# 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. 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 import unittest class TrtConvertPreluTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(batch, dim1, dim2, dim3): shape = [batch] if dim1 != 0: shape.append(dim1) if dim2 != 0: shape.append(dim2) if dim3 != 0: shape.append(dim3) return np.random.random(shape).astype(np.float32) def generate_alpha(attrs: List[Dict[str, Any]], dim1, dim2, dim3): if attrs[0]["mode"] == "all": return np.random.random(size=(1)).astype(np.float32) elif attrs[0]["mode"] == "channel" and attrs[0][ "data_format"] == "NCHW": shape = [1] if dim1 != 0: shape.append(dim1) if dim2 != 0: shape.append(1) if dim3 != 0: shape.append(1) return np.random.random(size=shape).astype(np.float32) elif attrs[0]["mode"] == "channel" and attrs[0][ "data_format"] == "NHWC": shape = [1] if dim1 != 0: shape.append(1) if dim2 != 0: shape.append(1) if dim3 != 0: shape.append(dim3) return np.random.random(size=shape).astype(np.float32) elif attrs[0]["mode"] == "element": shape = [1] if dim1 != 0: shape.append(dim1) if dim2 != 0: shape.append(dim2) if dim3 != 0: shape.append(dim3) return np.random.random(size=shape).astype(np.float32) for batch in [1, 4]: for dim1 in [0, 3]: for dim2 in [0, 16]: for dim3 in [0, 32]: self.dim1 = dim1 self.dim2 = dim2 self.dim3 = dim3 if dim1 == 0 and dim2 != 0: continue if dim1 == 0 and dim2 == 0 and dim3 != 0: continue for mode in ["all", "channel", "element"]: for data_format in ['NCHW', 'NHWC']: if mode == "channel" and dim1 == 0 and data_format == "NCHW": continue if mode == "channel" and dim3 == 0 and data_format == "NHWC": continue dics = [{ "mode": mode, "data_format": data_format }] ops_config = [{ "op_type": "prelu", "op_inputs": { "X": ["input_data"], "Alpha": ["alpha_weight"] }, "op_outputs": { "Out": ["output_data"] }, "op_attrs": dics[0] }] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "alpha_weight": TensorConfig( data_gen=partial(generate_alpha, dics, dim1, dim2, dim3)) }, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input, batch, dim1, dim2, dim3)), }, 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.dim1 == 0: self.dynamic_shape.min_input_shape = {"input_data": [1], } self.dynamic_shape.max_input_shape = {"input_data": [4], } self.dynamic_shape.opt_input_shape = {"input_data": [2], } else: if self.dim2 == 0 and self.dim3 == 0: self.dynamic_shape.min_input_shape = { "input_data": [1, 1], } self.dynamic_shape.max_input_shape = { "input_data": [4, 64], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 3], } elif self.dim2 != 0 and self.dim3 != 0: self.dynamic_shape.min_input_shape = { "input_data": [1, 1, 1, 1], } self.dynamic_shape.max_input_shape = { "input_data": [4, 64, 128, 128], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 3, 16, 32], } elif self.dim3 == 0: self.dynamic_shape.min_input_shape = { "input_data": [1, 1, 1], } self.dynamic_shape.max_input_shape = { "input_data": [4, 64, 256], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 3, 128], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] def generate_trt_nodes_num(attrs, dynamic_shape): if not dynamic_shape and self.dim1 == 0 and self.dim2 == 0 and self.dim3 == 0: return 0, 3 return 1, 2 # 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 # 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, True), 1e-5 def add_skip_trt_case(self): ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000: def teller(program_config, predictor_config): if not predictor_config.tensorrt_dynamic_shape_enabled(): return True return False self.add_skip_case( teller, SkipReasons.TRT_NOT_IMPLEMENTED, "Need to repair the case: the output of GPU and tensorrt has diff in trt6, the prelu static plugin has bug." ) def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()