Bump jaxlib from 0.1.61 to 0.3.5

[dependabot[bot]]

Bumps jaxlib from 0.1.61 to 0.3.5.

Release notes

Sourced from jaxlib's releases.

Jaxlib release v0.3.0

• Changes
  • Bazel 5.0.0 is now required to build jaxlib.
  • jaxlib version has been bumped to 0.3.0. Please see the design doc for the explanation.

Jaxlib v0.1.76

• New features
  • Includes precompiled SASS for NVidia compute capability 8.0 GPUS (e.g. A100). Removes precompiled SASS for compute capability 6.1 so as not to increase the number of compute capabilities: GPUs with compute capability 6.1 can use the 6.0 SASS.
  • With jaxlib 0.1.76, JAX uses the MHLO MLIR dialect as its primary target compiler IR by default.
• Breaking changes
  • Support for NumPy 1.18 has been dropped, per the deprecation policy. Please upgrade to a supported NumPy version.
• Bug fixes
  • Fixed a bug where apparently identical pytreedef objects constructed by different routes do not compare as equal (#9066).
  • The JAX jit cache requires two static arguments to have identical types for a cache hit (#9311).

Jaxlib release v0.1.75

• New features:
  • Support for python 3.10.

Jaxlib release v0.1.74

No release notes provided.

Jaxlib release v0.1.73

No release notes provided.

jaxlib release v0.1.72

No release notes provided.

Jaxlib release v0.1.71

No release notes provided.

jaxlib v0.1.70 release

No release notes provided.

Changelog

Sourced from jaxlib's changelog.

jax 0.3.5 (April 7, 2022)

• GitHub commits.
• Changes:
  • added {func}jax.random.loggamma & improved behavior of {func}jax.random.beta and {func}jax.random.dirichlet for small parameter values ({jax-issue}[#9906](https://github.com/google/jax/issues/9906)).
  • the private lax_numpy submodule is no longer exposed in the jax.numpy namespace ({jax-issue}[#10029](https://github.com/google/jax/issues/10029)).
  • added array creation routines {func}jax.numpy.frombuffer, {func}jax.numpy.fromfunction, and {func}jax.numpy.fromstring ({jax-issue}[#10049](https://github.com/google/jax/issues/10049)).
  • DeviceArray.copy() now returns a DeviceArray rather than a np.ndarray ({jax-issue}[#10069](https://github.com/google/jax/issues/10069))
  • added {func}jax.scipy.linalg.rsf2csf
• Deprecations:
  • {func}jax.nn.normalize is being deprecated. Use {func}jax.nn.standardize instead ({jax-issue}[#9899](https://github.com/google/jax/issues/9899)).
  • {func}jax.tree_util.tree_multimap is deprecated. Use {func}jax.tree_util.tree_map instead ({jax-issue}[#5746](https://github.com/google/jax/issues/5746)).
  • jax.experimental.sharded_jit is deprecated. Use pjit instead.

jaxlib 0.3.5 (April 7, 2022)

• Bug fixes
  • Fixed a bug where double-precision complex-to-real IRFFTs would mutate their input buffers on GPU ({jax-issue}[#9946](https://github.com/google/jax/issues/9946)).
  • Fixed incorrect constant-folding of complex scatters ({jax-issue}[#10159](https://github.com/google/jax/issues/10159))

jax 0.3.4 (March 18, 2022)

• GitHub commits.

jax 0.3.3 (March 17, 2022)

• GitHub commits.

jax 0.3.2 (March 16, 2022)

• GitHub commits.
• Changes:
  • The functions jax.ops.index_update, jax.ops.index_add, which were deprecated in 0.2.22, have been removed. Please use the .at property on JAX arrays instead, e.g., x.at[idx].set(y).
  • Moved jax.experimental.ann.approx_*_k into jax.lax. These functions are optimized alternatives to jax.lax.top_k.
  • {func}jax.numpy.broadcast_arrays and {func}jax.numpy.broadcast_to now require scalar or array-like inputs, and will fail if they are passed lists (part of {jax-issue}[#7737](https://github.com/google/jax/issues/7737)).
  • The standard jax[tpu] install can now be used with Cloud TPU v4 VMs.
  • pjit now works on CPU (in addition to previous TPU and GPU support).

jaxlib 0.3.2 (March 16, 2022)

• Changes

... (truncated)

Commits

• 700d1e1 Merge pull request #10177 from hawkinsp:jaxlib
• 96ba290 Jax 0.3.5 and jaxlib 0.3.5 release.
• 7ee6adb Merge pull request #10173 from jakevdp:bcoo-add-batchdim
• 6a7a346 Move PartitionSpec from sharded_jit.py to pxla.py. The public endpoint is via...
• 93a24f3 [sparse] add bcoo_add_batchdim
• bc658e7 [MHLO] Add direct MHLO lowerings for most linear algebra kernels.
• 4ed0660 Add deprecation warning for sharded_jit.
• 3bfa6af [MHLO] Add MHLO lowering for PRNG kernels.
• b9bb613 [MHLO] Prefer backend-specific HLO lowerings instead of non-backend-specific ...
• 4012267 Revert: implement jnp.trace in terms of jnp.diagonal
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Superseded by #289.