wehub-resource-sync
223 lines
7.2 KiB
Python
223 lines
7.2 KiB
Python
# Copyright (c) 2022 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 TestIdentityScaleCleanPass(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_gpu=True)
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yield config, ['relu', 'relu', 'scale'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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bias_after_scale = draw(st.booleans())
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n = draw(st.integers(min_value=1, max_value=4))
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c = draw(st.integers(min_value=1, max_value=20))
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h = draw(st.integers(min_value=1, max_value=20))
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w = draw(st.integers(min_value=1, max_value=20))
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relu_op1 = OpConfig(
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"relu", inputs={"X": ["relu_x"]}, outputs={"Out": ["relu_op1_out"]}
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)
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scale_op1 = OpConfig(
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"scale",
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inputs={"X": ["relu_op1_out"]},
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outputs={"Out": ["scale_op1_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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scale_op2 = OpConfig(
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"scale",
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inputs={"X": ["scale_op1_out"]},
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outputs={"Out": ["scale_op2_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={"X": ["relu_op1_out"]},
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outputs={"Out": ["relu_op2_out"]},
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)
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program_config = ProgramConfig(
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ops=[relu_op1, relu_op2, scale_op1, scale_op2],
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weights={},
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inputs={"relu_x": TensorConfig(shape=[n, c, h, w])},
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outputs=["scale_op2_out", "relu_op2_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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max_examples=25, passes=["identity_op_clean_pass"]
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)
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class TestIdentityScaleCleanPass_V1(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_gpu=True)
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yield config, ['relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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bias_after_scale = draw(st.booleans())
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n = draw(st.integers(min_value=1, max_value=4))
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c = draw(st.integers(min_value=1, max_value=20))
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h = draw(st.integers(min_value=1, max_value=20))
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w = draw(st.integers(min_value=1, max_value=20))
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relu_op1 = OpConfig(
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"relu", inputs={"X": ["relu_x"]}, outputs={"Out": ["relu_op1_out"]}
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)
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scale_op1 = OpConfig(
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"scale",
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inputs={"X": ["relu_op1_out"]},
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outputs={"Out": ["scale_op1_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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scale_op2 = OpConfig(
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"scale",
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inputs={"X": ["scale_op1_out"]},
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outputs={"Out": ["scale_op2_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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program_config = ProgramConfig(
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ops=[relu_op1, scale_op1, scale_op2],
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weights={},
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inputs={"relu_x": TensorConfig(shape=[n, c, h, w])},
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outputs=["scale_op2_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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max_examples=25, passes=["identity_op_clean_pass"]
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)
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class TestIdentityScaleCleanPass_V2(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_gpu=True)
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yield config, ['scale', 'relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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bias_after_scale = draw(st.booleans())
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n = draw(st.integers(min_value=1, max_value=4))
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c = draw(st.integers(min_value=1, max_value=20))
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h = draw(st.integers(min_value=1, max_value=20))
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w = draw(st.integers(min_value=1, max_value=20))
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scale_op1 = OpConfig(
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"scale",
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inputs={"X": ["scale_op1_in"]},
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outputs={"Out": ["scale_op1_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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scale_op2 = OpConfig(
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"scale",
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inputs={"X": ["scale_op1_out"]},
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outputs={"Out": ["scale_op2_out"]},
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bias=0.0,
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scale=1.0,
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bias_after_scale=True,
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={"X": ["scale_op2_out"]},
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outputs={"Out": ["relu_op1_out"]},
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)
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program_config = ProgramConfig(
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ops=[scale_op1, scale_op2, relu_op1],
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weights={},
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inputs={"scale_op1_in": TensorConfig(shape=[n, c, h, w])},
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outputs=["relu_op1_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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max_examples=25, passes=["identity_op_clean_pass"]
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)
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class TestIdentityCastCleanPass(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_gpu=True)
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yield config, ['relu', 'relu'], (1e-2, 1e-2)
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def sample_program_config(self, draw):
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n = draw(st.integers(min_value=1, max_value=4))
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c = draw(st.integers(min_value=1, max_value=20))
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h = draw(st.integers(min_value=1, max_value=20))
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w = draw(st.integers(min_value=1, max_value=20))
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relu_op_1 = OpConfig(
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"relu",
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inputs={"X": ["relu_op_1_in"]},
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outputs={"Out": ["relu_op_1_out"]},
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)
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cast_op_1 = OpConfig(
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"cast",
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inputs={"X": ["relu_op_1_out"]},
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outputs={"Out": ["cast_op_1_out"]},
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in_dtype=5,
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out_dtype=5,
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)
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relu_op_2 = OpConfig(
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"relu",
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inputs={"X": ["cast_op_1_out"]},
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outputs={"Out": ["relu_op_2_out"]},
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)
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cast_op_2 = OpConfig(
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"cast",
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inputs={"X": ["relu_op_2_out"]},
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outputs={"Out": ["cast_op_2_out"]},
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in_dtype=5,
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out_dtype=4,
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)
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cast_op_3 = OpConfig(
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"cast",
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inputs={"X": ["cast_op_2_out"]},
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outputs={"Out": ["cast_op_3_out"]},
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in_dtype=4,
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out_dtype=5,
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)
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program_config = ProgramConfig(
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ops=[relu_op_1, cast_op_1, relu_op_2, cast_op_2, cast_op_3],
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weights={},
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inputs={"relu_op_1_in": TensorConfig(shape=[n, c, h, w])},
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outputs=["cast_op_3_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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max_examples=25, passes=["identity_op_clean_pass"]
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)
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if __name__ == "__main__":
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unittest.main()
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