diff options
| author |  Michael Case <mikecase@google.com> | 2018-02-07 14:36:00 -0800 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-02-07 14:39:49 -0800 |
| commit | d90054e7c0f41f4bab81df0548577a73b939a87a (patch) | |
| tree | a15aea686a9d3f305e316d2a6ada0859ad8170d1 | |
| parent | 8461760f9f6cde8ed97507484d2a879140141032 (diff) | |
Merge changes from github.
PiperOrigin-RevId: 184897758
256 files changed, 4478 insertions, 898 deletions
diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md index 1a401997c6..2f3df7cda9 100644 --- a/ISSUE_TEMPLATE.md +++ b/ISSUE_TEMPLATE.md @@ -4,7 +4,7 @@ https://stackoverflow.com/questions/tagged/tensorflow If you open a GitHub issue, here is our policy: -1. It must be a bug or a feature request. +1. It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead). 2. The form below must be filled out. 3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorboard/issues). @@ -6,7 +6,7 @@ | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** | |-----------------|---------------------|------------------|-------------------|---------------| -| [](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [](https://ci.tensorflow.org/job/tensorflow-master-mac) | [](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [](https://ci.tensorflow.org/job/tensorflow-master-android) | +| [](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [](https://ci.tensorflow.org/job/tensorflow-master-mac) | [](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [](https://ci.tensorflow.org/job/tensorflow-master-android) [  ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | **TensorFlow** is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while @@ -27,7 +27,7 @@ guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's uphold this code.** **We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for -tracking requests and bugs. So please see +tracking requests and bugs. So please see [TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** diff --git a/RELEASE.md b/RELEASE.md index fdf10407fd..b11b1e40db 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,18 +1,39 @@ # Release 1.5.0 ## Breaking Changes -* Prebuilt binaries are now built against CUDA 9 and cuDNN 7. +* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7. * Our Linux binaries are built using ubuntu 16 containers, potentially introducing glibc incompatibility issues with ubuntu 14. * Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs. +## Known Bugs +* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or + `CUDA_ILLEGAL_ADDRESS` failures. + + Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9 + and CUDA 9.1 sometimes does not properly compute the carry bit when + decomposing 64-bit address calculations with large offsets (e.g. `load [x + + large_constant]`) into 32-bit arithmetic in SASS. + + As a result, these versions of `ptxas` miscompile most XLA programs which use + more than 4GB of temp memory. This results in garbage results and/or + `CUDA_ERROR_ILLEGAL_ADDRESS` failures. + + A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a + fix for CUDA 9.0.x. Until the fix is available, the only workaround is to + [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x + or disable XLA:GPU. + + TensorFlow will print a warning if you use XLA:GPU with a known-bad version of + CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122. + ## Major Features And Improvements * [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager) preview version is now available. * [TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/lite) dev preview is now available. -* CUDA 9 and cuDNN 7 support. +* CUDA 9.0 and cuDNN 7 support. * Accelerated Linear Algebra (XLA): * Add `complex64` support to XLA compiler. * `bfloat` support is now added to XLA infrastructure. @@ -523,7 +544,7 @@ answered questions, and were part of inspiring discussions. * Fixed LIBXSMM integration. * Make decode_jpeg/decode_png/decode_gif handle all formats, since users frequently try to decode an image as the wrong type. * Improve implicit broadcasting lowering. -* Improving stability of GCS/Bigquery clients by a faster retrying of stale transmissions. +* Improving stability of GCS/BigQuery clients by a faster retrying of stale transmissions. * Remove OpKernelConstruction::op_def() as part of minimizing proto dependencies. * VectorLaplaceDiag distribution added. * Android demo no longer requires libtensorflow_demo.so to run (libtensorflow_inference.so still required) @@ -41,12 +41,12 @@ load("//tensorflow:workspace.bzl", "tf_workspace") tf_workspace() new_http_archive( - name = "inception5h", + name = "inception_v1", build_file = "models.BUILD", - sha256 = "d13569f6a98159de37e92e9c8ec4dae8f674fbf475f69fe6199b514f756d4364", + sha256 = "7efe12a8363f09bc24d7b7a450304a15655a57a7751929b2c1593a71183bb105", urls = [ - "http://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip", - "http://download.tensorflow.org/models/inception5h.zip", + "http://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip", + "http://download.tensorflow.org/models/inception_v1.zip", ], ) diff --git a/configure.py b/configure.py index 083fed1710..27519b4aba 100644 --- a/configure.py +++ b/configure.py @@ -298,7 +298,7 @@ def get_var(environ_cp, System". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. - yes_reply: optionanl string for reply when feature is enabled. + yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. Returns: @@ -411,7 +411,7 @@ def set_action_env_var(environ_cp, System". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. - yes_reply: optionanl string for reply when feature is enabled. + yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. """ var = int( @@ -1354,6 +1354,7 @@ def main(): environ_cp['TF_NEED_GCP'] = '0' environ_cp['TF_NEED_HDFS'] = '0' environ_cp['TF_NEED_JEMALLOC'] = '0' + environ_cp['TF_NEED_KAFKA'] = '0' environ_cp['TF_NEED_OPENCL_SYCL'] = '0' environ_cp['TF_NEED_COMPUTECPP'] = '0' environ_cp['TF_NEED_OPENCL'] = '0' @@ -1372,6 +1373,8 @@ def main(): 'with_hdfs_support', True, 'hdfs') set_build_var(environ_cp, 'TF_NEED_S3', 'Amazon S3 File System', 'with_s3_support', True, 's3') + set_build_var(environ_cp, 'TF_NEED_KAFKA', 'Apache Kafka Platform', + 'with_kafka_support', False, 'kafka') set_build_var(environ_cp, 'TF_ENABLE_XLA', 'XLA JIT', 'with_xla_support', False, 'xla') set_build_var(environ_cp, 'TF_NEED_GDR', 'GDR', 'with_gdr_support', diff --git a/tensorflow/BUILD b/tensorflow/BUILD index e89667cbfd..a73e89bc1a 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -211,6 +211,12 @@ config_setting( visibility = ["//visibility:public"], ) +config_setting( + name = "with_kafka_support", + define_values = {"with_kafka_support": "true"}, + visibility = ["//visibility:public"], +) + # Crosses between platforms and file system libraries not supported on those # platforms due to limitations in nested select() statements. config_setting( diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD index c9ade5fb83..9060c19e9d 100644 --- a/tensorflow/cc/BUILD +++ b/tensorflow/cc/BUILD @@ -433,6 +433,7 @@ tf_gen_op_wrappers_cc( "linalg_ops", "logging_ops", "lookup_ops", + "manip_ops", "math_ops", "nn_ops", "no_op", diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc index 57244a4f0a..52a81a5028 100644 --- a/tensorflow/cc/tools/freeze_saved_model_test.cc +++ b/tensorflow/cc/tools/freeze_saved_model_test.cc @@ -71,7 +71,7 @@ class FreezeTest : public ::testing::Test { return Status::OK(); } - // Adds `graph_def` to `saved_model_bundle` and intializes a session with + // Adds `graph_def` to `saved_model_bundle` and initializes a session with // `init_node`. Status AddGraphDefToSavedModelBundle(const GraphDef& graph_def, const string& init_node, diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index 0540260efd..bc46918df9 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -132,7 +132,10 @@ tf_library( config = "test_graph_tfadd.config.pbtxt", cpp_class = "AddComp", graph = "test_graph_tfadd.pbtxt", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -143,7 +146,10 @@ tf_library( config = "test_graph_tfunknownop.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -155,7 +161,10 @@ tf_library( config = "test_graph_tfunknownop2.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) # A test of tf_library that includes a graph with an unknown op, but where @@ -166,7 +175,10 @@ tf_library( config = "test_graph_tfunknownop3.config.pbtxt", cpp_class = "UnknownOpAddComp", graph = "test_graph_tfunknownop.pbtxt", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) # Utility library for benchmark binaries, used by the *_benchmark rules that are diff --git a/tensorflow/compiler/aot/tests/BUILD b/tensorflow/compiler/aot/tests/BUILD index 7dfd49cc3b..43d8ae4108 100644 --- a/tensorflow/compiler/aot/tests/BUILD +++ b/tensorflow/compiler/aot/tests/BUILD @@ -74,7 +74,10 @@ tf_library( # compile but the others in this directory succeed, you may need to # expand the "required by all tf_library targets" list in tfcompile.bzl. include_standard_runtime_deps = False, - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -84,7 +87,10 @@ tf_library( cpp_class = "AddWithCkptComp", freeze_checkpoint = "test_graph_tfadd_with_ckpt.ckpt", graph = "test_graph_tfadd_with_ckpt.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -95,7 +101,10 @@ tf_library( freeze_checkpoint = "test_graph_tfadd_with_ckpt_saver.ckpt", freeze_saver = "test_graph_tfadd_with_ckpt_saver.saver", graph = "test_graph_tfadd_with_ckpt_saver.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -104,7 +113,10 @@ tf_library( config = "test_graph_tffunction.config.pbtxt", cpp_class = "FunctionComp", graph = "test_graph_tffunction.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -113,7 +125,10 @@ tf_library( config = "test_graph_tfgather.config.pbtxt", cpp_class = "GatherComp", graph = "test_graph_tfgather.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -122,7 +137,10 @@ tf_library( config = "test_graph_tfmatmul.config.pbtxt", cpp_class = "foo::bar::MatMulComp", graph = "test_graph_tfmatmul.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_library( @@ -131,7 +149,10 @@ tf_library( config = "test_graph_tfmatmulandadd.config.pbtxt", cpp_class = "MatMulAndAddComp", graph = "test_graph_tfmatmulandadd.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], tfcompile_flags = "--gen_name_to_index --gen_program_shape", ) @@ -141,13 +162,19 @@ tf_library( config = "test_graph_tfsplits.config.pbtxt", cpp_class = "SplitsComp", graph = "test_graph_tfsplits.pb", - tags = ["manual"], + tags = [ + "manual", + "notap", + ], ) tf_cc_test( name = "tfcompile_test", srcs = ["tfcompile_test.cc"], - tags = ["manual"], + tags = [ + "manual", + "notap", + ], deps = [ ":test_graph_tfadd", ":test_graph_tfadd_with_ckpt", diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 9d34cdfe10..30a6d3a74d 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -774,15 +774,15 @@ class BinaryOpsTest(XLATestCase): def DISABLED_testSparseMatMul(self): # Binary wrappers for sparse_matmul with different hints def SparseMatmulWrapperTF(a, b): - return tf.sparse_matmul(a, b, a_is_sparse=True) + return math_ops.sparse_matmul(a, b, a_is_sparse=True) def SparseMatmulWrapperFT(a, b): - return tf.sparse_matmul(a, b, b_is_sparse=True) + return math_ops.sparse_matmul(a, b, b_is_sparse=True) def SparseMatmulWrapperTT(a, b): - return tf.sparse_matmul(a, b, a_is_sparse=True, b_is_sparse=True) + return math_ops.sparse_matmul(a, b, a_is_sparse=True, b_is_sparse=True) - self._testMatMul(tf.sparse_matmul) + self._testMatMul(math_ops.sparse_matmul) self._testMatMul(SparseMatmulWrapperTF) self._testMatMul(SparseMatmulWrapperFT) self._testMatMul(SparseMatmulWrapperTT) diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 2ba572fd0e..d4fb5dd4e0 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -38,8 +38,22 @@ class PoolingOp : public XlaOpKernel { PoolingOp(OpKernelConstruction* ctx, int num_spatial_dims) : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { if (ctx->num_inputs() == 1) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); + std::vector<int32> ksize_int; + std::vector<int32> stride_int; + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_int)); + OP_REQUIRES(ctx, ksize_int.size() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_int)); + OP_REQUIRES(ctx, stride_int.size() == num_dims(), + errors::InvalidArgument("Sliding window stride field must " + "specify ", + num_dims(), " dimensions")); + for (int i = 0; i < num_dims(); ++i) { + ksize_.push_back(ksize_int[i]); + stride_.push_back(stride_int[i]); + } } Padding padding; OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding)); @@ -65,28 +79,33 @@ class PoolingOp : public XlaOpKernel { xla::ComputationDataHandle input = ctx->Input(0); const TensorShape input_shape = ctx->InputShape(0); + std::vector<int64> ksize = ksize_; + std::vector<int64> stride = stride_; if (ctx->num_inputs() != 1) { const TensorShape ksize_shape = ctx->InputShape(1); + // Validate input sizes. OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), errors::InvalidArgument("ksize must be a vector, not shape ", ksize_shape.DebugString())); - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &ksize_)); + OP_REQUIRES(ctx, ksize_shape.num_elements() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + ksize.clear(); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &ksize)); const TensorShape stride_shape = ctx->InputShape(2); + // Validate input sizes. OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), errors::InvalidArgument("stride must be a vector, not shape ", stride_shape.DebugString())); - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &stride_)); + OP_REQUIRES(ctx, stride_shape.num_elements() == num_dims(), + errors::InvalidArgument("Sliding window stride field must " + "specify ", + num_dims(), " dimensions")); + stride.clear(); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &stride)); } - - OP_REQUIRES(ctx, ksize_.size() == num_dims(), - errors::InvalidArgument("Sliding window ksize field must " - "specify ", - num_dims(), " dimensions")); - OP_REQUIRES(ctx, stride_.size() == num_dims(), - errors::InvalidArgument("Sliding window stride field must " - "specify ", - num_dims(), " dimensions")); OP_REQUIRES(ctx, input_shape.dims() == num_dims(), errors::InvalidArgument("Input to ", type_string(), " operator must have ", num_dims(), @@ -94,8 +113,8 @@ class PoolingOp : public XlaOpKernel { const DataType type = input_type(0); xla::ComputationDataHandle pooled = ctx->builder()->ReduceWindow( - input, InitValue(ctx->builder(), type), *Reduction(ctx, type), ksize_, - stride_, padding_); + input, InitValue(ctx->builder(), type), *Reduction(ctx, type), ksize, + stride, padding_); ctx->SetOutput(0, PostProcessOutput(ctx, pooled, type, input_shape)); } diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h index d82ba63e8a..ea4cdb7667 100644 --- a/tensorflow/compiler/xla/client/computation_builder.h +++ b/tensorflow/compiler/xla/client/computation_builder.h @@ -67,7 +67,7 @@ class ComputationBuilder { // OpMetadata is often applied to a series of XLA HLO instructions. As a // result, OpMetadata is set on the Computation Builder. All subsequent // instructions generated via this Computation Builder will have the same - // OpMetadata attached until a call to ClearOpMetdata. + // OpMetadata attached until a call to ClearOpMetadata. void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } // Clears the HloMetadata state. diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index 42e7f91f26..d9c4d094b8 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -2173,7 +2173,7 @@ bool HloParser::ParseConvolutionDimensionNumbers( // // {[2:3:4], [5:6:7], [8:9]} // -// The the parsed result will be: +// The parsed result will be: // // {/*starts=*/{2, 5, 8}, /*limits=*/{3, 6, 9}, /*strides=*/{4, 7, 1}} // diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 0451f00629..3ed8cef56c 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -50,6 +50,7 @@ py_library( "//tensorflow/contrib/image:single_image_random_dot_stereograms_py", "//tensorflow/contrib/input_pipeline:input_pipeline_py", "//tensorflow/contrib/integrate:integrate_py", + "//tensorflow/contrib/kafka", "//tensorflow/contrib/keras", "//tensorflow/contrib/kernel_methods", "//tensorflow/contrib/kfac", @@ -142,6 +143,7 @@ cc_library( "//tensorflow/contrib/factorization:all_ops", "//tensorflow/contrib/framework:all_ops", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib", + "//tensorflow/contrib/kafka:kafka_ops_op_lib", "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib", "//tensorflow/contrib/nccl:nccl_ops_op_lib", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_ops_op_lib", diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java index dc5b9fb887..abddadac5b 100644 --- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java +++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java @@ -194,6 +194,11 @@ public class TensorFlowInferenceInterface { * @param outputNames A list of output nodes which should be filled by the inference pass. */ public void run(String[] outputNames, boolean enableStats) { + run(outputNames, enableStats, new String[] {}); + } + + /** An overloaded version of runInference that allows supplying targetNodeNames as well */ + public void run(String[] outputNames, boolean enableStats, String[] targetNodeNames) { // Release any Tensors from the previous run calls. closeFetches(); @@ -204,6 +209,11 @@ public class TensorFlowInferenceInterface { runner.fetch(tid.name, tid.outputIndex); } + // Add targets. + for (String t : targetNodeNames) { + runner.addTarget(t); + } + // Run the session. try { if (enableStats) { diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index ad8c995eef..57a52bf4ca 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -6,6 +6,7 @@ tensorflow/core/example tensorflow/core/framework tensorflow/core/lib tensorflow/core/lib/core +tensorflow/core/profiler tensorflow/core/protobuf tensorflow/core/util tensorflow/examples @@ -219,6 +220,8 @@ tensorflow/contrib/input_pipeline/python/ops tensorflow/contrib/integrate tensorflow/contrib/integrate/python tensorflow/contrib/integrate/python/ops +tensorflow/contrib/kafka/python +tensorflow/contrib/kafka/python/ops tensorflow/contrib/keras tensorflow/contrib/keras/api tensorflow/contrib/keras/api/keras diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake index 138993db35..c42bc35ce7 100644 --- a/tensorflow/contrib/cmake/tf_core_ops.cmake +++ b/tensorflow/contrib/cmake/tf_core_ops.cmake @@ -30,6 +30,7 @@ set(tf_op_lib_names "list_ops" "lookup_ops" "logging_ops" + "manip_ops" "math_ops" "nn_ops" "no_op" diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 294b9c5941..34c466fa01 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -335,6 +335,7 @@ GENERATE_PYTHON_OP_LIB("list_ops") GENERATE_PYTHON_OP_LIB("logging_ops") GENERATE_PYTHON_OP_LIB("lookup_ops") GENERATE_PYTHON_OP_LIB("nn_ops") +GENERATE_PYTHON_OP_LIB("manip_ops") GENERATE_PYTHON_OP_LIB("parsing_ops") GENERATE_PYTHON_OP_LIB("random_ops") GENERATE_PYTHON_OP_LIB("remote_fused_graph_ops" diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index f67698eb99..53c2285699 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -31,7 +31,7 @@ from __future__ import division from __future__ import print_function import argparse -import io +import codecs import os import re import subprocess @@ -103,7 +103,7 @@ def main(): for lib_path in args.input: proc = subprocess.Popen([DUMPBIN, "/nologo", "/linkermember:1", lib_path], stdout=subprocess.PIPE) - for line in io.TextIOWrapper(proc.stdout, encoding="utf-8"): + for line in codecs.getreader("utf-8")(proc.stdout): cols = line.split() if len(cols) < 2: continue @@ -131,7 +131,7 @@ def main(): # We compare on undname but use the decorated name from candidates. dupes = 0 proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) - for idx, line in enumerate(io.TextIOWrapper(proc.stdout, encoding="utf-8")): + for idx, line in enumerate(codecs.getreader("utf-8")(proc.stdout)): decorated = candidates[idx] if decorated in taken: # Symbol is already in output, done. diff --git a/tensorflow/contrib/coder/README.md b/tensorflow/contrib/coder/README.md index e1e867db5a..c6c379c458 100644 --- a/tensorflow/contrib/coder/README.md +++ b/tensorflow/contrib/coder/README.md @@ -30,7 +30,7 @@ following sense: around, - The number of CDF axes does not extend, i.e., `CDF.ndim == data.ndim + 1`. -In the previous example where data has shape (10, 10), the followings are +In the previous example where data has shape (10, 10), the following are acceptable CDF shapes: - (10, 10, 65) diff --git a/tensorflow/contrib/coder/kernels/range_coder.cc b/tensorflow/contrib/coder/kernels/range_coder.cc index f4f076b6c4..21b35155ff 100644 --- a/tensorflow/contrib/coder/kernels/range_coder.cc +++ b/tensorflow/contrib/coder/kernels/range_coder.cc @@ -276,7 +276,7 @@ void RangeEncoder::Finalize(string* sink) { } } else if (base_ != 0) { // If base == 0, then pick 0 from [base, base + size) and no zeros are - // explcitly written. + // explicitly written. // // Otherwise, pick (base + (2^16 - base[16:0])), i.e., round up base to the // next multiple of 2^16. As 2^16 < size, this value should be in the diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py index 4fc5ff1bd1..933df6d71d 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py @@ -20,6 +20,7 @@ from __future__ import print_function import time +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import rnn as contrib_rnn from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops from tensorflow.contrib.rnn.python.ops import lstm_ops diff --git a/tensorflow/contrib/eager/python/evaluator.py b/tensorflow/contrib/eager/python/evaluator.py index 3faaeef590..68e7b5421f 100644 --- a/tensorflow/contrib/eager/python/evaluator.py +++ b/tensorflow/contrib/eager/python/evaluator.py @@ -178,7 +178,7 @@ class Evaluator(object): call_op: An op that updates evaluation state on a mini-batch of examples. Must generate an tf.errors.OutOfRangeError when done. results_op: A dictionary of tensors that compute the final evaluation - results from the evaulation state. + results from the evaluation state. sess: The Session to run the evaluation in. Defaults to the default Session. diff --git a/tensorflow/contrib/eager/python/examples/resnet50/README.md b/tensorflow/contrib/eager/python/examples/resnet50/README.md index db023e6c97..79e4600529 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/README.md +++ b/tensorflow/contrib/eager/python/examples/resnet50/README.md @@ -34,7 +34,7 @@ bazel run -c opt --config=cuda :resnet50_graph_test -- --benchmarks=. (Or remove the `--config=cuda` flag for running on CPU instead of GPU). -On October 31, 2017, the benchmarks demostrated comparable performance +On October 31, 2017, the benchmarks demonstrated comparable performance for eager and graph execution of this particular model when using a single NVIDIA Titan X (Pascal) GPU on a host with an Intel Xeon E5-1650 CPU @ 3.50GHz and a batch size of 32. diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py index b302a87e0e..9982fdb07e 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py @@ -97,7 +97,7 @@ class _ConvBlock(tfe.Network): Args: kernel_size: the kernel size of middle conv layer at main path - filters: list of integers, the filterss of 3 conv layer at main path + filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names data_format: data_format for the input ('channels_first' or diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py index 76e06269b6..0ff8746884 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py @@ -22,6 +22,7 @@ import gc import tempfile import time +from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf import tensorflow.contrib.eager as tfe diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md b/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md index 743ebb68ee..966177e91c 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/README.md @@ -40,7 +40,7 @@ bazel run -c opt --config=cuda :rnn_ptb_graph_test -- --benchmarks=. (Or remove the `--config=cuda` flag for running on CPU instead of GPU). -On October 31, 2017, the benchmarks demostrated slightly better performance +On October 31, 2017, the benchmarks demonstrated slightly better performance (3-6%) for graph execution over eager execution for this particular model when using a single NVIDIA Titan X (Pascal) GPU on a host with an Intel Xeon E5-1650 CPU @ 3.50GHz and a batch size of 32. diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py index 7b9637a9d5..5c5c59c877 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py @@ -88,7 +88,7 @@ class Embedding(tf.layers.Layer): class PTBModel(tfe.Network): - """LSTM for word language modelling. + """LSTM for word language modeling. Model described in: (Zaremba, et. al.) Recurrent Neural Network Regularization @@ -339,8 +339,7 @@ if __name__ == "__main__": "http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz") parser.add_argument( "--logdir", type=str, default="", help="Directory for checkpoint.") - parser.add_argument( - "--epoch", type=int, default=20, help="Number of epoches.") + parser.add_argument("--epoch", type=int, default=20, help="Number of epochs.") parser.add_argument("--batch-size", type=int, default=20, help="Batch size.") parser.add_argument( "--seq-len", type=int, default=35, help="Sequence length.") diff --git a/tensorflow/contrib/eager/python/examples/spinn/data.py b/tensorflow/contrib/eager/python/examples/spinn/data.py index a6e046320f..fcaae0a4f8 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/data.py +++ b/tensorflow/contrib/eager/python/examples/spinn/data.py @@ -51,11 +51,11 @@ def get_non_parenthesis_words(items): """Get the non-parenthesis items from a SNLI parsed sentence. Args: - items: Data items from a parsed SNLI setence, with parentheses. E.g., + items: Data items from a parsed SNLI sentence, with parentheses. E.g., ["(", "Man", "(", "(", "(", "(", "(", "wearing", "pass", ")", ... Returns: - A list of non-parenthis word items, all converted to lower case. E.g., + A list of non-parentheses word items, all converted to lower case. E.g., ["man", "wearing", "pass", ... """ return [x.lower() for x in items if x not in PARENTHESES and x] @@ -201,7 +201,7 @@ def load_word_vectors(data_root, vocab): def calculate_bins(length2count, min_bin_size): - """Cacluate bin boundaries given a histogram of lengths and mininum bin size. + """Calculate bin boundaries given a histogram of lengths and minimum bin size. Args: length2count: A `dict` mapping length to sentence count. @@ -335,9 +335,9 @@ class SnliData(object): # The sorting above and the batching here makes sure that sentences of # similar max lengths are batched together, minimizing the inefficiency # due to uneven max lengths. The sentences are batched differently in - # each call to get_generator() due to the shuffling before sotring + # each call to get_generator() due to the shuffling before sorting # above. The pad_and_reverse_word_ids() and pad_transitions() functions - # take care of any remaning unevenness of the max sentence lengths. + # take care of any remaining unevenness of the max sentence lengths. end = min(begin + batch_size, len(labels)) # Transpose, because the SPINN model requires time-major, instead of # batch-major. diff --git a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py index 84e25cf81a..7b2f09cba1 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py +++ b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py @@ -26,6 +26,7 @@ import tempfile import time import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf # pylint: disable=g-bad-import-order diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py index 8e6b947e5c..3329fc6c51 100644 --- a/tensorflow/contrib/eager/python/network_test.py +++ b/tensorflow/contrib/eager/python/network_test.py @@ -539,7 +539,7 @@ class NetworkTest(test.TestCase): # No issue here since the name is unique within its scope. name_conflict3 = MyNetwork(name="name_conflict") net2 = MyNetwork() # name=outside_scope/my_network_2 to avoid the - # variable_scope my_network_1 below. + # variable_scope my_network_1 below. vs_name_conflict = MyNetwork(name="vs_name_conflict") # conflict below with variable_scope.variable_scope("intervening_scope"): with variable_scope.variable_scope(captured_scope): @@ -688,7 +688,7 @@ class NetworkTest(test.TestCase): net2(one) # Layer names typically are globally unique rather than being unique within # the scope of their first use. However, within a Network they must be named - # locally so that previous Layer consutrciton does not interfere with + # locally so that previous Layer construction does not interfere with # variable naming (e.g. add a Layer construction before the Network, # suddenly your previously saved checkpoint is incompatible). self.assertEqual("dense", net1.l1.name) diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index 57b070ec6e..62421849c7 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -82,7 +82,7 @@ def restore_variables_on_create(save_path, map_func=None): map_func_wrapper = lambda self, x: x else: if not callable(map_func): - raise ValueError("map_func must be callaled.") + raise ValueError("map_func must be callable.") map_func_wrapper = lambda self, x: map_func(x) ckpt_var_cache = dict() diff --git a/tensorflow/contrib/ffmpeg/decode_video_op.cc b/tensorflow/contrib/ffmpeg/decode_video_op.cc index d44032968d..6f8ad486d1 100644 --- a/tensorflow/contrib/ffmpeg/decode_video_op.cc +++ b/tensorflow/contrib/ffmpeg/decode_video_op.cc @@ -102,16 +102,12 @@ REGISTER_OP("DecodeVideo") return Status::OK(); }) .Doc(R"doc( -Processes the contents of an audio file into a tensor using FFmpeg to decode +Processes the contents of an video file into a tensor using FFmpeg to decode the file. -One row of the tensor is created for each channel in the audio file. Each -channel contains audio samples starting at the beginning of the audio and -having `1/samples_per_second` time between them. If the `channel_count` is -different from the contents of the file, channels will be merged or created. - -contents: The binary audio file contents, as a string or rank-0 string - tensor. +contents: The binary contents of the video file to decode. This is a + scalar. +output: A rank-4 `Tensor` that has `[frames, height, width, 3]` RGB as output. )doc"); } // namespace ffmpeg diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index a9d47ac9b9..0754c3e0e3 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -25,6 +25,7 @@ import re from tensorflow.contrib.framework.python.ops import add_arg_scope as contrib_add_arg_scope from tensorflow.contrib.framework.python.ops import gen_variable_ops from tensorflow.contrib.util import loader +from tensorflow.core.protobuf import saver_pb2 from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import dtypes @@ -684,7 +685,8 @@ def assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=False, 'Variable %s missing in checkpoint %s', var, model_path) var_list = available_vars if var_list: - saver = tf_saver.Saver(var_list, reshape=reshape_variables) + saver = tf_saver.Saver(var_list, reshape=reshape_variables, + write_version=saver_pb2.SaverDef.V1) def callback(session): saver.restore(session, model_path) return callback diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py index 986a5ff6dc..fdfabd07c1 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py @@ -28,6 +28,7 @@ from __future__ import division from __future__ import print_function import functools +import os import sys import tarfile @@ -189,20 +190,34 @@ def get_graph_def_from_resource(filename): return graph_pb2.GraphDef.FromString(resource_loader.load_resource(filename)) -def get_graph_def_from_url_tarball(url, filename): - """Get a GraphDef proto from a tarball on the web.""" - def _progress(count, block_size, total_size): - sys.stdout.write('\r>> Downloading %s %.1f%%' % ( - url, float(count * block_size) / float(total_size) * 100.0)) - sys.stdout.flush() - tar_filename, _ = urllib.request.urlretrieve(url, reporthook=_progress) +def get_graph_def_from_url_tarball(url, filename, tar_filename=None): + """Get a GraphDef proto from a tarball on the web. + + Args: + url: Web address of tarball + filename: Filename of graph definition within tarball + tar_filename: Temporary download filename (None = always download) + + Returns: + A GraphDef loaded from a file in the downloaded tarball. + """ + if not (tar_filename and os.path.exists(tar_filename)): + + def _progress(count, block_size, total_size): + sys.stdout.write('\r>> Downloading %s %.1f%%' % + (url, + float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.flush() + + tar_filename, _ = urllib.request.urlretrieve(url, tar_filename, _progress) with tarfile.open(tar_filename, 'r:gz') as tar: proto_str = tar.extractfile(filename).read() return graph_pb2.GraphDef.FromString(proto_str) def _default_graph_def_fn(): - return get_graph_def_from_url_tarball(INCEPTION_URL, INCEPTION_FROZEN_GRAPH) + return get_graph_def_from_url_tarball(INCEPTION_URL, INCEPTION_FROZEN_GRAPH, + os.path.basename(INCEPTION_URL)) def run_inception(images, diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py index 7d2a7a254f..56ac45554d 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py @@ -620,7 +620,7 @@ class CombineAdversarialLossTest(test.TestCase): with self.test_session(use_gpu=True) as sess: for _ in range(10): # spot check closeness on more than one sample. gnorm_np, precond_gnorm_np = sess.run([gnorm, precond_gnorm]) - self.assertNear(gnorm_np, precond_gnorm_np, 1e-5) + self.assertNear(gnorm_np, precond_gnorm_np, 1e-4) class CycleConsistencyLossTest(test.TestCase): diff --git a/tensorflow/contrib/hvx/README.md b/tensorflow/contrib/hvx/README.md index 5a6f2f3086..163993a3f6 100644 --- a/tensorflow/contrib/hvx/README.md +++ b/tensorflow/contrib/hvx/README.md @@ -1,60 +1,67 @@ # TensorFlow Runtime with HVX Acceleration -## Description +This README explain how to build and use the TensorFlow runtime with HVX Acceleration. HVX is an extension of Hexagon, a DSP provided by Qualcomm, which can compute vector calculations faster using less energy than ARM processors. -This README explain how to build and use the TensorFlow Runtime with HVX Acceleration. HVX is an extension of Hexagon which is a DSP provided by qualcomm which can compute vector calculations faster using lower energy than ARM processors. +## Dependencies + +* [Android SDK](https://developer.android.com/studio/index.html). +* [Android NDK](https://developer.android.com/ndk/index.html). Save the path in `${NDK_ROOT}`. +* A rooted Qualcomm-based Android device connected to the computer (preferably, a [Snapdragon Development Board](https://developer.qualcomm.com/hardware/additional-snapdragon), but it could be a rooted phone with a Qualcomm SoC, albeit this guide may not work with it). The device needs to be rooted for development and testing purposes, and shouldn't be needed in production. See [Behold, The Snapdragon MDP](https://developer.qualcomm.com/blog/behold-snapdragon-mdp) for more information. +* [Hexagon SDK v3.0](https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools). Save the path in `${QUALCOMM_SDK}`. +* The current directory should be TensorFlow source code (`git clone https://github.com/tensorflow/tensorflow.git && cd tensorflow`), and saved into `${TF_ROOT_DIR}`. + +You may also need to add a test signature in the device to run HVX-based binaries. Follow the instructions in `${QUALCOMM_SDK}/docs/Tools_Signing.html`, using Python 2. + +Note that if the device is not rooted, you may not be able to get the serial number, push the test signature and/or run binary files that call HVX libraries. ## Quick Start Guide -We provides several tools to build and run inference with this runtime quickly. +We provide several tools to build and run inference with this runtime quickly. -#### All-in-one script to run inception model with prebuild hexagon library -If you don’t need to build your own implementation of hexagon HVX, we provide a shortcut to execute graphs by using pre-compiled binaries. +### Run inception model with a prebuilt Hexagon library +If you don’t need to build your own implementation of Hexagon HVX, we provide a shortcut to execute graphs by using pre-compiled binaries. + +```shell +./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh -p ``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -NDK_ROOT="/path/to/ndk" ./tensorflow/contrib/makefile/build_all_android.sh -X -``` -(-X downloads dependencies to hexagon HVX and graphs, and copy all dependencies to android and execute a test) -#### All-in-one script to run inception model by building entire libraries from source code - If you want to build your own implementation of hexagon HVX, we provide a sample all-in-one script to execute graphs which downloads source and build everything for hexagon. +The `-p` option makes the script download dependencies (i.e., Hexagon HVX binaries and graphs models), copy them to the Android device and execute a test. -``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -QUALCOMM_SDK="/path/to/qualcomm/sdk" NDK_ROOT="/path/to/ndk" ./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh +### Run inception model by building all from the source code + +If you want to build your own implementation of Hexagon HVX, we provide a sample all-in-one script to execute graphs which downloads the source and builds everything that's necessary. + +```shell +./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh ``` ## Building libraries If you've finished walking through the quick start guide, you may want to try building each binary manually. -#### Build libhexagon_nn_skel.so -Download hexagon nn library from codeaurora.org and build it. +### Build libhexagon\_nn\_skel.so -``` +Download Hexagon NN library from codeaurora.org and build it. + +```shell git clone https://source.codeaurora.org/quic/hexagon_nn/nnlib cd nnlib ``` -(Just follow instructions in README.HOW_TO_BUILD. You can find libhexagon_nn_skel.so in hexagon_Release_dynamic_toolv72_v60/ship) -Then copy the generated binary to GEN_LIBS_DIR +Just follow the instructions in `README.HOW_TO_BUILD`. You can find the file `libhexagon_nn_skel.so` in `hexagon_Release_dynamic_toolv72_v60/ship`. +Then copy the generated binary to `${GEN_LIBS_DIR}`. -``` +```shell GEN_LIBS_DIR="/path/to/a/dir/to/store/hexagon/libraries" cp -v "hexagon_Release_dynamic_toolv72_v60/ship/libhexagon_nn_skel.so" "${GEN_LIBS_DIR}" ``` -#### Build libhexagon_controller.so +### Build libhexagon\_controller.so + Download tensorflow and build hexagon controller. -``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -TF_ROOT_DIR="$(pwd)" -QUALCOMM_SDK="/path/to/qualcomm/sdk" +```shell GENERATED_NNLIB_DIRECTORY="/path/to/nnlib" GENERATED_HEXAGON_CONTROLLER_DIRECTORY="${QUALCOMM_SDK}/examples/common/generated_hexagon_controller" rm -rf "${GENERATED_HEXAGON_CONTROLLER_DIRECTORY}" @@ -70,12 +77,12 @@ make tree VERBOSE=1 V=android_Release cp -v "${GENERATED_HEXAGON_CONTROLLER_DIRECTORY}/android_Release/ship/libhexagon_controller.so" "${GEN_LIBS_DIR}" ``` -#### Build tensorflow linking hexagon library -Build tensorflow with the build_all_android.sh with specifying -x option. +### Build TensorFlow linking Hexagon library -``` +Build TensorFlow with `build_all_android.sh` specifying the `-x` option. + +```shell BUILD_ALL_ANDROID_PATH="${TF_ROOT_DIR}/tensorflow/contrib/makefile/build_all_android.sh" -NDK_ROOT="/path/to/ndk/root" CC_PREFIX=${CC_PREFIX} NDK_ROOT=${NDK_ROOT} "${BUILD_ALL_ANDROID_PATH}" \ -x "${GEN_LIBS_DIR}" \ @@ -83,11 +90,11 @@ CC_PREFIX=${CC_PREFIX} NDK_ROOT=${NDK_ROOT} "${BUILD_ALL_ANDROID_PATH}" \ -t hexagon_graph_execution ``` -#### Push binaries to your Android device +### Push binaries to your Android device Before running tests on your Android device, you need to push several binaries to it. -``` +```shell adb push "${GEN_LIBS_DIR}/libhexagon_controller.so" "/data/local/tmp" adb push "${GEN_LIBS_DIR}/libhexagon_nn_skel.so" "/vendor/lib/rfsa/adsp" adb push -p \ @@ -100,40 +107,54 @@ adb shell chmod "${ANDROID_EXEC_FILE_MODE}" \ adb wait-for-device ``` -#### Run tests on the device +### Run tests on the device Finally, you can run the inference tests on your device. -``` +```shell adb shell 'LD_LIBRARY_PATH=/data/local/tmp:$LD_LIBRARY_PATH' \ "/data/local/tmp/hexagon_graph_execution" ``` -#### Troubleshooting -If you're using the Open-Q 820 Snapdragon development kit, you may run into an issue with running the executable due to a missing testsig library. From the Hexagon SDK documentation: *Dynamic shared objects are required to be digitally signed and then authenticated at runtime before they are allowed to be loaded and executed.* Generating a testsig library is necessary to run the unsigned sample library built from this project. +### Troubleshooting + +#### Testsig issue + +If you're using the Open-Q 820 Snapdragon Development Kit, you may run into an issue with running the executable due to a missing `testsig` library. From the Hexagon SDK documentation: *Dynamic shared objects are required to be digitally signed and then authenticated at runtime before they are allowed to be loaded and executed.* Generating a testsig library is necessary to run the unsigned sample library built from this project. -If the lack of a testsig library is your problem, you will see errors of the type: +If the lack of a `testsig` library is your problem, you will see errors of the type: `vendor/qcom/proprietary/adsprpc/src/fastrpc_apps_user.c:169::error: -1: 0 == (nErr = remotectl_open(name, (int*)ph, dlerrstr, sizeof(dlerrstr), &dlerr))` -appearing in adb logcat. - -There are several ways to create the testsig library, the only prerequisite is Python and the correct version of the Hexagon-SDK. The following steps is one way to create this library: -1. Run adb as root: `adb root` -2. Run the command `adb shell cat /sys/devices/soc0/serial_number` -3. Convert the decimal number you get as output to hex -4. Run the python script: `python ${QUALCOMM_SDK}/tools/elfsigner/elfsigner.py -t $(SERIAL_NUMBER_HEX_VALUE)` -5. The output of the python script is a shared library stored in ${QUALCOMM_SDK}/tools/elfsigner/output/testsig-$(SERIAL_NUMBER_HEX_VALUE).so -6. Push the shared library to your device: +appearing in `adb logcat` or ["Expected: (version) >= (1), actual: 0 vs 1" while running a binary from adb](https://github.com/tensorflow/tensorflow/issues/11210). + +You need to add a test signature, as described at the beginning of this README. After rebooting your device, you should be able to run the sample application. + +#### Qualcomm SDK Linux installation fails with "Malformed \uxxxx encoding" + +The installation file is based on LaunchAnywhere, which fails in Linux if the `PS1` env variable contains non-common Unicode chars: + ``` -adb root -adb wait-for-device -adb remount -adb wait-for-device -adb shell mkdir /system/lib/rfsa -adb shell mkdir /system/lib/rfsa/adsp -adb push ${QUALCOMM_SDK}/tools/elfsigner/output/testsig-$(SERIAL_NUMBER_HEX_VALUE).so /system/lib/rfsa/adsp/ +Preparing to install... +Extracting the JRE from the installer archive... +Unpacking the JRE... +Extracting the installation resources from the installer archive... +Configuring the installer for this system's environment... + +Launching installer... + +An internal LaunchAnywhere application error has occurred and this application cannot proceed. (LAX) + +Stack Trace: +java.lang.IllegalArgumentException: Malformed \uxxxx encoding. + at java.util.Properties.loadConvert(Properties.java:574) + at java.util.Properties.load0(Properties.java:391) + at java.util.Properties.load(Properties.java:317) + at com.zerog.common.java.util.PropertiesUtil.loadProperties(Unknown Source) + at com.zerog.lax.LAX.<init>(Unknown Source) + at com.zerog.lax.LAX.main(Unknown Source) ``` -After rebooting your device, you should be able to run the sample application. +It can be solved by temporarily assigning the `PS1` environment variable to something simple, such as '$'. + +## Maintainers -Maintainers: -- Satoshi Kataoka (satok@google.com, github.com/satok16) +* Satoshi Kataoka (satok@google.com, github.com/satok16) diff --git a/tensorflow/contrib/kafka/BUILD b/tensorflow/contrib/kafka/BUILD new file mode 100644 index 0000000000..efb403462a --- /dev/null +++ b/tensorflow/contrib/kafka/BUILD @@ -0,0 +1,105 @@ +package( + default_visibility = ["//visibility:private"], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs") +load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") +load("//tensorflow:tensorflow.bzl", "tf_kernel_library") +load("//tensorflow:tensorflow.bzl", "tf_py_test") + +tf_kernel_library( + name = "kafka_kernels", + srcs = ["kernels/kafka_dataset_ops.cc"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core/kernels:bounds_check_lib", + "//tensorflow/core/kernels:dataset", + "//third_party/eigen3", + "@kafka", + ], +) + +tf_gen_op_libs( + op_lib_names = ["kafka_ops"], + deps = [ + "//tensorflow/core:lib", + ], +) + +tf_gen_op_wrapper_py( + name = "gen_kafka_ops", + out = "python/ops/gen_kafka_ops.py", + require_shape_functions = True, + deps = [":kafka_ops_op_lib"], +) + +py_library( + name = "kafka", + srcs = [ + "__init__.py", + "python/ops/kafka_dataset_ops.py", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":gen_kafka_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", + ], +) + +# The Kafka server has to be setup before running the test. +# The Kafka server is setup through Docker so the Docker engine +# has to be installed. +# +# Once the Docker engine is ready: +# To setup the Kafka server: +# $ bash tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh start kafka +# +# After the test is complete: +# To team down the Kafka server: +# $ bash tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh stop kafka +tf_py_test( + name = "kafka_test", + srcs = ["python/kernel_tests/kafka_test.py"], + additional_deps = [ + ":kafka", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], + tags = [ + "manual", + "notap", + ], +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/kafka/__init__.py b/tensorflow/contrib/kafka/__init__.py new file mode 100644 index 0000000000..4d755c4056 --- /dev/null +++ b/tensorflow/contrib/kafka/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Kafka Dataset. + +@@KafkaDataset +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kafka.python.ops.kafka_dataset_ops import KafkaDataset + +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + "KafkaDataset", +] + +remove_undocumented(__name__) diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc new file mode 100644 index 0000000000..88ef5f3571 --- /dev/null +++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc @@ -0,0 +1,321 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/kernels/dataset.h" + +#include "tensorflow/core/framework/tensor.h" + +#include "src-cpp/rdkafkacpp.h" + +namespace tensorflow { + +class KafkaDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + const Tensor* topics_tensor; + OP_REQUIRES_OK(ctx, ctx->input("topics", &topics_tensor)); + OP_REQUIRES( + ctx, topics_tensor->dims() <= 1, + errors::InvalidArgument("`topics` must be a scalar or a vector.")); + + std::vector<string> topics; + topics.reserve(topics_tensor->NumElements()); + for (int i = 0; i < topics_tensor->NumElements(); ++i) { + topics.push_back(topics_tensor->flat<string>()(i)); + } + + std::string servers = ""; + OP_REQUIRES_OK(ctx, + ParseScalarArgument<std::string>(ctx, "servers", &servers)); + std::string group = ""; + OP_REQUIRES_OK(ctx, ParseScalarArgument<std::string>(ctx, "group", &group)); + bool eof = false; + OP_REQUIRES_OK(ctx, ParseScalarArgument<bool>(ctx, "eof", &eof)); + int64 timeout = -1; + OP_REQUIRES_OK(ctx, ParseScalarArgument<int64>(ctx, "timeout", &timeout)); + OP_REQUIRES(ctx, (timeout > 0), + errors::InvalidArgument( + "Timeout value should be large than 0, got ", timeout)); + *output = new Dataset(ctx, std::move(topics), servers, group, eof, timeout); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, std::vector<string> topics, + const string& servers, const string& group, const bool eof, + const int64 timeout) + : GraphDatasetBase(ctx), + topics_(std::move(topics)), + servers_(servers), + group_(group), + eof_(eof), + timeout_(timeout) {} + + std::unique_ptr<IteratorBase> MakeIterator( + const string& prefix) const override { + return std::unique_ptr<IteratorBase>( + new Iterator({this, strings::StrCat(prefix, "::Kafka")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector<PartialTensorShape>& output_shapes() const override { + static std::vector<PartialTensorShape>* shapes = + new std::vector<PartialTensorShape>({{}}); + return *shapes; + } + + string DebugString() override { return "KafkaDatasetOp::Dataset"; } + + protected: + Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Node** output) const override { + Node* topics = nullptr; + TF_RETURN_IF_ERROR(b->AddVector(topics_, &topics)); + Node* servers = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(servers_, &servers)); + Node* group = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(group_, &group)); + Node* eof = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(eof_, &eof)); + Node* timeout = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(timeout_, &timeout)); + TF_RETURN_IF_ERROR( + b->AddDataset(this, {topics, servers, group, eof, timeout}, output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator<Dataset> { + public: + explicit Iterator(const Params& params) + : DatasetIterator<Dataset>(params) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector<Tensor>* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + do { + // We are currently processing a topic, so try to read the next line. + if (consumer_.get()) { + while (true) { + if (limit_ >= 0 && + (topic_partition_->offset() >= limit_ || offset_ >= limit_)) { + // EOF current topic + break; + } + std::unique_ptr<RdKafka::Message> message( + consumer_->consume(dataset()->timeout_)); + if (message->err() == RdKafka::ERR_NO_ERROR) { + // Produce the line as output. + Tensor line_tensor(cpu_allocator(), DT_STRING, {}); + line_tensor.scalar<string>()() = + std::string(static_cast<const char*>(message->payload()), + message->len()); + out_tensors->emplace_back(std::move(line_tensor)); + *end_of_sequence = false; + // Sync offset + offset_ = message->offset(); + return Status::OK(); + } + + if (message->err() == RdKafka::ERR__PARTITION_EOF && + dataset()->eof_) { + // EOF current topic + break; + } + if (message->err() != RdKafka::ERR__TIMED_OUT) { + return errors::Internal("Failed to consume:", + message->errstr()); + } + message.reset(nullptr); + consumer_->poll(0); + } + + // We have reached the end of the current topic, so maybe + // move on to next topic. + ResetStreamsLocked(); + ++current_topic_index_; + } + + // Iteration ends when there are no more topic to process. + if (current_topic_index_ == dataset()->topics_.size()) { + *end_of_sequence = true; + return Status::OK(); + } + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + } while (true); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("current_topic_index"), + current_topic_index_)); + + // `consumer_` is empty if + // 1. GetNext has not been called even once. + // 2. All topics have been read and iterator has been exhausted. + if (consumer_.get()) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("current_pos"), offset_)); + } + return Status::OK(); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + mutex_lock l(mu_); + ResetStreamsLocked(); + int64 current_topic_index; + TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("current_topic_index"), + ¤t_topic_index)); + current_topic_index_ = size_t(current_topic_index); + // The key "current_pos" is written only if the iterator was saved + // with an open topic. + if (reader->Contains(full_name("current_pos"))) { + int64 current_pos; + TF_RETURN_IF_ERROR( + reader->ReadScalar(full_name("current_pos"), ¤t_pos)); + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + topic_partition_->set_offset(current_pos); + if (topic_partition_->offset() != current_pos) { + return errors::Internal("Failed to restore to offset ", + current_pos); + } + offset_ = current_pos; + } + return Status::OK(); + } + + private: + // Sets up Kafka streams to read from the topic at + // `current_topic_index_`. + Status SetupStreamsLocked(Env* env) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + if (current_topic_index_ >= dataset()->topics_.size()) { + return errors::InvalidArgument( + "current_topic_index_:", current_topic_index_, + " >= topics_.size():", dataset()->topics_.size()); + } + + // Actually move on to next topic. + string entry = dataset()->topics_[current_topic_index_]; + + std::vector<string> parts = str_util::Split(entry, ":"); + if (parts.size() < 1) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + string topic = parts[0]; + int32 partition = 0; + if (parts.size() > 1) { + if (!strings::safe_strto32(parts[1], &partition)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + int64 offset = 0; + if (parts.size() > 2) { + if (!strings::safe_strto64(parts[2], &offset)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + + topic_partition_.reset( + RdKafka::TopicPartition::create(topic, partition, offset)); + + offset_ = topic_partition_->offset(); + limit_ = -1; + if (parts.size() > 3) { + if (!strings::safe_strto64(parts[3], &limit_)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + + std::unique_ptr<RdKafka::Conf> conf( + RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL)); + std::unique_ptr<RdKafka::Conf> topic_conf( + RdKafka::Conf::create(RdKafka::Conf::CONF_TOPIC)); + + std::string errstr; + + RdKafka::Conf::ConfResult result = + conf->set("default_topic_conf", topic_conf.get(), errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set default_topic_conf:", errstr); + } + + result = conf->set("bootstrap.servers", dataset()->servers_, errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set bootstrap.servers ", + dataset()->servers_, ":", errstr); + } + result = conf->set("group.id", dataset()->group_, errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set group.id ", dataset()->group_, + ":", errstr); + } + + consumer_.reset(RdKafka::KafkaConsumer::create(conf.get(), errstr)); + if (!consumer_.get()) { + return errors::Internal("Failed to create consumer:", errstr); + } + + std::vector<RdKafka::TopicPartition*> partitions; + partitions.emplace_back(topic_partition_.get()); + RdKafka::ErrorCode err = consumer_->assign(partitions); + if (err != RdKafka::ERR_NO_ERROR) { + return errors::Internal( + "Failed to assign partition [", topic_partition_->topic(), ", ", + topic_partition_->partition(), ", ", topic_partition_->offset(), + "]:", RdKafka::err2str(err)); + } + + return Status::OK(); + } + + // Resets all Kafka streams. + void ResetStreamsLocked() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + consumer_->unassign(); + consumer_->close(); + consumer_.reset(nullptr); + } + + mutex mu_; + size_t current_topic_index_ GUARDED_BY(mu_) = 0; + int64 offset_ GUARDED_BY(mu_) = 0; + int64 limit_ GUARDED_BY(mu_) = -1; + std::unique_ptr<RdKafka::TopicPartition> topic_partition_ GUARDED_BY(mu_); + std::unique_ptr<RdKafka::KafkaConsumer> consumer_ GUARDED_BY(mu_); + }; + + const std::vector<string> topics_; + const std::string servers_; + const std::string group_; + const bool eof_; + const int64 timeout_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("KafkaDataset").Device(DEVICE_CPU), + KafkaDatasetOp); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kafka/ops/kafka_ops.cc b/tensorflow/contrib/kafka/ops/kafka_ops.cc new file mode 100644 index 0000000000..8cdf16103b --- /dev/null +++ b/tensorflow/contrib/kafka/ops/kafka_ops.cc @@ -0,0 +1,44 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("KafkaDataset") + .Input("topics: string") + .Input("servers: string") + .Input("group: string") + .Input("eof: bool") + .Input("timeout: int64") + .Output("handle: variant") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that emits the messages of one or more Kafka topics. + +topics: A `tf.string` tensor containing one or more subscriptions, + in the format of [topic:partition:offset:length], + by default length is -1 for unlimited. +servers: A list of bootstrap servers. +group: The consumer group id. +eof: If True, the kafka reader will stop on EOF. +timeout: The timeout value for the Kafka Consumer to wait + (in millisecond). +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py new file mode 100644 index 0000000000..621911876f --- /dev/null +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py @@ -0,0 +1,115 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. +# ============================================================================== +"""Tests for KafkaDataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kafka.python.ops import kafka_dataset_ops +from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class KafkaDatasetTest(test.TestCase): + + def setUp(self): + # The Kafka server has to be setup before the test + # and tear down after the test manually. + # The docker engine has to be installed. + # + # To setup the Kafka server: + # $ bash kafka_test.sh start kafka + # + # To team down the Kafka server: + # $ bash kafka_test.sh stop kafka + pass + + def testKafkaDataset(self): + topics = array_ops.placeholder(dtypes.string, shape=[None]) + num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) + batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = kafka_dataset_ops.KafkaDataset( + topics, group="test", eof=True).repeat(num_epochs) + batch_dataset = repeat_dataset.batch(batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + init_op = iterator.make_initializer(repeat_dataset) + init_batch_op = iterator.make_initializer(batch_dataset) + get_next = iterator.get_next() + + with self.test_session() as sess: + # Basic test: read from topic 0. + sess.run(init_op, feed_dict={topics: ["test:0:0:4"], num_epochs: 1}) + for i in range(5): + self.assertEqual("D" + str(i), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Basic test: read from topic 1. + sess.run(init_op, feed_dict={topics: ["test:0:5:-1"], num_epochs: 1}) + for i in range(5): + self.assertEqual("D" + str(i + 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Basic test: read from both topics. + sess.run( + init_op, + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 1 + }) + for j in range(2): + for i in range(5): + self.assertEqual("D" + str(i + j * 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Test repeated iteration through both files. + sess.run( + init_op, + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10 + }) + for _ in range(10): + for j in range(2): + for i in range(5): + self.assertEqual("D" + str(i + j * 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Test batched and repeated iteration through both files. + sess.run( + init_batch_op, + feed_dict={ + topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10, + batch_size: 5 + }) + for _ in range(10): + self.assertAllEqual(["D" + str(i) for i in range(5)], + sess.run(get_next)) + self.assertAllEqual(["D" + str(i + 5) for i in range(5)], + sess.run(get_next)) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh new file mode 100644 index 0000000000..adf027b8e7 --- /dev/null +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh @@ -0,0 +1,48 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e +set -o pipefail + +if [ "$#" -ne 2 ]; then + echo "Usage: $0 start|stop <kafka container name>" >&2 + exit 1 +fi + +container=$2 +if [ "$1" == "start" ]; then + docker run -d --rm --net=host --name=$container spotify/kafka + echo Wait 5 secs until kafka is up and running + sleep 5 + echo Create test topic + docker exec $container bash -c '/opt/kafka_2.11-0.10.1.0/bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test' + echo Create test message + docker exec $container bash -c 'echo -e "D0\nD1\nD2\nD3\nD4\nD5\nD6\nD7\nD8\nD9" > /test' + echo Produce test message + docker exec $container bash -c '/opt/kafka_2.11-0.10.1.0/bin/kafka-console-producer.sh --topic test --broker-list 127.0.0.1:9092 < /test' + + echo Container $container started successfully +elif [ "$1" == "stop" ]; then + docker rm -f $container + + echo Container $container stopped successfully +else + echo "Usage: $0 start|stop <kafka container name>" >&2 + exit 1 +fi + + + diff --git a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py new file mode 100644 index 0000000000..8e51d27a34 --- /dev/null +++ b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py @@ -0,0 +1,74 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Kafka Dataset.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kafka.python.ops import gen_kafka_ops +from tensorflow.python.data.ops.readers import Dataset +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape + + +class KafkaDataset(Dataset): + """A Kafka Dataset that consumes the message. + """ + + def __init__(self, + topics, + servers="localhost", + group="", + eof=False, + timeout=1000): + """Create a KafkaReader. + + Args: + topics: A `tf.string` tensor containing one or more subscriptions, + in the format of [topic:partition:offset:length], + by default length is -1 for unlimited. + servers: A list of bootstrap servers. + group: The consumer group id. + eof: If True, the kafka reader will stop on EOF. + timeout: The timeout value for the Kafka Consumer to wait + (in millisecond). + """ + super(KafkaDataset, self).__init__() + self._topics = ops.convert_to_tensor( + topics, dtype=dtypes.string, name="topics") + self._servers = ops.convert_to_tensor( + servers, dtype=dtypes.string, name="servers") + self._group = ops.convert_to_tensor( + group, dtype=dtypes.string, name="group") + self._eof = ops.convert_to_tensor(eof, dtype=dtypes.bool, name="eof") + self._timeout = ops.convert_to_tensor( + timeout, dtype=dtypes.int64, name="timeout") + + def _as_variant_tensor(self): + return gen_kafka_ops.kafka_dataset(self._topics, self._servers, self._group, + self._eof, self._timeout) + + @property + def output_classes(self): + return ops.Tensor + + @property + def output_shapes(self): + return tensor_shape.scalar() + + @property + def output_types(self): + return dtypes.string diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py index 6c624929f2..ef419862b4 100644 --- a/tensorflow/contrib/layers/__init__.py +++ b/tensorflow/contrib/layers/__init__.py @@ -27,6 +27,7 @@ See the @{$python/contrib.layers} guide. @@convolution2d_transpose @@conv3d_transpose @@convolution3d_transpose +@@dense_to_sparse @@dropout @@elu @@embedding_lookup_unique diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index 7c52da7b49..1c3af19a6c 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -29,6 +29,7 @@ from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops @@ -58,12 +59,12 @@ __all__ = [ 'avg_pool2d', 'avg_pool3d', 'batch_norm', 'bias_add', 'conv2d', 'conv3d', 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', - 'convolution3d', 'convolution3d_transpose', 'dropout', 'elu', 'flatten', - 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', 'pool', - 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', - 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', 'softmax', - 'spatial_softmax', 'stack', 'unit_norm', 'legacy_fully_connected', - 'legacy_linear', 'legacy_relu', 'maxout' + 'convolution3d', 'convolution3d_transpose', 'dense_to_sparse', 'dropout', + 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', + 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', + 'repeat', 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', + 'softmax', 'spatial_softmax', 'stack', 'unit_norm', + 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout' ] DATA_FORMAT_NCHW = 'NCHW' @@ -1401,6 +1402,30 @@ def convolution3d_transpose( @add_arg_scope +def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None): + """Converts a dense tensor into a sparse tensor. + An example use would be to convert dense labels to sparse ones + so that they can be fed to the ctc_loss. + + Args: + tensor: An `int` `Tensor` to be converted to a `Sparse`. + eos_token: An integer. + It is part of the target label that signfies the end of a sentence. + outputs_collections: Collection to add the outputs. + scope: Optional scope for name_scope. + """ + with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc: + tensor = ops.convert_to_tensor(tensor) + indices = array_ops.where( + math_ops.not_equal(tensor, constant_op.constant(eos_token, + tensor.dtype))) + values = array_ops.gather_nd(tensor, indices) + shape = array_ops.shape(tensor, out_type=dtypes.int64) + outputs = sparse_tensor.SparseTensor(indices, values, shape) + return utils.collect_named_outputs(outputs_collections, sc.name, outputs) + + +@add_arg_scope def dropout(inputs, keep_prob=0.5, noise_shape=None, diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 49b23ce8fa..972ff10bf9 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -44,6 +44,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import random_ops +from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope @@ -1301,6 +1302,19 @@ class ConvolutionInPlaneTest(test.TestCase): self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5) +class DenseToSparseTest(test.TestCase): + + def testDenseFromConstantToSparse(self): + expected_constant = np.reshape(np.arange(24, dtype=np.int64), (3, 4, 2)) + tensor = constant_op.constant(expected_constant) + sparse = _layers.dense_to_sparse(tensor) + dense = sparse_ops.sparse_to_dense(sparse.indices, sparse.dense_shape, + sparse.values) + with self.test_session() as sess: + constant = sess.run(dense) + self.assertAllEqual(expected_constant, constant) + + class DropoutTest(test.TestCase): def testCreateDropout(self): diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py index 649996c49c..9a843168c2 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py @@ -151,7 +151,7 @@ def spirals(n_samples=100, # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes if extras > 0: - x_exrta, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, + x_extra, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, **kwargs) spir_x = np.append(spir_x, x_extra) spir_y = np.append(spir_y, y_extra) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py index 613d8d39a3..5809995c8c 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py @@ -136,6 +136,9 @@ class SyntheticTest(test.TestCase): self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data) + def test_spirals_synthetic(self): + synthetic.spirals(3) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py index 12f9bba531..2bd57597c2 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py @@ -1224,7 +1224,7 @@ class DNNRegressorTest(test.TestCase): self, predictions, expected_shape): predictions_nparray = np.array(predictions) self.assertAllEqual(expected_shape, predictions_nparray.shape) - self.assertTrue(np.issubdtype(predictions_nparray.dtype, np.float)) + self.assertTrue(np.issubdtype(predictions_nparray.dtype, np.floating)) def testPredict_AsIterableFalse(self): """Tests predict method with as_iterable=False.""" diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 0a097d5a69..19829e4991 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -5,25 +5,25 @@ def tflite_copts(): copts = [ "-DFARMHASH_NO_CXX_STRING", ] + select({ - "//tensorflow:android_arm64": [ + str(Label("//tensorflow:android_arm64")): [ "-std=c++11", "-O3", ], - "//tensorflow:android_arm": [ + str(Label("//tensorflow:android_arm")): [ "-mfpu=neon", "-mfloat-abi=softfp", "-std=c++11", "-O3", ], - "//tensorflow:android_x86": [ + str(Label("//tensorflow:android_x86")): [ "-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK", ], - "//tensorflow:ios_x86_64": [ + str(Label("//tensorflow:ios_x86_64")): [ "-msse4.1", ], "//conditions:default": [], }) + select({ - "//tensorflow:with_default_optimizations": [], + str(Label("//tensorflow:with_default_optimizations")): [], "//conditions:default": ["-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK"], }) diff --git a/tensorflow/contrib/lite/examples/label_image/BUILD b/tensorflow/contrib/lite/examples/label_image/BUILD index 476d85c031..959347b549 100644 --- a/tensorflow/contrib/lite/examples/label_image/BUILD +++ b/tensorflow/contrib/lite/examples/label_image/BUILD @@ -42,7 +42,15 @@ cc_library( "bitmap_helpers_impl.h", "label_image.h", ], - deps = ["//tensorflow/contrib/lite:string"], + deps = [ + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite:string", + "//tensorflow/contrib/lite:string_util", + "//tensorflow/contrib/lite/kernels:builtin_ops", + "//tensorflow/contrib/lite/schema:schema_fbs", + ], ) # TODO(ahentz): Test disabled as it has a memory leek from read_bmp diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h index 860e27e5ba..97343dde6b 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H -#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H +#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H_ +#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_H_ #include "tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h" #include "tensorflow/contrib/lite/examples/label_image/label_image.h" @@ -26,15 +26,15 @@ uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, int* channels, Settings* s); template <class T> -void downsize(T* out, uint8_t* in, int image_height, int image_width, - int image_channels, int wanted_height, int wanted_width, - int wanted_channels, Settings* s); +void resize(T* out, uint8_t* in, int image_height, int image_width, + int image_channels, int wanted_height, int wanted_width, + int wanted_channels, Settings* s); // explicit instantiation -template void downsize<uint8_t>(uint8_t*, unsigned char*, int, int, int, int, - int, int, Settings*); -template void downsize<float>(float*, unsigned char*, int, int, int, int, int, +template void resize<uint8_t>(uint8_t*, unsigned char*, int, int, int, int, int, int, Settings*); +template void resize<float>(float*, unsigned char*, int, int, int, int, int, + int, Settings*); } // namespace label_image } // namespace tflite diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h index 64a931082b..d57f597875 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h @@ -13,8 +13,14 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H -#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H +#ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ +#define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/string_util.h" +#include "tensorflow/contrib/lite/version.h" #include "tensorflow/contrib/lite/examples/label_image/label_image.h" @@ -22,28 +28,67 @@ namespace tflite { namespace label_image { template <class T> -void downsize(T* out, uint8_t* in, int image_height, int image_width, - int image_channels, int wanted_height, int wanted_width, - int wanted_channels, Settings* s) { - for (int y = 0; y < wanted_height; ++y) { - const int in_y = (y * image_height) / wanted_height; - uint8_t* in_row = in + (in_y * image_width * image_channels); - T* out_row = out + (y * wanted_width * wanted_channels); - for (int x = 0; x < wanted_width; ++x) { - const int in_x = (x * image_width) / wanted_width; - uint8_t* in_pixel = in_row + (in_x * image_channels); - T* out_pixel = out_row + (x * wanted_channels); - for (int c = 0; c < wanted_channels; ++c) { - if (s->input_floating) - out_pixel[c] = (in_pixel[c] - s->input_mean) / s->input_std; - else - out_pixel[c] = in_pixel[c]; - } - } +void resize(T* out, uint8_t* in, int image_height, int image_width, + int image_channels, int wanted_height, int wanted_width, + int wanted_channels, Settings* s) { + int number_of_pixels = image_height * image_width * image_channels; + std::unique_ptr<Interpreter> interpreter(new Interpreter); + + int base_index = 0; + + // two inputs: input and new_sizes + interpreter->AddTensors(2, &base_index); + // one output + interpreter->AddTensors(1, &base_index); + // set input and output tensors + interpreter->SetInputs({0, 1}); + interpreter->SetOutputs({2}); + + // set parameters of tensors + TfLiteQuantizationParams quant; + interpreter->SetTensorParametersReadWrite( + 0, kTfLiteFloat32, "input", + {1, image_height, image_width, image_channels}, quant); + interpreter->SetTensorParametersReadWrite(1, kTfLiteInt32, "new_size", {2}, + quant); + interpreter->SetTensorParametersReadWrite( + 2, kTfLiteFloat32, "output", + {1, wanted_height, wanted_width, wanted_channels}, quant); + + ops::builtin::BuiltinOpResolver resolver; + TfLiteRegistration* resize_op = + resolver.FindOp(BuiltinOperator_RESIZE_BILINEAR); + interpreter->AddNodeWithParameters({0, 1}, {2}, nullptr, 0, nullptr, + resize_op, nullptr); + + interpreter->AllocateTensors(); + + // fill input image + // in[] are integers, cannot do memcpy() directly + auto input = interpreter->typed_tensor<float>(0); + for (int i = 0; i < number_of_pixels; i++) { + input[i] = in[i]; + } + + // fill new_sizes + interpreter->typed_tensor<int>(1)[0] = wanted_height; + interpreter->typed_tensor<int>(1)[1] = wanted_width; + + interpreter->Invoke(); + + auto output = interpreter->typed_tensor<float>(2); + auto output_number_of_pixels = + wanted_height * wanted_height * wanted_channels; + + for (int i = 0; i < output_number_of_pixels; i++) { + if (s->input_floating) + out[i] = (output[i] - s->input_mean) / s->input_std; + else + out[i] = (uint8_t)output[i]; } } } // namespace label_image } // namespace tflite -#endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H +#endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H_ diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index 4d2e1ce0bc..a91467d345 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -148,14 +148,22 @@ void RunInference(Settings* s) { int wanted_width = dims->data[2]; int wanted_channels = dims->data[3]; - if (s->input_floating) { - downsize<float>(interpreter->typed_tensor<float>(input), in, image_height, + switch (interpreter->tensor(input)->type) { + case kTfLiteFloat32: + s->input_floating = true; + resize<float>(interpreter->typed_tensor<float>(input), in, image_height, image_width, image_channels, wanted_height, wanted_width, wanted_channels, s); - } else { - downsize<uint8_t>(interpreter->typed_tensor<uint8_t>(input), in, + break; + case kTfLiteUInt8: + resize<uint8_t>(interpreter->typed_tensor<uint8_t>(input), in, image_height, image_width, image_channels, wanted_height, wanted_width, wanted_channels, s); + break; + default: + LOG(FATAL) << "cannot handle input type " + << interpreter->tensor(input)->type << " yet"; + exit(-1); } struct timeval start_time, stop_time; @@ -177,13 +185,21 @@ void RunInference(Settings* s) { std::vector<std::pair<float, int>> top_results; - if (s->input_floating) { - get_top_n<float>(interpreter->typed_output_tensor<float>(0), output_size, - num_results, threshold, &top_results, s->input_floating); - } else { - get_top_n<uint8_t>(interpreter->typed_output_tensor<uint8_t>(0), - output_size, num_results, threshold, &top_results, - s->input_floating); + int output = interpreter->outputs()[0]; + switch (interpreter->tensor(output)->type) { + case kTfLiteFloat32: + get_top_n<float>(interpreter->typed_output_tensor<float>(0), output_size, + num_results, threshold, &top_results, true); + break; + case kTfLiteUInt8: + get_top_n<uint8_t>(interpreter->typed_output_tensor<uint8_t>(0), + output_size, num_results, threshold, &top_results, + false); + break; + default: + LOG(FATAL) << "cannot handle output type " + << interpreter->tensor(input)->type << " yet"; + exit(-1); } std::vector<string> labels; @@ -203,13 +219,11 @@ void display_usage() { LOG(INFO) << "label_image\n" << "--accelerated, -a: [0|1], use Android NNAPI or note\n" << "--count, -c: loop interpreter->Invoke() for certain times\n" - << "--input_floating, -f: [0|1] type of input layer is floating " - "point numbers\n" << "--input_mean, -b: input mean\n" << "--input_std, -s: input standard deviation\n" << "--image, -i: image_name.bmp\n" << "--labels, -l: labels for the model\n" - << "--tflite_mode, -m: model_name.tflite\n" + << "--tflite_model, -m: model_name.tflite\n" << "--threads, -t: number of threads\n" << "--verbose, -v: [0|1] print more information\n" << "\n"; @@ -223,7 +237,6 @@ int Main(int argc, char** argv) { static struct option long_options[] = { {"accelerated", required_argument, 0, 'a'}, {"count", required_argument, 0, 'c'}, - {"input_floating", required_argument, 0, 'f'}, {"verbose", required_argument, 0, 'v'}, {"image", required_argument, 0, 'i'}, {"labels", required_argument, 0, 'l'}, @@ -254,11 +267,6 @@ int Main(int argc, char** argv) { s.loop_count = strtol( // NOLINT(runtime/deprecated_fn) optarg, (char**)NULL, 10); break; - case 'f': - s.input_floating = strtol( // NOLINT(runtime/deprecated_fn) - optarg, (char**)NULL, 10); - s.input_layer_type = "float"; - break; case 'i': s.input_bmp_name = optarg; break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.h b/tensorflow/contrib/lite/examples/label_image/label_image.h index ce98e06fc1..4de32e33fb 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.h +++ b/tensorflow/contrib/lite/examples/label_image/label_image.h @@ -16,9 +16,11 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H #define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H -#include <string> #include "tensorflow/contrib/lite/string.h" +namespace tflite { +namespace label_image { + struct Settings { bool verbose = false; bool accel = false; @@ -33,4 +35,7 @@ struct Settings { int number_of_threads = 4; }; +} // namespace label_image +} // namespace tflite + #endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.md b/tensorflow/contrib/lite/examples/label_image/label_image.md index d6019d673f..9ce32cf101 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.md +++ b/tensorflow/contrib/lite/examples/label_image/label_image.md @@ -1,8 +1,12 @@ label_image for TensorFlow Lite inspired by TensorFlow's label_image. + +To build label_image for Android, run $TENSORFLOW_ROOT/configure +and set Android NDK or configure NDK setting in +$TENSORFLOW_ROOT/WORKSPACE first. To build it for android ARMv8: ``` -> bazel build --cxxopt=-std=c++11 \ +> bazel build --config monolithic --cxxopt=-std=c++11 \ --crosstool_top=//external:android/crosstool \ --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ --cpu=arm64-v8a \ @@ -10,13 +14,13 @@ To build it for android ARMv8: ``` or ``` -> bazel build --config android_arm64 --cxxopt=-std=c++11 \ +> bazel build --config android_arm64 --config monolithic --cxxopt=-std=c++11 \ //tensorflow/contrib/lite/examples/label_image:label_image ``` To build it for android arm-v7a: ``` -> bazel build --cxxopt=-std=c++11 \ +> bazel build --config monolithic --cxxopt=-std=c++11 \ --crosstool_top=//external:android/crosstool \ --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ --cpu=armeabi-v7a \ @@ -24,7 +28,7 @@ To build it for android arm-v7a: ``` or ``` -> bazel build --config android_arm --cxxopt=-std=c++11 \ +> bazel build --config android_arm --config monolithic --cxxopt=-std=c++11 \ //tensorflow/contrib/lite/examples/label_image:label_image ``` diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 4691a543e9..a6ccc99a51 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -278,6 +278,8 @@ cc_library( "optimized/neon_tensor_utils.cc", ], hdrs = [ + "common.h", + "optimized/cpu_check.h", "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", ], @@ -285,8 +287,11 @@ cc_library( deps = [ ":cpu_check", ":portable_tensor_utils", + ":types", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite/kernels:activation_functor", + "@arm_neon_2_x86_sse", + "@gemmlowp", ], ) @@ -306,14 +311,21 @@ cc_library( "tensor_utils.cc", ], hdrs = [ + "common.h", + "compatibility.h", + "optimized/cpu_check.h", + "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", "reference/portable_tensor_utils.h", "tensor_utils.h", + "types.h", ], copts = NEON_FLAGS_IF_APPLICABLE, deps = [ "//tensorflow/contrib/lite/kernels:activation_functor", "//tensorflow/contrib/lite:builtin_op_data", + "@arm_neon_2_x86_sse", + "@gemmlowp", ] + select({ ":arm": [ ":neon_tensor_utils", @@ -333,6 +345,18 @@ cc_library( ":ios_arm64": [ ":neon_tensor_utils", ], + ":x86_64": [ + ":neon_tensor_utils", + ], + ":x86": [ + ":neon_tensor_utils", + ], + ":k8": [ + ":neon_tensor_utils", + ], + ":darwin": [ + ":neon_tensor_utils", + ], "//conditions:default": [ ":portable_tensor_utils", ], diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h index 6cb556bf45..3a53d3ab07 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h @@ -34,7 +34,7 @@ inline bool TestCPUFeatureNeon() { #endif // __aarch64__ } -#elif __ARM_NEON +#elif defined USE_NEON || defined __ARM_NEON inline bool TestCPUFeatureNeon() { return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index bf0bdfb1fb..883c7f270d 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -16,11 +16,11 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h" #ifdef USE_NEON -#include <arm_neon.h> #define kFloatWeightsPerNeonLane 4 namespace tflite { diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc index 904a97803a..f4181b18a8 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" #ifndef USE_NEON #if defined(__ARM_NEON__) || defined(__ARM_NEON) diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index 7019c29959..76032771af 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -1571,7 +1571,7 @@ inline int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, } /** - * Specfifies which operands will be the model's inputs and outputs. + * Specifies which operands will be the model's inputs and outputs. * * An operand cannot be used for both input and output. Doing so will * return an error. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc index 2340f0e850..6961e23690 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc @@ -132,6 +132,7 @@ bool GraphTransformationsPass(int increment, Model* model, CHECK(increment == 1 || increment == -1); bool changed = false; if (model->operators.empty()) { + LOG(INFO) << "Model is empty!!!"; return false; } int op_index = increment == 1 ? 0 : model->operators.size() - 1; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc index 833c97c758..e79e2a32fc 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc @@ -189,7 +189,10 @@ bool ResolveConstantConcatenation::Run(Model* model, std::size_t op_index) { // Remove all the resolved arrays. for (const string& input_name : concat_op->inputs) { - model->EraseArray(input_name); + // Check to prevent removal of shared tensors + if (CountOpsWithInput(*model, input_name) == 1) { + model->EraseArray(input_name); + } } // Remove concatenate operator diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 8f12bc59fb..0bee694387 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -15,6 +15,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ #define TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ +#include <functional> #include <initializer_list> #include <memory> #include <string> diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 1add90fb82..ce0fde57f4 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -698,10 +698,11 @@ void CheckNonExistentIOArrays(const Model& model) { void CheckNoMissingArray(const Model& model) { for (const auto& op : model.operators) { for (const auto& input : op->inputs) { - CHECK(model.HasArray(input) || model.optional_arrays.count(input)); + CHECK(model.HasArray(input) || model.optional_arrays.count(input)) + << "Input: " << input << " missing for op: " << op->outputs[0] << "."; } for (const auto& output : op->outputs) { - CHECK(model.HasArray(output)); + CHECK(model.HasArray(output)) << "Output: " << output << " missing."; } } CheckNonExistentIOArrays(model); diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index dd5770dc99..81327407d4 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -377,10 +377,10 @@ $(MARCH_OPTION) \ ifeq ($(BUILD_FOR_TEGRA),1) NVCC := $(JETPACK)/cuda/bin/nvcc - NVCCFLAGS := -x=cu -D__CUDACC__ -DNVCC -DNVIDIA_TEGRA -ccbin $(NDK_ROOT)/toolchains/$(TOOLCHAIN)/prebuilt/$(ANDROID_HOST_OS_ARCH)/bin/$(BIN_PREFIX)-g++ --std c++11 --expt-relaxed-constexpr -m64 -gencode arch=compute_53,\"code=sm_53\" -gencode arch=compute_62,\"code=sm_62\" -DEIGEN_AVOID_STL_ARRAY -DTENSORFLOW_USE_EIGEN_THREADPOOL -DLANG_CXX11 -DEIGEN_HAS_C99_MATH -DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=5.3 + NVCCFLAGS := -x=cu -D__CUDACC__ -DNVCC -DANDROID_TEGRA -ccbin $(NDK_ROOT)/toolchains/$(TOOLCHAIN)/prebuilt/$(ANDROID_HOST_OS_ARCH)/bin/$(BIN_PREFIX)-g++ --std c++11 --expt-relaxed-constexpr -m64 -gencode arch=compute_53,\"code=sm_53\" -gencode arch=compute_62,\"code=sm_62\" -DEIGEN_AVOID_STL_ARRAY -DTENSORFLOW_USE_EIGEN_THREADPOOL -DLANG_CXX11 -DEIGEN_HAS_C99_MATH -DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=5.3 CXXFLAGS4NVCC =\ -DIS_SLIM_BUILD \ --DNVIDIA_TEGRA \ +-DANDROID_TEGRA \ -fno-exceptions \ -DNDEBUG $(OPTFLAGS) \ -march=armv8-a \ @@ -391,7 +391,7 @@ $(MARCH_OPTION) \ CXXFLAGS +=\ -DGOOGLE_CUDA=1 \ -D__ANDROID_TYPES_FULL__ \ --DNVIDIA_TEGRA \ +-DANDROID_TEGRA \ -DEIGEN_AVOID_STL_ARRAY \ -DEIGEN_HAS_C99_MATH \ -DLANG_CXX11 -DTENSORFLOW_USE_EIGEN_THREADPOOL -DTF_EXTRA_CUDA_CAPABILITIES=5.3 @@ -407,7 +407,7 @@ $(MARCH_OPTION) \ -I$(JETPACK)/cuda/extras/CUPTI/include - LIBS += \ + CUDA_LIBS := \ -ltfcuda \ -lcudart_static \ -lcudnn \ @@ -420,10 +420,10 @@ $(MARCH_OPTION) \ -lculibos \ -lcurand_static - OBJDIR := $(OBJDIR)Tegra/ - LIBDIR := $(LIBDIR)Tegra/ - BINDIR := $(BINDIR)Tegra/ - DEPDIR := $(DEPDIR)Tegra/ + OBJDIR := $(OBJDIR)android_arm64-v8a/ + LIBDIR := $(LIBDIR)android_arm64-v8a/ + BINDIR := $(BINDIR)android_arm64-v8a/ + DEPDIR := $(DEPDIR)android_arm64-v8a/ TEGRA_LIBS := \ -L$(JETPACK)/cuda/targets/aarch64-linux-androideabi/lib \ @@ -606,7 +606,8 @@ $(wildcard tensorflow/core/util/*/*.cc) \ tensorflow/core/util/version_info.cc # Remove duplicates (for version_info.cc) CORE_CC_ALL_SRCS := $(sort $(CORE_CC_ALL_SRCS)) -CORE_CC_EXCLUDE_SRCS := \ + +CORE_CC_EXCLUDE_SRCS_NON_GPU := \ $(wildcard tensorflow/core/*/*test.cc) \ $(wildcard tensorflow/core/*/*testutil*) \ $(wildcard tensorflow/core/*/*testlib*) \ @@ -626,49 +627,31 @@ $(wildcard tensorflow/core/lib/jpeg/*) \ $(wildcard tensorflow/core/lib/png/*) \ $(wildcard tensorflow/core/util/events_writer.*) \ $(wildcard tensorflow/core/util/reporter.*) \ -$(wildcard tensorflow/core/platform/default/cuda_libdevice_path.*) \ -$(wildcard tensorflow/core/platform/default/stream_executor.*) \ $(wildcard tensorflow/core/platform/default/test_benchmark.*) \ -$(wildcard tensorflow/core/platform/cuda.h) \ -$(wildcard tensorflow/core/platform/cuda_libdevice_path.*) \ $(wildcard tensorflow/core/platform/cloud/*) \ $(wildcard tensorflow/core/platform/google/*) \ $(wildcard tensorflow/core/platform/google/*/*) \ $(wildcard tensorflow/core/platform/jpeg.*) \ $(wildcard tensorflow/core/platform/png.*) \ $(wildcard tensorflow/core/platform/s3/*) \ -$(wildcard tensorflow/core/platform/stream_executor.*) \ $(wildcard tensorflow/core/platform/windows/*) \ -$(wildcard tensorflow/core/user_ops/*.cu.cc) \ -$(wildcard tensorflow/core/common_runtime/gpu/*) \ -$(wildcard tensorflow/core/common_runtime/gpu_device_factory.*) \ $(wildcard tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.*) \ $(wildcard tensorflow/core/grappler/inputs/file_input_yielder.*) \ -$(wildcard tensorflow/core/grappler/clusters/single_machine.*) +$(wildcard tensorflow/core/grappler/clusters/single_machine.*) \ +tensorflow/core/util/cuda_kernel_helper_test.cu.cc + +CORE_CC_EXCLUDE_SRCS := \ +$(CORE_CC_EXCLUDE_SRCS_NON_GPU) \ +$(wildcard tensorflow/core/platform/stream_executor.*) \ +$(wildcard tensorflow/core/platform/default/cuda_libdevice_path.*) \ +$(wildcard tensorflow/core/platform/cuda.h) \ +$(wildcard tensorflow/core/platform/cuda_libdevice_path.*) \ +$(wildcard tensorflow/core/user_ops/*.cu.cc) \ +$(wildcard tensorflow/core/common_runtime/gpu/*) \ +$(wildcard tensorflow/core/common_runtime/gpu_device_factory.*) ifeq ($(BUILD_FOR_TEGRA),1) -CORE_CC_ALL_SRCS := \ -$(wildcard tensorflow/core/*.cc) \ -$(wildcard tensorflow/core/common_runtime/*.cc) \ -$(wildcard tensorflow/core/common_runtime/gpu/*.cc) \ -$(wildcard tensorflow/core/framework/*.cc) \ -$(wildcard tensorflow/core/graph/*.cc) \ -$(wildcard tensorflow/core/platform/*.cc) \ -$(wildcard tensorflow/core/platform/*/*.cc) \ -$(wildcard tensorflow/core/platform/*/*/*.cc) \ -$(wildcard tensorflow/core/util/*.cc) \ -$(wildcard tensorflow/core/util/*/*.cc) \ -$(wildcard tensorflow/cc/training/*.cc) \ -$(wildcard tensorflow/stream_executor/*.cc) \ -$(wildcard tensorflow/stream_executor/*/*.cc) \ -$(wildcard tensorflow/core/grappler/optimizers/*.cc) \ -$(wildcard tensorflow/core/grappler/*.cc) \ -$(wildcard tensorflow/core/grappler/costs/*.cc) \ -$(wildcard tensorflow/core/grappler/clusters/*.cc) \ -$(wildcard tensorflow/core/grappler/utils/*.cc) \ -$(wildcard tensorflow/core/lib/core/*.cc) \ -$(wildcard tensorflow/core/lib/*/*.cc) \ -tensorflow/core/grappler/inputs/utils.cc \ +CORE_CC_ALL_SRCS := $(CORE_CC_ALL_SRCS) \ tensorflow/core/kernels/concat_lib_gpu.cc \ tensorflow/core/kernels/cuda_solvers.cc \ tensorflow/core/kernels/cudnn_pooling_gpu.cc \ @@ -677,28 +660,14 @@ tensorflow/core/kernels/fractional_avg_pool_op.cc \ tensorflow/core/kernels/fractional_max_pool_op.cc \ tensorflow/core/kernels/fractional_pool_common.cc \ tensorflow/core/kernels/pooling_ops_3d.cc \ -tensorflow/core/kernels/sparse_fill_empty_rows_op.cc +tensorflow/core/kernels/sparse_fill_empty_rows_op.cc \ +tensorflow/core/kernels/list_kernels.cc \ +$(wildcard tensorflow/core/common_runtime/gpu/*.cc) \ +$(wildcard tensorflow/stream_executor/*.cc) \ +$(wildcard tensorflow/stream_executor/*/*.cc) CORE_CC_EXCLUDE_SRCS := \ -$(wildcard tensorflow/core/*/*test.cc) \ -$(wildcard tensorflow/core/*/*testutil*) \ -$(wildcard tensorflow/core/*/*testlib*) \ -$(wildcard tensorflow/core/*/*/*test.cc) \ -$(wildcard tensorflow/core/*/*/*testutil*) \ -$(wildcard tensorflow/core/framework/op_gen_lib.cc) \ -$(wildcard tensorflow/core/lib/gif/*) \ -$(wildcard tensorflow/core/lib/jpeg/*) \ -$(wildcard tensorflow/core/lib/png/*) \ -$(wildcard tensorflow/core/lib/db/*) \ -$(wildcard tensorflow/core/platform/jpeg.*) \ -$(wildcard tensorflow/core/platform/png.*) \ -$(wildcard tensorflow/core/platform/cloud/*) \ -$(wildcard tensorflow/core/platform/s3/*) \ -$(wildcard tensorflow/core/platform/windows/*) \ -$(wildcard tensorflow/core/*/*/*testlib*) \ -$(wildcard tensorflow/cc/training/*test.cc) \ -tensorflow/core/lib/io/record_reader.cc \ -tensorflow/core/util/cuda_kernel_helper_test.cu.cc +$(CORE_CC_EXCLUDE_SRCS_NON_GPU) CUDA_CC_SRCS := $(wildcard tensorflow/core/kernels/*.cu.cc) CUDA_CC_OBJS := $(addprefix $(OBJDIR), $(CUDA_CC_SRCS:.cc=.o)) @@ -760,7 +729,7 @@ $(BENCHMARK_NAME): $(BENCHMARK_OBJS) $(LIB_PATH) $(CUDA_LIB_DEPS) @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) \ -o $(BENCHMARK_NAME) $(BENCHMARK_OBJS) \ - $(LIBFLAGS) $(TEGRA_LIBS) $(LIB_PATH) $(LDFLAGS) $(LIBS) + $(LIBFLAGS) $(TEGRA_LIBS) $(LIB_PATH) $(LDFLAGS) $(LIBS) $(CUDA_LIBS) # NVCC compilation rules for Tegra ifeq ($(BUILD_FOR_TEGRA),1) diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index 980a44a595..f67c516186 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -18,7 +18,7 @@ set -e usage() { - echo "Usage: NDK_ROOT=<path to ndk root> $(basename "$0") [-Es:t:Tx:a:X]" + echo "Usage: NDK_ROOT=<path to ndk root> $(basename "$0") [-Es:t:Tx:a]" echo "-E enable experimental hexnn ops" echo "-s [sub_makefiles] sub makefiles separated by white space" echo "-t [build_target] build target for Android makefile [default=all]" diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh index a18df256f9..2d99791839 100755 --- a/tensorflow/contrib/makefile/build_all_ios.sh +++ b/tensorflow/contrib/makefile/build_all_ios.sh @@ -96,7 +96,7 @@ if [[ "${ONLY_MAKE_TENSORFLOW}" != "true" ]]; then if [[ -z "${BUILD_ARCH}" ]]; then # Compile protobuf for the target iOS device architectures. - tensorflow/contrib/makefile/compile_ios_protobuf.sh -a ${DEFAULT_ARCH} + tensorflow/contrib/makefile/compile_ios_protobuf.sh else # Compile protobuf for the target iOS device architectures. tensorflow/contrib/makefile/compile_ios_protobuf.sh -a ${BUILD_ARCH} diff --git a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh index 861bb885c7..203ff4f890 100755 --- a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh +++ b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh @@ -76,6 +76,8 @@ GEN_LIBS_DIR="${GEN_DIR}/libs" GEN_DOWNLOAD_DIR="${GEN_DIR}/downloads" URL_BASE="https://storage.googleapis.com/download.tensorflow.org" +ARCH="armeabi-v7a" + source "${SCRIPT_DIR}/../build_helper.subr" rm -rf "${GEN_DIR}" @@ -219,7 +221,7 @@ if [[ "${BUILD_ONLY}" != "true" ]]; then adb push "${GEN_LIBS_DIR}/libhexagon_nn_skel.so" "/vendor/lib/rfsa/adsp" adb push -p \ - "${TF_ROOT_DIR}/tensorflow/contrib/makefile/gen/bin/hexagon_graph_execution" \ + "${TF_ROOT_DIR}/tensorflow/contrib/makefile/gen/bin/android_${ARCH}/hexagon_graph_execution" \ "/data/local/tmp/" adb wait-for-device adb shell chmod "${ANDROID_EXEC_FILE_MODE}" \ diff --git a/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in b/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in index d9277ed60c..3081084ee7 100644 --- a/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in +++ b/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in @@ -54,7 +54,7 @@ $(INFERENCE_SO_PATH): $(LIB_OBJS) $(INFERENCE_OBJS) $(CUDA_LIB_DEPS) -o $@ $(INFERENCE_OBJS) $(LIB_OBJS) $(TEGRA_LIBS) \ $(LIBFLAGS) $(LDFLAGS) \ -shared -Wl,-soname,$(INFERENCE_SO_NAME) \ - $(LIBS) + $(LIBS) $(CUDA_LIBS) $(INFERENCE_SO_NAME): $(INFERENCE_SO_PATH) diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt index 5f27566398..5a812af4e9 100644 --- a/tensorflow/contrib/makefile/tf_op_files.txt +++ b/tensorflow/contrib/makefile/tf_op_files.txt @@ -91,6 +91,7 @@ tensorflow/core/kernels/reduction_ops_max.cc tensorflow/core/kernels/reduction_ops_common.cc tensorflow/core/kernels/reduction_ops_any.cc tensorflow/core/kernels/reduction_ops_all.cc +tensorflow/core/kernels/roll_op.cc tensorflow/core/kernels/queue_ops.cc tensorflow/core/kernels/queue_base.cc tensorflow/core/kernels/pooling_ops_common.cc @@ -270,6 +271,7 @@ tensorflow/core/ops/parsing_ops.cc tensorflow/core/ops/no_op.cc tensorflow/core/ops/nn_ops.cc tensorflow/core/ops/nn_grad.cc +tensorflow/core/ops/manip_ops.cc tensorflow/core/ops/math_ops.cc tensorflow/core/ops/math_grad.cc tensorflow/core/ops/logging_ops.cc @@ -291,3 +293,4 @@ tensorflow/core/kernels/batchtospace_op.cc tensorflow/core/kernels/warn_about_ints.cc tensorflow/core/kernels/segment_reduction_ops.cc tensorflow/core/kernels/batch_util.cc +tensorflow/core/ops/audio_ops.cc diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc index c2c42b8ed7..6a7f5efecd 100644 --- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc +++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc @@ -151,7 +151,7 @@ MPIRemoteRendezvous::~MPIRemoteRendezvous() {} void MPIRendezvousMgr::AddRequest(RecvTensorRequest request, const int mpi_dst) { TF_CHECK_OK(recv_tensor_recent_request_ids_.TrackUnique( - req.request_id(), "RecvTensor (MPIRendezvousMgr)", req)); + request.request_id(), "RecvTensor (MPIRendezvousMgr)", request)); const int64 step_id = request.step_id(); const std::string& key = request.rendezvous_key(); Rendezvous::ParsedKey parsed; diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h index e665922135..5596601ddb 100644 --- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h +++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/contrib/mpi/mpi_msg.pb.h" #include "tensorflow/contrib/mpi/mpi_utils.h" #include "tensorflow/core/distributed_runtime/base_rendezvous_mgr.h" +#include "tensorflow/core/distributed_runtime/recent_request_ids.h" #include "tensorflow/core/distributed_runtime/request_id.h" #include "tensorflow/core/distributed_runtime/worker_env.h" #include "tensorflow/core/protobuf/worker.pb.h" diff --git a/tensorflow/contrib/ndlstm/__init__.py b/tensorflow/contrib/ndlstm/__init__.py index 52e83069cb..a5dd100b26 100644 --- a/tensorflow/contrib/ndlstm/__init__.py +++ b/tensorflow/contrib/ndlstm/__init__.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +"""Library of multidimensional LSTM models and related code.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function + +from tensorflow.contrib.ndlstm.python import lstm1d +from tensorflow.contrib.ndlstm.python import lstm2d diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py index d3c3531f40..2e2e9086c0 100644 --- a/tensorflow/contrib/ndlstm/python/lstm1d.py +++ b/tensorflow/contrib/ndlstm/python/lstm1d.py @@ -22,7 +22,6 @@ from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.framework.python.ops import variables from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import rnn @@ -85,18 +84,11 @@ def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): Output sequence (length, batch_size, noutput) """ with variable_scope.variable_scope(scope, "SeqLstm", [inputs]): - # TODO(tmb) make batch size, sequence_length dynamic - # example: sequence_length = tf.shape(inputs)[0] - _, batch_size, _ = _shape(inputs) - lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm_cell.state_size]) - sequence_length = int(inputs.get_shape()[0]) - sequence_lengths = math_ops.to_int64( - array_ops.fill([batch_size], sequence_length)) + lstm_cell = rnn_cell.BasicLSTMCell(noutput) if reverse: inputs = array_ops.reverse_v2(inputs, [0]) outputs, _ = rnn.dynamic_rnn( - lstm_cell, inputs, sequence_lengths, state, time_major=True) + lstm_cell, inputs, time_major=True, dtype=inputs.dtype) if reverse: outputs = array_ops.reverse_v2(outputs, [0]) return outputs diff --git a/tensorflow/contrib/opt/python/training/external_optimizer.py b/tensorflow/contrib/opt/python/training/external_optimizer.py index f243317f1d..82ebca7f20 100644 --- a/tensorflow/contrib/opt/python/training/external_optimizer.py +++ b/tensorflow/contrib/opt/python/training/external_optimizer.py @@ -397,10 +397,6 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface): 'automatically and cannot be injected manually'.format(kwarg)) minimize_kwargs.update(optimizer_kwargs) - if method == 'SLSQP': - # SLSQP doesn't support step callbacks. Obviate associated warning - # message. - del minimize_kwargs['callback'] import scipy.optimize # pylint: disable=g-import-not-at-top result = scipy.optimize.minimize(*minimize_args, **minimize_kwargs) diff --git a/tensorflow/contrib/opt/python/training/external_optimizer_test.py b/tensorflow/contrib/opt/python/training/external_optimizer_test.py index 0f597d0a24..953586ee70 100644 --- a/tensorflow/contrib/opt/python/training/external_optimizer_test.py +++ b/tensorflow/contrib/opt/python/training/external_optimizer_test.py @@ -299,6 +299,45 @@ class ScipyOptimizerInterfaceTest(TestCase): method = optimizer.optimizer_kwargs.get('method') self.assertEqual('SLSQP', method) + def test_callbacks(self): + vector_val = np.array([7., -2.], dtype=np.float32) + vector = variables.Variable(vector_val, 'vector') + + minimum_location_val = np.arange(2) + minimum_location = constant_op.constant( + minimum_location_val, dtype=dtypes.float32) + + loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2. + loss_val_first = ((vector_val - minimum_location_val)**2).sum() / 2. + + optimizer = external_optimizer.ScipyOptimizerInterface(loss, method='SLSQP') + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + + initial_vector_val = sess.run(vector) + + extra_fetches = [loss] + + step_callback = test.mock.Mock() + loss_callback = test.mock.Mock() + + optimizer.minimize( + sess, + fetches=extra_fetches, + loss_callback=loss_callback, + step_callback=step_callback) + + loss_val_last = sess.run(loss) + + call_first = test.mock.call(loss_val_first) + call_last = test.mock.call(loss_val_last) + loss_calls = [call_first, call_last] + loss_callback.assert_has_calls(loss_calls, any_order=True) + + args, _ = step_callback.call_args + self.assertAllClose(minimum_location_val, args[0]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py index 8ff6618912..85d40f3158 100644 --- a/tensorflow/contrib/py2tf/impl/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -86,8 +86,8 @@ def convert_inline(f, *args, **kwargs): def convert(recursive=False, arg_types=None): """Decorator that compiles a function to graph mode. - The decorator is dynamic - invoking compilation whenever the decorated fuction - is called. This means the parameter values are known at compilation. + The decorator is dynamic - invoking compilation whenever the decorated + function is called. This means the parameter values are known at compilation. Args: recursive: Whether to recusrively convert any functions that the decorator diff --git a/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py b/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py index b2360fec6c..0388079f20 100644 --- a/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py +++ b/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py @@ -61,7 +61,7 @@ def _compute_output_resolution(input_spatial_resolution, kernel_size, stride, stride: Stride (int). total_padding: Total padding to be applied (int). Returns: - output_resolution: Ouput dimension (int) or None. + output_resolution: Output dimension (int) or None. """ if (input_spatial_resolution is None) or (kernel_size is None) or ( stride is None) or (total_padding is None): diff --git a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc index b8b56c0e22..92879ab535 100644 --- a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc +++ b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc @@ -87,9 +87,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 2, 3] - [ 0, 0, 0] - [41,52,63]]. +the output will be [[ 1, 2, 3] + [ 0, 0, 0] + [41,52,63]]. ``` The data must be at least rank 1. The indices must be of shape (?,2) where the @@ -132,9 +132,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 2, 3] - [ 1, 1, 1] - [40,100,180]]. +the output will be [[ 1, 2, 3] + [ 1, 1, 1] + [40,100,180]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the @@ -189,9 +189,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 20, 3] - [ -BIG_VALUE, -BIG_VALUE, -BIG_VALUE] - [ 400, 20, 60]]. +the output will be [[ 1, 20, 3] + [ -BIG_VALUE, -BIG_VALUE, -BIG_VALUE] + [ 400, 20, 60]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the @@ -246,9 +246,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 20, 3] - [ +BIG_VALUE, +BIG_VALUE, +BIG_VALUE] - [ 1, 5, 3]]. +the output will be [[ 1, 20, 3] + [ +BIG_VALUE, +BIG_VALUE, +BIG_VALUE] + [ 1, 5, 3]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 09527e8473..0e62b315b6 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -157,6 +157,21 @@ class RNNCellTest(test.TestCase): # Smoke test self.assertAllClose(res[0], [[0.509682, 0.509682]]) + def testSRUCellWithDiffSize(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 3]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.SRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + # Smoke test + self.assertAllClose(res[0], [[0.55255556, 0.55255556]]) + def testBasicLSTMCell(self): for dtype in [dtypes.float16, dtypes.float32]: np_dtype = dtype.as_numpy_dtype diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py index c780e85d72..51933be29d 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py @@ -1635,6 +1635,5 @@ class WeightNormLSTMCellTest(test.TestCase): self.assertAllClose(expected_c, actual_c, 1e-5) self.assertAllClose(expected_h, actual_h, 1e-5) - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index 124e841fc2..fe07493d0f 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -2731,25 +2731,9 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): input_depth = inputs_shape[1].value - # Here the contributor believes that the following constraints - # are implied. The reasoning is explained here with reference to - # the paper https://arxiv.org/pdf/1709.02755.pdf upon which this - # implementation is based. - # In section 2.1 Equation 5, specifically: - # h_t = r_t \odot g(c_t) + (1 - r_t) \odot x_t - # the pointwise operation between r_t and x_t means they have - # the same shape (since we are implementing an RNN cell, braodcasting - # does not happen to input of a single timestep); by the same - # reasons, x_t has the same shape as h_t, essentially mandating that - # input_depth = unit_num. - if input_depth != self._num_units: - raise ValueError("SRU requires input_depth == num_units, got " - "input_depth = %s, num_units = %s" % (input_depth, - self._num_units)) - self._kernel = self.add_variable( rnn_cell_impl._WEIGHTS_VARIABLE_NAME, - shape=[input_depth, 3 * self._num_units]) + shape=[input_depth, 4 * self._num_units]) self._bias = self.add_variable( rnn_cell_impl._BIAS_VARIABLE_NAME, @@ -2762,8 +2746,8 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): """Simple recurrent unit (SRU) with num_units cells.""" U = math_ops.matmul(inputs, self._kernel) - x_bar, f_intermediate, r_intermediate = array_ops.split( - value=U, num_or_size_splits=3, axis=1) + x_bar, f_intermediate, r_intermediate, x_tx = array_ops.split( + value=U, num_or_size_splits=4, axis=1) f_r = math_ops.sigmoid( nn_ops.bias_add( @@ -2771,7 +2755,7 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): f, r = array_ops.split(value=f_r, num_or_size_splits=2, axis=1) c = f * state + (1.0 - f) * x_bar - h = r * self._activation(c) + (1.0 - r) * inputs + h = r * self._activation(c) + (1.0 - r) * x_tx return h, c diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py index 95dea312f3..d6b5eceb47 100644 --- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py +++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py @@ -924,8 +924,7 @@ class LuongMonotonicAttention(_BaseMonotonicAttentionMechanism): _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode, seed=sigmoid_noise_seed) super(LuongMonotonicAttention, self).__init__( - query_layer=layers_core.Dense( - num_units, name="query_layer", use_bias=False, dtype=dtype), + query_layer=None, memory_layer=layers_core.Dense( num_units, name="memory_layer", use_bias=False, dtype=dtype), memory=memory, diff --git a/tensorflow/contrib/session_bundle/bundle_shim.py b/tensorflow/contrib/session_bundle/bundle_shim.py index 062c9cc680..1db97020a2 100644 --- a/tensorflow/contrib/session_bundle/bundle_shim.py +++ b/tensorflow/contrib/session_bundle/bundle_shim.py @@ -82,7 +82,8 @@ def _convert_default_signature_to_signature_def(signatures): """ default_signature = signatures.default_signature signature_def = meta_graph_pb2.SignatureDef() - if default_signature.WhichOneof("type") == "regression_signature": + if (default_signature.WhichOneof("type") == + legacy_constants.REGRESSION_SIGNATURE): regression_signature = default_signature.regression_signature signature_def.method_name = signature_constants.REGRESS_METHOD_NAME _add_input_to_signature_def(regression_signature.input.tensor_name, @@ -91,7 +92,8 @@ def _convert_default_signature_to_signature_def(signatures): _add_output_to_signature_def(regression_signature.output.tensor_name, signature_constants.REGRESS_OUTPUTS, signature_def) - elif default_signature.WhichOneof("type") == "classification_signature": + elif (default_signature.WhichOneof("type") == + legacy_constants.CLASSIFICATION_SIGNATURE): classification_signature = default_signature.classification_signature signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME _add_input_to_signature_def(classification_signature.input.tensor_name, @@ -132,8 +134,9 @@ def _convert_named_signatures_to_signature_def(signatures): signature_constants.PREDICT_OUTPUTS] # TODO(pdudnik): what if there are other signatures? Mimic cr/140900781 once # it is submitted. - if (input_signature.WhichOneof("type") != "generic_signature" or - output_signature.WhichOneof("type") != "generic_signature"): + if (input_signature.WhichOneof("type") != legacy_constants.GENERIC_SIGNATURE + or output_signature.WhichOneof("type") != + legacy_constants.GENERIC_SIGNATURE): raise RuntimeError("Named input and output signatures can only be " "up-converted if they are generic signature. " "Input signature type is %s, output signature type is " diff --git a/tensorflow/contrib/session_bundle/constants.py b/tensorflow/contrib/session_bundle/constants.py index 6ced73241a..e833baee79 100644 --- a/tensorflow/contrib/session_bundle/constants.py +++ b/tensorflow/contrib/session_bundle/constants.py @@ -32,3 +32,6 @@ INIT_OP_KEY = "serving_init_op" SIGNATURES_KEY = "serving_signatures" ASSETS_KEY = "serving_assets" GRAPH_KEY = "serving_graph" +REGRESSION_SIGNATURE = "regression_signature" +CLASSIFICATION_SIGNATURE = "classification_signature" +GENERIC_SIGNATURE = "generic_signature" diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 870f504d10..8a267ddac7 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -29,7 +29,6 @@ from tensorflow.contrib.framework.python.ops import variables as variables_lib from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib -from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.wrappers import hooks from tensorflow.python.framework import constant_op @@ -236,7 +235,7 @@ class SingleEvaluationTest(test.TestCase): def _prepareCheckpoint(self, checkpoint_path): init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) - saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) + saver = saver_lib.Saver() with self.test_session() as sess: sess.run(init_op) saver.save(sess, checkpoint_path) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py index 930df2414b..a1282847be 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py @@ -45,32 +45,67 @@ def _get_linear_equations_tests(dtype_, use_static_shape_, shape_): low=-1.0, high=1.0, size=np.prod(shape_)).reshape(shape_).astype(dtype_) # Make a selfadjoint, positive definite. a_np = np.dot(a_np.T, a_np) + # jacobi preconditioner + jacobi_np = np.zeros_like(a_np) + jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = ( + 1.0 / a_np.diagonal()) rhs_np = np.random.uniform( low=-1.0, high=1.0, size=shape_[0]).astype(dtype_) + x_np = np.zeros_like(rhs_np) tol = 1e-6 if dtype_ == np.float64 else 1e-3 max_iter = 20 with self.test_session() as sess: if use_static_shape_: a = constant_op.constant(a_np) rhs = constant_op.constant(rhs_np) + x = constant_op.constant(x_np) + jacobi = constant_op.constant(jacobi_np) else: a = array_ops.placeholder(dtype_) rhs = array_ops.placeholder(dtype_) + x = array_ops.placeholder(dtype_) + jacobi = array_ops.placeholder(dtype_) operator = util.create_operator(a) - cg_graph = linear_equations.conjugate_gradient( - operator, rhs, tol=tol, max_iter=max_iter) - if use_static_shape_: - cg_val = sess.run(cg_graph) - else: - cg_val = sess.run(cg_graph, feed_dict={a: a_np, rhs: rhs_np}) - norm_r0 = np.linalg.norm(rhs_np) - norm_r = np.sqrt(cg_val.gamma) - self.assertLessEqual(norm_r, tol * norm_r0) - # Validate that we get an equally small residual norm with numpy - # using the computed solution. - r_np = rhs_np - np.dot(a_np, cg_val.x) - norm_r_np = np.linalg.norm(r_np) - self.assertLessEqual(norm_r_np, tol * norm_r0) + preconditioners = [ + None, util.identity_operator(a), + util.create_operator(jacobi) + ] + cg_results = [] + for preconditioner in preconditioners: + cg_graph = linear_equations.conjugate_gradient( + operator, + rhs, + preconditioner=preconditioner, + x=x, + tol=tol, + max_iter=max_iter) + if use_static_shape_: + cg_val = sess.run(cg_graph) + else: + cg_val = sess.run( + cg_graph, + feed_dict={ + a: a_np, + rhs: rhs_np, + x: x_np, + jacobi: jacobi_np + }) + norm_r0 = np.linalg.norm(rhs_np) + norm_r = np.linalg.norm(cg_val.r) + self.assertLessEqual(norm_r, tol * norm_r0) + # Validate that we get an equally small residual norm with numpy + # using the computed solution. + r_np = rhs_np - np.dot(a_np, cg_val.x) + norm_r_np = np.linalg.norm(r_np) + self.assertLessEqual(norm_r_np, tol * norm_r0) + cg_results.append(cg_val) + # Validate that we get same results using identity_preconditioner + # and None + self.assertEqual(cg_results[0].i, cg_results[1].i) + self.assertAlmostEqual(cg_results[0].gamma, cg_results[1].gamma) + self.assertAllClose(cg_results[0].r, cg_results[1].r, rtol=tol) + self.assertAllClose(cg_results[0].x, cg_results[1].x, rtol=tol) + self.assertAllClose(cg_results[0].p, cg_results[1].p, rtol=tol) return [test_conjugate_gradient] diff --git a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py index 1566984b27..5d7534657b 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py @@ -63,6 +63,43 @@ class UtilTest(test.TestCase): def testCreateOperatorUnknownShape(self): self._testCreateOperator(False) + def _testIdentityOperator(self, use_static_shape_): + for dtype in np.float32, np.float64: + a_np = np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=dtype) + x_np = np.array([[2.], [-3.]], dtype=dtype) + y_np = np.array([[2], [-3.], [5.]], dtype=dtype) + with self.test_session() as sess: + if use_static_shape_: + a = constant_op.constant(a_np, dtype=dtype) + x = constant_op.constant(x_np, dtype=dtype) + y = constant_op.constant(y_np, dtype=dtype) + else: + a = array_ops.placeholder(dtype) + x = array_ops.placeholder(dtype) + y = array_ops.placeholder(dtype) + id_op = util.identity_operator(a) + ax = id_op.apply(x) + aty = id_op.apply_adjoint(y) + op_shape = ops.convert_to_tensor(id_op.shape) + if use_static_shape_: + op_shape_val, ax_val, aty_val = sess.run([op_shape, ax, aty]) + else: + op_shape_val, ax_val, aty_val = sess.run( + [op_shape, ax, aty], feed_dict={ + a: a_np, + x: x_np, + y: y_np + }) + self.assertAllEqual(op_shape_val, [3, 2]) + self.assertAllClose(ax_val, x_np) + self.assertAllClose(aty_val, y_np) + + def testIdentityOperator(self): + self._testIdentityOperator(True) + + def testIdentityOperatorUnknownShape(self): + self._testIdentityOperator(False) + def testL2Norm(self): with self.test_session(): x_np = np.array([[2], [-3.], [5.]]) diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py index 8cba56eba6..2395707257 100644 --- a/tensorflow/contrib/solvers/python/ops/linear_equations.py +++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py @@ -26,11 +26,14 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops def conjugate_gradient(operator, rhs, + preconditioner=None, + x=None, tol=1e-4, max_iter=20, name="conjugate_gradient"): @@ -55,6 +58,15 @@ def conjugate_gradient(operator, vector with the result of applying the operator to `x`, i.e. if `operator` represents matrix `A`, `apply` should return `A * x`. rhs: A rank-1 `Tensor` of shape `[N]` containing the right-hand size vector. + preconditioner: An object representing a linear operator, see `operator` + for detail. The preconditioner should approximate the inverse of `A`. + An efficient preconditioner could dramatically improve the rate of + convergence. If `preconditioner` represents matrix `M`(`M` approximates + `A^{-1}`), the algorithm uses `preconditioner.apply(x)` to estimate + `A^{-1}x`. For this to be useful, the cost of applying `M` should be + much lower than computing `A^{-1}` directly. + x: A rank-1 `Tensor` of shape `[N]` containing the initial guess for the + solution. tol: A float scalar convergence tolerance. max_iter: An integer giving the maximum number of iterations. name: A name scope for the operation. @@ -65,35 +77,49 @@ def conjugate_gradient(operator, - x: A rank-1 `Tensor` of shape `[N]` containing the computed solution. - r: A rank-1 `Tensor` of shape `[M]` containing the residual vector. - p: A rank-1 `Tensor` of shape `[N]`. `A`-conjugate basis vector. - - gamma: \\(||r||_2^2\\) + - gamma: \\(r \dot M \dot r\\), equivalent to \\(||r||_2^2\\) when + `preconditioner=None`. """ # ephemeral class holding CG state. cg_state = collections.namedtuple("CGState", ["i", "x", "r", "p", "gamma"]) def stopping_criterion(i, state): - return math_ops.logical_and(i < max_iter, state.gamma > tol) + return math_ops.logical_and(i < max_iter, linalg_ops.norm(state.r) > tol) - # TODO(rmlarsen): add preconditioning - def cg_step(i, state): + def cg_step(i, state): # pylint: disable=missing-docstring z = operator.apply(state.p) alpha = state.gamma / util.dot(state.p, z) x = state.x + alpha * state.p r = state.r - alpha * z - gamma = util.l2norm_squared(r) - beta = gamma / state.gamma - p = r + beta * state.p + if preconditioner is None: + gamma = util.dot(r, r) + beta = gamma / state.gamma + p = r + beta * state.p + else: + q = preconditioner.apply(r) + gamma = util.dot(r, q) + beta = gamma / state.gamma + p = q + beta * state.p return i + 1, cg_state(i + 1, x, r, p, gamma) with ops.name_scope(name): n = operator.shape[1:] rhs = array_ops.expand_dims(rhs, -1) - gamma0 = util.l2norm_squared(rhs) - tol = tol * tol * gamma0 - x = array_ops.expand_dims( - array_ops.zeros( - n, dtype=rhs.dtype.base_dtype), -1) + if x is None: + x = array_ops.expand_dims( + array_ops.zeros(n, dtype=rhs.dtype.base_dtype), -1) + r0 = rhs + else: + x = array_ops.expand_dims(x, -1) + r0 = rhs - operator.apply(x) + if preconditioner is None: + p0 = r0 + else: + p0 = preconditioner.apply(r0) + gamma0 = util.dot(r0, p0) + tol *= linalg_ops.norm(r0) i = constant_op.constant(0, dtype=dtypes.int32) - state = cg_state(i=i, x=x, r=rhs, p=rhs, gamma=gamma0) + state = cg_state(i=i, x=x, r=r0, p=p0, gamma=gamma0) _, state = control_flow_ops.while_loop(stopping_criterion, cg_step, [i, state]) return cg_state( diff --git a/tensorflow/contrib/solvers/python/ops/util.py b/tensorflow/contrib/solvers/python/ops/util.py index 777e0c185d..96947e8eea 100644 --- a/tensorflow/contrib/solvers/python/ops/util.py +++ b/tensorflow/contrib/solvers/python/ops/util.py @@ -45,6 +45,23 @@ def create_operator(matrix): apply_adjoint=lambda v: math_ops.matmul(matrix, v, adjoint_a=True)) +def identity_operator(matrix): + """Creates a linear operator from a rank-2 identity tensor.""" + + linear_operator = collections.namedtuple( + "LinearOperator", ["shape", "dtype", "apply", "apply_adjoint"]) + shape = matrix.get_shape() + if shape.is_fully_defined(): + shape = shape.as_list() + else: + shape = array_ops.shape(matrix) + return linear_operator( + shape=shape, + dtype=matrix.dtype, + apply=lambda v: v, + apply_adjoint=lambda v: v) + + # TODO(rmlarsen): Measure if we should just call matmul. def dot(x, y): return math_ops.reduce_sum(math_ops.conj(x) * y) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 7970c20a26..78d237e6a2 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl import flags import os import subprocess @@ -24,13 +25,21 @@ import sys import tensorflow as tf -tf.flags.DEFINE_string('service_addr', '', - 'Address of TPU profiler service e.g. localhost:8466') -tf.flags.DEFINE_string('logdir', '', - 'Path of TensorBoard log directory e.g. /tmp/tb_log') -tf.flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') +flags.DEFINE_string( + 'service_addr', None, 'Address of TPU profiler service e.g. ' + 'localhost:8466') +flags.DEFINE_string( + 'logdir', None, 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' + 'gs://tb_bucket') +flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') +flags.DEFINE_integer( + 'num_tracing_attempts', 3, 'Automatically retry N times when no trace ' + 'event is collected.') +flags.DEFINE_boolean( + 'include_dataset_ops', True, 'Set to false to profile longer TPU ' + 'device traces.') -FLAGS = tf.flags.FLAGS +FLAGS = flags.FLAGS EXECUTABLE = 'data/capture_tpu_profile' @@ -42,10 +51,13 @@ def main(unused_argv=None): if not FLAGS.service_addr or not FLAGS.logdir: sys.exit('service_addr and logdir must be provided.') executable_path = os.path.join(os.path.dirname(__file__), EXECUTABLE) + logdir = os.path.expandvars(os.path.expanduser(FLAGS.logdir)) cmd = [executable_path] - cmd.append('--logdir='+FLAGS.logdir) + cmd.append('--logdir='+logdir) cmd.append('--service_addr='+FLAGS.service_addr) cmd.append('--duration_ms='+str(FLAGS.duration_ms)) + cmd.append('--num_tracing_attempts='+str(FLAGS.num_tracing_attempts)) + cmd.append('--include_dataset_ops='+str(FLAGS.include_dataset_ops).lower()) subprocess.call(cmd) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index 179d29602b..33ade16003 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,16 +20,12 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.3.0-a1' +_VERSION = '1.5.0-rc1' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', ] -REQUIRED_PACKAGES = [ - 'tensorflow >= 1.2.0', -] - setup( name='cloud_tpu_profiler', version=_VERSION.replace('-', ''), @@ -45,27 +41,22 @@ setup( entry_points={ 'console_scripts': CONSOLE_SCRIPTS, }, - install_requires=REQUIRED_PACKAGES, classifiers=[ # How mature is this project? Common values are # 3 - Alpha # 4 - Beta # 5 - Production/Stable - 'Development Status :: 3 - Alpha', - + 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: Apache Software License', - 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', - 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Artificial Intelligence', @@ -74,4 +65,5 @@ setup( 'Topic :: Software Development :: Libraries :: Python Modules', ], license='Apache 2.0', - keywords='tensorflow performance tpu',) + keywords='tensorflow performance tpu', +) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index c25aac3acf..7fa0b79766 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -454,6 +454,7 @@ tf_cuda_library( "framework/reader_interface.h", "framework/reader_op_kernel.h", "framework/register_types.h", + "framework/register_types_traits.h", "framework/resource_mgr.h", "framework/resource_op_kernel.h", "framework/selective_registration.h", @@ -611,6 +612,7 @@ tf_gen_op_libs( "list_ops", "lookup_ops", "logging_ops", + "manip_ops", "math_ops", "nn_ops", "no_op", @@ -693,6 +695,7 @@ cc_library( ":list_ops_op_lib", ":logging_ops_op_lib", ":lookup_ops_op_lib", + ":manip_ops_op_lib", ":math_ops_op_lib", ":nn_ops_op_lib", ":no_op_op_lib", @@ -831,6 +834,7 @@ cc_library( "//tensorflow/core/kernels:list_kernels", "//tensorflow/core/kernels:lookup", "//tensorflow/core/kernels:logging", + "//tensorflow/core/kernels:manip", "//tensorflow/core/kernels:math", "//tensorflow/core/kernels:multinomial_op", "//tensorflow/core/kernels:nn", @@ -1153,6 +1157,7 @@ cc_library( deps = [ ":protos_all_cc_impl", "//third_party/eigen3", + "@nsync//:nsync_cpp", "@protobuf_archive//:protobuf", ], alwayslink = 1, diff --git a/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt b/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt index 8da76684e5..97fd39f647 100644 --- a/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt @@ -16,5 +16,6 @@ END description: <<END Note that this routine only supports wildcard characters in the basename portion of the pattern, not in the directory portion. +Note also that the order of filenames returned can be non-deterministic. END } diff --git a/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt b/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt new file mode 100644 index 0000000000..b308ad1f9d --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt @@ -0,0 +1,52 @@ +op { + graph_op_name: "Roll" + in_arg { + name: "shift" + description: <<END +Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which +elements are shifted positively (towards larger indices) along the dimension +specified by `axis[i]`. Negative shifts will roll the elements in the opposite +direction. +END + } + in_arg { + name: "axis" + description: <<END +Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift +`shift[i]` should occur. If the same axis is referenced more than once, the +total shift for that axis will be the sum of all the shifts that belong to that +axis. +END + } + out_arg { + name: "output" + description: <<END +Has the same shape and size as the input. The elements are shifted +positively (towards larger indices) by the offsets of `shift` along the +dimensions of `axis`. +END + } + summary: "Rolls the elements of a tensor along an axis." + description: <<END +The elements are shifted positively (towards larger indices) by the offset of +`shift` along the dimension of `axis`. Negative `shift` values will shift +elements in the opposite direction. Elements that roll passed the last position +will wrap around to the first and vice versa. Multiple shifts along multiple +axes may be specified. + +For example: + +``` +# 't' is [0, 1, 2, 3, 4] +roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2] + +# shifting along multiple dimensions +# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] + +# shifting along the same axis multiple times +# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] +``` +END +} diff --git a/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt new file mode 100644 index 0000000000..97c380700a --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt @@ -0,0 +1,32 @@ +op { + graph_op_name: "UnravelIndex" + in_arg { + name: "indices" + description: <<END +An 0-D or 1-D `int` Tensor whose elements are indices into the +flattened version of an array of dimensions dims. +END + } + in_arg { + name: "dims" + description: <<END +An 1-D `int` Tensor. The shape of the array to use for unraveling +indices. +END + } + out_arg { + name: "output" + description: <<END +An 2-D (or 1-D if indices is 0-D) tensor where each row has the +same shape as the indices array. +END + } + summary: "Converts a flat index or array of flat indices into a tuple of" + description: <<END +coordinate arrays. + +@compatibility(numpy) +Equivalent to np.unravel_index +@end_compatibility +END +} diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 04b5541863..a9485a835e 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -762,7 +762,8 @@ int64 MinSystemMemory(int64 available_memory) { // is necessary. min_system_memory *= 2; #endif -#if defined(NVIDIA_TEGRA) + +#if defined(ANDROID_TEGRA) // 1GB system mem for NVIDIA Tegra devices since they use the same mem for RAM // and Video RAM min_system_memory = 1 << 30; diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD index f4ee841032..9e152aa082 100644 --- a/tensorflow/core/distributed_runtime/BUILD +++ b/tensorflow/core/distributed_runtime/BUILD @@ -145,6 +145,7 @@ cc_library( "//tensorflow/core:core_cpu_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:worker_proto_cc", ], ) diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc index dcc25e4426..878a1398c9 100644 --- a/tensorflow/core/distributed_runtime/master_session.cc +++ b/tensorflow/core/distributed_runtime/master_session.cc @@ -1448,6 +1448,7 @@ Status MasterSession::DoPartialRun(CallOptions* opts, const auto count = run_state->count; pss.collect_timeline = req.options().trace_level() == RunOptions::FULL_TRACE; + pss.collect_rpcs = req.options().trace_level() == RunOptions::FULL_TRACE; pss.report_tensor_allocations_upon_oom = req.options().report_tensor_allocations_upon_oom(); @@ -1610,6 +1611,7 @@ Status MasterSession::DoRunWithLocalExecution( TRACEPRINTF("stepid %llu", step_id); pss.collect_timeline = req.options().trace_level() == RunOptions::FULL_TRACE; + pss.collect_rpcs = req.options().trace_level() == RunOptions::FULL_TRACE; pss.report_tensor_allocations_upon_oom = req.options().report_tensor_allocations_upon_oom(); // Build the cost model every 'build_cost_model_every' steps after skipping an diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc index 95811476f7..b20e744a97 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc @@ -444,6 +444,24 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts, }); } +void GrpcWorker::LoggingAsync(const LoggingRequest* request, + LoggingResponse* response, StatusCallback done) { + auto env = this->env(); + if (env) { + auto session_mgr = (SessionMgr*)env->session_mgr; + if (session_mgr) { + session_mgr->SetLogging(request->rpc_logging()); + for (const auto& step_id : request->fetch_step_id()) { + session_mgr->RetrieveLogs(step_id, response); + } + if (request->clear()) { + session_mgr->ClearLogs(); + } + } + } + done(Status::OK()); +} + WorkerEnv* GrpcWorker::env() { return env_; } std::unique_ptr<GrpcWorker> NewGrpcWorker(WorkerEnv* env) { diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h index 78a21fd9f6..fbddbda9e6 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h @@ -40,6 +40,9 @@ class GrpcWorker : public Worker { ::grpc::ByteBuffer* response, StatusCallback done); + virtual void LoggingAsync(const LoggingRequest* request, + LoggingResponse* response, StatusCallback done); + WorkerEnv* env(); private: diff --git a/tensorflow/core/distributed_runtime/session_mgr.cc b/tensorflow/core/distributed_runtime/session_mgr.cc index 8db49e7f15..90664c3612 100644 --- a/tensorflow/core/distributed_runtime/session_mgr.cc +++ b/tensorflow/core/distributed_runtime/session_mgr.cc @@ -64,8 +64,13 @@ Status SessionMgr::CreateSession(const string& session, TF_RETURN_IF_ERROR(worker_cache_factory_(server_def, &worker_cache)); } + if (worker_cache != nullptr & default_worker_cache_.get() != nullptr) { + worker_cache->SetLogging(this->is_logging_active_); + } + CHECK(!worker_env_->local_devices.empty()) << "The WorkerEnv must have at least one device in `local_devices`."; + std::vector<Device*> renamed_devices; for (Device* d : worker_env_->local_devices) { renamed_devices.push_back(RenamedDevice::NewRenamedDevice( @@ -113,4 +118,77 @@ std::shared_ptr<WorkerSession> SessionMgr::LegacySession() { return legacy_session_; } +void SessionMgr::SetLogging(bool active) { + mutex_lock l(mu_); + this->is_logging_active_ = active; + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + worker_cache->SetLogging(active); + } + } + + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + worker_cache->SetLogging(active); + } + } + } +} + +void SessionMgr::RetrieveLogs(tensorflow::int64 step_id, + LoggingResponse* response) { + mutex_lock l(mu_); + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + auto step_stats = StepStats(); + if (worker_cache->RetrieveLogs(step_id, &step_stats)) { + auto* labeled_step_stats = response->add_step(); + labeled_step_stats->set_step_id(step_id); + labeled_step_stats->mutable_step_stats()->Swap(&step_stats); + } + } + } + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + auto step_stats = StepStats(); + if (worker_cache->RetrieveLogs(step_id, &step_stats)) { + auto* labeled_step_stats = response->add_step(); + labeled_step_stats->set_step_id(step_id); + labeled_step_stats->mutable_step_stats()->Swap(&step_stats); + } + } + } + } +} + +void SessionMgr::ClearLogs() { + mutex_lock l(mu_); + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + worker_cache->ClearLogs(); + } + } + + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + worker_cache->ClearLogs(); + } + } + } +} } // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/session_mgr.h b/tensorflow/core/distributed_runtime/session_mgr.h index 3ce260d12e..4c9702d522 100644 --- a/tensorflow/core/distributed_runtime/session_mgr.h +++ b/tensorflow/core/distributed_runtime/session_mgr.h @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/protobuf/tensorflow_server.pb.h" +#include "tensorflow/core/protobuf/worker.pb.h" namespace tensorflow { @@ -56,6 +57,12 @@ class SessionMgr { static string WorkerNameFromServerDef(const ServerDef& server_def); + void SetLogging(bool active); + + void RetrieveLogs(tensorflow::int64 step_id, LoggingResponse* response); + + void ClearLogs(); + private: const WorkerEnv* const worker_env_; // Not owned. @@ -75,6 +82,8 @@ class SessionMgr { std::unique_ptr<WorkerCacheInterface> default_worker_cache_; std::shared_ptr<WorkerSession> legacy_session_; + bool is_logging_active_ = false; + const WorkerCacheFactory worker_cache_factory_; std::shared_ptr<WorkerSession> WorkerSessionForSessionUnlocked( diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h index e448a60f5e..e90596980f 100644 --- a/tensorflow/core/framework/register_types.h +++ b/tensorflow/core/framework/register_types.h @@ -53,7 +53,7 @@ limitations under the License. */ #if !defined(IS_MOBILE_PLATFORM) || defined(SUPPORT_SELECTIVE_REGISTRATION) || \ - defined(NVIDIA_TEGRA) + defined(ANDROID_TEGRA) // All types are supported, so all macros are invoked. // diff --git a/tensorflow/core/framework/variant_op_registry.cc b/tensorflow/core/framework/variant_op_registry.cc index 395329da3b..ee07db1aee 100644 --- a/tensorflow/core/framework/variant_op_registry.cc +++ b/tensorflow/core/framework/variant_op_registry.cc @@ -182,7 +182,7 @@ Status VariantDeviceCopy( // Special casing UnaryOpFn per op and per device. UnaryVariantOpRegistry::VariantUnaryOpFn* UnaryVariantOpRegistry::GetUnaryOpFn( VariantUnaryOp op, StringPiece device, StringPiece type_name) { - auto found = unary_op_fns.find(std::make_tuple(op, device, type_name)); + auto found = unary_op_fns.find({op, device, type_name}); if (found == unary_op_fns.end()) return nullptr; return &found->second; } @@ -195,12 +195,10 @@ void UnaryVariantOpRegistry::RegisterUnaryOpFn( CHECK_EQ(existing, nullptr) << "Unary VariantUnaryOpFn for type_name: " << type_name << " already registered for device type: " << device; - unary_op_fns.insert( - std::pair<std::tuple<VariantUnaryOp, StringPiece, StringPiece>, - VariantUnaryOpFn>( - std::make_tuple(op, GetPersistentStringPiece(device), - GetPersistentStringPiece(type_name)), - unary_op_fn)); + unary_op_fns.insert(std::pair<FuncTuple<VariantUnaryOp>, VariantUnaryOpFn>( + {op, GetPersistentStringPiece(device), + GetPersistentStringPiece(type_name)}, + unary_op_fn)); } namespace { @@ -229,7 +227,7 @@ REGISTER_VARIANT_ZEROS_LIKE_TYPE(bool); UnaryVariantOpRegistry::VariantBinaryOpFn* UnaryVariantOpRegistry::GetBinaryOpFn(VariantBinaryOp op, StringPiece device, StringPiece type_name) { - auto found = binary_op_fns.find(std::make_tuple(op, device, type_name)); + auto found = binary_op_fns.find({op, device, type_name}); if (found == binary_op_fns.end()) return nullptr; return &found->second; } @@ -242,12 +240,10 @@ void UnaryVariantOpRegistry::RegisterBinaryOpFn( CHECK_EQ(existing, nullptr) << "Unary VariantBinaryOpFn for type_name: " << type_name << " already registered for device type: " << device; - binary_op_fns.insert( - std::pair<std::tuple<VariantBinaryOp, StringPiece, StringPiece>, - VariantBinaryOpFn>( - std::make_tuple(op, GetPersistentStringPiece(device), - GetPersistentStringPiece(type_name)), - add_fn)); + binary_op_fns.insert(std::pair<FuncTuple<VariantBinaryOp>, VariantBinaryOpFn>( + {op, GetPersistentStringPiece(device), + GetPersistentStringPiece(type_name)}, + add_fn)); } namespace { diff --git a/tensorflow/core/framework/variant_op_registry.h b/tensorflow/core/framework/variant_op_registry.h index 13f6908cae..e94100e994 100644 --- a/tensorflow/core/framework/variant_op_registry.h +++ b/tensorflow/core/framework/variant_op_registry.h @@ -166,6 +166,21 @@ class UnaryVariantOpRegistry { device_copy_fns; // Map std::tuple<Op, device, type_name> to function. + + // this breaks by falling victim to "too perfect forwarding" + // see https://stackoverflow.com/questions/44475317/variadic-template-issue + // and references therein + template <typename Op> + struct FuncTuple { + FuncTuple(const Op& op, const StringPiece& dev, const StringPiece& tname) + : op_type_(op), device_(dev), typename_(tname){}; + Op op_type_; + StringPiece device_, typename_; + }; + // friend declaration for operator== + // needed for clang + template <typename Op> + friend bool operator==(const FuncTuple<Op>& l, const FuncTuple<Op>& r); struct TupleHash { template <typename Op> std::size_t operator()( @@ -176,18 +191,25 @@ class UnaryVariantOpRegistry { ret = Hash64Combine(ret, sp_hasher_(std::get<2>(x))); return ret; } + + template <typename Op> + std::size_t operator()(const FuncTuple<Op>& x) const { + // The hash of an enum is just its value as a std::size_t. + std::size_t ret = static_cast<std::size_t>(x.op_type_); + ret = Hash64Combine(ret, sp_hasher_(x.device_)); + ret = Hash64Combine(ret, sp_hasher_(x.typename_)); + return ret; + } StringPieceHasher sp_hasher_; }; - std::unordered_map<std::tuple<VariantUnaryOp, StringPiece, StringPiece>, - VariantUnaryOpFn, TupleHash> + std::unordered_map<FuncTuple<VariantUnaryOp>, VariantUnaryOpFn, TupleHash> unary_op_fns; - std::unordered_map<std::tuple<VariantBinaryOp, StringPiece, StringPiece>, - VariantBinaryOpFn, TupleHash> + std::unordered_map<FuncTuple<VariantBinaryOp>, VariantBinaryOpFn, TupleHash> binary_op_fns; // Find or insert a string into a persistent string storage - // container; return the StringPiece pointing to the permanent - // string location. + // container; return the StringPiece pointing to the permanent string + // location. static StringPiece GetPersistentStringPiece(const string& str) { const auto string_storage = PersistentStringStorage(); auto found = string_storage->find(str); @@ -199,7 +221,12 @@ class UnaryVariantOpRegistry { } } }; - +template <typename Op> +inline bool operator==(const UnaryVariantOpRegistry::FuncTuple<Op>& lhs, + const UnaryVariantOpRegistry::FuncTuple<Op>& rhs) { + return (lhs.op_type_ == rhs.op_type_) && (lhs.device_ == rhs.device_) && + (lhs.typename_ == rhs.typename_); +} // Gets a TensorShape from a Tensor containing a scalar Variant. // Returns an Internal error if the Variant does not have a registered shape // function, or if it's a serialized Variant that cannot be decoded. diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 68c3136019..7d3be15299 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -42,7 +42,7 @@ limitations under the License. namespace tensorflow { -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // This pass implements rewriting of graph to support following scenarios: // (A) Merging nodes in the graph @@ -2211,7 +2211,7 @@ Status MklLayoutRewritePass::Run(const GraphOptimizationPassOptions& options) { return Status::OK(); } -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML // This pass implements rewriting of graph to support following scenarios: // (A) Merging nodes in the graph @@ -2452,9 +2452,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // NOTE: names are alphabetically sorted. rinfo_.push_back({csinfo_.addn, mkl_op_registry::GetMklOpName(csinfo_.addn), CopyAttrsAddN, AddNRewrite}); - /* rinfo_.push_back({csinfo_.add, - mkl_op_registry::GetMklOpName(csinfo_.add), - CopyAttrsDataType, AlwaysRewrite}); */ + rinfo_.push_back({csinfo_.add, mkl_op_registry::GetMklOpName(csinfo_.add), + CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.avg_pool, mkl_op_registry::GetMklOpName(csinfo_.avg_pool), CopyAttrsPooling, AlwaysRewrite}); @@ -2502,14 +2501,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.max_pool_grad, mkl_op_registry::GetMklOpName(csinfo_.max_pool_grad), CopyAttrsPooling, AlwaysRewrite}); - /* + rinfo_.push_back({csinfo_.maximum, mkl_op_registry::GetMklOpName(csinfo_.maximum), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.mul, mkl_op_registry::GetMklOpName(csinfo_.mul), CopyAttrsDataType, AlwaysRewrite}); - */ rinfo_.push_back({csinfo_.relu, mkl_op_registry::GetMklOpName(csinfo_.relu), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.relu_grad, @@ -2529,14 +2527,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.softmax, mkl_op_registry::GetMklOpName(csinfo_.softmax), CopyAttrsDataType, AlwaysRewrite}); - /* + rinfo_.push_back({csinfo_.squared_difference, mkl_op_registry::GetMklOpName(csinfo_.squared_difference), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.sub, mkl_op_registry::GetMklOpName(csinfo_.sub), CopyAttrsDataType, AlwaysRewrite}); - */ // Add info about which ops to add workspace edge to and the slots. wsinfo_.push_back({csinfo_.lrn, csinfo_.lrn_grad, 0, 2, 1, 3}); @@ -4317,7 +4314,7 @@ Status MklLayoutRewritePass::Run(const GraphOptimizationPassOptions& options) { return Status::OK(); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow #endif diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 320d5a48c7..5e2a465e22 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -38,7 +38,7 @@ limitations under the License. namespace tensorflow { -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML namespace { @@ -1899,7 +1899,7 @@ BENCHMARK(BM_MklLayoutRewritePass)->Arg(1000)->Arg(10000); } // namespace -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML namespace { @@ -3532,7 +3532,7 @@ BENCHMARK(BM_MklLayoutRewritePass)->Arg(1000)->Arg(10000); } // namespace -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow diff --git a/tensorflow/core/graph/testlib.cc b/tensorflow/core/graph/testlib.cc index d5b026eae3..0d88d1ff72 100644 --- a/tensorflow/core/graph/testlib.cc +++ b/tensorflow/core/graph/testlib.cc @@ -273,6 +273,16 @@ Node* Reverse(Graph* g, Node* tensor, Node* axis) { return Binary(g, "ReverseV2", tensor, axis); } +Node* Roll(Graph* g, Node* input, Node* shift, Node* axis) { + Node* ret; + TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Roll", g->op_registry()) + .Input(input) + .Input(shift) + .Input(axis) + .Finalize(g, &ret)); + return ret; +} + Node* Error(Graph* g, Node* input, const string& errmsg) { Node* ret; TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Error") diff --git a/tensorflow/core/graph/testlib.h b/tensorflow/core/graph/testlib.h index 06597778bb..eb9038d619 100644 --- a/tensorflow/core/graph/testlib.h +++ b/tensorflow/core/graph/testlib.h @@ -117,6 +117,10 @@ Node* RandomGamma(Graph* g, Node* shape, Node* alpha); // Output dtype determined by lam. Node* RandomPoisson(Graph* g, Node* shape, Node* lam); +// Rolls tensor by an offset of <shift> along the corresponding +// <axis> dimensions. +Node* Roll(Graph* g, Node* input, Node* shift, Node* axis); + // Generates random parameters from the truncated standard normal distribution // of the nput shape Node* TruncatedNormal(Graph* g, Node* input, DataType dtype); diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index fd99409c9b..e7192ec42f 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -629,6 +629,7 @@ cc_library( ":transpose_op", ":unique_op", ":unpack_op", + ":unravel_index_op", ":where_op", ], ) @@ -884,6 +885,12 @@ tf_kernel_library( ) tf_kernel_library( + name = "unravel_index_op", + prefix = "unravel_index_op", + deps = ARRAY_DEPS, +) + +tf_kernel_library( name = "where_op", srcs = ["where_op.cc"], hdrs = ["where_op.h"], @@ -2582,6 +2589,45 @@ tf_cc_tests( ], ) +cc_library( + name = "manip", + deps = [ + ":roll_op", + ], +) + +MANIP_DEPS = [ + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:manip_ops_op_lib", + "//third_party/eigen3", +] + +tf_kernel_library( + name = "roll_op", + prefix = "roll_op", + deps = MANIP_DEPS, +) + +tf_cc_test( + name = "roll_op_test", + size = "small", + srcs = ["roll_op_test.cc"], + deps = [ + ":ops_testutil", + ":ops_util", + ":roll_op", + "//tensorflow/core:core_cpu", + "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + MATH_DEPS = [ ":bounds_check", ":fill_functor", diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.cc b/tensorflow/core/kernels/compare_and_bitpack_op.cc index 9f626a274a..224fe534e3 100644 --- a/tensorflow/core/kernels/compare_and_bitpack_op.cc +++ b/tensorflow/core/kernels/compare_and_bitpack_op.cc @@ -110,7 +110,19 @@ struct ComputeShard<T, typename TTypes<bool>::ConstMatrix input, typename TTypes<uint8>::Matrix output, bool /*thresh*/, int64 start, int64 limit) { - // NOTE(ebrevdo): This assumes memory is little-endian. +#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ + for (int64 i = start; i < limit; ++i) { + uint8* out = output.data() + i; + const int64 block = *reinterpret_cast<const int64*>(input.data() + 8 * i); + *out = ((((block & (1LL << (7 * 8))) >> (7 * 8 - 7))) | + (((block & (1LL << (6 * 8))) >> (6 * 8 - 6))) | + (((block & (1LL << (5 * 8))) >> (5 * 8 - 5))) | + (((block & (1LL << (4 * 8))) >> (4 * 8 - 4))) | + (((block & (1LL << (3 * 8))) >> (3 * 8 - 3))) | + (((block & (1LL << (2 * 8))) >> (2 * 8 - 2))) | + (((block & (1LL << 8)) >> (1 * 8 - 1))) | (((block & (1LL))))); + } +#else for (int64 i = start; i < limit; ++i) { uint8* out = output.data() + i; const int64 block = *reinterpret_cast<const int64*>(input.data() + 8 * i); @@ -123,6 +135,7 @@ struct ComputeShard<T, (((block & (1LL << (2 * 8))) >> (2 * 8 - 5))) | (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7))); } +#endif } }; diff --git a/tensorflow/core/kernels/decode_bmp_op.cc b/tensorflow/core/kernels/decode_bmp_op.cc index c778278e8f..b7d120a617 100644 --- a/tensorflow/core/kernels/decode_bmp_op.cc +++ b/tensorflow/core/kernels/decode_bmp_op.cc @@ -39,6 +39,13 @@ class DecodeBmpOp : public OpKernel { errors::InvalidArgument("channels must be 0, 1, 3 or 4, got ", channels_)); } + inline int32 ByteSwapInt32ForBigEndian(int32 x) { +#if (__BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) + return le32toh(x); +#else + return x; +#endif + } void Compute(OpKernelContext* context) override { const Tensor& contents = context->input(0); @@ -56,14 +63,18 @@ class DecodeBmpOp : public OpKernel { input.size(), " bytes")); const uint8* img_bytes = reinterpret_cast<const uint8*>(input.data()); - const int32 header_size = internal::SubtleMustCopy( + int32 header_size_ = internal::SubtleMustCopy( *(reinterpret_cast<const int32*>(img_bytes + 10))); - const int32 width = internal::SubtleMustCopy( + const int32 header_size = ByteSwapInt32ForBigEndian(header_size_); + int32 width_ = internal::SubtleMustCopy( *(reinterpret_cast<const int32*>(img_bytes + 18))); - const int32 height = internal::SubtleMustCopy( + const int32 width = ByteSwapInt32ForBigEndian(width_); + int32 height_ = internal::SubtleMustCopy( *(reinterpret_cast<const int32*>(img_bytes + 22))); - const int32 bpp = internal::SubtleMustCopy( + const int32 height = ByteSwapInt32ForBigEndian(height_); + int32 bpp_ = internal::SubtleMustCopy( *(reinterpret_cast<const int32*>(img_bytes + 28))); + const int32 bpp = ByteSwapInt32ForBigEndian(bpp_); if (channels_) { OP_REQUIRES(context, (channels_ == bpp / 8), diff --git a/tensorflow/core/kernels/fractional_pool_common.h b/tensorflow/core/kernels/fractional_pool_common.h index df0bbbfa06..2d7a230fc0 100644 --- a/tensorflow/core/kernels/fractional_pool_common.h +++ b/tensorflow/core/kernels/fractional_pool_common.h @@ -57,7 +57,7 @@ static inline void RandomShuffle(Iter first, Iter last, const Random& uniform) { // * sum(generated_diff_pooling_sequence) = input_length // * Let's define floor(input_length / output_length) = K, then // K <= generated_diff_pooling_sequence[i] <= K+1 -// For example, when input_length = 10, output_length = 6, the followings are +// For example, when input_length = 10, output_length = 6, the following are // valid pooling sequence: // * [1, 2, 2, 1, 2, 2] // * [1, 1, 2, 2, 2, 2] diff --git a/tensorflow/core/kernels/mkl_aggregate_ops.cc b/tensorflow/core/kernels/mkl_aggregate_ops.cc index 89d37d2f87..b539b00009 100644 --- a/tensorflow/core/kernels/mkl_aggregate_ops.cc +++ b/tensorflow/core/kernels/mkl_aggregate_ops.cc @@ -28,7 +28,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; using mkldnn::sum; @@ -37,7 +37,7 @@ using mkldnn::sum; namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklAddNOp : public OpKernel { @@ -285,7 +285,7 @@ class MklAddNOp : public OpKernel { } MklAddNOpContext; }; -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML template <typename Device, typename T> class MklAddNOp : public OpKernel { public: @@ -317,8 +317,11 @@ class MklAddNOp : public OpKernel { : src2_tensor.dims(); // if the shapes of two tensors are not same raise op error TensorShape src1_shape, src2_shape; - src1_shape = src1_tensor.shape(); - src2_shape = src2_tensor.shape(); + src1_shape = input1_in_mkl_format ? src1_mkl_shape.GetTfShape() + : src1_tensor.shape(); + src2_shape = input2_in_mkl_format ? src2_mkl_shape.GetTfShape() + : src2_tensor.shape(); + if (!src1_shape.IsSameSize(src2_shape)) { ctx->SetStatus(errors::InvalidArgument( "Inputs to operation ", this->name(), " of type ", diff --git a/tensorflow/core/kernels/mkl_avgpooling_op.cc b/tensorflow/core/kernels/mkl_avgpooling_op.cc index a7c569ee05..d545d34fdf 100644 --- a/tensorflow/core/kernels/mkl_avgpooling_op.cc +++ b/tensorflow/core/kernels/mkl_avgpooling_op.cc @@ -24,7 +24,7 @@ #include "tensorflow/core/kernels/mkl_pooling_ops_common.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::algorithm; using mkldnn::engine; @@ -40,8 +40,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklAvgPoolingOp : public OpKernel { @@ -429,7 +428,7 @@ class MklAvgPoolingGradOp : public OpKernel { TensorFormat data_format_; }; // MklAvgPoolingGradOp -#else // INTEL_MKL_DNN is defined +#else template <typename Device, typename T> class MklAvgPoolingOp : public MklPoolingForwardOpBase<T> { @@ -466,6 +465,28 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase<T> { memory::dims output_dims_mkl_order; this->GetOutputDims(pool_params, &output_dims_mkl_order); + // If input is an empty tensor, allocate an empty output tensor and return + if (input_tensor.NumElements() == 0) { + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(false); + TensorShape output_tf_shape; + if (pool_params.data_format == TensorFormat::FORMAT_NCHW) { + output_tf_shape = MklDnnDimsToTFShape(output_dims_mkl_order); + } else { + memory::dims output_dims_NHWC_order; + output_dims_NHWC_order = {pool_params.tensor_in_batch, + static_cast<int>(pool_params.out_height), + static_cast<int>(pool_params.out_width), + pool_params.out_depth}; + output_tf_shape = MklDnnDimsToTFShape(output_dims_NHWC_order); + } + const int kOutputIndex = 0; + AllocateOutputSetMklShape(context, kOutputIndex, &output_tensor, + output_tf_shape, output_mkl_shape); + CHECK_NOTNULL(output_tensor); + return; + } + // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to AvgPool. if (dnn_shape_input.IsMklTensor()) { @@ -678,7 +699,7 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase<T> { } }; // MklAvgPoolingGradOp -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML REGISTER_KERNEL_BUILDER(Name("_MklAvgPool") .Device(DEVICE_CPU) diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 7da63604d2..f1f267e849 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -30,7 +30,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::concat; @@ -62,7 +62,7 @@ class EigenConcatBaseOp : public OpKernel { // we need to have empty Compute because Compute is pure virtual function. void Compute(OpKernelContext* c) {} -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Compute(OpKernelContext* c, const std::vector<Tensor>& values) { const Tensor* concat_dim_tensor; @@ -230,7 +230,7 @@ class EigenConcatBaseOp : public OpKernel { #endif }; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // -------------------------------------------------------------------------- // Mkl Concat Op diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index ef3f8cfec1..1401bc65a4 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -42,7 +42,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::convolution_backward_weights; @@ -55,7 +55,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, class T> class MklConv2DCustomBackpropFilterOp : public OpKernel { @@ -655,7 +655,7 @@ class MklConv2DCustomBackpropFilterOp TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index a6745489f4..eeed009531 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -44,7 +44,7 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::convolution_backward_data; @@ -56,7 +56,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, class T> class MklConv2DCustomBackpropInputOp : public OpKernel { @@ -493,7 +493,7 @@ class MklConv2DCustomBackpropInputOp } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #define REGISTER_MKL_CPU_KERNELS(T) \ REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropInput") \ diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index e44fba754b..2953426d58 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -41,7 +41,8 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML + #include "mkldnn.hpp" using mkldnn::prop_kind; @@ -58,8 +59,8 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +// MKL-DNN is now default. MKL-ML must be specified explicitly. +#ifdef INTEL_MKL_ML template <typename Device, typename T, bool biasEnabled> class MklConv2DOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_conv_ops.h b/tensorflow/core/kernels/mkl_conv_ops.h index 8b65eaea0d..9dd88221a8 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.h +++ b/tensorflow/core/kernels/mkl_conv_ops.h @@ -40,7 +40,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::prop_kind; @@ -52,7 +52,7 @@ using mkldnn::convolution_forward; namespace tensorflow { -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML class MklDnnConvUtil { protected: @@ -553,7 +553,7 @@ class MklConv2DBackpropCommonOp : public OpKernel { Padding padding_; TensorFormat data_format_; }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML ///////////////////////////////////////////////////////////////////// /// Dummy Mkl op that is just used for operators that are intermediate diff --git a/tensorflow/core/kernels/mkl_cwise_ops_common.cc b/tensorflow/core/kernels/mkl_cwise_ops_common.cc index c065724e0d..58f0c30f32 100644 --- a/tensorflow/core/kernels/mkl_cwise_ops_common.cc +++ b/tensorflow/core/kernels/mkl_cwise_ops_common.cc @@ -1,4 +1,4 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0(the "License"); you may not use this file except in compliance with the License. diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc index 0b6d838e09..8313224d7f 100644 --- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc +++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc @@ -25,7 +25,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::batch_normalization_backward; @@ -41,7 +41,7 @@ using mkldnn::use_scale_shift; namespace tensorflow { using CPUDevice = Eigen::ThreadPoolDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklFusedBatchNormOp : public OpKernel { @@ -683,7 +683,7 @@ class MklFusedBatchNormGradOp : public OpKernel { }; #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template <typename Device, typename T> class MklFusedBatchNormOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_identity_op.cc b/tensorflow/core/kernels/mkl_identity_op.cc index 9ee27ee21c..6c027f8e72 100644 --- a/tensorflow/core/kernels/mkl_identity_op.cc +++ b/tensorflow/core/kernels/mkl_identity_op.cc @@ -28,14 +28,14 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" #endif namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklIdentityOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc index 73d41efce1..5a8799ae93 100644 --- a/tensorflow/core/kernels/mkl_input_conversion_op.cc +++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc @@ -31,7 +31,7 @@ limitations under the License. #include "tensorflow/core/kernels/mkl_tfconv_op.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; @@ -59,7 +59,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; // convert the TF format input to MKL format /////////////////////////////////////////////////////////// -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklInputConversionOp : public OpKernel { public: @@ -293,14 +293,58 @@ class MklInputConversionOp : public OpKernel { // - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - // If both inputs are in MKL format if (input_shape_0.IsMklTensor() && input_shape_1.IsMklTensor()) { - // If both have the same shape, pass them through if (tf_shapes_are_same) { - VLOG(1) << "MklInputConversionOp: No conversion needed, " - << "copying MKL inputs with identical shapes to output"; - - ForwardMklTensorInToOut(context, 0, 0); - ForwardMklTensorInToOut(context, 1, 1); - return; + auto input0_md = input_shape_0.GetMklLayout(); + auto input1_md = input_shape_1.GetMklLayout(); + + // If both have the same shape and same format, pass them through + if (input0_md.data.format == input1_md.data.format) { + VLOG(1) << "MklInputConversionOp: No conversion needed, " + << "copying MKL inputs with identical shapes to output"; + + ForwardMklTensorInToOut(context, 0, 0); + ForwardMklTensorInToOut(context, 1, 1); + return; + } else { + VLOG(1) << "MklInputConversionOp: Shape is same, but format is " + "different, " + << "need to convert to same format"; + + // Convert input0, and keep input1 unchanged + // Create MklDnnShape for output mkl tensor based on input0 + Tensor* tensor_out; + MklDnnShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(true); + mkl_output_mkl_shape.SetElemType(MklDnnType<T>()); + mkl_output_mkl_shape.SetTfLayout(input_shape_0.GetDimension(), + input_shape_0.GetSizesAsMklDnnDims(), + input_shape_0.GetTfDataFormat()); + + // Get MKL layout from input1 as destination layout + mkl_output_mkl_shape.SetMklLayout(&input1_md); + + // Create output Mkl tensor for index 0 + AllocateOutputSetMklShape(context, 0, &tensor_out, + input_tensor_0.shape(), + mkl_output_mkl_shape); + + // Create MklDnnData object for input0 tesnsor + auto cpu_engine = engine(engine::cpu, 0); + MklDnnData<T> input(&cpu_engine); + input.SetUsrMem(input0_md, &input_tensor_0); + + // Create reorder from input0's layout to input1's layout + std::vector<primitive> net; + CHECK_EQ(input.CheckReorderToOpMem( + memory::primitive_desc(input1_md, cpu_engine), + tensor_out, &net), + true); + stream(stream::kind::eager).submit(net).wait(); + + // Input1 will be passed through + ForwardMklTensorInToOut(context, 1, 1); + return; + } } // Sanity check diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc index a8b45004b7..5f0a12a1fb 100644 --- a/tensorflow/core/kernels/mkl_lrn_op.cc +++ b/tensorflow/core/kernels/mkl_lrn_op.cc @@ -38,7 +38,7 @@ limitations under the License. #include "tensorflow/core/util/work_sharder.h" #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::lrn_across_channels; using mkldnn::lrn_backward; @@ -67,7 +67,7 @@ void GetBandMatrix(int depth, int depth_radius, } // namespace -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename T> class MklLRNOp : public OpKernel { @@ -1343,7 +1343,7 @@ class MklLRNGradOp : public OpKernel { float beta_; }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #define REGISTER_MKL_LRN_CPU(T) \ REGISTER_KERNEL_BUILDER(Name("_MklLRN") \ diff --git a/tensorflow/core/kernels/mkl_maxpooling_op.cc b/tensorflow/core/kernels/mkl_maxpooling_op.cc index 0de27ccd60..14607f26e0 100644 --- a/tensorflow/core/kernels/mkl_maxpooling_op.cc +++ b/tensorflow/core/kernels/mkl_maxpooling_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include <algorithm> #include "mkldnn.hpp" using mkldnn::algorithm; @@ -39,8 +39,8 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +// MKL-DNN is now default. MKL-ML must be specified explicitly. +#ifdef INTEL_MKL_ML // An implementation of MaxPooling (forward). template <typename Device, typename T> @@ -494,7 +494,7 @@ class MklMaxPoolingGradOp : public OpKernel { bool workspace_enabled_; }; // MklMaxPoolingGradOp -#else // INTEL_MKL_DNN is defined +#else // An implementation of MaxPooling (forward). template <typename Device, typename T> @@ -793,7 +793,7 @@ class MklMaxPoolingGradOp : public MklPoolingBackwardOpBase<T> { } }; // MklMaxPoolingGradOp -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML REGISTER_KERNEL_BUILDER(Name("_MklMaxPool") .Device(DEVICE_CPU) diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.cc b/tensorflow/core/kernels/mkl_pooling_ops_common.cc index ef8597b057..5ef6ce2a57 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.cc +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.cc @@ -42,7 +42,7 @@ void MklPoolParameters::Init(OpKernelContext* context, Init(context, ksize, stride, padding, data_format); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // Initialization for MKL format void MklPoolParameters::Init(OpKernelContext* context, const std::vector<int32>& ksize, @@ -72,7 +72,7 @@ void MklPoolParameters::Init(OpKernelContext* context, Init(context, ksize, stride, padding, data_format); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML // Common Initialization for TensorFlow and MKL formats void MklPoolParameters::Init(OpKernelContext* context, const std::vector<int32>& ksize, @@ -107,7 +107,7 @@ void MklPoolParameters::Init(OpKernelContext* context, OP_REQUIRES_OK(context, GetWindowedOutputSizeVerbose( tensor_in_cols, window_cols, col_stride, padding, &out_width, &pad_left, &pad_right)); -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // TF can work with int64, but mkldnn only supports int32 // Fail if the height or width are greater than MAX_INT diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index 880e45ab1e..279167aba2 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::memory; using mkldnn::pooling_backward; @@ -85,7 +85,7 @@ struct MklPoolParameters { void Init(OpKernelContext* context, const std::vector<int32>& ksize, const std::vector<int32>& stride, Padding padding, TensorFormat data_format, const TensorShape& tensor_in_shape); -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Init(OpKernelContext* context, const std::vector<int32>& ksize, const std::vector<int32>& stride, Padding padding, TensorFormat data_format, const MklShape* mkl_in_shape); @@ -102,7 +102,7 @@ struct MklPoolParameters { TensorFormat data_format); }; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template <class T> class MklPoolingOpBase : public OpKernel { @@ -395,7 +395,7 @@ class MklPoolingBackwardOpBase : public MklPoolingOpBase<T> { return grad_reorder_needed ? target_diff_dst_md : original_input_grad_md; } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML //------------------------------------------------------------------- // Utility functions diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 873aca30ca..51db3991e2 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -28,7 +28,7 @@ limitations under the License. #include "tensorflow/core/platform/default/logging.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::algorithm; @@ -58,7 +58,7 @@ struct MklReluHelpers { } }; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename Device, typename T> class MklReluOp : public OpKernel { @@ -368,7 +368,7 @@ void MklReluGradOp<Device, T>::Compute(OpKernelContext* context) { mkl_context.MklCleanup(); } -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML template <typename Device, typename T, algorithm alg_kind> class MklReluOpBase : public OpKernel { @@ -849,7 +849,7 @@ class MklTanhGradOp : public MklReluGradOpBase<Device, T, eltwise_tanh> { MklReluGradOp<CPUDevice, type>); TF_CALL_float(REGISTER_RELU_MKL_SUPPORTED_KERNELS_TYPES); -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // register dnn kernels for supported operations and supported types #define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ diff --git a/tensorflow/core/kernels/mkl_reshape_op.cc b/tensorflow/core/kernels/mkl_reshape_op.cc index 7d471e1e4c..5dbc4a2709 100644 --- a/tensorflow/core/kernels/mkl_reshape_op.cc +++ b/tensorflow/core/kernels/mkl_reshape_op.cc @@ -28,7 +28,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; #endif @@ -40,7 +40,7 @@ class MklReshapeOp : public OpKernel { public: explicit MklReshapeOp(OpKernelConstruction* context) : OpKernel(context) {} -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Compute(OpKernelContext* context) override { const Tensor& input = MklGetInput(context, 0); const Tensor& sizes = MklGetInput(context, 1); @@ -312,7 +312,7 @@ class MklReshapeOp : public OpKernel { } } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML private: const int kInputSlotIdx = 0; diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index c46eabdde1..aceef1e234 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -15,7 +15,7 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #ifdef INTEL_MKL -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" @@ -156,5 +156,5 @@ TF_CALL_float(REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES); } // namespace tensorflow -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #endif // INTEL_MKL diff --git a/tensorflow/core/kernels/mkl_tfconv_op.h b/tensorflow/core/kernels/mkl_tfconv_op.h index c4d5a45d3c..5fafa14b5d 100644 --- a/tensorflow/core/kernels/mkl_tfconv_op.h +++ b/tensorflow/core/kernels/mkl_tfconv_op.h @@ -35,7 +35,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML using mkldnn::stream; #endif @@ -61,7 +61,7 @@ class MklToTfOp : public OpKernel { VLOG(1) << "MKLToTFConversion complete successfully."; } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML static void ConvertMklToTf(OpKernel* op_kernel, OpKernelContext* context, string data_format_str, DataType op_data_type, bool has_avx512f, uint input_number) { diff --git a/tensorflow/core/kernels/roll_op.cc b/tensorflow/core/kernels/roll_op.cc new file mode 100644 index 0000000000..bcbdbee058 --- /dev/null +++ b/tensorflow/core/kernels/roll_op.cc @@ -0,0 +1,334 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/register_types_traits.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/platform/types.h" +#include "tensorflow/core/util/work_sharder.h" + +namespace tensorflow { + +#define EIGEN_USE_THREADS +using CPUDevice = Eigen::ThreadPoolDevice; + +// dim_size - the size of each dimension +// dim_range - the number of indices over in the flattened tensor +// you need to skip in order to make it over from one side of a dimension +// to the other. Used to make the shifts wrap around after a threshold. +// threshold - the index for each dimension that the roll starts to wrap +// back to the front +template <typename T> +void DoRoll(OpKernelContext* context, const int64 num_elements, + const int num_dims, const gtl::ArraySlice<int>& dim_size, + const T* input, T* output, const gtl::ArraySlice<int>& threshold, + const gtl::ArraySlice<int64>& dim_range) { + auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range]( + int64 start, int64 end) { + // array of indices for each dimension + gtl::InlinedVector<int, 4> indices(num_dims); + int offset = 0; // the shift along the flattened tensor for current element + // initialize indices and offset + for (int i = 0; i < num_dims; i++) { + // stride is the number of indices over in the flattened tensor + // you need to skip in order to make it over to an adjacent element + // along a dimension. dim_size[i] != 0 because we set it to max(dim, 1) + const int64 stride = dim_range[i] / dim_size[i]; + const int shift = dim_size[i] - threshold[i]; + const int indx = (start / stride) % dim_size[i]; + indices[i] = indx; + // calculate dimension index after the shift + const int shifted_indx = (indx + shift) % dim_size[i]; + offset += (shifted_indx - indx) * stride; + } + + for (int64 i = start; i < end; i++) { + output[i + offset] = input[i]; + // create next combination of indices + // while at it adjust offset if needed + for (int j = num_dims - 1; j >= 0; j--) { + const int indx = (indices[j] + 1) % dim_size[j]; + indices[j] = indx; + if (indx != 0) { + if (indx == threshold[j]) { // we've reached the threshold + // dim_range[j] = threshold[j] + shift[j] + // offset = shift[j] + ... other offsets + // offset - dim_range[j] = -threshold[j] + ... other offsets + // thus we undo our previous offset as well as add a new offset of + // -threshold[j] in one operation + offset -= dim_range[j]; // now wraps around + } + break; // indx != 0 don't need to carry + } else if (threshold[j] != 0) { // if threshold is 0 shift is 0 + offset += dim_range[j]; // indx became 0 so reverse wrap around + } + } + } + }; + // Shard + auto worker_threads = context->device()->tensorflow_cpu_worker_threads(); + // 15 - expiramentally determined with float and bool types + const int cost_per_element = 15 * sizeof(T); // rough esitmate + Shard(worker_threads->num_threads, worker_threads->workers, num_elements, + cost_per_element, std::move(work)); +} + +// dim_size - the size of each dimension +// dim_range - the number of indices over in the flattened tensor +// you need to skip in order to make it over from one side of a dimension +// to the other. Used to make the shifts wrap around after a threshold. +// threshold - the index for each dimension that the roll starts to wrap +// back to the front +// isd - inner shift dimension +template <typename T> +// Use memcpy to copy memory in groups when the data type supports memcpy +void DoRollWithMemcpy(OpKernelContext* context, const int64 num_elements, + const int num_dims, const gtl::ArraySlice<int>& dim_size, + const T* input, T* output, + const gtl::ArraySlice<int>& threshold, + const gtl::ArraySlice<int64>& dim_range, + const int64 isd) { + auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range, isd]( + int64 start, int64 end) { + // the number of indices over in the flattened tensor you need to skip in + // order to make it over from one side of the isd to the other + const int64 isd_range = std::max<int>(dim_range[isd], 1); + // the distance along the flattend tensor to the next element in the isd + const int64 isd_stride = isd_range / std::max<int>(dim_size[isd], 1); + + // start and end represent the i-th group currently so we will convert + // them into numbers representing the i-th elements. + // there are 2 groups per isd one for all elements before threshold[isd] + // and another for all elements after threshold[isd]. + const int64 start_remainder = (start % 2) * threshold[isd] * isd_stride; + const int64 end_remainder = (end % 2) * threshold[isd] * isd_stride; + start = (start / 2) * isd_range + start_remainder; + end = (end / 2) * isd_range + end_remainder; + + const T* in_ptr = &input[0]; + T* out_ptr = &output[0]; + in_ptr += start; + out_ptr += start; + + // array of indices for each dimension + // indicies = [i, j, k, l, m, n] + gtl::InlinedVector<int, 4> indicies(num_dims); + // the offset needed to make all inner non-shifting dimensions become 0 + int64 remainder_offset = 0; + // initialize indicies + for (int i = 0; i < num_dims; i++) { + // stride is the number of indices over in the flattened tensor + // you need to skip in order to make it over to an adjacent element + // along a dimension. dim_size[i] != 0 because we set it to max(dim, 1) + const int64 stride = dim_range[i] / dim_size[i]; + const int shift = dim_size[i] - threshold[i]; + const int indx = (start / stride) % dim_size[i]; + indicies[i] = indx; + // calculate dimension index after the shift + int out_indx = (indx + shift) % dim_size[i]; + if (i > isd) { + // trailing zeroes for indices after the inner shifted dimension + out_indx = 0; + remainder_offset += (out_indx - indx) * stride; + } + out_ptr += (out_indx - indx) * stride; + } + // set trailing zeroes for indices after the inner shifted dimension + for (int i = num_dims - 1; i > isd; i--) indicies[i] = 0; + + // the number of indices in the isd dimension the next group will skip + // to make it to the next threshold or end point + int isd_indx_skip = 0; + // the size of the next group + int64 group_size = 0; + // initialize isd_indx_skip and group_size + if (indicies[isd] < threshold[isd]) { + isd_indx_skip = threshold[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride + remainder_offset; + } else { + isd_indx_skip = dim_size[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride + remainder_offset; + } + + int64 i = start; + while (i < end) { + // copy group of elements + memcpy(out_ptr, in_ptr, group_size * sizeof(T)); + + // shift i and the pointers over to the next group position + i += group_size; + out_ptr += group_size; + in_ptr += group_size; + + // produce next combination of indices and adjust the out_ptr position + // to fix the offset if necessary + // the isd (inner shift dim) should skip to next threshold or endpoint + // all dimensions to the left increment by 1 when a digit is carried + // all dimensions to the right remain set to 0 + // +1 +1 +1 +isd_indx_skip + // indicies = [i, j, k, l, 0, 0] + // ^isd + for (int j = isd; j >= 0; j--) { + int inc = 1; + if (j == isd) inc = isd_indx_skip; + const int indx = (indicies[j] + inc) % dim_size[j]; + indicies[j] = indx; + if (indx != 0) { + if (indx == threshold[j]) { + out_ptr -= dim_range[j]; // now wraps around + } + break; // indx != 0 don't need to carry + } else if (threshold[j] != 0) { // if threshold is 0 shift is 0 + out_ptr += dim_range[j]; // indx became 0 so reverse wrap around + } + } + + // set isd_indx_skip and group_size for next iteration + if (indicies[isd] < threshold[isd]) { + isd_indx_skip = threshold[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride; + } else { + isd_indx_skip = dim_size[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride; + } + } + }; + // Shard + auto worker_threads = context->device()->tensorflow_cpu_worker_threads(); + const int64 ave_group_size = dim_range[isd] / 2; + const int total_work = 2 * num_elements / std::max<int>(dim_range[isd], 1); + // 25000 - expiramentally determined with float and bool types + const int cost_per_group = 25000 * sizeof(T) * ave_group_size; + Shard(worker_threads->num_threads, worker_threads->workers, total_work, + cost_per_group, std::move(work)); +} + +template <typename Device, typename T, typename Tshift, typename Taxis> +class RollOp : public OpKernel { + public: + explicit RollOp(OpKernelConstruction* context) : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + // Grab the input tensor + const Tensor& input = context->input(0); + const Tensor& shift = context->input(1); + const Tensor& axis = context->input(2); + + auto shift_flat = shift.flat<Tshift>(); + auto axis_flat = axis.flat<Taxis>(); + + OP_REQUIRES(context, TensorShapeUtils::IsVectorOrHigher(input.shape()), + errors::InvalidArgument("input must be 1-D or higher")); + OP_REQUIRES(context, shift.shape().dims() <= 1, + errors::InvalidArgument( + "shift must be a scalar or a 1-D vector. Found: ", + shift.shape().DebugString())); + OP_REQUIRES(context, axis.shape().dims() <= 1, + errors::InvalidArgument( + "axis must be a scalar or a 1-D vector. Found: ", + axis.shape().DebugString())); + OP_REQUIRES( + context, shift.shape() == axis.shape(), + errors::InvalidArgument("shift and axis must have the same size")); + const int64 num_elements = input.NumElements(); + const int num_shifts = static_cast<int>(shift_flat.size()); + const int num_dims = input.dims(); + + // if there are any duplicate axes, shift_mod_sum will have the + // total modulo sum of shifts for each dimension + gtl::InlinedVector<int, 4> shift_mod_sum(num_dims, 0); + for (int i = 0; i < num_shifts; i++) { + const int axis = axis_flat(i); + OP_REQUIRES(context, axis < num_dims, + errors::InvalidArgument("axis ", axis, " is out of range")); + const int ds = std::max<int>(static_cast<int>(input.dim_size(axis)), 1); + const int sum = shift_mod_sum[axis] + static_cast<int>(shift_flat(i)); + // modulo that works with negatives: ((x % y) + y) % y + shift_mod_sum[axis] = (sum % ds + ds) % ds; + } + // the size of each dimension + gtl::InlinedVector<int, 4> dim_size(num_dims); + // threshold[i] is the index that the roll starts to wrap back to the front + gtl::InlinedVector<int, 4> threshold(num_dims); + // dim_range is the number of indices over in the flattened tensor + // you need to skip in order to make it over from one side of a dimension + // to the other. Used to make the shifts wrap around after a threshold. + gtl::InlinedVector<int64, 4> dim_range(num_dims); + int64 dim_size_prod = 1; // dimension size product + // inner shift dimension (inner most shifted dimension) + int64 isd = 0; + for (int i = num_dims - 1; i >= 0; i--) { + if (isd == 0 && shift_mod_sum[i] != 0) isd = i; + const int ds = std::max<int>(static_cast<int>(input.dim_size(i)), 1); + dim_size[i] = ds; + threshold[i] = (ds - shift_mod_sum[i]) % ds; + dim_size_prod *= static_cast<int64>(input.dim_size(i)); + dim_range[i] = dim_size_prod; + } + + Tensor* output = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(0, input.shape(), &output)); + auto input_flat = input.flat<T>().data(); + auto output_flat = output->flat<T>().data(); + + if (std::is_same<Device, CPUDevice>::value) { + if (DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) { + // V2 copies memory in groups instead of element by element + DoRollWithMemcpy<T>(context, num_elements, num_dims, dim_size, + input_flat, output_flat, threshold, dim_range, isd); + } else { + // incase memcpy does not work for current data type + DoRoll<T>(context, num_elements, num_dims, dim_size, input_flat, + output_flat, threshold, dim_range); + } + } + } +}; + +// Register the CPU kernels. +#define REGISTER_CPU(type) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<int32>("Tshift") \ + .TypeConstraint<int32>("Taxis"), \ + RollOp<CPUDevice, type, int32, int32>) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<int64>("Tshift") \ + .TypeConstraint<int32>("Taxis"), \ + RollOp<CPUDevice, type, int64, int32>) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<int32>("Tshift") \ + .TypeConstraint<int64>("Taxis"), \ + RollOp<CPUDevice, type, int32, int64>) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<int64>("Tshift") \ + .TypeConstraint<int64>("Taxis"), \ + RollOp<CPUDevice, type, int64, int64>) + +TF_CALL_ALL_TYPES(REGISTER_CPU); +#undef REGISTER_CPU +} // namespace tensorflow diff --git a/tensorflow/core/kernels/roll_op_test.cc b/tensorflow/core/kernels/roll_op_test.cc new file mode 100644 index 0000000000..90b6f8d0f3 --- /dev/null +++ b/tensorflow/core/kernels/roll_op_test.cc @@ -0,0 +1,484 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include <functional> +#include <memory> + +#include "tensorflow/core/common_runtime/device.h" +#include "tensorflow/core/common_runtime/device_factory.h" +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/allocator.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/framework/types.pb.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" + +namespace tensorflow { +namespace { + +class RollOpTest : public OpsTestBase { + protected: + void MakeOp(DataType data_type, DataType index_type) { + TF_ASSERT_OK(NodeDefBuilder("myop", "Roll") + .Input(FakeInput(data_type)) + .Input(FakeInput(index_type)) + .Input(FakeInput(index_type)) + .Finalize(node_def())); + TF_ASSERT_OK(InitOp()); + } +}; + +TEST_F(RollOpTest, ScalarIndices) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({5}), {0, 1, 2, 3, 4}); + AddInputFromArray<int32>(TensorShape({}), {3}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5})); + test::FillValues<float>(&expected, {2, 3, 4, 0, 1}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ScalarIndices_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>(TensorShape({5}), {"a", "b", "c", "d", "e"}); + AddInputFromArray<int32>(TensorShape({}), {3}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5})); + test::FillValues<string>(&expected, {"c", "d", "e", "a", "b"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ScalarIndices_Complex) { + MakeOp(DT_COMPLEX64, DT_INT32); + + // Feed and run + AddInputFromArray<std::complex<float>>( + TensorShape({5}), {std::complex<float>(0, 10), std::complex<float>(1, 11), + std::complex<float>(2, 12), std::complex<float>(3, 13), + std::complex<float>(4, 14)}); + AddInputFromArray<int32>(TensorShape({}), {3}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_COMPLEX64, TensorShape({5})); + test::FillValues<std::complex<float>>( + &expected, {std::complex<float>(2, 12), std::complex<float>(3, 13), + std::complex<float>(4, 14), std::complex<float>(0, 10), + std::complex<float>(1, 11)}); + test::ExpectTensorEqual<std::complex<float>>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({3, 5}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray<int32>(TensorShape({2}), {2, -1}); + AddInputFromArray<int32>(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({3, 5})); + test::FillValues<float>(&expected, + {6, 7, 8, 9, 5, 11, 12, 13, 14, 10, 1, 2, 3, 4, 0}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>(TensorShape({3, 5}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray<int32>(TensorShape({2}), {2, -1}); + AddInputFromArray<int32>(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({3, 5})); + test::FillValues<string>(&expected, {"g", "h", "i", "j", "f", "l", "m", "n", + "o", "k", "b", "c", "d", "e", "a"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({2, 2, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray<int32>(TensorShape({3}), {1, -1, -1}); + AddInputFromArray<int32>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 3})); + test::FillValues<float>(&expected, {10, 11, 9, 7, 8, 6, 4, 5, 3, 1, 2, 0}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>( + TensorShape({2, 2, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray<int32>(TensorShape({3}), {1, -1, -1}); + AddInputFromArray<int32>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({2, 2, 3})); + test::FillValues<string>( + &expected, {"k", "l", "j", "h", "i", "g", "e", "f", "d", "b", "c", "a"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD64) { + MakeOp(DT_FLOAT, DT_INT64); + + // Feed and run + AddInputFromArray<float>(TensorShape({5, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray<int64>(TensorShape({2}), {-1, 4}); + AddInputFromArray<int64>(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3})); + test::FillValues<float>(&expected, + {5, 3, 4, 8, 6, 7, 11, 9, 10, 14, 12, 13, 2, 0, 1}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD64_NoMemcpy) { + MakeOp(DT_STRING, DT_INT64); + + // Feed and run + AddInputFromArray<string>(TensorShape({5, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray<int64>(TensorShape({2}), {-1, 4}); + AddInputFromArray<int64>(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5, 3})); + test::FillValues<string>(&expected, {"f", "d", "e", "i", "g", "h", "l", "j", + "k", "o", "m", "n", "c", "a", "b"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD64) { + MakeOp(DT_FLOAT, DT_INT64); + + // Feed and run + AddInputFromArray<float>(TensorShape({4, 1, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray<int64>(TensorShape({3}), {4, 3, 2}); + AddInputFromArray<int64>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({4, 1, 3})); + test::FillValues<float>(&expected, {1, 2, 0, 4, 5, 3, 7, 8, 6, 10, 11, 9}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD64_NoMemcpy) { + MakeOp(DT_STRING, DT_INT64); + + // Feed and run + AddInputFromArray<string>( + TensorShape({4, 1, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray<int64>(TensorShape({3}), {4, 3, 2}); + AddInputFromArray<int64>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({4, 1, 3})); + test::FillValues<string>( + &expected, {"b", "c", "a", "e", "f", "d", "h", "i", "g", "k", "l", "j"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroShift_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({2, 2, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray<int32>(TensorShape({3}), {0, 0, 0}); + AddInputFromArray<int32>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 3})); + test::FillValues<float>(&expected, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroShift_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>( + TensorShape({2, 2, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray<int32>(TensorShape({3}), {0, 0, 0}); + AddInputFromArray<int32>(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({2, 2, 3})); + test::FillValues<string>( + &expected, {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroSize_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({5, 0, 0}), {}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 0, 0})); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroSize_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>(TensorShape({5, 0, 0}), {}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5, 0, 0})); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, OneSize_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({1, 1, 1}), {5}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1})); + test::FillValues<float>(&expected, {5}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, OneSize_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>(TensorShape({1, 1, 1}), {"a"}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({1, 1, 1})); + test::FillValues<string>(&expected, {"a"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, MultiShifts_TwoD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({3, 5}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray<int32>(TensorShape({4}), {-2, 2, -1, 1}); + AddInputFromArray<int32>(TensorShape({4}), {1, 0, 0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({3, 5})); + test::FillValues<float>(&expected, + {11, 12, 13, 14, 10, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5}); + test::ExpectTensorEqual<float>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, MultiShifts_TwoD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray<string>(TensorShape({3, 5}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray<int32>(TensorShape({4}), {-2, 2, -1, 1}); + AddInputFromArray<int32>(TensorShape({4}), {1, 0, 0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({3, 5})); + test::FillValues<string>(&expected, {"l", "m", "n", "o", "k", "b", "c", "d", + "e", "a", "g", "h", "i", "j", "f"}); + test::ExpectTensorEqual<string>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Error_InputMustBeVectorOrHigher) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({}), {7}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {0}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()).contains("input must be 1-D or higher")) + << s; +} + +TEST_F(RollOpTest, Error_AxisMustBeScalarOrVector) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({1, 2}), {0, 1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("axis must be a scalar or a 1-D vector")) + << s; +} + +TEST_F(RollOpTest, Error_ShiftMustBeScalarOrVector) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray<int32>(TensorShape({1, 2}), {0, 1}); + AddInputFromArray<int32>(TensorShape({}), {1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("shift must be a scalar or a 1-D vector")) + << s; +} + +TEST_F(RollOpTest, Error_ShiftAndAxisMustBeSameSize) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray<int32>(TensorShape({1}), {1}); + AddInputFromArray<int32>(TensorShape({2}), {0, 1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("shift and axis must have the same size")) + << s; +} + +TEST_F(RollOpTest, Error_AxisOutOfRange) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray<float>(TensorShape({4}), {1, 2, 3, 4}); + AddInputFromArray<int32>(TensorShape({}), {1}); + AddInputFromArray<int32>(TensorShape({}), {1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()).contains("is out of range")) << s; +} + +// isd - (inner shift dimension) The inner most dimension to be shifted. +// All outer dimensions will also be shifted for testing. +static Graph* RollGraph(const TensorShape& shape, int isd) { + Graph* g = new Graph(OpRegistry::Global()); + Tensor input(DT_FLOAT, shape); + input.flat<float>().setRandom(); + const int dims = static_cast<int>(input.dims()); + Tensor shift(DT_INT32, TensorShape({dims})); + for (int i = 0; i < dims; i++) { + // shift the inner shift dimension and all outer dimensions + shift.flat<int32>()(i) = (i <= isd) ? 2 : 0; + } + Tensor axis(DT_INT32, TensorShape({dims})); + for (int i = 0; i < dims; i++) { + axis.flat<int32>()(i) = i; + } + test::graph::Roll(g, test::graph::Constant(g, input), + test::graph::Constant(g, shift), + test::graph::Constant(g, axis)); + return g; +} + +#define BM_ROLL_OUTER(DEVICE) \ + static void BM_##DEVICE##_roll_outer(int iters, int rows, int columns) { \ + TensorShape shape{rows, columns}; \ + const int64 num_items = static_cast<int64>(iters) * shape.num_elements(); \ + testing::ItemsProcessed(num_items); \ + testing::BytesProcessed(num_items * sizeof(float)); \ + testing::UseRealTime(); \ + test::Benchmark(#DEVICE, RollGraph(shape, 0)).Run(iters); \ + } \ + BENCHMARK(BM_##DEVICE##_roll_outer) \ + ->ArgPair(256, 256) \ + ->ArgPair(512, 512) \ + ->ArgPair(1024, 1024) \ + ->ArgPair(2048, 2048) + +#define BM_ROLL_ALL(DEVICE) \ + static void BM_##DEVICE##_roll_all(int iters, int rows, int columns) { \ + TensorShape shape{rows, columns}; \ + const int64 num_items = static_cast<int64>(iters) * shape.num_elements(); \ + testing::ItemsProcessed(num_items); \ + testing::BytesProcessed(num_items * sizeof(float)); \ + testing::UseRealTime(); \ + test::Benchmark(#DEVICE, RollGraph(shape, 1)).Run(iters); \ + } \ + BENCHMARK(BM_##DEVICE##_roll_all) \ + ->ArgPair(256, 256) \ + ->ArgPair(512, 512) \ + ->ArgPair(1024, 1024) \ + ->ArgPair(2048, 2048) + +BM_ROLL_OUTER(cpu); +BM_ROLL_ALL(cpu); +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/kernels/unravel_index_op.cc b/tensorflow/core/kernels/unravel_index_op.cc new file mode 100644 index 0000000000..a61272675b --- /dev/null +++ b/tensorflow/core/kernels/unravel_index_op.cc @@ -0,0 +1,122 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#define EIGEN_USE_THREADS + +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" + +namespace tensorflow { + +namespace { +template <typename T> +struct mod_op { + const T operator()(const T& a, const T& b) const { return a % b; } +}; +} // namespace + +typedef Eigen::ThreadPoolDevice CPUDevice; + +template <typename Tidx> +class UnravelIndexOp : public OpKernel { + public: + explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + const Tensor& indices_tensor = ctx->input(0); + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(indices_tensor.shape()) || + TensorShapeUtils::IsScalar(indices_tensor.shape()), + errors::InvalidArgument( + "The indices can only be scalar or vector, got \"", + indices_tensor.shape().DebugString(), "\"")); + + const Tensor& dims_tensor = ctx->input(1); + OP_REQUIRES( + ctx, TensorShapeUtils::IsVector(dims_tensor.shape()), + errors::InvalidArgument("The indices can only be 1-D, got \"", + dims_tensor.shape().DebugString(), "\"")); + + auto dims = dims_tensor.vec<Tidx>(); + + Eigen::array<bool, 1> reverse({true}); + + Tensor strides_tensor; + OP_REQUIRES_OK(ctx, + ctx->allocate_temp(DataTypeToEnum<Tidx>::value, + TensorShape({dims_tensor.NumElements()}), + &strides_tensor)); + + auto strides = strides_tensor.vec<Tidx>(); + strides = dims.reverse(reverse) + .scan(0, Eigen::internal::ProdReducer<Tidx>(), false) + .reverse(reverse); + + Tensor strides_shifted_tensor; + OP_REQUIRES_OK(ctx, + ctx->allocate_temp(DataTypeToEnum<Tidx>::value, + TensorShape({dims_tensor.NumElements()}), + &strides_shifted_tensor)); + + auto strides_shifted = strides_shifted_tensor.vec<Tidx>(); + strides_shifted = dims.reverse(reverse) + .scan(0, Eigen::internal::ProdReducer<Tidx>(), true) + .reverse(reverse); + + Tensor* output_tensor = nullptr; + if (TensorShapeUtils::IsScalar(indices_tensor.shape())) { + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), + &output_tensor)); + + auto output = output_tensor->vec<Tidx>(); + + output = output.constant(indices_tensor.scalar<Tidx>()()); + output = output.binaryExpr(strides, mod_op<Tidx>()) / strides_shifted; + } else { + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, + TensorShape({dims_tensor.NumElements(), + indices_tensor.NumElements()}), + &output_tensor)); + + auto output = output_tensor->matrix<Tidx>(); + + Eigen::array<int64, 2> reshape{{dims_tensor.NumElements(), 1}}; + Eigen::array<int64, 2> bcast({1, indices_tensor.NumElements()}); + Eigen::array<int64, 2> indices_reshape{{1, indices_tensor.NumElements()}}; + Eigen::array<int64, 2> indices_bcast({dims_tensor.NumElements(), 1}); + + output = indices_tensor.vec<Tidx>() + .reshape(indices_reshape) + .broadcast(indices_bcast); + output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), + mod_op<Tidx>()) / + strides_shifted.reshape(reshape).broadcast(bcast); + } + } +}; + +#define REGISTER_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("UnravelIndex").Device(DEVICE_CPU).TypeConstraint<type>("Tidx"), \ + UnravelIndexOp<type>); +TF_CALL_int32(REGISTER_KERNEL) TF_CALL_int64(REGISTER_KERNEL) +#undef REGISTER_KERNEL + +} // namespace tensorflow diff --git a/tensorflow/core/lib/io/random_inputstream.cc b/tensorflow/core/lib/io/random_inputstream.cc index 8b8c1392a1..09336e79cd 100644 --- a/tensorflow/core/lib/io/random_inputstream.cc +++ b/tensorflow/core/lib/io/random_inputstream.cc @@ -57,6 +57,43 @@ Status RandomAccessInputStream::ReadNBytes(int64 bytes_to_read, return Status::OK(); } +// To limit memory usage, the default implementation of SkipNBytes() only reads +// 8MB at a time. +static constexpr int64 kMaxSkipSize = 8 * 1024 * 1024; + +Status RandomAccessInputStream::SkipNBytes(int64 bytes_to_skip) { + if (bytes_to_skip < 0) { + return errors::InvalidArgument("Can't skip a negative number of bytes"); + } + std::unique_ptr<char[]> scratch(new char[kMaxSkipSize]); + // Try to read 1 bytes first, if we could complete the read then EOF is + // not reached yet and we could return. + if (bytes_to_skip > 0) { + StringPiece data; + Status s = file_->Read(pos_ + bytes_to_skip - 1, 1, &data, scratch.get()); + if ((s.ok() || errors::IsOutOfRange(s)) && data.size() == 1) { + pos_ += bytes_to_skip; + return Status::OK(); + } + } + // Read kDefaultSkipSize at a time till bytes_to_skip. + while (bytes_to_skip > 0) { + int64 bytes_to_read = std::min<int64>(kMaxSkipSize, bytes_to_skip); + StringPiece data; + Status s = file_->Read(pos_, bytes_to_read, &data, scratch.get()); + if (s.ok() || errors::IsOutOfRange(s)) { + pos_ += data.size(); + } else { + return s; + } + if (data.size() < bytes_to_read) { + return errors::OutOfRange("reached end of file"); + } + bytes_to_skip -= bytes_to_read; + } + return Status::OK(); +} + int64 RandomAccessInputStream::Tell() const { return pos_; } } // namespace io diff --git a/tensorflow/core/lib/io/random_inputstream.h b/tensorflow/core/lib/io/random_inputstream.h index 09ebe9ba49..bdbdbd71ff 100644 --- a/tensorflow/core/lib/io/random_inputstream.h +++ b/tensorflow/core/lib/io/random_inputstream.h @@ -34,6 +34,8 @@ class RandomAccessInputStream : public InputStreamInterface { Status ReadNBytes(int64 bytes_to_read, string* result) override; + Status SkipNBytes(int64 bytes_to_skip) override; + int64 Tell() const override; Status Seek(int64 position) { diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 87dfa77689..267ce88440 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -335,6 +335,13 @@ REGISTER_OP("Unpack") return Status::OK(); }); +REGISTER_OP("UnravelIndex") + .Input("indices: Tidx") + .Input("dims: Tidx") + .Output("output: Tidx") + .Attr("Tidx: {int32, int64} = DT_INT32") + .SetShapeFn([](InferenceContext* c) { return Status::OK(); }); + // -------------------------------------------------------------------------- // TODO(josh11b): Remove the >= 2 constraint, once we can rewrite the graph // in the N == 1 case to remove the node. diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index ef2ac267cc..a62e2d782b 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -586,6 +586,17 @@ REGISTER_OP("NonMaxSuppression") .Output("selected_indices: int32") .Attr("iou_threshold: float = 0.5") .SetShapeFn([](InferenceContext* c) { + // Get inputs and validate ranks. + ShapeHandle boxes; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &boxes)); + ShapeHandle scores; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &scores)); + ShapeHandle max_output_size; + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &max_output_size)); + // The boxes is a 2-D float Tensor of shape [num_boxes, 4]. + DimensionHandle unused; + TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused)); + c->set_output(0, c->Vector(c->UnknownDim())); return Status::OK(); }); @@ -597,6 +608,19 @@ REGISTER_OP("NonMaxSuppressionV2") .Input("iou_threshold: float") .Output("selected_indices: int32") .SetShapeFn([](InferenceContext* c) { + // Get inputs and validate ranks. + ShapeHandle boxes; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &boxes)); + ShapeHandle scores; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &scores)); + ShapeHandle max_output_size; + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &max_output_size)); + ShapeHandle iou_threshold; + TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &iou_threshold)); + // The boxes is a 2-D float Tensor of shape [num_boxes, 4]. + DimensionHandle unused; + TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused)); + c->set_output(0, c->Vector(c->UnknownDim())); return Status::OK(); }); diff --git a/tensorflow/core/ops/manip_ops.cc b/tensorflow/core/ops/manip_ops.cc new file mode 100644 index 0000000000..95b4774fe6 --- /dev/null +++ b/tensorflow/core/ops/manip_ops.cc @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +// -------------------------------------------------------------------------- +REGISTER_OP("Roll") + .Input("input: T") + .Input("shift: Tshift") + .Input("axis: Taxis") + .Output("output: T") + .Attr("T: type") + .Attr("Tshift: {int32,int64}") + .Attr("Taxis: {int32,int64}") + .SetShapeFn(shape_inference::UnchangedShape); + +} // namespace tensorflow diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 62661fe4bd..67481fd202 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1818,7 +1818,7 @@ REGISTER_OP("_MklMaxPool") .Input("input: T") .Input("mkl_input: uint8") .Output("output: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Output("workspace: T") #else .Output("workspace: uint8") @@ -1844,7 +1844,7 @@ REGISTER_OP("_MklMaxPoolGrad") .Input("orig_input: T") .Input("orig_output: T") .Input("grad: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Input("workspace: T") #else .Input("workspace: uint8") @@ -1916,7 +1916,7 @@ REGISTER_OP("_MklLRN") .Input("input: T") .Input("mkl_input: uint8") .Output("output: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Output("workspace: T") #else .Output("workspace: uint8") @@ -1944,7 +1944,7 @@ REGISTER_OP("_MklLRNGrad") .Input("input_grads: T") .Input("input_image: T") .Input("output_image: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Input("workspace: T") #else .Input("workspace: uint8") diff --git a/tensorflow/core/platform/cpu_feature_guard.cc b/tensorflow/core/platform/cpu_feature_guard.cc index b0d7b3a67a..b570658158 100644 --- a/tensorflow/core/platform/cpu_feature_guard.cc +++ b/tensorflow/core/platform/cpu_feature_guard.cc @@ -97,14 +97,17 @@ std::once_flag g_cpu_feature_guard_warn_once_flag; void InfoAboutUnusedCPUFeatures() { std::call_once(g_cpu_feature_guard_warn_once_flag, [] { string missing_instructions; -#ifdef PLATFORM_WINDOWS +#if defined(_MSC_VER) && !defined(__clang__) + #ifndef __AVX__ CheckIfFeatureUnused(CPUFeature::AVX, "AVX", missing_instructions); #endif // __AVX__ #ifndef __AVX2__ CheckIfFeatureUnused(CPUFeature::AVX2, "AVX2", missing_instructions); #endif // __AVX2__ -#else // ifdef platform windows + +#else // if defined(_MSC_VER) && !defined(__clang__) + #ifndef __SSE__ CheckIfFeatureUnused(CPUFeature::SSE, "SSE", missing_instructions); #endif // __SSE__ @@ -132,7 +135,7 @@ void InfoAboutUnusedCPUFeatures() { #ifndef __FMA__ CheckIfFeatureUnused(CPUFeature::FMA, "FMA", missing_instructions); #endif // __FMA__ -#endif // else of ifdef platform windows +#endif // else of if defined(_MSC_VER) && !defined(__clang__) if (!missing_instructions.empty()) { LOG(INFO) << "Your CPU supports instructions that this TensorFlow " << "binary was not compiled to use:" << missing_instructions; diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h index 2da20bb1b8..7b580c8bf6 100644 --- a/tensorflow/core/platform/profile_utils/cpu_utils.h +++ b/tensorflow/core/platform/profile_utils/cpu_utils.h @@ -42,7 +42,7 @@ namespace profile_utils { class CpuUtils { public: // Constant for invalid frequency. - // This value is returned when the furequency is not obtained somehow. + // This value is returned when the frequency is not obtained somehow. static constexpr int64 INVALID_FREQUENCY = -1; static constexpr uint64 DUMMY_CYCLE_CLOCK = 1; @@ -105,7 +105,7 @@ class CpuUtils { static int64 GetCycleCounterFrequency(); #endif - // Return micro secound per each clock + // Return micro second per each clock // As this method caches the cpu frequency internally, // the first call will incur overhead, but not subsequent calls. static double GetMicroSecPerClock(); diff --git a/tensorflow/core/platform/s3/s3_file_system.cc b/tensorflow/core/platform/s3/s3_file_system.cc index ebda3a2065..52bf0d4694 100644 --- a/tensorflow/core/platform/s3/s3_file_system.cc +++ b/tensorflow/core/platform/s3/s3_file_system.cc @@ -22,6 +22,7 @@ limitations under the License. #include <aws/core/Aws.h> #include <aws/core/config/AWSProfileConfigLoader.h> #include <aws/core/utils/FileSystemUtils.h> +#include <aws/core/utils/StringUtils.h> #include <aws/core/utils/logging/AWSLogging.h> #include <aws/core/utils/logging/LogSystemInterface.h> #include <aws/s3/S3Client.h> @@ -128,6 +129,15 @@ Aws::Client::ClientConfiguration& GetDefaultClientConfig() { return cfg; }; +void ShutdownClient(Aws::S3::S3Client* s3_client) { + if (s3_client != nullptr) { + delete s3_client; + Aws::SDKOptions options; + Aws::ShutdownAPI(options); + AWSLogSystem::ShutdownAWSLogging(); + } +} + Status ParseS3Path(const string& fname, bool empty_object_ok, string* bucket, string* object) { if (!bucket || !object) { @@ -155,12 +165,12 @@ Status ParseS3Path(const string& fname, bool empty_object_ok, string* bucket, class S3RandomAccessFile : public RandomAccessFile { public: - S3RandomAccessFile(const string& bucket, const string& object) - : bucket_(bucket), object_(object) {} + S3RandomAccessFile(const string& bucket, const string& object, + std::shared_ptr<Aws::S3::S3Client> s3_client) + : bucket_(bucket), object_(object), s3_client_(s3_client) {} Status Read(uint64 offset, size_t n, StringPiece* result, char* scratch) const override { - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); Aws::S3::Model::GetObjectRequest getObjectRequest; getObjectRequest.WithBucket(bucket_.c_str()).WithKey(object_.c_str()); string bytes = strings::StrCat("bytes=", offset, "-", offset + n - 1); @@ -168,7 +178,7 @@ class S3RandomAccessFile : public RandomAccessFile { getObjectRequest.SetResponseStreamFactory([]() { return Aws::New<Aws::StringStream>(kS3FileSystemAllocationTag); }); - auto getObjectOutcome = s3Client.GetObject(getObjectRequest); + auto getObjectOutcome = this->s3_client_->GetObject(getObjectRequest); if (!getObjectOutcome.IsSuccess()) { n = 0; *result = StringPiece(scratch, n); @@ -186,13 +196,16 @@ class S3RandomAccessFile : public RandomAccessFile { private: string bucket_; string object_; + std::shared_ptr<Aws::S3::S3Client> s3_client_; }; class S3WritableFile : public WritableFile { public: - S3WritableFile(const string& bucket, const string& object) + S3WritableFile(const string& bucket, const string& object, + std::shared_ptr<Aws::S3::S3Client> s3_client) : bucket_(bucket), object_(object), + s3_client_(s3_client), sync_needed_(true), outfile_(Aws::MakeShared<Aws::Utils::TempFile>( kS3FileSystemAllocationTag, "/tmp/s3_filesystem_XXXXXX", @@ -231,17 +244,13 @@ class S3WritableFile : public WritableFile { if (!sync_needed_) { return Status::OK(); } - Aws::Client::ClientConfiguration clientConfig = GetDefaultClientConfig(); - clientConfig.connectTimeoutMs = 300000; - clientConfig.requestTimeoutMs = 600000; - Aws::S3::S3Client s3Client(clientConfig); Aws::S3::Model::PutObjectRequest putObjectRequest; putObjectRequest.WithBucket(bucket_.c_str()).WithKey(object_.c_str()); long offset = outfile_->tellp(); outfile_->seekg(0); putObjectRequest.SetBody(outfile_); putObjectRequest.SetContentLength(offset); - auto putObjectOutcome = s3Client.PutObject(putObjectRequest); + auto putObjectOutcome = this->s3_client_->PutObject(putObjectRequest); outfile_->clear(); outfile_->seekp(offset); if (!putObjectOutcome.IsSuccess()) { @@ -256,6 +265,7 @@ class S3WritableFile : public WritableFile { private: string bucket_; string object_; + std::shared_ptr<Aws::S3::S3Client> s3_client_; bool sync_needed_; std::shared_ptr<Aws::Utils::TempFile> outfile_; }; @@ -274,31 +284,46 @@ class S3ReadOnlyMemoryRegion : public ReadOnlyMemoryRegion { } // namespace -S3FileSystem::S3FileSystem() { - AWSLogSystem::InitializeAWSLogging(); - - Aws::SDKOptions options; - options.cryptoOptions.sha256Factory_create_fn = []() { - return Aws::MakeShared<S3SHA256Factory>(S3CryptoAllocationTag); - }; - options.cryptoOptions.sha256HMACFactory_create_fn = []() { - return Aws::MakeShared<S3SHA256HmacFactory>(S3CryptoAllocationTag); - }; - Aws::InitAPI(options); -} - -S3FileSystem::~S3FileSystem() { - Aws::SDKOptions options; - Aws::ShutdownAPI(options); +S3FileSystem::S3FileSystem() + : s3_client_(nullptr, ShutdownClient), client_lock_() {} + +S3FileSystem::~S3FileSystem() {} + +// Initializes s3_client_, if needed, and returns it. +std::shared_ptr<Aws::S3::S3Client> S3FileSystem::GetS3Client() { + std::lock_guard<mutex> lock(this->client_lock_); + + if (this->s3_client_.get() == nullptr) { + AWSLogSystem::InitializeAWSLogging(); + + Aws::SDKOptions options; + options.cryptoOptions.sha256Factory_create_fn = []() { + return Aws::MakeShared<S3SHA256Factory>(S3CryptoAllocationTag); + }; + options.cryptoOptions.sha256HMACFactory_create_fn = []() { + return Aws::MakeShared<S3SHA256HmacFactory>(S3CryptoAllocationTag); + }; + Aws::InitAPI(options); + + // The creation of S3Client disables virtual addressing: + // S3Client(clientConfiguration, signPayloads, useVirtualAdressing = true) + // The purpose is to address the issue encountered when there is an `.` + // in the bucket name. Due to TLS hostname validation or DNS rules, + // the bucket may not be resolved. Disabling of virtual addressing + // should address the issue. See GitHub issue 16397 for details. + this->s3_client_ = std::shared_ptr<Aws::S3::S3Client>(new Aws::S3::S3Client( + GetDefaultClientConfig(), + Aws::Client::AWSAuthV4Signer::PayloadSigningPolicy::Never, false)); + } - AWSLogSystem::ShutdownAWSLogging(); + return this->s3_client_; } Status S3FileSystem::NewRandomAccessFile( const string& fname, std::unique_ptr<RandomAccessFile>* result) { string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(fname, false, &bucket, &object)); - result->reset(new S3RandomAccessFile(bucket, object)); + result->reset(new S3RandomAccessFile(bucket, object, this->GetS3Client())); return Status::OK(); } @@ -306,7 +331,7 @@ Status S3FileSystem::NewWritableFile(const string& fname, std::unique_ptr<WritableFile>* result) { string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(fname, false, &bucket, &object)); - result->reset(new S3WritableFile(bucket, object)); + result->reset(new S3WritableFile(bucket, object, this->GetS3Client())); return Status::OK(); } @@ -321,7 +346,7 @@ Status S3FileSystem::NewAppendableFile(const string& fname, string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(fname, false, &bucket, &object)); - result->reset(new S3WritableFile(bucket, object)); + result->reset(new S3WritableFile(bucket, object, this->GetS3Client())); while (true) { status = reader->Read(offset, kS3ReadAppendableFileBufferSize, &read_chunk, @@ -372,7 +397,6 @@ Status S3FileSystem::GetChildren(const string& dir, prefix.push_back('/'); } - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); Aws::S3::Model::ListObjectsRequest listObjectsRequest; listObjectsRequest.WithBucket(bucket.c_str()) .WithPrefix(prefix.c_str()) @@ -383,7 +407,8 @@ Status S3FileSystem::GetChildren(const string& dir, Aws::S3::Model::ListObjectsResult listObjectsResult; do { - auto listObjectsOutcome = s3Client.ListObjects(listObjectsRequest); + auto listObjectsOutcome = + this->GetS3Client()->ListObjects(listObjectsRequest); if (!listObjectsOutcome.IsSuccess()) { string error = strings::StrCat( listObjectsOutcome.GetError().GetExceptionName().c_str(), ": ", @@ -417,11 +442,10 @@ Status S3FileSystem::Stat(const string& fname, FileStatistics* stats) { string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(fname, true, &bucket, &object)); - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); if (object.empty()) { Aws::S3::Model::HeadBucketRequest headBucketRequest; headBucketRequest.WithBucket(bucket.c_str()); - auto headBucketOutcome = s3Client.HeadBucket(headBucketRequest); + auto headBucketOutcome = this->GetS3Client()->HeadBucket(headBucketRequest); if (!headBucketOutcome.IsSuccess()) { string error = strings::StrCat( headBucketOutcome.GetError().GetExceptionName().c_str(), ": ", @@ -439,7 +463,7 @@ Status S3FileSystem::Stat(const string& fname, FileStatistics* stats) { headObjectRequest.WithBucket(bucket.c_str()).WithKey(object.c_str()); headObjectRequest.SetResponseStreamFactory( []() { return Aws::New<Aws::StringStream>(kS3FileSystemAllocationTag); }); - auto headObjectOutcome = s3Client.HeadObject(headObjectRequest); + auto headObjectOutcome = this->GetS3Client()->HeadObject(headObjectRequest); if (headObjectOutcome.IsSuccess()) { stats->length = headObjectOutcome.GetResult().GetContentLength(); stats->is_directory = 0; @@ -457,7 +481,8 @@ Status S3FileSystem::Stat(const string& fname, FileStatistics* stats) { .WithMaxKeys(1); listObjectsRequest.SetResponseStreamFactory( []() { return Aws::New<Aws::StringStream>(kS3FileSystemAllocationTag); }); - auto listObjectsOutcome = s3Client.ListObjects(listObjectsRequest); + auto listObjectsOutcome = + this->GetS3Client()->ListObjects(listObjectsRequest); if (listObjectsOutcome.IsSuccess()) { if (listObjectsOutcome.GetResult().GetContents().size() > 0) { stats->length = 0; @@ -475,11 +500,11 @@ Status S3FileSystem::DeleteFile(const string& fname) { string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(fname, false, &bucket, &object)); - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); Aws::S3::Model::DeleteObjectRequest deleteObjectRequest; deleteObjectRequest.WithBucket(bucket.c_str()).WithKey(object.c_str()); - auto deleteObjectOutcome = s3Client.DeleteObject(deleteObjectRequest); + auto deleteObjectOutcome = + this->GetS3Client()->DeleteObject(deleteObjectRequest); if (!deleteObjectOutcome.IsSuccess()) { string error = strings::StrCat( deleteObjectOutcome.GetError().GetExceptionName().c_str(), ": ", @@ -494,10 +519,9 @@ Status S3FileSystem::CreateDir(const string& dirname) { TF_RETURN_IF_ERROR(ParseS3Path(dirname, true, &bucket, &object)); if (object.empty()) { - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); Aws::S3::Model::HeadBucketRequest headBucketRequest; headBucketRequest.WithBucket(bucket.c_str()); - auto headBucketOutcome = s3Client.HeadBucket(headBucketRequest); + auto headBucketOutcome = this->GetS3Client()->HeadBucket(headBucketRequest); if (!headBucketOutcome.IsSuccess()) { return errors::NotFound("The bucket ", bucket, " was not found."); } @@ -517,7 +541,6 @@ Status S3FileSystem::DeleteDir(const string& dirname) { string bucket, object; TF_RETURN_IF_ERROR(ParseS3Path(dirname, false, &bucket, &object)); - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); string prefix = object; if (prefix.back() != '/') { prefix.push_back('/'); @@ -528,7 +551,8 @@ Status S3FileSystem::DeleteDir(const string& dirname) { .WithMaxKeys(2); listObjectsRequest.SetResponseStreamFactory( []() { return Aws::New<Aws::StringStream>(kS3FileSystemAllocationTag); }); - auto listObjectsOutcome = s3Client.ListObjects(listObjectsRequest); + auto listObjectsOutcome = + this->GetS3Client()->ListObjects(listObjectsRequest); if (listObjectsOutcome.IsSuccess()) { auto contents = listObjectsOutcome.GetResult().GetContents(); if (contents.size() > 1 || @@ -568,8 +592,6 @@ Status S3FileSystem::RenameFile(const string& src, const string& target) { } } - Aws::S3::S3Client s3Client(GetDefaultClientConfig()); - Aws::S3::Model::CopyObjectRequest copyObjectRequest; Aws::S3::Model::DeleteObjectRequest deleteObjectRequest; @@ -582,7 +604,8 @@ Status S3FileSystem::RenameFile(const string& src, const string& target) { Aws::S3::Model::ListObjectsResult listObjectsResult; do { - auto listObjectsOutcome = s3Client.ListObjects(listObjectsRequest); + auto listObjectsOutcome = + this->GetS3Client()->ListObjects(listObjectsRequest); if (!listObjectsOutcome.IsSuccess()) { string error = strings::StrCat( listObjectsOutcome.GetError().GetExceptionName().c_str(), ": ", @@ -595,13 +618,15 @@ Status S3FileSystem::RenameFile(const string& src, const string& target) { Aws::String src_key = object.GetKey(); Aws::String target_key = src_key; target_key.replace(0, src_object.length(), target_object.c_str()); - Aws::String source = Aws::String(src_bucket.c_str()) + "/" + src_key; + Aws::String source = Aws::String(src_bucket.c_str()) + "/" + + Aws::Utils::StringUtils::URLEncode(src_key.c_str()); copyObjectRequest.SetBucket(target_bucket.c_str()); copyObjectRequest.SetKey(target_key); copyObjectRequest.SetCopySource(source); - auto copyObjectOutcome = s3Client.CopyObject(copyObjectRequest); + auto copyObjectOutcome = + this->GetS3Client()->CopyObject(copyObjectRequest); if (!copyObjectOutcome.IsSuccess()) { string error = strings::StrCat( copyObjectOutcome.GetError().GetExceptionName().c_str(), ": ", @@ -612,7 +637,8 @@ Status S3FileSystem::RenameFile(const string& src, const string& target) { deleteObjectRequest.SetBucket(src_bucket.c_str()); deleteObjectRequest.SetKey(src_key.c_str()); - auto deleteObjectOutcome = s3Client.DeleteObject(deleteObjectRequest); + auto deleteObjectOutcome = + this->GetS3Client()->DeleteObject(deleteObjectRequest); if (!deleteObjectOutcome.IsSuccess()) { string error = strings::StrCat( deleteObjectOutcome.GetError().GetExceptionName().c_str(), ": ", diff --git a/tensorflow/core/platform/s3/s3_file_system.h b/tensorflow/core/platform/s3/s3_file_system.h index 31ba3cecc5..31264be621 100644 --- a/tensorflow/core/platform/s3/s3_file_system.h +++ b/tensorflow/core/platform/s3/s3_file_system.h @@ -16,7 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_S3_S3_FILE_SYSTEM_H_ #define TENSORFLOW_CONTRIB_S3_S3_FILE_SYSTEM_H_ +#include <aws/s3/S3Client.h> #include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/mutex.h" namespace tensorflow { @@ -53,6 +55,26 @@ class S3FileSystem : public FileSystem { Status GetFileSize(const string& fname, uint64* size) override; Status RenameFile(const string& src, const string& target) override; + + private: + // Returns the member S3 client, initializing as-needed. + // When the client tries to access the object in S3, e.g., + // s3://bucket-name/path/to/object + // the behavior could be controlled by various environmental + // variables. + // By default S3 access regional endpoint, with region + // controlled by `AWS_REGION`. The endpoint could be overridden + // explicitly with `S3_ENDPOINT`. S3 uses HTTPS by default. + // If S3_USE_HTTPS=0 is specified, HTTP is used. Also, + // S3_VERIFY_SSL=0 could disable SSL verification in case + // HTTPS is used. + // This S3 Client does not support Virtual Hosted–Style Method + // for a bucket. + std::shared_ptr<Aws::S3::S3Client> GetS3Client(); + + std::shared_ptr<Aws::S3::S3Client> s3_client_; + // Lock held when checking for s3_client_ initialization. + mutex client_lock_; }; } // namespace tensorflow diff --git a/tensorflow/core/platform/s3/s3_file_system_test.cc b/tensorflow/core/platform/s3/s3_file_system_test.cc index 0b42f5fcec..d4411d9865 100644 --- a/tensorflow/core/platform/s3/s3_file_system_test.cc +++ b/tensorflow/core/platform/s3/s3_file_system_test.cc @@ -130,6 +130,8 @@ TEST_F(S3FileSystemTest, NewReadOnlyMemoryRegionFromFile) { TEST_F(S3FileSystemTest, FileExists) { const string fname = TmpDir("FileExists"); + // Ensure the file doesn't yet exist. + TF_ASSERT_OK(s3fs.DeleteFile(fname)); EXPECT_EQ(error::Code::NOT_FOUND, s3fs.FileExists(fname).code()); TF_ASSERT_OK(WriteString(fname, "test")); TF_EXPECT_OK(s3fs.FileExists(fname)); diff --git a/tensorflow/core/platform/windows/cpu_info.h b/tensorflow/core/platform/windows/cpu_info.h index d6e78dbc8f..f20939d3c0 100644 --- a/tensorflow/core/platform/windows/cpu_info.h +++ b/tensorflow/core/platform/windows/cpu_info.h @@ -22,8 +22,10 @@ limitations under the License. // Byte order defines provided by gcc. MSVC doesn't define those so // we define them here. // We assume that all windows platform out there are little endian. +#if defined(_MSC_VER) && !defined(__clang__) #define __ORDER_LITTLE_ENDIAN__ 0x4d2 #define __ORDER_BIG_ENDIAN__ 0x10e1 #define __BYTE_ORDER__ __ORDER_LITTLE_ENDIAN__ +#endif #endif // TENSORFLOW_PLATFORM_WINDOWS_CPU_INFO_H_ diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md index 460f935e4a..57d76eb4cb 100644 --- a/tensorflow/core/profiler/README.md +++ b/tensorflow/core/profiler/README.md @@ -240,8 +240,9 @@ Open a Chrome browser, enter URL chrome://tracing and load the timeline file. # can also generate memory profile using `-select bytes` tfprof> code -select accelerator_micros -max_depth 100000 -output pprof:outfile=<filename> -trim_name_regexes .*apply_op.* -# Use pprof to visualize the generated file. -pprof -png --nodecount=100 --sample_index=1 <filename> +# Use google-pprof, from the google-perftools package to visualize the generated file. +# On Ubuntu you can install it with `apt-get install it google-perftools`. +google-pprof --pdf --nodecount=100 <filename> ```  diff --git a/tensorflow/core/profiler/internal/tfprof_stats.h b/tensorflow/core/profiler/internal/tfprof_stats.h index 0790cb0ca6..db148c936c 100644 --- a/tensorflow/core/profiler/internal/tfprof_stats.h +++ b/tensorflow/core/profiler/internal/tfprof_stats.h @@ -83,7 +83,7 @@ class TFStats { const MultiGraphNodeProto& ShowMultiGraphNode(const string& cmd, const Options& opts) const; - // A a (partial) graph to existing graph. + // Add a (partial) graph to existing graph. void AddGraph(std::unique_ptr<GraphDef> graph); // Add a step of run time meta data. @@ -118,7 +118,7 @@ class TFStats { MultiGraphNodeProto empty_multi_graph_node_; std::map<int64, string> id_to_string_; - // Graph nodes covered by RunMetdata, that is traced with run time stats. + // Graph nodes covered by RunMetadata, that is traced with run time stats. std::set<int64> covered_nodes_; }; diff --git a/tensorflow/core/profiler/profiler.cc b/tensorflow/core/profiler/profiler.cc index 2cc212d589..808e3c853b 100644 --- a/tensorflow/core/profiler/profiler.cc +++ b/tensorflow/core/profiler/profiler.cc @@ -206,8 +206,12 @@ int Run(int argc, char** argv) { "graph_path,op_log_path,run_meta_path\n"); std::unique_ptr<GraphDef> graph(new GraphDef()); if (!FLAGS_graph_path.empty()) { - TF_CHECK_OK( - ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false)); + s = ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false); + if (!s.ok()) { + fprintf(stderr, "Failed to read graph_path: %s\n", + s.ToString().c_str()); + return 1; + } } std::unique_ptr<OpLogProto> op_log(new OpLogProto()); diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 67da7bf452..b02f899b87 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc1" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index 864e7e39c2..db4c5c35e3 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -35,7 +35,7 @@ limitations under the License. #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::engine; @@ -325,7 +325,7 @@ class MklShape { nullptr; // TF dimension corresponding to this MKL dimension }; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Forward decl TensorFormat MklDnnDataFormatToTFDataFormat(memory::format format); @@ -660,7 +660,7 @@ class MklDnnShape { typedef std::vector<MklShape> MklShapeList; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML typedef std::vector<MklDnnShape> MklDnnShapeList; #endif @@ -674,7 +674,7 @@ inline bool AreAllMklTensors(const MklShapeList& shapes) { return true; } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template <typename T> inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklShape& mkl_shape) { @@ -725,7 +725,7 @@ inline void GetMklShape(OpKernelContext* ctext, int n, MklShape* mklshape) { sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML inline void GetMklShape(OpKernelContext* ctext, int n, MklDnnShape* mklshape) { mklshape->DeSerializeMklDnnShape( ctext->input(GetTensorMetaDataIndex(n, ctext->num_inputs())) @@ -749,7 +749,7 @@ inline void GetMklInputList(OpKernelContext* ctext, StringPiece name, ctext->input_list(name, input_tensors); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void GetMklShapeList(OpKernelContext* ctext, StringPiece name, MklShapeList* mkl_shapes) { @@ -779,7 +779,7 @@ inline void GetMklShapeList(OpKernelContext* ctext, StringPiece name, #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML /// Get shape of input tensor pointed by 'input_idx' in TensorShape format. /// If the input tensor is in MKL layout, then obtains TensorShape from /// MklShape. @@ -814,7 +814,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, second_tensor->flat<uint8>().size() * sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Allocate the second output tensor that will contain // the MKL shape serialized inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, @@ -851,7 +851,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, second_tensor->flat<uint8>().size() * sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Allocate the output tensor, create a second output tensor that will contain // the MKL shape serialized inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, @@ -875,7 +875,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, // Allocates a temp tensor and returns the data buffer for temporary storage. // Currently -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template <typename T> inline void AllocTmpBuffer(OpKernelContext* context, Tensor* tensor_out, const memory::primitive_desc& pd, void** buf_out) { @@ -994,7 +994,7 @@ inline void CopyMklTensorInToOut(OpKernelContext* context, int idx_in, context->set_output(idx_meta_out, meta_output); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, int idx_in, int idx_out, const TensorShape& shape) { @@ -1032,7 +1032,7 @@ inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, int idx_in, } #endif -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void ForwardTfTensorInToOut(OpKernelContext* context, int idx_in, int idx_out) { @@ -1090,7 +1090,7 @@ inline void ForwardMklTensorInToOut(OpKernelContext* context, int idx_in, } } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML inline void ForwardMklTensorInToOutWithMklShape(OpKernelContext* context, int idx_in, int idx_out, const MklDnnShape& mkl_shape) { @@ -1132,7 +1132,7 @@ inline void SetDummyMklShapeOutput(OpKernelContext* context, AllocateOutputSetMklShape(context, idx_data_out, mkl_shape_output); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // We don't need these functions in MKLDNN. We have defined equality operator // on MklDnnShape class directly. @@ -1242,7 +1242,7 @@ inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) { // ------------------------------------------------------------------- -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML /// Return MKL-DNN data type (memory::data_type) for input type T /// @@ -1753,7 +1753,7 @@ class MklDnnData { } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow #endif // INTEL_MKL diff --git a/tensorflow/core/util/mkl_util_test.cc b/tensorflow/core/util/mkl_util_test.cc index 8b73eadb40..cd1d0713ad 100644 --- a/tensorflow/core/util/mkl_util_test.cc +++ b/tensorflow/core/util/mkl_util_test.cc @@ -22,7 +22,7 @@ limitations under the License. namespace tensorflow { namespace { -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML TEST(MklUtilTest, MklDnnTfShape) { auto cpu_engine = engine(engine::cpu, 0); @@ -84,7 +84,7 @@ TEST(MklUtilTest, MklDnnBlockedFormatTest) { EXPECT_EQ(b_md2.data.format, mkldnn_blocked); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace } // namespace tensorflow diff --git a/tensorflow/docs_src/about/bib.md b/tensorflow/docs_src/about/bib.md index c9f0c532c6..5593a3d95c 100644 --- a/tensorflow/docs_src/about/bib.md +++ b/tensorflow/docs_src/about/bib.md @@ -60,7 +60,7 @@ author={ Lukasz~Kaiser and Manjunath~Kudlur and Josh~Levenberg and - Dan~Man\'{e} and + Dandelion~Man\'{e} and Rajat~Monga and Sherry~Moore and Derek~Murray and diff --git a/tensorflow/docs_src/api_guides/python/contrib.signal.md b/tensorflow/docs_src/api_guides/python/contrib.signal.md index 85ef3ad134..0f7690f80a 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.signal.md +++ b/tensorflow/docs_src/api_guides/python/contrib.signal.md @@ -28,14 +28,14 @@ The `axis` parameter to @{tf.contrib.signal.frame} allows you to frame tensors with inner structure (e.g. a spectrogram): ```python -# `magnitude_spectrograms` is a [batch_size, ?, 127] tensor of spectrograms. We +# `magnitude_spectrograms` is a [batch_size, ?, 129] tensor of spectrograms. We # would like to produce overlapping fixed-size spectrogram patches; for example, # for use in a situation where a fixed size input is needed. magnitude_spectrograms = tf.abs(tf.contrib.signal.stft( signals, frame_length=256, frame_step=64, fft_length=256)) -# `spectrogram_patches` is a [batch_size, ?, 64, 127] tensor containing a -# variable number of [64, 127] spectrogram patches per batch item. +# `spectrogram_patches` is a [batch_size, ?, 64, 129] tensor containing a +# variable number of [64, 129] spectrogram patches per batch item. spectrogram_patches = tf.contrib.signal.frame( magnitude_spectrograms, frame_length=64, frame_step=16, axis=1) ``` diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md index 45cb9d829c..dae50a8f03 100644 --- a/tensorflow/docs_src/api_guides/python/regression_examples.md +++ b/tensorflow/docs_src/api_guides/python/regression_examples.md @@ -229,4 +229,4 @@ passed through to the `model_fn` when the `model_fn` is called. The `model_fn` returns an @{tf.estimator.EstimatorSpec$`EstimatorSpec`} which is a simple structure indicating to the `Estimator` which operations should be run to accomplish -varions tasks. +various tasks. diff --git a/tensorflow/docs_src/get_started/custom_estimators.md b/tensorflow/docs_src/get_started/custom_estimators.md index 6343cc4ee4..42a246678a 100644 --- a/tensorflow/docs_src/get_started/custom_estimators.md +++ b/tensorflow/docs_src/get_started/custom_estimators.md @@ -15,7 +15,7 @@ git clone https://github.com/tensorflow/models/ cd models/samples/core/get_started ``` -In this document we wil be looking at +In this document we will be looking at [`custom_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py). You can run it with the following command: @@ -161,7 +161,7 @@ classifier = tf.estimator.Estimator( To implement a typical model function, you must do the following: -* (Define the model)[#define_the_model]. +* [Define the model](#define_the_model). * Specify additional calculations for each of the [three different modes](#modes): * [Predict](#predict) diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/get_started/datasets_quickstart.md index ecfbf160f0..a8a2ab6e56 100644 --- a/tensorflow/docs_src/get_started/datasets_quickstart.md +++ b/tensorflow/docs_src/get_started/datasets_quickstart.md @@ -169,7 +169,7 @@ the number of examples in the `Dataset` ensures that the data is completely shuffled. The Iris data set only contains 150 examples. The @{tf.data.Dataset.repeat$`repeat`} method has the `Dataset` restart when -it reaches the end. To limit the number of epochss, set the `count` argument. +it reaches the end. To limit the number of epochs, set the `count` argument. The @{tf.data.Dataset.repeat$`batch`} method collects a number of examples and stacks them, to create batches. This adds a dimension to their shape. The new @@ -282,7 +282,7 @@ produce the necessary `(features, label)` pairs. We will start by building a function to parse a single line. -The following `iris_data.parse_line` function acomplishes this taks using the +The following `iris_data.parse_line` function accomplishes this task using the @{tf.decode_csv} function, and some simple python code: We must parse each of the lines in the dataset in order to generate the diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md index e3308ed716..ad3e1fe3e3 100644 --- a/tensorflow/docs_src/get_started/feature_columns.md +++ b/tensorflow/docs_src/get_started/feature_columns.md @@ -461,8 +461,8 @@ permitting a richer palette of numbers for every cell, an embedding column contains far fewer cells than an indicator column. Let's look at an example comparing indicator and embedding columns. Suppose our -input examples consists of different words from a limited palette of only 81 -words. Further suppose that the data set provides provides the following input +input examples consist of different words from a limited palette of only 81 +words. Further suppose that the data set provides the following input words in 4 separate examples: * `"dog"` diff --git a/tensorflow/docs_src/get_started/premade_estimators.md b/tensorflow/docs_src/get_started/premade_estimators.md index 45850a8996..4f01f997c3 100644 --- a/tensorflow/docs_src/get_started/premade_estimators.md +++ b/tensorflow/docs_src/get_started/premade_estimators.md @@ -372,7 +372,7 @@ Test set accuracy: 0.967 We now have a trained model that produces good evaluation results. We can now use the trained model to predict the species of an Iris flower -based on some unlabeled measurments. As with training and evaluation, we make +based on some unlabeled measurements. As with training and evaluation, we make predictions using a single function call: ```python diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index ba1a4118ae..14add7c77e 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 87cc647317..d2af9d9843 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 37e109a6e4..e5388c4b1e 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.5.0-rc1</version> + <version>1.5.0</version> </dependency> ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.5.0-rc1</version> + <version>1.5.0</version> </dependency> </dependencies> </project> @@ -123,12 +123,12 @@ instead: <dependency> <groupId>org.tensorflow</groupId> <artifactId>libtensorflow</artifactId> - <version>1.5.0-rc1</version> + <version>1.5.0</version> </dependency> <dependency> <groupId>org.tensorflow</groupId> <artifactId>libtensorflow_jni_gpu</artifactId> - <version>1.5.0-rc1</version> + <version>1.5.0</version> </dependency> ``` @@ -147,7 +147,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0-rc1.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0.zip). 3. Extract this .zip file. @@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -<pre><b>javac -cp libtensorflow-1.5.0-rc1.jar HelloTF.java</b></pre> +<pre><b>javac -cp libtensorflow-1.5.0.jar HelloTF.java</b></pre> ### Running @@ -239,11 +239,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -<pre><b>java -cp libtensorflow-1.5.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.5.0.jar:. -Djava.library.path=./jni HelloTF</b></pre> And the following command line executes the `HelloTF` program on Windows: -<pre><b>java -cp libtensorflow-1.5.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.5.0.jar;. -Djava.library.path=jni HelloTF</b></pre> If the program prints <tt>Hello from <i>version</i></tt>, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 03f12dff08..cd8c14599f 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -31,13 +31,13 @@ If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: - * CUDA® Toolkit 8.0. For details, see + * CUDA® Toolkit 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A). Ensure that you append the relevant Cuda pathnames to the `LD_LIBRARY_PATH` environment variable as described in the NVIDIA documentation. - * The NVIDIA drivers associated with CUDA Toolkit 8.0. - * cuDNN v6.0. For details, see + * The NVIDIA drivers associated with CUDA Toolkit 9.0. + * cuDNN v7.0. For details, see [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Ensure that you create the `CUDA_HOME` environment variable as described in the NVIDIA documentation. @@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv: Virtualenv environment: <pre>(tensorflow)$ <b>pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre> If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -293,7 +293,7 @@ take the following steps: <pre> $ <b>sudo pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b> </pre> If this step fails, see @@ -480,7 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre> <a name="ValidateYourInstallation"></a> @@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations. CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl </pre> @@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl </pre> diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index e13ddadab7..f49d3a2f08 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: <pre> $ <b>pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b></pre> If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -238,7 +238,7 @@ take the following steps: issue the following command: <pre> $ <b>sudo pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b> </pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b> </pre> If the preceding command fails, see [installation problems](#common-installation-problems). @@ -347,7 +347,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl</b></pre> <a name="ValidateYourInstallation"></a> @@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations. <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl </pre> @@ -528,5 +528,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-a <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py3-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl </pre> diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 485863bf2e..bc7d2080dc 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -133,7 +133,7 @@ The following NVIDIA <i>hardware</i> must be installed on your system: The following NVIDIA <i>software</i> must be installed on your system: - * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 8.0. + * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A). Ensure that you append the relevant Cuda pathnames to the @@ -289,11 +289,11 @@ Do you wish to build TensorFlow with CUDA support? [y/N] <b>Y</b> CUDA support will be enabled for TensorFlow Do you want to use clang as CUDA compiler? [y/N] nvcc will be used as CUDA compiler -Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: <b>8.0</b> -Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: +Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: <b>9.0</b> +Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: -Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: <b>6</b> -Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: +Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: <b>7</b> +Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. @@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.5.0rc1 on Linux: +for TensorFlow 1.5.0 on Linux: <pre> -$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0rc1-py2-none-any.whl</b> +$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0-py2-none-any.whl</b> </pre> ## Validate your installation @@ -461,8 +461,8 @@ Stack Overflow and specify the `tensorflow` tag. <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr> -<tr><td>tensorflow_gpu-1.5.0-rc1</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr> +<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr> +<tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow_gpu-1.4.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>6</td><td>8</td></tr> <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr> @@ -478,7 +478,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac** <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr> +<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.2.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr> @@ -491,8 +491,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows** <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.5.0-rc1</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> -<tr><td>tensorflow_gpu-1.5.0-rc1</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> +<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> +<tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow_gpu-1.4.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>6</td><td>8</td></tr> <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 8d0eb7966f..86a111c2ec 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -30,13 +30,13 @@ If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: - * CUDA® Toolkit 8.0. For details, see + * CUDA® Toolkit 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/) Ensure that you append the relevant Cuda pathnames to the `%PATH%` environment variable as described in the NVIDIA documentation. - * The NVIDIA drivers associated with CUDA Toolkit 8.0. - * cuDNN v6.0. For details, see + * The NVIDIA drivers associated with CUDA Toolkit 9.0. + * cuDNN v7.0. For details, see [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md index 2b4896c381..9049a5a9f3 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/programmers_guide/graphs.md @@ -125,14 +125,14 @@ an operation: @{tf.Tensor} accepts an optional `name` argument. For example, `tf.constant(42.0, name="answer")` creates a new @{tf.Operation} named `"answer"` and returns a @{tf.Tensor} named `"answer:0"`. If the default graph - already contained an operation named `"answer"`, the TensorFlow would append + already contains an operation named `"answer"`, then TensorFlow would append `"_1"`, `"_2"`, and so on to the name, in order to make it unique. * The @{tf.name_scope} function makes it possible to add a **name scope** prefix to all operations created in a particular context. The current name scope prefix is a `"/"`-delimited list of the names of all active @{tf.name_scope} context managers. If a name scope has already been used in the current - context, TensorFlow appens `"_1"`, `"_2"`, and so on. For example: + context, TensorFlow appends `"_1"`, `"_2"`, and so on. For example: ```python c_0 = tf.constant(0, name="c") # => operation named "c" diff --git a/tensorflow/examples/android/BUILD b/tensorflow/examples/android/BUILD index 46df5973e8..1214647797 100644 --- a/tensorflow/examples/android/BUILD +++ b/tensorflow/examples/android/BUILD @@ -92,7 +92,7 @@ android_binary( filegroup( name = "external_assets", srcs = [ - "@inception5h//:model_files", + "@inception_v1//:model_files", "@mobile_ssd//:model_files", "@speech_commands//:model_files", "@stylize//:model_files", diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle index f7bdf8b816..0767726aa9 100644 --- a/tensorflow/examples/android/build.gradle +++ b/tensorflow/examples/android/build.gradle @@ -56,10 +56,12 @@ def nativeOutDir = 'libs/' + cpuType def nativeBuildRule = 'buildNativeBazel' def demoLibPath = '../../../bazel-bin/tensorflow/examples/android/libtensorflow_demo.so' def inferenceLibPath = '../../../bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so' + +// Override for Makefile builds. if (nativeBuildSystem == 'makefile') { nativeBuildRule = 'buildNativeMake' - demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_demo.so' - inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_inference.so' + demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_demo.so' + inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_inference.so' } // If building with Bazel, this is the location of the bazel binary. @@ -154,7 +156,8 @@ task buildNativeMake(type: Exec) { '-s', \ 'tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in', \ '-t', \ - 'libtensorflow_inference.so libtensorflow_demo.so' \ + 'libtensorflow_inference.so libtensorflow_demo.so all' \ + , '-a', cpuType \ //, '-T' // Uncomment to skip protobuf and speed up subsequent builds. } diff --git a/tensorflow/examples/android/download-models.gradle b/tensorflow/examples/android/download-models.gradle index 0e2cf65f53..d3b67eab52 100644 --- a/tensorflow/examples/android/download-models.gradle +++ b/tensorflow/examples/android/download-models.gradle @@ -9,7 +9,7 @@ */ // hard coded model files // LINT.IfChange -def models = ['inception5h.zip', +def models = ['inception_v1.zip', 'object_detection/ssd_mobilenet_v1_android_export.zip', 'stylize_v1.zip', 'speech_commands_conv_actions.zip'] diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java index a317273acd..068c7b0d94 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java @@ -81,8 +81,11 @@ public class LegacyCameraConnectionFragment extends Fragment { try { Camera.Parameters parameters = camera.getParameters(); - parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); - + List<String> focusModes = parameters.getSupportedFocusModes(); + if (focusModes != null + && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) { + parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); + } List<Camera.Size> cameraSizes = parameters.getSupportedPreviewSizes(); Size[] sizes = new Size[cameraSizes.size()]; int i = 0; diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java index 2fe2ba539e..af6af2bc8f 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java @@ -199,7 +199,7 @@ public class MultiBoxTracker { final int w, final int h, final int rowStride, - final int sensorOrienation, + final int sensorOrientation, final byte[] frame, final long timestamp) { if (objectTracker == null && !initialized) { @@ -209,7 +209,7 @@ public class MultiBoxTracker { objectTracker = ObjectTracker.getInstance(w, h, rowStride, true); frameWidth = w; frameHeight = h; - this.sensorOrientation = sensorOrienation; + this.sensorOrientation = sensorOrientation; initialized = true; if (objectTracker == null) { diff --git a/tensorflow/examples/udacity/Dockerfile b/tensorflow/examples/udacity/Dockerfile index 3ca58566c1..00eb853e52 100644 --- a/tensorflow/examples/udacity/Dockerfile +++ b/tensorflow/examples/udacity/Dockerfile @@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ apt-get clean && \ rm -rf /var/lib/apt/lists/* -RUN pip install scikit-learn pyreadline Pillow +RUN pip install scikit-learn pyreadline Pillow imageio RUN rm -rf /notebooks/* ADD *.ipynb /notebooks/ WORKDIR /notebooks diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 2b4d5b8e0f..f5cd7885e7 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -76,6 +76,7 @@ py_library( ":layers", ":lib", ":list_ops", + ":manip_ops", ":math_ops", ":metrics", ":nn", @@ -1424,6 +1425,14 @@ tf_gen_op_wrapper_private_py( ) tf_gen_op_wrapper_private_py( + name = "manip_ops_gen", + visibility = [ + "//learning/brain/python/ops:__pkg__", + "//tensorflow/python/kernel_tests:__pkg__", + ], +) + +tf_gen_op_wrapper_private_py( name = "math_ops_gen", visibility = [ "//learning/brain/google/python/ops:__pkg__", @@ -1755,6 +1764,8 @@ py_library( ":linalg_grad", ":linalg_ops", ":logging_ops", + ":manip_grad", + ":manip_ops", ":math_grad", ":math_ops", ":platform", @@ -1878,6 +1889,29 @@ py_library( ) py_library( + name = "manip_grad", + srcs = ["ops/manip_grad.py"], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + ":framework_for_generated_wrappers", + ":manip_ops", + ], +) + +py_library( + name = "manip_ops", + srcs = ["ops/manip_ops.py"], + srcs_version = "PY2AND3", + deps = [ + ":dtypes", + ":framework_ops", + ":manip_ops_gen", + "//third_party/py/numpy", + ], +) + +py_library( name = "logging_ops", srcs = ["ops/logging_ops.py"], srcs_version = "PY2AND3", @@ -2339,6 +2373,8 @@ py_library( ":linalg_ops", ":logging_ops", ":lookup_ops", + ":manip_grad", + ":manip_ops", ":math_grad", ":math_ops", ":numerics", diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index bc9ddec2a5..ea7604d30f 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -84,6 +84,7 @@ from tensorflow.python.feature_column import feature_column_lib as feature_colum from tensorflow.python.layers import layers from tensorflow.python.ops import bitwise_ops as bitwise from tensorflow.python.ops import image_ops as image +from tensorflow.python.ops import manip_ops as manip from tensorflow.python.ops import metrics from tensorflow.python.ops import nn from tensorflow.python.ops import sets @@ -241,6 +242,7 @@ _allowed_symbols.extend([ 'linalg', 'logging', 'losses', + 'manip', 'metrics', 'newaxis', 'nn', diff --git a/tensorflow/python/client/session_benchmark.py b/tensorflow/python/client/session_benchmark.py index 721bca91b7..da74855193 100644 --- a/tensorflow/python/client/session_benchmark.py +++ b/tensorflow/python/client/session_benchmark.py @@ -22,6 +22,7 @@ import time import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 5d318531d5..c4b7e4919b 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -203,7 +203,7 @@ class Dataset(object): tensors: A nested structure of tensors. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return TensorDataset(tensors) @@ -216,7 +216,7 @@ class Dataset(object): 0th dimension. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return TensorSliceDataset(tensors) @@ -229,7 +229,7 @@ class Dataset(object): sparse_tensor: A `tf.SparseTensor`. Returns: - A `Dataset` of rank-(N-1) sparse tensors. + Dataset: A `Dataset` of rank-(N-1) sparse tensors. """ return SparseTensorSliceDataset(sparse_tensor) @@ -315,7 +315,7 @@ class Dataset(object): `generator`. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ if not callable(generator): raise TypeError("`generator` must be callable.") @@ -458,7 +458,7 @@ class Dataset(object): len(args) == 3 -> start = args[0], stop = args[1, stop = args[2] Returns: - A `RangeDataset`. + Dataset: A `RangeDataset`. Raises: ValueError: if len(args) == 0. @@ -502,7 +502,7 @@ class Dataset(object): datasets: A nested structure of datasets. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return ZipDataset(datasets) @@ -528,7 +528,7 @@ class Dataset(object): dataset: `Dataset` to be concatenated. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return ConcatenateDataset(self, dataset) @@ -540,7 +540,7 @@ class Dataset(object): maximum number elements that will be buffered when prefetching. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return PrefetchDataset(self, buffer_size) @@ -558,12 +558,14 @@ class Dataset(object): - /path/to/dir/b.py - /path/to/dir/c.py + NOTE: The order of the file names returned can be non-deterministic. + Args: file_pattern: A string or scalar string `tf.Tensor`, representing the filename pattern that will be matched. Returns: - A `Dataset` of strings corresponding to file names. + Dataset: A `Dataset` of strings corresponding to file names. """ return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern)) @@ -580,7 +582,7 @@ class Dataset(object): indefinitely. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return RepeatDataset(self, count) @@ -604,7 +606,7 @@ class Dataset(object): iterated over. (Defaults to `True`.) Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return ShuffleDataset(self, buffer_size, seed, reshuffle_each_iteration) @@ -617,7 +619,7 @@ class Dataset(object): If a filename is not provided, the dataset will be cached in memory. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return CacheDataset(self, filename) @@ -631,7 +633,7 @@ class Dataset(object): dataset, the new dataset will contain all elements of this dataset. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return TakeDataset(self, count) @@ -646,7 +648,7 @@ class Dataset(object): is -1, skips the entire dataset. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return SkipDataset(self, count) @@ -693,7 +695,7 @@ class Dataset(object): index: A `tf.int64` scalar `tf.Tensor`, representing the worker index. Returns: - A `Dataset`. + Dataset: A `Dataset`. Raises: ValueError: if `num_shards` or `index` are illegal values. Note: error @@ -737,7 +739,7 @@ class Dataset(object): consecutive elements of this dataset to combine in a single batch. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return BatchDataset(self, batch_size) @@ -766,7 +768,7 @@ class Dataset(object): the empty string for string types. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values) @@ -782,7 +784,7 @@ class Dataset(object): specified, elements will be processed sequentially. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ if num_parallel_calls is None: return MapDataset(self, map_func) @@ -798,7 +800,7 @@ class Dataset(object): `Dataset`. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return FlatMapDataset(self, map_func) @@ -867,7 +869,7 @@ class Dataset(object): input element before cycling to another input element. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return InterleaveDataset(self, map_func, cycle_length, block_length) @@ -880,7 +882,7 @@ class Dataset(object): scalar `tf.bool` tensor. Returns: - A `Dataset`. + Dataset: A `Dataset`. """ return FilterDataset(self, predicate) @@ -901,10 +903,11 @@ class Dataset(object): Args: transformation_func: A function that takes one `Dataset` argument and - returns a `Dataset`. + returns a `Dataset`. Returns: - The `Dataset` returned by applying `transformation_func` to this dataset. + Dataset: The `Dataset` returned by applying `transformation_func` to this + dataset. """ dataset = transformation_func(self) if not isinstance(dataset, Dataset): diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py index e387e35740..e90ce3fb40 100644 --- a/tensorflow/python/data/util/nest.py +++ b/tensorflow/python/data/util/nest.py @@ -266,7 +266,7 @@ def map_structure(func, *structure, **check_types_dict): and the return value will contain the results in the same structure. Args: - func: A callable that acceps as many arguments are there are structures. + func: A callable that accepts as many arguments are there are structures. *structure: scalar, or tuple or list of constructed scalars and/or other tuples/lists, or scalars. Note: numpy arrays are considered scalars. **check_types_dict: only valid keyword argument is `check_types`. If set to @@ -479,8 +479,8 @@ def map_structure_up_to(shallow_tree, func, *inputs): The `inputs`, can be thought of as having the same structure as `shallow_tree`, but with leaf nodes that are themselves tree structures. - This function therefore will return something with the same base structure as - `shallow_tree`. + This function, therefore, will return something with the same base structure + as `shallow_tree`. Examples: diff --git a/tensorflow/python/data/util/sparse.py b/tensorflow/python/data/util/sparse.py index 5ebcb4ea81..5e6d224709 100644 --- a/tensorflow/python/data/util/sparse.py +++ b/tensorflow/python/data/util/sparse.py @@ -141,7 +141,7 @@ def serialize_sparse_tensors(tensors): tensors: a tensor structure to serialize. Returns: - `tensors` with any sparse tensors replaced by the their serialized version. + `tensors` with any sparse tensors replaced by their serialized version. """ ret = nest.pack_sequence_as(tensors, [ diff --git a/tensorflow/python/debug/cli/tensor_format.py b/tensorflow/python/debug/cli/tensor_format.py index d4aea76d65..e0759a8bc1 100644 --- a/tensorflow/python/debug/cli/tensor_format.py +++ b/tensorflow/python/debug/cli/tensor_format.py @@ -535,7 +535,7 @@ def numeric_summary(tensor): if not isinstance(tensor, np.ndarray) or not np.size(tensor): return debugger_cli_common.RichTextLines([ "No numeric summary available due to empty tensor."]) - elif (np.issubdtype(tensor.dtype, np.float) or + elif (np.issubdtype(tensor.dtype, np.floating) or np.issubdtype(tensor.dtype, np.complex) or np.issubdtype(tensor.dtype, np.integer)): counts = [ diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py index c4b13a1045..8d355aa27f 100644 --- a/tensorflow/python/debug/lib/debug_data.py +++ b/tensorflow/python/debug/lib/debug_data.py @@ -222,7 +222,7 @@ def has_inf_or_nan(datum, tensor): # Also return False for data types that cannot be represented as numpy # arrays. return False - elif (np.issubdtype(tensor.dtype, np.float) or + elif (np.issubdtype(tensor.dtype, np.floating) or np.issubdtype(tensor.dtype, np.complex) or np.issubdtype(tensor.dtype, np.integer)): return np.any(np.isnan(tensor)) or np.any(np.isinf(tensor)) diff --git a/tensorflow/python/eager/execution_callbacks.py b/tensorflow/python/eager/execution_callbacks.py index 2f1654dda4..988442c971 100644 --- a/tensorflow/python/eager/execution_callbacks.py +++ b/tensorflow/python/eager/execution_callbacks.py @@ -153,7 +153,7 @@ def inf_nan_callback(op_type, continue numpy_dtype = output.dtype.as_numpy_dtype - if (np.issubdtype(numpy_dtype, np.float) or + if (np.issubdtype(numpy_dtype, np.floating) or np.issubdtype(numpy_dtype, np.complex) or np.issubdtype(numpy_dtype, np.integer)): try: diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py index 2bdec69303..706575985f 100644 --- a/tensorflow/python/estimator/canned/dnn_testing_utils.py +++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py @@ -877,7 +877,7 @@ class BaseDNNWarmStartingTest(object): # Create a second DNNClassifier, warm-started from the first. Use a # learning_rate = 0.0 optimizer to check values (use SGD so we don't have - # accumulator values that change). Use a a new FeatureColumn with a + # accumulator values that change). Use a new FeatureColumn with a # different vocabulary for occupation. new_vocab_list = ['doctor', 'consultant', 'engineer'] new_vocab_file = os.path.join(self._ckpt_and_vocab_dir, diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py index cccb9af4b2..3e9183cf1b 100644 --- a/tensorflow/python/estimator/canned/linear_testing_utils.py +++ b/tensorflow/python/estimator/canned/linear_testing_utils.py @@ -2003,7 +2003,7 @@ class BaseLinearWarmStartingTest(object): # Create a second LinearClassifier, warm-started from the first. Use a # learning_rate = 0.0 optimizer to check values (use SGD so we don't have - # accumulator values that change). Use a a new FeatureColumn with a + # accumulator values that change). Use a new FeatureColumn with a # different vocabulary for occupation. new_vocab_list = ['doctor', 'consultant', 'engineer'] new_vocab_file = os.path.join(self._ckpt_and_vocab_dir, diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 6da890cd22..17fab3df4d 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -55,6 +55,7 @@ from tensorflow.python.training import saver from tensorflow.python.training import training from tensorflow.python.training import training_util from tensorflow.python.util import compat +from tensorflow.python.util import compat_internal from tensorflow.python.util import nest @@ -179,7 +180,7 @@ class Estimator(object): self._config = config # Model directory. - model_dir = compat.path_to_str(model_dir) + model_dir = compat_internal.path_to_str(model_dir) if (model_dir is not None) and (self._config.model_dir is not None): if model_dir != self._config.model_dir: # TODO(alanyee): remove this suppression after it is no longer needed diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py index 61a537022b..0c636a8da1 100644 --- a/tensorflow/python/estimator/run_config.py +++ b/tensorflow/python/estimator/run_config.py @@ -27,7 +27,7 @@ import six from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib -from tensorflow.python.util import compat +from tensorflow.python.util import compat_internal _USE_DEFAULT = object() @@ -444,7 +444,8 @@ class RunConfig(object): if tf_config: logging.info('TF_CONFIG environment variable: %s', tf_config) - model_dir = _get_model_dir(tf_config, compat.path_to_str(model_dir)) + model_dir = _get_model_dir(tf_config, + compat_internal.path_to_str(model_dir)) RunConfig._replace( self, diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional.py b/tensorflow/python/keras/_impl/keras/layers/convolutional.py index b2ad4c4b65..2ee0732775 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional.py @@ -563,7 +563,7 @@ class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer): return dict(list(base_config.items()) + list(config.items())) -class Conv3DTranspose(tf_convolutional_layers.Conv3D, Layer): +class Conv3DTranspose(tf_convolutional_layers.Conv3DTranspose, Layer): """Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index c87b7652ad..3a6058054b 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -1602,6 +1602,19 @@ cuda_py_test( ) cuda_py_test( + name = "manip_ops_test", + size = "small", + srcs = ["manip_ops_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:manip_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + ], + tags = ["no_windows_gpu"], +) + +cuda_py_test( name = "matmul_op_test", size = "small", srcs = ["matmul_op_test.py"], diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index aae6d0a36e..7ec4624310 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1162,6 +1162,27 @@ class InvertPermutationTest(test_util.TensorFlowTestCase): self.assertAllEqual(y.eval(), [2, 4, 3, 0, 1]) +class UnravelIndexTest(test_util.TensorFlowTestCase): + + def testUnravelIndex(self): + with self.test_session(): + for dtype in [dtypes.int32, dtypes.int64]: + indices_1 = constant_op.constant(1621, dtype=dtype) + dims_1 = constant_op.constant([6, 7, 8, 9], dtype=dtype) + out_1 = array_ops.unravel_index(indices_1, dims_1) + self.assertAllEqual(out_1.eval(), [3, 1, 4, 1]) + + indices_2 = constant_op.constant([1621], dtype=dtype) + dims_2 = constant_op.constant([6, 7, 8, 9], dtype=dtype) + out_2 = array_ops.unravel_index(indices_2, dims_2) + self.assertAllEqual(out_2.eval(), [[3], [1], [4], [1]]) + + indices_3 = constant_op.constant([22, 41, 37], dtype=dtype) + dims_3 = constant_op.constant([7, 6], dtype=dtype) + out_3 = array_ops.unravel_index(indices_3, dims_3) + self.assertAllEqual(out_3.eval(), [[3, 6, 6], [4, 5, 1]]) + + class GuaranteeConstOpTest(test_util.TensorFlowTestCase): def testSimple(self): diff --git a/tensorflow/python/kernel_tests/constant_op_test.py b/tensorflow/python/kernel_tests/constant_op_test.py index 030c690167..16e56349c4 100644 --- a/tensorflow/python/kernel_tests/constant_op_test.py +++ b/tensorflow/python/kernel_tests/constant_op_test.py @@ -454,18 +454,19 @@ class ZerosLikeTest(test.TestCase): def testZerosLikeCPU(self): for dtype in [ - dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int8, - dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.uint16, dtypes_lib.int32, - dtypes_lib.int64, dtypes_lib.bool, dtypes_lib.complex64, - dtypes_lib.complex128, dtypes_lib.string + dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64, + dtypes_lib.int8, dtypes_lib.uint8, dtypes_lib.int16, dtypes_lib.uint16, + dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.bool, + dtypes_lib.complex64, dtypes_lib.complex128, dtypes_lib.string ]: self._compareZeros(dtype, fully_defined_shape=False, use_gpu=False) self._compareZeros(dtype, fully_defined_shape=True, use_gpu=False) def testZerosLikeGPU(self): for dtype in [ - dtypes_lib.float32, dtypes_lib.float64, dtypes_lib.int32, - dtypes_lib.bool, dtypes_lib.int64, dtypes_lib.string + dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64, + dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.complex64, + dtypes_lib.complex128, dtypes_lib.bool ]: self._compareZeros(dtype, fully_defined_shape=False, use_gpu=True) self._compareZeros(dtype, fully_defined_shape=True, use_gpu=True) diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 3e9bd3dade..edfb20d6a2 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -24,6 +24,7 @@ import time import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import layers from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op @@ -519,7 +520,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 2], padding="VALID") - # TODO this currently fails. + # TODO(yzhwang): this currently fails. # self._VerifyValues(tensor_in_sizes=[1, 8, 8, 1], # filter_in_sizes=[2, 2, 1, 1], # strides=[4, 4], padding="SAME", diff --git a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py index ead55cd03b..89fd26c544 100644 --- a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py +++ b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import os import time +from six.moves import xrange from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops diff --git a/tensorflow/python/kernel_tests/io_ops_test.py b/tensorflow/python/kernel_tests/io_ops_test.py index f91875c6f0..61944f7e31 100644 --- a/tensorflow/python/kernel_tests/io_ops_test.py +++ b/tensorflow/python/kernel_tests/io_ops_test.py @@ -1,4 +1,4 @@ -# -*- coding: utf-8 -*- +# -*- coding: utf-8 -*- # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index 00c6706593..197dbf44af 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -953,14 +953,14 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): # Compute the expected loss 'manually'. total = np.zeros((batch_size,)) for b in range(batch_size): - for i in range(dims): - for j in range(dims): + for i in range(dims - 1): + for j in range(i + 1, dims): x = self._predictions[b, i].item() - self._predictions[b, j].item() y = self._labels[b, i].item() - self._labels[b, j].item() diff = (x - y) total[b] += (diff * diff) - self._expected_losses = np.divide(total, 9.0) + self._expected_losses = np.divide(total, 3.0) def testValueErrorThrownWhenWeightIsNone(self): with self.test_session(): @@ -1059,8 +1059,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): [[4, 8, 12], [1, 2, 3], [4, 5, 6]], [[8, 1, 3], [7, 8, 9], [10, 11, 12]], ]) - self._test_valid_weights( - labels, predictions, expected_loss=122.22222) + self._test_valid_weights(labels, predictions, expected_loss=137.5) def test3dWeightedScalar(self): labels = np.array([ @@ -1073,8 +1072,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) weight = 3.0 self._test_valid_weights( - labels, predictions, expected_loss=weight * 122.22222, - weights=weight) + labels, predictions, expected_loss=weight * 137.5, weights=weight) def _test_invalid_weights( self, labels, predictions, weights=1.0): @@ -1124,7 +1122,9 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) self._test_valid_weights( # TODO(ptucker): This doesn't look right. - labels, predictions, expected_loss=9 * 122.22222, + labels, + predictions, + expected_loss=9 * 137.5, weights=np.ones((2, 3, 3))) def testLossWithAllZeroBatchSpecificWeights(self): diff --git a/tensorflow/python/kernel_tests/manip_ops_test.py b/tensorflow/python/kernel_tests/manip_ops_test.py new file mode 100644 index 0000000000..b8200ac0cb --- /dev/null +++ b/tensorflow/python/kernel_tests/manip_ops_test.py @@ -0,0 +1,138 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for manip_ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import test_util +from tensorflow.python.ops import gradient_checker +from tensorflow.python.ops import manip_ops +from tensorflow.python.platform import test as test_lib + +# pylint: disable=g-import-not-at-top +try: + from distutils.version import StrictVersion as Version + # numpy.roll for multiple shifts was introduced in numpy version 1.12.0 + NP_ROLL_CAN_MULTISHIFT = Version(np.version.version) >= Version("1.12.0") +except ImportError: + NP_ROLL_CAN_MULTISHIFT = False +# pylint: enable=g-import-not-at-top + + +class RollTest(test_util.TensorFlowTestCase): + + def _testRoll(self, np_input, shift, axis): + expected_roll = np.roll(np_input, shift, axis) + with self.test_session(): + roll = manip_ops.roll(np_input, shift, axis) + self.assertAllEqual(roll.eval(), expected_roll) + + def _testGradient(self, np_input, shift, axis): + with self.test_session(): + inx = constant_op.constant(np_input.tolist()) + xs = list(np_input.shape) + y = manip_ops.roll(inx, shift, axis) + # Expected y's shape to be the same + ys = xs + jacob_t, jacob_n = gradient_checker.compute_gradient( + inx, xs, y, ys, x_init_value=np_input) + self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5) + + def _testAll(self, np_input, shift, axis): + self._testRoll(np_input, shift, axis) + if np_input.dtype == np.float32: + self._testGradient(np_input, shift, axis) + + def testIntTypes(self): + for t in [np.int32, np.int64]: + self._testAll(np.random.randint(-100, 100, (5)).astype(t), 3, 0) + if NP_ROLL_CAN_MULTISHIFT: + self._testAll( + np.random.randint(-100, 100, (4, 4, 3)).astype(t), [1, -2, 3], + [0, 1, 2]) + self._testAll( + np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t), [0, 1, -2], + [1, 2, 3]) + + def testFloatTypes(self): + for t in [np.float32, np.float64]: + self._testAll(np.random.rand(5).astype(t), 2, 0) + if NP_ROLL_CAN_MULTISHIFT: + self._testAll(np.random.rand(3, 4).astype(t), [1, 2], [1, 0]) + self._testAll(np.random.rand(1, 3, 4).astype(t), [1, 0, -3], [0, 1, 2]) + + def testComplexTypes(self): + for t in [np.complex64, np.complex128]: + x = np.random.rand(4, 4).astype(t) + self._testAll(x + 1j * x, 2, 0) + if NP_ROLL_CAN_MULTISHIFT: + x = np.random.rand(2, 5).astype(t) + self._testAll(x + 1j * x, [1, 2], [1, 0]) + x = np.random.rand(3, 2, 1, 1).astype(t) + self._testAll(x + 1j * x, [2, 1, 1, 0], [0, 3, 1, 2]) + + def testRollInputMustVectorHigherRaises(self): + tensor = 7 + shift = 1 + axis = 0 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "input must be 1-D or higher"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollAxisMustBeScalarOrVectorRaises(self): + tensor = [[1, 2], [3, 4]] + shift = 1 + axis = [[0, 1]] + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "axis must be a scalar or a 1-D vector"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollShiftMustBeScalarOrVectorRaises(self): + tensor = [[1, 2], [3, 4]] + shift = [[0, 1]] + axis = 1 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "shift must be a scalar or a 1-D vector"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollShiftAndAxisMustBeSameSizeRaises(self): + tensor = [[1, 2], [3, 4]] + shift = [1] + axis = [0, 1] + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "shift and axis must have the same size"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollAxisOutOfRangeRaises(self): + tensor = [1, 2] + shift = 1 + axis = 1 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "is out of range"): + manip_ops.roll(tensor, shift, axis).eval() + + +if __name__ == "__main__": + test_lib.main() diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index 0c77d1db92..daa42938e6 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -23,6 +23,7 @@ import timeit import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import rnn as contrib_rnn from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session diff --git a/tensorflow/python/kernel_tests/tensordot_op_test.py b/tensorflow/python/kernel_tests/tensordot_op_test.py index f1670a47f5..8ad29afd0a 100644 --- a/tensorflow/python/kernel_tests/tensordot_op_test.py +++ b/tensorflow/python/kernel_tests/tensordot_op_test.py @@ -66,7 +66,7 @@ class TensordotTest(test_lib.TestCase): a = [[1, 2], [3, 4]] b = [[1, 2], [3, 4]] # Invalid static axes. - for axes_value in -1, 0, [1], [[1]], [[1], [0, 1]]: + for axes_value in -1, 3, [1], [[1]], [[1], [0, 1]]: with self.assertRaises(ValueError): math_ops.tensordot(a, b, axes_value) @@ -91,7 +91,7 @@ class TensordotTest(test_lib.TestCase): # Test case for 11950 def test_valid_axis(self): - for axes_value in [1, 2], [[1], [2]]: + for axes_value in [1, 2], [[1], [2]], [[], []], 0: with self.test_session() as sess: np_a = np.ones((3, 3)) np_b = np.array([2, 3, 1])[None, None] @@ -105,29 +105,29 @@ class TensordotTest(test_lib.TestCase): self.assertAllEqual(tf_ans, np_ans) def test_partial_shape_inference(self): - a = array_ops.placeholder(dtypes.float32) - b = array_ops.placeholder(dtypes.float32) - axes = ([1], [0]) - output = math_ops.tensordot(a, b, axes) - self.assertEqual(output.get_shape().ndims, None) - a.set_shape([None, 2]) - b.set_shape([2, 3]) - output = math_ops.tensordot(a, b, axes) - output_shape = output.get_shape() - self.assertEqual(output_shape.ndims, 2) - output_shape = output_shape.as_list() - self.assertEqual(output_shape[0], None) - self.assertEqual(output_shape[1], 3) - a = array_ops.placeholder(dtypes.float32) - b = array_ops.placeholder(dtypes.float32) - a.set_shape([2, 2]) - b.set_shape([2, None]) - output = math_ops.tensordot(a, b, axes) - output_shape = output.get_shape() - self.assertEqual(output_shape.ndims, 2) - output_shape = output_shape.as_list() - self.assertEqual(output_shape[0], 2) - self.assertEqual(output_shape[1], None) + for axes in ([1], [0]), 1: + a = array_ops.placeholder(dtypes.float32) + b = array_ops.placeholder(dtypes.float32) + output = math_ops.tensordot(a, b, axes) + self.assertEqual(output.get_shape().ndims, None) + a.set_shape([None, 2]) + b.set_shape([2, 3]) + output = math_ops.tensordot(a, b, axes) + output_shape = output.get_shape() + self.assertEqual(output_shape.ndims, 2) + output_shape = output_shape.as_list() + self.assertEqual(output_shape[0], None) + self.assertEqual(output_shape[1], 3) + a = array_ops.placeholder(dtypes.float32) + b = array_ops.placeholder(dtypes.float32) + a.set_shape([2, 2]) + b.set_shape([2, None]) + output = math_ops.tensordot(a, b, axes) + output_shape = output.get_shape() + self.assertEqual(output_shape.ndims, 2) + output_shape = output_shape.as_list() + self.assertEqual(output_shape[0], 2) + self.assertEqual(output_shape[1], None) def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_): @@ -196,8 +196,8 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_): low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype_) b_np = np.random.uniform( low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype_) - all_axes = [1] - if a_np.ndim > 1: + all_axes = [0, 1] + if a_np.ndim > 2: all_axes.append(a_np.ndim - 1) for axes in all_axes: np_ans = np.tensordot(a_np, b_np, axes=axes) diff --git a/tensorflow/python/kernel_tests/topk_op_test.py b/tensorflow/python/kernel_tests/topk_op_test.py index efb5b9f364..6ab931fdb9 100644 --- a/tensorflow/python/kernel_tests/topk_op_test.py +++ b/tensorflow/python/kernel_tests/topk_op_test.py @@ -58,7 +58,7 @@ class TopKTest(test.TestCase): # Do some special casing of equality of indices: if indices # are not the same, but values are floating type, ensure that # the values are within epsilon of each other. - if not np.issubdtype(np_expected_values.dtype, np.float): + if not np.issubdtype(np_expected_values.dtype, np.floating): # Values are not floating point type; check indices exactly self.assertAllEqual(np_expected_indices, indices) else: diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index 79c421f4c9..e8dba3cea3 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -1094,7 +1094,7 @@ class SeparableConv1D(_SeparableConv): strides = (1, 1, 1) + self.strides spatial_start_dim = 2 - # Explictly broadcast inputs and kernels to 4D. + # Explicitly broadcast inputs and kernels to 4D. # TODO(fchollet): refactor when a native separable_conv1d op is available. inputs = array_ops.expand_dims(inputs, spatial_start_dim) depthwise_kernel = array_ops.expand_dims(self.depthwise_kernel, 0) @@ -1904,6 +1904,7 @@ class Conv3DTranspose(Conv3D): dtype=self.dtype) else: self.bias = None + self.built = True def call(self, inputs): inputs_shape = array_ops.shape(inputs) @@ -1974,6 +1975,8 @@ class Conv3DTranspose(Conv3D): if self.use_bias: outputs_shape = outputs.shape.as_list() + if outputs_shape[0] is None: + outputs_shape[0] = -1 if self.data_format == 'channels_first': outputs_4d = array_ops.reshape(outputs, [ outputs_shape[0], outputs_shape[1], @@ -2007,11 +2010,11 @@ class Conv3DTranspose(Conv3D): output_shape[c_axis] = self.filters output_shape[d_axis] = utils.deconv_output_length( - output_shape[d_axis], stride_d, kernel_d, self.padding) + output_shape[d_axis], kernel_d, self.padding, stride_d) output_shape[h_axis] = utils.deconv_output_length( - output_shape[h_axis], stride_h, kernel_h, self.padding) + output_shape[h_axis], kernel_h, self.padding, stride_h) output_shape[w_axis] = utils.deconv_output_length( - output_shape[w_axis], stride_w, kernel_w, self.padding) + output_shape[w_axis], kernel_w, self.padding, stride_w) return tensor_shape.TensorShape(output_shape) diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py index e8be347799..7407d9a7b3 100644 --- a/tensorflow/python/layers/utils.py +++ b/tensorflow/python/layers/utils.py @@ -81,7 +81,7 @@ def normalize_tuple(value, n, name): for single_value in value_tuple: try: int(single_value) - except ValueError: + except (ValueError, TypeError): raise ValueError('The `' + name + '` argument must be a tuple of ' + str(n) + ' integers. Received: ' + str(value) + ' ' 'including element ' + str(single_value) + ' of type' + diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index e3902f5a8a..ad409ad7e5 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -35,6 +35,7 @@ See the @{$python/array_ops} guide. @@reshape @@squeeze @@expand_dims +@@unravel_index @@meshgrid @@slice @@strided_slice @@ -1589,9 +1590,9 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): Args: tensor: A `Tensor`. - dtype: A type for the returned `Tensor`. Must be `float32`, `float64`, - `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`, - `complex64`, `complex128` or `bool`. + dtype: A type for the returned `Tensor`. Must be `float16`, `float32`, + `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`, + `complex64`, `complex128`, `bool` or `string`. name: A name for the operation (optional). optimize: if true, attempt to statically determine the shape of 'tensor' and encode it as a constant. diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index 7dbccf1caf..ac03d30fcd 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -458,7 +458,7 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, For example, if `elems` is `(t1, [t2, t3])` and `initializer` is `[i1, i2]` then an appropriate signature for `fn` in `python2` is: - `fn = lambda (acc_p1, acc_p2), (t1 [t2, t3]):` and `fn` must return a list, + `fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list, `[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the one that works in `python3`, is: `fn = lambda a, t:`, where `a` and `t` correspond to the input tuples. diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 28b26a09a5..9f06c0ee1f 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -44,6 +44,7 @@ from tensorflow.python.ops import image_grad # pylint: disable=unused-import from tensorflow.python.ops import linalg_grad # pylint: disable=unused-import from tensorflow.python.ops import linalg_ops # pylint: disable=unused-import from tensorflow.python.ops import logging_ops # pylint: disable=unused-import +from tensorflow.python.ops import manip_grad # pylint: disable=unused-import from tensorflow.python.ops import math_grad # pylint: disable=unused-import from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index 3b0b5a978c..de12c5f63f 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -49,6 +49,10 @@ See the @{$python/image} guide. @@grayscale_to_rgb @@hsv_to_rgb @@rgb_to_hsv +@@rgb_to_yiq +@@yiq_to_rgb +@@rgb_to_yuv +@@yuv_to_rgb @@convert_image_dtype @@adjust_brightness @@random_brightness diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 2c231ef56c..14a38f25d1 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1508,7 +1508,7 @@ def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, - min_object_covered=None, + min_object_covered=0.1, aspect_ratio_range=None, area_range=None, max_attempts=None, @@ -1669,3 +1669,107 @@ def non_max_suppression(boxes, return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size, iou_threshold) # pylint: enable=protected-access + + +_rgb_to_yiq_kernel = [[0.299, 0.59590059, + 0.2115], [0.587, -0.27455667, -0.52273617], + [0.114, -0.32134392, 0.31119955]] + + +def rgb_to_yiq(images): + """Converts one or more images from RGB to YIQ. + + Outputs a tensor of the same shape as the `images` tensor, containing the YIQ + value of the pixels. + The output is only well defined if the value in images are in [0,1]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor( + _rgb_to_yiq_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) + + +_yiq_to_rgb_kernel = [[1, 1, 1], [0.95598634, -0.27201283, -1.10674021], + [0.6208248, -0.64720424, 1.70423049]] + + +def yiq_to_rgb(images): + """Converts one or more images from YIQ to RGB. + + Outputs a tensor of the same shape as the `images` tensor, containing the RGB + value of the pixels. + The output is only well defined if the Y value in images are in [0,1], + I value are in [-0.5957,0.5957] and Q value are in [-0.5226,0.5226]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor( + _yiq_to_rgb_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) + + +_rgb_to_yuv_kernel = [[0.299, -0.14714119, + 0.61497538], [0.587, -0.28886916, -0.51496512], + [0.114, 0.43601035, -0.10001026]] + + +def rgb_to_yuv(images): + """Converts one or more images from RGB to YUV. + + Outputs a tensor of the same shape as the `images` tensor, containing the YUV + value of the pixels. + The output is only well defined if the value in images are in [0,1]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor( + _rgb_to_yuv_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) + + +_yuv_to_rgb_kernel = [[1, 1, 1], [0, -0.394642334, 2.03206185], + [1.13988303, -0.58062185, 0]] + + +def yuv_to_rgb(images): + """Converts one or more images from YUV to RGB. + + Outputs a tensor of the same shape as the `images` tensor, containing the RGB + value of the pixels. + The output is only well defined if the Y value in images are in [0,1], + U and V value are in [-0.5,0.5]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor( + _yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 47dd8231c0..b12bd3d5b0 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -85,6 +85,64 @@ class RGBToHSVTest(test_util.TensorFlowTestCase): self.assertAllClose(rgb_tf, rgb_np) +class RGBToYIQTest(test_util.TensorFlowTestCase): + + def testBatch(self): + # Build an arbitrary RGB image + np.random.seed(7) + batch_size = 5 + shape = (batch_size, 2, 7, 3) + + for nptype in [np.float32, np.float64]: + inp = np.random.rand(*shape).astype(nptype) + + # Convert to YIQ and back, as a batch and individually + with self.test_session(use_gpu=True) as sess: + batch0 = constant_op.constant(inp) + batch1 = image_ops.rgb_to_yiq(batch0) + batch2 = image_ops.yiq_to_rgb(batch1) + split0 = array_ops.unstack(batch0) + split1 = list(map(image_ops.rgb_to_yiq, split0)) + split2 = list(map(image_ops.yiq_to_rgb, split1)) + join1 = array_ops.stack(split1) + join2 = array_ops.stack(split2) + batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2]) + + # Verify that processing batch elements together is the same as separate + self.assertAllClose(batch1, join1, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, join2, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, inp, rtol=1e-4, atol=1e-4) + + +class RGBToYUVTest(test_util.TensorFlowTestCase): + + def testBatch(self): + # Build an arbitrary RGB image + np.random.seed(7) + batch_size = 5 + shape = (batch_size, 2, 7, 3) + + for nptype in [np.float32, np.float64]: + inp = np.random.rand(*shape).astype(nptype) + + # Convert to YUV and back, as a batch and individually + with self.test_session(use_gpu=True) as sess: + batch0 = constant_op.constant(inp) + batch1 = image_ops.rgb_to_yuv(batch0) + batch2 = image_ops.yuv_to_rgb(batch1) + split0 = array_ops.unstack(batch0) + split1 = list(map(image_ops.rgb_to_yuv, split0)) + split2 = list(map(image_ops.yuv_to_rgb, split1)) + join1 = array_ops.stack(split1) + join2 = array_ops.stack(split2) + batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2]) + + # Verify that processing batch elements together is the same as separate + self.assertAllClose(batch1, join1, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, join2, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, inp, rtol=1e-4, atol=1e-4) + + class GrayscaleToRGBTest(test_util.TensorFlowTestCase): def _RGBToGrayscale(self, images): @@ -1839,6 +1897,26 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): self.assertAllEqual([3], end.get_shape().as_list()) self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list()) + def testDefaultMinObjectCovered(self): + # By default min_object_covered=0.1 if not provided + with self.test_session(use_gpu=True): + image_size = constant_op.constant( + [40, 50, 1], shape=[3], dtype=dtypes.int32) + bounding_box = constant_op.constant( + [0.0, 0.0, 1.0, 1.0], + shape=[4], + dtype=dtypes.float32, + ) + begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box( + image_size=image_size, + bounding_boxes=bounding_box, + aspect_ratio_range=(0.75, 1.33), + area_range=(0.05, 1.0)) + + self.assertAllEqual([3], begin.get_shape().as_list()) + self.assertAllEqual([3], end.get_shape().as_list()) + self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list()) + class ResizeImagesTest(test_util.TensorFlowTestCase): @@ -3092,6 +3170,40 @@ class NonMaxSuppressionTest(test_util.TensorFlowTestCase): boxes, scores, max_output_size, iou_threshold).eval() self.assertAllClose(selected_indices, [3, 0, 5]) + def testInvalidShape(self): + # The boxes should be 2D of shape [num_boxes, 4]. + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 2 but is rank 1"): + boxes = constant_op.constant([0.0, 0.0, 1.0, 1.0]) + scores = constant_op.constant([0.9]) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) + + with self.assertRaisesRegexp(ValueError, "Dimension must be 4 but is 3"): + boxes = constant_op.constant([[0.0, 0.0, 1.0]]) + scores = constant_op.constant([0.9]) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) + + # The scores should be 1D of shape [num_boxes]. + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 1 but is rank 2"): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([[0.9]]) + image_ops.non_max_suppression(boxes, scores, 3, 0.5) + + # The max_output_size should be a scaler (0-D). + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 0 but is rank 1"): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([0.9]) + image_ops.non_max_suppression(boxes, scores, [3], 0.5) + + # The iou_threshold should be a scaler (0-D). + with self.assertRaisesRegexp(ValueError, + "Shape must be rank 0 but is rank 2"): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([0.9]) + image_ops.non_max_suppression(boxes, scores, 3, [[0.5]]) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/linalg_grad.py b/tensorflow/python/ops/linalg_grad.py index 13a32c83d9..3cbbf3412a 100644 --- a/tensorflow/python/ops/linalg_grad.py +++ b/tensorflow/python/ops/linalg_grad.py @@ -277,20 +277,28 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): # https://j-towns.github.io/papers/svd-derivative.pdf a = op.inputs[0] a_shape = a.get_shape().with_rank_at_least(2) + grad_s_mat = array_ops.matrix_diag(grad_s) - if op.get_attr("compute_uv"): - # TODO(rmlarsen): Make this work with complex types. - if a.dtype.is_complex: - raise NotImplementedError( - "SVD gradient is not implemented for complex types and " - "compute_uv=True.") - grad_u_shape = grad_u.get_shape().with_rank_at_least(2) - grad_v_shape = grad_v.get_shape().with_rank_at_least(2) - m = a_shape[-2].merge_with(grad_u_shape[-2]) - n = a_shape[-1].merge_with(grad_v_shape[-2]) - batch_shape = a_shape[:-2].merge_with(grad_u_shape[:-2]).merge_with( - grad_v_shape[:-2]) - a_shape = batch_shape.concatenate([m, n]) + if not op.get_attr("compute_uv"): + s, u, v = linalg_ops.svd(a, compute_uv=True) + grad_a = math_ops.matmul(u, math_ops.matmul(grad_s_mat, v, adjoint_b=True)) + grad_a.set_shape(a_shape) + return grad_a + + full_matrices = op.get_attr("full_matrices") + + # TODO(rmlarsen): Make this work with complex types. + if a.dtype.is_complex: + raise NotImplementedError( + "SVD gradient is not implemented for complex types and " + "compute_uv=True.") + grad_u_shape = grad_u.get_shape().with_rank_at_least(2) + grad_v_shape = grad_v.get_shape().with_rank_at_least(2) + m = a_shape[-2].merge_with(grad_u_shape[-2]) + n = a_shape[-1].merge_with(grad_v_shape[-2]) + batch_shape = a_shape[:-2].merge_with(grad_u_shape[:-2]).merge_with( + grad_v_shape[:-2]) + a_shape = batch_shape.concatenate([m, n]) m = a_shape[-2].value n = a_shape[-1].value @@ -300,12 +308,9 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): "SVD gradient has not been implemented for input with unknown " "inner matrix shape.") - if not op.get_attr("compute_uv"): - s, u, v = linalg_ops.svd(a, compute_uv=True, full_matrices=True) - else: - s = op.outputs[0] - u = op.outputs[1] - v = op.outputs[2] + s = op.outputs[0] + u = op.outputs[1] + v = op.outputs[2] use_adjoint = False if m > n: @@ -317,19 +322,7 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): grad_u, grad_v = grad_v, grad_u with ops.control_dependencies([grad_s, grad_u, grad_v]): - grad_s_mat = array_ops.matrix_diag(grad_s) - if not op.get_attr("compute_uv"): - if use_adjoint: - grad_a = math_ops.matmul( - v[..., :, :m], math_ops.matmul(u, grad_s_mat), adjoint_b=True) - else: - grad_a = math_ops.matmul(u, - math_ops.matmul( - grad_s_mat, v[..., :, :m], adjoint_b=True)) - grad_a.set_shape(a_shape) - return grad_a - - if op.get_attr("full_matrices") and abs(m - n) > 1: + if full_matrices and abs(m - n) > 1: raise NotImplementedError( "svd gradient is not implemented for abs(m - n) > 1 " "when full_matrices is True") @@ -371,7 +364,7 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): gv1t_v1 = math_ops.matmul(gv1t, v1) term2_nous = gv1t - math_ops.matmul(gv1t_v1, v1, adjoint_b=True) - if op.get_attr("full_matrices"): + if full_matrices: v2 = v[..., :, m:n] grad_v2 = grad_v[..., :, m:n] diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index e75a9b22e4..84afbf0627 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -547,12 +547,13 @@ def mean_pairwise_squared_error( num_present_per_batch = _num_present(diffs, weights, per_batch=True) term1 = 2.0 * _safe_div(sum_squares_diff_per_batch, - num_present_per_batch) + num_present_per_batch - 1) sum_diff = math_ops.reduce_sum( diffs, reduction_indices=reduction_indices, keep_dims=True) - term2 = 2.0 * _safe_div(math_ops.square(sum_diff), - math_ops.square(num_present_per_batch)) + term2 = 2.0 * _safe_div( + math_ops.square(sum_diff), + math_ops.multiply(num_present_per_batch, num_present_per_batch - 1)) weighted_losses = math_ops.multiply(term1 - term2, weights) loss = math_ops.reduce_sum(weighted_losses) diff --git a/tensorflow/python/ops/manip_grad.py b/tensorflow/python/ops/manip_grad.py new file mode 100644 index 0000000000..bb2069359d --- /dev/null +++ b/tensorflow/python/ops/manip_grad.py @@ -0,0 +1,31 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Gradients for operators defined in manip_ops.py.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.ops import manip_ops + + +@ops.RegisterGradient("Roll") +def _RollGrad(op, grad): + # The gradient is just the roll reversed + shift = op.inputs[1] + axis = op.inputs[2] + roll_grad = manip_ops.roll(grad, -shift, axis) + return roll_grad, None, None diff --git a/tensorflow/python/ops/manip_ops.py b/tensorflow/python/ops/manip_ops.py new file mode 100644 index 0000000000..91e15b47b9 --- /dev/null +++ b/tensorflow/python/ops/manip_ops.py @@ -0,0 +1,38 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Operators for manipulating tensors. + +@@roll +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.ops import gen_manip_ops as _gen_manip_ops +from tensorflow.python.util.all_util import remove_undocumented + + +# pylint: disable=protected-access +def roll(input, shift, axis): # pylint: disable=redefined-builtin + return _gen_manip_ops.roll(input, shift, axis) + + +roll.__doc__ = _gen_manip_ops.roll.__doc__ +# pylint: enable=protected-access + +_allowed_symbols = ['roll'] + +remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 827e3caa36..9a8ac93de9 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -2826,10 +2826,14 @@ def tensordot(a, b, axes, name=None): """Generates two sets of contraction axes for the two tensor arguments.""" a_shape = a.get_shape() if isinstance(axes, compat.integral_types): - if axes < 1: - raise ValueError("'axes' must be at least 1.") + if axes < 0: + raise ValueError("'axes' must be at least 0.") if a_shape.ndims is not None: - return range(a_shape.ndims - axes, a_shape.ndims), range(axes) + if axes > a_shape.ndims: + raise ValueError("'axes' must not be larger than the number of " + "dimensions of tensor %s." % a) + return (list(xrange(a_shape.ndims - axes, a_shape.ndims)), + list(xrange(axes))) else: rank = array_ops.rank(a) return (range(rank - axes, rank, dtype=dtypes.int32), diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 24c6f64f0a..da80e72071 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -1127,6 +1127,12 @@ def raw_rnn(cell, loop_fn, def _copy_some_through(current, candidate): """Copy some tensors through via array_ops.where.""" def copy_fn(cur_i, cand_i): + # TensorArray and scalar get passed through. + if isinstance(cur_i, tensor_array_ops.TensorArray): + return cand_i + if cur_i.shape.ndims == 0: + return cand_i + # Otherwise propagate the old or the new value. with ops.colocate_with(cand_i): return array_ops.where(elements_finished, cur_i, cand_i) return nest.map_structure(copy_fn, current, candidate) diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index 30bf4e4ef1..009d1dc3b9 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -25,6 +25,7 @@ import sys as _sys # Imports the following modules so that @RegisterGradient get executed. from tensorflow.python.ops import array_grad from tensorflow.python.ops import data_flow_grad +from tensorflow.python.ops import manip_grad from tensorflow.python.ops import math_grad from tensorflow.python.ops import sparse_grad from tensorflow.python.ops import spectral_grad @@ -42,11 +43,13 @@ from tensorflow.python.ops.special_math_ops import * # TODO(vrv): Switch to import * once we're okay with exposing the module. from tensorflow.python.ops.confusion_matrix import confusion_matrix from tensorflow.python.ops.control_flow_ops import Assert +from tensorflow.python.ops.control_flow_ops import case +from tensorflow.python.ops.control_flow_ops import cond from tensorflow.python.ops.control_flow_ops import group from tensorflow.python.ops.control_flow_ops import no_op +# pylint: disable=redefined-builtin from tensorflow.python.ops.control_flow_ops import tuple -from tensorflow.python.ops.control_flow_ops import cond -from tensorflow.python.ops.control_flow_ops import case +# pylint: enable=redefined-builtin from tensorflow.python.ops.control_flow_ops import while_loop from tensorflow.python.ops.data_flow_ops import * from tensorflow.python.ops.functional_ops import * @@ -59,6 +62,7 @@ from tensorflow.python.ops.logging_ops import Print from tensorflow.python.ops.logging_ops import get_summary_op from tensorflow.python.ops.lookup_ops import initialize_all_tables from tensorflow.python.ops.lookup_ops import tables_initializer +from tensorflow.python.ops.manip_ops import * from tensorflow.python.ops.math_ops import * from tensorflow.python.ops.numerics import * from tensorflow.python.ops.parsing_ops import * @@ -105,6 +109,7 @@ from tensorflow.python.ops import init_ops as _init_ops from tensorflow.python.ops import io_ops as _io_ops from tensorflow.python.ops import linalg_ops as _linalg_ops from tensorflow.python.ops import logging_ops as _logging_ops +from tensorflow.python.ops import manip_ops as _manip_ops from tensorflow.python.ops import math_ops as _math_ops from tensorflow.python.ops import numerics as _numerics from tensorflow.python.ops import parsing_ops as _parsing_ops @@ -264,34 +269,36 @@ _allowed_symbols = (_allowed_symbols_array_ops + _allowed_symbols_misc + _allowed_symbols_partitioned_variables) -remove_undocumented(__name__, _allowed_symbols, - [_sys.modules[__name__], - _array_ops, - _check_ops, - _clip_ops, - _confusion_matrix, - _control_flow_ops, - _constant_op, - _data_flow_ops, - _functional_ops, - _gradients, - _histogram_ops, - _init_ops, - _io_ops, - _linalg_ops, - _logging_ops, - _math_ops, - _numerics, - _parsing_ops, - _partitioned_variables, - _random_ops, - _script_ops, - _session_ops, - _sparse_ops, - _special_math_ops, - _state_ops, - _string_ops, - _template, - _tensor_array_ops, - _variable_scope, - _variables,]) +remove_undocumented(__name__, _allowed_symbols, [ + _sys.modules[__name__], + _array_ops, + _check_ops, + _clip_ops, + _confusion_matrix, + _control_flow_ops, + _constant_op, + _data_flow_ops, + _functional_ops, + _gradients, + _histogram_ops, + _init_ops, + _io_ops, + _linalg_ops, + _logging_ops, + _manip_ops, + _math_ops, + _numerics, + _parsing_ops, + _partitioned_variables, + _random_ops, + _script_ops, + _session_ops, + _sparse_ops, + _special_math_ops, + _state_ops, + _string_ops, + _template, + _tensor_array_ops, + _variable_scope, + _variables, +]) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index ddfd6be6da..bebf1d5e0d 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -235,13 +235,10 @@ def load(sess, tags, export_dir, **saver_kwargs): asset_tensors_dictionary = _get_asset_tensors(export_dir, meta_graph_def_to_load) - main_op_tensor = _get_main_op_tensor(meta_graph_def_to_load) + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def_to_load) or + (_get_legacy_init_op_tensor(meta_graph_def_to_load))) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) - else: - legacy_init_op_tensor = _get_legacy_init_op_tensor(meta_graph_def_to_load) - if legacy_init_op_tensor is not None: - sess.run( - fetches=[legacy_init_op_tensor], feed_dict=asset_tensors_dictionary) return meta_graph_def_to_load diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py index 0ddf09260b..affa97062a 100644 --- a/tensorflow/python/tools/freeze_graph.py +++ b/tensorflow/python/tools/freeze_graph.py @@ -72,7 +72,8 @@ def freeze_graph_with_def_protos(input_graph_def, variable_names_blacklist="", input_meta_graph_def=None, input_saved_model_dir=None, - saved_model_tags=None): + saved_model_tags=None, + checkpoint_version=saver_pb2.SaverDef.V2): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. @@ -100,7 +101,8 @@ def freeze_graph_with_def_protos(input_graph_def, _ = importer.import_graph_def(input_graph_def, name="") with session.Session() as sess: if input_saver_def: - saver = saver_lib.Saver(saver_def=input_saver_def) + saver = saver_lib.Saver( + saver_def=input_saver_def, write_version=checkpoint_version) saver.restore(sess, input_checkpoint) elif input_meta_graph_def: restorer = saver_lib.import_meta_graph( @@ -124,7 +126,8 @@ def freeze_graph_with_def_protos(input_graph_def, # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor - saver = saver_lib.Saver(var_list=var_list) + saver = saver_lib.Saver( + var_list=var_list, write_version=checkpoint_version) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes.split(",")) @@ -217,7 +220,8 @@ def freeze_graph(input_graph, variable_names_blacklist="", input_meta_graph=None, input_saved_model_dir=None, - saved_model_tags=tag_constants.SERVING): + saved_model_tags=tag_constants.SERVING, + checkpoint_version=saver_pb2.SaverDef.V2): """Converts all variables in a graph and checkpoint into constants.""" input_graph_def = None if input_saved_model_dir: @@ -233,10 +237,21 @@ def freeze_graph(input_graph, if input_saver: input_saver_def = _parse_input_saver_proto(input_saver, input_binary) freeze_graph_with_def_protos( - input_graph_def, input_saver_def, input_checkpoint, output_node_names, - restore_op_name, filename_tensor_name, output_graph, clear_devices, - initializer_nodes, variable_names_whitelist, variable_names_blacklist, - input_meta_graph_def, input_saved_model_dir, saved_model_tags.split(",")) + input_graph_def, + input_saver_def, + input_checkpoint, + output_node_names, + restore_op_name, + filename_tensor_name, + output_graph, + clear_devices, + initializer_nodes, + variable_names_whitelist, + variable_names_blacklist, + input_meta_graph_def, + input_saved_model_dir, + saved_model_tags.split(","), + checkpoint_version=checkpoint_version) def main(unused_args): @@ -246,7 +261,7 @@ def main(unused_args): FLAGS.output_graph, FLAGS.clear_devices, FLAGS.initializer_nodes, FLAGS.variable_names_whitelist, FLAGS.variable_names_blacklist, FLAGS.input_meta_graph, FLAGS.input_saved_model_dir, - FLAGS.saved_model_tags) + FLAGS.saved_model_tags, FLAGS.checkpoint_version) if __name__ == "__main__": @@ -268,6 +283,11 @@ if __name__ == "__main__": default="", help="TensorFlow variables file to load.") parser.add_argument( + "--checkpoint_version", + type=int, + default=saver_pb2.SaverDef.V2, + help="Tensorflow variable file format") + parser.add_argument( "--output_graph", type=str, default="", diff --git a/tensorflow/python/tools/freeze_graph_test.py b/tensorflow/python/tools/freeze_graph_test.py index feeed7102c..91f0061ebc 100644 --- a/tensorflow/python/tools/freeze_graph_test.py +++ b/tensorflow/python/tools/freeze_graph_test.py @@ -84,9 +84,19 @@ class FreezeGraphTest(test_util.TensorFlowTestCase): input_meta_graph = checkpoint_meta_graph_file freeze_graph.freeze_graph( - input_graph_path, input_saver_def_path, input_binary, checkpoint_path, - output_node_names, restore_op_name, filename_tensor_name, - output_graph_path, clear_devices, "", "", input_meta_graph) + input_graph_path, + input_saver_def_path, + input_binary, + checkpoint_path, + output_node_names, + restore_op_name, + filename_tensor_name, + output_graph_path, + clear_devices, + "", + "", + input_meta_graph, + checkpoint_version=saver_write_version) # Now we make sure the variable is now a constant, and that the graph still # produces the expected result. diff --git a/tensorflow/python/tools/optimize_for_inference_lib.py b/tensorflow/python/tools/optimize_for_inference_lib.py index c2687bf557..9c19271222 100644 --- a/tensorflow/python/tools/optimize_for_inference_lib.py +++ b/tensorflow/python/tools/optimize_for_inference_lib.py @@ -349,6 +349,7 @@ def fold_batch_norms(input_graph_def): bias_add_op.op = "BiasAdd" bias_add_op.name = node.name bias_add_op.attr["T"].CopyFrom(conv_op.attr["T"]) + bias_add_op.attr["data_format"].CopyFrom(conv_op.attr["data_format"]) bias_add_op.input.extend([new_conv_op.name, offset_op.name]) new_ops.extend([scaled_weights_op, new_conv_op, offset_op, bias_add_op]) diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py index 7686bb0f14..084a4500f8 100644 --- a/tensorflow/python/tools/optimize_for_inference_test.py +++ b/tensorflow/python/tools/optimize_for_inference_test.py @@ -173,48 +173,56 @@ class OptimizeForInferenceTest(test.TestCase): self.assertNotEqual("BatchNormWithGlobalNormalization", node.op) def testFoldFusedBatchNorms(self): - with self.test_session() as sess: - inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6] - input_op = constant_op.constant( - np.array(inputs), shape=[1, 1, 6, 2], dtype=dtypes.float32) - weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4] - weights_op = constant_op.constant( - np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32) - conv_op = nn_ops.conv2d( - input_op, weights_op, [1, 1, 1, 1], padding="SAME", name="conv_op") - mean_op = constant_op.constant( - np.array([10, 20]), shape=[2], dtype=dtypes.float32) - variance_op = constant_op.constant( - np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32) - beta_op = constant_op.constant( - np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32) - gamma_op = constant_op.constant( - np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32) - ops.get_default_graph().graph_def_versions.producer = 9 - gen_nn_ops._fused_batch_norm( - conv_op, - gamma_op, - beta_op, - mean_op, - variance_op, - 0.00001, - is_training=False, - name="output") - original_graph_def = sess.graph_def - original_result = sess.run(["output:0"]) - optimized_graph_def = optimize_for_inference_lib.fold_batch_norms( - original_graph_def) - - with self.test_session() as sess: - _ = importer.import_graph_def( - optimized_graph_def, input_map={}, name="optimized") - optimized_result = sess.run(["optimized/output:0"]) - - self.assertAllClose( - original_result, optimized_result, rtol=1e-04, atol=1e-06) - - for node in optimized_graph_def.node: - self.assertNotEqual("FusedBatchNorm", node.op) + for data_format, use_gpu in [("NHWC", False), ("NCHW", True)]: + with self.test_session(use_gpu=use_gpu) as sess: + inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6] + input_op = constant_op.constant( + np.array(inputs), + shape=[1, 1, 6, 2] if data_format == "NHWC" else [1, 2, 1, 6], + dtype=dtypes.float32) + weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4] + weights_op = constant_op.constant( + np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32) + conv_op = nn_ops.conv2d( + input_op, + weights_op, [1, 1, 1, 1], + padding="SAME", + data_format=data_format, + name="conv_op") + mean_op = constant_op.constant( + np.array([10, 20]), shape=[2], dtype=dtypes.float32) + variance_op = constant_op.constant( + np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32) + beta_op = constant_op.constant( + np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32) + gamma_op = constant_op.constant( + np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32) + ops.get_default_graph().graph_def_versions.producer = 9 + gen_nn_ops._fused_batch_norm( + conv_op, + gamma_op, + beta_op, + mean_op, + variance_op, + 0.00001, + is_training=False, + data_format=data_format, + name="output") + original_graph_def = sess.graph_def + original_result = sess.run(["output:0"]) + optimized_graph_def = optimize_for_inference_lib.fold_batch_norms( + original_graph_def) + + with self.test_session(use_gpu=use_gpu) as sess: + _ = importer.import_graph_def( + optimized_graph_def, input_map={}, name="optimized") + optimized_result = sess.run(["optimized/output:0"]) + + self.assertAllClose( + original_result, optimized_result, rtol=1e-04, atol=1e-06) + + for node in optimized_graph_def.node: + self.assertNotEqual("FusedBatchNorm", node.op) def testFuseResizePadAndConv(self): with self.test_session() as sess: diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 667a4b1db8..33f6debbcb 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -31,6 +31,7 @@ import warnings import numpy as np +from six import integer_types from tensorflow.contrib.saved_model.python.saved_model import reader from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils from tensorflow.core.example import example_pb2 @@ -440,7 +441,7 @@ def _create_example_string(example_dict): elif isinstance(feature_list[0], str): example.features.feature[feature_name].bytes_list.value.extend( feature_list) - elif isinstance(feature_list[0], (int, long)): + elif isinstance(feature_list[0], integer_types): example.features.feature[feature_name].int64_list.value.extend( feature_list) else: diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 17e07e171a..aae757b99a 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -336,7 +336,7 @@ class CheckpointSaverListener(object): `CheckpointSaverHook`, as in this example: ```python - class ExampleCheckpointSaverListerner(CheckpointSaverListener): + class ExampleCheckpointSaverListener(CheckpointSaverListener): def begin(self): # You can add ops to the graph here. print('Starting the session.') @@ -352,7 +352,7 @@ class CheckpointSaverListener(object): print('Done with the session.') ... - listener = ExampleCheckpointSaverListerner() + listener = ExampleCheckpointSaverListener() saver_hook = tf.train.CheckpointSaverHook( checkpoint_dir, listeners=[listener]) with tf.train.MonitoredTrainingSession(chief_only_hooks=[saver_hook]): diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py index 992184ec9e..bd9985a7c5 100644 --- a/tensorflow/python/training/input.py +++ b/tensorflow/python/training/input.py @@ -58,6 +58,8 @@ _restore_sparse = sparse_ops._take_many_sparse_from_tensors_map def match_filenames_once(pattern, name=None): """Save the list of files matching pattern, so it is only computed once. + NOTE: The order of the files returned can be non-deterministic. + Args: pattern: A file pattern (glob), or 1D tensor of file patterns. name: A name for the operations (optional). diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 3888e9bba4..0c1c8e664b 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1597,9 +1597,9 @@ class Saver(object): [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). Returns: - A string: path prefix used for the checkpoint files. If the saver is - sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn' - is the number of shards created. + A string: path prefix used for the checkpoint files. If checkpoint + format is V1 and the saver is sharded, this string ends with: + '-?????-of-nnnnn' where 'nnnnn' is the number of shards created. If the saver is empty, returns None. Raises: @@ -1749,6 +1749,12 @@ class Saver(object): return if save_path is None: raise ValueError("Can't load save_path when it is None.") + if (os.path.isfile(save_path) and + self._write_version not in ( + saver_pb2.SaverDef.V1, saver_pb2.SaverDef.LEGACY)): + raise ValueError("The specified path: %s is a file." + " Please specify only the path prefix" + " to the checkpoint files." % save_path) logging.info("Restoring parameters from %s", save_path) if context.in_graph_mode(): sess.run(self.saver_def.restore_op_name, diff --git a/tensorflow/python/util/compat_internal.py b/tensorflow/python/util/compat_internal.py new file mode 100644 index 0000000000..fee1d6fab7 --- /dev/null +++ b/tensorflow/python/util/compat_internal.py @@ -0,0 +1,34 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Functions for Python 2 vs. 3 compatibility that are private to TensorFlow.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +def path_to_str(path): + """Returns the file system path representation of a `PathLike` object, + else as it is. + + Args: + path: An object that can be converted to path representation. + + Returns: + A `str` object. + """ + if hasattr(path, "__fspath__"): + path = as_str_any(path.__fspath__()) + return path diff --git a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc index f35542e18f..933c103f52 100644 --- a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc +++ b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc @@ -232,7 +232,7 @@ port::StatusOr<DriverVersion> Diagnostician::FindDsoVersion() { result = StringToDriverVersion(version); } #else -#if !defined(PLATFORM_WINDOWS) && !defined(NVIDIA_TEGRA) +#if !defined(PLATFORM_WINDOWS) && !defined(ANDROID_TEGRA) // Callback used when iterating through DSOs. Looks for the driver-interfacing // DSO and yields its version number into the callback data, when found. auto iterate_phdr = diff --git a/tensorflow/stream_executor/dso_loader.cc b/tensorflow/stream_executor/dso_loader.cc index 5210a81092..0c642912b1 100644 --- a/tensorflow/stream_executor/dso_loader.cc +++ b/tensorflow/stream_executor/dso_loader.cc @@ -96,10 +96,19 @@ string GetCudnnVersion() { return TF_CUDNN_VERSION; } } /* static */ port::Status DsoLoader::GetLibcuptiDsoHandle(void** dso_handle) { +#if defined(ANDROID_TEGRA) + // On Android devices the CUDA version number is not added to the library + // name. + return GetDsoHandle( + FindDsoPath(port::Env::Default()->FormatLibraryFileName("cupti", ""), + GetCudaCuptiLibraryPath()), + dso_handle); +#else return GetDsoHandle(FindDsoPath(port::Env::Default()->FormatLibraryFileName( "cupti", GetCudaVersion()), GetCudaCuptiLibraryPath()), dso_handle); +#endif } static mutex& GetRpathMutex() { diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index bf4a9fe6ce..411b393b0a 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -270,6 +270,8 @@ def _rpath_linkopts(name): clean_dep("//tensorflow:darwin"): [ "-Wl,%s" % (_make_search_paths("@loader_path", levels_to_root),), ], + clean_dep("//tensorflow:windows"): [], + clean_dep("//tensorflow:windows_msvc"): [], "//conditions:default": [ "-Wl,%s" % (_make_search_paths("$$ORIGIN", levels_to_root),), ], @@ -301,6 +303,7 @@ def tf_cc_shared_object( "-Wl,-install_name,@rpath/" + name.split("/")[-1], ], "//conditions:default": [ + "-Wl,-soname," + name.split("/")[-1], ], }), **kwargs) @@ -612,6 +615,8 @@ def tf_cc_test(name, "//tensorflow:android": [ "-pie", ], + clean_dep("//tensorflow:windows"): [], + clean_dep("//tensorflow:windows_msvc"): [], "//conditions:default": [ "-lpthread", "-lm" @@ -1264,6 +1269,8 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): "//conditions:default": [ "-lm", ], + clean_dep("//tensorflow:windows"): [], + clean_dep("//tensorflow:windows_msvc"): [], clean_dep("//tensorflow:darwin"): [], }),) diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index f32353c957..baedf596e8 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -169,12 +169,20 @@ tf_module { argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { + name: "rgb_to_yiq" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "rgb_to_yuv" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { name: "rot90" argspec: "args=[\'image\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\'], " } member_method { name: "sample_distorted_bounding_box" - argspec: "args=[\'image_size\', \'bounding_boxes\', \'seed\', \'seed2\', \'min_object_covered\', \'aspect_ratio_range\', \'area_range\', \'max_attempts\', \'use_image_if_no_bounding_boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'image_size\', \'bounding_boxes\', \'seed\', \'seed2\', \'min_object_covered\', \'aspect_ratio_range\', \'area_range\', \'max_attempts\', \'use_image_if_no_bounding_boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'0.1\', \'None\', \'None\', \'None\', \'None\', \'None\'], " } member_method { name: "total_variation" @@ -184,4 +192,12 @@ tf_module { name: "transpose_image" argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "yiq_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "yuv_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt index d898c54627..11e05f884d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.keras.layers.Conv3DTranspose" tf_class { is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv3DTranspose\'>" + is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>" is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>" is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>" is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt index a7001bbe34..58724a1e16 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.keras.layers.Convolution3DTranspose" tf_class { is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv3DTranspose\'>" + is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>" is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>" is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>" is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>" diff --git a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt new file mode 100644 index 0000000000..0b84165285 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.manip" +tf_module { + member_method { + name: "roll" + argspec: "args=[\'input\', \'shift\', \'axis\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index db1ed42185..e8890e9cc0 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -397,6 +397,10 @@ tf_module { mtype: "<type \'module\'>" } member { + name: "manip" + mtype: "<type \'module\'>" + } + member { name: "metrics" mtype: "<type \'module\'>" } @@ -2045,6 +2049,10 @@ tf_module { argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"<dtype: \'int32\'>\", \'None\'], " } member_method { + name: "unravel_index" + argspec: "args=[\'indices\', \'dims\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { name: "unsorted_segment_max" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 636d5a1e81..310c1b6248 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -322,7 +322,7 @@ do_external_licenses_check(){ EXTRA_LICENSES_FILE="$(mktemp)_extra_licenses.log" echo "Getting external dependencies for ${BUILD_TARGET}" - bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --no_implicit_deps --no_host_deps --keep_going \ + bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --keep_going \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort \ @@ -331,7 +331,7 @@ do_external_licenses_check(){ echo echo "Getting list of external licenses mentioned in ${LICENSES_TARGET}." - bazel query "deps(${LICENSES_TARGET})" --no_implicit_deps --no_host_deps --keep_going \ + bazel query "deps(${LICENSES_TARGET})" --keep_going \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort \ @@ -345,6 +345,18 @@ do_external_licenses_check(){ EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s') + # Blacklist + echo ${MISSING_LICENSES_FILE} + grep -e "@bazel_tools//third_party/" -e "@com_google_absl//absl" -e "@org_tensorflow//" -v ${MISSING_LICENSES_FILE} > temp.txt + mv temp.txt ${MISSING_LICENSES_FILE} + + # Whitelist + echo ${EXTRA_LICENSE_FILE} + grep -e "@bazel_tools//src/" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -v ${EXTRA_LICENSES_FILE} > temp.txt + mv temp.txt ${EXTRA_LICENSES_FILE} + + + echo echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s" echo diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index fa28e3d79c..583d1d5f09 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt \ +bazel build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh index 573c926203..94276c6c5c 100644 --- a/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh @@ -41,7 +41,7 @@ run_configure_for_gpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt \ +bazel build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ diff --git a/tensorflow/tools/docker/jupyter_notebook_config.py b/tensorflow/tools/docker/jupyter_notebook_config.py index 0acbf6fcee..05dcefb099 100644 --- a/tensorflow/tools/docker/jupyter_notebook_config.py +++ b/tensorflow/tools/docker/jupyter_notebook_config.py @@ -15,6 +15,7 @@ import os from IPython.lib import passwd +c = c # pylint:disable=undefined-variable c.NotebookApp.ip = '*' c.NotebookApp.port = int(os.getenv('PORT', 8888)) c.NotebookApp.open_browser = False diff --git a/tensorflow/tools/docs/pretty_docs.py b/tensorflow/tools/docs/pretty_docs.py index ac04f566d0..543b5fa6fe 100644 --- a/tensorflow/tools/docs/pretty_docs.py +++ b/tensorflow/tools/docs/pretty_docs.py @@ -327,7 +327,7 @@ class _Metadata(object): """ def __init__(self, name): - """Creata a Metadata builder. + """Create a Metadata builder. Args: name: The name of the page being described by the Metadata block. diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD index dbc81599de..35e55c0d31 100644 --- a/tensorflow/tools/lib_package/BUILD +++ b/tensorflow/tools/lib_package/BUILD @@ -99,6 +99,7 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", + "@aws//:LICENSE", "@boringssl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", @@ -112,8 +113,10 @@ genrule( "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", "@libxsmm_archive//:LICENSE", + "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", + "@nasm//:LICENSE", "@nsync//:LICENSE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", @@ -134,6 +137,7 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", + "@aws//:LICENSE", "@boringssl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", @@ -149,6 +153,7 @@ genrule( "@libxsmm_archive//:LICENSE", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", + "@nasm//:LICENSE", "@nsync//:LICENSE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index e4fa6694d8..a9c4a8de42 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -88,13 +88,20 @@ filegroup( "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", "//third_party/hadoop:LICENSE.txt", + "@absl_py//absl/flags:LICENSE", + "@arm_neon_2_x86_sse//:LICENSE", + "@astor_archive//:LICENSE", + "@aws//:LICENSE", "@boringssl//:LICENSE", + "@com_google_absl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", "@eigen_archive//:COPYING.MPL2", "@farmhash_archive//:COPYING", "@fft2d//:fft/readme.txt", + "@flatbuffers//:LICENSE.txt", + "@gast_archive//:PKG-INFO", "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", "@grpc//:LICENSE", @@ -105,11 +112,15 @@ filegroup( "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", "@grpc//third_party/nanopb:LICENSE.txt", + "@nasm//:LICENSE", "@nsync//:LICENSE", + "@pcre//:LICENCE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", "@six_archive//:LICENSE", "@snappy//:COPYING", + "@swig//:LICENSE", + "@termcolor_archive//:COPYING.txt", "@zlib_archive//:zlib.h", "@org_python_pypi_backports_weakref//:LICENSE", ] + if_mkl([ diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index ca8c272a08..dc31e4c5f7 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -137,8 +137,8 @@ function main() { fi fi fi - # Install toco as a binary in aux-bin. mkdir "${TMPDIR}/tensorflow/aux-bin" + # Install toco as a binary in aux-bin. cp bazel-bin/tensorflow/contrib/lite/toco/toco ${TMPDIR}/tensorflow/aux-bin/ fi diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 3cd4d12100..bc4315c600 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,7 +29,7 @@ from setuptools.dist import Distribution # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.5.0-rc1' +_VERSION = '1.5.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -39,7 +39,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.12.1', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'tensorflow-tensorboard >= 0.4.0', + 'tensorflow-tensorboard >= 1.5.0, < 1.6.0', 'termcolor >= 1.1.0', ] @@ -181,9 +181,10 @@ def find_files(pattern, root): matches = ['../' + x for x in find_files('*', 'external') if '.py' not in x] -so_lib_paths = [i for i in os.listdir('.') - if os.path.isdir(i) - and fnmatch.fnmatch(i, '_solib_*')] +so_lib_paths = [ + i for i in os.listdir('.') + if os.path.isdir(i) and fnmatch.fnmatch(i, '_solib_*') +] for path in so_lib_paths: matches.extend( diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 2c320cf68a..12d3c739cc 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -114,16 +114,17 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ], sha256 = "5996380e3e8b981f55d1c8d58e709c00dbb4806ba367be75d0925a68cc2f6478", strip_prefix = "abseil-cpp-720c017e30339fd1786ce4aac68bc8559736e53f", + build_file = str(Label("//third_party:com_google_absl.BUILD")), ) tf_http_archive( name = "eigen_archive", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/14e1418fcf12.tar.gz", - "https://bitbucket.org/eigen/eigen/get/14e1418fcf12.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/2355b229ea4c.tar.gz", + "https://bitbucket.org/eigen/eigen/get/2355b229ea4c.tar.gz", ], - sha256 = "2b526c6888639025323fd4f2600533c0f982d304ea48e4f1663e8066bd9f6368", - strip_prefix = "eigen-eigen-14e1418fcf12", + sha256 = "0cadb31a35b514bf2dfd6b5d38205da94ef326ec6908fc3fd7c269948467214f", + strip_prefix = "eigen-eigen-2355b229ea4c", build_file = str(Label("//third_party:eigen.BUILD")), ) @@ -556,6 +557,18 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ) tf_http_archive( + name = "kafka", + urls = [ + "https://mirror.bazel.build/github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", + "https://github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", + ], + sha256 = "dd035d57c8f19b0b612dd6eefe6e5eebad76f506e302cccb7c2066f25a83585e", + strip_prefix = "librdkafka-0.11.1", + build_file = str(Label("//third_party:kafka/BUILD")), + patch_file = str(Label("//third_party/kafka:config.patch")), + ) + + tf_http_archive( name = "aws", urls = [ "https://mirror.bazel.build/github.com/aws/aws-sdk-cpp/archive/1.3.15.tar.gz", diff --git a/third_party/com_google_absl.BUILD b/third_party/com_google_absl.BUILD new file mode 100644 index 0000000000..8fca145f75 --- /dev/null +++ b/third_party/com_google_absl.BUILD @@ -0,0 +1,5 @@ +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache + +exports_files(["LICENSE"]) diff --git a/third_party/flatbuffers/flatbuffers.BUILD b/third_party/flatbuffers/flatbuffers.BUILD index f6b8e6ddb0..824c97be60 100644 --- a/third_party/flatbuffers/flatbuffers.BUILD +++ b/third_party/flatbuffers/flatbuffers.BUILD @@ -4,6 +4,8 @@ package( licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE.txt"]) + config_setting( name = "freebsd", values = {"cpu": "freebsd"}, diff --git a/third_party/gast.BUILD b/third_party/gast.BUILD index 06db528ada..4866982e1f 100644 --- a/third_party/gast.BUILD +++ b/third_party/gast.BUILD @@ -3,7 +3,7 @@ licenses(["notice"]) # BSD 3-clause -exports_files(["LICENSE"]) +exports_files(["PKG-INFO"]) py_library( name = "gast", diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl index 8e1dd8a54f..255ae01190 100644 --- a/third_party/gpus/cuda_configure.bzl +++ b/third_party/gpus/cuda_configure.bzl @@ -826,7 +826,7 @@ def symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) - files = _read_dir(repository_ctx, src_dir) + files = '\n'.join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, '').splitlines() src_files = files.splitlines() diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 37924125cf..87a23925c4 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -34,6 +34,10 @@ libjpegturbo_copts = select({ "-mfloat-abi=softfp", "-fprefetch-loop-arrays", ], + ":linux_ppc64le": [ + "-mcpu=power8", + "-mtune=power8", + ], "//conditions:default": [], }) @@ -123,11 +127,51 @@ cc_library( ":k8": [":simd_x86_64"], ":armeabi-v7a": [":simd_armv7a"], ":arm64-v8a": [":simd_armv8a"], + ":linux_ppc64le": [":simd_altivec"], "//conditions:default": [":simd_none"], }), ) cc_library( + name = "simd_altivec", + srcs = [ + "jchuff.h", + "jconfig.h", + "jdct.h", + "jerror.h", + "jinclude.h", + "jmorecfg.h", + "jpegint.h", + "jpeglib.h", + "jsimd.h", + "jsimddct.h", + "simd/jccolor-altivec.c", + "simd/jcgray-altivec.c", + "simd/jcsample.h", + "simd/jcsample-altivec.c", + "simd/jdcolor-altivec.c", + "simd/jdmerge-altivec.c", + "simd/jdsample-altivec.c", + "simd/jfdctfst-altivec.c", + "simd/jfdctint-altivec.c", + "simd/jidctfst-altivec.c", + "simd/jidctint-altivec.c", + "simd/jquanti-altivec.c", + "simd/jsimd.h", + "simd/jsimd_altivec.h", + "simd/jsimd_powerpc.c", + ], + hdrs = [ + "simd/jccolext-altivec.c", # should have been named .inc + "simd/jcgryext-altivec.c", # should have been named .inc + "simd/jdcolext-altivec.c", # should have been named .inc + "simd/jdmrgext-altivec.c", # should have been named .inc + ], + copts = libjpegturbo_copts, + nocopts = libjpegturbo_nocopts, +) + +cc_library( name = "simd_x86_64", srcs = [ "jchuff.h", @@ -381,6 +425,7 @@ genrule( ":k8": "cp $(location jconfig_nowin_simd.h) $@", ":armeabi-v7a": "cp $(location jconfig_nowin_simd.h) $@", ":arm64-v8a": "cp $(location jconfig_nowin_simd.h) $@", + ":linux_ppc64le": "cp $(location jconfig_nowin_simd.h) $@", "//conditions:default": "cp $(location jconfig_nowin_nosimd.h) $@", }), ) @@ -498,3 +543,8 @@ config_setting( name = "windows_msvc", values = {"cpu": "x64_windows_msvc"}, ) + +config_setting( + name = "linux_ppc64le", + values = {"cpu": "ppc"}, +) diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD new file mode 100644 index 0000000000..a61a9e1f6c --- /dev/null +++ b/third_party/kafka/BUILD @@ -0,0 +1,147 @@ +# Description: +# Kafka C/C++ (librdkafka) client library + +licenses(["notice"]) # 2-clause BSD license + +exports_files(["LICENSE"]) + +cc_library( + name = "kafka", + srcs = [ + "config.h", + "src-cpp/ConfImpl.cpp", + "src-cpp/ConsumerImpl.cpp", + "src-cpp/HandleImpl.cpp", + "src-cpp/KafkaConsumerImpl.cpp", + "src-cpp/MessageImpl.cpp", + "src-cpp/MetadataImpl.cpp", + "src-cpp/QueueImpl.cpp", + "src-cpp/RdKafka.cpp", + "src-cpp/TopicImpl.cpp", + "src-cpp/TopicPartitionImpl.cpp", + "src/crc32c.c", + "src/crc32c.h", + "src/lz4.c", + "src/lz4.h", + "src/lz4frame.c", + "src/lz4frame.h", + "src/lz4frame_static.h", + "src/lz4hc.c", + "src/lz4hc.h", + "src/lz4opt.h", + "src/queue.h", + "src/rd.h", + "src/rdaddr.c", + "src/rdaddr.h", + "src/rdatomic.h", + "src/rdavg.h", + "src/rdavl.c", + "src/rdavl.h", + "src/rdbuf.c", + "src/rdbuf.h", + "src/rdcrc32.h", + "src/rddl.h", + "src/rdendian.h", + "src/rdgz.c", + "src/rdgz.h", + "src/rdinterval.h", + "src/rdkafka.c", + "src/rdkafka.h", + "src/rdkafka_assignor.c", + "src/rdkafka_assignor.h", + "src/rdkafka_broker.c", + "src/rdkafka_broker.h", + "src/rdkafka_buf.c", + "src/rdkafka_buf.h", + "src/rdkafka_cgrp.c", + "src/rdkafka_cgrp.h", + "src/rdkafka_conf.c", + "src/rdkafka_conf.h", + "src/rdkafka_event.h", + "src/rdkafka_feature.c", + "src/rdkafka_feature.h", + "src/rdkafka_int.h", + "src/rdkafka_interceptor.c", + "src/rdkafka_interceptor.h", + "src/rdkafka_lz4.c", + "src/rdkafka_lz4.h", + "src/rdkafka_metadata.c", + "src/rdkafka_metadata.h", + "src/rdkafka_metadata_cache.c", + "src/rdkafka_msg.c", + "src/rdkafka_msg.h", + "src/rdkafka_msgset.h", + "src/rdkafka_msgset_reader.c", + "src/rdkafka_msgset_writer.c", + "src/rdkafka_offset.c", + "src/rdkafka_offset.h", + "src/rdkafka_op.c", + "src/rdkafka_op.h", + "src/rdkafka_partition.c", + "src/rdkafka_partition.h", + "src/rdkafka_pattern.c", + "src/rdkafka_pattern.h", + "src/rdkafka_proto.h", + "src/rdkafka_queue.c", + "src/rdkafka_queue.h", + "src/rdkafka_range_assignor.c", + "src/rdkafka_request.c", + "src/rdkafka_request.h", + "src/rdkafka_roundrobin_assignor.c", + "src/rdkafka_sasl.c", + "src/rdkafka_sasl.h", + "src/rdkafka_sasl_int.h", + "src/rdkafka_sasl_plain.c", + "src/rdkafka_subscription.c", + "src/rdkafka_subscription.h", + "src/rdkafka_timer.c", + "src/rdkafka_timer.h", + "src/rdkafka_topic.c", + "src/rdkafka_topic.h", + "src/rdkafka_transport.c", + "src/rdkafka_transport.h", + "src/rdkafka_transport_int.h", + "src/rdlist.c", + "src/rdlist.h", + "src/rdlog.c", + "src/rdlog.h", + "src/rdports.c", + "src/rdports.h", + "src/rdposix.h", + "src/rdrand.c", + "src/rdrand.h", + "src/rdregex.c", + "src/rdregex.h", + "src/rdstring.c", + "src/rdstring.h", + "src/rdsysqueue.h", + "src/rdtime.h", + "src/rdtypes.h", + "src/rdunittest.c", + "src/rdunittest.h", + "src/rdvarint.c", + "src/rdvarint.h", + "src/snappy.c", + "src/snappy.h", + "src/tinycthread.c", + "src/tinycthread.h", + "src/xxhash.c", + "src/xxhash.h", + ], + hdrs = [ + "config.h", + ], + defines = [ + ], + includes = [ + "src", + "src-cpp", + ], + linkopts = [ + "-lpthread", + ], + visibility = ["//visibility:public"], + deps = [ + "@boringssl//:ssl", + ], +) diff --git a/third_party/kafka/config.patch b/third_party/kafka/config.patch new file mode 100644 index 0000000000..fa5c2d35b4 --- /dev/null +++ b/third_party/kafka/config.patch @@ -0,0 +1,44 @@ +diff -Naur a/config.h b/config.h +--- a/config.h 1970-01-01 00:00:00.000000000 +0000 ++++ b/config.h 2017-10-28 00:57:03.316957390 +0000 +@@ -0,0 +1,40 @@ ++#pragma once ++#define WITHOUT_OPTIMIZATION 0 ++#define ENABLE_DEVEL 0 ++#define ENABLE_REFCNT_DEBUG 0 ++#define ENABLE_SHAREDPTR_DEBUG 0 ++ ++#define HAVE_ATOMICS_32 1 ++#define HAVE_ATOMICS_32_SYNC 1 ++ ++#if (HAVE_ATOMICS_32) ++# if (HAVE_ATOMICS_32_SYNC) ++# define ATOMIC_OP32(OP1,OP2,PTR,VAL) __sync_ ## OP1 ## _and_ ## OP2(PTR, VAL) ++# else ++# define ATOMIC_OP32(OP1,OP2,PTR,VAL) __atomic_ ## OP1 ## _ ## OP2(PTR, VAL, __ATOMIC_SEQ_CST) ++# endif ++#endif ++ ++#define HAVE_ATOMICS_64 1 ++#define HAVE_ATOMICS_64_SYNC 1 ++ ++#if (HAVE_ATOMICS_64) ++# if (HAVE_ATOMICS_64_SYNC) ++# define ATOMIC_OP64(OP1,OP2,PTR,VAL) __sync_ ## OP1 ## _and_ ## OP2(PTR, VAL) ++# else ++# define ATOMIC_OP64(OP1,OP2,PTR,VAL) __atomic_ ## OP1 ## _ ## OP2(PTR, VAL, __ATOMIC_SEQ_CST) ++# endif ++#endif ++ ++ ++#define WITH_ZLIB 1 ++#define WITH_LIBDL 1 ++#define WITH_PLUGINS 0 ++#define WITH_SNAPPY 1 ++#define WITH_SOCKEM 1 ++#define WITH_SSL 1 ++#define WITH_SASL 0 ++#define WITH_SASL_SCRAM 0 ++#define WITH_SASL_CYRUS 0 ++#define HAVE_REGEX 1 ++#define HAVE_STRNDUP 1 diff --git a/third_party/pcre.BUILD b/third_party/pcre.BUILD index e2cdec4029..3a8e7a10b4 100644 --- a/third_party/pcre.BUILD +++ b/third_party/pcre.BUILD @@ -1,6 +1,6 @@ licenses(["notice"]) # BSD -exports_files(["COPYING"]) +exports_files(["LICENCE"]) cc_library( name = "pcre", diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl index c16eb3a12a..954f21f5f8 100644 --- a/third_party/py/python_configure.bzl +++ b/third_party/py/python_configure.bzl @@ -118,7 +118,7 @@ def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) - files = _read_dir(repository_ctx, src_dir) + files = '\n'.join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, '').splitlines() src_files = files.splitlines() diff --git a/third_party/termcolor.BUILD b/third_party/termcolor.BUILD index 6000e3289d..655d7cb85e 100644 --- a/third_party/termcolor.BUILD +++ b/third_party/termcolor.BUILD @@ -3,7 +3,7 @@ licenses(["notice"]) # MIT -exports_files(["LICENSE"]) +exports_files(["COPYING.txt"]) py_library( name = "termcolor", |