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[dynamic-shapes] implement bint arrays (opaque dtypes), add padding rules #12707
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sharadmv
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Co-authored-by: Sharad Vikram <sharad.vikram@gmail.com>
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…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
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…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
For reference, the "opaque dtypes" mention in the PR title here is to say that this builds on #11768 and subsequent developments. |
copybara-service bot
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…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
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Oct 11, 2022
…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
copybara-service bot
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Oct 11, 2022
…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
copybara-service bot
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Oct 11, 2022
…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480184773
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…rivial jax.numpy shape validation code failed in some downstream user tests. PiperOrigin-RevId: 480229237
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… 0.3.22 Adam Paszke (1): Support MANUAL collectives in top-level xmaps Artem Belevich (1): Handle FP8 types. Chao Chen (1): testSphHarmOrderZeroDegreeOne and test_custom_linear_solve_cholesky have been fixed in ROCm, no need to skip George Necula (3): Expand support for __jax_array__ in jnp.array. [jax2tf] Allow the use of DimPolynomial with jnp.array and binary operations [jax2tf] Implement jax2tf(pjit) for experimental_native_lowering Jake VanderPlas (22): pure_callback: fix batching rule for multiple arguments Split parts of lax_numpy_test.py into separate test files. Re-land google/jax#12588 with minor fixes [typing] add full annotations for lax_numpy setops Add types to jax/_src/numpy/util.py jnp.prod & jnp.sum: promote to default integer type rather than int64/uint64 jax.jacobian: propagate function signature to transformed function [typing] add annotations to jax.numpy.linalg [typing] add annotations to numpy.fft [typing] add types for jax.numpy.polynomial [typing] overloads for jnp.linalg.svd & jnp.linalg.qr test: fix LaxNumpyTest:testConcatenate Move promote_like_jnp to jax.test_util jnp.average: support tuple axis [typing] add type annotations to lax.linalg functions Update scipy version in jax.scipy.fft [typing] annotate lax.slicing [typing] add annotations to jax.scipy.fft [typing] add annotations to jax.scipy.ndimage [typing] add type annotations to jax.scipy.linalg changelog: add missing github commit links Remove deprecated functionality from jax.test_util Jason Furmanek (2): [ROCM] Add TENSORFLOW_ROCM_COMMIT parameter to ROCM ci build Add default setting for TENSORFLOW_ROCM_COMMIT Krishna Haridasan (1): Add unsafe_buffer_pointer to _DeviceArray Kuangyuan Chen (1): Turn on cpp pjit py default Matthew Johnson (9): improve custom_jvp/vjp error messages [dynamic-shapes] small fix to einsum (and indexing) improve jit(f).lower(duck_args) and pjit(f).lower(duck_args) make device_put work with Sharding 2nd arg add test, small fixes fix -O / PYTHONOPTIMIZE bug make device_put(prngkeyarray, sharding) for Array implement bint arrays (opaque dtypes), add padding rules Rolling forward google/jax#12707 after rollback, due to changes in relatively trivial jax.numpy shape validation code failed in some downstream user tests. Parker Schuh (1): Rename Executable to LoadedExecutable within jax. Peter Hawkins (10): Add an internal jtu.sample_product test decorator. Migrate api_test, lax_numpy_test, and lax_vmap_test to Switch lax_numpy_indexing_test to use jtu.sample_product. Use input-output aliasing for jaxlib GPU custom calls. Revert: Use input-output aliasing for jaxlib GPU custom calls. Reapply: Use input-output aliasing for jaxlib GPU custom calls. Copybara import of the project: Add changes accidentally omitted from Add input-output aliasing annotations for LAPACK calls on CPU. Fix compilation failure in lapack kernel under msan. Ran Ran (5): Set JAX_PLATFORMS=tpu,cpu on TPUs Add set up message for JAX_PLATFORMS Update set up message Update message and change log Address comments for change log Rishabh Kabra (1): Clarify docs for `fori_loop`, noting that negative or custom increments are not supported. Rohit Santhanam (1): [ROCm] Upgrade to ROCm 5.3 and associated enhancements Roy Frostig (1): add input/output sharding to executable protocol Serge Durand (1): Fix book links Sharad Vikram (2): Test that array layout is preserved in Python callbacks Fix collect_profile _src import Skye Wanderman-Milne (2): Update version and CHANGELOG for jax 0.3.21 release Update WORKSPACE and setup.py for jaxlib 0.3.22 release Sudhakar (1): Add multihost GPU CI run with last public jaxlib release Tianjian Lu (4): [sparse] Move broadcasting_vmap to sparse util. [sparse] Add conversions between BCSR and BCOO. [sparse] Bug fix in _validate_bcsr. [sparse] BCSR fromdense and todense. Yash Katariya (11): `get_device_buffers()` on ShardedBuffer if config.jax_array is enabled because jax.Arrray does not work with ShardedBuffer since jax.Array is like a ShardedBuffer. Create `Array`s from `__getitem__` and `__iter__`. This is done by `device_put`ting from the host to default device which is suboptimal. But there is a TODO to fix this! Add `host_local_array_to_global_array` and `global_array_to_host_local_array` for enabling transition to jax.Array. Add sharding to `DeviceArray` and `ShardedDeviceArray` as a compatibility change to rollout `jax.Array`. Add `addressable_shards` to SDA and DA as a compatibility API to match with `jax.Array`. This will aid in transition to `jax.Array`. Take `shardings` as a parameter to `deserialize` and `run_deserialization` instead of `mesh` and `pspecs`. Make `is_fully_replicated` and `is_fully_addressble` a property rather than a method. Lift `lambda x: x` to the top level so that we don't recompile on every invocation of `process_allgather`. Fix the type annotation of return type of `device_buffer` and `device_buffers` which return `ArrayImpl` instead of DeviceArray. Add weak_type attribute to `Array` since it exists on DA (but doesn't exist on SDA). Add support for calculating the device_assignment when there are no inputs to `jit` and `pjit`. dependabot[bot] (1): Bump styfle/cancel-workflow-action from 0.10.0 to 0.10.1 jax authors (1): Copybara import of the project:
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This was the last bit we needed to run a batch-shape-polymorphic transformer on XLA.
Co-authored-by: Sharad Vikram sharad.vikram@gmail.com