# Copyright (c) 2022 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 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 TrtConvertActivationTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8415: return False return True def sample_program_configs(self): self.trt_param.workspace_size = 1073741824 def generate_input1(dims, batch): if dims == 1: return np.zeros(batch).astype(np.float32) elif dims == 2: return np.ones((batch, 4)).astype(np.float32) elif dims == 3: return np.ones((batch, 4, 6)).astype(np.float32) else: return np.ones((batch, 4, 6, 8)).astype(np.float32) def generate_input2(dims, batch): if dims == 1: return np.zeros(batch).astype(np.float32) elif dims == 2: return np.ones((batch, 4)).astype(np.float32) elif dims == 3: return np.ones((batch, 4, 6)).astype(np.float32) else: return np.ones((batch, 4, 6, 8)).astype(np.float32) def generate_input3(dims, batch): if dims == 1: return np.zeros(batch).astype(np.float32) elif dims == 2: return np.ones((batch, 4)).astype(np.float32) elif dims == 3: return np.ones((batch, 4, 6)).astype(np.float32) else: return np.ones((batch, 4, 6, 8)).astype(np.float32) for dims in [1, 2, 3, 4]: for batch in [1, 2]: self.dims = dims dics = [{}] ops_config = [ { "op_type": "cast", "op_inputs": {"X": ["condition_data"]}, "op_outputs": {"Out": ["condition_data_bool"]}, "op_attrs": {"in_dtype": 5, "out_dtype": 0}, "outputs_dtype": {"condition_data_bool": np.bool_}, }, { "op_type": "where", "op_inputs": { "Condition": ["condition_data_bool"], "X": ["input_x_data"], "Y": ["input_y_data"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": {"condition_data_bool": np.bool_}, }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "condition_data": TensorConfig( data_gen=partial(generate_input1, dims, batch) ), "input_x_data": TensorConfig( data_gen=partial(generate_input2, dims, batch) ), "input_y_data": TensorConfig( data_gen=partial(generate_input3, dims, batch) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = { "condition_data": [1], "condition_data_bool": [1], "input_x_data": [1], "input_y_data": [1], } self.dynamic_shape.max_input_shape = { "condition_data": [2], "condition_data_bool": [2], "input_x_data": [2], "input_y_data": [2], } self.dynamic_shape.opt_input_shape = { "condition_data": [1], "condition_data_bool": [1], "input_x_data": [1], "input_y_data": [1], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "condition_data": [1, 4], "condition_data_bool": [1, 4], "input_x_data": [1, 4], "input_y_data": [1, 4], } self.dynamic_shape.max_input_shape = { "condition_data": [2, 4], "condition_data_bool": [2, 4], "input_x_data": [2, 4], "input_y_data": [2, 4], } self.dynamic_shape.opt_input_shape = { "condition_data": [1, 4], "condition_data_bool": [1, 4], "input_x_data": [1, 4], "input_y_data": [1, 4], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "condition_data": [1, 4, 6], "condition_data_bool": [1, 4, 6], "input_x_data": [1, 4, 6], "input_y_data": [1, 4, 6], } self.dynamic_shape.max_input_shape = { "condition_data": [2, 4, 6], "condition_data_bool": [2, 4, 6], "input_x_data": [2, 4, 6], "input_y_data": [2, 4, 6], } self.dynamic_shape.opt_input_shape = { "condition_data": [1, 4, 6], "condition_data_bool": [1, 4, 6], "input_x_data": [1, 4, 6], "input_y_data": [1, 4, 6], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "condition_data": [1, 4, 6, 8], "condition_data_bool": [1, 4, 6, 8], "input_x_data": [1, 4, 6, 8], "input_y_data": [1, 4, 6, 8], } self.dynamic_shape.max_input_shape = { "condition_data": [2, 4, 6, 8], "condition_data_bool": [2, 4, 6, 8], "input_x_data": [2, 4, 6, 8], "input_y_data": [2, 4, 6, 8], } self.dynamic_shape.opt_input_shape = { "condition_data": [1, 4, 6, 8], "condition_data_bool": [1, 4, 6, 8], "input_x_data": [1, 4, 6, 8], "input_y_data": [1, 4, 6, 8], } 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): if not dynamic_shape: return 0, 6 return 1, 4 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # for static_shape clear_dynamic_shape() 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, False ), 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, False ), 1e-5 # 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-5 def test(self): self.run_test() if __name__ == "__main__": unittest.main()