// coding=utf-8
//
// SPDX-FileCopyrightText: Copyright (c) 2025 The torch-harmonics Authors. All rights reserved.
// SPDX-License-Identifier: BSD-3-Clause
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#include "attention.cuh"
#include "c10/core/MemoryFormat.h"

#include <ATen/core/TensorAccessor.h>
#include <ATen/cuda/detail/TensorInfo.cuh>
#include <ATen/cuda/detail/KernelUtils.h>
#include <ATen/cuda/detail/IndexUtils.cuh>
#include <ATen/cuda/CUDAUtils.h>

#include <ctime>
#include <cub/cub.cuh>
#include <limits>

#ifndef WARP_SIZE
#define WARP_SIZE (32)
#endif
#ifndef FULL_MASK
#define FULL_MASK (0xFFFFFFFF)
#endif
#ifndef THREADS
#define THREADS (64)
#endif
#ifndef DIV_UP
#define DIV_UP(a,b) (((a)+((b)-1))/(b))
#endif
#ifndef CHECK_CUDA
#define CHECK_CUDA(call) {                                            \
    cudaError_t err = call;                                           \
    if( cudaSuccess != err) {                                         \
      fprintf(stderr, "Cuda error in file '%s' in line %i : %s.\\n",  \
              __FILE__, __LINE__, cudaGetErrorString( err) );         \
      exit(EXIT_FAILURE);                                             \
    }}
#endif

#include <iostream>
#include <chrono>
#include <string>

class ScopeTimer {
public:
  explicit ScopeTimer(const std::string& label = "")
    : label_(label), start_(std::chrono::high_resolution_clock::now()) {}

  ~ScopeTimer() {
    auto end = std::chrono::high_resolution_clock::now();
    auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start_);
    std::cout << label_ << "Elapsed time: " << elapsed.count() << " ms" << std::endl;
  }

private:
  std::string label_;
  std::chrono::high_resolution_clock::time_point start_;
};

static __device__ float __warp_sum(float val) {
#pragma unroll
  for(int i = WARP_SIZE/2; i; i /= 2) {
    val += __shfl_xor_sync(FULL_MASK, val, i);
  }
  return val;

}

// easier to understand version of manual shfl_xor_sync, performance appears similar
static __device__ float __warp_sum_cub(float val) {
  // use cub to reduce within a warp
  __shared__ typename cub::WarpReduce<float>::TempStorage temp_storage;
  
  // 1. Compute sum (initially only in lane 0)
  float sum = cub::WarpReduce<float>(temp_storage).Sum(val);
  // 2. Broadcast sum to all threads
  sum = __shfl_sync(0xFFFFFFFF, sum, 0);
  return sum;
}

// This kernel computes the backward pass for the S2 attention mechanism, using
// shared memory as a cache and one warp per output point, warp-parallel over
// channels, which should be layed out in the fastest dimension for coalesced
// memory access.
template<int BDIM_X>
__global__
__launch_bounds__(BDIM_X)
  void s2_attention_bwd_dkvq_kernel(
                                        int num_channels,
                                        int nlon_in,
                                        int nlat_out,
                                        int nlon_out,
                                        const torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> kx,
                                        const torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> vx,
                                        const torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> qy,
                                        const torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> dy,
                                        torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> dydk,
                                        torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> dydv,
                                        torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> dydq,
                                        const torch::PackedTensorAccessor64<int64_t, 1, torch::RestrictPtrTraits> psi_col_idx,
                                        const torch::PackedTensorAccessor64<int64_t, 1, torch::RestrictPtrTraits> psi_row_offset,
                                        const torch::PackedTensorAccessor32<float, 1, torch::RestrictPtrTraits> quad_weights) {

  extern __shared__ float sh[];
  float* sh_alpha_k = sh + threadIdx.y * num_channels * 5;
  float* sh_alpha_vw = sh_alpha_k + num_channels;
  float* sh_alpha_kvw = sh_alpha_vw + num_channels;
  float *sh_dy = sh_alpha_kvw + num_channels;
  float* sh_qy = sh_dy + num_channels;
  // (optionally, could use more shared memory for other intermediates)

  const uint64_t batchId = blockIdx.y;
  const uint64_t wid = uint64_t(blockIdx.x) * blockDim.y + threadIdx.y;
  if (wid >= uint64_t(nlat_out) * nlon_in) return;
  const int tidx = threadIdx.x;
  const int ho = wid / nlon_out;
  const int wo = wid - (ho * nlon_out);

  // Zero shared memory
  for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
    sh_alpha_k[chan] = 0.0f;
    sh_alpha_vw[chan] = 0.0f;
    sh_alpha_kvw[chan] = 0.0f;
    sh_dy[chan] = dy[batchId][chan][ho][wo];
    sh_qy[chan] = qy[batchId][chan][ho][wo];
  }
  float alpha_sum = 0.0f;
  float qdotk_max = -FLT_MAX;
  float integral = 0.0f;
  __syncthreads();

  const int64_t rbeg = psi_row_offset[ho];
  const int64_t rend = psi_row_offset[ho+1];
  const int rlen = rend - rbeg;

  // First pass: find qdotk_max
  for (int off = 0; off < rlen; off++) {
    const int64_t col = psi_col_idx[rbeg + off];
    const int hi = col / nlon_in;
    const int wi = col - (hi * nlon_in);
    const int wip = (wi + wo) - ((wi + wo) / nlon_in) * nlon_in;
    float qdotk = 0.0f;
    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
      qdotk += sh_qy[chan] * kx[batchId][chan][hi][wip];
    }
    qdotk = __warp_sum_cub(qdotk);
    qdotk_max = max(qdotk_max, qdotk);
  }

  // Second pass: accumulate alpha_sum, integral, and shared stats
  for (int off = 0; off < rlen; off++) {
    const int64_t col = psi_col_idx[rbeg + off];
    const int hi = col / nlon_in;
    const int wi = col - (hi * nlon_in);
    const int wip = (wi + wo) - ((wi + wo) / nlon_in) * nlon_in;
    float qdotk = 0.0f, gdotv = 0.0f;
    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
      qdotk += sh_qy[chan] * kx[batchId][chan][hi][wip];
      gdotv += sh_dy[chan] * vx[batchId][chan][hi][wip];
    }
    qdotk = __warp_sum_cub(qdotk);
    gdotv = __warp_sum_cub(gdotv);
    float alpha_inz = expf(qdotk - qdotk_max) * quad_weights[hi];
    alpha_sum += alpha_inz;
    integral += alpha_inz * gdotv;
    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
      float kxval = kx[batchId][chan][hi][wip];
      sh_alpha_k[chan] += alpha_inz * kxval;
      sh_alpha_vw[chan] += alpha_inz * gdotv;
      sh_alpha_kvw[chan] += alpha_inz * kxval * gdotv;
    }
  }

  integral /= alpha_sum;

  // Write dydq
  for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
    dydq[batchId][chan][ho][wo] = (sh_alpha_kvw[chan] * alpha_sum - sh_alpha_vw[chan] * sh_alpha_k[chan]) / (alpha_sum * alpha_sum);
  }

  // Third pass: accumulate gradients for k and v
  for (int off = 0; off < rlen; off++) {
    const int64_t col = psi_col_idx[rbeg + off];
    const int hi = col / nlon_in;
    const int wi = col - (hi * nlon_in);
    const int wip = (wi + wo) - ((wi + wo) / nlon_in) * nlon_in;
    float qdotk = 0.0f, gdotv = 0.0f;
    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
      qdotk += qy[batchId][chan][ho][wo] * kx[batchId][chan][hi][wip];
      gdotv += sh_dy[chan] * vx[batchId][chan][hi][wip];
    }
    qdotk = __warp_sum_cub(qdotk);
    gdotv = __warp_sum_cub(gdotv);
    float alpha_inz = expf(qdotk - qdotk_max) * quad_weights[hi];
    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
      float qyval = qy[batchId][chan][ho][wo];
      float dyval = sh_dy[chan];
      atomicAdd(&dydk[batchId][chan][hi][wip], qyval * (alpha_inz / alpha_sum) * (gdotv - integral));
      atomicAdd(&dydv[batchId][chan][hi][wip], (alpha_inz / alpha_sum) * dyval);
    }
  }
}




std::tuple<at::Tensor,at::Tensor,at::Tensor> s2_attention_bwd_dkvq_cuda(at::Tensor kx, at::Tensor vx,
                                                                        at::Tensor qy,
                                                                        at::Tensor dy,
                                                                        at::Tensor quad_weights,
                                                                        at::Tensor psi_col_idx,
                                                                        at::Tensor psi_row_off,
                                                                        int nlon_in, int nlat_out, int nlon_out) {

  CHECK_CUDA_TENSOR(kx);
  CHECK_CUDA_TENSOR(vx);
  CHECK_CUDA_TENSOR(qy);
  CHECK_CUDA_TENSOR(quad_weights);
  CHECK_CUDA_TENSOR(psi_col_idx);
  CHECK_CUDA_TENSOR(psi_row_off);
  CHECK_CUDA_TENSOR(dy);

  auto stream = at::cuda::getCurrentCUDAStream().stream();

  auto k_channel_first = kx.strides()[1] == 1;
  auto v_channel_first = vx.strides()[1] == 1;
  auto q_channel_first = qy.strides()[1] == 1;
  auto dy_channel_first = dy.strides()[1] == 1;

  // Transpose to [batch, ho, wo, channel]
  nvtxRangePush("s2_attention_bwd_dkvq_kernel_mbT permute inputs");
  // auto* permute_timer = new ScopeTimer("permute inputs");

  //Permute kx,vx,qy,dy to [batch, ho, wo, channel] in memory layout, but keep the original shape [batch, channel, ho, wo]
  auto kxP = at::Tensor();
  if (!k_channel_first) {
    // printf("Permuting kx from [batch, channel, ho, wo] to [batch, ho, wo, channel]\n");
    kxP = kx.permute({0, 2, 3, 1}).contiguous().permute({0, 3, 1, 2});
  } else {
    kxP = kx;
  }
  auto vxP = at::Tensor();
  if (!v_channel_first) {
    // printf("Permuting vx from [batch, channel, ho, wo] to [batch, ho, wo, channel]\n");
    vxP = vx.permute({0, 2, 3, 1}).contiguous().permute({0, 3, 1, 2});
  } else {
    vxP = vx;
  }
  auto qyP = at::Tensor();
  if (!q_channel_first) {
    // printf("Permuting qy from [batch, channel, ho, wo] to [batch, ho, wo, channel]\n");
    qyP = qy.permute({0, 2, 3, 1}).contiguous().permute({0, 3, 1, 2});
  } else {
    qyP = qy;
  }
  auto dyP = at::Tensor();
  if (!dy_channel_first) {
    // printf("Permuting dy from [batch, channel, ho, wo] to [batch, ho, wo, channel]\n");
    dyP = dy.permute({0, 2, 3, 1}).contiguous().permute({0, 3, 1, 2});
  } else {
    dyP = dy;
  }
  // cudaDeviceSynchronize();
  // delete permute_timer;
  nvtxRangePop();

  nvtxRangePush("s2_attention_bwd_dkvq_kernel_mbT output allocation & zero");
  auto dydk = torch::zeros_like(qyP);
  auto dydv = torch::zeros_like(qyP);
  auto dydq = torch::zeros_like(qyP);
  // print strdie of dydkP, dydvP, dydqP
  nvtxRangePop();

  size_t uo_num_channels = kx.size(1);
  const int batch_size = kx.size(0);

  dim3 block(WARP_SIZE, THREADS/WARP_SIZE);
  dim3 grid(DIV_UP(nlat_out*nlon_out, block.y), batch_size);
  size_t shared_size = sizeof(float) * uo_num_channels * 5 * block.y; // 4 arrays per warp

  cudaEvent_t start, stop;
  float milliseconds = 0;
  CHECK_CUDA(cudaEventCreate(&start));
  CHECK_CUDA(cudaEventCreate(&stop));
  CHECK_CUDA(cudaEventRecord(start, stream));

  s2_attention_bwd_dkvq_kernel<THREADS><<<
    grid, block, shared_size, stream>>>(
                                        uo_num_channels, nlon_in, nlat_out, nlon_out,
                                        kxP.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        vxP.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        qyP.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        dyP.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        dydk.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        dydv.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        dydq.packed_accessor32<float, 4, torch::RestrictPtrTraits>(),
                                        psi_col_idx.packed_accessor64<int64_t, 1, torch::RestrictPtrTraits>(),
                                        psi_row_off.packed_accessor64<int64_t, 1, torch::RestrictPtrTraits>(),
                                        quad_weights.packed_accessor32<float, 1, torch::RestrictPtrTraits>());
  
  CHECK_CUDA(cudaEventRecord(stop, stream));
  CHECK_CUDA(cudaEventSynchronize(stop));
  CHECK_CUDA(cudaEventElapsedTime(&milliseconds, start, stop));

  // [1, 256, 1, (721, 1440), (721, 1440), "equiangular", "equiangular", 1e-5, 1e-5],
  // s2_attention_bwd_kernel_mbT execution time: 63.280128 ms
  // printf("s2_attention_bwd_kernel_mbT execution time: %f ms\n", milliseconds);
  CHECK_CUDA(cudaEventDestroy(start));
  CHECK_CUDA(cudaEventDestroy(stop));
  
  C10_CUDA_KERNEL_LAUNCH_CHECK();

  // Permute outputs back to memory layout given by input. if input had channels
  // first, leave it in that layout, otherwise permute layout back to [batch,
  // channel, ho, wo]
  if(!k_channel_first) dydk = dydk.contiguous();
  if(!v_channel_first) dydv = dydv.contiguous();
  if(!q_channel_first) dydq = dydq.contiguous();

  // printf("dydk strides:[");
  // for(auto& stride : dydk.strides()) {
  //   printf("%ld,", stride);
  // }
  // printf("]\n");
  // cudaDeviceSynchronize();
  // delete permute_output_timer;
  // nvtxRangePop();
  return std::make_tuple(dydk, dydv, dydq);

}

