# 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. 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 TrtConvertDropoutTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): if dims == 1: return np.ones([64]).astype(np.float32) elif dims == 2: return np.ones([3, 64]).astype(np.float32) elif dims == 3: return np.ones([3, 64, 64]).astype(np.float32) else: return np.ones([batch, 3, 64, 64]).astype(np.float32) for dims in [1, 2, 3, 4]: for batch in [1, 2, 4]: for fix_seed in [False, True]: for dropout_implementation in [ "downgrade_in_infer", "upscale_in_train", ]: for dropout_prob in [np.random.random()]: for seed in [0, 64, 128, 512]: self.dims = dims dics = [ { "fix_seed": fix_seed, "dropout_implementation": dropout_implementation, "dropout_prob": dropout_prob, "seed": seed, "is_test": True, } ] ops_config = [ { "op_type": "dropout", "op_inputs": { "X": ["input_data"], }, "op_outputs": { "Out": ["dropout_output_data"] }, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input1, dims, batch, dics, ) ) }, outputs=["dropout_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 = {"input_data": [1]} self.dynamic_shape.max_input_shape = {"input_data": [128]} self.dynamic_shape.opt_input_shape = {"input_data": [64]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 64]} self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 64]} self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]} else: self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 32, 32] } self.dynamic_shape.max_input_shape = { "input_data": [4, 3, 64, 64] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 3, 64, 64] } 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 attrs[0]['dropout_implementation'] == "upscale_in_train": return 0, 2 elif self.dims == 1 and not dynamic_shape: return 0, 3 else: return 1, 2 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 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-3 # 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-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()