// !!! This is a file automatically generated by hipify!!!
#include "hip/hip_runtime.h"
/**
 * Copyright (c) 2020 by Contributors
 * @file array/cuda/ge_spmm.cuh
 * @brief GE-SpMM CUDA kernel function header.
 */
#ifndef DGL_ARRAY_CUDA_GE_SPMM_CUH_
#define DGL_ARRAY_CUDA_GE_SPMM_CUH_

#include "../../runtime/cuda/cuda_common.h"
#include "utils.h"
#include "atomic.cuh"
#include "macro.cuh"

namespace dgl {

using namespace cuda;

namespace aten {
namespace cuda {

/**
 * @brief CUDA kernel of GE-SpMM on Csr.
 * @note GE-SpMM: https://arxiv.org/pdf/2007.03179.pdf
 *       The grid dimension x and y are reordered for better performance.
 */
template <typename Idx, typename DType, typename BinaryOp>
__global__ void GESpMMKernel(
    const DType* __restrict__ ufeat, const DType* __restrict__ efeat,
    DType* __restrict__ out, const Idx* __restrict__ indptr,
    const Idx* __restrict__ indices, const int64_t num_rows,
    const int64_t num_cols, const int64_t feat_len) {
  const Idx rid =
      blockIdx.x * blockDim.y + threadIdx.y;        // over vertices dimension
  const Idx fid = (blockIdx.y * 64) + threadIdx.x;  // over feature dimension

  if (rid < num_rows && fid < feat_len) {
    const Idx low = __ldg(indptr + rid), high = __ldg(indptr + rid + 1);
    DType accum_0 = 0., accum_1 = 0.;

    if (blockIdx.y != gridDim.y - 1) {  // fid + 32 < feat_len
      for (Idx left = low; left < high; left += 32) {
        if (left + 32 <= high) {
#pragma unroll
          for (Idx i = 0; i < 32; ++i) {
            const Idx eid = left + i;
            const Idx cid = __ldg(indices + eid);
            const Idx offset = feat_len * cid + fid;
            if (BinaryOp::use_rhs) {
              accum_0 += BinaryOp::Call(ufeat + offset, efeat + eid);
              accum_1 += BinaryOp::Call(ufeat + offset + 32, efeat + eid);
            } else {
              accum_0 += ufeat[offset];
              accum_1 += ufeat[offset + 32];
            }
          }
        } else {
          for (Idx i = 0; left + i < high; ++i) {
            const Idx eid = left + i;
            const Idx cid = __ldg(indices + eid);
            const Idx offset = feat_len * cid + fid;
            if (BinaryOp::use_rhs) {
              accum_0 += BinaryOp::Call(ufeat + offset, efeat + eid);
              accum_1 += BinaryOp::Call(ufeat + offset + 32, efeat + eid);
            } else {
              accum_0 += ufeat[offset];
              accum_1 += ufeat[offset + 32];
            }
          }
        }

        out[feat_len * rid + fid] = accum_0;
        out[feat_len * rid + fid + 32] = accum_1;
      }
    } else {
      const Idx fid_0 = fid < feat_len ? fid : 0,
                fid_1 = fid + 32 < feat_len ? fid + 32 : 0;
      for (int left = low; left < high; left += 32) {
        if (left + 32 <= high) {
#pragma unroll
          for (int i = 0; i < 32; ++i) {
            const Idx eid = left + i;
            const Idx cid = __ldg(indices + eid);
            const Idx offset = feat_len * cid;
            if (BinaryOp::use_rhs) {
              accum_0 += BinaryOp::Call(ufeat + offset + fid_0, efeat + eid);
              accum_1 += BinaryOp::Call(ufeat + offset + fid_1, efeat + eid);
            } else {
              accum_0 += ufeat[offset + fid_0];
              accum_1 += ufeat[offset + fid_1];
            }
          }
        } else {
          for (int i = 0; i + left < high; ++i) {
            const Idx eid = left + i;
            const Idx cid = __ldg(indices + eid);
            const Idx offset = feat_len * cid;
            if (BinaryOp::use_rhs) {
              accum_0 += BinaryOp::Call(ufeat + offset + fid_0, efeat + eid);
              accum_1 += BinaryOp::Call(ufeat + offset + fid_1, efeat + eid);
            } else {
              accum_0 += ufeat[offset + fid_0];
              accum_1 += ufeat[offset + fid_1];
            }
          }
        }

        out[feat_len * rid + fid] = accum_0;
        if (fid + 32 < feat_len) out[feat_len * rid + fid + 32] = accum_1;
      }
    }
  }
}

template <typename Idx, typename DType, typename BinaryOp>
void GESpMMCsr(
    const CSRMatrix& csr, NDArray ufeat, NDArray efeat, NDArray out,
    int64_t feat_len) {
  const Idx* indptr = csr.indptr.Ptr<Idx>();
  const Idx* indices = csr.indices.Ptr<Idx>();
  const DType* ufeat_data = ufeat.Ptr<DType>();
  const DType* efeat_data = efeat.Ptr<DType>();
  DType* out_data = out.Ptr<DType>();

  hipStream_t stream = runtime::getCurrentHIPStreamMasqueradingAsCUDA();

  const int ntx = 32;
  const int nty = 32;
  const int nby = (feat_len + (ntx * 2) - 1) / (ntx * 2);
  const int nbx = (csr.num_rows + nty - 1) / nty;
  const dim3 nblks(nbx, nby);
  const dim3 nthrs(ntx, nty);
  const int sh_mem_size = 0;

  CUDA_KERNEL_CALL(
      (GESpMMKernel<Idx, DType, BinaryOp>), nblks, nthrs, sh_mem_size, stream,
      ufeat_data, efeat_data, out_data, indptr, indices, csr.num_rows,
      csr.num_cols, feat_len);
}

}  // namespace cuda
}  // namespace aten
}  // namespace dgl

#endif  // DGL_ARRAY_CUDA_GE_SPMM_CUH_
