wehub-resource-sync
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
# Copyright (c) 2023 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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import numpy as np
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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 TestReduceMaxFusePass(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_xpu=True)
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yield config, ["reduce_max"], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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s_axes = [2]
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batch_size = draw(st.integers(min_value=1, max_value=4))
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H = draw(st.integers(min_value=1, max_value=64))
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W = draw(st.integers(min_value=1, max_value=64))
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in_shape = [batch_size, H, W]
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transpose_op1 = OpConfig(
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type='transpose2',
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inputs={
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"X": ["transpose_in"],
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},
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outputs={"Out": ["transpose_out1"]},
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attrs={"axis": [0, 2, 1]},
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)
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unsqueeze2_op = OpConfig(
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type="unsqueeze2",
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inputs={"X": ["transpose_out1"]},
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outputs={"Out": ["unsqueeze_out"]},
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attrs={
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"axes": s_axes,
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},
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)
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pool_op = OpConfig(
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"pool2d",
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inputs={"X": ["unsqueeze_out"]},
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outputs={"Out": ["pool_out"]},
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ksize=[1, H],
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adaptive=False,
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pooling_type="max",
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data_format="NCHW",
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strides=[1, H],
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paddings=[0, 0],
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ceil_mode=False,
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global_pooling=False,
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padding_algorithm="EXPLICIT",
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exclusive=True,
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)
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squeeze2_op = OpConfig(
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"squeeze2",
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inputs={
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"X": ["pool_out"],
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},
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axes=s_axes,
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outputs={"Out": ["squeeze2_out"], "XShape": ["xshape"]},
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)
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transpose_op2 = OpConfig(
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type='transpose2',
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inputs={
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"X": ["squeeze2_out"],
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},
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outputs={"Out": ["transpose_out2"]},
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attrs={"axis": [0, 2, 1]},
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)
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ops = [
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transpose_op1,
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unsqueeze2_op,
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pool_op,
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squeeze2_op,
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transpose_op2,
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]
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"transpose_in": TensorConfig(shape=in_shape),
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},
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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=25,
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passes=["reduce_ops_fuse_pass"],
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)
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if __name__ == "__main__":
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np.random.seed(200)
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unittest.main()
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