Bump jax from 0.3.25 to 0.4.3 in /docker/openproblems-python-pytorch

[dependabot[bot]]

Bumps jax from 0.3.25 to 0.4.3.

Release notes

Sourced from jax's releases.

JAX release v0.4.3

No release notes provided.

jaxlib release v0.4.3

No release notes provided.

JAX release v0.4.2

No release notes provided.

jaxlib release v0.4.2

No release notes provided.

Jax release v0.4.1

• Changes
  • Support for Python 3.7 has been dropped, in accordance with JAX's {ref}version-support-policy.
  • We introduce jax.Array which is a unified array type that subsumes DeviceArray, ShardedDeviceArray, and GlobalDeviceArray types in JAX. The jax.Array type helps make parallelism a core feature of JAX, simplifies and unifies JAX internals, and allows us to unify jit and pjit. jax.Array has been enabled by default in JAX 0.4 and makes some breaking change to the pjit API. The jax.Array migration guide can help you migrate your codebase to jax.Array. You can also look at the Distributed arrays and automatic parallelization tutorial to understand the new concepts.
  • PartitionSpec and Mesh are now out of experimental. The new API endpoints are jax.sharding.PartitionSpec and jax.sharding.Mesh. jax.experimental.maps.Mesh and jax.experimental.PartitionSpec are deprecated and will be removed in 3 months.
  • with_sharding_constraints new public endpoint is jax.lax.with_sharding_constraint.
  • If using ABSL flags together with jax.config, the ABSL flag values are no longer read or written after the JAX configuration options are initially populated from the ABSL flags. This change improves performance of reading jax.config options, which are used pervasively in JAX.
  • The jax2tf.call_tf function now uses for TF lowering the first TF device of the same platform as used by the embedding JAX computation. Before, it was using the 0th device for the JAX-default backend.
  • A number of jax.numpy functions now have their arguments marked as positional-only, matching NumPy.
  • jnp.msort is now deprecated, following the deprecation of np.msort in numpy 1.24. It will be removed in a future release, in accordance with the {ref}api-compatibility policy. It can be replaced with jnp.sort(a, axis=0).

Jaxlib release v0.4.1

• Changes
  • Support for Python 3.7 has been dropped, in accordance with JAX's {ref}version-support-policy.
  • The behavior of XLA_PYTHON_CLIENT_MEM_FRACTION=.XX has been changed to allocate XX% of

... (truncated)

Changelog

Sourced from jax's changelog.

jax 0.4.3 (Feb 8, 2023)

• Breaking changes

  • Deleted {func}jax.scipy.linalg.polar_unitary, which was a deprecated JAX extension to the scipy API. Use {func}jax.scipy.linalg.polar instead.

• Changes

  • Added {func}jax.scipy.stats.rankdata.

jaxlib 0.4.3 (Feb 8, 2023)

jax 0.4.2 (Jan 24, 2023)

• Breaking changes

  • Deleted jax.experimental.callback
  • Operations with dimensions in presence of jax2tf shape polymorphism have been generalized to work in more scenarios, by converting the symbolic dimension to JAX arrays. Operations involving symbolic dimensions and np.ndarray now can raise errors when the result is used as a shape value ({jax-issue}[#14106](https://github.com/google/jax/issues/14106)).
  • jaxpr objects now raise an error on attribute setting in order to avoid problematic mutations ({jax-issue}14102)

• Changes

  • {func}jax2tf.call_tf has a new parameter has_side_effects (default True) that can be used to declare whether an instance can be removed or replicated by JAX optimizations such as dead-code elimination ({jax-issue}[#13980](https://github.com/google/jax/issues/13980)).
  • Added more support for floordiv and mod for jax2tf shape polymorphism. Previously, certain division operations resulted in errors in presence of symbolic dimensions ({jax-issue}[#14108](https://github.com/google/jax/issues/14108)).

jaxlib 0.4.2 (Jan 24, 2023)

• Changes
  • Set JAX_USE_PJRT_C_API_ON_TPU=1 to enable new Cloud TPU runtime, featuring automatic device memory defragmentation.

jax 0.4.1 (Dec 13, 2022)

• Changes
  • Support for Python 3.7 has been dropped, in accordance with JAX's {ref}version-support-policy.
  • We introduce jax.Array which is a unified array type that subsumes DeviceArray, ShardedDeviceArray, and GlobalDeviceArray types in JAX. The jax.Array type helps make parallelism a core feature of JAX, simplifies and unifies JAX internals, and allows us to unify jit and pjit. jax.Array has been enabled by default in JAX 0.4 and makes some breaking change to the pjit API. The jax.Array migration guide can help you migrate your codebase to jax.Array. You can also look at the Distributed arrays and automatic parallelization

... (truncated)

Commits

• 7783a5e Merge pull request #14343 from skye:cache_options_check
• 1228cbd Change the executable_build_options check in compilation_cache.py to be more ...
• a46f31b Merge pull request #14342 from skye:version
• 8ab1585 Update WORKSPACE and setup.py for jax/jaxlib 0.4.3 release
• eb13c05 Add option to run tests with persistent compilation cache enabled.
• 6860cb8 Move jax.interpreters.xla to jax._src.interpreters.xla.
• 9c827fb Merge pull request #14340 from ROCmSoftwarePlatform:rocm_reenable_linalg_spar...
• 01a10a1 [ROCm] Re-enable some linalg and sparse tests
• 8eb00c5 Merge pull request #14335 from jakevdp:doc-transformations
• a022a4e DOC: remove transformations.md
• Additional commits viewable in compare view

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Superseded by #832.