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b8ae8e3fa1
This CL renames occurrences of "mhlo" in: 1) names, 2) tests, 3) prose in order
to prepare for the upcoming migration.
Unchanged occurrences:
1) Public API that contains "mhlo", e.g. XlaLowering.mhlo and the "mhlo"
argument value in Lowering.as_text and Lowering.compiler_ir.
2) Documentation (changelog, JEPs, IR examples, etc).
3) One rare situation where prose says "StableHLO" and "MHLO" in one sentence,
so both are necessary to disambiguate.
PiperOrigin-RevId: 495771153
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
# Copyright 2021 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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from functools import partial
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import operator
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import jaxlib.mlir.ir as ir
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from .hlo_helpers import custom_call
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from jaxlib import xla_client
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try:
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from .cuda import _linalg as _cuda_linalg
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for _name, _value in _cuda_linalg.registrations().items():
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xla_client.register_custom_call_target(_name, _value, platform="CUDA")
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except ImportError:
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_cuda_linalg = None
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try:
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from .rocm import _linalg as _hip_linalg
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for _name, _value in _hip_linalg.registrations().items():
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xla_client.register_custom_call_target(_name, _value, platform="ROCM")
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except ImportError:
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_hip_linalg = None
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_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
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def _lu_pivots_to_permutation_hlo(platform, gpu_linalg, pivots, *, permutation_size):
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"""Kernel for the transformation of pivots to permutations on GPU."""
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typ = ir.RankedTensorType(pivots.type)
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dims = typ.shape
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i32_type = ir.IntegerType.get_signless(32)
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assert typ.element_type == i32_type, typ
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batch_size = _prod(dims[:-1])
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pivot_size = dims[-1]
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opaque = gpu_linalg.lu_pivots_to_permutation_descriptor(
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batch_size, pivot_size, permutation_size)
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pivots_layout = tuple(range(len(dims) - 1, -1, -1))
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permutations_layout = pivots_layout
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permutations_dims = list(dims)
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permutations_dims[-1] = permutation_size
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permutations_type = ir.RankedTensorType.get(permutations_dims, i32_type)
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return custom_call(
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f"{platform}_lu_pivots_to_permutation",
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[permutations_type],
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[pivots],
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backend_config=opaque,
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operand_layouts=[pivots_layout],
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result_layouts=[permutations_layout])
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cuda_lu_pivots_to_permutation = partial(_lu_pivots_to_permutation_hlo, "cu",
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_cuda_linalg)
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hip_lu_pivots_to_permutation = partial(
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_lu_pivots_to_permutation_hlo, "hip", _hip_linalg)
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