Merge pull request #5 from tensorflow/master

Sync master with tensorflow

Author: andrewstevens-infineon

.bazelrc

@@ -143,6 +143,11 @@ build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
 build:mkl --define=build_with_mkl_dnn_v1_only=true
 build:mkl -c opt
 
+# config to build OneDNN backend with a user specified threadpool.
+build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
+build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
+build:mkl_threadpool --define=build_with_mkldnn_threadpool=true
+build:mkl_threadpool -c opt
 # This config refers to building with CUDA available. It does not necessarily
 # mean that we build CUDA op kernels.
 build:using_cuda --define=using_cuda=true
@@ -235,10 +240,15 @@ build:c++17 --cxxopt=-std=c++1z
 build:c++17 --cxxopt=-stdlib=libc++
 build:c++1z --config=c++17
 
-# Enable using platform specific build settings
+# Enable using platform specific build settings, except when cross-compiling for
+# mobile platforms.
 build --enable_platform_specific_config
+build:android --noenable_platform_specific_config
+build:ios --noenable_platform_specific_config
 
 # Suppress C++ compiler warnings, otherwise build logs become 10s of MBs.
+build:android --copt=-w
+build:ios --copt=-w
 build:linux --copt=-w
 build:macos --copt=-w
 build:windows --copt=/w
@@ -258,6 +268,10 @@ build:macos --define=INCLUDEDIR=$(PREFIX)/include
 # TF_SYSTEM_LIBS do not work on windows.
 
 # By default, build TF in C++ 14 mode.
+build:android --cxxopt=-std=c++14
+build:android --host_cxxopt=-std=c++14
+build:ios --cxxopt=-std=c++14
+build:ios --host_cxxopt=-std=c++14
 build:linux --cxxopt=-std=c++14
 build:linux --host_cxxopt=-std=c++14
 build:macos --cxxopt=-std=c++14

.github/bot_config.yml

@@ -0,0 +1,87 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+#
+# THIS IS A GENERATED DOCKERFILE.
+#
+# This file was assembled from multiple pieces, whose use is documented
+# throughout. Please refer to the TensorFlow dockerfiles documentation
+# for more information.
+
+# A list of assignees
+assignees:
+   - amahendrakar
+   - ravikyram
+   - Saduf2019
+# A list of assignees for compiler folder
+compiler_assignees:
+   - joker-eph
+# Cuda Comment
+cuda_comment: >
+   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
+      * For TF-GPU - See point 1
+      * For TF-CPU - See point 2
+   -----------------------------------------------------------------------------------------------
+   
+   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
+   
+   
+   Make sure you are using compatible TF and CUDA versions.
+   Please refer following TF version and CUDA version compatibility table.
+   
+   | TF  | CUDA |
+   
+   | :-------------: | :-------------: |
+   
+   | 2.1.0 - 2.2.0  | 10.1 |
+   
+   | 1.13.1 - 2.0  | 10.0  |
+   
+   | 1.5.0 - 1.12.0 | 9.0 |
+   
+     * If you have above configuration and using _**Windows**_ platform -
+       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
+       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
+     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
+       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
+       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
+     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
+       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
+   
+   -----------------------------------------------------------------------------------------------
+   
+   **2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
+   
+   
+   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
+   
+   
+   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
+   
+   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
+   
+      * Try Google Colab to use TensorFlow.
+         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.
+         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
+         * All you need is a good internet connection and you are all set.
+      * Try to build TF from sources by changing CPU optimization flags.
+   
+   *Please let us know if this helps.*
+   
+windows_comment: >
+   From the stack trace it looks like you are hitting windows path length limit.
+      * Try to disable path length limit on Windows 10.
+        * Refer [disable path length limit instructions guide.](https://mspoweruser.com/ntfs-260-character-windows-10/)
+   
+   Please let us know if this helps.

README.md

@@ -142,6 +142,7 @@ Build Type                                                        | Status
 *   [Getting Started with TensorFlow 2 from Coursera](https://www.coursera.org/learn/getting-started-with-tensor-flow2)
 *   [Intro to TensorFlow for Deep Learning from Udacity](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)
 *   [Introduction to TensorFlow Lite from Udacity](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190)
+*   [Machine Learning with TensorFlow on GCP](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp)
 *   [TensorFlow Blog](https://blog.tensorflow.org)
 *   [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml)
 *   [TensorFlow Twitter](https://twitter.com/tensorflow)

RELEASE.md

@@ -1,3 +1,41 @@
+# Release 2.3.0
+
+## Breaking Changes
+
+*   `tf.image.extract_glimpse` has been updated to correctly process the case
+    where `centered=False` and `normalized=False`. This is a breaking change as
+    the output is different from (incorrect) previous versions. Note this
+    breaking change only impacts `tf.image.extract_glimpse` and
+    `tf.compat.v2.image.extract_glimpse` API endpoints. The behavior of
+    `tf.compat.v1.image.extract_glimpse` does not change. The behavior of
+    exsiting C++ kernel `ExtractGlimpse` does not change as well, so saved
+    models will not be impacted.
+
+# Release 2.1.1
+
+## Bug Fixes and Other Changes
+* Updates `sqlite3` to `3.31.01` to handle [CVE-2019-19880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19880), [CVE-2019-19244](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19244) and [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645)
+* Updates `curl` to `7.69.1` to handle [CVE-2019-15601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-15601)
+* Updates `libjpeg-turbo` to `2.0.4` to handle [CVE-2018-19664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-19664), [CVE-2018-20330](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-20330) and [CVE-2019-13960](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13960)
+* Updates Apache Spark to `2.4.5` to handle [CVE-2019-10099](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-10099), [CVE-2018-17190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17190) and [CVE-2018-11770](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-11770)
+* Fixes a versioning bug which causes Keras layers from TF 1.x to be used instead of those from TF 2.x
+
+# Release 2.0.2
+
+## Bug Fixes and Other Changes
+* Updates `sqlite3` to `3.31.01` to handle [CVE-2019-19880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19880), [CVE-2019-19244](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19244) and [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645)
+* Updates `curl` to `7.69.1` to handle [CVE-2019-15601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-15601)
+* Updates `libjpeg-turbo` to `2.0.4` to handle [CVE-2018-19664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-19664), [CVE-2018-20330](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-20330) and [CVE-2019-13960](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13960)
+* Updates Apache Spark to `2.4.5` to handle [CVE-2019-10099](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-10099), [CVE-2018-17190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17190) and [CVE-2018-11770](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-11770)
+
+# Release 1.15.3
+
+## Bug Fixes and Other Changes
+* Updates `sqlite3` to `3.31.01` to handle [CVE-2019-19880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19880), [CVE-2019-19244](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19244) and [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645)
+* Updates `curl` to `7.69.1` to handle [CVE-2019-15601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-15601)
+* Updates `libjpeg-turbo` to `2.0.4` to handle [CVE-2018-19664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-19664), [CVE-2018-20330](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-20330) and [CVE-2019-13960](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13960)
+* Updates Apache Spark to `2.4.5` to handle [CVE-2019-10099](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-10099), [CVE-2018-17190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17190) and [CVE-2018-11770](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-11770)
+
 # Release 2.2.0
 
 TensorFlow 2.2 discontinues support for Python 2, [previously announced](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ) as following [Python 2's EOL on January 1, 2020](https://www.python.org/dev/peps/pep-0373/#update).

SECURITY.md

@@ -64,7 +64,7 @@ your model, and we recommend you run the TensorFlow process in a sandbox.
 
 It is possible to write models that are secure in a sense that they can safely
 process untrusted inputs assuming there are no bugs. There are two main reasons
-to not rely on this: first, it is easy to write models which must not be exposed
+to not rely on this: First, it is easy to write models which must not be exposed
 to untrusted inputs, and second, there are bugs in any software system of
 sufficient complexity. Letting users control inputs could allow them to trigger
 bugs either in TensorFlow or in dependent libraries.
@@ -149,7 +149,7 @@ attack (or worse). Because TensorFlow behaves correctly, this is not a
 vulnerability in TensorFlow (although it would be a vulnerability of this
 hypothetical system).
 
-As a general rule, it is incorrect behavior for Tensorflow to access memory it
+As a general rule, it is incorrect behavior for TensorFlow to access memory it
 does not own, or to terminate in an unclean way. Bugs in TensorFlow that lead to
 such behaviors constitute a vulnerability.
 

WORKSPACE

@@ -114,6 +114,14 @@ http_archive(
     ],
 )
 
+http_archive(
+    name = "person_detect_data",
+    sha256 = "170542270da256994ce24d1e357f6e84a54fdaf7d28ff2b74725a40b70b082cf",
+    urls = [
+        "https://storage.googleapis.com/download.tensorflow.org/data/tf_lite_micro_person_data_grayscale_2020_05_24.zip",
+    ],
+)
+
 # Required for dependency @com_github_grpc_grpc
 
 load("@com_github_grpc_grpc//bazel:grpc_deps.bzl", "grpc_deps")

configure.py

@@ -1368,8 +1368,13 @@ def main():
   # environment variables.
   environ_cp = dict(os.environ)
 
-  current_bazel_version = check_bazel_version(_TF_MIN_BAZEL_VERSION,
-                                              _TF_MAX_BAZEL_VERSION)
+  try:
+    current_bazel_version = check_bazel_version(_TF_MIN_BAZEL_VERSION,
+                                                _TF_MAX_BAZEL_VERSION)
+  except subprocess.CalledProcessError as e:
+    print("Error checking bazel version: ", e.output.decode('UTF-8').strip())
+    raise e
+
   _TF_CURRENT_BAZEL_VERSION = convert_version_to_int(current_bazel_version)
 
   reset_tf_configure_bazelrc()
@@ -1387,7 +1392,6 @@ def main():
     # Windows.
     environ_cp['TF_DOWNLOAD_CLANG'] = '0'
     environ_cp['TF_NEED_MPI'] = '0'
-    environ_cp['TF_SET_ANDROID_WORKSPACE'] = '0'
 
   if is_macos():
     environ_cp['TF_NEED_TENSORRT'] = '0'

tensorflow/BUILD

@@ -524,7 +524,10 @@ package_group(
     ],
 )
 
-package_group(name = "ndarray_tensor_allow_list")
+package_group(
+    name = "ndarray_tensor_allow_list",
+    packages = ["//learning/pathways/..."],
+)
 
 # Packages that use composite tensors or dispatch.
 # TODO(b/154762408) Remove this package group once it's no longer needed.

tensorflow/c/BUILD

@@ -216,6 +216,7 @@ tf_cuda_library(
     ],
     visibility = [
         "//tensorflow/c:__subpackages__",
+        "//tensorflow/compiler/mlir/tensorflow/c:__subpackages__",
     ],
     deps = select({
         "//tensorflow:android": [
@@ -394,8 +395,14 @@ tf_cuda_library(
     deps = [
         ":tf_status",
         ":tf_status_internal",
-        "//tensorflow/core:lib",
-    ],
+    ] + select({
+        "//tensorflow:android": [
+            "//tensorflow/core:portable_tensorflow_lib_lite",  # TODO(annarev): exclude runtime srcs
+        ],
+        "//conditions:default": [
+            "//tensorflow/core:lib",
+        ],
+    }),
 )
 
 tf_cc_test(