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android / platform / external / tensorflow / 55633bf5c1b
commit55633bf5c1b9c9004a4bbc6b884dd6b6d5f9fab7[log] [tgz]
authorAdvait Jain <advaitjain@users.noreply.github.com>Thu Feb 11 15:52:01 2021 -0800
committerAdvait Jain <advaitjain@users.noreply.github.com>Fri Feb 12 14:23:00 2021 -0800
treef2aad67f1bd591f8fb6db2c97c2f6a8e3e456f81
parent9421ecff2a0ee4bd6a74116e339862aefa411feb [diff]
Use xa_nnlib for svdf for Fusion F1.

The code in this change is the subset of functionality needed for int8
svdf for Hifi4 copied from https://github.com/pnikam-cad/tensorflow/blob/a737c1e3945bc70022259479ad24133a343ec906/tensorflow/lite/micro/kernels/xtensa_hifi/svdf.cc

Note that the current change has not pulled in either the floating point
implementation or the Hifi5 implementation.

Profiled the keryword_benchmark with the following command:
```
make -f tensorflow/lite/micro/tools/make/Makefile TARGET=xtensa OPTIMIZED_KERNEL_DIR=xtensa TARGET_ARCH=fusion_f1 XTENSA_CORE=F1_190305_swupgrade run_keyword_benchmark -j8
```

gives a latency of 38516 ticks with this change vs 152642 ticks without this change.

Per OP latency with this change:
```
KeywordRunNIerations(1) took 38516 ticks (38 ms)
QUANTIZE took 3758 ticks (3 ms).
SVDF took 4753 ticks (4 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 4211 ticks (4 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 3145 ticks (3 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 4211 ticks (4 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 2890 ticks (2 ms).
SVDF took 3583 ticks (3 ms).
SVDF took 3054 ticks (3 ms).
FULLY_CONNECTED took 1091 ticks (1 ms).
SOFTMAX took 2042 ticks (2 ms).
QUANTIZE took 366 ticks (0 ms).
```

Without this change:
```
KeywordRunNIerations(1) took 152642 ticks (152 ms)
QUANTIZE took 3758 ticks (3 ms).
SVDF took 38003 ticks (38 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 18803 ticks (18 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 18803 ticks (18 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 18803 ticks (18 ms).
FULLY_CONNECTED took 1353 ticks (1 ms).
SVDF took 13907 ticks (13 ms).
SVDF took 15827 ticks (15 ms).
SVDF took 15827 ticks (15 ms).
FULLY_CONNECTED took 1091 ticks (1 ms).
SOFTMAX took 2042 ticks (2 ms).
QUANTIZE took 366 ticks (0 ms).
```

Also confirmed that the kernel_svdf_test passes with:
```
make -f tensorflow/lite/micro/tools/make/Makefile TARGET=xtensa OPTIMIZED_KERNEL_DIR=xtensa TARGET_ARCH=fusion_f1 XTENSA_CORE=F1_190305_swupgrade test_kernel_svdf_test -j8
```
1 file changed
tree: f2aad67f1bd591f8fb6db2c97c2f6a8e3e456f81
  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