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In presence of dynamic shapes the ThreeFry2x32Descriptor will contain the value n=-1, and the actual desired output length will be passed as an additional operand. If the shape is static then the length will be passed as part of the descriptor. PiperOrigin-RevId: 497945778
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
# Copyright 2019 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 itertools
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import operator
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from typing import Optional, Union
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import jaxlib.mlir.ir as ir
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from jaxlib import xla_client
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from .hlo_helpers import custom_call
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try:
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from .cuda import _prng as _cuda_prng
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for _name, _value in _cuda_prng.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_prng = None
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try:
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from .rocm import _prng as _hip_prng
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for _name, _value in _hip_prng.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_prng = None
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_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
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def _threefry2x32_lowering(prng, platform, keys, data,
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length: Optional[Union[int, ir.Value]] = None):
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"""ThreeFry2x32 kernel for GPU."""
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assert len(keys) == 2, keys
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assert len(data) == 2, data
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assert (ir.RankedTensorType(keys[0].type).element_type ==
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ir.IntegerType.get_unsigned(32)), keys[0].type
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typ = keys[0].type
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dims = ir.RankedTensorType(typ).shape
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for x in itertools.chain(keys, data):
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assert x.type == typ, (x.type, typ)
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ndims = len(dims)
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layout = tuple(range(ndims - 1, -1, -1))
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operand_layouts = [layout] * 4
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operands = [keys[0], keys[1], data[0], data[1]]
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if length is None:
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length = _prod(dims)
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if isinstance(length, int):
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opaque = prng.threefry2x32_descriptor(length)
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else:
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opaque = prng.threefry2x32_descriptor(-1)
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assert (ir.RankedTensorType(length.type).element_type ==
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ir.IntegerType.get_signless(64)), length
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assert (ir.RankedTensorType(length.type).shape ==
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[1]), (length, ir.RankedTensorType(length.type).shape)
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operands.append(length)
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operand_layouts.append((0,))
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return custom_call(
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f"{platform}_threefry2x32",
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[typ, typ],
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operands,
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backend_config=opaque,
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operand_layouts=operand_layouts,
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result_layouts=[layout] * 2)
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cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cu")
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rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "hip")
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