cxx_api.h

// Copyright (c) 2019 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 <memory>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/api/paddle_api.h"
#include "paddle/fluid/lite/core/op_lite.h"
#include "paddle/fluid/lite/core/optimizer.h"
#include "paddle/fluid/lite/core/program.h"
#include "paddle/fluid/lite/core/types.h"
#include "paddle/fluid/lite/model_parser/model_parser.h"

#ifdef LITE_WITH_X86
#include "paddle/fluid/framework/program_desc.h"
#endif

namespace paddle {
namespace lite {

/*
 * Predictor for inference, input a model, it will optimize and execute it.
 */
class Predictor {
 public:
  // Create an empty predictor.
  Predictor() { scope_ = std::make_shared<Scope>(); }
  // Create a predictor with the weight variable scope set.
  explicit Predictor(const std::shared_ptr<lite::Scope>& root_scope)
      : scope_(root_scope) {}

  // Build from a model, with places set for hardware config.
  void Build(const std::string& model_path, const Place& prefer_place,
             const std::vector<Place>& valid_places,
             const std::vector<std::string>& passes = {});

  void Build(const framework::proto::ProgramDesc& desc,
             const Place& prefer_place, const std::vector<Place>& valid_places,
             const std::vector<std::string>& passes = {});

  // Run the predictor for a single batch of data.
  void Run() { program_->Run(); }

  // Get offset-th col of feed inputs.
  lite::Tensor* GetInput(size_t offset);

  // Get offset-th col of fetch results.
  const lite::Tensor* GetOutput(size_t offset) const;

  const framework::proto::ProgramDesc& program_desc() const;
  const lite::Tensor* GetTensor(const std::string& name) const;
  const RuntimeProgram& runtime_program() const;

  // This method is disabled in mobile, for unnecessary dependencies required.
  void SaveModel(const std::string& dir);

#ifdef LITE_WITH_X86
  void Run(const std::vector<framework::Tensor>& tensors) {
    FeedVars(tensors);
    program_->Run();
  }

  void FeedVars(const std::vector<framework::Tensor>& tensors);
#endif

 private:
  Optimizer optimizer_;
  framework::proto::ProgramDesc program_desc_;
  std::shared_ptr<Scope> scope_;
  std::unique_ptr<RuntimeProgram> program_;
};

#ifdef LITE_WITH_X86
/*
 * An executor for training.
 *
 * Usage:
 *
 * CXXTrainer trainer(...);
 * trainer.RunStartupProgram(...);
 * auto exe = BuildMainProgramExecutor(...);
 *
 * for (auto& epoch : epoches) {
 *   auto* tensor0 = exe.GetInput(...);
 *   // fill data for tensor0
 *   exe.Run();
 * }
 */
class CXXTrainer {
 public:
  CXXTrainer(const std::shared_ptr<lite::Scope>& root_scope,
             const Place& preferred_place,
             const std::vector<Place>& valid_places)
      : scope_(root_scope),
        preferred_place_(preferred_place),
        valid_places_(valid_places),
        main_program_executor_(Predictor(scope_)) {}

  // Build the RuntimeProgram cache for the main program. The cache will run
  // multiple times for the epoches.
  // NOTE Just support to execute the 0-th block currently.
  Predictor& BuildMainProgramExecutor(const framework::proto::ProgramDesc& desc,
                                      int block_id = 0) {
    main_program_executor_.Build(desc, preferred_place_, valid_places_);
    return main_program_executor_;
  }

#ifdef LITE_WITH_X86
  Predictor& BuildMainProgramExecutor(framework::ProgramDesc& desc) {  // NOLINT
    return BuildMainProgramExecutor(*desc.Proto());
  }

  void RunStartupProgram(framework::ProgramDesc& desc) {  // NOLINT
    RunStartupProgram(*desc.Proto());
  }
#endif

  // Run the startup program. It just executes once, no cache needed.
  void RunStartupProgram(const framework::proto::ProgramDesc& desc,
                         int block_id = 0) {
    Predictor exe(scope_);
    exe.Build(desc, preferred_place_, valid_places_);
    exe.Run();
  }

 private:
  std::shared_ptr<lite::Scope> scope_;

  Place preferred_place_;
  std::vector<Place> valid_places_;

  // The training program.
  Predictor main_program_executor_;
};
#endif

}  // namespace lite
}  // namespace paddle