#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once

#include <ATen/Tensor.h>
#include <c10/core/Device.h>
#include <c10/cuda/CUDACachingAllocator.h>
#include <c10/cuda/CUDAGraphsC10Utils.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAStream.h>
#include <c10/util/flat_hash_map.h>

#include <limits>
#include <optional>
#include <stack>

#if defined(USE_ROCM) || !(defined(CUDA_VERSION) && CUDA_VERSION >= 12040)
// this type is not defined until CUDA 12.4, but we use it as a
// parameter type and return type in some below functions, so we give
// it the same definition as in CUDA 12.4.
typedef unsigned long long cudaGraphConditionalHandle;
#endif // defined(USE_ROCM) || !(defined(CUDA_VERSION) && CUDA_VERSION >= 12040)

namespace at {

struct Generator;
struct CUDAGeneratorImpl;
struct CUDAGeneratorState;

namespace cuda {

// Standalone way to get a unique mempool id usable as a pool=... argument
// to CUDAGraph::capture_begin
TORCH_CUDA_CPP_API MempoolId_t graph_pool_handle();

// Returns true if any CUDAGraph capture is currently active in this process.
// Used by ProcessGroupNCCL's ROCm watchdog workaround to avoid calling
// hipEventQuery during active capture on HIP runtimes without the
// event-query capture-mode fix (https://github.com/ROCm/clr/pull/3176).
// Not needed on CUDA/NVIDIA where cross-thread event query does not have this
// restriction.
#if defined(USE_ROCM)
TORCH_CUDA_CPP_API bool is_graph_capture_active();
#endif // defined(USE_ROCM)

struct TORCH_CUDA_CPP_API CUDAGraph {
  CUDAGraph(bool keep_graph=false);
  ~CUDAGraph();

  // Copy and move constructors and assignments are disabled. These
  // were disabled because pybind11 believed that CUDAGraph was copy
  // constructable because
  // pybind11::is_copy_constructible<CUDAGraph>::value originally
  // evaluated to true. However, it cannot generate a copy constructor
  // because CUDAGeneratorState, one of CUDAGraph's members, is an
  // incomplete type unless CUDAGeneratorImpl.h is included. However,
  // that would create a circular dependency between
  // CUDAGeneratorImpl.h and CUDAGraph.h. Disabling the copy and move
  // constructors is the most straightforward way to prevent pybind11
  // from trying to generate default implementations of them.
  //
  // We needed pybind11 to return a reference to a CUDAGraph as part
  // of wrapping CUDAGraph::get_currently_capturing_graph, which
  // unearthed the above problem.
  CUDAGraph(const CUDAGraph&) = delete;
  CUDAGraph& operator=(const CUDAGraph&) = delete;
  CUDAGraph(CUDAGraph&& other) = delete;
  CUDAGraph& operator=(CUDAGraph&& other) = delete;

  void register_generator_state(c10::intrusive_ptr<at::CUDAGeneratorState> state);
  void register_generator_state(const at::Generator& generator);
  void capture_begin(
      MempoolId_t pool = {0, 0},
      cudaStreamCaptureMode capture_mode = cudaStreamCaptureModeGlobal);
  void capture_end();
  void instantiate();
  void replay();
  void reset();
  MempoolId_t pool();
  void enable_debug_mode();
  void debug_dump(const std::string& debug_path);
  cudaGraph_t raw_cuda_graph();
  cudaGraphExec_t raw_cuda_graph_exec();

  static CUDAGraph* get_currently_capturing_graph();
  void begin_capture_to_if_node(const Tensor& scalar_cuda_pred_tensor);
  void end_capture_to_conditional_node();
  static void set_conditional_handle(
      cudaGraphConditionalHandle handle,
      const Tensor& scalar_cuda_pred_tensor);

 private:
  template <typename StreamType>
  std::function<bool(StreamType)> create_allocate_filter() const;
  std::function<bool(cudaStream_t)> create_child_allocate_filter();

 protected:
  cudaGraph_t graph_ = nullptr;
  cudaGraphExec_t graph_exec_ = nullptr;

  // internal states so reset() can do its best cleaning up

  // Set to true in capture_end if cudaStreamEndCapture succeeded
  // Set back to false after instantiate() unless keep_graph=True or
  // enable_debug_mode() was called on any CUDAGraph instance.
  bool has_graph_ = false;
  // Set to true in capture_end if cudaStreamEndCapture succeeded
  bool capture_ended_ = false;
  // Set to true in capture_end if cudaGraphInstantiate succeeded
  bool has_graph_exec_ = false;

  // the ID assigned by cuda during graph capture,
  // used to identify when a stream is participating in capture
  CaptureId_t capture_id_ = 0;

  // uuid used to request a particular private mempool from CUDACachingAllocator.
  // By default, this will be set to {id_, 0}.
  //
  // If capture_begin is called with "pool=other_graph.pool()", this graph's mempool_id_
  // will be set to the other graph's mempool_id_, and therefore share a mempool with the
  // other graph.
  //
  // If capture_begin is called with "pool=handle" where "handle" came from graph_pool_handle(),
  // it will share a mempool with any other captures that used "pool=handle".
  //
  // Sharing a mempool across graphs saves memory, and it's safe if you
  // know you'll replay those graphs in the same order you captured them.
  MempoolId_t mempool_id_;

  // Stream on which capture began
  at::cuda::CUDAStream capture_stream_;

  // multiple generator states and their wholegraph_increments in this graph
  // that are managed by the CUDA Graph
  ska::flat_hash_map<c10::intrusive_ptr<at::CUDAGeneratorState>, uint64_t>
      captured_generator_states_;

  // Device where capture occurred. Right now, for simplicity, we require all ops
  // in a capture to run on the same device, but this is a limitation of CUDAGraph,
  // not CUDA itself.  We can straightforwardly modify CUDAGraph to support multi-device
  // captures if needed.
  // init capture_dev_ as UNDEFINED_DEVICE to check that it stores the real device id in the destructor
  static constexpr c10::DeviceIndex UNDEFINED_DEVICE = -1;
  c10::DeviceIndex capture_dev_{UNDEFINED_DEVICE};

  bool keep_graph_;
  cudaStreamCaptureMode capture_mode_{};

#if !defined(USE_ROCM) && (defined(CUDA_VERSION) && CUDA_VERSION >= 12040)
  std::stack<at::cuda::CUDAStreamGuard> conditional_node_streams_;
  std::stack<CaptureId_t> conditional_graph_capture_ids_;
#endif // !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12040
};

template <>
std::function<bool(cudaStream_t)> CUDAGraph::create_allocate_filter<cudaStream_t>() const;
template <>
std::function<bool(c10::Stream)> CUDAGraph::create_allocate_filter<c10::Stream>() const;

} // namespace cuda
} // namespace at

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)