/*!
 *  Copyright (c) 2020 by Contributors
 * \file array/cuda/gather_mm.cu
 * \brief GatherMM C APIs and definitions.
 */
#include <dgl/array.h>
#include <algorithm>  // std::swap
#include "./utils.h"
#include "./functor.cuh"
#include "./atomic.cuh"

namespace dgl {
using namespace cuda;
namespace aten {

namespace {

/*! \brief Call cuBLAS GEMM API for dense matmul operation for float and double. */
template <typename DType>
cublasStatus_t cublasGemm(cublasHandle_t handle, cublasOperation_t transa,
    cublasOperation_t transb, int m, int n, int k,
    const DType* alpha, const DType* A, int lda,
    const DType* B, int ldb, const DType* beta,
    DType* C, int ldc) {
  LOG(INFO) << "Not supported dtype";
  return CUBLAS_STATUS_EXECUTION_FAILED;
}

#ifdef USE_FP16
template <>
cublasStatus_t cublasGemm<__half>(cublasHandle_t handle, cublasOperation_t transa,
    cublasOperation_t transb, int m, int n, int k,
    const __half* alpha, const __half* A, int lda,
    const __half* B, int ldb, const __half* beta,
    __half* C, int ldc) {
  return cublasHgemm(handle, transa, transb, m, n, k, alpha, A, lda,
      B, ldb, beta, C, ldc);
}
#endif

template <>
cublasStatus_t cublasGemm<float>(cublasHandle_t handle, cublasOperation_t transa,
    cublasOperation_t transb, int m, int n, int k,
    const float* alpha, const float* A, int lda,
    const float* B, int ldb, const float* beta,
    float* C, int ldc) {
  return cublasSgemm(handle, transa, transb, m, n, k, alpha, A, lda,
      B, ldb, beta, C, ldc);
}

template <>
cublasStatus_t cublasGemm<double>(cublasHandle_t handle, cublasOperation_t transa,
    cublasOperation_t transb, int m, int n, int k,
    const double* alpha, const double* A, int lda,
    const double* B, int ldb, const double* beta,
    double* C, int ldc) {
  return cublasDgemm(handle, transa, transb, m, n, k, alpha, A, lda,
      B, ldb, beta, C, ldc);
}

}  // namespace

namespace cuda {

/* \Note Each row of A multiplies a segment of matrix of B of dimension in_len * outlen.
  One warp is assigned to process one row of A. Each WARP sequentially multiplies
  one element of A and a row of B to compute partial result of the output. A
  is loaded in shared memory in a coalesced way. Output matrix is loaded in
  registers. B should get benefit from L2 cache.
*/
template <typename Idx, typename DType>
__global__ void GatherMMScatterKernel(
    const DType* __restrict__ A,
    const DType* __restrict__ B,
    DType* __restrict__ C,
    const Idx* __restrict__ idx_a,
    const Idx* __restrict__ idx_b,
    const Idx* __restrict__ idx_c,
    const int64_t num_rows,
    const int64_t in_len,
    const int64_t out_len) {

    unsigned int tId = threadIdx.x;
    unsigned int laneId = tId & 31;
    unsigned int gId = (blockIdx.x * blockDim.x + threadIdx.x);
    unsigned int warpId = gId >> 5;
    unsigned int row = warpId;
    if (row < num_rows) {
        const unsigned int local_row = row & 3;  // hardcoded for TB size 128 (4 warps)
        const Idx cur_rowA = (idx_a) ? idx_a[row] : row;
        const Idx cur_rowB = (idx_b) ? idx_b[row] : row;
        const Idx cur_rowC = (idx_c) ? idx_c[row] : row;
        const Idx B_offset = cur_rowB * in_len * out_len;
        const int sh_a_tile = 64;
        __shared__ DType sh_A[4 * sh_a_tile];
        int a_tile = sh_a_tile;
        for (unsigned int k_start = 0; k_start < in_len; k_start += 64) {
            if ((in_len - k_start) < a_tile) a_tile = in_len - k_start;
            // Load A in shared mem in a coalesced way
            for (unsigned int l = laneId; l < a_tile; l += 32)
                sh_A[local_row * sh_a_tile + l] = A[cur_rowA * in_len + (k_start + l)];
            __syncwarp();

            for (unsigned int outloop = 0; outloop < out_len; outloop +=32) {
                DType out_reg = 0;  // thread private
                const unsigned int l = laneId;
                if (l < out_len) {
                    // iterate over elements of a row of A
                    for (unsigned int i = 0; i < a_tile; i++) {
                        const DType a_val =  sh_A[local_row * sh_a_tile + i];
                        // iterate over elements of a row of B in parallel
                        out_reg += a_val * B[B_offset + ((i + k_start) * out_len + (outloop + l))];
                    }
                    if (idx_c) {
                      AtomicAdd(C + cur_rowC * out_len + (outloop + l), out_reg);
                    } else {
                      C[cur_rowC * out_len + (outloop + l)] += out_reg;
                    }
                }
            }
        }
    }
}


/* \Note Output matrix is accumulated via atomic operations. Rest of the strategies
  are similar to GatherMMKernel. One warp is assigned to process one row of A. Each
  WARP sequentially multiplies one element of A and a row of B to compute partial
  result of the output. A is loaded in shared memory in a coalesced way. B should
  get benefit from L2 cache.
*/
template <typename Idx, typename DType>
__global__ void GatherMMScatterKernel2(
    const DType* __restrict__ A,
    const DType* __restrict__ B,
    DType* __restrict__ C,
    const Idx* __restrict__ idx_a,
    const Idx* __restrict__ idx_b,
    const Idx* __restrict__ idx_c,
    const int64_t num_rows,
    const int64_t in_len,
    const int64_t out_len) {

    unsigned int tId = threadIdx.x;
    unsigned int laneId = tId & 31;
    unsigned int gId = (blockIdx.x * blockDim.x + threadIdx.x);
    unsigned int warpId = gId >> 5;
    unsigned int row = warpId;
    if (row < num_rows) {
        const unsigned int local_row = row & 3;  // hardcoded for TB size 128 (4 warps)
        const Idx row_a = (idx_a) ? idx_a[row] : row;
        const Idx row_b = (idx_b) ? idx_b[row] : row;
        const Idx row_c = (idx_c) ? idx_c[row] : row;
        const Idx C_offset = row_c * in_len * out_len;
        const int sh_a_tile = 64;
        __shared__ DType sh_A[4 * sh_a_tile];
        int a_tile = sh_a_tile;
        for (unsigned int k_start = 0; k_start < in_len; k_start += 64) {
            if ((in_len - k_start) < a_tile) a_tile = in_len - k_start;
            /* Load A in shared mem in a coalesced way */
            for (unsigned int l = laneId; l < a_tile; l += 32)
                sh_A[local_row * sh_a_tile + l] = A[row_a * in_len + (k_start + l)];
            __syncwarp();

            for (unsigned int outloop = 0; outloop < out_len; outloop +=32) {
                DType out_reg = 0;  // thread private
                const unsigned int l = laneId;
                if (l < out_len) {
                    const DType b_val = B[row_b * out_len + (outloop + l)];
                    /* iterate over elements of a row of A */
                    for (unsigned int i = 0; i < a_tile; i++) {
                        const DType a_val = sh_A[local_row * sh_a_tile + i];
                        const Idx C_idx = C_offset + ((i + k_start) * out_len + (outloop + l));
                        AtomicAdd(C + C_idx, a_val * b_val);
                    }
                }
            }
        }
    }
}

}  // namespace cuda

/*!
 * \brief Implementation of Gather_mm operator. The input matrix A is
 *        expected to be sorted according to relation type.
 * \param A The input dense matrix of dimension m x k
 * \param B The input dense matrix of dimension k x n
 * \param C The output dense matrix of dimension m x n
 * \param seglen_A The input vector of size R. Each element
 *        is the length of segments of input ``A``
 * \param a_trans Matrix A to be transposed
 * \param b_trans Matrix B to be transposed
 */
template <int XPU, typename IdType, int bits>
void SegmentMM(const NDArray A,
               const NDArray B,
               NDArray C,
               const NDArray seglen_A,
               bool a_trans, bool b_trans) {
    SWITCH_BITS(bits, DType, {
        auto device = runtime::DeviceAPI::Get(A->ctx);
        cudaStream_t stream = runtime::getCurrentCUDAStream();
        const DType *A_data = A.Ptr<DType>();
        const DType *B_data = B.Ptr<DType>();
        const IdType* seglen_A_data = seglen_A.Ptr<IdType>();
        DType *C_data = C.Ptr<DType>();
        int64_t A_offset = 0, B_offset = 0, C_offset = 0;
        int64_t m, n, k;
        int64_t num_rel = seglen_A.NumElements();
        DType alpha = 1., beta = 0.;

        auto* thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
        if (!thr_entry->cublas_handle)
            CUBLAS_CALL(cublasCreate(&(thr_entry->cublas_handle)));
        CUBLAS_CALL(cublasSetStream(thr_entry->cublas_handle, stream));

        IdType m_offset = 0;
        for (IdType etype = 0; etype < num_rel; ++etype) {
            m = seglen_A_data[etype];  // rows of A
            CHECK_LE(m_offset + m, A->shape[0]) << "Segment index out of bound of A->shape[0].";
            n = B->shape[2];  // cols of B
            k = B->shape[1];  // cols of A == rows of B
            int ldb = n, lda = k, ldc = n;
            cublasOperation_t transB = CUBLAS_OP_N;
            cublasOperation_t transA = CUBLAS_OP_N;
            if (b_trans) {
                transB = CUBLAS_OP_T;
                ldb = n, lda = n, ldc = k;
                std::swap(n, k);
            }
            CUBLAS_CALL(cublasGemm<DType>(
                thr_entry->cublas_handle,
                transB,
                transA,
                n, m, k,
                &alpha,
                B_data + B_offset, ldb,
                A_data + A_offset, lda,
                &beta,
                C_data + C_offset, ldc));
            A_offset += m * k;
            B_offset += k * n;
            C_offset += m * n;
            m_offset += m;
        }
    });
}

template <int XPU, typename IdType, int bits>
void SegmentMMBackwardB(const NDArray A,
                        const NDArray dC,
                        NDArray dB,
                        const NDArray seglen) {
    SWITCH_BITS(bits, DType, {
        auto device = runtime::DeviceAPI::Get(A->ctx);
        cudaStream_t stream = runtime::getCurrentCUDAStream();
        const DType *A_data = A.Ptr<DType>();
        const DType *dC_data = dC.Ptr<DType>();
        const IdType* seglen_data = seglen.Ptr<IdType>();
        DType *dB_data = dB.Ptr<DType>();
        int64_t A_offset = 0, dC_offset = 0, dB_offset = 0;
        int64_t m, n, k;
        int64_t num_rel = seglen.NumElements();
        DType alpha = 1., beta = 1.;

        auto* thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
        if (!thr_entry->cublas_handle)
            CUBLAS_CALL(cublasCreate(&(thr_entry->cublas_handle)));
        CUBLAS_CALL(cublasSetStream(thr_entry->cublas_handle, stream));

        IdType k_offset = 0;
        for (IdType etype = 0; etype < num_rel; ++etype) {
            m = dC->shape[1];
            n = A->shape[1];
            k = seglen_data[etype];
            CHECK_LE(k_offset + k, A->shape[0]) << "Segement index out of bound of A->shape[0].";
            int lddC = m, ldA = n, lddB = m;
            cublasOperation_t trans_dC = CUBLAS_OP_N;
            cublasOperation_t trans_A = CUBLAS_OP_T;
            CUBLAS_CALL(cublasGemm<DType>(
                thr_entry->cublas_handle,
                trans_dC,
                trans_A,
                m, n, k,
                &alpha,
                dC_data + dC_offset, lddC,
                A_data + A_offset, ldA,
                &beta,
                dB_data + dB_offset, lddB));
            dC_offset += m * k;
            A_offset += n * k;
            dB_offset += m * n;
            k_offset += k;
        }
    });
}

/*!
 * \brief Implementation of Gather_mm operator. The input matrix A is
 *        expected to be sorted according to relation type.
 * \param A The input dense matrix of dimension m x k
 * \param B The input dense matrix of dimension k x n
 * \param C The output dense matrix of dimension m x n
 * \param idx_a The input vector to gather left hand operand on
 * \param idx_b The input vector to gather right hand operand on
 */

template <int XPU, typename IdType, int bits>
void GatherMM(const NDArray A,
              const NDArray B,
              NDArray C,
              const NDArray idx_a,
              const NDArray idx_b) {
  SWITCH_BITS(bits, DType, {
        auto device = runtime::DeviceAPI::Get(A->ctx);
        cudaStream_t stream = runtime::getCurrentCUDAStream();
        int64_t out_len = B->shape[2];  // cols of B
        int64_t in_len = A->shape[1];  // cols of A
        const int64_t tot_num_rows = A->shape[0];
        const int ntx = 128;
        const int warp_size = 32;
        const int nbx = ((tot_num_rows * warp_size + ntx - 1) / ntx);
        const dim3 nblks(nbx);
        const dim3 nthrs(ntx);
        CUDA_KERNEL_CALL((cuda::GatherMMScatterKernel<IdType, DType>),
            nblks, nthrs, 0, stream,
            A.Ptr<DType>(),
            B.Ptr<DType>(),
            C.Ptr<DType>(),
            idx_a.Ptr<IdType>(),
            idx_b.Ptr<IdType>(),
            nullptr,
            tot_num_rows, in_len, out_len);
    });
}

/*!
 * \brief Implementation of Gather_mm operator. The input matrix A is
 *        expected to be sorted according to relation type.
 * \param A The input dense matrix of dimension m x k
 * \param B The input dense matrix of dimension k x n
 * \param C The output dense matrix of dimension m x n
 * \param idx_a The input vector to gather left hand operand on
 * \param idx_b The input vector to gather right hand operand on
 * \param idx_c The input vector to gather output operand on
 * \param num_rel The number of idx types in idx_b
 * \param a_trans Matrix A to be transposed
 * \param b_trans Matrix B to be transposed
 */
template <int XPU, typename IdType, int bits>
void GatherMMScatter(const NDArray A,
                     const NDArray B,
                     NDArray C,
                     const NDArray idx_a,
                     const NDArray idx_b,
                     const NDArray idx_c) {
    SWITCH_BITS(bits, DType, {
        auto device = runtime::DeviceAPI::Get(A->ctx);
        cudaStream_t stream = runtime::getCurrentCUDAStream();
        const IdType *idx_c_data = idx_c.Ptr<IdType>();
        int64_t out_len = (B->ndim == 2)? B->shape[1] : B->shape[2];  // cols of B
        int64_t in_len = A->shape[1];  // cols of A
        int64_t tot_num_rows = A->shape[0];
        const int ntx = 128;
        const int warp_size = 32;
        const int nbx = ((tot_num_rows * warp_size + ntx - 1) / ntx);
        const dim3 nblks(nbx);
        const dim3 nthrs(ntx);
        if (B->ndim == 3) {
          CUDA_KERNEL_CALL((cuda::GatherMMScatterKernel<IdType, DType>),
              nblks, nthrs, 0, stream,
              A.Ptr<DType>(),
              B.Ptr<DType>(),
              C.Ptr<DType>(),
              idx_a.Ptr<IdType>(),
              idx_b.Ptr<IdType>(),
              idx_c.Ptr<IdType>(),
              tot_num_rows, in_len, out_len);
        } else {
          // Custom kernel for W_grad[idx_c[i]] = H^T[i] * C.grad[i]
          // This kernel accesses rows of A in a transposed way w/o explicitly converting A
          CUDA_KERNEL_CALL((cuda::GatherMMScatterKernel2<IdType, DType>),
              nblks, nthrs, 0, stream,
              A.Ptr<DType>(),
              B.Ptr<DType>(),
              C.Ptr<DType>(),
              idx_a.Ptr<IdType>(),
              idx_b.Ptr<IdType>(),
              idx_c.Ptr<IdType>(),
              tot_num_rows, in_len, out_len);
        }
    });
}


template void GatherMM<kDLGPU, int32_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);
template void GatherMM<kDLGPU, int64_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);
template void GatherMM<kDLGPU, int32_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);
template void GatherMM<kDLGPU, int64_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);
template void GatherMM<kDLGPU, int32_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);
template void GatherMM<kDLGPU, int64_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b);

template void GatherMMScatter<kDLGPU, int32_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);
template void GatherMMScatter<kDLGPU, int64_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);
template void GatherMMScatter<kDLGPU, int32_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);
template void GatherMMScatter<kDLGPU, int64_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);
template void GatherMMScatter<kDLGPU, int32_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);
template void GatherMMScatter<kDLGPU, int64_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray idx_a, const NDArray idx_b, const NDArray idx_c);

template void SegmentMM<kDLGPU, int32_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);
template void SegmentMM<kDLGPU, int64_t, 16>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);
template void SegmentMM<kDLGPU, int32_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);
template void SegmentMM<kDLGPU, int64_t, 32>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);
template void SegmentMM<kDLGPU, int32_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);
template void SegmentMM<kDLGPU, int64_t, 64>(
    const NDArray A, const NDArray B, NDArray C,
    const NDArray seglen_A, bool a_trans, bool b_trans);

template void SegmentMMBackwardB<kDLGPU, int32_t, 16>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);
template void SegmentMMBackwardB<kDLGPU, int64_t, 16>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);
template void SegmentMMBackwardB<kDLGPU, int32_t, 32>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);
template void SegmentMMBackwardB<kDLGPU, int64_t, 32>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);
template void SegmentMMBackwardB<kDLGPU, int32_t, 64>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);
template void SegmentMMBackwardB<kDLGPU, int64_t, 64>(
    const NDArray A, const NDArray dC, NDArray dB, const NDArray seglen);

}  // namespace aten
}  // namespace dgl
