graph_executor_impl.h

#pragma once
#include <torch/csrc/jit/graph_executor.h>

#include <ATen/core/ivalue.h>
#include <c10/util/Exception.h>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/jit/argument_spec.h>
#include <torch/csrc/jit/autodiff.h>
#include <torch/csrc/jit/custom_operator.h>
#include <torch/csrc/jit/interpreter.h>
#include <torch/csrc/jit/ir.h>
#include <torch/csrc/jit/profiling_record.h>
#include <torch/csrc/jit/resource_guard.h>
#include <torch/csrc/jit/symbolic_variable.h>
#include <torch/csrc/jit/tracer.h>

#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/jit/script/compiler.h>
#include <torch/csrc/jit/script/logging.h>

#include <cstdint>
#include <iterator>
#include <memory>
#include <mutex>
#include <unordered_map>
#include <utility>
#include <vector>

namespace torch {
namespace jit {

struct ExecutionPlan {
  ExecutionPlan() = default;
  ExecutionPlan(std::shared_ptr<Graph> graph)
      : code(graph), graph(std::move(graph)) {}

  void run(Stack& stack) const;

  operator bool() const {
    return static_cast<bool>(graph);
  }

  ExecutionPlanState getDebugState() {
    ExecutionPlanState state;
    state.code = &code;
    state.graph = graph.get();
    return state;
  }

  Code code;
  std::shared_ptr<Graph> graph;
};

// a Graph can be created via tracing, or via a language-based frontend
// GraphExecutor runs it. It can run the same graph on many different sizes
// and different requires_grad states, and handles specializations for each
// situation. GraphExecutor is completely unaware of tracing or module
// parameters to keep the tracing concerns separated.
struct GraphExecutorImplBase {
  static std::shared_ptr<Graph> prepareGraph(
      const std::shared_ptr<Graph>& graph) {
    auto copy = graph->copy();
    EraseShapeInformation(copy);
    return copy;
  }

  GraphExecutorImplBase(const std::shared_ptr<Graph>& graph, bool optimize)
      : graph(prepareGraph(graph)),
        // until we have correct alias analysis any use of mutable operators
        // disables all optimization
        optimize(optimize),
        num_inputs(this->graph->inputs().size()),
        num_outputs(this->graph->outputs().size()) {}

  // entry point where execution begins
  virtual void run(Stack& stack) = 0;
  virtual GraphExecutorState getDebugState() = 0;
  virtual ~GraphExecutorImplBase() = default;

 protected:
  friend struct GraphExecutor;

  // The unoptimized starting graph. This field is effectively const, but we
  // can't make it so because Graph::copy() is not const (and making it const is
  // not that easy at this point).
  std::shared_ptr<Graph> graph;

  // If false, we'll run the graph as we get it, without any optimizations.
  // Useful for debugging.
  const bool optimize;
  const size_t num_inputs;
  const size_t num_outputs;

  // GraphExecutors can be accessed from multiple threads, so this thread needs
  // to be held every time we access the fallback or plan_cache.
  std::mutex compile_mutex;
};

} // namespace jit
} // namespace torch