Merge changes from github.

Change: 151046259

.gitignore

@@ -1,6 +1,7 @@
 .DS_Store
 .ipynb_checkpoints
 node_modules
+/.bazelrc
 /bazel-*
 /third_party/py/numpy/numpy_include
 /tools/bazel.rc
@@ -13,4 +14,4 @@ node_modules
 *.pyc
 __pycache__
 *.swp
-.vscode/
+.vscode/

RELEASE.md

@@ -1,3 +1,10 @@
+# Release 1.0.1
+
+## Bug Fixes and Other Changes
+* Change GraphConstructor to not increase the version when importing, but instead take the min of all versions.
+* Google Cloud Storage fixes.
+* Removed `tf.core` and `tf.python` modules from the API. These were never intended to be exposed. Please use the same objects through top-level `tf` module instead.
+
 # Release 1.0.0
 
 ## Major Features and Improvements
@@ -88,6 +95,8 @@ To help you upgrade your existing TensorFlow Python code to match the API change
   from the tensorflow::ops namespace to tensorflow.
 * Change arg order for `{softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits` to be (labels, predictions), and force use of named args.
 * tf.nn.rnn_cell.* and most functions in tf.nn.rnn.* (with the exception of dynamic_rnn and raw_rnn) are temporarily in tf.contrib.rnn.  They will be moved back into core for TF 1.1.
+* `tf.nn.sampled_softmax_loss` and `tf.nn.nce_loss` have both changed their API such that you need to switch the `inputs, labels` to `labels, inputs` parameters.
+* The shape keyword argument of the `SparseTensor` constructor changes its name to `dense_shape` between Tensorflow 0.12 and Tensorflow 1.0.
 
 ## Bug Fixes and Other Changes
 * Numerous C++ API updates.

WORKSPACE

@@ -14,12 +14,7 @@ load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories")
 
 closure_repositories()
 
-load("//tensorflow:workspace.bzl", "check_version", "tf_workspace")
-
-# We must check the bazel version before trying to parse any other BUILD files,
-# in case the parsing of those build files depends on the bazel version we
-# require here.
-check_version("0.4.2")
+load("//tensorflow:workspace.bzl", "tf_workspace")
 
 # Uncomment and update the paths in these entries to build the Android demo.
 #android_sdk_repository(

configure

@@ -8,6 +8,9 @@ pushd `dirname $0` > /dev/null
 SOURCE_BASE_DIR=`pwd -P`
 popd > /dev/null
 
+# This file contains customized config settings.
+touch .bazelrc
+
 PLATFORM="$(uname -s | tr 'A-Z' 'a-z')"
 
 function is_linux() {
@@ -36,15 +39,11 @@ function is_windows() {
 }
 
 function bazel_clean_and_fetch() {
-  # bazel clean --expunge currently doesn't work on Windows
-  # TODO(pcloudy): Re-enable it after bazel clean --expunge is fixed.
-  if ! is_windows; then
-    bazel clean --expunge
-  fi
   if [ -z "$TF_BAZEL_TARGETS" ]; then
-    TF_BAZEL_TARGETS="//tensorflow/... -//tensorflow/contrib/nccl/... -//tensorflow/examples/android/..."
+    bazel fetch "//tensorflow/... -//tensorflow/contrib/nccl/... -//tensorflow/examples/android/..."
+  else
+    bazel fetch $TF_BAZEL_TARGETS
   fi
-  bazel fetch "$TF_BAZEL_TARGETS"
 }
 
 function sed_hyphen_i() {
@@ -102,8 +101,8 @@ if false; then # Disable building with MKL for now
 
   if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
     DST=`dirname $0`
-    ARCHIVE_BASENAME=mklml_lnx_2017.0.2.20170110.tgz
-    GITHUB_RELEASE_TAG=v0.3
+    ARCHIVE_BASENAME=mklml_lnx_2017.0.2.20170209.tgz
+    GITHUB_RELEASE_TAG=v0.5
     MKLURL="https://github.com/01org/mkl-dnn/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME"
     if ! [ -e "$DST/third_party/mkl/$ARCHIVE_BASENAME" ]; then
       wget --no-check-certificate -P $DST/third_party/mkl/ $MKLURL
@@ -182,13 +181,12 @@ else
   TF_NEED_JEMALLOC=0
 fi
 
-if [ "$TF_NEED_JEMALLOC" == "1" ]; then
-  sed_hyphen_i -e "s/WITH_JEMALLOC = False/WITH_JEMALLOC = True/" tensorflow/core/platform/default/build_config.bzl
-else
-  sed_hyphen_i -e "s/WITH_JEMALLOC = True/WITH_JEMALLOC = False/" tensorflow/core/platform/default/build_config.bzl
+sed_hyphen_i -e "/with_jemalloc/d" .bazelrc
+if [[ "$TF_NEED_JEMALLOC" == "1" ]]; then
+  echo 'build --define with_jemalloc=true' >>.bazelrc
 fi
 
-while [ "$TF_NEED_GCP" == "" ]; do
+while [[ "$TF_NEED_GCP" == "" ]]; do
   read -p "Do you wish to build TensorFlow with "\
 "Google Cloud Platform support? [y/N] " INPUT
   case $INPUT in
@@ -202,23 +200,12 @@ while [ "$TF_NEED_GCP" == "" ]; do
   esac
 done
 
-if [ "$TF_NEED_GCP" == "1" ]; then
-  ## Verify that libcurl header files are available.
-  # Only check Linux, since on MacOS the header files are installed with XCode.
-  if is_linux && [[ ! -f "/usr/include/curl/curl.h" ]]; then
-    echo "ERROR: It appears that the development version of libcurl is not "\
-"available. Please install the libcurl3-dev package."
-    exit 1
-  fi
-
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/WITH_GCP_SUPPORT = False/WITH_GCP_SUPPORT = True/" tensorflow/core/platform/default/build_config.bzl
-else
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/WITH_GCP_SUPPORT = True/WITH_GCP_SUPPORT = False/" tensorflow/core/platform/default/build_config.bzl
+sed_hyphen_i -e "/with_gcp_support/d" .bazelrc
+if [[ "$TF_NEED_GCP" == "1" ]]; then
+  echo 'build --define with_gcp_support=true' >>.bazelrc
 fi
 
-while [ "$TF_NEED_HDFS" == "" ]; do
+while [[ "$TF_NEED_HDFS" == "" ]]; do
   read -p "Do you wish to build TensorFlow with "\
 "Hadoop File System support? [y/N] " INPUT
   case $INPUT in
@@ -232,16 +219,13 @@ while [ "$TF_NEED_HDFS" == "" ]; do
   esac
 done
 
-if [ "$TF_NEED_HDFS" == "1" ]; then
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/WITH_HDFS_SUPPORT = False/WITH_HDFS_SUPPORT = True/" tensorflow/core/platform/default/build_config.bzl
-else
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/WITH_HDFS_SUPPORT = True/WITH_HDFS_SUPPORT = False/" tensorflow/core/platform/default/build_config.bzl
+sed_hyphen_i -e "/with_hdfs_support/d" .bazelrc
+if [[ "$TF_NEED_HDFS" == "1" ]]; then
+  echo 'build --define with_hdfs_support=true' >>.bazelrc
 fi
 
 ## Enable XLA.
-while [ "$TF_ENABLE_XLA" == "" ]; do
+while [[ "$TF_ENABLE_XLA" == "" ]]; do
   read -p "Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] " INPUT
   case $INPUT in
     [Yy]* ) echo "XLA JIT support will be enabled for TensorFlow"; TF_ENABLE_XLA=1;;
@@ -251,12 +235,9 @@ while [ "$TF_ENABLE_XLA" == "" ]; do
   esac
 done
 
-if [ "$TF_ENABLE_XLA" == "1" ]; then
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/^WITH_XLA_SUPPORT = [FT].*/WITH_XLA_SUPPORT = True/" tensorflow/core/platform/default/build_config_root.bzl
-else
-  # Update Bazel build configuration.
-  sed_hyphen_i -e "s/^WITH_XLA_SUPPORT = [FT].*/WITH_XLA_SUPPORT = False/" tensorflow/core/platform/default/build_config_root.bzl
+sed_hyphen_i -e "/with_xla_support/d" .bazelrc
+if [[ "$TF_ENABLE_XLA" == "1" ]]; then
+  echo 'build --define with_xla_support=true' >>.bazelrc
 fi
 
 

tensorflow/BUILD

@@ -110,6 +110,34 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
+# TODO(jhseu): Enable on other platforms other than Linux.
+config_setting(
+    name = "with_jemalloc",
+    values = {
+        "cpu": "k8",
+        "define": "with_jemalloc=true",
+    },
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "with_gcp_support",
+    values = {"define": "with_gcp_support=true"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "with_hdfs_support",
+    values = {"define": "with_hdfs_support=true"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "with_xla_support",
+    values = {"define": "with_xla_support=true"},
+    visibility = ["//visibility:public"],
+)
+
 package_group(
     name = "internal",
     packages = ["//tensorflow/..."],
@@ -321,6 +349,8 @@ cc_binary(
     deps = [
         "//tensorflow/c:c_api",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:client_session",
+        "//tensorflow/cc:scope",
         "//tensorflow/core:tensorflow",
     ],
 )

tensorflow/compiler/xla/service/allocation_tracker.cc

@@ -138,7 +138,8 @@ tensorflow::Status AllocationTracker::DeallocateShape(
     TF_RET_CHECK(ShapeUtil::TupleElementCount(shape) == elements.size())
         << "tuple has unexpected number of elements: " << elements.size()
         << " != " << ShapeUtil::TupleElementCount(shape);
-    for (int i = 0; i < elements.size(); ++i) {
+    for (std::vector<se::DeviceMemoryBase>::size_type i = 0;
+         i < elements.size(); ++i) {
       VLOG(2) << "recursing onto the tuple elements";
       TF_RETURN_IF_ERROR(DeallocateShape(backend, device_ordinal, &elements[i],
                                          shape.tuple_shapes(i),

tensorflow/compiler/xla/service/generic_transfer_manager.cc

@@ -118,10 +118,10 @@ GenericTransferManager::ShallowCopyTupleFromDevice(
 
   // Create a DeviceMemoryBase from each void* pointer.
   std::vector<se::DeviceMemoryBase> destination;
-  for (int i = 0; i < element_pointers.size(); ++i) {
+  for (std::vector<void*>::size_type i = 0; i < element_pointers.size(); ++i) {
     if (element_pointers[i] == nullptr &&
         !ShapeUtil::HasZeroElements(shape.tuple_shapes(i))) {
-      return FailedPrecondition("tuple contains nullptr at element %d", i);
+      return FailedPrecondition("tuple contains nullptr at element %lu", i);
     }
     int64 buffer_size = ShapeUtil::ByteSizeOf(shape.tuple_shapes(i),
                                               /*pointer_size=*/sizeof(void*));

tensorflow/compiler/xla/service/service.cc

@@ -256,7 +256,8 @@ StatusOr<std::vector<const Allocation*>> Service::ResolveAndValidateArguments(
     tensorflow::gtl::ArraySlice<const GlobalDataHandle*> arguments,
     const Backend* backend, int device_ordinal) {
   std::vector<const Allocation*> allocations;
-  for (int i = 0; i < arguments.size(); ++i) {
+  for (tensorflow::gtl::ArraySlice<const GlobalDataHandle*>::size_type i = 0; 
+       i < arguments.size(); ++i) {
     auto allocation_status = allocation_tracker_.Resolve(*arguments[i]);
     if (!allocation_status.ok()) {
       return Status(allocation_status.status().code(),
@@ -269,7 +270,7 @@ StatusOr<std::vector<const Allocation*>> Service::ResolveAndValidateArguments(
     if (allocation->backend() != backend ||
         allocation->device_ordinal() != device_ordinal) {
       return InvalidArgument(
-          "argument %d is on device %s but computation will be executed "
+          "argument %lu is on device %s but computation will be executed "
           "on device %s",
           i,
           allocation->backend()
@@ -295,13 +296,14 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig(
                            program_shape.parameters_size(), arguments.size());
   }
 
-  for (int i = 0; i < arguments.size(); ++i) {
+  for (tensorflow::gtl::ArraySlice<const Allocation*>::size_type i = 0;
+       i < arguments.size(); ++i) {
     // Verify that shape of arguments matches the shape of the arguments in the
     // ProgramShape.
     if (!ShapeUtil::Compatible(arguments[i]->shape(),
                                program_shape.parameters(i))) {
       return InvalidArgument(
-          "computation expects parameter %d to have shape %s, given shape %s",
+          "computation expects parameter %lu to have shape %s, given shape %s",
           i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(),
           ShapeUtil::HumanString(arguments[i]->shape()).c_str());
     }
@@ -383,7 +385,8 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables(
                           hlo_dumper, std::move(executors)));
 
   if (!other_directory_path.empty()) {
-    for (int64 i = 0; i < versioned_handles.size(); ++i) {
+    for (std::vector<VersionedComputationHandle>::size_type i = 0;
+         i < versioned_handles.size(); ++i) {
       executables[i]->set_session_module(std::move(session_modules[i]));
     }
   }
@@ -523,7 +526,8 @@ Service::ExecuteParallelAndRegisterResult(
 
   // Asynchronously launch all executables.
   std::vector<GlobalDataHandle> result_handles;
-  for (int64 i = 0; i < executables.size(); i++) {
+  for (tensorflow::gtl::ArraySlice<Executable*>::size_type i = 0;
+       i < executables.size(); i++) {
     TF_ASSIGN_OR_RETURN(
         perftools::gputools::DeviceMemoryBase result,
         executables[i]->ExecuteAsyncOnStream(&run_options[i], arguments[i]));

tensorflow/contrib/android/BUILD

@@ -72,13 +72,17 @@ LINKER_SCRIPT = "//tensorflow/contrib/android:jni/version_script.lds"
 cc_binary(
     name = "libtensorflow_inference.so",
     srcs = [],
-    copts = tf_copts(),
+    copts = tf_copts() + [
+        "-ffunction-sections",
+        "-fdata-sections",
+    ],
     linkopts = if_android([
         "-landroid",
         "-llog",
         "-lm",
         "-z defs",
         "-s",
+        "-Wl,--gc-sections",
         "-Wl,--version-script",  # This line must be directly followed by LINKER_SCRIPT.
         LINKER_SCRIPT,
     ]),

tensorflow/contrib/cmake/CMakeLists.txt

@@ -56,9 +56,10 @@ mark_as_advanced(DOWNLOAD_LOCATION)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 add_definitions(-DEIGEN_AVOID_STL_ARRAY)
 if(WIN32)
-  add_definitions(-DNOMINMAX -D_WIN32_WINNT=0x0A00 -DLANG_CXX11 -DCOMPILER_MSVC -D__VERSION__=\"MSVC\")
+  add_definitions(-DNOMINMAX -D_WIN32_WINNT=0x0A00 -DLANG_CXX11 -DCOMPILER_MSVC)
   add_definitions(-DWIN32 -DOS_WIN -D_MBCS -DWIN64 -DWIN32_LEAN_AND_MEAN -DNOGDI -DPLATFORM_WINDOWS)
   add_definitions(-DTENSORFLOW_USE_EIGEN_THREADPOOL -DEIGEN_HAS_C99_MATH -D_ITERATOR_DEBUG_LEVEL=0)
+  add_definitions(-DTF_COMPILE_LIBRARY)
   add_definitions(-DNDEBUG /O2)  # Equivalent of -c opt in Bazel.
   add_definitions(/bigobj /nologo /EHsc /GF /FC /MP /Gm-)
   # Suppress warnings to reduce build log size.
@@ -190,6 +191,7 @@ if (tensorflow_ENABLE_GPU)
       ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h
       ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h ${CUDNN_HOME}/include/cudnn.h
       ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h
+      ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_runtime_api.h
       DESTINATION ${tensorflow_source_dir}/third_party/gpus/cuda/include
     )
     include_directories(${tensorflow_source_dir}/third_party/gpus)

tensorflow/contrib/cmake/README.md

@@ -13,7 +13,7 @@ Linux.
 Current Status
 --------------
 
-CMake can be used to build TensorFlow on Windows. See the [getting started documentation](https://www.tensorflow.org/get_started/os_setup.html#pip-installation-on-windows)
+CMake can be used to build TensorFlow on Windows. See the [getting started documentation](https://www.tensorflow.org/install/install_windows)
 for instructions on how to install a pre-built TensorFlow package on Windows.
 
 ### Current known limitations

tensorflow/contrib/cmake/tf_cc_ops.cmake

@@ -120,3 +120,43 @@ list(REMOVE_ITEM tf_cc_srcs ${tf_cc_test_srcs})
 
 add_library(tf_cc OBJECT ${tf_cc_srcs})
 add_dependencies(tf_cc tf_cc_framework tf_cc_ops)
+
+set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow_internal.lib")
+add_custom_target(tf_extension_ops)
+
+function(AddUserOps)
+  cmake_parse_arguments(_AT "" "" "TARGET;SOURCES;GPUSOURCES;DEPENDS;DISTCOPY" ${ARGN})
+  if (tensorflow_ENABLE_GPU AND _AT_GPUSOURCES)
+    # if gpu build is enabled and we have gpu specific code,
+    # hint to cmake that this needs to go to nvcc
+    set (gpu_source ${_AT_GPUSOURCES})
+    set (gpu_lib "${_AT_TARGET}_gpu")
+    set_source_files_properties(${gpu_source} PROPERTIES CUDA_SOURCE_PROPERTY_FORMAT OBJ)
+    cuda_compile(gpu_lib ${gpu_source})
+  endif()
+  # create shared library from source and cuda obj
+  add_library(${_AT_TARGET} SHARED ${_AT_SOURCES} ${gpu_lib})
+  target_link_libraries(${_AT_TARGET} ${pywrap_tensorflow_lib})
+  if(WIN32)
+    if (tensorflow_ENABLE_GPU AND _AT_GPUSOURCES)
+        # some ops call out to cuda directly; need to link libs for the cuda dlls
+        target_link_libraries(${_AT_TARGET} ${CUDA_LIBRARIES})
+    endif()
+    if (_AT_DISTCOPY)
+        add_custom_command(TARGET ${_AT_TARGET} POST_BUILD
+            COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_FILE:${_AT_TARGET}> ${_AT_DISTCOPY}/)
+    endif()
+  endif()
+  if (_AT_DEPENDS)
+    add_dependencies(${_AT_TARGET} ${_AT_DEPENDS})
+  endif()
+  # make sure TF_COMPILE_LIBRARY is not defined for this target
+  get_target_property(target_compile_flags  ${_AT_TARGET} COMPILE_FLAGS)
+  if(target_compile_flags STREQUAL "target_compile_flags-NOTFOUND")
+    set(target_compile_flags "/UTF_COMPILE_LIBRARY")
+  else()
+    set(target_compile_flags "${target_compile_flags} /UTF_COMPILE_LIBRARY")
+  endif()
+  set_target_properties(${_AT_TARGET} PROPERTIES COMPILE_FLAGS ${target_compile_flags})
+  add_dependencies(tf_extension_ops ${_AT_TARGET})
+endfunction(AddUserOps)