Merge changes from github.

PiperOrigin-RevId: 155709893

.gitignore

@@ -4,6 +4,9 @@ node_modules
 /.bazelrc
 /.tf_configure.bazelrc
 /bazel-*
+/bazel_pip
+/third_party/eigen3/mkl_include
+/third_party/mkl/*
 /tools/python_bin_path.sh
 /tools/git/gen
 /pip_test

CONTRIBUTING.md

@@ -40,7 +40,7 @@ TensorFlow coding style.
 * Include unit tests when you contribute new features, as they help to
   a) prove that your code works correctly, b) guard against future breaking
   changes to lower the maintenance cost.
-* Bug fixes also generally require unit tests, because the presense of bugs
+* Bug fixes also generally require unit tests, because the presence of bugs
   usually indicates insufficient test coverage.
 * Keep API compatibility in mind when you change code in core TensorFlow,
   e.g., code in [tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core) and  [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).

configure

@@ -147,17 +147,17 @@ function setup_python {
   # Set-up env variables used by python_configure.bzl
   write_action_env_to_bazelrc "PYTHON_BIN_PATH" "$PYTHON_BIN_PATH"
   write_action_env_to_bazelrc "PYTHON_LIB_PATH" "$PYTHON_LIB_PATH"
-  write_to_bazelrc "build --define PYTHON_BIN_PATH=$PYTHON_BIN_PATH"
-  write_to_bazelrc "build --define PYTHON_LIB_PATH=$PYTHON_LIB_PATH"
+  write_to_bazelrc "build --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
+  write_to_bazelrc "build --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
   write_to_bazelrc "build --force_python=py$python_major_version"
   write_to_bazelrc "build --host_force_python=py$python_major_version"
-  write_to_bazelrc "build --python${python_major_version}_path=$PYTHON_BIN_PATH"
+  write_to_bazelrc "build --python${python_major_version}_path=\"$PYTHON_BIN_PATH\""
   write_to_bazelrc "test --force_python=py$python_major_version"
   write_to_bazelrc "test --host_force_python=py$python_major_version"
-  write_to_bazelrc "test --define PYTHON_BIN_PATH=$PYTHON_BIN_PATH"
-  write_to_bazelrc "test --define PYTHON_LIB_PATH=$PYTHON_LIB_PATH"
-  write_to_bazelrc "run --define PYTHON_BIN_PATH=$PYTHON_BIN_PATH"
-  write_to_bazelrc "run --define PYTHON_LIB_PATH=$PYTHON_LIB_PATH"
+  write_to_bazelrc "test --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
+  write_to_bazelrc "test --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
+  write_to_bazelrc "run --define PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\""
+  write_to_bazelrc "run --define PYTHON_LIB_PATH=\"$PYTHON_LIB_PATH\""
 
   # Write tools/python_bin_path.sh
   echo "export PYTHON_BIN_PATH=\"$PYTHON_BIN_PATH\"" > tools/python_bin_path.sh
@@ -180,70 +180,99 @@ fi
 setup_python
 
 ## Set up MKL related environment settings
-if false; then # Disable building with MKL for now
-  while [ "$TF_NEED_MKL" == "" ]; do
+while [ "$TF_NEED_MKL" == "" ]; do
+  fromuser=""
+  read -p "Do you wish to build TensorFlow with MKL support? [y/N] " INPUT
+  fromuser="1"
+  case $INPUT in
+    [Yy]* ) echo "MKL support will be enabled for TensorFlow"; TF_NEED_MKL=1;;
+    [Nn]* ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
+    "" ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
+    * ) echo "Invalid selection: " $INPUT;;
+  esac
+done
+
+OSNAME=`uname -s`
+
+if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
+  while [ "$TF_DOWNLOAD_MKL" == "" ]; do
     fromuser=""
-    read -p "Do you wish to build TensorFlow with MKL support (experimental)? [y/N] " INPUT
+    read -p "Do you wish to download MKL LIB from the web? [Y/n] " INPUT
     fromuser="1"
     case $INPUT in
-      [Yy]* ) echo "MKL support (experimental) (will be enabled for TensorFlow"; TF_NEED_MKL=1;;
-      [Nn]* ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
-      "" ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
-      * ) echo "Invalid selection: " $INPUT;;
+      [Yy]* ) TF_DOWNLOAD_MKL=1;;
+      [Nn]* ) TF_DOWNLOAD_MKL=0;;
+      "" )    TF_DOWNLOAD_MKL=1;;
+      * )     echo "Invalid selection: " $INPUT; exit 1;;
     esac
   done
 
-  OSNAME=`uname -s`
-
-  if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
+  if [[ "$TF_DOWNLOAD_MKL" == "1" ]]; then
     DST=`dirname $0`
-    ARCHIVE_BASENAME=mklml_lnx_2017.0.2.20170209.tgz
-    GITHUB_RELEASE_TAG=v0.5
+    ARCHIVE_BASENAME=mklml_lnx_2018.0.20170425.tgz
+    GITHUB_RELEASE_TAG=v0.7
     MKLURL="https://github.com/01org/mkl-dnn/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME"
-    if ! [ -e "$DST/third_party/mkl/$ARCHIVE_BASENAME" ]; then
-      wget --no-check-certificate -P $DST/third_party/mkl/ $MKLURL
+    if ! [ -e "${DST}/third_party/mkl/${ARCHIVE_BASENAME}" ]; then
+      curl -fSsL -o "${DST}/third_party/mkl/${ARCHIVE_BASENAME}" "${MKLURL}"
     fi
     tar -xzf $DST/third_party/mkl/$ARCHIVE_BASENAME -C $DST/third_party/mkl/
     extracted_dir_name="${ARCHIVE_BASENAME%.*}"
     MKL_INSTALL_PATH=$DST/third_party/mkl/$extracted_dir_name
     MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
 
-    if [ "$OSNAME" == "Linux" ]; then
+  else
+    default_mkl_path=/opt/intel/mklml
+    fromuser=""
+    read -p "Please specify the location where MKL is installed. [Default is $default_mkl_path]: " MKL_INSTALL_PATH
+    fromuser="1"
+    if [ -z "$MKL_INSTALL_PATH" ]; then
+      MKL_INSTALL_PATH=$default_mkl_path
+    fi
+    # Result returned from "read" will be used unexpanded. That make "~" unuseable.
+    # Going through one more level of expansion to handle that.
+    MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
+  fi
+
+  if [ "$OSNAME" == "Linux" ]; then
       # Full MKL configuration
       MKL_RT_LIB_PATH="lib/intel64/libmkl_rt.so" #${TF_MKL_EXT}#TODO version?
       MKL_RT_OMP_LIB_PATH="../compiler/lib/intel64/libiomp5.so" #TODO VERSION?
 
       # MKL-ML configuration
       MKL_ML_LIB_PATH="lib/libmklml_intel.so" #${TF_MKL_EXT}#TODO version?
       MKL_ML_OMP_LIB_PATH="lib/libiomp5.so" #TODO VERSION?
-    elif [ "$OSNAME" == "Darwin" ]; then
+  elif [ "$OSNAME" == "Darwin" ]; then
       echo "Darwin is unsupported yet";
       exit 1
-    fi
+  fi
 
-    if [ -e "$MKL_INSTALL_PATH/${MKL_ML_LIB_PATH}" ]; then
+  if [ -e "$MKL_INSTALL_PATH/${MKL_ML_LIB_PATH}" ]; then
       ln -sf $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} third_party/mkl/
       ln -sf $MKL_INSTALL_PATH/${MKL_ML_OMP_LIB_PATH} third_party/mkl/
       ln -sf $MKL_INSTALL_PATH/include third_party/mkl/
       ln -sf $MKL_INSTALL_PATH/include third_party/eigen3/mkl_include
-    else
-      echo "ERROR: $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} does not exist";
-      exit 1
-    fi
-
-    if [ -z "$fromuser" ]; then
+      loc=$(locate -e libdl.so.2 | sed -n 1p)
+      ln -sf $loc third_party/mkl/libdl.so.2
+  elif [ -e "$MKL_INSTALL_PATH/${MKL_RT_LIB_PATH}" ]; then
+      ln -sf $MKL_INSTALL_PATH/${MKL_RT_LIB_PATH} third_party/mkl/
+      ln -sf $MKL_INSTALL_PATH/${MKL_RT_OMP_LIB_PATH} third_party/mkl/
+      ln -sf $MKL_INSTALL_PATH/include third_party/mkl/
+      ln -sf $MKL_INSTALL_PATH/include third_party/eigen3/mkl_include
+      loc=$(locate -e libdl.so.2 | sed -n 1p)
+      ln -sf $loc third_party/mkl/libdl.so.2
+  else
+      echo "ERROR: $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} nor $MKL_INSTALL_PATH/${MKL_RT_LIB_PATH} exists";
       exit 1
-    fi
+  fi
 
 cat > third_party/mkl/mkl.config <<EOF
 # MKL_INSTALL_PATH refers to the location of MKL root folder. The MKL header and library
 # files can be either in this directory, or under include/ and lib64/
 MKL_INSTALL_PATH=$MKL_INSTALL_PATH
 EOF
 
-  fi # TF_NEED_MKL
-  ################## MKL
-fi # Disable building with MKL for now
+fi # TF_NEED_MKL
+## End MKL setup
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then

tensorflow/compiler/xla/service/allocation_tracker.cc

@@ -64,8 +64,9 @@ GlobalDataHandle AllocationTracker::RegisterInternal(
     auto& allocation = FindOrDie(handle_to_allocation_, handle);
     int ref_count = allocation->ref_count();
     CHECK_GT(ref_count, 0);
-    VLOG(2) << "ref_count: " << ref_count << " -> " << ref_count + 1;
-    allocation->increment_ref_count();
+    VLOG(2) << "ref_count: " << ref_count << " -> " <<
+            (ref_count + initial_ref_count);
+    allocation->increment_ref_count(initial_ref_count);
   } else {
     handle = next_handle_++;
     VLOG(2) << "ref_count: " << initial_ref_count;

tensorflow/compiler/xla/service/allocation_tracker.h

@@ -63,10 +63,10 @@ class Allocation {
     CHECK_GE(ref_count_, 0);
     return ref_count_;
   }
-  void increment_ref_count() {
+  void increment_ref_count(int inc) {
     CHECK_GT(ref_count_, 0);
-    CHECK_LT(ref_count_, INT_MAX);
-    ++ref_count_;
+    CHECK_LE(ref_count_, INT_MAX - inc);
+    ref_count_ += inc;
   }
   void decrement_ref_count() {
     CHECK_GT(ref_count_, 0);

tensorflow/compiler/xla/service/cpu/BUILD

@@ -97,6 +97,7 @@ cc_library(
     name = "simple_orc_jit",
     srcs = ["simple_orc_jit.cc"],
     hdrs = ["simple_orc_jit.h"],
+    linkopts = ["-ldl"],
     deps = [
         ":compiler_functor",
         ":cpu_runtime",

tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h

@@ -17,6 +17,7 @@
 
 #include <memory>
 #include <vector>
+#include <cmath>
 
 #include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_buffer.h"
 #include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h"

tensorflow/contrib/cmake/CMakeLists.txt

@@ -229,6 +229,12 @@ if (tensorflow_ENABLE_GPU)
   endif()
 endif()
 
+# Find python executable
+include(FindPythonInterp)
+if(NOT ${PYTHONINTERP_FOUND})
+    message(FATAL_ERROR "CMake was unable to find a python interpreter.")
+endif()
+
 # Let's get to work!
 include(tf_core_framework.cmake)
 # NOTE: Disabled until issue #3996 is fixed.

tensorflow/contrib/cmake/tf_python.cmake

@@ -27,7 +27,6 @@
 
 # 1. Resolve the installed version of Python (for Python.h and python).
 # TODO(mrry): Parameterize the build script to enable Python 3 building.
-include(FindPythonInterp)
 if(NOT PYTHON_INCLUDE_DIR)
   set(PYTHON_NOT_FOUND false)
   exec_program("${PYTHON_EXECUTABLE}"

tensorflow/contrib/cmake/tf_shared_lib.cmake

@@ -82,6 +82,13 @@ target_link_libraries(tensorflow PRIVATE
     tf_protos_cc
 )
 
+# There is a bug in GCC 5 resulting in undefined reference to a __cpu_model function when
+# linking to the tensorflow library. Adding the following libraries fixes it.
+# See issue on github: https://github.com/tensorflow/tensorflow/issues/9593
+if(CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 5.0)
+    target_link_libraries(tensorflow PRIVATE gcc_s gcc)
+endif()
+
 if(WIN32)
   add_dependencies(tensorflow tensorflow_static)
 endif(WIN32)

tensorflow/contrib/cmake/tools/create_def_file.py

@@ -44,7 +44,7 @@
 DUMPBIN = "dumpbin.exe"
 
 # Exclude if matched
-EXCLUDE_RE = re.compile(r"deleting destructor|::internal::")
+EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::")
 
 # Include if matched before exclude
 INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|"
@@ -141,17 +141,17 @@ def main():
           continue
         if not INCLUDE_RE.search(line):
           continue
-          
+
       if "deleting destructor" in line:
         # Some of the symbols convered by INCLUDEPRE_RE export deleting
         # destructor symbols, which is a bad idea.
         # So we filter out such symbols here.
         continue
-          
+
       if DATA_EXCLUDE_RE.search(line):
         def_fp.write("\t" + decorated + "\n")
       else:
-        def_fp.write("\t" + decorated + " DATA\n")      
+        def_fp.write("\t" + decorated + " DATA\n")
       taken.add(decorated)
   exit_code = proc.wait()
   if exit_code != 0:

tensorflow/contrib/distributions/python/ops/operator_pd_identity.py

@@ -115,7 +115,7 @@ def _check_x(self, x):
     """Static check that the argument `x` is proper `shape`, `dtype`."""
     # x is a typical argument e.g. to matmul or solve.  In both cases, x should
     # have the same type/shape since this is a square matrix.  These checks are
-    # ususally not needed since we ususally have some tensor backing this
+    # usually not needed since we usually have some tensor backing this
     # distribution, and the calls to tf.matmul do a shape/type check.
     #
     # Static checks only for efficiency, the identity should be fast.

tensorflow/contrib/keras/python/keras/datasets/imdb.py

@@ -41,7 +41,7 @@ def load_data(path='imdb.npz',
       num_words: max number of words to include. Words are ranked
           by how often they occur (in the training set) and only
           the most frequent words are kept
-      skip_top: skip the top N most frequently occuring words
+      skip_top: skip the top N most frequently occurring words
           (which may not be informative).
       maxlen: truncate sequences after this length.
       seed: random seed for sample shuffling.

tensorflow/contrib/keras/python/keras/datasets/reuters.py

@@ -43,7 +43,7 @@ def load_data(path='reuters.npz',
       num_words: max number of words to include. Words are ranked
           by how often they occur (in the training set) and only
           the most frequent words are kept
-      skip_top: skip the top N most frequently occuring words
+      skip_top: skip the top N most frequently occurring words
           (which may not be informative).
       maxlen: truncate sequences after this length.
       test_split: Fraction of the dataset to be used as test data.

tensorflow/contrib/keras/python/keras/engine/topology.py

@@ -649,7 +649,7 @@ def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
                           'but was passed an input_mask: ' + str(mask))
       # masking not explicitly supported: return None as mask
       return None
-    # if masking is explictly supported, by default
+    # if masking is explicitly supported, by default
     # carry over the input mask
     return mask
 

tensorflow/contrib/keras/python/keras/engine/training.py

@@ -245,7 +245,7 @@ def _check_array_lengths(inputs, targets, weights):
 
 
 def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
-  """Does validation on the compatiblity of targets and loss functions.
+  """Does validation on the compatibility of targets and loss functions.
 
   This helps prevent users from using loss functions incorrectly.
 

tensorflow/contrib/keras/python/keras/layers/normalization.py

@@ -169,7 +169,7 @@ def call(self, inputs, training=None):
 
       def normalize_inference():
         if needs_broadcasting:
-          # In this case we must explictly broadcast all parameters.
+          # In this case we must explicitly broadcast all parameters.
           broadcast_moving_mean = K.reshape(self.moving_mean, broadcast_shape)
           broadcast_moving_variance = K.reshape(self.moving_variance,
                                                 broadcast_shape)

tensorflow/contrib/keras/python/keras/models.py

@@ -221,7 +221,7 @@ def convert_custom_objects(obj):
         obj: object, dict, or list.
 
     Returns:
-        The same structure, where occurences
+        The same structure, where occurrences
             of a custom object name have been replaced
             with the custom object.
     """

tensorflow/contrib/keras/python/keras/preprocessing/sequence.py

@@ -156,7 +156,7 @@ def skipgrams(sequence,
           of word indices (integers). If using a `sampling_table`,
           word indices are expected to match the rank
           of the words in a reference dataset (e.g. 10 would encode
-          the 10-th most frequently occuring token).
+          the 10-th most frequently occurring token).
           Note that index 0 is expected to be a non-word and will be skipped.
       vocabulary_size: int. maximum possible word index + 1
       window_size: int. actually half-window.

tensorflow/contrib/labeled_tensor/python/ops/core.py

@@ -810,7 +810,7 @@ def axis_order_scope(axis_order=None):
   Example usage:
 
     with lt.axis_order_scope(['x', 'y', 'z']):
-      # result is guranteed to have the correct axis order
+      # result is guaranteed to have the correct axis order
       result = w + b
 
   You can nest scopes, in which case only the inner-most scope applies, e.g.,

tensorflow/contrib/layers/python/layers/feature_column.py

@@ -451,7 +451,7 @@ def _get_input_sparse_tensor(self, input_tensor):
     return input_tensor
 
   def is_compatible(self, other_column):
-    """Check compatability of two sparse columns."""
+    """Check compatibility of two sparse columns."""
     if self.lookup_config and other_column.lookup_config:
       return self.lookup_config == other_column.lookup_config
     compatible = (self.length == other_column.length and
@@ -2129,7 +2129,7 @@ class _CrossedColumn(
         "columns", "hash_bucket_size", "hash_key", "combiner",
         "ckpt_to_load_from", "tensor_name_in_ckpt"
     ])):
-  """Represents a cross transformation also known as conjuction or combination.
+  """Represents a cross transformation also known as conjunction or combination.
 
   Instances of this class are immutable. It crosses given `columns`. Crossed
   column output will be hashed to hash_bucket_size.

tensorflow/contrib/layers/python/layers/feature_column_ops_test.py

@@ -1505,8 +1505,8 @@ def testEmbeddingColumnGradient(self):
     expected_input_shape = [4, 3, embedding_dimension]
     self.assertAllEqual(expected_input_shape, model_input.shape)
 
-    # `ids_tensor` consists of 7 instances of <empty>, 3 occurences of "b",
-    # 2 occurences of "c" and 1 instance of "a".
+    # `ids_tensor` consists of 7 instances of <empty>, 3 occurrences of "b",
+    # 2 occurrences of "c" and 1 instance of "a".
     expected_gradient_values = sorted([0., 3., 2., 1.] * embedding_dimension)
     actual_gradient_values = np.sort(gradients[0].values, axis=None)
     self.assertAllClose(expected_gradient_values, actual_gradient_values)

tensorflow/contrib/layers/python/layers/feature_column_test.py

@@ -173,7 +173,7 @@ def testSharedEmbeddingColumn(self):
     for i in range(len(b1_value)):
       self.assertAllClose(b1_value[i], b2_value[i])
 
-    # Test the case when a shared_embedding_name is explictly specified.
+    # Test the case when a shared_embedding_name is explicitly specified.
     d = fc.shared_embedding_columns(
         [a1, a2],
         dimension=4,