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android / platform / external / tensorflow / 3b94eb4fac3
commit3b94eb4fac3f81f33c4a5b92e5641d9a2c865909[log] [tgz]
authorA. Unique TensorFlower <gardener@tensorflow.org>Tue Dec 03 06:22:31 2019 -0800
committerTensorFlower Gardener <gardener@tensorflow.org>Tue Dec 03 06:26:37 2019 -0800
tree05d7f4d1e1df312434ea637369d318b2aa4276e1
parent2cfa79f98497cd7d9ce9d26de38ec046323d1d31 [diff]
Fix ViewOp to have at most one offset operand

As described in the documentation, ViewOp is expected to take an optional
dynamic offset followed by a list of dynamic sizes. However, the ViewOp parser
did not include a check for the offset being a single value and accepeted a
list of values instead.

Furthermore, several tests have been exercising the wrong syntax of a ViewOp,
passing multiple values to the dyanmic stride list, which was not caught by the
parser. The trailing values could have been erronously interpreted as dynamic
sizes. This is likely due to resyntaxing of the ViewOp, with the previous
syntax taking the list of sizes before the offset. Update the tests to use the
syntax with the offset preceding the sizes.

Worse, the conversion of ViewOp to the LLVM dialect assumed the wrong order of
operands with offset in the trailing position, and erronously relied on the
permissive parsing that interpreted trailing dynamic offset values as leading
dynamic sizes. Fix the lowering to use the correct order of operands.

PiperOrigin-RevId: 283532506
Change-Id: I4d80570132c0e1194d865f6282b87e6b89e9879d
2 files changed
tree: 05d7f4d1e1df312434ea637369d318b2aa4276e1
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .bazelversion
  7. .gitignore
  8. ACKNOWLEDGMENTS
  9. ADOPTERS.md
  10. arm_compiler.BUILD
  11. AUTHORS
  12. BUILD
  13. CODE_OF_CONDUCT.md
  14. CODEOWNERS
  15. configure
  16. configure.cmd
  17. configure.py
  18. CONTRIBUTING.md
  19. ISSUE_TEMPLATE.md
  20. ISSUES.md
  21. LICENSE
  22. models.BUILD
  23. README.md
  24. RELEASE.md
  25. SECURITY.md
  26. WORKSPACE
README.md
Documentation
Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release for CPU-only:

$ pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow-gpu

Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

CII Best Practices Contributor Covenant

Continuous build status

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1Status StatusPy2 Py3
Raspberry Pi 2 and 3Status StatusPy2 Py3

Community Supported Builds

Build TypeStatusArtifacts
Linux AMD ROCm GPU NightlyBuild StatusNightly
Linux AMD ROCm GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux s390x NightlyBuild StatusNightly
Linux s390x CPU Stable ReleaseBuild StatusRelease
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5, 3.6 and 3.7		Build Status1.14.0 PyPI
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6		Build Status1.13.1 PyPI

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0