Bump jaxlib from 0.1.56 to 0.1.63

[dependabot]

Bumps jaxlib from 0.1.56 to 0.1.63.

Changelog

Sourced from jaxlib's changelog.

jaxlib 0.1.63 (March 17 2021)

jax 0.2.10 (March 5 2021)

• GitHub commits.
• New features:
  • {func}jax.scipy.stats.chi2 is now available as a distribution with logpdf and pdf methods.
  • {func}jax.scipy.stats.betabinom is now available as a distribution with logpmf and pmf methods.
  • Added {func}jax.experimental.jax2tf.call_tf to call TensorFlow functions from JAX ({jax-issue}[#5627](https://github.com/google/jax/issues/5627)) and README).
  • Extended the batching rule for lax.pad to support batching of the padding values.
• Bug fixes:
  • {func}jax.numpy.take properly handles negative indices ({jax-issue}[#5768](https://github.com/google/jax/issues/5768))
• Breaking changes:
  • JAX's promotion rules were adjusted to make promotion more consistent and invariant to JIT. In particular, binary operations can now result in weakly-typed values when appropriate. The main user-visible effect of the change is that some operations result in outputs of different precision than before; for example the expression jnp.bfloat16(1) + 0.1 * jnp.arange(10) previously returned a float64 array, and now returns a bfloat16 array. JAX's type promotion behavior is described at {ref}type-promotion.
  • {func}jax.numpy.linspace now computes the floor of integer values, i.e., rounding towards -inf rather than 0. This change was made to match NumPy 1.20.0.
  • {func}jax.numpy.i0 no longer accepts complex numbers. Previously the function computed the absolute value of complex arguments. This change was made to match the semantics of NumPy 1.20.0.
  • Several {mod}jax.numpy functions no longer accept tuples or lists in place of array arguments: {func}jax.numpy.pad, :funcjax.numpy.ravel, {func}jax.numpy.repeat, {func}jax.numpy.reshape. In general, {mod}jax.numpy functions should be used with scalars or array arguments.

jaxlib 0.1.62 (March 9 2021)

• New features:
  • jaxlib wheels are now built to require AVX instructions on x86-64 machines by default. If you want to use JAX on a machine that doesn't support AVX, you can build a jaxlib from source using the --target_cpu_features flag to build.py. --target_cpu_features also replaces --enable_march_native.

jaxlib 0.1.61 (February 12 2021)

jaxlib 0.1.60 (Febuary 3 2021)

• Bug fixes:
  • Fixed a memory leak when converting CPU DeviceArrays to NumPy arrays. The memory leak was present in jaxlib releases 0.1.58 and 0.1.59.
  • bool, int8, and uint8 are now considered safe to cast to

... (truncated)

Commits

• 7ad5126 Update WORKSPACE for jaxlib 0.1.63 release, take 2
• 0a84db5 Merge pull request #6068 from jakevdp:fix-result-type
• 6e1cd39 Merge pull request #6075 from jakevdp:fix-formatting
• 4d8e954 Sharp Bits: fix formatting of code blocks within list
• 3b7de31 Merge pull request #6087 from jakevdp:pypi-extras
• d6408a4 Add extras_require to setup.py
• d2d7ecf Merge pull request #6082 from google:minjaxlib
• 328930b Increase minimum jaxlib version to 0.1.62.
• d326b07 Merge pull request #6086 from hawkinsp:numpy
• 2bf7dbc Merge pull request #5736 from tberghammer:changelist/357690519
• Additional commits viewable in compare view

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[dependabot]

Superseded by #185.