chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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 math
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import sys
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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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import paddle.inference as paddle_infer
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class ReverseRollPass(PassAutoScanTest):
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"""
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reshape2
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reshape2
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transpose2
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reshape2
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roll
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reshape2
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"""
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def sample_predictor_configs(self, program_config):
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# trt with dynamic_shape
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config = self.create_trt_inference_config()
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config.enable_tensorrt_engine(
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max_batch_size=4,
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workspace_size=102400,
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min_subgraph_size=0,
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precision_mode=paddle_infer.PrecisionType.Float32,
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use_static=False,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{
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"input0": [64, 9, 96],
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},
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{
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"input0": [512, 144, 768],
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},
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{
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"input0": [64, 49, 96],
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},
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)
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yield config, ['reverse_roll'], (1e-5, 1e-5)
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# trt dynamic_shape
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config = self.create_trt_inference_config()
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config.enable_tensorrt_engine(
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max_batch_size=1,
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workspace_size=102400,
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min_subgraph_size=0,
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precision_mode=paddle_infer.PrecisionType.Half,
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use_static=False,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{
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"input0": [64, 9, 96],
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},
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{
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"input0": [512, 144, 768],
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},
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{
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"input0": [64, 49, 96],
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},
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)
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yield config, ['reverse_roll'], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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batch_size = draw(st.integers(min_value=1, max_value=4))
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window_size = draw(st.sampled_from([3, 5, 7, 12]))
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dim = draw(st.sampled_from([96, 192, 384, 768]))
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window_number = 64
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def generate_input(attrs):
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return np.random.random(
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[
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attrs[0]["batch_size"] * attrs[1]["window_number"],
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attrs[1]["window_size"] * attrs[1]["window_size"],
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attrs[1]["dim"],
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]
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).astype(np.float32)
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attrs = [
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{"batch_size": batch_size},
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{
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"window_number": window_number,
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"window_size": window_size,
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"dim": dim,
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},
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]
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reshape2_00 = OpConfig(
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type="reshape2",
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inputs={"X": ["input0"]},
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outputs={
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"Out": ["reshape2_00_out"],
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"XShape": ["reshape2_00_outXshape"],
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},
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attrs={"shape": [-1, window_size, window_size, dim]},
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)
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reshape2_10 = OpConfig(
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type="reshape2",
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inputs={"X": ["reshape2_00_out"]},
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outputs={
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"Out": ["reshape2_10_out"],
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"XShape": ["reshape2_10_outXshape"],
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},
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attrs={
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"shape": [
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-1,
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int(math.sqrt(window_number)),
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int(math.sqrt(window_number)),
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window_size,
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window_size,
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dim,
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]
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},
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)
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transpose2_20 = OpConfig(
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type="transpose2",
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inputs={"X": ["reshape2_10_out"]},
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outputs={
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"Out": ["transpose2_20_out"],
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"XShape": ["transpose2_20_outXshape"],
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},
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attrs={"axis": [0, 1, 3, 2, 4, 5]},
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)
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reshape2_30 = OpConfig(
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type="reshape2",
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inputs={"X": ["transpose2_20_out"]},
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outputs={
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"Out": ["reshape2_30_out"],
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"XShape": ["reshape2_30_outXshape"],
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},
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attrs={
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"shape": [
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-1,
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int(math.sqrt(window_number)) * window_size,
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int(math.sqrt(window_number)) * window_size,
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dim,
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]
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},
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)
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roll_30_1 = OpConfig(
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type="roll",
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inputs={"X": ["reshape2_30_out"]},
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outputs={"Out": ["roll_30_1_out"]},
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attrs={
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"axis": [1, 2],
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"shifts": [
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math.floor(window_size // 2),
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math.floor(window_size // 2),
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],
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},
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)
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reshape2_40 = OpConfig(
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type="reshape2",
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inputs={"X": ["roll_30_1_out"]},
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outputs={
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"Out": ["reshape2_40_out"],
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"XShape": ["reshape2_40_outXshape"],
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},
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attrs={
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"shape": [-1, window_number * window_size * window_size, dim]
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},
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)
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program_config = ProgramConfig(
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ops=[
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reshape2_00,
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reshape2_10,
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transpose2_20,
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reshape2_30,
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roll_30_1,
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reshape2_40,
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],
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weights={},
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inputs={
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"input0": TensorConfig(data_gen=partial(generate_input, attrs)),
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},
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outputs=["reshape2_40_out"],
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)
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return program_config
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def test(self):
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max_examples = 50
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min_success_num = 50
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if sys.platform == "win32":
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max_examples = 5
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min_success_num = 5
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self.run_and_statistics(
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quant=False,
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max_examples=max_examples,
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passes=["reverse_roll_fuse_pass"],
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max_duration=250,
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min_success_num=min_success_num,
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)
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class ReverseRoll2Pass(PassAutoScanTest):
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"""
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reshape2
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reshape2
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transpose2
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reshape2
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reshape2
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"""
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def sample_predictor_configs(self, program_config):
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config = self.create_trt_inference_config()
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config.enable_tensorrt_engine(
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max_batch_size=4,
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workspace_size=102400,
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min_subgraph_size=0,
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precision_mode=paddle_infer.PrecisionType.Float32,
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use_static=False,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{
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"input0": [64, 9, 96],
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},
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{
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"input0": [512, 144, 768],
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},
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{
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"input0": [64, 49, 96],
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},
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)
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yield config, ['reverse_roll'], (1e-5, 1e-5)
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# trt dynamic_shape
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config = self.create_trt_inference_config()
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config.enable_tensorrt_engine(
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max_batch_size=1,
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workspace_size=102400,
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min_subgraph_size=0,
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precision_mode=paddle_infer.PrecisionType.Half,
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use_static=False,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{
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"input0": [64, 9, 96],
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},
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{
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"input0": [512, 144, 768],
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},
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{
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"input0": [64, 49, 96],
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},
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)
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yield config, ['reverse_roll'], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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batch_size = draw(st.integers(min_value=1, max_value=4))
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window_size = draw(st.sampled_from([3, 5, 7, 12]))
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dim = draw(st.sampled_from([96, 192, 384, 768]))
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window_number = 64
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def generate_input(attrs):
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return np.random.random(
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[
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attrs[0]["batch_size"] * attrs[1]["window_number"],
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attrs[1]["window_size"] * attrs[1]["window_size"],
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attrs[1]["dim"],
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]
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).astype(np.float32)
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attrs = [
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{"batch_size": batch_size},
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{
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"window_number": window_number,
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"window_size": window_size,
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"dim": dim,
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},
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]
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reshape2_00 = OpConfig(
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type="reshape2",
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inputs={"X": ["input0"]},
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outputs={
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"Out": ["reshape2_00_out"],
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"XShape": ["reshape2_00_outXshape"],
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},
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attrs={"shape": [-1, window_size, window_size, dim]},
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)
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reshape2_10 = OpConfig(
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type="reshape2",
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inputs={"X": ["reshape2_00_out"]},
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outputs={
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"Out": ["reshape2_10_out"],
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"XShape": ["reshape2_10_outXshape"],
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},
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attrs={
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"shape": [
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-1,
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int(math.sqrt(window_number)),
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int(math.sqrt(window_number)),
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window_size,
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window_size,
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dim,
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]
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},
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)
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transpose2_20 = OpConfig(
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type="transpose2",
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inputs={"X": ["reshape2_10_out"]},
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outputs={
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"Out": ["transpose2_20_out"],
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"XShape": ["transpose2_20_outXshape"],
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},
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attrs={"axis": [0, 1, 3, 2, 4, 5]},
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)
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reshape2_30 = OpConfig(
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type="reshape2",
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inputs={"X": ["transpose2_20_out"]},
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outputs={
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"Out": ["reshape2_30_out"],
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"XShape": ["reshape2_30_outXshape"],
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},
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attrs={
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"shape": [
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-1,
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int(math.sqrt(window_number)) * window_size,
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int(math.sqrt(window_number)) * window_size,
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dim,
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]
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},
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)
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reshape2_40 = OpConfig(
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type="reshape2",
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inputs={"X": ["reshape2_30_out"]},
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outputs={
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"Out": ["reshape2_40_out"],
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"XShape": ["reshape2_40_outXshape"],
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},
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attrs={
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"shape": [-1, window_number * window_size * window_size, dim]
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},
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)
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program_config = ProgramConfig(
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ops=[
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reshape2_00,
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reshape2_10,
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transpose2_20,
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reshape2_30,
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reshape2_40,
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],
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weights={},
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inputs={
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"input0": TensorConfig(data_gen=partial(generate_input, attrs)),
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},
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outputs=["reshape2_40_out"],
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)
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return program_config
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def test(self):
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max_examples = 50
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min_success_num = 50
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if sys.platform == "win32":
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max_examples = 5
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min_success_num = 5
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self.run_and_statistics(
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quant=False,
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max_examples=max_examples,
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passes=["reverse_roll_fuse_pass"],
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max_duration=250,
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min_success_num=min_success_num,
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)
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if __name__ == "__main__":
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unittest.main()
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