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graph_executor.h
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#pragma once
#include <torch/csrc/jit/argument_spec.h>
#include <torch/csrc/jit/autodiff.h>
#include <torch/csrc/jit/interpreter.h>
#include <torch/csrc/jit/ir.h>
#include <torch/csrc/jit/variable_tensor_list.h>
#include <memory>
namespace torch {
namespace jit {
struct GraphExecutorState;
// Notice that those structs don't manage lifetime of their members.
// They is only valid only right after you call getDebugState() and should never
// be used again once another GraphExecutor function is called.
struct ExecutionPlanState {
Code* code = nullptr;
const Graph* graph = nullptr;
};
struct GraphExecutorState {
const Graph* graph = nullptr;
ExecutionPlanState fallback; // XXX: members of this field are optional
std::unordered_map<ArgumentSpec, ExecutionPlanState> execution_plans;
};
struct GraphExecutorImpl;
struct TORCH_API GraphExecutor {
GraphExecutor() = default;
GraphExecutor(std::shared_ptr<Graph> graph, bool optimize = true);
void run(Stack& inputs);
explicit operator bool() const {
return pImpl != nullptr;
}
std::shared_ptr<Graph> graph() const;
GraphExecutorState getDebugState();
private:
std::shared_ptr<GraphExecutorImpl> pImpl;
};
// These passes need to run before it is valid to pass to the interpreter
// regardless of whether sizes have been specialized or not.
TORCH_API void runRequiredPasses(const std::shared_ptr<Graph>& g);
TORCH_API void debugSetAutodiffSubgraphInlining(bool state);
TORCH_API std::shared_ptr<Graph> lastExecutedOptimizedGraph();
namespace detail {
GraphExecutor* getGradExecutor(Operation& op);
// for debugging information we expose a way to get the last actually
// run graph. Previous approaches allowed querying the GraphExecutor
// for what graph it would run in certain circumstances (graphFor), but
// this is fragile because we sometimes change how these decisions are made.
// This interface still allows our tests to look at optimized graphs, but
// with less plumbing.
} // namespace detail
} // namespace jit
} // namespace torch