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).