wehub-resource-sync
154 lines
4.7 KiB
Python
154 lines
4.7 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 TestSqueeze2MatmulFusePass(PassAutoScanTest):
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r"""
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x_var
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squeeze2
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\
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squeeze2_out_var y_var
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\ /
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matmul bias_var
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\ /
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elementwise_add
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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 config, ["mul", "elementwise_add"], (1e-5, 1e-5)
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# for gpu
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config = self.create_inference_config(use_gpu=True)
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yield config, ["mul", "elementwise_add"], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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# 1. Generate shape of input:X of squeeze2
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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=2
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)
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)
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# axes of squeeze2 == [2, 3]
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x_shape += [1, 1]
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axes = [2, 3]
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# 2. Generate attr:transpose_X/transpose_Y/alpha of matmul
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alpha = 1.0
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transpose_X = False
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transpose_Y = False
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# 3. Generate legal shape of input:Y of matmul
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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), min_size=2, max_size=2
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)
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)
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y_shape[0] = x_shape[1]
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# 4. Generate legal attr:axis of elementwise_add
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axis = draw(st.integers(min_value=-1, max_value=1))
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if axis == 0 or axis == -1:
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if draw(st.booleans()):
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if axis == 0:
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bias_shape = [
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x_shape[0],
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]
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else:
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bias_shape = [
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y_shape[1],
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]
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else:
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bias_shape = [x_shape[0], y_shape[1]]
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elif axis == 1:
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bias_shape = [
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y_shape[1],
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]
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if draw(st.integers(min_value=1, max_value=10)) <= 1:
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bias_shape[-1] = 1
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if len(bias_shape) == 2 and draw(st.booleans()):
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bias_shape[0] = 1
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squeeze2_op = OpConfig(
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"squeeze2",
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inputs={
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"X": ["squeeze2_x"],
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},
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axes=axes,
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outputs={"Out": ["squeeze2_out"], "XShape": ["xshape"]},
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)
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matmul_op = OpConfig(
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"matmul",
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inputs={"X": ["squeeze2_out"], "Y": ["matmul_y"]},
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outputs={"Out": ["matmul_out"]},
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alpha=alpha,
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transpose_X=transpose_X,
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transpose_Y=transpose_Y,
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)
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add_op = OpConfig(
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"elementwise_add",
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inputs={"X": ["matmul_out"], "Y": ["bias"]},
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outputs={"Out": ["add_out"]},
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axis=axis,
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)
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ops = [squeeze2_op, matmul_op, add_op]
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if draw(st.integers(min_value=1, max_value=10)) <= 8:
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program_config = ProgramConfig(
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ops=ops,
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weights={
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"matmul_y": TensorConfig(shape=y_shape),
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"bias": TensorConfig(shape=bias_shape),
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},
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inputs={
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"squeeze2_x": TensorConfig(shape=x_shape),
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},
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outputs=ops[-1].outputs["Out"],
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)
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else:
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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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"squeeze2_x": TensorConfig(shape=x_shape),
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"matmul_y": TensorConfig(shape=y_shape),
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"bias": TensorConfig(shape=bias_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=50,
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max_duration=1000,
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passes=["gpu_cpu_squeeze2_matmul_fuse_pass"],
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)
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if __name__ == "__main__":
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unittest.main()
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