[shape_poly] Fix handling of shape polymorphism for pallas_call_batching

[gnecula]

The previous code was not handling the case of a symbolic batch dimension (one of the most common uses of shape polymorphism)

[gemini-code-assist]

Summary of Changes

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This pull request addresses a critical issue in JAX's Pallas library where pallas_call_batching failed to correctly handle symbolic batch dimensions, a common use case for shape polymorphism. The changes ensure that dynamic grid dimensions are properly recognized and integrated into the grid mapping and cost estimation, preventing crashes and enabling robust export of Pallas operations with symbolic shapes.

Highlights

• Enhanced Shape Polymorphism Support: Modified Pallas core logic to correctly identify and handle dynamic grid dimensions, ensuring proper behavior with symbolic shapes.
• Improved Batching with Symbolic Dimensions: Updated pallas_call_batching to correctly incorporate symbolic batch dimensions into the grid mapping and adjust cost estimation logic accordingly.
• Added Vmap Export Test: Introduced a new test case to validate the export of vmap-ped Pallas calls with symbolic shapes, specifically targeting the previously failing scenario.

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Changelog

• jax/_src/pallas/core.py
  • Updated get_grid_mapping to check if a grid dimension is not constant using not dim_check(d) in addition to checking for None.
• jax/_src/pallas/pallas_call.py
  • Introduced axis_size_is_dynamic boolean to determine if axis_size is a dynamic dimension.
  • Modified batched_grid_mapping to use pallas_core.dynamic_grid_dim for the batch axis if axis_size_is_dynamic.
  • Updated cost estimation logic to avoid scaling by axis_size if it's dynamic.
  • Conditionally prepended axis_size to dynamic_grid_args if axis_size_is_dynamic.
• tests/pallas/export_pallas_test.py
  • Added test_export_vmap to verify vmap functionality with symbolic shapes in Pallas calls, including a kernel and a batched add_vectors function.

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[gemini-code-assist]

Code Review

This pull request addresses an issue with shape polymorphism in pallas_call_batching when dealing with symbolic batch dimensions. The changes introduced are logical and correct. They properly detect dynamic grid dimensions and adjust the grid mapping, cost estimation, and dynamic grid arguments. The addition of a new test case for vmap with symbolic shapes effectively validates the fix. Overall, the implementation is solid and enhances the shape polymorphism capabilities of Pallas.

[bchetioui]

Should we use values of m, n that are larger than the block_size? I'm not sure how well this is covered elsewhere---just checking.

[gnecula]

I do not think that we need that extra coverage for this tests specifically. But I am now wondering what happens if we use values that are not divisible by 128! Let me try.