Bump jaxlib from 0.4.25 to 0.4.30

[dependabot]

Bumps jaxlib from 0.4.25 to 0.4.30.

Release notes

Sourced from jaxlib's releases.

Jaxlib release v0.4.30

No release notes provided.

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).
  • Fixed a bug where XLA:CPU miscompiled certain matmul fusions (openxla/xla#13301).
  • Fixes a compiler crash on GPU (google/jax#21396).

• Deprecations

  • jax.tree.map(f, None, non-None) now emits a DeprecationWarning, and will raise an error in a future version of jax. None is only a tree-prefix of itself. To preserve the current behavior, you can ask jax.tree.map to treat None as a leaf value by writing: jax.tree.map(lambda x, y: None if x is None else f(x, y), a, b, is_leaf=lambda x: x is None).

jaxlib v0.4.28

• Bug fixes

  • Fixes a memory corruption bug in the type name of Array and JIT Python objects in Python 3.10 or earlier.
  • Fixed a warning '+ptx84' is not a recognized feature for this target under CUDA 12.4.
  • Fixed a slow compilation problem on CPU.

• Changes

  • The Windows build is now built with Clang instead of MSVC.

Jaxlib release v0.4.27

No release notes provided.

Jaxlib release v0.4.26

No release notes provided.

Changelog

Sourced from jaxlib'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 #31.