paddle_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.

/*
 * This file defines PaddlePredictor, the api for lite. It supports multiple
 * hardware including ARM, X86, OpenCL, CUDA and so on.
 */

#ifndef PADDLE_LITE_API_H_  // NOLINT
#define PADDLE_LITE_API_H_
#include <memory>
#include <string>
#include <vector>
#include "paddle_place.h"  // NOLINT

namespace paddle {
namespace lite_api {

using shape_t = std::vector<int64_t>;
using lod_t = std::vector<std::vector<uint64_t>>;

enum class LiteModelType { kProtobuf = 0, kNaiveBuffer, UNK };

struct LITE_API Tensor {
  explicit Tensor(void* raw);
  explicit Tensor(const void* raw);

  void Resize(const shape_t& shape);

  /// Readonly data.
  template <typename T>
  const T* data() const;

  template <typename T>
  T* mutable_data() const;

  /// Shape of the tensor.
  shape_t shape() const;

  // LoD of the tensor
  lod_t lod() const;

  // Set LoD of the tensor
  void SetLoD(const lod_t& lod);

 private:
  void* raw_tensor_;
};

/// The PaddlePredictor defines the basic interfaces for different kinds of
/// predictors.
class LITE_API PaddlePredictor {
 public:
  PaddlePredictor() = default;

  /// Get i-th input.
  virtual std::unique_ptr<Tensor> GetInput(int i) = 0;

  /// Get i-th output.
  virtual std::unique_ptr<const Tensor> GetOutput(int i) const = 0;

  virtual void Run() = 0;

  virtual std::string GetVersion() const = 0;

  /// Get a readonly tensor, return null if no one called `name` exists.
  virtual std::unique_ptr<const Tensor> GetTensor(
      const std::string& name) const = 0;

  /// Persist the optimized model to disk. This API is only supported by
  /// CxxConfig, and the persisted model can be reused for MobileConfig.
  virtual void SaveOptimizedModel(
      const std::string& model_dir,
      LiteModelType model_type = LiteModelType::kProtobuf);

  virtual ~PaddlePredictor() = default;
};

/// Base class for all the configs.
class LITE_API ConfigBase {
  std::string model_dir_;

 public:
  void set_model_dir(const std::string& x) { model_dir_ = x; }

  const std::string& model_dir() const { return model_dir_; }
};

/// CxxConfig is the config for the Full feature predictor.
class LITE_API CxxConfig : public ConfigBase {
  Place preferred_place_;
  std::vector<Place> valid_places_;
  std::string model_file_;
  std::string param_file_;
  bool model_from_memory_{false};

 public:
  void set_preferred_place(const Place& x) { preferred_place_ = x; }
  void set_valid_places(const std::vector<Place>& x) { valid_places_ = x; }
  void set_model_file(const std::string& path) { model_file_ = path; }
  void set_param_file(const std::string& path) { param_file_ = path; }
  void set_model_buffer(const char* model_buffer,
                        size_t model_buffer_size,
                        const char* param_buffer,
                        size_t param_buffer_size) {
    model_file_ = std::string(model_buffer, model_buffer + model_buffer_size);
    param_file_ = std::string(param_buffer, param_buffer + param_buffer_size);
    model_from_memory_ = true;
  }

  const Place& preferred_place() const { return preferred_place_; }
  const std::vector<Place>& valid_places() const { return valid_places_; }
  std::string model_file() const { return model_file_; }
  std::string param_file() const { return param_file_; }
  bool model_from_memory() const { return model_from_memory_; }
};

/// MobileConfig is the config for the light weight predictor, it will skip
/// IR optimization or other unnecessary stages.
class LITE_API MobileConfig : public ConfigBase {
  PowerMode mode_{LITE_POWER_HIGH};
  int threads_{1};
  std::string model_buffer_;
  std::string param_buffer_;
  bool model_from_memory_{false};

 public:
  explicit MobileConfig(PowerMode mode = LITE_POWER_NO_BIND, int threads = 1);
  void set_power_mode(PowerMode mode);
  void set_threads(int threads);
  void set_model_buffer(const char* model_buffer,
                        size_t model_buffer_size,
                        const char* param_buffer,
                        size_t param_buffer_size) {
    model_buffer_ = std::string(model_buffer, model_buffer + model_buffer_size);
    param_buffer_ = std::string(param_buffer, param_buffer + param_buffer_size);
    model_from_memory_ = true;
  }

  PowerMode power_mode() const { return mode_; }
  int threads() const { return threads_; }
  bool model_from_memory() const { return model_from_memory_; }
  const std::string& model_buffer() const { return model_buffer_; }
  const std::string& param_buffer() const { return param_buffer_; }
};

template <typename ConfigT>
std::shared_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT&);

}  // namespace lite_api
}  // namespace paddle

#endif  // NOLINT