# Copyright (c) 2023 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. import unittest from functools import partial from typing import Any, Dict, List import numpy as np from program_config import ProgramConfig, TensorConfig from trt_layer_auto_scan_test import TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtConvertExpandASV2Test(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] if len(attrs[0]['target_shape']) < self.dims: return False if self.dims == 1: if len(attrs[0]['target_shape']) == 4: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: self.input_shape = [1, 8, 1, 32] return np.random.random([1, 8, 1, 32]).astype(np.float32) elif self.dims == 3: self.input_shape = [1, 32, 32] return np.random.random([1, 32, 32]).astype(np.float32) elif self.dims == 2: self.input_shape = [1, 32] return np.random.random([1, 32]).astype(np.float32) elif self.dims == 1: self.input_shape = [32] return np.random.random([32]).astype(np.float32) elif self.dims == 0: self.input_shape = [] return np.random.random([]).astype(np.float32) for dims in [0, 1, 2, 3, 4]: for shape in [ [10, 8, 32, 32], [2, 8, 32, 32], [8, 32, 32], [2, 32], [32], ]: dics = [ { "target_shape": shape, }, ] self.dims = dims ops_config = [ { "op_type": "expand_as_v2", "op_inputs": {"X": ["expand_v2_input"]}, "op_outputs": {"Out": ["expand_v2_out"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "expand_v2_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["expand_v2_out"], ) yield program_config 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 = { "expand_v2_input": [1, 8, 1, 32] } self.dynamic_shape.max_input_shape = { "expand_v2_input": [10, 8, 1, 32] } self.dynamic_shape.opt_input_shape = { "expand_v2_input": [1, 8, 1, 32] } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "expand_v2_input": [1, 32, 32] } self.dynamic_shape.max_input_shape = { "expand_v2_input": [8, 32, 32] } self.dynamic_shape.opt_input_shape = { "expand_v2_input": [1, 32, 32] } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "expand_v2_input": [1, 32] } self.dynamic_shape.max_input_shape = { "expand_v2_input": [4, 32] } self.dynamic_shape.opt_input_shape = { "expand_v2_input": [1, 32] } elif self.dims == 1: self.dynamic_shape.min_input_shape = {"expand_v2_input": [32]} self.dynamic_shape.max_input_shape = {"expand_v2_input": [64]} self.dynamic_shape.opt_input_shape = {"expand_v2_input": [32]} elif self.dims == 0: self.dynamic_shape.min_input_shape = {"expand_v2_input": []} self.dynamic_shape.max_input_shape = {"expand_v2_input": []} self.dynamic_shape.opt_input_shape = {"expand_v2_input": []} def clear_dynamic_shape(): self.dynamic_shape.min_input_shape = {} self.dynamic_shape.max_input_shape = {} self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() ver_num = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 if dynamic_shape and (ver_num > 8000 or self.dims > 0): return 1, 2 else: return 0, 3 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] clear_dynamic_shape() # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertExpandV2Test2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 1: self.input_shape = [1] return np.random.random([1]).astype(np.float32) for dims in [1]: for shape in [[10]]: dics = [ { "target_shape": shape, }, ] self.dims = dims dics_intput = [ {"X": ["expand_v2_input"], "Y": ["shapeT1_data"]}, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT1_data"]}, "op_attrs": { "dtype": 2, "str_value": "10", "shape": [1], }, }, { "op_type": "expand_as_v2", "op_inputs": dics_intput[0], "op_outputs": {"Out": ["expand_v2_out"]}, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "expand_v2_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["expand_v2_out"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 1: self.dynamic_shape.min_input_shape = {"expand_v2_input": [1]} self.dynamic_shape.max_input_shape = {"expand_v2_input": [1]} self.dynamic_shape.opt_input_shape = {"expand_v2_input": [1]} def clear_dynamic_shape(): self.dynamic_shape.min_input_shape = {} self.dynamic_shape.max_input_shape = {} self.dynamic_shape.opt_input_shape = {} clear_dynamic_shape() # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) # fill_constant will be folded by constnt folding pass! yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (1, 2), 1e-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()