wehub-resource-sync
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: RUF005
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"""Utilities to construct matmul workloads."""
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import tvm
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from tvm.script import relax as R
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from tvm.script.ir_builder import IRBuilder
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from tvm.script.ir_builder import relax as relax_builder
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def get_relax_matmul_module(
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x_shape,
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y_shape,
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in_dtype,
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out_dtype=None,
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transposed_y=False,
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bias_shape=None,
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activation=None,
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residual_bin_op=None,
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residual_activation=None,
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):
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"""Create a matmul op followd by epilogue operations."""
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out_dtype = out_dtype if out_dtype is not None else in_dtype
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with IRBuilder() as builder:
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with relax_builder.function():
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R.func_name("main")
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x = R.arg("x", R.Tensor(x_shape, in_dtype))
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y = R.arg("y", R.Tensor(y_shape, in_dtype))
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if bias_shape is not None:
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bias = R.arg("bias", R.Tensor(bias_shape, out_dtype))
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with R.dataflow() as frame:
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if transposed_y:
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axes = list(range(len(y_shape) - 2)) + [-1, -2]
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y = R.emit(R.permute_dims(y, axes=axes))
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result = R.emit(R.matmul(x, y, out_dtype=out_dtype))
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if bias_shape is not None:
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result = R.emit(result + bias)
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if activation is not None:
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result = R.emit(activation(result))
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if residual_bin_op is not None:
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result = R.emit(residual_bin_op(result, x))
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if residual_activation is not None:
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result = R.emit(residual_activation(result))
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R.output(result)
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R.func_ret_value(frame.output_vars[0])
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func = builder.get()
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return tvm.IRModule({"main": func})
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