mirror of
https://github.com/ROCm/jax.git
synced 2025-04-24 22:46:05 +00:00
f93c2a1aa5
On CPU and GPU, almost all of the primitives in lax.linalg are backed by custom calls that support simple semantics when batch dimensions are sharded. Before this change, all linalg operations on CPU and GPU will insert an `all-gather` before being executed when called on sharded inputs, even when that shouldn't be necessary. This change adds support for this type of partitioning, to cover a wide range of use cases. There are a few remaining GPU ops that don't support partitioning either because they are backed by HLO ops that don't partition properly (Cholesky factorization and triangular solves), or because they're still using descriptors with problem dimensions in kernel. I'm going to fix these in follow up changes. PiperOrigin-RevId: 731732301
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
# Copyright 2018 The JAX Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# https://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import numpy as np
|
|
|
|
from jaxlib import xla_client
|
|
|
|
from .cpu import _lapack
|
|
from .cpu._lapack import eig
|
|
from .cpu._lapack import schur
|
|
|
|
for _name, _value in _lapack.registrations().items():
|
|
api_version = 0
|
|
if _name.endswith("_ffi"):
|
|
api_version = 1
|
|
xla_client.register_custom_call_as_batch_partitionable(_name)
|
|
xla_client.register_custom_call_target(
|
|
_name, _value, platform="cpu", api_version=api_version
|
|
)
|
|
|
|
|
|
EigComputationMode = eig.ComputationMode
|
|
SchurComputationMode = schur.ComputationMode
|
|
SchurSort = schur.Sort
|
|
|
|
|
|
LAPACK_DTYPE_PREFIX = {
|
|
np.float32: "s",
|
|
np.float64: "d",
|
|
np.complex64: "c",
|
|
np.complex128: "z",
|
|
}
|
|
|
|
|
|
def prepare_lapack_call(fn_base, dtype):
|
|
"""Initializes the LAPACK library and returns the LAPACK target name."""
|
|
_lapack.initialize()
|
|
return build_lapack_fn_target(fn_base, dtype)
|
|
|
|
|
|
def build_lapack_fn_target(fn_base: str, dtype) -> str:
|
|
"""Builds the target name for a LAPACK function custom call."""
|
|
try:
|
|
prefix = (
|
|
LAPACK_DTYPE_PREFIX.get(dtype, None) or LAPACK_DTYPE_PREFIX[dtype.type]
|
|
)
|
|
return f"lapack_{prefix}{fn_base}"
|
|
except KeyError as err:
|
|
raise NotImplementedError(err, f"Unsupported dtype {dtype}.") from err
|