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94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
# Copyright 2022 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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# Helpers for building MLIR operators
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from typing import Dict, Optional, Sequence, Union
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import jaxlib.mlir.ir as ir
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import jaxlib.mlir.dialects.stablehlo as hlo
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import numpy as np
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def custom_call(
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call_target_name: Union[str, bytes],
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out_types: Sequence[ir.Type],
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operands: Sequence[ir.Value],
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operand_layouts: Sequence[Sequence[int]],
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result_layouts: Sequence[Sequence[int]],
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backend_config: Optional[str] = None,
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has_side_effect: bool = False,
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api_version: int = 2,
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operand_output_aliases: Dict[int, int] = {},
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indices_of_shape_operands: Sequence[int] = (),
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) -> Union[ir.Value, Sequence[ir.Value]]:
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"""Less-verbose helper for building a CustomCallOp.
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Once https://github.com/llvm/llvm-project/issues/54932 is fixed, this helper
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may be able to go away.
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Args:
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...
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operand_output_alias: a dictionary mapping input numbers -> output numbers
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that must alias.
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indices_of_shape_operands: in presence of dynamic shapes, must pass in the
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output shapes as some of the operands. These are the indices of those
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operands.
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"""
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i32_type = ir.IntegerType.get_signless(32)
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results = (out_types
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if len(out_types) == 1 else [ir.TupleType.get_tuple(out_types)])
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attributes = dict(
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call_target_name=ir.StringAttr.get(call_target_name),
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has_side_effect=ir.BoolAttr.get(has_side_effect),
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backend_config=ir.StringAttr.get(
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"" if backend_config is None else backend_config),
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api_version=ir.IntegerAttr.get(i32_type, api_version),
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called_computations=ir.ArrayAttr.get([]),
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operand_layouts=ir.ArrayAttr.get([
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ir.DenseIntElementsAttr.get(
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np.atleast_1d(np.asarray(l, dtype=np.int64)),
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type=ir.IndexType.get()) for l in operand_layouts
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]),
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result_layouts=ir.ArrayAttr.get([
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ir.DenseIntElementsAttr.get(
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np.atleast_1d(np.asarray(l, dtype=np.int64)),
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type=ir.IndexType.get()) for l in result_layouts
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]),
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output_operand_aliases=ir.ArrayAttr.get([
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hlo.OutputOperandAlias.get(
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output_tuple_indices=[] if len(out_types) == 1 else [output],
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operand_index=input,
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operand_tuple_indices=[])
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for input, output in operand_output_aliases.items()
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])
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)
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if indices_of_shape_operands:
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attributes["indices_of_shape_operands"] = ir.DenseIntElementsAttr.get(
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np.asarray(indices_of_shape_operands, dtype=np.int64))
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# TODO(necula): CustomCall constructor does not yet support
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# indices_of_shape_operands, so we use the generic builder
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# The generic builder is pickier about the type of the operands, and some
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# of the callers did not call .result
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operands = [opnd if isinstance(opnd, ir.Value) else opnd.result
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for opnd in operands]
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out = hlo.CustomCallOp.build_generic(results=results, operands=operands, attributes=attributes)
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if len(out_types) == 1:
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return out.result
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else:
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return [
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hlo.GetTupleElementOp(out, ir.IntegerAttr.get(i32_type, i)).result
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for i in range(len(out_types))
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]
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