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chromium / chromiumos / platform / tflite
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  1. c672f11 tflite: Add `per_os_targets = True` to delegates by Tommy Chiang · 2 weeks ago firmware-R143-16463.2.B main release-R143-16463.B
  2. 683bdd4 tflite: Fix native handle deallocation size mismatch by Tommy Chiang · 8 weeks ago firmware-R142-16433.2.B release-R142-16433.B
  3. 3e16540 tflite: Fix native handle deallocation size mismatch by Tommy Chiang · 8 weeks ago
  4. bd51e87 delegate: skip external_test if ubsan is enabled by George Burgess IV · 8 weeks ago
  5. 840c7a0 common: fix incorrect `reinterpret_cast` by George Burgess IV · 8 weeks ago

ChromeOS TFLite

This repository hosts the core ChromeOS TFLite components, enabling on-device machine learning (ODML) workloads accelerated by NPU.

The corresponding ebuild can be found at: tensorflow-9999.ebuild

TensorFlow Patch Management

Patches are stored in the patch/ directory and explicitly listed in WORKSPACE.bazel. A helper script, ./script/patcher.py, is included to facilitate patch management within a TFLite workspace.

The typical workflow:

  1. Eject (Download) TensorFlow Source Code

    Download the TensorFlow source code into a local git repository with patches applied as individual commits:

    ./script/patcher.py eject

    This creates a new local git repository at tensorflow/.

  2. Modify the TensorFlow Repository

    Make changes to the tensorflow/ repository as needed, following standard git workflows. Optionally, include a PATCH_NAME= tag in commit messages to specify the filename of the corresponding patch.

  3. Seal the Repository

    Regenerate the patch files and update the WORKSPACE.bazel file:

    ./script/patcher.py seal

    This updates the patches in the patch/ directory and reflects the changes in WORKSPACE.bazel.

It's preferred to submit changes to upstream TensorFlow first and cherry-pick them as patches. This helps minimize divergence and makes TensorFlow updates easier.