[Paddle Inference]Add split op TRT converter unittest. #35127
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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons | ||
| from program_config import TensorConfig, ProgramConfig | ||
| import numpy as np | ||
| import paddle.inference as paddle_infer | ||
| from functools import partial | ||
| from typing import Optional, List, Callable, Dict, Any, Set | ||
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| class TrtConvertSplitTest(TrtLayerAutoScanTest): | ||
| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| inputs = program_config.inputs | ||
| weights = program_config.weights | ||
| outputs = program_config.outputs | ||
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| attrs = [ | ||
| program_config.ops[i].attrs | ||
| for i in range(len(program_config.ops)) | ||
| ] | ||
| # the dimensions of input and axis match | ||
| if len(inputs['split_input'].shape) <= attrs[0]['axis']: | ||
| return False | ||
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| #Sections and num cannot both be equal to 0. | ||
| if len(attrs[0]['sections']) == 0: | ||
| if attrs[0]['num'] == 0: | ||
| return False | ||
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| #When sections and num are not both equal to 0, sections has higher priority. | ||
| #The sum of sections should be equal to the input size. | ||
| if len(attrs[0]['sections']) != 0: | ||
| if attrs[0]['num'] != 0: | ||
| return False | ||
| if len(outputs) != len(attrs[0]['sections']): | ||
| return False | ||
| sum = 0 | ||
| for num in attrs[0]['sections']: | ||
| sum += num | ||
| if sum != inputs['split_input'].shape[attrs[0]['axis']]: | ||
| return False | ||
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| #The size of num should be equal to the input dimension. | ||
| if attrs[0]['num'] != 0: | ||
| if len(outputs) != attrs[0]['num']: | ||
| return False | ||
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| #Test AxisTensor and SectionsTensorList | ||
| if self.num_input == 0: | ||
| if self.dims == 2 and attrs[0]['sections'] == [10, 14] and len( | ||
|
Member
There was a problem hiding this comment. 对于这些特殊处理的case，可以补上简单注释，方便后续其他同学的理解。 |
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| outputs) == 2: | ||
| return True | ||
| else: | ||
| return False | ||
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| return True | ||
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| def sample_program_configs(self): | ||
| def generate_input1(attrs: List[Dict[str, Any]], batch): | ||
| if self.dims == 4: | ||
| return np.ones([batch, 3, 3, 24]).astype(np.float32) | ||
| elif self.dims == 3: | ||
| return np.ones([batch, 3, 24]).astype(np.float32) | ||
| elif self.dims == 2: | ||
| return np.ones([batch, 24]).astype(np.float32) | ||
| elif self.dims == 1: | ||
| return np.ones([24]).astype(np.float32) | ||
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| def generate_AxisTensor(attrs: List[Dict[str, Any]]): | ||
| return np.ones([1]).astype(np.int32) | ||
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| def generate_SectionsTensorList1(attrs: List[Dict[str, Any]]): | ||
| return np.array([10]).astype(np.int32) | ||
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| def generate_SectionsTensorList2(attrs: List[Dict[str, Any]]): | ||
| return np.array([14]).astype(np.int32) | ||
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| for num_input in [0, 1]: | ||
| for dims in [1, 2, 3, 4]: | ||
| for batch in [3, 6, 9]: | ||
| for Out in [["output_var0", "output_var1"], | ||
| ["output_var0", "output_var1", "output_var2"]]: | ||
| for sections in [[], [1, 2], [2, 1], [10, 14], | ||
| [1, 1, 1], [2, 2, 2], [3, 3, 3], | ||
| [3, 7, 14]]: | ||
| for num in [0, 3]: | ||
| for axis in [0, 1, 2, 3]: | ||
| self.batch = batch | ||
| self.num_input = num_input | ||
| self.dims = dims | ||
| dics = [{ | ||
| "sections": sections, | ||
| "num": num, | ||
| "axis": axis | ||
| }, {}] | ||
|
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||
| dics_intput = [{ | ||
| "X": ["split_input"], | ||
| "AxisTensor": ["AxisTensor"], | ||
| "SectionsTensorList": [ | ||
| "SectionsTensorList1", | ||
| "SectionsTensorList2" | ||
| ] | ||
| }, { | ||
| "X": ["split_input"] | ||
| }] | ||
| dics_intputs = [{ | ||
| "AxisTensor": | ||
| TensorConfig(data_gen=partial( | ||
| generate_AxisTensor, dics)), | ||
| "SectionsTensorList1": TensorConfig( | ||
| data_gen=partial( | ||
| generate_SectionsTensorList1, | ||
| dics)), | ||
| "SectionsTensorList2": | ||
| TensorConfig(data_gen=partial( | ||
| generate_SectionsTensorList2, dics)) | ||
| }, {}] | ||
|
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| ops_config = [{ | ||
| "op_type": "split", | ||
| "op_inputs": dics_intput[num_input], | ||
| "op_outputs": { | ||
| "Out": Out | ||
| }, | ||
| "op_attrs": dics[0] | ||
| }] | ||
| ops = self.generate_op_config(ops_config) | ||
| program_config = ProgramConfig( | ||
| ops=ops, | ||
| weights=dics_intputs[num_input], | ||
| inputs={ | ||
| "split_input": | ||
| TensorConfig(data_gen=partial( | ||
| generate_input1, dics, batch)) | ||
| }, | ||
| outputs=Out) | ||
|
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| yield program_config | ||
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| def sample_predictor_configs( | ||
| self, program_config) -> (paddle_infer.Config, List[int], float): | ||
| def generate_dynamic_shape(attrs): | ||
| if self.dims == 4: | ||
| self.dynamic_shape.min_input_shape = { | ||
| "split_input": [1, 3, 3, 24] | ||
| } | ||
| self.dynamic_shape.max_input_shape = { | ||
| "split_input": [9, 3, 3, 24] | ||
| } | ||
| self.dynamic_shape.opt_input_shape = { | ||
| "split_input": [1, 3, 3, 24] | ||
| } | ||
| elif self.dims == 3: | ||
| self.dynamic_shape.min_input_shape = {"split_input": [1, 3, 24]} | ||
|
Member
There was a problem hiding this comment. 动态shape设置，除了batch维，其他维度也至少有一个维度需要变化。否则可能覆盖不全
Contributor
Author
There was a problem hiding this comment. 当sections 固定时，动态shape对应维度的不同会导致尺寸不匹配，所以固定动态shape各维度的尺寸 |
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| self.dynamic_shape.max_input_shape = {"split_input": [9, 3, 24]} | ||
| self.dynamic_shape.opt_input_shape = {"split_input": [1, 3, 24]} | ||
| elif self.dims == 2: | ||
| self.dynamic_shape.min_input_shape = {"split_input": [1, 24]} | ||
| self.dynamic_shape.max_input_shape = {"split_input": [9, 24]} | ||
| self.dynamic_shape.opt_input_shape = {"split_input": [1, 24]} | ||
| elif self.dims == 1: | ||
| self.dynamic_shape.min_input_shape = {"split_input": [24]} | ||
| self.dynamic_shape.max_input_shape = {"split_input": [24]} | ||
| self.dynamic_shape.opt_input_shape = {"split_input": [24]} | ||
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| def clear_dynamic_shape(): | ||
| self.dynamic_shape.min_input_shape = {} | ||
| self.dynamic_shape.max_input_shape = {} | ||
| self.dynamic_shape.opt_input_shape = {} | ||
|
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| def generate_trt_nodes_num(attrs, dynamic_shape): | ||
| if len(program_config.outputs) == 2: | ||
| if attrs[0]['axis'] != 0: | ||
| return 1, 3 | ||
| else: | ||
| return 0, 4 | ||
| else: | ||
| if attrs[0]['axis'] != 0: | ||
| return 1, 4 | ||
| else: | ||
| return 0, 5 | ||
|
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| attrs = [ | ||
| program_config.ops[i].attrs | ||
| for i in range(len(program_config.ops)) | ||
| ] | ||
| self.trt_param.max_batch_size = 9 | ||
| # for static_shape | ||
| clear_dynamic_shape() | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), 1e-5 | ||
|
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| # for dynamic_shape | ||
| generate_dynamic_shape(attrs) | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num(attrs, | ||
| True), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num(attrs, | ||
|
Member
There was a problem hiding this comment. FP16精度设置过低，可以实际跑下看看，因为有随机数种子，输入应该是确定的。 |
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| True), 1e-5 | ||
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| def add_skip_trt_case(self): | ||
| def teller1(program_config, predictor_config): | ||
| if len(program_config.weights) == 3: | ||
| return True | ||
| return False | ||
|
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| self.add_skip_case( | ||
| teller1, SkipReasons.TRT_NOT_SUPPORT, | ||
| "INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.") | ||
|
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| def test(self): | ||
| self.add_skip_trt_case() | ||
| self.run_test() | ||
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| if __name__ == "__main__": | ||
| unittest.main() | ||
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优先使用
const T&，下同