Support jax2tf in JaxLayer for tf backend

[wenyi-guo]

Support both tf and jax backend for JaxLayer

[gemini-code-assist]

Summary of Changes

Hello @wenyi-guo, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the interoperability of Keras with JAX and Flax by enabling JaxLayer and FlaxLayer to operate seamlessly within a TensorFlow backend environment. It achieves this by integrating jax2tf to convert JAX functions into TensorFlow-compatible graphs, allowing JAX/Flax models to be used as Keras layers even when Keras is configured to use TensorFlow. This change broadens the utility of these layers, offering greater flexibility for developers working across different deep learning frameworks.

Highlights

• TensorFlow Backend Support for JaxLayer: The JaxLayer and FlaxLayer classes now officially support the TensorFlow backend, allowing JAX and Flax models to be integrated into Keras models running on TensorFlow.
• jax2tf Integration: New internal utilities have been introduced to facilitate the conversion of JAX functions to TensorFlow graphs using jax2tf, including handling polymorphic shapes and the training argument, ensuring seamless execution.
• Unified Random Number Generation: The random number generation within JaxLayer and FlaxLayer has been standardized to consistently use JAX's PRNG keys and Keras's random module, replacing the previous SeedGenerator.
• Flexible Output Shape Computation: A new compute_output_shape_fn argument has been added to JaxLayer and FlaxLayer, providing a mechanism for users to explicitly define how the output shape is computed, with a robust fallback in the base Layer class.
• Expanded Test Coverage: The test suite for JaxLayer and FlaxLayer has been updated to include scenarios for the TensorFlow backend and now utilizes Keras's ops and random modules for consistent data generation.

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[gemini-code-assist]

Code Review

This pull request adds support for jax2tf in JaxLayer, enabling its use with both JAX and TensorFlow backends. This is a significant and well-implemented enhancement. The changes are primarily in keras/src/utils/jax_layer.py and its corresponding test file, with a minor supporting change in keras/src/layers/layer.py. The implementation correctly handles jax2tf conversion, state management, and RNG for both backends. I've identified a critical bug in a new helper function that could lead to infinite recursion and a minor issue in the test setup. With these fixes, this will be a solid contribution.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 17.10526% with 63 lines in your changes missing coverage. Please review.
✅ Project coverage is 61.56%. Comparing base (4d30a7f) to head (6210659).
⚠️ Report is 5 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/utils/jax_layer.py	17.10%	62 Missing and 1 partial ⚠️

❗ There is a different number of reports uploaded between BASE (4d30a7f) and HEAD (6210659). Click for more details.

HEAD has 6 uploads less than BASE

Flag	BASE (4d30a7f)	HEAD (6210659)
keras	5	2
keras-tensorflow	1	0
keras-torch	1	0
keras-jax	1	0

Additional details and impacted files

@@             Coverage Diff             @@
##           master   #21842       +/-   ##
===========================================
- Coverage   82.66%   61.56%   -21.10%     
===========================================
  Files         577      577               
  Lines       59477    59568       +91     
  Branches     9329     9345       +16     
===========================================
- Hits        49167    36674    -12493     
- Misses       7907    20569    +12662     
+ Partials     2403     2325       -78     

Flag	Coverage Δ
keras	61.55% <17.10%> (-20.94%)	⬇️
keras-jax	?
keras-numpy	57.51% <17.10%> (-0.04%)	⬇️
keras-openvino	34.33% <17.10%> (-0.01%)	⬇️
keras-tensorflow	?
keras-torch	?

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[hertschuh]

Thanks for the PR!

[hertschuh]

This is getting close!