wehub-resource-sync
152 lines
4.4 KiB
Python
152 lines
4.4 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# 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, 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 unittest
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import hypothesis.strategies as st
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from auto_scan_test import PassAutoScanTest
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from program_config import OpConfig, ProgramConfig, TensorConfig
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class TestMatmulScaleFusePass(PassAutoScanTest):
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r"""
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x_var y_var(persistable)
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\ /
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matmul
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scale
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"""
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def sample_predictor_configs(self, program_config):
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# cpu
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config = self.create_inference_config(use_gpu=False)
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yield (
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config,
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["matmul"],
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(1e-5, 1e-5),
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)
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# onednn
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config = self.create_inference_config(use_onednn=True)
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yield (
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config,
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["matmul"],
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(1e-5, 1e-5),
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)
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# gpu
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config = self.create_inference_config(use_gpu=True)
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yield (
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config,
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["matmul"],
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(1e-5, 1e-5),
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)
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def sample_program_config(self, draw):
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# 1. Generate shape and attr of matmul
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x_shape = draw(
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st.lists(
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st.integers(min_value=1, max_value=8), min_size=2, max_size=5
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)
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)
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x_shape_rank = len(x_shape)
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y_shape = draw(
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st.lists(
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st.integers(min_value=1, max_value=8),
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min_size=x_shape_rank,
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max_size=x_shape_rank,
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)
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)
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y_shape_rank = len(y_shape)
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y_shape[-2] = x_shape[-1]
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for i in range(y_shape_rank - 3, -1, -1):
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j = x_shape_rank - (y_shape_rank - i)
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if j < 0 or j >= x_shape_rank:
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break
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y_shape[i] = x_shape[j]
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transpose_X = False
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transpose_Y = False
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alpha = draw(st.floats(min_value=-2.0, max_value=2.0, width=32))
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# scale tensor
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scale_shape = [1]
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scale_value = draw(st.floats(min_value=-5.0, max_value=5.0, width=32))
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matmul_op = OpConfig(
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"matmul",
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inputs={"X": ["matmul_x"], "Y": ["matmul_y"]},
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outputs={"Out": ["matmul_out"]},
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transpose_X=transpose_X,
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transpose_Y=transpose_Y,
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alpha=alpha,
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head_number=1,
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)
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is_scale_tensor = draw(st.booleans())
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if is_scale_tensor:
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scale_op = OpConfig(
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"scale",
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inputs={"X": ["matmul_out"], "ScaleTensor": ["scale_tensor"]},
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outputs={"Out": ["scale_out"]},
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scale=scale_value,
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bias=0.0,
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bias_after_scale=draw(st.booleans()),
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)
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else:
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scale_op = OpConfig(
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"scale",
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inputs={
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"X": ["matmul_out"],
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},
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outputs={"Out": ["scale_out"]},
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scale=scale_value,
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bias=0.0,
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bias_after_scale=draw(st.booleans()),
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)
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ops = [matmul_op, scale_op]
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weights = {}
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inputs = {}
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if is_scale_tensor:
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weights = {
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"matmul_y": TensorConfig(shape=y_shape),
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"scale_tensor": TensorConfig(shape=scale_shape),
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}
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inputs = {
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"matmul_x": TensorConfig(shape=x_shape),
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}
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else:
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inputs = {
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"matmul_x": TensorConfig(shape=x_shape),
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"matmul_y": TensorConfig(shape=y_shape),
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}
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program_config = ProgramConfig(
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ops=ops,
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weights=weights,
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inputs=inputs,
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outputs=ops[-1].outputs["Out"],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=100,
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passes=["matmul_scale_fuse_pass"],
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)
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if __name__ == "__main__":
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unittest.main()
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