Out-of-bounds indices to jax.ops.segment_sum will now be handled with FILL_OR_DROP semantics, as documented. This primarily afects the reverse-mode derivative, where gradients corresponding to out-of-bounds indices will now be returned as 0. (#8634).
jax2tf will force the converted code to use XLA for the code fragments under jax.jit, e.g., most jax.numpy functions (#7839).
jax.api has been removed. Functions that were available as jax.api.*
were aliases for functions in jax.*; please use the functions in
jax.* instead.
jax.partial, jax.lax.partial, and jax.util.partial were accidental
exports that have now been removed. Use functools.partial from the Python
standard library instead.
Boolean scalar indices now raise a TypeError; previously this silently
returned wrong results (#7925 ).
Many more jax.numpy functions now require array-like inputs, and will error
if passed a list (#7747#7802#7907 ).
See #7737 for a discussion of the rationale behind this change.
When inside a transformation such as jax.jit, jax.numpy.array always
stages the array it produces into the traced computation. Previously
jax.numpy.array would sometimes produce a on-device array, even under
a jax.jit decorator. This change may break code that used JAX arrays to
perform shape or index computations that must be known statically; the
workaround is to perform such computations using classic NumPy arrays
instead.
jnp.ndarray is now a true base-class for JAX arrays. In particular, this
Out-of-bounds indices to jax.ops.segment_sum will now be handled with
FILL_OR_DROP semantics, as documented. This primarily afects the
reverse-mode derivative, where gradients corresponding to out-of-bounds
indices will now be returned as 0. (#8634).
jax2tf will force the converted code to use XLA for the code fragments
under jax.jit, e.g., most jax.numpy functions ({jax-issue}[#7839](https://github.com/google/jax/issues/7839)).
jaxlib 0.1.74 (Nov 17, 2021)
Enabled peer-to-peer copies between GPUs. Previously, GPU copies were bounced via
the host, which is usually slower.
Added experimental MLIR Python bindings for use by JAX.
jax.random.choice and jax.random.permutation now support
multidimensional arrays and an optional axis argument ({jax-issue}[#8158](https://github.com/google/jax/issues/8158))
Breaking changes:
jax.numpy.take and jax.numpy.take_along_axis now require array-like inputs
(see {jax-issue}[#7737](https://github.com/google/jax/issues/7737))
jaxlib 0.1.73 (Oct 18, 2021)
Multiple cuDNN versions are now supported for jaxlib GPU cuda11 wheels.
cuDNN 8.2 or newer. We recommend using the cuDNN 8.2 wheel if your cuDNN
installation is new enough, since it supports additional functionality.
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dependabot[bot]
commented
2 years ago
Bumps jax from 0.2.8 to 0.2.26.
Release notes
Sourced from jax's releases.
... (truncated)
Changelog
Sourced from jax's changelog.
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Commits
1b5630eUpdate jaxlib version number to 0.1.76a081f00Merge pull request #8850 from froystig:aot-transform3264045Restructures jax2tf converters eval directory.6a821d3Merge pull request #8843 from jakevdp:lax-dtype-errsb4db8abMerge pull request #8849 from jakevdp:x64-linalgb980acfdetect and err on transformation of AOT-compiled function calls2143f3dEnsure semantically equivalent PartitionSpecs are considered equivalent whenf8e18e9[x64] minor weak_type changes to linalg.py4dc3137Before this change, the local_to_global in the path of dispatching the pjitte...5779267Merge pull request #8847 from hawkinsp:tolDependabot 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
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