Merge changes from github.

PiperOrigin-RevId: 189945839

README.md

@@ -22,6 +22,10 @@ organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
+Keep up to date with release announcements and security updates by
+subscribing to
+[[email protected]](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
+
 ## Installation
 *See [Installing TensorFlow](https://www.tensorflow.org/get_started/os_setup.html) for instructions on how to install our release binaries or how to build from source.*
 

SECURITY.md

@@ -6,7 +6,7 @@ report vulnerabilities in TensorFlow.
 
 ## TensorFlow models are programs
 
-TensorFlow's runtime system interprets and executes programs. What machine 
+TensorFlow's runtime system interprets and executes programs. What machine
 learning practitioners term
 [**models**](https://developers.google.com/machine-learning/glossary/#model) are
 expressed as programs that TensorFlow executes.  TensorFlow programs are encoded
@@ -28,12 +28,12 @@ data you supply to TensorFlow to train a model, or to use a model to run
 inference on the data.
 
 **TensorFlow models are programs, and need to be treated as such from a security
-perspective.** 
+perspective.**
 
 ## Running untrusted models
 
 As a general rule: **Always** execute untrusted models inside a sandbox (e.g.,
-[nsjail](https://github.com/google/nsjail)). 
+[nsjail](https://github.com/google/nsjail)).
 
 There are several ways in which a model could become untrusted. Obviously, if an
 untrusted party supplies TensorFlow kernels, arbitrary code may be executed.
@@ -109,11 +109,11 @@ graphs known to the `ModelServer`. This means that an attacker may run
 graphs using untrusted inputs as described above, but they would not be able to
 execute arbitrary graphs. It is possible to safely expose a `ModelServer`
 directly to an untrusted network, **but only if the graphs it is configured to
-use have been carefully audited to be safe**. 
+use have been carefully audited to be safe**.
 
 Similar to best practices for other servers, we recommend running any
 `ModelServer` with appropriate privileges (i.e., using a separate user with
-reduced permisisons). In the spirit of defense in depth, we recommend
+reduced permissions). In the spirit of defense in depth, we recommend
 authenticating requests to any TensorFlow server connected to an untrusted
 network, as well as sandboxing the server to minimize the adverse effects of
 any breach.
@@ -129,11 +129,11 @@ with specially crafted inputs.
 ### What is a vulnerability?
 
 Given TensorFlow's flexibility, it is possible to specify computation graphs
-which exhibit unexpected or unwanted behaviors. The fact that TensorFlow models
+which exhibit unexpected or unwanted behavior. The fact that TensorFlow models
 can perform arbitrary computations means that they may read and write files,
 communicate via the network, produce deadlocks and infinite loops, or run out
 of memory. It is only when these behaviors are outside the specifications of the
-operations involved that such behavior is a vulnerability. 
+operations involved that such behavior is a vulnerability.
 
 A `FileWriter` writing a file is not unexpected behavior and therefore is not a
 vulnerability in TensorFlow. A `MatMul` allowing arbitrary binary code execution
@@ -168,7 +168,7 @@ below).
 
 Please use a descriptive subject line for your report email. After the initial
 reply to your report, the security team will endeavor to keep you informed of
-the progress being made towards a fix and announcement. 
+the progress being made towards a fix and announcement.
 
 If you believe that an existing (public) issue is security-related, please send
 an email to `[email protected]`. The email should include the issue ID and

configure.py

@@ -1048,7 +1048,10 @@ def is_compatible(tensorrt_lib, cuda_ver, cudnn_ver):
 
     for lib_file in possible_files:
       if is_compatible(lib_file, cuda_ver, cudnn_ver):
-        ver_str = nvinfer_pattern.search(lib_file).group(1)
+        matches = nvinfer_pattern.search(lib_file)
+        if len(matches.groups()) == 0:
+          continue
+        ver_str = matches.group(1)
         ver = convert_version_to_int(ver_str) if len(ver_str) else 0
         if ver > highest_ver[0]:
           highest_ver = [ver, ver_str, lib_file]

tensorflow/compiler/xla/service/generic_transfer_manager.cc

@@ -38,14 +38,7 @@ namespace xla {
 
 GenericTransferManager::GenericTransferManager(se::Platform::Id platform_id,
                                                size_t pointer_size)
-    : platform_id_(platform_id), pointer_size_(pointer_size) {
-  // We currently only support kHostPlatformId for CPU, kCudaPlatformId for
-  // GPU and kInterpreterPlatformId for Interpreter. Before supporting other
-  // platforms, we need to test this transfer manager on them.
-  CHECK(platform_id_ == se::host::kHostPlatformId ||
-        platform_id_ == se::interpreter::kInterpreterPlatformId ||
-        platform_id_ == se::cuda::kCudaPlatformId);
-}
+    : platform_id_(platform_id), pointer_size_(pointer_size) {}
 
 se::Platform::Id GenericTransferManager::PlatformId() const {
   return platform_id_;

tensorflow/compiler/xla/tests/convolution_test.cc

@@ -723,7 +723,7 @@ INSTANTIATE_TEST_CASE_P(
 );
 #endif
 
-TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) {
+XLA_TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) {
   ComputationBuilder builder(client_, TestName());
   Shape input_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2});
   Shape filter_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2});

tensorflow/contrib/BUILD

@@ -121,6 +121,7 @@ cc_library(
         "//tensorflow/contrib/coder:all_kernels",
         "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_kernels",
         "//tensorflow/contrib/data/kernels:dataset_kernels",
+        "//tensorflow/contrib/kafka:dataset_kernels",
         "//tensorflow/contrib/factorization/kernels:all_kernels",
         "//tensorflow/contrib/input_pipeline:input_pipeline_ops_kernels",
         "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel",
@@ -147,7 +148,7 @@ 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/kafka:dataset_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",

tensorflow/contrib/cmake/README.md

@@ -26,22 +26,24 @@ The CMake files in this directory can build the core TensorFlow runtime, an
 example C++ binary, and a PIP package containing the runtime and Python
 bindings.
 
-### Pre-requisites
+### Prerequisites
 
 * CMake version 3.5 or later.
 
 * [Git](https://git-scm.com)
 
 * [SWIG](http://www.swig.org/download.html)
 
-* Additional pre-requisites for Microsoft Windows:
+* Additional prerequisites for Microsoft Windows:
   - Visual Studio 2015
   - Python 3.5
-  - NumPy 1.11.0 or later
 
-* Additional pre-requisites for Linux:
+* Additional prerequisites for Linux:
   - Python 2.7 or later
   - [Docker](https://www.docker.com/) (for automated testing)
+
+* Python dependencies:
+  - wheel
   - NumPy 1.11.0 or later
 
 ### Known-good configurations
@@ -102,7 +104,7 @@ ops or APIs.
 Step-by-step Windows build
 ==========================
 
-1. Install the pre-requisites detailed above, and set up your environment.
+1. Install the prerequisites detailed above, and set up your environment.
 
    * The following commands assume that you are using the Windows Command
      Prompt (`cmd.exe`). You will need to set up your environment to use the

tensorflow/contrib/cmake/external/grpc.cmake

@@ -35,6 +35,7 @@ else()
   set(grpc_STATIC_LIBRARIES
       ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a
       ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc_unsecure.a
+      ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/third_party/cares/cares/lib/libcares.a
       ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgpr.a)
 endif()
 

tensorflow/contrib/cmake/external/protobuf.cmake

@@ -16,7 +16,7 @@ include (ExternalProject)
 
 set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src)
 set(PROTOBUF_URL https://github.com/google/protobuf.git)
-set(PROTOBUF_TAG 396336eb961b75f03b25824fe86cf6490fb75e3a)
+set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9)
 
 if(WIN32)
   if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")

tensorflow/contrib/cmake/tf_tests.cmake

@@ -478,6 +478,10 @@ if (tensorflow_BUILD_CC_TESTS)
     "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*_test.cc"
   )
 
+  list(REMOVE_ITEM tf_test_src_simple
+    ${tf_core_profiler_test_srcs}
+  )
+
   set(tf_test_lib tf_test_lib)
   add_library(${tf_test_lib} STATIC ${tf_src_testlib})
 

tensorflow/contrib/data/__init__.py

@@ -40,6 +40,7 @@
 @@rejection_resample
 @@scan
 @@shuffle_and_repeat
+@@sliding_window_batch
 @@sloppy_interleave
 @@unbatch
 
@@ -72,6 +73,9 @@
 from tensorflow.contrib.data.python.ops.resampling import rejection_resample
 from tensorflow.contrib.data.python.ops.scan_ops import scan
 from tensorflow.contrib.data.python.ops.shuffle_ops import shuffle_and_repeat
+from tensorflow.contrib.data.python.ops.sliding import sliding_window_batch
+from tensorflow.python.data.ops.iterator_ops import Iterator
+from tensorflow.python.ops.parsing_ops import parse_single_example_v2 as parse_single_example
 # pylint: enable=unused-import
 
 from tensorflow.python.util.all_util import remove_undocumented

tensorflow/contrib/data/python/kernel_tests/BUILD

@@ -498,6 +498,23 @@ py_test(
     ],
 )
 
+tf_py_test(
+    name = "slide_dataset_op_test",
+    size = "small",
+    srcs = ["slide_dataset_op_test.py"],
+    additional_deps = [
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/contrib/data/python/ops:transformation_ops",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//third_party/py/numpy",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(

tensorflow/contrib/data/python/kernel_tests/resample_test.py

@@ -45,12 +45,10 @@ def _testDistribution(self, initial_known):
                 target_dist=target_dist,
                 initial_dist=initial_dist,
                 class_func=lambda c, _: c,
-                seed=27)).make_initializable_iterator())
-    init_op = iterator.initializer
+                seed=27)).make_one_shot_iterator())
     get_next = iterator.get_next()
 
     with self.test_session() as sess:
-      sess.run(init_op)
       returned = []
       with self.assertRaises(errors.OutOfRangeError):
         while True: