PaddleX/deploy/ultra-infer/ultra_infer/runtime/backends/poros/common/compile.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.

#pragma once

#include <algorithm>
#include <set>
#include <string>
#include <unordered_map>

#include "iengine.h"      // NOLINT
#include "poros_module.h" // NOLINT
#include "torch/script.h" // NOLINT

namespace baidu {
namespace mirana {
namespace poros {

/**
 * @brief  compile graph
 *
 * @param [in] module : original module
 * @param [in] input_ivalues : prewarm datas
 * @param [in] options : Inference options
 * @return porosmodule
 * @retval !nullptr => succeed  nullptr => failed
 **/
std::unique_ptr<PorosModule>
Compile(const torch::jit::Module &module,
        const std::vector<std::vector<c10::IValue>> &prewarm_datas,
        const PorosOptions &options);

class Compiler {
public:
  typedef std::unordered_map<const torch::jit::Node *, IEngine *> engine_map_t;
  typedef std::vector<std::vector<c10::IValue>> ivalue_vec_t;

  Compiler() : _origin_module(NULL) {}
  ~Compiler();

  /**
   * @brief initial Compiler
   *
   * @param [in] options : poros options
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  int init(const PorosOptions &options);

  /**
   * @brief compile whole graph
   *
   * @param [in] origin_module
   * @param [in] prewarm_datas : ivalue_vec_t, vector of IValue
   * @param [out] optimized_module : optimized graph
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  int compile(const torch::jit::Module &origin_module,
              const ivalue_vec_t &prewarm_datas,
              torch::jit::Module *optimized_module);

private:
  /**
   * @brief preprocess this calculation graph
   *
   * @param [in] prewarm_datas : ivalue_vec_t, vector of IValue
   * @param [out] graph : preprcessed graph
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  int preprocess_graph(const ivalue_vec_t &prewarm_datas,
                       std::shared_ptr<torch::jit::Graph> &graph);

  /**
   * @brief segment this calculation graph
   *
   * @param [in/out] graph
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  int segment_graph(std::shared_ptr<torch::jit::Graph> &graph);

  // Split subgraph（block)
  // The divided subgraph, as a subgraph, is associated with the block
  int segment_block(torch::jit::Block &block, IEngine *engine,
                    int current_depth);

  // Subgraph optimization
  /**
   * @brief Subgraph optimization
   *
   * @param [in] prewarm_datas : ivalue_vec_t, vector of IValue
   * @param [in] opt_graph : ivalue_vec_t, vector of IValue
   * @param [out] optimized_module : optimized graph
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  int optimize_subgraph(const ivalue_vec_t &prewarm_datas,
                        const std::shared_ptr<torch::jit::Graph> &opt_graph,
                        torch::jit::Module *optimized_module);

  // Subgraph optimization(block)
  int optimize_subblock(torch::jit::Block *block,
                        torch::jit::Module *optimized_module);

  /**
   * @brief Compile the subgraph into a new graph based on the engine
   *
   * @param [in] engine : The engine used by the subgraph
   * @param [in] subgraph_node : Subgraph node
   * @return [out] module : Transformed model
   * @retval 0 => succeed  <0 => failed
   **/
  int transform(IEngine *engine, torch::jit::Node &subgraph_node,
                torch::jit::Module &module);

  /**
   * @brief Select engine based on subgraph and options
   *
   * @param [in] node : Jit Node
   * @return  int
   * @retval 0 => succeed  <0 => failed
   **/
  IEngine *select_engine(const torch::jit::Node *n);

  /**
   * @brief destroy
   *
   * @return  void
   **/
  void close();

private:
  int _max_segment_depth{5};   // Maximum subgraph segmentation depth
  ivalue_vec_t _prewarm_datas; // Prewarm datas
  PorosOptions _options;
  engine_map_t _engine_map; // The engine used to record the subgraph
  const torch::jit::Module *_origin_module; // Origin_module
  std::atomic<int> _engine_index = {0};     // Record engine index
};

/**
 * @brief  compile graph, internal use
 *
 * @param [in] module : Origin module
 * @param [in] input_ivalues : Prewarm datas
 * @param [in] options : Inference options
 * @return optimized_module
 * @retval !nullptr => succeed  nullptr => failed
 **/
std::unique_ptr<torch::jit::Module>
CompileGraph(const torch::jit::Module &module,
             const std::vector<std::vector<c10::IValue>> &prewarm_datas,
             const PorosOptions &options);

} // namespace poros
} // namespace mirana
} // namespace baidu