Update jax requirement from <=0.4.24 to <=0.4.30

[dependabot]

Updates the requirements on jax to permit the latest version.

Changelog

Sourced from jax's changelog.

jax 0.4.30 (June 18, 2024)

• Changes

  • JAX supports ml_dtypes >= 0.2. In 0.4.29 release, the ml_dtypes version was bumped to 0.4.0 but this has been rolled back in this release to give users of both TensorFlow and JAX more time to migrate to a newer TensorFlow release.
  • jax.experimental.mesh_utils can now create an efficient mesh for TPU v5e.
  • jax now depends on jaxlib directly. This change was enabled by the CUDA plugin switch: there are no longer multiple jaxlib variants. You can install a CPU-only jax with pip install jax, no extras required.
  • Added an API for exporting and serializing JAX functions. This used to exist in jax.experimental.export (which is being deprecated), and will now live in jax.export. See the documentation.

• Deprecations

  • Internal pretty-printing tools jax.core.pp_* are deprecated, and will be removed in a future release.
  • Hashing of tracers is deprecated, and will lead to a TypeError in a future JAX release. This previously was the case, but there was an inadvertent regression in the last several JAX releases.
  • jax.experimental.export is deprecated. Use {mod}jax.export instead. See the migration guide.
  • Passing an array in place of a dtype is now deprecated in most cases; e.g. for arrays x and y, x.astype(y) will raise a warning. To silence it use x.astype(y.dtype).
  • jax.xla_computation is deprecated and will be removed in a future release. Please use the AOT APIs to get the same functionality as jax.xla_computation.
    • jax.xla_computation(fn)(*args, **kwargs) can be replaced with jax.jit(fn).lower(*args, **kwargs).compiler_ir('hlo').
    • You can also use .out_info property of jax.stages.Lowered to get the output information (like tree structure, shape and dtype).
    • For cross-backend lowering, you can replace jax.xla_computation(fn, backend='tpu')(*args, **kwargs) with jax.jit(fn).trace(*args, **kwargs).lower(lowering_platforms=('tpu',)).compiler_ir('hlo').

jaxlib 0.4.30 (June 18, 2024)

• Support for monolithic CUDA jaxlibs has been dropped. You must use the plugin-based installation (pip install jax[cuda12] or pip install jax[cuda12_local]).

jax 0.4.29 (June 10, 2024)

• Changes
  • We anticipate that this will be the last release of JAX and jaxlib supporting a monolithic CUDA jaxlib. Future releases will use the CUDA plugin jaxlib (e.g. pip install jax[cuda12]).
  • JAX now requires ml_dtypes version 0.4.0 or newer.

... (truncated)

Commits

• f4158ac Merge pull request #21949 from hawkinsp:winwheel
• b0b0268 Add --allow-downgrade to Windows wheel builds.
• 5d35c99 Merge pull request #21945 from hawkinsp:release
• d7bc6b4 Update XLA dependency to use revision
• 3f4f79c Prepare for 0.4.30 release.
• dfcfb36 Pallas GPU no longer falls back to lax.pow for integer powers
• 5bfd6af Removed unnecessary skip in pallas_test.py::SoftmaxTest
• 3fd9326 [jax] Enable api_test with XLA:CPU thunks
• ed4958c [XLA:Mosaic] Add internal scratch VMEM
• 701c63e [Pallas/TPU] Add API for megacore partitioning of pipelines
• Additional commits viewable in compare view

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[dependabot]

Superseded by #12.