wehub-resource-sync
520 lines
15 KiB
Python
520 lines
15 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 math
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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 TestLayernormShiftPartitionPass(PassAutoScanTest):
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"""
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layer_norm
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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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# 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.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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"input_data": [1, 9, 96],
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},
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{
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"input_data": [4, 3136, 768],
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},
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{
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"input_data": [1, 784, 384],
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},
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)
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yield config, ['layernorm_shift_partition'], (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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"input_data": [1, 9, 96],
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},
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{
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"input_data": [4, 3136, 768],
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},
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{
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"input_data": [1, 784, 384],
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},
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)
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yield config, ['layernorm_shift_partition'], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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axis = [0, 1, 3, 2, 4, 5]
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epsilon = draw(st.floats(min_value=0.0000001, max_value=0.001))
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# begin_norm_axis has to be 2
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begin_norm_axis = 2
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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]))
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move_shape = draw(st.integers(min_value=1, max_value=8))
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dim = draw(st.sampled_from([96, 192, 384, 768]))
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def generate_input(attrs):
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return np.random.random(
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[attrs[1]["batch_size"], *attrs[1]["input_dim"]]
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).astype(np.float32)
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def generate_weight(attrs):
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return np.random.random(attrs[1]['input_dim'][-1]).astype(
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np.float32
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)
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attrs = [
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{
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'begin_norm_axis': begin_norm_axis,
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'epsilon': epsilon,
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},
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{
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'batch_size': batch_size,
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'input_dim': [(window_size * move_shape) ** 2, dim],
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},
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{
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'axis': axis,
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'input_resolution': window_size * move_shape,
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'move_shape': move_shape,
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'window_size': window_size,
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},
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]
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layer_norm_op = OpConfig(
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type="layer_norm",
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inputs={
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"X": ["input_data"],
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"Bias": ["layer_norm_bias"],
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"Scale": ["layer_norm_scale"],
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},
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outputs={
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"Y": ["layer_norm_output1"],
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"Mean": ["layer_norm_output2"],
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"Variance": ["layer_norm_output3"],
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},
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attrs={
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"begin_norm_axis": attrs[0]["begin_norm_axis"],
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"epsilon": attrs[0]["epsilon"],
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},
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)
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reshape_op2 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["layer_norm_output1"],
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},
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outputs={
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"Out": ["reshape_output2"],
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"XShape": ["reshape_output2_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["input_resolution"],
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attrs[2]["input_resolution"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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reshape_op3 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["reshape_output2"],
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},
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outputs={
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"Out": ["reshape_output3"],
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"XShape": ["reshape_output3_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["move_shape"],
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attrs[2]["window_size"],
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attrs[2]["move_shape"],
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attrs[2]["window_size"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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transpose_op4 = OpConfig(
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type='transpose2',
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inputs={
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"X": ["reshape_output3"],
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},
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outputs={"Out": ["transpose_output4"]},
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attrs={"axis": attrs[2]['axis']},
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)
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reshape_op5 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["transpose_output4"],
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},
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outputs={
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"Out": ["reshape_output5"],
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"XShape": ["reshape_output5_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["window_size"],
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attrs[2]["window_size"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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reshape_op6 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["reshape_output5"],
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},
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outputs={
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"Out": ["reshape_output6"],
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"XShape": ["reshape_output6_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["window_size"] ** 2,
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attrs[1]["input_dim"][-1],
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]
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},
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)
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program_config = ProgramConfig(
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ops=[
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layer_norm_op,
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reshape_op2,
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reshape_op3,
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transpose_op4,
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reshape_op5,
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reshape_op6,
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],
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weights={
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"layer_norm_bias": TensorConfig(
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data_gen=partial(generate_weight, attrs)
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),
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"layer_norm_scale": TensorConfig(
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data_gen=partial(generate_weight, attrs)
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),
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(generate_input, attrs)
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),
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},
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outputs=["reshape_output6"],
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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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passes=["layernorm_shift_partition_fuse_pass"],
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max_duration=250,
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min_success_num=50,
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)
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class TestLayernormShiftPartition2Pass(PassAutoScanTest):
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"""
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layer_norm
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reshape2
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roll
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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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# 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.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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"input_data": [1, 9, 96],
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},
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{
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"input_data": [4, 3136, 768],
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},
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{
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"input_data": [1, 784, 384],
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},
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)
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yield config, ['layernorm_shift_partition'], (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=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.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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"input_data": [1, 9, 96],
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},
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{
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"input_data": [4, 3136, 768],
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},
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{
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"input_data": [1, 784, 384],
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},
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)
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yield config, ['layernorm_shift_partition'], (1e-3, 1e-3)
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def sample_program_config(self, draw):
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axis = [0, 1, 3, 2, 4, 5]
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epsilon = draw(st.floats(min_value=0.0000001, max_value=0.001))
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# begin_norm_axis has to be 2
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begin_norm_axis = 2
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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]))
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move_shape = draw(st.integers(min_value=1, max_value=8))
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dim = draw(st.sampled_from([96, 192, 384, 768]))
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def generate_input(attrs):
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return np.random.random(
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[attrs[1]["batch_size"], *attrs[1]["input_dim"]]
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).astype(np.float32)
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def generate_weight(attrs):
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return np.random.random(attrs[1]['input_dim'][-1]).astype(
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np.float32
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)
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attrs = [
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{
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'begin_norm_axis': begin_norm_axis,
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'epsilon': epsilon,
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},
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{
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'batch_size': batch_size,
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'input_dim': [(window_size * move_shape) ** 2, dim],
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},
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{
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'axis': axis,
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'input_resolution': window_size * move_shape,
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'move_shape': move_shape,
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'window_size': window_size,
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},
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]
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layer_norm_op = OpConfig(
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type="layer_norm",
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inputs={
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"X": ["input_data"],
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"Bias": ["layer_norm_bias"],
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"Scale": ["layer_norm_scale"],
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},
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outputs={
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"Y": ["layer_norm_output1"],
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"Mean": ["layer_norm_output2"],
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"Variance": ["layer_norm_output3"],
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},
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attrs={
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"begin_norm_axis": attrs[0]["begin_norm_axis"],
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"epsilon": attrs[0]["epsilon"],
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},
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)
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reshape_op2 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["layer_norm_output1"],
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},
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outputs={
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"Out": ["reshape_output2"],
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"XShape": ["reshape_output2_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["input_resolution"],
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attrs[2]["input_resolution"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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roll_op1 = OpConfig(
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type="roll",
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inputs={"X": ["reshape_output2"]},
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outputs={"Out": ["roll_output1"]},
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attrs={
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"axis": [1, 2],
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"shifts": [
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-math.floor((attrs[2]["window_size"]) / 2.0),
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-math.floor((attrs[2]["window_size"]) / 2.0),
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],
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},
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)
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reshape_op3 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["roll_output1"],
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},
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outputs={
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"Out": ["reshape_output3"],
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"XShape": ["reshape_output3_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["move_shape"],
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attrs[2]["window_size"],
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attrs[2]["move_shape"],
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attrs[2]["window_size"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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transpose_op4 = OpConfig(
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type='transpose2',
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inputs={
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"X": ["reshape_output3"],
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},
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outputs={"Out": ["transpose_output4"]},
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attrs={"axis": attrs[2]['axis']},
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)
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reshape_op5 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["transpose_output4"],
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},
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outputs={
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"Out": ["reshape_output5"],
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"XShape": ["reshape_output5_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["window_size"],
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attrs[2]["window_size"],
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attrs[1]["input_dim"][-1],
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]
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},
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)
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reshape_op6 = OpConfig(
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type="reshape2",
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inputs={
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"X": ["reshape_output5"],
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},
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outputs={
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"Out": ["reshape_output6"],
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"XShape": ["reshape_output6_xshape"],
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},
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attrs={
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'shape': [
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-1,
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attrs[2]["window_size"] ** 2,
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attrs[1]["input_dim"][-1],
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]
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},
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)
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program_config = ProgramConfig(
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ops=[
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layer_norm_op,
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reshape_op2,
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roll_op1,
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reshape_op3,
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transpose_op4,
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reshape_op5,
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reshape_op6,
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],
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weights={
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"layer_norm_bias": TensorConfig(
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data_gen=partial(generate_weight, attrs)
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),
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"layer_norm_scale": TensorConfig(
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data_gen=partial(generate_weight, attrs)
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),
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(generate_input, attrs)
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),
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},
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outputs=["reshape_output6"],
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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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passes=["layernorm_shift_partition_fuse_pass"],
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max_duration=250,
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min_success_num=50,
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)
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if __name__ == "__main__":
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unittest.main()
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