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96 lines
2.7 KiB
Python
96 lines
2.7 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 importlib
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import numpy as np
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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 .gpu_common_utils import GpuLibNotLinkedError
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from jaxlib import xla_client
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for cuda_module_name in [".cuda", "jax_cuda12_plugin"]:
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try:
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_cuda_linalg = importlib.import_module(
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f"{cuda_module_name}._linalg", package="jaxlib"
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)
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except ImportError:
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_cuda_linalg = None
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else:
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break
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if _cuda_linalg:
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for _name, _value in _cuda_linalg.registrations().items():
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api_version = (1
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if _name.endswith("lu_pivots_to_permutation")
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or _name.endswith("_ffi") else 0)
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xla_client.register_custom_call_target(
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_name, _value, platform="CUDA", api_version=api_version
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)
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for rocm_module_name in [".rocm", "jax_rocm60_plugin"]:
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try:
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_hip_linalg = importlib.import_module(
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f"{rocm_module_name}._linalg", package="jaxlib"
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)
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except ImportError:
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_hip_linalg = None
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else:
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break
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if _hip_linalg:
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for _name, _value in _hip_linalg.registrations().items():
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api_version = (1
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if _name.endswith("lu_pivots_to_permutation")
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or _name.endswith("_ffi") else 0)
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xla_client.register_custom_call_target(
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_name, _value, platform="ROCM", api_version=api_version
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)
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_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
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def _cholesky_update_hlo(platform, gpu_linalg, r_matrix, w_vector, dtype):
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"""Cholesky update."""
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del platform
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r_type = ir.RankedTensorType(r_matrix.type)
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dims = r_type.shape
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assert dims[0] == dims[1]
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n = dims[0]
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if not gpu_linalg:
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raise GpuLibNotLinkedError()
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np_type = np.dtype(dtype)
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opaque = gpu_linalg.build_cholesky_update_descriptor(np_type, n)
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return custom_call(
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"cu_cholesky_update",
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operands = [r_matrix, w_vector],
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result_types=[
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ir.RankedTensorType.get((n, n), r_type.element_type),
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ir.RankedTensorType.get((n,), r_type.element_type),
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],
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operand_output_aliases={0: 0, 1: 1},
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backend_config=opaque,
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).results[:1]
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cuda_cholesky_update = partial(_cholesky_update_hlo, "cu", _cuda_linalg)
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