executor.h

/* 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 <map>
#include <memory>
#include <string>
#include <vector>

#include "common/types.h"
#include "framework/lod_tensor.h"
#include "framework/operator.h"
#include "framework/program/program.h"
#include "framework/tensor.h"
#ifdef PADDLE_EXECUTOR_MULTITHREAD
#include <condition_variable>
#include <mutex>
#include <thread>
#include "common/dep_core.h"
#endif
using std::string;

namespace paddle_mobile {
namespace framework {

template<typename Dtype = CPU, Precision P = Precision::FP32>
class Executor {
 public:
  typedef typename PrecisionTrait<P>::ptype Ptype;

  /*
   * @b init executor with program load by Loader class
   * @b 用 loader load 的 program 实例化 executor
   * */
  Executor(const framework::Program<Dtype> p, int batch_size = 1,
           bool use_optimize = true, bool loddable = false);

  /*
   * @b to predict
   * */
  std::shared_ptr<framework::Tensor> Predict(const framework::Tensor &t);

  /*
   * @b to predict
   * */
  std::shared_ptr<framework::LoDTensor> PredictLod(
          const framework::LoDTensor &t);

  /*
   * @b to predict with vector and dim
   *
   * @b 使用 输入 和 输入的维度信息 进行预测
   * */
  std::vector<Ptype> Predict(const std::vector<Ptype> &input,
                             const std::vector<int64_t> &dims);

 protected:
  Executor() = default;

  void InitMemory();

  void LoadMemory(const framework::VarDesc var_desc,
                  framework::LoDTensor *tensor, char **data);

  void InitCombineMemory();

  framework::Program<Dtype> program_;
  int batch_size_ = 1;
  std::shared_ptr<framework::ProgramDesc> to_predict_program_;

  std::shared_ptr<framework::Tensor> Predict(const framework::Tensor &t,
                                             int block_id);

  std::map<framework::BlockDesc,
          std::vector<std::shared_ptr<framework::OperatorBase<Dtype>>>>
          ops_of_block_;
  bool use_optimize_ = false;
  bool loddable_ = false;
#ifdef PADDLE_EXECUTOR_MULTITHREAD
  std::vector<depCore> depManager;
#endif
#ifdef PADDLE_MOBILE_PROFILE
  struct ProfInfo {
    int tid = 0;
    uint64_t runBegin = 0UL;
    uint64_t runEnd = 0UL;
  };
#endif

  bool varInputMemory(const std::shared_ptr<framework::VarDesc> &var_desc,
                      framework::Variable *var,
                      framework::LoDTensor *tensor) const;

#ifdef PADDLE_MOBILE_FPGA

  public:
   void InjectVariable(const framework::Tensor &t, string var_name);
   void FeedData(const framework::Tensor &t);
   std::shared_ptr<framework::Tensor> FetchResult(int id = -1);
   void Predict_From_To(int start = 0, int end = -1);
   void Predict_From(int start);
   void Predict_To(int end);
#endif
};

}
}  // namespace paddle_mobile