input.pfor.loop_len_vector).t + value = _flatten_first_two_dims(value) + flow_out = data_flow_ops.tensor_array_scatter_v3(handle, indices, value, + flow) + return _stack(flow_out, pfor_input.pfor.loop_len_vector) + + +@RegisterPFor("TensorArrayGradV3") +def _convert_tensor_array_grad_v3(pfor_input): + handle = pfor_input.unstacked_input(0) + flow, flow_stacked, _ = pfor_input.input(1) + if flow_stacked: + flow = _unstack_flow(flow) + source = pfor_input.get_attr("source") + # TODO(agarwal): For now, we assume that gradients are stacked if the + # TensorArrayGradV3 call is being done inside the pfor. Getting that wrong + # will give runtime error due to incorrect shape being written to the + # accumulator. It is difficult to know in advance if gradients written will be + # stacked or not. Note that flow being stacked is not indicative of the + # gradient being stacked or not. Revisit this later. + shape_to_prepend = pfor_input.pfor.loop_len_vector + grad_handle, flow_out = data_flow_ops.tensor_array_grad_with_shape( + handle=handle, + flow_in=flow, + shape_to_prepend=shape_to_prepend, + source=source) + flow_out = _stack(flow_out, pfor_input.pfor.loop_len_vector).t + return [wrap(grad_handle, False), wrap(flow_out, True)] + + +# StackV2 conversion is tricky since we don't have arrays of StackV2. So similar +# to TensorArrays, we convert them by changing the dimension of the elements +# inside the stack. +# +# We consider two cases: +# +# 1. StackV2 is constructed and used entirely inside the pfor loop. +# We keep a single Stack and perform the push/pop operations of all the +# iterations in lock-step. We also assume that all the iterations perform these +# operations. In case of dynamic control flow, if only some of the iterations +# try to perform a push/pop, then the conversion may not work correctly and may +# cause undefined behavior. +# TODO(agarwal): test StackV2 with dynamic control flow. +# +# 2. StackV2 is constructed outside the pfor loop. +# Performing stack push/pop in a parallel fashion is ill-defined. However given +# that reading stacks created externally is a common operation when computing +# jacobians, we provide some special semantics here as follows. +# - disallow push operations to the stack +# - pop operations are performed in lock step by all iterations, similar to the +# case when the stack is created inside. A single value is popped during the +# lock-step operation and broadcast to all the iterations. Values in the stack +# are assumed to be loop-invariant. +# +# Some other implementation details: +# We use an ugly logic to find whether values in Stack data structure are +# loop invariant or not. When converting push/pop operations, we keep track of +# whether the last conversion used a stacked value or not (see _stack_cache +# below). As a result if an unstacked value is written first, subsequent stacked +# writes are disallowed when they could have been allowed in theory. + +# Map from cache key based on StackV2 handle to a bool indicating whether values +# are stacked or not. +# TODO(agarwal): move _stack_cache inside pfor? +_stack_cache = {} + + +def _stack_cache_key(pfor_input): + """Create cache key corresponding to a stack handle.""" + op_type = pfor_input.op_type + assert op_type in ["StackPushV2", "StackPopV2"], op_type + orig_handle = pfor_input.op.inputs[0] + while orig_handle.op.type in ["Identity", "Enter"]: + orig_handle = orig_handle.op.inputs[0] + assert orig_handle.op.type == "StackV2", orig_handle.op + return ops.get_default_graph(), pfor_input.pfor, orig_handle + + +def _stack_handle_inside_pfor(handle, pfor_input): + while handle.op.type in ["Identity", "Enter"]: + handle = handle.op.inputs[0] + assert handle.op.type == "StackV2", ( + "Unable to find StackV2 op. Got %s" % handle.op) + return pfor_input.pfor.op_is_inside_loop(handle.op) + + +@RegisterPFor("StackPushV2") +def _convert_stack_push_v2(pfor_input): + handle = pfor_input.unstacked_input(0) + elem, elem_stacked, _ = pfor_input.input(1) + swap_memory = pfor_input.get_attr("swap_memory") + + if not _stack_handle_inside_pfor(pfor_input.op.inputs[0], pfor_input): + raise ValueError("StackPushV2 not allowed on stacks created outside pfor") + stack_cache_key = _stack_cache_key(pfor_input) + stacked = _stack_cache.get(stack_cache_key, None) + if stacked is None: + stacked = elem_stacked + _stack_cache[stack_cache_key] = stacked + else: + # If we previously made it unstacked then we can't revert to being stacked. + if not stacked and elem_stacked: + raise ValueError( + "It looks like the stack was previously determined to be loop" + " invariant, but we are now trying to push a loop dependent value" + " to it. This is currently unsupported.") + if stacked and not elem_stacked: + elem = _stack(elem, pfor_input.pfor.loop_len_vector).t + out = data_flow_ops.stack_push_v2(handle, elem, swap_memory=swap_memory) + return wrap(out, stacked) + + +# Note that inputs to this convertor will be unstacked. However it should get +# called since it is a stateful op. +@RegisterPFor("StackPopV2") +def _convert_stack_pop_v2(pfor_input): + handle = pfor_input.unstacked_input(0) + stack_cache_key = _stack_cache_key(pfor_input) + stacked = _stack_cache.get(stack_cache_key, None) + # If a StackPushV2 has not been converted yet, we default to unstacked since + # the push could be outside of pfor, or the covertor may not be called if the + # inputs are unconverted. + if stacked is None: + stacked = False + _stack_cache[stack_cache_key] = False + elem_type = pfor_input.get_attr("elem_type") + out = data_flow_ops.stack_pop_v2(handle, elem_type) + return wrap(out, stacked) diff --git a/tensorflow/python/ops/random_grad.py b/tensorflow/python/ops/random_grad.py new file mode 100644 index 0000000000000000000000000000000000000000..baa8e2e2cd33d37312b5b14bea3c248c06ff2e50 --- /dev/null +++ b/tensorflow/python/ops/random_grad.py @@ -0,0 +1,65 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Gradients for operators defined in random_ops.py.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_random_ops +from tensorflow.python.ops import math_ops + + +def add_leading_unit_dimensions(x, num_dimensions): + new_shape = array_ops.concat( + [array_ops.ones([num_dimensions], dtype=dtypes.int32), + array_ops.shape(x)], axis=0) + return array_ops.reshape(x, new_shape) + + +@ops.RegisterGradient("RandomGamma") +def _RandomGammaGrad(op, grad): # pylint: disable=invalid-name + """Returns the gradient of a Gamma sample w.r.t. alpha. + + The gradient is computed using implicit differentiation, see + "Implicit Reparameterization Gradients" (https://arxiv.org/abs/1805.08498). + + Args: + op: A `RandomGamma` operation. We assume that the inputs to the operation + are `shape` and `alpha` tensors, and the output is the `sample` tensor. + grad: The incoming gradient `dloss / dsample` of the same shape as + `op.outputs[0]`. + + Returns: + A `Tensor` with derivatives `dloss / dalpha` + """ + shape = op.inputs[0] + alpha = op.inputs[1] + sample = op.outputs[0] + + with ops.control_dependencies([grad]): + # Make the parameters alpha broadcastable with samples by appending + # unit dimensions. + num_sample_dimensions = array_ops.shape(shape)[0] + alpha_broadcastable = add_leading_unit_dimensions( + alpha, num_sample_dimensions) + partial_a = gen_random_ops.random_gamma_grad(alpha_broadcastable, sample) + + # The first input is shape; the second input is alpha. + return (None, math_ops.reduce_sum( + grad * partial_a, axis=math_ops.range(num_sample_dimensions))) diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index 6a2dd3f1cd55eea1d3b652a31cd2784c411c2ce0..b8738adf66e6ff51962ed44dce7cd4b95544e271 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -368,25 +368,41 @@ def random_gamma(shape, `alpha` is the shape parameter describing the distribution(s), and `beta` is the inverse scale parameter(s). - Example: + Note: Because internal calculations are done using `float64` and casting has + `floor` semantics, we must manually map zero outcomes to the smallest + possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This + means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise + should. This bias can only happen for small values of `alpha`, i.e., + `alpha << 1` or large values of `beta`, i.e., `beta >> 1`. - samples = tf.random_gamma([10], [0.5, 1.5]) - # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents - # the samples drawn from each distribution + The samples are differentiable w.r.t. alpha and beta. + The derivatives are computed using the approach described in the paper - samples = tf.random_gamma([7, 5], [0.5, 1.5]) - # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] - # represents the 7x5 samples drawn from each of the two distributions + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) - samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]]) - # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. + Example: - Note: Because internal calculations are done using `float64` and casting has - `floor` semantics, we must manually map zero outcomes to the smallest - possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This - means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise - should. This bias can only happen for small values of `alpha`, i.e., - `alpha << 1` or large values of `beta`, i.e., `beta >> 1`. + ```python + samples = tf.random_gamma([10], [0.5, 1.5]) + # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents + # the samples drawn from each distribution + + samples = tf.random_gamma([7, 5], [0.5, 1.5]) + # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] + # represents the 7x5 samples drawn from each of the two distributions + + alpha = tf.constant([[1.],[3.],[5.]]) + beta = tf.constant([[3., 4.]]) + samples = tf.random_gamma([30], alpha=alpha, beta=beta) + # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. + + loss = tf.reduce_mean(tf.square(samples)) + dloss_dalpha, dloss_dbeta = tf.gradients(loss, [alpha, beta]) + # unbiased stochastic derivatives of the loss function + alpha.shape == dloss_dalpha.shape # True + beta.shape == dloss_dbeta.shape # True + ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output samples @@ -406,8 +422,9 @@ def random_gamma(shape, name: Optional name for the operation. Returns: - samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))` - with values of type `dtype`. + samples: a `Tensor` of shape + `tf.concat([shape, tf.shape(alpha + beta)], axis=0)` with values of type + `dtype`. """ with ops.name_scope(name, "random_gamma", [shape, alpha, beta]): shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32) @@ -421,8 +438,6 @@ def random_gamma(shape, gen_random_ops.random_gamma( shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta) -ops.NotDifferentiable("RandomGamma") - @tf_export("random_poisson") def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): @@ -432,13 +447,15 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): Example: - samples = tf.random_poisson([0.5, 1.5], [10]) - # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents - # the samples drawn from each distribution + ```python + samples = tf.random_poisson([0.5, 1.5], [10]) + # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents + # the samples drawn from each distribution - samples = tf.random_poisson([12.2, 3.3], [7, 5]) - # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] - # represents the 7x5 samples drawn from each of the two distributions + samples = tf.random_poisson([12.2, 3.3], [7, 5]) + # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] + # represents the 7x5 samples drawn from each of the two distributions + ``` Args: lam: A Tensor or Python value or N-D array of type `dtype`. @@ -455,8 +472,8 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): name: Optional name for the operation. Returns: - samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with - values of type `dtype`. + samples: a `Tensor` of shape `tf.concat([shape, tf.shape(lam)], axis=0)` + with values of type `dtype`. """ with ops.name_scope(name, "random_poisson", [lam, shape]): shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index c137bfacb2e239598d78076a630c65f2253e1457..15cafbbde50335de0dc0cd8849425c07b4ac81d3 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -851,14 +851,15 @@ class ResourceVariable(variables.Variable): operator: string. The operator name. """ + tensor_oper = getattr(ops.Tensor, operator) def _run_op(a, *args): # pylint: disable=protected-access value = a._AsTensor() - return getattr(ops.Tensor, operator)(value, *args) + return tensor_oper(value, *args) # Propagate __doc__ to wrapper try: - _run_op.__doc__ = getattr(ops.Tensor, operator).__doc__ + _run_op.__doc__ = tensor_oper.__doc__ except AttributeError: pass @@ -998,32 +999,28 @@ class ResourceVariable(variables.Variable): def __imul__(self, unused_other): raise RuntimeError("Variable *= value not supported. Use " - "variable.assign_mul(value) to modify the variable " - "value and variable = variable * value to get a new " - "Tensor object.") + "`var.assign(var * value)` to modify the variable or " + "`var = var * value` to get a new Tensor object.") def __idiv__(self, unused_other): raise RuntimeError("Variable /= value not supported. Use " - "variable.assign_div(value) to modify the variable " - "value and variable = variable / value to get a new " - "Tensor object.") + "`var.assign(var / value)` to modify the variable or " + "`var = var / value` to get a new Tensor object.") def __itruediv__(self, unused_other): raise RuntimeError("Variable /= value not supported. Use " - "variable.assign_div(value) to modify the variable " - "value and variable = variable / value to get a new " - "Tensor object.") + "`var.assign(var / value)` to modify the variable or " + "`var = var / value` to get a new Tensor object.") def __irealdiv__(self, unused_other): raise RuntimeError("Variable /= value not supported. Use " - "variable.assign_div(value) to modify the variable " - "value and variable = variable / value to get a new " - "Tensor object.") + "`var.assign(var / value)` to modify the variable or " + "`var = var / value` to get a new Tensor object.") def __ipow__(self, unused_other): raise RuntimeError("Variable **= value not supported. Use " - "value and variable = variable ** value to get a new " - "Tensor object.") + "`var.assign(var ** value)` to modify the variable or " + "`var = var ** value` to get a new Tensor object.") pywrap_tensorflow.TFE_Py_RegisterResourceVariableType(ResourceVariable) @@ -1067,6 +1064,10 @@ class _UnreadVariable(ResourceVariable): self._graph_element = self.read_value() self._handle_deleter = deleter + @property + def name(self): + return self._parent_op.name + def value(self): return self._read_variable_op() diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 10d576c95bc4fd3147da44ee1522dc829bcab83d..deba133fb9910f28c7f902f334174734c3c742f7 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import tensor_array_ops @@ -131,6 +132,18 @@ def _maybe_tensor_shape_from_tensor(shape): return shape +def _should_cache(): + """Returns True if a default caching device should be set, otherwise False.""" + if context.executing_eagerly(): + return False + # Don't set a caching device when running in a loop, since it is possible that + # train steps could be wrapped in a tf.while_loop. In that scenario caching + # prevents forward computations in loop iterations from re-reading the + # updated weights. + ctxt = ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access + return control_flow_util.GetContainingWhileContext(ctxt) is None + + # pylint: disable=unused-argument def _rnn_step( time, sequence_length, min_sequence_length, max_sequence_length, @@ -558,7 +571,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - if not context.executing_eagerly(): + if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -828,7 +841,8 @@ def _dynamic_rnn_loop(cell, final_outputs = nest.pack_sequence_as( structure=cell.output_size, flat_sequence=final_outputs) if not in_graph_mode: - final_outputs = array_ops.stack(final_outputs, axis=0) + final_outputs = nest.map_structure_up_to( + cell.output_size, lambda x: array_ops.stack(x, axis=0), final_outputs) return (final_outputs, final_state) @@ -1014,7 +1028,7 @@ def raw_rnn(cell, loop_fn, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if not context.executing_eagerly(): + if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -1227,7 +1241,7 @@ def static_rnn(cell, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if not context.executing_eagerly(): + if _should_cache(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 05723c6960af3772d9576756ee94bd19f562edd1..82a044a0d4c8710f5ade0aa460f4354a0dd35deb 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -47,6 +47,7 @@ from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import tracking as checkpointable_tracking from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -1331,7 +1332,7 @@ class MultiRNNCell(RNNCell): return cur_inp, new_states -class _SlimRNNCell(RNNCell, checkpointable.NotCheckpointable): +class _SlimRNNCell(RNNCell, checkpointable_tracking.NotCheckpointable): """A simple wrapper for slim.rnn_cells.""" def __init__(self, cell_fn): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 16c73213d59821723483b72f18357fb3583f6777..1e3f662ff34f67d2b5f226427c8a03d82b9f2a7c 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Script Language Operators. See the @{$python/script_ops} guide.""" # pylint: disable=g-bad-name @@ -30,30 +29,55 @@ import numpy as np import six from tensorflow.python import pywrap_tensorflow +from tensorflow.python.eager import backprop from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_script_ops from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.util import compat from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export +# Map from EagerPyFunc token to tuple (tape, eager args, eager outputs); +# used for differentiation. +tape_cache = {} + class EagerFunc(object): """A wrapper for a function owned by an EagerPyFunc.""" - def __init__(self, func, Tout): + def __init__(self, func, Tout, is_grad_func): """Constructs an EagerFunc. Args: func: The function to wrap. Tout: A list of datatypes for the output; an empty list if the output is None. + is_grad_func: Whether this EagerFunc is the gradient of another + EagerPyFunc. """ self._func = func self._out_dtypes = Tout + self._is_grad_func = is_grad_func def _convert(self, value, dtype): + """Converts `value` to a tensor of type `dtype`, with error checking. + + Args: + value: The tensor to convert. + dtype: The desired dtype. + + Returns: + A tensor of type `dtype`, or a zeros tensor if value is None and + this function is in fact a grdient function. + + Raises: + RuntimeError: if `value` is a variable. + """ + if isinstance(value, resource_variable_ops.ResourceVariable): raise RuntimeError( "Attempting to return a variable from an eagerly executed py_func. " @@ -61,22 +85,39 @@ class EagerFunc(object): "be returned; to return the value of a variable, make sure to obtain " "the Tensor backing it by calling `.read_value()` on the variable in " "question: %s" % value) + if value is None and self._is_grad_func: + # Gradient functions may legitimately return a list that contains + # both Tensors and Python Nones. Unfortuantely this breaks the + # OpKernel, so for now we replace None objects with zeros, which is + # mathematically correct but will prevent short-circuiting gradient + # computations. + # + # TODO(akshayka): Make it possible to return a list of both Tensors and + # Nones from an EagerPyFunc. + return constant_op.constant(0.0, dtype=dtype) return ops.convert_to_tensor(value, dtype=dtype) - def __call__(self, on_gpu, args): + def __call__(self, device, token, args): """Passes `args` to `self._func`, which is executed eagerly.""" - with context.eager_mode(): + + with context.eager_mode(), backprop.GradientTape() as tape: + for tensor in args: + tape.watch(tensor) ret = self._func(*args) - maybe_copy_to_gpu = lambda x: x if not on_gpu else x.gpu() - if isinstance(ret, (tuple, list)): - return [ - maybe_copy_to_gpu(self._convert(x, dtype=dtype)) - for (x, dtype) in zip(ret, self._out_dtypes) - ] - elif ret is None: - return ret - else: - return maybe_copy_to_gpu(self._convert(ret, dtype=self._out_dtypes[0])) + # Use tf.identity to copy the returned tensors to device if neccesary. + with ops.device(device): + if isinstance(ret, (tuple, list)): + outputs = [ + array_ops.identity(self._convert(x, dtype=dtype)) + for (x, dtype) in zip(ret, self._out_dtypes) + ] + elif ret is None: + outputs = None + else: + outputs = array_ops.identity( + self._convert(ret, dtype=self._out_dtypes[0])) + tape_cache[compat.as_bytes(token)] = (tape, args, outputs) + return outputs class FuncRegistry(object): @@ -133,14 +174,14 @@ class FuncRegistry(object): else: return result - def __call__(self, token, on_gpu, args): + def __call__(self, token, device, args): """Calls the registered function for `token` with args. Args: token: A key into this `FuncRegistry` identifying which function to call. - on_gpu: A boolean indicating whether or not `token`'s corresponding - operation was placed on GPU; only used if the function registered for - `token` is an `EagerPyFunc`. + device: Name of the device on which outputs of `token`'s corresponding + operation should be placed. Used iff the function registered for `token` + is an EagerPyFunc. args: The arguments to pass to the function registered for `token`. Returns: @@ -153,7 +194,14 @@ class FuncRegistry(object): if func is None: raise ValueError("callback %s is not found" % token) if isinstance(func, EagerFunc): - return func(on_gpu, args) + # NB: Different invocations of the same py_func will share the same + # token, and the entries they stash in the tape_cache will collide. + # In practice, when executing a graph, this should only happen if + # the py_func is in a while_loop whose iterations are run in parallel + # or if the graph is being driven by concurrent session.run() calls. + # + # TODO(akshayka): Key the tape cache in a thread-safe way. + return func(device, token, args) else: ret = func(*args) # Strings seem to lead to a memory leak here if they're not wrapped in a @@ -184,7 +232,13 @@ _py_funcs = FuncRegistry() pywrap_tensorflow.InitializePyTrampoline(_py_funcs) -def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): +def _internal_py_func(func, + inp, + Tout, + stateful=None, + eager=False, + is_grad_func=False, + name=None): """See documentation for py_func and eager_py_func.""" is_list_or_tuple = False @@ -194,7 +248,7 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): Tout = [Tout] if eager: - func = EagerFunc(func, Tout) + func = EagerFunc(func, Tout, is_grad_func) token = _py_funcs.insert(func) # We tie the registered function's lifetime with the current default graph, @@ -231,34 +285,56 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): return result if is_list_or_tuple else result[0] +# TODO(akshayka): Implement higher-order derivatives. +@ops.RegisterGradient("EagerPyFunc") +def _EagerPyFuncGrad(op, dy): + """Computes the gradient of an EagerPyFunc.""" + + token = op.get_attr("token") + + def eagerly_executed_grad(dy): + tape, eager_inputs, eager_outputs = tape_cache.pop(compat.as_bytes(token)) + return tape.gradient(eager_outputs, eager_inputs, output_gradients=dy) + + with ops.control_dependencies(op.outputs): + return _internal_py_func( + func=eagerly_executed_grad, + inp=[dy] if isinstance(dy, ops.Tensor) else dy, + Tout=[tensor.dtype for tensor in op.inputs], + eager=True, + is_grad_func=True) + + def eager_py_func(func, inp, Tout, name=None): """Wraps a python function into a TensorFlow op that executes it eagerly. This function allows expressing computations in a TensorFlow graph as Python functions. In particular, it wraps a Python function `func` - in a TensorFlow operation that executes it with eager exeuction enabled. As a - consequence, `tf.contrib.eager.py_func` makes it possible to express control - flow using Python constructs (`if`, `while`, `for`, etc.), instead of - TensorFlow control flow constructs (@{tf.cond}, @{tf.while_loop}). For - example, you might use `tf.contrib.eager.py_func` to implement the log huber - function: + in a once-differentiable TensorFlow operation that executes it with eager + exeuction enabled. As a consequence, `tf.contrib.eager.py_func` makes it + possible to express control flow using Python constructs (`if`, `while`, + `for`, etc.), instead of TensorFlow control flow constructs (@{tf.cond}, + @{tf.while_loop}). For example, you might use `tf.contrib.eager.py_func` to + implement the log huber function: ```python def log_huber(x, m): if tf.abs(x) <= m: - return x ** 2 + return x**2 else: - return m ** 2 * (1 - 2 * tf.log(m) + tf.log(x ** 2)) + return m**2 * (1 - 2 * tf.log(m) + tf.log(x**2)) x = tf.placeholder(tf.float32) m = tf.placeholder(tf.float32) y = tf.contrib.eager.py_func(func=log_huber, inp=[x, m], Tout=tf.float32) + dy_dx = tf.gradients(y, x)[0] with tf.Session() as sess: # The session executes `log_huber` eagerly. Given the feed values below, - # it will take the second branch, so `output` evaluates to 7.24372. - output = sess.run(y, feed_dict={x: 3.0, m: 2.0}) + # it will take the first branch, so `y` evaluates to 1.0 and + # `dy_dx` evaluates to 2.0. + y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0}) ``` You can also use `tf.contrib.eager.py_func` to debug your models at runtime @@ -267,7 +343,7 @@ def eager_py_func(func, inp, Tout, name=None): or print statements as desired, and wrap those functions in `tf.contrib.eager.py_func`. - For more information on eager execution, see @{$programmers_guide/eager}. + For more information on eager execution, see @{$guide/eager}. `tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike the latter, the former lets you use TensorFlow operations in the wrapped @@ -277,10 +353,6 @@ def eager_py_func(func, inp, Tout, name=None): that take Tensors as inputs, execute TensorFlow operations in their bodies, and return Tensors as outputs. - `tf.contrib.eager.py_func` is not differentiable, though a gradient may be - implemented in the future; if you would like to differentiate through it, - please file an issue on Github. - Like @{tf.py_func}, `tf.contrib.eager.py_func` has the following limitations with respect to serialization and distribution: diff --git a/tensorflow/python/ops/sparse_grad.py b/tensorflow/python/ops/sparse_grad.py index 97353d6c747cb7e4d3c1fa92ad61af24fb17de91..1223b290ff6cfcfba27f40c05556c85b59e77148 100644 --- a/tensorflow/python/ops/sparse_grad.py +++ b/tensorflow/python/ops/sparse_grad.py @@ -116,6 +116,35 @@ def _SparseReduceSumGrad(op, out_grad): None, None) +@ops.RegisterGradient("SparseSlice") +def _SparseSliceGrad(op, *grads): + """The backward operator for the SparseSlice op. + + This op takes in the upstream gradient w.r.t. non-empty values of + the sliced `SparseTensor`, and outputs the gradients w.r.t. + the non-empty values of input `SparseTensor`. + + Args: + op: the SparseSlice op + *grads: the incoming gradients, one element per output of `op` + + Returns: + Gradient for each of the 5 input tensors of SparseSlice: + (indices, values, shape, start, size) + The gradients for the indices, shape, start and the size are None. + """ + backprop_val_grad = grads[1] + input_indices = op.inputs[0] + input_start = op.inputs[3] + output_indices = op.outputs[0] + + val_grad = gen_sparse_ops.sparse_slice_grad( + backprop_val_grad, input_indices, input_start, output_indices) + val_grad.set_shape(op.inputs[1].get_shape()) + # (indices, values, shape, start, size) + return (None, val_grad, None, None, None) + + @ops.RegisterGradient("SparseTensorDenseMatMul") def _SparseTensorDenseMatMulGrad(op, grad): """Gradients for the dense tensor in the SparseTensorDenseMatMul op. diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 6204adef3bb5dc96dab4a16bf05824d32627fccc..9a10abfcf736be783bfcd7907ec6f357912828ab 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -34,7 +34,7 @@ from tensorflow.python.util.tf_export import tf_export # TODO(b/27419586) Change docstring for required dtype of x once int allowed @tf_export('lbeta') -def lbeta(x, name='lbeta'): +def lbeta(x, name=None): r"""Computes \\(ln(|Beta(x)|)\\), reducing along the last dimension. Given one-dimensional `z = [z_0,...,z_{K-1}]`, we define @@ -64,7 +64,7 @@ def lbeta(x, name='lbeta'): # This is consistent with a convention that the sum over the empty set 0, and # the product is 1. # This is standard. See https://en.wikipedia.org/wiki/Empty_set. - with ops.name_scope(name, values=[x]): + with ops.name_scope(name, 'lbeta', [x]): x = ops.convert_to_tensor(x, name='x') # Note reduce_sum([]) = 0. @@ -82,6 +82,54 @@ def lbeta(x, name='lbeta'): return result +@tf_export('math.bessel_i0') +def bessel_i0(x, name=None): + """Computes the Bessel i0 function of `x` element-wise. + + Modified Bessel function of order 0. + + It is preferable to use the numerically stabler function `i0e(x)` instead. + + Args: + x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`, + `float32`, `float64`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`. + + @compatibility(scipy) + Equivalent to scipy.special.i0 + @end_compatibility + """ + with ops.name_scope(name, 'bessel_i0', [x]): + return math_ops.exp(math_ops.abs(x)) * math_ops.bessel_i0e(x) + + +@tf_export('math.bessel_i1') +def bessel_i1(x, name=None): + """Computes the Bessel i1 function of `x` element-wise. + + Modified Bessel function of order 1. + + It is preferable to use the numerically stabler function `i1e(x)` instead. + + Args: + x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`, + `float32`, `float64`. + name: A name for the operation (optional). + + Returns: + A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`. + + @compatibility(scipy) + Equivalent to scipy.special.i1 + @end_compatibility + """ + with ops.name_scope(name, 'bessel_i1', [x]): + return math_ops.exp(math_ops.abs(x)) * math_ops.bessel_i1e(x) + + @tf_export('einsum', 'linalg.einsum') def einsum(equation, *inputs, **kwargs): """A generalized contraction between tensors of arbitrary dimension. @@ -153,6 +201,8 @@ def einsum(equation, *inputs, **kwargs): indices in its subscript, or - the input shapes are inconsistent along a particular axis. """ + equation = equation.replace(' ', '') + name = kwargs.pop('name', None) if kwargs: raise TypeError('invalid keyword arguments for this function: ' + ', '.join( diff --git a/tensorflow/python/ops/special_math_ops_test.py b/tensorflow/python/ops/special_math_ops_test.py index 6118b54293dddd52fff5f9770c32b1365a70d60e..9bc4098d5b63c3e8ee4f9c14332e65b3d2875d8b 100644 --- a/tensorflow/python/ops/special_math_ops_test.py +++ b/tensorflow/python/ops/special_math_ops_test.py @@ -25,23 +25,25 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import special_math_ops from tensorflow.python.platform import test - +from tensorflow.python.platform import tf_logging class LBetaTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes def test_one_dimensional_arg(self): # Should evaluate to 1 and 1/2. x_one = [1, 1.] x_one_half = [2, 1.] with self.test_session(use_gpu=True): - self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_one)).eval()) - self.assertAllClose(0.5, - math_ops.exp( - special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose( + 1, self.evaluate(math_ops.exp(special_math_ops.lbeta(x_one)))) + self.assertAllClose( + 0.5, self.evaluate(math_ops.exp(special_math_ops.lbeta(x_one_half)))) self.assertEqual([], special_math_ops.lbeta(x_one).get_shape()) def test_one_dimensional_arg_dynamic(self): @@ -52,7 +54,8 @@ class LBetaTest(test.TestCase): ph = array_ops.placeholder(dtypes.float32) beta_ph = math_ops.exp(special_math_ops.lbeta(ph)) self.assertAllClose(1, beta_ph.eval(feed_dict={ph: x_one})) - self.assertAllClose(0.5, beta_ph.eval(feed_dict={ph: x_one_half})) + self.assertAllClose(0.5, + beta_ph.eval(feed_dict={ph: x_one_half})) def test_four_dimensional_arg_with_partial_shape_dynamic(self): x_ = np.ones((3, 2, 3, 4)) @@ -65,15 +68,17 @@ class LBetaTest(test.TestCase): with self.test_session(use_gpu=True): x_ph = array_ops.placeholder(dtypes.float32, [3, 2, 3, None]) beta_ph = math_ops.exp(special_math_ops.lbeta(x_ph)) - self.assertAllClose(expected_beta_x, beta_ph.eval(feed_dict={x_ph: x_})) + self.assertAllClose(expected_beta_x, + beta_ph.eval(feed_dict={x_ph: x_})) + @test_util.run_in_graph_and_eager_modes def test_two_dimensional_arg(self): # Should evaluate to 1/2. x_one_half = [[2, 1.], [2, 1.]] with self.test_session(use_gpu=True): - self.assertAllClose([0.5, 0.5], - math_ops.exp( - special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose( + [0.5, 0.5], + self.evaluate(math_ops.exp(special_math_ops.lbeta(x_one_half)))) self.assertEqual((2,), special_math_ops.lbeta(x_one_half).get_shape()) def test_two_dimensional_arg_dynamic(self): @@ -82,50 +87,59 @@ class LBetaTest(test.TestCase): with self.test_session(use_gpu=True): ph = array_ops.placeholder(dtypes.float32) beta_ph = math_ops.exp(special_math_ops.lbeta(ph)) - self.assertAllClose([0.5, 0.5], beta_ph.eval(feed_dict={ph: x_one_half})) + self.assertAllClose([0.5, 0.5], + beta_ph.eval(feed_dict={ph: x_one_half})) + @test_util.run_in_graph_and_eager_modes def test_two_dimensional_proper_shape(self): # Should evaluate to 1/2. x_one_half = [[2, 1.], [2, 1.]] with self.test_session(use_gpu=True): - self.assertAllClose([0.5, 0.5], - math_ops.exp( - special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose( + [0.5, 0.5], + self.evaluate(math_ops.exp(special_math_ops.lbeta(x_one_half)))) self.assertEqual( (2,), - array_ops.shape(special_math_ops.lbeta(x_one_half)).eval()) + self.evaluate(array_ops.shape(special_math_ops.lbeta(x_one_half)))) self.assertEqual( tensor_shape.TensorShape([2]), special_math_ops.lbeta(x_one_half).get_shape()) + @test_util.run_in_graph_and_eager_modes def test_complicated_shape(self): with self.test_session(use_gpu=True): x = ops.convert_to_tensor(np.random.rand(3, 2, 2)) - self.assertAllEqual((3, 2), - array_ops.shape(special_math_ops.lbeta(x)).eval()) + self.assertAllEqual( + (3, 2), self.evaluate(array_ops.shape(special_math_ops.lbeta(x)))) self.assertEqual( tensor_shape.TensorShape([3, 2]), special_math_ops.lbeta(x).get_shape()) + @test_util.run_in_graph_and_eager_modes def test_length_1_last_dimension_results_in_one(self): # If there is only one coefficient, the formula still works, and we get one # as the answer, always. x_a = [5.5] x_b = [0.1] with self.test_session(use_gpu=True): - self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_a)).eval()) - self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_b)).eval()) + self.assertAllClose( + 1, self.evaluate(math_ops.exp(special_math_ops.lbeta(x_a)))) + self.assertAllClose( + 1, self.evaluate(math_ops.exp(special_math_ops.lbeta(x_b)))) self.assertEqual((), special_math_ops.lbeta(x_a).get_shape()) + @test_util.run_in_graph_and_eager_modes def test_empty_rank1_returns_negative_infinity(self): with self.test_session(use_gpu=True): x = constant_op.constant([], shape=[0]) lbeta_x = special_math_ops.lbeta(x) expected_result = constant_op.constant(-np.inf, shape=()) - self.assertAllEqual(expected_result.eval(), lbeta_x.eval()) + self.assertAllEqual(self.evaluate(expected_result), + self.evaluate(lbeta_x)) self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape()) + @test_util.run_in_graph_and_eager_modes def test_empty_rank2_with_zero_last_dim_returns_negative_infinity(self): with self.test_session(use_gpu=True): event_size = 0 @@ -134,9 +148,11 @@ class LBetaTest(test.TestCase): lbeta_x = special_math_ops.lbeta(x) expected_result = constant_op.constant(-np.inf, shape=[batch_size]) - self.assertAllEqual(expected_result.eval(), lbeta_x.eval()) + self.assertAllEqual(self.evaluate(expected_result), + self.evaluate(lbeta_x)) self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape()) + @test_util.run_in_graph_and_eager_modes def test_empty_rank2_with_zero_batch_dim_returns_empty(self): with self.test_session(use_gpu=True): batch_size = 0 @@ -146,10 +162,40 @@ class LBetaTest(test.TestCase): expected_result = constant_op.constant([], shape=[batch_size]) - self.assertAllEqual(expected_result.eval(), lbeta_x.eval()) + self.assertAllEqual(self.evaluate(expected_result), + self.evaluate(lbeta_x)) self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape()) +class BesselTest(test.TestCase): + + @test_util.run_in_graph_and_eager_modes + def test_bessel_i0(self): + x_single = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32) + x_double = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self.assertAllClose(special.i0(x_single), + self.evaluate(special_math_ops.bessel_i0(x_single))) + self.assertAllClose(special.i0(x_double), + self.evaluate(special_math_ops.bessel_i0(x_double))) + except ImportError as e: + tf_logging.warn('Cannot test special functions: %s' % str(e)) + + @test_util.run_in_graph_and_eager_modes + def test_bessel_i1(self): + x_single = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32) + x_double = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64) + try: + from scipy import special # pylint: disable=g-import-not-at-top + self.assertAllClose(special.i1(x_single), + self.evaluate(special_math_ops.bessel_i1(x_single))) + self.assertAllClose(special.i1(x_double), + self.evaluate(special_math_ops.bessel_i1(x_double))) + except ImportError as e: + tf_logging.warn('Cannot test special functions: %s' % str(e)) + + class EinsumTest(test.TestCase): simple_cases = [ @@ -195,6 +241,12 @@ class EinsumTest(test.TestCase): 'iJ,Jk->ik', 'iJ,Ki->JK', 'iJk,Jklm->Jk' + 'ij, jk, kl -> il', + 'a, ab, abc -> abc', + 'ab, ab, cd, cd, ef, ef -> ', + 'abc, bac', + 'iJ, Ki -> JK', + 'iJk, Jklm -> Jk' ] long_cases = [ @@ -203,6 +255,8 @@ class EinsumTest(test.TestCase): 'ea,fb,gc,hd,abcd->efgh', 'ea,fb,abcd,gc,hd->efgh', 'abhe,hidj,jgba,hiab,gab', + 'efc, dbc, acf, fd -> abe', + 'abhe, hidj, jgba, hiab, gab', ] invalid_cases = [ @@ -273,14 +327,14 @@ class EinsumTest(test.TestCase): input_axes, _, _ = axes.partition('->') for idx in input_axes.split(','): - shape = [all_axes[ax] for ax in idx] + shape = [all_axes[ax] for ax in idx if ax.isalpha()] input_vals.append(np.random.random(shape)) input_tensors = [constant_op.constant(val) for val in input_vals] output_tensor = special_math_ops.einsum(axes, *input_tensors) with self.test_session(use_gpu=True): - output_value = output_tensor.eval() + output_value = self.evaluate(output_tensor) correct_value = np.einsum(axes, *input_vals) diff --git a/tensorflow/python/ops/spectral_ops.py b/tensorflow/python/ops/spectral_ops.py index 28054f50ef3b1227f12376b4b3700a7618270d65..293aace7282eb0f8dde9da75b0d353a560c0ecb9 100644 --- a/tensorflow/python/ops/spectral_ops.py +++ b/tensorflow/python/ops/spectral_ops.py @@ -167,8 +167,8 @@ def _validate_dct_arguments(dct_type, n, axis, norm): raise NotImplementedError("The DCT length argument is not implemented.") if axis != -1: raise NotImplementedError("axis must be -1. Got: %s" % axis) - if dct_type != 2: - raise ValueError("Only the Type II DCT is supported.") + if dct_type not in (2, 3): + raise ValueError("Only Types II and III (I)DCT are supported.") if norm not in (None, "ortho"): raise ValueError( "Unknown normalization. Expected None or 'ortho', got: %s" % norm) @@ -179,18 +179,20 @@ def _validate_dct_arguments(dct_type, n, axis, norm): def dct(input, type=2, n=None, axis=-1, norm=None, name=None): # pylint: disable=redefined-builtin """Computes the 1D [Discrete Cosine Transform (DCT)][dct] of `input`. - Currently only Type II is supported. Implemented using a length `2N` padded - @{tf.spectral.rfft}, as described here: https://dsp.stackexchange.com/a/10606 + Currently only Types II and III are supported. Type II is implemented using a + length `2N` padded @{tf.spectral.rfft}, as described here: + https://dsp.stackexchange.com/a/10606. Type III is a fairly straightforward + inverse of Type II (i.e. using a length `2N` padded @{tf.spectral.irfft}). @compatibility(scipy) - Equivalent to scipy.fftpack.dct for the Type-II DCT. + Equivalent to scipy.fftpack.dct for Type-II and Type-III DCT. https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html @end_compatibility Args: input: A `[..., samples]` `float32` `Tensor` containing the signals to take the DCT of. - type: The DCT type to perform. Must be 2. + type: The DCT type to perform. Must be 2 or 3. n: For future expansion. The length of the transform. Must be `None`. axis: For future expansion. The axis to compute the DCT along. Must be `-1`. norm: The normalization to apply. `None` for no normalization or `'ortho'` @@ -201,8 +203,8 @@ def dct(input, type=2, n=None, axis=-1, norm=None, name=None): # pylint: disabl A `[..., samples]` `float32` `Tensor` containing the DCT of `input`. Raises: - ValueError: If `type` is not `2`, `n` is not `None, `axis` is not `-1`, or - `norm` is not `None` or `'ortho'`. + ValueError: If `type` is not `2` or `3`, `n` is not `None, `axis` is not + `-1`, or `norm` is not `None` or `'ortho'`. [dct]: https://en.wikipedia.org/wiki/Discrete_cosine_transform """ @@ -214,22 +216,91 @@ def dct(input, type=2, n=None, axis=-1, norm=None, name=None): # pylint: disabl axis_dim = input.shape[-1].value or _array_ops.shape(input)[-1] axis_dim_float = _math_ops.to_float(axis_dim) - scale = 2.0 * _math_ops.exp(_math_ops.complex( - 0.0, -_math.pi * _math_ops.range(axis_dim_float) / - (2.0 * axis_dim_float))) - - # TODO(rjryan): Benchmark performance and memory usage of the various - # approaches to computing a DCT via the RFFT. - dct2 = _math_ops.real( - rfft(input, fft_length=[2 * axis_dim])[..., :axis_dim] * scale) - - if norm == "ortho": - n1 = 0.5 * _math_ops.rsqrt(axis_dim_float) - n2 = n1 * _math_ops.sqrt(2.0) - # Use tf.pad to make a vector of [n1, n2, n2, n2, ...]. - weights = _array_ops.pad( - _array_ops.expand_dims(n1, 0), [[0, axis_dim - 1]], - constant_values=n2) - dct2 *= weights - - return dct2 + if type == 2: + scale = 2.0 * _math_ops.exp( + _math_ops.complex( + 0.0, -_math_ops.range(axis_dim_float) * _math.pi * 0.5 / + axis_dim_float)) + + # TODO(rjryan): Benchmark performance and memory usage of the various + # approaches to computing a DCT via the RFFT. + dct2 = _math_ops.real( + rfft(input, fft_length=[2 * axis_dim])[..., :axis_dim] * scale) + + if norm == "ortho": + n1 = 0.5 * _math_ops.rsqrt(axis_dim_float) + n2 = n1 * _math_ops.sqrt(2.0) + # Use tf.pad to make a vector of [n1, n2, n2, n2, ...]. + weights = _array_ops.pad( + _array_ops.expand_dims(n1, 0), [[0, axis_dim - 1]], + constant_values=n2) + dct2 *= weights + + return dct2 + + elif type == 3: + if norm == "ortho": + n1 = _math_ops.sqrt(axis_dim_float) + n2 = n1 * _math_ops.sqrt(0.5) + # Use tf.pad to make a vector of [n1, n2, n2, n2, ...]. + weights = _array_ops.pad( + _array_ops.expand_dims(n1, 0), [[0, axis_dim - 1]], + constant_values=n2) + input *= weights + else: + input *= axis_dim_float + scale = 2.0 * _math_ops.exp( + _math_ops.complex( + 0.0, + _math_ops.range(axis_dim_float) * _math.pi * 0.5 / + axis_dim_float)) + dct3 = _math_ops.real( + irfft( + scale * _math_ops.complex(input, 0.0), + fft_length=[2 * axis_dim]))[..., :axis_dim] + + return dct3 + + +# TODO(rjryan): Implement `type`, `n` and `axis` parameters. +@tf_export("spectral.idct") +def idct(input, type=2, n=None, axis=-1, norm=None, name=None): # pylint: disable=redefined-builtin + """Computes the 1D [Inverse Discrete Cosine Transform (DCT)][idct] of `input`. + + Currently only Types II and III are supported. Type III is the inverse of + Type II, and vice versa. + + Note that you must re-normalize by 1/(2n) to obtain an inverse if `norm` is + not `'ortho'`. That is: + `signal == idct(dct(signal)) * 0.5 / signal.shape[-1]`. + When `norm='ortho'`, we have: + `signal == idct(dct(signal, norm='ortho'), norm='ortho')`. + + @compatibility(scipy) + Equivalent to scipy.fftpack.idct for Type-II and Type-III DCT. + https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.idct.html + @end_compatibility + + Args: + input: A `[..., samples]` `float32` `Tensor` containing the signals to take + the DCT of. + type: The IDCT type to perform. Must be 2 or 3. + n: For future expansion. The length of the transform. Must be `None`. + axis: For future expansion. The axis to compute the DCT along. Must be `-1`. + norm: The normalization to apply. `None` for no normalization or `'ortho'` + for orthonormal normalization. + name: An optional name for the operation. + + Returns: + A `[..., samples]` `float32` `Tensor` containing the IDCT of `input`. + + Raises: + ValueError: If `type` is not `2` or `3`, `n` is not `None, `axis` is not + `-1`, or `norm` is not `None` or `'ortho'`. + + [idct]: + https://en.wikipedia.org/wiki/Discrete_cosine_transform#Inverse_transforms + """ + _validate_dct_arguments(type, n, axis, norm) + inverse_type = {2: 3, 3: 2}[type] + return dct(input, type=inverse_type, n=n, axis=axis, norm=norm, name=name) diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index a2d24711e2291bafcf5736c6206ceb09ac210453..d0e5f700254fa5273cb707e59ac0d141fdc13627 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import cudnn_rnn_grad from tensorflow.python.ops import data_flow_grad from tensorflow.python.ops import manip_grad from tensorflow.python.ops import math_grad +from tensorflow.python.ops import random_grad from tensorflow.python.ops import sparse_grad from tensorflow.python.ops import spectral_grad from tensorflow.python.ops import state_grad diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 08b7cda73bdc739912ec58f161ec7113aeffd9e8..8cb6a0537e928effbcf4c475bcc4e974182da2a7 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -394,7 +394,7 @@ def scatter_add(ref, indices, updates, use_locking=False, name=None): A tensor of indices into the first dimension of `ref`. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to store in `ref`. - use_locking: An optional `bool`. Defaults to `True`. + use_locking: An optional `bool`. Defaults to `False`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). @@ -458,7 +458,7 @@ def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): A tensor of indices into ref. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to add to ref. - use_locking: An optional `bool`. Defaults to `True`. + use_locking: An optional `bool`. Defaults to `False`. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. diff --git a/tensorflow/python/ops/summary_ops_v2.py b/tensorflow/python/ops/summary_ops_v2.py index b80f84eb7cde264c5a7c83eafacc344adb50b80a..00150fe68820da711c76f642baced45163a8727c 100644 --- a/tensorflow/python/ops/summary_ops_v2.py +++ b/tensorflow/python/ops/summary_ops_v2.py @@ -306,10 +306,11 @@ def create_db_writer(db_uri, def _make_summary_writer(name, factory, **kwargs): resource = gen_summary_ops.summary_writer(shared_name=name) init_op_fn = lambda: factory(resource, **kwargs) - # TODO(apassos): Consider doing this instead. - # if not context.executing_eagerly(): - # ops.get_default_session().run(init_op) - ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, init_op_fn()) + init_op = init_op_fn() + if not context.executing_eagerly(): + # TODO(apassos): Consider doing this instead. + # ops.get_default_session().run(init_op) + ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, init_op) return SummaryWriter(resource, init_op_fn) @@ -380,7 +381,8 @@ def summary_writer_function(name, tensor, function, family=None): with ops.device("cpu:0"): op = smart_cond.smart_cond( should_record_summaries(), record, _nothing, name="") - ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access + if not context.executing_eagerly(): + ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access return op diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 355b0d961e2105bf19105dbc6f8a9ddfc41c0d30..161d9687d6b0af58a3e8aef5518d70432e70691c 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import util as checkpointable_util from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.deprecation import deprecated @@ -295,66 +296,6 @@ class Template(checkpointable.CheckpointableBase): # which is not the same as whether the scope has been created. self._variables_created = False - def _checkpointable_custom_creator(self, next_creator, name, initial_value, - checkpointable_parent=None, **kwargs): - """A variable creation hook which adds Checkpointable dependencies. - - Set during the `Template`'s first wrapped function execution. Ensures that - (a) `Template` objects depend on `Template`s created inside them which - create variables, and (b) that any variables not in a more deeply nested - `Template` are added as dependencies directly. - - The `checkpointable_parent` argument is passed between `Template` custom - creators but ignored when the variable object itself is created. This - argument indicates (if not `None`) that a more deeply nested `Template` has - already added the variable as a dependency, and that parent `Template`s - should add a dependency on that `Template` rather than on the variable - directly. - - Args: - next_creator: See `variable_scope.variable_creator_scope`; the next - creator in the chain. - name: The (full, scope-influenced) name of the variable. The scope name - for the Template itself is stripped for the purposes of object-based - dependency tracking, but scopes within Templates are respected. - initial_value: See `variable_scope.variable_creator_scope`. Taken - explicitly so the argument can be re-named and used with - `Checkpointable._add_variable_with_custom_getter`. - checkpointable_parent: If not None, a more deeply nested Template object - to add a dependency on (rather than depending on the variable directly). - **kwargs: Passed through to the next creator. - Returns: - The output of `next_creator`: the fetched/created variable object. - """ - def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): - inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which - # we don't want to propagate. - return next_creator( - initial_value=initializer, - name=name, - **inner_kwargs) - if name.startswith(self._variable_scope.name): - scope_stripped_name = name[len(self._variable_scope.name) + 1:] - if not checkpointable_parent: - return self._add_variable_with_custom_getter( - initializer=initial_value, - name=scope_stripped_name, - getter=_call_next_creator_renaming_initializer, - # Disable error checking for Checkpointable. Exceptions are instead - # raised if necessary when the object-based saver tries to - # save/restore the object. - overwrite=True, - checkpointable_parent=self, - **kwargs) - else: - self._track_checkpointable( - checkpointable_parent, - name=checkpointable_parent._variable_scope.name[ # pylint: disable=protected-access - len(self._variable_scope.name) + 1:], - overwrite=True) - return next_creator(name=name, initial_value=initial_value, - checkpointable_parent=self, **kwargs) - def _call_func(self, args, kwargs): try: vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) @@ -365,8 +306,7 @@ class Template(checkpointable.CheckpointableBase): else: # The first time we run, restore variables if necessary (via # Checkpointable). - with variable_scope.variable_creator_scope( - self._checkpointable_custom_creator): + with checkpointable_util.capture_dependencies(template=self): result = self._func(*args, **kwargs) if self._variables_created: @@ -634,8 +574,7 @@ class EagerTemplate(Template): else: # The first time we run, restore variables if necessary (via # Checkpointable). - with variable_scope.variable_creator_scope( - self._checkpointable_custom_creator): + with checkpointable_util.capture_dependencies(template=self): result = self._func(*args, **kwargs) if self._variables_created: diff --git a/tensorflow/python/ops/tensor_array_grad.py b/tensorflow/python/ops/tensor_array_grad.py index 1f70d695485ca0aab22c532099caad1b361d3637..d34134980400999ee2b0de9362423b2ec495868f 100644 --- a/tensorflow/python/ops/tensor_array_grad.py +++ b/tensorflow/python/ops/tensor_array_grad.py @@ -34,6 +34,7 @@ ops.NotDifferentiable("TensorArrayCloseV2") ops.NotDifferentiable("TensorArrayV3") ops.NotDifferentiable("TensorArrayGradV3") +ops.NotDifferentiable("TensorArrayGradWithShape") ops.NotDifferentiable("TensorArraySizeV3") ops.NotDifferentiable("TensorArrayCloseV3") diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 9a711edaa44f711aa4122ca282ca99ca23d17bdc..1e06bf07d5aaa88a4a30760450cffc32a20f4ca5 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1,4 +1,4 @@ - # Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -44,9 +44,11 @@ from tensorflow.python.util import function_utils from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -__all__ = ["AUTO_REUSE", "VariableScope", "get_variable_scope", - "get_variable", "get_local_variable", "variable_scope", - "variable_op_scope", "no_regularizer"] +__all__ = [ + "AUTO_REUSE", "VariableScope", "get_variable_scope", "get_variable", + "get_local_variable", "variable_scope", "variable_op_scope", + "no_regularizer", "VariableSynchronization", "VariableAggregation" +] class _PartitionInfo(object): @@ -188,6 +190,38 @@ class _ReuseMode(enum.Enum): # REUSE_FALSE = 2 # REUSE_TRUE = 3 + +@tf_export("VariableSynchronization") +class VariableSynchronization(enum.Enum): + """Indicates when a distributed variable will be synced.""" + + # Indicates that the synchronization will be determined by the current + # `DistributionStrategy` (eg. With `MirroredStrategy` this would be + # `ON_WRITE`). + AUTO = 0 + + # Indicates that there will only be one copy of the variable, so there is no + # need to sync. + NONE = 1 + + # Indicates that the variable will be aggregated across devices + # every time it is updated. + ON_WRITE = 2 + + # Indicates that the variable will be aggregated across devices + # when it is read (eg. when checkpointing or when evaluating an op that uses + # the variable). + ON_READ = 3 + + +@tf_export("VariableAggregation") +class VariableAggregation(enum.Enum): + """Indicates how a distributed variable will be aggregated.""" + NONE = 0 + SUM = 1 + MEAN = 2 + + AUTO_REUSE = _ReuseMode.AUTO_REUSE tf_export("AUTO_REUSE").export_constant(__name__, "AUTO_REUSE") AUTO_REUSE.__doc__ = """ @@ -214,11 +248,23 @@ class _VariableStore(object): self._partitioned_vars = {} # A dict of the stored PartitionedVariables. self._store_eager_variables = False - def get_variable(self, name, shape=None, dtype=dtypes.float32, - initializer=None, regularizer=None, reuse=None, - trainable=True, collections=None, caching_device=None, - partitioner=None, validate_shape=True, use_resource=None, - custom_getter=None, constraint=None): + def get_variable(self, + name, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + reuse=None, + trainable=True, + collections=None, + caching_device=None, + partitioner=None, + validate_shape=True, + use_resource=None, + custom_getter=None, + constraint=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): """Gets an existing variable with these parameters or create a new one. If a variable with the given name is already stored, we return the stored @@ -291,6 +337,14 @@ class _VariableStore(object): variable and return the Tensor for the projected value (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. Returns: The created or existing `Variable` (or `PartitionedVariable`, if a @@ -343,11 +397,22 @@ class _VariableStore(object): # it to custom_getter. # Note: the parameters of _true_getter, and their documentation, match # *exactly* item-for-item with the docstring of this method. - def _true_getter(name, shape=None, dtype=dtypes.float32, # pylint: disable=missing-docstring - initializer=None, regularizer=None, reuse=None, - trainable=True, collections=None, caching_device=None, - partitioner=None, validate_shape=True, use_resource=None, - constraint=None): + def _true_getter( # pylint: disable=missing-docstring + name, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + reuse=None, + trainable=True, + collections=None, + caching_device=None, + partitioner=None, + validate_shape=True, + use_resource=None, + constraint=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): is_scalar = (shape is not None and isinstance(shape, collections_lib.Sequence) and not shape) @@ -397,11 +462,20 @@ class _VariableStore(object): "name was already created with partitioning?" % name) return self._get_single_variable( - name=name, shape=shape, dtype=dtype, - initializer=initializer, regularizer=regularizer, reuse=reuse, - trainable=trainable, collections=collections, - caching_device=caching_device, validate_shape=validate_shape, - use_resource=use_resource, constraint=constraint) + name=name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + reuse=reuse, + trainable=trainable, + collections=collections, + caching_device=caching_device, + validate_shape=validate_shape, + use_resource=use_resource, + constraint=constraint, + synchronization=synchronization, + aggregation=aggregation) if custom_getter is not None: # Handle backwards compatibility with getter arguments that were added @@ -420,6 +494,8 @@ class _VariableStore(object): "partitioner": partitioner, "validate_shape": validate_shape, "use_resource": use_resource, + "synchronization": synchronization, + "aggregation": aggregation, } # `fn_args` can handle functions, `functools.partial`, `lambda`. if "constraint" in function_utils.fn_args(custom_getter): @@ -427,12 +503,21 @@ class _VariableStore(object): return custom_getter(**custom_getter_kwargs) else: return _true_getter( - name, shape=shape, dtype=dtype, - initializer=initializer, regularizer=regularizer, - reuse=reuse, trainable=trainable, collections=collections, - caching_device=caching_device, partitioner=partitioner, - validate_shape=validate_shape, use_resource=use_resource, - constraint=constraint) + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + reuse=reuse, + trainable=trainable, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + validate_shape=validate_shape, + use_resource=use_resource, + constraint=constraint, + synchronization=synchronization, + aggregation=aggregation) def _get_partitioned_variable( self, name, partitioner, shape=None, dtype=dtypes.float32, @@ -693,7 +778,9 @@ class _VariableStore(object): caching_device=None, validate_shape=True, use_resource=None, - constraint=None): + constraint=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): """Get or create a single Variable (e.g. a shard or entire variable). See the documentation of get_variable above (ignore partitioning components) @@ -713,6 +800,8 @@ class _VariableStore(object): validate_shape: see get_variable. use_resource: see get_variable. constraint: see get_variable. + synchronization: see get_variable. + aggregation: see get_variable. Returns: A Variable. See documentation of get_variable above. @@ -793,7 +882,9 @@ class _VariableStore(object): dtype=variable_dtype, validate_shape=validate_shape, constraint=constraint, - use_resource=use_resource) + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) if context.executing_eagerly() and self._store_eager_variables: if collections: ops.add_to_collections(collections, v) @@ -1052,7 +1143,9 @@ class VariableScope(object): validate_shape=True, use_resource=None, custom_getter=None, - constraint=None): + constraint=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): """Gets an existing variable with this name or create a new one.""" if regularizer is None: regularizer = self._regularizer @@ -1090,12 +1183,22 @@ class VariableScope(object): if dtype is None: dtype = self._dtype return var_store.get_variable( - full_name, shape=shape, dtype=dtype, initializer=initializer, - regularizer=regularizer, reuse=reuse, trainable=trainable, - collections=collections, caching_device=caching_device, - partitioner=partitioner, validate_shape=validate_shape, - use_resource=use_resource, custom_getter=custom_getter, - constraint=constraint) + full_name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + reuse=reuse, + trainable=trainable, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + validate_shape=validate_shape, + use_resource=use_resource, + custom_getter=custom_getter, + constraint=constraint, + synchronization=synchronization, + aggregation=aggregation) def _get_partitioned_variable(self, var_store, @@ -1326,14 +1429,28 @@ def get_variable(name, validate_shape=True, use_resource=None, custom_getter=None, - constraint=None): + constraint=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): return get_variable_scope().get_variable( - _get_default_variable_store(), name, shape=shape, dtype=dtype, - initializer=initializer, regularizer=regularizer, trainable=trainable, - collections=collections, caching_device=caching_device, - partitioner=partitioner, validate_shape=validate_shape, - use_resource=use_resource, custom_getter=custom_getter, - constraint=constraint) + _get_default_variable_store(), + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=trainable, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + validate_shape=validate_shape, + use_resource=use_resource, + custom_getter=custom_getter, + constraint=constraint, + synchronization=synchronization, + aggregation=aggregation) + + get_variable_or_local_docstring = ( """%s @@ -1430,29 +1547,44 @@ get_variable.__doc__ = get_variable_or_local_docstring % ( # The argument list for get_local_variable must match arguments to get_variable. # So, if you are updating the arguments, also update arguments to get_variable. @tf_export("get_local_variable") -def get_local_variable(name, - shape=None, - dtype=None, - initializer=None, - regularizer=None, - trainable=False, # pylint: disable=unused-argument - collections=None, - caching_device=None, - partitioner=None, - validate_shape=True, - use_resource=None, - custom_getter=None, - constraint=None): +def get_local_variable( # pylint: disable=missing-docstring + name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=False, # pylint: disable=unused-argument + collections=None, + caching_device=None, + partitioner=None, + validate_shape=True, + use_resource=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE, + custom_getter=None, + constraint=None): if collections: collections += [ops.GraphKeys.LOCAL_VARIABLES] else: collections = [ops.GraphKeys.LOCAL_VARIABLES] return get_variable( - name, shape=shape, dtype=dtype, initializer=initializer, - regularizer=regularizer, trainable=False, collections=collections, - caching_device=caching_device, partitioner=partitioner, - validate_shape=validate_shape, use_resource=use_resource, - custom_getter=custom_getter, constraint=constraint) + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=False, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + validate_shape=validate_shape, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation, + custom_getter=custom_getter, + constraint=constraint) + + get_local_variable.__doc__ = get_variable_or_local_docstring % ( "Gets an existing *local* variable or creates a new one.", "Behavior is the same as in `get_variable`, except that variables are\n" @@ -1925,7 +2057,8 @@ class variable_scope(object): for this scope as well as all sub-scopes; if tf.AUTO_REUSE, we create variables if they do not exist, and return them otherwise; if None, we inherit the parent scope's reuse flag. When eager execution is enabled, - this argument is always forced to be tf.AUTO_REUSE. + new variables are always created unless an EagerVariableStore or + template is currently active. dtype: type of variables created in this scope (defaults to the type in the passed scope, or inherited from parent scope). use_resource: If False, all variables will be regular Variables. If True, @@ -2213,6 +2346,12 @@ def default_variable_creator(next_creator=None, **kwargs): dtype = kwargs.get("dtype", None) constraint = kwargs.get("constraint", None) use_resource = kwargs.get("use_resource", None) + + # Enforce `ON_READ` variables to be not trainable. + synchronization = kwargs.get("synchronization", VariableSynchronization.AUTO) + if synchronization == VariableSynchronization.ON_READ: + trainable = False + if use_resource is None: use_resource = get_variable_scope().use_resource if use_resource or (use_resource is None and context.executing_eagerly()): @@ -2247,18 +2386,28 @@ def variable(initial_value=None, name=None, dtype=None, constraint=None, - use_resource=None): + use_resource=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access previous_getter = _make_getter(getter, previous_getter) - return previous_getter(initial_value=initial_value, - trainable=trainable, - collections=collections, - validate_shape=validate_shape, - caching_device=caching_device, - name=name, dtype=dtype, - constraint=constraint, - use_resource=use_resource) + + # Reset `aggregation` that is explicitly set as `None` to the enum None value. + if aggregation is None: + aggregation = VariableAggregation.NONE + return previous_getter( + initial_value=initial_value, + trainable=trainable, + collections=collections, + validate_shape=validate_shape, + caching_device=caching_device, + name=name, + dtype=dtype, + constraint=constraint, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) @tf_contextlib.contextmanager @@ -2310,6 +2459,14 @@ def variable_creator_scope(variable_creator): constraint: A constraint function to be applied to the variable after updates by some algorithms. use_resource: if True, a ResourceVariable is always created. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. This set may grow over time, so it's important the signature of creators is as mentioned above. diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index 4be9f5eb6864015cd9c3f6f3526285ebbdc180f9..9a09cdaa52425713cf18362dd8726fe7207c604f 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -1093,39 +1093,40 @@ class Variable(checkpointable.CheckpointableBase): def __imul__(self, other): logging.log_first_n( logging.WARN, - "Variable *= will be deprecated. Use variable.assign_mul" - " if you want assignment to the variable value or 'x = x * y'" + "Variable *= will be deprecated. Use `var.assign(var * other)`" + " if you want assignment to the variable value or `x = x * y`" " if you want a new python Tensor object.", 1) return self * other def __idiv__(self, other): logging.log_first_n( logging.WARN, - "Variable /= will be deprecated. Use variable.assign_div" - " if you want assignment to the variable value or 'x = x / y'" + "Variable /= will be deprecated. Use `var.assign(var / other)`" + " if you want assignment to the variable value or `x = x / y`" " if you want a new python Tensor object.", 1) return self / other def __itruediv__(self, other): logging.log_first_n( logging.WARN, - "Variable /= will be deprecated. Use variable.assign_div" - " if you want assignment to the variable value or 'x = x / y'" + "Variable /= will be deprecated. Use `var.assign(var / other)`" + " if you want assignment to the variable value or `x = x / y`" " if you want a new python Tensor object.", 1) return self / other def __irealdiv__(self, other): logging.log_first_n( logging.WARN, - "Variable /= will be deprecated. Use variable.assign_div" - " if you want assignment to the variable value or 'x = x / y'" + "Variable /= will be deprecated. Use `var.assign(var / other)`" + " if you want assignment to the variable value or `x = x / y`" " if you want a new python Tensor object.", 1) return self / other def __ipow__(self, other): logging.log_first_n( logging.WARN, - "Variable **= will be deprecated. Use 'x = x ** y'" + "Variable **= will be deprecated. Use `var.assign(var ** other)`" + " if you want assignment to the variable value or `x = x ** y`" " if you want a new python Tensor object.", 1) return self ** other @@ -1722,6 +1723,8 @@ def report_uninitialized_variables(var_list=None, var_list.append(op.outputs[0]) with ops.name_scope(name): # Run all operations on CPU + if var_list: + init_vars = [state_ops.is_variable_initialized(v) for v in var_list] with ops.device("/cpu:0"): if not var_list: # Return an empty tensor so we only need to check for returned tensor @@ -1729,9 +1732,7 @@ def report_uninitialized_variables(var_list=None, return array_ops.constant([], dtype=dtypes.string) else: # Get a 1-D boolean tensor listing whether each variable is initialized. - variables_mask = math_ops.logical_not( - array_ops.stack( - [state_ops.is_variable_initialized(v) for v in var_list])) + variables_mask = math_ops.logical_not(array_ops.stack(init_vars)) # Get a 1-D string tensor containing all the variable names. variable_names_tensor = array_ops.constant( [s.op.name for s in var_list]) diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py index 9e49188c1ef353d345c97ea0295aa1a68283605e..f9891f3b1e2e94f61329babd1409e3efacc7f5b3 100644 --- a/tensorflow/python/profiler/model_analyzer_test.py +++ b/tensorflow/python/profiler/model_analyzer_test.py @@ -707,8 +707,10 @@ class PrintModelAnalysisTest(test.TestCase): a = array_ops.constant(np.ones((100, 100))) b = array_ops.constant(np.ones((100, 100))) c = a * b + config = config_pb2.ConfigProto() + config.graph_options.rewrite_options.min_graph_nodes = -1 - with session.Session() as sess: + with session.Session(config=config) as sess: run_options = config_pb2.RunOptions( trace_level=config_pb2.RunOptions.FULL_TRACE) run_metadata = config_pb2.RunMetadata() diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i index 500dc30cc30f757965791e504bc79718bb7f7bd7..5d7535cf34f7396b7ff6aebd3984046e51c98347 100644 --- a/tensorflow/python/pywrap_tfe.i +++ b/tensorflow/python/pywrap_tfe.i @@ -59,6 +59,7 @@ limitations under the License. %rename("%s") TFE_ContextOptionsSetConfig; %rename("%s") TFE_ContextOptionsSetDevicePlacementPolicy; %rename("%s") TFE_ContextOptionsSetAsync; +%rename("%s") TFE_ContextOptionsSetServerDef; %rename("%s") TFE_DeleteContextOptions; %rename("%s") TFE_Py_TensorShapeSlice; %rename("%s") TFE_Py_TensorShapeOnDevice; diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 81786fbf435ffebba6217c0a03f06494195afc3c..076f2d8760fe00035ef5830a02d22e82c54dd768 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -87,6 +87,30 @@ py_library( "//tensorflow/python:platform", "//tensorflow/python:training", "//tensorflow/python:util", + "//tensorflow/python:variables", + ], +) + +py_test( + name = "loader_test", + size = "small", + srcs = ["loader_test.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:private"], + deps = [ + ":builder", + ":loader", + ":signature_def_utils", + ":utils", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:lib", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", ], ) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index bebf1d5e0d3cc6ac0e431230577704365d37a437..e5f649fdabb5cc2600a6fdd0e5ed9950d6bb23c2 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -28,6 +28,7 @@ from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver @@ -79,12 +80,14 @@ def _parse_saved_model(export_dir): constants.SAVED_MODEL_FILENAME_PB)) -def _get_asset_tensors(export_dir, meta_graph_def_to_load): +def _get_asset_tensors(export_dir, meta_graph_def_to_load, import_scope=None): """Gets the asset tensors, if defined in the meta graph def to load. Args: export_dir: Directory where the SavedModel is located. meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. + import_scope: Optional `string` -- if specified, prepend this followed by + '/' to all returned asset tensor names. Returns: A dictionary of asset tensors, keyed by the name of the asset tensor. The @@ -104,7 +107,10 @@ def _get_asset_tensors(export_dir, meta_graph_def_to_load): for asset_any_proto in assets_any_proto: asset_proto = meta_graph_pb2.AssetFileDef() asset_any_proto.Unpack(asset_proto) - asset_tensor_dict[asset_proto.tensor_info.name] = os.path.join( + tensor_name = asset_proto.tensor_info.name + if import_scope: + tensor_name = "%s/%s" % (import_scope, tensor_name) + asset_tensor_dict[tensor_name] = os.path.join( compat.as_bytes(assets_directory), compat.as_bytes(asset_proto.filename)) return asset_tensor_dict @@ -179,7 +185,7 @@ def maybe_saved_model_directory(export_dir): @tf_export("saved_model.loader.load") -def load(sess, tags, export_dir, **saver_kwargs): +def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): """Loads the model from a SavedModel as specified by tags. Args: @@ -189,6 +195,10 @@ def load(sess, tags, export_dir, **saver_kwargs): SavedModel `save()` API. export_dir: Directory in which the SavedModel protocol buffer and variables to be loaded are located. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. **saver_kwargs: Optional keyword arguments passed through to Saver. Returns: @@ -198,11 +208,56 @@ def load(sess, tags, export_dir, **saver_kwargs): Raises: RuntimeError: MetaGraphDef associated with the tags cannot be found. """ - with sess.graph.as_default(): - # Build the SavedModel protocol buffer and find requested meta graph def. - saved_model = _parse_saved_model(export_dir) + loader = SavedModelLoader(export_dir) + return loader.load(sess, tags, import_scope, **saver_kwargs) + + +class SavedModelLoader(object): + """Load graphs and restore variable values from a `SavedModel`.""" + + def __init__(self, export_dir): + """Creates a `SavedModelLoader`. + + Args: + export_dir: Directory in which the SavedModel protocol buffer and + variables to be loaded are located. + """ + self._export_dir = export_dir + self._variables_path = os.path.join( + compat.as_bytes(export_dir), + compat.as_bytes(constants.VARIABLES_DIRECTORY), + compat.as_bytes(constants.VARIABLES_FILENAME)) + self._saved_model = _parse_saved_model(export_dir) + + @property + def export_dir(self): + """Directory containing the SavedModel.""" + return self._export_dir + + @property + def variables_path(self): + """Path to variable checkpoint files.""" + return self._variables_path + + @property + def saved_model(self): + """SavedModel object parsed from the export directory.""" + return self._saved_model + + def get_meta_graph_def_from_tags(self, tags): + """Return MetaGraphDef with the exact specified tags. + + Args: + tags: A list or set of string tags that identify the MetaGraphDef. + + Returns: + MetaGraphDef with the same tags. + + Raises: + RuntimeError: if no metagraphs were found with the associated tags. + """ found_match = False - for meta_graph_def in saved_model.meta_graphs: + for meta_graph_def in self._saved_model.meta_graphs: if set(meta_graph_def.meta_info_def.tags) == set(tags): meta_graph_def_to_load = meta_graph_def found_match = True @@ -214,31 +269,100 @@ def load(sess, tags, export_dir, **saver_kwargs): " could not be found in SavedModel. To inspect available tag-sets in" " the SavedModel, please use the SavedModel CLI: `saved_model_cli`" ) - - # Build a saver by importing the meta graph def to load. - saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs) - - if saver: - # Build the checkpoint path where the variables are located. - variables_path = os.path.join( - compat.as_bytes(export_dir), - compat.as_bytes(constants.VARIABLES_DIRECTORY), - compat.as_bytes(constants.VARIABLES_FILENAME)) - - # Restore the variables using the built saver in the provided session. - saver.restore(sess, variables_path) - else: - tf_logging.info("The specified SavedModel has no variables; no " - "checkpoints were restored.") - - # Get asset tensors, if any. - asset_tensors_dictionary = _get_asset_tensors(export_dir, - meta_graph_def_to_load) - - main_op_tensor = ( - _get_main_op_tensor(meta_graph_def_to_load) or - (_get_legacy_init_op_tensor(meta_graph_def_to_load))) - if main_op_tensor is not None: - sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) - return meta_graph_def_to_load + + def load_graph(self, graph, tags, import_scope=None, **saver_kwargs): + """Load ops and nodes from SavedModel MetaGraph into graph. + + Args: + graph: tf.Graph object. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + Saver defined by the MetaGraph, which can be used to restore the variable + values. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with graph.as_default(): + return tf_saver.import_meta_graph( + meta_graph_def, import_scope=import_scope, **saver_kwargs) + + def restore_variables(self, sess, saver, import_scope=None): + """Restore SavedModel variable values into the session. + + Args: + sess: tf.Session to restore variable values. + saver: a tf.train.Saver object. Can be None if there are no variables in + graph. This may be the saver returned by the load_graph() function, or a + default `tf.train.Saver()`. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + + Raises: + ValueError: if no saver was passed to the saver argument, and there are + variables in the graph. + """ + with sess.graph.as_default(): + if (saver is None and + not variables._all_saveable_objects(scope=import_scope)): # pylint: disable=protected-access + tf_logging.info("The specified SavedModel has no variables; no " + "checkpoints were restored.") + elif isinstance(saver, tf_saver.Saver): + saver.restore(sess, self._variables_path) + else: + raise ValueError( + "No tf.train.Saver object was passed to the function " + "SavedModelLoader.restore_variables. Since there are variables in " + "the graph, a saver is required.") + + def run_init_ops(self, sess, tags, import_scope=None): + """Run initialization ops defined in the `MetaGraphDef`. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with sess.graph.as_default(): + # Get asset tensors, if any. + asset_tensors_dictionary = _get_asset_tensors( + self._export_dir, meta_graph_def, import_scope=import_scope) + + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def) or + (_get_legacy_init_op_tensor(meta_graph_def))) + if main_op_tensor is not None: + sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) + + def load(self, sess, tags, import_scope=None, **saver_kwargs): + """Load the MetaGraphDef graph and restore variable values into the session. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + `MetagraphDef` proto of the graph that was loaded. + """ + with sess.graph.as_default(): + saver = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) + self.restore_variables(sess, saver, import_scope) + self.run_init_ops(sess, tags, import_scope) + return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ce18859f6b9e4c141c4b27f3643c8d4004eb56f6 --- /dev/null +++ b/tensorflow/python/saved_model/loader_test.py @@ -0,0 +1,217 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for SavedModelLoader class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.client import session +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.saved_model import builder as saved_model_builder +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_def_utils +from tensorflow.python.saved_model import utils +from tensorflow.python.training import saver as tf_saver + + +def _get_export_dir(label): + return os.path.join(test.get_temp_dir(), label) + +SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model") +SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op") + + +class SavedModelLoaderTest(test.TestCase): + + def setUp(self): + """Write test SavedModels to a temp directory.""" + with session.Session(graph=ops.Graph()) as sess: + x = variables.Variable(5, name="x") + y = variables.Variable(11, name="y") + z = x + y + sess.run(variables.global_variables_initializer()) + + foo_sig_def = signature_def_utils.build_signature_def( + {"foo_input": utils.build_tensor_info(x)}, + {"foo_output": utils.build_tensor_info(z)}) + bar_sig_def = signature_def_utils.build_signature_def( + {"bar_x": utils.build_tensor_info(x), + "bar_y": utils.build_tensor_info(y)}, + {"bar_z": utils.build_tensor_info(z)}) + + builder = saved_model_builder.SavedModelBuilder(SIMPLE_ADD_SAVED_MODEL) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}) + builder.save() + + # Write SavedModel with a main_op + assign_op = control_flow_ops.group(state_ops.assign(y, 7)) + + builder = saved_model_builder.SavedModelBuilder(SAVED_MODEL_WITH_MAIN_OP) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}, + main_op=assign_op) + builder.save() + + def tearDown(self): + file_io.delete_recursively(test.get_temp_dir()) + + def test_load_function(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader2.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_load_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + graph = ops.Graph() + loader.load_graph(graph, ["foo_graph"]) + + x = graph.get_tensor_by_name("x:0") + y = graph.get_tensor_by_name("y:0") + + with self.assertRaises(KeyError): + graph.get_tensor_by_name("z:0") + + with self.test_session(graph=graph) as sess: + # Check that x and y are not initialized + with self.assertRaises(errors.FailedPreconditionError): + sess.run(x) + with self.assertRaises(errors.FailedPreconditionError): + sess.run(y) + + def test_load_with_import_scope(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") + + # The default saver should not work when the import scope is set. + with self.assertRaises(errors.NotFoundError): + loader.restore_variables(sess, tf_saver.Saver()) + + loader.restore_variables(sess, saver) + loader.run_init_ops(sess, ["foo_graph"]) + + self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval()) + + # Test combined load function. + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"], import_scope="baa") + self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval()) + + def test_restore_variables(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + x = variables.Variable(0, name="x") + y = variables.Variable(0, name="y") + z = x * y + + sess.run(variables.global_variables_initializer()) + + # There are variables to restore, so a saver must be created. + with self.assertRaises(ValueError): + loader.restore_variables(sess, None) + + loader.restore_variables(sess, tf_saver.Saver()) + self.assertEqual(55, z.eval()) + + def test_run_init_op(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + graph = ops.Graph() + saver = loader.load_graph(graph, ["foo_graph"]) + with self.test_session(graph=graph) as sess: + loader.restore_variables(sess, saver) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader.run_init_ops(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_parse_saved_model(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"]) + self.assertIsNotNone(meta_graph) + self.assertIn("foo", meta_graph.signature_def) + self.assertIn("bar", meta_graph.signature_def) + + def test_load_invalid_meta_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([""]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags(["not_a_graph"]) + + def test_load_saved_model_with_no_variables(self): + """Test that SavedModel runs saver when there appear to be no variables. + + When no variables are detected, this may mean that the variables were saved + to different collections, or the collections weren't saved to the + SavedModel. If the SavedModel MetaGraphDef contains a saver, it should still + run in either of these cases. + """ + path = _get_export_dir("no_variable_saved_model") + with session.Session(graph=ops.Graph()) as sess: + x = variables.Variable(5, name="x", collections=["not_global_variable"]) + y = variables.Variable(11, name="y", collections=["not_global_variable"]) + self.assertFalse(variables._all_saveable_objects()) + z = x + y + sess.run(variables.variables_initializer([x, y])) + + foo_sig_def = signature_def_utils.build_signature_def( + {"foo_input": utils.build_tensor_info(x)}, + {"foo_output": utils.build_tensor_info(z)}) + + builder = saved_model_builder.SavedModelBuilder(path) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def}, + saver=tf_saver.Saver([x, y])) + builder.save() + + loader = loader_impl.SavedModelLoader(path) + with self.test_session(graph=ops.Graph()) as sess: + saver = loader.load_graph(sess.graph, ["foo_graph"]) + self.assertFalse(variables._all_saveable_objects()) + self.assertIsNotNone(saver) + + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index effb38283bd053bf61506ceefad28c85ffadb3b0..fb4732aca21d4661aaea21a472475690687a42be 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -1197,6 +1197,59 @@ class SavedModelTest(test.TestCase): _validate_custom_saver("tag_1", "save_1/restore_all") _validate_custom_saver("tag_2", "save_2/restore_all") + def testImportScope(self): + export_dir = self._get_export_dir("test_scoped_assets") + builder = saved_model_builder.SavedModelBuilder(export_dir) + + # Build a SavedModel with a variable, an asset, and a constant tensor. + with self.test_session(graph=ops.Graph()) as sess: + self._init_and_validate_variable(sess, "v", 42) + asset_collection = self._build_asset_collection("foo.txt", "content_foo", + "asset_file_tensor") + constant_op.constant("constant value", name="constant_tensor_name") + builder.add_meta_graph_and_variables( + sess, ["tag_name"], assets_collection=asset_collection) + + # Save the asset file path for later comparison. + asset_file_path = asset_collection[0].eval() + + # Save the SavedModel to disk. + builder.save() + + with self.test_session(graph=ops.Graph()) as sess: + # Restore the SavedModel under an import_scope in a new graph/session. + graph_proto = loader.load( + sess, ["tag_name"], export_dir, import_scope="scope_name") + + # The loaded variable tensor should be scoped, but its contents should be + # unchanged. + self.assertEqual( + "scope_name/v:0", + ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].name) + self.assertEqual( + 42, + ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) + + # The loaded asset tensor should be scoped, but the asset file path and + # contents should be unchanged. + asset_collection = ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS) + self.assertEqual(1, len(asset_collection)) + self.assertEqual(asset_file_path, asset_collection[0].eval()) + self.assertEqual("scope_name/asset_file_tensor:0", + asset_collection[0].name) + # The static asset data inside graph_proto.collection_def should not be + # scoped. + self._validate_asset_collection(export_dir, graph_proto.collection_def, + "foo.txt", "content_foo", + "asset_file_tensor:0") + + # The constant tensor should be scoped, but its contents should be + # unchanged. + self.assertEqual( + compat.as_bytes("constant value"), + ops.get_default_graph().get_tensor_by_name( + "scope_name/constant_tensor_name:0").eval()) + def testClearDevices(self): export_dir = self._get_export_dir("test_clear_devices") builder = saved_model_builder.SavedModelBuilder(export_dir) diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 5b9d25d449d43d8420e0f30fa8b907d41171d5e5..38fed5335ef39e9832c8b47e3c872ada453aa645 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -15,7 +15,7 @@ """Command-line interface to inspect and execute a graph in a SavedModel. For detailed usages and examples, please refer to: -https://www.tensorflow.org/programmers_guide/saved_model_cli +https://www.tensorflow.org/guide/saved_model_cli """ @@ -720,7 +720,7 @@ def create_parser(): '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' \\\n' ' --outdir=/out\n\n' 'For more information about input file format, please see:\n' - 'https://www.tensorflow.org/programmers_guide/saved_model_cli\n') + 'https://www.tensorflow.org/guide/saved_model_cli\n') parser_run = subparsers.add_parser( 'run', description=run_msg, formatter_class=argparse.RawTextHelpFormatter) parser_run.add_argument( diff --git a/tensorflow/python/training/adadelta.py b/tensorflow/python/training/adadelta.py index c08e3cca007dc17f1112d53bf729c1accf61b5df..95eca76496992f7ac66643a4c94d7e9e812cecf8 100644 --- a/tensorflow/python/training/adadelta.py +++ b/tensorflow/python/training/adadelta.py @@ -46,6 +46,13 @@ class AdadeltaOptimizer(optimizer.Optimizer): use_locking: If `True` use locks for update operations. name: Optional name prefix for the operations created when applying gradients. Defaults to "Adadelta". + + @compatibility(eager) + When eager execution is enabled, `learning_rate`, `rho`, and `epsilon` can + each be a callable that takes no arguments and returns the actual value to + use. This can be useful for changing these values across different + invocations of optimizer functions. + @end_compatibility """ super(AdadeltaOptimizer, self).__init__(use_locking, name) self._lr = learning_rate @@ -63,9 +70,13 @@ class AdadeltaOptimizer(optimizer.Optimizer): self._zeros_slot(v, "accum_update", self._name) def _prepare(self): - self._lr_t = ops.convert_to_tensor(self._lr, name="lr") - self._rho_t = ops.convert_to_tensor(self._rho, name="rho") - self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon") + lr = self._call_if_callable(self._lr) + rho = self._call_if_callable(self._rho) + epsilon = self._call_if_callable(self._epsilon) + + self._lr_t = ops.convert_to_tensor(lr, name="lr") + self._rho_t = ops.convert_to_tensor(rho, name="rho") + self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") def _apply_dense(self, grad, var): accum = self.get_slot(var, "accum") diff --git a/tensorflow/python/training/adadelta_test.py b/tensorflow/python/training/adadelta_test.py index 50f435236b41fcda7ab5ea37a4e96b72dd1043e7..2678016d24b99b30cbf7021d67e33910051e2561 100644 --- a/tensorflow/python/training/adadelta_test.py +++ b/tensorflow/python/training/adadelta_test.py @@ -20,8 +20,10 @@ from __future__ import print_function import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import test_util from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -32,44 +34,52 @@ from tensorflow.python.training import adadelta class AdadeltaOptimizerTest(test.TestCase): - def doTestBasic(self, use_resource=False): + def doTestBasic(self, use_resource=False, use_callable_params=False): num_updates = 4 # number of ADADELTA steps to perform for dtype in [dtypes.half, dtypes.float32]: for grad in [0.2, 0.1, 0.01]: for lr in [1.0, 0.5, 0.1]: - with self.test_session(): - var0_init = [1.0, 2.0] - var1_init = [3.0, 4.0] - if use_resource: - var0 = resource_variable_ops.ResourceVariable( - var0_init, dtype=dtype) - var1 = resource_variable_ops.ResourceVariable( - var1_init, dtype=dtype) - else: - var0 = variables.Variable(var0_init, dtype=dtype) - var1 = variables.Variable(var1_init, dtype=dtype) - - grads = constant_op.constant([grad, grad], dtype=dtype) - - accum = 0.0 - accum_update = 0.0 - - # ADADELTA gradient optimizer - rho = 0.95 - epsilon = 1e-8 - adadelta_opt = adadelta.AdadeltaOptimizer(lr, rho, epsilon) + var0_init = [1.0, 2.0] + var1_init = [3.0, 4.0] + if use_resource: + var0 = resource_variable_ops.ResourceVariable( + var0_init, dtype=dtype) + var1 = resource_variable_ops.ResourceVariable( + var1_init, dtype=dtype) + else: + var0 = variables.Variable(var0_init, dtype=dtype) + var1 = variables.Variable(var1_init, dtype=dtype) + + grads = constant_op.constant([grad, grad], dtype=dtype) + + accum = 0.0 + accum_update = 0.0 + + # ADADELTA gradient optimizer + rho = 0.95 + epsilon = 1e-8 + if use_callable_params: + adadelta_opt = adadelta.AdadeltaOptimizer( + learning_rate=lambda: lr, # pylint: disable=cell-var-from-loop + rho=lambda: rho, # pylint: disable=cell-var-from-loop + epsilon=lambda: epsilon) # pylint: disable=cell-var-from-loop + else: + adadelta_opt = adadelta.AdadeltaOptimizer( + learning_rate=lr, rho=rho, epsilon=epsilon) + if not context.executing_eagerly(): adadelta_update = adadelta_opt.apply_gradients( zip([grads, grads], [var0, var1])) + self.evaluate(variables.global_variables_initializer()) + # TODO(lxuechen): This is hard to test in eager mode, + # since the optimizer is not fully initialized until the first + # call to `apply_gradients` opt_vars = adadelta_opt.variables() self.assertStartsWith(opt_vars[0].name, var0._shared_name) self.assertStartsWith(opt_vars[1].name, var0._shared_name) self.assertStartsWith(opt_vars[2].name, var1._shared_name) self.assertStartsWith(opt_vars[3].name, var1._shared_name) self.assertEqual(4, len(opt_vars)) - - variables.global_variables_initializer().run() - # Assign slots slot = [None] * 2 slot_update = [None] * 2 @@ -91,36 +101,42 @@ class AdadeltaOptimizerTest(test.TestCase): self.assertEquals(slot_update[1].get_shape(), var1.get_shape()) self.assertFalse(slot_update[1] in variables.trainable_variables()) - # Fetch params to validate initial values - self.assertAllClose(var0_init, var0.eval()) - self.assertAllClose(var1_init, var1.eval()) - - update = [None] * num_updates - tot_update = 0 - for step in range(num_updates): - # Run adadelta update for comparison - adadelta_update.run() - - # Perform initial update without previous accum values - accum = accum * rho + (grad**2) * (1 - rho) - update[step] = (np.sqrt(accum_update + epsilon) * - (1. / np.sqrt(accum + epsilon)) * grad) - accum_update = (accum_update * rho + (update[step]**2) * - (1.0 - rho)) - tot_update += update[step] * lr + # Fetch params to validate initial values + self.assertAllClose(var0_init, self.evaluate(var0)) + self.assertAllClose(var1_init, self.evaluate(var1)) + update = [None] * num_updates + tot_update = 0 + for step in range(num_updates): + # Run adadelta update for comparison + if not context.executing_eagerly(): + self.evaluate(adadelta_update) + else: + adadelta_opt.apply_gradients(zip([grads, grads], [var0, var1])) + + # Perform initial update without previous accum values + accum = accum * rho + (grad**2) * (1 - rho) + update[step] = ( + np.sqrt(accum_update + epsilon) * + (1. / np.sqrt(accum + epsilon)) * grad) + accum_update = ( + accum_update * rho + (update[step]**2) * (1.0 - rho)) + tot_update += update[step] * lr + + if not context.executing_eagerly(): # Check that the accumulators have been updated + # TODO(lxuechen): This is hard to test in eager mode for slot_idx in range(2): self.assertAllCloseAccordingToType( np.array([accum, accum], dtype=dtype.as_numpy_dtype()), - slot[slot_idx].eval(), + self.evaluate(slot[slot_idx]), rtol=1e-5) self.assertAllCloseAccordingToType( np.array( [accum_update, accum_update], dtype=dtype.as_numpy_dtype()), - slot_update[slot_idx].eval(), + self.evaluate(slot_update[slot_idx]), rtol=1e-5) # Check that the parameters have been updated @@ -128,22 +144,28 @@ class AdadeltaOptimizerTest(test.TestCase): np.array( [var0_init[0] - tot_update, var0_init[1] - tot_update], dtype=dtype.as_numpy_dtype()), - var0.eval(), + self.evaluate(var0), rtol=1e-5) self.assertAllCloseAccordingToType( np.array( [var1_init[0] - tot_update, var1_init[1] - tot_update], dtype=dtype.as_numpy_dtype()), - var1.eval(), + self.evaluate(var1), rtol=1e-5) def testBasic(self): - self.doTestBasic(use_resource=False) + with self.test_session(): + self.doTestBasic(use_resource=False) + @test_util.run_in_graph_and_eager_modes(reset_test=True) def testResourceBasic(self): self.doTestBasic(use_resource=True) + def testBasicCallableParams(self): + with context.eager_mode(): + self.doTestBasic(use_resource=True, use_callable_params=True) + def testMinimizeSparseResourceVariable(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.test_session(): diff --git a/tensorflow/python/training/adagrad.py b/tensorflow/python/training/adagrad.py index deb4e6f546379eff330235dbc302a30c44193830..6778f3c735a70fc32ed299bc9d800b270f06cc66 100644 --- a/tensorflow/python/training/adagrad.py +++ b/tensorflow/python/training/adagrad.py @@ -51,6 +51,13 @@ class AdagradOptimizer(optimizer.Optimizer): Raises: ValueError: If the `initial_accumulator_value` is invalid. + + @compatibility(eager) + When eager execution is enabled, `learning_rate` can be a callable that + takes no arguments and returns the actual value to use. This can be useful + for changing these values across different invocations of optimizer + functions. + @end_compatibility """ if initial_accumulator_value <= 0.0: raise ValueError("initial_accumulator_value must be positive: %s" % @@ -78,8 +85,9 @@ class AdagradOptimizer(optimizer.Optimizer): "accumulator", self._name) def _prepare(self): - self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, - name="learning_rate") + learning_rate = self._call_if_callable(self._learning_rate) + self._learning_rate_tensor = ops.convert_to_tensor( + learning_rate, name="learning_rate") def _apply_dense(self, grad, var): acc = self.get_slot(var, "accumulator") diff --git a/tensorflow/python/training/adagrad_test.py b/tensorflow/python/training/adagrad_test.py index 15b007b46dea6b3125c5f7bffe8782594bb23692..c9aec33d0916781e3d1a41b996083da92a4ae839 100644 --- a/tensorflow/python/training/adagrad_test.py +++ b/tensorflow/python/training/adagrad_test.py @@ -20,9 +20,11 @@ from __future__ import print_function import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -34,40 +36,63 @@ from tensorflow.python.training import adagrad class AdagradOptimizerTest(test.TestCase): - def doTestBasic(self, use_locking=False, use_resource=False): + def doTestBasic(self, + use_locking=False, + use_resource=False, + use_callable_params=False): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - if use_resource: - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) - else: - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - ada_opt = adagrad.AdagradOptimizer( - 3.0, initial_accumulator_value=0.1, use_locking=use_locking) + if use_resource: + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) + else: + var0 = variables.Variable([1.0, 2.0], dtype=dtype) + var1 = variables.Variable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + + learning_rate = lambda: 3.0 + if not use_callable_params: + learning_rate = learning_rate() + + ada_opt = adagrad.AdagradOptimizer( + learning_rate, initial_accumulator_value=0.1, use_locking=use_locking) + + if not context.executing_eagerly(): ada_update = ada_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - # Run 3 steps of adagrad - for _ in range(3): - ada_update.run() - # Validate updated params - self.assertAllCloseAccordingToType( - np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([2.715679168701172, 3.715679168701172]), var1.eval()) + self.evaluate(variables.global_variables_initializer()) + + # Fetch params to validate initial values + v0_val, v1_val = self.evaluate([var0, var1]) + self.assertAllClose([1.0, 2.0], v0_val) + self.assertAllClose([3.0, 4.0], v1_val) + + # Run 3 steps of adagrad + for _ in range(3): + if not context.executing_eagerly(): + self.evaluate(ada_update) + else: + ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + + # Validate updated params + v0_val, v1_val = self.evaluate([var0, var1]) + self.assertAllCloseAccordingToType( + np.array([-1.6026098728179932, -0.6026098728179932]), v0_val) + self.assertAllCloseAccordingToType( + np.array([2.715679168701172, 3.715679168701172]), v1_val) def testBasic(self): self.doTestBasic(use_locking=False) + @test_util.run_in_graph_and_eager_modes(reset_test=True) def testBasicResource(self): self.doTestBasic(use_locking=False, use_resource=True) + def testBasicCallableParams(self): + with context.eager_mode(): + self.doTestBasic( + use_locking=False, use_resource=True, use_callable_params=True) + def testBasicLocked(self): self.doTestBasic(use_locking=True) diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py index 6fa3ff66583ce07a6ee7b0d8158c851ea578637c..b65c88e972454da14dc5161a19cd26280d51d28f 100644 --- a/tensorflow/python/training/adam.py +++ b/tensorflow/python/training/adam.py @@ -85,6 +85,13 @@ class AdamOptimizer(optimizer.Optimizer): use_locking: If True use locks for update operations. name: Optional name for the operations created when applying gradients. Defaults to "Adam". + + @compatibility(eager) + When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and + `epsilon` can each be a callable that takes no arguments and returns the + actual value to use. This can be useful for changing these values across + different invocations of optimizer functions. + @end_compatibility """ super(AdamOptimizer, self).__init__(use_locking, name) self._lr = learning_rate @@ -128,10 +135,15 @@ class AdamOptimizer(optimizer.Optimizer): self._zeros_slot(v, "v", self._name) def _prepare(self): - self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate") - self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1") - self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2") - self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon") + lr = self._call_if_callable(self._lr) + beta1 = self._call_if_callable(self._beta1) + beta2 = self._call_if_callable(self._beta2) + epsilon = self._call_if_callable(self._epsilon) + + self._lr_t = ops.convert_to_tensor(lr, name="learning_rate") + self._beta1_t = ops.convert_to_tensor(beta1, name="beta1") + self._beta2_t = ops.convert_to_tensor(beta2, name="beta2") + self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") def _apply_dense(self, grad, var): m = self.get_slot(var, "m") diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py index bc68f24c6fda6748881022ca297ffa73d9c0632d..ccdc7e384da2ae792a681298c7076fc582d362df 100644 --- a/tensorflow/python/training/adam_test.py +++ b/tensorflow/python/training/adam_test.py @@ -150,7 +150,7 @@ class AdamOptimizerTest(test.TestCase): self.assertAllClose(aggregated_update_var.eval(), repeated_index_update_var.eval()) - def doTestBasic(self, use_resource=False): + def doTestBasic(self, use_resource=False, use_callable_params=False): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): with self.test_session(graph=ops.Graph()): # Initialize variables for numpy implementation. @@ -171,7 +171,17 @@ class AdamOptimizerTest(test.TestCase): grads0 = constant_op.constant(grads0_np) grads1 = constant_op.constant(grads1_np) - opt = adam.AdamOptimizer() + learning_rate = lambda: 0.001 + beta1 = lambda: 0.9 + beta2 = lambda: 0.999 + epsilon = lambda: 1e-8 + if not use_callable_params: + learning_rate = learning_rate() + beta1 = beta1() + beta2 = beta2() + epsilon = epsilon() + + opt = adam.AdamOptimizer(learning_rate=learning_rate) update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) opt_variables = opt.variables() beta1_power, beta2_power = opt._get_beta_accumulators() @@ -221,6 +231,10 @@ class AdamOptimizerTest(test.TestCase): def testResourceBasic(self): self.doTestBasic(use_resource=True) + def testBasicCallableParams(self): + with context.eager_mode(): + self.doTestBasic(use_resource=True, use_callable_params=True) + def testTensorLearningRate(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.test_session(): diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index e7f88de1d2290a49f3b7bdf47417016d7e7c9cea..5b372e82b3f637b78db4388b58b8d04a838fbe60 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -147,7 +147,7 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): partitioner=lambda shape, dtype: [5, 1]) # Initialize all variables in `new_scope_1` from `old_scope_1`. - init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/', 'new_scope_1'}) + init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'}) # Use names to specify which variables to initialize from checkpoint. init_from_checkpoint('/tmp/model.ckpt', @@ -219,8 +219,8 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): else: var_name = ",".join([v.name for v in var]) _set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt) - logging.info("Initialize variable %s from checkpoint %s with %s", - var_name, ckpt_dir_or_file, tensor_name_in_ckpt) + logging.debug("Initialize variable %s from checkpoint %s with %s", + var_name, ckpt_dir_or_file, tensor_name_in_ckpt) else: scopes = "" # TODO(vihanjain): Support list of 'current_var_or_name' here. @@ -261,8 +261,8 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): if var is None: var = _collect_partitioned_variable(var_name, store_vars) _set_variable_or_list_initializer(var, ckpt_file, full_tensor_name) - logging.info("Initialize variable %s from checkpoint %s with %s", - var_name, ckpt_dir_or_file, full_tensor_name) + logging.debug("Initialize variable %s from checkpoint %s with %s", + var_name, ckpt_dir_or_file, full_tensor_name) def _get_checkpoint_filename(ckpt_dir_or_file): diff --git a/tensorflow/python/training/checkpointable/BUILD b/tensorflow/python/training/checkpointable/BUILD index 87ba4dc91c89e03ac5f2a93bedca81878f5254a6..35007653a09f4b4990be19ef6b14bf6084a7f14c 100644 --- a/tensorflow/python/training/checkpointable/BUILD +++ b/tensorflow/python/training/checkpointable/BUILD @@ -42,21 +42,39 @@ py_test( ) py_library( - name = "data_structures_base", - srcs = ["data_structures_base.py"], + name = "tracking", + srcs = ["tracking.py"], srcs_version = "PY2AND3", deps = [ ":base", + ":data_structures", ], ) +py_test( + name = "tracking_test", + srcs = ["tracking_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":base", + ":tracking", + "//tensorflow/python:client_testlib", + ], +) + +py_library( + name = "layer_utils", + srcs = ["layer_utils.py"], + srcs_version = "PY2AND3", +) + py_library( name = "data_structures", srcs = ["data_structures.py"], srcs_version = "PY2AND3", deps = [ ":base", - ":data_structures_base", + ":layer_utils", ], ) @@ -83,6 +101,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":base", + ":tracking", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:control_flow_ops", diff --git a/tensorflow/python/training/checkpointable/base.py b/tensorflow/python/training/checkpointable/base.py index cfe7259e1b6d9932fff9e78049fa85554f022076..e9c8c219053bb612aabba1325de7ac0697262e8f 100644 --- a/tensorflow/python/training/checkpointable/base.py +++ b/tensorflow/python/training/checkpointable/base.py @@ -33,6 +33,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saveable_object from tensorflow.python.util import nest from tensorflow.python.util import serialization +from tensorflow.python.util import tf_decorator # Key where the object graph proto is saved in a TensorBundle @@ -340,6 +341,34 @@ _SlotVariableRestoration = collections.namedtuple( ]) +def no_automatic_dependency_tracking(method): + """Disables automatic dependency tracking on attribute assignment. + + Use to decorate any method of a Checkpointable object. Attribute assignment in + that method will not add dependencies (also respected in Model). Harmless if + used in a class which does not do automatic dependency tracking (which means + it's safe to use in base classes which may have subclasses which also inherit + from Checkpointable). + + Args: + method: The method to decorate. + Returns: + A decorated method which sets and un-sets automatic dependency tracking for + the object the method is called on (not thread safe). + """ + + def _method_wrapper(self, *args, **kwargs): + previous_value = getattr(self, "_setattr_tracking", True) + self._setattr_tracking = False # pylint: disable=protected-access + try: + method(self, *args, **kwargs) + finally: + self._setattr_tracking = previous_value # pylint: disable=protected-access + + return tf_decorator.make_decorator( + target=method, decorator_func=_method_wrapper) + + class CheckpointableBase(object): """Base class for `Checkpointable` objects without automatic dependencies. @@ -349,6 +378,11 @@ class CheckpointableBase(object): checks. """ + # CheckpointableBase does not do automatic dependency tracking, but uses the + # no_automatic_dependency_tracking decorator so it can avoid adding + # dependencies if a subclass is Checkpointable / inherits from Model (both of + # which have __setattr__ overrides). + @no_automatic_dependency_tracking def _maybe_initialize_checkpointable(self): """Initialize dependency management. @@ -386,6 +420,10 @@ class CheckpointableBase(object): # building. self._name_based_restores = set() + def _no_dependency(self, value): + """If automatic dependency tracking is enabled, ignores `value`.""" + return value + def _name_based_attribute_restore(self, checkpoint): """Restore the object's attributes from a name-based checkpoint.""" self._name_based_restores.add(checkpoint) @@ -733,86 +771,3 @@ class CheckpointableBase(object): return {OBJECT_CONFIG_JSON_KEY: functools.partial( PythonStringStateSaveable, state_callback=_state_callback)} - - -class NoDependency(object): - """Allows attribute assignment to `Checkpointable` objects with no dependency. - - Example usage: - ```python - obj = Checkpointable() - obj.has_dependency = tf.Variable(0., name="dep") - obj.no_dependency = NoDependency(tf.Variable(1., name="nodep")) - assert obj.no_dependency.name == "nodep:0" - ``` - - `obj` in this example has a dependency on the variable "dep", and both - attributes contain un-wrapped `Variable` objects. - - `NoDependency` also works with `tf.keras.Model`, but only for checkpoint - dependencies: wrapping a `Layer` in `NoDependency` will assign the (unwrapped) - `Layer` to the attribute without a checkpoint dependency, but the `Model` will - still track the `Layer` (so it will appear in `Model.layers`, and its - variables will appear in `Model.variables`). - """ - - def __init__(self, value): - self.value = value - - -class NotCheckpointable(object): - """Marks instances of child classes as unsaveable using an object-based API. - - Useful for marking objects which would otherwise look checkpointable because - of inheritance (e.g. through `Layer`) as not checkpointable. Inheriting from - `NotCheckpointable` does not prevent an object from being assigned to any - attributes, but will throw an error on save/restore. - """ - pass - - -class Checkpointable(CheckpointableBase): - """Manages dependencies on other objects. - - `Checkpointable` objects may have dependencies: other `Checkpointable` objects - which should be saved if the object declaring the dependency is saved. A - correctly saveable program has a dependency graph such that if changing a - global variable affects an object (e.g. changes the behavior of any of its - methods) then there is a chain of dependencies from the influenced object to - the variable. - - Dependency edges have names, and are created implicitly when a - `Checkpointable` object is assigned to an attribute of another - `Checkpointable` object. For example: - - ``` - obj = Checkpointable() - obj.v = ResourceVariable(0.) - ``` - - The `Checkpointable` object `obj` now has a dependency named "v" on a - variable. - - `Checkpointable` objects may specify `Tensor`s to be saved and restored - directly (e.g. a `Variable` indicating how to save itself) rather than through - dependencies on other objects. See - `Checkpointable._gather_saveables_for_checkpoint` for details. - """ - - def __setattr__(self, name, value): - """Support self.foo = checkpointable syntax.""" - # Perform the attribute assignment, and potentially call other __setattr__ - # overrides such as that for tf.keras.Model. - no_dependency = isinstance(value, NoDependency) - if no_dependency: - value = value.value - super(Checkpointable, self).__setattr__(name, value) - if not no_dependency and isinstance(value, CheckpointableBase): - self._track_checkpointable( - value, name=name, - # Allow the user to switch the Checkpointable which is tracked by this - # name, since assigning a new variable to an attribute has - # historically been fine (e.g. Adam did this). - # TODO(allenl): Should this be a warning once Checkpointable save/load - # is usable? - overwrite=True) diff --git a/tensorflow/python/training/checkpointable/base_test.py b/tensorflow/python/training/checkpointable/base_test.py index 0a274cdfed5af83a69513e9b26bf427f284a4df7..950e9c5b535a8314e1068b772f48a14b572df691 100644 --- a/tensorflow/python/training/checkpointable/base_test.py +++ b/tensorflow/python/training/checkpointable/base_test.py @@ -17,33 +17,25 @@ from __future__ import division from __future__ import print_function from tensorflow.python.platform import test -from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import base class InterfaceTests(test.TestCase): - def testMultipleAssignment(self): - root = checkpointable.Checkpointable() - root.leaf = checkpointable.Checkpointable() - root.leaf = root.leaf - duplicate_name_dep = checkpointable.Checkpointable() + def testOverwrite(self): + root = base.CheckpointableBase() + leaf = base.CheckpointableBase() + root._track_checkpointable(leaf, name="leaf") + (current_name, current_dependency), = root._checkpoint_dependencies + self.assertIs(leaf, current_dependency) + self.assertEqual("leaf", current_name) + duplicate_name_dep = base.CheckpointableBase() with self.assertRaises(ValueError): root._track_checkpointable(duplicate_name_dep, name="leaf") - # No error; we're overriding __setattr__, so we can't really stop people - # from doing this while maintaining backward compatibility. - root.leaf = duplicate_name_dep root._track_checkpointable(duplicate_name_dep, name="leaf", overwrite=True) - - def testNoDependency(self): - root = checkpointable.Checkpointable() - hasdep = checkpointable.Checkpointable() - root.hasdep = hasdep - nodep = checkpointable.Checkpointable() - root.nodep = checkpointable.NoDependency(nodep) - self.assertEqual(1, len(root._checkpoint_dependencies)) - self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep) - self.assertIs(root.hasdep, hasdep) - self.assertIs(root.nodep, nodep) + (current_name, current_dependency), = root._checkpoint_dependencies + self.assertIs(duplicate_name_dep, current_dependency) + self.assertEqual("leaf", current_name) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/checkpointable/data_structures.py b/tensorflow/python/training/checkpointable/data_structures.py index 69ed253fb2d874954ee7563cd8bb21add59a7318..019d43f09c10a4975a9b483593af30b5bbe06089 100644 --- a/tensorflow/python/training/checkpointable/data_structures.py +++ b/tensorflow/python/training/checkpointable/data_structures.py @@ -21,54 +21,127 @@ import collections import six -from tensorflow.python.keras.engine import base_layer -from tensorflow.python.keras.utils import layer_utils from tensorflow.python.ops import variables -from tensorflow.python.training.checkpointable import base as checkpointable_lib -from tensorflow.python.training.checkpointable import data_structures_base - - -# TODO(allenl): We could track regular Python data structures which get assigned -# to Checkpointable objects. Making this work with restore-on-create would be -# tricky; we'd need to re-create nested structures with our own wrapped objects -# on assignment to an attribute, and track the user's original structure to make -# sure they don't modify it except through the wrappers (since we could save the -# user's updated structure, but would have no way to support restore-on-create -# for those modifications). -# TODO(allenl): A dictionary data structure would be good too. -class CheckpointableDataStructure( - data_structures_base.CheckpointableDataStructureBase): +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import layer_utils + + +class NoDependency(object): + """Allows attribute assignment to `Checkpointable` objects with no dependency. + + Example usage: + ```python + obj = Checkpointable() + obj.has_dependency = tf.Variable(0., name="dep") + obj.no_dependency = NoDependency(tf.Variable(1., name="nodep")) + assert obj.no_dependency.name == "nodep:0" + ``` + + `obj` in this example has a dependency on the variable "dep", and both + attributes contain un-wrapped `Variable` objects. + + `NoDependency` also works with `tf.keras.Model`, but only for checkpoint + dependencies: wrapping a `Layer` in `NoDependency` will assign the (unwrapped) + `Layer` to the attribute without a checkpoint dependency, but the `Model` will + still track the `Layer` (so it will appear in `Model.layers`, and its + variables will appear in `Model.variables`). + """ + + def __init__(self, value): + self.value = value + + +def _wrap_or_unwrap(value): + """Wraps basic data structures, unwraps NoDependency objects.""" + if isinstance(value, NoDependency): + return value.value + if isinstance(value, base.CheckpointableBase): + return value # Skip conversion for already checkpointable objects. + elif isinstance(value, list): + return _ListWrapper(value) + else: + return value + # TODO(allenl): Handle other common data structures. Tuples will require + # special casing (tuple subclasses are not weak referenceable, so replacement + # with a wrapper that subclasses tuple on attribute assignment works poorly, + # and replacement with a wrapper that isn't a tuple is also problematic), + # probably a tree traversal where the leaves are non-tuples(/namedtuples) to + # come up with names. Dictionaries should look like lists. + + +def sticky_attribute_assignment(checkpointable, name, value): + """Adds dependencies, generally called from __setattr__. + + This behavior is shared between Checkpointable and Model. + + Respects NoDependency indicators, but otherwise makes checkpointable objects + out of common data structures and tracks objects by their attribute names. + + Args: + checkpointable: The object to add dependencies to (generally the one having + an attribute assigned). + name: The attribute name being assigned. + value: The value being assigned. Not necessarily a checkpointable object. + + Returns: + The value which should be stored in the attribute (unwrapped from a + NoDependency object if necessary). + """ + if isinstance(value, NoDependency): + add_dependency = False + else: + add_dependency = True + value = _wrap_or_unwrap(value) + if not add_dependency: + return value + if isinstance(value, base.CheckpointableBase): + checkpointable._track_checkpointable( # pylint: disable=protected-access + value, name=name, + # Allow the user to switch the Checkpointable which is tracked by this + # name, since assigning a new variable to an attribute has + # historically been fine (e.g. Adam did this). + overwrite=True) + return value + + +class CheckpointableDataStructure(base.CheckpointableBase): """Base class for data structures which contain checkpointable objects.""" def __init__(self): + # An append-only ordered set self._layers = [] + self.trainable = True self._extra_variables = [] def _track_value(self, value, name): """Add a dependency on `value`.""" - if isinstance(value, checkpointable_lib.CheckpointableBase): - self._track_checkpointable(value, name=name) - if isinstance(value, variables.Variable): - self._extra_variables.append(value) - else: + value = sticky_attribute_assignment( + checkpointable=self, value=value, name=name) + if isinstance(value, variables.Variable): + self._extra_variables.append(value) + if not isinstance(value, base.CheckpointableBase): raise ValueError( ("Only checkpointable objects (such as Layers or Optimizers) may be " "stored in a List object. Got %s, which does not inherit from " "CheckpointableBase.") % (value,)) - if isinstance(value, ( - base_layer.Layer, - data_structures_base.CheckpointableDataStructureBase)): - if value not in self._layers: + if (isinstance(value, CheckpointableDataStructure) + or layer_utils.is_layer(value)): + # Check for object-identity rather than with __eq__ to avoid + # de-duplicating empty container types. Automatically generated list + # wrappers keep things like "[] == []" true, which means "[] in [[]]" is + # also true. This becomes not true once one of the lists is mutated. + if not any((layer is value for layer in self._layers)): self._layers.append(value) if hasattr(value, "_use_resource_variables"): # In subclassed models, legacy layers (tf.layers) must always use # resource variables. value._use_resource_variables = True # pylint: disable=protected-access + return value @property def layers(self): - return self._layers + return layer_utils.filter_empty_layer_containers(self._layers) @property def trainable_weights(self): @@ -168,24 +241,28 @@ class List(CheckpointableDataStructure, collections.Sequence): def __init__(self, *args, **kwargs): """Construct a new sequence. Arguments are passed to `list()`.""" super(List, self).__init__() - self._storage = list(*args, **kwargs) + self._storage = self._make_storage(*args, **kwargs) for index, element in enumerate(self._storage): - self._track_value(element, name=self._name_element(index)) + self._storage[index] = self._track_value( + element, name=self._name_element(index)) + + def _make_storage(self, *args, **kwargs): + """Determines the backing storage (overridden in subclasses).""" + return list(*args, **kwargs) def _name_element(self, index): return "%d" % (index,) def append(self, value): """Add a new checkpointable value.""" - self._track_value(value, self._name_element(len(self._storage))) + value = self._track_value(value, self._name_element(len(self._storage))) self._storage.append(value) def extend(self, values): """Add a sequence of checkpointable values.""" - for index_offset, value in enumerate(values): - self._track_value( - value, name=self._name_element(len(self._storage) + index_offset)) - self._storage.extend(values) + for value in values: + self._storage.append(self._track_value( + value, name=self._name_element(len(self._storage)))) def __iadd__(self, values): self.extend(values) @@ -193,9 +270,12 @@ class List(CheckpointableDataStructure, collections.Sequence): def __add__(self, other): if isinstance(other, List): - return List(self._storage + other._storage) # pylint: disable=protected-access + return self.__class__(self._storage + other._storage) # pylint: disable=protected-access else: - return List(self._storage + other) + return self.__class__(self._storage + other) + + def __radd__(self, other): + return self + other def __getitem__(self, key): return self._storage[key] @@ -207,6 +287,144 @@ class List(CheckpointableDataStructure, collections.Sequence): return "List(%s)" % (repr(self._storage),) +class _ListWrapper(List, collections.MutableSequence, + # Shadowed, but there for isinstance checks. + list): + """Wraps the built-in `list` to support restore-on-create for variables. + + Unlike `List`, this sequence type is mutable in the same ways built-in lists + are. Instead of throwing an error immediately like `List`, it records + problematic mutations (e.g. assigning a new element to a position already + occupied, meaning both elements get the same names at different times) and + refuses to save. + + On assignment to an attribute of a Model or Checkpointable object, Python + lists are replaced with _ListWrapper. Wrapping a list in a + `tf.contrib.checkpoint.NoDependency` object prevents this. + """ + + def __init__(self, wrapped_list): + """Construct a new list wrapper. + + Args: + wrapped_list: The initial value of the data structure. A shallow copy may + be maintained for error checking. `wrapped_list` itself should not be + modified directly after constructing the `_ListWrapper`, and if changes + are detected the `_ListWrapper` will throw an exception on save. + """ + # Monotonic flags which indicate this object would not be restored properly, + # and therefore should throw an error on save to avoid giving the impression + # that restoring it will work. + self._non_append_mutation = False + self._external_modification = False + super(_ListWrapper, self).__init__(wrapped_list) + self._last_wrapped_list_snapshot = list(self._storage) + + def _make_storage(self, wrapped_list): + """Use the user's original list for storage.""" + return wrapped_list + + def _check_external_modification(self): + """Checks for any changes to the wrapped list not through the wrapper.""" + if self._external_modification or self._non_append_mutation: + return + if self._storage != self._last_wrapped_list_snapshot: + self._external_modification = True + self._last_wrapped_list_snapshot = None + + def _update_snapshot(self): + """Acknowledges tracked changes to the wrapped list.""" + if self._external_modification or self._non_append_mutation: + return + self._last_wrapped_list_snapshot = list(self._storage) + + @property + def _checkpoint_dependencies(self): + self._check_external_modification() + if self._non_append_mutation: + raise ValueError( + ("Unable to save the object %s (a list wrapper constructed to track " + "checkpointable TensorFlow objects). A list element was replaced " + "(__setitem__), deleted, or inserted. In order to support " + "restoration on object creation, tracking is exclusively for " + "append-only data structures.\n\nIf you don't need this list " + "checkpointed, wrap it in a tf.contrib.checkpoint.NoDependency " + "object; it will be automatically un-wrapped and subsequently " + "ignored." % (self,))) + if self._external_modification: + raise ValueError( + ("Unable to save the object %s (a list wrapper constructed to track " + "checkpointable TensorFlow objects). The wrapped list was modified " + "outside the wrapper (its final value was %s, its value when a " + "checkpoint dependency was added was %s), which breaks restoration " + "on object creation.\n\nIf you don't need this list checkpointed, " + "wrap it in a tf.contrib.checkpoint.NoDependency object; it will be " + "automatically un-wrapped and subsequently ignored." % ( + self, self._storage, self._last_wrapped_list_snapshot))) + return super(_ListWrapper, self)._checkpoint_dependencies + + def __delitem__(self, key): + self._non_append_mutation = True + del self._storage[key] + + def __setitem__(self, key, value): + self._non_append_mutation = True + self._storage[key] = value + + def append(self, value): + """Add a new checkpointable value.""" + self._check_external_modification() + super(_ListWrapper, self).append(value) + self._update_snapshot() + + def extend(self, values): + """Add a sequence of checkpointable values.""" + self._check_external_modification() + super(_ListWrapper, self).extend(values) + self._update_snapshot() + + def __eq__(self, other): + return self._storage == getattr(other, "_storage", other) + + def __ne__(self, other): + return self._storage != getattr(other, "_storage", other) + + def __lt__(self, other): + return self._storage < getattr(other, "_storage", other) + + def __le__(self, other): + return self._storage <= getattr(other, "_storage", other) + + def __gt__(self, other): + return self._storage > getattr(other, "_storage", other) + + def __ge__(self, other): + return self._storage >= getattr(other, "_storage", other) + + def __hash__(self): + # List wrappers need to compare like regular lists, and so like regular + # lists they don't belong in hash tables. + raise TypeError("unhashable type: 'ListWrapper'") + + def insert(self, index, obj): + self._non_append_mutation = True + self._storage.insert(index, obj) + + def _track_value(self, value, name): + """Allows storage of non-checkpointable objects.""" + try: + value = super(_ListWrapper, self)._track_value(value=value, name=name) + except ValueError: + # Even if this value isn't checkpointable, we need to make sure + # NoDependency objects get unwrapped. + value = sticky_attribute_assignment( + checkpointable=self, value=value, name=name) + return value + + def __repr__(self): + return "ListWrapper(%s)" % (repr(self._storage),) + + class Mapping(CheckpointableDataStructure, collections.Mapping): """An append-only checkpointable mapping data structure with string keys. @@ -221,8 +439,10 @@ class Mapping(CheckpointableDataStructure, collections.Mapping): """Construct a new sequence. Arguments are passed to `dict()`.""" super(Mapping, self).__init__() self._storage = dict(*args, **kwargs) - for key, value in self._storage.items(): - self._track_value(value, name=self._name_element(key)) + self._storage.update( + {key: self._track_value( + value, name=self._name_element(key)) + for key, value in self._storage.items()}) def _name_element(self, key): if not isinstance(key, six.string_types): @@ -232,13 +452,14 @@ class Mapping(CheckpointableDataStructure, collections.Mapping): return str(key) def __setitem__(self, key, value): + name = self._name_element(key) + value = self._track_value(value, name=name) current_value = self._storage.setdefault(key, value) if current_value is not value: raise ValueError( ("Mappings are an append-only data structure. Tried to overwrite the " "key '%s' with value %s, but it already contains %s") % (key, value, current_value)) - self._track_value(value, name=self._name_element(key)) def update(self, *args, **kwargs): for key, value in dict(*args, **kwargs).items(): diff --git a/tensorflow/python/training/checkpointable/data_structures_test.py b/tensorflow/python/training/checkpointable/data_structures_test.py index b05b3a88002e31560ed6c2005fdd29f56c5227a3..ec8c9da8090c968e8931f96949f5b982dd94f215 100644 --- a/tensorflow/python/training/checkpointable/data_structures_test.py +++ b/tensorflow/python/training/checkpointable/data_structures_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training.checkpointable import data_structures +from tensorflow.python.training.checkpointable import tracking class HasList(training.Model): @@ -66,7 +67,7 @@ class HasList(training.Model): class ListTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTracking(self): model = HasList() output = model(array_ops.ones([32, 2])) @@ -106,13 +107,26 @@ class ListTests(test.TestCase): model(model_input) self.assertEqual(0, len(model.updates)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLossesForwarded(self): model = HasList() model_input = array_ops.ones([32, 2]) model(model_input) self.assertEqual(2, len(model.losses)) + def testModelContainersCompareEqual(self): + class HasEqualContainers(training.Model): + + def __init__(self): + super(HasEqualContainers, self).__init__() + self.l1 = [] + self.l2 = [] + + model = HasEqualContainers() + model.l1.append(HasEqualContainers()) + model.l2.append(HasEqualContainers()) + self.assertEqual([model.l1, model.l2], model.layers) + def testNotCheckpointable(self): class NotCheckpointable(object): pass @@ -158,11 +172,62 @@ class ListTests(test.TestCase): self.assertEqual([v], l.trainable_weights) self.assertEqual([v2], l.non_trainable_weights) + def testListWrapperBasic(self): + # _ListWrapper, unlike List, compares like the built-in list type (since it + # is used to automatically replace lists). + a = tracking.Checkpointable() + b = tracking.Checkpointable() + self.assertEqual([a, a], + [a, a]) + self.assertEqual(data_structures._ListWrapper([a, a]), + data_structures._ListWrapper([a, a])) + self.assertEqual([a, a], + data_structures._ListWrapper([a, a])) + self.assertEqual(data_structures._ListWrapper([a, a]), + [a, a]) + self.assertNotEqual([a, a], + [b, a]) + self.assertNotEqual(data_structures._ListWrapper([a, a]), + data_structures._ListWrapper([b, a])) + self.assertNotEqual([a, a], + data_structures._ListWrapper([b, a])) + self.assertLess([a], [a, b]) + self.assertLess(data_structures._ListWrapper([a]), + data_structures._ListWrapper([a, b])) + self.assertLessEqual([a], [a, b]) + self.assertLessEqual(data_structures._ListWrapper([a]), + data_structures._ListWrapper([a, b])) + self.assertGreater([a, b], [a]) + self.assertGreater(data_structures._ListWrapper([a, b]), + data_structures._ListWrapper([a])) + self.assertGreaterEqual([a, b], [a]) + self.assertGreaterEqual(data_structures._ListWrapper([a, b]), + data_structures._ListWrapper([a])) + self.assertEqual([a], data_structures._ListWrapper([a])) + self.assertEqual([a], list(data_structures.List([a]))) + self.assertEqual([a, a], data_structures._ListWrapper([a]) + [a]) + self.assertEqual([a, a], [a] + data_structures._ListWrapper([a])) + self.assertIsInstance(data_structures._ListWrapper([a]), list) + + def testWrapperChangesList(self): + l = [] + l_wrapper = data_structures._ListWrapper(l) + l_wrapper.append(1) + self.assertEqual([1], l) + + def testListChangesWrapper(self): + l = [] + l_wrapper = data_structures._ListWrapper(l) + l.append(1) + self.assertEqual([1], l_wrapper) + def testHashing(self): has_sequences = set([data_structures.List(), data_structures.List()]) self.assertEqual(2, len(has_sequences)) self.assertNotIn(data_structures.List(), has_sequences) + with self.assertRaises(TypeError): + has_sequences.add(data_structures._ListWrapper([])) class HasMapping(training.Model): @@ -190,7 +255,7 @@ class HasMapping(training.Model): class MappingTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTracking(self): model = HasMapping() output = model(array_ops.ones([32, 2])) diff --git a/tensorflow/python/training/checkpointable/layer_utils.py b/tensorflow/python/training/checkpointable/layer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..978fcb2252cd4481b8286bdf3afd58b30ce6d665 --- /dev/null +++ b/tensorflow/python/training/checkpointable/layer_utils.py @@ -0,0 +1,93 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities related to layer/model functionality.""" + +# TODO(b/110718070): Move these functions back to tensorflow/python/keras/utils +# once __init__ files no longer require all of tf.keras to be imported together. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +def is_layer(obj): + """Implicit check for Layer-like objects.""" + # TODO(b/110718070): Replace with isinstance(obj, base_layer.Layer). + return (hasattr(obj, "call") + and hasattr(obj, "build") + and hasattr(obj, "variables")) + + +def filter_empty_layer_containers(layer_list): + """Filter out empty Layer-like containers.""" + return [layer for layer in layer_list + # Filter out only empty Checkpointable data structures. Empty Networks + # will still show up in Model.layers. + if is_layer(layer) or getattr(layer, "layers", True)] + + +def gather_trainable_weights(trainable, sub_layers, extra_variables): + """Lists the trainable weights for an object with sub-layers. + + Args: + trainable: Whether the object collecting the variables is trainable. + sub_layers: A flat list of Layer objects owned by this object, to collect + variables from. + extra_variables: Any extra variables to include. Their `.trainable` property + is used to categorize them. + + Returns: + A list of collected trainable weights/variables. + """ + if not trainable: + return [] + weights = [] + for layer in sub_layers: + weights += layer.trainable_weights + trainable_extra_variables = [ + v for v in extra_variables if v.trainable] + return weights + trainable_extra_variables + + +def gather_non_trainable_weights(trainable, sub_layers, extra_variables): + """Lists the non-trainable weights for an object with sub-layers. + + Args: + trainable: Whether the object collecting the variables is trainable. + sub_layers: A flat list of Layer objects owned by this object, to collect + variables from. + extra_variables: Any extra variables to include. Their `.trainable` property + is used to categorize them. + + Returns: + A list of collected non-trainable weights/variables. + """ + trainable_extra_variables = [] + non_trainable_extra_variables = [] + for v in extra_variables: + if v.trainable: + trainable_extra_variables.append(v) + else: + non_trainable_extra_variables.append(v) + weights = [] + for layer in sub_layers: + weights += layer.non_trainable_weights + if not trainable: + trainable_weights = [] + for layer in sub_layers: + trainable_weights += layer.trainable_weights + return (trainable_weights + trainable_extra_variables + + weights + non_trainable_extra_variables) + return weights + non_trainable_extra_variables diff --git a/tensorflow/python/training/checkpointable/tracking.py b/tensorflow/python/training/checkpointable/tracking.py new file mode 100644 index 0000000000000000000000000000000000000000..bd0bed9d46f2e75633e3bf1230eded3708ec1c8b --- /dev/null +++ b/tensorflow/python/training/checkpointable/tracking.py @@ -0,0 +1,72 @@ +"""Dependency tracking for checkpointable objects.""" +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import data_structures + + +class NotCheckpointable(object): + """Marks instances of child classes as unsaveable using an object-based API. + + Useful for marking objects which would otherwise look checkpointable because + of inheritance (e.g. through `Layer`) as not checkpointable. Inheriting from + `NotCheckpointable` does not prevent an object from being assigned to any + attributes, but will throw an error on save/restore. + """ + pass + + +class Checkpointable(base.CheckpointableBase): + """Manages dependencies on other objects. + + `Checkpointable` objects may have dependencies: other `Checkpointable` objects + which should be saved if the object declaring the dependency is saved. A + correctly saveable program has a dependency graph such that if changing a + global variable affects an object (e.g. changes the behavior of any of its + methods) then there is a chain of dependencies from the influenced object to + the variable. + + Dependency edges have names, and are created implicitly when a + `Checkpointable` object is assigned to an attribute of another + `Checkpointable` object. For example: + + ``` + obj = Checkpointable() + obj.v = ResourceVariable(0.) + ``` + + The `Checkpointable` object `obj` now has a dependency named "v" on a + variable. + + `Checkpointable` objects may specify `Tensor`s to be saved and restored + directly (e.g. a `Variable` indicating how to save itself) rather than through + dependencies on other objects. See + `Checkpointable._gather_saveables_for_checkpoint` for details. + """ + + def __setattr__(self, name, value): + """Support self.foo = checkpointable syntax.""" + if getattr(self, "_setattr_tracking", True): + value = data_structures.sticky_attribute_assignment( + checkpointable=self, value=value, name=name) + super(Checkpointable, self).__setattr__(name, value) + + def _no_dependency(self, value): + """Override to allow CheckpointableBase to disable dependency tracking.""" + return data_structures.NoDependency(value) diff --git a/tensorflow/python/training/checkpointable/tracking_test.py b/tensorflow/python/training/checkpointable/tracking_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f0178b074daae228acd2b3a6e290bc5f9f5add7a --- /dev/null +++ b/tensorflow/python/training/checkpointable/tracking_test.py @@ -0,0 +1,168 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import numpy + +from tensorflow.python.framework import test_util +from tensorflow.python.keras.engine import training +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import data_structures +from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util +from tensorflow.python.util import nest + + +class InterfaceTests(test.TestCase): + + def testMultipleAssignment(self): + root = tracking.Checkpointable() + root.leaf = tracking.Checkpointable() + root.leaf = root.leaf + duplicate_name_dep = tracking.Checkpointable() + with self.assertRaisesRegexp(ValueError, "already declared"): + root._track_checkpointable(duplicate_name_dep, name="leaf") + # No error; we're overriding __setattr__, so we can't really stop people + # from doing this while maintaining backward compatibility. + root.leaf = duplicate_name_dep + root._track_checkpointable(duplicate_name_dep, name="leaf", overwrite=True) + + def testNoDependency(self): + root = tracking.Checkpointable() + hasdep = tracking.Checkpointable() + root.hasdep = hasdep + nodep = tracking.Checkpointable() + root.nodep = data_structures.NoDependency(nodep) + self.assertEqual(1, len(root._checkpoint_dependencies)) + self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep) + self.assertIs(root.hasdep, hasdep) + self.assertIs(root.nodep, nodep) + + class NoDependencyModel(training.Model): + + @base.no_automatic_dependency_tracking + def __init__(self): + super(NoDependencyModel, self).__init__() + self.a = [] + self.b = tracking.Checkpointable() + + nodeps = NoDependencyModel() + self.assertEqual([nodeps], util.list_objects(nodeps)) + + def testListBasic(self): + a = tracking.Checkpointable() + b = tracking.Checkpointable() + a.l = [b] + c = tracking.Checkpointable() + a.l.append(c) + a_deps = util.list_objects(a) + self.assertIn(b, a_deps) + self.assertIn(c, a_deps) + direct_a_dep, = a._checkpoint_dependencies + self.assertEqual("l", direct_a_dep.name) + self.assertIn(b, direct_a_dep.ref) + self.assertIn(c, direct_a_dep.ref) + + @test_util.run_in_graph_and_eager_modes + def testMutationDirtiesList(self): + a = tracking.Checkpointable() + b = tracking.Checkpointable() + a.l = [b] + c = tracking.Checkpointable() + a.l.insert(0, c) + checkpoint = util.Checkpoint(a=a) + with self.assertRaisesRegexp(ValueError, "A list element was replaced"): + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + + @test_util.run_in_graph_and_eager_modes + def testOutOfBandEditDirtiesList(self): + a = tracking.Checkpointable() + b = tracking.Checkpointable() + held_reference = [b] + a.l = held_reference + c = tracking.Checkpointable() + held_reference.append(c) + checkpoint = util.Checkpoint(a=a) + with self.assertRaisesRegexp(ValueError, "The wrapped list was modified"): + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + + @test_util.run_in_graph_and_eager_modes + def testNestedLists(self): + a = tracking.Checkpointable() + a.l = [] + b = tracking.Checkpointable() + a.l.append([b]) + c = tracking.Checkpointable() + a.l[0].append(c) + a_deps = util.list_objects(a) + self.assertIn(b, a_deps) + self.assertIn(c, a_deps) + a.l[0].append(1) + d = tracking.Checkpointable() + a.l[0].append(d) + a_deps = util.list_objects(a) + self.assertIn(d, a_deps) + self.assertIn(b, a_deps) + self.assertIn(c, a_deps) + self.assertNotIn(1, a_deps) + e = tracking.Checkpointable() + f = tracking.Checkpointable() + a.l1 = [[], [e]] + a.l1[0].append(f) + a_deps = util.list_objects(a) + self.assertIn(e, a_deps) + self.assertIn(f, a_deps) + checkpoint = util.Checkpoint(a=a) + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + a.l[0].append(data_structures.NoDependency([])) + a.l[0][-1].append(5) + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + # Dirtying the inner list means the root object is unsaveable. + a.l[0][1] = 2 + with self.assertRaisesRegexp(ValueError, "A list element was replaced"): + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + + @test_util.run_in_graph_and_eager_modes + def testNoDepList(self): + a = training.Model() + a.l1 = data_structures.NoDependency([]) + a.l1.insert(1, 0) + self.assertTrue(isinstance(a.l1, list)) + checkpoint = util.Checkpoint(a=a) + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + a.l2 = [] + a.l2.insert(1, 0) + with self.assertRaisesRegexp(ValueError, "A list element was replaced"): + checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + + @test_util.run_in_graph_and_eager_modes + def testAssertions(self): + a = tracking.Checkpointable() + a.l = [numpy.zeros([2, 2])] + self.assertAllEqual([numpy.zeros([2, 2])], a.l) + self.assertAllClose([numpy.zeros([2, 2])], a.l) + nest.map_structure(self.assertAllClose, a.l, [numpy.zeros([2, 2])]) + a.tensors = [array_ops.ones([2, 2]), array_ops.zeros([3, 3])] + self.assertAllClose([numpy.ones([2, 2]), numpy.zeros([3, 3])], + self.evaluate(a.tensors)) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 96e6d10791f396ad7f9f73cce9356dd4cbe3ce9d..6ae5765b133cc72b67f3d9864d0f67abf33f0648 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -39,8 +39,11 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.training import optimizer as optimizer_lib from tensorflow.python.training import saveable_object as saveable_object_lib from tensorflow.python.training import saver as saver_lib -from tensorflow.python.training.checkpointable import base as checkpointable_lib +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import data_structures +from tensorflow.python.training.checkpointable import tracking from tensorflow.python.util import deprecation +from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export @@ -91,7 +94,7 @@ class _CheckpointRestoreCoordinator(object): # use them (for example because of inconsistent references when # loading). Used to make status assertions fail when loading checkpoints # that don't quite match. - self.all_python_objects = weakref.WeakSet() + self.all_python_objects = _ObjectIdentityWeakSet() self.save_path = save_path self.dtype_map = dtype_map # When graph building, contains a list of ops to run to restore objects from @@ -113,7 +116,7 @@ class _CheckpointRestoreCoordinator(object): # `node` refers to an `Optimizer`, since only these have slot variables. self.slot_restorations.setdefault( slot_reference.original_variable_node_id, []).append( - checkpointable_lib._SlotVariableRestoration( # pylint: disable=protected-access + base._SlotVariableRestoration( # pylint: disable=protected-access optimizer_id=node_index, slot_variable_id=slot_reference.slot_variable_node_id, slot_name=slot_reference.slot_name)) @@ -257,27 +260,145 @@ def object_metadata(save_path): reader = pywrap_tensorflow.NewCheckpointReader(save_path) try: object_graph_string = reader.get_tensor( - checkpointable_lib.OBJECT_GRAPH_PROTO_KEY) + base.OBJECT_GRAPH_PROTO_KEY) except errors_impl.NotFoundError: raise ValueError( ('The specified checkpoint "%s" does not appear to be object-based (it ' 'is missing the key "%s"). Likely it was created with a name-based ' 'saver and does not contain an object dependency graph.') % ( - save_path, checkpointable_lib.OBJECT_GRAPH_PROTO_KEY)) + save_path, base.OBJECT_GRAPH_PROTO_KEY)) object_graph_proto = ( checkpointable_object_graph_pb2.CheckpointableObjectGraph()) object_graph_proto.ParseFromString(object_graph_string) return object_graph_proto +class _ObjectIdentityWrapper(object): + """Wraps an object, mapping __eq__ on wrapper to "is" on wrapped. + + Since __eq__ is based on object identity, it's safe to also define __hash__ + based on object ids. This lets us add unhashable types like checkpointable + _ListWrapper objects to object-identity collections. + """ + + def __init__(self, wrapped): + self._wrapped = wrapped + + @property + def unwrapped(self): + return self._wrapped + + def __eq__(self, other): + if isinstance(other, _ObjectIdentityWrapper): + return self._wrapped is other._wrapped # pylint: disable=protected-access + return self._wrapped is other + + def __hash__(self): + # Wrapper id() is also fine for weakrefs. In fact, we rely on + # id(weakref.ref(a)) == id(weakref.ref(a)) and weakref.ref(a) is + # weakref.ref(a) in _WeakObjectIdentityWrapper. + return id(self._wrapped) + + +class _WeakObjectIdentityWrapper(_ObjectIdentityWrapper): + + def __init__(self, wrapped): + super(_WeakObjectIdentityWrapper, self).__init__(weakref.ref(wrapped)) + + @property + def unwrapped(self): + return self._wrapped() + + +class _ObjectIdentityDictionary(collections.MutableMapping): + """A mutable mapping data structure which compares using "is". + + This is necessary because we have checkpointable objects (_ListWrapper) which + have behavior identical to built-in Python lists (including being unhashable + and comparing based on the equality of their contents by default). + """ + + def __init__(self): + self._storage = {} + + def _wrap_key(self, key): + return _ObjectIdentityWrapper(key) + + def __getitem__(self, key): + return self._storage[self._wrap_key(key)] + + def __setitem__(self, key, value): + self._storage[self._wrap_key(key)] = value + + def __delitem__(self, key): + del self._storage[self._wrap_key(key)] + + def __len__(self): + return len(self._storage) + + def __iter__(self): + for key in self._storage: + yield key.unwrapped + + +class _ObjectIdentityWeakKeyDictionary(_ObjectIdentityDictionary): + """Like weakref.WeakKeyDictionary, but compares objects with "is".""" + + def _wrap_key(self, key): + return _WeakObjectIdentityWrapper(key) + + def __len__(self): + # Iterate, discarding old weak refs + return len(list(self._storage)) + + def __iter__(self): + keys = self._storage.keys() + for key in keys: + unwrapped = key.unwrapped + if unwrapped is None: + del self[key] + else: + yield unwrapped + + +class _ObjectIdentityWeakSet(collections.MutableSet): + """Like weakref.WeakSet, but compares objects with "is".""" + + def __init__(self): + self._storage = set() + + def __contains__(self, key): + return _WeakObjectIdentityWrapper(key) in self._storage + + def discard(self, key): + self._storage.discard(_WeakObjectIdentityWrapper(key)) + + def add(self, key): + self._storage.add(_WeakObjectIdentityWrapper(key)) + + def __len__(self): + # Iterate, discarding old weak refs + return len(list(self)) + + def __iter__(self): + keys = list(self._storage) + for key in keys: + unwrapped = key.unwrapped + if unwrapped is None: + self.discard(key) + else: + yield unwrapped + + def _breadth_first_checkpointable_traversal(root_checkpointable): """Find shortest paths to all variables owned by dependencies of root.""" bfs_sorted = [] to_visit = collections.deque([root_checkpointable]) - path_to_root = {root_checkpointable: ()} + path_to_root = _ObjectIdentityDictionary() + path_to_root[root_checkpointable] = () while to_visit: current_checkpointable = to_visit.popleft() - if isinstance(current_checkpointable, checkpointable_lib.NotCheckpointable): + if isinstance(current_checkpointable, tracking.NotCheckpointable): raise NotImplementedError( ("The object %s does not support object-based saving. File a feature " "request if this limitation bothers you. In the meantime, you can " @@ -335,7 +456,7 @@ def _slot_variable_naming_for_optimizer(optimizer_path): def _serialize_slot_variables(checkpointable_objects, node_ids, object_names): """Gather and name slot variables.""" non_slot_objects = list(checkpointable_objects) - slot_variables = {} + slot_variables = _ObjectIdentityDictionary() for checkpointable in non_slot_objects: if isinstance(checkpointable, optimizer_lib.Optimizer): naming_scheme = _slot_variable_naming_for_optimizer( @@ -498,11 +619,12 @@ def _serialize_object_graph(root_checkpointable, saveables_cache): """ checkpointable_objects, path_to_root = ( _breadth_first_checkpointable_traversal(root_checkpointable)) - object_names = { - obj: _object_prefix_from_path(path) - for obj, path in path_to_root.items()} - node_ids = {node: node_id for node_id, node - in enumerate(checkpointable_objects)} + object_names = _ObjectIdentityDictionary() + for obj, path in path_to_root.items(): + object_names[obj] = _object_prefix_from_path(path) + node_ids = _ObjectIdentityDictionary() + for node_id, node in enumerate(checkpointable_objects): + node_ids[node] = node_id slot_variables = _serialize_slot_variables( checkpointable_objects=checkpointable_objects, node_ids=node_ids, @@ -533,11 +655,12 @@ def list_objects(root_checkpointable): # to run. checkpointable_objects, path_to_root = ( _breadth_first_checkpointable_traversal(root_checkpointable)) - object_names = { - obj: _object_prefix_from_path(path) - for obj, path in path_to_root.items()} - node_ids = {node: node_id for node_id, node - in enumerate(checkpointable_objects)} + object_names = _ObjectIdentityDictionary() + for obj, path in path_to_root.items(): + object_names[obj] = _object_prefix_from_path(path) + node_ids = _ObjectIdentityDictionary() + for node_id, node in enumerate(checkpointable_objects): + node_ids[node] = node_id _serialize_slot_variables( checkpointable_objects=checkpointable_objects, node_ids=node_ids, @@ -564,6 +687,93 @@ def gather_initializers(root_checkpointable): if hasattr(c, "initializer") and c.initializer is not None] +@tf_contextlib.contextmanager +def capture_dependencies(template): + """Capture variables created within this scope as `Template` dependencies. + + Requires that `template.variable_scope` is active. + + This scope is intended as a compatibility measure, allowing a checkpointable + object to add dependencies on variables created in a block of code which is + not aware of object-based saving (and instead uses variable names + heavily). This is how `Template` objects add dependencies on variables and + sub-`Template`s. Where possible, use `tf.make_template` directly. + + Args: + template: The `Template` object to register dependencies with. + + Yields: + None (when used as a context manager). + """ + name_prefix = template.variable_scope.name + + def _checkpointable_custom_creator(next_creator, name, initial_value, + checkpointable_parent=None, **kwargs): + """A variable creation hook which adds Checkpointable dependencies. + + Set for example during a `Template`'s first wrapped function + execution. Ensures that (a) `template` depends on any checkpointable + objects using their own `capture_dependencies` scope inside this scope which + create variables, and (b) that any variables not in a more deeply nested + scope are added as dependencies directly. + + The `checkpointable_parent` argument is passed between custom creators but + ignored when the variable object itself is created. This argument indicates + (if not `None`) that a more deeply nested scope has already added the + variable as a dependency, and that parent scopes should add a dependency on + that object rather than on the variable directly. + + Args: + next_creator: See `variable_scope.variable_creator_scope`; the next + creator in the chain. + name: The (full, scope-influenced) name of the variable. The `name_prefix` + itself is stripped for the purposes of object-based dependency tracking, + but scopes opened within this scope are respected. + initial_value: See `variable_scope.variable_creator_scope`. Taken + explicitly so the argument can be re-named and used with + `Checkpointable._add_variable_with_custom_getter`. + checkpointable_parent: If not None, a more deeply nested checkpointable + object and its name prefix which were passed to `capture_dependencies` + to add a dependency on (rather than depending on the variable directly). + **kwargs: Passed through to the next creator. + + Returns: + The output of `next_creator`: the fetched/created variable object. + """ + def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): + inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which + # we don't want to propagate. + return next_creator( + initial_value=initializer, + name=name, + **inner_kwargs) + if name.startswith(name_prefix): + scope_stripped_name = name[len(name_prefix) + 1:] + if not checkpointable_parent: + return template._add_variable_with_custom_getter( # pylint: disable=protected-access + initializer=initial_value, + name=scope_stripped_name, + getter=_call_next_creator_renaming_initializer, + # Disable error checking for Checkpointable. Exceptions are instead + # raised if necessary when the object-based saver tries to + # save/restore the object. + overwrite=True, + checkpointable_parent=(template, name_prefix), + **kwargs) + else: + parent_object, parent_name_prefix = checkpointable_parent + template._track_checkpointable( # pylint: disable=protected-access + parent_object, + name=parent_name_prefix[len(name_prefix) + 1:], + overwrite=True) + return next_creator( + name=name, initial_value=initial_value, + checkpointable_parent=(template, name_prefix), **kwargs) + + with variable_scope.variable_creator_scope(_checkpointable_custom_creator): + yield + + class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject): def __init__(self, tensor, name): @@ -899,7 +1109,7 @@ class CheckpointableSaver(object): else: # Maps Checkpointable objects -> attribute names -> SaveableObjects, to # avoid re-creating SaveableObjects when graph building. - self._saveable_object_cache = weakref.WeakKeyDictionary() + self._saveable_object_cache = _ObjectIdentityWeakKeyDictionary() @property def _root_checkpointable(self): @@ -950,11 +1160,11 @@ class CheckpointableSaver(object): with ops.device("/cpu:0"): object_graph_tensor = constant_op.constant( graph_proto.SerializeToString(), dtype=dtypes.string) - assert checkpointable_lib.OBJECT_GRAPH_PROTO_KEY not in named_variables + assert base.OBJECT_GRAPH_PROTO_KEY not in named_variables named_variables.append( _NoRestoreSaveable( tensor=object_graph_tensor, - name=checkpointable_lib.OBJECT_GRAPH_PROTO_KEY)) + name=base.OBJECT_GRAPH_PROTO_KEY)) if (self._last_save_object_graph != graph_proto # When executing eagerly, we need to re-create SaveableObjects each time # save() is called so they pick up new Tensors passed to their @@ -1044,7 +1254,7 @@ class CheckpointableSaver(object): dtype_map = reader.get_variable_to_dtype_map() try: object_graph_string = reader.get_tensor( - checkpointable_lib.OBJECT_GRAPH_PROTO_KEY) + base.OBJECT_GRAPH_PROTO_KEY) except errors_impl.NotFoundError: # The object graph proto does not exist in this checkpoint. Try the # name-based compatibility mode. @@ -1090,7 +1300,7 @@ class CheckpointableSaver(object): "file a feature request if this limitation bothers you.") self._last_restore_checkpoint = checkpoint self._last_restore_object_graph = object_graph_proto - checkpointable_lib._CheckpointPosition( # pylint: disable=protected-access + base._CheckpointPosition( # pylint: disable=protected-access checkpoint=checkpoint, proto_id=0).restore(self._root_checkpointable) load_status = CheckpointLoadStatus( checkpoint, @@ -1100,7 +1310,7 @@ class CheckpointableSaver(object): @tf_export("train.Checkpoint") -class Checkpoint(checkpointable_lib.Checkpointable): +class Checkpoint(tracking.Checkpointable): """Groups checkpointable objects, saving and restoring them. `Checkpoint`'s constructor accepts keyword arguments whose values are types @@ -1202,7 +1412,7 @@ class Checkpoint(checkpointable_lib.Checkpointable): """ super(Checkpoint, self).__init__() for k, v in sorted(kwargs.items(), key=lambda item: item[0]): - if not isinstance(v, checkpointable_lib.CheckpointableBase): + if not isinstance(v, base.CheckpointableBase): raise ValueError( ("`Checkpoint` was expecting a checkpointable object (an object " "derived from `CheckpointableBase`), got %s. If you believe this " @@ -1221,7 +1431,7 @@ class Checkpoint(checkpointable_lib.Checkpointable): with ops.device("/cpu:0"): # add_variable creates a dependency named "save_counter"; NoDependency # prevents creating a second dependency named "_save_counter". - self._save_counter = checkpointable_lib.NoDependency( + self._save_counter = data_structures.NoDependency( add_variable(self, name="save_counter", initializer=0, dtype=dtypes.int64)) diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 8cdf5d78554b01874115d438e7f0fadaf5b6b91c..896ea47b974a334d34e520e6f3c2ad947dea12a2 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -44,11 +44,12 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.training import adam from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util -from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import tracking from tensorflow.python.training.checkpointable import util as checkpointable_utils -class NonLayerCheckpointable(checkpointable.Checkpointable): +class NonLayerCheckpointable(tracking.Checkpointable): def __init__(self): super(NonLayerCheckpointable, self).__init__() @@ -136,7 +137,7 @@ class InterfaceTests(test.TestCase): def testInitNotCalled(self): - class NoInit(checkpointable.Checkpointable): + class NoInit(tracking.Checkpointable): def __init__(self): pass @@ -145,7 +146,7 @@ class InterfaceTests(test.TestCase): checkpointable_utils.add_variable(NoInit(), "var", shape=[]) def testShapeDtype(self): - root = checkpointable.Checkpointable() + root = tracking.Checkpointable() v1 = checkpointable_utils.add_variable( root, name="v1", initializer=3., dtype=dtypes.float64) self.assertEqual(dtypes.float64, v1.dtype) @@ -177,7 +178,7 @@ class InterfaceTests(test.TestCase): def testNotCheckpointable(self): class CallsFunctionalStuff( - checkpointable.NotCheckpointable, checkpointable.Checkpointable): + tracking.NotCheckpointable, tracking.Checkpointable): pass test_dir = self.get_temp_dir() @@ -187,7 +188,7 @@ class InterfaceTests(test.TestCase): checkpoint.save(prefix) class CallsFunctionalStuffOtherMRO( - checkpointable.Checkpointable, checkpointable.NotCheckpointable): + tracking.Checkpointable, tracking.NotCheckpointable): pass checkpoint_reversed = checkpointable_utils.Checkpoint( @@ -217,7 +218,7 @@ class _MirroringSaveable(saver_lib.BaseSaverBuilder.SaveableObject): self._mirrored_variable.assign(tensor)) -class _OwnsMirroredVariables(checkpointable.CheckpointableBase): +class _OwnsMirroredVariables(base.CheckpointableBase): """A Checkpointable object which returns a more complex SaveableObject.""" def __init__(self): @@ -232,7 +233,7 @@ class _OwnsMirroredVariables(checkpointable.CheckpointableBase): primary_variable=self.non_dep_variable, mirrored_variable=self.mirrored, name=name) - return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} + return {base.VARIABLE_VALUE_KEY: _saveable_factory} # The Saver sorts by name before parsing, so we need a name property. @property @@ -355,7 +356,7 @@ class CheckpointingTests(test.TestCase): optimizer_node.slot_variables[0] .slot_variable_node_id].attributes[0].checkpoint_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturned(self): v = _OwnsMirroredVariables() checkpoint = checkpointable_utils.Checkpoint(v=v) @@ -375,7 +376,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(44., self.evaluate(v.non_dep_variable)) self.assertEqual(44., self.evaluate(v.mirrored)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturnedWithGlobalName(self): # The same object can also be saved using the name-based saver. v = _OwnsMirroredVariables() @@ -391,7 +392,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(42., self.evaluate(v.non_dep_variable)) self.assertEqual(42., self.evaluate(v.mirrored)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): model = MyModel() optimizer = adam.AdamOptimizer(0.001) @@ -512,7 +513,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(training_continuation + 1, session.run(root.save_counter)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAgnosticUsage(self): """Graph/eager agnostic usage.""" # Does create garbage when executing eagerly due to ops.Graph() creation. @@ -546,7 +547,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(root.save_counter)) # pylint: disable=cell-var-from-loop - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWithDefun(self): num_training_steps = 2 checkpoint_directory = self.get_temp_dir() @@ -590,7 +591,7 @@ class CheckpointingTests(test.TestCase): # pylint: enable=cell-var-from-loop def _get_checkpoint_name(self, name): - root = checkpointable.Checkpointable() + root = tracking.Checkpointable() checkpointable_utils.add_variable( root, name=name, shape=[1, 2], dtype=dtypes.float64) (named_variable,), _, _ = checkpointable_utils._serialize_object_graph( @@ -611,18 +612,18 @@ class CheckpointingTests(test.TestCase): @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) def testNumberedPath(self): - root = checkpointable.Checkpointable() - leaf = checkpointable.Checkpointable() + root = tracking.Checkpointable() + leaf = tracking.Checkpointable() root.leaf = leaf checkpointable_utils.add_variable(leaf, name="v", shape=[]) (named_variable,), _, _ = checkpointable_utils._serialize_object_graph( root, saveables_cache=None) self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", named_variable.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLocalNameValidation(self): - root = checkpointable.Checkpointable() - leaf = checkpointable.Checkpointable() + root = tracking.Checkpointable() + leaf = tracking.Checkpointable() # Dots are escaped, which avoids conflicts with reserved names. root._track_checkpointable(leaf, name=".ATTRIBUTES") checkpointable_utils.add_variable(checkpointable=leaf, name="a", shape=[]) @@ -660,16 +661,16 @@ class CheckpointingTests(test.TestCase): optimizer.apply_gradients( [(g, v) for g, v in zip(grad, model.vars)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLateDependencyTracking(self): - class Dependency(checkpointable.Checkpointable): + class Dependency(tracking.Checkpointable): def build(self): self.var = checkpointable_utils.add_variable( self, "var", initializer=0.) - class LateDependencies(checkpointable.Checkpointable): + class LateDependencies(tracking.Checkpointable): def add_dep(self): self.dep = Dependency() @@ -692,16 +693,16 @@ class CheckpointingTests(test.TestCase): status.run_restore_ops() self.assertEqual(123., self.evaluate(load_into.dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDepAfterVar(self): - class Dependency(checkpointable.Checkpointable): + class Dependency(tracking.Checkpointable): def build(self): self.var = checkpointable_utils.add_variable( self, "var", initializer=0.) - class DepAfterVar(checkpointable.Checkpointable): + class DepAfterVar(tracking.Checkpointable): def add_dep(self): dep = Dependency() @@ -724,11 +725,11 @@ class CheckpointingTests(test.TestCase): status.run_restore_ops() self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeferredSlotRestoration(self): checkpoint_directory = self.get_temp_dir() - root = checkpointable.Checkpointable() + root = tracking.Checkpointable() root.var = checkpointable_utils.add_variable( root, name="var", initializer=0.) optimizer = adam.AdamOptimizer(0.1) @@ -751,7 +752,7 @@ class CheckpointingTests(test.TestCase): 14.)) slots_path = checkpointable_utils.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "with_slots")) - new_root = checkpointable.Checkpointable() + new_root = tracking.Checkpointable() # Load the slot-containing checkpoint (deferred), then immediately overwrite # the non-slot variable (also deferred). slot_status = checkpointable_utils.CheckpointableSaver( @@ -789,11 +790,11 @@ class CheckpointingTests(test.TestCase): self.evaluate(train_op) slot_status.assert_consumed() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOverlappingRestores(self): checkpoint_directory = self.get_temp_dir() - save_root = checkpointable.Checkpointable() - save_root.dep = checkpointable.Checkpointable() + save_root = tracking.Checkpointable() + save_root.dep = tracking.Checkpointable() save_root.dep.var = checkpointable_utils.add_variable( save_root.dep, name="var", initializer=0.) self.evaluate(state_ops.assign(save_root.dep.var, 12.)) @@ -802,13 +803,13 @@ class CheckpointingTests(test.TestCase): self.evaluate(state_ops.assign(save_root.dep.var, 13.)) second_path = saver.save(os.path.join(checkpoint_directory, "second")) - first_root = checkpointable.Checkpointable() - second_root = checkpointable.Checkpointable() + first_root = tracking.Checkpointable() + second_root = tracking.Checkpointable() first_status = checkpointable_utils.CheckpointableSaver( first_root).restore(first_path) second_status = checkpointable_utils.CheckpointableSaver( second_root).restore(second_path) - load_dep = checkpointable.Checkpointable() + load_dep = tracking.Checkpointable() load_dep.var = checkpointable_utils.add_variable( load_dep, name="var", shape=[]) first_root.dep = load_dep @@ -822,13 +823,13 @@ class CheckpointingTests(test.TestCase): # Try again with the order of the restore() reversed. The last restore # determines the final value. - first_root = checkpointable.Checkpointable() - second_root = checkpointable.Checkpointable() + first_root = tracking.Checkpointable() + second_root = tracking.Checkpointable() second_status = checkpointable_utils.CheckpointableSaver( second_root).restore(second_path) first_status = checkpointable_utils.CheckpointableSaver( first_root).restore(first_path) - load_dep = checkpointable.Checkpointable() + load_dep = tracking.Checkpointable() load_dep.var = checkpointable_utils.add_variable( load_dep, name="var", shape=[]) first_root.dep = load_dep @@ -840,39 +841,39 @@ class CheckpointingTests(test.TestCase): second_status.run_restore_ops() self.assertEqual(12., self.evaluate(load_dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAmbiguousLoad(self): # Not OK to split one checkpoint object into two checkpoint_directory = self.get_temp_dir() - save_root = checkpointable.Checkpointable() - save_root.dep_one = checkpointable.Checkpointable() - save_root.dep_two = checkpointable.Checkpointable() - dep_three = checkpointable.Checkpointable() + save_root = tracking.Checkpointable() + save_root.dep_one = tracking.Checkpointable() + save_root.dep_two = tracking.Checkpointable() + dep_three = tracking.Checkpointable() save_root.dep_one.dep_three = dep_three save_root.dep_two.dep_three = dep_three checkpointable_utils.add_variable(dep_three, name="var", initializer=0.) self.evaluate(checkpointable_utils.gather_initializers(save_root)) save_path = checkpointable_utils.CheckpointableSaver(save_root).save( os.path.join(checkpoint_directory, "ckpt")) - load_root = checkpointable.Checkpointable() + load_root = tracking.Checkpointable() status = checkpointable_utils.CheckpointableSaver(load_root).restore( save_path) - load_root.dep_one = checkpointable.Checkpointable() - load_root.dep_two = checkpointable.Checkpointable() - load_root.dep_one.dep_three = checkpointable.Checkpointable() - load_root.dep_two.dep_three = checkpointable.Checkpointable() + load_root.dep_one = tracking.Checkpointable() + load_root.dep_two = tracking.Checkpointable() + load_root.dep_one.dep_three = tracking.Checkpointable() + load_root.dep_two.dep_three = tracking.Checkpointable() checkpointable_utils.add_variable( load_root.dep_one.dep_three, name="var", initializer=0.) with self.assertRaises(AssertionError): status.assert_consumed() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testObjectsCombined(self): # Currently fine to load two checkpoint objects into one Python object checkpoint_directory = self.get_temp_dir() - save_root = checkpointable.Checkpointable() - save_root.dep_one = checkpointable.Checkpointable() - save_root.dep_two = checkpointable.Checkpointable() + save_root = tracking.Checkpointable() + save_root.dep_one = tracking.Checkpointable() + save_root.dep_two = tracking.Checkpointable() checkpointable_utils.add_variable( save_root.dep_one, name="var1", initializer=32., dtype=dtypes.float64) checkpointable_utils.add_variable( @@ -880,8 +881,8 @@ class CheckpointingTests(test.TestCase): self.evaluate(checkpointable_utils.gather_initializers(save_root)) save_path = checkpointable_utils.CheckpointableSaver(save_root).save( os.path.join(checkpoint_directory, "ckpt")) - load_root = checkpointable.Checkpointable() - load_root.dep_one = checkpointable.Checkpointable() + load_root = tracking.Checkpointable() + load_root.dep_one = tracking.Checkpointable() load_root.dep_two = load_root.dep_one v1 = checkpointable_utils.add_variable( load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64) @@ -893,12 +894,12 @@ class CheckpointingTests(test.TestCase): self.assertEqual(32., self.evaluate(v1)) self.assertEqual(64., self.evaluate(v2)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDependencyLoop(self): # Note: this test creates garbage during eager execution because it # purposefully creates a reference cycle. - first = checkpointable.Checkpointable() - second = checkpointable.Checkpointable() + first = tracking.Checkpointable() + second = tracking.Checkpointable() first.second = second second.first = first first.v = checkpointable_utils.add_variable( @@ -911,10 +912,10 @@ class CheckpointingTests(test.TestCase): os.path.join(checkpoint_directory, "ckpt")) # Test deferred loading - first_load = checkpointable.Checkpointable() + first_load = tracking.Checkpointable() status = checkpointable_utils.CheckpointableSaver( first_load).restore(save_path) - second_load = checkpointable.Checkpointable() + second_load = tracking.Checkpointable() first_load.second = second_load second_load.first = first_load with self.assertRaises(AssertionError): @@ -939,13 +940,13 @@ class CheckpointingTests(test.TestCase): self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v)) self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRestoreOnAssign(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") save_graph = ops.Graph() with save_graph.as_default(), self.test_session(save_graph): - first = checkpointable.Checkpointable() + first = tracking.Checkpointable() first.var1 = variable_scope.get_variable( name="outside_var", initializer=0.) first.var2 = variable_scope.get_variable( @@ -956,7 +957,7 @@ class CheckpointingTests(test.TestCase): checkpoint_prefix) restore_graph = ops.Graph() with restore_graph.as_default(), self.test_session(restore_graph): - second = checkpointable.Checkpointable() + second = tracking.Checkpointable() second.var2 = variable_scope.get_variable( name="blah", initializer=0.) status = checkpointable_utils.CheckpointableSaver( @@ -978,7 +979,7 @@ class CheckpointingTests(test.TestCase): with graph.as_default(), self.test_session(graph): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) @@ -989,11 +990,11 @@ class CheckpointingTests(test.TestCase): saver.save(checkpoint_prefix) self.assertEqual(before_ops, graph.get_operations()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCheckpointCleanup(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) self.evaluate(checkpointable_utils.gather_initializers(obj)) saver = checkpointable_utils.Checkpoint(obj=obj) @@ -1009,11 +1010,11 @@ class CheckpointingTests(test.TestCase): expected_filenames, os.listdir(checkpoint_directory)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCheckpointCleanupChangingVarList(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) self.evaluate(checkpointable_utils.gather_initializers(obj)) checkpoint = checkpointable_utils.Checkpoint(obj=obj) @@ -1062,7 +1063,7 @@ class CheckpointingTests(test.TestCase): with graph.as_default(), self.test_session(graph): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - obj = checkpointable.Checkpointable() + obj = tracking.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) @@ -1132,7 +1133,7 @@ class CheckpointingTests(test.TestCase): beta1_power, _ = optimizer._get_beta_accumulators() self.assertAllEqual(3., self.evaluate(beta1_power)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_sequential(self): model = sequential.Sequential() checkpoint = checkpointable_utils.Checkpoint(model=model) @@ -1164,7 +1165,7 @@ class CheckpointingTests(test.TestCase): self.assertAllEqual([1., 2., 3., 4., 5.], self.evaluate(deferred_second_dense.bias)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_initialize_if_not_restoring(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -1243,9 +1244,21 @@ class CheckpointingTests(test.TestCase): self.assertEqual(42., self.evaluate(optimizer.variables()[0])) +class _ManualScope(tracking.Checkpointable): + + def __call__(self): + with variable_scope.variable_scope("ManualScope") as vs: + self.variable_scope = vs + with checkpointable_utils.capture_dependencies(template=self): + return self._build() + + def _build(self): + return variable_scope.get_variable(name="in_manual_scope", shape=[]) + + class TemplateTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore(self): def _templated(): @@ -1255,14 +1268,23 @@ class TemplateTests(test.TestCase): v2 = variable_scope.get_variable( "v2", shape=[1], initializer=init_ops.zeros_initializer(), use_resource=True) - return v, v + 1., v2 + manual = _ManualScope() + return v, v + 1., v2, manual, manual() save_template = template.make_template("s1", _templated) - v1_save, _, v2_save = save_template() + v1_save, _, v2_save, manual_scope, manual_scope_v = save_template() + six.assertCountEqual( + self, + [v1_save, v2_save, manual_scope, manual_scope_v, save_template], + checkpointable_utils.list_objects(save_template)) + manual_dep, = manual_scope._checkpoint_dependencies + self.assertEqual("in_manual_scope", manual_dep.name) + self.assertIs(manual_scope_v, manual_dep.ref) optimizer = adam.AdamOptimizer(0.0) save_root = checkpointable_utils.Checkpoint( my_template=save_template, optimizer=optimizer) optimizer.minimize(v1_save.read_value) + self.evaluate([v.initializer for v in save_template.variables]) self.evaluate([v.initializer for v in optimizer.variables()]) self.evaluate(v1_save.assign([12.])) self.evaluate(v2_save.assign([14.])) @@ -1275,17 +1297,19 @@ class TemplateTests(test.TestCase): load_root = checkpointable_utils.Checkpoint( my_template=load_template, optimizer=load_optimizer) status = load_root.restore(save_path) - var, var_plus_one, var2 = load_template() + var, var_plus_one, var2, _, _ = load_template() load_optimizer.minimize(var.read_value) - self.assertEqual(2, len(load_template._checkpoint_dependencies)) + self.assertEqual(3, len(load_template._checkpoint_dependencies)) self.assertEqual("v", load_template._checkpoint_dependencies[0].name) self.assertEqual("v2", load_template._checkpoint_dependencies[1].name) + self.assertEqual("ManualScope", + load_template._checkpoint_dependencies[2].name) status.assert_consumed().run_restore_ops() self.assertAllEqual([12.], self.evaluate(var)) self.assertAllEqual([13.], self.evaluate(var_plus_one)) self.assertAllEqual([14.], self.evaluate(var2)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore_nested(self): def _inner_template(): @@ -1386,7 +1410,7 @@ class CheckpointCompatibilityTests(test.TestCase): sess=session, save_path=checkpoint_prefix, global_step=root.optimizer_step) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadFromNameBasedSaver(self): """Save a name-based checkpoint, load it using the object-based API.""" with test_util.device(use_gpu=True): @@ -1448,7 +1472,7 @@ class CheckpointCompatibilityTests(test.TestCase): class PythonMetadataTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveLoad(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py index ab8b37bb655bfc3c222ed661b6d48f0ecdc3a858..d33fd7376a7244535f7a0f393dd6125b125b8018 100644 --- a/tensorflow/python/training/distribute.py +++ b/tensorflow/python/training/distribute.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import threading -import six from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import ops @@ -222,11 +221,11 @@ def has_distribution_strategy(): def get_loss_reduction(): - """Reduce `method_string` corresponding to the last loss reduction.""" + """Reduce `aggregation` corresponding to the last loss reduction.""" loss_reduction = ops.get_default_graph()._last_loss_reduction # pylint: disable=protected-access if loss_reduction == losses_impl.Reduction.SUM: - return "sum" - return "mean" + return variable_scope.VariableAggregation.SUM + return variable_scope.VariableAggregation.MEAN # ------------------------------------------------------------------------------ @@ -527,15 +526,21 @@ class DistributionStrategy(object): V(`v`), output will have locality V(`v`) as well. * `d.update_non_slot(d.non_slot_devices(), fn)`: in cross-tower context, like `d.update()` except with locality N. - * `d.fetch(t)`: Copy `t` with any locality to the client's CPU device. + * `d.read_var(v)`: Gets the (read-only) value of the variable `v` (on + the device determined by the current device scope), aggregating + across towers for tower-local variables. Frequently, this will be + done automatically when using `v` in an expression or fetching it in + a cross-tower context, but this function can be used to force that + conversion happens at a particular point in time (for example, to + add the result of the conversion to a graph collection). The standard pattern for updating variables is to: 1. Wrap your input dataset in `d.distribute_dataset()` and create an iterator. 2. Define each tower `d.call_for_each_tower()` up to the point of getting a list of gradient, variable pairs. - 3. Call `d.reduce("sum", t, v)` or `d.batch_reduce()` to sum the - gradients (with locality T) into values with locality V(`v`). + 3. Call `d.reduce(VariableAggregation.SUM, t, v)` or `d.batch_reduce()` to sum + the gradients (with locality T) into values with locality V(`v`). 4. Call `d.update(v)` for each variable to update its value. Steps 3 and 4 are done automatically by class `Optimizer` if you call @@ -609,18 +614,18 @@ class DistributionStrategy(object): # Note: should support "colocate_with" argument. raise NotImplementedError("must be implemented in descendants") - def tower_local_var_scope(self, reduce_method): + def tower_local_var_scope(self, aggregation): """Inside this scope, new variables will not be mirrored. There will still be one component variable per tower, but there is no requirement that they stay in sync. Instead, when saving them - or calling `fetch()`, we use the value that results when calling - `reduce()` on all the towers' variables. + or calling `read_var()`, we use the value that results when + calling `reduce()` on all the towers' variables. Note: tower-local implies not trainable. Instead, it is expected that each tower will directly update (using `assign_add()` or whatever) its local variable instance but only the aggregated - value (accessible using `fetch()`) will be exported from the + value (accessible using `read_var()`) will be exported from the model. When it is acceptable to only aggregate on export, we greatly reduce communication overhead by using tower-local variables. @@ -631,21 +636,41 @@ class DistributionStrategy(object): random numbers. Args: - reduce_method: String used as a `method_string` to `reduce()` - to get the value to save when checkpointing. + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. Returns: A context manager. """ + # TODO(psv): Remove this after adding support for synchronization and + # aggregation parameters in get_variable() and mirrored strategy. def create_tower_local_variable(next_creator, *args, **kwargs): _require_distribution_strategy_scope(self) kwargs["use_resource"] = True - kwargs["tower_local_reduce_method"] = reduce_method + + # Set synchronization to be ON_READ for tower local variables. + kwargs["synchronization"] = variable_scope.VariableSynchronization.ON_READ + kwargs["aggregation"] = aggregation return next_creator(*args, **kwargs) _require_distribution_strategy_scope(self) return variable_scope.variable_creator_scope(create_tower_local_variable) + def read_var(self, v): + """Reads the value of a variable. + + Returns the aggregate value of a tower-local variable, or the + (read-only) value of any other variable. + + Args: + v: A variable allocated within the scope of this `DistributionStrategy`. + + Returns: + A tensor representing the value of `v`, aggregated across towers if + necessary. + """ + raise NotImplementedError("must be implemented in descendants") + def colocate_vars_with(self, colocate_with_variable): """Scope that controls which devices variables will be created on. @@ -796,12 +821,12 @@ class DistributionStrategy(object): def _call_for_each_tower(self, fn, *args, **kwargs): raise NotImplementedError("must be implemented in descendants") - def reduce(self, method_string, value, destinations=None): + def reduce(self, aggregation, value, destinations=None): """Combine (via e.g. sum or mean) values across towers. Args: - method_string: A string indicating how to combine values, either - "sum" or "mean". + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. value: A per-device value with one value per tower. destinations: An optional mirrored variable, a device string, list of device strings. The return value will be copied to all @@ -816,18 +841,21 @@ class DistributionStrategy(object): # TODO(josh11b): Return an unwrapped value if colocate_with is a # single device. _require_cross_tower_context(self) - assert method_string in ("sum", "mean") - return self._reduce(method_string, value, destinations) + assert aggregation in [ + variable_scope.VariableAggregation.SUM, + variable_scope.VariableAggregation.MEAN + ] + return self._reduce(aggregation, value, destinations) - def _reduce(self, method_string, value, destinations): + def _reduce(self, aggregation, value, destinations): raise NotImplementedError("must be implemented in descendants") - def batch_reduce(self, method_string, value_destination_pairs): + def batch_reduce(self, aggregation, value_destination_pairs): """Combine multiple `reduce` calls into one for faster execution. Args: - method_string: A string indicating how to combine values, either - "sum" or "mean". + aggregation: Indicates how a variable will be aggregated. Accepted values + are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. value_destination_pairs: A sequence of (value, destinations) pairs. See `reduce()` for a description. @@ -836,12 +864,17 @@ class DistributionStrategy(object): """ # TODO(josh11b): More docstring _require_cross_tower_context(self) - assert method_string in ("sum", "mean") - return self._batch_reduce(method_string, value_destination_pairs) - - def _batch_reduce(self, method_string, value_destination_pairs): - return [self.reduce(method_string, t, destinations=v) - for t, v in value_destination_pairs] + assert aggregation in [ + variable_scope.VariableAggregation.SUM, + variable_scope.VariableAggregation.MEAN + ] + return self._batch_reduce(aggregation, value_destination_pairs) + + def _batch_reduce(self, aggregation, value_destination_pairs): + return [ + self.reduce(aggregation, t, destinations=v) + for t, v in value_destination_pairs + ] def update(self, var, fn, *args, **kwargs): """Run `fn` to update `var` using inputs mirrored to the same devices. @@ -897,30 +930,6 @@ class DistributionStrategy(object): def _update_non_slot(self, colocate_with, fn, *args, **kwargs): raise NotImplementedError("must be implemented in descendants") - def fetch(self, val, destination="/device:CPU:0", fn=lambda x: x): - """Return a copy of `val` or `fn(val)` on `destination`. - - This is useful for getting a mirrored value onto a device. It - will attempt to avoid a copy by checking if the value is already - on the destination device. - - Args: - val: Value (which may be mirrored) to copy. - destination: A device string to copy the value to. - fn: An optional function to apply to the value on the source - device, before copying. - - Returns: - A `Tensor` on `destination`. - """ - _require_cross_tower_context(self) - assert isinstance(destination, six.string_types) - destination = device_util.resolve(destination) - return self._fetch(val, destination, fn) - - def _fetch(self, val, destination, fn): - raise NotImplementedError("must be implemented in descendants") - def unwrap(self, value): """Returns the list of all per-device values contained in `value`. @@ -946,7 +955,7 @@ class DistributionStrategy(object): return control_flow_ops.group(value, name=name) # Special handling for the common case of one op. v, = value - if isinstance(v, ops.Tensor): + if hasattr(v, "op"): v = v.op return v @@ -1094,9 +1103,9 @@ class TowerContext(object): finally: _pop_per_thread_mode() - def tower_local_var_scope(self, reduce_method): + def tower_local_var_scope(self, aggregation): """Alias for distribution_strategy.tower_local_var_scope().""" - return self._distribution_strategy.tower_local_var_scope(reduce_method) + return self._distribution_strategy.tower_local_var_scope(aggregation) @property def is_single_tower(self): @@ -1144,13 +1153,12 @@ class _DefaultDistributionStrategy(DistributionStrategy): def creator(next_creator, *args, **kwargs): _require_distribution_strategy_scope(self) - kwargs.pop("tower_local_reduce_method", None) return next_creator(*args, **kwargs) return _CurrentDistributionContext( self, variable_scope.variable_creator_scope(creator)) - def tower_local_var_scope(self, reduce_method): + def tower_local_var_scope(self, aggregation): """Does not set to resource variables.""" def create_tower_local_variable(next_creator, *args, **kwargs): _require_distribution_strategy_scope(self) @@ -1180,9 +1188,9 @@ class _DefaultDistributionStrategy(DistributionStrategy): with TowerContext(self, tower_id=0): return fn(*args, **kwargs) - def _reduce(self, method_string, value, destinations): + def _reduce(self, aggregation, value, destinations): # TODO(josh11b): Use destinations? - del method_string, destinations + del aggregation, destinations return value def _update(self, var, fn, *args, **kwargs): @@ -1197,11 +1205,8 @@ class _DefaultDistributionStrategy(DistributionStrategy): with ops.colocate_with(colocate_with), UpdateContext(colocate_with): return fn(*args, **kwargs) - def _fetch(self, var, destination, fn): - with ops.colocate_with(var): - var = fn(var) - with ops.device(destination): - return array_ops.identity(var) + def read_var(self, tower_local_var): + return array_ops.identity(tower_local_var) def _unwrap(self, distributed_value): return [distributed_value] diff --git a/tensorflow/python/training/distribute_test.py b/tensorflow/python/training/distribute_test.py index 0a4f19c31f6714e1211f9deed9703c02192cc2c0..694145ede73c1c9121cbc4c4e2d6f61e93165d09 100644 --- a/tensorflow/python/training/distribute_test.py +++ b/tensorflow/python/training/distribute_test.py @@ -29,6 +29,14 @@ class _TestTowerContext(distribute.TowerContext): return kwargs["test_arg"] +def _get_test_variable(name, synchronization, aggregation): + return { + "name": name, + "synchronization": synchronization, + "aggregation": aggregation + } + + class _TestStrategy(distribute.DistributionStrategy): def _call_for_each_tower(self, fn, *args, **kwargs): @@ -36,7 +44,8 @@ class _TestStrategy(distribute.DistributionStrategy): return fn(*args, **kwargs) def _create_variable(self, next_creator, *args, **kwargs): - return kwargs["name"] + return _get_test_variable(kwargs["name"], kwargs["synchronization"], + kwargs["aggregation"]) def _assert_in_default_state(t): @@ -61,7 +70,11 @@ class TestStrategyTest(test.TestCase): self.assertTrue(distribute.has_distribution_strategy()) self.assertIs(dist, distribute.get_distribution_strategy()) self.assertEqual("foo", tower_context.merge_call(None, test_arg="foo")) - self.assertEqual("bar", variable_scope.variable(1.0, name="bar")) + expected_value = _get_test_variable( + "bar", variable_scope.VariableSynchronization.AUTO, + variable_scope.VariableAggregation.NONE) + self.assertDictEqual(expected_value, + variable_scope.variable(1.0, name="bar")) with self.assertRaises(RuntimeError): dist.call_for_each_tower(run_fn) @@ -77,7 +90,27 @@ class TestStrategyTest(test.TestCase): self.assertIs(dist, distribute.get_cross_tower_context()) self.assertTrue(distribute.has_distribution_strategy()) self.assertIs(dist, distribute.get_distribution_strategy()) - self.assertEqual("baz", variable_scope.variable(1.0, name="baz")) + expected_value = _get_test_variable( + "baz", variable_scope.VariableSynchronization.AUTO, + variable_scope.VariableAggregation.NONE) + self.assertDictEqual(expected_value, + variable_scope.variable(1.0, name="baz")) + _assert_in_default_state(self) + + def testSettingSynchronizationAndAggregation(self): + _assert_in_default_state(self) + dist = _TestStrategy() + with dist.scope(): + expected_value = _get_test_variable( + "baz", variable_scope.VariableSynchronization.ON_WRITE, + variable_scope.VariableAggregation.MEAN) + self.assertDictEqual( + expected_value, + variable_scope.variable( + 1.0, + name="baz", + synchronization=variable_scope.VariableSynchronization.ON_WRITE, + aggregation=variable_scope.VariableAggregation.MEAN)) _assert_in_default_state(self) diff --git a/tensorflow/python/training/gradient_descent.py b/tensorflow/python/training/gradient_descent.py index a07ad19a6ec73a92cf86d5829ef487314607b7a4..ef50f6315dd623647e000b9b713d3ae557c31427 100644 --- a/tensorflow/python/training/gradient_descent.py +++ b/tensorflow/python/training/gradient_descent.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -41,6 +40,13 @@ class GradientDescentOptimizer(optimizer.Optimizer): use_locking: If True use locks for update operations. name: Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent". + + @compatibility(eager) + When eager execution is enabled, `learning_rate` can be a callable that + takes no arguments and returns the actual value to use. This can be useful + for changing these values across different invocations of optimizer + functions. + @end_compatibility """ super(GradientDescentOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate @@ -71,7 +77,6 @@ class GradientDescentOptimizer(optimizer.Optimizer): return var.scatter_sub(delta, use_locking=self._use_locking) def _prepare(self): - if not context.executing_eagerly() or not isinstance( - self._learning_rate_tensor, ops.EagerTensor): - self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, - name="learning_rate") + learning_rate = self._call_if_callable(self._learning_rate) + self._learning_rate_tensor = ops.convert_to_tensor( + learning_rate, name="learning_rate") diff --git a/tensorflow/python/training/gradient_descent_test.py b/tensorflow/python/training/gradient_descent_test.py index f89a9c583812a60857062f53d4a74dd1e73e7663..b304e924212c49d84b7c85e01869603b47fc1222 100644 --- a/tensorflow/python/training/gradient_descent_test.py +++ b/tensorflow/python/training/gradient_descent_test.py @@ -83,6 +83,32 @@ class GradientDescentOptimizerTest(test.TestCase): self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval()) + def testBasicCallableParams(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.test_session(): + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + lr = lambda: 3.0 + sgd_op = gradient_descent.GradientDescentOptimizer(lr).apply_gradients( + zip([grads0, grads1], [var0, var1])) + # TODO(apassos) calling initialize_resources on all resources here + # doesn't work because the sessions and graph are reused across unit + # tests and this would mean trying to reinitialize variables. Figure out + # a long-term solution for this. + resources.initialize_resources([var0, var1]).run() + # Fetch params to validate initial values + self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval()) + self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) + # Run 1 step of sgd + sgd_op.run() + # Validate updated params + self.assertAllCloseAccordingToType([1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], + var0.eval()) + self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], + var1.eval()) + def testMinimizeResourceVariable(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.test_session(): diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index 10ab4c1137ff226d88902143d4f2281ad77de531..51190264e81ad177c56a6864b616aee52d954c43 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -19,6 +19,7 @@ from __future__ import print_function import math +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -87,6 +88,12 @@ def exponential_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for exponential_decay.") @@ -95,14 +102,22 @@ def exponential_decay(learning_rate, [learning_rate, global_step, decay_steps, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - return math_ops.multiply( - learning_rate, math_ops.pow(decay_rate, p), name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.piecewise_constant") @@ -141,48 +156,62 @@ def piecewise_constant(x, boundaries, values, name=None): ValueError: if types of `x` and `boundaries` do not match, or types of all `values` do not match or the number of elements in the lists does not match. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if len(boundaries) != len(values) - 1: raise ValueError( "The length of boundaries should be 1 less than the length of values") with ops.name_scope(name, "PiecewiseConstant", [x, boundaries, values, name]) as name: - x = ops.convert_to_tensor(x) - # Avoid explicit conversion to x's dtype. This could result in faulty - # comparisons, for example if floats are converted to integers. boundaries = ops.convert_n_to_tensor(boundaries) - for i, b in enumerate(boundaries): - if b.dtype.base_dtype != x.dtype.base_dtype: - # We can promote int32 boundaries to int64 without loss of precision. - # This covers the most common case where the user passes in boundaries - # as an array of Python integers. - if (b.dtype.base_dtype == dtypes.int32 and - x.dtype.base_dtype == dtypes.int64): - b = math_ops.cast(b, x.dtype.base_dtype) - boundaries[i] = b - else: - raise ValueError( - "Boundaries (%s) must have the same dtype as x (%s)." % - (b.dtype.base_dtype, x.dtype.base_dtype)) - # TODO(rdipietro): Ensure that boundaries' elements are strictly increasing. values = ops.convert_n_to_tensor(values) - for v in values[1:]: - if v.dtype.base_dtype != values[0].dtype.base_dtype: - raise ValueError( - "Values must have elements all with the same dtype (%s vs %s)." % - (values[0].dtype.base_dtype, v.dtype.base_dtype)) - pred_fn_pairs = [] - pred_fn_pairs.append((x <= boundaries[0], lambda: values[0])) - pred_fn_pairs.append((x > boundaries[-1], lambda: values[-1])) - for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): - # Need to bind v here; can do this with lambda v=v: ... - pred = (x > low) & (x <= high) - pred_fn_pairs.append((pred, lambda v=v: v)) - - # The default isn't needed here because our conditions are mutually - # exclusive and exhaustive, but tf.case requires it. - default = lambda: values[0] - return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + x_recomp = ops.convert_to_tensor(x) + # Avoid explicit conversion to x's dtype. This could result in faulty + # comparisons, for example if floats are converted to integers. + for i, b in enumerate(boundaries): + if b.dtype.base_dtype != x_recomp.dtype.base_dtype: + # We can promote int32 boundaries to int64 without loss of precision. + # This covers the most common case where the user passes in boundaries + # as an array of Python integers. + if (b.dtype.base_dtype == dtypes.int32 and + x_recomp.dtype.base_dtype == dtypes.int64): + b = math_ops.cast(b, x_recomp.dtype.base_dtype) + boundaries[i] = b + else: + raise ValueError( + "Boundaries (%s) must have the same dtype as x (%s)." % + (b.dtype.base_dtype, x_recomp.dtype.base_dtype)) + # TODO(rdipietro): Ensure that boundaries' elements strictly increases. + for v in values[1:]: + if v.dtype.base_dtype != values[0].dtype.base_dtype: + raise ValueError( + "Values must have elements all with the same dtype (%s vs %s)." % + (values[0].dtype.base_dtype, v.dtype.base_dtype)) + pred_fn_pairs = [] + pred_fn_pairs.append((x_recomp <= boundaries[0], lambda: values[0])) + pred_fn_pairs.append((x_recomp > boundaries[-1], lambda: values[-1])) + for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): + # Need to bind v here; can do this with lambda v=v: ... + pred = (x_recomp > low) & (x_recomp <= high) + pred_fn_pairs.append((pred, lambda v=v: v)) + + # The default isn't needed here because our conditions are mutually + # exclusive and exhaustive, but tf.case requires it. + default = lambda: values[0] + return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.polynomial_decay") @@ -263,6 +292,12 @@ def polynomial_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for polynomial_decay.") @@ -272,27 +307,35 @@ def polynomial_decay(learning_rate, ]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) - decay_steps = math_ops.cast(decay_steps, dtype) end_learning_rate = math_ops.cast(end_learning_rate, dtype) power = math_ops.cast(power, dtype) - if cycle: - # Find the first multiple of decay_steps that is bigger than global_step. - # If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond( - math_ops.equal(global_step, 0), lambda: 1.0, - lambda: math_ops.ceil(global_step / decay_steps)) - decay_steps = math_ops.multiply(decay_steps, multiplier) - else: - # Make sure that the global_step used is not bigger than decay_steps. - global_step = math_ops.minimum(global_step, decay_steps) - - p = math_ops.div(global_step, decay_steps) - return math_ops.add( - math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, - name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + decay_steps_recomp = math_ops.cast(decay_steps, dtype) + if cycle: + # Find the first multiple of decay_steps that is bigger than + # global_step. If global_step is zero set the multiplier to 1 + multiplier = control_flow_ops.cond( + math_ops.equal(global_step_recomp, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step_recomp / decay_steps)) + decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) + else: + # Make sure that the global_step used is not bigger than decay_steps. + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + + p = math_ops.div(global_step_recomp, decay_steps_recomp) + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.natural_exp_decay") @@ -350,6 +393,12 @@ def natural_exp_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for natural_exp_decay.") @@ -357,14 +406,23 @@ def natural_exp_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - exponent = math_ops.exp(math_ops.multiply(math_ops.negative(decay_rate), p)) - return math_ops.multiply(learning_rate, exponent, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + exponent = math_ops.exp( + math_ops.multiply(math_ops.negative(decay_rate), p)) + return math_ops.multiply(learning_rate, exponent, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.inverse_time_decay") @@ -432,6 +490,12 @@ def inverse_time_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for inverse_time_decay.") @@ -439,15 +503,23 @@ def inverse_time_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - const = math_ops.cast(constant_op.constant(1), learning_rate.dtype) - denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) - return math_ops.div(learning_rate, denom, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + const = math_ops.cast(constant_op.constant(1), dtype) + denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) + return math_ops.div(learning_rate, denom, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay") @@ -492,6 +564,12 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay requires global_step") @@ -499,15 +577,23 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) - completed_fraction = global_step / decay_steps - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + completed_fraction = global_step_recomp / decay_steps + cosine_decayed = 0.5 * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + + decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay_restarts") @@ -561,6 +647,12 @@ def cosine_decay_restarts(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay restarts requires global_step") @@ -568,40 +660,48 @@ def cosine_decay_restarts(learning_rate, learning_rate = ops.convert_to_tensor( learning_rate, name="initial_learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) first_decay_steps = math_ops.cast(first_decay_steps, dtype) alpha = math_ops.cast(alpha, dtype) t_mul = math_ops.cast(t_mul, dtype) m_mul = math_ops.cast(m_mul, dtype) - completed_fraction = global_step / first_decay_steps + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + completed_fraction = global_step_recomp / first_decay_steps - def compute_step(completed_fraction, geometric=False): - if geometric: - i_restart = math_ops.floor( - math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / - math_ops.log(t_mul)) + def compute_step(completed_fraction, geometric=False): + """Helper for `cond` operation.""" + if geometric: + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) - sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart - else: - i_restart = math_ops.floor(completed_fraction) - completed_fraction = completed_fraction - i_restart + else: + i_restart = math_ops.floor(completed_fraction) + completed_fraction -= i_restart + + return i_restart, completed_fraction - return i_restart, completed_fraction + i_restart, completed_fraction = control_flow_ops.cond( + math_ops.equal(t_mul, 1.0), + lambda: compute_step(completed_fraction, geometric=False), + lambda: compute_step(completed_fraction, geometric=True)) - i_restart, completed_fraction = control_flow_ops.cond( - math_ops.equal(t_mul, 1.0), - lambda: compute_step(completed_fraction, geometric=False), - lambda: compute_step(completed_fraction, geometric=True)) + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + decayed = (1 - alpha) * cosine_decayed + alpha - m_fac = m_mul**i_restart - cosine_decayed = 0.5 * m_fac * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed, name=name) - return math_ops.multiply(learning_rate, decayed, name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.linear_cosine_decay") @@ -664,6 +764,12 @@ def linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("linear cosine decay requires global_step") @@ -671,21 +777,28 @@ def linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) num_periods = math_ops.cast(num_periods, dtype) - global_step = math_ops.minimum(global_step, decay_steps) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + + linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta + return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) - linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta - return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.noisy_linear_cosine_decay") @@ -756,6 +869,12 @@ def noisy_linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("noisy linear cosine decay requires global_step") @@ -763,29 +882,36 @@ def noisy_linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) initial_variance = math_ops.cast(initial_variance, dtype) variance_decay = math_ops.cast(variance_decay, dtype) num_periods = math_ops.cast(num_periods, dtype) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - variance = initial_variance / ( - math_ops.pow(1.0 + global_step, variance_decay)) - std = math_ops.sqrt(variance) - noisy_linear_decayed = ( - linear_decayed + - random_ops.random_normal(linear_decayed.shape, stddev=std)) - - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - noisy_linear_cosine_decayed = ( - (alpha + noisy_linear_decayed) * cosine_decayed + beta) - - return math_ops.multiply( - learning_rate, noisy_linear_cosine_decayed, name=name) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + variance = initial_variance / ( + math_ops.pow(1.0 + global_step_recomp, variance_decay)) + std = math_ops.sqrt(variance) + noisy_linear_decayed = ( + linear_decayed + random_ops.random_normal( + linear_decayed.shape, stddev=std)) + + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + noisy_linear_cosine_decayed = ( + (alpha + noisy_linear_decayed) * cosine_decayed + beta) + + return math_ops.multiply( + learning_rate, noisy_linear_cosine_decayed, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index 60306e4f1239a759ea1f68492a1211d5f0858997..4f3cf01822c5b56c8fd05f859c3a1db302a57625 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -21,12 +21,9 @@ from __future__ import print_function import math from tensorflow.python.eager import context -from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util -from tensorflow.python.ops import gen_state_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import -from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import learning_rate_decay @@ -34,31 +31,35 @@ from tensorflow.python.training import learning_rate_decay class LRDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testContinuous(self): - with self.test_session(): - step = 5 - decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) - expected = .05 * 0.96 ** (5.0 / 10.0) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(variables.global_variables_initializer()) + step = 5 + decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) + expected = .05 * 0.96**(5.0 / 10.0) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testStaircase(self): - with self.test_session(): - step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") - assign_100 = state_ops.assign(step, 100) - assign_1 = state_ops.assign(step, 1) - assign_2 = state_ops.assign(step, 2) - decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96, - staircase=True) - # No change to learning rate - assign_1.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) - assign_2.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) + if context.executing_eagerly(): + step = resource_variable_ops.ResourceVariable(0) + self.evaluate(variables.global_variables_initializer()) + decayed_lr = learning_rate_decay.exponential_decay( + .1, step, 3, 0.96, staircase=True) + + # No change to learning rate due to staircase + expected = .1 + self.evaluate(step.assign(1)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + expected = .1 + self.evaluate(step.assign(2)) + self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6) + # Decayed learning rate - assign_100.op.run() expected = .1 * 0.96 ** (100 // 3) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(step.assign(100)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) def testVariables(self): with self.test_session(): @@ -79,38 +80,44 @@ class LRDecayTest(test_util.TensorFlowTestCase): expected = .1 * 0.96 ** (100 // 3) self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPiecewiseConstant(self): x = resource_variable_ops.ResourceVariable(-999) - def pc(): - return learning_rate_decay.piecewise_constant(x, [100, 110, 120], - [1.0, 0.1, 0.01, 0.001]) + decayed_lr = learning_rate_decay.piecewise_constant( + x, [100, 110, 120], [1.0, 0.1, 0.01, 0.001]) self.evaluate(variables.global_variables_initializer()) - self.assertAllClose(self.evaluate(pc()), 1.0, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6) self.evaluate(x.assign(100)) - self.assertAllClose(self.evaluate(pc()), 1.0, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6) self.evaluate(x.assign(105)) - self.assertAllClose(self.evaluate(pc()), 0.1, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6) self.evaluate(x.assign(110)) - self.assertAllClose(self.evaluate(pc()), 0.1, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6) self.evaluate(x.assign(120)) - self.assertAllClose(self.evaluate(pc()), 0.01, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.01, 1e-6) self.evaluate(x.assign(999)) - self.assertAllClose(self.evaluate(pc()), 0.001, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.001, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPiecewiseConstantEdgeCases(self): x_int = resource_variable_ops.ResourceVariable( 0, dtype=variables.dtypes.int32) boundaries, values = [-1.0, 1.0], [1, 2, 3] with self.assertRaises(ValueError): - learning_rate_decay.piecewise_constant(x_int, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x_int, boundaries, values) + if context.executing_eagerly(): + decayed_lr() + x = resource_variable_ops.ResourceVariable(0.0) boundaries, values = [-1.0, 1.0], [1.0, 2, 3] with self.assertRaises(ValueError): - learning_rate_decay.piecewise_constant(x, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x, boundaries, values) + if context.executing_eagerly(): + decayed_lr() # Test that ref types are valid. if not context.executing_eagerly(): @@ -123,221 +130,205 @@ class LRDecayTest(test_util.TensorFlowTestCase): x_int64 = resource_variable_ops.ResourceVariable( 0, dtype=variables.dtypes.int64) boundaries, values = [1, 2, 3], [0.4, 0.5, 0.6, 0.7] - def pc(): - return learning_rate_decay.piecewise_constant(x_int64, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x_int64, boundaries, values) self.evaluate(variables.global_variables_initializer()) - self.assertAllClose(self.evaluate(pc()), 0.4, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6) self.evaluate(x_int64.assign(1)) - self.assertAllClose(self.evaluate(pc()), 0.4, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6) self.evaluate(x_int64.assign(2)) - self.assertAllClose(self.evaluate(pc()), 0.5, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.5, 1e-6) self.evaluate(x_int64.assign(3)) - self.assertAllClose(self.evaluate(pc()), 0.6, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.6, 1e-6) self.evaluate(x_int64.assign(4)) - self.assertAllClose(self.evaluate(pc()), 0.7, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.7, 1e-6) class LinearDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = lr * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 5 + lr = 0.05 + end_lr = 0.0 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = lr * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 10 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = (lr + end_lr) * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 5 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = (lr + end_lr) * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - cycle=True) - expected = (lr - end_lr) * 0.25 + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, cycle=True) + expected = (lr - end_lr) * 0.25 + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class SqrtDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = lr * 0.5 ** power - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 5 + lr = 0.05 + end_lr = 0.0 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = lr * 0.5**power + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 10 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = (lr - end_lr) * 0.5 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 5 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = (lr - end_lr) * 0.5**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power, cycle=True) - expected = (lr - end_lr) * 0.25 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power, cycle=True) + expected = (lr - end_lr) * 0.25**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class PolynomialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testBeginWithCycle(self): - with self.test_session(): - lr = 0.001 - decay_steps = 10 - step = 0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, - decay_steps, cycle=True) - expected = lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + lr = 0.001 + decay_steps = 10 + step = 0 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, decay_steps, cycle=True) + expected = lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class ExponentialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, - k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, k, + decay_rate) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + @test_util.run_in_graph_and_eager_modes def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class InverseDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, step, k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class CosineDecayTest(test_util.TensorFlowTestCase): @@ -348,34 +339,35 @@ class CosineDecayTest(test_util.TensorFlowTestCase): decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps) + expected = self.np_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps, alpha) - expected = self.np_cosine_decay(step, num_training_steps, alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps, alpha) + expected = self.np_cosine_decay(step, num_training_steps, alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class CosineDecayRestartsTest(test_util.TensorFlowTestCase): + def np_cosine_decay_restarts(self, step, decay_steps, t_mul=2.0, m_mul=1.0, alpha=0.0): fac = 1.0 while step >= decay_steps: - step = step - decay_steps + step -= decay_steps decay_steps *= t_mul fac *= m_mul @@ -383,51 +375,51 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay_restarts(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay_restarts(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, alpha=alpha) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - alpha=alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, alpha=alpha) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, alpha=alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testMMul(self): num_training_steps = 1000 initial_lr = 1.0 m_mul = 0.9 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, m_mul=m_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - m_mul=m_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, m_mul=m_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, m_mul=m_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testTMul(self): num_training_steps = 1000 initial_lr = 1.0 t_mul = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, t_mul=t_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - t_mul=t_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, t_mul=t_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, t_mul=t_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class LinearCosineDecayTest(test_util.TensorFlowTestCase): @@ -444,65 +436,63 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) return (alpha + linear_decayed) * cosine_decayed + beta + @test_util.run_in_graph_and_eager_modes def testDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_linear_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_linear_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes def testNonDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - expected = self.np_linear_cosine_decay( - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + expected = self.np_linear_cosine_decay( + step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes def testDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, step, num_training_steps) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, step, num_training_steps) + # Cannot be deterministically tested + self.evaluate(decayed_lr) + @test_util.run_in_graph_and_eager_modes def testNonDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, - step, - num_training_steps, - initial_variance=0.5, - variance_decay=0.1, - alpha=0.1, - beta=1e-4, - num_periods=5) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, + step, + num_training_steps, + initial_variance=0.5, + variance_decay=0.1, + alpha=0.1, + beta=1e-4, + num_periods=5) + # Cannot be deterministically tested + self.evaluate(decayed_lr) if __name__ == "__main__": diff --git a/tensorflow/python/training/momentum.py b/tensorflow/python/training/momentum.py index bd9fa79d8feac68c149f787ee8501bdddb173d33..cb3ec6f053e2e7f5aa80152ed233c8fbb6920be0 100644 --- a/tensorflow/python/training/momentum.py +++ b/tensorflow/python/training/momentum.py @@ -61,8 +61,8 @@ class MomentumOptimizer(optimizer.Optimizer): variable(s) track the values called `theta_t + mu*v_t` in the paper. @compatibility(eager) - When eager execution is enabled, learning_rate and momentum can each be a - callable that takes no arguments and returns the actual value to use. This + When eager execution is enabled, `learning_rate` and `momentum` can each be + a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. @end_compatibility diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index fece3370f343173de46bc447c478264864708dca..7b06bffa4b29b92dd8d3df5d8eaa6ebec1ea44b1 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -298,7 +298,8 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name stop_grace_period_secs=120, log_step_count_steps=100, max_wait_secs=7200, - save_checkpoint_steps=USE_DEFAULT): + save_checkpoint_steps=USE_DEFAULT, + summary_dir=None): """Creates a `MonitoredSession` for training. For a chief, this utility sets proper session initializer/restorer. It also @@ -348,6 +349,8 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name `save_checkpoint_steps` and `save_checkpoint_secs` are set to `None`, then the default checkpoint saver isn't used. If both are provided, then only `save_checkpoint_secs` is used. Default not enabled. + summary_dir: A string. Optional path to a directory where to + save summaries. If None, checkpoint_dir is used instead. Returns: A `MonitoredSession` object. @@ -388,11 +391,12 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name master=master, config=config) - if checkpoint_dir: + summary_dir = summary_dir or checkpoint_dir + if summary_dir: if log_step_count_steps and log_step_count_steps > 0: all_hooks.append( basic_session_run_hooks.StepCounterHook( - output_dir=checkpoint_dir, every_n_steps=log_step_count_steps)) + output_dir=summary_dir, every_n_steps=log_step_count_steps)) if (save_summaries_steps and save_summaries_steps > 0) or ( save_summaries_secs and save_summaries_secs > 0): @@ -400,7 +404,9 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name scaffold=scaffold, save_steps=save_summaries_steps, save_secs=save_summaries_secs, - output_dir=checkpoint_dir)) + output_dir=summary_dir)) + + if checkpoint_dir: if (save_checkpoint_secs and save_checkpoint_secs > 0) or ( save_checkpoint_steps and save_checkpoint_steps > 0): all_hooks.append(basic_session_run_hooks.CheckpointSaverHook( diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index a9287a0f0d0391cc6e0b297cce18eebaf9f64291..971ed5c8b5ed3bd78b0d467e5c3fa4b7a72c96a1 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -461,7 +461,8 @@ class Optimizer( # Have to be careful to call distribute_lib.get_loss_reduction() # *after* loss() is evaluated, so we know what loss reduction it uses. # TODO(josh11b): Test that we handle weight decay in a reasonable way. - if distribute_lib.get_loss_reduction() == "mean": + if (distribute_lib.get_loss_reduction() == + variable_scope.VariableAggregation.MEAN): num_towers = distribute_lib.get_distribution_strategy().num_towers if num_towers > 1: loss_value *= (1. / num_towers) @@ -478,7 +479,8 @@ class Optimizer( "be a function when eager execution is enabled.") # Scale loss if using a "mean" loss reduction and multiple towers. - if distribute_lib.get_loss_reduction() == "mean": + if (distribute_lib.get_loss_reduction() == + variable_scope.VariableAggregation.MEAN): num_towers = distribute_lib.get_distribution_strategy().num_towers if num_towers > 1: loss *= (1. / num_towers) @@ -649,7 +651,8 @@ class Optimizer( towers. If `global_step` was not None, that operation also increments `global_step`. """ - reduced_grads = distribution.batch_reduce("sum", grads_and_vars) + reduced_grads = distribution.batch_reduce( + variable_scope.VariableAggregation.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) # Note that this is called in a cross-tower context. @@ -730,15 +733,15 @@ class Optimizer( if not named_slots: return None - if hasattr(var, "_mirrored_container"): + if hasattr(var, "_distributed_container"): # NOTE: If this isn't patched, then there is no `handle` in # `_resource_apply_dense`. - mirrored_container = var._mirrored_container() - assert mirrored_container is not None + distributed_container = var._distributed_container() + assert distributed_container is not None if context.executing_eagerly(): - key = mirrored_container._unique_id + key = distributed_container._unique_id else: - key = (mirrored_container.graph, mirrored_container._shared_name) + key = (distributed_container.graph, distributed_container._shared_name) # pylint: enable=protected-access mirrored_slot = named_slots.get(key, None) if mirrored_slot is None: return None @@ -839,7 +842,7 @@ class Optimizer( def _get_non_slot_variable(self, name, graph=None): non_slot = self._non_slot_dict.get((name, graph), None) - if hasattr(non_slot, "_mirrored_container"): + if hasattr(non_slot, "_distributed_container"): # This is a mirrored non-slot. In order to enable code like `_finish` # to assign to a non-slot, return the current context replica. return non_slot.get() @@ -1211,3 +1214,7 @@ class Optimizer( self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) + + def _call_if_callable(self, param): + """Call the function if param is callable.""" + return param() if callable(param) else param diff --git a/tensorflow/python/training/optimizer_test.py b/tensorflow/python/training/optimizer_test.py index 0cab6410e83ca1880a0a4a80d2cfa5c17517af95..dfe9176beaf27f3cfa945eee8693ba7c5e9551fa 100644 --- a/tensorflow/python/training/optimizer_test.py +++ b/tensorflow/python/training/optimizer_test.py @@ -34,7 +34,7 @@ from tensorflow.python.training import gradient_descent class OptimizerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -112,7 +112,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoVariables(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: # pylint: disable=cell-var-from-loop @@ -127,7 +127,7 @@ class OptimizerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No.*variables'): sgd_op.minimize(loss) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -145,7 +145,7 @@ class OptimizerTest(test.TestCase): # var1 has no gradient sgd_op.minimize(loss, var_list=[var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -161,7 +161,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.minimize(loss, var_list=[var0, var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -175,7 +175,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.apply_gradients([(None, var0), (None, var1)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -215,7 +215,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([-14., -13.], self.evaluate(var0)) self.assertAllClose([-6., -5.], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeGradientsWithTensors(self): x = ops.convert_to_tensor(1.0) def f(): diff --git a/tensorflow/python/training/rmsprop.py b/tensorflow/python/training/rmsprop.py index 341b970c92e42b4fe392d91f57219d713d2513e5..f38c9861d64aa258cde07ccd3041d3c50932c33b 100644 --- a/tensorflow/python/training/rmsprop.py +++ b/tensorflow/python/training/rmsprop.py @@ -92,6 +92,13 @@ class RMSPropOptimizer(optimizer.Optimizer): computation and memory. Defaults to False. name: Optional name prefix for the operations created when applying gradients. Defaults to "RMSProp". + + @compatibility(eager) + When eager execution is enabled, `learning_rate`, `decay`, `momentum`, and + `epsilon` can each be a callable that takes no arguments and returns the + actual value to use. This can be useful for changing these values across + different invocations of optimizer functions. + @end_compatibility """ super(RMSPropOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate @@ -120,12 +127,15 @@ class RMSPropOptimizer(optimizer.Optimizer): self._zeros_slot(v, "momentum", self._name) def _prepare(self): - self._learning_rate_tensor = ops.convert_to_tensor( - self._learning_rate, name="learning_rate") - self._decay_tensor = ops.convert_to_tensor(self._decay, name="decay") - self._momentum_tensor = ops.convert_to_tensor( - self._momentum, name="momentum") - self._epsilon_tensor = ops.convert_to_tensor(self._epsilon, name="epsilon") + lr = self._call_if_callable(self._learning_rate) + decay = self._call_if_callable(self._decay) + momentum = self._call_if_callable(self._momentum) + epsilon = self._call_if_callable(self._epsilon) + + self._learning_rate_tensor = ops.convert_to_tensor(lr, name="learning_rate") + self._decay_tensor = ops.convert_to_tensor(decay, name="decay") + self._momentum_tensor = ops.convert_to_tensor(momentum, name="momentum") + self._epsilon_tensor = ops.convert_to_tensor(epsilon, name="epsilon") def _apply_dense(self, grad, var): rms = self.get_slot(var, "rms") diff --git a/tensorflow/python/training/rmsprop_test.py b/tensorflow/python/training/rmsprop_test.py index ee5385596c8b11e607969f94153f7e4f5d2d4cdd..604332738456bfc8b3ff24242f6032bf95273072 100644 --- a/tensorflow/python/training/rmsprop_test.py +++ b/tensorflow/python/training/rmsprop_test.py @@ -24,6 +24,7 @@ import math import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -141,7 +142,7 @@ class RMSPropOptimizerTest(test.TestCase): self.assertAllClose([3.0, 4.0], var1.eval()) # Run 4 steps of RMSProp - for t in range(1, 5): + for _ in range(1, 5): update.run() var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( @@ -261,7 +262,7 @@ class RMSPropOptimizerTest(test.TestCase): self.assertAllClose([3.0, 4.0], var1.eval()) # Run 4 steps of RMSProp - for t in range(1, 5): + for _ in range(1, 5): update.run() var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( @@ -444,6 +445,55 @@ class RMSPropOptimizerTest(test.TestCase): (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) ]), var1.eval()) + def testCallableParams(self): + with context.eager_mode(): + for dtype in [dtypes.half, dtypes.float32]: + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + + learning_rate = lambda: 2.0 + decay = lambda: 0.9 + momentum = lambda: 0.0 + epsilon = lambda: 1.0 + opt = rmsprop.RMSPropOptimizer(learning_rate, decay, momentum, epsilon) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + # Step 1: the rms accumulators where 1. So we should see a normal + # update: v -= grad * learning_rate + opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) + ]), self.evaluate(var0)) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) + ]), self.evaluate(var1)) + # Step 2: the root mean square accumulators contain the previous update. + opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) + ]), self.evaluate(var0)) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) + ]), self.evaluate(var1)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 4d464135fd03330134c0a371853d6bc8a228cd21..1ee975fbe48e8ba724d8f40040b122c5c02aa352 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -22,7 +22,6 @@ from __future__ import print_function import collections import os.path import re -import sys import time import uuid @@ -206,21 +205,19 @@ class BaseSaverBuilder(object): filename_tensor: String Tensor. saveables: List of BaseSaverBuilder.SaveableObject objects. preferred_shard: Int. Shard to open first when loading a sharded file. - restore_sequentially: Bool. If true, each restore is sequential. + restore_sequentially: Unused. Bool. If true, each restore is sequential. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ + del restore_sequentially all_tensors = [] - assign_ops = [] for saveable in saveables: - restore_control_inputs = assign_ops[-1:] if restore_sequentially else [] with ops.device(_set_cpu0(saveable.device) if saveable.device else None): - with ops.control_dependencies(restore_control_inputs): - all_tensors.extend( - self.restore_op(filename_tensor, saveable, preferred_shard)) + all_tensors.extend( + self.restore_op(filename_tensor, saveable, preferred_shard)) return all_tensors # pylint: disable=unused-argument @@ -1045,8 +1042,8 @@ def get_checkpoint_state(checkpoint_dir, latest_filename=None): ckpt = CheckpointState() text_format.Merge(file_content, ckpt) if not ckpt.model_checkpoint_path: - raise ValueError("Invalid checkpoint state loaded from %s", - checkpoint_dir) + raise ValueError("Invalid checkpoint state loaded from " + + checkpoint_dir) # For relative model_checkpoint_path and all_model_checkpoint_paths, # prepend checkpoint_dir. if not os.path.isabs(ckpt.model_checkpoint_path): @@ -1373,23 +1370,6 @@ class Saver(object): name, _ = p return name - def _MetaGraphFilename(self, checkpoint_filename, meta_graph_suffix="meta"): - """Returns the meta graph filename. - - Args: - checkpoint_filename: Name of the checkpoint file. - meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. - - Returns: - MetaGraph file name. - """ - # If the checkpoint_filename is sharded, the checkpoint_filename could - # be of format model.ckpt-step#-?????-of-shard#. For example, - # model.ckpt-123456-?????-of-00005, or model.ckpt-123456-00001-of-00002. - basename = re.sub(r"-[\d\?]+-of-\d+$", "", checkpoint_filename) - meta_graph_filename = ".".join([basename, meta_graph_suffix]) - return meta_graph_filename - def _RecordLastCheckpoint(self, latest_save_path): """Manages the list of the latest checkpoints.""" if not self.saver_def.max_to_keep: @@ -1430,24 +1410,12 @@ class Saver(object): # Otherwise delete the files. try: - checkpoint_prefix = self._CheckpointFilename(p) - self._delete_file_if_exists( - self._MetaGraphFilename(checkpoint_prefix, meta_graph_suffix)) - if self.saver_def.version == saver_pb2.SaverDef.V2: - # V2 has a metadata file and some data files. - self._delete_file_if_exists(checkpoint_prefix + ".index") - self._delete_file_if_exists(checkpoint_prefix + - ".data-?????-of-?????") - else: - # V1, Legacy. Exact match on the data file. - self._delete_file_if_exists(checkpoint_prefix) + remove_checkpoint( + self._CheckpointFilename(p), self.saver_def.version, + meta_graph_suffix) except Exception as e: # pylint: disable=broad-except logging.warning("Ignoring: %s", str(e)) - def _delete_file_if_exists(self, filespec): - for pathname in file_io.get_matching_files(filespec): - file_io.delete_file(pathname) - def as_saver_def(self): """Generates a `SaverDef` representation of this saver. @@ -1669,7 +1637,7 @@ class Saver(object): raise exc if write_meta_graph: - meta_graph_filename = self._MetaGraphFilename( + meta_graph_filename = _meta_graph_filename( checkpoint_file, meta_graph_suffix=meta_graph_suffix) if not context.executing_eagerly(): with sess.graph.as_default(): @@ -1737,12 +1705,17 @@ class Saver(object): save_path: Path where parameters were previously saved. Raises: - ValueError: If save_path is None. + ValueError: If save_path is None or not a valid checkpoint. """ if self._is_empty: return if save_path is None: raise ValueError("Can't load save_path when it is None.") + + if not checkpoint_exists(compat.as_text(save_path)): + raise ValueError("The passed save_path is not a valid checkpoint: " + + compat.as_text(save_path)) + logging.info("Restoring parameters from %s", compat.as_text(save_path)) try: if context.executing_eagerly(): @@ -1750,23 +1723,24 @@ class Saver(object): else: sess.run(self.saver_def.restore_op_name, {self.saver_def.filename_tensor_name: save_path}) - except errors.NotFoundError: - exception_type, exception_value, exception_traceback = sys.exc_info() - # The checkpoint would not be loaded successfully as is. Try to parse it - # as an object-based checkpoint. - should_reraise = False + except errors.NotFoundError as err: + # There are three common conditions that might cause this error: + # 0. The file is missing. We ignore here, as this is checked above. + # 1. This is an object-based checkpoint trying name-based loading. + # 2. The graph has been altered and a variable or other name is missing. + + # 1. The checkpoint would not be loaded successfully as is. Try to parse + # it as an object-based checkpoint. try: reader = pywrap_tensorflow.NewCheckpointReader(save_path) object_graph_string = reader.get_tensor( checkpointable.OBJECT_GRAPH_PROTO_KEY) except errors.NotFoundError: - # This is not an object-based checkpoint, or the checkpoint doesn't - # exist. Re-raise the original exception, but do it outside the except - # block so the object graph lookup isn't included in the stack trace. - should_reraise = True - if should_reraise: - six.reraise(exception_type, exception_value, exception_traceback) - del exception_traceback # avoid reference cycles + # 2. This is not an object-based checkpoint, which likely means there + # is a graph mismatch. Re-raise the original error with + # a helpful message (b/110263146) + raise _wrap_restore_error_with_msg( + err, "a Variable name or other graph key that is missing") # This is an object-based checkpoint. We'll print a warning and then do # the restore. @@ -1778,6 +1752,11 @@ class Saver(object): self._restore_from_object_based_checkpoint( sess=sess, save_path=save_path, object_graph_string=object_graph_string) + except errors.InvalidArgumentError as err: + # There is a mismatch between the graph and the checkpoint being loaded. + # We add a more reasonable error message here to help users (b/110263146) + raise _wrap_restore_error_with_msg( + err, "a mismatch between the current graph and the graph") def _restore_from_object_based_checkpoint(self, sess, save_path, object_graph_string): @@ -1970,7 +1949,7 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, return Saver(saver_def=meta_graph_def.saver_def, name=scope) else: - if variables._all_saveable_objects(): # pylint: disable=protected-access + if variables._all_saveable_objects(scope=import_scope): # pylint: disable=protected-access # Return the default saver instance for all graph variables. return Saver() else: @@ -2121,6 +2100,63 @@ def get_checkpoint_mtimes(checkpoint_prefixes): return mtimes +@tf_export("train.remove_checkpoint") +def remove_checkpoint(checkpoint_prefix, + checkpoint_format_version=saver_pb2.SaverDef.V2, + meta_graph_suffix="meta"): + """Removes a checkpoint given by `checkpoint_prefix`. + + Args: + checkpoint_prefix: The prefix of a V1 or V2 checkpoint. Typically the result + of `Saver.save()` or that of `tf.train.latest_checkpoint()`, regardless of + sharded/non-sharded or V1/V2. + checkpoint_format_version: `SaverDef.CheckpointFormatVersion`, defaults to + `SaverDef.V2`. + meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. + """ + _delete_file_if_exists( + _meta_graph_filename(checkpoint_prefix, meta_graph_suffix)) + if checkpoint_format_version == saver_pb2.SaverDef.V2: + # V2 has a metadata file and some data files. + _delete_file_if_exists(checkpoint_prefix + ".index") + _delete_file_if_exists(checkpoint_prefix + ".data-?????-of-?????") + else: + # V1, Legacy. Exact match on the data file. + _delete_file_if_exists(checkpoint_prefix) + + +def _delete_file_if_exists(filespec): + """Deletes files matching `filespec`.""" + for pathname in file_io.get_matching_files(filespec): + file_io.delete_file(pathname) + + +def _meta_graph_filename(checkpoint_filename, meta_graph_suffix="meta"): + """Returns the meta graph filename. + + Args: + checkpoint_filename: Name of the checkpoint file. + meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. + + Returns: + MetaGraph file name. + """ + # If the checkpoint_filename is sharded, the checkpoint_filename could + # be of format model.ckpt-step#-?????-of-shard#. For example, + # model.ckpt-123456-?????-of-00005, or model.ckpt-123456-00001-of-00002. + basename = re.sub(r"-[\d\?]+-of-\d+$", "", checkpoint_filename) + meta_graph_filename = ".".join([basename, meta_graph_suffix]) + return meta_graph_filename + + +def _wrap_restore_error_with_msg(err, extra_verbiage): + err_msg = ("Restoring from checkpoint failed. This is most likely " + "due to {} from the checkpoint. Please ensure that you " + "have not altered the graph expected based on the checkpoint. " + "Original error:\n\n{}").format(extra_verbiage, err.message) + return err.__class__(err.node_def, err.op, err_msg) + + ops.register_proto_function( ops.GraphKeys.SAVERS, proto_type=saver_pb2.SaverDef, diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index f1991093e0b519da7448809e759a1cd5c57b80d9..ae9c244aaf372dcbcf365cf3e6a21ae77d9ae7d0 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -24,10 +24,8 @@ import math import os import random import shutil -import sys import tempfile import time -import traceback import numpy as np import six @@ -79,7 +77,8 @@ from tensorflow.python.training import saver as saver_module from tensorflow.python.training import saver_test_utils from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState -from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import base as checkpointable_base +from tensorflow.python.training.checkpointable import tracking as checkpointable_tracking from tensorflow.python.training.checkpointable import util as checkpointable_utils from tensorflow.python.util import compat @@ -171,7 +170,7 @@ class SaverTest(test.TestCase): def testBasic(self): self.basicSaveRestore(variables.Variable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceBasic(self): self.basicSaveRestore(resource_variable_ops.ResourceVariable) @@ -252,7 +251,7 @@ class SaverTest(test.TestCase): self.assertAllEqual(w3.eval(), 3.0) self.assertAllEqual(w4.eval(), 4.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceSaveRestoreCachingDevice(self): save_path = os.path.join(self.get_temp_dir(), "resource_cache") with self.test_session(graph=ops_lib.Graph()) as sess: @@ -368,8 +367,8 @@ class SaverTest(test.TestCase): for ver in (saver_pb2.SaverDef.V1, saver_pb2.SaverDef.V2): with self.test_session() as sess: save = saver_module.Saver({"v0": v0}, write_version=ver) - with self.assertRaisesRegexp(errors.NotFoundError, - "Failed to find any matching files for"): + with self.assertRaisesRegexp( + ValueError, "The passed save_path is not a valid checkpoint:"): save.restore(sess, "invalid path") def testInt64(self): @@ -671,7 +670,7 @@ class SaverTest(test.TestCase): save.restore(sess, save_path) self.assertAllClose([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], var.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveWithGlobalStep(self, pad_step_number=False): save_path = os.path.join(self.get_temp_dir(), "ckpt_with_global_step") global_step_int = 5 @@ -809,7 +808,7 @@ class SaveRestoreShardedTest(test.TestCase): self.assertEqual(save_path + "-?????-of-00002", val) else: self.assertEqual(save_path, val) - meta_graph_filename = save._MetaGraphFilename(val) + meta_graph_filename = saver_module._meta_graph_filename(val) self.assertEqual(save_path + ".meta", meta_graph_filename) if save._write_version is saver_pb2.SaverDef.V1: @@ -1185,13 +1184,13 @@ class MaxToKeepTest(test.TestCase): self.assertEqual([s3, s2], save.last_checkpoints) self.assertFalse(saver_module.checkpoint_exists(s1)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) self.assertTrue(saver_module.checkpoint_exists(s3)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) self.assertCheckpointState( model_checkpoint_path=s2, all_model_checkpoint_paths=[s3, s2], @@ -1202,13 +1201,13 @@ class MaxToKeepTest(test.TestCase): self.assertEqual([s2, s1], save.last_checkpoints) self.assertFalse(saver_module.checkpoint_exists(s3)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) self.assertTrue(saver_module.checkpoint_exists(s1)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) self.assertCheckpointState( model_checkpoint_path=s1, all_model_checkpoint_paths=[s2, s1], @@ -1222,14 +1221,14 @@ class MaxToKeepTest(test.TestCase): # Created by the first helper. self.assertTrue(saver_module.checkpoint_exists(s1)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) # Deleted by the first helper. self.assertFalse(saver_module.checkpoint_exists(s3)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) self.assertCheckpointState( model_checkpoint_path=s2, all_model_checkpoint_paths=[s3, s2], @@ -1240,13 +1239,13 @@ class MaxToKeepTest(test.TestCase): self.assertEqual([s2, s1], save2.last_checkpoints) self.assertFalse(saver_module.checkpoint_exists(s3)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) self.assertTrue(saver_module.checkpoint_exists(s1)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) self.assertCheckpointState( model_checkpoint_path=s1, all_model_checkpoint_paths=[s2, s1], @@ -1260,14 +1259,14 @@ class MaxToKeepTest(test.TestCase): # Created by the first helper. self.assertTrue(saver_module.checkpoint_exists(s1)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) # Deleted by the first helper. self.assertFalse(saver_module.checkpoint_exists(s3)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) # Even though the file for s1 exists, this saver isn't aware of it, which # is why it doesn't end up in the checkpoint state. self.assertCheckpointState( @@ -1280,13 +1279,13 @@ class MaxToKeepTest(test.TestCase): self.assertEqual([s2, s1], save3.last_checkpoints) self.assertFalse(saver_module.checkpoint_exists(s3)) self.assertFalse( - saver_module.checkpoint_exists(save._MetaGraphFilename(s3))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s3))) self.assertTrue(saver_module.checkpoint_exists(s2)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s2))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s2))) self.assertTrue(saver_module.checkpoint_exists(s1)) self.assertTrue( - saver_module.checkpoint_exists(save._MetaGraphFilename(s1))) + saver_module.checkpoint_exists(saver_module._meta_graph_filename(s1))) self.assertCheckpointState( model_checkpoint_path=s1, all_model_checkpoint_paths=[s2, s1], @@ -1317,7 +1316,7 @@ class MaxToKeepTest(test.TestCase): else: self.assertEqual(4, len(gfile.Glob(s1 + "*"))) - self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1))) + self.assertTrue(gfile.Exists(saver_module._meta_graph_filename(s1))) s2 = save.save(sess, os.path.join(save_dir, "s2")) self.assertEqual([s1, s2], save.last_checkpoints) @@ -1325,27 +1324,27 @@ class MaxToKeepTest(test.TestCase): self.assertEqual(2, len(gfile.Glob(s1))) else: self.assertEqual(4, len(gfile.Glob(s1 + "*"))) - self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1))) + self.assertTrue(gfile.Exists(saver_module._meta_graph_filename(s1))) if save._write_version is saver_pb2.SaverDef.V1: self.assertEqual(2, len(gfile.Glob(s2))) else: self.assertEqual(4, len(gfile.Glob(s2 + "*"))) - self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2))) + self.assertTrue(gfile.Exists(saver_module._meta_graph_filename(s2))) s3 = save.save(sess, os.path.join(save_dir, "s3")) self.assertEqual([s2, s3], save.last_checkpoints) self.assertEqual(0, len(gfile.Glob(s1 + "*"))) - self.assertFalse(gfile.Exists(save._MetaGraphFilename(s1))) + self.assertFalse(gfile.Exists(saver_module._meta_graph_filename(s1))) if save._write_version is saver_pb2.SaverDef.V1: self.assertEqual(2, len(gfile.Glob(s2))) else: self.assertEqual(4, len(gfile.Glob(s2 + "*"))) - self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2))) + self.assertTrue(gfile.Exists(saver_module._meta_graph_filename(s2))) if save._write_version is saver_pb2.SaverDef.V1: self.assertEqual(2, len(gfile.Glob(s3))) else: self.assertEqual(4, len(gfile.Glob(s3 + "*"))) - self.assertTrue(gfile.Exists(save._MetaGraphFilename(s3))) + self.assertTrue(gfile.Exists(saver_module._meta_graph_filename(s3))) def testNoMaxToKeep(self): save_dir = self._get_test_dir("no_max_to_keep") @@ -1385,7 +1384,7 @@ class MaxToKeepTest(test.TestCase): s1 = save.save(sess, os.path.join(save_dir, "s1"), write_meta_graph=False) self.assertTrue(saver_module.checkpoint_exists(s1)) - self.assertFalse(gfile.Exists(save._MetaGraphFilename(s1))) + self.assertFalse(gfile.Exists(saver_module._meta_graph_filename(s1))) class KeepCheckpointEveryNHoursTest(test.TestCase): @@ -1395,7 +1394,7 @@ class KeepCheckpointEveryNHoursTest(test.TestCase): gfile.MakeDirs(test_dir) return test_dir - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes @test.mock.patch.object(saver_module, "time") def testNonSharded(self, mock_time): save_dir = self._get_test_dir("keep_checkpoint_every_n_hours") @@ -1515,7 +1514,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): self.assertEqual(10.0, self.evaluate(v0)) self.assertEqual(20.0, self.evaluate(v1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonReshapeResourceVariable(self): self._testNonReshape(resource_variable_ops.ResourceVariable) @@ -2339,6 +2338,46 @@ class MetaGraphTest(test.TestCase): 10, size=[1, 10]) }) + def testImportIntoNamescopeWithoutVariables(self): + # Save a simple graph that contains no variables into a checkpoint. + test_dir = self._get_test_dir("no_vars_graph") + filename = os.path.join(test_dir, "ckpt") + graph_1 = ops_lib.Graph() + with session.Session(graph=graph_1) as sess: + constant_op.constant([1, 2, 3], name="x") + constant_op.constant([1, 2, 3], name="y") + saver = saver_module.Saver(allow_empty=True) + saver.save(sess, filename) + + # Create a fresh graph. + graph_2 = ops_lib.Graph() + with session.Session(graph=graph_2) as sess: + # Restore the above checkpoint under scope "subgraph_1". + new_saver_1 = saver_module.import_meta_graph( + filename + ".meta", graph=graph_2, import_scope="subgraph_1") + # There are no variables to restore, so import_meta_graph should not + # return a Saver. + self.assertIsNone(new_saver_1) + + # Create a variable in graph_2 under scope "my_scope". + variables.Variable(array_ops.zeros([10]), name="my_scope/my_var") + sess.run(variables.global_variables_initializer()) + # Restore the checkpoint into a different scope "subgraph_2". + new_saver_2 = saver_module.import_meta_graph( + filename + ".meta", graph=graph_2, import_scope="subgraph_2") + # Because the variable does not live in scope "subgraph_2", + # import_meta_graph should not attempt to restore the variable. So, + # import_meta_graph still won't return a Saver instance. + self.assertIsNone(new_saver_2) + + # However, if we restore the checkpoint under scope "my_scope", + # import_meta_graph will detect the variable and return a Saver for + # restoring it. This should happen even when the variable does not + # originate from graph_1. + new_saver_3 = saver_module.import_meta_graph( + filename + ".meta", graph=graph_2, import_scope="my_scope") + self.assertIsInstance(new_saver_3, saver_module.Saver) + def testImportIntoImplicitNamescope(self): # Test that we can import a meta graph into an implicit namescope. test_dir = self._get_test_dir("import_into_namescope") @@ -2581,6 +2620,20 @@ class SaverUtilsTest(test.TestCase): self.assertEqual(2, len(mtimes)) self.assertTrue(mtimes[1] >= mtimes[0]) + def testRemoveCheckpoint(self): + for sharded in (False, True): + for version in (saver_pb2.SaverDef.V2, saver_pb2.SaverDef.V1): + with self.test_session(graph=ops_lib.Graph()) as sess: + unused_v = variables.Variable(1.0, name="v") + variables.global_variables_initializer().run() + saver = saver_module.Saver(sharded=sharded, write_version=version) + + path = os.path.join(self._base_dir, "%s-%s" % (sharded, version)) + ckpt_prefix = saver.save(sess, path) + self.assertTrue(saver_module.checkpoint_exists(ckpt_prefix)) + saver_module.remove_checkpoint(ckpt_prefix, version) + self.assertFalse(saver_module.checkpoint_exists(ckpt_prefix)) + class ScopedGraphTest(test.TestCase): @@ -2885,7 +2938,7 @@ class ScopedGraphTest(test.TestCase): self.assertEqual(2.0, var_dict2["variable2:0"].eval()) -class _OwnsAVariableSimple(checkpointable.CheckpointableBase): +class _OwnsAVariableSimple(checkpointable_base.CheckpointableBase): """A Checkpointable object which can be saved using a tf.train.Saver.""" def __init__(self): @@ -2893,7 +2946,7 @@ class _OwnsAVariableSimple(checkpointable.CheckpointableBase): name="non_dep_variable", initializer=6., use_resource=True) def _gather_saveables_for_checkpoint(self): - return {checkpointable.VARIABLE_VALUE_KEY: self.non_dep_variable} + return {checkpointable_base.VARIABLE_VALUE_KEY: self.non_dep_variable} # The Saver sorts by name before parsing, so we need a name property. @property @@ -2918,7 +2971,7 @@ class _MirroringSaveable( self._mirrored_variable.assign(tensor)) -class _OwnsMirroredVariables(checkpointable.CheckpointableBase): +class _OwnsMirroredVariables(checkpointable_base.CheckpointableBase): """A Checkpointable object which returns a more complex SaveableObject.""" def __init__(self): @@ -2933,7 +2986,7 @@ class _OwnsMirroredVariables(checkpointable.CheckpointableBase): primary_variable=self.non_dep_variable, mirrored_variable=self.mirrored, name=name) - return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} + return {checkpointable_base.VARIABLE_VALUE_KEY: _saveable_factory} # The Saver sorts by name before parsing, so we need a name property. @property @@ -2941,7 +2994,7 @@ class _OwnsMirroredVariables(checkpointable.CheckpointableBase): return self.non_dep_variable.name -class NonLayerCheckpointable(checkpointable.Checkpointable): +class NonLayerCheckpointable(checkpointable_tracking.Checkpointable): def __init__(self): super(NonLayerCheckpointable, self).__init__() @@ -2967,7 +3020,7 @@ class MyModel(training.Model): class CheckpointableCompatibilityTests(test.TestCase): # TODO(allenl): Track down python3 reference cycles in these tests. - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNotSaveableButIsCheckpointable(self): v = _OwnsAVariableSimple() saver = saver_module.Saver(var_list=[v]) @@ -2980,7 +3033,7 @@ class CheckpointableCompatibilityTests(test.TestCase): saver.restore(sess, save_path) self.assertEqual(42., self.evaluate(v.non_dep_variable)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturned(self): v = _OwnsMirroredVariables() saver = saver_module.Saver(var_list=[v]) @@ -3084,27 +3137,33 @@ class CheckpointableCompatibilityTests(test.TestCase): errors.NotFoundError, "Key b not found in checkpoint"): b_saver.restore(sess=sess, save_path=save_path) - def testCheckpointNotFoundErrorRaised(self): - # Restore does some tricky exception handling to figure out if it should - # load an object-based checkpoint. Tests that the exception handling isn't - # too broad. - a = resource_variable_ops.ResourceVariable(1., name="a") - saver = saver_module.Saver([a]) - with self.test_session() as sess: - with self.assertRaisesRegexp( - errors.NotFoundError, - "Failed to find any matching files for path_which_does_not_exist"): - saver.restore(sess=sess, save_path="path_which_does_not_exist") - try: - saver.restore(sess=sess, save_path="path_which_does_not_exist") - except errors.NotFoundError: - # Make sure we don't have a confusing "During handling of the above - # exception" block in Python 3. - # pylint: disable=no-value-for-parameter - exception_string = "\n".join( - traceback.format_exception(*sys.exc_info())) - # pylint: enable=no-value-for-parameter - self.assertNotIn("NewCheckpointReader", exception_string) + with self.assertRaises(errors.NotFoundError) as cs: + b_saver.restore(sess=sess, save_path=save_path) + + # Make sure we don't have a confusing "During handling of the above + # exception" block in Python 3. + self.assertNotIn("NewCheckpointReader", cs.exception.message) + + def testGraphChangedForRestoreErrorRaised(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + + with ops_lib.Graph().as_default() as g: + a = variables.Variable(1., name="a") + a_saver = saver_module.Saver([a]) + + with self.test_session(graph=g) as sess: + sess.run(a.initializer) + save_path = a_saver.save(sess=sess, save_path=checkpoint_prefix) + + with ops_lib.Graph().as_default() as g: + a = variables.Variable([1.], name="a") + a_saver = saver_module.Saver([a]) + with self.test_session(graph=g) as sess: + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "a mismatch between the current graph and the graph"): + a_saver.restore(sess=sess, save_path=save_path) def testLoadFromObjectBasedGraph(self): checkpoint_directory = self.get_temp_dir() diff --git a/tensorflow/python/util/lock_util.py b/tensorflow/python/util/lock_util.py new file mode 100644 index 0000000000000000000000000000000000000000..0424960666323870fb1db83804857dd838cfe9ae --- /dev/null +++ b/tensorflow/python/util/lock_util.py @@ -0,0 +1,128 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Locking related utils.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import threading + + +class GroupLock(object): + """A lock to allow many members of a group to access a resource exclusively. + + This lock provides a way to allow access to a resource by multiple threads + belonging to a logical group at the same time, while restricting access to + threads from all other groups. You can think of this as an extension of a + reader-writer lock, where you allow multiple writers at the same time. We + made it generic to support multiple groups instead of just two - readers and + writers. + + Simple usage example with two groups accessing the same resource: + + ```python + lock = GroupLock(num_groups=2) + + # In a member of group 0: + with lock.group(0): + # do stuff, access the resource + # ... + + # In a member of group 1: + with lock.group(1): + # do stuff, access the resource + # ... + ``` + + Using as a context manager with `.group(group_id)` is the easiest way. You + can also use the `acquire` and `release` method directly. + """ + + def __init__(self, num_groups=2): + """Initialize a group lock. + + Args: + num_groups: The number of groups that will be accessing the resource under + consideration. Should be a positive number. + + Returns: + A group lock that can then be used to synchronize code. + + Raises: + ValueError: If num_groups is less than 1. + """ + if num_groups < 1: + raise ValueError("num_groups must be a positive integer, got {}".format( + num_groups)) + self._ready = threading.Condition(threading.Lock()) + self._num_groups = num_groups + self._group_member_counts = [0] * self._num_groups + + def group(self, group_id): + """Enter a context where the lock is with group `group_id`. + + Args: + group_id: The group for which to acquire and release the lock. + + Returns: + A context manager which will acquire the lock for `group_id`. + """ + self._validate_group_id(group_id) + return self._Context(self, group_id) + + def acquire(self, group_id): + """Acquire the group lock for a specific group `group_id`.""" + self._validate_group_id(group_id) + + self._ready.acquire() + while self._another_group_active(group_id): + self._ready.wait() + self._group_member_counts[group_id] += 1 + self._ready.release() + + def release(self, group_id): + """Release the group lock for a specific group `group_id`.""" + self._validate_group_id(group_id) + + self._ready.acquire() + self._group_member_counts[group_id] -= 1 + if self._group_member_counts[group_id] == 0: + self._ready.notifyAll() + self._ready.release() + + def _another_group_active(self, group_id): + return any( + c > 0 for g, c in enumerate(self._group_member_counts) if g != group_id) + + def _validate_group_id(self, group_id): + if group_id < 0 or group_id >= self._num_groups: + raise ValueError( + "group_id={} should be between 0 and num_groups={}".format( + group_id, self._num_groups)) + + class _Context(object): + """Context manager helper for `GroupLock`.""" + + def __init__(self, lock, group_id): + self._lock = lock + self._group_id = group_id + + def __enter__(self): + self._lock.acquire(self._group_id) + + def __exit__(self, type_arg, value_arg, traceback_arg): + del type_arg, value_arg, traceback_arg + self._lock.release(self._group_id) diff --git a/tensorflow/python/util/lock_util_test.py b/tensorflow/python/util/lock_util_test.py new file mode 100644 index 0000000000000000000000000000000000000000..cda8f952259c9e117e0bd7ff3cac35e764856f43 --- /dev/null +++ b/tensorflow/python/util/lock_util_test.py @@ -0,0 +1,63 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for lock_util.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import random +import time + +from absl.testing import parameterized + +from tensorflow.python.platform import test +from tensorflow.python.util import lock_util + + +class GroupLockTest(test.TestCase, parameterized.TestCase): + + @parameterized.parameters(1, 2, 3, 5, 10) + def testGroups(self, num_groups): + lock = lock_util.GroupLock(num_groups) + num_threads = 10 + finished = set() + + def thread_fn(thread_id): + time.sleep(random.random() * 0.1) + group_id = thread_id % num_groups + with lock.group(group_id): + time.sleep(random.random() * 0.1) + self.assertGreater(lock._group_member_counts[group_id], 0) + for g, c in enumerate(lock._group_member_counts): + if g != group_id: + self.assertEqual(0, c) + finished.add(thread_id) + + threads = [ + self.checkedThread(target=thread_fn, args=(i,)) + for i in range(num_threads) + ] + + for i in range(num_threads): + threads[i].start() + for i in range(num_threads): + threads[i].join() + + self.assertEqual(set(range(num_threads)), finished) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py index 1104768ae8f69598f686eb2ffee8b69e43051011..d63f59a8c8e836d3f8ad3686da0b0b3f010a9225 100644 --- a/tensorflow/python/util/nest.py +++ b/tensorflow/python/util/nest.py @@ -167,11 +167,14 @@ def assert_same_structure(nest1, nest2, check_types=True): Args: nest1: an arbitrarily nested structure. nest2: an arbitrarily nested structure. - check_types: if `True` (default) types of sequences are checked as - well, including the keys of dictionaries. If set to `False`, for example - a list and a tuple of objects will look the same if they have the same + check_types: if `True` (default) types of sequences are checked as well, + including the keys of dictionaries. If set to `False`, for example a + list and a tuple of objects will look the same if they have the same size. Note that namedtuples with identical name and fields are always - considered to have the same shallow structure. + considered to have the same shallow structure. Two types will also be + considered the same if they are both list subtypes (which allows "list" + and "_ListWrapper" from checkpointable dependency tracking to compare + equal). Raises: ValueError: If the two structures do not have the same number of elements or diff --git a/tensorflow/python/util/serialization_test.py b/tensorflow/python/util/serialization_test.py index 5000bcfad05900e63bc72c1bd0e31e30434b74ae..9d9cac272592f6b73b4c78f38310d7b89a89e05d 100644 --- a/tensorflow/python/util/serialization_test.py +++ b/tensorflow/python/util/serialization_test.py @@ -47,7 +47,7 @@ class SerializationTests(test.TestCase): self.assertIs(round_trip[0], None) self.assertEqual(round_trip[1], 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_serialize_sequential(self): model = sequential.Sequential() model.add(core.Dense(4)) @@ -61,7 +61,7 @@ class SerializationTests(test.TestCase): self.assertAllEqual([1, 1], input_round_trip[0]["config"]["batch_input_shape"]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_serialize_model(self): x = input_layer.Input(shape=[3]) y = core.Dense(10)(x) diff --git a/tensorflow/python/util/tf_export.py b/tensorflow/python/util/tf_export.py index bf3961c6920c4c6ade0593b28f9eb1fd23ea8e0d..e154ffb68a4f0ccdebf5320cad7d3da056117197 100644 --- a/tensorflow/python/util/tf_export.py +++ b/tensorflow/python/util/tf_export.py @@ -41,17 +41,35 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections +import functools import sys from tensorflow.python.util import tf_decorator +ESTIMATOR_API_NAME = 'estimator' +TENSORFLOW_API_NAME = 'tensorflow' + +_Attributes = collections.namedtuple( + 'ExportedApiAttributes', ['names', 'constants']) + +# Attribute values must be unique to each API. +API_ATTRS = { + TENSORFLOW_API_NAME: _Attributes( + '_tf_api_names', + '_tf_api_constants'), + ESTIMATOR_API_NAME: _Attributes( + '_estimator_api_names', + '_estimator_api_constants') +} + class SymbolAlreadyExposedError(Exception): """Raised when adding API names to symbol that already has API names.""" pass -class tf_export(object): # pylint: disable=invalid-name +class api_export(object): # pylint: disable=invalid-name """Provides ways to export symbols to the TensorFlow API.""" def __init__(self, *args, **kwargs): @@ -63,15 +81,12 @@ class tf_export(object): # pylint: disable=invalid-name overrides: List of symbols that this is overriding (those overrided api exports will be removed). Note: passing overrides has no effect on exporting a constant. - allow_multiple_exports: Allows exporting the same symbol multiple - times with multiple `tf_export` usages. Prefer however, to list all - of the exported names in a single `tf_export` usage when possible. - + api_name: Name of the API you want to generate (e.g. `tensorflow` or + `estimator`). Default is `tensorflow`. """ self._names = args + self._api_name = kwargs.get('api_name', TENSORFLOW_API_NAME) self._overrides = kwargs.get('overrides', []) - self._allow_multiple_exports = kwargs.get( - 'allow_multiple_exports', False) def __call__(self, func): """Calls this decorator. @@ -86,25 +101,24 @@ class tf_export(object): # pylint: disable=invalid-name SymbolAlreadyExposedError: Raised when a symbol already has API names and kwarg `allow_multiple_exports` not set. """ + api_names_attr = API_ATTRS[self._api_name].names + # Undecorate overridden names for f in self._overrides: _, undecorated_f = tf_decorator.unwrap(f) - del undecorated_f._tf_api_names # pylint: disable=protected-access + delattr(undecorated_f, api_names_attr) _, undecorated_func = tf_decorator.unwrap(func) # Check for an existing api. We check if attribute name is in # __dict__ instead of using hasattr to verify that subclasses have # their own _tf_api_names as opposed to just inheriting it. - if '_tf_api_names' in undecorated_func.__dict__: - if self._allow_multiple_exports: - undecorated_func._tf_api_names += self._names # pylint: disable=protected-access - else: - raise SymbolAlreadyExposedError( - 'Symbol %s is already exposed as %s.' % - (undecorated_func.__name__, undecorated_func._tf_api_names)) # pylint: disable=protected-access - else: - undecorated_func._tf_api_names = self._names # pylint: disable=protected-access + if api_names_attr in undecorated_func.__dict__: + raise SymbolAlreadyExposedError( + 'Symbol %s is already exposed as %s.' % + (undecorated_func.__name__, getattr( + undecorated_func, api_names_attr))) # pylint: disable=protected-access + setattr(undecorated_func, api_names_attr, self._names) return func def export_constant(self, module_name, name): @@ -126,8 +140,12 @@ class tf_export(object): # pylint: disable=invalid-name name: (string) Current constant name. """ module = sys.modules[module_name] - if not hasattr(module, '_tf_api_constants'): - module._tf_api_constants = [] # pylint: disable=protected-access + if not hasattr(module, API_ATTRS[self._api_name].constants): + setattr(module, API_ATTRS[self._api_name].constants, []) # pylint: disable=protected-access - module._tf_api_constants.append((self._names, name)) + getattr(module, API_ATTRS[self._api_name].constants).append( + (self._names, name)) + +tf_export = functools.partial(api_export, api_name=TENSORFLOW_API_NAME) +estimator_export = functools.partial(tf_export, api_name=ESTIMATOR_API_NAME) diff --git a/tensorflow/python/util/tf_export_test.py b/tensorflow/python/util/tf_export_test.py index ace3f054ba952f012aa5ca642e490b1f45f8ba1d..b9e26ecb33383f5aa936a6bc92acea6d91eb996e 100644 --- a/tensorflow/python/util/tf_export_test.py +++ b/tensorflow/python/util/tf_export_test.py @@ -128,13 +128,6 @@ class ValidateExportTest(test.TestCase): with self.assertRaises(tf_export.SymbolAlreadyExposedError): export_decorator(_test_function) - def testEAllowMultipleExports(self): - _test_function._tf_api_names = ['name1', 'name2'] - tf_export.tf_export('nameRed', 'nameBlue', allow_multiple_exports=True)( - _test_function) - self.assertEquals(['name1', 'name2', 'nameRed', 'nameBlue'], - _test_function._tf_api_names) - def testOverridesFunction(self): _test_function2._tf_api_names = ['abc'] diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc index c79d8a84458800937e3e51a8dae26605bd834233..366f8a0deb533c3ee258ea618136d44a28160f8f 100644 --- a/tensorflow/python/util/util.cc +++ b/tensorflow/python/util/util.cc @@ -394,7 +394,11 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types, type2->tp_name); return true; } - } else if (type1 != type2) { + } else if (type1 != type2 + /* If both sequences are list types, don't complain. This allows + one to be a list subclass (e.g. _ListWrapper used for automatic + dependency tracking.) */ + && !(PyList_Check(o1) && PyList_Check(o2))) { *is_type_error = true; *error_msg = tensorflow::strings::StrCat( "The two namedtuples don't have the same sequence type. " diff --git a/tensorflow/stream_executor/BUILD b/tensorflow/stream_executor/BUILD index c68cda01002b1c5bbc2facb95b1eba214fbad7cb..e742f8e8d51d0217b631ebdc23ee65263c1ce0f0 100644 --- a/tensorflow/stream_executor/BUILD +++ b/tensorflow/stream_executor/BUILD @@ -2,6 +2,7 @@ licenses(["restricted"]) load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured") load("//tensorflow/core:platform/default/build_config_root.bzl", "if_static") +load("//tensorflow:tensorflow.bzl", "cc_header_only_library") STREAM_EXECUTOR_HEADERS = glob([ "*.h", @@ -33,7 +34,6 @@ cc_library( }), visibility = ["//visibility:public"], deps = [ - "//tensorflow/compiler/xla:statusor", "//tensorflow/core:lib", "//tensorflow/core:ptr_util", "@local_config_cuda//cuda:cuda_headers", @@ -48,11 +48,18 @@ cc_library( deps = [ "//tensorflow/core:lib", "//tensorflow/core:ptr_util", - "//tensorflow/compiler/xla:statusor", "@local_config_cuda//cuda:cuda_headers", ] + if_static([":stream_executor_impl"]), ) +cc_header_only_library( + name = "stream_executor_headers_lib", + visibility = ["//visibility:public"], + deps = [ + ":stream_executor", + ], +) + cc_library( name = "cuda_platform", srcs = if_cuda_is_configured( diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 08fe153b5909d36eae7848862932bb1359c29fe0..874bf0e8cb481bf9e506e6d9b71c19afbe89d644 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -2155,10 +2155,7 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( const HostOrDeviceScalar &beta, DeviceMemory *c, int ldc, blas::ComputationType computation_type, blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) { -// CUDA < version 8 and GPUs < sm_50 don't support cublasGemmEx. -#if CUDA_VERSION < 8000 - return false; -#else + // GPUs < sm_50 don't support cublasGemmEx. int cc_major, cc_minor; if (stream->parent()->GetDeviceDescription().cuda_compute_capability( &cc_major, &cc_minor) && @@ -2184,6 +2181,15 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( } } + // Return false if we might be hitting a cuBLAS bug that produces the wrong + // result. See nvbugs/2156201, b/79126339. +#if CUDA_VERSION >= 9000 && CUDA_VERSION < 9020 + if ((algorithm == CUBLAS_GEMM_DEFAULT || algorithm >= CUBLAS_GEMM_ALGO13) && + std::max({m, n, k}) >= 2097153 && cc_major < 7) { + return false; + } +#endif + cudaDataType_t cuda_in_type = CUDADataType::type; // Since we are converting 'algorithm' to cublasGemmAlgo_t by static_cast, // we do the following compile-time check on the default value: @@ -2213,7 +2219,6 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( timer->GetElapsedMilliseconds()); } return result; -#endif } bool CUDABlas::GetBlasGemmAlgorithms( diff --git a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc index 46e5deed8474dfa0c0ce6402bd6e5e2675491b31..124d5905b91cbf839437e763728cc76ad0d671dc 100644 --- a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc +++ b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc @@ -124,15 +124,20 @@ void Diagnostician::LogDiagnosticInformation() { #ifdef __APPLE__ CFStringRef kext_ids[1]; kext_ids[0] = kDriverKextIdentifier; - CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void**)kext_ids, 1, &kCFTypeArrayCallBacks); - CFDictionaryRef kext_infos = KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); + CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void **)kext_ids, 1, + &kCFTypeArrayCallBacks); + CFDictionaryRef kext_infos = + KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); CFRelease(kext_id_query); CFDictionaryRef cuda_driver_info = nullptr; - if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, (const void**)&cuda_driver_info)) { - bool started = CFBooleanGetValue((CFBooleanRef)CFDictionaryGetValue(cuda_driver_info, CFSTR("OSBundleStarted"))); + if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, + (const void **)&cuda_driver_info)) { + bool started = CFBooleanGetValue((CFBooleanRef)CFDictionaryGetValue( + cuda_driver_info, CFSTR("OSBundleStarted"))); if (!started) { - LOG(INFO) << "kernel driver is installed, but does not appear to be running on this host " + LOG(INFO) << "kernel driver is installed, but does not appear to be " + "running on this host " << "(" << port::Hostname() << ")"; } } else { @@ -210,27 +215,27 @@ port::StatusOr Diagnostician::FindDsoVersion() { "was unable to find libcuda.so DSO loaded into this program")); #if defined(__APPLE__) - // OSX CUDA libraries have names like: libcuda_310.41.15_mercury.dylib - const string prefix("libcuda_"); - const string suffix("_mercury.dylib"); - for (uint32_t image_index = 0; image_index < _dyld_image_count(); ++image_index) { - const string path(_dyld_get_image_name(image_index)); - const size_t suffix_pos = path.rfind(suffix); - const size_t prefix_pos = path.rfind(prefix, suffix_pos); - if (prefix_pos == string::npos || - suffix_pos == string::npos) { - // no match - continue; - } - const size_t start = prefix_pos + prefix.size(); - if (start >= suffix_pos) { - // version not included - continue; - } - const size_t length = suffix_pos - start; - const string version = path.substr(start, length); - result = StringToDriverVersion(version); + // OSX CUDA libraries have names like: libcuda_310.41.15_mercury.dylib + const string prefix("libcuda_"); + const string suffix("_mercury.dylib"); + for (uint32_t image_index = 0; image_index < _dyld_image_count(); + ++image_index) { + const string path(_dyld_get_image_name(image_index)); + const size_t suffix_pos = path.rfind(suffix); + const size_t prefix_pos = path.rfind(prefix, suffix_pos); + if (prefix_pos == string::npos || suffix_pos == string::npos) { + // no match + continue; + } + const size_t start = prefix_pos + prefix.size(); + if (start >= suffix_pos) { + // version not included + continue; } + const size_t length = suffix_pos - start; + const string version = path.substr(start, length); + result = StringToDriverVersion(version); + } #else #if !defined(PLATFORM_WINDOWS) && !defined(ANDROID_TEGRA) // Callback used when iterating through DSOs. Looks for the driver-interfacing @@ -313,12 +318,15 @@ port::StatusOr Diagnostician::FindKernelDriverVersion() { #if defined(__APPLE__) CFStringRef kext_ids[1]; kext_ids[0] = kDriverKextIdentifier; - CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void**)kext_ids, 1, &kCFTypeArrayCallBacks); - CFDictionaryRef kext_infos = KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); + CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void **)kext_ids, 1, + &kCFTypeArrayCallBacks); + CFDictionaryRef kext_infos = + KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); CFRelease(kext_id_query); CFDictionaryRef cuda_driver_info = nullptr; - if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, (const void**)&cuda_driver_info)) { + if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, + (const void **)&cuda_driver_info)) { // NOTE: OSX CUDA driver does not currently store the same driver version // in kCFBundleVersionKey as is returned by cuDriverGetVersion CFRelease(kext_infos); diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index f6564df0d077190b274f72cbf437dcd063ca0a4c..84916385a89b6e2bafb8a3c0a8f435ec9626e816 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -495,10 +495,10 @@ PersistentRnnPlan CreatePersistentRnnPlan(cudnnRNNDescriptor_t rnn_desc, // Turns a BatchDescriptor structure into a cudnn tensor handle within a // scope. -class ScopedTensorDescriptor { +class CudnnTensorDescriptor { public: - ScopedTensorDescriptor(const dnn::BatchDescriptor& batch_descriptor, - cudnnDataType_t elem_type) + CudnnTensorDescriptor(const dnn::BatchDescriptor& batch_descriptor, + cudnnDataType_t elem_type) : handle_(CreateTensorDescriptor()) { switch (batch_descriptor.layout()) { case dnn::DataLayout::kBatchYXDepth: @@ -540,15 +540,15 @@ class ScopedTensorDescriptor { private: TensorDescriptor handle_; - SE_DISALLOW_COPY_AND_ASSIGN(ScopedTensorDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnTensorDescriptor); }; // Turns a FilterDescriptor structure into a cudnn filter handle within a // scope. -class ScopedFilterDescriptor { +class CudnnFilterDescriptor { public: - ScopedFilterDescriptor(const dnn::FilterDescriptor& filter_descriptor, - cudnnDataType_t elem_type) + CudnnFilterDescriptor(const dnn::FilterDescriptor& filter_descriptor, + cudnnDataType_t elem_type) : handle_(CreateFilterDescriptor()) { // TODO(b/23032134): Even if the filter layout is not supported, // cudnnSetFilter4DDescriptor_v4 will return CUDNN_STATUS_SUCCESS because @@ -586,7 +586,7 @@ class ScopedFilterDescriptor { private: FilterDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedFilterDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnFilterDescriptor); }; // A helper function to decide whether to enable the TENSOR_OP_MATH math type @@ -636,9 +636,9 @@ bool BatchnormSpatialPersistentEnabled() { // Turns a ConvolutionDescriptor structure into a cudnn convolution handle // within a scope. -class ScopedConvolutionDescriptor { +class CudnnConvolutionDescriptor { public: - ScopedConvolutionDescriptor( + CudnnConvolutionDescriptor( const dnn::ConvolutionDescriptor& convolution_descriptor, cudnnDataType_t data_type) : handle_(CreateConvolutionDescriptor()) { @@ -700,14 +700,14 @@ class ScopedConvolutionDescriptor { private: ConvolutionDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedConvolutionDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnConvolutionDescriptor); }; // Turns a PoolingDescriptor structure into a cudnn pooling descriptor handle // within a scope. -class ScopedPoolingDescriptor { +class CudnnPoolingDescriptor { public: - explicit ScopedPoolingDescriptor( + explicit CudnnPoolingDescriptor( const dnn::PoolingDescriptor& pooling_descriptor) : handle_(CreatePoolingDescriptor()) { const std::vector strides64 = pooling_descriptor.strides(); @@ -739,13 +739,13 @@ class ScopedPoolingDescriptor { private: PoolingDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedPoolingDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnPoolingDescriptor); }; // Turns a NormalizeDescriptor structure into a cudnn LRN descriptor handle. -class ScopedNormalizeDescriptor { +class CudnnNormalizeDescriptor { public: - explicit ScopedNormalizeDescriptor( + explicit CudnnNormalizeDescriptor( const dnn::NormalizeDescriptor& normalize_descriptor) : handle_(CreateLrnDescriptor()) { // The range specifies that the indices in the closed range @@ -777,16 +777,16 @@ class ScopedNormalizeDescriptor { private: LrnDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedNormalizeDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnNormalizeDescriptor); }; // Turns a ActivationDescriptor structure into a cudnn activation // descriptor handle within a scope. -class ScopedActivationDescriptor { +class CudnnActivationDescriptor { public: - ScopedActivationDescriptor(dnn::ActivationMode activation_mode, - cudnnNanPropagation_t nan_propagation, - double value_max) + CudnnActivationDescriptor(dnn::ActivationMode activation_mode, + cudnnNanPropagation_t nan_propagation, + double value_max) : handle_(CreateActivationDescriptor()) { double relu_ceiling = 0.0; cudnnActivationMode_t mode; @@ -822,7 +822,7 @@ class ScopedActivationDescriptor { private: ActivationDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedActivationDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnActivationDescriptor); }; cudnnDataType_t ToCudnnDataType( @@ -888,21 +888,21 @@ int CudnnDataTypeToByteSize(cudnnDataType_t data_type) { } } -class ScopedDropoutDescriptor { - explicit ScopedDropoutDescriptor(DropoutDescriptor handle) +class CudnnDropoutDescriptor { + explicit CudnnDropoutDescriptor(DropoutDescriptor handle) : handle_(std::move(handle)) {} public: - ScopedDropoutDescriptor(ScopedDropoutDescriptor&&) = default; + CudnnDropoutDescriptor(CudnnDropoutDescriptor&&) = default; - static port::StatusOr Create( + static port::StatusOr Create( const CudnnHandle& cudnn, float dropout, uint64 seed, ScratchAllocator* state_allocator) { DropoutDescriptor handle = CreateDropoutDescriptor(); if (dropout == 0.0f) { // Return 'empty' dropout descriptor. - return ScopedDropoutDescriptor(std::move(handle)); + return CudnnDropoutDescriptor(std::move(handle)); } DeviceMemory state_memory; @@ -917,14 +917,14 @@ class ScopedDropoutDescriptor { handle.get(), cudnn.handle(), dropout, state_memory.opaque(), state_memory.size(), seed)); - return ScopedDropoutDescriptor(std::move(handle)); + return CudnnDropoutDescriptor(std::move(handle)); } cudnnDropoutDescriptor_t handle() const { return handle_.get(); } private: DropoutDescriptor handle_; // Owned. - SE_DISALLOW_COPY_AND_ASSIGN(ScopedDropoutDescriptor); + SE_DISALLOW_COPY_AND_ASSIGN(CudnnDropoutDescriptor); }; class CudnnRnnParamsDescriptor { @@ -973,7 +973,7 @@ class CudnnRnnDescriptor : public dnn::RnnDescriptor { cudnnRNNMode_t rnn_mode, cudnnDataType_t data_type, cudnnDataType_t compute_type, const dnn::AlgorithmConfig& algorithm_config, - ScopedDropoutDescriptor dropout_desc, + CudnnDropoutDescriptor dropout_desc, CudnnRnnParamsDescriptor params_desc) : rnn_desc_(std::move(rnn_desc)), rnn_plan_(std::move(rnn_plan)), @@ -1002,8 +1002,8 @@ class CudnnRnnDescriptor : public dnn::RnnDescriptor { const dnn::AlgorithmConfig& algorithm_config, float dropout, uint64 seed, ScratchAllocator* state_allocator) { SE_ASSIGN_OR_RETURN( - ScopedDropoutDescriptor dropout_desc, - ScopedDropoutDescriptor::Create(cudnn, dropout, seed, state_allocator)); + CudnnDropoutDescriptor dropout_desc, + CudnnDropoutDescriptor::Create(cudnn, dropout, seed, state_allocator)); cuda::RnnDescriptor rnn_desc = CreateRnnDescriptor(); cudnnRNNAlgo_t rnn_algo = ToCudnnRNNAlgo(algorithm_config.algorithm()); @@ -1097,7 +1097,7 @@ class CudnnRnnDescriptor : public dnn::RnnDescriptor { cudnnDataType_t data_type_; cudnnDataType_t compute_type_; dnn::AlgorithmConfig algorithm_config_; - ScopedDropoutDescriptor dropout_desc_; + CudnnDropoutDescriptor dropout_desc_; CudnnRnnParamsDescriptor params_desc_; SE_DISALLOW_COPY_AND_ASSIGN(CudnnRnnDescriptor); }; @@ -1926,10 +1926,9 @@ namespace { // and backward filter. port::StatusOr GetCudnnConvolutionForwardAlgo( - const CudnnHandle& cudnn, const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, bool specify_workspace_limit, + const CudnnHandle& cudnn, const CudnnTensorDescriptor& input_nd, + const CudnnFilterDescriptor& filter, const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, bool specify_workspace_limit, size_t memory_limit_bytes) { cudnnConvolutionFwdPreference_t preference = specify_workspace_limit ? CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT @@ -1943,10 +1942,10 @@ port::StatusOr GetCudnnConvolutionForwardAlgo( port::StatusOr GetCudnnConvolutionBackwardDataAlgo(const CudnnHandle& cudnn, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, + const CudnnTensorDescriptor& input_nd, + const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, bool specify_workspace_limit, size_t memory_limit_bytes) { cudnnConvolutionBwdDataPreference_t preference = @@ -1962,10 +1961,10 @@ GetCudnnConvolutionBackwardDataAlgo(const CudnnHandle& cudnn, port::StatusOr GetCudnnConvolutionBackwardFilterAlgo(const CudnnHandle& cudnn, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, + const CudnnTensorDescriptor& input_nd, + const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, bool specify_workspace_limit, size_t memory_limit_bytes) { cudnnConvolutionBwdFilterPreference_t preference = @@ -1982,10 +1981,9 @@ GetCudnnConvolutionBackwardFilterAlgo(const CudnnHandle& cudnn, port::StatusOr> AllocateCudnnConvolutionForwardWorkspace( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmDesc& algorithm_desc, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of @@ -2025,10 +2023,9 @@ port::StatusOr> AllocateCudnnConvolutionBackwardDataWorkspace( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmDesc& algorithm_desc, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of @@ -2070,10 +2067,9 @@ port::StatusOr> AllocateCudnnConvolutionBackwardFilterWorkspace( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmDesc& algorithm_desc, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of @@ -2114,11 +2110,10 @@ AllocateCudnnConvolutionBackwardFilterWorkspace( port::StatusOr GetCudnnConvolutionForwardAlgorithm( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmConfig& algorithm_config, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, - ScratchAllocator* scratch_allocator, DeviceMemory* scratch) { + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator, + DeviceMemory* scratch) { dnn::AlgorithmDesc algo_desc = algorithm_config.algorithm(); if (algorithm_config.algorithm().is_default()) { // Pick fastest algorithm within memory limit according to cuDNN's @@ -2164,11 +2159,10 @@ port::StatusOr GetCudnnConvolutionForwardAlgorithm( port::StatusOr GetCudnnConvolutionBackwardDataAlgorithm( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmConfig& algorithm_config, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, - ScratchAllocator* scratch_allocator, DeviceMemory* scratch) { + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator, + DeviceMemory* scratch) { dnn::AlgorithmDesc algo_desc = algorithm_config.algorithm(); if (algorithm_config.algorithm().is_default()) { // Pick fastest algorithm within memory limit according to cuDNN's @@ -2214,11 +2208,10 @@ port::StatusOr GetCudnnConvolutionBackwardDataAlgorithm( port::StatusOr GetCudnnConvolutionBackwardFilterAlgorithm( Stream* stream, const CudnnHandle& cudnn, const dnn::AlgorithmConfig& algorithm_config, - const ScopedTensorDescriptor& input_nd, - const ScopedFilterDescriptor& filter, - const ScopedConvolutionDescriptor& conv, - const ScopedTensorDescriptor& output_nd, - ScratchAllocator* scratch_allocator, DeviceMemory* scratch) { + const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, + const CudnnConvolutionDescriptor& conv, + const CudnnTensorDescriptor& output_nd, ScratchAllocator* scratch_allocator, + DeviceMemory* scratch) { dnn::AlgorithmDesc algo_desc = algorithm_config.algorithm(); if (algorithm_config.algorithm().is_default()) { // Pick fastest algorithm within memory limit according to cuDNN's @@ -2291,9 +2284,7 @@ class CudnnEnvVar { // algorithm through an env-var "TF_ENABLE_FFT_TILING_FORWARD=1". struct FftTilingForward { static constexpr const char* kName = "TF_ENABLE_FFT_TILING_FORWARD"; - // TODO(csigg): Enabling this algo causes XLA test failures, for example in - // platforms/xla/tests/internal:convolution_test_gpu. See b/80018418. - static constexpr bool kDefaultFlag = false; // CUDNN_VERSION >= 7000; + static constexpr bool kDefaultFlag = CUDNN_VERSION >= 7000; }; // A helper struct to decide whether to enable the WINOGRAD_NONFUSED algorithms. @@ -2389,11 +2380,11 @@ port::Status CudnnSupport::DoConvolveImpl( const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { cudnnDataType_t cudnn_type = GetCudnnDataType(); - ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type); - ScopedTensorDescriptor output_nd(output_descriptor, cudnn_type); - ScopedFilterDescriptor filter(filter_descriptor, cudnn_type); - ScopedConvolutionDescriptor conv(convolution_descriptor, - GetConvComputeType()); + CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); + CudnnTensorDescriptor output_nd(output_descriptor, cudnn_type); + CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); + CudnnConvolutionDescriptor conv(convolution_descriptor, + GetConvComputeType()); auto cudnn = cudnn_->GetHandle(parent_, stream); // Alpha is the scaling factor for input. @@ -2426,6 +2417,33 @@ port::Status CudnnSupport::DoConvolveImpl( } } + // Report an error if we might be hitting a cuDNN bug that accesses illegal + // memory. See nvbugs/2138754, b/80018418. + SE_RETURN_IF_ERROR([&] { + if (algo_desc.algo_id() != CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING) { + return port::Status::OK(); + } + if (input_descriptor.ndims() < 3) { + return port::Status::OK(); + } + // Checks that a*b is within the valid range (as provided by NVIDIA). + auto check_sizes = [](size_t a, size_t b) { + if ((a * b * 4608 - 1) >> 31 == 0) { + return port::Status::OK(); + } + return port::Status( + port::error::FAILED_PRECONDITION, + "This configuration potentially accesses illegal memory."); + }; + SE_RETURN_IF_ERROR(check_sizes(input_descriptor.feature_map_count(), + output_descriptor.feature_map_count())); + SE_RETURN_IF_ERROR(check_sizes(input_descriptor.count(), + input_descriptor.feature_map_count())); + SE_RETURN_IF_ERROR(check_sizes(input_descriptor.count(), + output_descriptor.feature_map_count())); + return port::Status::OK(); + }()); + RETURN_IF_CUDNN_ERROR(cudnnConvolutionForward( cudnn.handle(), /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), @@ -2468,14 +2486,14 @@ port::Status CudnnSupport::DoFusedConvolveImpl( "Relu activation."); } - ScopedTensorDescriptor conv_input_nd( + CudnnTensorDescriptor conv_input_nd( conv_input_descriptor, static_cast(cudnn_data_type)); - ScopedTensorDescriptor output_nd( + CudnnTensorDescriptor output_nd( output_descriptor, static_cast(cudnn_data_type)); - ScopedFilterDescriptor filter(filter_descriptor, - static_cast(cudnn_data_type)); - ScopedTensorDescriptor bias_nd(bias_descriptor, CUDNN_DATA_FLOAT); - ScopedConvolutionDescriptor conv( + CudnnFilterDescriptor filter(filter_descriptor, + static_cast(cudnn_data_type)); + CudnnTensorDescriptor bias_nd(bias_descriptor, CUDNN_DATA_FLOAT); + CudnnConvolutionDescriptor conv( convolution_descriptor, static_cast(cudnn_compute_type)); auto cudnn = cudnn_->GetHandle(parent_, stream); @@ -2503,7 +2521,7 @@ port::Status CudnnSupport::DoFusedConvolveImpl( // activation descriptor. Note that this will change the nan propagation // behavior from separate conv, bias, and relu (which by default is // CUDNN_PROPAGATE_NAN. - ScopedActivationDescriptor activation_desc( + CudnnActivationDescriptor activation_desc( activation_mode, CUDNN_NOT_PROPAGATE_NAN, output_descriptor.value_max()); auto side_input_data_ptr = (side_input_scale == 0) ? output_data->opaque() : side_input_data.opaque(); @@ -2715,8 +2733,8 @@ port::Status CudnnSupport::DoBatchNormalizationForwardImpl( DeviceMemory* saved_mean, DeviceMemory* saved_inv_var, bool is_training, std::function&()> var_to_inv_var, std::function inv_var_to_var) { - ScopedTensorDescriptor x_descriptor(x_desc, ToCudnnDataType(input_data_type)); - ScopedTensorDescriptor scale_offset_descriptor( + CudnnTensorDescriptor x_descriptor(x_desc, ToCudnnDataType(input_data_type)); + CudnnTensorDescriptor scale_offset_descriptor( scale_offset_desc, ToCudnnDataType(scale_data_type)); cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL; #if CUDNN_VERSION >= 7000 @@ -2800,9 +2818,9 @@ port::Status CudnnSupport::DoBatchNormalizationBackwardImpl( const dnn::BatchDescriptor& scale_offset_desc, const double epsilon, DeviceMemory* x_backprop, DeviceMemory* scale_backprop, DeviceMemory* offset_backprop) { - ScopedTensorDescriptor x_descriptor( + CudnnTensorDescriptor x_descriptor( x_desc, static_cast(cudnn_input_type)); - ScopedTensorDescriptor scale_offset_descriptor( + CudnnTensorDescriptor scale_offset_descriptor( scale_offset_desc, static_cast(cudnn_scale_type)); cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL; #if CUDNN_VERSION >= 7000 @@ -2992,9 +3010,9 @@ bool CudnnSupport::DoTransformTensor(Stream* stream, dnn::DataType output_type, float scale, DeviceMemoryBase* output_data) { float beta = 0.0f; - ScopedTensorDescriptor input_tensor_desc( + CudnnTensorDescriptor input_tensor_desc( input_desc, ToCudnnDataType(input_type, input_desc.layout())); - ScopedTensorDescriptor output_tensor_desc( + CudnnTensorDescriptor output_tensor_desc( output_desc, ToCudnnDataType(output_type, output_desc.layout())); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3031,11 +3049,11 @@ port::Status CudnnSupport::DoConvolveBackwardDataImpl( auto cudnn = cudnn_->GetHandle(parent_, stream); - ScopedTensorDescriptor out_back_nd(output_descriptor, cudnn_type); - ScopedTensorDescriptor in_back_nd(input_descriptor, cudnn_type); - ScopedFilterDescriptor filter(filter_descriptor, cudnn_type); - ScopedConvolutionDescriptor conv(convolution_descriptor, - GetConvComputeType()); + CudnnTensorDescriptor out_back_nd(output_descriptor, cudnn_type); + CudnnTensorDescriptor in_back_nd(input_descriptor, cudnn_type); + CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); + CudnnConvolutionDescriptor conv(convolution_descriptor, + GetConvComputeType()); const bool is_profiling = output_profile_result != nullptr; @@ -3056,6 +3074,22 @@ port::Status CudnnSupport::DoConvolveBackwardDataImpl( } } + // Cudnn 7.1.4 has a bug if the workspace of the following convolution is not + // zero-initialized. + // TODO(timshen): Add an nvbugs/ link. + if (CUDNN_VERSION >= 7000 && + algorithm_config.algorithm().algo_id() == + CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 && + cudnn_type == CUDNN_DATA_HALF && + algorithm_config.algorithm().tensor_ops_enabled() && + input_descriptor.layout() == dnn::DataLayout::kBatchYXDepth && + filter_descriptor.layout() == dnn::FilterLayout::kOutputInputYX && + output_descriptor.layout() == dnn::DataLayout::kBatchDepthYX && + (convolution_descriptor.vertical_filter_stride() > 1 || + convolution_descriptor.horizontal_filter_stride() > 1)) { + stream->ThenMemZero(&scratch, scratch.size()); + } + RETURN_IF_CUDNN_ERROR( cudnnConvolutionBackwardData(cudnn.handle(), /*alpha=*/alpha, @@ -3167,11 +3201,11 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( auto cudnn = cudnn_->GetHandle(parent_, stream); - ScopedTensorDescriptor out_back_nd(output_descriptor, cudnn_type); - ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type); - ScopedFilterDescriptor filter(filter_descriptor, cudnn_type); - ScopedConvolutionDescriptor conv(convolution_descriptor, - GetConvComputeType()); + CudnnTensorDescriptor out_back_nd(output_descriptor, cudnn_type); + CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); + CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); + CudnnConvolutionDescriptor conv(convolution_descriptor, + GetConvComputeType()); const bool is_profiling = output_profile_result != nullptr; @@ -3192,6 +3226,34 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( } } + // Report an error if we might be hitting a cuDNN bug that produces incorrect + // results. See nvbugs/2072856 + SE_RETURN_IF_ERROR([&] { + if (algo_desc.algo_id() != CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING) { + return port::Status::OK(); + } + if (output_descriptor.height() > 1 && output_descriptor.width() > 1) { + return port::Status::OK(); + } + int convolution_size = output_descriptor.height() > 1 + ? filter_descriptor.input_filter_height() + : filter_descriptor.input_filter_width(); + if (convolution_size <= 32) { + return port::Status::OK(); + } + cudnnConvolutionMode_t convolution_mode; + cudnnDataType_t compute_type; + RETURN_IF_CUDNN_ERROR(cudnnGetConvolutionNdDescriptor( + conv.handle(), 0, nullptr, nullptr, nullptr, nullptr, &convolution_mode, + &compute_type)); + if (convolution_mode != CUDNN_CONVOLUTION) { + return port::Status::OK(); + } + return port::Status( + port::error::FAILED_PRECONDITION, + "This configuration potentially produces incorrect results."); + }()); + RETURN_IF_CUDNN_ERROR(cudnnConvolutionBackwardFilter( cudnn.handle(), /*alpha=*/alpha, @@ -3285,8 +3347,8 @@ port::Status CudnnSupport::DoConvolveBackwardBiasImpl( const dnn::BatchDescriptor& bias_descriptor, DeviceMemory* backward_bias_data) { cudnnDataType_t cudnn_type = GetCudnnDataType(); - ScopedTensorDescriptor input_nd(input_descriptor, cudnn_type); - ScopedTensorDescriptor bias_nd(bias_descriptor, cudnn_type); + CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); + CudnnTensorDescriptor bias_nd(bias_descriptor, cudnn_type); // Alpha is the scaling factor for input. float alpha = 1.0; @@ -3473,7 +3535,7 @@ bool CudnnSupport::DoBiasAdd(Stream* stream, const DeviceMemory& biases, const dnn::BatchDescriptor& dimensions, DeviceMemory* output_data) { - ScopedTensorDescriptor input_descriptor(dimensions, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor input_descriptor(dimensions, CUDNN_DATA_FLOAT); dnn::BatchDescriptor bias_dimensions; bias_dimensions.set_count(1) @@ -3481,7 +3543,7 @@ bool CudnnSupport::DoBiasAdd(Stream* stream, .set_height(1) .set_width(1) .set_layout(dnn::DataLayout::kBatchYXDepth); - ScopedTensorDescriptor bias_descriptor(bias_dimensions, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor bias_descriptor(bias_dimensions, CUDNN_DATA_FLOAT); // cudnnAddTensor after R3 is in-place, so we need to copy input_data to // output_data before doing the addition, unless the input and @@ -3517,10 +3579,10 @@ bool CudnnSupport::DoActivate(Stream* stream, const DeviceMemory& input_data, DeviceMemory* output_data, uint64 options) { - ScopedActivationDescriptor activation_desc( + CudnnActivationDescriptor activation_desc( activation_mode, CUDNN_PROPAGATE_NAN, dimensions.value_max()); - ScopedTensorDescriptor input_nd(dimensions, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor input_nd(dimensions, CUDNN_DATA_FLOAT); // Alpha is the input scaling factor. float alpha = 1.0; // Beta is the output scaling factor. @@ -3547,9 +3609,9 @@ bool CudnnSupport::DoPoolForward( // Beta is the scaling factor for output. double beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3572,9 +3634,9 @@ bool CudnnSupport::DoPoolForward( // Beta is the scaling factor for output. float beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3597,9 +3659,9 @@ bool CudnnSupport::DoPoolForward( // Beta is the scaling factor for output. float beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { RETURN_IF_CUDNN_ERROR(cudnnPoolingForward( @@ -3623,9 +3685,9 @@ bool CudnnSupport::DoPoolBackward( // Beta is the scaling factor for output. double beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_DOUBLE); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_DOUBLE); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3652,9 +3714,9 @@ bool CudnnSupport::DoPoolBackward( // Beta is the scaling factor for output. float beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_FLOAT); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3681,9 +3743,9 @@ bool CudnnSupport::DoPoolBackward( // Beta is the scaling factor for output. float beta = 0.0; - ScopedTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF); - ScopedTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF); - ScopedPoolingDescriptor pooling_desc(pooling_dimensions); + CudnnTensorDescriptor src_desc(input_dimensions, CUDNN_DATA_HALF); + CudnnTensorDescriptor dest_desc(output_dimensions, CUDNN_DATA_HALF); + CudnnPoolingDescriptor pooling_desc(pooling_dimensions); auto cudnn = cudnn_->GetHandle(parent_, stream); auto status = [&] { @@ -3718,8 +3780,8 @@ bool CudnnSupport::DoNormalizeWithDimensions( return false; } - ScopedTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT); - ScopedNormalizeDescriptor normalize(normalize_descriptor); + CudnnTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT); + CudnnNormalizeDescriptor normalize(normalize_descriptor); // Alpha is the scaling factor for input. float alpha = 1.0f; @@ -3755,8 +3817,8 @@ bool CudnnSupport::DoNormalizeBackwardWithDimensions( return false; } - ScopedTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT); - ScopedNormalizeDescriptor normalize(normalize_descriptor); + CudnnTensorDescriptor dims(dimensions, CUDNN_DATA_FLOAT); + CudnnNormalizeDescriptor normalize(normalize_descriptor); float alpha = 1.0f; float beta = 0.0f; @@ -3879,9 +3941,9 @@ bool CudnnSupport::DeriveOutputBatchDescriptor( const dnn::FilterDescriptor& filter_descriptor, const dnn::ConvolutionDescriptor& convolution_descriptor, dnn::BatchDescriptor* output_batch_descriptor) { - ScopedTensorDescriptor input_nd(batch_descriptor, CUDNN_DATA_FLOAT); - ScopedFilterDescriptor filter(filter_descriptor, CUDNN_DATA_FLOAT); - ScopedConvolutionDescriptor conv(convolution_descriptor, CUDNN_DATA_FLOAT); + CudnnTensorDescriptor input_nd(batch_descriptor, CUDNN_DATA_FLOAT); + CudnnFilterDescriptor filter(filter_descriptor, CUDNN_DATA_FLOAT); + CudnnConvolutionDescriptor conv(convolution_descriptor, CUDNN_DATA_FLOAT); int dn = batch_descriptor.ndims() + 2; std::vector dims(dn); // in BDYX diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc index f2be68bc421c1fbc31ea5a054b91130c11949635..f11022ef1dfd4a1a08d035f5328724d93ac808be 100644 --- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc +++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc @@ -180,11 +180,11 @@ bool CUDAExecutor::FindOnDiskForComputeCapability( static string GetBinaryDir(bool strip_exe) { char exe_path[PATH_MAX] = {0}; #if defined(__APPLE__) - uint32_t buffer_size = 0U; - _NSGetExecutablePath(nullptr, &buffer_size); - char unresolved_path[buffer_size]; - _NSGetExecutablePath(unresolved_path, &buffer_size); - CHECK_ERR(realpath(unresolved_path, exe_path) ? 1 : -1); + uint32_t buffer_size = 0U; + _NSGetExecutablePath(nullptr, &buffer_size); + char unresolved_path[buffer_size]; + _NSGetExecutablePath(unresolved_path, &buffer_size); + CHECK_ERR(realpath(unresolved_path, exe_path) ? 1 : -1); #else #if defined(PLATFORM_WINDOWS) HMODULE hModule = GetModuleHandle(NULL); diff --git a/tensorflow/stream_executor/host/host_gpu_executor.cc b/tensorflow/stream_executor/host/host_gpu_executor.cc index 2c4819651acaa2c6ee99c720b2c3d80e5c2ea1a9..c8a629733006e17b7642a59afb8e0cb468f2c538 100644 --- a/tensorflow/stream_executor/host/host_gpu_executor.cc +++ b/tensorflow/stream_executor/host/host_gpu_executor.cc @@ -95,7 +95,7 @@ bool HostExecutor::MemcpyDeviceToDevice(Stream *stream, // the nature of the HostExecutor) memcpy on the stream (HostStream) // associated with the HostExecutor. AsHostStream(stream)->EnqueueTask( - [src_mem, dst_mem, size]() { memcpy(src_mem, dst_mem, size); }); + [src_mem, dst_mem, size]() { memcpy(dst_mem, src_mem, size); }); return true; } diff --git a/tensorflow/compiler/xla/statusor.cc b/tensorflow/stream_executor/lib/statusor.cc similarity index 89% rename from tensorflow/compiler/xla/statusor.cc rename to tensorflow/stream_executor/lib/statusor.cc index 72ab67ff810e0ec384a22da092363cc7446435bb..e0e851f96ef6fe18ec32ff7d3fd1d1aed18b0343 100644 --- a/tensorflow/compiler/xla/statusor.cc +++ b/tensorflow/stream_executor/lib/statusor.cc @@ -13,12 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/stream_executor/lib/statusor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/platform/logging.h" -namespace xla { +namespace stream_executor { +namespace port { namespace internal_statusor { void Helper::HandleInvalidStatusCtorArg(Status* status) { @@ -35,4 +36,5 @@ void Helper::Crash(const Status& status) { } } // namespace internal_statusor -} // namespace xla +} // namespace port +} // namespace stream_executor diff --git a/tensorflow/stream_executor/lib/statusor.h b/tensorflow/stream_executor/lib/statusor.h index dab59096740102b94c0ff63c089b83ce052ea264..3c716acb462f1ca25e1d86408386d9eca37265b7 100644 --- a/tensorflow/stream_executor/lib/statusor.h +++ b/tensorflow/stream_executor/lib/statusor.h @@ -1,4 +1,4 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -13,19 +13,297 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// IWYU pragma: private, include "third_party/tensorflow/stream_executor/stream_executor.h" - +// StatusOr is the union of a Status object and a T object. StatusOr models +// the concept of an object that is either a value, or an error Status +// explaining why such a value is not present. To this end, StatusOr does not +// allow its Status value to be Status::OK. +// +// The primary use-case for StatusOr is as the return value of a +// function which may fail. +// +// Example client usage for a StatusOr, where T is not a pointer: +// +// StatusOr result = DoBigCalculationThatCouldFail(); +// if (result.ok()) { +// float answer = result.ValueOrDie(); +// printf("Big calculation yielded: %f", answer); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example client usage for a StatusOr: +// +// StatusOr result = FooFactory::MakeNewFoo(arg); +// if (result.ok()) { +// std::unique_ptr foo(result.ValueOrDie()); +// foo->DoSomethingCool(); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example client usage for a StatusOr>: +// +// StatusOr> result = FooFactory::MakeNewFoo(arg); +// if (result.ok()) { +// std::unique_ptr foo = std::move(result.ValueOrDie()); +// foo->DoSomethingCool(); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example factory implementation returning StatusOr: +// +// StatusOr FooFactory::MakeNewFoo(int arg) { +// if (arg <= 0) { +// return tensorflow::InvalidArgument("Arg must be positive"); +// } else { +// return new Foo(arg); +// } +// } +// +// Note that the assignment operators require that destroying the currently +// stored value cannot invalidate the argument; in other words, the argument +// cannot be an alias for the current value, or anything owned by the current +// value. #ifndef TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_H_ #define TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_H_ -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/status.h" +#include "tensorflow/stream_executor/lib/statusor_internals.h" namespace stream_executor { namespace port { -// Use XLA's StatusOr so we don't duplicate code. +#if defined(__clang__) +// Only clang supports warn_unused_result as a type annotation. +template +class TF_MUST_USE_RESULT StatusOr; +#endif + +template +class StatusOr : private internal_statusor::StatusOrData, + private internal_statusor::TraitsBase< + std::is_copy_constructible::value, + std::is_move_constructible::value> { + template + friend class StatusOr; + + typedef internal_statusor::StatusOrData Base; + + public: + typedef T element_type; + + // Constructs a new StatusOr with Status::UNKNOWN status. This is marked + // 'explicit' to try to catch cases like 'return {};', where people think + // StatusOr> will be initialized with an empty vector, + // instead of a Status::UNKNOWN status. + explicit StatusOr(); + + // StatusOr will be copy constructible/assignable if T is copy + // constructible. + StatusOr(const StatusOr&) = default; + StatusOr& operator=(const StatusOr&) = default; + + // StatusOr will be move constructible/assignable if T is move + // constructible. + StatusOr(StatusOr&&) = default; + StatusOr& operator=(StatusOr&&) = default; + + // Conversion copy/move constructor, T must be convertible from U. + template ::value>::type* = nullptr> + StatusOr(const StatusOr& other); + template ::value>::type* = nullptr> + StatusOr(StatusOr&& other); + + // Conversion copy/move assignment operator, T must be convertible from U. + template ::value>::type* = nullptr> + StatusOr& operator=(const StatusOr& other); + template ::value>::type* = nullptr> + StatusOr& operator=(StatusOr&& other); + + // Constructs a new StatusOr with the given value. After calling this + // constructor, calls to ValueOrDie() will succeed, and calls to status() will + // return OK. + // + // NOTE: Not explicit - we want to use StatusOr as a return type + // so it is convenient and sensible to be able to do 'return T()' + // when the return type is StatusOr. + // + // REQUIRES: T is copy constructible. + StatusOr(const T& value); + + // Constructs a new StatusOr with the given non-ok status. After calling + // this constructor, calls to ValueOrDie() will CHECK-fail. + // + // NOTE: Not explicit - we want to use StatusOr as a return + // value, so it is convenient and sensible to be able to do 'return + // Status()' when the return type is StatusOr. + // + // REQUIRES: !status.ok(). This requirement is DCHECKed. + // In optimized builds, passing Status::OK() here will have the effect + // of passing tensorflow::error::INTERNAL as a fallback. + StatusOr(const Status& status); + StatusOr& operator=(const Status& status); + + // TODO(b/62186997): Add operator=(T) overloads. + + // Similar to the `const T&` overload. + // + // REQUIRES: T is move constructible. + StatusOr(T&& value); + + // RValue versions of the operations declared above. + StatusOr(Status&& status); + StatusOr& operator=(Status&& status); + + // Returns this->status().ok() + bool ok() const { return this->status_.ok(); } + + // Returns a reference to our status. If this contains a T, then + // returns Status::OK(). + const Status& status() const &; + Status status() &&; + + // Returns a reference to our current value, or CHECK-fails if !this->ok(). + // + // Note: for value types that are cheap to copy, prefer simple code: + // + // T value = statusor.ValueOrDie(); + // + // Otherwise, if the value type is expensive to copy, but can be left + // in the StatusOr, simply assign to a reference: + // + // T& value = statusor.ValueOrDie(); // or `const T&` + // + // Otherwise, if the value type supports an efficient move, it can be + // used as follows: + // + // T value = std::move(statusor).ValueOrDie(); + // + // The std::move on statusor instead of on the whole expression enables + // warnings about possible uses of the statusor object after the move. + // C++ style guide waiver for ref-qualified overloads granted in cl/143176389 + // See go/ref-qualifiers for more details on such overloads. + const T& ValueOrDie() const &; + T& ValueOrDie() &; + const T&& ValueOrDie() const &&; + T&& ValueOrDie() &&; + + T ConsumeValueOrDie() { return std::move(ValueOrDie()); } + + // Ignores any errors. This method does nothing except potentially suppress + // complaints from any tools that are checking that errors are not dropped on + // the floor. + void IgnoreError() const; +}; + +//////////////////////////////////////////////////////////////////////////////// +// Implementation details for StatusOr + +template +StatusOr::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {} + +template +StatusOr::StatusOr(const T& value) : Base(value) {} + +template +StatusOr::StatusOr(const Status& status) : Base(status) {} + +template +StatusOr& StatusOr::operator=(const Status& status) { + this->Assign(status); + return *this; +} + +template +StatusOr::StatusOr(T&& value) : Base(std::move(value)) {} + +template +StatusOr::StatusOr(Status&& status) : Base(std::move(status)) {} + +template +StatusOr& StatusOr::operator=(Status&& status) { + this->Assign(std::move(status)); + return *this; +} + +template +template ::value>::type*> +inline StatusOr::StatusOr(const StatusOr& other) + : Base(static_cast::Base&>(other)) {} + +template +template ::value>::type*> +inline StatusOr& StatusOr::operator=(const StatusOr& other) { + if (other.ok()) + this->Assign(other.ValueOrDie()); + else + this->Assign(other.status()); + return *this; +} + +template +template ::value>::type*> +inline StatusOr::StatusOr(StatusOr&& other) + : Base(static_cast::Base&&>(other)) {} + +template +template ::value>::type*> +inline StatusOr& StatusOr::operator=(StatusOr&& other) { + if (other.ok()) { + this->Assign(std::move(other).ValueOrDie()); + } else { + this->Assign(std::move(other).status()); + } + return *this; +} + +template +const Status& StatusOr::status() const & { + return this->status_; +} +template +Status StatusOr::status() && { + return ok() ? Status::OK() : std::move(this->status_); +} + +template +const T& StatusOr::ValueOrDie() const & { + this->EnsureOk(); + return this->data_; +} + +template +T& StatusOr::ValueOrDie() & { + this->EnsureOk(); + return this->data_; +} + +template +const T&& StatusOr::ValueOrDie() const && { + this->EnsureOk(); + return std::move(this->data_); +} + +template +T&& StatusOr::ValueOrDie() && { + this->EnsureOk(); + return std::move(this->data_); +} + template -using StatusOr = ::xla::StatusOr; +void StatusOr::IgnoreError() const { + // no-op +} } // namespace port } // namespace stream_executor diff --git a/tensorflow/compiler/xla/statusor_internals.h b/tensorflow/stream_executor/lib/statusor_internals.h similarity index 94% rename from tensorflow/compiler/xla/statusor_internals.h rename to tensorflow/stream_executor/lib/statusor_internals.h index 14636bd144bc0a155fc96c5a350c658fd2dadfe6..09f88f5825f57c8e654bd079616a074e84de4f30 100644 --- a/tensorflow/compiler/xla/statusor_internals.h +++ b/tensorflow/stream_executor/lib/statusor_internals.h @@ -13,13 +13,15 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ -#define TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ +#ifndef TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ +#define TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ + -#include "tensorflow/compiler/xla/status.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/status.h" -namespace xla { +namespace stream_executor { +namespace port { namespace internal_statusor { class Helper { @@ -240,6 +242,7 @@ struct TraitsBase { }; } // namespace internal_statusor -} // namespace xla +} // namespace port +} // namespace stream_executor -#endif // TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ +#endif // TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/stream_executor/lib/statusor_test.cc similarity index 99% rename from tensorflow/compiler/xla/statusor_test.cc rename to tensorflow/stream_executor/lib/statusor_test.cc index 377a618ffbd99316d409130df8a39f352664dee0..56584e189208b2576f10650fd56bca6d04ecc6c1 100644 --- a/tensorflow/compiler/xla/statusor_test.cc +++ b/tensorflow/stream_executor/lib/statusor_test.cc @@ -15,18 +15,18 @@ limitations under the License. // Unit tests for StatusOr -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/stream_executor/lib/statusor.h" #include #include -#include "tensorflow/compiler/xla/test.h" -#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/platform/test.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/test_benchmark.h" -namespace xla { +namespace stream_executor { +namespace port { namespace { class Base1 { @@ -672,4 +672,5 @@ void BM_StatusOrFactoryFailLongMsg(int iters) { BENCHMARK(BM_StatusOrFactoryFailLongMsg); } // namespace -} // namespace xla +} // namespace port +} // namespace stream_executor diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index 4a98cfe16460ff860b6b73fedc21e98b5a3ed9fd..0cd0790a72b49bb259b9c72268535b5d74531cf5 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -192,6 +192,7 @@ string ToVlogString(dnn::DataType data_type) { case dnn::DataType::kInt8: return "dnn::DataType::kInt8"; } + return "unknown DataType"; } // Used together with PARAM to VLOG calls made to the stream. Intended diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index 3da1b856d6a41fa0c8d5a77feac33932da392422..e8885e1eb682d9ee67c6b7594f96c0911c7c1fa2 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -25,6 +25,7 @@ limitations under the License. #include #include +#include "tensorflow/core/platform/macros.h" #include "tensorflow/stream_executor/blas.h" #include "tensorflow/stream_executor/device_memory.h" #include "tensorflow/stream_executor/dnn.h" @@ -156,14 +157,13 @@ class Stream { const TypedKernel &kernel, Args... args); // Record a "start" event for the interval timer at this point in the - // stream's - // execution (relative to the previously and subsequently enqueued items in - // the stream's execution). Streams may be started/stopped multiple times. + // stream's execution (relative to the previously and subsequently enqueued + // items in the stream's execution). Streams may be started/stopped multiple + // times. Stream &ThenStartTimer(Timer *t); // Record a "stop" event for the interval timer at this point in the - // stream's - // execution. See also Stream::ThenStartTimer. + // stream's execution. See also Stream::ThenStartTimer. Stream &ThenStopTimer(Timer *t); // TODO(leary) If work is added to the stream that is being depended upon, @@ -179,8 +179,7 @@ class Stream { // // Checks that a stream does not wait for itself, and it is up to the // user to guarantee that a stream does not come to wait on itself in a - // cyclic - // manner; in that case, behavior is undefined. + // cyclic manner; in that case, behavior is undefined. // // N.B. Base recursion case for the variadic ThenWaitFor. Stream &ThenWaitFor(Stream *other); @@ -1351,33 +1350,39 @@ class Stream { DeviceMemory> *x, int incx); // See BlasSupport::DoBlasGemm. - Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, uint64 m, - uint64 n, uint64 k, float alpha, - const DeviceMemory &a, int lda, - const DeviceMemory &b, int ldb, float beta, - DeviceMemory *c, int ldc); - Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, uint64 m, - uint64 n, uint64 k, float alpha, - const DeviceMemory &a, int lda, - const DeviceMemory &b, int ldb, float beta, - DeviceMemory *c, int ldc); - Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, uint64 m, - uint64 n, uint64 k, double alpha, - const DeviceMemory &a, int lda, - const DeviceMemory &b, int ldb, double beta, - DeviceMemory *c, int ldc); - Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, uint64 m, - uint64 n, uint64 k, std::complex alpha, - const DeviceMemory> &a, int lda, - const DeviceMemory> &b, int ldb, - std::complex beta, - DeviceMemory> *c, int ldc); - Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, uint64 m, - uint64 n, uint64 k, std::complex alpha, - const DeviceMemory> &a, int lda, - const DeviceMemory> &b, int ldb, - std::complex beta, - DeviceMemory> *c, int ldc); + TF_EXPORT Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, + uint64 m, uint64 n, uint64 k, float alpha, + const DeviceMemory &a, int lda, + const DeviceMemory &b, int ldb, + float beta, DeviceMemory *c, + int ldc); + TF_EXPORT Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, + uint64 m, uint64 n, uint64 k, float alpha, + const DeviceMemory &a, int lda, + const DeviceMemory &b, int ldb, + float beta, DeviceMemory *c, int ldc); + TF_EXPORT Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, + uint64 m, uint64 n, uint64 k, double alpha, + const DeviceMemory &a, int lda, + const DeviceMemory &b, int ldb, + double beta, DeviceMemory *c, int ldc); + TF_EXPORT Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, + uint64 m, uint64 n, uint64 k, + std::complex alpha, + const DeviceMemory> &a, + int lda, + const DeviceMemory> &b, + int ldb, std::complex beta, + DeviceMemory> *c, int ldc); + TF_EXPORT Stream &ThenBlasGemm(blas::Transpose transa, blas::Transpose transb, + uint64 m, uint64 n, uint64 k, + std::complex alpha, + const DeviceMemory> &a, + int lda, + const DeviceMemory> &b, + int ldb, std::complex beta, + DeviceMemory> *c, + int ldc); Stream &ThenBlasGemmWithProfiling(blas::Transpose transa, blas::Transpose transb, uint64 m, uint64 n, diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc index b222a4d82a3e87a52c44427627e7aaacd0ed5c0d..000795ff0048dddb0eb4a08956e6de6f5e336f28 100644 --- a/tensorflow/stream_executor/stream_executor_pimpl.cc +++ b/tensorflow/stream_executor/stream_executor_pimpl.cc @@ -610,7 +610,7 @@ port::Status StreamExecutor::SynchronousMemcpyD2H( port::Status StreamExecutor::SynchronousMemcpyH2D( const void *host_src, int64 size, DeviceMemoryBase *device_dst) { VLOG(1) << "Called StreamExecutor::SynchronousMemcpyH2D(host_src=" << host_src - << ", size=" << size << ", device_dst" << device_dst->opaque() << ")" + << ", size=" << size << ", device_dst=" << device_dst->opaque() << ")" << StackTraceIfVLOG10(); port::Status result; diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index b59f8e1f987567727ef3d4051618edd377d06f89..e4632c48112d40fb96b4c2b510da93678b11efc4 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -148,6 +148,12 @@ def if_windows(a): "//conditions:default": [], }) +def if_not_windows_cuda(a): + return select({ + clean_dep("//tensorflow:with_cuda_support_windows_override"): [], + "//conditions:default": a, + }) + def if_linux_x86_64(a): return select({ clean_dep("//tensorflow:linux_x86_64"): a, @@ -241,6 +247,9 @@ def tf_opts_nortti_if_android(): # LINT.ThenChange(//tensorflow/contrib/android/cmake/CMakeLists.txt) +def tf_features_nomodules_if_android(): + return if_android(["-use_header_modules"]) + # Given a list of "op_lib_names" (a list of files in the ops directory # without their .cc extensions), generate a library for that file. def tf_gen_op_libs(op_lib_names, deps=None, is_external=True): @@ -919,6 +928,7 @@ def tf_gpu_kernel_library(srcs, hdrs=[], **kwargs): copts = copts + _cuda_copts() + if_cuda(cuda_copts) + tf_copts() + kwargs["features"] = kwargs.get("features", []) + ["-use_header_modules"] native.cc_library( srcs=srcs, @@ -959,6 +969,7 @@ def tf_cuda_library(deps=None, cuda_deps=None, copts=tf_copts(), **kwargs): if not cuda_deps: cuda_deps = [] + kwargs["features"] = kwargs.get("features", []) + ["-use_header_modules"] native.cc_library( deps=deps + if_cuda(cuda_deps + [ clean_dep("//tensorflow/core:cuda"), @@ -1301,6 +1312,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): name=basename + "_gpu", srcs=gpu_srcs, copts=_cuda_copts() + if_tensorrt(["-DGOOGLE_TENSORRT=1"]), + features = if_cuda(["-use_header_modules"]), deps=deps + if_cuda(cuda_deps)) cuda_deps.extend([":" + basename + "_gpu"]) diff --git a/tensorflow/tf_framework_version_script.lds b/tensorflow/tf_framework_version_script.lds new file mode 100644 index 0000000000000000000000000000000000000000..d4977f88c0c340fa236b746efcefd607f4752359 --- /dev/null +++ b/tensorflow/tf_framework_version_script.lds @@ -0,0 +1,11 @@ +VERS_1.0 { + # Hide libjpeg symbols to avoid symbol conflict with OpenCV + local: + jpeg_*; + jinit_*; + jdiv_round_up; + jround_up; + jzero_far; + jcopy_*; + jsimd_*; +}; diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index f0c5877a90836eaba58ba5e3a49db822f324b3c5..8c760e6f52598a5e7399c9250adf99283572d3a4 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -3,24 +3,69 @@ licenses(["notice"]) # Apache 2.0 -exports_files(["LICENSE"]) +load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES") +load("//tensorflow/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES") -py_binary( - name = "create_python_api", - srcs = ["create_python_api.py"], +exports_files( + [ + "LICENSE", + "create_python_api.py", + ], +) + +py_library( + name = "doc_srcs", + srcs = ["doc_srcs.py"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ - "//tensorflow/python:no_contrib", + "//tensorflow/python:util", ], ) py_test( name = "create_python_api_test", - srcs = ["create_python_api_test.py"], + srcs = [ + "create_python_api.py", + "create_python_api_test.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":doc_srcs", + "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", + ], +) + +py_test( + name = "tensorflow_doc_srcs_test", + srcs = ["doc_srcs_test.py"], + args = [ + "--package=tensorflow.python", + "--api_name=tensorflow", + ] + TENSORFLOW_API_INIT_FILES, + main = "doc_srcs_test.py", srcs_version = "PY2AND3", deps = [ - ":create_python_api", + ":doc_srcs", "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", + ], +) + +py_test( + name = "estimator_doc_srcs_test", + srcs = ["doc_srcs_test.py"], + args = [ + "--package=tensorflow.python.estimator", + "--api_name=estimator", + ] + ESTIMATOR_API_INIT_FILES, + main = "doc_srcs_test.py", + srcs_version = "PY2AND3", + deps = [ + ":doc_srcs", + "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", + "//tensorflow/python/estimator:estimator_py", ], ) diff --git a/tensorflow/tools/api/generator/api_gen.bzl b/tensorflow/tools/api/generator/api_gen.bzl index fe3e4d14340ae6eb20b105a30f066aa7ff048842..d746b5d3e4f7745d78563eac65ccdf822511a7ef 100644 --- a/tensorflow/tools/api/generator/api_gen.bzl +++ b/tensorflow/tools/api/generator/api_gen.bzl @@ -8,16 +8,16 @@ TENSORFLOW_API_INIT_FILES = [ "bitwise/__init__.py", "compat/__init__.py", "data/__init__.py", + "debugging/__init__.py", "distributions/__init__.py", "distributions/bijectors/__init__.py", + "dtypes/__init__.py", "errors/__init__.py", - "estimator/__init__.py", - "estimator/export/__init__.py", - "estimator/inputs/__init__.py", "feature_column/__init__.py", "gfile/__init__.py", "graph_util/__init__.py", "image/__init__.py", + "io/__init__.py", "initializers/__init__.py", "keras/__init__.py", "keras/activations/__init__.py", @@ -68,6 +68,7 @@ TENSORFLOW_API_INIT_FILES = [ "nn/rnn_cell/__init__.py", "profiler/__init__.py", "python_io/__init__.py", + "quantization/__init__.py", "resource_loader/__init__.py", "strings/__init__.py", "saved_model/__init__.py", @@ -91,6 +92,16 @@ TENSORFLOW_API_INIT_FILES = [ # END GENERATED FILES ] +# keep sorted +ESTIMATOR_API_INIT_FILES = [ + # BEGIN GENERATED ESTIMATOR FILES + "__init__.py", + "estimator/__init__.py", + "estimator/export/__init__.py", + "estimator/inputs/__init__.py", + # END GENERATED ESTIMATOR FILES +] + # Creates a genrule that generates a directory structure with __init__.py # files that import all exported modules (i.e. modules with tf_export # decorators). @@ -107,19 +118,44 @@ TENSORFLOW_API_INIT_FILES = [ # template will be replaced with root imports collected by this genrule. # srcs: genrule sources. If passing root_init_template, the template file # must be included in sources. -def gen_api_init_files(name, - output_files=TENSORFLOW_API_INIT_FILES, - root_init_template=None, - srcs=[]): - root_init_template_flag = "" - if root_init_template: - root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" - native.genrule( - name = name, - outs = output_files, - cmd = ( - "$(location //tensorflow/tools/api/generator:create_python_api) " + - root_init_template_flag + " --apidir=$(@D) $(OUTS)"), - srcs = srcs, - tools = ["//tensorflow/tools/api/generator:create_python_api"], - ) +# api_name: Name of the project that you want to generate API files for +# (e.g. "tensorflow" or "estimator"). +# package: Python package containing the @tf_export decorators you want to +# process +# package_dep: Python library target containing your package. + +def gen_api_init_files( + name, + output_files = TENSORFLOW_API_INIT_FILES, + root_init_template = None, + srcs = [], + api_name = "tensorflow", + package = "tensorflow.python", + package_dep = "//tensorflow/python:no_contrib"): + root_init_template_flag = "" + if root_init_template: + root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" + + api_gen_binary_target = "create_" + package + "_api" + native.py_binary( + name = "create_" + package + "_api", + srcs = ["//tensorflow/tools/api/generator:create_python_api.py"], + main = "//tensorflow/tools/api/generator:create_python_api.py", + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + package_dep, + "//tensorflow/tools/api/generator:doc_srcs", + ], + ) + + native.genrule( + name = name, + outs = output_files, + cmd = ( + "$(location :" + api_gen_binary_target + ") " + + root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)"), + srcs = srcs, + tools = [":" + api_gen_binary_target ], + visibility = ["//tensorflow:__pkg__"], + ) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 9f210ad42b316929c40b9e03654ffcc710b3ed0b..48d7dcd09eb38f53031afde70fe2e1a9b660ad1a 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -25,10 +25,11 @@ import os import sys from tensorflow.python.util import tf_decorator +from tensorflow.python.util import tf_export +from tensorflow.tools.api.generator import doc_srcs +API_ATTRS = tf_export.API_ATTRS -_API_CONSTANTS_ATTR = '_tf_api_constants' -_API_NAMES_ATTR = '_tf_api_names' _DEFAULT_PACKAGE = 'tensorflow.python' _GENFILES_DIR_SUFFIX = 'genfiles/' _SYMBOLS_TO_SKIP_EXPLICITLY = { @@ -36,10 +37,9 @@ _SYMBOLS_TO_SKIP_EXPLICITLY = { # would have side effects. 'tensorflow.python.platform.flags.FLAGS' } -_GENERATED_FILE_HEADER = """\"\"\"Imports for Python API. - -This file is MACHINE GENERATED! Do not edit. -Generated by: tensorflow/tools/api/generator/create_python_api.py script. +_GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. +# Generated by: tensorflow/tools/api/generator/create_python_api.py script. +\"\"\"%s \"\"\" from __future__ import print_function @@ -159,12 +159,13 @@ __all__.remove('print_function') return module_text_map -def get_api_init_text(package): +def get_api_init_text(package, api_name): """Get a map from destination module to __init__.py code for that module. Args: package: Base python package containing python with target tf_export decorators. + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). Returns: A dictionary where @@ -179,7 +180,7 @@ def get_api_init_text(package): for module in list(sys.modules.values()): # Only look at tensorflow modules. if (not module or not hasattr(module, '__name__') or - package not in module.__name__): + module.__name__ is None or package not in module.__name__): continue # Do not generate __init__.py files for contrib modules for now. if '.contrib.' in module.__name__ or module.__name__.endswith('.contrib'): @@ -192,7 +193,7 @@ def get_api_init_text(package): attr = getattr(module, module_contents_name) # If attr is _tf_api_constants attribute, then add the constants. - if module_contents_name == _API_CONSTANTS_ATTR: + if module_contents_name == API_ATTRS[api_name].constants: for exports, value in attr: for export in exports: names = export.split('.') @@ -201,15 +202,12 @@ def get_api_init_text(package): -1, dest_module, module.__name__, value, names[-1]) continue - try: - _, attr = tf_decorator.unwrap(attr) - except Exception as e: - print('5555: %s %s' % (module, module_contents_name), file=sys.stderr) - raise e + _, attr = tf_decorator.unwrap(attr) # If attr is a symbol with _tf_api_names attribute, then # add import for it. - if hasattr(attr, '__dict__') and _API_NAMES_ATTR in attr.__dict__: - for export in attr._tf_api_names: # pylint: disable=protected-access + if (hasattr(attr, '__dict__') and + API_ATTRS[api_name].names in attr.__dict__): + for export in getattr(attr, API_ATTRS[api_name].names): # pylint: disable=protected-access names = export.split('.') dest_module = '.'.join(names[:-1]) module_code_builder.add_import( @@ -246,7 +244,7 @@ def get_module(dir_path, relative_to_dir): relative_to_dir: Get module relative to this directory. Returns: - module that corresponds to the given directory. + Name of module that corresponds to the given directory. """ dir_path = dir_path[len(relative_to_dir):] # Convert path separators to '/' for easier parsing below. @@ -254,8 +252,49 @@ def get_module(dir_path, relative_to_dir): return dir_path.replace('/', '.').strip('.') +def get_module_docstring(module_name, package, api_name): + """Get docstring for the given module. + + This method looks for docstring in the following order: + 1. Checks if module has a docstring specified in doc_srcs. + 2. Checks if module has a docstring source module specified + in doc_srcs. If it does, gets docstring from that module. + 3. Checks if module with module_name exists under base package. + If it does, gets docstring from that module. + 4. Returns a default docstring. + + Args: + module_name: module name relative to tensorflow + (excluding 'tensorflow.' prefix) to get a docstring for. + package: Base python package containing python with target tf_export + decorators. + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). + + Returns: + One-line docstring to describe the module. + """ + # Module under base package to get a docstring from. + docstring_module_name = module_name + + doc_sources = doc_srcs.get_doc_sources(api_name) + + if module_name in doc_sources: + docsrc = doc_sources[module_name] + if docsrc.docstring: + return docsrc.docstring + if docsrc.docstring_module_name: + docstring_module_name = docsrc.docstring_module_name + + docstring_module_name = package + '.' + docstring_module_name + if (docstring_module_name in sys.modules and + sys.modules[docstring_module_name].__doc__): + return sys.modules[docstring_module_name].__doc__ + + return 'Public API for tf.%s namespace.' % module_name + + def create_api_files( - output_files, package, root_init_template, output_dir): + output_files, package, root_init_template, output_dir, api_name): """Creates __init__.py files for the Python API. Args: @@ -267,6 +306,7 @@ def create_api_files( "#API IMPORTS PLACEHOLDER" comment in the template file will be replaced with imports. output_dir: output API root directory. + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). Raises: ValueError: if an output file is not under api/ directory, @@ -283,7 +323,7 @@ def create_api_files( os.makedirs(os.path.dirname(file_path)) open(file_path, 'a').close() - module_text_map = get_api_init_text(package) + module_text_map = get_api_init_text(package, api_name) # Add imports to output files. missing_output_files = [] @@ -296,7 +336,10 @@ def create_api_files( continue contents = '' if module or not root_init_template: - contents = _GENERATED_FILE_HEADER + text + _GENERATED_FILE_FOOTER + contents = ( + _GENERATED_FILE_HEADER % + get_module_docstring(module, package, api_name) + + text + _GENERATED_FILE_FOOTER) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: @@ -309,7 +352,7 @@ def create_api_files( raise ValueError( 'Missing outputs for python_api_gen genrule:\n%s.' 'Make sure all required outputs are in the ' - 'tensorflow/tools/api/generator/BUILD file.' % + 'tensorflow/tools/api/generator/api_gen.bzl file.' % ',\n'.join(sorted(missing_output_files))) @@ -334,6 +377,10 @@ def main(): help='Directory where generated output files are placed. ' 'gendir should be a prefix of apidir. Also, apidir ' 'should be a prefix of every directory in outputs.') + parser.add_argument( + '--apiname', required=True, type=str, + choices=API_ATTRS.keys(), + help='The API you want to generate.') args = parser.parse_args() @@ -347,8 +394,8 @@ def main(): # Populate `sys.modules` with modules containing tf_export(). importlib.import_module(args.package) - create_api_files( - outputs, args.package, args.root_init_template, args.apidir) + create_api_files(outputs, args.package, args.root_init_template, + args.apidir, args.apiname) if __name__ == '__main__': diff --git a/tensorflow/tools/api/generator/create_python_api_test.py b/tensorflow/tools/api/generator/create_python_api_test.py index 986340cf6d4a1bb18841d781dcd11c0208279ec8..651ec9d040302a4343ae6e0053cf6a4b37a971d4 100644 --- a/tensorflow/tools/api/generator/create_python_api_test.py +++ b/tensorflow/tools/api/generator/create_python_api_test.py @@ -57,7 +57,8 @@ class CreatePythonApiTest(test.TestCase): def testFunctionImportIsAdded(self): imports = create_python_api.get_api_init_text( - package=create_python_api._DEFAULT_PACKAGE) + package=create_python_api._DEFAULT_PACKAGE, + api_name='tensorflow') expected_import = ( 'from tensorflow.python.test_module ' 'import test_op as test_op1') @@ -73,7 +74,8 @@ class CreatePythonApiTest(test.TestCase): def testClassImportIsAdded(self): imports = create_python_api.get_api_init_text( - package=create_python_api._DEFAULT_PACKAGE) + package=create_python_api._DEFAULT_PACKAGE, + api_name='tensorflow') expected_import = ('from tensorflow.python.test_module ' 'import TestClass') self.assertTrue( @@ -82,7 +84,8 @@ class CreatePythonApiTest(test.TestCase): def testConstantIsAdded(self): imports = create_python_api.get_api_init_text( - package=create_python_api._DEFAULT_PACKAGE) + package=create_python_api._DEFAULT_PACKAGE, + api_name='tensorflow') expected = ('from tensorflow.python.test_module ' 'import _TEST_CONSTANT') self.assertTrue(expected in str(imports), diff --git a/tensorflow/tools/api/generator/doc_srcs.py b/tensorflow/tools/api/generator/doc_srcs.py new file mode 100644 index 0000000000000000000000000000000000000000..ad1988494dae4a9d3ee96af5af76f02c52c0dff4 --- /dev/null +++ b/tensorflow/tools/api/generator/doc_srcs.py @@ -0,0 +1,92 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Specifies sources of doc strings for API modules.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from tensorflow.python.util import tf_export + + +# Specifies docstring source for a module. +# Only one of docstring or docstring_module_name should be set. +# * If docstring is set, then we will use this docstring when +# for the module. +# * If docstring_module_name is set, then we will copy the docstring +# from docstring source module. +DocSource = collections.namedtuple( + 'DocSource', ['docstring', 'docstring_module_name']) +# Each attribute of DocSource is optional. +DocSource.__new__.__defaults__ = (None,) * len(DocSource._fields) + +_TENSORFLOW_DOC_SOURCES = { + 'app': DocSource(docstring_module_name='platform.app'), + 'compat': DocSource(docstring_module_name='util.compat'), + 'distributions': DocSource( + docstring_module_name='ops.distributions.distributions'), + 'bitwise': DocSource(docstring_module_name='ops.bitwise_ops'), + 'errors': DocSource(docstring_module_name='framework.errors'), + 'gfile': DocSource(docstring_module_name='platform.gfile'), + 'graph_util': DocSource(docstring_module_name='framework.graph_util'), + 'image': DocSource(docstring_module_name='ops.image_ops'), + 'keras.estimator': DocSource(docstring_module_name='keras.estimator'), + 'linalg': DocSource(docstring_module_name='ops.linalg_ops'), + 'logging': DocSource(docstring_module_name='ops.logging_ops'), + 'losses': DocSource(docstring_module_name='ops.losses.losses'), + 'manip': DocSource(docstring_module_name='ops.manip_ops'), + 'math': DocSource(docstring_module_name='ops.math_ops'), + 'metrics': DocSource(docstring_module_name='ops.metrics'), + 'nn': DocSource(docstring_module_name='ops.nn_ops'), + 'nn.rnn_cell': DocSource(docstring_module_name='ops.rnn_cell'), + 'python_io': DocSource(docstring_module_name='lib.io.python_io'), + 'resource_loader': DocSource( + docstring_module_name='platform.resource_loader'), + 'sets': DocSource(docstring_module_name='ops.sets'), + 'sparse': DocSource(docstring_module_name='ops.sparse_ops'), + 'spectral': DocSource(docstring_module_name='ops.spectral_ops'), + 'strings': DocSource(docstring_module_name='ops.string_ops'), + 'sysconfig': DocSource(docstring_module_name='platform.sysconfig'), + 'test': DocSource(docstring_module_name='platform.test'), + 'train': DocSource(docstring_module_name='training.training'), + 'train.queue_runner': DocSource( + docstring_module_name='training.queue_runner'), +} + +_ESTIMATOR_DOC_SOURCES = { + 'estimator': DocSource( + docstring_module_name='estimator_lib'), + 'estimator.export': DocSource( + docstring_module_name='export.export_lib'), + 'estimator.inputs': DocSource( + docstring_module_name='inputs.inputs'), +} + + +def get_doc_sources(api_name): + """Get a map from module to a DocSource object. + + Args: + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). + + Returns: + Map from module name to DocSource object. + """ + if api_name == tf_export.TENSORFLOW_API_NAME: + return _TENSORFLOW_DOC_SOURCES + if api_name == tf_export.ESTIMATOR_API_NAME: + return _ESTIMATOR_DOC_SOURCES + return {} diff --git a/tensorflow/tools/api/generator/doc_srcs_test.py b/tensorflow/tools/api/generator/doc_srcs_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dbff904abe6251ad180140c4c7c404f051b17d55 --- /dev/null +++ b/tensorflow/tools/api/generator/doc_srcs_test.py @@ -0,0 +1,83 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Tests for tensorflow.tools.api.generator.doc_srcs.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import importlib +import sys + +from tensorflow.python.platform import test +from tensorflow.tools.api.generator import doc_srcs + + +FLAGS = None + + +class DocSrcsTest(test.TestCase): + + def testModulesAreValidAPIModules(self): + for module_name in doc_srcs.get_doc_sources(FLAGS.api_name): + # Convert module_name to corresponding __init__.py file path. + file_path = module_name.replace('.', '/') + if file_path: + file_path += '/' + file_path += '__init__.py' + + self.assertIn( + file_path, FLAGS.outputs, + msg='%s is not a valid API module' % module_name) + + def testHaveDocstringOrDocstringModule(self): + for module_name, docsrc in doc_srcs.get_doc_sources(FLAGS.api_name).items(): + self.assertFalse( + docsrc.docstring and docsrc.docstring_module_name, + msg=('%s contains DocSource has both a docstring and a ' + 'docstring_module_name. Only one of "docstring" or ' + '"docstring_module_name" should be set.') % (module_name)) + + def testDocstringModulesAreValidModules(self): + for _, docsrc in doc_srcs.get_doc_sources(FLAGS.api_name).items(): + if docsrc.docstring_module_name: + doc_module_name = '.'.join([ + FLAGS.package, docsrc.docstring_module_name]) + self.assertIn( + doc_module_name, sys.modules, + msg=('docsources_module %s is not a valid module under %s.' % + (docsrc.docstring_module_name, FLAGS.package))) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + 'outputs', metavar='O', type=str, nargs='+', + help='create_python_api output files.') + parser.add_argument( + '--package', type=str, + help='Base package that imports modules containing the target tf_export ' + 'decorators.') + parser.add_argument( + '--api_name', type=str, + help='API name: tensorflow or estimator') + FLAGS, unparsed = parser.parse_known_args() + + importlib.import_module(FLAGS.package) + + # Now update argv, so that unittest library does not get confused. + sys.argv = [sys.argv[0]] + unparsed + test.main() diff --git a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt index f819b174c0b701153af4709fade9313efa7f7fb6..353e63127de174a79c209a05327da2de20bf0dd7 100644 --- a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt @@ -72,6 +72,12 @@ tf_proto { label: LABEL_OPTIONAL type: TYPE_BOOL } + field { + name: "num_dev_to_dev_copy_streams" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } nested_type { name: "VirtualDevices" field { diff --git a/tensorflow/tools/api/golden/tensorflow.-variable-aggregation.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable-aggregation.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..36b534af360835e3c1cbd1f0fb12a38c42232abf --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.-variable-aggregation.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.VariableAggregation" +tf_class { + is_instance: "" + member { + name: "MEAN" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "SUM" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt index 8e539069da05fbb192c383d3f5acff78ab9bfeff..ec1f72453fdb540463503a626d75d481907a3676 100644 --- a/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt @@ -56,7 +56,7 @@ tf_class { } member_method { name: "get_variable" - argspec: "args=[\'self\', \'var_store\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'reuse\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'var_store\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'reuse\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " } member_method { name: "global_variables" diff --git a/tensorflow/tools/api/golden/tensorflow.-variable-synchronization.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable-synchronization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7589bb28888774839a3011e1e5581f004313f81d --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.-variable-synchronization.pbtxt @@ -0,0 +1,20 @@ +path: "tensorflow.VariableSynchronization" +tf_class { + is_instance: "" + member { + name: "AUTO" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "ON_READ" + mtype: "" + } + member { + name: "ON_WRITE" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt index 8e7e945ed1bc26669d7c7f0ed3c2002df9f1883b..834f0954d5bba655a8eb923672d89bac6bb80808 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt @@ -24,7 +24,7 @@ tf_class { } member_method { name: "batch" - argspec: "args=[\'self\', \'batch_size\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " } member_method { name: "cache" @@ -80,7 +80,7 @@ tf_class { } member_method { name: "padded_batch" - argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " } member_method { name: "prefetch" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt index 5cfb2fd2f0c6a7b733e70445aa130e96c512205e..4d854a4ceea3907d7d795d0a19d081f4069c9ba9 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt @@ -25,7 +25,7 @@ tf_class { } member_method { name: "batch" - argspec: "args=[\'self\', \'batch_size\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " } member_method { name: "cache" @@ -81,7 +81,7 @@ tf_class { } member_method { name: "padded_batch" - argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " } member_method { name: "prefetch" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt index 3327e5b274b43c0b424933cb086c894d47ad25cb..601f095a60ae481b895a535efa37341611499499 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt @@ -25,7 +25,7 @@ tf_class { } member_method { name: "batch" - argspec: "args=[\'self\', \'batch_size\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " } member_method { name: "cache" @@ -81,7 +81,7 @@ tf_class { } member_method { name: "padded_batch" - argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " } member_method { name: "prefetch" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt index 9d59375282b39564456b4c8aa49435c3836c58ea..587829a4c078e8ab945f66c64f5adad21223dfb1 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt @@ -25,7 +25,7 @@ tf_class { } member_method { name: "batch" - argspec: "args=[\'self\', \'batch_size\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " } member_method { name: "cache" @@ -81,7 +81,7 @@ tf_class { } member_method { name: "padded_batch" - argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " } member_method { name: "prefetch" diff --git a/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt b/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d9efe97821904f5891148b72a0c31e02c9562bd7 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.debugging" +tf_module { + member_method { + name: "check_numerics" + argspec: "args=[\'tensor\', \'message\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_finite" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_inf" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_nan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt b/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..98e1feed002ceb4f455aa5ec361d26a159fdad1a --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.dtypes" +tf_module { + member_method { + name: "as_string" + argspec: "args=[\'input\', \'precision\', \'scientific\', \'shortest\', \'width\', \'fill\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'False\', \'False\', \'-1\', \'\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt index 111914f643a3b192d496c5b0857b4429da12b1d6..0c6b7e4a821ad47c20b6f6074b575bf83c403653 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], " + argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " } member_method { name: "eval_dir" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt index 67e4ee02d0581207e7dd316196aeb782930e7602..49a3e898c5e7b4528a4dd39a906512f273f2b608 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], " + argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " } member_method { name: "eval_dir" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt index e1289b975e721e94f4a63889f3e0b76b0db23d81..4b81613c9292d3ee8d5fd7f175776436f8f68393 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'1\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], " + argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'1\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " } member_method { name: "eval_dir" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt index d030b2f51f019ecc179a09b76c4484e60ada9dd0..f50e375f7cd392567f5c87536c95eb1f6809bc97 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\'], " + argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " } member_method { name: "eval_dir" diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 32fb9183e6e3bb8f6682ed64096d918d93418291..e89b4dbffdfe85f471fb1dd1b976cc701d526c64 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "adjust_hue" argspec: "args=[\'image\', \'delta\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "adjust_jpeg_quality" + argspec: "args=[\'image\', \'jpeg_quality\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "adjust_saturation" argspec: "args=[\'image\', \'saturation_factor\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -80,6 +84,10 @@ tf_module { name: "extract_glimpse" argspec: "args=[\'input\', \'size\', \'offsets\', \'centered\', \'normalized\', \'uniform_noise\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'True\', \'None\'], " } + member_method { + name: "extract_image_patches" + argspec: "args=[\'images\', \'ksizes\', \'strides\', \'rates\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "extract_jpeg_shape" argspec: "args=[\'contents\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " @@ -144,6 +152,10 @@ tf_module { name: "random_hue" argspec: "args=[\'image\', \'max_delta\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "random_jpeg_quality" + argspec: "args=[\'image\', \'min_jpeg_quality\', \'max_jpeg_quality\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "random_saturation" argspec: "args=[\'image\', \'lower\', \'upper\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -164,9 +176,13 @@ tf_module { name: "resize_image_with_crop_or_pad" argspec: "args=[\'image\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "resize_image_with_pad" + argspec: "args=[\'image\', \'target_height\', \'target_width\', \'method\'], varargs=None, keywords=None, defaults=[\'0\'], " + } member_method { name: "resize_images" - argspec: "args=[\'images\', \'size\', \'method\', \'align_corners\'], varargs=None, keywords=None, defaults=[\'0\', \'False\'], " + argspec: "args=[\'images\', \'size\', \'method\', \'align_corners\', \'preserve_aspect_ratio\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\'], " } member_method { name: "resize_nearest_neighbor" diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt b/tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt index a6b6e5eceb62654c9ad567a361f7558a2865e57a..86340913e2506c96499aae05a3ed0d5273c93bba 100644 --- a/tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'normal\', \'None\', \"\"], " + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.io.pbtxt b/tensorflow/tools/api/golden/tensorflow.io.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3a36c168aa703721421b662185fc852fa3d6a3ec --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.io.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.io" +tf_module { + member_method { + name: "decode_base64" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_compressed" + argspec: "args=[\'bytes\', \'compression_type\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } + member_method { + name: "decode_json_example" + argspec: "args=[\'json_examples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_raw" + argspec: "args=[\'bytes\', \'out_type\', \'little_endian\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "encode_base64" + argspec: "args=[\'input\', \'pad\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "matching_files" + argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "parse_tensor" + argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_file" + argspec: "args=[\'filename\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "write_file" + argspec: "args=[\'filename\', \'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-early-stopping.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-early-stopping.pbtxt index 7b0ad85eaac5b83835a9e1c4b152e38e7051a2f6..f71292856cd29b2e52194bec8a586686fbfad667 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-early-stopping.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-early-stopping.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\'], " + argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.-variance-scaling.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.-variance-scaling.pbtxt index 32a6f6ee88815b3dc70e9cca855f73099554953b..03f4064b9ef5093044a9cbb897043d643cf7f83e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.initializers.-variance-scaling.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.-variance-scaling.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'normal\', \'None\', \"\"], " + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..56e32e9d3690a92c3f6e41bf2b5164c6bf62f443 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Minimum" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f3a96ab895dc9dbf8e2362dbcbfdccdf6af749ec --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.ReLU" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'max_value\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..35ad87ad5d91f1cc5d413b0adc8e9e5d1403726a --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Subtract" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt index 709eb5be55ef180ce9836def4bef601ea4315be0..9d7e5bb8c7808689bedd8abb835e61c1f38fdb1d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt @@ -280,6 +280,10 @@ tf_module { name: "Maximum" mtype: "" } + member { + name: "Minimum" + mtype: "" + } member { name: "Multiply" mtype: "" @@ -296,6 +300,10 @@ tf_module { name: "RNN" mtype: "" } + member { + name: "ReLU" + mtype: "" + } member { name: "RepeatVector" mtype: "" @@ -348,6 +356,10 @@ tf_module { name: "StackedRNNCells" mtype: "" } + member { + name: "Subtract" + mtype: "" + } member { name: "ThresholdedReLU" mtype: "" @@ -408,8 +420,16 @@ tf_module { name: "maximum" argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" } + member_method { + name: "minimum" + argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" + } member_method { name: "multiply" argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" } + member_method { + name: "subtract" + argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt index 00b9238543367546cff96b736f73440214e99e22..3b5845f99a474ed976b91dab4f80ac2f231e7fc1 100644 --- a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt @@ -68,6 +68,10 @@ tf_module { name: "cholesky_solve" argspec: "args=[\'chol\', \'rhs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "cross" + argspec: "args=[\'a\', \'b\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "det" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -140,6 +144,14 @@ tf_module { name: "svd" argspec: "args=[\'tensor\', \'full_matrices\', \'compute_uv\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'None\'], " } + member_method { + name: "tensor_diag" + argspec: "args=[\'diagonal\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tensor_diag_part" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "tensordot" argspec: "args=[\'a\', \'b\', \'axes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt index 0b84165285102daf0a8e3dd6542bfc391e50f77b..9add462396ea526ae94678e969c9acf5bce86df1 100644 --- a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt @@ -1,7 +1,35 @@ path: "tensorflow.manip" tf_module { + member_method { + name: "batch_to_space_nd" + argspec: "args=[\'input\', \'block_shape\', \'crops\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "gather_nd" + argspec: "args=[\'params\', \'indices\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reshape" + argspec: "args=[\'tensor\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reverse" + argspec: "args=[\'tensor\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "roll" argspec: "args=[\'input\', \'shift\', \'axis\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "scatter_nd" + argspec: "args=[\'indices\', \'updates\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "space_to_batch_nd" + argspec: "args=[\'input\', \'block_shape\', \'paddings\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tile" + argspec: "args=[\'input\', \'multiples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } } diff --git a/tensorflow/tools/api/golden/tensorflow.math.pbtxt b/tensorflow/tools/api/golden/tensorflow.math.pbtxt index 897718c05e0d10a6f961f33b8c65f5dab1d03f5b..a308c76ebc08df06c0c360579451ea70e60695d4 100644 --- a/tensorflow/tools/api/golden/tensorflow.math.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.math.pbtxt @@ -1,7 +1,239 @@ path: "tensorflow.math" tf_module { + member_method { + name: "acos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "acosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "asin" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "asinh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atan2" + argspec: "args=[\'y\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atanh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bessel_i0" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bessel_i0e" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bessel_i1" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bessel_i1e" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "betainc" + argspec: "args=[\'a\', \'b\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ceil" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "digamma" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "erfc" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "exp" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "expm1" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "floor" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "greater" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "greater_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "igamma" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "igammac" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "invert_permutation" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "less" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "less_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lgamma" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "log" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "log1p" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_and" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_not" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_or" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "maximum" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "minimum" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "not_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "polygamma" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "polyval" argspec: "args=[\'coeffs\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "reciprocal" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rint" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rsqrt" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_max" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_mean" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_min" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_prod" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_sum" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "sin" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "sinh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "softplus" + argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "softsign" + argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "squared_difference" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_max" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_min" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_prod" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_sum" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "zeta" + argspec: "args=[\'x\', \'q\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } } diff --git a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt index 455590d866a4c1ebea65ccff51e34f2e0b0479d7..d9e5b0d0fca8bbcf82feb34304f2a1e4f43f48dd 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt @@ -260,6 +260,10 @@ tf_module { name: "relu_layer" argspec: "args=[\'x\', \'weights\', \'biases\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "safe_embedding_lookup_sparse" + argspec: "args=[\'embedding_weights\', \'sparse_ids\', \'sparse_weights\', \'combiner\', \'default_id\', \'name\', \'partition_strategy\', \'max_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'mean\', \'None\', \'None\', \'div\', \'None\'], " + } member_method { name: "sampled_softmax_loss" argspec: "args=[\'weights\', \'biases\', \'labels\', \'inputs\', \'num_sampled\', \'num_classes\', \'num_true\', \'sampled_values\', \'remove_accidental_hits\', \'partition_strategy\', \'name\', \'seed\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'True\', \'mod\', \'sampled_softmax_loss\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index 01b80581188e65d228aaa669254d9951546ecfa0..9ec20f095574cd1d08ed45659dba3663e09ab8d9 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -260,10 +260,18 @@ tf_module { name: "Variable" mtype: "" } + member { + name: "VariableAggregation" + mtype: "" + } member { name: "VariableScope" mtype: "" } + member { + name: "VariableSynchronization" + mtype: "" + } member { name: "WholeFileReader" mtype: "" @@ -308,6 +316,10 @@ tf_module { name: "data" mtype: "" } + member { + name: "debugging" + mtype: "" + } member { name: "distributions" mtype: "" @@ -316,6 +328,10 @@ tf_module { name: "double" mtype: "" } + member { + name: "dtypes" + mtype: "" + } member { name: "errors" mtype: "" @@ -380,6 +396,10 @@ tf_module { name: "int8" mtype: "" } + member { + name: "io" + mtype: "" + } member { name: "keras" mtype: "" @@ -456,6 +476,10 @@ tf_module { name: "qint8" mtype: "" } + member { + name: "quantization" + mtype: "" + } member { name: "quint16" mtype: "" @@ -1134,7 +1158,7 @@ tf_module { } member_method { name: "get_local_variable" - argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'False\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'synchronization\', \'aggregation\', \'custom_getter\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'False\', \'None\', \'None\', \'None\', \'True\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\', \'None\'], " } member_method { name: "get_seed" @@ -1150,7 +1174,7 @@ tf_module { } member_method { name: "get_variable" - argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " } member_method { name: "get_variable_scope" @@ -1294,7 +1318,7 @@ tf_module { } member_method { name: "lbeta" - argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'lbeta\'], " + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "less" diff --git a/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt b/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6d865efed0bfdada8dde64e86ddb5d2b2b364c79 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.quantization" +tf_module { + member_method { + name: "dequantize" + argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "quantized_concat" + argspec: "args=[\'concat_dim\', \'values\', \'input_mins\', \'input_maxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt b/tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt index 896e2160c693039ab5582be13286f387c08d8f37..511e6b4712d3c55746a39fe9098fa3b649bc75dc 100644 --- a/tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt @@ -2,7 +2,7 @@ path: "tensorflow.saved_model.loader" tf_module { member_method { name: "load" - argspec: "args=[\'sess\', \'tags\', \'export_dir\'], varargs=None, keywords=saver_kwargs, defaults=None" + argspec: "args=[\'sess\', \'tags\', \'export_dir\', \'import_scope\'], varargs=None, keywords=saver_kwargs, defaults=[\'None\'], " } member_method { name: "maybe_saved_model_directory" diff --git a/tensorflow/tools/api/golden/tensorflow.spectral.pbtxt b/tensorflow/tools/api/golden/tensorflow.spectral.pbtxt index 4f306540ccfdeac8ce59a394ec77b24284f13ceb..6a421ef12d58dc047905ec916cbe777b4ce19b9a 100644 --- a/tensorflow/tools/api/golden/tensorflow.spectral.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.spectral.pbtxt @@ -16,6 +16,10 @@ tf_module { name: "fft3d" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "idct" + argspec: "args=[\'input\', \'type\', \'n\', \'axis\', \'norm\', \'name\'], varargs=None, keywords=None, defaults=[\'2\', \'None\', \'-1\', \'None\', \'None\'], " + } member_method { name: "ifft" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index b641c39feb6bcc4b5b73ba81ce0f0d4a499007ea..9a831fed2692b30db6ce991c86f46a42908c0789 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -1,11 +1,43 @@ path: "tensorflow.strings" tf_module { + member_method { + name: "join" + argspec: "args=[\'inputs\', \'separator\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } member_method { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "regex_replace" + argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } member_method { name: "split" argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " } + member_method { + name: "strip" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "substr" + argspec: "args=[\'input\', \'pos\', \'len\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket" + argspec: "args=[\'string_tensor\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_fast" + argspec: "args=[\'input\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_strong" + argspec: "args=[\'input\', \'num_buckets\', \'key\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_number" + argspec: "args=[\'string_tensor\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } } diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt index ddc553d7c984b24fe33c03bb90e00e7e81f55d26..2d067e4eff13208cb03ca01b7b8a8018a1e99097 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt @@ -1,7 +1,7 @@ path: "tensorflow.train.Checkpoint" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.pbtxt index 9fb18e77afd7c9c989ad5e967be291406e7239aa..b0fb04d7d4d71e8cb2630ca79284e0ade1db8571 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.pbtxt @@ -242,7 +242,7 @@ tf_module { } member_method { name: "MonitoredTrainingSession" - argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\', \'max_wait_secs\', \'save_checkpoint_steps\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'\', \'\', \'\', \'None\', \'120\', \'100\', \'7200\', \'\'], " + argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\', \'max_wait_secs\', \'save_checkpoint_steps\', \'summary_dir\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'\', \'\', \'\', \'None\', \'120\', \'100\', \'7200\', \'\', \'None\'], " } member_method { name: "NewCheckpointReader" @@ -400,6 +400,10 @@ tf_module { name: "range_input_producer" argspec: "args=[\'limit\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\'], " } + member_method { + name: "remove_checkpoint" + argspec: "args=[\'checkpoint_prefix\', \'checkpoint_format_version\', \'meta_graph_suffix\'], varargs=None, keywords=None, defaults=[\'2\', \'meta\'], " + } member_method { name: "replica_device_setter" argspec: "args=[\'ps_tasks\', \'ps_device\', \'worker_device\', \'merge_devices\', \'cluster\', \'ps_ops\', \'ps_strategy\'], varargs=None, keywords=None, defaults=[\'0\', \'/job:ps\', \'/job:worker\', \'True\', \'None\', \'None\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt b/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt index a58398d645e8397dc8e61a6e0241710c3e34218f..09d7bc03b4f238923db6778ec32ce78ae76eed61 100644 --- a/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'normal\', \'None\', \"\"], " + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " } member_method { name: "from_config" diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake index d5dea4f3e41841aed5aeac02fcca850dbfdfaeb3..e8c319982839b7b5adc17d6fb7ac364660ac76fe 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cmake +++ b/tensorflow/tools/ci_build/Dockerfile.cmake @@ -28,6 +28,8 @@ RUN pip install --upgrade astor RUN pip install --upgrade gast RUN pip install --upgrade numpy RUN pip install --upgrade termcolor +RUN pip install keras_applications==1.0.2 +RUN pip install keras_preprocessing==1.0.1 # Install golang RUN apt-get install -t xenial-backports -y golang-1.9 diff --git a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le new file mode 100644 index 0000000000000000000000000000000000000000..e879c34bbdadd7b90973fda0f7c3fdb71a385856 --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le @@ -0,0 +1,20 @@ +FROM ubuntu:16.04 + +LABEL maintainer="William Irons " + +# Copy and run the install scripts. +COPY install/*.sh /install/ +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa +RUN /install/install_deb_packages.sh +RUN apt-get update && apt-get install -y libopenblas-dev +RUN /install/install_hdf5_ppc64le.sh +RUN /install/install_pip_packages.sh +RUN /install/install_bazel_from_source.sh +RUN /install/install_proto3.sh +RUN /install/install_buildifier_from_source.sh +RUN /install/install_auditwheel.sh +RUN /install/install_golang_ppc64le.sh + +# Set up the master bazelrc configuration file. +COPY install/.bazelrc /etc/bazel.bazelrc diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le new file mode 100644 index 0000000000000000000000000000000000000000..89671387472a15c112a09fa2fa7a9798446d135b --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le @@ -0,0 +1,28 @@ +FROM nvidia/cuda-ppc64le:9.0-cudnn7-devel-ubuntu16.04 + +LABEL maintainer="William Irons " + +# In the Ubuntu 16.04 images, cudnn is placed in system paths. Move them to +# /usr/local/cuda +RUN cp -P /usr/include/cudnn.h /usr/local/cuda/include +RUN cp -P /usr/lib/powerpc64le-linux-gnu/libcudnn* /usr/local/cuda/lib64 + +# Copy and run the install scripts. +COPY install/*.sh /install/ +ARG DEBIAN_FRONTEND=noninteractive +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa +RUN /install/install_deb_packages.sh +RUN apt-get update && apt-get install -y libopenblas-dev +RUN /install/install_hdf5_ppc64le.sh +RUN /install/install_pip_packages.sh +RUN /install/install_bazel_from_source.sh +RUN /install/install_golang_ppc64le.sh + +# Set up the master bazelrc configuration file. +COPY install/.bazelrc /etc/bazel.bazelrc +ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH + +# Configure the build for our CUDA configuration. +ENV TF_NEED_CUDA 1 +ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0 diff --git a/tensorflow/tools/ci_build/Dockerfile.rbe.cpu b/tensorflow/tools/ci_build/Dockerfile.rbe.cpu index 3bc52b9ed611a0f0a4a269a2864d5b349ee9232c..7e5860aeec186d908e5d2884bd690b2e5e43cffa 100644 --- a/tensorflow/tools/ci_build/Dockerfile.rbe.cpu +++ b/tensorflow/tools/ci_build/Dockerfile.rbe.cpu @@ -1,4 +1,4 @@ -FROM launcher.gcr.io/google/rbe-debian8:r327695 +FROM launcher.gcr.io/google/rbe-ubuntu16-04:r327695 LABEL maintainer="Yu Yi " # Copy install scripts @@ -9,6 +9,6 @@ ENV CC /usr/local/bin/clang ENV CXX /usr/local/bin/clang++ ENV AR /usr/bin/ar -# Run pip install script for RBE Debian8 container. +# Run pip install script for RBE Ubuntu 16-04 container. RUN /install/install_pip_packages_remote.sh RUN /install/install_pip_packages.sh diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 1f0fd0387af28bf15e5c42fa14f5c1a1ee5a8cfb..f6a50d3d4c4f948e37ff841a880b373f1034fd76 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -79,7 +79,7 @@ if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then fi # Use nvidia-docker if the container is GPU. -if [[ "${CONTAINER_TYPE}" == "gpu" ]]; then +if [[ "${CONTAINER_TYPE}" == gpu* ]]; then DOCKER_BINARY="nvidia-docker" else DOCKER_BINARY="docker" @@ -99,7 +99,7 @@ BUILD_TAG="${BUILD_TAG:-tf_ci}" # Add extra params for cuda devices and libraries for GPU container. # And clear them if we are not building for GPU. -if [[ "${CONTAINER_TYPE}" != "gpu" ]]; then +if [[ "${CONTAINER_TYPE}" != gpu* ]]; then GPU_EXTRA_PARAMS="" fi diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index e621f85652588f7b5cba6dc5128f857f9eb0fe09..08e2c3edd2d22fbb7b9912c9ce7ec561dc5a7113 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -59,6 +59,9 @@ # TF_BUILD_BAZEL_CLEAN: # Will perform "bazel clean", if and only if this variable # is set to any non-empty and non-0 value +# TF_BAZEL_BUILD_ONLY: +# If it is set to any non-empty value that is not "0", Bazel +# will only build specified targets # TF_GPU_COUNT: # Run this many parallel tests for serial builds. # For now, only can be edited for PIP builds. @@ -94,10 +97,6 @@ # # This script can be used by Jenkins parameterized / matrix builds. -# TODO(jhseu): Temporary for the gRPC pull request due to the -# protobuf -> protobuf_archive rename. Remove later. -TF_BUILD_BAZEL_CLEAN=1 - # Helper function: Convert to lower case to_lower () { echo "$1" | tr '[:upper:]' '[:lower:]' @@ -132,7 +131,7 @@ BAZEL_CMD="bazel test" BAZEL_BUILD_ONLY_CMD="bazel build" BAZEL_CLEAN_CMD="bazel clean" -DEFAULT_BAZEL_CONFIGS="--config=gcp --config=hdfs" +DEFAULT_BAZEL_CONFIGS="" PIP_CMD="${CI_BUILD_DIR}/builds/pip.sh" PIP_TEST_TUTORIALS_FLAG="--test_tutorials" @@ -262,9 +261,9 @@ function set_script_variable() { # Process container type -if [[ ${CTYPE} == "cpu" ]] || [[ ${CTYPE} == "debian.jessie.cpu" ]]; then +if [[ ${CTYPE} == cpu* ]] || [[ ${CTYPE} == "debian.jessie.cpu" ]]; then : -elif [[ ${CTYPE} == "gpu" ]]; then +elif [[ ${CTYPE} == gpu* ]]; then set_script_variable TF_NEED_CUDA 1 if [[ $TF_CUDA_CLANG == "1" ]]; then @@ -414,6 +413,11 @@ fi # this flag, and it only affects a few tests. EXTRA_ARGS="${EXTRA_ARGS} --distinct_host_configuration=false" +if [[ ! -z "${TF_BAZEL_BUILD_ONLY}" ]] && + [[ "${TF_BAZEL_BUILD_ONLY}" != "0" ]];then + BAZEL_CMD=${BAZEL_BUILD_ONLY_CMD} +fi + # Process PIP install-test option if [[ ${TF_BUILD_IS_PIP} == "no_pip" ]] || [[ ${TF_BUILD_IS_PIP} == "both" ]]; then @@ -422,12 +426,12 @@ if [[ ${TF_BUILD_IS_PIP} == "no_pip" ]] || BAZEL_TARGET=${TF_BUILD_BAZEL_TARGET} fi - if [[ ${CTYPE} == "cpu" ]] || \ + if [[ ${CTYPE} == cpu* ]] || \ [[ ${CTYPE} == "debian.jessie.cpu" ]]; then # CPU only command, fully parallel. NO_PIP_MAIN_CMD="${MAIN_CMD} ${BAZEL_CMD} ${OPT_FLAG} ${EXTRA_ARGS} -- "\ "${BAZEL_TARGET}" - elif [[ ${CTYPE} == "gpu" ]]; then + elif [[ ${CTYPE} == gpu* ]]; then # GPU only command, run as many jobs as the GPU count only. NO_PIP_MAIN_CMD="${BAZEL_CMD} ${OPT_FLAG} "\ "--local_test_jobs=${TF_GPU_COUNT} "\ diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 05676f9551d4a1e0cb55d0693f99e458381887df..db37edf8097844646236aace5e3517a8080d70cb 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -349,12 +349,12 @@ do_external_licenses_check(){ # Blacklist echo ${MISSING_LICENSES_FILE} - grep -e "@bazel_tools//third_party/" -e "@com_google_absl//absl" -e "@org_tensorflow//" -v ${MISSING_LICENSES_FILE} > temp.txt + grep -e "@bazel_tools//third_party/" -e "@com_google_absl//absl" -e "@org_tensorflow//" -e "@com_github_googlecloudplatform_google_cloud_cpp//google" -v ${MISSING_LICENSES_FILE} > temp.txt mv temp.txt ${MISSING_LICENSES_FILE} # Whitelist echo ${EXTRA_LICENSE_FILE} - grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -v ${EXTRA_LICENSES_FILE} > temp.txt + grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -e "@com_github_googlecloudplatform_google_cloud_cpp//" -v ${EXTRA_LICENSES_FILE} > temp.txt mv temp.txt ${EXTRA_LICENSES_FILE} @@ -543,7 +543,7 @@ SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "d SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "Check that python files have certain __future__ imports" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test" "Code Link Check: Check there are no broken links" "Test entries in /tensorflow/contrib/cmake/python_{modules|protos|protos_cc}.txt for validity and consistency" "Check file names for cases") INCREMENTAL_FLAG="" -DEFAULT_BAZEL_CONFIGS="--config=hdfs --config=gcp" +DEFAULT_BAZEL_CONFIGS="" # Parse command-line arguments BAZEL_FLAGS=${DEFAULT_BAZEL_CONFIGS} diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh new file mode 100755 index 0000000000000000000000000000000000000000..ddad00c5f01a78164903702b03c816c427aeb0b8 --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh @@ -0,0 +1,40 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# This script is to be used to install bzel on non x86_64 systems +# It will compile bazel from source and install it in /usr/local/bin + +# Select bazel version. +BAZEL_VERSION="0.11.0" + +set +e +local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') + +if [[ "$local_bazel_ver" == "$BAZEL_VERSION" ]]; then + exit 0 +fi + +set -e + +# Compile bazel from source +mkdir -p /bazel +cd /bazel + +curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip +unzip bazel-$BAZEL_VERSION-dist.zip +bash ./compile.sh +cp output/bazel /usr/local/bin/ +rm -rf /bazel diff --git a/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh b/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh new file mode 100755 index 0000000000000000000000000000000000000000..a93c258fad1ca62b0c95f22560110ba231aa0053 --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e +BUILDTOOLS_VERSION="0.11.1" + +# Clone buildtools +git clone -b $BUILDTOOLS_VERSION https://github.com/bazelbuild/buildtools +cd buildtools + +# Build buildifier +bazel build //buildifier +sudo mv bazel-bin/buildifier/linux*stripped/buildifier /usr/local/bin + +# Build buildozer +bazel build //buildozer +sudo mv bazel-bin/buildozer/linux*stripped/buildozer /usr/local/bin diff --git a/tensorflow/tools/ci_build/install/install_golang_ppc64le.sh b/tensorflow/tools/ci_build/install/install_golang_ppc64le.sh new file mode 100755 index 0000000000000000000000000000000000000000..47d23a59b3ee9152ef9812fbe939e20ee7c2b40a --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_golang_ppc64le.sh @@ -0,0 +1,22 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -ex + +GOLANG_URL="https://storage.googleapis.com/golang/go1.10.linux-ppc64le.tar.gz" + +sudo mkdir -p /usr/local +wget -q -O - "${GOLANG_URL}" | sudo tar -C /usr/local -xz diff --git a/tensorflow/tools/ci_build/install/install_hdf5_ppc64le.sh b/tensorflow/tools/ci_build/install/install_hdf5_ppc64le.sh new file mode 100755 index 0000000000000000000000000000000000000000..4989d986b8eb0690f63ecff41f7107371724bc3a --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_hdf5_ppc64le.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + + +#This is required because pypi doesn't have a pre-built h5py binary for ppc64le +#It has to be compiled from source during the install +apt-get update +apt-get install -y libhdf5-dev + +#h5py is not expecting the shared libraries to have _serial in the name. +ln -s /usr/lib/powerpc64le-linux-gnu/libhdf5_serial.so /usr/lib/powerpc64le-linux-gnu/libhdf5.so +ln -s /usr/lib/powerpc64le-linux-gnu/libhdf5_serial_hl.so /usr/lib/powerpc64le-linux-gnu/libhdf5_hl.so + +#pip is not installed yet, so use easy_install +#CPATH is the location of hdf5.h +CPATH=/usr/include/hdf5/serial/ easy_install -U h5py +CPATH=/usr/include/hdf5/serial/ easy_install3 -U h5py diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index b3d3f23ec8fd16c78b525159abeeb22776e3eac5..221b5b80fb48979af09cb99a5c35cbe5fc4e5ca1 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -51,8 +51,8 @@ pip2 install --upgrade markdown==2.6.8 pip3 install --upgrade markdown==2.6.8 # Install protobuf. -pip2 install --upgrade protobuf==3.3.0 -pip3 install --upgrade protobuf==3.3.0 +pip2 install --upgrade protobuf==3.6.0 +pip3 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* @@ -113,3 +113,13 @@ pip3 install --upgrade termcolor # Install last working version of setuptools. pip2 install --upgrade setuptools==39.1.0 pip3 install --upgrade setuptools==39.1.0 + +# Keras +pip2 install keras_applications==1.0.2 +pip3 install keras_applications==1.0.2 +pip2 install keras_preprocessing==1.0.1 +pip3 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip2 install --upgrade setuptools==39.1.0 +pip3 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh index 7934002b2c982cd10216016f8614b70b77b58e29..821d50baff325106fceca368d46042401d13c336 100755 --- a/tensorflow/tools/ci_build/install/install_proto3.sh +++ b/tensorflow/tools/ci_build/install/install_proto3.sh @@ -17,7 +17,7 @@ # Install protobuf3. # Select protobuf version. -PROTOBUF_VERSION="3.3.0" +PROTOBUF_VERSION="3.6.0" protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g') local_protobuf_ver=$(protoc --version) local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g') diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index 61d34c7304708be3290b116b7e528560ea907031..45a30c6e82c336a0171c7602e09f2184f1459175 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -48,7 +48,7 @@ pip3.5 install --upgrade absl-py pip3.5 install --upgrade six==1.10.0 # Install protobuf. -pip3.5 install --upgrade protobuf==3.3.0 +pip3.5 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* @@ -84,4 +84,11 @@ pip3.5 install --upgrade termcolor # Install last working version of setuptools. pip3.5 install --upgrade setuptools==39.1.0 +# Keras +pip3.5 install keras_applications==1.0.2 +pip3.5 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip3.5 install --upgrade setuptools==39.1.0 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index fe2d2cf11c3a172cb795f91184e474a0d7d15167..d66b2aa18a7d77dd697031cfd2616712d586280a 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -60,7 +60,7 @@ pip3 install --upgrade absl-py pip3 install --upgrade six==1.10.0 # Install protobuf. -pip3 install --upgrade protobuf==3.3.0 +pip3 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* @@ -100,4 +100,8 @@ pip3 install --upgrade termcolor # Install last working version of setuptools. pip3 install --upgrade setuptools==39.1.0 +# Keras +pip3.5 install keras_applications==1.0.2 +pip3.5 install keras_preprocessing==1.0.1 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh b/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh new file mode 100755 index 0000000000000000000000000000000000000000..50ee07e727b309c1370edc993928d7165e9eb6cc --- /dev/null +++ b/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================== + +set -e +set -x + +N_JOBS=$(grep -c ^processor /proc/cpuinfo) + +echo "" +echo "Bazel will use ${N_JOBS} concurrent job(s)." +echo "" + +# Run configure. +export PYTHON_BIN_PATH=`which python2` + +export TF_NEED_CUDA=1 +export TF_CUDA_VERSION=9.0 +export TF_CUDNN_VERSION=7 +export TF_CUDA_COMPUTE_CAPABILITIES=3.7 + +yes "" | $PYTHON_BIN_PATH configure.py + +# Run bazel test command. Double test timeouts to avoid flakes. +# Setting KMP_BLOCKTIME to 0 lets OpenMP threads to sleep right after parallel execution +# in an MKL primitive. This reduces the effects of an oversubscription of OpenMP threads +# caused by executing multiple tests concurrently. +bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test \ + --test_lang_filters=cc,py -k --jobs="${N_JOBS}" \ + --test_timeout 300,450,1200,3600 --build_tests_only --test_env=KMP_BLOCKTIME=0\ + --config=mkl --config=opt --test_output=errors --local_test_jobs=8 \ + --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \ + //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/... + diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh new file mode 100755 index 0000000000000000000000000000000000000000..68354bf7c1cd6717bd0e27dc872703bb723925c4 --- /dev/null +++ b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Usage: basic_mkl_test.sh + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" + +BUILD_TAG=mkl-gpu-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh gpu tensorflow/tools/ci_build/linux/gpu/run_mkl.sh diff --git a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh new file mode 100755 index 0000000000000000000000000000000000000000..ad22ebe4eb304fe6b6f8613f43f2c7c001111503 --- /dev/null +++ b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh @@ -0,0 +1,53 @@ +#!/usr/bin/env bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# Build a whl and container with Intel(R) MKL support +# Usage: build-dev-container.sh + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" + +TF_DOCKER_BUILD_DEVEL_BRANCH=${TF_DOCKER_BUILD_DEVEL_BRANCH:-master} +TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME:-intel-mkl/tensorflow} +TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION:-nightly} + +echo "TF_DOCKER_BUILD_DEVEL_BRANCH=${TF_DOCKER_BUILD_DEVEL_BRANCH}" +echo "TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME}" +echo "TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION}" + +# build the python 2 container and whl +TF_DOCKER_BUILD_TYPE="MKL" \ + TF_DOCKER_BUILD_IS_DEVEL="YES" \ + TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \ + TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ + TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \ + ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh + +# build the python 3 container and whl +TF_DOCKER_BUILD_TYPE="MKL" \ + TF_DOCKER_BUILD_IS_DEVEL="YES" \ + TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \ + TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ + TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \ + TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \ + ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh + diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index b8bce57c87ab39ab2f51288163187f2e87c9135d..3d27e84b81c586729aff21d0859383c24f436a11 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -65,6 +65,10 @@ OPENBLAS_SRC_PATH=/tmp/openblas_src/ sudo rm -rf ${OPENBLAS_SRC_PATH} git clone https://github.com/xianyi/OpenBLAS ${OPENBLAS_SRC_PATH} cd ${OPENBLAS_SRC_PATH} +# The commit after this introduced Fortran compile issues. In theory they should +# be solvable using NOFORTRAN=1 on the make command, but my initial tries didn't +# work, so pinning to the last know good version. +git checkout 5a6a2bed9aff0ba8a18651d5514d029c8cae336a # If this path is changed, you'll also need to update # cxx_builtin_include_directory in third_party/toolchains/cpus/arm/CROSSTOOL.tpl OPENBLAS_INSTALL_PATH=/tmp/openblas_install/ diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py index 00bfcfd49bd1d90dccf094de21173ca9e4307319..642dde36a7caae35df764d5d7513df972e1e5615 100755 --- a/tensorflow/tools/ci_build/update_version.py +++ b/tensorflow/tools/ci_build/update_version.py @@ -37,7 +37,7 @@ SETUP_PY = "%s/tools/pip_package/setup.py" % TF_SRC_DIR README_MD = "./README.md" DEVEL_DOCKERFILE = "%s/tools/docker/Dockerfile.devel" % TF_SRC_DIR GPU_DEVEL_DOCKERFILE = "%s/tools/docker/Dockerfile.devel-gpu" % TF_SRC_DIR -CPU_MKL_DEVEL_DOCKERFILE = "%s/tools/docker/Dockerfile.devel-cpu-mkl" % TF_SRC_DIR +CPU_MKL_DEVEL_DOCKERFILE = "%s/tools/docker/Dockerfile.devel-mkl" % TF_SRC_DIR RELEVANT_FILES = [TF_SRC_DIR, VERSION_H, SETUP_PY, diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh index a3e07737a4fa79de80cf667d058517772db9f103..e10483e7fdc55926d678b157cffbd98b5d57def6 100644 --- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh +++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh @@ -30,10 +30,17 @@ function run_configure_for_gpu_build { yes "" | ./configure } -function set_gcs_remote_cache_options { - echo "build --experimental_remote_spawn_cache" >> "${TMP_BAZELRC}" +function set_remote_cache_options { + echo "build --remote_instance_name=projects/tensorflow-testing-cpu" >> "${TMP_BAZELRC}" echo "build --experimental_remote_platform_override='properties:{name:\"build\" value:\"windows-x64\"}'" >> "${TMP_BAZELRC}" - echo "build --remote_http_cache=https://storage.googleapis.com/$GCS_BUCKET_NAME" >> "${TMP_BAZELRC}" + echo "build --remote_cache=remotebuildexecution.googleapis.com" >> "${TMP_BAZELRC}" + echo "build --tls_enabled=true" >> "${TMP_BAZELRC}" + echo "build --remote_timeout=3600" >> "${TMP_BAZELRC}" + echo "build --auth_enabled=true" >> "${TMP_BAZELRC}" + echo "build --spawn_strategy=remote" >> "${TMP_BAZELRC}" + echo "build --strategy=Javac=remote" >> "${TMP_BAZELRC}" + echo "build --strategy=Closure=remote" >> "${TMP_BAZELRC}" + echo "build --genrule_strategy=remote" >> "${TMP_BAZELRC}" echo "build --google_credentials=$GOOGLE_CLOUD_CREDENTIAL" >> "${TMP_BAZELRC}" } diff --git a/tensorflow/tools/ci_build/windows/bazel/common_env.sh b/tensorflow/tools/ci_build/windows/bazel/common_env.sh index eefa8ee2d504945991c91e1574b6a74330ba3a8d..8a237e4e28376771742ba93b795950d368660196 100644 --- a/tensorflow/tools/ci_build/windows/bazel/common_env.sh +++ b/tensorflow/tools/ci_build/windows/bazel/common_env.sh @@ -49,3 +49,15 @@ export PATH="/c/Program Files/Git/cmd:$PATH" # Make sure we have pip in PATH export PATH="/c/${PYTHON_BASE_PATH}/Scripts:$PATH" + +# Setting default values to CUDA related environment variables +export TF_CUDA_VERSION=${TF_CUDA_VERSION:-9.0} +export TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-7.0} +export TF_CUDA_COMPUTE_CAPABILITIES=${TF_CUDA_COMPUTE_CAPABILITIES:-3.7} +export CUDA_INSTALL_PATH=${CUDA_INSTALL_PATH:-"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${TF_CUDA_VERSION}"} +export CUDNN_INSTALL_PATH=${CUDNN_INSTALL_PATH:-"C:/tools/cuda"} + +# Add Cuda and Cudnn dll directories into PATH +export PATH="$(cygpath -u "${CUDA_INSTALL_PATH}")/bin:$PATH" +export PATH="$(cygpath -u "${CUDA_INSTALL_PATH}")/extras/CUPTI/libx64:$PATH" +export PATH="$(cygpath -u "${CUDNN_INSTALL_PATH}")/bin:$PATH" diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 0b13b97209fa6cd6c629a64fdd54a0423535a9a3..ed7340146789078bf12fc3bbfba46fb0f740ba54 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -59,8 +59,8 @@ release_build=0 for ARG in "$@"; do if [[ "$ARG" == --skip_test ]]; then skip_test=1 - elif [[ "$ARG" == --enable_gcs_remote_cache ]]; then - set_gcs_remote_cache_options + elif [[ "$ARG" == --enable_remote_cache ]]; then + set_remote_cache_options elif [[ "$ARG" == --release_build ]]; then release_build=1 fi @@ -77,7 +77,12 @@ fi # to distinct them. This helps avoid building the same targets twice. echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" -echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +# Enable short object file path to avoid long path issue on Windows. +echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" + +if ! grep -q "import %workspace%/${TMP_BAZELRC}" .bazelrc; then + echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +fi run_configure_for_cpu_build diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index 922bb67bbf6ce34f55acad6d3399bd810032abd0..fe3bce428fb2feb053cb1b8c097f707dd2762a20 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -42,9 +42,58 @@ source "tensorflow/tools/ci_build/windows/bazel/common_env.sh" \ source "tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh" \ || { echo "Failed to source bazel_test_lib.sh" >&2; exit 1; } +# Recreate an empty bazelrc file under source root +export TMP_BAZELRC=.tmp.bazelrc +rm -f "${TMP_BAZELRC}" +touch "${TMP_BAZELRC}" + +function cleanup { + # Remove all options in .tmp.bazelrc + echo "" > "${TMP_BAZELRC}" +} +trap cleanup EXIT + +skip_test=0 +release_build=0 + +for ARG in "$@"; do + if [[ "$ARG" == --skip_test ]]; then + skip_test=1 + elif [[ "$ARG" == --enable_remote_cache ]]; then + set_remote_cache_options + elif [[ "$ARG" == --release_build ]]; then + release_build=1 + fi +done + +if [[ "$release_build" != 1 ]]; then + # --define=override_eigen_strong_inline=true speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc + # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 + # Because this hurts the performance of TF, we don't enable it in release build. + echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}" +fi + +# The host and target platforms are the same in Windows build. So we don't have +# to distinct them. This helps avoid building the same targets twice. +echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" + +# Enable short object file path to avoid long path issue on Windows. +echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" + +# Disable nvcc warnings to reduce log file size. +echo "build --copt=-nvcc_options=disable-warnings" >> "${TMP_BAZELRC}" + +if ! grep -q "import %workspace%/${TMP_BAZELRC}" .bazelrc; then + echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +fi + run_configure_for_gpu_build -bazel build -c opt tensorflow/tools/pip_package:build_pip_package || exit $? +bazel build --announce_rc --config=opt tensorflow/tools/pip_package:build_pip_package || exit $? + +if [[ "$skip_test" == 1 ]]; then + exit 0 +fi # Create a python test directory to avoid package name conflict PY_TEST_DIR="py_test_dir" @@ -59,8 +108,11 @@ reinstall_tensorflow_pip ${PIP_NAME} # Define no_tensorflow_py_deps=true so that every py_test has no deps anymore, # which will result testing system installed tensorflow # GPU tests are very flaky when running concurrently, so set local_test_jobs=1 -bazel test -c opt -k --test_output=errors \ +bazel test --announce_rc --config=opt -k --test_output=errors \ --define=no_tensorflow_py_deps=true --test_lang_filters=py \ - --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,no_oss \ - --build_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,no_oss \ - --local_test_jobs=1 --build_tests_only //${PY_TEST_DIR}/tensorflow/python/... + --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,-no_oss \ + --build_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,-no_oss --build_tests_only \ + --local_test_jobs=1 --test_timeout="300,450,1200,3600" \ + --flaky_test_attempts=3 \ + //${PY_TEST_DIR}/tensorflow/python/... \ + //${PY_TEST_DIR}/tensorflow/contrib/... diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index 583d1d5f09527861015458c636af2259b34d45f8..fdbd1120b20ea4461a4ec5f84c666d8b62309905 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt --copt=/arch:AVX \ +bazel --output_user_root=${TMPDIR} build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl deleted file mode 100644 index 6796ad70e5d22ca683343680b142081d8d58a9e4..0000000000000000000000000000000000000000 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ /dev/null @@ -1,83 +0,0 @@ -FROM tensorflow/tensorflow:latest-devel - -LABEL maintainer="Clayne Robison" - -# These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.9 -ARG WHL_DIR=/whl - -RUN apt-get update && apt-get install -y --no-install-recommends \ - golang \ - vim \ - emacs \ - && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* - -RUN pip --no-cache-dir install --upgrade \ - pip setuptools - -RUN pip --no-cache-dir install wheel - -# Download and build TensorFlow. -WORKDIR / -RUN rm -rf tensorflow && \ - git clone https://github.com/tensorflow/tensorflow.git && \ - cd tensorflow && \ - git checkout ${TF_BRANCH} -WORKDIR /tensorflow - -# Configure the build for CPU with MKL by accepting default build options and -# setting library locations -ENV CI_BUILD_PYTHON=python \ - LD_LIBRARY_PATH=${LD_LIBRARY_PATH} \ - PYTHON_BIN_PATH=/usr/bin/python \ - PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \ - CC_OPT_FLAGS='-march=native' \ - TF_NEED_JEMALLOC=0 \ - TF_NEED_GCP=1 \ - TF_NEED_CUDA=0 \ - TF_NEED_HDFS=0 \ - TF_NEED_S3=1 \ - TF_NEED_OPENCL=0 \ - TF_NEED_GDR=0 \ - TF_ENABLE_XLA=0 \ - TF_NEED_VERBS=0 \ - TF_NEED_MPI=0 -RUN ./configure - -# Build and Install TensorFlow. -# The 'mkl' option builds with Intel(R) Math Kernel Library (MKL), which detects -# the platform it is currently running on and takes appropriately optimized -# paths. The -march=native option is for code that is not in MKL, and assumes -# this container will be run on the same architecture on which it is built. -RUN LD_LIBRARY_PATH=${LD_LIBRARY_PATH} \ - bazel build --config=mkl \ - --config="opt" \ - --copt="-march=broadwell" \ - --copt="-O3" \ - //tensorflow/tools/pip_package:build_pip_package && \ - mkdir ${WHL_DIR} && \ - bazel-bin/tensorflow/tools/pip_package/build_pip_package ${WHL_DIR} - -# Clean up Bazel cache when done, but leave the whl. -# This will upgrade the default Tensorflow version with the Intel MKL version -RUN pip --no-cache-dir install --upgrade ${WHL_DIR}/tensorflow-*.whl && \ - rm -rf /root/.cache - -WORKDIR /root - -#add welcome message with instructions - -RUN echo '[ ! -z "$TERM" -a -r /etc/motd ] && cat /etc/issue && cat /etc/motd' \ - >> /etc/bash.bashrc \ - ; echo "\ -||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\ -| \n\ -| Docker container running Ubuntu \n\ -| with TensorFlow ${TF_BRANCH} optimized for CPU \n\ -| with Intel(R) MKL \n\ -| \n\ -||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\ -\n "\ - > /etc/motd diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl new file mode 100755 index 0000000000000000000000000000000000000000..6dca0e393fa8d61ec819a5f9b5a2e5ffd3c7be92 --- /dev/null +++ b/tensorflow/tools/docker/Dockerfile.devel-mkl @@ -0,0 +1,128 @@ +FROM ubuntu:16.04 + +LABEL maintainer="Clayne Robison " + +# These parameters can be overridden by parameterized_docker_build.sh +ARG TF_BUILD_VERSION=r1.9 +ARG PYTHON="python" +ARG PYTHON3_DEV="" +ARG WHL_DIR="/tmp/pip" +ARG PIP="pip" + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + curl \ + git \ + libcurl3-dev \ + libfreetype6-dev \ + libhdf5-serial-dev \ + libpng12-dev \ + libzmq3-dev \ + pkg-config \ + python-dev \ + ${PYTHON3_DEV} \ + rsync \ + software-properties-common \ + unzip \ + zip \ + zlib1g-dev \ + openjdk-8-jdk \ + openjdk-8-jre-headless \ + && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \ + ${PYTHON} get-pip.py && \ + rm get-pip.py + +RUN ${PIP} --no-cache-dir install \ + Pillow \ + h5py \ + ipykernel \ + jupyter \ + matplotlib \ + mock \ + numpy \ + scipy \ + sklearn \ + pandas \ + && \ + ${PYTHON} -m ipykernel.kernelspec + +RUN if [ "${PYTHON}" = "python3" ]; then \ + ln -s -f /usr/bin/python3 /usr/bin/python; \ + fi + +# Set up our notebook config. +COPY jupyter_notebook_config.py /root/.jupyter/ + +# Jupyter has issues with being run directly: +# https://github.com/ipython/ipython/issues/7062 +# We just add a little wrapper script. +COPY run_jupyter.sh / + +# Set up Bazel. + +# Running bazel inside a `docker build` command causes trouble, cf: +# https://github.com/bazelbuild/bazel/issues/134 +# The easiest solution is to set up a bazelrc file forcing --batch. +RUN echo "startup --batch" >>/etc/bazel.bazelrc +# Similarly, we need to workaround sandboxing issues: +# https://github.com/bazelbuild/bazel/issues/418 +RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ + >>/etc/bazel.bazelrc +# Install the most recent bazel release. +ENV BAZEL_VERSION 0.11.0 +WORKDIR / +RUN mkdir /bazel && \ + cd /bazel && \ + curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \ + curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \ + chmod +x bazel-*.sh && \ + ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \ + cd / && \ + rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh + +# Download and build TensorFlow. +WORKDIR /tensorflow + +# Download and build TensorFlow. +# Enable checking out both tags and branches +RUN export TAG_PREFIX="v" && \ + echo ${TF_BUILD_VERSION} | grep -q ^${TAG_PREFIX}; \ + if [ $? -eq 0 ]; then \ + git clone --depth=1 https://github.com/tensorflow/tensorflow.git . && \ + git fetch --tags && \ + git checkout ${TF_BUILD_VERSION}; \ + else \ + git clone --depth=1 --branch=${TF_BUILD_VERSION} https://github.com/tensorflow/tensorflow.git . ; \ + fi + +RUN yes "" | ${PYTHON} configure.py + +ENV CI_BUILD_PYTHON ${PYTHON} + +# Set bazel build parameters in .bazelrc in parameterized_docker_build.sh +# Use --copt=-march values to get optimized builds appropriate for the hardware +# platform of your choice. +# For ivy-bridge or sandy-bridge +# --copt=-march="avx" \ +# For haswell, broadwell, or skylake +# --copt=-march="avx2" \ +COPY .bazelrc /root/.bazelrc + +RUN tensorflow/tools/ci_build/builds/configured CPU \ + bazel --bazelrc=/root/.bazelrc build -c opt \ + tensorflow/tools/pip_package:build_pip_package && \ + bazel-bin/tensorflow/tools/pip_package/build_pip_package "${WHL_DIR}" && \ + ${PIP} --no-cache-dir install --upgrade "${WHL_DIR}"/tensorflow-*.whl && \ + rm -rf /root/.cache +# Clean up Bazel cache when done. + +# TensorBoard +EXPOSE 6006 +# IPython +EXPOSE 8888 + +WORKDIR /root diff --git a/tensorflow/tools/docker/Dockerfile.mkl b/tensorflow/tools/docker/Dockerfile.mkl new file mode 100755 index 0000000000000000000000000000000000000000..139395d49102fe2de3e241936095613da3f21bf8 --- /dev/null +++ b/tensorflow/tools/docker/Dockerfile.mkl @@ -0,0 +1,75 @@ +FROM ubuntu:16.04 + +LABEL maintainer="Clayne Robison " + +# This parameter MUST be set by parameterized_docker_build.sh +ARG TF_WHL_URL + +# Optional parameters +ARG TF_BUILD_VERSION=r1.9 +ARG PYTHON="python" +ARG PYTHON_DEV="python-dev" +ARG PIP="pip" + +# Pick up some TF dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + curl \ + libfreetype6-dev \ + libhdf5-serial-dev \ + libpng12-dev \ + libzmq3-dev \ + pkg-config \ + python \ + ${PYTHON_DEV} \ + rsync \ + software-properties-common \ + unzip \ + && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -O https://bootstrap.pypa.io/get-pip.py && \ + python get-pip.py && \ + rm get-pip.py + +RUN ${PIP} --no-cache-dir install \ + Pillow \ + h5py \ + ipykernel \ + jupyter \ + matplotlib \ + numpy \ + pandas \ + scipy \ + sklearn \ + && \ + python -m ipykernel.kernelspec + +COPY ${TF_WHL_URL} / +RUN ${PIP} install --no-cache-dir --force-reinstall /${TF_WHL_URL} && \ + rm -rf /${TF_WHL_URL} + +RUN if [ "${PYTHON}" = "python3" ]; then \ + ln -s -f /usr/bin/python3 /usr/bin/python; \ + fi + +# Set up our notebook config. +COPY jupyter_notebook_config.py /root/.jupyter/ + +# Copy sample notebooks. +COPY notebooks /notebooks + +# Jupyter has issues with being run directly: +# https://github.com/ipython/ipython/issues/7062 +# We just add a little wrapper script. +COPY run_jupyter.sh / + +# TensorBoard +EXPOSE 6006 +# IPython +EXPOSE 8888 + +WORKDIR "/notebooks" + +CMD ["/run_jupyter.sh", "--allow-root"] diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh index 05de25f2cb11d76f223a31bc12329e6ab7368e8a..4681c5fd61158e0be998d72bb4329f204808eda7 100755 --- a/tensorflow/tools/docker/parameterized_docker_build.sh +++ b/tensorflow/tools/docker/parameterized_docker_build.sh @@ -19,8 +19,8 @@ # parameterized_docker_build.sh # # The script obeys the following environment variables: -# TF_DOCKER_BUILD_TYPE: (CPU | GPU) -# CPU or GPU image +# TF_DOCKER_BUILD_TYPE: (CPU | GPU | MKL) +# CPU, GPU, or MKL image # # TF_DOCKER_BUILD_IS_DEVEL: (NO | YES) # Is this developer image @@ -87,6 +87,15 @@ # TF_DOCKER_BUILD_OPTIONS # (Optional) # Specifies the desired build options. Defaults to OPT. +# +# TF_DOCKER_BUILD_ARGS +# (Optional) +# A list (array) of docker build args. Will be passed to docker build +# command as list of --build-arg parameters. +# +# TF_BAZEL_BUILD_OPTIONS +# (Optional) +# Bazel compiler flags to be passed to the bazelrc file # Script directory SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" @@ -116,6 +125,8 @@ echo " TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME}" echo " TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION}" echo " TF_DOCKER_BUILD_PORT=${TF_DOCKER_BUILD_PORT}" echo " TF_DOCKER_BUILD_PUSH_CMD=${TF_DOCKER_BUILD_PUSH_CMD}" +echo " TF_DOCKER_BUILD_ARGS=${TF_DOCKER_BUILD_ARGS[@]:-()}" +echo " TF_BAZEL_BUILD_OPTIONS=${TF_BAZEL_BUILD_OPTIONS}" CONTAINER_PORT=${TF_DOCKER_BUILD_PORT:-8888} @@ -149,6 +160,15 @@ fi if [[ ${TF_DOCKER_BUILD_TYPE} == "cpu" ]]; then DOCKER_BINARY="docker" +elif [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + DOCKER_BINARY="docker" + FINAL_TAG="${FINAL_TAG}-mkl" + if [[ ${ORIG_DOCKERFILE} == *"."* ]]; then + # There is already a dot in the tag, use "-" + ORIG_DOCKERFILE="${ORIG_DOCKERFILE}-mkl" + else + ORIG_DOCKERFILE="${ORIG_DOCKERFILE}.mkl" + fi elif [[ ${TF_DOCKER_BUILD_TYPE} == "gpu" ]]; then DOCKER_BINARY="nvidia-docker" @@ -203,6 +223,10 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then export TF_BUILD_OPTIONS=${TF_DOCKER_BUILD_OPTIONS} export TF_BUILD_IS_PIP="PIP" + if [[ "${TF_DOCKER_BUILD_TYPE}" == "mkl" ]]; then + die "FAIL: Non-development MKL builds require a pre-built pip whl." + fi + if [[ "${TF_DOCKER_BUILD_TYPE}" == "gpu" ]]; then export TF_BUILD_APPEND_CI_DOCKER_EXTRA_PARAMS=\ "${TF_BUILD_APPEND_CI_DOCKER_EXTRA_PARAMS} -e TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2" @@ -255,25 +279,39 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then # Use string replacement to put the correct file name into the Dockerfile PIP_WHL=$(basename "${PIP_WHL}") - # Modify the non-devel Dockerfile to point to the correct pip whl file - # location - sed -e "/# --- DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/,"\ + if [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + TF_DOCKER_BUILD_ARGS+=("--build-arg TF_WHL_URL=${PIP_WHL}" ) + cp "${ORIG_DOCKERFILE}" "${DOCKERFILE}" + else + # Modify the non-devel Dockerfile to point to the correct pip whl file + # location + sed -e "/# --- DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/,"\ "/# --- ~ DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/c"\ "COPY ${PIP_WHL} /\n"\ "RUN pip --no-cache-dir install /${PIP_WHL}" "${ORIG_DOCKERFILE}" \ - > "${DOCKERFILE}" + > "${DOCKERFILE}" + fi echo "Using local pip wheel from: ${TF_DOCKER_BUILD_CENTRAL_PIP}" echo - else echo "Downloading pip wheel from: ${TF_DOCKER_BUILD_CENTRAL_PIP}" - echo - - # Modify the non-devel Dockerfile to point to the correct pip whl URL. - sed -e "/# --- DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/,"\ + if [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + pushd "${TMP_DIR}/" + curl -O ${TF_DOCKER_BUILD_CENTRAL_PIP} + popd + PIP_WHL_PATH=`find ${TMP_DIR} -name "*.whl"` + PIP_WHL=$(basename "${PIP_WHL_PATH}") + echo "PIP_WHL= ${PIP_WHL}" + echo + TF_DOCKER_BUILD_ARGS+=("--build-arg TF_WHL_URL=${PIP_WHL}") + cp "${ORIG_DOCKERFILE}" "${DOCKERFILE}" + else + # Modify the non-devel Dockerfile to point to the correct pip whl URL. + sed -e "/# --- DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/,"\ "/# --- ~ DO NOT EDIT OR DELETE BETWEEN THE LINES --- #/c"\ "RUN pip --no-cache-dir install ${TF_DOCKER_BUILD_CENTRAL_PIP}" "${ORIG_DOCKERFILE}" \ - > "${DOCKERFILE}" + > "${DOCKERFILE}" + fi fi echo "Modified Dockerfile at: ${DOCKERFILE}" @@ -281,36 +319,66 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then # Modify python/pip version if necessary. if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then - if sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \ - sed -i -e 's/python-dev/python3-dev/g' "${DOCKERFILE}" && \ - sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \ - sed -i -e 's^# RUN ln -s -f /usr/bin/python3 /usr/bin/python#^RUN ln -s -f /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" - then - echo "Modified Dockerfile for python version "\ -"${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" + if [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + TF_DOCKER_BUILD_ARGS+=("--build-arg PYTHON=${TF_DOCKER_BUILD_PYTHON_VERSION}") + TF_DOCKER_BUILD_ARGS+=("--build-arg PYTHON_DEV=python3-dev") + TF_DOCKER_BUILD_ARGS+=("--build-arg PIP=pip3") + cp "${ORIG_DOCKERFILE}" "${DOCKERFILE}" else - die "FAILED to modify ${DOCKERFILE} for python3" + if sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \ + sed -i -e 's/python-dev/python3-dev/g' "${DOCKERFILE}" && \ + sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \ + sed -i -e 's^# RUN ln -s -f /usr/bin/python3 /usr/bin/python#^RUN ln -s -f /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" + then + echo "Modified Dockerfile for python version "\ + "${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" + else + die "FAILED to modify ${DOCKERFILE} for python3" + fi fi fi -else +else # TF_DOCKER_BUILD_IS_DEVEL == 'yes' DOCKERFILE="${TMP_DIR}/Dockerfile" - # Modify the devel Dockerfile to specify the git branch - sed "s/^RUN git clone --branch=.* --depth=1/RUN git clone --branch=${TF_DOCKER_BUILD_DEVEL_BRANCH} --depth=1/" \ - "${ORIG_DOCKERFILE}" > "${DOCKERFILE}" + # Set up Dockerfile ARGS for mkl build + if [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + if [[ -z "${TF_BAZEL_BUILD_OPTIONS// }" ]]; then + TF_BAZEL_BUILD_OPTIONS=("--config=mkl --copt=-mavx --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0") + else + TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" + fi + TF_DOCKER_BUILD_ARGS+=("--build-arg TF_BUILD_VERSION=${TF_DOCKER_BUILD_DEVEL_BRANCH}") + echo "TF_DOCKER_BUILD_ARGS=${TF_DOCKER_BUILD_ARGS[@]}" + + # Pass the build options to bazel using the user-specific .bazelrc file + echo "build ${TF_BAZEL_BUILD_OPTIONS}" >> ${TMP_DIR}/.bazelrc + cp "${ORIG_DOCKERFILE}" "${DOCKERFILE}" + else + # Modify the devel Dockerfile to specify the git branch + sed "s/^RUN git clone --branch=.* --depth=1/RUN git clone --branch=${TF_DOCKER_BUILD_DEVEL_BRANCH} --depth=1/" \ + "${ORIG_DOCKERFILE}" > "${DOCKERFILE}" + fi # Modify python/pip version if necessary. if [[ "${TF_DOCKER_BUILD_PYTHON_VERSION}" == "python3" ]]; then - if sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \ - sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \ - sed -i -e 's^/tmp/pip^/tmp/pip3^g' "${DOCKERFILE}" && \ - sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \ - sed -i -e 's/ENV CI_BUILD_PYTHON python/ENV CI_BUILD_PYTHON python3/g' "${DOCKERFILE}" && \ - sed -i -e 's^# RUN ln -s -f /usr/bin/python3 /usr/bin/python#^RUN ln -s -f /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" - then - echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" + if [[ ${TF_DOCKER_BUILD_TYPE} == "mkl" ]]; then + TF_DOCKER_BUILD_ARGS+=("--build-arg PYTHON=${TF_DOCKER_BUILD_PYTHON_VERSION}") + TF_DOCKER_BUILD_ARGS+=("--build-arg PYTHON3_DEV=python3-dev") + TF_DOCKER_BUILD_ARGS+=("--build-arg WHL_DIR=/tmp/pip3") + TF_DOCKER_BUILD_ARGS+=("--build-arg PIP=pip3") + cp "${ORIG_DOCKERFILE}" "${DOCKERFILE}" else - die "FAILED to modify ${DOCKERFILE} for python3" + if sed -i -e 's/python-dev/python-dev python3-dev/g' "${DOCKERFILE}" && \ + sed -i -e 's/python /python3 /g' "${DOCKERFILE}" && \ + sed -i -e 's^/tmp/pip^/tmp/pip3^g' "${DOCKERFILE}" && \ + sed -i -e 's/pip /pip3 /g' "${DOCKERFILE}" && \ + sed -i -e 's/ENV CI_BUILD_PYTHON python/ENV CI_BUILD_PYTHON python3/g' "${DOCKERFILE}" && \ + sed -i -e 's^# RUN ln -s -f /usr/bin/python3 /usr/bin/python#^RUN ln -s -f /usr/bin/python3 /usr/bin/python^' "${DOCKERFILE}" + then + echo "Modified Dockerfile further for python version ${TF_DOCKER_BUILD_PYTHON_VERSION} at: ${DOCKERFILE}" + else + die "FAILED to modify ${DOCKERFILE} for python3" + fi fi fi fi @@ -319,8 +387,11 @@ fi # Intermediate image name with tag IMG="${USER}/tensorflow:${FINAL_TAG}" echo "Building docker image with image name and tag: ${IMG}" +echo "TF_DOCKER_BUILD_ARGS=${TF_DOCKER_BUILD_ARGS[@]}" +CMD="${DOCKER_BINARY} build ${TF_DOCKER_BUILD_ARGS[@]} --no-cache --pull -t ${IMG} -f ${DOCKERFILE} ${TMP_DIR}" +echo "CMD=${CMD}" +${CMD} -"${DOCKER_BINARY}" build --no-cache --pull -t "${IMG}" -f "${DOCKERFILE}" "${TMP_DIR}" if [[ $? == "0" ]]; then echo "${DOCKER_BINARY} build of ${IMG} succeeded" else @@ -340,7 +411,7 @@ fi DOCKER_RUN_LOG="${TMP_DIR}/docker_run.log" echo "" echo "Running docker container from image ${IMG}..." -echo " (Log file is at: ${DOCKER_RUN_LOG}" +echo " Log file is at: ${DOCKER_RUN_LOG}" echo "" if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then @@ -386,7 +457,6 @@ if [[ "${TF_DOCKER_BUILD_IS_DEVEL}" == "no" ]]; then # Stop the running docker container sleep 1 "${DOCKER_BINARY}" stop --time=0 ${CONTAINER_ID} - fi diff --git a/tensorflow/tools/docs/BUILD b/tensorflow/tools/docs/BUILD index 58b5ef8345c9de83e2d50cd01fe11e11f51fe298..2403e2d966929b86976bf6a31f8144d9b4f58bc6 100644 --- a/tensorflow/tools/docs/BUILD +++ b/tensorflow/tools/docs/BUILD @@ -37,7 +37,11 @@ py_library( srcs = ["parser.py"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = ["@astor_archive//:astor"], + deps = [ + "//tensorflow/python:platform", + "//tensorflow/python:util", + "@astor_archive//:astor", + ], ) py_test( @@ -92,6 +96,7 @@ py_binary( deps = [ ":generate_lib", "//tensorflow:tensorflow_py", + "//tensorflow/python:util", "//tensorflow/python/debug:debug_py", ], ) diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index 853ec6194f8327f13b3eb6ac7792511c9c4494cd..e7634cd5dcf19d5f21b0bd42b282dfe928659a52 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -21,6 +21,7 @@ from __future__ import print_function import argparse import fnmatch import os +import shutil import six @@ -81,12 +82,8 @@ def write_docs(output_dir, raise ValueError("'output_dir' must be an absolute path.\n" " output_dir='%s'" % output_dir) - try: - if not os.path.exists(output_dir): - os.makedirs(output_dir) - except OSError as e: - print('Creating output dir "%s" failed: %s' % (output_dir, e)) - raise + if not os.path.exists(output_dir): + os.makedirs(output_dir) # These dictionaries are used for table-of-contents generation below # They will contain, after the for-loop below:: @@ -129,8 +126,6 @@ def write_docs(output_dir, module_children.setdefault(subname, []).append(full_name) break - print('Writing docs for %s (%r).' % (full_name, py_object)) - # Generate docs for `py_object`, resolving references. page_info = parser.docs_for_object(full_name, py_object, parser_config) @@ -151,10 +146,9 @@ def write_docs(output_dir, text = text.encode('utf-8') with open(path, 'wb') as f: f.write(text) - except OSError as e: - print('Cannot write documentation for %s to %s: %s' % (full_name, - directory, e)) - raise + except OSError: + raise OSError( + 'Cannot write documentation for %s to %s' % (full_name, directory)) if yaml_toc: # Generate table of contents @@ -394,16 +388,40 @@ def _build_guide_index(guide_src_dir): class _UpdateTags(py_guide_parser.PyGuideParser): - """Rewrites a Python guide so that each section has an explicit tag.""" + """Rewrites a Python guide so that each section has an explicit id tag. + + "section" here refers to blocks delimited by second level headings. + """ def process_section(self, line_number, section_title, tag): self.replace_line(line_number, '

%s

' % (tag, section_title)) +def update_id_tags_inplace(src_dir): + """Set explicit ids on all second-level headings to ensure back-links work. + + Args: + src_dir: The directory of md-files to convert (inplace). + """ + tag_updater = _UpdateTags() + + for dirpath, _, filenames in os.walk(src_dir): + for base_name in filenames: + if not base_name.endswith('.md'): + continue + full_path = os.path.join(src_dir, dirpath, base_name) + + # Tag updater loads the file, makes the replacements, and returns the + # modified file contents + content = tag_updater.process(full_path) + with open(full_path, 'w') as f: + f.write(content) + + EXCLUDED = set(['__init__.py', 'OWNERS', 'README.txt']) -def _other_docs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): +def replace_refs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): """Fix @{} references in all files under `src_dir` matching `file_pattern`. A matching directory structure, with the modified files is @@ -424,7 +442,6 @@ def _other_docs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): using fnmatch. Non-matching files are copied unchanged. """ # Iterate through all the source files and process them. - tag_updater = _UpdateTags() for dirpath, _, filenames in os.walk(src_dir): # How to get from `dirpath` to api_docs/python/ relative_path_to_root = os.path.relpath( @@ -433,41 +450,32 @@ def _other_docs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): # Make the directory under output_dir. new_dir = os.path.join(output_dir, os.path.relpath(path=dirpath, start=src_dir)) - try: - if not os.path.exists(new_dir): - os.makedirs(new_dir) - except OSError as e: - print('Creating output dir "%s" failed: %s' % (new_dir, e)) - raise + if not os.path.exists(new_dir): + os.makedirs(new_dir) for base_name in filenames: if base_name in EXCLUDED: - print('Skipping excluded file %s...' % base_name) continue full_in_path = os.path.join(dirpath, base_name) + # Set the `current_doc_full_name` so bad files can be reported on errors. reference_resolver.current_doc_full_name = full_in_path suffix = os.path.relpath(path=full_in_path, start=src_dir) full_out_path = os.path.join(output_dir, suffix) + # Copy files that do not match the file_pattern, unmodified. if not fnmatch.fnmatch(base_name, file_pattern): - print('Copying un-matched file %s...' % suffix) - open(full_out_path, 'wb').write(open(full_in_path, 'rb').read()) + shutil.copyfile(full_in_path, full_out_path) continue - if dirpath.endswith('/api_guides/python'): - print('Processing Python guide %s...' % base_name) - content = tag_updater.process(full_in_path) - else: - print('Processing doc %s...' % suffix) - content = open(full_in_path, 'rb').read().decode('utf-8') + + with open(full_in_path, 'rb') as f: + content = f.read().decode('utf-8') content = reference_resolver.replace_references(content, relative_path_to_root) with open(full_out_path, 'wb') as f: f.write(content.encode('utf-8')) - print('Done.') - class DocGenerator(object): """Main entry point for generating docs.""" @@ -554,15 +562,43 @@ class DocGenerator(object): self._do_not_descend_map) def build(self, flags): - """Actually build the docs.""" + """Build all the docs. + + This produces two outputs + + python api docs: + + * generated from modules set with `set_py_modules`. + * written to '{FLAGS.output_dir}/api_docs/python/' + + non-api docs: + + * Everything in '{FLAGS.src_dir}' is copied to '{FLAGS.output_dir}'. + * '@{}' references in '.md' files are replaced with links. + * '.md' files under 'api_guides/python' have explicit ids set for their + second level headings. + + Args: + flags: + * src_dir: Where to fetch the non-api-docs. + * base_dir: Base of the docs directory (Used to build correct + relative links). + * output_dir: Where to write the resulting docs. + + Returns: + The number of errors encountered while processing. + """ + # Extract the python api from the _py_modules doc_index = build_doc_index(flags.src_dir) visitor = self.run_extraction() reference_resolver = self.make_reference_resolver(visitor, doc_index) + # Build the guide_index for the api_docs back links. root_title = getattr(flags, 'root_title', 'TensorFlow') guide_index = _build_guide_index( os.path.join(flags.src_dir, 'api_guides/python')) + # Write the api docs. parser_config = self.make_parser_config(visitor, reference_resolver, guide_index, flags.base_dir) output_dir = os.path.join(flags.output_dir, 'api_docs/python') @@ -573,8 +609,16 @@ class DocGenerator(object): yaml_toc=self.yaml_toc, root_title=root_title, search_hints=getattr(flags, 'search_hints', True)) - _other_docs(flags.src_dir, flags.output_dir, reference_resolver) + # Replace all the @{} references in files under `FLAGS.src_dir` + replace_refs(flags.src_dir, flags.output_dir, reference_resolver, '*.md') + # Fix the tags in the guide dir. + guide_dir = os.path.join(flags.output_dir, 'api_guides/python') + if os.path.exists(guide_dir): + update_id_tags_inplace(guide_dir) + + # Report all errors found by the reference resolver, and return the error + # code. parser_config.reference_resolver.log_errors() return parser_config.reference_resolver.num_errors() diff --git a/tensorflow/tools/docs/generate_lib_test.py b/tensorflow/tools/docs/generate_lib_test.py index ea6d28a02b1f3c07fe8783fd59e345dade1fc804..7a6f9fd9f799db5a14015d77e5297955c76a51cd 100644 --- a/tensorflow/tools/docs/generate_lib_test.py +++ b/tensorflow/tools/docs/generate_lib_test.py @@ -51,7 +51,9 @@ class DummyVisitor(object): class GenerateTest(googletest.TestCase): - def test_write(self): + def get_test_objects(self): + # These are all mutable objects, so rebuild them for each test. + # Don't cache the objects. module = sys.modules[__name__] index = { @@ -98,6 +100,11 @@ class GenerateTest(googletest.TestCase): guide_index={}, base_dir=base_dir) + return reference_resolver, parser_config + + def test_write(self): + _, parser_config = self.get_test_objects() + output_dir = googletest.GetTempDir() generate_lib.write_docs(output_dir, parser_config, yaml_toc=True) @@ -127,6 +134,107 @@ class GenerateTest(googletest.TestCase): os.path.exists( os.path.join(output_dir, 'tf/TestModule/test_function.md'))) + def test_update_id_tags_inplace(self): + test_dir = googletest.GetTempDir() + test_sub_dir = os.path.join(test_dir, 'a/b') + os.makedirs(test_sub_dir) + + test_path1 = os.path.join(test_dir, 'file1.md') + test_path2 = os.path.join(test_sub_dir, 'file2.md') + test_path3 = os.path.join(test_sub_dir, 'file3.notmd') + + with open(test_path1, 'w') as f: + f.write('## abc&123') + + with open(test_path2, 'w') as f: + f.write('# A Level 1 Heading\n') + f.write('## A Level 2 Heading') + + with open(test_path3, 'w') as f: + f.write("## don\'t change this") + + generate_lib.update_id_tags_inplace(test_dir) + + with open(test_path1) as f: + content = f.read() + + self.assertEqual(content, '

abc&123

') + + with open(test_path2) as f: + content = f.read() + + self.assertEqual( + content, '# A Level 1 Heading\n' + '

A Level 2 Heading

') + + with open(test_path3) as f: + content = f.read() + + self.assertEqual(content, "## don\'t change this") + + def test_replace_refes(self): + test_dir = googletest.GetTempDir() + test_in_dir = os.path.join(test_dir, 'in') + test_in_dir_a = os.path.join(test_dir, 'in/a') + test_in_dir_b = os.path.join(test_dir, 'in/b') + os.makedirs(test_in_dir) + os.makedirs(test_in_dir_a) + os.makedirs(test_in_dir_b) + + test_out_dir = os.path.join(test_dir, 'out') + os.makedirs(test_out_dir) + + test_path1 = os.path.join(test_in_dir_a, 'file1.md') + test_path2 = os.path.join(test_in_dir_b, 'file2.md') + test_path3 = os.path.join(test_in_dir_b, 'file3.notmd') + test_path4 = os.path.join(test_in_dir_b, 'OWNERS') + + with open(test_path1, 'w') as f: + f.write('Use `tf.test_function` to test things.') + + with open(test_path2, 'w') as f: + f.write('Use @{tf.TestModule.TestClass.ChildClass} to test things.\n' + "`tf.whatever` doesn't exist") + + with open(test_path3, 'w') as f: + file3_content = ( + 'Not a .md file. Should be copied unchanged:' + '@{tf.TestModule.TestClass.ChildClass}, `tf.test_function`') + f.write(file3_content) + + with open(test_path4, 'w') as f: + f.write('') + + reference_resolver, _ = self.get_test_objects() + generate_lib.replace_refs(test_in_dir, test_out_dir, reference_resolver, + '*.md') + + with open(os.path.join(test_out_dir, 'a/file1.md')) as f: + content = f.read() + self.assertEqual( + content, + 'Use ' + 'tf.test_function to test things.') + + with open(os.path.join(test_out_dir, 'b/file2.md')) as f: + content = f.read() + self.assertEqual( + content, + 'Use ' + '' + 'tf.TestModule.TestClass.ChildClass ' + 'to test things.\n' + '`tf.whatever` doesn\'t exist') + + with open(os.path.join(test_out_dir, 'b/file3.notmd')) as f: + content = f.read() + self.assertEqual(content, file3_content) + + with self.assertRaises(IOError): + # This should fail. The OWNERS file should not be copied + with open(os.path.join(test_out_dir, 'b/OWNERS')) as f: + content = f.read() + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py index 50c90527413d0904c78dab199a68678f6cc91845..ffb93027ed48dd2106c702758917c0846f20cb1c 100644 --- a/tensorflow/tools/docs/parser.py +++ b/tensorflow/tools/docs/parser.py @@ -25,12 +25,12 @@ import itertools import json import os import re -import sys import astor import six from google.protobuf.message import Message as ProtoMessage +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect @@ -53,7 +53,7 @@ class _Errors(object): template = 'ERROR:\n output file name: %s\n %s\n\n' for full_name, message in self._errors: - print(template % (full_name, message), file=sys.stderr) + logging.warn(template, full_name, message) def append(self, full_name, message): """Add an error to the collection. @@ -761,8 +761,9 @@ def _generate_signature(func, reverse_index): lookup_text = public_name + default_text[len(internal_name):] break if default_text is lookup_text: - print('WARNING: Using default arg, failed lookup: %s, repr: %r' % - (default_text, default)) + logging.warn( + 'WARNING: Using default arg, failed lookup: %s, repr: %r', + default_text, default) else: default_text = lookup_text else: @@ -1165,7 +1166,7 @@ class _ClassPageInfo(object): if short_name in [ '__class__', '__base__', '__weakref__', '__doc__', '__module__', '__dict__', '__abstractmethods__', '__slots__', '__getnewargs__', - '__str__', '__repr__', '__hash__' + '__str__', '__repr__', '__hash__', '__reduce__' ]: continue @@ -1213,8 +1214,6 @@ class _ClassPageInfo(object): if not child_doc.brief.strip() and short_name in [ '__del__', '__copy__' ]: - print('Skipping %s, defined in %s, no docstring.' % (child_name, - defining_class)) continue try: @@ -1371,7 +1370,8 @@ class _ModulePageInfo(object): for name in member_names: if name in ['__builtins__', '__doc__', '__file__', - '__name__', '__path__', '__package__']: + '__name__', '__path__', '__package__', + '__cached__', '__loader__', '__spec__']: continue member_full_name = self.full_name + '.' + name if self.full_name else name diff --git a/tensorflow/tools/docs/py_guide_parser.py b/tensorflow/tools/docs/py_guide_parser.py index 328f42d18f1efb0fd82725a4683abad2df0d5a19..b00694dc40322161f180410630bb4dcfd8c2fb18 100644 --- a/tensorflow/tools/docs/py_guide_parser.py +++ b/tensorflow/tools/docs/py_guide_parser.py @@ -44,7 +44,8 @@ class PyGuideParser(object): def process(self, full_path): """Read and process the file at `full_path`.""" - md_string = open(full_path, 'rb').read().decode('utf-8') + with open(full_path, 'rb') as f: + md_string = f.read().decode('utf-8') self._lines = md_string.split('\n') seen = set() diff --git a/tensorflow/tools/git/gen_git_source.py b/tensorflow/tools/git/gen_git_source.py index 73dee98bae8946b747e1b28bd14b0a26edc62736..cc2288a7fa9202efcd077e54b941cc278b25993c 100755 --- a/tensorflow/tools/git/gen_git_source.py +++ b/tensorflow/tools/git/gen_git_source.py @@ -164,14 +164,17 @@ def get_git_version(git_base_path, git_tag_override): "git", str("--git-dir=%s/.git" % git_base_path), str("--work-tree=" + git_base_path), "describe", "--long", "--tags" ]).strip()) - if git_tag_override: + if git_tag_override and val: split_val = val.split("-") - if len(split_val) != 3: + if len(split_val) < 3: raise Exception( ("Expected git version in format 'TAG-COMMITS AFTER TAG-HASH' " "but got '%s'") % val) - split_val[0] = git_tag_override - val = bytes("-".join(split_val)) + # There might be "-" in the tag name. But we can be sure that the final + # two "-" are those inserted by the git describe command. + abbrev_commit = split_val[-1] + val = bytes( + "-".join([git_tag_override, "0", abbrev_commit])) return val if val else unknown_label except (subprocess.CalledProcessError, OSError): return unknown_label diff --git a/tensorflow/tools/graph_transforms/fold_constants_lib.cc b/tensorflow/tools/graph_transforms/fold_constants_lib.cc index 85660f94a85dce29360525f7bb7474494b3f010f..f85841187670fef0fdc9237886237f84057d6bd5 100644 --- a/tensorflow/tools/graph_transforms/fold_constants_lib.cc +++ b/tensorflow/tools/graph_transforms/fold_constants_lib.cc @@ -117,6 +117,31 @@ Status ReplaceSendRecvs(const GraphDef& original_graph_def, return Status::OK(); } +Status RewriteInputsAsPlaceholders(const TransformFuncContext& context, + GraphDef* graph_def) { + std::unordered_set input_names; + for (const string& input_name : context.input_names) { + input_names.insert(ParseTensorName(input_name).first.ToString()); + } + + for (NodeDef& node : *graph_def->mutable_node()) { + if (input_names.find(node.name()) == input_names.end()) { + continue; + } + if (node.op() == "PlaceholderWithDefault") { + node.set_op("Placeholder"); + node.clear_input(); + } else if (node.op() != "Placeholder") { + return errors::InvalidArgument( + "Input '", node.name(), + "' was expected to be a Placeholder or PlaceholderWithDefault op, " + "but was ", + node.op()); + } + } + return Status::OK(); +} + Status RemoveUnusedNodes(const GraphDef& input_graph_def, const TransformFuncContext& context, GraphDef* output_graph_def) { @@ -165,6 +190,7 @@ Status RemoveUnusedNodes(const GraphDef& input_graph_def, input_graph_def, [&](const NodeDef& node) { return used_nodes.count(node.name()) > 0; }, output_graph_def); + TF_RETURN_IF_ERROR(RewriteInputsAsPlaceholders(context, output_graph_def)); return Status::OK(); } diff --git a/tensorflow/tools/graph_transforms/fold_constants_test.cc b/tensorflow/tools/graph_transforms/fold_constants_test.cc index a082399a87dbaad913be421fe273ba89b6f7340e..dcdc3c29069c212c499aa21e420b47f239ce62f2 100644 --- a/tensorflow/tools/graph_transforms/fold_constants_test.cc +++ b/tensorflow/tools/graph_transforms/fold_constants_test.cc @@ -330,48 +330,6 @@ class ConstantFoldingTest : public ::testing::Test { EXPECT_EQ(0, node_map.count("unused")); } - void TestRemoveUnusedNodesMultipleOutputs() { - using namespace ::tensorflow::ops; // NOLINT(build/namespaces) - auto root = tensorflow::Scope::NewRootScope(); - - // a b - // \ / - // shape_n - // \ / - // c - auto a = Placeholder(root.WithOpName("a"), DT_FLOAT); - auto b = Placeholder(root.WithOpName("b"), DT_FLOAT); - auto shape_n = ShapeN(root.WithOpName("shape_n"), {Output(a), Output(b)}); - auto c = Add(root.WithOpName("c"), shape_n[0], shape_n[1]); - - GraphDef graph_def; - TF_ASSERT_OK(root.ToGraphDef(&graph_def)); - GraphDef result_graph_def; - TF_ASSERT_OK(graph_transforms::RemoveUnusedNodes( - graph_def, {{shape_n[0].name()}, {"c"}}, &result_graph_def)); - - // Only one output of shape_n node is fed input. Hence the graph search - // should propagate to inputs of shape_n. Nothing to remove here. - std::map node_map; - graph_transforms::MapNamesToNodes(result_graph_def, &node_map); - EXPECT_EQ(1, node_map.count("a")); - EXPECT_EQ(1, node_map.count("b")); - EXPECT_EQ(1, node_map.count("c")); - - result_graph_def.Clear(); - TF_ASSERT_OK(graph_transforms::RemoveUnusedNodes( - graph_def, {{shape_n[0].name(), shape_n[1].name()}, {"c"}}, - &result_graph_def)); - - // Both outputs of shape_n node are fed inputs. shape_n does not function - // and inputs to shape_n should be removed. - node_map.clear(); - graph_transforms::MapNamesToNodes(result_graph_def, &node_map); - EXPECT_EQ(0, node_map.count("a")); - EXPECT_EQ(0, node_map.count("b")); - EXPECT_EQ(1, node_map.count("c")); - } - void TestMaxConstantSizeInBytes() { auto root = tensorflow::Scope::NewRootScope(); @@ -431,10 +389,6 @@ TEST_F(ConstantFoldingTest, TestReplaceSendRecvsPrefixNames) { TEST_F(ConstantFoldingTest, TestRemoveUnusedNodes) { TestRemoveUnusedNodes(); } -TEST_F(ConstantFoldingTest, TestRemoveUnusedNodesMultipleOutputs) { - TestRemoveUnusedNodesMultipleOutputs(); -} - TEST_F(ConstantFoldingTest, TestMaxConstantSizeInBytes) { TestMaxConstantSizeInBytes(); } diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD index 77f83b77a0214110e520c85d15ffa38bce65955f..173f418dc8d998bc51d208a04c8671bacf364cdc 100644 --- a/tensorflow/tools/lib_package/BUILD +++ b/tensorflow/tools/lib_package/BUILD @@ -115,6 +115,7 @@ genrule( "//third_party/fft2d:LICENSE", "@aws//:LICENSE", "@boringssl//:LICENSE", + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", @@ -130,7 +131,7 @@ genrule( "@highwayhash//:LICENSE", "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", - "@libxsmm_archive//:LICENSE", + "@libxsmm_archive//:LICENSE.md", "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", @@ -156,6 +157,7 @@ genrule( "//third_party/fft2d:LICENSE", "@aws//:LICENSE", "@boringssl//:LICENSE", + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", @@ -168,7 +170,7 @@ genrule( "@highwayhash//:LICENSE", "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", - "@libxsmm_archive//:LICENSE", + "@libxsmm_archive//:LICENSE.md", "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 9d4148c07f37fb511836425e1b6ffceb7c259777..c9d53f46c3cff9eceb6eb03a872d05e8afd06047 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -57,15 +57,18 @@ COMMON_PIP_DEPS = [ "//tensorflow:tensorflow_py", "//tensorflow/contrib/autograph:autograph", "//tensorflow/contrib/autograph/converters:converters", - "//tensorflow/contrib/autograph/converters:test_lib", + "//tensorflow/contrib/autograph/core:core", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl:impl", + "//tensorflow/contrib/autograph/lang:lang", "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", + "//tensorflow/contrib/autograph/pyct/common_transformers:common_transformers", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/constrained_optimization:constrained_optimization_pip", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/kernel_tests/serialization:dataset_serialization_test_base", "//tensorflow/contrib/data/python/ops:contrib_op_loader", "//tensorflow/contrib/eager/python/examples:examples_pip", "//tensorflow/contrib/eager/python:evaluator", @@ -91,6 +94,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/timeseries:timeseries_pip", "//tensorflow/contrib/tpu", "//tensorflow/examples/tutorials/mnist:package", + "//tensorflow/python:cond_v2", "//tensorflow/python:distributed_framework_test_lib", "//tensorflow/python:meta_graph_testdata", "//tensorflow/python:spectral_ops_test_util", @@ -126,6 +130,8 @@ filegroup( "@astor_archive//:LICENSE", "@aws//:LICENSE", "@boringssl//:LICENSE", + "@com_github_googleapis_googleapis//:LICENSE", + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_google_absl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", @@ -143,7 +149,7 @@ filegroup( "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", "@kafka//:LICENSE", - "@libxsmm_archive//:LICENSE", + "@libxsmm_archive//:LICENSE.md", "@lmdb//:LICENSE", "@local_config_nccl//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index f7e42ce5362163a23ae1060c47e3e02869372556..9e41514cfa1a70d649eab6fd23a599db4afae2a8 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -24,9 +24,15 @@ function real_path() { function cp_external() { local src_dir=$1 local dest_dir=$2 - for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do - cp -R "$f" "$dest_dir" + + pushd . + cd "$src_dir" + for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do + mkdir -p "${dest_dir}/$(dirname ${f})" + cp "${f}" "${dest_dir}/$(dirname ${f})/" done + popd + mkdir -p "${dest_dir}/local_config_cuda/cuda/cuda/" cp "${src_dir}/local_config_cuda/cuda/cuda/cuda_config.h" "${dest_dir}/local_config_cuda/cuda/cuda/" } @@ -49,6 +55,8 @@ function prepare_src() { TMPDIR="$1" mkdir -p "$TMPDIR" + EXTERNAL_INCLUDES="${TMPDIR}/tensorflow/include/external" + echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then @@ -66,10 +74,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}/" RUNFILES=bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow else RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow @@ -78,10 +85,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then @@ -96,10 +102,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9cf5bc5064596cc76737f5cb7a591f2..c630ca04b885d35da6550d4e5f3e6912b5fd7a00 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -53,7 +53,7 @@ REQUIRED_PACKAGES = [ 'gast >= 0.2.0', 'numpy >= 1.13.3', 'six >= 1.10.0', - 'protobuf >= 3.4.0', + 'protobuf >= 3.6.0', 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', @@ -84,7 +84,7 @@ else: if 'tf_nightly' in project_name: for i, pkg in enumerate(REQUIRED_PACKAGES): if 'tensorboard' in pkg: - REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.9.0a0, < 1.10.0a0' + REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.10.0a0, < 1.11.0a0' break # weakref.finalize and enum were introduced in Python 3.4 @@ -170,8 +170,9 @@ class InstallHeaders(Command): # symlink within the directory hierarchy. # NOTE(keveman): Figure out how to customize bdist_wheel package so # we can do the symlink. - if 'external/eigen_archive/' in install_dir: - extra_dir = install_dir.replace('external/eigen_archive', '') + if 'tensorflow/include/external/eigen_archive/' in install_dir: + extra_dir = install_dir.replace( + 'tensorflow/include/external/eigen_archive', '') if not os.path.exists(extra_dir): self.mkpath(extra_dir) self.copy_file(header, extra_dir) @@ -204,13 +205,12 @@ def find_files(pattern, root): yield os.path.join(dirpath, filename) -matches = ['../' + x for x in find_files('*', 'external') if '.py' not in x] - so_lib_paths = [ i for i in os.listdir('.') if os.path.isdir(i) and fnmatch.fnmatch(i, '_solib_*') ] +matches = [] for path in so_lib_paths: matches.extend( ['../' + x for x in find_files('*', path) if '.py' not in x] @@ -225,7 +225,7 @@ headers = (list(find_files('*.h', 'tensorflow/core')) + list(find_files('*.h', 'tensorflow/stream_executor')) + list(find_files('*.h', 'google/protobuf_archive/src')) + list(find_files('*', 'third_party/eigen3')) + - list(find_files('*', 'external/eigen_archive'))) + list(find_files('*', 'tensorflow/include/external/eigen_archive'))) setup( name=project_name, diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 8483a4d767bb8f35387d5cedd5beed48eb7ba26d..c4ae21b645201f1a67f62ccc3eb44239c3ca5a55 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -107,13 +107,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "eigen_archive", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/6913f0cf7d06.tar.gz", - "https://bitbucket.org/eigen/eigen/get/6913f0cf7d06.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/fd6845384b86.tar.gz", + "https://bitbucket.org/eigen/eigen/get/fd6845384b86.tar.gz", ], - sha256 = "791b836cacd03e20bae5bdd25f1c4a5505a0a9975ba94a61eb4e2631fbd1d53a", - strip_prefix = "eigen-eigen-6913f0cf7d06", + sha256 = "d956415d784fa4e42b6a2a45c32556d6aec9d0a3d8ef48baee2522ab762556a9", + strip_prefix = "eigen-eigen-fd6845384b86", build_file = clean_dep("//third_party:eigen.BUILD"), - patch_file = clean_dep("//third_party:eigen_fix_cuda_compilation.patch") ) tf_http_archive( @@ -132,11 +131,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "libxsmm_archive", urls = [ - "https://mirror.bazel.build/github.com/hfp/libxsmm/archive/1.8.1.tar.gz", - "https://github.com/hfp/libxsmm/archive/1.8.1.tar.gz", + "https://mirror.bazel.build/github.com/hfp/libxsmm/archive/1.9.tar.gz", + "https://github.com/hfp/libxsmm/archive/1.9.tar.gz", ], - sha256 = "2ade869c3f42f23b5263c7d594aa3c7e5e61ac6a3afcaf5d6e42899d2a7986ce", - strip_prefix = "libxsmm-1.8.1", + sha256 = "cd8532021352b4a0290d209f7f9bfd7c2411e08286a893af3577a43457287bfa", + strip_prefix = "libxsmm-1.9", build_file = clean_dep("//third_party:libxsmm.BUILD"), ) @@ -156,12 +155,33 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "com_googlesource_code_re2", urls = [ - "https://mirror.bazel.build/github.com/google/re2/archive/26cd968b735e227361c9703683266f01e5df7857.tar.gz", - "https://github.com/google/re2/archive/26cd968b735e227361c9703683266f01e5df7857.tar.gz", + "https://mirror.bazel.build/github.com/google/re2/archive/2018-04-01.tar.gz", + "https://github.com/google/re2/archive/2018-04-01.tar.gz", ], - sha256 = "e57eeb837ac40b5be37b2c6197438766e73343ffb32368efea793dfd8b28653b", - strip_prefix = "re2-26cd968b735e227361c9703683266f01e5df7857", + sha256 = "2f945446b71336e7f5a2bcace1abcf0b23fbba368266c6a1be33de3de3b3c912", + strip_prefix = "re2-2018-04-01", + ) + + tf_http_archive( + name = "com_github_googlecloudplatform_google_cloud_cpp", + urls = [ + "https://mirror.bazel.build/github.com/GoogleCloudPlatform/google-cloud-cpp/archive/53f822805e77ea7715f5b52c592a162c515c7219.tar.gz", + "https://github.com/GoogleCloudPlatform/google-cloud-cpp/archive/53f822805e77ea7715f5b52c592a162c515c7219.tar.gz", + ], + sha256 = "06853bfca77ef4aec09db5ab48c548f68ef2e18f17404cbce61f8d9b820f951b", + strip_prefix = "google-cloud-cpp-53f822805e77ea7715f5b52c592a162c515c7219", + ) + + tf_http_archive( + name = "com_github_googleapis_googleapis", + urls = [ + "https://mirror.bazel.build/github.com/googleapis/googleapis/archive/f81082ea1e2f85c43649bee26e0d9871d4b41cdb.zip", + "https://github.com/googleapis/googleapis/archive/f81082ea1e2f85c43649bee26e0d9871d4b41cdb.zip", + ], + sha256 = "824870d87a176f26bcef663e92051f532fac756d1a06b404055dc078425f4378", + strip_prefix="googleapis-f81082ea1e2f85c43649bee26e0d9871d4b41cdb", + build_file = clean_dep("//third_party:googleapis.BUILD"), ) tf_http_archive( @@ -201,6 +221,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): urls = [ "https://mirror.bazel.build/www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2", "http://pkgs.fedoraproject.org/repo/pkgs/nasm/nasm-2.12.02.tar.bz2/d15843c3fb7db39af80571ee27ec6fad/nasm-2.12.02.tar.bz2", + "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2", ], sha256 = "00b0891c678c065446ca59bcee64719d0096d54d6886e6e472aeee2e170ae324", strip_prefix = "nasm-2.12.02", @@ -299,11 +320,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "absl_py", urls = [ - "https://mirror.bazel.build/github.com/abseil/abseil-py/archive/ea8c4d2ddbf3fba610c4d613260561699b776db8.tar.gz", - "https://github.com/abseil/abseil-py/archive/ea8c4d2ddbf3fba610c4d613260561699b776db8.tar.gz", + "https://mirror.bazel.build/github.com/abseil/abseil-py/archive/pypi-v0.2.2.tar.gz", + "https://github.com/abseil/abseil-py/archive/pypi-v0.2.2.tar.gz", ], - sha256 = "c30b48e0d2580ef1412e55c5c0e1dab8db2ee4ab56e2075eccff29c90c7c7059", - strip_prefix = "abseil-py-ea8c4d2ddbf3fba610c4d613260561699b776db8", + sha256 = "95160f778a62c7a60ddeadc7bf2d83f85a23a27359814aca12cf949e896fa82c", + strip_prefix = "abseil-py-pypi-v0.2.2", ) tf_http_archive( @@ -331,11 +352,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "protobuf_archive", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) # We need to import the protobuf library under the names com_google_protobuf @@ -344,31 +365,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "com_google_protobuf", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) tf_http_archive( name = "com_google_protobuf_cc", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) tf_http_archive( name = "nsync", urls = [ - "https://mirror.bazel.build/github.com/google/nsync/archive/0559ce013feac8db639ee1bf776aca0325d28777.tar.gz", - "https://github.com/google/nsync/archive/0559ce013feac8db639ee1bf776aca0325d28777.tar.gz", + "https://mirror.bazel.build/github.com/google/nsync/archive/1.20.0.tar.gz", + "https://github.com/google/nsync/archive/1.20.0.tar.gz", ], - sha256 = "6284454c5cd8b1dae2eeb8cf5eb63004de930b5427ed5f6b1aa793513df6b361", - strip_prefix = "nsync-0559ce013feac8db639ee1bf776aca0325d28777", + sha256 = "0c1b03962b2f8450f21e74a5a46116bf2d6009a807c57eb4207e974a8c4bb7dd", + strip_prefix = "nsync-1.20.0", ) tf_http_archive( @@ -393,12 +414,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "pcre", - sha256 = "ccdf7e788769838f8285b3ee672ed573358202305ee361cfec7a4a4fb005bbc7", + sha256 = "69acbc2fbdefb955d42a4c606dfde800c2885711d2979e356c0636efde9ec3b5", urls = [ - "https://mirror.bazel.build/ftp.exim.org/pub/pcre/pcre-8.39.tar.gz", - "http://ftp.exim.org/pub/pcre/pcre-8.39.tar.gz", + "https://mirror.bazel.build/ftp.exim.org/pub/pcre/pcre-8.42.tar.gz", + "http://ftp.exim.org/pub/pcre/pcre-8.42.tar.gz", ], - strip_prefix = "pcre-8.39", + strip_prefix = "pcre-8.42", build_file = clean_dep("//third_party:pcre.BUILD"), ) @@ -416,12 +437,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "curl", - sha256 = "ff3e80c1ca6a068428726cd7dd19037a47cc538ce58ef61c59587191039b2ca6", + sha256 = "e9c37986337743f37fd14fe8737f246e97aec94b39d1b71e8a5973f72a9fc4f5", urls = [ - "https://mirror.bazel.build/curl.haxx.se/download/curl-7.49.1.tar.gz", - "https://curl.haxx.se/download/curl-7.49.1.tar.gz", + "https://mirror.bazel.build/curl.haxx.se/download/curl-7.60.0.tar.gz", + "https://curl.haxx.se/download/curl-7.60.0.tar.gz", ], - strip_prefix = "curl-7.49.1", + strip_prefix = "curl-7.60.0", build_file = clean_dep("//third_party:curl.BUILD"), ) @@ -452,33 +473,33 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/40c66c3d40377cf85640b3a35e6ec5c5b1cbc41f.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/40c66c3d40377cf85640b3a35e6ec5c5b1cbc41f.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/fe1e7736763a8577ac081eca525e05d3b52de414.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/fe1e7736763a8577ac081eca525e05d3b52de414.tar.gz", ], - sha256 = "6f782a0d2e9d7946bdf20807e0fcd8f5eaed8afd93bdd610cdefbe9435ca551f", - strip_prefix = "llvm-40c66c3d40377cf85640b3a35e6ec5c5b1cbc41f", - build_file = clean_dep("//third_party/llvm:llvm.BUILD"), + sha256 = "77b9a98d3c0be94561fed32f44a7a8c78421e01a74bad009964d8bbaf066ed6c", + strip_prefix = "llvm-fe1e7736763a8577ac081eca525e05d3b52de414", + build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) tf_http_archive( name = "lmdb", urls = [ - "https://mirror.bazel.build/github.com/LMDB/lmdb/archive/LMDB_0.9.19.tar.gz", - "https://github.com/LMDB/lmdb/archive/LMDB_0.9.19.tar.gz", + "https://mirror.bazel.build/github.com/LMDB/lmdb/archive/LMDB_0.9.22.tar.gz", + "https://github.com/LMDB/lmdb/archive/LMDB_0.9.22.tar.gz", ], - sha256 = "108532fb94c6f227558d45be3f3347b52539f0f58290a7bb31ec06c462d05326", - strip_prefix = "lmdb-LMDB_0.9.19/libraries/liblmdb", + sha256 = "f3927859882eb608868c8c31586bb7eb84562a40a6bf5cc3e13b6b564641ea28", + strip_prefix = "lmdb-LMDB_0.9.22/libraries/liblmdb", build_file = clean_dep("//third_party:lmdb.BUILD"), ) tf_http_archive( name = "jsoncpp_git", urls = [ - "https://mirror.bazel.build/github.com/open-source-parsers/jsoncpp/archive/11086dd6a7eba04289944367ca82cea71299ed70.tar.gz", - "https://github.com/open-source-parsers/jsoncpp/archive/11086dd6a7eba04289944367ca82cea71299ed70.tar.gz", + "https://mirror.bazel.build/github.com/open-source-parsers/jsoncpp/archive/1.8.4.tar.gz", + "https://github.com/open-source-parsers/jsoncpp/archive/1.8.4.tar.gz", ], - sha256 = "07d34db40593d257324ec5fb9debc4dc33f29f8fb44e33a2eeb35503e61d0fe2", - strip_prefix = "jsoncpp-11086dd6a7eba04289944367ca82cea71299ed70", + sha256 = "c49deac9e0933bcb7044f08516861a2d560988540b23de2ac1ad443b219afdb6", + strip_prefix = "jsoncpp-1.8.4", build_file = clean_dep("//third_party:jsoncpp.BUILD"), ) @@ -538,11 +559,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "kafka", urls = [ - "https://mirror.bazel.build/github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", - "https://github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", + "https://mirror.bazel.build/github.com/edenhill/librdkafka/archive/v0.11.4.tar.gz", + "https://github.com/edenhill/librdkafka/archive/v0.11.4.tar.gz", ], - sha256 = "dd035d57c8f19b0b612dd6eefe6e5eebad76f506e302cccb7c2066f25a83585e", - strip_prefix = "librdkafka-0.11.1", + sha256 = "9d8f1eb7b0e29e9ab1168347c939cb7ae5dff00a39cef99e7ef033fd8f92737c", + strip_prefix = "librdkafka-0.11.4", build_file = clean_dep("//third_party:kafka/BUILD"), patch_file = clean_dep("//third_party/kafka:config.patch"), ) @@ -628,6 +649,16 @@ def tf_workspace(path_prefix="", tf_repo_name=""): licenses = ["notice"], # Apache 2.0 ) + java_import_external( + name = "com_squareup_javapoet", + jar_sha256 = "5bb5abdfe4366c15c0da3332c57d484e238bd48260d6f9d6acf2b08fdde1efea", + jar_urls = [ + "http://mirror.bazel.build/repo1.maven.org/maven2/com/squareup/javapoet/1.9.0/javapoet-1.9.0.jar", + "http://repo1.maven.org/maven2/com/squareup/javapoet/1.9.0/javapoet-1.9.0.jar", + ], + licenses = ["notice"], # Apache 2.0 + ) + tf_http_archive( name = "com_google_pprof", urls = [ @@ -685,11 +716,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "flatbuffers", - strip_prefix = "flatbuffers-971a68110e4fc1bace10fcb6deeb189e7e1a34ce", - sha256 = "874088d2ee0d9f8524191f77209556415f03dd44e156276edf19e5b90ceb5f55", + strip_prefix = "flatbuffers-1.9.0", + sha256 = "5ca5491e4260cacae30f1a5786d109230db3f3a6e5a0eb45d0d0608293d247e3", urls = [ - "https://mirror.bazel.build/github.com/google/flatbuffers/archive/971a68110e4fc1bace10fcb6deeb189e7e1a34ce.tar.gz", - "https://github.com/google/flatbuffers/archive/971a68110e4fc1bace10fcb6deeb189e7e1a34ce.tar.gz", + "https://mirror.bazel.build/github.com/google/flatbuffers/archive/v1.9.0.tar.gz", + "https://github.com/google/flatbuffers/archive/v1.9.0.tar.gz", ], build_file = clean_dep("//third_party/flatbuffers:flatbuffers.BUILD"), ) @@ -723,6 +754,14 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ], build_file = str(Label("//third_party:tflite_mobilenet.BUILD")), ) + tf_http_archive( + name = "tflite_mobilenet_ssd_quant", + sha256 = "a809cd290b4d6a2e8a9d5dad076e0bd695b8091974e0eed1052b480b2f21b6dc", + urls = ["https://mirror.bazel.build/storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_0.75_quant_2018_06_29.zip", + "https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_0.75_quant_2018_06_29.zip", + ], + build_file = str(Label("//third_party:tflite_mobilenet.BUILD")), + ) tf_http_archive( name = "tflite_conv_actions_frozen", @@ -755,6 +794,16 @@ def tf_workspace(path_prefix="", tf_repo_name=""): strip_prefix = "ovic", ) + tf_http_archive( + name = "build_bazel_rules_android", + sha256 = "cd06d15dd8bb59926e4d65f9003bfc20f9da4b2519985c27e190cddc8b7a7806", + urls = [ + "https://mirror.bazel.build/github.com/bazelbuild/rules_android/archive/v0.1.1.zip", + "https://github.com/bazelbuild/rules_android/archive/v0.1.1.zip", + ], + strip_prefix = "rules_android-0.1.1", + ) + ############################################################################## # BIND DEFINITIONS # @@ -779,10 +828,13 @@ def tf_workspace(path_prefix="", tf_repo_name=""): actual = "@grpc//:grpc_python_plugin", ) - # gRPC has three empty C++ functions which it wants the user to define - # at build time. https://github.com/grpc/grpc/issues/13590 native.bind( name = "grpc_lib", + actual = "@grpc//:grpc++", + ) + + native.bind( + name = "grpc_lib_unsecure", actual = "@grpc//:grpc++_unsecure", ) diff --git a/third_party/aws.BUILD b/third_party/aws.BUILD index 2dc921933c310aa9ce2bf21798f1b5143386a12d..5426f79e4650a1ce4dcb4a8408691310c864f06c 100644 --- a/third_party/aws.BUILD +++ b/third_party/aws.BUILD @@ -46,6 +46,8 @@ cc_library( "aws-cpp-sdk-core/source/utils/xml/**/*.cpp", "aws-cpp-sdk-core/source/utils/crypto/*.cpp", "aws-cpp-sdk-core/source/utils/crypto/factory/**/*.cpp", + "aws-cpp-sdk-kinesis/include/**/*.h", + "aws-cpp-sdk-kinesis/source/**/*.cpp", "aws-cpp-sdk-s3/include/**/*.h", "aws-cpp-sdk-s3/source/**/*.cpp", ]), @@ -72,6 +74,7 @@ cc_library( }), includes = [ "aws-cpp-sdk-core/include/", + "aws-cpp-sdk-kinesis/include/", "aws-cpp-sdk-s3/include/", ], deps = [ diff --git a/third_party/clang_toolchain/download_clang.bzl b/third_party/clang_toolchain/download_clang.bzl index a203245005cc215250380239e5ac4d1dbc209d97..a014a806a69ecf9d7e43c51daf3672fc5750e706 100644 --- a/third_party/clang_toolchain/download_clang.bzl +++ b/third_party/clang_toolchain/download_clang.bzl @@ -35,18 +35,18 @@ def download_clang(repo_ctx, out_folder): # Latest CLANG_REVISION and CLANG_SUB_REVISION of the Chromiums's release # can be found in https://chromium.googlesource.com/chromium/src/tools/clang/+/master/scripts/update.py - CLANG_REVISION = '332838' + CLANG_REVISION = '335091' CLANG_SUB_REVISION = 1 package_version = '%s-%s' % (CLANG_REVISION, CLANG_SUB_REVISION) checksums = { 'Linux_x64': - 'b9ef55de7500778f366039dbe62d1632074a3ef3673022eabf4e59d405730968', + '17002b75293fccfdd175eacdc9ee47d97b58d7e98fef343384fbbef1b68ce99f', 'Mac': - '30d808512763c98cecf15f7bb654d845de3e8d065a95f5c5b6b3459254cc98d6', + '9351e46d28315daaa06a1eb55bd0370ed4aaeb693a2a3e82e48d2737d7723468', 'Win': - '277e799a190b22727c26b09986c0cedbd667a189f425318f421addf6a21ca4bd', + 'e78a1e469224d6f6751b4df4374bf58893ac03900ec924e4c8264888ba4aeb1e', } platform_folder = _get_platform_folder(repo_ctx.os.name) diff --git a/third_party/curl.BUILD b/third_party/curl.BUILD index 4def6f94892329e0d8b594b824babd60ea259351..1638b7216162abca208267ff804c6d92231081f6 100644 --- a/third_party/curl.BUILD +++ b/third_party/curl.BUILD @@ -7,6 +7,7 @@ exports_files(["COPYING"]) CURL_WIN_COPTS = [ "/Iexternal/curl/lib", + "/DBUILDING_LIBCURL", "/DHAVE_CONFIG_H", "/DCURL_DISABLE_FTP", "/DCURL_DISABLE_NTLM", @@ -49,6 +50,8 @@ cc_library( "lib/curl_addrinfo.c", "lib/curl_addrinfo.h", "lib/curl_base64.h", + "lib/curl_ctype.c", + "lib/curl_ctype.h", "lib/curl_des.h", "lib/curl_endian.h", "lib/curl_fnmatch.c", @@ -75,6 +78,7 @@ cc_library( "lib/curl_sec.h", "lib/curl_setup.h", "lib/curl_setup_once.h", + "lib/curl_sha256.h", "lib/curl_sspi.c", "lib/curl_sspi.h", "lib/curl_threads.c", @@ -134,6 +138,8 @@ cc_library( "lib/md5.c", "lib/memdebug.c", "lib/memdebug.h", + "lib/mime.c", + "lib/mime.h", "lib/mprintf.c", "lib/multi.c", "lib/multihandle.h", @@ -153,8 +159,8 @@ cc_library( "lib/pop3.h", "lib/progress.c", "lib/progress.h", - "lib/rawstr.c", - "lib/rawstr.h", + "lib/rand.c", + "lib/rand.h", "lib/rtsp.c", "lib/rtsp.h", "lib/security.c", @@ -162,8 +168,11 @@ cc_library( "lib/select.h", "lib/sendf.c", "lib/sendf.h", + "lib/setopt.c", + "lib/setopt.h", "lib/setup-os400.h", "lib/setup-vms.h", + "lib/sha256.c", "lib/share.c", "lib/share.h", "lib/sigpipe.h", @@ -179,10 +188,10 @@ cc_library( "lib/splay.c", "lib/splay.h", "lib/ssh.h", + "lib/strcase.c", + "lib/strcase.h", "lib/strdup.c", "lib/strdup.h", - "lib/strequal.c", - "lib/strequal.h", "lib/strerror.c", "lib/strerror.h", "lib/strtok.c", @@ -241,13 +250,12 @@ cc_library( }), hdrs = [ "include/curl/curl.h", - "include/curl/curlbuild.h", - "include/curl/curlrules.h", "include/curl/curlver.h", "include/curl/easy.h", "include/curl/mprintf.h", "include/curl/multi.h", "include/curl/stdcheaders.h", + "include/curl/system.h", "include/curl/typecheck-gcc.h", ], copts = select({ @@ -256,6 +264,7 @@ cc_library( "//conditions:default": [ "-Iexternal/curl/lib", "-D_GNU_SOURCE", + "-DBUILDING_LIBCURL", "-DHAVE_CONFIG_H", "-DCURL_DISABLE_FTP", "-DCURL_DISABLE_NTLM", # turning it off in configure is not enough @@ -676,6 +685,7 @@ genrule( "# define SIZEOF_INT 4", "# define SIZEOF_LONG 8", "# define SIZEOF_OFF_T 8", + "# define SIZEOF_CURL_OFF_T 8", "# define SIZEOF_SHORT 2", "# define SIZEOF_SIZE_T 8", "# define SIZEOF_TIME_T 8", diff --git a/third_party/eigen.BUILD b/third_party/eigen.BUILD index e54c1a4501d46b6b68a9b8fcc9ce0b1af0535ef4..759f8a9be92e14537d334c3ec37f036d369d8796 100644 --- a/third_party/eigen.BUILD +++ b/third_party/eigen.BUILD @@ -69,3 +69,9 @@ cc_library( includes = ["."], visibility = ["//visibility:public"], ) + +filegroup( + name = "eigen_header_files", + srcs = EIGEN_MPL2_HEADER_FILES, + visibility = ["//visibility:public"], +) diff --git a/third_party/eigen3/BUILD b/third_party/eigen3/BUILD index f661093bc9f68b845f3000b0a931c66773fb3339..203991b50f56086aa76932595f6797ae3bbf58db 100644 --- a/third_party/eigen3/BUILD +++ b/third_party/eigen3/BUILD @@ -17,21 +17,23 @@ load("//tensorflow:tensorflow.bzl", "if_mkl") # INTEL_MKL end load("//tensorflow:tensorflow.bzl", "if_mkl") +EIGEN3_THIRD_PARTY_HEADERS = [ + "Eigen/Core", + "Eigen/LU", + "Eigen/Cholesky", + "Eigen/Eigenvalues", + "Eigen/QR", + "Eigen/SVD", + "unsupported/Eigen/MatrixFunctions", + "unsupported/Eigen/SpecialFunctions", + "unsupported/Eigen/CXX11/ThreadPool", + "unsupported/Eigen/CXX11/Tensor", + "unsupported/Eigen/CXX11/FixedPoint", +] + glob(["unsupported/Eigen/CXX11/src/FixedPoint/*.h"]) + cc_library( name = "eigen3", - hdrs = glob(["unsupported/Eigen/CXX11/src/FixedPoint/*.h"]) + [ - "Eigen/Core", - "Eigen/LU", - "Eigen/Cholesky", - "Eigen/Eigenvalues", - "Eigen/QR", - "Eigen/SVD", - "unsupported/Eigen/MatrixFunctions", - "unsupported/Eigen/SpecialFunctions", - "unsupported/Eigen/CXX11/ThreadPool", - "unsupported/Eigen/CXX11/Tensor", - "unsupported/Eigen/CXX11/FixedPoint", - ], + hdrs = EIGEN3_THIRD_PARTY_HEADERS, includes = if_mkl(["./mkl_include"]), visibility = ["//visibility:public"], deps = [ @@ -48,3 +50,35 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +filegroup( + name = "eigen_third_party_header_files", + srcs = EIGEN3_THIRD_PARTY_HEADERS, + visibility = ["//visibility:public"], +) + +genrule( + name = "install_eigen_headers", + srcs = [ + "@eigen_archive//:eigen_header_files", + ":eigen_third_party_header_files", + ], + outs = ["include"], + cmd = """ + mkdir $@ + for f in $(locations @eigen_archive//:eigen_header_files) ; do + d="$${f%/*}" + d="$${d#*external/eigen_archive/}" + + mkdir -p "$@/$${d}" + cp "$${f}" "$@/$${d}/" + done + + for f in $(locations :eigen_third_party_header_files) ; do + d="$${f%/*}" + + mkdir -p "$@/$${d}" + cp "$${f}" "$@/$${d}/" + done + """, +) diff --git a/third_party/eigen_fix_cuda_compilation.patch b/third_party/eigen_fix_cuda_compilation.patch deleted file mode 100644 index b921a7c31d5c96c79cd3033b13c60a8f7e63ba75..0000000000000000000000000000000000000000 --- a/third_party/eigen_fix_cuda_compilation.patch +++ /dev/null @@ -1,38 +0,0 @@ -diff --git a/Eigen/src/Core/ProductEvaluators.h b/Eigen/src/Core/ProductEvaluators.h ---- a/Eigen/src/Core/ProductEvaluators.h -+++ b/Eigen/src/Core/ProductEvaluators.h -@@ -137,7 +137,7 @@ struct Assignment::type> - { - typedef Product SrcXprType; -- static EIGEN_STRONG_INLINE -+ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE - void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op &) - { - Index dstRows = src.rows(); -@@ -390,7 +390,7 @@ struct generic_product_impl::Scalar Scalar; - - template -- static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) -+ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) - { - // Same as: dst.noalias() = lhs.lazyProduct(rhs); - // but easier on the compiler side -@@ -398,14 +398,14 @@ struct generic_product_impl -- static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) -+ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) - { - // dst.noalias() += lhs.lazyProduct(rhs); - call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op()); - } - - template -- static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) -+ static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) - { - // dst.noalias() -= lhs.lazyProduct(rhs); - call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op()); diff --git a/third_party/examples/eager/spinn/README.md b/third_party/examples/eager/spinn/README.md index fbb1fde837b92bc521698d0a517a946da0438dbc..e2fd8009a052d7cbfd01b48af7da6b891ad08c74 100644 --- a/third_party/examples/eager/spinn/README.md +++ b/third_party/examples/eager/spinn/README.md @@ -22,7 +22,7 @@ Other eager execution examples can be found under [tensorflow/contrib/eager/pyth - [`data.py`](../../../../tensorflow/contrib/eager/python/examples/spinn/data.py): Pipeline for loading and preprocessing the [SNLI](https://nlp.stanford.edu/projects/snli/) data and [GloVe](https://nlp.stanford.edu/projects/glove/) word embedding, written - using the [`tf.data`](https://www.tensorflow.org/programmers_guide/datasets) + using the [`tf.data`](https://www.tensorflow.org/guide/datasets) API. - [`spinn.py`](./spinn.py): Model definition and training routines. This example illustrates how one might perform the following actions with diff --git a/third_party/flatbuffers/flatbuffers.BUILD b/third_party/flatbuffers/flatbuffers.BUILD index 824c97be60e7ef148a363b964ed330ba3c5fcb0c..639dff2cd01056cf70e727b39c0a0c537c763c9e 100644 --- a/third_party/flatbuffers/flatbuffers.BUILD +++ b/third_party/flatbuffers/flatbuffers.BUILD @@ -98,6 +98,8 @@ cc_binary( "grpc/src/compiler/cpp_generator.h", "grpc/src/compiler/go_generator.cc", "grpc/src/compiler/go_generator.h", + "grpc/src/compiler/java_generator.cc", + "grpc/src/compiler/java_generator.h", "grpc/src/compiler/schema_interface.h", "src/flatc_main.cpp", "src/idl_gen_cpp.cpp", diff --git a/third_party/googleapis.BUILD b/third_party/googleapis.BUILD new file mode 100644 index 0000000000000000000000000000000000000000..95e999af1886576317aa59d133e8d5c88ba368d3 --- /dev/null +++ b/third_party/googleapis.BUILD @@ -0,0 +1,45 @@ +# Copyright 2018 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +package(default_visibility = ["//visibility:public"]) +licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE"]) + +load("@protobuf_archive//:protobuf.bzl", "cc_proto_library") + +cc_proto_library( + name = "bigtable_protos", + srcs = [ + "google/bigtable/admin/v2/bigtable_instance_admin.proto", + "google/bigtable/admin/v2/bigtable_table_admin.proto", + "google/bigtable/admin/v2/common.proto", + "google/bigtable/admin/v2/instance.proto", + "google/bigtable/admin/v2/table.proto", + "google/bigtable/v2/bigtable.proto", + "google/bigtable/v2/data.proto", + "google/iam/v1/iam_policy.proto", + "google/iam/v1/policy.proto", + "google/longrunning/operations.proto", + "google/rpc/status.proto", + "google/rpc/error_details.proto", + "google/api/annotations.proto", + "google/api/auth.proto", + "google/api/http.proto", + ], + include = ".", + protoc = "@protobuf_archive//:protoc", + default_runtime = "@protobuf_archive//:protobuf", + deps = ["@protobuf_archive//:cc_wkt_protos"], + use_grpc_plugin = True, +) diff --git a/third_party/gpus/crosstool/CROSSTOOL.tpl b/third_party/gpus/crosstool/CROSSTOOL.tpl index 60b19daf1d781055fbd141343ec3fd260a49b76b..1424ff6511dfe0e7e8eef2843201e825e09a91f1 100644 --- a/third_party/gpus/crosstool/CROSSTOOL.tpl +++ b/third_party/gpus/crosstool/CROSSTOOL.tpl @@ -295,3 +295,245 @@ toolchain { %{host_compiler_includes} } + +toolchain { + abi_version: "local" + abi_libc_version: "local" + compiler: "compiler" + host_system_name: "local" + needsPic: true + target_libc: "macosx" + target_cpu: "darwin" + target_system_name: "local" + toolchain_identifier: "local_darwin" + feature { + name: "c++11" + flag_set { + action: "c++-compile" + flag_group { + flag: "-std=c++11" + } + } + } + + feature { + name: "stdlib" + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-lc++" + } + } + } + + feature { + name: "determinism" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Make C++ compilation deterministic. Use linkstamping instead of these + # compiler symbols. + flag: "-Wno-builtin-macro-redefined" + flag: "-D__DATE__=\"redacted\"" + flag: "-D__TIMESTAMP__=\"redacted\"" + flag: "-D__TIME__=\"redacted\"" + } + } + } + + # This feature will be enabled for builds that support pic by bazel. + feature { + name: "pic" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + expand_if_all_available: "pic" + flag: "-fPIC" + } + flag_group { + expand_if_none_available: "pic" + flag: "-fPIE" + } + } + } + + # Security hardening on by default. + feature { + name: "hardening" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases. + # We need to undef it before redefining it as some distributions now + # have it enabled by default. + flag: "-U_FORTIFY_SOURCE" + flag: "-D_FORTIFY_SOURCE=1" + flag: "-fstack-protector" + } + } + flag_set { + action: "c++-link-executable" + flag_group { + flag: "-pie" + } + } + } + + feature { + name: "warnings" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # All warnings are enabled. Maybe enable -Werror as well? + flag: "-Wall" + %{host_compiler_warnings} + } + } + } + + # Keep stack frames for debugging, even in opt mode. + feature { + name: "frame-pointer" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-fno-omit-frame-pointer" + } + } + } + + feature { + name: "no-canonical-prefixes" + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag:"-no-canonical-prefixes" + } + } + } + + feature { + name: "disable-assertions" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-DNDEBUG" + } + } + } + + feature { + name: "linker-bin-path" + + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-B/usr/bin/" + } + } + } + + feature { + name: "undefined-dynamic" + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-executable" + flag_group { + flag: "-undefined" + flag: "dynamic_lookup" + } + } + } + + feature { + name: "common" + implies: "stdlib" + implies: "c++11" + implies: "determinism" + implies: "hardening" + implies: "warnings" + implies: "frame-pointer" + implies: "no-canonical-prefixes" + implies: "linker-bin-path" + implies: "undefined-dynamic" + } + + feature { + name: "opt" + implies: "common" + implies: "disable-assertions" + + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # No debug symbols. + # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt + # or even generally? However, that can't happen here, as it requires + # special handling in Bazel. + flag: "-g0" + + # Conservative choice for -O + # -O3 can increase binary size and even slow down the resulting binaries. + # Profile first and / or use FDO if you need better performance than this. + flag: "-O2" + + # Removal of unused code and data at link time (can this increase binary size in some cases?). + flag: "-ffunction-sections" + flag: "-fdata-sections" + } + } + } + + feature { + name: "fastbuild" + implies: "common" + } + + feature { + name: "dbg" + implies: "common" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-g" + } + } + } + + # Set clang as a C/C++ compiler. + tool_path { name: "gcc" path: "%{host_compiler_path}" } + + # Use the default system toolchain for everything else. + tool_path { name: "ar" path: "/usr/bin/libtool" } + tool_path { name: "compat-ld" path: "/usr/bin/ld" } + tool_path { name: "cpp" path: "/usr/bin/cpp" } + tool_path { name: "dwp" path: "/usr/bin/dwp" } + tool_path { name: "gcov" path: "/usr/bin/gcov" } + tool_path { name: "ld" path: "/usr/bin/ld" } + tool_path { name: "nm" path: "/usr/bin/nm" } + tool_path { name: "objcopy" path: "/usr/bin/objcopy" } + tool_path { name: "objdump" path: "/usr/bin/objdump" } + tool_path { name: "strip" path: "/usr/bin/strip" } + + # Enabled dynamic linking. + linking_mode_flags { mode: DYNAMIC } + +%{host_compiler_includes} +} diff --git a/third_party/jsoncpp.BUILD b/third_party/jsoncpp.BUILD index 65f98410b289a7e324c9ed89e33de1c6010fa21a..cf3cba05556a0bb22a632475c6ab810b8230f355 100644 --- a/third_party/jsoncpp.BUILD +++ b/third_party/jsoncpp.BUILD @@ -6,7 +6,6 @@ cc_library( name = "jsoncpp", srcs = [ "include/json/assertions.h", - "src/lib_json/json_batchallocator.h", "src/lib_json/json_reader.cpp", "src/lib_json/json_tool.h", "src/lib_json/json_value.cpp", @@ -20,9 +19,13 @@ cc_library( "include/json/json.h", "include/json/reader.h", "include/json/value.h", + "include/json/version.h", "include/json/writer.h", ], - copts = ["-DJSON_USE_EXCEPTION=0"], + copts = [ + "-DJSON_USE_EXCEPTION=0", + "-DJSON_HAS_INT64", + ], includes = ["include"], visibility = ["//visibility:public"], deps = [":private"], diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD index a839ca717e695f35fac684b510f0a022010e0710..75792b0d87366c304ca29f95f943114ee482dfcd 100644 --- a/third_party/kafka/BUILD +++ b/third_party/kafka/BUILD @@ -60,6 +60,8 @@ cc_library( "src/rdkafka_event.h", "src/rdkafka_feature.c", "src/rdkafka_feature.h", + "src/rdkafka_header.c", + "src/rdkafka_header.h", "src/rdkafka_int.h", "src/rdkafka_interceptor.c", "src/rdkafka_interceptor.h", @@ -93,7 +95,6 @@ cc_library( "src/rdkafka_sasl_int.h", "src/rdkafka_sasl_plain.c", "src/rdkafka_subscription.c", - "src/rdkafka_subscription.h", "src/rdkafka_timer.c", "src/rdkafka_timer.h", "src/rdkafka_topic.c", @@ -105,6 +106,8 @@ cc_library( "src/rdlist.h", "src/rdlog.c", "src/rdlog.h", + "src/rdmurmur2.c", + "src/rdmurmur2.h", "src/rdports.c", "src/rdports.h", "src/rdposix.h", diff --git a/third_party/libxsmm.BUILD b/third_party/libxsmm.BUILD index 78ed1f4e168891367ddc2249da726a6ef16dd5d5..ee49d281abcd54b566edde119f4a5b3e6b07d2a3 100644 --- a/third_party/libxsmm.BUILD +++ b/third_party/libxsmm.BUILD @@ -3,7 +3,7 @@ licenses(["notice"]) # BSD 3-clause -exports_files(["LICENSE"]) +exports_files(["LICENSE.md"]) # Arguments to ./scripts/libxsmm_interface.py, see that file for detailed description. # precision: SP & DP diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.autogenerated.BUILD similarity index 89% rename from third_party/llvm/llvm.BUILD rename to third_party/llvm/llvm.autogenerated.BUILD index e1c22c815196cc9be0af763ae6400ecb40555e4e..d931932d9d517cb5f0638a87569b697e35e158f6 100644 --- a/third_party/llvm/llvm.BUILD +++ b/third_party/llvm/llvm.autogenerated.BUILD @@ -8,10 +8,13 @@ exports_files(["LICENSE.TXT"]) load( "@org_tensorflow//third_party/llvm:llvm.bzl", + "LLVM_COPTS", + "LLVM_DEFINES", + "LLVM_LINKOPTS", "cmake_var_string", "expand_cmake_vars", "gentbl", - "llvm_target_cmake_vars", + "llvm_all_cmake_vars", ) load( "@org_tensorflow//third_party:common.bzl", @@ -39,147 +42,25 @@ llvm_target_asm_printers = llvm_targets llvm_target_disassemblers = llvm_targets -# TODO(phawkins): the set of CMake variables was hardcoded for expediency. -# However, we should really detect many of these via configure-time tests. - -# The set of CMake variables common to all targets. -cmake_vars = { - # Headers - "HAVE_DIRENT_H": 1, - "HAVE_DLFCN_H": 1, - "HAVE_ERRNO_H": 1, - "HAVE_EXECINFO_H": 1, - "HAVE_FCNTL_H": 1, - "HAVE_INTTYPES_H": 1, - "HAVE_PTHREAD_H": 1, - "HAVE_SIGNAL_H": 1, - "HAVE_STDINT_H": 1, - "HAVE_SYS_IOCTL_H": 1, - "HAVE_SYS_MMAN_H": 1, - "HAVE_SYS_PARAM_H": 1, - "HAVE_SYS_RESOURCE_H": 1, - "HAVE_SYS_STAT_H": 1, - "HAVE_SYS_TIME_H": 1, - "HAVE_SYS_TYPES_H": 1, - "HAVE_TERMIOS_H": 1, - "HAVE_UNISTD_H": 1, - "HAVE_ZLIB_H": 1, - - # Features - "HAVE_BACKTRACE": 1, - "BACKTRACE_HEADER": "execinfo.h", - "HAVE_DLOPEN": 1, - "HAVE_FUTIMES": 1, - "HAVE_GETCWD": 1, - "HAVE_GETPAGESIZE": 1, - "HAVE_GETRLIMIT": 1, - "HAVE_GETRUSAGE": 1, - "HAVE_GETTIMEOFDAY": 1, - "HAVE_INT64_T": 1, - "HAVE_ISATTY": 1, - "HAVE_LIBEDIT": 1, - "HAVE_LIBPTHREAD": 1, - "HAVE_LIBZ": 1, - "HAVE_MKDTEMP": 1, - "HAVE_MKSTEMP": 1, - "HAVE_MKTEMP": 1, - "HAVE_PREAD": 1, - "HAVE_PTHREAD_GETSPECIFIC": 1, - "HAVE_PTHREAD_MUTEX_LOCK": 1, - "HAVE_PTHREAD_RWLOCK_INIT": 1, - "HAVE_REALPATH": 1, - "HAVE_SBRK": 1, - "HAVE_SETENV": 1, - "HAVE_SETRLIMIT": 1, - "HAVE_SIGALTSTACK": 1, - "HAVE_STRERROR": 1, - "HAVE_STRERROR_R": 1, - "HAVE_STRTOLL": 1, - "HAVE_SYSCONF": 1, - "HAVE_UINT64_T": 1, - "HAVE__UNWIND_BACKTRACE": 1, - - # LLVM features - "ENABLE_BACKTRACES": 1, - "LLVM_BINDIR": "/dev/null", - "LLVM_DISABLE_ABI_BREAKING_CHECKS_ENFORCING": 0, - "LLVM_ENABLE_ABI_BREAKING_CHECKS": 0, - "LLVM_ENABLE_THREADS": 1, - "LLVM_ENABLE_ZLIB": 1, - "LLVM_HAS_ATOMICS": 1, - "LLVM_INCLUDEDIR": "/dev/null", - "LLVM_INFODIR": "/dev/null", - "LLVM_MANDIR": "/dev/null", - "LLVM_NATIVE_TARGET": 1, - "LLVM_NATIVE_TARGETINFO": 1, - "LLVM_NATIVE_TARGETMC": 1, - "LLVM_NATIVE_ASMPRINTER": 1, - "LLVM_NATIVE_ASMPARSER": 1, - "LLVM_NATIVE_DISASSEMBLER": 1, - "LLVM_ON_UNIX": 1, - "LLVM_PREFIX": "/dev/null", - "LLVM_VERSION_MAJOR": 0, - "LLVM_VERSION_MINOR": 0, - "LLVM_VERSION_PATCH": 0, - "LTDL_SHLIB_EXT": ".so", - "PACKAGE_NAME": "llvm", - "PACKAGE_STRING": "llvm tensorflow-trunk", - "PACKAGE_VERSION": "tensorflow-trunk", - "RETSIGTYPE": "void", -} - -# CMake variables specific to the Linux platform -linux_cmake_vars = { - "HAVE_MALLOC_H": 1, - "HAVE_LINK_H": 1, - "HAVE_MALLINFO": 1, - "HAVE_FUTIMENS": 1, -} - -# CMake variables specific to the Darwin (Mac OS X) platform. -darwin_cmake_vars = { - "HAVE_MALLOC_MALLOC_H": 1, -} - -# Select a set of CMake variables based on the platform. -# TODO(phawkins): use a better method to select the right host triple, rather -# than hardcoding x86_64. -all_cmake_vars = select({ - "@org_tensorflow//tensorflow:darwin": cmake_var_string( - cmake_vars + llvm_target_cmake_vars("X86", "x86_64-apple-darwin") + - darwin_cmake_vars, - ), - "@org_tensorflow//tensorflow:linux_ppc64le": cmake_var_string( - cmake_vars + - llvm_target_cmake_vars("PowerPC", "powerpc64le-unknown-linux_gnu") + - linux_cmake_vars, - ), - "//conditions:default": cmake_var_string( - cmake_vars + - llvm_target_cmake_vars("X86", "x86_64-unknown-linux_gnu") + - linux_cmake_vars, - ), -}) - # Performs CMake variable substitutions on configuration header files. expand_cmake_vars( name = "config_gen", src = "include/llvm/Config/config.h.cmake", - cmake_vars = all_cmake_vars, + cmake_vars = llvm_all_cmake_vars, dst = "include/llvm/Config/config.h", ) expand_cmake_vars( name = "llvm_config_gen", src = "include/llvm/Config/llvm-config.h.cmake", - cmake_vars = all_cmake_vars, + cmake_vars = llvm_all_cmake_vars, dst = "include/llvm/Config/llvm-config.h", ) expand_cmake_vars( name = "abi_breaking_gen", src = "include/llvm/Config/abi-breaking.h.cmake", - cmake_vars = all_cmake_vars, + cmake_vars = llvm_all_cmake_vars, dst = "include/llvm/Config/abi-breaking.h", ) @@ -240,14 +121,7 @@ cc_library( "include/llvm/Config/config.h", "include/llvm/Config/llvm-config.h", ], - defines = [ - "LLVM_ENABLE_STATS", - "__STDC_LIMIT_MACROS", - "__STDC_CONSTANT_MACROS", - "__STDC_FORMAT_MACROS", - "_DEBUG", - "LLVM_BUILD_GLOBAL_ISEL", - ], + defines = LLVM_DEFINES, includes = ["include"], ) @@ -262,17 +136,6 @@ genrule( ) # Rules that apply the LLVM tblgen tool. -gentbl( - name = "intrinsics_gen", - tbl_outs = [("-gen-intrinsic", "include/llvm/IR/Intrinsics.inc")], - tblgen = ":llvm-tblgen", - td_file = "include/llvm/IR/Intrinsics.td", - td_srcs = glob([ - "include/llvm/CodeGen/*.td", - "include/llvm/IR/Intrinsics*.td", - ]), -) - gentbl( name = "attributes_gen", tbl_outs = [("-gen-attrs", "include/llvm/IR/Attributes.inc")], @@ -292,6 +155,42 @@ gentbl( ], ) +gentbl( + name = "instcombine_transforms_gen", + tbl_outs = [( + "-gen-searchable-tables", + "lib/Transforms/InstCombine/InstCombineTables.inc", + )], + tblgen = ":llvm-tblgen", + td_file = "lib/Transforms/InstCombine/InstCombineTables.td", + td_srcs = glob([ + "include/llvm/CodeGen/*.td", + "include/llvm/IR/Intrinsics*.td", + ]) + ["include/llvm/TableGen/SearchableTable.td"], +) + +gentbl( + name = "intrinsic_enums_gen", + tbl_outs = [("-gen-intrinsic-enums", "include/llvm/IR/IntrinsicEnums.inc")], + tblgen = ":llvm-tblgen", + td_file = "include/llvm/IR/Intrinsics.td", + td_srcs = glob([ + "include/llvm/CodeGen/*.td", + "include/llvm/IR/Intrinsics*.td", + ]), +) + +gentbl( + name = "intrinsics_impl_gen", + tbl_outs = [("-gen-intrinsic-impl", "include/llvm/IR/IntrinsicImpl.inc")], + tblgen = ":llvm-tblgen", + td_file = "include/llvm/IR/Intrinsics.td", + td_srcs = glob([ + "include/llvm/CodeGen/*.td", + "include/llvm/IR/Intrinsics*.td", + ]), +) + # Binary targets used by Tensorflow. cc_binary( name = "llvm-tblgen", @@ -299,11 +198,7 @@ cc_binary( "utils/TableGen/*.cpp", "utils/TableGen/*.h", ]), - linkopts = [ - "-lm", - "-ldl", - "-lpthread", - ], + linkopts = LLVM_LINKOPTS, stamp = 0, deps = [ ":config", @@ -319,11 +214,7 @@ cc_binary( "utils/FileCheck/*.cpp", "utils/FileCheck/*.h", ]), - linkopts = [ - "-ldl", - "-lm", - "-lpthread", - ], + linkopts = LLVM_LINKOPTS, stamp = 0, deps = [":support"], ) @@ -494,7 +385,8 @@ cc_library( "include/llvm/Target/AArch64/AsmParser/*.inc", "lib/Target/AArch64/AsmParser/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_desc", ":aarch64_info", @@ -519,7 +411,8 @@ cc_library( "include/llvm/Target/AArch64/InstPrinter/*.inc", "lib/Target/AArch64/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_target_gen", ":aarch64_utils", @@ -542,7 +435,8 @@ cc_library( "include/llvm/Target/AArch64/*.inc", "lib/Target/AArch64/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_asm_printer", ":aarch64_desc", @@ -575,14 +469,16 @@ cc_library( "include/llvm/Target/AArch64/MCTargetDesc/*.inc", "lib/Target/AArch64/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_asm_printer", ":aarch64_info", ":aarch64_target_gen", ":attributes_gen", ":config", - ":intrinsics_gen", + ":intrinsic_enums_gen", + ":intrinsics_impl_gen", ":mc", ":support", ], @@ -601,7 +497,8 @@ cc_library( "include/llvm/Target/AArch64/Disassembler/*.inc", "lib/Target/AArch64/Disassembler/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_desc", ":aarch64_info", @@ -629,7 +526,8 @@ cc_library( "lib/Target/AArch64/AArch64*.h", "lib/Target/AArch64/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":code_gen", ":config", @@ -652,7 +550,8 @@ cc_library( "include/llvm/Target/AArch64/Utils/*.inc", "lib/Target/AArch64/Utils/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AArch64"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"], + defines = LLVM_DEFINES, deps = [ ":aarch64_target_gen", ":config", @@ -674,6 +573,8 @@ cc_library( "include/llvm/Transforms/AggressiveInstCombine/*.def", "include/llvm/Transforms/AggressiveInstCombine/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -698,6 +599,8 @@ cc_library( "include/llvm/Analysis/*.def", "include/llvm/Analysis/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":binary_format", ":config", @@ -721,7 +624,8 @@ cc_library( "include/llvm/Target/AMDGPU/MCTargetDesc/*.inc", "lib/Target/AMDGPU/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_asm_printer", ":amdgpu_info", @@ -746,7 +650,8 @@ cc_library( "include/llvm/Target/AMDGPU/Disassembler/*.inc", "lib/Target/AMDGPU/Disassembler/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_desc", ":amdgpu_info", @@ -771,7 +676,8 @@ cc_library( "include/llvm/Target/AMDGPU/TargetInfo/*.inc", "lib/Target/AMDGPU/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_target_gen", ":config", @@ -793,7 +699,8 @@ cc_library( "include/llvm/Target/AMDGPU/Utils/*.inc", "lib/Target/AMDGPU/Utils/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_target_gen", ":config", @@ -816,7 +723,8 @@ cc_library( "include/llvm/Target/AMDGPU/AsmParser/*.inc", "lib/Target/AMDGPU/AsmParser/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_desc", ":amdgpu_info", @@ -841,7 +749,8 @@ cc_library( "include/llvm/Target/AMDGPU/InstPrinter/*.inc", "lib/Target/AMDGPU/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_utils", ":config", @@ -863,7 +772,8 @@ cc_library( "include/llvm/Target/AMDGPU/*.inc", "lib/Target/AMDGPU/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/AMDGPU"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"], + defines = LLVM_DEFINES, deps = [ ":amdgpu_asm_printer", ":amdgpu_desc", @@ -899,7 +809,8 @@ cc_library( "include/llvm/Target/ARM/AsmParser/*.inc", "lib/Target/ARM/AsmParser/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_desc", ":arm_info", @@ -925,7 +836,8 @@ cc_library( "lib/Target/ARM/*.h", "lib/Target/ARM/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_info", ":arm_target_gen", @@ -949,7 +861,8 @@ cc_library( "include/llvm/Target/ARM/*.inc", "lib/Target/ARM/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":analysis", ":arm_asm_printer", @@ -984,14 +897,16 @@ cc_library( "include/llvm/Target/ARM/MCTargetDesc/*.inc", "lib/Target/ARM/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_asm_printer", ":arm_info", ":arm_target_gen", ":attributes_gen", ":config", - ":intrinsics_gen", + ":intrinsic_enums_gen", + ":intrinsics_impl_gen", ":mc", ":mc_disassembler", ":support", @@ -1011,7 +926,8 @@ cc_library( "include/llvm/Target/ARM/Disassembler/*.inc", "lib/Target/ARM/Disassembler/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_desc", ":arm_info", @@ -1036,7 +952,8 @@ cc_library( "include/llvm/Target/ARM/TargetInfo/*.inc", "lib/Target/ARM/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_target_gen", ":config", @@ -1059,7 +976,8 @@ cc_library( "include/llvm/Target/ARM/Utils/*.inc", "lib/Target/ARM/Utils/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/ARM"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"], + defines = LLVM_DEFINES, deps = [ ":arm_target_gen", ":config", @@ -1081,6 +999,8 @@ cc_library( "include/llvm/AsmParser/*.def", "include/llvm/AsmParser/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":binary_format", ":config", @@ -1103,6 +1023,8 @@ cc_library( "include/llvm/CodeGen/AsmPrinter/*.inc", "lib/CodeGen/AsmPrinter/*.def", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":binary_format", @@ -1133,6 +1055,8 @@ cc_library( "include/llvm/BinaryFormat/ELFRelocs/*.def", "include/llvm/BinaryFormat/WasmRelocs/*.def", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":support", @@ -1153,6 +1077,8 @@ cc_library( "include/llvm/Bitcode/Reader/*.inc", "include/llvm/Bitcode/BitstreamReader.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":core", @@ -1176,6 +1102,8 @@ cc_library( "include/llvm/Bitcode/BitcodeWriterPass.h", "include/llvm/Bitcode/BitstreamWriter.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -1200,6 +1128,8 @@ cc_library( "include/llvm/CodeGen/*.inc", "include/llvm/CodeGen/**/*.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":bit_reader", @@ -1237,12 +1167,15 @@ cc_library( "include/llvm/*.h", "include/llvm/Analysis/*.def", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":attributes_compat_gen", ":attributes_gen", ":binary_format", ":config", - ":intrinsics_gen", + ":intrinsic_enums_gen", + ":intrinsics_impl_gen", ":support", ], ) @@ -1260,6 +1193,8 @@ cc_library( "include/llvm/DebugInfo/CodeView/*.def", "include/llvm/DebugInfo/CodeView/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":binary_format", ":config", @@ -1281,6 +1216,8 @@ cc_library( "include/llvm/DebugInfo/MSF/*.def", "include/llvm/DebugInfo/MSF/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":support", @@ -1300,6 +1237,8 @@ cc_library( "include/llvm/Demangle/*.def", "include/llvm/Demangle/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [":config"], ) @@ -1316,6 +1255,8 @@ cc_library( "include/llvm/ExecutionEngine/*.def", "include/llvm/ExecutionEngine/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":core", @@ -1340,6 +1281,8 @@ cc_library( "include/llvm/CodeGen/GlobalISel/*.def", "include/llvm/CodeGen/GlobalISel/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":code_gen", @@ -1369,6 +1312,8 @@ cc_library( "include/llvm/Transforms/InstrProfiling.h", "include/llvm/Transforms/PGOInstrumentation.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -1393,10 +1338,13 @@ cc_library( "include/llvm/Transforms/InstCombine/*.def", "include/llvm/Transforms/InstCombine/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", ":core", + ":instcombine_transforms_gen", ":support", ":transform_utils", ], @@ -1418,6 +1366,8 @@ cc_library( "include/llvm/Transforms/IPO/*.def", "include/llvm/Transforms/IPO/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":aggressive_inst_combine", ":analysis", @@ -1451,6 +1401,8 @@ cc_library( "include/llvm/IRReader/*.def", "include/llvm/IRReader/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":asm_parser", ":bit_reader", @@ -1473,6 +1425,8 @@ cc_library( "include/llvm/Linker/*.def", "include/llvm/Linker/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":core", @@ -1494,6 +1448,8 @@ cc_library( "include/llvm/MC/*.def", "include/llvm/MC/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":binary_format", ":config", @@ -1515,6 +1471,8 @@ cc_library( "include/llvm/MC/MCDisassembler/*.def", "include/llvm/MC/MCDisassembler/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -1535,6 +1493,8 @@ cc_library( "include/llvm/MC/MCParser/*.def", "include/llvm/MC/MCParser/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -1555,7 +1515,8 @@ cc_library( "include/llvm/Target/NVPTX/InstPrinter/*.inc", "lib/Target/NVPTX/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/NVPTX"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"], + defines = LLVM_DEFINES, deps = [ "nvptx_target_gen", ":attributes_gen", @@ -1579,7 +1540,8 @@ cc_library( "include/llvm/Target/NVPTX/*.inc", "lib/Target/NVPTX/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/NVPTX"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"], + defines = LLVM_DEFINES, deps = [ ":analysis", ":asm_printer", @@ -1613,7 +1575,8 @@ cc_library( "include/llvm/Target/NVPTX/MCTargetDesc/*.inc", "lib/Target/NVPTX/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/NVPTX"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"], + defines = LLVM_DEFINES, deps = [ "nvptx_target_gen", ":config", @@ -1639,7 +1602,8 @@ cc_library( "lib/Target/NVPTX/NVPTX.h", "lib/Target/NVPTX/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/NVPTX"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"], + defines = LLVM_DEFINES, deps = [ "nvptx_target_gen", ":attributes_gen", @@ -1663,6 +1627,8 @@ cc_library( "include/llvm/Object/*.def", "include/llvm/Object/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":binary_format", ":bit_reader", @@ -1688,6 +1654,8 @@ cc_library( "include/llvm/Transforms/ObjCARC/*.def", "include/llvm/Transforms/ObjCARC/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -1710,13 +1678,17 @@ cc_library( "include/llvm/ExecutionEngine/Orc/*.def", "include/llvm/ExecutionEngine/Orc/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":core", ":execution_engine", + ":mc", ":object", ":runtime_dyld", ":support", + ":target", ":transform_utils", ], ) @@ -1734,7 +1706,8 @@ cc_library( "include/llvm/Target/PowerPC/AsmParser/*.inc", "lib/Target/PowerPC/AsmParser/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -1758,11 +1731,13 @@ cc_library( "include/llvm/Target/PowerPC/InstPrinter/*.inc", "lib/Target/PowerPC/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":attributes_gen", ":config", - ":intrinsics_gen", + ":intrinsic_enums_gen", + ":intrinsics_impl_gen", ":mc", ":powerpc_info", ":powerpc_target_gen", @@ -1783,7 +1758,8 @@ cc_library( "include/llvm/Target/PowerPC/*.inc", "lib/Target/PowerPC/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":analysis", ":asm_printer", @@ -1815,11 +1791,13 @@ cc_library( "include/llvm/Target/PowerPC/MCTargetDesc/*.inc", "lib/Target/PowerPC/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":attributes_gen", ":config", - ":intrinsics_gen", + ":intrinsic_enums_gen", + ":intrinsics_impl_gen", ":mc", ":powerpc_asm_printer", ":powerpc_info", @@ -1841,7 +1819,8 @@ cc_library( "include/llvm/Target/PowerPC/Disassembler/*.inc", "lib/Target/PowerPC/Disassembler/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc_disassembler", @@ -1865,12 +1844,12 @@ cc_library( "lib/Target/PowerPC/PPC*.h", "lib/Target/PowerPC/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/PowerPC"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"], + defines = LLVM_DEFINES, deps = [ ":attributes_gen", ":config", ":core", - ":intrinsics_gen", ":powerpc_target_gen", ":support", ":target", @@ -1890,6 +1869,8 @@ cc_library( "include/llvm/ProfileData/*.def", "include/llvm/ProfileData/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":core", @@ -1918,6 +1899,8 @@ cc_library( "include/llvm/ExecutionEngine/RTDyldMemoryManager.h", "include/llvm/ExecutionEngine/RuntimeDyld*.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -1945,6 +1928,8 @@ cc_library( "include/llvm/Transforms/IPO.h", "include/llvm/Transforms/IPO/SCCP.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":aggressive_inst_combine", ":analysis", @@ -1970,6 +1955,8 @@ cc_library( "include/llvm/CodeGen/SelectionDAG/*.def", "include/llvm/CodeGen/SelectionDAG/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":code_gen", @@ -2007,6 +1994,8 @@ cc_library( "include/llvm/BinaryFormat/MachO.def", "include/llvm/Support/VCSRevision.h", ], + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":demangle", @@ -2029,6 +2018,8 @@ cc_library( "include/llvm/TableGen/*.inc", "include/llvm/Target/*.def", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -2054,6 +2045,8 @@ cc_library( "include/llvm/CodeGen/*.def", "include/llvm/CodeGen/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -2078,6 +2071,8 @@ cc_library( "include/llvm/Transforms/Utils/*.def", "include/llvm/Transforms/Utils/*.inc", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -2101,6 +2096,8 @@ cc_library( "include/llvm/Transforms/Vectorize/*.inc", "include/llvm/Transforms/Vectorize.h", ]), + copts = LLVM_COPTS, + defines = LLVM_DEFINES, deps = [ ":analysis", ":config", @@ -2124,7 +2121,8 @@ cc_library( "include/llvm/Target/X86/AsmParser/*.inc", "lib/Target/X86/AsmParser/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -2149,7 +2147,8 @@ cc_library( "include/llvm/Target/X86/InstPrinter/*.inc", "lib/Target/X86/InstPrinter/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -2173,7 +2172,8 @@ cc_library( "include/llvm/Target/X86/*.inc", "lib/Target/X86/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":analysis", ":asm_printer", @@ -2206,7 +2206,8 @@ cc_library( "include/llvm/Target/X86/MCTargetDesc/*.inc", "lib/Target/X86/MCTargetDesc/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -2231,7 +2232,8 @@ cc_library( "include/llvm/Target/X86/Disassembler/*.inc", "lib/Target/X86/Disassembler/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc_disassembler", @@ -2254,7 +2256,8 @@ cc_library( "include/llvm/Target/X86/TargetInfo/*.inc", "lib/Target/X86/TargetInfo/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":config", ":mc", @@ -2276,7 +2279,8 @@ cc_library( "include/llvm/Target/X86/Utils/*.inc", "lib/Target/X86/Utils/*.h", ]), - copts = ["-Iexternal/llvm/lib/Target/X86"], + copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"], + defines = LLVM_DEFINES, deps = [ ":code_gen", ":config", diff --git a/third_party/llvm/llvm.bzl b/third_party/llvm/llvm.bzl index 0efcf319bd99be79263a1b9cd23544523a4c8076..2e809e5f147d9e2b359dbf8fcc57575572bc64cd 100644 --- a/third_party/llvm/llvm.bzl +++ b/third_party/llvm/llvm.bzl @@ -105,3 +105,136 @@ def expand_cmake_vars(name, src, dst, cmake_vars): "< $< > $@") ) +# TODO(phawkins): the set of CMake variables was hardcoded for expediency. +# However, we should really detect many of these via configure-time tests. + +# The set of CMake variables common to all targets. +cmake_vars = { + # Headers + "HAVE_DIRENT_H": 1, + "HAVE_DLFCN_H": 1, + "HAVE_ERRNO_H": 1, + "HAVE_EXECINFO_H": 1, + "HAVE_FCNTL_H": 1, + "HAVE_INTTYPES_H": 1, + "HAVE_PTHREAD_H": 1, + "HAVE_SIGNAL_H": 1, + "HAVE_STDINT_H": 1, + "HAVE_SYS_IOCTL_H": 1, + "HAVE_SYS_MMAN_H": 1, + "HAVE_SYS_PARAM_H": 1, + "HAVE_SYS_RESOURCE_H": 1, + "HAVE_SYS_STAT_H": 1, + "HAVE_SYS_TIME_H": 1, + "HAVE_SYS_TYPES_H": 1, + "HAVE_TERMIOS_H": 1, + "HAVE_UNISTD_H": 1, + "HAVE_ZLIB_H": 1, + + # Features + "HAVE_BACKTRACE": 1, + "BACKTRACE_HEADER": "execinfo.h", + "HAVE_DLOPEN": 1, + "HAVE_FUTIMES": 1, + "HAVE_GETCWD": 1, + "HAVE_GETPAGESIZE": 1, + "HAVE_GETRLIMIT": 1, + "HAVE_GETRUSAGE": 1, + "HAVE_GETTIMEOFDAY": 1, + "HAVE_INT64_T": 1, + "HAVE_ISATTY": 1, + "HAVE_LIBEDIT": 1, + "HAVE_LIBPTHREAD": 1, + "HAVE_LIBZ": 1, + "HAVE_MKDTEMP": 1, + "HAVE_MKSTEMP": 1, + "HAVE_MKTEMP": 1, + "HAVE_PREAD": 1, + "HAVE_PTHREAD_GETSPECIFIC": 1, + "HAVE_PTHREAD_MUTEX_LOCK": 1, + "HAVE_PTHREAD_RWLOCK_INIT": 1, + "HAVE_REALPATH": 1, + "HAVE_SBRK": 1, + "HAVE_SETENV": 1, + "HAVE_SETRLIMIT": 1, + "HAVE_SIGALTSTACK": 1, + "HAVE_STRERROR": 1, + "HAVE_STRERROR_R": 1, + "HAVE_STRTOLL": 1, + "HAVE_SYSCONF": 1, + "HAVE_UINT64_T": 1, + "HAVE__UNWIND_BACKTRACE": 1, + + # LLVM features + "ENABLE_BACKTRACES": 1, + "LLVM_BINDIR": "/dev/null", + "LLVM_DISABLE_ABI_BREAKING_CHECKS_ENFORCING": 0, + "LLVM_ENABLE_ABI_BREAKING_CHECKS": 0, + "LLVM_ENABLE_THREADS": 1, + "LLVM_ENABLE_ZLIB": 1, + "LLVM_HAS_ATOMICS": 1, + "LLVM_INCLUDEDIR": "/dev/null", + "LLVM_INFODIR": "/dev/null", + "LLVM_MANDIR": "/dev/null", + "LLVM_NATIVE_TARGET": 1, + "LLVM_NATIVE_TARGETINFO": 1, + "LLVM_NATIVE_TARGETMC": 1, + "LLVM_NATIVE_ASMPRINTER": 1, + "LLVM_NATIVE_ASMPARSER": 1, + "LLVM_NATIVE_DISASSEMBLER": 1, + "LLVM_ON_UNIX": 1, + "LLVM_PREFIX": "/dev/null", + "LLVM_VERSION_MAJOR": 0, + "LLVM_VERSION_MINOR": 0, + "LLVM_VERSION_PATCH": 0, + "LTDL_SHLIB_EXT": ".so", + "PACKAGE_NAME": "llvm", + "PACKAGE_STRING": "llvm tensorflow-trunk", + "PACKAGE_VERSION": "tensorflow-trunk", + "RETSIGTYPE": "void", +} + +# CMake variables specific to the Linux platform +linux_cmake_vars = { + "HAVE_MALLOC_H": 1, + "HAVE_LINK_H": 1, + "HAVE_MALLINFO": 1, + "HAVE_FUTIMENS": 1, +} + +# CMake variables specific to the Darwin (Mac OS X) platform. +darwin_cmake_vars = { + "HAVE_MALLOC_MALLOC_H": 1, +} + +# Select a set of CMake variables based on the platform. +# TODO(phawkins): use a better method to select the right host triple, rather +# than hardcoding x86_64. +llvm_all_cmake_vars = select({ + "@org_tensorflow//tensorflow:darwin": cmake_var_string( + cmake_vars + llvm_target_cmake_vars("X86", "x86_64-apple-darwin") + + darwin_cmake_vars), + "@org_tensorflow//tensorflow:linux_ppc64le": cmake_var_string( + cmake_vars + + llvm_target_cmake_vars("PowerPC", "powerpc64le-unknown-linux_gnu") + + linux_cmake_vars, + ), + "//conditions:default": cmake_var_string( + cmake_vars + + llvm_target_cmake_vars("X86", "x86_64-unknown-linux_gnu") + + linux_cmake_vars), + +}) + +LLVM_LINKOPTS = ["-ldl", "-lm", "-lpthread"] + +LLVM_DEFINES = [ + "LLVM_ENABLE_STATS", + "__STDC_LIMIT_MACROS", + "__STDC_CONSTANT_MACROS", + "__STDC_FORMAT_MACROS", + "_DEBUG", + "LLVM_BUILD_GLOBAL_ISEL", +] + +LLVM_COPTS = [] diff --git a/third_party/repo.bzl b/third_party/repo.bzl index cb67d3e9617dd1e9374d07cb1536cedf4bc74ae8..9cee1fcc4b5c2b05ecc09b4f372eadeca9e91be8 100644 --- a/third_party/repo.bzl +++ b/third_party/repo.bzl @@ -16,7 +16,6 @@ _SINGLE_URL_WHITELIST = depset([ "arm_compiler", - "ortools_archive", ]) def _is_windows(ctx): diff --git a/third_party/sqlite.BUILD b/third_party/sqlite.BUILD index 6da795358927f5cb8db7cb0d7ea653b80f8b5226..2876f305f1f74e8bba9a364b1ef582f42c72c313 100644 --- a/third_party/sqlite.BUILD +++ b/third_party/sqlite.BUILD @@ -5,6 +5,7 @@ licenses(["unencumbered"]) # Public Domain SQLITE_COPTS = [ "-Os", + "-DSQLITE_ENABLE_JSON1", "-DHAVE_DECL_STRERROR_R=1", "-DHAVE_STDINT_H=1", "-DHAVE_INTTYPES_H=1", diff --git a/third_party/toolchains/BUILD b/third_party/toolchains/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..fc3183a754369fc30dbce40c2bf7b6828ea497c3 --- /dev/null +++ b/third_party/toolchains/BUILD @@ -0,0 +1,22 @@ +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +# Platform for use with remote execution with +# custom container based off RBE Ubuntu16_04 +# http://gcr.io/cloud-marketplace/google/rbe-ubuntu16-04 +# Built with //tensorflow/tools/ci_build/Dockerfile.rbe.cpu +platform( + name = "rbe_ubuntu16_04-tf", + constraint_values = [ + "@bazel_tools//platforms:x86_64", + "@bazel_tools//platforms:linux", + "@bazel_tools//tools/cpp:clang", + "@bazel_toolchains//constraints:xenial", + ], + remote_execution_properties = """ + properties: { + name: "container-image" + value:"docker://gcr.io/asci-toolchain/nosla-ubuntu16_04-tf@sha256:800a7b68cabef15419695c188ed33ed70adf678c2371b97b236f3ae26c38274d" + }""", +) diff --git a/third_party/toolchains/clang6/CROSSTOOL.tpl b/third_party/toolchains/clang6/CROSSTOOL.tpl index 6b7e5a88086f8e5e67fa86a0e9377c3c2afd535d..ffba9850bb80a880d5b95afacbad296ec1f2df54 100644 --- a/third_party/toolchains/clang6/CROSSTOOL.tpl +++ b/third_party/toolchains/clang6/CROSSTOOL.tpl @@ -76,9 +76,6 @@ toolchain { # This adds a little bit more durability to our Clang build. # - # At the moment, this only only be needed for: - # - add_boringssl_s390x.patch: --Wa,--noexecstack - # # Folks who do maintenance work on TF Bazel Clang should consider # commenting out these lines, while doing that work, to gain a better # understanding of what the intersection of support looks like between GCC

+

Learn and use ML

+
+

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

+
    +
  1. Basic classification
    2. +
  2. Text classification
    3. +
  3. Regression
    4. +
  4. Overfitting and underfitting
    5. +
  5. Save and load
    6. +
+
+
+ Read the Keras guide +
+