[XLA:GPU] Simplify heuristics for promotion to int8x32.

Previously, we would upgrade to int8x32 if doing so did not result in a > 2.5x
increase in the input/filter/output size of the conv.

Now we only consider the input/output sizes.  Sometimes the filter increases by
more than 2.5x and we observe that it's still worthwhile to pad.  Anyway I
think this makes more sense, since the increase in the filter size is really a
function of the input/output increases.

As a result we're able to switch the heuristic to the more intuitive "don't
expand input/output by 2x or more".

PiperOrigin-RevId: 390144519
Change-Id: I96cdb0a95dc627157cdcd02d4eb1c593cedd148f
3 files changed
tree: ee42254609980b9f5af1964860dc364754a8f391
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .bazelversion
  7. .gitignore
  8. .zenodo.json
  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 DOI

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.

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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

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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.

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You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
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Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly Binary Official GCS
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Apache License 2.0