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android / platform / external / tensorflow / 37e3282cde6
commit37e3282cde6cf04d69c8a5f48454b707ec545a53[log] [tgz]
authorGeorge Karpenkov <cheshire@google.com>Fri Aug 30 12:13:57 2019 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Fri Aug 30 12:19:22 2019 -0700
tree645b4f9f4765eb1fc30733e637ce9ebcac1cd5b4
parent7d4060879a15bcfa806eef4152e0b905239e783c [diff]
[XLA GPU] [NFC] Remove codegen logic from KernelCodegenInfo/KernelMappingScheme/ReductionCodegenInfo

The summary of the changes is:

 - All the actual codegen logic is now in ir_emitter_unnested, making it easier
   to follow.
 - KernelCodegenInfo abstraction is removed entirely, it's easier to pass the
   lane_id explicitly than to have a whole separate abstraction around it
   (and index_type can be curried into passed callbacks).
 - KernelMappingScheme is now a POD (plain datastructure, no business logic),
   which just holds the relevant data.
 - ReductionCodegenInfo is now a POD, all logic is moved into helper functions.
 - The logic for checking whether two shapes form a 021 transpose is moved into
   ShapeUtil, since that's where utilities which operate exclusively on shapes go.

Overall, the change removes ~120 LOC, and removes the number of layers of abstraction,
and separates the emission logic from datatypes holding the auxiliary information.

PiperOrigin-RevId: 266430439
9 files changed
tree: 645b4f9f4765eb1fc30733e637ce9ebcac1cd5b4
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .gitignore
  7. ACKNOWLEDGMENTS
  8. ADOPTERS.md
  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. ISSUES.md
  20. LICENSE
  21. models.BUILD
  22. README.md
  23. RELEASE.md
  24. SECURITY.md
  25. 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 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.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backwards 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:

$ 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.enable_eager_execution()
>>> 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
Linux s390x NightlyBuild StatusNightly
Linux s390x CPU Stable ReleaseBuild StatusRelease
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease
Linux CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5, and 3.6		Build Status1.13.1 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