llama.cpp/ggml/src/ggml-sycl/mmvq.cpp

#include "mmvq.hpp"
#include "vecdotq.hpp"


template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
                          const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
                                       const void *__restrict__ vy,
                                       float *__restrict__ dst, const int ncols,
                                       const int nrows,
                                       const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
                                      const void *__restrict__ vy,
                                      float *__restrict__ dst, const int ncols,
                                      const int nrows,
                                      const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid, ksigns64);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
                                     const void *__restrict__ vy,
                                     float *__restrict__ dst, const int ncols,
                                     const int nrows,
                                     const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq2_s_q8_1(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
                                       const void *__restrict__ vy,
                                       float *__restrict__ dst, const int ncols,
                                       const int nrows,
                                       const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid, ksigns64);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
                                     const void *__restrict__ vy,
                                     float *__restrict__ dst, const int ncols,
                                     const int nrows,
                                     const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
                                     const void *__restrict__ vy,
                                     float *__restrict__ dst, const int ncols,
                                     const int nrows,
                                     const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_gpu);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
                                     const void *__restrict__ vy,
                                     float *__restrict__ dst, const int ncols,
                                     const int nrows,
                                     const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq1_m_q8_1(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
                                      const void *__restrict__ vy,
                                      float *__restrict__ dst, const int ncols,
                                      const int nrows,
                                      const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq4_nl_q8_1(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}


template <int qk, int qi, typename block_q_t, int vdr>
static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
                                      const void *__restrict__ vy,
                                      float *__restrict__ dst, const int ncols,
                                      const int nrows,
                                      const sycl::nd_item<3> &item_ct1) {
    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
                    item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

    const int blocks_per_row = ncols / qk;
    const int blocks_per_warp = vdr * WARP_SIZE / qi;

// partial sum for each thread
    float tmp = 0.0f;

    const block_q_t  * x = (const block_q_t  *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
         i += blocks_per_warp) {
        const int ibx = row*blocks_per_row + i; // x block index

        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx

        const int iqs =
            vdr *
            (item_ct1.get_local_id(2) %
             (qi / vdr)); // x block quant index when casting the quants to int

        tmp += vec_dot_iq4_xs_q8_1(&x[ibx], &y[iby], iqs);
    }

    // sum up partial sums and write back result
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
        dst[row] = tmp;
    }
}

static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK4_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK4_1 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK5_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK5_1 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK8_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
                                       dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}


static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {
        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
                                         float *dst, const int ncols,
                                         const int nrows,
                                         dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
            auto ksigns64_ptr_ct1 = &ksigns64[0];

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
                                         float *dst, const int ncols,
                                         const int nrows,
                                         dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
            auto ksigns64_ptr_ct1 = &ksigns64[0];

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
            auto ksigns64_ptr_ct1 = &ksigns64[0];

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
            auto ksigns64_ptr_ct1 = &ksigns64[0];

            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {
        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK4_NL == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
                                          float *dst, const int ncols,
                                          const int nrows,
                                          dpct::queue_ptr stream) {
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
    }
}

void ggml_sycl_op_mul_mat_vec_q(
    ggml_backend_sycl_context & ctx,
    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
    const int64_t src1_ncols, const int64_t src1_padded_col_size,
    const dpct::queue_ptr &stream) {

    const int64_t ne10 = src1->ne[0];
    GGML_ASSERT(ne10 % QK8_1 == 0);

    const int64_t ne00 = src0->ne[0];
    const int64_t row_diff = row_high - row_low;

    int id;
    SYCL_CHECK(
        CHECK_TRY_ERROR(id = get_current_device_id()));
    const size_t q8_1_ts = sizeof(block_q8_1);
    const size_t q8_1_bs = QK8_1;
    // the main device has a larger memory buffer to hold the results from all GPUs
    // nrows_dst == nrows of the matrix that the kernel writes into
    const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
    for (int i = 0; i < src1_ncols; i++)
    {
        const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
        const char* src1_ddq_i_bs = src1_ddq_i + src1_ddq_i_offset;
        float* dst_dd_i_bs = dst_dd_i + i * dst->ne[0];
        switch (src0->type) {
        case GGML_TYPE_Q4_0:
            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q4_1:
            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_0:
            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_1:
            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q8_0:
            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q2_K:
            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q3_K:
            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q4_K:
            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q5_K:
            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_Q6_K:
            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ1_S:
            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ1_M:
            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ2_XXS:
            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ2_XS:
            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ2_S:
            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ3_XXS:
            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ3_S:
            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ4_NL:
            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        case GGML_TYPE_IQ4_XS:
            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
            break;
        default:
            GGML_ABORT("fatal error");
            break;
        }
    }
    (void) src1;
    (void) dst;
    (void) src1_ddf_i;
}