Merge branch 'master' into nccl2_configure
diff --git a/WORKSPACE b/WORKSPACE
index 1160587..1796182 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -9,27 +9,11 @@
"https://github.com/bazelbuild/rules_closure/archive/dbb96841cc0a5fb2664c37822803b06dab20c7d1.tar.gz", # 2018-04-13
],
)
+
load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories")
+
closure_repositories()
-http_archive(
- name = "io_bazel_rules_python",
- strip_prefix = "rules_python-8b5d0683a7d878b28fffe464779c8a53659fc645",
- urls = [
- "https://github.com/bazelbuild/rules_python/archive/8b5d0683a7d878b28fffe464779c8a53659fc645.tar.gz",
- ],
-)
-load("@io_bazel_rules_python//python:pip.bzl", "pip_repositories")
-pip_repositories()
-
-load("@io_bazel_rules_python//python:pip.bzl", "pip_import")
-pip_import(
- name = "pip_deps",
- requirements = "//tensorflow:requirements.txt",
-)
-load("@pip_deps//:requirements.bzl", "pip_install")
-pip_install()
-
# 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.
diff --git a/configure.py b/configure.py
index b1ab55b..e195d65 100644
--- a/configure.py
+++ b/configure.py
@@ -41,7 +41,6 @@
_DEFAULT_CUDA_PATH_LINUX = '/opt/cuda'
_DEFAULT_CUDA_PATH_WIN = ('C:/Program Files/NVIDIA GPU Computing '
'Toolkit/CUDA/v%s' % _DEFAULT_CUDA_VERSION)
-_DEFAULT_TENSORRT_PATH_LINUX = '/usr/lib/%s-linux-gnu' % platform.machine()
_TF_OPENCL_VERSION = '1.2'
_DEFAULT_COMPUTECPP_TOOLKIT_PATH = '/usr/local/computecpp'
_DEFAULT_TRISYCL_INCLUDE_DIR = '/usr/local/triSYCL/include'
@@ -61,6 +60,12 @@
""
]
+if platform.machine() == 'ppc64le':
+ _DEFAULT_TENSORRT_PATH_LINUX = '/usr/lib/powerpc64le-linux-gnu/'
+else:
+ _DEFAULT_TENSORRT_PATH_LINUX = '/usr/lib/%s-linux-gnu' % platform.machine()
+
+
class UserInputError(Exception):
pass
@@ -159,14 +164,18 @@
if environ_cp.get('PYTHONPATH'):
python_paths = environ_cp.get('PYTHONPATH').split(':')
try:
- library_paths = run_shell(
- [python_bin_path, '-c',
- 'import site; print("\\n".join(site.getsitepackages()))']).split('\n')
+ library_paths = run_shell([
+ python_bin_path, '-c',
+ 'import site; print("\\n".join(site.getsitepackages()))'
+ ]).split('\n')
except subprocess.CalledProcessError:
- library_paths = [run_shell(
- [python_bin_path, '-c',
- 'from distutils.sysconfig import get_python_lib;'
- 'print(get_python_lib())'])]
+ library_paths = [
+ run_shell([
+ python_bin_path, '-c',
+ 'from distutils.sysconfig import get_python_lib;'
+ 'print(get_python_lib())'
+ ])
+ ]
all_paths = set(python_paths + library_paths)
@@ -193,8 +202,7 @@
environ_cp, 'PYTHON_BIN_PATH', ask_python_bin_path,
default_python_bin_path)
# Check if the path is valid
- if os.path.isfile(python_bin_path) and os.access(
- python_bin_path, os.X_OK):
+ if os.path.isfile(python_bin_path) and os.access(python_bin_path, os.X_OK):
break
elif not os.path.exists(python_bin_path):
print('Invalid python path: %s cannot be found.' % python_bin_path)
@@ -236,8 +244,9 @@
environ_cp['PYTHON_BIN_PATH'] = python_bin_path
# Write tools/python_bin_path.sh
- with open(os.path.join(
- _TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'), 'w') as f:
+ with open(
+ os.path.join(_TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'),
+ 'w') as f:
f.write('export PYTHON_BIN_PATH="%s"' % python_bin_path)
@@ -256,7 +265,7 @@
continue
f.write('%s\n' % l)
if is_windows():
- tf_bazelrc_path = _TF_BAZELRC.replace("\\", "/")
+ tf_bazelrc_path = _TF_BAZELRC.replace('\\', '/')
else:
tf_bazelrc_path = _TF_BAZELRC
f.write('import %s\n' % tf_bazelrc_path)
@@ -267,8 +276,8 @@
These files could interfere with Bazel parsing.
"""
- makefile_download_dir = os.path.join(
- _TF_WORKSPACE_ROOT, 'tensorflow', 'contrib', 'makefile', 'downloads')
+ makefile_download_dir = os.path.join(_TF_WORKSPACE_ROOT, 'tensorflow',
+ 'contrib', 'makefile', 'downloads')
if os.path.isdir(makefile_download_dir):
for root, _, filenames in os.walk(makefile_download_dir):
for f in filenames:
@@ -336,9 +345,8 @@
'Environment variable %s must be set as a boolean indicator.\n'
'The following are accepted as TRUE : %s.\n'
'The following are accepted as FALSE: %s.\n'
- 'Current value is %s.' % (
- var_name, ', '.join(true_strings), ', '.join(false_strings),
- var))
+ 'Current value is %s.' % (var_name, ', '.join(true_strings),
+ ', '.join(false_strings), var))
while var is None:
user_input_origin = get_input(question)
@@ -361,8 +369,12 @@
return var
-def set_build_var(environ_cp, var_name, query_item, option_name,
- enabled_by_default, bazel_config_name=None):
+def set_build_var(environ_cp,
+ var_name,
+ query_item,
+ option_name,
+ enabled_by_default,
+ bazel_config_name=None):
"""Set if query_item will be enabled for the build.
Ask user if query_item will be enabled. Default is used if no input is given.
@@ -385,8 +397,8 @@
elif bazel_config_name is not None:
# TODO(mikecase): Migrate all users of configure.py to use --config Bazel
# options and not to set build configs through environment variables.
- write_to_bazelrc('build:%s --define %s=true'
- % (bazel_config_name, option_name))
+ write_to_bazelrc(
+ 'build:%s --define %s=true' % (bazel_config_name, option_name))
def set_action_env_var(environ_cp,
@@ -453,7 +465,8 @@
if which('bazel') is None:
print('Cannot find bazel. Please install bazel.')
sys.exit(0)
- curr_version = run_shell(['bazel', '--batch', '--bazelrc=/dev/null', 'version'])
+ curr_version = run_shell(
+ ['bazel', '--batch', '--bazelrc=/dev/null', 'version'])
for line in curr_version.split('\n'):
if 'Build label: ' in line:
@@ -505,6 +518,7 @@
write_to_bazelrc('build:opt --host_copt=-march=native')
write_to_bazelrc('build:opt --define with_default_optimizations=true')
+
def set_tf_cuda_clang(environ_cp):
"""set TF_CUDA_CLANG action_env.
@@ -587,16 +601,14 @@
clang_cuda_compiler_path)
-def prompt_loop_or_load_from_env(
- environ_cp,
- var_name,
- var_default,
- ask_for_var,
- check_success,
- error_msg,
- suppress_default_error=False,
- n_ask_attempts=_DEFAULT_PROMPT_ASK_ATTEMPTS
-):
+def prompt_loop_or_load_from_env(environ_cp,
+ var_name,
+ var_default,
+ ask_for_var,
+ check_success,
+ error_msg,
+ suppress_default_error=False,
+ n_ask_attempts=_DEFAULT_PROMPT_ASK_ATTEMPTS):
"""Loop over user prompts for an ENV param until receiving a valid response.
For the env param var_name, read from the environment or verify user input
@@ -635,9 +647,7 @@
)
for _ in range(n_ask_attempts):
- val = get_from_env_or_user_or_default(environ_cp,
- var_name,
- full_query,
+ val = get_from_env_or_user_or_default(environ_cp, var_name, full_query,
default)
if check_success(val):
break
@@ -645,9 +655,9 @@
print(error_msg % val)
environ_cp[var_name] = ''
else:
- raise UserInputError('Invalid %s setting was provided %d times in a row. '
- 'Assuming to be a scripting mistake.' %
- (var_name, n_ask_attempts))
+ raise UserInputError(
+ 'Invalid %s setting was provided %d times in a row. '
+ 'Assuming to be a scripting mistake.' % (var_name, n_ask_attempts))
environ_cp[var_name] = val
return val
@@ -656,8 +666,8 @@
def create_android_ndk_rule(environ_cp):
"""Set ANDROID_NDK_HOME and write Android NDK WORKSPACE rule."""
if is_windows() or is_cygwin():
- default_ndk_path = cygpath('%s/Android/Sdk/ndk-bundle' %
- environ_cp['APPDATA'])
+ default_ndk_path = cygpath(
+ '%s/Android/Sdk/ndk-bundle' % environ_cp['APPDATA'])
elif is_macos():
default_ndk_path = '%s/library/Android/Sdk/ndk-bundle' % environ_cp['HOME']
else:
@@ -674,8 +684,7 @@
ask_for_var='Please specify the home path of the Android NDK to use.',
check_success=valid_ndk_path,
error_msg=('The path %s or its child file "source.properties" '
- 'does not exist.')
- )
+ 'does not exist.'))
write_action_env_to_bazelrc('ANDROID_NDK_HOME', android_ndk_home_path)
write_action_env_to_bazelrc('ANDROID_NDK_API_LEVEL',
check_ndk_level(android_ndk_home_path))
@@ -709,9 +718,9 @@
api_levels = [x.replace('android-', '') for x in api_levels]
def valid_api_level(api_level):
- return os.path.exists(os.path.join(android_sdk_home_path,
- 'platforms',
- 'android-' + api_level))
+ return os.path.exists(
+ os.path.join(android_sdk_home_path, 'platforms',
+ 'android-' + api_level))
android_api_level = prompt_loop_or_load_from_env(
environ_cp,
@@ -726,9 +735,8 @@
versions = sorted(os.listdir(build_tools))
def valid_build_tools(version):
- return os.path.exists(os.path.join(android_sdk_home_path,
- 'build-tools',
- version))
+ return os.path.exists(
+ os.path.join(android_sdk_home_path, 'build-tools', version))
android_build_tools_version = prompt_loop_or_load_from_env(
environ_cp,
@@ -742,10 +750,8 @@
write_action_env_to_bazelrc('ANDROID_BUILD_TOOLS_VERSION',
android_build_tools_version)
- write_action_env_to_bazelrc('ANDROID_SDK_API_LEVEL',
- android_api_level)
- write_action_env_to_bazelrc('ANDROID_SDK_HOME',
- android_sdk_home_path)
+ write_action_env_to_bazelrc('ANDROID_SDK_API_LEVEL', android_api_level)
+ write_action_env_to_bazelrc('ANDROID_SDK_HOME', android_sdk_home_path)
def check_ndk_level(android_ndk_home_path):
@@ -804,6 +810,7 @@
Args:
version_str: String, the version string.
sequence_count: int, an integer.
+
Returns:
string, reformatted version string.
"""
@@ -847,12 +854,19 @@
if is_windows():
cuda_rt_lib_paths = ['lib/x64/cudart.lib']
elif is_linux():
- cuda_rt_lib_paths = ['%s/libcudart.so.%s' % (x, tf_cuda_version)
- for x in ['lib64', 'lib/x86_64-linux-gnu']]
+ cuda_rt_lib_paths = [
+ '%s/libcudart.so.%s' % (x, tf_cuda_version) for x in [
+ 'lib64',
+ 'lib/powerpc64le-linux-gnu',
+ 'lib/x86_64-linux-gnu',
+ ]
+ ]
elif is_macos():
cuda_rt_lib_paths = ['lib/libcudart.%s.dylib' % tf_cuda_version]
- cuda_toolkit_paths_full = [os.path.join(cuda_toolkit_path, x) for x in cuda_rt_lib_paths]
+ cuda_toolkit_paths_full = [
+ os.path.join(cuda_toolkit_path, x) for x in cuda_rt_lib_paths
+ ]
if any([os.path.exists(x) for x in cuda_toolkit_paths_full]):
break
@@ -925,8 +939,8 @@
cudnn_path_from_ldconfig)
if cudnn_path_from_ldconfig:
cudnn_path_from_ldconfig = cudnn_path_from_ldconfig.group(1)
- if os.path.exists('%s.%s' % (cudnn_path_from_ldconfig,
- tf_cudnn_version)):
+ if os.path.exists(
+ '%s.%s' % (cudnn_path_from_ldconfig, tf_cudnn_version)):
cudnn_install_path = os.path.dirname(cudnn_path_from_ldconfig)
break
@@ -1224,6 +1238,7 @@
Args:
environ_cp: copy of the os.environ.
+
Returns:
string of native cuda compute capabilities, separated by comma.
"""
@@ -1348,8 +1363,7 @@
else:
sycl_rt_lib_path = ''
- sycl_rt_lib_path_full = os.path.join(toolkit_path,
- sycl_rt_lib_path)
+ sycl_rt_lib_path_full = os.path.join(toolkit_path, sycl_rt_lib_path)
exists = os.path.exists(sycl_rt_lib_path_full)
if not exists:
print('Invalid SYCL %s library path. %s cannot be found' %
@@ -1377,8 +1391,8 @@
ask_trisycl_include_dir = ('Please specify the location of the triSYCL '
'include directory. (Use --config=sycl_trisycl '
'when building with Bazel) '
- '[Default is %s]: '
- ) % (_DEFAULT_TRISYCL_INCLUDE_DIR)
+ '[Default is %s]: ') % (
+ _DEFAULT_TRISYCL_INCLUDE_DIR)
while True:
trisycl_include_dir = get_from_env_or_user_or_default(
@@ -1387,13 +1401,12 @@
if os.path.exists(trisycl_include_dir):
break
- print('Invalid triSYCL include directory, %s cannot be found'
- % (trisycl_include_dir))
+ print('Invalid triSYCL include directory, %s cannot be found' %
+ (trisycl_include_dir))
# Set TRISYCL_INCLUDE_DIR
environ_cp['TRISYCL_INCLUDE_DIR'] = trisycl_include_dir
- write_action_env_to_bazelrc('TRISYCL_INCLUDE_DIR',
- trisycl_include_dir)
+ write_action_env_to_bazelrc('TRISYCL_INCLUDE_DIR', trisycl_include_dir)
def set_mpi_home(environ_cp):
@@ -1403,8 +1416,9 @@
default_mpi_home = os.path.dirname(os.path.dirname(default_mpi_home))
def valid_mpi_path(mpi_home):
- exists = (os.path.exists(os.path.join(mpi_home, 'include')) and
- os.path.exists(os.path.join(mpi_home, 'lib')))
+ exists = (
+ os.path.exists(os.path.join(mpi_home, 'include')) and
+ os.path.exists(os.path.join(mpi_home, 'lib')))
if not exists:
print('Invalid path to the MPI Toolkit. %s or %s cannot be found' %
(os.path.join(mpi_home, 'include'),
@@ -1492,11 +1506,9 @@
if get_var(
environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline',
- True,
- ('Would you like to override eigen strong inline for some C++ '
- 'compilation to reduce the compilation time?'),
- 'Eigen strong inline overridden.',
- 'Not overriding eigen strong inline, '
+ True, ('Would you like to override eigen strong inline for some C++ '
+ 'compilation to reduce the compilation time?'),
+ 'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
'some compilations could take more than 20 mins.'):
# Due to a known MSVC compiler issue
# https://github.com/tensorflow/tensorflow/issues/10521
@@ -1513,10 +1525,11 @@
def main():
parser = argparse.ArgumentParser()
- parser.add_argument("--workspace",
- type=str,
- default=_TF_WORKSPACE_ROOT,
- help="The absolute path to your active Bazel workspace.")
+ parser.add_argument(
+ '--workspace',
+ type=str,
+ default=_TF_WORKSPACE_ROOT,
+ help='The absolute path to your active Bazel workspace.')
args = parser.parse_args()
# Make a copy of os.environ to be clear when functions and getting and setting
@@ -1558,7 +1571,7 @@
# runtime to allow the Tensorflow testcases which compare numpy
# results to Tensorflow results to succeed.
if is_ppc64le():
- write_action_env_to_bazelrc("OMP_NUM_THREADS", 1)
+ write_action_env_to_bazelrc('OMP_NUM_THREADS', 1)
set_build_var(environ_cp, 'TF_NEED_JEMALLOC', 'jemalloc as malloc',
'with_jemalloc', True)
@@ -1572,12 +1585,12 @@
'with_kafka_support', True, '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',
- False, 'gdr')
+ set_build_var(environ_cp, 'TF_NEED_GDR', 'GDR', 'with_gdr_support', False,
+ 'gdr')
set_build_var(environ_cp, 'TF_NEED_VERBS', 'VERBS', 'with_verbs_support',
False, 'verbs')
- set_build_var(environ_cp, 'TF_NEED_NGRAPH', 'nGraph',
- 'with_ngraph_support', False, 'ngraph')
+ set_build_var(environ_cp, 'TF_NEED_NGRAPH', 'nGraph', 'with_ngraph_support',
+ False, 'ngraph')
set_action_env_var(environ_cp, 'TF_NEED_OPENCL_SYCL', 'OpenCL SYCL', False)
if environ_cp.get('TF_NEED_OPENCL_SYCL') == '1':
@@ -1643,13 +1656,10 @@
# Add a config option to build TensorFlow 2.0 API.
write_to_bazelrc('build:v2 --define=tf_api_version=2')
- if get_var(
- environ_cp, 'TF_SET_ANDROID_WORKSPACE', 'android workspace',
- False,
- ('Would you like to interactively configure ./WORKSPACE for '
- 'Android builds?'),
- 'Searching for NDK and SDK installations.',
- 'Not configuring the WORKSPACE for Android builds.'):
+ if get_var(environ_cp, 'TF_SET_ANDROID_WORKSPACE', 'android workspace', False,
+ ('Would you like to interactively configure ./WORKSPACE for '
+ 'Android builds?'), 'Searching for NDK and SDK installations.',
+ 'Not configuring the WORKSPACE for Android builds.'):
create_android_ndk_rule(environ_cp)
create_android_sdk_rule(environ_cp)
@@ -1663,5 +1673,6 @@
config_info_line('mkl', 'Build with MKL support.')
config_info_line('monolithic', 'Config for mostly static monolithic build.')
+
if __name__ == '__main__':
main()
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index ebcabb4..c6d6f04 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -353,7 +353,7 @@
# MKL Support
if (tensorflow_ENABLE_MKL_SUPPORT)
- add_definitions(-DINTEL_MKL -DEIGEN_USE_VML)
+ add_definitions(-DINTEL_MKL -DEIGEN_USE_VML -DENABLE_MKL)
include(mkl)
list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkl_STATIC_LIBRARIES})
list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkl_copy_shared_to_destination)
diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
index 2ed4f69..efd6185 100644
--- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc
@@ -108,7 +108,7 @@
EXPECT_EQ(2, shape.dim(0).size());
EXPECT_EQ(1, shape.dim(1).size());
if (node->name() == y->name()) {
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
// if MKL is used, it goes through various additional
// graph rewrite pass. In TF, everytime a graph pass
// happens, "constant" nodes are allocated
@@ -120,13 +120,13 @@
EXPECT_EQ(29, cm->AllocationId(node, 0));
#else
EXPECT_EQ(21, cm->AllocationId(node, 0));
-#endif
+#endif // INTEL_MKL && ENABLE_MKL
} else {
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
EXPECT_EQ(30, cm->AllocationId(node, 0));
#else
EXPECT_EQ(22, cm->AllocationId(node, 0));
-#endif
+#endif // INTEL_MKL && ENABLE_MKL
}
}
EXPECT_LE(0, cm->MaxExecutionTime(node));
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator_test.cc b/tensorflow/core/common_runtime/mkl_cpu_allocator_test.cc
index a67411c..e08ab57 100644
--- a/tensorflow/core/common_runtime/mkl_cpu_allocator_test.cc
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator_test.cc
@@ -13,7 +13,7 @@
limitations under the License.
==============================================================================*/
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#include "tensorflow/core/common_runtime/mkl_cpu_allocator.h"
@@ -50,4 +50,4 @@
} // namespace tensorflow
-#endif // INTEL_MKL
+#endif // INTEL_MKL && ENABLE_MKL
diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc
index 0fbc20b..8587d17 100644
--- a/tensorflow/core/common_runtime/threadpool_device.cc
+++ b/tensorflow/core/common_runtime/threadpool_device.cc
@@ -113,8 +113,11 @@
}
};
+#ifdef ENABLE_MKL
REGISTER_MEM_ALLOCATOR("MklCPUAllocator", 200, MklCPUAllocatorFactory);
+#endif // ENABLE_MKL
+
} // namespace
-#endif
+#endif // INTEL_MKL
} // namespace tensorflow
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 3df6776..1dea6da 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -813,7 +813,7 @@
}
Tensor Tensor::SubSlice(int64 index) const {
- CHECK_GE(dims(), 2); // Crash ok.
+ CHECK_GE(dims(), 1); // Crash ok.
CHECK_LE(0, index); // Crash ok.
int64 dim0_size = shape_.dim_size(0);
CHECK_LE(index, dim0_size); // Crash ok.
diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h
index 8a0c70f..d0f9eb5 100644
--- a/tensorflow/core/framework/tensor.h
+++ b/tensorflow/core/framework/tensor.h
@@ -219,7 +219,7 @@
/// must check the returned tensor's alignment before calling certain
/// methods that have alignment requirement (e.g., `flat()`, `tensor()`).
///
- /// REQUIRES: `dims()` >= 2
+ /// REQUIRES: `dims()` >= 1
/// REQUIRES: `0 <= dim0_start < dim_size(0)`
Tensor SubSlice(int64 index) const;
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index 0bfa53e..c596604 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -1246,6 +1246,9 @@
EXPECT_EQ(&tx(5, j, k), &ty(j, k));
}
}
+ Tensor z = y.SubSlice(3).SubSlice(31);
+ auto tz = z.unaligned_flat<float>();
+ EXPECT_EQ(*tz.data(), 5.0);
}
{
// Test unaligned access via a SubSlice.
diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index f5b0105..37b88f1 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -977,7 +977,9 @@
// nodes. Do not change the ordering of the Mkl passes.
const OptimizationPassRegistry::Grouping kMklLayoutRewritePassGroup =
OptimizationPassRegistry::POST_PARTITIONING;
+#ifdef ENABLE_MKL
REGISTER_OPTIMIZATION(kMklLayoutRewritePassGroup, 1, MklLayoutRewritePass);
+#endif // ENABLE_MKL
//////////////////////////////////////////////////////////////////////////
// Helper functions for creating new node
@@ -3150,7 +3152,9 @@
// nodes. Do not change the ordering of the Mkl passes.
const OptimizationPassRegistry::Grouping kMklLayoutRewritePassGroup =
OptimizationPassRegistry::POST_PARTITIONING;
+#ifdef ENABLE_MKL
REGISTER_OPTIMIZATION(kMklLayoutRewritePassGroup, 1, MklLayoutRewritePass);
+#endif // ENABLE_MKL
//////////////////////////////////////////////////////////////////////////
// Helper functions for creating new node
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc
index e8bac84..f42a4ee 100644
--- a/tensorflow/core/graph/mkl_layout_pass_test.cc
+++ b/tensorflow/core/graph/mkl_layout_pass_test.cc
@@ -13,7 +13,7 @@
limitations under the License.
==============================================================================*/
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#include "tensorflow/core/graph/mkl_layout_pass.h"
#include "tensorflow/core/graph/mkl_graph_util.h"
@@ -3586,4 +3586,4 @@
} // namespace tensorflow
-#endif /* INTEL_MKL */
+#endif // INTEL_MKL && ENABLE_MKL
diff --git a/tensorflow/core/graph/mkl_tfconversion_pass.cc b/tensorflow/core/graph/mkl_tfconversion_pass.cc
index b67a321..8c5ffd7 100644
--- a/tensorflow/core/graph/mkl_tfconversion_pass.cc
+++ b/tensorflow/core/graph/mkl_tfconversion_pass.cc
@@ -133,7 +133,9 @@
// complete picture of inputs and outputs of the nodes in the graphs.
const OptimizationPassRegistry::Grouping kMklTfConvPassGroup =
OptimizationPassRegistry::POST_PARTITIONING;
+#ifdef ENABLE_MKL
REGISTER_OPTIMIZATION(kMklTfConvPassGroup, 2, MklToTfConversionPass);
+#endif // ENABLE_MKL
Status MklToTfConversionPass::InsertConversionNodeOnEdge(
std::unique_ptr<Graph>* g, Edge* e) {
diff --git a/tensorflow/core/graph/mkl_tfconversion_pass_test.cc b/tensorflow/core/graph/mkl_tfconversion_pass_test.cc
index ebcb6de..319437a 100644
--- a/tensorflow/core/graph/mkl_tfconversion_pass_test.cc
+++ b/tensorflow/core/graph/mkl_tfconversion_pass_test.cc
@@ -13,7 +13,7 @@
limitations under the License.
==============================================================================*/
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#include "tensorflow/core/graph/mkl_tfconversion_pass.h"
#include "tensorflow/core/graph/mkl_graph_util.h"
@@ -304,4 +304,4 @@
} // namespace
} // namespace tensorflow
-#endif /* INTEL_MKL */
+#endif // INTEL_MKL && ENABLE_MKL
diff --git a/tensorflow/core/kernels/batch_matmul_op_complex.cc b/tensorflow/core/kernels/batch_matmul_op_complex.cc
index 54c45bf..f48bd0c 100644
--- a/tensorflow/core/kernels/batch_matmul_op_complex.cc
+++ b/tensorflow/core/kernels/batch_matmul_op_complex.cc
@@ -17,14 +17,18 @@
namespace tensorflow {
-#if !defined(INTEL_MKL) || defined(INTEL_MKL_DNN_ONLY)
+// MKL_ML registers its own complex64/128 kernels in mkl_batch_matmul_op.cc
+// if defined(INTEL_MKL) && !defined(INTEL_MKL_DNN_ONLY) && defined(ENABLE_MKL).
+// Anything else (the complement) should register the TF ones.
+// (MKL-DNN doesn't implement these kernels either.)
+#if !defined(INTEL_MKL) || defined(INTEL_MKL_DNN_ONLY) || !defined(ENABLE_MKL)
TF_CALL_complex64(REGISTER_BATCH_MATMUL_CPU);
TF_CALL_complex128(REGISTER_BATCH_MATMUL_CPU);
-#endif
+#endif // !INTEL_MKL || INTEL_MKL_DNN_ONLY || !ENABLE_MKL
#if GOOGLE_CUDA
TF_CALL_complex64(REGISTER_BATCH_MATMUL_GPU);
TF_CALL_complex128(REGISTER_BATCH_MATMUL_GPU);
-#endif
+#endif // GOOGLE_CUDA
} // namespace tensorflow
diff --git a/tensorflow/core/kernels/batch_matmul_op_real.cc b/tensorflow/core/kernels/batch_matmul_op_real.cc
index 584b507..25ae795 100644
--- a/tensorflow/core/kernels/batch_matmul_op_real.cc
+++ b/tensorflow/core/kernels/batch_matmul_op_real.cc
@@ -21,10 +21,15 @@
namespace tensorflow {
-#if !defined(INTEL_MKL) || defined(INTEL_MKL_DNN_ONLY)
+// MKL_ML registers its own float and double kernels in mkl_batch_matmul_op.cc
+// if defined(INTEL_MKL) && !defined(INTEL_MKL_DNN_ONLY) && defined(ENABLE_MKL).
+// Anything else (the complement) should register the TF ones.
+// (MKL-DNN doesn't implement these kernels either.)
+#if !defined(INTEL_MKL) || defined(INTEL_MKL_DNN_ONLY) || !defined(ENABLE_MKL)
TF_CALL_float(REGISTER_BATCH_MATMUL_CPU);
TF_CALL_double(REGISTER_BATCH_MATMUL_CPU);
-#endif
+#endif // !INTEL_MKL || INTEL_MKL_DNN_ONLY || !ENABLE_MKL
+
TF_CALL_half(REGISTER_BATCH_MATMUL_CPU);
TF_CALL_int32(REGISTER_BATCH_MATMUL_CPU);
diff --git a/tensorflow/core/kernels/cwise_ops_common.cc b/tensorflow/core/kernels/cwise_ops_common.cc
index 980edff..8ad3b4d1f 100644
--- a/tensorflow/core/kernels/cwise_ops_common.cc
+++ b/tensorflow/core/kernels/cwise_ops_common.cc
@@ -20,9 +20,9 @@
BinaryOpShared::BinaryOpShared(OpKernelConstruction* ctx, DataType out,
DataType in)
: OpKernel(ctx) {
-#ifndef INTEL_MKL
+#if !defined(INTEL_MKL) || !defined(ENABLE_MKL)
OP_REQUIRES_OK(ctx, ctx->MatchSignature({in, in}, {out}));
-#endif
+#endif // !INTEL_MKL || !ENABLE_MKL
}
void BinaryOpShared::SetUnimplementedError(OpKernelContext* ctx) {
diff --git a/tensorflow/core/kernels/gather_nd_op_cpu_impl.h b/tensorflow/core/kernels/gather_nd_op_cpu_impl.h
index 277ee2b..1c78de2 100644
--- a/tensorflow/core/kernels/gather_nd_op_cpu_impl.h
+++ b/tensorflow/core/kernels/gather_nd_op_cpu_impl.h
@@ -114,7 +114,7 @@
generator::GatherNdSliceGenerator<T, Index, IXDIM> gather_nd_generator(
slice_size, Tindices, Tparams, Tout, &error_loc);
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
// Eigen implementation below is not highly performant. gather_nd_generator
// does not seem to be called in parallel, leading to very poor performance.
// Additionally, since it uses scalar (Tscratch) to invoke 'generate', it
@@ -126,12 +126,12 @@
const Eigen::array<Eigen::DenseIndex, 1> loc{i};
gather_nd_generator(loc);
}
-#else // INTEL_MKL
+#else // INTEL_MKL && ENABLE_MKL
Tscratch.device(d) = Tscratch.reshape(reshape_dims)
.broadcast(broadcast_dims)
.generate(gather_nd_generator)
.sum();
-#endif
+#endif // INTEL_MKL && ENABLE_MKL
// error_loc() returns -1 if there's no out-of-bounds index,
// otherwise it returns the location of an OOB index in Tindices.
diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc
index 79967aa..4ad390a 100644
--- a/tensorflow/core/kernels/matmul_op.cc
+++ b/tensorflow/core/kernels/matmul_op.cc
@@ -578,7 +578,7 @@
.Label("cublas"), \
MatMulOp<GPUDevice, T, true /* cublas */>)
-#if defined(INTEL_MKL)
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
// MKL does not support half, bfloat16 and int32 types for
// matrix-multiplication, so register the kernel to use default Eigen based
@@ -606,9 +606,9 @@
TF_CALL_complex64(REGISTER_CPU_EIGEN);
TF_CALL_complex128(REGISTER_CPU_EIGEN);
TF_CALL_double(REGISTER_CPU_EIGEN);
-#endif
+#endif // INTEL_MKL_DNN_ONLY
-#else // INTEL MKL
+#else // INTEL_MKL && ENABLE_MKL
TF_CALL_float(REGISTER_CPU);
TF_CALL_double(REGISTER_CPU);
TF_CALL_half(REGISTER_CPU);
@@ -616,7 +616,7 @@
TF_CALL_int32(REGISTER_CPU);
TF_CALL_complex64(REGISTER_CPU);
TF_CALL_complex128(REGISTER_CPU);
-#endif
+#endif // INTEL_MKL && ENABLE_MKL
#if GOOGLE_CUDA
TF_CALL_float(REGISTER_GPU);
diff --git a/tensorflow/core/kernels/mkl_batch_matmul_op.cc b/tensorflow/core/kernels/mkl_batch_matmul_op.cc
index 0841395..bc135de 100644
--- a/tensorflow/core/kernels/mkl_batch_matmul_op.cc
+++ b/tensorflow/core/kernels/mkl_batch_matmul_op.cc
@@ -223,10 +223,12 @@
Name("BatchMatMul").Device(DEVICE_CPU).TypeConstraint<TYPE>("T"), \
BatchMatMulMkl<CPUDevice, TYPE>)
+#ifdef ENABLE_MKL
TF_CALL_float(REGISTER_BATCH_MATMUL_MKL);
TF_CALL_double(REGISTER_BATCH_MATMUL_MKL);
TF_CALL_complex64(REGISTER_BATCH_MATMUL_MKL);
TF_CALL_complex128(REGISTER_BATCH_MATMUL_MKL);
+#endif // ENABLE_MKL
} // end namespace tensorflow
#endif
diff --git a/tensorflow/core/kernels/mkl_matmul_op.cc b/tensorflow/core/kernels/mkl_matmul_op.cc
index 077d62c..f4788f4 100644
--- a/tensorflow/core/kernels/mkl_matmul_op.cc
+++ b/tensorflow/core/kernels/mkl_matmul_op.cc
@@ -217,7 +217,7 @@
reinterpret_cast<const MKL_Complex16*>(b), ldb, &beta,
reinterpret_cast<MKL_Complex16*>(c), ldc);
}
-#endif
+#endif // !INTEL_MKL_DNN_ONLY
};
#define REGISTER_CPU(T) \
@@ -225,6 +225,7 @@
Name("MatMul").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
MklMatMulOp<CPUDevice, T, false /* cublas, ignored for CPU */>);
+#ifdef ENABLE_MKL
// TODO(inteltf) Consider template specialization when adding/removing
// additional types
TF_CALL_float(REGISTER_CPU);
@@ -233,7 +234,8 @@
TF_CALL_double(REGISTER_CPU);
TF_CALL_complex64(REGISTER_CPU);
TF_CALL_complex128(REGISTER_CPU);
-#endif
+#endif // !INTEL_MKL_DNN_ONLY
+#endif // ENABLE_MKL
} // namespace tensorflow
#endif // INTEL_MKL
diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc
index 7759447..97f77e4 100644
--- a/tensorflow/core/kernels/slice_op.cc
+++ b/tensorflow/core/kernels/slice_op.cc
@@ -411,7 +411,7 @@
context->input(0).tensor<T, NDIM>(), indices, sizes);
}
};
-#endif
+#endif // INTEL_MKL
// Forward declarations of the functor specializations for declared in the
// sharded source files.
@@ -440,19 +440,7 @@
#undef DECLARE_CPU_SPEC
} // namespace functor
-#ifndef INTEL_MKL
-#define REGISTER_SLICE(type) \
- REGISTER_KERNEL_BUILDER(Name("Slice") \
- .Device(DEVICE_CPU) \
- .TypeConstraint<type>("T") \
- .HostMemory("begin") \
- .HostMemory("size"), \
- SliceOp<CPUDevice, type>)
-
-TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
-TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
-#undef REGISTER_SLICE
-#else
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#define REGISTER_SLICE(type) \
REGISTER_KERNEL_BUILDER(Name("Slice") \
.Device(DEVICE_CPU) \
@@ -460,11 +448,19 @@
.HostMemory("begin") \
.HostMemory("size"), \
MklSliceOp<CPUDevice, type>)
+#else
+#define REGISTER_SLICE(type) \
+ REGISTER_KERNEL_BUILDER(Name("Slice") \
+ .Device(DEVICE_CPU) \
+ .TypeConstraint<type>("T") \
+ .HostMemory("begin") \
+ .HostMemory("size"), \
+ SliceOp<CPUDevice, type>)
+#endif // INTEL_MKL && ENABLE_MKL
TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
#undef REGISTER_SLICE
-#endif // INTEL_MKL
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc
index 0f0f65c..48e392c 100644
--- a/tensorflow/core/kernels/transpose_op.cc
+++ b/tensorflow/core/kernels/transpose_op.cc
@@ -218,7 +218,7 @@
perm, out);
}
-#if defined(INTEL_MKL)
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
#define REGISTER(T) \
REGISTER_KERNEL_BUILDER(Name("Transpose") \
.Device(DEVICE_CPU) \
@@ -230,11 +230,8 @@
.TypeConstraint<T>("T") \
.HostMemory("perm"), \
MklConjugateTransposeCpuOp);
-TF_CALL_ALL_TYPES(REGISTER);
-#undef REGISTER
-#else // INTEL_MKL
-
+#else // INTEL_MKL && ENABLE_MKL
#define REGISTER(T) \
REGISTER_KERNEL_BUILDER(Name("Transpose") \
.Device(DEVICE_CPU) \
@@ -246,9 +243,10 @@
.TypeConstraint<T>("T") \
.HostMemory("perm"), \
ConjugateTransposeCpuOp);
+#endif // INTEL_MKL && ENABLE_MKL
+
TF_CALL_ALL_TYPES(REGISTER)
#undef REGISTER
-#endif // INTEL_MKL
#if GOOGLE_CUDA
Status TransposeGpuOp::DoTranspose(OpKernelContext* ctx, const Tensor& in,
diff --git a/tensorflow/core/util/port.cc b/tensorflow/core/util/port.cc
index c081cea..e01058d 100644
--- a/tensorflow/core/util/port.cc
+++ b/tensorflow/core/util/port.cc
@@ -38,10 +38,10 @@
}
bool IsMklEnabled() {
-#ifdef INTEL_MKL
+#if defined(INTEL_MKL) && defined(ENABLE_MKL)
return true;
#else
return false;
-#endif
+#endif // INTEL_MKL && ENABLE_MKL
}
} // end namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 79f1446..410b3a5 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -333,6 +333,7 @@
"//tensorflow/core:lib",
"//tensorflow/core:lib_internal",
"//third_party/python_runtime:headers",
+ "@com_google_absl//absl/memory",
],
)
diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py
index 74fe1fe..ce23073 100644
--- a/tensorflow/python/compat/compat.py
+++ b/tensorflow/python/compat/compat.py
@@ -26,7 +26,7 @@
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export
-_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 9, 25)
+_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 9, 26)
@tf_export("compat.forward_compatible")
diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index ac011a2..4a72c4b 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -7,7 +7,6 @@
package(default_visibility = ["//visibility:public"])
-load("@pip_deps//:requirements.bzl", "requirement")
load("//tensorflow:tensorflow.bzl", "py_test")
load("//tensorflow:tensorflow.bzl", "cuda_py_test")
@@ -63,7 +62,6 @@
":backend",
":engine",
":layers",
- requirement("keras_applications"),
"//tensorflow/python/saved_model",
"//tensorflow/python:training",
],
diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc
index 562bbdc..2087957 100644
--- a/tensorflow/python/util/util.cc
+++ b/tensorflow/python/util/util.cc
@@ -15,9 +15,11 @@
#include "tensorflow/python/util/util.h"
#include <functional>
+#include <memory>
#include <unordered_map>
#include <vector>
+#include "absl/memory/memory.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/logging.h"
@@ -222,93 +224,136 @@
return check_cache->CachedLookup(o);
}
-// Implements the same idea as tensorflow.util.nest._yield_value
-// During construction we check if the iterable is a dictionary.
-// If so, we construct a sequence from its sorted keys that will be used
-// for iteration.
-// If not, we construct a sequence directly from the iterable.
-// At each step, we get the next element from the sequence and use it
-// either as a key or return it directly.
-//
-// 'iterable' must not be modified while ValIterator is used.
-class ValIterator {
+// ValueIterator interface
+class ValueIterator {
public:
- explicit ValIterator(PyObject* iterable)
- : dict_(nullptr),
- mapping_(nullptr),
- last_mapping_element_(nullptr),
- seq_(nullptr),
- index_(0) {
- if (PyDict_Check(iterable)) {
- dict_ = iterable;
- // PyDict_Keys returns a list, which can be used with
- // PySequence_Fast_GET_ITEM.
- seq_ = PyDict_Keys(iterable);
- // Iterate through dictionaries in a deterministic order by sorting the
- // keys. Notice this means that we ignore the original order of
- // `OrderedDict` instances. This is intentional, to avoid potential
- // bugs caused by mixing ordered and plain dicts (e.g., flattening
- // a dict but using a corresponding `OrderedDict` to pack it back).
- PyList_Sort(seq_);
- } else if (IsMappingHelper(iterable)) {
- mapping_ = iterable;
- seq_ = MappingKeys(iterable);
- PyList_Sort(seq_);
+ virtual ~ValueIterator() {}
+ virtual Safe_PyObjectPtr next() = 0;
+
+ bool valid() const { return is_valid_; }
+
+ protected:
+ void invalidate() { is_valid_ = false; }
+
+ private:
+ bool is_valid_ = true;
+};
+
+using ValueIteratorPtr = std::unique_ptr<ValueIterator>;
+
+// Iterate through dictionaries in a deterministic order by sorting the
+// keys. Notice this means that we ignore the original order of
+// `OrderedDict` instances. This is intentional, to avoid potential
+// bugs caused by mixing ordered and plain dicts (e.g., flattening
+// a dict but using a corresponding `OrderedDict` to pack it back).
+class DictValueIterator : public ValueIterator {
+ public:
+ explicit DictValueIterator(PyObject* dict)
+ : dict_(dict), keys_(PyDict_Keys(dict)) {
+ if (PyList_Sort(keys_.get()) == -1) {
+ invalidate();
} else {
- seq_ = PySequence_Fast(iterable, "");
+ iter_.reset(PyObject_GetIter(keys_.get()));
}
- size_ = PySequence_Fast_GET_SIZE(seq_);
}
- ~ValIterator() { Py_DECREF(seq_); }
-
- // Return a borrowed reference to the next element from iterable.
- // Return nullptr when iteration is over.
- PyObject* next() {
- if (TF_PREDICT_FALSE(seq_ == nullptr)) {
- return nullptr;
- }
- PyObject* element = nullptr;
- if (index_ < size_) {
- // Both PySequence_Fast_GET_ITEM and PyDict_GetItem return borrowed
- // references. For general mappings, ValIterator keeps a reference to the
- // last retrieved element (and decrefs it before producing the next
- // element) to abstract away the borrowed/new difference.
- element = PySequence_Fast_GET_ITEM(seq_, index_);
- ++index_;
- if (dict_ != nullptr) {
- element = PyDict_GetItem(dict_, element);
- if (element == nullptr) {
- PyErr_SetString(PyExc_RuntimeError,
- "Dictionary was modified during iteration over it");
- return nullptr;
- }
- } else if (mapping_ != nullptr) {
- element = PyObject_GetItem(mapping_, element);
- if (element == nullptr) {
- PyErr_SetString(PyExc_RuntimeError,
- "Mapping was modified during iteration over it");
- return nullptr;
- }
- last_mapping_element_.reset(element);
+ Safe_PyObjectPtr next() override {
+ Safe_PyObjectPtr result;
+ Safe_PyObjectPtr key(PyIter_Next(iter_.get()));
+ if (key) {
+ // PyDict_GetItem returns a borrowed reference.
+ PyObject* elem = PyDict_GetItem(dict_, key.get());
+ if (elem) {
+ Py_INCREF(elem);
+ result.reset(elem);
+ } else {
+ PyErr_SetString(PyExc_RuntimeError,
+ "Dictionary was modified during iteration over it");
}
}
- return element;
+ return result;
}
private:
- // Special casing for things that pass PyDict_Check (faster, no Python calls)
PyObject* dict_;
+ Safe_PyObjectPtr keys_;
+ Safe_PyObjectPtr iter_;
+};
- // General mappings which have custom Python logic
+// Iterate over mapping objects by sorting the keys first
+class MappingValueIterator : public ValueIterator {
+ public:
+ explicit MappingValueIterator(PyObject* mapping)
+ : mapping_(mapping), keys_(MappingKeys(mapping)) {
+ if (!keys_ || PyList_Sort(keys_.get()) == -1) {
+ invalidate();
+ } else {
+ iter_.reset(PyObject_GetIter(keys_.get()));
+ }
+ }
+
+ Safe_PyObjectPtr next() override {
+ Safe_PyObjectPtr result;
+ Safe_PyObjectPtr key(PyIter_Next(iter_.get()));
+ if (key) {
+ // Unlike PyDict_GetItem, PyObject_GetItem returns a new reference.
+ PyObject* elem = PyObject_GetItem(mapping_, key.get());
+ if (elem) {
+ result.reset(elem);
+ } else {
+ PyErr_SetString(PyExc_RuntimeError,
+ "Mapping was modified during iteration over it");
+ }
+ }
+ return result;
+ }
+
+ private:
PyObject* mapping_;
- Safe_PyObjectPtr last_mapping_element_;
+ Safe_PyObjectPtr keys_;
+ Safe_PyObjectPtr iter_;
+};
- PyObject* seq_;
- Py_ssize_t size_;
+// Iterate over a sequence, by index.
+class SequenceValueIterator : public ValueIterator {
+ public:
+ explicit SequenceValueIterator(PyObject* iterable)
+ : seq_(PySequence_Fast(iterable, "")),
+ size_(PySequence_Fast_GET_SIZE(seq_.get())),
+ index_(0) {}
+
+ Safe_PyObjectPtr next() override {
+ Safe_PyObjectPtr result;
+ if (index_ < size_) {
+ // PySequence_Fast_GET_ITEM returns a borrowed reference.
+ PyObject* elem = PySequence_Fast_GET_ITEM(seq_.get(), index_);
+ ++index_;
+ Py_INCREF(elem);
+ result.reset(elem);
+ }
+
+ return result;
+ }
+
+ private:
+ Safe_PyObjectPtr seq_;
+ const Py_ssize_t size_;
Py_ssize_t index_;
};
+// Just return itself as a single item.
+class SparseTensorValueIterator : public ValueIterator {
+ public:
+ explicit SparseTensorValueIterator(PyObject* tensor) : tensor_(tensor) {
+ Py_INCREF(tensor);
+ }
+
+ Safe_PyObjectPtr next() override { return std::move(tensor_); }
+
+ private:
+ Safe_PyObjectPtr tensor_;
+};
+
bool IsSparseTensorValueType(PyObject* o) {
if (TF_PREDICT_FALSE(SparseTensorValueType == nullptr)) {
return false;
@@ -322,93 +367,33 @@
!IsSparseTensorValueType(o);
}
-bool GetNextValuesForDict(PyObject* nested,
- std::vector<Safe_PyObjectPtr>* next_values) {
- Safe_PyObjectPtr keys(PyDict_Keys(nested));
- if (PyList_Sort(keys.get()) == -1) return false;
- Py_ssize_t size = PyList_Size(keys.get());
- for (Py_ssize_t i = 0; i < size; ++i) {
- // We know that key and item will not be deleted because nested owns
- // a reference to them and callers of flatten must not modify nested
- // while the method is running.
- PyObject* key = PyList_GET_ITEM(keys.get(), i);
- PyObject* item = PyDict_GetItem(nested, key);
- Py_INCREF(item);
- next_values->emplace_back(item);
- }
- return true;
-}
-
-bool GetNextValuesForMapping(PyObject* nested,
- std::vector<Safe_PyObjectPtr>* next_values) {
- Safe_PyObjectPtr keys(MappingKeys(nested));
- if (keys.get() == nullptr) {
- return false;
- }
- if (PyList_Sort(keys.get()) == -1) return false;
- Py_ssize_t size = PyList_Size(keys.get());
- for (Py_ssize_t i = 0; i < size; ++i) {
- PyObject* key = PyList_GET_ITEM(keys.get(), i);
- // Unlike PyDict_GetItem, PyObject_GetItem returns a new reference.
- PyObject* item = PyObject_GetItem(nested, key);
- next_values->emplace_back(item);
- }
- return true;
-}
-
-bool GetNextValuesForIterable(PyObject* nested,
- std::vector<Safe_PyObjectPtr>* next_values) {
- PyObject* item;
- PyObject* iterator = PyObject_GetIter(nested);
- if (iterator == nullptr || PyErr_Occurred()) {
- return false;
- }
- while ((item = PyIter_Next(iterator)) != nullptr) {
- next_values->emplace_back(item);
- }
- Py_DECREF(iterator);
- return true;
-}
-
-// GetNextValues returns the values that the FlattenHelper function will recurse
-// over next.
-bool GetNextValues(PyObject* nested,
- std::vector<Safe_PyObjectPtr>* next_values) {
+ValueIteratorPtr GetValueIterator(PyObject* nested) {
if (PyDict_Check(nested)) {
- // if nested is dictionary, sort it by key and recurse on each value
- return GetNextValuesForDict(nested, next_values);
+ return absl::make_unique<DictValueIterator>(nested);
} else if (IsMappingHelper(nested)) {
- // same treatment as dictionaries, but for custom mapping types
- return GetNextValuesForMapping(nested, next_values);
+ return absl::make_unique<MappingValueIterator>(nested);
+ } else {
+ return absl::make_unique<SequenceValueIterator>(nested);
}
- // iterate and recurse
- return GetNextValuesForIterable(nested, next_values);
}
-// Similar to above, just specialized for the functions in the data pacakage.
-bool GetNextValuesForData(PyObject* nested,
- std::vector<Safe_PyObjectPtr>* next_values) {
+// Similar to above, just specialized for the functions in the data package.
+ValueIteratorPtr GetValueIteratorForData(PyObject* nested) {
if (PyDict_Check(nested)) {
- // if nested is dictionary, sort it by key and recurse on each value
- return GetNextValuesForDict(nested, next_values);
+ return absl::make_unique<DictValueIterator>(nested);
} else if (IsMappingHelper(nested)) {
- // same treatment as dictionaries, but for custom mapping types
- return GetNextValuesForMapping(nested, next_values);
+ return absl::make_unique<MappingValueIterator>(nested);
} else if (IsSparseTensorValueType(nested)) {
- // if nested is a SparseTensorValue, just return itself as a single item
- Py_INCREF(nested);
- next_values->emplace_back(nested);
- return true;
+ return absl::make_unique<SparseTensorValueIterator>(nested);
+ } else {
+ return absl::make_unique<SequenceValueIterator>(nested);
}
- // iterate and recurse
- return GetNextValuesForIterable(nested, next_values);
}
bool FlattenHelper(
PyObject* nested, PyObject* list,
const std::function<int(PyObject*)>& is_sequence_helper,
- const std::function<bool(PyObject*, std::vector<Safe_PyObjectPtr>*)>&
- next_values_getter) {
+ const std::function<ValueIteratorPtr(PyObject*)>& value_iterator_getter) {
// if nested is not a sequence, append itself and exit
int is_seq = is_sequence_helper(nested);
if (is_seq == -1) return false;
@@ -416,16 +401,15 @@
return PyList_Append(list, nested) != -1;
}
- std::vector<Safe_PyObjectPtr> next_values;
- // Get the next values to recurse over.
- if (!next_values_getter(nested, &next_values)) return false;
+ ValueIteratorPtr iter = value_iterator_getter(nested);
+ if (!iter->valid()) return false;
- for (const auto& item : next_values) {
+ for (Safe_PyObjectPtr item = iter->next(); item; item = iter->next()) {
if (Py_EnterRecursiveCall(" in flatten")) {
return false;
}
- const bool success =
- FlattenHelper(item.get(), list, is_sequence_helper, next_values_getter);
+ const bool success = FlattenHelper(item.get(), list, is_sequence_helper,
+ value_iterator_getter);
Py_LeaveRecursiveCall();
if (!success) {
return false;
@@ -579,22 +563,25 @@
}
}
- ValIterator iter1(o1);
- ValIterator iter2(o2);
+ ValueIteratorPtr iter1 = GetValueIterator(o1);
+ ValueIteratorPtr iter2 = GetValueIterator(o2);
+
+ if (!iter1->valid() || !iter2->valid()) return false;
while (true) {
- PyObject* v1 = iter1.next();
- PyObject* v2 = iter2.next();
- if (v1 != nullptr && v2 != nullptr) {
+ Safe_PyObjectPtr v1 = iter1->next();
+ Safe_PyObjectPtr v2 = iter2->next();
+ if (v1 && v2) {
if (Py_EnterRecursiveCall(" in assert_same_structure")) {
return false;
}
- bool no_internal_errors = AssertSameStructureHelper(
- v1, v2, check_types, error_msg, is_type_error, is_sequence_helper);
+ bool no_internal_errors =
+ AssertSameStructureHelper(v1.get(), v2.get(), check_types, error_msg,
+ is_type_error, is_sequence_helper);
Py_LeaveRecursiveCall();
if (!no_internal_errors) return false;
if (!error_msg->empty()) return true;
- } else if (v1 == nullptr && v2 == nullptr) {
+ } else if (!v1 && !v2) {
// Done with all recursive calls. Structure matched.
return true;
} else {
@@ -655,7 +642,7 @@
PyObject* Flatten(PyObject* nested) {
PyObject* list = PyList_New(0);
- if (FlattenHelper(nested, list, IsSequenceHelper, GetNextValues)) {
+ if (FlattenHelper(nested, list, IsSequenceHelper, GetValueIterator)) {
return list;
} else {
Py_DECREF(list);
@@ -668,7 +655,7 @@
PyObject* FlattenForData(PyObject* nested) {
PyObject* list = PyList_New(0);
if (FlattenHelper(nested, list, IsSequenceForDataHelper,
- GetNextValuesForData)) {
+ GetValueIteratorForData)) {
return list;
} else {
Py_DECREF(list);
diff --git a/tensorflow/requirements.txt b/tensorflow/requirements.txt
deleted file mode 100644
index 6e111ed..0000000
--- a/tensorflow/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-keras_applications >= 1.0.5
-keras_preprocessing >= 1.0.3
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 7ddaf78..d6c75d6 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -22,6 +22,7 @@
)
load(
"//third_party/mkl:build_defs.bzl",
+ "if_enable_mkl",
"if_mkl",
"if_mkl_lnx_x64",
"if_mkl_ml",
@@ -237,6 +238,7 @@
if_tensorrt(["-DGOOGLE_TENSORRT=1"]) +
if_mkl(["-DINTEL_MKL=1", "-DEIGEN_USE_VML"]) +
if_mkl_open_source_only(["-DINTEL_MKL_DNN_ONLY"]) +
+ if_enable_mkl(["-DENABLE_MKL"]) +
if_ngraph(["-DINTEL_NGRAPH=1"]) +
if_mkl_lnx_x64(["-fopenmp"]) +
if_android_arm(["-mfpu=neon"]) +
@@ -1082,6 +1084,7 @@
]),
copts = (copts + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) +
if_mkl_open_source_only(["-DINTEL_MKL_DNN_ONLY"]) +
+ if_enable_mkl(["-DENABLE_MKL"]) +
if_tensorrt(["-DGOOGLE_TENSORRT=1"])),
**kwargs
)
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 5648b15..f5fdd3a 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -48,6 +48,7 @@
CUDA_LIB_PATHS = [
"lib64/",
"lib64/stubs/",
+ "lib/powerpc64le-linux-gnu/",
"lib/x86_64-linux-gnu/",
"lib/x64/",
"lib/",
@@ -70,6 +71,7 @@
# the other CUDA libraries but rather in a special extras/CUPTI directory.
CUPTI_LIB_PATHS = [
"extras/CUPTI/lib64/",
+ "lib/powerpc64le-linux-gnu/",
"lib/x86_64-linux-gnu/",
"lib64/",
"extras/CUPTI/libx64/",
diff --git a/third_party/mkl/BUILD b/third_party/mkl/BUILD
index efff7fd..15a3e5c 100644
--- a/third_party/mkl/BUILD
+++ b/third_party/mkl/BUILD
@@ -1,26 +1,26 @@
licenses(["notice"]) # 3-Clause BSD
config_setting(
- name = "using_mkl",
+ name = "build_with_mkl",
define_values = {
- "using_mkl": "true",
+ "build_with_mkl": "true",
},
visibility = ["//visibility:public"],
)
config_setting(
- name = "using_mkl_ml_only",
+ name = "build_with_mkl_ml_only",
define_values = {
- "using_mkl": "true",
- "using_mkl_ml_only": "true",
+ "build_with_mkl": "true",
+ "build_with_mkl_ml_only": "true",
},
visibility = ["//visibility:public"],
)
config_setting(
- name = "using_mkl_lnx_x64",
+ name = "build_with_mkl_lnx_x64",
define_values = {
- "using_mkl": "true",
+ "build_with_mkl": "true",
},
values = {
"cpu": "k8",
@@ -28,6 +28,15 @@
visibility = ["//visibility:public"],
)
+config_setting(
+ name = "enable_mkl",
+ define_values = {
+ "enable_mkl": "true",
+ "build_with_mkl": "true",
+ },
+ visibility = ["//visibility:public"],
+)
+
load(
"//third_party/mkl:build_defs.bzl",
"if_mkl",
diff --git a/third_party/mkl/build_defs.bzl b/third_party/mkl/build_defs.bzl
index b645c0f..bb798e7 100644
--- a/third_party/mkl/build_defs.bzl
+++ b/third_party/mkl/build_defs.bzl
@@ -1,9 +1,11 @@
# -*- Python -*-
"""Skylark macros for MKL.
-if_mkl is a conditional to check if MKL is enabled or not.
-if_mkl_ml is a conditional to check if MKL-ML is enabled.
+
+if_mkl is a conditional to check if we are building with MKL.
+if_mkl_ml is a conditional to check if we are building with MKL-ML.
if_mkl_ml_only is a conditional to check for MKL-ML-only (no MKL-DNN) mode.
if_mkl_lnx_x64 is a conditional to check for MKL
+if_enable_mkl is a conditional to check if building with MKL and MKL is enabled.
mkl_repository is a repository rule for creating MKL repository rule that can
be pointed to either a local folder, or download it from the internet.
@@ -24,7 +26,7 @@
a select evaluating to either if_true or if_false as appropriate.
"""
return select({
- str(Label("//third_party/mkl:using_mkl")): if_true,
+ str(Label("//third_party/mkl:build_with_mkl")): if_true,
"//conditions:default": if_false,
})
@@ -40,8 +42,8 @@
a select evaluating to either if_true or if_false as appropriate.
"""
return select({
- str(Label("//third_party/mkl_dnn:using_mkl_dnn_only")): if_false,
- str(Label("//third_party/mkl:using_mkl")): if_true,
+ str(Label("//third_party/mkl_dnn:build_with_mkl_dnn_only")): if_false,
+ str(Label("//third_party/mkl:build_with_mkl")): if_true,
"//conditions:default": if_false,
})
@@ -56,12 +58,12 @@
a select evaluating to either if_true or if_false as appropriate.
"""
return select({
- str(Label("//third_party/mkl:using_mkl_ml_only")): if_true,
+ str(Label("//third_party/mkl:build_with_mkl_ml_only")): if_true,
"//conditions:default": if_false,
})
def if_mkl_lnx_x64(if_true, if_false = []):
- """Shorthand to select() on if MKL is on and the target is Linux x86-64.
+ """Shorthand to select() if building with MKL and the target is Linux x86-64.
Args:
if_true: expression to evaluate if building with MKL is enabled and the
@@ -73,7 +75,24 @@
a select evaluating to either if_true or if_false as appropriate.
"""
return select({
- str(Label("//third_party/mkl:using_mkl_lnx_x64")): if_true,
+ str(Label("//third_party/mkl:build_with_mkl_lnx_x64")): if_true,
+ "//conditions:default": if_false,
+ })
+
+def if_enable_mkl(if_true, if_false = []):
+ """Shorthand to select() if we are building with MKL and MKL is enabled.
+
+ This is only effective when built with MKL.
+
+ Args:
+ if_true: expression to evaluate if building with MKL and MKL is enabled
+ if_false: expression to evaluate if building without MKL or MKL is not enabled.
+
+ Returns:
+ A select evaluating to either if_true or if_false as appropriate.
+ """
+ return select({
+ "//third_party/mkl:enable_mkl": if_true,
"//conditions:default": if_false,
})
@@ -87,9 +106,9 @@
inclusion in the deps attribute of rules.
"""
return select({
- str(Label("//third_party/mkl_dnn:using_mkl_dnn_only")): ["@mkl_dnn"],
- str(Label("//third_party/mkl:using_mkl_ml_only")): ["//third_party/mkl:intel_binary_blob"],
- str(Label("//third_party/mkl:using_mkl")): [
+ str(Label("//third_party/mkl_dnn:build_with_mkl_dnn_only")): ["@mkl_dnn"],
+ str(Label("//third_party/mkl:build_with_mkl_ml_only")): ["//third_party/mkl:intel_binary_blob"],
+ str(Label("//third_party/mkl:build_with_mkl")): [
"//third_party/mkl:intel_binary_blob",
"@mkl_dnn",
],
diff --git a/third_party/mkl_dnn/BUILD b/third_party/mkl_dnn/BUILD
index 3e567fa..58ecda5 100644
--- a/third_party/mkl_dnn/BUILD
+++ b/third_party/mkl_dnn/BUILD
@@ -3,10 +3,10 @@
exports_files(["LICENSE"])
config_setting(
- name = "using_mkl_dnn_only",
+ name = "build_with_mkl_dnn_only",
define_values = {
- "using_mkl": "true",
- "using_mkl_dnn_only": "true",
+ "build_with_mkl": "true",
+ "build_with_mkl_dnn_only": "true",
},
visibility = ["//visibility:public"],
)
diff --git a/third_party/mkl_dnn/build_defs.bzl b/third_party/mkl_dnn/build_defs.bzl
index 7ce2a7d..6388f31 100644
--- a/third_party/mkl_dnn/build_defs.bzl
+++ b/third_party/mkl_dnn/build_defs.bzl
@@ -8,6 +8,6 @@
"""
return select({
- str(Label("//third_party/mkl_dnn:using_mkl_dnn_only")): if_true,
+ str(Label("//third_party/mkl_dnn:build_with_mkl_dnn_only")): if_true,
"//conditions:default": if_false,
})
diff --git a/tools/bazel.rc b/tools/bazel.rc
index ccf6262..6747c7e 100644
--- a/tools/bazel.rc
+++ b/tools/bazel.rc
@@ -24,12 +24,13 @@
# Please note that MKL on MacOS or windows is still not supported.
# If you would like to use a local MKL instead of downloading, please set the
# environment variable "TF_MKL_ROOT" every time before build.
-build:mkl --define=using_mkl=true
+build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl -c opt
# This config option is used to enable MKL-DNN open source library only,
# without depending on MKL binary version.
-build:mkl_open_source_only --define=using_mkl_dnn_only=true
+build:mkl_open_source_only --define=build_with_mkl_dnn_only=true
+build:mkl_open_source_only --define=build_with_mkl=true --define=enable_mkl=true
build:download_clang --crosstool_top=@local_config_download_clang//:toolchain
build:download_clang --define=using_clang=true