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8081e7a23d4f60b9edafc12af24926734372c9b2
whisper.cpp/ggml/src/ggml-sycl/mmvq.cpp
Svetlozar Georgiev 8081e7a23d sycl: reordered Q4_K MMVQ (llama/13109)
2025-05-19 14:58:39 +03:00

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#include "mmvq.hpp"
#include "ggml.h"
#include "common.hpp"
#include "quants.hpp"
#include "vecdotq.hpp"
template <typename reorder_vec_dot_q_sycl>
static void mul_mat_vec_q_reorder(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
const int ncols, const int nrows, const sycl::nd_item<3> & nd_item) {
using block_type = ggml_sycl_reordered::block_q_t<reorder_vec_dot_q_sycl::gtype>;
using block_traits = typename block_type::traits;
const auto sg = nd_item.get_sub_group();
const int sg_range = sg.get_group_linear_range();
const int workgroup_id = nd_item.get_group_linear_id();
const int sg_id = sg.get_group_linear_id();
const int row = workgroup_id * sg_range + sg_id;
if (row >= nrows) {
return;
}
const int blocks_per_row = ncols / block_traits::qk;
constexpr int blocks_per_subgroup = ceil_div(block_traits::vdr_mmvq * WARP_SIZE, block_traits::qi);
constexpr int block_elements_per_subgroup = block_traits::qi / block_traits::vdr_mmvq;
const int nblocks = nrows * (ncols / block_traits::qk);
static_assert(blocks_per_subgroup > 0);
static_assert(block_elements_per_subgroup > 0);
const block_q8_1 * y = (const block_q8_1 *) vy;
float partial_sum = 0.0f;
for (int i = sg.get_local_linear_id() / block_elements_per_subgroup; i < blocks_per_row; i += blocks_per_subgroup) {
const int ibx = row * blocks_per_row + i; // x block index
// TODO: Generalize offsets, right now only works for quantizations that don't split high and low bits
const int bx_offset = block_type::get_block_offset(ibx);
const int d_offset = block_type::get_d_offset(nrows, ncols, ibx);
// Y block index that aligns with ibx
const int iby = i * block_type::block_to_q8_1_ratio();
#pragma unroll
for (int elem = 0; elem < block_elements_per_subgroup; elem += WARP_SIZE) {
// x block quant index when casting the quants to int
const int iqs = elem + block_traits::vdr_mmvq * (sg.get_local_linear_id() % block_elements_per_subgroup);
partial_sum += reorder_vec_dot_q_sycl()(vx, bx_offset, d_offset, &y[iby], iqs, nblocks);
}
}
auto sum = sycl::reduce_over_group(nd_item.get_sub_group(), partial_sum, std::plus<>());
if (sg.leader()) {
dst[row] = sum;
}
}
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;
constexpr int blocks_per_warp = (vdr * WARP_SIZE + qi - 1) / qi; // Ensuring blocks_per_warp > 0
assert(blocks_per_warp > 0);
// 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
for (size_t elem = 0; elem < qi / vdr; elem += WARP_SIZE) {
const int iqs = elem + 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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;
assert(blocks_per_warp>0);
// 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 reorder_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 = ceil_div(nrows, GGML_SYCL_MMV_Y);
constexpr size_t num_subgroups = 16;
GGML_ASSERT(block_num_y % num_subgroups == 0);
const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, (block_num_y * WARP_SIZE));
const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
[=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0>>(vx, vy, dst, ncols, nrows,
nd_item);
});
});
}
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) [[sycl::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)
[[sycl::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)
[[sycl::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)
[[sycl::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)
[[sycl::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)
[[sycl::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)
[[sycl::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)
[[sycl::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 reorder_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 = ceil_div(nrows, GGML_SYCL_MMV_Y);
constexpr size_t num_subgroups = 16;
GGML_ASSERT(block_num_y % num_subgroups == 0);
const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
[=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K>>(vx, vy, dst, ncols,
nrows, nd_item);
});
});
}
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)
[[sycl::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)
[[sycl::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)
[[sycl::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) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::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) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::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) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::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) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::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) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::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)
[[sycl::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)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
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)
[[sycl::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
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:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
GGML_SYCL_DEBUG("Calling reorder_mul_mat_vec_q4_0_q8_1_sycl\n");
reorder_mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
} else {
GGML_SYCL_DEBUG("Calling mul_mat_vec_q4_0_q8_1_sycl\n");
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:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
reorder_mul_mat_vec_q4_k_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
} else {
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");
}
}
GGML_UNUSED(src1);
GGML_UNUSED(dst);
GGML_UNUSED(src1_ddf_i);
GGML_UNUSED(ctx);
}
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