Class Args

tensorflow/core/common_runtime/executor.h:94–130

      RendezvousFactory;

  struct Args {
    int64_t step_id = 0;
    int64_t round_step_id = 0;
    Rendezvous* rendezvous = nullptr;
    Rendezvous* global_rendezvous = nullptr;
    StepStatsCollectorInterface* stats_collector = nullptr;
    CallFrameInterface* call_frame = nullptr;
    CancellationManager* cancellation_manager = nullptr;
    SessionState* session_state = nullptr;
    // Unique session identifier. Can be empty.
    string session_handle;
    TensorStore* tensor_store = nullptr;
    ScopedStepContainer* step_container = nullptr;
    CollectiveExecutor* collective_executor = nullptr;
    thread::ThreadPoolInterface* user_intra_op_threadpool = nullptr;
    int64_t start_time_usecs = 0;
    // The deadline for the kernel to complete by. Empty if unspecified.
    absl::optional<absl::Time> deadline;

    // If true, calls Sync() on the device.
    bool sync_on_finish = false;

    typedef std::function<void()> Closure;
    typedef std::function<void(Closure)> Runner;
    // CostRunner will schedule task via cost value which compute by CostModel.
    typedef std::function<void(Closure, int64)> CostRunner;
    Runner runner = nullptr;
    CostRunner cost_runner = nullptr;

    ExecutorPolicy executor_policy = ExecutorPolicy::USE_NORMAL_EXECUTOR;

    // store refs to cpu tensors that will be sent to gpu,
    // and release them when the session run finishes.
    std::unique_ptr<mutex> ref_send_inputs_mu_ptr;
    std::vector<std::unique_ptr<TensorReference>>* ref_send_inputs_ptr = nullptr;
    bool merge_compute_and_copy_stream = false;
  };
  typedef std::function<void(const Status&)> DoneCallback;
  virtual void RunAsync(const Args& args, DoneCallback done) = 0;