// coding=utf-8
//
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#include "attention.cuh"
#include <ATen/cuda/detail/TensorInfo.cuh>
#include <ATen/cuda/detail/KernelUtils.h>
#include <ATen/cuda/detail/IndexUtils.cuh>
#include <ATen/cuda/CUDAUtils.h>

#include <cuda_runtime.h>

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

using BlockReduceFloat256 = cub::BlockReduce<float, 256>;
using BlockReduceFloat512 = cub::BlockReduce<float, 512>;

#define WARP_SIZE (32)
#define FULL_MASK (0xFFFFFFFF)
#define THREADS (64)
#define DIV_UP(a, b) (((a) + ((b)-1)) / (b))

#define NNZ_TRESH (32)

#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);                                                                                         \
        }                                                                                                               \
    }

#define CHECK_ERROR(errorMessage)                                                                                      \
    {                                                                                                                  \
        cudaError_t err = cudaGetLastError();                                                                          \
        if (cudaSuccess != err) {                                                                                      \
            fprintf(stderr, "Cuda error: %s in file '%s' in line %i : %s.\n", errorMessage, __FILE__, __LINE__,        \
                    cudaGetErrorString(err));                                                                          \
            exit(EXIT_FAILURE);                                                                                        \
        }                                                                                                              \
    }

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;
}

// one warp per (ho,wo)
template <int BDIM_X>
__global__ __launch_bounds__(BDIM_X) void s2_attention_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,
    torch::PackedTensorAccessor32<float, 4, torch::RestrictPtrTraits> y,
    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 *shy = sh + threadIdx.y * num_channels;

    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);

    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
#if 0
    // useless read, y is always zeroed before kernel is called
    shy[chan] = y[batchId][chan][ho][wo];
#else
        shy[chan] = 0;
#endif
    }
    float alpha_sum = 0.0f;
    float qdotk_max = -FLT_MAX;

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

    const int rlen = rend - rbeg;

    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 += qy[batchId][chan][ho][wo] * kx[batchId][chan][hi][wip];
        }
        qdotk = __warp_sum_cub(qdotk);

        float qdotk_max_tmp;
        float alpha;
        float exp_save;

        qdotk_max_tmp = max(qdotk_max, qdotk);
        alpha = expf(qdotk - qdotk_max_tmp) * quad_weights[hi];
        exp_save = expf(qdotk_max - qdotk_max_tmp);

        alpha_sum = alpha + alpha_sum * exp_save;

        for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) {
            shy[chan] = shy[chan] * exp_save + vx[batchId][chan][hi][wip] * alpha;
        }
        qdotk_max = qdotk_max_tmp;
    }

    for (int chan = tidx; chan < num_channels; chan += WARP_SIZE) { y[batchId][chan][ho][wo] = shy[chan] / alpha_sum; }

    return;
}

torch::Tensor s2_attention_fwd_cuda(at::Tensor kx, at::Tensor vx, at::Tensor qy, 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);

    // TODO: check sizes

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

    size_t uo_num_channels = kx.size(1);

    const int batch_size = kx.size(0);

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

    // transpose inputs so that channels are in the last dimension, allowing for
    // coalesced memory access
    nvtxRangePush("s2_attention_fwd_kernel_mbT 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;
    }
    cudaDeviceSynchronize();
    nvtxRangePop();
    torch::Tensor y = torch::empty_like(qy);

    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 * block.y;

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

    s2_attention_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>(),
        y.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));
    // printf("s2_attention_kernel_fwd execution time: %f ms\n", milliseconds);
    CHECK_CUDA(cudaEventDestroy(start));
    CHECK_CUDA(cudaEventDestroy(stop));

    // match output layout to input layout
    if (!q_channel_first) y = y.contiguous();

    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return y;
}
