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android / platform / external / tensorflow / 0047edb7b1d
commit0047edb7b1d35d588c3e3fb5bfe7de60c67c351c[log] [tgz]
authorSmit Hinsu <hinsu@google.com>Wed Mar 10 15:17:22 2021 -0800
committerTensorFlower Gardener <gardener@tensorflow.org>Wed Mar 10 15:23:55 2021 -0800
tree8b02f4728653ce16620a54d2ffa22a2572695f45
parent464dc50569cba1bbf7f3817438dd9abc2a39bf2c [diff]
Return failure for ops with zero results in TF dialect folding hook

Currently, the hook returns success for ops like tf.Yield that doesn't have any side effect and zero results. That will cause canonicalizer or any other pass using the greedy rewriter to not converge. Canonicalizer pass doesn't handle return value of the greedy rewriter so this change doesn't have any observable effect for canonicalizer.

Added test is passing before this change as well because the canonicalizer pass ignores convergence issue in the rewriter.

Ran into this issue while using greedy rewriter to replace _XlaHostComputeMlir op by XlaHostCompute op.

PiperOrigin-RevId: 362152694
Change-Id: I30a3829de7c0a75fa8f0b137246a50aedc0db918
2 files changed
tree: 8b02f4728653ce16620a54d2ffa22a2572695f45
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  6. .bazelrc
  7. .bazelversion
  8. .gitignore
  9. ACKNOWLEDGMENTS
  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

Python PyPI

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, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu 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()
b'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:

Fuzzing Status 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 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly Binary Official GCS

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 aarch64 CPU Nightly (Linaro)Build StatusNightly
Linux aarch64 CPU Stable Release (Linaro)Build StatusRelease 1.x & 2.x
Linux aarch64 CPU Nightly (OpenLab)
Python 3.6		Build StatusNightly
Linux aarch64 CPU Stable Release (OpenLab)Build StatusRelease 1.15 / 2.x
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) NightlyBuild StatusNightly
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable ReleaseBuild StatusRelease 1.15 / 2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6		Build Status1.13.1 PyPI

Community Supported Containers

Container TypeStatusArtifacts
TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro)
Debian		StaticRelease 2.3

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0