FastDeploy/fastdeploy/runtime.h

// Copyright (c) 2022 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.

/*! \file runtime.h
    \brief A brief file description.

    More details
 */

#pragma once

#include <map>
#include <vector>
#include <algorithm>

#include "fastdeploy/backends/backend.h"
#include "fastdeploy/utils/perf.h"

/** \brief All C++ FastDeploy APIs are defined inside this namespace
*
*/
namespace fastdeploy {

/*! Inference backend supported in FastDeploy */
enum Backend {
  UNKNOWN,  ///< Unknown inference backend
  ORT,  ///< ONNX Runtime, support Paddle/ONNX format model, CPU / Nvidia GPU
  TRT,  ///< TensorRT, support Paddle/ONNX format model, Nvidia GPU only
  PDINFER,  ///< Paddle Inference, support Paddle format model, CPU / Nvidia GPU
  POROS,  ///< Poros, support TorchScript format model, CPU / Nvidia GPU
  OPENVINO,  ///< Intel OpenVINO, support Paddle/ONNX format, CPU only
  LITE,  ///< Paddle Lite, support Paddle format model, ARM CPU only
};

/*! Deep learning model format */
enum ModelFormat {
  AUTOREC,  ///< Auto recognize the model format by model file name
  PADDLE,  ///< Model with paddlepaddle format
  ONNX,  ///< Model with ONNX format
  TORCHSCRIPT,  ///< Model with TorchScript format
};

FASTDEPLOY_DECL std::ostream& operator<<(std::ostream& out,
                                         const Backend& backend);
FASTDEPLOY_DECL std::ostream& operator<<(std::ostream& out,
                                         const ModelFormat& format);

/*! Paddle Lite power mode for mobile device. */
enum LitePowerMode {
  LITE_POWER_HIGH = 0,  ///< Use Lite Backend with high power mode
  LITE_POWER_LOW = 1,  ///< Use Lite Backend with low power mode
  LITE_POWER_FULL = 2,  ///< Use Lite Backend with full power mode
  LITE_POWER_NO_BIND = 3,  ///< Use Lite Backend with no bind power mode
  LITE_POWER_RAND_HIGH = 4,  ///< Use Lite Backend with rand high mode
  LITE_POWER_RAND_LOW = 5  ///< Use Lite Backend with rand low power mode
};

FASTDEPLOY_DECL std::string Str(const Backend& b);
FASTDEPLOY_DECL std::string Str(const ModelFormat& f);

/**
 * @brief Get all the available inference backend in FastDeploy
 */
FASTDEPLOY_DECL std::vector<Backend> GetAvailableBackends();

/**
 * @brief Check if the inference backend available
 */
FASTDEPLOY_DECL bool IsBackendAvailable(const Backend& backend);

bool CheckModelFormat(const std::string& model_file,
                      const ModelFormat& model_format);
ModelFormat GuessModelFormat(const std::string& model_file);

/*! @brief Option object used when create a new Runtime object
 */
struct FASTDEPLOY_DECL RuntimeOption {
   /** \brief Set path of model file and parameter file
   *
   * \param[in] model_path Path of model file, e.g ResNet50/model.pdmodel for Paddle format model / ResNet50/model.onnx for ONNX format model
   * \param[in] params_path Path of parameter file, this only used when the model format is Paddle, e.g Resnet50/model.pdiparams
   * \param[in] format Format of the loaded model
   */
  void SetModelPath(const std::string& model_path,
                    const std::string& params_path = "",
                    const ModelFormat& format = ModelFormat::PADDLE);

  /// Use cpu to inference, the runtime will inference on CPU by default
  void UseCpu();

  /// Use Nvidia GPU to inference
  void UseGpu(int gpu_id = 0);

  void SetExternalStream(void* external_stream);

  /*
   * @brief Set number of cpu threads while inference on CPU, by default it will decided by the different backends
   */
  void SetCpuThreadNum(int thread_num);

  /// Set ORT graph opt level, default is decide by ONNX Runtime itself
  void SetOrtGraphOptLevel(int level = -1);

  /// Set Paddle Inference as inference backend, support CPU/GPU
  void UsePaddleBackend();

  /// Set ONNX Runtime as inference backend, support CPU/GPU
  void UseOrtBackend();

  /// Set TensorRT as inference backend, only support GPU
  void UseTrtBackend();

  /// Set Poros backend as inference backend, support CPU/GPU
  void UsePorosBackend();

  /// Set OpenVINO as inference backend, only support CPU
  void UseOpenVINOBackend();

  /// Set Paddle Lite as inference backend, only support arm cpu
  void UseLiteBackend();

  /// Set mkldnn switch while using Paddle Inference as inference backend
  void SetPaddleMKLDNN(bool pd_mkldnn = true);

  /*
   * @brief If TensorRT backend is used, EnablePaddleToTrt will change to use Paddle Inference backend, and use its integrated TensorRT instead.
   */
  void EnablePaddleToTrt();

  /**
   * @brief Delete pass by name while using Paddle Inference as inference backend, this can be called multiple times to delete a set of passes
   */
  void DeletePaddleBackendPass(const std::string& delete_pass_name);

  /**
   * @brief Enable print debug information while using Paddle Inference as inference backend, the backend disable the debug information by default
   */
  void EnablePaddleLogInfo();

  /**
   * @brief Disable print debug information while using Paddle Inference as inference backend
   */
  void DisablePaddleLogInfo();

  /**
   * @brief Set shape cache size while using Paddle Inference with mkldnn, by default it will cache all the difference shape
   */
  void SetPaddleMKLDNNCacheSize(int size);

  /**
   * @brief Set optimzed model dir for Paddle Lite backend.
   */
  void SetLiteOptimizedModelDir(const std::string& optimized_model_dir);

  /**
   * @brief enable half precision while use paddle lite backend
   */
  void EnableLiteFP16();

  /**
   * @brief disable half precision, change to full precision(float32)
   */
  void DisableLiteFP16();

   /**
   * @brief enable int8 precision while use paddle lite backend
   */
  void EnableLiteInt8();

  /**
   * @brief disable int8 precision, change to full precision(float32)
   */
  void DisableLiteInt8();

  /**
   * @brief Set power mode while using Paddle Lite as inference backend, mode(0: LITE_POWER_HIGH; 1: LITE_POWER_LOW; 2: LITE_POWER_FULL; 3: LITE_POWER_NO_BIND, 4: LITE_POWER_RAND_HIGH; 5: LITE_POWER_RAND_LOW, refer [paddle lite](https://paddle-lite.readthedocs.io/zh/latest/api_reference/cxx_api_doc.html#set-power-mode) for more details)
   */
  void SetLitePowerMode(LitePowerMode mode);

  /** \brief Set shape range of input tensor for the model that contain dynamic input shape while using TensorRT backend
   *
   * \param[in] input_name The name of input for the model which is dynamic shape
   * \param[in] min_shape The minimal shape for the input tensor
   * \param[in] opt_shape The optimized shape for the input tensor, just set the most common shape, if set as default value, it will keep same with min_shape
   * \param[in] max_shape The maximum shape for the input tensor, if set as default value, it will keep same with min_shape
   */
  void SetTrtInputShape(
      const std::string& input_name, const std::vector<int32_t>& min_shape,
      const std::vector<int32_t>& opt_shape = std::vector<int32_t>(),
      const std::vector<int32_t>& max_shape = std::vector<int32_t>());

  /// Set max_workspace_size for TensorRT, default 1<<30
  void SetTrtMaxWorkspaceSize(size_t trt_max_workspace_size);

  /**
   * @brief Enable FP16 inference while using TensorRT backend. Notice: not all the GPU device support FP16, on those device doesn't support FP16, FastDeploy will fallback to FP32 automaticly
   */
  void EnableTrtFP16();

  /// Disable FP16 inference while using TensorRT backend
  void DisableTrtFP16();

  /**
   * @brief Set cache file path while use TensorRT backend. Loadding a Paddle/ONNX model and initialize TensorRT will take a long time, by this interface it will save the tensorrt engine to `cache_file_path`, and load it directly while execute the code again
   */
  void SetTrtCacheFile(const std::string& cache_file_path);

  /**
   * @brief Enable pinned memory. Pinned memory can be utilized to speedup the data transfer between CPU and GPU. Currently it's only suppurted in TRT backend and Paddle Inference backend.
   */
  void EnablePinnedMemory();

  /**
   * @brief Disable pinned memory
   */
  void DisablePinnedMemory();

  /**
   * @brief Enable to collect shape in paddle trt backend
   */
  void EnablePaddleTrtCollectShape();

  /**
   * @brief Disable to collect shape in paddle trt backend
   */
  void DisablePaddleTrtCollectShape();

  Backend backend = Backend::UNKNOWN;
  // for cpu inference and preprocess
  // default will let the backend choose their own default value
  int cpu_thread_num = -1;
  int device_id = 0;

  Device device = Device::CPU;

  void* external_stream_ = nullptr;

  bool enable_pinned_memory = false;

  // ======Only for ORT Backend========
  // -1 means use default value by ort
  // 0: ORT_DISABLE_ALL 1: ORT_ENABLE_BASIC 2: ORT_ENABLE_EXTENDED 3:
  // ORT_ENABLE_ALL
  int ort_graph_opt_level = -1;
  int ort_inter_op_num_threads = -1;
  // 0: ORT_SEQUENTIAL 1: ORT_PARALLEL
  int ort_execution_mode = -1;

  // ======Only for Paddle Backend=====
  bool pd_enable_mkldnn = true;
  bool pd_enable_log_info = false;
  bool pd_enable_trt = false;
  bool pd_collect_shape = false;
  int pd_mkldnn_cache_size = 1;
  std::vector<std::string> pd_delete_pass_names;

  // ======Only for Paddle-Lite Backend=====
  // 0: LITE_POWER_HIGH 1: LITE_POWER_LOW 2: LITE_POWER_FULL
  // 3: LITE_POWER_NO_BIND 4: LITE_POWER_RAND_HIGH
  // 5: LITE_POWER_RAND_LOW
  LitePowerMode lite_power_mode = LitePowerMode::LITE_POWER_NO_BIND;
  // enable int8 or not
  bool lite_enable_int8 = false;
  // enable fp16 or not
  bool lite_enable_fp16 = false;
  // optimized model dir for CxxConfig
  std::string lite_optimized_model_dir = "";

  // ======Only for Trt Backend=======
  std::map<std::string, std::vector<int32_t>> trt_max_shape;
  std::map<std::string, std::vector<int32_t>> trt_min_shape;
  std::map<std::string, std::vector<int32_t>> trt_opt_shape;
  std::string trt_serialize_file = "";
  bool trt_enable_fp16 = false;
  bool trt_enable_int8 = false;
  size_t trt_max_batch_size = 32;
  size_t trt_max_workspace_size = 1 << 30;

  // ======Only for Poros Backend=======
  bool is_dynamic = false;
  bool long_to_int = true;
  bool use_nvidia_tf32 = false;
  int unconst_ops_thres = -1;
  std::string poros_file = "";

  std::string model_file = "";   // Path of model file
  std::string params_file = "";  // Path of parameters file, can be empty
  ModelFormat model_format = ModelFormat::AUTOREC;  // format of input model

  // inside parameters, only for inside usage
  // remove multiclass_nms in Paddle2ONNX
  bool remove_multiclass_nms_ = false;
  // for Paddle2ONNX to export custom operators
  std::map<std::string, std::string> custom_op_info_;
};

/*! @brief Runtime object used to inference the loaded model on different devices
 */
struct FASTDEPLOY_DECL Runtime {
 public:
  /// Intialize a Runtime object with RuntimeOption
  bool Init(const RuntimeOption& _option);

  /** \brief Inference the model by the input data, and write to the output
   *
   * \param[in] input_tensors Notice the FDTensor::name should keep same with the model's input
   * \param[in] output_tensors Inference results
   * \return true if the inference successed, otherwise false
   */
  bool Infer(std::vector<FDTensor>& input_tensors,
             std::vector<FDTensor>* output_tensors);

  /** \brief Compile TorchScript Module, only for Poros backend
   *
   * \param[in] prewarm_tensors Prewarm datas for compile
   * \param[in] _option Runtime option
   * \return true if compile successed, otherwise false
   */
  bool Compile(std::vector<std::vector<FDTensor>>& prewarm_tensors,
               const RuntimeOption& _option);

  /** \brief Get number of inputs
   */
  int NumInputs() { return backend_->NumInputs(); }
  /** \brief Get number of outputs
   */
  int NumOutputs() { return backend_->NumOutputs(); }
  /** \brief Get input information by index
   */
  TensorInfo GetInputInfo(int index);
  /** \brief Get output information by index
   */
  TensorInfo GetOutputInfo(int index);
  /** \brief Get all the input information
   */
  std::vector<TensorInfo> GetInputInfos();
  /** \brief Get all the output information
   */
  std::vector<TensorInfo> GetOutputInfos();

  RuntimeOption option;

 private:
  void CreateOrtBackend();
  void CreatePaddleBackend();
  void CreateTrtBackend();
  void CreateOpenVINOBackend();
  void CreateLiteBackend();
  std::unique_ptr<BaseBackend> backend_;
};
}  // namespace fastdeploy