/* Copyright (c) 2018 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. */ #pragma once #include namespace paddle { namespace inference { struct Buffer; enum class DeviceType { UNK = -1, CPU, GPU }; /* * EngineBase is the base class of all inference engines. An inference engine * takes a paddle program as input, and outputs the result in fluid Tensor * format. It can be used to optimize performance of computation sub-blocks, for * example, break down the original block into sub-blocks and execute each * sub-blocks in different engines. * * For example: * When inference, the resnet50 model can put most of the model into subgraph * and run it on a TensorRT engine. * * There are several engines such as TensorRT and other frameworks, so an * EngineBase is put forward to give an unified interface for all the * different engine implemention. */ class EngineBase { public: using DescType = ::paddle::framework::proto::BlockDesc; // Build the model and do some preparation, for example, in TensorRT, run // createInferBuilder, buildCudaEngine. virtual void Build(const DescType& paddle_model) = 0; // Execute the engine, that will run the inference network. virtual void Execute(int batch_size) = 0; virtual ~EngineBase() {} }; // class EngineBase struct Buffer { void* buffer{nullptr}; // buffer should be allocated only once. size_t max_size; // buffer allocated space. size_t size; // data size. DeviceType device{DeviceType::UNK}; // tells which device this buffer is on. }; } // namespace inference } // namespace paddle