wehub-resource-sync
819 lines
22 KiB
Python
819 lines
22 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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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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class TestDeleteRepeatedShapeCastPass(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, ['shape', 'cast', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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x_shape = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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shape_op0 = OpConfig(
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"shape",
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inputs={
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"Input": ["shape_x"],
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},
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outputs={"Out": ["shape0_out"]},
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)
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cast_op0 = OpConfig(
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"cast",
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inputs={
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"X": ["shape0_out"],
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},
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in_dtype=2,
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out_dtype=5,
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outputs={"Out": ["cast0_out"]},
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)
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relu_op0 = OpConfig(
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"relu",
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inputs={
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"X": ["cast0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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shape_op1 = OpConfig(
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"shape",
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inputs={
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"Input": ["shape_x"],
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},
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outputs={"Out": ["shape1_out"]},
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)
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cast_op1 = OpConfig(
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"cast",
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inputs={
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"X": ["shape1_out"],
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},
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in_dtype=2,
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out_dtype=5,
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outputs={"Out": ["cast1_out"]},
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={
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"X": ["cast1_out"],
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},
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outputs={"Out": ["relu1_out"]},
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)
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shape_op2 = OpConfig(
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"shape",
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inputs={
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"Input": ["shape_x"],
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},
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outputs={"Out": ["shape2_out"]},
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)
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cast_op2 = OpConfig(
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"cast",
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inputs={
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"X": ["shape2_out"],
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},
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in_dtype=2,
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out_dtype=5,
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outputs={"Out": ["cast2_out"]},
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={
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"X": ["cast2_out"],
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},
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outputs={"Out": ["relu2_out"]},
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)
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ops = [
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shape_op0,
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cast_op0,
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relu_op0,
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shape_op1,
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cast_op1,
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relu_op1,
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shape_op2,
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cast_op2,
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relu_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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"shape_x": TensorConfig(shape=x_shape),
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},
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outputs=["relu0_out", "relu1_out", "relu2_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=["delete_repeated_ops_pass"],
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)
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class TestDeleteRepeatedSlicePass(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, ['slice', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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slice_x = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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slice_op0 = OpConfig(
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"slice",
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inputs={
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"Input": ["slice_x"],
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},
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starts=[0],
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ends=[1],
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axes=[0],
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decrease_axis=[0],
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outputs={"Out": ["slice0_out"]},
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)
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relu_op0 = OpConfig(
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"relu",
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inputs={
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"X": ["slice0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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slice_op1 = OpConfig(
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"slice",
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inputs={
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"Input": ["slice_x"],
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},
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starts=[0],
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ends=[1],
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axes=[0],
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decrease_axis=[0],
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outputs={"Out": ["slice1_out"]},
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={
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"X": ["slice1_out"],
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},
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outputs={"Out": ["relu1_out"]},
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)
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slice_op2 = OpConfig(
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"slice",
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inputs={
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"Input": ["slice_x"],
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},
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starts=[0],
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ends=[1],
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axes=[0],
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decrease_axis=[0],
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outputs={"Out": ["slice2_out"]},
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={
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"X": ["slice2_out"],
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},
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outputs={"Out": ["relu2_out"]},
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)
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ops = [slice_op0, relu_op0, slice_op1, relu_op1, slice_op2, relu_op2]
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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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"slice_x": TensorConfig(shape=slice_x),
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},
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outputs=["relu0_out", "relu1_out", "relu2_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=["delete_repeated_ops_pass"],
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)
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class TestDeleteRepeatedAddPass(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, ['elementwise_add', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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add_x = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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add_op0 = OpConfig(
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"elementwise_add",
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inputs={
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"X": ["add_x"],
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"Y": ["add_y"],
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},
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axis=-1,
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outputs={"Out": ["add0_out"]},
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)
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relu_op0 = OpConfig(
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"relu",
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inputs={
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"X": ["add0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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add_op1 = OpConfig(
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"elementwise_add",
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inputs={
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"X": ["add_x"],
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"Y": ["add_y"],
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},
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axis=-1,
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outputs={"Out": ["add1_out"]},
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={
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"X": ["add1_out"],
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},
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outputs={"Out": ["relu1_out"]},
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)
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add_op2 = OpConfig(
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"elementwise_add",
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inputs={
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"X": ["add_x"],
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"Y": ["add_y"],
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},
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axis=-1,
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outputs={"Out": ["add2_out"]},
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={
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"X": ["add2_out"],
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},
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outputs={"Out": ["relu2_out"]},
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)
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ops = [add_op0, relu_op0, add_op1, relu_op1, add_op2, relu_op2]
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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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"add_x": TensorConfig(shape=add_x),
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"add_y": TensorConfig(shape=add_x),
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},
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outputs=["relu0_out", "relu1_out", "relu2_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=["delete_repeated_ops_pass"],
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)
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class TestDeleteRepeatedScalePass(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, ['scale', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
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def sample_program_config(self, draw):
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scale_x = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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scale_op0 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale0_out"]},
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)
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relu_op0 = OpConfig(
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"relu",
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inputs={
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"X": ["scale0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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scale_op1 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale1_out"]},
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={
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"X": ["scale1_out"],
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},
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outputs={"Out": ["relu1_out"]},
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)
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scale_op2 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale2_out"]},
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={
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"X": ["scale2_out"],
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},
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outputs={"Out": ["relu2_out"]},
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)
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ops = [scale_op0, relu_op0, scale_op1, relu_op1, scale_op2, relu_op2]
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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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"scale_x": TensorConfig(shape=scale_x),
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},
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outputs=["relu0_out", "relu1_out", "relu2_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=["delete_repeated_ops_pass"],
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)
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class TestDeleteRepeatedSqueezePass(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 (
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config,
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['scale', 'squeeze2', 'relu', 'relu', 'relu'],
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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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scale_x = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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scale_x[0] = 1
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axis = 0
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scale_op0 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale0_out"]},
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)
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squeeze_op0 = OpConfig(
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"squeeze2",
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inputs={
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"X": ["scale0_out"],
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},
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axes=[axis],
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outputs={"Out": ["squeeze0_out"]},
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)
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relu_op0 = OpConfig(
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"relu",
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inputs={
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"X": ["squeeze0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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scale_op1 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale1_out"]},
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)
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squeeze_op1 = OpConfig(
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"squeeze2",
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inputs={
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"X": ["scale1_out"],
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},
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axes=[axis],
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outputs={"Out": ["squeeze1_out"]},
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)
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relu_op1 = OpConfig(
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"relu",
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inputs={
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"X": ["squeeze1_out"],
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},
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outputs={"Out": ["relu1_out"]},
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)
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scale_op2 = OpConfig(
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"scale",
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inputs={
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"X": ["scale_x"],
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},
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale2_out"]},
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)
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squeeze_op2 = OpConfig(
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"squeeze2",
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inputs={
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"X": ["scale2_out"],
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},
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axes=[axis],
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outputs={"Out": ["squeeze2_out"]},
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)
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relu_op2 = OpConfig(
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"relu",
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inputs={
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"X": ["squeeze2_out"],
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},
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outputs={"Out": ["relu2_out"]},
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)
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ops = [
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scale_op0,
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squeeze_op0,
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relu_op0,
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scale_op1,
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squeeze_op1,
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relu_op1,
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scale_op2,
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squeeze_op2,
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relu_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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"scale_x": TensorConfig(shape=scale_x),
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},
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outputs=["relu0_out", "relu1_out", "relu2_out"],
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)
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return program_config
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|
|
|
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class TestDeleteRepeatedUnSqueezePass(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 (
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config,
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['scale', 'unsqueeze2', 'relu', 'relu', 'relu'],
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(1e-5, 1e-5),
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)
|
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|
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def sample_program_config(self, draw):
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scale_x = draw(
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st.lists(
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st.integers(min_value=1, max_value=20), min_size=2, max_size=4
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)
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)
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axis = 0
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scale_op0 = OpConfig(
|
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"scale",
|
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inputs={
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"X": ["scale_x"],
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},
|
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale0_out"]},
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)
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unsqueeze_op0 = OpConfig(
|
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"unsqueeze2",
|
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inputs={
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"X": ["scale0_out"],
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},
|
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axes=[axis],
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outputs={"Out": ["unsqueeze0_out"]},
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)
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relu_op0 = OpConfig(
|
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"relu",
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inputs={
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"X": ["unsqueeze0_out"],
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},
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outputs={"Out": ["relu0_out"]},
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)
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scale_op1 = OpConfig(
|
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"scale",
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inputs={
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"X": ["scale_x"],
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},
|
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scale=2.0,
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bias=1.0,
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bias_after_scale=True,
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outputs={"Out": ["scale1_out"]},
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)
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unsqueeze_op1 = OpConfig(
|
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"unsqueeze2",
|
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inputs={
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"X": ["scale1_out"],
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},
|
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axes=[axis],
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outputs={"Out": ["unsqueeze1_out"]},
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)
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relu_op1 = OpConfig(
|
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"relu",
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inputs={
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"X": ["unsqueeze1_out"],
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},
|
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outputs={"Out": ["relu1_out"]},
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)
|
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scale_op2 = OpConfig(
|
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"scale",
|
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inputs={
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"X": ["scale_x"],
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},
|
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scale=2.0,
|
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bias=1.0,
|
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bias_after_scale=True,
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outputs={"Out": ["scale2_out"]},
|
|
)
|
|
unsqueeze_op2 = OpConfig(
|
|
"unsqueeze2",
|
|
inputs={
|
|
"X": ["scale2_out"],
|
|
},
|
|
axes=[axis],
|
|
outputs={"Out": ["unsqueeze2_out"]},
|
|
)
|
|
relu_op2 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["unsqueeze2_out"],
|
|
},
|
|
outputs={"Out": ["relu2_out"]},
|
|
)
|
|
ops = [
|
|
scale_op0,
|
|
unsqueeze_op0,
|
|
relu_op0,
|
|
scale_op1,
|
|
unsqueeze_op1,
|
|
relu_op1,
|
|
scale_op2,
|
|
unsqueeze_op2,
|
|
relu_op2,
|
|
]
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"scale_x": TensorConfig(shape=scale_x),
|
|
},
|
|
outputs=["relu0_out", "relu1_out", "relu2_out"],
|
|
)
|
|
return program_config
|
|
|
|
|
|
class TestDeleteRepeatedGatherPass(PassAutoScanTest):
|
|
def sample_predictor_configs(self, program_config):
|
|
config = self.create_inference_config(use_xpu=True)
|
|
yield config, ['scale', 'gather', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
|
|
|
|
def sample_program_config(self, draw):
|
|
scale_x = draw(
|
|
st.lists(
|
|
st.integers(min_value=3, max_value=20), min_size=2, max_size=4
|
|
)
|
|
)
|
|
axis = 0
|
|
|
|
def generate_index(*args, **kwargs):
|
|
return np.array([0]).astype(np.int64)
|
|
|
|
gather_index = np.array([0]).astype(np.int64)
|
|
scale_op0 = OpConfig(
|
|
"scale",
|
|
inputs={
|
|
"X": ["scale_x"],
|
|
},
|
|
scale=2.0,
|
|
bias=1.0,
|
|
bias_after_scale=True,
|
|
outputs={"Out": ["scale0_out"]},
|
|
)
|
|
gather_op0 = OpConfig(
|
|
"gather",
|
|
inputs={"X": ["scale0_out"], "Index": ["gather_index"]},
|
|
axis=axis,
|
|
outputs={"Out": ["gather0_out"]},
|
|
)
|
|
relu_op0 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["gather0_out"],
|
|
},
|
|
outputs={"Out": ["relu0_out"]},
|
|
)
|
|
scale_op1 = OpConfig(
|
|
"scale",
|
|
inputs={
|
|
"X": ["scale_x"],
|
|
},
|
|
scale=2.0,
|
|
bias=1.0,
|
|
bias_after_scale=True,
|
|
outputs={"Out": ["scale1_out"]},
|
|
)
|
|
gather_op1 = OpConfig(
|
|
"gather",
|
|
inputs={"X": ["scale1_out"], "Index": ["gather_index"]},
|
|
axis=axis,
|
|
outputs={"Out": ["gather1_out"]},
|
|
)
|
|
relu_op1 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["gather1_out"],
|
|
},
|
|
outputs={"Out": ["relu1_out"]},
|
|
)
|
|
scale_op2 = OpConfig(
|
|
"scale",
|
|
inputs={
|
|
"X": ["scale_x"],
|
|
},
|
|
scale=2.0,
|
|
bias=1.0,
|
|
bias_after_scale=True,
|
|
outputs={"Out": ["scale2_out"]},
|
|
)
|
|
gather_op2 = OpConfig(
|
|
"gather",
|
|
inputs={"X": ["scale2_out"], "Index": ["gather_index"]},
|
|
axis=axis,
|
|
outputs={"Out": ["gather2_out"]},
|
|
)
|
|
relu_op2 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["gather2_out"],
|
|
},
|
|
outputs={"Out": ["relu2_out"]},
|
|
)
|
|
|
|
ops = [
|
|
scale_op0,
|
|
gather_op0,
|
|
relu_op0,
|
|
scale_op1,
|
|
gather_op1,
|
|
relu_op1,
|
|
scale_op2,
|
|
gather_op2,
|
|
relu_op2,
|
|
]
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"scale_x": TensorConfig(shape=scale_x),
|
|
"gather_index": TensorConfig(data_gen=partial(generate_index)),
|
|
},
|
|
outputs=["relu0_out", "relu1_out", "relu2_out"],
|
|
)
|
|
return program_config
|
|
|
|
def test(self):
|
|
self.run_and_statistics(
|
|
quant=False,
|
|
max_examples=25,
|
|
passes=["delete_repeated_ops_pass"],
|
|
)
|
|
|
|
|
|
class TestDeleteRepeatedTransposePass(PassAutoScanTest):
|
|
def sample_predictor_configs(self, program_config):
|
|
config = self.create_inference_config(use_xpu=True)
|
|
yield config, ['transpose2', 'relu', 'relu', 'relu'], (1e-5, 1e-5)
|
|
|
|
def sample_program_config(self, draw):
|
|
batch_size = draw(st.integers(min_value=1, max_value=4))
|
|
H = draw(st.integers(min_value=1, max_value=64))
|
|
W = draw(st.integers(min_value=1, max_value=64))
|
|
in_shape = [batch_size, H, W]
|
|
axis = [0, 2, 1]
|
|
|
|
transpose_op0 = OpConfig(
|
|
type='transpose2',
|
|
inputs={
|
|
"X": ["transpose_x"],
|
|
},
|
|
outputs={"Out": ["transpose_output0"]},
|
|
attrs={"axis": axis},
|
|
)
|
|
relu_op0 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["transpose_output0"],
|
|
},
|
|
outputs={"Out": ["relu0_out"]},
|
|
)
|
|
transpose_op1 = OpConfig(
|
|
type='transpose2',
|
|
inputs={
|
|
"X": ["transpose_x"],
|
|
},
|
|
outputs={"Out": ["transpose_output1"]},
|
|
attrs={"axis": axis},
|
|
)
|
|
relu_op1 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["transpose_output1"],
|
|
},
|
|
outputs={"Out": ["relu1_out"]},
|
|
)
|
|
transpose_op2 = OpConfig(
|
|
type='transpose2',
|
|
inputs={
|
|
"X": ["transpose_x"],
|
|
},
|
|
outputs={"Out": ["transpose_output2"]},
|
|
attrs={"axis": axis},
|
|
)
|
|
relu_op2 = OpConfig(
|
|
"relu",
|
|
inputs={
|
|
"X": ["transpose_output2"],
|
|
},
|
|
outputs={"Out": ["relu2_out"]},
|
|
)
|
|
|
|
ops = [
|
|
transpose_op0,
|
|
relu_op0,
|
|
transpose_op1,
|
|
relu_op1,
|
|
transpose_op2,
|
|
relu_op2,
|
|
]
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"transpose_x": TensorConfig(shape=in_shape),
|
|
},
|
|
outputs=["relu0_out", "relu1_out", "relu2_out"],
|
|
)
|
|
return program_config
|
|
|
|
def test(self):
|
|
self.run_and_statistics(
|
|
quant=False,
|
|
max_examples=25,
|
|
passes=["delete_repeated_ops_pass"],
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|