[XLA] Automated g4 rollback of changelist 352846603.

*** Reason for rollback ***

fix breakage due to transitive property of liverange analysis after rollback

Original CL description:
[XLA] remove extraneous copies in copy_insertion related to nested conditionals and while loops. The change increases the precision of LiveRangeBefore analysis inside copy_insertion to accommodate disjoint branches inside conditionals that never overlap.

The breakage is due to the fact that when we allow def-use values that are in exclusive conditional branches to share buffers, the LiveRangeBefore relation is no longer transitive. In particular, suppose op_a's live range is before that of op_b, and live range of ob_b is before that of op_c, we may not have live range of op_a before op_c, because op_a and op_c may be in the same branch and overlapping with each other. This is fixed by modifying copy_insertion.cc to check all related HloValues without assuming they are ordered. This will lengthen the compilation time a bit, but because the number of copy instructions removed are fairly limited, the cost should be negligible.

PiperOrigin-RevId: 353953760
Change-Id: Ia110e1a13047bf1d3dec37668bbe21fb10b47a5f
5 files changed
tree: a5c686cc1098310af047e745cd87b60b5b2e764d
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .bazelversion
  7. .gitignore
  8. ACKNOWLEDGMENTS
  9. arm_compiler.BUILD
  10. AUTHORS
  11. BUILD
  12. CODE_OF_CONDUCT.md
  13. CODEOWNERS
  14. configure
  15. configure.cmd
  16. configure.py
  17. CONTRIBUTING.md
  18. ISSUE_TEMPLATE.md
  19. LICENSE
  20. models.BUILD
  21. README.md
  22. RELEASE.md
  23. SECURITY.md
  24. 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:

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Linux aarch64 CPU Stable Release (Linaro)Build StatusRelease 1.x & 2.x
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Python 3.6
Build StatusNightly
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Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable ReleaseBuild StatusRelease 1.15 / 2.x
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Python 2.7, 3.6
Build Status1.13.1 PyPI

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TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro)
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Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0