{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 (Unreleased)
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
bfloat16 NumPy extension type.
... (truncated)
Commits
6ec9046 Merge pull request #5950 from google:update-pypi
7615d67 Merge pull request #5945 from jakevdp:simplify-wrapped-doc
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dependabot[bot]
commented
3 years ago
Bumps jax from 0.2.8 to 0.2.10.
Changelog
Sourced from jax's changelog.
... (truncated)
Commits
6ec9046Merge pull request #5950 from google:update-pypi7615d67Merge pull request #5945 from jakevdp:simplify-wrapped-doc206acc1update jax for pypi8a8080cRewrite numpy docstring parser & remove extraneous sections76c1ec3Merge pull request #5936 from apaszke:vmap-of-xmapfcaf47eMerge pull request #5928 from apaszke:xmap-no-vmap6884f21Fix batching formula of xmap249cd8bMerge pull request #5946 from skye:pjit_test6f83cc6Merge pull request #5940 from jakevdp:fix-doc49cca16Ignore warnings in pjit_test.pyDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
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