chore(deps): update jax[cpu] requirement from <0.5,>=0.4 to >=0.4,<0.6

[dependabot]

Updates the requirements on jax[cpu] to permit the latest version.

Release notes

Sourced from jax[cpu]'s releases.

JAX v0.5.0

As of this release, JAX now uses effort-based versioning. Since this release makes a breaking change to PRNG key semantics that may require users to update their code, we are bumping the "meso" version of JAX to signify this.

• Breaking changes

  • Enable jax_threefry_partitionable by default (see the update note).

  • This release drops support for Mac x86 wheels. Mac ARM of course remains supported. For a recent discussion, see Will macOS x86 still be supported for the next few years? jax-ml/jax#22936.

    Two key factors motivated this decision:

    • The Mac x86 build (only) has a number of test failures and crashes. We would prefer to ship no release than a broken release.
    • Mac x86 hardware is end-of-life and cannot be easily obtained for developers at this point. So it is difficult for us to fix this kind of problem even if we wanted to.

    We are open to readding support for Mac x86 if the community is willing to help support that platform: in particular, we would need the JAX test suite to pass cleanly on Mac x86 before we could ship releases again.

• Changes:

  • The minimum NumPy version is now 1.25. NumPy 1.25 will remain the minimum supported version until June 2025.
  • The minimum SciPy version is now 1.11. SciPy 1.11 will remain the minimum supported version until June 2025.
  • jax.numpy.einsum now defaults to optimize='auto' rather than optimize='optimal'. This avoids exponentially-scaling trace-time in the case of many arguments ([#25214](https://github.com/jax-ml/jax/issues/25214)).
  • jax.numpy.linalg.solve no longer supports batched 1D arguments on the right hand side. To recover the previous behavior in these cases, use solve(a, b[..., None]).squeeze(-1).

• New Features

  • jax.numpy.fft.fftn, jax.numpy.fft.rfftn, jax.numpy.fft.ifftn, and jax.numpy.fft.irfftn now support transforms in more than 3 dimensions, which was previously the limit. See [#25606](https://github.com/jax-ml/jax/issues/25606) for more details.
  • Support added for user defined state in the FFI via the new jax.ffi.register_ffi_type_id function.
  • The AOT lowering .as_text() method now supports the debug_info option to include debugging information, e.g., source location, in the output.

• Deprecations

  • From jax.interpreters.xla, abstractify and pytype_aval_mappings are now deprecated, having been replaced by symbols of the same name in jax.core.

... (truncated)

Changelog

Sourced from jax[cpu]'s changelog.

jax 0.5.0 (Jan 17, 2025)

As of this release, JAX now uses effort-based versioning. Since this release makes a breaking change to PRNG key semantics that may require users to update their code, we are bumping the "meso" version of JAX to signify this.

• Breaking changes

  • Enable jax_threefry_partitionable by default (see the update note).

  • This release drops support for Mac x86 wheels. Mac ARM of course remains supported. For a recent discussion, see Will macOS x86 still be supported for the next few years? jax-ml/jax#22936.

    Two key factors motivated this decision:

    • The Mac x86 build (only) has a number of test failures and crashes. We would prefer to ship no release than a broken release.
    • Mac x86 hardware is end-of-life and cannot be easily obtained for developers at this point. So it is difficult for us to fix this kind of problem even if we wanted to.

    We are open to readding support for Mac x86 if the community is willing to help support that platform: in particular, we would need the JAX test suite to pass cleanly on Mac x86 before we could ship releases again.

• Changes:

  • The minimum NumPy version is now 1.25. NumPy 1.25 will remain the minimum supported version until June 2025.
  • The minimum SciPy version is now 1.11. SciPy 1.11 will remain the minimum supported version until June 2025.
  • {func}jax.numpy.einsum now defaults to optimize='auto' rather than optimize='optimal'. This avoids exponentially-scaling trace-time in the case of many arguments ({jax-issue}[#25214](https://github.com/jax-ml/jax/issues/25214)).
  • {func}jax.numpy.linalg.solve no longer supports batched 1D arguments on the right hand side. To recover the previous behavior in these cases, use solve(a, b[..., None]).squeeze(-1).

• New Features

  • {func}jax.numpy.fft.fftn, {func}jax.numpy.fft.rfftn, {func}jax.numpy.fft.ifftn, and {func}jax.numpy.fft.irfftn now support transforms in more than 3 dimensions, which was previously the limit. See {jax-issue}[#25606](https://github.com/jax-ml/jax/issues/25606) for more details.
  • Support added for user defined state in the FFI via the new {func}jax.ffi.register_ffi_type_id function.
  • The AOT lowering .as_text() method now supports the debug_info option to include debugging information, e.g., source location, in the output.

• Deprecations

... (truncated)

Commits

• c25fb92 Release JAX 0.5.0
• a527aba Reverts f1b894d14a28ac22a037fb79177b991275c75a18
• ce85b89 [sharding_in_types] Error out for reshape for splits like this: (4, 6, 8) -...
• 7cac76d Update XLA dependency to use revision
• d3be190 [Mosaic GPU] Delete unused declarations of mosaic_gpu_memcpy_async_h2d.
• d34c40f [mosaic_gpu] Added a serialization pass
• af66719 [sharding_in_types] Rename .at[...].get(out_spec) to `.at[...].get(out_shar...
• 97cd748 Rename out_type -> out_sharding parameter on einsum
• 49224d6 Replace Auto/User/Collective AxisTypes names with Hidden/Visible/Collective.
• bd22bfe [Mosaic TPU] Use large to compact 2nd minor retiling for conversions going bo...
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[coveralls]

Pull Request Test Coverage Report for Build 12835378898

Details

• 0 of 0 changed or added relevant lines in 0 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 93.373%

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Totals
Change from base Build 12761119841:	0.0%
Covered Lines:	1395
Relevant Lines:	1494

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