Bump jax from 0.4.25 to 0.4.30

[dependabot]

Bumps jax from 0.4.25 to 0.4.30.

Release notes

Sourced from jax's releases.

Jax release v0.4.30

No release notes provided.

Jaxlib release v0.4.30

No release notes provided.

JAX v0.4.29

• 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.
  • Removed backwards-compatibility support for old usage of the jax.experimental.export API. It is not possible anymore to use from jax.experimental.export import export, and instead you should use from jax.experimental import export. The removed functionality has been deprecated since 0.4.24.

• Deprecations

  • jax.sharding.XLACompatibleSharding is deprecated. Please use jax.sharding.Sharding.
  • jax.experimental.Exported.in_shardings has been renamed as jax.experimental.Exported.in_shardings_hlo. Same for out_shardings. The old names will be removed after 3 months.
  • Removed a number of previously-deprecated APIs:
    • from {mod}jax.core: non_negative_dim, DimSize, Shape
    • from {mod}jax.lax: tie_in
    • from {mod}jax.nn: normalize
    • from {mod}jax.interpreters.xla: backend_specific_translations, translations, register_translation, xla_destructure, TranslationRule, TranslationContext, XlaOp.
  • The tol argument of {func}jax.numpy.linalg.matrix_rank is being deprecated and will soon be removed. Use rtol instead.
  • The rcond argument of {func}jax.numpy.linalg.pinv is being deprecated and will soon be removed. Use rtol instead.
  • The deprecated jax.config submodule has been removed. To configure JAX use import jax and then reference the config object via jax.config.
  • {mod}jax.random APIs no longer accept batched keys, where previously some did unintentionally. Going forward, we recommend explicit use of {func}jax.vmap in such cases.

• New Functionality

  • Added {func}jax.experimental.Exported.in_shardings_jax to construct shardings that can be used with the JAX APIs from the HloShardings that are stored in the Exported objects.

Jaxlib release v0.4.29

• Bug fixes
  • Fixed a bug where XLA sharded some concatenation operations incorrectly, which manifested as an incorrect output for cumulative reductions (#21403).

... (truncated)

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 #32.