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/*! \file
    \brief Benchmark helpers for Distributed GEMM

    A delay kernel to gate all GEMMs across devices, controlled by a flag that
    the host will set off once it launches DistGEMM across all devices.

    DistGpuTimer extends cutlass's existing cudaEvent-based timer to multiple devices.
*/

#pragma once

#include <iostream>
#include <cuda/atomic>
#include <cuda/std/atomic>

#include "cute/layout.hpp"
#include "cute/tensor.hpp"
#include "cutlass/cutlass.h"
#include "cutlass/cuda_host_adapter.hpp"


namespace cutlass {

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Delay kernel
/////////////////////////////////////////////////////////////////////////////////////////////////

using AtomicBoolean = cuda::atomic<bool>;

__global__ void delay_kernel(const AtomicBoolean* atomic_flag_ptr) {
  while (not atomic_flag_ptr->load()) {
    __nanosleep(40);
  }
}


/////////////////////////////////////////////////////////////////////////////////////////////////
/// Distributed GPU Timer
/// Sets up cuda events for multiple processors.
/////////////////////////////////////////////////////////////////////////////////////////////////
template <int NP>
struct DistGpuTimer {
  int _primary_device;
  cudaEvent_t _start[NP];
  cudaEvent_t _stop[NP];

  /// Constructor
  DistGpuTimer()
  {
    CUDA_CHECK(cudaGetDevice(&_primary_device));
    for (int device = 0; device < NP; ++device) {
      CUDA_CHECK(cudaSetDevice(device));
      CUDA_CHECK(cudaEventCreate(&_start[device]));
      CUDA_CHECK(cudaEventCreate(&_stop[device]));
    }
    CUDA_CHECK(cudaSetDevice(_primary_device));
  }

  /// Destructor
  ~DistGpuTimer()
  {
    for (int device = 0; device < NP; ++device) {
      CUDA_CHECK(cudaSetDevice(device));
      CUDA_CHECK(cudaEventDestroy(_start[device]));
      CUDA_CHECK(cudaEventDestroy(_stop[device]));
    }
    CUDA_CHECK(cudaSetDevice(_primary_device));
  }

  /// Start the timer for a given stream (defaults to the default stream)
  void start(int device, cudaStream_t stream) {
    assert(device >= 0 && device < NP);
    CUDA_CHECK(cudaEventRecord(_start[device], stream));
  }

  /// Stop the timer
  void stop(int device, cudaStream_t stream) {
    assert(device >= 0 && device < NP);
    CUDA_CHECK(cudaEventRecord(_stop[device], stream));
  }

  /// Return the elapsed time (in milliseconds)
  float elapsed_millis(int device) {
    assert(device >= 0 && device < NP);
    float elapsed = 0.0;
    CUDA_CHECK(cudaEventSynchronize(_stop[device]));
    CUDA_CHECK(cudaEventElapsedTime(&elapsed, _start[device], _stop[device]));
    return elapsed;
  }
};

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Generic device-to-device data movement kernel based for CuTe tensors.
///
///   NOTE: this kernel assigns one element copy to every thread, and is by no means
///   an efficient way of copying tensors. It should only be used for convenience in
///   reference checks.
/////////////////////////////////////////////////////////////////////////////////////////////////

template <typename TensorSource, typename TensorDestination>
void device_copy(TensorSource      tensor_source,
                 TensorDestination tensor_destination,
                 cudaStream_t stream);


template <typename TensorSource, typename TensorDestination>
__global__ void device_copy_kernel(TensorSource const tensor_source, 
                                   TensorDestination tensor_destination) {
  auto linear_idx = blockIdx.x * blockDim.x + threadIdx.x;
  using ElementSrc = typename TensorSource::value_type;
  using ElementDst = typename TensorDestination::value_type;
  NumericConverter<ElementDst, ElementSrc> converter;
  if (linear_idx < size(tensor_source)) {
    tensor_destination(linear_idx) = converter(tensor_source(linear_idx));
  }
}

template <typename TensorSource, typename TensorDestination>
void device_copy(TensorSource      tensor_source,
                 TensorDestination tensor_destination,
                 cudaStream_t stream) {
  
  assert(tensor_source.size() == tensor_destination.size());

  auto numel = tensor_source.size();
  static constexpr int NumThreads = 128;
  auto grid_size = cute::ceil_div(numel, NumThreads);

  dim3 grid(grid_size);
  dim3 block(NumThreads);
  device_copy_kernel<<<grid, block, 0, stream>>>(tensor_source, tensor_destination);
}

} //namespace cutlass
