The new "typed" API that XLA provides for foreign function calls is
header-only and packaging it as part of jaxlib could simplify the open
source workflow for building custom calls.
It's not completely obvious that we need to include this, because jaxlib
isn't strictly required as a _build_ dependency for FFI calls, although
it typically will be required as a _run time_ dependency. Also, it
probably wouldn't be too painful for external projects to use the
headers directly from the openxla/xla repo.
All that being said, I wanted to figure out how to do this, and it has
been requested a few times.
The current supported values for compute type is `device_host`, `device`. `device_sparse` will be allowed in follow up CL. Using `device_host` means that the device's PJRT client will be orchestrating the execution of the computation on the host.
`cpu` as a compute_type is reserved for pure CPU only computations without a device's pjrt client orchestrating the computation.
PiperOrigin-RevId: 634909918
Metadata loader was using incorrect warp assignment, which resulted in incorrect addresses with num_warps>4. This was previously missed, as the autotuner rarely selected such configs.
PiperOrigin-RevId: 633513110
This change is in preparation for adding support for emitting https://tc39.es/source-map/ source map information for jaxprs, so the relationship between a jaxpr and its Python code can be visualized using tooling for that purpose.
This change adds a new `source_map()` pretty printer document, which causes the pretty-printer to populate a source_map side output during pretty printing.
The change also teaches the core jaxpr pretty printer to populate source map information on each equation.
Usage:
from jax.experimental.sparse import nm
res = nm.nm_spmm(lhs, rhs, nm.nm_pack(mask))
where:
lhs.shape = [M, K/2]
rhs.shape = [K, N]
`mask` has the same shape as `lhs` with boolean type
If batch dimensions are present, the `dimension_numbers` argument has to be set to:
((lhs_contracting_dims, rhs_contracting_dims), (lhs_batch_dims, rhs_batch_dims))
The lowering only works on nVidia GPUs, that provide hardware support for sparse dots.
PiperOrigin-RevId: 627640553
* Added a noop config_tags_overrides parameter to jax_test()
* Updated BUILD files necessary to run Pallas tests via Bazel
* Changed PallasTest to skip "large" test cases
PiperOrigin-RevId: 608534008
The guarantee provided by this API is that the sharding of `x` and `y` will be the same! What the sharding will be is decided by GSPMD.
The flow of sharding is bidirectional i.e. SPMD will choose what the sharding should be of `x` and `y` depending on it's propagation algorithm. It might end up being that the sharding chosen is not of `x` and `y` but something better. At the end of propagation `x` and `y` will be sharded alike.
The API can be made variadic in the future i.e. `*args = shard_alike(*args)` depending on use cases.
Fixes: https://github.com/google/jax/issues/15600
PiperOrigin-RevId: 592375936
