wehub-resource-sync
125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
# Copyright (c) 2024 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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from functools import partial
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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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from paddle.base import core
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@unittest.skipIf(
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core.get_xpu_device_version(0) == core.XPUVersion.XPU3,
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"Unsupported on XPU3",
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)
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class TestXpuUnSqueezPad3dUnsqueezeFusePass2(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, ["pad2d_xpu"], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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x_shape = draw(st.sampled_from([[6, 6, 6, 6]]))
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# x_shape = draw(st.sampled_from([[1, 1, 3, 3]]))
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# 1.unsqeeze
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axes = [2]
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# 2.pad3d
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data_format = draw(st.sampled_from(['NCDHW', 'NDHWC']))
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value = 0.0
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mode = draw(st.sampled_from(['constant', 'reflect', 'replicate']))
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paddings = draw(
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st.sampled_from(
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[
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[1, 1, 1, 1, 0, 0],
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[2, 2, 2, 2, 0, 0],
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[0, 1, 2, 3, 0, 0],
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[4, 3, 2, 1, 0, 0],
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[2, 3, 4, 5, 0, 0],
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[1, 2, 2, 1, 0, 0],
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[2, 0, 1, 1, 0, 0],
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[1, 1, 2, 0, 0, 0],
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]
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)
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)
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if data_format == 'NDHWC':
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axes = [1]
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unsqueeze_op = OpConfig(
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"unsqueeze2",
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inputs={
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"X": ["unsqueeze_input"],
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},
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outputs={"Out": ["unsqueeze_out"]},
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axes=axes,
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)
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pad3d_op = OpConfig(
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"pad3d",
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inputs={
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"X": ["unsqueeze_out"],
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},
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outputs={"Out": ["pad3d_out"]},
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attrs={
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"paddings": paddings,
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"mode": mode,
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"pad_value": value,
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"data_format": data_format,
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},
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)
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squeeze_op = OpConfig(
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"squeeze2",
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inputs={
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"X": ["pad3d_out"],
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},
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outputs={"Out": ["squeeze_out"]},
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axes=axes,
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)
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ops = [
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unsqueeze_op,
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pad3d_op,
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squeeze_op,
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]
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def generate_data(shape):
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return np.random.random(shape).astype(np.float32)
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program_config = ProgramConfig(
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ops=ops,
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inputs={
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"unsqueeze_input": TensorConfig(
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data_gen=partial(generate_data, x_shape)
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),
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},
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weights={},
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outputs=["squeeze_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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min_success_num=1,
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passes=["pad2d_xpu_fuse_pass"],
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)
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if __name__ == "__main__":
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unittest.main()
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