ONNX-opt 0

#if PROTOBUF_VERSION != 5029002
#error "Protobuf C++ gencode is built with an incompatible version of"
#error "Protobuf C++ headers/runtime. See"
#error "https://protobuf.dev/support/cross-version-runtime-guarantee/#cpp"
...
...
#endif // ps: 这个endif在文件末尾

// onnx_optimizer_exec.cpp

/*
 * SPDX-License-Identifier: Apache-2.0
 */
...

  try {
    ONNX_NAMESPACE::ModelProto model;
    onnx::optimization::loadModel(&model, model_in_path, true);
    onnx::checker::check_model(model);
    auto new_model = onnx::optimization::Optimize(
        model, onnx::optimization::GetFuseAndEliminationPass());
    onnx::checker::check_model(new_model);
    bool save_external_data = !model_data_path.empty();
    onnx::optimization::saveModel(&new_model, model_out_path,
                                  save_external_data, model_data_path);

  } catch (std::exception& e) {
    std::cout << e.what() << std::endl;
    return -1;
  }
  return 0;

...

class Pass {
  PassType pass_type;
  PassEfficiency pass_efficiency;
  PassOptimizationType pass_optimization_type;
    ...
};

// Enum that represents the type of optimization it is.
enum PassType {
  // Class of optimizations that fuses operations.
  Fuse = 0,
  // Class of optimizations that removes useless operations.
  Nop = 1,
  // Class of optimizations that includes some form of separation.
  Separate = 2,
  // Immutable pass, also sometimes referred to as an analysis pass.
  Immutable = 3,
  // Class of optimizations that replaces nodes with other.
  Replace = 4,
  // Other type of pass.
  Other = 5
};
enum PassOptimizationType {
  // Is not optimizing anything. Most likely will be used in an immutable pass.
  None = 0,
  // Optimizes for compute.
  Compute = 1,
  // Optimizes for memory.
  Memory = 2,
  // Optimizes for both compute and memory.
  ComputeMemory = 3,
  // Optimizes for stability (e.g. log-sum-exp trick).
  Stability = 4
};
enum PassEfficiency {
  // A partially efficient optimization pass cannot guarantee that running two
  // consecutive passes
  // will return the same result as running a single pass.
  Partial = 0,
  // A completely efficient optimization guarantees that running two consecutive
  // passes is equivalent
  // to running a single pass.
  Complete = 1
};

std::shared_ptr<PassManagerAnalysis> FixedPointPassManager::run(Graph& graph) {
  bool fixed_point_optimization_done;

  do {
    fixed_point_optimization_done = false;
    for (const std::shared_ptr<Pass>& pass : this->passes) {
      std::shared_ptr<PostPassAnalysis> analysis = pass->runPass(graph);
      if (pass->getPassAnalysisType() == PassAnalysisType::Empty) {
        continue;
      }
      std::shared_ptr<CountBasedPassAnalysis> count_analysis =
          std::static_pointer_cast<CountBasedPassAnalysis>(analysis);

      while (count_analysis->fixedPointOptimizationNeeded()) {
        count_analysis = std::static_pointer_cast<CountBasedPassAnalysis>(
            pass->runPass(graph));
        fixed_point_optimization_done = true;
      }
    }
  } while (fixed_point_optimization_done);

  return std::shared_ptr<PassManagerAnalysis>(new EmptyPassManagerAnalysis());
}

class PredicateBasedPass : public Pass {
 public:
  explicit PredicateBasedPass(PassType pass_type,
                              PassEfficiency pass_efficiency,
                              PassOptimizationType pass_optimization_type)
      : Pass(pass_type, pass_efficiency, pass_optimization_type) {}
  ~PredicateBasedPass() override;

  virtual bool patternMatchPredicate(Node *node) = 0;
  virtual bool runTransform(Node *node, Graph &graph,
                            NodeDestroyType &destroy_current) = 0;

  std::shared_ptr<PostPassAnalysis> runPass(Graph &graph) override;
  PassAnalysisType getPassAnalysisType() const override;

  static int getOpsetVersion(const Graph &g) {
  Graph &mut_g = const_cast<Graph &>(g);
    for (const OpSetID &opset : mut_g.opset_versions_mutable()) {
      if (opset.domain() == "") {
        return opset.version();
      }
    }
    return 0;
  }

 private:
  unsigned int _runPassInternal(Graph &graph);
};

// The most general pass which allows the user to run a pass given only a graph.
class FullGraphBasedPass : public Pass {
 public:
  explicit FullGraphBasedPass(PassType pass_type,
                              PassEfficiency pass_efficiency,
                              PassOptimizationType pass_optimization_type)
      : Pass(pass_type, pass_efficiency, pass_optimization_type) {}
  ~FullGraphBasedPass() override;
};

unsigned int EliminateDead(Graph& graph) {
unsigned int nodes_removed = 0;
auto nodes = graph.nodes().reverse();
for (auto it = nodes.begin(); it != nodes.end(); it++) {
    auto node = *it;
    if (!node->hasUses()) {
    nodes_removed++;
    it.destroyCurrent();
    }
}
return nodes_removed;
}

// Graph represents one "function" of computation.
// It uses a simple ownership model where the graph owns all the nodes inside it.
// All references inside the graph are raw pointers.
// Destroying the Graph will invalidate any pointers to nodes in the graph.
struct Graph;

// Node is the base class of the IR graph. It represents one computation
// and dependencies on a list of Values. The "prim-ops", so to speak.
struct Node;

// A Value represents an input or output to node that is either a
// Tensor or an opaque Handle object, as determined by type().
struct Value;

struct Value final {
  Node* node_;
  size_t offset_;
  size_t unique_ = 0; // unique id
  size_t stage_ = 0; // 0-forward, 1-backward, 2-double-backward,...
  use_list uses_in_current_graph_;
  bool has_unique_name_{false};
  std::string unique_name_;
  int32_t elem_type_{ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED};
  bool has_sizes_{false};
  std::vector<Dimension> sizes_;
  ...
};

struct Node : public Attributes<Node> {
  ...
  std::array<Node*, 2> next_in_graph{nullptr, nullptr};
  Node*& next();
  Node*& prev();
  ...
  std::vector<Value*> inputs_;
  std::vector<Value*> outputs_;
  Graph* graph_;
  size_t stage_;
  ...
};